diff --git a/data/preprocess/__init__.py b/data/preprocess/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/data/preprocess/catalog.py b/data/preprocess/catalog.py
new file mode 100644
index 0000000..091bcaa
--- /dev/null
+++ b/data/preprocess/catalog.py
@@ -0,0 +1,80 @@
+import os
+import numpy as np
+import pandas as pd
+from tqdm import tqdm
+from dla_cnn.desi.DesiMock import DesiMock
+import json
+
+# Prepare the wavelengths of some important emission lines
+
+lines = json.load(open('./wavelength.json', 'r'))
+
+# prepare for the data path
+def generate_suffix(prefix):
+ suffix = {}
+ for preid in os.listdir(prefix):
+ suffix[preid] = os.listdir(prefix+preid)
+ return suffix
+
+
+def generate_seperated_catalog(prefix):
+
+ # prefix = './desi-0.2-100/spectra-16/' # this need to be specialized
+ suffix = generate_suffix(prefix=prefix)
+ # generate a catalog (csv format) under each folder
+
+ data = {}
+ for suffix1 in tqdm(suffix.keys()):
+ for suffix2 in tqdm(suffix[suffix1]):
+ path = prefix + suffix1 + '/' + suffix2 + '/'
+ if len(os.listdir(path)) == 3:
+ path_spectra = path + 'spectra-16-' + suffix2 +'.fits'
+ path_truth = path + 'truth-16-' + suffix2 +'.fits'
+ path_zbest = path + 'zbest-16-' + suffix2 +'.fits'
+ data = DesiMock()
+ data.read_fits_file(path_spectra, path_truth, path_zbest)
+ total = pd.DataFrame()
+ for id in data.data:
+ sline = data.get_sightline(id=id)
+ wav_max, wav_min = 10**np.max(sline.loglam - np.log10(1+sline.z_qso)), 10**np.min(sline.loglam - np.log10(1+sline.z_qso))
+ info = pd.DataFrame()
+ info['id'] = np.ones(1, dtype='i8') * int(id)
+ info['z_qso'] = np.ones(1) * sline.z_qso
+ info['snr'] = np.ones(1) * sline.s2n
+ for name in names:
+ info[name] = [lines[name] >= wav_min and lines[name] <= wav_max]
+ total = pd.concat([total, info])
+ total['file'] = np.ones(len(total), dtype='i8') * int(suffix2)
+ total = total[['file', 'id', 'z_qso', 'snr', 'LyBETA', 'LyALPHA', 'MgII1', 'CIV1', 'MgII2', 'CIV2']]
+ total.to_csv(prefix + suffix1 + '/' + suffix2 + '/catalog.csv', index=False)
+
+# delete all the catalog
+def delete_all_calalog(prefix):
+ suffix = generate_suffix(prefix=prefix)
+ for suffix1 in suffix.keys():
+ for suffix2 in suffix[suffix1]:
+ path = prefix + suffix1 + '/' + suffix2 + '/'
+ files = os.listdir(path)
+ if len(files) == 4:
+ for file in files:
+ if '.csv' in file:
+ os.remove(path + file)
+ if 'catalog_total.csv' in os.listdir(prefix):
+ os.remove(prefix+'catalog_total.csv')
+
+# generate a total catalog
+# this should be done AFTER the catalog of each folder has been generated
+def generate_total_catalog(prefix):
+ suffix = generate_suffix(prefix=prefix)
+ catalog = pd.DataFrame()
+ for suffix1 in suffix.keys():
+ for suffix2 in suffix[suffix1]:
+ path = prefix + suffix1 + '/' + suffix2 + '/'
+ files = os.listdir(path)
+ if len(files) == 4:
+ for file in files:
+ if '.csv' in file:
+ this = pd.read_csv(path+file)
+ catalog = pd.concat([catalog, this])
+
+ catalog.to_csv(prefix+'catalog_total.csv')
\ No newline at end of file
diff --git a/data/preprocess/dla_cnn/CODE_NOTES.txt b/data/preprocess/dla_cnn/CODE_NOTES.txt
new file mode 100644
index 0000000..58620f7
--- /dev/null
+++ b/data/preprocess/dla_cnn/CODE_NOTES.txt
@@ -0,0 +1,3 @@
+This file contains snippets of executables, notes essentially
+
+stdbuf -o 0 python localize_model.py -i 10000000 -c '../models/training/model_gensample_v7.0' -r '../data/v7gensample/train_*' -e '../data/v7gensample/test_mix_23559_10000.npz' | tee ../tmp/stdout_train_7.0.txt
diff --git a/data/preprocess/dla_cnn/Timer.py b/data/preprocess/dla_cnn/Timer.py
new file mode 100644
index 0000000..cbd9794
--- /dev/null
+++ b/data/preprocess/dla_cnn/Timer.py
@@ -0,0 +1,20 @@
+import time
+
+
+class Timer:
+ def __init__(self, disp=None):
+ self.disp = disp
+
+
+ def __enter__(self, disp=None):
+ self.start = time.time()
+ if (self.disp is not None):
+ print ("%s [Begin]" % self.disp)
+ return self
+
+
+ def __exit__(self, *args):
+ self.end = time.time()
+ self.interval = self.end - self.start
+ if(self.disp is not None):
+ print ("%s [%.1fs]" % (self.disp, self.interval))
\ No newline at end of file
diff --git a/data/preprocess/dla_cnn/__init__.py b/data/preprocess/dla_cnn/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/data/preprocess/dla_cnn/absorption.py b/data/preprocess/dla_cnn/absorption.py
new file mode 100644
index 0000000..165f913
--- /dev/null
+++ b/data/preprocess/dla_cnn/absorption.py
@@ -0,0 +1,225 @@
+""" Module for loading spectra, either fake or real
+"""
+from __future__ import print_function, absolute_import, division, unicode_literals
+
+import os, sys
+import numpy as np
+import pdb
+
+from dla_cnn.data_loader import REST_RANGE
+
+
+# Raise warnings to errors for debugging
+import warnings
+#warnings.filterwarnings('error')
+
+def add_abs_to_sightline(sightline):
+ from dla_cnn.data_loader import get_lam_data
+ #if REST_RANGE is None:
+ # from dla_cnn.data_loader import REST_RANGE
+ #
+ dlas = []
+ subdlas = []
+ lybs = []
+
+ # Loop through peaks which identify a DLA
+ # (peaks, peaks_uncentered, smoothed_sample, ixs_left, ixs_right, offset_hist, offset_conv_sum, peaks_offset) \
+ # = peaks_data[ix]
+ lam, lam_rest, ix_dla_range = get_lam_data(sightline.loglam, sightline.z_qso, REST_RANGE)
+ for peak in sightline.prediction.peaks_ixs:
+ peak_lam_rest = lam_rest[ix_dla_range][peak]
+ peak_lam_spectrum = peak_lam_rest * (1 + sightline.z_qso)
+
+ # mean_col_density_prediction = np.mean(density_data[peak-40:peak+40])
+ # std_col_density_prediction = np.std(density_data[peak-40:peak+40])
+ z_dla = float(peak_lam_spectrum) / 1215.67 - 1
+ _, mean_col_density_prediction, std_col_density_prediction, bias_correction = \
+ sightline.prediction.get_coldensity_for_peak(peak)
+
+ absorber_type = "LYB" if sightline.is_lyb(peak) else "DLA" if mean_col_density_prediction >= 20.3 else "SUBDLA"
+ dla_sub_lyb = lybs if absorber_type == "LYB" else dlas if absorber_type == "DLA" else subdlas
+
+ # Should add S/N at peak
+ abs_dict = {
+ 'rest': float(peak_lam_rest),
+ 'spectrum': float(peak_lam_spectrum),
+ 'z_dla':float(z_dla),
+ 'dla_confidence': min(1.0,float(sightline.prediction.offset_conv_sum[peak])),
+ 'column_density': float(mean_col_density_prediction),
+ 'std_column_density': float(std_col_density_prediction),
+ 'column_density_bias_adjust': float(bias_correction),
+ 'type': absorber_type
+ }
+ #get_s2n_for_absorbers(sightline, lam, [abs_dict]) # SLOWED CODE DOWN TOO MUCH
+ dla_sub_lyb.append(abs_dict)
+ # Save
+ sightline.dlas = dlas
+ sightline.subdlas = subdlas
+ sightline.lybs = lybs
+
+def generate_voigt_model(sightline, absorber):
+ """ Generate a continuum-scaled Voigt profile for the absorber
+ :param sightline:
+ :param absorber:
+ :param REST_RANGE:
+ :return:
+ voigt_wave, voigt_model
+ """
+ from dla_cnn.data_loader import get_lam_data
+ #from dla_cnn.data_loader import generate_voigt_profile
+ #from dla_cnn.data_loader import get_peaks_for_voigt_scaling
+ from scipy.stats import chisquare
+ from scipy.optimize import minimize
+ #if REST_RANGE is None:
+ # from dla_cnn.data_loader import REST_RANGE
+ # Wavelengths
+ full_lam, full_lam_rest, full_ix_dla_range = get_lam_data(sightline.loglam, sightline.z_qso, REST_RANGE)
+ # Generate the voigt model using astropy, linetools, etc.
+ voigt_flux, voigt_wave = generate_voigt_profile(absorber['z_dla'],
+ absorber['column_density'], full_lam)
+ # get peaks
+ ixs_mypeaks = get_peaks_for_voigt_scaling(sightline, voigt_flux)
+ # get indexes where voigt profile is between 0.2 and 0.95
+ observed_values = sightline.flux[ixs_mypeaks]
+ expected_values = voigt_flux[ixs_mypeaks]
+ # Minimize scale variable using chi square measure
+ opt = minimize(lambda scale: chisquare(observed_values, expected_values * scale)[0], 1)
+ opt_scale = opt.x[0]
+ #from IPython import embed; embed()
+
+ # Return
+ return voigt_wave, voigt_flux*opt_scale, ixs_mypeaks
+
+def generate_voigt_profile(dla_z, mean_col_density_prediction, full_lam):
+ from linetools.spectralline import AbsLine
+ from linetools.analysis.voigt import voigt_from_abslines
+ from astropy import units as u
+ with open(os.devnull, 'w') as devnull:
+ with warnings.catch_warnings():
+ warnings.simplefilter("ignore")
+ # Hack to avoid AbsLine spamming us with print statements
+ stdout = sys.stdout
+ sys.stdout = devnull
+
+ abslin = AbsLine(1215.670 * 0.1 * u.nm, z=dla_z)
+ abslin.attrib['N'] = 10 ** mean_col_density_prediction / u.cm ** 2 # log N
+ abslin.attrib['b'] = 25. * u.km / u.s # b
+ # print dla_z, mean_col_density_prediction, full_lam, full_lam.shape
+ # try:
+ #vmodel = voigt_from_abslines(full_lam.astype(np.float16) * u.AA, abslin, fwhm=3, debug=False)
+ vmodel = voigt_from_abslines(full_lam * u.AA, abslin, fwhm=3, debug=False)
+ # except TypeError as e:
+ # import pdb; pdb.set_trace()
+ voigt_flux = vmodel.data['flux'].data[0]
+ voigt_wave = vmodel.data['wave'].data[0]
+ # clear some bad values at beginning / end of voigt_flux
+ voigt_flux[0:10] = 1
+ voigt_flux[-10:len(voigt_flux) + 1] = 1
+
+ sys.stdout = stdout
+
+ return voigt_flux, voigt_wave
+
+# Returns peaks used for voigt scaling, removes outlier and ensures enough points for good scaling
+def get_peaks_for_voigt_scaling(sightline, voigt_flux):
+ from scipy.signal import find_peaks_cwt
+ iteration_count = 0
+ ixs_mypeaks_outliers_removed = []
+
+ # Loop to try different find_peak values if we don't get enough peaks with one try
+ while iteration_count < 10 and len(ixs_mypeaks_outliers_removed) < 5:
+ peaks = np.array(find_peaks_cwt(sightline.flux, np.arange(1, 2+iteration_count)))
+ ixs = np.where((voigt_flux > 0.2) & (voigt_flux < 0.95))
+ ixs_mypeaks = np.intersect1d(ixs, peaks)
+
+ # Remove any points > 1.5 standard deviations from the mean (poor mans outlier removal)
+ peaks_mean = np.mean(sightline.flux[ixs_mypeaks]) if len(ixs_mypeaks)>0 else 0
+ peaks_std = np.std(sightline.flux[ixs_mypeaks]) if len(ixs_mypeaks)>0 else 0
+
+ ixs_mypeaks_outliers_removed = ixs_mypeaks[np.abs(sightline.flux[ixs_mypeaks] - peaks_mean) < (peaks_std * 1.5)]
+ iteration_count += 1
+
+
+ return ixs_mypeaks_outliers_removed
+
+
+# Estimate S/N at an absorber
+def get_s2n_for_absorbers(sightline, lam, absorbers, nsamp=20):
+ from scipy.stats import chisquare
+ from scipy.optimize import minimize
+ if len(absorbers) == 0:
+ return
+ # Loop on the DLAs
+ for jj in range(len(absorbers)):
+ # Find the peak
+ isys = absorbers[jj]
+ # Get the Voigt (to avoid it)
+ voigt_flux, voigt_wave = generate_voigt_profile(isys['z_dla'], isys['column_density'], lam)
+ # get peaks
+ ixs_mypeaks = get_peaks_for_voigt_scaling(sightline, voigt_flux)
+ if len(ixs_mypeaks) < 2:
+ s2n = 1. # KLUDGE
+ else:
+ # get indexes where voigt profile is between 0.2 and 0.95
+ observed_values = sightline.flux[ixs_mypeaks]
+ expected_values = voigt_flux[ixs_mypeaks]
+ # Minimize scale variable using chi square measure for signal
+ opt = minimize(lambda scale: chisquare(observed_values, expected_values * scale)[0], 1)
+ opt_scale = opt.x[0]
+ # Noise
+ core = voigt_flux < 0.8
+ rough_noise = np.median(sightline.sig[core])
+ if rough_noise == 0: # Occasional bad data in error array
+ s2n = 0.1
+ else:
+ s2n = opt_scale/rough_noise
+ isys['s2n'] = s2n
+ ''' Another algorithm
+ # Core
+ core = np.where(voigt_flux < 0.8)[0]
+ # Fluxes -- Take +/-nsamp away from core
+ flux_for_stats = np.concatenate([sightline.flux[core[0]-nsamp:core[0]], sightline.flux[core[1]:core[1]+nsamp]])
+ # Sort
+ asrt = np.argsort(flux_for_stats)
+ rough_signal = flux_for_stats[asrt][int(0.9*len(flux_for_stats))]
+ rough_noise = np.median(sightline.sig[core])
+ #
+ s2n = rough_signal/rough_noise
+ '''
+ return
+
+
+def voigt_from_sightline(sightline, inp):
+ """ Wrapper to generate the Voigt for a given sightline and
+
+ Parameters
+ ----------
+ sightline
+ inp : int, float (coming)
+
+ Returns
+ -------
+ voigt_wave
+ voigt_model
+ ixs_mypeaks
+ """
+ from dla_cnn.data_loader import get_lam_data
+ # Setup
+ peaks_offset = sightline.prediction.peaks_ixs
+ full_lam, full_lam_rest, full_ix_dla_range = get_lam_data(sightline.loglam,
+ sightline.z_qso, REST_RANGE)
+ # Input
+ if isinstance(inp,int):
+ peakid = inp
+ else:
+ raise IOError("Only taking peakid so far")
+ # Grab peak
+ peak = peaks_offset[peakid]
+ # z, NHI
+ lam_rest = full_lam_rest[full_ix_dla_range]
+ dla_z = lam_rest[peak] * (1 + sightline.z_qso) / 1215.67 - 1
+ density_pred_per_this_dla, mean_col_density_prediction, std_col_density_prediction, bias_correction = \
+ sightline.prediction.get_coldensity_for_peak(peak)
+ # Absorber and voigt
+ absorber = dict(z_dla=dla_z, column_density=mean_col_density_prediction)
+ return generate_voigt_model(sightline, absorber)
diff --git a/data/preprocess/dla_cnn/catalogs.py b/data/preprocess/dla_cnn/catalogs.py
new file mode 100644
index 0000000..51772b1
--- /dev/null
+++ b/data/preprocess/dla_cnn/catalogs.py
@@ -0,0 +1,188 @@
+""" Module of routines related to DLA catalogs
+However I/O is in io.py
+"""
+from __future__ import print_function, absolute_import, division, unicode_literals
+
+from pkg_resources import resource_filename
+
+import os
+import numpy as np
+
+from astropy.table import Table
+from astropy.coordinates import SkyCoord, match_coordinates_sky
+from astropy import units as u
+
+from linetools import utils as ltu
+
+def generate_boss_tables():
+ """
+ Returns
+ -------
+
+ """
+ # Load JSON file
+ dr12_json = resource_filename('dla_cnn', 'catalogs/boss_dr12/predictions_DR12.json')
+ dr12 = ltu.loadjson(dr12_json)
+
+ # Load Garnett Table 2 for BALs
+ tbl2_garnett_file = '/media/xavier/ExtraDrive2/Projects/ML_DLA_results/garnett16/ascii_catalog/table2.dat'
+ tbl2_garnett = Table.read(tbl2_garnett_file, format='cds')
+ tbl2_garnett_coords = SkyCoord(ra=tbl2_garnett['RAdeg'], dec=tbl2_garnett['DEdeg'], unit='deg')
+
+
+ # Parse into tables
+ s_plates = []
+ s_fibers = []
+ s_mjds = []
+ s_ra = []
+ s_dec = []
+ s_zem = []
+
+ a_zabs = []
+ a_NHI = []
+ a_sigNHI = []
+ a_conf = []
+ a_plates = []
+ a_fibers = []
+ a_mjds = []
+ a_ra = []
+ a_dec = []
+ a_zem = []
+ for sline in dr12:
+ # Plate/fiber
+ plate, mjd, fiber = [int(spl) for spl in sline['id'].split('-')]
+ s_plates.append(plate)
+ s_mjds.append(mjd)
+ s_fibers.append(fiber)
+ # RA/DEC/zem
+ s_ra.append(sline['ra'])
+ s_dec.append(sline['dec'])
+ s_zem.append(sline['z_qso'])
+ # DLAs/SLLS
+ for abs in sline['dlas']+sline['subdlas']:
+ a_plates.append(plate)
+ a_mjds.append(mjd)
+ a_fibers.append(fiber)
+ # RA/DEC/zem
+ a_ra.append(sline['ra'])
+ a_dec.append(sline['dec'])
+ a_zem.append(sline['z_qso'])
+ # Absorber
+ a_zabs.append(abs['z_dla'])
+ a_NHI.append(abs['column_density'])
+ a_sigNHI.append(abs['std_column_density'])
+ a_conf.append(abs['dla_confidence'])
+ # Sightline tables
+ sline_tbl = Table()
+ sline_tbl['Plate'] = s_plates
+ sline_tbl['Fiber'] = s_fibers
+ sline_tbl['MJD'] = s_mjds
+ sline_tbl['RA'] = s_ra
+ sline_tbl['DEC'] = s_dec
+ sline_tbl['zem'] = s_zem
+
+ # Match and fill BAL flag
+ dr12_sline_coord = SkyCoord(ra=sline_tbl['RA'], dec=sline_tbl['DEC'], unit='deg')
+ sline_tbl['flg_BAL'] = -1
+ idx, d2d, d3d = match_coordinates_sky(dr12_sline_coord, tbl2_garnett_coords, nthneighbor=1)
+ in_garnett = d2d < 1*u.arcsec # Check
+ sline_tbl['flg_BAL'][in_garnett] = tbl2_garnett['f_BAL'][idx[in_garnett]]
+ print("There were {:d} DR12 sightlines not in Garnett".format(np.sum(~in_garnett)))
+
+ # Write
+ dr12_sline = resource_filename('dla_cnn', 'catalogs/boss_dr12/DR12_sightlines.fits')
+ sline_tbl.write(dr12_sline, overwrite=True)
+ print("Wrote {:s}".format(dr12_sline))
+
+ # DLA/SLLS table
+ abs_tbl = Table()
+ abs_tbl['Plate'] = a_plates
+ abs_tbl['Fiber'] = a_fibers
+ abs_tbl['MJD'] = a_mjds
+ abs_tbl['RA'] = a_ra
+ abs_tbl['DEC'] = a_dec
+ abs_tbl['zem'] = a_zem
+ #
+ abs_tbl['zabs'] = a_zabs
+ abs_tbl['NHI'] = a_NHI
+ abs_tbl['sigNHI'] = a_sigNHI
+ abs_tbl['conf'] = a_conf
+ # BAL
+ dr12_abs_coord = SkyCoord(ra=abs_tbl['RA'], dec=abs_tbl['DEC'], unit='deg')
+ idx, d2d, d3d = match_coordinates_sky(dr12_abs_coord, tbl2_garnett_coords, nthneighbor=1)
+ in_garnett = d2d < 1*u.arcsec # Check
+ abs_tbl['flg_BAL'] = -1
+ abs_tbl['flg_BAL'][in_garnett] = tbl2_garnett['f_BAL'][idx[in_garnett]]
+ abs_tbl['SNR'] = 0.
+ abs_tbl['SNR'][in_garnett] = tbl2_garnett['SNRSpec'][idx[in_garnett]]
+ print("There were {:d} DR12 absorbers not covered by Garnett".format(np.sum(~in_garnett)))
+
+ dr12_abs = resource_filename('dla_cnn', 'catalogs/boss_dr12/DR12_DLA_SLLS.fits')
+ abs_tbl.write(dr12_abs, overwrite=True)
+ print("Wrote {:s}".format(dr12_abs))
+
+ # Garnett
+ ml_path = os.getenv('PROJECT_ML')
+ g16_dlas = Table.read(ml_path + '/garnett16/ascii_catalog/table3.dat', format='cds')
+ tbl3_garnett_coords = SkyCoord(ra=g16_dlas['RAdeg'], dec=g16_dlas['DEdeg'], unit='deg')
+ idx, d2d, d3d = match_coordinates_sky(tbl3_garnett_coords, tbl2_garnett_coords, nthneighbor=1)
+ in_garnett = d2d < 1*u.arcsec # Check
+ g16_dlas['flg_BAL'] = -1
+ g16_dlas['flg_BAL'][in_garnett] = tbl2_garnett['f_BAL'][idx[in_garnett]]
+ g16_dlas['SNR'] = 0.
+ g16_dlas['SNR'][in_garnett] = tbl2_garnett['SNRSpec'][idx[in_garnett]]
+ g16_outfile = resource_filename('dla_cnn', 'catalogs/boss_dr12/DR12_DLA_garnett16.fits')
+ g16_dlas.write(g16_outfile, overwrite=True)
+ print("Wrote {:s}".format(g16_outfile))
+
+
+def match_boss_catalogs(dr12_dla, g16_dlas, dztoler=0.015, reverse=False):
+ """ Match our ML catalog against G16 or vice-versa
+
+ Parameters
+ ----------
+ dr12_dla
+ g16_dlas
+ dztoler
+ reverse
+
+ Returns
+ -------
+
+ """
+ # Indices
+ dr12_to_g16 = np.zeros(len(dr12_dla)).astype(int) -1
+ # Search around
+ if reverse:
+ dr12_dla_coords = SkyCoord(ra=dr12_dla['RAdeg'], dec=dr12_dla['DEdeg'], unit='deg')
+ g16_coord = SkyCoord(ra=g16_dlas['RA'], dec=g16_dlas['DEC'], unit='deg')
+ else:
+ dr12_dla_coords = SkyCoord(ra=dr12_dla['RA'], dec=dr12_dla['DEC'], unit='deg')
+ g16_coord = SkyCoord(ra=g16_dlas['RAdeg'], dec=g16_dlas['DEdeg'], unit='deg')
+ idx_g16, idx_dr12, d2d, d3d = dr12_dla_coords.search_around_sky(g16_coord, 1*u.arcsec)
+
+ # Loop to match
+ for kk,idx in enumerate(idx_dr12):
+ if reverse:
+ dz = np.abs(dr12_dla['z_DLA'][idx] - g16_dlas['zabs'][idx_g16[kk]])
+ else:
+ dz = np.abs(dr12_dla['zabs'][idx] - g16_dlas['z_DLA'][idx_g16[kk]])
+ if dz < dztoler:
+ dr12_to_g16[idx] = idx_g16[kk]
+ # Return
+ return dr12_to_g16
+
+def main(flg_cat):
+ import os
+
+ # BOSS tables
+ if (flg_cat & 2**0):
+ generate_boss_tables()
+
+# Test
+if __name__ == '__main__':
+ flg_cat = 0
+ flg_cat += 2**0 # BOSS Tables
+ #flg_cat += 2**7 # Training set of high NHI systems
+
+ main(flg_cat)
diff --git a/data/preprocess/dla_cnn/data_loader.py b/data/preprocess/dla_cnn/data_loader.py
new file mode 100644
index 0000000..d7722a0
--- /dev/null
+++ b/data/preprocess/dla_cnn/data_loader.py
@@ -0,0 +1,701 @@
+""" Module for loading spectra, either fake or real
+"""
+from __future__ import print_function, absolute_import, division, unicode_literals
+
+import numpy as np
+import os, urllib, math, json, timeit, multiprocessing, gc, sys
+import re, h5py, itertools, glob
+from traceback import print_exc
+import pdb
+
+from pkg_resources import resource_filename
+
+from scipy.stats import chisquare
+from scipy.optimize import minimize
+
+from astropy.io import fits
+from astropy.table import Table
+from multiprocessing import Process, Value, Array, Pool
+from dla_cnn.data_model.Sightline import Sightline
+from dla_cnn.data_model.Dla import Dla
+from dla_cnn.data_model.Id_GENSAMPLES import Id_GENSAMPLES
+from dla_cnn.data_model.Id_DR12 import Id_DR12
+from dla_cnn.data_model.Id_old_DR12 import Id_old_DR12 # FITS files
+#from dla_cnn.data_model.Id_DR7 import Id_DR7
+from dla_cnn.data_model.Prediction import Prediction
+from dla_cnn.data_model.DataMarker import Marker
+import code, traceback, threading
+from dla_cnn.localize_model import predictions_ann as predictions_ann_c2
+import scipy.signal as signal
+from scipy.spatial.distance import cdist
+from operator import itemgetter, attrgetter, methodcaller
+
+from dla_cnn.Timer import Timer
+from dla_cnn import defs
+from dla_cnn.data_model import data_utils
+from dla_cnn.spectra_utils import get_lam_data
+
+# Raise warnings to errors for debugging
+import warnings
+#warnings.filterwarnings('error')
+
+
+# DLAs from the DR9 catalog range from 920 to 1214, adding 120 on the right for variance in ly-a
+# the last number is the number of pixels in SDSS sightlines that span the range
+# REST_RANGE = [920, 1334, 1614]
+# REST_RANGE = [911, 1346, 1696]
+REST_RANGE = defs.REST_RANGE
+cache = {} # Cache for files and resources that should be opened once and kept open
+TF_DEVICE = os.getenv('TF_DEVICE', '')
+lock = threading.Lock()
+
+default_model = resource_filename('dla_cnn', "models/model_gensample_v7.1")
+
+
+# Rads fits file locally based on plate-mjd-fiber or online if option is set
+def read_fits_file(plate, mjd, fiber, fits_base_dir="../../BOSS_dat_all", download_if_notfound=False):
+ # Open the fits file
+ fits_filename = "%s/spec-%04d-%05d-%04d.fits" % (fits_base_dir, int(plate), int(mjd), int(fiber))
+ if os.path.isfile(fits_filename) and fits_base_dir is not None:
+ return read_fits_filename(fits_filename)
+ elif download_if_notfound:
+ url = "http://dr12.sdss3.org/sas/dr12/boss/spectro/redux/v5_7_0/spectra/%04d/spec-%04d-%05d-%04d.fits" % \
+ (plate, plate, mjd, fiber)
+ r = urllib.urlretrieve(url)
+ data = read_fits_filename(r[0])
+ os.remove(r[0])
+ return data
+ else:
+ raise Exception("File not found in [%s], and download_if_notfound is False" % fits_base_dir)
+
+
+def read_fits_filename(fits_filename):
+ with fits.open(fits_filename) as fits_file:
+ data1 = fits_file[1].data.copy()
+ z_qso = fits_file[3].data['LINEZ'][0].copy()
+
+ raw_data = {}
+ # Pad loglam and flux_normalized to sufficiently below 920A rest that we don't have issues falling off the left
+ (loglam_padded, flux_padded) = data_utils.pad_loglam_flux(data1['loglam'], data1['flux'], z_qso)
+ raw_data['flux'] = flux_padded
+ raw_data['loglam'] = loglam_padded
+ raw_data['plate'] = fits_file[2].data['PLATE'].copy()
+ raw_data['mjd'] = fits_file[2].data['MJD'].copy()
+ raw_data['fiber'] = fits_file[2].data['FIBERID'].copy()
+ raw_data['ra'] = fits_file[2].data['RA'].copy()
+ raw_data['dec'] = fits_file[2].data['DEC'].copy()
+
+ for hdu in fits_file:
+ del hdu.data
+ gc.collect() # Workaround for issue with mmap numpy not releasing the fits file: https://goo.gl/IEfAPh
+
+ return raw_data, z_qso
+
+
+def read_custom_hdf5(sightline):
+ """ Read custom HDF5 files made for this project
+ Parameters
+ ----------
+ sightline : Sightline
+
+ Returns
+ -------
+
+ """
+ global cache
+ fs = sightline.id.hdf5_datafile
+ json_datafile = sightline.id.json_datafile
+ if ~cache.has_key(fs) or ~cache.has_key(json_datafile):
+ with lock:
+ if not cache.has_key(fs) or not cache.has_key(json_datafile):
+ # print "Cache miss: [%s] and/or [%s] not found in cache" % (fs, json_datafile)
+ cache[fs] = h5py.File(fs, "r")
+ cache[json_datafile] = json.load(open(json_datafile))
+ f = cache[fs]
+ j = cache[json_datafile]
+
+ ix = sightline.id.ix
+ lam, flux, sig, _ = f['data'][ix]
+
+ # print "DEBUG> read_custom_hdf5 [%s] --- index: [%d]" % (sightline.id.hdf5_datafile, ix)
+
+ # Trim leading or training 0's and non finite values to clean up the data
+ # Can't use np.non_zero here because of the Inf values
+ first = 0
+ for i in flux:
+ if i == 0 or ~np.isfinite(i):
+ first += 1
+ else:
+ break
+ last = len(lam)
+ for i in flux[::-1]:
+ if i == 0 or ~np.isfinite(i):
+ last -= 1
+ else:
+ break
+ lam = lam[first:last]
+ flux = flux[first:last]
+ sig = sig[first:last]
+ assert np.all(np.isfinite(lam) & np.isfinite(flux) & np.isfinite(sig))
+
+ loglam = np.log10(lam)
+
+ # z_qso
+ if len(f['meta'].shape) == 1: # This was for the dr5 no-dla sightlines lacking a JSON file
+ z_qso = f['cut_meta']['zem_GROUP'][ix]
+ else:
+ meta = json.loads(f['meta'].value)
+ # Two different runs named this key different things
+ z_qso = meta['headers'][sightline.id.ix]['zem'] \
+ if meta['headers'][sightline.id.ix].has_key('zem') else meta['headers'][sightline.id.ix]['zem_GROUP']
+
+ # Pad loglam and flux_normalized to sufficiently below 920A rest that we don't have issues falling off the left
+ (loglam_padded, flux_padded, sig_padded) = data_utils.pad_loglam_flux(loglam, flux, z_qso, sig=sig)
+ assert(np.all(np.logical_and(np.isfinite(loglam_padded), np.isfinite(flux_padded))))
+
+ # sightline id
+ sightline.id.sightlineid = j[str(ix)]['sl'] if j[str(ix)].has_key('sl') else -1
+
+ sightline.dlas = []
+ for dla_ix in range(0,int(j[str(ix)]['nDLA'])):
+ central_wavelength = (1 + float(j[str(ix)][str(dla_ix)]['zabs'])) * 1215.67
+ col_density = float(j[str(ix)][str(dla_ix)]['NHI'])
+ sightline.dlas.append(Dla(central_wavelength, col_density))
+ sightline.flux = flux_padded
+ sightline.sig = sig_padded
+ sightline.loglam = loglam_padded
+ sightline.z_qso = z_qso
+
+ if not validate_sightline(sightline):
+ print("error validating sightline! bug! exiting")
+ exit()
+
+ return sightline
+
+
+# Reads spectra out of IgmSpec library for DR7 or DR12 (plate & fiber only)
+def read_igmspec(plate, fiber, ra=-1, dec=-1, mjd=-1, table_name='SDSS_DR7'):
+ with open(os.devnull, 'w') as devnull:
+ with warnings.catch_warnings():
+ warnings.simplefilter("ignore")
+ # Hack to avoid specdb spamming us with print statements
+ stdout = sys.stdout
+ sys.stdout = devnull
+
+ from specdb.specdb import IgmSpec # Custom package only used in this optional read function
+
+ # global igmtables, igmsp
+ global cache
+ if table_name not in cache.keys(): # ~cache.has_key(table_name):
+ with lock:
+ #if ~cache.has_key(table_name):
+ if table_name not in cache:
+ cache['igmsp'] = IgmSpec()
+ cache[table_name] = Table(cache['igmsp'].hdf[table_name + "/meta"].value)
+ igmsp = cache['igmsp']
+ mtbl = cache[table_name]
+
+ print("Plate/Fiber: ", plate, fiber)
+ plate = int(plate)
+ fiber = int(fiber)
+
+ # Find plate/fiber
+ if table_name == 'SDSS_DR7':
+ imt = np.where((mtbl['PLATE'] == plate) & (mtbl['FIBER'] == fiber))[0]
+ elif table_name == 'BOSS_DR12':
+ imt = np.where((mtbl['PLATE'] == plate) & (mtbl['FIBERID'] == fiber) & (mtbl['MJD'] == mjd))[0]
+ igmid = mtbl['IGM_ID'][imt]
+ # print "imt, igmid", imt, igmid, type(imt), type(igmid), type(mtbl), np.shape(mtbl), "end-print"
+ assert np.shape(igmid)[0] == 1, "Expected igmid to contain exactly 1 value, found %d" % np.shape(igmid)[0]
+
+ raw_data = {}
+ # spec, meta = igmsp.idb.grab_spec([table_name], igmid)
+ # spec, meta = igmsp.allspec_of_ID(igmid, groups=[table_name])
+ spec, meta = igmsp.spectra_from_ID(igmid, groups=[table_name])
+
+ z_qso = meta['zem_GROUP'][0]
+ flux = np.array(spec[0].flux)
+ sig = np.array(spec[0].sig)
+ loglam = np.log10(np.array(spec[0].wavelength))
+ (loglam_padded, flux_padded, sig_padded) = data_utils.pad_loglam_flux(loglam, flux, z_qso, sig=sig)
+ # Sanity check that we're getting the log10 values
+ assert np.all(loglam < 10), "Loglam values > 10, example: %f" % loglam[0]
+
+ raw_data['flux'] = flux_padded
+ raw_data['sig'] = sig_padded
+ raw_data['loglam'] = loglam_padded
+ raw_data['plate'] = plate
+ raw_data['mjd'] = 0
+ raw_data['fiber'] = fiber
+ raw_data['ra'] = ra
+ raw_data['dec'] = dec
+ assert np.shape(raw_data['flux']) == np.shape(raw_data['loglam'])
+ sys.stdout = stdout
+
+ return raw_data, z_qso
+
+
+
+
+def scan_flux_sample(flux_normalized, loglam, z_qso, central_wavelength, #col_density, plate, mjd, fiber, ra, dec,
+ exclude_positive_samples=False, kernel=400, stride=5,
+ pos_sample_kernel_percent=0.3, testing=None):
+ # Split from rest frame 920A to 1214A (the range of DLAs in DR9 catalog)
+ # pos_sample_kernel_percent is the percent of the kernel where positive samples can be found
+ # e.g. the central wavelength is within this percentage of the center of the kernel window
+
+ # Pre allocate space for generating samples
+ samples_buffer = np.zeros((10000, kernel), dtype=np.float32)
+ offsets_buffer = np.zeros((10000,), dtype=np.float32)
+ buffer_count = 0
+
+ lam, lam_rest, ix_dla_range = get_lam_data(loglam, z_qso, REST_RANGE)
+ ix_from = np.nonzero(ix_dla_range)[0][0]
+ ix_to = np.shape(lam_rest)[0] - np.nonzero(np.flipud(ix_dla_range))[0][0]
+ ix_central = np.nonzero(lam >= central_wavelength)[0][0]
+
+ assert (ix_to > ix_central)
+
+ # Scan across the data set generating negative samples
+ # (skip positive samples where lam is near the central wavelength)
+ for position in range(ix_from, ix_to, stride):
+ if abs(position - ix_central) > kernel * pos_sample_kernel_percent:
+ # Add a negative sample (not within pos_sample_kernel_percent of the central_wavelength)
+ try:
+ samples_buffer[buffer_count, :] = flux_normalized[position - kernel // 2:position - kernel // 2 + kernel]
+ except (IndexError, ValueError): # Running off the red side of the spectrum (I think)
+ # Kludge to pad with data at end of spectrum
+ samples_buffer[buffer_count, :] = flux_normalized[-kernel:]
+ offsets_buffer[buffer_count] = 0
+ buffer_count += 1
+ elif not exclude_positive_samples:
+ # Add a positive sample (is within pos_sample_kernel_percent of the central_wavelength)
+ samples_buffer[buffer_count, :] = flux_normalized[position - kernel // 2:position - kernel // 2 + kernel]
+ offsets_buffer[buffer_count] = position - ix_central
+ buffer_count += 1
+
+ # return samples_buffer[0:buffer_count, :]
+ return samples_buffer[0:buffer_count, :], offsets_buffer[0:buffer_count] #neg_flux, neg_offsets
+
+
+def read_sightline(id):
+ sightline = Sightline(id=id)
+ if isinstance(id, Id_old_DR12, ):
+ data1, z_qso = read_fits_file(id.plate, id.mjd, id.fiber)
+ sightline.id.ra = data1['ra']
+ sightline.id.dec = data1['dec']
+ sightline.flux = data1['flux']
+ sightline.loglam = data1['loglam']
+ sightline.z_qso = z_qso
+ elif isinstance(id, (Id_DR7, Id_DR12)):
+ if isinstance(id, Id_DR7):
+ data1, z_qso = read_igmspec(id.plate, id.fiber, id.ra, id.dec)
+ elif isinstance(id, Id_DR12):
+ data1, z_qso = read_igmspec(id.plate, id.fiber, id.ra, id.dec, mjd=id.mjd, table_name='BOSS_DR12')
+ sightline.id.ra = data1['ra']
+ sightline.id.dec = data1['dec']
+ sightline.flux = data1['flux']
+ sightline.sig = data1['sig']
+ sightline.loglam = data1['loglam']
+ sightline.z_qso = z_qso
+ elif isinstance(id, Id_GENSAMPLES):
+ read_custom_hdf5(sightline)
+ else:
+ raise Exception("%s not implemented yet" % type(id).__name__)
+ return sightline
+
+
+
+
+# Length 1 for non array elements
+def pseudolen(p):
+ return len(p) if hasattr(p,'__len__') else 1
+
+
+# Set save_file parameters to null to return the results and not write them to disk
+def preprocess_gensample_from_single_hdf5_file(kernel=400, stride=3, pos_sample_kernel_percent=0.3, percent_test=0.0,
+ datafile='../data/training_100',
+ save_file="../data/localize",
+ ignore_sightline_markers=None):#"../data/ignore_data_dr5_markers.csv"):
+ hdf5_datafile = datafile + ".hdf5"
+ json_datafile = datafile + ".json"
+ train_save_file = save_file + "_train" if percent_test > 0.0 else save_file
+ test_save_file = save_file + "_test"
+
+ with open(json_datafile, 'r') as fj:
+ n = len(json.load(fj))
+ n_train = int((1-percent_test)*n)
+ ids_train = [Id_GENSAMPLES(i, hdf5_datafile, json_datafile) for i in range(0,n_train)]
+ ids_test = [Id_GENSAMPLES(i, hdf5_datafile, json_datafile) for i in range(n_train,n)]
+
+ markers_csv = np.loadtxt(ignore_sightline_markers, delimiter=',') if ignore_sightline_markers != None else []
+ markers = {}
+ for m in markers_csv:
+ markers[m[0]] = [] if not markers.has_key(m[0]) else markers[m[0]]
+ markers[m[0]].append(Marker(m[1]))
+
+
+ prepare_localization_training_set(kernel, stride, pos_sample_kernel_percent,
+ ids_train, ids_test,
+ train_save_file=train_save_file,
+ test_save_file=test_save_file,
+ ignore_sightline_markers=markers)
+
+
+def preprocess_overlapping_dla_sightlines_from_gensample(kernel=400, stride=3, pos_sample_kernel_percent=0.3, percent_test=0.0,
+ datafile='../data/gensample_hdf5_files/dlas/training*',
+ save_file = "../data/gensample/train_overlapdlas"):
+ hdf5_datafiles = sorted(glob.glob(datafile + ".hdf5"))
+ json_datafiles = sorted(glob.glob(datafile + ".json"))
+
+ ids = []
+ for hdf5_datafile, json_datafile in zip(hdf5_datafiles, json_datafiles):
+ with open(json_datafile, 'r') as f:
+ j = json.load(f)
+ for i in range(5000):
+ n_dlas = j[str(i)]['nDLA']
+ if n_dlas > 1:
+ dlas = np.array([j[str(i)][str(n)]['zabs'] for n in range(n_dlas)]) # array of DLA zabs values
+ dlas = np.reshape(dlas, (len(dlas),1)) # reshape for use in cdist
+ distances = cdist(dlas, dlas, 'cityblock') # get distances between each dla
+
+ if np.min(distances[distances>0.0][:]) < 0.2: # if there's at least one pair
+ ids.append(Id_GENSAMPLES(ix=i, hdf5_datafile=hdf5_datafile, json_datafile=json_datafile))
+
+ prepare_localization_training_set(kernel, stride, pos_sample_kernel_percent,
+ ids, [],
+ train_save_file=save_file,
+ test_save_file=None)
+
+
+
+def validate_sightline(sightline):
+ # check that all DLAs are in range
+ lam, lam_rest, ix_dla_range = get_lam_data(sightline.loglam, sightline.z_qso, REST_RANGE)
+ ix_from = np.nonzero(ix_dla_range)[0][0]
+ ix_to = np.shape(lam_rest)[0] - np.nonzero(np.flipud(ix_dla_range))[0][0]
+ for dla in sightline.dlas:
+ ix_central = np.nonzero(lam >= dla.central_wavelength)[0][0]
+
+ if ix_to > ix_central and ix_from < ix_central:
+ continue
+ else:
+ return False
+ return True
+
+
+def find_nearest(array,value):
+ idx = (np.abs(array-value)).argmin()
+ return array[idx]
+
+
+# Expects a sightline with the prediction object complete, updates the peaks_ixs of the sightline object
+def compute_peaks(sightline):
+ PEAK_THRESH = 0.2 # Threshold to accept a peak
+ PEAK_SEPARATION_THRESH = 0.1 # Peaks must be separated by a valley at least this low
+
+ # Translate relative offsets to histogram
+ offset_to_ix = np.arange(len(sightline.prediction.offsets)) + sightline.prediction.offsets
+ offset_to_ix[offset_to_ix < 0] = 0
+ offset_to_ix[offset_to_ix >= len(sightline.prediction.offsets)] = len(sightline.prediction.offsets)
+ offset_hist, ignore_offset_range = np.histogram(offset_to_ix, bins=np.arange(0,len(sightline.prediction.offsets)+1))
+
+ # Somewhat arbitrary normalization
+ offset_hist = offset_hist / 80.0
+
+ po = np.pad(offset_hist, 2, 'constant', constant_values=np.mean(offset_hist))
+ offset_conv_sum = (po[:-4] + po[1:-3] + po[2:-2] + po[3:-1] + po[4:])
+ smooth_conv_sum = signal.medfilt(offset_conv_sum, 9)
+ # ensures a 0 value at the beginning and end exists to avoid an unnecessarily pathalogical case below
+ smooth_conv_sum[0] = 0
+ smooth_conv_sum[-1] = 0
+
+ peaks_ixs = []
+ while True:
+ peak = np.argmax(smooth_conv_sum) # Returns the first occurace of the max
+ # exit if we're no longer finding valid peaks
+ if smooth_conv_sum[peak] < PEAK_THRESH:
+ break
+ # skip this peak if it's off the end or beginning of the sightline
+ if peak <= 10 or peak >= REST_RANGE[2]-10:
+ smooth_conv_sum[max(0,peak-15):peak+15] = 0
+ continue
+ # move to the middle of the peak if there are multiple equal values
+ ridge = 1
+ while smooth_conv_sum[peak] == smooth_conv_sum[peak+ridge]:
+ ridge += 1
+ peak = peak + ridge//2
+ peaks_ixs.append(peak)
+
+ # clear points around the peak, that is, anything above PEAK_THRESH in order for a new DLA to be identified the peak has to dip below PEAK_THRESH
+ clear_left = smooth_conv_sum[0:peak+1] < PEAK_SEPARATION_THRESH # something like: 1 0 0 1 1 1 0 0 0 0
+ clear_left = np.nonzero(clear_left)[0][-1]+1 # Take the last value and increment 1
+ clear_right = smooth_conv_sum[peak:] < PEAK_SEPARATION_THRESH # something like 0 0 0 0 1 1 1 0 0 1
+ clear_right = np.nonzero(clear_right)[0][0]+peak # Take the first value & add the peak offset
+ smooth_conv_sum[clear_left:clear_right] = 0
+
+ sightline.prediction.peaks_ixs = peaks_ixs
+ sightline.prediction.offset_hist = offset_hist
+ sightline.prediction.offset_conv_sum = offset_conv_sum
+ return sightline
+
+
+
+# Generates a catalog from plate/mjd/fiber from a CSV file
+def process_catalog_dr12(csv_plate_mjd_fiber="../data/dr12_test_set.csv",
+ kernel_size=400, pfiber=None, make_pdf=False,
+ model_checkpoint=default_model,
+ output_dir="../tmp/visuals_dr12"):
+ #csv = np.genfromtxt(csv_plate_mjd_fiber, delimiter=',')
+ csv = Table.read(csv_plate_mjd_fiber)
+ ids = [Id_DR12(c[0],c[1],c[2],c[3],c[4]) for c in csv]
+ if pfiber is not None:
+ plates = np.array([iid.plate for iid in ids])
+ fibers = np.array([iid.fiber for iid in ids])
+ imt = np.where((plates==pfiber[0]) & (fibers==pfiber[1]))[0]
+ if len(imt) != 1:
+ print("Plate/Fiber not in DR12!!")
+ pdb.set_trace()
+ else:
+ ids = [ids[imt[0]]]
+ process_catalog(ids, kernel_size, model_checkpoint, CHUNK_SIZE=500,
+ make_pdf=make_pdf, output_dir=output_dir)
+
+
+def process_catalog_gensample(gensample_files_glob="../data/gensample_hdf5_files/test_mix_23559_10000.hdf5",
+ json_files_glob= "../data/gensample_hdf5_files/test_mix_23559_10000.json",
+ kernel_size=400, debug=False,
+ model_checkpoint=default_model,
+ output_dir="../tmp/visuals_gensample96451/"):
+ """ Generate a DLA catalog from a general sample
+ Usually used for validation
+
+ Parameters
+ ----------
+ gensample_files_glob
+ json_files_glob
+ kernel_size
+ model_checkpoint
+ output_dir
+
+ Returns
+ -------
+
+ """
+ expanded_datafiles = sorted(glob.glob(gensample_files_glob))
+ expanded_json = sorted(glob.glob(json_files_glob))
+ ids = []
+ for hdf5_datafile, json_datafile in zip(expanded_datafiles, expanded_json):
+ with open(json_datafile, 'r') as fj:
+ n = len(json.load(fj))
+ ids.extend([Id_GENSAMPLES(i, hdf5_datafile, json_datafile) for i in range(0, n)])
+ process_catalog(ids, kernel_size, model_checkpoint, output_dir=output_dir, debug=debug)
+
+
+# Process a directory of fits files in format ".*plate-mjd-fiber.*"
+def process_catalog_fits_pmf(fits_dir="../../BOSS_dat_all",
+ model_checkpoint=default_model,
+ output_dir="../tmp/visuals/",
+ kernel_size=400):
+ ids = []
+ for f in glob.glob(fits_dir + "/*.fits"):
+ match = re.match(r'.*-(\d+)-(\d+)-(\d+)\..*', f)
+ if not match:
+ print("Match failed on: ", f)
+ exit()
+ ids.append(Id_DR12(int(match.group(1)),int(match.group(2)),int(match.group(3))))
+
+ process_catalog(ids, kernel_size=kernel_size, model_path=model_checkpoint, output_dir=output_dir)
+
+
+def process_catalog_csv_pmf(csv="../data/boss_catalog.csv",
+ model_checkpoint=default_model,
+ output_dir="../tmp/visuals/",
+ kernel_size=400):
+ pmf = np.loadtxt(csv, dtype=np.int64, delimiter=',')
+ ids = [Id_DR12(row[0],row[1],row[2]) for row in pmf]
+ process_catalog(ids, model_path=model_checkpoint, output_dir=output_dir, kernel_size=kernel_size)
+
+# This function processes a full catalog of sightlines, it's not meant to call directly,
+# for each catalog there will be a helper function dedicated to that catalog type:
+# process_catalog_gensample
+# process_catalog_dr12
+# process_catalog_dr5
+
+def process_catalog(ids, kernel_size, model_path="", debug=False,
+ CHUNK_SIZE=1000, output_dir="../tmp/visuals/",
+ data=None, data_read_sightline=None,
+ make_pdf=False, num_cores=None, verbose=False):
+ from dla_cnn.plots import generate_pdf
+ from dla_cnn.absorption import add_abs_to_sightline
+ if num_cores is None:
+ num_cores = multiprocessing.cpu_count() - 1
+ # num_cores = 24
+ # p = None
+ p = Pool(num_cores) # a thread pool we'll reuse
+ if debug:
+ num_cores = 1
+ p = None
+ sightlines_processed_count = 0
+
+ sightline_results = [] # array of map objects containing the classification, and an array of DLAs
+
+ ids.sort(key=methodcaller('id_string'))
+
+ # We'll handle the full process in batches so as to not exceed memory constraints
+ done = False
+ for sss,ids_batch in enumerate(np.array_split(ids, np.arange(CHUNK_SIZE,len(ids),CHUNK_SIZE))):
+ num_sightlines = len(ids_batch)
+ #if sss < 46: # debugging
+ # sightlines_processed_count += num_sightlines
+ # continue
+ if done:
+ break
+ # # Workaround for segfaults occuring in matplotlib, kill multiprocess pool every iteration
+ # if p is not None:
+ # p.close()
+ # p.join()
+ # time.sleep(5)
+
+ report_timer = timeit.default_timer()
+
+ # Batch read files
+ process_timer = timeit.default_timer()
+ print("Reading {:d} sightlines with {:d} cores".format(num_sightlines, num_cores))
+ if debug:
+ sightlines_batch = []
+ for iid in ids_batch:
+ if data is None:
+ sightlines_batch.append(read_sightline(iid)) # This approach will phase out
+ else:
+ sightlines_batch.append(data_read_sightline(iid))
+ else:
+ if data is None:
+ sightlines_batch = p.map(read_sightline, ids_batch) # This approach will phase out
+ else:
+ sightlines_batch = p.map(data_read_sightline, ids_batch)
+ print("Spectrum/Fits read done in {:0.1f}".format(timeit.default_timer() - process_timer))
+
+ ##################################################################
+ # Process model
+ ##################################################################
+ print("Model predictions begin")
+ fluxes = np.vstack([scan_flux_sample(s.flux, s.loglam, s.z_qso, -1, stride=1)[0] for s in sightlines_batch])
+ #fluxes = np.vstack([scan_flux_sample(s.flux, s.loglam, s.z_qso, -1, stride=1, testing=s)[0] for s in sightlines_batch])
+ with open(model_path+"_hyperparams.json",'r') as f:
+ hyperparameters = json.load(f)
+ loc_pred, loc_conf, offsets, density_data_flat = predictions_ann_c2(hyperparameters, fluxes, model_path)
+
+ # Add results from predictions and peaks_data to data model for easier processing later.
+ for sl, lp, lc, of, dd in zip(sightlines_batch,
+ np.split(loc_pred, num_sightlines),
+ np.split(loc_conf, num_sightlines),
+ np.split(offsets, num_sightlines),
+ np.split(density_data_flat, num_sightlines)):
+ sl.prediction = Prediction(loc_pred=lp, loc_conf=lc, offsets=of, density_data=dd)
+
+ with Timer(disp="Compute peaks"):
+ sightlines_batch = map(compute_peaks, sightlines_batch)
+ sightlines_batch = sorted(sightlines_batch, key=lambda s: s.id.id_string())
+
+ ##################################################################
+ # Process output for each sightline
+ ##################################################################
+ assert num_sightlines * REST_RANGE[2] == density_data_flat.shape[0]
+ for sightline in sightlines_batch:
+ smoothed_sample = sightline.prediction.smoothed_loc_conf()
+
+ # Add absorbers
+ add_abs_to_sightline(sightline)
+
+ # Store classification level data in results
+ sightline_json = ({
+ 'id': sightline.id.id_string(),
+ 'ra': float(sightline.id.ra),
+ 'dec': float(sightline.id.dec),
+ 'z_qso': float(sightline.z_qso),
+ 'num_dlas': len(sightline.dlas),
+ 'num_subdlas': len(sightline.subdlas),
+ 'num_lyb': len(sightline.lybs),
+ 'dlas': sightline.dlas,
+ 'subdlas': sightline.subdlas,
+ 'lyb': sightline.lybs
+ })
+
+ sightline_results.append(sightline_json)
+
+ ##################################################################
+ # Process pdfs for each sightline
+ ##################################################################
+ if not os.path.exists(output_dir):
+ os.makedirs(output_dir)
+
+ # print "Processing PDFs"
+ if make_pdf:
+ if debug:
+ for sightline in sightlines_batch:
+ generate_pdf(sightline, output_dir)
+ else:
+ p.starmap(generate_pdf, zip(sightlines_batch, itertools.repeat(output_dir))) # TODO
+
+ print("Processed {:d} sightlines for reporting on {:d} cores in {:0.2f}s".format(
+ num_sightlines, num_cores, timeit.default_timer() - report_timer))
+
+ runtime = timeit.default_timer() - process_timer
+ print("Processed {:d} of {:d} in {:0.0f}s - {:0.2f}s per sample".format(
+ sightlines_processed_count + num_sightlines, len(ids), runtime, runtime/num_sightlines))
+ sightlines_processed_count += num_sightlines
+ if debug:
+ done = True
+
+
+ # Write JSON string
+ with open(output_dir + "/predictions.json", 'w') as outfile:
+ json.dump(sightline_results, outfile, indent=4)
+
+# Add S/N after the fact
+def add_s2n_after(ids, json_file, debug=False, CHUNK_SIZE=1000):
+ from linetools import utils as ltu
+ from dla_cnn.absorption import get_s2n_for_absorbers # Needs to be here
+
+ # Load json file
+ predictions = ltu.loadjson(json_file)
+ jids = [ii['id'] for ii in predictions]
+
+ num_cores = multiprocessing.cpu_count() - 2
+ p = Pool(num_cores) # a thread pool we'll reuse
+ sightlines_processed_count = 0
+
+ # IDs
+ ids.sort(key=methodcaller('id_string'))
+ for sss,ids_batch in enumerate(np.array_split(ids, np.arange(CHUNK_SIZE,len(ids),CHUNK_SIZE))):
+ num_sightlines = len(ids_batch)
+ # Read batch
+ process_timer = timeit.default_timer()
+ print("Reading {:d} sightlines with {:d} cores".format(num_sightlines, num_cores))
+ sightlines_batch = p.map(read_sightline, ids_batch)
+ print("Done reading")
+
+ for sightline in sightlines_batch:
+ jidx = jids.index(sightline.id.id_string())
+ # Any absorbers?
+ if (predictions[jidx]['num_dlas'])+ (predictions[jidx]['num_subdlas']) == 0:
+ continue
+ lam, lam_rest, ix_dla_range = get_lam_data(sightline.loglam, sightline.z_qso, REST_RANGE)
+ # DLAs, subDLAs
+ get_s2n_for_absorbers(sightline, lam, predictions[jidx]['dlas'])
+ get_s2n_for_absorbers(sightline, lam, predictions[jidx]['subdlas'])
+
+ runtime = timeit.default_timer() - process_timer
+ print("Processed {:d} of {:d} in {:0.0f}s - {:0.2f}s per sample".format(
+ sightlines_processed_count + num_sightlines, len(ids), runtime, runtime/num_sightlines))
+ sightlines_processed_count += num_sightlines
+ # Write
+ print("About to over-write your JSON file. Continue at your own risk!")
+ # Return new predictions
+ return predictions
+
+
+
+
+
diff --git a/data/preprocess/dla_cnn/data_model/Data.py b/data/preprocess/dla_cnn/data_model/Data.py
new file mode 100644
index 0000000..d3cc8f5
--- /dev/null
+++ b/data/preprocess/dla_cnn/data_model/Data.py
@@ -0,0 +1,29 @@
+""" Parent class for a set of Data, training or real"""
+
+from abc import ABCMeta
+
+from dla_cnn.data_model import Id
+from dla_cnn.data_model import Sightline
+
+
+class Data(object):
+ __metaclass__ = ABCMeta
+
+ def __init__(self):
+ self.catalog_file = None
+ self.catalog = None
+
+ # Required methods -- All of these are dummy
+ def gen_ID(self):
+ return Id.Id()
+
+ def load_data(self, id):
+ raw_data = {}
+ z_qso = 0.
+ return raw_data, z_qso
+
+ def load_catalog(self):
+ self.cat = None
+
+ def load_sightline(self, id):
+ return Sightline.Sightline(id)
diff --git a/data/preprocess/dla_cnn/data_model/Dla.py b/data/preprocess/dla_cnn/data_model/Dla.py
new file mode 100644
index 0000000..1da939b
--- /dev/null
+++ b/data/preprocess/dla_cnn/data_model/Dla.py
@@ -0,0 +1,8 @@
+""" Simple object to hold DLAs"""
+
+class Dla():
+ def __init__(self, central_wavelength, col_density=0, id = None):
+ self.central_wavelength = central_wavelength # observed wavelength (1+zDLA)*1215.6701
+ self.col_density = col_density # log10 column density
+ self.id = id # str, the id of DLA
+
diff --git a/data/preprocess/dla_cnn/data_model/Sightline.py b/data/preprocess/dla_cnn/data_model/Sightline.py
new file mode 100644
index 0000000..062d25b
--- /dev/null
+++ b/data/preprocess/dla_cnn/data_model/Sightline.py
@@ -0,0 +1,134 @@
+import numpy as np
+
+
+class Sightline(object):
+
+ def __init__(self, id, ra=None,dec=None,dlas=None, flux=None, loglam=None,error=None, z_qso=None, split_point_br = None, split_point_rz = None,s2n = None,normalize = False):
+ """
+
+ Args:
+ id (int): Index identifier for the sightline
+ dlas (list): List of DLAs
+ flux (np.ndarray):
+ wavelength (np.ndarray):
+ observed wavelength values
+ z_qso (float):
+ Quasar redshift
+ normalize(bool):
+ if True, the Sightlne has been normalized, default not.
+ split_point_br(int):
+ the split index of b channel and r channel for desi spectra
+ split_point_rz(int):
+ the split index of r channel and z channel for desi spectra
+ s2n(float):
+ the s/n of lymann forest part(about 1070 -1170 A in rest frame (avoid dlas +- 3000 km/s))
+
+ """
+ self.ra=ra
+ self.dec=dec
+ self.flux = flux
+ self.loglam = loglam
+ self.id = id
+ self.dlas = dlas
+ self.z_qso = z_qso
+ self.data_makers = []
+ self.error = error # error = 1./ np.sqrt(ivar)
+ self.normalize = normalize
+ self.split_point_br = split_point_br
+ self.split_point_rz = split_point_rz
+ self.s2n = s2n
+
+ # Attributes
+ self.prediction = None
+ self.classification = None
+ self.offsets = None
+ self.column_density = None
+
+
+ # Returns the data in the legacy data1, qso_z format for code that hasn't been updated to the new format yet
+ def get_legacy_data1_format(self):
+ raw_data = {}
+ raw_data['flux'] = self.flux
+ raw_data['loglam'] = self.loglam
+ raw_data['plate'] = self.id.plate if hasattr(self.id, 'plate') else 0
+ raw_data['mjd'] = self.id.mjd if hasattr(self.id, 'mjd') else 0
+ raw_data['fiber'] = self.id.fiber if hasattr(self.id, 'fiber') else 0
+ raw_data['ra'] = self.id.ra if hasattr(self.id, 'ra') else 0
+ raw_data['dec'] = self.id.dec if hasattr(self.id, 'dec') else 0
+ return raw_data, self.z_qso
+
+
+ # Clears all fields of the DLA
+ def clear(self):
+ self.flux = None
+ self.loglam = None
+ self.id = None
+ self.dlas = None
+ self.z_qso = None
+ self.prediction = None
+ self.data_markers = []
+
+
+ def is_lyb(self, peakix):
+ """
+ Returns true if the given peakix (from peaks_ixs) is the ly-b of another DLA in the set peaks_ixs in prediction
+ :param peakix:
+ :return: boolean
+ """
+ assert self.prediction is not None and peakix in self.prediction.peaks_ixs
+
+ lambda_higher = (10**self.loglam[peakix]) / (1025.722/1215.67)
+
+ # An array of how close each peak is to beign the ly-b of peakix in spectrum reference frame
+ peak_difference_spectrum = np.abs(10**self.loglam[self.prediction.peaks_ixs] - lambda_higher)
+ nearest_peak_ix = np.argmin(peak_difference_spectrum)
+
+ # get the column density of the identfied nearest peak
+ _, potential_lya_nhi, _, _ = \
+ self.prediction.get_coldensity_for_peak(self.prediction.peaks_ixs[nearest_peak_ix])
+ _, potential_lyb_nhi, _, _ = \
+ self.prediction.get_coldensity_for_peak(peakix)
+
+ # Validations: check that the nearest peak is close enough to match
+ # sanity check that the LyB is at least 0.3 less than the DLA
+ is_nearest_peak_within_range = peak_difference_spectrum[nearest_peak_ix] <= 15
+ is_nearest_peak_larger_coldensity = potential_lyb_nhi < potential_lya_nhi - 0.3
+
+ return is_nearest_peak_within_range and is_nearest_peak_larger_coldensity
+
+
+ def get_lyb_index(self, peakix):
+ """
+ Returns the index location of the Ly-B absorption for a given peak index value
+ :param peakix:
+ :return: index location of Ly-B
+ """
+ spectrum_higher = 10**self.loglam[peakix]
+ spectrum_lambda_lower = spectrum_higher * (1025.722 / 1215.67)
+ log_lambda_lower = np.log10(spectrum_lambda_lower)
+ ix_lambda_lower = (np.abs(self.loglam - log_lambda_lower)).argmin()
+ return ix_lambda_lower
+
+ def process(self, model_path):
+ """ The following should follow the algorithm in process_catalog
+ :param model_path:
+ :return:
+ """
+ import json
+ from dla_cnn.data_loader import scan_flux_sample
+ from dla_cnn.localize_model import predictions_ann as predictions_ann_c2
+ from dla_cnn.data_loader import compute_peaks, get_lam_data
+ #from dla_cnn.data_loader import add_abs_to_sightline
+ from dla_cnn.absorption import add_abs_to_sightline
+ from dla_cnn.data_model.Prediction import Prediction
+ # Fluxes
+ fluxes = scan_flux_sample(self.flux, self.loglam, self.z_qso, -1, stride=1)[0]
+ # Model
+ with open(model_path+"_hyperparams.json",'r') as f:
+ hyperparameters = json.load(f)
+ loc_pred, loc_conf, offsets, density_data_flat = predictions_ann_c2(hyperparameters, fluxes, model_path)
+ self.prediction = Prediction(loc_pred=loc_pred, loc_conf=loc_conf, offsets=offsets, density_data=density_data_flat)
+ # Peaks
+ _ = compute_peaks(self)
+ # Absorbers?
+ add_abs_to_sightline(self)
diff --git a/data/preprocess/dla_cnn/data_model/__init__.py b/data/preprocess/dla_cnn/data_model/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/data/preprocess/dla_cnn/data_model/data_utils.py b/data/preprocess/dla_cnn/data_model/data_utils.py
new file mode 100644
index 0000000..7327e19
--- /dev/null
+++ b/data/preprocess/dla_cnn/data_model/data_utils.py
@@ -0,0 +1,23 @@
+""" Utilities for data """
+
+import numpy as np
+import math
+
+from dla_cnn import defs
+
+def pad_loglam_flux(loglam, flux, z_qso, kernel=1800, sig=None):
+ # kernel = 1800 # Overriding left padding to increase it
+ assert np.shape(loglam) == np.shape(flux)
+ pad_loglam_upper = loglam[0] - 0.0001
+ pad_loglam_lower = (math.floor(math.log10(defs.REST_RANGE[0] * (1 + z_qso)) * 10000) - kernel / 2) / 10000
+ pad_loglam = np.linspace(pad_loglam_lower, pad_loglam_upper, max(0, int((pad_loglam_upper - pad_loglam_lower + 0.0001) * 10000)), dtype=np.float32)
+ pad_value = np.mean(flux[0:50])
+ flux_padded = np.hstack((pad_loglam*0+pad_value, flux))
+ loglam_padded = np.hstack((pad_loglam, loglam))
+ assert (10**loglam_padded[0])/(1+z_qso) <= defs.REST_RANGE[0]
+ # Error array
+ if sig is not None:
+ sig_padded = np.hstack((pad_loglam*0+pad_value, sig))
+ return loglam_padded, flux_padded, sig_padded
+ else:
+ return loglam_padded, flux_padded
diff --git a/data/preprocess/dla_cnn/defs.py b/data/preprocess/dla_cnn/defs.py
new file mode 100644
index 0000000..7546df2
--- /dev/null
+++ b/data/preprocess/dla_cnn/defs.py
@@ -0,0 +1,3 @@
+""" Definitions used throughout the package"""
+
+REST_RANGE = [900, 1346, 1748]
diff --git a/data/preprocess/dla_cnn/desi/DesiMock.py b/data/preprocess/dla_cnn/desi/DesiMock.py
new file mode 100644
index 0000000..6cca7cc
--- /dev/null
+++ b/data/preprocess/dla_cnn/desi/DesiMock.py
@@ -0,0 +1,147 @@
+from astropy.io import fits
+import numpy as np
+from dla_cnn.data_model.Sightline import Sightline
+from dla_cnn.data_model.Dla import Dla
+from dla_cnn.desi import preprocess
+from .defs import best_v
+
+
+class DesiMock:
+ """
+ a class to load all spectrum from a mock DESI data v9 fits file, each file contains about 1186 spectrum.
+ --------------------------------------------------------------------------------------------------
+ attributes:
+ wavelength: array-like, the wavelength of all spectrum (all spectrum share same wavelength array)
+ data: dict, using each spectra's id as its key and a dict of all data we need of this spectra as its value
+ its format like spectra_id: {'FLUX':flux,'ERROR':error,'z_qso':z_qso, 'RA':ra, 'DEC':dec, 'DLAS':a tuple of Dla objects containing the information of dla}
+ split_point_br: int,the length of the flux_b,the split point of b channel data and r channel data
+ split_point_rz: int,the length of the flux_b and flux_r, the split point of r channel data and z channel data
+ data_size: int,the point number of all data points of wavelength and flux
+ """
+
+ def __init__(self, wavelength = None, data = {}, split_point_br = None, split_point_rz = None, data_size = None):
+ self.wavelength = wavelength
+ self.data = data
+ self.split_point_br = split_point_br
+ self.split_point_rz = split_point_rz
+ self.data_size = data_size
+
+ def read_fits_file(self, spec_path, truth_path, zbest_path):
+ """
+ read Desi Mock spectrum from a fits file, load all spectrum as a DesiMock object
+ ------------------------------------------------------------------------------------------------
+ parameters:
+
+ spec_path: str, spectrum file path
+ truth_path: str, truth file path
+ zbest_path: str, zbest file path
+ ------------------------------------------------------------------------------------------------
+ return:
+
+ self.wavelength,self.data(contained all information we need),self.split_point_br,self.split_point_rz,self.data_size
+ """
+ spec = fits.open(spec_path)
+ truth = fits.open(truth_path)
+ zbest = fits.open(zbest_path)
+
+ # spec[2].data ,spec[7].data and spec[12].data are the wavelength data for the b, r and z cameras.
+ self.wavelength = np.hstack((spec[2].data.copy(), spec[7].data.copy(), spec[12].data.copy()))
+ self.data_size = len(self.wavelength)
+
+ dlas_data = truth[3].data[truth[3].data.copy()['NHI']>19.3]
+ spec_dlas = {}
+ # item[2] is the spec_id, item[3] is the dla_id, and item[0] is NHI, item[1] is z_qso
+ for item in dlas_data:
+ if item[2] not in spec_dlas:
+ spec_dlas[item[2]] = [Dla((item[1]+1)*1215.6701, item[0], '00'+str(item[3]-item[2]*1000))]
+ else:
+ spec_dlas[item[2]].append(Dla((item[1]+1)*1215.6701, item[0], '00'+str(item[3]-item[2]*1000)))
+
+ test = np.array([True if item in dlas_data['TARGETID'] else False for item in spec[1].data['TARGETID'].copy()])
+ for item in spec[1].data['TARGETID'].copy()[~test]:
+ spec_dlas[item] = []
+
+ # read data from the fits file above, one can directly get those varibles meanings by their names.
+ spec_id = spec[1].data['TARGETID'].copy()
+ flux_b = spec[3].data.copy()
+ flux_r = spec[8].data.copy()
+ flux_z = spec[13].data.copy()
+ flux = np.hstack((flux_b,flux_r,flux_z))
+ ivar_b = spec[4].data.copy()
+ ivar_r = spec[9].data.copy()
+ ivar_z = spec[14].data.copy()
+ error = 1./np.sqrt(np.hstack((ivar_b,ivar_r,ivar_z)))
+ self.split_point_br = flux_b.shape[1]
+ self.split_point_rz = flux_b.shape[1]+flux_z.shape[1]
+ z_qso = zbest[1].data['Z'].copy()
+ ra = spec[1].data['TARGET_RA'].copy()
+ dec = spec[1].data['TARGET_DEC'].copy()
+
+ self.data = {spec_id[i]:{'FLUX':flux[i],'ERROR': error[i], 'z_qso':z_qso[i] , 'RA': ra[i], 'DEC':dec[i], 'DLAS':spec_dlas[spec_id[i]]} for i in range(len(spec_id))}
+
+ def get_sightline(self, id, camera = 'all', rebin=False, normalize=False):
+ """
+ using id(int) as index to retrive each spectra in DesiMock's dataset, return a Sightline object.
+ ---------------------------------------------------------------------------------------------------
+ parameters:
+ id: spectra's id , a unique number for each spectra.
+ camera: str, 'b' : Load up the wavelength and data for the blue camera., 'r': Load up the wavelength and data for the r camera,
+ 'z' : Load up the wavelength and data for the z camera, 'all': Load up the wavelength and data for all cameras.
+ rebin: bool, if True rebin the spectra to the best dlambda/lambda, default False,
+ normalize: bool, if True normalize the spectra, using the slice of flux from wavelength ~1070 to 1170, default False.
+ ---------------------------------------------------------------------------------------------------
+ return:
+ sightline: dla_cnn.data_model.Sightline.Sightline object
+ """
+ assert camera in ['all', 'r', 'z', 'b'], "No such camera! The parameter 'camera' must be in ['all', 'r', 'b', 'z']"
+ sightline = Sightline(id)
+
+ # this inside method can get the data(wavelength, flux, error) from the start_point(int) to end_point(int)
+ def get_data(start_point=0, end_point=self.data_size):
+ """
+
+ Parameters
+ ----------
+ start_point: int, the start index of the slice of the data(wavelength, flux, error), default 0
+ end_point: int, the end index of the slice of the data(wavelength, flux, error), default the length of the data array
+
+ Returns
+ -------
+
+ """
+ sightline.flux = self.data[id]['FLUX'][start_point:end_point]
+ sightline.error = self.data[id]['ERROR'][start_point:end_point]
+ sightline.z_qso = self.data[id]['z_qso']
+ sightline.ra = self.data[id]['RA']
+ sightline.dec = self.data[id]['DEC']
+ sightline.dlas = self.data[id]['DLAS']
+ sightline.loglam = np.log10(self.wavelength[start_point:end_point])
+ sightline.split_point_br = self.split_point_br
+ sightline.split_point_rz = self.split_point_rz
+ sightline.s2n = preprocess.estimate_s2n(sightline)
+
+ # invoke the inside function above to select different camera's data.
+ if camera == 'all':
+ get_data()
+ #this part is to deal with the overlap between different cameras.
+ if rebin:
+ sortedindex = np.argsort(sightline.loglam)
+ sightline.flux = sightline.flux[sortedindex]
+ sightline.loglam = sightline.loglam[sortedindex]
+ sightline.error = sightline.error[sortedindex]
+ elif camera == 'b':
+ get_data(end_point = self.split_point_br)
+ elif camera == 'r':
+ get_data(start_point= self.split_point_br, end_point= self.split_point_rz)
+ else:
+ get_data(start_point=self.split_point_rz)
+
+ # if the parameter rebin is True, then rebin this sightline using rebin method in preprocess.py and the v we determined previously(defs.py/best_v) .
+ if rebin:
+ preprocess.rebin(sightline, best_v[camera])
+ #if the parameter normalize is True, then normalize this sightline using the method in preprocess.py
+ if normalize:
+ preprocess.normalize(sightline, self.wavelength, self.data[id]['FLUX'])
+
+ # Return the Sightline object
+ return sightline
diff --git a/data/preprocess/dla_cnn/desi/__init__.py b/data/preprocess/dla_cnn/desi/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/data/preprocess/dla_cnn/desi/defs.py b/data/preprocess/dla_cnn/desi/defs.py
new file mode 100644
index 0000000..453c126
--- /dev/null
+++ b/data/preprocess/dla_cnn/desi/defs.py
@@ -0,0 +1,6 @@
+""" Definitions used throughout the package"""
+
+# Need to fill in TBD with the correct value
+REST_RANGE = [900, 1346, "TBD"]
+kernel = 400 # SDSS value -- UPDATE!!
+best_v = {'b': 62996, 'r': 44859, 'z': 34720}#the best value of rebining for each channel, its unit is m*s^(-1).
diff --git a/data/preprocess/dla_cnn/desi/desi_mocks.py b/data/preprocess/dla_cnn/desi/desi_mocks.py
new file mode 100644
index 0000000..7b8ac77
--- /dev/null
+++ b/data/preprocess/dla_cnn/desi/desi_mocks.py
@@ -0,0 +1,240 @@
+""" Classes and methods for DESI mocks. """
+
+from pkg_resources import resource_filename
+import pdb
+
+import numpy as np
+
+from specdb.specdb import SpecDB
+from specdb import cat_utils
+
+from dla_cnn.data_model import Data
+from dla_cnn.data_model import Id
+from dla_cnn.data_model import Sightline
+from dla_cnn.data_model import data_utils
+
+cache = {} # Cache for multiprocessing
+
+class DESIMockv9(Data.Data):
+
+ def __init__(self, mock_catalog_file):
+ super(DESIMockv9,self).__init__()
+
+ # Catalog
+ self.load_catalog(mock_catalog_file)
+
+class DESIMockSpecDB(Data.Data):
+ """
+ Data class for DESI Mocks using specDB files
+
+ Deprecated
+ """
+
+ def __init__(self, db_file):
+ super(DESIMockSpecDB,self).__init__()
+
+ # Load IGMSpec which holds our data
+ self.specdb = SpecDB(db_file=db_file)
+
+ # For multi-processing
+ global cache
+ cache['specdb'] = self.specdb
+
+ # Catalog
+ self.load_catalog()
+
+ def gen_ID(self, plate, fiber, group_id, ra=None, dec=None, group=None):
+ """
+
+ Args:
+ plate: int
+ fiber: int
+ group_id: int
+ ra: float, optional
+ dec: float, optional
+
+ Returns:
+ Id: Id object
+
+ """
+ return Id_DESI(plate, fiber, ra=ra, dec=dec, group_id=group_id, group=group)
+
+ def load_catalog(self):
+ """
+ Load up the source catalog
+
+ Uses self.catalog_file
+
+ Args:
+ csv: bool, optional
+
+ Returns:
+
+ """
+ # Load it
+ self.catalog = self.specdb.cat
+
+ def load_IDs(self, pfiber=None):
+ """
+ Load up a list of Id objects from the catalog
+
+ Args:
+ pfiber: tuple, optional
+ Plate, fiber to restrict IDs to a single sightline
+
+ Returns:
+ ids : list of Id objects
+
+ """
+ ids = []
+ # Loop on groups
+ for group in self.specdb.groups:
+ flag = self.specdb.group_dict[group]
+ in_group = np.where((2**self.catalog['flag_group'].data & (2**flag)) > 0)[0]
+ # Grab group ids
+ meta = self.specdb[group].meta
+ midx = cat_utils.match_ids(self.catalog['DESI_ID'][in_group],
+ meta['DESI_ID'])
+ sub_meta = meta[midx]
+ # Load em
+ sub_ids = [self.gen_ID(c['PLATE'],c['FIBERID'],c['GROUP_ID'], ra=c['RA_GROUP'],
+ dec=c['DEC_GROUP'], group=group) for c in sub_meta]
+ # Save im
+ ids += sub_ids
+ # Single ID using plate/fiber?
+ if pfiber is not None:
+ plates = np.array([iid.plate for iid in ids])
+ fibers = np.array([iid.fiber for iid in ids])
+ imt = np.where((plates==pfiber[0]) & (fibers==pfiber[1]))[0]
+ if len(imt) != 1:
+ print("Plate/Fiber not in DR7!!")
+ pdb.set_trace()
+ else:
+ ids = [ids[imt[0]]]
+ return ids
+
+
+def load_data(id):
+ """
+ Load the spectrum for a single object
+
+ Needs to be a stand-alone method for multi-processing
+
+ Args:
+ id:
+
+ Returns:
+ raw_data: dict
+ Contains the spectral info
+ z_qso: float
+ Quasar redshift
+
+ """
+ global cache
+ data, meta = cache['specdb'][id.group].grab_specmeta(id.group_id, use_XSpec=False)
+ z_qso = meta['zem_GROUP'][0]
+
+ flux = data['flux'].flatten() #np.array(spec[0].flux)
+ sig = data['sig'].flatten() # np.array(spec[0].sig)
+ loglam = np.log10(data['wave'].flatten())
+
+ gdi = np.isfinite(loglam)
+
+ (loglam_padded, flux_padded, sig_padded) = data_utils.pad_loglam_flux(
+ loglam[gdi], flux[gdi], z_qso, sig=sig[gdi]) # Sanity check that we're getting the log10 values
+ assert np.all(loglam < 10), "Loglam values > 10, example: %f" % loglam[0]
+
+ raw_data = {}
+ raw_data['flux'] = flux_padded
+ raw_data['sig'] = sig_padded
+ raw_data['loglam'] = loglam_padded
+ raw_data['plate'] = id.plate
+ raw_data['mjd'] = 0
+ raw_data['fiber'] = id.fiber
+ raw_data['ra'] = id.ra
+ raw_data['dec'] = id.dec
+ assert np.shape(raw_data['flux']) == np.shape(raw_data['loglam'])
+ #sys.stdout = stdout
+ # Return
+ return raw_data, z_qso
+
+
+def read_sightline(id):
+ """
+ Instantiate a Sightline object for a given Id
+ Fills in the spectrum with a call to load_data()
+
+ Needs to be a stand-alone method for multi-processing
+
+ Args:
+ id: Id object
+
+ Returns:
+ sightline: Sightline object
+
+ """
+ sightline = Sightline.Sightline(id=id)
+ # Data
+ data1, z_qso = load_data(id)
+ # Fill
+ sightline.id.ra = data1['ra']
+ sightline.id.dec = data1['dec']
+ sightline.flux = data1['flux']
+ sightline.sig = data1['sig']
+ sightline.loglam = data1['loglam']
+ sightline.z_qso = z_qso
+ # Giddy up
+ return sightline
+
+class Id_DESI(Id.Id):
+ """
+ SDSS-DR7 specific Id object
+ """
+ def __init__(self, plate, fiber, ra=0, dec=0, group_id=-1, group=None):
+ super(Id_DESI,self).__init__()
+ self.plate = plate
+ self.fiber = fiber
+ self.ra = ra
+ self.dec = dec
+ self.group_id = group_id
+ self.group = group
+
+ def id_string(self):
+ return "%05d-%05d" % (self.plate, self.fiber)
+
+
+def process_catalog_desi_mock(kernel_size=400, pfiber=None, make_pdf=False,
+ model_checkpoint=None, #default_model,
+ output_dir="../tmp/visuals_dr7",
+ debug=False):
+ """ Runs a SDSS DR7 DLA search using the SDSSDR7 data object
+
+ Parameters
+ ----------
+ kernel_size
+ pfiber: tuple, optional
+ plate, fiber (int)
+ Restrict the run to a single sightline
+ make_pdf: bool, optional
+ Generate PDFs
+ model_checkpoint
+ output_dir
+
+ Returns
+ -------
+ Nothing
+ Code generates predictions.json which contains
+ the information on DLAs in each processed sightline
+
+ """
+ from dla_cnn.data_loader import process_catalog
+ # Hard-coding for now
+ specdb_file = '/home/xavier/Projects/DESI_SANDBOX/docs/nb/z2.8_specdb_test.hdf'
+ # Instantiate the data object
+ data = DESIMock(specdb_file)
+ # Load the IDs
+ ids = data.load_IDs(pfiber=pfiber)
+ # Run
+ process_catalog(ids, kernel_size, model_checkpoint, make_pdf=make_pdf,
+ CHUNK_SIZE=500, output_dir=output_dir, data=data, debug=debug,
+ data_read_sightline=read_sightline)
diff --git a/data/preprocess/dla_cnn/desi/io.py b/data/preprocess/dla_cnn/desi/io.py
new file mode 100644
index 0000000..32510ad
--- /dev/null
+++ b/data/preprocess/dla_cnn/desi/io.py
@@ -0,0 +1,29 @@
+""" Methods for I/O for DESI project"""
+
+from dla_cnn.data_model import Sightline
+
+
+def read_mock_spectrum(id, dla_catalog):
+ sightline = Sightline(id=id)
+
+ """ Rest of this needs building """
+
+
+def read_sightline(id):
+ """
+ Read Sightline from hard drive
+
+ May need separate methods for Mocks vs. real data
+
+ Parameters
+ ----------
+ id: int
+
+ Returns
+ -------
+ dla_cnn.data_model.Sightline
+
+ """
+ sightline = Sightline(id=id)
+
+ """ Rest of this needs building """
diff --git a/data/preprocess/dla_cnn/desi/model.py b/data/preprocess/dla_cnn/desi/model.py
new file mode 100644
index 0000000..7ba99d6
--- /dev/null
+++ b/data/preprocess/dla_cnn/desi/model.py
@@ -0,0 +1,221 @@
+""" TensorFlow Models for DLA finder"""
+
+"""
+0. Convert the model to TF 2.1
+"""
+
+
+def weight_variable(shape):
+ initial = tf.truncated_normal(shape, stddev=0.1)
+ return tf.Variable(initial)
+
+
+def bias_variable(shape):
+ initial = tf.constant(0.0, shape=shape)
+ return tf.Variable(initial)
+
+
+def conv1d(x, W, s):
+ return tf.nn.conv2d(x, W, strides=s, padding='SAME')
+
+
+def pooling_layer_parameterized(pool_method, h_conv, pool_kernel, pool_stride):
+ if pool_method == 1:
+ return tf.nn.max_pool(h_conv, ksize=[1, pool_kernel, 1, 1], strides=[1, pool_stride, 1, 1], padding='SAME')
+ elif pool_method == 2:
+ return tf.nn.avg_pool(h_conv, ksize=[1, pool_kernel, 1, 1], strides=[1, pool_stride, 1, 1], padding='SAME')
+
+def variable_summaries(var, name, collection):
+ """Attach a lot of summaries to a Tensor."""
+ with tf.name_scope('summaries') as r:
+ mean = tf.reduce_mean(var)
+ tf.add_to_collection(collection, tf.summary.scalar('mean/' + name, mean))
+ with tf.name_scope('stddev'):
+ stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
+ tf.add_to_collection(collection, tf.summary.scalar('stddev/' + name, stddev))
+ tf.add_to_collection(collection, tf.summary.scalar('max/' + name, tf.reduce_max(var)))
+ tf.add_to_collection(collection, tf.summary.scalar('min/' + name, tf.reduce_min(var)))
+ tf.add_to_collection(collection, tf.summary.histogram(name, var))
+
+def build_model(hyperparameters):
+ """
+ This is Model_v5 from SDSS
+
+ Parameters
+ ----------
+ hyperparameters
+
+ Returns
+ -------
+
+ """
+ learning_rate = hyperparameters['learning_rate']
+ batch_size = hyperparameters['batch_size']
+ l2_regularization_penalty = hyperparameters['l2_regularization_penalty']
+ fc1_n_neurons = hyperparameters['fc1_n_neurons']
+ fc2_1_n_neurons = hyperparameters['fc2_1_n_neurons']
+ fc2_2_n_neurons = hyperparameters['fc2_2_n_neurons']
+ fc2_3_n_neurons = hyperparameters['fc2_3_n_neurons']
+ conv1_kernel = hyperparameters['conv1_kernel']
+ conv2_kernel = hyperparameters['conv2_kernel']
+ conv3_kernel = hyperparameters['conv3_kernel']
+ conv1_filters = hyperparameters['conv1_filters']
+ conv2_filters = hyperparameters['conv2_filters']
+ conv3_filters = hyperparameters['conv3_filters']
+ conv1_stride = hyperparameters['conv1_stride']
+ conv2_stride = hyperparameters['conv2_stride']
+ conv3_stride = hyperparameters['conv3_stride']
+ pool1_kernel = hyperparameters['pool1_kernel']
+ pool2_kernel = hyperparameters['pool2_kernel']
+ pool3_kernel = hyperparameters['pool3_kernel']
+ pool1_stride = hyperparameters['pool1_stride']
+ pool2_stride = hyperparameters['pool2_stride']
+ pool3_stride = hyperparameters['pool3_stride']
+ pool1_method = 1 # Removed from hyperparameter search because it never chose anything but this method
+ pool2_method = 1
+ pool3_method = 1
+
+ INPUT_SIZE = 400
+ tfo = {} # Tensorflow objects
+
+ x = tf.placeholder(tf.float32, shape=[None, INPUT_SIZE], name='x')
+ label_classifier = tf.placeholder(tf.float32, shape=[None], name='label_classifier')
+ label_offset = tf.placeholder(tf.float32, shape=[None], name='label_offset')
+ label_coldensity = tf.placeholder(tf.float32, shape=[None], name='label_coldensity')
+ keep_prob = tf.placeholder(tf.float32, name='keep_prob')
+ global_step = tf.Variable(0, name='global_step', trainable=False)
+
+ x_4d = tf.reshape(x, [-1, INPUT_SIZE, 1, 1])
+
+ # First Convolutional Layer
+ # Kernel size (16,1)
+ # Stride (4,1)
+ # number of filters = 4 (features?)
+ # Neuron activation = ReLU (rectified linear unit)
+ W_conv1 = weight_variable([conv1_kernel, 1, 1, conv1_filters])
+ b_conv1 = bias_variable([conv1_filters])
+
+ # https://www.tensorflow.org/versions/r0.10/api_docs/python/nn.html#convolution
+ # out_height = ceil(float(in_height) / float(strides[1])) = ceil(1024./4.) = 256
+ # out_width = ceil(float(in_width) / float(strides[2])) = 1
+ # shape of h_conv1: [-1, 256, 1, 4]
+ stride1 = [1, conv1_stride, 1, 1]
+ h_conv1 = tf.nn.relu(conv1d(x_4d, W_conv1, stride1) + b_conv1)
+
+ # Kernel size (8,1)
+ # Stride (2,1)
+ # Pooling type = Max Pooling
+ # out_height = ceil(float(in_height) / float(strides[1])) = ceil(256./2.) = 128
+ # out_width = ceil(float(in_width) / float(strides[2])) = 1
+ # shape of h_pool1: [-1, 128, 1, 4]
+ h_pool1 = pooling_layer_parameterized(pool1_method, h_conv1, pool1_kernel, pool1_stride)
+
+ # Second Convolutional Layer
+ # Kernel size (16,1)
+ # Stride (2,1)
+ # number of filters=8
+ # Neuron activation = ReLU (rectified linear unit)
+ W_conv2 = weight_variable([conv2_kernel, 1, conv1_filters, conv2_filters])
+ b_conv2 = bias_variable([conv2_filters])
+ stride2 = [1, conv2_stride, 1, 1]
+ h_conv2 = tf.nn.relu(conv1d(h_pool1, W_conv2, stride2) + b_conv2)
+ h_pool2 = pooling_layer_parameterized(pool2_method, h_conv2, pool2_kernel, pool2_stride)
+
+ # Third convolutional layer
+ W_conv3 = weight_variable([conv3_kernel, 1, conv2_filters, conv3_filters])
+ b_conv3 = bias_variable([conv3_filters])
+ stride3 = [1, conv3_stride, 1, 1]
+ h_conv3 = tf.nn.relu(conv1d(h_pool2, W_conv3, stride3) + b_conv3)
+ h_pool3 = pooling_layer_parameterized(pool3_method, h_conv3, pool3_kernel, pool3_stride)
+
+
+ # FC1: first fully connected layer, shared
+ inputsize_fc1 = int(math.ceil(math.ceil(math.ceil(math.ceil(math.ceil(math.ceil(
+ INPUT_SIZE / conv1_stride) / pool1_stride) / conv2_stride) / pool2_stride) / conv3_stride) / pool3_stride)) * conv3_filters
+ # batch_size = x.get_shape().as_list()[0]
+ h_pool3_flat = tf.reshape(h_pool3, [-1, inputsize_fc1])
+
+ W_fc1 = weight_variable([inputsize_fc1, fc1_n_neurons])
+ b_fc1 = bias_variable([fc1_n_neurons])
+ h_fc1 = tf.nn.relu(tf.matmul(h_pool3_flat, W_fc1) + b_fc1)
+
+ # Dropout FC1
+ h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
+
+ W_fc2_1 = weight_variable([fc1_n_neurons, fc2_1_n_neurons])
+ b_fc2_1 = bias_variable([fc2_1_n_neurons])
+ W_fc2_2 = weight_variable([fc1_n_neurons, fc2_2_n_neurons])
+ b_fc2_2 = bias_variable([fc2_2_n_neurons])
+ W_fc2_3 = weight_variable([fc1_n_neurons, fc2_3_n_neurons])
+ b_fc2_3 = bias_variable([fc2_3_n_neurons])
+
+ # FC2 activations
+ h_fc2_1 = tf.nn.relu(tf.matmul(h_fc1_drop, W_fc2_1) + b_fc2_1)
+ h_fc2_2 = tf.nn.relu(tf.matmul(h_fc1_drop, W_fc2_2) + b_fc2_2)
+ h_fc2_3 = tf.nn.relu(tf.matmul(h_fc1_drop, W_fc2_3) + b_fc2_3)
+
+ # FC2 Dropout [1-3]
+ h_fc2_1_drop = tf.nn.dropout(h_fc2_1, keep_prob)
+ h_fc2_2_drop = tf.nn.dropout(h_fc2_2, keep_prob)
+ h_fc2_3_drop = tf.nn.dropout(h_fc2_3, keep_prob)
+
+ # Readout Layer
+ W_fc3_1 = weight_variable([fc2_1_n_neurons, 1])
+ b_fc3_1 = bias_variable([1])
+ W_fc3_2 = weight_variable([fc2_2_n_neurons, 1])
+ b_fc3_2 = bias_variable([1])
+ W_fc3_3 = weight_variable([fc2_3_n_neurons, 1])
+ b_fc3_3 = bias_variable([1])
+
+ # y_fc4 = tf.add(tf.matmul(h_fc3_drop, W_fc4), b_fc4)
+ # y_nn = tf.reshape(y_fc4, [-1])
+ y_fc4_1 = tf.add(tf.matmul(h_fc2_1_drop, W_fc3_1), b_fc3_1)
+ y_nn_classifier = tf.reshape(y_fc4_1, [-1], name='y_nn_classifer')
+ y_fc4_2 = tf.add(tf.matmul(h_fc2_2_drop, W_fc3_2), b_fc3_2)
+ y_nn_offset = tf.reshape(y_fc4_2, [-1], name='y_nn_offset')
+ y_fc4_3 = tf.add(tf.matmul(h_fc2_3_drop, W_fc3_3), b_fc3_3)
+ y_nn_coldensity = tf.reshape(y_fc4_3, [-1], name='y_nn_coldensity')
+
+ # Train and Evaluate the model
+ loss_classifier = tf.add(tf.nn.sigmoid_cross_entropy_with_logits(logits=y_nn_classifier, labels=label_classifier),
+ l2_regularization_penalty * (tf.nn.l2_loss(W_conv1) + tf.nn.l2_loss(W_conv2) +
+ tf.nn.l2_loss(W_fc1) + tf.nn.l2_loss(W_fc2_1)),
+ name='loss_classifier')
+ loss_offset_regression = tf.add(tf.reduce_sum(tf.nn.l2_loss(y_nn_offset - label_offset)),
+ l2_regularization_penalty * (tf.nn.l2_loss(W_conv1) + tf.nn.l2_loss(W_conv2) +
+ tf.nn.l2_loss(W_fc1) + tf.nn.l2_loss(W_fc2_2)),
+ name='loss_offset_regression')
+ epsilon = 1e-6
+ loss_coldensity_regression = tf.reduce_sum(
+ tf.multiply(tf.square(y_nn_coldensity - label_coldensity),
+ tf.div(label_coldensity,label_coldensity+epsilon)) +
+ l2_regularization_penalty * (tf.nn.l2_loss(W_conv1) + tf.nn.l2_loss(W_conv2) +
+ tf.nn.l2_loss(W_fc1) + tf.nn.l2_loss(W_fc2_1)),
+ name='loss_coldensity_regression')
+
+ optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
+
+ cost_all_samples_lossfns_AB = loss_classifier + loss_offset_regression
+ cost_pos_samples_lossfns_ABC = loss_classifier + loss_offset_regression + loss_coldensity_regression
+ # train_step_AB = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost_all_samples_lossfns_AB, global_step=global_step, name='train_step_AB')
+ train_step_ABC = optimizer.minimize(cost_pos_samples_lossfns_ABC, global_step=global_step, name='train_step_ABC')
+ # train_step_C = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss_coldensity_regression, global_step=global_step, name='train_step_C')
+ output_classifier = tf.sigmoid(y_nn_classifier, name='output_classifier')
+ prediction = tf.round(output_classifier, name='prediction')
+ correct_prediction = tf.equal(prediction, label_classifier)
+ accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name='accuracy')
+ rmse_offset = tf.sqrt(tf.reduce_mean(tf.square(tf.subtract(y_nn_offset,label_offset))), name='rmse_offset')
+ rmse_coldensity = tf.sqrt(tf.reduce_mean(tf.square(tf.subtract(y_nn_coldensity,label_coldensity))), name='rmse_coldensity')
+
+ variable_summaries(loss_classifier, 'loss_classifier', 'SUMMARY_A')
+ variable_summaries(loss_offset_regression, 'loss_offset_regression', 'SUMMARY_B')
+ variable_summaries(loss_coldensity_regression, 'loss_coldensity_regression', 'SUMMARY_C')
+ variable_summaries(accuracy, 'classification_accuracy', 'SUMMARY_A')
+ variable_summaries(rmse_offset, 'rmse_offset', 'SUMMARY_B')
+ variable_summaries(rmse_coldensity, 'rmse_coldensity', 'SUMMARY_C')
+ # tb_summaries = tf.merge_all_summaries()
+
+ return train_step_ABC, tfo #, accuracy , loss_classifier, loss_offset_regression, loss_coldensity_regression, \
+ #x, label_classifier, label_offset, label_coldensity, keep_prob, prediction, output_classifier, y_nn_offset, \
+ #rmse_offset, y_nn_coldensity, rmse_coldensity
+
diff --git a/data/preprocess/dla_cnn/desi/model_v2.py b/data/preprocess/dla_cnn/desi/model_v2.py
new file mode 100644
index 0000000..9c7c289
--- /dev/null
+++ b/data/preprocess/dla_cnn/desi/model_v2.py
@@ -0,0 +1,334 @@
+""" TensorFlow Models for DLA finder"""
+
+"""
+0. Convert the model to TF 2.1
+
+used the tf_upgrade_v2 --intree model_v1 --outtree model_v2 to update this code to TF2.1
+But tf.summary.scalar requires manual check.
+The TF 1.x summary API cannot be automatically migrated to TF 2.0, so symbols have been converted to tf.compat.v1.summary.*
+and must be migrated manually.
+The method I have used can be found at https://www.tensorflow.org/guide/upgrade
+The report.txt is at https://docs.google.com/document/d/1l07KDAfWWZyv9eps71q1d5Qqa46SMUm73u0FZcU-tlY/edit?usp=sharing
+
+"""
+
+"""
+tf.random.truncated_normal:Outputs random values from a truncated normal distribution.(shape:The shape of the output tensor.
+stddev:The standard deviation of the normal distribution, before truncation)
+tf.Variable:
+tf.constant:generating constant
+tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None, name=None)
+"""
+
+def weight_variable(shape):
+
+ """
+
+ Generating random number according to the tensor shape,The standard deviation is 0.1，define a variable function
+ parameter_shape:the shape of the output tensor，A 1-D integer Tensor or Python array
+ return:tf.Variable (initial_value is random number）A tensor of the specified shape filled with random truncated normal values.
+
+ """
+ initial = tf.random.truncated_normal(shape, stddev=0.1) #Outputs random values from a truncated normal distribution，shape:The shape of the output tensor.stddev:The standard deviation of the normal distribution, before truncation
+ return tf.Variable(initial)
+
+
+def bias_variable(shape):
+
+ """
+
+ Generating constant 0 according to the tensor shape,define a variable function
+ parameter_shape:the shape of the output tensor，A 1-D integer Tensor or Python array
+ return:tf.Variable（initial_value is a constant）A tensor of the specified shape filled with random truncated normal values.
+
+ """
+ initial = tf.constant(0.0, shape=shape) #generating constant
+ return tf.Variable(initial)
+
+
+def conv1d(x, W, s):
+
+ """
+
+ achieve the convolution
+ input:
+ parameter_x:the input image need to do the convolution,must a tensor format
+ parameter_W:the core of the CNN,a tensor format
+ parameter_s:the batch_size on each dimension,a one-dimensional vector
+ return:tf.nn.conv2d(padding defines which convolution method will be used,"SAME"or"VALID")
+
+ """
+ return tf.nn.conv2d(input=x, filters=W, strides=s, padding='SAME')#tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None, name=None)
+
+
+def pooling_layer_parameterized(pool_method, h_conv, pool_kernel, pool_stride):
+
+ """
+
+ define pooling parameter,which method should be used
+ input: pool_method 1 or 2
+ parameter_pool_method:1 or 2,determines use max_pool or avg_pool
+ parameter_h_conv:the input need to be pooled,shape:[batch, height, width, channels]
+ parameter_pool_kernel:int format,the height of the pool window. ksize:[1,height,width,1],1-D CNN,width=1
+ parameter_pool_stride:int format,size each window slides on each dimension. strides:[1,stride,stride,1]
+ return: pool_method=1,use the max set
+ pool_method=2,use the average set
+
+ """
+ if pool_method == 1:
+ return tf.nn.max_pool2d(input=h_conv, ksize=[1, pool_kernel, 1, 1], strides=[1, pool_stride, 1, 1], padding='SAME')
+ elif pool_method == 2:
+ return tf.nn.avg_pool2d(input=h_conv, ksize=[1, pool_kernel, 1, 1], strides=[1, pool_stride, 1, 1], padding='SAME')
+
+def variable_summaries(var, name, collection):
+ """
+
+ Attach a lot of summaries to a Tensor.
+
+ parameter_var:tensor format
+ parameter_name:string format,the name of information
+ parameter_collection:value=None
+
+ display information of the tensor and draw a histogram about tensor and name
+ """
+ writer = tf.summary.create_file_writer("summary_file")
+ #tf.summary.experimental.set_step()
+ with tf.name_scope('summaries') as r:
+ #sets a default value for the step parameter
+ tf.summary.experimental.set_step(step=1) #An int64-castable default step value, or None to unset.
+ # I make it 1 to test,thsi value is variable
+ mean = tf.reduce_mean(input_tensor=var) #reduce the dimension of input tensor
+ with writer.as_default():
+ tf.summary.scalar('mean/' + name,mean,step=step)
+ with tf.name_scope('stddev'):
+ stddev = tf.sqrt(tf.reduce_mean(input_tensor=tf.square(var - mean)))
+ with writer.as_default(): #display the information
+ tf.summary.scalar('stddev/' + name, stddev,step=step) #tf.summary.scalar(tags, values, step)
+ tf.summary.scalar('max/' + name, tf.reduce_max(input_tensor=var),step=step)
+ tf.summary.scalar('min/' + name, tf.reduce_min(input_tensor=var),step=step)
+ tf.summary.histogram(name, var)
+
+
+ """
+ every tf.summary.scalar command needs a new parameter "step" but I have no idea how to pass value to this parameter,
+ maybe use tf.summary.experimental.set_step()?
+ the parameter "collection" is not used, I changed this part using writer.as_default(), but I have no idea whether I keep the logic correct
+ if these summary commands do not work well,we can use the following code:
+
+ def variable_summaries(var, name, collection):
+ with tf.compat.v1.name_scope('summaries') as r:
+ mean = tf.reduce_mean(input_tensor=var)
+ tf.compat.v1.add_to_collection(collection,tf.compat.v1.summary.scalar('mean/'+name,mean))
+ with tf.compat.v1.name_scope('stddev'):
+ stddev = tf.sqrt(tf.reduce_mean(input_tensor=tf.square(var - mean)))
+ tf.compat.v1.add_to_collection(collection,tf.compat.v1.summary.scalar('stddev/'+name, stddev))
+ tf.compat.v1.add_to_collection(collection,tf.compat.v1.summary.scalar('max/'+name, tf.reduce_max(input_tensor=var)))
+ tf.compat.v1.add_to_collection(collection,tf.compat.v1.summary.scalar('min/'+name, tf.reduce_min(input_tensor=var)))
+ tf.compat.v1.add_to_collection(collection,tf.compat.v1.summary.histogram(name, var))
+
+ tf.summary has many differences with tf.add_to_collection,using tf.compat.v1. can make tf.add_to_collection work with TF2.1
+ this may need to be changed into TF2.1 totally in the future
+
+ """
+
+
+
+def build_model(hyperparameters):
+ """
+ This is Model_v5 from SDSS
+
+ three conv layers,three pool layers,two full connected layers
+
+ Parameters
+ ----------
+ hyperparameters：use the hyperparameters in dla_cnn/models/model_gensample_v7.1_hyperparams.json
+
+ Returns
+ -------
+
+●
+
+ """
+ learning_rate = hyperparameters['learning_rate']
+ batch_size = hyperparameters['batch_size']
+ l2_regularization_penalty = hyperparameters['l2_regularization_penalty']
+ fc1_n_neurons = hyperparameters['fc1_n_neurons']
+ fc2_1_n_neurons = hyperparameters['fc2_1_n_neurons']
+ fc2_2_n_neurons = hyperparameters['fc2_2_n_neurons']
+ fc2_3_n_neurons = hyperparameters['fc2_3_n_neurons']
+ conv1_kernel = hyperparameters['conv1_kernel']
+ conv2_kernel = hyperparameters['conv2_kernel']
+ conv3_kernel = hyperparameters['conv3_kernel']
+ conv1_filters = hyperparameters['conv1_filters']
+ conv2_filters = hyperparameters['conv2_filters']
+ conv3_filters = hyperparameters['conv3_filters']
+ conv1_stride = hyperparameters['conv1_stride']
+ conv2_stride = hyperparameters['conv2_stride']
+ conv3_stride = hyperparameters['conv3_stride']
+ pool1_kernel = hyperparameters['pool1_kernel']
+ pool2_kernel = hyperparameters['pool2_kernel']
+ pool3_kernel = hyperparameters['pool3_kernel']
+ pool1_stride = hyperparameters['pool1_stride']
+ pool2_stride = hyperparameters['pool2_stride']
+ pool3_stride = hyperparameters['pool3_stride']
+ pool1_method = 1 # Removed from hyperparameter search because it never chose anything but this method
+ pool2_method = 1
+ pool3_method = 1
+
+ INPUT_SIZE = hyperparameters['INPUT_SIZE'] # the pixel of input image,need a new hyperparameter
+ tfo = {} # Tensorflow objects
+
+ x = tf.Variable(tf.ones(shape=[None, INPUT_SIZE]), name='x')
+ label_classifier = tf.Variable(tf.zeros(shape=[None]), name='label_classifier')
+ label_offset = tf.Variable(tf.zeros(shape=[None]), name='label_offset')
+ label_coldensity = tf.Variable(tf.zeros(shape=[None]), name='label_coldensity')
+ keep_prob = tf.Variable( name='keep_prob')
+ global_step = tf.Variable(0, name='global_step', trainable=False)
+
+ # I converted "tf.placeholder" to "tf.Variable" because tf.placeholder is not used anymore in TF2.1,but if tf.Variable is
+ #incompatible with the nump version,you can still use tf.placeholder like the following code
+ """"
+ tf.compat.v1.disable_eager_execution()
+ x = tf.compat.v1.placeholder(tf.float32, shape=[None, INPUT_SIZE], name='x')
+ label_classifier = tf.compat.v1.placeholder(tf.float32, shape=[None], name='label_classifier')
+ label_offset = tf.compat.v1.placeholder(tf.float32, shape=[None], name='label_offset')
+ label_coldensity = tf.compat.v1.placeholder(tf.float32, shape=[None], name='label_coldensity')
+ keep_prob = tf.compat.v1.placeholder(tf.float32, name='keep_prob')
+ global_step = tf.Variable(0, name='global_step', trainable=False)
+ """"
+
+
+ x_4d = tf.reshape(x, [-1, INPUT_SIZE, 1, 1])
+
+ # First Convolutional Layer
+ # Kernel size (16,1)
+ # Stride (4,1)
+ # number of filters = 4 (features?)
+ # Neuron activation = ReLU (rectified linear unit)
+ W_conv1 = weight_variable([conv1_kernel, 1, 1, conv1_filters])
+ b_conv1 = bias_variable([conv1_filters])
+
+ # https://www.tensorflow.org/versions/r0.10/api_docs/python/nn.html#convolution
+ # out_height = ceil(float(in_height) / float(strides[1])) = ceil(1024./4.) = 256
+ # out_width = ceil(float(in_width) / float(strides[2])) = 1
+ # shape of h_conv1: [-1, 256, 1, 4]
+ stride1 = [1, conv1_stride, 1, 1]
+ h_conv1 = tf.nn.relu(conv1d(x_4d, W_conv1, stride1) + b_conv1)
+
+ # Kernel size (8,1)
+ # Stride (2,1)
+ # Pooling type = Max Pooling
+ # out_height = ceil(float(in_height) / float(strides[1])) = ceil(256./2.) = 128
+ # out_width = ceil(float(in_width) / float(strides[2])) = 1
+ # shape of h_pool1: [-1, 128, 1, 4]
+ h_pool1 = pooling_layer_parameterized(pool1_method, h_conv1, pool1_kernel, pool1_stride)
+
+ # Second Convolutional Layer
+ # Kernel size (16,1)
+ # Stride (2,1)
+ # number of filters=8
+ # Neuron activation = ReLU (rectified linear unit)
+ W_conv2 = weight_variable([conv2_kernel, 1, conv1_filters, conv2_filters])
+ b_conv2 = bias_variable([conv2_filters])
+ stride2 = [1, conv2_stride, 1, 1]
+ h_conv2 = tf.nn.relu(conv1d(h_pool1, W_conv2, stride2) + b_conv2)
+ h_pool2 = pooling_layer_parameterized(pool2_method, h_conv2, pool2_kernel, pool2_stride)
+
+ # Third convolutional layer
+ W_conv3 = weight_variable([conv3_kernel, 1, conv2_filters, conv3_filters])
+ b_conv3 = bias_variable([conv3_filters])
+ stride3 = [1, conv3_stride, 1, 1]
+ h_conv3 = tf.nn.relu(conv1d(h_pool2, W_conv3, stride3) + b_conv3)
+ h_pool3 = pooling_layer_parameterized(pool3_method, h_conv3, pool3_kernel, pool3_stride)
+
+
+ # FC1: first fully connected layer, shared
+ inputsize_fc1 = int(math.ceil(math.ceil(math.ceil(math.ceil(math.ceil(math.ceil(
+ INPUT_SIZE / conv1_stride) / pool1_stride) / conv2_stride) / pool2_stride) / conv3_stride) / pool3_stride)) * conv3_filters
+ # batch_size = x.get_shape().as_list()[0]
+ h_pool3_flat = tf.reshape(h_pool3, [-1, inputsize_fc1])
+
+ W_fc1 = weight_variable([inputsize_fc1, fc1_n_neurons])
+ b_fc1 = bias_variable([fc1_n_neurons])
+ h_fc1 = tf.nn.relu(tf.matmul(h_pool3_flat, W_fc1) + b_fc1)
+
+ # Dropout FC1
+ h_fc1_drop = tf.nn.dropout(h_fc1, 1 - (keep_prob))
+
+ W_fc2_1 = weight_variable([fc1_n_neurons, fc2_1_n_neurons])
+ b_fc2_1 = bias_variable([fc2_1_n_neurons])
+ W_fc2_2 = weight_variable([fc1_n_neurons, fc2_2_n_neurons])
+ b_fc2_2 = bias_variable([fc2_2_n_neurons])
+ W_fc2_3 = weight_variable([fc1_n_neurons, fc2_3_n_neurons])
+ b_fc2_3 = bias_variable([fc2_3_n_neurons])
+
+ # FC2 activations
+ h_fc2_1 = tf.nn.relu(tf.matmul(h_fc1_drop, W_fc2_1) + b_fc2_1)
+ h_fc2_2 = tf.nn.relu(tf.matmul(h_fc1_drop, W_fc2_2) + b_fc2_2)
+ h_fc2_3 = tf.nn.relu(tf.matmul(h_fc1_drop, W_fc2_3) + b_fc2_3)
+
+ # FC2 Dropout [1-3]
+ h_fc2_1_drop = tf.nn.dropout(h_fc2_1, 1 - (keep_prob))
+ h_fc2_2_drop = tf.nn.dropout(h_fc2_2, 1 - (keep_prob))
+ h_fc2_3_drop = tf.nn.dropout(h_fc2_3, 1 - (keep_prob))
+
+ # Readout Layer
+ W_fc3_1 = weight_variable([fc2_1_n_neurons, 1])
+ b_fc3_1 = bias_variable([1])
+ W_fc3_2 = weight_variable([fc2_2_n_neurons, 1])
+ b_fc3_2 = bias_variable([1])
+ W_fc3_3 = weight_variable([fc2_3_n_neurons, 1])
+ b_fc3_3 = bias_variable([1])
+
+ # y_fc4 = tf.add(tf.matmul(h_fc3_drop, W_fc4), b_fc4)
+ # y_nn = tf.reshape(y_fc4, [-1])
+ y_fc4_1 = tf.add(tf.matmul(h_fc2_1_drop, W_fc3_1), b_fc3_1)
+ y_nn_classifier = tf.reshape(y_fc4_1, [-1], name='y_nn_classifer')
+ y_fc4_2 = tf.add(tf.matmul(h_fc2_2_drop, W_fc3_2), b_fc3_2)
+ y_nn_offset = tf.reshape(y_fc4_2, [-1], name='y_nn_offset')
+ y_fc4_3 = tf.add(tf.matmul(h_fc2_3_drop, W_fc3_3), b_fc3_3)
+ y_nn_coldensity = tf.reshape(y_fc4_3, [-1], name='y_nn_coldensity')
+
+ # Train and Evaluate the model
+ loss_classifier = tf.add(tf.nn.sigmoid_cross_entropy_with_logits(logits=y_nn_classifier, labels=label_classifier),
+ l2_regularization_penalty * (tf.nn.l2_loss(W_conv1) + tf.nn.l2_loss(W_conv2) +
+ tf.nn.l2_loss(W_fc1) + tf.nn.l2_loss(W_fc2_1)),
+ name='loss_classifier')
+ loss_offset_regression = tf.add(tf.reduce_sum(input_tensor=tf.nn.l2_loss(y_nn_offset - label_offset)),
+ l2_regularization_penalty * (tf.nn.l2_loss(W_conv1) + tf.nn.l2_loss(W_conv2) +
+ tf.nn.l2_loss(W_fc1) + tf.nn.l2_loss(W_fc2_2)),
+ name='loss_offset_regression')
+ epsilon = 1e-6
+ loss_coldensity_regression = tf.reduce_sum(
+ input_tensor=tf.multiply(tf.square(y_nn_coldensity - label_coldensity),
+ tf.compat.v1.div(label_coldensity,label_coldensity+epsilon)) +
+ l2_regularization_penalty * (tf.nn.l2_loss(W_conv1) + tf.nn.l2_loss(W_conv2) +
+ tf.nn.l2_loss(W_fc1) + tf.nn.l2_loss(W_fc2_1)),
+ name='loss_coldensity_regression')
+
+ optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)
+
+ cost_all_samples_lossfns_AB = loss_classifier + loss_offset_regression
+ cost_pos_samples_lossfns_ABC = loss_classifier + loss_offset_regression + loss_coldensity_regression
+ # train_step_AB = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost_all_samples_lossfns_AB, global_step=global_step, name='train_step_AB')
+ train_step_ABC = optimizer.minimize(cost_pos_samples_lossfns_ABC, global_step=global_step, name='train_step_ABC')
+ # train_step_C = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss_coldensity_regression, global_step=global_step, name='train_step_C')
+ output_classifier = tf.sigmoid(y_nn_classifier, name='output_classifier')
+ prediction = tf.round(output_classifier, name='prediction')
+ correct_prediction = tf.equal(prediction, label_classifier)
+ accuracy = tf.reduce_mean(input_tensor=tf.cast(correct_prediction, tf.float32), name='accuracy')
+ rmse_offset = tf.sqrt(tf.reduce_mean(input_tensor=tf.square(tf.subtract(y_nn_offset,label_offset))), name='rmse_offset')
+ rmse_coldensity = tf.sqrt(tf.reduce_mean(input_tensor=tf.square(tf.subtract(y_nn_coldensity,label_coldensity))), name='rmse_coldensity')
+
+ variable_summaries(loss_classifier, 'loss_classifier', 'SUMMARY_A')
+ variable_summaries(loss_offset_regression, 'loss_offset_regression', 'SUMMARY_B')
+ variable_summaries(loss_coldensity_regression, 'loss_coldensity_regression', 'SUMMARY_C')
+ variable_summaries(accuracy, 'classification_accuracy', 'SUMMARY_A')
+ variable_summaries(rmse_offset, 'rmse_offset', 'SUMMARY_B')
+ variable_summaries(rmse_coldensity, 'rmse_coldensity', 'SUMMARY_C')
+ # tb_summaries = tf.merge_all_summaries()
+
+ return train_step_ABC, tfo #, accuracy , loss_classifier, loss_offset_regression, loss_coldensity_regression, \
+ #x, label_classifier, label_offset, label_coldensity, keep_prob, prediction, output_classifier, y_nn_offset, \
+ #rmse_offset, y_nn_coldensity, rmse_coldensity
+
diff --git a/data/preprocess/dla_cnn/desi/nb/Generate Summary Table.ipynb b/data/preprocess/dla_cnn/desi/nb/Generate Summary Table.ipynb
new file mode 100644
index 0000000..1675c95
--- /dev/null
+++ b/data/preprocess/dla_cnn/desi/nb/Generate Summary Table.ipynb
@@ -0,0 +1,1458 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-04-17T11:32:57.373158Z",
+ "start_time": "2020-04-17T11:32:57.037263Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "from dla_cnn.desi.preprocess import write_summary_table"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-04-17T11:33:51.372093Z",
+ "start_time": "2020-04-17T11:33:06.453752Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "from os.path import join, exists\n",
+ "files = list(set(range(700,800)).difference(set([709, 711, 712, 749, 750, 753, 766, 767, 770, 784, 789, 790])))\n",
+ "path = \"F:\\Astronamy\\desi\"#here\n",
+ "write_summary_table(files, 16, path, r\"F:\\Astronamy\\summary_table.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-04-17T11:34:35.974302Z",
+ "start_time": "2020-04-17T11:34:35.422315Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-04-17T11:35:03.957952Z",
+ "start_time": "2020-04-17T11:35:03.859190Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "summary_table = pd.read_csv(r\"F:\\Astronamy\\summary_table.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-04-17T11:35:07.091572Z",
+ "start_time": "2020-04-17T11:35:07.018685Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>id</th>\n",
+ " <th>z_qso</th>\n",
+ " <th>s2n</th>\n",
+ " <th>wavelength_start_b</th>\n",
+ " <th>wavelength_end_b</th>\n",
+ " <th>pixel_start_b</th>\n",
+ " <th>pixel_end_b</th>\n",
+ " <th>wavelength_start_r</th>\n",
+ " <th>wavelength_end_r</th>\n",
+ " <th>pixel_start_r</th>\n",
+ " <th>pixel_end_r</th>\n",
+ " <th>wavelength_start_z</th>\n",
+ " <th>wavelength_end_z</th>\n",
+ " <th>pixel_start_z</th>\n",
+ " <th>pixel_end_z</th>\n",
+ " <th>dlas_col_density</th>\n",
+ " <th>dlas_central_wavelength</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>160316239</td>\n",
+ " <td>3.180780</td>\n",
+ " <td>17.694805</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>21.293353071210017,19.796839436340605</td>\n",
+ " <td>3478.0773459716816,4953.865379121646</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>160316854</td>\n",
+ " <td>2.487049</td>\n",
+ " <td>15.366678</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>21.343129690035312</td>\n",
+ " <td>3630.7131575745166</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>160317436</td>\n",
+ " <td>2.858960</td>\n",
+ " <td>31.047950</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>20.70361123215101</td>\n",
+ " <td>4627.556241285893</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>160317443</td>\n",
+ " <td>2.856237</td>\n",
+ " <td>17.321907</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.33942414613816,20.037905540420695</td>\n",
+ " <td>3987.636661361166,4360.8683708942535</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>160317741</td>\n",
+ " <td>2.825150</td>\n",
+ " <td>11.683969</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.707905027091194</td>\n",
+ " <td>3597.2945715173582</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>160318048</td>\n",
+ " <td>2.621608</td>\n",
+ " <td>11.949551</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.757254138529014</td>\n",
+ " <td>3552.2921152384993</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>160319900</td>\n",
+ " <td>3.033517</td>\n",
+ " <td>66.647804</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.88427904095016</td>\n",
+ " <td>4298.953230731651</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>160320253</td>\n",
+ " <td>2.997435</td>\n",
+ " <td>6.947001</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.387604842719583</td>\n",
+ " <td>4677.554973401055</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>160368131</td>\n",
+ " <td>3.223414</td>\n",
+ " <td>13.278096</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.553346559875575</td>\n",
+ " <td>4855.820991073565</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>160368133</td>\n",
+ " <td>2.580355</td>\n",
+ " <td>10.817702</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.952848512501276</td>\n",
+ " <td>3638.385820714504</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>160368213</td>\n",
+ " <td>3.298373</td>\n",
+ " <td>26.773186</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.512331774958067,20.433905504017524</td>\n",
+ " <td>5069.3631600997915,5216.411049352369</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td>160368279</td>\n",
+ " <td>3.250392</td>\n",
+ " <td>6.861768</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>20.650883527949457,19.488072319915062</td>\n",
+ " <td>3592.525509286231,5124.757900302609</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12</th>\n",
+ " <td>160368280</td>\n",
+ " <td>3.227425</td>\n",
+ " <td>10.458097</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>20.259147765228953,19.607615222376918</td>\n",
+ " <td>3640.8480763440434,4750.6428033916545</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13</th>\n",
+ " <td>160368308</td>\n",
+ " <td>2.955006</td>\n",
+ " <td>9.533985</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>20.584869565147827,20.659651488810283,20.14771...</td>\n",
+ " <td>3929.0877891763103,4257.004817992117,4541.9423...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14</th>\n",
+ " <td>160368396</td>\n",
+ " <td>2.653312</td>\n",
+ " <td>20.078917</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.639027757864913</td>\n",
+ " <td>4308.170289008891</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>15</th>\n",
+ " <td>160368399</td>\n",
+ " <td>2.434552</td>\n",
+ " <td>114.683150</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>20.60419116762859</td>\n",
+ " <td>3689.613745020405</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16</th>\n",
+ " <td>160368518</td>\n",
+ " <td>2.758813</td>\n",
+ " <td>12.857859</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.501674606860423,19.8539212560881,20.4871135...</td>\n",
+ " <td>4096.832772607261,4245.995512416156,4344.52468...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17</th>\n",
+ " <td>160368606</td>\n",
+ " <td>3.023304</td>\n",
+ " <td>64.678375</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>20.4805080488606</td>\n",
+ " <td>4594.458098335094</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>18</th>\n",
+ " <td>160368820</td>\n",
+ " <td>3.031016</td>\n",
+ " <td>20.688532</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.919462531956167</td>\n",
+ " <td>4677.623035319407</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>19</th>\n",
+ " <td>160368937</td>\n",
+ " <td>2.747197</td>\n",
+ " <td>5.723158</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>20.14830412876717,19.532897720686627</td>\n",
+ " <td>4176.053147401865,4217.899235374931</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>20</th>\n",
+ " <td>160368998</td>\n",
+ " <td>2.931968</td>\n",
+ " <td>24.937086</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>20.533187235858342,19.92404477677225</td>\n",
+ " <td>3633.4652130131917,4167.436622605713</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>21</th>\n",
+ " <td>160369124</td>\n",
+ " <td>2.744710</td>\n",
+ " <td>24.653072</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.428069548786244</td>\n",
+ " <td>4051.050336254714</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>22</th>\n",
+ " <td>160369181</td>\n",
+ " <td>3.205858</td>\n",
+ " <td>22.615282</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.44380344629662</td>\n",
+ " <td>3598.399895610419</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>23</th>\n",
+ " <td>160369182</td>\n",
+ " <td>2.844948</td>\n",
+ " <td>41.629760</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>20.19267711200964</td>\n",
+ " <td>4253.8208957154875</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>24</th>\n",
+ " <td>160369183</td>\n",
+ " <td>2.715789</td>\n",
+ " <td>16.852566</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>20.06812554408301</td>\n",
+ " <td>3913.8303492892965</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25</th>\n",
+ " <td>160369185</td>\n",
+ " <td>2.579895</td>\n",
+ " <td>47.671803</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.7547538529594,20.353208806497555</td>\n",
+ " <td>3541.711331139962,4213.9206268484795</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>26</th>\n",
+ " <td>160369255</td>\n",
+ " <td>2.625570</td>\n",
+ " <td>15.187897</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.373018476476535</td>\n",
+ " <td>4255.45122229734</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>27</th>\n",
+ " <td>160369396</td>\n",
+ " <td>3.456144</td>\n",
+ " <td>8.093833</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.782059775623473</td>\n",
+ " <td>3884.6727163837922</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>28</th>\n",
+ " <td>160369397</td>\n",
+ " <td>2.754010</td>\n",
+ " <td>9.720048</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>20.058796447762813</td>\n",
+ " <td>3623.5597714519536</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>29</th>\n",
+ " <td>160369471</td>\n",
+ " <td>2.468736</td>\n",
+ " <td>9.449290</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.917397444466143</td>\n",
+ " <td>3926.0691457162743</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17053</th>\n",
+ " <td>150129977</td>\n",
+ " <td>2.713104</td>\n",
+ " <td>22.024029</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>20.1183595858312</td>\n",
+ " <td>4470.193050318297</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17054</th>\n",
+ " <td>150130323</td>\n",
+ " <td>2.721788</td>\n",
+ " <td>5.454872</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>20.01587376246629</td>\n",
+ " <td>3533.195667775869</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17055</th>\n",
+ " <td>150130418</td>\n",
+ " <td>2.718720</td>\n",
+ " <td>7.207655</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>20.84906853633227,20.113267242418864</td>\n",
+ " <td>3565.6060267110033,4477.247665089136</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17056</th>\n",
+ " <td>150130948</td>\n",
+ " <td>2.738994</td>\n",
+ " <td>7.722400</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.673035616069367</td>\n",
+ " <td>3844.973795492256</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17057</th>\n",
+ " <td>150131115</td>\n",
+ " <td>2.781793</td>\n",
+ " <td>21.984898</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.584396855006997</td>\n",
+ " <td>4209.790539468387</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17058</th>\n",
+ " <td>150131119</td>\n",
+ " <td>2.765700</td>\n",
+ " <td>21.303370</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>20.28609668409351</td>\n",
+ " <td>4458.276335152068</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17059</th>\n",
+ " <td>150131204</td>\n",
+ " <td>2.790346</td>\n",
+ " <td>37.849390</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>20.50960687038486</td>\n",
+ " <td>3712.2569436317913</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17060</th>\n",
+ " <td>150131406</td>\n",
+ " <td>2.757230</td>\n",
+ " <td>5.127159</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>20.577839394268256,21.273016364168104,21.59926...</td>\n",
+ " <td>4042.193710315947,4182.876572883094,4232.55486...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17061</th>\n",
+ " <td>150131480</td>\n",
+ " <td>2.765510</td>\n",
+ " <td>39.704468</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.73213655424343</td>\n",
+ " <td>3569.111962044157</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17062</th>\n",
+ " <td>150131565</td>\n",
+ " <td>2.800318</td>\n",
+ " <td>12.393608</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>20.299866097283573</td>\n",
+ " <td>4117.370314193351</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17063</th>\n",
+ " <td>150131656</td>\n",
+ " <td>2.793201</td>\n",
+ " <td>12.650562</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.35815218749913,20.801396680089375,19.656906...</td>\n",
+ " <td>4178.161439809847,4271.110393889822,4550.58610...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17064</th>\n",
+ " <td>150131824</td>\n",
+ " <td>2.812400</td>\n",
+ " <td>10.701872</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.40743920289456</td>\n",
+ " <td>3687.6952923864446</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17065</th>\n",
+ " <td>150131832</td>\n",
+ " <td>2.774528</td>\n",
+ " <td>16.158115</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>20.128771525867318</td>\n",
+ " <td>4091.3774981804254</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17066</th>\n",
+ " <td>150132317</td>\n",
+ " <td>2.837232</td>\n",
+ " <td>38.260890</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.827642970691283</td>\n",
+ " <td>4312.95634027278</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17067</th>\n",
+ " <td>150151028</td>\n",
+ " <td>2.344810</td>\n",
+ " <td>24.808086</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.86241448198075</td>\n",
+ " <td>4008.5945511054238</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17068</th>\n",
+ " <td>150151034</td>\n",
+ " <td>2.335801</td>\n",
+ " <td>10.096772</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.464423249102502</td>\n",
+ " <td>3708.6844184048414</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17069</th>\n",
+ " <td>150152243</td>\n",
+ " <td>2.333949</td>\n",
+ " <td>7.303610</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>21.670137001953027</td>\n",
+ " <td>4017.3447912620622</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17070</th>\n",
+ " <td>150152914</td>\n",
+ " <td>2.352317</td>\n",
+ " <td>9.456176</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>20.424733843589834</td>\n",
+ " <td>3797.389132984765</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17071</th>\n",
+ " <td>150153246</td>\n",
+ " <td>2.355282</td>\n",
+ " <td>6.044281</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.510748755780654</td>\n",
+ " <td>3952.609896628724</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17072</th>\n",
+ " <td>150153559</td>\n",
+ " <td>2.361471</td>\n",
+ " <td>5.537011</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.727095350262825</td>\n",
+ " <td>3565.8167164501647</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17073</th>\n",
+ " <td>150153700</td>\n",
+ " <td>2.366615</td>\n",
+ " <td>10.798866</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>20.249240865229485</td>\n",
+ " <td>3770.5919255668236</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17074</th>\n",
+ " <td>150153871</td>\n",
+ " <td>2.376021</td>\n",
+ " <td>8.196203</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.837653379106328</td>\n",
+ " <td>4011.99690309991</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17075</th>\n",
+ " <td>150154384</td>\n",
+ " <td>2.376430</td>\n",
+ " <td>56.989235</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>20.274854103253084</td>\n",
+ " <td>3580.97589047444</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17076</th>\n",
+ " <td>150154561</td>\n",
+ " <td>2.375011</td>\n",
+ " <td>120.311410</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>20.499474672078613</td>\n",
+ " <td>3984.987215197593</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17077</th>\n",
+ " <td>150155261</td>\n",
+ " <td>2.384696</td>\n",
+ " <td>14.905687</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>21.445886984514548</td>\n",
+ " <td>4109.726071896773</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17078</th>\n",
+ " <td>150155415</td>\n",
+ " <td>2.399774</td>\n",
+ " <td>7.211618</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>20.088161430101906</td>\n",
+ " <td>3851.739635966748</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17079</th>\n",
+ " <td>150155603</td>\n",
+ " <td>2.395128</td>\n",
+ " <td>4.932624</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
+ " <td>6894</td>\n",
+ " <td>19.979439304582566</td>\n",
+ " <td>4006.3402984476224</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17080</th>\n",
+ " <td>150155612</td>\n",
+ " <td>2.384966</td>\n",
+ " <td>22.181450</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
+ " <td>5625.399902</td>\n",
+ " <td>7717.399902</td>\n",
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+ " <td>7718.399902</td>\n",
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+ " <td>21.009513734053993</td>\n",
+ " <td>3855.942270810273</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17081</th>\n",
+ " <td>150155901</td>\n",
+ " <td>2.404521</td>\n",
+ " <td>62.175780</td>\n",
+ " <td>3569.399902</td>\n",
+ " <td>5948.399902</td>\n",
+ " <td>0</td>\n",
+ " <td>2379</td>\n",
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+ " <td>7717.399902</td>\n",
+ " <td>2380</td>\n",
+ " <td>4778</td>\n",
+ " <td>7718.399902</td>\n",
+ " <td>9833.400391</td>\n",
+ " <td>4779</td>\n",
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+ " <td>20.184401417107516,20.25850430554559</td>\n",
+ " <td>4023.3793085492016,4119.803095804543</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17082</th>\n",
+ " <td>150156518</td>\n",
+ " <td>2.406876</td>\n",
+ " <td>7.158979</td>\n",
+ " <td>3569.399902</td>\n",
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+ " <td>9833.400391</td>\n",
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+ " <td>6894</td>\n",
+ " <td>20.417713061263647</td>\n",
+ " <td>3800.1902826707824</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>17083 rows × 17 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " id z_qso s2n wavelength_start_b wavelength_end_b \\\n",
+ "0 160316239 3.180780 17.694805 3569.399902 5948.399902 \n",
+ "1 160316854 2.487049 15.366678 3569.399902 5948.399902 \n",
+ "2 160317436 2.858960 31.047950 3569.399902 5948.399902 \n",
+ "3 160317443 2.856237 17.321907 3569.399902 5948.399902 \n",
+ "4 160317741 2.825150 11.683969 3569.399902 5948.399902 \n",
+ "... ... ... ... ... ... \n",
+ "17078 150155415 2.399774 7.211618 3569.399902 5948.399902 \n",
+ "17079 150155603 2.395128 4.932624 3569.399902 5948.399902 \n",
+ "17080 150155612 2.384966 22.181450 3569.399902 5948.399902 \n",
+ "17081 150155901 2.404521 62.175780 3569.399902 5948.399902 \n",
+ "17082 150156518 2.406876 7.158979 3569.399902 5948.399902 \n",
+ "\n",
+ " pixel_start_b pixel_end_b wavelength_start_r wavelength_end_r \\\n",
+ "0 0 2379 5625.399902 7717.399902 \n",
+ "1 0 2379 5625.399902 7717.399902 \n",
+ "2 0 2379 5625.399902 7717.399902 \n",
+ "3 0 2379 5625.399902 7717.399902 \n",
+ "4 0 2379 5625.399902 7717.399902 \n",
+ "... ... ... ... ... \n",
+ "17078 0 2379 5625.399902 7717.399902 \n",
+ "17079 0 2379 5625.399902 7717.399902 \n",
+ "17080 0 2379 5625.399902 7717.399902 \n",
+ "17081 0 2379 5625.399902 7717.399902 \n",
+ "17082 0 2379 5625.399902 7717.399902 \n",
+ "\n",
+ " pixel_start_r pixel_end_r wavelength_start_z wavelength_end_z \\\n",
+ "0 2380 4778 7718.399902 9833.400391 \n",
+ "1 2380 4778 7718.399902 9833.400391 \n",
+ "2 2380 4778 7718.399902 9833.400391 \n",
+ "3 2380 4778 7718.399902 9833.400391 \n",
+ "4 2380 4778 7718.399902 9833.400391 \n",
+ "... ... ... ... ... \n",
+ "17078 2380 4778 7718.399902 9833.400391 \n",
+ "17079 2380 4778 7718.399902 9833.400391 \n",
+ "17080 2380 4778 7718.399902 9833.400391 \n",
+ "17081 2380 4778 7718.399902 9833.400391 \n",
+ "17082 2380 4778 7718.399902 9833.400391 \n",
+ "\n",
+ " pixel_start_z pixel_end_z dlas_col_density \\\n",
+ "0 4779 6894 21.293353071210017,19.796839436340605 \n",
+ "1 4779 6894 21.343129690035312 \n",
+ "2 4779 6894 20.70361123215101 \n",
+ "3 4779 6894 19.33942414613816,20.037905540420695 \n",
+ "4 4779 6894 19.707905027091194 \n",
+ "... ... ... ... \n",
+ "17078 4779 6894 20.088161430101906 \n",
+ "17079 4779 6894 19.979439304582566 \n",
+ "17080 4779 6894 21.009513734053993 \n",
+ "17081 4779 6894 20.184401417107516,20.25850430554559 \n",
+ "17082 4779 6894 20.417713061263647 \n",
+ "\n",
+ " dlas_central_wavelength \n",
+ "0 3478.0773459716816,4953.865379121646 \n",
+ "1 3630.7131575745166 \n",
+ "2 4627.556241285893 \n",
+ "3 3987.636661361166,4360.8683708942535 \n",
+ "4 3597.2945715173582 \n",
+ "... ... \n",
+ "17078 3851.739635966748 \n",
+ "17079 4006.3402984476224 \n",
+ "17080 3855.942270810273 \n",
+ "17081 4023.3793085492016,4119.803095804543 \n",
+ "17082 3800.1902826707824 \n",
+ "\n",
+ "[17083 rows x 17 columns]"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "summary_table"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.7.4 64-bit",
+ "language": "python",
+ "name": "python37464bit9a38927acc0145af89620e6e1adc3dca"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.4"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/data/preprocess/dla_cnn/desi/nb/Usage of DesiMock.ipynb b/data/preprocess/dla_cnn/desi/nb/Usage of DesiMock.ipynb
new file mode 100644
index 0000000..196e3fd
--- /dev/null
+++ b/data/preprocess/dla_cnn/desi/nb/Usage of DesiMock.ipynb
@@ -0,0 +1,159 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-02-28T07:57:05.020121Z",
+ "start_time": "2020-02-28T07:57:05.012838Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "from astropy.io import fits\n",
+ "from dla_cnn.desi.DesiMock import DesiMock\n",
+ "from os.path import join\n",
+ "from matplotlib import pyplot as plt\n",
+ "import sympy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-02-28T07:57:05.657083Z",
+ "start_time": "2020-02-28T07:57:05.650145Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "file_num = [705,706,711,718,723,731,735,743,747,748,761,773,777,785,789,791]\n",
+ "path = r\"F:\\Astronamy\\desi-0.2-100\\desi-0.2-100\\desi-0.2-100\\spectra-16\\7\"\n",
+ "file_path = join(path,str(file_num[0]))\n",
+ "spectra = join(file_path,\"spectra-16-%s.fits\"%file_num[0])\n",
+ "truth = join(file_path,\"truth-16-%s.fits\"%file_num[0])\n",
+ "zbest = join(file_path,\"zbest-16-%s.fits\"%file_num[0])#generate file path"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-02-28T07:57:06.682797Z",
+ "start_time": "2020-02-28T07:57:06.331742Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "specs = DesiMock()\n",
+ "specs.read_fits_file(spectra,truth,zbest)#use DesiMock.read_fits_file(spectra, truth, zbest) to load all data from the fits file"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-02-28T07:57:07.287115Z",
+ "start_time": "2020-02-28T07:57:07.282150Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "keys = list(specs.data.keys())#get spectra id"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-02-28T07:57:08.257763Z",
+ "start_time": "2020-02-28T07:57:08.251811Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "sightline = specs.get_sightline(keys[0])#use DesiMock.get_sightline(id) to grab the specific spectra"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-02-28T07:57:10.918240Z",
+ "start_time": "2020-02-28T07:57:10.386651Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1296x720 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "max_flux = max(sightline.flux)\n",
+ "plt.figure(figsize=(18,10))\n",
+ "plt.title('spec-%s'%(str(sightline.id)),fontdict=None,loc='center',pad='20',fontsize=30,color='black')\n",
+ "plt.xlabel('Wavelength'+'['+'$\\AA$'+']',fontsize=18)\n",
+ "plt.ylabel('Flux',fontsize=18)\n",
+ "plt.xlim([4000,8000])\n",
+ "wavelength = np.exp(np.log(10)*sightline.loglam)\n",
+ "plt.plot(wavelength,sightline.flux)\n",
+ "plt.plot(wavelength,sightline.error,color='red',ls='--',linewidth=1)\n",
+ "for i in range(len(sightline.dlas)):\n",
+ " plt.axvline(x=sightline.dlas[i].central_wavelength,ls=\"-\",c=\"red\",linewidth=1)\n",
+ " plt.text(sightline.dlas[i].central_wavelength,max_flux-i,'%s'%(sightline.dlas[i].id),fontsize=20,color='green')\n",
+ " plt.text(5400,max_flux-4-i,'%s,central_wavelength=%f,col_density=%f'%(sightline.dlas[i].id,sightline.dlas[i].central_wavelength,sightline.dlas[i].col_density),fontsize=20,color='red')\n",
+ "plt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.4"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/data/preprocess/dla_cnn/desi/nb/method of estimating s2n.ipynb b/data/preprocess/dla_cnn/desi/nb/method of estimating s2n.ipynb
new file mode 100644
index 0000000..1a91601
--- /dev/null
+++ b/data/preprocess/dla_cnn/desi/nb/method of estimating s2n.ipynb
@@ -0,0 +1,1744 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This notebook shows the method of estimating s/n using `dla_cnn.desi.preprocess.py/estimae_s2n`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-04-03T14:42:03.920913Z",
+ "start_time": "2020-04-03T14:42:02.943316Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib notebook\n",
+ "from dla_cnn.desi.DesiMock import DesiMock\n",
+ "from dla_cnn.data_model.Sightline import Sightline\n",
+ "from dla_cnn.desi.preprocess import estimate_s2n\n",
+ "from os.path import join\n",
+ "from matplotlib import pyplot as plt\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-04-03T14:42:30.868878Z",
+ "start_time": "2020-04-03T14:42:24.026045Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "s2n = []\n",
+ "z_qso = []\n",
+ "file_num = [705,706,718,723,731,735,743,747,761,773,777,785,791]\n",
+ "for num in file_num:\n",
+ " path = r\"F:\\Astronamy\\desi-0.2-100\\desi-0.2-100\\desi-0.2-100\\spectra-16\\7\"\n",
+ " file_path = join(path,str(num))\n",
+ " spectra = join(file_path,\"spectra-16-%s.fits\"%num)\n",
+ " truth = join(file_path,\"truth-16-%s.fits\"%num)\n",
+ " zbest = join(file_path,\"zbest-16-%s.fits\"%num)\n",
+ " spec = DesiMock()\n",
+ " spec.read_fits_file(spectra,truth,zbest)\n",
+ " for key,value in spec.data.items():\n",
+ " if value['z_qso']>=2.33:\n",
+ " sightline = spec.get_sightline(key,camera='b')\n",
+ " s2n.append(estimate_s2n(sightline))\n",
+ " z_qso.append(sightline.z_qso)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-04-03T14:42:36.279082Z",
+ "start_time": "2020-04-03T14:42:36.271085Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "6533"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(z_qso)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-04-03T14:42:38.171908Z",
+ "start_time": "2020-04-03T14:42:38.005357Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support. ' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option);\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div id='89842d7e-2e96-47e8-b071-bc37f581a4c9'></div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from matplotlib import pyplot as plt\n",
+ "fig = plt.figure()\n",
+ "plt.hist(s2n,bins=50,range = [5,125],density=True,histtype='barstacked')\n",
+ "plt.title(\"s/n distribution\")\n",
+ "plt.xlabel(\"s2n\")\n",
+ "plt.ylabel(\"frequency\")\n",
+ "plt.savefig(\"F:/Astronamy/1.jpg\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-04-03T14:42:44.025123Z",
+ "start_time": "2020-04-03T14:42:43.870500Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support. ' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option);\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
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+ "output_type": "display_data"
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width=\"640\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig2 = plt.figure()\n",
+ "plt.scatter(z_qso,s2n,marker='x',color='g')\n",
+ "plt.xlabel('z_qso')\n",
+ "plt.ylabel('s/n')\n",
+ "plt.title(\"scatter of s/n and z_qso\")\n",
+ "plt.savefig(r\"F:/Astronamy/2.jpg\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.7.4 64-bit",
+ "language": "python",
+ "name": "python37464bit9a38927acc0145af89620e6e1adc3dca"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.4"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/data/preprocess/dla_cnn/desi/nb/methods of normalization.ipynb b/data/preprocess/dla_cnn/desi/nb/methods of normalization.ipynb
new file mode 100644
index 0000000..d6d781d
--- /dev/null
+++ b/data/preprocess/dla_cnn/desi/nb/methods of normalization.ipynb
@@ -0,0 +1,12877 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-03-04T11:10:14.836897Z",
+ "start_time": "2020-03-04T11:10:13.947027Z"
+ }
+ },
+ "source": [
+ "This notebook will show the all methods of normalization."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-03-20T07:42:25.468165Z",
+ "start_time": "2020-03-20T07:42:24.578514Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib notebook\n",
+ "from dla_cnn.desi.DesiMock import DesiMock\n",
+ "from dla_cnn.data_model.Sightline import Sightline\n",
+ "from os.path import join\n",
+ "from matplotlib import pyplot as plt\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-03-20T07:42:25.965002Z",
+ "start_time": "2020-03-20T07:42:25.958013Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "file_num = [705,706,711,718,723,731,735,743,747,748,761,773,777,785,789,791]\n",
+ "path = r\"F:\\Astronamy\\desi-0.2-100\\desi-0.2-100\\desi-0.2-100\\spectra-16\\7\"\n",
+ "file_path = join(path,str(file_num[0]))\n",
+ "spectra = join(file_path,\"spectra-16-%s.fits\"%file_num[0])\n",
+ "truth = join(file_path,\"truth-16-%s.fits\"%file_num[0])\n",
+ "zbest = join(file_path,\"zbest-16-%s.fits\"%file_num[0])#generate file path"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### The methods of normalization"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-03-20T07:42:27.855479Z",
+ "start_time": "2020-03-20T07:42:27.140266Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "spec = DesiMock()\n",
+ "spec.read_fits_file(spectra,truth,zbest)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-03-20T07:42:28.895712Z",
+ "start_time": "2020-03-20T07:42:28.529698Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "nspec = DesiMock()\n",
+ "nspec.read_fits_file(spectra,truth,zbest)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-03-20T07:42:29.473340Z",
+ "start_time": "2020-03-20T07:42:29.411501Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support. ' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
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+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
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+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
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+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
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+ "\n",
+ " this.imageObj.onload = function() {\n",
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+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
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+ " fig.ws.close();\n",
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+ " this.ws.onmessage = this._make_on_message_function(this);\n",
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+ " this.ondownload = ondownload;\n",
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+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
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+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option);\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
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\" width=\"640\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x17b1abecd48>"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "spec_id = list(spec.data.keys())\n",
+ "nspec_id = list(spec.data.keys())\n",
+ "sightline = spec.get_sightline(spec_id[0],camera='z')\n",
+ "new_sightline = nspec.get_sightline(spec_id[0],camera='z',normalize=True)\n",
+ "fig,axes = plt.subplots(1,1,sharex=True, sharey= True)\n",
+ "axes.plot(10**sightline.loglam,sightline.flux,'r',label = 'Original Spectra')\n",
+ "axes.plot(10**sightline.loglam,sightline.error,'b',label = 'Original Error')\n",
+ "axes.set(**{'title':'Original Spectra && Normalized Spectra'})\n",
+ "axes.plot(10**new_sightline.loglam,new_sightline.flux,'g', label = 'Normalized Spectra')\n",
+ "axes.plot(10**new_sightline.loglam,new_sightline.error,'y--',label = 'Normalized Error')\n",
+ "axes.legend(loc = 'best')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-03-20T07:43:16.129091Z",
+ "start_time": "2020-03-20T07:43:16.046312Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "selected_spectrum = {}\n",
+ "for key,value in spec.data.items():\n",
+ " z_qso = float(\"%.1f\"%value['z_qso'])\n",
+ " if z_qso<=4 and z_qso>=2.33 and z_qso not in selected_spectrum:\n",
+ " selected_spectrum[z_qso] = key\n",
+ " if len(selected_spectrum)==21:\n",
+ " break"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-03-20T07:43:19.100086Z",
+ "start_time": "2020-03-20T07:43:16.814951Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support. ' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option);\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\" 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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support. ' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option);\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\" width=\"640\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support. ' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option);\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<img 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\" width=\"640\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support. ' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option);\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
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\" width=\"640\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support. ' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option);\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<img 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+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support. ' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option);\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
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SZkyZZzmSaPvsBZvAyyr9Avx8fF6uPniiy+63H7Xrl16HSZOnOhyHfgPARbgB5s2bZKwsDCpUKGCXLp0KdcA6/Dhw/pyT6ezPv3006KUkqJFi0pKSoph2ejRo/V9nDp1yuX26enpUqxYMVFKSa9evZyWV65cWZTyfFr4nDlz9OdZt26d2/WQ41oCLNqG9U2ZMkUPIo4fP56nbWkfoenq1atSokQJUUpJ8+bNnZbTLqzPNl+eq8tHbb766iuXn+HatWv1xz1dwmWbw7NKlSpOy3r27ClKKSlWrJhkZGS43P7kyZP687z66quGZSkpKVKkSBFRyvPlSOPHj9f3cfjwYbfrQWN/9srMmTPdrjdp0iR9vR9//FF/nL7DerwNsKzSL8ycOVNf9vPPP7vdxy233CJKKbn//vvdrgP/IMACfCw9PV3/69WXX34pIpJrgLVw4UJ9+Zo1a9zu+5NPPtHXc5yw13apQKVKlTzWz3Zqeb169QyPnzlzRt/3qFGj3G7/119/6etNmjTJ43NB4yrA8nY+D9qG9bVp00aU0k63t5eRkZHrBKO0j9BkfwkF3ymhyTaAi4iIkN27d7tcx3YZjeNnM3HiRP09P3TokNvnGDlypL7e2bNnDctscyTdd999Hutpm6etffv2hse3b9+u79vThM6rV6/W11u0aJHH54Ix1PF011r7s/OmTJmiP07fYT3eBlhW6Rdsc3VFRER4nGy+e/fuopSS6667zmNd4XsEWICP2X5UtmzZUn8stwDL278wrlmzRl9vzpw5hmW2vyTExMR4rF+PHj30LzL708Z/+uknr/5ql56erp+a6+n0dOSw//zr1q2r3zWqSJEi0rBhQxk1apQkJCS43Ja2YX2lS5fW37OUlBQZNWqU1KhRQ8LDw6VQoUJSu3ZtGTlypMs7PtE+QlP79u1FKe2v3K5uJ067sL7z58/rfUeVKlVk3rx5cvr0aUlJSZHdu3dL7969RSltjhf7M2xEcs6eCQ8Pd3uWhIh2FznbZ/jTTz/pj2dlZUnhwoVFKdcTxNu77777RCklt9xyi+Fx+6Bl7dq1brc/dOiQvt748eM9PhdEli1b5tVxZ38G1jPPPKM/Tt9hPd4GWFbpF2xniFWuXNljHUaNGqXv4+TJkx7XhW8RYAE+dOjQISlatKgULlzYMHFmbgHWkCFD9OWu7ghjs2fPHn09x3kFbNetd+rUyWMdhw4dqu/DflJX+/kIli9f7nEftru/uLtGHkaebmtsKzfeeKN89913TtvSNqztypUr+nv7/PPP62dvuiqVK1eWvXv3GranfYSeM2fO6HOQdOvWzeU6tIvQsHfvXqlVq5bL/iIiIkI6d+7s8uws29xBpUqV8rh/+zDEfj6bixcv6o8PGzbM4z5slzreeOONhsft58H5/fff3W6flJSkr+duPhvksB/Y2wdTjuzvxGZ/d1v6DuvxNsCySr9gmx+rcePGHuswdepUfR/uzmKFfxBgAT5ku+3rK6+8Yng8twDL/lbBnv7qcfDgQX09x7tO2QY03bt391jHV155Rd/H6dOn9cftb3u7evVqj/uw3UHkwQcf9LgeNMePH5c2bdrI559/Lr///rtcunRJUlJSZMeOHfL888/rt5qOioqS3377zbAtbcPa4uLi9PfW9pfLTp06yZ49eyQtLU1OnDghr732mv5X5ptvvlkuX76sb0/7CD32P7rdXSJEuwgd+/fvl7vvvttliHXXXXfJkiVLnLax/VapWLGix33/8MMP+r7mzZunP37q1Cn98ZEjR3rcR7du3fT+zd7rr7+u7+Ovv/5yu316erpXgQxy1K9fX3/PDx486LR8x44d+neK43FH32E93gZYVukXbr31VlFKSdOmTT3W4eOPP9b3sXnzZo/rwrcIsAAfsX3hVqtWTa5cuWJYlpcAKzMz0+1z2F/v7zi5pu0HQ48ePTzW0/7adfvJNe1/MHia90Ak59p1T5NrwnvffPONfrvoZs2aGZbRNqzN/gefUkpatGjhct4r+8s9Jk+erD9O+wg9d955pyilpHz58m4/c9pFaIiNjZWwsDApW7asfPzxx3L8+HE5d+6cbNmyRT/DJiwszNBniOQMVHObo8h+nhn7SZ3t+y1PcxSJ5MwzExkZaXjcfqDqab6djIwMfT1Pkzojx4oVK/T3rHLlyvLll19KfHy8nD59Wj777DMpW7ashIWF6ZNlt2nTRt+WvsN68hpgBXu/YAuwcpuHy34eN0+TvcP3CLAAH7hw4YKUK1dOlFKybNkyp+V5uYTQ/uwKR96csv3EE094rKs3p2yvWLHC4z44Zdt8Xbp0cflDjrZhbfan3CtlnE/CXlpampQqVUqUMt4xh/YRWg4cOKC/10OHDnW7Hu3C+t566y1RSpsHzX7KAntPPfWUKKXNaWN/iYztUqHSpUt7fA5vLhUaPny4x314c6nQH3/84XZ7LiHMn3fffddwlpV9CQsLk4kTJ+rfKV27dtW3o++wnrxeQhjs/YLtEsImTZp4rAOXEAYuAizAB2xf+I8++qjL5XmZxP3IkSNun8f+FrfuJs3M7XawtlvcFi5c2O2kmbNmzXK7fUZGBpNmFgD7iStXrlypP07bsDb7SU/Dw8M93jGnXbt2opRxfgraR2ixn3R2586dbtejXVhbenq6HhL07dvX7XonTpxwGXjaJmuOiIjweJmY/e3o3U3W3KtXL491jYmJEaU8T9a8bt06t9sfPnxYX49J3PNmx44d0rNnT6lSpYoULlxYSpcuLe3bt5cNGzbI5cuX9TO/7cMG+g7ryesk7sHeL9gmca9SpYrHOowePVrfB5O4BxYCLMAHHnvsMZd/5cqtHD16VEREFixYoD/m6a4b9nf+2L59u2GZbXCb2103bHf+qFu3ruHxxMREfd+jR492u739BKETJ07M5Z2Bt+znFLA/JZu2YX316tVzCqZccXXKPe0jdGRnZ0u1atVEKefbzjuiXVjbzp079ffsgw8+8LhumTJlRCklbdu21R+bMGGCvr2nO83ZB6ZnzpwxLLPdcCK3O81VqlRJlFLy8MMPGx7ftm2bvm9Pd5qzv9vdwoULPT4XvPfjjz/q7+v8+fP1x+k7rMfbAMsq/cLAgQNFKSWFChWStLQ0t/uw3QUzOjraY13hewRYgA9ca4Bl/5cEx8kw7dnmJihSpIikpKQYltn/JcF+Mkx7GRkZUqxYMVHK9S1uK1euLEp5ngzTfm4CTz9ukDezZ8/W31f7M7BoG9b35JNPilLaZR1Xr151u17btm1FKePp/bSP0GE/4JwwYYLHdWkX1rZ582avA6yyZcuKUkratWunP2Z/9oz9JMyObHPiuAoibGfPFC9e3O3ZGqdPn3YbRKSkpOhzMHmanN1+ThxPg2rkzfDhw0Up7eynxMRE/XH6DuvxNsCySr9gf4aYp8nZbXNl5Ra2wfcIsAAfOHLkiOzcudNtWblypd6ZjhkzRn/c/i8DtWvXFqWUNGrUyOVzZGRk6PNs2f8Qtdm6dav+HFOnTnW5j6VLl+rrLFiwwGn5gAED9B80jn9VsenQoYMopaREiRKSnp7uzdsDL3Tq1En/bOznwBKhbVjd4sWL9fd+/fr1LtdJTU2VG2+8UZRS0qpVK8My2kdosA0Yw8PDvbrcgXZhXUeOHNHfd08Tm9tfQti/f3/98fT0dClRooQopaRjx44utz1z5ox+OdDAgQOdls+fP1/ft6u5P0VE3nvvPX2dbdu2OS1/6KGHRCkl5cqVczvYbdiwoSilpE6dOm5fJ/Lm/Pnz+vxXXbp0cVpO32Et3gZYVukX4uPj9ctj3c0VaT+HW25/EILvEWABASC3ObBEjJMJLlq0yGn5xIkT9eXLly93uY/GjRuLUtodRC5evGhYlp6ernf4FStWdHmmx+7du/VO/7nnnnNavnXrVn2+gWHDhnnxyiEiuQ4258+fr7/vzZs3d1pO27C2tLQ0qV69uiil3TXHfi4Qm3Hjxumf8YwZMwzLaB/Wl5qaqs955O3dtmgX1mbrM4oXLy4HDx50uY5tEnellCxevNiwzDaBdnh4uGzdutVp20GDBolS2pmhriY4vnr1qn4HuIYNGzoFCBcvXtSXu5tM2X4y6EmTJjktX7hwob582rRpbt8LeC8jI0OfrDsqKsrlnd7oO6zF2wBLxDr9gm2S+OjoaDl27JjT8vbt24tS2k0w4uPjXe4D/kOABQQAbwKstLQ0qVu3rv6j4v3335dTp07JsWPHZMyYMRIREZHr4GXjxo36eg0bNpT169fLmTNnZOvWrdKyZUu9Do4TbtqzTeKolJLBgwfLwYMHJT4+XubOnav/xa5ChQpu/yIGZ6VLl5YnnnhC5syZI3v37pWzZ89KYmKibNq0Sfr166f/SCtevLjs2rXLaXvahvUtWbJEbwcPPfSQbN68Wc6fPy/79u2TYcOG6csaN27s9IOQ9mF99j/YZ8+e7dU2tAtrs79Mpnz58jJjxgw5ceKEnD9/Xn755Rfp3Lmzvrxhw4ZOwfiZM2ekQoUKopR2WfLcuXMlPj5eDh48KIMHD9a39XSGl/2Eyy1btpStW7fKmTNnZN26dXpAERERIRs3bnS7D9vlSBERETJ27Fg5duyYnDp1SqZPny5RUVGilDZHkqe5bGB07NgxadSokUyfPl22b98uiYmJcuLECVmwYIE0atRIDyAc/xhiQ98R3Pbu3StbtmzRS9++fUUp7Ww2+8e3bNnidOMYq/QL+/fvl+joaFFKSfXq1WX58uWSmJgoe/bsMdz1+/XXX8/fm4wCRYAFBABvAiwR7bKAGjVq6Os6lsaNG8v58+c9Ptcnn3wikZGRLrcPCwuT2NhYj9unpqbqc+24KmXLlnWasBOe2U7J9lRuuukm2bBhg9t90Das77333pNChQq5fX8bNGjg9mw+2oe1Pfroo6KUFnJ7urW9I9qFtY0cOVIPt92VunXryvHjx11uv337dn2OLFelbdu2Hu+MKqKd3eGuDpGR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width=\"640\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support. ' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option);\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
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width=\"640\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support. ' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option);\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
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8vLCokWLDAVY27dvV83bZq9o/TBh9jWeOwRYyuXZGeKf1ezZs2V7zz33nMvtOfLLL7+o/t7v3r1reN2YmBgI8Wiu3tOnTwNggEX5EwMsoiw6d+4sT+ZDhgxxev379++rLjI/++wzmzrKL4yXX35ZXvQ0bdoUkyZNQmxsLF544QXVU46MBDKnT59G0aJFZb3AwEAMGDAAs2fPxrhx4+TNU0hIiGpYkBkB1qBBg2TPtXr16mHcuHGIi4vDyy+/LHvQCPGou7Ne74LVq1dDCAEfHx80bdoUI0aMwMyZMzFnzhy89tprqFWrluri4+uvv9b9HMwKsJST+w4dOjTb7VgpLzq7d+8uJ+OuVKkSxowZg9mzZ+OVV15BSEiI6nM8duyY3Tatk4ZbLyKHDh2K2bNnY/r06XjmmWfg7e0tl9vrVXjx4kWUL19e1vX09ESLFi0wfvx4zJkzBxMmTEDnzp3l9pTHzqpVq7BgwQLVvmtNPqp8ihCgDrCsQzY9PT3l04dmzJiBHj16qG7MzDxWnHXixAkZYpQpUyZHhgq3bt1a7vsHH3xgevuOZA2wMjMzUadOHflveg+qMBJgZWZmqoZbCyHQsmVLTJkyBTNmzMCzzz6rmli2SpUqunORZOdcqlxn5MiR8PX1haenJ7p06YJp06Zh+vTpaNy4sWr/vv/+e+zcuRNeXl6yrvUR7lnrbt++Xfd9tf64UL58efTq1QuTJk3C3LlzMXXqVPTo0UP1d9yoUSO7T7ZyNcCy/r3aK/Pnz1fdAMfFxdm087///Q/FixeXdUJDQ/HSSy8hNjYWsbGx6NOnjyrA6dKli+53wKFDh1RhYrFixTBkyBDExcVh1KhRqFixojzHde/eXdbLboCVmpqqOjdu3LgxW+0oKY+vESNGwMfHB0IINGnSRD6I4dlnn5X/LoRArVq1cP/+fd02lfN0CfEoRI6Ojpbn5AYNGshlHh4e+OSTT3Tb+u6771QhrMViwbPPPospU6bI97lJkyZyjh2rO3fuYMGCBaoeohEREZrHzX//+1/VNpXB0ODBgyGEQEBAAPr164fY2FhMnDgRzZs3x7lz5+Q6/fv3hxACQUFB6NSpE2JiYhAXF4fY2Fi8/PLLKF26tGyzaNGiuHjxogufWvb3MyMjA127dpXreXt7o1OnTpg8eTLmzJmDkSNHyuPW+nr05ix6+PAhmjdvrvr+7dSpE6ZPn46pU6eibdu2cvjXmDFj7P59nzt3TnU+CQ8Px6uvvopZs2Zhzpw5GDt2LDp16oSAgAAI4TjAMuMaTy/Ash5bCxYsQLt27WSdPn362BxbAOR/W+uFhIRoHodac9Eq/45cDbBu3ryp+mzfeecdl9ozYtCgQXJ7zkwc//vvv8vvR+X8fQywKD9igEWkkJGRobpYt3cRaE/Hjh1lG6+//rrNcuUXhvUC8ttvv7XbppFARvnFX79+fVy5csWmzuLFi+VFrqMvcWcCLCEEChUqpPnr7+nTp1UXmz/99JNme99//z1Wr15tt9fbZ599Jm+GwsPDdXsTmRVgzZw5U3VjMG/ePJeGLChvKq2fwejRo22Gkdy4cUN1IduwYUPNi8Lr16/LOZl8fHzw3nvvab4nx44dQ0REhN2b7IyMDDRq1EjWqVatmu6ww+TkZKxatQpHjx61WebsHFhZe+KULFnSZnhiVmYeK9lhvbkSQqBGjRpODfU0YseOHbJ9Hx8f/Oc//zG1fUeyBliA+rHb1atX13w/jQRYb7/9tqzj5+enee47evSo6qmeL7zwgmZb2TmXZl2nePHimpPTjx07VvX3V6FCBZQoUULz2IyOjpZ1W7ZsqbvtyZMn448//tBdfuPGDdX3x9q1a3Xr5sZTCJXvQbVq1WweWJCSkoLIyEhZZ+bMmZpDkS5evKgajv3ee+/Z1Hn48CHq1q0r67Rt29amR1RaWhqGDx9u8x3myhxYrVq1UgUhX3zxhUvnCuXx5eHhAW9vb83vxcOHD6u+F/V67+3evVuGSWFhYbrfn1988YUMpgIDA3H9+nWbOpcuXVL9yNWtWzfd3l9Xr17VfLpZdubAytrbr3Hjxg6D/+XLl+Obb77RHWKZlpaGSZMmyTZffPFFQ/ti9n7GxsbK+k2bNlWFW1YPHz7ElClTZL1GjRpptqWcFDwoKEiz9/V3330Hi8WiOv61/r6VQePQoUN1w/C0tDR8/vnn+Ouvv2yWmX2NZ6Tnf07PgaV8PdkJsB48eIAzZ85gzZo1cjieEAKVK1fOkSkFlJKTk2XgaO991trnmjVrQgiB2rVrq/6mGGBRfsQAi0jh0qVLqi83Z5/sYaW8cGjXrp3N8qw3UEaGODkKZA4cOCCXBwYG2h2SoHzqiL0vcWcDrKVLl+puUzkxfnR0tL2X6pA1hBNCaM5Rk3XfXAmwEhISUKpUKZub5K5du2LWrFn49ttv7Q5xykp5UymEQIcOHXTr3rp1SzUf2w8//GBTZ9y4cXZvCpWOHj0qf4HTusn+7LPPZFulSpVy6nUpuRpgOTNXjiNGjhVnZGZm2vz9CCEQFRWV7fcrqx07dqguUoV49KjrLVu2mNK+EVoBFqDukbh+/Xqb9RwFWGlpaaobnQ8//FB3Hw4fPix7x3h6euKff/6xuz2j59Ks6+gdF6mpqaqbfSH05/9KSUmRPQ89PDzsPr3KkeTkZDnPmt6NLpDzAdaGDRtUwY7W9+GyZctknUmTJtlt79q1awgMDIQQj3ruZA3k//Of/8i2wsLCkJSUpNlOZmYm2rRpo/pcXAmwfvrpJ5uhX6Ghoejfvz+WLFmCn3/+2akb06zH15w5c3Tr7tmzR/W9ohXIP/300/IcoPWDgdLy5ctlezNmzLBZrjx3tWnTJltBnasBVvHixU0N/Lt16ybDFb1jxihn9zMhIUH2cqpSpYrD46R3796633Pp6emqB4TYG57+wQcfqI4xrb/vLl26yOXZfb/NvsbLrwFWjRo1bL7zlaVRo0a58tAm5Tm5UqVKhh8kMW3aNAjxaPjp/v37VcsYYFF+xACLSOHIkSOqL6WsvzYbpfwV7cknn7RZrvzCqFChgqEvIUeBjHVsuxAC48aNs9vW1atXVRfsZgRYoaGhmr+8WyUkJMi6rVu3trt/jly9elW2NXXqVIf75kqABTy6kVZ2E9cqVatWRXR0NP73v//ZbStrgKXV80NJ+UjprBcXDx8+lD0GK1asaOg4at++vQwEsh7fynmvnHkaV1auBFjNmzfP9na1GDlWnKGcl6Vnz56qG/hq1arZnTT+/v37sm7dunU16+zdu1f2oihZsiS2bdsmwwxvb298+umndvevV69echuO5lqxRy/AUs6/UaFCBTx48EC1nqMA69tvv5XLIyMjHe6Hcr4xrRvy7JxLlevYm88LAJ577jlZ9+mnnzZcVzk3XHZYH3Pu4+OjO/FzTgZYv/76KwoVKiSPu507d2rWs/6qb7FYDIU8r776qtynrGGM8mleS5Yscbh/yvOoKwEW8GhuM3vzoHl7e+Ppp5/G/PnzHQbVyuMrODjY4cTdHTp00P2uUv4wZeTmMi0tTQ7nqlevnmrZgwcPVK9Rq8eNEa4GWNOmTcvWdvUo5+fTO06NcnY/Fy5c6NR1hvK4jYmJUS3btm2bXFanTh2HbVWtWtXu37dymPalS5cctqfF7Gs8dwiwskMvwAoJCcGnn36aa08kVc6Nppyb0Z4///xTDlfWeqoxAyzKjxhgESkofw0VQtidf8Qe6/w8QjzqVpyV8gvD6LxKjgKZJk2ayOVGJv1WDhUzI8B6/vnnHW7T2puhZs2aDuvev38fe/fuxQcffIClS5di4cKFcl6DN998U263T58+DvfN1QDLuj9LlixBVFSU7k2OEI+CocGDB+vOZ6K8qQwNDXW43b///lvWf+KJJ1TL9u/fL5cZna9N2WNL+QvwgwcP5HA7Ly8vl37JdiXAMvLkuqxcPVaM2rJliyr4sIY3yrk4KleurDuJ7YULF2S9fv362SxPSkqS8495e3vLX0qPHz8u5xjy8vKyOyeWde4sT09Ph5Nl26MXYAHqG6Osc344CrDeeOMNudxIoKjsldO+fXub5dk5lyrXcdRrSPnDwOTJkw3X/fzzzx3ux9mzZ7F161asWrUKixYtUs3dogyT9YKGnAqwLly4oOp1umzZMs16169fl3W0ehprUQa+WXvwKYeMag3Dykq5j64GWMCjCd3Hjh2resqoVvH397cbsCmPr759+zrc7sqVK3WPYeW55aOPPjL0OurXry/PFcprGGX47Ci4tcfVACvr+cSIhIQE7NixA2vXrsWSJUtUfyvWsFcIgRUrVjjdtiv7qezlZOSJt8nJybJ+1l7QU6dOlcu0wvqsJkyYYPfvW7m8Y8eOTj2h0srsa7z8GmCtW7dOHm/Tp0/Hiy++KHvH+/j4oH///k4/8MlZZ8+elcNGPT09ceHCBYfrPHz4EE899ZR8j7R+ZGCARfkRAywiBbN6YCmHLTnqgbVo0SJDbToKZJQX80aGr1gny7Z3IeFMgOXo6VsA5M15xYoVdeucP38eAwYMUE0+aq/oDcEzO8BSunjxIjZu3IiYmBi0atVKc19btGih+Wul8qZS64Y8q4yMDNkTwtPTU/VL39q1aw29R3pF+YCBU6dOyX830jPGHlcCLGee6mfWsWJEZmam/MXbw8PDpneT8uajQoUK8gk/SsowRisUiIuLk8uz/jp/8OBB+XAIT09PrF69WnM/rU9vc+UGFbAfYB06dEheSJcqVUp1UewowFL2stGbCF7p4sWLsn5ERITN8uycS5XrOLrhtQ69cLau3lDGzMxMrFu3TvZcMlL0bqRzIsC6d++earJ+ew98UD4tMzvFOiEz8Gi4pvXfAwMDDe2rMuQzI8CyyszMxNGjR7Fq1SoMHToU9erV03y6nF6gqTy+7A0ftPrvf/8r67dq1Uq1TNkDMTtF+SRL5Q9r9j5XR1wNsJx50uOePXvQpk0bOQeYo5KdH0Bc2U9l6OpsqVGjhqqtvn37OvU9+OGHH9r9+z579qxqKLqPjw/at2+PefPmYc+ePTa9Z7WYfY2XXwMsLQ8ePFB970dGRmrOO2cW5fti9AcD5Q9433zzjcN2GWBRfsEAi0hBebMkhNCcc8UI5WSdbdu2tVmu/MJYs2aNoTYdBTJ6IYeeiRMnOryQcCbAMnKD5Ki93377zeFjwrMWvQvonAywskpNTcXnn3+umsxYCO0bauVNpZFfNAGo5sVQhqrKi5PsFGUPiN9++83pmxI9rgRYWvN8aTHzWDG6PWs7zZo106yjHF5YtmxZnDhxQnO5h4eH5pCOatWq2T337NmzR4Z1Hh4eePfdd1XLDx06JNfXelqcM+wFWIA6iFJuy1GApRwuZWSus5SUFFk/ODjYZnl2zqXO3CCZWTcjIwP9+vVz+u80u+dnZwOszMxM1TDINm3a6E6gDQCffvqpS+cfZS8T5VBfez9wKClv+M0MsLQkJiZiyZIlNnOiaU3IrzwOHM1LCKh72WYdOqZ8KnJ2irIn27x585w6HvS4GmAZ7dm+fPly1UTlRoorrys7+2n0xxOtEh4ermpLeW40Mjn39u3bHb7uPXv22ExMby1+fn7o3r273YdemH2NV5ACLKvRo0fL/RgwYECObCMzM1M+sVoIoTmRflYnT56Uvert9QRlgEX5EQMsIoWMjAzVTbGj+Wb0KIfYjBw50ma5M1/QVmYHWMru5e4QYKWkpKgutJo0aYJ169bh6NGjuHnzpurXwszMTIcX0LkZYFklJyernrRVrVo1mzrZCbCUveuUAZZyfqyuXbtqPkbaXlHO16UMaOw9Rc0IVwIsI5Oqmn2sGLF06VLZjt4EtZmZmapHXIeGhsr3+MGDB3IYoNavp0lJSXK9okWL6u7Hjh075N+6EAILFy6Uy1577TUI8Wj4oZEhWPY4CrBOnjwpJ1gPDg6WvRWcCbCM3KQpe+aEhITYLM/OuTSvAizlBNv+/v4YP348fvjhB5w/f0y16r4AACAASURBVB7JycmqCbWVvblyK8BSDu+sUqWKwx4oyrmHGjdu7PT557///a9sKzsBljJEzekAy+rkyZNyInohBIYNG2ZTx9kA66+//pL1swZYyr+XUaNGOf0eK4c1KQMsI0PU9LgaYBmhfOCIh4cH+vfvjy+++AKnTp1CUlKSKlzatWuXU8e5mftpPRf7+vo6/dmsWrVK1Zaz50blnFn2XveDBw+wceNG9O3bV3d4bOfOnTWHlzHAcuzq1asyaPXx8cmRJxEqe7sGBwcbmh7AOrw1JCTE7vBRBliUHzHAIspCGT69+uqrTq+fkpKiCsGUw7SsciLAyushhK5e3CifgNejRw+7IdytW7fcMsACgK1bt6ouDLNezJg5hPC9996TbY0ZM8al/XaXIYRGAiyzjxUjZs+ebejmLyMjQ3VjXbx4cRw6dEg1rHjXrl026ymfgJr1l/mstmzZIsMjIR5N5nrp0iXZG8CMX4EdBVgAMHjwYFnHOrzEUYDl7DAZZ4YQunuAZZ0I2NvbGwcOHLDblrI3X24EWJs2bZJ1g4KCDE3w/d1338l1nn32WYf17XGnIYSOjBkzRm73qaeesllu5hDC559/Xi7bunWrS/vtLkMIjVA+LfHtt9+2W/fLL7/MswBL+URVV+dAMnsIoZ5//vkHq1atQvv27VU93F555RWbugywjClRooTcF0fn9uxQDiXWCs211K5dW3Ut6mypXbu26a+DyCwMsIiyUE6oGhAQ4PRk1srH3FosFs1fsXMiwMrrSdxdvbhRTi7u6Aleysn23S3AOnHihOoiIOvErsqbytKlSztsTzm8pGrVqqplP/30k1zm6AlpjqSlpckAJC8ncTcSYJl9rBixYsUK2c6LL75ot25aWhq6du0q64eEhMinReqFlsqhcr6+vroPAbD68MMPVfPCWJ+SWaRIkWwPfVYyEmBdvHhRPjHRz88PV65ccRhgKXv5GHnK11dffSXrO5rE3Z0DLOXkzUaewqoMZ3I6wNq/f7/qAQ7fffedw/0DHs1BZ20/NDTU5Sdx5fUk7kYpfzioUqWKzXLlceDsJO5ZfzSbOXOmXDZ+/HiX9lt5LszLSdyNsE5EX6RIEYdzNSl/XMjtAKtVq1ayvr2heEaYPYm7EZs3b5Yhlre3N+7evatazgDLmJCQELkvv//+u6ltJyUlqYaqGm2fARYVZAywiLK4f/++6teU1157zfC6SUlJqovw119/XbNeTgRYyidgjRs3zm5bV69eVU1K6w4BlrI3h3JomxblnAPuFmApn/QkhLB5hLryptLIxYhy2EfW7t3Kx6J7eHjYPJbeWe3atZPbWrlyZbbbqVy5smxHOSxKj7MBltnHihG///67bCckJMThAx5SUlLkEwGtxc/PT3Nyd6vq1avLunqTgCspb6StZd68eU6/Ni1GAiwAiI6OlvWGDx/uMMBSDnupVauWw/0YMGCA3Zu6/BJgKXvY9ezZ02478fHxqmGiORlgXb58WTWsyN7T9bRUqVJFrqs3SbBRyp6Ljnrd/Prrr6rjPjcDrFmzZsntNmzY0Ga58jgICQmx+Q7IqmPHjrrHjbJ3VtmyZQ1NvK0n6/eFkV52WpTHst58gFk5GwxZv0OKFy/usK7yoQO5HWApH7zRq1cvl7atPDfWrVvXYf0nnnjClNet/N45fPiwapm7B1jWnsjlypVzuG855fTp06pzkdb8lq5Q9px0pnf81KlTMXDgQIdFGXRVqlRJ/ruRpwQT5RUGWEQalL+Ienh42DzuW8uDBw/wzDPPyPVKlCiBK1euaNbNiQDrwIEDcnlQUJDdMe/K7vnuEmCNHz9etvXBBx/otnH8+HHZWyA3Aqzdu3fbncg4q5deekluV+sXrKwBVqdOnXTbun37tqqXwY4dO2zqKIcaNW3aVPPJh1q0ekt8/vnnsq1SpUohPj7eUFtZPfnkk7Id5VOw9DgbYJl9rBiRmZmJiIgI2ZaRYXpXrlyRN4xCPOqlcvHiRd36M2bMkHVLly5tt67Vs88+qzqe9J5O6CyjAdaNGzfkk658fHxUQx20Aqy0tDTVsJtNmzbptn306FF5g+Lp6anZsyy/BFjJycmyp0PlypXt9lZSnkNyMsBKSUmRPV2EEBgyZIjd16dFOTS2atWqhp/cq/X6lcOvy5UrZ3cuGWXY7kqAlZ6erjmkV09qaqoqOBg1apRNHeVxIITA/Pnzddvbu3evPC78/Pw03z9lQONMLyyt93jkyJGyrbZt2xr6gSEr5Xx9WZ+kp8fZYOjpp5+W9S9cuKBbb/369ar3OrcDrKtXr8peqB4eHk6FuFk/n7S0NNX3vb0ho8rhg2YGWFkfPOLuAZb1oQoWi8XhvuUU5fVL9erVTW+/cePGsn3lk1vNwjmwKD9igEWko3fv3vKk7unpiZiYGN1hVUePHlV9yXh6emLbtm26bedEgAUAbdq0kXWefvppXL161abOkiVL5MWWOwVYyqFCYWFhOHPmjE2d/fv3o2zZsqp9z+kAq0GDBqhYsSIWLFhg95e15ORkVbAihMDSpUtt6ilvKq2vIzo62iYkS0hIQMuWLWXdBg0aaN6QXLt2TXXR27p1a7s3c2fOnMHUqVM1h8FlZGSohpZWq1YNR44c0X29q1at0uz1pZy3RWsOuKycDbDMPlaMUgZ8QjyahD8hIUGz7vfff6+60bWWWrVq6c6VkpSUpOoNEx4ejj179mjWvXjxoiosshZvb298//33Lr1OwHiABajPZ8qenXqPtH/77bdlHX9/f80ha8eOHVPdTOoN28wvARYA1QMepkyZYhMepKamyh6DZpyfHQVYyr/TFi1aGA6/le7fv6+6Aa5duzb+/PNP3fpXrlzBggULNIeDPnz4UNUboEOHDjbzOaalpcmHFSjfo+wGWNahu02aNMEHH3xgM4RK6eLFi6qJtj09PW16rADq48DDwwM+Pj745JNPbOr9+eefqr93vZ7TO3fuVA0XHjVqlN0h3r/99hsGDhyo2Zvu0qVLqicpdu/eXfeHiqtXr+KNN97QXGYNob29vTWvM7JyNhhS9ihv37695ufywQcfoFChQqrjILcDLED9w0ORIkWwevVq3ScYPnjwAFu3bkWrVq0050pauHChbCskJET1oAOrH374Af7+/g5fd5s2bbB06VK7PyIpHyxRqlQpm/129wBLeb2yf/9+h/sH/P9hh/b24eWXX8Z7771nd16zW7duqeaSFULg/fff162vPLaMfledPHlS9d1u74fp7GKARfkRAywiHampqaoLfCH+/2OHJ06ciLi4OIwaNQpPP/206kKicOHC2Lx5s922cyrAOnXqlGosfmBgIAYOHIjZs2dj/Pjx8pfckJAQvPLKK7Ke3uPsczPAevjwIWrWrCnbK1SoEPr27Yvp06djypQpaNeunbyInzJlisNQwswAS3kzEhkZiZdeeglTpkzBvHnzMGXKFPTs2VM1cb8Qjx5Br3URq7yp7N69u3w0cuXKlREdHY24uDgMHjxYdZPh7+9vd3jgnj17ZC8Y64VO27ZtMWHCBMydOxdvvPEGBg4cKCeRFkKgS5cumm1duHABZcuWVd2ktWzZUrY1ceJEdO7cWfYs0roA/Pe//606BkeOHInFixdj6dKlWLp0qc0Etc4GWGYfK86YPHmy6nO2WCzo2bMnpk+fjlmzZmHYsGE2wVX//v1Vvbfatm2rGxYcOHBA9VkK8WiY0tixYzFnzhyMGzcObdq0ga+vr1xeuXJl1bkqICBA88baGc4EWElJSfIJi8qiF2BlZmaqHpYhxKPJq9944w3MnDkTzz33nM3r03swRX4KsD755BPVa65VqxZef/11zJ49G0OHDpVhRsmSJVXDZHMiwFIOTfPw8MCECROcfnKg1YkTJxAaGqpqr0mTJoiJicHcuXMxbdo0DBkyBPXq1ZN/l3o9d/744w/VfC/FixfHq6++iri4OIwePVr+HYWHh6N79+6ynqsBlvJc0rBhQwwfPhwzZszA3LlzMXbsWLRt2xY+Pj6qunrzFCmPg+HDh8v1mjZtiilTpiA2NtbmGI+MjERycrLufi5ZskR1nREUFISePXti6tSp8rzco0cP1blbr7fGtm3bVENULRYLnnvuOUydOhVz5szBqFGj0KRJE/lZaVE+bbVSpUqYPHky3n77bXmOzzo03tlg6OzZs6res6VKlcKQIUMwe/ZsxMTEICoqSn4/TZw40anrEHuyE2BlZGSgZ8+eqmOjfPnyGDRoEGJjYzF79mxER0ejQ4cOqusErcDl4cOHqvlMPT090aVLF8yYMQPTpk1Du3bt5HGgHB6v9bqtr8XLywsNGzbEsGHDMHPmTMyaNQvDhw9XDf8VQtg8FRFw/wBLOZw3NDQUMTExWLJkiTwOf/jhB5t1lK9Zbx+s33+FChVC8+bNMXLkSMycORNz587FhAkT0KVLF9XxKcSjXtn2etZmJ8CaNGmSXOeZZ54xtI6zGGBRfsQAi8iOzMxMLFu2DCVLlrS5OdMqzZs3x8GDBx22m1MBFgDs27dPNUQnaylWrBh2796tmkxZbx6m3AywgEe/Nim/5LXK//3f/+Hhw4fy/3M6wIqOjlYNA3NUvLy8MHz4cN3HHCtvKgcOHIjDhw+r5k3LWooXL27okdrHjx9H3bp1De2jp6cnxo4dq9vWpUuX0KxZM0NtafUQSk9PV12EZy1ZPzNnAyzA3GPFWWvWrLEJmbRKhQoV8PHHHwN41EtTuY69IYh//fWXahimXvH19cWIESNw9+5dPHz4UDWsqkyZMnaH3jjiTIAFAG+99ZbN/ukFWMCjHwiU81vplaZNmxp+BLi7B1iA+oZEq5QrVw6///67oRs9VwKsrEOZjRa98/ylS5ds5nyzV3r37q37Pv7www+qAF/r7+rIkSOq84YrQwhbtGih6jnoqAQHB2PZsmW6bWY9Dj766CM5zEyr1K5dW3e6AaUvv/xSFRTaKxaLxW7v159//tnh+VOIRz+GaDl37pxqrlBHx0l2gqEtW7bYhATKUrhwYaxdu9app206kp39BB6FWDNmzLD7OStLmTJldB+2cfv2bTRv3lx3XS8vLyxcuNDh61bORWmvFCpUCAsXLtTcF3cPsG7fvm0TxCmLViCjXK63D8rJ+R0VPz8/zJ8/3+FwXGcDrIyMDISFhcl1jDyZMjsYYFF+xACLyIC7d+9iw4YN6NOnD6pWrYqQkBD4+PggNDQU9erVQ0xMjKGQwSonAyzgUdfmGTNmoHbt2vD390dAQAAiIyMxYcIEOa/O66+/LtvTm8w1twMsALh58yamTZuG2rVrw8/PD35+foiIiED//v2xc+dOWc+63dyYxP3+/fvYtm0bJk2ahA4dOqBixYqwWCzw8vJCQEAAwsPD0blzZ8yZM0dzOJtS1gALeDSP0MyZM1G7dm0EBQXBz88PkZGRmDRpEq5fv254PzMzM/H111/jlVdeQbVq1RAcHAwvLy8EBgaievXq6NWrF5YvX254ktFt27Zh4MCBqFy5Mvz9/eHt7Y3ixYujSZMmmDRpkt1ePqmpqVi8eDGaN2+O4sWLy7mMtD6z7ARYgHnHSnbcunULixYtQvv27VG6dGkUKlQIAQEBqFy5Mvr164ePPvrIppfV1q1bVb0o9IbnAP//sxwwYACqVKmCgIAA+Pr6okyZMmjVqhXmzp1rM0fWzZs3VTctNWvWNDwnUVbOBlgpKSmq3h9C2A+wrH799VcMGTIEVatWhb+/PwoXLozy5cujd+/ehi7Y81uABTwKaLp3745SpUrBx8cHJUuWRIMGDTBv3jwkJibatJUfAiyrn3/+GcOHD0dUVBSKFi0KLy8v+Pv7o0qVKujWrRveeustnDx50m4bwKOJ7CdMmIDq1avDz88PQUFBqFOnDmJjY+V7ZEaAZXX9+nVs3LgRI0aMQLNmzVC6dGkULlwY3t7eCA4ORrVq1dC7d2+sWbNGtzegldZxcOzYMQwbNgyVK1eGn58fgoOD0ahRI7z77rtODd1MSUnB6tWr8dxzzyE8PBwWiwXe3t4oVqwYnnrqKQwaNAibNm0y9BTZBw8eYM2aNXjmmWdQtmxZFC5cGIUKFULZsmXRsWNHLFy40G6wdvnyZYwbNw5169ZFYGCgapijGQEW8Kh335AhQ1CxYkX4+voiKCgIkZGRGDNmDP7++28Axp+2aUR299Pq6tWriIuLQ+vWrVGmTBkUKlQIhQoVQunSpdG8eXPExMRgx44dukMMrTIzM7F+/Xq0atUKxYoVQ+HChVGxYkUMGDBA/uDo6HUnJydjy5YtGD16NJo2bYrSpUvD19cXPj4+8nt86tSpdv923D3AAh6FWDNnzkSDBg0QEhKiCqOzG2AlJyfjq6++wtixY9GqVSuUL18eRYoUgZeXF4KCglClShX06NEDy5cvl+cjR5wNsJST+hcvXjxbQ7yNYIBF+REDLKLHlLW3hqenp93JcslcWgEWEREVDNkJVYmIiMgYBlhEj6E7d+7IYXHVqlXL6915rDDAIiIquBhgERER5RwGWESPIeVE1BMmTMjr3XmsMMAiIiq4GGARERHlHAZYRAXMa6+9pjs3UXp6OubPny/nqvD19dWdSJRyBgMsIqKCiwEWERFRzmGARVTAWJ9aUqNGDbz66quYNWsWYmNjMXjwYJun3c2fPz+vd/exwwCLiKjgYoBFRESUcxhgERUwysfu6hUfHx/MmTMnr3f1scQAi4io4GKARURElHMYYBEVMH///TcWLVqETp06oVq1aihWrJh8bPJTTz2F8ePH4/Tp03m9m48tBlhERAUXAywiIqKcwwCLiIiIiIiIiIjcGgMsIiIiIiIiIiJyawywCrizZ89izJgxqFWrFvz9/eHj44PQ0FB07NgRGzZsQEZGRl7vIhERERERERGRXQywCrAvv/wSFovF7mTezZo1w507d/J6V4mIiIiIiIiIdDHAKqDOnDmDwoULQwiBkiVLYsWKFThx4gSuXbuGX375Bd27d5ch1osvvpjXu0tEREREREREpIsBVgE1adIkCCHg6emJffv2adbp0qULhBDw8fFhLywiIiIiIiIiclsMsAqozp07QwiBJ554QrfOxx9/LHthHT58OBf3joiIiIiIiIjIOAZYBdRzzz0HIQSqV6+uW2fTpk0ywLp8+bIp283IyEB8fDzi4+Nx9+5d3Lt3j4WFhYWFhYWFhYWFhYUlx8rdu3flfSgfVFZwMcAqoGbPng0hBLy8vHDkyBHNOj169IAQAjVr1jRtu/Hx8XYnjWdhYWFhYWFhYWFhYWFhyakSHx9v2v0tuRcGWAXUzZs3Ubx4cQghUL58eXz88ce4cuUKkpOTceTIEQwcOBBCCFgsFvz888+mbZcBFgsLCwsLCwsLCwsLC0teFQZYBRcDrALs2LFjqFatmuYftZeXF3r16qXbO8see103r127pjpx5HVXUhYWFhYWFhYWFhYWFpaCXZQdKe7evZsDd9fkDhhgFXB///03GjZsqBliNWjQAFu2bHG6TaPJ971793LgFRERERERERH9f/fu3eN96GOAAVYBNmPGDHh4eKBkyZJYtWoVzp8/j8TEROzduxe9evWCEAIeHh5YtGiRU+0ywCIiIiIiIiJ3wQDr8cAAq4CaN28ehBDw8/PD33//rVnn5ZdfhhACnp6eTg0lNNp1kycOIiIiIiIiymkMsB4PDLAKoLS0NAQHB0MIgUGDBunWu3Dhgvwjj46ONmXbPHEQERERERFRbuJ96OOBAVYBdOjQIfnHu3z5crt1S5QoASEEOnbsaMq2eeIgIiIiIiKi3MT70McDA6wC6NdffzUcYJUsWRJCCHTu3NmUbfPEQURERERERLmJ96GPBwZYBdCZM2fkH++QIUN06ymHEA4bNsyUbfPEQURERERERLmJ96GPBwZYBVTFihUhhIDFYsHJkyc161gncRdC4IsvvjBluzxxEBERERERUW7ifejjgQFWAbV27Vr5BxwaGoo1a9bgwoULuHnzJn777Tf06tVLLq9bty4yMjJM2S5PHERERERERJSbeB/6eGCAVYBNmTIFHh4e8g9Zq9SoUQPnz583bZs8cRAREREREVFu4n3o44EBVgG3f/9+DBo0CE888QQsFgu8vb1RokQJtGnTBitWrEBKSoqp2+OJg4iIiAqizMxMpKSkID4+HufOncOZM2dYWFhYWEws586dQ3x8PFJSUpCZmenUOZr3oY8HBlhkKp44iIiIqKDJzMzExYsXcfz4cRYWFhaWXCgXL150KsTifejjgQEWmYonDiIiIipItMKrEydO4PTp03neW4GFhYWloJTTp0/jxIkT2Q6xeB/6eGCARabiiYOIiIgKkpSUFHkzderUKSQnJzs9tIWIiBzLzMxEcnIyTp06Jc+7Rqe84X3o44EBFpmKJw4iIiIqSOLj4+WNVHJycl7vDhFRgZecnCzPu/Hx8YbW4X3o44EBFpmKJw4iIiIqSM6dOyeHDbLnFRFRzsvMzJTDCc+dO2doHd6HPh4YYJGpeOIgIiKiguTMmTM4fvw4Tp8+nde7QkT02Dh9+jSOHz+OM2fOGKrP+9DHAwMsMhVPHERERFSQWAMsozdRRETkOmfPvbwPfTwwwCJT8cRBREREBQkDLCKi3McAi7QwwCJT8cRBREREBQkDLCKi3McAi7QwwCJT8cRBREREBQkDLCKi3McAi7QwwCJT8cRBREREBQkDrNxz8OBBREdHIyoqCsWKFYOvry/CwsLQunVrLFq0CDdv3jRtW2fPnpXXrLt37zatXavdu3fL9s+ePWt6+64ya/8OHDiAoUOHIjIyEgEBAfD19UWZMmVQp04d9OvXD6tXr8Y///xj3o7TY4MBFmlhgEWm4omDiIiIChIGWDkvOTkZAwcOhIeHh7yO1CrFihXDhg0bTNkmAyzX9i8zMxMxMTEOPzMhBCpUqGD6/ucld/9sCwoGWKSFARaZiicOIiIiKkgYYOWs27dvo2HDhvL6sUGDBvjwww9x+vRpJCQk4PDhw4iLi0NISIisM2/ePJe3ywDLtf2bNWuWXL9GjRpYu3Ytjh49ioSEBFy4cAHbt2/HhAkTEBERwQCLsoUBFmlhgEWm4omDiIiIChIGWDmrb9++8tpxzJgxyMjI0Kx35coVVK9eHUIIeHh4YPv27bm8pwWLKyHMvXv34OfnByEEGjZsiNTUVN26mZmZ+Pnnn13cW/fCACt3MMAiLQywyFQ8cRAREVFBwgAr52zfvl1eN3bt2tVh/XPnzsFisUAIgfLlyyM9PT0X9rJgciWE+f777+W6H330Uc7soBtjgJU7GGCRFgZYZCqeOIiIiKggYYCVczp06CB7VBmd6Hvq1KnyWnPTpk02y63L1q1bh/T0dCxevBj169dHcHAwhBD48ssvARgbQpiRkYGVK1eifv36sFgsCA4ORuPGjbF+/XoAwPTp03XneHIUcij3MzMzE2vWrEHDhg0RFBQEPz8/1KtXD8uWLdPtkQYAp06dwltvvYW2bdsiNDQUPj4+CAwMRFRUFMaPH48rV67orutKCPPRRx/Jdb/66iun1rXK+v7fvXsX06ZNQ/Xq1VGkSBEUK1YMXbp0MTS88969e1i4cCGaNm0qJ/8vU6YMevXqZaj3V3JyMhYtWoSWLVuiZMmS8PX1RdmyZdGsWTPMnz8fFy5ckHWdme8r62u8d+8eZs6ciaioKAQEBEAIgUOHDsn6rnyeBREDLNLCAItMxRMHERERFSQMsHJGUlISvLy8IIRAy5YtDa+nDAV69+5ts9y6bMWKFWjatKlNwGA0wHrw4AG6dOmiG1S88MILpgRYa9asQffu3XW3M2DAAM334fbt2w7DlGLFimHv3r2a67sSYH377bdy3dGjRzu1rpXy/d+8eTMiIyM1X4OHhwcWLFig286hQ4dQtmxZu+/DxIkTddffv38/wsLC7K4/cOBAWT+7AdZnn32GatWq2dS3Bliufp4FEQMs0sIAi0zFEwcREREVJAywcoZyGNr06dOdWrdcuXIQQiAsLMxmmbXNsLAweHt7Y/LkyTh27BgSEhKwd+9enDp1CoDjAGvMmDFyea9evbBv3z4kJibi8OHDGDRoEIQQiIiIcDnAioiIgLe3N6ZMmYJjx44hMTER+/btQ6tWrWSdbdu22ax/+/ZtREVFITY2Fj/++CNOnjyJxMRE/PXXX1i3bp0MhMqWLat5Te5KgHXr1i0UKVIEQgh4eXlh6tSpuHz5slNtKN//8PBwFCpUCHFxcTh9+jSuX7+Or7/+GjVq1JB1tOY8O3funJzcv0qVKli/fj3Onj2Lmzdv4uDBgxg8eLBcf+XKlTbrnzx5EoGBgRBCICAgALNmzcLRo0dx8+ZNnD9/Hlu3bsVLL72EoUOHynXu3r2rCvCOHTuGu3fvypKcnKz5GsPCwmCxWDB//nycOnUKN27cwK5duxAfHw/A9c+zIGKARVoYYJGpeOIgIjLHmj/WIO7nOMz9ZS7m/TIPSalJeb1LRI8lBlg547333pPXjFpDAe2xDj0UQtjMg6XssfL+++/rtmEvwDp//rzsHfb8889rrj9y5EjNXjdWRgMsIQQ2btxoszw5ORllypSBENo9zRy5d+8eKleuDCEEVq9e7fT+OTJ37lybnlJPPPEEXnzxRSxduhRHjx61u77y/RdC4JNPPrGpk5iYiAoVKkAIgcjISJvl1h5yERERuH37tuZ23njjDdl7SRkuAUCbNm0ghIC/vz+OHDmiu69ZjzGj753yNXp4eOCHH37QreuIo8+zIGKARVoYYJGpeOIgIjJHnZV1IGYIWa4kPV5zXxC5C8M3USVLAhZLwSwlS5r+vs6bN89u7xp7+vTpI9e19mCxsv57zZo17bZhL8CyhjOenp64ePGi5vp37tyRvZBcCbCaNGmiu4+jRo2CEAIVK1a0+1r0jBs3N2s+dQAAIABJREFUDkII9O3b1+n9M+Ldd9+Vc4tplRo1auDTTz/VXFf5/jdq1Eh3G++//76st2/fPvnv//zzDzw8PCCEwNdff627/v379+XE/9bhowBw/Phx2e6bb77p1OvOToBl5CEFjtj7PAsiBlikhQEWmYonDiIic0StiFIFWFfvXs3rXSJ6LBm+ibJYACEKZrFYTH9flQHWd99959S6ffv2leteu3ZNtcz67+PGjbPbhr0A61//+heEEKhXr57dNjp16uRygBUbG6vb/rJlyyCEgJ+fn26db775Bn369EFERAT8/Pw0g6Qnn3zS6f0zKikpCevWrUOvXr1055J67bXXbNZTvv/z58/Xbf/WrVuy3uLFi+W/r1q1CkIIeHt749q1a6phfFlLvXr1IITAlClT5PrvvvuubPfqVee+X7MTYC1btsxQ29n9PAsiBlikhQEWmYonDiIic9RaXksVYMXfi3e8EhGZjgFWzgRYyiGEWsPH7FEOIUxLS1Mts/778uXL7bZhL8CKioqCveGDVqNHj3Y5wFqzZo1u++vWrZP1skpPT0fv3r01A46spVKlSk7vX3ZdvXoVmzZtQufOnVX78Nlnn6nqKd//rVu32m2zVKlSEEI9YXxMTIyh164sr776qlzfOsdZyWz0LsxOgPXtt9/abdPVz7MgYoBFWhhgkal44iAiMkeNZTVUAdb1e9fzepeIHkscQpgzQwi/++47ec04Y8YMp9YtX748hLA/ifu6devstmEvwKpUqRKEEBg8eLDdNqzzK7kSYNnbT3sB1qxZs+SyHj16YOvWrfjnn3+QmJgoex5NnDgx2/tnBmUvp1atWqmWKd9/R3NDaX0eygnajRbl0wSt62cnDMpOgKX1oAAlVz/PgogBFmlhgEWm4omDiMgckcsiVQFWQnJCXu8S0WOJk7jnjDt37sDT0xNCCLRu3drweufPn5fXmlqTm5sRYFl7YPXr189uG2b0wMpugFW2bFmHvcSsc2jlVYAFADVr1oQQAsHBwap/d7UHlvW9L1u2bLb2K7d7YDkKsFz9PAsiBlikhQEWmYonDiIicyjDKzFD4HKSc48oJyJzMMDKOe3atYMQjyZLN/r+Tp8+XV5raj29z4wA65lnnoEQjufAsg6Ty+0AKzExUf77li1bdNe3PmUvLwMs67A4X19f1b+7OgfWO++8AyEePd3v+nXneyhb5xcTInfmwLIXYJnxeRZEDLBICwMsMhVPHERErsvIzLAJsP4X/7+83i2ixxIDrJzz7bffyuvGbt26Oax/4cIF+Pv7QwiBcuXKIT093aaOGQHWnDlzZLB26dIlzfWTkpLkJNu5HWBduXJF/vvmzZs117106RK8vb3zPMBq3LgxhBAIDw9X/bvRpxAq3wPlUwiVTxFcuHCh0/v1119/yfUXLFjg1Lp79uyR6/7zzz+69YwGWGZ8ngURAyzSwgCLTMUTBxGR607fPG0TYB27fiyvd4voscQAK2f16tVLXjuOHTsWGRkZmvWuXr2KGjVqyF4327Zt06xnRoB17tw5Obyxf//+muu//vrrcv3cDrDS09NhsVgghMBLL71ks97Dhw/RtWtXl/bPnj179mDBggUOr/W//fZbeHh4QAiBIUOGqJYp338htCfyT0xMRHh4OIQQiIyMtFlufQpkQEAA/vjjD7v7cu7cOaSmpqr+rW3bthBCIDAwEEePHtVdN2tQqgy/fvnlF931jAZYZnyeBREDLNLCAItMxRMHEZHrrt+7bhNgHb9+PK93i+ixxAArZ926dQv169eX148NGzbExo0bcebMGSQkJODPP//EnDlzULRoUVln7ty5uu2ZEWAB6oCqT58+OHDgABITE/Hnn39iyJAhEEIgIiIiTwIsAHjhhRfkspiYGBw/fhwJCQnYvXu3HGpWvXr1HAmwvvrqKxn8DB48GJ988glOnjyJmzdvIj4+Hnv27MGoUaPg6+sLIQQsFovN34/y/Q8PD0ehQoUwZ84cnDlzBjdu3MA333wjA0shBLZv326zH+fOnUPx4sUhhECRIkUwceJE7N+/HwkJCbhx4wb+/PNPvP/++/jXv/4Fb29v3LhxQ7X+qVOnEBgYCCEEgoKCEBcXh2PHjuHWrVu4ePEivv76awwaNAjDhg1TrZeeni7X69q1K06dOoXU1FSkp6fj4cOHmq/R0RxYrn6eBREDLNLCAItMxRMHEZHr4u/FM8AichMMsHLe3bt38cILL8jeOnqlaNGiWL9+vd22zAqwUlNT0bFjR9196d+/P6ZNmwYhtJ9kl9MB1uXLl1GuXDnd/Rs1apScL8zsAGvXrl3w8vKy+1lZS5kyZfDTTz/ZtKF8/zdv3izDmazFw8PD7hC/Y8eOoWrVqg73w8vLCzdv3rRZ//fff0doaKjddZVPL7SaMGGCZl3le+1MgOXq51kQMcAiLQywyFQ8cRARue7a3WsMsIjcBAOs3PPHH39g9OjRqFWrFkJCQuDr64vSpUujVatWeOuttzQDiKzMCrAAICMjA8uXL0e9evVgsVgQGBiIhg0bYs2aNQCA6OhoCCFQp04dm3VzOsACgGvXrmH48OEoV64cfHx8ULJkSbRv3x5ffPEFAORYgAUA169fx7p16/DSSy+hbt26CAkJgZeXF4oUKYLy5cujS5cuWLZsGZKSkjTXz/r+JyUlYfLkyXjiiSdQuHBhFC1aFJ07d3YY/ADAgwcPsHbtWnTp0gWlS5eGr68vChcujPDwcDzzzDNYvXq1Te8rpaSkJMybNw+NGjWSx125cuXQvHlzLFiwQHMetIyMDCxZsgT169dHQECADF+zG2ABrn2eBREDLNLCAItMxRMHEZHrriRdsQmw9pzfk9e7RfRYYoBFerp16wYhBLp06ZLXu5LvOBvu0OOHARZpYYBFpuKJg4jIdZeTLtsEWH0/75vXu0X0WGKARVru3bsn50GaNm1aXu9OvsMAixxhgEVaGGCRqXjiICJy3aU7l2wCLDGDX9lEeYEB1uMpNTUVycnJustHjhwpr3ntPcGOtDHAIkcYYJEWXg2TqXjiICJy3ZFrRxhgEbkJBliPp7Nnz6J06dKYOXMm9u3bhxs3biA+Ph47d+5E9+7d5fVur1698npX8yUGWOQIAyzSwqthMhVPHERErjt09RADLCI3wQDr8aQMWPRKkyZNcOvWrbze1XyJARY5wgCLtPBqmEzFEwcRkesOXjnIAIvITTDAejylpqZi7dq16NGjB6pUqYLg4GDVk+HWrVuHhw8f5vVu5lsMsMgRBlikhVfDZCqeOIiIXHfg8gEGWERuggEWEVHuY4BFWng1TKbiiYOIyHX7L+9ngEXkJhhgERHlPgZYpIVXw2QqnjiIiFy379I+BlhEboIBFhFR7mOARVp4NUym4omDiMh1x64fY4BF5CYYYBER5T4GWKSFV8NkKp44iIhcl3g/kQEWkZtggEVElPsYYJEWXg2TqXjiICJyXUp6CgMsIjfBAIuIKPcxwCItvBomU/HEQUTkuszMTAZYRG6CARYRUe5jgEVaeDVMpuKJg4jIHAywiNwDAywiotzHAIu08GqYTMUTBxGROWour6kKr04lnsrrXSJ6LDHAIiLKfQywSAsDLDIVTxxERK5Le5jGHlhEboIBFhFR7mOARVp4NUym4omDiMh1R64dYYBF5CYYYBER5T4GWKSFV8NkKp44iIhcd/jqYQZYRG6CARYRUe5jgEVaeDVMpuKJg4jIdQevHGSAReQmGGAREeU+BlikhVfDZCqeOIiIXPfHlT8YYBG5CQZYuefgwYOIjo5GVFQUihUrBl9fX4SFhaF169ZYtGgRbt68adq2zp49K69Zd+/ebVq7Vrt375btnz171vT2XeXq/g0cOFCub6SsW7fO9NdABRsDLNLCq2EyFU8cRESuO3D5AAMsIjfBACvnJScnY+DAgfDw8LAbghQrVgwbNmwwZZsMsBhgkXtjgEVaeDVMpuKJg4jIdfsu7WOAReQmGGDlrNu3b6Nhw4by+rFBgwb48MMPcfr0aSQkJODw4cOIi4tDSEiIrDNv3jyXt8sAy7wA6+7duw5Lenq6+S+CCjQGWKSFV8NkKp44iIhcxwCLyH0wwMpZffv2ldeOY8aMQUZGhma9K1euoHr16hBCwMPDA9u3b8/lPS1YzAywiHICAyzSwjMOmYonDiIi1zHAInIfDLByzvbt2+V1Y9euXR3WP3fuHCwWC4QQKF++PHv1uIABFrk7BlikhWccMhVPHERErmOAReQ+GGDlnA4dOsgeVf/884+hdaZOnSqvNTdt2mSzXDnnUnp6OhYvXoz69esjODgYQgh8+eWXAIwNIczIyMDKlStRv359WCwWBAcHo3Hjxli/fj0AYPr06RBCoEKFCjbrOgqIlPuZmZmJNWvWoGHDhggKCoKfnx/q1auHZcuW6fZIA4BTp07hrbfeQtu2bREaGgofHx8EBgYiKioK48ePx5UrV3TXzesAa926dar1z5w5g//7v/9DREQEChUqhKCgIJtttWjRAgDw008/4bnnnkOZMmXg5eWFbt262bS/a9cu9O7dG2FhYfD19UXRokXRrFkzLF26FA8ePNDdL6PHD+U8BlikhVfDZCqeOIiIXMcAi8h9MMDKGUlJSfDy8oIQAi1btjS8njJ46t27t81y67IVK1agadOmNpOJGw2wHjx4gC5duuhOSv7CCy+YEmCtWbMG3bt3193OgAEDNN+H27dvO5w4vVixYti7d6/m+u4UYO3ZswdBQUGqfdcLsN555x14enqq6ioDrIyMDIwYMcLu+xIVFaUb7hk9fijnMcAiLbwaJlPxxEFE5DoGWETugwFWzvj+++/lNeP06dOdWrdcuXIQQiAsLMxmmbXNsLAweHt7Y/LkyTh27BgSEhKwd+9enDp1CoDjAGvMmDFyea9evbBv3z4kJibi8OHDGDRoEIQQiIiIcDnAioiIgLe3N6ZMmYJjx44hMTER+/btQ6tWrWSdbdu22ax/+/ZtREVFITY2Fj/++CNOnjyJxMRE/PXXX1i3bh0iIyMhhEDZsmU1r8ndKcAKCwtDxYoV8fHHH+Py5cu4cuUKtm7darOtUqVKwcvLCy1btsTOnTtx48YNnD59Gjt37pR1Z86cKdtt164dfv75ZyQkJOCvv/7ChAkTZGhar149pKWl2eyX0eOHch4DLNLCq2EyFU8cRESuY4BF5D4YYOWM9957z+5QQHusQw+FEDbzYCl7y7z//vu6bdgLsM6fPy+Djueff15z/ZEjR8r1XQmwhBDYuHGjzfLk5GSUKVNGt6eZI/fu3UPlypUhhMDq/8fefUdHVef/H/8EUuiEIiKdgAsiIioqSlNhFRsoiKC4qKuuwioior/VVUBF8Ksg4lIXEFDBAiiCCKgURYoaQJReQgkQQgo1PZn37w+cuzOZe6femTvkPh/n3HPWmVs+d5K8+bxfe++d6dMDHp8vgX4LYWmuAVadOnUkLS3Nr2Pdcssths8+O3bsmMTFxYlSSrp37y7FxcUe60yePNntKqvS/P39QfgRYEEPs2GYisIBAKEjwAKih79NVJ06IpUrl82lTh3zP9e33npLmzMG+o2Cffv21bZNT093e8/5euvWrb3uw1uANWbMGFFKSbly5SQ1NVV3+9OnT0vFihVDDrA6dOhgOMZnn31WlFLStGlTr+di5IUXXhCllPTr1y/g8fniGir5s5TmGmCNHTvW72MlJycbrvf2229r6+3Zs8dwvSuvvFKUUtKuXTuP9/z9/UH4EWBBD7NhmIrCAQChI8ACooe/TVTlyiJKlc2lcmXzP1fXAGvFihUBbduvXz9t2+PHj7u953z9hRde8LoPbwFWjx49xHmbmTe33357yAHW66+/brj/SZMmiVJKKlWqZLjO0qVLpW/fvpKUlCSVKlXSDY+uvvrqgMfni5kB1vbt2/061kUXXeR1PefPrU2bNl7Xe+ONN0QpJeXLl/foV/z9/UH4EWBBD7NhmIrCAQCh+y3tNwIsIEoQYIUnwHK9hfCzzz4LaFvXWwhLP8fI+frkyZO97sNbgNWmTRvxdvug05AhQ0IOsGbMmGG4/9Lf1OeqqKhI7r//fr/Co2bNmgU8Pl/MfAZWbm6uX8e67rrrvK7nvLJK74ozV1988YV27N27d7u95+/vD8KPAAt6mA3DVBQOAAjdmLVjdAMsh8Nh9dAA2+EWwvDcQrhixQptzjhy5MiAtm3UqJEo5f0h7rNmzfK6D28BVrNmzUQpJY8//rjXfbzyyishB1jexuktwHJeRaSUkt69e8tXX30l+/btk6ysLO25U//617+CHp8vZgZY/h6rS5cuXtfz9+f23XffaccufUuiv78/CD8CLOghwIKpKBwAELpP//hUN8AqLPb8xiQA4cVD3MPj9OnTUq5cOVHq/IO5/XXo0CFtrqn3cHMzAiznFVgPPvig132YcQVWsAFWgwYNfF4l5nyGll0CLH+vwPryyy99XoFFgGU9AizoIcCCqSgcABA6owDrbIHnNzkBCC8CrPD561//Kkqdf1i6v5/viBEjtLmm3rf3mRFg3X333aKU72dg3XHHHZYEWFlZWdrrixYtMty+a9eutgqwevbsKUr5fgbWqFGjRCnvz8AiwLIeARb0EGDBVBQOAAidUYB18ORBq4cG2A4BVvh888032ryxZ8+ePtc/fPiwVKlSRZRS0rBhQykqKvJYx4wAa/To0VqwduTIEd3tz5w5oz00PdIB1rFjx7TXFy5cqLvtkSNHJDY21lYB1jvvvKPt09u3ELZt21aU8v4thARY1iPAgh4CLJiKwgEAoTMKsNYeWmv10ADbIcAKrz59+mhzx2HDhklJSYnuemlpaXL55ZeLUkpiYmJk2bJluuuZEWAdPHhQu72xf//+utsPHjxY2z7SAVZRUZFUrlxZlFLyyCOPeGxXXFwsd911V0jj8yUaA6y0tDSJi4sTpZR0795diouLPdaZOnWqdtwpU6Z4vE+AFT0IsKCHAAumonAAQOje3/g+ARYQJQiwwuvkyZNy7bXXavPH9u3byyeffCIpKSmSmZkpv//+u4wePVpq1qyprTNmzBjD/ZkRYIm4B1R9+/aV5ORkycrKkt9//12eeOIJUUpJUlKSJQGWiMhDDz2kvff888/Ljh07JDMzU1avXq3dOnjZZZdFJMByPjTe25Kfn+/3uRkdy1eAJSLy+uuva/u99dZbZe3atZKZmSm7du2Sl156ScqXL6/dHlr6GyxFCLCiCQEW9BBgwVQUDgAI3bPLniXAAqIEAVb4nT17Vh566CGJiYnR5pF6S82aNWX27Nle92VWgJWfny/du3c3HEv//v1l+PDhopSSZs2aeWwf7gDr6NGj0rBhQ8PxPfvss9rzwsIdYPmzPPzww36fm9Gx/AmwSkpK5J///KfXsbRp00aOHTumuz0BVvQgwIIeAiyYisIBAKHTC68IsABrEGBFzqZNm2TIkCFyxRVXSI0aNSQ+Pl4uueQSufnmm2XcuHGSnZ3tcx9mBVgi58OQyZMnyzXXXCOVK1eWatWqSfv27WXGjBkiIjJ06FBRSknbtm09tg13gCUicvz4cRk0aJA0bNhQ4uLipE6dOnLrrbfKF198ISJiywDLadWqVdKnTx+pV6+exMXFSY0aNaRjx47yn//8RwoKCgy3I8CKHgRY0EOABVNROAAgdEYB1j8W/8PqoQG2Q4AFI85vvbvzzjutHgpQ5hBgQQ8BFkxF4QCA0BkFWGok/2wDkUaABT3nzp2TatWqiVJKhg8fbvVwgDKHAAt6mAnDVBQOAAgdARYQPQiw7Ck/P19ycnIM33/66ae1Oe8ff/wRwZEB9kCABT3MhGEqCgcAhI4AC4geBFj2dODAAbnkkkvktddek19++UUyMjIkPT1dVq5cKffcc4823+3Tp4/VQwXKJAIs6GEmDFNROAAgdARYQPQgwLIn14e8Gy0dOnSQkydPWj1UoEwiwIIeZsIwFYUDAEJHgAVEDwIse8rPz5eZM2dK79695dJLL5XExES3b/qbNWuWFBcXWz1MoMwiwIIeZsIwFYUDAELX4N0GBFhAlCDAAoDII8CCHmbCMBWFAwBC12pSKwIsIEoQYAFA5BFgQQ8zYZiKwgEAoWvxnxYEWECUIMACgMgjwIIeZsIwFYUDAELX/P3mBFhAlCDAAoDII8CCHmbCMBWFAwBC1/S9pgRYQJQgwAKAyCPAgh5mwjAVhQMAQtdofCMCLCBKEGABQOQRYEEPM2GYisIBAKEb/M1gwwArIyfD6uEBtkKABQCRR4AFPQRYMBWFAwBC9+2+b2Xk6pG6Adb3+7+3eniArRBgAUDkEWBBDwEWTEXhAADz6AVY3+771uphAbZCgAUAkUeABT0EWDAVhQMAzKMXYC3fu9zqYQG2QoAFAJFHgAU9BFgwFYUDAMyjF2DtOLHD6mEBtkKABQCRR4AFPQRYMBWFAwDMoxdgbT+x3ephAbZCgAUAkUeABT0EWDAVhQMAzKMXYP2R/ofVwwJshQALACKPAAt6CLBgKgoHAIRud+ZuWZmyUjfA2np8q9XDA2yFACt8RowYoc0bY2JiZPPmzV7Xf/jhh0UpJS1atIjQCC9szs921qxZHu85P8suXbpEfFyhmDVrlnZewXI4HLJo0SK57777pEmTJlKxYkWpWLGiNG7cWK677joZNGiQzJ8/XzIyMkwcOQJFgAU9BFgwFYUDAEL3z6X/1A2v1EglW9K2WD08wFYIsMLHNcBSSkmPHj28rk+AFRgCLE+nTp2Srl27uv3eGS0PP/ywuYO3mPPvrXHjxlYPxS8EWNBDgAVTUTgAIHSDvh5kGGBtOrbJ6uEBtkKAFT6lAyyllCQnJxuuT4AVGAIsT926ddO27969uyxevFj27Nkj2dnZsnfvXpk/f778/e9/lxo1ahBgWYwAC3oIsGAqCgcAhG7g1wMNA6wlu5dYPTzAVgiwwsfZUMfFxUmDBg1EKSV33XWX4foEWIHxFmBdqEIJsFatWqVtO2TIEK/r5uTkyC+//BLsMKMSARbKAgIsmIrCAQChe2rJU4YB1mfbPrN6eICtEGCFj7OhTkhIkMmTJ2tzSKPggAArMARY7l5++WVt26NHj4ZhdNGNAAtlAQEWTEXhAIDQPbnkScMAa0/mHquHB9gKAVb4uAZYBQUF0qhRI1FKyR133KG7vj8BVn5+vowfP15uvPFGqVGjhsTHx0uDBg2kX79+snbtWsPtSgcjKSkpMnDgQElKSpKEhASpXr26xzict9+tX79e7r33Xqlbt65UrFhRWrduLRMmTJDi4mJtm9TUVBk8eLA0a9ZMKlSoIPXq1ZOnnnrK64PCMzIyZPbs2dK7d29p0qSJJCQkSMWKFSUpKUkeeeQRnw+9D+YWQtfPwddidPthVlaWjBgxQtq1ayeJiYmSkJAgjRs3locffli2bvX9RSQLFiyQLl26SPXq1aVKlSrStm1bGTt2rBQWFoYUYD3xxBPatmfPng14exHP35N9+/bJ448/Lo0aNZKEhASpX7++PProo37Vi8OHD8uwYcPkiiuukGrVqkmFChWkefPmMmjQIL+2T01NlRdffFGuuuoqSUxMlAoVKkizZs3kzjvvlFmzZsmZM2dERGT16tUBPe8rkL8Fh8MhGzdulJdeekmuu+46SUxMlNjYWKlZs6Z06tRJJkyYILm5uYF8xBoCLOghwIKpKBwAELp/LP6HYYCVkk0TDUQSAVb4uAZYIiJTp07V5pEbN270WN9XgHXo0CFp2bKl10b9hRdeEIfD4bGta9P+008/SfXq1d22MwqwZs6cKeXLl9c91oABA0REJDk5WerUqaO7TsuWLeX06dO659O2bVuv51K+fHmZMmWK4edrRYD17bffSmJiouE25cqVk8mTJxuOeeDAgV6PN23atKADrBdffFHbdtGiRQFvL+L++WzcuNHj98S5VK5cWX744QfD/Xz00UdSoUIFw3OtWLGifPnll4bbf/DBB5KQkOD15+P8uYcSYPn6W1i0aJHPfbdt21ZOnDgR8GdNgAU9BFgwFYUDAEL3xOInDAOs7Se2Wz08wFYIsMKndIBVWFgojRs3FqXOP2C7NG8BVl5enrRq1UqUUhIfHy/Dhw+X3bt3S0ZGhqxatUo6duyozVHfffddj+1dm/b69etL06ZNZd68eXL06FE5duyYfPXVVx7jqFevnsTHx8udd94p69atk8zMTNm2bZv06tVL29fnn38ujRs3llatWsmiRYskPT1dDh06JMOHD9fWefHFF3U/nzvvvFOeeeYZ+eabb+SPP/6QjIwMSUlJkaVLl0r37t21EGvTJv0v9wgmwCoqKpKzZ88aLgsXLpSYmBhRyvM5Ur/++qvEx8eLUkratWsn8+fPl9TUVMnKypL169fLPffcI0opiYmJkWXLlnmMaeLEidqYO3XqJGvWrJGMjAzZvn27PPfccxITEyNNmjQJOsBasWKFtm3t2rXlgw8+CPhKLNffkyZNmki9evXk448/lrS0NElNTZWpU6dKjRo1RCklNWrUkLS0NI99uIY+3bp1k6VLl0paWppkZmbK999/L507d9b+Ln777TeP7T/99FNt+6SkJJk5c6akpKRIdna27N69W2bNmiW33nqrzJ49W0REiouL5ezZs/LSSy+JUkoaNWrk8XPNz8/XPUdffwtff/21dO3aVaZMmSIbNmyQgwcPSkZGhvz2228yatQoqVWrliil5N577w3ocxYhwII+AiyYisIBAKHzFmC98cMbVg8PsBUCrPApHWCJiEyfPl2bS27YsMFtfW8B1tixY7Xt5s6d6/F+QUGBFmJVrFhRsrKy3N53bdrr1KmjGzyUHoezMS99RVdhYaEkJSWJUkpiY2MlKSlJTp065bGf/v37i1JKLr74YsNjefPAAw+IUkr69++v+34wAZY3u3dfp3t2AAAgAElEQVTv1q6u6ty5sxQWFmrvORwOueKKK0QpJe3bt3d7z9Xf/vY3UUpJq1at3D633Nxcbd/t27eXgoICj21HjRrldmVPMJzBn3OJi4uTa6+9VgYNGiQffvihHDlyxOv2rr8nVatWlX379nms8/PPP2tX5Q0aNMjtvdzcXLnoootEKSX33Xef7jGKiorkpptuEqWU3H777W7vnTlzRgvIrrzySjl58qThWIuKitz+299nYAXyt+DL9u3bJTY2VmJiYmTPnsAegUCABT0EWDAVhQMAQvf4V48bBliXjL3E6uEBthJoE1X5zcpBLe9teE93f9/s+Sbofe7K2KW7z8HfDPZ7H+GkF2AVFhZK06ZNRSklt956q9v63gIs59VXHTp0MDze5s2btXnqxIkT3d5zbdrHjh3rddyuAdb+/ft11xk2bJjXAElEZMmSJdo6hw4d8npMPUuXLhWllNStW1f3fTMDrFOnTkmLFi1EKSVNmzb1eHbXypUrteNt27bNcD+HDx/W1tuyZYv2uutVRUa33hUVFUn9+vVDCrByc3Nl0KBBEhsbq3u7W0xMjNx6662GV7W5/p689NJLhscZMGCAKKWkWrVqbkHSzJkzteAsOzvbcPsff/xRG4/rev582YGRYAIsX38L/rj22mtFKSVTp04NaDsCLOghwIKpKBwAELrHvnrMMMBq+G5Dq4cH2EqgTZTR366v5Z117+jub8nuJUHvc2fGTt19evuiiNJLOOkFWCL/a/KVUrJu3TrtdaMAKysrS1tf7/ZAV82aNROllPTt29ftddemfft277dqO8fRvHlzw3Vcgwajq3p27NihrfPzzz/rrrN161YZNGiQtGnTRqpWrardvld6cT6w25VZAVZxcbF25VLVqlV1AyrnN/zVrVvX6y2IZ8+e1a5Amj59urb94MGDRSklNWvW1H1GmdOTTz4ZUoDldPjwYXn77bfl1ltv1X2OVVxcnHz66ace27n+nhj9zEREvvzyS92g7sEHHxSllFx33XVeP6MTJ05o23/33Xfa9vfdd59hiOtLMAGWr78FkfNXN06bNk1uu+02ueSSS7TbSEsvQ4cODWi8BFjQQ4AFU1E4ACB0f1/0d8NmMmZkjNXDA2yFACt8jAKsoqIiLWjq1q2b9rpRgLVlyxZt/rl8+XKvx+zRo4copeSGG25we921aff1rWnOcXTs2NFwHdf9uX4boasDBw5o66xZs8bj/XHjxhk+IL70kpqa6rG9WQHW0KFDRanzD2BfsmSJ7jq9e/f2a5yuy+jRo7XtjX4upY0fP96UAMuVw+GQ3bt3y8SJE7XbIJU6f6vp4cOH3dZ1/bkaPXxfRGTnzp3aeq4PjL/mmmsC/pzmzZunbX/VVVeJUkruv//+gM8zmADL19/CsWPH5PLLL/frPB577LGAxkuABT0EWDAVhQMAQvfookcNm8kHFz5o9fAAW+EWwvAxCrBE3JvotWvXiohxgLV27Vpt3Z9++snrMZ3PnWrdurXh8XzxJ/zxZ3+uAdbq1asNz+mqq66SOXPmyLZt2yQjI0O7Ssd5C6FSSg4cOOCxfzMCrNmzZ2v7efvttw3X69atW8DBzIgRI7Ttu3bt6hFY6nF9Rlo4FBcXS79+/bRjvPbaa27v+xNMioikpqZq63388cfa682bNw/4c3L9+Tm3DzQMEgkuwPLF+XOLi4uT559/XlavXi2HDx+WkydPar+nHTp0EKXcv+nQHwRY0EOABVNROAAgdI8sesQwwJr6a2DPkAAQGh7iHj7eAqzi4mKtWb/55ptFxJwrsHr27Kl7pU+0BVjOoC0pKUny8vJ0t3e9TS0cAdb69eslISFBlFIyYMAAw/VERPuGQW9XpXlj5RVYpWVkZGjHuOeee9zeC/UKrLZt24pSSh566KGgxhbpK7C82bt3r7bef//7X8P1rrzySgIsmIYAC6aicABA6B7+8mHDAGta8jSrhwfYCgFW+HgLsERE5syZo80r16xZ4/UZWM5nQ/l6BpYzFOvXr5/b69EWYDlvZRsyZIjh9m+88UbYAqzU1FSpW7euFirl5+cbjkPkf7cZJiYmer0yyUikn4HlS506dUQpzy8SCPUZWL169RKlPK8A9FefPn1EKSUtW7YMeFuzA6yFCxdq6+l9y6bI+S9lqFixIgEWTEOABVNROAAgdAdOHpDko8ny69FfCbAAixFghY+vAKu4uFj+8pe/aEGLt28hdD6Hx9u3EP7222/aPNXbtxD6EokAy3nezzzzjO62DofD7dlDZgZYubm5cvXVV4tSSho1aiTHjx83PAenZcuWacdbsGCBz/VLi9S3EPrj3Llz2oPIH3nkEbf3/P0WQufnW/pbCKdMmaJtn5ycHPDYpk6dqm3/66+/BrTtqFGjRCklDRo08Lqev38L8+bN09bLysrSXeezzz7T1iHAghkIsGAqCgcAmItbCAFrEWCFj68AS0Tko48+0uaWSUlJhgHWuHHjtPXmzp3r8X5hYaF07txZlFJSqVIlj4Y72gKs2267TTvXgoICj23feustbVuzA6y+ffuKUkoqV64sv/32m+H4XZWUlGiBWr169XTH42rXLvfns+Xm5kpiYqIopaR9+/a65/zmm2+6nXOgpk+fLvPnz5eSkhKv6/2///f/DH+XXH+uVatWlX379nls//PPP0tsbKwopWTQoEFu7507d05q164tSim54oorDIMfp9Kf09mzZ6VGjRqi1Plnoxld+SQibsGZyP/CswoVKni8Z3SO3mzYsEFbb/bs2R7vp6enS+PGjQmwYCoCLJiKwgEA5iodYE36ZZLVQwJshQArfPwJsIqLi6VFixZuwYVegJWXl6cFKPHx8TJixAjZs2ePZGZmypo1a6RLly7a9nq3GUZbgDVjxgztve7du8vGjRslIyNDtm7dKoMGDRKllFx22WWmB1iuz5j68MMPtQdx6y2lv6EuOTlZKlSoIEqdvxVw9OjRsnXrVsnOzpb09HRJTk6WyZMnS9euXaVq1aoeY5o4caJ27M6dO8uaNWskMzNTdu7cKUOHDpWYmBhp0qRJ0AHW888/r11V9tJLL8ny5cvl4MGDcurUKUlNTZWvv/5ae0aaUkratGnjEfS4/lybNGki9evXl7lz50paWpocOXJEpk2bpgVMiYmJkpaW5jGOJUuWaLe8NmzYUP7zn//Izp075eTJk5KWliYbNmyQcePGyfXXX697q6HrVU3NmzeXWbNmyYEDB+TkyZOyZ88e+fjjj+X222/3CJV+/vlnbbtXX31V0tLSpKioSIqKitxCPX//FoqKirSAqlq1ajJ58mQ5ePCgpKWlyaeffipJSUlSqVIlbR0CLJiBAAumonAAgLlKB1gTNk6wekiArRBghY8/AZaIyNy5c30GWCIiBw8elJYtW7qtW3oZNmyY7jOWoi3AKi4u1q7C0ltuvPFG+frrr00PsJyv+7Ponf+PP/6oPTvL21KzZk3dz2TgwIGG23Tq1EmmTZsWdID12muv+X1u1157rRw9etRjH64/1w0bNki1atV0t69cubKsWbPGcCwLFy6UqlWr+hzH1Vdfrbv99OnTtdscjRa9n3v79u1113UNlwL5W/juu++0B/2XXuLj4+XTTz/VwmMCLJiBAAumonAAgLlKB1jzt8+3ekiArRBghY+/AVZJSYnb1UZGAZbI+Suxxo8fLzfeeKMkJiZKfHy8NGjQQPr27Str16413C7aAiyR87c9jhkzRlq1aiUJCQlSvXp1adeunYwbN04KCwtl9erVURdgiZzvB9577z255ZZbpE6dOhIbGyuVKlWSSy+9VO6//36ZN2+enDlzxvBzmT9/vnTp0kWqVasmlStXljZt2shbb70lhYWFAf2c9OzatUvGjx8vvXv3lpYtW0rVqlWlfPnyUrVqVWnRooU88MAD8sUXXxjeZlj6+Hv27JFHH31UGjZsKPHx8VKvXj159NFHZf/+/T7HkpmZKaNGjZIOHTpIrVq1JDY2VqpUqSKXXXaZDBgwQBYtWmT4DZQi539/hgwZIpdffrlUqVJFKlWqJM2bN5e7775b5syZI2fPnvXYJjs7W4YOHSotW7bUrpYLJcASEdm8ebPce++9UrNmTYmPj5eGDRvKQw89JJs3bxYRIcCCqQiwbGL58uXywAMPSJMmTaRChQpSs2ZNueKKK+SJJ56QZcuWmXYcCgcAmKt0gAUgsgiwADiFGqDBfwRY0MNfXhl37tw57etaA/1/T4I9HoUDAELz0daP5Lnlz8nQ5UMJsACLEWABcCLAihwCLOjhL68MKygokJtuukmUUlK+fHkZNGiQbNiwQU6cOCFpaWmyatUq+ec//ym9evUy7ZgUDgAIXb8F/TyCKwIswBoEWACcCLAihwALevjLK8OcDyqMjY2VxYsXR+SYFA4ACF3f+X11w6u2U9taPTTAdgiwADgRYEUOARb08JdXRmVmZmoP5nv22WcjdlwKBwCE7v759+sGWP0W9JOC4gKrhwfYCgEWACcCrMghwIIe/vLKqPHjx4tSSmJiYuTQoUMROy6FAwBC1+fzPoa3EA5bMczq4QG2QoAFwIkAK3IIsKCHv7wy6rbbbhOllLRt6367SVFRkTgcjrAdl8IBAKG77/P7DAMsnoMFRBYBFgBEHgEW9DALLqNq164tSil5/PHHJScnR1555RVJSkqScuXKSWxsrFx22WXy73//W7Kzs009LoUDAELX+7PeBFhAlCDAAoDII8CCHmbBZVBubq72xzt48GBp1aqV9t+ll4YNG8r27dsD2v+5c+cMl/T0dAoHAISo12e9CLCAKEGABQCRR4AFPcyCy6C0tDTtjzc+Pl6UUtK7d2/5/fffpaCgQA4fPiwjRoyQcuXKiVJKmjVrFtAfuVEYVnqhcABAcC6fdDkBFhAlCLAAIPIIsKCHWXAZdPToUbcg6ZZbbtF97tXYsWO1dd59912/90+ABQDh89HWj7yGVwRYQGQRYAFA5BFgQQ+z4DLo1KlTbkHSTz/9pLteQUGB1KpVS5RS0rlzZ7/3zy2EABA+vsIrAiwgsgiwACDyCLCgh1lwGVRSUqLdOliuXDnJz883XPeOO+4QpZTUqlXLlGNTOAAgNARYQHQhwAKAyCPAgh5mwWVU69at/Qqm+vfvL0opiYuLM+W4FA4ACA0BFhBdCLAAIPIIsKCHWXAZ9cgjj4hSSmJiYiQvL89wve7du4tSSmrXrm3KcSkcABAaAiwguhBgAUDkEWBBD7PgMuqLL77Q/oBXr16tu05+fr7UqFFDlFLSrVs3U45L4QCA0BBgAdGFAAsAIo8AC3qYBZdRBQUF0rRpU1FKSceOHaWkpMRjnTfeeEP7I58xY4Ypx6VwAEBoSodVL33/EgEWYCECLACIPAIs6GEWXIYtWrRIYmJiRCklt99+u6xfv16ys7Nl586d8vzzz2vvXXPNNVJYWGjKMSkcABCa0mHVY189RoAFWIgACwAijwALepgFl3ETJkyQ2NhY7Y+59NK2bVs5cuSIacejcABAaFpPbu0WVr2z7h0CLMBCBFgAEHkEWNDDLNgGNm3aJAMGDJBGjRpJfHy8JCYmSqdOnWTSpElSUFBg6rEoHAAQmtIBVurpVKn9dm3tv2dtmWX1EAFbIcACgMgjwIIeAiyYisIBAKF5fc3rWlj1yspXpLC4UFKyU+TVVa/KJ398Ig6Hw+ohArZCgBU+I0aM0OaNMTExsnnzZq/rP/zww6KUkhYtWkRohBc252c7a9Ysj/ecn2WXLl0iPq5QzJo1SzuvYDRu3NjwzhS95cCBA+aeAPxGgAU9BFgwFYUDAEIT93qc2xVY5wqopYCVCLDCxzXAUkpJjx49vK5PgBUYAixPBFgXDgIs6CHAgqkoHAAQmtLPu1IjlYxZO0a+3PmlfLnzS1mVssrqIQK2QoAVPqUDLKWUJCcnG65PgBUYAixPzgCrY8eOcvbsWZ8LVz1bhwALegiwYCoKBwAE79iZY7oBluvSdmpbq4cJ2AoBVvg4A6y4uDhp0KCBKKXkrrvuMlyfACsw3gKsC5VZAdaFFtzZEQEW9BBgwVQUDgAI3tNLn/YZYF0+6XKrhwnYCgFW+DgDrISEBJk8ebI2h/zll1901yfACgwBlicCrAsHARb0EGDBVBQOAAier/BKjVTS/P3mVg8TsBUCrPBxDbAKCgqkUaNGopSSO+64Q3d9fwKs/Px8GT9+vNx4441So0YNiY+PlwYNGki/fv1k7dq1htuVDkZSUlJk4MCBkpSUJAkJCVK9enWPcThDkPXr18u9994rdevWlYoVK0rr1q1lwoQJUlxcrG2TmpoqgwcPlmbNmkmFChWkXr168tRTT0lGRobhmDIyMmT27NnSu3dvadKkiSQkJEjFihUlKSlJHnnkEZ8PvQ/mFkLXz8HXYhQCZWVlyYgRI6Rdu3aSmJgoCQkJ0rhxY3n44Ydl69atXscsIrJgwQLp0qWLVK9eXapUqSJt27aVsWPHSmFhoeUBVunP7YcffpBevXpJvXr1pHz58tKzZ0+PY40YMUJEzn+2nTt3llq1aolSSsaPH++27+LiYvnggw+ka9euUrt2bYmLi5O6detKz549ZfHixYZjWr16tdszu06cOCHDhg2TFi1aSKVKlUQpJSdPngzqfK1EgAU9BFgwFYUDAILnT4DVaHwjq4cJ2AoBVvi4BlgiIlOnTtXmkRs3bvRY31eAdejQIWnZsqXX0OWFF17Qfa6RazDy008/SfXq1d22MwqwZs6cKeXLl9c91oABA0REJDk5WerUqaO7TsuWLeX06dO659O2bVuv51K+fHmZMmWK4edrRYD17bffSmJiouE25cqVk8mTJxuOeeDAgV6PN23atKgJsN5//30pV66c2xj1Aqzhw4dLnz59PM7HNcDKzs6WDh06eP28H3zwQSksLPQYk2uAtWLFCqlXr57HtgRYKCsIsGAqCgcABO+eT+/xGWBd/M7FVg8TsBUCrPApHWAVFhZqTX/37t091vcWYOXl5UmrVq1EKSXx8fEyfPhw2b17t2RkZMiqVaukY8eO2hz13Xff9djeNbipX7++NG3aVObNmydHjx6VY8eOyVdffeUxjnr16kl8fLzceeedsm7dOsnMzJRt27ZJr169tH19/vnn0rhxY2nVqpUsWrRI0tPT5dChQzJ8+HBtnRdffFH387nzzjvlmWeekW+++Ub++OMPycjIkJSUFFm6dKl0795dC7E2bdqku30wAVZRUZHXh5ovXLhQYmJiRCklQ4YMcdv2119/lfj4eFFKSbt27WT+/PmSmpoqWVlZsn79ernnnntEKSUxMTGybNkyjzFNnDhRG3OnTp1kzZo1kpGRIdu3b5fnnntOYmJipEmTJlERYF188cVSvnx5uemmm2TlypWSkZEh+/fvl5UrV3ocq379+qKUkqeeeko2b94smZmZsmXLFtmyZYuIiDgcDunatav22TzzzDPy+++/S2ZmpmzYsEH73JRSMnjwYI8xuQZY9evXl4suukimTp0qhw4dkvT0dFmyZInk5eUFdb5WIsCCHgIsmIrCAQDBG7F6hM8AK/GtRKuHCdhKoE3UDz9UDmpJTX1Pd3+Zmd8Evc+cnF26+9yzZ7Df+win0gGWiMj06dO1ueSGDRvc1vcWYI0dO1bbbu7cuR7vFxQUaCFWxYoVJSsry+191wCrTp06kpaWZjhu5ziUUnLvvfd6XNFVWFgoSUlJopSS2NhYSUpKklOnTnnsp3///loYEowHHnhAlFLSv39/3feDCbC82b17t3Z1VefOnd2uBnI4HHLFFVeIUkrat2+ve6WQiMjf/vY3UUpJq1at3D633Nxcbd/t27eXgoICj21HjRrldlVRMAL5FsLc3FyP7V1/9rfccosUFRX5PJbzKiwjCxYs0NZ78803Pd53OBzSt29fLeDasWOH2/uuAVaFChVk+/btAXwi0YsAC3oIsGAqCgcABM8owEp4I0H739XGVLN6mICtBNpErV6tgloOHXpHd38ZGUuC3ue5czt197lr15N+7yOc9AKswsJCadq0qSil5NZbb3Vb31uA5bz6qkOHDobH27x5szZPnThxott7rgHW2LFjvY7bNcTYv3+/7jrDhg3zGiCJiCxZskRb59ChQ16PqWfp0qWilJK6devqvm9mgHXq1Clp0aKFKKWkadOmHs/uWrlypXa8bdu2Ge7n8OHD2nrOK5BERD799FPt9R9++EF326KiIu1qplADLH8Wvc/G9WefnJzs17Fq1aqlG8g53XHHHaKUksaNG7s9N83V8ePHJS4uTpRSMmzYMLf3XAOsp59+2veHcIEgwIIeAiyYisIBAMHTC7C+3fetx2un8/WflwLAfARY4aMXYImIzJw5U5tPrlu3TnvdKMDKysryenugq2bNmolSSvr27ev2umuA5esKFuc4mjc3/lIN129VPHLkiO46O3bs0Nb5+eefddfZunWrDBo0SNq0aSNVq1bVbt8rvZw5c8ZjW7MCrOLiYu2WxapVq+oGVC+//LIWpvm6sumiiy4SpZRMnz5d237w4MGilJKaNWvqPqPM6cknn4yKAOuiiy7y+1h9+vQxXMfhcGjPW9O7PdCV8zbD66+/3u111wBr6dKlPsd1oSDAgh4CLJiKwgEAwdMLsDakbvB4LTMn0+qhArbBLYThYxRgFRUVaUFTt27dtNeNAqwtW7Zo88/ly5d7PWaPHj1EKSU33HCD2+uuAZberWOunOPo2LGj4Tqu+zO6qubAgQPaOmvWrPF4f9y4cYYPiC+9pKamemxvVoA1dOhQUer8A9iXLFmiu07v3r39Doacy+jRo7XtjX4upY0fP96UACvUZ2Bdd911fh/L6BlnIiInT57Uzmfq1Kle9+cM+S655BK3110DrNK3F17ICLCghwALpqJwAEDw9AKsF759weO1VSmrrB4qYBs8xD18jAIsEfcAaO3atSJiHGCtXbtWW/enn37yekznc6dat25teDxf/Al//Nmfa4C1evVqw3O66qqrZM6cObJt2zbJyMjQrmRy3kKolJIDBw547N+MAGv27Nnaft5++23D9bp16xZwgDVixAhte+fVRa6BpR7XZ6QFw8xvIfT3WK7nWVpqaqp2Ph9//LHX/f373/8WpZRUqVLF7XXXAEvv9+BCRYAFPQRYMBWFAwCCd/e8u30+xF2NVPLFji+sHipgGwRY4eMtwCouLpbmzZuLUkpuvvlmETHnCqyePXv6vALLl0gEWM6gLSkpyfAb5L788suwBljr16+XhIQEUUrJgAEDDNcTEe2b8rxdlebNhXYFllkBViBXYD377LM+r8AiwKIPLesIsGAqCgcABM+f8EqNVPLeBv1bjQCYjwArfLwFWCIic+bM0eaVa9as8foMLOezoXw9A8sZivXr18/t9WgLsJzf6DdkyBDD7d94442wBVipqalSt25dLVTKz883HIfI/24zTExMNLxl0ptIPwMrWgIsh8Ohffuir2dgOa9ya9++vdvrBFjn0YfaAwEWTEXhAIDg+RtgPb207HzLEBDtCLDCx1eAVVxcLH/5y1+0wMDbtxBefvnlopT3byH87bfftHmqt28h9CUSAZbzvJ955hndbR0Oh3bOZgdYubm5cvXVV4tSSho1aiTHjx83PAenZcuWacdbsGCBz/VLi/S3EEZLgCUicuedd4pS3r+FMD09XeLj40Up799CSIBFH1rWEWDBVBQOAAhepw86+RVgPfbVY1YPFbANAqzw8RVgiYh89NFH2twyKSnJMMAaN26ctt7cuXM93i8sLJTOnTuLUkoqVaokWVlZbu9HW4B12223aedaUFDgse1bb72lbWt2gNW3b19RSknlypXlt99+Mxy/q5KSEi1Qq1evns8gZdcu9y8YyM3N1a5Eat++ve45v/nmm27nHIxoDLAWLlyondObb76pu86DDz4oSimJiYnxeFA7AdZ59KH2QIAFU1E4ACB4L3//sl8B1gMLHrB6qIBtEGCFjz8BVnFxsbRo0cItuNALsPLy8rQAJT4+XkaMGCF79uyRzMxMWbNmjXTp0kXbXu82w2gLsGbMmKG91717d9m4caNkZGTI1q1bZdCgQaKUkssuu8z0AMv1GVMffvih9sB4vaX0tzUmJydLhQoVtFsBR48eLVu3bpXs7GxJT0+X5ORkmTx5snTt2lWqVq3qMaaJEydqx+7cubOsWbNGMjMzZefOnTJ06FCJiYmRJk2amBJgdezY0eu5OZeioiK/Pjdvx/IVYDkcDu0h9jExMfLMM8/Itm3bJCsrS37++Wfp1auXds56txkSYJ1HH2oPBFgwFYUDAILnb4DV85OeVg8VsA0CrPDxJ8ASEZk7d67PAEtE5ODBg9KyZUu3dUsvw4YN033GUrQFWMXFxdpVWHrLjTfeKF9//bXpAZbzdX8WvfP/8ccftWdneVtq1qyp+5kMHDjQcJtOnTrJtGnTTAmw/F1Kf3bhCLBEzj/HrUOHDl7H8uCDD0phYaHHtgRY59GH2gMBFkxF4QCA4PkbYP190d+tHipgGwRY4eNvgFVSUuJ2tZFRgCVy/kqs8ePHy4033iiJiYkSHx8vDRo0kL59+8ratWsNt4u2AEvk/G2PY8aMkVatWklCQoJUr15d2rVrJ+PGjZPCwkKfwYUVAZbI+X7gvffek1tuuUXq1KkjsbGxUqlSJbn00kvl/vvvl3nz5smZM2cMP5f58+dLly5dpFq1alK5cmVp06aNvPXWW1JYWBjQz0lPtAZYIudDy5kzZ0rXrl2lVq1aEhcXJxdffLH06NFDFi9ebLgdAdZ59KH2QIAFU1E4ACB4L33/kl8B1vzt860eKmAbBFgAEHkEWNBDgAVTUTgAIHhGAdZfP/yr238v2B74NzwBCA4BFgBEHgEW9BBgwVQUDgAIXt/5fXUDrO4fd3f776HLh1o9VC7ukgIAACAASURBVMA2CLAAIPIIsKCHAAumonAAQPCMbhms/XZtj9cARAYBFgBEHgEW9DADhqkoHAAQPH+ef0WABUQWARYARB4BFvQwA4apKBwAEDwCLCD6EGABQOQRYEEPM2CYisIBAMEjwAKiDwEWAEQeARb0MAOGqSgcABA8vaDq+RXPE2ABFiLAAoDII8CCHmbAMBWFAwCC9++V//YIqqb+OpUAC7AQARYARB4BFvQwA4apKBwAELxRP4zyCKqm/DqFAAuwEAEWAEQeARb0MAOGqSgcABC8N3980yOoGrF6BAEWYCECLACIPAIs6GEGDFNROAAgeHoB1utrXifAAizkbKL2799v9VAAwDb2799PgAUPzIBhKgoHAARPL8B6f+P7BFiAhQ4ePCg7duyQ3bt3i8PhsHo4AFDmORwO2b17t+zYsUMOHjzo1zb0ofbADBimonAAQPD0AiyjBUBkpKeny44dO2THjh2Sk5Nj9XAAoMzLycnR6m56erpf29CH2gMzYJiKwgEAwWs5sSUBFhBl8vLytEZq7969kpOTw5VYABAGDodDcnJyZO/evVrdzcvL82tb+lB7YAYMU1E4ACB4/oZXBFhA5DgcDklNTdWaKefthPv375eUlBQWFhYWFhOW/fv3a7cNOpfU1FS//w8D+lB7YAYMU1E4ACB4BFhAdHLohFgsLCwsLOFbAgmvROhD7YIZMExF4QCA4A34cgABFhClHA6H5OXlSXp6uhw8eNDyqxVYWFhYytpy8OBBSU9Pl7y8vIBv1aYPtQdmwDAVhQMAgvf8iucJsAAAAAJEH2oPzIBhKgoHAASPAAsAACBw9KH2wAwYpqJwAEDwjAKs+uPqE2ABAAAYoA+1B2bAMBWFAwCCN3T5UN0Aq+7YugRYAAAABuhD7YEZMExF4QCA4D23/DndAKv227UJsAAAAAzQh9oDM2CYisIBAMFrM6WNboD12FePEWABAAAYoA+1B2bAMBWFAwCCZ/TA9ldXverxWqBfLw0AAFBW0YfaAwEWTEXhAIDgGQVYw1cN93gttzDX6uECAABEBfpQeyDAgqkoHAAQPKMA64YZN3i8lleUZ/VwAQAAogJ9qD0QYMFUFA4ACJ5eeHXp+5fK9dOv5wosAAAAA/Sh9kCABVNROAAgePXG1dN9WPu9n97r8Xr6uXSLRwsAABAd6EPtgQALpqJwAEDwRv84WjfAenrp0x6vL9u7zOLRAgAARAf6UHsgwIKpKBwAEDyjACsrN8vj9e/2f2fxaAEAAKIDfag9EGDBVBQOAAjemLVjPIKq34//LiKez8daf3i9xaMFAACIDvSh9kCABVNROAAgeHoB1uoDq0XEM8Bad3idtYMFAACIEvSh9kCABVNROAAgeG//9LZugOVwODxeX3tordXDBQAAiAr0ofZAgAVTUTgAIHjvrHvHI6hSI5Wknk71eO2Hgz9YPVwAAICoQB9qDwRYMBWFAwCCpxdeqZFKUrJTPF4btmKY1cMFAACICvSh9kCABVNROAAgeEYB1rb0bbqvAwAAgD7ULpj9wlQUDgAIXsVRFXWDqq3HtxJgAQAAGKAPtQdmvzAVhQMAgjf4m8G6QdUf6X8QYAEAABigD7UHZr8wFYUDAII38OuB3EIIAAAQIPpQe2D2C1NROAAgeE8ueVI3qNpxYgcBFgAAgAH6UHtg9gtTUTgAIHhGAdbOjJ0EWAAAAAboQ+2B2S9MReEAgOD9Y/E/dIOqgycPEmABAAAYoA+1B2a/MBWFAwCC98TiJ3SDqqNnjhJgAQAAGKAPtQdmvzAVhQMAghf7eqxuUHXszDGP16qMrmL1cAEAAKICfag9EGDBVBQOAAieXnjlDLCav9/c7bVKb1ayergAAABRgT7UHgiwYCoKBwAEz1uANXT5ULfXXln5itXDBQAAiAr0ofZAgAVTUTgAIHjeAqxb5tzCM7AAAAB00IfaA7NfmIrCAQDB0wuvnlv+nJzOP81D3AEAAAzQh9oDs1+YisIBAMG7efbNhiHV8FXDCbAAAAB00IfaA7NfmIrCAQDBG/3jaMOQauvxrW6v3zz7ZgtHCgAAED3oQ+2BAAumonAAQPC8BVi7Mna5vd54fGPrBgoAABBF6EPtgQALpqJwAEDwxqwd4xFgrdi3QkRE9mfv5xZCAAAAHfSh9sDsF6aicABA4BwOh0z9darug9qdAdbhU4cJsAAAAHTQh9oDs1+YisIBAIHbfGyzbnilRioZs3aMFBYXyr6sfQRYAAAAOuhD7YHZL0xF4QCAwHWd09UwwFIjlaSfS5dzBecIsAAAAHTQh9oDs1+YisIBAIG7bvp1PgMsESHAAgAA0EEfag/MfmEqCgcABM7fAOvxrx6Xu+fdLb0+6yX9F/a3eNQAAADRgT7UHgiwYCoKBwAEzugB7qUDLAAAAHiiD7UHAiyYisIBAIGbljyNAAsAACBI9KH2QIAFU1E4ACBwvq7AOnHuhNVDBAAAiFr0ofZAgAVTUTgAIHAEWAAAAMGjD7UHAiyYisIBAIEjwAIAAAgefag9EGDBVBQOAAjclF+n+BVgZeZkStrZNDl65qiknk6VEkeJxSMHAACwHn2oPRBgwVQUDgAI3N3z7vYrwLr0/UvdXj+df9rikQMAAFiPPtQeCLBgKgoHAATummnXeA2wMnIyRESkxX9auL1+Mu+kxSMHAACwHn2oPRBgwVQUDgAIXO/PevsVYF028TK317NysyweOQAAgPXoQ+2BAAumonAAQOCmb5ruV4B1+aTLdV8HAACwM/pQeyDAgqkoHAAQOH8DrNKvj1031uKRAwAAWI8+1B4IsGAqCgcABOaLHV94Da9WpqyUguICEfEMsF5d9arFowcAALAefag9EGDBVBQOAPCfw+GQiqMqeg2wXJV+7/kVz1s0cgAAgOhBH2oPBFgwFYUDAPxXVFLkNbzyFWA1f7+5RSMHAACIHvSh9kCABVNROADAf/4EWOnn0rX1fQVcAAAAdkQfag/MfGEqCgcA+K+wuNBngLVk9xJtfQIsAAAAT/Sh9sDMF6aicACA/wiwAAAAQkcfag/MfGEqCgcA+K+guMBngHXtf6+V3MJcESHAAgAA0EMfag/MfGEqCgcA+C+/KN9ngKVGKsnOzRYRAiwAAAA99KH2wMwXpqJwAID/CLAAAABCRx9qD8x8YSoKBwD4L68ojwALAAAgRPSh9sDMF6aicACA/3ILcwmwAAAAQkQfag/MfGEqCgcA+M/fAOtk3kkRIcACAADQQx9qD8x8YSoKBwD4L6cwhwALAAAgRPSh9sDMF6aicACA/8wIsHILcy0+CwAAAGvRh9oDARZMReEAgMCN3zDea4B1Ku+UiIj85T9/8XhvV8Yui0cPAABgLfpQeyDAgqkoHAAQuHfWveNXgKX33t6svRaPHgAAwFr0ofZAgAVTUTgAwH8Oh0PWHFjj8xZCbwFWxw86WnwWAAAA1qIPtQcCLJiKwgEA/ikuKfbr+Ve+Aiwe5A4AAOyOPtQemPXCVBQOAPDP17u/9jvAOp1/WkRE3tvwHgEWAABAKfSh9sCs10b27t0rFSpU0P6wZ82aZfoxKBwA4J/bP7494ADr233fEmABAACUQh9qD8x6baRbt27aHzUBFgBY6655d/kVXlUcVVELsB776jECLAAAgFLoQ+2BWa9NfPTRR6KUkqZNmxJgAUAU+L+f/s+vAMvVPZ/eQ4AFAABQCn2oPTDrtYHs7GypU6eOlC9fXhYtWkSABQBR4KOtHwUcYN00+yYCLAAAgFLoQ+2BWa8NPPbYY6KUkiFDhsiBAwcIsAAgCgQTYL30/UsEWAAAAKXQh9oDs94ybu3atRITEyOXXHKJnD59mgALAKKEvwHW5mObtW1eWfkKARYAAEAp9KH2wKy3DCssLJRWrVqJUko++eQTERECLACIEv4GWAt3LNS2efn7lwmwAAAASqEPtQdmvWXYqFGjRCklXbt21V4jwAKA6BBMgPXvlf8mwAIAACiFPtQemPWWUfv27ZMKFSpIfHy87Nq1S3vdjADr3Llzhkt6ejqFAwD88Nbat/wKsNRIJTmFOSIicuT0EQIsAACAUgiw7IFZbxn117/+VZRS8vLLL7u9bkaA5dze10LhAABjA78e6HeAdbbgrLad3vsHTh6w7kQAAAAsRoBlDwRYZdDcuXNFKSVNmjSR3Nxct/cIsAAgOvxz6T9NC7D6L+xv4ZkAAABYiwDLHgiwypiTJ0/KxRdfLEopWbx4scf73EIIANHB6IHswQRY3EYIAADsjADLHpjxljFDhgwRpZT06NFD930e4g4A0WFL2ha/A6xzBf+rpwRYAAAA7uhD7YEZbxnTs2dPv2/xc10OHDhgyvEpHADgn9+P/x5UgBX7eiwBFgAAgAv6UHtgxlvGEGABwIVh+4ntQQVYg74eRIAFAADggj7UHpjxljEpKSmyZcsWw2Xp0qXaH/Zrr72mvV5QUGDK8SkcAOCfXRm7ggqwUrJTCLAAAABc0IfaAzNem+EZWAAQHfZm7fU7wMopzNG2O5N/hgALAADABX2oPTDjtRkCLACIDkZXUhFgAQAABIY+1B6Y8doMARYARId/ffcvAiwAAAAT0IfaAzNemyHAAoDo0Hpya9MCrG4fdrPwTAAAAKxFH2oPBFgwFYUDAPzjb3ilRirJLczVttMLsB5d9KiFZwIAAGAt+lB7IMCCqSgcAOAfMwOsvvP7WngmAAAA1qIPtQcCLJiKwgEA/vE3vOrzeR8pKC7QtuMZWAAAAO7oQ+2BGS9MReEAAP8MXT7UrwCrtMLiQt31TueftuAsAAAArEcfag8EWDAVhQMAfCsuKZaKoyoGFWCJ6F+9lZmTGeGzAAAAiA70ofZAgAVTUTgAwLePt37s9y2EevTWy8rNivBZAAAARAf6UHsgwIKpKBwA4FsgD3D/atdXfm1PgAUAAOyKPtQeCLBgKgoHAPgWSID1yR+f+LU9ARYAALAr+lB7IMCCqSgcAOBbIAHWhI0TxOFw+NyeAAsAANgVfag9EGDBVBQOAPAtkABLjVSSX5SvbWv0LYTHzx6X4pJiC88KAADAGvSh9kCABVNROADAt1ACrJzCHMP1rp9+PVdiAQAA26EPtQcCLJiKwgEAvoUSYOUX5Xtd97U1r1l4ZgAAAJFHH2oPBFgwFYUDAHwb/M3goAOsopIir+u2+287C88MAAAg8uhD7YEAC6aicACAb6+teS2gAKuguEDb1uFweF13xOoR1p0YAACABehD7YEAC6aicACAbx9v/Vi6fdgtqABLxPstiARYAADAbuhD7YEAC6aicACAf0asHkGABQAAYAL6UHsgwIKpKBwA4J8Xvn2BAAsAAMAE9KH2QIAFU1E4AMA/w1YM8zvAKiwudNuWAAsAAOB/6EPtgQALpqJwAIBvk36ZFNBD3AMJsJ5a8pRFZwUAAGAN+lB7IMCCqSgcAOBb/4X9wxZgqZH80w4AAOyFPtQemOXCVBQOAPDt/vn3BxRgFZUUuW1/5ZQrCbAAAAD+RB9qD8xyYSoKBwD4Fkh4pRdgrTu8jgALAADgT/Sh9sAsF6aicACAb6EGWLsydhFgAQAA/Ik+1B6Y5cJUFA4A8C2Q8GrOb3OkxFHitv3vx38nwAIAAPgTfag9MMuFqSgcAOBbIAGWnl+P/kqABQAA8Cf6UHtglgtTUTgAwLdQA6zT+acN179hxg0RPhsAAABr0YfaAwEWTEXhAADfAgmwsnOzA9rHsBXDInw2AAAA1qIPtQcCLJiKwgEAvgX6DKxA9kGABQAA7IY+1B4IsGAqCgcA+BbOAOv5Fc9H+GwAAACsRR9qDwRYMBWFAwB8CyTA6jKrizgcjoD2AQAAYCf0ofbALBemonAAgG+BBFhqpJISR4nHPtpObUuABQAAIPShdsEsF6aicACAb2YEWHfMvYMACwAAQOhD7YJZLkxF4QAA38wIsFalrPIZYJ3MOym5hbmRPDUAAICIow+1BwIsmIrCAQC+jd8wPuQAK/V0qtcA6+XvXxY1UslFb18kG1I3RPoUAQAAIoY+1B4IsGAqCgcA+BZogKX3EPfT+acN1z925pjbf1855UoLzhIAACAy6EPtgQALpqJwAIBvW9K2yJBlQ0IKsESMb0XUu70QAACgrKIPtQdmtDAVhQMA/PPUkqfCFmAt3LHQ7b87ftAxwmcHAAAQOfSh9kCABVNROADAP08uedLvAMtI/Bvxuus/t/w5AiwAAGAb9KH2QIAFU1E4AMA//1j8j5ADrJiRMbrr1x1blwALAADYBn2oPRBgwVQUDgDwrc/nfQJ6iLsRf7cnwAIAAGUZfag9EGDBVBQOAPDtjrl3EGABAACYhD7UHgiwYCoKBwD41nZq24gGWDfMuCGCZwcAABBZ9KH2QIAFU1E4AMC3QMIrMwIsb/sAAAC40NGH2gMzWpiKwgEAvhFgAQAAmIc+1B6Y0cJUFA4A8C2Q4CkzJ9NwP13ndCXAAgAAtkcfag/MaGEqCgcA+GbWlVNbj28lwAIAALZHH2oPzGhhKgoHAPhm5q1/BFgAAMDu6EPtgRktTEXhAADfAgmwtqRtMWVfZwvORujsAAAAIos+1B4IsGAqCgcA+BZIgDV903RT9pWdmx2hswMAAIgs+lB7IMCCqSgcAOCbFQFWVm5WhM4OAAAgsuhD7YEAC6aicACAb4EEWL6eX+XvPrx9myEAAMCFjD7UHgiwYCoKBwD4ZkWAlZGTEaGzAwAAiCz6UHsgwIKpKBwA4N3ZgrOmBljPLnvWr33kFeVF6AwBAAAiiz7UHgiwYCoKBwB4N3zVcFMDrGErhvm1j+0ntkfoDAEAACKLPtQeCLBgKgoHABjbfGyz1B1b19QAS8S/2wgbjW8UgTMEAACIPPpQeyDAgqkoHACgb+bmmQEHVwRYAAAAvtGH2gMBFkxF4QAAfcGGV2YFWA3fbRiBswQAAIg8+lB7IMCCqSgcAKDP6gCr+fvNI3CWAAAAkUcfag8EWDAVhQMA9FkdYNX6v1oROEsAAIDIow+1BwIsmIrCAQD6whlgtftvO1P2AwAAcCGiD7UHZrMwFYUDAPSFM8Dq83kfAiwAAGBb9KH2wGwWpqJwAIA+AiwAAIDwoA+1B2azMBWFAwD0JU1ICiq8ins9zue++87vS4AFAABsiz7UHpjNwlQUDgDQ1+S9JgRYAAAAYUAfag/MZmEqCgdgjbyiPJm1ZZYs2b1EHA6H1cOBjkbjGwUVYHX6oJPPfT+w4AECLAAAYFv0ofbAbBamKguFIyU7RW6efbO0mtRKFu1cZPVwAL90+7CbFlK8s+4dq4cDHQ3ebRCW51+JiFw97WoCLAAAYFtloQ+Fb8xmYaqyUDj+9sXftGavxls1pLC40OohAV6tSlnlEVScLThr9bBQStrZNEk9nSpdZnUxPcDqOqdrwPv69eivMi15mhw7cywcpwsAABAxZaEPhW8EWDBVWSgcpRu+1pNbc0sWotayvct0g4qYkTEy+sfRVg8PLjYd2yTf7f9O+i/sb3mA9cPBH6T8a+VFjVRy0dsXEXgCAIALWlnoQ+EbARZMVRYKh17T98PBH6weFuBhZcpKn4HFybyTVg8Tf+o8q3NQtxAu3LHQ574DDbAuff9St9fHrhsbzlMHAAAIq7LQh8I3AiyYqiwUDr2m79r/Xmv1sAA3RldelV5SslOsHir+dNPsm4IKsCb9MsnnvgMNsAZ+PTDgkAwAACBalYU+FL4RYMFUZaFwGDV+JY4Sq4cGaOqNq+dXYHHk9BGrh4o/+RsyhSvAin09VlufAAsAAJQlZaEPhW8EWDBVWSgc102/Trf5W394vdVDAzT+hh9Hzxy1eqj4018//GtQAZbrlVNGLp90OVdgAQAA2yoLfSh8I8CCqcpC4bh++vW6jd/3+7+3emiAxt/gg2+Yix7Bhlf+BFj+7udcwfm6TIAFAADKkrLQh8I3AiyYqiwUjvYz2us2fitTVlo9NEBDgHXhiYYAa0PqBhEhwAIAAGVLWehD4RsBFkxVFgrHDTNu0G38Nh/bbPXQABER+cfif/gdWKSdTbN6uPhTNARYW49vFRECLAAAULaUhT4UvhFgwVQXYuE4ePKgHD97XPvvG2feqNv4HTp1iAe5IyoEEnxsOrbJ6uHiT9EQYDlrXenXn1v+XLhPH7CMw+Hg328AKOMuxD4UgSPAgqkutMLx6qpXRY1UkvBGgnYFQoeZHQybv9jXY2XCxgkWjxp2F0jwsWzvMquHiz+FM8B64dsX/NrPiXMndMfy6KJHw336gGUGfT0o4L8pAMCF5ULrQxEc/gWHqS6kwnGu4JzHhDb9XLpfTeDZgrMBHSunMEd6fdZLKr9ZWfot6Cf5RflhOivYgd7vZLUx1eT2j2/3eH3pnqVWDxd/CmeAdSrvlF/7yczJ1B3L3xf9PdynD1jmb1/8jQALAMq4C6kPRfD4FxymupAKR0p2iseEtu7YupI0IclnE5h8NDmgY83YNMNt+0/++CRMZwU70Pud/PnIz7rvLdm9xOLRwimcAZaIyJRfp/jcT3Zutu5YCLBQ1uzO3C3f7vtWvtv/ndQdW5cACwDKuAupD0Xw+BccprqQCodegOXvEmiAVWV0FbftkyYkhemsYAdGv5cXv3Oxx2uLdi6yergQkaKSorAHWAdOHvC5n9+P/647lse+eizMnwAQWUOXDw35bwoAcOG4kPpQBI9/wWGqC6lwHDtzLGIBVuU3KxNgwTSB/K5+seMLq4cLEfls22dhD7BERCb9Mimo/RNgoax5bvlzBFgAYCMXUh+K4PEvOEx1IRWOrNwsywKsZhOahemsYAeB/K4u2L7A6uFCRJbtXRb2ACuvKE8yczKD2v/jXz0e5k8AiKwhy4YQYAGAjVxIfSiCx7/gMNWFVDiCbfTUSCWbjm3yuf+ikiJZsH2BLNi+wGN7AiyEIpDf1fnb51s9XIjIzM0zwx5gBXv1lRqpZPIvk8P8CQCRdfW0qwmwAMBGLqQ+FMHjX3CY6kIqHKEEWL8c+UX6L+wvrSa1kok/T9Td/5n8M4bb3/bRbRE+W5Qlgfyurti3wurhQkQ+2PxB0PWm0puV/DpGKAHWsr3LwvwJAJFlRigMALhwXEh9KILHv+Aw1YVUODJyMoJu9p755hm3/z548qDH/r0FWEahF+CPQH5Xv9nzjdXDhYjM2jIr7AHW5F8mB32M5XuXh/kTACKLAAsA7OVC6kMRPP4Fh6kupMIRSoBVenl22bNu+96fvV96fNLDcP1Jv0yy6KxRFgTyu/n/2Tvv8CiK/4+fSFEEUQERbEhRxIIgKtjLVxQUFUHAhqKoIIrSxE4KNaGG3ksoKUBCD2l0AkkICQECqUB6b6Qn9/79gXe/29ndu623t5d5Pc/neZLd2ZnPzM7O7rxvyt4re7V2lwJg47mNktuXuSfmCkqDFNbFGB2pR3E2qIBFoVAojQs99UMp0qFvcIqi6KnhyC7PFtXBazO7DV7b8Bpe3/g6Bm4ZyDi378o+Rtw9l/W0GtfyyOXYcG4DXljzAsbuHYsbNY5dVhTHQky9DUwI1NpdCoBNsZski0tCeX7N81TAolD+gwpYFAqF0rjQUz+UIh36Bqcoip4aji1xW0R18GKyYszXfhP4De8oFyHC2J+hfzL+X3x6sSCfc2/k4rUNr+HVDa/i1Q2v4rs93yleLhTHR0y93Xlpp9buUgBMOTRFdQHrhTUvSE4jODlYxdxTKPaHClgUCoXSuNBTP5QiHfoGpyiKnhqONrPbsD5oM0ozeD92IzMizdd+u/tbxrk9l/eYzxVXFfPGMW7fOIzfPx59V/eV9DF9veQ645q+q/sqXi4Ux0eMMOF3wU9rdxsdPvE+eHzp43jH+x2kl6YDAEYFjJIsLlXVVQlKt9/afpLTWBezTs0ioVDsjpB6f73kutZuUigUCkUh9NQPpUiHClgURdFTw8H3iyzfh+7YvWOx9fxWbDu/DU8se4Jxbvfl3eZ4S6pKeON4bMljSClKwejA0YoIWM+uelaVsqE4FmN2j4HBxYBbXG5BE9cmokYPbju/TWv3GxUVtRVoObOlufx/3PcjAOC34N8ki0sLIxYKSrv/2v6S0/g3/F81i4VCsTtC6v3YvWO1dpNCoVAoCqGnfihFOlTAoiiKnhqO22fcLkrAsmYmASs0JVTyWjdCSC9NpwJWI4RryqrQelVWXaa1+42KgIQAzmdbzugooQLWK+tfkZwGnWpKcTaUfPdSKBQKxfHRUz+UIh365qYoip4ajjtm3sH6kF16Zqmkzp9poWypnUehH9FpxWmMa7os7qJmEVEcBHJx7tXRqwXXq/Kacq3db1RwCVjVddWy2obB2wYLSvt0+mkqYFEo/0EFLAqFQmlc6KkfSpEOfXNTFEVPDceds++U1am0tMeXPg5AfQErPDWcfnw3Qsh7/nXg11TAclC4BKzdl3fLbmOEYDQa4XLYBT2X9USXxV2ogEVp1FABi0KhUBoXeuqHUqRD39wURdFTw3HXnLsUE7BMH8FUwKKogRwBy+OEBw4mHdQ6C40GLrFq56WddhGwLAlKCqICFqVR80/4P1TAolAolEaEnvqhFOnQNzdFUfTUcNwz9x6HEbCETgUMSw2jH9+NEPKef7HrC1H1a+SOkVpnodGQkJ/AKPvXNryGiUETqYBFodiZNWfX2Kz3w/2Ha+0mhUKhUBRCT/1QinRo75eiKHpqONyPuisqYCUWJMq6PiYrBuU15Tidfhr91vbD6xtfR2x2LMPn0JRQKmA1QoTUn07zO/Ge+3D7h1pnodHAJWA9uOBBhxewfOJ9VCgNCkU71sest1nvP/H7RGs3KRQKhaIQeuqHUqRDe78URdFTw5FSlKKogNVxXkdF4zO4GNBvbT+GzyEpIVTAaoQIqSvvb3uf99wA7wFaZ6HRwCVgdfPq5vAC1i8Hf1GhNCgU7dgcpZP52gAAIABJREFUu9lmvX9i2RNau0mhUCgUhdBTP5QiHdr7pSiKnhqO5MJkxQUnNcwSKmA1TpSuRxT14BKwXl7/st3v3/Frx6mARWn0cI1aJq2kqkRrNykUCoWiAHrqh1KkQ3s1FEXRU8OhRwErODmYChONECXqUWpRquD0quuq8UfoHxi8bTCCk4NVzJnzQQpYBhcD3t78tiYCpJidVn89+KvCJUGhaEtNfQ1e2/Cazbr/R+gfWrtKoVAoFAXQUz+UIh3a+6Uoip4aDj0KWIeSD1EBywEwGo24WnwVpdWldklPiXo0NXiq4PQWRiw0X9fMrRmKq4pVzJ1zwSVgKd0OCCUmK0Zw/BODJipcEhSKtlhbF9DShvkN09pVCoVCoSiAnvqhFOnQ3i9FUfTUcOhRwOJa14Zif77c9SUMLga082iHyIxI1dNToh7tvbJXcnqeJz1VzJ1zEZUZ5TACltFopAIWpdEitO4P9R2qtasUCkUn1DXUwf2oOz7x+wThqeFau0Mh0FM/lCId2vulKIqeGg7fC76ai1NCrL1He0w+NBkAEJ4aTgUsjSivKce3u7/Fs6ueZZR/39V9VU9biXp0JO2I5PSmH56uXuacjCsFVxxGwAKADp4dBMU/KWiSgqVAoWiP0GfrudXPae0qhULRCcsjl5vbjmZuzVBUWaS1SxQL9NQPpUiH9n4piqKnhkPM+jBa2/d7vgcAnM06yzj+7pZ3NS5FoLK2Euti1iEwIRBGo1Frdzgpqy7DhAMTMMxvGM5ln5MUR1FlkeLighBis2MVqUPfBH6DC7kXBKVJBSzpqCZg/fWXJH+ETqMyieQUirMg5vly1HcXhUJxLMi2Y8bRGVq7RLFAT/1QinSogEVRFD01HG1mt9FcmBJqY3aPAQBEZ0azzm2J24L6hnrNynGA9wCzL7OOzdLMD2v8evBXs4/3zbtPUnldyrukiYDV3au7onVp8LbB+Dvsb6vrWlEBSzqqCVgGA1Ak/pfeBxY8ICj+0JRQFUqDQtEOMc/XpbxLWrtLoVB0ANl2zDs5T2uXKBboqR9KkQ4VsCiKoqeGQ08jsL4J/AYAEJkRyXn+h70/2MxvWXUZFpxagE2xm9BgbFCkDLPKsuwq5kiF9PHU9VOi47iQe4FfXFARterUAO8BMBqNnHWBDPtv+L+q5tGZUEPAumvafwLW2bOi/XlwwYOC0pDyTFAojoyYZ8weaxlSKBT989nOzxhtR0hKiNYuUSzQUz+UIh3H7G1SdIueGg49CVgmkeRMxhnJIsor618xh/0n/B9FypBrxzVHhPQxqTBJdBx8Atbdc+7mDF/XUIdZx2bhE79PZI1uUbNOtZ7VGq1ntcauS7uspkkFLOGoKmCdPy/an4cWPiQojYj0CBVKg0LRDjHPWHxuvNbuUigUHUAKWHT0smOhp34oRTqO2duk6BY9NRx6mkJocDFge/x25N3I4z1vjfTSdFHhbXH82nGM2T0Gkw9NVizeBmMDdl3ahf2J+xVfj+TpFU8zfMwuzxYdh7UphNMPT2dNS1wVvYoRpuXMltgev110uvaoW03dmlpNkwpYwonLiVP8/sTf+5+AdfCgaH86L+osKA0qYFGciYKKAlHP2MW8i1q7TKFQdAAVsBwbPfVDKdKhAhZFUfTUcNwx8w7NxCip9uH2D3nPWeNi3kWb4YurijHv5DxsidtiVUDKvZGLW11vleSHNcbsHmOO44/QPyTHw0XPZT0ZPuZX5IuOg2u0maUFJwczwnOFWRW9SnS69qpbwM17+1fYX2jm1gx9VvXBC2teQL+1/RCUFISgpCCUVpeK9r+xsefyHuXvjcHCRPLIokcEpXE6/bQKpaEukRmRuvSboj6pRaminrG4nDitXaZQKDrg0x2fMtoOKmA5Fnrqh1KkQwUsJ6WyshK7du3CDz/8gD59+qBNmzZo2rQp2rVrhzfffBPLli1DZWWl4unqqeFoObOl3cQBewkQfHBNfyN5dtWz5nPWFmOfFDSJ14dnVj6DHRd3oPOizuiyuAu6eXWDxwkPQfdDTH7E8uiSRxlxW1vAnI/L+Zetln/XxV3NYStrKznDCJ26WVNfg+mHp+Mjn4/sVn+MRiN6r+xt/n/KoSkAbtYd03TbLou74EaNYz/XWuNoAlaXxV0EpXEm44wKpaEebkfcRD9XlMZDYkGiqGfsbJb49eUoFErjgxSwwlLDtHaJYoGe+qEU6VABy0lp3bq1+QHmsx49eiAxMVHRdPXUcFgbRaRHs4YtAUvICC0Tw/yG8frQZ1UfbDi3gXFsWsg0m/fCaDSKyo9YyE58eU256DhsrW3UcV5Hc9g1Z9dwhnl1w6uC0vI67WX3+sOVPwCYGjyVccz3gq/osmtMqCFgmacQGsQ/F10XdxWUxrLIZSqUhnqo2V5Q9I+1Kd9cpjcBl0KhaMPgbYMZbUdQUpDWLlEs0FM/lCId+tXnpBgMBjRv3hwjR46Ej48PUlJSUFRUhNjYWIwbNw633HILDAYDOnfujPJy8Z15PvTUcNhbIFDbrJFRmmE1/IlrJwTHJ3btsIFbBtq8F/UN9ap2SB9e+DAj7qq6KtFx2BKwOs3vZA7LF+bl9S/bTOdGzQ1N6k9EegTrWFpxGmudM+84b9Fl15jYe2Wv4vdm7kvSBaxpIdMEpbEyaqUKpaEearYXFP0jdi26k9dPau0yhULRAWTb8Vvwb1q7RLFAT/1QinToV5+T8uOPPyI7m3+h6jlz5pgf8Llz5yqWrp4aDi1EAjWtwdjAm9eSqhJG2O5e3W2WQXJhsmLltu38Nqv3ora+VtUOKRn3xKCJouOwNYWwqVtTfLrjU7y16S3eMC+ue9FmOt/t+U6T+nMk7Qjn8SauTRj/rz27FotPL8Y/4f+gslb5ach6x9EELL7RgKTtvLRThdJQj1azWpl9bzmzpdbuUBwMvpHCPZb24G3/KBQKxRZk20EFLMdCT/1QinSogNVIqaurQ9u2bWEwGPD6668rFq+eGg4tRAI1ra6hjjevpdWljLDdvLpxTtuzNNOaIIWVhYjKjMKZjDM4k3FGsn/WqK6rFhUeuDlqy/+iPwISAmzuWijWHy5sLeIuxPqt7WczHa3qT1hqmKTrlN4xUu+oIWA1+ZcKWCSWm3BQAYtiSVZZFmcdXxSxCADwid8nrHN0GhCFQhEC2XZQAcux0FM/lCIdKmA1Yvr16weDwYBHH31UsTj11HBoJRSoYffNu8/qCKyy6jJG+K6Lu6Kuoc5qnNGZ0QCApm5NFfHRGlyLntvCcqTS5EOTRd9rsYhdU4XLnl/zvM10tKpDUvPXmKbeVNZWYuSOkbh7zt0YFTAKNfU1rDBqCFgGF+kCFtf6dlymNwHLchOOO2beobU7FAfin/B/OOv4iqgVAIBlkcs4z5dVl2nsOYVCcXTIdoMKWI6FnvqhFOlQAasR88gjj8BgMKBv376KxamnhkMroUANG7R1kNW8lteUs66xnILDZVGZUZxT+yR3wK0gZQqhmPBc/pRWl9pMwxKuhfDF2nOrn7OaRmhKqGZ1SGr+xu0bJ6oc9Yx3nDcj7/4X/Vlh+KZiyn5+DNIELEBYW6c3Aev2GbebfW81q5XW7lAciCmHpnDW8bicOHOY51Y/xzq/+PRiDb2mUCh6gGw3pgZPFXTdmYwz6O7VHR3nddTd+1ZP6KkfSpEOFbAaKTExMeYHfPz48YrFq5eGQ0lhxhHsHe93rOa3orZCdJyRGZHIKc9RrgNug+buzc1h28xuYzO8mPi5/PE67WUzDROVtZUISAiQXQbPrnrWajqWnXJ72xe7vpB03ejA0YLLUe/cOftORt4fXvgwK4wSU005nx8DFbAsuW3GbWbfW89qrbU7FAfibNZZzjoemx1rDvPSupdY59t5tNPQawqFogcGeA9gtBt+F/wEXddnVR/zNS3cW1idNUGRjl76oRR5UAGrkfLWW2/BYDDglltuQVxcnO0LLLhx4wav5ebm6qLhIEdS6N3e2vSW1fzmV+SLjvNMxhmrAlZSYRKul1wXHF9YahgWnFqAjNIMln+FlYVo5tYMt824zSziLD2zFAUVBbx5YnXwcXPtk8iMSNZ6YFz++MT7CK4vUteHIq33yt6K3iNHsL/D/hZcjnrnrjl3MfL+0MKHWGFUF7AKC0X5zNeZJ23N2TVKFZNd4Hr+KRSAv87HZMWYw3C16UJ+OKFQKI2b0YGjGe3G/sT9gq4j25uiyiKVPW2cUAGrcUC/+hohHh4e5od73Djx039M19oyR244fjn4i+YdfyXt7jl3W82vrR30uMz3gq9VAetGzQ3Oxddt2b2e96Kqrsrs22/Bv8HgYkB7j/aISI/Alrgt5rDdvLqxfqUyGo1IK05jxRuRHoHWs1rD4GLAGxvfYCwuzpc/oRy7esxmvvqv7W8zTK8VvTjjXx65XPM6JNVM68o0Br7c9SUj778c/IUVRnUBa/58UT4vPr1YcBqTD03G/sT92HtlLypqK5QqNlVglQ+lUVPXUIfAhED4XvDlrd+mdR0BILEgkTNMfUO9hrmgUCiODilg7buyT9B1ZFtDBSx1oAJW44B+9TUygoKCcOutt8JgMOCpp55CZWWl6DicQcD6+cDPmnf8Fe/gWkHqAt0+8T685wZvG4xbXG6RFO/KqJUAgIzSDMbxp1c8zQpr+euW0WjE4G2DOeN8cvmTjP9DUkKQVJgEz5OeuGfuPazwyyOXC64v4/ePt5mnVze8ajPMU8uf4oyfb2t3PZiYctQ7bkfcGHlfemYpK4zqAtadd4ry2eu0l6T0UotSGfGcTj+N3Zd3o7a+VlYZKgWrfCiNmqG+Q23W6ajMKHN4vk1Mdl3apWEuKBSKo/NN4DeMNoMKWI4FFbAaB/SrrxERHR2N1q1bw2Aw4MEHH8T169clxeMMUwh/2v+T5h1/pa28ppw3v0J3IiPNcl0qJW3KoSkAgMCEQJthP93xKa6X3Kyr4anhgtP49eCvVheqf3DBg4LrixDB83+b/2czzBPLnkBMVgz+DP2TIcw9uuRRzeuPWBvhPwIj/EcgODlY4lOoP1wOuzDKYFnkMlYY1QUsg7jX9sSgiZLSmxYyzTyKcX3MevPx97a+p0hZyoVVPpRGS96NPEF1+kzGGcZ1XGFM7yYTl/MvY13MOpagS6FQGiekgLX3yl5B15FtDRWw1IEKWI0D+tXXSLhy5Qrat28Pg8GA9u3bIyEhQZV09NJw/LjvR80FAKUtpzyHN7/xufGa+0d2jgFhApbBxYAFpxYAACYFTRKcRhPXJjbDCKG2vhYj/EfYjGvQ1kE2w7y39T2GKBiWGgYA6ObVTfN7IsbcjrjJfAL1yfTD0xnlwDX6TK1nTaqA1Xtlb8lp/hP+DwD2h7cjdOalPMsU50ToFHlyxCRXGEsB63L+ZfOajK1ntTb/kEKhUBov3+7+ltFmUAHLsdBLP5QiD/rV1wi4fv06HnroIRgMBrRp0wYxMTG2L5KIXhqOsXvHai4CKG3Z5dm8+Y3LidPcP0v7M/RPAMDuy7sFhfc44QFAnID1xLInbIYxUddQB8+Tnvh297eMhX6LKosYO8dYs5YzW9oM03lRZ9YxMaPKHMWmH54O4OaUTu84b8w/NR8lVSUqPKmOQWZZJufU1RVRK9BgbMD+xP0ITw0HAFzIvaBKmTMELIv13WwhtP5ae0bIY6eun1KlnMXA9yxTGh9CBSzPk56M67jCWApY7297n3Hu293f2jtrFArFwSDbDNMPrGKvK6wUtyELRRh66YdS5EG/+pycvLw8PPbYYzAYDLj99ttx/PhxVdPTS8OxKGKR5iKA0pZVlsWb39jsWM39Iy0iPUKwgDXr2CwAwORDkwXH//L6l22GMbEwYqH5WKtZrVBZe3NtOPej7pqXkyNaO492WBW9Cl0WdzEfe37N84o/p3UNdTiXfQ5ns84iKjMKcTnidkxVih/2/sBZDquiV2HM7jHm/2ccnYHUolRVypwhYEVH23b6P55d9ay8dMH+8CanYmkB37NMaXxcKbgiqC57x3kzruMK082rm/l8C/cWjHOPLnnU3lmjUCgOBtlmTA2eKui6++ffz7jO2rIfFOnopR9KkQf96nNiSktL0adPHxgMBjRr1gwHDx5UPU29NBzLIpdpLgIobdYErOjMaM39I62JaxPB98H9qDsAwOOEh+D43978ts0wJsjjm2M3AwDe3fKuYvkd7j8cr6x/RfNyV9OuFFxR9Dk9ce0E7z2zJ3z5/WznZ6xjdhGw9gvbthsA+q7uKy9djvxb7uamFY5QLyiOQXFVsc163M6jHWrqaxjXWavzAFhrQD625DF7Z41CoTgYZHshVMCy/MHP4HJzJ2+K8uilH0qRB/3qc1Kqqqrw6quvwmAwoEmTJvDz87NLunppOJxxBFZmWSZvfsm1e8QYubufkvbYkscEhXtj4xsAgHUx6wTH/UfoHzbDmCCPt5rVCk8tf0rRvH4d+DX+CvtL83qipkVmRCr6nB6/dpz3ntkTvvzeMfMO1jE1BKyWfxICVkCAYN+fW/2c5HRNGx2Qx89ln1OrqAVz95y7zf40c2umtTsUDSmpKrFZl7nWbeMLa6KZWzPG8R5Le9gzWxQKxQG5b959jHZhS9wWQdcdv3YcwcnBCE4ORkhKCOoa6lT2tHGil34oRR5UwHJC6uvr8cEHH5gf4IULF6K8vJzXKioqFEtbLw2H1F35HNkySjN48zvz2EzN/ZNjTVybAABWRq0UfM2m2E02w5iwRx5GBYxi7ObmjKb0oqTHrh5jpZGQn4DeK3ujg2cHbDi3QdH0+ODL720zbmMdU0PAaj+VELDWrxfs+7yT82SlXddQxzqm1VROS5xBwMq7kYfFpxcjKClIa1d0TVl1mc16zIWtsKSA9fjSx+2VJQqF4qAM9R3KaBeOX1N3aRaKOPTSD6XIgwpYTkhaWpr54RViDz/8sGJp66Xh2Hdln+adfaUtvTSdN79ipt45qtXU18DrtJfg8P4X/W2GMTFyx0jV/e+9sjdcj7hqXo5qWll1mWLPaEpRCmcan+741Px3c/fmdlk8nlzM2WS3ut7KOpZWnKZ4ufYaSwhYQ4cK9r28plxW2kFJQaxjyYXJKpa2MCwFrKZuTbV2RzT1DfWMTR22nt+qtUu6xVYdt1zXyhK+HUON/22S0NStKeM4FbAoFAoVsBwbvfRDKfKgApYTQgUs2wzxGaJ5Z19ps7bFd0BCgOb+ybVT109h/qn5isZpgmstI2riraquSrFnNCYrRlCaOy/tVCxNPvjai68CvmL832NpD0RmRCpapt1/JsQrgwG49VZR/stJ/4U1L6DjvI6MY/UN9SqVtHDumXuP2R89Cli7Lu1ilTVFGjdqblitw9vOb+O9lgzbZnYbs4A1KmAU49ykoEn2yhKFQnFQPvb9mNEuUAHLsdBLP5QiD/rFRFEUvTQc5AvIGexS3iXe/DrD1LWjV49ixtEZisY5ZvcYNBgb8OWuLzXPnzOYkms6RGVGCUpz7dm1iqXJx3D/4Zxpk8Lnk8ufxMGkg4qWaWwHDgHL01OU/3LS77+2P0b4j8D/Nv8Pr298HS+vf9ncwdcSvQtYXG0yRRoVtRVW67C1DiYZdoT/CPM58n3jddrLHtmhUFThYt5FnM06aza6BpM0yP7DiWsnBF332c7PMMB7gNlo+auDXvqhFHnQLyaKouil4SCHADuD/bT/J978bji3QXP/5NqRtCOqxOt+1B29VvTSPH/OYEoKG6fTTwtKc+mZpYqlycfnOz/nTHuY3zDG/71W9FJUwPpuMId4ZTAAS5aI8t+R7qsSGI1Glo96gxRoB20dpLVLuoVvurHJrK2VR4b9yOcj8zkqYFGcCbKuJxUmKRLvpbxLSCtOUyQuPSBVwLp//v2M66iApQ566YdS5KG/rz6KQ6OXhsMZBSxrW3w7wwis8NRwzX2gxm9Kj4I5ef2koHTnnZynaLpcfB34NWfa5C6afVb1UXx9vU8+MWDH44SA9cUXovyX60OrWa3QcmZLjPAfgeq6apVKWTj1DfUsH/UGOUX23S3vau2SbkksSLRaf1dHr+a9lgzbZ1Uf87my6jJklGaYrbym3B7ZoVBUgazrSghY00Kmmd//Qnfj0zud5ndilKO1KcqWkOVP2xN10Es/lCIP/X31URwavTQczjjipuvirqx85t7IxZa4LfjI5yPN/ZNrYalhmvtAzbrNPTFXsWeUawdCLruYd1GxNPnosbSHIF+eX/M8xuweo3i5ur/KMQpLBO092ivmi9CPdTXRq4BVXFWM7PJsAEBsdizD/7c3v62xd/rF1gismKwY3mvvnH0nK7wjbFJAoSgNWc/lClhcU3cbA2SepxyaIum6gooClT2VRk19Df4M/RODtg7Cnst7tHZHNHrph1Lk0ThaG4rd0EvDoXVHXw17aOFDjDwK2VpcTxaSEoKey3pq7gc165Z7I5f3ubtWcg1FlUWCntFfDv7CGT+5tX1WWZastkAIQvN+x8w7GDvLKWVyBazg5GDFfHlgwQMqlfLNqYE+8T5YF7PO6kivuoY6hk+d5ndSzSelsNyA4u+wv1k74L216S2tXdQtyYXJvPV1iM8Qq9fOPTGXdc17W9+zk+cUiv0g67lcASujNIMVZ2OAzLNUAauwslBlT6WxLHKZ2ccmrk2QdyNPa5dEoZd+KEUejaO1odgNvTQcWnfy1bD759/PyOPCiIWa+6SkBScH4/Glj2vuBzXrxvcxN+HABBhcDLhz9p0ITw1X7BnNKc9RpE1QwheDiwH3zbtPlXKVI2CJzYMtu5B7QYVSBn4P+d2chuVaRCS19bUMf8i2z9G4mHeRVYbHrx1nHeuzqg9SilK0dld3JBUm8dbVBmOD1Wu947y5nzfcHAXqedIT807Ow7yT8xCfG2+P7FAoqkDW8cSCRFnxZZZlcj43zo7Qbx5b1znqCCzSz7/C/tLaJVHopR9KkUfjaG0odkMvDYdanXetzeOEh3nkwr2e92ruj5IWlBSkuQ/UbNunOz5lPW/kL7XkaEESrkW6+YxrxNfFvIvwveAL/4v+2Hlpp6QpQTFZMXhm5TPo5tVNVP6VnK5naY4kYJ3POS86fSk+VtRWcIarqa9hhbWHkCmVwdsGs/zdFLuJs2y/Dvxaa3d1B98aWG3ntrV57c5LO7mfN/z/+j4mWx+zXu2sUCiqQdZxuQJWVlkW53Pj7JB5dnYB69eDv2rtkij00g+lyKNxtDYUu6GXhkONDqaj2OBtgwE4n4DF19Gg5lhmuQ29Ca6d+Ub4j8BL615CcHIwKzzXGkd8xjW83f2oOyPMkjPidu0DgDc3vSkp/08uf1KVcnUkASsuJ050+lJ8LK4q5gzHJWBtjt2sik9KwFWGfDtbtpndRmt3dceVgiucZdnOo53Na/namsiMSPwW/BvjmLXdDCkUR4es41TAkgaZZ6EjlMjrHHUKIeknFbAojkjjaG0odkMvDYcaHUxHsuKqYnTw7KC5H0raCP8RmvtAzbZxTeeyNnrurjl3oaa+hhGenCJmzWKzY1nbUbsecWWEWXpmqd3aiKnBU1UpV0cSsGKzY0Wnz0dkRiS6LO6Cu+fczUqHb5em6rpqVlhHE7AajA24kHsBhZWFnGX4TeA3nMc/2P6B7LQnBU1C75W90WdVH/RZ1Uc1wdFR2H15N2dZdvfqLuh6rmufW/0cHl3yKOPYZzs/UzknFIp6kHVcroCVXZ7Nfk9JJCw1DIsiFtllTUu5jNs3jpHnHRd3CLqOLCu9CFiTgiZp7ZIo9NIPpciDClgURdFLw6FGB9ORLLMs0+kErEFbB2nuAzVhRgpKthYRD0sNY4TnEiisWb+1/XCj5v/bm+mHpzPOL49cbrc2YmLQRFXKVK6A9fDChxXz5ejVo6LT5+OtTW/xpsMnlFXVVbHCjts3TjGf5GI0GjHAewAMLjensYkp2499P5adPhmn50lPBXLlmFzIvWD9uRGA0HvTd3VflXNDoagHWZ/lLuJeUFHAiK/1rNaS4rEUoO+bd5/VDTwcgR/3/cjIt7MJWF/u+pLh56HkQ1q7JAq99EMp8qACFkVR9NJwqNHBdCTLLMtUbTFpreyD7R9o7gM1YXbs6jHG8xaSEmI1fEhKCCO8T7yP6DS9TnuZrx/mN4xxTqy4IWYNLtJ+PvCzKmXKErAyM0Xl6cV1LyrmyzMrnxGVtjWspTNyx0jOayprK1lh39j4hmI+ycVWfbdmw/2Hy06fjNNZBSyuesB6bgTAdd1zq5/jPEah6BWyPssdgUUKWI8vfVxSPLfNuI0Rz5qza2T5pTbOLmB9sesLhp9UwKI4IlTAoiiKXhoONTqYjmQ55Tn4M/RPzf0wuBjwVcBXisQzxGeI5nmhJsxCU0IZz1toSqjV8Ja7EpZVl0lKs8fSHqiuq+bc3e2RRY+Iah/ErMFFGjm9QCljCViTJ4vKk5IClsHF+qdDeU05ojOjeacAWmItjYURCzmvqaitYIV1JAHLcjdFsca1CYJYyDjnnZynQK4cgw+3f4g+q/qg14peNsvy7c1vC4pT6L15fs3zKueOQlEPsj7LFbDyK/IZ8fVc1lMRvxacWiDLL7UhBSz/i/6CriPzqRcBi2udUkdGL/1QijyogEVRFL00HGp0MB3JautrWQtZ691mHZuluQ/UhJnnSU/EZMWYn7cjaUeshreckrY5drOkNCccmIDeK3tznrOngEWum6OUsQSsceJGldlLwMqvyEfXxV1hcDGg6+KunIvsW2ItjcWnF3NeI1TAKq4qRlhqGPIr8m2WT0VtBTJKM2A0Gm2GtcUfoX9ILtcvd30pO30yzvmn5suO01Ew1S0hdiH3gqA4hcb3wpoXVM4dhaIeZH2WK2CVVpdihP8IDPcfjk/8PsHEoImS4iHXA9x7Za8sv9Rm/P7xDH+pgOVY6KUfSpEHFbAoiqKXhmPx6cWqdDKDW6jUAAAgAElEQVQdxeob6uF2xE1zP5Sy51Y/J2laGTVtze2IGwAgJivGargT106Yn81T109JSuuldS/xnus0v5PoNqLH0h6al5+lsQQsg7jXt70ErGkh0xjhpoVMM5/Lr8jHJ36f4JmVz8An3geX8y9bTWPuibmcadyoucEKSwpY+RX56DivIwwuBnSc1xHppem8PifkJ+CBBQ/A4GLAJ36fyBax3t/2vuRybeHeQlbagHMLWN29usuuoyRC4+u3tp+KOaNQ1IWsz3IFLLlU1FYgsSARr298neFXYEKgpn7ZwtkFLHKHXCpgURwRKmBRFEUvDcfSM0s175CqaUajkbWQtZ4NsL6THTXHNQCIzoy2GiYiPcL8bHJNAZRr93reK7qN6Lmsp+ZlxyhHg2MJWJMPTWbtHgkAQ32HMsKZpsRxLbbd1K2p1TRcj7hy5qW8ppwVlhSwftr/E+P8H6F/8JbN4G2DGWHJNdys4XHCAxODJmLyocmYGjwVpdWlijwz1qhvqEdiQSLKqss4z5PxOfqUHDGIEZaFIjS+/mv7m6+pa6hDSVWJIiP2KBR7QNZnrQWssNQwzuds56WdmvplC9LfP0P/lHRdQUWByp5KgxSwyDVKHR299EMp8qACFkVR9NJwLDmzxC6dTq0MANJL0zX3QwkzLcD91PKnNPelsdids+9ULC7A9pQ8S/Hh6NWjiuen7dy2otuIJ5Y9ofl9YJSjQZ6AJSSNx5c+jvYe7QX7NMRnCCONsuoy/Bv+LyPMoohF2HFxh6Q884lOXCOwTHXNxGsbXmOc+2znZ4LLxnLU2LWSa3A57ALXI65wP+rOWrD36RVPM67NKc9R5Jnho76h3jxi4YEFD3B2Qsn4nEnAun/+/ZLqpjWE3pcX172ImvoaxOfGm+/7e1vfQ219rYo5ZhORHoG1Z9eaLbkw2a7pU/TJ2L1jMXDLQLOVVJXIjvPb3d/i68Cv8VXAVxi/f7yoa8NTwzmfM78LfrL9UhPS3ymHpgi6jvxRzFEFLPKHJb2N4NVLP5QiDypgURRFLw2Hs08hBICrxVc190MJWx+zHoDzr1vmSGZrZIwYWx29WtDoH9Nw+sNph1XJ05IzS9BgbBDcRnSa30nz+2BpSgtYbkfc0MK9BQwuBgzaOogR9lz2OcF+mX4t/zvsb87zQgUHLuNaU6W4qpg1YspcRhaQ0zx8L/jyls3XgV8zwu5P3G8+t+vSLsa5W11vZVz75PInGee5RpqJvtdWIP0Z6jvU5r12JgFLaBkKXf9KTJyd5nfiHAFmjw73xbyL2HVpF3Zc3MFqm7ad38Z7XUFFATbHbsa57HOq+0hpfNzqequ5Hor9oYhvbUxr9dkRIP0VKmD1WtELzd2bo7l7c7Rwb+GwAta3u79l5M/R1yQj0Us/lCIPKmBRFEUvDceiiEWyOxmObADw6oZXNfeDGjWhu2FGZkQCAHZf3i0pHSFr4wzzGya4jSDXctLSOk1SXsA6nX4aacVpOHn9JOob6hlhz+ecF+xbE9cmyC7PViXf98y9h7VOiOdJT97wlqORyN0grU1LIT/Y91zeYz7H9WOHJWpMNbXGqIBRNsOT5/l2c9QjSpSh1Dj5bID3AJVy+//8FfYXb/p8Hf6quio8uOBBGFxu/ihhuVkGhaIElj923T3nblHX8o22XhezTiVvlYEc3StmyrkesPY+1AN66YdS5EEFLIqi6KXhqKmvUaXDZc1WRK2wW1qpRal2zx81aqTdPedu/B7yu6Cwh9MOAwCG+w9X1aequipBbURBRYHm5WcyJQQscgpuTnkOb9iLeRc1z7OlPbHsCTy/5nnsvbKXd6dJg8vN0VGmkVY/7P2BcS4gIQCZZZkYuWMkBm4ZyNglkyssAPhf9OdMxxI18msNcscurvDkeSpgWYcUO8WaPQSsf8L/4U3f86Qn5zXLI5czwj288GHV/aQ0HriWBrBGeU05VkStQEBCAIxGI+96l7fPuN1OObDO0atH4XfBj7XWI7lJh+nHN2dhzO4xjPxRAYviiFABi6Ioemk45p+ab/dO2OjA0XZLK7kw2e75o0aNtLZz25pHANiy49eOA1B/qmhlbaXN9qGqrkrzsjPZkBEG/P6WfAGr14pejHizy7N5w9raHVBL67+2v80wAPDdnu8YxwITAjFyx0jz/928upkX4CZ3wbJlsdmxqKytVG2jjIzSDCyMWIjw1HDWvSHzZcqvJeR5PQpY9Q31rCm/RqNRcBmK4VrJNVn36+3Nb9tMo66hDlvPb4X/RX9RU5lNWKtrg7cN5rzm+z3fyyoXCsUamWWZouqXZds9/9R8nLh2QpHnVw1WRq00+/KO9zuMc+9tfY/hKxWwHAu99EMp8tC+laA4FXppOLh+xXYmSyxI1NwHatTELAh+8vpJAOoLWEKoqK2QFPfjSx9X3N+y5jzilUHc6/uZlc8w4s0sy+QNm1SYpHndkWPHrh5jtfG3uNzCCpdemi5KFLG0fmv7qea/5bQcsvPANVqIhDy/KGKRqLqiNRvPbUQL9xZoO7ctwlLDzMc/8ftEcBmKJTg5WPL9em/rezbj/yrgK3P4SUGTRPvndsSNN30+AUuI2EmhSCWjNENw/UrIT2CF/fnAz4o9v0pD+mO5UQIpYEVlRgmKs7a+lmGOCilg7b68W2uXRKGXfihFHtq3EhSnQi8NBznH29nMkUdQUGs81sGzg+Cwz6953i7CshD4drmzZUrvXDjhXSvilcEAFBUJbvP6rOrDiDujNIM3bFpxmuZ1xx6WXpquyK6BalrrWa0Z9+an/T+xwpCQ500C1pWCKwhKCkJ1XbXgemNvyGlJnRd1BgBEZUaJKjexhKaESr5HrkdcbcYv1L8GYwMqaitQVl2GkqoSlFWXAeBe+8xkLWe25IyL7IhypXsu+xxGB47GzGMzHbpTTZHPML9heG71c2Yz1S2pcO10zQfXeldc9VPq86s0pD+W61xJFbDaebQzX9PEtYlarsuGFL6pgEVxRLRvJShOhV4aDmsvTiVM7JQUpe1S3iVN06emvP20/yc8vPBhzf3QuwlBqoD19IqnFfWVIWCNGsUWsAIDBbd5z656lhH39ZLrvGGvl1zX/D7ZwworC1VbgF5Js+TXg79aPQ+wO19ep70QnByMZm7NYHC5OXrMNH3S0SisLOTM37yT8ySXmRDCU8Ml35+mbk2txs01yo+P+Nx4Rri3Nr0FwPbIVK51sGwJWNV11Wgzu4353Ozjs0WXG0U/kHXBctMLKXC9J/jgW7Cdz6RMs1US0h/LDRAGbR3EOOdsAhaZdykjRrVEL/1QijyogEVRFL00HFxD65WyPZf3yPoYVsKU2MqdmmPZzwd+VmWKWmMzLqrrqhkfzOU15ZLitrbAuFS7/S8D3F41ACdPsgWsQYMEt3l9V/dlxHut5BpvWK61TZzRCioKdDFd0hKv015WzwNAdnk2rhRcQWJBIpIKk3Cj5gZrOq/l1DylSCtOw65Lu+B3wQ/bzm9DbHas6Di4BKwxu8dgavBUweU198Rc0ekeTjus2D0i4Vvseu3Ztei1ohe+2PUFSqpKALA3UHh94+sAgCauTUT7wDXS3JLt8dtF5YOib8h7LVfA4lo7jo8zGWdEPU89l/XEjRrt+hCkP0fSjvCeEzpFm7yO3GHXUSD9/PXgr1q7JAq99EMp8qBvK4qi6KXhuGfuPap1Nuob6lFSVYLQlFCz9VjaQ7X0uOx8znm7pkdNfZtwYILmPujduLa7XnJmCZq5NUPbuW3N58uqyyTFT4pEStk/bxiAc+dkrYNFxnni2gnesI4+rU4pGx04Gq9ueFVzP2yZJRW1FTiTcQbRmdE4l30O53PO40LuBc7dNbfHb4fHCQ/MOjaLFacaC7uT28s/ufxJ0XHI2f3T/ag7Y6SEGMSOECGtvqGeN26uXY9JkXjO8TkAgFPXT3Hef8t10fjsn/B/GOnaErBWRa+yep7iXJD3+krBFVnxXS2+yll/jEYjAhMC4X/R3/xcSPmhwLSjrBbcNecuhi+WP/iQfk4+NFlQnOR1uTdy1XJfFqSfVMCiOCL0bUVRFL00HGp2NpILk5FSlIKkwiTzr6pDfYeqmiZpsdmxdk2Pmvp224zbNPfBkY0c1s9lY/eOxeX8y+Z2gBxpNcB7AACgtLpU8/xY2sihBuDiRUUFrJCUEN6w+RX5mueZ2v/bs6ueRXppOus+5ZTnoJtXNxhcbq6/ZnrfmHh5/cu8caqxsHuXxV0Yadwx8w7B1xqNRvy470dZ5SSHY1ePyUr7s52f8cbNtavpX2F/sY5FZUZx7lYMAM3dm4sug8WnFzPOPbjgQcZ5KmA1Lsh7rZaANeXQFPP/3+7+FsDN9ffEPlO9V/aWnWepkMsBWO7aS/opVcDKKc9Ry31ZkH5SAYviiNC3FUVR9NJwqNnZsLSVUSsBiBOwxOzcxmcxWTF2yyM1ao5g7297X1C44ORgczuwOXYz6zwAlFSVKOLTbW4GfLrOgE/WGtDMVV5cuHZNUQHLckoESVFlESv8wC0DNb/HzmA9lvbA1OCpiMyIFHXdu1veZd0nclrdzGMzGeetrcU4MWgiVkStwLnsc4LrkC1MYprJ+BYX5+LEtROyy1YOaqbPNSX50x2fiopX6A8YwE0BPiQlBKEpoQhPDUdcThynX1TAalyQ91ruFEKuzT640gEgaWOhNza+ITvPUnlq+VMMXyzFJtJPKmA5Fnrph1LkQd9WFEXRS8Mh5iU6wn+E5MWZV0StAAB87Pux7I9jMRadGW3X9Oxhr6x/RXMfDC4GdJrfSXMfqLHtg+0fCAp3KPkQACAkJYTzPKCcgDVrhwGHD9+0qdvkxYWsLFkCluXi3+092lud8lRZW4kJByZgYtBETDk0BZ4nPRvNtEK1LC4nDvG58eZpI1zTymxZbX0tzuecR0lVCeoa6ljnR+4Yidr6WvwW/Btr10k+a+bWDMevHQcA5N3Iw6W8S5IXeOeKP6ssS9C1w/2Hyy5jOZy8flLx9Osa6hCRHoH5p+bLjveOmXcIDns26yzjGJf4CQCro1crWoYUx4a813IFrNSiVM76w3UsIT9BdL1/c9ObsvMslSeXP8nwxXK6H+knFbAcC730QynyoG8riqLopeEQ+gId5jdM9DWW1n9tf+RX5IsWsEy7RUk1sb/uK2FPLHvC7mkqbR4nPPDh9g95z3++83PJdYGaPLO1bt1HPh8JiicoKQgAcP/8+znPA0BxVbEiPpvEK5PJiQvZ2bIErNLqUozfPx5DfIYI3jXJkrwbeZrXAb1aN69urPKsra9VJS1yt0kh9sbGN3Ds6jG0ntUaBpebo4OOXzuOR5c8iq6LuyIkJQSHkg8hODkYoSmhSMhPYOXn77C/OeP+Lfg3m/WqrLoMI/xHyM67HCLSIxRN32g04q1Nb9m9rhmNRkRlRjGODdrKvdkDFbCkYTQa8f2e79HUrSn6r+3vsGsZkZD3WqyANTFoIt7xfgcDvAdggPcAzlGLAHCv572sY+TmBPZ4puXQc1lPhh/5Ffnmc6SYbFq/zhZk3iynJToSpJ9UwKI4IvRtRVEUvTQcQl+eQ32Hmq95x/sdSS/gMxlnRAtYt7reKusj9nT6abt/OD+/5nm7p6m0zTw2k/F/B88O8In3weX8y0gsSDR/cGjtZ2O0/mv7Wz0/wHuAoHgOJh20eg8B4FDyIUV8VlTAyskBQkMlC1hykbO4NjUD5p2ch4URC+F3wQ8A9850Who54sCafbbzM8w4OgPTD09HaXUpKmsrrYa3JL00Ha5HXLE9fjsmBU2CwcWANrPbsDq9Ym119GpZ9VvsLmnW8jn7+GzN7mNmWSbrB6z3t73PmecjaUcY4QZuGSirDBsLx68dZ5TbtJBpWrskCLKuiF0D68V1LzKu53pPAsB98+5jHJtzfA6+CvhK1jNlb8gdnwsqCsznTO2WybbEbREUJ5k3vQhYnic9tXZJFHrph1LkQQUsiqLopeEQ+vK0FLDazG6j6IfmH6F/8J67xeUWWXFz7WSktpEfN85g0w9Pl1V/qDmeHUg8YPUeKnV/b3FRQcCqqmILWBKne4mlsLJQ83vnDPbSupcAAA3GBs19MZmc982grYOw7fw2q2FMUxLrGupUmYI9+dBkzl0YxaDEqGUAyL2Rq+m9zCjNYP2ANXjbYM48cy1c/2fon8gsy5RVls7O5zs/57z3jg7ps1wBKygpiPH/i+teBAB0nNdRsfqsFaQfkRmR5nOkgOUd5y0pTqHTq+3NML9hDD+5dm92ZPTSD6XIQx+tLkU36KXhEPryfHbVs+ZryG11HdmUWJBWrOlhO3opdjbrLI5fO44T107g5PWTqk39UcLazm2ruQ9yzXIHIzVs35V9vG1Al8VdRLUP1qyJi8ICVu5/01QMhIBVU2OXNlOpaZWkuR9117zO2dNeWf8KgJvTkLT2xV5mWiPG94KvKvHLHX0FwOYoMiHWb20/zcs6vTQd62LWMY6182iHuJw4Voc5MiMS3by6oevirozw3b26S14HzRlpMDZgwoEJaO/RHh/7foyN5zYyymvs3rFauygIsq5Y7sgr5fq1Z9cy/jeJ83zT86XY1vNbsT5mPeoa6tQoEl5azWrF8CM8Ndx8bvKhyYxzziZgkZtOUQGL4ohQAYuiKHppOMS8QE1wrcHz0rqXNP9g5TKu7Y357H+b/6dImm9sfIN1rPfK3vhp/0+al4ccI6fWuBx20dwnLnto4UP4Lfg3zf2Qa1y/bitpe6/s5W0DOi/qLLp9sGaW4tXOQ/LiQl7ezYaodWumgFVcbJc2s7S6VJX7AQChKaGa1zt72esbX5f0HtK7AcCc43NUibudRztF6rjWZaSEXS+5bvM+cHH7jNsZ4S7lXVKkTJ0Bcqrlm5veZPz/w94ftHZREGRd4FrLTsz1K6NWMv43CVgPLnhQ8XpdUlWiRpHwYm0UEilgbY7dLChOMk+OOtKRFLBMm3zoBb30QynyoAIWRVH00nDcPeduwS9OE+Tolk7zO0naWUVta+HeAoDwj/GWM1sqkq5SQpijGbk4/Xd7vtPcJy4D1BMZnMn2XN7D+3w8tPAhUc+OLVNUwMr/bxFZAzECa8UKu7SZZdVlqtwP4OZopBfWvKB53bCXmdDaD3vawC0DMWjrINXLVCrOMiLuWsk1SeVE/hiXVJgku0zlUlZdhqTCJKs7ptoDUrAi7fs936uSbkBCAD72/RgLTi1Ag7FBdnyk31wCVml1KQorCwVdH5AQAO84b2yO3YxNsZvM60s+vPBhxeu1vXfsI3dFPZx22HyOHCVOBSzHQi/9UIo8qIBFURS9NBzW1p/i++Br59GOcbzjvI4AgNGBo2FwublLmtCd0NQ0sQKW0O25bRm5a4uzmF7yNe/kPM190NKEruOz+/Ju3s7qAwseAAA0dWuqiE+KClgF/y0ia5C+E6EcymvKVblvJqrrqllrqjirmWjv0V5zX5zF5OJIa5LJsbTiNEnl9PL6lxnhtJ42lFiQiAcWPACDy80RT1qKWLZ2k/xuz3eKp5lUmMR4p/nE+8iOk/SbHGW3+/JutJzZEk1cm2BRxCKb14elhglKR6l6bU8+3fEpI/3QlFDzuSE+QxjnVkWvEhQnmSdHFbDITaeogEVxRKiARVEUvTQcv4f8LvrDmOxs3DfvPgA3f7m9WnwVxVXFqk2REGM9lvYAIOwjwvOkp2ICFrmDny0T0nkjRUNq6ttw/+HYn7hfUx+k7GgpVMAKTAjk7ax+uetLwc+OEFNUwCr871dxgzYCFgDF17n7N/xfVhpa1js17LnVz7Hv5X/Q9k05k4uj7Qop1ZIKkwSXU96NPHT36s4bNiYrRna5SoUcBRKYEKiZL7YErDG7xyieJikidPPqJiu+7PJslt+kgEWu81pTz1xfkbzecl0oa+GUsOTCZFn5F8sXu75gpB+cHMybv0lBkwTFSV6XUZqhlvuyIHd0thTv9IBe+qEUeVABi6Ioemk4xKwVZOId73cYx385+Asr3rkn5mr+ATs1eCoACBIBfOJ9NBOw3I64aV5W1Ni24NQClFSVaOqDmoshByQEoK6hjnX8va3vISI9AgAVsPgoqizC9MPTMXbvWNllM/3wdFTXVbPS0LLeKWEPL3wYfVf3Rf+1/THr2Cz8cvAX9r38D2fYdMFRTC5cbYIebVPsJqvnLRdnt7VelmlXOXtiNBo58zA6cLTdfTGhhYBFrr3ZzK2ZrPjInfN+OfiLTYEq90au1fPhqeF4ZuUzaOfRDm3ntsU9c+9BTX2NKvVa7I6JchkVMIqRvml6JFc5CBWw+qzqw7jOUQWsDp4dGH4uOLVAa5dEoZd+KEUeVMCiKIpeGo6pwVMFvzhN/LjvR8bxHRd3sOL1POkp+0U9/fB01gKSYmxayDQAELSmzPb47YoJWLOOzRIVvrHtPqYXm3lsJlKLUjX3Qy3bdWkX70f2qIBRABxUwCoqutnIGDgELH9/9RtNC+oa6tB6VmtR/v+w9wdBO5s9tuQxzeuIHFsZtZKRn58P/My+l//BtTEINevG98OHXLjEHHJhc2ew1ze+bl4QO6M0w2Z4e7P1/FZOP77d/a3dfTFhS8BSo5z+Cf+HEb/QaWp8kP72WdWHFeap5U8xwpDrTpFxhKeGsxZsr66rVqXeil1wXi7kiG7LEYCkbzOPzRQU55RDU/Cx78f42PdjDPUdioKKArXcl8X4/eMZ+fO/aN/vC7nopR9KkQcVsCiKopeGY0XUCsEvThPkbnp+F/xY8SqxDtGh5ENoMDbAO85b0lo8/db2Q0FFgc1RLPd63ouy6jJ8FfCVIh8YVMByDnP2USE7L+1EVV0V57mvAr5SbDHnFm5MAevwYelxdZlgsduggUPAMtj/Ve53wQ+d5ndCz2U9cTr9tHnh3ltdb0V4ajhq6mvMGzv0WdVH8C5SetmR8N/wfzlFvNXRqxn54RqZakLMZiLUbhrfxilyicuJY8XJNe3KWezRJY8K+pHL7YgbiiqLZJevUEzrXpGmxBpQUtFCwCLXaV0Xs05WfFw+l1WXMcL0Xd2Xcf5ayTWrcYSnhuOhhQ8xjvG9W+XauH3jZOVfLGT6vhd8zefe3vw241xcTpwqPryw5gV0mt8J98+/Hw8seACVtZWCrgtICEB3r+7ov7Y/LudfFp0uKWBtO79NdBxaopd+KEUeVMCiKIqeGg6h02BMTDgwweYH1fxT82W/qE0jqICbw+n5RDFrIyCErGHUf21/AMqMGjO4iBewqDmmObuAtT9xPypqKzjPjQ4crdhizrcpLWCV/CcAGRxDwCIprCzE5tjNOJd9TnZcjl4H+63tBwCc6w2R04mKq4rRwr2F+fyWuC3mc1rnQ4/GVW4vr39Zdp07n3OeFa8zC1gGFwNr9AyfvbTuJdnla4v6hnpU1VXh852fc/qgZSf6/W3vC6qXSkLOEtgUu0lWfFw+d/fqjtr6WnMYciF/ctoeef3OSztZx+QsP/DzgZ/RalYr3vPkml1qEJ0ZzVoDyuDC/N4nBayxe8eyRhdPPjQZzdyaoe/qvkgvTZfkC1d7ZIu6hjrG+3O4/3DR6ZI/1htcDEgpSpGSBU3QUz+UIh3tv3opToWeGo4f9v5g84X60MKHzOF/PfirzQ+qKwVXZH9UmqYxmVgUsYgz3PFrx3nj8L3gazMd0/oWSglYs4/PViQeamxrPas1DiQewPTD01VPy9HFAzlm2u66vqEe57LPcU5His6MViSt292Z4tWWA9Li8X7agPDOBqC09GaD0KGDQwpYSrL49GLN64otM0Eu9rv0zFJWfqIzozF+/3isi1nH6OhonQe92f7E/QCAfVf2MY6fvH5Sdp2Lz41npVdQUaB5ntU0cvSMNbM1Cqu2vhYNxgbRZd7MrZmg9IWuM6QG8bnxWBixEAtOLcCCUwvQeVFnln9KMzFoIiP+iUETZcV3q+utnOVqKYyZRsyajBxZRF7rddpLkXo4xGcIxu8fj5KqEjyx7AnecCP8R8gqAyEcSj7Embbl9z5ZTmQ5kmL4j/t+FOXDtvPbOEfuXi+5bvPa2OxY2XWTS8D6dMenouPRCj31QynSca6vXorm6KnhECJgdV7U2RyeXATT8pd0S17b8Jqsl/nPB35mxLcwYiFnuIj0CN44poVMs5lOE9cmWB29Gh4nPBT7CFEiHmrc5nrElXOaS2Oxz3Z+JnrdJdK847wZz9buy7tZYV5c96Ii/rYkBCzv/eLjeH6MAXVN/hOoyv6b7jFtGreAZVrk3QkgBSyuDoPWZmJ/4n40cW0Cg8vNaVlVdVWC80nG6XHCA928ummeN0e0n/b/xBBIcspzcDbrLGshaqk0RgFLjJHrIVnyd9jfuMXlFjyy6BFczLsouMwHbhkoyocmrk0w1HeoYvdcKlyjxJSG6/sUuCmO+MT7CJ6SbYJPKLT83iRHmp3JOMOIg7xWKQHLElK4s7QX1ryg+nRWPgFr6/mt5jB87yMTUw5NYZ2rra/Fjos74HfBD7su7cLuy7t5feDLvxABi+sbUSxc6zaqUcfVQk/9UIp09FMjKbpATw3H93u+t/li7bq4qzk8ua3xlENTOON9Zf0rsl7m2+O3M+LjE7C4fmkxGTmHnc8+9v1YsZ0Tvwn8RpF4qHHbv+H/ck5zUcrazm3LWnfDkWxz7GZ0WdxFVhyWH6EAEJgQqJq/dxAC1mYJAtb4QRYCVXn5TadTUrgFrH//VbaB1JCt57ei14peeHrF03hy+ZOiRXalREg+azmzJcPfqMwoeMd5i+5UkvHyHdd6V1Ax1mNpD8XjtFz7pa6hDr1W9MIzK59B75W9MXDLQHmV7T8u5F5gpVteU655eTqKNXNrhgu5F1jlllacxgj3se/Hgstcqi+W6xFpgT0ELK6RSIfTDpvXRe26uCtj+p8tvt39LWdZbji3wRyG3Dzo6NWjjDjIa9fFrFOkbnSwDCUAACAASURBVFliaw3K1rNaIyw1TFbZWiM4OZgzXcsfv6wJWAn5CazF8Lns9hm38/rAdw25JhkXSghYr2541eZ9cmT01A+lSEc/NZKiC/TUcAgRsLp7dTeH77msJ+Ncj6U9OOMtqSpBQEKA5Jc5ubYW3xRCgP9FJ3R9r6G+QxUTsMg1wqgpa9MPT0dpdSn2XN4D1yOuqqQRkhKieT75TIn15UhxeNelXaLjeHfLu4LCkQLWJgkCVo/xBoQ9YkDSPRYCFuCw62CpidCpzmP3jlVUwCJ3ozK4GLD3yl5F8nTfvPvMcbbzaGc+TqZXWl2q+fMn1IR03sTY+9veZ5QZKZgYXJSp91wCFte9aMw2du9YVrlxbYhjSXJhMu8i11L9eHTJo4x4jEYjojKjWGs2qcWCUwsY/ogR7UwYjUYkFSbxit5c+X5k0SOM/zvN74SpwVMF7Wb3e8jvnHF+t+c75Ffkc6Z5MOkgIw5SVFNqww2SP0P/tHmN2OmqQuH7JjAtPwDwL+r//Z7vRW28VN9Qz+kDX/irxVdt+q+EgCX0PjkqeuqHUqSjnxpJ0QV6ajiEvGAeW/KYOTz5a9MfoX/wxn0m44zklzn56yLfCCxreRAizhlcDOg4ryNG7hipyEeIkI8OatLN5bCLuU7klOeokoajCljN3ZvzDu0Xal0Wd0HejTwAN6cL/RH6B2v9Isv0yGOd5nfCyqiVgqe9tJohX8Ay2bT/GQDL9tTQ+AQs4ObomwZjA/wu+LHKaJjfMHjHeaO+oV71EVhKcSj5EDrO64j75t3H6CyS6ZVVl4mebqWF3et5r6Lx3T3nbpb4kVqUqsr9uJh3kTNercvU0YyEayHvvqv7Yvz+8dgcu9m89pJpDaf8inxMCpokaJkDoX78uO9HGFxuTjGUu9g5H6XVpUguTObd5OOldS8Jnj5pNBoxeNtgGFwM6ODZgVPgE1MWA7wH2EyTT8AyuNxcKuNGzQ3W8YCEAEYc5I+4SghYXAuzJxYk2ryOXA5AKfimzwUmBJrDKPUs8Y2o4gtPBSxh6KkfSpGOfmokRRfoqeEQ8oJp6tbUHH7N2TWMc3+F/SUqPaFrYwkVsGrra3njGLN7jOCXqLVFM8XY9ZLrir3YqbHN9YiruU6otTsW3/B5rS00JVS2uPbgggcB3Nwt7/YZt1sNy/ULa+dFnUU9V61nKLcL4bT/GYCKiv9vFPbvb5QClglykwpy3UByNy0h9tzq5/DmpjcFhVUbMr3S6lJczr+Mtza9hQcWPIA7Z9+JJ5c/yQrXb20/bIrdxDkN58EFD8L1iCuWRy7X/HkWYgsjFnKuu5RSlKLK/eCLV+tycDTLKc/Bn6F/ov/a/ph/aj7njm18Vl5TLvgZs2UZpRkAgLNZZ1WpD5bE58ajg2cHGFwMeG/re1b9is2OtRnfkbQjjGve3PQmK4zY8rAF+f1KGtcPROQ6r+5H3fHjvh8xbt84jNs3jndaolCbf2o+p69C155TAz4BS43NN8QKWGnFaTb9V0vAaj2rteh4tEJP/VCKdBrPFy/FLuip4RDygvlh7w/m8KuiVzHO/RP+DyvOwspC7LuyD0vOLMHEoImM9QX4Xoyk+V3wY8RJDlc32byT83jjELMe1bGrxxR5GQstU6Gm9K/51Gyb3FFOallxVTHCUsNkx9N5UWdBU5yUWDT8TgUFrLa/GYDK/18HCGVl3AKWZRgnZnv8dkb5TDgwgXH+1PVTgso1NjsW3wR+g68Dv0Z6aToajA2C1j1Sm+5e3Rnp8U01uZx/GQ8seAAGFwMGbR1ktZOVVZZlPncg8YDg3d/EmvtRd0Xi4ZtalVyYrMr9yCjNYMTZZ1UfznJs7CZndOO1kmuK+TF+/3j8Fvwb5zk+autr4XrEFSN3jMTOSzvx6Y5PMXDLQERnRlutG1L8G+Y3zDylMbkwGZllmeb41sesZ4S1/KHUhC2hjDS+NkJoPrie26dXPI21Z9cyfK+tr8WEAxPQdXFX2fcwuTBZVpmrAdcOfGRaStVhvhFVfOFTi1Jt+n8p75LscuJK2x47QCqFnvqhFOlQAYuiKHpqOD7d8anNF4zpVz6AvdbD9MPTWXEeSDzACPPlri/N5349+Kugl5r/RX9GnHwCFt/ILIOLAV8Hfi34JWpr0UwuazO7DetYZW0lRgWMUuzlTs3+FpQUpLkPXAawf7VW09rObSs7Di4Bq4WbjDKostjdjk/A6tYN+PNPoFb44r56hNyl8N0t7zLO803zsbSFEQt54yfDjts3zvz332F/q509HEk7gg6eHdBqVive3W5N3Ki5gavFV1lrwpB5ME2fNSFkmo5Ye3fLu6hvqFdklFdZdRlnfpMKkzjbB7lklmUy4nxm5TOc5UhNul0tvmqXdLiIzIjE/fPv5wzfZXEXhvhr4nDaYTy08CHJftw24zZ84vcJDC43p6Wbvu2OXj3KCDfMbxgr7aVnlopKq6K2ghUHidxy7b+2v6L3ydpuko8teczm9S1ntsSL617E+pj1iu1OKKROKfk8iPEhpSjFpv9XCq4wrnl5/cuKlMFw/+Gi49EKPfVDKdKhAhZFUfTUcHCtpWHtpbUschnjnOWULhPkFKzPd35uPjcpaJKgl9rXgV8z4uQTsMhOnKU9u+pZwS9RAHhp3UuCw3fz6sa5fXJkRiS+CvhK0Q8cRzSh64vp0ZRaD02Ocf2yC0CxkYL2sjYzFRawqqv/v1EoL+dfB8tgAAKYa5c4Gzsu7mCVz6nrpxhhTOvicBn5IwEJGb6sugynrp/Cyesn1cyWopB5KKwsZIXhms411Hco0kvTJdVRS5GsvUd7Wc9PXUMdZ77UErDIadm9VvTiLEdq0k3uLrJCzZLa+lpB6XKJCXyjceT6duLaCcaxIT5DWGlzfWNZs+KqYqv1OzIjUvP7T5q1hdj7ru4rOr41Z9fYfM5tIaROKZV/U50zGo2ora81i6h84a2NWAOAvBt5eHn9y7jF5RaMDhyNqroqVNdVW71GaBlwiayOip76oRTpUAGLoih6aziEfGyYWHJmCeOc+1F3Vnxci1qadoiZcmiKoJfaqIBRjDj5BCwhI8iEGCB8yo3B5eZC3+dzzrOOR2VGiRr5pSfr5tUN3b2649MdnwqqN9SUNYD90e/odheHgNVcKQGrvt66gHXbbYq1kY5IXUMdq3xCU0IZYawJWOQ0bZK/w/42hx28bbCaWVENMs9cO5VxTZVNLEgU9OMOaeP3j2fEbVozyGRb4raIfua5UGsXwtwbuYw4n1r+FGc5UtOPfbD9A87R4nyWkJ/AqBNqfM8YjUZEpEcwjn24/UNGug3GBgQmBIqKl2u9OEs2x27W5B60cG/B+e07OnC0VX//Cf9HUnpyERKv0J2IbVlacRrKqsvMPyS8u+VdVNRW8IZPKkyy6vv0w9MZ4Y9ePapYGQz1HSopLi3QWz+UIg0qYFEURW8Nh5iXITky5OGFD7PiC08NZ8VxreQaGowNOJx2GJ4nPTHEZ4jVNXYs190CgJisGM5ww/2HK/ISBW5+MAkdPRWSEsJZdtGZ0YKGfevJPvL5yLzFtJh6o5WZdjZyNgPEiayOYHdzCFjNXGWUQQ0x3cJgRcAyOP+rndwRy9QumbAmYOXeyLUat9FohP9Ff6yLWSfpF2xHgMwzl4BFLsC99uxaANwLmtsycnoLeb6sugytZ7UW9cxzYTQaGWuE2eoICyXvRh4j/SeWPcGZD2rObXsu7zHXCdP0PyUtJiuGtUv1+9veZ9RFchqYELtech1VdVVIKUrhnJrnHeetSXkGJASw1vwyuBjgccLD6vModCF30uQiJN6/wv5SpGzSitOwKGIR4xhXWZnMtKaaUN+5Rk1V1VWhtt76EgNc68+uil4lvjA1Qm/9UIo0nP8rl2JX9NZwiHkZko36ffPuY8VnbZ0eEr41scih7Hy/yCgpYAFAWXWZoPCmkQ7kca7dgBzJ3t78tuhrTCOuxNYbrUyNKQ+OYABwOv205n6IMcUFLHJdK0PjFrDI8tmfuJ9xfmrwVFaY5u7NMffEXI08ti8fbv/QnO9uXt041/gZuGUgo3zOZp0FwD1Nz5aRnSLyfFl1GecOWQYXAy7nX2b8PzV4qtW8Xcq7hBH+IzB271jF1r4hO8ymXUu1bkeo2d/icuIQmx2Ldh7tFI/7nrn3IDozmnFs4JaBjLrI95xYs+TCZPOO0g8ueBC+F3zNQn19Q71mZXml4Ao2ntvIOt5xXkerz6OQdQy57IU1L2DO8Tmc7Z0QrMV9Ke8SAGUFrDtm3sE4Zm1xfHKEIEmvFb0Y4cldHl2PuOIWl1tw//z7zW09F+9ve5+VtrU1Ix0NvfVDKdJw/q9cil3RW8Nh6wVjiRABi1ycky8uAPjl4C+sMN5x3qxwVXVVnPEpLWAJKQ+Dy80pJlxhY7Ji8MPeHxjH7p9/P2ukhJpmbYe5kqoSDPUdKjpOl8MumBYyDb+H/I6/w/5GSVWJ3fIj1pxBwNp3ZR/rWEZphkOu4WFw4V9v7jY3hQWsOmJNoOefpwKWhR1IPMA4T3aabIkizkZKUQoGbhmIV9a/goj0CM4w5E5nUZlRAGyPAGnu3hzveL9j/p9rSiZ5TVl1GWrra1kdthVRKwAAF3IvYFTAKPwR+gcqa7XZTdPyXWUaCa11+0LN+a3L4i6Mesi3bIQ145pyd6/nvYjNjtU0bz/t/4lz9BfX97MlUjYXsjRyTUShWItz0NZBAJQTsFKLUnHbjNsYx8gdaC3tYt5Fq76T399DfIbAJ94HAJBTnsM4139tf954+HbA1At664dSpKGfGknRBXprOKy9XMjF1MlfJbjWRjl+7ThvfCQTDkxgnN8ev12UnyP8R6DlzJaMYx4nPERP5RNaHpYvcIA9/eRc9jnWWgebYjexfm1U00y/QHJZeU05hvkNEx3n3XPuNv/d1K2pwy5U/+O+H7EuZp3mfsg1rsVbIzMi7VqPlDJSwLrDXXpcqCe2Sa+pESZg1dYCDfyL5eoVsnxIAWtT7CbG+cYmYAmBnHJ8JuMMgJsLXzd3b85bF1vNamUzbvIa01TM7fHb0XFeR/Rc1hOx2bGq5k8sacVp+H7P95gWMg3lNeUAqIBFzT5mida+KG1bz29lHbMlYMkthxfWvMCKr66hDtGZ0VbXCxNynyzXSJRjqUWp+Hb3t4xjpAhlsve3vY9rJdeslhfXBkNt57YFAOy+vJt1bnX0as54qIBF0QP6qZEUXaC3hoPvxdJqVivWr8AJ+Qlo5tYMBhcDmrk14xzOe/L6SasvPkuauDZhnPc86SnKz5E7RqLf2n6sFyIAvLL+FcEvUSHlYXAxYHnkcvPOUIWVhazzsdmxmHxoMuPY5tjNNkfO+F7wxQtrXlDkg+DxpY/znquorZAkYJGLwCrlq9KWVJjE6rQ7i0VnRvOuBefIRgpYrWZIj4tThDLYELBmzgSaNAHuvx+IibHaFuoNsnxsCVhTDk3RyFPHhewsZZRmmM9tPb8V7Tzacf4YIkTAshxFMm7fODWzoSpatyHUGoc5c537cteXNvPMxXOrn5OVru8FXwA3pyNW11XjtQ2vweBy85suOjMaEekRmHVsFmKy/v/dKOQ+KSlgzTs5j3HM44QHK9y+K/tYZROXE4feK3uju1d387ptY3aP4fWZa2T7u1ve5Sz3QVsHSbpfjoLe+qEUaeinRlJ0gd4aDr4Xy5zjczjDn806i7kn5vLOHyd3l7HW+JPnv9/zvSg/R+4YiZzyHFwvuY700nRklGaYBSah0wtvn3G7oPJo5taMsQhwSEoIK0xcThwmBU1iHPOO87ZaJgYXA3zifTg/VN7Y+IboDwK+kWcf+XzE+WEgxMhRbmJNarpirO3ctpgYNFGx+O6ac5fqPouxs1lnNZ8KIcUUHYHFtabHpk38AlZiIvP/N97gbV/0CFk+pIBF7rpFBSw2yYXJ6LmsJ251vRX/hP/DG25F1ApGWdpagNnEmYwzCE8Nl7wejSPANapBL+Z6xFVzH6SaGutPObKlFqWiwdiAmvoazX2xlwmB/KYUa0K/I5MLkwEIE7AeXviwIvlPKUphTRedc3wOJhyYgCE+Q/CRz0f4YPsH5tGglnzk85H5mpYzW6K0upQ1msvS54NJBznPZZVlseLmErB6r+wttQm1O3rrh1KkQQUsiqLoreHge7HwCVi2sLbQtK20+bapTSxI5IyPb4FxALzr8pC2LHIZ4zq+XaLIXW3cjrixwpzPOc8SUd7Y+AY6L+rMGefEoInILs9GVV0Vr7/dvLqJ+iD49eCvsgUnJe2znZ+hqq7K6tpojmYbzm0AANbaDFpaTFYMzuec19wPsaa6gNXQYHsxd0tzIsjyOZh0kHHe94Iv7pl7D+6Zew/azm2Lf8P/1chT/XOj5oZ5q/eX1r2EkqoSrV2yG/UN9bod2cr1ntaDJRUm6W7TDqXM2tRdZzMh5N7Itcu3iOl72la4L3d9ifHeBmw9YMCWAwZs3m9AzwXS0ozKjGLtQjjr2CxB5ULGFZYahm8Cv+Et5+DkYM5zEw5MYMXd3qM9Z9iUohRklmWiuKpYYOupDXrrh1Kk4VxftBTN0VvDwfdikSpgWZsuZyvt2cdnc8a5Onq11ReumHyRtvTMUvM11XXVeHDBg6wwf4T+wYqf68M4Pjeed2dFLjP9it9gbOBdu0qsgNVjaQ/FP2yk2v82/89cXgn5CZr7I9Q2ntsIALh9xu2a+2Kyc9nnkFWWpbkfYk1RAYv3YacClsGFLWBRlKXB2IDymnI0GJ1vPTUh8HXqHNnmn5qvuQ9y2rrEgkRsiduCoKQg7LuyD0fSjlhdJoCavkwo8bnxdvFH6Gj5v32Z7/Xei6SnOTpwNOP/GUdnCCoTMp7QlFD0X9uft5yt7WxpiZDdyHss7SH43mmB3vqhFGk41xctRXP01nBwNc5ep70QnRktKT5rC03bSnvr+a2ccfIJWJ/t/ExUvrhsS9wW8zV5N/I4w/we8jsrfi4B60LuBd4RXHwfCwCwKnoVbxixApYjmeWi/JfyLmnuj1D7af9P2HBug+Z+WFpcThwAoO3ctpr7ItSaubIFrJYSBaw2v1MBi4QsI1LAOnX9FFq4t0AL9xa4bcZtGOE/QiNPKc6AHgUsJdpxoaO5lTRb6HlqJDXh99mSy/mXMcJ/hOY+G1wM+IcQsHotVC7u5u7NzVOuY7NjEZMVg9jsWMTnxjPKg7wuNCXUajlbE7BCUkJwJO0IojOjWevqKnHv7I3e+qEUaTh2LaToDr01HORC6gYXA4b7D0dKUYqk+KwtNE1CnrcUkyzxPOnJGd/nOz/HkbQj8DrthU/8PkGrWa0wxGeI4DUDXl7/MiOd/Ip8znDTQqaxfHI/6s4KdzHvoqgXtTMLWO9tfY9RXsVVxdh4biM2nNuA9THrHXqagCN21M7nnAcA3DP3Hs19EWodZrMFrNslClhP/GjlVe3pKVzAKiuz2n7pCbKMSAGL3BH2Y9+PNfKU4gxw7eKlhN3rea8q8bbzaIeozCjZ8QzxGWLXdvPvsL9t3ou6hjq4HHaxq1/UlDex8H2j2tumEwLW0woKWAYXA7ad3wYA5k2jTGZa4xbgnkJorZyVXkPUkdFbP5QiDceuhRTdobeGg299pqjMKEnxncs+J7jBJ897x3lzxsk3rHl19GqM3z9e8guIFLAKKgo4w/0W/BvLJy4BS8ooo1azWlk9r1cB6/1t7zPKa8O5DZgWMg2/Bf+GqcFT0Xtlb8195DNHFLBMI7DunnO35r4ItfvmKCdgPTnOyqvaaBQuYI0ebasJ0w1kGQUlBTHOkwIW3xqDFIpQlG4jfg/5HfG58Xh1w6vot7YfzmScYYURs6OwpUVnRiuybuCH2z+0a7sZkBAg+H446gL7Dy98GB08O2juhyMY3zt74JaBkp7BUQGjNM+Tix/zvf6UwgKWwcXA2d5EpEfwtkXhqeFW4/sr7C/F/XNU9NYPpUjDsWshRXforeHgE7DOZJyRFF+DsQH3z79fUINPnt8cu5kzTj4Bi29RRqFGClhFlUWCX1ZcAlZCfoJNQaqxWNfFXRnlxbctsRi7VnIN2eXZoq55Y+Mb6Le2HzrO6yj4mv9j7zrDoyq68FAtFBGQpkhRUBDELljAgqCIoFIEURCkiV2KAiIBUQi9S5Pee+8hJJBKGiGUAAmQnkB6L7vv92O2zb1z25Zs1m/f5zkPZOqZuXNn5r47c46jTgTYIuHJ4QBQobwjVlaIb8whsO6frr2evGoqrv/Vrft/d41Q2E9CAsv3ti8T7yaw3LAFKbkpdp0/FgYsZE5UGCFM1355e81lv7/5fQDShpstpfWS1oppXvv3NYfNo0uClphOwrdd1laTjbWcohyMOjSq3Od+nuj0Osz3n4/FgYtRVFqExxc87nSdnC1D9w/lOkAYsm8IEnMSrXoPpZwalacI1/VPV5dPvb63faHT60RrG/EgDjshKiU874UVBa72HeqGdfjv7GbdqBBwtYlDyh1uYHyg3csUQhhvNJ4thOd5T255J26esHkRsjxdlVmYyU1TZ1YdkU57r+wVpcstzsWh6EN2WyArygbwpVUvmf7fclFL1flupt9ESVkJAODDLR/arMeiwEWaP6IG7h6IVotbOb0PbRUjgfXQzIecrkvNGQTz9xF4naG/xFabxk9nDwJrVE+VxNPmzf/3BJZxjBgh9PzZd2dfJ2nqxn8Bwus8tsirq1/l1qHX60VppRycyEmPLT0AAPuu7pNNN/rQaMVrhk8uftLqU2BqJCYjBmFJYdh+aTtyi3Otejbl/fHOEyEqeVRyuk7OFgCIy4pjwhrPbWzVM7bEmtA1Tm2XcF0fs6l86p3sNRkfbf3I6c+VeBBsiNhg83N0FFztO9QN6/Df2c26USHgahPHCytf4E7OAXN+BH77DUjS/ivDx9s/llzMLSGMn352Orc8KQLLHr9EDT8w3FRPVmEWN01dz7oincp0ZaJ0RsOTuy7vwtgTY232gCRFYI07Ma5cSZnbmbexNXIrjt04JmuvS+oZAUDPrT1t1mNx4GKk5qVqyjNw90DV1zDrz66PLZFbyq1ftUh4cjiKy4qdrgfxIOi9it28fryKn65cCayyMusIrPx84M4dQOd6nuWEZHJeMbve/HrqV1F/uuGGtVAisLT84CJFYAHifYE1nnWN17P2XNkjm87D2wMB8QFOnU9jM2Lt8nwSshNwOe0yjl4/6pR2KD1HVxXhVWxr+uSbw9+AeFCTEadjTtvleas5OegoEa7r35YTgaUkjeY2Kre6+u3sZ5fn6Ai42neoG9bBvaNzw65wtYlDimxa95zFB1/jxkBJiaZyeYu+EML4T7Z/wi1LisDilaFVRhwcYapH6gphPc96XL1q/FWDSSc8+i88AaFVms5vyg0PSQzBW+vfsrrcAbsHaEpveVRayiOklNxIvwEAdvnVbEnQEuj1ehNRGBx+WDHPQx7344lFT6iuY/fl3Tbr6QgJSwpDfHa81fnt+Wv4phMNmM3rzuP8dPa6Qvhzd4L9T6lYqolGAismBmjenIZ160ZJMBeCEoHFu1rkhhvWQsnxhtQPQDzpuKajZD1jT4xl0nbb1M2qeQoAYjJiZNNMPD1RksC6kHgB/nH+knm7rOtiFbkmlFuZt+z6nK7evWq3uV5rf1tCKp3UCX1ny/iT4yXbpSZ/bnGubJ/EZMTgbv5duz3nMl0Z98RieUhFJbDKU/rv6m+3Z2lvuNp3qBvWwb2jc8OucLWJQ2py/vgzwUffQw9pKle4cWy9pLVi3VIE1qxzs2zaWCiJEekF6dz4+rPrc/US2lUq07EfwDuidtik12PzH+OGhyaFWkVgPfvPs1bpkZybbGrT6tDVmvIaCaw31r5h83NaErTE3LlXrgBEXT6ep00p2XV5l13GlL0lNCkUiTmJTtXhl+O/4FbmLRz1ql6uBBbxoCSWIohGAmvAADZ8/37lOioQAuMDcfbWWZMI5x83geWGCCtXAi+8AIwcCWjcnyi9o4CYfJKSt9a/JVmPcN387sh3eH/z+5rnDCNGHBwB4kFQe2ZtUZpJpydJklQlZSWyBJh/nL9dzBjczrxt3bOUQdeNXVXXf/DaQXjf8ra5HUJIGZgHgEmnJ9lcn73lYspFUViTeU0ASP/Qa5Q3174JAFgYsNAUNtdvrt2fKw/O6Cvhuv4dh8Cq7EFQewbdBzzm6fzna29xE1huOBvuHZ0bdoWrTRxSk3PvAZwPv9WrgR07gOJi6yrT6YDcXNN1HWGdUl4IZ56bydUx+l60XRYiI6S8EDaY04CrV5N5TZh0QoO0ttrDkjKGn5iTqJnA+nLvlwCAL/Z+oVmPlNwUU5u02l64mX4TgLRdNC2yNGipuXN79FBNYAnl6PWjkq7RKzKBpdWAPU92Ru20KX+juY2w/yRxbQKrtJSf/ssvleuoYMjNjUBGxmnoOYaf3QSWGwCAc+eAWbOAY8fY8b5kiXJeCyi9owDw8/GfVb3PRiPrPAivwH1/9HuUlJVgSdASTXOGEXq9HjEZMdz1fdLpSfCL8+PmL9WVypopCEoIQkFJgSh86P6horB3N7yL371+55ZzJ+uOdc9VBiVlJThy/YiqE9MAkF2UbfPaIkRWYRbaLG3DpPli7xemeEfaFbNGLqddFoUdu3EMABCVGoV2y9tJ5u2yroupXTfSb5hMJ5QHpPbHjhThuv69gMB66C82ft1hc1zdvwkaznT+87ZVPtv1Wbk9Y61wte9QN6yDe0fnhl3hahOH1OTci0dgGeV96c1nUk4S3tv4Ht7d8C7e2fAOvts/Cpg3D5gzB+jUieZ/5x0gL09Up5RXjxk+M7g6bru0DV3WdbF5ITJCisCq+XdNrl5CgslosNwIuesHaqSuZ11R2J4re7gG5JXEuHH8cu+XmvOm5qWa2vRv2L+a8sZkxACwD4FlL8krzkO/nf24cbYSmYBnFAAAIABJREFUPI6SzMJMzfa/HCX7TlhPYEkZfFcjypOZSgJLSoYMUa6jAiE5eT28vSvB25sgKkq8mXYTWG7g/HmgUiW7ODRQ837+eOxHJmxjxEaU6cqw+eJmJrzytMqS9ZTpytB7W28QD4I2S9sgLisOACVlbJ0vhGkmnZ6E25m3ufnLdGWy1/FCk0K5ZQ7dP1R0HW1VyCp8sPkDbjnG9jkCpbpShCaFIjw5HBHJEXhl9SvcfsorFu/HiAclZtTa2+SB54HPiJiMGNSfXR/Eg+CdDe/gRvoNNJzT0Clr2uv/vo6knCSurjq9DoWlhSgoKRBdo72YchHhyeGIvhftsGeoBL1ej62RWzHffz7O3jqLoIQgSbLUXiIisDaz8VWn8fcIby8jOOlFw75cW/7P2Z7iJrDccDbcOzo37AqXmjju3sUj4/mT80cDFT727twB4uOBs2eBoiJTkbEZsUw5HX95iJ9/xQpRnZYnfSwx7ew0ro47onZgrt9cVYvNw7MelowzQq/Xo/nC5rJpLCFMI7RvYG/jsCMPjgRgnQeaQXsGAbCOwErLSzO1aW3YWk15jQZqpfpVTjqu6eiQjUdecR767+ovOaYcUaetklGQgbv5d52uB/EgNp3AsqVeRfzyi20E1rBhynVUIAj7Njsvhol3E1huoEMHdddpVUDN+/nD0R9E8ykA0Q8Dtf6uJVuXXq9HekE6isvMp71LdaWq54oO/3RQ1YZJpydJzqs6vQ5RqVGSdUQkRwCAaC05FH0IUalRqOdZD8SDoNOaTijVlSIqNQpVp1cVlROfHa/pOdgCnoF3ACgsLeS2cdLpSei1rZfV8zPvBy9L5BXnISYjxmQ/VLgHU7q6Z60M2D0ARaVFmH1+NqacmYL0gnTuD0QAcOT6Ecly/OL8kFecx4zTioIyXZnDnP0oEVjCNHtO0LATXmx4VRt+0HK2uAksN5wN947ODbvCpSaOmBjUnMifnHsqEVgrVwK1atH/v/CCyQDyrcxbTDmvDJfI30x8IseSKLHEH2f+4Oq4cvAzKLt9C6tDV0vaizKKHDFhiTOxZxTTGCFMIzw2HpQQZNMCKSTdjAbnbSGwBu8brDmv5XOJy4rDwWsHVec1Gqi1hsBylOQV5+GzXZ85XQ8tci//nqSNtvKWaTvZTejQ9fx05U5g3btnG4E1fLhyHRUIwr71v7GeiefZoHHj/wxKY15LUSrez++OfMeE7YzaCYCenjI6Hag8rTKO3ziuuSk8z79SEhgfqKoNk05PkjzZqtfrEZEcIVnHpdRLAOiVMeMJ4+6bupvImPSCdIQmhTK26UISQ0TlJGQnaO4La3HsxjHuc5PycKvlmh8PwhNsllftpOAX54c31r6BHlt6KBrhV5Jh+4fhh6M/wPO8Jzqv6wziQb1lGk0bCGFpL9PovEeN5+U/zvxh3QNxMJT2oNPOTrPKc59w7fmBQ2B5nTHH7zMQWFuOsvnus9KkQEWQMYfHOPvxSsKlvkPdsBruHZ0bdoVLTRze3pKT84efa/z4274dAHBn0XTxxkZN/rNnJdWc7DWZq+NXvQkwdCgA/smg5gub4/EFj6Pp/KayRkqF2Bq5VdXmTJhGSGAFJwRbvTi2WtwKiwMXM2HDD9APbFsIrCH7hmjOy/Ocozav0UBteRFYNSYpp8kvydfsidHZkpaXhszCTKfrQTyowVbLTejby/jpyp3AAoCXX7aewBo5Ul0dFQRuAssNRSiN+ZdfBrKy1BWl4v0cc3gME7b78m5T/oyCDGyM2IgLiResaopaj2uzz89W3YbJXpORkpsiCu++qTsAIDw5XLKeq3evmsrNL8nHnaw7Ik/EPDy34jmmnMScRKv6wxpIEVhayEEt87PQrpSc8X4p2KLTDJ8ZpnLKdGWIyYhBbnGuZF2W3nqNznvUEFhTzkzR3K7ygNQedFXIKoQkhpjS8Wy5yckBwSnsYevFaU5bEFj7T9KwzQICy1qbmOUhj8wk6LBA2uzBhJMTnPhk5eFS36FuWA33js4Nu8KlJo6u0p5qemglsFpTL4PxtcVlLX6FYPszCvmrVKE6xccDCewvkrxfLYkHwdDe5l+R14evV1xcXlr1kqiMDREbROnsRWCV6kqRUZCBpJwkxGfHq7bXNXjfYBSVFolOOn194GsA2j0BEg+Cz/d8DkA7gfW71+8i2168tkuJ0UBteRFYP3Vn/+7yFcHnn5r/fmX1KwCAnlt7los+9pLUvFTkFOU4XQ/iQa8LWG5C36pIBFbHjtYTWN98o66OCgJh3569vpGJdxNYbqga956e6opS8X4Kw5YFL7Nvc1TME+vC16nOP9lrMtf2UXZRNgC6hhuvAgpF6pSXEnxu++DgtYM4cO0A9l/dj8LSQqvKsQZSBJZaclDr/Cy8gvn2+rc162yLTkYCq7isGH5xfjh/5zzO3Tlnsl8mV9cjsx8BAKy4IDZ3IZTfvX7X3K7ygBSBJXQ49N7G9zT16+B17NrTexUbX/dv/tq/6Qgb9sCftu9HHCFPzSM4corquPIg9agoTDPq0CgnPVVluNR3qBtWw72jc8OucKmJg0hP4B8MsuIDMCICibVkNjhK+f/8kzU4O2AAEBvLdW9MPFgCa2PERiZu3Ilxouby7DEIXc8D9iOwlNILpen8pmi2oJnJPa/Qi2Grxa3ge9uXcdUsFKNRVKFYS2BxPTCmpanObzRQ6ywC660hBFn3Efzi0QmjD402XdeQupZaUSU5N1nS0G55y6C11KvQ+sMEG44QvLyIn84pBJanp/UE1rffqqujgmD5AbZvT0WzXlw9z3sy/ffovEedpKkbToPasa+mKJl3s+Gchtw0iwMX27c5hnJrzyBYsp+O+0nbzR+Y1f+sbiKf1LRhstdkJOYkMmHPr3ieySNlAykoIciubSsPHL9xXHJetXVd4OFS6iUmzTsb3tGssy06GQks3im7yJRI2bqMex81BNZkr8ma21UeuJB4gatvViF76lJrv36xll17PhEQWM1n89f+jQICq0YFJbCEayvvRzprT5KWB1zqO9QNq+EmsNywK1xq4iDSE3j/vlZ8ANavj+SaMhscreUZJHX4QHie+VNU3rBe5s33zqidqOdZD4/MfgQN5zSEh7eHqLnCq3cTT0/kdou1BJbxupxkd6tcPKv/WR0AcDj6MDdeaCjXUt5e/zY33FoCS9T2qVNx9En1eY0GalssbFEuG4+fOQSWaSzp9aZmnIo5VS762EvCk8M1H/N3tlSbJt7E8n7JJB4E9SYol6cKOTn0NCixYq754Qd1dVQQLBVssk8LCCyh4ezvjnznJE3dcBrUjv38fOWiVLyfwrClQUvt2xxDucPWs2P/uYU0nHei2hJGO1xG2Xtlr+iaG2+u4bU3OCHYqjasDVuLRYGLsDBgIRYELOD+iOYo8K5EGtF4bmOb5nseIlMimTTvbnhXk746vU51/bPPzxYZyTfuyXh2zub6zRXVZxlvJLD+ufCPYt2N5jbS1K7yQkZBBlffnKIcJp3WZ/35v+z712c1Gy9FYG1wEQJLycbXoehDTnqi6uBS36FuWA03geWGXeFSEweRnsDnd7SObEqpIbPBsaI8kyxbJirvayOBFRGhqrnCq3eTTk/iptsSuUXV5kxky2L+NKBEfN3O1N0qF0+ji3GpX37lCCypa4rfHBgJeHhg8ATtXmlMyMoCCEHXL9XnTchOALZtw7OT+dcw7CEvjCQ49iTB0Sfpv5Zxb1sSWDVqAE8+CYwciZP71HmvrChSWFqIotIip+uhVYQbQVtsXqhGXh7g7w8sXaptjvnpJ/V1VAAYT6AYxctNYLkhhNqx/+yzykWpeD+FYfa+Qth9U3fuvDJtp7o54tMdnzL6nb9zHr63fRXnGl57r6RdsaoNQocz5enB7nbmbabul1e9bIprOKehTXM9D8LT8103dtWkr5RxeaEYHdwk5iSi87rOqOdZDzPPzTSVw7smKiSwhNcojacKlwcvt+/6VI64l3+Pq6vQDpjWZz1AQGD1W8PGt5jDJ7DWHWbDVgTNsWnMOUr+PSR/Aqs8HS9YA5f6DnXDalTMWccNl4VLTRxEegK3lsBKe1BmgRekTalBkFiLIMEgSmW/OutJprwNHSzi//pLsbmrlnzF5J/sNZl6T9yyhUpZGZCRgR0hG5Q3JwkJ6PDno0yapJoEGDeOnvSZOxd47jngu++AQmrjQu3i2dbzcUCvh1esFzf+x2M/SuZ9Y+0borB+O/vhzG+fAYRg8MfaF3MTIiJwtb62vAkvPw0QgoDHHLfZ+O1d8ziIeoSNe2cwfyydeML5myS1sjGC2jYqKStxui7Eg+BRT4K+q+mmtf8aghclrhASDycRWEaUlmqbv375RXsdTsRiEYHFnj4REljfHnGtK5Ju2AFaxn+hvC0mNe+nMMzeBFZucS7uny6eVyZuVzdH9NnRh9HP97Yv7ubfZcIeX/C4YtufWPQE9BYnerWg6fymTFlFpUVWlWMNRF6iDTYhAaDBnAaq5+IPt3zInNiSes7rwtfZNI+rXfNWhaySLYdHYM3xm8Ok4ZFlAN9umF3Wp3KAcGwbJa+Y/TbRuhb3XEmw4zDBgX0Eu/cT9FjBxreUILCEYbsi12C+/3yr9wSOkt938E94GiUlN8VJT1QdXOo71A2rUTFnHTdcFi41cRDpCdxaAuveAzILvCCtaFGtJl92aGOCWhNp2nbfEJRUFqSRQ1ERVr4oWKSW9QUGDRLVc6g1m27Y/qH047ZRI6B/fyApCSAEz45m0yXXNJRx6BBb5hy6UVK7eL40ggC/U6OgbZa2EcXLEVgfbP5AFJaYk2jSxSYCKyoK29ppy5toICb9HUhg/drV3NeXGrBxUgTWcRkC69nRBF984tgNklpZG7bW1P328BRlD3lzKbu5+4njQtsoTiWwAKCgQP38NX68dXU4CQv3sX17RkBg7bq8i+k/N4H1f4aUFG3rd1iYbHFy7+Zr/77GTbM8eLndmzXr1ADRvPLLFnVzRL+d/Rj9fG77ADB7Oq4zqw68b3mL8o09MZbJl5STZLX+jy94nCmrPAms2IxYpu5XV79qipOyn8mTF1a+gJTcFPzt+zd2RO2QJPN49qO0QO2atzJkpWw5agisUl0pV1edXoenlz7tmPXJweBdnSQetp/AajSLIK8ZQV5TghU9CKoKPPU9wSGwrt+7Lgq7lHRO1qars+QPAYHVYQEbfy//npOeqDq41HeoG1ajYs46brgsXGriIASer/Mn8AVWElgZ98ss8IK0wvic6srlx9Um8GpBUFCVDS+tTHDw2kEcjj6Mo9ePot3ydnh8weNotqAZmi9sjkDfbZjema1v3Hv8Og63YtMNXdaNm679N2y6lBoyup8+zfUKRjzolUHLvzt+bSbktBJYvxz/BS+sfIEJi8+ON+nxpRXEjGmDPXYstj+jLa/xOfk1VU7bbEEzPO7ZGE1/JhjxEcGjv6irY4IFgbWlvfK4AxFfNbSUg61pmhY/suFS74oWkfJoJSXrw9ebXld7eIqyh7yxhN3c/bxFOq3TCSwAaNlS3fz166/A6dPAtm1AUfl9VFoLYd+eiV7HxAcnBGPwvsEYvG8whuwbYjrJ58b/AfR67ev37NmyRT4y+xHJd1OKwPrnwj92b9q045+Ixv73m9XNEQsDFmLA7gEYuHsgPt/zOXMNMC0vTWQbyAidXoeNERuxMmSlzYSTsI8yCzNtKk8LYjJimLo7ruloitOyNr2w8gVV9a0MWWnTPK52zVtxYYVsOUJD/cSD2swSoutGs2fur/Z/ZQrPLMxU9P5cEZGcmyzSs+WiliLCUapNby+j9q0eFNiq2r6Vff8az2LjeQRWTlEOh8DyqZAE1tSdrJ7tBQSW0Ah+RYNLfYe6YTUq5qzjhsvCpSYOQk89fdtDPIFbS2Bl3SezwAvSCuNzVRBYUpItUy/xIPA5s1YU9uZQfllHBATWV7356doJCKxUOQLrqacQd2IXOs1ogQdnPADiQdBtUzd8sPkDPL3kKaacalMMeaZMQZu/moj0liOweBKXFWfSw5qTRbnFucDNmwAh2KGBwHpkvLn951UQWJby8WcEj/2sLu14CyJyxYvieN7zmPyOdHmHDARWyx/Y8NmvOXbTxBMT6VBcDJSVichOZ8jrAgLrl4pOYFlj1L17d/vMsQ6EiMC6toaJv5d/D6diTuFUzCmcjjmNy2mXnaSpG+WGu3eB7Gx6lV3rmCcE8PFhisvNvYhz5+rC17cWTngR/LWb/24aCaxq06sx4QHxAXZv4q+HuokJrE02zhHlCGHfJeYkllvdRaVFuJB4AcEJwQhKCEJUapSkXnIy/MBwVfUJ7YtVnV5Vs85q9FE66ZeQnSDK43neU5QuszATU72nYua5mSgsFV+pdcj65ECU6coQnx2PI9ePoNHcRqjnWQ9esV6idLz2WNq5Wn7AHP7EoicQvJx9/xrOZPM+OVe89uv1elFYZOJZLApcpOoZr7iwAlO9pzpsX3M78zZeWvUSiAe1qWepZ7v5bNqSMmlbtxUBLvUd6obVqJizjhsuC5eZOKKimI1r5v0EsXXMknWffYmkQZ86lsDKlDn5RTwI/M+I7Vp1/opflvB0zuCP+emE5cXXVqlvo0aMvRHPIU+KyjKmffpbcT0//Sw+lSUndzo0N5W36FXtC3te1l2gSxeAEOxsqy7PG0MJrte1nsDqPYCgqRUElu/jbBzjhdBCGo+VLq/bFwR9+ovDR3/omI2TnGy6uAmYPx+oUgVo2BBb93jgAQMB6izZdozd3G05yk9Xe4b9CKwXR9qwVLdta9288sMPlDgEgPh4oHdv4JVXgOPHbZt77QRh33pH/8vEC223DNk3xDmKulE+mDFDeUzXrAk8/7x8mmKzUfHc3IvMGJu9h/9+GgmsV1e/yoTHZsTavZmzj74oGvsfraT1uQKEfVeeBJYc+u/qr2oufmDGAybvwkrQ6/WmMVFlWhUcuHZAs16e5z2Z+p9f8Txq/V2LCVOytaaWwFKCXL+4MixtlVX2INh7Qrx2t5hD41eFrEKIgMB66S/2eTw9r4ooPyBes/Kyb2Jx4GLFMffSqpdMhOLoQ6OZuFp/1+KejtsQId7zy0lKbgr0ej1KdaWYuacqo2dbCwLr0XmPOvNRqYLLfIe6YRNce9Zxo8LBZSYOge2nH96n9qVqTCJ4cJL5FIpWya0uXhjeHEpw82HHElh7n5ZfnEJPqiewTrZk033xiToCy5KwUZIr9Qm832yKs80oWSPaDBnSPfWdOO6n7to+/iMb0CuWIPTEnZa8xIMg38I22S6VBNaonmx7SypT+2h3G9VGWq3KiuRUrwEEj/+kri7Lq6BCW1u9B2gnsB7+VXsfOUq2+K9kde/SBTq9DtPPTneaTvtPspvQncf56RrPsh+B9dIIG5bqZ5+1el7BdwbPfUOHmsPq1pX1NlpeEBFYsSeZ+KPXjzJ96Caw/sPIzVU3nufNo+nz8qTTTJliKjYvaDczxkLOv8h9P40EVsc1HZnwmIwYuzd16r5morHfZh6tzxUg7LuKQmDlFudiqvdUTPaajEupl+Bz20ek66A9gzR7XywsLcT+q/sRkazOW7QQBSUF+GT7J7h/xv3ou7MvCksL0XldZ3ad39ZbtgyeLa1ZFl4K1UJujXJl6PV6bIzYiPEnx+PQtb3itcWb4GWDs5axJ8biwgo2Lm3KUJy9dRb38u+hVFeK9MwAVQRWYcolWQLrjzN/4Ordq8yJJ6FzowG7B+DAtQOivGtC12jaY1i+h/tOVmL07LPanK7JvCbl/ny0wmW+Q92wCa4967hR4eAyE4dg0zqyJzuZ73vaug++wqqchV0irTCdLQTWhg7yi9Ol78S/LqolsHoOVKd/toZTa59/Kq+vMV2r78VxvGtySrLX8Dx5BKOSWNob291GXZ5vPmTbO/kdgmfGELQdQ9CGc6pMKB8NVE9gje1mrkfo7dAaAuuh36TbpLXvbJVtbz4s1v/aNfwxSHy1tLxEisCq5EFw33SC6tMJqk0jaOppHwKr+xcEP3e3Yal+4QXrCSxCqHdSYdiFC/abi62E5a/kXmcIzt46y8QfuX6E6UdLmy4VBnv2AJ06UYIws/xsAf3ncPWqurG80cIO2oED0ukMKOjennl/Q7fW5b6jTy99GgDQaU0nJtwRBNbvex51E1jlBKGuFxKdP+8BQJ1ZdRi9ms5vqphH2JaZP7+suV65deq/gmPR+7gEVuelhv3WibG4sJKNK/p9NFNGdvYFJv7ixR7cK4SFKZFYErRE1Je9t/XGb6d+Q0FJgUi/0zGnmbQDdw9Eqa6U8Yqp5lSXUOKy4kx1CPX8fYc5XaO5jRz+DGyFy3yHumET/juzjhsVAi4zcQg2rKMEBNZeKwksEILKf5jLqTdBPYGl5IVQTtYrEFhX67N6EQ9qZ4lXVkhjzuaEk25PG4Lqv9P4395Vr6ucAXHL+oRkjFGu1Cd4fZi2xbl/X1pmjhUEVmFVts1q8jz6C9vmIRq9H340UBzm+zgta0wPah/rsZ/pNcPzTaUJrF5WEFhSovb6pD1lxzMc/QcP5hqrLy+RIrA6LBBveu1BYBlPD1qNCRNsI7AaNBCHRVh3msCesHwOpzkE1uHow0w/Dt0/1EmaSiAzE6he3dynU6c6WyPXxbVryuO4bl3WOQGPmDWKTgcAKGzMXgUKWX+/5HsKiAmsm+k37d5Uj72NRfPKo55mHSo6hP2WmpfqbJUkIdT1YspFZ6sEwD4E1t9vECAjQ5QuPz8amZlnodeXKZbBjP8FC5jrt66KY9F7uGv3SxYnsIRx19c+z5SRnR3MxEdG9oROVyomsJIjsTRoKXcukcKpmFNM2s/3fA4AuJ15G5O9JmNd+DpVxv/bLW8H39u+8IvzQ2B8IOOcQY7Aajinof073c5wme9QN2yCa6x4brgMXGbiMGxUC+sTJPQiGPc5O7nvaWP9B1+lqeZyLA15KxFYpg9VK2Ttc/KLVURDsQe+40/wy0qqyabrMFq63sRaBDc0XB0EoXaZ5HSd8SZN59WCH28LgVVSmeD3t7XltSSwlK5qWsrCV815v+qtnbgQytrnlPs28FE2z0cSp+d4BFb98XybY0ZxBmm0s63696e8RIrA+mCFPHllDYHVr59FmzUiOzsIYWFvIiyoI8IWEtweZAOJJZSrVx0wKQNITgYGDwY+/RS4LGN0Xa/HQYvncNKL4HbKNSbJzqidTF8qXbNxKDIzgaefNvdfv37AkiXifl20iBIrtiImhto+q1QJmDjR9vIqOtScwLp+XZxvxQp+2rlzAQDFddn3N3izPIH12r+vMWE30m/YvalzDjwimlcsdajoGLJviEnf9svbO1sdWbRb3o55nml5ac5WCYAdCaybLMF69+4hnD1b1XRqSIiBuwdyx37bMYb35vvv7dZGZyHwzmnu2l17Bm0rj8C6uvEZpozs7EABgfURdLpiJmzvEQKkp2P/1f3cuUQKJ2+eZNIO2jOIm05uX/Hciucw5cwUbj5AnsB6ZPYj2ju1nOEy36Fu2ATXWPHccBm4ysRx9zXC3GP3HM9O8LtkPpyV5M/OBNM7E3h0oZ7bpNJta2c+wTTrdds+KJVOYBVWpcbYLcOkCKzEWmy650fZ8cOXEHT6WqzfjDcJ+vYjmPiumfQ505zflqs2EFggBBcbastbVMWcd58GAot4EKQ/QPMNtYLAEhpN39JeuW+DbCCwiIc8Eeot8TwcIdPPTsf1uvTEnFRby0sXoewXGHg1ElgfrRRverUQWL92lR+3WhEfv5Cp++wpO77Hx47ZcTK+CwwfDvTpAzz5JFuPFEpLcdSCwDrhRZC5YiGT5OsDX4v6s1wRFwd4egLLlmnr2zlzbK/7iy/YMm/Yn0hxGoqKaB9NnUpPkBQUKPdprIwxdak8d+4g81nxO9xkHv/6MgBRmM9tH+l6rcTcg3wCq0JekeUgqzALPx37CUP3D3WIkXt7ov3y9szzTMlNcbZKAIBBewYxeo04OEIxj3Bs/vWmeF7w9a3JjKvcXPbEWXJuMm6k38Bcv7lMWSMtbX5aA70e2LSJeg+9os3GmL1xId6Lu3bXkiGwrggIrKws1gZWZGQvFBZnM2HbjhEgPR3hyeFMX3bb1E1WvxM3TzDpv9z7JTed3B5GCXIEVrmvo1bAVb5D3bANFX8kuuFScJWJ49aX4gVKckF2oKTWIEioZXs5SjawQMQE1rEn+WXF12bTvTDSvm3mEVjezcXppAgTawgs40mWrPsocaclb7EFgbX/KW15M+6n+b7upZ0oERJYW9sp9+32Z/jPXihNfuHX+e/z/PA23xLoKmlvg7WyeWw3xbZqKa/NT9XsotfLIwgOHecTWH1WW09gbW0nXWer7ykZrhXR0d+I6i+pTVBWjeDKbwTn9xBcG0ugq6I8rkTy7LPylR88CIweTf8VIiUFiIyk17T0epOHT65cuybODwDFxThmQWAdO02Q2fUNJsmw/cPQZzXB+sMEE7cTPPinQh/GxgInTzIeUk3Yto3aEhs4EEhPZ+MyM4ExY+ipsbAwGlZYaNs8aSuE5dmDFKsoGDnS3K4XX1Tuy+HD5cvz9JTMe+9V8Tv8xd4vRO/oj8d+BABR+N4re+3e/I2n24p0eugvgm2Xttm9rv93dPinA/M8k3OTna0SAOBW5i3TKay6nnWRkJ2gmEc4NnkElnBcpaZu55a198pe0V7F6rnr2jWgcWNz/lq1qFMGJyEkjn8Cq8aftK1cAmtTW6aMnJxwBAQ8gYCAlggIaIFr10YivyidybP5KAHS0xGWFMb05fub35fVb4bPDCZ9i4UtuOnk9jFK8NzDtm/MJm35nQ1X+Q51wzZU/JHohkvBVSaOOwP4H5ivL6FudN8Yal/SxtGy8Vn5D28QsR2moxIEVpyAwHpphH117cghsM42E6c724zflmv1rCe9cUJyAAAgAElEQVSwxvTQTlqUWFztPNhaW95MA4H16nDt9QoJrG0qCCyegXteur79+HWu4RBYnb42t0NrG6yVTc8qt7XuBHVl3T+ZeqJ8Yn4zm3Qy1itFYA34V5nAauopXbZc3d/20L5UX7s2SlT/zREEKe+yYXdlTokqyg8/AH5+QHAwkJ9Pf0WfN49NExQExMcD99+vvfyhEnarIiNFbctvQoDVq2ldAH7c8SET32e1TB/6+QEPPEDrfP559hpfejpQrZpZp99/Z/N++605rkkT6qHR2v40ihL27wd69QL+/JPvEVJYnuFK3H8CWvqxUiXl8vR6yfwxwzkE1r8fMu9ms78eQWYhNcD/8faPmTj/OH/7tTs/Hxg8GDuXVRPpNGP/69L5ysqAqCi3kwAr8NyK55jnWZEMzt/JuoOdUTtVkVcARGvKnNeIiMAKDe3EjKusLP743XV5F1PWhEEExbVVzl2WyMsDatcWv3tLlmgrx44IiTvBXbsf1EBgAUBq6g4kJq5GYuJKJCT8g7zCZCbPJsMVwhvpN9B9U3d029QN7218DxNPs1e+i4oSkZMTDr1eD0B8sljqSp/sXkYBH68SzHlrzXm/OfyNFb1avnCV71A3bIP2XbEbbsjAVSaOG6OlPzJ/30Hw2jB6MuFOf4LLvxNkPG/jB4mDRQ2BJbTDdKQVv6zbD7HpXi4HAsuHQ2D5SBBYBVWV7WgJxRYCy9I22SGNBFaWwTNjyx+01zv8I/bv/n3pFb8NHahx82LOyRm1BJaUR8HVL4jD1ncw5+s1QHs7rJGNKgis+uPVlzf7NQLP1wke/f1BvLTqJat0Mo0BCQJroAoCq+18cbmVptJy5er+qrf2pToi4j1R/f47CHyOivUqruP8OUxWBg0CmjalhNm5cwARtyHL8tp3dDRWrq0pSoO7d+kJnv792asq7duz9fXsCaxaBfz9N1+fZMNJjDNnHNPe3Fxg3z7+CbS4ODbtunXiNMLy5s/XPH6sxoULwIwZlLi0BdHRwJ07bFhQkLZ+PHlSXV0S+c+eEo8zri1JAxHQa1svJi4gPsBch14PnD1LyVIlBAZSO2m//UavSALA+vUAITjAcRaxfWsV2l9ClJWZTzg2bChvV84NEQ5FH8LGiI3YdHETNl/cjPySfGerZDVSclNwoy7BdYPkVSMiAisysrcsgVVamoXo6DE4FdARb61ohKZzH8Wfm2ha30MEme0V1qnYWEr4//knvQrMswNICNCsmZ1brx7nL7zDXbuNp6fVElgBAS2YNLn5t5m/NxwhwL17srpkZHjBx+dBeHsTXLrUBwCwPHg5M8d4eHtw80rtJWrPrK3YB70FBNbgdQRrw9ZiY8RGlOnsYKPRwXCV71A3bIP2XbEbbsjAVSaOG2PkPzTnrSEIXWr+2/cwNfju9A85CVFDYAntMB2WILDCGqkjQawVHoFl9LBnKb6Pi9OVVaJxSja/hNLXQGB9awWBZawThPaZlrzGk0vWEFhKngvvPijus9Mcw/e8Z6CFyNtgQWBpvUJpjXzWlz8ehNJgnON1EfZjo7HSRtzVnMD6cAW/7OIqKurXCH//pqL6/XbSuUwYHriRQGeDEwlniIjAEnis3O0pToNBg8xpnnqKkgqA09siKdWrU8LOEr/9Jk4nBK+skBBg8mSgb19xmfbCtWtA1aq0vipV6MkfazBlilnv4cOBxERg1y5tffe6zKkkIaTG2GnxGOLaknzzTQDAR1s/YuKCEixIvJ9+Mpc9YwYNKy4GfH1NpwYB0A/7WrXMaadNY3T0lphnMjoQSpBZYt8+tk0ffihu+4EDtK+GDOF6pXPjPwThGBc4NoiK6suMqcxMXyb++vUfTXHnzzdA/u3zTPrIGZy5yAi9HmjZ0lz3qFHy76/lO1GOkFq75QmsNqJyAgKeYNLk5N1g/l53mNAfVADo9Xrk5V1GcTHrJMDf/zGWBMuNxIoLK5g55o8zf3Db0WZpG+4+4qGZDyn2gdCe55B1Ms+1AsJVvkPdsA2uNSrdqPBwlYkjp7Xyx6ZQbg2uAB80EiJHYBmvQwrDe3zOL4tHUthTVx6BdY5DWJwTEFif9jfHrZMwNl53Al9/I4FljS0qnQWBdUSCwJrfUWxAnXiYjbhbQ2C9pnBN0mhfy1L0hHqNJB70ZI+UMwItRJ4lgaXViL01onYcSRmid6ReNSbZRmB5e/PL5hGPovo1IizsTVHd5/cQRE3l65V6bjq9puTrCxw96vQ5TUmE+t95kY2PGs8hsITlPPyw09uhSh58ENi5kxI5770njm/eHGjQgD43QF15d+4Ap06pv1pWUAB4ecl/WPbuzdbz1lvK5ebkUBtdy5YB27cD3ZTt36kS3tVKKSxezB9jXsoElsmWZHAwPtzSA5+uJpi4jaDDAoLgUT3pc+NdU0xJMZN9hACzZtETfwMHitMCZp3W8d9f/+0EaN3a3Capd1jY95ZXeydNUt9nbrgehGNBQGBdvjyAJUUzvJh44ZiLuzSV+fviTDq+9JEXkT70WWT3a0+vl5eVUccfWt9hJxCqUut293/o+z7uxDhR3GUOgRUY+CSTJq/gtihffnwgACAqqh+8vQl8fWsjK+u8pC7JyRuxOnQ1M/9M9prMbYd/nD/u+/M+0T6izqw6in3QU0BgLdpHFPNUJLjKd6gbtsG1RqUbFR6uMnHoCcHVCdoIrKg/CHJaEeiqVYAPGoHIEVhGA5vC8C5f8cvi2Xmyp648e1Dnm4rTnW/KprEksKS85dWbwG+rkcAa0Ief77GfpftPb6FTaWXqlVCYZtI7/OuF9wwEVosf5cmJqlO0EypSHvpyq1OPhQGPST+D7z5QX4/ldb49bRxPFH3Vm2BYL3qF8lID6TY8KmGI3lFy7En674xd7LxgvBao5gpheRJY587VFRNY+6T1io21+CU3Odnpc5qSCPUPf5ONv/aLCgLLLUDduvQ6ZXw88Pbb9PrO6tXAli3mNBs2AG3a0P/XrEk/SnfsoGlKS+mYWbqUX35oKDsws7OBf/+lRGlMjOPapRVvvKE4xry9CZ7+lqDNPIIfNhPM3E2w+3sCfWUCPP44xu151pTu9BmC4Nb2b5fPJOl32NTuvDxKUir1i+FaIiOpqdr77v8Q+oXzUfDW0ygbOYT2tytA+KwFBNaVK18y4yk9/TgTLxxvt0N/Zv42nsC6Or+RKSxhZAOgc2frx/yoUfTadDlB6t3qZiCwVoWsUklgtWbSFBWnY95eNl/ubW/k5IQxYUFBz0jqkpKyGf+G/cvsC4Q2syxx7e41kcF9NQRWjxXi9mdnB1vXoU6Aq3yHumEbrFjl3XBDGi4zcRgWx6K62kgs08L+gvM+OHTVCFLeIch4zhwmR2CNMnhUFIa/NYRfPo+Isaf+PALLTwWB9cln5jgpb3n1x/PbqkRgxTwsDqs7geDhX/ltUEt6GK8QKhFYPM+MSlJQ1fpnoIXAsjSortWDo61y/AnpNpT3CSzj2BESWK/8TcMrGoHFq/vcAZUEVnq60+Y3tSIisDqz8bxr4s7W+f9WatYERowAIiLMRJgjJSlJ+57A319xjHl7E8z5TBxmPJ0tDPcda/+27R2nQGAVFQHffSddhiVWr+anGT9ee/85AoWFQEKC+aqvA1FScg/Xr/+I69e/Q2Gh4JShTkft79WvD3zyCfTBgYiYQ/vcfxtBwappjlEqJ4deC1WwlaQawucsILCuXh3GjKd79w4z8aI1Y28v5u9L0whKS3PsP+8arug6Gnq9XpHA8or1Eq0tVzc8IyorKOhpJk1i4gpsEHj3y711BgkJS8X9JdHfKSlbsT58PbMvmHBygmK7qk6vCuJB0HouwbojVXDhwnO4cOFFxMT8xk2/c5m4/SEhL1vfseUMl/kOdcMmEOUkbrihHi4zcRgWxrLqBCHLrSOx9JXssDBbIeFzzTok9KJhwut2ljLSQGD1E3iem9CVX35EQ87Hsx315xFY/pzTQn4CAutjCwLrdAtKaHX7gq+rMKyP4fTWZ33FcW3H0JNVvL6b9Tq/DWoIj1eHm9MrEVj9+2q36cQz4q5Wun6pvp7N7ZXHh1ZpMI7qoOSd8WRL6TbYqoO1IiSw3jDYxnAFAsv3SCVJvaKjvzVn1uuBVq3M/d2wIft3BZC1Alte5wXzWXJ3Nv7mKOfrbJJ16+gVMmfr8V+SzZtt2xOEhIjKDF0sfk+ClkiTR8Kwo3Ps387tExUIrFu35MuYPBl47jngxx+B5cul0737LpCVZVufWovISOCvv4B69aguH35ISSSHVvmRqR9DQwX203x9UVSX4N7LBCW1CO6+zvb75UnE/gplZwPt2tH216kjIps0o7hY/IwFhv/FhMkW2fiY3e8zf0dNJSgqTLQ/gUUIvd7uYOh0JZLv1vsGAut0zGnc7SgksNoBoARYcvIGhIfzDcELJTf2FLKzg9n5Jehpuv6uWydKn5q6XXSFcMzhMbJtKiotAvGgNrw6CeauqKj+3Dx8fVV4dK0gcJnvUDdsggNmXTf+n+EyE4dgcSQeBH/t1kZgxfUjyG5NED6P4OIsgoIm7HUzR0hBE/6m9UIT6Q/fRwynkv5+gw1f0FG6Hkv7S/+q9cBYp46qdDzSgnfdzf8xcboJXc3X8kDEngqN3gaF+eQIrHbfEJRIEFjEg9+GnW3N1/4qTRXnGduNIKWGOX1zBQLrs770OQn1ksujs4FA1WIEf+/T5nzhjQgWvWo7CRTRkJY3qqd8utMtpNtgDzLKGun+D8HIDQSjNhKM3kiwy/Befa6SwGo1V1zm4I+pl0S5erWCV/f58/Ul9bp6dRhbQGAgvfrxwQfAzZv0ZIeV480R8vt2Vn+f7mx80geCj63hztGTKxLrkFtsEFsJjvBwUZlKzl6UCCyvI/Zv55YfFAgsLdK1q3Ka998XXwPlobSUki6rVgEnTqjv94sXgUWL6HXG/fuBQ4dY22BGmTIFCAuz/vkqQNiXlga1C/p2wrn9NNxvJ7X1JOp7e8PSkQEhwNSpNhVX1aMKs56s70BE3kmFbUqKWyUbn7CVtZl1+XeC4sObmLDgf+009j/5hHohPX3aYSfyysoKJN+tHgYHLKdiTuHeq4K1cya9lpeQsFzTN0TOzePIzb3EhIWHvwPMncudT1ITNqHvzr7Ke4PDh6ln3bffRmKkH95cSnCU45AiJOQVbj/wdI2+Nsohfe4IuMx3qBs2wQGzrhv/z3CZiUOwQXp9GMEDfxK0X0Bt2iydTXDlV4LclgS3b89UvSAFbamOPBUe1BSldm1ueFY7/qb1Vh3pD98mv9A0f73Jhi981VBuzZqiegqq0pM3Z5tp0LlxY1XpXuEQWIGPitMFcAgs4kFw+RFzGuHJMyNRJcxjDO/PIbA6jKaeBqd3liAOBHp9/wE9DdZhNEGznwjmdWLTT35HnEeJwBrQh15/NP5d+Q96da/yH2aCQ0kvLbKzrXrC5oqF980uX9mHBFJLYHlVQAJLKMbTdYPWqt+4Csvo/BUlv+Xq0QpevSEhr8oQWF8pFzp0qH0+Royydau5bD8/4LPPVOedLCCwfN9n4xM/FBBYw+yotxYZMYL9+777zG12hj7/VbEVFy+Kyrz+vW0E1pkT9m/n1G/tSGBpkXffZU/B5OcDGzcCPj7AH38AlSuz6f/9l6aLi6PeKS1hJCB27rROl+Ji6knz8GFqIBygdtxssJUk7MuCglumuMtLGik//7IyYO1a6uVzwwZlkkWnozpLGSpv0kTc7uvX6Rz53HP0+uf587T/VRA6wvVkytuGMvftow4aOH2QFD6TZr5xA5g5E4EbBITKW+zfVyaKzXJcWOWg8Xj5sprHqgm8649GCdjzCcKTw5FTlIN7Lwva/RsBPvtME3nl7U2Qc+Mo8vKuMGFhYV1MbRQRWNGr0G9nP/m9QWkpvepqKCNxUC+c4jijMI1bDnjp4nbzT2tVRLjMd6gbNsEOq74bbpjhMhOHYOM0rQu7ICx72RxXVJQAX9+HVC9Kl6bZuDB/+ilw8CCXxBJ6T4ywuKJQ+zeZj19CMENAYC0yElgSZJlmeeIJVel4BFYuxyB5IMerH/EguNjQnEZ4zdBoJ0vo6XDqWzRceI2SeBA8b3G1SKrvLKXNt2x8j8/Zv98cKs7Dq9dSPv+U2twy/l3lD5rv5sMEoY0pudH6O3N8u29se1bWElhvDbEP6WM0zj76Q/l03s2l2+AIMsoW0UJgDVnH5jU6VJArXyu0bqavXRuuXKhOBxw/Tj8cq1enz6J6dfrhJnxGbduKwxYtop7qPD3pCS8e9u9XNYYnKhBYCR+x8bFf2WGO0yJPPQXExlI7NrVqmcP37DG3tTz1+a8I79TQr79CpytFWto+ZGae1fyuAAAuXRKVe2Gl+vcHhBPuAAJrwXAnEVhGuXsX+PJLdWm3bTP/WDjBYKtnxw7H6VatGvVkyUNpKbWptX493au0bEnJ5BEjgNxcDoF1kxJD06apMjMBQk1SFNU3nMQfNgy4fZvOmV5e9IqqEXo9PdlKCCUbIiLE+mpp99ixisPbci3ptIRg+1KC+D4GUxgdOgAREWICy/MtSsgZ6gnYLCA1+oj7ofARAYG1QkM7eB445WTrViAqym4nskpKMiSfb8zBXqZ06S8KCKyJEu+/gmRfP4T8/GgBgWV2JiFMn3vzJPrv6i+/NxAQ8Uk1FcYtB0Ki0tubIP4TftqKCJf5DnXDJrjOiHTDJeAyE0dZGTPJe3RhFwRLAgsAcnMv4uZPD6hemLLaEqS8S1BquEaW14wgYjaB7xGCNAm7SiYZPNisZ2kpMGOGKS7rGbaeyD/N+R5SILD+FJwwWvyKIe/LL9tn8yg8aSAhw3opk0QgBLESp8rCGpnTCE9p9R5Aw2/UJTjVktpQOtmSIKEWDZcikmIelo6z1Cm+tjheePXvgcnitux/Sp6c6DmQGow3/l11iriMiw0p2dZ2jDyxo0Z2PKOemLE88WYvAiuuNi3v5+7y6XyaSbfhncHlS1AZ5TFPgnl7qSzYRzBig4GEWqpt83r6jLlMo0MFuXq1Qutm+s6dOdrn0ZIS8wkInY6eEMjOZtNcvQrs3k2JHLUYN05xDE/cJiCwerDx6S/THxMi/ySI/IvOx3aZ54xSXEzbbvlBPmIE/ypbdDS9ArR/Pxv++uvmvB070rn+8cftq+d/Sc6do9dZheG5ubh06VPzx1b8Ik3DGAA90SEoV8v7w0vvc8z+fbDm9/IlsLLb0CtzV34jKH5YPq2uKkH20wTFdQxhNWo4Z5wYSbMRI1TvS4R9mZ9/DfjzT4AQ3Bmg/PwLGhMEbKH/v/i3hI3U/oZTLL6+bHjXruxYLCnR3ma9nuZLSDB7BrWAcR1pPIvVO7WzdB8kCX4U8BfMuTwCq6Ah+3fIMhW6T5oELFlC59NPP7X+uR84QPvBSkKruDhN9hnfuPETACC/KT2def07es04pSu//5QkO/ogCvJvMGGhoZ1Q+iBB0cMEKW8TJHxMkNqFIO0NAt2tG1gStATEg6D6dIKfNhNsPvageb5LTwcaNWL6JNkKAktIVHp7G+ztughc5jvUDZvgOiPSDZeAS00c339vmuSFBNbylwwLwLhx5vSPPIKih7UvUiU16K9QlmGFDWQWYUsCCwDmzTPFZbzAlnP5d3O+re0I2kvYTQIRX5FbYiSwgoPts2n081OVLphjr0sqbZ1fxWkvNDHHBwlOaX00UL5uKZIqpLEMcWCR/ybHW6HQ+DqPwNr7tDw58dR39Epiyx/otcT2Np6wUpLtMgTWr11p/I5n6EmtrPvM+d4eYhv58/hPBI/9TH8VBCFYI+FN0ijnjNdxhw6lVyWI/XSxVlrPZd/BmbtpeO0Z2ueGGn/SvG8PoW2yJ4Hl41NDky45OSHKhZYX7t4FWrSgz7pZM2rwPDUVeOst0/P/VfAxda4Hod6q0tIoaab0HtSuDYwZY/77m2+AZ57hp33oIfZv4QeiXm+6hqMJUVH04/Xdd6nhaiP692fri421zRW9pTzyiOPmluefp0Sdvcrr0wfIzGT7LCZGlI734akZnDGj5f3hpQ9dYv8+3jpfWofUzsr573YkuPEN/TEMhP6gFrqUniIpqcWm1VcmOG/hOe0yZ20ziq6y2SHOuf0EuTLXvyua6DnPLq+ZOV5I1BjtYVnKtZ/Zv+/J2BjlisIYV5SPPuKH3bkDELqGNJtNsPIgq+dZi1OCwjYFbKanyozx/jvE8cI8+QI7rYrvgNBjaFQU8OSTtj9XgYF6APRHB19fID6e2rw7c4aeyjMQXnq9HrcbEOw/Kf2OFRUlIOFjgsgBtXG2f3PE9qtssn2rdf339q4Mf++GTFiIzzOIFOwlstsY2nTzJgpKCtBiYQv0FJwOzcu7zHwrGMUqAmurOF3ih/y0FREu9R3qhtVwnRHphkvApSaOxETTJC9JYJWUmNMbbDxZ2qGSM4osJ6GLKakVspwg5wmCpO7UOGjoYgLd0QOsngaDjsbNp2U5V341L1T/vCRPwgg/+H8yGj0GgKVL1W8M6tShLqWF4aGhqssQnlqSSsc7VWZpL0tIhn34uXy9UgRWqEoCi3cqrKEKAmtPG3ly4v7J7N/GEzmOkm3tpHU58YR0PlvJH2F5SgTW+aaGtIGBVCzy2us0mFZ5UkBgzdpjeIbTtc8Dqw7SvEa7afYksM6dq6tJl9JSDSekygM5OdRor/BEV3Y2yq5HifQ/t2sIk6ywfSOc20/ge4jA56jgQ6pHD1o2QE/1GO3IlJRQOzqTJpnTfvwxtVXTowe99nPliuPbnppKT458/jnrfaywkApgPmXg40OvZp45A6xYQe0Nca7DIT6eng67c0f7VR214u9Pyb3Fi20vSwp6PXtKbeBA0TUc44dZUVEyIiK6w9//ccTHL0Zq6g5ERHRFTMxv0OlKUFJyD1lZfigry6OkqUAHLe8PL/31b+3fx+slvCBa6iGUsuoEuioEmR0s0p6mNj59jpvDhI4OCuvLl1/6ILWDlPMkQVpnNl34XMeuYfYUfSVxO3MtPOAm9GLjIv9k/46YLc5v+eOiKnnvPUr+DhrkkDY2mqU8Zs7vE8clWTjH8FPh6Ci/Kft32EIZ50ZyJ6VWry6/MVCvHj2BmZiIuNoEG49It+/ezrHw3lONCSszlBOs0pGLkoQsY//OfNag59WrAICswizxeIsaIGpX0cME13rJ14WkJGDmTLqGFBUBer2IqPT2FpzGO3PGhsXN8XCp71A3rAZxtgJu/LfgchOHYUJe9jK9mmWUbe0I3eRb4sABU/qsZwjuHPsK+fnRKCy8Y5dFy1L0lgv7nDmmem9/Lk5bavDKp0QcCMPeGWxYjDjtk5RWreimY/p0cRzHCK6UrHmengBb+rLguqZAeHa9/C08FvI8FcrV2/IHcfoxPei1RDWky+2HxPGNxrJ/PzhJXO9uBQKr5kT2b6NNJEfJB4MqBoH1rwKBZXrWISEiL2FqCawXR9qXwHpHsLlcuI+GV/Kw/n336aviHdaIGzd+0qRDhSOwZFBSkinS/9zeoUyaop+/YOIvrCRA69bAli0SpVpAr6fX/TZupL/auyL27jW/L/Pm8dMIr+scPkwNNvNsmlkKz97h7Nls2e+/z8ZXq0ZtIvHKCwlh/z54UL5tAQHAO+/QE1rx8Sgry2OedUBACwBATMxvkuP95s3xOH+enn4IDGyN0tJcWp5Rh4ULNb0/IOKwFAuHHqX3E9x7lZ5QsWXu3rRaWQ9LiR1Kw/12qm+HUQoaScfrKxEErzGH88gNR65h1krhIwRXxxFE/0BQXNscflFA8OS0Mscl9WDjhCdkwueJ235puvPbaimjNyo/83MHxXF+uygBmtqFIHw+QdgC+fGT11wcVna/hF5KcEI/3WpE13Sp9mW1JTi0rw4TdmzwU9BVI6pspVkjJrMjFy+aukaYJnJrc/N724QSqFrfd3TpAvz0E/x2idNFzhCkrcDrost9h7phFVTMIG64oR4uN3HILWbCX/6Li4HRo+mvv6NHMxN4WVmhXResu3ctTmHNnm3aePHSJhlsv9hMYKWlyffHxIn01BrAJ7A4NkRsFSGxQzwsrpUR7QTWZxwvhKk1+NcaeeXFcWxgTXyX/XtsN0G9R4/iTHOCTl8TvDqcb8ReeNKs81eO3aQ99rN0e0+2lM5nK/kTV5stb+1z8ulNp+3Cw+nJF4u8aj0ibuhgXwJLeL1g53FznC3vfLrMCco3h6pfqu/ePWRV/a5FYKWL9D+39xsmTfGPg5n44DXO2+7o9TpkZfmhoCCmfCvOygLu3ZOOv3KFGrMmBPjlFzYuNJReyWzWDDh5kp68JYQapI+Job/Wnz5NDXqvWiW2/XXlCv0geuklem3HiPx8elJs0yZqr8fyx5rcXFquRuh0xcyz9vd/DIA2O3BxcXPp6bF9++ipNo35QQguT6In/UKW0RPWxh+X9JXNZM/ZkwSZ7ZXn6LL7COL6EcR/SqCrZg4/JnM6xNubkgxG5y5Cj3BaP2iFp2ks43OeUFdWTmuCjOclbELJiYOuuoZanGCzdLhz8W9W9+ynzHHJ7wk+5v9i/w5bKG77xZmGZ1+JnlArquvYNV1J1Dxz38PiuPN7tDkzyOWMi9IHJfRSwqhR5d5PESPk21dch+DgwTqi8Nih2vpJi5gIrMcek3yeEZ6G8UYIAmXISrn33SiW14Yl065erXmeLi+43HeoG1ZBxQzihhvq4XITh9RC1rev5qLS0vbafeEKC3sD17e9htIHCYLWyS9Clh+81acTdFxM0HkptbOjq0zw+hKCrssJqk2jaXqNIshqQ6DTGYi49HTp/khJYRu7fr04zbVrfA9RHFn+EsEv3QjGv0cJIKNNJKHUmCT+mM+32MxLEVgXmlCbYFvaE2xubyZOPF/np/duro7ASqgljk+qSfDMGPr/Vt8TpNSwyHPyJAAg48nHcKsOvYIYw0bnVg0AACAASURBVLGjVW8C+zfPk6E95dFfpNt7yoEEVnQ9trx1CgRWsPG0QliYyC7I68PU1XmqpWsQWN7eBE1m2UZgZWdfsLru0tJczXOesyC0ebT2MIH/rvlMmpLvhzBpgtY5b7tjNDB+9mw13L2rcLqovGH0zqaEuDh6gurWLYerpAS9Xo+UlC24ePED3Lt3DHq9jnnWfn6NAGgjoEJDO4nq0ZJfbr7NfJZN67+dklplhrVMV5ng5iiCwA3UOLSuKiVXjOmNpgLUkEbe3ga7Rs8/j9SD4zS1QXjtLbe5OI3xOlja68rlJfQ2///qBJXrk9HQOQCcPcvGpaVZTWroqlEdpJ6b8FpgVltz3J3PFMYO51qnkcC6ZLha7nOUXuF05LouJ2rGrs9R29ewnFbiMKFtNRBCT2haoLg4DTk5YdDry8yBOh312jpzZvn1E+cUGtOWmgRHjtTkxlmeSLSnRP9ET28WWeyfLs5k01wdR8PzH9NWtpaxwjhx6NfPIXO7PeBy36FuWAXn7ejc+E/C5SYOqYXsk0+sKi7j4nqHLGDe3gTep6XjdFXoh66RnPrngJayq8DPrwnO+T6MuD7010JRfwhRXCxOEx1Nba8IDR5zROhB7hLHqH1RFfGH/Nrn2DRCL4RGwqnV92yYRxcabhWBNcBgW6BKFSR1f00UX1CVStQjBHnVBO24cQMAMK5PbU0kyevDHLtJazxWum5LG2NCsZX8ia3Dlpd9H8H1ugRffsJPbzLYHxpKT2to1OX5UfSDq6wSQWllgmpTHEdg1f1byzsnLcL6Vr5IcOApdUu1LfXm5V22as5zBoqKkhjdVx0k8N+9gE3zI3uFMGCrc7Y7OTmhjB5hYW84RQ9K+mxFfPwilJSkO0UHe+HmTZaYSU3dBW/vyqa/z52rB0Db+xAS8qqoHms+BHXVCGKHEERNNRMW8Z+K0wcdqAdvb4LIiz0ReLgeE8cjh0DERrRl9cnJQdrNtZracOU3dn7mkRHZrWlc6f3a5xjuVbKAAOqBs0YN6vVPCL0eyMszezsFKLGRnEzXBePJQAVJeVv+uRXVp9c7CxrR0+6Wp96kiJ3Lk+nJ2ezW4riL86qJCMeg9Y5d1+VEzdg9e8K2tcvbm3qhFIYVdWmPtDcJ4j+hfVs44mMUxQSZHmdOThjOnXsY3t4E4eFvsSY0jIiPp3YBpRxt2EnOKOydy2TGvdyPzPaQlLcJChtSu3MRnmzcLcONitwW2srMb0rHvK6yurGSY3j/RZ4zKxBc7jvUDavgnB2dG/9ZuNzEIbWQffSRVcWV3bzCNYRZHjKdc2/dWknuLugPA3S6YpSVFZj6LvtpguSuBlsSRq8vCQmAlxewcqVk/wqvf12tL04T0ZBPNqFTJ1OawEf5aYRhH39GgLp1Mfs1fvozzWUIrLQ0ansgJgbJaxaK4ouqyGyIYmMBAOMFBFaVP+RJEkd7IRTa7bIU4SkpS7GV/IkTEFi+jxPUnSCdPrSxIW1IiMjIspr6XhzJ1vcg50SfvQisD1fY593bfowz5onyUl1ammvbO5+8wao5zxkoLIxjdP/nAMHulcuZNDeXdRC10RmIj19SIfS4dWu6qf7AwCfZkw4uBmF/Bge3w9mz9zFhvGumchIS8jIKCm7h+vUfcfv2DKvMAmQ/TXBO8AFc/JDYCLiSxAwXhyl9WApFpytFWto+TXmujbWYb+bORdEcsQ0xozMEXVXtcwxzgsMoCQn2GRS5uUBEBD0tPn68qB7/7WJ9glcb4mvX5q53BY3odUy5NhXVpdc9heEXTzwvMmzv7WX72p3ahY6PnNYs6aAkUvrf+tKQ5tgxzc9TrVyN/IIbbkRYWGcmPD39hPLzHjrU7vui/EdVvFdWjHt7SUpXKrw4owMGng0yb2+CQ6cUxvHDymPF25vA+7RFn8WU85V4lXC571A3rIJzdlJu/GfhchOH1GLWo4d15SUkIOsZavDw6njq1rqkNkHCRwSXLw9EfPwihy1ux2ROaGkVn6PUdkNyV+lf5Xj35IODn0V6+nHcvv0X0v0XSfav8PrXdY59iIs/DuATWN9/b0oTpJLA6j2O2kT57uenmHCjx0CvFjIEVrr5tELquqWi+GI5AisuDgAwoc9DTJ5nRysTJXbbmL3yivn/tWsDWVloMKGSZL33HpAuy1rSp8ksgg0G2y03LUgluet9rb8juPyIIW1wMPVKZ6HLc6OU6315BKu/Iwmsz+3kgcjbmxqR3XSEYP1h6gWqrLr8Uq3TlSI4uL1NdV6+/Ll1c54TUFAQy+i+9ADBqaVHmDQxS+1HYBUVJeDGjZ9NxIY4PgnZ2cFcUqiiEFj+/k0ZHTIzfRTzlJRkQqcrLQft1EOv1/PXLJ8azN+XLvXRNP6Dg581GXX39iaIjv7WLu/yzREECX4TNOWJ9Rlmc71RUf1QXJyiOV/AZoKUMU/Rzi4o4K7zec0J9P7+mssu7PumeF0x2tW0NzIyqL21t99GQWO+PqFLiHFQAYSeKiurZr4meXmSyr6eQhAU1IYJi44eg4x5g9g59ndC7Tn26AH07AmMHSu/dgucHqQKTpH5HGONzVtDYKX1rANERgI5OXYZ71rECGF4TMxvys83J4cSlUOHAgsWUDt8e/dSZ0RXrgC9emneK2W3UdZZL9OX9pC4ftJxyd0Ikj7gx90YTdvAs0GmRiyveSqlLbuP0BOT8fGOeXdthMt9h7phFdwElht2hStNHFFRgAf5g+/it1s36wqVM4RuQEHBLdNmJ+oPuik5t58uDL6+tSUXDZ+j9HhwWTWCrHblu9GQEp7XGqHoqtJfd26OpG2ImEMwaC3BswvMpMANIYF18iQidy/jkzo//mhKF9yEuoduN5+gyjRlAqvn+Ee5ZcoSWFlZpmd3d9MKUbxOzjitYXP+q4DAekGFZzyryKqGDcVhgYHU+9qjj9LNHYD6v0oTWHLlK+lc51d6gksY/tMWdkwYPXGdeEJF+x96iBp2Flxb5ZGXQnlV4Bb+4V8dR2ANXe/4962kJB1FRcnQ6/W4d+8wvL2JzcSVUaKiPrNyJi1/5OdfF+l/Zu183Lt3BGlpu6HXlyFmiX0ILL1ej+DgdqYyrl//wSKuDNnZgfD1rQVvb4Lw8LdF119SU7czOly//p1NbVeLkpIMXL36NcLD30V6+klRX6Sl7ZXNf/36D/D2JvDzexS5uZfKRWc10OmKHPJuxcZOEYRVtku510/0QkLCck15AgNb26Xu/PxrVuXzOfsAysryAQDnTtXgprl1RdrDo7Q+18uPwLJAXOhErj5hi+icUFBwS9ZEg1aJjOyJpKR1uLeJJUGvTpCYgwoMp9rz8+m1yNhYIDOT2qez6CspG0uJH3LW6/vvp1cvi4qA+HhJXZOS/kV+/jWUFmg7sWgPAYDs7CBReEzMJKZ7srICcPv2X8jJCdf24C9c4O9ldu+m5h18fKi3cUO4mn11SQ3H9kl8H+m4pPelT3Ne/95AwnGucKoRy+u9SmmLHyb0tGMFhSt9h7phPdwElht2hatMHNevA40a0fn6ZzJPvMC9+651BWdlKRJYAP0oKrOwsaCrTKDPoV4Pi4tT4efXmLtwmLy5tFa/wf1hM0GizzisX0Rw8KQ4PjhY/KGnVvy3ycdfjuyPjEPTVZelq0x/4bp79wD8T3TCj5sJ5u4l+Hs3gdcZuoHL/WMQQJQ9LO05QdB8toHAGtsE2dkX8PGSanhrGcGXawleWGggoKoReA2lp16+3URQWUik5JqNW6duX4z7p2sgmgzG73/rU4fJ0/0LBxBYjRsDs2bJjjsjGo2vbFW9DcYp673wVXGY8Nkk9KblHXtSRfu3bze+NCJ9traT16XT12z611QafreUjl8T9O9L0LcfdT4gRWAN3+DYja2j5dKlPtbNeQ5AaWkurlz5EoGBrXDnzixRfF7eZdm2XLnypd1OYBUU3BLPw6U5iIjoBm/vKqK4kJCXEBjYCtHRY6DTFSMlZTMTf+PGz9x6dLpSlJbmoLg41S6nniztRBkJNktJS9stmbeoKBGBga1w9mxVeHsTXLs23GZ97IWSkntOf1e0yMWL7yMhYZnT9dAqUVH9EBOjnaSSk9zcSGS2J7jbkSDlXXq6tDwIrPDwdyR1Cg3thOzsQLu18dIls/3UtKA5TFz01Ie0KW5Y88ruJ0j4WL5ePSHUM9yAAcCQIdTxiUmRNEW909NPlfsYKy3Nho/PA6Lw2NgpJtVzcsJhJJPPnq2mzZNrcrJ4H9O2Lev5FADatQMItVmnpPOdAerbd/q0eH1QEkvHB0JJ/JAgToLguvYLbZ/wlJ5qOU1/IAch6Np1s2zagsQQbeO4nOEq36Fu2AY3geWGXeEKE8etW0DTpvxv9G/JEtwizZDz+vsoLaU/4KxbR38MU4WCAn7BK1eK0wrTGH+FA70ic+fOHNy5Mxt37x5CaV4qcOgQcPgwldxcpKXtZhYVnieclxcZyIDLl/Fzd/r/dvMJpu4gODi3rsGmVb5DNykpKZsRGzsVocGvKKZN7koQ/XMlxXQ5rak7cqV0J70Ias8gmLlS/AEnJ12WmgmM0pxkREX1k01/fg89XXZlIjWyaXymRUmXEB09hpvHcxvBS4sI3l5GsMfg9njmboLWc1kCR1eZEpd5TQkKGlPvVaa4agQFQ7tDv2838uMDoV/5jyoC6/ePanLJmgbjZAisr76SNf5ulCEfi8OW7Gfbnt+UlnmklQKBtWMHq/iyZUCVKiadeB4dLeW5UWwbjN4itYjRg6VRpAisES5OYHl7V5ztQHz8QvZ9F/zynpt7UbEt/ofFJ0fUgkdaWUpS0hpV/ZmauhNJSeuYsJs3J5jqSU7eiLi4uaJ8GRlephMw1kKNbpb9ff58A4SGvo7CwjuIi1vApA0IaGmTLvZEYeEdp78nbpGXkJBXueEFBTHM32dPEujTHe9QQElfqTXaGomI6A4AuHVrGvz8GjFxN7y1e24rmeuhqt7C4xtly4n/RD5/dnYwFnsusKrN1orUycTY2KkmvS9efJ+Ju3JlsKhtXKPvRnz5JYrqExQb7W9aEntG+PkBDzyAjBeUdbaHoXvZPpEhsBLGPyVJoEVNoTcdbKk73+DA57nnzsimy6tAJ3J5cIXvUDdsR8XZsbrhMOzbtw/du3dHgwYNcN9996FFixb45ptvcPPmTbvX5QoTx5072g+3EAI0bw6MHAns2iVTeFmZOKOPhK2RL74wp+nYUVMbEhMpqRYfvwQBAS0QdfhVFN9HsMXCW87eE2avhCgsxC/d2A/zzf2fNpWXmXlO82IX7f0JUL8+Ej6Vv2rh59dItJGzlNWHCCIGEWS8QJD9lHK9l0+/jeI69BfH8CnS6dYfJnhlMW3rL3MeltXBUpYfoKRSZQ+CWhMJ9EVFKC5OxblzdWXzndtPELKMniQrNdiRunt7m+Z+/XaTmcApqscnJqUk8ERD0yYk+ymDgf3iNOh0JcjNjUBkZG/4+zfDqqnVUe9vgof/Iqj3N0HVaQQ1JxLcqiMx+CtVAhISsOZ5ZcKH51FwpeCqaVF9egx9yrvmNNWmUT0YAmvfPvHgv3cPmDoVIJRcUtKHd6VRiyQIXIALCaz9Jwk+XW2fzauzJTf3ItPVOl0p9HqdYWorREnJPdl5SfZDAvRklRQKC++YrkcKTzZdufIFcnJCEBTUFt7eYntHaoXqqMOVK1/A27sSwsI6o6QkQ6DHbcVyIiK6q6rv/PkGSExcxYTFxEwGAOTmRsrmjYubL9uXSlBuQ1eEhnZCVNRnTLjldUmjhIZ24taRnR2M9PRTpjFSHlA6fecW58u5c3VEYQEBzblrcFlZIXS6Ity8OR5hYZ2RnCxPxFgDZZ2VfzRTK2FhnQEAAQHNRXExMRM1684rR0rkvIuWPiCfNylpHXr0UEfM20ssHUsIwwEgK0tsZy0w8ElTmzIzfZl9WXr6cVG79boyBHg3we3gH+U7+tYtpHvNcvq7IydxcUsR8/cT3LjQ460R920Dm8ovKqKnITt0OCubLjv7guZxXJ5whe9QN2yHm8D6D0Ov12PYsGGmF1koNWvWxJEjR5QL0gBXmTjWrrWOxLKUFi2o3cjh/2PvzuNsqh8/jn/MYCyTJfsaWoRUKF8l2iP5tlgiUkpa+Kakb/2KikpChfh+vy2oRIUWEqWIkCVZo+wky5ixm2EWc9+/P05z3Dt3mTsc93LP6/l4fB7qrJ8599zPvZ/3PedzHpK6dpWee0769ltp+80PKsMUshZ67LHglTh8WPq//7MGJd+1K+y6P/OMtelzzpFmzfp74rhxyog3ajDcaNx0a8DsnFvkTH/rbd6+vW/HfHjnC3y2m5LytTZseEIHpg2Ux1hPp1nXx+hgvRPNxPHjaUpK+kT79s20Oi1ZWdbYRJKOHPlNCxdW9foy8b08nmzt3j0urA/PBd8X06qZl+e53E9ziyk7zuiXMAbNzrmSamz3K5SWtk4vhvkY8mc/NaowyKjE/xllZx7TihXX57nO3B+M1vW2jldGzlU7+/Zpz55Jmv1jePsd/pXvWF4ek78nWK39uaWOFzFKPc9o75VGK99JDGu9jyYZrbrrxMmdVdRocR5XFL35pVHfz6zbMVu9Y4Vhpr9R5zbWvwUHGDUdafR/edxmmrvc/4FRu/etpy15pn9tn3uZmfuVkjJVK1ferLUr2im5qdHmRkZt3zeq/eaJ8zrxVaMCf//3Q/80Wl7RP5Qq0N+o0uu+oVmxV6zXvOLrJ+pfcIDRrkTfN36gOo+edvJfGs+ksmnTM8rOztTWra/4TF+37hHNm1dSc+YYLVx4nt9A5unpO7RixXVasKCCtm8f5tdmZWbut7e1eHFtZWbu1/HjR5WaulYeT7bf/k5XkeQ3HtTChVXtL+6SdOjQUsf2N3duIe3YMcpn2uLFF+qvv97Wzp3vhFx35853Arb/O3b8R4sW1dAvv9TXsmVXa88e/19UAo0r41TZsuUF7d79of7888TtUb//3kWSlJ2dqT17JmvnzneUlXXQr15OOHTol6i/Tyihy/z5ZcJeNiMjOdeVOHFKTV3r6DmTdz2cC7B+/fUKSdKCBf5hwtKlDeXxeLRt22tatqyp1q7tpMOHA1wR9Lf8jveWs+9AMkqGXnf16jvUosUH+drfqZY//xwccHrOZ8jKlTf5zVu0qKb9Ny1b1tTvdczISPL5uw8fXm7P93iylZGxJ+gxyhlX8kwts2aN0B9vVw847+jRzfr11ytPafs5bXb9+vNCLvfXX8N17Nifeb/xouRs6Yfi1BBgxbCBAwfab+K7775bq1atUnJysqZOnaoaNWrYIdb69esd2+fZ0nBkZEi1a596iBVOefJJ6e23pdtuOzFt7FhrDMlNm6x/16+XVq3yeeBdQCkpPndQ6dpr/54xbpyOFgxxO5bkN+2mXqUD7+S338K6DS0/srMzdfToZmVlHdFfT53n/+Vpzj3yeDw+47YEKyt+bqyM++/QqtdCLzdzlnV10ZXdjY61v1Pbt78V9gf5tB+Mqg42Kvl/Rn/88UD+vgjMtsZJkDFK2f1FvtZ/YeKJcbt08cWSMUqrasIaYHb79jetK2A6dZLHGO29IVG/zq0X1n6/esMKy7wfaPDbgPD/5t4TrFtTy/3b6J62RiUHGn0V4lL7Pdflvc3Vrxh5vvd9nPYff3QLu06XDTN6soXRygq+5/3D+bzVb9lLJ8aeywhwif7sH6195escOYmyalWrsK8iPNmydGkDbdnyok8QHax4h1g5A37nLkuW1NHixbX9pm/Z8qKWLm1w2o9Z7hLsCXZz5hgtW9ZUx4+naufO9x3dZ6C/P5yye/c4HTv2p/bu/Ubp6buVnr5THo9HGzY8kes1a+TX3q5adVvEj21GRoqWLj3xA8SqVbdJsn70SEmZpqNHt/rVM+fKraysw36haLCr+fbvD317C+XsKkePbtKSJRf7TFu7trM8Ho/27JmkjRv76PDhZfn6vpFbXldsbtqUv6dE5lXGjXtVwR4CEOh7yL59MwPWO7+D/8+ZY425tnfvdL/3z7EKea87e7ZzQV44JdDt03PmGP355xB5PNkB5y1cWN3+mwLN37btNZ82JGccvzlzrKvj1q/v6Xec09N36bff7oro334yZerUt7RuVI2A85z40SI7O1OSdMklC/JcdsuWfqf0njydzpZ+KE4NAVaMSkpKUvHixWWM0W233eb3YbZlyxZ7frt27Rzb79nUcBw+LPXtG5kQ62TKtGnS3LnSkiVW4Pbjj4GXS0+XhnZfr26F3wwaYM2eLV3TqYTPtOc71deWLUEOzo03ntjB+PHOHvjGjX3GSbrkMSP98osk60qNdeu669dfG2vxj9fqwQ+N+nxi9MwnRg88//f4T8uXSxs3ymOMkkqcuNrG3pZk3UKXYLSmnFFmnJHaWgNU51x9U3CAUZXBRvWHWY8E3/LZUJV5zejO94xav2uU8PdA7aWeNcrMPKBNm57Rpk3Pavuv/6dtnY1WDjFa9nbgD/ZfRhully0gvfWWPJ7jYYVyc+YYjZlm7d8OHf9+2mJalfC/gBw6tFjpx3bo158vzdcXl5lf+p9YB+uFv/7k74wavW70Qy2jDu2MKr9udMv/Ai8bzhgSU743Ou8Zoy3ffmqfNsePH9XcuYXDrlOPj43+davRb+V93wtPjs/fl7olI63jcbyw0eYHjT4a69vZeuzlU/vVM6/y4kSjUgMDf1RnZ2ed1n0HK4sXX+hTj6VLGzq6/TVr7na8zt9+W1SbN/cNuUx2drrf2IJnQ/ntt7Zavbq11q17RJs2/Tsqddi6dUCeyxw8uNA+Z7ZvfzOs7a5efYfPracbNjwe9eNNOb3FupXwY/v/588vpfT0Xfb32GPH/tLy5c00Z451O296+g77M+L48aM+bVOwIMS7bNz4VFT/3t9/v9eub2bmXvtvOJUratav76n09J3KyjqktLT1WnLZBVF/XXMX76s4wz83rg35uubcfigFvpo2d1C1Zk27U3qQUSTL9Okvad34Rqd1H7t2jQlruTPpwR65nU39UJw8AqwYNWTIEPsNvDLI406ffPJJGWMUFxenPXuCX1abH2djwzFtmtSi6Fz1Nm/qEfM/n378E09Yt+pFO8wKqxQ5EHwsHyOZsn/IPFva+v9etWQKHw66rdKlPepzx0a90G2HevWyxqAfPlzq0MG66qtkSallyxPLX3SRdSfkW29JixZZY22//771NOhdu6yQ7dgx6dAhydPxHv2VUFy9Who9cIc1ELeW+f/Cum7u5z5/Q7MH/t7Z4sXWAj/8oKQrLvZZpr5XgOVT2rSxJgc4NipSRNu++8xv+n13Gj16W64mcutW/23/4x/2fx8vYqzB0nJdSretbhXFvXhi23PP+3v5BKPseCtk86tXero0aJD0zDPy7Nolz/vvatcdhfy+SKxc2UIpKdOsR5RLf1/J9my+v7gc9Rp83hNntC+MAU29y9zvjZKuN2rf3goJ67518l+ibvqv0e3vGq2bNcI+hrmvPMmrzP7R6MVHjNaW8z2un357EvXpa1S3pxX0eU8fPbq+mtT6Pt/bC7d4h7PHfC9QkccjZWZa/2ZlSe+8I1WpskF16y5UgQLZios7rrZth6lLl/CeAjpixONas6Zd2HXztnfvjNN2DJwq06fnfTvt0aNblJ6+22/6/Pmlo15/J0rPnqM0eXK107Lt33/vEnL+vHkl7TGAMjJSlJz8RdjbPnp0i922zZ9fWjNmXHVaj9OoUa9pzZreUX+9KCdX1qzpYLdNWVkHo16fcIok7dz5jubOLay5cwv73Xoci+XXX/N+sE+gsmvXGJ/b0r1LVtYRHT26WZs39z3tVyy7rcyeXUDTp4/Qnj0TdeQMHsj9bOyHIv8IsGJUs2bNZIzRhRdeGHSZBQsW2G/ysWPHOrLfs7bh8Lq8yWOMMlesCbjYsWPW7YBRD6sClRv6hg6wjGTO2SlT6weZhEPRr69XKXnOcV16qXTBBSemVar9m+/f8MA1MkYqUcKjJk2ku++Wqtfc6btMz4tljFTWJOtqs0D3mnHqYD5Vy/LL1Lx54ADrvhu265ablvpMO/ehC9TZfKwHzBg99JDUp4819li3e9J0u5mix8x/9LQZon7mZd3bMlk9zCg9ZwaqkxmvXu126sknpR49pAEDrGHO+pYYoe5Vu+qqm69Wxws76WXTT0NNH71untEw84QGmWd99n/O00U1dqw1xto771hPwhw+XPrfsGMa9366fv5Zmj9fGjlS+vBDa6zz99+3lv3mG2n2bGn7dungQY9SUrZo585Z2r17q9atk9askXp2L6Nb/mfdrlhwgNHA2+OVVejv2wgHvKzsEqWUnhCnpP43afHi2lq40P+2T++ybKTRqtespwu2vdv6G14KMN7YzJmFdd99A7RoUZ08vyxN+d5oy09T7PdeSsrUfH/h+uhdoz8anWcf1/OHntwXt3vH/n2Fn1co9+23RVWr1ipVMX9pyz2hH2QQqhR54kKZEn9Z532htLzfw44Vj6666mv169dRb711napW3aQvvpCmTFmjzZv/5/Prv/dtGHPmWE/t8ubxeLR2baew/t4FC8qFFbBOm1Yt7GM4fob/tO+/T/D5/2uu+VJz5/oPLu1dDhyYL0n6+ecTVzHNmFFb3bv7/pL/00/FTrkzEOny8cdN1aLFB/rhh4KOb/vXXxtr/frH8lxu8eKL7KtLjh3bFtZg/H/9NdLnXNuy5cWIHK9vvsn7NtqzvYwfX+uk1ps1Kz7PZaZMOTG49v33v3TKdV2x4sawl01K+sQ+X7KyDuvHH4ue9H7D+VudKMuWNbH/e/Hii7RkSR1t3z5My5dfp9mz4zR1aml16fKymjX7QsZ41KtXz3xtv02bEXriCeeetuhEyf3+nzSpspo2/UoLFrwQcr3Vq+/Qpk3PasGCCn7zJCkpaULU/7bTUX78MSHoeG7SSwAAIABJREFUvK++Cv0dzYlSqNAxGSP9/vsp97hOq7O2H4p8IcCKUYmJiTLG6N577w26THp6uuLj42WM0b/+9S9H9ntWNxwTJkgPPmhdkhWG1FTrgpwcW7ZIt94axSDouXMi2PmNQDl3g+/f8GDTwMt1bX5imWavht5msGNTerPvtG5NIvz3enz3/3SF039sn0u09vVsKZlzdireZKmQyVCRhGx7uQIFpMKFpYIFpXPPlSqY3SpVcoXfMSxpDqioSVNZk6xzb+pmD4Le9n2jDqOtQdkrXTg/cF2uekPFHq+juP7WIOoPfGj0j7+fHlm34XpVry5VqWKVatU8uuiiX1WmzE7VKLFXdeOX+dSj9ECrVB1sVPQVowp33Kormi73WabYK9ZA+fEDrP9u8T+r1H3rxK2j3iUu0PkSrDQY47d+kZdPDPJ+13sBzr/+Rqbicmv9iAZY4ZX4eCku7rgqVtyi+PhMGSM1bmxdeJhTqla1lj333F267LJN6tx5lVq3TlHHjsv0wAMzdP/9I9SnT3fVqbPY3m7lypvUpMk3evzxoerRo7eeeeYBde36om666WNdcsl8GeNRgQLZuuKKmfrnP9/RDTd8opo1V6t+/Xlq2PAHVXzdqOxrXse55jzVqrVKzZtP/ns/1vrWeWz9W6RIqq64YqbatBmhbt2eV6dOr6lp06/Uvv2P6tFjljp0SFbp0lb9SpRIUZEiqXZ9ExLSdPHFS3TppT+pRIm96tWrp374oaCef76z2rd/U48+2kc33zxOdesu1D//+Y4efLCvBg26VS1afKD77uuvV1/9p154oYO++KK8pkwpoxEjrolYx2fGjOI6//wVqlZtnWbMOLknOIYqjz/+oSZOXKnZs0N39mfOrKmvvsrWlCnS11+HN+7Q99830+efT9WXX0rffBN8DLMzucyYUUxdurys889fqQIFslWoULp9Ttao8ZsaNfpeo0Y1Oa11cCKIiVSYE6h89FEv/fHHp5ozJ/Rt5HPnnqNt27K0fr31Q86yZb5jA735Zt4PZDlTyqZNM1S0qFSq1B5VqbLBp11OSEiLev3CKS+9FPyqXu/2onfvR3TxxUv05JN5B+HW63yX+vVbleu1baNDh6TMzAN+P7qcOI+i9/pPm1Yy7GUj/UTIvMpDDz3nc/5t23Z6umFOOKv7oQibiXYF4LwdO3bYb94XXngh5LJVq1aVMUY33XSTI/um4bBukxs/3noi4fjx0nffWU8L/OAD6c8/pc8+kxo0sO40M0a6/XapRQvrNrxwOpQVK0qPPhpgXk4gkbv0qRi1zu8plaoLw+vEF0qVufI/MvUmyhhP6G0GOj5GMvHpVohQaZlM5aUyZX+P7N9aIDvyr1npTTL1J8iU2J6/9UpuC/26NH4715VzzWQKHM9ju57Ar01edct93AKV0pvyXiY/JVR9Ch+Ruad1/reZE2AVPCbT9dozKsA6Y0vu41N5afTrlM8SH5+p9u39x4KyQrhFKl06SeXKbdfFFy9Ro0bfq2nTKbr55nFq1uwLtWkzQrfeOka33/5fdegwRHffPVQ33fSxWrYcq1tu+Ug33zxO1ar9oXr1ftY55+yz91mlygbdeeco9ez5hGbN8r9y8IMP6uiRR55Wv3736KGH/k833PCJ7rjjP+rd+xG1bTtcvXr11EMPPafevR/Ro4/20RVXzLS3Xb3672rVarSaN/9cHTu+rn79Otq3bj788DMqXvyA19/v0UsvtdecOUbPPPOAevR4UkOG3KI5c4zGjbtQzz/fWR98UFfPP9/Z/uXfGKlo0cN67rl7NWTILapR4zede+4ujRhxjQYNaqUmTaapWLFDql9/nm68cYJat35XDz7YV7fc8pHatBmhcuW2Kz4+U6VLJ6lSpc0yxqNmzb7Qm29er1Gjmqhx4xmqX3+eHnjgBbVtO0xFihxRx46v+x2jOnUWq2DBDMXFZal58891ww2fqHHjGbr22kkqWjT4rfl5lXr1ftbLL9+pjh0H64orZmr48Ob2PqdMOVclSqSo6p1P2tMuG2ZUa6jRnXeO0uef+98uNXBgaw0f3lwtWnwoYzxq3nyyXwg1fnwtXXXV15owoabGjLlEt9/+XyUm7lezZr63eNatu1BXXvmdJkyoqW+/PXFF04wZxdSnz0MaOdL/ts4PPqirK6/81mf5vEqgc3LOHKMHH+wbdoB2/vkr7WP6wAMv2NO/+KKCypXbrs6dX434gOUnU955p1Ee54xHTz9tPdhk4kTnrhjs3v1ZlS27Q4899pS6dn1Rd97pezvjrFlx9ns3r9eyWLFDYe93woSaqlZtnaZMyfsplpUrb5IxstvPxx9/3OfYvPHGzX7rvPbabRo58uqw6jJjRnHNmBH8Stv33rtczzzTVc8910X33uvsU3SbN/9cxki3357/QfznzDG6777+qlNnkS666Fc1uvpzfRFg3NFnn71fzz57f8jtXH/9p2rQYLYuuWSB37nXt2+0e1rB0Q91BxPtCsB5K1eutN+8I0eODLlsw4YNZYxRgwYNwt5+ampq0LJnzx4aDgccPy6tXi3t3y/t3SutWCEtXChlZ59YxuOR3nhDqlZNGjJEer1TrYAd30sfKWCt9/77kjHKNAX1k2mm3z9dqZkzpW7drNvxChU68eH0+OO+H1adO0vPPSddc43v9MqVrXL55VY9atVysINXYrvv39K36KlvM8DxiURHNc9S4HiuAKtS9OsUtK7ZMhVWyVT6VabqIpkqS3znNx7p+7c0fznvbRbdG/i1OWdH3uvmFRiV2hK5AMtIJj7jJAKsFSfOg/h0mVv/dWaep2dSyX18zsIAK6cUK3ZI11//mVq1Gq1q1dZFvT6UM7g0GxjBtiH0D0LFih1SwYIZ9rKFCx8NuWzZsjtUqFC6ihY9rHPP3aU6dRarRo3fdOGFy5SYuF/GWFcWlS6dpISENBkjxcVl2fUoXTpJl1/+o2rWXK06dRbrxhsn6JprvlTjxjPUsuVYNWz4gy67bI7i4k78YFKz5mo1afKN6tRZ7HNFZZUqG1St2h9q3HiGunR5We3bv6mGDX+QMR7VrLlaF1+8RI0bz9Cll/6kCy5Yrtat39Wtt45R8+af6+abx6lOncWqW3ehHnnkaQ0ZcouGD2+mzp1f1cMPP6N3322gIUNu1pAht+jRR/uoVavRatNmhB2IfPddEY0dm/cTgps0mZbv16xQoWOqUWONrrzyW7Vr95ZathyrSy5ZYIfaZcrsVPXqv+vOO0fqgQf66d57X9Fzz92rZ57pqq5dX1T9+vMCbvfii5eoc+eBuueeQbrwwmUyRipXbrvuvfcVNW/+uQoWzNA55+xTkSKpqlVrlSpX3mS/DiVLJishIU233/5fPfdcF335ZTnNmWP0/POddc45+3TTTR/r/vtf+jtUtrZ7zz2D1Lv3I7r//pfUuPEM9erVU88884BuueWjv88J65wrUWKvT8BtjFS48FE99NBz6tu3kx5++Bl17Pi6atVaJWM8ql79dzVsOEvt2r2lPn26a+LEqpo5M0EffFBXHToM0bvvNtBjjz2lKlU2KDFxv667bqKuueZL9et3j95//zINH95MHTsO9jrvc457um69dYw6dXpNtWqtUseOg9WkyTd2XatVW6fLLpuj4sUP6IYbPtGtt45RyZLJKlgwwz7X834vetS27XC9+eb1mjixqv797wftq5qLFDmiBg1mq2LFLf7rPnrZibbizvsCbv+ii07cJj9gwF151ql69aDdl6gjwHIHE+0KwHk///yz/eZ9//33Qy579dVXyxijiy66KOzt52w7r0LDEVmprw0I2Dmu2+Pvt/mRI1KnTtYnzzPPWAnYaXbsmPW0x5yBpiXr1stdv+3Vqrte0paWjylpzu/69ltrbLG5c6WPP5Zee00aPShZV91RW6a/UXzfQmp/YRd98YU1/8svpcmTravcttZtpY3mfC0wV2t6+w/0/ffWdr7/JEVj+qzV6FHHNHWqNG+eAh6f6dOleRO2a6a5WZNNW00w9+gT01HfmVv0Tev/6csvpbFjpdGjpf/9T/rsxvc02bTVOHOv3rnrWw0aJD3/vDVw/bPPSk8/Lb3yijVm1YgRVm74xtBs9bp4pl6IH6g3ar+nQS8d0/PPWy9D797Say/73jqW+FQJPfus9O9/S089ZS3Tu7cVNjZsKF12mTWQ/nnnSf/8pxUw3n+/FUQ2ayZddZV04YXWmGLnn28FizVrSo0aWfMuOf+oypsknWe2qrb5Q6VLHleRIlKxYlLRolLZslJiolS6tFSkiP7uSIT5JfrKUb7H+NoBea8TLMBK3JX3uoXSZJq8JXPFf61bSHNvI5JXYBlZV2Hld5s5AVadz4Mus3OnlJJi3bY8dKhUp87p6rSeJSX3MTqLAywKJewS0QCL4nTJuXLPGOs26jvu+I/uvHOk7rxzlFq1Gq127d5Sq1aj7SuMKJRTLiW3ydzeTaZFb5mEg45s8/LLrbtNzkQEWO5gol0BOM87wBo9enTIZZs2baq8BnvPjQDrDPXXX4EDrMfjo12zk+a56UatLm/0VwljjVYeSEqK1L+/lSDlpGRBBDo+tjvvPPHpfMcd0qef+l7yZlfKI82caY2i7pDs41l676rCereR0TuNjMb98zzHth3UvHnWcfvllzwXPX7c+tfjkY7vPaBMU1CHTaL+MlV0pPC5OnbMOlRZWdLgx67zOb49ri2nua/O144dVnh54ICUlmYtu3+/9NeqfZrX/4OAr82yRb8rNdV6IGN6uvVwx8WLpd9+s8ac271b2j/wv9poLtDygleqX/db/LYx49t5jgZYaWnSjh3W3/DX18u1ve87OrZsrVXH/oO001RQ8WcL5WubKye+LUn6/JngV5OdTtnZ0tGj1uubUzIyrNcrKUnasME65uvXW3/71q0nypYtVtm40Xptfv3VumJ09WprsNf16615330nzZljnW4LF1rj00ydaoXNs2ZJkyadCKWnTZNWrrTe2itWWOvNmyf99JNVFi3yfy9PnLFQK1da2//5Z2npUmnBAmnTJumPP6xp8+ZZ/86aZb29v/3WegDC4sVWWbRI9gMSfvnF2vfcuVZdp0+3nvUxcaI0Zow0bpz19vn6a6t+U6da0z/7TJoyxfpbvvjC2s9331nTv/rKCupHj7YetvDJJ9Itt1jBsnfnoGNH6aWXrAC7UyfpxRetK2BzQu9evaxAul07qWdPK+R++mmr9Oljlaeekq6/3hqv7NJLrQD8vvukcuVkj/PlXQoVskLrcuWsUDwxUSpf3tpH69bWOHjnnCN16WLt/6qrrPWKFbPC88cek+rXt6bdfbdV/379rLqULGlNb9VKevhhqXt3K4yvXVtq2tR64MUrr1jrtGlj3Sbfvbv19zz8sPTAA9a/XbtaVwe3bm0ds5dfto7HU09JN90klSlj7eeCC6z6lyhh/X/hwtaxaNTICv+fftq6mvi666z5Vatax6ZqVevvv+QS64eCq692pgNZt671Oi5ZIm3ebJ2PKSnWe+/jj60fGCZODHN717wWNMDKeWJytWonli9SxNr/P/5h/Qhx/vn+2zznHOuHC2Os16psWet4duhgHY/Kla1zplgxa5mbb7a25f3QFe/9X3ONVK+e77nVpYv125n38omJ/nU57zypSRPraceVK5+YXry4NfRCw4bW+ey9Tq1a1uuaU664wqrD+edbf8ull1qvfcmS1jkRH38GhAphlho1rDbD47HOnyeesL4KeTxW21quXOD1zjvP+rdyZeu1qFDBmlakiFSqVPD9XXPNifdCtP/2UKViRet19p5Wu/aJNuCcc6xlChb0XaZxY6vtDLXtIkU8uuPKnSGXqVAh8PQLL7Tam0svPfGeikRJSLD+vfhiq03s0MF6f9atG3ydSy4J/FmQkGAdx6uusn4cffNN67Pywguttv5MHv9KIsByCxPtCsB53rcQjho1KuSy3EIYWwIGWE8Wjna1Tl5GhnW51aJFjmyu9SP+A93bjh+3eqLResTKF19Y37arV3c0HDstnnwy55ue1YP38t8pvk/DfGlMlzw3tzct8BVYyYeTwqvP7t1ScrIOpR9Swkvx9voDJ/fSyt0rHQuvSryaGFZ1przbO1/bXTHZutX7nd7NohJgnY0ee+8O+9ic17+UPBG4ohSIttdeaUHbcBbL+YEgLc36MWftWuvJwL//Lq1bZwXuSUnW0BFHj0a7togZmzZZvz65AAGWO/DJF4O8B3F/8cUXQy5brVo1MYh77AjU8R16e9loV+uMUWlweb78O2XjRmnXLr/Jby/2HcT9hdn98tyUxxN4EPd9R/flu1rfbfxOLcfeqCe/eVxpmWnKPJ4ZcNuv/vRKnsHSreNv9fn/r9d9nXcFJGVsXJevAGvapwMkSb3H3B1w/mUvV873cYh1h9IPqde0Hur4aTut2bMm2tUBIuK1V1vyGQYAQdAPdQc++WJUYmKijDHq0iX41Q/p6emKj4+XMUY9e/Z0ZL80HNEVqPP7xphu0a7WGeP1+a/z5f80W7Nnjc/x/WHzD2GtF+jczTie4Uidcm9395HdkqROX3QKuN9OI5pr4faF8ng8mrRmktpNaqdRS0aFfZVP+h9rAm43WPnms1ckSY/PCDB4e3+jrpM6O3IcAJzdBr1xJ59hABAE/VB34JMvRjVr1kx5Dc7uPVbWmDFjHNkvDUd0Ber8vjl/SLSrdcaYvWW2z7Ep/XrpaFcpJk35Y4ru++o+fbTyo7BDn85fdD5tHbN3lr5jb7Pj5x3t6VnZWZq0ZpLffq8be+0p7S95/Yp8BVjTJw2UJPX4pkfgAGtK11OqD4DYMGi2/8NaAAAW+qHuwCdfjBo8eLD9Bl69enXAZXr37i1jjOLi4pSUFOZYM3mg4YiugAHWwjejXa0zhsfj0VWjr5Lpb1To5UL6buN30a4S/rY2ee1p7Zgt3blUMzfNVLbHf2D+3PsdNH/QKe1r+8Ht+QuwPn9NkvTw1w/7zdt5eKf2H91/SvUBEBsGzR9EgAUAQdAPdQc++WJUUlKSihUrJmOM/vnPf/rN37p1q4oXLy5jjNq2bevYfmk4oitQ5/ithW9Fu1pnlPSsdM3YMEO/J0dpsHYE1ejdRvZ5+8KPL0Rsv60mtPJ5z3y44sNT2t6fB//MV4A144vBkqRuU7vROQUQFLfBA0Bw9EPdgU++GPbqq6/ab+IOHTpo9erVSk5O1rRp01SzZk0ZY5SYmKh169Y5tk8ajugK1DketmhYtKsFhGX/0f0avGCwxiwfo6zsrIjt9/lZz/u8Z95f9v4pbS85NTl/AdbXVsj81sK3fKZXeqOSE38egBhBgAUAwdEPdQc++WKYx+PRgw8+aL+Rc5fExERNnz7d0X3ScERXoM5x8w+aR7tawBnt6ZlPOxr6pqSlBA2rzht2nt+0b9d/I0lKzUhV2SFlZfobxQ2I0+wts5348wDEiNHLRhNgAUAQ9EPdgU8+F/jyyy/VokULlStXTgkJCapZs6YeffRRbdq0yfF90XBEV6AO8wVvXxDtagFntNKvl/Z5z1R9q+opbS9YgDV/3fcBp3uPxbbj0A7955f/aNFfi071zwIQY37e/rNP23HXZ3dFu0oAcMagH+oOBFhwFA1HdAXqHD867dFoVws4o/1r+r983jNPfffUKW0vWICVun9PwOm/7fnNob8EQCwjwAKA4OiHugMBFhxFwxFdld+sHPApZgCC23V4l/3eqfpWVSUdObWnsuY3wPrz4J8O/SUAYtmxrGPavH+zXXYf2R3tKgHAGYN+qDsQYMFRNBzRVeXNKoyPAZyEfUf3ac7WOdp3dN8pbytUgPWfX/5DgAUAAOAw+qHuQO8WjqLhiK7zR5zv0zEuP7R8tKsEuM7WA1sDBlhZhw9Kkgq+XJAACwAAwEH0Q92BAAuOouGIroHzBvp0jEcsHhHtKgGusy5lnV94VepZI/3dJl753pUEWAAAAA6iH+oOBFhwFA1HdA2aP8inYzxs0bBoVwlwnT9S/vALsEr+HwEWAADA6UI/1B0IsOAoGo7oGrxgsE/H+M2Fb0a7SoDrZBzPUIH+BYIGWO0ntdeFb1+oi0ZepNoja2v7we1RrjEAAMDZjX6oOxBgwVE0HNE19OehPp3moT8PjXaVAFf6ZPUnQQOspmOa+szbuG9jlGsLAABwdqMf6g4EWHAUDUd0vbnwTZ+O8eAFg6NdJcC1EvoFDrCuHnM1ARYAAICD6Ie6AwEWHEXDEV3DFg3z6RgPmj8o2lUCXCv3OFiZhw5Ikq4afZXP9E37NkW5pgAAAGc3+qHuQIAFR9FwRFfuKztqjagV7SoBrpU7wErdv0eS1GR0E5/pm/dvjnJNAQAAzm70Q92BAAuOouGIrnd/fdenY/z6/NejXSXAtcINsLbs3xLlmgIAAJzd6Ie6AwEWHEXDEV1pmWm68aMbZfobXT3mah04diDaVQJcK1iAlXv6mj1rolxTAACAsxv9UHcgwIKjaDiiL9uTrSMZR3Q8+3i0qwK4WrgB1k/bfopyTQEAAM5u9EPdgQALjqLhAABLuAHWvG3zolxTAACAsxv9UHcgwIKjaDgAwFKrFwEWAABAJNAPdQcCLDiKhgMALOEGWEt2LIlyTQEAAM5u9EPdgQALjqLhAABLsACr5/Se9rTKb1ZWtic7yjUFAAA4u9EPdQcCLDiKhgMALP4BVpIk6eCxg+rxTQ+1mdhGy3ctj3ItAQAAzn70Q92BAAuOouEAAItfgLVvd7SrBAAAEJPoh7oDARYcRcMBABYCLAAAgMigH+oOBFhwFA0HAFhyD9aeundXtKsEAAAQk+iHugMBFhxFwwEAFr8AK2VntKsEAAAQk+iHugMBFhxFwwEAFgIsAACAyKAf6g4EWHAUDQcAWHIHWJ79+6NdJQAAgJhEP9QdCLDgKBoOALAUf/5EeFX8eSMRYAEAAJwW9EPdgQALjqLhAACLd4BVjAALAADgtKEf6g4EWHAUDQcAWBKfyxVg7dsX7SoBAADEJPqh7kCABUfRcACAxS/A2rs32lUCAACISfRD3YEAC46i4QAACwEWAABAZNAPdQcCLDiKhgMALLmfQnhs91/RrhIAAEBMoh/qDgRYcBQNBwBYcgdYqTu3RrtKAAAAMYl+qDsQYMFRNBwAYCHAAgAAiAz6oe5AgAVH0XAAgMUvwDp2ONpVAgAAiEn0Q92BAAuOouEAAItfgJVBmwgAAHA60A91BwIsOIqGAwAsBFgAAACRQT/UHQiw4CgaDgCw1BpRiwALAAAgAuiHugMBFhxFwwEAFgIsAACAyKAf6g4EWHAUDQcAWAiwAAAAIoN+qDsQYMFRNBwAYCHAAgAAiAz6oe5AgAVH0XAAgIUACwAAIDLoh7oDARYcRcMBAJYSg0oQYAEAAEQA/VB3IMCCo2g4AMDiHV4RYAEAAJw+9EPdgQALjqLhAAALARYAAEBk0A91BwIsOIqGAwAsuQOso5lHo10lAACAmEQ/1B0IsOAoGg4AsJQcVNIOrxJeSYh2dQAAAGIW/VB3IMCCo2g4AMDiHWAVfqVwtKsDAAAQs+iHugMBFhxFwwEAllKvlyLAAgAAiAD6oe5AgAVH0XAAgIUACwAAIDLoh7oDARYcRcMBABYCLAAAgMigH+oOBFhwFA0HAFgIsAAAACKDfqg7EGDBUTQcAGDJCa9ySmoGbSIAAMDpQD/UHQiw4CgaDgCwEGABAABEBv1QdyDAgqNoOADAQoAFAAAQGfRD3YEAC46i4QAACwEWAABAZNAPdQcCLDiKhgMALARYAAAAkUE/1B0IsOAoGg4AsBBgAQAARAb9UHcgwIKjaDgAwFJrRC0CLAAAgAigH+oOBFhwFA0HAFgIsAAAACKDfqg7EGDBUTQcAGAhwAIAAIgM+qHuQIAFR9FwAICFAAsAACAy6Ie6AwEWHEXDAQAWAiwAAIDIoB/qDgRYcBQNBwBYeAohAABAZNAPdQcCLDiKhgMALARYAAAAkUE/1B0IsOAoGg4AsBBgAQAARAb9UHcgwIKjaDgAwJI7wMo8nhntKgEAAMQk+qHuQIAFR9FwAIClzOAydngVPyA+2tUBAACIWfRD3YEAC46i4QAAi3eAFTcgLtrVAQAAiFn0Q92BAAuOouEAAEvZIWUJsAAAACKAfqg7EGDBUTQcAGAhwAIAAIgM+qHuQIAFR9FwAICFAAsAACAy6Ie6AwEWHEXDAQCW3E8hzMrOinaVAAAAYhL9UHcgwIKjaDgAwJI7wErNoE0EAAA4HeiHugMBFhxFwwEAFgIsAACAyKAf6g4EWHAUDQcAWAiwAAAAIoN+qDsQYMFRNBwAYCHAAgAAiAz6oe5AgAVH0XAAgIUACwAAIDLoh7oDARYcRcMBAJZSr5ciwAIAAIgA+qHuQIAFR9FwAICl1ohaBFgAAAARQD/UHQiw4CgaDgCwEGABAABEBv1QdyDAgqNoOADAQoAFAAAQGfRD3YEAC46i4QAACwEWAABAZNAPdQcCLDiKhgMALARYAAAAkUE/1B0IsOAoGg4AsHiHVwRYAAAApw/9UHcgwIKjaDgAwEKABQAAEBn0Q92BAAuOouEAAAsBFgAAQGTQD3UHAiw4ioYDACy5A6xsT3a0qwQAABCT6Ie6AwEWHEXDAQCWCkMr+ARYAAAAOD3oh7oD36jhKBoOALDkDrA8Hk+0qwQAABCT6Ie6AwEWHEXDAQCWim9UJMACAACIAPqh7kCABUfRcACAhQALAAAgMuiHugMBFhxFwwEAFgIsAACAyKAf6g4EWHAUDQcAWHI/hfBo5tFoVwkAACAm0Q91BwIsOIqGAwAsuQOsw+mHo10lAACAmEQ/1B0IsOAoGg4AsBBgAQAARAb9UHcgwIKjaDgAwEKABQAAEBn0Q92BAAuOouEAAAsBFgAAQGTQD3UHAiw4ioYDACwEWAAAAJFBP9TDhfy5AAAgAElEQVQdCLDgKBoOALDUGlHLJ8BKzaBNBAAAOB3oh7oDARYcRcMBABYCLAAAgMigH+oOBFhwFA0HAFgIsAAAACKDfqg7EGDBUTQcAGAhwAIAAIgM+qHuQIAFR9FwAICFAAsAACAy6Ie6AwEWHEXDAQAWAiwAAIDIoB/qDgRYcBQNBwBYvMMr09/owLED0a4SAABATKIf6g4EWHAUDQcAWAiwAAAAIoN+qDsQYMFRNBwAYMkdYB08djDaVQIAAIhJ9EPdgQALjqLhAABL1beq+gRYmcczo10lAACAmEQ/1B0IsGLQoUOHNH78eHXt2lWXXHKJEhMTVahQIVWsWFGtWrXSxx9/rKysrNOybxoOALDkDrAyjmdEu0oAAAAxiX6oOxBgxZidO3cqISHBfvMGK02aNNGePXsc3z8NBwBYqr1VjQALAAAgAuiHugMBVozZunWrjDFKTEzUQw89pKlTp+rPP//Uvn37tHjxYt199932G7tx48bKzs52dP80HABgIcACAACIDPqh7kCAFWN27typvn376uDB4IMFP/roo/abe+LEiY7un4YDACwEWAAAAJFBP9QdCLBcKCUlRXFxcTLGqGvXro5um4YDACwEWAAAAJFBP9QdCLBcqmLFijLG6JZbbnF0uzQcAGDxDq9Mf6P9R/dHu0oAAAAxiX6oOxBguVBmZqY90Hu7du0c3TYNBwBYcgdY+47ui3aVAAAAYhL9UHcgwHKhL7/80n5zDx061NFt03AAgIUACwAAIDLoh7oDAZbLZGRkqE6dOjLGqFixYkpKSsr3NlJTU4OWPXv20HAAgAiwAAAAIoUAyx0IsFymR48e9ht78ODBJ7WNnPXzKjQcANyMAAsAACAyCLDcgQArytLS0vTHH3+cUvF4PGHta/To0fab+uabb1Z2dvZJ1ZkACwDyljvAOnjsYLSrBAAAEJMIsNyBACvK5syZE3YgFKwcOXIkz/1Mnz5dBQsWlDFGl112mQ4ePPmOFLcQAkDeao2o5RNgpWbQJgIAAJwOBFjuQIAVZZEIsBYsWKBixYrJGKMLLrjgpMa9ChcNBwBYCLAAAAAig36oOxBgxbiVK1eqVKlSMsaoatWq2rZt22ndHw0HAFgIsAAAACKDfqg7EGDFsA0bNqhChQoyxqhcuXJat27dad8nDQcAWAiwAAAAIoN+qDsQYMWov/76S+edd56MMSpZsqSWL18ekf3ScACAhQALAAAgMuiHugMBVgzau3ev6tSpI2OMihYtqvnz50ds3zQcAGDJ/RTCQ+mHol0lAACAmEQ/1B0IsGJMamqqGjduLGOM4uPjNWnSJB05ciRoOXr0qOP7p+EAAP8AKyUtJdpVAgAAiEn0Q92BACvG5Pephtdee62j+6fhAAALARYAAEBk0A91BwKsGEOABQBnhtwB1oFjB6JdJQAAgJhEP9QdCLDgKBoOALDUGF7DJ8A6munsLdsAAACw0A91BwIsOIqGAwAsBFgAAACRQT/UHQiw4CgaDgCw5A6w0jLTol0lAACAmEQ/1B0IsOAoGg4AsNQcXpMACwAAIALoh7oDARYcRcMBABYCLAAAgMigH+oOBFhwFA0HAFhyP4XwcPrhaFcJAAAgJtEPdQcCLDiKhgMALLkDrOTU5GhXCQAAICbRD3UHAiw4ioYDACwEWAAAAJFBP9QdCLDgKBoOALDkDrCOZByJdpUAAABiEv1QdyDAgqNoOADAMnDeQDu86jC5Q7SrAwAAELPoh7oDARYcRcMBABaPx6OZm2bq87WfKys7K9rVAQAAiFn0Q92BAAuOouEAAAAAAEQS/VB3IMCCo2g4AAAAAACRRD/UHQiw4CgaDgAAAABAJNEPdQcCLDiKhgMAAAAAEEn0Q92BAAuOouEAAAAAAEQS/VB3IMCCo2g4AAAAAACRRD/UHQiw4CgaDgAAAABAJNEPdQcCLDiKhgMAAAAAEEn0Q92BAAuOouEAAAAAAEQS/VB3IMCCo2g4AAAAAACRRD/UHQiw4CgaDgAAAABAJNEPdQcCLDiKhgMAAAAAEEn0Q92BAAuOouEAAAAAAEQS/VB3IMCCo2g4AAAAAACRRD/UHQiw4CgaDgAAAABAJNEPdQcCLDiKhgMAAAAAEEn0Q92BAAuOouEAAAAAAEQS/VB3IMCCo2g4AAAAAACRRD/UHQiw4CgaDgAAAABAJNEPdQcCLDiKhgMAAAAAEEn0Q92BAAuOouEAAAAAAEQS/VB3IMCCo2g4AAAAAACRRD/UHQiw4CgaDgAAAABAJNEPdQcCLDiKhgMAAAAAEEn0Q92BAAuOouEAAAAAAEQS/VB3IMCCo2g4AAAAAACRRD/UHQiw4CgaDgAAAABAJNEPdQcCLDjqyJEjdsOxZ88epaamUigUCoVCoVAoFAqFctrKnj177H7okSNHot0txmlCgAVHeTccFAqFQqFQKBQKhUKhRLLs2bMn2t1inCYEWHAUARaFQqFQKBQKhUKhUKJVCLBiFwEWHJWdna09e/Zoz549OnLkSNQvJXV78Q4UuaWT4l04NyjBCucGJVjh3KAEK5wblGCFc4MSrDh9bhw5csTuh2ZnZ0e7W4zThAALiGGpqQxmiMA4NxAM5waC4dxAMJwbCIZzA8FwbuBkEGABMYwPBgTDuYFgODcQDOcGguHcQDCcGwiGcwMngwALiGF8MCAYzg0Ew7mBYDg3EAznBoLh3EAwnBs4GQRYQAzjgwHBcG4gGM4NBMO5gWA4NxAM5waC4dzAySDAAmIYHwwIhnMDwXBuIBjODQTDuYFgODcQDOcGTgYBFhDD+GBAMJwbCIZzA8FwbiAYzg0Ew7mBYDg3cDIIsIAYxgcDguHcQDCcGwiGcwPBcG4gGM4NBMO5gZNBgAUAAAAAAIAzGgEWAAAAAAAAzmgEWAAAAAAAADijEWABAAAAAADgjEaABQAAAAAAgDMaARYAAAAAAADOaARYAAAAAAAAOKMRYAEAAAAAAOCMRoAFAAAAAACAMxoBFhBlGzduVJEiRWSMkTFGH3zwQcjlDx8+rAEDBqh+/fpKTExUyZIldeWVV2rYsGHKzMwMa5/Lly9Xly5dVK1aNSUkJKhSpUpq06aN5syZE9b6Ho9HY8eOVfPmzVWmTBkVLVpUtWvX1r///W8lJSWFtQ2csHXrVvv1D1WKFy8ecjucG+7w3Xff6Z577lGNGjVUpEgRnXvuuapfv766d++ub7/9Nuh6nB+x5bzzzgur3cjrs4XzIrZt3bpVvXv3tl/fQoUKqWLFimrZsqU++ugjZWdnh1w/KSlJ//73v1W7dm0VLVpUZcqUUfPmzTV27Fh5PJ6w6jBnzhy1adNGlSpVUkJCgqpVq6YuXbpo+fLlYa2fmZmpYcOG6corr1TJkiWVmJio+vXra8CAATp8+HBY24C/bdu2qVevXrr44otVrFgxlShRQpdffrlefvnlsI4rbcfZx+Px6Pfff9eHH36oHj166IorrlDhwoVljFFCQkLY24mVdmHTpk167LHHVLNmTSUkJKh8+fJq0aKFvvrqq7DWR3QQYAFRdtNNN4XVyZCkLVu2qFatWkE7KI0aNdL+/ftD7m/06NEqVKhQwPULFCig/v37h1w/PT1dLVu2DFqH8uXLa+nSpSdzKFzLiQCLcyP2paamqk2bNiHPkWuvvTbgupwfsSe/Adavv/7qtw3Oi9j21VdfqXjx4iHPi2bNmunQoUMB11+6dKnKly8fdN2WLVsqPT09ZB369++vAgUKBFy/UKFCGj16dMj19+/fr0aNGgWtw/nnn68tW7ac9DFyq8mTJ6to0aJBj2uNGjW0bt26oOvTdpydQn3fDDfAipV2Yfr06UpMTAy6jW7duoUdxiGyCLCAKPr4449ljFHNmjXtBjNYgJWRkaF69erJGKOiRYtq5MiR2rFjh7Zu3ar+/fsrLi5OxhjdfPPNQff3008/KT4+XsYYXX755Zo9e7aSk5O1aNEi3XDDDXYdJkyYEHQb3bt3t5fr2bOn1q9fr927d2v8+PE699xzZYxRpUqVlJKScqqHxzW8v1DMmDFDR44cCVhSU1MDrs+5EfsyMjJ03XXXyRij+Ph49ejRQ4sWLVJycrJ2796tH3/8UT179lSbNm0Crsv5EXvS0tKCthVHjhzR4cOHVaVKFRljVKdOHb/1OS9i25YtW+yru8uXL6///e9/Wr9+vZKSkjR//nzdeeed9nHv0qWL3/opKSmqVKmSjDEqU6aMxo8fr927d2vdunXq2bOnvW737t2D1mHChAn2cjfccIMWL16s5ORkzZ49W5dffrndnv30009Bt3HzzTfLGKO4uDj1799fW7du1Y4dOzRy5Ej776tXr54yMjIcOW5usGjRIjs4qlKliiZMmKDdu3dr586dGjt2rMqVK2eHAEeOHPFbn7bj7OX9fbNKlSq666671KxZs7ADrFhpF9atW2eHVzVq1NDUqVOVnJysVatWqX379nb9Bg4cmPdBRcQRYAFRsn//fpUvX17x8fGaMmVKngHWyJEj7WUmTZrkN3/w4MH2/G+++SbgNq644goZY1S5cmUdOHDAZ15GRoYuu+wyGWNUtWrVgL+erF692v7FpEePHn7zFy5caH9xefrpp8M4CpB8v1CEe9m8N86N2DdgwAAZY1SwYEF9/fXX+VqX88Od5s6da7+ur732mt98zovY9txzz9kdvF9++SXgMrfddpt9xUPuq7D69Oljr79o0SK/dR977DEZY10ps3r1ar/5x44dU9WqVe2AIvctZQcOHFDlypVljNGVV14ZsH7Tpk2zz8EhQ4b4zZ84caI9f9SoUUGPBXw1bdrUft0DXWX166+/2u/Ll156yW8+bcfZ6/Dhw5oyZYp2795tT3vppZfCDrBipV1o27atjLHubMh9pZbH41GrVq3s+dyKeuYhwAKipFu3bjLG6Mknn/QJMIIFWHXr1pUxRg0aNAg4PzMz0/7V7LbbbvOb/8svv9j7GDFiRMBteAdpkydP9pvfo0cPGWNUuHBhJScnB9xGzq+6pUqVCnsMBLc71QCLcyO27d271/5F8Yknnsj3+pwf7pTzGVOgQAFt377dbz7nRWzL6YDVrl076DKffPKJ/fqsXLnSnp6ZmalSpUrJGKO77ror4LrJycn2VTw9e/b0mz9p0iR721OnTg24jeHDh9vLBLrVK+dvKF++fNDXPueKjXr16gX9O3HCX3/9ZR/zhx56KOhy7dq1kzFG5cqV8xsnjbYjtoQbYMVKu5CUlGSHm7179w64/sqVK+06DB06NOAyiB4CLCAK5s+frwIFCqhSpUo6dOhQngHW5s2b7fmhLmd96KGHZIxRkSJFlJaW5jPvhRdesLexc+fOgOtnZmaqWLFiMsbovvvu85tfrVo1GRP6svDx48fb+/nxxx+DLocTTiXA4tyIfcOGDbODiD///DNf63J+uNOxY8dUsmRJGWN0/fXX+83nvIh9OePlBbp9NMdnn30W8DWcPXu2PT3ULVw5Y3hWr17db16XLl1kjFGxYsWUlZUVcP0dO3bY+3nxxRd95qWlpSkhIUHGhL4d6dVXX7W3sXnz5qDLweJ99cqYMWOCLvfGG2/Yy82bN8+eTtsRe8INsGKlXRgzZow97+effw66jQsuuEDGGDVv3jzoMogOAiwgwjIzM+1frz799FNJyjPAmjx5sj1/1qxZQbf9/vvv28vlHrA351aBqlWrhqxfzqXll1xyic/0lJQUe9v9+vULuv7GjRvt5d54442Q+4IlUIAV7ngenBuxr0WLFjLGutzeW1ZWVp4DjHJ+uJP3LRR8prhTTgcuPj5eq1atCrhMzm00uV+boUOH2sd806ZNQffRt29fe7m9e/f6zMsZI+maa64JWc+ccdpat27tM33p0qX2tkMN6PzDDz/Yy33++ech9wXfUCfUU2u9r84bNmyYPZ22I/aEG2DFSruQM1ZXfHx8yMHmO3fuLGOMzjnnnJB1ReQRYAERlvOl8sYbb7Sn5RVghfsL46xZs+zlxo8f7zMv55eEZs2ahazfvffea3+QeV82vmDBgrB+tcvMzLQvzQ11eTpO8H7969WrZz81KiEhQQ0aNFC/fv20Z8+egOtybsS+smXL2scsLS1N/fr1U61atRQXF6eCBQuqTp066tu3b8AnPnF+uFPr1q1ljPUrd6DHiXNexL79+/fbbUf16tX1ySefaNeuXUpLS9OqVat0//33yxhrjBfvK2ykE1fPxMXFBb1KQrKeIpfzGi5YsMCenp2drcKFC8uYwAPEe7vmmmtkjNEFF1zgM907aJk9e3bQ9Tdt2mQv9+qrr4bcF6Svv/46rPed9xVYDz/8sD2dtiP2hBtgxUq7kHOFWLVq1ULWoV+/fvY2duzYEXJZRBYBFhBBmzZtUpEiRVS4cGGfgTPzCrCefPJJe36gJ8LkWL16tb1c7nEFcu5bb9u2bcg6PvXUU/Y2vAd19R6PYNq0aSG3kfP0l2D3yMNXqMca55TSpUvru+++81uXcyO2HT161D62vXr1sq/eDFSqVaumtWvX+qzP+eE+KSkp9hgknTp1CrgM54U7rF27VhdffHHA9iI+Pl7t27cPeHVWzthBZcqUCbl97zDEezybgwcP2tP79OkTchs5tzqWLl3aZ7r3ODi//fZb0PUPHz5sLxdsPBuc4N2x9w6mcvN+Epv3021pO2JPuAFWrLQLOeNjNWrUKGQd3n77bXsbwa5iRXQQYAERlPPY1+eff95nel4BlvejgkP96rFhwwZ7udxPncrp0HTu3DlkHZ9//nl7G7t27bKnez/29ocffgi5jZwniNxyyy0hl4Plzz//VIsWLfTRRx/pt99+06FDh5SWlqbly5erV69e9qOmixYtqmXLlvmsy7kR23bv3m0f25xfLtu2bavVq1crIyND27dv10svvWT/ynz++ecrNTXVXp/zw328v3QHu0WI88I91q1bpyZNmgQMsf7xj39oypQpfuvkfFepUqVKyG1///339rY++eQTe/rOnTvt6X379g25jU6dOtntm7eBAwfa29i4cWPQ9TMzM8MKZHBC/fr17WO+YcMGv/nLly+3P1Nyv+9oO2JPuAFWrLQLF154oYwxatq0acg6vPfee/Y2Fi5cGHJZRBYBFhAhOR+4NWrU0NGjR33m5SfAOn78eNB9eN/vn3twzZwvDPfee2/Ienrfu+49uKb3F4ZQ4x5IJ+5dDzW4JsL31Vdf2Y+Lvvbaa33mcW7ENu8vfMYY3XDDDQHHvfK+3eOtt96yp3N+uE/jxo1ljFHFihWDvuacF+7Qv39/FShQQOXLl9d7772nP//8U/v27dOiRYvsK2wKFCjg02ZIJzqqeY1R5D3OjPegzt7tVqgxiqQT48wUKlTIZ7p3RzXUeDtZWVn2cqEGdcYJ33zzjX3MqlWrpk8//VRJSUnatWuXPvzwQ5UvX14FChSwB8tu0aKFvS5tR+zJb4B1trcLOQFWXuNweY/jFmqwd0QeARYQAQcOHFCFChVkjNHXX3/tNz8/txB6X12RWziXbLdr1y5kXcO5ZPubb74JuQ0u2XZehw4dAn6R49yIbd6X3BvjO56Et4yMDJUpU0bG+D4xh/PDXdavX28f66eeeirocpwXse/111+XMdY4aN5DFnh74IEHZIw1po33LTI5twqVLVs25D7CuVXo6aefDrmNcG4VWrNmTdD1uYXw5AwfPtznKivvUqBAAQ0dOtT+TOnYsaO9Hm1H7MnvLYRne7uQcwvhFVdcEbIO3EJ45iLAAiIg5wP/9ttvDzg/P4O4b9myJeh+vB9xG2zQzLweB5vziNvChQsHHTRz7NixQdfPyspi0MzTwHvgyunTp9vTOTdim/egp3FxcSGfmNOqVSsZ4zs+BeeHu3gPOrtixYqgy3FexLbMzEw7JOjWrVvQ5bZv3x4w8MwZrDk+Pj7kbWLej6MPNljzfffdF7KuzZo1kzGhB2v+8ccfg66/efNmezkGcc+f5cuXq0uXLqpevboKFy6ssmXLqnXr1po7d65SU1PtK7+9wwbajtiT30Hcz/Z2IWcQ9+rVq4eswwsvvGBvg0HczywEWEAE3HHHHQF/5cqrbN26VZI0adIke1qop254P/lj6dKlPvNyOrd5PXUj58kf9erV85menJxsb/uFF14Iur73AKFDhw7N48ggXN5jCnhfks25EfsuueQSv2AqkECX3HN+uIfH41GNGjVkjP9j53PjvIhtK1assI/Zf//735DLlitXTsYYtWzZ0p42ZMgQe/1QT5rzDkxTUlJ85uU8cCKvJ81VrVpVxhjddtttPtN/+eUXe9uhnjTn/bS7yZMnh9wXwjdv3jz7uE6cONGeTtsRe8INsGKlXejRo4eMMSpYsKAyMjKCbiPnKZiJiYkh64rII8ACIuBUAyzvXxJyD4bpLWdsgoSEBKWlpfnM8/4lwXswTG9ZWVkqVqyYjAn8iNtq1arJmNCDYXqPTRDqyw3yZ9y4cfZx9b4Ci3Mj9nXt2lXGWLd1HDt2LOhyLVu2lDG+l/dzfriHd4dzyJAhIZflvIhtCxcuDDvAKl++vIwxatWqlT3N++oZ70GYc8sZEydQEJFz9Uzx4sWDXq2xa9euoEFEWlqaPQZTqMHZvcfECdWpRv48/fTTMsa6+ik5OdmeTtsRe8INsGKlXfC+QizU4Ow5Y2XlFbYh8giwgAjYsmWLVqxYEbRMnz7dbkwHDBhgT/f+ZaBOnToyxqhhw4YB95GVlWWPs+X9RTTHkiVL7H28/fbbAbcxdepUe5lJkyb5zX/sscfsLzS5f1XJcdddd8kYo5IlSyozMzOcw4MwtG3b1n5tvMfAkjg3Yt2XX35pH/s5c+YEXCY9PV2lS5eWMUY33XSTzzzOD3fI6TDGxcWFdbsD50Xs2rJli33cQw1s7n0L4aOPPmpPz8zMVMmSJWWMUZs2bQKum5KSYt8O1KNHD7/5EydOtLcdaOxPSRoxYoS9zC+//OI3/9Zbb5UxRhUqVAja2W3QoIGMMapbt27QvxP5s3//fnv8qw4dOvjNp+2ILeEGWLHSLiQlJdm3xwYbK9J7DLe8fhBC5BFgAWeAvMbAknwHE/z888/95g8dOtSeP23atIDbaNSokYyxniBy8OBBn3mZmZl2g1+lSpWAV3qsWrXKbvT/9a9/+c1fsmSJPd5Anz59wvjLISnPzubEiRPt43799df7zefciG0ZGRmqWbOmjLGemuM9FkiOV155xX6NR48e7TOP8yP2paen22Mehfu0Lc6L2JbTZhQvXlwbNmwIuEzOIO7GGH355Zc+83IG0I6Li9OSJUv81u3Zs6eMsa4MDTTA8bFjx+wnwDVo0MAvQDh48KA9P9hgyt6DQb/xxht+8ydPnmzPHzlyZNBjgfBlZWXZg3UXLVo04JPeaDtiS7gBlhQ77ULOIPGJiYnatm2b3/zWrVvLGOshGElJSQG3geghwALOAOEEWBkZGapXr579peI///mPdu7cqW3btmnAgAGKj4/Ps/Py008/2cs1aNBAc+bMUUpKipYsWaIbb7zRrkPuATe95QziaIzR448/rg0bNigpKUkTJkywf7GrVKlS0F/E4K9s2bJq166dxo8fr7Vr12rv3r1KTk7W/Pnz1b17d/tLWvHixbVy5Uq/9Tk3Yt+UKVPs8+DWW2/VwoULtX//fv3xxx/q06ePPa9Ro0Z+Xwg5P2Kf9xf2cePGhbUO50Vs875NpmLFiho9erS2b9+u/fv3a/HixWrfvr09v0GDBn7BeEpKiipVqiRjrNuSJ0yYoKSkJG3YsEGPP/64vW6oK7y8B1y+8cYbtWTJEqWkpOjHH3+0A4r4+Hj99NNPQbeRcztSfHy8Xn75ZW3btk07d+7UqFGjVLRoURljjZEUaiwb+Nq2bZsaNmyoUaNGaenSpUpOTtb27ds1adIkNWzY0A4gcv8YkoO24+y2du1aLVq0yC7dunWTMdbVbN7TFy1a5PfgmFhpF9at+//27t+lrTUM4LgFf1RTQhEsQUQwgx0kUKiLQ3DoECfBXRx0cewQ6NClDm4iiIhDwf/Aye51Fx2cOri4ijo4iC4+dxBDa5MovdzL0/D5wLt5opy8HJMv73nPj3j16lV0dXXF2NhY7O3txdnZWRwfH//y1O/V1dU/O8n8pwQsSOA5ASvi/raAcrnc+NnH4/3793F5edn2d339+jV6enqaHv/ixYv48uVL2+Nvbm4ae+00G2/evPltw07ae1iS3W4MDw/H/v5+y9cwNzrfxsZGdHd3tzy/7969a7maz/zobLOzs9HVdR+52z3a/jHzorN9/vy5EbdbjYmJiTg9PW16/MHBQWOPrGZjZmam7ZNRI+5Xd7T6G3p6elpGkgcXFxeN1TrNRrlcbvs0PH7382fOZqNQKDz5vrh2/L2mp6ef/Mz5MB724v1Zp1wXvn371ohYzcbi4mLc3d09eT75/wlYkMBzA1ZExNXVVaysrESlUolCoRDFYjEmJydjfX392ff4Hx0dxfz8fIyMjERvb2+USqWYm5trub/OY3d3d7GzsxPVajUGBwejv78/xsfHo16vW2r7B3Z3d+Pjx48xNTUVo6OjMTAw0HhfarVabG5uxtXV1ZOvY250vsPDw1hYWGg89vz169dRrVZja2vryRUI5kdnOj8/b3wJbLbZ8VPMi852cHAQS0tL8fbt2ygUCtHd3R1DQ0Px4cOH2N7ebvtgiIj7/WLq9XqMj49Hf39/DA4ORrVajZ2dnWd/ufv+/XvMzc1FqVSK3t7eGBkZifn5+Tg6OnrW8be3t7G+vh6Tk5NRLBajUChEpVKJlZWVZ/1v5FfX19extrYWMzMzMTo6Gi9fvoxisRiVSiU+ffrUMmg+5trxd/q3ASuic64LJycnsby8HGNjY9HX1xdDQ0NRq9V+u6WaXAQsAAAAAFITsAAAAABITcACAAAAIDUBCzbCpAMAAAGoSURBVAAAAIDUBCwAAAAAUhOwAAAAAEhNwAIAAAAgNQELAAAAgNQELAAAAABSE7AAAAAASE3AAgAAACA1AQsAAACA1AQsAAAAAFITsAAAAABITcACAAAAIDUBCwAAAIDUBCwAAAAAUhOwAAAAAEhNwAIAAAAgNQELAAAAgNQELAAAAABSE7AAAAAASE3AAgAAACA1AQsAAACA1AQsAAAAAFITsAAAAABITcACAAAAIDUBCwAAAIDUBCwAAAAAUhOwAAAAAEhNwAIAAAAgNQELAAAAgNQELAAAAABSE7AAAAAASE3AAgAAACA1AQsAAACA1AQsAAAAAFITsAAAAABITcACAAAAIDUBCwAAAIDUBCwAAAAAUhOwAAAAAEhNwAIAAAAgNQELAAAAgNQELAAAAABSE7AAAAAASE3AAgAAACA1AQsAAACA1AQsAAAAAFITsAAAAABITcACAAAAIDUBCwAAAIDUBCwAAAAAUhOwAAAAAEhNwAIAAAAgNQELAAAAgNQELAAAAABSE7AAAAAASE3AAgAAACA1AQsAAACA1AQsAAAAAFL7B4n3BsDlVlIaAAAAAElFTkSuQmCC\" width=\"640\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support. ' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option);\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
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sLKystCtWzfF/aSf48iRIzU9V2RkpBjq2Ksg7QSehIEjR460+p0UFRWF48ePW2zD/fv3zeYlkhYPDw8sXrxYU4C1a9cu2bxtlorSDxN6X+M5Q4AlfbwgQ/xNzZ49W6zv1Vdftbs+a3799VfZ3/uDBw9Ut+3SpYu47Zw5czTX+yzP4UpPBwZYRCbat28vnsQHDx5s8/6PHj2SXWR++eWXZttIv8TffPNN8aKncePGmDhxIhITE/H666/LVjnSEsgkJyejePHi4nZBQUHo168fZs+ejbFjx4o3T6GhobJhQXoEWAMHDhR7rtWpUwdjx47FnDlz8Oabb4o9aAThSXdntd4Fa9asgSAI8PLyQuPGjTF8+HDMnDkTc+fOxTvvvIPnn39edvHx/fffq34OegVY0sl9hwwZUuB6jKQXnV26dBEn465UqRJGjx6N2bNn46233kJoaKjsczx58qTFOo2ThhsvIocMGYLZs2dj+vTp6NSpEzw9PcXHLfUqvHz5MsqXLy9u6+7ujqZNm2LcuHGYO3cuxo8fj/bt24vPJz12Vq9ejYULF8rarjT5qHQVIUAeYBm7vLu7u4urD82YMQPdunWT3ZjpeazY6syZM2KIER4e7pChwi1atBDbvnHjRt3rt8Y0wDIYDKhVq5b4b2oLVWgJsAwGg2y4tSAIaNasGSZPnowZM2aga9euYk8HQRBQpUoV1blICnIule4zYsQIeHt7w93dHR06dMC0adMwffp0NGzYUNa+H3/8EXv27IGHh4e4rXEJd9Ntd+3apfq+Gn9cKF++PHr06IGJEydi3rx5mDp1Krp16yb7O27QoIHFla3sDbCMf6+WyoIFC2Q3wEo3Qf/73/9QsmRJcZuwsDAMGDAAiYmJSExMRK9evWQBTocOHVS/A/766y9ZmFiiRAkMHjwYc+bMwciRI1GxYkXxHCe9OStogJWdnS07N27evLlA9UhJj6/hw4fDy8sLgiCgUaNG4kIMXbt2Ff9dEAQ8//zzePTokWqd0nm6BOFJiBwfHy+ek+vVqyc+5ubmhs8//1y1rh9++EEWwvr7+6Nr166YPHmy+D43atRInGPH6P79+1i4cKGsh2h0dLTicfPf//5X9pzSYGjQoEEQBAGBgYHo06cPEhMTMWHCBDRp0gSpqaniPn379oUgCAgODka7du2QkJCAOXPmIDExEW+++SbKli0r1lm8eHFcvnzZjk+t4O3Mz89Hx44dxf08PT3Rrl07TJo0CXPnzsWIESPE49b4etTmLHr8+DGaNGki+/5t164dpk+fjqlTp6JVq1binJyjR4+2+PedmpoqO59ERUXh7bffxqxZszB37lyMGTMG7dq1Q2BgIATBeoClxzWeWoBlPLYWLlyI1q1bi9v06tXL7NgCIP63cbvQ0FDF41BpLlrp35G9AdadO3dkn+2HH35oV31aDBw4UHw+axPHL1q0SPa3qvaDvMFgQNeuXcVtP/nkE0c0nUg3DLCIJPLz82UX65YuAi155ZVXxDreffdds8elX+LGC8gdO3ZYrFNLICP94q9bty6uXbtmts2SJUvEi1xrX+K2BFiCIMDHx0fx19/k5GTZxeb+/fsV6/vxxx+xZs0ai73evvzyS/FmKCoqSrU3kV4B1syZM2U3BvPnz7dryIL0ptL4GYwaNcpsGMnt27dlF7L169dXvCi8deuWOCeTl5cXVq1apfienDx5EtHR0RZvsvPz89GgQQNxm5iYGNVhh5mZmVi9ejVOnDhh9pitc2CZ9sQpXbq02fBEU3oeKwVhvLkSBAHVq1e3aainFrt37xbr9/Lywn/+8x9d67fGNMACgO3bt4v/Vq1aNcX3U0uA9cEHH4jb+Pn5KZ77Tpw4IVvV8/XXX1esqyDnUtN9SpYsqTg5/ZgxY2R/fxUqVECpUqUUj834+Hhx22bNmqk+96RJk/Dnn3+qPn779m3Z94elG4nCWIVQ+h7ExMSYLViQlZWF2NhYcZuZM2cqDkW6fPmybDi20nCWx48fo3bt2uI2rVq1MusRlZubi2HDhpl9h9kzB1bz5s1lQcjXX39t17lCeny5ubnB09NT8Xvx2LFjsu9Ftd57+/btE8OkiIgI1e/Pr7/+WgymgoKCcOvWLbNtrly5IvuRq3Pnzqq9v65fv664ullB5sAy7e3XsGFDq8H/ihUrsH37dtUhlrm5uZg4caJY5xtvvKGpLXq3MzExUdy+cePGsnDL6PHjx5g8ebK4XYMGDRTrkk4KHhwcrNj7+ocffoC/v7/s+Ff6+5YGjUOGDFENw3Nzc/HVV1/h9OnTZo/pfY2npee/o+fAkr6eggRYOTk5uHDhAtauXYtKlSqJdVWuXNkhUwpIZWZmioGjpffZ6O7du7IpD6pUqYKkpCQkJycjKysLV69exfbt22XXmn369Hlm5/ejpwcDLCKJK1euyL7c1H4ls0Z64dC6dWuzx01voLQMcbIWyBw5ckR8PCgoyOKQhOHDh2v6Erc1wFq2bJnqc0onxo+Pj7f0Uq0yhnCCICjOUWPaNnsCrLS0NJQpU8bsJrljx46YNWsWduzYYXGIkynpTaUgCGjbtq3qtnfv3pXNx/bTTz+ZbTN27FiLN4VSJ06cEHuoKN1kf/nll2JdZcqUsel1SdkbYNkyV441Wo4VWxgMBrO/H0EQEBcXV+D3y9Tu3btlF6mCIMDb2xvffvutLvVroRRgAfIeievXrzfbz1qAlZubK7vR2bRpk2objh07JvaOcXd3x/nz5y0+n9Zzqek+asdFdna27GZfENTn/8rKyhJ7Hrq5uVlcvcqazMxM8aZD7UYXcHyAtWHDBlmwo/R9uHz5cnGbiRMnWqzvxo0bCAoKgiA86Q1gepP0n//8R6wrIiICGRkZivUYDAa0bNlS9rnYE2Dt37/fbOhXWFgY+vbti6VLl+KXX36x6cbU9PiaO3eu6rYHDhyQfa8oBfIvvfSSeA5Q+sFAasWKFWJ9M2bMMHtceu5q2bJlgYI6ewOskiVL6hr4d+7cWQxX1I4ZrWxtZ1pamtjLqUqVKlaPk549e6p+z+Xl5ckWCLE0PH3jxo2yY0zp77tDhw7i4wV9v/W+xntaA6zq1aubfedLS4MGDQpl0SbpOblSpUqagqZ//vlHNhWGWomJicHy5csd/hqI9MAAi0ji+PHjshO66a/NWkl/RXvhhRfMHpd+QVeoUEHTl5C1QCYhIUF8fOzYsRbrun79uuyCXY8AKywsTPGXd6O0tDRx2xYtWlhsnzXXr18X65o6darVttkTYAFPbqSl3cSVStWqVREfH4///e9/FusyDbCUen5ISZeUNp3P4fHjx2KPwYoVK2o6jtq0aSMGAqbHt3TeK1tW4zJlT4DVpEmTAj+vEi3Hii2k87J0795ddgMfExNjcdL4R48eidvWrl1bcZuDBw+KvShKly6NnTt3imGGp6cnvvjiC4vt69Gjh/gc1uZasUQtwJLOk1GhQgXk5OTI9rMWYO3YsUN8PDY21mo7pPONKd2QF+RcKt3H0nxeAPDqq6+K27700kuat5XODVcQQ4YMgSA86X2nNvGzIwOs3377DT4+PuJxt2fPHsXtatSoAUF4Er5oCXnefvttsU2mYYx0Na+lS5dabZ/0PGpPgAU8mdvM0jxonp6eeOmll7BgwQKrQbX0+AoJCbE6cbd0omXT7yrpD1NaJqTOzc0Vh3PVqVNH9lhOTo7sNSr1uNHC3gBr2rRpBXpeNdL5+dSOU61sbad0iJaW6wzpcZuQkCB7bOfOneJjtWrVslpX1apVLf59S4dpX7lyxWp9SvS+xnOGAKsg1AKs0NBQfPHFF4XWY0k6N5p0bkZrHj58iFWrVsmGekuLt7c3+vfv75BVlYkcgQEWkYT011BBECzOP2KJcX4eQXjSrdiU9Ata67xK1gKZRo0aiY9rmfRbOlRMjwDrtddes/qcxt4MNWrUsLrto0ePcPDgQWzcuBHLli3DokWLxHkN3nvvPfF5e/XqZbVt9gZYxvYsXboUcXFxqjc5gvAkGBo0aJDqfCbSm8qwsDCrz/vPP/+I2z/33HOyxw4fPiw+pnW+NmmPLekvwDk5OeJwOw8PD7t+ybYnwNKycp0pe48Vrb799ltZ8GEMb6RzcVSuXFl1EttLly6J2/Xp08fs8YyMDHH+MU9PTxw+fBjAk5WtjBeeHh4eFufEMs6d5e7ubnWybEvUAixAfmNkOueHtQBrypQp4uNaAkVpr5w2bdqYPV6Qc6l0H2u9hqQ/DEyaNEnztl999ZXVdqSkpGDbtm1YvXo1Fi9eLJu7RRomqwUNjgqwLl26JOt1qvar/K1bt8RtlHoaK5EGvqY9+KRDRpWGYZmSttHeAAt4MqH7mDFjZENulEpAQIDFgE16fPXu3dvq865cuVL1GJaeWz799FNNr6Nu3briuUJ6DSMNn60Ft5bYG2CZnk+0SEtLw+7du/HJJ59g6dKlsr8VY9grCAI+/vhjm+u2p53SXk5aVrzNzMwUtzftBT116lTxMaWw3tT48eMt/n1LH3/llVdsWqHSSO9rvKc1wEpKShKPt+nTp+ONN94Qe8d7eXmhb9++Ni/4ZKuUlBRx2Ki7uzsuXbqkaT+DwYBFixaJ863WqFEDo0ePxty5czFu3DjZ/HkeHh6y+SKJnBUDLCIJvXpgSYctWeuBtXjxYk11WgtkpBfzWoavGCfLtnQhYUuAZW31LQDizXnFihVVt7l48SL69esnm3zUUlEbgqd3gCV1+fJlbN68GQkJCWjevLliW5s2bar4a6X0plLphtxUfn6+2BPC3d1d9kvfJ598ouk9UivSBQbOnTsn/ruWnjGW2BNg2bKqn17HihYGg0H8xdvNzc2sd5P05qNChQpITk42q0MaxiiFAnPmzBEfN/11/ujRo+LiEO7u7lizZo1iO42rt9lzgwpYDrD++usv8UK6TJkysp431gIsaS8btYngpS5fvixuHx0dbfZ4Qc6l0n2s3fBOmzatQNuqDWU0GAxISkoSey5pKWo30o4IsB4+fCibrN/Sgg/S1TILUowTMgNPhmsa/z0oKEhTW6Uhnx4BlpHBYMCJEyewevVqDBkyBHXq1FFcXU4t0JQeX5aGDxr997//Fbdv3ry57DFpD8SCFOlKltIf1ix9rtbYG2DZstLjgQMH0LJlS3EOMGulID+A2NNOaehqa6levbqsrt69e9v0Pbhp0yaLf98pKSmyoeheXl5o06YN5s+fjwMHDpj1nlWi9zXe0xpgKcnJyZF978fGxirOO6cX6fui9QeD7OxscbELDw8PrF27VnG77777TtY703ShHSJnwwCLSEJ6syQIguKcK1pIJ+ts1aqV2ePSLyK1LxRT1gIZtZBDzYQJE6xeSNgSYGm5QbJW3++//251mXDTonYB7cgAy1R2dja++uor2WTGgqB8Qy29qdTyiyYA2bwY0lBV2ruoIEXaA+L333+3+aZEjT0BltI8X0r0PFa0Pp+xnpdffllxG+nwwsjISJw5c0bxcTc3N8UhHTExMRbPPQcOHBDDOjc3N3z00Ueyx//66y9xf2tLZltjKcAC5EGU9LmsBVjS4VJa5jrLysoStw8JCTF7vCDnUltukPTcNj8/H3369LH577Sg52dbAyyDwSAbBtmyZUvVCbQB4IsvvrDr/CPtZSId6mvpBw4p6Q2/ngGWkvT0dCxdutRsTjSlCfmlx4G1eQkBeS9b06Fj0lWRC1KkPdnmz59v0/Ggxt4AS2vP9hUrVsgmKtdS7HldBWmn1h9PlEpUVJSsLum50drk3ACwa9cuq6/7wIEDZhPTG4ufnx+6dOlicdELva/xXCnAMho1apTYjn79+jnkOQwGg9iDShAExYn0lUjn47XW41k6h1mFChUKPAKFqDAwwCKSyM/Pl90UW5tvRo10iM2IESPMHrflC9pI7wBL2r3cGQKsrKws2YVWo0aNkJSUhBMnTuDOnTuyXwsNBoPVC+jCDLCMMjMzZSttxcTEmG1TkABL2rtOGmBJ5/dkFUAAACAASURBVMfq2LGj4jLSlop0vi5pQGNpFTUt7AmwtEyqqvexosWyZcvEetQmqDUYDLIlrsPCwsT3OCcnRxwGqPTraUZGhrhf8eLFVduxe/du8W9dEARZd/933nkHgvBk+KGWIViWWAuwzp49K06wHhISIvZWsCXA0nKTJu2ZExoaavZ4Qc6lRRVgSSfYDggIwLhx4/DTTz/h4sWLyMzMlE2oLe3NVVgBlnR4Z5UqVaz2QJHOPdSwYUObzz///e9/xboKEmBJQ1RHB1hGZ8+eFSeiFwQBQ4cONdvG1gDr9OnT4vamAZb072XkyJE2v8fSYU3SAEvLEDU19gZYWkgXHHFzc0Pfvn3x9ddf49y5c8jIyJDdXO/du9em41zPdhrPxd7e3jZ/NqtXr5bVZeu5UTpnlqXXnZOTg82bN6N3796qw2Pbt2+vOIcdAyzrrl+/LgatXl5eDlmJUNrbNSQkRNP0ALm5ueL9jJubm9W5+/Ly8mTntiNHjujVfCLdMcAiMiENn95++22b98/KypKFYNJhWkaOCLCKegihvRc30hXwunXrZjGEu3v3rlMGWACwbds22YWh6cWMnkMIV61aJdY1evRou9rtLEMItQRYeh8rWsyePVvTzV9+fr7sxrpkyZL466+/ZMOK9+7da7afdAVU01/mTX377bdieCQITyZzvXLlitgbQI9fga0FWAAwaNAgcRvj8BJrAZatw2RsGULo7AGWcSJgT09PqzcH0t58hRFgbdmyRdw2ODhY0wTfP/zwg7hP165drW5viTMNIbRm9OjR4vO++OKLZo/rOYTwtddeEx/btm2bXe12liGEWkhXS/zggw8sbvvNN98UWYAlXVHV3jmQ9B5CqOb8+fNYvXo12rRpI+vh9tZbb5ltywBLm1KlSjk0+JEOJVYKzZVIp0QpXbq0pn2M8+cJgoDPP//cniYTORQDLCIT0glVAwMDbZ7MWrrMrb+/v+Kv2I4IsIp6End7L26kk4tbW8FLOtm+swVYZ86ckQVYphO7Sm8qy5Yta7U+6fCSqlWryh7bv3+/+Ji1FdKsyc3NFQOQopzEXUuApfexosXHH38s1vPGG29Y3DY3NxcdO3YUtw8NDRVXi1QLLaVD5by9vVUXATDatGmTbF4Y4yqZvr6+BR76LKUlwLp8+bK4YqKfnx+uXbtmNcCS9vLRssrXd999J25vbRJ3Zw6wpJM3a1mFVRrOODrAOnz4sGwBhx9++MFq+4Anc9AZ6w8LC7N7Ja6insRdK+kPB1WqVDF7XHoc2DqJu+mPZjNnzhQfGzdunF3tlp4Li3ISdy2MN9K+vr5W52qS/rhQ2AFW8+bNxe0tDcXTQu9J3LXYunWrGGJ5enriwYMHsscZYGkTGhoqtuWPP/7Qte6MjAzZUFWt9UuDca0B1osvvijuU9ARKESFgQEWkYlHjx7Jfk155513NO+bkZEhuwh/9913FbdzRIAlXQFr7NixFuu6fv26bFJaZwiwpL05pEPblEjnHHC2AEu60pMgCGZLqEtvKrVcjEiHfZguoy5dFt3Nzc1sWXpbtW7dWnyulStXFrieypUri/VIh0WpsTXA0vtY0eKPP/6QBVLWFnjIysoSVwQ0Fj8/P8XJ3Y2qVasmbqs2CbiU9EbaWObPn2/za1OiJcACgPj4eHG7YcOGWQ2wpMNenn/+eavt6Nevn8WbuqclwJL2sOvevbvFem7evCkbJurIAOvq1auyYUWWVtdTUqVKFXHf7du327SvKWnPRWu9bn777TfZcV+YAdasWbPE561fv77Z49LjIDQ01Ow7wNQrr7yietxIb0IjIyM1TbytxvT7QksvOyXSY1ltPkBTtgZDxu+QkiVLWt1WuuhAYQdY0oU3evToYddzS8+NtWvXtrr9c889p8vrln7vHDt2TPaYswdYxp7I5cqVs9o2R0lOTpadi5Tmt7SHtOekLb3jpT+mah1CKJ30X8sP4URFhQEWkQLpL6Jubm5my30rycnJQadOncT9SpUqhWvXrilu64gA68iRI+LjwcHBFpdMlnbPt+cGSc+Lm3Hjxol1bdy4UbWOU6dOib0FLIUSegVY+/btsziRsakBAwaIz1uzZk3F+qTvfbt27VTrunfvnqyXwe7du822kQ41aty4seLKh0qUekt89dVXYl1lypSxesGj5oUXXhDrka6CpcbWAEvvY0ULg8GA6OhosS4tw/SuXbsmW9knLCwMly9fVt1+xowZ4rZly5a1uK1R165dZceT2uqEttIaYN2+fVu86PXy8pINdVAKsHJzc2XDbiytdnTixAnxBsXd3V2xZ9nTEmBlZmaKPR0qV65ssbeS9BziyAArKytLNmRk8ODBFl+fEunQ2KpVq2peuVfp9UuHX5crV87iXDLSsN2eACsvL09xSK+a7OxsWXAwcuRIs22kx4EgCFiwYIFqfQcPHhSPCz8/P8X3TxrQ2NILS+k9HjFihFhXq1atNP3AYEo6X5/pSnpqbA2GXnrpJXH7S5cuqW63fv162Xtd2AHW9evXxV6obm5uNoW4pp9Pbm6u7Pve0pBR6fBBe1+3NMAyXXjE2QMs46IK/v7+VtvmKNLrl2rVqulef8OGDcX6pSu3WvP48WPZohPWPj/pD2LFihXTNM8WUVFhgEWkomfPnuLJ3N3dHQkJCarDqk6cOCH7knF3d8fOnTtV63ZEgAUALVu2FLd56aWXcP36dbNtli5dKl5sOVOAJR0qFBERgQsXLphtc/jwYURGRsra7ugAq169eqhYsSIWLlxo8Ze1zMxMWbAiCAKWLVtmtp30ptL4OuLj481CsrS0NDRr1kzctl69eoo3JDdu3JBd9LZo0cLizdyFCxcwdepUxWFw+fn5sqGlMTExOH78uOrrXb16tWKvL+m8LUpzwJmyNcDS+1jRShrwCcKTSfjT0tIUt/3xxx9lN7rG8vzzz6vOlZKRkSHrDRMVFYUDBw4obnv58mVZWGQsnp6e+PHHH+16nYD2AAuQn8+kPTvVlrT/4IMPxG0CAgIUh6ydPHlSdjOpNmzzaQmwAMgWeJg8ebJZeJCdnS32GNTj/GwtwJL+nTZt2lRz+C316NEj2Q1wzZo18ffff6tuf+3aNSxcuFBxOOjjx49Rs2ZNsa62bduazeeYm5srLlYgfY8KGmAZh+42atQIGzduNBtCJXX58mXZRNvu7u5mPVYA+XHg5uYGLy8vxflk/v77b9nfu1rP6T179siGC48cOdLiEO/ff/8d/fv3V+xNd+XKFdlNbZcuXVR/qLh+/TqmTJmi+JgxhPb09FS8zjBlazAk7VHepk0bxc9l48aN8PHxkR0HhR1gAfIfHnx9fbFmzRrVFdxycnKwbds2NG/eXHGupEWLFol1hYaGyhY6MPrpp58QEBBg9XW3bNkSy5Yts/gjknRhiTJlypi129kDLOn1yuHDh622D/j/ww4tteHNN9/EqlWrLM5rdvfuXdlcsoIgYN26darbS48trd9VZ8+elX23W/phWsm7774r21/tebdt2yYbpqh1ni2iosIAi0hFdna27AJfEP7/ssMTJkzAnDlzMHLkSLz00kuyC4lixYph69atFut2VIB17tw52Vj8oKAg9O/fH7Nnz8a4cePEX3JDQ0Px1ltvidupLWdfmAHW48ePUaNGDbE+Hx8f9O7dG9OnT8fkyZPRunVr8SJ+8uTJVkMJPQMs6c1IbGwsBgwYgMmTJ2P+/PmYPHkyunfvLpu4XxCeLEGvdBErvans0qWLuDRy5cqVER8fjzlz5mDQoEGym4yAgACLwwMPHDgg6/rt6emJVq1aYfz48Zg3bx6mTJmC/v37i5NIC4KADh06KNZ16dIlREZGym7SmjVrJtY1YcIEtG/fXuxZpHQB+O9//1t2DI4YMQJLlizBsmXLsGzZMrMJam0NsPQ+VmwhXZZaEJ788tu9e3dMnz4ds2bNwtChQ82Cq759+8p6b7Vq1Uo1LDhy5IjssxSEJ8OUxowZg7lz52Ls2LFo2bIlvL29xccrV64sO1cFBgYq3ljbwpYAKyMjQ1xhUVrUAiyDwSBbLEMQnkxePWXKFMycOROvvvqq2etTW5jiaQqwPv/8c9lrfv755/Huu+9i9uzZGDJkiBhmlC5dWjZM1hEBlnRompubG8aPH2/zyoFGZ86cQVhYmKy+Ro0aISEhAfPmzcO0adMwePBg1KlTR/y7VOu58+eff8pupEqWLIm3334bc+bMwahRo8S/o6ioKHTp0kXczt4AS3ouqV+/PoYNG4YZM2Zg3rx5GDNmDFq1agUvLy/ZtmrzFEmPg2HDhon7NW7cGJMnT0ZiYqLZMR4bG4vMzEzVdi5dulR2nREcHIzu3btj6tSp4nm5W7dusnO3Wm+NnTt3yoao+vv749VXX8XUqVMxd+5cjBw5Eo0aNRI/KyXS1VYrVaqESZMm4YMPPhDP8aZD420NhlJSUmS9Z8uUKYPBgwdj9uzZSEhIQFxcnPj9NGHCBJuuQywpSICVn5+P7t27y46N8uXLY+DAgUhMTMTs2bMRHx+Ptm3byq4TlAKXx48fy+YzdXd3R4cOHTBjxgxMmzYNrVu3Fo8D6fB4pddtfC0eHh6oX78+hg4dipkzZ2LWrFkYNmyYbPivIAhmqyICzh9gSYfzhoWFISEhAUuXLhWPw59++slsH+lrVmuD8fvPx8cHTZo0wYgRIzBz5kzMmzcP48ePR4cOHWTHpyA86ZVtqWdtQQKsiRMnivt06tRJ0z5SaWlpqFSpkqydcXFxiI+Px7x58zBu3DjZNa4gPFksRUvPeaKixACLyAKDwYDly5ejdOnSZjdnSqVJkyY4evSo1XodFWABwKFDh2RDdExLiRIlsG/fPtlkymrzMBVmgAU8+bVJ+iWvVP7v//4Pjx8/Fv/f0QFWfHy8bBiYteLh4YFhw4apdr+W3lT2798fx44dk82bZlpKliypaUntU6dOoXbt2pra6O7ujjFjxqjWdeXKFbz88sua6lLqIZSXlye7CDctpp+ZrQEWoO+xYqu1a9eahUxKpUKFCvjss88APOmlKd3H0hDE06dPy4ZhqhVvb28MHz4cDx48wOPHj2XDqsLDwy0OvbHGlgALAN5//32z9qkFWMCTHwik81uplcaNG1v81flpCrAA+Q2JUilXrhz++OMPTTd69gRYpkOZtRa18/yVK1fM5nyzVHr27Kn6Pv7000+yAF/p7+r48eOy84Y9QwibNm0q6zlorYSEhGD58uWqdZoeB59++qk4zEyp1KxZU3W6AalvvvlGFhRaKv7+/hZ7v/7yyy9Wz5+C8OTHECWpqamyuUKtHScFCYa+/fZbs5BAWooVK4ZPPvnEptU2rSlIO4EnIdaMGTMsfs7SEh4errrYxr1799CkSRPVfT08PLBo0SKrr1s6F6Wl4uPjg0WLFim2xdkDrHv37pkFcdJiOm8ooC3Akk7Ob634+flhwYIFVofj2hpg5efnIyIiQtxHy8qUSlJTUy1ej0lLw4YNNS2gQVTUGGARafDgwQNs2LABvXr1QtWqVREaGgovLy+EhYWhTp06SEhI0BQyGDkywAKedG2eMWMGatasiYCAAAQGBiI2Nhbjx48X59WRdi1Wm8y1sAMsALhz5w6mTZuGmjVrws/PD35+foiOjkbfvn2xZ88ecTvj8xbGJO6PHj3Czp07MXHiRLRt2xYVK1aEv78/PDw8EBgYiKioKLRv3x5z585VHM4mZRpgAU/mEZo5cyZq1qyJ4OBg+Pn5ITY2FhMnTsStW7c0t9NgMOD777/HW2+9hZiYGISEhMDDwwNBQUGoVq0aevTogRUrVmieZHTnzp3o378/KleujICAAHh6eqJkyZJo1KgRJk6caLGXT3Z2NpYsWYImTZqgZMmS4lxGSp9ZQQIsQL9jpSDu3r2LxYsXo02bNihbtix8fHwQGBiIypUro0+fPvj000/Nellt27ZN1otCbXgO8P8/y379+qFKlSoIDAyEt7c3wsPD0bx5c8ybN89sjqw7d+7Iblpq1KiheU4iU7YGWFlZWbLeH4JgOcAy+u233zB48GBUrVoVAQEBKFasGMqXL4+ePXtqumB/2gIs4ElA06VLF5QpUwZeXl4oXbo06tWrh/nz5yM9Pd2srqchwDL65ZdfMGzYMMTFxaF48eLw8PBAQEAAqlSpgs6dO+P999/H2bNnLdYBPJnIfvz48ahWrRr8/PwQHByMWrVqITExUXyP9AiwjG7duoXNmzdj+PDhePnll1G2bFkUK1YMnp6eCAkJQUxMDHr27Im1a9eq9gY0UjoOTp48iaFDh6Jy5crw8/NDSEgIGjRogI8++simoZtZWVlYs2YNXn31VURFRcHf3x+enp4oUaIEXnzxRQwcOBBbtmzRtIpsTk4O1q5di06dOiEyMhLFihWDj48PIiMj8corr2DRokUWg7WrV69i7NixqF27NoKCgmTDHPUIsIAnvfsGDx6MihUrwtvbG8HBwYiNjcXo0aPxzz//ANC+2qYWBW2n0fXr1zFnzhy0aNEC4eHh8PHxgY+PD8qWLYsmTZogISEBu3fvVh1iaGQwGLB+/Xo0b94cJUqUQLFixVCxYkX069dP/MHR2uvOzMzEt99+i1GjRqFx48YoW7YsvL294eXlJX6PT5061eLfjrMHWMCTEGvmzJmoV68eQkNDZWF0QQOszMxMfPfddxgzZgyaN2+O8uXLw9fXFx4eHggODkaVKlXQrVs3rFixQjwfWWNrgCWd1L9kyZIFGuJtZDAYsHPnTgwYMACxsbEIDg4Wrw1jYmLQr18/bN++3e6VZIkKCwMsomeUsbeGu7u7xclySV9KARYREbmGgoSqREREpA0DLKJn0P3798VhcTExMUXdnGcKAywiItfFAIuIiMhxGGARPYOkE1GPHz++qJvzTGGARUTkuhhgEREROQ4DLCIX884776jOTZSXl4cFCxaIc1V4e3urTiRKjsEAi4jIdTHAIiIichwGWEQuxrhqSfXq1fH2229j1qxZSExMxKBBg8xWu1uwYEFRN/eZwwCLiMh1McAiIiJyHAZYRC5GuuyuWvHy8sLcuXOLuqnPJAZYRESuiwEWERGR4zDAInIx//zzDxYvXox27dohJiYGJUqUEJdNfvHFFzFu3DgkJycXdTOfWQywiIhcFwMsIiIix2GARURERERERERETo0BlgtKSUmxOoRMEAT4+/sXdVOJiIiIiIiIiKxigOWCGGARERERERERkSthgOWCpAHWjh078ODBA8Xy8OHDom4qEREREREREZFVDLBckDTA2rdvX1E3h4iIiIiIiIjILgywXBADLCIiIiIiIiJyJQywXBADLCIiIiIiIiJyJQywXJBSgJWTk1Moz52fn4+bN2/i5s2b4jxbLCwsLCwsLCwsLCwsLCyOKg8ePBDvQ/Pz8wvl3pcKHwMsFyQNsKpXrw5/f38IggAfHx/Url0bU6ZMwc2bNx3y3Ddv3tS0AiILCwsLCwsLCwsLCwsLi97FUfe6VPQYYLkgaYClVkJDQ7Fr164C1W8p+b5w4UKRn7BYWFhYWFhYWFhYWFhYns3CAMt1McByQRcvXkTbtm2xYcMGnDhxAvfv30dmZiaOHj2Kd999Fx4eHhAEAb6+vvjzzz9trt+WE0dRdyVlYWFhYWFhYWFhYWFhce0iHQn04MEDB9xlkzNggPUM+uabb+Dm5gZBENC0aVOb99caYD18+FD/xhMRERERERFJPHz4kPehzwAGWM+oXr16iX/gV69etWlfrck3TxxERERERETkaAywng0MsJ5RmzZtEv/At2/frlu9PHEQERERERFRYeJ96LOBAdYz6scffxT/wD/99FPd6uWJg4iIiIiIiAoT70OfDQywnlEbN25kDywiIiIiIiJ66vE+9NnAAOsZ1a1btwLPgWUJTxxERERERERUmHgf+mxggOWCrly5YvHxzz//XFyFsHnz5ro+N08cREREREREVJh4H/psYIDlgkqWLInu3btj06ZNOHnyJNLS0nDr1i38+uuvGDx4sBhe+fv749ixY7o+N08cREREREREVJh4H/psYIDlgoKDg8U/XrUSHh6On3/+Wffn5omDiIiIiIiIChPvQ58NDLBc0NatWzFq1Cg0aNAA5cuXh5+fH7y9vREWFoa2bdti2bJlyMjIcMhz88RBREREREREhYn3oc8GBlikK544iIiIyBUZDAZkZWXh5s2bSE1NxYULF1hYWFhYdCypqam4efMmsrKyYDAYbDpH8z702cAAi3TFEwcRERG5GoPBgMuXL+PUqVMsLCwsLIVQLl++bFOIxfvQZwMDLNIVTxxERETkSpTCqzNnziA5ObnIeyuwsLCwuEpJTk7GmTNnChxi8T702cAAi3TFEwcRERG5kqysLPFm6ty5c8jMzLR5aAsREVlnMBiQmZmJc+fOiefdrKwsTfvyPvTZwACLdMUTBxEREbmSmzdvijdSmZmZRd0cIiKXl5mZKZ53b968qWkf3oc+Gxhgka544iAiIiJXkpqaKg4bZM8rIiLHMxgM4nDC1NRUTfvwPvTZwACLdMUTBxEREbmSCxcu4NSpU0hOTi7qphARPTOSk5Nx6tQpXLhwQdP2vA99NjDAIl3xxEFERESuxBhgab2JIiIi+9l67uV96LOBARbpiicOIiIiciUMsIiICh8DLFLCAIt0xRMHERERuRIGWEREhY8BFilhgEW64omDiIiIXAkDLCKiwscAi5QwwCJd8cRBREREroQBVuE5evQo4uPjERcXhxIlSsDb2xsRERFo0aIFFi9ejDt37uj2XCkpKeI16759+3Sr12jfvn1i/SkpKbrXby+92nfkyBEMGTIEsbGxCAwMhLe3N8LDw1GrVi306dMHa9aswfnz5/VrOD0zGGCREgZYpCueOIiIiMiVMMByvMzMTPTv3x9ubm7idaRSKVGiBDZs2KDLczLAsq99BoMBCQkJVj8zQRBQoUIF3dtflJz9s3UVDLBICQMs0hVPHERERORKGGA51r1791C/fn3x+rFevXrYtGkTkpOTkZaWhmPHjmHOnDkIDQ0Vt5k/f77dz8sAy772zZo1S9y/evXq+OSTT3DixAmkpaXh0qVL2LVrF8aPH4/o6GgGWFQgDLBICQMs0hVPHERERORKGGA5Vu/evcVrx9GjRyM/P19xu2vXrqFatWoQBAFubm7YtWtXIbfUtdgTwjx8+BB+fn4QBAH169dHdna26rYGgwG//PKLna11LgywCgcDLFLCAIt0xRMHERERuRIGWI6za9cu8bqxY8eOVrdPTU2Fv78/BEFA+fLlkZeXVwitdE32hDA//vijuO+nn37qmAY6MQZYhYMBFilhgEW64omDiIiIXAkDLMdp27at2KNK60TfU6dOFa81t2zZYva48bGkpCTk5eVhyZIlqFu3LkJCQiAIAr755hsA2oYQ5ufnY+XKlahbty78/f0REhKChg0bYv369QCA6dOnq87xZC3kkLbTYDBg7dq1qF+/PoKDg+Hn54c6depg+fLlqj3SAODcuXN4//330apVK4SFhcHLywtBQUGIi4vDuHHjcO3aNdV97QlhPv30U3Hf7777zqZ9jUzf/wcPHmDatGmoVq0afH19UaJECXTo0EHT8M6HDx9i0aJFaNy4sTj5f3h4OHr06KGp91dmZiYWL16MZs2aoXTp0vD29kZkZCRefvllLFiwAJcuXRK3tWW+L9PX+PDhQ8ycORNxcXEIDAyEIAj466+/xO3t+TxdEQMsUsIAi3TFEwcRUeH4OeVnbDmxBdl56kM3iMh+DLAcIyMjAx4eHhAEAc2aNdO8nzQU6Nmzp9njxsc+/vhjNG7c2Cxg0Bpg5eTkoEOHDqpBxeuvv65LgLV27Vp06dJF9Xn69eun+D7cu3fPaphSokQJHDx4UHF/ewKsHTt2iPuOGjXKpn2NpO//1q1bERsbq/ga3NzcsHDhQtV6/vrrL0RGRlp8HyZMmKC6/+HDhxEREWFx//79+4vbFzTA+vLLLxETE2O2vTHAsvfzdEUMsEgJAyzSFU8cRESOt+rIKggzBAgzBLTc0LKom0Pk0hhgOYZ0GNr06dNt2rdcuXIQBAERERFmjxnrjIiIgKenJyZNmoSTJ08iLS0NBw8exLlz5wBYD7BGjx4tPt6jRw8cOnQI6enpOHbsGAYOHAhBEBAdHW13gBUdHQ1PT09MnjwZJ0+eRHp6Og4dOoTmzZuL2+zcudNs/3v37iEuLg6JiYn4+eefcfbsWaSnp+P06dNISkoSA6HIyEjFa3J7Aqy7d+/C19cXgiDAw8MDU6dOxdWrV22qQ/r+R0VFwcfHB3PmzEFycjJu3bqF77//HtWrVxe3UZrzLDU1VZzcv0qVKli/fj1SUlJw584dHD16FIMGDRL3X7lypdn+Z8+eRVBQEARBQGBgIGbNmoUTJ07gzp07uHjxIrZt24YBAwZgyJAh4j4PHjyQBXgnT57EgwcPxJKZman4GiMiIuDv748FCxbg3LlzuH37Nvbu3YubN28CsP/zdEUMsEgJAyzSFU8cRESOZwyvjOVM2pmibhKRy2KA5RirVq0SrxmVhgJaYhx6KAiC2TxY0h4r69atU63DUoB18eJFsXfYa6+9prj/iBEjFHvdGGkNsARBwObNm80ez8zMRHh4OARBuaeZNQ8fPkTlypUhCALWrFljc/usmTdvnllPqeeeew5vvPEGli1bhhMnTljcX/r+C4KAzz//3Gyb9PR0VKhQAYIgIDY21uxxYw+56Oho3Lt3T/F5pkyZIvZekoZLANCyZUsIgoCAgAAcP35cta2mx5jW9076Gt3c3PDTTz+pbmuNtc/TFTHAIiUMsEhXPHEQETmeaYC1P3V/UTeJyGVpvokqXRrw93fNUrq07u/r/PnzLfausaRXr17ivsYeLEbGf69Ro4bFOiwFWMZw4ULVxgAAIABJREFUxt3dHZcvX1bc//79+2IvJHsCrEaNGqm2ceTIkRAEARUrVrT4WtSMHTsWgiCgd+/eNrdPi48++kicW0ypVK9eHV988YXivtL3v0GDBqrPsW7dOnG7Q4cOif9+/vx5uLm5QRAEfP/996r7P3r0SJz43zh8FABOnTol1vvee+/Z9LoLEmBpWaTAGkufpytigEVKGGCRrnjiICJyPNMA65dU11qinMiZaL6J8vcHBME1i7+/7u+rNMD64YcfbNq3d+/e4r43btyQPWb897Fjx1qsw1KA9a9//QuCIKBOnToW62jXrp3dAVZiYqJq/cuXL4cgCPDz81PdZvv27ejVqxeio6Ph5+enGCS98MILNrdPq4yMDCQlJaFHjx6qc0m98847ZvtJ3/8FCxao1n/37l1xuyVLloj/vnr1agiCAE9PT9y4cUM2jM+01KlTB4IgYPLkyeL+H330kVjv9evXbXrNBQmwli9frqnugn6erogBFilhgEW64omDiMjxaqyoIQuwLt9X7iFARPZjgOWYAEs6hFBp+Jgl0iGEubm5sseM/75ixQqLdVgKsOLi4mBp+KDRqFGj7A6w1q5dq1p/UlKSuJ2pvLw89OzZUzHgMC2VKlWyuX0Fdf36dWzZsgXt27eXteHLL7+UbSd9/7dt22axzjJlykAQ5BPGJyQkaHrt0vL222+L+xvnOCtdgN6FBQmwduzYYbFOez9PV8QAi5QwwCJd8cRBROR4tVbWkgVYVzNsmzyXiLTjEELHDCH84YcfxGvGGTNm2LRv+fLlIQiWJ3FPSkqyWIelAKtSpUoQBAGDBg2yWIdxfiV7AixL7bQUYM2aNUt8rFu3bti2bRvOnz+P9PR0sefRhAkTCtw+PUh7OTVv3lz2mPT9tzY3lNLnIZ2gXWuRriZo3L8gYVBBAiylhQKk7P08XREDLFLCAIt0xRMHEZFjLTiwwGwI4fUHtg1/ICLtOIm7Y9y/fx/u7u4QBAEtWrTQvN/FixfFa02lyc31CLCMPbD69OljsQ49emAVNMCKjIy02kvMOIdWUQVYAFCjRg0IgoCQkBDZv9vbA8v43kdGRhaoXYXdA8tagGXv5+mKGGCREgZYpCueOIiIHCv6g2izAOvGgxvWdySiAmGA5TitW7eGIDyZLF3r+zt9+nTxWlNp9T49AqxOnTpBEKzPgWUcJlfYAVZ6err4799++63q/sZV9ooywDIOi/P29pb9u71zYH344YcQhCer+926dcvmdhnnFxOEwpkDy1KApcfn6YoYYJESBlikK544iIgcSynAuvXQ9ot3ItKGAZbj7NixQ7xu7Ny5s9XtL126hICAAAiCgHLlyiEvL89sGz0CrLlz54rB2pUrVxT3z8jIECfZLuwA69q1a+K/b926VXHfK1euwNPTs8gDrIYNG0IQBERFRcn+XesqhNL3QLoKoXQVwUWLFtncrtOnT4v7L1y40KZ9Dxw4IO57/vx51e20Blh6fJ6uiAEWKWGARbriiYOIyLGUAqzbmbeLullELosBlmP16NFDvHYcM2YM8vPzFbe7fv06qlevLva62blzp+J2egRYqamp4vDGvn37Ku7/7rvvivsXdoCVl5cHf39/CIKAAQMGmO33+PFjdOzY0a72WXLgwAEsXLjQ6rX+jh074ObmBkEQMHjwYNlj0vdfEJQn8k9PT0dUVBQEQUBsbKzZ48ZVIAMDA/Hnn39abEtqaiqys7Nl/9aqVSsIgoCgoCCcOHFCdV/ToFQafv3666+q+2kNsPT4PF0RAyxSwgCLdMUTBxGRYykFWCdvnSzqZhG5LAZYjnX37l3UrVtXvH6sX78+Nm/ejAsXLiAtLQ1///035s6di+LFi4vbzJs3T7U+PQIsQB5Q9erVC0eOHEF6ejr+/vtvDB48GIIgIDo6ukgCLAB4/fXXxccSEhJw6tQppKWlYd++feJQs2rVqjkkwPruu+/E4GfQoEH4/PPPcfbsWdy5cwc3b97EgQMHMHLkSHh7e0MQBPj7+5v9/Ujf/6ioKPj4+GDu3Lm4cOECbt++je3bt4uBpSAI2LVrl1k7UlNTUbJkSQiCAF9fX0yYMAGHDx9GWloabt++jb///hvr1q3Dv/71L3h6euL2bfmPPefOnUNQUBAEQUBwcDDmzJmDkydP4u7du7h8+TK+//57DBw4EEOHDpXtl5eXJ+7XsWNHnDt3DtnZ2cjLy8Pjx48VX6O1ObDs/TxdEQMsUsIAi3TFEwcRkWOZhlfCDAFHrx0t6mYRuSwGWI734MEDvP7662JvHbVSvHhxrF+/3mJdegVY2dnZeOWVV1Tb0rdvX0ybNg2CoLySnaMDrKtXr6JcuXKq7Rs5cqQ4X5jeAdbevXvh4eFh8bMylvDwcOzfv9+sDun7v3XrVjGcMS1ubm4Wh/idPHkSVatWtdoODw8P3Llzx2z/P/74A2FhYRb3la5eaDR+/HjFbaXvtS0Blr2fpytigEVKGGCRrnjiICJyLKUA69j1Y0XdLCKXxQCr8Pz5558YNWoUnn/+eYSGhsLb2xtly5ZF8+bN8f777ysGEKb0CrAAID8/HytWrECdOnXg7++PoKAg1K9fH2vXrgUAxMfHQxAE1KpVy2xfRwdYAHDjxg0MGzYM5cqVg5eXF0qXLo02bdrg66+/BgCHBVgAcOvWLSQlJWHAgAGoXbs2QkND4eHhAV9fX5QvXx4dOnTA8uXLkZGRobi/6fufkZGBSZMm4bnnnkOxYsVQvHhxtG/f3mrwAwA5OTn45JNP0KFDB5QtWxbe3t4oVqwYoqKi0KlTJ6xZs8as95VURkYG5s+fjwYNGojHXbly5dCkSRMsXLhQcR60/Px8LF26FHXr1kVgYKAYvhY0wALs+zxdEQMsUsIAi3TFEwcRkWMpBVj/3P6nqJtF5LIYYJGazp07QxAEdOjQoaib8tSxNdyhZw8DLFLCAIt0xRMHEZFjKQVYKXdTirpZRC6LARYpefjwoTgP0rRp04q6OU8dBlhkDQMsUsIAi3TFEwcRkWMxwCIqXAywnk3Z2dnIzMxUfXzEiBHiNa+lFexIGQMssoYBFilhgEW64omDiMixGGARFS4GWM+mlJQUlC1bFjNnzsShQ4dw+/Zt3Lx5E3v27EGXLl3E690ePXoUdVOfSgywyBoGWKSEARbpiicOIiLHYoBFVLgYYD2bpAGLWmnUqBHu3r1b1E19KjHAImsYYJESBlikK544iIgciwEWUeFigPVsys7OxieffIJu3bqhSpUqCAkJka0Ml5SUhMePHxd1M59aDLDIGgZYpIQBFumKJw4iIsdSCrCS7yQXdbOIXBYDLCKiwscAi5QwwCJd8cRBRORYSgHW+fTzRd0sIpfFAIuIqPAxwCIlDLBIVzxxEBE5llKAdeEOb6yJHIUBFhFR4WOARUoYYJGueOIgInIspQDr98u/F3WziFwWAywiosLHAIuUMMAiXfHEQUTkOBfvXVQMsLxneRd104hcFgMsIqLCxwCLlDDAIl3xxEFE5DgDvh2gGGAJM/h1TuQoDLCIiAofAyxSwite0hVPHEREjlNxaUXF8KrNv9sUddOIXBYDLCKiwscAi5QwwCJd8cRBROQ4HT7toBhg9d3at6ibRuSyGGARERU+BlikhAEW6YonDiIix0n4IYEBFlEhY4BFRFT4GGCREgZYpCueOIiIHGfkzpGKAdbrX79e1E0jclkMsIiICh8DLFLCAIt0xRMHEZHjdN7cmQEWUSFjgEVEVPgYYJESBlikK544iIgc51+b/6UYYDVY26Com0bkshhgEREVPgZYpIQBFumKJw4iIsfp9FknxQBLmMGvcyJHYYBFRFT4GGCREl7xkq544iAicpwqH1ZhgEVUyBhgFZ6jR48iPj4ecXFxKFGiBLy9vREREYEWLVpg8eLFuHPnjm7PlZKSIl6z7tu3T7d6jfbt2yfWn5KSonv99rK3ff379xf311KSkpJ0fw3k2hhgkRJe8ZKueOIgInIctfCKARaR4zDAcrzMzEz0798fbm5uFkOQEiVKYMOGDbo8JwMsBljk3BhgkRJe8ZKueOIgInIcBlhEhY8BlmPdu3cP9evXF68f69Wrh02bNiE5ORlpaWk4duwY5syZg9DQUHGb+fPn2/28DLD0C7AePHhgteTl5en/IsilMcAiJbziJV3xxEFE5DgMsIgKHwMsx+rdu7d47Th69Gjk5+crbnft2jVUq1YNgiDAzc0Nu3btKuSWuhY9AywiR2CARUp4xiFd8cRBROQ4DLCICh8DLMfZtWuXeN3YsWNHq9unpqbC398fgiCgfPny7NVjBwZY5OwYYJESnnFIVzxxEBE5Tr019RTDq5YbWhZ104hcFgMsx2nbtq3Yo+r8+fOa9pk6dap4rbllyxazx6VzLuXl5WHJkiWoW7cuQkJCIAgCvvnmGwDahhDm5+dj5cqVqFu3Lvz9/RESEoKGDRti/fr1AIDp06dDEARUqFDBbF9rAZG0nQaDAWvXrkX9+vURHBwMPz8/1KlTB8uXL1ftkQYA586dw/vvv49WrVohLCwMXl5eCAoKQlxcHMaNG4dr166p7lvUAVZSUpJs/wsXLuD//u//EB0dDR8fHwQHB5s9V9OmTQEA+/fvx6uvvorw8HB4eHigc+fOZvXv3bsXPXv2REREBLy9vVG8eHG8/PLLWLZsGXJyclTbpfX4IcdjgEVKGGCRrnjiICJynBdXv8gAi6iQMcByjIyMDHh4eEAQBDRr1kzzftLgqWfPnmaPGx/7+OOP0bhxY7PJxLUGWDk5OejQoYPqpOSvv/66LgHW2rVr0aVLF9Xn6devn+L7cO/ePasTp5coUQIHDx5U3N+ZAqwDBw4gODhY1na1AOvDDz+Eu7u7bFtpgJWfn4/hw4dbfF/i4uJUwz2txw85HgMsUsIAi3TFEwcRkeO8sOoFxQCr1cZWRd00IpfFAMsxfvzxR/Gacfr06TbtW65cOQiCgIiICLPHjHVGRETA09MTkyZNwsmTJ5GWloaDBw/i3LlzAKwHWKNHjxYf79GjBw4dOoT09HQcO3YMAwcOhCAIiI6OtjvAio6OhqenJyZPnoyTJ08iPT0dhw4dQvPmzcVtdu7cabb/vXv3EBcXh8TERPz88884e/Ys0tPTcfr0aSQlJSE2NhaCICAyMlLxmtyZAqyIiAhUrFgRn332Ga5evYpr165h27ZtZs9VpkwZeHh4oFmzZtizZw9u376N5ORk7NmzR9x25syZYr2tW7fGL7/8grS0NJw+fRrjx48XQ9M6deogNzfXrF1ajx9yPAZYpIQBFumKJw4iIscZ9v0wxQCr9cbWRd00IpfFAMsxVq1aZXEooCXGoYeCIJjNgyXtLbNu3TrVOiwFWBcvXhSDjtdee01x/xEjRoj72xNgCYKAzZs3mz2emZmJ8PBw1Z5m1jx8+BCVK1eGIAhYs2aNze2zxtZVCE1JA6zS/4+9Ow2PosrfPn4iJGELBJRFdgIjiIDgoDKAIMsAioKICIoCo86j4IAMoo76HxZZhy0gyDJsQRZRQEBAUATCDhpZZAdZA4SYhC2QPfk9L7hSk05Xr6nqTnd/P9dVL6g6p87p6vTpOjdV1RUqSFxcnFNttW3b1uazz65evSrBwcGilJJOnTpJVlaWVZmZM2daXGWVn7N/PzAfARb0EGDBUAwcAGCef2z4Bw9xBzzM2UlUhQoiJUv651KhgvHHdfz48do5o6u/KNizZ0+tbnx8vMW23PUNGjSwuw97Ada4ceNEKSX33XefxMbG6ta/deuWFC9evMABVosWLWz28b333hOllNSqVcvua7Hlgw8+EKWU9OrVy+X+OZI3VHJmyS9vgDVp0iSn24qJibFZbsKECVq506dP2yz36KOPilJKmjZtarXN2b8fmI8AC3o444WhGDgAwDwEWIDnOTuJKllSRCn/XEqWNP645g2wfvjhB5fq9urVS6t77do1i2256z/44AO7+7AXYHXp0kVybzOz55lnnilwgPXZZ5/Z3P8XX3whSikpUaKEzTIbNmyQnj17SkREhJQoUUI3PHrsscdc7p8jRgZYx44dc6qt8uXL2y2X+741atTIbrlRo0aJUkqKFCliNV9x9u8H5iPAgh7OeGEoBg4AMM+f5/yZAAvwMAIscwKsvLcQfv311y7VzXsLYf7nGOWunzlzpt192AuwGjVqJPZuH8w1ePDgAgdY8+bNs7n//L/Ul1dmZqa8/PLLToVHtWvXdrl/jhj5DKyUlBSn2nriiSfslsu9skrvirO8vv32W63tU6dOWWxz9u8H5iPAgh7OeGEoBg4AMM8Tc58gwAI8jFsIzbmF8IcfftDOGUeMGOFS3erVq4tS9h/ivnDhQrv7sBdg1a5dW5RS8tZbb9ndx//93/8VOMCy1097AVbuVURKKenevbusXbtWfv/9d0lKStKeO/Wvf/3L7f45YmSA5WxbrVu3tlvO2fdt8+bNWtv5b0l09u8H5iPAgh7OeGEoBg4AMM/j/32cAAvwMB7ibo5bt27JfffdJ0rdezC3sy5evKida+o93NyIACv3CqxXX33V7j6MuALL3QCratWqDq8Sy32GVqAEWM5egbV69WqHV2ARYHkfARb0cMYLQzFwAIB5QkaFEGABHkaAZZ6//vWvotS9h6U7e3yHDx+unWvq/XqfEQHW888/L0o5fgbWs88+65UAKykpSVu/Zs0am/XbtWsXUAFW165dRSnHz8AaPXq0KGX/GVgEWN5HgAU9nPHCUAwcAGCePqv7EGABHkaAZZ7vv/9eO2/s2rWrw/KXLl2SUqVKiVJKqlWrJpmZmVZljAiwxo4dqwVrly9f1q1/+/Zt7aHpng6wrl69qq1ftWqVbt3Lly9L0aJFAyrAmjhxorZPe79C2LhxY1HK/q8QEmB5HwEW9HDGC0MxcACAefqv70+ABXgYAZa5evTooZ07Dh06VLKzs3XLxcXFySOPPCJKKQkKCpKNGzfqljMiwLpw4YJ2e2Pv3r116w8aNEir7+kAKzMzU0qWLClKKenXr59VvaysLHnuuecK1D9HCmOAFRcXJ8HBwaKUkk6dOklWVpZVmdmzZ2vtzpo1y2o7AVbhQYAFPZzxwlAMHABgnte/fZ0AC/AwAixz3bhxQx5//HHt/LFZs2by1Vdfyblz5yQxMVF+++03GTt2rJQrV04rM27cOJv7MyLAErEMqHr27CkxMTGSlJQkv/32m/z9738XpZRERER4JcASEXnttde0be+//74cP35cEhMTZdu2bdqtgw8//LBHAqzch8bbW9LS0px+bbbachRgiYh89tln2n47dOggO3fulMTERDl58qR8/PHHUqRIEe320Py/YClCgFWYEGBBD2e8MBQDBwCY55klzxBgAR5GgGW+5ORkee211yQoKEg7j9RbypUrJ1FRUXb3ZVSAlZaWJp06dbLZl969e8uwYcNEKSW1a9e2qm92gHXlyhWpVq2azf6999572vPCzA6wnFn69u3r9Guz1ZYzAVZ2dra8++67dvvSqFEjuXr1qm59AqzCgwALejjjhaEYOADAPM8ufZYAC/AwAizP+fXXX2Xw4MHSsGFDKVu2rISEhMiDDz4obdq0kcmTJ8v169cd7sOoAEvkXhgyc+ZM+fOf/ywlS5aU0qVLS7NmzWTevHkiIjJkyBBRSknjxo2t6podYImIXLt2TQYMGCDVqlWT4OBgqVChgnTo0EG+/fZbEZGADLBybd26VXr06CGVK1eW4OBgKVu2rLRs2VKmT58u6enpNusRYBUeBFjQwxkvDMXAAQDmIcACPI8AC7bk/upd586dvd0VwO8QYEEPZ7wwFAMHAJjHVnilRiiJvxPv7e4BfokAC3ru3LkjpUuXFqWUDBs2zNvdAfwOARb0EGDBUAwcAGAeewHWkE1DvN09wC8RYAWmtLQ0uXv3rs3t//jHP7Rz3iNHjniwZ0BgIMCCHgIsGIqBAwDMYy/A6ra8m7e7B/glAqzAdP78eXnwwQdl5MiR8vPPP0tCQoLEx8fLli1b5IUXXtDOd3v06OHtrgJ+iQALegiwYCgGDgAwj70A6/llz3u7e4BfIsAKTHkf8m5radGihdy4ccPbXQX8EgEW9BBgBZAzZ85IsWLFTP11DQYOADCPvQCr4+KO3u4e4JcIsAJTWlqazJ8/X7p37y5/+tOfJDw83OKX/hYuXChZWVne7ibgtwiwoIcAK4C0b9/e4n+NCLAAwLeUHV/WZoDVJqqNt7sH+CUCLADwPAIs6CHAChCLFy8WpZTUqlWLAAsAfFT4+HCbAVbLBS293T3ALxFgAYDnEWBBDwFWALh+/bpUqFBBihQpImvWrCHAAgAfVT2yus0A69Mtn3q7e4BfIsACAM8jwIIeAqwA8Oabb4pSSgYPHmzxQEoCLADwLd2Wd7MZYE3aPcnb3QP8EgEWAHgeARb0EGD5uZ07d0pQUJA8+OCDcuvWLQIsAPBhXb/qajPAmvnzTG93D/BLBFgA4HkEWNBDgOXHMjIypH79+qKUkq+++kpEhAALAHxYl6+62AywklKSvN09wC8RYAGA5xFgQQ8Blh8bPXq0KKWkXbt22joCLADwXe9ueJcAC/AwAiwA8DwCLOghwPJTv//+uxQrVkxCQkLk5MmT2nojAqw7d+7YXOLj4xk4AMAkA78fSIAFeBgBFgB4HgEW9BBg+am//vWvopSSTz75xGK9EQFWbn1HCwMHABjL3hVYOy7s8Hb3AL9EgAUAnkeABT0EWH5o6dKlopSSmjVrSkpKisU2AiwA8F2ReyNtBlirjq/ydvcAv0SABQCeR4AFPQRYfubGjRtSsWJFUUrJd999Z7WdWwgBwLetOr5KGs1qZBVgrT6x2ttdA/wSARYAeB4BFvQQYPmZwYMHi1JKunTporudh7gDgG/7y7y/6F6B1W15N293DfBLBFgA4HkEWNBDgOVnunbt6vQtfnmX8+fPG9I+AwcAmOda8jWbtxC2W9TO8Q4AuIwACwA8jwALegiw/AwBFgD4r7PXz9oMsNouauvt7gF+iQDLPMOHD9fOG4OCguTAgQN2y/ft21eUUlK3bl0P9dC32bvrIvdYtm7d2uP9KoiFCxdqr8tdOTk5smbNGnnppZekZs2aUrx4cSlevLjUqFFDnnjiCRkwYICsWLFCEhISDOw5XEWABT0EWH7m3LlzcvDgQZvLhg0btA/2yJEjtfXp6emGtM/AAQDmsRdgtYlq4+3uAX6JAMs8eQMse4/AyEWA5RoCLGs3b96Udu3aOfUf/H379jW2816W+3mrUaOGt7viFAIs6CHACjA8AwsAfNeJhBM2A6w/z/mzt7sH+CUCLPPkD7CUUhITE2OzPAGWawiwrLVv316r36lTJ/nuu+/k9OnTcv36dTlz5oysWLFC3njjDSlbtiwBlpcRYEEPAVaAIcACAN/VblE7mwFWw5kNvd09wC8RYJknd0IdHBwsVatWFaWUPPfcczbLE2C5xsxzfm8pSIC1detWre7gwYPtlr179678/PPP7nazUCLAgj8gwAowBFgA4LtaLmhpM8CqOqWqt7sH+CUCLPPkTqhDQ0Nl5syZ2jmkreCAAMs1BFiWPvnkE63ulStXTOhd4UaABX9AgBVgCLAAwHe1mN/CZoBVfHRxb3cP8EsEWObJG2Clp6dL9erVRSklzz77rG55ZwKstLQ0iYyMlObNm0vZsmUlJCREqlatKr169ZKdO3farJc/GDl37pz0799fIiIiJDQ0VMqUKWPVj9zb7/bs2SPdunWTSpUqSfHixaVBgwYybdo0ycrK0urExsbKoEGDpHbt2lKsWDGpXLmyvPPOO3YfFJ6QkCBRUVHSvXt3qVmzpoSGhkrx4sUlIiJC+vXr5/Ch9+7cQpj3ODhabN1+mJSUJMOHD5emTZtKeHi4hIaGSo0aNaRv375y+PBhu30WEVm5cqW0bt1aypQpI6VKlZLGjRvLpEmTJCMjo0AB1t///netbnJyssv1Raz/Tn7//Xd56623pHr16hIaGipVqlSRv/3tb06NF5cuXZKhQ4dKw4YNpXTp0lKsWDGpU6eODBgwwKn6sbGx8uGHH0qTJk0kPDxcihUrJrVr15bOnTvLwoUL5fbt2yIism3bNpee9+XKZyEnJ0f27dsnH3/8sTzxxBMSHh4uRYsWlXLlyslTTz0l06ZNk5SUFFcOsYYAC3oIsGAoBg4AME/z+c1tBljFRhfzdvcAv0SAZZ68AZaIyOzZs7XzyH379lmVdxRgXbx4UerVq2d3ov7BBx9ITk6OVd28k/Zdu3ZJmTJlLOrZCrDmz58vRYoU0W2rT58+IiISExMjFSpU0C1Tr149uXXrlu7rady4sd3XUqRIEZk1a5bN4+uNAOvHH3+U8PBwm3Xuu+8+mTlzps0+9+/f3257c+bMcTvA+vDDD7W6a9ascbm+iOXx2bdvn9XfSe5SsmRJ2b59u839LF68WIoVK2bztRYvXlxWr15ts/6CBQskNDTU7vuT+74XJMBy9FlYs2aNw303btxY/vjjD5ePNQEW9BBgwVAMHABgnmbzmtkMsNQIvtIBMxBgmSd/gJWRkSE1atQQpe49YDs/ewFWamqq1K9fX5RSEhISIsOGDZNTp05JQkKCbN26VVq2bKmdo06ZMsWqft5Je5UqVaRWrVqybNkyuXLlily9elXWrl1r1Y/KlStLSEiIdO7cWXbv3i2JiYly9OhRefHFF7V9ffPNN1KjRg2pX7++rFmzRuLj4+XixYsybNgwrcyHH36oe3w6d+4sAwcOlO+//16OHDkiCQkJcu7cOdmwYYN06tRJC7F+/fVX3fruBFiZmZmSnJxsc1m1apUEBQWJUtbPkfrll18kJCRElFLStGlTWbFihcTGxkpSUpLs2bNHXnjhBVFKSVBQkGzcuNGqTzNmzND6/NRTT0l0dLSDN4voAAAgAElEQVQkJCTIsWPH5J///KcEBQVJzZo13Q6wfvjhB63uAw88IAsWLHD5Sqy8fyc1a9aUypUry5IlSyQuLk5iY2Nl9uzZUrZsWVFKSdmyZSUuLs5qH3lDn/bt28uGDRskLi5OEhMT5aeffpJWrVppn4tDhw5Z1V++fLlWPyIiQubPny/nzp2T69evy6lTp2ThwoXSoUMHiYqKEhGRrKwsSU5Olo8//liUUlK9enWr9zUtLU33NTr6LKxfv17atWsns2bNkr1798qFCxckISFBDh06JKNHj5b7779flFLSrVs3l46zCAEW9HG2C0MxcACAefqt6Wc3wLqWfM3bXQT8DgGWefIHWCIic+fO1c4l9+7da1HeXoA1adIkrd7SpUuttqenp2shVvHixSUpKclie95Je4UKFXSDh/z9yJ2Y57+iKyMjQyIiIkQpJUWLFpWIiAi5efOm1X569+4tSimpWLGizbbseeWVV0QpJb1799bd7k6AZc+pU6e0q6tatWolGRkZ2racnBxp2LChKKWkWbNmFtvyev3110UpJfXr17c4bikpKdq+mzVrJunp6VZ1R48ebXFljztyg7/cJTg4WB5//HEZMGCAfPnll3L58mW79fP+nYSFhcnvv/9uVWb//v3aVXkDBgyw2JaSkiLly5cXpZS89NJLum1kZmbK008/LUopeeaZZyy23b59WwvIHn30Ublx44bNvmZmZlr829lnYLnyWXDk2LFjUrRoUQkKCpLTp0+7VJcAC3oIsGAoBg4AMM+nWz61G2BtPGP9P9oACsbVSVTJMSXdWqbunaq7v+9Pf+/2Pk8mnNTd56DvBzm9DzPpBVgZGRlSq1YtUUpJhw4dLMrbC7Byr75q0aKFzfYOHDignafOmDHDYlveSfukSZPs9jtvgHX27FndMkOHDrUbIImIrFu3Titz8eJFu23q2bBhgyilpFKlSrrbjQywbt68KXXr1hWllNSqVcvq2V1btmzR2jt69KjN/Vy6dEkrd/DgQW193quKbN16l5mZKVWqVClQgJWSkiIDBgyQokWL6t7uFhQUJB06dLB5VVvev5OPP/7YZjt9+vQRpZSULl3aIkiaP3++Fpxdv37dZv0dO3Zo/clbzpkfO7DFnQDL0WfBGY8//rgopWT27Nku1SPAgh4CLBiKgQMAzPPJT5/YDbB+OvuTt7sI+B1XJ1H2PqP2lom7J+rub92pdW7v80TCCd19vr3ubaf3YSa9AEvkf5N8pZTs3r1bW28rwEpKStLK690emFft2rVFKSU9e/a0WJ930n7s2DG7+8jtR506dWyWyRs02Lqq5/jx41qZ/fv365Y5fPiwDBgwQBo1aiRhYWHa7Xv5l9wHdudlVICVlZWlXbkUFhamG1Dl/sJfpUqV7N6CmJycrF2BNHfuXK3+oEGDRCkl5cqV031GWa633367QAFWrkuXLsmECROkQ4cOus+xCg4OluXLl1vVy/t3Yus9ExFZvXq1blD36quvilJKnnjiCbvH6I8//tDqb968Wav/0ksv2QxxHXEnwHL0WRC5d3XjnDlzpGPHjvLggw9qt5HmX4YMGeJSfwmwoIcAC4Zi4AAA8zgKsLZfsP3AWADuIcAyj60AKzMzUwua2rdvr623FWAdPHhQO//ctGmT3Ta7dOkiSin5y1/+YrE+76Td0a+m5fajZcuWNsvk3V/eXyPMK++vg0dHR1ttnzx5ss0HxOdfYmNjreobFWANGTJElLr3APZ169bplunevbtT/cy7jB07Vqtv633JLzIy0pAAK6+cnBw5deqUzJgxQ7sNUql7t5peunTJomze99XWw/dFRE6cOKGVy/vA+D//+c8uH6dly5Zp9Zs0aSJKKXn55Zddfp3uBFiOPgtXr16VRx55xKnX8eabb7rUXwIs6CHAgqEYOADAPB//9LHdiebe2L2OdwLAJdxCaB5bAZaI5SR6586dImI7wNq5c6dWdteuXXbbzH3uVIMGDWy254gz4Y8z+8sbYG3bts3ma2rSpIksWrRIjh49KgkJCdpVOrm3ECql5Pz581b7NyLAioqK0vYzYcIEm+Xat2/vcjAzfPhwrX67du2sAks9eZ+RZoasrCzp1auX1sbIkSMttjsTTIqIxMbGauWWLFmira9Tp47Lxynv+5db39UwSMS9AMuR3PctODhY3n//fdm2bZtcunRJbty4of2dtmjRQpSy/KVDZxBgQQ8BFgzFwAEA5vnX5n/ZDbBirsR4u4uA3+Eh7uaxF2BlZWVpk/U2bdqIiDFXYHXt2lX3Sp/CFmDlBm0RERGSmpqqWz/vbWpmBFh79uyR0NBQUUpJnz59bJYTEe0XBu1dlWaPN6/Ayi8hIUFr44UXXrDYVtArsBo3bixKKXnttdfc6punr8Cy58yZM1q5//73vzbLPfroowRYMAwBFgzFwAEA5nEUYMXesr6FBEDBEGCZx16AJSKyaNEi7bwyOjra7jOwcp8N5egZWLmhWK9evSzWF7YAK/dWtsGDB9usP2rUKNMCrNjYWKlUqZIWKqWlpdnsh8j/bjMMDw+3e2WSLZ5+BpYjFSpUEKWsf0igoM/AevHFF0Up6ysAndWjRw9RSkm9evVcrmt0gLVq1SqtnN6vbIrc+1GG4sWLE2DBMARYMBQDBwCY56PNH9kNsDKzMx3vBIBLCLDM4yjAysrKkoceekgLWuz9CmHuc3js/QrhoUOHtPNUe79C6IgnAqzc1z1w4EDdujk5ORbPHjIywEpJSZHHHntMlFJSvXp1uXbtms3XkGvjxo1aeytXrnRYPj9P/QqhM+7cuaM9iLxfv34W25z9FcLc45v/VwhnzZql1Y+Jcf2q6dmzZ2v1f/nlF5fqjh49WpRSUrVqVbvlnP0sLFu2TCuXlJSkW+brr7/WyhBgwQgEWDAUAwcAmMdRgJWV7fr/egOwjwDLPI4CLBGRxYsXa+eWERERNgOsyZMna+WWLl1qtT0jI0NatWolSikpUaKE1YS7sAVYHTt21F5renq6Vd3x48drdY0OsHr27ClKKSlZsqQcOnTIZv/zys7O1gK1ypUr6/Ynr5MnLZ/PlpKSIuHh4aKUkmbNmum+5jFjxli8ZlfNnTtXVqxYIdnZ2XbLffTRRzb/lvK+r2FhYfL7779b1d+/f78ULVpUlFIyYMAAi2137tyRBx54QJRS0rBhQ5vBT678xyk5OVnKli0rSt17NpqtK59ExCI4E/lfeFasWDGrbbZeoz179+7VykVFRVltj4+Plxo1ahBgwVAEWDAUAwcAmGfz2c0yYtsIGb5tuG6AlZ1j/6QcgOsIsMzjTICVlZUldevWtQgu9AKs1NRULUAJCQmR4cOHy+nTpyUxMVGio6OldevWWn292wwLW4A1b948bVunTp1k3759kpCQIIcPH5YBAwaIUkoefvhhwwOsvM+Y+vLLL7UHcest+X+hLiYmRooVKyZK3bsVcOzYsXL48GG5fv26xMfHS0xMjMycOVPatWsnYWFhVn2aMWOG1narVq0kOjpaEhMT5cSJEzJkyBAJCgqSmjVruh1gvf/++9pVZR9//LFs2rRJLly4IDdv3pTY2FhZv3699ow0pZQ0atTIKujJ+77WrFlTqlSpIkuXLpW4uDi5fPmyzJkzRwuYwsPDJS4uzqof69at0255rVatmkyfPl1OnDghN27ckLi4ONm7d69MnjxZnnzySd1bDfNe1VSnTh1ZuHChnD9/Xm7cuCGnT5+WJUuWyDPPPGMVKu3fv1+r9+9//1vi4uIkMzNTMjMzLUI9Zz8LmZmZWkBVunRpmTlzply4cEHi4uJk+fLlEhERISVKlNDKEGDBCARYMBQDBwB4hqd/7h4IVARY5nEmwBIRWbp0qcMAS0TkwoULUq9ePYuy+ZehQ4fqPmOpsAVYWVlZ2lVYekvz5s1l/fr1hgdYueudWfRe/44dO7RnZ9lbypUrp3tM+vfvb7POU089JXPmzHE7wBo5cqTTr+3xxx+XK1euWO0j7/u6d+9eKV26tG79kiVLSnR0tM2+rFq1SsLCwhz247HHHtOtP3fuXO02R1uL3vverFkz3bJ5wyVXPgubN2/WHvSffwkJCZHly5dr4TEBFozA2S4MxcABAJ5BgAV4BgGWeZwNsLKzsy2uNrIVYIncuxIrMjJSmjdvLuHh4RISEiJVq1aVnj17ys6dO23WK2wBlsi92x7HjRsn9evXl9DQUClTpow0bdpUJk+eLBkZGbJt27ZCF2CJ3JsPTJ06Vdq2bSsVKlSQokWLSokSJeRPf/qTvPzyy7Js2TK5ffu2zeOyYsUKad26tZQuXVpKliwpjRo1kvHjx0tGRoZL75OekydPSmRkpHTv3l3q1asnYWFhUqRIEQkLC5O6devKK6+8It9++63N2wzzt3/69Gn529/+JtWqVZOQkBCpXLmy/O1vf5OzZ8867EtiYqKMHj1aWrRoIffff78ULVpUSpUqJQ8//LD06dNH1qxZY/MXKEXu/f0MHjxYHnnkESlVqpSUKFFC6tSpI88//7wsWrRIkpOTrepcv35dhgwZIvXq1dOulitIgCUicuDAAenWrZuUK1dOQkJCpFq1avLaa6/JgQMHREQIsGAoznZhKAYOAPAMvQArJSPFcUUALiHAApCroAEanEeABT188mAoBg4A8Ay9AOtU4ilvdwvwOwRYAHIRYHkOARb08MmDoRg4AMAzCLAAzyDAApCLAMtzCLCgh08eDMXAAQDmOZN0Rrae2ypbz23VDbBOJpx0vBMALiHAApCLAMtzCLCgh08eDMXAAQDmef+H93WDq9zl+B/Hvd1FwO8QYAHIRYDlOQRY0MMnD4Zi4AAA4yWnJ8uOCzvk9W9fJ8ACPIwAC0AuAizPIcCCHj55MBQDBwAYKzk9WR6a/pDd4IoACzAPARYAeB4BFvQQYMFQDBwAYKyJuyc6FV4RYAHmIMACAM8jwIIeAiwYioEDAIz11tq3CLAALyLAAgDPI8CCHgIsGIqBAwCMFXUwigAL8CICLADwPAIs6CHAgqEYOADAWARYgHcRYAGA5xFgQQ8BFgzFwAEAxiLAAryLAAsAPI8AC3oIsGAoBg4AMBYBFuBdBFgA4HkEWNBDgAVDMXAAgLEIsADvIsACAM8jwIIeAiwYioEDAIz1+b7PCbAALyLAAgDPI8CCHgIsGIqBAwCM1Xx+c6cDrNtpt73dXcDvEGABgOcRYEEPARYMxcABAMb6y7y/OB1gZWVnebu7gN8hwAIAzyPAgh4CLBiKgQMAjNVsXjMCLMCLCLAAwPMIsKCHAAuGYuAAAGM9OfdJq6Dqlyu/SI3IGhbrJu6eKDk5Od7uLuB3CLAAwPMIsKCHAAuGYuAAAGPpBVgiIm0XtdVdD8BYBFgA4HkEWNDD2S4MxcABAMZ6Yu4TukFVm6g2BFiABxBgmWf48OHaeWNQUJAcOHDAbvm+ffuKUkrq1q3roR76ttxju3DhQqttuceydevWHu9XQSxcuFB7Xe6oUaOGVt+Z5fz588a+ADiNAAt6ONuFoRg4AMBYk/dMtgipPtr8kYjoB1gJdxO83FvA/xBgmSdvgKWUki5dutgtT4DlGgIsawRYvoMAC3oIsGAoBg4AMNb0/dMtQqrPoj8TEf1bCJf9tszLvQX8DwGWefIHWEopiYmJsVmeAMs1BFjWcgOsli1bSnJyssOFZ0t6DwEW9BBgwVAMHABgrPwB1qjto0REpPS40lYBVuioUC/3FvA/BFjmyQ2wgoODpWrVqqKUkueee85meQIs19gLsHyVUQGWrwV3gYgAC3oIsGAoBg4AMNbn+z63CqqGbBpitY7nYAHmIMAyT26AFRoaKjNnztTOIX/++Wfd8gRYriHAskaA5TsIsKCHM10YioEDAIw1bd80m2EVARZgPgIs8+QNsNLT06V69eqilJJnn31Wt7wzAVZaWppERkZK8+bNpWzZshISEiJVq1aVXr16yc6dO23Wyx+MnDt3Tvr37y8RERESGhoqZcqUsepHbgiyZ88e6datm1SqVEmKFy8uDRo0kGnTpklWVpZWJzY2VgYNGiS1a9eWYsWKSeXKleWdd96RhATbzy5MSEiQqKgo6d69u9SsWVNCQ0OlePHiEhERIf369XP40Ht3biHMexwcLbZCoKSkJBk+fLg0bdpUwsPDJTQ0VGrUqCF9+/aVw4cP2+2ziMjKlSuldevWUqZMGSlVqpQ0btxYJk2aJBkZGV4PsPIft+3bt8uLL74olStXliJFikjXrl2t2ho+fLiI3Du2rVq1kvvvv1+UUhIZGWmx76ysLFmwYIG0a9dOHnjgAQkODpZKlSpJ165d5bvvvrPZp23btlk8s+uPP/6QoUOHSt26daVEiRKilJIbN2649Xq9iQALejjThaEYOADAWFP3TiXAAryIAMs8eQMsEZHZs2dr55H79u2zKu8owLp48aLUq1fPbujywQcf6D7XKG8wsmvXLilTpoxFPVsB1vz586VIkSK6bfXp00dERGJiYqRChQq6ZerVqye3bt3SfT2NGze2+1qKFCkis2bNsnl8vRFg/fjjjxIeHm6zzn333SczZ8602ef+/fvbbW/OnDmFJsD6/PPP5b777rPoo16ANWzYMOnRo4fV68kbYF2/fl1atGhh93i/+uqrkpGRYdWnvAHWDz/8IJUrV7aqS4AFf8GZLgzFwAEAxur6VVcCLMCLCLDMkz/AysjI0Cb9nTp1sipvL8BKTU2V+vXri1JKQkJCZNiwYXLq1ClJSEiQrVu3SsuWLbVz1ClTpljVzxvcVKlSRWrVqiXLli2TK1euyNWrV2Xt2rVW/ahcubKEhIRI586dZffu3ZKYmChHjx6VF198UdvXN998IzVq1JD69evLmjVrJD4+Xi5evCjDhg3Tynz44Ye6x6dz584ycOBA+f777+XIkSOSkJAg586dkw0bNkinTp20EOvXX3/Vre9OgJWZmWn3oearVq2SoKAgUUrJ4MGDLer+8ssvEhISIkopadq0qaxYsUJiY2MlKSlJ9uzZIy+88IIopSQoKEg2btxo1acZM2ZofX7qqackOjpaEhIS5NixY/LPf/5TgoKCpGbNmoUiwKpYsaIUKVJEnn76admyZYskJCTI2bNnZcuWLVZtValSRZRS8s4778iBAwckMTFRDh48KAcPHhQRkZycHGnXrp12bAYOHCi//fabJCYmyt69e7XjppSSQYMGWfUpb4BVpUoVKV++vMyePVsuXrwo8fHxsm7dOklNTXXr9XoTARb0cKYLQzFwAICxGs9uTIAFeJGrk6jt20u6tcTGTtXdX2Li927v8+7dk7r7PH16kNP7MFP+AEtEZO7cudq55N69ey3K2wuwJk2apNVbunSp1fb09HQtxCpevLgkJSVZbM8bYFWoUEHi4uJs9ju3H0op6datm9UVXRkZGRIRESFKKSlatKhERETIzZs3rfbTu3dvLQxxxyuvvCJKKendu7fudncCLHtOnTqlXV3VqlUri6uBcnJypGHDhqKUkmbNmuleKSQi8vrrr4tSSurXr29x3FJSUrR9N2vWTNLT063qjh492uKqIne48iuEKSkpVvXzvvdt27aVzMxMh23lXoVly8qVK7VyY8aMsdqek5MjPXv21AKu48ePW2zPG2AVK1ZMjh075sIRKbwIsKCHM10YioEDAIz16ZZPCbAAL3J1ErVtm3JruXhxou7+EhLWub3PO3dO6O7z5Mm3nd6HmfQCrIyMDKlVq5YopaRDhw4W5e0FWLlXX7Vo0cJmewcOHNDOU2fMmGGxLW+ANWnSJLv9zhtinD17VrfM0KFD7QZIIiLr1q3Tyly8eNFum3o2bNggSimpVKmS7nYjA6ybN29K3bp1RSkltWrVsnp215YtW7T2jh49anM/ly5d0srlXoEkIrJ8+XJt/fbt23XrZmZmalczFTTAcmbROzZ53/uYmBin2rr//vt1A7lczz77rCilpEaNGhbPTcvr2rVrEhwcLEopGTp0qMW2vAHWP/7xD8cHwUcQYEEPZ7owFAMHABgr6mAUARbgRQRY5tELsERE5s+fr51P7t69W1tvK8BKSkqye3tgXrVr1xallPTs2dNifd4Ay9EVLLn9qFOnjs0yeX9V8fLly7pljh8/rpXZv3+/bpnDhw/LgAEDpFGjRhIWFqbdvpd/uX37tlVdowKsrKws7ZbFsLAw3YDqk08+0cI0R1c2lS9fXpRSMnfuXK3+oEGDRCkl5cqV031GWa633367UARY5cuXd7qtHj162CyTk5OjPW9N7/bAvHJvM3zyySct1ucNsDZs2OCwX76CAAt6ONOFoRg4AMBYBFiAd3ELoXlsBViZmZla0NS+fXttva0A6+DBg9r556ZNm+y22aVLF1FKyV/+8heL9XkDLL1bx/LK7UfLli1tlsm7P1tX1Zw/f14rEx0dbbV98uTJNh8Qn3+JjY21qm9UgDVkyBBR6t4D2NetW6dbpnv37k4HQ7nL2LFjtfq23pf8IiMjDQmwCvoMrCeeeMLptmw940xE5MaNG9rrmT17tt395YZ8Dz74oMX6vAFW/tsLfRkBFvRwpgtDMXAAgLEIsADv4iHu5rEVYIlYBkA7d+4UEdsB1s6dO7Wyu3btsttm7nOnGjRoYLM9R5wJf5zZX94Aa9u2bTZfU5MmTWTRokVy9OhRSUhI0K5kyr2FUCkl58+ft9q/EQFWVFSUtp8JEybYLNe+fXuXA6zhw4dr9XOvLsobWOrJ+4w0dxj5K4TOtpX3deYXGxurvZ4lS5bY3d+nn34qSikpVaqUxfq8AZbe34GvIsCCHs50YSgGDgAwFgEW4F0EWOaxF2BlZWVJnTp1RCklbdq0ERFjrsDq2rWrwyuwHPFEgJUbtEVERNj8BbnVq1ebGmDt2bNHQkNDRSklffr0sVlORLRfyrN3VZo9vnYFllEBlitXYL333nsOr8AiwGIe6u8404WhGDgAwFgEWIB3EWCZx16AJSKyaNEi7bwyOjra7jOwcp8N5egZWLmhWK9evSzWF7YAK/cX/QYPHmyz/qhRo0wLsGJjY6VSpUpaqJSWlmazHyL/u80wPDzc5i2T9nj6GViFJcDKycnRfn3R0TOwcq9ya9asmcV6Aqx7mIcGBs50YSgGDgAw1qDvBxFgAV5EgGUeRwFWVlaWPPTQQ1pgYO9XCB955BFRyv6vEB46dEg7T7X3K4SOeCLAyn3dAwcO1K2bk5OjvWajA6yUlBR57LHHRCkl1atXl2vXrtl8Dbk2btyotbdy5UqH5fPz9K8QFpYAS0Skc+fOopT9XyGMj4+XkJAQUcr+rxASYDEP9Xec6cJQDBwAYKz6X9QnwAK8iADLPI4CLBGRxYsXa+eWERERNgOsyZMna+WWLl1qtT0jI0NatWolSikpUaKEJCUlWWwvbAFWx44dtdeanp5uVXf8+PFaXaMDrJ49e4pSSkqWLCmHDh2y2f+8srOztUCtcuXKDoOUkyctf2AgJSVFuxKpWbNmuq95zJgxFq/ZHYUxwFq1apX2msaMGaNb5tVXXxWllAQFBVk9qJ0A6x7moYGBM10YioEDAIz18IyHCbAALyLAMo8zAVZWVpbUrVvXIrjQC7BSU1O1ACUkJESGDx8up0+flsTERImOjpbWrVtr9fVuMyxsAda8efO0bZ06dZJ9+/ZJQkKCHD58WAYMGCBKKXn44YcND7DyPmPqyy+/1B4Yr7fk/7XGmJgYKVasmHYr4NixY+Xw4cNy/fp1iY+Pl5iYGJk5c6a0a9dOwsLCrPo0Y8YMre1WrVpJdHS0JCYmyokTJ2TIkCESFBQkNWvWNCTAatmypd3XlrtkZmY6ddzsteUowMrJydEeYh8UFCQDBw6Uo0ePSlJSkuzfv19efPFF7TXr3WZIgHUP89DAwJkuDMXAAQDG0guwKk6sSIAFeAgBlnmcCbBERJYuXeowwBIRuXDhgtSrV8+ibP5l6NChus9YKmwBVlZWlnYVlt7SvHlzWb9+veEBVu56Zxa9179jxw7t2Vn2lnLlyukek/79+9us89RTT8mcOXMMCbCcXfIfOzMCLJF7z3Fr0aKF3b68+uqrkpGRYVWXAOse5qGBgTNdGIqBAwCMVW9GPYuAKvizYBERSU5P1g2wYm/FernHgH8hwDKPswFWdna2xdVGtgIskXtXYkVGRkrz5s0lPDxcQkJCpGrVqtKzZ0/ZuXOnzXqFLcASuXfb47hx46R+/foSGhoqZcqUkaZNm8rkyZMlIyPDYXDhjQBL5N58YOrUqdK2bVupUKGCFC1aVEqUKCF/+tOf5OWXX5Zly5bJ7du3bR6XFStWSOvWraV06dJSsmRJadSokYwfP14yMjJcep/0FNYAS+ReaDl//nxp166d3H///RIcHCwVK1aULl26yHfffWezHgHWPcxDAwMBFgzFwAEAxqo7va5FQBUyKkTb1mxeM6sAa/KeyV7sLeB/CLAAwPMIsKCHAAuGYuAAAGO1mN/CIqCqO/1/Vx48HfW0VYAVuTfSi70F/A8BFgB4HgEW9BBgwVAMHABgrNHbR1sEVNP2TbO5Lf92AAVHgAUAnkeABT0EWDAUAwcAGCt/SPX5vs+1bV8f/doqwPrp7E9e7C3gfwiwAMDzCLCghwALhmLgAABjjdo+ymaAte38NqsAa/uF7V7sLeB/CLAAwPMIsKCHAAuGYuAAAGN9Fv2ZVUj10PSHpO70ujJs6zCrbdvOb/N2lwG/QoAFAJ5HgAU9BFgwFAMHABhrZPRIq5Aqdxm8cbDVupHRI73dZcCvEGABgOcRYEEPARYMxcABAMYasW2EzQBr4PcDrdYV/ayot7sM+BUCLADwPAIs6CHAgqEYOADAWA9Nf8hmgBU+PtxqXdCIIG93GfArBFgA4HkEWNBDgAVDMXAAgLHqfF7HZoBlawFgHAIsAPA8Aizo4SwXhmLgAE8HHiYAACAASURBVABjfXnoSwIswIsIsADA8wiwoIezXBiKgQMAjLXst2U2g6oHJz1ote7pqKe93WXAr+ROos6ePevtrgBAwDh79iwBFqwQYMFQDBwAYCx7AdbHP31stW7ur3O93WXAr1y4cEGOHz8up06dkpycHG93BwD8Xk5Ojpw6dUqOHz8uFy5ccKoO89DAQIAFQzFwAICxvjrylc0Aa/T20VbrFh1a5O0uA34lPj5ejh8/LsePH5e7d+96uzsA4Pfu3r2rjbvx8fFO1WEeGhgIsGAoBg4AMNbyI8ttBlhjdoyxWvfloS+93WXAr6SmpmoTqTNnzsjdu3e5EgsATJCTkyN3796VM2fOaONuamqqU3WZhwYGAiwYioEDAIxlL8Aat3McARZgspycHImNjdUmU7m3E549e1bOnTvHwsLCwmLAcvbsWe22wdwlNjbW6f8wYB4aGAiwYCgGDgAw1jNLnrEZYI3fOd5q3Y4LO7zdZcDv5OiEWCwsLCws5i2uhFcizEMDBQEWDMXAAQDGqvN5HZsB1oRdE6zW/Xr1V293GfBLOTk5kpqaKvHx8XLhwgWvX63AwsLC4m/LhQsXJD4+XlJTU12+VZt5aGAgwIKhGDgAwFj2AqyJuydarSszroy3uwwAAOBRzEMDAwEWDMXAAQDG0guw3lz7pry59k1ZcniJbrCVkpFisaRnpXv7ZQAAAJiGeWhgIMCCoRg4AMBY+QOs4qOLa9t+vfqrboD1/777fxb/7remnxdfAQAAgLmYhwYGAiw/dOnSJfn888+ld+/e0rBhQ6lUqZIEBwdLqVKlpEGDBjJw4EA5ceKEKW0zcADwZ20XtbUIhj788UPT28wfYJUYU0LbduDqAd0Aq/eq3gRYAAAgYDAPDQwEWH5o+vTp2ofX1hIcHCzTp083vG0GDgD+LH9Q9NSCp0xvs8tXXSzafGXlK9q2g3EHdQOsXit7Wfy77vS6cuX2FdP7CgAA4A3MQwMDAZYfmjdvnnTu3FkmT54sW7dulZMnT0piYqIcO3ZM5s2bJ3Xq1NE+3OvXrze0bQYOAP7MGwHWBz9+YNHmokOLtG1H4o/oBlgvr3jZat2UPVNM7ysAAIA3MA8NDARYAejGjRtSuXJlUUpJ69atDd03AwcAf+aNAGvoD0NtBlgZWRm6AdZL37xktW7ynsmm9xUAAMAbmIcGBgKsADVw4EBRSkmpUqUM3S8DBwB/5o0A6/0f3rdo899b/y2nE0/LmaQzkpKRohtg6f1yIQEWAADwV8xDAwMBVoAaMmSIKKXkgQceMHS/DBwA/Jk3Aqwhm4bohlRqhJKdF3fqri/3n3JW6x7/7+Om9xUAAMAbmIcGBgKsAJSamqo9B6tTp06G7puBA4A/yx8KtVrYyvQ23QmwOizuYLWu6pSqpvcVAADAG5iHBgYCrACRnZ0tV69elbVr18rjjz8uSikJCQmR/fv3G9oOAwcAf+aNAOu1b1+zGWDturhLd33pcaWt1lWZXMX0vgIAAHgD89DAQIDl5zp27Kh9kPMudevWlejoaLf2eefOHZtLfHw8AwcAv+WNAKv2tNouB1h6CwEWAADwVwRYgYEAy8/pBVjly5eXWbNmSXJyslv71AvE9BYGDgD+xhsB1rNLn7UZSu2+tJsACwAABDwCrMBAgOXnUlNTJTk5WW7duiXnzp2TL7/8Uh5++GFRSkn9+vXl3LlzLu+TAAtAoPJGgLXu1DpDAqzKkyub3lcAAABvIMAKDARYASglJUWefPJJUUpJgwYNJDs726X63EIIIFDlD4VaL2xtepvrT623G2DV/6I+ARYAAAhoBFiBgQArQG3btk37gG/evNmw/TJwAPBnhS3A2nNpjzy/7HmnAqwHJz1oel8BAAC8gXloYCDAClApKSnaB3zChAmG7ZeBA4A/80aAteH0BrsBlrO3EBJgAQAAf8U8NDAQYAWo27dvax/wiRMnGrZfBg4A/qzJ7CYWodB7G98zvU17Adbe2L1OB1iVJlUyva8AAADewDw0MBBgBaj169drH/BVq1YZtl8GDgAwlr2HuBNgAQAAMA8NFARYfujEiRN2t1+/fl0eeeQRUUpJWFiY3Lp1y7C2GTgAwFgR0yJshlL7Yvc5HWC9/8P73n4pAAAApmAeGhgIsPxQkSJFpGvXrhIVFSVHjhyRhIQESUpKkiNHjsi0adOkWrVq2od7xowZhrbNwAEAxnIUYD025zGnAqzIvZHefikAAACmYB4aGAiw/FDuB9feUqxYMZkyZYrhbTNwAICx7AVY+y/vl+DPggmwAABAQGMeGhgIsPzQjh07ZMSIEdK2bVupXbu2lCpVSkJCQqR8+fLSqlUrGT58uFy8eNGUthk4AMBYtabWsgqjVp9YLatPrJbrKdel5JiSDsOrmlNryrpT67z9UgAAAEzBPDQwEGDBUAwcAPzZpZuXZF/sPm2JvxNvepv5A6xSY0tZbK8wsYLDAOvtdW+b3k8AAABvYR4aGAiwYCgGDgD+LGxsmEUw1P3r7qa36SjAemDCAwRYAAAgoDEPDQwEWDAUAwcAf5Y/GHo66mnT23xl5SsWbU7fP91i+/3/ud+pZ2BtO7/N9L4CAAB4A/PQwECABUMxcADwZ94IsN5a+5ZFm2tPrrXbJ1vL6O2jTe8rAACANzAPDQwEWDAUAwcAf1YYAqzvTn5nsT10VKhTAdao7aNM7ysAAIA3MA8NDARYMBQDBwB/5o0A6821b1q02SaqjUzZM0Ui90ZK/J14qTK5ilMB1ic/fWJ6XwEAALyBeWhgIMCCoRg4APgzbwRYb6x5w2Yo9evVX536FUI1Qkm5/5Qzva8AAADewDw0MBBgwVAMHAD8mTcCrL+t+ZvdAKvSpEoEWAAAIKAxDw0MBFgwFAMHAH/mjQDrqQVP2QylDlw9IJ2XdnYqwCo7vqzpfQUAAPAG5qGBgQALhmLgAODPvBFg1Zpay26A1W15NwIsAAAQ0JiHBgYCLBiKgQOAP/NGgPXplk9thlIH4w5K7Wm1nQqwwseHm95XAAAAb2AeGhgIsGAoBg4A/swbAdb2C9tthlKH4g45FV6pEUqKjCxiel8BAAC8gXloYCDAgqEYOAD4M28EWDsu7LAbYHX/urvTIRYAAIA/Yh4aGDibhaEYOAD4M08HWKO3j7YbSB2+dlheXfUqARYAAAhozEMDA2ezMBQDBwB/5ukAq//6/g4DrCazmxBgAQCAgMY8NDBwNgtDMXAA8Gcjto2QChMraMvXR782tb2inxW1G0j9du03qTK5CgEWAAAIaMxDAwNnszAUAwcAGMdRIPXbtd/kwUkPEmABAICAxjw0MHA2C0MxcACAcRwFUkfij0ilSZWcCq9aL2zt7ZcDAABgCuahgYEAC4Zi4AAA4zgTYJWfUN6pAMsTv5gIAADgDcxDAwMBFgzFwAEAxnEUSh2NP+r07YNtotp4++UAAACYgnloYCDAgqEYOADAOHpBVMkxJSUzO1MyszMlJyfH6QBr/M7x3n45AAAApmAeGhgIsGAoBg4A/mzNiTXyzrp3tGXXxV2mtqcXRJUZV8aiTMWJFZ0KsNadWmdqXwEAALyFeWhgIMCCoRg4APgzT9+WpxdE1fm8jkWZub/OdSrAWn9qval9BQAA8BbmoYGBAAuGYuAA4M8KQ4AVfT7aoszms5udCrA6Lelkal8BAAC8hXloYCDAgqEYOAD4s8IQYG2/sN2izOjto50KsJrMbmJqXwEAALyFeWhgIMCCoXx54LibcVfG7xwv/9n1H0nJSPF2dwAUQoUhwNpxYYdFmfd/eJ8ACwAABDRfnofCeQRYMJQvDxwvffOSNtHruaKnt7sDoBAqDAFWyKgQabuorbRb1E4u3bwkCw4scCrAqhFZw9S+AgAAeIsvz0PhPAIsGMqXB478kz0AyC//ONF2UVuPtpd/OZlwUtKz0qXMuDJOhVgAAAD+yJfnoXAeZ7MwlC8PHEz0ADhSGAMsEZGz188SYAEAgIDly/NQOI+zWRjKlweOEmNKaJO8kmNKers7AAqhwhZgnUo8JSIisbdinQqw5sTMkbTMNFP7DAAA4Gm+PA+F8wiwYChfHjhKjinpcoCVkZUhdzPuSkpGiqRkpEhmdqbJvQTgTZ4MsLKys5wOsFIyUpwKsNQIJT2+6WFanwEAALzBl+ehcB4BFgzlywNH3gCrxJgSTtUZt3OcxcQwcm+kyb0E4E2eDLDSMtMchlGnE0/b7Ju95cDVA3I95bppfQcAAPAkX56HwnkEWDCULw8cpcaWcjnAembJMxaTwu5fdze5lwC8KX8Q1G5RO9PaSs1MNS3AUiOUVJpUyaI+AACAr/LleSicR4AFQ/nywJE3wCo+urhTdcLHh1tMCKtNqWZyL0U2ndkkn/z0ifxy5RfT2wJgyZMBljO3BZ5JOmOzb84sLyx/wbT+AwAAeIovz0PhPAIsGMqXB478Eztn5A+wqkdWN7WPOy/u1Noq+llRuXjzoqntAbDkyQDrbsZd0wMsZ8c6AACAwsyX56FwHmeuMJQvDxzuTOrKjCvj0QCr7vS6Fu31W9PP1PYAWMo/TnRa0sm0tu6k33EYPv2e9LvNvhFgAQCAQOHL81A4jzNXGMqXBw4jAqwakTU82sfm85ub2h4AS/ti98nCgwu15dz1c6a1lZyeTIAFAADgBF+eh8J5nLnCUL48cBBgAShMbqfddhg+nb1+VitPgAUAAAKVL89D4TzOXGEoXx443JnUlR5X2qsBVov5LUxtD4D33Eq7RYAFAADgBF+eh8J5nLnCUL48cBgRYNWcWtOjfSTAAvzXzdSbDsOnvLcwPjzjYQIswGDpWemSlZ3l7W4AABzw5XkonMeZKwzlqwPHhtMbfCLAqhFZw6K917993dT2AHjHlnNbpMnsJjZDp46LO0rHxR0lLjlOq+NOeNVxcUcvvkqgcPvwxw9FjVASMS1CTiSc8HZ3AAB2+Oo8FK4hwIKhfHHguHr7qgSNCLKY1FWYWMGpumFjwzwaYH20+SOL9hYeXGhqewA8Lz0r3er5es4E7NxCCBjnTNIZi8/Jc8ue83aXAAB2+OI8FK7jzBWG8sWBY8imIVYTuiIjizhVt9TYUhb1ak2tZWpfc/83mAAL8I6+q/tafAYn7p5oeBvrT613K3T61+Z/uRxerTy20vD+A/5g+v7phL0A4EN8cR4K1/FtDEP54sDxxpo3bE4QM7MzZcS2ETJi2wgZGT1Spu6dalE3f4AVMS3C1L7mD7CiDkaZ2h4AS/nHiXaL2hnexpoTa+yGTk9HPa1bb+reqS4HWBtObzC8/4A/IMACAN/ii/NQuI5vYxjKlwaOjWc2ypgdY+xe4ZCWmWaxrnpkdYt9eDrA2npuq4zbOU5bfrv2m6ntAbDkiQBr9YnVdkOnTWc26dYjwAKMM2P/DAIsAPAhvjQPhfv4NoahfGngeGj6Qw5v0UnNTLUbYPVc0dNiOw9VB/yb2QFWXHKcdF7a2e7YFHMlRrfutH3TXA6wvjv5naH9B/wFARYA+BZfmofCfXwbw1C+NHA4mtiJiCSnJ9s9gf3i5y+k7Piy2pL/FkOjJacny7Xka9pyN+Ouqe0BsGRmgHUn/Y5UnFjR4djUJqqNpGSkSGpmquTk5Gj1P9/3ucsB1p8+/5Nh/Qf8yRc/f0GABQA+xJfmoXAf38YwlC8NHEYEWM66lXZLRm8fLZP3TJa0zDS3+/zvrf+26MucmDm65ZYfWS5NZjeRnit6SuLdRLfbA2DJzABryp4pLgdQl25e0urP/XWuy/Ufmv6QYf0H/MnMn2cSYAGAD/GleSjcx7cxDOVLA4czAdbttNuGnMB2WNxBq//W2rfc7rMzAdbN1JsS/FmwVuaTnz5xuz0AlvKPB+2/bG/Yvvut6VegAOuDHz9wuT6TckDfT2d/svicmP2MSwBAwfjSPBTu48wVhvKlgcNTAVZGVoZhE0ZnAqx9sftMm2ADgc7MAKvv6r4uh0+xt2K1+k9HPe1WgJX3NkQA9xyMOyjVI6tryxtr3vB2lwAAdvjSPBTuI8CCoXxp4PBUgJX/QfAFCbBeXvGyxX6GbR1mVebnyz9blHlu2XNutwfAkqsBVkpGipxMOCmpmakO913QAKvVwlZuBVj7L+8v8HEBAADwJl+ah8J9BFgwlC8NHM4EWLfSbumuz87Jlnm/zpMXv35Rpu6dKl8f/Vq+OfqNnLt+zqodIwOs/PtpuaClVZn8AVbnpZ3dbg+AJVcCrMS7iVL/i/qiRih55ItHJCklye6++6zu43L4dPnWZa1+96+7uxVgPR31tGHHBwAAwBt8aR4K9xFgwVC+NHAUJMD6z67/6NZZcGCBVTspGSkeDbB+ufILARZgElcCrE+3fGpRdvi24Xb3PW7nOJfDpyu3r2j1j/9x3K0Aa8qeKUYdHsCvZGZnWiwAgMLLl+ahcB8BFgzlSwOHo0ndpZuXbAZYturoBUreDrCeXfqs2+0BsJT/M/jXL/9qs+wDEx6wKFt5cmW7+155bGWBAqzM7Ey3AqxFhxYZdnwAf1JmXBntcxI6KtTb3QEA2OFL81C4jwALhvKlgcPRpK7pf5vKzdSbLgVYdafXtWonLTPNoky1KdUM67NegBVzJYYACzBJYQuwrt6+qtVPz0p3K8D68tCXhh0fwF/EJccZ9p9PAADz+dI8FO7j2xiG8qWBw5mJ3YbTG1wKsOrNqGfVTv5nYFWPrG5Yn/UCrGW/LeOkGzCJKwFWr5W9LMq+/u3rdve94tiKAgVYer94SoAFuOdQ3CG+SwHAh/jSPBTu49sYhvKlgcOdiZ4RAVaNyBqG9VkvwJq+fzon3YBJKk6saPHZ6r++v82y43eOtyjr6FlT3538TipMrODxAGvx4cWGHR/AXyw6tIjvUgDwIb40D4X7+DaGoXxp4ChIgFVzak2nA6z8z8AiwAJ8V3J6siTcTdCWtMw0m2VdDbBybT231a0Ay91nYC05vKTAxwXwN3N/nct3KQD4EF+ah8J9fBvDUL40cLgz0as4saLdug/PeNiqnfwBVs2pNQ3rMwEWUHjl/1VBRwHWH3f+kB9+/0HeWvuWWwFWVnaWW+PaoO8HmX0oAJ9DgAUAvsWX5qFwH9/GMJSvDBx30u+4HWDlvyUw71L/i/pWbWXnZMsvV36Rny//LPsv75eDcQfd7jcBFuA78gdYkXsj7ZZff2q9y2OSEQGWved4AYFq3q/z+C4FAB/iK/NQFAzfxjCUrwwcU/ZMcXpy13tVb/lo80fy0eaPZNT2UXI77bZLAVZOTo488sUj2tJ6YWu3+52/vacWPGVVhgALKBw8HWBl52S7FWB1WNzB7EMB+Jz5B+bzXQoAPsRX5qEoGL6NYShfGTgGbxzs9OTu8q3LFnWT05NdDrDylqk0qZLb/W4T1cZiX8O3DbcqQ4AFFA59V/e1+By+s+4du+XdCbDupP9vnM0/1hBgAe4jwAIA3+Ir81AUDN/GMJSvDByuBFhXbl+xqGvvQcmPfPGIVVtGBljvbXzPYl/LfltmVYYACzDPrbRbcvnWZW3JGyDlV3Z8WYvPYbUp1ezu29UAK39g7m6A1XFxR0OODeBPFhxYwHcpAPgQX5mHomD4NoahfGXgKEiAZe82nSIji8gfd/6wKG9mgPXVka+syqw8tpKTbsAkD0560OKz1W9NP5tlXQ2w3t3wrkvBk96PRrgTYHVa0qnAxwXwNwRYAOBbfGUeioLh2xiG8pWB46PNH7k0wWv636by+H8fl54rejq8yuGTnz6xai9/mebzm8uf5/xZdl7c6VK/nQmwdlzYYVHmpW9ecvs4AbCU/7Ns7/a77l93tyj7yspX7O779W9f90qANW7nuAIfF8DfLDy4kAALAHyIr8xDUTB8G8NQvjJwbD672a2JXu4tgq4EWGmZaTbLVplcRbKys5zu99XbV+VI/BFtuZF6w6rM9gvbCbAAk7gSYE3YNcGi7KTdk+zu29UAq96Meg7758yy8ODCgh4WwO8QYAGAb/GVeSgKhm9jGMpXBo6fzv7k1kQv9wTW3vZPt3xq0Za9Xy1UI5QcuHrA0NeWP8Dq8U0PQ/cPBDICLCAwRB2MIsACAB/iK/NQFAzfxjCUrwwcZgZYL6942aKtW2m37JbX++VCWw5cPSDzfp2nLcf+OGZVJvp8NAEWYBIzA6zXvn3N5fEoJSPFbv8IsAD3EGABgG/xlXkoCoZvYxjKVwaOLee2uB1gpWamOhVy5bqZetOl8vZ8Fv2ZRb0Z+2dYlckfYOUP1AC4r7AFWFdvX7XbPwIswD2LDy8mwAIAH+Ir81AUDN/GMJSvDBxbz211O8C6k37HKwHWhtMbpNGsRg4DrG3nt1mUaRPVxpBjBqDwB1h9V/clwAIMsP/yfovPSbn/lPN2lwAAdvjKPBQFQ4AFQ/nKwOFMqGRrSU5PdimQupF6w5AAS6+eXoD127XfLMq0/7K9IccMgGsB1ltr37Io++6Gd+3u24gAq8HMBi7v46PNHxlybAB/cv7GeRmwfoC2zPpllre7BACww1fmoSgYAiwYylcGjvxXKZkZYF1PuW5agPXGmjesyhFgAebJ/xnsuLijzbKlx5W2KFtzak27++69qneBA6yqU6q6vI/nlz1vyLEBAADwFl+Zh6JgCLBgKF8ZOL47+Z1fBFitF7a2Kpc/wGq3qJ0Rhww+4srtKxI2NkxKjS0lJceUlJYLWnq7S36lsAdY1aZUc3kfDWY2MOTYAAAAeIuvzENRMARYMJSvDBxrT651O8BqMruJwzKf/PSJPDbnMRm+bbgk3E3waIB1JP4IAVYA+/nyz279fcE5rgRYYWPDXAqwvvj5C4vy9b+o73KAVT2yulvjWkZWhiHHB/AnG05vkNUnVmsLAKDw8pV5KAqGmQ0M5SsDR0ECLFeXFcdWeDXAaruorcFHD4WZ3g8UwDgFCbBqTa1ld99fHvrS5fElf4BVI7KGW+MUk3PA2gMTHtA+I/eNvM/b3QEA2OEr81AUDDMbP5WSkiLffvutvP322/LYY49JmTJlpGjRovLAAw9I27Zt5YsvvpCUlBTD2/WVgWP1idUeC7DKji/r0QDraPxRAqwAtuXcFgIsE5kZYC06tKjAAZa749SiQ4sMOT6Av7h08xJjKQD4EF+Zh6Jg+Db2U2FhYdoH2NZSr149OX36tKHt+srAMWn3JI8FWMVGFzMtwFIjlHRb3s2i3Iz9MzjpDmA/nf2J999ErgRY7//wvkXZrl91lSfnPilPzn1Sms1rJiOjR1qU92aAFXUwypTjBfiq/M+TZCwFgMLNV+ahKBi+jf2UUkpCQkKkV69esnz5cjl79qxcv35dDh06JP3795egoCBRSknNmjUlOTnZsHZ9ZeB4b+N7HguwKk2qZHf769++7lSfbdXPf1XH5/s+56Q7gBFgmSv/g9bn/TrPZtmR0SMtynb5qovFv/us7mNRfvel3fLGmjfkkS8e8XiAtfDgQjMOF+Czlv22jLEUAHyIr8xDUTB8G/upAQMGSFxcnM3t48eP1z7g//nPfwxr11cGjkHfD/JYgDVk0xCb20JGhTjdZ2cDrGn7pnHSHcA2n92s+/7fTrstuy7uksS7iV7uYeDIH2A9v+x5uwFWrq+Pfu3xAGvBgQWeOCSAz/jqyFd8lwKAD/GVeSgKhm/jAJWZmSn333+/KKXk6aefNmy/vjJwDPx+oGmB1X0j77P49z83/VMqTqwoFSZWkPITyluVH719tFN9ttVexLQIi3IEWIHtx99/tHr/b6belDqf1xE14t4VgedvnPd2NwPCiG0j7AZYfVf3tSh/MuGkDPx+oBQfXdzjAdaPv//owSMDFH7LjyznuxQAfIivzENRMHwbB7BmzZqJUkoeeughw/bpKwPHPzb8w7QAq8jIIhb/HrV9lBz/47g8/t/HbZZ3hq32ak+rbVGOACuw6QVYo7ePtvi3rSt/UDAnEk7Ii1+/KG+seUP+uPOHywGW3tVzrgZYVadUdWvc2hu714NHCij8CLAAwLf4yjwUBcO3cQCrVauWKKWkadOmhu3TVwYOMwOs4M+CLf6d/zYifw+wklKS5Im5T8gTc5+QJ+c+Kb1X9fZo+4FOL8B6bM5jTMQ8IO/xbTizocMAq9+afhb1vRlg7b6025OHCij09G7lBQAUXr4yD0XB8G0coA4cOKB9wN99913D9usrA4dZAVaRkUXk0y2fWqwbtnWYw3rOcLa+twOsa8nXLNpuNKuRR9sPdD/8/gMBlhccuHrA6hjbuuoydyn6WVGLfbgTYGVmZ1rsw90Aa9fFXZ48XEChR4AFAL7FV+ahKBi+jQNUu3btRCklQUFBcvjwYZfq3rlzx+YSHx/vEwPHuxveNSXAemDCAzInZo7Fuv/b8n8BFWDFJccRYHmRXoDVd3Vfi38/PONhb3fTZ8VciZHZv8zWlqPxR0VEZMKuCW6NGXl9e/xbl+rWnFrTqn/uBlinE0975PgBvuKbo98QYAGADyHACgx8GwegCRMmaB/u/v37u1w/t66jpTAPHAPWDzAlwNJbKk2qVKAA6/Kty7L48GLp+lVXp+p/d/I7i22tFrYy81BayR9gPTrrUY+2H+g2ndlk9fex6NAii39/8OMH3u6mz8p/bJ9Z8oyIGBNgvbrqVZfq1oisYdW/R2c96nIfenzTwxOHDvApBFgA4FsIsAID38YBZtOmTVKkSBFRSknDhg0lJSXF5X34Q4A1fud4jwVYpceVdlgmJydHcnJyrPp55fYVKTW2lLafnit6SplxZeyeVOcPMDz9wO4TCScK9Ul/WmaabDi9QY7/cdzbXTHFxjMbrY7/4sOLLf79/g/ve7ubPiv/se20pJOIiEzaPalQBFglxpRwuQ8NZjaQG6k3PHH4AJ+x4tiKQv1dBgCwRIAVGPg2DiAxMTESFhYmSimpVq2aXLp06f+zd+ZxVRXvH79pQE51zwAAIABJREFUmktpmraZmtmmlZaWlW3fsrI0y2/Ltyw1yzSXXLLFLLWLoiIquICIC+4Li4jsgqAIioIiKKCACIgsIoKA7HCf3x/+7vWe9Z59uTzv1+t5vfTOnJln5szMOfPhzIygdOxhCeHh7MOKCVg9XXrajNPaoTU85vIYnCk8Q/BzjN8YQrx5h+dBeW0560s1WcAYvW+0klUL6SXpmn3pN5lM8Nrm18BgvL3/UFhWmNouSU7U5ShK/e8+t5vw/1/Df1XbTd3CJGC5J7hTwjou6SirgNXLtZdN/7ha1o0sReoPQfQCClgIgiD6AgWslgE+jVsIGRkZ0L17dzAYDNC9e3e4cOGCLPnoZeBQUsDisgeW2d7Y8gbBT3J4P7d+NgWsEbtHqPrSrWUBiyzudF7WWW2XJOds0VkYsmkIvLLxFRjsORj+iPgD9p7fSyj3zNCZarupW5gELLdTbpSw1g6teQlYZMFaiID1wPIHBI1Tl25cUqT+EEQv+KX5afZZhiAIglDRyzwUEQc+jVsAV65cgV69eoHBYIDOnTtDUlKSbHnpZeAQctqXUOMyiWV6QaYLf2frO4zXlNWU2UxTbrS8hNA13lWzvskJeS+XX0J+Udsl3cIkYJH3nhPS3/kKWAajgbL07+DFg4L8yC7LVqwOEUQP7E/f3yKfFwiCIHpFL/NQRBz4NLZzSkpK4JlnngGDwQDt27eH2NhYWfPTy8BBt8xKK3ar/k693WW8i9ck+MCFA7ThyUXJitWtWgLWjZobkF2WDVk3siCzNBNu1t6kxGmpAhb5ZMzv9n+ntku6hUnAismNES1g8V1CaDAaoLCykJDG9errgvwwn6aIIMht6PYTRBAEQbSLXuahiDjwaWzHVFRUwKBBg8BgMECbNm0gLEz+/X70MnCcLTqrulDFZGFZYVDdUA0AAK0cWkkiYD297mnaTeLlQC0Ba97heYQ8N5/ZTInTUgWsIZuGtMhyy4EYAavr8q6yC1glt0qgi1MX3ul4nvZUrA4RRA+cunqK0EdG7RmltksIgiAIC3qZhyLiwFmMnVJbWwtvv/02GAwGaNWqFfj4+CiSr14Gjlv1t1QXqtisv3t/qG2shYkHJ9qM+8DyByzl8k/3Z4xXcquEdz01NDWAV5IX7ErZBU3NTZyuUUvA+ifqH0Kem85sosRpCQLW1Yqr8OneT+HVTa9CxKUIAEABS0qYBKyEqwk2+2p7x/aE/4/zH0dIW4iAVVxVTEgjNi9W0JiDAhaCECmvLYfQzFCLnS44rbZLFi5evwgZpRlqu4EgCKIp9DIPRcSBsxg7pKmpCT799FNLB3Z1dYWqqipGq66ulixvvQwc+87vU12ksmXrE9ZTln7RWdflXS3lYvuy7EbNDd71ZD2hnhE6g9M1F69fVEUsIW+WTzchbwkC1vcHvieU71juMYq4t+TYErXd1C3k9vPxro8B4PYSVr59nCxgCdkD69qta4Q0hApYGxI3KFaHCIIIZ8XxFZZ+63LCRW13EARBNINe5qGIOOxv9oZATk6OpfNysd69e0uWt14GDvKpbFq00ftGw8bTG23G6+LUxVKuc8XnGOPR7QllCyFiT0Zphioi0cLohYQ8PRI9KHFcTrjYvYDFpW0N3zlcbTd1C5OAVdtYK1rAmhAwgXca16uvE9KIy4tDAQtB7Bh7f4YhCIIIRS/zUEQc+OSzQ1DAso0eBKzP9n4Gm89sthnvfqf7LeVKKU5hjEc+rYwLXF+Uw7LCYNSeUTA/aj6cv3ZelRds8tcr86PmU+KEZ4UT4ryy8RUAAKhuqIa4vDjK1yx6hGv7QoTBJGBdrbgqWsDakLiBdxql1aWENIQKWHSCL4K0dOaEz4GJBydarNnUrLZLOJYjCIIwoJd5KCIOfPIhkqKXgUMvAtaWpC0243Ve1tlSLqmXEHJ5US6tLoXWDq0tcf6K/EuVF2xynkM2DaHEKawsBM/TnrAhcQN4JHrAoUuHoLqhGvq59QOD0QDdnLvpel+RyrpKFLBkhknA4rIHlhwCFrlfH79yHAUsBJGApuYmSj/RgoBlvZfefUvvU9sdBEEQzaCXeSgiDpzFIJKil4HD7ZSb6gIVFwGrsq4ScspzILc8lzFej1U9LOVKKkxijEf+UsPMtVvX4AvvL2CQ5yDwT/cnhHERPRyOOrCWo7drb8nuGxvkfM1fV9li7cm1hOs+3fupbD4mXE2A2WGzYU74HPjt0G8QnBEsWdplNWXQd01fFLBkhlyP7RzbAQA/Aau9Y3vosKQDTAqcREjbI9GD9zhB/rISBSwEkYaiqiJNCljtHNuhgIUgCEKDXuahiDhwFoNIil4GjqnBU1UXqGzZpMBJEJsXC56nPVk3c39+/fOWcp0pPMMYj7xXjpnZYbMtcTos6cBb9Pj90O+s1/Ry7SXLPSRDzvfljS9zuu6jXR8pJu5sO7uNkM8fEX9IlrZTrBOv9oUIg6kuEwsSeffxKUFTCGkLEbDIe9sFXAgQNN6sT1ivWB0iiB6g+4MQClj2ia3n4/Erx2FZ7DI4V3xOBe8QBOGKXuahiDhwFoNIil4GDiGbJStt3qneMCVois141gJWaGYoY7ySWyW0dTHYczAnfx5c8SDt9bYErJ4uPWW5h2TI+Q72HMzpuo93fWzz5VUqtp7dSsjnz4g/JUv71U2v8mpfiDCec3+Oti6FLP8jC1iXyy6Df7o/zAqbxTmNiroKQhr70/cLGm/cE9wVq0ME0QOBFwMp/URrAlanZZ3UdscuYHs+ni44Da0cWoHBaIB7Ft8DeTfzVPISQRBb6GUeiogDZzGIpOhl4Pj+wPeKCVFCzSfVh5OA9cL6Fyzl8k71ZoxXXFVMWxfDtg/j5M9DKx6ivZ5OwJofNR/mR82HBdELYOXxlbLcQzNup9ygl2svig8vbXiJNv7Viqvw/YHvYfyB8XDl5hVFv8DySvIi5DM3cq5kabvGu/JqX4gwFkQvoK1L66PtuRpZwAIAuFV/Czad2cQ5jar6KsL1QgUst1NuitQfgugFOgGrqblJbbdQwJIBtufjKxtfIYR9f+B7dZxEEMQmepmHIuLAWQwiKXoZOMYfGK+oGCXEfFJ94Oegn23Gsxaw0krSGONl3ciCxuZGSl2IFbD+iPhDNXGkuKqY0d8XN7xIiZ9fkU+Jp6SA5RjjSMhn1J5RkqW96sQqXu0LEcbC6IW0dekc58y7j08NnkpIW8hG8LfqieOsUAHLO9VbsTpEED0QlBFE6SdaELDaLm5r8QeXEEoD2/ORHMb0xzEEQdRHL/NQRBw4i0EkRS8Dh9YFrE7LOsGGxA0wYvcIm3EHeAwAAICGpga4XHaZNe6zbs9CbnkuoS7e3/E+J5/4CFglt0qg5FYJXK++TtlkWkrYvnoZ6DGQEj8sK4wST8klhB/u/FC2vPgKKIgwpBawcstzYWH0Qth9bjfE5MbwTqO6oZrgHwpYCCINwRnBlH6iBQELx3LpYatTchjdH8cQBNEGepmHIuLAJx8iKXoZOMb5jxMsLsltN2pugE+qD+GvrGw2wGMArDy+Eu5edDen+BMCJhDqgquA9fDKh2nrcn3CetbrXt30qmz3ceXxlYz5Wn+ZZoZO8FJSwPpgxwey5YWbuCvDv0f+pa1LOgHr6ZUGWH3AACv8DdBzue17IkRYr22sJfjnl+YnaNz50udLNaoTQTQLnYAVlxentls4lssAW52Sw1DAQhDtopd5KCIOfPIhkqKXgWOs/1hBkzwu9sSaJ0RdX1VfJZtvdC9ndKIKH9GDvHH8f7b9h/B/OQUslxMujL5ab25vhk5kIO/hJefLqZwC1pJjSwS3AYQ7fASszUEGOHLktjnvl74f030VKVTAwjaBIERCMkMofeTvw3+r7Rb2Wxlgq1NyGN3X3QiCaAO9zEMRceCTD5EUvQwc3+3/TjZxyHxajVbtw50fEuqC63VMe21cu3UNQjJDIDgjGIIzguHAhQOE6+QUsKIuRzH6+++Rfynx6USGklslcDL/JMTnx0N8fjxliSUfNp3ZBA8sfwAGeAyA9JJ0SricAtYPAT9wvpfdnLtJlm9Lg0n4oWtbZvHKbFL3ZbqTQVHAQhBpoDvVd97heWq7hf1WBtjqlBxm3rYBQRDtoZd5KCIOfPIhkqKXgUNOAUvr9tPBnwh1wfW6e5fey6lu6fbhIlPXWAf5Ffmi9xM5lnuM0d9dKbso8elEBjMmkwni8uLgdMFpQb5U1lUS0h29bzQljpwCFt928Ln351BQWSBZ/i0FJuGHbnmqGgJWYkGi4PQQBLkDnYClhX6iNX/sAbY6JYfRbU+AIIg20Ms8FBEHPvkQSdHLwOF52lMRsUiLNilwEqEuuF7XcUlHSL2WCglXE8BkMhHSyLuZB9ll2ZBdlg2Hsw+zvgwWVhbCs27PgsFogDe93qTs4cMHtk2vyQJWZmkm62RkTvgcy28rj6/k7cuhS4dsTizkErBMJpOgtvBDwA+S5N+SYGpDaghY3Z27U/zLKc8RnF5hZaHS1YkgmoXu0A+pxmwxaM0fe4CtTn86+BMhbH7UfJW8RBDEFnqZhyLiwCcfIil6GTiYXkxbgk0OnEyoCyFp/Br+KyGN3q69WeNbMyN0BiGM7isSrrAJWLvP7SbEnR81n9G3ZlOz6ElBeFa4zTSkFLBMJhNsPbsVvvb9mrKPFx9D+MFUh+QDBe4yqiNg2TqJ1FZ6FXUVSlcpgmgSrQpY1ge2tFnURm137AKt3WMEQYShl3koIg4cpRFJ0cvA0ZIFrJ+DfibUhZA07ll8j2X5X1lNmc34tvITytGco4x5kgWsRUcXMebd1Nwk2ie6NvXO1neg75q+Fl/oTnwUCtvySSbr5twNX9RFwtSG1BCw7ne6n+Jfdlm2qDRdTrgoXaUIokno/iihhTGzw5IOFl+4Lu1H2NHaPUYQRBh6mYci4sBRGpEUvQwcTHtbtASbEjSFUBdC06lrrAMA9q+g6F4GpZwQkAWsfm79IKU4BVKKU6CspowQd+mxpbR5l9wq4bT8zxZ0J1aZrb1je6huqIa7jHdJVvbn3J/jfc/80/3xRV0kTO2XLGC2MsovYNHdP7EClk+qj9JViiCahO65oIUxEwUs6dHaPUYQRBh6mYci4sBRGpEUvQwccXlxskwm9WCdlnUi1IXQdMwClleSF69J9kMrHpLsZXHv+b2UtFzjXcHlhAvE5cUR4jIJWPMOz5NkkhJ9OZq1DoIzgiWdDN3vdD/ve/bO1ncky9+ay2WXwTnOGaIvR0uSnpZhuofkyS5ZwPIJl6c/k7l045Ko9PzS/JSuUgTRJLF5sTb7S+q1VMX9IvuQWZqpuA/2hhzPRQRBlEcv81BEHDhKI5Kil4GjsLJQlsmkWnbpxiX4xu8b3pPe1GupgvLr5drLksYfEX/wmmTTxRfK5jObGfP8J+ofQtwlx5bQxvt418c2feZCclEyax1IfaKVEAFLyvzNVNRVQOdlnS3pRVyKEJ2mlmGqQ60IWBmlGdDFqYvg9A5cOKB0lSKIJjl19ZTN/hJ4MVBxv8g+nLhyQnEf7A1ynZIPqkEQRB/oZR6KiAMFLERS9DJwpJekyzKZVMPuMt4FAAD/2fYf3pNepq+CbFlv196WNLgIWDuSd0DfNX1h2PZhtEJbQ1ODoPuYcDWBMU+uAtZHuz5irSOunCk8w1oHUm8IPCtsliTtRyzkeqXbWNyeINffA8sfAACAtJI0wu+tjAaYv88A871v2+xd8vR/MkmFSaLSO3jxoNJViiCaZaDHQM31F7IPx68cV9yHlkRaSRoEXAiwWG55rtouIQjCgF7moYg4UMBCJEUvAweXZW9asMdXP855EitEwBK6X87jqx+3pMFFwLpn8T2s4W96vQnNpmbe9zHvZh5jmn8f/psQl0nAGr5zOOU3syjBh8SCRNYySr0hMFN5+JgUYtOUoCmSlUkPkMs6YvcIAACoa6xTZYwgI1bACs4IVrpKEUSz6EHAIi+XR6SlnWM7Qn2/sP4FtV1CEIQBvcxDEXHY90wDURy9DBxbkraoMtnka8O2D+M8iaXb38jWpDenPEeQX31W97GkwUXA4mJClmLkV+QzpsdVwPpw54eU37ou78rbF1vLTaTeENgxxlGSei+oLBDsAwBA4MVAQno/HfxJVHpah1x/ZgGLLkwJI3O26Kyo9MKywpSsTgTRNAM8BrD2FxSw7J+7F91NqO9+bv3UdglBEAb0Mg9FxIECFiIpehk49CJgcTWTySRIwMotzxWVp5T1+Puh33nfx4LKAsb0Ptr1ESEuk4D1wY4PKL91cerC25dLNy7B+APjYaz/WPh2/7eUNCOzIym/fe37Ne98zCyOWSxJvf8S8guYTCaobqgW9BUceRnqjwE/Ci6THiDX38jdIwHg9kb2avR9MmIFrO8PfK9wjSKIdrHVXwIuBKjukxr7cLUk2ixqQ6jvZ92eVdslBEEY0Ms8FBEHCliIpOhl4GDb/FuL9uiqR1nDm03NvJYQllaXAgD7EjxbRn6pE2tCli4VVRUxptd3TV9CXCYBq+vyrpTf7ne6n7cvTc1NUFxVDEVVRbSHBLRd3Jbym3OcM+98zEglYLVd3BZG7RkFBqMBhmwaAterr/Pyg7w5/YSACYLLpAfI9WcWsFKKUyhhXsF3bPUBecYGMrYOE7Bl/933X6WrFEE0QUxuDPRd0xf6rO4DkdmR0NDUYLO/aEHAEvMcQWxDfnajgIUg2kUv81BEHChgIZKil4FDbwLW175fS5rewuiFACBOwJLauP4V+eDFgzA3ci78dug3+G7/d4zpPbHmCWhoaoDoy9GQdSOL15I7j0QP3m2quKqYko57gju4nXJjzOfjXR/zzsfMoqOLZLkP86Pm8/KDvLfX+APjCeFJhUkwxm8M/BX5F9Q01Agur1agqzMAegHL+hTC/Yfk6Tdk2A424GIoYCEthdrGWsgpz4HG5kYAAOjn1s/SD3q69ISq+iqb/UULAtbyuOWK+2BvlNWUQVlNGZTXlsPN2puEMLKA9cy6Z1TyEkEQW+hlHoqIAwUsRFL0MnBsOrNJlsmkXuyXkF+goakB+rv3V90XvhOB6SHTOaX3xJonLMsq71l8D68v1JYeW8q7TR3NOUpIw/qkRqZ83vJ6yxKnsq4SxvmPg+fcn4N1p9YR0vZN84Vn3Z6FYduHWU5Aen/H+7Lch+fXP8+r3BGXIgjXj/UfawlraGqAB5Y/YAkjnwypR+jqDOD21xvk360FrCNH5Ok3ZPad3ycqvc+9P1e6ShFEcQorC+GZdc+AwWiAoVuGQm1jLaUvnMw/abO/HLhwQHHfyT44xTop7oO9Qa7TpuYmSxj5EJqn1z2toqcIgrChl3koIg4UsBBJ0cvA0dIFrBmhM2Dv+b2q+2Ft+9P3M96vf6L+gQdXPAgPrnhQEV+eWvsU7zY1yHMQJR0zTPmsOrHKEmfViVWEsOyybAC4/ZXAfUvvs/z+7rZ3ISY3Bro4dZGl7OTN721xOPsw4fpv939rCSPvj2VdJ3rl56Cfacu0On414bc2DsIFLPLG+GxGRqyAZQ/3CEFsMTN0JqHNbzy9kdIPyJt305kWBKxlscsU98HeINeptYBFPoUQBSwE0S56mYci4sA3VURS9DJw0L2stiSbGToTXONdVffD2nxSfRjvF3myIbcJeUGlS4ctzGA0gMsJF8Y4U4KmAAAwnl4ol/H9SmpO+BzGcvuk+jCG6RW6vdQAAJbFLiP81naRcAHrMZfHOMclI4WAVVZTpnS1IoiikNv821vfFtRXUMCyD8h1yiZgCfkDF4IgyqCXeSgiDv3PJhBNoZeBo6ULWLPCZsGO5B2q+2Ft+87vY7xfs8JmSZKHywkXTvGE7HFBl86NmhtQXlvOmM8AjwGW66cETaGEP7zyYcXvw4LoBbzKTee3GXsUsJYeW0pbJvLvYgQsPkaGrs752tmis0pXK4IoyoLoBYQ2vyFxg6C+4p/ur7jvZB+ELHlHiJDr1LwvGgBAe8f2hLAn1z6poqcIgrChl3koIg79zyYQTaGXgcPztKfiwoCWbHbYbLjf6X7V/bC2PyP+JNyj/Ip8+GzvZzB0y1AY6DFQkjy+8P6CUzy+pwwxnYZ4l/Eu6LCkA2teZv6M+FP1e2Aw3tngnyvTgqcxlqklC1j3qCRgVTdU22xztiypMEnpakUQRTEeMRLavHuCO0Wo4GJaELCWHFuiuA/2BrlOrQUs8niKAhaCaBe9zEMRceh/NoFoCr0MHC1dwKITHdS2iQcnAgDA6YLT8PfhvyXf7+qRlY/wig8AUFVfBRMPToSBHgNhfcJ6xvYk5uQ3MwujF6p+DwxGA/x75F9K+SrqKmBa8DQYuXskxObFEsLoNtU3Y48CFnmpoLlM5KWFaglYAAB+aX6UjYf5WGJBopJViiCKszhmMaHNrz25ltOeV2TTgoDlGOOouA/2BrlOUcBCEH2il3koIg79zyYQTaGXgUPocgF7sVc2vqK6D2T7IeAHyK/IhzaL2qjui8F4e2hcc3IN4bfLZZdp25NXkpeofADo91ZSw346+BOlfHMj51rCOy/rDPVN9ZawX0J+YSyTGgJWbWMtVNZVypa+U6wTbZnI96+dAgLWvUvvZfQzszRTcLrx+fGy1R+CaAG6LymFGFnQVwKyD6vjVyvug71BrlM2Aavvmr4qeoogCBt6mYci4kABC5EUvQwcLVnAGn9gPIw/MF51P8g2IWCCpF+Gkf/Czmaj9oyi/HYs9xjltze2vEHbnlo7tBbs5+fen8PofaNVr39rI0MOD7gQYAkj7yv24oYXLWG+ab4205aSk/knLV/u8d3LiytM9eUY40j4Tc8ClhqTcgRRipTiFMF94+GVD8Ozbs+CwWiANovaQGFloeL+k31CwVk85DptaGqwhHVc0pEQ9sSaJ1T0FEEQNvQyD0XEgQIWIil6GThasoD1Z8Sf4HbKTXU/yDbOfxy8v+N9ydKj+1KGyf7n+z/Kb3cZ76Lde+u7/d9B3zV9YVbYLPBK8gKTyaR63UltZMjh1idG7krZRQibEz7HEpZekg6Pr34cerv2ht6uvWGs/1hZ+/UAjwEEX4qriiXPg6m+1BCwOi7pyOinGAHrSM4RyesNQbTC8J3DJeuDHokeivtP9gEFLPGQ69RawLp36b2EsD6r+6joKYIgbOhlHoqIAwUsRFL0MnA0NjdKtoRAbzY3ci4EXAhQ3Q+yzTs8Dz7a9ZFk6a04vkIRv+ub6lWvO6nNvAQvvSQdJgVOooT7pvla+tLOlJ2EsF/Df4WahhowmUyK92uyn+FZ4bLnYTDSC1jtF+tXwIq4FCF5vSGIVpC6H6rtPwpY4iHXqbWAFZYVBmP9x8I4/3Ewzn8cBGUEqegpgiBs6GUeiogDBSxEUvQ0cEwNnqq6UKCGzY2cCwcuHFDdD7LF5sXCyN0jJUuPzxJCMVbXWKd63UltG09vhMbmRuju3J023FrA2pG8gzbOhzs/hJqGGkX7NNkHJQUscntTQsDqsKQDo59iBKzhO4dDUVWR5HWHIFpA6n6otv8oYImHXKfW+zwiCKIf9DQPRYSDAhYiKXoaOOYdnqe6UKCG/RX5l2YFrM/2fiZJWj1detIuC5TD7FHAMhhvn67FFBaTG2PpR0wClsFogM1nNrP2QfcEd+jv3h++3f8t3Ky9KbpPk/NXUsAi7wXWQQEBy5w3HeFZ4aLSfcvrLXCMcYR/ov6BspoyyesRQdTifqf7FemDckHOHwUs8ZDrFAUsBNEnepqHIsJBAQuRFD0NHOQT5lqSTQ6crLoPZGu7uK2kE4pZYbMU8bu2sVb1upPDxvmPYwxLLkq29KPtydsZ4z2z7hnG/pdbnkuIu+L4Cl79N7ssG5xinQjL3cj5H79ynOeoYBum9kZelqu2gEW3gb5Qe2/7e5LXI4KoRU+Xnor0Qbkg56/0l672CLlOUcBCEH2ip3koIhwUsBBJ0dPAsfbkWtVFArVs6Jahqvsgpw3bPkyxvGoaalQvrxw21n8sY9jZorOWfsQmBA/yHASbzmyC0upSSv/7NfxXSnyuVNZVEr6iCMkMAQCAlze+TEgv72ae+IGCBF05P9nzCeW3jiQBa0eIPPeJCSkFLIOReKw8guiZx1weU6QPygU5/0OXDinug71BrlMUsBBEn+hpHooIBwUsRFL0NHC0ZAGrw5IOqvtgL1bdUK26D3IYm4CVVJhk6UdzI+dySm+w52D46eBPlutmhs6kxOHKGL8xhOtaObQCAIDBnoMJv1+5eUW6AeP/4Vp/ZAFru8ICll+an6T51DXWSV6XCKIGPVb1UKQPygU5/+CMYMV9sDdeWP8C9FjVA3qs6gF9VvdBwR5BdIqe5qGIcFDAQiRFLwNH4MVA1YQBNPuy6oZqinBiD8ZVwPor8i9e6ZphE7BqGmpgTvgc+GjXRxCWFUbpv0zpDvIcRPgtvyJf8rGDaznvdjDAsPUGeP//beg6ee4TE1ILWL+E/AKvbHwFnOOcVTlhEkGk4tFVj0rWLx5zeUxR300mE8UHPBVPXtacXAN9VveBPqv7wOOrH4edKTvVdglBEAb0Mg9FxIECFiIpehk43E65KSoGoNmv3aq/BW9seUN1P6Q2h6MOjGFnCs9Y+hLbJu50ZoZNwDIeMVp+a7OoDZTXlhP6L/m6R1c9CgAAz7k/R/i9oLJA8rFD7ftibSN3j2T0c3/6ftnyTSxIlLxeEUQp7E3ACrwYqKgPLQ3ypv89XXqq7RKCIAzoZR6KiAMFLERS9DJwrDu1TvXJJ5p92Oh9o1X3QQ6jE6ZG7xsNn3t/Tviy6dKNS7zSNcMmYJF/dznhQui/dOkWVBZQfiusLJR87FD7vljbqD2jGP08V3xOtnz/s+0/ktcrgiiFlH1hQsAERX2nE7De3vo2nLp6SlE/WhJdnLoQ6ltp0RJBEO7oZR6KiAMFLERS9DJwtHQBq71Q3dagAAAgAElEQVRje9V9QNO2bT27lTEstzzX0pcul13mle7DKx+G7s7dKb+/uOFFAAAoqipi9Me8dI0u3DvVm/KbmksIDUYDPLTsjj24TPp7xEfAenDFg/DgigfBYDRQJmRCDJcRInpF6n6oJHQClsFogNYOreF0wWlFfWkpoICFIPpBL/NQRBwoYCGSopeBoyVv4G4wGmDIpiGq+4Amj206s0mSdLYkbWEMsz7dL7c8V5L8xviNAQCA4qpixjjm0w/pwrYnb6f8JsdXCXzKZL2J+/5D0t/rT/d+yujn+WvnKfGHbBoCvVx7QXZZNvRy7SUqb7dTbpLXLYIogdT9UEmaTc2Mfryy8RVFfWkpoICFIPpBL/NQRBwoYCGSopeBo6ULWK9uelV1H/jaqD2j4AvvL1T3o6XYtrPboLtzd+jm3I0SZn26X3BGsCT5mQWs8tpyxjgf7PgAALhPQFOKUyQfO/iUSWsCltkOXToEGaUZMClwEu3XcFzs410fS163CKIEUvdDJWETsJT2xZ6YHjIdfgz4ESYenAjTQ6ZDU3OTJazr8q6EOu6xqoeKniIIwoZe5qGIOPBph0iKXgaONSfXSP4Sqyd7ffPrqvvA15bHLW/xSz+Vsi+8vyAsv2M63S/vZp5keX7j9w0AAFTUVTDGMe+FxTXN5KJk2v5f21gLhZWFgpbBcc27jQNRwDpyRPr7xCZgpV5LZbzO+hStKzevsOYxNXgqYVN9s5nFRATRG1L3QyVBAUseyPVY01BjCXtg+QOEMBSwEES76GUeiogDn3aIpOhl4GjpAtbQLUNV9wFNu3bh+gUoriqG4TuHw4c7P6SEX624CgAAEwImiM4rJjcGjuUegwvXLwAAQGVdJWNcryQvAOA+ATUvObQmpzwH+q7pCwajAUbsHgHNpmZeYwfXvDs5UgWsdou4Xeuf7s8p3md7P2P0k03A2pG8wxLvVv0t1jwmB06mPZFy+M7hvOoNQbRCP7d+ko6XSoICljyQ65FNwDKfeosgiPbQyzwUEQc+7RBJ0cvAsTp+taQvsHqzN7a8oboPaPIYneAkxGJyYxjDzhSeAQDql1lCjAyboLLt7DYA4C4iJRUmUdKfEz6HECcyO5LX2ME1bzECFldjE7DCssIYr7MWsGyVaXLgZPif7/9ow17a8BKvukMQLdDfvb+k/bCpuQm2nd0GGxI3QF1jnay+NzU38RpPEW6Q67G6odoSRl5G/8jKR1T0VFs0NTfBsdxjkF2WrbYrCAIA+pmHIuLApx0iKXoZOFq6gCX1Czya/dn4A+MZw/zT/QFAGgFrccxiWByzGMKywgAAoLqhmjGuWXjhmrZZaLNmavBUQpz96ft5jR1c81ZCwBq9bzSjnwEXAmzWo5kPdnzAGPfnoJ/h410f04Y9vvpxXnWHIFrgOffnJO2HM0NnWv79uffnsvrOJmAN9Bgoa972DLkurQUs8j6BKGDd4aNdH4HBaIC2i9tanuEIoiZ6mYci4kABC5EUvQwcrvGukr7AoqHZm/0a/itjmJQCltmmBU8DAPY9mXal7AIA7iJSfH48pe+TBSy/ND9eYwfXvDsvUVfAirgUwXjd9uTthLjf7f+OMe7PQT8zhvV06cmr7hBECzy//nlZx07rDcBNJhOkXkuFwspCSXxnE7DmR82XJI+WCLku2QSsh1c+rKKn2uFM4RlCvdy39D61XUIQ3cxDEXGggIVIil4GDhSw0NDYbUrQFNbwMX5joL1je8nye879Odh8ZjNEX45mjLP73G6oaajhnGb05WhK358WPI0QxzfNl9fYwTVvOgGrw2Jp71FRVRGjn5mlmYzXkQWsHqt6CMofNzNG9MgL61+Qdey0FrDM+wR2WNIBDl06JNr3xuZGxnxRwBIOuS5v1d95f31wxYOEMBSwbrPt7DZKvdkLm89shsmBk2FS4CSYFDjJskcnon30Mg9FxGE/ow2iCfQycLiccJH1BRYNTe822HOw6j6INToBa3rIdEIcn1QfXmMH17zvpxGwOjlKWz422AQs815ifMtEZ194fwHTQ6bDzdqbvOoRQdRC7nHHfDBEdlk24feuy7uK9h0FLHkg1yWbgPXQiodU9FQ77EjeweuZpCfG+I0hlCvqcpTaLiEc0cs8FBGH/Yw2iCbQy8CBAhYamv3bkZwjlL7/S8gvhDjeqd68xg6ueXdpIQKW2WaEzuBVjwiiFnKPOw+vfBgcjjpA4MVAXn2WC3QCVnfn7jBqzyjYmbJTgtppmZDr1FrAemjFQ4QwFLBu45PqI3n71grkcrnGu6rtEsIRvcxDEXHYz2iDaAK9DBx+aX6Uo5HR0NDuWJtFbSRP89FVjypaBjoBa0boDEIcJgHrasVV+Nr3axi5eyQkFyVbfueat54ELPJXaXL4gyBaQanxZ1nsMsn7CJ2ARbfXH8Idk8lEqdOq+ipLOHnJad81fVX0VjuklaQR6uXdbe+q7ZJkkNsDft2oH/QyD0XEgW+ciKToaeCQatKGhmaPJuX+VmZbEL1A0TIczTlK6ffkODNDZ9KOD1/6fGmJ08+tH5hMJtrrmYxOwLpPIwLW1rNbCXFzynNk9wdBtIJS4w95828p+kizqZmSpudpT0guSoaCygIJaqflYUvAulFzA84UnoGkwiRIKkyC/Ip8Fb3VDukl6YQ6e2frO2q7JBnk9oACln7Q0zwUEQ6+cSKSoqeBg+10LTQ0NGlt05lNMO/wPEXzjMmNofR7cpxJgZNoxwdyvBs1N2h/ZzK1BayM0gzG68gCFp9yCfUHQbSCUuNPN+dusvQRpvx+P/S7JOm3NGwJWAg9F69fJNTZ21vfVtslySC3h3+i/lHbJYQjepqHIsLBN05EUvQ0cDjHOSv2Iotm2x5f/bjqPqDJZ7+G/woLoxcqnm87x3Zwv9P9AAAQlhVGCf856Gfa8YEcr7S6lPZ3Juu6VH4Byz3BnXF8C84IZrwOBSykpdDU3AQJVxPgZP5JOHHlBJwuOK3Y2NN1eVdZ+ghTfr8d+k2S9FsadF+1VdZVqu2W5iH/keRNrzfVdkkyyO3h78N/q+0SwhE9zUMR4eAbJyIpeho4NiRuUOxFFg1NSVsYvRAySzOhp0tP1X0xW6dlnWDpsaWq5W8ymaCLUxfK71ODp9KOD+S6q2+qp/1LPZPRCVj3SixgfenzJeP4xiZg+ab5UuJL4Q+CaI3qhmpFxpd2ju0ov33j940sfYTJhznhcyRJv6WBApYwyMvU39jyhtouSQa5PZAFrKamGqivL7ZsLYBoBz3NQxHh4BsnIil6GjhQwEKzV1t3ah0AALy3/T3VfSFb1+Vd4ZWNryie77nic7S/TwueRhgXLl6/COsT1lPiNTQ10E50mOwBlQWsyOxISvzuzt2hu3N3OFd8jhJfCn+uVlyVdpBGEJHUNtaqNtbNCZ9D+U0KmPIjj2UIN1DAEsalG5cIdTZ0y1C1XZIMcnuYd3ieJay6+iKcONELjhwxQFraNyhiaQw9zUMR4aCAhUiKXgaOZlMzuCe4q/Zii4Ymp7mdcgMAgGHbh6nuC511WtZJ8Tw/3vUx5TQpg9EA00Omg2OMIzy44kF4cu2TjNc3NTdBU3MT5/yUELC+8vmKcYxrbG4k7MFja3mRFP5MD5ku6TjNRm1jLfwZ8Sd8sucTiMyOVCxfRF8UVxWrNs5tSdoCu8/thl0pu2BXyi7J2ilbngh/TCYTRGZHQlBGEARlBEFwRjA0NTep7ZbmCbgQYLftj1wuawHr3LnPCM/18vJYFT1FyOhlHoqIw35GG0QT6GXgwP2v0OzZ5kfNh+yybOizuo/qvtDZ3YvuVjzPIZuGwHPuz1F+H7plKKfrL5ddhoamBs753WWkCljtFklbJjYBCwAg4WoC/Hfff2FG6AybXxRI5ZNYsm5kwfbk7ZBTnsMab3ncckue9yy+ByrqKihxDmcfhve2vwc/BPwAZTVlon1D9EfezTzVxjm6pbpisfUVKCI9n+79lFDHY/3Hqu2SJjh48aDdtj9yuawFLPJz/dKlPynX36i5ASuPr4S95/fiF1oKo5d5KCIO+xltEE2gl4EDBSw0NPXMWnxQygZ7DoZ+bv0EXz90y1Cob6rndY13GPFFt7WDtGX6n+//JBsTpfJJDFk3sqDjko5gMN7+Su/KzSuc/V1zcg0h/ML1C4TwPyL+EOUbok+u3Lyi2jjnk+ojeXlsjUGI9Dzm8hihjrs4dVHbJU1APhBhxO4RarskGeR+9VfkX5YwqoBFfLaYTCYY4DHAcu2K4yuUdr9Fo5d5KCIOfNohkqKXgUONCTQamhJG/msx2m17acNL8PS6pym/91jVg3MadY11vPL0CW95AtbZorOCffjS50tCWj8E/MAY99VNrxLihmaGWsKY7hPS8uD7BdbxK8cl6wsoYNkH5AM9zKfatnQSCxIJ9dJSBKzU1K8Jz/WyssOEa5OLkrFfqohe5qGIOLBXIZKil4EDBSw0e7R/j/wLANKJEfZkU4Km0O5x1du1N+c0ahpqeOVJ/kttK4nL9LXv15KNiVL5NMZvjGAfyHuj9XbtzRj3a9+viXWdc8QStiVpC61vXKlrrIP5UfPhu/3fwZnCM4LLg6hLUmESzA6bzav9phSnSNYXZobOFOy7yWSCIzlHIDaPuL8OCljKgwIWPWQBa+TukWq7JAnkL8sMRgPMjZxrCU9L+4YkYEURro++HI39UkX0Mg9FxIG9CpEUvQwcTrFOkk4k0dC0YJMDJ8O1W9dU90OLtj5hPTyx5gnK752XdeacRnVDNa88W6KAZTAKf60g34terr0sYcdyj4Ffmh80NDUAAMAYvzGEuFGX70wiHI46iPLLeMRouaaLUxeob6oXXCZbVNZVWsqESMf5a+ehzaI2vNsu3eRVqD297mnLxuBBGUFw/Mpxm35X1FVAxKUIwhIk4xGjJdzWV6CI9KCARY89CliLYxbT9iuHow6WONQvsKIJaRzLPYb9UkX0Mg9FxIG9CpEUvQwcKGCh2au9uOFF1X3QotGdOtrduTuvNKrqq3jFJwtYUpfpG79vJBsTpfRrxO4RkF2WzduH+53uJ6RjFrA8Ej0IaQMARZywPuFt0dFFFJ8GegyEHck7YMXxFVBeW87qB3l5olxfYc0InQEG4+0vzTYkboDZYbNhVtgsmBk6Ew5dOiRLni0FoSewAgC8t/092cYhW9B9vWF9HQpY0kN3umxNQ40lvJdrL0JY52WdoaKuAjaf2QzRl6NZUrZv7FHAYupXMbkxljipqf8jCVhHCGnQLUNGlEMv81BEHNirEEnRy8CxLHaZbC+oaGhq2kCPgar7oEVbd2qd6DQq6yo5x+28RP4vsKQUsDxPe0rqW981fXmfvkQWsHq69AQA6qSCbpmXteDD9Fd0sw32HMzqx4c7PyTEF7OvFxPpJemsPjrGOEqeZ0ui7eK2vNvsm15vAgBAQ1MDhGaGyjIO2WLr2a2s16GAJZym5iZ4Ys0T0HdNX3hy7ZOE+02uR2uRm7zMvNOyTvDC+hcs/9+RvEOtIqlKwtUEQr3Yo4D13vb34L3t70FKcYolDlnAKi8/Skjj1NVT2C9VRC/zUEQc2KsQSdHLwIECFpq9mvXSE7Q7JoWAZesIe2t7aBlVwGq3SNoyidlvig66Te7FWEpxCtQ21sKpq6fgevV1m/l3cepCuP4xl8cAgH5DXfJvYVlhlnQcYxxt+pZeks7ox/CdwwlxkwqTxFcuiV0pu1j9e2TlI5Ln2ZIQImCN8x9nuZ5PX+djtrAlYNU21opKvyVDFqp6rOpB+7vBaCCMV4+vflz0fbVHQjJD7K4euJQnNfUrVgErqTDJ7upFT+hlHoqIA3sVIil6GTiWHlsqy8spGpraRt6vA+22rT25VnQadEtNmEyPAta7296V1L9JgZMs/35wxYOQUZrBmv9bXm8Rrv9076cAQJ1U/HboN8pv7gnulnSWHFti07cTV04Q8s4pz4HCykIAAPh418eEuHIsIWRaKmZtiHCECFjjD4y3XG8ymSQfg7jc021nt9FeN9Z/LDzr9qzNdxeEGboN8Jl+35++33IdClhUVhxfYZf1wKU85Od6bu4SQjjd17WIcuhlHoqIA3sVIil6GThQwEJDa1m26sQq0WnY+vrB2pQQsL7d/62k46LQfYO42md7P4PZYbOhvWN7eGPLG1BcVUzIn26iSPdXfjqbHzXfkg7XL2ynh0yH2sZay6btbRe3Be9UbxixewQhXmJBIsHPgsoC2J++H65WXBVc14cuHbLpHyKcQZ6DeLfPCQETLNdrTcDiYl94fyFnleoeuuWXAPQC1oELByzX9Vndx676asLVBPj90O/gn+4v6Hq2kzr1zn1L77OUpb1je9o45Od6VtYcQnhmaSahTsxLVRFl0Ms8FBGH/kcbRFPoZeDg8hd6NDS92hi/MdDesT0YjAZOL99o3IzPKYRKCFi/H/pd8rGR6QQ/OaynS0/4Oehn2JC4Aa5XXxeV1t+H/7aUIepyFOfr6Pb+st7fxmA0QMLVBEvaBZUF8MDyB8BgvH1CYW55rqB65iLM2SLqchQ8sPwBaO/YHnal7BLkh72yIHoB7zb0Y8CPhDTkaPO22J68XXDa1iIuQoXuDxAA9AJWwIUAy3X2JGAVVhbC3YvutvgdnhXOO42Ru0fqvh6YkELAyinPgafXPW0xqb+URtjRyzwUEYf+RxtEU+hl4EABC82ebUvSFrhy8wp0c+6mui/2ZFX1VdDKoRWnuA87yS9gycEfEX+oXs9CbN7heYRy8BGxbJn1/lr93fsTwsb6jxVUzwEXAkTfX/Jy4Ss3rwjyxV5oNjXDjuQd4HbKTdDXhD8d/ImQnhzt1BYoYMlHTUMN7f2g+zLr4MWDlutsCVgf7PhArSLxorS6lOJ7N+duvNMhn9LKp31rHWsBy2C8vRfhIysfIQh9tgQsRF30Mg9FxKH/0QbRFHoZOLhs8ouGplfzSvICAOpePmjiLCgjiHPcR1DAUtT+ivyLUI7gjGDJ0t56disAADQ2Nwq6DwWVBeCX5gd5N/Msv/mm+drMN78inzVdcvy7F90Np66e4n/T7YQ/I/4UdZ9f3PAiIT052qktdiTvEJw2Cljs0H1BC2BbwHpizROs9a4XAev7A98LapNk2E46VpKm5ibYdGYTuMa7wq16aeYbnZZ1oi2X9XLLjIxphOf6tWu+kuSNSINe5qGIOFDAQiRFLwMHClho9mxSCFgPr3xY9XJozeZGzuUcV68CllgRQC37M+JP2HNuD7y44UX42vdriLgUIVnaZgErPj+eNty8+TsdhZWFliWHnZd1hvlR82H8gfGc8p0eMp31XtFdQxZhWhL3LL5HkvtsRo52aguhAtaoPaMIS10RKlX1VbT3g07ACrwYaLmu75q+rHX//o731SoSL4S2STJaEbBmh8225Dt853BJ0mQSsOaE3/nKKjPzF5KA5SNJ3og06GUeiogDBSxEUvQycJy6egqGbhkqywsqmm3jusmyvdmNmhuK5GOeiJE3o+ZjR3KOqF5fWjPrU/VsGQpYyppTrBNhb5d3tr7D6brJgZNtxjH3p7i8ONrwacHTLPUXmhkKr21+Db70+RKKq4rhs72fiSrXSxteguyybNp71du1N6920dDUALWNtZK2FzXwSfWBgR4DYZDnIHh548uw8fRGALAtNLDZ0mNLobG5kZCPHO3UFjtTdgpKNygjSJa6ticq6ypp7wfd3ljWpxDODJ1JCDML0mZraQIW0z6JT659UgavmSHnX1VfJTrNzss605aNKGDNIAlY+yjp7Dm3B3am7IQdyTtg33lqOCIfepmHIuJAAQuRFD0NHD8E/CDLCyoaGpMB3N68ucOSDrLms+3sNgAQJ2BFX46WxBePRA/V610q+9LnS85xUcBS1sb5jxN0HRdR0vxF45nCM7Th3/h9Ay9vfFnW8tEJT3SnNjK1i2O5x6Cbczdo7dAaXE64yNJ2lMLtlBuhvA5HHQAA4Mm1Twqq2wXRC2jzkeM+2kKogDXYczCEZIZIWs/2RkVdBe39oBOwZofNZkznXPE5Qtxh24cpVQRRCG2TZBbHLKZNp++avjJ4zQw5/xs1NyRP02y/hv9qiZOZOZPwXC8u3ktJx/qPKV2cuoj2C+GOnuahiHBQwLJTTCYTpKenw7Zt22DatGnw8ssvQ9u2bcFgMMA999wjW756GjhQwEJT2gAAZoXNgraL28qaj1nAYjstyJa9vvl1SXyZd3ie6vUulT3m8hjc62iAVf4GiIo2wPx9BmjjQB9XEQErLg7A1xegvl6ycZHPMkkt2Ri/MYKu++ngTzbjbEnaAgAAl8suq1a+7w98T7lXTHvz0PHU2qcIcaT4WkEt1p5cSyiLY4wjAAA8ve5pQXVrvVzMGjnuoy12peyiXPPa5tc4t2WEmZu1N2nvB93m7taCBZlTV08R4j6x5gmliiAKujbz2ubXeKez6Ogi2rT6rO4jg9fMkPMvrS6VPE2zWQuamZmzSALWHko6bRa1sVx7v9P9ov1CuKOneSgiHBSw7JScnBxLByYbCli32ZK0RbXJCFrLtNXxqxXJZ3vydgAQJ2Ch0dsoT6IoNdyDPp4SAtb0EQYAgwHgA+k2EdargPWN3zfC7ueeUZzi5d3Mg+yybFXLSIZpyRwd5DgnrpyQrM2YyS7Lho93fQxDtwyFY7nHJE/fDHkcXXpsKQAAPLPuGUnq1YwS95AMnYDFdenyN37fwE8Hf4JBnoMsyyr5YDKZIDgjGLYnb4e6xjre12ud8tpy2vvBV8DaenYr7/uqBejazC8hv/BOh0nAenz144zXlNeWQ2xeLMTmxUJcXhxklGaIKQoAyCNgWQtP1mYtYGVl/UoSsHYT0si6kQX9XQywzM8Av+02wL2O+mgf9oKe5qGIcLBX2SnWAlaPHj3gv//9L7z11lsoYFmBApZ8ZuvUHjR5bUfyDgAA+GTPJ6r7onVr59iOV3wHH6Io5RNOH08JAWv86P8XsAwGgKwsScZFlxMuqt8TJY2r6PHhzg/h0o1Lqvp67dY1wpdTTEvm6CDHofuiSyxfeH9hSb+3a28wmUyS5wEA8HPQz4SyfOH9BQAAPOv2rKB6ZUKOe2gLOgHr90O/C8rrys0rhLSbTc3gHOcMI3aPgK1nt1Luj3Ocs+Xaj3Z9JODOaJuymjLa+8FXwNp7fi/v+6oF6NqIrYMi6GDaA6uXay/GayKzIwlxv93/rZiiAIA8Apb10j9rmxU2yxKH/FzPzv6bkEZ6yXkIiLgTPn2nPtqHvaCneSgiHOxVdkplZSUEBARAUVGR5bd///0XBSwrvJK8FJ18tCQjL1dpScZlSZLchgKWfGb9YqopAev4cUnGxaTCJNXrWEnj89VO1o0s1f1dEL0ApgRNYV0ySQc5jln0kRJyHiW3SiTPA4BeqKLboJurcS2PFGYLpn3WhNjyuOWEtA9ePEgITylOYS0v2+maeqS0upT2ftDtgWW9aTeZhKsJhLgjd49UqgiiGOAxgFJO6wMouMIkYLG1b9d4V959wRbk9OQUsGaGzrTEIT/XMzNnEtJIyd1NiYMoh57moYhwsFe1IFDAusOF6xfg072fyvKCioYClto+7EzZCQD6F7DIpz1pwQ4c4iZgPbBUfgHre2sB6/RpScZGugn0kmNL4OGVD6te92rb0ZyjqvtgyyYFTqK9r+R486Pm82oX7gnu8ML6F2BCwASorKukjUNe0ijXPlt0e10J/frKYNSWgFXfVA8Xr1+UJK+wrDBC2vcuvZcQPnzncNbyXrh+QdgN0ihV9VWM94P8+5JjSyxhueW5cLrgtMUiLkUQ4n6y5xM1isObgR4DCX4P9hwM6xPW807HKdaJd/v+J+of3n3BFuT0pNjEvbVDa9py8RGwknN2ooClInqZhyLiwF7VgkAB6w7/HvlXkckEWsszrvuVyGm7UnYBAHDe30er9tuh31T3gWxcBSyDkSpgtZLYF4KAdf68JGPj6YLThDy6OHWhfLmBpl1j2tOGHM+88TkAwNJjS6Hjko7w4oYXIbssm3ItefP61fGrafPos7oPIV51Q7UkbZJMd+fuktYZE3LcHy7QfREkxMKzwlnLQ97AmxwuxT5FWsP6ABXrzbXJZTfvqwYA8NKGl1jrWS8C1osbXiT4XVBZICidkMwQ3u1bDgHroRUPEdKTYsmyMAFrBiGNlDxvFLBURC/zUEQc2KtaEChg3WFh9EJZXk7R0JzjnCkTOaVt97nbm4rqXcCaEz5HdR/IxkfAsl5ueDhael8mfGYlYMXHSzI2JhYkEvIYsXsEBGUEqV7vaLbtevV1xvtKjutw1AEAAIqqigi/j/UfS7l2avBUyvV09HbtTYhT21hLaVvrE9aDR6IHeJ72hLSSNE5tsrG5EZYcWwKf7v0U/NP9Ja0zthPC5LhHZEqrS+F69XWLAdDvySTEIi5FQG1jLfim+cLJ/JOUcLKA9cL6Fwjh125d43R/9ESf1X2gx6oe0GNVD+jn1s/yO7lu1pxcYwmz9XWfXgSs69XXITYvFpxinWB53HLYfGYznC7g/+VuaGYobT2M3jea8Zq/D//NaQzhQ0hmCPil+YFvmi/4pPqITg8AoJVDK9qyzQi9I1KRxam8POJS3ZQrB1DAUhG9zEMRcWCvakGggHUHFLDQ5LKISxEQcCFAlbyfWvsUbDy9EbJu3N7Qe/S+0arXhxj7NfxX1X0g27x9xBfTZX7McRUVsDp1kmRspNvfJTgjWPV6R7NtH+36CEbuHgmHsw9DfkU+FFUVWZbxkeMujF4IAACbzmyihJ2/dh6ampssbeLHgB8pcejo6dKTEKe+qZ4QbjxiJIRzXb60I3mHLPV179J7ISgjiDFfOfIkY/012V3GuwAAoLqhWpK8IrMj4U2vNxnDbQlYxVXFnO6PPfD9ge8JZbdeftnPrR9rPQ/bPgxi82J1IYmnNuAAACAASURBVPiRT1CcGzmXdxphWWG09aC0gCUHTPfYWsBKSfmI8A5QWXmGkMa5/BBaAau5uREKCjzhypVV0NSk3fmR3tHLPBQRhzZHEEQWpBKwbt26xWjXrl3TxcCxIHqBLC+naGjx+fGqCVhOsU6Edm59KpgebXbYbNV9INtwD+KL6eTtzHHJL7FS+0IQsAzSPM7TS9Lhtc2vwZBNQ2Cw52CYFTaLsmTkLuNdqt8HNG5mnqDShQ3dMpTxurKaMkubmBAwgRJOR49VPQhxGpsbCeHkpfseiR6c2qQc9ZJekg51jXWK50uGTsC6VX9LkrysTxWkM2sBa+mxpZTwlixghWaGWsL6u/fnVN8PLH9A0/uG3ai5ASuOr6AdH/gQnhVOW/7/7vsv4zXzDs/jNIaoDdO9tV6anZw8nCRgJRHSSGEQsLKyZlv+n5z8oaLlakmggNUy0OYIgsiCVAKWeWCwZVoeOFDAatnGdNKMFMa0h4IS1md1H9iQuAGaTc0AcHs/o5DMEMYXTq3brLBZqvtANrKA9fMO5rhyC1g/yCBgBV4MJOQx8eBEyl/cv/L5SvX7YDAa4D13A3yxyQAdFqvvi1btn6h/AIC/GGN9opfnaU/K/TdzteIqvLvtXXhk5SOUNMh7YJGfuxsSN3Bqk3LUS97NPFXytZVHfkU+7RdvQsxWOq9vfh0AqMtIzVZUVUTxV4+M8x8H3Zy7QdflXaGLUxfaZXNkkVaIgGUwans54WMuj1H8/SvyL0Ick8kEa06ugTe93oR/ov6BhqYGSjqHLh2iLbs9C1hrT661xElO/pAkYJ0lpEEWsFYfuF1W8vsAfoUlDyhgtQy0OYIgsoAC1h3mR82X5eVUr/bO1ndU90FJ80ryUlVoktvIXz4AyDMZa2n2ptebMMDVAAu8DTD//23Yeub41i+r+w9J748cAhZ5v6sfA36EPef2EH7Twimj33ndqdt1Aeq3Da2aeakg3+v+jPgTlscth/PXzlOW8P126DdLe2Fb5pt3Mw/qGuvAO9UbjuUeg3H+4wjhU4OncmqTctRLfkW+KvnayoNpfyEhRj4Vkmzbk7cDAFD6t9nsRcAinzi97ew2uHj9okXEpDudcEfyDsv1z7k/J+oeawXyF5IG4+2vnK0hn0Lrl+ZHSYdu2bHBaN8CVlxenCVOcvIHhGd7VVUyIY2U/GBaASs+/nGSgCXPIRctHRSwWgbaHEEQWcAlhHdAAYtodPsT2KvNCZ8DACDrV1hqG91fTdX2yR4MADjvB/WIE/ULrDYO3PPqvKyzzTg/fiq9gEVeLvhDwA+aHC/Jdfuok/o+adGmBE0R1f+3nt3Kum8O27W55bkwbPswxvB7l97LqU3KUS9cTmCTI99VJ1ax5uGd6i1ZXu9tf4813CzSMAlYhZWFXIcNTfPZ3s9oyzfYczAA0AtYW5K2WK5/fv3zvOpdqzy66lGb/pL7ayuHVpR0mJYeP7TiIca85RCwvvT5Ep5e9zQ8tfYp6LumryTtlemeXq24aomTnPw+ScBKIaSRfCWIQcDqo9oXWEVVRXCj5oZi+akJClgtA+2OtIjk4Cbud6A70relWk+XnnD8ynFwjXeF/2z7j2TpTg6cDFOCpsC04Gmqn8pnbZvObAKAliNgpZekw4bEDar7ZA/2te/XnOPSCVjtFnHP60ufL2H4zuGsXzvJIWCRvwD5/sD3mlxyTa7bp1eq75NWDUC4GOMc50z54sK8LNFWujG5MTbTP3DhAK3gbo0cdcLl6yI58nWOc2bNQ0oBi87e8noL3vR6E97Y8gaEZIZATUMN7D2/lzbulZtXLH6aTCbwPO0JY/3HQnBGsOX30wWnBZ1kpyRMQt7LG18GAIDKukpKmLWARd7cns2mBU9Tq5g2YfLZmtc3v84aDgDwxpY3OKVlzV+Rf3GOS0dRVRGcLToLyUXJcK74HJRWl1LS47Is2BZ0ZRrnP44QJynpHdISQmL7P1+UAEPWGuDV/7f+LmYB6wnCdY2NVaL95YJTrBMYjAZo59gO/NP9FclTTfQyD0XEgQJWCwIFrDuggHXHHlzxIPwR8Qf8EPADTAiYIKmIZbZ7Ft+jejnN1t+9P/x75F9os6iN6r7IZeZTCAGo+9egCbeOSzpCz+UGGLzGAINW37YuS+jjihWwzPaW11uMYRQBq459U2oukPdLG39gPApYOrf96ftFXU8WwBdGL4Tz187D5bLLrNdFX47mlP4bW96w7NtHhxzPDy4nxslxL5bFLmPNQ64TF81m5sL1C/DEmifAYDTAwysfpo1rvcySvDde1o0sWByz2PJ/4xGjgNFGGWzVB52A5ZXkZbmej4Bl/uJRi9hqEwDaFbAcYxwJ1645uYaSXm55Lq806aAr01j/sYQ45GdPRgZxKXTqtVTC9f3d+wMAQHx8X14CVrOpGdwT3OG3Q7/B5bLLgspT11gnqN7j8+NhWewySClOsR1ZY+hlHoqIAwWsFgQKWHdAAUtZa7u4reo+6MEnqWz3ud2Wto4ClnR296K74ZedxJfXd93p40olYLHZRLKA5cHtVDc2yBv0jvUfS3tCmdqGApZyturEKtrfbe0j2HV5V855WG+aTea3Q79JWp7hO4fDrXrb7ydy1KVjjCNrHuT9mqQ2M0zL6qzNetlUp2WdCGHf7v+WMW2tYas+6ASsyOxIy/V8BKxJgZPUKqZNbLUJAG4C1pteb3JKyxqxAtbC6IWEa63FU7MJFXms6ebcjZLuvvP7CHHIz54z54kCV+q1VHh9nQHeWGeAN9cZ4GfvAQBAJ2BVsvpifWLkIysfgabmJt7lKa4q5l3vZ4vOWsb2tovbQk55Du981UQv81BEHNp94iCSgwLWHVrSnk9CrJdrL1HXkzeFf3DFg6qXiWxsR8jr3cwCVtTlKMtf2fVoI3ePVN0Hss3QkIDl058kYL33nuix0eGoAyWfGzU3CEtuneOcVV+CS67bp1DAks1+CPhB9jzmR81nbJO/H/pd0rxMJhOnviBHOT/e9THE58fDiuMrCBNUpYxP2SrrKhnj0y3L0yq26qOiroLy++Hsw5brB3gM4Fy/Ew9OVKuYNrHVJgDo97cio4aAxWUbiuyybF5p0tHduTsl3cUxiwmnspKfPRvCnyWkUVRVBIejW1vCDx9pDyaTCQ5EdiQJWBWsvpD9CMoI4l2ea7euUdIZtn0YzI+az7h0m/zV9xi/MbzzVRO9zEMRcWj3iYNIDgpYd0ABS14jC1hPrn0SXtv8mup+WRsAwE8Hf1LdDznMLGDNCpului9i7PsD36vuA9l+20N8eR25gT6eEgIWQbwyGABefVX02EgnYAEAHLx4EIZtHwYzQ2dCdUO16GVpYu3AIWLdMi3lRBNvT697WvY8FkQvAAAAn1QfMBgNMMhzEFy5eQXqm+olzcf6dDlbyFVWusm8UmbeA8tWvNYOrVnrgm5zfq3CVk4AaQWsCQET1CqmTdjqwAx5qR7dF2Vsy9qZIB9+8vz653n5butETYORuHWCUOgELPK4ER1NfPbM2duRks7Ro/dYwo8d6wzRl6Mp7wONjTdZfREzdpmhE7DMtjNlJ+015O01+rn1452vmuhlHoqIQ7tPHEQ0aWlpEB8fb7GJEyeCwWCAtm3bEn6Pj4+HOgn2TgHQz8BBdyIKmnRG3kfrybVPQlhWGOxM2Qm7Unap7p/BaN8C1p5zewCA/Yh7PdiUoCm8lm8oYQERxJdQn3D6eKoIWIMHix4bmQQsOkqrS1UTprcGE+u2k6P6bQNNuH3u/Tll0/fOyzrD6vjVkuaz7ew2zn1BrrIy7TmlJWuzqA1rXdAJWK9sfAWGbBoCr256FWaGzhQ8BkkNWzkB6AWsqMtRlusHegzkXG/jD4xXq5g2YasDM+sT1hPC/j3yLyWdt7e+zSkta1KvpcLbW9+Gt7e+De9sfYdwEAQXnln3jM26/znoZ15p0sG0WsA13tUSxz/+c8Kzx/3wMEo6MTHtrASsTvBr+K+U94GGhnJWX8g+CBGwSm6V2Gz/ZMjbazzr9ixtPK2il3koIg4UsOyYd955x9KJbVlOTo4keepl4NCjgJV1I0t1H7hahyUdKL8lFiRaTG3/zA/uiQcnKpqnUssWzQLWnPA5qtezGJsWPI33EeZym6YFrL59RY+Ni44uoubDQrOpmdMXHUIsvSSdMWwbScC6DwUsNA5mvTm3LdT2lcm6LTXAd16399eRK497Ft/DWhe9XXuzXv/Bjg8EjT9ywOYnAMDN2puU36MvR1uuf3HDi7zqTqtw8ZcsYNFtzs+2fxobAz0GwgvrX4ABHgPgna3v8PK9v3t/TnVvffCAEJgELOvN+f3jvyA8e9aTBKz6+hLKs3922GwaAauM1ReyD0IErOvV13m3VfKhGU+ve5p3vmqil3koIg7tjrSIaFDAYqa0uhR+DPhRtRdQIZZ3M091H7hYP7d+Nr/KmBk6U3U/AUDxNgAAspzySDazgCX15sdK2/SQ6ShgsRhFwDKIf6STBebhO4fbvGbE7hGy1HVmaSZj2PYQYt3eaycCltp7i9m7bTqziXNfUNtXOmvtYADf8Dvt/kMPadJ1inWC+5beZ/l/e8f2rHUxOXCyzTS1gi0f6QQs6y/1Dl06BE+ufRKeXPskPLX2Kfhu/3eqlbumoQYWRi+EacHTeJ+6x8Vf9wR3QhidgMX2B2A27jLeZYnXzbkbZ7+9U705t2OHow6c0yVjMpkY07X+uss//kuSgEXce7Kq6jzl2f9LyC80AtYNVn/IPjAt+WOjqr6Kd1udtrM1bA+5/YzdFmyA4Zsf452vmuhlHoqIQztPGMQu0NPAMdZ/rOovo3wstzxXdR+4mEeiBzjFOqnuhy0DUEfAUmJTdbOAJfXmx0rbjNAZqvtANvLeS5oTsNLTRY+N4/zHgcFogMdcHoPUa6k244/aMwpaObSyeSodX8spz2EMIwtYHRer3zaksHaO7VT3wZ5tQ+IGzv1AbV/p7D136rjyqJP4dJfFLoOOSzpa/t9xSUfWupgbOddmmlrBlo90AtbssNmM6dn6Gl5OrP/498y6ZzgfSADATcA6W3QWVh5fCSuPr4QVx1fA8SvHKelIIWB1d+7Oyefy2nLKnkxstjhmMef6INPQ1MCY7uTAyZZ4+0kClsfhdwnpVFWdo/TRKUFTaAWsSzcuQUhmiMWsT1Ik+8B2UisTjc2NrPV18fpFyCjNIGzovsivHcHPMbueElCb6qGneSgiHO08YRC7QE8Dh1T7AwHY/kxXCmObzGnN9HDyHQAocroWOU8l8jELWH9E/KF6PYuxR1c9qroPZNPSF1iX76cRsE6dEj02mkwmKK4qhpqGGt7XZpRmSFa+9JJ0+CXkF8b67bncAL2cDdDb2QCtNNA2pLBOyzrJ9kUbmgEOXDjAuS2r7SudfbOFOq4MWy8+3TF+Yyi/Wfd/ctjcyLk2vxbUCrZ8LK8tp/zOJmAdyTmiSrkr6yopeRVWFnK+Xip/2Q4h4JN/9OVo+DHgR5h4cCJMCpxEe8oeeUmjLVt6bCnv8phhOzCCKGB9xSpgXS8/RemjgRcDaQSsUso2Fn9E/GFJ591t7xLCuPwxiUxTcxOnesspz7Fcszf6eYKfO+Jn8a9MFdHTPBQRjnaeMIhdoKeBY0fyDtEvfV2XdwUAFLDIxuXIY7WN6bQZOe39He8rko9ZwPoz4k/V61mMafFrFC0JWJNG0QhYaWlqDqsAIN3EPzgjmPHE2PFbDTB/nwHmextggbf9nELYeVlnuFV/C1YcXwHf7v9WdX/szSIuRSjejqU0PgKW2D8AWC9RI4fNjZxr84vLfm794Fm3Z0Ut65ICNh8B+AtY25O3M6b39+G/ZSnD1rNbab9Eul59nXMabHVg5p+ofyy/f7f/O/BN86WkI0TAOld8jhKX/CXXWP+xlOv4HuLgFOvEuT7IsAlY1qcx+saNIPS/tRFDCemcyfWl9NGgjCDKb/X111lP/CNvN5FWwv/ZbjKZwDnOGVYcXwGvbHyFU18Pj+5I8NP7xFcCalM99DQPRYSDAhYiKXoaOKQQsH46+BMAKCNg6cn0IGBZL5ewN9t7fi8AsL9o6sHaObYD3zRf1f2wNi0JWD/RCVgSLCEUi1TlC88KhwXRC2jD1gVIv4xKK2bGZDLByxtfVt0fe7KY3BjF27GU9qkndwHrS58vReVl/VUGOcwp1gmu3boGxVXFUFRVBBtPb2RMZ2rwVCmHF96wlRGAXsD6NfxXxvQOXjzImB7f0/XElqGshn0jcC5pmKE7jXF6yHRKOmzLR5nwSfWhxKXbHN+atJI03m3W+rRAvtQ11jGma37XBwBK//t1N9HvM7k+nL7AamqqpYjAz7k/Z0mHLGCll4h7tu87v4+xfHk38xjLFxD3hqh8lUZP81BEOChgIZKip4FjZ8pO0S+T5o0dS6tLRadlT/b46sdV98GW3bv0XlnSVfpkQzo7ePEgAOjztE1re33z6wAAkF+RD4M9B6vuj8GorT2w7F3AisyOBMcYR9qwtSQBq8dy9duGVGZNdUO16v7Yi207uw2u3bqmeDuW0p5zoY4r47fKkxebgLU8bjlcuXnFEs4kNBuMxBPc1GDY9mGMvl28fhFqG2spv7MJUbF5sYzpzY+aL0sZmPK7WXuTcxpMX4Cb2ZK0hRI2LXgaJR22L7uZoDtQZoDHAMpvkdmRcLrgNDQ0NcAnez7h3WbPXzvPr2KtECpgzeEgYB28eBBCD9/5f0R0awAg7gtmMBrghfUvWNLxTvW27EW24vgKKK0uFVw2c3pM5bPuy2TfTyV/LipfpdHTPBQRDgpYiKToZeDwTfOlHBVrMN7+SyGfh6X5r1NiBCyHow6SvGyi8TN7FrACLwYCADAuv9KLrTm5xtJnm03N4HLCRXWfuH6B1XExdaJ5DwpYvCz6cjR0XtaZvm2QBKzH7FTAkrI+W7LRTcS5tuOOiw3QxkH9MhiM9ALWjF3y5MW2obTBeHu564XrF2y2UesT3KSivLYccspzOG1i/t729xh967CkAxRUFlB+X3VileV671Rv+Hb/tzDGbwx84/cN6zub0gJWVX0V5zQ+2PEBbRpm6L6ioxMfAy4EMPrDdD/mhM+hxH1h/QuSt1lzexSCVALW6Zx9NgWsQ1F3AwD1vg7wGCDYfzoamhog4lIERFyKgEmBkxjLl1+Rz1i+86nfgvGIEWaHzYaCygJJ/ZMDvcxDEXGggIVIil4GDqbNrfnuMzUpcBJUN1RDfkU+7wft6H2jYdWJVZB3M0/SB/jofaMlfymwR7M+MlxK04KAZd4M1Xo/Cz0aeTnA+WvnVfeJq4BlMBogJPJOvNDD0vtCK2ClpKgxpBLgu/Eukx3LPQadlnWiDVt9gHgfeupUwJoeMp3yGxm1fbQH80ry4t2ODcbb4tCRIwbYF2aAx53VLwetgLVTnrxsCVgGowF6u/aG5KJk1nSs9w+SgpP5J6Hr8q5gMBpgQsAEm/HZBCyDkf4Pl9YCFp9lvK9vfp0gBkgFU37VDdWc06AbawxGA/il+QEAgOdpT0rYsO3DKOkkFSYx+sNHwHp+/fOSt9nM0kxhFQxA+yWe2cQKWCGZIRAeZSVgRbcBAOp9Hegx0JJOYWUhZN3IgszSTMgszYTaxlpe5WlqbmK9V9ZmvUE82fetEU9Z4g3yHCSkahVFL/NQRBwoYCGSopeB4/dDv1MG8K98vlJ0uYaZKzevSJ7u/3z/JyqNfm79FKsHtUwuASvwYiAczj4MTrFOqpUtOCMYAADmR81XvZ7FmPUkAkDYnhhSmy0Bq8+KOxuKyy1gTfyURsDq0kW5gZSBpuYm6O3aW3T5jl85zihgkV+ytSAu8DHzaVN0fzQh87Xv16r7q3dbn7CedzvuuZzYxlb6q18OJQWs7LJsS12sO7VOcDovbnhR+GBCw0CPgYT0be0NFJYVBl5JXrRL5AxGA7y66VW43+l+wm9bkraAyWSCtSfXCirzYy6P8VquagumfCrqKkSn096xPdyqv0UrYBmM1PHobNFZRn+YoDv1+zn35yRvs9aiK1/MAtYrawywK9QAvuEGeMvtdroTD060xIuIakXof2+usy1gBVwIgMjoO9cdPtKO9n68tOElSzojd48khCUWJHIuS2Nzo03h1tqiL0dbriX7Pt+bGJfPyZdqoJd5KCIOFLAQSdHLwEEnYBmMBmhoapD8gWrrQS/k6y1b6X7l8xWva3q59lKs3GrbM+uegfPXzjNOjMXa2aKzAACQei1V8bJNDZ4KhZWFlr/Use1LogdbeXwlod+ml6Sr7tPojcSXuwVWL3fzve+IVQNdDXDI+i+uUdL78iOdgGXQxmO9qr4Khm4ZKqp8O1N2wiDPQbRh5JfsvivUb698zHyiFBcBK7ssW3V/9W41DTW82/A7btR2pnY51BKw3BPcRaXFhslkAq8kL5gaPBXi8+Mtv1fUVdBuUk5Oe3LgZIi+HM1pOWEXpy6U61/d9CpMC55G+M03zZd1s3YuJuWG7uS9ksxmfXocF5h83Xd+H+NG/GTYvrhjYnbYbErcZ92elbzNctkT7Fb9LfBI9ID96fsJbaamoQYMRgP4hRP71+TtBvgx4EdLvN2HOhPCH1pmW8Ban7Ce+D4Q3Zb2fgz2HGxJhyxgJVxNsFk2M6GZobzqjY+AxbfNKY1e5qGIOLTxpovYDXoZOOg2lDQYb3/+LPUDlc6+8rlzLK3UAlZGaQb0d+/P6xqmT8vt0cwvlXIJWF5JXhCaGSr65VeIkY8rNx4xQmuH1nD3ortVr3ch5hznTCjPhesXVPfpjXXU5QMG4+0vgKx/9w6Tf/JLu4TQoJ3HelNzExRUFoBfmh986fMlzDs8D97yeotz+Xal7IKF0Qtpw8h1+4TOBKyU4ttLPclf4D659knauuT7Rwm0O/bfff8V1H6fXUVsY46+6pdFSQHLelmRR6KHqLTYsN5c+u5Fd0PJrRIIvBgIHZZ0AIPRAOMPjLfEZdunaErQFEgrSYMxfmNgVtgsy9dJl8suw8n8kxCXF0d73WubX6O8A/mm+dKKXULKXXKrRPTXWEzpW2++bQu2k7f3nt/LWcBKKU7hfZ9nhc1SpH9w4U2vNy3xl8ctt/xe01ADrYzU/nXkiAHudbyzv1ZYzOOEsLHeHxDST8zZQwjfHmIAt1NulDSbmm5R/H9l4yuWdMib2PMRsNg2bKezIzlHLNfaErCO5R7j7Ica6GUeiohDO2+6iF2gl4GDScACAGizqI3sD9mGpgaLL1ILWH1W9+F9Dfmvj/ZsZgGLaXNovdsnez6h3RdDbb+E2N+H/yaUIaM0Q3WfyALWb/8vYH2wnv7FV04Ba9oIbQtYdIzYPYJz+fac2wP/HvmXNkysgKX2SalJhUkAAJQNpK2XkFiDywiFm9BT8LQoYI3zUk7AMhgNkHczDwDoN/jmY8vjlsPK4ythyKYhMDV4KiyPWw6hmaEAQH02zY2cS1ni/1fkX1BYWWhze4TWDq0t/54aPBUAgHXzaoPx9hJH8rYJPqk+ktTftrPboM2iNtDKoZWgZaxmmNLPr8iH+qZ6OH/tPFTWVbKmQXdwkdn2nd8HJ66coA0jw7YXJRMzQmco0j9icmNY66C4qpgQf8imIZaw6oZqRgGr+zIDPLrqUWg2NUPI0Z7EP2IFf0fIgyxgLfO7fSANOc26uqsU/639IS9rtf5KyhZyClgdlnTg9LWjWuhlHoqIQ9tvuoju0MvAQbehpPnhy2WwTyxIFPWQtRYYiqqKFHmws9n/sXfd4VUUX3tAKYoKSBMBpdsoKlJEpShdrFhQlOKPXhQRFBThhhY6oYWe0CEhgVASCARuqKGG3pJAQg0hBEJJL+/3x2Zvdmdn2929ubl+932e80BmZmfOzM7O7L73zDl6oy+6svzXCSxiIcwXWWfrZI+UnFBS1Ieoe1FO16mCJ0Eb73x5YwaX3m6BYwmsV4ZK02QJrMfaI1MVNDqu7qi5z37n/BxGYDl1Dk2tgKycLADS9V/oxFeIHwJ+cLrerip9t/S1a64WRgKrPWOdcSSBRSzcnOStocwWQLo3/bTxJ2ZZeyyiAKDvlr6KZV6e8bIkrd2qdg7rrz2Qq2/P1T1otKgRiIXzu6V0vKv4+OKy9fid88PGCxs16TzlwBTd/RscPNihc5QXPoCNHOigSZWmVbLlpWSm4GkP9t5dfhJXfsyeMViwWZz3+7ZuuJdyz3bc9VjsGgmBNStiFhZtUSewmi1tJnvP15xZo9g3IfQSsHoILGIhOHDtgGZdChqu8h3qhjG4CSw3TIWrLByFicAqSL9bcuIIAqvclHIoP7U8yk8t7/T+CYUPc03/uvVfEpZjV2frZI/QBFZ0UrTTdZIT2jKLJSXGmdvmIDkCa8kSAMCjRydw+fIg3Lq1pND8YqqHwIp/HA+L1cLMo8e2hosQWGUml0FoTKhtPGiLgPre9Znj1i2wm9N1dwVZfnI5Ju2bBGIhKD+1PCpMrYBhO4bZNVcLI4H13uyCJ7AcKYB0b+oW2M00/0gA0G9rP8UyBWmNac86rOTagialykwug/jH8cx6lE4XrDmzBm1XtpUdQwD4a9dfqpEDM7IzcPTmUfy2/TesPbPW1t+CssDacmmL4lhevX9Vcs2KUysAcL6xiqkQWMRCsJAioip4cpZ/JcaXwLqz63Ds6mqE7SHYnSeTAjh/njSBlZYmPX3RfFlzm66se6QVegksPT6wiIWLqlhY4SrfoW4Yg5vAcsNUuMrCwYqIwm/UahttuSnlDJNOhY3AuvvkLqKTom0he82wTuri18UmVWdWdXof6Y3XDP8WhVWEBFbghUCJLwVXkVFho0TPbdyDOKfrJCe1p4tf+lYES1+CS9pJYJ1uzA6yIEtgtWqFrKxk7N1b0tZ2fPyKAlhZ1aH1COHEfRMBAB7hHsx8MwgsuahbjpTopGjReNx9cleUxqQDZAAAIABJREFUX8+7HnPcft74s9PnuCvJrUe3DM/VwkhgNf6PEVi9N/eWpHUL7KZKlGgVAE4/LiyUjOwM3fNQr2/Wg9cPMutRukbOSTw/hgA0vcddvX9VRJRtvLARANuJuyPki3VfYMmJJbajrzTkfgQLuhiEeUfm4SkZAqucgMDaQkUibjpHXFfk7UhJ/d03dZcQX2lp0gjkHyz7QPZ+rT2zVvOc2XB+g65xExJYAaFPideX1dLyapZuzoSrfIe6YQxuAssNU+EqCweLwOLD5Obk5sj+evHW/Ldsv/AY2WTpiEizImYVyOYuJ02WNEGFqRVsFlNKpuauLmfunAHw3yaw9l/bb5tbSub+hVnemv8W7j65K3l2my1t5lS92ngTDF5FMGgVwcBVBNWmcOl1prNffM0gsO69yfZrN7ijDIFFCOLiPCXtFwaoEVgveL6Amw9v2sr/HfY3s5zeI4QNFjQQ/V11ZtUCC9pBLASvznoV1b2qI/ZBrGg87qXcE5V7c/6bzHErOaGkU+e9q4kZod7rUQ7T14Q4v1//NQKLJT8G/oi3F75tSl2A86yPa86uKUm7/ei2yKF5F78uSEpNUpyHubm5+GDZB6jvXV9Tu8L9Xwila5Sss3gdtLQ949AM0d/tV7UHUHBO3IXCW39lZmci4kYEDt84jFWnVzHLCn2ghe2RPmMvCggsOm/EWnFdNIHVYXUH9N3SV3L0MC3tmkSPj3w+kr1fBUVg9V8tjrL47VJp+aCLQZp1KWi4yneoG8ZQON5o3fjPwFUWDtavQb039xaVkdvIlfK1Cm9dwMPZBBYtQkeo9siLU150eh/khCewCrOORmXD+Q22uTV5/2Sn62OPpGelM5/dpkuaisp1DehaoHqNWid+CW08m0uv60ACa1vLysz0gZ0I8MsvTAIrKupXKYGVk2PG8mkISgSWT6SPhOApNbEUsyzdt1oqBBbrYxIouI9bOdx8KPWDwkJBzvH/gsgdo9KDoYENJfPM2f3qtFA69xvMcr5eZsoPAT+g8eLGptTlzGen+bLmkjSWxdn4veNV56IelwdyUeLkyvfY1EN1DOmjznIyfu940d89g3riUuIl9N/av8DHP+JGBACplStLhJG7d+2WPmNlJ+aXpfP+ZBBY/VcSDF/LkVuzt1VBny294S0hsOLw1fqvRNf6RPrI3i89BJZen4lHbh6xXdt/TRlVAmvzpc2adSlouMp3qBvG4Caw3DAVrrJw2Etgnbh9AsdvHUdSapLhzVUIrwivAt/claSoR1FD19O+FGrPqc38gHSG8OHrpx6Y6nRdHCU8gSX3a6MriByaLGkiKjfvyLwC1YsmsN7LI7CazdFPYI3ePVpTm2O+YlsLRr5EgOnT2QRWYGspgbXP+eGv5QisKjOqMMvLOY+m+1Z1ivIYPj3uaeYca7igYYHMm85rO+OztZ+JPhQA4H7qfU1z/6XpLzntWXRFufP4juG5+sOKKoWOwOrGiELIB5L4r0jntZ1NO0LYZmUbp/VDDwmnBj0W43KR+JTaVqrvYfpDeB/1dvq80CvtVrUDACQ8SVAtKySwdjIIrI/mcXkvz3hZSmCtE9f1KP0R9ux7UVSmz+ZfsJuy7EpNjcX15Os4GX8SkbcjcfzWcTxIeyB7v/QQWPb4kOMt3vuuLovQ3QShuwl2hBF0WSIte/TmUc26FDRc5TvUDWNwE1humApXWThY5sx9tvQRlVFa6M3wKSTE7MOznb7Zmyk0gVXPu55dY/bnzj9N1+1k/EnbuBfGyF7dN3U3XAdPYHnu93R6f+wVOdAfBSxiwpEyc5P4JbT3Ci7dHgJr99XdhnR5WIIAc+YwCazYn6Xt40328bSChJwTdzkCiyb368ypA2IhCBL4IQnbY/8cO3rzKCpMraD72r92/WVXmywnw138utjypxyYwhyHsCthhn9Y+P8kCU8SDM/VL5aVK3QE1sBV/30C678iNDnecEFDfLziY2ZZNeixGN92eRuzDrnySnkT903EK7PYPhgLu7TwbQFA3Xqs9pzaor9DGQTWx/Pz8ydsEOcNpo7wAsDBg5VFZfpu7impMzU1VvGe03quO7tOlJ+bm4v0rHRJYIBH6Y/sGi+e9K/hxXZZIBTeuq0wwlW+Q90wBjeB5YapcJWFwyiB9cmKTyRpn675VNdmwYP2gVIYxOgLCx2Cup53PTzOeIwHaQ+QnJaM1+a+plrHN/7fYO2Ztab3LTw23Db29PGzbZe3OX3s1eaeFgk4HwDAdf1f8ePAwnuL3xOVK2h/bZtCxS+h/ju4dC1RCGkCyxprNaRLcgkCBAYyCaykxuK2L4wkQNWqZi6jdsEn0ofZFzkC61H6I9t622lNJ9x+dBtvzHtDkcCi/bAQC2GS1TweZzxG/ON4pl7f+H+DGw9v4PlJz4vS5X7VD4kKwek7p2XvGevjMisnCxvOb8DOmJ2KY3f4xmHVObHq9KoCsyorzMLyn6cX7zOeaWf3a2qg/uOzriJzDs9xug5myuZLm0V/15xdEy18WzDLqqHclHKa251yYApG7ByBX0N+FVkiypXn816YwEWcmxvE/SBDLAQVp1V0+jgakYuJF2XXdl7qzq0r+nv+ZukzJiSwOiwQ5/VZkZ/H72MHD4ottfpu7iGp84tVTVDaszT+3fOv6F6/Nf8tWV0HBw9GRnYGUjJTbO/ZrZa3EgXusfd0wZX7VwBA02mJA9cOGF5fHQVX+Q51wxjcBJYbpsJVFo6J+yZKFuS+W/qKylSaVkl28Wb9iqYWqpn10gAAiSmJmsofun6owDZ9RzjKHmsdaxMtfou2XNrikL4JX+hoPfZc3SPrc8cMmbRvksTnAWteKOVPPzhd9ddYnsBy5WOScmi0qJGoXInxJQpUryAqAhFPYP24TD+BZdSS80FJAhw8qJ3AevllM5dRu7Dp4iZmXz5e8bHsNbm5ucjOybb90nzz4U1sFtyHXbvFdS05sURSf2hMqOocY+mVlZMFAOi9uTc6remEjqs7osPqDkjNTGWuk8dvHZeti1iMhx9n+dCh++QmsAgSUxINjTMANGVYVTq7X9M2Fj6dzBLWD4OuLBcTL2ouu+niJuYczM3N1ex/iiUtfVva6pErA3Dr1YCV+XMqJIyguJ0+GwubRNyIUMwXHrcraiF4bYb0GfvEO798e4rA6isgsJ6d+CyePDknub5v0M+StMqTxXOFh5a5svj4YlHarIhZtuvVIqnLyaXESwCARvNfRZUpBC9P5qSYh7Ss8IfgwgZX+Q51wxjcBJYbpsKVFg7+Q+CFCfkflkIoLfQsAmvAtgG6NgseLCe+LDlw7UCBbfhVZlRhpg8JGSJLGJy/ex7nEs7hXMI5/BH6h2L9X6z7QlUHRxFYQny/4XtR3p6re/DcpOccNq4Ad2RJrYxSfuTtSNGRI5bwBNa0g9MKbM448j7xYFm9PDPhmQLVywiB9baXubrcL0mAw4e1E1gvvWTa+mkvNl7YyOzLpH2TNNeR8CRBFMo8VEBgtV3ZFmlZaUhMScS9lHu4l3IPKZkpiEmKUZ1jWuchj+O3jkvKqxFYIVEh+gaMwsBtA1WfGzriYmESmoB2lNxLuWdonAGgSSEksGb8hwms/+8y9cBU0fzLzc3Fdxu+M1zvrUe3kJObI5sPcOsVPa9aznP+mJghet6DXpjA3rvbCAisdhSB1W+luI7k5AjJ9Z1WtpSkvSwgsISWuWo6Pj/peQzbMUyUNmDbANv1g4MH2zVOPIE1b8szqhaeu6/uNry+Ogqu9B3qhv1wE1humApXWTju3w/Dgu3VsSKYW6B5B41CKIUVbr28tcTsWK8jdh5aCax9cfsKbMOXI7AGBw+WJbACLwQi4HwANpzfgD5b+ijW32F1B1UdaPN7s0QImsCyxlolR4XMbrsgCazpB6cX2Jxx5H3iwToK4EiLOZbIHSFkWWvQ0nmRubokPUOAo0e1E1gVK5q2htqL6KRozIqYJTmmPHn/ZM113H1yF4u2EKwO4WRFMFfH6/NetwVpYKH9qva29paeWCrJ1zoPla45duuYbF3Ewh2TMgK/c36qz43eH1MKUlIzU7Hxwka0Wt7Koe0kpSYZGmcAqDmt8JFFHv5SnRr+x6IQ/n+WnNwcZGZnwivCyzSLtHJTyikGHuJ/BKLnVev5xtt2NSk9UZ3AaustzusvIbAOSa7vHfS9IoH12tzXMGDbAOyM2amqY+e1nTHz0ExRmud+T9u6NSh4kF19/2f3P8jJzcGy7WIH9DUpAutU/CmkZKYYXl8dBVf5DnXDGNwElhumwlUWjitXRooW6I4LCd5d9K6ozBvz3pBd6FstbyWpU+5ojJzw0EpgGfWXo0dG7ByBL9d/ycyj/VsxXwI8Syvmv7f4PVxMvIhT8adkfx1zNIG1/9p+lJ9aXjLGL3i+4LBxTc9Kx8HrB1X1U8o/GX9SM4HF8gXkKsIC6yiFIy3mWCJHYL01U53A6rTQXF1W1yfA8ePaCazy5Q2vnWaBnptyzstZYIVFj38cr3pdRnYGAs4HyPrv0DoPla7hCax1Z9cxrQNnHpqpuZ8s+J/zV31ubj265fTnlyW1Ztey9SM3Nxd/h/0tKaNG8GuV+6n3DY0zAIk1hu8254/hv37Kx5vc4haWjNkzRrWMKxFYtAWSWVJWA4HVhiKwBlAE1rYzkyXX+59ZKkmrwoicq8UViUe4h2T/FP4ANGLnCEPzxD+smkjPGhSBVWlaJVOivDoKrvId6oYxuAksN0yFqywcsbEW0QL9xWKCX0N+FZURhtWlhUVgKZlo0/J32N+267QSWEYjlumRxccXY3jocLuvVyOwiCV/6dkbt5eZH3QxyCF948FyThoeG65Jd0cKYB6BRf9K50rCAou4cKTFHEvkjhC+zvCbQUsLk49k/PwVASIjmQTWvabitk/OJEDJkvYvmiaDtg6kj88oITElEWP8CLbtIgjexflrib2zw7BOWueh0jXC8OLZOdmSfKME1onbJ9B/a38M2DZAEi1qVNgoW7kr969I2v5n9z+YemAqFhxbUODPc8kJJbE3bq+oLxvOb2CON+v6l6a/pKs9YTh6e0ETWGtDCnbMWDLWTWC5xQ6Ri/4qFFex7Ptuw3cYuWukQ+qWI7DaCp6xMdQz+NtqcR2sH7P8zyzRRGBpCQY11jpW8QegsCthhsaA1pMm6IiF4Paj24bXV0fBVb5D3TAGN4HlhqlwlYXj2rUpogW66zIpgaW0wPuf82fWq3WDEB5v0EpgFaTMPTLX7jDxxEI0WTHxkDsaqdeiTavwkCOwjNZv1P+M2jyKSYpRJbA2X9oMAJgVMcup80hJSk4oqWjqzgIr4IEjLeZYIkdg1WIcN6LlzZnm6vLTVwQ4fZpJYF0eKm0/vbzzt/yIGxFMP07TDk7TXEdiSiImBoj7Fhsv9S316NFxREUNQXz8CkmocRZoneTWeaVrhAQWK1/obNcosnOybT5yGi9uLPlVnA4YwVue3Xh4o8Cel+bLmsP/nD+ik6Il+ushsIKjgvG139ea201OSzY8vg1nSZ+hglxrWPLZIqlO3Xycr5dbCrd08euCNivbKJaZEyR/xK0wSBe/LhixcwSS05JNeVdjSblJ7L37w7n5Zei8kevEddRjEliLRH9vDOUcxtPtf7b2M1Ud/93zr+QHICGBtefqHrv7X2xcMYnuo9dLy2mxeHYWXOU71A1jcP7brBv/KbjKwnHjhpdkkfYKbYGcvIhTgDyJ8Pm6z5GZncmsV853lFCeHve06BqzCSwtOgil5ISSkjSlML5aRItVDA85AssM56W0lJpYytYuK8qknDWYHvkh4AdD1yvNPa3CWzro9ctWkDIkZAgAoOrMqorzQ4h7Kfck5QraYk6OwGL5y3E0gdXtawKcPcsksG59Km3/0h/O3/J9In2YfRmzZ4zmOu6l3MOEDcoEVmZmEsLDi9vyExL8VOuldeItGfVco0ZgeUV4ae6nFuTm5iIjOwM5uTmSPJro3n9tPwDgWvI1u+dcv6398DD9oWq5pkuaIic3R5E4vHD3guia1+e9DgAYun2oKJ23WtMT1U0YVt5e7Dr4ZqEjsOSOKjtbL7cUbll+crnqu8lcisCqXMgIrLgHcQCAmKQYvDzjZYe0Ud4OAmuUFgLrlLfob95vIy1aAhz9s/sfiesNoQWzEXcjDRc0lBJYftJy7iOEbjgbzn+bdeM/BVdZOHx2N5P9yIz75Rlg61bm4p6amSp+Ic/NBebPBzp3BhYsQJ05dVQ3iGLjiol0MZvACokKwcYLG2WjfdFy5s4ZSZowrLCjhIfc0UvawboZ8tyk52ztsvL3xe1Dfe/6htroGtDV8LgY7ee+uH0AgNmHZzv8PtorPIFVbWY1xfkhxP3U+5JyZSeXLVC95XxgaSGwXpthri4/fk2A8+eZBNbdj6TtH1pHgBTnOl/1PenL7AtPXmhBUmqSlMC6Eywqc/Xqv5L+q4HWyRpr1X1NQRNYSijqUVTU9vKTywEAsQ9i7Z5zw0OHM/tFH2nhozEqITopWnRN82XNmXU3XNDQdo1Wv36P0h8ZHr8TJ5oXOqLITWC5xR7xifTBj4E/KpaZv1k8p17SQGB9sZggMJQjvyp5OrYPd5/cBQBE3YtyWBsVPNnPl/D4vz0Elt+peaK/l8v405PzPSuUUWGjJD/wdl7b2bZuGbFOe2fhO5oIrIQnCYbXV0fBVb5D3TAGN4HlhqlwlYVj0U7prwy8hO8kyKxahrm4CxFwPgAzVw3EzGYEs5oRJD5LUGcK25pEKFossD5Y9gGe8njKrg1ICLWybVe2ZZbTQsQZFTU9px6YqlpH14CuCLoYhCM3j+hul5V3Kv6UYQLrp40/Ma3a9OhndGx5AmvukbkOv4/2Cn9kl45GRywET3k8xXxuH6Q9kJSVI8AcJXIWWB/NUyew6pvsU+SHLgS4eBH44w8JgXW1B4PA8iPIKlMMaSN6Im3MAKQFLUFWlvEPfT2YsHcCsy/hseGa60hKTcJ4isCKowisixd/0U1gNV/WXKQT69gbjSZLmoiu4a0EeND9LEgCi277n93/AGD7x+Ll4xUfM9OrzayGKjOqYMLeCcy65x6Zi+ycbJyKP2X70FQD/SH6oc+HzLobLGhgu0brseiM7AzD43f0aL1CRxS9N9tNYLlFv6i5HSAWAm+KwFIjpGgfccPXOrYPPGKSYhzWRkUZAqulQQLLI2yg6G+5gBBaLLC+9f8W1b2qi9Ken/S8bXzUThKUGk9QzIOd12hRI00E1rbL2wyvr46Cq3yHumEMbgLLDVPhKgvHQgUCy2oleFyLW6SfHU9QVeBoUQh6Qd9TnaDu8BKqm48WAqtXUC+7Qygr6ciSXkG9HPrSYa+eTzKeqNbRY1MPAEBmdqbuduXyjBJYl+9dBgDEP47Xfe33G77XfN/k5N1F7yLydiQAYN6ReU65t1qEJ7BenfWqKD3iRgTOJZxjPrfJacmisnXm1FGMFuoIWREsXit4x85fLVYnsFgvgkbkhy4EiIoCHj+WEFgXRkrbP+hPENdNnHYjtJ/mddMMsJwJe+73ZB6Bk8P91PuSviUmiQmwixf/p5vA+mDZByK9ou5FqV7TeHFj0TWFicCij9euObMGgNTySSgsPznVvapL6qbLzD0yV7d+lxIvier4yOcjZt31vevbrplzeI7qc9FmZRs7R0wMq/WpQkcUyRHlztbLLYVb6IAPLFlAEVgVVAisutPF5ZdudZz+Qn+ErFMDZkmd6eznq5UgIqP/DnFeRWqc6jN85700Wfy3j4GIpsXHF5cQWMRC8Ob8NzErYhYepj/E/zb/j3ntkNV5FmHbpdEFiYU7paDlvcVMX45mw1W+Q90wBjeB5YapcJWFQ8kCy2oluN2R4G0vzjTaaiWYFMAt2ojPd1xIL+gz3te2+WghsHps6oHWy1vbtbkJoaV8+anl0S2wm8NeCOzVU4v+PYN6AuAsMvS2K5dXz7ueoX7xBBZNtmiR7zZ8p/m+sY4qWqwW0bjOPzq/wO+rVvlt+28ApARWamaq7HNL+96pPac2Pl/3eYHqTTt2HpP3cvfdUnUCy+wPza5dCBATww0OdZTw/N8MAmsDg8D6sWAjE3Za00nSD714kPZA0rfk5IOiMpcu9dZNYLX0bYmnxz2NYuOKofj44poILO+j3hgVNgojd43EyF0jJdHv6L7OPjxbX2cNIDgq2HaMsObsmsjK8/F4+MZh5nxq6dtS9nmiQefPOzJPt360T6sWvi0AAEUsRUTp9bzr2a7RYlVqFoG1f3+ZQkcUuQkstzhKNlPWxdWnKpevQR2bn7bRcbqdij9ley5Pxp90WDty+3ZrAYG1ivoR69nx4jq0EFjLNJB97RcQ9F5B0GcFQb+VnH8uPo9FYPFSa3YtZnq1KWIdZsjcLy0E1oxDM0xZYx0BV/kOdcMY3ASWG6bCVRaOf0K+1vSxKZRGswnw9de2OugFfXRrbRukFgKr+6buaOnb0q4N2IacHM3X6InuZJYIIZevVkevoF4A9P0ip9amUQLrh4AfMHDbQAzcNpD5sa4k3/p/C0DqfJklWgiskKgQfLHuC01+FQpahm4fCgCSF7GUTHkfTY8zHovK1ppdC3/u/LNA9aYJrLF5L3ddlxU8gdWvMwGuXhU8SAICazSDwAoguPinOC1qSMG+BjiOwDogKnPpUh/dBJYjQPfV96RvgbZ/5OYR+J70xf3U+7a09WfXS/QavXs0klKTkJ2TjXcXvat6j+j8+Ufn69aNduLe0rclAEisB8Zax9qukfOhFpMUg5ikGFy5f8W0EO+HDlYrdERRd183geUWxwg9p9otUC5ffao2QsSoFPUoKvphyxkE1hsC/5WrQ8R5z1AEVgP6HcGfoOxEaZ205RYtnlSk3TrTjfXtS4aVuJYxYEUhFDqNL2xwle9QN4zBTWC5YSpcZeEYsK0//lynj8D6c23ex2Ee6AV9TCttm8jHsxuJdGERWD9t/MmuDerthW9zlf79N0C0X/eN/zcOeyGQEyHovLArYcx0WhxBYJnZx/jH8dgevR0T903UVL6LXxebfiN2jlAsq+QsnhUhJuB8QIHfYyXhCSw6PTgqWKI7D/pYaQ2vGjh+6zj6bOlTYHpLXk7zCKwfnEBgDW1PgDjBkTWST2Ad95a2ff0bAmuYND07Ow05Oemy424mWEcI9SI5LVnShwcP9ovKXLkyqlAQWB1WdxD19cTtE07RQ4h1Z9cp3oP3l76veo/o/FWnV+nWIyM7A1fuX0FMUgyik6Jx4+ENAMD15Ou2KGO1ZtcSkW8pmSnMZ8ERiNr8ia7nt5gHZykxYyPBx/OVy9orS7ey15V+K52/phe0qO2RbtEnHv7iOdVkjjh/rB939Mx/B0HADoIP5rIJrKc9CD7xJmg2x5g+48LHIfZBrMSn3qn4Uw4bA7l9+2mBzyiawCo5TlwH60eupz0Idu0Wp6tZuNEEVl2DBBYdAGL8Bm1jwJ9AEcrk/ZMdsuaaAVf5DnXDGNwElhumwlUWjv5b+4NYuF8WtBJYv60WEFiRkZIF3dJS2yZi+a6SSJfElERJGfoIhVZpv6o9Z5FBiC4CyxER/9SEx7mEc5K8Y7eOAVAnk0p7lgZQeAksPlLLriu7NJVfcmKJTb/hocMVy7o6gfX7jt+Z473h/AbZ55b+eOV98+yL21dgehe1cE5QeSmR9/LqNALrxo38AXr+eduzvy9Y2vb1LvJ63b27UXnRNAmOI7D2icrcubNOlB8V9atZXdAFur9aovM5Gruv7la8BwO3DRTltVvVTlJH25VtRWWER3zMQFJqEg5eP8iMJsiyTnYELge20PX8frZIXFbNh5A9MkrmnWXwKuev6QUtGdkZTtfhvyT0j7o0gbVuuzi/9Xzx37M2ceXGCQJsdPOxX5/QmFDmc3n6zmmHjYHc/viUAQKLdzMwfaM4vcY07lhgNx/u/aHrMo4U5OuZEigubzSKMU1gefhrHwPh8UViIZi4b6JD1lwz4CrfoW4Yg5vAcsNUuMrCwRNYXy/RTmD18BUQWM2aSRb9cS20bSKWluLHjj4WZUTar2oPzJ2rm8AatmMYXpv7GurOrYu6c+uaokuPTT3QM6gnegb1lOQVsRSx9f/g9YOSfD4UvVob/JGTswlnNenUbGkzW7uVp1cW5ZWfWl5Tm3rk4HXOL0/YlTDVsgO3DRTNCzUCS0niH8eDxobzG0ztmxH5fN3nyM3NZY53rdm1EHQxiPncpmWlScpn5WSh+6buTu9TG4bFU4EQWLdu5Q9QnuUlCLttZQIrUG3ZNAUrTq0Q9YEPxKAHKZkpDAJrr6jMnTtrRfnR0UNN6oE+0EcmCwOBlZObg5qza9p0mnN4jijfK8JLpDPrY6XdqnaiMifjTxaU+vh3z7+SZyE4KhgB5wMQeCEQGy9s1BUUQA7X/b5hPr/lJxG8P5eg9ESxDn+sFZcVfoyaJcPXsp9fPQRWifElmMdEXU2A/Hc5Z0gxD+4HDWePg1kygppbtAUVPefoI2lem7gfdYzuec2WNsP7S9/H4RuHmc8l60dPs0RufxTe5zUqBFb1qQQTNuRL12Vc+jSKwKo5jTuaKEwTWkXROhi16qSjI1p0EFi/rxGXmbRvkuH11VFwle9QN4zBTWC5YSpcZeHoF9QbxELQaaF2AmtFMMH9hnmPDJEu+hM+Ut9A3hhEML8xAe7dszmET89KN7zpfu33Nb72+xrjwsfZRWDR2Hxps2GdSowvYRM6j3dWDrAJrCM3j8jmCYX3u6KVwGqypImt3aozq0ryI25EGO43Ldsub2NaPLDk0zWf2vSjo5vpESGB9XfY36b3yah0Dehq00+uDAtZOVmScslpyaKPcWdJ0zn6Cawyk8sYanNoewLcFvj7iY9XJLCS33A+gZWRnWGL9lfDq4Ykap9W0Pon3Nslyr9zZzVFYP1uhvq6QRNYvHWpsxF2NV6MAAAgAElEQVT/OB4T9k7AmjNrbGQyD9pR+rjwcZLrj906hh3ROxAaE4qdMTuZllKOAovAqjKjiujvzOxMw+08XCE+hjphA0HlyfnOrgNDxSTW72scT2D9KUNg7dytvY4ufl3wIO2B09dMI9J2ZVsAwI2HNwxHDrZH/smzhFuylaDcpIJt21FCE7DNqflLzzk+op1QXpxkjMBiWY/TyMnNsR0xNlOKMvrISxGFceiyRFv9UymLqprTpKSS0CqKbqe1QQJLzv0BLSz3Krv3iMu4CSw3nA03geWGqXCVhaPf1BYgFu6cvnCRPhggv4FZrQQJrQmQmsokhyZ9qL6B0KHuQQiQlMT0kcSyXGJJqYmlxJ0zgcDKzM7EsB3DHPai9P2G721tHbp+SJIv/OVNqR69BNZ7i9+z1VtQBNavIb9iz9U9msp+sOwDTf1Wky3nNwHBwcCJExi5a6Qp/ZCLbGOP/BDwg2o/5VBnTh1Ruav3r5p+z5Rk2BruCMHqEC4aUa28UNT2EFheEV52HxcmFgaBlZOjSGDJpRckgQVwRGRMUgyeZNi3R+Tm5kr0v5GwTVQmLe0aEhI2ICHBDwkJ6/HoUaQZquuG91FvDNw2EIOCB2Fw8GC7CbuChPdRb9E8EzpRLwwYvXu05FmgP2j5iItGcH/NCMk8Y33c8W0u2CxO/2ie+evPXwq+O7XW8Y3/NwCA2YdnF+jaaaZ0WN3Bdp9ycnNM+SFQq7zrRRE5/5Hjm8NUCFh6vo3xk6Y9P0H8t+82fToAQExSDCpNq4SXpr+EytMr46v1X0mezf3X9qOFbws0WtTItofW864nWbv0yFMe2p4tOu8fhpNzer508yFYS1lu1ZomjVhocSCBJXe0kZb2C9wElhuFH24Cyw1T4SoLR99R3C92tBNKLYLLl5nk0OQPNGzOhCEtWkiO1RALwbXka2i3qh0aLGggSm82h/vVb0ogQaU8Hxtrz6zF2jNrsTdurykEFo9tl7fZtVE2Wyo9Yllzdk3U8KqB6l7VMSRkiK0NFoEVcSPClq/UDh+6XatJ+buL3rXVW21mNUm+XHh5I9J3S19YY62ayn7o8yEAwCfSx1CbQT81BghBxlME/Wa01nzd8pPL8cyEZ5h5tefUNm1Mfgz8UfX+yoHWQy4qmaNkwgbxesBHJ3IGgTWoE0VgAXYTWM6yULIHubk5Ev2v39nqbLX+M1h0fJFono3ePdrZKonAIrBoyc7JNtzOvfW/S+YZ7f/Gas33dUWna7XM0COjTCCwmi5pauujI9bIkhNKOnwdFlorm9GX6lM5eXUqQbUpymW/XyqdE47ub0HIUIrA+lCFwBrLILBeoAgsHx0E1oBtAwAAFxMvitJb+LZQfE4fpD1A5O1IpGWlAbDfJ2YxBQLrhQny48AisGZu4iw0ld4Fak1TJpXMJrDepojX0QYILHcUQjecDTeB5YapcJWFo+/fHIFVbhJBi3kE274mSGyu7QM0rSKbHJraXH0DYRJYhHNGWmFqBVu5ladWivRtvqw5iIWLZMIfX7BauV9jhfV/vu5zYM4cXQTWX7v+kh2n4KhguzZK+tiM0PKJBsvqqdbsWrZ8pXZ4AismKUbbBs5HaQTn+J0+duIIAqv/1v4Ijw3XVPYjn49U+6xFNr/G3X+ft/Vd53vSF0EXg0wfA1oaLGhguw9frf+KWUYOtCXY0O1DHa6vUCZSVpqvGyCw9l7cjqIeRe3WJbaMPgLrdkd5vS5e/J/smBc25OZmS/RPeHBKUiYr6zGyspKRmXkfWVkPnaSt62HpiaWieTYqbJSzVRJBC4Flhg+sy1s/lswzlhP1rbu4Nul0RzhW/1sh8IyeenjYo0N4bDjemPeGbP7UA1Mdvg5/vu5zyf2if+zTI1sE71WhKscxm1M/fP6xVlsblaZVYqaX9izt8PHSIvR8auOtnD/WX5pWZqL476Vb1dutNrMaXpn1Cv4O+xsA+71TL16c8qLu/hcfJ/9sfbpQfhxGMwgsn23q7wK1p3MkoTBt8075dloZJLBop/tyawZNqlmt3I/mwjKzD8+2f2F1MFzlO9QNY3ATWG6YCldZOPr8XU+0GAe9RvC4lnTRDt3N3nhyi0gX/envq28gcgQWAFxLvoZx4eMQeEF6lIcPF01HEaE3IL0EVpnJZST+T4Swm8Ca9o7obyUCS87PFQ+lduYemQtAO4ElJE4A4LO1n4nyj9w8YugFgSUDtg3A3ri9msq+MusV1T5rkS11ufvvq5PAWn5yOTZd3GT6GNBSz7ue7R7IRcCUA+3vqoZXDYfrK5SVVHS/cXlOV5vZQWAdnDrYYQTW4RXStq/8okBgHfhCdswLG3JyskS6++8geJguJqgePTouKnP6dCcnaet6GL93vGiedVzd0dkqibDq9CrVZ0NpX9OKC8Efanqm+eeaTuu13Pz1x3uzsg5ahYc9OqRkpuBS4iXZ/CkHpjh8HWYdKztw7YDd9W3dlT+OO8KUy9Jr/Z8aCKydMTuZ491vaz/NLhAcLfR8ar9AOZ9FYJWlCCya+OClpW9L5ObmIuFJAhJTEpGUmoTktGQAwPKTy2Xnq1bQQXq0SEkFAqvzovxybSnXIyPXSetatlV9zagznR1I6pWpnM9dOr2lwSPJ7RiWVXJlv6OsDL9bKs73ivCyc1V1PFzlO9QNY3ATWG6YCldZOPqMfEu0GG9+jSDnKeniTkcNEUobb87kfHIgd6xo/qfqG4gsgXX7NpCczFb2wgVsmPE/EIvU4SO9AX229jNg9mzNBNYnKz5RHKeQqBC7NsrWvcQf5dW9qsu2IefgnIdSOyN2jgAAXLl/RbNuyMkBzpzB1eijkrwjN4/Y9eJDLNyv0u8sfEeSPmbPGM0EFt9vIy8pxJJPYC1vqO+65SeXIzUz1XD7WsT7qDcA4Fv/bxXvPw2asDLTN5cWCdopfv78d3Dp9kQhDKltTJerCgTWmQnSti8PVSCwtn6kuBYUJmRnZ2ggsCIpAquDTG1u0KAtaJWeR2eA/tGDZQ1kBs5ub2yIwJoYYP76s367cwmsYuOKITc3V3LMSygFQWDxfrxo1POuZ1d9wQICK0SFwGo8W53AoGXXFS7IxLSD00Tp0UnRdt0HR4heAsvCILDKUU7cF29htzX1wFRkZGeI0vgf8MwgsBYeW6i7/88wIijy8pmAwFK7/0UtBOsUnlNe6k7njhnT6bSbArMILNY7ilzZrsvE5b6lCKxZEbN035OCgqt8h7phDIXrrcQNl4erLBz+wzrgj3YEw/LkbEX2sZtvlypvQCFh+f/f6qO+gcgSWLz8+y+QmQnwvx6fPg2UKoUL5QleG6xugdXStyXQuLFpBNb26O2i8iy/Ubzw/q2qT62Csn9J8xstamSTkKgQWxssP1svTnkRgLaXOgC6HHnjyy8BQnDx1VKSvCM3j9ht0bP76m4mgeUR7oEnGU9w+s5pnL5zGv7n/FX7Y+QlhVjyCawVOgmsFadWmNK+Fvlz558AgC5+XWTHgQW63KuzXi0QfXmRI7C6+egnsGr8ZkyXK2XlCaxjOiKsWq0EF7d8wB7wQogn6cki3ddvJ5IoeI8fnxKVOXWqnZO0dT303dJXNM+emfCMs1USgba0cRSBpef5IRZp2jINx6f0ip+TCSyfSB8AUj9pQpm8f7Jp/S03pRyenfisYh+EoMtojcIrfJcL3iXNFx5PbEQRGH+rOPEmFu79AOD8NX3j/w1qeNXAlANTbHp7hHuYPlf0il4Cy4NBYJWnCKxFMgTWW/PfQlpWmiiN/6HTDAJLaX7KSSkFAuuLxfnl3qPu/6i8+z9irdRCW0m6+XDRQ7WWb2EngdVpIUeULdyibc1ov0Dqv4v25zfz0Ezd96Sg4CrfoW4Yg5vAcsNUuMzCMWAAk0BKakxgFWwo1aeyrZ7kpPJk5Y1ElcDi5YcfOEuhTz+1pa1swDl9VNuAhPWobWxMAistDRg1CujSBds9/ycq331Td1mn0zh6FLhzBzh/HvMaK7e7+vRqW3OrT6+W5JebUg6AgwisvLG5UF6adzDGKjmixgt93JCWXVd24d1F70rSPcI9RMN78byyNZbWfisJT2CtbCDNa/szQdXf2dc5g8DS6wOLLmevxZy9sol6seMJLNYvqSypquIgWI/EKBBYej6+rVaCy5ta6VtDnYgn6fdFuq/bTvA447GozJ07a0Vl9u17wUnauh5uPLwhWuc3XtjobJVEOJdwDm1XtkWblW3QZmUbdA3oqnn90AOjBJbeCGy0fLX+K8nxMv8dxgks3nckAAwJGaJLpzuP7wAAvCK8ZMt47ve0u8/1veuL/h4UPAgA8PuO3zXd496be9vy68ypg9zcXE3tCt1FbKUIrIXWr3D8eGMcOfo2gnZXwPC14nH/V8YZtlD2XN2jONeS05LRfVN30/YGe8QMAquCp7Z5+db8t5CSmSJKqzm7JgBzCCx7ohHSERSF8qWAwJIjMKcG6t939Yi9BNYGnWsG6/ji1xSBtfTEUt33pKDgMt+hbhiCm8Byw1S4zMLRp48seZT4AXfU5ocx+Yv1B3MJIuapbzDdVKywNBNYhADbtwNFi9r+XtGQM01mfTzLtaG2sX2y4hMgMRHo1g344AMgOBgYPdp2/Y5a4vJNljSRtVACIUDp0sDq1boILNYRwvJTywPQSGDl5iL2knbn60oE1r4Pq6HWDLZFz5ITSxTr3RG9A40WNZKkW6yW/Hn34AEu1i6jrJ/GfivJ1jwCa3V9fdetPLUS2TnZhtvXIjyB9cW6L2THgQW6XLkp5QpEX17kLLBY1pEs0XLcRKvElCVAfLx4gP73P7sIrOiNytaYhQkpGY8waBXnJHvIKoKevlICKypqiKSPbmjHnqt78EvQL1h6Yqkp/qQcCZY/JjOg5/lpvqy5JG1j2AvwPemr2QqIlvjH3LMdGhNqS1vMsKLQQ2CNtY5FZnamrY9hV8J06ZTwJAGAPIFVdWZV/Lb9N011sX68KDVRbBn98oyXAQALji0QpctZBSanJePXkF/RLbAbLiVeAqBtP6XHsqgg72zUKPGav7OM6G8tlnbhseGa5tydx3fgud9TRMSZJSXHEQxcRbBgMxcAqPpU5TEQEljHbh2T5NNE3rzNXGRsLfOynnc9PM54LEqrPac2AHMIrLlH5uoeHzqColC+EhBY71LR/PgohNMVXI44k8DSS3qzrMiEBB6xmBMkw1Fwme9QNwzB/UbnhqlwmYWjVy9VAum9PuIF+/oLBOf+Vd9kPPy5o4dFGBuDLgKLEt6f0e49ghfkUOU21Da2T1Z8Agwbln/Ns8+Krg+txagf7BdC23WlS6sSWBWmVrDdipzcHLw29zVmvpbNGd2741I5bRv5i/+WsOnZ7Wtp/oFqBLVHsyMChceGK36IjN87XpHACroYhICx38LzQ5X+aOy3kvAE1hqdBNaq06vw1vy3DLevRfjol0rjwAJd7pVZrxSIvrzIEVg1GdaRclJqvDm69P+UAPfviwcoMNA+AivgY9OWV0cjPSsd1aYQNJjFRUxqOIsg+cl1UZmoqF/dBNb/E1xKvITi47ijMh/MVV4/9EDP88MqH2otAwB4lP4IJSeU1P188wTW8VvHbWlLFZxDP+2hXqekj7FWXTrdfXIXgDKBdeX+FTw/6XnVulg/Xjw36TnR3/yxsrSsNFScVtGWvuXSFs33UUu/6LGsJrCUHbJK/f6r1b83bq+uuafkY8xe6e4r1fu1GfJjwBNY15KvMef3sDXSv1+arG1s6nnXw8P0h6K0unPrAmAH9tGLWRGzdI9PMQ/5+7sxlKD0RK4cTWDxUQh9NUQe1CKLZEjqZnPsu+9yfvNYP4DLlf9hmbiMmkWhM+Ey36FuGIL7jc4NU+EyC0f37nYRWJnPad+EpgSKry9qIcgoax95da00wY95hIuSqTux2EFgKbTLJLACA5l1Ca9TI7CKji3ClX3mGeD8edxPvS/KrzitIgCNBBYhSC6hbSMv96fy2MSWIagzhJ1njbUCgCyJ1TOoJzpPlDqQtewaDQCaXuinHZymud9KwhNYa+vpu64gCayRu0Yq9lUOdLnNlzbrbtvIUQ0zCKy5QQQlRhsfw09/JAC9zgYE2EdgDSTArVsmL7SOQXpWOkauE+t/684mUZmoqN+YJIMbro/ElESERIUgOCoYwVHB8D/nLzq+082ncBBYu63FbHUduXkEvwT9Ivss+5/zx5oza7D50maERIUgNCYU6VnpALgjk3w5JQKr9wr1NYOGngAjxEJwL+UeAGDd2XXM/FqzawHgjvXL+a7ipfPazpI0+gcJYQTjmw9vYuahmTZ/UlqhpV/0WL4isE4K26N+/9Xq339tvy6do+5FGd4f1PpotXJRLeXyy+YRNvw8pPNpC6yhawhe1khg1feujw3nNzDnZ25uLt5f+j6IhaCoR1EEnA/QNXYAZ8lmT1RpucjjVmv+kWDaQmmMn/z42iMTA9jpxceJdW09n2DQKoLXZyj3Sc6h/JoQ7fNkFOXn7bO1n+m+JwUFl/kOdcMQ3G90bpgKl1k4unVTJY3qDRAv2MveIbo/DPkXgI/m5YVp3k0Q008/gTWrWb4e21Si5eghsD5e8bFiuztrsutn1SW8br4KgfXUGEH5WrWQmZ0pytflA4sQPCmm0JbHU/n1qhBYSmP2+brPAcgTWN39ujKvt7QigIeH5GgES0/eLFvvSxct+1/h+rJOJ4HF8kfmKDGLwKIdwWqRXkG97NbbDALLaiUIrWt8DDv9SLigD0Js2GA/gVWunHw01EKEjOwM/EUTWAluAuv/C4RH6oiF4BOfdxxyr/U8P6zy28OKSOqkfTzxcuuRMnnMl2s2R1kPtTWDxvXk67rWnKTUJK6vDMutdgsItu+rjaio35CdnYqeQT0V66KDBRALQXBUsOjvQ9cPGb6PWvpFj2N1kwmsA9cO6NL5ScYTw/uDWh9p3WnH3cU8xHOGvq67r/jvX1cTVJkirb+ojD6+J31l52daVho2XtiIE7dPGLr3T497WtcY7QhTvs+vz5CmmU1gjZeJQlhMYGHZfG5++q7d+d8aLNktM3/Xbdc+T4avFZf5Yt0Xhu6LI+Ey36FuGIL7jc4NU+EqC0fsz51x7GWCYy8THK/MWfDQ5A3z5Y8QXO2pfSPqtJD9YpBeXh+B9WebfB2EH9C7dov1G9peJ4Hl20qx3b2vsseATqsyzACBRQg6jJb61QLUXzxrjn5BlcAq6lHU9n8lAqvuYOlYs3SSI7BGLvqWWe/q+gRxpdXvxcBtA23zU88LF0tOV+L6sv4tfdfNChtvuG2twhNYcmHP5UCXy8zOVIyOabaYRWBd7WVcl04/kvyIpTz8/e0isOLb5j0bnp4mr7bmIzM7U0Jg3aYIrOjooZI+uvHfAE1gfbq8gXn3+uJF4Nw5AI4hsOTWu1PxpxTVKjOZ852o5mtPbc148uQ8bt/2QVpa/pHbboHdNK03jRY1sv3IEh4bLsqrSPk+unlzPuYdmYcufl2Yvq74o/V0enZONibum4jWy1vDK8LLFP9rav162qOoZBxrTMvP36UhUtyX67+0lS9qySdtTtw+gWO3jkl89KmB9g9lhqjNGTpAycpggmfH5z9LW3YVF+W3ni8uP2QVd8yOrv8pmaOtyyKXad737YVc0CE5Gbpa+T5XZliYjTWZwGI5x6fHcesucd7AVfrv+3odBNafFIH1td/Xpt8rs+Aq36FuGIP7jc4NU+EqC0evP2qLFuPgOtoJrLTyBIc1+ESwWjlzZO/N0vS4H/QRWMLjjAGUQ8bFjQhq/MZFl7v1vD4C65OBz6m2XWRsfvnyI7i03p+J6xnbSh+B9fS/4vIVRjDGGuovnkM6qltgFR0j1Z81Nk17c+nZRQiWvCtz/wEMCh7EzLv7W29mvevqERypon4vBm7pBwwfDrRqZdfLqVDOVOT64v+mvuvGdtDvp8VeGRU2CoC8RYIc6HJZOVlosqRJgektF4VQL4Gl5WNTTTp2Y4yTnQRW8ht5z0b9+uYvuCYjMzsTf9IE1h1xpLzo6N8lfXTjv4Ed0TtEz0EH33r673V6OjB0KPD22xxpm50NzJwp2pf0PD+AlMBatLWYpFk5sujozaOK6vLl1AisHWFcFDFWGzWmEezdWxJWK8H+/WWQkcH5s1KL1Pf9hu/RNaArJu2bZNOHPno4iPFOJETVmVVF5fmjhlrXfSNQW0efskh1ryUgsHZqILAGri6J7WH51lrtFhj7yH+U/sj0vUttH6J/nLFaCfqsEFhgXZiOTSf+wvtzOV9zH1ME1qA8EoU+slZMhsBaemKpQ++/1giUQqk/S/k+00St1Uow1l95fPXIoi1cfcK08RsIvqP86tLXjdug/77L+cBilf2LCj7zjf83pt4rM+Eq36FuGIP7jc4NU+EqC0fPKe+LFmM9BBYIQU4xgj+87N+krvYiuNeU4F5jguxiBOllCe62IEipwiaRGvXl2mcdH8gpKk8+qW3WH3dXJ8+E5SsO59IWvCdOpwkstaNrNIFVcThjrBcuhEfvfKLx1aHSMr924K5/XFz7C4oagaV4/6Oj0X5Ve0n6sHW9ZK/b9wrB0ZfV9RrYp4rm+6YmPIG1QSeB9ZeC9ZnZwhNYDRY0EKUXG1cM7Ve1l3126Xpyc3MRdDGowPSWs8D6eon+deDlycZ06cAisPz87CKw7jUWzP9Hjxy19JqCzOx0iaPb2wk0gTVM8YPaDdcFbQVblXFsSRWzZqnufXqeH0BKYI1Y/5ykWe+j3sxnOSUzRVFdvpzw6JCcyFlWCP2EWa0E0dG/S+oXynMTuGNisbHjERc3CfHxq2zl98XtA7EQlJvERbejHXrT94C3IOPllVmvMNt1BNTWUZbz7trT8/OV/CLxMnu72Arw04UEUfei7NaZdnCuJlfuX4HfOT+8NP0lSd7xW8dRT4b4/EkQOXszg8AK2CG9J10DuoJYOBJLWDZoJ1fPmhBxOu27iZfFxxc79P6nZqbq3lMbqhBY5SdJ0/qt5K6193tAKIu2EPzrJ05720uqJ33dBBkCi45cLpQNOggs2geWo55VM+Aq36FuGEPhnYFuuCRcZeHoOaW5aCEOqa2PwOLza0wjeG82Z75rxuZltRI8fE2ewGo5T1z219UEYTUcR2DlUnW89AebwBrTSnzd9trK7dIEViUWgZXX/rY6nDNylhVTgRNYL7yA9j9J0yd8Utx2XdXfxXmZRbmjqmp6Deyk/b7xcr8kwYOSnMXY2FbcfRjTiuBOKa6egDf0vbwpHZ80W/4O+xvIzESDGWJrSD7ylhyEUar4UOqbLm5ymJ4jdo4QjxFl+cOHUO/ua9/zbkS39j8xtvD9++0jsJpQz39qKufUPS7OEUuwIaSlJ0r0T0zcKirjJrD+u6AJLJbfHUWsX69KXplBYA1nEFgLjy1E8fHFRfr7RPqo9pkv+9E8dV2CdhK8OOVFRN6ORKnxBF8uJnifQXwdP/6epH6h0A65T5xoZiu//9p+WyCFwFAuMAVd/5QDUzB692j8s/sfSd2vznoVAPD2wrdtaW/Me0PD3dcPtXW0+Dip7nUEBJaaX6TtYQRLdn8k3hfCOxvS+UHaA117QXZOtu3ayfsn29Lre9dHbm6ubGQ7YXQ5+lia1UoQan3eVu/w0OEYuG0gio/jfC69OlVcdmWe5d9qisAqKUNgLTy2UJJmJqKTonWN4bPjpfs7LS9OYrvxKGIx/u5vtRIsYQRpYEUfpMuwCKxvlqhHRmSNA6vcP24Cy41ChsI7A91wSbjKwtFjcjPRQmwvgcVLLTuODym+DFO68ARWp4XictM2chH/HEVgZRehXmjzfF0tbCROpwksVvRCoRSjCKyX/lAeaxCCE5WlZX7LI7Cuv6D9JcUQgUUIm8D6iMvLeIrxYlmE87Omppc9BJbaB1igTgJrfAt95Y3I6AXfAu+/j4b9xem3H93mHtKYGCA0lDvqI4AwmmPJf7h+btwn/SXXDPlr118SPx30sz41L9poD1/7nvUKnvbrxySwcnOBetIjVWryiLEG2sTTE7h5EwgOBpKSCmCFVkZa+l2J/omJW0RloqP/kK6rbvwnoJXAunVrIS5fHoioqCGIjh6K1NRYrgKVddMsAmvgmhdM6zPf1xYaCKxtuwjCY8PxIPWBYtTCY8feRm5uLnJyMtFoYT0J0aBk2bb9wjJVPSpOqyi7dlX3qg4AOHj9IN6c/yZen/c69sbtNW28WGMnJyUZBFZdAYG1XYXA+nEZwfY9z4vSNh/40JDOdGRmNRH6CsvNzcWq06swef9kW9RIOd0DI77DgWsHcOHuBWb+wYMvAQCysh6jrOcLIBaCb5ey61qdF9WOJrCeGc/WmWWNaCZikmJ0jSFrHaGlzESCLZSlmhYLPa0i97z6biNoNDtf1598xPlfLaaer6na2mONA6ucH8Oqs7DCVb5D3TCGwjsD3XBJuMrC0cOzqWgh3m6QwHragyBM4UVRr6RVFLf1bh6BFcJ4kfLs4jgCK7MotcHLEFj/thZfF/6qcrvFR4vLv/in8liDcJZmdBneaf2lcvJtCcmxZv9zLIHFfLEkbPKNFiGBNb8xZ132awf58vUH5Jc/WI07BrstT1KKcemxZTjrrKXvEHz/jboOWizFzBJ+zjw/Spwe9yAO6NFDfP/79wfCwgAApT1L28qWGG0fUadV6OiYxCL1dTVtI5fe09f+591e/YZ0lNnCHzzQv+aoBZZ4/nnx34GBBbdgU0jPUCewYmKGS8q48d+AVgLrzJnPRGkXL/bErVuLcKsTQdyPXETgmD4EV/5H8IjhRkDP8wNISYJvfV+06RwfvxzHjjXChQs9kJWlP9In39cFDJ+atISEETxMf4hbibsMrUWvMD6Ceaw40E21rkrTKsmuXW/Of9PgLNA/dnJS1EIQTvk2fH1Gfr4agdV1mTRtY3hd3Hp0Czcf3vn8LusAACAASURBVMSNhzd0O6M/m3BW8z5QaVol1frkHINfiBmPFr4t0Gp5K2Z+fPwKmzXrrt0En3hzDr3lxmIbw4qrlAyBNf/ofEmambhy/4qu/bSaBgLrhQlsSzV7JDCUYOYmcdqyrQTBMvW3mJevKz3nvl0q7suo9fqfd160li2scJXvUDeMofDOQDdcEq6ycHT3FDt9NkpgEQtBYFdzNjWrlSChpbitd/qx/TRYrQRj1thPYM2hjw1RQlsUVfudS6ePpn1GOaVXc1pOE1hqYw1C8MYgaZl+ndUJrOA6BK8N5gifI1Xk2xSSW0o6tf1Zmq5EYB2vTBD5kvq9EBJYQhE60RdKg/75ZZr2FufFlpHWk1OEc/avpMODktpf9owK7zeNTj8Qs0d+TkZEiHyp8JZ8eo9KahWWD42XJxPMCeJkdlB+9B8jBFY3H/v0G9pefgvXq8PD11UILJacOVOAq3Y+MpgE1mZRmdhYiyg/MtKYRYQbhQc0gUV/eB44wH3Qnz7dSfP8j28vnd96np/E4H8kaXOtXQEA6enxsFqL2NLj4ibq7rNt3ZQhImh5nPEY12+v1r0OCMf1nbllJPkZ2RkAgE3HpQQxLSUnlMTzE7jjZvTaFR4bbt6EUAF/xLyohcBrE8H0jQSeAeLIamfGi3V/QweB9T3DIskzQNzfK/ev6NJZD4HFH8dUwvgNbN3PRI+z1dPNR5ofG+th977Gy3MTOCu3zosIPlvEHWltPZ9g7pG5kr6Yiav3ryqO2/DQ4fCK8EJIVAi+2/Cd5EgkSxrOYpN09kj9WdzRw7ITOcuu0hO5sfLfwS4vJLC+oXxuCo+CEgsXGVGLDpMCCN6cKb5Wy9rw+47f1W+Ak+Aq36FuGIObwHLDVLjKwtF9UmPRYryjlnECa/1b2sz7tcgtisx4px+RfQHZtEM/geX9nrYP1H2vsMeAJjrqDhZfd6ai8gtXCR0E1pCOBB/8Il+XGoGV/hS7byX/EZf752NtOrEiLCoRWBFVjRFYwgiUQqkyjODeMwRJzxC8NVCcxyKwQDjfWJ4fEnzeVb5/Wl+ajcqa+uz2Ds0fpTgny/6VX5b3pabXWb1WSUlO1Fy213Jjz7w9+g39upRkbbt/fw/Onv1ad/uxP9tBYBECWCzAwoVcFLcCApvAChKViYvzFOVfvz6zwPRzw7HwifQRPQfFxxVBSsplPHlyAU+enENqKkcWnDrVXvP8j29jjMDaz/Ax5LP/J8DPDzcGVJTk6YXtmV+tTZ+UzBTcvRto9zoU+yAW95KPSvLTs7gj3feSrIr1xMSMQPO5+Vbj3XwI2q1qhyZLmmDC3gnIyc0xdU6oITUzFS9SDrjXCY5FnaWOEb41k/NNVmFqBablu1BYR+p4AuujeVwk6n3H2iEz855mfc8lnNO8D7Rd2Va1vkkBbN3PRE+01fMjw5Ls+PGmuuYPyx/sCxO4I5l0+vqz60X9KD+1vJFbLEHsg1jmeP208SfMipgl8hvWxa+LpmN3H81jn4SwR4asIvg7z1JqdQhHrrZfwDlXZ5VvNV/+XYP+EWy0RgLLauUswZ4SRIpUWxuIhWD83vGm3isz4SrfoW4Yg5vAcsNUuMrC0X3ie6LF2AwCy/9N7lclMzY2q5XgWtf8thoN0HZN1BDuyNqT6gQXRnIvE7T/gVY98o+YqQnLGTuLwHqNIrAelCRY0ZDA7y2CLXWllj+vD9I+1kovbn0+48pctoPAem2wuNz9ktp02s8g9ZQIrPBX9R8h1EJgKclVAYF1tiJ3rDA4T+KfI1jZQHpNy54FR2D17aw8zsPaaSdlQTgC1RF6PilbSnNZo88+b8mlR34fVEu0rmVkJNjdfqyGiKSqRFYBISPjjkT/u3c3icrExU0S5d+44VVg+tG4ccMLe/eWwpEjb+DJkwtO0+O/gozsDFSeXtn2HGy6uAl9tvRBz6Ce6BXUC4OCBwEATp1qo3n+3/lYOqdvdzT2TK/Z/QlQqhRufCnN04uZh2aiNoMEkJO0rDRVAuv06Q6yH6nXk6/jZJy/JD8jKw0AkJX1SLHuK1dGSj70j1zfb+o80IP09JsICSsq0of32dQ1oCvOUhbu9WYSXEq8hIzsDOzdrjzWtDWM1coRWM+MF/tHunJlpGZ99fhvarqkqWp9UwLl9efr+YFBYB09Wt/QM2C1cpZFtP9Wq5UgOCpY1I+eQT2N3GIJ4h7EMcfrQdoDSdmv1n8lcQ/Akhbz1C3yePHezFn76Rmr7r4cocTKaz1f6sJAeB2xcGThbxpJbqG8K4h0qDZPiIVgXPg4U++VmXCV71A3jMFNYLlhKlxl4fh5QiPRYswisDr9KF6wJ32o/CEd8AYXucboZi+U1Je5tt7/Vfs1SY0JjiwXp20M5Ta1Ye24o2QgBNklCNLL2Udg3VchsGjZ+Lq4fL/OymPJt6NGqExtrk5gZcgQWK9TRxK1Elgs/15KBNaYVsYILN6Bvx45VyH/+v6fivMC3yBozrBoO1FZvg89v+COLOrVQ06EBJacny+5udRO4IOsSZ7fspeHmacbL590J1j8rjS9kifnG6baFE6ezvvlko4Qao/o1XHYoDqidc1I24YJLEKA6Gjji3NUFODjA1y9KlskPf22RP87CQGiMnFXx4nyb1x3DoFFW4tduNBDvnBODpCSwv3rhiyePLmIoydaY8ve17H70hIAQInxJWzPxQuenPP0kyc/1jz/E1oxCKwOxp7nNTPLA4TgzifSPEXcuYPzG97GhR0tcOFCD0RF/QYA2B/xhua207PScffuRsUy5859L7sGnYw/ici49ZL8zKxUAEB2dookb0UwQYi1HCIiauLatWmS/CPXQh03KVRAE9pWK+cYm1gIdkaH4txY6ficucEdSz689inFcey9QprmGcBZ0+i67wLk5uaizpw6mvYBLf7EpskQKeHhJW31sHx5HTmifc7JyTPj2QRWSFQIiIUjuCYHEgTvfhZxcZ5232Map+JPMccrOU3qg45YtAVj0rPPt/Em6MCYA0rSzUcc5VAoH8+Xt6zqtZzrGx05VKu0FBxPbDaHXWa44MitR7iHaffJbLjKd6gbxuAmsNwwFa6ycPw84V3RhhbKILDoD/+NlI8YelP0zzvGxAonHbaH+0XPno3loD/BtOXs8LosOcx4meJlUV+C7GIE5/8R1B9AcPNzgod1pWMQIkNgJT0jTqMtqmihnWz3/7TgCKzMotoIrKRntOm0p7o0fXZTZQJLbxTChyU4S6k7pTi/Y3qJDWFUTdY8rkrVWfYv5X5fK62dwJrVTL2MkMB8UkxbFEperpYh+Po7zu/ahfKOI7BiyhL8/JU03Ytyulp1CpduBoElNOPXIn8Mrita14y0HT1Afsx1ycyZXPRIf39g7VogK0v7whwdDTz7LFfPc88B165x6bm5XF1eXsDDh0hPvynR/07sivx6cnOR06EtsotxRH12SYKc+V7AhQvA/v3qBNGDB8AvvwCtWgE7dmjXn4H4+JUSXW1ITgaWLgX27eOIq08+4fresiXw6JGhdv/LiIxsaRvL48ebAACKjy9uey5Ke5YGcnJ0zf+7LaRzOULBWbUWWfs3V09aeXH6sWPv2vqSmXkf0dHDcPlyf6SlXUN25mPk1H5VVP7AvgoA9D3fWdkZqgTW+fM/StKIhfPJsyuyNw6f6S3Jj7kyCgCQk5MuSl+fdxzvs7Wf2fpGX3v85oECnCViHDxYWaLP0q3cUcm0jBScGyMdn7M3gwEA5yc8I76vjOOitHgGEHzKIG30IOpeFHps6qG6D7TwbaFaF+0sXChDV3P1fMc4Cnn0qP6ItkIZv4Grm+VUnCewaP+RqanyP17oQeTtSOZ4PUx/KClLLNp+gG6lw9J6MOM4pZr8uEwa5ZCXbisrYcbmssy8CXnjrMURPUvaLcgfn6YyBJbVSlB+ElfGTWC54Wy4CSw3TIWrLBw/jX9HtKGZQWCtaMj9W3wcQVtvgsazuehIpSdyDtiJhYt8ZO+LgO82+6/VKofWcZYY2XlHDK3VxX188U8u/R5FYL2hQmCdqsT5shrSkWBwR85fmBkE1pQP8kkNuTJZMgRWi54Elf/g5OVh2i2wWNEQVzSUv+bf1mwCqx0VzVDol+yr742RLzyBdfN5glJ/S+dxFQbhU+5Pgs2vcZZHwvTmv3B1NdRIYC14T71MVypy5vKG8vdfi1RmEGBa5cev5e+1HgJLz4utnPjv0Kd7yxEVROuakbbPjDeJwGLJxx8Do0cDmZnA7dvAjBlAeDjw8CEQFwckJnKkEuva5GSgSxdRWtqqGRL9E3x75Q/Evn3K+pQrx5Fhx49z5a9dy8/z9gaGDcv/+7nnOHJJiPh4ICiI64sKbt1aLNEVJ04AK1YARYoAhOBxLYITcwmOexMkv5HX7rffatvMLlwAIiNVi6Wnx+PcuW9w8mQrJCU5zxLGDNDjefHacunz6y89/qYkl4ZJ54kZe2pGaYL0F8VpR482tPXl3LlvJNdc7Sn+++CWEsD06brazc5OR2ZmkiT97t1NuHs3AAkJfrh+XfocFbVwvqGU6gaAnJws5tpVdWZVW99278nP3xRKEHlbfZ46ChER1SX92LPqaQBAVtp9nGSQF2dubAcAnJ/0rCh93LiiiuNjtXIWRUYJLB7C47JyogY1fd+YwfblZVQ8/NlHE61Wgu3R20Es0vSYmD/tGicaJ26fYI4VKyJkv639mH66aDHTTQhLWFZwvARHDsXS0BrMPP4IoBY/Xixp650/Pk0UCKx38tqxWC2m3CNHwFW+Q90wBjeB5YapcJWF46dxb4s2tJ01pS+v/TpLP/yVCI7dDGKD9VGcUoUg9SXu/9kl9G0yA1cRPDvevg1KjxxbRJBdnOBANbH+X31vH4GlJnJjpZXASiglXya7iLk67awpTV/ZQJnAop3a1/hNf9t6hI+qybKg2/S61PqMl+nvEyxqJE4b3VofgUVfzxLa6b8zCKzTlfKv79iN3T6LwJpNWVhWySOwzHqx1dOHpn+VE61rRto94/mU4wgskyW1klj33XsI0sePAK5fB4YPBwhBblGCO62541vxbdgWNihSBDh8WL3N9evzB3n8eHHe3Ln5eTk5wLhxXPpXXwFZWUiLOyLS9fj2GrZrH9UlOE05lj66VFC3tzfg6wvcvcveyBYssJFgIATo1o0j/Bg4f/4HWxt795ZEZuZ9g7uo80DP3SNHP0F3X4K/1uWnZZXUv+c9rklwecW7uNKbIKuUOc/z4xoEGWWoe3y0nmxfrFaCS39I03KJvnazs1MlfqoOH64tGsc7d9ZJrnt/rnrdAHfETZgWGMqtSTyBxco/FX+q4CYJBaW+pD2+ysw7fYMjep80fxn3G0qJRSWZHMg+NmcPqsyoYlvzOy0k+HU1Qe3p1H5lZ/95ET47QgkPL25o/lsUomaeSziHAdsGSNI1+wq7fx8YMgTo1QuIjZVkH791nP0ex8C15Gv4fnUD1f58PF/s14wlckfwjEpw5G9YvONVZl7dvPmgxY8XS4QWWOUnyZdrMocr88qsV7TdIyfAVb5D3TAG+1ZTN9yQgassHN08Goo2NC0E1iYVAutwFY5QKTKWs1SqPUScP+BT9sfR5d/0bTRvexF8sdgxGyQtByiy5ss8Amv+N5y/i6Fr8p3EG/kgZb5kKOTxMjmPwLr7rHyZHBkCK+1pznLrahkual9CKW06eX4oTV+lQGCNbs35pBKmte6hfzz0yPY3iwOE7cMs6DWOVGNd1/9Tzpdbw/7ckcH6AwjmNeZ0eruftrZZfqNoKT9CfLRzhUECq1UP/WN0pmL+9YM7ivPqDOHSWQTWRsrBql/e0RlnEFidh1cRrWtG2j2zq4nTiSmtklpZrPu8zQS5deuIyuQ8LS5zyE+mvoYN1dusUYM70vfkiW5d0+njY4u49NwinMUr617kytUXkOfn6+FDICmJXcaT7UMmfM/TojYSE7eav7GmpnIE3rBh8tZpublARgb3rx2giRGrleDADmmUP9aRMCU54kuwX0BOR02Q1mmPJLQiyHyBauvIG7b+aK0np6i+dq8e6IMnaWILrI07i3O+5TZsABISkJAg9XHlo8HSG5BaYFmtBNt2EewIK4Lw8OI4dqyRKM9/B8GZO2fsuudGkZISrdiXtIdRzLy1+zvizp01uNWzPK5/Q3D3Q+3jbyaBVXduXRALRzII6+rmQ/Bi3pEuNajp+zfjiJ/VShAZ+aGh+T9WgcC6l3IPS08slaRfufK3toHp1i1/3XvnHUn2k4wnzH2TZYEFAMnJh1T708Zb2VcWf2SSttQ2Q4JPDIGflU2y8YSmnkAPQvlysXiMRsrsTR/MFbyjFVK4yneoG8ZQeGegGy4JV1k4ulkaiBbrXSYRWCAEj4oTpD4tJQkWNWJ/lOQUJbjXmHvRvfKL+kbTbI68o8abjGhHhmQbwYi1nAP4shM5AuvMBPnytzpzH2Z0Hx8V53w68UJHQXxroDyBofQB3/ZnrkyiAoEl9zF4uIq43Oddle9vw/5cetPe0rzV9eV19WpKEPUi58uq2u8ErwyVHqGjRS8ZQ0uo5Wfg/fexo5Y0b7MCgdW3s7xOWgksOviBnEx/P79uowTW8Lb6x0jo6F7O6oxFYG2iCCz+6IyS6b8eqeipvQ/Dh4qd9xpp98yZzpylkY5xd5akvCzWfU6Q9DmnP/gP+jtH14yyYj14C6uUavL3IkdjlFhZ8fHhjm3++y93bJMxN+4u68VZq4WFAXv2AFWrcteWLcv5L7tzR0wyeXgAlSsDnTtz5BkL/fvn69CokTT/4UPOvxchQMeOQHq69k376FFgwQLk3IiT9CU8xIRnLy+y2MmTn+DUqTY4fryJKc/z3Q8JMp+Tpp8//yMuXOiu/flU2HflJPnWaTSdQ/DebLGj8bMeBGc9SyDySDO7+gQAd+8GKJahnX/7becsbtLSruPixV64dKkfMjIStN9/A7hx3UtWz9zcHKQ+uKSp3yd0WNaYSWC1XdkWxEIQvEta38ZQghLj1OtV03eMjHPwyMiPDM3/uVtflM07GX8SxCJNv3LlH20DQ697D6TRBecemSvZN3Ny2T4Qk5MPqPanwwJlK6dlWwkazbZ/vJTEZ9+32HKA/czWnMb17bUZ9tc/cNsAFPUoCmJh+yyzWgmmbRS8oxVSuMp3qBvGUHhnoBsuCVdZOC5YBsFanXPIvae61IE3CBd18MevCX7owsn1F8T5cgQWLzRJsFCGwKLl4evKm0zDWYS56bMkYIf479Eym5IWmb6RYK6GiCoRa7l+JDbnCLnUygSjPhGPxbJ3xH2e11iewFD7iAdRJrDkxjmiqrgcTWBVHC7OH9KRS2/cR9rG2nryuj4urv8DVKm/pypxDszl8l8YSZC+YhkAYOePTSX5W+pyjuVZ1/b5zD6d7JEZAgJrZQNjBNYIOwis8wICa+k74rxRn3DpeggsViQqe0VrH377Pd+SIyVF20eYnJw+3YmriHckXoglpapYd69N0jL0kauDAY7T5/7b3I8HGaWleRm09Y0vl/64uvy9yHpWub3s4gT3G6pHkQUhwOrVAIBD1AfqvSYaru3QAcjO5vxsCdMnT87fTJOTgd69gQ8+kF4fHw88fgwsWgRs3QrMmiXO//RTbRv23r1A0aJc3yszPogZYe33b9Y3/yNCOOfIR468jjt31uLECf3kzn6Gc++7HxHc/cC8tUGP3PT+ntsTJhDsYhx7Ct+t7suJJcjNRUKCn/J4RtQS/b1uO8H5u+dFFj1nznxuyvucGuICvpbVMycnE6lJ57TdX0aAHjn5fBF7T7AHZ+6cUXzn67hQvV41fQfKOByPPGHMAmvzrmKyebyPKtrn2oMH4dIOZGUBf/4J1K8PjBzJrUv0epOYyOw77UMsOyebWe7Bg32q/fl0of1+pozK2K2NsfkA+35Un0ow/+h8vGlnsCirlWDbqX9x/NZx2YiVvJQY5yaw3HA+Cu8MdMMl4TILx5gxqi/v9LEk4QcviPRjMvIlcT5NEmglsEAIYn9mbxybQvMdwmv5leerPLNg/pqiFoLVZvxarXdjDCSo5Jmvy9rvCdLL5vfX521jBBbtk0sLEXKIIrC+oAis1tT95y12/mwjbWNLXbauwuh+ekSpv7trcFZcdPrnXQne7UuwtS4BVq0CAOzq/qGk3FYFAqt3ARBYT3lw83BWs/y6jRJYrHuiJnwEQxACX2r+/dmGS/9JB4HF+rXdiGjpQ/9hrwFQPiKjVS5fHsitjaNHS8f4rbfsmsdmyYMGBDe6cMfxQAieUNZLszZJLbBud5D20RG6CY8VHVpPkPOUOD+T8qV0eAWX/riG/L1gEWG85DxNcDTP2m/fVs4BvFy5K//j/Ck9qUZwYaS4jaTGGvv46adAz57S9BIlgI8+Avr0kb+2SxegWjX1NjwEEa0yM4GoKCBBYJ3z6quy4yknesgGq5Xg8OE6or8PHWL7mnEludnpORCLedahvNwb2tyu687fOS59JgsAcd3kdcrOTkPKvdOmj32v5eK/w/f9H3tnHR3F1YbxS3AI7g4tUgpfKbS0FIciLS3urqVAi5UiRUpwdytQLLhDcQ0SJLiHIIGQYAEixGX3+f64Wbkzs76bTTbv75z3QObqzN658syV/HjyZKTV97D2xlqDaTVabvo5mspv/43uitcvnyyK8+fzyWbU2cOuvryK7mvl1yMjH2jzrVarEBAwB7dOVkVQSyPLqxnjgrkC+nuIMQ+G+MR4RX+hoWdM5rnFSucJWPOONcSmKxPQVeGZlZjFsPr6aqtPO/fyYjh0rQ8A4ORp4/4KzaA9sAjnkzytB5FmSDUVh9IgTWKGBAyNRWTiS+M0Jt1rSSoSrPjaRCdeYmrGv7CHVWbwG8awbzlD5fm6+PqsN90gfblQPujNPY0fl1x7CcMYK4/ctdY+nSMeEXxhF984VzMDpvFy/iWwwjzdczA2gC80Q3fUee/1cnfNqYlKJhWwNPt7aaxuL9HdN2nwPKmePJ3HeZXzWr+n4fQvlGDY+TnD9kr8VMb3Wc27Z0MmxJ806+LkwKYyfwfLGRaw+rRgOF+SH1iwuyLfD+tjJuvzpLGhmxgOn2TajlH1RXxppSa/zhCwHuYznP6IJvy6JQLWZzZM3TdkU3byo72V8m/vtB49GszrRiVxf+JEi34Pe9r76ro8eu/lm3M/78p/h9838uVRn81juFnuE1xdxQWiD18xPPlFfo/WpK9247OVguswvGnEl3rru/uOFNMI/UJ0l24mfmlTUvvxieHfIqaA4fy8bir6vTlX7icxo+jnzHG5gPWuhnXPw9km3RDdXiYdoF+4UCxZ28a0YHdf7JRdSw78exrOk5/fIES+9XHYPWvs+gvbT/40FPf43YWhNrAsToOpDyz+/n8bdbfXklp9u/REeRlqVNRjbb7fvz8suIVXNFI/9OuneO95ZuYR2s4EVYKiv5APp0zmufUqZtZphY6wfRd7Yd3NdWAeDAcky0kHejJsubMFVRZYH/+hqz2MljONFZvFMOjgICtLseNJNeNQwiaSp/Ug0gyppuIYO9ZkR7lhD3HAeLegZR3tifVtE7CkNu87MT7pV6Anv8obmtzTjA/i6/dkCK3inMZY37b/Lj/C+UPSjDXmwZdATN/F8O8BvinoZ/P4NWk8mn0ndh7lexH07yp/jvE5GPx7M7RYwJAu6Tmk82DoPoYhsrTOX60+4rN6lCRSKQlYj0wIWJEZGXq2YujdkotE4xX2oBrzvS5tqVuPVgzF/rBAwNqyBQBwakQbmb9D5eRlU2O9WzLU6yVe08xU6tzWegFr+Gbxd6q3lGGx3hImWwWs0RYKWJ8O4XuxacJv/p/oPqwpv26JgGXt6T/m2G8bHSteeXkxPHr0O68bNafo6durV+Jpdx4eDhUp4nMw3JrNZxhJ8yndh2bidoYBngxe/6XXXru0Wbk+tCYvxjaDjy7MBTOh3pLU828aKOfjY3nDv0V0EcP5efCX6fuSilxeXvIN44NrWfibZOfiXWQpx/3u5lhMfsPPzRa7cqWy8Le3t302cSfT2eOXcsEiObhlom729x1j1/v8V6HeuvJkldX5j4kJwKPz7Y2mGRenvHxOgykB68mT0cleHk4dqK94PWbtDOD5cyAxEReP5hbcri8xUUc8fy6794WXFmrbzsGHBxt8Rqr4aBwabDzPhja7t6d1U5hh5eXFsNuzDdbfXK/YB9h8LD8239mMqTutT3feka8UD8mQWqnZDL8d+s3q8uxoUs04lLCJ5Gk9iDRDqqk4xowx2VH+XiJg3bFQwBrwkxi+bwvbOu5zv5MPpn/byHDqJMO15Xy21u0Zukbm1hzTA3mNwPJWMsi6pTBFevw2eeOt2Y9rgoENQO1hM3bZLy7phspeXgzzFNb7x7vrnlG2KVws8/mE4VVThmW/8mVwmn0AmAeDXz4+Y076fNd+yZ/vB8kSx4q/yf3+0UT3W0vd/mwsF9SMCj5btwIATo/uKPN39FOGUgpLEDVl3NDSWb981gtYv0n21xjgqTvdEMyIgJUli1nvRv+f5eGlp4Dqm3S/Oukm7m078Ou9WqYMAcvLiyFXkhg9YrNj4tfuR/PqlXa/ITDGZ18BwIoVQPnyQJs2wPv3fF8iK+oxcyygo+33o3S6qzV5UTMm7LF0fp/OTWlT53ffieFf/qScj7DPDec9qoTh/LxsJvp9PEDux2e9PE5vST33tq7ePabjm9x7neIzWi9vYIjRa+9UGbko6OXFZ3OFVnbM726OSU+gdJSdP2948+m0ZPbccuBJqzyya9qZQ/fv8w8v+gcEPHwI+PjwUyv1OXpUVyYWLgSuXQPOndMdOvDiBa+jkkiO57RuDcPknbpT2qTuV30XWtVVVanizEo/OvqZ0XhMCViPHg1N9rK15M/citf19/Y7I2lvL3uaqCNattQeXKHP9VfX4R3gkvBq4QAAIABJREFUzU8gTEwEjhzh5Uaf+Hjsrey8d01jz59Pl13zPMSwfcXvmH5uukEBa/s94/vSmbKp/1WEWq0y6e/TOcaFQGeTasahhE0wZ2eAcC1STcUxapTJjnKj7uLA9XYhyzra2yqJ4TWbgFtrFX5XHoz76TX2sfkZHg1meDyQizWmhIUGPZMGKG4Mz3ow3JzPENSCD2gCOjIcX8HQdrVuphLz4IOXxQozU5RmPKRmyzKZixOm/OWfzmcpXZAIbf8eYPjVk2/W/KIy3/fp++UMK/9jmL6WIfsU3bOrsZgLkXcn8wGqvptGwDL1WwrlZds2AMCZcV1l/k58wpdWSq9rTt6TXu+jJ7yWGWqdgLVcsqHy4I0My/VmqhgUsDJnBoYMMflutOhkWX6uFBXDL5McIlC7N78+vGnKEbCSwxITY3j9+M8/XKxq3VoYCCoSEwP89ptdRYpbdljaLF0y5+VlfvqJmfgyxaf9+JK1s3qD+LOH9fKpIPQH1xXjMiRghRiZ+Ro5rKXBvEnje9pH7ueywobMFyTH2b9pqPOvdMLdzXl8ttf9sfwDib6bz1r7/daWmnT/M0fZuXPKewKRWW+3FURlVcBT4OJFIFMm/hvnyMHFhVmzxN++Wzfg668tKy+7dgHnTG/MbQ/r0kZsJ6Tuj6rnAvbskdedicobimt49szDrPTvj0+656lTFeP5yYSA5Xe6ucOeTd2lfBsB6fXFBvqMcXoHJp2V9MEubzTjd//8c0AlWVIZFcXbte3bebum8fvpp0B0NBdLv/0Wez9TnkGXnBYQMFN2bf1Bhh3/DEbHnR2VBazj+c2aPWXMPPZXgkqVYNJf+bkMQw4PMd4vcCKpZhxK2ARzdgYI1yK1VBw7RzfHH/Xd0aR+DUysx/Asd1Jj1qmTtmGTDlxXV7Oso721shh+yA+2ddzNEi7MDKMxY3s0gTGcKaWc3voqcpFD7caX5l1fzAdx0k10F+1jOKZwEpK1Jj25xhy7O5nBb4hpf0/7MuSdzjBph2m/B09wAcueHZjF+3TPtvRshhn9+J5n//zHl9+ZLAfbtwMAzk7sJfN3ujTfXF7/WvHhhstM57Y6t0J/Gi9Phkx6fzN3i0tqNxoTsGJj+aln+fMbLKf/fGV5nvTDx2TQzdhy/0t3IENaE7BevVprXYWqUvHBwfDhfCCwf79NdV1AJ9vv5e5k+TVN/LF5GF434mKIUvoP/9CFubZMXpd9qM73slJK9833Ylwvmyvn48PXhvP+8eNN4MkTICCAz0DRiy+wlej3WXd5/qXLBZXsdWPu92M5656vLb+vLWZs7zCy1GVXV+l+14/l+MebuNz2LzPJcS99uvD9+Kov4h+kpO5xORniczLg55+BIkXEPJYvD0yYwE/ak3D5clmz86CN79UrLspERWnj+Xml8bD3/nbcs6k5JT3arhav+fdkWDxI7vf2TN0+gkpiteYQDE1dcG0Fv/Ze6VCKfv2g9rmExMRoqBs2MFxG+vTRilq7PrPuZE572vmzuWTX1h1k2Dm9Gzpub6/Yp9p+ohBWXplvU7rj91cxa8bf5/MZhu7sa11fIRlILeNQwjaYszNAuBappeJoPqqSOMAundSQvXsHVKmiOJCfWseyTtMWiYA1VCpg+fvzDkZQkFnxOUPA8s8t+q/Rl1+XCg76y980FlWc4cq/DOcOMvyymPvLO115VtNKBy5B1Ldbs+QDQCW7N5HPmDK1fNFjB0OZOQz3CjNcMLH3QPe1fMll+3+56GXMb8V5/HkZWy528AQ/fUgzY+hdTZ2bz4lieP/+IM5N6Sf7zc+UYsgtEbBKDjNcZjo5QMCavVs8lfNQOQPleuZM8cX191cspyFZ5Ht3WSJggfHN6g+X1ROzGS/Xpu5l9zGGzmt4BzM5yrCjLSYm0D6V7I0bQI0air9XQnaG9zWUNyuPy8nfP0fc25NfuTimEaTOHGP4qHCKn6l4Xv1o3P36Er6c+8NXfEar1A2MQeW5FvHehxH79ArCKjHcm8Dzc34/w+XLFfD27U7ds4yM1ObtRXsxvoBO8vxfNGOfljPHmLA00lKzt8hgroUb2TuMzP52QmGJvb3s2nL+m4Z+oSuL3rsZErPYt8yYyoc9Zo/XNOP0t3MHzcivtzdfBpc1K9C8Obx3m58Hxfjy5QOuXkVzEwLW/fHmpfGiHcPHsvwQiJ1mfODz8mKoNZHPaM8/naHADIaXpfmeeksMfEwMbM3zrvQRQn/JtP7eZhd28I+o0vvXF7zPHmG4O8n48z9eMnOyvFfbLPgAO2oLQ5GZfFXHkNHKM0MvXqiCaaeH2JQn790/IzExxqS/HusY+i372j79BAeQWsahhG0wZ2eAcC1SQ8Xx/DmQvXNLcWBa2gu7WBvs2wfEeV0AKlaUDVyn6C8NqVZN5i7d5N3QxtBa02CmgGVoE21jYUwN4jVLCA3ZE8kyxO+SBKxNkntTErD0raRkz6UGcxleNeMndMVnZ6gySHna9rUVyg1on/V8GvNaD4aQanzJ48eyDAnZjHfELuxgiGvXCM+fTzPaQL//lmHwKvMa/QbLGNaOMN0J1j990Zg9HshPuskymSHPNNPT2SctYdjU2LD75sN8Flc6D4byv/PTM2v05b9D0Zn89JpNO3jHMD6HvIx0bKf7HY0JWOkMXFcSfebuYVhVjX9h9RvGcGsww4pZDCvnMIzowrCgBgO++QYICRFfXiPviiqdXHC1RMBSsum1GcoN5UJhm9V8FmFydG6dbffvd8WbN5sQGLgIAQGzkJAQZrJeDQk5haCgFYiP/yA6JCYCK1cibFF/+N3ujaCxlbQzhM4d4ktswfheU6+aJf+9Ph5k+YDXXHvSn+H+OPHas94ZxOPeVSqgRAlZvXX5cgXxOc6bBzCG511Efy/ayvOvf3Kjo8yeAoMlFlHa+e8HmX3s+mL+m96UnOqmJMraYqbyoXRiqaU2zUwRDIwhphDvYyRkNZ33yJLm50F/6Z2+xefNYDKsJYf5fGB5AMYwUmFJqJKdrM8/nHmV5haXnufrvz7K/l8k9Tmky569vPgWGYZ+V6XDL8Iqin7uTDX+vI8Wk89+coQtsaAv0XUt77N0a82wbKmyn+t7i2OS1zgstqGPEtCeIXHpXLP8dlrDLBx5JR+pYRxK2E7KLYFEqiQ1VBzPngHp27cTB7SlzmjbsC+/BJYuhXEBq08fmfvV/+UTGkKjAlbOnLoM2ShgLVWaOp1kpgbxpmZgPc4r+q+VtN+KVMAaLhXnJFZ8uOg/z2jR/YsB/Pr3yxn2Jwk9t+bwQW1EGYaQqgwny/JZUTmm6uKZ0MBwmqr0/Ovbm+8ZwiswRHzKTxRDcDAA/vvmnMrQbwMXKKoMYgitkp5P82cMRysz9F4l379JY2O36WZAzTFz4L3FzA1x+23g8WacxMWndQp77Wjs7GG+pOnxQNNLh7bv4Pv7zP2O77M13YwN8let0n3ZrDuNz546eILPDmu6gqHlKn4ypH6YagsZCs/k/244pBzvXg/Ds9auL2a4f68L4uP5/ktv3mzBhQtF4OXFl275DWMI/4wLHq8bcyFM7cYQmNN0mc84iX8Jjs9k3uDnscIyh5Ri3y1JnnRu3WpstE59+3ab1u/ly2WhkhxTHhv72mDc9yby5/xkeFanPMOrPlWQMKg38P33Zg94bbF7e75EcPBeqFSxAIC4uGC8OjNK0a/Ax49Ar17w7yn6CZIeDnL8OEK+y+LQe3jWw0Cdm5ELas96MG096ghzRjkhs7/dnK/8e96anbzl5Ul/2+5j01KGKWYKWOHl+UEIXl78sAVVBvuV94vb+TsoDR+b13TY0C/MTyd79lAs+6klppi57PDV/5TvK/BnZf8BHbm7dO89Ly+GhyMMP5ePZeVphHwp+rk3wfBzDv+MYcnML5z+XihZ4ZkMfTsaFrBuLGDYbmN/4HUj/kHZHL991kvapxREahiHEraTcksgkSpJLRVHmb4NDApYWpMMfHPU/RNHWFMEpSsOPH8uc782uocQgVGR5/BhXWZevjSrEybdJFRjc2oaDmOrgCU9ea5O0ubWJmeXSazwCNF/gZEMSJ9e615lgM4t51S+n5B6jriJ64lP5Pn/u74VHdokFGflnD8vdmgyMwRnE8PPrCUPN60OH7A1XcHwvwUMP6xg6LWOz3BSuzGca8z3DdDcX6PlDDs8xQ7B3doMwbUZwsOvoOgf3G/BGQz7TMzcurLGvCVDGnvaj2FuHd2sqTlmLBHRfCkescb8dMZu44KkIfcjBo6K1rfLl8sBAJ4+HWdWmrf0TmqU7vmhdNqklxfvJIdV0v2+qvR8BsClLc7tsJqywjMZsv+dAQBw40Zdh6cXGLgYarUK797tw/nzeXH79o8G/QYH79K+Z4mJkSbjfmTi6HJH2+XL5ZCQEIbIkgx3piRPmtev14RarUZ4+GWDfpTwvdxa8KPdVD59er5fFoCw92cdmnffPxnuTOP71UToLcHU/x2vL5XUu3nzAjdv8g82U6YAkyYB6dJZJUi8aejc8kJmH9MIVdLrN+cl1cUZ+KwitRVlxBIB6Gm/5Ltnn3Xi3yFVTec/pqD58Wv2thPCFzIdzpJllMWL+1l0z349+OETQS348kDNKYOBbZX9P+/M3W8slLs9+ItB1ag+wPhyQCFcV4ZEiYD3vobox3eU/PkkZuYfxZz9PpiyiZOyYouBvTZvz7Qt7udd+bOIy22e/8NbP7f3sMxupJZxKGEbJGARdiW1VBxNhot7YLFSZ00KWKzeJK3bZ5/J3a9N/EWI4Nt+onv+kXIRBYDNAlbbDhK//frZTcDyzS/6r9eLX5fu72VKwCooWXpWeAQDLlzgG5fOmYMiI+R5Q3S0EIdHPbmfifXtLGBduGAy/IesygKWoecNxnChhPz6i5wM3/RjyD5WIsQBuFmY4fgn3E58wvAhB8OvHZU7EglZGaKKmt9RubSJYXZdPpttpJkiTXTSCZy//szwo4nTjDTWdrXxjc0PrDV9TP3btzuhVqsRHx+K8PCrRv1e9mQ4t5zvsZFzqm4mn1nPZAs/iVP/d/bv7fwOa86pDNmm8KWkSmXru1H5hKrkzZvNePlyFdTqRJQvf82ueXn0aAjevTtg1nHrjx8PBwAEBi52+jNMyebn9xsCAxcadE9MjEFkpC8/+j2Ju3fbyPxh61a+eX4SHz/a97c3ZR+q8Zmd+tfOn3ZHYsVPEe8xAjfPV8dF72J4/Xq92PY9egQwLvKrGQOKFeNLTmfOFOvd778HAgOB+Hjgl1+gcmPw3uv834/MNgupymcMXZfMKgmryEWXy0kfee5Osk3ECmxjovxWZ9g84BunPIMbC5RnDkntrJmzt728uACovxm+Jf0Dc2z06J42hdds0v7CgID1rBsXuQyFP+OVAS/aczFL6qa/r6Kayfcr9Bsqf7ap5QRtz94VcXSSspuhQ0XMNY2wF2vmgUS+276yx1DMIaSWcShhGyRgEXYltVQc7UZ/YpOApehe5BoKsLdoxI5jHhuO8r+L7pnH8wb1fbPuYmZCQ+UdFjc3IFs2swQs6XI8xMTwAcDff8v8ZhvL0LwzQ4tO3C5oTuEaO1aMY8UKgPHT2PTDl03af+BsKfG6ZnN3Q5ZvlOi/6B96Vc/Fi8rCDwAwhsiMDKFZlPdfatkpyd+9e/YRsC5dMisOabjptY0LWPH58wgn//VsZSJ/Ctef52Jo3J2h32yGE2v4V0p1Orm/mPwMF0/lM9jx+FiOzyLLMMnw8j6pab6Ir6zNUGk+wxEzNn+uvohPM7elUxUYuBAA8OBBd5N+zx1guDiSb3bKPPgSyb/MnJkm/Qr+rIfpMPuO8/3D7NU51d/UteO/psVn5sFQc2Reg3WcripJwNChg4S0Fi2qbbd8K1l4+BUAQETEHYemk1bs6tWvoFLFAwDu3m0tc1erEwWRKyLibrLn8fHhn8zyFxp6FgAQEuKFCxeKaq9772G4c+o7eHmlx/1b7RDfogFvB9u2hTomBgCgUsXh9esN8PLiJ485+3chs92UZtlElOYnAQvl5guxjg79H8MDz4p40U4+60ZqCSaWRT0czjB+eFenPYM70yRteEF+uETo/7j4lJiZWbSZu5eXuAzTkn20ksu8dxue5ezfkwsqxsKfOSGfgeXlxWdcae5bKqp7eTFc/YcvI7y4jZ96Ceb8Z2GuHej9uUEByx7mO4ohqph5fu+PT7nyQWoZhxK2kXJLIJEqSS0VR7vRZcTBYMlzZghYHiYErOui++/lRfdxWbRuP/7ITzoODuYTjWIaNkMAK4EElrSsbudOLqYULMg78Yyhq7kClh4TGoh++/+s0MEbMgR48wZo2pR/AZ8zhx/nPGqUbKmgRpB5Jtks+5t+xjuQiqfeaTh71qiA1bSb4QH8sKZJ/u7ft1nA+nwQAy5etErA0szA6tlKvD61DgN+/x04fx77KzBUHsjQqLt40p25ApZFtn8/j8fTE2o3hvCK/EQ0zelOM2qL+VxQg38FPqewYfx4D77ZPhjD8q8Z6hnYg0FqRWea3vT85cuV2v1AlCwxkQ9cExMj8eBBN7PSrbuU31Pp2QynTpv2f+6AKASG/s+4/8Mn+ewyzbMzdlKkKUtM2odrQxXzBCup1frTtIBlzHLnfosGDXT7V/n69sWLFwtw+7Z5YoSSXbxYShBTHNXZtsROnnTDvHkNnZ4Pa+327WZ4/XoDAJh8D27e/B43btRzep4N2bVrXyM+PtSkvzNnMuLatW+dnl+y5LfoIvJr5w5wIQqM4Vl30c1vyzdAjx5IqFgKsXn5gSReXlwkeVuXt3vOvidjpvlABMZPEz5zzD7xxuXiB7NITy5N6WbL7OfHQzNqn6WamfYf9rnz79cSO7bI8WkcOGB8I/tLm7momlJJLeNQwjZSbgkk7MbevXvRtGlTFCxYEJkzZ0aZMmUwcOBAPHnyxO5ppZaKo+2o0sJAsEr9Axg0CGjd2ohAVWumcQGr8A3RfXA5gwKWMStXLBKPHiVlNC4OiIoCvvnG4CDWmIB1pajod+MXCgmqVAaf0/Yt48wSsL7tx4CMuo6D/obISsJOn5bpdImEhRkVsIwN4F+7J/l78MBmAatnKwY8fWqTgDWsrbtwfcz3DBgxArh82by8NWqkvW+b7MABHs+WLZj7HRcuB/7E8PuPDK/cGcY1FPM/I2kGGZYt017LMIn/21LvRKjlX+vCaE7myz6Fn5ioOYXwdiGdn2qSr+sr/+Oi1oI/GDB6NAAgqij/EhryJd+81cuL7+sVHy85gRB81saNBUnxneR7hmHFCnwsx/cxudJMl3bOqQytDJwk2Xs9w/3BDD5r+ewr6fKUqGJ8Q1PvTQxHTzL86smQe5rhpXz5p/P0Mg2ugBxTGaYkbU5/8ITyIQDZsoULP9d6IwLWz50ZWndUdqs9Io/iexsRYXmR6dpVHo9KFY+goGW4caMufHwq4d699nj5cjXu3++CixdLKj7bd+/2acOr1Wq8erVWcH/0aIjsmim7cKEY/P3/NunvzJkMePfuAK5cqazXGc+JggUDUKzYI9y61cTpAxBr7OrVqoiOfpZUXV4y6f/ixeJOz7Mhe/VqDSIj7zs9H6bszJmMmD+/vkPTeDDa+fdpb1u//rNkT9Pf/2+8fLnSoPvt6XyGn7OfjSG7fr4KMGYMwOw7szCopfPvzRn28eMN3u9izs+LK9r98fKxRkoitYxDCdtIuSWQsBm1Wo0+ffpoX2Spubu749ChQ3ZNM7VUHG1HlhIGgue3zda63bsHrF8PdGiZVfDTt2xrzJljRMByf2VCwMpq8aDS3Z3/2ynPUYMD3HSjcwth3r/Xtt1gTA1WdwrYoM/BfhqAnBleozTzxxQ2Dj+xA8ifMRTDhgELFwL//MP1G39/3XOKPnYI2cfq0vqzMQOWLIG/koDl4wMMHgxs2gQsWybcyJyaov/ZddyE38OggFWvnsH7/rGrXiPq62uzgKWdzTVypOGwZ84oCljTazPg+XM06KlwH2PHAteumZe3W7eSMmejgKV5r3fuRL1eYn4e5GfIMk68Vk5zNPXIkbL86wtYy6obFxSZB19euqqa7u9aS7gAVH6u7trIxknPxdi9GkLq7/177f8T3PgBAdI85ZzKUH8ZnxWmuZbgZvo5DvzJ9P0K9ltFxev6IpZUvGIMYF+uMxhnbNKR4xV/k7vlG5dReDRxcdYXmY4dzas7lUhI+IjExCio1YnCdbVajejop3j//hCCgpYhKspP66ZSxSMhIRzv3u2Dr29vPH8+DR8/3kRIyElERt5HdPQzJCZGIS4uWDsTLyHhI+LjP+Dt6+dwc0vU5j1Llkgc/u8EEhN1bc4ffygXqaiohwgMXIiwsAuIiXmB+PgPUKliERbmjbCwi1CpYvHx401ERz9FXNxb3L79E86fz4eHD/sjPPwKPnw4hsuXy2p/z0uXPkVQ0FI8fNgfly6Vga9vL7x+vQF+fgNx/nw++PhUxPHjlXDypOmj7I2ZZlabWq12+gDGFouJeW72PTx75oEzZzI7Pc+22PUTnyH4uReK5g/m7TED1rGeOMiaYTIbjwdliuN4p/JOz2dqtvPn88Dff6LBsnJ9CcOLJra9f460K1e+4JVTcLBd41WaUW2OzTDjdGIyw1akQ29UWeD8fDjCLnvqNuFPqaSWcShhGym3BBI2M23aNO1L3KFDB9y+fRvBwcHYv38/SpcurRWx/Pz8TEdmJqml4mgzsqQoYO2YK/PTq6U4WDxclr8uajXw558Aq7IBbGI67t6mi3xQOLiszQKWYIYGzaPy2qx3KNm33wK/twhA7XKjUaBPWXza5lvkzvoIWbMCLM9TMQ99a6BJE2D4cK7VzO/vi9nsT1xhX+MDy4NptdwE//NrpxeetUEB6+pVg/e9SXM0M8A3MDZ0I//7n+7/zZoZTHNWx+LcITERWLXKsKjSoIFwaiLzYNjTuAQAoH5PhfuYMAG4ccP0A//lF23ezpZiWF2N4d+qDGuqMgRaeiS95pTLfftQu7eYH798cjGkUfekcMOGyfKvf0jAUjMErBuFGbZVMu5nZGPGTyEDbBewAL7UNunvQ+X4Us36PZmw75i+9Wtu3nPs1NZCAUsqWkstY6RyUkYErLgkAeszBQFL+54AuHLFtve9TRvDjzyl8faNWpb/vbtE8ezPP80vUqZISOCTGi9elLt9+MD3Ivf15W2DlMBAcXLqgAH8enz8e5w+nd7sgUNk5AO9OJeZGS49vLzSOWWg4+vbV3btzp0W2nsICjL3Hsy3JisYTpqxbNiUbd9uWlAqVOg5atfeI7u+YEE9oczFxQHr1pl+/2rV2mdW3saOdd5eTVIbNGiY4vV58xrIrjVtuB7RLAse9zBe5s+fN324h9QePOiedHCB4biLpA+0230PW5vd7s/y7t02Bme1ennJNyyvX387vv76mEH/jwfyk5HvrithUT7+2MxQeT7DMoWZw65oV/61X1wHD+ZA9uyhfGzgwWdtO/v+bLExW/l+p3uPMVTvPBju7iHozf61vUF1MKllHErYRsotgYRNvHnzBtmzZwdjDD/99JOwJwkA+Pv7a93btWtnt3RTS8XR5s8SwiDQe9cCmR/psrfD1XOLHhjDjYJu8CrNoErHcHbWJWRkcbpOqVTAGpvNNlHJ0KC4Yyvb4rXGZALWd8b953oOVuYk2KfHwModAsvtb/LeFi4E+vc3fN/tv2iOyuyOYnq5WCh+ZSswlC3AHz3fYWYlTyypthZLJn1A375JM9Qk8TVp0glt2vCZ/Bs2AKdZffixcrjPKuIMq4u7rBJ27wa8K/+Kn9vkEMJuHv07Hj4EavXMIItXPW481NeuI5ZlwgtWHIGsmG6vM30bNEhbtHKOEeNY8bWFP9CRIwAA/z1rZPl5nJfhXEnx2p2Cujz0lgi3S77Rxdv/Z+MCTuWBfDmedOmq7Fl3deMHDQCGZ7wZom1bnZ8GDfi1IkUU4/j6F+X0H+Q34xmmS4eOf5Q0eh8aq9mHoUTvUlzINubXoIC11nCY3P5gOQNRru1Xyu52eqf1tF2no1YDGzcCAwcCe/YADRsCOXMCf/3FBYEOHeT5//ZbNRo1AmrV4kLT6NHmFykl4uK47hwcLBa5pUt1+cuRQ4y/eXPgxAk+EfW//3g8SedhyPIREwPkzg1FQeL48XKyNluDSgU0aaJGiRK++PHHE7h9ezU+fDiK8HAfvH9/BB8+HMfbtzvw118b+GCKqTF79gu0aXMHpUvfQ4ECL9ChwxxcvbocFy+WkKXdp884zJu3AQsWjEPNmvtRqlQYrl+PwZMnz7B06R2cOhWI0NDzCAvzhq9vH1y6VAZeXgw+Pp/B3388AgJmIj4+NCmvcQgNPQ8/v4EIDt4FlSpB7zdWIyTkJO7ebY0nT0bixYsF8Pcfj7dvtyMq6jE+fryRtAeWTpQ4ezY7zp/nB1RMnNgOBQsGIGPGGFRv+C/cp+reiUIzGFq3XowmTTagXbv5qFz5PL799hB69JiEihUva3+HjBljkT17KL766jjq1NmN8uWvokQJXzCmRsuWH1G9+hGUKOGLggUD0KzZv6hbdxcKFHjhsHb1009vomnT9ciR4wMYUyNLlggUK/YIAwaMQIMGW5EunQqMAeUrXEathhvgNpzXT5knMzT7h6FTr7/QrNlqlCp1H198cRaffeaDGjUOYPXqKsJv3KXLdOzYUUy4tmtXYe3/N236FB07zhbc50mW4HXrNgWMAcOGDdBe+/XXkahS5QwyZIhDpUoXBP/16u1AwCf10b7geqOD5kePBls80H7yZLRJP82arcaIEf3Mim/fPsOHoHh4tEXpDr8mu5jwzacn0LXrVKxcWRW9e0+AZjZf8+b/yPxevFgC8UG+UD94YHE6TVbwd6jbWueIJinJvl/OtxBQcpu9m++xOWILQ5UFfEuFPHne8HfZ/RXYF55gHnzbAVP7qFK3AAAgAElEQVTp/LWNYer2DFiypKZd8j11Z1JdODo3smULx4QJHbF1aynMX/k5DpzQpdl7PcORI1kV4+g3h5/Uns6DIf0kBtayl7aeimJZLW9Qk5HUMg4lbCPllkDCJmbPnq19gW9pliVJGDZsGBhjcHNzw9u3b+2SbmqpOFqPKC4KWHsWyvxIBawj03qJHqS9Tx8f4IcfcJrVRyN2XFHA0v8Sb7EpDV7HZQXL+8j8OL5aCfbrlzr7fId1eSnhLebjL3fbOu9K92bMzYPxDoItaTYaLcb35Vrzw7btJIYtc4pf71VP8T4yZVTJ4sjJwlCIvUZ6loD8LBjZM8QiQ4akPfslcRRu1APVmQ/+x25rw1did8EY8DW7ggbsFL5hl1GO+aEt24nWP8VhyBBgyMC9srh+63oSc93/xoBK9VGveVkMK98Q/7I+2MI64fxPM9CjcUHB//gqn+BZnqrwZ6VRYER6WXz/658Jnp8Uxh/VymNDlp/xlJXB0aK5jQo5mYcUw/J2p3D8OLDFMwHeJTrhOSuJ06w+4lhGXGFf49EjPhkuKIgva713j//78kEYnvSbgcQhwxH25B0iIoDoQqWRyNxke1kZSv9a/iwIZbkQx8QXMoplxTuWD+9ZXsS8CkGHEaVMC1gt+qAL22S6vHoYEbAq7DdLKJPZ+Ey2vQMGbMAAPqlx4UI+80ilAk6eBDw9+YpNJe7e5WJTRITcTa0G/PyAlSuBO3f4LM0xY4CDB7l7ZCRfeTxyJBeXy5Sx/z0xBrRvDyxZwkWlWrWAoUP5/ehrRbGxQKFCxuPp29e89CZPVr4eE8NXCzPGRRR9geHo0SwoXtwPR48qP+djx8S4/vxT7ufoUdN5a9KEn9uRO7dpvzVrin8fOsSf04YNwIIFYpkIDwdev1bOuz3QWzGss7yPHCbspmjrLxG1cwYa8KtG4cL+wvJlN7cElClzB5kzRwl+M2WKhkYcyVH6IgrM0MXv5sGQMWMM3NwShDBubomyeJSsVFH+gS9PntcYN64L9u7NDy8vhoCAlZg/fzm6dDmHTZvu4sGDHrh1qwlCQk7jzZutuHy5Ci5cKCYbZF+4UARv3mxCfPwHPHky0uigvkcPD7i7h2DYsIGYOLEdatQ4iIUL6wp+1q2riAIFXiBdOhW6dZuiEytmN8bSpTUwblwX5MoVDFZ7OrJNYdh6xHB6I5eUQ4UKV7B/fx6bBYk9ewoIS6alliFDHDJkiMOHD5EICTmNhARdJZyYGI3r12to4/r115GoWvWUYr5m7WbINJkhx1SGJfsYdu3Ni379/kKhQs/h6VnOYP6OH8+IBg22gjE1GjReBy8vhgMn+InFgzdx8dPTzBOPjVmdOrtx8OBU2fXq1Y9g0KDhJsOPGd3d7LQW7+Pl/dM58nvV7A9qss5Jup57GsPorfw5TNdbnrn1CN8/VCM2MQbMmdNIlpfDRk59Pno8I7JnD8OcET/j5CmGsdv4lgn6cWqt4m7kmMr37NTkLV06FTJlipbFO3h1Ltn4RRNPNXYNKpbOSC3tXFLLOJSwDebsDBCOoU6dOmCMoVy5cgb9eHt7a1/ytWvX2iXd1FJxSAWsC3sXy/zIBKwpPUUP0tbq6lU+Mtm8GTh2DNPbFBDCT62ZFQAfLO3ezQce3t58ILBqFTBrFh/wlC5toAOo1GDmf2BZh1cavlc96zrOtWbad8AwLqvh+JTu24Pxadq2pJn/AZ8N9ukxLkBJ9zAzZu06inkpfZpfNyBgWWzSOBqNti6ewjflceUKMB7mk+Ng9SeCNRwL1ngkWJFrOreuP8rj619NHkdRw0s/mQcDG/KJbb+dCcvKolCAvTWcvt57kydrNIrkjEQ2JheX0rVvZ56QZOy30zdDAlaRa9YJWP2+dehzNGTp0vHZR9mzK7tXqABUqwZ88424gjcl2+efA5984rz0c+T4gK++Oo6cOd9pr40Zw8XEwYOBUaOAuXOVw27ZAuzYwWd97d2b/HnPkEE5Xak4NnkyF+COHuXbJPbqBVSuzP/96y9uGr9NmvAZuPPm8fvbsoVPLG3USCEP+fxIwPIwJmBZaQXuO/y5fvGF/NrJk0BAAFCnju5avXr85OboaCAkhC/L7NCBH9gcF8ctJIR/8Hj+nAviR44YEDwFU6NAgZfKexMastozwDz4ASbVFzFUaDdce4hJ/ukMJWYxsJY9hTCFCj1DhQpXsGNHrMXCTbNm/1r0TL/8ki9pfvmSC8q3b8v9ZMkSgXz5XoIxLmbmzPle/lt/ckLr380tESVLPkDZsjeQM+d7fP75RRQq9Bz58r1Elix67VqmCIPtVbFZXBCaOv1HeHkxLFlcC19/fQw1a+5Hhw5zkDfvK3z55WlUrHgZU6ZEws0tEfnzByJ9+ngh7/Xrq7B4sQqVK4v35O4egitXhuHChaLw8mK4f78Lfmr8WMife/oojB4NDBkSgqlTL+Cff/bB27slRo/uidKl7+KTBotRZUHSrCO933TS9B+xcGEdVKlyBmxUPtP9AAawbk10biMLGHwuWit5HqzgHeTPH4Sq1Q+h1Gyd2FR1IcNXXx1HxoyxKP6DB36eWQpdJ9VAoaIPdekNk8wYlwpYnx4T3Vt307qVKnUfGz353o7jx3dC+iHGt0DZzto7fJxmLallHErYBnN2BgjH4O7uDsYYunXrZtBPbGws0qdPD8YYfv/9d7ukm1oqjs7DSyL7WIZsSXZp/zKZnxm1+T46GvOZ1F/0IG2trl0Tw7fKLzQACyT7PpkiPJzPZggO5h2RX+oXFuJb/A3DSLepYAwYNozv03L2LO+8fPklHyhI8yhtMGsMKYBly/jAqHx5ndfff+eDEv3gFdl95GKh/G97C1jfzRPjGlxW52aosa+y3u6dabOtXQcxL6XO8Ou96trnuUjj+H6MdfEUuiWPK0eQ9fddZ5o8PiUBy5Qg42ABy+Bz1Fh+X/PCp3QBq1ML570DZGQpxdKqgJUuUTQm3xvOJitwL20+V1OWJGBprcZ8+XPSW3IlteLF/bB1aymtQHXsWCZBsNqwoQKmTm2OSZNao1Gjjcl3X51aiPdQ7LLlcWSMFOMYWkb+bLKEOPQ+8uYFypa1Mnz5A8ptrf4p4+3bm+4HMIDleQLWqSVYl2b8Y6lSGKn1qsvDZoi2/N3r1lT0L93a45PjoruegCUzE4dQ/VDgmskxjLNILeNQwjaYszNA2J+goCDtyzthwgSjfosXLw7GGBo1amSXtFNNxdGunVhbnzsn8/JLc4YiIxgKJ9llj36iB2mN//Ch4DyjpfiVZn6dDDZleUIDeWNX/ncG5MljOJAkj9Lw9YblMp7o2bPy+wQwc6p8Jg7ABTc/Py6mqdX8K/vffwNHs7TETVYFr1kh7GJt0DX7XnTpArRqBVStCrCq/0oEm790SRpo6LM0GIFSubmgli2bPJvu7rq9vTNnBmrU4F/7v/+ezw5pxfbgR3YI37DLaMYOapfkZcjAv/K3bhSO6tnuolyGpyiUKwZ58+riztq+mZifUme5m4KAlTMnkD696YFF1qxqZMiQdPKk9H6tFbAK35DHZclMM6nVnm5exy2lCFgt+iqnb2oWmsY6tDWv06kfxpg/ErDIyOxv+R5aPthLzZYpgs+O0pgtHyWMWcG7aeu5mmvSD3jSD3Aekj6MUZP2DewsQlpidhGwosQ4hpaWPxsHC1g2mbUCVvVlxuPN9dy8Nl2zKiKdyvJ375vFov+6U0R3SwSs38uLfsdlEdwrl4uxaTzjSFLNOJSwCebsDBD259atW9qXd8mSJUb9VqtWDYwxVK1a1ez4IyMjDdrbt29TR8XRpo1YW3t7y7y07SA2Hucm9BQ96O8S/MUXsvAzW+QVws+vm1HmxxLGGxKwFi0yHOjBA+E+peHrmhKw1GqgYkVdHKtX83ub+oMsLqN07iw+b70NywFgdTUxrrENdfEZauizj2V8zYC1SFvsr74yO+jezxhm1GaYVodhQp1M8PMYAwCo81t2xeeiVgPqs+cQVe9HqDt3gfr1G6hUQMj5e/Bfegiqj+K7Io1jwJ/18OxJIo79/h+m1T+OFk1jUDhPLMZWPYzDY8/j3j2+LHXrVl4cxo3jy3G69Zd/9Zs1xRczut7FSDYLQ9kCDGPzMahFIPr148JdQ3YStdk5fM7uoT47jf/lCkCJEkClSkCxuoMVf4sv2Q18za4gC4tG0SIqlC5lYk+noaXN6kwWLAgULsz/X7EiUKCA6F6kCF/2VbRo0t5hDMjuFoWsjO/H4taip2L6WXL5oUwZvtdRunQ8XJYsQLFiPL7SpYHixYGM0g69IdPPt5J7xV1geR/zTqnSvVopYDXvrCtfwcF89XKVKny/sG3beNytWlneiVcShPUtY0YgU6YUMNggI2PgS+c8LBzspWYre9i8ushWK3gnbT1Xc63WLPGZfDdX/rGk/H/Oz6el1rm5eA/WCFjSmUPDSsnLUGoUsIpc1/mRCli/VAfLEGM83lwB5rftmjBjs+mumbPP7LeLxHjqTBXdLRKwKoh+x2cW3GvXVj51NyVAAlbagDk7A4T9uXDhgvblXZ0kOBiiZs2aYIyhfPnyZseviduUpeiKo2VLsbZWOB+9XXuxUTk7vrvoIT4emD+fKwVv3sjCz2yeRwg/r14mm7I8rqFyY4ePH40HjIoCLl8GPnyQhTUpYAFcJFqyRLfjMoCZUywUsHx9dRvm5M4NSA4NWNW8qBDXOHMFLFv44QexDAQGmh/2u+/EsEkCaJ2hOS17LgaQxjF6ekOr4jm3d6EsrveBfnzDkN9+4+tGR47ku6VrmD5dvDe9QyBm/pBDFt+gZgzo2BH4+mu+EQ6AnZvHGe2glRlq/W+n6TRJO09qddK1iAhg8WJg61b0aqmcfuCZ/4RwCQlQpPXwokbvQ/sba57V+PGK7ltLFUabLKtx+3KUYqfPb/NiNO5uZudWz0rb8BwtJTZW/swTEvj1V6+ADx/4sufgYL6Jd2Ag8OwZN39/rqWHhXF/Dx7wKvPlS+529y4/8e/ZM+DJEx4+OpqnERLCr797x69FR/P/x8WqEbH3BHD4MILfqhEQAISGcv8RETxvmpmgKhUv8vHxPN07d4DHj/l+Oa9fAy9e8Hw8f87j1uy1k5jIZ5Reu8b3kfn4kVdl164B58/zTe1v3eL35O8PnDkD3L/P43v4ELh+nd/rxYu8Cr56lS/tPnWKb46/dCmfHSodPDRoAHTrBnTvDlSvzpurKlWAIUP4yYw9e/JwP/zA94/q3x/45Re+uXz//nzfrP79+cmSdetyYbZoUT77lDGYPEzkhx+4OJwnDz9ZUSMiM8bjHDCAx6e51qoV0KkTkCsX/7t3b75/lzTehg2BLl2Arl2BHj101z/9FKhfn9+r5lqVKvz0x/Ll+YzZ1q15XvTjGz1a71vUL9V170bjP2Vp58vHn1nhwjy/mTPrqvFMmbh4/9lnXMDWCOXGliFpZvcq2f79vHqdMoUfhPDwIS87KhUv09q6CrwpHDcOuHRJuSwomlRA0bN06ZJm8er5L12abytgKL4sWcS/CxXivzErdFtxcF2rFvdXooRyfA0bAqVKidcqVTLv3tKn5/FK9zfS/B5FiigfPJAzJ//d8ueXu2XLBpQsqfvIYbP1qSU+k171wJr3E6/ZScBKnx744w9eXjw9+XuwYl4k1A/9EPBcbdZzTa9w6LGiyQQsH8vznCFGjEO6L5MHc5iAlTOnaT/Fi5vwY42ApZmBr2dZWLR4zRoBS39vWLMELLG/V7leF/TqpVfuJQJW4TbfG45LImBlGs/Qu9pN7NsWg7i45OhxWA8JWGkD5uwMEPZHX8D6999/jfqtVasWTG32LsUlBKxevXhPqGBB3uO5Jl/P3V4iYJ35q4tFScz6WSJg1c9sU5bHGhKwLEAatv58+cwxc7BYwAL4iHHTJj5qlLByufj1cvyMJgbzrDGbBazERD4zLF06vmusJVy9Cu2awubNtaORZoPz2vT7aKg3Q+w8nLz3n+lACpy8sUuWn5CQl8YDvXvHR3O5c/MRoh6zOpWQxRfuKRfJY+OikXNsOoO/XZk/LNsPzlqkBzFo7OXDq2aFbzWssMF7EH7jHTu4IqFWG/dnqE6Mjwfy5zcrLXuULyLloVaLOjJhGe+H9MPE+gyLvnNDwpZNzs6OQxnx11fJUh/c8tqWJuqbmBiduGgOk7b8KjyTcZ4D0bdtRuHa5vG9EBYmxqtScbH++nU+Y3rUKGDGDC5yLlsGzJwJDB/OBeo//+RCaECA3W9XkcREoHln8bf22bFA8BMbyw3gTZZSfRUbKy4hLDFcXkb9fZ/hxQv+wSEujn9U0JxeGxsrflDSPL+oKO7fFBERPL4XL/jHlJgY5XxqPlypVPzDi0rFr+3z/FvxvTq9fytevuTxtZV+3P6uKBARIX7guX6dq8CtWwMHDiDg3gWz2/SEBCAuMh5Zxumu5fiLaZ99QpwKsX7PERIUJdzTgvmisDZ95s+C+5GVIwX37/qK73NICJ+9HR0NfDY8k+A30/jU8+6TgJU2SD0lkjAb/SWES5cuNeo3zS4hNIMOA8Q9rLx2zLYo/OD27kL4Hh1sW0L41/e2d1hzThdn0PTb39eqvMyc0tSmfEhZ6bNciGvCYd3Z8IYaefdxTj7GVzOVRK+HemCv+GW875TqVkV9+/UtFJzMf6tmK2pDbeVc7cOP5EtNwmPDrYoLAEas7yLENXL+jwb9Xrp9CG0nV8aoJS2RfqIoZpUZ5251Hiyhe2vlsvPq4yuzwg8cqPD1WGLS2WRGxaaICAMpAfBT2IjaDCMIAnz0efs2nwrn4vwxpmqy1Ac3n12i+kaBD9Ef8L9ZpcE8GL6YVQYh0SH4ZZnYJ9p/fYuzs2kxP0sFrG3zLI4jPjHeZJsV8jHYAbm3Dwf3z1HM8/VTOlFc6nZmzwIjMXICHsjfJaPvsEolCFg5x5h+9w7d2Y2OvxVEh3YM7QcVwN4bYhnctGyA2e/zZ6PF8UumCU7ub1sACVhpA2qNXBD9Tdz//vtvo35LlCiBNLmJuxl0WNVEqMBPPz1lUfjl48UOzcQx39qUH3t0WNfcWKMNl35SerwIe2FVXhZN+dmuHdsEVQIiYyMQGRmKiNiPiE2I1boZuu/fVjS3KU1HsP+huPdTvx3dTQcyQGhMKB6+ewiV2oJPwxKuvZTvrRQZZ/17WWRuESGu/LPzmxWug6e4NGHCtl+tzoMlNOyhXHbeRMiX/CoxuU9Zg+VPY9NrWyBgmVjuSwIWQRCm+GN08ghYfu/lojrBiUmIwZMPT7R9lV/++0V4Tvt89zk5h5Yj/a1PbJpscRwJqgSTbdbHWBPbXjiRgw+VlxAaE7BO7zEt9FksYAHwKs1wsgzDiU8YTpc2891TqfgUKoUphRuX9Tf7fa447xNRwJpk2wf45MSVxqGEYag1clHc3d3BGEP37oYH0LGxsUifPj0YY/jtt9/skq4rVRzSin7MiTEWhV92WpyN43HwT9OBLMiPNR3KRFUipp2bhpZbW+K/h9YtSwOA/VO6CXn4pbnjqhKpaKKx4UeGOSxNa9nnu0/IY///+js1P2qFJW3R8dFWx1d0nrgnlLkC1sjj4tT1dTfXWZ0HSzD0zvi99zMr/JReYicuwwSGVgPFZaIWCVjhxme/WSpefbvaNlGcIIjUxx9LmxuuY+xIYLi4Of5XK80/6CSt0W2P2CfaeX+ns7NkMdKydMTT+AdwJRJVibJ4is0rJvwdFR9lOiInceTxEWUB6+RGrR+rBCzfyxYJWImqRPQbVBx9WzD0bcHw57DPbb63jUvNF7C+Xf2t4K/gnII2p59cuNI4lDAMCVguSp06dWBqc3b9vbLWrFljl3RdqeKwVcBa6rNUCD/pzCSb8lNcYS8Be3dYzWX/5K5CHvo5UMA6/uQ4lvgswVcrxX0/Rh0f5bA0rWXPgz1CHn89kDwzjYwhLS/xifFWx1VmYRkhrqLzipoVbvwpcXPzlddWWp0HSzDUQQyLCTMr/NQepYVwi75lWNhRvGaJgBXz3vjML0sFrH+u/mP1syEIInUy/MjQZBGwVGoVQmNCERoTirCYMETEGVkCncbpvKuz8Dsc8Dvg7CxZTNnBYll6u2OtxXGo1CpZmaywpALcp7sj+7TsyD4te4oWsHzf+TpEwHr7+JZFApZ0Jluxueb1tYxxePlws+uLlddWCv7Gnxpvc/rJhSuNQwnDkIDlosyaNUv7At+5c0fRz/Dhw8EYg5ubG94onKJnDa5UcUgr+tEnRpsOpMcSnyVC+MlnLJ+OrU/PvyqmGAHrv8ni10ZHClgaVl1bJaQ57tQ4h6dpKe+i3sHrmRdO+5/GKf9T8H3n6+wsYeEl3ck0gw8PtimuE09PCL/B0cdHzQo35ewUIdziy4ttyoe52DrIO96xOob+wDCsKcPwpgznStomYEUEyw8wMDcs82BoPqEs0k9Kzzu084ohJiHGqudCEETqZfhR+UDUmf0BAnga8lT7gafZ5mY2Lf13FpUGiWUpeJvlH7aVZn3b8tEsuXn47qHlAtbuuaYjDgpCtf7WC1jmfiw0xqvV84U4v/zVcH3xz9V/BL8TTk+wOf3kwpXGoYRhqLVzUd68eYNs2bKBMYbmzeV7BT179gzZs2cHYwxt27a1W7quVHFIGxVLZ/wsvrxYCD/l7BSb8tNrc4cU02E9sGGskIe+bR2/Pn7ZlWVCmh5eHg5P01W4/uo6vAO8rd4MXkOiKhF/n/4b1VZWw7hT45CgSjAdCMAsb3E57ZwLc2zKh7nYPMi7elU8W3r7dizsZIOA9TbQqvxqbOGEJrj68irW3liLd1HvrHkkBEGkcoYdGaZYP/Tc29PZWUvTRMdHIyg8KFWKVwBQeaBEwNpi/BRzQxSbKi6zN7efkBJQ2vfNlIB1yhwB6+VLRGRiCM/MTX+DdqX+iXQpZpG5RWy+t1er5pktYK24ukLw+/dpy5eTOgtXGocShiEBy4WZOnWq9iXu2LEj7ty5g+DgYBw4cABlypQBYwzu7u54+PCh3dJ0pYqjkWcjsZHyt2wT979Pi8fxDrNxz6Ze+3qlHAFLstdTn6VNHJ6m/kwi5sEw7dw0h6dJ2IelPkvhPt0d+WblQ9F5RbHUx/jpqPbCVgErNDoEZ2YNxLkmn+H86M54+OoOZncWTyYc38ACAeuN8UMTTApYE5ta9RwIgnAdlASseRfnCYefEISlSAWst+eOWBWPdK/M1CRghcaEyj6WMg+GGycNb+J++7/VpiN+9Ur4GNayk3EBS7oU0x4C1st/xBMWLRGwJnpNtDn95MKVxqGEYUjAcmHUajX69OmjfZGl5u7ujkOHDtk1TVeqOO6+vYvCcwuDeTD8vOVni2evlJhfQmgAcs/MbVN+UpKAddDvoChg7evj8DSrrawmpNlgfQOHp0mkbmwVsM48OyOE67CzA2oOzWE0PqMC1usAq/KrsYvzhlv1HAiCcB2GKuyBRRC28r/pYp/1zcfXVsUjFbASVYl2zqnjadRdfL/unNyidZPuRxt/8rjpCF+/FgSsUY0sE7Ds8Y6/XDFbiK+qEQErLjEOYTFhCIsJQ2hMqE2H/yQ3rjQOJQxDrV4aYM+ePWjatCkKFCiAzJkzo0yZMhgwYACePHli97RcreIIjw3Hkw9PrJoSLhWw8szMY1NeUpKAdfHFRSEPyXE6UeYpmYU0Kyyp4PA0idRNww0NbXpnTvufFsJ12tUJE7sVF67V7WW+gPXx1TOj6Un9Z/JIL/wd9Mq80xMJgnBdSMAiHMEXK74QytTrCOsELOnJ0alRwPq5M0O2sTq7e2qr1k0mYJ04ZjpCCwUspb3EbCVo+UyzBazUjKuNQwllXLP0Ek6DKg4dxeeLA11bBazaa2unGAHr1mvxRJXGno0dnuaE0xOENBdcWuDwNInUja0C1smnJ4VwXXZ3wfZW5YRrg3+0n4BVbrEYd9lFZUUBK9z4JvAEQbg+JGARjkBaps49P2dVPFIBK1XuCaYnNoExwMdH62SNgKV+/RqqdExrIxsr9xFaddS9y/Z+xwOWTRfi+3yQa9YbNA5NG7hm6SWcBlUcOr779zuhsaiyoopN8Rn7WpPc3Hx9U8hDk42O3wMrODIYlZdXBvNg+Gb1N4iMS9vlizDN9xu+l70vDTc0NDv85jubhbDfb/jeNgHr5TOj6UkFrLPPz8I7wFtrtMcNQRBDDg9JEf0AwrWQlqkDfgfsEo+rC1hxJ0yfxhz49KbRvoHG/vrecQKW5/xeaaLeoHFo2sA1Sy/hNKji0HEl6IrQUFx4ccGm+JQau29Wf2On3FrGjVc3hHw03Zg8m0vHJsTiRdiLVHUsM+E8pAcxMA+G+8H3zQ7fdGNTWXibBKwgf6PpSQWs8Nhw3HlzB7sf7CbBliAIAMCGWxuEeqLqP1WdnSXCBXCUgGXr6cdOwYiAVUIqYJ00PQPLHAEr4wSGcR3y67JgbwFrXk8SsAiXwTVLL+E0qOIQ2XFvB3rs7YEtd7aY9myClCxg/bDpB6fkgyCM0dizsVBO/3v4n0Xhm2xsYl8B663xUwilAtbO+zuRYXIGMA+GL1Z8kTq/ZBMEYVcuvriIb1d/q7VJZyY5O0uEC0AClo5X7gyBOXWmvnRJ6+b+l3h/EccPmowv0P+W0b7BiCYMyJgR0EtH3z3zlMw239OGeT1IwCJcBtcsvYTToIrDcUgbnkaejfAu6p1T8nL91XUSsIgUj3RTWp8gH9OB9JAKYLYKWBFxEUbTW3VtFWZ5z9KaNPzyK8stfgYEQRAEYQppe2PpBx9D8aRGSg4T7yH2om4/MOn9nd4912R8gc9uG+0blJySD7h1SwjjecsTmadkhvt0d6t/C302eI5wid/GFDQOTRu4ZuklnAZVHI5D2vD02NvDaXnZeHtjmmgIidSNtIyuubHGovBKSxAndism/F2/p/kClqXLAKXh6x6YEgoAACAASURBVK6ra1F4giAIgjAHaXuz/+F+u8STGrFEwDplBwGr1IJSiuFiE2IRlxhnl3s67HdQSLPY1Lx2iTelQePQtEHqrFmIFAtVHI4j/+z8QuPj4eXhtLxMOzfNJTophGvTeVdnoYxaeiy40ibwNYe6Gy37jhSwkuOwBIIgCCLtIW1vTjw9YVU82aZl08aRfVp2O+cyeZA+i/snt2rd2nQQ3bz3LDIZX+DzO0b7BsyDYfSJ0fC85emwe/J65iWk13FnR4el5UxoHJo2oFEnYVeo4nAcgw4OEhqfnfd3Oi0vJGARqYHHHx6jyooqcJ/ujtnesy0O33BDwxQlYDVY38DieyAIgiAIU0gPLbnx6oZV8WSfll0bR7Zp2eycy+RB2vZeP7Ze62aVgBVgWsBiHgwtt7Z02D2RgEW4EjTqJOwKVRyOY+DBgULjs+v+LqflZfq56SRgES5Pg/UN7CZgVVhSAVHxURalTzOwCIIgiORAKmDdfH3Tqnjcp+vayKxTs9o5l8mDtO29dnSd1q11R8cJWK22tXLYPZGARbgSNOok7ApVHI5jwIEBQuOz+8Fup+WFZmARaQF7CVi+73ytSl8az8jjI+1xWwRBEAQhYC8BK8f0HNo4skzNYudcJg+WCFgX9i42GV/Qi3tmCViO7EuTgEW4EjTqJOwKVRyO49cDvwqNz54He5yWFxKwiLRA/fX17SJgjTs1Dhtvb4RKrbIofWk8o46PsuftEQRBEAQA+wlYOWfk1MaReUpmO+cyeZALWGu1blYJWIH3U5yA1WFnB4el5UxoHJo2oFEnYVeo4nAc/f/rLzQ+e333Oi0vJGARaYF66+rZRcDSWIIqwaL0peFHnxhtz9sjCIIgCABAk41N7CJg5ZqRSxtHpimZ7JzL5MEVBaz9D/eniX47jUPTBq5ZegmnQRWH45B+HVt/c73pQA6CBCwiLSAt5933dMfQXoWFaz93Fsv+X3t/d5iANebEGHveHkEQBEEAAKr+U1UUbV5esyoeV+gbygSsI2sMup3eNddkfEFBD5wuYK27uc4lfhtT0Dg0beCapZdwGlRxOA5pw9N3f1+n5cXzlqeQF7dJbk7LC0E4in+u/iOU8wmnJ2BRh1LCtWl1xGbUJ8jHbgKW2yQ3IfxSn6X2vD2CIAiCACDvY1q7z6oriCSWCFinds0xGV/QS1+nC1hrb6x1id/GFDQOTRu4ZuklnAZVHI5j/sX5QsPz6P0jp+XltP9pIS+uuhkkkbZZeW2lUM7HnxpvUsA65X/KbgJWj709hPBHHh+x5+0RBEEQBAC5MHP08VG7xJMasWgG1m4zZmCRgJVs0Dg0beCapZdwGlRxOI6wmDB02NkBny76FLO8Zzk1LyRgEWmBVddWCeV83KlxNglY4bHhFqUvFbCsHVAQBEEQhDFqralll4+kG25tQMbJGeE2yS3Vzhq2t4AV8f6V0wWsc8/PkYBFuAyuWXoJp0EVR9ogrZxmQqRtgsKDcOLpCZx8ehKn/E/hachTmwSssJgwi9Lvvqc7CVgEQRCEw6m9trbQ3vi997M6ruDIYLyOeG3H3CUvFglYZuyBhYgI9GzFUKc3N2cIWE8+PBHSqbmmpsPSciY0Dk0bkIBF2BWqONIGZ56dIQGLSJPYImCFxoRalFa3Pd2E8MeeHLPnrRAEQRAEAKDO2jpCe/Pw3UNnZ8lpyASsw/8adDNnDyxERACMaS3z+OQXsB5/eEwCFuEykIBF2BWqONIGUgGr/Y72zs4SQSQLw3oWEsp+645iM/og2PBpQ7YKWMefHLfnrRAEQRAEAKDuurokYCUhbbtvXz1o0C3g4GbTEUZGCgLWsKbOF7BqranlsLScCY1D0wYkYBF2hSqOtIF0CWHzLc2dnSWCSBZqDM1utMMZFB5kNwHrfvB9eD3zwmn/0zjtfxoh0SH2vBWCIAiCACAXsHzf+To7S06jwfQKwrO4H3xf61Z6qNiuRx8/bDrCqChBwJrQIPkFrEfvH5GARbgMJGARdoUqjrRBRFwEskzNom0I51+c7+wsEUSysKZNGaET+EtzxwlY7Xe0F8KffX7WnrdCEARBEADkSwgfBD9wdpacxqQzk9BheQN0mP8dOmxvh8DwQK2bTMA6dsh0hClQwKq9trbD0nImNA5NG5CARdgVqjjSDt4B3ui0qxMmn5mM+MR4Z2eHIJKFLS0/FTqBw5qSgEUQBEGkbqTt1fZ7252dpRSJVMCKOnbQZJiYjyHYX4FprW4v5T7Cn8f+dFi+vQO8hbTcJrk5LC1nQuPQtAEJWIRdoYqDIAhXZsuYn0QBq39JwZ0ELIIgCCK1IW2vtt3d5uwspUjKWCFgvXrnb7BfkFwC1r/X/0222V7OhMahaQPXLL2E06CKgyAIV2bLhZWigLW5u+AeGB5IAhZBEASRqiAByzwcJWC13tYa62+ud1i+V19fTQIW4TK4ZuklnAZVHARBuDJb7mwRBawjwwT3u2/vkoBFEARBpCoarG8gtDcvP750dpacxuY7m7Hi6gqt6W+TIW3Xnx3cZDK+V++fGRWv2u1o58jbAUACFuFauGbpJZwGVRwEQbgypgSsE09PGOykRsRFWJSWNPxSn6X2vBWCIAiCAADcfnMbJReURDqPdJjoNdHZ2XEqnyz6RGh7I+N04xlpu3x611yT8b368NyogPW/5f9z5O0AIAGLcC1cs/QSToMqDoIgXJnue7oLHcAS80sI7sYELP1OsDlIw086M8met0IQBEEQWhJViYhLjHN2NpxO7pm5hbY3LCZM6yZtl89c2WEyPlMC1pDDQxx5OwCA8wHnhTTbbm/r8DSdAY1D0wYkYBF2hSoOgiBcmZprahr9innK/5TDBKzJZybb81YIgiAIgpAgbXuvvbymdWuxqIbgdjnwssn4XoUEGBWw8s3Kh0rLKmHcqXEOuycSsAhXggQswq5QxUEQhCtjSsDyfedLAhZBEARBpFKMClhbW1ghYL1QjLPH3h7CtZ57ezrsnqQCVpvtbRyWljOhcWjagAQswq5QxUEQhCtjjYA14tgIjDg2ArEJsRalJY1nic8Se94KQRAEQRASpG3v1ZdXtW7NtzS3WMB6HSo/nTgkOgR7ffcK1zrs7OCwe/IO8CYBi3AZSMAi7ApVHARBuDKWClj11tWzOq1Flxdp48k6NavFM7gIgiAIgrAMSwQsnyAfk/G9DgtSFLC67O5itD9hT0jAIlwJErAIu0IVB0EQrow1M7A67uyIjjs7Wrw5boIqAbO8Z6HH3h5mdZIJgiAIgrANuwtY4S9lcYbGhKLzrs4kYNkZGoemDUjAIuwKVRwEQbgytuyBFZMQ46RcEwRBEARhDsYErPrr61u+hDDitdMFrE23NyVbWs6ExqFpA9csvYTToIqDIAhXZs6FOUIHcOTxkYI7CVgEQRAEkXoxJmBJ3U75nzIZnyEBq9OuTskmKq26tooELMJlcM3SSzgNqjgIgnBllvgsETqAU85OEdxP+Z8iAYsgCIIgUinJIWCFxYQlq4C18tpKErAIl8E1Sy/hNKjiIAjClTElYB19fNSggEWbsBMEQRBEysYSAeu0/2mT8RkSsDru7EgClp2hcWjawDVLL+E0qOIgCMKVsUXAeh/13km5JgiCIAjCHOwtYCWoEhQFrA47OySbqHT15VUhnSorqjgsLWdC49C0AQlYhF2hioMgCFeGBCyCIAiCcF3svYQQ4HtQzfKepbXYhNhkFbDuvLkjpNPIs5HD0nImNA5NG5CARdgVqjgIgnBlbBGw3kW9c1KuCYIgCIIwB0cIWEq039E+2QSs229uk4BFuAwkYBF2hSoOgiBcGemeFeUWlxPcn4Y8JQGLIAiCIFIpX638Smi7fd/5at0Kzy0suIVEh1idjjMFrMaejR2WljOhcWjagAQswq5QxUEQhCvTcmtLoROYe2Zuwf156HMSsAiCIAgilVJ3XV2h7X747qHWrezisoLbx9iPVqfTbke7ZBOwbr2+RQIW4TKQgEXYFao4CIJwZTbd3iR0Av84+ofg/iz0GQlYBEEQBJFKOfr4KDxveWotLCZM62ZPAWvEsRFCXHlm5rFH9hWRClhNNjZxWFrOhMahaQMSsAi7QhUHQRCuDAlYBEEQBJE2kQpY4bHhJsOEx4bjl/9+0ZqHlwcAYOvdrUJcQ48MdVi+d97fmWyzvZwJjUPTBq5ZegmnQRUHQRCuDAlYBEEQBJE2Kbe4nMUC1tvIt7L+QIIqIVkFrBVXV5CARbgMrll6CadBFQdBEK4MCVgEQRAEkTaxl4AVEReBpyFPsebGGq1dCrzksHwvv7KcBCzCZXDN0ks4Dao4CIJwZUwJWOcDzhsUsN5HvXdSrgmCIAiCMIf+//XHT5t/0lpknG48I23Xr7+6bjI+QwJWckICFuFKuGbpJZwGVRwEQbgypgSsI4+PGBSw9DvBBEEQBEGkPMovKS+03fqbuEvb9dP+p03GlxIErM13NpOARbgMrll6CadBFQdBEK5M7bW1jXYCScAiCIIgiNSLtO0OCAsw6HbxxUWT8aUEAety4GUh/RZbWyRr+skFjUPTBiRgEXaFKg6CIFyZ7/79zqiAZWwJIQlYBEEQBJGykbbdV4KuaN2abW4muF17ec1kfMGRwU7vD5CARbgSJGARdoUqDoIgXBlTApbvO18SsAiCIAgilUICVuqFxqFpAxKwCLtCFQdBEK6MNQKW1zMveD3zQqIq0Um5JgiCIAjCHKRtuE+Qj9bNVQSs5luaJ2v6yQWNQ9MGJGARdoUqDoIgXBlLBax66+o5J6MEQRAEQVhMcghYUfFRjrwFGT5BPiRgES4DCViEXaGKgyAIV4YELIIgCIJwXYwJWD9u+jFVClgXX1wU0m/k2ShZ008uaByaNiABi7ArVHEQBOHK/LT5J6ETmGdmHsGdBCyCIAiCSL0YE7CkbpcCL5mM713UO6cLWEt9lhr9+OYq0Dg0beCapZdwGlRxEAThykg7gZPOTBLc77y5I7iXW1zOSTklCIIgCMJSLBGwTvmfMhlfShCwVl9fTQIW4TK4ZuklnAZVHARBuDLLriwzKmAd9DuYJjqJBEEQBOGKJIeAFR0f7chbkBERF4EsU7No0/fw8kjW9JMLGoemDahnTdgVqjgIgnBlSMAiCIIgCNfF3gJWSHSI0wUsADjy+AgabmiIAQcG4GPsx2RPPzmgcWjagHrWhF2hioMgCFeGBCyCIAiCcF3sLWABQOmFpZ0uYKUFaByaNqCeNWFXqOIgCMKVIQGLIAiCIFwXSwSsk09PmhXn4w//b+/e46Mo7z2OPyGQEEJBBSLIRaAeETEVBFGrSAUVRF/tAfS0pxW1IlbhSG3Fc04LarDqqcLLG16qXLyBFujhIqA9Qrh4IUAkIIggl3CRQC4QgSRAEpLf+SOvfdzd7OxuwmR28zyf9+v1/LPPzOxk88yz83x35pld8k3hN7KtcJtsK9wmVdVVDbX7VmMcagfOrOEqOg4AJosUYGXmZhJgAQDQSAV/h3+Z96VjXTRPIYR3GIfagTNruIqOA4DJrpt1XdiAavfR3QF1P5350xjtKQAAqKvg7/mdR3bqukteuSSgrvhkcQz3FMEYh9qBAAuuouMAYLLxH40POHntP71/QD0BFgAAjRcBVuPFONQOBFhwFR0HAJO9u/ndgJPXCf83IaB+19FdBFgAADRSJ06fkOKTxbqcqTqj6wiw4hvjUDsQYMFVdBwATFbXAOvamdfGaE8BAICbggOsoyePRlyn+GSxdJjaQZfr37regz21E+NQOxBgwVV0HABMRoAFAICder7Ss84B1tGTR2vNnVlZVenB3tqHcagdCLDgKjoOACYjwAIAwE5uBVjlZ8o92Fv7MA61AwEWXEXHAcBkkQKsJd8uCfuUQgAAEL8uffXSgO/wI2VHdF3w93tmbmbE7RFgeYdxqB04s4ar6DgAmOyZT58JOAkdOXdkQD0BFgAAjRcBVuPFONQOnFnDVXQcAEx29YyrwwZUBFgAADRewd/hWwu26ro+f+sTULfx0MaI2yPA8g7jUDtwZg1X0XEAMFmkACs7L5sACwCARir4O3z9wfW6bsh7QwLqNh3eFHF7oQKsijMVDfknWItxqB04s4ar6DgAmCxSgLW9aHtA3cC3BsZmRwEAQJ0Ff8ev+26drrv5vZsJsOIY41A7EGDBVXQcAExGgAUAgLnqEmBtPrw54vaKTxYTYHmEcagdCLDgKjoOACYjwAIAwFxeBFiVVZUN+SdYi3GoHQiw4Co6DgAmI8ACAMBcBFiNF+NQOxBgGejMmTOyadMmeeONN2T06NGSnp4uiYmJopSSHj16NOh703EAMNnvlvwu4CT02pnXBtQTYAEA0HiFC7CC6z7d92nE7RFgeYdxqB0IsAy0atUqffAGFwIsAKi/1za8FnASmrEqI6B+7YG1PIUQAIBGqi4B1oo9KyJujwDLO4xD7cCZtYH8A6zu3bvLr371K7n88ssJsADgLEUKsBZtX0SABQBAI1WXACszNzPi9kIFWGeqzjTkn2AtxqF24MzaQHl5efLxxx/L0aNH9Wt33303ARYAnCUCLAAAzOV2gFVWUUaA5RHGoXbgzNoSBFgAcPYIsAAAMJfbAZaIyDUzrpHuL3WXbi92k24vdiPAaiCMQ+3AmbUlCLAA4OwRYAEAYC6358CCdxiH2oEza0sQYAHA2Xt85eMBJ6+jFowKqP/iwBcEWAAANFLB3+FbC7Y61n1d8HUM9xTBGIfagTNrSxBgAcDZu2r6VWEDqu1F2wPqrn/r+hjtKQAAqKtrZlwT8D2+p3iPrrvstcsC6gpLC2O4pwjGONQOBFiWcDPAKi0tdSwFBQV0HACMNeWLKWGvwAoOsAa+NTA2OwoAAOrs/g/vl5/O/KkuB48f1HUEWPGNAMsOBFiWcDPA8nUMkQodBwDTvL3p7YCT1//85D8D6gmwAAAwU3CAVVBaEHGdM1VnZOOhjbpw22HDIcCyAwFWjJWVlcn27dvPqlRXV0d8HwIsADh7BFgAANgp/bX0OgdYx04dC1jn4mkXe7CndiLAsgMBVoytWrUq6kDIqZSUlER8H24hBICzR4AFAICd3AiwVIaK6uID1B0Blh0IsGKsMQZY4dBxADBZpAArOy87oD7lqZQY7SkAAKirNfvWyJJvl+hyqvKUrgsOonYf3R1xe6ECLDQMxqF24AiyBAEWAJy9tza9FTbAWrh9ISeqAAA0Upe/fnnAd/jhksO6Lvj7PTM3M+L2CLC8wzjUDhxBliDAAoCzN+bDMQEnoT95/ScB9cG3ECb/JTlGewoAAOqqLgHW6r2rI26PAMs7jEPtwBFkCQIsADh7V8+4OuKJ6Ii5I0RlKEn6S5Is/XZpDPYSAADUR/B3fNZ3Wbpu8DuDA+q2FmyNuD0CLO8wDrUDR5AlCLAA4OxFE2BVVVdJ1ndZklucG4M9BAAA9UWA1XgxDrUDR5ChcnJyJCsrS5dhw4aJUkouvPDCgNezsrIib6wO6DgAmCyaAAsAADROwd/xaw+s1XUEWPGNcagdOIIMdeGFF0b9FEM30XEAMBkBFgAA5nI7wDp++jjnDR5hHGoHjiBDEWABgPsIsAAAMFe4AGvQO4MIsOIY41A7cATBVXQcAExGgAUAgLnqEmB9XfB1xO0RYHmHcagdOILgKjoOACZ7as1TASeh9y66N9a7BAAAXBIuwAquy8zNjLg9AizvMA61A0cQXEXHAcBkf8v+W8BJ6OMrH4/1LgEAAJfUJcBasWdFxO0RYHmHcagdOILgKjoOACYjwAIAwFwEWI0X41A7cATBVXQcAExGgAUAgLncDrAqqyoJsDzCONQOHEFwFR0HAJMRYAEAYK66BFjL9yyPapt/WvEnGf/ReHnoo4dk/EfjG2rXrcc41A4EWHAVHQcAkxFgAQBgroYIsOANxqF2IMCCq+g4AJjs3kX3Bpy89n2jb6x3CQAAuKTdc+0Cvud3Hd2l61r/T+uAutzi3BjuKYIxDrUDARZcRccBwGSD3hkUcPLa9Mmmsd4lAADgkv7T+wd8z+/7fp+u6/233gF1h04ciuGeIhjjUDsQYMFVdBwATDYrZ1bAyet/L//vWO8SAABwyVub3pJnPn1Gl2Onjuk6Aqz4xjjUDgRYcBUdBwCTEWABAGCn4AAr70RexHXKz5TL5NWTdXl53cse7KmdGIfagQALrqLjAGAyAiwAAOzU52996hxglZSX1Jr8HQ2DcagdOILgKjoOACYjwAIAwE4EWPGNcagdOILgKjoOACYjwAIAwFzPfPqM/G7J73QpPlms64KDqI2HNkbcHgGWdxiH2oEjCK6i4wBgspk5MwmwAAAwVN83+gZ8z393/DtdFxxErdizIuL2CLC8wzjUDhxBcBUdBwCT3fTuTQEnoSlPpcR6lwAAgEuCw6adR3Y61q37bl3E7RFgeYdxqB04guAqOg4AJrtq+lWciAIAYKjg7/gvDnyh63729s8C6r4p/Cbi9giwvMM41A4cQXAVHQcAkxFgAQBgruDv+M/3f67rCLDiG+NQO3AEwVV0HABMRoAFAIC5wgVYA98aWOcAq7S8lPMGjzAOtQNHEFxFxwHAZARYAACYqy4B1vai7RG3R4DlHcahduAIgqvoOACYjAALAABzEWA1XoxD7cARBFfRcQAwWfD8F4mTE2O9SwAAwCXBYVN2Xrau+9EzPwqo23x4c8TtEWB5h3GoHTiC4Co6DgAme+PLNwJOQh9b+VisdwkAALjk9x//Xn/Hd36+s1RXV+u64CBq+Z7lEbdHgOUdxqF24AiCq+g4AJiMAAsAAHOVlpfKo588KqMWjKo1STsBVnxjHGoHjiC4io4DgMkIsAAAsFNwELViz4qI6xBgeYdxqB04guAqOg4AJiPAAgDATvUJsEREWv1PK71O86eaN/Be2otxqB0IsOAqOg4AJiPAAgDATvUNsFbmrpRPdn8i/7f7/+ST3Z808F7ai3GoHQiw4Co6DgAmI8ACAMBOwQHW6r2rY71L8MM41A4EWHAVHQcAk9347o0BJ68tn2kZ610CAAAe6PtG34BzgAPHDsR6l+CHcagdCLDgKjoOACa7a+FdASev6a+lx3qXAACAB/q92Y8AK44xDrUDARZcRccBwGTTN04POHmdmDkx1rsEAAA8QIAV3xiH2oEAC66i4wBgMgIsAADsVJ8A61TlKbnyzSt1+cUHv/BgT+3EONQOBFhwFR0HAJMRYAEAYKf6BFgnK07WmvwdDYNxqB04guAqOg4AJiPAAgDATgRY8Y1xqB04guAqOg4AJiPAAgDATsFB1Jp9ayKuQ4DlHcahduAIgqvoOACYbMoXUwJOQu//8P5Y7xIAAPBAcBCVmZsZcR0CLO8wDrUDRxBcRccBwGRXTb+KE1EAACx07l/PDfj+31qwNeI6BFjeYRxqB44guIqOA4DJCLAAALDTdbOuC/j+33lkZ8R1CLC8wzjUDhxBcBUdBwCTEWABAGAnAqz4xjjUDhxBcBUdBwCTzcyZGXAS+ugnj8Z6lwAAgAfqE2CdqjxFgOURxqF24AiCq+g4AJjsVOUpueHtG0RlKPnJ6z+RgtKCWO8SAADwAAFWfGMcageOILiKjgOA6c5UnZGC0gKpOFMR610BAAAeuXbmtQRYcYxxqB04guAqOg4AAAAApgkOotYfXB9xHQIs7zAOtQNHEFxFxwEAAADANMFB1PI9yyOuQ4DlHcahduAIgqvoOAAAAACYhgArvjEOtQNHEFxFxwEAAADANPUJsCrOVBBgeYRxqB04guAqOg4AAAAApqlPgCUi0up/WkmzJ5tJsyebSZtn2zTwXtqLcagdCLDgKjoOAAAAAKapb4AFbzAOtQMBFlxFxwEAAADANMEB1oaDG2K9S/DDONQOBFhwFR0HAAAAANNcNf2qgAArtzg31rsEP4xD7UCABVfRcQAAAAAwzdUzribAimOMQ+1AgAVX0XEAAAAAME19Aqzq6mopKC3QpaisyIM9tRPjUDsQYMFVdBwAAAAATFOfAKv8THnAOp2f7+zBntqJcagdCLDgKjoOAAAAAKZxI8BSGQy/GwrjUDtwBMFVdBwAAAAATNPuuXYBQdSOoh0R1yHA8g7jUDtwBMFVdBwAAAAATBMcRK3euzriOgRY3mEcageOILiKjgMAAACAaR766CEdQrV9rq1UVlVGXIcAyzuMQ+3AEQRX0XEAAAAAME3xyWK5b/F9csvsW+SLA19EtQ4BlncYh9qBIwiuouMAAAAAAAIsLzEOtQNHEFxFxwEAAAAABFheYhxqB44guIqOAwAAAABEKs5UEGB5hHGoHTiC4Co6DgAAAAAgwPIS41A7cATBVXQcAAAAAECA5SXGoXbgCIKr6DgAAAAAgADLS4xD7cARBFfRcQAAAAAAAZaXGIfagSMIrqLjAAAAAAACLC8xDrUDRxBcRccBAAAAACJV1VUEWB5hHGoHjiC4io4DAAAAAGqMnDtShv99uAz/+3C5e+Hdsd4dYzEOtQMBFlxFxwEAAAAA8BLjUDsQYMFVdBwAAAAAAC8xDrUDARZcRccBAAAAAPAS41A7EGDBVXQcAAAAAAAvMQ61AwEWXEXHAQAAAAA1TyGcmTNTlw+2fhDrXTIW41A7EGDBVXQcAAAAACBypuqMqAylS4unW8R6l4zFONQOBFhwFR0HAAAAANQOsFQGw++GwjjUDhxBBjp+/LjMnj1b7rnnHrnsssukZcuW0qxZM2nfvr0MGzZM3nvvPamsrGyQ96bjAAAAAAACLC8xDrUDR5Bh8vLyJDk5WR+8TuXqq6+WgoIC19+fjgMAAAAACLC8xDjUDhxBhtm7d68opaRly5Zy3333yeLFi2X//v1y9OhRWbdunfzbv/2bPrD79+8vVVVVrr4/HQcAAAAAEGB5iXGoHTiCDJOXlycTJ06UY8eOOS7zwAMPs35TzgAAHmJJREFU6IN77ty5rr4/HQcAAAAAEGB5iXGoHTiCLFRUVCRNmjQRpZTcc889rm6bjgMAAAAACLC8xDjUDhxBlmrfvr0opeTmm292dbt0HAAAAAAgUlVdRYDlEcahduAIslBFRYWe6P322293ddt0HAAAAABQ47eLfqvDq0mZk2K9O8ZiHGoHAiwLLViwQB/cU6ZMcXXbdBwAAAAAUONM1RlZ8M0CWbZzmVRXV8d6d4zFONQOBFiWKS8vl549e4pSSlq0aCH5+fl13kZpaaljKSgooOMAAAAAAHiGAMsOBFiWGTt2rD6wn3322Xptw7d+pELHAQAAAABoaARYdiDAirGysjLZvn37WZVoL0WdMWOGPqhvuukmqaqqqtc+E2ABAAAAAOIFAZYdCLBibNWqVVEHQk6lpKQk4vssW7ZMmjZtKkopufzyy+XYsWP13mduIQQAAAAAxAsCLDsQYMWYFwHW559/Li1atBCllFx00UX1mvcqWnQcAAAAAAAvMQ61AwGW4TZv3iznnHOOKKWkU6dOsm/fvgZ9PzoOAAAAAICXGIfagQDLYDt37pTzzz9flFLSrl072bFjR4O/Jx0HAAAAAMBLjEPtQIBlqO+++04uvPBCUUpJ69atJScnx5P3peMAAAAAAHiJcagdCLAMdOTIEenZs6copSQlJUU+++wzz96bjgMAAAAA4CXGoXYgwDJMaWmp9O/fX5RSkpiYKPPmzZOSkhLHcvLkSdffn44DAAAAAOAVxqF2IMAyTF2fajhw4EBX35+OAwAAAADgJcahdiDAMgwBFgAAAADAJoxD7UCABVfRcQAAAAAAvMQ41A4EWHAVHQcAAAAAwEuMQ+1AgAVX0XEAAAAAALzEONQOBFhwFR0HAAAAAMBLjEPtQIAFV9FxAAAAAAC8xDjUDgRYcBUdBwAAAADAS4xD7UCABVfRcQAAAAAAvMQ41A4EWHAVHQcAAAAAwEuMQ+1AgAVX0XEAAAAAALzEONQOBFhwFR0HAAAAAMBLjEPtQIAFV9FxAAAAAAC8xDjUDgRYcBUdBwAAAADAS4xD7UCABVfRcQAAAAAAvMQ41A4EWHAVHQcAAAAAwEuMQ+1AgAVX0XEAAAAAALzEONQOBFhwVUlJie44CgoKpLS0lEKhUCgUCoVCoVAolAYrBQUFehxaUlIS62ExGggBFlzl33FQKBQKhUKhUCgUCoXiZSkoKIj1sBgNhAALriLAolAoFAqFQqFQKBRKrAoBlrkIsOCqqqoqKSgokIKCAikpKYn5paS2F/9AkVs6Kf6FtkFxKrQNilOhbVCcCm2D4lRoGxSn4nbbKCkp0ePQqqqqWA+L0UAIsACDlZYymSFCo23ACW0DTmgbcELbgBPaBpzQNlAfBFiAwfhigBPaBpzQNuCEtgEntA04oW3ACW0D9UGABRiMLwY4oW3ACW0DTmgbcELbgBPaBpzQNlAfBFiAwfhigBPaBpzQNuCEtgEntA04oW3ACW0D9UGABRiMLwY4oW3ACW0DTmgbcELbgBPaBpzQNlAfBFiAwfhigBPaBpzQNuCEtgEntA04oW3ACW0D9UGABRiMLwY4oW3ACW0DTmgbcELbgBPaBpzQNlAfBFgAAAAAAACIawRYAAAAAAAAiGsEWAAAAAAAAIhrBFgAAAAAAACIawRYAAAAAAAAiGsEWAAAAAAAAIhrBFgAAAAAAACIawRYAAAAAAAAiGsEWAAAAAAAAIhrBFhAjO3atUuaN28uSilRSslbb70VdvkTJ07I5MmTJT09XVq2bCmtW7eWK6+8Ul544QWpqKiI6j1zcnJk1KhR0rlzZ0lOTpYOHTrIiBEjZNWqVVGtX11dLbNmzZLrr79e2rRpIykpKdKjRw959NFHJT8/P6pt4Ad79+7V//9wJTU1Nex2aBt2+Oc//yn//u//Ll27dpXmzZvLeeedJ+np6TJmzBj5+OOPHdejfZjlwgsvjKrfiPTdQrsw2969e+UPf/iD/v82a9ZM2rdvL0OHDpV33nlHqqqqwq6fn58vjz76qPTo0UNSUlKkTZs2cv3118usWbOkuro6qn1YtWqVjBgxQjp06CDJycnSuXNnGTVqlOTk5ES1fkVFhbzwwgty5ZVXSuvWraVly5aSnp4ukydPlhMnTkS1DdS2b98+GT9+vFxyySXSokULadWqlfTu3VuefPLJqD5X+o7Gp7q6Wr755ht5++23ZezYsdKvXz9JSkoSpZQkJydHvR1T+oXdu3fLgw8+KN26dZPk5GRJS0uTIUOGyMKFC6NaH7FBgAXE2I033hjVIENEJDc3V7p37+44QOnbt68UFxeHfb8ZM2ZIs2bNQq6fkJAgGRkZYdc/ffq0DB061HEf0tLSJDs7uz4fhbXcCLBoG+YrLS2VESNGhG0jAwcODLku7cM8dQ2wvvzyy1rboF2YbeHChZKamhq2XQwYMECOHz8ecv3s7GxJS0tzXHfo0KFy+vTpsPuQkZEhCQkJIddv1qyZzJgxI+z6xcXF0rdvX8d9+PGPfyy5ubn1/oxsNX/+fElJSXH8XLt27So7duxwXJ++o3EKd74ZbYBlSr+wbNkyadmypeM2Ro8eHXUYB28RYAEx9N5774lSSrp166Y7TKcAq7y8XHr16iVKKUlJSZFp06bJwYMHZe/evZKRkSFNmjQRpZTcdNNNju+3Zs0aSUxMFKWU9O7dWzIzM6WwsFCysrJk0KBBeh/mzJnjuI0xY8bo5caNGyfffvutHD58WGbPni3nnXeeKKWkQ4cOUlRUdLYfjzX8Tyg++ugjKSkpCVlKS0tDrk/bMF95ebn87Gc/E6WUJCYmytixYyUrK0sKCwvl8OHDsnLlShk3bpyMGDEi5Lq0D/OUlZU59hUlJSVy4sQJ6dixoyilpGfPnrXWp12YLTc3V1/dnZaWJq+//rp8++23kp+fL5999pn867/+q/7cR40aVWv9oqIi6dChgyilpE2bNjJ79mw5fPiw7NixQ8aNG6fXHTNmjOM+zJkzRy83aNAgWbdunRQWFkpmZqb07t1b92dr1qxx3MZNN90kSilp0qSJZGRkyN69e+XgwYMybdo0/ff16tVLysvLXfncbJCVlaWDo44dO8qcOXPk8OHDkpeXJ7NmzZJ27drpEKCkpKTW+vQdjZf/+WbHjh1l+PDhMmDAgKgDLFP6hR07dujwqmvXrrJ48WIpLCyUr776Su644w69f08//XTkDxWeI8ACYqS4uFjS0tIkMTFRFi1aFDHAmjZtml5m3rx5teqfffZZXb906dKQ2+jXr58opeSCCy6Q77//PqCuvLxcLr/8clFKSadOnUL+erJlyxb9i8nYsWNr1a9du1afuEyYMCGKTwEigScU0V4274+2Yb7JkyeLUkqaNm0qH374YZ3WpX3YafXq1fr/+swzz9Sqp12Y7U9/+pMe4G3YsCHkMrfeequ+4iH4KqxHHnlEr5+VlVVr3QcffFCUqrlSZsuWLbXqT506JZ06ddIBRfAtZd9//71ccMEFopSSK6+8MuT+LVmyRLfB5557rlb93Llzdf0rr7zi+Fkg0LXXXqv/76Gusvryyy/1cfnEE0/UqqfvaLxOnDghixYtksOHD+vXnnjiiagDLFP6hZEjR4pSNXc2BF+pVV1dLcOGDdP13IoafwiwgBgZPXq0KKXk4YcfDggwnAKsSy+9VJRS0qdPn5D1FRUV+lezW2+9tVb9hg0b9Hu89NJLIbfhH6TNnz+/Vv3YsWNFKSVJSUlSWFgYchu+X3XPOeecqOdAsN3ZBli0DbMdOXJE/6L4+9//vs7r0z7s5PuOSUhIkAMHDtSqp12YzTcA69Gjh+My77//vv7/bN68Wb9eUVEh55xzjiilZPjw4SHXLSws1FfxjBs3rlb9vHnz9LYXL14cchsvvviiXibUrV6+vyEtLc3xf++7YqNXr16Ofyd+8N133+nP/L777nNc7vbbbxellLRr167WPGn0HWaJNsAypV/Iz8/X4eYf/vCHkOtv3rxZ78OUKVNCLoPYIcACYuCzzz6ThIQE6dChgxw/fjxigLVnzx5dH+5y1vvuu0+UUtK8eXMpKysLqHvsscf0NvLy8kKuX1FRIS1atBCllNx111216jt37ixKhb8sfPbs2fp9Vq5c6bgcfnA2ARZtw3wvvPCCDiL2799fp3VpH3Y6deqUtG7dWpRScsMNN9Sqp12YzzdfXqjbR33+/ve/h/wfZmZm6tfD3cLlm8OzS5cutepGjRolSilp0aKFVFZWhlz/4MGD+n0ef/zxgLqysjJJTk4WpcLfjvTUU0/pbezZs8dxOdTwv3pl5syZjstNnTpVL/fpp5/q1+k7zBNtgGVKvzBz5kxd98UXXzhu46KLLhKllFx//fWOyyA2CLAAj1VUVOhfrz744AMRkYgB1vz583X9ihUrHLc9ffp0vVzwhL2+WwU6deoUdv98l5ZfdtllAa8XFRXpbU+aNMlx/V27dunlpk6dGva9UCNUgBXtfB60DfMNGTJElKq53N5fZWVlxAlGaR928r+Fgu8UO/kGcImJifLVV1+FXMZ3G03w/2bKlCn6M9+9e7fje0ycOFEvd+TIkYA63xxJ1113Xdj99M3TdttttwW8np2drbcdbkLn5cuX6+X+8Y9/hH0vBIY64Z5a63913gsvvKBfp+8wT7QBlin9gm+ursTExLCTzf/mN78RpZT86Ec/Cruv8B4BFuAx30nl4MGD9WuRAqxof2FcsWKFXm727NkBdb5fEgYMGBB2/+688079ReZ/2fjnn38e1a92FRUV+tLccJen4wf+//9evXrpp0YlJydLnz59ZNKkSVJQUBByXdqG+dq2bas/s7KyMpk0aZJ0795dmjRpIk2bNpWePXvKxIkTQz7xifZhp9tuu02UqvmVO9TjxGkX5isuLtZ9R5cuXeT999+XQ4cOSVlZmXz11Vdy9913i1I1c7z4X2Ej8sPVM02aNHG8SkKk5ilyvv/h559/rl+vqqqSpKQkUSr0BPH+rrvuOlFKyUUXXRTwun/QkpmZ6bj+7t279XJPPfVU2PeCyIcffhjVced/Bdb999+vX6fvME+0AZYp/YLvCrHOnTuH3YdJkybpbRw8eDDssvAWARbgod27d0vz5s0lKSkpYOLMSAHWww8/rOtDPRHGZ8uWLXq54HkFfPetjxw5Muw+/vGPf9Tb8J/U1X8+giVLloTdhu/pL073yCNQuMca+8q5554r//znP2utS9sw28mTJ/VnO378eH31ZqjSuXNn2bZtW8D6tA/7FBUV6TlIfv3rX4dchnZhh23btskll1wSsr9ITEyUO+64I+TVWb65g9q0aRN2+/5hiP98NseOHdOvP/LII2G34bvV8dxzzw143X8enK1btzquf+LECb2c03w2+IH/wN4/mArm/yQ2/6fb0neYJ9oAy5R+wTc/Vt++fcPuw8svv6y34XQVK2KDAAvwkO+xr3/+858DXo8UYPk/Kjjcrx47d+7UywU/dco3oPnNb34Tdh///Oc/620cOnRIv+7/2Nvly5eH3YbvCSI333xz2OVQY//+/TJkyBB55513ZOvWrXL8+HEpKyuTnJwcGT9+vH7UdEpKimzcuDFgXdqG2Q4fPqw/W98vlyNHjpQtW7ZIeXm5HDhwQJ544gn9K/OPf/xjKS0t1evTPuzjf9LtdIsQ7cIeO3bskKuvvjpkiHXVVVfJokWLaq3jO1fp2LFj2G1/8sknelvvv/++fj0vL0+/PnHixLDb+PWvf637N39PP/203sauXbsc16+oqIgqkMEP0tPT9We+c+fOWvU5OTn6OyX4uKPvME+0AZYp/cK//Mu/iFJKrr322rD78Oabb+ptrF27Nuyy8BYBFuAR3xdu165d5eTJkwF1dQmwzpw54/ge/vf7B0+u6TthuPPOO8Pup/+96/6Ta/qfMISb90Dkh3vXw02uiegtXLhQPy564MCBAXW0DbP5n/AppWTQoEEh573yv93j+eef16/TPuzTv39/UUpJ+/btHf/ntAs7ZGRkSEJCgqSlpcmbb74p+/fvl6NHj0pWVpa+wiYhISGgzxD5YaAaaY4i/3lm/Cd19u+3ws1RJPLDPDPNmjULeN1/oBpuvp3Kykq9XLhJnfGDpUuX6s+sc+fO8sEHH0h+fr4cOnRI3n77bUlLS5OEhAQ9WfaQIUP0uvQd5qlrgNXY+wVfgBVpHi7/edzCTfYO7xFgAR74/vvv5fzzzxellHz44Ye16utyC6H/1RXBorlk+/bbbw+7r9Fcsr106dKw2+CSbff98pe/DHkiR9swm/8l90oFzifhr7y8XNq0aSNKBT4xh/Zhl2+//VZ/1n/84x8dl6NdmO+vf/2rKFUzD5r/lAX+fvvb34pSNXPa+N8i47tVqG3btmHfI5pbhSZMmBB2G9HcKvT11187rs8thPXz4osvBlxl5V8SEhJkypQp+jvlV7/6lV6PvsM8db2FsLH3C75bCPv16xd2H7iFMH4RYAEe8H3h//znPw9ZX5dJ3HNzcx3fx/8Rt06TZkZ6HKzvEbdJSUmOk2bOmjXLcf3KykomzWwA/hNXLlu2TL9O2zCb/6SnTZo0CfvEnGHDholSgfNT0D7s4j/p7KZNmxyXo12YraKiQocEo0ePdlzuwIEDIQNP32TNiYmJYW8T838cvdNkzXfddVfYfR0wYIAoFX6y5pUrVzquv2fPHr0ck7jXTU5OjowaNUq6dOkiSUlJ0rZtW7nttttk9erVUlpaqq/89g8b6DvMU9dJ3Bt7v+CbxL1Lly5h9+Gxxx7T22AS9/hCgAV44Be/+EXIX7kilb1794qIyLx58/Rr4Z664f/kj+zs7IA63+A20lM3fE/+6NWrV8DrhYWFetuPPfaY4/r+E4ROmTIlwieDaPnPKeB/STZtw3yXXXZZrWAqlFCX3NM+7FFdXS1du3YVpWo/dj4Y7cJsmzZt0p/Za6+9FnbZdu3aiVJKhg4dql977rnn9PrhnjTnH5gWFRUF1PkeOBHpSXOdOnUSpZTceuutAa9v2LBBbzvck+b8n3Y3f/78sO+F6H366af6c507d65+nb7DPNEGWKb0C2PHjhWllDRt2lTKy8sdt+F7CmbLli3D7iu8R4AFeOBsAyz/XxKCJ8P055ubIDk5WcrKygLq/H9J8J8M019lZaW0aNFClAr9iNvOnTuLUuEnw/SfmyDcyQ3q5t1339Wfq/8VWLQN891zzz2iVM1tHadOnXJcbujQoaJU4OX9tA97+A84n3vuubDL0i7Mtnbt2qgDrLS0NFFKybBhw/Rr/lfP+E/CHMw3J06oIMJ39Uxqaqrj1RqHDh1yDCLKysr0HEzhJmf3nxMn3KAadTNhwgRRqubqp8LCQv06fYd5og2wTOkX/K8QCzc5u2+urEhhG7xHgAV4IDc3VzZt2uRYli1bpjvTyZMn69f9fxno2bOnKKXkiiuuCPkelZWVep4t/xNRn/Xr1+v3ePnll0NuY/HixXqZefPm1ap/8MEH9QlN8K8qPsOHDxellLRu3VoqKiqi+XgQhZEjR+r/jf8cWCK0DdMtWLBAf/arVq0Kuczp06fl3HPPFaWU3HjjjQF1tA87+AaMTZo0iep2B9qFuXJzc/XnHm5ic/9bCB944AH9ekVFhbRu3VqUUjJixIiQ6xYVFenbgcaOHVurfu7cuXrboeb+FBF56aWX9DIbNmyoVX/LLbeIUkrOP/98x8Funz59RCkll156qePfibopLi7W81/98pe/rFVP32GWaAMsU/qF/Px8fXus01yR/nO4RfpBCN4jwALiQKQ5sEQCJxP8xz/+Uat+ypQpun7JkiUht9G3b19RquYJIseOHQuoq6io0B1+x44dQ17p8dVXX+lO/z/+4z9q1a9fv17PN/DII49E8ZdDRCIONufOnas/9xtuuKFWPW3DbOXl5dKtWzdRquapOf5zgfj85S9/0f/jGTNmBNTRPsx3+vRpPedRtE/bol2YzddnpKamys6dO0Mu45vEXSklCxYsCKjzTaDdpEkTWb9+fa11x40bJ0rVXBkaaoLjU6dO6SfA9enTp1aAcOzYMV3vNJmy/2TQU6dOrVU/f/58XT9t2jTHzwLRq6ys1JN1p6SkhHzSG32HWaINsETM6Rd8k8S3bNlS9u3bV6v+tttuE6VqHoKRn58fchuIHQIsIA5EE2CVl5dLr1699EnFq6++Knl5ebJv3z6ZPHmyJCYmRhy8rFmzRi/Xp08fWbVqlRQVFcn69etl8ODBeh+CJ9z055vEUSklDz30kOzcuVPy8/Nlzpw5+he7Dh06OP4ihtratm0rt99+u8yePVu2bdsmR44ckcLCQvnss89kzJgx+iQtNTVVNm/eXGt92ob5Fi1apNvBLbfcImvXrpXi4mLZvn27PPLII7qub9++tU4IaR/m8z9hf/fdd6Nah3ZhNv/bZNq3by8zZsyQAwcOSHFxsaxbt07uuOMOXd+nT59awXhRUZF06NBBlKq5LXnOnDmSn58vO3fulIceekivG+4KL/8JlwcPHizr16+XoqIiWblypQ4oEhMTZc2aNY7b8N2OlJiYKE8++aTs27dP8vLy5JVXXpGUlBRRqmaOpHBz2SDQvn375IorrpBXXnlFsrOzpbCwUA4cOCDz5s2TK664QgcQwT+G+NB3NG7btm2TrKwsXUaPHi1K1VzN5v96VlZWrQfHmNIv7NixQ1q2bClKKenWrZssWbJECgsLZcuWLQFP/X766afr9yGjQRFgAXEgmgBLpOa2gO7du+tlg0vfvn2luLg47HtNnz5dmjVrFnL9hIQEycjICLv+6dOn9Vw7oUpaWlqtCTsRnu+S7HDlggsukNWrVztug7ZhvpdeekmaNm3q+Pn27t3b8Wo+2ofZfv7zn4tSNSF3uEfbB6NdmG3ixIk63HYqvXr1kv3794dcPzs7W8+RFaoMHTo07JNRRWqu7nDah2bNmjmGJD5Hjx7VV+uEKt27dw/7NDzU5n/OGaqkpqZG/L/QdzReAwcOjHjO6Su+uXj9mdIvLF26VIdYocq9994r1dXVET9PeI8AC4gD0QZYIiInTpyQyZMnS3p6uqSmpkqrVq2kX79+8vzzz0d9j39OTo7ceeed0qlTJ0lKSpL27dvL8OHDHefXCVZdXS2zZs2SAQMGyHnnnScpKSly8cUXy4QJE7jUth7+93//Vx5++GG55pprpEuXLtKiRQv9fxkyZIhMmzZNTpw4EXE7tA3zbdy4Ue666y792PNzzjlHBgwYIK+++mrEKxBoH2Y6cuSIHgSGmuw4EtqF2bKzs2X06NHSo0cPSU1NlaZNm0q7du1k8ODB8vrrr4d9MIRIzXwxEyZMkIsvvlhSUlLkvPPOkwEDBsisWbOiHtytWrVKhg8fLu3bt5ekpCTp1KmT3HnnnZKTkxPV+uXl5fL8889Lv379pFWrVpKamirp6ekyefLkqL4bEejkyZMydepUGTp0qHTp0kWaN28urVq1kvT0dPmv//ovx0AzGH1H43S2AZaIOf3C7t275YEHHpBu3bpJcnKytGvXToYMGVLrlmrEFwIsAAAAAAAAxDUCLAAAAAAAAMQ1AiwAAAAAAADENQIsAAAAAAAAxDUCLAAAAAAAAMQ1AiwAAAAAAADENQIsAAAAAAAAxDUCLAAAAAAAAMQ1AiwAAAAAAADENQIsAAAAAAAAxDUCLAAAAAAAAMQ1AiwAAAAAAADENQIsAAAAAAAAxDUCLAAAAAAAAMQ1AiwAAAAAAADENQIsAAAAAAAAxDUCLAAAAAAAAMQ1AiwAAAAAAADENQIsAAAAAAAAxDUCLAAAAAAAAMQ1AiwAAAAAAADENQIsAAAAAAAAxDUCLAAAAAAAAMQ1AiwAAAAAAADENQIsAAAAAAAAxDUCLAAAAAAAAMQ1AiwAAAAAAADENQIsAAAAAAAAxDUCLAAAAAAAAMQ1AiwAAAAAAADENQIsAAAAAAAAxDUCLAAAAAAAAMQ1AiwAAAAAAADENQIsAAAAAAAAxDUCLAAAAAAAAMQ1AiwAAAAAAADENQIsAAAAAAAAxDUCLAAAAAAAAMQ1AiwAAAAAAADENQIsAAAAAAAAxDUCLAAAAAAAAMQ1AiwAAAAAAADENQIsAAAAAAAAxDUCLAAAAAAAAMQ1AiwAAAAAAADENQIsAAAAAAAAxDUCLAAAAAAAAMQ1AiwAAAAAAADENQIsAAAAAAAAxDUCLAAAAAAAAMQ1AiwAAAAAAADENQIsAAAAAAAAxDUCLAAAAAAAAMQ1AiwAAAAAAADENQIsAAAAAAAAxDUCLAAAAAAAAMQ1AiwAAAAAAADENQIsAAAAAAAAxDUCLAAAAAAAAMQ1AiwAAAAAAADENQIsAAAAAAAAxLX/B4NYu5RCBfbjAAAAAElFTkSuQmCC\" width=\"640\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support. ' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option);\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
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\" width=\"640\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support. ' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option);\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
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\" width=\"640\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support. ' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option);\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
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\" width=\"640\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support. ' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option);\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<img 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width=\"640\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support. ' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option);\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
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\" width=\"640\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support. ' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option);\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<img 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\" width=\"640\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "for key,value in selected_spectrum.items():\n",
+ " sightline = spec.get_sightline(value,camera='all')\n",
+ " new_sightline = nspec.get_sightline(value,camera='all',normalize=True)\n",
+ " fig,axes = plt.subplots(1,1,sharex=True, sharey= True)\n",
+ " axes.plot(10**sightline.loglam,sightline.flux,'r',label = 'Original Spectra')\n",
+ " axes.plot(10**sightline.loglam,sightline.error,'b',label = 'Original Error')\n",
+ " axes.set(**{'title':'Original Spectra && Normalized Spectra redshift:%.2f'%sightline.z_qso})\n",
+ " axes.plot(10**new_sightline.loglam,new_sightline.flux,'g--', label = 'Normalized Spectra')\n",
+ " axes.plot(10**new_sightline.loglam,new_sightline.error,'y--',label = 'Normalized Error')\n",
+ " axes.legend(loc = 'best')\n",
+ " plt.savefig(\"F:\\\\Astronamy\\\\normalized figure\\\\id-%i-z_qso-%.2f.png\"%(value,sightline.z_qso))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.7.4 64-bit",
+ "language": "python",
+ "name": "python37464bit9a38927acc0145af89620e6e1adc3dca"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.4"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/data/preprocess/dla_cnn/desi/nb/methods of rebining &&determine the best v for each camera .ipynb b/data/preprocess/dla_cnn/desi/nb/methods of rebining &&determine the best v for each camera .ipynb
new file mode 100644
index 0000000..692d098
--- /dev/null
+++ b/data/preprocess/dla_cnn/desi/nb/methods of rebining &&determine the best v for each camera .ipynb
@@ -0,0 +1,1970 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-03-04T11:10:14.836897Z",
+ "start_time": "2020-03-04T11:10:13.947027Z"
+ }
+ },
+ "source": [
+ "This notebook will show the methods of rebining, and the process of chosing the best `v` for rebining."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-03-12T14:20:32.400474Z",
+ "start_time": "2020-03-12T14:20:31.450298Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib notebook\n",
+ "from dla_cnn.desi.DesiMock import DesiMock\n",
+ "from dla_cnn.desi.preprocess import _rebin\n",
+ "from dla_cnn.data_model.Sightline import Sightline\n",
+ "from os.path import join\n",
+ "from matplotlib import pyplot as plt\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-03-12T14:20:32.929976Z",
+ "start_time": "2020-03-12T14:20:32.923953Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "file_num = [705,706,711,718,723,731,735,743,747,748,761,773,777,785,789,791]\n",
+ "path = r\"F:\\Astronamy\\desi-0.2-100\\desi-0.2-100\\desi-0.2-100\\spectra-16\\7\"\n",
+ "file_path = join(path,str(file_num[0]))\n",
+ "spectra = join(file_path,\"spectra-16-%s.fits\"%file_num[0])\n",
+ "truth = join(file_path,\"truth-16-%s.fits\"%file_num[0])\n",
+ "zbest = join(file_path,\"zbest-16-%s.fits\"%file_num[0])#generate file path"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-03-12T14:20:34.397124Z",
+ "start_time": "2020-03-12T14:20:33.671033Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "spec = DesiMock()\n",
+ "spec.read_fits_file(spectra,truth,zbest)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-03-12T14:20:34.775871Z",
+ "start_time": "2020-03-12T14:20:34.770895Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "data = spec.data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-03-12T14:20:35.573196Z",
+ "start_time": "2020-03-12T14:20:35.569192Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "spec_id = list(data.keys())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### The methods of rebining"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-03-12T14:20:37.133548Z",
+ "start_time": "2020-03-12T14:20:37.028819Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1447\n"
+ ]
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support. ' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option);\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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1MAcQVw4APhGH+oOBFhZ2DfffKPSpUt7LuQrR7Zs2RQVFZXiq7N8OXnyZIrjyJEjFA4AAIBQdDHA+upG7wFW09jLbxNMSLig8PjLzxUYHhbEhQOAbwRY7kCAlcXt3r1blSpV8hpi/fvf/9a6devSPKe3ubwNCgcAAEAI2bBBMkb/K2IPrsLGGr19u9HONjVsmxceFmbb7tyZU0FaOAD4RoDlDgRYWdi4ceMUFhamIkWKaMGCBTpw4IB+//137dixQ1FRUTLGKCwsTNOmTUvTvARYAAAAmdD69ZIx+raQPcCq3sVIxkjNmtk2v2dgTtt2h378X5AWDgC+EWC5AwFWFvX000/LGKNrr71Wu3fv9rpN165dZYxReHh4mt5KyFsIAQAAMqF1gVDjxwAAIABJREFU6yRj9F1Be4BVtevFAKtFC9vm1Qdcb9tu18frg7RwAPCNAMsdCLCyoHPnzilfvnwyxqh79+4pbvfTTz95LvJBgwY5cmwKBwAAQIhau1YyRt8XsAdYD3e7GGC1bGnbvHlMUdt27298LkgLBwDf6EPdgQArC9q5c6fn4p0zZ47PbQsXLixjjCIiIhw5NoUDAAAgNL29bKxuGZj85u3/7nExwIqKsm3/Ua9IbbzLaPstRt8UNjq17tUgrRwAfKMPdQcCrCzoo48+8jvAKlKkiIwxioyMdOTYFA4AAIDQtH7pCK+fPlix58UA69FH7TsMHmw9fmksXRqUdQNAauhD3YEAKwvat2+f5+Lt2bNnittd+RbC3r17O3JsCgcAAEBoWrdkmNcAy4wzmlDNaEOPqvYdJkywB1gzZgRn4QCQCvpQdyDAyqJKliwpY4zy5MmjPXv2eN3m0k3cjTFas2aNI8elcAAAAISmtYuHphhgmXFGXQbebt/h2WftAdb48cFZOACkgj7UHQiwsqjFixd7LuCiRYtq0aJF+umnn3T8+HF9/PHHioqK8jxfoUIFJSQkOHJcCgcAAEBoWr14iM8Aq+ugO+w7LFtmD7AGDw7OwgEgFfSh7kCAlYWNGjVKYWFhngvZ27j33nt14MABx45J4QAAAAhNry0a5DPA6pY0wFq3zh5g9egRnIUDQCroQ92BACuL++yzz9S9e3eVKlVKefLkUfbs2VW4cGHVrl1bc+fO1enTpx09HoUDAAAgNL26cKAtsMoz0h5gdR98l23742+t15yKRvE1jHo1MprQ594grRwAfKMPdQcCLDiKwgEAABCaVi2ItQVWt8baA6weSQKs/R+8bnv+/iHXBWnlAOAbfag7EGDBURQOAACA0PTK/AE+A6yeQ+62bX/q5322528ami1IKwcA3+hD3YEAC46icAAAAISmlfOjbYHUbckCrFL2HRITdX3c5edzjDFKOOPs7ScAwAn0oe5AgAVHUTgAAABC08vz+vsMsB4bWjrZPncNuca2zbHdO4OwcgDwjT7UHQiw4CgKBwAAQGhaMbevzwCr19AyyfZ5ZFA+2zbf/OfVIKwcAHyjD3UHAiw4isIBAAAQmpYnCbCSjt7D7km2T6vBt9i2effVSUFYOQD4Rh/qDgRYcBSFAwAAIDS9NKd3KgHWvcn26Te8nG2bl+dHB2HlAOAbfag7EGDBURQOAACA0PTZwnHq38CoexPvAVaf4WWT7TNhXE3bNtMnNQ/CygHAN/pQdyDAgqMoHAAAACFq2TLJGJ3J5j3A6juiXLJdFszoaNtm+PD7g7BwAPCNPtQdCLDgKAoHAABAiHr+eckYnfUSYOUaZRQzonyyXTase8a2XZfeRYOwcADwjT7UHQiw4CgKBwAAQIhaulQyRufC7eHVTYOMZIwUE5Nsl093bbZtG9Erb+DXDQCpoA91BwIsOIrCAQAAEKKWLJGM0fkkAVbxSwFWbGyyXX46vMe2bfnoHEFYOAD4Rh/qDgRYcBSFAwAAIEQtXiwZowth9gCr2OCLAdbAgcl2OXPutG3bokPDgrBwAPCNPtQdCLDgKAoHAABAaPp9wUx9daPRVzfaA6yiPgIsSSo8LExFhhiV7mdUtatRYkJCgFcOAL7Rh7oDARYcReEAAAAITYtmdvb66YM3DrkYYA0a5HW/xFtvsZ6/NE6dCvDKAcA3+lB3IMCCoygcAAAAoWnhjE5eA6wilwKswYO973jvvfYA6/DhwC4cAFJBH+oOBFhwFIUDAAAgNC2Y3tFrgGXGGT3Y02h6XE3vO1aqZA+w9uwJ7MIBIBX0oe5AgAVHUTgAAABC0/zp7VMMsMw4o4EjH/C+Y8OG9gDrww8Du3AASAV9qDsQYMFRFA4AAIDQNG9aO58B1qBRFb3v2LOnPcB67bXALhwAUkEf6g4EWHAUhQMAACA0zZ3a1meANXjUg953jI/3hFfnw43+mTklsAsHgFTQh7oDARYcReEAAAAITXOmtLEFVncM8C/A2jSznx7qYVR8kFF4vFH86CoBXjkA+EYf6g4EWHAUhQMAACA0PTflUZ8B1pBRD3ndb+2yONt23QbfFeCVA4Bv9KHuQIAFR1E4AAAAQtPsKa19BlhDR//b636fvb3Mtl3d2IIBXjkA+EYf6g4EWHAUhQMAACA0zZoc5TPAGja6ktf9Dn2/07ZdqUE5A7xyAPCNPtQdCLDgKAoHAABAaHp2UitbEHVndJIAa4z3ACvh/DldO/LydjnGGJ3/+0SAVw8AKaMPdQcCLDiKwgEAABCaZk5q6TPAGj6mcor7VhiUx7bt7ndeCeDKAcA3+lB3IMCCoygcAAAAoWnGM81tIVTSMWLMwynu227oHbZt1y8dEcCVA4Bv9KHuQIAFR1E4AAAAQsgXX0ht20ojRmj6hIY+A6y4+JQDrLgxD9u2nTe1bQBPAgB8ow91BwIsOIrCAQAAECLOnZMKFZKMkYzRj0Vz6c07jDbfmVKAVSXFqZLeP2vs2GoBPBEA8I0+1B0IsOAoCgcAAECI2LTJE14lHTeMSB5gjfQRYK1aPMi27WNDSwfwRADAN/pQdyDAgqMoHAAAACHilVdSDLDyDbeHV62ijFbEt0hxqg82PmfbvknsjQE8EQDwjT7UHQiw4CgKBwAAQIhYuTLFACv/FQFW9jEXHx87NsWpfti51RZgPRibJ3DnAQCpoA91BwIsOIrCAQAAECJWrEgxwCow7HIYlS0+9QDr5PHDtgDr5iHZAnceAJAK+lB3IMCCoygcAAAAIeKll1IMsApeEWCFXwqwxo3zOd11cfZXbSUkXAjQiQCAb/Sh7kCABUdROAAAAELECy/YQquPbjZ6oqrRk4/Y738VNvbiNuPH+5xueKt8iqtt9OxDRqvuMTp/9EiATgQAfKMPdQcCLDiKwgEAABAili3T+XCjL4oa/XZt8uDqyiFjpMcf9z1f1ar2V3J9/XVgzgMAUkEf6g4EWHAUhQMAACA0JCxZrOpdrIAq/3DrkwZTCrCO5DE6OX607wmjouwB1jvvBOZEACAV9KHuQIAFR1E4AAAAQsMbs2NTDKy8jfHja/mesHdve4C1enVgTgQAUkEf6g4EWHAUhQMAACA0DJlSL20B1uOpBFhDhtgDrGXLAnMiAJAK+lB3IMCCoygcAAAAoWHo5LQFWI8/Xtv3hOPG2QOs554LzIkAQCroQ92BAAuOonAAAACEhqGT6qYpwJrweB2f8x2eNFb/uc1ow91GL/7L6IsnowN0JgDgG32oOxBgwVEUDgAAgNAw9Jk6PgOr0v3sf544oa7P+eZPa2/bflR8lQCdCQD4Rh/qDgRYcBSFAwAAIDRMm9zCZ4BVJlmAVc/nfCvn9rdt3z/uvgCdCQD4Rh/qDgRYLvHmm2+qbdu2KlGihHLlyqUCBQqoXLly6tmzpzZv3uzYcSgcAAAAoWHl5C5pCrCemFjf53xvvDDGtn3HoXcG6EwAwDf6UHcgwMriTp48qRYtWnguZm+jevXqjh6PwgEAABB8yyd1TFOA9WQqAdbn6+fZtq8xqGCAzgQAfKMPdQcCrCzs7NmzqlGjhowxypYtm/r27asdO3bot99+06FDh/Tuu++qX79+atGihWPHpHAAAACEhheebpemAOupiRE+5/vjx9227W8dnC1AZwIAvtGHugMBVhY2fvx4GWOUPXt2bdiwISDHpHAAAACEhqVPP5qmTyF86okGqc6Zf0SYZ/uwsUZn//krAGcCAL7Rh7oDAVYWdezYMeXKlUvGGMXExATsuBQOAACA0PDrM2P09u1Gb95htOkuoxatfQdYTz8RmeqcFWNy2/b57pv3A3AmAOAbfag7EGBlUdOnT5cxRmFhYTpw4EDAjkvhAAAACA1nnn5C228xeu82o60ljep3MMo+xihbvPcA6xk/AqzW/YrY9tn85uwAnAkA+EYf6g4EWFlU/fr1ZYxR+fLlbY+fP39eiYmJGXZcCgcAAEBo+OnJ4bawqWJPIxlr3BWdPMCa9GTDVOccEV3Gts9zsVUCcCYA4Bt9qDsQYGVRhQoVkjFGPXr00D///KPRo0fr9ttvV3h4uLJnz64yZcpo1KhROn78uKPHpXAAAACEhoMT7QFW+V6XA6y7+3sJsJ5qlOqcC6Iftu0zsPUNATgTAPCNPtQdCLCyoFOnTnku3gEDBuiee+7x/DnpuOWWW/TNN9+kaf6TJ0+mOI4cOULhAAAACAGHxw+zhU3l+lwOsEolCbBm/Nvof5OGpDrnf+aPtO0X2fu6AJwJAPhGgOUOBFhZ0KFDhzwXb86cOWWMUcuWLbVr1y6dPXtWP/30k8aOHavw8HAZY3THHXek6SJPKQxLOigcAAAAwXMsfogtbCrT73KAVbqfPcD6PbeRpk1Ldc7Dx3+y7Xf7wGwBOBMA8I0Ayx0IsLKggwcP2oKkWrVqeb3v1ZQpUzzbTPPjB5ZLCLAAAABCWGKi1KeP3rgz+dsELwVY9/S1P34st5GmT/dj6kTdEBfm2S9srNH5c2cCcFIAkDICLHcgwMqC/vzzT1uQtH37dq/bnT17VgULFpQxRtWqVfN7ft5CCAAAEMI+/lgyRqNrphxg3ZskwPrtWiPNmOHX9PfG5vTsV2io0W8/pu12FADgNAIsdyDAyoISEhI8bx0MDw/XmTMp/1YsMjJSxhgVLFjQkWNTOAAAAIKsVy/JGI3xEmBV7m70cLfkjx/J43+AtTviQf10vdHZbBcDsV27MviEAMA3+lB3IMDKosqWLetXMNW+fXsZY5QjRw5HjkvhAAAACLKePVMMsFIah/MYaeZM/+Z/9FHPK7lkjLRlS8aeDwCkgj7UHQiwsqguXbrIGKOwsDCdPn06xe0iIiJkjFGhQoUcOS6FAwAAIMh69JCMUXwN/wOsJx8x+npanH/zDxhgD7AmTszY8wGAVNCHugMBVha1Zs0azwW8bds2r9ucOXNG+fPnlzFGderUceS4FA4AAIAg69Ytza/AMuOMZkxq6d/806fbA6zIyIw9HwBIBX2oOxBgZVFnz55VyZIlZYzRI488ooSEhGTbTJgwwXORL1q0yJHjUjgAAACCrEsXyRiNrJW2AGumvwHWN9/YA6zSpTP2fAAgFfSh7kCAlYWtW7dOYWFhMsaoQYMG+uijj3T8+HH93//9nwYPHux57oEHHtC5c+ccOSaFAwAAIMg6d5aM0fA6aQywJrfyb/7z5+0BVpEiGXo6AJAa+lB3IMDK4mbOnKns2bN7Luako3z58vrll18cOx6FAwAAIMg6dZKM0ZC6aQuwnp0S5fchBje+RhHtjcr1MSo01Ojs+ZQ/9RoAMhp9qDsQYLnA559/rk6dOunWW29Vzpw5lS9fPlWtWlXPPfeczp496+ixKBwAAABB1qGDZIwG1fMdWN3X2/7nWVNa+32I8gNy2vb98addGXhCAOAbfag7EGDBURQOAACAIFm9WurTRypcWDJGMRFpC7BmT3nU70M17JvPtu/295dn4IkBgG/0oe5AgAVHUTgAAACC4IMP7PelMkb9G/gOsMr3ShJgTfU/wOo18C7bvssW9MvAkwMA3+hD3YEAC46icAAAAATBQw8lC7D6NExbgPXctDZ+H27WE01t+/aLr5iBJwcAvtGHugMBFhxF4QAAAAiC3Lm1u6DRynuNfs9tBViPNUpbgDVnWju/D/fZ6lm2fSsNyZ+BJwcAvtGHugMBFhxF4QAAAAi8z+7Oq1yjrDCp+CCjkzmM+kYaXTPa/08hnJuGAOv0z/sVNvbyvgVGhGfg2QGAb/Sh7kCABUdROAAAAAKvZo8cXkOppm2MnqniZ4A1vX2ajnnboHDb/sd+3ZtBZwcAvtGHugMBFhxF4QAAAAg8f19l5WvMm9EhTcesE1PAtv+ODXMy6OwAwDf6UHcgwIKjKBwAAACBl5agalQtoyrdkj8+f0bHNB2zz8j77J9EOCVtARgAOIU+1B0IsOAoCgcAAEDgpSXAGlbHqGrX5I8vmNEpTcec/mw72/6jh9yfQWcHAL7Rh7oDARYcReEAAAAIvNIDc/odYA2sb1Sti/2xD28xOr1oXpqOuXHDVNscrfsVyaCzAwDf6EPdgQALjqJwAAAABN6mqkX9DrDatTCqniTA+raQkZYsSdMx9+z91DZHhf45MujsAMA3+lB3IMCCoygcAAAAgffBA4X9DrBuHmhUo7P9sW8Kpz3AOnfhnLLFX54jz0ijxPPnM+gMASBl9KHuQIAFR1E4AAAAAm9X6QJ+B1i5Rxnd/5j9sa8LG2np0jQft03X69S1qdFTjxi9Vsbowi8/O39yAJAK+lB3IMCCoygcAAAAgbf3tuvTdCP3pON/RYz0/PNpP3Dt2pIxl8e2bY6fGwCkhj7UHQiw4CgKBwAAQOD9WDzPVQVYu4oYadmytB+4Vy97gDV0qPMnBwCpoA91BwIsOIrCAQAAEHhHi+RRw3ZGjdumL8C6M9ro3Xkj0n7gNWvsAVazZs6fHACkgj7UHQiw4CgKBwAAQAAlJOjd8V1sN1JP76uwnn+uZ9qP/9139gCrUiXnzxEAUkEf6g4EWHAUhQMAACBw5o1rdFVvHbxyLJvTK+0LOHHCHmCVKOH8SQJAKuhD3YEAC46icAAAAAROmX7OhFdmnNELc3qnfQGJifqhaE795zajFWWNplY2Sjh9yvkTBQAf6EPdgQALjqJwAAAABM7VBFYVezoQYEkqHR1um+eLwe0cPksA8I0+1B0IsOAoCgcAAEDgOBlgvTi3T7rW0Kd7Uds8TzbO7/BZAoBv9KHuQIAFR1E4AAAAAsfJAOuleX3TtYb1Y9vY5qnWhRYDQGDRh7oD313gKAoHAABAgCQmOhpgLZ/fP13L+OuPI8ox5vI82ccY/XniN4dPFgBSRh/qDgRYcBSFAwAAIEAuXFCBYekPsB50KMCSpBqP5bTNteH5OAdPFAB8ow91BwIsOIrCAQAAECDnzinPSOcCrBULotO9lPjGeW1zTZ/Q0METBQDf6EPdgQALjqJwAAAABMiZM8o+Jv0BVtLx8oIB6V7KwqntbXMNiXvAwRMFAN/oQ92BAAuOonAAAAAExoWTfzsWXplxRisXxqR7LZtfnmCbq+2g25w7UQBIBX2oOxBgwVEUDgAAgMD454/fHA6wYtO9ll3/ecU21yOx1zt4pgDgG32oOxBgwVEUDgAAgMD45/fD6tjcqEXrtAVVtw8waulln1cWDUz3Wk4c+ck213VxRufPnnbwbAEgZfSh7kCABUdROAAAAALkzz/1deG0v9KqZIxRq6jkj7/zfPxVLafcwFy2+T575wWHThQAfKMPdQcCLDiKwgEAABAgx49rd8G0B1glYoyikgRY/7nNSK+9dlXL6T+snG3O4oOMlJjo0MkCQMroQ92BAAuOonAAAAAEyLFj2ps/7QHWjUOMWreyP7athJFWr76q5bw2LybZsX56d61DJwsAKaMPdQcCLDiKwgEAABAgR4/qp+vTHmAVGGb0aJIA690SRlqz5qqW88eeXTLjjHo0NlpcwejrwkYXxl3d2xIBwB/0oe5AgAVHUTgAAAAC5MgR/ZrXmU8g3FrSSGuv/tVSH915jVbdYyRzcfTs6cCJAoBv9KHuQIAFR1E4AAAAAuCTT3Q2um+6buLubbzjUICl99+/HF4ZIzVtevVzAkAq6EPdgQALjqJwAAAAZLBdu6Ts2bX9FmfCKzPO6O3bjbRu3dWvbfdue4BVpszVzwkAqaAPdQcCLDiKwgEAAJDB2raVjNEHtzoXYJlxRutfGHn1azt+3B5gGSN9//3VzwsAPtCHugMBFhxF4QAAAMhgd9whGaP3bnM2wFrnRICVmCgVL+4Jr45ea7S0S/mrnxcAfKAPdQcCLDiKwgEAAJDBHnpIMkbbSjj9CqxRzqxv40YlhBktKW9UcJhRrlFGF47/7szcAOAFfag7EGDBURQOAACAjLWneXWtLW0UE+FsgLXhxTGOrbFln4K2ub9bt9ixuQEgKfpQdyDAgqMoHAAAABlrcv/7HQmsKnW3//n1l+IdW2PbkaVsc6+ZE+3Y3ACQFH2oOxBgwVEUDgAAgIxVZ3hxn8FUvQ5GOcakHmBVThpgLR/r2BonPl7HNveEof92bG4ASIo+1B0IsOAoCgcAAEDGqjyisCOvwEoaYG10MMBaO72Xbe42LY11g3cAyAD0oe5AgAVHUTgAAAAy0LPP6pGuztzzKmmAtWnFeMeWuWfh07a57+5vpN+5kTuAjEEf6g4EWC7y/fffK1euXJ4Le+nSpY4fg8IBAACQQTZulIxR9S4ZE2C98fLjji31woZ1yj/cPv+3/3nNsfkB4Er0oe5AgOUiderU8VzUBFgAAACZzA03SMaoVidnAqx8wzMuwNLJk+rczD7/hNmtnZsfAK5AH+oOBFgu8eKLL8oYo5IlSxJgAQAAZEbGSMaobkdnAqykY/PKiY4ud8NjNWzzlx92vaPzA8Al9KHuQIDlAsePH1eRIkWULVs2rVu3jgALAAAgE/rzGqMX/pUx4ZUZZ/TmK084ut7T3+7SdXH2Y/yw4w1HjwEAEn2oWxBguUD37t1ljFFsbKz2799PgAUAAJDZnD+v/xVxLqxq/mjyx95a9aTjy27b5TrbMYY2vdbxYwAAfag7EGBlcR988IHCwsJUrFgxnThxggALAAAgE0r86y+tL+VMeHVdnFHfyMAEWKt7VbMdI2+c0d9HDzp+HADuRh/qDgRYWdi5c+d0zz33yBijl19+WZIIsAAAADKhE7/ud+zVV3njjPolCbDu62209/UXHF/3mbWvJbtZ/GebFjp+HADuRh/qDgRYWdjEiRNljFHt2rU9jxFgAQAAZD6Hvt/pWICVZ6RR/wb2x16510jvvOP8whMT1a2J/Vgrpnd3/jgAXI0+1B0IsLKoH374Qbly5VLOnDm1e/duz+NOBFgnT55McRw5coTCAQAA4LB9X73nWIB17Uij6CQB1sp7jbR1a4asfduzg7TgfqO1pY1G1DaaW9Hony8/y5BjAXAnAix3IMDKourWrStjjEaOHGl73IkA69L+qQ0KBwAAgDO+/fh1xwKs3KOMYiLsj71cNuMCLK1fLxmjti0vH69396IZcywArkSA5Q4EWFnQ8uXLZYxRiRIldOrUKdtzBFgAAACZz+fbVjgWYHkby8sZ6d13M2bxH30kGaOtJY3GVTd6uJt1I/kLx3/PmOMBcB0CLHcgwMpi/vjjD914440yxmjDhg3JnucthAAAAJnP9s3zU7whuxMB1kvljLRtW8Ys/ocfJGNUto/9mLs3v5QxxwPgOgRY7kCAlcXExsbKGKMmTZp4fZ6buAMAAGQ+b6+ZkqGvwOrUzOj4269nzOL//lsKC1P7FvZjvjJ/QMYcD4Dr0Ie6AwFWFtO0aVO/3+J35di/f78jx6dwAAAAOO/1leMzNMAy44z+s25Gxp1A06aa9LD9eI89mifjjgfAVehD3YEAK4shwAIAAMh6Vi0bFoAAa2bGncCZM3qrUZlkxzz9+5GMOyYA16APdQcCrCxm37592rlzZ4pj06ZNngt7/PjxnsfPnj3ryPEpHAAAAM5bNr/vVYVTd0Uble7ne5v31j+boedwZOnsZMfc9MKYDD0mAHegD3UHAiyX4R5YAAAAmc/8ZztdVYBVtWvym6gnHe9vmJ2xJ3HokPINtx9zdPN8GXtMAK5AH+oOBFguQ4AFAACQ+cyY3NLvsGrh/f5tV62L/c8fvP5chp/HK10etB2z4DCj83/9meHHBZC10Ye6AwGWyxBgAQAAZDKJiXpqYoRfodTyckYy6Quwtm+ck+Gn8sN765KtY0t0Q+no0Qw/NoCsiz7UHQiwXIYACwAAIHO48NcJxfQpqQq9w1RkSOqBVOl+Rp2bGbVu5V+AVT1pgLVpbsafVGKianVKvpYhkTmUuGdPxh8fQJZEH+oOBFhwFIUDAADAGc/G1bKFPLH1jZ59KP33wUo6anS2//nDTfMCcl5fvTjF63o2j24TkOMDyHroQ92BAAuOonAAAAA4I2nA83QV6+2BDdtlTID10RvzA3NiiYnq0LuI57h5Rhq1ijL6ujCtCYD0oQ91B75LwFEUDgAAAAf88YeeesQeMI2vbgVYDdp7D6Ryjbq6QGvH5oUBO709/31Lua9Yb7HBRqezG+nDDwO2BgBZB32oOxBgwVEUDgAAAAcsX64FST5NMK62FWDV6+A9gCo47CoDrDcXBfQUtw1/VP/uYfRgT6NPi1vnJkOIBSDt6EPdgQALjqJwAAAAOKBvX60uYw+YHmtkvN4A/dK4LfbqAqyPAxxg6cSJy6HVlSM8XNq7N7BrAZCp0Ye6AwEWHEXhAAAAcEDbttpWwh4wtWxtVKp/ygHUPX39D6tG1Ur+2CdvLQnsOSYm6kw2ozWljepf8aqyAzcYqVevwK4FQKZGH+oOBFhwFIUDAALgxx+1qXV5jW1dWP+3fGawVwMgI0RGalcR/wOpX64zmv+A9+dG1rJulJ5agPXplucDfprnXliq6+KSr+XAI+UCvhYAmRd9qDsQYMFRFA4AyHgbe1b3NHl544yOdWgprV4d7GUBcFKVKtqfz/8A60w2o9fKeH/uxDVGt8b6DrB6NDb65+MPgnKqUVHJ19y3c+GgrAVA5kQf6g4EWHAUhQMAMtiWLXqoh73Re6LqxfvGvPpqsFcHwCE9O+VXRAqfNuhtnMputOku78/9nTP5/bFG17T/eea/jfTf/wblXL/v3TrZmu/tS5sCwH/0oe7AdwY4isIBABkr8faSyRq9uh2vuPkxgCyhyPDwNN2A/WQOo3dLeH+uUdvkjyUNsGb820iffx6ck71wQfu8vNps37trgrMeAJkOfag78JMuHEXhAIAMlJCgno2TN3lVul0RYCUmBnuVAByQd6T/4ZUZZ/RXTqMdN/u/fdIAa3olI33xRfBOeP4b+MMlAAAgAElEQVR8tWydfJ27VnKfPwCpow91BwIsOIrCAQAOOHtW5xo10NeFjRLCjLRxoyTp0HefK2ys92a0+CCjG0YYnT9F7QUyu8Tz55UtPm0B1olrjL66MW37XDmmVTLSzp1BPe/1Q5skW1fVvrmDuiYAmQN9qDsQYMFRFA4AuHq/z5/hueHyfb2te9soIUFbX5ucehN7aH+wlw/gKp09diRN4dP1I4yatjGal8KnEJpxRoPqGd08MOXnp1Y20ldfBfW8z506qSLDwmzrChtrdPzg3qCuC0Doow91BwIsOIrCAQBXb0KdnLYGbtZDRvrrL82Z0ibVRvbg7k+DvXwAV+nPPbvS9SqqJ6oalenn/blmj1qv1PS1/88fbwn2qWvdtMeSrWtV67K8PRqAT/Sh7kCABUdROADg6hUYZm/eso8x0tGjih9ZOdUGds+nm4O9fABX6ddP3klXgLW0vHUvvGKDkz/X/FHvj185dn4QGp9kOrbHHbZ1PdrKSM8/H+xlAQhh9KHuQIAFR1E4AODq5R+evLG8cOBHxQ4tl2oDu7NvC2nlSun8+WCfBoB02vvCTNt1Xam7UekUXll15Vh2nxVgFfUSVLVs7f3xK8eX218L9qlLkvb98JltXdfFGZ3NxodUAEgZfag7EGDBURQOALhKy5bphhHJG8sf4vurexP/XoVRtavRL+OHBPtMAKTHuHH6urD9mq7Z2eiOAalf+y9WyCYZoyJDkj/XKsroRi+PXzm++nBNsM/e46EeRtW7WG+hPnjdxU9ZXbw42MsCEKLoQ92BAAuOonAAwFVITFRigfzJPmmwVyOrgYuK8i/AKtPPaHdBI50+HewzApAWZ84oMTxMiyvYr+mG7Yxu8XED9ktjeVwjyXi/D1ZUVPJgq07HJAHWR2uD/RXwODtnlhVaXTly5Aj2sgCEKPpQdyDAgqMoHABwFU6c0F857Q1lyZjLzVv9Dv4FWI+2Mhpf3UgzZwb7jACkxZEjionw/uqp1N7+Z8YZrXiyrZQvn2T8qxW1O9n/vGvH+mB/BS5LSEgeYBkjXbgQ7JUBCEH0oe5AgAVHUTgA4Cr8+KN+uc7eUJbvZTVtv+dO/e0/V47RNS82e9wzBsg0/tr/ndfruWNzk+zDHbyNlU91kO64w+8Aq1aSAOt/H28I9pfAbtUqW3h1NpvRiQfKBntVAEIQfag7EGDBURQOALgKO3fqm4v3vrkt1vrvQz2M3rvN6Hov98XyNaZXutj0vfBCsM8KgJ8O/G97mq7zpGPVM5307/65lDfOv+1rdrb/+ZtPNwX7S2CXmCgZo1+uM4qvYYX4UVFGOnAg2CsDEGLoQ92BAAuOonAAQPrt2fRCsgaz+CCje/umvZG99Glkato02KcFwE8/f/l+qtf2/nwpP/fapK4qG3uN33Wiehf7n//vs83B/hIk80XfFsoWb1/nj5XvkY4dC/bSAIQQ+lB3IMCCoygcAJB+bzw/KlmD2c3LJw9OqWy0N7/vxnTjXRcDrGLFgn1aAPy075M3fV7XeUZa13W+4d6fXzOlu+6Pye13gFW1q/3P332+JdhfgmQSjx9XxZ7J176tW61gLw1ACKEPdQcCLDiKwgEA6bdidu9kTdrKe72/uurPa3w3pk8+YrTpLqNV94ZxHywgk/ju/bXJruVrR17+/1yjrADruhTeIrh2ak9VjLk2za/YvDS+3/lOsL8EXk2pmi3ZWmt1oo0BcBl9qDtQ+eEoCgcApN+Cqe39ajLXlzL6voD/TWnCP9RjIDP4+u0Vtmu3Zmf7q61yjjb6Na+P2jC9tx6KyZPuAGvvl9uC/SXw6s9lC7yuN/Hvv4O9NAAhgj7UHQiw4CgKBwD46csvpQkTpPff9zw0++mWfgdYY2v435SeOLgviCcKIFX/93/Sq6/qi1dm2K7deh3snz6YLd6ob2TK1/rrM/qqckxev+pC2Njkj+3f9X7qaw2G06d1+4Dk6901eUiwVwYgRNCHugMBFhxF4QAAPxw4IOXIYd2jKixM+uQTSdK0CQ39ajxX3mt0Z7T/AdbPX38U5BMGkKLt2/X2neF6vJrR8nL2a7dRW6PCQ+2PRTfwfp1fF2e0fdYwVYm93q+64C0EP/C/7cH+aqRoVePbk613QISR/vvfYC8NQAigD3UHAiw4isIBAKk73ruLjuU2SjQXb7Rer54k6en42n41nv/P3lWHVZW08RdEEQsDu7u7W8TuxlZMkG6Q8Nq5tq5rx7p2rLHqp+7aq2vrrt2Niq0gcX/fH3Pr5L0XLiJyfs/zPnDnzJl5Z86cOTPvvLGqBmFyU9MFWNcmegEJCancagUKFIjhj7alRLWhSEXo7kwo4G/ae/5vXgKWL0dTH3vBtf/yEsryhN5iAqyn186kdndIQr17Nwb04PJbZ6RmDr2naJkqUJDeoexD0wcUAZYCi0KZOBQoUKDAOMb3dACpmEPmUl5Mowp79mD82MYmC6U69yMU9mP/F/QnFPGVznu6MAHOzoozdwUKvjeo1bLveZ9e+vfcGF3JR8CKFWjum1NwDUSoNUpegBXWkoBHj1K7R2TxdeN6Ds/5Aghx1gR4eKQ2awoUKEhlKPvQ9AFFgKXAolAmDgUKFCgwjlH9s3M2YZsqE2BtjTCPSiYLsOxDCNF2hL9KsIiElcZI541wJFxzIGD//tRuugIFCgzw4cRhk995Y3Q5PwGrVqGFby5RAVZtIwKssS0JePw4tbtEHmo16ozU81x7FOFFVgKqVk1tzhQoUJDKUPah6QOKAEuBRaFMHAoUKFBgHB2HZBRsMM8UJgS2Nn2zmidIYzqjodIiDo4NqYkLAXnyACNGAKcUn1gKFHwPOL1snMUEWBcLELBmDZr75RYVYBkKfsQEWKFOBDx5ktpdYhSbRjXWmVx2cyYkWhFgZ5fabClQoCCVoexD0wcUAZYCi0KZOBQoUKBAHnHv38I+RLj5zBdAGNnZ9M1q/gC2KY3NQLiZR+jomU9NXZiGRY8+hCOlMwCvX6d2VyhQkO6xcuYAiwmwzhckYN06SQFWXZ4Aq9Ug7u8QJwKePk3tLjGOd+9w3YFwvBjhoT3hf6UIS2oT1DExqc2ZAgUKUhHKPjR9QBFgKbAolIlDgQIFCuRx4/ReyQ1oQROdNZOKUMiPbUrPF+SmtxtACDCiyWUXRng/Y2Jqd4UCBekeM8e3tYjwyjaccK4gAevXo4V/HlEBlm9b+TKCWxHw7Flqd4lpiIxEOQ8u/3/v/jm1uVKgQEEqQtmHpg8oAiwFFoUycShQoECBPI7tWii5geRHCZOjYgEZgGXLcLUsN+KY02BCuKPx+9eHdkrtrlCgIN1jcmgTiwiwMoWzoBDHV4xDC38HUQEWiOWTKiOoFQHPn6d2l5iMMR14/HfMBCQmpjZbChQoSCUo+9D0AUWApcCiUCYOCRw4APTrB8yZoyyuFChI59i2OtgiG9aS/jYAgBtn93HSmw0lTGxm/P4Vqi6p3BMKFCgY51uT814ursMESUmdF46vGo8lw2tw0rKM1QuwMkbI3//h0Z3U7hKTsXWZn4D/o15dU5stBQoUpBKUfWj6gCLAUmBRKBOHCJ4+xe8VrNBmIDPr+bJmRWpzpECBgm+FT5/wKcQPUcP7AteuAQCWzB1kEQFWWb+MAIC7l49w0hsOJ8xsZPz+X6f2S82eUaBAAYDQMeU57+XymsxXHf99DXEybV44uWYS3vbuzEkr56EXYNkYE2A9vpvaXWIybp/YJeC/7UBla6NAQXqFsg9NH1BmeQUWhTJxCPEyYAxnwTinAQEXLgAtWrCIYGfOiN+YmAicOwc8evRtGVagQIHFcNq3Dxw0ztV9eucAEhIwcVIb2Q1kvgDCpKbMQbFcvoq+mQAAj66d5qTXGUlYUM/4RnfJlB6p3DsKFCjwdy3FeS+HdRF/X00VYP29biqinbtw5wp3vQArQ6T8/Z+e3k/tLjEZiV8+o6S3sA3488/UZk2BAgWpAGUfmj6gCLAUWBTKxCHE7lHNQSqmtp8rmDlRRZEiUBPhgT3hc0YCjh0DTpwADPrsc+9uWFWDsL9iRmD//lRsgQIFCpKKnn24G6v7f+2AV1hto5vQsJaEM4Xl81TxtQUAPLtzkZNe3ZWwrJbxje4MVetU7h0juHMHn+ZMx+XtPyvRxRT8sHjepSUu5yecLUQ4WVRc+8ocAdbpX6fjVb+unLTKY/QCLGsjAqwvz9PWodnNtXMEbfi5b5nUZkuBAgWpAGUfmj6gCLAUWBRpduKIi2Oho+Pj2e+vX4GoKECtTnbRG125DlrHdCCoidDdmf3OE8ROR23DCd1dsiA2+iVw7RqauujvWdAmZ9Iq//oV2LoVOH482e1QoECB+eBvrFas8kK/wFJGN6EL6xJe2xHWVyVsriSep4aPHQDg1aMbgmt1RxJ69+amFfG35vzOE0S48utPFpnnBPj8GfjpJ0YfPwKvXgF797J51RT8/Tdu5CHYhzBenVyzQP3xo+X5VKAgtdG6tU64BJI2/zVVgPXPb7Pwvn9PDOtCGNqVMKg7uxdE+GLDzZs7iFDMh5sWG/U0tXvEbLQczG1Dq0HK9kaBgvSINLsPVWAWlBlegUWRJieON2+A6tXZ4rFBA+D6daB8efbb0RG4fRtYvhxYvRpYvNjsCD1Lx9QXLDB3lpdefG6YOgDX1/wkSE8KbndtgpGdCR7tCc8nBAHu7sCIEcDjx9yMsbHApEmApyfw4EGS6lKgQIEQ/Pd4wdz+aOWX1+gmdFtF/YY20Uo8T22fLACAdy8eCK5VcyVM4Dlyb+iZRZAvUzjh4jQfyze8Vy/9ptzJCdcr5UO4I2FrrcxQ379v9PY7HRpw+LQNJ5xdN83yfCpQkNpo2ZIjwJrSJHkCrHMbZwP9+3PKBBFga4tmY+wE+Yv4cn/HvXyR2j1iNuYOqSBoV2J8XGqzpUCBgm+MNLkPVWA2FAGWAosiTU4cEyZwFnkJ+fNiZiN2anmyKAkXgfnyAefPA0ePAgkJRouf5SE0F6riJr34LBRkg0PLw4SLsb17mHDNxH5NvHkD1Vz19w/oQThdmLCvDCGhcSNuZj8/ffvKlTNNI+PjR8QtWYyPa5czAZgCBXzExgKbNwM7d+q1G390qNWIunAcD84eBL5+FbzHKlULVPfJbHQTeqyYfs6JsxbPU98nKwDg85sowbW6I5kZomFaZ488ouUMGZZbuj3v3rH5zpx3PDGRM2e+tyXkCDEQ0lch4OVL2SK6OXN57NCf8HnGZNN5UKDge8bu3UzzytsbqFMHr7IQSnoLhUlJEWBd2DwP11w643BJwv9KEfaXJrzKQsDFi2julUO45vDj/k54/Sq1e8d8nD4taNfyaX1SmysFChR8Y6TJfagCs6EIsBRYFGly4uAJqBbW1S+Aso4lRNvptSBe2/GEWV2Nh2se51XNpEWnlnKHWuPA8lBB+soamjpz5QL+/Ve6wl27gKlT8d+CSMk6+vUkrkCBL6S7etVou/4Y2hhZxzLNiCWBLYGEBCSOHIHHhbMjpoADkD8/ULgwW6grSBI+3b6GDaGd8ffcgLQhAHr6FOjYEahSBZg7lzumundPbe6+CbYHdYZtOHvPfN1LC949L/dSyB9gfB64kk/fd3yzHy019s4OAIj7/FF4bRghsDU3baBXUcn6RPHwId6WKID9pQkva1UAvnwxrRNevtTxHm9NmNxUWF/D4YQJA4thnUcznA0ayARlBuBvqh2HEODrm5xHo0DBdwH1ixcIbJcBhfwIfXoRPmZiawtz1glydGnrQgz255op7y/N3nExAVYBf+5v9Zs3qdxDSUPLoVaCtiV++ZzabClQoOAbIk3uQxWYDUWApcCiSJMTB094k2WscEHYxIWQPZRpNWjzqbX33JUJOb17N3zbmrf4zBVqjb3LggTpZT0N+KxdW7y+337jtCVPkHQ90S/u49mFo2jmkQ1ZxzJ/ORcLEP4uQvhw7KBsl3199QJ5A/VlZQonvNi4Aq0Gsd+lvQgP7TV8FCuWfB87T58CYWHAxIlAdHTyyrIUHj5kUSKNte3LF2DcOGDIEODSJZOLV8fHo96YTLo+/m2Sc7LY/SZwc0NsBsJ1B4P3w5Bev5a+NzGRafmkhD8mC0N98SIu+PfH8WljkPDCwKT40ydkC5V/v116Z8KVfIQdFQizGhLKeErnreBOeJGV8CGTRFmj8jN+4uMF11oOJni346Ytd2+EO7kIT7Jz060jCfHX/gV69mRaIf/8AwB4FeCmE7blDSQ8yW0j/wy1uHwZ0xoTSngbj3impd892wAqFdCgAT7ZC82c/s1LgHMaeAcUKDCCkxNHccb24jqEd7bmrROGdpW+dnnbYgz15wrP/yijEWB5CwVY+XgCdb4wOa3gwHxvQdtOFiVg2bLUZk2BAgXfCGlyH6rAbCgCrB8UX758wfbt2zF69GjUqlUL9vb2sLGxgYODA1q2bIlFixbhi6mn6WYgLU4c2g333rKE+SaEnh/TgZA/gKn7Hy5JwJEj4gVfuAAQwUVmoSlGuYMIK/pXFL32WxUDYUCciH8HnsCg1ijpes6f2IJeo3OLXju1ZhITKEjgwq6lnPylvAhLanPLcOtowIvBWHt5eBdmD6+EXd7tEHvmFH52q4v5Y2rjy61rworOnQN69+a2a/BgYb7nz4EpU4CNGy0nADl7FqhQAShUiDnCN8QffwC2towfY5vqyEgu/8Z8qMXEAIsX44pHb8Fzwefv+zT5Xk42fklFaDdARIglJex98wbxDevjbWaNVuN3rG2WsHABao7WP5NRw/OxdyUhAY+WzRI8M6tx3N+9enP7ZE8DcbM+LT3NTnibWZjeuZ/GRx8AqNWC6+0HEFw7ctO2+bfX1WtoXkwqwqX87J3t2YdwoW5RQK1GZAtunhmNCBg92ngn7d8PVXPz5r1evQlRWVn0xLXVuNdKeWn6q1UrIw8nAffD3LGqa3HcifSQncMUKEgtOPsVE4z/EZ3Ne1/k6OqOXzDcvywnbU9ZrQDLXpA/UzjvO/PhQyr3UNJReQxrQ61RhKsGWqxYtCi1WVOgQME3QFrchyowH4oA6wdF9uzZdS+wFFWoUAG3bt2yaL1pceJYXT3pC8UcIYT/8hIwTcS58KxZALGNmTll8k1n+HQvp2ZBdu6csE5iQoNevQmjO8mXkzvESlY7IsirItSGgoSvX4ElS4D583H0Jy9O3u7ObOPLL+N5Nmb+hGfPAADxnz6gtI+1aH3dx+TR16VWM79JxDRQBncjzK1POFqc/X50fI9eyJGQAJQsqV+oLlwo/8BjY4EdO8T7zxCtWunLzJ+f6++sRAlcd2Bhz9VEwIkTwCOJ0ONEOFVE70/tbWbCm0N7uHkSEoDp04HWraEmwpHihAhHYR+FDyj0fWgoxcYCGzYAQ4eyvxoM7M7l968ShAsF2Fjs1I+AayJCSgA3ZwSjhDe7Z2RnYgJCLV68APbtY8EWvgOICaSv/G8dFgwRCp2txhFOFOWmtRvAFWAdaCFt1kcqwsRmQmfspGICKkOBDv96g9EZMKQbi8ZVbwR7P6PD/XX1dujPzW/ol6+oLyHh2VNRfkAmLBtWrkzSvMrXBNFS176aehs2lK328bZVOg04uzDCnZ0rk/u4FSiwOHp6FTDrvRjTgUUqNjX/f7tWYEgAVwNrWS1pAZbgHU/D0T5PqIajmqvG5xfxyNkZePIktVlUoEBBCiIt7kMVmA9FgPWDgoiQKVMm9O3bFxs3bsTdu3fx5s0bXLp0CW5ubrCysgIRoUSJEvhowcVKWpw4/ihj/kaLT+4diEUrNEREBECENgOTX74hba+gWYzNn8+pbtt8N5T3IFR3tVxdhxcH6isYPly3ENxfmptvUHfmy4N/v3UkO909UJqAT5+wyctJtr74OI2j6NBQgAg38oibdJKKMHxobrbQ3r9fuFCVQ7NmLI+VFbBpk3Q+fpmlSzPzqps3sbymXrOmS19C/x5MUHDUozMA4OvJYwju64AGw5k5pRj/MyZ10Ne1Z4+unnARwZUhvf5HQuPvW+HQIWHfHDsGQChE4dPd0X24zsDVauDWLbh34OY717EmMGwY4nPnRGQLQvOhhF9aZDfNfC0F8eHYQVGh71z/xhwn5Voa1YlwMw83rfEwbt/91bZ8kt7NNgMJaNtWxxv/eg3PjJzfTV0ImDFDV+/wLvLln9i/TDT9p4YmLBsmT8aR4pabh/r21PRX1aqy1Qb3deDcN3SEQ3IfuQIFFscQL6EGlhxtr0Do2N/0/Nf3rIZjsFBIBpgowEojazcpqIsVxYdM+jn2tR1bozn3IlxqU40dXm3e/N1rNCtQoMB8pMV9qALzoQiwflCMGTMGz2VMlaZNm6Z7wadPn26xetPMxDFxInMqXbo0ThY1fWEoR8+WzeHWMWECdpa33CbOkIr4EsI9KuuqinkfjZzBlq+nojsB3boxLRgHB92CcEcFbr5RndgmU6qcwn6Em4M7Gu/Du5dZg4g5tjX0syVGp6Z7AEuXmi7AungRd3Mxc42A1oT3ma10l77s3Yl13Utjm0sDxL94JixTS23aSEaKsgsjPPjtZ1xpVgGlJARXnI3GPxpNo9KldeUbu+fYulSOxFarFl7bMa27Ir5MePmpc1skfvlslPfjxYjjiHtXpDMaDBfm692bmbPx050GE06umgB1KpmGHRrYWLRdrqMKi6aPb074nJGZJx8tzrTRHthzx9OJrjWT9G62HExAB70Q1K8N93ozj6yc33VHEvDLL7p6jfnmmzelq+S1xET56Ku7/DpicDfLzkXdnAkuA7PJ1lvKW+jEWYGCZEOtBvbuBdavt0jE3fXD6po19neXYwclpua/+cc6SQGWk3dOTlp3Z+H9aV6w8/YtM/3XzHX9e3Dbt7M8YUoTwp0mlRUzYwUKfjCkmX2ogmRBWd2lU8THxyNPnjwgIrRo0cJi5aaZiaNSJUlTmqTSkzVc07WZka0suoHj01C3grq6ju7/JUXqsBqn2Wh7e3M23RuqpEybNkX0YA0iwsbKpt0THeojLcB68ABXujXEH61KIG7HVqi3bOGYSmUZS1g80xmvr19Av/62unSf/rkF7W08jJnCRRuJFjV4WC7Aygrx1sxnkFzeJXMGMD6trXXmn8bau37hKMu+C3FxTAg4diwTLi1axDWX1GLPHqBPH4AIwa24PE3rkAOfH94xyvvhkpo+Vavx6f4tSe06Y9R2IEG9apVl+8EEbKwsLlTtNSy7KJ/LakkIQQ3oH+emSeqD5kMJ6NxZx9uuctzrnTy4/u2quhEz9yTC7AZ6X2VSFBxWX/Lay6cG2qb79uFLsUJQlywBHD8OAHD1EkZfNKTePLNqmwgWJMNYmwsHWMs+n8ruJgqwYmLwZFYkHqv8vp+gECmBmJjv2p9cWkHieBX+LEG4k4uALl2SX2CHDkYPZwxpf2nTXRHUHkV4fXg3WkoIsFp75eKkjRfxVWdytNHvGbGxUPt4w0dGUF/Ij/Bh8dzU5lSBAgUWRJrZhypIFhQBVjpGgwYNQEQoV66cxcpMMxOHs7NuA2ksapip9GUVN9JNWHijZJV3KzfhdGFCWEvx613cc+vq2rdunNnll/cwnqfWKGa+8GcJtoAe2Zn5Zmo0zDJ9xienwQTMnIm7uUy/Z0abrEiwYpHanmfTRD/UYKtbc1hrTL5audjgwboFomXkEtFe69qXacycKsJNN2bKsaqWNY4XM433yRNaM0YzZNBpnRm7J2J8MgTOb94Au3ZxzV0NTEN1lDMnE1o2bgysWQPcvYsPma1w3YEQY0Nw4G2+mrtnxetr50xqcxMXAmrUwOHGBZM8TloNYg6/cfNm0vvCTCSePIEIR+ZPqhzv3RneMyN+qS3k81BJocDqugNhQT3CL7UJK2sQlrtKC4pIJXQCr6XGw4hpkWqwtSL3+gBPrlZYWU9CzO4dJgeVGBhQSjR9aS1CbAYCpk9H/Kso9OqbAaRiG+doOwJOnsQQjyKyZV+NdEVbA9PqgNamzcM5Q+SXLLXH2AjuSYx6oc9w9ChQrRp+qc2iI1qNI8ztXyqlhkyq4uWK+Rjbxgbj29jiw64tLPHpU7wO9kLs5Alp2lH3N4Vaja4G2k97ylLy+87REUUltHjF6M8SQi0iKdpRgYATJ+AYlF9wDQDaeHEF26ICrJgYC3Tcd4CEBNQbId9fq6sT8PPPqc2pAgUKLIQ0sw9VkCwoAqx0jJIlS4KIUKdOHYuVmWYmjsmTdRvKPEY0EUwltcEi6On1f+A6MJcgT5uBhNtnD2DFvKGCa3xfSa/tGH+Tm0psYL2YOU3s6BFm+ccgFdM6e5uZcCWfuPN1PjV1sUwfGaMefVibxczKpIhvAlHITz+t8bU6Voa0TVH+xzfXCypMye8bUpMxamMDEIs4Z3i9xmihJlo7n7xJG/NfvgA1azL+MmbU+a3S8htnzaJcbqiiEVCQnu53aiJpNkkqQl2/7Hh87k+T2pw7iJV5NIk+ktobOkE35rDfEoiLA06cwO/dKsiO2zs8oatNBBOAPrQnfDXoT75z844h8v5w+MJCQxrjWVrH5vqqvGve3ChkRX0JscdMe0akIvgOzIvlNQnjWujTVM2544Lv58q3LQE2NnAekVO27Pt/7UB83jz4rQphW0VCohWJ+hDjU8YIkg1iUNUnk+Cei/tXs4tqNf6tUwyOQ7jXcwYTEqNT17eaxREfj5bD9cK8vj0JePQIM3sXhk0E0zw95NYutblME7h/7hBnvNiFEfDwITfTly/CNDk0aICynqbPeStrEJoNNS3vtopMiCwlwGprRIA1ojNZxEzye8HRLtVl+2tYF71rgGdbVuKTnyfTNtbizBlg7Vrg/fvUa4QCBQpMRprZhypIFhQBVjrFhQsXdC+4u7u7xcpNMxPH5s26TZg5qvxSlDmMgLlzoY6Px2Bv8Q1paS+NUEqDUn5cbQH+gvZJdgJy5azzeqsAACAASURBVMJPEzuIllfJ1xa4f9+oAKoMr9y9ZTWR8zQUY0Oob+SU0pTNpSXIq51pZnSGVMWNCQu0v+1DCHj5Eti9W5DXJaCsMM1EjRRT6Ehxfb+aUu4gv5JsMBBha0WhH7FmQwnPsvGeu3fGpI35SZPEna8T63PDcVTFjfA4hz6fSuSU3pAqB9jh5vGdgnSpiJo1Ryevn+OsNbzxAhkkCfPnAzVqACNHMufFly/rI0omJmLB8KpoOVienxZDGD9ykT8dAlmfLqvFTe8dVk62bDlNyWE+JXXNWFmDpWk1mYL8qwnyv/7nqMl97NWOO1biNX3+NQOLBHqgtHiABZBxfz3Pr5xi5oYG5dubOMfEfXgn+SjL8ObU7RUI77b+yi5++iQpiH905qCwsKgo4Pz578YE796q2eg6xBZObllxarv8uH97+YygjR8yse+U9nc5D4Ja0cIyisMbpgjH77kjuuuPju1Gh8EZUNaT4O9WGgmxJmgvVa+Oqm6mjXdzaUslAv7+Gy0lBFjtvPJw0vgCrOFdiEUc/oGwuI50f93NxeafgNbsd5axmu949eq6Q6X/lSLEVqkA3LuHxCuXv49IwAoUfEu8esU0FVeuZL4AtQeg3yHSzD5UQbKgCLDSKZycnEBEsLKywuXLl82699OnT5IUFRWVNiaOXbssakKYK5iAqVPx38HfJPNsqEKAn5+OhQq+XG2BCrww2bdzE1C8OBZO7S5aXmkvwtdWjqJR0QypUAg3GlkjXhQ0U30vpQR16sf8GZ0vqOclzlo6fwF/QmyFMpw0uzDu5jdjhLQWVAc/4aJeykTLGJXwJsyrz+ojFcG7HVcwaMxJNqkIHbzzstN7IpOcvpOKUNTfSmZgyyBfPo4Z2+ZKhHe27DffATipCCFO+rYY0/DLGUy4sH0xJ621iw38xifPjFaMynpaUAPr5k1x31SZMgG7duHIsnCTeCrvoR+7cua1HzORYDM1eJxQ0GRIjWXKc/Mrr2vKktosguUYTUTHyCCho+iHl0wXYLUbIOyXxzkIJb3l7wMRWg+Sz/Pu3jXGdLVqurLFzHjF6O3Tu5KPs5h/Bk7eGBsClixhFx8/Fi3PPoQQu283p5zYY3/CtRMT5M6tT0wDIzURG4vOg/RtyzKW8OTqScnsJ9dOErRzT1lh268fWI9Hl44hwrsalgQ6IvbFk2/YqG8P9c2beNq9FT43qM0iWtavzzRsZLBh7ghBv/2zb7nuei/3vJxrs8eboNlWrhzqjkz+XDi1CcGjPTdtU2UCzpyRFGC1NyLAGtaFmNbpj4SEBJxyKoeGBprd2UKZbzEQ4XJ+bh9UdWPp5wsSshoI6Yv5MI39Nf6tUrtFChR8W5w7x10PtG+f2hxJQhFgpQ8oAqx0iBkzZuhebjc3N7Pv195rjL7riWP/ft1EbEwAZAoV8iPA1RX7F/lJ5tkytB7HOXY1n8yyZe4qR3hTsyKWzeovmYe/8BKjzp7c0PJiAizUqYOy/kLzm5SkU0XEN8i/yTiID/OuCgAowduo8kmrocNPr+WbxWL81x/B6nieTePcl9eWCc2Ml9HKOxe+/HPSZC2wUZ2YDzKEhprlpyQ+5rNOWPV3EYJtOOkW5J8yigtxe/fWt8WUQAc/Da/E+d3VrxBCJjtafNwU9TXo5xkzkj4HqNVA6dL4uQ4za6zuSujTiwk0y3sQblUtjCYBuU3iKctYvfByTgPpfAGtudqCpCK4jq8nW3aLIdLXfAKq6pqjfb5afz1zPYUCrOc3TPNT5jiEOernj2m5SKNaAhk3Of76UhMht149XdlRWU17/q8eXJN8pPmDrDl5E60IGD+eXbxyRVTT1LsdAbyAAEvcuH1X3Idw8edxSR9ryYT63j2BFuy0SCd9hsRE4NIl4ONHAMCKWQME7RTT1N2x2Au13PVaa+M9qkpwkEagVgOPH+v6wRCJTx6jUz/WzqxjNQdKpNG00eLxY6BvX+ao/d9/AQBzIloL+u3I5pksf0yMQMM5XyAxX4MyeF6ukODAKikUlZVpShqmbaxMwNmzkiaErXy4cxpfi9KlK303WocWx6tXwLVreDE9Am8zs/XG5kpCTeG8gYQvNvJmm1sqERsnt26ldqsUKEhZJCayID+G64Fu3VKbK0koAqz0AUWAlc6wf/9+ZMiQAUSEqlWr4ksSos38EAKsw4eNavuYS6cLs8WQ1PUda8dyWOg/wvjmuJlXDqiXLkWEo/h1mwjSLcrFaNbYFjgbPIiTNqQbYUltwvx6TAAGIqBZM9Tyy2qxvjBGa6rrP4QXCxCGdtVrjvDJLowwtiVhq2szqJ8+BQA0884hW/7bzKxsvgP8XCHCKGVJpS79NBvfhASgd2+8zcxObLuaEe7c0Tsnov4UmjqK0fTGPIHC7NkmD/djm2fBJoJETeH4GyBDajOQaZqZwp9P31xw6cqEHF36EiZ71sC4qSnjc2xqEybIO6AalPQ5YPFiRNvphXl8GtKN4ORqusAzWuOzbmFd89riO7GJ7PVKY6SvBYfU1rdHMy7qjCTkDyA87NpCkP/1w+tG+dFqJYiRKe3R8iB13TqSoNZ8dyKGFINzLyZc6dTPNLPb57cv6pr85t5/cPMshS6j7XF63VTO+63TxNQe0hw5IirE+bkOAXPm6Pvx5k3UEDFxzR9AeHXvatLHWzIQLWL62c+rMLsYH4+J/QqjpDehX39bfLh5BZPGO3Hy/tSQEb8Mj5FcR/+Nh1sBnz+bx9zXr9+PSVX//uyZ58kDXLnCuXRqfhCnrTlCmFnse1uCq2cpdHDLgSMlrfTjXSPYCg6sKei3P1ZqvuWPH4tqDj7ft0WWzcIB1oJ7zCVthNNltZjAJetYpk04vjkh9p+/4RiYT/huAtjUT17jc0g3Eo9C+6MhOhpbnKtI9sO5gixYiFxf6TTH27dn0XsVKPgBET9iGLZX4AWlcXZObbYkoQiw0gcUAVY6wrlz55A9e3YQEYoWLYpHWj8vZuKHMCHU+P55b5u8RaQhebYnLK8pfX33+nFcHho1wkQjWjqdvPICa9cixMl8ftoPIMRFPQdevMCGGhnQr6fQUfy8+pqPUbt2mOxSxuw6kkqTm+o/hNsqyuet6E5A3bqcrps6qZ3sPaW82AlpNVduujnO4Y3RyIn1dPwkLF+mMyU0h5p658CD/RtNyju3vohQwUQsnNZDtmxzfZx1cc+DpT9xtTy8u2Xm8jZiBCbP6GxymZOasqiX5vARFF4/Sa//502/4kVWEjj05lM1d/GNZpax4maVSQkIMXVqR/xalWlOiG2G5d79yLBG+kZpAgEcL0Z4lYWA7kLT4/cvHhrlJ8FKOM7u5BI6n5eivWXlx5NHB9IJPBr423OurTGhjsfXzwBv3uCWUw1OukMg28hnDmNadNlCNfz36MH6Z8cOUWHtnyUMtLRu3QKyZMGfJcTrnje9R5LGGwdqNTvRNgP/7F4i4KWKdyYAwKVtXNPdOYPKwjuUK3QJbE34vbywPdXHcMf3wO7E/H6ZiDNTPTCoOzNB/rx+tVltsjguXhTOj1+/Aq9fA5s3Y2NPYRCGW7kJrh31v23DmXm17v7PnzHKT+ijbt8iX1bnnTuifuB2LwuUZTVvEPcghS9E921r3C+l1u8eiGmPGl57/vdBtJQQYL3v4CRbbuNhZPb4TKs4cGKNaB/06k2YLaNJq6URnZlZuG9bwqDuhHP1i/642msK0iVi//kbzYfqx7zuIHXw4NRmTRKKACt9QBFgpRPcvHkTefPmBREhb968uH79eorUk2YmjtOnAWLOiE3ZlJlCIzrLmw/t3zyFy0OLFghsLV9mP5+iwKZNoj6K5KicByHuZ4MTwXPnAF9fTB9YUpA3cxhhoHcxJPQQ97WVUqRdfK+rJp+vqhsBBw5wui7m49sk1RnhaJo5nCkUPruTAUMxZkWV0lIj72y48ftKk/L+XIfwb17mKNy3rUbLzEREBtax6LN7/ewuTuxahGI+bPPUfChhGl9DzN0dM3/qaXKZsxvIOywnFdMOMPztGVLdeON5+HNFuMn+lvjmQVoq7Ec4MdvXIn25cGZvXZ8Zbqa19HcR6XsnRzrqG2YQmAJEwIkTcDfQamwxhIAvX2R5sTHwH3c5P2FtNeZTx5RIpcYoYwShoD8BdnY6lhv7cyO1zqtvvJz7W5bhdesmHN80WjpbSM+/1uk8Gjdmla1dK+rM+bUdAf7+AIBPfp4Y2lUo6NdSl9E5zB5vHPz6K2u/gwNwUMRxvASubF4gMN/MGcyep9tkrp8560hCvwDhPE8qYaAQPk1oRsy5vjHExeHD5HEo4M+9/+2/55LROcnEuHFCAdbEiUDlygCJHy5tqkyCceTR3uD+d+8w2Kc45/q+MqQLHhF39ZJoP04LbSrLqqGA1zacPTPt7zyaKK1/lDH+Lmj55DuEj/rnT2wc0ZCT5jiEfS/edZQXYJGKvh+NuhTG3xfFtZ8vD22H59mYubqxvvq9PJvXtM/ybPtqqd0sBQosg8uXBYdKFdw1887IkanNnSTSzD5UQbKgCLDSAR49eoRixYqBiGBvb48LFy6kWF1pZuI4fx4gwv2cyd+YaamiO8G5l/T1Q9tncXlo21Y2ahmpCKP8ywH/+x8825vHy51cxCLx8TBndm/R/P39S+DdIPFrcnS+IPOhNLkpYWIz8yI6VnRnDqErGvEFUnNMBtFHeMSlJbYa0d7iU1ArxrOcphypWBSi6w7yeRYsH8Xhp4W3vVm8kIpQzysLLrq0NynvpKZs06r93acXAR4eJp2Wu7mXsNg4JxUhPi5WXOPBkPz8MH+utP82Pi2prd8IaMlwYzfDpx52rRnLud6pn/mbLWMRBQ1JKopiZb/MAIA6Hsn3G7d0zkA8zU6iwmy7MCaUkbp31kQDR6rx8UBgIFCrFos4mZiIr37emFefvZ/vurQBAFledFpLREa1Q82hLzYG4yJ3bh3Lzf25ZtR8h9JidCcXMyEVu3a2EHPcvq0iYWUNdqCwtF0+VtmKFbjHm++1fuy0i/EFbXJyrvMFNOU9CHj/3qzxpkNiIqJK5MWoTkxb426N4qbf++uvADHBqSE/n8+ewrjJXB9NU5sQWvk5iPaPMcfhq2oQrpbOjr961UHi6b+l+XF1FT14GNuSgB07RH1QpThUKtn5aK6IcJQvECcV0wLV3RcVhd6eBTjX/ypBwPTpAIDoM0cE99uGM9N1SVPMxERONEi+AK2gP6t7g4wvSN1Y1PBZmWdm/PLcURwdyvU/2Ls3W+6/7dTK6DuWXvDf7VOi7X/w6AoQG4uvs2fiQgHCiposEuGZwtx8pb2Yn72PmVgAjcxhzP+f2NpLgYI0gdhYYOdO4OhRoGtXuIkcqoE0a8/vFGlmH6ogWUg/X6p0ipcvX6J8+fIgItjZ2eG4KaeryUCamTiuXAGImZkZTsxVfGyRScInjjk0WGRhfGQXL/R5ly7oZ8QpskdgFeDKFaOCLj5FOXcSbfai+YNE848IqoDogT3RfoB59ej85SxZAvzxByr4ZDTrflOonm828Wfo4gIQNwKhMfJpy/gV8wdjSP8UIjzLJp9ny/ZJHHbae4tvGuWo9ijmzN6UvGKaY+4dCJ93bDY63Ht6Ch36JpVsIjSfjTt35AVYY8diycKhRsvLE0Qo4su0IQzNcbKMJfyu6oc2g6zg75wLX/69hIObpgruf/7nLqPtN4Sp7azqxhUYGlITH3sAQG+/osnuz1ULhuNcQW5aiyHMP5+aCFfySd+7YFp3+cYmJACrVzP/LBq/U2Ll1BzNBMmGm/dwCb975pJ1JG9c5M+vY8/Jn/vOmBK580YeJrwVu+bSlQmHDNPKehIT7i1ZAhBzTq+95tqRCU7/GNYMADB0eB5BmXYGwoZM4YSEnduZX60VK8wTnn7+zNFkqzPSDOHrokUACQVQd/74FSERXA2sZbWEAg1TqWN/fVTWfkOySQvHieAvoRWcYEVAyZLm+9JKCtaswbPyhXG9cXmoG9SHmti8fb4gYXc5wtaKem08MS3m3r2FaTYRBtp7jx6hswd3TJwuTExYBuDen9s51xwCDUxwfX1FWVYPHcKJfJsjhB3kFPZjB0BV3YhjsiNFVd0IOHkSIKGfvNcXTuLPoS04ac692Lz9prPQKT2f0gseR90Sbf/bj68k7znq0hLVXNn7e74ge95La3F9ePbtbY3TM7wR/+6N/h3/USI7xscDL16kDz9p6Q0xMVztZWJm5aJzvEZr+XtEmtmHKkgW0s+XKh3i/fv3qFWrFogIGTNmxL59+1K8zjQzcdy4gZt5hBNzO+88OLhpCnr4FEI5I+ZMsgtAInR35qad2PcLl4devUT96BiSd3B1ICrK5Ch1Wjqye4Fos5ctFoYEJxXBPbQ68xVD3A2eMfq1quZDp4nYJBVZMX9A0vuyka+E2c6YMQBJO+EWozEdGL/TG8vnK+Rn3F/WsePrOex08ylgMh9aqu5KOFzStLxSUavmNLc1OtwdvXOazZsUZQnTfDaiouQFWOPHY9XPo42W99Ce+aJbVkt4DQBnoXxy9yJBnrDhJY22X4vEr7Emt7OsJ0kKs7t4OAAABvmXSnZ//vqzG07zTva79OX25fFi4vcundXf5LZrwS+j+VB9PV8zsGcRlVVem9Qcyh7KGxcFCuh4aeMv9NNjacoXQEDDhlg9YwDyBAm1qkhF6O7FhGqdvPMKrvHfu8c5DNqyfr1UN+sRF4e3vm7Y2CSXoOzPUU+4eaOigFmzgN9/56bPnAmQMEDEsd+mCco0VytVjh6dO6zn4eFDdjqv2eT4SAgbV9bQ9E3VqsDTp8C9e0x42r07M5/s04f5pkou3r/HkgY2und0TAc2fvn8iEVYJRXTqpEKuvJaE5ABt29jWY8S8GjPXAQM6MG0thEcDAC4tHcF575Wg3hjffVqLs8JCYLAMYX9hHOnmOkyf36sNUozPwYFCcbom8unccilOSetfw+WP7ozVwOLHxWVVOlnW/D+i7grgoREGeGMWg389Rfg6Kh7Xvy1niF5t2MO4ZEhw3e96TcJHz4wf6REgLU1W4Pt3JluTE5/WNy7BwQEIKp6GTQaxg4PgzRRiLuIBCWKtmMHlN8r0sw+VEGykH6+VOkMMTExaNasGYgI1tbW2LzZuJaGJZBmJo67d0WFGN19CuqyiG2oTSUQoTMvOuCZg7zF7IABRoUk/iG1gIQEs+v/a/8S0WavWeoumt8nvA7QoQNAhFAzHMbrnMBrQknX8TE9apup1Mw3l/gz9PeHmswra3gXxu/kpsJrMxoRivuYXtbNO2c47PT1LWZ227ZVJOwpm/w+MoYGPtks9jzsQ61YoV++cDZdHzKxzf2LrGwDGDN1ImI2rcf8epZr19UT2wV5ygRkMnkBffX4NpPr5pszGlKEI+NtRKDQMbS5tHmZD04U5ab16MPd0B4tLn7vmvnDTWq3IfhlGAqw5CIeJpUK8TfoBfVzbAd/84W+5pJtOKt3YR9xv1Ck0ggeNm0SNQN24JlFX3NgWj4fMhGQN6/R/v6yeT2KScwrL84f1WeMi0N0xRJYUI/5P8Ly5fprU6cCJNQuW+fjKChTbjNtLv25QeO3UXNYgCxZgO3bcTu3tIlpCW8WvVTbb6W9WGTOD5kI2ysQbucmYIt8lD5TEOXcSVC33DvLp4HdpTX5ltVi2jWxly8AlSoJhfNeXgCAY1tnC97b97ZMM/lJdo3QgjMYvuBTRm5dWcfqhfja8vnfoSv5SNz8FUBssD/y8Q6I3l05i6i+nbG9AhNobq7EfOkBwO8DuP4QDTUMScUCoKQXJKoTBc8++1gz2v/1KxAZadQlAanYHA4iRI9xwac504G3b1OuYZZAYiJidu/As19mASEhQPHiiLdmgo2mLkxgvLq6JmDINzgcV2ABPH0KDB6MxG5dgUuXWFp8PFCokKjW9d9FmDY4fyzfzk1AZGTqtkUGaWYfqiBZSD9fqnSEhIQEdOnSRfcCz5kzBx8/fpSkzxZU9U8zE8ejR6KL1/6+xXVZFiRx413dlZ0ENx7GTT9/ZAOHhYmB9Y2WFRTKou9V8jHP187efTxzRQ02rPIXryeyIdCyJUDMf4ep9Uxpoll0P3wIAGjknZ1znR+VreFwwr5q5gm5nHzziD/D8HDE2Jj3bMY3ZyZIHiI+xSY1JVRxM72sd5/fcNgZ6l/aLF66O7O+s4TGhDHU8LVLdh2kIhTzIVT2YdHPoFYDxMzclooIe1VT2gA7diRPEMzDu8d3BHmauhDW17FF3ASVbB+cPSgecSop9EdP5qh3THDVZJe1Y1UwBvTgpvXpZbBZLltWMire0sXJF2CRimklaU0ILdVHWs2Xwn4s0t/fRQgXCxAeldWbEHbxL2RSWbVGGfffJEexGeQDbJCKRR4rYoLTZu2G3yFQY05mBEvcpef5hL80Gk6XLyPBihvEYGVrA+HYxIkAMQG84f3hQ4VCc/53Jzm0a94Y4MkTwMqKI8DZUUE8v6FpHJ+0mlKZwwin6xUye9xycO+eJA+WpIfH9wDFiwsFWBqfaSc3zkQpLzbGHQKZhlYpTQCAHCGE//IScPWqnu/37xFtJ17XL7WZdtfl/MJr8dYk0Bhv6sLG3vjwpoL8H/67APTsKeQbQr9zfCrjmb62BfzowUUCxH1uyiHu3BnJ+SlDJBMC9O6tN2PNE0Q43KYs+4bevq0z7051bN0KdOkCVKuGv0qwd9U6kplaJ1qRaBAM7djFkyfGy1eQqnjm3AH1RjDXCLM7OwDv3gHLlunmB/5z5Ufx1lJYS0JUiGdqN0cSaWYfqiBZSF9fqnSC+/fv615eU6h48eIWqzvNTBwvXoj6FPL3razLMqMJN9R1CW+mecGP+MMnsYXM6E6ER/9wo06NGy88Pe/GOz0fG9YQAPDfqd+RP8hatl4tZYwgvD1xSLTZ234NE/8gqZqxU2WSdpAsRuGOmoXxixcAgBY8U7VNlbn5W3rlxF8755q1iWjrl0/8GU6ejLeZTS+niYv89cgWhBqjuWmGTsQNyTacoOZp/bgGVjKrXUO6iZ9umUs1RstP44n/XjWrPKkIbFsqaZ51pUr6wjNlAojwzlYYQXDStI7Anj1YVDfpbRM2RnhirqXB3fSCVDG4BlZMdl9rKXo302j1HVs72WXt+TVSkNa/h8Gmc/9+HJIwM521xPxQ1lJ85A4iNLKg8APENj1888f2w/Qmr939ixgtx9CcMqm8vM1s3Gw4KaQ1y5JDnzHiZpJlPNmzBQDUqYOd5bnX8wbqy37vNRqPczANH8M8w12EPrv+KsE2mnIO8WuOJrQzwd/h+hmDdH6WDCnRiuDWkQk+2w9gAQi2VmSaR6b0m2/3LOKddeYM0LEj86H1++/SmpWLF1vsebYYIq15+PbIfiBvXpwoykzKq7my5xborZkDt23j9MtC3lzXZiABtrZ6IVZ0NJ5L+FacV58kA0yoifllNEwr58HGx6Tw5oL8H69fxpHBzeDblpmwebYnHCylEWD5yQuwtOWmF/BNKMv5ZUpSOTHbN2NZO6EJspbqjmRmqIZplyvlwcRmTJvxfpFsLDCKHK5cAebNY/4nLQB1fDwOhvWD4xB2oLapMtOuqS4iuNhYWd43W/0RhI/LFhmvVMG3QWIiCzbh5ATMnw/1jh0cE/SMEUyz82o+Nr+YY83QeBjhRsDQ1G6hJNLMPlRBspC+vlTpBIoAywRER8NJZLGIMWN0WWJzZcerLIRHOdjp5795CSM7GzdTcBosHhYbN29yWFg0vZfg3l9qc39HRDTR5Y+JjsLjovacBbOYltjcppkkT/R2b54kyrNqohPb/NvY4GUWdmpozsfs00t2+tbGm7uh4mvmdPTOi7P/W23WBqOTf0HRtlyeGWCWuaPc5jx7qGlhy7VUzEc4dfqE1BDNe6EA8ynE12z720Tn7caoiK/8ND5xTBWTy9KahIr5YNH5O6umDxO+aQgzRxET9E2b0RXYvx/Hi4kHNTBGUoI5uffvwx87JPvBVSSaTlLILkzPV3B4g2SXd2DTFEFaiyGEp9k15hkfPuCAhGDg+qGNss9eDEGt2Iad7+g7y1jpDXRSSDtH8Q8KSvrpNRx6BxSXvL+6K8G3iy1e22fUlZVUgW9UVqZhmZR75cZbzmDjSyh3V3HT4hqjifmPSUgAiEXwE/QhAIwcKao91nYgYcSA7IL0OiOZCe/t3NI+nj5mYv1pLGjHkkndgAMHEG/NhICPczDza6fBTDDyU0O2CULHjvgvb9LeIURFAbt2ATdu6ATiOqpZUzzqY3g4jkn4hTOXXDuSaKQtUhESDh8E7O3xv1Lc9AHeRRkfGzboeN1YWbyMUZ0I6tGaiLUvXuCBvXi+6Y2ZwIufnjGCRM17tONjSoTwIOzzrf8wexRXO3RaY9MEWBXc09e2gL/WKe9v3KekHO65dEMvkeAApGLrR7m+dxxCiPnvMhvzJ08Cnz4xIa5aDVy/juMlM2BlDcKbzMQ05itVYho0V64g1qkFiwz37Blw7Rq7R3OwqMObN4zu3EFCWKggiJDVOHaAmWWskLfWg4y/S869CHB1ZQF2dpkXXCVFkJiINzs2YF2bAjjRqhzUV66kNkemITqaPSdzcOsWi0Ds4AAMGsTM20mv5S/m+1FLXfoKzdOlSBekwt09ZdpuAaSZfaiCZCF9fakUpDjSzMTx4YMg8hepCAgI0Ofp1o2zmL4qEw3MkPgbw6xjNWXcvcthYf/yEE6+VoNIoK2iGtecy3fTphyeZvM2NlObEFvQSODAjlmiPE+e3I5lOHMGCA7GVk8n5A9gpjvHizG/L58zMh8mYvd//fgOANDJh6ttMLMRN18vn0L4dO5vk/qRVEyw9NeIVqJtWfxTX5PLIRUzX5S6trq6uIZHUxe2UeM7oa87Ujh1hoSJCzOicmQAiEWVi3Bkm7/V1YXaXkmlzGEEdXy8+AP//Nmssn6ulWYEmQAAIABJREFUw/qhhLfwms45c82auuJvndojWdasWT2BQ4cAYiYw5vBhF0Y4Ulz887Spb1WB2YeWDq8dLzn2D5YyjwcpyhZmpSszclyzJJVxoijTWou2I3w9uF82Lz5/xj4R4eoE54LSUeLkQPr5I5dBlMUMkeKRLpNK2jry8nxI5Q+y1rHSL0DcL5WTpz0Twn/+rAsuYa52qCE9zkEY18L8+/5oUgDD3KUjTWYOM76Eut9VfIw0dSFg/Xq87dYOR4qLC+dw/TpAQu2xWQ0183+rrJLtBUkLyWMzsOvGBJafx40FtmyR3fwsmtAZ+PoVUcNNn4/rudqwznnxAsiXT6cBIDB5IwKqVxdqYml8cvH9TCaFXLoy/0S/VuWmZwsl4I8/gCxZBD7qtE7/sXq1js9ImfF1viATROPxY9zKLZ5nfHMSDeqSI4SVHyEhwJoe6SRIj7lzA3NHVuOkTW5qmgCr0pj0tS3gB4GpkEwBFgDgyRMkLv2FE+241SBCiAkHbhOa6cf+pfxMe9+zPfeQKF8AO1gFMY1Lw+/10K5srruU3+AdqlYNXwN8jQboaT6U+QJ9m5lpVJn7Lm2uRLibi5k6F/UlbKhCUEdHJ78/jeHSJWDIECBPHqgd8iCxZg3g+HHsb8CNcuvegVjAoZkzmS/GPn2AR49Snj8p3LjBHKhrcfUqULo0e2YZMwJr1gjviYkBJkxgc6ChJt7gwRyfmf16MpNVY5HOzaH2A4Rm1N8j0sw+VEGykL6+VApSHGlm4oiNFZxOZ4gkqCPC9Xlu3WJOWImADBlwoUDSJv0M2jDyPPOm+OhXaDyCmQUW8SVcqV1E4PB60ngnLt8GkW9+qc00AAzzzxrfTrbZf+0VRnEjFWHGtM7CzFrHvQYkZiJiHakXoPTwlfdpM8i3BHDjBmfjUdWN8G9R8eiFLl2JLUxEsHyBi1nPQW5BptUu4qd30/ipem/LHK4vrsMWhwvrCqdOVaTQlINUhOiHN5hg0MAZ8J1clltUkIrw6YX4ImyLTxuzytEKqcR8AS2prRkHdevqyn8bIx7FiVSEeXP6AkePmm3+1WgYc2qMHBLRJ6Oi8CgHW7gLnuNiV8mxf1Hi/T1RlJlEDetiGn9X6xbXlTlpovGQ9GL0TyGD9+qvv2TzRr99Jv5eJNUJMJHZzyQppK2D72DaUIA1MFDcb1w7z9x6fvv00ZW1hKehyjdblaK7ucQ1nOSoqC8Bnz7hol9/7C0rrqFZyI+ATZtku/tduxai5XfoT3jVryuKyvjdwvz5AAkdps+vx/pjbOsMovfdycWuix3SkIqNd5C8vyy7MALs7QFPT1GNTC3tXs2iUSXEfTW5b9uPZCaEXyJCdFGu2g0gfMrI+Iq200c5vFCAgGPHuJ06YgRAJKnpYi7tLM+EeoZphf0I2LED54vaYE117rW2XprxOXKkbmzKafiFOhFQoADe7two8GNmjIr4svLFBLAAMCtSOAd9vX8HC0ZwBVgTm7H8zfyE0TDFyk0v4GsWHWpbzqLlx+zZidc5mWahqVqKz7OxQ0O5PMO7EGJshEEmtGQdSThVRD8PTzFR+L+3LOGLjbxAVopyBrO5hy/w/nf3CmHHxMYyLbO//mIBKx4/Nq1DExKYYH/bNmDcOFZGsWJItBL6COST1TimMXorN5t/644k7O+ged4p5YcsIYEJzeLiuOkTJui/xWFhzIeYra1QgM9DTIAPFtZlwtA71YvpozRr8r+2E48smlwa1YnH12Dz3Rd8K6SZfaiCZCF9fakUpDjSzMSRmCi6gfsycRw33/nzwJQpwJkzOFnN+MJPiuKtial285Bw8jiuj+yOD/NnArNmCTWqJrbl3tC6te4DUlJES2bexE6yzT55aJUof3Nm9hRm9vUVfEyj7QiHef54shhE7TEWiW+Uf3ng7l08yc4EBn17ssUE7t6FS1dh/sHdCBgu7qh6rURERSmqZ+REsdlQYZpLQFlg8mQgd25uX9SqJeBn2sS2ouV+eGEguFSrsbUiIaC1NB8L65pv7vTwynHRPqrhLS4YlCKtII+vcUYq/aYZDRvqyo9LiJMsa9G8gbh7cDO69GXmTqbyENaSABsb+Uhl9vb4V2QzsPSnAZK33M8pzD+ys/6ZmqpC/6RGKV2ZM6cII6GRip2Cy5VhuLHA0aOyeaPfvxCk/exaR7pvjMFgHJszNsylEZ3FTbPyBesFWEMDy4ne29nLQc9vv346fvk+9UZ3EvqFSiq1H8CEmQdLEXaVI/zRTeMPMSQEIKGAg1QaIVfevLKacIlNGoua2PbpZVyohuXLRbV7tJqSXu3E77vmwK6LOQTXHagQMzeUqtteo/kj9t4Y0sOLR/RDy8S+7jwqO5CQIAhgsaS2uLnxs8UzuJ06ZAhAJAh+kFTaXInwMosw/fG6RcgcJkxv6pUdOHeO8y7xtY0NKULjK/JmY/OjllZ0Z/fuKsdNL+HNvrtzVMLvTvyjB/AZwxUOOw1WBFhi4M8fx1pbVoAFgJnJFi0KEPMXJ9f32yqyIEByByoOgUzItKqGdJ5O/QjrqzKTLzWxqJimBKkJdWLj7WMm8evZQtm8m7hmNa47cN1NRDgy7U+x+yb0L6w/dPn6FShaFI9zENZUZ4F1kCcP8OABO+hVq1l0vNOngStX8GC0M6b3LYpVnk0xvF9WtBvANNWuOeiF8YvqsnVGbZlAJMGtWN6r+Qi/l2d+SEnFhDPOvZhLjrg3r4HXr8VNlwHG2+TJQJ06wNixTIv09Gl8OXsKe12dcMVvILv/0iVg2zbcKZ4df5QhxJQqxkwD1WomtNPMG9ccCP8rxYT3V/MxSwfBwXEDB7h1ssJaj6YcVxSVxxDidmzF7YUT8DYz0/S0xHwoRlqNPx0tXWr598RCSDP7UAXJQvr6UilIcaSpiYOEziofBI2SzP6/6tmSPPl/zETAy5fy/MybJ1gEz5jcgZPlbadWeJyDhdQW2xQtmtxNtoqzxzaK8rdodj9h5uBgUZMONYn4+NKgv29x2X7wDqrGVKb55QKIt+YKEazGsd8YJf5MNq4KSLGPtZZ8I+uzymJj9ardtrZsYcXDnKldRcuIecN97nKRukhFWFdN2OdjOsjfc/7wr6J9ZG57tY7a+dEjSaU3W0LTppw6Mkmc9v2y0AVXDv0mWZfUQnPcqLJctXoR/N2gCLxFNu/zpnSVvOedrTC/R3t9H5uqan+vtl6ANW9GT9E8ozoJ03IGs41HQX9N9Drt8z1xQra+91+4Wm5lPAkYIC2oMwoD0+iUfn/EKK+BAGtEkPiGvoeXPlIhBg3S8bu3LDff0K7MT1hS+GjsZqv7v9IYzSEDGVA7jTbr+PGyY+SdreZwQsrheI0aHFNNcwi//QaQ0PRoRU3Gj5TvJq35kJjGh12Yvo3GNrSlJAI6cMoyaLdUvqw8nzptRmcFWrdGWZ5mV6ZwtvHm389/r119yogKlpJK66qJa8XuXegtmr/2KALq1eOMl3n1pcv3bsfyiAndjVG9EezeRCv9eiVTOIvsCQDzVe0F9yQ8eQwnfwdBOgA4+ioCLEMYHjZkiCTsb1k8ZSpKSGBuJBITEfcqCnNda6D1IOYmQVv/b1XYs/4gITzSkn8bFlTAlPGTPZQJlVZKCLuqu7KDgV+rMg17nSnv4cMC7VnrSIJ6yhSmUQQAajVWhrZDrVHswCI2g/FAID5tWfvu5RSal2tpyuhKzHSY2BzGnz8MqYob01ibW18f+VaK7uVkbYuzFrrf0FIJb8LucgRYWyPRIQ8e5WBCwLseA/Ap0Btxvl74YsOERYlWhGPFWITbiu76Mkp5MfPmIJ7J5prqxIkOKhaBOnso4boDe2ZNjQQe+pb0yVCwVrLk9xM5UwRpah+qIMlIX18qBSmONDVxFC4sWCwfHyVtgvdT80xJnvyPFyMkvH4ly861+RFw78B8odQfwez1b88K5eTp5V1Qtp6l03rL1nH59O/i980fIswcESEqwPrK00QwdCLeKUDehDB4bD224SlTRl+mq8bsa8MGJFqxjSTHH4qBY31DbFs3NsU/2nuX+OsrfPcO2LGDmZaKYMXcIYL7S3oT4j994OST8t9EKhaeXqzPpTQttLR/6zRRnsxt7+/lWX32IeLXS3gT+rlxo0LmCrESzbti8UjcOLZd9FrN0dL+liaqHEXbYogKAeKaZVNVTpL3JFoJ8xua8plqjnS9bkldmUtmDxDNwzdlCGwt/lxBBJw6JVvf5ziuH7OS3sSEOknF9etArlypJsByCNELsCYF1kctEUFmX+/CujyefhWRLZREBRZ9ewo1BUp7mebLa/MvXtgxZTDmtrVnzvL5z6Wzxqx6xgyAmElZYT9hOT37aPLXr880LfgoXRqFRO4zhbByJaLthOlaIffT7ExYxXc0H+LENHZW1BGaGOYIYRuvLzbM0bu5ppWGtGUu93Bh5KCcovn4JkXNhzI/LabWM2xcDU49Q7zlD0rMpWyh4lEZN88eLppfqxW1qC7TwpMzsdRSq0GEPWXN581psH5MfrFhm+sbefQHP4vGC7VA1c+fw8lPXIDVz70AJ00suEV6wsucmfAiK+tbNRFQqpTxmyyFxETgxg08H9Uf0Xb655xoJR08oldvtgbju5uQoqpurF1iawjnXoTYo4cl2RvNO4jpGVhcPOOBAzre11YzztOQboSTRUnWJ9ffRZizemNlhTuy+dBYvqYu+v4Vs17g0/qqpmmsJYW06ywpP6jdnFk00JSo25h5YcYI8XlKtybfuZOthb9jpKl9qIIkI319qRSkONLUxLF9u2BhPS2ypWR2scgs5tCbV/LOIn+bO4KT37sdMR8oBujnLR92ftVMec2M+Lu3RTUWVi0Sccg4aZLohpuvyVLWUz+NHB4mjIhkSKrIZizjrVvA0KGAvz/3Yzh7trBOLy/RtuzeOCFFPvBaajzKBuqYGNn+NMTjrStxoDThYgG2sfyqcZTM930gpzmgi/THI/828rxuWO4j4Ed94IColl5NkUWTbTjTprCJMB5ls6VPbk49+YLEBVhrl7rj/mmhg3LP9oQXWaWjGk2d0NpoXzfyySF6b8TYBqL5E77GYnxzbt4MkQR1xQq6Pu7mbNq4uFy3mK7clQuGCa77tSFBdMywljICrDNnZOv7msD1LVTUlyT9wpmMZ8+Aw4dT9P3REn9z39jNwEmypyczkeD51hvkU1yXZXSAtNlV537sNN0wrbCfac9yx1rmuwm7d4s/lx492PUFCwRCez7p7uE/F7UayJoVxX3M77fO/aRNUX+rwvzfaOeYnhJmlJnDjdczkGc6K6ftwKcDfy3nNPfWT2Gi5sdiZEwT1ZBUoY049fT1kj8osRStGFZTNL24D+v3Cc3MK09K40SOujsTkC+f+BgFsGRCF07+jv0JePlSUoDV3517CCamLZqukDMnt08LFUodPlauBLJlY896/XqcWhbJfZfDCJc71wOmT2f5MmTAntbFUcWNUMc9E0KcuO+U4xBC34F2OnPi04UJy2sSLpXKgs/DBuHZmEHMzE0GDxdO5hy4nZrrL515/Xqge3egUSPczSXvc+tVFiZ4kltTNxgurWGqpSYu+gPPyU1J8qCgkB/TftQ+Yykzx29FFdzFD2RSmuzC2KGFXJ7XdmysCOYEou868qAh0tQ+VEGSkc6+VApSGmlt4tj3m14IkiGS8Oia0DRMi0wmbAbk6MM7eRPCLQvHcPKP6UDA4sWcPIN95E+ef509VL7BT5/iuohz0Murpwuynpw6BsGtCP0N/IwU8RUKQAwFWLcGdZTlb/pEeSfzOH4cN/Kw8Ozz6zGn6ScDnEWz7t82PcU+9Ef9eyH+2r/yvPJx5oz4JoNnWiS3QfylNgGRkTq/O1oyFj1o8Vyh4DKuSiW0GciiL1ZxYw6bY2xINBjBe1tWT93R1pz0HCEk0Bhr6+PAqSd3sLgA67flPnh6QejfaVtFVpejSNQ1UhFmTmxvtKvbeucRvdc/sLpo/qe3zovm/zghHDhyBGoZfvh0pq5+g7N+CfPDZhvOVP/HttRrZhjeM765/lmGO7JNZud+TNBy9+gO2foSEhM4vwv6EwtTbgHwx1VuEdNRLR0uKe9zRYr2VrHl/D7drLSeAR8fgJhWiWGeYb56LQj3wMqy5fOjszkEMv9Sxvja8xuLWHls608o48kEg3kD2aYJRICzZt5ZvhxvjWgCxGnMD9XEXVJFPb+DHBLajHL0KguhjQl+47TjSsr8tYyvjVGtH1N9v4nRsZO/CcbUg0gvi0a+IhUhOLAmp44envmSXJapgmpSSTuFzhvInrUx025L0OCgsqzRFSpwvysZMwIAlk3sxsk/tiUBr15JCrAG8ARYfA28XMHpbFvQvDm3Xx2NawCnGGJimO8nQ5w5A7WHOzBxIotkKYXnz3G3cxMcrp8PcQvmsbLi4tj64+lT5nfKXMTH45J3X6ja2eGvfg2l/UJJQP3smWigjWyhTOBkbOzzI0TyKdTJ4KCQiEXxi4pCwu878HXlMiRu3IBPU8ZDvWUL81UVFMTyWVnhWbaUf3e/Jyruow/SU1fG9yGIcM97sDB90iQ2ptIA0to+VEHSkM6+VApSGmlt4lAnJmLTUm94hVTH8T2LZPOKabOYQzExH2XL37nEl5N/RGcSOEoc7isetUtLm+YaCW376pWo7T+2bRNkXTi9l8lt0+Lq4Hay+eZP6y7P36lT+K0K9x7vsNqiWf/cNT/FPvZJwv37QuFVhgyCbPah4gIfUjFfCli3Dli4kFNOuEgIdUOaPIUr9ImP/YL6I9jGNsKR+bgASftgea0xX2g02oaTfjs34QzvNK6TD9eEMKeECeHmVQF4/d9ZQfqucqwuKf8Oc6Z0MdrV3Xy4ZjAuXZnK/8VRItE0AZw9tFa0rkcTAvDwzP/MercNNYjUW7ZwzV01NIcfjKEJYXV1ZvLGL+/Snxsk6yrqS1Cr1Zy0vIEkGdjAXLzMwkzuSMUcwvpJaPptrsSceYv5JzJG0Wt/wc7yzGH+tooEHDqkZ8DfHyDC9grce0b76R0pewVVNbvOJib4DjmwhZnd/vn7XE56356a5zhwIADg/uq5Ah9UfCrpzZ5VKS/u3HH1b3GTbTma2IxMNu8M1zgHl9LUquVlixgb+TKSI8A6e/Z3wZg6uNXyBwtjWxJn897RU1yA/S3JWKAGS5F7SDXWaK0PRi3Z2QEAVk7i+uELbkVAdDRcPIpy0rW+Kge6c7XX+AFDOvdLZ9sCnjN+nDyZ2hz9UEg8dBDuPEFvIT/CzTzMv5VUEI4pTcQDOvTtyb5DIU7M9xW6dQNeybvnkMKZ+cE6dwl5A4VBLYxFNcwZLO1uIaVoTXX2HRW75tmeBaNYX5X53/qnEOGBPfM5BtL0FYBLJTLjaj72TeaXgdWrgZUrOd/QSU3T1pyQ1vahCpKGtDUqFXz3+JEnjuR+eOIT4mTL/2N5MCf/kG7E1MoN4OonHrVLS9sXGFHxff8eS2tx7/FoT8CuXYKsy2cPNLltWpwf0kY237LZA+X5++cfgQ+FgPB6olmP7/uFk6/FEOakc1c55uS4hYkaNXLtMQufPgkFWLa2gmy5JQQ+pNJsXnftAn75BS+zsIXH1wziIdS1VNiPsCSCK8C6c5FrHlZjNONHTPuKVJqFIBGajeZqy1x3YJHZDNN6+BTk1JVTYgG3fe1YfLh7TZA+vTFhWS3p9iwwJuQE0I8X7fJgKU1/9+0rmv/3tWGidV0JH4XXD4Q8ylE5Hxt9wSLmZ2J+O2Y1thZdLJKKcPXYVo4DWC3ZhRH2l2d18a99HGVE09JU2Nvjc0Ym2IyxIUzvLq7Z8nt55jQ6Ke/Sx+jnLCBEjRpMu9AwYp/mRJwfXdAjoJIui1+wuBmXHMlFotLSnzvnAABO7l8mfF4NCYfcWATY/WsiTK7XJoKQGKs/pT7ME46ZQrMbmC7ACtJE1RIzAyMVob5XFnzMxLTS7EPYmOLn4ZsQmkP/XhX6zzm+e1GSy+NTxggD05+5c3V1tPLk+traV8Yy9ZlDvm1Tvo7cQYStoRoH9sWLc+eaHDkAAGsm9+bcE9CagLdv8aC7I8ekbG9ZjQDLgyvA4mvCdOyfDrcFW7cylwYbNqQ2Jz8mPnzAka418HOjjLhfpbDg0Oe6A3eN81ND9k7z/UC9+/e8xVlL/PgB6mfPgKgofH37GgMGZkGWsUywFqcxTzxZVPz9HN+c+WBcV03oQ29rRRII7kjFNLWbDWXam4bvZ+/ezDdgZAsWmdCnLdOeH9KNmX5GOBJ2ltdr+vKd+G/X+k+9cQNYu1a4Fr10Sf/tdXMDiPAsG9eM07O95t3ftAkfMzFN8s2VhJrF3zt+5H2oAj3S1qhU8N3jR544/AKqJWsxqpaKUqXBwTVcnwf9exD7EBnAw7+ibB27f/aVb0RMDH6uw70nwpGAffsEWdfNH2Fy27Q45dJaNt/2xZ7y/J0/jxU1ufeERDYSzXrmMFerplM/FkXvtyrsrykmOMbaYxbUambWYbhoyJpVkC1fkLVkvSM7E3D7NnbP9zCZ1zeZCfDw4NRxZOc8Tp7uzqTzgyFWhjZEspNrFk76lXxCwYWzTxFOXVICrF3rx+HrqxeCdGPO0n+eIR+IAACG+5fl1lWOe8LIx4xIJ9G6jgb3ReyXj5K8NHVhJkeGocKXOdrrCzZwXqslMafb85vZoktf8TqundiJqjxnsUeKE55nt9K9//x7AgOqGe0jk7BunZ73cuUwL1z8/T1QmlFS3qXY2M/S9YeGAkQ4VJIt3hsNY6fgs/z173xQaB3Z8q0jSWCmJ/XuB7ViggeP9oTbBzcBAM4fEdeAGx1YEQBwZot5QqgX967qeN/4a6jZ/bW0FtswmJLXty17dh7txa838couGJ98/2yGJuLm0t1bZwSP9J//rZa9p7wHob2Iw3Q+/dSQRQbU8V68uK6OZp7ZOXnFBFhJMXc1h9ZUT9nyScU0KRAZyRo9fz73WU6dCgD4dXJf4Zh4/x7o2BF/liCM7sQ22NpN6CB3eQFWh/QowFLwbZCQwP6+fw/s3Qu8fg3cucMOMiIjcbQ4E8y0Hcg0tB461cGU0ZWw0aMFEp8/+zY8vnoFdfNm3HetQwfc7tECi+sQ8gcwba3gVtzItXHz52JeAysM68IO/bTpT7OzA9WxLQm36pVmZp379wPEtJqPFTMoJ39+oGpVbt3u7sxnbN267HfjxsC5c7ibix1M5g1kAi5MMwjks2GDUIBliMePgVatAGKBg+qNYAK058011g6fPzM/a9p7J0z4Nn1vIfzI+1AFeihfKgUWxY88cTz97zRK/p+9qwyPImnCHRwOl3C4E9zdDi64S3ANTkLcibA4JBAhARIsBHcL7nC4ux/u7gRI9v1+9GazPd2zu7GPg533efqBzFRXV/dMz0zVljjzVZ2IyriEtIZwYNlkrs/VyACGxslVPpzGuSXB02m++geJjUVAA7affwMCbNvGka4JtzfqI3uSjnvx/sF/y9Jl9CHA6tV6xbtzZCvXz1fVREh77p81DF2rfnySXGNLTeu2P12S8ViUJtvNlo0jKeAmb8AiKgL1jx+IDORzEKyqKE4C/Ck9AfqyObC2r5jA0AzoTOU5VFQ85r8aRbHVqKzM8a1lCGdQ7O9UnBkrl4eY5/ZVk6COieGOuw4vrnf+86b3MbjMo90qMX1WakIk0VqcY62Xnbh6Z4B7I6jVatkQwjMFKN/VFej95deU4Ef+fAmM9+/nPhQ/p+f5VLAXPzeIiuDG8a1cXrnHrRsCN25oh5H28XCtxk8yqdi1i+bae/MGF1U2CK/Jy7i7JL0XEruXiIogTh0nP7a3N/+hTQjg4KAlGTOmnl7+GXx4w2wHQahjg8GSMU6eBABcOR4t5OvoTvOp3dy2NFHzPXooIS/UzAg2yb8xedaWVabl6I0dz07GeEVUBE0dcnBrq/rLeN6G2pP7fJ7AC4fW6O1zqiD1LjDEOz4vn7blTci9V9eeNbQfLUxDi7r2oB5Ezi3pOCk1T2krY8eHVqdGi6pKgMmT6aTfvwfq1aNrUbs28PYtAGD5lD5MH4fWBPj4EWjbVqjENnPKrXfMNn0VtUDBT8Ldu8DQoUCVKkBUFJ8P7P8FtRq4fBkID6ch7/GGN7WaGp+6dgWKFqV7KmdOIIh68+LhQxqSmk3yw0GhQvTHKN0cZlOmJJxPnx548CBhjMhI+i0TFSWWLy5OW0lYTQiQmy2sw/wwpbP3OcTEAMWLJ9DophI5fBjo1o16T3/W8yPUfxC/sx6qIAHKm0pBiuJ3f3B8fP4QFzbP0+aNSUwzhCOrpnN9ls9lK/C5u8uH07i0JMCCBXrHiFPHcf1m1yLAhg0cbfQCD+E4ZewSKotVHUnwuVVC5caxoyvpXQP1Jj5nii4uH9nI9Rk3TlwZ8vIJseIZ3zL4GO/JoNvWVEj6YzG4Qz6GVwUbnlcRV3ljRpWRlH7BDD58E0RcCvt7GgK0a8eMsT7SnaEZ3p4A//yDPSXE48aXZW9vk1Pv2hAVwWDnUsxYIg8sywEED7etBAAuEeu4kWIvwvpD6K//O0PEVSd1IfXKidQkJ0XTpkL6Rg7iqoUznGjVwqxjxGGdJwoJjCu6H4tHjwKEVp48XIQaUk4KFGeHHtmE/ImK4PapnVzujfvObI4raZ8xbjUMrlGSMGkSYs14GS/no2GEid1L6Xz176VpfpbI6kUrMqXzpSELIARwc9PS+Pk01DvGH2MI/pEYZkVV+eoOlVzHc+cAALcvHBDy9fCsBQB4+c/ORM15YVRCRVDfILZCnCini7R5WYrLmCeltXDMw92/+qqDJaZl9CF4++wed01vnNyut18ngSdipVGEqwis690AQvCpXsI9X90hE0P7OBu/T+WM9cltFqOpYamcIOw3sc1sLNGbf29+dUIr88bjxw/g+XNGsb/t74msXjQUKIcnwd8DCFX0jX5oAAAgAElEQVQ427QRKrEnuzfQK1NrxYClQEHysHw5H74nxadPNJSvYUNg5crEjxERAaRLRz2lNm5kz0VGGmfAAmj+sFmzgAMHEi/DfxS/ux6qgEJ5UylIUZjCg+NHzJckfawawsn1YVyf1QvZssXeHrVl+Vt1J8AT/W7W0oTQREXDC0S5H3YtUQnHaTYsA75kTodreQli05rRyi8atHL7U+8axERv5MbRxbUTvAfWpAkthbQ3z+0xuOaiamT6KkcF1yWIM0v6Y7GicyaOpxTFXdIJx87gQ7C2Pa2+Ni+gN8+HELi14PupicatXAfL5rAVLR08qceO3LzT+NHQrYZOYkOPbhvpbMGMJTJgRVYj2g+i0DpUUUvvS6vgTLYVh+K+yaT50Jo3z+A6j/VtxPVfWZEAdesK6as5ZuboCzoTvB/UGwBg7iE2Kh4pwivGyKETQqhJAFx3qPx6Zfck8LPiK4LFt7tn9uJDBppL42s6TVlwOzYkVNrHx72WwTVKEvz9uRDIsqPpvAvJlCjXbdKKlZm99e+liSrWYzOkrmaNx4zR0ozzbaJ3zJyeZtgrMcyKquDVHC65jpep99CjW+IKlT5j6gMAfpzjz5u7ysvj5VNfK7vT1GbMOVfB/k3N1tbJHNMa0tx6Y5tS49WM+snnq71Ogl/m7136J9H8LEbThMjxf6fzJfiSjlantG1LfzAZEJ8TUq1GBccMTP+XWfh9ureE4SpmP7vZt+aNrV6WNKT2QDFNbsKwMP17NjCQyW2W1YvQamEyBqxbXZsalEuBAgXJwPfv1Lu4cWOukniK4uNHagiTYv164w1YvyFMQQ9VoBiwFKQwTOXBkdgP1ZxGlKY+F80nE14X5cnQqLzqy44xeWBJGc76Zd9oQYS/AB1aFSAcp7l9LuDsWRpzrwnDiUdLAwasd5vX6JXt5uldXB//Se2EtHevHEn0dcjlQb01dBNXxjfLAZoXfRNxyKIxqOGUheMrRWnn9BxNQAOCJ/kya9czYloPjmZDuQTPN4Z//AfKvn3aMeaHDGRoPH2op5Gh9WnqrD+8hKgI7FwqMPNZWYUPiZxbg1AXdAAgVBGLTxTvb1NNyPdjfKWcyEiD6zx1HJ+rqepIgqXtigrppWu+sJpG6dWEzhZz468JUREcLCYwYIWGavnePb4DTQbpX68MPgQTehWUPX//wiF+jJEjGfmlffzcaxtcoyRh5kzcyMPLaJ3Eims5vPQ/96aOZ4s+zKivmf/YsVqaSWPlw5KJiiCfRxouB1JfQV6n+EIG2qYJ0Xz16JaQ7zifxlSAJ09QQycpfHpf/eth61JeK/vICWz4o1yy9dRqHZ0KIJ2OUTGXB0FoneTzDWigWcNv37hr+vT2+UTzy+3OJpj/YwzvQWXVXTNmSAhKO7M/AmirbAlanBk1hmX1Sv31Fr1X4tslc4IISXiuS0uC5v3ZY+f+lMwhPFz/ng0KQiadtcvuqbkurVsLldib3ZoanIcCBQp+YcTEaEMMQUhCGLKJwFT0UFOH8qZSkKIwlQdHYj9s87oZ3mpXdizh+m1ayua0muQt9kbI4Unw4c71JMl+IT+hv+RIcHKjuJpUK/vcAq4UPZyL6l2H55v1V/m5c+Eg1ydwSichrfrJE8yvbtz667YfaWhZYGmVwr8GaV72R48atY4i1HD+w6AyYOHMeg+Usic44NMPePxYSzNnSjeOTwkHGWUj/iOlZk1t/9AAtjLVOFUzAIbv23bO+Q3SuEgSiC+tzNPMqk2AY8cogUSJChwh9sD6mk5DIylcIMLMyZ2EPEq7pOeJv37l6GLNCNCypTa3RXkX3nOOqKgXB8qVAywtqWyNG9NcNBrcOLXDqHtuUr9isuceXT7KK5pDhzJTkPbxc0slD6zNm2XDTJPS8rmb6R1u+oS2DL1/vGFk0iQtzbRx+iubFnRLy4U3/iEwJFQaJVnjO3cAAB/f8IUGiIpgsl9C6PKNDg3QpSdBi/40vG9VRfny7wN1csQN9Ete4Q/dtr00wf7i+mmke7Gbc2HGuJHNiwhznMm1qiPpc1H32NimCSHHTEVJDd4+uaOXp8jgm96XDaXL7c6H4rbvnXDtiriwRvOKNjRc28tS3pCVxz3l7mtR21COesOKwlcLuFAZuAIlljQ3m+6xg8VotdiL5rQa2duIEP17NjgYGXQ8zXJ6EBpi2KqV0IB1Q2LAyi1Zl6KOilqgQMEvjwsXgCFDaK6t7/oroP9uMBU91NShvKkUpChM5cHh4i72IpFrBYxIDH5zH5/8dstKtvqHvy/vjeDcLSvuLphhtOzS/p8dbIR0F7ZFCufSzj6vkB4Arrjwycdzu9NwrZL2BI+36Ddg3b9ylOsf6m8lJn71KlHKWHyrOZygiJP43Np+NcVjGYkazlk5nlJUdMrInH+ZhQC7dzM0syZ34fgUlAnhyu2eEOYVj4DJ7Rga/0ltARg2YPVw0W+AJCoCT3c2/5LIiBhclwCnTlECiRI104avKjeko8aoRAiwbJnBdRaFWBIVNWYwUKsxYijrFZjZm9BkqzrKd02B4TF+Hg+aVKW0nz5xCrtcuJTUgDJ+cCnZ9Xx67RSvaFpbM+NIQ6FUbqnkgXXxYopWbyvolkbvcEGT2jP0kxtp5u/vr6WZITFySVtR17RYU8E4eY4Xoon5L5oTfL9PDVg/vvEGTqIiCBjXKkHQHz9owv4dO4BDCR5zbfvw/awcCgBnzgBbtqCHV2mj5OopCHUmKvqssu5EqwxeMqd51vTxkRqwejnzhlOpwUJfK2/LF8LQVgZMI762X96+1MtTarARtQKuZrjQms332Lx/wv7I7ybOWde3Kz0fXJeGW49qR41bKyvytEkp8KGv3c1Jx+7XhT+X142ek76vfJsRzKxDQwmHtyfo34WGTOrSzJk5QP+enTmT8bLL7a4xLLZsKTRgXZcYsBpbJ4TcZvWioYsKFChQ8KvCVPRQU4fyplKQojCVB8frG+cxdEg+oz9uizgZ3mp3D2/h+m1fM4WhCRrbiqN59fCGDEcxdMNfOvQmwM6dQrobAoMaURF0dDCXZ/74MRZWN4PlAFqx7XvWzAkfz9myAV++6JXt0Y1T3HhzZvQSE79/j8B6KauEWLuWMXYZhajpYtiAVcWJ9fZ5mpVwCTQvhI9jaArLGNziWx53zRprfmkbP441dMYbAfMKqhjqtkFuZQyukZ9HHUbWDeV4moAGhIaZAoCra8I9YG6O2TPYsu8VbQiWVyJYXIXmfsGqVQbXednMYULZcnmyHj83dq3gaMwFXkGNnXLIzreRA19JMh5Pbp8T9skvyZHkN8JClv+Lm+d5RbN/f2acIMl9PsW1nsE1ShJ+/EAdPfm8Etsajsqod7jQyZ25Ppm9CeZN66mlCZnUUe8YRd3ScaG1OT3YnEqi9uzfi9ox0goSaQdNbC8v+ODBAOHDv4hKkwTbzAwg4mqIotZCwIeoCK7lZe8LabVFaZMasPq7lEzW9SvmSLiCJQ+za+TJkEG4NHHfvyX7vinunAY3jrHvw8bWCeuQy1NswPp7AK1eWNRRP//M3tRwlBiZphvIHRafSF6UKy6HJz2nJgTf0tLKsW8z0Yql0r0/5m+277ywwfr3bGgo472Wx53QSmbSRNK2tgCAa1ZNGf5NBlG5LucjePYH+0OIAgUKFPxqMBU91NShvKkUpChM7cGxZcU4oz+ADeHRid1cn70bgxiasPHtOZr3zx8kSuaYtDQJ7/T6mrxDEu+feNw/Kq4m1cXhT/0D7NlDFfDQUODePaBFC6BCBWDtWoOyPb1zkRtvflB/MfGXLxjfJHmKkrTZuFcyKKM+1HLhq81J8bBhZdzPQfAoGzVexZoR4MgRlujzZ/TqRfO85HYnmNZbv2dURh+N4vHyJQDAy4fNlTYvhP6Kv6JSwrHc7oQJL0rnSzDCTVwhULdN8GITxsd7POjSVLAh+HBaE4r58SPg4gL07AmcO4e5IbyXXnxrOpAgdo1hA9b6cAf5ddBBwPjWHE1Jl3Qcv1aO8knWmznklJXjtUz+JGnr6lBA9tzrfy/zBqzevZlxpMrzNJdUMmAB2GLB5zQjKnFBBH1t3F8EKF1a71izp1oJ+86b3kdLM2tKV73jFHJPx3kn5vIguJGHoE9X6oXzMgtfNe7No9vaMUS5i8KmdJEXPCICIOJwOF1Di8jApds2lCPYVZKgncCTi6io15XufXGmgH5+UgOWtathg7SxbZsmhDEmrUaeLFlklye5Y1k4psP9s/uZY+VsCV5lptcyoAENZbQckDT+Wb0InFsaT1/QmRqo9NHEJ5IXncsyRhzWeKwwvWazalPvw6dZCdybs30XzRkpu84A8Cx0CjceAPpjRnzoc/ny2hD1pQNr8PRS2RQoUKDgF4Wp6aGmCuVNpSBFYWoPjgM7I2Q/aFv2o78IExVNvm0Iz88d5ngc372Iobkx1Y2j+fLuVeKEln6s7tljtDxERWDlUDBx4yUCLx5c48ZbNFPmF+gfP7iP/eQ2F88a4rGMhGoUawDq1U1w3evW5a+BJBk+AKjPnsWtEd3xZvoEnFg4waDsrfoR4DZVyh092fCbpXM0YaKEYH05AtVfBHdysomNs4whsPWorHeMke0ItnlJQjoJVcCktHdP7hKuUWTYUL1j7FkyzuA6f1y9VNZrJTYuVkvn680XPajsyHsFdXaSNzC1cJDP+fbx9VOj7qsmzrlkz727f5O/H7p3Z8bxb8D2me5cX0ai5EPdsAGqj+DlPPsnDYU2di+5ttAoznow17+XsO+i4EFamhPTneDbjFahE9H+6Z4WZSQeWBl8eKVc6kn08WVCzrncHrxHT4R/T5HIFIsXA0QchlZDp9phQz3hcourJNA1GyimkSb0vpBf/5pLDVjD3coZfb0MtcfZ6HusnC3NJ9bcOq3s8iR3rKoOGfHs6knhuWojCB5kp55W5/5MGv8cngSza1EDWEl7w/Ql7Qleqzz00rwfaQ0QNpdXfEvvKzZgderF0omeo0vnjpZdZwDYG8wb87WIiwOePqVJnTWYNIy/JxQDlgIFCn4XmJoeaqpQ3lQKUhSm9uA4um+x7AetrsLVp6vhraa+dUtr8IpvF49uZImCgzHuL5ozY1Q7gmEdCH585UuZ64X0Y1XGgPXumriaVC/HwokbLxF4/eRf/gN+1ggxsVoNm7bitU9q8/FOnmHg9fH9yKcJ08vqRXB9zkSeqEED/hqcO6eX78XVoQZlrzWM0Nw7AOdJtS7SnTKSjJteJ3dKTk+Cta5tZZXpdn00/by9WeEIwbwaAoX33EHhXJbMGal3HgdWTjO80Nu2yVaC+/g1Icn6KFfeo6yuXSaOXS89xQfaOsjnfPv+5ZNR95Wlg7wB68OjO/z90IX1/pnSiO0TmIoGLERHY0kVXk4Q6i1o7F4a0Z4AVaroHWrhjH7CvsvChicQhYUBhCYOFxkHzD3SYJUkx9HcGrxSLg0ri/nwVjtEQTfe62xhoIznJwCsXg0QwlQnjG/afHQy57VzrExDZ9dUkM/JJ23SXGjSdi8HTX5+qiBNAP7Itj9HU2F0GgTWI4l+dl7Ly/5d2EU+v1li+ErbnZwE6iqV8e7OVb10edwJFlVN2hh5PMyYe+Nzev30FVwz49PXD3ppYj69Bwjh3uG67U8XghOFEsaVGmVFhQFWLXDWu4f2hThyffRh5hD+RwrFgKVAgYLfBaamh5oqlDeVghSFqT04Th9ZK/uxeqQIDSHp1oP+YmwQ9+6hvjRh7kWJEWD2bP5jU61OnNDS/rduCcliHt/n8sjYtSGIHJZKCaQBvH/5iP+AnyP/C7SUtspIArOxSS+ZPsmvWbLn8HyqL9a0Loq7tn3EOb+aNIFaeg0uX9bL88aeVQZlb2RNgL17AQC9nViDzO41GqNQ1araMdWE7Z/fzQxwccFpmTClVv00so4dK7kIRGjweHmZ9yoDgBURdnrncXh9sOFF3rdPmCyZqAhevLqvJevnyK7DhnIELy2KcOwGusonWe/okF9WDHVcnLCPbiLmDD4EYb3F/OsPIfj6/DFQvz57P+gkMQeASY3ZfiFuTQyvUVIRG4udpXhZYWUlGyIlar27EaCGfo/GqMCBwr6rw+0SiObM0WuIyuuRBt/TJCj/fw8geJeRV8ql3mNx379ph/Dsmp2TYUnIEFm5f2zeiFeZaaiiSP5OvRKqnJYdLaZZJqjeaWwb8zcNA5c7P6SjZt52/F7rMCC9Ud5c0iY1eBV3ljdgSfM4JaZdz0OA2rUR8+SBQdp8BnL6yTVz9zTMvSF9FhIVDQ9dW556tW3oUh6xcbF6ecbF/gAIwfn8RJhTLb7tK54wrl0b9lyrfjz9ukUeevfQ/jBXro8+LLKuztMTSVOgQIGCXxSmpoeaKpQ3lYIUhak9OC6e2ir7oRrQgLCGCkN4/BhVR7I8Ht+9yNLMn5/8j80lS7RJhtGunTzd9+/CsAf0kkmqngL49PYFp6B8X7FUll665iv9B+DltLH4MDs4SYpN4Pg2qTY3LZo1w98DqKEtvS8N3btycqveLrfP7TUo+1+DCLB5MwDAc3hJVB9Bw6byuhGc3h1FGR0+DKRJg1eZadJe3f5FXNIA7u6yiaItB2iu/xS2sAAWL0ZMWgJzSeLy9zck964Ga+Y7653H8ehww2t47Bisuov737t3QUvW2Y4ttHCkCAHy8wap3XbtZOXp5lBArygir5iH5QuheX+CEg4EC6oTrPTsIOQNQoC3b6nnXKZM9O8CBYAPH5gxPC3ZfmPGpF4OLAC4vpTfP7h4Ecgl70kmbW36EhouqwfLgocI+26Y75pANG8e8/yR5j26WD631gjBGYZ1Wh6d6ntp/CSGfwsLzpiwcpa4OisA7FsTIJRb5BW4uAoNp5QeT44B631Gfk66rX8XzbydnLhz7QdlAIjhfFqGWmkn+RBCNSHYXZJgaxlqTJMzNovaRXMCNG4M9Zs3yZJPrtUZSjCneU4apht/f5QtyxndZtTXuX+aNgUgTvav3R+Alv6EnmT7O0ol8PVuw1akLeHA029a6qt3Dx0I9xLLIoP3Xdshs07uQ/vWigFLgQIFvw9MTQ81VShvKgUpClN7cFy/uE/vx3KiPgpfvOByubx99Yil0eReSfbH5pkzwNattDy8PuTMyY/Xt2/SxjQCXz+/Z+Zf1JEAa9bI0kvXe8PEftpzjq0Sr9zMnqwncXNKoXlzNLZmx71+XhzGGY8PAs80UTseOYF2aNiQvWY3dCpVHj0Kl9Z8yFQp53TAmDH4p6iYd9OBGl7BEg+p2FigcmUUk3jGfL1zUziX6EVj9M7h9M5Fhtfw3Dn8NUjc/+qVBK9FS1s2qf5FcwJkz86xi3GwlZWnp4GQ2WwSb7+0fgQ4eBDImJGuV+XKWLd+Esc3jZ9mPd9rQh5v3KAVGF+84MaQemba+NU0vEbJwJcDe8RK8Qf9YVRSQwEaNdI7zsrQEcK+W6N8EogWLtTex7Fm1DCmS3uqQg5Zo9XtXDT07ZI520ea7B9VqsCpFTUmx4fVrotwlJX76JZwodyiULTQOgRelvxxab6qsqOp15oxaxtfvS67p8w9a6VZAzc+Z2I7a3pfGqpoaKiVc+SLIWghuBb6QuuY/V+A0MIfnz4ZDJlMSouqSoCiRYEnTwBra6BbN+DyZUyUejnW1ZG/eXMAYAw/wv3RooW2j77Ksa37EnTsRWA5PJNBebetGK93Dx1aMFYsixw6d8bKijSPWfveBE+yKgYsBQoU/D4wNT3UVKG8qRSkKEztwXHn+nG9H5+J+ih8+5Yrwf3t+1eWZuXK/+/HZpky/HgDBqTacN+/fWXmX8iZABs2yNLrVpLK6kXweUrCx/7LLPSX/8bWhnPHxLdI/z6yY6UUZgyy4Mb99+oR/Z3Uau1c0/pR5be44Nf6yPiE95Urs9fs6VOGnadfA65veacMgJ8f9hcXr02j+OpqERG8fCtWcCFasY8eCqfydeNavR4K5/evNLyI16/DQiY06/SpTVqyuqNZBfFOTgI48oaJt+72svL0cyqmV5S8kgTgGeKNI7duUSPx58/YvDWQoak/RBMqRQhgxHPy3J8J+Z8yeRPcG+9ieI2Sg+/f0cAmYe1a2Oeix2VCJuXagJH6K5aunSU2HO5aNiGBSGO0t5XJ2fRPxWxAkSLs/T5wIEDYKpu6LauX5LlZuzYXUqZevVpW7rN7lwr5/kjDH5vQhFbMkx6vJkmUX9GGek4Zs67xlQBF1ROJiqBzT81cvHjvnLaDqaefnKHa2FbJIYP8hSWEa6LwOFE7WpgAHToA374hh4yBLjltQXUClCzJiaz6i6cNjjditW4NAHrlAQBcvUq9tQjh3uWiVsMxs0GaXWum6t1D/yyeKJZFDl26yBp8FQOWAgUKfnWYmh5qqlDeVApSFKb24Hh096Lsh2dG3UpYixcbZvbxI0rZE+2vvGn9CNSS/FaPVs1DRE2CsNoEQfVoiEaqol49/uPW2jrVhouLi0WrfgRt+xB06E1DchAdLUv/auZU9O9CjSvbShPgfkL+o3h5pfmD9LWVQUNTbW7xaOuYjxv3/u0zhjva2OCSucYIQwg2l+XlnxXQg9IWLcpeM0kuLl/VX7xC7ZgZGD8eu0uK1yazN00M/WGRwIC1di0TzpTWj9DQOBG2bsX1PPLX4O6JnQaX4svt67L9/zm4REtXwSE9c+5FnszA3bscv6e+fKhVfBvkzCu7upAmAM8g9e4BcO7gKvTtSjCwM8HQDjT5tNy1EYLQ5NwBDag3EQICDPdJJm6d34fe9oXQz64wHp49kCCKkXuJqAg62ufTO8bGcPG671+TML9Xi8MRWkd+jN0VMwM7diSsZ44cNARzzhzZXHi5PCXXqFEj/jm3UVJAQwdXj27ieJqNFSe5d21BOO8eUas6kmBwR+PWNdaMylh2tNhQ0ja+4IKvL3euzRBqwDpVkOYMLG9r/PXUbdXt+WqeCTcJbxRp39s4vgeKEZpvTa2WDZFMTouoSegPMxJ4C/J29YsPxdSE2udz56tVxjcpCrinNShLM8ecBmn2rZ+hdw8dXjHNoCwMunZVDFgKFCj4bWFqeqipQnlTKUhRmNqD4/Wzu7IfnlnGEGD5cpp3yBjExDAeAF/S8dtz97IJzBh1h6byFtYJidC2YcNSd0zpeAfF1ewAAF+/Am5ugKUlXWtd9OgBEIIKNsYrN5tm2afu3AB0cPqTG/fJw6uGO8bFAWvXMvdIpVEsn+mTNDnNdEM/06XjEv2Pn9Cck6GO4x94MXEMJjfSv0YbFrjxsm3axITSZfYmwLdvPB0A7NiB8zIJpP8aRKDWDXeUwfc3r2Tl27ljlpauqAtrXPp677aQ310VX8krvg1z5pVdXZRwScfQp/UT7Mlr1+SVRbl10oW0z8yZhvukEuTWSWQI6WIvnwAfAK5HBcKvKd/v8MaE+W2ex4fB6bboyhpDyt69wLRpjIEyp4zHTH43M1aQ5s35Nd6yRVbuO+cPcDxrDSP4kIEfy6YtwbSGhp89tYYRDG9v3HNKmuvrojlPM6I9wS7VAO54/VEZsNGCVj9cXolgTwmSJE+n2vaZ9dwkVK5H2QjO/klwrLB8vi5Rm2xXjV4/mST5yWmzaxGgXDlOZI/mPG2vbpo17tQJAFDQlQ+9JipaqEWK/O5iWmZ/OBU0SPPP5jD5dQZwbK0gX50+KAYsBQoU/MYwNT3UVKG8qRSkKEzuwfHlC2rKlErPJg1TMYS4OIMfkruXJzJcILno3BmvMlPvjzMFaIWlJzZ6ysunBGrWTJh/wYI0mXxS8PAhYGWFyqPE18dJkCNrZbh8xcOUgkhpefXinvEMrl4FRo0C/P0xsxNrDJugakaNVWl0qmzlycOxmDKpDSeDt3VRBE1qb1Ch2rp0LC/Ttm1MmGYOTyJfHXPPHi6EsJgj9Sb8ko4A94xbi0E2YuVvw4aEJPO5dcL7RB6N2iWdIB9COMqVV3Z1YeHEenlttBDsydu3tdcj1kxigDCUhw7gnwvhRiS6TyUENBCvU5WR/LH2dvy9x0BjkJV655zYOldLsi1Sf860tZXlczHl8RB7zBR2ZSvoPelsiUvm1Cvpn6IEbzMRYKe8J+CTW2cZftVG0Ovy7A9+rIaDCRxaGzas1B9CcCMPHV+a80zaNlkQrCuv8cAltJ+Ibvr41tyxYq6sZ5DlAFrowZB8nLx2WeSvq+Y+FSW1N6YNc7UAAK6oSUq0mXUIULEiJ7JLSzF9eE2CvQMaAwC21MujrXYZ31xait/VrYZkNCjLMNeyBmmObRV4vOrgypZIhr7pQMWApUCBAtOFyemhJgrlTaUgRWFyD44fP2Q9fHJ5JGF7eXgkfESGhnKn96yYzI2TmvC1q8SN5+ReNVXHxNmzNI+IpSVw5Uqy2VVzEucZuSzwWoiYnnoVFuNh5VyYG/fD+5dJ4jVv5iCGzxjvelB/+MAaSEqU4PpNn9qR6TetIQFat0bYlC4GFaqD64M4fi+3rTX+vty/n8uz1a2HjrxPnhg197jnz7C5dw3OQLlsxRgtjW6OoD/GyMu0dmJf2fnau1XSK4dtv9zo0pN6awzoTPAuo2Cc+/e184sPa0vjZ8DQpwupgrlokeE+qQQ1oYYTqadeDYEhv41tTv3MNm4ECEFLSX6ks7sSQq53LR6r937sOkA+lC2/q7hPCWe2gl4vR3ZP7itOgH37ZPm+eXKHoc/uSZPFry+XdMNKE9ssQJYsACEYZKThp7gDvR/u5hSfD5nYAf4Sg+PgvlnZcQcRLn+dMa2xXVb56zp3LkBoyGxS1sJuTHUAwIyeRZK8nrJyWxPs/5sPC9bXp5dTEUpUrBgWVGfPeVqKjT5XyuUxKMseNysmMbzox7BTu/Tv9eu7lnPz04tu3RQDlgIFCn5bmJweaqJQ3lQKUhQm9+BQq4Wlr4mK/qqdJJw5A1y+LDy1Z9UUbpzUxAi38gRNmU0AACAASURBVNx4bh41UnXMlEYtxyzC6/P89EG07c+GedxeNz/V5enlXJSThUvWbySWzB3N8HHyqIrHN07DbCw1jhRxIug2LAfXLyTAiuk3sTEBunRBuH8P5rhvM4Lm/VlZz++M4vg93r7a+Pvy0CFMaMLSjm6jozy9epWoNXg1uBceZid4nZngazoCtU7OtAe50uBWbpo36lxZvvpgPKwn1JJVMp2ceW8NBroeg3IK4NOn2nO6CiuXTFwOUv6bNxvXLzVAxF4/DQbza9fCVn7NAdAwPULQbCDb7/LBtVqSfcv5Co66bYZTXVn2BWUSaZd1Ss/QDXIqwZzfVpoA//wjy/fL+9cpblixdMgFjB4NEIJOvYzrU9iJXosnWcXnZ0/pyhkaR/fKzl03aQVRY1ozOwPXdvNm1BqWtLXwHdeU8jhwAEX0VPMjKoKS9vyxDAaKdlR35MMf9dH3dy5OiUqVQkRN9pxPM/GeP1vOcH6ri7P8sL4cQe1htHLkzlI8zfl9yzneuri1fx13PfXCyop/nugWa+mV+j/iKFCgQEFqweT0UBOFYsBSkKIwxQfHnzK/Xmf2TvnttWf1VG6c1ISzVw1uPE+vOqk6ZkqjrmNW4fWJu34NFzfO1SZBHt0ti1EV4ZKLvi4lOFnkQtsMYe1iT4bPSJdyuHo8mjnW2JFXNGcH9mFoxjYlQO/eWDCd9UTytKTKle6xu0e2cvzebt9g9H356dBejnajhY4y9f594hbB1pZVxlatosdjY9njBQrIshg4kTdg2bWh/07w/Uv/+HXrGjZgvXoFED4XktlYI/evn18C73z5kh5WmxKwtAQIwXFJGGi7PoQLrxowJLd+Xjt3AoQWYdDtd+NYQv6pgyv5JNXxraydGT7duyXLvqiMYaaiI1tBb4QLH8p1e99aGa602IQh40RiW2uHPICDA0BoLjhj+uR3pffEq8zi83On9UR0WeoJ1bsbgVV3gjlt2CIStYfxFRGNaS3sDHjXAajp9EeS1iLQvwtl8O+/aNtHP22N4YQziMsZLuNbLUc+/FFf1cDBzqUpkYUFwmqz58b9Jd7zx8pnMzjPm9GLgJ49aX8zM9wZ3oOjeX9Kfw7NO4e3MPQG82J2784/r86do4nq+/Qx2gNWgQIFCv6LMEU91BShGLAUpChM8cFxPwetzibKI5LS2L0mkRWHkglfv0bceL4+DVN1zJRGA0exIoE7dwAAX48ewtvZgcDjx/8XeQa4lkqxa7h1DRtSOtCpOI5vn88ca+/IJ9LmQg//JoC1NaKC2OOuLQgsRrOyvj5/jBdk2zY0GZRAY9tWfk6fjx7k5n/RXEeZ+ppIbzRvb3F+qC9f2OPFismyGDCpNidTOVtaSfTG4U36x69f37AB6/17gIgNBUYhJgZQqYBBg4CLF43rk1q4dg2wtORCp4t4ZMDySuwxx7559fPauxcgBCsqEfg3oJ6AY5sSvL54QktyZG2QcP82tib4fEK/ci8tckBUNGn5YUs2Mb+jZ3WO7vKB1Xp5p/dNnFEmrR/Nb3WiEEEPK/58BwdzwMUFIOJ8YqKWx53eb6Lk8URFEDm9H3dvPq5akqGpNoLmwUrMXIiKoI2dAeMkgBpOYu9XQ23hLE2hkEePUM9APrDKo+g9o3ssvGFGlLaT71PPkQ9/3FtCnn64S1nNDVUJwXXZc5Mai/f8gQqG5/5g5xqa+3LvXuD8ebw9sIM5X8aOAJcu6V3j+yd3M31qDUuCAUuBAgUKfhOYoh5qilDeXApSFCb54NB8BLbum0TlNBHYvfb/a8CaOoFPAjx+bNNUHTOl0dgxBzeHzj0JTfL+EzDYjff2SCr2bQlj+PRwKIhdq1ijVp/48BcdLJo1jKFxa0GAUaOwfCZ7PLugOtmPF894QTZtwptMBOObEATVI/iWVn5OsadPcjxfZdZRpmJjE7cI06ezytjUqfT427fs8bJlZVkcDHRgFdb21NDx1N/P8PiNGhlWCD9+BIg4x82vikze7DxKuWfComps0nT3XvqTuH/Yux0HitG13lmKJlEHIUwlwRMbZwmVf2/3WgZlbGzN9/ucngCNGzN0Eya25OhuHNEfppnVi+etr3XonXB/uAsq3nV1LKDNgWgobE63lbQnsmHsS4OsuXvzRRXegJ6U1tE+n8H1r+acNA+s9Yu9KIOXL1F2tGF6XcNwaTuCKxa59XpUNRR4pYIQRFYT09u6VtBMqBpXxMC/gXjP76qQyaDcLw6w3qxxx4/BbGzC+bxuhBqM9eDRWfYHgeojFAOWAgUKTBcmqYeaIJQ3l4IUhUk+ODQfgW3+DwasW+f2/F8V4DB/K268qeNbpeqYKY2mjnwukqt5CfBMYIj5P2CYe7kUu4bH9i9hlWS7vNgYyYYVDnPnq+gti2BzZ/WwIvjkbIfVs231KlxZxhBx0vHt241Xis6f5/iqjeknh/nz2XE9Penx58/Z45Ury7JQz5qFfl2oLFVGEjzKRoDx441KsP65aUO4tSDwsqT5cMJqC+agVgOlS3PhmKm9f1MTafzYeZR3zoi5ddgKdz7d9RuwzmxfyNC37qu5Vo8eJdBsmSe8F1Ue9QzK2FGQS+phdgK0acPQhYcN4uhenT+ql3dijTI9rBLuxTF/8+d7OhbGIe9+8BacS2pbGTqC25dvq8hXvsvnRrReayXtqReiHG2gdXmD61/VzXAYnajt2xQMAHjz4n6i+/bqRoDixZHHXZ6miaMg/LF1a9zOJaZ3dKsCANjUrjRXGCConvi5FV0xnUFZPx49wHY6cwY5Pai3Xh536v2qvnFD7xrH3ryBx9loBcyXWQjei4pI6KJHD8WApUCBgt8WJqmHmiCUN5eCFIVJPjg0H4GiXB2pgY6Of4KoaP6c5eG2qTJGPCJDrHnFZVL7VB0zpdHCka8G9T0NAV6//inyDHevkGL3yfmTmxk+ze1yYGWEHXPMzpOvGrl6gTMng7tvPWyYyx/XbeYeacSCfPsG5MqVoBB5eMgLffo0U3WuY69kKlNr17L9R4ygxx88YI/X0uOxExEBEJoEPtYscXI8aF2fWaOS9jJ9Dx36rTywckq88xqMzIBZDdIzx8Z10R9mdmH3Uoa+RX/N2j9/nkCzfZHwXpwwppFBGaXJ4YmK4EJ+QpV4Haxd4cfRqW/J59YCEm/AGtg54V70bcafH+BUHNP8LJNk8JFr6+bYAf37M/vgo8pLSCtN5L6yIoG1TDXEEg4E6NjR4PpX8eS9X41pZw+uBAB8//Ip0X079iJA+fIY1Ikmw2/Vj3B5Kps7Cgyrly/jXg4xTzd3WhWxjS3/Y0hoHfHzYmkDce5F3fbj7Gm2061b+JhBYtDXhLrL4t69xBmk4nNuGUuvQIECBb8QTFIPNUEoby4FKQqTfHBoPgLb/Z8MWLHfv2Hnqsk4f3BVqvDXxeq5DvwH+7RuqT5uSqK9ozmvnBICfPjwU+QZkoIeWDeuHGL4NLT7A1GhQ5ljrt61uX4bongl1k/VFNGRYuU2vhV3zyCQQoOjR4FOnQA7O/1ru3cvXmYhcGpF4NKSeg1oFalRoxK9Bhc3zUW/LjSBeOu+BFNsqLfEowv/oF8XgiEdCWzaEoT1LCHPROrFlQilrrlDbqOvZzVHPqzoV8XyHmyF0gOdqyGoSQbm2JQOufTyuHyArV7ZbKBm7d++TaDZvUx4L9r6Gq6GKvUSIyqC/cUJMHgwQ7c2WhCaff++Xt6JNayk9aPGjoiafOW80W0IHg7qihmqVnp5rKmQuDE3z3Ol4Zh//knX1cICMY/FXk3SfGFru1cUerARlab6YdeuBtd/x2g2BN2tBcHSynTf65P7/hWN95tajYaC6pa6raIkF1uL/oSrDHqyIEvT1tFcKO+jbSuFY3h50mdoR1t+r4fXFD8vvlWtxMkmvR+4MPaXL/nnkIH7EI8eKQYsBQoUKNDAJPVQE4Ty5lKQojDJB4fmI7BDb/4j9VfH1iW8Z0LEjD4/W6xEoZtjQf66EJL4ZOEpBBs31gPLYNUoPXjw4BLDq4Z9RkQEspUEfcY25vptWTaWW5PJk1pjx9JxepXFCq589a5EQxraV64csH49sG4dTWicSOzbEsrI2MOhIADg/OG1rGJrr8eYcvBgkpW6Vg68h58cqjv8PgasmJPHYNOWoLwtgfffBOp9+zDEijVgDe79h14e149sYugbW2vWXufdcX3/GuG92GJsSYMyduvB91tXngD29gzdk4tHGJrWfYnBamyqNklLUC5qwXWpUS1kYgdZmuYa7zRpCJuu4WZAZ/bY9khvKuzr18CxY8CnT4iL+SrsLw1v3eTYBlUlyeTP/knwMQNBTFpCDSEG8MPVGUM6EuTyoBUQP6VP2F/j/hLPw2wswfc3r7Q8HmUjGNmOwKG1+B1bX5LkveFgQnOc6ezlw0VYmi6O4oqkz07tF8rkN6Y+AMDKJh93bn51QvPwSWFvj6/pqMFUbq749o3t8/07/xwyVFzk8+fEPbt69VIMWAoUKPhtYZJ6qAlCeXMpSFGY5IOjd2+A0MTPuh+nyyr/+tvr4Hq+AtjC4EE/W6xEoY9zMV5xICTxycJTCC4ulRlZllRJ+n3y6u0TZPMiMHelIUDtBmfCTO8WDP8J45tz/Z5uWYk6Q9k1CfTvgj2rp+pVtGu6ZEvO1BPg6kqvQZ48wGH9leQM4fieRYyM7R1ocukTexcLjwuhVgPVqycodMuXGz1+W0deqZVDjd/IgAUAWLMG6NsXiIoC1Go0H8yGEA7qnVlv99snd3Drcagoq9jf/meT8F4MVxkOZb6Wl+9Xxo7gtLc1S/j8OZw1XkH53AiOFyLUG0YPTpfnw8niW5YxBOkSUaUwoAENfZ3t3505bjGael1ttCA4Vpjem3LJydv0JejTlT22e8k4XnC1WthfavzaOnEgcuiEiab1I/iRRsfo0bevwfXHkSO8sUTTgurJrwdjyNbp06sbT9uqH/t3tREEsE5IXn8lH+/x1tOpiFDc1xdPCOWZ4E1/BOhj8yd3LqpZbmp4kuL5cyBTJoDQZ7y0n8Vomb0vXStjcjU6OFBaMzPqTaoPigFLgQIFvzFMUg81QShvLgUpCpN8cDx5AvTpA9u27Afqmgq//vY6vXMR9+G9dNaIny1WojDIpbTYgPWTMN2xDiqNoopWzeEE0WWTIYtaDaRNq1VE1H/wXiEBUwSK/v79XB6eOcF9cXBDsF5Fu66rIPlxUvHiBa3Ol0xcPLKekdHSgXpaHdrCVq/r5lhQP6NPn4AlSxJtUGvvlN9oo1RN+9/MgCXBxUrmSKsJ28voQ3CnvNjTJR53z+7j1iODD2GS5987zhu5iIrgxcQxhgUi4jxOQdI8fl+/AoRWw/wc7yX07p1e1kcryed3CqxHEFLXsOHKbCxBZm+NAcvWFnOD+jHnfZrxhp/iMhUHO/Yi6N6dPXZg1TTxskj6tuvDG4c2z3GExWiC3Jpk6Nm8JLIMHGh4/QFxyBohmFlHjwFLcg3jW5eePG3/rmbcsccLQ4Ds2QFCK1xKzw9wFocTf7xxCdVH8PRTfJsBAAaOLsydWxY6XH7uly8Dvr5Ya9ec6+dlaaQBS6eggSzUauD0aYMVCwFof3BTDFgKFCj4HWGSeqgJQnlzKUhRmPKDw8GJzQmzvNKvv72uHeG9H1bPdfjZYiUKrq5V/lMGLAwenLIKRLZsWj4HivHKV2hAd77P4cNaj5P4tmT2SBzZGq5X4W7opr+q3M/A7bNsZc76jtRLbNcaNq9RX+diqTL+AAeBh58MfncDFrZswamC1CBz0ZwAmzfrJX946Qi3Htm82DV5dJo3cl00J8A0sXGGweLFWFCdv49XzhnN00r3pIH314Eq8gasyY0IpjbUb7z6llaSrNvREVFhwxga55a84ae0nZhftx4EnSUGniPrQ4SyS/t2FYRantk4h6lC2KcrwfU8BLdzEdzPQfB2aD/D6w9Q48qDB0DFisw8bNqK5+E4VOId1bIlLuQnWFWRp51Zh+BW/vTc8e1RPsCqVUDevDhUlO831KWMWFZNPqlpkms33a8FAGCYYynmeBk7grPzxhtcgu3L+NDss8NkPAiLFWOveUyMcetsLPr0UQxYChQo+G1hynqoKUF5cylIUZjyg8PVgTVgLU5GaNh/BSIFc8NC958tVuJgbw+zsQny53UjQKtWP0+eLl1SVoHIn1/LZ14NXlmbG9yf73P8OBfyun6BK07tjtKrdG8Z0iR5sqYCnlw/xchY1YmGrUUv8WWOD3Ytmyrjq2wrGW2Uqm33mxuwvn+nSfxLlABsbcWhVTp4ev00tx553dk1+Xb1Ek4UIjhTgOB8foJbuTV7RpR3SIpv37ChHH8f39qymKeV7klpfiIJdlWXN2D5NiNQyeQ90l536Xhubtgy34Oh0a1cGN90jUq6rVc3Ars2NKl52dG0UuC5LeJwMmnf2u65uGMPDm/FnZw0NN7LkmBOLfa8g1tlw+uviyVLmHnsKMXPod8Ic7w8fYjtd/06nFqJ53zJnAD9+/PvqMUa7zy1GsdWzeDO27pWEMuoyc83sTFLHzK+HQDA1q0ic3x1BWJUuPG/m/nn6p3L/4iJ9+4FcucG0qQBgoONXV3joRiwFChQ8BvDlPVQU4Ly5lKQojDlB4enPZuce0H1X397vXlwg/vw3rrE72eLlSjEubky8hdwIcCrV4Y7phYqV05ZBaJECS0f/wa8krc4ZAjf58wZ9JXky9m1bDzenT6MUvY8j/j2ZIDhymP/b7x7cIuRsYwdAdauxcp5bAVNO48qqTL+oWEtWWPAMPnrWed3N2AlEi/uXOLWo6CrGUt0+7Y4j5KRyv1TO2tk9k7gX9qOQH3jBk9oZZXAu1Ytg3y31Mouu09cWxBZo0t8W1+OoHc3gkGdCEa0J9g/pg+Orwjg6JZVZuddeZSYXyFn6vW7qCqtdPghA6GVQQUY3JHtO9TNguP3+cJpmuxeM254Tfa8j7vhNWLw5QvQpAnlV7Uq1BkzoKUmf5W5K8HFMtllu3q4VRfO+WZuAty7xx1fGOWo7XtmXRh33sm9qnigt28BwhsfZ03pAgBw967Dv+NXrzY4dfWBA5wMb188kO/w9StNzp4a6NtXMWApUKDgt4Up66GmBOXNpSBFYcoPDlc7VgmIqPnrb6/vnz6gjB39Rb/BYPor/4volT9brEQhxseTuS5FnH7ydXF0ZJWHLl2Sx08nNMfLklfyVs0YzPe5eBGderF0R9fPBK5dw9AO8kr3vcH/PQPW9zevODmPVciO+UGsZ4bnmLqpMr56yGA070/HSOdLsMlC/v4aOIA1erQaYCZLawp48/AWd+1KOKdliZ4+FRuwwsKMG+TMGRwoRqv01R9CcLhSDibHlhbPnwMjR9LcTnfvGmQ7sbXxVQhHtuOPjW3K/j3bry1ub17E0S2ozs77Sj6Cy/kIQvXkkCIqgn9zEeDUKaHsX9IRPM5GDUDn8xOcdu/H9cft23SdNm8GNm/mzk/wqG/c+usiNpYmJf/+HciVC3FmBNfyErzNRIBChWS7+XnUEc7x/oZFwPfv3PHVSxPyo13aNI877+khY3zTVPRTE5qw/ks6gvcZCWJCZgAA/Me3YvhMa0hoBVVDOHYMHs0T+jUbSMT34P8DigFLgQIFvzFMWQ81JShvLgUpClN+cDRxy8t83A7q9JtsL+nH7q5dP1uiROHTeDaUrITDT74u165pq1MhQwbg3r3k8atdW3ttRkjCAgs7EZwPcOG6xF29wil1F7YvAv79F70Flb7i243h3ZIna2ogJkabODy+ZfAhCBzzN6twj22WOuOPHIkfaWiy6Gt59SuE47rkZmQKbZAudWT6RfDh+QPuHivnmJ4lio0VG7CMMDJpceQIUK8eDWv88CFFZK9um46TfWJjPvyMqAj2ChKJe0qMzQvGdcb7fds5uh2lBHMnNHxNnwHral4CnD8vFl7C6+0oa96AJUkeLj0f4PVX8hYwc2ZWjtKlZUknejbkxs/mRfD84DYAwGyd8MacHgRfd2/X9r2+ja1Gmt+V4JnbKPFA37+L77WICADAwoA+DC+3FsRgnjcAwMmT+JGGIKoqTe7/IcNPfAeFh7Nzq1Pn58miQIECBSkMU9ZDTQm/iYatQB82bNiAVq1awdzcHBkzZkSJEiUwatQo3L59O8XHMuUHx7yhbJjDxMa/yfaSfsyvWPGzJUoU3k1RMdelrFz58v8nLl2iHiQ3byablZVNXpSzJSjmKFacMWUK10d98ybvsXFwI/DwITr24vnEt0uj/oMGLLUa2bx4WYc7lWH+DpzYLnXGHz3aaI+G0LZ5UcCFGlHL2xJE1UovS2sKiH3/jrtuVR0y8oTS9W3T5v8vrASNhvMGrHpD5L2hdENz7VvzIYZLJ/WE+sQJZJXcy5/S8wYVNdFvvCIq6lmFK1fEwkvXs39/NBnEyoeXL9kuEv6hXpbJW0CpJ+q4cbKkAd5NmbH9G2j6aAx0cdMDEFGTrunVYlmY8Ls7u1czfesOJYCXl3ggtVpswIqMBABsnuPI8BrUiQDbthme64ED/x2vp69fqbGQEOCPP2j1QgUKBFCrqYPh2LH0NwAFCn4FmLIeakr4D2hyClILarUagwcP1m5kacuaNSu2bt2aomOa8oPjdbe2qKYpwV17GA0/+C0g/fA+cOBnS5QonPUYyCgdNYb/JtdFgzqu8smkz/4p4zEnyBvz4Mox4PlzWA6QV4rP2P73QggBIL8rL2sDJ3ZdIvx7ps7gTk7GK6clS7J0WbKkjky/Cr5+5a5bHXvBmkjXNzHeV6mEw5VzMnLPq0FQZaR431zOR6v3DetA4P03NS5JadZMHQAcPQoXneqgfk0l854/H7C0xLHChg1YJwoR4NYtsfDjxrF89+7F20y0emJYbYLvaQjw8SPTRcp/nlcyC2E8fAgULkzHL1UKePdOljTEl80zN6GJRu74H+HUaiAoCLC25gwyD/ezlXRrDCeAn3wex8LOBJm9CdL7EmTy1oyjSdR+dF2wlk/frgRt+xB82r7J8FzPnfvvGLAA6oW4ZQtw587PlUPBfxrh4UDBgrfRocMcFC16A4cOGe6jQMHPhinroaaE30uTU8Bg0qRJ2k3co0cPXLhwAS9evMCmTZtQvHhxrRHrhiihbRLxqzw43r4FZswA/v03BZl26IAfaQgeZdMpj/47YNWqhI9uCwsgLu5nS5QoLLRtwCgwVUb+JtdFg1Yu+WWV2HvmGcXX6/FjdO+eQFfEiSDu6xfg7VtZLxKiIvind8P//wSNQAkHXtZ8nmlZDxdRMvuUgLu78cqpmRlLlzdv6sj0q+DHD+66NbLPxtPpVrD7mRVEdaDOlxeuLQgKOtNk7J/SE9kCCCcLsvfHhUr5OJrNYXbA6dNQExqO+k9RglgznX7xieXbtRPmv9KttEpUBLtLEvnw5LdvgYYN6f3Yqxfw4wd/D0sqSErHWzIuBbwx37wBjh8HDHwrhKvaM2P7NtPI+PSpwSGeH97J9K00igATJsjS53FPoE3rpxln3ToAwL8nd3LrcCo6wvA81Wq2eEdqVBdUoCCF0azZQ2zYkBf79xPs2ZMGzs7XfrZIChKJ/7Aalmr4VfRQBcnD76XJKdDi2bNn+OOPP0AIQbt27aCWJAy9c+eO9ryVlVWKjfurPDiWLWO/1Xv0+AQXl/sYMiQG9vZfcewYjaA4fRqIjqa/RJ08ydsC4uJ0XhDt2jFM35Ns+PHj/z611MHBg/TX/7dvf7YkicYdZ2tk9ElQ8k4V/L0ee71cissanN7flgkhevYM93IQdOpF0GQQVZYBAJ8/y/LK7knwsUm9/9/EEoGKNoY9UjZEOKXO4KdOsQ+TgQPlaaUl7O3sUkemXwjS6naW9jnFhKdO0XxD3779fwWUQ758nNGngIv43jtQjAALFwJ//w1YWeF6VCBHs3PxWPpCKVo0gWenToClJdC0KfXiAYDOnRFYj+1b2o7Ati0/7ut/L8vLr1bT/GLxkBqwJN8MnMeY/8AUX1I5RE7uwYzt3lwjoxH5zN6cP8b0tRhNgKlTZenzu5kx9GpC6EcAAPWHD6gzNOFcXjeCby+fGTeJN2+o4Wrt2p+XwF2BgkSgXLkT8PdvgW3b/sD+/QTjx8uE3ir4z+HrV+Cvv+hjsmVL4NixxBmzvnyhTpq6UeixsfQx9l9/fP0qeqiC5OH30uQUaOHv76/dwOdlErk6OjqCEII0adLg+fPnKTLur/LgSKiYroal5TLs30+Ytnp1QcyY0QyhoQ2walVhbN6cEyEhjTBqlDNsbBwxbFgU6tRJ+Nb/4w+gQtZ7aJJvBxo3WovuHQLhYG8DBwcbdO8+HcOHq2BjswSjRx+Ap+dpqFTn4OS0Gy4uW6FSncKUKS8wZw41rB05Aty/T73Drl2j6ZJ0dTa1Gnj9mqZPeviQ/v3jB/1/TAw7z//6i+b/guvXsSZPBbjWKYwjRQiwZs3PlihFMdKtglBpTutHoJazoH76JPYaio3l+Pg1pbleThYk1BvvPwhpEndR27VyUuoJMGwYXcMyZeTDtgCaLyd+vfPkAV69Sj2ZfhGcLMhep7b2eX62SMYhLIzbQ9k9xfdevy4E3x/d13a9t289R3N08yx68vZtwMYGmDiRaiESRA9uxPWdXYt6JUmPv3uYiDyXHTokzKVJE+60xWiW978R8kaglMayoMHM2A6tNXLqGuBk8On+baZvcQdC3a9lUMg1DUNv1Z3g3uYl2vNvWzbBxMYEw9sTXBrw3/AGVKAgNVC37lbmuzgqKvU9sJVv1pRBSEjC49zc/D569AhAq1bRuHWLXeC4OFoYNj5t4M2bNEK/Vi32d7bFi9WoXv09CKG/3VhZcU66/xn8KnqoguRBMWD9pmjcuDEIIShTpowszeHDh7WbfOHChSky7q/w4Pj6lRqcCAEyZfqIyMgKnAHLUJs9uw6n/xMCWFkF7vV9ZgAAIABJREFUJprX/v0EPj69hfwaN16HWbPqwte3F9zdB8Pbuz/8/HpixoxmmDevKkJCGkGl6gtf334ICWmEsWN7Y8yYKRg9ei+KF0/gU7IkUKUK0KwZ4OCwBh4e0bC1PQ9b2+vw9NwHD4/FGD/+MEJCLmLRopeIinoHleodXFzWwt19IYKCTmHq1G9o3pz+mrN2LXDhArBv3zfs3/8Fx46pMW/eQyxZcg+7d6uxfXssli69g8DAG4iIuIUrV+Jw7x4QHf0Zs2dfwbx5TxAdTXPwnjlDjXTx0SBqNfsRo1bTdCzGvixjY+nL+Plz4OJFwNFxE+bPr4LAwKYI7L0aXz7qV3q+fqVjJiZSMjb25314eY2pK1Sa83qk0d9x1Ch6c6RJo01SDABRE6y0BiGX3nnYn/F0kiP/l9C8v9hw0NiaJrdP60dwZvfi1BXi0ycY5XK5ezdNrJ8KRTR+RfxTlL1mXWx/kbDKDx+Axo0BQhBXuRIWVtNvQP3xISHH07Oju7jzb88eNWpYX/vKXN9NFgTBdfkxP714ZJhhPF68oIazYcO4CoQAsLskQZYxlO/gjgTYscN43snE2rls8vTh7Qmt5GoEvn98z63LySBXWfqK9nxy/uvbdJ4dDx8Czs6Ary/9JUmBgt8QcXFAyZIXmO/U+fN5w3ZKIjr6Ozp02Ig2bfbh8OFUHeq3h4UF/WzLlu01Vq4soqNrROLOHSAqCpg0KRYdOy6Fl1d/eHgMgo3NeAQHN8H06ZZo0mQNo4tkzPgZzs7DkS7dNxAClCv3s2coj19BD1WQfCgGrN8UWbNmBSEE/fr1k6WJiYlB2rRpQQjB6NGjU2TcX+HBERNDHUlKllRj7NjuSTI4OTsPFxqcHBxsksTP0XGUkJ/IO8yYFhZWT8iPEGDVqsJJ4lmmzBmOV7lyJ7B3rxl27MikpdP9f3zbuDEPIiPLY/v2zNi/n2DdOnOhbFmzAq6uwzFuXA94ew/BlCk9EBzcFOHhNTFjxt/w8hqEsWMHYMqUAZg4cTDGjRsFPz97BAT0RPPm+5A1K8svU6ZPGDeuCyNLUNBf8PXtj3HjhkOl8sL48W7w9R0HD48g+PgMRGhofXTqNAuEANmzA2XLAjVq0JQxPXsC06evxPTpgZgwwRVTp/bGtGldMXFiJ0ya1BETJvTFhAm2mDjRA5MnW2P8+EGYNq0zJk7sgjZtNiJbNpq3O2dOWr28dWvAxcUfkyfbYNIkG4wf7wyVSgVvb0/4+Dhi0qRRmDTJHpMne2HatAmYPn0mgoKiMHVqCCZMUKFTJ6Bx/U0gzd1BOg8AaW2HXG08UGuYFVoMsUJQ0DIEBS3G7NmrMX/+Vixdegjr1p3DunXXsHDBXiwLWIc1s3Zi1qzNmDlzHVSqV/DxAbzsr2K683YsX/wDCxeoMT/8ASIjDyAycicWLNiC+fPXIyJiO+bM2Ybw8JWYPXsNZsxYi6CgxZg4cRECAuYhMHA5wsK2IiSERp9On07zLM+ZQ//etesFTp5UY+PGT1i58gkiIq5j/vxTWLz4GKKijiAq6jCiog5j69br2LHjPlavPov1688iOvopoqOBo0dp+pwTJwD/4tVBRlYCcSoE4lgExD03GvTPiE8kCz6TzPhC0uPlmTt48oSmBbp5E7h8Gbh2LQ4nT37DkSNxuHaNGiJjYqjHY0wM8PKlGo8exeDu3Y+4desHXr8G3r+ntqdr1+Jw9y7VYeMNmP/+q8bVq3G4fZt6UT55Qg2zly7R8R48oMbYuDjg2rUfOH36PS5d+o6nT+m5d++Amzfj8OABcP58LI4d+4qDB79iz54YHDsWi8uXgcOHqWH22DFg505g2zY1Dh+Ow+HDcTh+XI0LF4CzZ6lx+NIlGgJw/Tp1DLtzh8r16BGd39u3NCTgzRtqj/nyhRqK443IMTFUpqdPqd7++DH9tfbFC+o89uYNPf/hA12z+H5qNbXnPXlCf829e5fSvn5N/33xgp579gzYWCw7iK8mX9lYAqshhfDtGzUkx8bS9Xr/Xhw5+OMHHSt+7HjExSXMI1URFwe8fw/19+8GPQB1hfl47QJ//plxoWhTHGtyfc8UIJhenx9T1gMzKRg3Ds/+ILiWl0BdpLBR3k8phcOrAri5te1j5OerWs31jV7gIUveemhmjv7ZgZQtdvMzEO+5He+lrVYDFy9+xf79LxEd/QBTptyAv/9prFx5AZs23cOOHY+xe/dT7Nv3HAcOvMThw29w4kQMzp6lz5+rV+kz5e5d+mx4+pQ+E969o3s/JuaXS5epQAcvXwJZs75hvp1WriyRauN9+wZMnmyl803s/l/9vcwoxO+3jx/pfnjzBnj6VI0HD2Jx9+53/PvvV9y48RlXr37A5cvvcOHCa5w79w1nz9Jo+RMnaDTGgQPArl1vsGLFecyfvxcrVhxHVNQJBASsgJ/faEye3AHjxo3EhAnjMXGiL8aPH4vx493QrVsQCFEjd+4n8PHpzVzHJUtKY9eu9Hq/9+3tbbnv84IFb2HoUE8QAowc+bNXWB6/gh6qIPlQDFi/IR49eqTdvL6+vnppCxcuDEIImjdvniJj/0oPjk+fHmLXrorYvTsDtmwpjG3bShptzIk3cEibv3+LJBmHhgwZI+RXu/aOJPHbsiUbCFFz/NKn/4q9e80SzW/nzgzaX150m7n5/STJt2NHJlkDm+6vRYlpbdosEPJr23Zeonk5Oo4U8kqT5gd27syQJPmaNl0l5DllSpsk8Vu6tKSQX/fu05PEb/9+gkqVDgt59ukzOUn81q79U/Y6r1hRLEk87ezshPzMze9j586M2Lw5J6KiLBAVVRYrVhTFmjUFsG5dfmzenBNbt2bFjh2ZsHt3OmYflChxEYQAGTKwedYnTOikpdmzJy1WrSqMNWsKYOfOjNi/n2D79sxYty4/Fi8ui4ULK2mPR0ZWkJ13aGh9rF5diPmAjI7Ojh07MiE6Oodmv2XEnj1pmHl36BAu5Pf338tl12rvXjPs2ZMGe/akxe7d6bBzZwbs2JEJ27ZlwdatWZEv3wMhTxsbJyxbVgIrVxbBxo25sX17Zm2f6Ojs2LAhL6KiymLp0pKIiKiO2bNrY86cWvDwGIj06YGMGXmemzblws6dGbFjRyYtv23b/sDWrVmxZk0BRC6ywMKFFbBuXX6sW2eu/bdbt2AQAqRNC+TPD/z5J3W+adlyExYsqITFi8sgKqosFi+2wOLF5REZWQFLl5bEqlWFsWJFMSxbVgpLl1pgyZJKWLSoGhYsqIo8eV4gWzZqSM6Shbbs2YFhwyYgMrIyIiMrY+HCyliwoDIWLKiEhQtpi4yshEWLKmHBghpYsKAWwsObYObMVvD2HgWS/QFI7psg5hdoy3MdJP95+Exsg7DwaliypAIWLKiF+fNrIyqqPCLmVkVwaB1MC6mLWWG1MXduXZ1WD4MGhaJwYaBIEZoWq1gxIEcOoGyZk4haXBqRi0shcnFJLFxcCovml8e8iKqYOas2Jk5vggn+TREcWgdz5jTRtL8wZ85fqFDhDgoWpEX/KlcG6tennrnW1rMQFtYCgYFdMWtWW8yf/xfCw9tj7tx2mDOnN2bOHIqwsM4IDWiLQL8eCJrRDSEhPaBSuaBCBaBCBaBiRaBSJcq3alVgypTBCAtrh7CwDpg9uxNCQroiMNAKgYE9EBjYG0FBfREc3B/BwYMQEjIYM2cOxcyZI+DgsAwtWwJt2wIdOwJdugBdmz1HrrpzMHRMWwz2bQEbv9Zw82qPkBArhIT01PAZhpkzhyIkpDumTx+IqVNHYvJkO/j7j0Dd/JtgVnoLSKVlSFfoH9Sr9A6NGwPDh2/GjBnDMX26DQICHBAQ4AoXZysMcu+K7i6dMdizI2x82yJo2ggEBzsgKMgBQUH2mDnTC9bWQO/eQOfONP1l8+Y0CnPSpBAEBbkhNNQF06d7wN/fByEhzggNdUFoqDdCQ30QGuqMsLARCAuzRlCQLUJDXeHruxLNm9MfSvr2pbn1GzQA2rS5jZCQbggJaYtZs5ohMNAKISFWmD27KebNq4PQ0L8QHl4P8+bVwvz5NRAVVQEREfUQGVkJvXsvh7k53TMJ3wDAmDEjsHp1oUQ/e62tfYXPi7JlT2HFimKIirJAZGQFLFhQCfPnV8b8+VUwd241zJ1bHXPn1sDcubUwd25t7T0+d2591K9/ExYW9P6pUoXeO1WrAra2YQgPb445c1pgzpyWmD27FWbNaoNZs9pi1ix6X4WFdURYWCeEhXVBaGhXhIV1g7//CDRtSr3NLS2BFi2o83Dr1sC0aW4IDR2E0FBrhIYORmjoUISFDcPs2cMxe/ZIhIWNQmioDWbOtEVIiB1CQuwRHOwAD4816N4d6NGDtp496fWxtn6K4GBXTXNDUJC7pnloW3CwuNnaXkf//kD//kC/frT17Qs4Ox9CYKA3ZszwwYwZvpgxwweBgWMQGOiFwEBPBAZ6IDDQHUFBbggKckVQkAuCg10QHOyKPn0oj/79gQEDaDrGQYOAKVPCEBLigsBAFwQEuGDqVPrv9On0X39/F0ybRo+PH0//9vBwQevWC7FtWxbmHpgxYySmTHFEQIAdQkJsEBpqg5AQe/TseRXVqlGn1Pbt6b7t0gXo3fsVgoNH620hIaMwe3Z97n6rVesybG2ps/jw4YCf3xaEhHhr1tAFQUGOCAwcjaCgkQgKGoqgoMEIChqEoKD+CA7ui+Dg3ggJ6YmpU4ehdm2gZk38r707j6ui3P8A/gCCbO5KYmJuWe65VLdy6WqmZeVWWamtWqaZ/nLJXFLLunXz1nUpNRFXRBHZQZTlgLgvaKCCiCyKbIcdlJ3P748TczmeOQc8IOeAn/fr9bx6dWbmOzPy8MB8mHkGgwap+lf//qo+9+OPH2Lz5lHYsuVFbNkyAlu3DsOffz6Pbdv+AUfHp+HoOAROTgOxY0c/7Nr1JPbseRzOzt0xe/Y3aNkSaNFC9USHlZXq51LXrtfh69sS/v42OHzYCkeOWGj8vqGtPfecd739TnfkSHO88IIHTE3LsWHDCwgMbHZf269d+4aWa5LDGDVqH1xcDH0Fp11jug4l/THAaoIuXbokffNu3LhR57qDBw+GEAKDBg2qdf3CwkKtLT09vVEPHOXlRcjLi0BMzAokJGxGVlYglEovFBZehlLpg9jYX3Hs2EKEh1/Axo3A0qWAqyvw++/Atm2V8PR8Gz4+feDvPwGJib8hIuI3XLz4LSIjv0Ng4EwcPvwW/Pxega/vKAQFvYPg4Onw9x+DXbt24ZtvgI8/Vt2a27696qJg/PgzeocRdnZJGj98unS5qletLVuGyP4wMzcv0vv45AIxIQA3t4561Zs4cZNsPUfHAfdda9myabK1HBxi9D7fnj0jZGsuXDhTr3q///6s1osJfY+xX79w2ZoffbRCr3r79nXVGuS4unbSq+bMmd/U+3n36HFRtubSpe/rVc/Rsb/W8/b0bKtXTW0Blr53aSoUAu3b35KtuWzZNL3q/fHH01rPW+7OzNq0adN+kK334osH9D7vTp3iZGsuWDBbr3pOTn119PP7DwoUCoHPPlssW69379N6n3evXudla86Zs0Cvert29dJ63s7O3fSqqe1u5Mceu6L3effocUnLuLZSr3oeHu21nvfWrYP0qvn11x/I1uvR45Le5z1woEK2pr5/aJs+/XvZeoMGBet9jN27/yVbc968eXrV27/fod775Pz5c2Trde16We/zfuYZf9ma77zzk171goJMtZ73+vXD9Kq5du0b2Ljx+Vqt+9Zb/5Hd9yOPJOi17yVLPtKoNXfufL1qHTr0iNZ/m127ntCr5qJFn8jW69Tput594oUXPGRr6js1SVCQKV59dRtmzvzmvrfdvn0whFA9fTBpkipAXLNGNeVHUlJJbd6fYTAMsB4OwtAHQPXvxIkT0jfvtm3bdK77/PPPQwiBXr161bp+Ve2aGgeOuquoKEZOzjFkZLhDqfSBUhmA7Oxg5Oaewp07McjLO43r13chOvogcnKOIz5+D06f/hmBgYtw5swNZGWpHqXx9QW+/hr4889bCApajv3735XugAkIsIWvb2e4uw+Fp+dQeHv3gK9vVxw9agNPzz7w9JyMjRv/jenTgfnzVT/MnnlG9RfOiRPL4O3dAQqFgJ9fe3h7d5F+APr7t4a7e1/4+7eWPgsJMYG3dw+4uw/E22/nYOhQ1V/te/RQ/QXLwkJ1p4Y+P6ynTv03LC1Vd2l07aq6a+HFF4FPPjl+37V+/vkNWFtr/iIxYoSb3r+cWFnlQwho1P3gg1V61fvhh9dkf9kxNS2Dj09LvWr2739Mtubs2Qv1qrdz55Naf3F0d++gV81p09bK1vvHP3z1/tr06nVOtqa+vzBrC3yFqNS4s6q27bXXtsrWrEuA1a7dbdmaK1a8o1e9zZuHav16V92dVl9f78GDA/U+b20XzfoGWC4uj2k9b32DWu0B1im9z1vb9/cnnyzTq96BA521nreLSxe9aso9uiIE0LVrlN7n/dRTIbI1Z85cqlc9T892Ws97584n9aqp7YL40Udj9aoXHGwCa+s82Zo//PCaXjXfeecn2Xr3TvZ9P63q7td725dfztWrnotLFx19Ur+7frUHWPr3SW0B1rvv/kuveg8qwKrt8axa9absvvUNsHr3PqVR67PPFutVy929g9Z/mx07eutVc8mSD2Xr2dvf0LtPDB9+SLbm5Mnr9a7500/jYGlZiF9+eUnt8yNHrODt3RHe3g5QKAT8/cfh6NEvcPnyBiiVXsjNPWXoyx+9McB6OAhDHwDVv+oBlqOjo851X3jhBdQ02fu9GGA1HZX1MElMRUUpSkuz1WqVluZI/19ZWYHi4hSUlChRUSEzkc09cnKO4/LlzQgP/xk3bviioCASRUVJyMkJh1Lph/T0w4iLC0B8vC9u3DiIa9cckZBwGAUFKTrOswI5OeeRnh6ExMTDSE31RXj4JgQG/gvOzrOwb99r8PFZhNhY57/3d/Pvc1PN63Hzpmo+IVfXcHh6LoWPz484dWorEhMDkJkZjtzcU8jNPYmUlCO4fPkATp/ehr/+Oohr13xx8+ZhJCYGIDW1Arl/z+OclwccPQqEhQGxsdcRGemGixcP4uTJ7Th1ahOuXNmOmBhnREUdxKVL+3DypCOCg3+Fh8cieHouhL//cvj4HMa2bcCWLcD/2fyONnahMGlxC31ae2PT7DFw2fAsDh2aC0/PhXBzm4sDBz7A/v2TsX//GLi4vAAXl4lwdn4V+/c/jwMHpsHDYzacnGKxZYtqfuLPPlMFlkuWAJs3/wFX1zdx4MA0HDjwIfbv/xQHD86Am9v7OHhwLlxdZ+HAgbfh4vIe3NzmwNf3G3h5fQkXl+/x/feqGgsWAIsWqWovWgQcODAAvr7t4O3dDZ6eA3Ho0HC4ub0KV9cJOHBgEg4cmIz9+yfCxeU57N8/GPv3vwQXl5fx3XcHMGOG6vGiN95QPa7wyivA3LkH4O9vjaAgM9lf5I4etUBAgDX8/VvCz68tfH07wMvLHp6eDvjkkwg8+aTql0VbW8DeHnj0UWDevH/D3b0z3Ny6qQWxAQG28PZ+BAEBNvfsozm8vR/B1q1jMXSo6lGFvn1Vj1WMHAn84x/51X6BbI5Dh7rDx8cOQUFmCAhQParh69sWAQHW8PNrC0/Px+Dl1QXe3l2waJEzhg9XBcdPP616xGraNGDFCnd4e9vDx6cjfH0fgY+PHXx97eDr2wG+vu3h59cOvr7t4OfXBv7+reHv3xKHD9vi8GFrDBqUhiefVE342rOnKvjt3Bn48ce3peMMDjZFQIANDh+2RUCANY4csdT6OISj4xBpnreuXVWPaTzzjOqNRl5edvD3bwl//xbw92/xdz0bBARY6fwF/Msvv0fPnurBr5UV0KvXeb1/qX/22ZOwsACaNVM9/mFtrXrs8auv9AuwdN2Ro+8dpXIBlqkpMHjwSb3Pe+jQI7LHOG3aWr3qeXq21Xre+s61qO0R4brciTRihJtszc8+W6TneWsPsPQNSbTNq9m+fbJe9eT+eNCyJdCsWSXWrn2j2rl0wt69vf9+5HYI9u//J9zchuLgwcFwcxsIN7f+cHPrA3f3Xli6dDvGjVM9mjdypOpxx2efBT744JDeX5uuXaNkz1vf+UR1hcnGFKpqC7D0fVz/QQVYNja52Lu3R43raguz9QmwPvhglWytjz9ertd56Pp+dXLqq1fNFSveR/Pmqp9FNjaqRwlbtQJ69Ur8+zF5a/j7t4CfXyv4+LSFt3d7eHk9Ak9Pe3h4dIa7exccOtQNbm49cPBgLxw8+CQ+/TQAY8eqfra//rrq8eS33waWLz8EF5cR2LlzIpydX4er66twc3sPvr7fIDZ2H8LC1iMs7L84fXoLzpzZjHPntuLChYO4ffsUKirKkZMDVFSUIy/vEvLyLqCoKBEVFfU4P6KRYYD1cGCA1QRVf4Rw06ZNOtflI4RETUBlpeZvZvPmGfqoGtz6RcNhulqgzQ8CtmsFPp4sULnu36isrN1swjVNPFxeXqgRwlZWlqO0NBNFRUk1/lJYWVmJ8vK7KCq6hYqKYo3lxvJLZUVFqXSuciF3RUUJSkuzUVaWj6KiJBQVJaKoKAnFxal67a/sTiEKM+JRUpKGiooy2X2Wl6smma96S2h5eQmKi1NRVpaL0tIclJZmo7Q0CyUlSpSVFaCiogTl5UUoKytASUkOcnKUyM5ORVFRCioq5F9pWlaWh5KS9Htaxj0tHXfv3kJeXiKUyqvIzLyA3NyrKM7Iw643n8crk+zx3azxUMakIWV3IG6eD0NGxnUUFqYjOTkeqanXUFKixJ07SUhKisblyxdw+/ZVFBQkoLAwAQUF8cjPj0dhYSaKi1Xne/eu6gWgpaWqPlJUlIVkdy/c2OOMW0lpyMtLQ1HRLWRmRuPKlXBcvx6GzMwryM6OQWZmNJTKaGRkXEVJSQFKS1UBemqq6mUGyclAdnYK0tIikJQUhhs3zuLatSuIijqJiIjTOH78CI4dc0dUVBjOnlVAoTiAK1eOIjIyBDExJ6BUqkJ+pVL15te0NFXt+PhIxMWdQ2zsGZw9ewpXrx5HfPwxxMaG4urVYERFBeLixSM4f/4wTp/2xYkT3jh2zBPnz/+FixeB8+dVL2k4flwV9AcHZ8Lf5094uG2Gn58TQkOdcelSEKKiFLhw4QhOnPBAWJgrLl8OwZUr/oiKcsNff7ng4sVDuHIlGSEhgLe3agLyuDjViw3++usyzp/3wJkzB3Hy5D4cP74bx45tx751s7BsydP4ZvEQeB/4FxSKfQgN3YewMBeEhrrAy8sd3t6qF4oePw6cPat6acKpU0BgoDsCA3fC3383FIrtUCi2IChoNwICdsPP70/4+m7FkSO7ERLijvBwX4SH74e//y4EBJxARITqDyXe3qp29Spw6VIJLlw4hpSU81AqIxEZGYBz50Jw+3Yk8vOvIyYmHDdunMGRI24ICwvAnTvJyMq6hqKidMTGAkFBqpc3ZGX97+26SUl/ISRkPxISruj1/XqvysoKlJffRWlpNkpKlH9/n6ShuDgVxcUpKC5ORlHRLRQV3URRUSLu3k1AQcEN5OTEIS+vBDk5qhc7pKb+r928eRsJCZFISLiE+PiLiI+PwI0b53Hjxjlcv34WsbFnEBNzCtHRJ3H16glcvhyOyMhjiIw8jatXVS/NiIpSvS354kXVo0+nTytw8qQfTpzwxfHjPggP90ZYmCeCgjwQGOiO4GA3hIQchELhCoXiAEJDXRAaug+hoecQGqqaUFuhAEJCqloOQkJ2Izh4F4KDd/7ddkgtKMgJQUE7EBS0A4GB6u3IkVs4cgRSO3pU1QIDLyIo6E8EBW1FcPAWBAf/ieDgbQgOdkRIyHaEhDghJGQHFIqdUCh2/b3/PQgO3ovAQFWNI0eAw4cBf3/Azw84fDgAgYF7EBq6BydO7MGpU3tw/PgehIer/v/kSdVnp0+r/hsWtgfBwXsQFKTA5ctAaWkpzp8PQljYLoSG7sbx4844fXo/QkMPQqE4CIViH06cOIiYmHJER6u+b0+cULXjxwsRHu5aQ3NDeLgnwsO9ER9/GXl5qu8pX1/VC5fc3AAPD8DH5xwCAv7E0aNOCA7ejdBQZxw/7opTp9xx9qwXzp/3RUSEPy5dCsBffx1FVFQwrlxRIDr6BJKS/vciktRU1VilVAIpKdeQkhKFtLQrUCpjkJUV+3e/jEd+fiLu3Ln59x9gM1BamoWysry/f75o/vwm48AA6+HAAKsJqj6J+7fffqtzXQcHBzysk7gTNSnvv3/vLRCGPqIGV5KtVHt7WOuvBfLW/2zowyKixuaBv8KSiIjqG69DHw4MsJooW1tbCCEwY8YMresUFxfDzMwMQgjMnTu3XvbLgYPIQNLSgLFjVfexv/uu6r3UD6GAj0bA9hsB028F/jVMANHRhj4kIiIiInrAeB36cGCA1UQNHz4cNU3OXn2urO3bt9fLfjlwEJFBRUSg0L49si2F6n3iRERERNTk8Tr04cAAq4n6+eefpW/gyMhI2XX+7//+D0IImJqaIi0trV72y4GDiAwuP1814QUfAyIiIiJ6KPA69OHAAKuJSktLg7W1NYQQeP311zWWJyQkwMbGBkIITJkypd72y4GDiIiIiIiIGhKvQx8ODLCasLVr10rfxFOnTkVkZCQyMjLg4+ODbt26QQgBW1tbxMTE1Ns+OXAQERERERFRQ+J16MOBAVYTVllZiY8//lj6Rr632draws/Pr173yYGDiIiIiIiIGhKvQx8ODLAeAu7u7hg7diw6dOiA5s2bo1u3bpg9ezbi4uLqfV8cOIiIiIiIiKgh8Tr04cAAi+oVBw4iIiIiIiJqSLwOfTgwwKJ6xYGDiIiIiIiIGhKvQx8ODLCoXnHgICIiIiIioobE69CHAwMsqlc8IKzeAAAe60lEQVQcOIiIiIiIiKgh8Tr04cAAi+oVBw4iIiIiIiJqSLwOfTgwwKJ6xYGDiIiIiIiIGhKvQx8ODLCoXnHgICIiIiIioobE69CHAwMsqlcFBQXSwJGeno7CwkI2NjY2NjY2NjY2NjY2tgfW0tPTpevQgoICQ18W0wPCAIvqVfWBg42NjY2NjY2NjY2NjY2tIVt6erqhL4vpAWGARfWKARYbGxsbGxsbGxsbGxuboRoDrKaLARbVq4qKCqSnpyM9PR0FBQX1cgsoH0Vka4yNfZitKTT2Y7am0NiP2Rp7Yx9mawrtQffjgoIC6Tq0oqLC0JfF9IAwwCKjVFjISfiocWMfpqaA/ZiaAvZjauzYh6kpYD+m+sAAi4wSBzhq7NiHqSlgP6amgP2YGjv2YWoK2I+pPjDAIqPEAY4aO/ZhagrYj6kpYD+mxo59mJoC9mOqDwywyChxgKPGjn2YmgL2Y2oK2I+psWMfpqaA/ZjqAwMsMkoc4KixYx+mpoD9mJoC9mNq7NiHqSlgP6b6wACLjBIHOGrs2IepKWA/pqaA/ZgaO/ZhagrYj6k+MMAio8QBjho79mFqCtiPqSlgP6bGjn2YmgL2Y6oPDLCIiIiIiIiIiMioMcAiIiIiIiIiIiKjxgCLiIiIiIiIiIiMGgMsIiIiIiIiIiIyagywiIiIiIiIiIjIqDHAIiIiIiIiIiIio8YAi4iIiIiIiIiIjBoDLCIiIiIiIiIiMmoMsIiIiIiIiIiIyKgxwCKdbt68iQ0bNmDatGno378/OnbsCHNzc9ja2qJfv36YN28eoqOjddZYtWoVhBA1trlz59Z4PBEREZgxYwYcHBzQvHlz2NvbY/LkyVAoFLU6n8rKSjg5OWHEiBFo164drKys8MQTT2Dx4sVIS0urVQ1qOq5fvw5LS0upD+7YsUPn+vn5+VizZg369+8PW1tbtGrVCk8//TR+++03lJaW1mqf7MNU32rbjzkWk6ElJCTUqg/a2NjorMOxmAyprv2YYzEZq4CAALz77rvo2rUrLC0t0bZtW/Tv3x+zZs3C4cOHtW7HMZkaEgMs0mnjxo01/oA1NzfHxo0btdaorx/Ujo6OMDc3l93WxMQEq1ev1rl9cXExxo0bp3X/dnZ2OHfunF7/TtQ4vfTSS2p9QFeAFR8fj+7du2vtP0OGDEF2drbO/bEP04NQ237MsZgMrT4CLI7FZGjGEmCxH1N9KSwsxOTJk3X2x5EjR8puyzGZGhoDLNLJ0dER48ePx3/+8x+EhIQgJiYGmZmZuHLlChwdHdGzZ09pcPD19ZWtUfWDukuXLigoKNDaSkpKtB5HWFgYzMzMIITAU089heDgYGRkZODUqVMYNWqUdAzOzs5aa8yaNUvtl4Jr164hNTUVe/fuRdu2bSGEgL29PZRKZZ3/3cj47dmzB0IIdOvWrcYL/5KSEvTt2xdCCFhZWWHjxo1ITk5GQkICVq9eDVNTUwghMGbMGK37Yx+mB+F++jHHYjK06hf+/v7+WvtgYWGh7PYci8kY1LUfcywmY1JSUoIXX3wRQgiYmZlhzpw5OHXqFDIyMpCamoqQkBDMnTsXkydPlt2WYzI1NAZYVCc5OTno1KmTzmS+6gf1Y489pvd+hg4dCiEEOnXqhJycHLVlJSUlGDhwIIQQ6Ny5M4qLizW2j4yMhImJCYQQmDNnjsbykydPSoPsokWL9D5Oahyys7NhZ2cHMzMzeHp61njhX/1ORFdXV43lP//8c41BLvsw1bf77ccci8nQql/41/axkOo4FpMxqGs/5lhMxmTNmjUQQqBZs2bw9va+r205JpMhMMCiOps3bx6EELC1tZVdXtcf1GfPnpUGv/Xr18uuU/3i7eDBgxrL58yZAyEELCwskJGRIVtj4sSJEEKgdevWtX5emxqnTz75BEIILFiwQO0XUW0X/n369IEQAoMGDZJdXlpaig4dOkAIgfHjx2ssZx+mB+F++zHHYjK0ul74cywmY2DoAIv9mOpLZmamNIfm/Pnz73t7jslkCAywqM6++uorCCHQvn172eV1/UG9cuVKaeC6ffu27DqlpaWwtraGEALvv/++xnIHB4cab2Hdu3evtJ+QkBC9jpWMX3h4OExMTGBvb4+8vLwaL/xv3LghLf/hhx+01p05cyaEELC0tMSdO3fUlrEPU327334McCwmw6vLhT/HYjIWhg6w2I+pvvz2228QQjXPVFJS0n1tyzGZDIUBFtVJUVGRNA/WuHHjZNe59wd1RUUFysrKar2P8ePHS7eO6vLCCy9ACIF+/fqpfa5UKqVBa8WKFVq3v379urTeunXran181HiUlpZKfy1ycXEBgBov/A8ePCgtDwoK0lp727Zt0nrnz59XW8Y+TPVJn34McCwmw5O78Nc1z091HIvJWNSlHwMci8l4jB07FkKo5p6qrqysDJWVlTq35ZhMhsIAi+5bRUUFUlJS4OXlhaefflq6bfPMmTOy61f9oLayskKfPn2kifratm2LcePGYd++fSgvL9e6v6qAbPjw4TqPa/r06RBCoHnz5qioqJA+P378uDRobd++Xev2paWl0jPSM2fOrOFfgRqjtWvXQgiB0aNHS5/VdOFftY0QAjdu3NBaOygoSFpv7969asvYh6k+6dOPAY7FZHjV+2nfvn1hY2Mj9ZVBgwZhxYoVSE9Pl92WYzEZi7r0Y4BjMRmP9u3bS1/fO3fuYMWKFejevTtMTU3RrFkz9O7dG8uXL5d9iyDHZDIUBlhUa1Up/b3tiSeeQGhoqNbtavO64BEjRmh9M0Tr1q0hhMCUKVN0Hl/Vo4xCCOTl5UmfV3922sfHR2eNqjdVTJo0Sed61PjExcXB0tISFhYWiImJkT6v6cJ/wYIF0vKCggKt9SMjI6X17n2On32Y6ou+/RjgWEyGV72famtt2rRBQECAxrYci8lY1KUfAxyLyTjcvXtX6gdffvmldGe3XHNwcMCVK1fUtueYTIbCAItqTS7A6tChAzZv3qxz4Prtt98wb948HDlyBPHx8SguLkZmZia8vLzw3HPPSbWef/552b84mZubQwiBadOm6Ty+ZcuWSbVSUlKkz52dnaXPAwMDddaoeqPiyy+/XMO/BjU2Y8aMgRACy5YtU/u8pgv/6q/m1XWLf2xsrLTejz/+qLaMfZjqi779GOBYTIaXlJSEsWPHYteuXYiKikJeXh7u3LmDiIgIfPnll9KdKFZWVrhw4YLathyLyVjUpR8DHIvJOKSmpkr9wMLCQgqSIiMjUVJSgps3b2LVqlXSnUs9evRAYWGhtD3HZDIUBlhUa0VFRSgoKEBeXh7i4+Oxe/du9O7dG0II9OnTB/Hx8fdds7y8HG+99ZbOC6+qwW369Ok6ay1fvlyqU30iwOqDm65ntAHg0UcfhRC6JwKkxqeqD3Tt2hV3795VW3Y/AZauW/qrP19/72SW7MNUH+rSj2vCsZiMgYeHh/Q69JEjR6ot41hMjYWuflwTjsXUUG7fvq12U8KoUaNk571at26dtM6vv/4qfc4xmQyFARbVyd27d/Hss89CCNXEetWfS66tzMxM6e0ScoNK1e2lb775ps46tbm91NfXV2cN3l7a9OTk5OCRRx6BEALe3t4ay+/nEcLqf3m6V21ukWYfJn3VtR/XBsdiMgZTp06VvVDhWEyNibZ+XBsci6kh5ObmqgVYx48fl12vpKQE7dq1gxCqR1urcEwmQ2GARXWmUCikwaOm2ze1eemllyCE6pHEe1VN8Fd90JQzY8YMCKG6DVbbBH9OTk5aty8rK+MEf01Q1Q/YN954Q3b5/Uzirusuw+DgYGk9bZNUsg+Tvuraj2uLYzEZWvXXnfv5+UmfcyymxkRbP64tjsX0oFVUVEiPDpqamqK4uFjruq+++iqEEGjXrp30GcdkMhQGWFRn1ScB/Pe//61Xjffeew9CCJibm2ssqxo0HRwcdNYYNmwYhFC9Eaa6jIwM6fhWrlypdfu4uDhpvV9++UWv8yDjM2HCBLW/MNW2JSQkAABcXV2lz4KDg7Xux9HRUVrv3LlzasvYh6mu6tqPa4tjMRna0aNHpa+/s7Oz9DnHYmpMtPXj2uJYTA2hX79+GsGUnGnTpmn0R47JZCgMsKjO8vPz6zwojBo1SutfmlauXCnVrz5xX3VlZWXS7dYzZszQWO7g4AAhdE/cV/05al0DMTUudb3wv3HjhvTZvZNPVlc1F0Dz5s1x584dtWXsw1RXDRVgcSwmQ9u9e7f09a9+5wrHYmpMtPXj2uJYTA3hww8/hBACJiYmKCoq0rreuHHjIIRA+/btpc84JpOhMMCiOvP19ZUGhUOHDt339hkZGbC0tNQ6+Jw5c0aqv2HDBtkaXl5e0jqurq4ayz///HMIobr1VNtriSdNmgQhBFq1aoXS0tL7Pg8yTvHx8bh48aLW5ufnJ/WdNWvWSJ+XlJRINapeVjB48GDZfZSVlUnzE7366qsay9mHqa7qox/XhGMxGYMpU6ZIfejeuYM4FlNjoasf14RjMTUUd3d3qZ8oFArZdYqLi9GmTRsIIfDSSy+pLeOYTIbAAIt0io6O1rk8Ozsbffv2hRACLVq0UJtYDwCUSqXOZ6pLS0vV7izYtWuX7HpDhgyBEAKdO3dGbm6uRo1BgwZBCIFHH31U9i8If/31l/RGmC+++EJj+ZkzZ6RnoxcuXKjznKlpqc3cQRs2bJDWcXNz01j+yy+/SMt9fHxka7AP04NUUz/mWEzGIDk5WefyAwcOSP3jn//8p8ZyjsVkDOrSjzkWkzEpKSlBt27dIITAsGHDZF/G9f3330v90dHRUW0Zx2QyBAZYpJOZmRkmTJiAnTt3IioqCkqlEllZWYiKisL69eul2zaFENi0aZPG9h4eHrC3t8fixYtx5MgRJCUlITc3F0lJSXBxccHgwYOl7UeOHKn1LYZhYWEwMzODEAKDBg2CQqGAUqnEmTNnMHr0aKnGvZMDVjdz5kxpvXnz5iE2NhZpaWlwdnaW3q5hb2+vNb2npqk2AVZJSYkU1FpZWeH333/H7du3kZiYiDVr1kh9U9eredmH6UGqqR9zLCZj0L59e7z55pvYu3cvrly5gszMTGRkZCA8PByzZs2SLkJsbGxw6dIlje05FpMxqEs/5lhMxsbT01Pqs6+88gpOnjyJ7OxsREdHY+HChdKyIUOGaNy9xDGZDIEBFulUNRjoapaWlvj1119lt/fw8KhVjfHjxyMnJ0fnsWzbtg3m5uay25uYmGD16tU6ty8uLpae4ZZrdnZ2GpMLUtNX27e3xcfHo3v37lr7z5AhQ5Cdna1zX+zD9KDUJsDiWEyG1qpVqxr7YKdOnRAaGqq1BsdiMrS69GOOxWSM1q9fj2bNmmntC0899ZTWOw85JlNDY4BFOh07dgyrV6/GqFGj0KNHD9ja2sLCwgIdOnTAiBEjsGrVKiQlJWndPiMjA5s2bcJ7772Hfv36wc7ODubm5rC1tUWvXr0wY8YMHD16tNbHExERgenTp6Nz586wsLBAx44dMWnSJK3Pbd+rsrISTk5OGD58ONq2bQsrKyv06tULixYtQlpaWq2Pg5qO2gZYgOqFBWvWrEH//v1hY2ODli1bYujQofj1119r/Uw9+zA9CDX1Y47FZAwOHTqEBQsW4LnnnkOXLl1gbW0t9Z+xY8di48aNyM/Pr7EOx2IypLr0Y47FZKwuXLiA999/H126dIGFhQVat26N4cOH4/fff69xPk2OydSQGGAREREREREREZFRY4BFRERERERERERGjQEWEREREREREREZNQZYRERERERERERk1BhgERERERERERGRUWOARURERERERERERo0BFhERERERERERGTUGWEREREREREREZNQYYBERERERERERkVFjgEVEREREREREREaNARYRERERERERERk1BlhERERERERERGTUGGAREREREREREZFRY4BFRERERERERERGjQEWEREREREREREZNQZYRERERERERERk1BhgERERERERERGRUWOARURERGQAjz32GIQQWLVqlaEPxaAUCgWEEBBCICEhwdCHQ0REREaKARYRERGRATT1AGvHjh1SMKULAywiIiKqDQZYRERERAbAAEuFARYRERHVBgMsIiIiIgNggKXCAIuIiIhqgwEWERERkQEwwFJhgEVERES1wQCLiIiIHrhnnnkGQggsWLBAdnmPHj2kECMmJkZjeWBgIIQQMDExgVKplD6/fv06/vOf/+Cll15Cx44dYW5ujpYtW2LAgAFYsmQJUlJSZPc3dOhQCCEwZcqUGo/9ueeegxACr776quzyq1ev4vPPP0evXr1gY2MDa2tr9OnTB0uWLEF6errWurUJsAoLC7Fu3ToMGzYM7dq1g4WFBTp16oS33noLx44d07rdBx98ACEERo4cCQCIiIjA1KlTYW9vDwsLC3Tp0gWzZ8/W+u9Tpbi4GD/99BP69+8PKysrtG/fHqNHj4aXl5fsfgAgISFB+lpqa9XXvzfAKigowMqVK/Hkk0/C0tISrVu3xpgxY3D06FGdx0pERERNGwMsIiIieuCWLFkCIQSeeuopjWXJyclq4caWLVs01lm+fDmEEOjbt6/0WW5ubo1BSbt27XDq1CmNer/99huEEGjevDlyc3O1Hnd8fLxUy9nZWWP5Tz/9BDMzM637b9u2LU6ePClbu6YA6+LFi+jcubPO81u6dKnsttWDpX379sHCwkJ2+86dO+PWrVuyNXJzc6WgT66tWLGi3gOsEydOoFevXrLbmJiYYPv27fJfKCIiImryGGARERHRA+fn5wchBExNTZGdna22bO/evRBCwNzcHEIIvPPOOxrbDxs2DEIIzJkzR/osNzcXAwYMwHfffYfQ0FDExsYiKysL0dHR2LFjB/r06SOFNIWFhWr1UlNTpeBJVyiydu1aCCFgY2OjUWP9+vVSuPL2228jJCQE6enpSE9Ph5eXF/r37y+FaMnJyRq1dQVYiYmJaNOmDYQQePzxx7Fz504kJCQgOzsbERERmDlzps7ArypYevTRR9G8eXOMGjUKISEhUCqVSExMxHfffQcTExMIITB16lTZc580aZK0jzlz5iAyMhJZWVk4ffo0JkyYACEEunXrphFIVVZWoqCgAFu2bJG2LygoUGt3796V1q8eYHXv3h12dnbYunUrkpKSkJmZCW9vb+nfysbGRu0OPCIiInp4MMAiIiKiBy4/P18KjDw9PdWWVYUxs2bNghAC9vb2asvv3r0r3UHk6upa630WFhaiZ8+eEEJg27ZtGsvHjBkDIQRGjx6ttUbfvn0hhMC0adPUPk9OTkbz5s0hhMDChQtlty0oKEDv3r0hhMDnn3+usVxXgDV+/Hgp0NF2h9iKFSukgOzOnTtqy6oCLCEExo8fj/Lyco3tFyxYIAWHeXl5astOnDih8y6vyspKvP7667J3VFXRZw6sFi1aIDY2VmOdixcvSuv88ccfOusRERFR08QAi4iIiBpE1eNo986D9fjjj0MIgXPnzqFTp04a82AFBwdL4UVaWtp97XPx4sVa7+rauXOndFeY3FxQ1UMTf39/tWUrV66UwrbS0lKt+9+9ezeEEGjTpg0qKyvVlmkLsOLi4qS7o3x9fbXWvnv3LmxsbCCEgIeHh9qy6gGWXCB07/mFhYWpLfvss88ghECrVq3U7paqLjY2tt4DrOXLl2tdb+DAgRBC4KOPPtJZj4iIiJomBlhERETUIBYtWgQh1OfBun37NoQQaNmyJcrLy/Huu+9qPBZXFRb17t1btq6fnx+mTp2K7t27w9raWnb+pMGDB2tsl5+fDysrKwgh8Ouvv2osrwq/7OzsUFZWprbs+eefhxACb775psbjcdXb+fPnpWO4fv26Wg1tAdaff/4JIQSaNWuGtLQ0nfWHDBkiG/xUBVjdu3eX/2IAuHPnjnRs997ZNmDAAAhR8yT3VXeY1VeApWti+rfeegtCaJ9Mn4iIiJo2BlhERETUIHx8fDTmwXJ2dpYecwOArVu3atwxNXz4cAghMHv2bLV6ZWVlePvtt2ucMFwIgR49esgeU9X2Q4YMUfu8srISDg4OEEJg3rx5Gtu1a9euVvut3u6dzF1bgLVw4cL7rv3pp5+q1agKsIYNG6bjKwJp+507d6p93rJlSwgh8M033+jcfuLEifUaYMXFxWldr+qcXnzxRZ31iIiIqGligEVEREQNIjc3F6ampmrzYH366acQQuCXX34BAMTExKjNg1VUVCTNNbV//361et9//70UfEyZMgVeXl6Ii4tDVlaWdIfS0qVLIYTAY489JntMXl5eUo3qjy2GhoZKn58+fVpju2bNmt13yKRQKNRqaAuwqk/QXtv2wQcfqNWQezugnKrtd+zYofZ51Xxla9eu1bn99OnT6zXASkhI0Lpebc+JiIiImiYGWERERNRgBg8eDCH+Nw9Wr169IIRq/qsq9vb2UqAUEhIihRupqalqtTp37gwhBN59912t+5s/f77OAKu0tBRt27aFEALffvut9HnVhPLa7txq3bo1hBBYsWJFbU9dg7YAq2py9c6dO+tdu64BVtUdWMuWLdO5fX3fgcUAi4iIiLRhgEVEREQN5quvvoIQqnmwUlNTIcT/5r+q8s4770AI1TxYq1atghACTzzxhFqdrKwsKfS4962G1Y0ePVpngAX8b8Lynj17AgBKSkpkQ63qqoK411577T7OXp22AGvDhg0QQsDExAQZGRl61a5rgNW/f3/pzjZd+vTpwwCLiIiIGgQDLCIiImowVY/smZqa4o8//oAQ/5v/qsqWLVsghGoerJEjR8rO8ZSSkiKFHocOHZLdV3JysvSon64A69ixY1KtM2fOwNPTU/r/a9euyW7z9ddfQwiB5s2bIzk5+f7+Ef6mLcC6evWqtP9169bpVbuuAVbVo52tW7dGUVGR7LZxcXHS9nL72bt3r7S8ekB5LwZYREREVBsMsIiIiKjB5OTkSPNgVQU4VfNfVYmOjoYQAo888ggsLS0hhMC+ffvU1ikrK4ONjQ2EEPjwww819lNeXo7XXntNCkZ0BViVlZXo0qULhBCYP3++NLH7008/rXWbmzdvwsLCAkIIjB49Gnfv3tV53nJBmLYACwBeeeUVCCHQokULXLhwQWftxMREFBcXq31W1wArPDxcWnbvGw4B1b9Z1eOD2vZz+PBhafmtW7e0HgMDLCIiIqoNBlhERETUoJ566im1Ccirz39VpWPHjmrr3L59W2OdqgnEhRBYuHAhrl69iszMTCgUCunRwd69e9cYYAGQJnu3s7ODlZUVhBD473//q3ObTZs2Sfvv3bs3nJyccOPGDeTk5CA5ORlhYWH4/vvv0bdvX427zADdAVZiYiLat28PIQSsrKywdOlSnDt3DpmZmVAqlYiMjISTkxPeeOMNNGvWDEqlUm37ugZYADBhwgRp+RdffIHLly8jKysLZ8+exaRJkyCEQLdu3bTuJzU1VQorP/nkE9y8eROlpaUoKytTuyOLARYRERHVBgMsIiIialBVk5QLIdCqVSvZx8umTp0qrfP444/L1rl9+zYcHBzUgq7qbf78+dIcWjUFWFFRUWrbmpmZaUwaL2fTpk3SnVi62uTJkzW21RVgAcCVK1ekSe51NTMzM2RnZ6ttWx8BVnZ2tjTXl1xbtmwZ3n//fekuNDnVv47VW/XjYoBFREREtcEAi4iIiBqUh4eHFFjI3ZkEAJs3b5bWmTlzptZaaWlpmDNnDhwcHGBubg47Ozu8/PLLcHd3B4BaB1gAMGDAAGmfL7/8cq3P5+bNm1i6dCmGDBmCNm3awMzMDC1btsSAAQMwe/ZsBAYGoqysTGO7mgIsQDWh/Pbt2zF+/HjY29vDwsIClpaW6Nq1K15//XVs27ZN4+4roH4CLAAoLi7Gjz/+iL59+8LS0hJt27bFP//5T+nfd/LkyRBCYOLEibLbFxUVYfXq1RgwYACsra0ZYBEREZHeGGARERERkV4GDhwIIQTmzp1r6EMhIiKiJo4BFhERERHdt8TERGmOKycnJ0MfDhERETVxDLCIiIiISEN+fr7so4+A6i2PVZO8W1hYIDMzs4GPjoiIiB42DLCIiIiISINCoUDPnj2xbt06XLp0CdnZ2UhJSYGPjw9GjBghzVu1ePFiQx8qERERPQQYYBERERGRhuqTq+t6u2JxcbGhD5WIiIgeAgywiIiIiEhDbm4uNm3ahPHjx6N79+5o0aIFLCws0KlTJ0yYMEF6EyERERFRQ2CARURERERERERERo0BFhERERERERERGTUGWEREREREREREZNQYYBERERERERERkVFjgEVEREREREREREaNARYRERERERERERk1BlhERERERERERGTUGGAREREREREREZFRY4BFRERERERERERGjQEWEREREREREREZNQZYRERERERERERk1BhgERERERERERGRUWOARURERERERERERo0BFhERERERERERGTUGWEREREREREREZNQYYBERERERERERkVFjgEVEREREREREREaNARYRERERERERERm1/wdUrNL1+V+AMAAAAABJRU5ErkJggg==\" width=\"640\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Show the two methods of rebinning the spectra and draw the spectra for each method.\n",
+ "\n",
+ "sightline = spec.get_sightline(spec_id[0], camera= 'b')\n",
+ "\n",
+ "wavelength = 10**sightline.loglam\n",
+ "flux = sightline.flux\n",
+ "error = sightline.error\n",
+ "\n",
+ "_rebin(sightline, v = 40000)#directly invoke _rebin\n",
+ "\n",
+ "new_wavelength = 10**sightline.loglam\n",
+ "new_flux = sightline.flux\n",
+ "new_error = sightline.error\n",
+ "\n",
+ "print(len(new_wavelength)-len(wavelength))\n",
+ "\n",
+ "fig,axs = plt.subplots(2,1,sharex = True, sharey= True)\n",
+ "\n",
+ "axs[0].plot(wavelength, flux, 'r',label = 'original spectra')\n",
+ "axs[0].plot(new_wavelength, new_flux, 'g--', label = 'rebinned spectra')\n",
+ "axs[0].plot(wavelength,error,'b',label = 'original error')\n",
+ "axs[0].plot(new_wavelength,new_error,'y--',label = 'rebinned error')\n",
+ "axs[0].legend(loc = 'best')\n",
+ "\n",
+ "orignal_sightline = spec.get_sightline(spec_id[0], camera= 'b')\n",
+ "rebinned_sightline = spec.get_sightline(spec_id[0],camera= 'b', rebin = True)\n",
+ "#one can also rebin the spectra using the parameter 'v'(int) in function DesiMock.get_sightline.\n",
+ "axs[1].plot(10**orignal_sightline.loglam, orignal_sightline.flux, 'r',label = 'original spectra')\n",
+ "axs[1].plot(10**rebinned_sightline.loglam, rebinned_sightline.flux, 'g--', label = 'rebinned spectra')\n",
+ "axs[1].plot(10**orignal_sightline.loglam, orignal_sightline.error,'b',label = 'original error')\n",
+ "axs[1].plot(10**rebinned_sightline.loglam, rebinned_sightline.error,'y--',label = 'rebinned error')\n",
+ "axs[0].set(**{'title':'orignal spectrum and rebinned spectrum', 'ylabel':'Relative Flux'})\n",
+ "axs[1].set(**{'xlabel':'wavelength'})\n",
+ "plt.subplots_adjust(wspace=0,hspace=0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Determine the best $\\frac{\\delta\\lambda}{\\lambda}$ for each channel."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-03-12T14:20:45.759868Z",
+ "start_time": "2020-03-12T14:20:45.749894Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[1. 1. 1. ... 1. 1. 1.]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#for 'b' channel\n",
+ "c = 2.9979246e8\n",
+ "best_v = {}\n",
+ "sightline = spec.get_sightline(spec_id[0], camera= 'b')\n",
+ "wavelength = 10**sightline.loglam\n",
+ "dlambda = (wavelength - np.roll(wavelength,1))[1:]\n",
+ "print(dlambda)\n",
+ "dlnlambda = np.array(dlambda/wavelength[1:])\n",
+ "v = c*(np.exp(dlnlambda)-1)\n",
+ "vm = np.median(v)\n",
+ "best_v['b'] = int(vm)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-03-12T14:20:46.786594Z",
+ "start_time": "2020-03-12T14:20:46.775604Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[1. 1. 1. ... 1. 1. 1.]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#for 'r' channel\n",
+ "sightline = spec.get_sightline(spec_id[0], camera= 'r')\n",
+ "wavelength = 10**sightline.loglam\n",
+ "dlambda = (wavelength - np.roll(wavelength,1))[1:]\n",
+ "print(dlambda)\n",
+ "dlnlambda = np.array(dlambda/wavelength[1:])\n",
+ "v = c*(np.exp(dlnlambda)-1)\n",
+ "vm = np.median(v)\n",
+ "best_v['r'] = int(vm)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-03-12T14:20:47.697147Z",
+ "start_time": "2020-03-12T14:20:47.686176Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[1. 1. 1. ... 1. 1. 1.]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#for 'z' channel\n",
+ "sightline = spec.get_sightline(spec_id[0], camera= 'z')\n",
+ "wavelength = 10**sightline.loglam\n",
+ "dlambda = (wavelength - np.roll(wavelength,1))[1:]\n",
+ "print(dlambda)\n",
+ "dlnlambda = np.array(dlambda/wavelength[1:])\n",
+ "v = c*(np.exp(dlnlambda)-1)\n",
+ "vm = np.median(v)\n",
+ "best_v['z'] = int(vm)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-03-12T14:20:48.633643Z",
+ "start_time": "2020-03-12T14:20:48.627659Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'b': 62996, 'r': 44859, 'z': 34720}\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(best_v)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-03-12T14:20:51.145199Z",
+ "start_time": "2020-03-12T14:20:51.036484Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support. ' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option);\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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0WDRrA5BA6wJE2/0u941w/Kqwgijm3sZdbnL6NjBvSHuOQDAG3VobCDAgqMYOAAAQDR48v57ggZYkybd7He91Pr1JGM8bePGEPccAOCNOjQ2EGDBUQwcAAAgGkydeXvQAGvK5Ib+V+zc2R5gvfVWaDsOAPBBHRobCLDgKAYOAAAQDWbOaB40wLpvciP/K06ebA+wJk4Mab8BAL6oQ2MDARYcxcABAACiwezpLYIGWNOTmvhf8cMP7QFW48ah7TgAwAd1aGwgwIKjGDgAAEA0+HlGXNAA65v7+vtf8dAhe4BVrlxoOw4A8EEdGhsIsOAoBg4AABANXNOnBwyvyiQYadq0wCuXKCGXMdpT2OizikanjxwMXccBAD6oQ2MDARYcxcABAACiwvTpyjfJf4B18SgjzZgRcNW+fUqp2BjP8j9/8XoIOw4A8EYdGhsIsOAoBg4AABAVpk5V4fH+A6ySo410//0BV+0Rf5lt+VefHR3CjgMAvFGHxgYCLDiKgQMAAESFpCRdOMZ/gFU80UizZgVc9f6kW23LT5veLIQdBwB4ow6NDQRYcBQDBwAAiApTpujiUf4DrAvHGGnOnICrvrFwlG359gkVQ9dvAIAP6tDYQIAFRzFwAACAaPDDpL4+wdXI240erG/0RB2jXXMmB1x3+/cf2dYrnZhHrtTUEPYeAJARdWhsIMCCoxg4AABANJg6qWHAuxCaJKM3ZvUMvHJKiiok5LYtv33dp6HrPADAhjo0NhBgwVEMHAAAIBokTWwQNMB6b06/oOvfHV/Otvxbz40LUc8BAN6oQ2MDARYcxcABAACiweSJNwUNsD55cFjQ9e+b1tS2/PSkJiHqOQDAG3VobCDAilAnTpzQ66+/rgEDBqh27doqWrSo8ubNqxIlSqhJkyZ67LHHdOLEiUy3s3v3bo0ePVpVqlRRwYIFVbx4cTVs2FCLFi2Sy+VyvN8MHAAAIBpMmFA/aID11cOjg67/xvPjbct3GFEuRD0HAHijDo0NBFgR6vzzz3d/AAO1qlWrauvWrQG38d1336lUqVIB12/evLlOnTrlaL8ZOAAAQDQYN/6GoAHW2scDT+IuSdvWf2pb/qqR+UPUcwCAN+rQ2ECAFaGMMcqfP786duyol19+Wb/99psOHDigdevWadCgQcqVK5eMMapUqZKOHj3qs/6+fftUpkwZGWNUvHhxLV26VLt27dLmzZs1ZMgQ94e7X7/g8zucLQYOAAAQDcaMqxs0wNr01Myg66empqjweM/yeSYbnTx6MES9BwBkRB0aGwiwItTgwYO1a9eugM/PmjXL/QGdPXu2z/MJCQkyxih37tz66quvfJ4fNGiQjDHKlSuXfvrpJ8f6zcABAACiwehxdYIGWL8/Ny/TbdwwoohtnR8/fiEEPQcAeKMOjQ0EWFEqOTlZxYsXlzFGjRs3tj135swZFStWTMYYtWvXzu/6e/fuVb58+WSM0ZAhQxzrFwMHAACIBvFjawcNsP5auiDTbfRLrGpbZ9HDvULQcwCAN+rQ2ECAFcXq1asnY4wqV65se3z16tXuD+8LLwT+S+Ctt94qY4wqVKjgWJ8YOAAAQDQYMbZW0ABr77JnM93GY//r5F4+92SjaaOuz/mOAwB8UIfGBgKsKHbppZfKGKPrrrvO9vgDDzzg/vBu27Yt4PoTJkxwL7d//35H+sTAAQAAwu74cSkuTmrQQFq61O8iw8fUCBpgHXn9pUx38+unr2tePaMvyhsdz2ekevWcPhIAQBZQh8YGAqwo9eOPP7o/oN6XAPbt29c9/1VycnLAbSxcuNC9jS+++MKRfjFwAACAcPtj7kTd3MuoTILR43WN9OefPssMGVPdFlidN9Hz//X6GOmddzLf0cmTUt68kjFWK1hQCvLdCwCQM6hDYwMBVpRq2rSpexL29evX255r27at++6Dwbz11lvuD/mKFSuyvO9jx44FbHv27GHgAAAAYdX/Tk8YlW+S0a4Z43yWGZRYzRZgPVfTaGxTo2EtjP4pYqT33svazmrW9ARYxkgbNjh8NACAzBBgxQYCrCg0Z84c94dz0KBBPs83a9ZMxhiVLVs26HZWrVrl3s6LL76Y5f2nr5NZY+AAAADh4H054KIJLX2WGZB4tW2ZN6oaexC1alXWdta7t3295593+GgAAJkhwIoNBFhR5v3331eePHlkjFH16tV14sQJn2XSA6xy5coF3daHH36YpcnevRFgAQCASOVKSfEJsOY2yu9zaV+/0fY7CK6o4hVgffRR1nb46KP29YYPz4GjAgAEQ4AVGwiwosj333+v888/X8YYlS9fXjt27PC7XPolhCVKlAi6PS4hBAAA/zV/b/7OJ8Aa0tJIc+bYljvRt6cOnWf0b0GjfYWMTuXxCrA+/TRrO/zqK9t6Z2rXyoGjAgAEQ4AVGwiwosSWLVtUsmRJGWNUsmRJbdq0KeCy6ZO458mTJ+gk7s888wyTuAMAgP+U95fP9gmwutyVFjBt3CiNHy+9+qrvpX/ebc2arO3wzBktvj6/+t9pVLu/UaHxRid3/5WzBwkAsKEOjQ0EWFFgx44dqlChgowxKlq0qH788cegy2ecI+u3334LuNzEiRPdy+3bt8+RvjJwAACAcJp9/50+AdZd7a1QamfpQhrfxOip2kapRQoHD7C+/DLL+6yXUMy2vy+fn56DRwgA8EYdGhsIsCLc3r17VaVKFRljVLBgQa3Jwl8DV69enaXJ2dPnyipfvrxj/WXgAAAA4TRwxJW2MKnQeKPH6hql5jKqOMLz+AM3BgmvjJG+/jrL+xw5vYFtn7NG1cvBIwQAeKMOjQ0EWBHs8OHDql27towxypcvn97L4u2cz5w5o6JFi8oYo7vuusvvMvv27VP+/PlljNHgwYMd6zMDBwAACKf2Qy/2OQPr2gFG/6tnfB4PGmB9912W97n8nQds271xSIEcPEIAgDfq0NhAgBWhTp48qYYNG8oYo9y5c2vZsmVntX58fLx73W+++cbn+SFDhsgYo1y5cmn9+vVOdZuBAwAAhFXToRf4BFWBWtAAK5MpGzI6cvygCky0b/uv//sgB48SAJARdWhsIMCKQCkpKWrdurX7Azhv3jwdPXo0YDt+/LjPNvbt26cyZcq470b4wgsvaPfu3dq6dauGDRvm3na/fv0c7TsDBwAACJcDf2xSkXFWgFRidOYBVuuORm07GN3d3mhjSa8A6yz/wNd2VHnbtudPui2HjhIA4I06NDYQYEWg7du3uz98WWkVK1b0u53vvvtOpUqVCrhe8+bNderUKUf7zsABAADCwuVSj94XZfnsK++2poJXgLV161ntfumL42zbazS0SA4dKADAG3VobCDAikBOBViStHv3bo0aNUqVK1dWwYIFddFFF6lBgwZatGiRXC6X431n4AAAAOHg2rkz2+GVSTL68qYKnvCqShXpLL8nHTq0R/kmebaXZ7LRwT8259DRAgAyog6NDQRYcBQDBwAACIfd//fhOQVYX7/yoJSYKPXpI23blq0+NI0vYdvmssecu1EOACAw6tDYQIAFRzFwAACAcPjktbmZhlS5pgR+7tt3njznPvxv7j22bXYbUfHcDwwAkCnq0NhAgAVHMXAAAIBweGFe70wDrAITAj/3/cqF59yHzes+sm3zgnFGJ08edeDoAADBUIfGBgIsOIqBAwAAhNqR334JGlzlnWR0Oo9RsTGBl1n7/rOO9KX6iPNs2/38nccc2S4AIDDq0NhAgAVHMXAAAIBQe3h8U59Aqkv/Eroo0ajgBKNnrrUmZ794VOAAa/2qJY70ZdS4OrbtTp/U2JHtAgACow6NDQRYcBQDBwAACLXqg3wDqbcXjZWrwHk6k9sKr07lMZpb3+iBG40WXOe7/M8fvehIX958Ocm9zWJjjB5oV0pKTXVk2wAA/6hDYwMBFhzFwAEAAEKtylDfQOqnVUuka6+VjBVgLakRfH6sjatfdqQvJ44eVLOeufVsLaPj+ax9a9UqR7YNAPCPOjQ2EGDBUQwcAAAg1GoPzmMLoy4fbuTauVOqU8cdYC2qFTzA2vTpa851KC7OvV8ZIzVr5ty2AQA+qENjAwEWHMXAAQAAQq1sQi5bGHW0yc3WE3XrukOkp2oHD7A2f7bcuQ798os9wMqVSzp+3LntAwBsqENjAwEWHMXAAQAAQsrl0sQmRiNvNxrawmpK+w5yz4CLVLefUe3+wcMrk2S0dc2bzvarVi17iLV6tbPbBwC4UYfGBgIsOIqBAwAAhNSpU/agqHx591NXji6QaXCV3n798m1n+9Wzp71fXbo4u30AgBt1aGwgwIKjGDgAAEBIHTxoD4oqV3Y/VXVUwaCh1VVDjOr3MarXx2jn1w5PtL50qbtPfxY1eqRRQSWfOuHsPgAAkqhDYwUBFhzFwAEAAELqn3/sAVbNmu6nqo0qFDTA+qFMhvU2bHC2Xy6Xnr6lqOpkuHxx2dJxzu4DACCJOjRWEGDBUQwcAAAgpDZtsgdY9eu7n6oxqnDQAOvbSzKs98svjndt8qSbbfurE1/E8X0AAKhDYwUBFhzFwAEAAEIl+b13Nf96o0F3GK0tnRZENWnifv7ahCJBA6yvymUIsDZtcrx/ezd+q4ITvO52+N17ju8HAGIddWhsIMCCoxg4AABASLhcmtfUfobV12WNtre7xb1I3YQLggZYX5TPEGBt2ZIj3ew+spJtnxPGXp8j+wGAWEYdGhsIsOAoBg4AABASR4/6DaXqJV7kXqRefNGgAdZnFTMEWL/+miPdfOPZMbZ9nj/O6N/tzl+uCACxjDo0NhBgwVEMHAAAIBQObPvZbyjVcHRJ9zI3xRcLGmB9UilDgLVtW470Mzn5tCqPOs+238ce7Jgj+wKAWEUdGhsIsOAoBg4AABAK27/5wG8o1TSxtHuZhvEXBg2wTJJRvklG+Sca7dn4bY719en5PW37vC2ueI7tCwBiEXVobCDAgqMYOAAAQChsWvWi30CqRWJZ9zK3xBfPNMBKb/s2/5hjff1351bbvoqNNXKlpubY/gAg1lCHxgYCLDiKgQMAAITC2lfn+w2iWieWdy9za3yJLAdY+7esy9H+Xhmfz7a/tZ++nKP7A4BYQh0aGwiw4CgGDgAAEApfPXOf3yDqnjGXupe5Pb5UlgOsA9t+ztH+dhxRzmefa99+Okf3CQCxgjo0NhBgwVEMHAAAIBQ+eXSU3yCq85gr3Mu0jL/Y9lzt/oEDrIO/5+ydAZ9/sIfPPsskGO3fuj5H9wsAsYA6NDYQYMFRDBwAACAUVj440G8Q1WNsVfcyMwdW0z33GnW4x6jzXUbNugUOsA7/sSVH+3v0nz9UIT63z36nN+DrOACcK+rQ2MBvTDiKgQMAAITC6/d39xtE9RtbzbPQvfdKxmhtaaMvyhtVHxQ4wDq687cc7/Of6z/XTb2t/dXrY7SoltGxfEb6++8c3zcA/JdRh8YGAiw4ioEDAACEwov33es3iBo8vqZnoQ4dJGNUeWjmc2Ad+3t7SPp95t99Wn+xkUyGlpgYkn0DwH8VdWhsIMCCoxg4AABAKCyaeKffIGrEhDqehTp2lIzR1YPty9zXyHe9E7t2hK7z3bvbAyxjpOefD93+AeA/hjo0NhBgwVEMHAAAIBSeGHur3wArceINnoU6d5ZM8EsH09vJPSG8jO/YMal0ad8QKzk5dH0AgP8Q6tDYQIAFRzFwAACAkBgyRItr+gZREybf5Fmma1cNuDPz8MokGZ3evye0/V+yxCfA2tmmcWj7AAD/EdShsYEAC45i4AAQKQ7t3aFWPc+TSTLq2v18ndq5PdxdAuCkPn207GrfIOq+KZ4Q6FiPTrqxd9YCrOQD+0Pbf5fLHVydyW00tqlR/olGXy37X2j7AQD/AdShsYEAC45i4AAQEVwudehbzFac9hpSLty9AuCkLl20ooo9hCo30mjrfXHuRfomXJml8MokGaUcOhj6Y1i3TtsuNLq+r6cfl43Mo5NHw9AXAIhi1KGxgQALjmLgADu0bFMAACAASURBVBB2mzbpQO9Oyj3ZXpzmnmz0509rwt07AE65+269f7n9c96tnZFmzHAvMiChSpYDLNeRI2E5jO9GtFfeSfa+zJ/ZNix9AYBoRR0aGwiw4CgGDgBhtW+fVKSIVl0WuEht27uQls3oHO6eAjhXd9yhTyrZP9/t7zHS7NnuRQaPuipoaNWgl9GMBkbTGhrp+PGwHcr06bfZ+lVknNH3yx4KW38AINpQh8YGAiw4ioEDQFgtXCgZoyP5jdaWNqoxMHDhuuHjl8PdWwBZdeSIlJpq/b/LJe3cKdWrpy/L2z/XbToaae5c92rDRlULGmDNq5dhEvWTJ8N0cNLxff+oVGIuW9/KJBjt2bo2bH0CgGhCHRobCLDgKAYOAGFz8KB21btGBwt4CtKUXEYVRvgvXG/oa5Syb2+4ew0gE2um91f1QUZXxOXWQ/e3latLZ8kYTW1odP44++e6eRcjzZvnXnfE6OpBA6y59TMEWKdOhfEopaWP9vfpX5u+RTzBHQAgIOrQ2ECABUcxcAAIh7+3/qAqcXncRV/VIUYD7jT6uqxRy86Bi9f7GhnpwIFwdx9AALt/+VYXjLV/bqc2NLqlh//PdJPuRnrkEff6CYk1gwZYs2/KEGCdORPGI7U8FHeDTx8fHts48xUBIMZRh8YGAiw4ioEDQDh0Hl/Vb3H6yPVGd7cPPnHzqqaVOMMBiFBzprXI8iTs6W3z/Cnu9cck1g667MybMwRYKSnhO9A0rtRUNR9+kU8/l83sGu6uAUBEow6NDQRYcBQDB4BQO3nyaMDi9OnaRl3bZV7w/vPl++E+DAB+1IkreNYB1usPD3SvP37MdUGXndYwQ4AVIUH2r9+8pxKj7f28KNHowI6t4e4aAEQs6tDYQIAFRzFwAAi1lS9NC1icLq5p1K9V5gXvG0+NDPdhAPDy2zfvn3V4ZZKM3n5kmHsbk8b6XpKXsdXrY/TB5UarLzXW5PAR4qP5I3z6env3XDp9YF+4uwYAEYk6NDYQYMFRDBwAQiX1r536aVgHTbzl7IrbaQ19H1s47OZwHw4AL1MnNsxWgPXBglHubSSNrZ+ldQqPj7yvxHf1LuzTz+79SkZU0AYAkYI6NDZE3m9rRDUGDgChkHriuBoNKpCt4vbZWkZDWtofm32Tke64I2IuIQJi3dH9/6j4mFzZ+ox//NQ493amjbspS+sUGRd5X4lTTp1U80Hn2/pZp7/RgVVvhbtrABBxqENjQ+T9tkZUY+AAEAofPDcpW4WtSTJ6/DqjZ24vZXtsdLO0OXDeojAEIsGCBztm+zP+xTNT3NuZOT5rZ3FdMDYyvxIf2/uXru9rdFtX6zJHl0kbq44cCXfXACCiUIfGhsj8bY2oxcABIBSmT77lrAramTcbrbrMaE0Fo92Fjf66uYY+vMzo3nuts7Geqm30cSWj5NxG2rgx3IcHxLxufex34hvawuifIkY922T+ef/muRnu7cye0DhLY0SxMZH7lfj0p6s9E81nbIRYAOBGHRobIve3NaISAweAUOgysuJZBVj3NTKqPNTomkFGtfsbPdXlKslYRWv6MrknpwVYZctKjF9A+OzaJZcxWnex0bimRlWH5dKBAlZo0ysLAdbapXPdm5o7santuRoD/a9zYQQHWJKkKVP8h1jJyeHuGQBEBOrQ2BDhv60RbRg4AOS0E3v/VrMeuc8qwBrVzP7vaYOq6Xg++2OXxFsF4c4LjFImTwz3YQKx66WX7CFN69ZSnjySMep0d+af9w3LHnVvasPEAXq2ltHzNYxeqG40KcBNH4onRvhX4pQU/wHW/Pnh7hkARATq0NgQ4b+tEW0YOADkpI9fnqXiiWc/J453gDV75PXadqH9sbr9rIJwd2GjQXcYHfhzc7gPF4hN/fvbQ5p586QCBSRj1K5D5p/3LSuXeLY1ebJkjFZeYZ3NVSnO/zolR0fBV+I1a2yvyw9ljOr1MdrywQvh7hkAhB11aGyIgt/WiCYMHABywqlD/+q5x/qfdXAVKMD637hb9EMZ+2MNe1pF7rAWVpE7PrFOuA8biE1XXmkPsNatk4oUkYxRy86Zf943f/66Z1tJSZLxHQO8W6nRucJ3vGdjwQLJGL10jVGBCVbfqww1ch0/Hu6eAUBYUYfGBgIsOIqBA0BOuCehfLbDK5Nk3WUw478fndZKn1QKvk7tuALhPmwg9uzcaQ+viheXUlOlCy+UjNHGkkafVDIqkxD4s7vp+/c925s6VTJGY5sG/7yXHhUlAZakjyd19en/4JZGcrnC3TUACBvq0NhAgAVHMXAAOGepqUp5YameblhEN/Q16tapwDmFV/7aEw92Uk2vyZzbDCzme0nRjz+G+9UAYsvLL9sDrLvvliT1bl9Ad7c3at3RapWHBv587/vjF8/2ZszQLyWM7m4ffEy4JIoCLFdysvq18j2Gl8e3CXfXACBsqENjAwEWHMXAAeBcbXj+QZUYnbUgqsIIozUVjD6reHYB1rOP9PF5LG50dV2caJ8c/lSetLsSHj4c7pcFiAlvz+6j5l2MhrQ0erC+0bqpgyVJxcd6Ppu5Jxvd3Mv/Z7tHWyOdOOHZ4KxZKjIu8zGhbEL0BFiS9M9PX6r8SPsxFBlntP2zFeHuGgCEBXVobCDAgqMYOACcqy5xWb9csEEvz5kaVYdkPcB6YcFgn8cmTaiv2iMK2R77vZhRSi6jv7u0DvfLAsSE+yY1tH0G5861zsAqnZjH9nizbvbP73M1jT6taOQy9kvpfpszLktjQvmE3OE65GxbO2OY8ky2H8c1g40O7vw13F0DgJCjDo0NBFhwFAMHQio1VTp9WpJ0ZtsWHfzmM+tW44gOLpf09dc6/MIi7Vj8iHTggCTpsuFZD6JqDvQEWNUHZX29ZU+P8HksfvINajayuO2xEqONzh9ndPlwI61fH3HvL1dKilbM6K4HBtXQ7vdeDXd3gHOzfLn6tPYKm58eLkkqN9oeYN3Zyb7c5xXSxoIGDWybfGFOtyyNCRXjoy/AkqRHOl/hcyx3Di+h1JMnMl8ZAP5DqENjAwEWHMXAgZD5/Xf9ePMVerFWHq2uW0LFEz1f3o/eeJ1VyDz6aOxNanv4sHTqVLh74Z/LZYWOaT4adZcKj/f83AZ2KKyDa7/yKcZevdqoy13+i86qQzwBVu3+WQ+w3nxurHJ7nbnwxJQ71Cq+dMB1DhQw+rvKJTq9+29nXo8zZ7Rhyf/065uLlJp8Rod/3aCU5DPSX39JjRpJxig1l9E3V1+gte3qy7Vrl88mFs3u5CnAR+ZS8t7d0u+/S/v2SV9+aXu9gYh2+LCGts7r87l765VpkqRLR+WzPe49p9V7VxipXTtp61bbZl9+sFeWxoRKURpgSdKwjkV9jmdwp6LSmTPh7hoAhAx1aGwgwIKj/nMDx86d2nRPY93XobRWPNA39sKQCLZsVEvlmuK/EOl0d4YJgJcskSS5zpzRyZNHw9zrbEpJ0a43lujAh2/7Pudy6fU5vdR3WEXd3iOPKsUZXdfPaF3ZvFYxt25d6Pvr5cTevzW+iVG1wdbt3qcOq6Ej67/TBWN9f3bP97vB9u9m3ayfo/dZGe6iM87zs76hb9YDrHeXTtGqy4z7PZR7stGOpPigdztcU8HoymFGD99g5Bo5QpOTGqlKfH4NGVNdp48ctA7W5dLisS3Uvkt+Pdy3ulIPH3K/Dif3/O2em+efbWvVqHdun33U7Wf09/nW8STnNmra3fPcA4NreV5Ul0s6cEAFJvgp4k2G1rChfdw6fVr68ENp+/YQ/OSBrNv6ygK/n7vVHzwhSboyIX/Qz/RrV/ufw+rVh/oFXe+aQUbrLzbaeFWJUB6uo1JPn1LrfkV8jm1Ey9yR+wcNAHDYf64OhV8EWHBUVA0cycnS9Ok61a6V9O67kqTD33+pjyt5bt39fRmj8yZ6vgwuvNZI8+c714eUFOn996XVq7P2l9KNG6Unn5T++MP3ub//lhITpdmz7RPYZtHxw/v185rlOnnkwFmvm1Ncu3fr6zlxWr94jvZv/E4dh5VR3aHn6bXpndW0R66ABUneSUaHz0v7Odatq1eeGKbiY6zlbxhynjauWCg9/LAUH+/z1/pINH54NZkkozyTjWaOudF68NQp6c039Wq/m/y+Bjf2Tjv+UqWkvXtD3+nTp6VDhySXS72GV8xysNSqd0Hbv8fcah3H4JaB19lY0lrGe1LnJTWM1pY2WnWZ7zofvnK/ZIzeqmw08najr8saafp0dRl1WZb6ee0A38fa9S+qZbO6+zxeZ0g+1RicS7knG12SYPTunH6q1yfwtjvcYx3P/5Uzev9yz+MXjDU6feSgTu/foxmdy6u3n1Dv0bqe8MpljJ6tZV1q1bZvEX2+eKpUp44n3HrlldC/L4AAHoy73u/n4ZuvXpMkXR0f/G6ki6/1H2Atf2RQ0PXadUj7PJQpE8rDddyh/X/pypG+Z7DNGlwj3F0DgJCIqjoU2UaABUdF08BxZsFjat7F8yWv5kDP/6fmMjqWz+iWHr5fdmfebKTNm/XtO0+q3YCiGnhXfu0pbBWOJslo+KBKOnn4X0nSL1+9pTnTW+qH1Ut9O/DHH9pXyGjiLUb9Whmt7X6b1KqVVL269OKLPot/84Bnzp76fYz++PxtJR8/qjceG6Y3HhqoP4sa7SlsfRH/9hLrLKRGPY1u6ptbBScYtYgroeOH9/t9Lf74eY2ujLcuz6gyMp8O/bNdkpT83rv6Or6DDrz/hrR7t/TJJ9Lx4079CIJKPX1K93bzX7B4X/rlr6272AqxHq1rlG+S/bm2HbzOUvn335Ack19HjlhBWvfu0tSp1uVjGfz50xrbmWa5JxvtXP+51L69thcztkvwvIMfV8Zj/PBDv7s/uvcvLX2gmyZ0K6vHxzTRgU/fk7Ztk95+W1q50gp6vR07pt9njNK7XW/Qx/Ht9M//fSAdOyZNny7Fxen3V59Sy85GZeONBt5hVChAH7Pys325mtX/+Nuyvo309llFa90NJX2f+3LFo/b3gDHSzJnqk1j1rPeT3WML1AqPN9pd2GjAnb7PrXlxlqaOviHgut3aGf1b0PrZJzWyP3fBWKND53kdc2Ki9OabOjF8sMb3vVR9R1yun56aZl2GKOvMRSDHvfii5tXz/57e+NNqSVKN+IKZfnb8Wf7Y0KDrtO6Y9lkoWzaUR5wjfnl7kfJP9D3GrRcZ65JiAPgPi6Y6FNlHgAVHRdPAseT2wHPdxN9m9NANgb/wbq9RQQuu8xSkvdpYZ7ykP3/9gDx6/el49/MFJxj9vGa5Z+e7dknG6NYMd1EqnpjhrKHcuaUNG9yL//bjR377UWq071lIlYcG/4J/aOc2n9ei/YhytmUWPthFv772lO2xS+OMrhhmlNitjFKPeV2Kd+aM9M470ooV1plB338vde0qNW8uPf+8tcy330p161pngCxZInXoYP176lTPPD1Hj0rz5sk1d65a9S0U9Dgya1cPDvxchRFeRXy1atLMmVKVKlLjxtr54XJ9Nq6zUp5YIL36qrRvn/Z8+7GemdBCXz83w/rZ7NrluTQrOVkbl87TR48m6NT+PZ7X5eBBnf78k8Ch36+/+oYoxlj7TPNomzI+/V9Sw1pu4i2Bj/HFa4xO5rXuyrW4ZtoZOT/+KJ086d6268QJ3d7Pf1G47GprnV9KGI1uZoV+He6xv2fTW57JRivqXuDuf4suWfsZmaTM7xy49SJrm+OaZu99IGO0pbjv42s/Wmq9RzO+7hs3avDY6uf0vgvW/q+c0RN1srbsF+WtCeT9/Vyzur+LR/k+tqKKn/ebMerZxnfZRj2Nio61Lkl9uZrRlFuMvp8++D93KfWudV/oxRkd9dvKF8LdldhVvLgmBRjP/txuXQZdZ2ThzD/vfix/Ii7oOnd0TvsclC8fyiPOMT+sXqqCGS4tTv99IWOk++8Pd/cAIMdEUx2K7CPAgqOiaeBo2DP4F+Fglyw9Ucdoc3GjlVcYv/P4+Gv9RlW2drx3r9ZUMErwc0bJa1dl+KLZrp1VKP78szbWKOP3r6rZaUNHVLH6sX+/tGKFXH/95bPMZfG+lyFkbI/OucfzQrpc0q23+g9i0tuGDVZYFej5l16yttW0qWT8X/LldPu4khXwePdlzo3WXE13tTcqNcqo/T1WmFklQzD4dmXP8n/ecbO9GBp6kVJTU+TasUP97y2gvJOMrh+QWy/XO1+v3FBEJ2bP8Lx2pUq5t3P4PKNWnaw5nR653kg//SStWaOWnQMUdUWtgCHQ8dUYaNzvmYITjLYXsy57KzLOaFpDI11yiT7r2yzoazShif0S2mAtfR//FjQB5ybzbi07Gw1tEfj588cZncqT9dDHX5Mx+r2Y7+M7v1stbdokXXqp9TMYOlSSNHJ8nRx/72WlPVnH6OdWgc+0ykrznuQ6vfVsY4X0f59vtO1C60y1rJ4dVmSc0daX0m6OEMFB1lvzh2hgz5J6+6aSSln0jLRli5SQYAXVGX437f51ncolWHOR5Z9otO61x6wndu503xXzvyz52BFt+/xNpR63XpMj27fowaTb9NC0O3XyQIguPT5yRC5jnVns7z33774dkqQbRp6f+efdj+VPjQy6TvMuaeN5hQqhOd4QWD6ts0yS9UeOjL/fzuQ2eqq29Yeu3t2KavdHb4a7qwDgmGiqQ5F9BFhwVDQNHDcPCX45wpNBiuZ6fayQ4/xxRqUTsl5Qxt9bVE839J1oNb2NbWp0JL/RguuMnq5t9HPPlmrS3QojnCqMLxpjdGT9d/qhjHWZ0VuVz34b1QfnktaulX77TT8/MVULrzXacYFvGOQyVsj3YLMi7nmK/LZu3ay7pqX9e0KTnA0H0luxMdYX+vT9/l7MKuQLj7fCpGA/fxnrcqwKI/wEB4OLa2jnYn7XvXKY0emtm6wz1TK8BgPvsC/3230jlDoiTj3a+u/D6TzBj+3SOPu/77nX/u+/z7cuM/S3bu3+1llVN/a27gAYLCjL2Jp0t87UCrZMpTij6Q2sCdFlzu6Mouw0GaOdF/gen3seuTNnrDP/0oybdGNI3nsd7gke3pkko/2/b9CTTwSfgPpc2tl8znJNseYKMklG7fpdoEYDzlOJRKN+I6/QiV07Ao6zqcln5EpJseb7S+NKTtaB3zZkHoC5XNLnn0uvv561iag3bJBq19aHl/mGqEurZxhvBg1yrzJmgm9IePVgK8h2GSOVKCHNmiX9+Wfm+48iu9d9oRHdL7aNFx92sr8WXfr7Tmr+7+a12v3Bcv+XFmfX99/rm7KB33unTllnsN480vdOexlb/7Z5/W5++TOjgq6XfqMIVark3DGFm8ulxRPu1KrLjE5k+ENNSi7fs15LjjbqO7yS9r/zqnT77VKLFta8nAAQZaKpDkX2EWDBUdE0cNQbEThIMklGpzt3ULmRgZ/PeHcwp1rNgVZwkFMFa3q7vavRl+WtL7RXDMveNsY3MerbyvPviiOM9he0tnkyr9FvFxpb+FJwgtGmEv4DrJNVr5C++sr9b+8zR+Ka58zrUH2QvR8Lr836usfzeeY9O9u2/Ol4a46htP0eKOC7zMKeNXSqXSsNusP/Nrq2C7z96d0q6Zq44HfsWnmF76TnGdukW6xwc+cF1nxwb2cj6PTX+vUsbnvNX68aeNkyY4KfCZhZyz/R2sfuwn5+7gEmtk9KanzW+8hO38qPNEoMECCaJKPbulq/nt98YVK2j//qwcbnLoXZaXknWXdqG9rCf2Dfd8Rl1ot3/LjemdRBQ4ZfocmDqurKDGPLxaOM1sS106yB1+jCMdZjtwwurB0/fOx+7V0pKXpvyRT1SbhSU7qU1fF8nvfJ8XzW2STNuhlNG1ZTJw/usy617dxZWrrU+nkaK3SaebNvHy8eZRXvJ/NaZ3i6TpyQUlODnkH7fA2vseo/cvnVzyufU5Fxmf/cz5tolLLdmgtNf/2l58a1UJ7J1vthbpfLPJd+n6sVK3Qyr3UZvb9+uNKCzsYjLwrY17LxRtsvKeR388ufTcz0WIe2MLq/9UXOHE8k+eMP6xL5DO/jr8r5fw3q9/HMm/hnUaNaA4yqxOXRjf3zKGHE1Xr2gS7avexZuQ4fDvdRAYBf0VSHIvsIsOCoqBk47rsv6Nw7gwZXlM6c0eYrL9TI262CqPNd1p3Ktl5kXSbV4R77Og/ceO6FYmbN+0wSf61Zt+BnD6W3XFOsyeOd7N/8660vv69U8//81IZWGLKphHW2Wd9W1tll+SYZrRrfUQPu9D/X0fM1cu41vXCM0SXxVhjl/TMN1nq1sS4zzOry9fsYd0E/bUID/d87T6hbO6P7Gllnh3gvP3TY5ZrWMOvbn97mIi1oWUqvtr9GZ37/NdOA9oPLfQvGtXM9Zyrc1Nu6fO/3YtaxVhlqnT2VsfBt3dH67+CWgc/m8m4jh1W2FVMrrwi8bN3EC8/pZ3tpnLWPfwvaH686xNguI8to5YxeWd7+TWl3ejzQoK5u62rd7c+J9+SNvY3+mjlWkrTmnceytY26I4tIq1dr35InsrzOnZ2s8cP78e8u8b0RQsZWYIJR8oH9+mRk2yxfPpreqg02Ov3BSqWcOa12wy+2PTeqmfX6puayX8JrkowG9ixhex9tKmF0771WsLvtwsCva/oNBbaueVP7f/s5aN9eqeYVYOXN6zlb78UXpZYtpcGDpYMHQ/SLKxMZAqXkf/7S8L7lVHdQXj09ta3nbLdduzSwe+AgyLtt/WiZUl5/TeP9nK33wOBa2vPea+fe7xdekIzRGwHC7HTNRpbweW5yY6MfS6fdnGDqVL+bX754bJaOtXpc/nM/lki1das74P2hTODLNb8o7zl7um6/wK9Vn2EV9fMXr4f7qICcl3b28L+ff6C1E/pIt93mmXrAWNM/bO3aUqtm9NbGBVOlTZuUsup925ndCJ2oqUNxTgiw4KioGTgaN1aZIJf+TZh4o7XcypVSjRpS48Z6tK51hk76Ly3vObJeqWYU18t3wm2nWvrE430DhE6PPthBZ056JguvNDpfjvUlUEufDPfDbMxh1Xlo4Nfu40r+J5nOrPmb5D5S2g3Dz22S+owtrnV+pZw8YXuLNx0RvEh9xutss/KJ+SRJfyx9VG07GDXsabT+YmNNxD9ihHTCvn33R6l3nrPq66RE+1xoH1cKvGzP0ZXP6XWpMtTax5H89scvG24Cnz0yb17QGwBkbDUGph1H69ZS/vzakYWAObP2erNy0qJF0unTkqQfP38lW9tpNbK0JOuspqyehfVJJd/5xoqNsYrZapm8Jn9986Ha9PM/R1H7IMHwlMbW+2z+rb7rlhxthVc39fZdL+8kz11Xz+S2fqbpz93ROfMbCXy54lF9/U7wcO/weVa43Ke19Xm4JN5o/YdLpcceswdbCQk+b6Pko4c1Y1gt9e1eTGsfmyRJ+vax8RrS8QI906mqUk+nXRJ58qRSDh3Us72v1bh2F+iTGf0yvbQyNfmM/v7+Ex09sFun/t2r5EMHtOXxaWrYL59Kj8qluRNu0axh17qPI/dko42vPq6N98er091n9z5a8dw4LW0f+M6c5000+vBx3+M/GweffFjbLjTaVcRoRgPfn3O6FvEX++x/Xr20n8E11wS8m+zy58dl6VhrDv8PB1iSdRfhfPm0sWTgM73dr6fJ2vQCZeONBg2qoLWvPiJX2pgFRLvUkyf06cJJ6ni37yW33lcQJOf2XF7v3aYPqqbkI4fCfTgxJWrqUJwTAiw4KmoGjnvuUeHxgb+UPTizle86LVroZF7PKfbed4Dr3tZo0nDfO5id7RkJgdrNvaz9jmrm//kXnhxq627NeOcCkqJZnKg+p9q/aZcmBvqr8W1drUuMavf3LUic6kO37p4Cu1RiLtUa4Owx9oq/PNvrrlk+z+ft2npk4Lts+n1/jbjAWtHlkt5915r3Z0fguY3SfbtktirFBd5uTa/522ZPtibqn3mz//nD0tsj1xv9PqrvOb2m1wzyXNJqkqyziAqNt8KYgB5/3BaGeLe72ht9X8a6s2F6gKJOnaTSpXUkv3XHr5k3W/vOrH/+Ll9bs2yurTtb13+crWPvEu+Zz8f7ZxCoyRgdrlHFdvfCYS2sx4Od1Tn/eqMDq95S+bTJ0LParhqS+Zl73nPDZWxvVrHCq2F+5hLbXzD4dj944T69tmB4wOcrpZ295z0H3d3trT8ifFYxQxFTq5b1QqemWmFv5coacbtnnTIJ1llh6XeGyz/Ruoyx6QDfeRhzTzZauXhiwLfnP0seD/q7K1Dr1i57v4uem9cj0wD04sTcOnUi+2cbLJh9b8Bt1+vj+ayOHOYbaD8x+15p+/agc6QtXzI+S8daK+68bB9D1Ni/X/r5Z8nl0rZP39CNCfazXHu2sc8JmdUw3yRZZ2KOv6OAXJ9/HtE3eAB8HDumlOcX6+fZCbq/+2UqG+//PT6jgW+AtaGksV0u76+VTTvL/+RvW8J9pP95UVOH4pwQYMFR0TJwJA8IPjHyc4/08V2pXz93cHVRgLk6Jk6+2eexs5nkPVhr3dFIF1yg++L93yXt7Zfus3W34Uj/k4ifbfvuEuuX9B0B7oYXipacO3gf2nawnvcOsG4fem6vwfZiVoG8u/qlksulb5bP1//ua6GtX72rXoPL+iw/NcAlfz+Wznxf8x/qnO1+rv1gsc/btXN8pbPaRsv4i7P9eUp55y39M3ao3+3e1tWaZ610gnX54eNz7pWM/9AhY5t1k9HOpOB3D/Nu3l8ia/dPuxzm8cfdwbO7BbJwYcAvrybJuoOXzzxuffr4zDPz7SXW/tMLwNv7nOezLe/3a/87jfR//2frzt9/BL/MzSRZN33wfmx6d0+A1T3AjQAytpv6p02AnZystXfUUbd2RqM7XKhD7dtIxqh3a//rJd6adszLlvnMq5R7srXeiir+1x14R/BLEzNr45sYW9iW3gqNNzpavmh/ewAAIABJREFUprh6TagWcN3lT43UQzNaB3z++r5GevppTY+r7fNckXFGn1ewzg5z/8xr1pR69JCM8XsmXvrYVWScdalcsJ9J4fFG2xbOkU6d0o9xHfTGHZdr72NztOGlh7P9WgVrj1xvnQnn77nHprbK0jZWPj8p2+PH3KktfLZXZahRidFpv/fSde7sc/fexfP9/K728vnLs7N0DLWHx0CA5eWnb9/xeR3eHH6btG6d9i99Solj66hRz6zfjbZ7W6+xsXfvLP0hBAi506elP//U75+vUL0Afxz1bp9V9HwflbHuHH62f1C4tZvRr1OGEfLmkGipQ3FuCLDgqGgZOE4NGaiJt1hFtL9C4q0lfr6MJyZKJvidwxY+3MPnseuCzCNxNq1zt0LSkSOa9UAbn+dKjTL69K35tu7eObykbZnGPazLAy73Orvk8euM/ini/7K/Dy43OnNtTclY88s4XThltW0ubgVp995rzWOT16vo7XS39WXCO8DoEVfxnPZ7JH/aF5VZs3zeDku71bItO6yF/7NJ0i/JyOyMnI9fyVqR5a9t+3qlT//6jQ582Y+/1iHx0nP6TB0/fczvdlde4XW6/eJn3fMQBWuTbjHa/UBSlvpecILRwzdYl6JlfLxeHyM9/LB1Zkzt2p5+3Hln4ANZskQlRgfel8/E3sZIw4ZJDRv6vUGBu/XynVsr41k6JUenhUEbNti6c+jgrqDH3qajJzBLf10LTDDa/MQM9za2jOuf6WvYKr6MZ6cul7Rzp3WHRknas0djx9X1u96cGz2X8WV8vExiHmvd33/Xt5f43+cj12f/s2mSrLnZKvo5i++5mkaaO1cJU+oHXHfxQ72UGOCYTJJRq7hSkqRXnxoRcJkKI6ybG3j/rO/3M4l8eisyzhq/Hq1r9OtFnrOy/LXSCZ6zpkqO9g08nWjpl9nK+L8xRINBBbK0nYQ2BQNeZpyZqRMb+rwv0vvkMhm+pvbo4XPp/qtPjch0+z+8Ot+2TqCx+LrhBbLV/2h24tghn9fhoWl3+F3WdeaMvn5rgR6acrvfS4pzTbF+V3t/HtZU8Cwzu10pHfl1g45uXCudPBnio/Uv+d99ent2H/UaWl7XDcitOv2tS4Zv72rUon9hNe5hfffYPOhe6a23pE8/lXbtCne3kV3JyVJcnGSMHqx/duNll7uMll3teW9/Vc6o5Vn+cbfaYOsMXPcfQAoXlrZwZpZToqUOxbkhwIKjombg6NnT9gUrfTLq9PbFu0/4rjNlStAzEUyS0bvP+94xLNC18SbJupRkesdLsvRLr39CZUnSvP+1tz3uPqX5229t3e0+oJRtufRCy/uudnX7WdvY43WntqsHp2134ULJZG8OKqfb0LTLmfYWss7qeOkao0W1jFZf6v+shzGJ9rMngk1K66+57monjRvnd8Lv5EceVu/W1qnhPdoaHc1vtLimdTZYnbRC89FmRaUxY6Rrr9W71wefkHzLFyuy/brs3vSdT/9GjLs2S+u27WDN8/Pp6HvP6SPlcrl85lXJP9GaV8lW0Lz+epb+mh9/m9GBJ+Zl6Ri6trO2/WlF3+fcNm+W7rrLutwv2BkBb70V9A5tr1SzLkk8keFyYo0ZI7VtGzzAGjBAEzJMhF1upNG+BnX0Q5m0CajTl/PqW3LKGb/9SGpk3fAgY6D2ezFrnr7N15S2zQd0/NC+TF/DLgnBA8z/3e//bKXH6lr73lfI/vhVCWlhQEqK35sw3N7V6GU/N3vI7FKMrLQPLjfS/v2aPu22gMs8fv/dGjwi8BxrfRKulCT98UHgOcgqjrCOO/31P3yedYZeVvq46jL/Z4+FutUaYH+fJjWy7pCZnUukF45uenaDRpoxY+1nFT9zrddnJ12fPurl9XvoncUTMt1+8rtva18h626vh8+z7pDrr/91hxfMVv+jnffrsHhu10zXST11Uk9PaKGqQ4wqp91gocM9/se+2TcFfs9cNtxzKfVDrUtp/YqndOD4v9ZliAsXSl9+aU2ifeSItG+f/864XNZdSL3PaMn475Mnpbffltq00b5CRqObmaCXvnu3i0dZNzXJeCbv4fOMXr3a6O2nRun4X9s5oyZSuVzSd99J1av7vDezciOe0c2MPhrfUT/Nn6jdzz3mP6h3uXR67y5tePMp/TqogxY2KBxwe4XG28/iytg+rV9Grt27Q/8a/YdETR2Kc0KABUdFzcAxZoz7F8aJvL6/YLb/+LHPKp9M76vhzYP/ovvybd87hg1v7juPSnorOtboxE8/ZukLVHxiTUnSxien67maVnjzelVrLh4ZI/30k62/o7raA6z0oqDLXf63/0sJ+7/T59zS6dPSN99o8D3OzamV3da7deCA4PurfC8XnJHUJNv7qjgik+Hx2DGpTBlr//nySeXK+fbrY/v7aELidQH3d/jPrdnu6/HdO326N37SjVlad8F1aX2dkHkhmJmMp9IXnBDgZ/X++yqWhTnVBtz5/+yddXgUxxvHJwkhAULx4BJci7a4uxYtxSlSrIHgFA36K1a8uMQISUhCILhTCgVaihZ3iSeEuNx9f3/M5XJ7u3uWgwjv53nepyU78+7M3uy7O+/OvC9DjMdeg/pgtZDrniWxAs5YlPfu6TxXQLX0jKOWCxny/8KweXF3pIz+EcH5GO4X49sM3uXX6vfkyUisWRVLW/GtgveKMe4c1b4+H8QBX7VX9qWtuhLJokXAyZNAZKRIx59ldV/DMTOr67wurpulV3HtrSf9EaDp1K9k299nIEPyYX/JzHZSWzl8lg3GrrldcNHArWC3vfhq1HW/9pYts8q5I0ZOlP94sGAeT+ShfPVKNoD9+sZ8BdqSVtyRZUwQ/8eF+cpXU+/5NKk3LmPbMJuOyyUcQ40aqf/fWF1FZjEo4uMkx48uHGcIt3oe0Mz+6OycXnD8ePygFYT+rJd4dayI48cFfQzOJ93+xo5fpgNLO0bevd0GXFMt3t84j4d9Wwm31apkmMTKPn3yeyOxnp+7cudqjUkMjiOLY8zIwug4jId0KDCHO8PG9OTx+jTjtkk5C4xNZqBO2KEhcdY8yUVamQJzuB30H1gXIQd2I+nNS776NzUViDP+vhCgVHI9SiU+BL1E6L1rcF01BO5dSiE+X25R2yIL5cGykZWwZHYTvP73YsbOndkoFJLPRTXx8Uh9/gyPD+9F2JUzcBvbGO2Hc6ejwxS+yjQtFIampFrwLNRSv3e3wQw7h9RA6oeMZZiND3qNi+unwqmzcBvuHXtxex4W4cfyzGOY2j8/njlPQfyUSVAeP04ZDY0g28xDiQxBDizCrGQbw/HsmU4HllQ2nfX/k58MMWeGnwfmx4M/xatohvfmq0nk6gHiL6BSsmBeU94Qd3fpyevjx4L2rv5OuIVwRQvpiWaanK8g/HfPQcKv3zMWNjF5kmQuaTOCb+sTvZD26YP/rh8TlV+/gq8asZTJuKQtgzWce6uGGrClLjyc/x737wODB4t/kxvClVFyq1jyzmNQfvxocsB/pUQA4yVL2htUd0NjVVuXLtXfXz0UnpOe9dFyoYyT5eJFlJypP9j3sD4MSYf9DepDx2EMmDULa34oJ3l/GUNy7Eed5zpZiWFhG+HftvyvD+bMbSz4207tWFkzZnCHpoUF/3exYsC7d+Lro0rZrcnRKsLzjejNnWTeNRm2N+TxwpSMAdskVo6q0JcZdPKcejqvi8+eGZL1vGpJ29BuU9NjqgXnYzhRiWFNU77t7/r2hUBcnOTXb20HexXH9N/wv2vieD1SEvnyAQBg+29DZMssnNsUA8cXlT3uu3Kk+rx/D++g95x37bnT0tB7NlS1cuuPcnxbdCfHgnhyYr/shEpK4t6+AM6fx3s7Bt8afDVc65HG2Y52s0sAY8YAuXIBbdrwDJjMNAcWc2bwXT/O0FtNzajJFQQ61Nsy69QRTtwcHTG6F4+NlW8utzFXArboP8GJE4J7LNaaYV478ZbJppPzGd32nMCRBQPV2/K7D7MEojIwaVcqgYsXgfLlMzaOaoifHYdkYunpkxcFxbqMWX3FnBnajpB+nmluA9cl9jMYuo7NiyF9GSaPLYN/Dm7i19nPD/j3X6T+cwPh3i64d9IN3tscEfi7E0IDPPHntAHY36IgRvVikiuDLReqsgVrtSs4HxNk2rZSfWxpNoqh/SQ7bBxYAednDsAbfxfE3fuX/27BwSJHW+TCmfBtUQSvfuybvqVciuRkxNz8C8q0+zUykn/8jI4G7t1D/JkTuHNoB24F7MCzm2dxZtkoXFwyGq5LB2Dn/wbg0MZJmPtzTQz68St8P9QGbhOa49+fegGWlsK+Va2K74fx5BdVHA3/HaUcon+XTD9efRJ/jj703QHlJ5q7xP5xDiub84802qEVwOQTNDFn7vxqNZJhZ6eiODZ3AG46j4dSbkWiNtevA+3aAd27A9euyWdgliPtt8wmZJt5KJEhyIFFmJVsZTi8vIAuXYA5c9BnsJX6QTFtivRKhK1rfpB9uPQYzPD+uA+PG8P4l523+Rkul+UvDbq2xQDArD52eh/Aixe04g3x9pZ2DGhtPTr/TTGM7cmDzm/6luHfErxc2xHS+vfXFv57eG8GOKQ7cRYubmvSy+OnkPWNtfq+fDke3bsoKvdwxwqcr8C39+naxsCc+aoWxW9rcaVibtyolp+nHDeC12O/x7XS3LFwshJPCY/79wVldqwbpn6Z1Dx38VmWgEKB/Dq2rukSKVb/r4dBddNiGGH1aqP6K0XJWVYC3c1H8RhwNSemO1Bx4wYcplsZ1LZ/j+zUW8ZyIcPV+sUAAKFdWwtWo8zsaNojbqKjfEbI/hKx4Hxd5mDN8u6iv9eYxIPYT+vEgLlzufJr14CtW4H377mzSvs+lsCjjlDvlC5MlCkx2oZx54MM+iZ/s+c30XlNjngskqy3sA2fHGj/XTMLIiZNSu9f4cJ8OxCA7iOsRfV+a5KeeTDPPAavHuk2KOrJXfzWhDvt5PpRYE66E9B1s3yyjmmz6qLn+AKyx19670hvv1KJbT1Lpo/r0Qydxuu32bokyUrjN+/XT32q/64d1Vu3zgSGAeOL8ApxcYLxc8fe8DZ0GMawzqmJuo8A+Ao+ZrrjgTkzhD25rXMsATzuUMCcPrjeuQ4G9hfWP+v/G88sqO3MnTpV3bZkSy7Kv/7Sey7NPmnKk8LC8zabbKdfV07k40c8nTwMl/p9g9TjR82rW6lEkPce7BhWE01HC+O66ZI/yol/L4WFcVkRmTPDD/3EeoJ0rHys75QHs8ZWwIGRjbBlTnt4Lh0IpxHF+bNEYgylrZoxVrTfYQzZyiYl47uL2/SqQPq2TkOl4U8MV8oI9YTlTd/mbL2AO17KOwlXEtnOY6g/TrgdOjyPuE1y23Z1yWiZFfeldCRZkROp1fupFgyPx3+PV8c8oVQ9kz4LSiV/X//pJ3Vbkqx4nENj+tRoLBOteLxdnI+Jhj/x58Tu+jysQLNR6fUG9md4vHwasG8f/wAbG4sPyxfinvNExO7aiuit64GQECAlBS/njIdTZwbnzjaIsy8EDB0KuLlxZ2cWJVvNQwmTIQcWYVayq+FIPH4Eu/tXgueP3yD1jXR8nN0bRko+RB7Pm5AeeDkmRvSQrK8jlsivzfkt+O7XeaIJqbasWtqFn8PfX9qBFRoqbHC9epLl5ILKa29DGdKXAS7p2e1+Xd7NpBesjIiu4Jg1J3Jn3P9aMODBA6TGxQpebH4YlBt48kTd740SAaOnd+IP8596MITVq6oaDInGf6EC8POsOgLd3jUZ8Py5oEyyhyuSrLizQbNs5em5AUDwxdQYkWLTGvnU9JqytJVqbGzaZHSftakwI5fseSZ2U53n3j3UmJrboLY9OuMlcvalif0Mfv9cLssAex5wG+3awaUuHxvf/cDjupmCMj4ep9dPxqUJ3dB5qPC8Uqv5Htw9j33bJ8r2o8WPTLgVSpMWLdLvz27dJIuE5NMfbHbjtwwhLr/L9knbQa0tzs5tdF6T0wdXGTUmF06pm145MpKv8unShQdAVnFXIqveMlVMvxgbC8Ta5QZ8fdP1RESor5VU8HbmzPDb9+XUxUPdd+BqGely42ZUR/sJ8nFKRKsaX77EhR61caDZV4jdsBr9poqzkBojR6ry7ahgjE8KVHwMfYMNjfl9KXu/MwZ06pTeNpZu318VMLwN10sx7lzU5OJFgPFVSqb0q+JkhmsLRukcSwDQc1JhWR1/nXOTrLN8bkvY/ZL+AWBnAwbc1u8sw6lTks/Cx1oOrBaT8+vXRWQIRVIiUhPikfgxCtcPbYHrhGaoN47HwSrnxCfalR0ZnhYS/16JVvo/RGmL5UKGf0oK9bwoyFezfD2eof1whl+62eD9hSOGdyI0FJg9G8pCBXG9ki0qTjYuE92I3kyUFfdVAf5MM6ZvBWcLY/CB8dWwxq4uY87c8f9fUaGuX4281syZO7ek7rVEK+N11ZkgrUvb4W2IfDtGVT9PHv6xTmKlc6bw+DHQqhU+5mboOsS4PjUfJb42m78xvP7lsvxjq62ORCLaov4QaWNjctKOT012nYcSxkEOLMKs5GTD4b51gqRBf/v3+fRCSqXogdJ6rHiVQZocraK6BT98QGyd6nhQVLw9SX2e/67xsoGB0g6s6Ghhg7XifvxXlIm2IumSTlMKC9QFrV8m+IpjDikwhz+E5Y53NyC7y/De6Wbs4tIxaD6KodcQS7w6sJ3/URVce6dEcOX9PzUFSpcGypYFTp/O0PiY8YswELHr10ycqejwYYDx1XmaZetN5bFXqk7Rv7VOW2pPkDbjuzaONKj+/LaqMbJzZ4b6DwDVpul2TPX6gQHh4ajvlMegtr29clI2S1vjMRpjf/583oDWrcX3RUb4+298rRUfptIUC1FbkpMScOTIWp192bqst/Q5Xr4Ehg/n6ebfvZMuwxhultB/vU7slY9jJjX+NWXfiu91Xoo/AsXx/eRkamcGDDAsKcBOx+aCur+0Z3ylTXCweCtTQgLA+Mo67XMOGl8M20bUQuo9jViAAQFItZBe8TGkL0Nc0QIIzctETq7ug3WMG9VKpSHTKxh9r0rJ6mYMFyZpZMVMSQEYX130tJA4Jpj1AtUW6j590ut06aIe7x9sDD/3WQcGTJsm7N+1a+pJviE6zjik///I71QJCTp21Pmbv7lxVqfOO9cDJest1cpWuPFbJto6L8np0watnmk55Sv9uohPj1LJPyQB/KPg2rXA8uWAry/8nH/AJKcqGDGxFCYNL4pZv3yDVYPLw6mXDXqNscPgvtwJPqc9j7W3fUh1pKz6lW/nj4j4NIHW4+OBsDBc+n0OhvTVHbKg6EzuOBWswFTJn2V54hND798S03niDjDG32Nq1gRq1cKpYc0xYlxxtB1rrffDqKY4TEnf2gzG3xcddWTdluuf5PspMz5pheVC1cpiDR1Rtgzthuuu13wUX6X8P8f6OPv7TDy7egypcdlgPqJU4sL8oeg2WJxpW0ps54nH0YbGhl/fZqOMX6nXKy3+ZuvWmX21ZMnJ81AiHXJgEWYlJxsO793TJA16xBNh8HR8/XX6A6VSJfSaVET2YeBVS+MWTEgA/vsPGDJEtJT4hNeK9HKq7RD1xvEYBw5T+FalxDitJdCpqcCSJUDTpsDy5eg0rZhRD6pxkysK9W3fLhuM3lRxmGaFs3MHyertMUi/jpmz6gvb+f49j02VRkoK4O8P95/EMbz8dk43fUBoMX+BcCK+owETBx49dw5gDA+0AuanTZw2dCkkCAqrT6wW8sx7UjzYt9ogHdYLuHPvzd4NGb4GX0/V7ZgqO5W3tdkUw7ZfRf13E1/JbKusOz79HkNICG9A8+ZmdWCl3v5XdN5WY8WrzADg6t+HdPZl3f++M70hjAeH13e9LnmskFVxqBpfddBoLJ/4aNar9jPTuwLvxhlXg8fl/1rIrybTZv9G8Ta/HfO7ShdWKmW3okRGh4jLnzwpyo6YJtqB8Ker4hQWm8lwbcYgve0ePUM+g6Hm/RlePL/ejH5TtOOP5U4Pyvy8YHrsm8KzGCLStucMGZJe/v59oGBBgDEoW7XEgVr8a37tCbyeXFytY5UZTySgye3bABPGh5GT8k4Mnt/mRXjHFljdjAf0T7RiQPHi0MUlPcH4Q5/dlay3elFHQbmVzZnujKJpPHokOaHWTlzSekoB/boIwgBSYj/i1T/n8PCcDzy3TID3lomYOaMuJnTnH7eSteJ4xlozuH3N3+WYM3fe5JvLHdhje/L4nz9+x7CqOcPVXc7478geJAfJfPDQIinoLSIvnsC5paPRb1whNB/F40ZWduQOpwIaSVXUK6UZX51jqM3XtHmpEoH8wfjKs1wL+PnKOaVvgy85naH1KEtRYP3BPxVFVP0a6XEjGQN69cKOsQ3Qb0IRLPzRAZcCt+Ba4HbEPHuAuPevEP/ovv4Lkg1ITU1ByoVziOjVEQHVeAKDb8eIr7d2cPp1TQz/rcb14OPwUDXdux0E7xhpW3t/NT7Rw+ciJ89DiXTIgUWYlZxsOALc5ksa9IQwrVU2587xAKZlygDHj6PbZGkHVvfBDDH5bcQnmjJFsOok31zhbRp84iB21xfrUyh0L4keNkM+ro+U3PHTCgi9bx++N2Hpti5xmGaFec6tZI/3Hqhfx/8WtDXo9/PdJFxB13MQQ5y/t0F1DWHZYnHQ9NgYrWxwN24AjG/d0SzXbXJRfrxqVSzRsXVI9BvZM6BhQ8n2fPR0Meq3OLV3foavwTdTdTumFi5tDwBoN0WcMVJb6o9jQGoq9rTIh62NGJa3FB6vPokBAQHCIM9NmpjVgRV6+6qoXY1/Eq+SA4AnL/7R2Z/fV+te4aSTUaMMiiFyw0eHE1LjmmgHXS/nxHjKeh28uXYa47vzoMVz2ktvyU2TTd8yoFUrg7oWsF38YcB5sfw9fU/GkZeQLLGd4eJF2Rg11SeJJ1gRRfIirklDHstQDz/PrqP392DODEn16iD6qB++0pF5c15ago40ChUStOvNVzzrrGC70E8/Cet8+ADcvatewaUpyZYMe+qJz+tXnfHMlRq8vnnBqK1M078vyCvm1sqElpIie+32b5LOaJkmSpkg4psXC+P6ObdmgKGBjDVWqWH7dkmncFunQobpIoiMkpLCP7wolXxl2JkzwIMH/N9pYmI4A1OIvnkVj2eMgnL+PB46Yts2XK+QW71Cp+0Ihq2j6+Lc6on4y38zDq0Yjr2L++CBxwY8XL8AL/46gTe3/8DrPwK5LYqN5R9lHz7UaQu0Ubx+haBj3lBKZNIlOCmnTuBut0a4W8JSlNDIv7r8Fsu0EBWD+gljZyVa8dWK+my9xSIeK/fvkkwUHiMrkZPnoUQ65MAizEpONhz+HgukX7Z1ZWYB0NlJOtPV8pYMaNxYVD51zixBueIzhLfpn0d+F+myma//Vtbe4qYtlR0ZdrctiKF9GPxmfydean/ggGwGw7xGxH/QFIdpVli2vLPs8QED9Ad8/f1//aQ7rMWxbdMF9Ub0Zjw2ipmQCuItcio+eAAwvnVHs9zAKaX5ccYwt510P+1+YWrHpsUi1ZewfPlk+xC8f4dRv8UVz4wHcVe0ayv4mqste3b/DADoNkX3asAS0xneeKq2gBYtqv5CLRg7U5h4i2ajRmZ1YCkS4gVx1boNTv9CniZ55vFzREaH6OzT7nXDTW/Ikyd46yC/kjNN7h/eLa9j+HD1NVGy9LgXVgt5rBGlu7veNmhe11QLHb9zPYmVPTIk+XpjQVth/TUresiWj7SVfrFWSm0NunYNl8vKt/NURY1xkiePQe1NY8bcRgbdV1GNagNKJQKcf0DtydIx4n517iBUXqaM7DYctUydKt84mTrfadlvz9oMWCFctRf08G+d9vh/LXjMQItF3An4bONiXrFaNeH5NFfBarFyWVed10xuwrt7WT9BudkdmOHp5ZOTucP72jUok5JwqRxfJZumq9Q0hn6OuleOEcQXRXw8X8U/YgTwzz+Z3RpCF9euAX37AhMmcIdoaip2LuyBjsN4du3o/Xv5atX+/UXPhVhrHifuxeh+UCxbCrevedzOck78Q+/+2gydh/JVgRO7MaSeOJbZvdVJTp6HEumQA4swKznZcHjumy79sq2HflNKStbzqMOAP/8UlY+cJ1yRUHGy8Bx/n9wn0lXgFwu97VgusUJIU+pMyc1fWOR+N39/dNIKaN1+OA827Pq1WF+ZqTxIsa5zVpmaC2tX9tZZRt+WOo9NP0m3V4vzu4UOyO/7M+DSJYPqGsLmVf31jw1VlsqzDnw7XcHZ3IEwelJZfpzxjHWSv88Ehvulc2NpK4YLLcpyZ5iOSeJ79206Ewhoy91DO2R1GUzXrjpjeAQc4JPd3k7S90TauEm+cjldp8qBlWIpLFdqGuMvaposXSp8OdPcZmUiJ0a2RO0JPF7EfQc7zO5uI2jH2P58FaVCqdB5fd03GzZO5Qh79UDvb/jitI+8gr/+Ar76il+Xnj1xqiJfgaKOoeHvr7sBr1+LXnzlsmZ61WJ8K68hnDiB+VoOrG1rfpAtntisseh8aU5EEf/9h4M15K/Xt5px1AoZt/pGe8uwnLz/toagXt2ptqIyG5drbS/VdgYx/pVcEO9knny8M0kH1qlTmDXvW8F5Z3VgiFy9RFA16s0TyX44dVbp+eEHYNIkxDZrhBTnBemxirRXPz55Itu86XPlP6Zs+lb+merx6xCxjTVidUcaytRUgQ77Gao2G7jtlSAIItty/z5PphIfD2zcCAweLI4BGxkJvH2b/u/QUJ7xNasEv9dBTp6HEumQA4swKznZcOzZPl708tx0tP5baJRjeckXdb/qTDKg6IMFE1BaY9VHq5HCc9w5f0Ckq/gsS73tkFohpClr5unZinfsGFqNFNZ5oJG1Rlvf8N7Sf9eUgSPtsGXdYJ1lxgzXvd3sqIthW9/+cv+Vi0FqAAAgAElEQVRVUK/nIMa39JkJn13irVDaxIW9R6ehPM5OZUfuAFQyhpQKqsxpBQpgUjfpfjYay4CnT4EjR4C4OP0NOnBA7/UXOD9OZXw7ZWrv73Se489jfFXVd9PFGdzOOqgy6Y0bKlRarJjsGIuJ0FqBFRkJlCvHyxcpYp5l7q9f84ltw4bA0aN4XcxGncq96EyGWw1KqYvqSkjgs31KhpoRE/ZO728YfPWMbiVhYcCdO5LJJnD8uO66oaGiOqVl0pkHLhtheMcuXoRTZ2F9901jZYsrb98WBUkuPFvagR/76qlkMNx+Ginr1f0pWdLwNkN6y7CUPGlcRVBPKv7bztXCmFvnO1SG69cMW77hmcAibfl2OebMgzwXnsWwb2l/+cZp/7aq7aELFrYUnXv1KmFygYTIUNm+LG3FgPHjpc/ZtavwnNevyzZvwsyakvoP1lBlZ5PBY/UwvTbWUDTHUJFZqjb36mWyPoIgCCLzycnzUCIdcmARZiUnG46/zrmJXp7vFdN/C60cL46VUnYqQ/LwodIVnJ0BxuOdnHVgOFdBeI5HV46I9JWfYaW3Hev/J17pNLQP3wrSdKINwu7JTzgA4P1xb7HTo2D6hKX5KL6VMe3YH6pgj9oB6b1q8aDha5synG5VBnu2iIM4a8rs8ZUFE05tuRygO/h0Gre9NwnqdRjGeMwYM3Hh0Aa9k6vkxHhRmbYjGN8yBAC//qozUL5RqLJQ/tBPXp+mRPx1PsPXIHZgX1n9FosY4m/yTJqDtTK4VZiimvCePy926hYuLOvASo6XsDFRUfxrYnBwhvsjCeMxhR4X5hmRULeu+tBde4ZOQ3mAXO22XvD8X4ZOmxwXo/P3c5jCoExKMqofAjlxQnf56GhRnTRHnqiv3qsMb8f16xjTU1g/YOcMnVW0V/mVniHtwE+Ni9U77nsO4s72Gf2NC+C9dnkPvbqZM8PuzvaCeh0mFxKVcd84RljGUei0bz9cfK33rdKxulArhhauXAEAXFkyVhTQffNvwtVuyqQk2b70GMSAyZMlT3ngp2aY0ZEHBh7cl+HBIfmspiOnVRTpvlVc1dZKlWTrua4dkTGbqIG1hmOz4GzVufv2NVkfQRAEkfnk5HkokQ45sAizkpMNh1KpRMtxucGceSaVY5WZQTF2on6ZinwaMaLKOzG8y8+Aq1elKyxZojOOz8t/xCnIq0/NrbcdklvcfvkFye/e8GCbenhwar+ofkIuVftU27wEUru25CoNQfadmjXhuctJ5wRw3pQ6slm0mDPD6yt6Jt4qHgfsFdRrPkq1oslMvLh32aDJlXaZGpMYkD8/ACD2zwuw0pGC2yiSk4FixeBfXaij0Vix3kKzTduKo03YIPEKLMuFfIuXf/X06/2Lk9Cpu72hjr4VKCDrwFJ+pgC3AlauFI5zN7f0Yxp/v1g+PfvbN2MZUl69yNBplQqF5NgoPItvh723TNqxIIv2/arPgZWUhBRLnglrShcm2vanKTeO6Q4IL+DuXZGT9azHUp1VbLQcWJWm5ZItq72NVi7FfdmZ8jqk2LJSbE8NuW+/m2wvOu673UlQ5seJ4hWKojqbJsg3zt8fsLTkv2uzZulO4ZUrMbuDUM/uTaOEdZVKdXw0bWkzggEzZ0qecsicKoKyJ3fPlW3e907C/h2opRqD+fPzFaYy7F0lXq1rKpp9zP+L6vwDBpisjyAIgsh8cvI8lEiHHFiEWcnphiMpIRanKjI8LKJ64XV21l8pJAQ3yllhbE8e30NhwR1HUtsHAQDLlul0YL2/K86MVs9JfwBi7x1iR5E6fokBxLx4JJg4thvOAFtbwN6e76fXbvOqVUCfPupUyWkSk1ujzNdf45DbPJ0TtelzGsgem9CdCffp6+DNCfEKMkPrGkobjex6i+a3kCyj3YaOwxjQpw8A4OVfJ2X7ami2RQH37+NALfE1085I9uOcGvp1GcCbgV0Eehv8xBCal+Fj2m+uSnl/dkJnLGrDt0j51GR4WUD+UeT6rY3k9Wg+KpMeXyEhwNdf8/60a8cdhWlo3QOheXnGyaSa1eTvdyOQ2rJ3aFB94OhR47NVad+v+hxYSiWuljHMYfPf5UOGt+PpU/QYJKx/7fBWnVW0HSy6HFh/lk1PBNF2BA9ALtXmqjMkMsLqYPe64QZdizk9hbZ50JQyojLH9gm3QS+aoTvhBnNmOLlzju4G3r4NHD4stPHr12NyF6Ge/dt/FlWVizv4zVgGzJfesj1+fj1B2YM6HGzve7TBXXt+b1wozxA1ZigPxq7neeQ9p5fZHFiaH5XyzlXdA4MG6a9IEARBZFly+jyU4JADizArX4ThCAvjTqZduwyfNF69yrOD7N2rfyJ76JBwYlmkiOBwxJM7opf4Jk52epuQ8DESheZYqOusWNrZsLZrsM65C2zm8y1fN5dMEPZfe0K8Zw8AoLpW1raIPBpl/Pxw5uAqnRO1pc5tRX+LtmEIsmNAhw4yLRUT/O8f4smPdhDwDBIbEYQtK/vDc/N4KGWCXWq3ofdAps7wE/Xwluj49yPzYePCLkiNNyDulQQudYX6ZnRk2PyN8G8LV3Y1uc+aOI+rJr7Gab911arpY3/QIJ1OWk2kVt+N/I7h1RwdK1A+NcnJPCaU9r08c6awT02b8kyjcqstjaTxpNyia3F8r45g3rrQvv6PH+uvYoDDhjkzvLonTk4hh/LtW1H9+xd0BKMH8JVW8PiK03RsoWYM7+0YzlfgmRPlEkvUm5bX4DYDwIEtE/Veh6IzGR6UE+od4yTePnfea6WgzK614mDl2nJlv7COQfz+O8Zqbdc8tHe2qFhaunVtKefEoFyyREIxMGtxC0HZPSt1OINathSOvYcPDWp+/KRxgsQBi9qY/gqrOYZs5qvaMWyYyfoIgiCIzOeLmIcS5MAizAsZDjOQmpqehcrCgn9F1yD29TPRxKLtlIIGqb51/gB+nFEFy5d2QnKCaQ4RZXQ0Ul9IBMfWnhCfPAkA+Hq8sK1BdhrxRpKT8e/xvTonattWDkAp7aD2Z84AHh4GbX1UtzslRRBHZlQvxrOwfGa0+9d9ePrqkdSg96LjscGvM3Q+zVTxzJlhXjuGhW2Ef9u8IePZ+gDgB8dSYgfW9OnAjz8Ks5K1a2ewA6uwlgOr1DTGs3eaYUWT2YmL49nhRo7kmX7MTL/JJUTX99z+FaYp8/RM32bWs6dBVQxxXuVawJAUGqRfWRqRkWIH2K2LOqs0mmAlKP+iqr184R070sdY27a42aa6ZLubTv3K8DYDCNw1S+d1GNeDr8BDW+HKycnTaojK/qW14uzUgRV6r/PdAPkYU7Ls2oWhfYR6TnqIHVIOU+TPu3uF9Da7pb92E5TbsKS7fDsaNhTe+68NtHE3buByWYa+33NHfOxvvxpWT4JCc4T9quzIsGRKfZP1EQRBEJkPzUO/DMiBRZgVMhxmIjoacHUFrl0THUoJDRZNKrpOKZoJjdRCc+tjmTLqdLvawYevDmzOy6scEG/+OKpzoua6YRQO96mF8k4MtSYy3BjR0eQmPhvbH6N6MUzrxBDVunGGu2wK2v1r6aixei5GHKhbbiWXoWz6VqhvyQ8lMb678G/eO5z0KzKAsRPE26MkadLECAeWhUBfyZn6M27mVCbNESeE+NNvg+kK79wBTp0yODW2IQ6suuOZcFulPhISRDrCX/6ns0qrsdaC8g+qFtZ9jjt3gMuXAaUSQa//k2x3+6lFdOvQIsnPRxRfS1N8a6jGtdbqu19m1BeVvX3GQ1Dm4ZXDeq/zi7O+RrUXAODmJkqIccl/vahYjUny5/VfNUpCMbBh3Q+CckvntZRvR/Xqwnvf0JWwSiUwaxZQqhT/APLxo2H1JCg6y0LUN6fptU3WRxAEQWQ+NA/9MiAHFmFWyHB8epQfP4pevPs4GZcC/pOQmgqsXw9MnQo8e6b+s3Zbu0wrLqgW//Cezomaz7bJPO7QtGk8dlhUlOlt/PgRWL6c6wkLM11PBig3TThxGjKuWPpBhUIQo2dYn4yb6Ig2jXG7OI8380c5hpdOI9FnoPAaX/QwcRWPFvNHORjmwPLzE05gd++W1VlUy4FV4gt2YC10biO6vn8bEzA9g+i6T3PPZ1jRgiHZ3jhHEJRKka7Ej7rv8V4jhFspL31teAbB1JRkyWD4PaaWMK7dJ07gG4mECGlyon5+9SpUTZb+0lxU9vGVQEGZuBDxtkptCbuhe5WaFCEeO0R6bpzcKyqn67wXN0yT1L1n+3hBuVmzdKxmKldOeP8bsZLWXBSfKXZgzZpR77O3gyAIgjAfNA/9MiAHFmFWyHB8BlJTRS/eQ6eWz+xWyeL6tdYX/GndhAUk+qMpgXt/yZyGfyICd84U9O/mgXWC4ymLFmBfXYY99RmSt27J+And3NInivb2wPPnaD5KeI0fHN6T8fMAeNe5mcA5sLK5zCMmIQHo1w/InZtvX9MxgS0221LQ1uIzLczS1uxI4PpJovvj7rkDn+382ufWjpX0eyMGrDDeGbqmabqOcT2Y3u2h4/sKHVgHWhi2hToNe4nVN99PK2dcoy9cQK2JYnv1vxYMzq0Znu9ZK1ltrXNnUZ03t/8QFlIqUUFjG1+J6QxVHNP/XXA2Q0pMtHHtBXDC3Vl07nt/iFdy6bLHN3dIx8A66DZXUG78tKryDdHMWmthkSnbgUvNsBT1bd7Mhp+9HQRBEIT5oHnolwE5sL4A/P390blzZ9jb28PGxgYODg6YMGECnqpS2psTMhyfB+0Xb52ThUwm2ZLhh34MBeYwDO3DkLhFvOVp0vd2shOmM57LM6HVnw5FUiLWT/4WvUbaYp9TG+ntVv/9J4wZlVGuXAH271evOtO+xuHnj5nnPC1bYn9thqajuSMiJnfGHzHaDiz7L9iBBV9fkfPx8dWjn+302uOm39TSWP5DaVT7meHH7xjiAg6aprhjR1wtw3DGgUHZto3e4ks7CB1Ya7sa58CqMs1a1JcRMyob1+arVwVOJubMEGetsaro9GnJau9WLxRkwGPODGHP7orKecz/DrbzeIDxfXUZTlRiKDSbwWohw9Zh1Y1rq4oLh9aL+v3s5llROV0xsB6fkw6wf+rQWlFZx8lVkPL6pajs5B5W+Lkrw8yODIs7WJvUl4xSbrqVqL2LZzfJlLYQBEEQ5oHmoV8G5MDKwSiVSowaNUp9I2uLnZ0djh417+SHDMfnQfvF282prf5KmcXSpemTuoIFJeOWKE4cx8EGtvCqJZ4wPbpmJucKoabVyPTrW86JQXnpknkU9+gh3BpkY5NhlZ3H5hGMh87Dv9wthPjjD9G2tVe3zfTbGcCwvsKVSwcW9eMO2Hv3MhSPCO/e8UD/I0caFND77wo2+L0Rw6FqDDdKMURWL2/U6Ro55RXZmQmzahnX5n//hf0MoQ6FhcbYlwvirxlUnjEoGYMyMlJcLiUFMdMdEd25DRAQAHh4IKFMCSTWrcVjeplA0JVTon6/f3JTVG5/bWnnVeXJDEqZ1ZJXz+yTrPObYyNhQYUCtvPSj9vNzRyHdI0pucSr535pkSltIQiCIMwDzUO/DMiBlYNZvny5+ib+/vvvcfv2bYSGhiIgIAAVKlRQO7EePXpktnOS4fg8lJkqfPFOmjgus5skT2IiMH8+MHAgzx4nx4cPQGioaFKhSDEiIDRhENdLMdSewFBxMsOxyoyv0DIHf/8tdGB5e2dY5e2lPwvGw78zv+BU9w8f4kVBJohplvjqmf56ZuL+74tRZBb/HRr9ZIGU4Pef7dwCrK2F46yqcStQ208pJLIzM38xcvvYgweoO55voywwh+GrOVpJCeRi9e3fL05gkJho3LlN5ckTdB+c3meHKQyKiHBxudWr8VdpBq9aDO/yM/zUOxcG/GCFu9uXyqq+//cxSQeWzXzha2ZyRJjgeKmZVubupUE0mpRb1NY1c/Wv/iMIgiCyLjQP/TIgB1YOJTg4GPny5QNjDN27d4dSK8bE8+fP1cf79+9vtvOS4fg8RORhiLZhSLTiX/DhZJ4sclmBMTPT09xPN3ZSSRiGo6MwLlZKinn0KpU8kH+zZsDcucZlo5MjKgpnW5TGjI4MZ74txlfrfKlERIidH+ESDohPRWoqwlYvxvVRXZB8VnqL3GdB+xrUMm711MSfxckGfp1vpPPi4UNxO9KkZk35uE4uLgZn4DQ7ISEIzcvwfX+GtiMYLpeVcZ4pFMDWrcDMmTwhh1Kp18n2+snfstsOP34I5YX++w97vxU6jqpPy/0JOqqf1uNtRe3cON/0DLcEQRBE5kPz0C8DcmDlUFatWqW+gW/duiVZxsnJCYwxWFpaIiQkxCznJcPxmdCeAM2dm9ktMhupKcnw2zcbR9wWQJFqJscKISQyEhg/HujVC7h2LbNbo5/YWOD6db5K70tGoRDf+1+ina1SRXgNevQwrv6IEZjTXui82OncyzgdUs7Erl2Btm2BGzfk6/n7Z54DKz5efG4zBVD/EPFO1oH1x3kXAMDBCa1FxxpPyWeW8xvLP62qoP1wYVu2LeymvyJBEASRZaF56JcBObByKC1btgRjDFWqVJEtc/nyZfVNvmePebKQkeH4TGhPQpYty+wWEQTxOdC+91NTM7tFn5/jx4XXwNiYUI6OGNNT6LzwXzHc+HaMGcPPnyuX4dtl4+KAAgXS275tm/HnNRWl8pM5z1JTU2CxSNqBtWnDEADA1TJM5DTqOLmQ2dpgFC1bipyYe5x7Z05bCIIgCLNA89AvA3Jg5VDs7OzAGMPQoUNlyyQmJsLKygqMMfz8889mOS8Zjs+E9iTkt98yu0UEQXwOFi5Mv+9Hjszs1mQOSiWwfTuPq2dKnLX587G2KUOHYQz1x/FEBn/uXWJaO/75h2+zM4Z79/i27x07Pr8DUjPRQt++ZlVdYjoTBbZnzgyjZ9cA4uKwuLX4WH/HEmZtg8F0747pnbSSoSwdkDltIQiCIMwCzUO/DMiBlQN5+/at+uZdsGCBzrJlypQBYwwdOnQwy7mzi+FYswZo0wYYNCgaGzYswrp1zti0aQm2bl2KDRuWY8uWFdi+/X/YunUlfv99NX7/fS22bVuPXbu2Y98+N+zffxB7977DypXAr7+my6pVwObN9+HishOurrvh5rYH7u574eGxD76+rvDxcUNAgDv8/PbD09MTHh4H4OnpDS8vf/j4HIO7eyLc3ABfX77IIDAQOHQIOHr0Jo4dO42jR08jMPA0Att1xbE6TXCjVA38WaEOTm91weHDlxAQcAXHj1/HmTP/4MyZmzh9+ilOnuS6Tp8Gzp8HLl0C/vxTiatXH+P69cf499/3uHHjCa5de4Bbt57i/v1XuH37PW7ffo7Ll+/h/Pk7uHTpDi5fvoO//rqLW7ci8PIlcPcuT7T18CHw9Cnw7FkMnj9/jTdvovDoUTD++ecZbt58irt3n+PRoxd4/vwlnjx5jdu3X+G//4Jw/34w7t8PwpMn4Xj9OhXBwTycT3g4EBoKhIQA794l4O3bj3j16gNevozC8+cRePYsHE+ehOHx4xA8fBiMBw+CcP/+O9y79xZ37nD9t269x+3bwL//8vnln39yefQICA6ORVRUKt68icWbNzEqicWbN3EqicebNwl49y4BQUGJCAlJQmhoEiIiUhERwdsXFsbbyNuZiuDgeISExCIkJBZhYbGIiopDZGQcIiPjVZKAqKhEREUl4sOHRHz4kISPH5WIieE7wGJj+cKMuDi+yyc+HkhI4JKYyI9/+MD/m5rK//7hA5foaC4fPwr1pOlNTNS9S0ip5CGwzClp51MozLZDySAUCiApKV2Sk9PblJr6+dvzSVAqgRMn+Fa0L3H1lTlwdRV/BPj338xu1echKgqYN487QqOjzao6xZJfyzv2QsdQ/al5gevXJbMbjppSwaxtMJhBg+DYVaMt83Nj76IxiIgAIiNTkZKShI8fkxAUlKh6FiQgODgBoaEJCAtLRHh4EqKikvHhQwqio1MRE6NAbKxSbbOTktLtTra3OcQXSWhoChYuXIc1a3ph9er1SEz89AM5Npa/++UUlEr+3pGayu1BUhK3DwkJ/D0vNhaIieHvb9HR3DxHRgLh4ckIDU1AcHA8goLi8P59LN694++rr19/xKtX0Xjx4gOePYvC06eRePIkAo8ehePRoyg8fszfy318gCVLeDhDFxfg0qUIvHkTgydPInD3bhBu3HiJy5cf4eLF/3D9+lP89ddDXLhwCydOXEVAwHm4ufnBw+Mw/PwisX8/sGcPD3G6YAF//ciqZJd5KJExyIGVA7l165b65t20aZPOsg0aNABjDPXr1zdYf2xsrKyEhIRkC8MxfDifsxQr9hrnzzOTpEULP8n4vb17bzZZp51dpKTOlSs7m6Rv9eoOkvpy5UoyuY0jRiyS1Nmpk4vJOu3tX0nqXLRogEn6tm5tJBtf+fDhgibpHD7cWVJfw4anTO535co3JXWOGzcDp05ZGy2enuVk+71vX02TdDo5jZfUV6bMI0G5kydz4+RJG5w4YYsTJ2xx/HgeHDuWF8eO5cPRo3YIDMyPI0e+QmDgV2jWLBDW1oCNDWBrC+TJA+TNC/Tvvw3+/vbw8yuukhLw9S2JgwdL4eDBUjhypIBKp1isrBSS7Vyzpr1WG6XamU/VvgI4fLggDh8uBGfngciXj7fLzg4oXBjInx+wt4+Fn18Jtfj6ltQpfn4lVX0oiV69XJA/PwTy1VdA27aB8PIqpyHlBXLgQAUNcVBJBXh7l0WlSu9RtCjUUqwYl9mzJ+ltW9p11ZT16zuiSBGgSBEI9BYtCri61oSPTxktKSsSb+9yAhk1arW6XZrSpMl1eHlVMEm++eampM7Ro1dqXCMunp4VsX9/ZXh4VIW7ezW4udaA296qcNtXBe4uleHhWgnbtzeR1FesGLBuXWdJnWKpJBAnp5mS+qpUeQ8Pj6pwc6sOd/dq8PCogv37KxskHTocl9TZt68bPDyqGiBV1OdLa2fJkomSOhcu/FFnW9J0qcW1IjxcK2LR7IFgeUMEUsgiBEVyvYOLa2WBuLpWhrt7NdV1qKrRtoo4cMABXl4V4O1dDt9/vwvFiwNly/IQaFWqAKVKAe3anVCPRW9vTSktEi+vcmrb4u1VBpUcLoN99QqswEuBzZg3b7BJ9nzXrq9l7a+fXzGRnTx+PI9K0u3k0aP5ERj4lUoKYOzYper72t4eKFGCS/Pm1+DvX1xlh9LsTCkcPFgaBw+W0bgPy8PLK81uVMSBAxXRpMlNtZ40KV4c+PHHtThwwEE1NmvCza2WQNzd06S2QHbsaImqVXky0GrVhLJuXW+4udXRIV9LysyZC1CzJkTStOlbuLrWM0n69DmDmjWBGjWEMnSoJ1xc6psktWoloXp1iMTZeSJcXBoIZN8+/eLsPEF0DatVA2rUSIKbWx3VNa8FD4+a8PCogf37q8PTsxo8PaviwIEqOHCgMry8KsHbuyK8vR3g41MBw4fvU98zmtK3byAOHSqOgAB7BAQUw+HDRXHkSBEcOVIYgYGFROP76FE7uLnVxeDB/qhdm+fOSJOaNYEffvDH/v01sX9/DXh68nYdOMDbpd0mH5/yOHiwHHx9y8LXtwzq1o1AqVLp90vp0rzfS5aMU42JunB1rQcXl/qq37Ou6u914OHBr8f+/enXY8mS0ahSBahcmUulSlyqVk2Etzc/v49PORw8WBYHD5aBr29p+PqWUt1LJeDvXxz+/vY4dKgYDh0qioCAIujXbyfy5+fvA2nvBra2QJs2/jh2LJ/qPs6L48fz4MQJW5w8aYOTJ3Pj1ClrnD6dC2fOWOHMGUucPWuBs2ctcP48Q/HiLyTtxZw5w02yQXv21JK1QZs2NTVJZ6NGJ0W6Ro/O7FmcPOTA+jIgB1YO5M8//1TfvDt37tRZtlmzZmCMoaoRacjTdOuTrGw4unblRrh48ZcmOx9atfKRcWBtMlmnjU2cWR1Yq1Z1ktRnbZ1ochv79VsvqbNDB3eTdRYp8k5S58KF35ukb9u2hrIP8WPH8pmkU86B1aDBGZP7XaXKP5I6J06capI+H5+Ssv12da1iks7p08fKOrBM7Xfz5ockdQ4YsNZknYwpJXWuX9/KJH3/+183SX22tjEmt7Fnz62SOtu2PWCyzqJF30jqNHUSvnNnXdkxdORIAZN0jho1T1Jf7dp/mNzvGjWuSuocO3a2Sfr8/IrJ9nvPnlom6Zw9e4Skvqz23LG2TjDrc2fNmvaS+iwtU0xuY79+6yR1Nm/ub7LOUqWefMZ75yuTdMrdO3XqXDK533L3zpgxc0zS5+9fVLbfe/fWNEnnrFkjJfWVKPHc5H63anVQUmefPhtN1mltnSipc9WqTibpk/vomJF3tr59N0jqbNXqoEn6unbdLamvc+e9JrexQIFQSZ2rV3cwSZ/8u2+CyW3s02ejzHX0MVlniRLPJXXOnj3CJH1799aU1JcvXxTOnLEySWeTJkck7pnMnsXJQw6sLwOW2Q0gzI+mA2vXrl06yzZv3hz6gr1rkxMcWN98kzaReGHyg6dtW0/JB0XfvhtM1mlpmWrWicTKlZ0l9eXOHW9yGzt2dJXU2a7dfpN1FiwYIqlzwYKBJunbvr2B7Av1yZM2JukcPnyxpL569c6Z3O8v1YHVtOlhSZ0DBqwxWae5HVgrVnSX1Jcnz0eT2yjnwMrIvSPnwJo7d4hJ+r5UB9ahQ4Vl+21uB1bJks9M7jc5sISSEQeW3MpfU++dHTvqyY6hwMD8Jun88cf5kvq+/vqiyf2uUeMvSZ3jx083SR85sMQ6ze3AysiqebmPji1b+pqkr0uXPZL6OnfeZ3Ib5d4B16xpb5K+Vas6SurLSo7A8+cZSpZ8Jqlz1qyRJunbu7eGpL4WLfxMbmPLlr4Sfc7sWZw85MD6MmCZ3QDC/GhuIdy8ebPOsl/qFsLgYJ646uzZaBw79jtOnFQ9RqgAACAASURBVNiCI0c2w9d3I44c2QA/v3Xw8fkNvr5r4ee3Cr6+v8LHZxkOHJgPFxcn7NkzHkuXXsfKlcDq1Tym1tq1/P+3bLkCd/e5cHP7BW5us+HmNgsuLjOxbdsM7No1Ddu2TcWuXU7Yt28yXF0d4eIyCS4uP8HFZRScnHhs33HjgAEDgGHDgLFjgTVrVmLPniFq2bt3EPbu7Ql392/h4tIWe/d+BxeX/nB17Q1X1x5wcekCV9fO+O23eRg6lG+ZHDKExzzu1w/o2zcBbm4t4ObWDN7eNeHu3hiurq3g6toUbm6N4OlZF25u38LVtQ1cXTvAxSVN2mDw4EuoXx9o147HEWvZEmjWDBg8+DTc3BrA27syPD1rw929MdzcmsLVtQlcXb+Fi8s3cHNrCHf3RvD0rA1Pzzrw9Pwanp610KzZB9Spw5fe16zJl6TXqQMsWuQEb+/K8PKqAi+vajhwoDoOHKgBT89aah3799fF/v314OHRAB4eDeHh0QgbNw5Fx45Ap05Aly5At248dnGzZoCXVyX4+5fAwYMO8PGpBB+fijh40EElFeDrW14lacvby8LPrwwmTFiH6tX5loO0NtauDfTocVW1JN4B3t4VVf9NWyafvlReU3x9yyAgwB6tWt1CxYqAgwOXChW4TJs2F0eOFBJIYKD439ri4VEDFSqk66tYkW+5sbcHtm9vhMOHCwkkIID/V/tcmjJ79hSULg2RNGjwRKMtBREYWABHj36lkvw4dswOx4/zZfUnTuTByZO2KrFB+/ZHUbAgT8b21VfpS/KHDl2t80Xq1ClrHDtmJylFiihRogRQpgzvc5kyvJ2//dZd3cYjR6TbeexYPvWS/1On+JL/FSt6I29e3i5bW8DSkv+3QAHTHVj9+m1FnjxQi60tl65dTV+9aG//BpaWvH0WFunyyy9DTdK3c2ddWFlBrVNTt6kOrNGj5wnaliZ16pjuwKpZ86qkzp9+Ms2BdfhwQUl9FhYZc2BJ6Std+onJ/W7d2kdSZ58+pjuwcudOkNSZEQeWBVOAaYmVpemT8P79pR1YLVqY7sAqXPi9Wo+tdQyqVOHPoHnzHOHjUwYHD5ZVPQPK4eDB8jh4UPO5UAZ+fqXh51cK/v4l4e9fAps3d0D58kL7U7Ik36Ln7V1ObX8CA7nt0bQ/x4+nbz3S3H40cqQzLC3N68CqXv2a5LX8+efJJukjB9and2BZWSWb3EY556+2U+PsWQucOWOJM2escPp0Lpw6ZS2pr3PnvZL6MhJGolCh4M/iwDLFEZh2XQYM2CgIeWBnx8MAdOhwSL0N+NgxO9W9zbcAHzlSEEeOFMLhw4Vx+HARHD5cBAEBRREQUAyHDtmjUaMXqFiRb2+sUiV9G+7cuTM07A5/v/TxqQhv70rw8akEb+/K8PauAm/vqvDyqgYvr+rw8qqBrVt7oHlz/l7euDF/v6hcGRg0yAWentXg41MRXl7V4elZFx4e38LdvQXc3FrD3b0pXF1bwsWlI1xcesHF5Xu4uIyAm9sQzJt3GVOmALNnA0uXAr//zuPqZlXIgfVlQA6sHIhmEPeFCxfqLFu2bFl8iUHcCYLI2iiVSiiVCiiVqRqS+dGQebuUGu1La6NCJAqFAklJmn+Tbr9SmQqFIhEKRSJSUxO0JF5D4gSiUCTq0CndHmGbpUUOhSJZS5IkJFFCUmT7Leyf4SLXToUiSXSdUlJikJLyESkpH5CcHIXk5HAkJ0eoJBLJyVFISfko22/xtY8VCT+HUFJTE2T7zdsSoTr3B6SkRKvaqFsUimSZfieq9MiL8Fxp54uRHUP82sm1JVpWUlP5sz/s2V24LOiFjU0s4Na7IlJ/HIHkvy8gOSkCyXEhqt9CKLx9H1XXL1Z17ROgUKRAoeABkKOiuCQnQzWutcdlsqq8UOLjY5CcnASlUgmFIgnJySm4ecwLt/y9oUjOHgkRFKpEFDw4tBIKhUIlqVAoUpCamozU1CSkpnJbkpISj5SUOKSkxKokBgpFqlqHpigUyUhJiUViYhTi48MQGxuGmJgwREeHISoqDJGRYQgPD0VYmFDCw8Pw4UP676IpYWHhCAkJkZTg4GBJCQoKRkjIB3VSF00JC0tBUNB7kyQ0NF6tJyIiXUJDYxAc/M5gCQlJl4gIJSIjIZLQ0DCEhLw1WsLCwiSvY2SkEmFhwQgLC1Ff8/DwcISHRyA8PBLh4VGIiPiAiIhoRER8REREDCIiYhEZGYeoqBR1shdN+fBBiQ8flJLH0iQujo+7iIgoBAUF4d27IAQHx6mT2ISFpQsPOh6G4OBwhIREICQkEqGhUQgLi0ZY2EeEhcUgPDwWERFxiIjgyW0iIxMRGZmkbkdSEh+L0dH8OoaEROL9+yC8e/ce79+/w/v3bxEU9A5BQe8RHByEkJAQhIaGIjQ0DGFh4QgL49cjIiJGkODm48e0YOlKxMTEIzY2HnFxCUhISERiYhKSkpKRkpKiujfkn9VE1ofmoV8G5MDKodjZ2YExhmHDhsmWSUxMhJWVFRhjmDRpklnOS4aDIAiCIAiCIAiC+JzQPPTLgBxYOZSWLVtCX3B2zVhZu3fvNst5yXAQBEEQBEEQBEEQnxOah34ZkAMrh7Jy5Ur1DXznzh3JMlOnTgVjDJaWlggODjbLeclwEARBEARBEARBEJ8Tmod+GZADK4cSHByMvHnzgjGGnj17io6/ePEC+fLlA2MM/fr1M9t5yXAQBEEQBEEQBEEQnxOah34ZkAMrB7Ns2TL1TTxw4EDcuXMHoaGhOHLkCBwcHMAYg52dHR4+fGi2c5LhIAiCIAiCIAiCID4nNA/9MiAHVg5GqVRi1KhR6htZW+zs7HD06FGznjMmJkatPyQkBLGxsSQkJCQkJCQkJCQkJCQkn0xCQkLU89CYmBizznGJrAM5sL4A/Pz80LlzZxQrVgw2NjZwcHDA+PHj8fTpU7OfS9NwkJCQkJCQkJCQkJCQkJB8TgkJCTH7PJfIGpADizAr5MAiISEhISEhISEhISEhySwhB1bOhRxYhFlRKBQICQlBSEgIYmJizLIElLYikmRHoTFMkhOExjFJThAaxyTZXWgMk+QE+dTjOCYmRj0PVSgUmT0tJj4R5MAisiSxsRSEj8je0BgmcgI0jomcAI1jIrtDY5jICdA4JswBObCILAkZOCK7Q2OYyAnQOCZyAjSOiewOjWEiJ0DjmDAH5MAisiRk4IjsDo1hIidA45jICdA4JrI7NIaJnACNY8IckAOLyJKQgSOyOzSGiZwAjWMiJ0DjmMju0BgmcgI0jglzQA4sIktCBo7I7tAYJnICNI6JnACNYyK7Q2OYyAnQOCbMATmwiCwJGTgiu0NjmMgJ0DgmcgI0jonsDo1hIidA45gwB+TAIrIkZOCI7A6NYSInQOOYyAnQOCayOzSGiZwAjWPCHJADiyAIgiAIgiAIgiAIgsjSkAOLIAiCIAiCIAiCIAiCyNKQA4sgCIIgCIIgCIIgCILI0pADiyAIgiAIgiAIgiAIgsjSkAOLIAiCIAiCIAiCIAiCyNKQA4sgCIIgCIIgCIIgCILI0pADiyAIgiAIgiAIgiAIgsjSkAOLIAiCIAiCIAiCIAiCyNKQA4sgCIIgCIIgCIIgCILI0pADi9DJ69evsXHjRgwZMgR16tRBiRIlYG1tDTs7O9SuXRuOjo548OCBTh2LFi0CY0yvTJo0SW97bt68iWHDhqFs2bKwsbFByZIl0bdvX5w/f96g/iiVSuzZswetWrVCkSJFkCdPHlSrVg0zZ85EcHCwQTqInMOTJ09ga2urHoN79+7VWf7jx49YvHgx6tSpAzs7OxQoUADffPMN1q1bh+TkZIPOSWOYMDeGjmOyxURm8+LFC4PGYL58+XTqIVtMZCYZHcdki4msyokTJzBo0CBUqFABtra2KFy4MOrUqYOxY8fi+PHjsvXIJhOfE3JgETrZtGmT3gestbU1Nm3aJKvDXA/qXbt2wdraWrKuhYUFnJ2dddZPTExEly5dZM9vb2+PGzdumHSdiOxJhw4dBGNAlwPr+fPnqFixouz4adiwISIjI3Wej8Yw8SkwdByTLSYyG3M4sMgWE5lNVnFg0TgmzEVsbCz69u2rczy2bt1asi7ZZOJzQw4sQie7du1C9+7dsXbtWpw7dw4PHz5EeHg47t+/j127dqFy5cpq4xAYGCipI+1BXa5cOcTExMhKUlKSbDsuXrwIKysrMMZQr149nD17FqGhobh69SratWunboOHh4esjrFjxwpeCh49eoSgoCC4u7ujcOHCYIyhZMmSCAsLy/B1I7I+bm5uYIzBwcFB78Q/KSkJtWrVAmMMefLkwaZNm/D27Vu8ePECzs7OsLS0BGMMHTt2lD0fjWHiU2DMOCZbTGQ2mhP/Y8eOyY7B2NhYyfpki4msQEbHMdliIiuRlJSENm3agDEGKysrTJw4EVevXkVoaCiCgoJw7tw5TJo0CX379pWsSzaZ+NyQA4vIEFFRUShVqpROz3zag7p8+fImn6dRo0ZgjKFUqVKIiooSHEtKSkLdunXBGEOZMmWQmJgoqn/nzh1YWFiAMYaJEyeKjl+5ckVtZGfMmGFyO4nsQWRkJOzt7WFlZYVDhw7pnfhrrkT09vYWHV+5cqVeRy6NYcLcGDuOyRYTmY3mxN/QbSGakC0msgIZHcdki4msxOLFi8EYQ65cuXD48GGj6pJNJjIDcmARGcbR0RGMMdjZ2Ukez+iD+vr162rjt2HDBskympM3Hx8f0fGJEyeCMYbcuXMjNDRUUkfv3r3BGEPBggUN3q9NZE9Gjx4NxhicnJwEL6JyE/+aNWuCMYb69etLHk9OTkaxYsXAGEP37t1Fx2kME58CY8cx2WIis8noxJ9sMZEVyGwHFo1jwlyEh4erY2hOmTLF6Ppkk4nMgBxYRIaZNm0aGGMoWrSo5PGMPqgXLFigNlzv3r2TLJOcnIy8efOCMYbhw4eLjpctW1bvElZ3d3f1ec6dO2dSW4mszx9//AELCwuULFkS0dHReif+z549Ux9fvny5rN4xY8aAMQZbW1vExcUJjtEYJsyNseMYIFtMZD4ZmfiTLSayCpntwKJxTJiLdevWgTEeZ+rVq1dG1SWbTGQW5MAiMkRCQoI6DlaXLl0ky2g/qBUKBVJSUgw+R/fu3dVLR3XRvHlzMMZQu3Ztwd/DwsLURmv+/Pmy9Z88eaIut2bNGoPbR2QfkpOT1V+LPD09AUDvxN/Hx0d9/MyZM7K6d+7cqS73999/C47RGCbMiSnjGCBbTGQ+UhN/XXF+NCFbTGQVMjKOAbLFRNahc+fOYIzHntIkJSUFSqVSZ12yyURmQQ4swmgUCgXev3+PgIAAfPPNN+plm9euXZMsn/agzpMnD2rWrKkO1Fe4cGF06dIF+/fvR2pqquz50hxkLVu21NmuoUOHgjEGGxsbKBQK9d8vX76sNlq7d++WrZ+cnKzeIz1mzBg9V4HIjixbtgyMMbRv3179N30T/7Q6jDE8e/ZMVveZM2fU5dzd3QXHaAwT5sSUcQyQLSYyH81xWqtWLeTLl089VurXr4/58+cjJCREsi7ZYiKrkJFxDJAtJrIORYsWVf++cXFxmD9/PipWrAhLS0vkypULNWrUwLx58ySzCJJNJjILcmARBpPmpdeWatWq4cKFC7L1DEkX3KpVK9nMEAULFgRjDP369dPZvrStjIwxREdHq/+uuXf6yJEjOnWkZaro06ePznJE9uPp06ewtbVF7ty58fDhQ/Xf9U38nZyc1MdjYmJk9d+5c0ddTnsfP41hwlyYOo4BssVE5qM5TuWkUKFCOHHihKgu2WIiq5CRcQyQLSayBvHx8epxMHnyZPXKbikpW7Ys7t+/L6hPNpnILMiBRRiMlAOrWLFi2Lp1q07DtW7dOjg6OuLkyZN4/vw5EhMTER4ejoCAADRt2lStq1mzZpJfnKytrcEYw5AhQ3S2b+7cuWpd79+/V//dw8ND/ffTp0/r1JGWUbFTp056rgaR3ejYsSMYY5g7d67g7/om/pqpeXUt8X/8+LG63IoVKwTHaAwT5sLUcQyQLSYyn1evXqFz585wcXHB3bt3ER0djbi4ONy8eROTJ09Wr0TJkycP/vnnH0FdssVEViEj4xggW0xkDYKCgtTjIHfu3GpH0p07d5CUlITXr19j0aJF6pVLlSpVQmxsrLo+2WQisyAHFmEwCQkJiImJQXR0NJ4/fw5XV1fUqFEDjDHUrFkTz58/N1pnamoqBgwYoHPilWbchg4dqlPXvHnz1Ho0AwFqGjdde7QBoHTp0mBMdyBAIvuRNgYqVKiA+Ph4wTFjHFi6lvRr7q/XDmZJY5gwBxkZx/ogW0xkBfz9/dXp0Fu3bi04RraYyC7oGsf6IFtMfC7evXsnWJTQrl07ybhXa9asUZf57bff1H8nm0xkFuTAIjJEfHw8GjduDMZ4YD3NfcmGEh4ers4uIWVU0paX9u/fX6ceQ5aXBgYG6tRBy0tzHlFRUShevDgYYzh8+LDouDFbCDW/PGljyBJpGsOEqWR0HBsC2WIiKzBw4EDJiQrZYiI7ITeODYFsMfE5+PDhg8CBdfnyZclySUlJKFKkCBjjW1vTIJtMZBbkwCIyzPnz59XGQ9/yTTk6dOgAxviWRG3SAvxpGk0phg0bBsb4Mli5AH979uyRrZ+SkkIB/nIgaQ/YXr16SR43Joi7rlWGZ8+eVZeTC1JJY5gwlYyOY0MhW0xkNprpzo8ePar+O9liIjshN44NhWwx8alRKBTqrYOWlpZITEyULdutWzcwxlCkSBH138gmE5kFObCIDKMZBHDVqlUm6Rg8eDAYY7C2thYdSzOaZcuW1amjRYsWYIxnhNEkNDRU3b4FCxbI1n/69Km63OrVq03qB5H1+O677wRfmAyVFy9eAAC8vb3Vfzt79qzseXbt2qUud+PGDcExGsNERsnoODYUssVEZnPq1Cn17+/h4aH+O9liIjshN44NhWwx8TmoXbu2yDElxZAhQ0TjkWwykVmQA4vIMB8/fsywUWjXrp3sl6YFCxao9WsG7tMkJSVFvdx62LBhouNly5YFY7oD92nuo9ZliInsRUYn/s+ePVP/TTv4pCZpsQBsbGwQFxcnOEZjmMgon8uBRbaYyGxcXV3Vv7/myhWyxUR2Qm4cGwrZYuJzMHLkSDDGYGFhgYSEBNlyXbp0AWMMRYsWVf+NbDKRWZADi8gwgYGBaqPg6+trdP3Q0FDY2trKGp9r166p9W/cuFFSR0BAgLqMt7e36PiECRPAGF96KpeWuE+fPmCMoUCBAkhOTja6H0TW5Pnz5/j3339l5ejRo+qxs3jxYvXfk5KS1DrSkhU0aNBA8hwpKSnq+ETdunUTHacxTGQUc4xjfZAtJrIC/fr1U48h7dhBZIuJ7IKucawPssXE58LPz089Ts6fPy9ZJjExEYUKFQJjDB06dBAcI5tMZAbkwCJ08uDBA53HIyMjUatWLTDGkD9/fkFgPQAICwvTuac6OTlZsLLAxcVFslzDhg3BGEOZMmXw4cMHkY769euDMYbSpUtLfkG4ffu2OiPMzz//LDp+7do19d7o6dOn6+wzkbMwJHbQxo0b1WUOHjwoOr569Wr18SNHjkjqoDFMfEr0jWOyxURW4O3btzqPe3l5qcdH27ZtRcfJFhNZgYyMY7LFRFYiKSkJDg4OYIyhRYsWksm4li5dqh6Pu3btEhwjm0xkBuTAInRiZWWF7777Dvv27cPdu3cRFhaGiIgI3L17Fxs2bFAv22SMYfPmzaL6/v7+KFmyJGbOnImTJ0/i1atX+PDhA169egVPT080aNBAXb9169ayWQwvXrwIKysrMMZQv359nD9/HmFhYbh27Rrat2+v1qEdHFCTMWPGqMs5Ojri8ePHCA4OhoeHhzq7RsmSJWW990TOxBAHVlJSktpRmydPHmzZsgXv3r3Dy5cvsXjxYvXY1JWal8Yw8SnRN47JFhNZgaJFi6J///5wd3fH/fv3ER4ejtDQUPzxxx8YO3asehKSL18+3Lp1S1SfbDGRFcjIOCZbTGQ1Dh06pB6zXbt2xZUrVxAZGYkHDx5g+vTp6mMNGzYUrV4im0xkBuTAInSSZgx0ia2tLX777TfJ+v7+/gbp6N69O6KionS2ZefOnbC2tpasb2FhAWdnZ531ExMT1Xu4pcTe3l4UXJDI+Riave358+eoWLGi7Php2LAhIiMjdZ6LxjDxqTDEgUW2mMhsChQooHcMlipVChcuXJDVQbaYyGwyMo7JFhNZkQ0bNiBXrlyyY6FevXqyKw/JJhOfG3JgETq5dOkSnJ2d0a5dO1SqVAl2dnbInTs3ihUrhlat/s/efUdHUb1vAH9D6L1JE5AiIAIKiDSpIk16772GEloSOgZQUfSHKIIgCiooTb6AiAIiXVQQFKQX6SW0QEIgZXee3x83yWbYlCXM7iSZ53POPUfI7M7dyc6V9zn33qmHt956CxcvXkzw9Tdv3sSnn36K7t27o2LFiihQoAAyZMiA7Nmzo2zZsujVqxe2bt3qcn8OHTqEnj17omjRosiYMSMKFSqEdu3aJbhu+3GapmHJkiWoW7cu8ubNiyxZsqBs2bLw8/PDjRs3XO4HpR2uBliAemDB9OnTUalSJWTLlg05c+ZEtWrVMGfOHJfX1PM7TO6Q1PeYYzGlBGvXrsXo0aNRq1YtFC9eHFmzZo39/jRt2hTz5s1DSEhIku/DsZjM9DTfY47FlFIdPHgQvXv3RvHixZExY0bkzp0bdevWxfz585PcT5NjMnkSAywiIiIiIiIiIkrRGGCRoex2O4KCghAUFITQ0FA8ePCAjY2NjY2NjY2NjY2Njc1tLTQ0NLYOTWj/OEr9GGCRoYKCglxa28/GxsbGxsbGxsbGxsbGZnQLCgoyuywmN2GARYZigMXGxsbGxsbGxsbGxsZmVmOAlXYxwCJDhYaG6gYOs6eSsrGxsbGxsbGxsbGxsaXtFnciRWhoqNllMbkJAywy1IMHD2IHjgcPHpjdHSIiIiIiIkrjWIdaAwMsMhQHDiIiIiIiIvIk1qHWwACLDMWBg4iIiIiIiDyJdag1MMAiQ3HgICIiIiIiIk9iHWoNDLDIUBw4iIhMFBEBaJrZvSAiIiLyKNah1sAAiwzFgYOIyCRDhgAiwIsvAhcvmt0bIiIiIo9hHWoNDLDIUBw4iIhMsHWrCq9EEJxZEJVOgLffBoKCzO4ZERERkduxDrUGBlhkKA4cREQedu1abHj1WTWBBKpWyUdwr3lDs3tHRERE5HasQ62BARYZigMHEZEHXb4cG15BBKfzOgKsmHZ5+QKze0lERETkVqxDrYEBFhmKAwcRkWeEBN9A96EFcDGXI8D6+XnBltLOIdaDq+fN7i4RERGR27AOtQYGWGQoDhxERJ4xYuLLkEBB0TGC4/lVgPVOXefwSgIFq+YNNbu7RERERG7DOtQaGGCRoThwEBG53/3/TugCqjzjBafyCSLTCRr0cQ6wBvuVM7vLRERERG7DOtQaGGCRoThwEBG5WUQEGvfSB1QF/QRRoffVzzUN9YZl0f88IB2iwh+a228iIiIiN2Edag0MsMhQHDiIiNzrwIIpTjOsxox5UXeM9uiR0zGfvt/BpB4TERERuRfrUGtggEWG4sBBRORefUaXcAqnbvzzm9Nxb02qrTumyqhMJvSWiIiIyP1Yh1oDAywyFAcOIiL3uXvrEjJN0YdXB+ZNjPfY0OAgZJ+kP/bEn5s83GMiIiIi92Mdag0MsMhQHDiIiNxn67JAXSBVwycDYLcneHzXUUX1Sw39X/Jgb4mIiIg8g3WoNTDAIkNx4CAicp9PZrXVBVLjA6omevz27z/UHV9sXDpoUVEe6i0RERGRZ7AOtQYGWGQoDhxERO4zzL+CLpBaOn9Qosfb7TYU9fdG9YGC918TnM0jwO7dHuotERERkWewDrUGBlhkKA4cRERuEhKCij76Pa1+37Qo6Zf5+QIijvbOOx7oLBEREZHnsA61BgZYZCgOHERE7nHoQz9deJVzgiAi9F7SL/zf//QBVteu7u8sERERkQexDrUGBlhkKA4cRETuMdCvrC7A6tHexf+Fnz2rD7BEgLAw93aWiIiIyINYh1oDAywyFAcOIiLjaXY7Cvt56QKs/d/McvHFGlC6tD7AeuEF93aYiIiIyINYh1oDAywyFAcOIiI32LULl3MKFr0iaN1VUGGYQLt/3/XXz5jhPAsrIsJ9/SUiIiLyINah1sAAiwzFgYOIyA3Gj9eFT/a6dZ7s9QcPYn05wcx6gkGtBE17Cm6d+9c9fSUiIiLyMNah1sAAiwzFgYOIyA2qVtXPntq69YnfolFv/RMM/9rxrRs6SkREROR5rEOtgQEWGYoDBxGRwU6f1odXWbMC4eFP/Db9RpfUBVj/+26aGzpLRERE5HmsQ62BARYZigMHEZHBHt+/ql27ZL3NW+Or6wKsmeNrGdxRIiIiInOwDrUGBlhkKA4cRETGsufLqw+wfvghWe/z1ezuugCr7Jj00Gw2g3tLRERE5HmsQ62BARYZigMHEZFxtvu2ig2cBrcUXMshQHBwst4r6PJJZJ6s3wfr9I7vDe4xERERkeexDrUGBlhkKA4cREQGuXdPFzZJoKDy6MxP9Zbdxz+ve7+Fc3oY1FkiIiIi87AOtQYGWGQoDhxERMa4+/c+pwBrypiXn+o9l8zto3u/en0FWkSEQT0mIiIiMgfrUGtggEWG4sBBRGSMA+sXOAVYZ/dseKr3vHloD7yn6d9zxxdTDOoxERERkTlYh1oDAywyFAcOIiJjrPxsuC5oqj7QmP9lt+nmpXvfCVNqGvK+RERERGZhHWoNDLDIUBw4iIiMMdq3nC5omjX9DUPe96fFAbr3fXNcYUPel4iIiMgsrEOtgQEWGYoDBxGRMUqMTacLmvaumG3IcD//OwAAIABJREFU+948/LvufYv6eQM2myHvTURERGQG1qHWwACLDMWBg4jo6V3977AuZCrgL7BHRRrz5lFRKOSvX0Z458+dxrw3ERERkQlYh1oDAywyFAcOIqKnt/qLsbqAqY1vAUPfv4l/Yd3771z+tqHvT0RERORJrEOtgQEWGYoDBxHR02vpm18XML0/o6mh779kSktMbCRYUVFw7BlB1Px5hr4/ERERkSexDrUGBlhkKA4cRERPJ/zWDWSe7AivvN4SXDjxh7EnmT8fEHG0KVOMfX8iIiIiD2Idag0MsMhQHDiIiJ7OrvUf62ZfvTo2h/EnWb9eH2A1amT8OYiIiIg8hHWoNTDAIkNx4CAiejr/C+yCwuMcAZb/pFeNP8mNG0D69PoQ68wZ489DRERE5AGsQ62BARYZigMHEdFT2LkTEIHdS7C7uGBkc8H+Ze+751zt2+sDrLlz3XMeIiIiIjdjHWoNDLDIUBw4iIiSKSoKKFRIHyqJAJGR7jnf8uXO5yIiIiJKhViHWgP/tUqG4sBBRJQ8wUf240gB8VioZDt6BBvLCqY2FDTrIagwTBB16oTbzkdERETkLqxDrYEBFhmKAwcRUfKsXTYJEiho2Eew7gWBzUvUPlVuotlsKD5adBvGb1s23W3nIyIiInIX1qHWwACLDMWBg4goecYH1tGFSXNqCvDOO+4955iKunPOer+VW89HRERE5A6sQ62BARYZigMHEdGTu3Fghy5IkkDBjvKZgfv33Xre7z8eojtnV/+Sbj0fERERkTuwDrUGBlhkKA4cRERPSNPQaWAupwAr5N5Nt5/67M/f6s5ZbKwXtIgIt5+XiIiIyEisQ62BARYZigMHEdGTuXPmiFN4ld/fM/971iIjUcTfW3fuf3791iPnJiIiIjIK61BrYIBFhuLAQUT0ZL7/drJTgDV2ZFmPnX/Q+Bd1514wu5PHzk1ERERkBNah1sAAiwzFgYOI6AnYbKg6zNspwLqza7PHurBkwWDdufuP4j5YRERElLqwDrUGBlhkKA4cRESuO7r+c6fw6td3B3m0D//+vUV3/rKj03v0/ERERERPi3WoNTDAIkNx4EjhTpwAjh41uxdEFO3dMdV04dEz/gLbo4ce7YPNFoW8E7x0/Th3YKtH+0BERET0NFiHWgMDLDIUB44UbNgwQAQQwdHKRbB0aE3cWfed2b0isqwbh/c5zb5aMW+IKX3pPLaYrh8TxlczpR9EREREycE61BoYYJGhOHCkEGFhwPjx0Fq8iZ86vozFVQX/5RZoIvjzWUH6qY5CNfh/3wH//Wd2j4ksZ9aE13Shkfc0QfC5Y6b0ZePqt3V9KTkuPTS73ZS+EBERET0p1qHWwACLDMWBI4WYNg33Mwk6d9TP7qg+UPD8SHGa9XH0GQFefRVo1AioVw/46y+zPwFR2qZpgAhO5hO8/5q6D6sMMe9/yXZbFAoGpNONC4fXLzKtP0RERERPgnWoNTDAIkNx4DDRoUNA9epA5crYXVyQZ7w+pMo0RfBRTYFvM+cASwIFAW9I7BJDVKxo9qchSrsiI2Pvtah0gqkNBS26C/7bsc7Ubg0eVzZ2PEg3TfBlFQEiIkztExEREZErWIdaAwMsMhQHDhPVrh1bFL80NP6QKqm2rWScEOvePbM/EVHaNHiw4z6Lad27m90r/PL1NLTpKlhaWXAra3S/fv3V7G4RERERJYl1qDUwwCJDceAwSfRyJIjgt2KJh1SDWyb+87frCrp0FBzb9b3Zn4oo7Tl/HvczCexejwVYGzaY3TMgJMQ5WFuwwOxeERERESWJdag1MMAiQ3HgMMmGDQjLINhSOuFgqsxIwem8qnCu5JP0bKy5PcuoYIyIDGE//x8a9nHcY7uLCyK8o4Mim83s7imtW+sDrGHDzO4RERERUZJYh1oDAywyFAcOz7u24wcUGasK4tr9Bb3aCTJM1YdRGacITjaoCFSoAIjgck7BjHpJh1h/fDnD7I9HlGZsnNHT6R5r1U2AvXvN7prD/v36AKt0aQbZRERElOKxDrUGBlhkKA4cHhYcjCHxLAls1FtQMc4sq/XlBNi3T20efe0aYLcDt2/jfibBxzUEbbsIxjWJP8S6veBDsz8lUZowon8hp/trVp0U9r9hmw0oUEAfYh07ZnaviIiIiBLFOtQaUti/nCm148DhQVeu4FThDAnOnnpxmHriYHju7MAHH8T/HlOmqAI1e3Yce6V4vO/TtKcAR4969rMRpUGVx2Z1ur/u/bLR7G45698fEEFIRsGqCoINb/cyu0dEREREiWIdag0MsMhQHDg8Q7PZsLNPgwTDq28rCdCrF/Djj0m/2e3bwIMHiAi+jSLjvOJ9v2PPCLB5s/s/GFEa9fCrL5zuq4gDf5jdrXhdXL0YLboLMk1R/Xx1WAazu0RERESUKNah1sAAiwzFgcONrl0DFi5EyI9rUWtElkT3rtIePkzWKbZ/OTXe9/ugdvRSosOHDf5QRGlf0Oa1TvdUNd8sZncrQXduXXIOsbcsN7tbRERERAliHWoNDLDIUBw43OTBA6BAAVzJIbEbtifUVr/4dLf191UyO20Af6iQCrC0UiWhpZSnpRGldJqGR6NHoHZ/5/t08LhyZvcuUWVGpdP1t+ugvGZ3iYiIiChBrEOtgQEWGYoDh5ssXIiwDILnRyYcXFUdLNhWUoCBA5/qVNonn2BQK8f7Xs4p2FNcbfJe2lfw1w8LDfpQRGnbhe8+S/B+/X31HLO7l6i3hpTV9TfrJEHo9Ytmd4uIiIgoXqxDrYEBFhmKA4ebDBqEFt3jL4TfqSuYUU+AWrWA3r2BoKCnO1dICNCyJUY3FUxp6Px0wonNM6qnGBJRoob1KxDvPTtzxEuAppndvURpR47A6y19v5d/MsjsbhERERHFi3WoNTDAIkNx4HCPGw2qxVsIPzc6zqPuDaaFhiLCbwy2ltKfM8dEwbl6ldTm70TkTNOAjz5CnX7O9+yfK//P7N657KP32ur63mF0EbO7RERERBQv1qHWwACLDMWBww0++ABXcwiGvelcDBfwc1+AFSPirz+RL8D53BeqluZMLKLH2e1ArlyACKLSCSr5OO6Zz4fVNLt3T+Tyyf26ez77JEHEw1Czu0VERETkhHWoNTDAIkNx4DCYpuFiLsGmMoJH6QW5JuhDpAxTo8MrLy+3duOb8c2cAqzywwX2pUvcel6iVKdTJ0eoLAJNBIcLCi7lFCAy0uzePbHKo/UPdfgqsK3ZXSIiIiJywjrUGhhgpUHnz5+PvXkTa9myZTP83Klu4FixAihcGKhYETh82Oze6GkavvFtAAkUFB4nmFNThUaPB0kQARa6eWN1TUNFH+dzHywswNdfu/fcRKmEdvYsbmQTRHiLLsRCtmzA9etmdy9Zpkx9TXfPlxgtsIU/MrtbRERERDqprg6lZGGAlQYxwHJRRASQM6ejyGzRwuwexdIiIzFkyLO6wvHdOoKxj22oPru2ABMmAOHhbu9TwMz6TgHWtAbR1+6PP9x+fqIUy2bD8b4tY++LsiME/xaIE2Cl4qW2p3etQ/qp+vu+Ve8MwL17ZneNiIiIKFaqqkMp2RhgpUFxA6yffvoJoaGh8TZ33NipauA4elQ/S8KN+0g9keBgrGzwTLybtvdvLajfV/13hWGCG8s+81i3zmz/Pt4+fVEl+tpt2+axvhClGKGhWFnB+T4Nzhx9X5w/b3YPn9rIDlmd7vtf3xlodreIiIiIYqWqOpSSLYVU7GSkuAHWjh07PHruVDVwHD+e4gIs2/VrOFkqFyoPcQ6KJFDwML1Aq1sH13x6IuKLReppZ56iaVjZprRTn0Y0j7521at7ri9EKUFkJJZWjv9efWG4IHz1CrN7aIize39w+nxjh5cxu1tEREREsVJVHUrJZn7FToZjgOWiEyfcF2D9/jswaxZw5IjLL4k4fQKv+njHWwxLoKB3v9zA/fvG9TE5QkMR2CaXrl8TGwkmvy6o3V8QfP28uf0j8qC7Pv2QfWL89+vgXnlS9dJBHU1Dk8FZYj/bolcEl16rZHaviIiIiGKlqjqUko0BVhrEAMtFri4hvH4dOHhQ7ZnlioMHAW9v9X4ZMwJbtwJTpwLr1zsfGxUF7cIFhK5ahm8rxV8IS6Cg98B8uHZwV/I/q4HCb91A5wE5kd/fuZ8lRglOfTfP7C4SuZfNhsXDayV4v87pVx5aWgmvomnBwfi9qGBPcYHNK3q8vHvX7G4RERERAUhldSglGwOsNCi+ACvC1fDlKaWqgePQoaQDrN9+U08QEwGaNweiopJ826jXG2BUM0HJUQKfFo89keyzOHtW3bkDW4XyaNNVFb15A1R7vBge3i2XZ5cKukLTgIoV8W4d5/4WGeeF8Fs3zO4hkdusmz8ywfBqRb28gM1mdhfdI18+5zFz1Sqze0VERESUuupQSjYGWGlQ3ACrQoUKyJYtG0QEmTJlQpUqVTBlyhQEBQW55dypaeAI2fsr5lUXfFlFsKKiYEcJcQ6KevTQF2tffx3/m925A3TrBlStip+e1xe035dXr9VEcC27IPzt6UBYGPDJJ/j65YRnXcW0/6qVdv/FSI7//sMfNYvF2+cPOhYxu3dExnv4ELh6FUsa5HL6zhf0E4RlEGDnTrN76T79+jkHWAULpp2lkkRERJRqpaY6lJKPAVYaFDfASqjlyZMHmzdvTtb7P3jwIMEWFBSUagaOM5u/cypCQ0Ju6w96vFhLaJnhxImxP3+9t/49W3RXS26a9XD83dAWgt+KxR9YlRnp+O+DhQVYutTt1yK5oqIikHeCV7yf48i6RcDt20m/CVFqcPw4ULQoIIKodOK0hHZAa3Fphmaq9vAhIAK7l+DfAmovrAZ9BA+PHDS7Z0RERGRxDLCsgQFWGnTx4kU0bdoUX3/9Nf7991/cv38fYWFhOHToEHx9feHt7Q0RQZYsWXDw4JMXHkmFY6ll4Diy4XOn0OXK1RO6Y65nFyyvpDYpLzFKMLWh4ELN8lj56VCcPRJnT6o4Ade0Bvr3zO8v2FI66ZlWMW1vMcHdzNHv98orqmhMwd6dUj/Bz/Igg6iZaR5awkrkDhEhwVheNT0+riG4mVXdm6Ob6r/rW+aPNbubntGjB97o9dgefW35TwkiIiIyFwMsa+C/Oi1o3bp18PLygoigfv36T/z6tBJg/bF6jlPgciqfAK+9hoivl2DG6+lR0C/hoCn9VMGyL3zVm4laPvQgg2BcE+djy45wPcCa/6oAbdoAp0+nihkd965fSPCzjH8jOoibOtXsbhIliz0qErV9s8V+p18cJgj3FtzPJKjbT5BximDI0GehWSWkPXUKAW843+tH18w3u2dERERkYQywrIEBlkV16dIl9ga/evXqE702rSwh3L5sZryhS982Ar/GroVN+f0F9vEB2FZSkGe8wOst14OqxNrxVamrGPxn/SJ4T3P0f3RTwf4iagP7byup63noy7fN7iaRy4I3rMInLQugZzv9vVnKVy2fgwiQOTMiD+4HIiPN7q5HXVzzhdOY9ZJvRthtKT9wJyIiorSJAZY1MMCyqOXLl8fe4Js2bTLsfVPTwLHpywmGhE1n8grq9zUmuIqdCbblW7MvT7LsXDINz/gLpjQUHHtG0LLbY9eqfQMgPNzsbhIl6t7WH5B1UsL3Z4/20QHWtGlmd9UcmoYF9bM6XZc9qz8wu2dERERkUampDqXkY4BlUVu3bo29wb/91riwJDUNHGsW+hoSNhUZm/zXZpoi6NZBkC7O7KVGfbyA4GCzL0+yncyn9g17PLyKaTP7loQ90iLLrSjVubruG6Sfmvh9+3tRAdasAWw2s7trGnv4I6fgPv1UgZYKlj0TERFR2pOa6lBKPgZYFvXNN99YfgbWNx8PMHTW1JO0Aa0FR5+JnsVRsSL2TOmNRiNzocvQ/Lj5v+VmX5qn078/III1Lyb8+asOFkTNnI6o7dug/forEBpqdq+JAE3D7G7FE713DxYWYORIs3uaIqz5KsDp+vRuK7DdS70BPBEREaVOqakOpeRjgGVRHTp0SPYeWIlJTQPHhBaZniqEqt9XMKL5k73mZD7BnSyie2phmnP0KJAtG25nEd2+WI+3rh0cP88yWfDz5C5m95ys6t49YONGIGtWQNTTRuP7zv5QOx/w8cd8qma0Swd3JLjv37Gda8zuHhEREVlIaqpDKfnSYPVMV65cSfTnq1atin0KYcOGDQ09d2oaOGoMdC66+rZxPYz6t4BgcdUnC7Civl2mD68mTzb7MrjHhQvAsmWYWe/Jrs+1wtmBmzeBFSuARYuAzZuB27fN/jQUQ9MAPz+gWDFg0KA0sYTux9kD8eIw9RCGWXXUfTnlsQDrze4CzW43u6spj92OgR0yJHg/379wyuweEhERkUWkpjqUko8BVhqUP39+dOzYEcuXL8exY8dw+/Zt3Lx5E3v27MGgQYNiw6ts2bLhn3/+MfTcqWngKOinL7buZBH8l9v1sEUTwU/Pu358x06iAoB+/WKXDuLyZbMvg3tt2oSrOQTLXnK+Hs+Njv86rS0viEonGNdEUG2QoFc7wfUcXmpmF6DCsaVLgdOnTf1oVnLv6EGM6FsQn77qCF+v5BCMbSIYPKQITu3dYHYXXadpsO3ZhS/fLIyiY/TfvULjBP8UVE/P7N1W7W83rkMOaCl8LDPVpUv4skr89/JXMzuY3TsiIiKyiNRUh1LyMcBKg3LlyhV78ybUihQpgp07dxp+7tQycNgfPdQtb8s9XoBixXCokGthVM0BAnzzDQ4XdO34ImMF59d/5ejAo0eAVWZ0REYCAQHYU9z1sO/915z/bstrBYECBWIDlP9yC7bM7ItHF89h39R+eLdDAez/yE/N4HqcpqlGT+7RI9Ttp34HH9ZS1z4om6BYnPDH6y3BqZXzU/w1vv7nr+jQO3OC37ujzwj+LhQd0lWqBJw9a5379GnYbFjUJJ/uWga8EX0dT5wwu3dERERkAamlDqWnwwArDVq7di1Gjx6NWrVqoXjx4siaNSsyZsyIQoUKoWnTppg3bx5CQkLccu7UMnDcOX9cV2yVGSnApk3YXsK1gKVFd3XrhB7ej+wTHX9f2lewp7jg+ZGCAn6CVRUEF3IJogKnpvji3t0eXDqHXBMc16pxL9cDrbhtT3HB7uKCjFMSPmbfx/64fnAnHlw9D9vNIHzapST6tBVsH9zE8r+HJ/XhxAax13VTGTXz8L/cglHNnK/76g/6mt3dRHUYnj/R79YXVaJDlxw5gPPnze5u6mK346MuxVFkrKBRb8G17HGWSvfvz/uOiIiI3Cq11KH0dBhgkaFSxcARGYmTg9rrCtfa/QWIjMT/htaPt7Ad/qb+z33aRt86NhsWVxXkmqBCsOP5H9ugvV07cz9rCrPOvyUq+gga9/bC+ZZ1sKSy87Vu31mclnfGbf3aCFp3dS3syjnBeV+znyd1Bq5c0c2sObHhS4weXBwfjamFiDvxzOCyqMtXTyDrZMe1W/iKYHZtFU7Ed73zBgjCw9wTjiebpgE3buD+7atJfl++elmATp0YtiSXpgElSujHwJi2cqXZvSMiIqI0LFXUofTUGGCRoVLFwHH4MA4XVIFTnvGqcG3tkxsA8NPwJsgyWV/UzqynZpx8Ul0wrYEKs5a9FOfWEYHdK06hdv8+8PXXahPy8HCTPmQKdvWqeuobADx44PTEt3p9Ew4YKg8R9GyXvJlbjwcVMTO4pjYUPDvW8bPA8TX0/T1+HLalS3B12QIgONjjl8sst/dtc7puOSYKLuUU7C0mutl0cduGz0ab3XUgKkotJX34EKhcGRDBmhcT/j5UG+qNAwum4NamNWrJKyXfH3/ogitNBH8VFmwsK8CBA2b3joiIiNKoVFGH0lNjgEWGSjUDR9++sQVWZDpByLABAICH772NsiMche30+gJt506gWzfnGQUxYjZlFwEaNTLpA6Vi9+5BGzkCt7MItpQWhGRUG94nFDbkmJjwz2LagmqCuTUE+QKSF3CNaepYAhWUTVDK1/Gz0y1q4csqguHtM2P3nFFmXz23iAoLjfe6/FArL/DKK0D37rj3wxo8P9L5GO9pAtuJ48C1a6b03f7fOaytkw+B9QXbSjru1/XlBPuK6vs6dXAZXNr/C7SwMFP6mmadOoU7WQSdO6ql1DHXe36nEmb3jIiIiNKoVFOH0lNhgEWGSjUDR0iIKsRFgOzZHTMD7t3Dw5xZsKe44GZWAb77Tv19r14JB1gPHwKzZwPTpztmFlHyaRrw77/QLl/GlNY5nGZgXcsuqJ/ILK1VFdQMq8QCqgGtkw6xKvoIgjOrzaiTeq/Nk7sCe/cCVaviUvFcmNmjGGZNeA37vpuNieMqY9k7XWAPf2T2lXWJLSIcrw7P6PQ5m/rkgBYVpTt277J34r0mgfWj75Hlyz3S5/D/TsN+XQVmk30rxvbD6y3B8kqOe3ZvMUcfN9UtBAQFeaR/VmTr3NFpll6GqYIzAQMtNZORiIiIPCPV1KH0VBhgkaFS1cARFgbs3q32Q4rryBFg3DhVfMfshRNnxpZTgEVuY5/7EZZWFgxtIfiu6bPQLl0CNA2233/D1v71cci/p1qSGBmJqGP/Av/3f7HLltp2STh0er134qFUwBuCh+nV79nVWVvry6kN+wuNi//nBf0EISOHAMOHA2PHquD0hRcAf3+gbl1or1TF4eEdcXz+dCDm3omMBFatAubMcW/YEv1UzEv7fk7w8x375bt4X7r23V6xxzw3WoVGz46NXlZbrJh7+rp4sWrHj2Px+MaJ/l4yTlFL2CCCJj3V343qX4RPF3S3I0cwsnn8v5NMUwT73hlqdg+JiIgoDUlVdSglG6twMlSaHTgGDmSAZQa7XQU4H37oHDTGR9OANWsAEYRlUPtbVRmiL579GwuOPiNIPzXxQKrYGLVv0vgkZmBJoGBgK8GRAoKodIKPaqqZJvEdV6efIMJb/z36vryg/HD9caPaZcFfE/viYIlMsMXsr1amDBAR4fisDx4Ae/ao2YRPY/lyIGdORHgLKgxLYFbbrJ6JvsXKsU1ijz1YWF2LcG81G86nV17smucH2GyqXb+e8BvZbMDWrapPoaGICAnGJ4HN8e6QCjjd8GX82foVRKVzXLtT+RL/vbTrIvj0VcH+Iur4B+/NxKUta9R5yO2izpxCm0QeuPBro1KJfx+IiIiIXJRm61DSYRVOhkqzA8fkyQywUqPr14Evv8ShzvXQvb0Ko0IzCjB4MKL+2Ieb3dtgWPQTJvMGCOZ0LYFqg9Sfs01Se3JBBJ9XTbgI31xazfi6nFMtO0wsUBndVIUuMd+hvwonHY7pHhAggvBSz+Fq91YY21TwymDBi8PUMrlZ3Yvj8MLpwLp1QOfOahahvz8uDeuJt/uUxFtDyuLUyvn4e2x3rG9RGjWHeEMCBd3bq6dn7ike//n/XftZ0tf53j3dfljLKwluZBOUi7OfXIM+Ktxa86L6edi+Xeq1q1er2Wi1a8Pevh12lBBsKiN40KY5+vkUdupPJR/H7LhfSyZ9/Xq3Fewul0XN3CLPu38fg1ol/PsZ20Rgv3bV7F4SERFRKpdm61DSYRVOhkqzA8eNG4C3tyNIWLTI7B5RcsUsC43z50enjuHR6eOxfxV6/SJsZ88An3wS+zu/mkMtQSs6xlF8Z5gq6NJR/czmJejRPvEwpZKP4O9CjocHrKgoqDHQ9QBr3QsqWEvo2CyTBbuecxwfnFlQcpTjiYsJtZwT1NMF+7XR/32/Prldvqxh509jeOfsaNRbsKGcOn+Et+CD2vGfs+wIwe7ijr7avRLfvD9uW/SKek2L7kkf6/WWYP6kxkZ9eygZQu/eQHlf7wR/R3N7lzW7i0RERJTKpdk6lHQYYJGh0vTA8fPPQNu2wIwZam8ispawMGDJErW87eFD3Di4C2En/wX699fNkLqXSfBxjcQDlSMF1PI2V8KaqznU+0alE0xoJPE++e/xdiObes25PGpmWO3+Sb/Gt5ngenZBfn/H3x1Y9t6TXSObTd0n0YHUtAYqHEvonO266Gej+TcWtOqW8PEBbwh+KSX4sYzgv9wJH9e8hyDy/Flc/ux9XF42X7/0kswRGRm7B9nj7fmRAuTIAfzxh3PATEREROSCNF2HUiwGWGQoDhxkSZqmluwVLgx07Qqtfz/Mqy7o2U7wvxcEFz99BzN6PYfm/TLi2/6vAmvWYFMZ1wKsk9FLDq9nV8GMK6+RQMdMrzN5XTu++Gi1b9WZvIK5NQR/TRuY/DBhzx5ABPOqJ37O50fql0f+VViQY2Lir5na0HF8yGuv4o+3fXD//Elc2PcTAgeVwVeDqkO7cMHY3y8Z4v6RA3inUyHd77Oij5r5Z/NSS0M/DqgPjSEWERERPSHWodbAAIsMxYGDCEBwMDByJNCxI3DgQIKH/bp4Et7zq4U9Cych/MJZbApohxWTWuEbn9ewsGd53A0cD8yeDeTLh9Uvuh5eZZwiuBI9c2tmPddek22SILRSOWDjRuOuw7VryJrIkse9xRz7jJ3KJ3jGP+l+Pj9SEJRNgBIlgKgo4/pKnnP9Ov4tIHiQQR9g+jVWv+MxTQWP9u40u5cJs9mAI0dwZ8USbO1bD/c+muUc9t6+DSxcCGzezCdeEhEReQDrUGtggEWG4sBB5D5/fPMuhvbIheY9BF+MbYDwa5dw8f+mYUuT0sgX4Ah5PuxXDrh5E+jUCRdyCX57tRD2fjQGK/2b49XhGeE9TdC3Vw58W9kbWSarwGtp3Rxu2eg85ORhtO2WLt4wqtYAx35Wp/IJerUT9Gkr+LmcNwa39YZf59z4Y+ZQFQZoGuxrViPqhbLA668De/ca3lfyoNGjdeHVb8XU8tq4348Xhwn2zhlj3LLCzz8HatYERo1y/q7fvaueNtuokXoSZoxLl4CVK/FoyedY92YprKmXH+Hegou5BAX9VD+6qMzNAAAgAElEQVQzTBVs7lYdcyY0wDs+FfF2XfVwhI1loz/fyy8j8uQxnNm6ElpQkHqCaHw0Dfj1V2D7di6lJCIiekKsQ62BARYZigMHkTnun/gHy4bXxfbhLYArVxI+UNMQefOGKpAvXUJI2zcRWqc68Msv7utceDhw4gRw7hx+690wtvCvPETw6PBB952XUq5794A334wNsBr2SXjWXX5/QejNRL7TrjhxAr8VUw9amFFPELFgntobLSgIAHDarx/q9lPn+qimAJs24VT5AujYST0IIW5/ercVfFdRPTQhsVmQb3YXnM8tuJBLLdGN+/NqgwSjBz+H/1YtAs6dU4FanEAPAQEGXGQD2e04v3Ih/p46CLalS/D3d3NwZ8t6Bm1EZI7bt4Hff+cThkmHdag1MMAiQ3HgIKJE3b+PyMav48jzOWEPfMvs3pDZ9u0D8ubFL6USXzqa3189uROFCwNz5jiCk3v3gDNnklxOemfkQN0sxUo+6qmXTXsKdpTP/ET7y5XyFRwqJHhpaOL9DYp+mML/Xkj6PYuMFTTqrR68EBti9emjwl+THhpy/9IZ7Jg1BB80y5ngZ/y9xcsMsYjIs06dAvLmVeNkxYpqxjkRWIdaBQMsMhQHDiJyCYteiqFpQHg4/lm/MNGQp31nR7iz8znBhx0Ko+pgQbkRgo+b5oK2axdujx0Ke+9eQJs2wKBB0FavxomPp2J2q7wJvm+uCYK367q2t9yPZdQTQe1ezjOz4mtTG6pZWG/XTfoBBS26Cx6ld3zGcG9Bsx5qiWLu8YLtY9pB80SYpWnY9P5AZEtk/7qYlmOi4Hh+4ZM+ichj9vp3wdZScfZR7N/f7C5RCsE61BoYYJGhOHAQEVFyHfg8EDknJByYXMgl+KyaYEjLhI/JOUGwu7gqbK7kEKSblngI06ar6w87aN9ZoEUHTN+XT/r4Lh0FYRnUfl6uvH9pX8EPZR3nmPy68zFDWgoWDHwZl7Ybv4Tv0fXLqObj7VJfc00QLKmswjzUqWNoP4iIEtLaJzckUJB+qqDmAMGRAgKEhJjdLUoBWIdaAwMsMhQHDiIiSja7HQ83rceeHnWdApN366hleZHpBC27JR6udOrkmMk0q07ix5YZKbicM/Fjio8WLK4qeJQnB9C2rXq64JYtWDO4Dvp3zozFHUuhgJ/j+Lr9BJED+gHFigGigremPV0LsXJNUP2BCGxegsa9Ej72xWHqs+4b0zH5s6BCQlQQdvEipjZ0rY8SqPYTs3vFWfK4YYOx3wVKOex2tUxr924122XZMuDWLSAszOyekcXYr11FnvH6sehGNlFjMlke61BrYIBFhuLAQURET03T8H2/mijoJxjQWgU5MYFOUsHK1lKOGUwQ9d+dOiV8/Jim6ri/CguKjdH/7OuXBbaXKgEbNyY52+nPt33QsI+geQ/B8ekjHT+4fBlYvBiYPBmavx9uBwbg+kcz0WpwjtjAampDwbQGgnfqRi/Ji+739PqCdl1cC5TeqStA0aJqNtS8ecDSpWqT+rCwhPs+bZpu8/i5NQTZE1jqWMBPbUz/RRX11EjdpvPR7dFgLuVJa4L++Q0+7TKiWwfB34Ucv+vj+QXzXxWc+nAiN9Imj/nn8xm6cemF4dHfyenTze4apQCsQ62BARYZigMHEREZ5u+/gUyZYovmP59NOsjZVtJRZJ/IL6g+UP/zUc1UaFVkrJrddGtgd+DCBfVUq2HDAC8vNbOoXz8VPj2Jw4eBf/5x/fiYYMluBzp3doRBP/+sAqH0gi+rqH2+XAmxfinl+Oz7igomNBIMbCVY2Cg3whZ9iod/7sWf7/ti/aimuFGuaGwwGLd9XMPxfv1HPofb5/5VTxH9+mvg2jXV3xs3nF73ML3g+ZGCxf6vq89jFE2D7fYtHP9iFkJ2bVXX7Pp1YNs2XGhVD6HVK+PdAWXRbERuLJvUEr+OaQv/9tmxY/ibgM1mXD+s5NEj2JcuwYXRffHCcMf34dmxaknsL6UE3nGW5npPEwS2yY2Ik8fM7jmlcZ8ENteNeYNbch8scmAdag0MsMhQHDiIiMhQf/wBDB+uAhSbDRE/bsD/ur6MSYNKYUBrfYBTdXD0srb27YG5c4GKFXG+biX0GZAPpXwF341uBO3uXWD/fmD2bODoUefzXbli3oyS8HBHqBUVBRw8CDx8iIj7d7Hy3e66MCG+tr+IKubuZVLhlSuh17oXnEOs03kFB1q9kviss88+071m2Jv6JZSXP/8w+dchIgK4fx/B23/CgJ6OpyDmnCBY/aI639LKSX+2Q4VEfXc++wzgv0kSZb94AUdn+2NBrfSoPMT5WpYfrvaL21pKPWCge3vnY97oJbh55A+zPwqlYQP9yuq+c8teih6DmjUzu2uUArAOtQYGWGQoDhxEROQxUVHYX0TQpKdaane5dsW0HVSEhODakk8wp0tx5PfXhwc/Pe94euG86oIsk5MOeAa2knhnYWHECBWmJaVbN2gJhEnFxgg2+bfFja/mA6dPq+MTC8Q0DfYb1/FhLbWsMqE+P+MvCMmongaZ2NLQmHY3c/RnqlGDTz993MWLQOvWsHmpBxS4Enh+VFNwMp+gbSJLWwd2yYJHVy6Y/ekoDWrhm1/3XZteX/B5VcHcdkXM7hqlAKxDrYEBFhmKAwcREXnU+fNA165Ahw5qqZtVhIcjeNuPmNzIC9tLOMKnCO+kn7wYt5UYJdgR8/pt256sD5oGnDyJaW1zJ3meGgMFVYYICo8T9G0jOJ/bsddXYH3X+xt3mWRkOvV+SQUuunBu0yYGWceP43yDyvgvt/Oy0aRawBvqd1a7f9LH/vPjFwjZ/IMKyogM8MqoLPF+17JNEmhWv6+JdahFMMAiQ3HgICIi8iBNU8FTYCCwdi0wcSJOdmjgciDx/EjB7d1bnqoLUY/C8Mnsji7v1SWBgqPPqPBk4SuuvybbJDX7KiaoG9RKUDaJcy6prH9a4vnc6nWfVBcc7tMM2smTBv0iUo95nUvA6y11fWbVUZuz920jTrP6Ylru8YJJjQQ7uteG9s8/wPPP41p2wbgmghHN1Xcosd9Bo96CcJ/BZn9sSgOe9UuX4Pfs1iXr3ctusW0bsHJl8p9sayLWodbAAIsMxYGDiIjIfA///Rur+9VAl46Clt3UUptNEzviwNK3sWPuaGz7YBj2L56OR5fPG3ZOzWZDz3aJhxne0wSbS8d5cmF6wc7nJMn9vQqPk9gZQzHtXB5BaV/HMUXHqBb3dVWGqJlaMa/5pLr+52VGCjbO6ImwoCuGXYcUIzxc7aUWFQXtt9+Amzexenpn3ed/t44KBR+/3m26qu/N2vKCiHXf69/36lVgwQK1P52mwf4wDPWHZ0v09/d/taJ/B7/8gjML3sb3b3dH8AUGDoa6dAm4e1f99+XLiJw5HQ+XLlbfgTTAbrch/dSEv2N7Ns43u4spy7JlQLt26l51dXba//2fY4wtWhSYORPYt8+9/TQQ61BrYIBFhuLAQUREZF0Prp5Hf78yKDI24UJz0uuCyzlVkTSlofPPqwwRLKgmWNvuBSyb0RGndq5Vb37+PPD9944i/fp1RPbphUu1KyJi7erYPtxauwy+g4tj6NBiseeBCIIzC/IFJNyvFUNeQ/j5Mx6/Zm7xxRe4VigbQjImvlfYsDcFfo31f7d8XGMgLEw9mdNFwedPoFWfjE7vX2uAYHklQWj0zLlr2QUN+qifFR8tuPT5h+rpltu3A/7+wO7dbrwoLpo9G1rJEkCnTsC9e2b3xjVjx6rveY4cwJYt+LRlAaSfKijop/ao+7JdCXz1smB2bcHXLwvWvpIVG30a4epPq3Dn8B/GPjnUTW5eO5toSLrgox5md9Gz7HbnYCosTC3ZPXBAv3T6vfeAcuWAfPmAr74CIiPVcXEfZBIV5bQfYlQ6AdKlU08ETgVYh1oDAywyFAcOIiIiAoA7m9fh7bpq/6uYIjPukwQhgsMFHUvQag4QHK9QALhxw7hOhIUBAQGACCa68GTGjFMEtwvlBHx9U9/Mldu3gZMncefLT9G4l2vLMv0aqz2tjj0juP2W/9M/gVPTsGahb+xSr4xT9L/vx59YGfN7rzFQ0LiXYFoDgW3XDkMuxxO7dw/49lvMqiPIGyCo31dw893J5vQlHhf+3IrxI17A+0MqIvToIfWXFy8iaFgfhGYUfFtJhVOn8z7ZPngSKBjbRIBq1YD69YGXXlIzcVKY/b9/n+hnGDbpZbO76H5XrgBz50KbNQt/1imJdTVyIWzp5+pnZ87gwguFsLGs4EEG/T0XklEt923aU+DfWPBrSXXfxz60A9A9WTY4s+C1/uphIOOaCNCypXlP530CrEOtgQEWGYoDBxERETnRNODsWccTCbdsAQYNAlatUsHBrVuqudHtn9ZiYctCLhX0t7MI0Lq1CsBSoogIdd1iZmC8/37spvh+jVUQ5MrnPBa9Fxm6dzc0sNMiInC2QmHdDLiYjfcrDEu6XzNb5sC+ZhVg79VTzS5at04tkQsNTXw5lCtPz4zbz4cPcWnHBtz67gsga1YcKSCx+4NJoGBUs5RRKtmOHsFLQx39GtFccLFMAQxq9WRBVXwt6yTBkQKO31FQNsGYpoJHe3ea/bF1vvpypK7frwxLr/tz3bF5zO6i+1y5olq5coCovf1iPvebPb1wdHgnNOrt+LtKPoKH6VVL6Dsy+fU49+bRo7r7dPwb+mMPFBEge3Y1AzYFYx1qDSljVKY0gwMHERERpWQPd29H/9YJF/S+zQTh3qqQu59J7QcV2CQTfuz4Mm4FjACOHdPPRrDZ3NbXiAf3cW3nRjz4+08cXjwTf8wcinFd8+pClgrDBN+XV+FVzKyKLaUTDy26+jyDA8tnq8/irqe3XbkCDBgA5MwJvPhibHHsarAy7E0VeAVlUzNHio0RdO0g+KW0F+a1LoSFHUsgYsc2bHtvMJb2qICwuLNO1q5VYZfdjl8X+KPuiGxo3yM95tYQLB5YBbs+HInZtQUlR6lz5ZionnA5srlzPx5+uQgRP6wzZpndnj1Aw4ZAhgzQPvwQ9t/34e7vOwBNQ/C5Y7i2axOwciWu1auCf9rXRuSWn4Hnn8e2kq5ds+Y91PLb+D5HfC3zZMGe4vqQcWqcZb0HP56gNvV+wmDQHQLeqq3r+6wA/Z/zTEyXep5EGB4OLF4MvPUWcPly/MdcugQ8eoQLsydhQGvBgNaCC7nU7yipPQMlUPDNS4JuHRL+eZbJag9CiOCvwuphDSVHCT6v6nzs2CbR34+sWVP0slrWodbAAIsMxYGDiIiIUjybDbun93faq8u3mb6Y92+cdKH47FjB3mKi9m/68Ufgm2+A69dVWBESknAf1qwBXnsNGDIEePBABUlXrwK1agFZsuByyXyo6ONaENG6q+Cn5x39jm8z/ZwT1Mwa++lTnrvOcWka8NdfOP/ZLBQfk/RnKjxOfZb2nV27BuVGOPbaOpUv8f3OHm9DW6jZKuUTCAayThKs6VnVEVxqmtrE/vffgY4dEZw/O+w+Q3GwVTW06Sro2M0b62f2wMM9O6AFBwN//qk2xBZBWAbBe6+51q/KQwQ3sqnPk9SxpX0F818V3MyqrsHs2s7H5B6vHlwQdzZX8dEq7IgJP2sO0L+m6mDBz+W8gYcPzfneROvsW1jXr20r3kWp0fqnEl4JSiV72E2f7hhnqlXThcgPd2/H9PoqjGzZTf+7qDEw/ocuxNca9BGsqJj4MWOaqvfLMz7x4/q2iTMubtxo4oVLHOtQa2CARYbiwEFERESpRdS5MxjWITPyBqhZDTEzryCCk/kEGRJ56tnjbV51ffgV85TFbSXVkr4uA3Nj+Yj6+Glmb3xQW1B2hApdyoxUs43+Kah/7c/P65ezJdQq+gj2FXW8LsJb8NxoQatugo1l1Sbq91Z/o2bSJBaoeVD4qWPYNa0PVnepiLZdEg6w7F76sCWhlm6aesJkzDX4u5AKnZJ6XaYpau+omNftKZ748WVGCr6rqH+y5Zm8gje7J/66iY1UQBbzmgJ+rn+vOnRWr2nVLeljG/dyzKqJWWrWqptgQa/ysN+5rZs5ePOHFVhc1fHdsXkJ3mqQ8Hu/M+gFUzd7154tgqs5BFtLCT6qKbh94Tja+OTR9XHz1gWm9c/JhQvAoUP6GY5RUYja/BPsXo+NFUuW4MCUfpj/qqDaoMR/xxvLqrA6qe9CvgC1x2Bix3hPEyx6xbXvYWxfv/zSvGuaBNah1sAAiwzFgYOIiIhSlchI4Nw5tZSnTp3YQq17e9dDBgmM3uw4TlEalU4QWF8SfSJj3DY2ntcPf9P187fqFmcvo5Urn+gpgqZ78AAh+3ag4GPBjiZqdlBSn715D8GylxzX7qOarl+38sMFu+Mso0uqoN/1nP73ZPNy7TwxM5y+qOLa8bnHq0DpThb1upn1Ej9+dm0VXsb2bd06dW0T28ctOFgt86xdG3jhBSxM4rPnnCC4981iYx+04IqwMH3gU7AgAGDymJd0/ftgQU/P9isha9cCGTKovvbvr65XixZYHGd5Xp1+govRSwK/reT699V7muD3oipMTeiY/q3VRu6aqBmi8R3j9ZYgv/+T3Sfh3qL220uhWIdaAwMsMhQHDiIiIkrV/v03dqnXJ9VdL/Bm1nOegbWvqOMpi0m17BMFdzM7XquJmn2T2PF54yyTyzpJzU7Brl1mX8FkC9m/Bx8NfgnfjmwAbfSo2H3IEirCY1qmKY69nO5nUns7ufp7ezw41ETQrEfCx784zDED62Iu9bS2pM6xvJLjvTNOca1fy+Is64OopYRx9z4a10TwYMtGaFFRapbPpUvAnDnAvn3Jvv5aVBR+/L8hLvUv3TQ1sxBz5qh94Nz5lLoTJ/T3Vo0aAICV73TX9anPtJfc14fE3L0LzJsH/Pyz+nONGrr+ns+d8PLUUc3U92mCC09JlUA1QyvCW7C/iOCZ6ACq5gDBjX2/4I/axWMDz5j2eVXHTM4RzdX+eLPqCP4t4Po9IoGCWgOi39PPz5xr7ALWodbAAIsMxYGDiIiI0oQbN1QwoGmIOH0C+4a1wm/FBJ9VU3tjrehUXlfgxd0/K6YgTTftyYrEwPr64tPupTZoX9SyMHa+Oxib+r6G7wfVwaND+1Xfdu6ENnECLvdtj8gP3nfrhvKmeOMNQAQn8qtCv0tH/ayRfAEC/96Fcan5a0CjRrHXbXsJdWyDPoKPu5bEzV0/IXT/XkRu26pm89hsCD+4H/dWfqVm3i1dCrRqFfv664Wyo/IQNdtFAh2bvce0919zzJJz5fcad2aUJoLXe8d/3IvDBJtLC87mifM9yJYNmDsX+PFHRPqPxdXalaC9+65bL7st/BF2L5yY5LLFFRUfC23d9ZS6zZv15+ncGQBwdP3nuv5UHp3FuHPabOrzbNgQ/4MOYpZTnjoFFC+OyznVZuh3M4tuieDeYolfw29eUvtQHSiilprG/H18S2d9Wgjub96gQuopU3C3Qikc79kM9osXVF9u3wZy53YK0k/nVcsJNRHgvfdiv7tPEvRuLBv9fn36GHeNDcY61BoYYJGhOHAQERGRVWi3b+PwwFb4+XnBuTzRGzKLwF6rJgZEP+nw8WVx8c1k8Z4maNdF7aeETp2AFSuAjz8Gjhwx+yOa64cfAC8vVTinT6/CpoULAV9f4PDh+F9z5Yp6AmFyREXFPnFPCw5G5OkTsUFF+MmjWP/REEz0Katbrtikp/73ufpFNYOrYR9BvzaCd+uo2VO6UOHkSVz7dhEif1gPDB2KB6+9qpZmRUWpfmgasH272m/o0qXkfRYDaA8eYGy/Z+P93vZo7zzj8GBh9bS8meNeRdj1BPqdnCcFfvCB/lwBAQCAyLBQXQiTfqrg0bHDaoP9p9yv6+ag7viwlpoJZ/Md4fhBaCgiGzXA3XzZcCmn2r/u8WtTwE+wo0TSs5wqDFNBUoS3oFMnx3jxRRWBdusWzq1aiJHNVbB9s/bL6uEQSXb8JvDtt2oPruigK/a65cmj7o2dO4H27eNdovzns4Jjz6gliAtfUctzP6sW59q3aPFU19WdWIdaAwMsMhQHDiIiIiIAmob7//4F+9kz0C5cwO+vFMTa8oKrHZrAfv+e2ntryxZomzdD8/SeQqnJwYNqT6+U+O/Ks2fx18cTUMBfBZGTm2ZQM/cOHdKHKPfvx87mS1aAYyZNw2+BA5yCjjN59UscDxTRP/kx93jBG70EXXyewQKfaviyV0V06agedvCoUzugbFn12qJFgXv3gBMnoL33HvDTTyq4DApSpz97Fksrq9lN8c30qjkqGyRQUHSMoG0XwbXs0cd07aoOCAtD5DdLgddfh71iBWDMGKeZitqjR/ilY1V8/Fp6HJ8wAFpYGGrHWRpa2leFTJHpBAFvJD1zKVP0MtHioxM+pm3fzLj08yo1o6l4cWijfHH1xxUIu/PYWHDjBnD06NP9DrdsUTOvzp/Xf24RVB/o6NPLQwXa/fvqnJ9+6hRQQgSoXv3p+uJGrEOtgQEWGYoDBxERERFZScTffyH40w+dAoI0JSwMV4d0x+I38mLRwCo4VSKHLtj4t4DamNzVJWlxZ7FFpVMbj+ea4Ph5vzaCGXE2ri/gp5Zu3s4iuk3kj/Z90xFaPdb+XjAN5UY4z47aWkp/3JCWSfe3Zzu11O/78kkfO6ap/rM83g6O627iLzKO8eMRlU7wcQ3B5NcFV3OIPmDdsAGYOBF45x3g11/VjNDoYDElYh1qDQywyFAcOIiIiIiILODuXaBsWWwvoZbvuRpeSaCaxXQrqyNEqujj2uveGJFT34fvvnMKrs7mkST37zoXZ5+xU/kEnTsmfe77mQRfvfxknzNu21hWoOXMoWZfpgS3bjkHf6kY61BrSN3fUkpxOHAQEREREVlHxNvT0TKJwChu69FeMLSF4NeSjuAk4A3XXrtl8QT9yTUt9j0u5VSztl4ZnPT7fBNnBpgmKlxK7Pjfiqlj59ZI/LhnxwoGt1SfbYNPQ8zsUQyHCkWfq0SJp18OaKSoKH14lSeP2T16KqxDrYEBFhmKAwcRERERkbXYI8Jxafs6hN+5ifVj30SDPmpfqiJjBWVGOgc9fz6rn/nj0yLp0KlZr3Tx7yE2dCggKmRyNUR7aaggLIPj/DUGJnxsrQGC8OgnSW4uLfigtmBhNS9MayBYVUEwronq/3+5BTh+HNi/H3j0SN/HR49S5lNCP/xQPSghUyZg1Sqze/NUWIdaAwMsMhQHDiIiIiIiiwsOBk6dcvzZZsOjgX1RKXqpYLZJggu5HAFShLfEPrkzvlZxmBcenEpg9tLNm0C/frB7JbwP16TXBZWHOP78em91zpjzL64a/+uGtIwTtI0YEf/5z59XG88/fGj4ZfSI69eB27fN7sVTYx1qDQywyFAcOIiIiIiIKD5hP/+AH4Y1wqnPZ6m/OHsW2L5dLWcDcHr7GjQdngtVhggWdSuL0AuncXjNp4gKup70m9vtiGjdAr3a6UOo50YL7JERQFAQfh3bDmv710LUt8uAkBD1ujt3EHX8KPp0zoiCfgLfbrlh79YVd5rVd4RX5cql6A3MiXWoVTDAIkNx4CAiIiIiItNcuYLDBQWvDlJLBXf0f92114WGAgcP6jdZt9mAy5eBiAj39JUMwzrUGhhgkaE4cBARERERkamCgoDp04EZM4A7d8zuDXkA61BrYIBFhuLAQURERERERJ7EOtQaGGCRoThwEBERERERkSexDrUGBlhkKA4cRERERERE5EmsQ62BARYZigMHEREREREReRLrUGtggEWG4sBBREREREREnsQ61BoYYJGhOHAQERERERGRJ7EOtQYGWGQoDhxERERERETkSaxDrYEBFhmKAwcRERERERF5EutQa2CARYbiwEFERERERESexDrUGhhgkaE4cBAREREREZEnsQ61BgZYZCgOHERERERERORJrEOtgQEWGYoDBxEREREREXkS61BrYIBFhuLAQURERERERJ7EOtQaGGCRoThwEBERERERkSexDrUGBlhkKA4cRERERERE5EmsQ62BARYZigMHEREREREReRLrUGtggEWG4sBBREREREREnsQ61BoYYJGhOHAQERERERGRJ7EOtQYGWGQoDhxERERERETkSaxDrYEBFhmKAwcRERERERF5EutQa2CARYbiwEFERERERESexDrUGhhgkaE4cBAREREREZEnsQ61BgZYZCgOHERERERERORJrEOtgQEWGYoDBxEREREREXkS61BrYIBFhuLAQURERERERJ7EOtQaGGCRoThwEBERERERkSexDrUGBlhkKA4cRERERERE5EmsQ62BARYZigMHEREREREReRLrUGtggEWG4sBBREREREREnsQ61BoYYJGhOHAQERERERGRJ7EOtQYGWGQoDhxERERERETkSaxDrYEBFhmKAwcRERERERF5EutQa2CARYbiwEFERERERESexDrUGhhgkaE4cBAREREREZEnsQ61BgZYZCgOHERERERERORJrEOtgQEWGYoDBxEREREREXkS61BrYIBFhuLAQURERERERJ7EOtQaGGCRoThwEBERERERkSexDrUGBlhkKA4cRERERERE5EmsQ62BARYZigMHEREREREReRLrUGtggEWG4sBBREREREREnsQ61BoYYJGhOHAQERERERGRJ7EOtQYGWGQoDhxERERERETkSaxDrYEBFhmKAwcRERERERF5EutQa2CARYbiwEFERERERESexDrUGhhgkaE4cBAREREREZEnsQ61BgZYZKi0PHCsWQM0bgwEBADh4Wb3hoiIiIiIiIC0XYeSAwMsMlRaHTguXgTSpQNEVJs71+weEREREREREZB261DSY4BlAevWrUPTpk1RoEABZMqUCSVLloSPjw/Onj1r+LnS6sAxd64jvIppREREREREZL60WoeSHsvwNEzTNNYilLgAACAASURBVPTv3z/2Rn68Zc+eHZs2bTL0nKlp4LDZXD924kTnAMtud1/fiIiIiIiIyDWpqQ6l5GOAlYa98847sTdx586dcfjwYdy8eRMbNmxAiRIlYkOsU6dOGXbO1DJw2O1A7dqAnx8QHJz08SNH3kHGjA91Adb16+7vJxGlLHY7sGMHcPCg2T0hIiIiohippQ6lp8MAK426ceMGsmXLBhFBixYtoGma7uf//fdf7M87duxo2HlTy8Dx9deOICpHjmBUrAi8/jrQtSswciQwcybw2WfAokXAd98BI0b4Ytu2dPj667IIDOyAsmX/wp9/mv0piMiTNE2NESIaRID5883uEREREdGTCw0FuncHsmRR9VChQsC775rdq6eTWupQejoMsNKo2bNnx97A//zzT7zHjB49GiKCdOnSISgoyJDzpoaB48ED4NlnY8KrO9ixQ/DTT9mwYsVzWLjwFcye3RiBgR0QENAP/v794es7HDt2iK5VqPAbRID+/e2YPh2YMQP4+GNg7Vpg507g0CHg3Dng1i0gMtLsT0xERti8WcPIkSOwfn0+zJjRFtWrX3HLeSIigK++UgHZ/ftuOQURERFZ2IwZdgweHIANG/Jgxw7B8uWlUK/eGuzYAXzx/+zdd1QUVxsG8AtYEDH23o2998QvUWOJPfYSa+wFe40Fu8aYoknUxI7YkN6bQogtlthNNPYSG6CIgFJ3n++PgYV1C8uysCz7/M55Twwzc+fO7tw7e9+dubtb+gI/K9Ot5AWmMA6l7GMCK59q3749hBCoU6eOxnVOnTqlaOR79+41yH5NoeO4exdo0UJKYLVoEaqSnNIlbGzewNIyBX5+xRAYWAQeHmVx6FBN7N7dBFu2tMN3332O1asHYMmS0ZgzZxrs7BZi2rTVmDbtR9jZ7UCfPs/Rr5/0zcekScDs2cCyZcC338Zj27ZEODpKybCjR4EzZ4Dr14GHD4GXL6XBrTm5fBlYuTIOgYFyvHmj2yOfRDnhhx/8lPqBLVv+h4QEw+9nzpz0O0Q7d5bu/CIiorxJLgcCAgAvL/3nR335UvrSMzOPHkl3zugiMVH60tbcxMcDERGGKUsuz7kvolNSgHfvsraNTIZsfe7I+HnCzm6byvjG398WH3zwUvEZpEoVGb7++ikmTAjD2LF3sWwZ4Oys//5zmimMQyn7mMDKp2xtbSGEwKhRozSuk5CQACsrKwghMGPGDIPs11Q6jpQUwMEBmD79hywnrzZv7gghgFq1ruqV/AoLE2jQ4KzKpPBCABMnLkFYmEBwcCF4eZWCk1M1ODg0xK+/tsWPP3bCxo3dsWFDL3zzTV+sXz8Q69YNwZo1w7Fw4RxUrgxUrw58+CFQrx7QqBHQtCkwf/4G2NsvwfLl9lixYiVWrlyLVau+werVG7F69Q9YvXozVq/+BWvWbMOaNduxdu1OrFu3G4sWncHEicCUKcC0acCMGVKibdGiKPz44xFs2uSCn35yxc8/u+OXXzywdasntm3zxm+/+WD7dj/s3OmPXbsCsWdPEPbuPYp9+45hx45X2LED2LUL2L4dWLcOWLQI+P77/+DgcAaOjudw4MB5HDr0F5ycLmL79j/x/fc9EBYm4ONTAhs29MSCBZMxb95RBAQ8gr8/FOHqCvz4o/TYZ2BgBAICnsHH5z94eDzA4cO3sWvXaezZ8xdCQyMRFvYKYWFRCA19jaNHo/Hnn9Hw9X0GF5f7OH9emt/o4kXpTrpz54BTp5Jw/nwkTp8Oh49PBIKD3+DUqbe4eDEBly4lZYjkDJGCCxdScP26HP/+C0XcupUet2+nx5076XH3bnrcuyfF/fvp8eBBejx8mB4PHkjL792Tyrl1C/jrL+Cff5TXzbh92jZpkba/e/eU63H3LnD7thy3b8tw+7YMt26l4PLlZFy+nIzz55Nx65Yc9+5J5T1+DDx5Iv375k2pDlevSn9/9Ei1rnfvSvVNey1u3ZJe+5s3gWfPgKdPpXjyBPjvPynSynr0CLh/X46rVxOwf/817N9/HadOJeLcuXe4eDEh9RjluH8/CWfOvMLmzVexZ89dXLoEhIXJcO4ccOECcOIEEBgInDwp1fXaNeDYsVhs23YRrq5PceWKDPb2qndjDh/+AocPS6/xtWtSwvXCBeDcude4eDEBLi7P8OOPZ+Ds/ARnzybgxIkk+PvH49ixOJw6FY2jR18hICASp09Hw8fnHrZv//u9fkGOZcsiERLyAkePPkNw8BMEBT1GYOBDeHrex8GDdxEQ8AgBAU8QEPAUAQHPEBDwHAEBL+Drm4CdOwFPT+D336W5u44fB44fj8fJk69x+rR07p89G41z56Lx11/RuHAhGpcuRePyZfVx5Qpw5Yr0nnp5SeHhAXh4vMGRIw/g6xuBS5dkuHlTen//+w949EiGBw/i8ddfb7Blywls2nQaoaHxOH4cOHsWuHFDej8fPpTmFvzvP+DiRTkePpTjyRPg5EkZduy4Cw+PF4iIAMLDAReXcHzzzUUcORKOmzfl2LbtIbZt+wf//JOCV6+kc+rBA+l8unRJOndOnEjA3r23ERb2DpGRUJT14oW03+fP08+3K1ekc+L5cyAqSrojrlEjoHZtYPNmadu07dP+ff++tG50NBATIw0y376VBigJCdIg6No1GSZPBoYNk8rPeE3atesG5s93wPr11xEbKw0+nZ3D4ej4AOHhuZfFfPwY2LgR2LIlZwe/b99K58/WrfexbVs4tmy5iRs3UpCcrDlpK5cDV68mYeFCdyxevAX79z9XnDNp28TESO9pcjLw7Jkc16+nKAaJKSnAzZsyXL8uR0SE9P/h4YC//1s8eSIV8PSpdEf1vn2qA9f//pPuso6Lk8o/dOgpNm26g9u31Vc4KUk6LxITpQgMlNqgLkmO5GRpP+Hh0nmoTyI7KUk6//RNqsjlUj3Mwbt30heJ9esDAwb8grFjN2PFilC8fg2cPy/DjRsyjXejzJiR3mcPHiy1ndmzpXMFkF7H27elBFV8PODi8hp+fuFISpKWOTr+h7lz7TB37lT89ttjAFL7+O036UeEHjyQ3ofp049jw4aeWLZsBPbseapUh8hIOX7++V/s2XMTsbFynD2bhC+/dMDYsSuwevVlteePTAZcvJiMffte48EDOcLD5Viz5gQWL/ZCQEBMpq/ZmzfStSA+HggPl2PXrqfw8IjR+EWrTCadk/HxwKtX0nl96xYQGCjHpUvSddTZGdi6Nb2dpKRI/fJ//0ltKO2cjI+XXqPYWKnNp33BuWXLE5QrFwUhgE8+kd4Lb29pf0+eSNeD+/dV+//g4Bhs3XoToaFxuHdPjrt3k7Bly1OMHr0ZdnYLMGnSady4Afz9t5SsXLoUsLeXITj4Ddzdb2LVKg9MmrQOs2dvxPr1PvjhhxD8+utJODqex4EDtzF9+kXMnPkzhg3bg9Gjj6F8+TjFOfPBB9KX2WPGADNmHIe9vT/s7U9h48ZArF3rj0WLfoO9/UGsXOmDCRPWYtGiCZg8eTfWr/8DmzeH4qefQjB/vvR5eswYoHdvoGtXoGVLYOHCjVi6dBFWrFiLNWsmYe3afli4cB6WLJmj8nnGxaUyqlW7ofQZpFs3R8XylSuHQAhg0KBMTw2jMZVxKGUPE1j50JMnTxSNd/ny5VrXrVKlCoQQ6Nq1q0H2bWodx6NHG3H6dFX88Ye1Tomn33+3wNSpYbC1Bb744je9E1g1arw/SJVi5syZepV36FBNteUJATg7V9GrzOnTZ6str3btS3ofd8uWx9SWOXr0miyX5e5eTuMx//RTB73qt3Xrx2rLa9TotN7H3KTJCbVljhtnr3Gb0FALRYSEWGYIK3h7l9R43L/+2hbBwYXfi0IIDi6Eo0cLaozFi8eoLa9s2ccIDbXQ+VhDQixx9GhBBAcXRvv27mrL7NVrFwICbBAYWARBQdYICrJWqmtanY4dK4CQEKvUkI7f1jZKbZnfffe5ljpZaVw2cuR6teUVLx6BkBBLnY65Xr3zass4ePBDvc6XHTtaKJVTtGi03udemzZBaus2YsQ3epV39GhBnfuZo0cLIijIWuPrf+xYAQQEFMW4cfZqyytRIhzBwYXh72+rtF1AgA18fYu/97eiGl+/jOHqWlGxXmBgEQQEFFUb/v62SjF69Bq15c2ebQcvr9Lw8ioNT88yivDwKAsPj7Jwdy+XIcrD3b08jh0rgPLlHyqVY20NWFvHwcenhFL/dvBgLcX/BwVZ4/DhGjh0qCYOH66Bw4drwMmpOpycqmPDhl6wsIDacHSsBxeXynBxqQQXl0pwda2oCDe3CnBzq6CoW1pdf/qpPdLmeUsLCwvA0hLo1MkZBw7U1hJ1NIat7RtYWkIRaWU7OtZVHGdwcGG4uFSCr29xHDlSDQcO1MbBg7Vx8OCHikh7v0NCLPHRR/4q9Rw5cj2cnavAxaUS/PyKKc43f39beHmVUvQrPj4lcORINbi5VVD0/wULylXe57JlX8LRsT4cHevj0KGa8PMrBmfnKnB3L69U77R1HB3rY/9+6b+VK99We+7MmDErdb16OHCgbmpIr9PBg7VTj1s6Xk/PMggIKApPz7LYtKkrbG2hFEWLAjY2wN69jbFvXwNFODgoR8ZlGaNPn52wsFCtY9OmJ+DoWE8R+/enR3qd1Uft2rdgYwOV+Oqrb+Dg0AiOjg2wf399le0y7mP//rTXsQE2b+6ictzFikmxadPn2LevERwcGqvEvn2aY8KEdSheHIpIO+6SJZ8r9S9eXqVx7FgBHD1aEE5O1eHo2EARDg4N4ORUDV988Zva97lQoXc4cKA2Dh+ugYCAovDyKgVHx3qK66qbWwWl8z8tJk5crFJWsWKvYGennHTYt6++ItLO47AwgcOHayit5+ZWAdbWcShUCChYELCykq7vnp5lFOu4ulZUOqdDQqzg6Jj+njg61se+fQ2wfXtrpfabFiNHrlNs6+JSGQ4OjbF3b2M4ODTC3r2N4ODQEA4ODeHoWA/OzlXg41MCPj4l4OdXDJ9+6qH29evQwRWHD9eAj08JeHuXxMGDH8LJqTqcnavAza0CPD3LwNu7JPz8iiEgwAbHjhVAWJjA4MGbNJYnfSYqDB+fEvD1/QABAUURFJT5GGDJktEq5ZUv/1Cva+mmTZ9pvE45ODTIcnlBQdYayztypKrO5YSEWKJ//y1K23fq5KRY3qnTEQgBDBhg7NGbZqY2DiX9CGNXgAzvypUrisa7ZcsWreu2bNkSQgi0aNFC5/Lj4uI0Rnh4uEl2HHK5HMnJsXj37j5iYi7j9evjiIz0RUSEB168OIzHjzfj0aONiI29DkD69ufPP5cgNLQCQkJss3yxeX8AkxaLFo3T62K4f38dnQZtWQk7u7lqy6tT56Je5YWFCbRsGaK2zDFjVme5LE/PMhqPee/eRnrVb/78SWrLa9DgjN7HrCmBNX78Mr3K8/X9QONx79rVTK8yly0boba8MmX+0/u4O3VyUltmdhK/NjZv1Ja5YUNPvcqbOXOm2vIqVbqr0/aBgUVQoECi2jJ27GihV53eTyZWqXJL79dLUwJr5Mj1epWnLYG1c2dzvcqcPHmR2vL0vcPVyam6xjo6OVXXq0xNSbZly0boVd6iRWPVljd8+Aa9ytOWtEtL4GQ12rYNUFte79479T4fixaNVlvm5s0d9SqvT58dasubPHmRXuXt2tVMbXklS77Q+5g1fVm1evVAvcr77bfWGt9rbcl6bTF8+Aa15bVpE5iN476utsyZM2foVV5OtOtZs6arLa9Ll0NZLis01AJffvkt3k/8FiiQqFfd1CVLhFBOJugazs5VVO6qka7vT/Sqm7+/rdq6lS//QO9zsF+/bWrLHDjwZ73K2727icp7oe/rFxYm4OhYD9bWsSrlVahwX+/X0NIyRaW8QoXe6fzlWcbQdm3etu0jncv56quVsLCQKW3fvr07wsKkJH2RIjEQAujb18iDNi2YwDIPwtgVIMM7ffq0ovHu2rVL67r/+9//IIRA3bp1dS4/rezMwpw6DrlchpSUOCQmvsC7d3cRG3sF0dGn8OpVECIi3PD8+T48frwF//67AZcvL8O1a7E4fVqa48rLCzh0SHr0zdl5NoKCKuPo0eIIDdX9g8C+ffU1Xrzc3cvpdYGdOnW+2vLq1r2gV3lhYQLNm/+utsyvvlqZ5bK8vEprOebyetVv4MCf1ZZXv/45vY+5ceOTasvUP4FVXONxGz6Bpd8H3LAwgS5dDqktMzsJLGvrOLVlrlv3hV7lrVgxVG15WbnLsE2bQLVl6DsoHzr0e6VyGjc+qffr1bp1sNq6ZfymPCuh7UPypk2f6VXmpElfqy2vTZsgvcrTltjW9644TQms5cuH6VVeSIglqlf/R+3gxcWlcpbL27mzucZj1jeBtXt3E7UDrJxIYK1cOTjLZWlKfAoBTJy4WK/67dnTWG1579+Rk5XQlMhZtWqQXuWlTWGgLkwhgTV7tp1e5eVmAmv16gF6H/e8eZOVyrK0TNGrHE13RWf1c0NAgE3qF6aqyZwSJcL1qltAQFGN78XSpSP1KlNTH6vvtSosTKBu3b9UyuvU6UiWywkKskatWlfVvoaVKt3Ru341a15TKa9evfN6lRUaaqHxPdHlfHZ3b434+DfYvh3o2RMYNw4IDgbs7YFvvz2GwMCa8PAYi99+k+PXX6VHKPMqJrDMgzB2BcjwMiawdu/erXXdTz75BJlN9v4+JrByh1wuR0pKPJKSXiI+/iHi4v5GbOwVxMRcwJs3ZxEdfQqvX/+BqKgQvH59SjEnQGysNA9ARIT0bP/Nm074++/duHp1By5e/BXnzv2CP//cjFOnfsCJExtx/Pg3+OOPtfj991UIDV2OkBB7HD26FIGB/vD2lua3cXOT5iVwcgKcnB7Bx2cmvL1nwMvLDp6e0+DhMQXu7pPh7j4Rbm4T4Oo6Hi4uY+Hi8hWcnUfjyJFRcHYegY0b/8Hy5dI8E/b20pxVu3cDe/YchotLX7i49IGLS284O/eCs3MPODt3h4vLAAQEfARX167w8akNL69GcHVti0OHPse4cVCJiROBI0f+B3f3ZnBzawU3t7Zwc/sfXFw+xZEjH8PdvQE8POrDw6MePD3rwtOzDjw8asPdvT6mTTuNnj2lC3iPHlL06QNMmHAdLi6N4OLSBB4eDeHlVRteXjXh5VUV3t5VUqOyInx8KilixIhz6NAB6NABaN8+PZYs2QAfn/Lw8SkPX99yGaKsIvz8ysDPrwx8fcvAz680/PxKw82tJj76CPjoI6Bt2/Ro0wbYu/dTBAQUR2DgBwgIKJ767+IICCgBf/8S8PcvqTa+/XYSPv4YimjXTorPP3+eoT7lUutbAd7eFVOjUobjLQ8/v9Lw9y+JgIDiGDfOCW3bSsfdpYtUXuvWgJ3dbgQF2SIoyBaBgcUQGPiBUn2lupZEQEBJ+PuXgq9v6dQogzZtEtCyJdCqlRStW0vH/d134+DtXREuLq3h4dEcvr4V4eVVHd7eVeDnVwo+PuXg6VkF7u414epaHT4+0uv6229D0Lmz9H4PGgT07w907w6MHXsafn5l4O1dEQcP1oG7e034+paHj08luLs3hKtrd7i6fgwPj1oYMmQH2rUDPvtMOs5u3aTyNm+eCm/vyvD0/BBHjjSEh0cNeHtXhY9PFfj4VIOnZy14eNSBh0cDeHg0hIdHbbi4tMCsWScxbJg0p8qkScD8+aFwd28IN7cmcHNrBlfXFnB1bQUXl7ZwcfkIrq7t4OLSBm5uLeDm1hzu7s3g7t4U7u5NsHDhn5gyBRg1Chg+XJp3acgQYOXK3+DhUTc16sDdva4i3Nw0h4tLQ3TrJh1j9+7SXBvjxwMDBwI//TQBR47Uh4fHh/D1rYCAgBIIDCwGP7/S8PauAA+PanB1lR6Ncncvj8DAYggOtsH69SvQoYP0wxqtWwNNmgDNmwNTprgiKKgogoOLICjIFgEBxVIfN7XG0aOFUs8dG/j7F8fRo4Xh6/sBfH2Lw9+/BOrXl86L5s2lctu3l+YD3LevBby8ysLfvwQCA20RFFRUTdggKMgGwcFSBAXZYO7c1ahXT5qnpFQpaS6sxo2BH34YhpAQK4SGWmYIi0wfuz1ypAY6dvRFhQpAyZLSY1C2tkCVKm+xZMkolfXTHovRFHv3NkfZslAb/v4f6DUgOnKkKmrUuI7y5YFy5YAyZaQYNkz/BFa1atGKctKiTp1krFw5Fb6+JRAcXFixbmbJGGfnqvjf/7xRr540L1mNGtLxlikDzJq1VGlg5+lZHj4+pRAUVCT19SwIf/+SCAlRfl337WuIYsWkR6zSBn8ffABUq6acwDp6tKDOx9ys2XWUKiU9omZrmzHpnrWkXVrdv/uup9IjeUWKSP+1tdU/gTVx4gbF+VKunBRlywJduuifwGrY8LrKI3+2tsDChVP1PB+rqxx3Whw5ol8Ca+7c6ShcGEpRvjwwb946uLvXTn2vC6W+/lLfo628gwdroW3bAJQtm/4+Fyki16tuS5Z8hWLF0suxtZXOxY4dA7JUTkiIFcaNW45y5VIU52GJEkDp0kCNGlEIDbXItH95PwIDbVCmTHq/ULCgVL8iRYCPPvLPUt1CQy1x7FgBrFgxHxUqABUrSn1s2nHPmfO11jKk7QsiKKgIAgKKwc+vBHx8ymDHjnZo1+53NG0KVKokHXfdusCYMW6pnzmK4dixgggOtkZQUNHUa1UpHDpUBz4+ZeDjUx6enlXg4VEDjo7tMG5cCL74Qo727aUfVvn8c+l8adjwPjw8Pky9HveDp+dY+PuPhbf3V/DxGQ5390FwcekNN7d28PBohEOH2sLRsQ/27u2LDRuOY/586TPrkCHSdbVz59NwdOyFffs64ciRTnB27gU3t27Ys2conJwGw9X1S7i790JQUF14eAyAh8cQeHp+CU/PUdi3T449ewAXF2luyYsXpTlGL1wIwaVLu3Hu3GZcu+aAx48P4MaN3bh5cz/u3HFBZOTvkMnyz69DMYFlHpjAyocyPkK4detWrevyEUIiIqKcJ5fLIZNlPiO2dDdvBJKToyGXS7Nvy2TJSEmJS423qfEOKSnvIJNp/kkqmSwJMlkyZLJkyOUpqSHLEHLIszA7uEyWiOTkGA3xRmvosh+prompX+DEvVd+rCK0lSWVkZR6rMrrZTxeaR/vUl9naX/qyOUyJCVFISnpFZKTYxRTDiQnR6fuR/p/aR3lkMvVz/qtvP5rJCW9RnJydIbXK/2YU1LiFfXVNtBMSnqJpKRXWY6UFPU/gyaTJSnqpi6k+qoPbcedmBiRWtcopXKUy097bdLqGaXluF8hMTEiy5GcnPkk5XJ5ClJS4hXnTUrKW7Wvc9o5pP6Y3yAl5a2iDSclvUw9b2Sp5Umvg7QPWWrd3mh4T5JT10vRcL7JIJMlIykpCsnJMZDJNP98Xsb6SudwNJKSXkEmS8jw/++/Fy+1vhdSHROQlBSFxMTwDK93JBITIxWvXdo+0vojzWUlpvZxUptOTo5BSkq84vUjeh8TWOaBCax8KOMk7itWrNC6btWqVWHOk7gTERERERGRaeM41DwwgZVP2draQgiB0aNHa1wnISEBVlZWEEJg+vTpBtkvOw4iIiIiIiLKTRyHmgcmsPKp9u3bI7PJ2TPOlbVnzx6D7JcdBxEREREREeUmjkPNAxNY+dTGjRsVDfjatWtq15k7dy6EELC0tMSLFy8Msl92HERERERERJSbOA41D0xg5VMvXryAjY0NhBD44osvVJY/ePAARYsWhRACgwYNMth+Y2NjFR1HeHi41gnfGQwGg8FgMBgMBoPByG5k/DGx2NhYg41vKW9hAisfW7dunaIRDxs2DNeuXUNERAR8fX1Rs2ZNCCFga2uLf//912D7zNhxMBgMBoPBYDAYDAaDkZsRHh5usPEt5S1MYOVjcrkc48eP19iwbW1t4e/vb9B9MoHFYDAYDAaDwWAwGAxjBRNY+RcTWGbAw8MD3bt3R9myZVG4cGHUrFkTU6dOxd27dw2+L5lMhvDwcISHhyM2Ntbot5IyGLkZGRO4fISWwVAOtg8GQ3OwfTAYmoPtg6FLxMbGKsahMpnM4ONcyhuYwCIiMpC4OE4eSaQJ2weRZmwfRJqxfRBRGiawiIgMhB+wiDRj+yDSjO2DSDO2DyJKwwQWEZGB8AMWkWZsH0SasX0Qacb2QURpmMAiIjIQfsAi0oztg0gztg8izdg+iCgNE1hERAbCD1hEmrF9EGnG9kGkGdsHEaVhAouIyED4AYtIM7YPIs3YPog0Y/sgojRMYBERGQg/YBFpxvZBpBnbB5FmbB9ElIYJLCIiIiIiIiIiytOYwCIiIiIiIiIiojyNCSwiIiIiIiIiIsrTmMAiIiIiIiIiIqI8jQksIiIiIiIiIiLK05jAIiIiIiIiIiKiPI0JLCIiIiIiIiIiytOYwCIiIiIiIiIiojyNCSwiIiIiIiIiIsrTmMAiIiIiIiIiIqI8jQksIjIbQUFBGD58OGrUqAFra2uUKlUKTZo0waRJkxAYGKhxu5iYGKxevRpNmjSBra0tihcvjjZt2mDz5s1ISkrSad+XLl3C6NGjUbVqVRQuXBgVK1bEwIEDERYWptP2crkce/fuRYcOHVC6dGkUKVIE9erVw8KFC/HixQudyiDSJqvtY+XKlRBCZBrTp0/PdN9sH5SXVK9eXadzOy0cHBzUlsNrB+VH2W0fvHYQUXYwgUVE+V5cXBwGDhyo9YNSx44d1W57//591KpVS+N2rVq1QlRUlNb97969GwULFlS7vYWFBVatWqV1+4SEBPTo0UNjHcqVK4e//vpL35eHzJy+7cNQgxC2D8prsjpAv3DhgkoZvHZQfpXd9sFrBxFlBxNYRJSvJSYm4rPPPoMQAlZWVrCzs8OZM2cQERGB58+f4/fff8f06dMxcOBAtds2GlqfBgAAIABJREFUatQIQggUKVIEW7ZswZMnT/DgwQOsWrUKlpaWEELg888/17j/48ePw8rKCkIING/eHKGhoYiIiMCZM2fQuXNnxQelQ4cOaSxj0qRJSh/obt26hefPn+PgwYMoVaoUhBCoWLEiIiMjDfKakfnITvtIG4RUq1YNsbGxGiMxMVHj/tk+KC96+/at1nM6JiYGlStXhhACDRo0UNme1w7Kz7LbPnjtIKLsYAKLiPK11atXQwiBAgUKwMfHJ0vbbtmyRfHhxsXFRWX5xo0bFcv9/PzUltG6dWsIIVCpUiW8fv1aaVliYiKaNWsGIQSqVKmChIQEle2vXbsGCwsLCCFgZ2ensvzPP/9UDIYWLFiQpeMjyk77SBuEVK9eXe/9s32QKfrjjz8Uff8333yjspzXDjJnmbUPXjuIKDuYwCKifOvly5ewtraGEAKzZ8/O8vYNGzaEEAItWrRQuzwpKQlly5aFEAK9e/dWWX7+/HnFh7iff/5ZbRleXl6KdVxdXVWW29nZQQiBQoUKISIiQm0Z/fv3hxACJUqU0HleFaLsto/sDkLYPshUTZgwQfGY0uPHj1WW89pB5iyz9sFrBxFlBxNYRJRvbd68WfEh6tGjR1na9t69e4oPP+vXr9e43sSJEyGEgLW1Nd6+fau0bPny5Yoynj59qnb7pKQk2NjYQAiBMWPGqCyvWrVqpo+aHDx4ULGf33//XccjJHOXnfYBZH8QwvZBpig+Ph7FixeHEAKdOnVSWc5rB5mzzNoHwGsHEWUPE1hElG91795dMT9CRsnJyZDL5Vq3dXV1VXxwCQkJ0bjerl27FOu9P1Fp7969Fbewa/PJJ59ACIHGjRsr/T0yMlJRtr29vcbt79y5o1jvhx9+0LovojTZaR+A6iBEJpMhOTlZ5/2zfZApcnZ2VpxP6n59kNcOMmeZtQ+A1w4iyh4msIgo3ypTpgyEEJg4cSLevn0Le3t71KpVC5aWlihQoAAaNGiAZcuWqf0lqHXr1ik+uNy7d0/jPkJCQhTrHTx4UGlZ7dq1IYRA+/bttdZz1KhREEKgcOHCkMlkir+fOnVKUfaePXs0bp+UlKSYq2HixIla90WUJjvtA0gfhBQpUgQNGzZUTKhbqlQp9OjRA4cPH0ZKSorG/bN9kCnq06cPhBCwsbFBTEyMynJeO8icZdY+AF47iCh7mMAionzp3bt3ig8os2bNUsxJoi6qVq2Kf/75R2n7OXPmKJbHxsZq3M+1a9c0zsVQokQJCCEwaNAgrXWdN2+eoow3b94o/p5xDgdfX1+tZaT9Ys6AAQO0rkcEZL99ALr9FHqHDh00/oIT2weZmsjISBQsWBBCCIwYMULtOrx2kLnSpX0AvHYQUfYwgUVE+dLz588VH1AKFSqk+LBz7do1JCYm4vHjx1i5cqXi27UPP/wQcXFxiu0z/ryytlvbb9++rVjv/V/bSfsgN3LkSK11Xbp0qaKMZ8+eKf5+6NAhxd+PHTumtYxKlSpBCIFu3bppXY8IyH77AKQ5tGbOnIng4GDcv38fCQkJePnyJby9vdGuXTtF+f/73//UfpvO9kGm5pdfflGcc4GBgWrX4bWDzJUu7QPgtYOIsocJLCLKl54+far0bV7nzp3Vzuvzww8/KNbZtGmT4u8ZByHabmXPOEfC+xP2pn3IGjVqlNa6Llu2TFFGxglJM37I0jaXCgBUrlwZQmifkJQoTXbbR2ZSUlIwZMgQxbbq5kJh+yBT07ZtWwghUKFCBY3XBV47yFzp0j4yw2sHEWWGCSwiypeio6OVBuinTp1Su15iYiJKly6tuGU9TcbHQN6/8yQjXR4DGTx4sNa66nKbu5+fn9YyeJs7ZUV224cuXr58qfgVKHUf/tk+yJTcunVLcb7NmzdP43q8dpA50rV96ILXDiLShgksIsqXZDKZ4tEoS0tLJCQkaFy3V69eEEKgdOnSir9lnIj3/v37GrcNDQ1VrKdpIt7MBv6jR4+GENKjXJomGt27d6/G7ZOTkznRKGVJdtuHrrp27QohBMqWLauyjO2DTIm9vb3ifLt8+bLG9XjtIHOka/vQFa8dRKQJE1hElG81btxYp4H3yJEjIYRAwYIFFX9zcXFRfMAJDQ3VuO3u3bsV6/31119Ky9IG/lWrVtW6/08//RRCCDRq1Ejp7xEREYqyly9frnH7u3fvKtb7/vvvte6LKE122oeuRowYoXFbtg8yFXK5HDVq1IAQAo0bN9a6Lq8dZG6y0j50xWsHEWnCBBYR5Vtjx46FEAIWFhaIj4/XuF6PHj0ghECZMmUUf7t3757ig8v7E+xmlDbfSeHChfH27VulZcuXL1eUkXEC0YySk5MVt8qPHj1aZXnVqlUhhPYJRDPO56BtwESUUXbah646d+6s8Vt0tg8yFSdOnFCcQ999953WdXntIHOTlfahK147iEgTJrCIKN/y8PBQfPgICwtTu05CQgJKliwJIQS6du2qtKxBgwYQQqBly5Zqt01OTkb58uUhhECvXr1Ulp87d06x/19++UVtGd7e3op1XFxcVJZPmzZNcQu8pp+UHjBgAIQQKF68OJKSktSuQ/S+7LaPzERERMDa2lrjIIHtg0xFWrLJ0tIST548yXR9XjvInGS1fWSG1w4i0oYJLCLKtxITE1GzZk0IIfDpp58qzYGQZu3atYoPObt371ZalvEnod3c3FS2/f777xXLfX191dahVatWEEKgSpUqiI6OVlqWlJSEFi1aQAiBypUrq70L5urVq7CwsIAQAjNmzFBZfu7cOcUcDfPnz9f6ehBllJ32ERkZqXXerKSkJPTr10+xraOjo9r12D4or0tISFBMGq3rL5Hx2kHmIqvtg9cOIsouJrCIKF/z8vJSfEjp2bMn/vzzT0RFReHmzZuYP3++YlmrVq1UvmFLTExEo0aNIIRAkSJFsG3bNjx9+hQPHz7E6tWrYWVllemHtuPHjyvWa9GiBcLCwhAZGYlz586hS5cuig9p70/im9HEiRMV682cORO3b9/GixcvcOjQIcUvxFWsWFHjt4hEmujbPjw9PVGxYkUsXLgQwcHBePToEaKjo/Ho0SM4OTmhZcuWinO2Y8eOapNjANsH5X2urq6K82v//v06bcNrB5mLrLYPXjuIKLuYwCKifO/nn39GgQIFFB9U3o/mzZtrvO39/v37qFWrlsZtW7VqhaioKK3737VrFwoWLKh2ewsLC6xatUrr9gkJCYp5iNRFuXLlVCYBJtKVPu3D09NT4/oZo3fv3nj9+rXW/bN9UF7Wt29fCCFQtGhRxMXF6bwdrx1kDrLaPnjtIKLsYgKLiMzCxYsXMWbMGFSrVg2FChVCiRIl0L59e2zbtg2JiYlat42JicHq1avRpEkTFC1aFB988AFat26NTZs26TwvwqVLlzBq1ChUqVIFhQoVQoUKFTBgwACNcw+9Ty6XY+/evWjfvj1KlSqFIkWKoG7duliwYAFevHihUxlEmmS1fURERGDr1q0YMWIEGjdujHLlyqFgwYKwtbVF3bp1MXr0aBw9elTn/bN9UF708uVLxQBZ3UTQmeG1g/IzfdoHrx1ElF1MYJFByWQyhIeHIzw8HLGxsYiLi2MwGAwGg8FgMBgMBiPHIjY2VjEO1fT4KZk+JrDIoMLDw3W6NZjBYDAYDAaDwWAwGAxDR3h4uLGHxZRDmMAig2ICi8FgMBgMBoPBYDAYxgomsPIvJrDIoGJjY5U6DmPfSspgMBgMBoPBYDAYjPwdGW+kiI2NNfawmHIIE1hkUHFxcYqOIy5O91/rISIiIiIiItIHx6HmgQksMih2HERERERERJSbOA41D0xgkUGx4yAiIiIiIqLcxHGoeWACiwyKHQcRERERERHlJo5DzQMTWGRQ7DiIiIiIiIgoN3Ecah6YwCKDYsdBlImYGGDLFmDBAuD0aWPXhoiIiIjI5HEcah6YwCKDYsdBpEVcHM59aI0OYwUsVwgcaCogDwkxdq2IiIiIiEwax6HmgQksMih2HESavT64G2KVUIpfh9YydrWIiIiIiEwax6HmgQksMih2HEQayOVwaC5UElifjmM3TERERESUHRyHmgeOnMig2HEQaRAYiBWfqSawxCoByOXGrh0RERERkcniONQ8MIFFBsWOg0iDRo0ws6f6BNaeDUONXTsiIiIiIpPFcah5YAKLDIodB5F6ZysLNLJTn8BqN4FdMRERERGRvjgONQ8cNZFBseMgUnXLd5/axFValF4kIHd0NHY1iYiIiIhMEseh5oEJLDIodhxEqsZMLqs1gdVqskDcB9bGriYRERERkUniONQ8MIFFBsWOg+g9cjnKLVCfuHpuKwCRIYiIiIiIKMs4DjUPHDGRQbHjIFKW/DJCJXFltULAqXF64kouBG6VFnjz+I6xq0tEREREZHI4DjUPTGCRQbHjIFJ295SPSgLrwa7vgWPH4NpQoMdIgZJfS393cbI3dnWJiIiIiEwOx6HmgQksMih2HETKAvavUEpeDZlZQbHshyFVlJbNX9fBiDUlIiIiIjJNHIeaByawyKDYcRAp++m7gUpJqmUrPlEsO7lbObnVfIoA5HIj1paIiIiIyPRwHGoemMAig2LHQaRs2teNlZJU+38er1j29vJ5FFiu/HjhlaMHjFhbIiIiIiLTw3GoeWACiwyKHQeRsi52tkoJqrPBe9IXyuXoN6aQ0vL1qzobr7JERERERCaI41DzwAQWGRQ7DiJlVedZKCWoomIilJZ77VmotPyLeRWNVFMiIiIiItPEcah5YAKLDIodB1G6xIS3SsmpMl9bqKzz/MZ5pXUqLrIyQk2JiIiIiEwXx6HmgQksMih2HETpXv53Cw2mC5ReJCWnms4urLqSXI4q85Xv0noafjf3K0tEREREZKI4DjUPTGCRQbHjIMpg3DhACEAIJFsKRHVoq3a1/nallBJY3s5rcrmiRERERESmi+NQ88AEFhkUOw6iDFKTV4ro0UPtauvsOyglsFas7JDLFSUiIiIiMl0ch5oHJrDIoNhxEKWKi1NNYE2frnZVz72LlBJYYxfWyeXKEhERERGZLo5DzQMTWGRQ7DiIUj18qJrAunFD7apnA3YqJbB6zi2by5UlIiIiIjJdHIeaByawyKDYcRBJLnlvh1glMKOnwMrPBLz61tW47sOrJ5QSWC3n2uRiTYmIiIiITBvHoeaBCSwyKHYcRJIxM6soJaW+nF9d47rxb14prdt8miXk8fG5V1kiIiIiIhPGcah5YAKLDIodB5Hk03FCKSm1eHEbresvGVYajs0EHhZPfdxw585cqikRERERkWnjONQ8MIFFBsWOgwh4cCZQKXklVgk8u3tF+0bLlinPl9WnT+5UloiIiIjIxHEcah6YwCKDYsdBBGxa20spefXZFOvMN3r8WDmBVaZMzleUiIiIiCgf4DjUPDCBRQbFjoMIGD27mlIC6/CiXrptWLWqchIrKipnK0pERERElA9wHGoemMAig2LHQQR8MstWKYF19byvbht26qScwPrjj5ytKBERERFRPsBxqHlgAosMih0HmTu5XK4y/1XM6xe6bWxnp0hexRYSuDFnVM5WloiIiIgoH+A41DwwgUUGxY6DzN2RnbOUkldV51novG1csC+++59A5zECBZcLNJ0qALk8B2tLRERERGT6OA41D0xgkUGx4yBzN3h6OaUE1sCJxXTeNunNa3ywWPnurX/ct+dgbYmIiIiITB/HoeaBCSwyKHYcZO4qLbRSSkD5LBucpe2HDlZOYK1b3SWHakpERERElD9wHGoemMAig2LHQebs+X83lZJPheyz/gig67IBSmV8Oa9aDtWWiIiIiCh/4DjUPDCBRQbFjoPM2eIhJZWST61nWWe5jDsXjymV0Xhu1ssgIiIiIjInHIeaByawyKDYcZDZunMHV8sL/PyRwJj+UvKpg12RLBeTkpgA62XpCayCywWSEuNzoMJERERERPkDx6HmgQksM3Lnzh1YW1srGraDg4PB98GOg8zWmjWAEIi0ERg2WGBsP4GXIT56FdVijrXSXVg3/gowcGWJiIiIiPIPjkPNAxNYZqRr166KRs0EFpEBvX0LCKEaKSl6FTd6bg2lBJaLwwIDV5iIiIiIKP/gONQ8MIFlJg4cOAAhBGrWrMkEFpGh/fwzXhYR8Kon8LRYavLqyBG9i9u4prtSAmv64iYGrCwRERERUf7Ccah5YALLDERFRaFcuXKwsrKCl5cXE1hEBva0mIDtEinZVHqRwL+lBfDund7l/RXsoJTAasKJ3ImIiIiINOI41DwwgWUGJkyYACEE5syZgwcPHjCBRWQg8nfvELZlvlKySawS6DM8e11rSlIiii5NL89yhcC7qAgD1ZqIiIiIKH/hONQ8MIGVz508eRIWFhaoWLEi3rx5wwQWkaHI5Vg0tbZK8kqsEqg5O/tda4u5NkplPr5+ygCVJiIiIiLKfzgONQ9MYOVjSUlJaNiwIYQQcHJyAgAmsIiyKyUFiIrCrblj1CavxCqB0LEds72bjnNLKJV5bFz2yyQiIiIiyo84DjUPTGDlY+vWrYMQAl26dFH8jQksoqwLdvkGrefYoPUkgQ5jBXqPEJjWW33ySqwSwL//ZnuffeaWVyn32ZWTBjgaIiIiIqL8heNQ88AEVj519+5dWFtbo1ChQvg3w2DaEAmsuLg4jREeHs6Og/KHlBQ8CjyCGCdHVJyvOVn1ftSbYZhuNWBKZ+xqKTCzZ3rZC0eWM0jZRERERET5CRNY5oEJrHzq888/hxACS5cuVfq7IRJYadtnFuw4yFTJZTIMtCudabJqbD+BzmOU/3ZhyKeGqYSdHe6WFJjVQ9qP4u6u8+cNUz4RERERUT7BBJZ5YAIrHzp06BCEEKhRowbevXuntIwJLKLMnfHfodPdVuvbC0BIIW/WFLJFC4GoKMNUYtYsJFsKFF+svM+ddh8ZpnwiIiIionyCCSzzwARWPvP69WuUL18eQgj4+PioLOcjhESZ++3HL3VKYJValJ7AQkKCYSuxaBEgBD77Snmf3Uex2yYiIiIiyogJLPPAkVA+M2fOHAgh0LdvX7XLOYk7UeYmL6yvUwJrTvfU5NXp04avhIsLIAS++VR5n+0msNsmIiIiIsqI41DzwJFQPtOvXz+dH/HLGA8ePDDI/tlxkKm7c9JbY8Kq2hyBRV0Fmk4VGDK+GKJuXs65iiQkAP37401h1XqccPsRuHgRuHcv5/ZPRERERGQiOA41D0xg5TNMYBHpycEBKFcOA4eqJoz2tBC4WFFAHhEhrZuSAshkuVIteXIybJYq16f6HAGZhQAKFJDu1CIiIiIiMmMch5oHJrDymfv37+Py5csaw9/fX9GwV69erfh7YmKiQfbPjuM9cjlw7hxw9qz0b8qbYmOBokXh2Ez9nVfJlgL4+GOjVa/sIguVOk3pI7C/qYCsqI3R6kVERERElBdwHGoemMAyM5wDK3f9s8oOXw4S+GK4QGi3OpDHxxu7SqTOn3/iTBX1yatVHVPnufLwMFr1es8qo/Gxxvnd2I0T5ZrTp4GGDYG6dfGqewd49amDJxuWSHdl8ksKIiIio+E41Dxw5GNmmMDKPbKUZNSboZxsmDmnnrGrRer4+6tNDu1pkZq86tYNSE42WvV+XN1d62TyRJQ7TlYT6DNcoKFdevsruFzg77IC69sLtB8nsGFFZyQnG+auZiIiItINx6HmgSMfM8MEVu55/M+fapMNj2+dN3bVKCO5HPebVFF5n6aPKiUlrV6+NPqdFXGRTzF+XCk0niYwvi8TWES54o8/gEGDgCVLgIcPsXBCVbV9eospAna9BIpmmKuu8TSBpUNKw29eH8iNmPwmIiIyFxyHmgeOfMwME1i554Tbj2oHOwd3zjB21SiDkO2LUNhe+T1qN0EgMe6Nsaumqk0b/FFdTQLrTR6sK5Epi44GbGwAIRBQW8B6meY7INtMEjhRTeDbT9QvbzpVINxhK/Dvv8Y+KiIionyL41DzwAQWGRQ7jnQHd85QGcjUny7w7tefjV01SkgA/v4biI9Hu5lFlN4jm6UCb3duM3YNNXpaTHWAnNL3C2NXiyh/OXYMEAKPimtOXGWMRCsBz/ra1ymwXOBxCQsmnImIiHIAx6HmgQksMih2HOm2/jRSZQBzspoAvv/e2FUzbzExSGzWGGcrC/jVUR1k9h8mEBnmb+xaaiZU6/yghABevzZ2zYhMn1wOxyU98eUgAedG0hx4owdoT0y9KiKtd7Fi5omuLwcJbG0jMK1/QZwb+zlkZ/409hETERHlCxyHmgcmsMig2HGkW/9tb6WBy7r2qROCr1pl7KqZtZRDB9FjpOYBptUKged/nzV2NTXr2lWlzis/E/ixf3nEvXxm7NoRmbSQ3csU7cpipUBITYGzldX3FbZLBII+LgP06gWMGwd8/z12z+2Igst1u2tLrBIovUjgod8h4Pp1PDu4HVPHlsH0CRXx7FyosV8KIiIik8JxqHlgAosMih1Huq/XdFAaqCh+0W7hQmNXzax5jm6jdUC5ul8JQCYzdjU1++cflTqn3SFiZ1fD2LUjMlkR968rtavPvpKSVwlW6X/btLwr4v8Iwd8n3fEm8onacp49uI5ty7X/cmjG+Hy0dG347Kv0v/WaUTKXj56IiMi0cRxqHpjAIoNix5Fuqn0LpUGKW4PUBJadnbGrZtbGLqqndhDZbKpA/Mhh0txYedz7dS+/IMP8XdGRxq4ekUmRJyZi8/RWavuFEl8LRNoI4NSpLJf7r/tOjBqg3Mc0slOfxBo8RPVv8Teu5cDREhER5U8ch5oHJrDIoNhxpBu+uI7SYORordQE1pgxxq6aWTsyu4vaAaT3viXGrprO5ve3Uap7uQXp/7ZZKrBnSQ+kRL0ydjWJ8j65HD8Nr6nx7qj+wy0hT07O3i5OnUJkszo4X0n3RwvFKoHhQ60MdJBERET5H8eh5oEJLDIodhzp+i6qqjQYaTJNYNQAgU2Tmxq7aubr+HFACAwdrDxQbDBdQJ6XHxt8zwvnveg7XKChXfrdV+/H9wv+Z+xqEuVpKc+eouPkQhoTSAOGCSQ8uGO4Hcpk2DGtbZaSWNumtkTM5XOGqwMREVE+xXGoeWACiwyKHUe6l8O+wL2SAg7NlQckPWaXNnbVzNOlS9IdcELganmBwvbp78mTa1l/PMjobtwAQkNxwvk7tQPfSgsskJKSvTtHiPKllBSgRw/82E5L4uizojn3C4HJyRg0o5zOSayP7Qqn3wVmQol2IiKi3MRxqHlgAosMih1HBj16AELgma3yYKT+vELGrpl5efoUr379EZ+OE/ixnUB8ASmJ9fgDAa96AlF7thm7htkjk2HqpIpqB75OW6cau3ZEec+BA7hXUqCQvWqb6TbWClGPbwFyeY5WIebpA6z4Wve7sTbObInEFk2BYsWAevWA3buB+PgcrSMREZEp4TjUPDCBRQbFjiODTz8FhIBcCFgvSx+IWC8zrcfVTJZcjite29FxvKXSQLDIMoFuowROVxVAxYrGrqVByGUyfL/0M7UDX7edc41dPaK8IzISEAK/tVZtKws+z/2PRHFnT2LfV811TmQNGyzQe4TAJ+MFjg1tnev1JSIiyqs4DjUPTGCRQbHjyKBZM8Uja/WnKw9Cnt29bOza5T8Z75i4eBE3ylloHARaLxN4VsYaePfOePXNAVdOuqk93qsuW4xdNaI84dbADkiwkvrlh8UF1nSQ2siSLgJ4+tRo9Wo2NWsTvItVAmUWCiT8fdVodSYiIspLOA41D0xgkUGx48jgww8VCayeI5UHHqf9txu7dvlHSgowZIj0Wn/2GXD6NCAEbpTRPPD7a/qAHH9EyFjqzFQ9XrteAilt28Bzx1yEOn+bb4+dSJvwe9fQdKrAH9WFom9Ou0tWHhtr1Lo9mD8BpRdlPYlVboHAqa+HG7XuREREeQHHoeaBCSwyKHYcGZQvrxggTemjPOhw3T7L2LXLFZF/n8fdk94Gf2QyMfIFQuYPwKWZg4Fff0WKhcCj4unzW6VFt1GqAz772U0MWpe8ZtXU+opjLWwvMKOnwL+lBYYPSn8NNqztbuxqEuWqpHdxKn3B3O5S8go3bxq7esDr17hRtyS+/UTg9xoCSZa6J7GKLxZ4fYt3YhERkXnjONQ8MIFFBsWOI93YQQUwrp9098v7A45dG4YYu3o5LmTHYhRdKh3v+OE2kL98CcjliPgzBD/ObotDa4dClpwkPbbTsqWUdJo6NfO7g+RyjJyW/gteazoIVJ6X/tp+1V8alPrVUT/YS3n3NndeACOJCQ1El6+kxyen9xKQWQjEFFJ9Hd4+f4zT+9fD62c7JMfFGLvaRAYXduRbtJ1lgw/nFVDbF/T9UgDXrxu7mukiI4FDh4BbtwAA//1zBsPmVNEpieWz92sjV56IiMi4OA41D0xgkUGx45AkJ8bDcoXmwcZ3q7sZu4oG9ezeVQydVxUdZtgi5NvJwOLF6Dpa+Zin9RZIsRBoOzH9b/O7CciqVMaeFtIcNA9KCMgHDUT0/1oCK1dKjwemefkSSEnBzT+keZ4aTBcY01/969tgunQH1kcTVZeZhWfPIN+0SXEn2tXy6u/aUAzkZ5Yxdo2JDEqWmIBKCzTPgydWCYTfz0PJK22Sk/HkuC8iv56p8Vi+/UQAnp54e+cGIs6GAvyhECIiMjMch5oHMxnNUW5hxyF5dOOM1oHT0mUfG7uK+jl3Dli7FrhwQbpT6s0bICkJQ2eUVzo+p8bqj/unj3R/LKbTVwJOvavDd3BTJKZOupxiofv2+5sKPP5AKCUSf1n3hbFfwdwVFQUIgZqzM3+9Hv973ti1JTKYF/9e0Hq+O2+dZuwqZtnf3y1Ueyz1Zgi0mKLc103oK5Dy9Imxq0xERJRrOA41D0xgkUGx45Ac99umdfA07evGxq5ilsmuXsHBpgKTvhCDuFr/AAAgAElEQVQ41ERgZ0uBzmOEXhMP6xPhRYXikURd4mxlARw8CLcZndFttAW+HlsF8eHG+5UxY3nSvJZOr1fQihHGriqRwfx72lvjuT51YQNjV08vshPH0TTDrxV+Ml7gma3A56PVH6f9gOLGrjIREVGu4TjUPDCBRQbFjkOyf+cMpYFE/dnKc7AMX1DT2FXUmTwxEWd2LFd65CyzaD9OoM/w3ElsqYtPpxaGPDnZ2C9dnuDWvSosVur2usn/+MPY1SUyiCv+e1Fsieo5/vHsooiPiTJ29fQjl+N1p3bY0lbAq540119Abe1t+nYpAfTuDfz3H399lIiI8jWOQ80DE1hkUKbYcUQ9vYdTfr/i3ZtXBitz7dquSoOIL6crP2LXY34Fg+3L0OQPH+LOkimI/u0nxD64hQoLVAdFBZcL2Gi5G6rJNM3LsnIXlb4hT0oy9suYZzzb/j1Kfq38+lyuoP51m9lTADEx0qOHRCZKfu0aLlYUeG4r8M2n6ef3rK+bGbtq2ZeUBAQFATduADVrIrqw9r5w4FCBhZ8LVJ8jMG5cScTe5K8VEhFR/mSK41DKOiawyKBMreP457QXSi6WJvptZCfwevgA4MyZbJc7dV49pUHEhmUdle8CmF/CALXPAZGR6D5KqmPpRQLj+6oOiM5XEpjVQ/p37ZnqB02rO6r/e/U5us3HpGvsbClNBJ/xb99NbWLsVzFviY3FjR6tsb69wB8DWuCl/Tz89JFA2YXqX9NqcwSqzhUI2GR6cwSReZNfuYKjvesr3Xm1vr304xAv6lUxdvUM79IlQAiNd1juaCX9AunXXaUvHdL+/v3QKkiKZpKaiIjyF1Mbh5J+mMAigzKpjuP+fQwYpvyBf317abJw7NiRraJHTK+oVK7LbzNQda5A06kCHcYK2E2saKCDMJw/p/bROXE0dLBAy8mqf9/cowRkFtJr+LxGGZybOxQTx5VBn+ECazqkr1dpnrS91QqBLmP0S15ZrRBI9vIAoqOBGzdwY2Q33JjUH3hqfvNcZUouB+LipP/K5YAQ+KGd9te3+hzeyUamZd2XldSeyxcqCqBLF2NXL2e8fYtNH6tvwxO/kH74AkLg5/d+QGPLql7GrjkREZFBmdQ4lPTGBBYZlEl1HJ9+qvKB/9fWqQkskb2m0Wd6KaVyz/ltTy9XCKBs2TwxH8ndy6F4+eQO7h511jlxVGmewNNiAqe+/AT15hZE1fmWOPLjeOXjSUlRu78/Dn+D7St6I+LiCWDyZLyrUgEQmvfV70uBqb2lO7puN6uKb0dWR/3pAuMnV0DMi0e59CrlQ6tX41UR6W4rbe/1xC8Etgz/kI8dUd6XnIzK89Sfx5cqCMDNzdg1zDlnz+JkhxrwaVIIKR9/hJSmTZBglX69GTZY/euCvH6NJiIiygKTGoeS3pjAIoMymY4j9S6UQvbpH+YL2Uu/dDe2n3T3iW9dAdmokcCdO5mX9+4dEBqqmCi3w1jlgcLNa78DtWopJ7EePsz543yfXI77u3/AkdbWikcFsxJjhhaEfN8+4PhxwyXgLl+G7XuTLTebKvDq5iVp+Zs3HGgZWlwcUK4cYgsJbGsjvd7xBTS/76PsKuaJhCuRJm8f39V4/t4uJYDERGNXMXdduAAcPAgsX640D1jG+MWulbFrSUREZDAmMw6lbGECiwzKZDqOhAS8Lahb0ub7YVW1l5WcjDetGmN3C4HQ+oWBX39F8ynKZTx5eB3o2hUpFgL+dQROVxXAlSu5c6ypboW56fWontI39i9e5EjdXBsq78d9TJsc2Q9lcPEi8NVXwOrVQGIiwrt9ovF9t1oh8LKIANatM3atidS6umONxrs4ceCAsatnVLd2bkCB5erb9vBBAk+ddxu7ikRERNlmMuNQyhYmsMigTKbjaNkSt0vplrQpuFzgldNeaa4lQPomf/58oGVL4LvvIPPxRtOp6es7NhOoNUu5jJjXL4D+/TF8UIZvv6e2yLXDjZk9LdvJq7UT6+ZY/WQWAtN6C1SZKzClj0DiF5yfJbf9+/UEje9924kCbwqn3jm4axfC715F9Asj3EFIpI67O35UM6dbvy8F5MePG7t2xpeQAK8BDTS2b8sVAk4bRhq7lkRERNliMuNQyhYmsMigTKXjSGnTOkvJmxPVBFC1Kr5b2hHV5gj0HyYQW0ga0J/o2VBp3WJLBIosU06AyVKS8bJvV9U7mnT0+vkDDJhTEVUWWGHNmtTJiF+/lh5dPHdOmnQ+IiJ9g8REnJzRF2u/rISroz6HY7OsJ6xulBGY212aJ2lyPysk3bhu4Hchg6FDlR+v3Lkz5/ZFal1aP1PlUU6xSkoqPrcVSLYUSo/GWi8TCPyRv1RIRvb2LVCxIoa+N8/T2h42QGyssWuXp0Q7/Kaxv288TUAuhEF+hZeIiMgYTGUcStnDBBYZlMl0HO3aZSmZM3iI8q/oiVUCk/tIyZbtrdRvY50hiQUA13u2yjyBlZAAnD8PvHql9OeNSzsqbRc4pQsGDBPoPkpgUVeBsgsFRg4UuLdmLrBtG7ZMaaHXXVZe9QSulROQd+4k7TguDvD0BJ48ydn349YtoHRpKXlVv76UmKNc9fT2RVitUD4ftrQVOF5dmsxd3fnSfpwATpwwdtXJXMlkeFpM4FURgXXtBQ42EZjXTcBipcD980eNXbs86anTTpT8Or0N150hsKeFUJr0HW3b5tjj4kRERDnFZMahlC1MYJFBmUzH0b693o/SZYyHxQVGDNS+Tp/hUjMbOrW09gRWUhLOd2mAr/oLTOgr8I/9FODBA/y39Rud62O1QqDHSP2OJejDDAOY589z/z0JDwdOnpTuqCCjcPhlPJrMtcaX86rh1bN7gJ8fwmpoP2/kQuCG/VS8DPXlRO+Uq76ZWA8WKwUKLBc40FTgq/7SOVl5noBcJjN29fKs6KhnCHPagJk9Be6VFEp3v74qIrC2g5Sc9ls+DG9uXTN2dYmIiHRiMuNQyhYmsMigTKbj6NzZIAksnRNDADpPt1VZVnC5wPzFLQEAj/0OqSzvNcJw9Si3QPvyxx+kDmIaNTLmO0N5zK1FmufGej9C7XoCU6YAH38MzJsHJCcbu/qUD8mfP1e683VMf4HA2gI1Z0vJK78xHxu7iqbh1SvlR7dTI/hD5Xb9y+JOxq4pERFRpkxmHErZwgQWGZTJdBzdumF9+5xPXnUekzooAPDr9I80rje7lyUa2WV/fz1GSvvUtPxkNYHC9qp//2R8hgFMUJCR3xzKS2KjXsBipe7nYKKVdFfHmg4Cbn1qQZ6YCMyYAZQtCwweLD2WSqSvq1dVJmzv9JXAL21T+68JE4xzB6mpevcOmD5d0f87NFdt0xYrBf5ZP5tzihERUZ5mMuNQyhYmsMigTKbj6NULr60FGkzP2QTW+L4C6NEDABB37UKOJ8weFde8bOIXAqhQAXcuHIXfroWIvf8v9m4ajR9WdcObYB9g6VLA19fIbwzlRUOG6H4Oft1VoPoc9csGDxHwryNwdlQn4OZNYx8WmRq5HEs7qz+3trYRwPr1xq6h6fr9d8iF9ra9fEgZ6Vd4iYiI8iCTGYdStjCBRQZlMh1Hv36AkO4WuVMq5xJKfb8UQGioYreGfCTw/Rg1QPoGfWZP1WVlFkrzdeHBA+O95mSyEtu0xIGm0iT/90sItJmk+Ty0Warb+bpgeJn0ObPkcs6fRdqlpOCu6w6151KRZQJPihlp7r785OpVPJg4SGu7DRzfAYiKMnZNiYiIVJjMOJSyhQksMiiT6TiGDlWa8yOnkkpilUBEmJ9itwc2j9N5u0FDpUj7/6GDxf/Zu++wKI43DuADiL3X6E9jx1iiiUajMbao0VijscXeooI19t6ixl7pHL333hTF3hVLsGNFxUZH6u3398fAwXGFAw8Q7/08zzzK3u7s3N7u3M57M7PI0mIIb8akniLFNjMMnaSFNF8v4O1bRJ8JRIPs+a7qrGS40LcVMhxsKUBAii4sjA8BrFYNcHGBkJyslmtj6aS62K3fCQPnVMLoWVXx9Kh7ab9T8jkSBKxZ8I3C88ita5Xif1KqBtk9o43S69bye+kfZgghhJDPQZlph5JPQgEsolZlpuKIiJAKYImGNPzkxnirhfKX3z/tLdmtOD0Nc8dVVim/11XzTa6b3WsMjCFFlyHJ01nh23sX4oXA+b8iwcIQoKdxkWKgsGfVQIbV/Yt2DQ2Ykn2ub9qEV2HeSLp6Xv7O4+KAtLSSfcOk1NwPdlB4zmToz6GHBahbWhoe7VqFvT3kH/NaqxjEWgx4/760S0oIIYRIlJl2KPkkFMAialVmKg5BANatAxo1AkaPRsaraAxfVA9sM0P/+dVwfmRn+PaojZvNKqnc+L70P4YfZ8suf3zSW3rfDx6g+yzpdTb0y/2/1iYGy0PTgV69coNX5coBjx/z+bSqVQPmzaMeVaRU/dNb+hy2HqeHqAB7ICUFmXP/gk2nogWx7teRXfa/pfzfbnN18GL6aKBCBX5diESl8+YzMyGOesSvQbEY8PQE/vwTz8b+iuv/LkLmuzeIuhGON09u8/VtbYF27YDRo4HExNIpcxk2bXpNuedKVIPyQGpqaRfvi/bIeJvkeA+azLC5D0NAa94bOGrCILy8d4WvGBMDREWVbmEJIYRotDLTDiWfhAJYRK3KesWRkZoivUAshm+bghvdWpsYMn/oDIHxuahyln+1nCErPk46z5cv0Wme9PavcnpbmZjkrnfyJFCrFl++dm3xv3lCCiFNh2H+EIaWixgWDWbImjNbegVBwKl+LTCmEBPArxjInyinbJ2K6xjeVma40Jhhf3cGtwGN8PbOFfiuHoXxkypghUFrJL15AWRk8GBRQYHely8hDjuG+DsREPL2VkxOBiIj8TLiFNaOq4vhfzIs+I1hyiiGdga8HJNHMcRW5O9fUVkD+jWW7kk5d676P4wvzYMHuPPX79DZqPg8uPP7z8CLF6VdUo1wyccIFdcx/D6eYdYIhke1ZH+sSdHNPr9XruRBXQosEkIIKWFlvR1KVEMBLKJWX2TFwRhGjVfeqB75VzVJQzn62kmMn1UD/Wfp4uyh5TLZZXx4J7N9XMXsm//8v2AnJgLv3pXEuySkcPT1pQMzp0/LrhMbCxw8iMer52LShPLY3135dVRhvWqBrr+GM4ycoPj1Zb8yPKjN8Pcghj0/MbwdOxQvt6+GcPZsbtkEAZf+Hiu1Xdv5DNbfMcRU4e8ptRxD1TWK9zNwCsNXywoub97jZNaF4aLZRsyfVAvLJ9TGf0c2QMjK4mW6dg1wcNDsXlovXiCmivLj6Te3b2mXUuPE7t+Baw0ZMrWl52bMmzK1+Tl+vX0dWHRmiFkwo7SLTQghRIN8ke1QIoMCWEStvsiKY9EimeFQ6/vxOaq29WLYvrI7El4+Vj2/zEyZG/80HQbMmVN874EQdYuKAlq25IGZP/4ouKdTYiLfJj0dWYkJ8F03BoFz+sF4aS+Ve2jlpMGTlL9ec5Xi1/SHMqBNG4AxfDdX/jr1VvCeVe7tlO+nylqGDvoMs4cztFmgeL3A1gwC4z24FK3TbxpDaEu+Xlab1kBOUEuDJD++j3YG8o9P32kMh7tlBwJPnCjtomomR0c8r674XN/bg+HM10zSc67eCoakaxdKu9SEEEI0xBfZDiUyKIBF1OqLrDjevUOaDm/4tljEh1B8qJTdkGrfvkhZ5r/xFy5d0sgGKynj0tKAt28/eT62zA/vcMZiA25M+bXIE8AXJv3wF0O6Dp9wXtE6M0fIn48rf5o9nA8rVLaO8Q+8zjhQQA+0nFR7JYPv30OQ9eI50m7fUNOH9fmbNLuW0uMS3iy73r12rbSLqrFiX0ahxmr5n4+83or7fm8AfPxY2sUmhBCiAb7IdiiRQQEsolZfbMVx+bL0cCnG+ITMkZFFym7RzNynHk6cW1fNhSWkjBKLgbAwvFilj2Nmq/Ax9i1S3kTDYWEflNvAr5clgxh+naw4yNFgOcO/Pyt+fdFghiPdGCK+YrjSiKHRUuWBp0mjFb+us5Fh3lDpwJO89VotZLj4P+VBq+qr+ZxaFdfJBgUcVgzixycgIDdwfvIkcP48YG/P5+sqS6KiAFdX4M0bySIXI32lx+dRrTx178OHpVh4EubwD3oYVFApGPvbJAY0a0ZBLEIIIcXui22HEikUwCJq9cVWHIIArFkDNGzIA1cJCZ+UnTjyPzhN7gSbKd8iQ4N6WBBSZM+eAXFxQGAgkhbro8cs2cby2l/4PDwNlitvVH8slxsMGaFkLi3bTnx+uvFj+N8tFzFob2Ro/DfDmv4MadcuQzAywpP/VUH80vlAfDyObp+FnrO1JXnUWclwcXI/3Gwj/0l6OSmwNYPDt4pf39yHwbwzQ3Q1Xu64irx8p5oyCN91+iyeSpp5NxIfjgfwnnmKREYClStLjv/9DQZouEJb6bF5XTXfjwc0L+BnQZyWqlIQK+Irfm4n2JfSU0MJIV8WQQDCw3n6lO8+sRgwN+dzel6+rK7SkVL0xbZDiRQKYBG1ooqDEFISxEmJSHSxQ+q5U4gIsUba29fA5s0AY7DvqLxBbdE5TzCkbl3Yzewis07bJbr4GP8euH6d3yRnZPAdZ2YC8fHKC/fxIyAIEL9+BSQlSRZnxsdi9eL2MvsaOIXhXWUGgyHKy11jNe9Blv+phyMnMKBPHwhiMc447IDo37F4d+cqAEDIyACio4ENG/hTTjMzgf/+w+Nls5AiMuE38Gpw39UYTZfw8sxY0CR3YvociYlAXBxSO3dEYGs+nNLkh4KDH2wzA7Zvz/28Bg9WS3mJeqjy+eXtiZgadb+0i0wIKevWrMn9Tli9uuj52Nnl5lOhAn/wDCnTqB2qGSiARdSKKg5CSGnL/JiMFQat0XWOFjZN+xo+e2bjRqAlXj28jkO7RuOY/Ra5gZtMZ0cc7FcZWyY0RPyNi8VXwIwMvLt8ElmB/nizcwNQowZ/ettXBQcBZoyUv/x5dQa3fJPON1uiheqr+UMnJDfpdepg5hhdsM0M1dYwXDFer5a3tGheM6l9n7Xagrhn97FhccdCBTnkBrAEAfD3B2xtlffuIiVu75ZBSj+7PtOl/9YfytQWNCWEaCiWr1fuJ+QTWY/hWAuGLC0G7NqlvjKSUkHtUM1AASyiVlRxEEJIIcXHA4cOAa6uOPTv70UK8gS1YnKHVeakMWP5Dfqt+tLLh01kPDCU0xBo04ZPzB8fD+zfz3+hTk0FXrxAfJ/uWPsL7yn21NVM6i3k39/ICQxzhykvs/5QhuaLpZctW94RtVdp5fZOW1y7lD4UoooPjyPRZSGfD6uOgjng8qerDRmwezc/rwghpLDyB7BUHUYYFQWMG8d7a795A4+2fFoAtplhyEQGTJlSvOUmxY7aoZqBAlhErajiIISQTyekpmLkzEoqB7CUzZ+VN/0yVXZZOwMG7294QyBTm2FnT+nAUnsDPtfWzBG5y+ovZ5g3jGHUZF1c+Fp2Dqt2BqqVZ0M//q/BpFp4eswDAOBgoo/aq7TQYlk5XAq1KuVPghQkKyUZifdvQUhKwpY+uZ/tzzMUf+6hLbMbntHRpV18Qsjn7NUrHlj64w/gzh0+nD9/AEtZMPzFC9iNaoFR4/k8kmAMaToMou9l66X/6jHg7FmIE+I/i3klSeFRO1QzUACLqBVVHIQQoh7C1au4N20Y9vZgmDitKnxGtUPfafIDAvOHMHTQL3zPrZzUYhFDQgXZ+bVyUrU1hc+z2WLlr9dcxSdoP7NrPg0N/FKMGIErjRguN2JI0eVP15T32bdeyBBThf9/cx8GcXAQkJ5e2qUn5PP0/DkwezYwf/7n8xCLwEBg3z4gJqZYd3NuYi80/pt/B9kNawIkJuJBbYbusxiaLuE/3iicuyojA+75htYfa8HQS0FwPX+v4aenfIv1vRH1o3aoZqAAFlErqjgIIaT4pKckwvvIfJkb72MtFAcLctLXSxjCm8l/rfZKPoSi4bLCB6rmDWUot6Hw2039/RPnLyGfn5QU4PBhwMwMO3YMUfr5H+4m/ffICQyJV89DoEAWIdJ69sztbTRqVGmXBnBwQHQ1hguNGTK/bswfDlIcsrKk6ojKaxkyHt7D2LG5y6qsZUh6IufhEHFxuPvnQJl6p9VC1b+jWi9kiIk4gxfngqlHVhlB7VDNQHeORK2o4iCEkOJ3Z3RvzBrBsLEv7+mS1k4P3fPNgaWzkWHLim6YPrkqpo9kuFOXN4B2/Fz0nlryUnArxUExu44M00cybB1UEabzfsDp4Z2wZBDDul8Y4ioyYO7c0j6UpJg8sN6nNLCZd0hq3tRlvi6eXzpW2sUn5POQlaW+ScvliYjgw/PmzFG5d9fZJjxwxDbzYemCm5vq+7t3D3BwAN68KXBVhxWyD4l4fNxTZtkZ1z3AihVA9+5AjRp4VZWh22z1fs8dXNiNB7EyMgBfX8DLi382Oe9p+3bg+HHVjwMpFtQO1QwUwCJqRRUHIYSUgIwM3gg4dAhISgIACP7+iFowCVmhwYiKOIFnkef5uoLAn/wWHQ1UrIjQlvJv0MuvL9qNvWt7+cvNujCgenXAxka67C4uQIcOwLBhwMuXJXzgSIm5fBlhzRlmD2ew7cTnn1H1nOr6FwPq1gXc3YFt2xDcqQpMBtdFvJdTab8rQkpWSkrxBbCysmA1sC5aLWQYPInh1axx0q9HRgI7dgBnzkgt/m6u9PV68l/Vfoh45eOAX6by3sDm/WtKvrsAIPW/G0iytwTOnQNOnwZcXeXWDWHbZ8tdvq8HPzYCY+g5U73Bq85z+BD7qw358EPtjQwrBjIk/TWNB/0qV879bHx8gGXLgKlT+ZxdeQh37uDSvOF4sm4+8PFjkT4yohy1QzUDBbCIWlHFQQghn7ETJ5AwcrDk1/OcNH1+Y+DFC9yaNwo+bRiG/Sn9+i9TGe4cWCdzY/9m9UIcby677jtnSz6shOa20lyCAHTsKGnYpS4ykDR8my9m+LaAOdtONWU4km+YYad5DGIdbT73DiGaIDa22AJY7y6FQzdPL8n5Q/Lk/eIFrjctD/uODG+rMOA8/0FEiIuTuVa3bxkAfPgAhIYC27bxHylyeidlZgK+vhBOn0af6dLb/bdgHO76WqL+8txlxj8wPKmpuF5Y30/+8u/mMjyvznskn2/MMHG04jz29WC40ohh2a+F6IH1o2yd1XcaQ3wFHjTL+WxiKzIs/ZXhr+EMj2ox4OZNfhyysjBhamWwzQy6Gxj89FjxDb0syLVrwI0bpbPvYkbtUM1AASyiVlRxEELI5y/UeRtaLi2HCusZJi5tiqyMPPMOXb8OMIYPlRjEWgwYO5b/Uv7xIyzy9KLZMrgSkJ6OrLl/4WU1hov/Y3Brx3BiYOvSe2Pk8/L+PbBrF2BvDwgCBMZwrw5DlhZv7D2rweeZKUxviMuNshuLjx/zfYjFeHvxONIf3ivd90o0V1QUsHs3EB4uvVwQgISETwtUvH6tvgBWfDxgbg7Y2gJpafCwWy1zfeUIM/gNWptyl88ezuAz62f4DW8j97qsspbPoTh6HMPjmgy36jOMH1P43k66BcynOGMkw5JBsst1NjKENefHZ/Io2dfX9Gd4U4UhYdhAoFUroGVLwNUVo/Vry/wAo2i/ysrVYxbDq6qyPZmjqzHgxg3cW/OX1PIO+gzw9JT+fJ48AWbO5JP1v31b9M9ZmQ0bpM+lxo2BpUt5L+0vALVDNQMFsL5QHz9+hJeXF+bOnYvOnTujRo0aKFeuHOrWrYtffvkFRkZG+FgM3Vep4iCEkLJBEIulA1d5BQQA8+bx4RB5paUhZccWxBjkGR6RHfACY4C2NnDhQvEWnJRdnTpJN56WLQMAxD5/INMgrLlKfkPRojPfNkWX4eTGqRg0VRtsM0OdlQzXbXeW8hvMJyuLeiF+6eLjgWrVcs/pHTv4crGYzy3FGB8yHR1dtPyfPlU9gJWeDri6ygz5y+E3rTt6T+fBqJTli+G0d5rM9ZU2/DeFQSBVU8+ZDI2WFn17ZSm0JcP2XvJfG/4nPz75HxBxZt0UhYfsfeQV/DOvHXbO1EPsyWDg1CkknDoqtX3FdQz/1eNP6y1KmS83YrDvKLscjAGvXuUWJu9nPHp00c6XguQ/l3KShUXx7K+EUTtUM1AA6wtVrVo1yQWsKH3zzTd48OCBWvdLFQchhGggb29gxgw+sS0hioSH5zaYypfnvUuyHd7wq0qNwaETlQ8/nLuoBZy2jMHJ2QPxeNcalSaLlhAE9T1t7OFDQE+Pv9fFi5Wv++wZYG0NYetWvFuzWHrunC/16Wfv3gH//AOYmJTeUCp12LYNYDzAYdGZ4UV1xofThYRIBwiWLy9a/vfuAYwhqXx23ozx4FhsLJD3Hj45GXfqMvSfyvDTTIbzhquksnl994rU0PG5wxhub9aXuX4Mu/IhcfKurY191T85emGTomF/0ydUQuKEUcCvvwL+/ni/aQVu/9ELYkeHIh32tPdvsEi/GQZM04K/HsPbygWXre80xa8tGiy7rIM+w6M/BwFxcXwSeMbw62T+2qoBDG/9nHlhxGLA0BDQ1wecnYFVq3hPusL2mhKLFQew2JcREqB2qGb4Ms5WIoMxhvLly2PChAlwcXFBVFQUYmNjcePGDejr60NLSwuMMTRr1gxJeSZR/FRUcRBCCCFEoaNHgU2bgFu3pJenpSF4+3TUX87QZQ7DYjkNvqIk/aEMmDCBDxtq1owHlRwdpRt/T5/iTd+u2NyHYe+Qmki9dE75e3j8mE8w//49/zszE3jxgv8/JQXx507wHox5GoeCiQlSvdykemQJqakIWfUHfNvw4Ued5vEyd5nDh/BadC+Pk00Z0LMnb7h+KQQByT90hHMHhutfMWDt2tIuUdGNH48bDRgqZA8dq76a4V1lJQGCmzd50Ovs2QKzTnV1hFs7HszIOZ939mR47tZXPTkAACAASURBVGuPFYN1sK0XQ8L82XzlESNkhr9dWzEZQtOv5fZKYpsZxoxlWDlAdnmXOYqvpyETiy84NXQiQ0wVhrbzFa8TV5FhV0+efNrwObOij/soP5DqEB2N6z+3xNAivH/dDbLzSuZNzRbzp/mCyX99/BiGj+X46x/LMdxowJChzYADB3jZ3r5F1skT/KEohoa853RO4DsmBtizhy+T90CAvGnvXv5gmDw/LMDXF6hfnw81PHGi+I/zJ6J2qGagANYXysDAAK/zVkD57Ny5U3KB79q1S237pYqDEEIIIerQcZ56GsaR9XgDzb0dn6um/1SGiLa1ELVrNXz+nYYBU6TXn6PfWLYwgoCTB5ag6ZLc9Vb3Z3je5zsIjCFZl+HQj7mv1V+eu987dRlq5RkS2c6gcOW3+o7hXBOGERP4U9D2ru0LISiIPyUuKwsPQhyR9qYYn+iZng6sXAn06AEcOfLJ2QmRkVI9efz0eK+l94N7I7wZw5s6FSUBweS4t7gZZI3U9zGfvN9iMW4cpuebH2nJIPkBgkfrDRBXMc+yS5cUZiskJ6P3bB2Zc6H6an4O5/w9cwTvMeWvJ//cMerKgz71Vsh//atlhTsXVXmaaIX1fBJzVfJr8jefPzHvccrUZrg4rT9uNJBe96eZ+Y5p+/bA8+cl+GEDsLCA+LtO2NhX9r20VDLE8Oslyo+D1iaGh7WVr3O0BUOrPHMGtl7I8GOe66idAUNieX5sMurVhvj2LZxsynt1DfuT4X0lHjDOCRC2Xpg9RxdjCGnJn65YcR3Dtl4MuHoVyMqCvx7v8TpgCsPTHu1K9lgXAbVDNQMFsDRUZmYm6tSpA8YY+vbtq7Z8qeIghBBCiDqcG9YJ1dbIBgcKG8Da14Phfh2GcgVMEJ037Z7eGpkic1zp3xbuXavgSR0ddFAwdLHVQoZL/yt8uVQuy08ME/5g+G2S9PKhE5lkWFiN1QwPT3rh7vYlcFnUH+9PhwKCgKsOu3HabB2ET5mLy9ZWOnAQEcHndDp4UPJ0OoXi4gA/Pz58NCQEfjumyw1inGrKG8+KjkHLRQwPfa0BAB8f3MHHQ3uR0PtHvBrwI/YZfA+zjUMRHx1V9PdYRBGTB8iU9X9LZYNXc4fx1yqvZbxXHeM961Iun0PcUT+ZudJuHXNQ+fzYJCeYwjYz9JvGe+o4dWCopOTY5k+dFASOp/3O4NFW/muHZrRD88V87qzrX/E56oZn9zr6bi5DXO9ugLMzfpopvd3dc768J2P58rnHa+tWAEDKxjU40YwhsDXf79EWeZqtycmlPvG4IBbjlbc9bjTgD6aIrMekJr7Pm/QWFHzcC/MZKUqV1zK4tFc8h6AqSWsTQ1Qt2bnGhv3J+PDFYphDWV2oHaoZKIClwbp37w7GGPT09NSWJ1UchBBCCFGLq1fxvqo2/PV4gOPw6MYKG139lMw/89skPrH0pzYOlaXR43gqjrznDWW4XZ+h+WLF63Scx+cvUhQE0l/QXHJYM328kLx6KQ9E5ZeVxYdI5gxByn56pEdbPv+QZ1sGYUB/oF49HmzQ0oIQFoZ96/rhtzlVIFrSBy/9HHFl8VjE/9JTEpRI1uW90hSVX5UJstf0Z3Btr/j1chsYYlbOl54YO790+Q+uEOLj8dzPHkJCguJtIyL4nFR5WPzVRW5ZzjdmCGjN8Lw6H+aW97XBkxhiKzLMH5K7rP5yhs19eI+o+ArK36eq6bu5vNfeqgEM7QvR6+9CY3695V2ms5Hh3kYD+A9uIbN+vRV5mpOCAEyaJPnck8oziMf8wc+rgACZXkqxT7LnewsI4PNXLV7Mn3oLAJs3l615mtLSYNKvGtrO54G8+AoMZ75msO3EkFCh4OPe9a9P/8z7TitcoF5R+n6u/OUJFbJ7vql5DmV1oXaoZvjMawJSnJo3bw7GGH744Qe15UkVByGEEELUJjISsLEBXr2CkJyM7ev7oO/8qjiy9GdkhATB6xsGu468oezWTn7QI7D1p/VIUCWV2yD/SWOqpjorFb829Xf1NEoTyzPcq8N7POUsc908FhAExL16jGOmK/HLdC20N2AQfc+AXr3wdNpIucHBHrN4Lw0wBtMuivd5+mu+zuBJn1b2fT140Kf66oLXvdEgO9gxaRJSr1wABAEfdm+BQZ6Akc5GBif9XoCPDz5eOosu+jqShnvSz90g3roFglgMZGQg88Z1BHasBJf2DGk6DDFrF2H3lkHYu/U3/F2EHoFsM0NDJUP3lv7KsLeHes7L1f0Zyq9Xff3usxgwZAgQGIik8rx314LfGK50qgckJuKY7wGZbbrNltOcjImR7SF14gQuNObBUKOuPG+lPQO3bClbASyAD2ncsAEQiXLff0CAwvmt8qa/BykOHLm0z51nTVGquI7PDba1t3rOHXnpQW0GlCuXO//fZ4baoZqhDNQEpDhcv35dcoHPnz9fbflSxUEIIYSQEpOYCHTtKmngXuzbClNHa0kaXI2W8p4wnxJcUjUVpQdW/eU8QHWlEQ8ayRu+5dSBP+Gu8tpPK9+t+sonk86bym1g8PpG+Tq9ZjBMGaV8nVYL+cTTa/orXmfGSD6sbtYIxesM+5P3alKl7BNH83PBp03B6y77lQ95GzJRep4yRWncGIbQlsV7HgmMwbcNk3pqoKL0jZIJz2eNYEhv0wpmXRjqrmDouFAXZ1z34Ib9Hmhv5OtUWctg04lP3N53GsOdQxtyry2xGLC2BpYtA27fBgCcDjaV2Y9RVxWbk2/fArq6ucGoCROUr+/oWPYCWEro5gtCb5vRSurvSaMZhNat4KfHP9dqa3jP0qTsea2CWxV8PhgMYXhSs/jOzaprGFJ83Ev7UCpE7VDNULZrAlJk/fv3B2MMWlpauHnzZqG2TU5OVpjevHlDFQchhBBCSlbOkLfs/0fsXY4G2UPWRk5gyLSxwlMv62INPDh+yzB2rOzy5QMZsu5E8jKmpCDR2hTpbs58WNX58zKN9N/H525bbwXD+ygePEB6utLeFU0LmCgajPeI+nmGau+n9kqGUeOVr2P9nfLXXdvz4Y/K1umgz3tzCYzP9SVvnft1ePl9VQhKsc28t1ZSedXWNfmBD/lSNgdRhfV8/jWfNgxXGxbfOcQ28+GUEV/x95tQgfcWk7eevx5fJ02HYe0vfFmvGfxYCa1bSV0e4uQkCFlZkuvj8q5F2Dm0Jm7XZ0DVqsCSJcCTJwVeZpeO20mVYehEBowfr/p1amPDh6CNGQMoG64JABkZQKNGudeGi4vq+/kMOf1QQXLc+kxnuHlgtcxnmn7jGl/55UsgOJgP581TNzyszSdf19rEA5Tvl84DDh7EDfs9uLj5L4jDjgENGxa5Z2CjpcqHKou+/7xDBxTA0gyf91lIisXu3bslF7e+vn6ht8/ZtqBEFQchhBBCSkVWFhIcrfB482IId+9KFudvkC0ezHDJfBN6T+d/GwzhwYyvlvGJt9f9Ir1+ewP+VMGTv7RA7IF/8f38cmCbeeBKPKA/BD8/XFvwB36bxJ/q1XVRJcQ/uau4nG/eyASw3i6YibW/MKwZqIVHBzdKr5+YiOvLJ2P5UF10nyVdNkUTSLPNDL2n87zfV2KSwF5BaeQE5a+rMixzz098Qu89P0kvd5zTA4sH86GI//7MgB07gDlzAAMDRLSrDZf2DDv66mCDQVvcdDkEeHpKjk9QK4YxcgKFOenrJQzpOnxoqSrvU3cDk+kdIy+1N2DY0K/4e2BVXMcnBc95v/nn0GKbebBPcs6MGiU9CTpjQKtW8s+3T3TzrKdUOQZMYcCjR8WyLwDA69fA3r1AUFDx7aOkeHnhchMdeH3DkDbmd8SeDJb5XLOePZXeRhCA1q1zP9fWrSEM+hVpPboCJ07I309mJlLv3kaz7ECU9kb5c6Dt/onhvz7tcPBHPrQ4Zx/yzsnLxuuQeCZM+oeCzxAFsDQDBbA0TEhICHR0dMAYw7fffouPRXiSBAWwCCGEEFIW5e0h1WxxdqBAEIBvv5UdrpQ9P8+K5Z3ANvPhM6f3LpLKT0hPR8KTe7INO0FASvQT3suqIBMm5O7T3JwvS0lR6Wlf//mK4Dq/LxIqMIQ158MRj3RjyDh5Am0W5DZgQ1oyoEoVgPGhhMrm3FI11VAwH9WsiVUxzKAmfpuUPQfWokXAo0cQLl/GW+O9fH6konj+HOjQAahcmc+PFBcHISYGD876SoZetlrIJyF/X4mh/1TV38vE0QWvU20N7xl1+uuiHa/e0xlcVwxBwolgxIb6Yse0Fug8R3qdlZMaIPPHrlLn4bvK0uu0WsiAatX46xUqAHfvAmvWSJ+7u3cX7RgX4N7VEKmy9JpBTclCuX8fOHMGEIshvHgh9fCCrvN05NcXFy4AvXrxSe7zPURAmQQ7c7iNbI27+mOB8+dlrlc/PcaHicbH8x5fW7cCEydiZ0/p9USjmxe8s88EBbA0A9U6GuTq1auoVq0aGGNo0qQJnj9/XqR8aAghIYQQQsqiF01qYPwYhl8nM1z6X555dTp0UDzfjliMN5dOIOXe7eIplFgMHD0KXL1a9DzS0gAnJ+Cff3ivFQBJDyPhtmkMbtrskttzIv1jEpbMyn2y47iplTHhD/70ugPdeU+nnF5ejZby5ZXX8nm6non24fbcUZg3lA9x+1afYfpf9ZD4+qnMftROwZMEERPDAzd6ekCNGkhcsxRnPA/gjrcFziwehQuLRiPj8UOkR96S9E5hmxnazmfI0FYe8Jo3lOFZjTznxrlz+Hhor8L15Q37c943Q7bMEycCjAdSM+1tc5fHxwMWFsDx47j+bT2pfPrN1OFDy0Si3IDGmzdAy5a8bG3bAnFx6j/uAJ7cPiNVlq5/UVPyU4QtHYU2Cxg6LCyHi56Him9Hb99KzVfXcR7vpShPXIiP5ProNI8h7Vhw8ZVLzSiApRmo1tEQ9+/fR7169cAYQ7169XD3rpLu7J+AKg5CCCGEfLYUTQw9bFiJDMH6HAkJCbxniCAA794BBw4A7u5AbCzvCRYfz3s/ZWYi68olCE/zBKnevQMyM+U/de4zFnshHMvmNsfMseVxty4DZs1C6uMH8Nz/F1wPzUHq+xg89rZCrLUJsl48B9auBZo3B6ZNk+oZFzpIeiLuzX34E+NuNJANYOXvvQeAH/Nbt4BXrxSW9UlNPsfVlFF8ovWVY2vJXzE5medVhNEVqnr9MAJV1vL50RouY/htEjUlP1lsbLF+ZgCA1FQk6/Lhupv7MMRXYHwuMnkEAVkH9uPO7z9DOHTosx82mBe1QzUD1Toa4Pnz5/j666/BGEONGjVw/fr1YtsXVRyEEEII+WxlZkoHqnr35ssjIqSXh4aWbjlJ2XDlCm7XZzDsyudGy3sO5Z2Qvt4KhrRN64q2j/wB15xztjTImbONlBFjxkh/bseOlXaJ1I7aoZqBap0v3Nu3b9GmTRswxlCpUiWcOXOmWPdHFQchhBBCPmv29kCNGkCTJvwpgDkcHXkjz8SkTPU6IKXsm29kgzqM4WkNhsGTGLrMyZ6DzMGhaPnnz3vcOPWWvzDevaMAVlklCHxY6qhRwPHjpV2aYkHtUM1Atc4XLCEhAZ07dwZjDLq6uggOLv4xzFRxEEIIIYQQjREVxYNKbdrwydWbNAG8vICAgNwgT4MGRR8mpqMjHTDaskW95S+M9HTpsujolF5ZCMmH2qGagQJYX6jU1FT07t0bjDFoa2vDzc2tRPZLFQchhBBCCCEATp4EDh0CoqOLnoeRUW7AqHJl4MMH9ZWvKHbuzH0Cort76ZaFkDyoHaoZKID1BcrKysKIESMkF/CBAweQlJSkMKWkpKht31RxEEIIIYQQoiZZWcCePcDkyUAxTwWislev+HBCQj4j1A7VDBTA+gI9efJEcvGqkpo2baq2fVPFQQghhBBCCCGkJFE7VDNQAOsLRAEsQgghhBBCCCGagtqhmoECWEStqOIghBBCCCGEEFKSqB2qGSiARdSKKg5CCCGEEEIIISWJ2qGagQJYRK2o4iCEEEIIIYQQUpKoHaoZKIBF1IoqDkIIIYQQQgghJYnaoZqBAlhErajiIIQQQgghhBBSkqgdqhkogEXUiioOQgghhBBCCCElidqhmoECWEStqOIghBBCCCGEEFKSqB2qGSiARdSKKg5CCCGEEEIIISWJ2qGagQJYRK2o4iCEEEIIIYQQUpKoHaoZKIBF1IoqDkIIIYQQQgghJYnaoZqBAlhErajiIIQQQgghhBBSkqgdqhkogEXUiioOQgghhBBCCCElidqhmoECWEStqOIghBBCCCGEEFKSqB2qGSiARdSKKg5CCCGEEEIIISWJ2qGagQJYRK2o4iCEEEIIIYQQUpKoHaoZKIBF1IoqDkIIIYQQQgghJYnaoZqBAlhErajiIIQQQgghhBBSkqgdqhkogEXUiioOQgghhBBCCCElidqhmoECWEStqOIghBBCCCGEEFKSqB2qGSiARdSKKg5CCCGEEEIIISWJ2qGagQJYRK2o4iCEEEIIIYQQUpKoHaoZKIBF1IoqDkIIIYQQQgghJYnaoZqBAlhErajiIIQQQgghhBBSkqgdqhkogEXUiioOQgghhBBCCCElidqhmoECWEStqOIghBBCCCGEEFKSqB2qGSiARdSKKg5CCCGEEEIIISWJ2qGagQJYRK2o4iCEEEIIIYQQUpKoHaoZKIBF1IoqDkIIIYQQQgghJYnaoZqBAlhErajiIIQQQgghhBBSkqgdqhkogEXUiioOQgghhBBCCCElidqhmoECWEStqOIghBBCCCGEEFKSqB2qGSiARdSKKg5CCCGEEEIIISWJ2qGagQJYRK2o4iCEEEIIIYQQUpKoHaoZKIBF1IoqDkIIIYQQQgghJYnaoZqBAlhErajiIIQQQgghhBBSkqgdqhkogEXUiioOQgghhBBCCCElidqhmoECWEStqOIghBBCCCGEEFKSqB2qGSiARdSKKg5CCCGEEEIIISWJ2qGagQJYRK2o4iCEEEIIIYQQUpKoHaoZKIBF1IoqDkIIIYQQQgghJYnaoZqBAlhErajiIIQQQgghhBBSkqgdqhkogEXUiioOQgghhBBCCCElidqhmoECWEStqOIghBBCCCGEEFKSqB2qGSiARdSKKg5CCCGEEEIIISWJ2qGagQJYRK2o4iCEEEIIIYQQUpKoHaoZKIBF1IoqDkIIIYQQQgghJYnaoZqBAlhErajiIIQQQgghhBBSkqgdqhkogEXUiioOQgghhBBCCCElidqhmoECWEStqOIghBBCCCFEfcLDgUOHgNevS7skhHy+qB2qGSiARdSKKg5CCCGEEELUw8cHqFUrBt27B0BP7x1SU0u7RIR8nqgdqhkogKUBvL29MWjQINSvXx8VKlRA8+bNoa+vj0ePHql9X1RxEEIIIYQQoh4DB16Bn19NhIczeHrWh7PzW7Xkm5EBbNoUgkOHBsLERB9xcfEqb3v6NGBhAcSrvgkhxY7aoZqBAlhfMEEQMHPmTMmFnD9VrVoVgYGBat1nWas4EhPT4eDgBAcHU7x/H1faxSGEEEIIIUTi8OGeCA9nknTokLFa8vXz+4DAwKqSfK2sVqu0nZUVwBhPenpAVpZaikNKwKZNQMOGwB9/AB8/lnZp1K+stUNJ0VAA6wu2fft2yUU8btw43Lx5E2/fvoWvry+aNWsmCWLdv39fbfssaxXHkSOrJF/c1tYDIQhCaReJEEIIIYSUQVZWl7FhgzFcXV8jLAyYOhUwNgbE4qLnmTd4FR7OYGa2TC1lNTOzlMm7IGJxbvAqJ50/r5bikGJ25Yr057ZyZWmXSP3KWjuUFA0FsL5QMTExqFKlChhjGDp0qExg5vHjx5LXx4wZo7b9lpWKIzkZeP8+FUFBVaS+uCMirha4bVoa8OIFIAg8JSV9REZGeqHLIAgCUlM/32NECCGElDZ7+6dYuXINNm3ah/fvU9SSpyAAz56pHlRIThZw9epzpKTQj1xEvhcvAJHopOR+0tn5a5Qv/1ESLNi2rWj5JiWlyQSZzM3Hq6XMFharZPIWi5Wf49HRsgEsT89PiM4VgSAA9+8/x717X9ZkYIIAPHwIZGYWT/47dsh+dl+astIOJZ/mCzx1CQDs3r1bcgHfuHFD7jpLliwBYwza2tp48+aNWvZbViqOjRuB9u3PyXxxe3p+jYCAQwgJsURYmBtOnQqCu7srdu/ejF9+uSCp8HV0MrBs2WypbY8fD1B5/5GRkXB07ISwMB1YWf1FPb8IISXu6FFfWFsbwM/PFlmfOAYkKws4dgy4ebPgdW/dErBixVMcOhRdbDfq5Mvw/Hka7O31JN+zRkarPjnP1NQs7NkzEx4eX2HjxgWIi1P+/fvyZTwcHDogPJzBwuJHxMSkqbSfGzcew9BwNoyN9fHixatPLjf5fO3fz+8Nzc2/k7ovHDDAXipYUJRbvUePnsncq9ra9ihUHhkZwKlTPDiSl0g0VSbv16/5/FqCAPj5XYONjSVevMh99OHZs8CAAQ44cqQHHBxaICioMpydTeTuNyuL7zuvc+duwsqqO6ytuyAo6FyBZU9IEOP48fN4/PgDAB50NjQ0QHg4Q1BQFRgbXyzEkSh9jx8/g5/fKbx6JR30y8wE5s1zhqlpF+zY8SdevIhV+77nzZMOXtWtG43Zs80xffotqKkZWOrKSjuUfBoKYH2hevXqBcYYWrdurXCds2fPSi5yKysrtey3rFQcU6cCWlpibNkyWubLW14aMcJYqtIfPtwERkY/yqzn6HgQ5uYbERx8TGlQytq6l9R2Pj4HkVrIx8pkZQkICzuLsLBgZOS/QyhD3r//gKNH/XDpUjRu3VJtTP7Tp2nYv38DjI2n4+LFiELv8/nzZLi5ReDJE8Wt56SkjxCJRDA0NENMjPL50QSBP956yhTgxIlCF0cusViAmdlhWFn1hEg0Da9efShyXnfvvoSTkyMuX36gdL3jx4ExY4DNm1X7BVAQgMTEkv3lleRKS0vFmTMP8P594SeyOH36jFQd5OCwC7GxRWtgAfy8yakflX2dJCeLsXs3r3fDwnRgbGxZYN5RUYnYsOEwtm5djrCw64UqV2amgEyKkhW7iAhg3z7AxOQYjhwZBkPDUbh+/S7277eAqWkfWFhsRFpa4U8uFxdHqfPU17cWTEw2wcJiCW7fflbg9gkJQFSU9DIHB+t8vVlsJa9du/YWJiZOOHPmjmSZiclaqfUtLCzk7isqCviQXU0LAiAS5c5bZGnZu1DXliAAISHP4eUVgA8fElXfkJS4+HigQgVe9+W/JzQ27gZd3VRJ3RgTA0RE3MWePWuwc6cR3r0ruO6+cOGinB9b/4f374EnT6Tr7JQU4PLlRKSn5y4UBGDUqNz62d09d30bm19l8r5yhY9EsLYOy3Nv2xpRUQkAAEdHYPToQ1Lb2Nhskin3qVN8rqXq1QFb25zyZcLFpYVkO3v7tkrnz7p/Pw42Nh0RHs4QGloep05F4NSpu1L7trb+FpmZql9cYjEfSZHDwwP45Rfg77+llxdGWlo6zp17jHfveAaCwN+/uzuknhgZHBycp074Dg8fpkAsBq5dA8zMIhAWpp3n3FkitY+YmFQkJHzad9mSJXbo188ZrVpdR+3ar+Ds3FSyv3/+Mf+kvD8XZaUdSj4NBbC+UFWrVgVjDJMnT1a4TlpaGnR0dMAYw4IFC9Sy37JScfTuzb/IGzV6pFIAKySkIlq0uCm5AahQIQW//SY7d0D+FBpaEf7+NeHt3QDduj1A585AuXLAwIF22LNnAA4ezA1keXs3gJubGfz8PODv74cnT54jNjYWqakp+PAhDocPZ+DIEeDdO/7luG/fijw3Ad2Qnp6BjAwB/v5hcHQ0xbt3cYiLewVT080QifrC2HgRwsOD8PDhW6Rkj8KIiAD27OGPaH78OAmWlv/iyJFVCAu7DLFYQGambHAiNTVDcrPw7Bnw/HnRP4fY2Di4uLSRHOMePfzQrRsQFwe8fq14YtBdu/TzNGjqwNHxHebPBzw9c9fJzATS03O635/C69f856W7d5/D3b1J9k1XO9y+/QIAcOEC8M8/wMXsH/MsLCblGQrQCgkJueezWCwgICAELi5+SEpKh4mJ9K9af/7JA1qxsQJcXc1hbj4TLi62iI5+hZMnQ3H7diSiop7B0nIUXF1bw95+u0zAMyDAW+Z8cnDoBDc3Q7x7lwxra2O4udkiLU06eHnzJhAUlBuAunPnDVxc+E3K0aO6cHHxAQAkJKTAx8cRAQGeSE5OQXQ0UKVK7ntYvlz2uF+//gHh4XcgFgs4eTISTk568POrib17/1XaOMt5LTMzE56e7nB19UJaGgUVVPHhgxg7drjAxGQhDA2HIzCwOhwdv4GtrQlcXRtnn8ff4v79GGRm8ms6JqbgfEWiOfmC783BGNCmTe72794lIilJ+iKMjHyBw4c3w9z8X7x+/R4AcO+e9PnfoAE//zIyMmFmNgeurs1gYrIUyclpCA4OkNqvl1c9ZGRI1zNOTodga/sz7Oy2Ij09A0eOTJes7+9fAw8fyg9cCAJw8uQZhIYG4+PHDJw/fwkODu0RHFwJNjZbC3/wS5hYnIVnz57h3r2HePbsCd68icnzGn/i17RpwIABwNy5z7Bz51aYmk6HuflKWFltgouLJS5ciMCNG/eRnl5ysypfvAhUrAj8738PEBpaXuH3oUh0AK9eAePHAyNGALduFZy3paW+TD45+7C3b4v4+AzExyfByckOvr4npeohQ0P+fcsYMGtWbj1kaPhzvmBAQwDA3btv4eTUXBJcDQ3lDfn8+7e27itVRrEYGDqU76d8ecDaGrhz57nMdpcvK+6e6OMDjB4N6Ovz73gzMxuEhelIvn/evUtQ6bOIigKuXuXv9cOHeJiZLYKVVU84OdmptL0y9+4l4dYteuRcfh4e/LOvXv293PM+LEwbtrZtx3gZkwAAIABJREFU8P33x2Fk9Bre3vXzBDEGQBAEpKSkITLyhVTgCeC9l0QiB5k8jx/Xgo5OBhgD5s7ln/erVx+xZ88oHD+uBScnPdy9y2/OwsOl6+fGjXPzd3b+VirfadM2wd+f1ztGRr9LvWZktB8AsH27gL59XfMFdefKHJd27XL3WbEiEBsLhIYek3kvjx4p/sIyMtqSrw4ZCjs7E5k8bt9WbS7f8PBIbNo0Ff/8Mwa2tqdx+3aWpI7IOZaqev8+HZGRtxEWdgZ2dh0l38U3bz6VChi2bZsBX98wmJs7wtm5hVS5zc13YsSInPmoZki95ub2PwiCALFYjP37F0jqpW3bjLFxI2BjU/ihhh4eTRTWz0FBlSESOcDZuRUcHFrh55+98MMPwN69uVOmZGV9/qNFyko7lHwaCmB9gaKjoyUX74YNG5Su27hxYzDGMGDAALXsu6xUHEOHArVqyf/FTFGys2uNypUT8vTCMlV52/BwhkaNHkm27dDhbJ4ATC2Vtm/T5nKemxABU6dulnp6TM5NTUH59O/vKDMGvnz5jxgwQPYmKSeFhFSCm1tTBARUR1iYNoKCqmDTprEoVy4djAGtWvEGwvTpwOTJqTAzGwdT0z9gYjISFhYDYWU1EFZW/SAS/Q4rq36wte0KE5MpCAysJtnHvn390LJlhFS5Bg3ygJXVaFhYjIKFxQRYWCyBqelYmfJNmLBTsk3nzsDixUD9+oCubhocHZtL1nN27iT3/a1ZY4KKFZMleTRrlont24dJGhDh4Qzu7o0hEv0Nc/M1cHZuJZNHly5H8x1XAQ4OLZR+FnmTmVlfzJjBj+G0acCgQdaSAIWy5OHRBDY2G7Fz53Xo6UnfMG7ZAnh715XZxtx8OZydv5b8HRpaAQsWbJc6R1u2jIBItAm2tv/Azm4H9u41QEhIRYSHMxw7Vk4mz507/8KePcC///L9rl8PLF0qwNBwFHx86sLc/Fd4ejaSrO/i8jWsrU3h5GQBR0djODkdhLu7CK6uNnByssWBA7GYN48fj0WLgK5dgRYtgDVrzsPZ2QQuLqZwdTWDq6s5XF0t4OpqDnd3Q3h47IeHx064uW2Fre1qWFouhLX1DkyfnoUFCwAjI8DcHDAx4cnO7ik8PS3g4WEDDw97uLvnTXZwdbWBSGQFZ2cL2NkdhI3NXri52cHLywHOzklwcoJMcnc/DW9vS3h7m8Pb2yL7/zbw9raBj48hvL2t4O1tm53s4eXlAC8vJ3h4BMLREXBw4L9YT5gANGv2H44e1S3wPHBwaIkaNeIln9/UqXxYi4MD4O5+Bx4ebnB1dYOTkxvs7V3k5pG3p8DKlbzh4OnZEObmK+Dn5wZ3d0v4+NSTrB8Wpg0nJycsW7YPtWu/kjr/N26MgpNTO6n8nZy+gafn/2T2u3fvFXh4AM7OwOLF7lKvubk1lVnf2fkbODruhYvLDbi48B4BdnZAv34RknM0f90YHs5gaTkbLi5mcHY2gaOjERwcDGFvfxj79ydi1y5g4UJg4ECgb19gwwYBhw7thIXFbzAyWgR3dwf4+bkhIMA9O3kgIMAzT/KQvObtfQfm5vzYOzvzeUdMTAB39yfw8LCGm5spnJ0Pwd7+X1hazoaV1QA4O+vJ/UzCwxmsrXugfv1nUvV1p04nMWPGhgLPi+PHtWBt/TNWr3aDgYGAVav49bRsGf/xYsuWJ7C33w93d1F2soSHhxU8PKzh4WEDLy8b+PjYwsfHDt7e9vD0dICHhwPc3R1hZxeN/fuBypV5uZYsmVdgeRgTpM4TS8uXCA72QEiIK/z8rGBjsxb29tsREOAJf393me319K6iVq3XmDWL94qysuov9b1nbDwYHh4esLTchdDQCvj+++OSfW7fDhw7JiAoqLJMvt7eLvDzq43wcIa1ayehQ4czYEyAuflHBZ/JP3B3t0dAgAc2bnRFgwZPpd7X7Nl2MtvY2fWCj48NHB23wdVVBD8/D1hZPcKUKdLfx40aPZTZ1spqMI4fP46wsGMICwuBj48vXF1tEBISAhcXAW5uwNixuXl07/4a3t71pPKwsNiEgIAgeHsHwtXVH/b2vrC19YabWwACA30RGPgWPj7Azp38SWWjR/MnzI0f/xLGxv0l55Op6TCcOHEUR4/6w8/PDX5+tvDwOAwHh3/g4WEJb28HeHl5wNERCA7mKSAAcHYWYGg4Gy4urWBpOQamprNhadkT5uYz4eNjieBgN4SEeCEkxB+hoSE4evQojh0LwrFjJxEWdhrHj6fjxAkekDlxgufr7Q14ep6Ek5MxzM2XwNJyHszNJ8LL6yu4uTWHufkqeHt7w9/fF76+vvDx8YOXVwA8Pb1hYZEEe3sePDx6lOdlbg74+T3BsWMhOH78BEJDjyMg4Ci8vYNhY+MFU9ONEIm2wNBwKw4f1oeV1TbMmuWItm0v4quvHuP3348ouY7byV3u6dkCwcH8nFy//k/8+GMsxowB+vcHqlUDpk7dAjOzzpL1fX1rYdAgG+jqpoExoEqVOOzYIb8ucHe3xbZt49GvnwuqVo2VnB/t2gF2dkEydUVOUKxfP0Fufm5uRgrfn6XlWtjY/ABr659gY9MD4eEMv/9uKLn+tLSAadM2ydluAIyN18PYeDKsrbvB3r491q61wOHDQKVKiTh4sBe2bRuOjRvHKdz3rFlOWLeOf+fZ2wPW1o9gZ7cH5uab4eh4CJ6e5nBx2S+zXWhoeezb1w+9e3tI3vukSbx+NDJygIPDVri5bYWT01Z4em7Nvq+YD2vr/ggIqC6TX1iYDv7446DkOOvpXS2wTqxX7zkYk33SZHg4g4PDj/Dw+Ermc8r5Qb1hQx5gMje/AmdnQ7i6WsPBwQJ+fo7w9bWAp6c93NzM4ei4F/b2fxVYlvzvRU/vChjj5+HevUPg61sHhw6dLXJPtZJQVtqh5NNQAOsLdOPGDcnFe+TIEaXrdu7cGYwxfP/99yrnn5ycrDC9efOmTFUcSUn8V4WzZx/C3t4GxsYHcfjwNhw5sgqGhvNhbDwVxsZjcfjwMMyevQZ160ajRg3+xVSpUmKhvgzq1n0h+VLT0cmAj0/tQm3fseMpqZvcJk3uoW7daJUaDcryYQzQ1s7CwYO9C5VPYGBVaGtnyg2GFe5LUhsbN46DgcEStG59TZJP3bov4OnZQKU8Nm0aK9X4zkk9eviptH1oaAWZuSoaNoxCgwZPsXv3QJXfy/79faUCWYpuWOWlAwf6yJR/165BmDNnJTw96xe4/ZAhFjLbMwaVgh85qWdPH8l2gwdbFepzNDf/Tu7+x47dV6h8clLbthfl5rdly6gi5de8+S25+fXs6VOk/MLDGb766rHcPNesmVKk/Kyt28rN748/Dha4radnA4XlmThxR4Hb9+3rKhXArFXrNdzcGqlcdj4csCvmzl2BOnVeYsAAe5W3/e67E5L91q//DDY236i03eDBVjLvtVWr64UKHIeHM5ngQ05StRz505QpW+Xm16ePW5HyCwvTkanftLUzJUEcVdLEiTvklqlNm8tFPv+7d/eX5PPTT77Ys2eA0vUtLTugYsWkIl9/QUFVMGKEMZydm8LN7X/w96+h0nYmJj/ghx9Csxv78WjV6rrCdY8c+QkrV06Hvv5S1K79Ck2a3FNpP/m/V/X0rqhUtqFDzWU+k0mTthXqc6he/b1MHoWtv8PDGbp2DZF7jtStGw0fnzqFysvHp47cvMaP313k861mzTdy89y2bXiR8tPTu1rk+jZ/mjVrLZo2jYS2dqbCdVT50XPRovnQ0hJLladChRTUr587D1a3bkHZrwno3DlM5Xu348e1IBJ1RMOGUdDSEsu87uraWLLPmjXfyP2hqijJza0RBg2ykdTPqmzTr5+LpCzypuvIm9zdG6Jdu/NSx+zHHwMLXU4Pj69Qt260JI/C3PuFhzPY27fCpEnbUa3aB+jqpmHu3BVSQwIVpSZN7mbXTXEq7Uck+hYVKqRIvd8xY2SDc5+SgoIqY86clahSJfdHsbFj98LWtg3KlUuHnh7vef05ogCWZmClXQCifufOnZNcvIrmasjx008/gTEGPT09lfPPybug9CVXHElJfNhEfHwmoqPjYGl5CCYmU2Fu/iNCQyvg6FFd+PtXR0BANclwhxo13kp94QwZYlGoL5T8N5fVqn3Av/8OUbkHV05q2DBK5obtzz//LdKXXN6AU04qVy69SHl5e9eV/KJYvvxHmJp2KdT2pqZd0KDBE0k5dHVTpXpfqZKWLftL8tSgKlXi4eVVr1Dbh4czTJ78T/b+06R6cBWUdu4cLHMsJ0zYqfL2v/9+RO7NuCrBr5w0YICDZLuhQ80L9b4tLTvI3b+2dhaMjbsW+jjK9mjjycWlSZHOrwULFsnNrzgCWCtXTi9SflZW7eXm16LFTdjby/b6y588PevLDfypEsAKD+dByJ9/9pJs167d+UIFQMPD+S/a1au/Uyl4HBamA23tLJnyyus9JS/lBCVyUk7Dr0GDJ7C1bfPJn2P+CZlVTYoCWD/95Fuk/ESijlL51KnzEkeO9ChUHuvXT5BbpsaN7xf5/M8bwFqxYmaB6xsadpfZf/fu/irvb926iWjQ4KlKPY3zp379nKX2W9D6rq6NJedTxYrJBa4fHFxJ5jtalXJNmLBL5pgYGPxdqPfWvv05mTx695btvVbYY8Tr70zs2/dLofPy8PhKwfdBZpG+D8LDGWrVipGb56ZNY4qUX8+e3nLzGzduT5HyCw/nwdLCXpv5z6Pq1d/JuRfYhfBwPoS6YsUkaGtn4cCBPoXOPyioCnR109CwYZTMa4sWzZfaZ95e2p+afvwxEIwBXbocLXBdfi+U21OzoIBu/l6djAHffnu60GUUib6VyquwASyeR0doaYmho5Oh0vE7elRX6ofgfv2cC9wm74iDnFSUoGtBifeey91HgwZP0bnzMTAGNG0KyVQknxsKYGkGVtoFIOqXN4AlEomUrtuzZ08UNNl7fhTAKjxBECM2VsCNG8Dp03zIS2gocPToBdjYHIGR0RqYmc2Aqek4GBmNh0jUD/b2neDk1Aaurs3g6KiXPRSCz1NUsybv0tu4cRS2bBmF4OBKcr+AXF2/hrl5b9jafgcLizEwMxuF7t1T0aCB9A3ltm0jCh0ICw9nUl2l8zYiC5vP0aO66NHDT5JHt25BhQr+hIcz+PtXzx72wfMoyi/QYWE6UuUYNMimUI2l9ev/lAyrbNnyRqH2vXXr7zLHsnz5jzhy5CeVth83bo/cm/HCBHyGDTOTbDd8uOw8E8qSjc03cvevq5uKkSMVDztQlHr18pTJq1atmELnk5N8fWvJ7aVX1KBCeLjiwMfy5bOKlJ+iIGD37gEq5xESUlEmsKxqAMvfv7rkBlVXNw09e/rAwkL+sFtFae7c5WAM6NTppNL1jh/XkurxJ9349lBpX82a3c7XOM7CmjWTC33c8/Y8y5sK+uVfUZo6dYvc/Lp2DVG6XWBgVTg7t4SDwzdSw9xWrZomlc+PPwbCwaFlocqkKDj6KddU9+4Bknzq1HlZYNDQ1raNzP67dQtSaV+uro0l19vChQsKVU5T0y6S4UE5qaBtBg2yllq/detrkuGpilJoaHlJr56KFZNUKtvs2atljsk//4ws1PuT1xOxKPXa8OEmMvnMn7+4SOeGs/PXcs+3ChVSMGSIqEh51qr1Wm6ea9dOKlJ+in70UbW+lJfmz1+Ln38+W+Ttw8Pl1x85wfScnkyMAQYGSwqd95gx+8GYbO90Z+emUqMEtLTEauuBtXHjOOQEhhYsWFTg+vnr9cqVE+T+GHnsWDn89JOv3M+wsPdfoaEVZALBhQ1gBQdXkuqJ2aHDmQJ/jMlfJ2ppibF9+zCF63t61pf7fpUNXS1KMjBYItMTsG7dF5Jr0Ne3tFtVilEASzOw0i4AUb+8QwgNDQ2VrqvpQwi/FJmZYiQnZyIuLgupqQIyMsSIi0tVOrH2x4/8kcr5x7InJaXi1asPuHnzER48iMa1a3dx9eoD/PfffURE3MejR29x+fItnD9/EefPP8Ht24CfH/9CCwwEwsKA06fDcfHiaUREnMPt29dx8eI1XL16E8ePh+P8+Uu4fv0KQkN94O7uBS8vfzx8yCeDTknhc2VcvQpERj6Cj48nfHz8EBIShuDgAHh5OcLNzRbOzj64cOEJLl26A1dXRwQHh+LMmQSYmfHJ042MAFdXAceOXYGzswhBQf5wd3eDl5cTQkNDcfnyDTx69Bbu7s5wcjKBo6M5HB3tcPToE3h48ACjoyNw5gxw9eoLeHg4w8PDCe7utvDwEMHNTQRfX0+EhR2Dk5M1PD298fp1AuLi+COm4+KA06ffwMvLA/fuPcXLl3EICjqKkJDj+PDhI16+TMGZMxG4e/cR3r9PwdmzVxAYGAl/f34Mg4L4/B5nzvCn/IWFXcL16w9x794TeHg4wc3NFpcv30Jo6DG4uFjAwcEQtrbXYWcHSfmNjflcJnZ21nBxMcXFi7cQGno0e96fA3B1tUFk5BO4uNjAxmYXbG33wczsJk6f5vM4iUT/wcHBGPb2R2BvfwD29nvg4GAFNzc72NoehoXFETg6OiAyMgYnThyFk5Mddu7kc1AYGvK5RGxscspyDlZW/8DScgMcHA7C3t4GItFWWFuvgY3NWtjYbIKV1XZYWKyGldUK2NiswL5917B9O7BuHbBmDZ9b6+DBV7Cy2gpLy42wtNwAS8v1sLRcB0vLtbC0XAcLiy0wM/sXpqb7YGp6BCKRKWxtrWBmtg9GRuZYvz4Rq1fzPDduBDZsADZvvgNz87UQiZbD0nKZnLQCDg6rIRKthaXlFtjaboVItBQWFkuxYsV7LFkCmbRrlwfMzFbD1HQdTE3XwcxsNczNV8LcfDlMTDZl/38FzM2Xw8JiGSws/oZItARGRjvx99/8aUjLl/Ny7tgB3L4N3Lv3AaGhYYiOToSnpzNEojXw8HDCgwexcHKyzT6Gy7FnjzeWL/9/e3ceXUV9/398QmQJ4YBliaDgglZU5FQEamsFz9elpmJbAbcvAv3JphAWgbAoW6BoC+RHWCJJ7pYEkgCJstO6gIDwEyGKgtoDiGEpS0gQMBhJAuT1++N6pzfk3kuaG3AueT7O+ZzDmZnPzCR5c+cz7zvz/pRr9Gh3/5gY6a9/fU8u1zClpQ3TokXDtHjxcKWmjpPTOUsbNuQqI2OOsrPTtH79aU2Z4q4hN3u2tGzZcaWm/k02W4xstmGy20fI4RivnJwlstvnyuEYqbS04XK5hstme1U2W7ZmzChXTIw0evRhOZ3jlJb2hhYtipfTOVkOx6uy2SbJ5ZqjLVu+kt3u/tsOHOie3nvECGn8eE/9kcVyuabK6Rwuu/01LV3qUkbG2z/97kbJZpuukSOLNGyY+3zHjJHGjZOmTi2T0/mOXK43lJaWoCVLFisry/nT32+iXK7JSk2dqtTUOKWlTVd6+gzFx3+n+Hh37E6a5P6djRolJSWlKDn5VSUnvya7fZhsthilpMTIZhsqm23IT+0Vr38PVUpKjOLi1mncOPd+YmLc5zZypDRhwj45HGPldE6Sy/VXpaXNUnZ2lrZsydXRo8d18WLFYvbff1+iffuOacWKQs2bJ/3rp4nxvD/Xi4qKVFJyUf/+9486dKhQ7777//TBB5/qnXdWKiPjPWVmJstun6f09Lc0Z460YIH7/6TN5q4JNW9eqRyOODkcr/3UJsjhGC+HY5zs9nGy28cqJSVWNtsY2e2j5XCMltM5Si7Xq5o+/WuNGCENG+auQXb8eLHee2+bjh0r0IoVy2W3v66srFR99NFu2WyvKTHxdU2c6J6tcNo099991KivlZw8RAsXDtPChSOVlDRBSUnjZbcPks02VHb7GGVlperw4WL94x/uL4CKii5q2TKXbLZXZLe/LIdjklJSZsjhGC+bbYhcrhilp78qlytOKSlOTZ9+Vn/5i7u2Te/e7rZw4avKyFih1NR4paYOkN0+WDZbrGy2YZo5c7VZJ2zcOHdh55EjpfT0L2SzTVVa2mtavHiq7PahSkkZav7fSEhYpHHjLurll6Vx404qJWWcXK4EpaWlKClphJKShis5eZQWLpwou32obLbBmjNnpWbPlhwOdy2mN9+U4uMXKiVlkByOaVq4cJ5SUoYqJaXPT3Ug/1c2Wx/Z7f2UkjJSDseLiotbqf793bUT+/WTnnhCion5QnZ7H9lsQ5SUNE422yDZ7f3kcPwfuVz9lZ4+UBkZg5SZ+bIWLeonu72/JkxYr3793D9rTIw0eHC5Ro9++6ff6ctKSxutefMmyGZ7XikpLygl5SXZbDFKSopVUtIUORyzZbfHym4fpcTEv2rYMKlPH/c5vfSSu07m8OHlmjHjXblc0+RwJGrevBlKTo5VUtIwJSXFKDn5ZaWk9Jfd3k92+4tKSeknp/NZOZ291K/fKT3/vPTcc+6JAPr1kwYNkmbOzJTDEauUlHFKSRmilJQRyshwaMkSh+z2sXK5BsnhGCinc5BcroFKTe2vtLQBio9/V6NGueOwd2+pf393Ef3Jk1fLZusrh+N5OZ3/K5frRaWl9VN6+ktatGik7PZX5XDEyOEYqaSk4VqwYITmzXtNmZnvqLTUPanKV19dUEbG98rMXKXPPtsrlyv5p8+/8UpOnqWlS1fo5MkSOZ1O2WyvKCPjLS1d+g/NmjVbL7zwmUaOdMfEt99K69eXyuWao02bcnXsmPv/z/z50smTF7VixRLZ7a/K6RyrpKQhSk5eoJycrbLbF8pmmySbbbiczklKT5+rL774SuXl7rFFfPxWpacPU1raGC1dmq3Cwh+1ZYv7c3/uXGnVqjItWZKixYtT5HKtlt0eJ4djsmy2OM2f/1fZbH/TmjUbtXr1+7LbX9fChVNltw+X3R6rt96aLqfzTS1YMEErV26U3e6uvTdlipSY+KmyshZr3bqtyslZKpfrDaWmzlJGxv9VTs48rV27RXPmlCg21h2DnuvCihWnlJa2UMnJY2WzDdaiRfO1deuXWrTI/Tk2ebL7s23aNPc1eMqU75SWNlOpqX+X0/m6UlImKSlpijIzp2rlyvlavHie7PZYpaa+royMBOXm7te6de6ffckS9+dZevoqpaYmKjk5UQ5HohYscP87I8Ohdes+0ieffK0LFy5q9+5vtXTpKn322XEtXy6lpLhnH8zLk/bu/U45Ocu0alW21qzZqMzM+crJceqDD7ZryZJ5crmWa9Ys9zGTktwTCX38cbneffczZWdna926dXrnnQxt2bJdFy6U6x//KNbcue79u1zucdHMmVJCwk45nXFyOmN/GnuMUHLyBDkcI+VwvK7U1L/J6XxLmZmLtHr1BhUVlWn79q+0detOFReXaOXK9/X220u0a9ch5eeX6ptv3BMCFRa6x/fLl0u5ue7ze+65oG5XrjgSWLUDCaxrkHcR9ylTpgTctk2bNqqNRdwBAAAAAFUT6ItxK+A+tHYggXWNatSokQzDUN++ff1uU1JSovDwcBmGoZiYmBo5Lh8cAAAAAICrifvQ2oEE1jWqa9euulxxdu9aWU6ns0aOywcHAAAAAOBq4j60diCBdY2aOXOm+R949+7dPrcZNWqUDMNQnTp1lJ+fXyPH5YMDAAAAAHA1cR9aO5DAukbl5+erYcOGMgxDf/zjHyutP3DggCIjI2UYhnr16lVjx+WDAwAAAABwNXEfWjuQwLqGzZgxw/xP/Pzzz2v37t0qKCjQmjVrdNttt8kwDDVq1Eh79uypsWOePXvWPOaJEycCzlhIo9FoNBqNRqPRaDRasO3EiRPmfejZs2dr7P4W1kIC6xpWXl6u/v37m/+RL22NGjXSunXravSY3h8cNBqNRqPRaDQajUajXc124sSJGr3HhXWQwKoFli9frieeeEItWrRQ/fr1ddttt+mVV17R/v37a/xYJLBoNBqNRqPRaDQajfZzNRJY1y4SWKhRFy9e1IkTJ3TixAmdPXv2Z3+U9Od+fJXXKGmh1ohfWqg3YpgWyo34pYVyI35pP2c7e/aseR968eLFn/u2GFcICSyghv3wAwUEEbqIX4Q6YhihjPhFKCN+AVxpJLCAGsbFG6GM+EWoI4YRyohfhDLiF8CVRgILqGFcvBHKiF+EOmIYoYz4RSgjfgFcaSSwgBrGxRuhjPhFqCOGEcqIX4Qy4hfAlUYCC6hhXLwRyohfhDpiGKGM+EUoI34BXGkksIAaxsUboYz4RagjhhHKiF+EMuIXwJVGAguoYVy8EcqIX4Q6YhihjPhFKCN+AVxpJLAAAAAAAABgaSSwAAAAAAAAYGkksAAAAAAAAGBpJLAAAAAAAABgaSSwAAAAAAAAYGkksAAAAAAAAGBpJLAAAAAAAABgaSSwAAAAAAAAYGkksAAAAAAAAGBpJLBwTbvllltkGEaVW2pqaoX+U6dOrVK/mJiYy57Lzp071bdvX7Vp00b169dXq1at1LNnT23cuLFKP0t5eblcLpe6deumZs2aKSIiQu3atdPYsWOVn59fjd8OQsmBAwc0atQodejQQY0aNVLdunXVsmVLRUdHKz09XRcvXgzYPz8/X2PHjlW7du0UERGhZs2aqVu3bnK5XCovL6/SOWzcuFE9e/ZUq1atVL9+fbVp00Z9+/bVzp07q9S/rKxMCQkJ6tKli5o0aaJGjRqpQ4cOmjZtmoqKiqq0D4Sm6sbvX/7ylyp9Bs+ePfuy50D8oroOHjyoESNG6K677lLDhg3VuHFj3XfffZo+fXqV/vZFRUWaNm2aGf9NmjRRly5dlJCQoLKysiqdA2MIBKO6Mcw4GIDVkMDCNe2/TWB9+umnFfrX1IXb4XCobt26PvuGhYUpLi4uYP+SkhJFR0f7PX5UVJRyc3OD/n3BmlasWKHIyMiAMdi1a1d9//33Pvvn5uYqKirKb9/o6GiVlJQEPIe4uDiFhYX57F+3bl0gVxh3AAAUDUlEQVQ5HI6A/U+dOqVOnTr5PYfbb79deXl51f4dwbqCid+aSmARv6iunJwcRURE+P3b33rrrdqzZ4/f/nl5eWrbtq3f/p06ddKpU6cCngNjCAQjmBhmHAzAakhg4ZpWXFyss2fP+m1FRUW66aabZBiG7r777kr9PRfum2++OeB+SktL/Z7D5s2bFR4eLsMwdN9992nDhg0qKCjQtm3b9Mgjj5gX38zMTL/7GDRoUIVBwt69e3X8+HFlZGSoadOmMgxDrVq1UmFhYY383mAdeXl5atCggTlAS0pK0t69e5Wfn68tW7bo6aefNmOjb9++lfoXFhaqVatWMgxDzZo1U0ZGho4fP649e/YoJibG7Dto0CC/55CZmWlu98gjj+iTTz5RQUGBNmzYoPvuu0+GYSg8PFybN2/2u4/HH39chmGoTp06iouL04EDB3TkyBEtWLDA/Pnat28f8P8SQk+w8etJYD300EMBP4MDPcVC/KK6tm3bZt5033TTTcrMzNTx48d19OhRuVwutWjRwkxgnj17tlL/0tJStW/fXoZhKCIiQgsWLNCRI0d04MABxcXFqU6dOjIMQ48//rjfc2AMgWAEG8OMgwFYDQks1GqbNm0yL4hvvvlmpfWeC/ctt9xS7WN07txZhmHoxhtv1OnTpyusKy0t1a9+9SsZhqHWrVv7fApm9+7d5pMDQ4cOrbT+448/NgfBsbGx1T5PWNNrr71m3jjv2LHD5zbdu3c3nyS59CmWMWPGmP23bdtWqe+QIUPMb0B3795daf25c+fUunVrc+B5aaLg9OnTuvHGG2UYhrp06eLz/NasWWP+P5s1a1al9cuWLTPXJyYm+v1dIPQEG7+eBNbDDz9creMTvwjG7373OzM2fT2h8umnn5rX56lTp1Zav2DBAjM2srOzK62fOXOmuX7t2rU+z4ExBIIRbAwzDgZgNSSwUKsNGDDAvHk/fPhwpfXBXrh37NhhDk7nzZvnc5uVK1ea2+Tk5FRaP3ToUBmGoXr16qmgoMDnPjxPMVx//fVVrqeB0PDkk0/KMAy1a9fO7zZZWVlmDH3xxRfm8rKyMl1//fUyDEM9evTw2begoMD8dtbXKwDZ2dnmvletWuVzH3PnzjW38fUIv+dniIqK8hufnidh2rdv7/fnROgJJn6l4BNYxC+q69///rcZFwMHDvS73TPPPCPDMNSiRYtKtdzuueceGYahjh07+uxbVlZmPgHTvXv3SusZQyAYNRHDjIMBWA0JLNRa586dU5MmTWQYhv7nf/7H5zbBXrgnT55sXpSPHj3qc5uysjI1bNhQhmGoX79+lda3adPmsq8YZGRkmMf58MMPq3WusKaePXvKMHy/4uqxdOlSn3G2YcOGKj2a/9hjj5mvCFyqb9++MgxDDRs21Pnz5332P3LkiHmcKVOmVFhXXFys+vXryzACv6Y4Y8YMcx/ffvut3+0QWoKJXyn4BBbxi+ryfvLO6XT63S4+Pt7c7qOPPjKXf/vtt+byN954w2//gQMHyjAMNWjQQMXFxRXWMYZAMIKNYYlxMADrIYGFWsv7tY9LZx/0uPTCffHiRb83Qb54Xo1p3bp1wO08j3jfe++9FZYXFhaa5zhp0iS//b/55htzu/j4+CqfH6zPc2McHh6uXbt2+dymV69ePuNn9uzZZlzs37/f7zEmTpxobnfy5MkK6zz1Wx566KGA5+mpJffUU09VWJ6bm2vuO1Ch7A8++MDc7u233w54LISOYOJXqpzAunDhgi5cuFDl4xO/qC7vG+J//vOffrfzfoIwISHBXJ6Tk2MuX79+vd/+drvd3O7SiWQYQyAYwcawxDgYgPWQwEKt9dRTT5nfzPubQthz4Y6IiNA999xjFqFs2rSpoqOjlZWVFfBm6o477pBhuGfYCqRPnz4yDEP169ev8Pj21q1bq/TtWVlZmfn+f6DHxBF6Tp06pebNm5tPSGVlZenYsWMqLi7Wrl27zBv8yMjISt+cer7Zr1OnTsABp8PhMONs69at5vKLFy+qXr16MgzfBba9PfTQQzIMQ3fccUeF5d4D6A0bNvjtv3//fnO7GTNmBDwWQkcw8Sv9J4HVtGlTtW3bVnXq1FFYWJiioqL09NNPa926dX6PTfwiGKtXr/6vn14ZPHiwubyqT+WtX7/e3C4jI6PCOsYQCEawMSwxDgZgPSSwUCsVFhaadX969+7td7uqTB/crVs3v7OeeOoP9erVK+D5jB492tyfdxFj77oAa9asCbgPzyws/modIXR9/fXXuuuuu3zGX3h4uJ599lmfT7d4akI0a9Ys4P69B7nedYLOnDljLh8zZkzAfXheFfvFL35RYbl3faEvv/zSb/+ioiJzu1GjRgU8FkJLdeNX+k8CK1Dr1auXfvzxx0p9iV8EwzspeelNvbdnn33W3K5nz57m8ldffdVc7mt2N4/du3eb211aI4gxBIIRbAxLjIMBWA8JLNRK8+fPNy+IgR6rTkhI0PDhw/Xee+8pLy9PJSUlOnnypFatWqXf/va35j4efPBBn99AeZJkL774YsDzef311819HTt2zFzuPf37Bx98EHAfnpm0fv/731/mp0co2rNnj37zm9/4HDw+8MADWrlyZaU+jz/+uAzDPXV2IO+//765r6ysLHP50aNHzeUTJ04MuI/evXvLMNxFVr298cYb5j6++eYbv/3LysqqNNBGaKpO/ErS+PHjNWHCBG3atEmHDx9WaWmpjh8/rqysLLNAtmEYeu655yr1JX4RrA4dOphxsW/fvkrrd+7caT71cen1d9CgQebyQE/A7tu3z9zu0tmQGUMgWMHEsMQ4GID1kMBCrfTrX/9ahmGoZcuW/1U9FW8XLlyo8K2Vrzpangt3nz59Au7LuwaRd5FL7wt3oBoa0n9quAQqconQFBcXZ742ZbPZdOjQIX333Xfatm2bGYNhYWGaM2dOhX6eBNblak941+/xLvbunQAIVHtCkl588UUZhnuqbm/eCYBAdbjOnz9vbheoWDZCT3Xj93KKi4vNuimGUblwL/GLYK1du9b8u7Zp00ZLlixRfn6+jh07prS0NEVFRSksLMws9P/EE0+Yfb0TWIHGGd61ey4t9s4YAsEKJoYvh3EwgJ8DCSzUOnv37jUvhqNHjw5qXydPnjRnTvF1wfQ8Ov3MM88E3E9VHp1eu3ZtwH3w6PS16e9//7sMw12rbc+ePT63eemll2QY7lpX3q9ieV4hbN68ecBjVOUVwtjY2ID7qMorWF999ZXf/ryCdW0KJn6r4l//+pffxBHxi5owd+7cCk+oeLewsDDNnj1bzZo1k2EYeuGFF8x+3q8Q/vDDD373X5VXCBlDIBjVjeGqYBwM4GojgYVaZ9KkSebF8PPPPw96f4899pgMw1CLFi0qrfMUr+zWrVvAfXimeq9Xr57f4pUul8tv//Pnz1O88hpUVlZmDv4GDBjgd7vDhw/7TMp6iriHh4cHfIXF6XSa/f0Vcfc1tbW3rl27yjACF8EONLW195TzFMG+NgQbv1Xl+Zzt0qVLheXEL2rKzp071bdvX918882qV6+emjdvrqeeekqbNm3SDz/8oLCwsEqJUu8i7nl5eX73vWHDBnM7f0XcGUMgWNWJ4apiHAzgaiKBhVqlvLxct956qwzD95Tt1eGpnXLpqyeS9OSTT5qPbQfimQGrffv2FZYXFBSYF+7Jkyf77e9dqHP27NnV+0FgOZ9//rn5d124cGHAbVu0aCHDMBQdHW0umzVrltk/0CxY3kndSwuxeuoMXW4GodatW8swDHXv3r3C8h07dpj7DjSDkPdMXDk5OQGPhdAQbPxW1YMPPijDMPTLX/6y0jriF1faRx99ZP7tly1bZi7Pzs42lweawdJ7Ftjc3NwK6xhD4GrwF8NVxTgYwNVEAgu1ivdFetasWTWyz0ceecTvN0+TJ082j+ddlNLb+fPnzcevfU313qZNGxlG4KKU3jUCAg2UEVo+/vjjKicAoqKiZBiGnnzySXOZ9zf73sXZL+WpleVrgOn5VjQyMtLvU1zHjh3zO8AsLi42a2sEKm7tXWsoULINoSPY+K2qtm3b+nwCSyJ+ceXFxsaaT44UFBSYy72fyru0OLs3T62s+vXrq7i4uMI6xhC4GvzFcFUxDgZwNZHAQq3iGSjWqVNHR44cCXp/BQUFatCggd8L6/bt280L6vz5833uY9WqVeY22dnZldYPGTLEHFj4m6a4R48eMgxDTZo0UVlZWXA/FCwjLy/PjI1AhaG9X8F65ZVXzOVlZWVq0qSJDKPy1NgehYWF5mtWQ4cOrbR+2bJl5r5Xr17tcx/z5s0zt9mxY0el9X/4wx9kGIZuuOEGv0mEjh07yjAM3XPPPX5/ToSWYOO3Kr788kuzr68EE/GLK+nUqVNm7aDnn3++0vq7775bhmHo/vvv99n//PnzuuGGG/wmbxlD4Eq7XAxfDuNgAFcbCSzUGiUlJWY9lqrMUFJYWKiSkhK/68vKyvTnP//ZvOimp6f73K5Tp04yDPdMcGfOnKm0D8+Nz0033aRz585V6r9r1y6zNsGwYcMqrd++fbv53v+YMWMu+3MhtNx2223mEyS+psCW/lME2zAMLV++vMI6T2HUOnXqaPv27ZX6xsTEyDDchVx9FdA+d+6cObNPx44dKw0Mz5w5Y67v3Lmzz/PzLhIfHx9faX1OTo65fsGCBX5/Fwg9wcTv8ePHA87edvbsWT3wwANm382bN1fahvjFlXL+/HlzooyIiAifs1TOnz/fjI2333670vrZs2eb69esWePzOIwhcKVcLoYZBwOwIhJYqDW8bzIWLVp02e1XrFihVq1aaezYsXrvvfd06NAhnTlzRocOHdKSJUt0//33m/t7+OGHKxSd9LZ582aFh4ebN1AbN25UYWGhtm/frkcffdTcx6XFW715inEbhqHhw4dr3759ys/PV2ZmpvnNWatWrfx+M4XQ5V1gvWXLlnI4HDp8+LBOnTqlTz75pMIU1h07dqwUh4WFhWrVqpUMwz0bYWZmpvLz87Vv3z4NHz68Sk/IeBeyfvTRR7V9+3YVFhbqww8/NAee4eHhPhMIHp7XFMPDwzV9+nQdPHhQR48eVWJioiIiImQY7toXpaWlNfa7w88vmPhNSEjQ7bffrri4OG3cuFFHjhzR6dOntX//fjkcDrM4sGEEnqKd+EV1HTx4UPfff78SExOVm5urgoICHT58WNnZ2eYYICwsTA6Hw2f/0tJStW/f3kwQvPXWWzp69KgOHjyoadOmmWODQF+qMYZAMIKJYcbBAKyIBBZqjT/96U/mkwCBprT2WLFihXmxDNS6d++u06dPB9yX3W5X3bp1ffYPCwtTXFxcwP4lJSWKjo72ew5RUVGVir/i2jFx4kTz20d/rX379jp06JDP/rm5uWaNIV8tOjo64LeskjR16lS/51C3bl2/N3Ae3333nfktrK/Wtm3bgDN1IXRVN34TEhKq9Bn80ksvXTZxRPyiOg4cOBAw9iIjIy8bO3l5eWadNl+tU6dOOnXqVMB9MIZAdQUTw4yDAVgRCSzUCidPnjQvnL4KRPpSUFCgxMRE9e7dW/fee6+ioqJUt25dNWrUSHfeeaf69u2r999/v8rnsHPnTvXp00etW7dWvXr11LJlS/Xo0UMbN26sUv/y8nK5XC517dpVTZs2VUREhO68807FxsYqPz+/yueB0JSbm6sBAwaoXbt2ioyM1HXXXacWLVro0UcfVVJSks/H7r3l5+crNjZWd955pyIiItS0aVN17dpVLpdL5eXlVTqHjRs3qkePHmrZsqXq1aun1q1bq0+fPtq5c2eV+peWlmrOnDnq3LmzGjdurMjISHXo0EHTpk1TUVFRlfaB0FSd+D148KDi4+PVq1cv3XXXXWrevLmuu+46NW7cWO3bt9fgwYN9vhbrD/GL/9aPP/6o+Ph4RUdH6+abb1aDBg3UuHFjdejQQePHj/f7pcGlioqKNG3aNHXo0EGRkZFq3LixOnfurDlz5lS5Xg9jCFRHMDHMOBiAFZHAAgAAAAAAgKWRwAIAAAAAAIClkcACAAAAAACApZHAAgAAAAAAgKWRwAIAAAAAAIClkcACAAAAAACApZHAAgAAAAAAgKWRwAIAAAAAAIClkcACAAAAAACApZHAAgAAAAAAgKWRwAIAAAAAAIClkcACAAAAAACApZHAAgAAAAAAgKWRwAIAAAAAAIClkcACAAAAAACApZHAAgAAAAAAgKWRwAIAAAAAAIClkcACAAAAAACApZHAAgAAAAAAgKWRwAIAAAAAAIClkcACAAAAAACApZHAAgAAAAAAgKWRwAIAAAAAAIClkcACAAAAAACApZHAAgAAAAAAgKWRwAIAAAAAAIClkcACAAAAAACApZHAAgAAAAAAgKWRwAIAAAAAAIClkcACAAAAAACApZHAAgAAAAAAgKWRwAIAAAAAAIClkcACAAAAAACApZHAAgAAAAAAgKWRwAIAAAAAAIClkcACAAAAAACApZHAAgAAAAAAgKWRwAIAAAAAAIClkcACAAAAAACApZHAAgAAAAAAgKWRwAIAAAAAAIClkcACAAAAAACApZHAAgAAAAAAgKWRwAIAAAAAAIClkcACAAAAAACApZHAAgAAAAAAgKWRwAIAAAAAAIClkcACAAAAAACApZHAAgAAAAAAgKWRwAIAAAAAAIClkcACAAAAAACApZHAAgAAAAAAgKWRwAIAAAAAAIClkcACAAAAAACApZHAAgAAAAAAgKWRwAIAAAAAAIClkcACAAAAAACApZHAAgAAAAAAgKWRwAIAAAAAAIClkcACAAAAAACApZHAAgAAAAAAgKX9f7ZUh211DJBGAAAAAElFTkSuQmCC\" width=\"640\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x1eb6eac79c8>]"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rbsightline = spec.get_sightline(spec_id[1],camera='b',rebin = True)\n",
+ "bsightline = spec.get_sightline(spec_id[1],camera='b')\n",
+ "\n",
+ "rrsightline = spec.get_sightline(spec_id[1],camera='r',rebin = True)\n",
+ "rsightline = spec.get_sightline(spec_id[1],camera='r')\n",
+ "\n",
+ "rzsightline = spec.get_sightline(spec_id[1],camera='z',rebin = True)\n",
+ "zsightline = spec.get_sightline(spec_id[1],camera='z')\n",
+ "\n",
+ "\n",
+ "figs,axes = plt.subplots(3,1)\n",
+ "axes[0].plot(10**bsightline.loglam, bsightline.flux, 'r',label = 'original_spectra_b')\n",
+ "axes[0].plot(10**rbsightline.loglam, rbsightline.flux, 'g--', label = 'rebinned_spectra_b')\n",
+ "axes[0].plot(10**bsightline.loglam, bsightline.error,'b',label = 'original_error_b')\n",
+ "axes[0].plot(10**rbsightline.loglam, rbsightline.error,'y--',label = 'rebinned_error_b')\n",
+ "\n",
+ "axes[1].plot(10**rsightline.loglam, rsightline.flux, 'r',label = 'original_spectra_r')\n",
+ "axes[1].plot(10**rrsightline.loglam, rrsightline.flux, 'g--', label = 'rebinned_spectra_r')\n",
+ "axes[1].plot(10**rsightline.loglam, rsightline.error,'b',label = 'original_error_r')\n",
+ "axes[1].plot(10**rrsightline.loglam, rrsightline.error,'y--',label = 'rebinned_error_r')\n",
+ "\n",
+ "axes[2].plot(10**zsightline.loglam, zsightline.flux, 'r',label = 'original_spectra_z')\n",
+ "axes[2].plot(10**rzsightline.loglam, rzsightline.flux, 'g--', label = 'rebinned_spectra_z')\n",
+ "axes[2].plot(10**zsightline.loglam, zsightline.error,'b',label = 'original_error_z')\n",
+ "axes[2].plot(10**rzsightline.loglam, rzsightline.error,'y--',label = 'rebinned_error_z')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-03-12T14:21:03.198931Z",
+ "start_time": "2020-03-12T14:21:03.192910Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'b': 62996, 'r': 44859, 'z': 34720}\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(best_v)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.7.4 64-bit",
+ "language": "python",
+ "name": "python37464bit9a38927acc0145af89620e6e1adc3dca"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.4"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/data/preprocess/dla_cnn/desi/nb/training_modelv2.ipynb b/data/preprocess/dla_cnn/desi/nb/training_modelv2.ipynb
new file mode 100644
index 0000000..5c5f67c
--- /dev/null
+++ b/data/preprocess/dla_cnn/desi/nb/training_modelv2.ipynb
@@ -0,0 +1,209 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import tensorflow as tf\n",
+ "import numpy as np\n",
+ "import random, os, sys, traceback, math, json, timeit, gc, multiprocessing, gzip, pickle\n",
+ "import peakutils, re, scipy, getopt, argparse, fasteners\n",
+ "import glob, sys, gzip, pickle, os, multiprocessing.dummy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:From /Users/samwang/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "If using Keras pass *_constraint arguments to layers.\n",
+ "WARNING:tensorflow:From /Users/samwang/Desktop/trial/dla_cnn-master/modell1.py:271: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "Deprecated in favor of operator or tf.math.divide.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from dla_cnn.desi.DesiMock import DesiMock\n",
+ "from dla_cnn.desi.model_v2 import build_model\n",
+ "from src.Dataset import Dataset\n",
+ "from dla_cnn.desi.train_v2 import train_ann_test_batch\n",
+ "from dla_cnn.desi.train_v2 import train_ann\n",
+ "from dla_cnn.desi.train_v2 import calc_normalized_score\n",
+ "from dla_cnn.desi.train_v2 import main"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "DEBUG> init dataset file loop, counting samples in [local_train.npy]: 484\n",
+ "DEBUG> get_next_buffer called 1 files in set\n",
+ "DEBUG> Enter file load loop\n",
+ "DEBUG> File load loop complete\n",
+ "DEBUG> Enter file load loop\n",
+ "DEBUG> File load loop complete\n",
+ "DEBUG> init dataset file loop, counting samples in [local_train.npy]: 484\n",
+ "DEBUG> get_next_buffer called 1 files in set\n",
+ "DEBUG> Enter file load loop\n",
+ "DEBUG> File load loop complete\n",
+ "DEBUG> Enter file load loop\n",
+ "DEBUG> File load loop complete\n"
+ ]
+ }
+ ],
+ "source": [
+ "#load the npy file to a class Dataset\n",
+ "#Dataset in src/Dataset.py ,this code needs to be updated\n",
+ "train_datafiles='local_train.npy'\n",
+ "test_datafiles='local_test.npy'\n",
+ "train_dataset=Dataset(train_datafiles)\n",
+ "test_dataset=Dataset(test_datafiles)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# hyperparameters"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'fc2_3_n_neurons': 150, 'pool3_stride': 4, 'conv3_kernel': 16, 'conv1_stride': 3, 'pool1_stride': 4, 'conv3_filters': 96, 'conv2_stride': 1, 'conv1_filters': 100, 'fc2_1_n_neurons': 200, 'dropout_keep_prob': 0.98, 'pool2_kernel': 6, 'pool3_kernel': 6, 'conv2_filters': 96, 'l2_regularization_penalty': 0.005, 'pool2_stride': 4, 'conv2_kernel': 16, 'fc1_n_neurons': 350, 'learning_rate': 2e-05, 'batch_size': 700, 'conv1_kernel': 32, 'training_iters': 1000000, 'fc2_2_n_neurons': 350, 'pool1_kernel': 7, 'conv3_stride': 1, 'INPUT_SIZE': 400}\n"
+ ]
+ }
+ ],
+ "source": [
+ "#load the hyperparameters\n",
+ "#may need to do hyperparameter search\n",
+ "import json\n",
+ "with open(\"dla_cnn/models/model_gensample_v7.1_hyperparams.json\",'r', encoding='UTF-8') as f:\n",
+ " load_dict = json.load(f)\n",
+ "print(load_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# build_model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(<tf.Operation 'train_step_ABC_1' type=AssignAddVariableOp>, {})"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#build the training model using build_model in model_v2\n",
+ "import tensorflow as tf\n",
+ "import math\n",
+ "from dla_cnn.desi.model_v2 import weight_variable\n",
+ "from dla_cnn.desi.model_v2 import bias_variable\n",
+ "from dla_cnn.desi.model_v2 import conv1d\n",
+ "from dla_cnn.desi.model_v2 import pooling_layer_parameterized\n",
+ "from dla_cnn.desi.model_v2 import variable_summaries\n",
+ "hyperparameters=load_dict\n",
+ "build_model(hyperparameters)\n",
+ "#the function variable_summaries and tf.Variable may not work with TF2.1\n",
+ "#if so,you can find the replace code among the comments in model_v2(using tf.compat.v1) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def save_checkpoint(sess, save_filename):\n",
+ " if save_filename is not None:\n",
+ " tf.compat.v1.train.Saver().save(sess, save_filename + \".ckpt\")\n",
+ " with open(checkpoint_filename + \"_hyperparams.json\", 'w') as fp:\n",
+ " json.dump(hyperparameters, fp)\n",
+ " print(\"Model saved in file: %s\" % save_filename + \".ckpt\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# start training"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "(best_accuracy, last_accuracy, last_objective, best_offset_rmse, last_offset_rmse, best_coldensity_rmse,\n",
+ " last_coldensity_rmse) = train_ann(hyperparameters, train_dataset, test_dataset,\n",
+ " save_filename=checkpoint_filename, load_filename=args['loadmodel'])\n",
+ "#this command may strat training,but this will take so long for my computer without GPU,so I run it using the terminal\n",
+ "#I successfully run this code with the terminal,but the train.py and Dataset.py need to update\n",
+ "#after updating these two codes,the following commands in the terminal will start training\n",
+ "#python train.py -r'local_train.npy' -e'local_test.npy'"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/data/preprocess/dla_cnn/desi/preprocess.py b/data/preprocess/dla_cnn/desi/preprocess.py
new file mode 100644
index 0000000..a8ffddd
--- /dev/null
+++ b/data/preprocess/dla_cnn/desi/preprocess.py
@@ -0,0 +1,225 @@
+""" Code for pre-processing DESI data"""
+
+''' Basic Recipe
+0. Load the DESI mock spectrum
+1. Resample to a constant dlambda/lambda dispersion
+2. Renomalize the flux?
+3. Generate a Sightline object with DLAs
+4. Add labels
+5. Write to disk (numpy or TF)
+'''
+
+import numpy as np
+from dla_cnn.spectra_utils import get_lam_data
+from dla_cnn.data_model.DataMarker import Marker
+from scipy.interpolate import interp1d
+from os.path import join,exists
+from os import remove
+import csv
+# Set defined items
+#from dla_cnn.desi import defs
+#REST_RANGE = defs.REST_RANGE
+#kernel = defs.kernel
+
+
+def label_sightline(sightline, kernel=400, REST_RANGE=[900,1316], pos_sample_kernel_percent=0.3):
+ """
+ Add labels to input sightline based on the DLAs along that sightline
+
+ Parameters
+ ----------
+ sightline: dla_cnn.data_model.Sightline
+ pos_sample_kernel_percent: float
+ kernel: int
+ REST_RANGE: list
+
+ Returns
+ -------
+ classification: np.ndarray
+ is 1 / 0 / -1 for DLA/nonDLA/border
+ offsets_array: np.ndarray
+ offset
+ column_density: np.ndarray
+
+ """
+ lam, lam_rest, ix_dla_range = get_lam_data(sightline.loglam, sightline.z_qso, REST_RANGE)
+ samplerangepx = int(kernel*pos_sample_kernel_percent/2) #60
+ #kernelrangepx = int(kernel/2) #200
+ ix_dlas=[]
+ coldensity_dlas=[]
+ for dla in sightline.dlas:
+ if 912<(dla.central_wavelength/(1+sightline.z_qso))<1220:
+ ix_dlas.append(np.abs(lam[ix_dla_range]-dla.central_wavelength).argmin())
+ coldensity_dlas.append(dla.col_density) # column densities matching ix_dlas
+
+ '''
+ # FLUXES - Produce a 1748x400 matrix of flux values
+ fluxes_matrix = np.vstack(map(lambda f,r:f[r-kernelrangepx:r+kernelrangepx],
+ zip(itertools.repeat(sightline.flux), np.nonzero(ix_dla_range)[0])))
+ '''
+
+ # CLASSIFICATION (1 = positive sample, 0 = negative sample, -1 = border sample not used
+ # Start with all samples zero
+ classification = np.zeros((np.sum(ix_dla_range)), dtype=np.float32)
+ # overlay samples that are too close to a known DLA, write these for all DLAs before overlaying positive sample 1's
+ for ix_dla in ix_dlas:
+ classification[ix_dla-samplerangepx*2:ix_dla+samplerangepx*2+1] = -1
+ # Mark out Ly-B areas
+ lyb_ix = sightline.get_lyb_index(ix_dla)
+ classification[lyb_ix-samplerangepx:lyb_ix+samplerangepx+1] = -1
+ # mark out bad samples from custom defined markers
+ #for marker in sightline.data_markers:
+ #assert marker.marker_type == Marker.IGNORE_FEATURE # we assume there are no other marker types for now
+ #ixloc = np.abs(lam_rest - marker.lam_rest_location).argmin()
+ #classification[ixloc-samplerangepx:ixloc+samplerangepx+1] = -1
+ # overlay samples that are positive
+ for ix_dla in ix_dlas:
+ classification[ix_dla-samplerangepx:ix_dla+samplerangepx+1] = 1
+
+ # OFFSETS & COLUMN DENSITY
+ offsets_array = np.full([np.sum(ix_dla_range)], np.nan, dtype=np.float32) # Start all NaN markers
+ column_density = np.full([np.sum(ix_dla_range)], np.nan, dtype=np.float32)
+ # Add DLAs, this loop will work from the DLA outward updating the offset values and not update it
+ # if it would overwrite something set by another nearby DLA
+ for i in range(int(samplerangepx+1)):
+ for ix_dla,j in zip(ix_dlas,range(len(ix_dlas))):
+ offsets_array[ix_dla+i] = -i if np.isnan(offsets_array[ix_dla+i]) else offsets_array[ix_dla+i]
+ offsets_array[ix_dla-i] = i if np.isnan(offsets_array[ix_dla-i]) else offsets_array[ix_dla-i]
+ column_density[ix_dla+i] = coldensity_dlas[j] if np.isnan(column_density[ix_dla+i]) else column_density[ix_dla+i]
+ column_density[ix_dla-i] = coldensity_dlas[j] if np.isnan(column_density[ix_dla-i]) else column_density[ix_dla-i]
+ offsets_array = np.nan_to_num(offsets_array)
+ column_density = np.nan_to_num(column_density)
+
+ # Append these to the Sightline
+ sightline.classification = classification
+ sightline.offsets = offsets_array
+ sightline.column_density = column_density
+
+ # classification is 1 / 0 / -1 for DLA/nonDLA/border
+ # offsets_array is offset
+ return classification, offsets_array, column_density
+
+def rebin(sightline, v):
+ """
+ Resample and rebin the input Sightline object's data to a constant dlambda/lambda dispersion.
+ Parameters
+ ----------
+ sightline: :class:`dla_cnn.data_model.Sightline.Sightline`
+ v: float, and np.log(1+v/c) is dlambda/lambda, its unit is m/s, c is the velocity of light
+ Returns
+ -------
+ :class:`dla_cnn.data_model.Sightline.Sightline`:
+ """
+ # TODO -- Add inline comments
+ c = 2.9979246e8
+ dlnlambda = np.log(1+v/c)
+ wavelength = 10**sightline.loglam
+ max_wavelength = wavelength[-1]
+ min_wavelength = wavelength[0]
+ pixels_number = int(np.round(np.log(max_wavelength/min_wavelength)/dlnlambda))+1
+ new_wavelength = wavelength[0]*np.exp(dlnlambda*np.arange(pixels_number))
+
+ npix = len(wavelength)
+ wvh = (wavelength + np.roll(wavelength, -1)) / 2.
+ wvh[npix - 1] = wavelength[npix - 1] + \
+ (wavelength[npix - 1] - wavelength[npix - 2]) / 2.
+ dwv = wvh - np.roll(wvh, 1)
+ dwv[0] = 2 * (wvh[0] - wavelength[0])
+ med_dwv = np.median(dwv)
+
+ cumsum = np.cumsum(sightline.flux * dwv)
+ cumvar = np.cumsum(sightline.error * dwv, dtype=np.float64)
+
+ fcum = interp1d(wvh, cumsum,bounds_error=False)
+ fvar = interp1d(wvh, cumvar,bounds_error=False)
+
+ nnew = len(new_wavelength)
+ nwvh = (new_wavelength + np.roll(new_wavelength, -1)) / 2.
+ nwvh[nnew - 1] = new_wavelength[nnew - 1] + \
+ (new_wavelength[nnew - 1] - new_wavelength[nnew - 2]) / 2.
+
+ bwv = np.zeros(nnew + 1)
+ bwv[0] = new_wavelength[0] - (new_wavelength[1] - new_wavelength[0]) / 2.
+ bwv[1:] = nwvh
+
+ newcum = fcum(bwv)
+ newvar = fvar(bwv)
+
+ new_fx = (np.roll(newcum, -1) - newcum)[:-1]
+ new_var = (np.roll(newvar, -1) - newvar)[:-1]
+
+ # Normalize (preserve counts and flambda)
+ new_dwv = bwv - np.roll(bwv, 1)
+ new_fx = new_fx / new_dwv[1:]
+ # Preserve S/N (crudely)
+ med_newdwv = np.median(new_dwv)
+ new_var = new_var / (med_newdwv/med_dwv) / new_dwv[1:]
+
+ left = 0
+ while np.isnan(new_fx[left])|np.isnan(new_var[left]):
+ left = left+1
+ right = len(new_fx)
+ while np.isnan(new_fx[right-1])|np.isnan(new_var[right-1]):
+ right = right-1
+
+ test = np.sum((np.isnan(new_fx[left:right]))|(np.isnan(new_var[left:right])))
+ assert test==0, 'Missing value in this spectra!'
+
+ sightline.loglam = np.log10(new_wavelength[left:right])
+ sightline.flux = new_fx[left:right]
+ sightline.error = new_var[left:right]
+
+ return sightline
+
+
+def normalize(sightline, full_wavelength, full_flux):
+ """
+ Normalize this spectra using the lymann-forest part, using the median of the flux array with wavelength in rest frame between max(3800/(1+z_qso),1070)
+ and 1170. Normalize the error array at the same time to maintain the s/n. And for those spectrum cannot be normalzied, this function will assert error, when encounter this case.
+ ---------------------------------------------------
+ parameters:
+ sightline: :class:`dla_cnn.data_model.sightline.Sightline` object, the spectrum to be normalized;
+ full_wavelength: numpy.ndarray, the whole wavelength array of this sightline, since the sightline may not contain the blue channel,
+ we pass the wavelength array to this function
+ full_flux:numpy.ndarray,the whole flux wavelength array of this sightline, take it as a parameter to solve the same problem above.
+ """
+ # determine the blue limit and red limit of the slice we use to normalize this spectra, and when cannot find such a slice, this function will assert error
+ blue_limit = max(3800/(1+sightline.z_qso),1070)
+ red_limit = 1170
+ rest_wavelength = full_wavelength/(sightline.z_qso+1)
+ assert blue_limit <= red_limit,"No Lymann-alpha forest, Please check this spectra: %i"%sightline.id#when no lymann alpha forest exists, assert error.
+ #use the slice we chose above to normalize this spectra, normalize both flux and error array using the same factor to maintain the s/n.
+ good_pix = (rest_wavelength>=blue_limit)&(rest_wavelength<=red_limit)
+ sightline.flux = sightline.flux/np.median(full_flux[good_pix])
+ # sightline.error = sightline.error / np.median(full_error[good_pix])
+ sightline.error = sightline.error/np.median(full_flux[good_pix])
+
+def estimate_s2n(sightline):
+ """
+ Estimate the s/n of a given sightline, using the lymann forest part and excluding dlas.
+ -------------------------------------------------------------------------------------
+ parameters；
+ sightline: class:`dla_cnn.data_model.sightline.Sightline` object, we use it to estimate the s/n,
+ and since we use the lymann forest part, the sightline's wavelength range should contain 1070~1170
+
+ --------------------------------------------------------------------------------------
+ return:
+ s/n : float, the s/n of the given sightline.
+ """
+ #determine the lymann forest part of this sightline
+ blue_limit = max(3800/(1+sightline.z_qso),1070)
+ red_limit = 1170
+ wavelength = 10**sightline.loglam
+ rest_wavelength = wavelength/(sightline.z_qso+1)
+ #lymann forest part of this sightline, contain dlas
+ test = (rest_wavelength>blue_limit)&(rest_wavelength<red_limit)
+ #when excluding the part of dla, we remove the part between central_wavelength+-delta
+ dwv = rest_wavelength[1]-rest_wavelength[0]
+ dv = dwv/rest_wavelength[0] * 3e5 # km/s
+ delta = int(np.round(3000./dv))
+ for dla in sightline.dlas:
+ test = test&((wavelength>dla.central_wavelength+delta)|(wavelength<dla.central_wavelength-delta))
+ s2n = sightline.flux/sightline.error
+ #return s/n
+ return np.median(s2n[test])
+
diff --git a/data/preprocess/dla_cnn/desi/scripts/__init__.py b/data/preprocess/dla_cnn/desi/scripts/__init__.py
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/data/preprocess/dla_cnn/desi/scripts/__init__.py
@@ -0,0 +1 @@
+
diff --git a/data/preprocess/dla_cnn/desi/scripts/generate_summary_table.py b/data/preprocess/dla_cnn/desi/scripts/generate_summary_table.py
new file mode 100644
index 0000000..1933dc4
--- /dev/null
+++ b/data/preprocess/dla_cnn/desi/scripts/generate_summary_table.py
@@ -0,0 +1,144 @@
+"""
+Script to generate summary table for given DESI FITS files
+"""
+import argparse
+from dla_cnn.desi.DesiMock import DesiMock
+from dla_cnn.desi.training_sets import select_samples_50p_pos_neg
+from dla_cnn.desi.preprocess import label_sightline
+from dla_cnn.desi.training_sets import split_sightline_into_samples
+import csv
+from os.path import exists,join
+from os import remove
+
+
+def generate_summary_table(sightlines, output_dir, mode = "w"):
+ """
+ Generate a csv file to store some necessary information of the given sightlines. The necessary information means the id, z_qso,
+ s/n of thelymann forest part(avoid dlas+- 3000km/s), the wavelength range and corresponding pixel number of each channel. And the csv file's format is like:
+
+ id(int)， z_qso(float), s2n(float), wavelength_start_b(float), wavelength_end_b(float), pixel_start_b(int), pixel_end_b(int), wavelength_start_r(float), wavelength_end_r(float), pixel_start_r(int), pixel_end_r(int), wavelength_start_z(float), wavelength_end_z(float), pixel_start_z(int), pixel_end_z(int),dlas_col_density(str),dlas_central_wavelength(str)
+
+ "wavelength_start_b" means the start wavelength value of b channel, "wavelength_end_b" means the end wavelength value of b channel, "pixel_start_b" means the start pixel number of b channel, "pixel_end_b" means the end pixel number of b channel
+ so do the other two channels.Besides, "dlas_col_density" means the col_density array of the sightline, and "dlas_central_wavelength" means the central wavelength array means the central wavelength array of the given sightline. Due to the uncertainty of the dlas' number, we chose to use str format to store the two arrays,
+ each array is written in the format like "value1,value2, value3", and one can use `str.split(",")` to get the data, the column density and central wavelength which have the same index in the two arrays corrspond to the same dla.
+ -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+ parameters:
+ sightlines: list of `dla_cnn.data_model.Sightline.Sightline` object, the sightline contained should contain the all data of b,r,z channel, and shouldn't be rebinned,
+ output_dir: str, where the output csv file is stored, its format should be "xxxx.csv",
+ mode: str, possible values "w", "a", "w" means writing to the csv file directly(overwrite the previous content), "a" means adding more data to the csv file(remaining the previous content)
+ --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+ return:
+ None
+
+ """
+ #the header of the summary table, each element's meaning can refer to above comment
+ headers = ["id","z_qso","s2n","wavelength_start_b","wavelength_end_b","pixel_start_b","pixel_end_b","wavelength_start_r",\
+ "wavelength_end_r","pixel_start_r","pixel_end_r","wavelength_start_z","wavelength_end_z","pixel_start_z","pixel_end_z","dlas_col_density","dlas_central_wavelength"]
+ #open the csv file
+ with open(output_dir, mode=mode,newline="") as summary_table:
+ summary_table_writer = csv.DictWriter(summary_table,headers)
+ if mode == "w":
+ summary_table_writer.writeheader()
+ for sightline in sightlines:
+ #test if this sightline can be used for sample
+ label_sightline(sightline, kernel=400, REST_RANGE=[900,1346], pos_sample_kernel_percent=0.3)
+ flux,classification,offsets,column_density = split_sightline_into_samples(sightline, REST_RANGE=[900,1346], kernel=400)
+ sample_masks=select_samples_50p_pos_neg(sightline,kernel=400)
+ if sample_masks.size!=0:
+ dlas_col_density = ""
+ dlas_central_wavelength = ""
+ for dla in sightline.dlas:
+ if dla.central_wavelength/(sightline.z_qso+1) <1220 and dla.central_wavelength/(sightline.z_qso+1)>912:
+ dlas_col_density += str(dla.col_density)+","
+ dlas_central_wavelength += str(dla.central_wavelength)+","
+ if dlas_central_wavelength!="":
+ #for each sightline, read its information and write to the csv file
+ info = {"id":sightline.id, "z_qso":sightline.z_qso, "s2n": sightline.s2n, "wavelength_start_b":10**sightline.loglam[0],\
+ "wavelength_end_b":10**sightline.loglam[sightline.split_point_br-1],"pixel_start_b":0,"pixel_end_b":sightline.split_point_br-1,\
+ "wavelength_start_r":10**sightline.loglam[sightline.split_point_br],"wavelength_end_r":10**sightline.loglam[sightline.split_point_rz-1],\
+ "pixel_start_r":sightline.split_point_br,"pixel_end_r":sightline.split_point_rz-1,"wavelength_start_z":10**sightline.loglam[sightline.split_point_rz],\
+ "wavelength_end_z":10**sightline.loglam[-1],"pixel_start_z":sightline.split_point_rz,"pixel_end_z":len(sightline.loglam)-1}
+ info["dlas_col_density"] = dlas_col_density[:-1]
+ info["dlas_central_wavelength"] = dlas_central_wavelength[:-1]
+ #write to the csv file
+ summary_table_writer.writerow(info)
+
+def write_summary_table(nums, version,path, output_path):
+ """
+ Directly read data from fits files and write the summary table, the summary table contains all available sightlines(dlas!=[] and z_qso>2.33) in the given fits files.
+ -----------------------------------------------------------------------------------------------------------------------------------------
+ parameters:
+ nums: list, the given fits files' id, its elements' format is int, and one should make sure all fits files are available before invoking this funciton, otherwise some sightlines can be missed;
+ version: int, the version of the data set we use, e.g. if the version is v9.16, then version = 16
+ path: str, the dir of the folder which stores the given fits file, the folder's structure is like folder-fits files' id - fits files , if you are still confused, you can check the below code about read data from the fits file;
+ output_path: str, the dir where the summary table is generated, and if there have been a summary table, then we will remove it and generate a new summary table;
+ ------------------------------------------------------------------------------------------------------------------------------------------
+ retrun:
+ None
+ """
+ #if exists summary table before, remove it
+ if exists(output_path):
+ remove(output_path)
+ def write_as_summary_table(num):
+ """
+ write summary table for a single given fits file, if there have been a summary table then directly write after it, otherwise create a new one
+ ---------------------------------------------------------------------------------------------------------------------------------------------
+ parameter:
+ num: int, the id of the given fits file, e.g. 700
+ ---------------------------------------------------------------------------------------------------------------------------------------------
+ return:
+ None
+ """
+ #read data from fits file
+ file_path = join(path,str(num))
+ spectra = join(file_path,"spectra-%i-%i.fits"%(version,num))
+ truth = join(file_path,"truth-%i-%i.fits"%(version,num))
+ zbest = join(file_path,"zbest-%i-%i.fits"%(version,num))
+ spec = DesiMock()
+ spec.read_fits_file(spectra,truth,zbest)
+ sightlines = []
+
+ for key in spec.data.keys():
+ if spec.data[key]["z_qso"]>2.33 and spec.data[key]["DLAS"]!=[]:
+ sightlines.append(spec.get_sightline(key))
+ #generate summary table
+ if exists(output_path):
+ generate_summary_table(sightlines,output_path,"a")
+ else:
+ generate_summary_table(sightlines,output_path,"w")
+ bad_files = [] #store the fits files with problems
+ #for each id in nums, invoking the `write_as_summary_table` funciton
+ for num in nums:
+ #try:
+ write_as_summary_table(num)
+ #except:
+ #if have problems append to the bad_files
+ #bad_files.append(num)
+ #assert bad_files==[], "these fits files have some problems, check them please, fits files' id :%s"%str(bad_files)
+
+def parser(options = None):
+ parser = argparse.ArgumentParser(description = "read the given FITS files and generate the summary table")
+ parser.add_argument("numbers",type=str,help="Numbers of FITS files, splited by , such as '700,701,702'")
+ parser.add_argument("version",type=int,help="version of the given dataset")
+ parser.add_argument("path", type=str, help= "the path of the FITS files' folder" )
+ parser.add_argument("output_path",type=str,help="the path of the summary table")
+
+ if options is None:
+ args = parser.parse_args()
+ else:
+ args = parser.parse_args(options)
+ return args
+
+def main(args = None):
+ if args is None:
+ pargs = parser()
+ else:
+ pargs = args
+ numbers = pargs.numbers.split(",")
+ numbers = list(map(int,list(numbers)))
+ write_summary_table(numbers,pargs.version,pargs.path,pargs.output_path)
+
+# Command line execution
+if __name__ == "__main__":
+ args = parser()
+ main(args)
diff --git a/data/preprocess/dla_cnn/desi/train.py b/data/preprocess/dla_cnn/desi/train.py
new file mode 100644
index 0000000..58a93f1
--- /dev/null
+++ b/data/preprocess/dla_cnn/desi/train.py
@@ -0,0 +1,216 @@
+""" Methods for training the Model"""
+#!python
+
+"""
+0. Update all methods to TF 2.1 including using tf.Dataset
+"""
+import numpy as np
+import math
+import re
+
+from dla_cnn.desi.model import build_model
+
+
+tensor_regex = re.compile('.*:\d*')
+# Get a tensor by name, convenience method
+def t(tensor_name):
+ tensor_name = tensor_name+":0" if not tensor_regex.match(tensor_name) else tensor_name
+ return tf.get_default_graph().get_tensor_by_name(tensor_name)
+
+
+# Called from train_ann to perform a test of the train or test data, needs to separate pos/neg to get accurate #'s
+def train_ann_test_batch(sess, ixs, data, summary_writer=None): #inputs: label_classifier, label_offset, label_coldensity
+ MAX_BATCH_SIZE = 40000.0
+
+ classifier_accuracy = 0.0
+ classifier_loss_value = 0.0
+ result_rmse_offset = 0.0
+ result_loss_offset_regression = 0.0
+ result_rmse_coldensity = 0.0
+ result_loss_coldensity_regression = 0.0
+
+ # split x and labels into pos/neg
+ pos_ixs = ixs[data['labels_classifier'][ixs]==1]
+ neg_ixs = ixs[data['labels_classifier'][ixs]==0]
+ pos_ixs_split = np.array_split(pos_ixs, math.ceil(len(pos_ixs)/MAX_BATCH_SIZE)) if len(pos_ixs) > 0 else []
+ neg_ixs_split = np.array_split(neg_ixs, math.ceil(len(neg_ixs)/MAX_BATCH_SIZE)) if len(neg_ixs) > 0 else []
+
+ sum_samples = float(len(ixs)) # forces cast of percent len(pos_ix)/sum_samples to a float value
+ sum_pos_only = float(len(pos_ixs))
+
+ # Process pos and neg samples separately
+ # pos
+ for pos_ix in pos_ixs_split:
+ tensors = [t('accuracy'), t('loss_classifier'), t('rmse_offset'), t('loss_offset_regression'),
+ t('rmse_coldensity'), t('loss_coldensity_regression'), t('global_step')]
+ if summary_writer is not None:
+ tensors.extend(tf.get_collection('SUMMARY_A'))
+ tensors.extend(tf.get_collection('SUMMARY_B'))
+ tensors.extend(tf.get_collection('SUMMARY_C'))
+ tensors.extend(tf.get_collection('SUMMARY_shared'))
+
+ run = sess.run(tensors, feed_dict={t('x'): data['fluxes'][pos_ix],
+ t('label_classifier'): data['labels_classifier'][pos_ix],
+ t('label_offset'): data['labels_offset'][pos_ix],
+ t('label_coldensity'): data['col_density'][pos_ix],
+ t('keep_prob'): 1.0})
+ weight_wrt_total_samples = len(pos_ix)/sum_samples
+ weight_wrt_pos_samples = len(pos_ix)/sum_pos_only
+ # print "DEBUG> Processing %d positive samples" % len(pos_ix), weight_wrt_total_samples, sum_samples, weight_wrt_pos_samples, sum_pos_only
+ classifier_accuracy += run[0] * weight_wrt_total_samples
+ classifier_loss_value += np.sum(run[1]) * weight_wrt_total_samples
+ result_rmse_offset += run[2] * weight_wrt_total_samples
+ result_loss_offset_regression += run[3] * weight_wrt_total_samples
+ result_rmse_coldensity += run[4] * weight_wrt_pos_samples
+ result_loss_coldensity_regression += run[5] * weight_wrt_pos_samples
+
+ for i in range(7, len(tensors)):
+ summary_writer.add_summary(run[i], run[6])
+
+ # neg
+ for neg_ix in neg_ixs_split:
+ tensors = [t('accuracy'), t('loss_classifier'), t('rmse_offset'), t('loss_offset_regression'), t('global_step')]
+ if summary_writer is not None:
+ tensors.extend(tf.get_collection('SUMMARY_A'))
+ tensors.extend(tf.get_collection('SUMMARY_B'))
+ tensors.extend(tf.get_collection('SUMMARY_shared'))
+
+ run = sess.run(tensors, feed_dict={t('x'): data['fluxes'][neg_ix],
+ t('label_classifier'): data['labels_classifier'][neg_ix],
+ t('label_offset'): data['labels_offset'][neg_ix],
+ t('keep_prob'): 1.0})
+ weight_wrt_total_samples = len(neg_ix)/sum_samples
+ # print "DEBUG> Processing %d negative samples" % len(neg_ix), weight_wrt_total_samples, sum_samples
+ classifier_accuracy += run[0] * weight_wrt_total_samples
+ classifier_loss_value += np.sum(run[1]) * weight_wrt_total_samples
+ result_rmse_offset += run[2] * weight_wrt_total_samples
+ result_loss_offset_regression += run[3] * weight_wrt_total_samples
+
+ for i in range(5,len(tensors)):
+ summary_writer.add_summary(run[i], run[4])
+
+ return classifier_accuracy, classifier_loss_value, \
+ result_rmse_offset, result_loss_offset_regression, \
+ result_rmse_coldensity, result_loss_coldensity_regression
+
+
+
+def train_ann(hyperparameters, train_dataset, test_dataset, save_filename=None, load_filename=None, tblogs = "../tmp/tblogs", TF_DEVICE=''):
+ """
+ Perform training
+
+ Parameters
+ ----------
+ hyperparameters
+ train_dataset
+ test_dataset
+ save_filename
+ load_filename
+ tblogs
+ TF_DEVICE
+
+ Returns
+ -------
+
+ """
+ training_iters = hyperparameters['training_iters']
+ batch_size = hyperparameters['batch_size']
+ dropout_keep_prob = hyperparameters['dropout_keep_prob']
+
+ # Predefine variables that need to be returned from local scope
+ best_accuracy = 0.0
+ best_offset_rmse = 999999999
+ best_density_rmse = 999999999
+ test_accuracy = None
+ loss_value = None
+
+ with tf.Graph().as_default():
+ train_step_ABC, tb_summaries = build_model(hyperparameters)
+
+ with tf.device(TF_DEVICE), tf.Session() as sess:
+ # Restore or initialize model
+ if load_filename is not None:
+ tf.train.Saver().restore(sess, load_filename+".ckpt")
+ print("Model loaded from checkpoint: %s"%load_filename)
+ else:
+ print("Initializing variables")
+ sess.run(tf.initialize_all_variables())
+
+ summary_writer = tf.train.SummaryWriter(tblogs, sess.graph)
+
+ for i in range(training_iters):
+ # Grab a batch
+ batch_fluxes, batch_labels_classifier, batch_labels_offset, batch_col_density = train_dataset.next_batch(batch_size)
+ # Train
+ sess.run(train_step_ABC, feed_dict={t('x'): batch_fluxes,
+ t('label_classifier'): batch_labels_classifier,
+ t('label_offset'): batch_labels_offset,
+ t('label_coldensity'): batch_col_density,
+ t('keep_prob'): dropout_keep_prob})
+
+ if i % 200 == 0: # i%2 = 1 on 200ths iteration, that's important so we have the full batch pos/neg
+ train_accuracy, loss_value, result_rmse_offset, result_loss_offset_regression, \
+ result_rmse_coldensity, result_loss_coldensity_regression \
+ = train_ann_test_batch(sess, np.random.permutation(train_dataset.fluxes.shape[0])[0:10000],
+ train_dataset.data, summary_writer=summary_writer)
+ # = train_ann_test_batch(sess, batch_ix, data_train) # Note this batch_ix must come from train_step_ABC
+ print("step {:06d}, classify-offset-density acc/loss - RMSE/loss {:0.3f}/{:0.3f} - {:0.3f}/{:0.3f} - {:0.3f}/{:0.3f}".format(
+ i, train_accuracy, float(np.mean(loss_value)), result_rmse_offset,
+ result_loss_offset_regression, result_rmse_coldensity,
+ result_loss_coldensity_regression))
+ # Run on test
+ if i % 5000 == 0 or i == training_iters - 1:
+ test_accuracy, _, result_rmse_offset, _, result_rmse_coldensity, _ = train_ann_test_batch(
+ sess, np.arange(test_dataset.fluxes.shape[0]), test_dataset.data)
+ best_accuracy = test_accuracy if test_accuracy > best_accuracy else best_accuracy
+ best_offset_rmse = result_rmse_offset if result_rmse_offset < best_offset_rmse else best_offset_rmse
+ best_density_rmse = result_rmse_coldensity if result_rmse_coldensity < best_density_rmse else best_density_rmse
+ print(" test accuracy/offset RMSE/density RMSE: {:0.3f} / {:0.3f} / {:0.3f}".format(
+ test_accuracy, result_rmse_offset, result_rmse_coldensity))
+ save_checkpoint(sess, (save_filename + "_" + str(i)) if save_filename is not None else None)
+
+ # Save checkpoint
+ save_checkpoint(sess, save_filename)
+
+ return best_accuracy, test_accuracy, np.mean(loss_value), best_offset_rmse, result_rmse_offset, \
+ best_density_rmse, result_rmse_coldensity
+
+
+
+def calc_normalized_score(best_accuracy, best_offset_rmse, best_coldensity_rmse):
+ """
+ Generate the normalized score from the 3 loss functions
+
+ This should be used for a hyperparameter search
+
+ Parameters
+ ----------
+ best_accuracy
+ best_offset_rmse
+ best_coldensity_rmse
+
+ Returns
+ -------
+
+ """
+ # These mean & std dev are used to normalize the score from all 3 loss functions for hyperparam optimize
+ # TODO -- Reconsider these for DESI
+ mean_best_accuracy = 0.02
+ std_best_accuracy = 0.0535
+ mean_best_offset_rmse = 4.0
+ std_best_offset_rmse = 1.56
+ mean_best_coldensity_rmse = 0.33
+ std_best_coldensity_rmse = 0.1
+ normalized_score = (1 - best_accuracy - mean_best_accuracy) / std_best_accuracy + \
+ (best_offset_rmse - mean_best_offset_rmse) / std_best_offset_rmse + \
+ (best_coldensity_rmse - mean_best_coldensity_rmse) / std_best_coldensity_rmse
+ #
+ return normalized_score
+
+
+def main():
+ # ANN Training
+ (best_accuracy, last_accuracy, last_objective, best_offset_rmse, last_offset_rmse, best_coldensity_rmse,
+ last_coldensity_rmse) = train_ann(hyperparameters, train_dataset, test_dataset,
+ save_filename=checkpoint_filename, load_filename=args['loadmodel'])
+
diff --git a/data/preprocess/dla_cnn/desi/training_sets.py b/data/preprocess/dla_cnn/desi/training_sets.py
new file mode 100644
index 0000000..6516e74
--- /dev/null
+++ b/data/preprocess/dla_cnn/desi/training_sets.py
@@ -0,0 +1,112 @@
+""" Code to build/load/write DESI Training sets"""
+
+'''
+1. Load up the Sightlines
+2. Split into samples of kernel length
+3. Grab DLAs and non-DLA samples
+4. Hold in memory or write to disk??
+5. Convert to TF Dataset
+'''
+
+import multiprocessing
+from multiprocessing import Pool
+import itertools
+
+import numpy as np
+
+from dla_cnn.Timer import Timer
+from dla_cnn.desi.io import read_sightline
+from dla_cnn.spectra_utils import get_lam_data
+from dla_cnn.training_set import select_samples_50p_pos_neg
+
+
+def split_sightline_into_samples(sightline, REST_RANGE, kernel=400):
+ """
+ Split the sightline into a series of snippets, each with length kernel
+
+ Parameters
+ ----------
+ sightline: dla_cnn.data_model.Sightline
+ REST_RANGE: list
+ kernel: int, optional
+
+ Returns
+ -------
+
+ """
+ lam, lam_rest, ix_dla_range = get_lam_data(sightline.loglam, sightline.z_qso, REST_RANGE)
+ #samplerangepx = int(kernel*pos_sample_kernel_percent/2) #60
+ kernelrangepx = int(kernel/2) #200
+ #ix_dlas = [(np.abs(lam[ix_dla_range]-dla.central_wavelength).argmin()) for dla in sightline.dlas]
+ #coldensity_dlas = [dla.col_density for dla in sightline.dlas] # column densities matching ix_dlas
+
+ # FLUXES - Produce a 1748x400 matrix of flux values
+ fluxes_matrix = np.vstack(map(lambda f,r:f[r-kernelrangepx:r+kernelrangepx],
+ zip(itertools.repeat(sightline.flux), np.nonzero(ix_dla_range)[0])))
+ # Return
+ return fluxes_matrix, sightline.classification, sightline.offsets, sightline.column_density
+
+
+def prepare_training_test_set(ids_train, ids_test,
+ train_save_file="../data/localize_train.npy",
+ test_save_file="../data/localize_test.npy",
+ ignore_sightline_markers={},
+ save=False):
+ """
+ Build a Training set for DESI
+
+ and a test set, as desired
+
+ Args:
+ ids_train: list
+ ids_test: list (can be empty)
+ train_save_file: str
+ test_save_file: str
+ ignore_sightline_markers:
+ save: bool
+
+ Returns:
+
+ """
+ num_cores = multiprocessing.cpu_count() - 1
+ p = Pool(num_cores, maxtasksperchild=10) # a thread pool we'll reuse
+
+ # Training data
+ with Timer(disp="read_sightlines"):
+ sightlines_train = p.map(read_sightline, ids_train)
+ # add the ignore markers to the sightline
+ for s in sightlines_train:
+ if hasattr(s.id, 'sightlineid') and s.id.sightlineid >= 0:
+ s.data_markers = ignore_sightline_markers[s.id.sightlineid] if ignore_sightline_markers.has_key(
+ s.id.sightlineid) else []
+ with Timer(disp="split_sightlines_into_samples"):
+ data_split = p.map(split_sightline_into_samples, sightlines_train)
+ with Timer(disp="select_samples_50p_pos_neg"):
+ sample_masks = p.map(select_samples_50p_pos_neg, data_split[1])
+ with Timer(disp="zip and stack"):
+ zip_data_masks = zip(data_split, sample_masks)
+ data_train = {}
+ data_train['flux'] = np.vstack([d[0][m] for d, m in zip_data_masks])
+ data_train['labels_classifier'] = np.hstack([d[1][m] for d, m in zip_data_masks])
+ data_train['labels_offset'] = np.hstack([d[2][m] for d, m in zip_data_masks])
+ data_train['col_density'] = np.hstack([d[3][m] for d, m in zip_data_masks])
+ if save:
+ with Timer(disp="save train data files"):
+ save_tf_dataset(train_save_file, data_train)
+
+ # Same for test data if it exists
+ data_test = {}
+ if len(ids_test) > 0:
+ sightlines_test = p.map(read_sightline, ids_test)
+ data_split = map(split_sightline_into_samples, sightlines_test)
+ sample_masks = map(select_samples_50p_pos_neg, data_split)
+ zip_data_masks = zip(data_split, sample_masks)
+ data_test['flux'] = np.vstack([d[0][m] for d, m in zip_data_masks])
+ data_test['labels_classifier'] = np.hstack([d[1][m] for d, m in zip_data_masks])
+ data_test['labels_offset'] = np.hstack([d[2][m] for d, m in zip_data_masks])
+ data_test['col_density'] = np.hstack([d[3][m] for d, m in zip_data_masks])
+ if save:
+ save_tf_dataset(test_save_file, data_test)
+
+ # Return
+ return data_train, data_test
diff --git a/data/preprocess/dla_cnn/gen_catalogs.sh b/data/preprocess/dla_cnn/gen_catalogs.sh
new file mode 100644
index 0000000..5225ff8
--- /dev/null
+++ b/data/preprocess/dla_cnn/gen_catalogs.sh
@@ -0,0 +1,18 @@
+#/bin/bash
+if [[ $1 == "" ]]; then echo "Usage: get_catalogs vX.X.X"; exit; fi
+version=$1
+
+# gensample 10k test DLAs
+nice python -c "import data_loader as d; d.process_catalog_gensample(gensample_files_glob=\"../data/gensample_hdf5_files/test_dlas_96629_10000.hdf5\", json_files_glob=\"../data/gensample_hdf5_files/test_dlas_96629_10000.json\", output_dir=\"../tmp/model_${version}_data_testdlas10k96629/\")"
+
+# gensample 5k test DLAs
+nice python -c "import data_loader as d; d.process_catalog_gensample(gensample_files_glob=\"../data/gensample_hdf5_files/test_dlas_96451_5000.hdf5\", json_files_glob=\"../data/gensample_hdf5_files/test_dlas_96451_5000.json\", output_dir=\"../tmp/model_${version}_data_testdlas5k96451/\")"
+
+# gensample 10k mixed
+nice python -c "import data_loader as d; d.process_catalog_gensample(gensample_files_glob=\"../data/gensample_hdf5_files/test_mix_23559_10000.hdf5\", json_files_glob=\"../data/gensample_hdf5_files/test_mix_23559_10000.json\", output_dir=\"../tmp/model_${version}_data_genmix23559/\")"
+
+# DR5
+nice python -c "import data_loader as d; d.process_catalog_dr7(output_dir=\"../tmp/model_${version}_data_dr5/\")"
+
+# DR12
+nice python -c "import data_loader as d; d.process_catalog_csv_pmf(output_dir=\"../tmp/model_${version}_data_dr12/\")"
diff --git a/data/preprocess/dla_cnn/io.py b/data/preprocess/dla_cnn/io.py
new file mode 100644
index 0000000..0b8b322
--- /dev/null
+++ b/data/preprocess/dla_cnn/io.py
@@ -0,0 +1,237 @@
+""" Module for loading files and results from CNN
+"""
+from __future__ import print_function, absolute_import, division, unicode_literals
+
+import os, sys
+import numpy as np
+
+from pkg_resources import resource_filename
+
+from scipy.signal import find_peaks_cwt
+
+from astropy.io import fits
+from astropy.table import Table
+from astropy import units as u
+from astropy.coordinates import SkyCoord
+
+from linetools import utils as ltu
+
+from pyigm.abssys.dla import DLASystem
+from pyigm.abssys.lls import LLSSystem
+from pyigm.surveys.llssurvey import LLSSurvey
+from pyigm.surveys.dlasurvey import DLASurvey
+
+dr7_file = resource_filename('dla_cnn', 'catalogs/sdss_dr7/predictions_SDSSDR7.json')
+
+
+def load_ml_file(pred_file):
+ """ Load the search results from the CNN into a DLASurvey object
+ Parameters
+ ----------
+ pred_file
+
+ Returns
+ -------
+ ml_llssurvey: LLSSurvey
+ ml_dlasusrvey: DLASurvey
+ """
+ print("Loading {:s}. Please be patient..".format(pred_file))
+ # Read
+ ml_results = ltu.loadjson(pred_file)
+ use_platef = False
+ if 'plate' in ml_results[0].keys():
+ use_platef = True
+ else:
+ if 'id' in ml_results[0].keys():
+ use_id = True
+ # Init
+ idict = dict(ra=[], dec=[], plate=[], fiber=[])
+ if use_platef:
+ for key in ['plate', 'fiber', 'mjd']:
+ idict[key] = []
+ dlasystems = []
+ llssystems = []
+
+ # Generate coords to speed things up
+ for obj in ml_results:
+ for key in ['ra', 'dec']:
+ idict[key].append(obj[key])
+ ml_coords = SkyCoord(ra=idict['ra'], dec=idict['dec'], unit='deg')
+ ra_names = ml_coords.icrs.ra.to_string(unit=u.hour,sep='',pad=True)
+ dec_names = ml_coords.icrs.dec.to_string(sep='',pad=True,alwayssign=True)
+ vlim = [-500., 500.]*u.km/u.s
+ dcoord = SkyCoord(ra=0., dec=0., unit='deg')
+
+ # Loop on list
+ didx, lidx = [], []
+ print("Looping on sightlines..")
+ for tt,obj in enumerate(ml_results):
+ #if (tt % 100) == 0:
+ # print('tt: {:d}'.format(tt))
+ # Sightline
+ if use_id:
+ plate, fiber = [int(spl) for spl in obj['id'].split('-')]
+ idict['plate'].append(plate)
+ idict['fiber'].append(fiber)
+
+ # Systems
+ for ss,syskey in enumerate(['dlas', 'subdlas']):
+ for idla in obj[syskey]:
+ name = 'J{:s}{:s}_z{:.3f}'.format(ra_names[tt], dec_names[tt], idla['z_dla'])
+ if ss == 0:
+ isys = DLASystem(dcoord, idla['z_dla'], vlim, NHI=idla['column_density'], zem=obj['z_qso'], name=name)
+ else:
+ isys = LLSSystem(dcoord, idla['z_dla'], vlim, NHI=idla['column_density'], zem=obj['z_qso'], name=name)
+ isys.confidence = idla['dla_confidence']
+ isys.s2n = idla['s2n']
+ if use_platef:
+ isys.plate = obj['plate']
+ isys.fiber = obj['fiber']
+ elif use_id:
+ isys.plate = plate
+ isys.fiber = fiber
+ # Save
+ if ss == 0:
+ didx.append(tt)
+ dlasystems.append(isys)
+ else:
+ lidx.append(tt)
+ llssystems.append(isys)
+ # Generate sightline tables
+ sightlines = Table()
+ sightlines['RA'] = idict['ra']
+ sightlines['DEC'] = idict['dec']
+ sightlines['PLATE'] = idict['plate']
+ sightlines['FIBERID'] = idict['fiber']
+ # Surveys
+ ml_llssurvey = LLSSurvey()
+ ml_llssurvey.sightlines = sightlines.copy()
+ ml_llssurvey._abs_sys = llssystems
+ ml_llssurvey.coords = ml_coords[np.array(lidx)]
+
+ ml_dlasurvey = DLASurvey()
+ ml_dlasurvey.sightlines = sightlines.copy()
+ ml_dlasurvey._abs_sys = dlasystems
+ ml_dlasurvey.coords = ml_coords[np.array(didx)]
+
+ # Return
+ return ml_llssurvey, ml_dlasurvey
+
+def load_ml_dr7():
+ """ Load the search results from the SDSS DR7 dataset into
+ a DLASurvey object
+ """
+ print("Loading SDSSDR7 model. Please be patient..")
+ # Read
+ ml_results = ltu.loadjson(dr7_file)
+ use_platef = False
+ if 'plate' in ml_results[0].keys():
+ use_platef = True
+ else:
+ if 'id' in ml_results[0].keys():
+ use_id = True
+ # Init
+ #idict = dict(plate=[], fiber=[], classification_confidence=[], # FOR v2
+ # classification=[], ra=[], dec=[])
+ idict = dict(ra=[], dec=[], plate=[], fiber=[])
+ if use_platef:
+ for key in ['plate', 'fiber', 'mjd']:
+ idict[key] = []
+ dlasystems = []
+ llssystems = []
+
+ # Generate coords to speed things up
+ for obj in ml_results:
+ for key in ['ra', 'dec']:
+ idict[key].append(obj[key])
+ ml_coords = SkyCoord(ra=idict['ra'], dec=idict['dec'], unit='deg')
+ ra_names = ml_coords.icrs.ra.to_string(unit=u.hour,sep='',pad=True)
+ dec_names = ml_coords.icrs.dec.to_string(sep='',pad=True,alwayssign=True)
+ vlim = [-500., 500.]*u.km/u.s
+ dcoord = SkyCoord(ra=0., dec=0., unit='deg')
+
+ # Loop on list
+ didx, lidx = [], []
+ print("Looping on sightlines..")
+ for tt,obj in enumerate(ml_results):
+ #if (tt % 100) == 0:
+ # print('tt: {:d}'.format(tt))
+ # Sightline
+ if use_id:
+ plate, fiber = [int(spl) for spl in obj['id'].split('-')]
+ idict['plate'].append(plate)
+ idict['fiber'].append(fiber)
+
+ # Systems
+ for ss,syskey in enumerate(['dlas', 'subdlas']):
+ for idla in obj[syskey]:
+ name = 'J{:s}{:s}_z{:.3f}'.format(ra_names[tt], dec_names[tt], idla['z_dla'])
+ if ss == 0:
+ isys = DLASystem(dcoord, idla['z_dla'], vlim, NHI=idla['column_density'], zem=obj['z_qso'], name=name)
+ else:
+ isys = LLSSystem(dcoord, idla['z_dla'], vlim, NHI=idla['column_density'], zem=obj['z_qso'], name=name)
+ isys.confidence = idla['dla_confidence']
+ isys.s2n = idla['s2n']
+ if use_platef:
+ isys.plate = obj['plate']
+ isys.fiber = obj['fiber']
+ elif use_id:
+ isys.plate = plate
+ isys.fiber = fiber
+ # Save
+ if ss == 0:
+ didx.append(tt)
+ dlasystems.append(isys)
+ else:
+ lidx.append(tt)
+ llssystems.append(isys)
+ # Generate sightline tables
+ sightlines = Table()
+ sightlines['RA'] = idict['ra']
+ sightlines['DEC'] = idict['dec']
+ sightlines['PLATE'] = idict['plate']
+ sightlines['FIBERID'] = idict['fiber']
+ # Surveys
+ ml_llssurvey = LLSSurvey()
+ ml_llssurvey.sightlines = sightlines.copy()
+ ml_llssurvey._abs_sys = llssystems
+ ml_llssurvey.coords = ml_coords[np.array(lidx)]
+
+ ml_dlasurvey = DLASurvey()
+ ml_dlasurvey.sightlines = sightlines.copy()
+ ml_dlasurvey._abs_sys = dlasystems
+ ml_dlasurvey.coords = ml_coords[np.array(didx)]
+
+ # Return
+ return ml_llssurvey, ml_dlasurvey
+
+
+def load_ml_dr12():
+ # Sightlines
+ dr12_sline_file = resource_filename('dla_cnn', 'catalogs/boss_dr12/DR12_sightlines.fits.gz')
+ dr12_slines = Table.read(dr12_sline_file)
+ # Absorbers
+ dr12_abs_file = resource_filename('dla_cnn', 'catalogs/boss_dr12/DR12_DLA_SLLS.fits.gz')
+ dr12_abs = Table.read(dr12_abs_file)
+ # Return
+ return dr12_slines, dr12_abs
+
+
+def load_garnett16(orig=False):
+ """ Read one of the Garnett files
+ Parameters
+ ----------
+ orig : bool, optional
+ Use the original files, i.e. prior to full publication in MNRAS
+
+ Returns
+ -------
+ g16_abs : Table
+ Table describing the BOSS sightlines
+ """
+ if orig:
+ g16_abs_file = resource_filename('dla_cnn', 'catalogs/boss_dr12/DR12_DLA_garnett16_orig.fits.gz')
+ else:
+ g16_abs_file = resource_filename('dla_cnn', 'catalogs/boss_dr12/MNRAS/merged_g16.fits.gz')
+ g16_abs = Table.read(g16_abs_file)
+ return g16_abs
diff --git a/data/preprocess/dla_cnn/plots.py b/data/preprocess/dla_cnn/plots.py
new file mode 100644
index 0000000..fa06dc8
--- /dev/null
+++ b/data/preprocess/dla_cnn/plots.py
@@ -0,0 +1,189 @@
+""" Module for loading spectra, either fake or real
+"""
+from __future__ import print_function, absolute_import, division, unicode_literals
+
+import matplotlib
+matplotlib.use('Agg')
+from matplotlib.backends.backend_pdf import PdfPages
+from matplotlib import pyplot as plt
+import numpy as np
+import pdb
+
+
+import code, traceback, threading
+
+#from dla_cnn.absorption import generate_voigt_model
+
+# Raise warnings to errors for debugging
+#import warnings
+#warnings.filterwarnings('error')
+
+# Generates a PDF visuals for a sightline and predictions
+def generate_pdf(sightline, path):
+ from dla_cnn.data_loader import get_lam_data
+ from dla_cnn.data_loader import REST_RANGE
+ from dla_cnn.absorption import generate_voigt_model
+
+ loc_conf = sightline.prediction.loc_conf
+ peaks_offset = sightline.prediction.peaks_ixs
+ offset_conv_sum = sightline.prediction.offset_conv_sum
+ # smoothed_sample = sightline.prediction.smoothed_loc_conf()
+
+ PLOT_LEFT_BUFFER = 50 # The number of pixels to plot left of the predicted sightline
+ dlas_counter = 0
+
+ filename = path + "/dla-spec-%s.pdf"%sightline.id.id_string()
+ pp = PdfPages(filename)
+
+ full_lam, full_lam_rest, full_ix_dla_range = get_lam_data(sightline.loglam, sightline.z_qso, REST_RANGE)
+ lam_rest = full_lam_rest[full_ix_dla_range]
+
+ xlim = [REST_RANGE[0]-PLOT_LEFT_BUFFER, lam_rest[-1]]
+ y = sightline.flux
+ y_plot_range = np.mean(y[y > 0]) * 3.0
+ ylim = [-2, y_plot_range]
+
+ n_dlas = len(sightline.prediction.peaks_ixs)
+
+ # Plot DLA range
+ n_rows = 3 + (1 if n_dlas>0 else 0) + n_dlas
+ fig = plt.figure(figsize=(20, (3.75 * (4+n_dlas)) + 0.15))
+ axtxt = fig.add_subplot(n_rows,1,1)
+ axsl = fig.add_subplot(n_rows,1,2)
+ axloc = fig.add_subplot(n_rows,1,3)
+
+ axsl.set_xlabel("Rest frame sightline in region of interest for DLAs with z_qso = [%0.4f]" % sightline.z_qso)
+ axsl.set_ylabel("Flux")
+ axsl.set_ylim(ylim)
+ axsl.set_xlim(xlim)
+ axsl.plot(full_lam_rest, sightline.flux, '-k')
+
+ # Plot 0-line
+ axsl.axhline(0, color='grey')
+
+ # Plot z_qso line over sightline
+ # axsl.plot((1216, 1216), (ylim[0], ylim[1]), 'k-', linewidth=2, color='grey', alpha=0.4)
+
+ # Plot observer frame ticks
+ axupper = axsl.twiny()
+ axupper.set_xlim(xlim)
+ xticks = np.array(axsl.get_xticks())[1:-1]
+ axupper.set_xticks(xticks)
+ axupper.set_xticklabels((xticks * (1 + sightline.z_qso)).astype(np.int32))
+
+ # Sanity check
+ if sightline.dlas and len(sightline.dlas) > 9:
+ print("number of sightlines for {:s} is {:d}".format(
+ sightline.id.id_string(), len(sightline.dlas)))
+
+ # Plot given DLA markers over location plot
+ #for dla in sightline.dlas if sightline.dlas is not None else []:
+ # dla_rest = dla.central_wavelength / (1+sightline.z_qso)
+ # axsl.plot((dla_rest, dla_rest), (ylim[0], ylim[1]), 'g--')
+
+ # Plot localization
+ axloc.set_xlabel("DLA Localization confidence & localization prediction(s)")
+ axloc.set_ylabel("Identification")
+ axloc.plot(lam_rest, loc_conf, color='deepskyblue')
+ axloc.set_ylim([0, 1])
+ axloc.set_xlim(xlim)
+
+ # Classification results
+ textresult = "Classified %s (%0.5f ra / %0.5f dec) with %d DLAs/sub dlas/Ly-B\n" \
+ % (sightline.id.id_string(), sightline.id.ra, sightline.id.dec, n_dlas)
+
+ # Plot localization histogram
+ axloc.scatter(lam_rest, sightline.prediction.offset_hist, s=6, color='orange')
+ axloc.plot(lam_rest, sightline.prediction.offset_conv_sum, color='green')
+ axloc.plot(lam_rest, sightline.prediction.smoothed_conv_sum(), color='yellow', linestyle='-', linewidth=0.25)
+
+ # Plot '+' peak markers
+ if len(peaks_offset) > 0:
+ axloc.plot(lam_rest[peaks_offset], np.minimum(1, offset_conv_sum[peaks_offset]), '+', mew=5, ms=10, color='green', alpha=1)
+
+ #
+ # For loop over each DLA identified
+ #
+ for dlaix, peak in zip(range(0,n_dlas), peaks_offset):
+ # Some calculations that will be used multiple times
+ dla_z = lam_rest[peak] * (1 + sightline.z_qso) / 1215.67 - 1
+
+ # Sightline plot transparent marker boxes
+ axsl.fill_between(lam_rest[peak - 10:peak + 10], y_plot_range, -2, color='green', lw=0, alpha=0.1)
+ axsl.fill_between(lam_rest[peak - 30:peak + 30], y_plot_range, -2, color='green', lw=0, alpha=0.1)
+ axsl.fill_between(lam_rest[peak - 50:peak + 50], y_plot_range, -2, color='green', lw=0, alpha=0.1)
+ axsl.fill_between(lam_rest[peak - 70:peak + 70], y_plot_range, -2, color='green', lw=0, alpha=0.1)
+
+ # Plot column density measures with bar plots
+ # density_pred_per_this_dla = sightline.prediction.density_data[peak-40:peak+40]
+ dlas_counter += 1
+ # mean_col_density_prediction = float(np.mean(density_pred_per_this_dla))
+ density_pred_per_this_dla, mean_col_density_prediction, std_col_density_prediction, bias_correction = \
+ sightline.prediction.get_coldensity_for_peak(peak)
+
+ pltix = fig.add_subplot(n_rows, 1, 5+dlaix)
+ pltix.bar(np.arange(0, density_pred_per_this_dla.shape[0]), density_pred_per_this_dla, 0.25)
+ pltix.set_xlabel("Individual Column Density estimates for peak @ %0.0fA, +/- 0.3 of mean. Bias adjustment of %0.3f added. " %
+ (lam_rest[peak], float(bias_correction)) +
+ "Mean: %0.3f - Median: %0.3f - Stddev: %0.3f" %
+ (mean_col_density_prediction, float(np.median(density_pred_per_this_dla)),
+ float(std_col_density_prediction)))
+ pltix.set_ylim([mean_col_density_prediction - 0.3, mean_col_density_prediction + 0.3])
+ pltix.plot(np.arange(0, density_pred_per_this_dla.shape[0]),
+ np.ones((density_pred_per_this_dla.shape[0]), np.float32) * mean_col_density_prediction)
+ pltix.set_ylabel("Column Density")
+
+ # Add DLA to test result
+ absorber_type = "Ly-b" if sightline.is_lyb(peak) else "DLA" if mean_col_density_prediction >= 20.3 else "sub dla"
+ dla_text = \
+ "%s at: %0.0fA rest / %0.0fA observed / %0.4f z, w/ confidence %0.2f, has Column Density: %0.3f" \
+ % (absorber_type,
+ lam_rest[peak],
+ lam_rest[peak] * (1 + sightline.z_qso),
+ dla_z,
+ min(1.0, float(sightline.prediction.offset_conv_sum[peak])),
+ mean_col_density_prediction)
+ textresult += " > " + dla_text + "\n"
+
+ #
+ # Plot DLA zoom view with voigt overlay
+ #
+
+ dla_min_text = \
+ "%0.0fA rest / %0.0fA observed - NHI %0.3f" \
+ % (lam_rest[peak],
+ lam_rest[peak] * (1 + sightline.z_qso),
+ mean_col_density_prediction)
+
+ # Generate the Voigt profile
+ absorber = dict(z_dla=dla_z, column_density=mean_col_density_prediction)
+ voigt_wave, voigt_model, ixs_mypeaks = generate_voigt_model(sightline, absorber)
+ inax = fig.add_subplot(n_rows, n_dlas, n_dlas*3+dlaix+1)
+ inax.plot(full_lam, sightline.flux, '-k', lw=1.2)
+ inax.plot(full_lam[ixs_mypeaks], sightline.flux[ixs_mypeaks], '+', mew=5, ms=10, color='green', alpha=1)
+ inax.plot(voigt_wave, voigt_model, 'g--', lw=3.0)
+ inax.set_ylim(ylim)
+ # convert peak to index into full_lam range for plotting
+ peak_full_lam = np.nonzero(np.cumsum(full_ix_dla_range) > peak)[0][0]
+ inax.set_xlim([full_lam[peak_full_lam-150],full_lam[peak_full_lam+150]])
+ inax.axhline(0, color='grey')
+
+ #
+ # Plot legend on location graph
+ #
+ axloc.legend(['DLA classifier', 'Localization', 'DLA peak', 'Localization histogram'],
+ bbox_to_anchor=(1.0, 1.05))
+
+
+ # Display text
+ axtxt.text(0, 0, textresult, family='monospace', fontsize='xx-large')
+ axtxt.get_xaxis().set_visible(False)
+ axtxt.get_yaxis().set_visible(False)
+ axtxt.set_frame_on(False)
+
+ fig.tight_layout()
+ pp.savefig(figure=fig)
+ pp.close()
+ plt.close('all')
+ print("Wrote figure to {:s}".format(filename))
+
diff --git a/data/preprocess/dla_cnn/preprocess.sh b/data/preprocess/dla_cnn/preprocess.sh
new file mode 100644
index 0000000..0381af3
--- /dev/null
+++ b/data/preprocess/dla_cnn/preprocess.sh
@@ -0,0 +1,38 @@
+#!/bin/bash
+bdird=../data/gensample_hdf5_files/dlas
+bdirs=../data/gensample_hdf5_files/slls
+bdirh=../data/gensample_hdf5_files/high_nhi
+bdirt=../data/gensample_hdf5_files
+bout=../data/v71gensample
+
+# DLAs x2
+for f in ${bdird}/*.hdf5; do
+ bname=$(basename ${f} .hdf5)
+ echo "Processing: ${bdird}/${bname} to ${bout}/train_dla_${bname}_1"
+ nice python -c "import data_loader as d; d.preprocess_gensample_from_single_hdf5_file(datafile=\"${bdird}/${bname}\", save_file=\"${bout}/train_dla_${bname}_1\")"
+ echo "Processing: ${bdird}/${bname} to ${bout}/train_dla_${bname}_2"
+ nice python -c "import data_loader as d; d.preprocess_gensample_from_single_hdf5_file(datafile=\"${bdird}/${bname}\", save_file=\"${bout}/train_dla_${bname}_2\")"
+done
+
+# High NHI x2
+nice python -c "import data_loader as d; d.preprocess_gensample_from_single_hdf5_file(datafile=\"${bdirh}/${bname}\", save_file=\"${bout}/train_dla_${bname}_1\")"
+nice python -c "import data_loader as d; d.preprocess_gensample_from_single_hdf5_file(datafile=\"${bdirh}/${bname}\", save_file=\"${bout}/train_dla_${bname}_2\")"
+
+# Dual DLAs x2
+echo "Processing: ${bout}/train_overlapdlas_1"
+nice python -c "import data_loader as d; d.preprocess_overlapping_dla_sightlines_from_gensample(save_file=\"${bout}/train_overlapdlas_1\")"
+echo "Processing: ${bout}/train_overlapdlas_2"
+nice python -c "import data_loader as d; d.preprocess_overlapping_dla_sightlines_from_gensample(save_file=\"${bout}/train_overlapdlas_2\")"
+
+# SLLS x1
+for f in ${bdirs}/*.hdf5; do
+ bname=$(basename ${f} .hdf5)
+ echo "Processing: ${bdirs}/${bname} to ${bout}/train_slls_${bname}_1"
+ nice python -c "import data_loader as d; d.preprocess_gensample_from_single_hdf5_file(datafile=\"${bdirs}/${bname}\", save_file=\"${bout}/train_slls_${bname}\")"
+done
+
+# Test files
+nice python -c "import data_loader as d; d.preprocess_gensample_from_single_hdf5_file(datafile=\"${bdirt}/test_dlas_96451_5000\", save_file=\"${bout}/test_dlas_96451_5000\")"
+nice python -c "import data_loader as d; d.preprocess_gensample_from_single_hdf5_file(datafile=\"${bdirt}/test_dlas_96629_10000\", save_file=\"${bout}/test_dlas_96629_10000\")"
+nice python -c "import data_loader as d; d.preprocess_gensample_from_single_hdf5_file(datafile=\"${bdirt}/test_mix_23559_10000\", save_file=\"${bout}/test_mix_23559_10000\")"
+nice python -c "import data_loader as d; d.preprocess_gensample_from_single_hdf5_file(datafile=\"${bdirt}/test_slls_94047_5000\", save_file=\"${bout}/test_slls_94047_5000\")"
diff --git a/data/preprocess/dla_cnn/scripts/__init__.py b/data/preprocess/dla_cnn/scripts/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/data/preprocess/dla_cnn/scripts/analyze_sl.py b/data/preprocess/dla_cnn/scripts/analyze_sl.py
new file mode 100644
index 0000000..b3fdfa1
--- /dev/null
+++ b/data/preprocess/dla_cnn/scripts/analyze_sl.py
@@ -0,0 +1,51 @@
+#!/usr/bin/env python
+
+"""
+Script to generate a PDF of desired sightline
+Requires specdb for the spectral data
+"""
+
+import pdb
+
+def parser(options=None):
+ import argparse
+ # Parse
+ parser = argparse.ArgumentParser(
+ description='Analyze the desired sightline and generate a PDF (v1.0)')
+ parser.add_argument("plate", type=int, help="Plate")
+ parser.add_argument("fiber", type=int, help="Fiber")
+ parser.add_argument("survey", type=str, help="SDSS_DR7, DESI_MOCK")
+
+ if options is None:
+ args = parser.parse_args()
+ else:
+ args = parser.parse_args(options)
+ return args
+
+
+def main(args=None):
+ from pkg_resources import resource_filename
+ from dla_cnn.data_model.sdss_dr7 import process_catalog_dr7
+ from dla_cnn.data_model.desi_mocks import process_catalog_desi_mock
+
+ if args is None:
+ pargs = parser()
+ else:
+ pargs = args
+ default_model = resource_filename('dla_cnn', "models/model_gensample_v7.1")
+ if pargs.survey == 'SDSS_DR7':
+ process_catalog_dr7(kernel_size=400, model_checkpoint=default_model,
+ output_dir="./", pfiber=(pargs.plate, pargs.fiber),
+ make_pdf=True)
+ elif pargs.survey == 'DESI_MOCK':
+ process_catalog_desi_mock(kernel_size=400, model_checkpoint=default_model,
+ output_dir="./", pfiber=(pargs.plate, pargs.fiber),
+ make_pdf=True)
+ #
+ print("See predictions.json file for outputs")
+
+# Command line execution
+if __name__ == '__main__':
+ args = parser()
+ main(args)
+
diff --git a/data/preprocess/dla_cnn/sdss/Model_v4.py b/data/preprocess/dla_cnn/sdss/Model_v4.py
new file mode 100644
index 0000000..b1b5671
--- /dev/null
+++ b/data/preprocess/dla_cnn/sdss/Model_v4.py
@@ -0,0 +1,195 @@
+import tensorflow as tf
+import math
+
+
+def weight_variable(shape):
+ initial = tf.truncated_normal(shape, stddev=0.1)
+ return tf.Variable(initial)
+
+
+def bias_variable(shape):
+ initial = tf.constant(0.1, shape=shape)
+ return tf.Variable(initial)
+
+
+def conv1d(x, W, s):
+ return tf.nn.conv2d(x, W, strides=s, padding='SAME')
+
+
+def pooling_layer_parameterized(pool_method, h_conv, pool_kernel, pool_stride):
+ if pool_method == 1:
+ return tf.nn.max_pool(h_conv, ksize=[1, pool_kernel, 1, 1], strides=[1, pool_stride, 1, 1], padding='SAME')
+ elif pool_method == 2:
+ return tf.nn.avg_pool(h_conv, ksize=[1, pool_kernel, 1, 1], strides=[1, pool_stride, 1, 1], padding='SAME')
+
+
+def variable_summaries(var, name, collection):
+ """Attach a lot of summaries to a Tensor."""
+ with tf.name_scope('summaries') as r:
+ mean = tf.reduce_mean(var)
+ tf.add_to_collection(collection, tf.scalar_summary('mean/' + name, mean))
+ with tf.name_scope('stddev'):
+ stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
+ tf.add_to_collection(collection, tf.scalar_summary('stddev/' + name, stddev))
+ tf.add_to_collection(collection, tf.scalar_summary('max/' + name, tf.reduce_max(var)))
+ tf.add_to_collection(collection, tf.scalar_summary('min/' + name, tf.reduce_min(var)))
+ tf.add_to_collection(collection, tf.histogram_summary(name, var))
+
+
+def build_model(hyperparameters):
+ learning_rate = hyperparameters['learning_rate']
+ l2_regularization_penalty = hyperparameters['l2_regularization_penalty']
+ fc1_n_neurons = hyperparameters['fc1_n_neurons']
+ fc2_1_n_neurons = hyperparameters['fc2_1_n_neurons']
+ fc2_2_n_neurons = hyperparameters['fc2_2_n_neurons']
+ fc2_3_n_neurons = hyperparameters['fc2_3_n_neurons']
+ conv1_kernel = hyperparameters['conv1_kernel']
+ conv2_kernel = hyperparameters['conv2_kernel']
+ conv1_filters = hyperparameters['conv1_filters']
+ conv2_filters = hyperparameters['conv2_filters']
+ conv1_stride = hyperparameters['conv1_stride']
+ conv2_stride = hyperparameters['conv2_stride']
+ pool1_kernel = hyperparameters['pool1_kernel']
+ pool2_kernel = hyperparameters['pool2_kernel']
+ pool1_stride = hyperparameters['pool1_stride']
+ pool2_stride = hyperparameters['pool2_stride']
+ pool1_method = 1
+ pool2_method = 1
+
+ INPUT_SIZE = 400
+ tfo = {} # Tensorflow objects
+
+ x = tf.placeholder(tf.float32, shape=[None, INPUT_SIZE], name='x')
+ label_classifier = tf.placeholder(tf.float32, shape=[None], name='label_classifier')
+ label_offset = tf.placeholder(tf.float32, shape=[None], name='label_offset')
+ label_coldensity = tf.placeholder(tf.float32, shape=[None], name='label_coldensity')
+ keep_prob = tf.placeholder(tf.float32, name='keep_prob')
+ global_step = tf.Variable(0, name='global_step', trainable=False)
+
+ # First Convolutional Layer
+ # Kernel size (16,1)
+ # Stride (4,1)
+ # number of filters = 4 (features?)
+ # Neuron activation = ReLU (rectified linear unit)
+
+ W_conv1 = weight_variable([conv1_kernel, 1, 1, conv1_filters])
+ b_conv1 = bias_variable([conv1_filters])
+ x_4d = tf.reshape(x, [-1, INPUT_SIZE, 1, 1])
+
+ # https://www.tensorflow.org/versions/r0.10/api_docs/python/nn.html#convolution
+ # out_height = ceil(float(in_height) / float(strides[1])) = ceil(1024./4.) = 256
+ # out_width = ceil(float(in_width) / float(strides[2])) = 1
+ # shape of h_conv1: [-1, 256, 1, 4]
+ stride1 = [1, conv1_stride, 1, 1]
+ h_conv1 = tf.nn.relu(conv1d(x_4d, W_conv1, stride1) + b_conv1)
+
+ # Kernel size (8,1)
+ # Stride (2,1)
+ # Pooling type = Max Pooling
+ # out_height = ceil(float(in_height) / float(strides[1])) = ceil(256./2.) = 128
+ # out_width = ceil(float(in_width) / float(strides[2])) = 1
+ # shape of h_pool1: [-1, 128, 1, 4]
+ h_pool1 = pooling_layer_parameterized(pool1_method, h_conv1, pool1_kernel, pool1_stride)
+
+ # Second Convolutional Layer
+ # Kernel size (16,1)
+ # Stride (2,1)
+ # number of filters=8
+ # Neuron activation = ReLU (rectified linear unit)
+ W_conv2 = weight_variable([conv2_kernel, 1, conv1_filters, conv2_filters])
+ b_conv2 = bias_variable([conv2_filters])
+ # out_height = ceil(float(in_height) / float(strides[1])) = ceil(128./2.) = 64
+ # out_width = ceil(float(in_width) / float(strides[2])) = 1
+ # shape of h_conv1: [-1, 64, 1, 8]
+ stride2 = [1, conv2_stride, 1, 1]
+ h_conv2 = tf.nn.relu(conv1d(h_pool1, W_conv2, stride2) + b_conv2)
+ h_pool2 = pooling_layer_parameterized(pool2_method, h_conv2, pool2_kernel, pool2_stride)
+
+ # FC1: first fully connected layer, shared
+ inputsize_fc1 = int(math.ceil(math.ceil(math.ceil(math.ceil(
+ INPUT_SIZE / conv1_stride) / pool1_stride) / conv2_stride) / pool2_stride)) * conv2_filters
+ h_pool2_flat = tf.reshape(h_pool2, [-1, inputsize_fc1])
+
+ W_fc1 = weight_variable([inputsize_fc1, fc1_n_neurons])
+ b_fc1 = bias_variable([fc1_n_neurons])
+ h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
+
+ # Dropout FC1
+ h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
+
+ W_fc2_1 = weight_variable([fc1_n_neurons, fc2_1_n_neurons])
+ b_fc2_1 = bias_variable([fc2_1_n_neurons])
+ W_fc2_2 = weight_variable([fc1_n_neurons, fc2_2_n_neurons])
+ b_fc2_2 = bias_variable([fc2_2_n_neurons])
+ W_fc2_3 = weight_variable([fc1_n_neurons, fc2_3_n_neurons])
+ b_fc2_3 = bias_variable([fc2_3_n_neurons])
+
+ # FC2 activations
+ h_fc2_1 = tf.nn.relu(tf.matmul(h_fc1_drop, W_fc2_1) + b_fc2_1)
+ h_fc2_2 = tf.nn.relu(tf.matmul(h_fc1_drop, W_fc2_2) + b_fc2_2)
+ h_fc2_3 = tf.nn.relu(tf.matmul(h_fc1_drop, W_fc2_3) + b_fc2_3)
+
+ # FC2 Dropout [1-3]
+ h_fc2_1_drop = tf.nn.dropout(h_fc2_1, keep_prob)
+ h_fc2_2_drop = tf.nn.dropout(h_fc2_2, keep_prob)
+ h_fc2_3_drop = tf.nn.dropout(h_fc2_3, keep_prob)
+
+ # Readout Layer
+ W_fc3_1 = weight_variable([fc2_1_n_neurons, 1])
+ b_fc3_1 = bias_variable([1])
+ W_fc3_2 = weight_variable([fc2_2_n_neurons, 1])
+ b_fc3_2 = bias_variable([1])
+ W_fc3_3 = weight_variable([fc2_3_n_neurons, 1])
+ b_fc3_3 = bias_variable([1])
+
+ # y_fc4 = tf.add(tf.matmul(h_fc3_drop, W_fc4), b_fc4)
+ # y_nn = tf.reshape(y_fc4, [-1])
+ y_fc4_1 = tf.add(tf.matmul(h_fc2_1_drop, W_fc3_1), b_fc3_1)
+ y_nn_classifier = tf.reshape(y_fc4_1, [-1], name='y_nn_classifer')
+ y_fc4_2 = tf.add(tf.matmul(h_fc2_2_drop, W_fc3_2), b_fc3_2)
+ y_nn_offset = tf.reshape(y_fc4_2, [-1], name='y_nn_offset')
+ y_fc4_3 = tf.add(tf.matmul(h_fc2_3_drop, W_fc3_3), b_fc3_3)
+ y_nn_coldensity = tf.reshape(y_fc4_3, [-1], name='y_nn_coldensity')
+
+ # Train and Evaluate the model
+ loss_classifier = tf.add(tf.nn.sigmoid_cross_entropy_with_logits(y_nn_classifier, label_classifier),
+ l2_regularization_penalty * (tf.nn.l2_loss(W_conv1) + tf.nn.l2_loss(W_conv2) +
+ tf.nn.l2_loss(W_fc1) + tf.nn.l2_loss(W_fc2_1)),
+ name='loss_classifier')
+ loss_offset_regression = tf.add(tf.reduce_sum(tf.nn.l2_loss(y_nn_offset - label_offset)),
+ l2_regularization_penalty * (tf.nn.l2_loss(W_conv1) + tf.nn.l2_loss(W_conv2) +
+ tf.nn.l2_loss(W_fc1) + tf.nn.l2_loss(W_fc2_2)),
+ name='loss_offset_regression')
+ epsilon = 1e-6
+ loss_coldensity_regression = tf.reduce_sum(
+ tf.mul(tf.square(y_nn_coldensity - label_coldensity),
+ tf.div(label_coldensity,label_coldensity+epsilon)) +
+ l2_regularization_penalty * (tf.nn.l2_loss(W_conv1) + tf.nn.l2_loss(W_conv2) +
+ tf.nn.l2_loss(W_fc1) + tf.nn.l2_loss(W_fc2_1)),
+ name='loss_coldensity_regression')
+
+ optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
+
+ cost_all_samples_lossfns_AB = loss_classifier + loss_offset_regression
+ cost_pos_samples_lossfns_ABC = loss_classifier + loss_offset_regression + loss_coldensity_regression
+ # train_step_AB = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost_all_samples_lossfns_AB, global_step=global_step, name='train_step_AB')
+ train_step_ABC = optimizer.minimize(cost_pos_samples_lossfns_ABC, global_step=global_step, name='train_step_ABC')
+ # train_step_C = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss_coldensity_regression, global_step=global_step, name='train_step_C')
+ output_classifier = tf.sigmoid(y_nn_classifier, name='output_classifier')
+ prediction = tf.round(output_classifier, name='prediction')
+ correct_prediction = tf.equal(prediction, label_classifier)
+ accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name='accuracy')
+ rmse_offset = tf.sqrt(tf.reduce_mean(tf.square(tf.sub(y_nn_offset,label_offset))), name='rmse_offset')
+ rmse_coldensity = tf.sqrt(tf.reduce_mean(tf.square(tf.sub(y_nn_coldensity,label_coldensity))), name='rmse_coldensity')
+
+ variable_summaries(loss_classifier, 'loss_classifier', 'SUMMARY_A')
+ variable_summaries(loss_offset_regression, 'loss_offset_regression', 'SUMMARY_B')
+ variable_summaries(loss_coldensity_regression, 'loss_coldensity_regression', 'SUMMARY_C')
+ variable_summaries(accuracy, 'classification_accuracy', 'SUMMARY_A')
+ variable_summaries(rmse_offset, 'rmse_offset', 'SUMMARY_B')
+ variable_summaries(rmse_coldensity, 'rmse_coldensity', 'SUMMARY_C')
+ # tb_summaries = tf.merge_all_summaries()
+
+ return train_step_ABC, tfo #, accuracy , loss_classifier, loss_offset_regression, loss_coldensity_regression, \
+ #x, label_classifier, label_offset, label_coldensity, keep_prob, prediction, output_classifier, y_nn_offset, \
+ #rmse_offset, y_nn_coldensity, rmse_coldensity
\ No newline at end of file
diff --git a/data/preprocess/dla_cnn/sdss/Model_v5.py b/data/preprocess/dla_cnn/sdss/Model_v5.py
new file mode 100644
index 0000000..9b56c9e
--- /dev/null
+++ b/data/preprocess/dla_cnn/sdss/Model_v5.py
@@ -0,0 +1,208 @@
+import tensorflow as tf
+import math
+
+
+def weight_variable(shape):
+ initial = tf.truncated_normal(shape, stddev=0.1)
+ return tf.Variable(initial)
+
+
+def bias_variable(shape):
+ initial = tf.constant(0.0, shape=shape)
+ return tf.Variable(initial)
+
+
+def conv1d(x, W, s):
+ return tf.nn.conv2d(x, W, strides=s, padding='SAME')
+
+
+def pooling_layer_parameterized(pool_method, h_conv, pool_kernel, pool_stride):
+ if pool_method == 1:
+ return tf.nn.max_pool(h_conv, ksize=[1, pool_kernel, 1, 1], strides=[1, pool_stride, 1, 1], padding='SAME')
+ elif pool_method == 2:
+ return tf.nn.avg_pool(h_conv, ksize=[1, pool_kernel, 1, 1], strides=[1, pool_stride, 1, 1], padding='SAME')
+
+
+def variable_summaries(var, name, collection):
+ """Attach a lot of summaries to a Tensor."""
+ with tf.name_scope('summaries') as r:
+ mean = tf.reduce_mean(var)
+ tf.add_to_collection(collection, tf.summary.scalar('mean/' + name, mean))
+ with tf.name_scope('stddev'):
+ stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
+ tf.add_to_collection(collection, tf.summary.scalar('stddev/' + name, stddev))
+ tf.add_to_collection(collection, tf.summary.scalar('max/' + name, tf.reduce_max(var)))
+ tf.add_to_collection(collection, tf.summary.scalar('min/' + name, tf.reduce_min(var)))
+ tf.add_to_collection(collection, tf.summary.histogram(name, var))
+
+
+def build_model(hyperparameters):
+ learning_rate = hyperparameters['learning_rate']
+ batch_size = hyperparameters['batch_size']
+ l2_regularization_penalty = hyperparameters['l2_regularization_penalty']
+ fc1_n_neurons = hyperparameters['fc1_n_neurons']
+ fc2_1_n_neurons = hyperparameters['fc2_1_n_neurons']
+ fc2_2_n_neurons = hyperparameters['fc2_2_n_neurons']
+ fc2_3_n_neurons = hyperparameters['fc2_3_n_neurons']
+ conv1_kernel = hyperparameters['conv1_kernel']
+ conv2_kernel = hyperparameters['conv2_kernel']
+ conv3_kernel = hyperparameters['conv3_kernel']
+ conv1_filters = hyperparameters['conv1_filters']
+ conv2_filters = hyperparameters['conv2_filters']
+ conv3_filters = hyperparameters['conv3_filters']
+ conv1_stride = hyperparameters['conv1_stride']
+ conv2_stride = hyperparameters['conv2_stride']
+ conv3_stride = hyperparameters['conv3_stride']
+ pool1_kernel = hyperparameters['pool1_kernel']
+ pool2_kernel = hyperparameters['pool2_kernel']
+ pool3_kernel = hyperparameters['pool3_kernel']
+ pool1_stride = hyperparameters['pool1_stride']
+ pool2_stride = hyperparameters['pool2_stride']
+ pool3_stride = hyperparameters['pool3_stride']
+ pool1_method = 1 # Removed from hyperparameter search because it never chose anything but this method
+ pool2_method = 1
+ pool3_method = 1
+
+ INPUT_SIZE = 400
+ tfo = {} # Tensorflow objects
+
+ x = tf.placeholder(tf.float32, shape=[None, INPUT_SIZE], name='x')
+ label_classifier = tf.placeholder(tf.float32, shape=[None], name='label_classifier')
+ label_offset = tf.placeholder(tf.float32, shape=[None], name='label_offset')
+ label_coldensity = tf.placeholder(tf.float32, shape=[None], name='label_coldensity')
+ keep_prob = tf.placeholder(tf.float32, name='keep_prob')
+ global_step = tf.Variable(0, name='global_step', trainable=False)
+
+ x_4d = tf.reshape(x, [-1, INPUT_SIZE, 1, 1])
+
+ # First Convolutional Layer
+ # Kernel size (16,1)
+ # Stride (4,1)
+ # number of filters = 4 (features?)
+ # Neuron activation = ReLU (rectified linear unit)
+ W_conv1 = weight_variable([conv1_kernel, 1, 1, conv1_filters])
+ b_conv1 = bias_variable([conv1_filters])
+
+ # https://www.tensorflow.org/versions/r0.10/api_docs/python/nn.html#convolution
+ # out_height = ceil(float(in_height) / float(strides[1])) = ceil(1024./4.) = 256
+ # out_width = ceil(float(in_width) / float(strides[2])) = 1
+ # shape of h_conv1: [-1, 256, 1, 4]
+ stride1 = [1, conv1_stride, 1, 1]
+ h_conv1 = tf.nn.relu(conv1d(x_4d, W_conv1, stride1) + b_conv1)
+
+ # Kernel size (8,1)
+ # Stride (2,1)
+ # Pooling type = Max Pooling
+ # out_height = ceil(float(in_height) / float(strides[1])) = ceil(256./2.) = 128
+ # out_width = ceil(float(in_width) / float(strides[2])) = 1
+ # shape of h_pool1: [-1, 128, 1, 4]
+ h_pool1 = pooling_layer_parameterized(pool1_method, h_conv1, pool1_kernel, pool1_stride)
+
+ # Second Convolutional Layer
+ # Kernel size (16,1)
+ # Stride (2,1)
+ # number of filters=8
+ # Neuron activation = ReLU (rectified linear unit)
+ W_conv2 = weight_variable([conv2_kernel, 1, conv1_filters, conv2_filters])
+ b_conv2 = bias_variable([conv2_filters])
+ stride2 = [1, conv2_stride, 1, 1]
+ h_conv2 = tf.nn.relu(conv1d(h_pool1, W_conv2, stride2) + b_conv2)
+ h_pool2 = pooling_layer_parameterized(pool2_method, h_conv2, pool2_kernel, pool2_stride)
+
+ # Third convolutional layer
+ W_conv3 = weight_variable([conv3_kernel, 1, conv2_filters, conv3_filters])
+ b_conv3 = bias_variable([conv3_filters])
+ stride3 = [1, conv3_stride, 1, 1]
+ h_conv3 = tf.nn.relu(conv1d(h_pool2, W_conv3, stride3) + b_conv3)
+ h_pool3 = pooling_layer_parameterized(pool3_method, h_conv3, pool3_kernel, pool3_stride)
+
+
+ # FC1: first fully connected layer, shared
+ inputsize_fc1 = int(math.ceil(math.ceil(math.ceil(math.ceil(math.ceil(math.ceil(
+ INPUT_SIZE / conv1_stride) / pool1_stride) / conv2_stride) / pool2_stride) / conv3_stride) / pool3_stride)) * conv3_filters
+ # batch_size = x.get_shape().as_list()[0]
+ h_pool3_flat = tf.reshape(h_pool3, [-1, inputsize_fc1])
+
+ W_fc1 = weight_variable([inputsize_fc1, fc1_n_neurons])
+ b_fc1 = bias_variable([fc1_n_neurons])
+ h_fc1 = tf.nn.relu(tf.matmul(h_pool3_flat, W_fc1) + b_fc1)
+
+ # Dropout FC1
+ h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
+
+ W_fc2_1 = weight_variable([fc1_n_neurons, fc2_1_n_neurons])
+ b_fc2_1 = bias_variable([fc2_1_n_neurons])
+ W_fc2_2 = weight_variable([fc1_n_neurons, fc2_2_n_neurons])
+ b_fc2_2 = bias_variable([fc2_2_n_neurons])
+ W_fc2_3 = weight_variable([fc1_n_neurons, fc2_3_n_neurons])
+ b_fc2_3 = bias_variable([fc2_3_n_neurons])
+
+ # FC2 activations
+ h_fc2_1 = tf.nn.relu(tf.matmul(h_fc1_drop, W_fc2_1) + b_fc2_1)
+ h_fc2_2 = tf.nn.relu(tf.matmul(h_fc1_drop, W_fc2_2) + b_fc2_2)
+ h_fc2_3 = tf.nn.relu(tf.matmul(h_fc1_drop, W_fc2_3) + b_fc2_3)
+
+ # FC2 Dropout [1-3]
+ h_fc2_1_drop = tf.nn.dropout(h_fc2_1, keep_prob)
+ h_fc2_2_drop = tf.nn.dropout(h_fc2_2, keep_prob)
+ h_fc2_3_drop = tf.nn.dropout(h_fc2_3, keep_prob)
+
+ # Readout Layer
+ W_fc3_1 = weight_variable([fc2_1_n_neurons, 1])
+ b_fc3_1 = bias_variable([1])
+ W_fc3_2 = weight_variable([fc2_2_n_neurons, 1])
+ b_fc3_2 = bias_variable([1])
+ W_fc3_3 = weight_variable([fc2_3_n_neurons, 1])
+ b_fc3_3 = bias_variable([1])
+
+ # y_fc4 = tf.add(tf.matmul(h_fc3_drop, W_fc4), b_fc4)
+ # y_nn = tf.reshape(y_fc4, [-1])
+ y_fc4_1 = tf.add(tf.matmul(h_fc2_1_drop, W_fc3_1), b_fc3_1)
+ y_nn_classifier = tf.reshape(y_fc4_1, [-1], name='y_nn_classifer')
+ y_fc4_2 = tf.add(tf.matmul(h_fc2_2_drop, W_fc3_2), b_fc3_2)
+ y_nn_offset = tf.reshape(y_fc4_2, [-1], name='y_nn_offset')
+ y_fc4_3 = tf.add(tf.matmul(h_fc2_3_drop, W_fc3_3), b_fc3_3)
+ y_nn_coldensity = tf.reshape(y_fc4_3, [-1], name='y_nn_coldensity')
+
+ # Train and Evaluate the model
+ loss_classifier = tf.add(tf.nn.sigmoid_cross_entropy_with_logits(logits=y_nn_classifier, labels=label_classifier),
+ l2_regularization_penalty * (tf.nn.l2_loss(W_conv1) + tf.nn.l2_loss(W_conv2) +
+ tf.nn.l2_loss(W_fc1) + tf.nn.l2_loss(W_fc2_1)),
+ name='loss_classifier')
+ loss_offset_regression = tf.add(tf.reduce_sum(tf.nn.l2_loss(y_nn_offset - label_offset)),
+ l2_regularization_penalty * (tf.nn.l2_loss(W_conv1) + tf.nn.l2_loss(W_conv2) +
+ tf.nn.l2_loss(W_fc1) + tf.nn.l2_loss(W_fc2_2)),
+ name='loss_offset_regression')
+ epsilon = 1e-6
+ loss_coldensity_regression = tf.reduce_sum(
+ tf.multiply(tf.square(y_nn_coldensity - label_coldensity),
+ tf.div(label_coldensity,label_coldensity+epsilon)) +
+ l2_regularization_penalty * (tf.nn.l2_loss(W_conv1) + tf.nn.l2_loss(W_conv2) +
+ tf.nn.l2_loss(W_fc1) + tf.nn.l2_loss(W_fc2_1)),
+ name='loss_coldensity_regression')
+
+ optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
+
+ cost_all_samples_lossfns_AB = loss_classifier + loss_offset_regression
+ cost_pos_samples_lossfns_ABC = loss_classifier + loss_offset_regression + loss_coldensity_regression
+ # train_step_AB = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost_all_samples_lossfns_AB, global_step=global_step, name='train_step_AB')
+ train_step_ABC = optimizer.minimize(cost_pos_samples_lossfns_ABC, global_step=global_step, name='train_step_ABC')
+ # train_step_C = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss_coldensity_regression, global_step=global_step, name='train_step_C')
+ output_classifier = tf.sigmoid(y_nn_classifier, name='output_classifier')
+ prediction = tf.round(output_classifier, name='prediction')
+ correct_prediction = tf.equal(prediction, label_classifier)
+ accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name='accuracy')
+ rmse_offset = tf.sqrt(tf.reduce_mean(tf.square(tf.subtract(y_nn_offset,label_offset))), name='rmse_offset')
+ rmse_coldensity = tf.sqrt(tf.reduce_mean(tf.square(tf.subtract(y_nn_coldensity,label_coldensity))), name='rmse_coldensity')
+
+ variable_summaries(loss_classifier, 'loss_classifier', 'SUMMARY_A')
+ variable_summaries(loss_offset_regression, 'loss_offset_regression', 'SUMMARY_B')
+ variable_summaries(loss_coldensity_regression, 'loss_coldensity_regression', 'SUMMARY_C')
+ variable_summaries(accuracy, 'classification_accuracy', 'SUMMARY_A')
+ variable_summaries(rmse_offset, 'rmse_offset', 'SUMMARY_B')
+ variable_summaries(rmse_coldensity, 'rmse_coldensity', 'SUMMARY_C')
+ # tb_summaries = tf.merge_all_summaries()
+
+ return train_step_ABC, tfo #, accuracy , loss_classifier, loss_offset_regression, loss_coldensity_regression, \
+ #x, label_classifier, label_offset, label_coldensity, keep_prob, prediction, output_classifier, y_nn_offset, \
+ #rmse_offset, y_nn_coldensity, rmse_coldensity
diff --git a/data/preprocess/dla_cnn/sdss/__init__.py b/data/preprocess/dla_cnn/sdss/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/data/preprocess/dla_cnn/sdss/localize_model.py b/data/preprocess/dla_cnn/sdss/localize_model.py
new file mode 100644
index 0000000..4c90a8f
--- /dev/null
+++ b/data/preprocess/dla_cnn/sdss/localize_model.py
@@ -0,0 +1,406 @@
+""" This code was for TF 1.X and the SDSS model"""
+#!python
+from __future__ import print_function, absolute_import, division, unicode_literals
+
+from dla_cnn.sdss.Model_v5 import build_model
+import tensorflow as tf
+import numpy as np
+import random, os, sys, traceback, math, json, timeit, gc, multiprocessing, gzip, pickle
+import peakutils, re, scipy, getopt, argparse, fasteners
+
+# Mean and std deviation of distribution of column densities
+# COL_DENSITY_MEAN = 20.488289796394628
+# COL_DENSITY_STD = 0.31015769579662766
+tensor_regex = re.compile('.*:\d*')
+
+
+
+# def predictions_ann_multiprocess(param_tuple):
+# return predictions_ann(param_tuple[0], param_tuple[1], param_tuple[2], param_tuple[3])
+
+
+def predictions_ann(hyperparameters, flux, checkpoint_filename, TF_DEVICE=''):
+ timer = timeit.default_timer()
+ BATCH_SIZE = 4000
+ n_samples = flux.shape[0]
+ pred = np.zeros((n_samples,), dtype=np.float32)
+ conf = np.copy(pred)
+ offset = np.copy(pred)
+ coldensity = np.copy(pred)
+
+ with tf.Graph().as_default():
+ build_model(hyperparameters)
+
+ with tf.device(TF_DEVICE), tf.Session() as sess:
+ tf.train.Saver().restore(sess, checkpoint_filename+".ckpt")
+ for i in range(0,n_samples,BATCH_SIZE):
+ pred[i:i+BATCH_SIZE], conf[i:i+BATCH_SIZE], offset[i:i+BATCH_SIZE], coldensity[i:i+BATCH_SIZE] = \
+ sess.run([t('prediction'), t('output_classifier'), t('y_nn_offset'), t('y_nn_coldensity')],
+ feed_dict={t('x'): flux[i:i+BATCH_SIZE,:],
+ t('keep_prob'): 1.0})
+
+ print("Localize Model processed {:d} samples in chunks of {:d} in {:0.1f} seconds".format(
+ n_samples, BATCH_SIZE, timeit.default_timer() - timer))
+
+ # coldensity_rescaled = coldensity * COL_DENSITY_STD + COL_DENSITY_MEAN
+ return pred, conf, offset, coldensity
+
+
+def train_ann(hyperparameters, train_dataset, test_dataset, save_filename=None, load_filename=None, tblogs = "../tmp/tblogs", TF_DEVICE=''):
+ training_iters = hyperparameters['training_iters']
+ batch_size = hyperparameters['batch_size']
+ dropout_keep_prob = hyperparameters['dropout_keep_prob']
+
+ # Predefine variables that need to be returned from local scope
+ best_accuracy = 0.0
+ best_offset_rmse = 999999999
+ best_density_rmse = 999999999
+ test_accuracy = None
+ loss_value = None
+
+ with tf.Graph().as_default():
+ train_step_ABC, tb_summaries = build_model(hyperparameters)
+
+ with tf.device(TF_DEVICE), tf.Session() as sess:
+ # Restore or initialize model
+ if load_filename is not None:
+ tf.train.Saver().restore(sess, load_filename+".ckpt")
+ print("Model loaded from checkpoint: %s"%load_filename)
+ else:
+ print("Initializing variables")
+ sess.run(tf.initialize_all_variables())
+
+ summary_writer = tf.train.SummaryWriter(tblogs, sess.graph)
+
+ for i in range(training_iters):
+ batch_fluxes, batch_labels_classifier, batch_labels_offset, batch_col_density = train_dataset.next_batch(batch_size)
+ sess.run(train_step_ABC, feed_dict={t('x'): batch_fluxes,
+ t('label_classifier'): batch_labels_classifier,
+ t('label_offset'): batch_labels_offset,
+ t('label_coldensity'): batch_col_density,
+ t('keep_prob'): dropout_keep_prob})
+
+ if i % 200 == 0: # i%2 = 1 on 200ths iteration, that's important so we have the full batch pos/neg
+ train_accuracy, loss_value, result_rmse_offset, result_loss_offset_regression, \
+ result_rmse_coldensity, result_loss_coldensity_regression \
+ = train_ann_test_batch(sess, np.random.permutation(train_dataset.fluxes.shape[0])[0:10000],
+ train_dataset.data, summary_writer=summary_writer)
+ # = train_ann_test_batch(sess, batch_ix, data_train) # Note this batch_ix must come from train_step_ABC
+ print("step {:06d}, classify-offset-density acc/loss - RMSE/loss {:0.3f}/{:0.3f} - {:0.3f}/{:0.3f} - {:0.3f}/{:0.3f}".format(
+ i, train_accuracy, float(np.mean(loss_value)), result_rmse_offset,
+ result_loss_offset_regression, result_rmse_coldensity,
+ result_loss_coldensity_regression))
+ if i % 5000 == 0 or i == training_iters - 1:
+ test_accuracy, _, result_rmse_offset, _, result_rmse_coldensity, _ = train_ann_test_batch(
+ sess, np.arange(test_dataset.fluxes.shape[0]), test_dataset.data)
+ best_accuracy = test_accuracy if test_accuracy > best_accuracy else best_accuracy
+ best_offset_rmse = result_rmse_offset if result_rmse_offset < best_offset_rmse else best_offset_rmse
+ best_density_rmse = result_rmse_coldensity if result_rmse_coldensity < best_density_rmse else best_density_rmse
+ print(" test accuracy/offset RMSE/density RMSE: {:0.3f} / {:0.3f} / {:0.3f}".format(
+ test_accuracy, result_rmse_offset, result_rmse_coldensity))
+ save_checkpoint(sess, (save_filename + "_" + str(i)) if save_filename is not None else None)
+
+ # Save checkpoint
+ save_checkpoint(sess, save_filename)
+
+ return best_accuracy, test_accuracy, np.mean(loss_value), best_offset_rmse, result_rmse_offset, \
+ best_density_rmse, result_rmse_coldensity
+
+
+def save_checkpoint(sess, save_filename):
+ if save_filename is not None:
+ tf.train.Saver().save(sess, save_filename + ".ckpt")
+ with open(checkpoint_filename + "_hyperparams.json", 'w') as fp:
+ json.dump(hyperparameters, fp)
+ print("Model saved in file: %s" % save_filename + ".ckpt")
+
+
+# Get a tensor by name, convenience method
+def t(tensor_name):
+ tensor_name = tensor_name+":0" if not tensor_regex.match(tensor_name) else tensor_name
+ return tf.get_default_graph().get_tensor_by_name(tensor_name)
+
+
+# Called from train_ann to perform a test of the train or test data, needs to separate pos/neg to get accurate #'s
+def train_ann_test_batch(sess, ixs, data, summary_writer=None): #inputs: label_classifier, label_offset, label_coldensity
+ MAX_BATCH_SIZE = 40000.0
+
+ classifier_accuracy = 0.0
+ classifier_loss_value = 0.0
+ result_rmse_offset = 0.0
+ result_loss_offset_regression = 0.0
+ result_rmse_coldensity = 0.0
+ result_loss_coldensity_regression = 0.0
+
+ # split x and labels into pos/neg
+ pos_ixs = ixs[data['labels_classifier'][ixs]==1]
+ neg_ixs = ixs[data['labels_classifier'][ixs]==0]
+ pos_ixs_split = np.array_split(pos_ixs, math.ceil(len(pos_ixs)/MAX_BATCH_SIZE)) if len(pos_ixs) > 0 else []
+ neg_ixs_split = np.array_split(neg_ixs, math.ceil(len(neg_ixs)/MAX_BATCH_SIZE)) if len(neg_ixs) > 0 else []
+
+ sum_samples = float(len(ixs)) # forces cast of percent len(pos_ix)/sum_samples to a float value
+ sum_pos_only = float(len(pos_ixs))
+
+ # Process pos and neg samples separately
+ # pos
+ for pos_ix in pos_ixs_split:
+ tensors = [t('accuracy'), t('loss_classifier'), t('rmse_offset'), t('loss_offset_regression'),
+ t('rmse_coldensity'), t('loss_coldensity_regression'), t('global_step')]
+ if summary_writer is not None:
+ tensors.extend(tf.get_collection('SUMMARY_A'))
+ tensors.extend(tf.get_collection('SUMMARY_B'))
+ tensors.extend(tf.get_collection('SUMMARY_C'))
+ tensors.extend(tf.get_collection('SUMMARY_shared'))
+
+ run = sess.run(tensors, feed_dict={t('x'): data['fluxes'][pos_ix],
+ t('label_classifier'): data['labels_classifier'][pos_ix],
+ t('label_offset'): data['labels_offset'][pos_ix],
+ t('label_coldensity'): data['col_density'][pos_ix],
+ t('keep_prob'): 1.0})
+ weight_wrt_total_samples = len(pos_ix)/sum_samples
+ weight_wrt_pos_samples = len(pos_ix)/sum_pos_only
+ # print "DEBUG> Processing %d positive samples" % len(pos_ix), weight_wrt_total_samples, sum_samples, weight_wrt_pos_samples, sum_pos_only
+ classifier_accuracy += run[0] * weight_wrt_total_samples
+ classifier_loss_value += np.sum(run[1]) * weight_wrt_total_samples
+ result_rmse_offset += run[2] * weight_wrt_total_samples
+ result_loss_offset_regression += run[3] * weight_wrt_total_samples
+ result_rmse_coldensity += run[4] * weight_wrt_pos_samples
+ result_loss_coldensity_regression += run[5] * weight_wrt_pos_samples
+
+ for i in range(7, len(tensors)):
+ summary_writer.add_summary(run[i], run[6])
+
+ # neg
+ for neg_ix in neg_ixs_split:
+ tensors = [t('accuracy'), t('loss_classifier'), t('rmse_offset'), t('loss_offset_regression'), t('global_step')]
+ if summary_writer is not None:
+ tensors.extend(tf.get_collection('SUMMARY_A'))
+ tensors.extend(tf.get_collection('SUMMARY_B'))
+ tensors.extend(tf.get_collection('SUMMARY_shared'))
+
+ run = sess.run(tensors, feed_dict={t('x'): data['fluxes'][neg_ix],
+ t('label_classifier'): data['labels_classifier'][neg_ix],
+ t('label_offset'): data['labels_offset'][neg_ix],
+ t('keep_prob'): 1.0})
+ weight_wrt_total_samples = len(neg_ix)/sum_samples
+ # print "DEBUG> Processing %d negative samples" % len(neg_ix), weight_wrt_total_samples, sum_samples
+ classifier_accuracy += run[0] * weight_wrt_total_samples
+ classifier_loss_value += np.sum(run[1]) * weight_wrt_total_samples
+ result_rmse_offset += run[2] * weight_wrt_total_samples
+ result_loss_offset_regression += run[3] * weight_wrt_total_samples
+
+ for i in range(5,len(tensors)):
+ summary_writer.add_summary(run[i], run[4])
+
+ return classifier_accuracy, classifier_loss_value, \
+ result_rmse_offset, result_loss_offset_regression, \
+ result_rmse_coldensity, result_loss_coldensity_regression
+
+
+def load_and_save_current_best_hyperparameters(parameters, batch_results_file, best_param_ix):
+ # Filename based on batch_results_file
+ best_filename = os.path.splitext(batch_results_file)[0] + '_bestparams.pickle'
+
+ # Lock file
+ with fasteners.InterProcessLock(best_filename):
+ if os.path.getsize(best_filename) == 0:
+ # Create the file if it doesn't exist
+ with open(best_filename, 'w') as fh:
+ r = np.array([])
+ for i in range(0,100):
+ r = np.hstack((r,np.random.permutation(len(parameters))))
+ best_params = [p[0] for p in parameters]
+ pickle.dump((best_params, r), fh)
+
+ # Read current best parameters (pickle)
+ with open(best_filename, 'r') as fh:
+ (best_params, r) = pickle.load(fh) # load the best parameters currently on disk
+
+ # take all parameters from the version loaded from file except for the parameter we were updating here
+ for j in range(len(best_params)):
+ parameters[j][0] = best_params[j] if j != best_param_ix else parameters[j][0]
+
+ # take the next index from file and roll it
+ next_param_index = r[0]
+ r = np.roll(r,1)
+
+ # Save best parameters file (pickle)
+ with open(best_filename, 'w') as fh:
+ best_params_out = [p[0] for p in parameters] # grab all best params
+ best_params_out[best_param_ix] = parameters[best_param_ix][0] # update the file with this rounds best choice param
+ pickle.dump((best_params_out, r), fh)
+
+ # Return updated parameters and next parameter index
+ return parameters, int(next_param_index)
+
+
+if __name__ == '__main__':
+ #
+ # Execute batch mode
+ #
+ from Dataset import Dataset
+
+ DEFAULT_TRAINING_ITERS = 30000
+ parser = argparse.ArgumentParser()
+ parser.add_argument('-s', '--hyperparamsearch', help='Run hyperparam search', required=False, action='store_true', default=False)
+ parser.add_argument('-o', '--output_file', help='Hyperparam csv result file location', required=False, default='../tmp/batch_results.csv')
+ parser.add_argument('-i', '--iterations', help='Number of training iterations', required=False, default=DEFAULT_TRAINING_ITERS)
+ parser.add_argument('-l', '--loadmodel', help='Specify a model name to load', required=False, default=None)
+ parser.add_argument('-c', '--checkpoint_file', help='Name of the checkpoint file to save (without file extension)', required=False, default="../models/training/current")
+ parser.add_argument('-r', '--train_dataset_filename', help='File name of the training dataset without extension', required=False, default="../data/gensample/train_*.npz")
+ parser.add_argument('-e', '--test_dataset_filename', help='File name of the testing dataset without extension', required=False, default="../data/gensample/test_mix_23559.npz")
+ args = vars(parser.parse_args())
+
+ RUN_SINGLE_ITERATION = not args['hyperparamsearch']
+ checkpoint_filename = args['checkpoint_file'] if RUN_SINGLE_ITERATION else None
+ batch_results_file = args['output_file']
+ tf.logging.set_verbosity(tf.logging.DEBUG)
+
+ train_dataset = Dataset(args['train_dataset_filename'])
+ test_dataset = Dataset(args['test_dataset_filename'])
+
+ exception_counter = 0
+ iteration_num = 0
+
+ parameter_names = ["learning_rate", "training_iters", "batch_size", "l2_regularization_penalty", "dropout_keep_prob",
+ "fc1_n_neurons", "fc2_1_n_neurons", "fc2_2_n_neurons", "fc2_3_n_neurons",
+ "conv1_kernel", "conv2_kernel", "conv3_kernel",
+ "conv1_filters", "conv2_filters", "conv3_filters",
+ "conv1_stride", "conv2_stride", "conv3_stride",
+ "pool1_kernel", "pool2_kernel", "pool3_kernel",
+ "pool1_stride", "pool2_stride", "pool3_stride"]
+ parameters = [
+ # First column: Keeps the best best parameter based on accuracy score
+ # Other columns contain the parameter options to try
+
+ # learning_rate
+ [0.00002, 0.0005, 0.0007, 0.0010, 0.0030, 0.0050, 0.0070],
+ # training_iters
+ [int(args['iterations'])],
+ # batch_size
+ [700, 400, 500, 600, 700, 850, 1000],
+ # l2_regularization_penalty
+ [0.005, 0.01, 0.008, 0.005, 0.003],
+ # dropout_keep_prob
+ [0.98, 0.75, 0.9, 0.95, 0.98, 1],
+ # fc1_n_neurons
+ [350, 200, 350, 500, 700, 900, 1500],
+ # fc2_1_n_neurons
+ [200, 200, 350, 500, 700, 900, 1500],
+ # fc2_2_n_neurons
+ [350, 200, 350, 500, 700, 900, 1500],
+ # fc2_3_n_neurons
+ [150, 200, 350, 500, 700, 900, 1500],
+ # conv1_kernel
+ [32, 20, 22, 24, 26, 28, 32, 40, 48, 54],
+ # conv2_kernel
+ [16, 10, 14, 16, 20, 24, 28, 32, 34],
+ # conv3_kernel
+ [16, 10, 14, 16, 20, 24, 28, 32, 34],
+ # conv1_filters
+ [100, 64, 80, 90, 100, 110, 120, 140, 160, 200],
+ # conv2_filters
+ [96, 80, 96, 128, 192, 256],
+ # conv3_filters
+ [96, 80, 96, 128, 192, 256],
+ # conv1_stride
+ [3, 2, 3, 4, 5, 6, 8],
+ # conv2_stride
+ [1, 1, 2, 3, 4, 5, 6],
+ # conv3_stride
+ [1, 1, 2, 3, 4, 5, 6],
+ # pool1_kernel
+ [7, 3, 4, 5, 6, 7, 8, 9],
+ # pool2_kernel
+ [6, 4, 5, 6, 7, 8, 9, 10],
+ # pool3_kernel
+ [6, 4, 5, 6, 7, 8, 9, 10],
+ # pool1_stride
+ [4, 1, 2, 4, 5, 6],
+ # pool2_stride
+ [4, 1, 2, 3, 4, 5, 6, 7, 8],
+ # pool3_stride
+ [4, 1, 2, 3, 4, 5, 6, 7, 8]
+ ]
+
+ # Random permutation of parameters out some artibrarily long distance
+ r = np.random.permutation(1000)
+
+ # Write out CSV header
+ os.remove(batch_results_file) if os.path.exists(batch_results_file) else None
+ with open(batch_results_file, "a") as csvoutput:
+ csvoutput.write("iteration_num,normalized_score,best_accuracy,last_accuracy,last_objective,best_offset_rmse,last_offset_rmse,best_coldensity_rmse,last_coldensity_rmse," + ",".join(parameter_names) + "\n")
+
+ while (RUN_SINGLE_ITERATION and iteration_num < 1) or not RUN_SINGLE_ITERATION:
+ iteration_best_result = 9999999
+ i = -1 # index of next parameter to change
+
+ # # For distributed HP search we save the list of current best hyperparameters & load results that might
+ # # have been generated by another iteration using the same batch_results_file
+ # if not RUN_SINGLE_ITERATION:
+ # (parameters, i) = load_and_save_current_best_hyperparameters(parameters, batch_results_file, i)
+
+ i = r[0]%len(parameters) # Choose a random parameter to change
+ r = np.roll(r,1)
+ hyperparameters = {} # Dictionary that stores all hyperparameters used in this iteration
+
+ for j in (range(1,len(parameters[i])) if not RUN_SINGLE_ITERATION else range(0,1)):
+ iteration_num += 1
+
+ for k in range(0,len(parameter_names)):
+ hyperparameters[parameter_names[k]] = parameters[k][j] if i == k else parameters[k][0]
+
+ try:
+ # Print parameters during training
+ print("----------------------------------------------------------------------------------------------")
+ print("PARAMETERS (varying %s): " % parameter_names[i])
+ for k in range(0,len(parameters)):
+ sys.stdout.write("{:<30}{:<15}\n".format( parameter_names[k], parameters[k][j] if k==i else parameters[k][0] ))
+
+ # ANN Training
+ (best_accuracy, last_accuracy, last_objective, best_offset_rmse, last_offset_rmse, best_coldensity_rmse,
+ last_coldensity_rmse) = train_ann(hyperparameters, train_dataset, test_dataset,
+ save_filename=checkpoint_filename, load_filename=args['loadmodel'])
+
+ # These mean & std dev are used to normalize the score from all 3 loss functions for hyperparam optimize
+ mean_best_accuracy = 0.02
+ std_best_accuracy = 0.0535
+ mean_best_offset_rmse = 4.0
+ std_best_offset_rmse = 1.56
+ mean_best_coldensity_rmse = 0.33
+ std_best_coldensity_rmse = 0.1
+ normalized_score = (1-best_accuracy-mean_best_accuracy)/std_best_accuracy + \
+ (best_offset_rmse-mean_best_offset_rmse)/std_best_offset_rmse + \
+ (best_coldensity_rmse-mean_best_coldensity_rmse)/std_best_coldensity_rmse
+
+ # Save results and parameters to CSV
+ with open(batch_results_file, "a") as csvoutput:
+ csvoutput.write("%d,%07f,%07f,%07f,%07f,%07f,%07f,%07f,%07f" % \
+ (iteration_num, normalized_score, best_accuracy, last_accuracy,
+ float(last_objective), best_offset_rmse, last_offset_rmse,
+ best_coldensity_rmse, last_coldensity_rmse) )
+ for hp in parameter_names:
+ csvoutput.write(",%07f" % hyperparameters[hp])
+ csvoutput.write("\n")
+
+ # Keep a running tab of the best parameters based on overall accuracy
+ if normalized_score <= iteration_best_result:
+ iteration_best_result = normalized_score
+ parameters[i][0] = parameters[i][j]
+ print("Best result for parameter [%s] with iteration_best_result [%0.2f] now set to [%f]" % (parameter_names[i], iteration_best_result, parameters[i][0]))
+
+ exception_counter = 0 # reset exception counter on success
+
+ # Log and ignore exceptions
+ except Exception as e:
+ exception_counter += 1
+ with open(batch_results_file, "a") as csvoutput:
+ csvoutput.write("%d,error,error,error,error,error,error,error,error" % iteration_num)
+ for hp in parameter_names:
+ csvoutput.write(",%07f" % hyperparameters[hp])
+ csvoutput.write("\n")
+
+ traceback.print_exc()
+ if exception_counter > 20:
+ exit() # circuit breaker
+
diff --git a/data/preprocess/dla_cnn/sdss/training_sets.py b/data/preprocess/dla_cnn/sdss/training_sets.py
new file mode 100644
index 0000000..43c7480
--- /dev/null
+++ b/data/preprocess/dla_cnn/sdss/training_sets.py
@@ -0,0 +1,240 @@
+""" Training sets for SDSS/BOSS. Parks et al. 2018"""
+
+import numpy as np
+
+from dla_cnn.data_model import Sightline
+from dla_cnn.data_model import Id_DR12
+from dla_cnn.data_model import Dla
+
+from dla_cnn.training_set import save_np_dataset
+
+def preprocess_data_from_dr9(kernel=400, stride=3, pos_sample_kernel_percent=0.3,
+ train_keys_csv="../data/dr9_train_set.csv",
+ test_keys_csv="../data/dr9_test_set.csv"):
+ """
+ Custom training sets for SDSS DR9
+
+ Args:
+ kernel:
+ stride:
+ pos_sample_kernel_percent:
+ train_keys_csv:
+ test_keys_csv:
+
+ Returns:
+
+ """
+ dr9_train = np.genfromtxt(train_keys_csv, delimiter=',')
+ dr9_test = np.genfromtxt(test_keys_csv, delimiter=',')
+
+ # Dedup ---(there aren't any in dr9_train, so skipping for now)
+ # dr9_train_keys = np.vstack({tuple(row) for row in dr9_train[:,0:3]})
+
+ sightlines_train = [Sightline(Id_DR12(s[0],s[1],s[2]),[Dla(s[3],s[4])]) for s in dr9_train]
+ sightlines_test = [Sightline(Id_DR12(s[0],s[1],s[2]),[Dla(s[3],s[4])]) for s in dr9_test]
+
+ prepare_localization_training_set(kernel, stride, pos_sample_kernel_percent,
+ sightlines_train, sightlines_test)
+
+
+def prepare_localization_training_set(ids_train, ids_test,
+ train_save_file="../data/localize_train.npy",
+ test_save_file="../data/localize_test.npy",
+ ignore_sightline_markers={}):
+ """
+ Build a Training set
+
+ Args:
+ ids_train:
+ ids_test:
+ train_save_file:
+ test_save_file:
+ ignore_sightline_markers:
+
+ Returns:
+
+ """
+ num_cores = multiprocessing.cpu_count() - 1
+ p = Pool(num_cores, maxtasksperchild=10) # a thread pool we'll reuse
+
+ # Training data
+ with Timer(disp="read_sightlines"):
+ sightlines_train = p.map(read_sightline, ids_train)
+ # add the ignore markers to the sightline
+ for s in sightlines_train:
+ if hasattr(s.id, 'sightlineid') and s.id.sightlineid >= 0:
+ s.data_markers = ignore_sightline_markers[s.id.sightlineid] if ignore_sightline_markers.has_key(
+ s.id.sightlineid) else []
+ with Timer(disp="split_sightlines_into_samples"):
+ data_split = p.map(split_sightline_into_samples, sightlines_train)
+ with Timer(disp="select_samples_50p_pos_neg"):
+ sample_masks = p.map(select_samples_50p_pos_neg, data_split)
+ with Timer(disp="zip and stack"):
+ zip_data_masks = zip(data_split, sample_masks)
+ data_train = {}
+ data_train['flux'] = np.vstack([d[0][m] for d, m in zip_data_masks])
+ data_train['labels_classifier'] = np.hstack([d[1][m] for d, m in zip_data_masks])
+ data_train['labels_offset'] = np.hstack([d[2][m] for d, m in zip_data_masks])
+ data_train['col_density'] = np.hstack([d[3][m] for d, m in zip_data_masks])
+ with Timer(disp="save train data files"):
+ save_np_dataset(train_save_file, data_train)
+
+ # Same for test data if it exists
+ if len(ids_test) > 0:
+ sightlines_test = p.map(read_sightline, ids_test)
+ data_split = map(split_sightline_into_samples, sightlines_test)
+ sample_masks = map(select_samples_50p_pos_neg, data_split)
+ zip_data_masks = zip(data_split, sample_masks)
+ data_test = {}
+ data_test['flux'] = np.vstack([d[0][m] for d, m in zip_data_masks])
+ data_test['labels_classifier'] = np.hstack([d[1][m] for d, m in zip_data_masks])
+ data_test['labels_offset'] = np.hstack([d[2][m] for d, m in zip_data_masks])
+ data_test['col_density'] = np.hstack([d[3][m] for d, m in zip_data_masks])
+ save_np_dataset(test_save_file, data_test)
+
+
+def split_sightline_into_samples(sightline,
+ kernel=400, pos_sample_kernel_percent=0.3):
+ lam, lam_rest, ix_dla_range = get_lam_data(sightline.loglam, sightline.z_qso, REST_RANGE)
+ samplerangepx = int(kernel*pos_sample_kernel_percent/2) #60
+ kernelrangepx = int(kernel/2) #200
+ ix_dlas = [(np.abs(lam[ix_dla_range]-dla.central_wavelength).argmin()) for dla in sightline.dlas]
+ coldensity_dlas = [dla.col_density for dla in sightline.dlas] # column densities matching ix_dlas
+
+ # FLUXES - Produce a 1748x400 matrix of flux values
+ fluxes_matrix = np.vstack(map(lambda f,r:f[r-kernelrangepx:r+kernelrangepx],
+ zip(itertools.repeat(sightline.flux), np.nonzero(ix_dla_range)[0])))
+
+ # CLASSIFICATION (1 = positive sample, 0 = negative sample, -1 = border sample not used
+ # Start with all samples negative
+ classification = np.zeros((REST_RANGE[2]), dtype=np.float32)
+ # overlay samples that are too close to a known DLA, write these for all DLAs before overlaying positive sample 1's
+ for ix_dla in ix_dlas:
+ classification[ix_dla-samplerangepx*2:ix_dla+samplerangepx*2+1] = -1
+ # Mark out Ly-B areas
+ lyb_ix = sightline.get_lyb_index(ix_dla)
+ classification[lyb_ix-samplerangepx:lyb_ix+samplerangepx+1] = -1
+ # mark out bad samples from custom defined markers
+ for marker in sightline.data_markers:
+ assert marker.marker_type == Marker.IGNORE_FEATURE # we assume there are no other marker types for now
+ ixloc = np.abs(lam_rest - marker.lam_rest_location).argmin()
+ classification[ixloc-samplerangepx:ixloc+samplerangepx+1] = -1
+ # overlay samples that are positive
+ for ix_dla in ix_dlas:
+ classification[ix_dla-samplerangepx:ix_dla+samplerangepx+1] = 1
+
+ # OFFSETS & COLUMN DENSITY
+ offsets_array = np.full([REST_RANGE[2]], np.nan, dtype=np.float32) # Start all NaN markers
+ column_density = np.full([REST_RANGE[2]], np.nan, dtype=np.float32)
+ # Add DLAs, this loop will work from the DLA outward updating the offset values and not update it
+ # if it would overwrite something set by another nearby DLA
+ for i in range(int(samplerangepx+1)):
+ for ix_dla,j in zip(ix_dlas,range(len(ix_dlas))):
+ offsets_array[ix_dla+i] = -i if np.isnan(offsets_array[ix_dla+i]) else offsets_array[ix_dla+i]
+ offsets_array[ix_dla-i] = i if np.isnan(offsets_array[ix_dla-i]) else offsets_array[ix_dla-i]
+ column_density[ix_dla+i] = coldensity_dlas[j] if np.isnan(column_density[ix_dla+i]) else column_density[ix_dla+i]
+ column_density[ix_dla-i] = coldensity_dlas[j] if np.isnan(column_density[ix_dla-i]) else column_density[ix_dla-i]
+ offsets_array = np.nan_to_num(offsets_array)
+ column_density = np.nan_to_num(column_density)
+
+ # fluxes is 1748x400 of fluxes
+ # classification is 1 / 0 / -1 for DLA/nonDLA/border
+ # offsets_array is offset
+ return fluxes_matrix, classification, offsets_array, column_density
+
+
+def main(flg_tst, sdss=None, ml_survey=None):
+ import os
+
+ # Sightlines
+ flg_tst = int(flg_tst)
+ if (flg_tst % 2**1) >= 2**0:
+ if sdss is None:
+ sdss = DLASurvey.load_SDSS_DR5(sample='all')
+ slines, sdict = grab_sightlines(sdss, flg_bal=0)
+
+ # Test case of 100 sightlines
+ if (flg_tst % 2**2) >= 2**1:
+ # Make training set
+ _, _ = make_set(100, slines, outroot='results/training_100')
+
+ # Production runs
+ if (flg_tst % 2**3) >= 2**2:
+ #training_prod(123456, 5, 10, outpath=os.getenv('DROPBOX_DIR')+'/MachineLearning/DLAs/') # TEST
+ #training_prod(123456, 10, 500, outpath=os.getenv('DROPBOX_DIR')+'/MachineLearning/DLAs/') # TEST
+ training_prod(12345, 10, 5000, outpath=os.getenv('DROPBOX_DIR')+'/MachineLearning/DLAs/')
+
+ # Production runs -- 100k more
+ if (flg_tst % 2**4) >= 2**3:
+ # python src/training_set.py
+ training_prod(22345, 10, 10000, outpath=os.getenv('DROPBOX_DIR')+'/MachineLearning/DLAs/')
+
+ # Production runs -- 100k more
+ if flg_tst & (2**4):
+ # python src/training_set.py
+ if False:
+ if sdss is None:
+ sdss = DLASurvey.load_SDSS_DR5(sample='all')
+ slines, sdict = grab_sightlines(sdss, flg_bal=0)
+ _, _ = make_set(100, slines, outroot='results/slls_training_100',slls=True)
+ #training_prod(22343, 10, 100, slls=True, outpath=os.getenv('DROPBOX_DIR')+'/MachineLearning/SLLSs/')
+ training_prod(22343, 10, 5000, slls=True, outpath=os.getenv('DROPBOX_DIR')+'/MachineLearning/SLLSs/')
+
+ # Mixed systems for testing
+ if flg_tst & (2**5):
+ # python src/training_set.py
+ if sdss is None:
+ sdss = DLASurvey.load_SDSS_DR5(sample='all')
+ slines, sdict = grab_sightlines(sdss, flg_bal=0)
+ ntrials = 10000
+ seed=23559
+ _, _ = make_set(ntrials, slines, seed=seed, mix=True,
+ outroot=os.getenv('DROPBOX_DIR')+'/MachineLearning/Mix/mix_test_{:d}_{:d}'.format(seed,ntrials))
+
+ # DR5 DLA-free sightlines
+ if flg_tst & (2**6):
+ write_sdss_sightlines()
+
+ # High NHI systems for testing
+ if flg_tst & (2**7):
+ # python src/training_set.py
+ if sdss is None:
+ sdss = DLASurvey.load_SDSS_DR5(sample='all')
+ slines, sdict = grab_sightlines(sdss, flg_bal=0)
+ ntrials = 20000
+ seed=83559
+ _, _ = make_set(ntrials, slines, seed=seed, high=True,
+ outroot=os.getenv('DROPBOX_DIR')+'/MachineLearning/HighNHI/high_train_{:d}_{:d}'.format(seed,ntrials))
+
+ # Low S/N
+ if flg_tst & (2**8):
+ # python src/training_set.py
+ if sdss is None:
+ sdss = DLASurvey.load_SDSS_DR5(sample='all')
+ slines, sdict = grab_sightlines(sdss, flg_bal=0)
+ ntrials = 10000
+ seed=83557
+ _, _ = make_set(ntrials, slines, seed=seed, low_s2n=True,
+ outroot=os.getenv('DROPBOX_DIR')+'/MachineLearning/LowS2N/lows2n_train_{:d}_{:d}'.format(seed,ntrials))
+
+# Test
+if __name__ == '__main__':
+
+ import sys
+ if len(sys.argv) == 1:
+ # Run from above src/
+ # I.e. python src/training_set.py
+ flg_tst = 0
+ #flg_tst += 2**0 # Grab sightlines
+ #flg_tst += 2**1 # First 100
+ #flg_tst += 2**2 # Production run of training - fixed
+ #flg_tst += 2**3 # Another production run of training - fixed seed
+ #flg_tst += 2**4 # A production run with SLLS
+ #flg_tst += 2**5 # A test run with a mix of SLLS and DLAs
+ #flg_tst += 2**6 # Write SDSS DR5 sightlines without DLAs
+ #flg_tst += 2**7 # Training set of high NHI systems
+ flg_tst += 2**8 # Low S/N
+ else:
+ flg_tst = sys.argv[1]
+
+ main(flg_tst)
diff --git a/data/preprocess/dla_cnn/spectra_utils.py b/data/preprocess/dla_cnn/spectra_utils.py
new file mode 100644
index 0000000..3b2f131
--- /dev/null
+++ b/data/preprocess/dla_cnn/spectra_utils.py
@@ -0,0 +1,42 @@
+""" Methods for fussing with a spectrum """
+
+import numpy as np
+
+
+def get_lam_data(loglam, z_qso, REST_RANGE):
+ """
+ Generate wavelengths from the log10 wavelengths
+
+ Parameters
+ ----------
+ loglam: np.ndarray
+ z_qso: float
+ REST_RANGE: list
+ Lowest rest wavelength to search, highest rest wavelength, number of pixels in the search
+
+ Returns
+ -------
+ lam: np.ndarray
+ lam_rest: np.ndarray
+ ix_dla_range: np.ndarray
+ Indices listing where to search for the DLA
+ """
+ lam = 10.0 ** loglam
+ lam_rest = lam / (1.0 + z_qso)
+ ix_dla_range = np.logical_and(lam_rest >= REST_RANGE[0], lam_rest <= REST_RANGE[1])
+
+ # ix_dla_range may be 1 pixels shorter or longer due to rounding error, we force it to a consistent size here
+ size_ix_dla_range = np.sum(ix_dla_range)
+ assert size_ix_dla_range >= REST_RANGE[2] - 2 and size_ix_dla_range <= REST_RANGE[2] + 2, \
+ "Size of DLA range assertion error, size_ix_dla_range: [%d]" % size_ix_dla_range
+ b = np.nonzero(ix_dla_range)[0][0]
+ if size_ix_dla_range < REST_RANGE[2]:
+ # Add a one to the left or right sides, making sure we don't exceed bounds on the left
+ ix_dla_range[max(b - 1, 0):max(b - 1, 0) + REST_RANGE[2]] = 1
+ if size_ix_dla_range > REST_RANGE[2]:
+ ix_dla_range[b + REST_RANGE[2]:] = 0 # Delete 1 or 2 zeros from right side
+ assert np.sum(ix_dla_range) == REST_RANGE[2], \
+ "Size of ix_dla_range: %d, %d, %d, %d, %d" % \
+ (np.sum(ix_dla_range), b, REST_RANGE[2], size_ix_dla_range, np.nonzero(np.flipud(ix_dla_range))[0][0])
+
+ return lam, lam_rest, ix_dla_range
diff --git a/data/preprocess/dla_cnn/training_set.py b/data/preprocess/dla_cnn/training_set.py
new file mode 100644
index 0000000..a378d38
--- /dev/null
+++ b/data/preprocess/dla_cnn/training_set.py
@@ -0,0 +1,479 @@
+""" Module to vette results against Human catalogs
+ SDSS-DR5 (JXP) and BOSS (Notredaeme)
+"""
+from __future__ import print_function, absolute_import, division, unicode_literals
+
+import numpy as np
+import pdb
+
+from scipy import interpolate
+
+
+from astropy.coordinates import SkyCoord, match_coordinates_sky
+from astropy import units as u
+from astropy.table import Table
+from astropy.io.fits import Header
+
+from specdb.specdb import IgmSpec
+
+from linetools import utils as ltu
+from linetools.spectra.xspectrum1d import XSpectrum1D
+from linetools.lists.linelist import LineList
+from pyigm.surveys.dlasurvey import DLASurvey
+
+llist = LineList('HI')
+
+def grab_sightlines(dlasurvey=None, flg_bal=None, zmin=2.3, s2n=5., DX=0.,
+ igmsp_survey='SDSS_DR7', update_zem=True):
+ """ Grab a set of sightlines without DLAs from a DLA survey
+ Insist that all have spectra occur in igmspec
+ Update sightline zem with igmspec zem
+
+ Parameters
+ ----------
+ dlas : DLASurvey
+ Usually SDSS or BOSS
+ flg_bal : int, optional
+ Maximum BAL flag (0=No signature, 1=Weak BAL, 2=BAL)
+ s2n : float, optional
+ Minimum S/N as defined in some manner
+ DX : float, optional
+ Restrict on DX
+ zmin : float, optional
+ Minimum redshift for zem
+ update_zem : bool, optional
+ Update zem in sightlines?
+
+ Returns
+ -------
+ final : Table
+ astropy Table of good sightlines
+ sdict : dict
+ dict describing the sightlines
+ """
+ #1) REMOVE 910, 526 z=2.88; NHI=21.19
+ import warnings
+ warnings.warn("Someday remove 910, 526 which has a *strong* DLA")
+ igmsp = IgmSpec()
+ # Init
+ if dlasurvey is None:
+ print("Using the DR5 sample for the sightlines")
+ dlasurvey = DLASurvey.load_SDSS_DR5(sample='all')
+ igmsp_survey = 'SDSS_DR7'
+ nsight = len(dlasurvey.sightlines)
+ keep = np.array([True]*nsight)
+ meta = igmsp[igmsp_survey].meta
+
+ # Avoid DLAs
+ dla_coord = dlasurvey.coord
+ sl_coord = SkyCoord(ra=dlasurvey.sightlines['RA'], dec=dlasurvey.sightlines['DEC'])
+ idx, d2d, d3d = match_coordinates_sky(sl_coord, dla_coord, nthneighbor=1)
+ clear = d2d > 1*u.arcsec
+ keep = keep & clear
+
+ # BAL
+ if flg_bal is not None:
+ gd_bal = dlasurvey.sightlines['FLG_BAL'] <= flg_bal
+ keep = keep & gd_bal
+
+ # S/N
+ if s2n > 0.:
+ gd_s2n = dlasurvey.sightlines['S2N'] > s2n
+ keep = keep & gd_s2n
+
+ # Cut on DX
+ if DX > 0.:
+ gd_DX = dlasurvey.sightlines['DX'] > DX
+ keep = keep & gd_DX
+
+ # igmsp
+ qso_coord = SkyCoord(ra=meta['RA_GROUP'], dec=meta['DEC_GROUP'], unit='deg')
+ idxq, d2dq, d3dq = match_coordinates_sky(sl_coord, qso_coord, nthneighbor=1)
+ in_igmsp = d2dq < 1*u.arcsec
+ keep = keep & in_igmsp
+
+ # Check zem and dz
+ #igm_id = meta['IGM_ID'][idxq]
+ #cat_rows = match_ids(igm_id, igmsp.cat['IGM_ID'])
+ #zem = igmsp.cat['zem'][cat_rows]
+ zem = meta['zem_GROUP'][idxq]
+ dz = np.abs(zem - dlasurvey.sightlines['ZEM'])
+ gd_dz = dz < 0.1
+ keep = keep & gd_dz #& gd_zlim
+ if zmin is not None:
+ gd_zmin = zem > zmin
+ keep = keep & gd_zmin #& gd_zlim
+ #gd_zlim = (zem-dlasurvey.sightlines['Z_START']) > 0.1
+ #pdb.set_trace()
+
+ # Assess
+ final = dlasurvey.sightlines[keep]
+ #final_coords = SkyCoord(ra=final['RA'], dec=final['DEC'], unit='deg')
+ #matches, meta = igmsp.meta_from_coords(final_coords, groups=['SDSS_DR7'], tol=1*u.arcsec)
+ #idxq2, d2dq2, d3dq2 = match_coordinates_sky(final_coords, qso_coord, nthneighbor=1)
+ #in_igmsp2 = d2dq2 < 1*u.arcsec
+ #pdb.set_trace()
+ sdict = {}
+ sdict['n'] = len(final)
+ print("We have {:d} sightlines for analysis".format(sdict['n']))
+
+ def qck_stats(idict, tbl, istr, key):
+ idict[istr+'min'] = np.min(tbl[key])
+ idict[istr+'max'] = np.max(tbl[key])
+ idict[istr+'median'] = np.median(tbl[key])
+ qck_stats(sdict, final, 'z', 'ZEM')
+ qck_stats(sdict, final, 'i', 'MAG')
+
+ print("Min z = {:g}, Median z = {:g}, Max z = {:g}".format(sdict['zmin'], sdict['zmedian'], sdict['zmax']))
+
+ # Return
+ return final, sdict
+
+
+def init_fNHI(slls=False, mix=False, high=False):
+ """ Generate the interpolator for log NHI
+
+ Returns
+ -------
+ fNHI : scipy.interpolate.interp1d function
+ """
+ from pyigm.fN.fnmodel import FNModel
+ # f(N)
+ fN_model = FNModel.default_model()
+ # Integrate on NHI
+ if slls:
+ lX, cum_lX, lX_NHI = fN_model.calculate_lox(fN_model.zpivot,
+ 19.5, NHI_max=20.3, cumul=True)
+ elif high:
+ lX, cum_lX, lX_NHI = fN_model.calculate_lox(fN_model.zpivot,
+ 21.2, NHI_max=22.5, cumul=True)
+ elif mix:
+ lX, cum_lX, lX_NHI = fN_model.calculate_lox(fN_model.zpivot,
+ 19.5, NHI_max=22.5, cumul=True)
+ else:
+ lX, cum_lX, lX_NHI = fN_model.calculate_lox(fN_model.zpivot,
+ 20.3, NHI_max=22.5, cumul=True)
+ # Interpolator
+ cum_lX /= cum_lX[-1] # Normalize
+ fNHI = interpolate.interp1d(cum_lX, lX_NHI,
+ bounds_error=False,fill_value=lX_NHI[0])
+ return fNHI
+
+
+def insert_dlas(spec, zem, fNHI=None, rstate=None, slls=False,
+ mix=False, high=False, low_s2n=False, noise_boost=4.):
+ """ Insert a DLA into input spectrum
+ Also adjusts the noise
+ Will also add noise 'everywhere' if requested
+ Parameters
+ ----------
+ spec
+ fNHI
+ rstate
+ low_s2n : bool, optional
+ Reduce the S/N everywhere. By a factor of noise_boost
+ noise_boost : float, optional
+ Factor to *increase* the noise by
+
+ Returns
+ -------
+ final_spec : XSpectrum1D
+ dlas : list
+ List of DLAs inserted
+
+ """
+ from pyigm.fN import dla as pyi_fd
+ from pyigm.abssys.dla import DLASystem
+ from pyigm.abssys.lls import LLSSystem
+ from pyigm.abssys.utils import hi_model
+
+ # Init
+ if rstate is None:
+ rstate = np.random.RandomState()
+ if fNHI is None:
+ fNHI = init_fNHI(slls=slls, mix=mix, high=high)
+
+ # Allowed redshift placement
+ ## Cut on zem and 910A rest-frame
+ zlya = spec.wavelength.value/1215.67 - 1
+ dz = np.roll(zlya,-1)-zlya
+ dz[-1] = dz[-2]
+ gdz = (zlya < zem) & (spec.wavelength > 910.*u.AA*(1+zem))
+
+ # l(z) -- Uses DLA for SLLS too which is fine
+ lz = pyi_fd.lX(zlya[gdz], extrap=True, calc_lz=True)
+ cum_lz = np.cumsum(lz*dz[gdz])
+ tot_lz = cum_lz[-1]
+ fzdla = interpolate.interp1d(cum_lz/tot_lz, zlya[gdz],
+ bounds_error=False,fill_value=np.min(zlya[gdz]))#
+
+ # n DLA
+ nDLA = 0
+ while nDLA == 0:
+ nval = rstate.poisson(tot_lz, 100)
+ gdv = nval > 0
+ if np.sum(gdv) == 0:
+ continue
+ else:
+ nDLA = nval[np.where(gdv)[0][0]]
+
+ # Generate DLAs
+ dlas = []
+ for jj in range(nDLA):
+ # Random z
+ zabs = float(fzdla(rstate.random_sample()))
+ # Random NHI
+ NHI = float(fNHI(rstate.random_sample()))
+ if (slls or mix):
+ dla = LLSSystem((0.,0), zabs, None, NHI=NHI)
+ else:
+ dla = DLASystem((0.,0), zabs, (None,None), NHI)
+ dlas.append(dla)
+
+ # Insert
+ vmodel, _ = hi_model(dlas, spec, fwhm=3., llist=llist)
+ # Add noise
+ rand = rstate.randn(spec.npix)
+ noise = rand * spec.sig * (1-vmodel.flux.value)
+ # More noise??
+ if low_s2n:
+ rand2 = rstate.randn(spec.npix)
+ more_noise = noise_boost * rand2 * spec.sig
+ noise += more_noise
+ else:
+ s2n_boost=1.
+
+ final_spec = XSpectrum1D.from_tuple((vmodel.wavelength,
+ spec.flux.value*vmodel.flux.value+noise,
+ noise_boost*spec.sig))
+
+ # Return
+ return final_spec, dlas
+
+
+def make_set(ntrain, slines, outroot=None, tol=1*u.arcsec, igmsp_survey='SDSS_DR7',
+ frac_without=0., seed=1234, zmin=None, zmax=4.5, high=False,
+ slls=False, mix=False, low_s2n=False):
+ """ Generate a training set
+
+ Parameters
+ ----------
+ ntrain : int
+ Number of training sightlines to generate
+ slines : Table
+ Table of sightlines without DLAs (usually from SDSS or BOSS)
+ igmsp_survey : str, optional
+ Dataset name for spectra
+ frac_without : float, optional
+ Fraction of sightlines (on average) without a DLA
+ seed : int, optional
+ outroot : str, optional
+ Root for output filenames
+ root+'.fits' for spectra
+ root+'.json' for DLA info
+ zmin : float, optional
+ Minimum redshift for training; defaults to min(slines['ZEM'])
+ zmax : float, optional
+ Maximum redshift to train on
+ mix : bool, optional
+ Mix of SLLS and DLAs
+ low_s2n : bool, optional
+ Reduce the S/N artificially, i.e. add noise
+
+ Returns
+ -------
+
+ """
+ from linetools.spectra.utils import collate
+
+ # Init and checks
+ igmsp = IgmSpec()
+ assert igmsp_survey in igmsp.groups
+ rstate = np.random.RandomState(seed)
+ rfrac = rstate.random_sample(ntrain)
+ if zmin is None:
+ zmin = np.min(slines['ZEM'])
+ rzem = zmin + rstate.random_sample(ntrain)*(zmax-zmin)
+ fNHI = init_fNHI(slls=slls, mix=mix, high=high)
+
+ all_spec = []
+ full_dict = {}
+ # Begin looping
+ for qq in range(ntrain):
+ print("qq = {:d}".format(qq))
+ full_dict[qq] = {}
+ # Grab sightline
+ isl = np.argmin(np.abs(slines['ZEM']-rzem[qq]))
+ full_dict[qq]['sl'] = isl # sightline
+ specl, meta = igmsp.spectra_from_coord((slines['RA'][isl], slines['DEC'][isl]),
+ groups=['SDSS_DR7'], tol=tol, verbose=False)
+ assert len(meta) == 1
+ spec = specl
+ # Meta data for header
+ mdict = {}
+ for key in meta.keys():
+ mdict[key] = meta[key][0]
+ mhead = Header(mdict)
+ # Clear?
+ if rfrac[qq] < frac_without:
+ spec.meta['headers'][0] = mdict.copy() #mhead
+ all_spec.append(spec)
+ full_dict[qq]['nDLA'] = 0
+ continue
+ # Insert at least one DLA
+ spec, dlas = insert_dlas(spec, mhead['zem_GROUP'], rstate=rstate,
+ fNHI=fNHI, slls=slls, mix=mix, high=high,
+ low_s2n=low_s2n)
+ spec.meta['headers'][0] = mdict.copy() #mhead
+ all_spec.append(spec)
+ full_dict[qq]['nDLA'] = len(dlas)
+ for kk,dla in enumerate(dlas):
+ full_dict[qq][kk] = {}
+ full_dict[qq][kk]['NHI'] = dla.NHI
+ full_dict[qq][kk]['zabs'] = dla.zabs
+
+ # Generate one object
+ final_spec = collate(all_spec)
+ # Write?
+ if outroot is not None:
+ # Spectra
+ final_spec.write_to_hdf5(outroot+'.hdf5')
+ # Dict -> JSON
+ gdict = ltu.jsonify(full_dict)
+ ltu.savejson(outroot+'.json', gdict, overwrite=True)#, easy_to_read=True)
+ # Return
+ return final_spec, full_dict
+
+
+def training_prod(seed, nruns, nsline, nproc=10, outpath='./', slls=False):
+ """ Perform a full production run of training sightlines
+
+ Parameters
+ ----------
+ seed
+ nsline
+ outpath
+
+ Returns
+ -------
+
+ """
+ from subprocess import Popen
+ rstate = np.random.RandomState(seed)
+ # Generate individual seeds
+ seeds = np.round(100000*rstate.random_sample(nruns)).astype(int)
+
+ # Start looping on processor
+ # Loop on the systems
+ nrun = -1
+ while(nrun < nruns):
+ proc = []
+ for ss in range(nproc):
+ nrun += 1
+ if nrun == nruns:
+ break
+ # Run
+ script = ['./scripts/dlaml_trainingset.py', str(seeds[nrun]), str(nsline), str(outpath)]
+ if slls:
+ script = ['./scripts/dlaml_trainingset.py', str(seeds[nrun]), str(nsline), str(outpath), str('--slls')]
+ proc.append(Popen(script))
+ exit_codes = [p.wait() for p in proc]
+
+
+def write_sdss_sightlines():
+ """ Writes the SDSS DR5 sightlines that have no (or very few) DLAs
+ Returns
+ -------
+ None : Writes to Dropbox
+
+ """
+ import os
+ import h5py
+ outfile=os.getenv('DROPBOX_DIR')+'/MachineLearning/DR5/SDSS_DR5_noDLAs.hdf5'
+ # Load
+ sdss = DLASurvey.load_SDSS_DR5(sample='all')
+ slines, sdict = grab_sightlines(sdss, flg_bal=0)
+ coords = SkyCoord(ra=slines['RA'], dec=slines['DEC'], unit='deg')
+ # Load spectra -- RA/DEC in igmsp is not identical to RA_GROUP, DEC_GROUP in SDSS_DR7
+ igmsp = IgmSpec()
+ sdss_meta = igmsp['SDSS_DR7'].meta
+ qso_coord = SkyCoord(ra=sdss_meta['RA_GROUP'], dec=sdss_meta['DEC_GROUP'], unit='deg')
+ idxq, d2dq, d3dq = match_coordinates_sky(coords, qso_coord, nthneighbor=1)
+ in_igmsp = d2dq < 1*u.arcsec # Check
+ # Cut meta
+ cut_meta = sdss_meta[idxq[in_igmsp]]
+ assert len(slines) == len(cut_meta)
+ # Grab
+ spectra = igmsp['SDSS_DR7'].spec_from_meta(cut_meta)
+ # Write
+ hdf = h5py.File(outfile,'w')
+ spectra.write_to_hdf5(outfile, hdf5=hdf, clobber=True, fill_val=0.)
+ # Add table (meta is already used)
+ hdf['cut_meta'] = cut_meta
+ hdf.close()
+
+
+def save_np_dataset(save_file, data):
+ """
+ Write input dataset (typically training or test) to a Numpy file
+
+ Args:
+ save_file (str):
+ data (dict): dict holding the flux and labels for the dataset
+
+ """
+ print("Writing %s.npy to disk" % save_file)
+ # np.save(save_file+".npy", data['flux'])
+ # data['flux'] = None
+ # print "Writing %s.pickle to disk" % save_file
+ # with gzip.GzipFile(filename=save_file+".pickle", mode='wb', compresslevel=2) as f:
+ # pickle.dump([data], f, protocol=-1)
+ np.savez_compressed(save_file,
+ flux=data['flux'],
+ labels_classifier=data['labels_classifier'],
+ labels_offset=data['labels_offset'],
+ col_density=data['col_density'])
+
+
+# Receives data in the tuple form returned from split_sightline_into_samples:
+# (fluxes_matrix, classification, offsets_array, column_density)
+# Returns indexes of pos & neg samples that are 50% positive and 50% negative and no boarder
+def select_samples_50p_pos_neg(classification):
+ """
+ For a given sightline, generate the indices for DLAs and for without
+ Split 50/50 to have equal representation
+
+ Parameters
+ ----------
+ classification: np.ndarray
+ Array of classification values. 1=DLA; 0=Not; -1=not analyzed
+
+ Returns
+ -------
+ idx: np.ndarray
+ positive + negative indices
+
+ """
+ #classification = data[1]
+ num_pos = np.sum(classification==1, dtype=np.float64)
+ num_neg = np.sum(classification==0, dtype=np.float64)
+ n_samples = int(min(num_pos, num_neg))
+
+ r = np.random.permutation(len(classification))
+
+ pos_ixs = r[classification[r]==1][0:n_samples]
+ neg_ixs = r[classification[r]==0][0:n_samples]
+ # num_total = data[0].shape[0]
+ # ratio_neg = num_pos / num_neg
+
+ # pos_mask = classification == 1 # Take all positive samples
+
+ # neg_ixs_by_ratio = np.linspace(1,num_total-1,round(ratio_neg*num_total), dtype=np.int32) # get all samples by ratio
+ # neg_mask = np.zeros((num_total),dtype=np.bool) # create a 0 vector of negative samples
+ # neg_mask[neg_ixs_by_ratio] = True # set the vector to positives, selecting for the appropriate ratio across the whole sightline
+ # neg_mask[pos_mask] = False # remove previously positive samples from the set
+ # neg_mask[classification == -1] = False # remove border samples from the set, what remains is still in the right ratio
+
+ # return pos_mask | neg_mask
+ return np.hstack((pos_ixs,neg_ixs))
+
diff --git a/data/preprocess/dla_cnn/vette_results.py b/data/preprocess/dla_cnn/vette_results.py
new file mode 100644
index 0000000..f192d10
--- /dev/null
+++ b/data/preprocess/dla_cnn/vette_results.py
@@ -0,0 +1,509 @@
+""" Module to vette results against Human catalogs
+ SDSS-DR5 (JXP) and BOSS (Notredaeme)
+"""
+from __future__ import print_function, absolute_import, division, unicode_literals
+
+import numpy as np
+import pdb
+
+
+import matplotlib as mpl
+mpl.rcParams['font.family'] = 'stixgeneral'
+from matplotlib.backends.backend_pdf import PdfPages
+from matplotlib import pyplot as plt
+import matplotlib.gridspec as gridspec
+
+from astropy.table import Table
+from astropy.coordinates import SkyCoord, match_coordinates_sky
+from astropy import units as u
+
+from linetools import utils as ltu
+from pyigm.surveys.llssurvey import LLSSurvey
+from pyigm.surveys.dlasurvey import DLASurvey
+
+
+def json_to_sdss_dlasurvey(json_file, sdss_survey, add_pf=True, debug=False):
+ """ Convert JSON output file to a DLASurvey object
+ Assumes SDSS bookkeeping for sightlines (i.e. PLATE, FIBER)
+
+ Parameters
+ ----------
+ json_file : str
+ Full path to the JSON results file
+ sdss_survey : DLASurvey
+ SDSS survey, usually human (e.g. JXP for DR5)
+ add_pf : bool, optional
+ Add plate/fiber to DLAs in sdss_survey
+
+ Returns
+ -------
+ ml_survey : LLSSurvey
+ Survey object for the LLS
+
+ """
+ print("Loading SDSS Survey from JSON file {:s}".format(json_file))
+ # imports
+ from pyigm.abssys.dla import DLASystem
+ from pyigm.abssys.lls import LLSSystem
+ # Fiber key
+ for fkey in ['FIBER', 'FIBER_ID', 'FIB']:
+ if fkey in sdss_survey.sightlines.keys():
+ break
+ # Read
+ ml_results = ltu.loadjson(json_file)
+ use_platef = False
+ if 'plate' in ml_results[0].keys():
+ use_platef = True
+ else:
+ if 'id' in ml_results[0].keys():
+ use_id = True
+ # Init
+ #idict = dict(plate=[], fiber=[], classification_confidence=[], # FOR v2
+ # classification=[], ra=[], dec=[])
+ idict = dict(ra=[], dec=[])
+ if use_platef:
+ for key in ['plate', 'fiber', 'mjd']:
+ idict[key] = []
+ ml_tbl = Table()
+ ml_survey = LLSSurvey()
+ systems = []
+ in_ml = np.array([False]*len(sdss_survey.sightlines))
+ # Loop
+ for obj in ml_results:
+ # Sightline
+ for key in idict.keys():
+ idict[key].append(obj[key])
+ # DLAs
+ #if debug:
+ # if (obj['plate'] == 1366) & (obj['fiber'] == 614):
+ # sv_coord = SkyCoord(ra=obj['ra'], dec=obj['dec'], unit='deg')
+ # print("GOT A MATCH IN RESULTS FILE")
+ for idla in obj['dlas']:
+ """
+ dla = DLASystem((sdss_survey.sightlines['RA'][mt[0]],
+ sdss_survey.sightlines['DEC'][mt[0]]),
+ idla['spectrum']/(1215.6701)-1., None,
+ idla['column_density'])
+ """
+ if idla['z_dla'] < 1.8:
+ continue
+ isys = LLSSystem((obj['ra'],obj['dec']),
+ idla['z_dla'], None, NHI=idla['column_density'], zem=obj['z_qso'])
+ isys.confidence = idla['dla_confidence']
+ if use_platef:
+ isys.plate = obj['plate']
+ isys.fiber = obj['fiber']
+ elif use_id:
+ plate, fiber = [int(spl) for spl in obj['id'].split('-')]
+ isys.plate = plate
+ isys.fiber = fiber
+ # Save
+ systems.append(isys)
+ # Connect to sightlines
+ ml_coord = SkyCoord(ra=idict['ra'], dec=idict['dec'], unit='deg')
+ s_coord = SkyCoord(ra=sdss_survey.sightlines['RA'], dec=sdss_survey.sightlines['DEC'], unit='deg')
+ idx, d2d, d3d = match_coordinates_sky(s_coord, ml_coord, nthneighbor=1)
+ used = d2d < 1.*u.arcsec
+ for iidx in np.where(~used)[0]:
+ print("Sightline RA={:g}, DEC={:g} was not used".format(sdss_survey.sightlines['RA'][iidx],
+ sdss_survey.sightlines['DEC'][iidx]))
+ # Add plate/fiber to statistical DLAs
+ if add_pf:
+ dla_coord = sdss_survey.coord
+ idx2, d2d, d3d = match_coordinates_sky(dla_coord, s_coord, nthneighbor=1)
+ if np.min(d2d.to('arcsec').value) > 1.:
+ raise ValueError("Bad match to sightlines")
+ for jj,igd in enumerate(np.where(sdss_survey.mask)[0]):
+ dla = sdss_survey._abs_sys[igd]
+ try:
+ dla.plate = sdss_survey.sightlines['PLATE'][idx2[jj]]
+ except IndexError:
+ pdb.set_trace()
+ dla.fiber = sdss_survey.sightlines[fkey][idx2[jj]]
+ # Finish
+ ml_survey._abs_sys = systems
+ if debug:
+ ml2_coord = ml_survey.coord
+ minsep = np.min(sv_coord.separation(ml2_coord))
+ minsep2 = np.min(sv_coord.separation(s_coord))
+ tmp = sdss_survey.sightlines[used]
+ t_coord = SkyCoord(ra=tmp['RA'], dec=tmp['DEC'], unit='deg')
+ minsep3 = np.min(sv_coord.separation(t_coord))
+ pdb.set_trace()
+ ml_survey.sightlines = sdss_survey.sightlines[used]
+ for key in idict.keys():
+ ml_tbl[key] = idict[key]
+ ml_survey.ml_tbl = ml_tbl
+ # Return
+ return ml_survey
+
+
+def vette_dlasurvey(ml_survey, sdss_survey, fig_root='tmp', lyb_cut=True,
+ dz_toler=0.03, debug=False):
+ """
+ Parameters
+ ----------
+ ml_survey : IGMSurvey
+ Survey describing the Machine Learning results
+ sdss_survey : DLASurvey
+ SDSS survey, usually human (e.g. JXP for DR5)
+ fig_root : str, optional
+ Root string for figures generated
+ lyb_cut : bool, optional
+ Cut surveys at Lyb in QSO rest-frame.
+ Recommended until LLS, Lyb and OVI is dealt with
+ dz_toler : float, optional
+ Tolerance for matching in redshift
+
+ Returns
+ -------
+ false_neg : list
+ List of systems that are false negatives from SDSS -> ML
+ midx : list
+ List of indices matching SDSS -> ML
+ """
+ from pyigm.surveys import dlasurvey as pyis_ds
+ reload(pyis_ds)
+ # Cut at Lyb
+ if lyb_cut:
+ for survey in [ml_survey, sdss_survey]:
+ # Alter Z_START
+ zlyb = (1+survey.sightlines['ZEM']).data*1026./1215.6701 - 1.
+ survey.sightlines['Z_START'] = np.maximum(survey.sightlines['Z_START'], zlyb)
+ # Mask
+ mask = pyis_ds.dla_stat(survey, survey.sightlines, zem_tol=0.2) # Errors in zem!
+ survey.mask = mask
+ print("Done cutting on Lyb")
+
+ # Setup coords
+ ml_coords = ml_survey.coord
+ ml_z = ml_survey.zabs
+ s_coords = sdss_survey.coord
+ s_z = sdss_survey.zabs
+# if debug:
+# miss_coord = SkyCoord(ra=174.35545833333333,dec=44.585,unit='deg')
+# minsep = np.min(miss_coord.separation(ml_coords))
+# s_coord = SkyCoord(ra=ml_survey.sightlines['RA'], dec=ml_survey.sightlines['DEC'], unit='deg')
+# isl = np.argmin(miss_coord.separation(s_coord))
+
+ # Match from SDSS and record false negatives
+ false_neg = []
+ midx = []
+ for igd in np.where(sdss_survey.mask)[0]:
+ isys = sdss_survey._abs_sys[igd]
+ # Match?
+ gd_radec = np.where(isys.coord.separation(ml_coords) < 1*u.arcsec)[0]
+ sep = isys.coord.separation(ml_coords)
+ if len(gd_radec) == 0:
+ false_neg.append(isys)
+ midx.append(-1)
+ else:
+ gdz = np.abs(ml_z[gd_radec] - isys.zabs) < dz_toler
+ # Only require one match
+ if np.sum(gdz) > 0:
+ iz = np.argmin(np.abs(ml_z[gd_radec] - isys.zabs))
+ midx.append(gd_radec[iz])
+ else:
+ false_neg.append(isys)
+ midx.append(-1)
+ if debug:
+ if (isys.plate == 1366) & (isys.fiber == 614):
+ pdb.set_trace()
+
+ # Match from ML and record false positives
+ false_pos = []
+ pidx = []
+ for igd in np.where(ml_survey.mask)[0]:
+ isys = ml_survey._abs_sys[igd]
+ # Match?
+ gd_radec = np.where(isys.coord.separation(s_coords) < 1*u.arcsec)[0]
+ sep = isys.coord.separation(s_coords)
+ if len(gd_radec) == 0:
+ false_pos.append(isys)
+ pidx.append(-1)
+ else:
+ gdz = np.abs(s_z[gd_radec] - isys.zabs) < dz_toler
+ # Only require one match
+ if np.sum(gdz) > 0:
+ iz = np.argmin(np.abs(s_z[gd_radec] - isys.zabs))
+ pidx.append(gd_radec[iz])
+ else:
+ false_pos.append(isys)
+ pidx.append(-1)
+
+ # Return
+ return false_neg, np.array(midx), false_pos
+
+def mk_false_neg_table(false_neg, outfil):
+ """ Generate a simple CSV file of false negatives
+
+ Parameters
+ ----------
+ false_neg : list
+ List of false negative systems
+ outfil : str
+
+ Returns
+ -------
+
+ """
+ # Parse
+ ra, dec = [], []
+ zabs, zem = [], []
+ NHI = []
+ plate, fiber = [], []
+ for ifneg in false_neg:
+ ra.append(ifneg.coord.ra.value)
+ dec.append(ifneg.coord.dec.value)
+ zabs.append(ifneg.zabs)
+ zem.append(ifneg.zem)
+ NHI.append(ifneg.NHI)
+ plate.append(ifneg.plate)
+ fiber.append(ifneg.fiber)
+ # Generate a Table
+ fneg_tbl = Table()
+ fneg_tbl['RA'] = ra
+ fneg_tbl['DEC'] = dec
+ fneg_tbl['zabs'] = zabs
+ fneg_tbl['zem'] = zem
+ fneg_tbl['NHI'] = NHI
+ fneg_tbl['plate'] = plate
+ fneg_tbl['fiber'] = fiber
+ # Write
+ print("Writing false negative file: {:s}".format(outfil))
+ fneg_tbl.write(outfil, format='ascii.csv')#, overwrite=True)
+
+
+def fig_dzdnhi(ml_survey, sdss_survey, midx, outfil='fig_dzdnhi.pdf'):
+ """ Compare zabs and NHI between SDSS and ML
+
+ Parameters
+ ----------
+ ml_survey : IGMSurvey
+ Survey describing the Machine Learning results
+ This should be masked according to the vetting
+ sdss_survey : DLASurvey
+ SDSS survey, usually human (e.g. JXP for DR5)
+ This should be masked according to the vetting
+ midx : list
+ List of indices matching SDSS -> ML
+ outfil : str, optional
+ Input None to plot to screen
+
+ Returns
+ -------
+
+ """
+ # z, NHI
+ z_sdss = sdss_survey.zabs
+ z_ml = ml_survey.zabs
+ NHI_sdss = sdss_survey.NHI
+ NHI_ml = ml_survey.NHI
+ # deltas
+ dz = []
+ dNHI = []
+ for qq,idx in enumerate(midx):
+ if idx < 0:
+ continue
+ # Match
+ dz.append(z_sdss[qq]-z_ml[idx])
+ dNHI.append(NHI_sdss[qq]-NHI_ml[idx])
+
+ # Figure
+ if outfil is not None:
+ pp = PdfPages(outfil)
+ fig = plt.figure(figsize=(8, 5))
+ plt.clf()
+ gs = gridspec.GridSpec(1, 2)
+ # dz
+ ax = plt.subplot(gs[0])
+ ax.hist(dz, color='green', bins=20)#, normed=True)#, bins=20 , zorder=1)
+ #ax.text(0.05, 0.74, lbl3, transform=ax.transAxes, color=wcolor, size=csz, ha='left')
+ ax.set_xlim(-0.03, 0.03)
+ ax.set_xlabel(r'$\delta z$ [SDSS-ML]')
+ # NHI
+ ax = plt.subplot(gs[1])
+ ax.hist(dNHI, color='blue', bins=20)#, normed=True)#, bins=20 , zorder=1)
+ #ax.text(0.05, 0.74, lbl3, transform=ax.transAxes, color=wcolor, size=csz, ha='left')
+ #ax.set_xlim(-0.03, 0.03)
+ ax.set_xlabel(r'$\Delta \log N_{\rm HI}$ [SDSS-ML]')
+ #
+ # End
+ plt.tight_layout(pad=0.2,h_pad=0.,w_pad=0.1)
+ if outfil is not None:
+ print('Writing {:s}'.format(outfil))
+ pp.savefig()
+ pp.close()
+ plt.close()
+ else:
+ plt.show()
+
+
+def fig_falseneg(ml_survey, sdss_survey, false_neg, outfil='fig_falseneg.pdf'):
+ """ Figure on false negatives
+
+ Parameters
+ ----------
+ ml_survey : IGMSurvey
+ Survey describing the Machine Learning results
+ This should be masked according to the vetting
+ sdss_survey : DLASurvey
+ SDSS survey, usually human (e.g. JXP for DR5)
+ This should be masked according to the vetting
+ midx : list
+ List of indices matching SDSS -> ML
+ false_neg : list
+ List of false negatives
+ outfil : str, optional
+ Input None to plot to screen
+
+ Returns
+ -------
+
+ """
+ # Generate some lists
+ zabs_false = [isys.zabs for isys in false_neg]
+ zem_false = [isys.zem for isys in false_neg]
+ NHI_false = [isys.NHI for isys in false_neg]
+
+ # Figure
+ if outfil is not None:
+ pp = PdfPages(outfil)
+ fig = plt.figure(figsize=(8, 5))
+ plt.clf()
+ gs = gridspec.GridSpec(2, 2)
+ # zabs
+ ax = plt.subplot(gs[0])
+ ax.hist(zabs_false, color='green', bins=20)#, normed=True)#, bins=20 , zorder=1)
+ ax.set_xlabel(r'$z_{\rm abs}$')
+ # zem
+ ax = plt.subplot(gs[1])
+ ax.hist(zem_false, color='red', bins=20)#, normed=True)#, bins=20 , zorder=1)
+ ax.set_xlabel(r'$z_{\rm qso}$')
+ # NHI
+ ax = plt.subplot(gs[2])
+ ax.hist(NHI_false, color='blue', bins=20)#, normed=True)#, bins=20 , zorder=1)
+ ax.set_xlabel(r'$\log \, N_{\rm HI}$')
+ # End
+ plt.tight_layout(pad=0.2,h_pad=0.,w_pad=0.1)
+ if outfil is not None:
+ print('Writing {:s}'.format(outfil))
+ pp.savefig()
+ pp.close()
+ plt.close()
+ else:
+ plt.show()
+
+
+def dr5_for_david():
+ """ Generate a Table for David
+ """
+ # imports
+ from pyigm.abssys.dla import DLASystem
+ from pyigm.abssys.lls import LLSSystem
+ sdss_survey = DLASurvey.load_SDSS_DR5()
+ # Fiber key
+ for fkey in ['FIBER', 'FIBER_ID', 'FIB']:
+ if fkey in sdss_survey.sightlines.keys():
+ break
+ # Init
+ #idict = dict(plate=[], fiber=[], classification_confidence=[], # FOR v2
+ # classification=[], ra=[], dec=[])
+ # Connect to sightlines
+ s_coord = SkyCoord(ra=sdss_survey.sightlines['RA'], dec=sdss_survey.sightlines['DEC'], unit='deg')
+ # Add plate/fiber to statistical DLAs
+ dla_coord = sdss_survey.coord
+ idx2, d2d, d3d = match_coordinates_sky(dla_coord, s_coord, nthneighbor=1)
+ if np.min(d2d.to('arcsec').value) > 1.:
+ raise ValueError("Bad match to sightlines")
+ plates, fibers = [], []
+ for jj,igd in enumerate(np.where(sdss_survey.mask)[0]):
+ dla = sdss_survey._abs_sys[igd]
+ try:
+ dla.plate = sdss_survey.sightlines['PLATE'][idx2[jj]]
+ except IndexError:
+ pdb.set_trace()
+ dla.fiber = sdss_survey.sightlines[fkey][idx2[jj]]
+ plates.append(sdss_survey.sightlines['PLATE'][idx2[jj]])
+ fibers.append(sdss_survey.sightlines[fkey][idx2[jj]])
+ # Write
+ dtbl = Table()
+ dtbl['plate'] = plates
+ dtbl['fiber'] = fibers
+ dtbl['zabs'] = sdss_survey.zabs
+ dtbl['NHI'] = sdss_survey.NHI
+ dtbl.write('results/dr5_for_david.ascii', format='ascii')
+ # Write sightline info
+ stbl = sdss_survey.sightlines[['PLATE', 'FIB', 'Z_START', 'Z_END', 'RA', 'DEC']]
+ gdsl = stbl['Z_END'] > stbl['Z_START']
+ stbl[gdsl].write('results/dr5_sightlines_for_david.ascii', format='ascii')
+
+def main(flg_tst, sdss=None, ml_survey=None):
+
+ # Load JSON for DR5
+ if (flg_tst % 2**1) >= 2**0:
+ if sdss is None:
+ sdss = DLASurvey.load_SDSS_DR5()
+ #ml_survey = json_to_sdss_dlasurvey('../results/dr5_v1_predictions.json', sdss)
+ ml_survey = json_to_sdss_dlasurvey('../results/dr5_v2_results.json', sdss)
+
+ # Vette
+ if (flg_tst % 2**2) >= 2**1:
+ if ml_survey is None:
+ sdss = DLASurvey.load_SDSS_DR5()
+ ml_survey = json_to_sdss_dlasurvey('../results/dr5_v2_results.json', sdss)
+ vette_dlasurvey(ml_survey, sdss)
+
+ # Vette v5 and generate CSV
+ if (flg_tst % 2**3) >= 2**2:
+ if ml_survey is None:
+ sdss = DLASurvey.load_SDSS_DR5()
+ ml_survey = json_to_sdss_dlasurvey('../results/dr5_v5_predictions.json', sdss)
+ false_neg, midx, _ = vette_dlasurvey(ml_survey, sdss)
+ # CSV of false negatives
+ mk_false_neg_table(false_neg, '../results/false_negative_DR5_v5.csv')
+
+ # Vette v6 and generate CSV
+ if (flg_tst % 2**4) >= 2**3:
+ if ml_survey is None:
+ sdss = DLASurvey.load_SDSS_DR5()
+ ml_survey = json_to_sdss_dlasurvey('../results/dr5_v6.1_results.json', sdss)
+ false_neg, midx, _ = vette_dlasurvey(ml_survey, sdss)
+ # CSV of false negatives
+ mk_false_neg_table(false_neg, '../results/false_negative_DR5_v6.1.csv')
+
+ # Vette gensample v2
+ if (flg_tst % 2**5) >= 2**4:
+ if ml_survey is None:
+ sdss = DLASurvey.load_SDSS_DR5()
+ ml_survey = json_to_sdss_dlasurvey('../results/results_catalog_dr7_model_gensample_v2.json',sdss)
+ false_neg, midx, false_pos = vette_dlasurvey(ml_survey, sdss)
+ # CSV of false negatives
+ mk_false_neg_table(false_neg, '../results/false_negative_DR5_v2_gen.csv')
+ mk_false_neg_table(false_pos, '../results/false_positives_DR5_v2_gen.csv')
+
+ # Vette gensample v4.3.1
+ if flg_tst & (2**5):
+ if ml_survey is None:
+ sdss = DLASurvey.load_SDSS_DR5()
+ ml_survey = json_to_sdss_dlasurvey('../results/results_model_4.3.1_data_dr5.json',sdss)
+ false_neg, midx, false_pos = vette_dlasurvey(ml_survey, sdss)
+ # CSV of false negatives
+ mk_false_neg_table(false_neg, '../results/false_negative_DR5_v4.3.1_gen.csv')
+ mk_false_neg_table(false_pos, '../results/false_positives_DR5_v4.3.1_gen.csv')
+
+ if flg_tst & (2**6):
+ dr5_for_david()
+
+# Test
+if __name__ == '__main__':
+ flg_tst = 0
+ #flg_tst += 2**0 # Load JSON for DR5
+ #flg_tst += 2**1 # Vette
+ #flg_tst += 2**2 # v5
+ #flg_tst += 2**3 # v6.1
+ #flg_tst += 2**4 # v2 of gensample
+ #flg_tst += 2**5 # v4.3.1 of gensample
+ flg_tst += 2**6 # Generate DR5 table for David
+
+ main(flg_tst)
diff --git a/data/preprocess/nb/test.ipynb b/data/preprocess/nb/test.ipynb
new file mode 100644
index 0000000..fd81ce0
--- /dev/null
+++ b/data/preprocess/nb/test.ipynb
@@ -0,0 +1,50 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "ModuleNotFoundError",
+ "evalue": "No module named 'QFA'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mQFA\u001b[39;00m\n",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'QFA'"
+ ]
+ }
+ ],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "test",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.15"
+ },
+ "orig_nbformat": 4,
+ "vscode": {
+ "interpreter": {
+ "hash": "dbb886185122a257babc2157e12aad50346e07d484718b574e3134464536f446"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/data/preprocess/preprocess.py b/data/preprocess/preprocess.py
new file mode 100644
index 0000000..b5b3764
--- /dev/null
+++ b/data/preprocess/preprocess.py
@@ -0,0 +1,350 @@
+from dla_cnn.desi.DesiMock import DesiMock
+import numpy as np
+from scipy.interpolate import interp1d
+from astropy.stats import sigma_clip
+from scipy.optimize import curve_fit
+from astropy import cosmology
+from astropy import units as u
+import json
+
+# read the frequently-used wavelength
+wlh = json.load(open('wavelength.json', 'r'))
+
+def get_spilt_point(data:DesiMock, id):
+ '''
+ Get the spilt point in the overlapping area. Before this point we reserve the spectrum from the last camera, and after this point we reserve the ones from the next camera.
+
+ -----
+ ### Parameters:
+ `data` and `id`: the original dataset from which the sightline is extracted by `DesiMock().get_sightline()` and the id of this sightline.
+ '''
+ line_b = data.get_sightline(id=id, camera='b')
+ line_z = data.get_sightline(id=id, camera='z')
+ line_r = data.get_sightline(id=id, camera='r')
+ spilt_loglam_br = np.average([np.max(line_b.loglam), np.min(line_r.loglam)])
+ spilt_loglam_rz = np.average([np.min(line_z.loglam), np.max(line_r.loglam)])
+ return spilt_loglam_br, spilt_loglam_rz
+
+def get_between(array, max, min, maxif=False, minif=False):
+ '''
+ Get the indices of a part of `array` whose value is between `max` and `min`.
+
+ ------
+ ### Parameters:
+ `array`: the array which we will process the above procedure on.
+ `max` and `min`: the upper and lower limit for the new child array.
+ `maxif` and `minif`: whether the values EQUAL to `max` and `min` can be included in the new child array.
+ '''
+ if max >= min:
+ if max >= np.min(array) and min <= np.max(array):
+ if maxif:
+ if minif:
+ return np.intersect1d(np.where(array>=min)[0], np.where(array<=max)[0])
+ else:
+ return np.intersect1d(np.where(array>min)[0], np.where(array<=max)[0])
+ else:
+ if minif:
+ return np.intersect1d(np.where(array>=min)[0], np.where(array<max)[0])
+ else:
+ return np.intersect1d(np.where(array>min)[0], np.where(array<max)[0])
+ else:
+ raise ValueError('min~max out of range')
+ else:
+ raise ValueError('max < min, will return nothing')
+
+def W(N):
+ '''
+ Calculate the equivalent width W of DLA.
+
+ ------
+
+ ### Parameters:
+ `N`: the column density of the DLA. (in the form of 10^x)
+ '''
+ return 10**(0.5*(N-24.440751700479186))
+
+
+def wing_correction(z_dla, nhi_dla, wav, dla_indicator):
+ '''
+ Correct the wing of the DLA absorption.
+
+ -----
+
+ ### Parameters:
+ `z_dla`: the redshift of the DLA.
+ `nhi_dla`: the column density of the DLA. (in the form of 10^x)
+ `wav`: the observed wavelength of the sightline.
+ `dla_indicator`(Bool array): the indicator along the wavelenght axis. It is `True` if there is a DLA at this wavelength, and `False` if not.
+ '''
+ w = W(nhi_dla)
+ wav_dla = wav/(1+z_dla)
+ dwav = wav/(1+z_dla) - 1215.67
+ correction = np.exp(w**2 / (4*np.pi) * (wav_dla * dla_indicator / dwav)**2)
+ correction[np.isnan(correction)] = 0.
+ return correction
+
+def overlap(sightline, data:DesiMock, id):
+ '''
+ Deal with the overlapping area between different cameras so that the result will not contain the overlaps.
+
+ ---
+ ### Parameters
+ `sightline`: the spectra that is waiting to be rebinned. It must have been clipped by `clip()`.
+ `data` and `id`: the original dataset from which the sightline is extracted by `DesiMock().get_sightline()` and the id of this sightline.
+ '''
+ spilt_loglam_br, spilt_loglam_rz = get_spilt_point(data, id)
+ line_r = data.get_sightline(id=id, camera='r')
+ line_b = data.get_sightline(id=id, camera='b')
+ line_z = data.get_sightline(id=id, camera='z')
+
+ loglam_r = line_r.loglam[0:np.where(line_r.loglam == np.max(line_r.loglam))[0][0]]
+ indice_r = get_between(loglam_r, max=spilt_loglam_rz, min=spilt_loglam_br)
+ indice_b = get_between(line_b.loglam, max=spilt_loglam_br, min=0, maxif=True)
+ indice_z = get_between(line_z.loglam, max=np.Infinity, min=spilt_loglam_rz, minif=True)
+
+ loglam_r, loglam_b, loglam_z = loglam_r[indice_r], line_b.loglam[indice_b], line_z.loglam[indice_z]
+ flux_r, flux_b, flux_z = line_r.flux[indice_r], line_b.flux[indice_b], line_z.flux[indice_z]
+ error_r, error_b, error_z = line_r.error[indice_r], line_b.error[indice_b], line_z.error[indice_z]
+ sightline.loglam = np.concatenate((loglam_b, loglam_r, loglam_z))
+ sightline.flux = np.concatenate((flux_b, flux_r, flux_z))
+ sightline.error = np.concatenate((error_b, error_r, error_z))
+
+def normalize(sightline, full_wavelength, full_flux):
+ """
+ Normalize this spectra using the lymann-forest part, using the median of the flux array with wavelength in rest frame between max(3800/(1+z_qso),1070)
+ and 1170. Normalize the error array at the same time to maintain the s/n. And for those spectrum cannot be normalzied, this function will assert error, when encounter this case.
+ ---------------------------------------------------
+ parameters:
+ sightline: :class:`dla_cnn.data_model.sightline.Sightline` object, the spectrum to be normalized;(It must have been processed by `overlap()`)
+ full_wavelength: numpy.ndarray, the whole wavelength array of this sightline, since the sightline may not contain the blue channel,
+ we pass the wavelength array to this function
+ full_flux:numpy.ndarray,the whole flux wavelength array of this sightline, take it as a parameter to solve the same problem above.
+ """
+ # determine the blue limit and red limit of the slice we use to normalize this spectra, and when cannot find such a slice, this function will assert error
+ blue_limit = max(3800/(1+sightline.z_qso),1070)
+ red_limit = 1170
+ rest_wavelength = full_wavelength/(sightline.z_qso+1)
+ assert blue_limit <= red_limit,"No Lymann-alpha forest, Please check this spectra: %i"%sightline.id#when no lymann alpha forest exists, assert error.
+ #use the slice we chose above to normalize this spectra, normalize both flux and error array using the same factor to maintain the s/n.
+ good_pix = (rest_wavelength>=blue_limit)&(rest_wavelength<=red_limit)
+ norm_factor = np.median(full_flux[good_pix])
+ sightline.flux_norm = sightline.flux/norm_factor
+ # sightline.error = sightline.error / np.median(full_error[good_pix])
+ sightline.error_norm = sightline.error/norm_factor
+ return norm_factor
+
+def clip(sightline, Nsigma_flat=3, Nsigma_steep=3, unit_default=100, slope=2e-3, ratio=0.5):
+ '''
+ Clip the abnormal points in the spectra.
+
+ ---
+
+ ### Parameters
+ `sightline`: the spectra that is waiting to be cliped. It must have been normalized by `normalize()`.
+ `unit_default`: the default length of each bin that is used to conduct sigmaclip.
+ `slope`: the critical value that decides whether a smaller bin will be applied. If the fit slope of current bin exceeds this value, a smaller bin will be used.
+ `ratio`: how small the smaller bin will be compared with the default bin length.
+ `Nsigma_flat` and `Nsigma_steep`: the critical value in different areas that decides whether a point is abnormal. If the point is abnormal, it will be clipped.
+ '''
+
+ wavs = 10**sightline.loglam
+ flux = sightline.flux_norm
+ error = sightline.error_norm
+ zero_point = np.where(wavs / (1+sightline.z_qso) >= wlh['LyALPHA'])[0][0]
+ sightline.points_num = len(wavs)
+
+ wavs_new = wavs[0:zero_point]
+ flux_new = flux[0:zero_point]
+ error_new = error[0:zero_point]
+
+ unit = unit_default
+ judge, start, end = True, zero_point, zero_point + unit
+ while judge:
+
+ if end > len(wavs):
+ end = len(wavs)
+ judge = False
+ if start == end:
+ break
+ else:
+ judge = True
+ subwavs, subflux, suberror = wavs[start:end], flux[start:end], error[start:end]
+ if end - start >= 3:
+ slope_fit = curve_fit(lambda t,a,b: a*t+b, subwavs, subflux)[0][0]
+
+ if np.abs(slope_fit) >= slope:
+ unit = int(unit_default*ratio)
+ end = start + unit
+ if end >= len(wavs):
+ end = len(wavs)
+ judge = False
+ if start == end:
+ break
+ else:
+ judge = True
+ subwavs, subflux, suberror = wavs[start:end], flux[start:end], error[start:end]
+
+ elif np.abs(slope_fit) < slope and unit != unit_default:
+ unit = unit_default
+ end = start + unit
+ if end >= len(wavs):
+ end = len(wavs)
+ judge = False
+ if start == end:
+ break
+ else:
+ judge = True
+ subwavs, subflux, suberror = wavs[start:end], flux[start:end], error[start:end]
+
+ mask = np.invert(sigma_clip(subflux, sigma=3).mask)
+ flux_cliped = subflux[mask]
+ wavs_cliped = subwavs[mask]
+ error_cliped = suberror[mask]
+ else:
+ flux_cliped, wavs_cliped, error_cliped = subflux, subwavs, suberror
+ wavs_new = np.concatenate((wavs_new, wavs_cliped))
+ flux_new = np.concatenate((flux_new, flux_cliped))
+ error_new = np.concatenate((error_new, error_cliped))
+ start = start + unit
+ end = end + unit
+
+ sightline.loglam_cliped = np.log10(wavs_new)
+ sightline.flux_cliped = flux_new
+ sightline.error_cliped = error_new
+
+def get_dloglambda(sightline):
+ '''
+ Generate the step length of restframe grid used in `rebin()`.
+
+ ----
+
+ ### Attention
+ For the mock data, this function generate the same value for all the spectrum. I am not sure whether this characristic will remain the same for the actual data.
+ '''
+ wavelength = 10**sightline.loglam_cliped
+ pixels_number = sightline.points_num
+ max_wavelength = wavelength[-1]
+ min_wavelength = wavelength[0]
+ dloglambda = np.log10(max_wavelength/min_wavelength)/pixels_number
+ return dloglambda
+
+def rebin(sightline, loglam_start, dloglambda, max_index_up:int=int(1e6), max_index_down:int=int(1e6)):
+ '''
+ Rebin to the same restframe grid.
+
+ --------
+
+ ### Parameters:
+ `sightline`: the spectra that is waiting to be rebinned. It must have been clipped by `clip()`.
+ `loglam_start`: the start point of this restframe grid. Notice that it is not the smallest wavelength in restframe. Instead it is the point where the grid starts to "grow". See the definition of `max_index_up` and `max_index_down` for more details.
+ `dloglambda`: the step length of this restframe grid. It can be derived with `get_dlnlambda()`.
+ `max_index_up` and `max_index_down`: because different spectra has different range of wavelength in restframe, so it is necessary to make the restframe grid large enough to contain all of these spectrum. These parameters determine the size of this grid, which are usually very big. `max_index_up` determines how long this restframe will grow rightwards, and `max_index_down` determines the leftwards. You can change the default value if you think it is too big.
+ '''
+
+ wavelength = 10**sightline.loglam_cliped / (1+sightline.z_qso)
+ flux = sightline.flux_cliped
+ error = sightline.error_cliped
+
+ max_wavelength = wavelength[-1]
+ min_wavelength = wavelength[0]
+ new_wavelength_total = 10**loglam_start * 10**(dloglambda * np.union1d(np.arange(max_index_up), -np.arange(max_index_down)))
+ indices = get_between(new_wavelength_total, max_wavelength, min_wavelength, maxif=True, minif=True)
+ new_wavelength = new_wavelength_total[indices]
+
+ # below is the code from dla_cnn.data_model.Spectrum.rebin()
+ npix = len(wavelength)
+ wvh = (wavelength + np.roll(wavelength, -1)) / 2.
+ wvh[npix - 1] = wavelength[npix - 1] + \
+ (wavelength[npix - 1] - wavelength[npix - 2]) / 2.
+ dwv = wvh - np.roll(wvh, 1)
+ dwv[0] = 2 * (wvh[0] - wavelength[0])
+ med_dwv = np.median(dwv)
+
+ cumsum = np.cumsum(flux * dwv)
+ cumvar = np.cumsum(error * dwv, dtype=np.float64)
+
+ fcum = interp1d(wvh, cumsum,bounds_error=False)
+ fvar = interp1d(wvh, cumvar,bounds_error=False)
+
+ nnew = len(new_wavelength)
+ nwvh = (new_wavelength + np.roll(new_wavelength, -1)) / 2.
+ nwvh[nnew - 1] = new_wavelength[nnew - 1] + \
+ (new_wavelength[nnew - 1] - new_wavelength[nnew - 2]) / 2.
+
+ bwv = np.zeros(nnew + 1)
+ bwv[0] = new_wavelength[0] - (new_wavelength[1] - new_wavelength[0]) / 2.
+ bwv[1:] = nwvh
+
+ newcum = fcum(bwv)
+ newvar = fvar(bwv)
+
+ new_fx = (np.roll(newcum, -1) - newcum)[:-1]
+ new_var = (np.roll(newvar, -1) - newvar)[:-1]
+
+ # Normalize (preserve counts and flambda)
+ new_dwv = bwv - np.roll(bwv, 1)
+ new_fx = new_fx / new_dwv[1:]
+ # Preserve S/N (crudely)
+ med_newdwv = np.median(new_dwv)
+ new_var = new_var / (med_newdwv/med_dwv) / new_dwv[1:]
+
+ left = 0
+ while np.isnan(new_fx[left])|np.isnan(new_var[left]):
+ left = left+1
+ right = len(new_fx)
+ while np.isnan(new_fx[right-1])|np.isnan(new_var[right-1]):
+ right = right-1
+
+ test = np.sum((np.isnan(new_fx[left:right]))|(np.isnan(new_var[left:right])))
+ assert test==0, 'Missing value in this spectra!'
+
+ sightline.loglam_rebin_restframe = np.log10(new_wavelength[left:right])
+ sightline.flux_rebin_restframe = new_fx[left:right]
+ sightline.error_rebin_restframe = new_var[left:right]
+
+def mask_dla(sightline):
+ '''
+ Mask the DLA region.
+
+ ----
+
+ ### Parameters:
+ `sightline`: the spectra that is waiting to be masked. It must have been rebinned by `rebin()`.
+ '''
+ interp_flux = sightline.flux_rebin_restframe
+ interp_error = sightline.error_rebin_restframe
+ lamda = 10**sightline.loglam_rebin_restframe
+ z = sightline.z_qso
+ dla_indicator = np.ones_like(interp_flux, dtype=bool)
+ dla_correction = np.ones_like(interp_flux)
+ if len(sightline.dlas) > 0:
+ for dla in sightline.dlas:
+ z_dla = (dla.central_wavelength / wlh['LyALPHA']) - 1
+ nhi = dla.col_density
+ w = W(nhi)
+ dla_indicator *= ~((lamda*(1+z) < 1215.67 * (1+z_dla) * (1.+w) ) & (lamda*(1+z)>1215.67*(1+z_dla)*(1.-w)))
+ dla_correction *= wing_correction(z_dla, nhi, lamda*(1+z), dla_indicator)
+ sightline.loglam_mask = sightline.loglam_rebin_restframe[dla_indicator]
+ sightline.flux_mask = interp_flux[dla_indicator] * dla_correction[dla_indicator]
+ sightline.error_mask = interp_error[dla_indicator] * dla_correction[dla_indicator]
+ else:
+ sightline.loglam_mask = sightline.loglam_rebin_restframe
+ sightline.flux_mask = interp_flux
+ sightline.error_mask = interp_error
+
+def get_bolometric_lum(sightline, norm_factor):
+ '''
+ Get the bolometric luminosity of the sightline.
+
+ -----
+
+ ### Parameters:
+ `sightline`: the spectra that is going to be calculated.
+ `norm_factor`: the normalization factor of the spectra. Come from `normalize()`.
+ '''
+ universe = cosmology.LambdaCDM(H0=70, Om0=0.28, Ode0=0.72)
+ z = sightline.z_qso
+ dL = universe.luminosity_distance(z=z)
+ L1280 = 4*np.pi*dL*dL*norm_factor*1e-17*u.erg/u.centimeter/u.centimeter/u.s/u.angstrom
+ lum = np.log10(((1280.*u.angstrom*(1.+z)*L1280).to(u.erg/u.s)).value)
+ return lum
\ No newline at end of file
diff --git a/data/preprocess/wavelength.json b/data/preprocess/wavelength.json
new file mode 100644
index 0000000..3b342d9
--- /dev/null
+++ b/data/preprocess/wavelength.json
@@ -0,0 +1,12 @@
+{
+ "LyALPHA" : 1215.67,
+ "LyBETA" : 1025.72,
+ "MgII1" : 1025.97,
+ "MgII2" : 1026.11,
+ "MgII3" : 1239.93,
+ "MgII4" : 1240.39,
+ "MgII5" : 2796.35,
+ "MgII6" : 2803.53,
+ "CIV1" : 1548.20,
+ "CIV2" : 1550.78
+}
\ No newline at end of file