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{
"filename": "README.md",
"repo_name": "revoltek/LiLF",
"repo_path": "LiLF_extracted/LiLF-master/README.md",
"type": "Markdown"
}
|
# LiLF
Library for Low Frequencies
LiLF is a set of functions linked together in pipelines for the reduction of low-frequency interferometric data in radio astronomy. It is built upon LOFAR software. LiLF can be used on both LOFAR and uGMRT data.
- LOFAR: http://lofar.org/
- uGMRT: http://www.ncra.tifr.res.in/ncra/gmrt
### Files:
- ~/.stagingrc >> with the login and pass for LTA archive, for example:
```
user = <username>
password = <password>
```
- ~/.surveys >> with the pass for the surveydb
- ~/.awe/Environment.cfg >> if you want to use the LiLF/scripts/LOFAR_stager.py to stage and download, your LTA credentials should be also in this file, for example:
```
[global]
database_user : <your username>
database_password : <your password>
```
### Container:
Check here if you want a container that can run the pipeline:
LiLF/container/docker_build.sh
For the stable pipeline, better use "singularity build pill20220805.simg docker://revoltek/pill:20220805"
# LBA data reduction How-To:
To calibrate LOFAR LBA data is possible to use the script PILL.py (in the pipeline dir) that automatically does all the different pipeline steps, or to run the single steps by hand to check the intermediate results.
If you use these scripts, please cite:
- [de Gasperin+ 2019](https://ui.adsabs.harvard.edu/abs/2019A%26A...622A...5D/abstract) (DIE calibration)
- [de Gasperin+ 2020](https://ui.adsabs.harvard.edu/abs/2020A%26A...642A..85D/abstract) (DDE calibration)
Information on the ionosphere systematic effects can be found here:
- [deGasperin+ 2018](https://ui.adsabs.harvard.edu/abs/2018A%26A...615A.179D/abstract)
If you demixed A-team sources, here is the paper describing the models:
- [de Gasperin+ 2020b](https://ui.adsabs.harvard.edu/abs/2020A%26A...635A.150D/abstract)
### Environment
The preferred way is to use the singularity container as described above, enter in the singularity and go to your working directory.
Check the path for LiLF, let's say it is /opt/LiLF/, and include it in your ~/.bashrc as follow:
`export PYTHONPATH=/opt/LiLF:$PYTHONPATH`
`export PATH=/opt/LiLF/scripts:$PATH`
I also reccommended to set:
`ulimit -n 4000`
as the pipeline requires to open many files at the same time.
### Run the pipelines:
* To use PILL run
`python3 /opt/LiLF/pipelines/PILL.py`
* To run the pipeline step-by-step follow these commands:
1. On you working directory create a `Download` directory and put here the html.txt files obtained from a data staging request on Long Term Archive (LTA). Then run: `python3 /opt/LiLF/pipelines/LOFAR_preprocess.py` to download the data from LTA, unpack, averaged to 4 chan/sb and 4 sec and finally arrange the data in sub-directories that you can find in `Download/mss` The subdirectories are called `id000_CAL` and `id000_TARGET`, where 000 is the id of your observation and CAL and TARGET are the name of the calibrator and target. If your observation is split in more than one night, you will have a calibrator and target directory for every observation. Inside that directories you will find all the ms files. Copy them in a directory called data-bkp (Don't change the name otherwise the pipeline doesn't find the ms files). So to summarize you will have `Download/mss/id000_CAL/data-bkp` and `Download/mss/id000_TARGET/data-bkp`.
2. In your cal directory `Download/mss/id000_CAL/` run the calibrator pipeline that will estimate the contribution of systematic effects on your observations: `python3 /opt/LiLF/pipelines/LOFAR_cal.py`. Do it for every observations if you have more than one.
3. In your target directory `Download/mss/id000_TARGET` run the split pipeline to apply the calibrator solutions and split the data to MS of 1h to run the next steps in parallel: `python3 /opt/LiLF/pipelines/LOFAR_timesplit.py`. You can find the new MS in `Download/mss/id000_TARGET/mss` named as TC00.MS, TC01.MS etc., one for every hour of observation. Now if you have more than one observation, copy all the MS files in a single directory as `Download/mss/TARGET/mss`, pay attention to rename the files with an increasing number, as for each observation the name start from T00.MS.
4. In your target directory, run the self pipeline that performs the direction-indipendent calibration: `python3 /opt/LiLF/pipelines/LOFAR_self.py`. In the self directory you can find some plots useful to understand the quality of the ionosphere and calibration solutions, you can find some examples in the papers mentioned below. The images are in the `img` directory.
5. In your target directory, run the dd-serial pipeline that performs the direction-dependent calibration: `python3 /opt/LiLF/pipelines/LOFAR_dd-serial.py`. The pipeline selects the DD-calibrators, calibrates them one after the other (check the plots in `ddcal/` and the images of the varius steps of self-calibration), then it transfers the solutions to the associated facet and with DDFacet creates an image of the widefield. It performs two major cycles called c00 and c01. In the `/ddcal/c00/skymodels/` directory you can find the `all-c00.reg` file that indicates all the source selected as DD-calibrators, load it on the image you obtain from the previous step to check which sources it selects, they are indicated with a red circle and named ddcal000. To have a good image of your source is important that it is selected as a calibrator. The images of the single calibrators are named as `ddcalM-c01-ddcal0059-cdd00-MFS-image.fits, ddcalM-c01-ddcal0059-cdd01-MFS-image.fits` where cdd are the different steps of selfcal. You can use the best of them as final image of your source. The image of the widefield instead is `wideDD-c01.app.restored.fits`. You can re-image it using DDFacet with your prefered parameters (for example try to use --Deconv-Mode SSD).
# Extraction of LBA data:
Usage: python LiLF/pipelines/target_extraction.py -p [/path/to/observation] --radec [RA and DEC in deg]
You can extract a target of interest to improve selfcalibration and try to fix ionospheric effects.
If you wish to extract only one target, simply run the command above indicating the path to the directory of
the observation [-p], which must contain the subdirectories /ddcal and /mss-avg obtained from the calibration pipeline (e.g. if one
has /example1/example2/myobs/ddcal, it will be [-p /example1/example2], and RA and DEC where to center the extraction (--radec).
Optionally one can add redshift [--z] and target name [--name].
The pipeline is able to process multiple pointings of the same target altogether: it will look in the path specified with -p for directories
calibrated with LiLF (see previous steps), check if the input coordinates are covered by a specific observation, and move on to the next ones.
All observations for which the beam sensitivity is above 30% at the coordinates position will be used for the extraction.
The code will then create an extraction region based on the flux density within a given area (larger if low flux and vice-versa),
run the selfcalibration and produce images. Outputs will be stored in the /img subdirectory within the target directory, while extracted .MS files
can be found in the /mss-extract subdirectory. A final, nominal-resolution image will be produced ('extractM-final-MFS-image.fits'), as well as high-resolution,
low-resolution and source-subtracted images. The source subtraction is still on beta version, please check it carefully before using the relative image.
Multiple default parameters can be changed through the command line - see below for a brief description.
If you wish to extract more than one target, prepare a .fits file with a minimum of 4 columns: Name, RA, DEC, z (the column names must exactly match these ones,
but the order can be different). Run the extraction script through:
python LiLF/pipelines/target_extraction.py -p [/path/to/observation] --l [/path/to/fits/file]
Coordinates must be in degrees, while the purpose of the name is just to create a subdirectory
in which all the results will be stored. Redshift is mandatory only if one wants to perform the subtraction of compact sources, otherwise just put -99.
An optional column can also be added with the name EXTREG. This must be a .reg file that the pipeline uses as extraction region. The script is usually able to create a good one by itself,
use this option only if needed - always try a run without it first.
Another optional .reg file can be provided in a column named 'MASKREG'; the script will use it as cleaning mask.
Finally, one can specify a subtraction region under the column 'SUBREG'; every source within this region will be subtracted.
The extraction will be run for each object in a different directory named after the 'Name' column.
###Command line parameters
`-p`, `--path`: Path where to look for observations. It must lead to a directory where subdirectories contain /ddcal and /mss-avg derived from calibration.
`--radec`: RA/DEC where to center the extraction in deg. Use if you wish to extract only one target.
`--z`: Redshift of the target. Not necessary unless one wants to perform compact source subtraction.
`--name`: Name of the target. Will be used to create the directory containing the extracted data.
`-l`,`--list`: Name of .fits file which lists Name, RA, DEC and z. Optionally an extraction region and a mask region can be added. Use only for multiple extractions.
`--beamcut`: Beam sensitivity threshold. Default is 0.3.
`--noselfcal`: Do not perform selfcalibration.
`--extreg`: Provide an optional extraction region. If not, one will be created automatically.
`--maskreg`: Provide an optional user mask for cleaning.
`--ampcal`: Perform amplitude calibration. Can be set to True, False or auto. Default is auto.
`--ampsol`: How to solve for amplitudes. Can be set to diagonal or fulljones. Default is diagonal.
`--phsol`: How to solve for phases. Can be set to tecandphase or phase. Default is tecandphase.
`--maxniter`: Maximum number of selfcalibration cycles to perform. Default is 10.
`--subreg`: Provide an optional mask for sources one wishes to completely subtract
# lilf.config specifications:
### PiLL
working_dir: dir path [./] # working directory
download_file: str # html.txt file for downloading if no project/target/obsid provided
project: str # LOFAR project
target: str # observation target
obsid: str # unique observation ID
### flag
stations: str [DE*;FR*;SE*;UK*;IE*;PL*] # For LOFAR, by default flag all IS.
antennas: str, # uGMRT
### model
sourcedb: str # model sourcedb
apparent: bool [False] # is model in apparent sky?
userReg: str # user defined region for cleaning
### LOFAR_preprocess
fix_table: bool [True] # fix bug in some old observations
renameavg: bool [True] # rename and average the data
flagelev: bool [True] # flag anything below 30 deg elevation
keep_IS: bool [False] # keep the LOFAR international stations?
### LOFAR_demix
data_dir: dir path [../cals-bkp/]
demix_model: skaydb file [/home/fdg/scripts/model/demix_all.skydb]
### LOFAR_cal
imaging: bool [False] # perform test imaging of the calibrator data
skymodel: skydb file ["LiLF_dir"/models/calib-simple.skydb']
data_dir: dir path [../cals-bkp/]
### LOFAR_timesplit
data_dir: dir path [../tgts-bkp/]
cal_dir: dir path [../cals/]
ngroups: int [1] # number of frequency groups to create
initc: int [0] # init number for time chunk numbering
### LOFAR_self
### LOFAR_dd
maxIter: int [2] # iterations
minCalFlux60: float [1.5] # apparent flux [Jy] at 60 MHz to be considered a calibrator
removeExtendedCutoff: float [0.0001] # remove extended sources from possible DD-calibrator list
|
revoltekREPO_NAMELiLFPATH_START.@LiLF_extracted@LiLF-master@README.md@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "exoplanet-dev/celerite2",
"repo_path": "celerite2_extracted/celerite2-main/python/celerite2/__init__.py",
"type": "Python"
}
|
# -*- coding: utf-8 -*-
__all__ = ["__version__", "terms", "GaussianProcess"]
import celerite2.terms as terms
from celerite2.celerite2_version import __version__
from celerite2.numpy import GaussianProcess
__uri__ = "https://celerite2.readthedocs.io"
__author__ = "Daniel Foreman-Mackey"
__email__ = "foreman.mackey@gmail.com"
__license__ = "MIT"
__description__ = "Fast and scalable Gaussian Processes in 1D"
__bibtex__ = (
__citation__
) = r"""
@article{celerite1,
author = {{Foreman-Mackey}, D. and {Agol}, E. and {Ambikasaran}, S. and
{Angus}, R.},
title = "{Fast and Scalable Gaussian Process Modeling with Applications to
Astronomical Time Series}",
journal = {\aj},
year = 2017,
month = dec,
volume = 154,
pages = {220},
doi = {10.3847/1538-3881/aa9332},
adsurl = {http://adsabs.harvard.edu/abs/2017AJ....154..220F},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{celerite2,
author = {{Foreman-Mackey}, D.},
title = "{Scalable Backpropagation for Gaussian Processes using Celerite}",
journal = {Research Notes of the American Astronomical Society},
year = 2018,
month = feb,
volume = 2,
number = 1,
pages = {31},
doi = {10.3847/2515-5172/aaaf6c},
adsurl = {http://adsabs.harvard.edu/abs/2018RNAAS...2a..31F},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
"""
|
exoplanet-devREPO_NAMEcelerite2PATH_START.@celerite2_extracted@celerite2-main@python@celerite2@__init__.py@.PATH_END.py
|
{
"filename": "_mode.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/scatterternary/_mode.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ModeValidator(_plotly_utils.basevalidators.FlaglistValidator):
def __init__(self, plotly_name="mode", parent_name="scatterternary", **kwargs):
super(ModeValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
extras=kwargs.pop("extras", ["none"]),
flags=kwargs.pop("flags", ["lines", "markers", "text"]),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@scatterternary@_mode.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "crossbario/crossbar",
"repo_path": "crossbar_extracted/crossbar-master/crossbar/bridge/mqtt/__init__.py",
"type": "Python"
}
|
#####################################################################################
#
# Copyright (c) typedef int GmbH
# SPDX-License-Identifier: EUPL-1.2
#
#####################################################################################
|
crossbarioREPO_NAMEcrossbarPATH_START.@crossbar_extracted@crossbar-master@crossbar@bridge@mqtt@__init__.py@.PATH_END.py
|
{
"filename": "_enabled.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/scattersmith/marker/colorbar/tickformatstop/_enabled.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class EnabledValidator(_plotly_utils.basevalidators.BooleanValidator):
def __init__(
self,
plotly_name="enabled",
parent_name="scattersmith.marker.colorbar.tickformatstop",
**kwargs,
):
super(EnabledValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "colorbars"),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@scattersmith@marker@colorbar@tickformatstop@_enabled.py@.PATH_END.py
|
{
"filename": "README.md",
"repo_name": "cajohare/AxionLimits",
"repo_path": "AxionLimits_extracted/AxionLimits-master/limit_data/AxionElectron/README.md",
"type": "Markdown"
}
|
# AxionLimits
All data files have ma [eV] in the first column and g_ae [dimensionless] in the second column. Projected limits are in the Projections folder
# Limits:
### Underground detectors
* DARWIN: [projection](https://github.com/cajohare/AxionLimits/raw/master/limit_data/AxionElectron/Projections/DARWIN.txt), [reference](https://arxiv.org/abs/1606.07001)
* EDELWEISS: [limit](https://github.com/cajohare/AxionLimits/raw/master/limit_data/AxionElectron/EDELWEISS.txt), [projection](https://github.com/cajohare/AxionLimits/raw/master/limit_data/AxionElectron/Projections/EDELWEISS.txt), [reference](https://arxiv.org/abs/1808.02340)
* LUX: [limit](https://github.com/cajohare/AxionLimits/raw/master/limit_data/AxionElectron/LUX.txt), [reference](https://arxiv.org/abs/1704.02297)
* PandaX-II: [limit](https://github.com/cajohare/AxionLimits/raw/master/limit_data/AxionElectron/PandaX.txt), [reference](https://arxiv.org/abs/1707.07921)
* Semiconductors (absorption): [projection](https://github.com/cajohare/AxionLimits/raw/master/limit_data/AxionElectron/Projections/SemiconductorAbsorption.txt), [reference](https://arxiv.org/abs/1608.02123)
* SuperCDMS: [limit](https://github.com/cajohare/AxionLimits/raw/master/limit_data/AxionElectron/SuperCDMS.txt), [reference](https://arxiv.org/abs/1911.11905)
* XENON1T: [limit](https://github.com/cajohare/AxionLimits/raw/master/limit_data/AxionElectron/XENON1T.txt), [reference](https://arxiv.org/abs/1907.11485)
### Haloscopes:
* Electron spin magnetometers: [projection](https://github.com/cajohare/AxionLimits/raw/master/limit_data/AxionElectron/Projections/ElectronSpinMagnetometers.txt), (in prep.)
### Astro bounds:
* Red giant branch: [limit](https://github.com/cajohare/AxionLimits/raw/master/limit_data/AxionElectron/RedGiants.txt), [reference](https://arxiv.org/abs/1708.02111)
* Solar neutrinos: [limit](https://github.com/cajohare/AxionLimits/raw/master/limit_data/AxionElectron/SolarNu.txt), [reference](https://arxiv.org/abs/0807.2926)
* White dwarf hint: [limit](https://github.com/cajohare/AxionLimits/raw/master/limit_data/AxionElectron/WDhint.txt), [reference](https://arxiv.org/abs/1708.02111)
|
cajohareREPO_NAMEAxionLimitsPATH_START.@AxionLimits_extracted@AxionLimits-master@limit_data@AxionElectron@README.md@.PATH_END.py
|
{
"filename": "Calibration.py",
"repo_name": "GBTSpectroscopy/gbtpipe",
"repo_path": "gbtpipe_extracted/gbtpipe-master/gbtpipe/Calibration.py",
"type": "Python"
}
|
"""Calibration methods for the GBT Pipeline.
This module contains all the core calibration methods for GBT
single dish mapping needed by the pipeline. It includes both
position-switched and frequency-switched calibration methods.
"""
# Copyright (C) 2007 Associated Universities, Inc. Washington DC, USA.
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
#
# Correspondence concerning GBT software should be addressed as follows:
# GBT Operations
# National Radio Astronomy Observatory
# P. O. Box 2
# Green Bank, WV 24944-0002 USA
# $Id$
import numpy as np
import math
from .smoothing import *
from .Pipeutils import Pipeutils
class Calibration(object):
def __init__(self, smoothing_window_size=0):
# set calibration constants
self.BB = .0132 # Ruze equation parameter
self.UNDER_2GHZ_TAU_0 = 0.008
self.SMOOTHING_WINDOW = smoothing_window_size
self.pu = Pipeutils()
# ------------- Unit methods: do not depend on any other pipeline methods
def total_power(self, cal_on, cal_off, t_on, t_off):
r"""Calculate the total power of spectrum with noise diode-switching.
Args:
cal_on(1d array): Spectrum *with* noise diode applied.
cal_off(1d array): Spectrum *without* noise diode applied.
t_on(float): Exposure time of the spectrum *with* noise diode.
t_off(float): Exposure time of the spectrum *without* noise diode.
Returns:
1d array and float:
A spectrum and a total exposure time.
The spectrum is the average of the input spectra.
The exposure time is the sum of the input exposure times.
"""
return np.ma.mean((cal_on, cal_off), axis=0), t_on + t_off
def tsky_correction(self, tsky_sig, tsky_ref, spillover):
r"""Correction factor for sky brightness variation between reference and current integration.
Args:
tsky_sig(float): Sky brightness at current temperature, \
frequency and elevation.
tsky_ref(float): Sky brightness at reference temperature, \
frequency and elevation.
spillover(float): Spillover factor.
Returns:
float:
A sky brightness correction factor.
.. math::
spillover * (tsky\_{sig} - tsky\_{ref})
"""
return spillover * (tsky_sig - tsky_ref)
def aperture_efficiency(self, reference_eta_a, freq_hz):
r"""Determine telescope aperture efficiency at a given frequency.
EtaA model is from memo by Jim Condon, provided by Ron Maddalena.
Args:
reference_eta_a(float): Reference aperture efficiency.
freq_hz(float): Frequency in Hertz.
Returns:
float:
Point or main beam efficiency (ranges from 0 to 1).
.. testsetup::
from Calibration import Calibration
.. doctest:: :hide:
>>> cal = Calibration()
>>> print '{0:.6f}'.format(cal.aperture_efficiency(.71, 23e9))
0.647483
>>> print '{0:.6f}'.format(cal.aperture_efficiency(.91, 23e9))
0.829872
"""
freq_ghz = float(freq_hz)/1e9
return reference_eta_a * np.exp(-((self.BB * freq_ghz)**2))
def main_beam_efficiency(self, reference_eta_b, freq_hz):
r"""Determine main beam efficiency, given a reference etaB value and frequency.
This is the same equation as is used to determine aperture efficiency.
The only difference is the reference value.
Args:
reference_eta_b(float): The main beam efficiency. \
For the GBT, the default is :math:`1.28 * \eta_A`, where :math:`\eta_A` is aperture efficiency.
freq_hz(float): The frequency in Hertz.
Returns:
float:
An aperture efficiency at a given frequency.
"""
return self.aperture_efficiency(reference_eta_b, freq_hz)
def elevation_adjusted_opacity(self, zenith_opacity, elev):
r"""Compute elevation-corrected opacities.
We need to estimate the number of atmospheres along the
line of site at an input elevation
This comes from a model reported by Ron Maddalena:
:math:`A = \frac{1}{\sin(elev)}` is a good approximation down to about 15 deg but
starts to get pretty poor below that. Here's a quick-to-calculate,
better approximation that I determined from multiple years worth of
weather data and which is good down to elev = 1 deg:
.. math::
A = -0.023437 + \frac{1.0140}{\sin( \frac{pi}{180} * (elev + \frac{5.1774}{elev + 3.3543} )}
Args:
zenith_opacity(float): Opacity at zenith based only on time.
elev(float): Elevation angle of integration or scan.
Returns:
float:
Elevation-adjusted opacity
.. testsetup::
from Calibration import Calibration
.. doctest:: :hide:
>>> cal = Calibration()
>>> print ['{0:.6f}'.format(cal.elevation_adjusted_opacity(1, el)) for el in range(90)]
['37.621216', '26.523488', '19.566942', '15.217485', '12.341207', '10.331365', '8.861127', '7.745094', '6.872195', '6.172545', '5.600276', '5.124171', '4.722318', '4.378917', '4.082311', '3.823718', '3.596410', '3.395144', '3.215779', '3.055004', '2.910137', '2.778989', '2.659751', '2.550918', '2.451229', '2.359617', '2.275175', '2.197126', '2.124803', '2.057628', '1.995099', '1.936775', '1.882273', '1.831253', '1.783416', '1.738495', '1.696253', '1.656478', '1.618982', '1.583595', '1.550162', '1.518545', '1.488619', '1.460271', '1.433397', '1.407903', '1.383703', '1.360719', '1.338878', '1.318115', '1.298369', '1.279585', '1.261710', '1.244698', '1.228504', '1.213089', '1.198415', '1.184446', '1.171152', '1.158501', '1.146467', '1.135024', '1.124146', '1.113814', '1.104005', '1.094700', '1.085882', '1.077533', '1.069639', '1.062184', '1.055156', '1.048543', '1.042331', '1.036512', '1.031074', '1.026009', '1.021309', '1.016966', '1.012972', '1.009322', '1.006009', '1.003029', '1.000376', '0.998047', '0.996038', '0.994346', '0.992968', '0.991902', '0.991147', '0.990701']
"""
deg2rad = (np.pi/180) # factor to convert degrees to radians
num_atmospheres = (-0.023437 + 1.0140 /
np.sin(deg2rad * (elev + 5.1774 / (elev + 3.3543))))
corrected_opacity = zenith_opacity * num_atmospheres
return corrected_opacity
def _tatm(self, freq_hz, tmp_c):
"""Estimates the atmospheric effective temperature.
Keyword arguments:
freq_hz -- input frequency in Hz
where: tmp_c -- input ground temperature in Celsius
Returns:
air_temp_k -- output Air Temperature in Kelvin
Based on local ground temperature measurements. These estimates
come from a model reported by Ron Maddalena
Using Tatm = 270 is too rough an approximation since Tatm can vary
from 244 to 290, depending upon the weather conditions and observing
frequency. One can derive an approximation for the default Tatm that
is accurate to about 3.5 K from the equation:
TATM = (A0 + A1*FREQ + A2*FREQ^2 +A3*FREQ^3 + A4*FREQ^4 + A5*FREQ^5)
+ (B0 + B1*FREQ + B2*FREQ^2 + B3*FREQ^3 + B4*FREQ^4 +
B5*FREQ^5)*TMPC
where TMPC = ground-level air temperature in C and Freq is in GHz. The
A and B coefficients are:
A0= 259.69185966 +/- 0.117749542
A1= -1.66599001 +/- 0.0313805607
A2= 0.226962192 +/- 0.00289457549
A3= -0.0100909636 +/- 0.00011905765
A4= 0.00018402955 +/- 0.00000223708
A5= -0.00000119516 +/- 0.00000001564
B0= 0.42557717 +/- 0.0078863791
B1= 0.033932476 +/- 0.00210078949
B2= 0.0002579834 +/- 0.00019368682
B3= -0.00006539032 +/- 0.00000796362
B4= 0.00000157104 +/- 0.00000014959
B5= -0.00000001182 +/- 0.00000000105
tatm model is provided by Ron Maddalena
>>> print '{0:.6f}'.format(Calibration()._tatm(23e9, 40))
298.885174
>>> print '{0:.6f}'.format(Calibration()._tatm(23e9, 30))
289.780603
>>> print '{0:.6f}'.format(Calibration()._tatm(1.42e9, 30))
271.978666
"""
# where TMPC = ground-level air temperature in C and Freq is in GHz.
# The A and B coefficients are:
aaa = [259.69185966, -1.66599001, 0.226962192, -0.0100909636, 0.00018402955, -0.00000119516]
bbb = [0.42557717, 0.033932476, 0.0002579834, -0.00006539032, 0.00000157104, -0.00000001182]
freq_ghz = float(freq_hz)/1e9
air_temp_k_A = air_temp_k_B = 0
for idx, term in enumerate(zip(aaa, bbb)):
if idx > 0:
air_temp_k_A = air_temp_k_A + term[0] * (freq_ghz**idx)
air_temp_k_B = air_temp_k_B + term[1] * (freq_ghz**idx)
else:
air_temp_k_A = term[0]
air_temp_k_B = term[1]
air_temp_k = air_temp_k_A + (air_temp_k_B * float(tmp_c))
return air_temp_k
def Tatm_from_coeffs(self, coeffs, freq):
for idx, term in enumerate(coeffs):
if idx > 0:
result = result + term*freq**idx
else:
result = term
return result
def zenith_opacity(self, coeffs, freq_ghz):
r"""Interpolate low and high opacities across a vector of frequencies.
Args:
coeffs(1d array): Opacitiy coefficients from archived text file, \
produced by GBT weather prediction code.
freq_ghz(float): Frequency value in GHz.
Returns:
float:
A zenith opacity at requested frequency.
"""
# interpolate between the coefficients based on time for a
# given frequency
def _interpolated_zenith_opacity(freq):
# for frequencies < 2 GHz, return a default zenith opacity
if np.array(freq).mean() < 2:
result = np.ones(np.array(freq).shape)*self.UNDER_2GHZ_TAU_0
return result
result = 0
for idx, term in enumerate(coeffs):
if idx > 0:
result = result + term*freq**idx
else:
result = term
return result
zenith_opacity = _interpolated_zenith_opacity(freq_ghz)
return zenith_opacity
def tsys(self, tcal, cal_on, cal_off):
r"""Calculate the system temperature for an integration.
Args:
tcal(float): Lab-measured receiver calibration temperature.
cal_on(1d array): Spectrum *with* noise diode applied.
cal_off(1d array): Spectrum *without* noise diode applied.
Returns:
float:
.. math::
tcal * \frac{cal\_{off}}{cal\_{on} - cal\_{off}} + \frac{tcal}{2}
"""
nchan = len(cal_off)
low = int(.1 * nchan)
high = int(.9 * nchan)
cal_off = (cal_off[low:high]).mean()
cal_on = (cal_on[low:high]).mean()
return float(tcal * (cal_off / (cal_on - cal_off)) + tcal / 2)
def antenna_temp(self, tsys, sig, ref, t_sig, t_ref):
r"""Calibrate a spectrum to units of antenna temperature.
Args:
tsys(float): System temperature of the reference scan.
sig(1d array): Signal ("on") spectrum.
ref(1d array): Reference ("off") spectrum.
t_sig(float): Exposure time of the signal spectrum.
t_ref(float): Exposure time of the reference spectrum.
Returns:
1d array or float:
A calibrated spectrum with an exposure time.
The spectrum is
.. math:: tsys * \frac{sig - ref}{ref}.
The exposure time is
.. math::
\frac{t\_{sig} * t\_{ref} * window\_{size}}{t\_{sig} + (t\_{ref} * window\_{size})}
where the window size is an optional smoothing kernel size for the
reference spectrum.
"""
if self.SMOOTHING_WINDOW > 1:
ref = smoothing.boxcar(ref, self.SMOOTHING_WINDOW)
window_size = self.SMOOTHING_WINDOW
else:
window_size = 1
ref = self.pu.masked_array(ref)
spectrum = tsys * ((sig-ref)/ref)
exposure_time = (t_sig * t_ref * window_size / (t_sig + t_ref*window_size))
return spectrum, exposure_time
def _ta_fs_one_state(self, sigref_state, sigid, refid, scale):
sig = sigref_state[sigid]['TP']
ref = sigref_state[refid]['TP']
ref_cal_on = sigref_state[refid]['cal_on']
ref_cal_off = sigref_state[refid]['cal_off']
tcal = ref_cal_off['TCAL'] * scale
tsys = self.tsys(tcal, ref_cal_on['DATA'], ref_cal_off['DATA'])
a_temp_params = {'tsys': tsys, 'sig': sig, 'ref': ref,
't_sig': sigref_state[sigid]['EXPOSURE'],
't_ref': sigref_state[refid]['EXPOSURE']}
antenna_temp, exposure = self.antenna_temp(**a_temp_params)
return antenna_temp, tsys, exposure
def ta_fs(self, sigref_state, scale):
r"""Calibrate a frequency-switched integration to units of antenna temperature.
Args:
sigref_state(struct): A structure holding the noise diode off and on \
integrations (which are full rows from the FITS table, including the DATA column), \
a total power integration, FITS table row number and \
exposure time.
scale(float): A relative beam scaling factor. Default is 1, or no scaling.
Returns:
1d array, float, float:
An averaged spectrum calibrated to units of antenna temperature, \
a system temperature and a total exposure time for the spectrum.
"""
ta0, tsys0, exposure0 = self._ta_fs_one_state(sigref_state, 0, 1, scale)
ta1, tsys1, exposure1 = self._ta_fs_one_state(sigref_state, 1, 0, scale)
# shift in frequency
sig_centerfreq = sigref_state[0]['cal_off']['OBSFREQ']
ref_centerfreq = sigref_state[1]['cal_off']['OBSFREQ']
sig_delta = sigref_state[0]['cal_off']['CDELT1']
channel_shift = -((sig_centerfreq-ref_centerfreq)/sig_delta)
# do integer channel shift to second spectrum
ta1_ishifted = np.roll(ta1, int(channel_shift))
if channel_shift > 0:
ta1_ishifted[:channel_shift] = float('nan')
elif channel_shift < 0:
ta1_ishifted[channel_shift:] = float('nan')
# do fractional channel shift
fractional_shift = channel_shift - int(channel_shift)
# doMessage(logger, msg.DBG, 'Fractional channel shift is',
# fractional_shift)
xxp = range(len(ta1_ishifted))
yyp = ta1_ishifted
xxx = xxp-fractional_shift
yyy = np.interp(xxx, xxp, yyp)
ta1_shifted = self.pu.masked_array(yyy)
exposures = np.array([exposure0, exposure1])
tsyss = np.array([tsys0, tsys1])
tas = [ta0, ta1_shifted]
# average shifted spectra
ta = self.average_spectra(tas, tsyss, exposures)
# average tsys
tsys = self.average_tsys(tsyss, exposures)
# only sum the exposure if frequency switch is "in band" (i.e.
# overlapping channels); otherwise use the exposure from the
# first state only
if abs(channel_shift) < len(ta1):
exposure_sum = exposure0 + exposure1
else:
exposure_sum = exposure0
return ta, tsys, exposure_sum
def ta_star(self, antenna_temp, opacity, spillover):
r"""Calibrate a spectrum to units of **ta***.
Args:
antenna_temp(1d array): Spectrum calibrated to units of antenna temperature.
opacity(float): Elevation-adjusted atmospheric opacity.
spillover(float): Correction factor for rear-spillover, ohmic loss and blockage efficiency.
Returns:
1d array:
A calibrated spectrum.
.. math::
antenna\_{temp} * e^{opacity} * \frac{1}{spillover}
"""
# opacity is corrected for elevation
return antenna_temp * np.exp(opacity) * 1 / spillover
def jansky(self, spectrum, aperture_efficiency):
r"""Calibrate a spectrum to units of **Jansky**.
Args:
spectrum(1d array): A spectrum previously calibrated to **ta***.
aperture_efficiency(float): The aperture efficiency factor.
Returns:
1d array:
.. math::
\frac{spectrum}{2.85 * aperture\_{efficiency}}
"""
return spectrum / (2.85 * aperture_efficiency)
def interpolate_by_time(self, reference1, reference2,
first_ref_timestamp, second_ref_timestamp,
integration_timestamp):
r"""Calculate interpolated value(s).
This function can be used to calculate a single interpolated value
or an array of values at a specified time.
Args:
reference1(float or 1d array): Value(s) for first time.
reference2(float or 1d array): Value(s) for second time.
first_ref_timestamp(float): First time.
second_ref_timestamp(float): Second time.
integration_timestamp(float): The time for which we want a value.
Returns:
float or 1d array:
Interpolated value(s) for a specific time.
.. testsetup::
from Calibration import Calibration
import numpy as np
.. doctest:: :hide:
>>> cal = Calibration()
>>> cal.interpolate_by_time(1, 2, 0, 100, 75)
1.75
>>> cal.interpolate_by_time(np.array([1, 2]), np.array([2, 3]), 0, 100, 75)
array([ 1.75, 2.75])
"""
time_btwn_ref_scans = float(second_ref_timestamp) - float(first_ref_timestamp)
aa1 = (second_ref_timestamp - integration_timestamp) / time_btwn_ref_scans
aa2 = (integration_timestamp - first_ref_timestamp) / time_btwn_ref_scans
return aa1 * reference1 + aa2 * reference2
def make_weights(self, tsyss, exposures):
r"""Create weights for integration averaging.
Args:
tsyss(1d array): A list of system temperatures.
exposures(1d array): A list of exposure times. \
The number of exposure times must match the number of system temperatures.
Returns:
1d array:
A list of weights. The weights are computed with the following formula.
.. math::
\frac{exposure\ time}{tsys^2}
"""
return exposures / tsyss**2
def average_tsys(self, tsyss, exposures):
r"""Compute a weighted average multiple system temperatures.
Args:
tsyss(1d array): The system temperatures to average.
exposures(1d array): The exposure times corresponding to each system temperature.
Returns:
1d array:
A weighted average system temperature. See the *make_weights* method to see how
the weights are computed.
"""
weights = self.make_weights(tsyss, exposures)
return np.sqrt(np.average(tsyss**2, axis=0, weights=weights))
def average_spectra(self, specs, tsyss, exposures):
r"""Perform a weighted average of two spectra.
Args:
specs(two 1d arrays): The two input spectra to be averaged.
tsyss(two floats): System temperatures corresponding to each input spectrum.
exposures(two floats): Exposure times corresponding to each input spectrum.
Returns:
1d array:
A weighted average spectrum. See the *make_weights* method to see how the weights
are computed.
"""
weights = self.make_weights(tsyss, exposures)
if float('nan') in specs[0] or float('nan') in specs[1]:
weight0 = np.ma.array([weights[0]] * len(specs[0]), mask=specs[0].mask)
weight1 = np.ma.array([weights[1]] * len(specs[1]), mask=specs[1].mask)
weights = [weight0.filled(0), weight1.filled(0)]
return np.ma.average(specs, axis=0, weights=weights)
def getReferenceAverage(self, crefs, tsyss, exposures, timestamps,
tambients, elevations):
r"""Average the total power integrations from a reference scan.
Args:
crefs(stack of 1d arrays): The total power integrations (spectra) for a single reference scan.
tsyss(1d array): The system temperatures; one for each input spectrum.
exposures(1d array): The exposure times; one for each input spectrum.
timestamps(1d array): The timestamps; one for each input spectrum.
tambients(1d array): Ambient temperatures in Kelvin; one for each input spectrum.
elevations(1d array): Elevation in degrees; one for each input spectrum.
Returns:
1d array, float, float, float, float, float:
An average value for each of the input parameters. An average spectrum along with
average system temperature, exposure time, timestamp, ambient temperature and elevation.
"""
# convert to numpy arrays
crefs = np.array(crefs)
tsyss = np.array(tsyss)
exposures = np.array(exposures)
timestamps = np.array(timestamps)
tambients = np.array(tambients)
elevations = np.array(elevations)
avg_tsys = self.average_tsys(tsyss, exposures)
avg_tsys80 = avg_tsys.mean(0) # single value for mid 80% of band
avg_cref = self.average_spectra(crefs, tsyss, exposures)
exposure = np.sum(exposures)
avg_timestamp = timestamps.mean()
avg_tambient = tambients.mean()
avg_elevation = elevations.mean()
return avg_cref, avg_tsys80, avg_timestamp, avg_tambient, avg_elevation, exposure
def tsky(self, ambient_temp_k, freq_hz, tau):
r"""Determine the sky brightness temperature at a frequency.
Args:
ambient_temp_k(float): Mean ambient temperature in Kelvin.
freq_hz(float): Frequency in Hz.
tau(float): Atmospheric opacity value.
Returns:
float:
The sky model temperature contribution at frequency channel.
"""
ambient_temp_c = ambient_temp_k - 273.15 # convert to Celsius
airTemp = self._tatm(freq_hz, ambient_temp_c)
tsky = airTemp * (1 - np.exp(-tau))
return tsky
if __name__ == "__main__":
import doctest
doctest.testmod()
|
GBTSpectroscopyREPO_NAMEgbtpipePATH_START.@gbtpipe_extracted@gbtpipe-master@gbtpipe@Calibration.py@.PATH_END.py
|
{
"filename": "_violin.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/graph_objs/_violin.py",
"type": "Python"
}
|
from plotly.basedatatypes import BaseTraceType as _BaseTraceType
import copy as _copy
class Violin(_BaseTraceType):
# class properties
# --------------------
_parent_path_str = ""
_path_str = "violin"
_valid_props = {
"alignmentgroup",
"bandwidth",
"box",
"customdata",
"customdatasrc",
"fillcolor",
"hoverinfo",
"hoverinfosrc",
"hoverlabel",
"hoveron",
"hovertemplate",
"hovertemplatesrc",
"hovertext",
"hovertextsrc",
"ids",
"idssrc",
"jitter",
"legend",
"legendgroup",
"legendgrouptitle",
"legendrank",
"legendwidth",
"line",
"marker",
"meanline",
"meta",
"metasrc",
"name",
"offsetgroup",
"opacity",
"orientation",
"pointpos",
"points",
"quartilemethod",
"scalegroup",
"scalemode",
"selected",
"selectedpoints",
"showlegend",
"side",
"span",
"spanmode",
"stream",
"text",
"textsrc",
"type",
"uid",
"uirevision",
"unselected",
"visible",
"width",
"x",
"x0",
"xaxis",
"xhoverformat",
"xsrc",
"y",
"y0",
"yaxis",
"yhoverformat",
"ysrc",
"zorder",
}
# alignmentgroup
# --------------
@property
def alignmentgroup(self):
"""
Set several traces linked to the same position axis or matching
axes to the same alignmentgroup. This controls whether bars
compute their positional range dependently or independently.
The 'alignmentgroup' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["alignmentgroup"]
@alignmentgroup.setter
def alignmentgroup(self, val):
self["alignmentgroup"] = val
# bandwidth
# ---------
@property
def bandwidth(self):
"""
Sets the bandwidth used to compute the kernel density estimate.
By default, the bandwidth is determined by Silverman's rule of
thumb.
The 'bandwidth' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
"""
return self["bandwidth"]
@bandwidth.setter
def bandwidth(self, val):
self["bandwidth"] = val
# box
# ---
@property
def box(self):
"""
The 'box' property is an instance of Box
that may be specified as:
- An instance of :class:`plotly.graph_objs.violin.Box`
- A dict of string/value properties that will be passed
to the Box constructor
Supported dict properties:
fillcolor
Sets the inner box plot fill color.
line
:class:`plotly.graph_objects.violin.box.Line`
instance or dict with compatible properties
visible
Determines if an miniature box plot is drawn
inside the violins.
width
Sets the width of the inner box plots relative
to the violins' width. For example, with 1, the
inner box plots are as wide as the violins.
Returns
-------
plotly.graph_objs.violin.Box
"""
return self["box"]
@box.setter
def box(self, val):
self["box"] = val
# customdata
# ----------
@property
def customdata(self):
"""
Assigns extra data each datum. This may be useful when
listening to hover, click and selection events. Note that,
"scatter" traces also appends customdata items in the markers
DOM elements
The 'customdata' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray
"""
return self["customdata"]
@customdata.setter
def customdata(self, val):
self["customdata"] = val
# customdatasrc
# -------------
@property
def customdatasrc(self):
"""
Sets the source reference on Chart Studio Cloud for
`customdata`.
The 'customdatasrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["customdatasrc"]
@customdatasrc.setter
def customdatasrc(self, val):
self["customdatasrc"] = val
# fillcolor
# ---------
@property
def fillcolor(self):
"""
Sets the fill color. Defaults to a half-transparent variant of
the line color, marker color, or marker line color, whichever
is available.
The 'fillcolor' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
Returns
-------
str
"""
return self["fillcolor"]
@fillcolor.setter
def fillcolor(self, val):
self["fillcolor"] = val
# hoverinfo
# ---------
@property
def hoverinfo(self):
"""
Determines which trace information appear on hover. If `none`
or `skip` are set, no information is displayed upon hovering.
But, if `none` is set, click and hover events are still fired.
The 'hoverinfo' property is a flaglist and may be specified
as a string containing:
- Any combination of ['x', 'y', 'z', 'text', 'name'] joined with '+' characters
(e.g. 'x+y')
OR exactly one of ['all', 'none', 'skip'] (e.g. 'skip')
- A list or array of the above
Returns
-------
Any|numpy.ndarray
"""
return self["hoverinfo"]
@hoverinfo.setter
def hoverinfo(self, val):
self["hoverinfo"] = val
# hoverinfosrc
# ------------
@property
def hoverinfosrc(self):
"""
Sets the source reference on Chart Studio Cloud for
`hoverinfo`.
The 'hoverinfosrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["hoverinfosrc"]
@hoverinfosrc.setter
def hoverinfosrc(self, val):
self["hoverinfosrc"] = val
# hoverlabel
# ----------
@property
def hoverlabel(self):
"""
The 'hoverlabel' property is an instance of Hoverlabel
that may be specified as:
- An instance of :class:`plotly.graph_objs.violin.Hoverlabel`
- A dict of string/value properties that will be passed
to the Hoverlabel constructor
Supported dict properties:
align
Sets the horizontal alignment of the text
content within hover label box. Has an effect
only if the hover label text spans more two or
more lines
alignsrc
Sets the source reference on Chart Studio Cloud
for `align`.
bgcolor
Sets the background color of the hover labels
for this trace
bgcolorsrc
Sets the source reference on Chart Studio Cloud
for `bgcolor`.
bordercolor
Sets the border color of the hover labels for
this trace.
bordercolorsrc
Sets the source reference on Chart Studio Cloud
for `bordercolor`.
font
Sets the font used in hover labels.
namelength
Sets the default length (in number of
characters) of the trace name in the hover
labels for all traces. -1 shows the whole name
regardless of length. 0-3 shows the first 0-3
characters, and an integer >3 will show the
whole name if it is less than that many
characters, but if it is longer, will truncate
to `namelength - 3` characters and add an
ellipsis.
namelengthsrc
Sets the source reference on Chart Studio Cloud
for `namelength`.
Returns
-------
plotly.graph_objs.violin.Hoverlabel
"""
return self["hoverlabel"]
@hoverlabel.setter
def hoverlabel(self, val):
self["hoverlabel"] = val
# hoveron
# -------
@property
def hoveron(self):
"""
Do the hover effects highlight individual violins or sample
points or the kernel density estimate or any combination of
them?
The 'hoveron' property is a flaglist and may be specified
as a string containing:
- Any combination of ['violins', 'points', 'kde'] joined with '+' characters
(e.g. 'violins+points')
OR exactly one of ['all'] (e.g. 'all')
Returns
-------
Any
"""
return self["hoveron"]
@hoveron.setter
def hoveron(self, val):
self["hoveron"] = val
# hovertemplate
# -------------
@property
def hovertemplate(self):
"""
Template string used for rendering the information that appear
on hover box. Note that this will override `hoverinfo`.
Variables are inserted using %{variable}, for example "y: %{y}"
as well as %{xother}, {%_xother}, {%_xother_}, {%xother_}. When
showing info for several points, "xother" will be added to
those with different x positions from the first point. An
underscore before or after "(x|y)other" will add a space on
that side, only when this field is shown. Numbers are formatted
using d3-format's syntax %{variable:d3-format}, for example
"Price: %{y:$.2f}".
https://github.com/d3/d3-format/tree/v1.4.5#d3-format for
details on the formatting syntax. Dates are formatted using
d3-time-format's syntax %{variable|d3-time-format}, for example
"Day: %{2019-01-01|%A}". https://github.com/d3/d3-time-
format/tree/v2.2.3#locale_format for details on the date
formatting syntax. The variables available in `hovertemplate`
are the ones emitted as event data described at this link
https://plotly.com/javascript/plotlyjs-events/#event-data.
Additionally, every attributes that can be specified per-point
(the ones that are `arrayOk: true`) are available. Anything
contained in tag `<extra>` is displayed in the secondary box,
for example "<extra>{fullData.name}</extra>". To hide the
secondary box completely, use an empty tag `<extra></extra>`.
The 'hovertemplate' property is a string and must be specified as:
- A string
- A number that will be converted to a string
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
str|numpy.ndarray
"""
return self["hovertemplate"]
@hovertemplate.setter
def hovertemplate(self, val):
self["hovertemplate"] = val
# hovertemplatesrc
# ----------------
@property
def hovertemplatesrc(self):
"""
Sets the source reference on Chart Studio Cloud for
`hovertemplate`.
The 'hovertemplatesrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["hovertemplatesrc"]
@hovertemplatesrc.setter
def hovertemplatesrc(self, val):
self["hovertemplatesrc"] = val
# hovertext
# ---------
@property
def hovertext(self):
"""
Same as `text`.
The 'hovertext' property is a string and must be specified as:
- A string
- A number that will be converted to a string
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
str|numpy.ndarray
"""
return self["hovertext"]
@hovertext.setter
def hovertext(self, val):
self["hovertext"] = val
# hovertextsrc
# ------------
@property
def hovertextsrc(self):
"""
Sets the source reference on Chart Studio Cloud for
`hovertext`.
The 'hovertextsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["hovertextsrc"]
@hovertextsrc.setter
def hovertextsrc(self, val):
self["hovertextsrc"] = val
# ids
# ---
@property
def ids(self):
"""
Assigns id labels to each datum. These ids for object constancy
of data points during animation. Should be an array of strings,
not numbers or any other type.
The 'ids' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray
"""
return self["ids"]
@ids.setter
def ids(self, val):
self["ids"] = val
# idssrc
# ------
@property
def idssrc(self):
"""
Sets the source reference on Chart Studio Cloud for `ids`.
The 'idssrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["idssrc"]
@idssrc.setter
def idssrc(self, val):
self["idssrc"] = val
# jitter
# ------
@property
def jitter(self):
"""
Sets the amount of jitter in the sample points drawn. If 0, the
sample points align along the distribution axis. If 1, the
sample points are drawn in a random jitter of width equal to
the width of the violins.
The 'jitter' property is a number and may be specified as:
- An int or float in the interval [0, 1]
Returns
-------
int|float
"""
return self["jitter"]
@jitter.setter
def jitter(self, val):
self["jitter"] = val
# legend
# ------
@property
def legend(self):
"""
Sets the reference to a legend to show this trace in.
References to these legends are "legend", "legend2", "legend3",
etc. Settings for these legends are set in the layout, under
`layout.legend`, `layout.legend2`, etc.
The 'legend' property is an identifier of a particular
subplot, of type 'legend', that may be specified as the string 'legend'
optionally followed by an integer >= 1
(e.g. 'legend', 'legend1', 'legend2', 'legend3', etc.)
Returns
-------
str
"""
return self["legend"]
@legend.setter
def legend(self, val):
self["legend"] = val
# legendgroup
# -----------
@property
def legendgroup(self):
"""
Sets the legend group for this trace. Traces and shapes part of
the same legend group hide/show at the same time when toggling
legend items.
The 'legendgroup' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["legendgroup"]
@legendgroup.setter
def legendgroup(self, val):
self["legendgroup"] = val
# legendgrouptitle
# ----------------
@property
def legendgrouptitle(self):
"""
The 'legendgrouptitle' property is an instance of Legendgrouptitle
that may be specified as:
- An instance of :class:`plotly.graph_objs.violin.Legendgrouptitle`
- A dict of string/value properties that will be passed
to the Legendgrouptitle constructor
Supported dict properties:
font
Sets this legend group's title font.
text
Sets the title of the legend group.
Returns
-------
plotly.graph_objs.violin.Legendgrouptitle
"""
return self["legendgrouptitle"]
@legendgrouptitle.setter
def legendgrouptitle(self, val):
self["legendgrouptitle"] = val
# legendrank
# ----------
@property
def legendrank(self):
"""
Sets the legend rank for this trace. Items and groups with
smaller ranks are presented on top/left side while with
"reversed" `legend.traceorder` they are on bottom/right side.
The default legendrank is 1000, so that you can use ranks less
than 1000 to place certain items before all unranked items, and
ranks greater than 1000 to go after all unranked items. When
having unranked or equal rank items shapes would be displayed
after traces i.e. according to their order in data and layout.
The 'legendrank' property is a number and may be specified as:
- An int or float
Returns
-------
int|float
"""
return self["legendrank"]
@legendrank.setter
def legendrank(self, val):
self["legendrank"] = val
# legendwidth
# -----------
@property
def legendwidth(self):
"""
Sets the width (in px or fraction) of the legend for this
trace.
The 'legendwidth' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
"""
return self["legendwidth"]
@legendwidth.setter
def legendwidth(self, val):
self["legendwidth"] = val
# line
# ----
@property
def line(self):
"""
The 'line' property is an instance of Line
that may be specified as:
- An instance of :class:`plotly.graph_objs.violin.Line`
- A dict of string/value properties that will be passed
to the Line constructor
Supported dict properties:
color
Sets the color of line bounding the violin(s).
width
Sets the width (in px) of line bounding the
violin(s).
Returns
-------
plotly.graph_objs.violin.Line
"""
return self["line"]
@line.setter
def line(self, val):
self["line"] = val
# marker
# ------
@property
def marker(self):
"""
The 'marker' property is an instance of Marker
that may be specified as:
- An instance of :class:`plotly.graph_objs.violin.Marker`
- A dict of string/value properties that will be passed
to the Marker constructor
Supported dict properties:
angle
Sets the marker angle in respect to `angleref`.
color
Sets the marker color. It accepts either a
specific color or an array of numbers that are
mapped to the colorscale relative to the max
and min values of the array or relative to
`marker.cmin` and `marker.cmax` if set.
line
:class:`plotly.graph_objects.violin.marker.Line
` instance or dict with compatible properties
opacity
Sets the marker opacity.
outliercolor
Sets the color of the outlier sample points.
size
Sets the marker size (in px).
symbol
Sets the marker symbol type. Adding 100 is
equivalent to appending "-open" to a symbol
name. Adding 200 is equivalent to appending
"-dot" to a symbol name. Adding 300 is
equivalent to appending "-open-dot" or "dot-
open" to a symbol name.
Returns
-------
plotly.graph_objs.violin.Marker
"""
return self["marker"]
@marker.setter
def marker(self, val):
self["marker"] = val
# meanline
# --------
@property
def meanline(self):
"""
The 'meanline' property is an instance of Meanline
that may be specified as:
- An instance of :class:`plotly.graph_objs.violin.Meanline`
- A dict of string/value properties that will be passed
to the Meanline constructor
Supported dict properties:
color
Sets the mean line color.
visible
Determines if a line corresponding to the
sample's mean is shown inside the violins. If
`box.visible` is turned on, the mean line is
drawn inside the inner box. Otherwise, the mean
line is drawn from one side of the violin to
other.
width
Sets the mean line width.
Returns
-------
plotly.graph_objs.violin.Meanline
"""
return self["meanline"]
@meanline.setter
def meanline(self, val):
self["meanline"] = val
# meta
# ----
@property
def meta(self):
"""
Assigns extra meta information associated with this trace that
can be used in various text attributes. Attributes such as
trace `name`, graph, axis and colorbar `title.text`, annotation
`text` `rangeselector`, `updatemenues` and `sliders` `label`
text all support `meta`. To access the trace `meta` values in
an attribute in the same trace, simply use `%{meta[i]}` where
`i` is the index or key of the `meta` item in question. To
access trace `meta` in layout attributes, use
`%{data[n[.meta[i]}` where `i` is the index or key of the
`meta` and `n` is the trace index.
The 'meta' property accepts values of any type
Returns
-------
Any|numpy.ndarray
"""
return self["meta"]
@meta.setter
def meta(self, val):
self["meta"] = val
# metasrc
# -------
@property
def metasrc(self):
"""
Sets the source reference on Chart Studio Cloud for `meta`.
The 'metasrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["metasrc"]
@metasrc.setter
def metasrc(self, val):
self["metasrc"] = val
# name
# ----
@property
def name(self):
"""
Sets the trace name. The trace name appears as the legend item
and on hover. For violin traces, the name will also be used for
the position coordinate, if `x` and `x0` (`y` and `y0` if
horizontal) are missing and the position axis is categorical.
Note that the trace name is also used as a default value for
attribute `scalegroup` (please see its description for
details).
The 'name' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["name"]
@name.setter
def name(self, val):
self["name"] = val
# offsetgroup
# -----------
@property
def offsetgroup(self):
"""
Set several traces linked to the same position axis or matching
axes to the same offsetgroup where bars of the same position
coordinate will line up.
The 'offsetgroup' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["offsetgroup"]
@offsetgroup.setter
def offsetgroup(self, val):
self["offsetgroup"] = val
# opacity
# -------
@property
def opacity(self):
"""
Sets the opacity of the trace.
The 'opacity' property is a number and may be specified as:
- An int or float in the interval [0, 1]
Returns
-------
int|float
"""
return self["opacity"]
@opacity.setter
def opacity(self, val):
self["opacity"] = val
# orientation
# -----------
@property
def orientation(self):
"""
Sets the orientation of the violin(s). If "v" ("h"), the
distribution is visualized along the vertical (horizontal).
The 'orientation' property is an enumeration that may be specified as:
- One of the following enumeration values:
['v', 'h']
Returns
-------
Any
"""
return self["orientation"]
@orientation.setter
def orientation(self, val):
self["orientation"] = val
# pointpos
# --------
@property
def pointpos(self):
"""
Sets the position of the sample points in relation to the
violins. If 0, the sample points are places over the center of
the violins. Positive (negative) values correspond to positions
to the right (left) for vertical violins and above (below) for
horizontal violins.
The 'pointpos' property is a number and may be specified as:
- An int or float in the interval [-2, 2]
Returns
-------
int|float
"""
return self["pointpos"]
@pointpos.setter
def pointpos(self, val):
self["pointpos"] = val
# points
# ------
@property
def points(self):
"""
If "outliers", only the sample points lying outside the
whiskers are shown If "suspectedoutliers", the outlier points
are shown and points either less than 4*Q1-3*Q3 or greater than
4*Q3-3*Q1 are highlighted (see `outliercolor`) If "all", all
sample points are shown If False, only the violins are shown
with no sample points. Defaults to "suspectedoutliers" when
`marker.outliercolor` or `marker.line.outliercolor` is set,
otherwise defaults to "outliers".
The 'points' property is an enumeration that may be specified as:
- One of the following enumeration values:
['all', 'outliers', 'suspectedoutliers', False]
Returns
-------
Any
"""
return self["points"]
@points.setter
def points(self, val):
self["points"] = val
# quartilemethod
# --------------
@property
def quartilemethod(self):
"""
Sets the method used to compute the sample's Q1 and Q3
quartiles. The "linear" method uses the 25th percentile for Q1
and 75th percentile for Q3 as computed using method #10 (listed
on http://jse.amstat.org/v14n3/langford.html). The "exclusive"
method uses the median to divide the ordered dataset into two
halves if the sample is odd, it does not include the median in
either half - Q1 is then the median of the lower half and Q3
the median of the upper half. The "inclusive" method also uses
the median to divide the ordered dataset into two halves but if
the sample is odd, it includes the median in both halves - Q1
is then the median of the lower half and Q3 the median of the
upper half.
The 'quartilemethod' property is an enumeration that may be specified as:
- One of the following enumeration values:
['linear', 'exclusive', 'inclusive']
Returns
-------
Any
"""
return self["quartilemethod"]
@quartilemethod.setter
def quartilemethod(self, val):
self["quartilemethod"] = val
# scalegroup
# ----------
@property
def scalegroup(self):
"""
If there are multiple violins that should be sized according to
to some metric (see `scalemode`), link them by providing a non-
empty group id here shared by every trace in the same group. If
a violin's `width` is undefined, `scalegroup` will default to
the trace's name. In this case, violins with the same names
will be linked together
The 'scalegroup' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["scalegroup"]
@scalegroup.setter
def scalegroup(self, val):
self["scalegroup"] = val
# scalemode
# ---------
@property
def scalemode(self):
"""
Sets the metric by which the width of each violin is
determined. "width" means each violin has the same (max) width
"count" means the violins are scaled by the number of sample
points making up each violin.
The 'scalemode' property is an enumeration that may be specified as:
- One of the following enumeration values:
['width', 'count']
Returns
-------
Any
"""
return self["scalemode"]
@scalemode.setter
def scalemode(self, val):
self["scalemode"] = val
# selected
# --------
@property
def selected(self):
"""
The 'selected' property is an instance of Selected
that may be specified as:
- An instance of :class:`plotly.graph_objs.violin.Selected`
- A dict of string/value properties that will be passed
to the Selected constructor
Supported dict properties:
marker
:class:`plotly.graph_objects.violin.selected.Ma
rker` instance or dict with compatible
properties
Returns
-------
plotly.graph_objs.violin.Selected
"""
return self["selected"]
@selected.setter
def selected(self, val):
self["selected"] = val
# selectedpoints
# --------------
@property
def selectedpoints(self):
"""
Array containing integer indices of selected points. Has an
effect only for traces that support selections. Note that an
empty array means an empty selection where the `unselected` are
turned on for all points, whereas, any other non-array values
means no selection all where the `selected` and `unselected`
styles have no effect.
The 'selectedpoints' property accepts values of any type
Returns
-------
Any
"""
return self["selectedpoints"]
@selectedpoints.setter
def selectedpoints(self, val):
self["selectedpoints"] = val
# showlegend
# ----------
@property
def showlegend(self):
"""
Determines whether or not an item corresponding to this trace
is shown in the legend.
The 'showlegend' property must be specified as a bool
(either True, or False)
Returns
-------
bool
"""
return self["showlegend"]
@showlegend.setter
def showlegend(self, val):
self["showlegend"] = val
# side
# ----
@property
def side(self):
"""
Determines on which side of the position value the density
function making up one half of a violin is plotted. Useful when
comparing two violin traces under "overlay" mode, where one
trace has `side` set to "positive" and the other to "negative".
The 'side' property is an enumeration that may be specified as:
- One of the following enumeration values:
['both', 'positive', 'negative']
Returns
-------
Any
"""
return self["side"]
@side.setter
def side(self, val):
self["side"] = val
# span
# ----
@property
def span(self):
"""
Sets the span in data space for which the density function will
be computed. Has an effect only when `spanmode` is set to
"manual".
The 'span' property is an info array that may be specified as:
* a list or tuple of 2 elements where:
(0) The 'span[0]' property accepts values of any type
(1) The 'span[1]' property accepts values of any type
Returns
-------
list
"""
return self["span"]
@span.setter
def span(self, val):
self["span"] = val
# spanmode
# --------
@property
def spanmode(self):
"""
Sets the method by which the span in data space where the
density function will be computed. "soft" means the span goes
from the sample's minimum value minus two bandwidths to the
sample's maximum value plus two bandwidths. "hard" means the
span goes from the sample's minimum to its maximum value. For
custom span settings, use mode "manual" and fill in the `span`
attribute.
The 'spanmode' property is an enumeration that may be specified as:
- One of the following enumeration values:
['soft', 'hard', 'manual']
Returns
-------
Any
"""
return self["spanmode"]
@spanmode.setter
def spanmode(self, val):
self["spanmode"] = val
# stream
# ------
@property
def stream(self):
"""
The 'stream' property is an instance of Stream
that may be specified as:
- An instance of :class:`plotly.graph_objs.violin.Stream`
- A dict of string/value properties that will be passed
to the Stream constructor
Supported dict properties:
maxpoints
Sets the maximum number of points to keep on
the plots from an incoming stream. If
`maxpoints` is set to 50, only the newest 50
points will be displayed on the plot.
token
The stream id number links a data trace on a
plot with a stream. See https://chart-
studio.plotly.com/settings for more details.
Returns
-------
plotly.graph_objs.violin.Stream
"""
return self["stream"]
@stream.setter
def stream(self, val):
self["stream"] = val
# text
# ----
@property
def text(self):
"""
Sets the text elements associated with each sample value. If a
single string, the same string appears over all the data
points. If an array of string, the items are mapped in order to
the this trace's (x,y) coordinates. To be seen, trace
`hoverinfo` must contain a "text" flag.
The 'text' property is a string and must be specified as:
- A string
- A number that will be converted to a string
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
str|numpy.ndarray
"""
return self["text"]
@text.setter
def text(self, val):
self["text"] = val
# textsrc
# -------
@property
def textsrc(self):
"""
Sets the source reference on Chart Studio Cloud for `text`.
The 'textsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["textsrc"]
@textsrc.setter
def textsrc(self, val):
self["textsrc"] = val
# uid
# ---
@property
def uid(self):
"""
Assign an id to this trace, Use this to provide object
constancy between traces during animations and transitions.
The 'uid' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["uid"]
@uid.setter
def uid(self, val):
self["uid"] = val
# uirevision
# ----------
@property
def uirevision(self):
"""
Controls persistence of some user-driven changes to the trace:
`constraintrange` in `parcoords` traces, as well as some
`editable: true` modifications such as `name` and
`colorbar.title`. Defaults to `layout.uirevision`. Note that
other user-driven trace attribute changes are controlled by
`layout` attributes: `trace.visible` is controlled by
`layout.legend.uirevision`, `selectedpoints` is controlled by
`layout.selectionrevision`, and `colorbar.(x|y)` (accessible
with `config: {editable: true}`) is controlled by
`layout.editrevision`. Trace changes are tracked by `uid`,
which only falls back on trace index if no `uid` is provided.
So if your app can add/remove traces before the end of the
`data` array, such that the same trace has a different index,
you can still preserve user-driven changes if you give each
trace a `uid` that stays with it as it moves.
The 'uirevision' property accepts values of any type
Returns
-------
Any
"""
return self["uirevision"]
@uirevision.setter
def uirevision(self, val):
self["uirevision"] = val
# unselected
# ----------
@property
def unselected(self):
"""
The 'unselected' property is an instance of Unselected
that may be specified as:
- An instance of :class:`plotly.graph_objs.violin.Unselected`
- A dict of string/value properties that will be passed
to the Unselected constructor
Supported dict properties:
marker
:class:`plotly.graph_objects.violin.unselected.
Marker` instance or dict with compatible
properties
Returns
-------
plotly.graph_objs.violin.Unselected
"""
return self["unselected"]
@unselected.setter
def unselected(self, val):
self["unselected"] = val
# visible
# -------
@property
def visible(self):
"""
Determines whether or not this trace is visible. If
"legendonly", the trace is not drawn, but can appear as a
legend item (provided that the legend itself is visible).
The 'visible' property is an enumeration that may be specified as:
- One of the following enumeration values:
[True, False, 'legendonly']
Returns
-------
Any
"""
return self["visible"]
@visible.setter
def visible(self, val):
self["visible"] = val
# width
# -----
@property
def width(self):
"""
Sets the width of the violin in data coordinates. If 0 (default
value) the width is automatically selected based on the
positions of other violin traces in the same subplot.
The 'width' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
"""
return self["width"]
@width.setter
def width(self, val):
self["width"] = val
# x
# -
@property
def x(self):
"""
Sets the x sample data or coordinates. See overview for more
info.
The 'x' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray
"""
return self["x"]
@x.setter
def x(self, val):
self["x"] = val
# x0
# --
@property
def x0(self):
"""
Sets the x coordinate for single-box traces or the starting
coordinate for multi-box traces set using q1/median/q3. See
overview for more info.
The 'x0' property accepts values of any type
Returns
-------
Any
"""
return self["x0"]
@x0.setter
def x0(self, val):
self["x0"] = val
# xaxis
# -----
@property
def xaxis(self):
"""
Sets a reference between this trace's x coordinates and a 2D
cartesian x axis. If "x" (the default value), the x coordinates
refer to `layout.xaxis`. If "x2", the x coordinates refer to
`layout.xaxis2`, and so on.
The 'xaxis' property is an identifier of a particular
subplot, of type 'x', that may be specified as the string 'x'
optionally followed by an integer >= 1
(e.g. 'x', 'x1', 'x2', 'x3', etc.)
Returns
-------
str
"""
return self["xaxis"]
@xaxis.setter
def xaxis(self, val):
self["xaxis"] = val
# xhoverformat
# ------------
@property
def xhoverformat(self):
"""
Sets the hover text formatting rulefor `x` using d3 formatting
mini-languages which are very similar to those in Python. For
numbers, see:
https://github.com/d3/d3-format/tree/v1.4.5#d3-format. And for
dates see: https://github.com/d3/d3-time-
format/tree/v2.2.3#locale_format. We add two items to d3's date
formatter: "%h" for half of the year as a decimal number as
well as "%{n}f" for fractional seconds with n digits. For
example, *2016-10-13 09:15:23.456* with tickformat
"%H~%M~%S.%2f" would display *09~15~23.46*By default the values
are formatted using `xaxis.hoverformat`.
The 'xhoverformat' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["xhoverformat"]
@xhoverformat.setter
def xhoverformat(self, val):
self["xhoverformat"] = val
# xsrc
# ----
@property
def xsrc(self):
"""
Sets the source reference on Chart Studio Cloud for `x`.
The 'xsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["xsrc"]
@xsrc.setter
def xsrc(self, val):
self["xsrc"] = val
# y
# -
@property
def y(self):
"""
Sets the y sample data or coordinates. See overview for more
info.
The 'y' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray
"""
return self["y"]
@y.setter
def y(self, val):
self["y"] = val
# y0
# --
@property
def y0(self):
"""
Sets the y coordinate for single-box traces or the starting
coordinate for multi-box traces set using q1/median/q3. See
overview for more info.
The 'y0' property accepts values of any type
Returns
-------
Any
"""
return self["y0"]
@y0.setter
def y0(self, val):
self["y0"] = val
# yaxis
# -----
@property
def yaxis(self):
"""
Sets a reference between this trace's y coordinates and a 2D
cartesian y axis. If "y" (the default value), the y coordinates
refer to `layout.yaxis`. If "y2", the y coordinates refer to
`layout.yaxis2`, and so on.
The 'yaxis' property is an identifier of a particular
subplot, of type 'y', that may be specified as the string 'y'
optionally followed by an integer >= 1
(e.g. 'y', 'y1', 'y2', 'y3', etc.)
Returns
-------
str
"""
return self["yaxis"]
@yaxis.setter
def yaxis(self, val):
self["yaxis"] = val
# yhoverformat
# ------------
@property
def yhoverformat(self):
"""
Sets the hover text formatting rulefor `y` using d3 formatting
mini-languages which are very similar to those in Python. For
numbers, see:
https://github.com/d3/d3-format/tree/v1.4.5#d3-format. And for
dates see: https://github.com/d3/d3-time-
format/tree/v2.2.3#locale_format. We add two items to d3's date
formatter: "%h" for half of the year as a decimal number as
well as "%{n}f" for fractional seconds with n digits. For
example, *2016-10-13 09:15:23.456* with tickformat
"%H~%M~%S.%2f" would display *09~15~23.46*By default the values
are formatted using `yaxis.hoverformat`.
The 'yhoverformat' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["yhoverformat"]
@yhoverformat.setter
def yhoverformat(self, val):
self["yhoverformat"] = val
# ysrc
# ----
@property
def ysrc(self):
"""
Sets the source reference on Chart Studio Cloud for `y`.
The 'ysrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["ysrc"]
@ysrc.setter
def ysrc(self, val):
self["ysrc"] = val
# zorder
# ------
@property
def zorder(self):
"""
Sets the layer on which this trace is displayed, relative to
other SVG traces on the same subplot. SVG traces with higher
`zorder` appear in front of those with lower `zorder`.
The 'zorder' property is a integer and may be specified as:
- An int (or float that will be cast to an int)
Returns
-------
int
"""
return self["zorder"]
@zorder.setter
def zorder(self, val):
self["zorder"] = val
# type
# ----
@property
def type(self):
return self._props["type"]
# Self properties description
# ---------------------------
@property
def _prop_descriptions(self):
return """\
alignmentgroup
Set several traces linked to the same position axis or
matching axes to the same alignmentgroup. This controls
whether bars compute their positional range dependently
or independently.
bandwidth
Sets the bandwidth used to compute the kernel density
estimate. By default, the bandwidth is determined by
Silverman's rule of thumb.
box
:class:`plotly.graph_objects.violin.Box` instance or
dict with compatible properties
customdata
Assigns extra data each datum. This may be useful when
listening to hover, click and selection events. Note
that, "scatter" traces also appends customdata items in
the markers DOM elements
customdatasrc
Sets the source reference on Chart Studio Cloud for
`customdata`.
fillcolor
Sets the fill color. Defaults to a half-transparent
variant of the line color, marker color, or marker line
color, whichever is available.
hoverinfo
Determines which trace information appear on hover. If
`none` or `skip` are set, no information is displayed
upon hovering. But, if `none` is set, click and hover
events are still fired.
hoverinfosrc
Sets the source reference on Chart Studio Cloud for
`hoverinfo`.
hoverlabel
:class:`plotly.graph_objects.violin.Hoverlabel`
instance or dict with compatible properties
hoveron
Do the hover effects highlight individual violins or
sample points or the kernel density estimate or any
combination of them?
hovertemplate
Template string used for rendering the information that
appear on hover box. Note that this will override
`hoverinfo`. Variables are inserted using %{variable},
for example "y: %{y}" as well as %{xother}, {%_xother},
{%_xother_}, {%xother_}. When showing info for several
points, "xother" will be added to those with different
x positions from the first point. An underscore before
or after "(x|y)other" will add a space on that side,
only when this field is shown. Numbers are formatted
using d3-format's syntax %{variable:d3-format}, for
example "Price: %{y:$.2f}".
https://github.com/d3/d3-format/tree/v1.4.5#d3-format
for details on the formatting syntax. Dates are
formatted using d3-time-format's syntax
%{variable|d3-time-format}, for example "Day:
%{2019-01-01|%A}". https://github.com/d3/d3-time-
format/tree/v2.2.3#locale_format for details on the
date formatting syntax. The variables available in
`hovertemplate` are the ones emitted as event data
described at this link
https://plotly.com/javascript/plotlyjs-events/#event-
data. Additionally, every attributes that can be
specified per-point (the ones that are `arrayOk: true`)
are available. Anything contained in tag `<extra>` is
displayed in the secondary box, for example
"<extra>{fullData.name}</extra>". To hide the secondary
box completely, use an empty tag `<extra></extra>`.
hovertemplatesrc
Sets the source reference on Chart Studio Cloud for
`hovertemplate`.
hovertext
Same as `text`.
hovertextsrc
Sets the source reference on Chart Studio Cloud for
`hovertext`.
ids
Assigns id labels to each datum. These ids for object
constancy of data points during animation. Should be an
array of strings, not numbers or any other type.
idssrc
Sets the source reference on Chart Studio Cloud for
`ids`.
jitter
Sets the amount of jitter in the sample points drawn.
If 0, the sample points align along the distribution
axis. If 1, the sample points are drawn in a random
jitter of width equal to the width of the violins.
legend
Sets the reference to a legend to show this trace in.
References to these legends are "legend", "legend2",
"legend3", etc. Settings for these legends are set in
the layout, under `layout.legend`, `layout.legend2`,
etc.
legendgroup
Sets the legend group for this trace. Traces and shapes
part of the same legend group hide/show at the same
time when toggling legend items.
legendgrouptitle
:class:`plotly.graph_objects.violin.Legendgrouptitle`
instance or dict with compatible properties
legendrank
Sets the legend rank for this trace. Items and groups
with smaller ranks are presented on top/left side while
with "reversed" `legend.traceorder` they are on
bottom/right side. The default legendrank is 1000, so
that you can use ranks less than 1000 to place certain
items before all unranked items, and ranks greater than
1000 to go after all unranked items. When having
unranked or equal rank items shapes would be displayed
after traces i.e. according to their order in data and
layout.
legendwidth
Sets the width (in px or fraction) of the legend for
this trace.
line
:class:`plotly.graph_objects.violin.Line` instance or
dict with compatible properties
marker
:class:`plotly.graph_objects.violin.Marker` instance or
dict with compatible properties
meanline
:class:`plotly.graph_objects.violin.Meanline` instance
or dict with compatible properties
meta
Assigns extra meta information associated with this
trace that can be used in various text attributes.
Attributes such as trace `name`, graph, axis and
colorbar `title.text`, annotation `text`
`rangeselector`, `updatemenues` and `sliders` `label`
text all support `meta`. To access the trace `meta`
values in an attribute in the same trace, simply use
`%{meta[i]}` where `i` is the index or key of the
`meta` item in question. To access trace `meta` in
layout attributes, use `%{data[n[.meta[i]}` where `i`
is the index or key of the `meta` and `n` is the trace
index.
metasrc
Sets the source reference on Chart Studio Cloud for
`meta`.
name
Sets the trace name. The trace name appears as the
legend item and on hover. For violin traces, the name
will also be used for the position coordinate, if `x`
and `x0` (`y` and `y0` if horizontal) are missing and
the position axis is categorical. Note that the trace
name is also used as a default value for attribute
`scalegroup` (please see its description for details).
offsetgroup
Set several traces linked to the same position axis or
matching axes to the same offsetgroup where bars of the
same position coordinate will line up.
opacity
Sets the opacity of the trace.
orientation
Sets the orientation of the violin(s). If "v" ("h"),
the distribution is visualized along the vertical
(horizontal).
pointpos
Sets the position of the sample points in relation to
the violins. If 0, the sample points are places over
the center of the violins. Positive (negative) values
correspond to positions to the right (left) for
vertical violins and above (below) for horizontal
violins.
points
If "outliers", only the sample points lying outside the
whiskers are shown If "suspectedoutliers", the outlier
points are shown and points either less than 4*Q1-3*Q3
or greater than 4*Q3-3*Q1 are highlighted (see
`outliercolor`) If "all", all sample points are shown
If False, only the violins are shown with no sample
points. Defaults to "suspectedoutliers" when
`marker.outliercolor` or `marker.line.outliercolor` is
set, otherwise defaults to "outliers".
quartilemethod
Sets the method used to compute the sample's Q1 and Q3
quartiles. The "linear" method uses the 25th percentile
for Q1 and 75th percentile for Q3 as computed using
method #10 (listed on
http://jse.amstat.org/v14n3/langford.html). The
"exclusive" method uses the median to divide the
ordered dataset into two halves if the sample is odd,
it does not include the median in either half - Q1 is
then the median of the lower half and Q3 the median of
the upper half. The "inclusive" method also uses the
median to divide the ordered dataset into two halves
but if the sample is odd, it includes the median in
both halves - Q1 is then the median of the lower half
and Q3 the median of the upper half.
scalegroup
If there are multiple violins that should be sized
according to to some metric (see `scalemode`), link
them by providing a non-empty group id here shared by
every trace in the same group. If a violin's `width` is
undefined, `scalegroup` will default to the trace's
name. In this case, violins with the same names will be
linked together
scalemode
Sets the metric by which the width of each violin is
determined. "width" means each violin has the same
(max) width "count" means the violins are scaled by the
number of sample points making up each violin.
selected
:class:`plotly.graph_objects.violin.Selected` instance
or dict with compatible properties
selectedpoints
Array containing integer indices of selected points.
Has an effect only for traces that support selections.
Note that an empty array means an empty selection where
the `unselected` are turned on for all points, whereas,
any other non-array values means no selection all where
the `selected` and `unselected` styles have no effect.
showlegend
Determines whether or not an item corresponding to this
trace is shown in the legend.
side
Determines on which side of the position value the
density function making up one half of a violin is
plotted. Useful when comparing two violin traces under
"overlay" mode, where one trace has `side` set to
"positive" and the other to "negative".
span
Sets the span in data space for which the density
function will be computed. Has an effect only when
`spanmode` is set to "manual".
spanmode
Sets the method by which the span in data space where
the density function will be computed. "soft" means the
span goes from the sample's minimum value minus two
bandwidths to the sample's maximum value plus two
bandwidths. "hard" means the span goes from the
sample's minimum to its maximum value. For custom span
settings, use mode "manual" and fill in the `span`
attribute.
stream
:class:`plotly.graph_objects.violin.Stream` instance or
dict with compatible properties
text
Sets the text elements associated with each sample
value. If a single string, the same string appears over
all the data points. If an array of string, the items
are mapped in order to the this trace's (x,y)
coordinates. To be seen, trace `hoverinfo` must contain
a "text" flag.
textsrc
Sets the source reference on Chart Studio Cloud for
`text`.
uid
Assign an id to this trace, Use this to provide object
constancy between traces during animations and
transitions.
uirevision
Controls persistence of some user-driven changes to the
trace: `constraintrange` in `parcoords` traces, as well
as some `editable: true` modifications such as `name`
and `colorbar.title`. Defaults to `layout.uirevision`.
Note that other user-driven trace attribute changes are
controlled by `layout` attributes: `trace.visible` is
controlled by `layout.legend.uirevision`,
`selectedpoints` is controlled by
`layout.selectionrevision`, and `colorbar.(x|y)`
(accessible with `config: {editable: true}`) is
controlled by `layout.editrevision`. Trace changes are
tracked by `uid`, which only falls back on trace index
if no `uid` is provided. So if your app can add/remove
traces before the end of the `data` array, such that
the same trace has a different index, you can still
preserve user-driven changes if you give each trace a
`uid` that stays with it as it moves.
unselected
:class:`plotly.graph_objects.violin.Unselected`
instance or dict with compatible properties
visible
Determines whether or not this trace is visible. If
"legendonly", the trace is not drawn, but can appear as
a legend item (provided that the legend itself is
visible).
width
Sets the width of the violin in data coordinates. If 0
(default value) the width is automatically selected
based on the positions of other violin traces in the
same subplot.
x
Sets the x sample data or coordinates. See overview for
more info.
x0
Sets the x coordinate for single-box traces or the
starting coordinate for multi-box traces set using
q1/median/q3. See overview for more info.
xaxis
Sets a reference between this trace's x coordinates and
a 2D cartesian x axis. If "x" (the default value), the
x coordinates refer to `layout.xaxis`. If "x2", the x
coordinates refer to `layout.xaxis2`, and so on.
xhoverformat
Sets the hover text formatting rulefor `x` using d3
formatting mini-languages which are very similar to
those in Python. For numbers, see:
https://github.com/d3/d3-format/tree/v1.4.5#d3-format.
And for dates see: https://github.com/d3/d3-time-
format/tree/v2.2.3#locale_format. We add two items to
d3's date formatter: "%h" for half of the year as a
decimal number as well as "%{n}f" for fractional
seconds with n digits. For example, *2016-10-13
09:15:23.456* with tickformat "%H~%M~%S.%2f" would
display *09~15~23.46*By default the values are
formatted using `xaxis.hoverformat`.
xsrc
Sets the source reference on Chart Studio Cloud for
`x`.
y
Sets the y sample data or coordinates. See overview for
more info.
y0
Sets the y coordinate for single-box traces or the
starting coordinate for multi-box traces set using
q1/median/q3. See overview for more info.
yaxis
Sets a reference between this trace's y coordinates and
a 2D cartesian y axis. If "y" (the default value), the
y coordinates refer to `layout.yaxis`. If "y2", the y
coordinates refer to `layout.yaxis2`, and so on.
yhoverformat
Sets the hover text formatting rulefor `y` using d3
formatting mini-languages which are very similar to
those in Python. For numbers, see:
https://github.com/d3/d3-format/tree/v1.4.5#d3-format.
And for dates see: https://github.com/d3/d3-time-
format/tree/v2.2.3#locale_format. We add two items to
d3's date formatter: "%h" for half of the year as a
decimal number as well as "%{n}f" for fractional
seconds with n digits. For example, *2016-10-13
09:15:23.456* with tickformat "%H~%M~%S.%2f" would
display *09~15~23.46*By default the values are
formatted using `yaxis.hoverformat`.
ysrc
Sets the source reference on Chart Studio Cloud for
`y`.
zorder
Sets the layer on which this trace is displayed,
relative to other SVG traces on the same subplot. SVG
traces with higher `zorder` appear in front of those
with lower `zorder`.
"""
def __init__(
self,
arg=None,
alignmentgroup=None,
bandwidth=None,
box=None,
customdata=None,
customdatasrc=None,
fillcolor=None,
hoverinfo=None,
hoverinfosrc=None,
hoverlabel=None,
hoveron=None,
hovertemplate=None,
hovertemplatesrc=None,
hovertext=None,
hovertextsrc=None,
ids=None,
idssrc=None,
jitter=None,
legend=None,
legendgroup=None,
legendgrouptitle=None,
legendrank=None,
legendwidth=None,
line=None,
marker=None,
meanline=None,
meta=None,
metasrc=None,
name=None,
offsetgroup=None,
opacity=None,
orientation=None,
pointpos=None,
points=None,
quartilemethod=None,
scalegroup=None,
scalemode=None,
selected=None,
selectedpoints=None,
showlegend=None,
side=None,
span=None,
spanmode=None,
stream=None,
text=None,
textsrc=None,
uid=None,
uirevision=None,
unselected=None,
visible=None,
width=None,
x=None,
x0=None,
xaxis=None,
xhoverformat=None,
xsrc=None,
y=None,
y0=None,
yaxis=None,
yhoverformat=None,
ysrc=None,
zorder=None,
**kwargs,
):
"""
Construct a new Violin object
In vertical (horizontal) violin plots, statistics are computed
using `y` (`x`) values. By supplying an `x` (`y`) array, one
violin per distinct x (y) value is drawn If no `x` (`y`) list
is provided, a single violin is drawn. That violin position is
then positioned with with `name` or with `x0` (`y0`) if
provided.
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of :class:`plotly.graph_objs.Violin`
alignmentgroup
Set several traces linked to the same position axis or
matching axes to the same alignmentgroup. This controls
whether bars compute their positional range dependently
or independently.
bandwidth
Sets the bandwidth used to compute the kernel density
estimate. By default, the bandwidth is determined by
Silverman's rule of thumb.
box
:class:`plotly.graph_objects.violin.Box` instance or
dict with compatible properties
customdata
Assigns extra data each datum. This may be useful when
listening to hover, click and selection events. Note
that, "scatter" traces also appends customdata items in
the markers DOM elements
customdatasrc
Sets the source reference on Chart Studio Cloud for
`customdata`.
fillcolor
Sets the fill color. Defaults to a half-transparent
variant of the line color, marker color, or marker line
color, whichever is available.
hoverinfo
Determines which trace information appear on hover. If
`none` or `skip` are set, no information is displayed
upon hovering. But, if `none` is set, click and hover
events are still fired.
hoverinfosrc
Sets the source reference on Chart Studio Cloud for
`hoverinfo`.
hoverlabel
:class:`plotly.graph_objects.violin.Hoverlabel`
instance or dict with compatible properties
hoveron
Do the hover effects highlight individual violins or
sample points or the kernel density estimate or any
combination of them?
hovertemplate
Template string used for rendering the information that
appear on hover box. Note that this will override
`hoverinfo`. Variables are inserted using %{variable},
for example "y: %{y}" as well as %{xother}, {%_xother},
{%_xother_}, {%xother_}. When showing info for several
points, "xother" will be added to those with different
x positions from the first point. An underscore before
or after "(x|y)other" will add a space on that side,
only when this field is shown. Numbers are formatted
using d3-format's syntax %{variable:d3-format}, for
example "Price: %{y:$.2f}".
https://github.com/d3/d3-format/tree/v1.4.5#d3-format
for details on the formatting syntax. Dates are
formatted using d3-time-format's syntax
%{variable|d3-time-format}, for example "Day:
%{2019-01-01|%A}". https://github.com/d3/d3-time-
format/tree/v2.2.3#locale_format for details on the
date formatting syntax. The variables available in
`hovertemplate` are the ones emitted as event data
described at this link
https://plotly.com/javascript/plotlyjs-events/#event-
data. Additionally, every attributes that can be
specified per-point (the ones that are `arrayOk: true`)
are available. Anything contained in tag `<extra>` is
displayed in the secondary box, for example
"<extra>{fullData.name}</extra>". To hide the secondary
box completely, use an empty tag `<extra></extra>`.
hovertemplatesrc
Sets the source reference on Chart Studio Cloud for
`hovertemplate`.
hovertext
Same as `text`.
hovertextsrc
Sets the source reference on Chart Studio Cloud for
`hovertext`.
ids
Assigns id labels to each datum. These ids for object
constancy of data points during animation. Should be an
array of strings, not numbers or any other type.
idssrc
Sets the source reference on Chart Studio Cloud for
`ids`.
jitter
Sets the amount of jitter in the sample points drawn.
If 0, the sample points align along the distribution
axis. If 1, the sample points are drawn in a random
jitter of width equal to the width of the violins.
legend
Sets the reference to a legend to show this trace in.
References to these legends are "legend", "legend2",
"legend3", etc. Settings for these legends are set in
the layout, under `layout.legend`, `layout.legend2`,
etc.
legendgroup
Sets the legend group for this trace. Traces and shapes
part of the same legend group hide/show at the same
time when toggling legend items.
legendgrouptitle
:class:`plotly.graph_objects.violin.Legendgrouptitle`
instance or dict with compatible properties
legendrank
Sets the legend rank for this trace. Items and groups
with smaller ranks are presented on top/left side while
with "reversed" `legend.traceorder` they are on
bottom/right side. The default legendrank is 1000, so
that you can use ranks less than 1000 to place certain
items before all unranked items, and ranks greater than
1000 to go after all unranked items. When having
unranked or equal rank items shapes would be displayed
after traces i.e. according to their order in data and
layout.
legendwidth
Sets the width (in px or fraction) of the legend for
this trace.
line
:class:`plotly.graph_objects.violin.Line` instance or
dict with compatible properties
marker
:class:`plotly.graph_objects.violin.Marker` instance or
dict with compatible properties
meanline
:class:`plotly.graph_objects.violin.Meanline` instance
or dict with compatible properties
meta
Assigns extra meta information associated with this
trace that can be used in various text attributes.
Attributes such as trace `name`, graph, axis and
colorbar `title.text`, annotation `text`
`rangeselector`, `updatemenues` and `sliders` `label`
text all support `meta`. To access the trace `meta`
values in an attribute in the same trace, simply use
`%{meta[i]}` where `i` is the index or key of the
`meta` item in question. To access trace `meta` in
layout attributes, use `%{data[n[.meta[i]}` where `i`
is the index or key of the `meta` and `n` is the trace
index.
metasrc
Sets the source reference on Chart Studio Cloud for
`meta`.
name
Sets the trace name. The trace name appears as the
legend item and on hover. For violin traces, the name
will also be used for the position coordinate, if `x`
and `x0` (`y` and `y0` if horizontal) are missing and
the position axis is categorical. Note that the trace
name is also used as a default value for attribute
`scalegroup` (please see its description for details).
offsetgroup
Set several traces linked to the same position axis or
matching axes to the same offsetgroup where bars of the
same position coordinate will line up.
opacity
Sets the opacity of the trace.
orientation
Sets the orientation of the violin(s). If "v" ("h"),
the distribution is visualized along the vertical
(horizontal).
pointpos
Sets the position of the sample points in relation to
the violins. If 0, the sample points are places over
the center of the violins. Positive (negative) values
correspond to positions to the right (left) for
vertical violins and above (below) for horizontal
violins.
points
If "outliers", only the sample points lying outside the
whiskers are shown If "suspectedoutliers", the outlier
points are shown and points either less than 4*Q1-3*Q3
or greater than 4*Q3-3*Q1 are highlighted (see
`outliercolor`) If "all", all sample points are shown
If False, only the violins are shown with no sample
points. Defaults to "suspectedoutliers" when
`marker.outliercolor` or `marker.line.outliercolor` is
set, otherwise defaults to "outliers".
quartilemethod
Sets the method used to compute the sample's Q1 and Q3
quartiles. The "linear" method uses the 25th percentile
for Q1 and 75th percentile for Q3 as computed using
method #10 (listed on
http://jse.amstat.org/v14n3/langford.html). The
"exclusive" method uses the median to divide the
ordered dataset into two halves if the sample is odd,
it does not include the median in either half - Q1 is
then the median of the lower half and Q3 the median of
the upper half. The "inclusive" method also uses the
median to divide the ordered dataset into two halves
but if the sample is odd, it includes the median in
both halves - Q1 is then the median of the lower half
and Q3 the median of the upper half.
scalegroup
If there are multiple violins that should be sized
according to to some metric (see `scalemode`), link
them by providing a non-empty group id here shared by
every trace in the same group. If a violin's `width` is
undefined, `scalegroup` will default to the trace's
name. In this case, violins with the same names will be
linked together
scalemode
Sets the metric by which the width of each violin is
determined. "width" means each violin has the same
(max) width "count" means the violins are scaled by the
number of sample points making up each violin.
selected
:class:`plotly.graph_objects.violin.Selected` instance
or dict with compatible properties
selectedpoints
Array containing integer indices of selected points.
Has an effect only for traces that support selections.
Note that an empty array means an empty selection where
the `unselected` are turned on for all points, whereas,
any other non-array values means no selection all where
the `selected` and `unselected` styles have no effect.
showlegend
Determines whether or not an item corresponding to this
trace is shown in the legend.
side
Determines on which side of the position value the
density function making up one half of a violin is
plotted. Useful when comparing two violin traces under
"overlay" mode, where one trace has `side` set to
"positive" and the other to "negative".
span
Sets the span in data space for which the density
function will be computed. Has an effect only when
`spanmode` is set to "manual".
spanmode
Sets the method by which the span in data space where
the density function will be computed. "soft" means the
span goes from the sample's minimum value minus two
bandwidths to the sample's maximum value plus two
bandwidths. "hard" means the span goes from the
sample's minimum to its maximum value. For custom span
settings, use mode "manual" and fill in the `span`
attribute.
stream
:class:`plotly.graph_objects.violin.Stream` instance or
dict with compatible properties
text
Sets the text elements associated with each sample
value. If a single string, the same string appears over
all the data points. If an array of string, the items
are mapped in order to the this trace's (x,y)
coordinates. To be seen, trace `hoverinfo` must contain
a "text" flag.
textsrc
Sets the source reference on Chart Studio Cloud for
`text`.
uid
Assign an id to this trace, Use this to provide object
constancy between traces during animations and
transitions.
uirevision
Controls persistence of some user-driven changes to the
trace: `constraintrange` in `parcoords` traces, as well
as some `editable: true` modifications such as `name`
and `colorbar.title`. Defaults to `layout.uirevision`.
Note that other user-driven trace attribute changes are
controlled by `layout` attributes: `trace.visible` is
controlled by `layout.legend.uirevision`,
`selectedpoints` is controlled by
`layout.selectionrevision`, and `colorbar.(x|y)`
(accessible with `config: {editable: true}`) is
controlled by `layout.editrevision`. Trace changes are
tracked by `uid`, which only falls back on trace index
if no `uid` is provided. So if your app can add/remove
traces before the end of the `data` array, such that
the same trace has a different index, you can still
preserve user-driven changes if you give each trace a
`uid` that stays with it as it moves.
unselected
:class:`plotly.graph_objects.violin.Unselected`
instance or dict with compatible properties
visible
Determines whether or not this trace is visible. If
"legendonly", the trace is not drawn, but can appear as
a legend item (provided that the legend itself is
visible).
width
Sets the width of the violin in data coordinates. If 0
(default value) the width is automatically selected
based on the positions of other violin traces in the
same subplot.
x
Sets the x sample data or coordinates. See overview for
more info.
x0
Sets the x coordinate for single-box traces or the
starting coordinate for multi-box traces set using
q1/median/q3. See overview for more info.
xaxis
Sets a reference between this trace's x coordinates and
a 2D cartesian x axis. If "x" (the default value), the
x coordinates refer to `layout.xaxis`. If "x2", the x
coordinates refer to `layout.xaxis2`, and so on.
xhoverformat
Sets the hover text formatting rulefor `x` using d3
formatting mini-languages which are very similar to
those in Python. For numbers, see:
https://github.com/d3/d3-format/tree/v1.4.5#d3-format.
And for dates see: https://github.com/d3/d3-time-
format/tree/v2.2.3#locale_format. We add two items to
d3's date formatter: "%h" for half of the year as a
decimal number as well as "%{n}f" for fractional
seconds with n digits. For example, *2016-10-13
09:15:23.456* with tickformat "%H~%M~%S.%2f" would
display *09~15~23.46*By default the values are
formatted using `xaxis.hoverformat`.
xsrc
Sets the source reference on Chart Studio Cloud for
`x`.
y
Sets the y sample data or coordinates. See overview for
more info.
y0
Sets the y coordinate for single-box traces or the
starting coordinate for multi-box traces set using
q1/median/q3. See overview for more info.
yaxis
Sets a reference between this trace's y coordinates and
a 2D cartesian y axis. If "y" (the default value), the
y coordinates refer to `layout.yaxis`. If "y2", the y
coordinates refer to `layout.yaxis2`, and so on.
yhoverformat
Sets the hover text formatting rulefor `y` using d3
formatting mini-languages which are very similar to
those in Python. For numbers, see:
https://github.com/d3/d3-format/tree/v1.4.5#d3-format.
And for dates see: https://github.com/d3/d3-time-
format/tree/v2.2.3#locale_format. We add two items to
d3's date formatter: "%h" for half of the year as a
decimal number as well as "%{n}f" for fractional
seconds with n digits. For example, *2016-10-13
09:15:23.456* with tickformat "%H~%M~%S.%2f" would
display *09~15~23.46*By default the values are
formatted using `yaxis.hoverformat`.
ysrc
Sets the source reference on Chart Studio Cloud for
`y`.
zorder
Sets the layer on which this trace is displayed,
relative to other SVG traces on the same subplot. SVG
traces with higher `zorder` appear in front of those
with lower `zorder`.
Returns
-------
Violin
"""
super(Violin, self).__init__("violin")
if "_parent" in kwargs:
self._parent = kwargs["_parent"]
return
# Validate arg
# ------------
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError(
"""\
The first argument to the plotly.graph_objs.Violin
constructor must be a dict or
an instance of :class:`plotly.graph_objs.Violin`"""
)
# Handle skip_invalid
# -------------------
self._skip_invalid = kwargs.pop("skip_invalid", False)
self._validate = kwargs.pop("_validate", True)
# Populate data dict with properties
# ----------------------------------
_v = arg.pop("alignmentgroup", None)
_v = alignmentgroup if alignmentgroup is not None else _v
if _v is not None:
self["alignmentgroup"] = _v
_v = arg.pop("bandwidth", None)
_v = bandwidth if bandwidth is not None else _v
if _v is not None:
self["bandwidth"] = _v
_v = arg.pop("box", None)
_v = box if box is not None else _v
if _v is not None:
self["box"] = _v
_v = arg.pop("customdata", None)
_v = customdata if customdata is not None else _v
if _v is not None:
self["customdata"] = _v
_v = arg.pop("customdatasrc", None)
_v = customdatasrc if customdatasrc is not None else _v
if _v is not None:
self["customdatasrc"] = _v
_v = arg.pop("fillcolor", None)
_v = fillcolor if fillcolor is not None else _v
if _v is not None:
self["fillcolor"] = _v
_v = arg.pop("hoverinfo", None)
_v = hoverinfo if hoverinfo is not None else _v
if _v is not None:
self["hoverinfo"] = _v
_v = arg.pop("hoverinfosrc", None)
_v = hoverinfosrc if hoverinfosrc is not None else _v
if _v is not None:
self["hoverinfosrc"] = _v
_v = arg.pop("hoverlabel", None)
_v = hoverlabel if hoverlabel is not None else _v
if _v is not None:
self["hoverlabel"] = _v
_v = arg.pop("hoveron", None)
_v = hoveron if hoveron is not None else _v
if _v is not None:
self["hoveron"] = _v
_v = arg.pop("hovertemplate", None)
_v = hovertemplate if hovertemplate is not None else _v
if _v is not None:
self["hovertemplate"] = _v
_v = arg.pop("hovertemplatesrc", None)
_v = hovertemplatesrc if hovertemplatesrc is not None else _v
if _v is not None:
self["hovertemplatesrc"] = _v
_v = arg.pop("hovertext", None)
_v = hovertext if hovertext is not None else _v
if _v is not None:
self["hovertext"] = _v
_v = arg.pop("hovertextsrc", None)
_v = hovertextsrc if hovertextsrc is not None else _v
if _v is not None:
self["hovertextsrc"] = _v
_v = arg.pop("ids", None)
_v = ids if ids is not None else _v
if _v is not None:
self["ids"] = _v
_v = arg.pop("idssrc", None)
_v = idssrc if idssrc is not None else _v
if _v is not None:
self["idssrc"] = _v
_v = arg.pop("jitter", None)
_v = jitter if jitter is not None else _v
if _v is not None:
self["jitter"] = _v
_v = arg.pop("legend", None)
_v = legend if legend is not None else _v
if _v is not None:
self["legend"] = _v
_v = arg.pop("legendgroup", None)
_v = legendgroup if legendgroup is not None else _v
if _v is not None:
self["legendgroup"] = _v
_v = arg.pop("legendgrouptitle", None)
_v = legendgrouptitle if legendgrouptitle is not None else _v
if _v is not None:
self["legendgrouptitle"] = _v
_v = arg.pop("legendrank", None)
_v = legendrank if legendrank is not None else _v
if _v is not None:
self["legendrank"] = _v
_v = arg.pop("legendwidth", None)
_v = legendwidth if legendwidth is not None else _v
if _v is not None:
self["legendwidth"] = _v
_v = arg.pop("line", None)
_v = line if line is not None else _v
if _v is not None:
self["line"] = _v
_v = arg.pop("marker", None)
_v = marker if marker is not None else _v
if _v is not None:
self["marker"] = _v
_v = arg.pop("meanline", None)
_v = meanline if meanline is not None else _v
if _v is not None:
self["meanline"] = _v
_v = arg.pop("meta", None)
_v = meta if meta is not None else _v
if _v is not None:
self["meta"] = _v
_v = arg.pop("metasrc", None)
_v = metasrc if metasrc is not None else _v
if _v is not None:
self["metasrc"] = _v
_v = arg.pop("name", None)
_v = name if name is not None else _v
if _v is not None:
self["name"] = _v
_v = arg.pop("offsetgroup", None)
_v = offsetgroup if offsetgroup is not None else _v
if _v is not None:
self["offsetgroup"] = _v
_v = arg.pop("opacity", None)
_v = opacity if opacity is not None else _v
if _v is not None:
self["opacity"] = _v
_v = arg.pop("orientation", None)
_v = orientation if orientation is not None else _v
if _v is not None:
self["orientation"] = _v
_v = arg.pop("pointpos", None)
_v = pointpos if pointpos is not None else _v
if _v is not None:
self["pointpos"] = _v
_v = arg.pop("points", None)
_v = points if points is not None else _v
if _v is not None:
self["points"] = _v
_v = arg.pop("quartilemethod", None)
_v = quartilemethod if quartilemethod is not None else _v
if _v is not None:
self["quartilemethod"] = _v
_v = arg.pop("scalegroup", None)
_v = scalegroup if scalegroup is not None else _v
if _v is not None:
self["scalegroup"] = _v
_v = arg.pop("scalemode", None)
_v = scalemode if scalemode is not None else _v
if _v is not None:
self["scalemode"] = _v
_v = arg.pop("selected", None)
_v = selected if selected is not None else _v
if _v is not None:
self["selected"] = _v
_v = arg.pop("selectedpoints", None)
_v = selectedpoints if selectedpoints is not None else _v
if _v is not None:
self["selectedpoints"] = _v
_v = arg.pop("showlegend", None)
_v = showlegend if showlegend is not None else _v
if _v is not None:
self["showlegend"] = _v
_v = arg.pop("side", None)
_v = side if side is not None else _v
if _v is not None:
self["side"] = _v
_v = arg.pop("span", None)
_v = span if span is not None else _v
if _v is not None:
self["span"] = _v
_v = arg.pop("spanmode", None)
_v = spanmode if spanmode is not None else _v
if _v is not None:
self["spanmode"] = _v
_v = arg.pop("stream", None)
_v = stream if stream is not None else _v
if _v is not None:
self["stream"] = _v
_v = arg.pop("text", None)
_v = text if text is not None else _v
if _v is not None:
self["text"] = _v
_v = arg.pop("textsrc", None)
_v = textsrc if textsrc is not None else _v
if _v is not None:
self["textsrc"] = _v
_v = arg.pop("uid", None)
_v = uid if uid is not None else _v
if _v is not None:
self["uid"] = _v
_v = arg.pop("uirevision", None)
_v = uirevision if uirevision is not None else _v
if _v is not None:
self["uirevision"] = _v
_v = arg.pop("unselected", None)
_v = unselected if unselected is not None else _v
if _v is not None:
self["unselected"] = _v
_v = arg.pop("visible", None)
_v = visible if visible is not None else _v
if _v is not None:
self["visible"] = _v
_v = arg.pop("width", None)
_v = width if width is not None else _v
if _v is not None:
self["width"] = _v
_v = arg.pop("x", None)
_v = x if x is not None else _v
if _v is not None:
self["x"] = _v
_v = arg.pop("x0", None)
_v = x0 if x0 is not None else _v
if _v is not None:
self["x0"] = _v
_v = arg.pop("xaxis", None)
_v = xaxis if xaxis is not None else _v
if _v is not None:
self["xaxis"] = _v
_v = arg.pop("xhoverformat", None)
_v = xhoverformat if xhoverformat is not None else _v
if _v is not None:
self["xhoverformat"] = _v
_v = arg.pop("xsrc", None)
_v = xsrc if xsrc is not None else _v
if _v is not None:
self["xsrc"] = _v
_v = arg.pop("y", None)
_v = y if y is not None else _v
if _v is not None:
self["y"] = _v
_v = arg.pop("y0", None)
_v = y0 if y0 is not None else _v
if _v is not None:
self["y0"] = _v
_v = arg.pop("yaxis", None)
_v = yaxis if yaxis is not None else _v
if _v is not None:
self["yaxis"] = _v
_v = arg.pop("yhoverformat", None)
_v = yhoverformat if yhoverformat is not None else _v
if _v is not None:
self["yhoverformat"] = _v
_v = arg.pop("ysrc", None)
_v = ysrc if ysrc is not None else _v
if _v is not None:
self["ysrc"] = _v
_v = arg.pop("zorder", None)
_v = zorder if zorder is not None else _v
if _v is not None:
self["zorder"] = _v
# Read-only literals
# ------------------
self._props["type"] = "violin"
arg.pop("type", None)
# Process unknown kwargs
# ----------------------
self._process_kwargs(**dict(arg, **kwargs))
# Reset skip_invalid
# ------------------
self._skip_invalid = False
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@graph_objs@_violin.py@.PATH_END.py
|
{
"filename": "simple_example.py",
"repo_name": "rbuehler/vasca",
"repo_path": "vasca_extracted/vasca-main/docs/tutorials/simple_example.py",
"type": "Python"
}
|
# ---
# jupyter:
# jupytext:
# text_represenation:
# extension: .py
# format_name: percent
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.16.4
# kernelspec:
# display_name: vasca-github
# language: python
# name: vasca-github
# ---
# %% tags=["remove-cell"]
# ruff: noqa: T201
# %% [markdown]
# # Test Tutorial
#
# The contents of this page are edited in a python file which is converted to a markdown
# file prior to the sphinx build and then executed during build time. See how long it
# took to run this notebook [below](#execution-statistics).
# %% [markdown]
# ## Simple Test
# This is a simple test function
# Let's try what happens to myst-style sphinx admonitions:
# :::{hint}
# Pairing `.py` files with `.ipynb` files can make version control of notebooks better.
# The python file is used for version control the notebook file is excluded from it, but
# can be used for developing the notebook contents in a proper Jupyter environment. This
# allows to seamlessly use all the tools and extensions in my code editor the help me
# develop Python code.
# :::
# %%
# Just an inline code comment explaining that the function below is very simple.
def f(x: float) -> float:
return 3 * x + 1
# %% tags=["raises-exception"]
assert f(4) == 12
print(f(4))
# %% tags=["hide-output"]
# The output of this cell will be collapsed by default
# using the `hide-output` cell metadata tag
print(1 == 1)
for i in range(20):
print(f"Number: {i}")
# %%
# %%time
import numpy as np
print(f"{np.pi:1.5f}")
# %%
# Simple plot example
import matplotlib.pyplot as plt
Fs = 8000
f = 5
sample = 8000
x = np.arange(sample)
y = np.sin(2 * np.pi * f * x / Fs)
plt.plot(x, y)
plt.xlabel("samples")
plt.ylabel("amplitude")
# %% [markdown]
# ## Test Markdown Features
#
# Lets put a relative link to one of the sections in the user guide in this documentation
# [here](../user_guide/index.md#using-vasca)
#
# This is a simple and elegant definitions list:
#
# **First Item**
# : The first item in this list is bold
#
# [Second Item](https://en.wikipedia.org/wiki/Tidal_disruption_event)
# : The second item links to TDEs
#
# Third Item
# : Definition text contains a link to another [tutorial](tutorial_pipe.md)
# ## Execution Statistics
# ```{nb-exec-table}
# ```
|
rbuehlerREPO_NAMEvascaPATH_START.@vasca_extracted@vasca-main@docs@tutorials@simple_example.py@.PATH_END.py
|
{
"filename": "_gradient.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/scattergeo/marker/_gradient.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class GradientValidator(_plotly_utils.basevalidators.CompoundValidator):
def __init__(
self, plotly_name="gradient", parent_name="scattergeo.marker", **kwargs
):
super(GradientValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
data_class_str=kwargs.pop("data_class_str", "Gradient"),
data_docs=kwargs.pop(
"data_docs",
"""
color
Sets the final color of the gradient fill: the
center color for radial, the right for
horizontal, or the bottom for vertical.
colorsrc
Sets the source reference on Chart Studio Cloud
for `color`.
type
Sets the type of gradient used to fill the
markers
typesrc
Sets the source reference on Chart Studio Cloud
for `type`.
""",
),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@scattergeo@marker@_gradient.py@.PATH_END.py
|
{
"filename": "grid.py",
"repo_name": "clemson-cal/sailfish",
"repo_path": "sailfish_extracted/sailfish-master/ideas/grid.py",
"type": "Python"
}
|
from numpy import zeros, linspace, meshgrid, exp
def copy_guard_zones(grid):
for i, j in grid:
cc = grid.get((i, j))
lc = grid.get((i - 1, j), None)
rc = grid.get((i + 1, j), None)
cl = grid.get((i, j - 1), None)
cr = grid.get((i, j + 1), None)
if lc is not None:
cc[:+2, 2:-2] = lc[-4:-2, 2:-2]
if rc is not None:
cc[-2:, 2:-2] = rc[+2:+4, 2:-2]
if cl is not None:
cc[2:-2, :+2] = cl[2:-2, -4:-2]
if cr is not None:
cc[2:-2, -2:] = cr[2:-2, +2:+4]
def cell_center_coordinates(i, j, ni_patches, nj_patches, ni, nj):
dx = 1.0 / ni_patches
dy = 1.0 / nj_patches
ddx = dx / ni
ddy = dy / nj
x0 = -0.5 + (i + 0) * dx
x1 = -0.5 + (i + 1) * dx
y0 = -0.5 + (j + 0) * dy
y1 = -0.5 + (j + 1) * dy
xv = linspace(x0 - 2 * ddx, x1 + 2 * ddy, ni + 5)
yv = linspace(y0 - 2 * ddx, y1 + 2 * ddy, nj + 5)
xc = 0.5 * (xv[1:] + xv[:-1])
yc = 0.5 * (yv[1:] + yv[:-1])
return meshgrid(xc, yc, indexing="ij")
def initial_patches(ni_patches, nj_patches):
for i in range(ni_patches):
for j in range(nj_patches):
yield i, j
def initial_data(x, y):
z = exp(-(x**2 + y**2) / 0.01)
z[:+2, :] = 0.0
z[-2:, :] = 0.0
z[:, :+2] = 0.0
z[:, -2:] = 0.0
return z
if __name__ == "__main__":
patches = set(initial_patches(8, 8))
coordinate = {ij: cell_center_coordinates(*ij, 8, 8, 10, 10) for ij in patches}
primitives = {ij: initial_data(*xy) for ij, xy in coordinate.items()}
copy_guard_zones(primitives)
import matplotlib.pyplot as plt
for i, j in patches:
if i % 2 == 0 or j % 2 == 0:
continue
z = primitives[(i, j)]
x, y = coordinate[(i, j)]
plt.pcolormesh(x, y, z, vmin=0, vmax=1)
plt.axis("equal")
plt.show()
|
clemson-calREPO_NAMEsailfishPATH_START.@sailfish_extracted@sailfish-master@ideas@grid.py@.PATH_END.py
|
{
"filename": "_cmax.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/scatter3d/marker/_cmax.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class CmaxValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(self, plotly_name="cmax", parent_name="scatter3d.marker", **kwargs):
super(CmaxValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
implied_edits=kwargs.pop("implied_edits", {"cauto": False}),
role=kwargs.pop("role", "info"),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@scatter3d@marker@_cmax.py@.PATH_END.py
|
{
"filename": "syn_phot.py",
"repo_name": "tomasstolker/species",
"repo_path": "species_extracted/species-main/species/phot/syn_phot.py",
"type": "Python"
}
|
"""
Module with functionalities for calculating synthetic photometry.
"""
import os
import math
import warnings
import configparser
from typing import List, Optional, Union, Tuple
import h5py
import numpy as np
from typeguard import typechecked
from species.data.spec_data.spec_vega import add_vega
from species.read.read_filter import ReadFilter
from species.util.convert_util import apparent_to_absolute, parallax_to_distance
class SyntheticPhotometry:
"""
Class for calculating synthetic photometry from a spectrum and also
for converting between magnitudes and fluxes. Any filter from the
`SVO Filter Profile Service <http://svo2.cab.inta-csic.es/svo/
theory/fps/>`_ will be automatically downloaded and added to the
database. Also the detector type (energy- or photon-counting) will
be fetched. For a photon-counting detector, an additional
wavelength factor is included in the integral for calculating the
synthetic photometry, although typically the impact of the factor
on the calculated flux is negligible. It is also important to note
that by default the magnitude of Vega is set to 0.03 for all
filters. The value can be adjusted in the `configuration file
<https://species.readthedocs.io/en/latest/configuration.html>`_.
"""
@typechecked
def __init__(self, filter_name: str, zero_point: Optional[float] = None) -> None:
"""
Parameters
----------
filter_name : str
Filter name by which the profile is stored in database.
Any filter from the `SVO Filter Profile Service
<http://svo2.cab.inta-csic.es/svo/theory/fps/>`_ will be
automatically downloaded and added to the database.
zero_point : float, None
Zero-point flux (:math:`\\mathrm{W}`
:math:`\\mathrm{m}^{-2}` :math:`\\mu\\mathrm{m}^{-1}`) for
``filter_name``. This flux is equalized to the magnitude of
Vega, which by default is set to 0.03 for all filters. The
value can be adjusted in the `configuration file <https://
species.readthedocs.io/en/latest/configuration.html>`_.
By default, the argument of ``zero_point`` is set to
``None``, in which case the zero point is calculated
internally. The zero point can be accessed through
``zero_point`` attribute from instance of
:class:`~species.phot.syn_phot.SyntheticPhotometry`.
Returns
-------
NoneType
None
"""
self.filter_name = filter_name
self.zero_point = zero_point
self.filter_interp = None
self.wavel_range = None
config_file = os.path.join(os.getcwd(), "species_config.ini")
config = configparser.ConfigParser()
config.read(config_file)
self.database = config["species"]["database"]
self.data_folder = config["species"]["data_folder"]
self.vega_mag = float(config["species"]["vega_mag"])
read_filt = ReadFilter(self.filter_name)
self.det_type = read_filt.detector_type()
if self.zero_point is None:
self.zero_point = self.calc_zero_point()
else:
warnings.warn(
"Please note that a manually provided zero-point flux "
"is by default equalized to a magnitude of 0.03 for "
"all filters. The magnitude of Vega can be adjusted "
"in the configuration file (see https://species."
"readthedocs.io/en/latest/configuration.html) by "
"setting the 'vega_mag' parameter. Currently the "
f"parameter is set to {self.vega_mag}."
)
@typechecked
def calc_zero_point(self) -> Union[float, np.float64]:
"""
Internal function for calculating the zero point of the
provided ``filter_name``. The zero point is here defined
as the flux of Vega, which by default is set to a
magnitude of 0.03 for all filters.
Returns
-------
float
Zero-point flux (:math:`\\mathrm{W}`
:math:`\\mathrm{m}^{-2}` :math:`\\mu\\mathrm{m}^{-1}`).
"""
if self.wavel_range is None:
read_filt = ReadFilter(self.filter_name)
self.wavel_range = read_filt.wavelength_range()
with h5py.File(self.database, "r") as hdf5_file:
vega_found = "spectra/calibration/vega" in hdf5_file
if not vega_found:
with h5py.File(self.database, "a") as hdf5_file:
add_vega(self.data_folder, hdf5_file)
with h5py.File(self.database, "r") as hdf5_file:
vega_spec = np.array(hdf5_file["spectra/calibration/vega"])
wavelength = vega_spec[0,]
flux = vega_spec[1,]
wavelength_crop = wavelength[
(wavelength > self.wavel_range[0]) & (wavelength < self.wavel_range[1])
]
flux_crop = flux[
(wavelength > self.wavel_range[0]) & (wavelength < self.wavel_range[1])
]
return self.spectrum_to_flux(wavelength_crop, flux_crop)[0]
@typechecked
def spectrum_to_flux(
self,
wavelength: np.ndarray,
flux: np.ndarray,
error: Optional[np.ndarray] = None,
threshold: Optional[float] = 0.01,
) -> Tuple[
Union[float, np.float32, np.float64],
Optional[Union[float, np.float32, np.float64]],
]:
"""
Function for calculating the average flux from a spectrum and
a filter profile. The uncertainty is propagated by sampling
200 random values from the error distributions.
Parameters
----------
wavelength : np.ndarray
Wavelength points (um).
flux : np.ndarray
Flux (:math:`\\mathrm{W}` :math:`\\mathrm{m}^{-2}`
:math:`\\mu\\mathrm{m}^{-1}`).
error : np.ndarray, None
Uncertainty (:math:`\\mathrm{W}` :math:`\\mathrm{m}^{-2}`
:math:`\\mu\\mathrm{m}^{-1}`). Not used if set to ``None``.
threshold : float, None
Transmission threshold (value between 0 and 1). If the
minimum transmission value is larger than the threshold,
a NaN is returned. This will happen if the input spectrum
does not cover the full wavelength range of the filter
profile. The parameter is not used if set to ``None``
(default: 0.01).
Returns
-------
float
Average flux (:math:`\\mathrm{W}` :math:`\\mathrm{m}^{-2}`
:math:`\\mu\\mathrm{m}^{-1}`).
float, None
Uncertainty (:math:`\\mathrm{W}` :math:`\\mathrm{m}^{-2}`
:math:`\\mu\\mathrm{m}^{-1}`).
"""
# Remove fluxes that are a NaN
nan_idx = np.isnan(flux)
if np.sum(nan_idx) > 0:
warnings.warn(
f"Found {np.sum(nan_idx)} fluxes with NaN. Removing "
"these spectral fluxes from the input data before "
"calculating synthetic photometry."
)
wavelength = wavelength[~nan_idx]
flux = flux[~nan_idx]
if error is not None:
error = error[~nan_idx]
if error is not None:
# The error calculation requires the original
# spectrum because spectrum_to_flux is used
wavel_error = wavelength.copy()
flux_error = flux.copy()
if self.filter_interp is None:
# Interpolate filter profile
read_filt = ReadFilter(self.filter_name)
self.filter_interp = read_filt.interpolate_filter()
if self.wavel_range is None:
# Set the wavel_range attribute to the
# wavelength range of the filter profile
self.wavel_range = read_filt.wavelength_range()
if wavelength.size == 0:
syn_flux = np.nan
if error is not None:
error_flux = np.nan
else:
error_flux = None
indices = None
warnings.warn(
f"Calculation of the mean flux for {self.filter_name} "
"is not possible because the wavelength array is "
"empty. Returning a NaN for the flux."
)
else:
indices = np.where(
(self.wavel_range[0] <= wavelength)
& (wavelength <= self.wavel_range[1])
)[0]
if indices is not None and indices.size < 2:
syn_flux = np.nan
if error is not None:
error_flux = np.nan
else:
error_flux = None
warnings.warn(
"Calculating a synthetic flux requires more than "
"one wavelength point. Photometry is set to NaN."
)
else:
if threshold is None and (
wavelength[0] > self.wavel_range[0]
or wavelength[-1] < self.wavel_range[1]
):
warnings.warn(
f"The filter profile of {self.filter_name} "
f"({self.wavel_range[0]:.4f}-"
f"{self.wavel_range[1]:.4f}) extends beyond "
f"the wavelength range of the spectrum "
f"({wavelength[0]:.4f}-{wavelength[-1]:.4f}). "
"The synthetic flux is set to NaN. Setting "
"the 'threshold' parameter will loosen the "
"wavelength constraints."
)
syn_flux = np.nan
else:
wavelength = wavelength[indices]
flux = flux[indices]
transmission = self.filter_interp(wavelength)
if np.sum(wavelength) == 0.0:
# The wavelength array looks empty but it is and
# empty array inside another array so the size
# of the wavelength array is 1. The sum however
# is 0 so that is used to check if it is empty
warnings.warn(
f"The filter profile of {self.filter_name} "
f"({self.wavel_range[0]:.4f}-{self.wavel_range[1]:.4f}) "
f"lies outside the wavelength range of the spectrum. "
f"The returned synthetic flux is therefore set to NaN."
)
syn_flux = np.nan
elif (
threshold is not None
and (transmission[0] > threshold or transmission[-1] > threshold)
and (
wavelength[0] < self.wavel_range[0]
or wavelength[-1] > self.wavel_range[-1]
)
):
warnings.warn(
f"The filter profile of {self.filter_name} "
f"({self.wavel_range[0]:.4f}-{self.wavel_range[1]:.4f}) "
f"extends beyond the wavelength range of the spectrum "
f"({wavelength[0]:.4f}-{wavelength[-1]:.4f}). The flux "
f"is set to NaN. Increasing the 'threshold' parameter "
f"({threshold}) will loosen the wavelength constraint."
)
syn_flux = np.nan
else:
indices = np.isnan(transmission)
indices = np.logical_not(indices)
if self.det_type == "energy":
# Energy counting detector
integrand1 = transmission[indices] * flux[indices]
integrand2 = transmission[indices]
elif self.det_type == "photon":
# Photon counting detector
integrand1 = (
wavelength[indices] * transmission[indices] * flux[indices]
)
integrand2 = wavelength[indices] * transmission[indices]
integral1 = np.trapz(integrand1, x=wavelength[indices])
integral2 = np.trapz(integrand2, x=wavelength[indices])
syn_flux = integral1 / integral2
if error is not None and not np.any(np.isnan(error)) and not np.isnan(syn_flux):
phot_random = np.zeros(200)
for i in range(200):
# Use the original spectrum size (i.e. wavel_error and flux_error)
spec_random = (
flux_error
+ np.random.normal(loc=0.0, scale=1.0, size=wavel_error.shape[0])
* error
)
phot_random[i] = self.spectrum_to_flux(
wavel_error, spec_random, error=None, threshold=threshold
)[0]
nan_idx = np.isnan(phot_random)
if np.sum(nan_idx) > 0:
warnings.warn(
f"{np.sum(nan_idx)} out of 200 samples "
"that are used for estimating the "
"uncertainty on the synthetic flux "
"are NaN so removing these samples."
)
phot_random = phot_random[~nan_idx]
error_flux = np.std(phot_random)
elif error is not None and np.any(np.isnan(error)):
warnings.warn("Spectum contains NaN so cannot calculate the error.")
error_flux = None
else:
error_flux = None
return syn_flux, error_flux
@typechecked
def spectrum_to_magnitude(
self,
wavelength: np.ndarray,
flux: np.ndarray,
error: Optional[Union[np.ndarray, List[np.ndarray]]] = None,
parallax: Optional[Tuple[float, Optional[float]]] = None,
distance: Optional[Tuple[float, Optional[float]]] = None,
threshold: Optional[float] = 0.01,
) -> Tuple[
Tuple[float, Optional[float]], Optional[Tuple[Optional[float], Optional[float]]]
]:
"""
Function for calculating the apparent and absolute magnitude
from a spectrum and a filter profile. The uncertainty is
propagated by sampling 200 random values from the error
distributions.
Parameters
----------
wavelength : np.ndarray
Wavelength points (um).
flux : np.ndarray
Flux (:math:`\\mathrm{W}` :math:`\\mathrm{m}^{-2}`
:math:`\\mu\\mathrm{m}^{-1}`).
error : np.ndarray, list(np.ndarray), None
Uncertainty (:math:`\\mathrm{W}` :math:`\\mathrm{m}^{-2}`
:math:`\\mu\\mathrm{m}^{-1}`).
parallax : tuple(float, float), None
Parallax and uncertainty (mas). No absolute magnitude is
calculated if set to ``None``. No error on the absolute
magnitude is calculated if the ``error`` parameter is
set to ``None``.
distance : tuple(float, float), None
Distance and uncertainty (pc). No absolute magnitude is
calculated if set to ``None``. No error on the absolute
magnitude is calculated if the ``error`` parameter is
set to ``None``. This parameter is ignored if the
``parallax`` parameter is used.
threshold : float, None
Transmission threshold (value between 0 and 1). If the
minimum transmission value is larger than the threshold,
a NaN is returned. This will happen if the input spectrum
does not cover the full wavelength range of the filter
profile. The parameter is not used if set to ``None``
(default: 0.01).
Returns
-------
tuple(float, float)
Apparent magnitude and uncertainty.
tuple(float, float)
Absolute magnitude and uncertainty.
"""
# Remove fluxes that are a NaN
nan_idx = np.isnan(flux)
if np.sum(nan_idx) > 0:
warnings.warn(
f"Found {np.sum(nan_idx)} fluxes with NaN. Removing "
"these spectral fluxes from the input data before "
"calculating synthetic photometry."
)
wavelength = wavelength[~nan_idx]
flux = flux[~nan_idx]
if error is not None:
error = error[~nan_idx]
if parallax is not None:
distance = parallax_to_distance(parallax)
syn_flux = self.spectrum_to_flux(
wavelength, flux, error=error, threshold=threshold
)
app_mag = self.vega_mag - 2.5 * math.log10(syn_flux[0] / self.zero_point)
if error is not None and not np.any(np.isnan(error)):
mag_random = np.zeros(200)
for i in range(200):
spec_random = (
flux
+ np.random.normal(loc=0.0, scale=1.0, size=wavelength.shape[0])
* error
)
flux_random = self.spectrum_to_flux(
wavelength, spec_random, error=None, threshold=threshold
)
mag_random[i] = self.vega_mag - 2.5 * np.log10(
flux_random[0] / self.zero_point
)
nan_idx = np.isnan(mag_random)
if np.sum(nan_idx) > 0:
warnings.warn(
f"{np.sum(nan_idx)} out of 200 samples "
"that are used for estimating the "
"uncertainty on the synthetic magnitude "
"are NaN so removing these samples."
)
mag_random = mag_random[~nan_idx]
error_app_mag = np.std(mag_random)
elif error is not None and np.any(np.isnan(error)):
warnings.warn("Spectum contains NaN so cannot calculate the error.")
error_app_mag = None
else:
error_app_mag = None
if distance is None:
abs_mag = None
error_abs_mag = None
else:
abs_mag = app_mag - 5.0 * np.log10(distance[0]) + 5.0
if error_app_mag is not None and distance[1] is not None:
error_dist = distance[1] * (5.0 / (distance[0] * math.log(10.0)))
error_abs_mag = math.sqrt(error_app_mag**2 + error_dist**2)
else:
error_abs_mag = None
return (app_mag, error_app_mag), (abs_mag, error_abs_mag)
@typechecked
def magnitude_to_flux(
self,
magnitude: float,
error: Optional[float] = None,
zp_flux: Optional[float] = None,
) -> Tuple[
Union[float, np.float32, np.float64],
Optional[Union[float, np.float32, np.float64]],
]:
"""
Function for converting a magnitude to a flux.
Parameters
----------
magnitude : float
Magnitude.
error : float, None
Error on the magnitude. Not used if set to ``None``.
zp_flux : float, None
DEPRECATED: Zero-point flux (:math:`\\mathrm{W}`
:math:`\\mathrm{m}^{-2}` :math:`\\mu\\mathrm{m}^{-1}`).
This parameter is deprecated and will be removed in a
future release. Please use the zero_point parameter
of the constructor of
:class:`~species.phot.syn_phot.SyntheticPhotometry`
instead. By default, the zero point is calculated
internally and stored as the ``zero_point`` attribute
of an instance from
:class:`~species.phot.syn_phot.SyntheticPhotometry`.
Returns
-------
float
Flux (:math:`\\mathrm{W}` :math:`\\mathrm{m}^{-2}`
:math:`\\mu\\mathrm{m}^{-1}`).
float, None
Error (:math:`\\mathrm{W}` :math:`\\mathrm{m}^{-2}`
:math:`\\mu\\mathrm{m}^{-1}`). The returned value is
``None`` if the argument of ``error`` is ``None``.
"""
if zp_flux is None:
flux = 10.0 ** (-0.4 * (magnitude - self.vega_mag)) * self.zero_point
else:
flux = 10.0 ** (-0.4 * (magnitude - self.vega_mag)) * zp_flux
warnings.warn(
"The 'zp_flux' parameter is deprecated "
"and will be removed in a future release. "
"Please use the 'zero_point' parameter "
"of the SyntheticPhotometry constructor "
"instead.",
DeprecationWarning,
)
if error is None:
error_flux = None
else:
error_upper = flux * (10.0 ** (0.4 * error) - 1.0)
error_lower = flux * (1.0 - 10.0 ** (-0.4 * error))
error_flux = (error_lower + error_upper) / 2.0
return flux, error_flux
@typechecked
def flux_to_magnitude(
self,
flux: float,
error: Optional[Union[float, np.ndarray]] = None,
parallax: Optional[
Union[
Tuple[float, Optional[float]], Tuple[np.ndarray, Optional[np.ndarray]]
]
] = None,
distance: Optional[
Union[
Tuple[float, Optional[float]], Tuple[np.ndarray, Optional[np.ndarray]]
]
] = None,
) -> Tuple[
Union[Tuple[float, Optional[float]], Tuple[np.ndarray, Optional[np.ndarray]]],
Union[
Tuple[Optional[float], Optional[float]],
Tuple[Optional[np.ndarray], Optional[np.ndarray]],
],
]:
"""
Function for converting a flux into a magnitude.
Parameters
----------
flux : float, np.ndarray
Flux (:math:`\\mathrm{W}` :math:`\\mathrm{m}^{-2}`
:math:`\\mu\\mathrm{m}^{-1}`).
error : float, np.ndarray, None
Uncertainty (:math:`\\mathrm{W}` :math:`\\mathrm{m}^{-2}`
:math:`\\mu\\mathrm{m}^{-1}`). Not used if set to None.
parallax : tuple(float, float), , tuple(np.ndarray, np.ndarray), None
Parallax and uncertainty (mas). The returned absolute
magnitude is set to ``None`` in case ``parallax`` and
``distance`` are set to ``None``. The error is not
propagated into the error on the absolute magnitude
in case the parallax uncertainty is set to ``None``,
for example ``parallax=(10., None)``.
distance : tuple(float, float), tuple(np.ndarray, np.ndarray), None
Distance and uncertainty (pc). The returned absolute
magnitude is set to ``None`` in case ``distance`` and
``parallax`` are set to ``None``. The error is not
propagated into the error on the absolute magnitude in
case the distance uncertainty is set to ``None``, for
example ``distance=(20., None)``. This parameter is
ignored if the ``parallax`` parameter is used.
Returns
-------
tuple(float, float), tuple(np.ndarray, np.ndarray)
Apparent magnitude and uncertainty.
tuple(float, float), tuple(np.ndarray, np.ndarray)
Absolute magnitude and uncertainty.
"""
if parallax is not None:
distance = parallax_to_distance(parallax)
if flux <= 0.0:
raise ValueError(
"Converting a flux into a magnitude "
"is only possible if the argument of "
"'flux' has a positive value."
)
app_mag = self.vega_mag - 2.5 * np.log10(flux / self.zero_point)
if error is None:
error_app_mag = None
error_abs_mag = None
else:
if flux + error > 0.0:
error_app_lower = app_mag - (
self.vega_mag - 2.5 * np.log10((flux + error) / self.zero_point)
)
else:
error_app_lower = np.nan
if flux - error > 0.0:
error_app_upper = (
self.vega_mag - 2.5 * np.log10((flux - error) / self.zero_point)
) - app_mag
else:
error_app_upper = np.nan
error_app_mag = np.nanmean([error_app_lower, error_app_upper])
if np.isnan(error_app_mag):
error_app_mag = None
warnings.warn(
"This warning should not have occurred "
"since either error_app_lower and/or "
"error_app_upper should not be NaN."
)
if distance is None:
abs_mag = None
error_abs_mag = None
else:
abs_mag, error_abs_mag = apparent_to_absolute(
(app_mag, error_app_mag), distance
)
return (app_mag, error_app_mag), (abs_mag, error_abs_mag)
|
tomasstolkerREPO_NAMEspeciesPATH_START.@species_extracted@species-main@species@phot@syn_phot.py@.PATH_END.py
|
{
"filename": "atomic.py",
"repo_name": "atomdb/pyatomdb",
"repo_path": "pyatomdb_extracted/pyatomdb-master/pyatomdb/pyatomdb/atomic.py",
"type": "Python"
}
|
"""
atomic.py contains routines related to basic atomic data, e.g. converting
integer nuclear charge to element symbols, etc.
Version -.1 - initial release
Adam Foster July 17th 2015
"""
llist = 'spdfghiklmnoqrtuvwxyzABCDEFGHIJKLMNOP'
import re, numpy
################################################################################
#
# Python Module
#
# Name: adbatomic.py
#
# Decription: Codes for simple atomic data related tasks
#
# Module contcents (and 1 line description: see individual modules for more):
#
# z0toelsymb
# Converts z0 to element symbol (eg 6 -> C)
#
# z0toelname
# Converts z0 to element name (eg 6 -> Carbon)
#
# int2roman
# Converts integer to Roman Numerals (eg 6 -> VI)
#
# spectroscopic_name
# Converts z0,ionstage to spectroscopic name (eg 6,3 -> C IV)
#
# Check individual codes for author and update details
#
#
# First Version:
# Adam Foster, 28-Jul-2009
#
################################################################################
#*******************************************************************************
#
# Routine z0toelsymb
#
# Converts z0 to element symbol
#
# input: z0 (integer)
#
# returns: Element symbol (first letter capitalised)
#
# First Version:
# Adam Foster, 28-Jul-2009
#
#*******************************************************************************
def Ztoelsymb(Z) :
"""
Returns element symbol of element with nuclear charge Z.
PARAMETERS
----------
Z - nuclear charge of element (e.g. 6 for carbon)
RETURNS
-------
element symbol (e.g. "C" for carbon)
Version 0.1 28 July 2009
Adam Foster
"""
elsymb=('H' , 'He', 'Li', 'Be', 'B' , 'C' , 'N' , 'O' , 'F' , 'Ne',
'Na', 'Mg', 'Al', 'Si', 'P' , 'S' , 'Cl', 'Ar', 'K' , 'Ca',
'Sc', 'Ti', 'V' , 'Cr', 'Mn', 'Fe', 'Co', 'Ni', 'Cu', 'Zn',
'Ga', 'Ge', 'As', 'Se', 'Br', 'Kr', 'Rb', 'Sr', 'Y' , 'Zr',
'Nb', 'Mo', 'Tc', 'Ru', 'Rh', 'Pd', 'Ag', 'Cd', 'In', 'Sn',
'Sb', 'Te', 'I' , 'Xe', 'Cs', 'Ba', 'La', 'Ce', 'Pr', 'Nd',
'Pm', 'Sm', 'Eu', 'Gd', 'Tb', 'Dy', 'Ho', 'Er', 'Tm', 'Yb',
'Lu', 'Hf', 'Ta', 'W' , 'Re', 'Os', 'Ir', 'Pt', 'Au', 'Hg',
'Tl', 'Pb', 'Bi', 'Po', 'At', 'Rn', 'Fr', 'Ra', 'Ac', 'Th',
'Pa', 'U')
if Z < 1 :
print("Z must be between 1 and 92. You have given Z= " + repr(z0))
ret=-1
elif Z > 92 :
print("Z must be between 1 and 92. You have given Z= " + repr(z0))
ret=-1
else :
ret=elsymb[Z-1]
return ret
#*******************************************************************************
#*******************************************************************************
#*******************************************************************************
def z0toelsymb(z0):
"""
Returns element symbol of element with nuclear charge z0.
(wrapper to Ztoelsymb for compatibility purposes)
Parameters
----------
z0 : int
nuclear charge of element (e.g. 6 for carbon)
Returns
-------
str
element symbol (e.g. "C" for carbon)
"""
#
# Version 0.1 28 July 2009
# Adam Foster
#
ret = Ztoelsymb(z0)
return ret
#*******************************************************************************
#*******************************************************************************
#*******************************************************************************
#*******************************************************************************
#
# Routine z0toelname
#
# Converts z0 to element name
#
# input: z0 (integer)
#
# returns: Element name (first letter capitalised)
#
# First Version:
# Adam Foster, 28-Jul-2009
#
#*******************************************************************************
def z0toelname(z0):
"""
Returns element name of element with nuclear charge z0.
(wrapper to Ztoelname for compatibility purposes)
Parameters
----------
z0 : int
nuclear charge of element (e.g. 6 for carbon)
Returns
-------
str
element name (e.g. "Carbon" for carbon)
"""
#
# Version 0.1 28 July 2009
# Adam Foster
#
ret = Ztoelname(Z)
return ret
def Ztoelname(Z):
"""
Returns element name of element with nuclear charge Z.
Parameters
----------
Z : int
nuclear charge of element (e.g. 6 for carbon)
Returns
-------
str
element name (e.g. "Carbon" for carbon)
"""
#
# Version 0.1 28 July 2009
# Adam Foster
#
elname=('Hydrogen' , 'Helium' , 'Lithium' , 'Beryllium' ,
'Boron' , 'Carbon' , 'Nitrogen' , 'Oxygen' ,
'Fluorine' , 'Neon' , 'Sodium' , 'Magnesium' ,
'Aluminum' , 'Silicon' , 'Phosphorus' , 'Sulfur' ,
'Chlorine' , 'Argon' , 'Potassium' , 'Calcium' ,
'Scandium' , 'Titanium' , 'Vanadium' , 'Chromium' ,
'Manganese' , 'Iron' , 'Cobalt' , 'Nickel' ,
'Copper' , 'Zinc' , 'Gallium' , 'Germanium' ,
'Arsenic' , 'Selenium' , 'Bromine' , 'Krypton' ,
'Rubidium' , 'Strontium' , 'Yttrium' , 'Zirconium' ,
'Niobium' , 'Molybdenum' , 'Technetium' , 'Ruthenium' ,
'Rhodium' , 'Palladium' , 'Silver' , 'Cadmium' ,
'Indium' , 'Tin' , 'Antimony' , 'Tellurium' ,
'Iodine' , 'Xenon' , 'Cesium' , 'Barium' ,
'Lanthanum' , 'Cerium' , 'Praseodymium', 'Neodymium' ,
'Promethium' , 'Samarium' , 'Europium' , 'Gadolinium' ,
'Terbium' , 'Dysprosium' , 'Holmium' , 'Erbium' ,
'Thulium' , 'Ytterbium' , 'Lutetium' , 'Hafnium' ,
'Tantalum' , 'Tungsten' , 'Rhenium' , 'Osmium' ,
'Iridium' , 'Platinum' , 'Gold' , 'Mercury' ,
'Thallium' , 'Lead' , 'Bismuth' , 'Polonium' ,
'Astatine' , 'Radon' , 'Francium' , 'Radium' ,
'Actinium' , 'Thorium' , 'Protactinium', 'Uranium')
if Z < 1 :
print("Z must be between 1 and 92. You have given Z= " + repr(Z))
ret=-1
elif Z > 92 :
print("Z must be between 1 and 92. You have given Z= " + repr(Z))
ret=-1
else :
ret=elname[Z-1]
return ret
#*******************************************************************************
#*******************************************************************************
#*******************************************************************************
#*******************************************************************************
#
# Routine int2roman
#
# Converts a number to roman numeral (pilfered off the internet)
#
# input: number (integer)
#
# returns: Roman numeral (string)
#
# First Version:
# Adam Foster, 28-Jul-2009
#
#*******************************************************************************
def int2roman(number):
numerals = { 1 : "I" , 4 : "IV", 5 : "V" , 9 : "IX", 10 : "X" ,
40 : "XL", 50 : "L" , 90 : "XC", 100 : "C" , 400 : "CD",
500 : "D" , 900 : "CM", 1000 : "M" }
result = ""
for value, numeral in sorted(list(numerals.items()), reverse=True):
while number >= value:
result += numeral
number -= value
return result
#*******************************************************************************
#*******************************************************************************
#*******************************************************************************
#*******************************************************************************
#
# Routine int_to_roman
#
# Converts a integer to a roman numeral (pilfered off the internet)
#
# input: Roman numeral (string)
#
# returns: number (integer)
#
# First Version:
# Adam Foster, 03-Nov-2011
#
#*******************************************************************************
def int_to_roman(input):
"""
Convert an integer to Roman numerals.
"""
if type(input) != type(1):
raise TypeError("expected integer, got %s" % type(input))
if not 0 < input < 4000:
raise ValueError("Argument must be between 1 and 3999")
ints = (1000, 900, 500, 400, 100, 90, 50, 40, 10, 9, 5, 4, 1)
nums = ('M', 'CM', 'D', 'CD','C', 'XC','L','XL','X','IX','V','IV','I')
result = ""
for i in range(len(ints)):
count = int(input / ints[i])
result += nums[i] * count
input -= ints[i] * count
return result
#*******************************************************************************
#*******************************************************************************
#*******************************************************************************
#*******************************************************************************
#
# Routine roman2int
#
# Converts a roman numeral to an integer (pilfered off the internet)
#
# input: Roman numeral (string)
#
# returns: number (integer)
#
# First Version:
# Adam Foster, 03-Nov-2011
#
#*******************************************************************************
def roman_to_int(input):
"""
Convert a roman numeral to an integer.
"""
if type(input) != type(""):
raise TypeError("expected string, got %s" % type(input))
input = input.upper()
nums = ['M', 'D', 'C', 'L', 'X', 'V', 'I']
ints = [1000, 500, 100, 50, 10, 5, 1]
places = []
for c in input:
if not c in nums:
raise ValueError("input is not a valid roman numeral: %s" % input)
for i in range(len(input)):
c = input[i]
value = ints[nums.index(c)]
# If the next place holds a larger number, this value is negative.
try:
nextvalue = ints[nums.index(input[i +1])]
if nextvalue > value:
value *= -1
except IndexError:
# there is no next place.
pass
places.append(value)
sum = 0
for n in places: sum += n
# Easiest test for validity...
if int_to_roman(sum) == input:
return sum
else:
raise ValueError('input is not a valid roman numeral: %s' % input)
#*******************************************************************************
#
# Routine spectroscopic_name
#
# Converts z0, ioncharge to element symbol
#
# input: z0 (integer), ioncharge (integer)
#
# returns: Spectroscopic name (e.g. C IV or Mg XI)
#
# First Version:
# Adam Foster, 28-Jul-2009
#
#*******************************************************************************
def spectroscopic_name(Z,z1) :
"""
Converts Z,z1 to spectroscopic name, e.g. 6,5 to "C V"
Parameters
----------
Z : int
nuclear charge (e.g. 6 for C)
z1 : int
ion charge +1 (e.g. 5 for C4+)
Returns
-------
str
spectroscopic symbol for ion (e.g. "C V" for C+4)
"""
#
# Version 0.1 28 July 2009
# Adam Foster
#
# get element symbol
elsymb = Ztoelsymb(Z)
# convert z1 to spectroscopic
roman = int2roman(z1)
ret = elsymb + ' ' + roman
return ret
#*******************************************************************************
#*******************************************************************************
#*******************************************************************************
#*******************************************************************************
#
# Routine spectroscopictoz0
#
# Converts z0, ioncharge to element symbol
#
# input: z0 (integer), ioncharge (integer)
#
# returns: Spectroscopic name (e.g. C IV or Mg XI)
#
# First Version:
# Adam Foster, 28-Jul-2009
#
#*******************************************************************************
def spectroscopictoz0(name):
"""
Converts spectroscopic name to Z, z1, e.g. "C V" to 6,5
Parameters
----------
name : str
Ion name, e.g. "C V"
Returns
-------
int, int
Z, z1 for the ion. (e.g. 6,5 for C V)
"""
#
# Version 0.1 28 July 2009
# Adam Foster
#
# convert name (e.g. Fe VIII) to z0 & ioncharge (=0 for neutral)
# get element symbol
d = name.split()
elsymb = d[0]
chargesymb = d[1]
z0 = elsymb_to_z0(elsymb)
z1 = roman_to_int(chargesymb)
z=z1-1
return z0,z
#*******************************************************************************
#*******************************************************************************
#*******************************************************************************
#*******************************************************************************
#
# Routine occup_to_cfg
#
# Converts occupancy vector to configuration string
# (e.g. [2,1,0,1] -> 1s2 2s1 3s1)
#
# input: occupancy (list)
#
# returns: configuration string
#
# First Version:
# Adam Foster, 24-Nov-2009
#
#*******************************************************************************
def occup_to_cfg(occlist) :
# l_list = ['s','p','d','f','g','h','i','k','l','m','n','o','q','r',
# 't','u','v','w','x','y','z', 'A','B','C','D','E','F','G','H','I','J']
cfgstr=''
l=0
n=0
for j, i in enumerate(occlist) :
if (l+1 >= n):
l = 0
n += 1
else:
l += 1
if (i > 0):
cfgstr = cfgstr+' '+repr(n)+llist[l]+repr(i)
# return minus leading blank
return cfgstr.strip()
#*******************************************************************************
#*******************************************************************************
#*******************************************************************************
#*******************************************************************************
#
# Routine elsymb_to_z0
#
# Converts occupancy element symbol to z0
# (e.g. 'He' -> 2) (case insensitive)
#
# input: occupancy (list)
#
# returns: configuration string
#
# First Version:
# Adam Foster, 24-Nov-2009
#
#*******************************************************************************
def elsymb_to_Z(elsymb) :
"""
Converts element symbol to nuclear charge, e.g. "C" -> 6
Parameters
----------
elsymb : str
Element symbol, e.g. "C". Case insensitive.
Returns
-------
int
Z for the ion. (e.g. 6 for C)
"""
#
# Version 0.1 28 July 2009
# Adam Foster
#
ellist=('h' , 'he', 'li', 'be', 'b' , 'c' , 'n' , 'o' , 'f' , 'ne',
'na', 'mg', 'al', 'si', 'p' , 's' , 'cl', 'ar', 'k' , 'ca',
'sc', 'ti', 'v' , 'cr', 'mn', 'fe', 'co', 'ni', 'cu', 'zn',
'ga', 'ge', 'as', 'se', 'br', 'kr', 'rb', 'sr', 'y' , 'zr',
'nb', 'mo', 'tc', 'ru', 'rh', 'pd', 'ag', 'cd', 'in', 'sn',
'sb', 'te', 'i' , 'xe', 'cs', 'ba', 'la', 'ce', 'pr', 'nd',
'pm', 'sm', 'eu', 'gd', 'tb', 'dy', 'ho', 'er', 'tm', 'yb',
'lu', 'hf', 'ta', 'w' , 're', 'os', 'ir', 'pt', 'au', 'hg',
'tl', 'pb', 'bi', 'po', 'at', 'rn', 'fr', 'ra', 'ac', 'th',
'pa', 'u')
try:
ind=ellist.index(elsymb.lower().strip())
except ValueError:
print("elsymb_to_z0 error: invalid element symbol '"+elsymb+"', returning -1")
ind=-1
return ind+1
def elsymb_to_z0(elsymb) :
"""
Converts element symbol to nuclear charge, e.g. "C" -> 6
(wrapper to elsymb_to_Z, retained for consistency)
Parameters
----------
elsymb : str
Element symbol, e.g. "C". Case insensitive.
Returns
-------
int
Z for the ion. (e.g. 6 for C)
"""
ret= elsymb_to_Z(elsymb)
return ret
#*******************************************************************************
#*******************************************************************************
#*******************************************************************************
#*******************************************************************************
#
# Routine z0_to_mass
#
# Return atomic mass of element with atomic number z0
#
# input: z0
#
# returns: atomic mass (float)
#
# First Version:
# Adam Foster, 4-Apr-2010
#
#*******************************************************************************
def z0_to_mass(z0):
"""
Converts element symbol to atomic mass, e.g. "C" -> 12.0107
(wrapper to Z_to_mass, retained for consistency)
Isotope fractions based on those found in earth's crust samples, your
astrophysical object may vary.
Parameters
----------
z0 : int
nuclear charge, e.g 6 for C
Returns
-------
float
mass in a.m.u. for the element. (e.g. 12.0107 for C)
References
----------
Atomic masses are taken from:
Pure Appl. Chem. 81 NO 11, 2131-2156 (2009)
Masses for Technetium, Promethium, Polonium, Astatine, Radon,
Francium, Radium & Actinum are estimates. If you need these you
probably aren't doing astronomy...
"""
#
# Version 0.1 28 July 2009
# Adam Foster
#
ret = Z_to_mass(z0)
return ret
def Z_to_mass(Z, raw = False):
"""
Converts element symbol to atomic mass, e.g. "C" -> 12.0107
Isotope fractions based on those found in earth's crust samples, your
astrophysical object may vary.
Parameters
----------
Z : int
nuclear charge, e.g 6 for C
raw : bool
if true, ignore Z, and return the entire mass list as an array with
a 0 at the beginning so ret[12] = mass of carbon.
Returns
-------
float
mass in a.m.u. for the element. (e.g. 12.0107 for C)
References
----------
Atomic masses are taken from:
Pure Appl. Chem. 81 NO 11, 2131-2156 (2009)
Masses for Technetium, Promethium, Polonium, Astatine, Radon,
Francium, Radium & Actinum are estimates. If you need these you
probably aren't doing astronomy...
"""
# Version 0.1 28 July 2009
# Adam Foster
#
masslist=( 1.00794 , 4.002602, 6.941 , 9.012182 , 10.811 ,
12.0107 , 14.0067 , 15.9994 , 18.9984032, 20.1797 ,
22.98976928, 24.3050 , 26.9815386, 28.0855 , 30.973762 ,
32.065 , 35.453 , 39.948 , 39.0983 , 40.078 ,
44.955912 , 47.867 , 50.9415 , 51.9961 , 54.938045 ,
55.845 , 58.933195, 58.6934 , 63.546 , 65.38 ,
69.723 , 72.64 , 74.92160 , 78.96 , 79.904 ,
83.798 , 85.4678 , 87.62 , 88.90585 , 91.224 ,
92.90638 , 95.96 , 98.000 , 101.07 , 102.90550 ,
106.42 , 107.8682 , 112.411 , 114.818 , 118.710 ,
121.760 , 127.60 , 126.90447 , 131.293 , 132.9054519,
137.327 , 138.90547 , 140.116 , 140.90765 , 144.242 ,
145.000 , 150.36 , 151.964 , 157.25 , 158.92535 ,
162.500 , 164.93032 , 167.259 , 168.93421 , 173.054 ,
174.9668 , 178.49 , 180.94788 , 183.84 , 186.207 ,
190.23 , 192.217 , 195.084 , 196.966569 , 200.59 ,
204.3833 , 207.2 , 208.98040 , 209.000 , 210.000 ,
222.000 , 223.000 , 226.000 , 227.00 , 232.03806 ,
231.03588 , 238.02891)
if raw==True:
n = numpy.append(0,numpy.array(masslist))
return n
if Z < 1 :
print("Z must be between 1 and 92. You have given Z= " + repr(Z))
ret=-1
elif Z > 92 :
print("Z must be between 1 and 92. You have given Z= " + repr(Z))
ret=-1
else :
ret=masslist[Z-1]
return ret
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------
def config_to_occup(cfgstr, nel=-1, shlmax=-1, noccup=[-1]):
if len(cfgstr)==0:
cfgstr = '1s2'
cfgsplit = cfgstr.split(' ')
n = []
l = []
o = []
for cfg in cfgsplit:
cfg=cfg.lower()
ntmp = re.search("^[0-9]+",cfg)
n.append(int(ntmp.group(0)))
ltmp = re.search("[a-zA-Z]",cfg)
l.append(llist.index(ltmp.group(0)))
otmp = re.search("[0-9]+$",cfg)
o.append(int(otmp.group(0)))
# find the max nl shell
if shlmax == -1:
maxshl = -1
for i in range(len(n)):
shlind = 0
shlind=sum(range(1,n[i]+1))
maxshl = max([maxshl, shlind])
else:
maxshl=shlmax
occup = numpy.zeros(maxshl, dtype=int)
for i in range(len(n)):
shlind = 0
if n[i] > 1:
for iin in range(1,n[i]):
shlind = shlind + iin
shlind = shlind + l[i]
occup[shlind] = occup[shlind] + o[i]
inext = 0
lnext = 0
nnext = 1
onext = 2
if noccup[0]==-1:
firstoccup = min(numpy.where(occup>0)[0])
if firstoccup > 0:
for i in range(len(occup)):
if ((occup[i] == 0) &(occup.sum() < nel)):
if (nel-occup.sum()==6) &\
(4*lnext+2 != 6):
pass
elif (occup.sum()+4*lnext+2 <= nel):
occup[i] = occup[i] + 4*lnext+2
else:
break
if nnext-lnext == 1:
nnext += 1
lnext = 0
else:
lnext += 1
inext = 0
lnext = 0
nnext = 1
onext = 2
while (sum(occup) < nel):
if occup[inext] == 0:
if (onext > (nel-sum(occup))):
occup[inext] += nel-sum(occup)
else:
occup[inext] += onext
if nnext-lnext == 1:
nnext += 1
lnext = 0
else:
lnext += 1
onext = 4*lnext+2
inext += 1
else:
# we have an array defining the number of electrons total in each N shell
# such as in FAC
inext = 0
nnext = 1
shell_n = numpy.zeros(len(occup), dtype=int)
shell_l = numpy.zeros(len(occup), dtype=int)
while inext < len(shell_n):
shell_n[inext:inext+nnext]=nnext
shell_l[inext:inext+nnext]=numpy.arange(nnext)
inext += nnext
nnext += 1
for i_n in range(len(noccup)):
nnext = i_n+1
i = numpy.where(shell_n == nnext)[0]
nel_tot = sum(occup[i])
nel_targ = noccup[i_n]
# if the number of electrons match
if nel_tot == nel_targ: continue
if nel_tot > nel_targ:
print("ERROR: more electron in n=%i shell than there should be for %s" %\
(nnext, cfgstr))
print(" %i vs %i" %(nel_tot, nel_targ))
while nel_tot< nel_targ:
#find empty l shells
lposs = []
for il in i:
if ((occup[il]==0) &(shell_l[il]*4+2 <= (nel_targ-nel_tot))):
lposs.append(shell_l[il])
# get number of occupancies
shell_occup = numpy.array(lposs)*4+2
delta_nel = nel_targ - nel_tot
k = numpy.where(shell_occup == delta_nel)[0]
if len(k) ==1:
k = k[0]
ind = numpy.where((shell_n==nnext) & (shell_l==lposs[k]))[0][0]
occup[ind] =shell_occup[k]
else:
ind = numpy.where((shell_n==nnext) & (shell_l==lposs[0]))[0][0]
occup[ind] = shell_occup[0]
nel_tot = sum(occup[i])
if ((nel > 0) & (sum(occup) != nel)):
return occup,False
else:
return occup, True
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------
def occup_to_config(occup):
s = ''
lnext = 0
nnext = 1
for i,j in enumerate(occup):
if j > 0:
s = s+ repr(nnext)+llist[lnext]+repr(j)+' '
if nnext-lnext==1:
lnext = 0
nnext += 1
else:
lnext=lnext+1
s = s[:-1]
return s
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------
def parse_config(cfgstr):
# returns n shell, l shell and occupancy for each part of the configuration
# e.g. [[1,0,2],[2,1,1]] for 1s2 2p1
#split on space
try:
c = cfgstr.decode('ascii').split()
except AttributeError:
c = cfgstr.split()
ret=[]
for ic in c:
cfg = []
ntmp = re.search("^[0-9]+",ic)
cfg.append(int(ntmp.group(0)))
ltmp = re.search("[a-zA-Z]",ic)
cfg.append(llist.index(ltmp.group(0)))
otmp = re.search("[0-9]+$",ic)
cfg.append(int(otmp.group(0)))
ret.append(cfg)
return ret
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------
def get_parity(cfgstr):
d = parse_config(cfgstr)
evenparity = True
for i in d:
if i[1]*i[2] % 2 == 1:
evenparity = not(evenparity)
if evenparity:
return 0
else:
return 1
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------
def get_maxn(cfgstr):
d = parse_config(cfgstr)
maxn = max([c[0] for c in d])
return maxn
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------
def parse_eissner(cfgstr, nel=0, levelmap=None, lmax_set=None):
if levelmap is not None:
# levelmap is a list of n, l for each level
# with i['N'] and i['L'] giving the infos
shelllist='123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
cfg = cfgstr.strip()
try:
cfg = cfg.decode('ascii')
except AttributeError:
pass
cfgcopy = cfg+' '
cfgcopy = cfgcopy[:-1]
if len(cfg)%3 == 0:
# find the initial split. Want configuration to start with 5 (or 6, or 7)
if cfg[0] in['5','6','7']:
pass
elif cfg[-1] in ['5','6','7']:
cfg='5'+cfg[:-1]
elif (cfg[-1].islower() and cfg[-2].islower()):
cfg='5'+cfg
else:
print("Invalid configuration (1) %s" %(cfg))
elif len(cfg)%3 == 2:
if not cfg[0] in ['5','6','7']:
cfg = '5'+cfg
else:
print("Invalid configuration (2) %s" %(cfg))
elif len(cfg)%3 == 1:
if not (cfg[-2].islower() and cfg[-1].islower()):
print("Invalid configuration (3) %s" %(cfg))
ret = ""
i=0
while i < len(cfg):
cfgtmp = cfg[i:i+3]
if cfgtmp[-1].islower():
if len(cfg)>=i+4:
if cfg[i+3].islower():
cfgtmp=cfg[i:i+4]
#if re.search('[a-z][a-zA-Z]',''):
# cfgtmp = cfg[i:i+4]
i += len(cfgtmp)
nelec = int(cfgtmp[:2])-50
if len(cfgtmp)==3:
ishell = shelllist.index(cfgtmp[2])
else:
ishell = shelllist.index(cfgtmp[3])-35+len(shelllist)+(26*(shelllist.index(cfgtmp[2])-35))
try:
n = levelmap[ishell]['N']
l = levelmap[ishell]['L']
except IndexError:
# too many shells?
if ishell >= len(levelmap):
last_n = levelmap[-1]['N']
last_l = levelmap[-1]['L']
if last_l < last_n -1:
n=last_n
l=last_l+1
else:
n=last_n+1
l=0
levelmap= numpy.append(levelmap, \
numpy.zeros(1, \
dtype=numpy.dtype({'names':['N','L'],\
'formats':[int, int]})))
levelmap[-1]['N'] = n
levelmap[-1]['L'] = l
lsymb = llist[l]
ret += "%i%s%i "%(n,lsymb,nelec)
ret = ret[:-1]
else:
shelllist='123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
cfg = cfgstr.strip()
try:
cfg = cfg.decode('ascii')
except AttributeError:
pass
cfgcopy = cfg+' '
cfgcopy = cfgcopy[:-1]
# now deal with double letters
# for i in range(len(cfg)-1):
# if cfg[i].islower() and cfg[i+1].islower():
# cfg=cfg[:i]+'$^'+cfg[i+1:]
# cfg = re.sub('^', '', cfgcopy)
if len(cfg)%3 == 0:
# find the initial split. Want configuration to start with 5 (or 6, or 7)
if cfg[0] in['5','6','7']:
pass
elif cfg[-1] in ['5','6','7']:
cfg='5'+cfg[:-1]
elif (cfg[-1].islower() and cfg[-2].islower()):
cfg='5'+cfg
else:
print("Invalid configuration (1) %s" %(cfg))
elif len(cfg)%3 == 2:
if not cfg[0] in ['5','6','7']:
cfg = '5'+cfg
else:
print("Invalid configuration (2) %s" %(cfg))
elif len(cfg)%3 == 1:
if not (cfg[-2].islower() and cfg[-1].islower()):
print("Invalid configuration (3) %s" %(cfg))
ret = ""
i=0
while i < len(cfg):
cfgtmp = cfg[i:i+3]
if cfgtmp[-1].islower():
if len(cfg)>=i+4:
if cfg[i+3].islower():
cfgtmp=cfg[i:i+4]
#if re.search('[a-z][a-zA-Z]',''):
# cfgtmp = cfg[i:i+4]
i += len(cfgtmp)
nelec = int(cfgtmp[:2])-50
if len(cfgtmp)==3:
ishell = shelllist.index(cfgtmp[2])
else:
ishell = shelllist.index(cfgtmp[3])-35+len(shelllist)+(26*(shelllist.index(cfgtmp[2])-35))
n=1
l=0
for ii in range(ishell):
if l < n-1:
l+=1
else:
n+=1
l=0
lsymb = llist[l]
ret += "%i%s%i "%(n,lsymb,nelec)
ret = ret[:-1]
return ret
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------
def shorten_config(cfgstr, nel=0):
"""
Shorten the configuration as required
PARAMETERS
----------
cfgstr : string
configuration string. Should be simplified already e.g. '1s2 2s2 3p1'
RETURNS
-------
cfgshrt : string
shortened configuration, e.g. '3p1'
"""
# get n, l, occupancy for each shell
cfglist = parse_config(cfgstr)
status = numpy.zeros(len(cfglist), dtype=int)
# 1 = empty
# 2 = partial
# 3 = full
for i in range(len(cfglist)):
if cfglist[i][2] == 0:
status[i] = 1
elif cfglist[i][2] == cfglist[i][1]*4+2:
status[i] = 3
else:
status[i] = 2
# find the first shell which isn't full or empty
i = numpy.where(status==2)[0]
if len(i)==0:
# We have nothing!
# find the first empty shell
ii = numpy.where(status==1)[0]
if len(ii) == 0:
# none!
# blank out everything else except the last shell
status[:-1]= 0
else:
i = i[i<=ii[0]]=0
i[ii] = 0
else:
pass
|
atomdbREPO_NAMEpyatomdbPATH_START.@pyatomdb_extracted@pyatomdb-master@pyatomdb@pyatomdb@atomic.py@.PATH_END.py
|
{
"filename": "model.py",
"repo_name": "triton-inference-server/server",
"repo_path": "server_extracted/server-main/qa/L0_model_config/autofill_noplatform/python/no_return/model.py",
"type": "Python"
}
|
# Copyright 2022-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of NVIDIA CORPORATION nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
class TritonPythonModel:
@staticmethod
def auto_complete_config(auto_complete_model_config):
input0 = {"name": "INPUT0", "data_type": "TYPE_FP32", "dims": [4]}
input1 = {"name": "INPUT1", "data_type": "TYPE_FP32", "dims": [4]}
output0 = {"name": "OUTPUT0", "data_type": "TYPE_FP32", "dims": [4]}
output1 = {"name": "OUTPUT1", "data_type": "TYPE_FP32", "dims": [4]}
auto_complete_model_config.set_max_batch_size(0)
auto_complete_model_config.add_input(input0)
auto_complete_model_config.add_input(input1)
auto_complete_model_config.add_output(output0)
auto_complete_model_config.add_output(output1)
def execute(self, requests):
pass
|
triton-inference-serverREPO_NAMEserverPATH_START.@server_extracted@server-main@qa@L0_model_config@autofill_noplatform@python@no_return@model.py@.PATH_END.py
|
{
"filename": "Documentation.md",
"repo_name": "hpc4cmb/toast",
"repo_path": "toast_extracted/toast-main/src/libtoast/gtest/googletest/docs/Documentation.md",
"type": "Markdown"
}
|
This page lists all documentation wiki pages for Google Test **(the SVN trunk version)**
-- **if you use a released version of Google Test, please read the
documentation for that specific version instead.**
* [Primer](Primer.md) -- start here if you are new to Google Test.
* [Samples](Samples.md) -- learn from examples.
* [AdvancedGuide](AdvancedGuide.md) -- learn more about Google Test.
* [XcodeGuide](XcodeGuide.md) -- how to use Google Test in Xcode on Mac.
* [Frequently-Asked Questions](FAQ.md) -- check here before asking a question on the mailing list.
To contribute code to Google Test, read:
* [DevGuide](DevGuide.md) -- read this _before_ writing your first patch.
* [PumpManual](PumpManual.md) -- how we generate some of Google Test's source files.
|
hpc4cmbREPO_NAMEtoastPATH_START.@toast_extracted@toast-main@src@libtoast@gtest@googletest@docs@Documentation.md@.PATH_END.py
|
{
"filename": "210_DataLoading_Automatic.ipynb",
"repo_name": "rometsch/fargocpt",
"repo_path": "fargocpt_extracted/fargocpt-master/examples/210_DataLoading_Automatic.ipynb",
"type": "Jupyter Notebook"
}
|
## Loading data from the simulation output - the automatic way
This notebook teaches you how to load data from the simulation output using a helper tool.
We will use the data from the simulation in the quickstart example, so make sure you ran this beforehand.
We'll first create a Data object and ask what's available in the output.
```python
from fargocpt import Loader
l = Loader("example_dirs/100_quickstart/output/out")
l
```
Loader
====================
| output_dir: example_dirs/100_quickstart/output/out
| snapshots: 0 ... 10
| special_snapshots: ['reference']
| snapshot_time: 0.0 5.02257e+06 s ... 62.8 5.02257e+06 s
| monitor_number: 0 ... 200
| units: Units
| target_units = None
| gas: Hydro
| nbody: Nbody
| params: Params
| particles = None
____________________
We can explore this data object further by looking at some of its member variables.
```python
l.nbody
```
[ Nbody
====================
| filepath: example_dirs/100_quickstart/output/out/monitor/nbody0.dat
| varnames:
| time
| snapshot_number
| monitor_number
| x
| y
| vx
| vy
| mass
| physical_time
| omega_frame
| mdcp
| eccentricity
| angular_momentum
| semi_major_axis
| omega_kepler
| mean_anomaly
| eccentric_anomaly
| true_anomaly
| pericenter_angle
| torque
| accreted_mass
| accretion_rate
____________________,
Nbody
====================
| filepath: example_dirs/100_quickstart/output/out/monitor/nbody1.dat
| varnames:
| time
| snapshot_number
| monitor_number
| x
| y
| vx
| vy
| mass
| physical_time
| omega_frame
| mdcp
| eccentricity
| angular_momentum
| semi_major_axis
| omega_kepler
| mean_anomaly
| eccentric_anomaly
| true_anomaly
| pericenter_angle
| torque
| accreted_mass
| accretion_rate
____________________]
```python
l.gas
```
Hydro
====================
| output_dir: example_dirs/100_quickstart/output/out
| units: Units
| target_units= None
| grid: Grid
| timestepping: Scalar
| scalars: Scalar
| vars1D: Vars1D
| vars2D: Vars2D
____________________
```python
l.units
```
Units
====================
| base:
| length: 1.49598e+13 cm
| time: 5.02257e+06 s
| mass: 1.98847e+33 g
| temperature: 106700 K
| derived:
| energy: 1.76408e+46 erg
| energy surface density: 7.88257e+19 erg / cm2
| density: 5.9394e-07 g / cm3
| mass surface density: 8.88522e+06 g / cm2
| opacity: 1.12546e-07 cm2 / g
| energy flux: 1.56943e+13 erg / (s cm2)
| velocity: 2.97851e+06 cm / s
| angular momentum: 8.86021e+52 cm2 g / s
| kinematic viscosity: 4.45579e+19 cm2 / s
| dynamic viscosity: 2.64648e+13 P
| acceleration: 0.593026 cm / s2
| stress: 7.88257e+19 g / s2
| pressure: 7.88257e+19 dyn / cm
| power: 3.51231e+39 erg / s
| potential: 8.87155e+12 erg / g
| torque: 1.76408e+46 erg
| force: 1.17921e+33 dyn
| mass accretion rate: 3.95907e+26 g / s
____________________
To see all at once, run the following cell.
```python
l.print(recursive=True)
```
Loader
====================
| output_dir: example_dirs/100_quickstart/output/out
| snapshots: 0 ... 10
| special_snapshots: ['reference']
| snapshot_time: 0.0 5.02257e+06 s ... 62.8 5.02257e+06 s
| monitor_number: 0 ... 200
| units: Units
| target_units = None
| gas: Hydro
| nbody: Nbody
| params: Params
| particles = None
____________________
. Units
. ====================
. | base:
. | length: 1.49598e+13 cm
. | time: 5.02257e+06 s
. | mass: 1.98847e+33 g
. | temperature: 106700 K
. | derived:
. | energy: 1.76408e+46 erg
. | energy surface density: 7.88257e+19 erg / cm2
. | density: 5.9394e-07 g / cm3
. | mass surface density: 8.88522e+06 g / cm2
. | opacity: 1.12546e-07 cm2 / g
. | energy flux: 1.56943e+13 erg / (s cm2)
. | velocity: 2.97851e+06 cm / s
. | angular momentum: 8.86021e+52 cm2 g / s
. | kinematic viscosity: 4.45579e+19 cm2 / s
. | dynamic viscosity: 2.64648e+13 P
. | acceleration: 0.593026 cm / s2
. | stress: 7.88257e+19 g / s2
. | pressure: 7.88257e+19 dyn / cm
. | power: 3.51231e+39 erg / s
. | potential: 8.87155e+12 erg / g
. | torque: 1.76408e+46 erg
. | force: 1.17921e+33 dyn
. | mass accretion rate: 3.95907e+26 g / s
. ____________________
.
. Params
. ====================
. | filename: example_dirs/100_quickstart/output/out/parameters/setup.yml
. | params:
. | Disk: True
. | DiskFeedback: True
. | SelfGravity: False
. | IntegrateParticles: False
. | l0: 1 au
. | m0: 1 solMass
. | mu: 2.35
. | HydroFrameCenter: primary
. | IndirectTermMode: 0
. | OmegaFrame: 1.0
. | Frame: F
. | MonitorTimestep: 0.314
. | Nmonitor: 20
. | Nsnapshots: 10
. | FirstDT: 0.1
. | nbody: [{'name': 'Star', 'semi-major axis': '0.0 au', 'mass': '1.0 solMass', 'eccentricity': 0.0, 'radius': '1.0 solRadius', 'temperature': '5778 K'}, {'name': 'Jupiter', 'semi-major axis': '1.0 au', 'mass': '1 jupiterMass', 'cubic smoothing factor': 0.3, 'accretion efficiency': '2', 'accretion method': 'kley', 'eccentricity': 0, 'radius': '0.01 solRadius', 'ramp-up time': 0.0}]
. | NumberOfParticles: 2000
. | ParticleGasDragEnabled: True
. | ParticleDustDiffusion: True
. | ParticleDiskGravityEnabled: False
. | ParticleMinimumEscapeRadius: 0.4
. | ParticleMaximumEscapeRadius: 2.5
. | ParticleMinimumRadius: 0.4
. | ParticleMaximumRadius: 2.5
. | ParticleSurfaceDensitySlope: 0.5
. | ParticleSpeciesNumber: 7
. | ParticleRadius: 1 cm
. | ParticleRadiusIncreaseFactor: 1e-1
. | ParticleEccentricity: 0.03
. | ParticleDensity: 2.65 g/cm3
. | ParticleIntegrator: Midpoint
. | CartesianParticles: True
. | Transport: FARGO
. | Integrator: Euler
. | CFL: 0.5
. | CFLmaxVar: 1.1
. | cps: 2
. | Nrad: 128
. | Naz: 384
. | Rmin: 0.4
. | Rmax: 2.5
. | RadialSpacing: Logarithmic
. | ThicknessSmoothing: 0.6
. | ThicknessSmoothingSG: 0.6
. | MassAccretionRadius: 0.3
. | Sigma0: 200 g/cm2
. | SigmaSlope: 0.5
. | SigmaFloor: 1e-9
. | AspectRatio: 0.05
. | FlaringIndex: 0.0
. | AspectRatioMode: 0
. | RandomSeed: 1337
. | RandomSigma: False
. | RandomFactor: 0.1
. | FeatureSize: 0.05
. | ViscousAlpha: 0.001
. | ArtificialViscosity: TW
. | ArtificialViscosityDissipation: True
. | ArtificialViscosityFactor: 1.41
. | EquationOfState: isothermal
. | AdiabaticIndex: 1.4
. | HydrogenMassFraction: 0.75
. | SurfaceCooling: thermal
. | RadiativeDiffusion: False
. | CoolingBetaLocal: False
. | CoolingRadiativeFactor: 1.0
. | CoolingBeta: 10
. | CoolingBetaRampUp: 0.0
. | CoolingBetaReference: floor
. | ScurveType: Kimura
. | RadiativeDiffusionOmega: 1.5
. | RadiativeDiffusionAutoOmega: False
. | RadiativeDiffusionMaxIterations: 50000
. | RadiativeDiffusionTolerance: 1.5
. | RadiativeDiffusionInnerBoundary: zerogradient
. | RadiativeDiffusionOuterBoundary: zerogradient
. | Opacity: Lin
. | KappaConst: 2e-06
. | HeatingViscous: True
. | MinimumTemperature: 3 K
. | MaximumTemperature: 1e100 K
. | HeatingCoolingCFLlimit: 1.0
. | InnerBoundary: Reflecting
. | OuterBoundary: Reflecting
. | Damping: True
. | DampingInnerLimit: 1.1
. | DampingOuterLimit: 0.9
. | DampingTimeFactor: 0.1
. | DampingTimeRadiusOuter: 2.5
. | DampingEnergyInner: Initial
. | DampingVRadialInner: Initial
. | DampingVAzimuthalInner: Initial
. | DampingSurfaceDensityInner: Initial
. | DampingEnergyOuter: Initial
. | DampingVRadialOuter: Initial
. | DampingVAzimuthalOuter: Initial
. | DampingSurfaceDensityOuter: Initial
. | RocheLobeOverflow: False
. | ROFplanet: 1
. | ROFrampingtime: 5
. | ROFtemperature: 1500.0
. | ROFvalue: 1.5e-10 solMass/yr
. | ROFVariableTransfer: False
. | ROFgamma: 0.5
. | ROFaveragingtime: 10
. | OutputDir: output/out
. | LogAfterRealSeconds: 10
. | LogAfterSteps: 0
. | WriteAtEveryTimestep: True
. | WriteDensity: True
. | WriteVelocity: True
. | WriteEnergy: True
. | WriteTemperature: False
. | WriteSoundspeed: False
. | WriteEccentricityChange: False
. | WriteEffectiveGamma: False
. | WriteFirstAdiabaticIndex: False
. | WriteMeanMolecularWeight: False
. | WriteToomre: False
. | WriteQMinus: False
. | WriteQPlus: False
. | WriteTauCool: False
. | WriteViscosity: False
. | WriteAlpha: False
. | WriteKappa: False
. | WriteAlphaGrav: False
. | WriteAlphaGravMean: False
. | WriteAlphaReynolds: False
. | WriteAlphaReynoldsMean: False
. | WriteEccentricity: False
. | WriteTReynolds: False
. | WriteTGravitational: False
. | WritepdV: False
. | WriteDiskQuantities: True
. | WriteRadialLuminosity: False
. | WriteRadialDissipation: False
. | WriteLightCurves: False
. | WriteLightcurvesRadii: 0.4,5.2
. | WriteMassFlow: False
. | WriteGasTorques: False
. | WritePressure: False
. | WriteScaleHeight: False
. | WriteAspectratio: False
. | WriteTorques: False
. | WriteVerticalOpticalDepth: False
. | WriteSGAccelRad: False
. | WriteSGAccelAzi: False
. ____________________
.
. Nbody
. ====================
. | filepath: example_dirs/100_quickstart/output/out/monitor/nbody0.dat
. | varnames:
. | time
. | snapshot_number
. | monitor_number
. | x
. | y
. | vx
. | vy
. | mass
. | physical_time
. | omega_frame
. | mdcp
. | eccentricity
. | angular_momentum
. | semi_major_axis
. | omega_kepler
. | mean_anomaly
. | eccentric_anomaly
. | true_anomaly
. | pericenter_angle
. | torque
. | accreted_mass
. | accretion_rate
. ____________________
.
. Nbody
. ====================
. | filepath: example_dirs/100_quickstart/output/out/monitor/nbody1.dat
. | varnames:
. | time
. | snapshot_number
. | monitor_number
. | x
. | y
. | vx
. | vy
. | mass
. | physical_time
. | omega_frame
. | mdcp
. | eccentricity
. | angular_momentum
. | semi_major_axis
. | omega_kepler
. | mean_anomaly
. | eccentric_anomaly
. | true_anomaly
. | pericenter_angle
. | torque
. | accreted_mass
. | accretion_rate
. ____________________
.
. Hydro
. ====================
. | output_dir: example_dirs/100_quickstart/output/out
. | units: Units
. | target_units= None
. | grid: Grid
. | timestepping: Scalar
. | scalars: Scalar
. | vars1D: Vars1D
. | vars2D: Vars2D
. ____________________
.
. . Grid
. . ====================
. . | radi: 0.3899474657238236 1.49598e+13 cm ... 2.564448003640175 1.49598e+13 cm
. . | phii: 0.0 ... 6.283185307179586
. . | Nrad: 74
. . | Naz: 251
. . | Spacing: Logarithmic
. . ____________________
. .
. . Scalars
. . ====================
. . | filepath: example_dirs/100_quickstart/output/out/monitor/Quantities.dat
. . | varnames:
. . | snapshot_number
. . | monitor_number
. . | time
. . | mass
. . | radius
. . | angular_momentum
. . | total_energy
. . | internal_energy
. . | kinematic_energy
. . | potential_energy
. . | radial_kinetic_energy
. . | azimuthal_kinetic_energy
. . | eccentricity
. . | periastron
. . | viscous_dissipation
. . | luminosity
. . | pdivv
. . | inner_boundary_mass_inflow
. . | inner_boundary_mass_outflow
. . | outer_boundary_mass_inflow
. . | outer_boundary_mass_outflow
. . | wave_damping_inner_mass_creation
. . | wave_damping_inner_mass_removal
. . | wave_damping_outer_mass_creation
. . | wave_damping_outer_mass_removal
. . | density_floor_mass_creation
. . | aspect
. . | indirect_term_nbody_x
. . | indirect_term_nbody_y
. . | indirect_term_disk_x
. . | indirect_term_disk_y
. . | frame_angle
. . | advection_torque
. . | viscous_torque
. . | gravitational_torque
. . ____________________
. .
. . TimeStepInfo
. . ====================
. . | filepath: example_dirs/100_quickstart/output/out/monitor/timestepLogging.dat
. . | varnames:
. . | snapshot_number
. . | monitor_number
. . | hydrostep_number
. . | Number_of_Hydrosteps_in_last_monitor_timestep
. . | time
. . | walltime
. . | walltime_per_hydrostep
. . | mean_dt
. . | min_dt
. . | std_dev_dt
. . ____________________
. .
. . Vars1D
. . ====================
. . | output_dir: example_dirs/100_quickstart/output/out
. . | target_units= None
. . | grid: Grid
. . | var_names:
. . | Sigma
. . | vrad
. . | vazi
. . | energy
. . ____________________
. .
. . Vars2D
. . ====================
. . | output_dir: example_dirs/100_quickstart/output/out
. . | target_units= None
. . | grid: Grid
. . | var_names:
. . | Sigma
. . | vrad
. . | vazi
. . | energy
. . ____________________
. .
Scalar quantities can be loaded directly by accessing a member:
```python
print(l.gas.scalars.mass[:10])
print(l.nbody[1].x[:10])
```
[0.00034884 0.00034884 0.00034884 0.00034884 0.00034884 0.00034884
0.00034884 0.00034884 0.00034885 0.00034886] 1.98847e+33 g
[1. 0.99999997 0.99999969 0.99999869 0.99999637 0.99999203
0.99998523 0.99997591 0.99996434 0.9999511 ] 1.49598e+13 cm
We can also use a getter function to do this. See its use below.
```python
print(l.gas.scalars.get("mass")[:10])
```
[0.00034884 0.00034884 0.00034884 0.00034884 0.00034884 0.00034884
0.00034884 0.00034884 0.00034885 0.00034886] 1.98847e+33 g
Let's plot some data for the second nbody object, the planet in this case.
```python
import matplotlib.pyplot as plt
for varname in ["mass", "eccentricity"]:
fig, ax = plt.subplots()
x = l.nbody[1].get(varname)
t = l.nbody[1].time.to("yr")
ax.plot(t, x)
ax.set_ylabel(f"{varname} [{x.unit}]")
ax.set_xlabel(f"time [yr]")
```
## 2D data
To obtain 2D data for the gas, let's inspect
```python
l.gas
```
Hydro
====================
| output_dir: example_dirs/100_quickstart/output/out
| units: Units
| target_units= None
| grid: Grid
| timestepping: Scalar
| scalars: Scalar
| vars1D: Vars1D
| vars2D: Vars2D
____________________
There seems to be a `vars2D` member.
```python
l.gas.vars2D
```
Vars2D
====================
| output_dir: example_dirs/100_quickstart/output/out
| target_units= None
| grid: Grid
| var_names:
| Sigma
| vrad
| vazi
| energy
____________________
And some data is available.
Here, we can't just access the data via member variables, because we need to specify a snaphost number.
```python
vals = l.gas.vars2D.get("Sigma", 5)
print(vals.shape)
```
(74, 251)
To get the data along with the grid it is defined on.
```python
R, PHI, vals = l.gas.vars2D.get("Sigma", 5, grid=True)
print(R.shape, PHI.shape, vals.shape)
```
(74, 251) (74, 251) (74, 251)
This returns meshgrids for radius and azimuth coordinates and the values as 2D array.
If you want to use this for plotting with pcolormesh, add the following. This returns meshgrids with the coordinates of the cell edges (1 larger in each direction) as needed by plt.pcolormesh.
```python
R, PHI, vals = l.gas.vars2D.get("Sigma", 5, grid_for_plot=True)
print(R.shape, PHI.shape, vals.shape)
```
(75, 252) (75, 252) (74, 251)
Now in action!
```python
import numpy as np
import matplotlib.colors as mplcolors
def plot_field(loader, name, N, ax=None, dataunit=None, vmin=None, vmax=None, cmap="viridis"):
R, PHI, vals = loader.gas.vars2D.get(name, N, grid_for_plot=True)
if dataunit is None:
dataunit = vals.unit
Z = vals.to_value(dataunit)
X = R*np.cos(PHI)
Y = R*np.sin(PHI)
if ax is None:
fig, ax = plt.subplots(dpi=150)
else:
fig = ax.get_figure()
norm = mplcolors.Normalize(vmin=vmin, vmax=vmax)
pcm = ax.pcolormesh(X,Y,Z, norm=norm, cmap=cmap)
ax.set_aspect("equal")
t = loader.snapshot_time[N].to_value("kyr")
ax.set_title(f" t={t:.2e}kyr, N={N}")
cbar = fig.colorbar(pcm, ax=ax)
cbar.set_label(f"{name} [{dataunit}]")
return fig
```
```python
plot_field(l, "Sigma", l.snapshots[-1], dataunit="g/cm2", cmap="magma", vmax=800);
```
We can also reduce the data to azimuthal averages, minimums or maximums as follows
```python
from matplotlib import colormaps
name = "Sigma"
dataunit = "g/cm2"
Nfirst = l.snapshots[0]
Nlast = l.snapshots[-1]
fig, ax = plt.subplots(dpi=150)
cmap = colormaps.get_cmap("viridis")
inds = np.linspace(Nfirst, Nlast, 10, dtype=int)
for k, n in enumerate(inds):
color = cmap(k/(len(inds)-1))
r, vals = l.gas.vars2D.avg(name, n) # here, we automatically get a grid, i.e. an array of radii
r = r.to_value("au")
y = vals.to_value(dataunit)
t = l.snapshot_time[n].to_value("yr")
# ax.plot(r, (profile-profile0)/profile0, label=f"t={t:.3f}yr")
line, = ax.plot(r, y, label=f"t={t:.3f}yr", color=color)
ax.legend()
ax.set_yscale("log")
ax.set_xlabel("r [au]")
ax.set_ylabel(fr"$\Sigma$ [{dataunit}]")
```
Text(0, 0.5, '$\\Sigma$ [g/cm2]')
|
rometschREPO_NAMEfargocptPATH_START.@fargocpt_extracted@fargocpt-master@examples@210_DataLoading_Automatic.ipynb@.PATH_END.py
|
{
"filename": "slices_test.py",
"repo_name": "tensorflow/tensorflow",
"repo_path": "tensorflow_extracted/tensorflow-master/tensorflow/python/autograph/converters/slices_test.py",
"type": "Python"
}
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for slices module."""
from tensorflow.python.autograph.converters import directives as directives_converter
from tensorflow.python.autograph.converters import slices
from tensorflow.python.autograph.core import converter_testing
from tensorflow.python.autograph.lang import directives
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import list_ops
from tensorflow.python.platform import test
class SliceTest(converter_testing.TestCase):
def test_index_access(self):
def f(l):
directives.set_element_type(l, dtypes.int32)
return l[1]
tr = self.transform(f, (directives_converter, slices))
tl = list_ops.tensor_list_from_tensor(
[1, 2], element_shape=constant_op.constant([], dtype=dtypes.int32))
y = tr(tl)
self.assertEqual(2, self.evaluate(y))
def test_index_access_multiple_definitions(self):
def f(l):
directives.set_element_type(l, dtypes.int32)
if l:
l = []
return l[1]
self.transform(f, (directives_converter, slices))
if __name__ == '__main__':
test.main()
|
tensorflowREPO_NAMEtensorflowPATH_START.@tensorflow_extracted@tensorflow-master@tensorflow@python@autograph@converters@slices_test.py@.PATH_END.py
|
{
"filename": "utils.py",
"repo_name": "1313e/CMasher",
"repo_path": "CMasher_extracted/CMasher-master/src/cmasher/utils.py",
"type": "Python"
}
|
"""
Utils
=====
Utility functions for registering and manipulating colormaps in various ways.
"""
import os
from collections import OrderedDict
from collections.abc import Callable
from importlib.util import find_spec
from pathlib import Path
from textwrap import dedent
from typing import TYPE_CHECKING
import matplotlib as mpl
import numpy as np
from colorspacious import cspace_converter
from matplotlib.colors import (
Colormap,
LinearSegmentedColormap,
ListedColormap as LC,
to_hex,
to_rgb,
)
from cmasher import cm as cmrcm
from ._known_cmap_types import _CMASHER_BUILTIN_MAP_TYPES
if TYPE_CHECKING:
from typing import TypeAlias
from matplotlib.artist import Artist
from numpy.typing import NDArray
_HAS_VISCM = find_spec("viscm") is not None
# All declaration
__all__ = [
"combine_cmaps",
"create_cmap_mod",
"create_cmap_overview",
"get_bibtex",
"get_cmap_list",
"get_cmap_type",
"get_sub_cmap",
"import_cmaps",
"register_cmap",
"set_cmap_legend_entry",
"take_cmap_colors",
"view_cmap",
]
# %% GLOBALS
# Obtain the colorspace converter for showing cmaps in gray-scale
cspace_convert = cspace_converter("sRGB1", "CAM02-UCS")
# Type aliases
CMAP = str | Colormap
RED: "TypeAlias" = float
GREEN: "TypeAlias" = float
BLUE: "TypeAlias" = float
RGB = list[tuple[RED, GREEN, BLUE]]
# %% HELPER FUNCTIONS
# Define function for obtaining the sorting order for lightness ranking
def _get_cmap_lightness_rank(
cmap: Colormap,
) -> tuple[int, int, float, float, float, str]:
"""
Returns a tuple of objects used for sorting the provided `cmap` based
on its lightness profile.
Parameters
----------
cmap : :obj:`~matplotlib.colors.Colormap` object
The registered name of the colormap in :mod:`matplotlib.cm` or its
corresponding :obj:`~matplotlib.colors.Colormap` object.
Returns
-------
L_slope : int
The slope type of lightness profile of `cmap`.
L_type : int
The range type of lightness profile of `cmap`.
This is only used for sequential colormaps.
L_start : float
The starting lightness value of `cmap`.
For diverging/cyclic colormaps, this is the central lightness value.
L_rng : float
The lightness range of `cmap`.
L_rmse : float
The RMSE of the lightness profile of `cmap`.
For diverging/cyclic colormaps, this is the max RMSE of either half.
name : str
The name of `cmap`.
For qualitative and miscellaneous colormaps, this is the only value
that is used.
"""
# Obtain the colormap
cm_type = get_cmap_type(cmap)
# Determine lightness profile stats for sequential/diverging/cyclic
if cm_type in ("sequential", "diverging", "cyclic"):
# Get RGB values for colormap
rgb = cmap(np.arange(cmap.N))[:, :3]
# Get lightness values of colormap
lab = cspace_converter("sRGB1", "CAM02-UCS")(rgb)
L = lab[:, 0]
# If cyclic colormap, add first L at the end
if cm_type == "cyclic":
L = np.r_[L, [L[0]]]
# Determine number of values that will be in deltas
N_deltas = len(L) - 1
# Determine the deltas of the lightness profile
deltas = np.diff(L)
derivs = N_deltas * deltas
# Set lightness profile type to 0
L_type = 0
# Determine the RMSE of the lightness profile of a sequential colormap
if cm_type == "sequential":
# Take RMSE of entire lightness profile
L_rmse = np.around(np.std(derivs), 0)
# Calculate starting lightness value
L_start = np.around(L[0], 0)
# Determine type of lightness profile
L_type += (not np.allclose(rgb[0], [0, 0, 0])) * 2
L_type += np.allclose(rgb[0], [0, 0, 0]) == np.allclose(rgb[-1], [1, 1, 1])
# Diverging/cyclic colormaps
else:
# Determine the center of the colormap
central_i = [int(np.ceil(N_deltas / 2)), int(np.floor(N_deltas / 2))]
# Calculate RMSE of both halves
L_rmse = np.max(
[
np.around(np.std(derivs[: central_i[0]]), 0),
np.around(np.std(derivs[central_i[1] :]), 0),
]
)
# Calculate central lightness value
L_start = np.around(np.average(L[central_i]), 0)
# Determine lightness range
L_rng = np.around(np.max(L) - np.min(L), 0)
# Determine if cmap goes from dark to light or the opposite
L_slope = (L_start > L[-1]) * 2 - 1
# For qualitative/misc colormaps, set all lightness values to zero
else:
L_slope = L_type = L_start = L_rng = L_rmse = 0
# Return lightness contributions to the rank
return (L_slope, L_type, L_start, L_rng, L_rmse, cmap.name)
# Define function for obtaining the sorting order for perceptual ranking
def _get_cmap_perceptual_rank(
cmap: Colormap,
) -> tuple[int, int, float, float, float, float, str]:
"""
In addition to returning the lightness rank as given by
:func:`~_get_cmap_lightness_rank`, also returns the length of the
perceptual profile, also known as the perceptual range, of the provided
`cmap`.
Parameters
----------
cmap : :obj:`~matplotlib.colors.Colormap` object
The registered name of the colormap in :mod:`matplotlib.cm` or its
corresponding :obj:`~matplotlib.colors.Colormap` object.
Returns
-------
*L_rank : objects
The values returned by :func:`~_get_cmap_lightness_rank`, except for
the name of the colormap.
P_rng : float
The perceptual range of `cmap`.
name : str
The name of `cmap`.
For qualitative and miscellaneous colormaps, this is the only value
that is used.
"""
# Obtain the colormap
cm_type = get_cmap_type(cmap)
# Determine perceptual range for sequential/diverging/cyclic
if cm_type in ("sequential", "diverging", "cyclic"):
# Get RGB values for colormap
rgb = cmap(np.arange(cmap.N))[:, :3]
# Get lab values of colormap
lab = cspace_converter("sRGB1", "CAM02-UCS")(rgb)
# If cyclic colormap, add first lab at the end
if cm_type == "cyclic":
lab = np.r_[lab, [lab[0]]]
# Determine the deltas of the lightness profile
deltas = np.sqrt(np.sum(np.diff(lab, axis=0) ** 2, axis=-1))
# Determine perceptual range
P_rng = np.around(np.sum(deltas), 0)
# For qualitative/misc colormaps, set all values to zero
else:
P_rng = 0
# Return perceptual contributions to the rank
return (*_get_cmap_lightness_rank(cmap)[:-1], P_rng, cmap.name)
# %% FUNCTIONS
# This function combines multiple colormaps at given nodes
def combine_cmaps(
*cmaps: Colormap | str,
nodes: list[float] | np.ndarray | None = None,
n_rgb_levels: int = 256,
combined_cmap_name: str = "combined_cmap",
) -> LinearSegmentedColormap:
"""Create a composite matplotlib colormap by combining multiple colormaps.
Parameters
----------
*cmaps: Colormap or colormap name (str) to be combined.
nodes: list or numpy array of nodes (float). Defaults: equal divisions.
The blending points between colormaps, in the range [0, 1].
n_rgb_levels: int. Defaults: 256.
Number of RGB levels for each colormap segment.
combined_cmap_name: str. Defaults: "combined_cmap".
name of the combined Colormap.
Returns
-------
Colormap: The composite colormap.
Raises
------
TypeError: If the list contains mixed datatypes or invalid
colormap names.
ValueError: If the cmaps contain only one single colormap,
or if the number of nodes is not one less than the number
of colormaps, or if the nodes do not contain incrementing values
between 0.0 and 1.0.
Note
----
The colormaps are combined from low value to high value end.
References
----------
- https://stackoverflow.com/questions/31051488/combining-two-matplotlib-colormaps/31052741#31052741
Examples
--------
Using predefined colormap names::
>>> custom_cmap_1 = combine_cmaps(
["ocean", "prism", "coolwarm"], nodes=[0.2, 0.75]
)
Using Colormap objects::
>>> cmap_0 = plt.get_cmap("Blues")
>>> cmap_1 = plt.get_cmap("Oranges")
>>> cmap_2 = plt.get_cmap("Greens")
>>> custom_cmap_2 = combine_cmaps([cmap_0, cmap_1, cmap_2])
"""
# Check colormap datatype and convert to list[Colormap]
if len(cmaps) <= 1:
raise ValueError("Expected at least two colormaps to combine.")
for cm in cmaps:
if not isinstance(cm, Colormap | str):
raise TypeError(f"Unsupported colormap type: {type(cm)}.")
_cmaps: list[Colormap] = [
cm if isinstance(cm, Colormap) else mpl.colormaps[cm] for cm in cmaps
]
# Generate default nodes for equal separation
if nodes is None:
nodes_arr = np.linspace(0, 1, len(_cmaps) + 1)
elif isinstance(nodes, list | np.ndarray):
nodes_arr = np.concatenate([[0.0], nodes, [1.0]])
else:
raise TypeError(f"Unsupported nodes type: {type(nodes)}, expect list of float.")
# Check nodes length
if len(nodes_arr) != len(_cmaps) + 1:
raise ValueError(
"Number of nodes should be one less than the number of colormaps."
)
# Check node values
if any((nodes_arr < 0) | (nodes_arr > 1)) or any(np.diff(nodes_arr) <= 0):
raise ValueError(
"Nodes should only contain increasing values between 0.0 and 1.0."
)
# Generate composite colormap
combined_cmap_segments = []
for i, cmap in enumerate(_cmaps):
start_position = nodes_arr[i]
end_position = nodes_arr[i + 1]
# Calculate the length of the segment
segment_length = int(n_rgb_levels * (end_position - start_position))
# Append the segment to the combined colormap segments
combined_cmap_segments.append(cmap(np.linspace(0, 1, segment_length)))
# Combine the segments (from bottom to top)
return LinearSegmentedColormap.from_list(
combined_cmap_name, np.vstack(combined_cmap_segments)
)
# This function creates a standalone module of a CMasher colormap
def create_cmap_mod(
cmap: str,
*,
save_dir: str | os.PathLike[str] = ".",
_copy_name: str | None = None,
) -> str:
"""
Creates a standalone Python module of the provided *CMasher* `cmap` and
saves it in the given `save_dir` as '<`cmap`>.py'.
A standalone colormap module can be used to quickly share a colormap with
someone without adding the *CMasher* dependency.
Importing the created module allows the colormap to be used in the same way
as usual through *MPL* (including the 'cmr.' prefix).
Parameters
----------
cmap : str
The name of the *CMasher* colormap a standalone Python module must be
made for. An added 'cmr.' prefix will be ignored.
Optional
--------
save_dir: str or os.PathLike[str] Default: '.'
The path to the directory where the module must be saved.
By default, the current directory is used.
Returns
-------
cmap_path : str
The path to the Python file containing the colormap module.
Example
-------
Creating a standalone Python module of the 'rainforest' colormap::
>>> create_cmap_mod('rainforest')
One can now import the 'rainforest' colormap in any script by moving the
created 'rainforest.py' file to the proper working directory and importing
it with ``import rainforest``.
Note
----
Unlike other *CMasher* utility functions, `cmap` solely accepts names of
colormaps that are registered in *CMasher* (:mod:`cmasher.cm`).
"""
# Get absolute value to provided save_dir
save_dir = Path(save_dir).resolve()
# Remove any 'cmr.' prefix from provided cmap
name = cmap.removeprefix("cmr.")
# Obtain the CMasher colormap associated with the provided cmap
if (_cmap := cmrcm.cmap_d.get(name, None)) is None:
raise ValueError(f"{name!r} is not a valid cmasher colormap name")
cm_type = get_cmap_type(cmap)
# Obtain the RGB tuples of provided cmap
rgb = np.array(_cmap.colors)
# Convert RGB values to string
array_str = np.array2string(
rgb,
max_line_width=79,
prefix="cm_data = ",
separator=", ",
threshold=rgb.size,
formatter={"float": lambda x: f"{x:.8f}"},
)
# Create Python module template and add obtained RGB data to it
cm_py_file = dedent(
"""
import matplotlib as mpl
from matplotlib.colors import ListedColormap
# All declaration
__all__ = ["cmap"]
# Author declaration
__author__ = "Ellert van der Velden (@1313e)"
# Package declaration
__package__ = "cmasher"
# %% GLOBALS AND DEFINITIONS
# Type of this colormap
cm_type = '{0}'
# RGB-values of this colormap
cm_data = {1}
# Create ListedColormap object for this colormap
assert len(cm_data) == {3}
cmap = ListedColormap(cm_data, name='cmr.{2}')
cmap_r = cmap.reversed()
# Register (reversed) cmap in MPL
mpl.colormaps.register(cmap=cmap)
mpl.colormaps.register(cmap=cmap_r)
"""
)
# If this colormap is cyclic, add code to register shifted version as well
if cm_type == "cyclic":
cm_py_file += dedent(
"""
# Shift the entire colormap by half of its length
cm_data_s = list(cm_data[{4}:])
cm_data_s.extend(cm_data[:{4}])
# Create ListedColormap object for this shifted version
cmap_s = ListedColormap(cm_data_s, name='cmr.{2}_s', N={3})
cmap_s_r = cmap_s.reversed()
# Register shifted versions in MPL as well
mpl.colormaps.register(cmap=cmap_s)
mpl.colormaps.register(cmap=cmap_s_r)
"""
)
# Format py-file string
cm_py_file = cm_py_file.format(
cm_type, array_str, _copy_name or name, len(rgb), len(rgb) // 2
)
# Obtain the path to the module
cmap_path = save_dir / f"{_copy_name or name}.py"
# Create Python module
with open(cmap_path, "w") as f:
f.write(cm_py_file[1:])
# Return cmap_path
return str(cmap_path.resolve())
# This function creates an overview plot of all colormaps specified
def create_cmap_overview(
cmaps: list[CMAP] | dict[str, list[Colormap]] | None = None,
*,
savefig: str | os.PathLike[str] | None = None,
use_types: bool = True,
sort: str | Callable | None = "alphabetical",
show_grayscale: bool = True,
show_info: bool = False,
plot_profile: bool | float = False,
dark_mode: bool = False,
title: str | None = "Colormap Overview",
wscale: float = 1,
hscale: float = 1,
) -> None:
"""
Creates an overview plot containing all colormaps defined in the provided
`cmaps`.
Optional
--------
cmaps : list of {str; :obj:`~matplotlib.colors.Colormap` objects}, dict \
of lists or None. Default: None
A list of all colormaps that must be included in the overview plot.
If dict of lists, the keys define categories for the colormaps.
If *None*, all colormaps defined in *CMasher* are used instead.
savefig : str, os.PathLike or None. Default: None
If not *None*, the path where the overview plot must be saved to.
Else, the plot will simply be shown.
use_types : bool. Default: True
Whether all colormaps in `cmaps` should be categorized into their
colormap types (sequential; diverging; cyclic; qualitative; misc).
If `cmaps` is a dict, this value is ignored.
sort : {'alphabetical'/'name'; 'lightness'; 'perceptual'}, function or \
None. Default: 'alphabetical'
String or function indicating how the colormaps should be sorted in the
overview.
If 'alphabetical', the colormaps are sorted alphabetically on their
name.
If 'lightness', the colormaps are sorted based on their lightness
profile, which is given by :func:`~_get_cmap_lightness_rank`.
If 'perceptual', the colormaps sorted based on their perceptual range
in addition to their lightness profile, which is given by
:func:`~_get_cmap_perceptual_rank`. Note that this is only meaningful
if all `cmaps` are perceptually uniform sequential.
If function, a function definition that takes a
:obj:`~matplotlib.colors.Colormap` object and returns the sorted
position of that colormap.
If *None*, the colormaps retain the order they were given in.
show_grayscale : bool. Default: True
Whether to show the grayscale versions of the given `cmaps` in the
overview.
show_info : bool. Default: False
Whether the statistics information of all sequential, diverging and
cyclic colormaps should be shown under their names. This is a series of
numbers representing, in order, the starting (sequential) or central
(diverging/cyclic) lightness value; the final/outer lightness value;
and the perceptual range of the colormap.
plot_profile : bool or float. Default: False
Whether the lightness profiles of all non-qualitative colormaps should
be plotted. If not *False*, the lightness profile of a colormap is
plotted on top of its gray-scale version and `plot_profile` is used for
setting the alpha (opacity) value.
If `plot_profile` is *True*, it will be set to `0.25`.
If `show_grayscale` is *False*, this value is ignored.
dark_mode : bool. Default: False
Whether the colormap overview should be created using mostly dark
colors.
title : str or None. Default: "Colormap Overview"
String to be used as the title of the colormap overview.
If empty or *None*, no title will be used.
wscale, hscale : float. Default: (1, 1)
Floats that determine with what factor the colormap subplot dimensions
in the overview should be scaled with.
The default values uses the default dimensions for the subplots (which
are determined by other input arguments).
Notes
-----
The colormaps in `cmaps` can either be provided as their registered name in
:mod:`matplotlib.cm`, or their corresponding
:obj:`~matplotlib.colors.Colormap` object.
Any provided reversed colormaps (colormaps that end their name with '_r')
are ignored if their normal versions were provided as well.
When `sort` is 'lightness' or 'perceptual', qualitative and miscellaneous
colormaps are solely sorted on their names, as the lightness/perceptual
profile of these colormaps is meaningless.
If `plot_profile` is not set to *False*, the lightness profiles are plotted
on top of the gray-scale colormap versions, where the y-axis ranges from 0%
lightness to 100% lightness.
The lightness profile transitions between black and white at 50% lightness.
"""
import matplotlib.pyplot as plt
from matplotlib.axes import Axes
# If cmaps is None, use cmap_d.values
if cmaps is None:
cmaps = list(cmrcm.cmap_d.values())
# If sort is a string, obtain proper function
if isinstance(sort, str):
# Convert sort to lowercase
sort = sort.lower()
# Check what string was provided and obtain sorting function
if sort in ("alphabetical", "name"):
def sort_key(x):
return x.name
elif sort == "lightness":
sort_key = _get_cmap_lightness_rank
elif sort == "perceptual":
sort_key = _get_cmap_perceptual_rank
else:
raise ValueError(
"Input argument 'sort' has invalid string value " f"{sort!r}!"
)
# Create empty list of cmaps
cmaps_list: list[Colormap | tuple[str, bool]] = []
# Define empty dict of colormaps
cmaps_dict: OrderedDict[str, list[Colormap]] = OrderedDict()
# If cmaps is a dict, it has cm_types defined
if isinstance(cmaps, dict):
# Set use_types to True
use_types = True
# Save provided cmaps as something else
input_cmaps = cmaps
# Loop over all cm_types
for cm_type, maps in input_cmaps.items():
# Add empty list of colormaps to cmaps_dict with this cm_type
cmaps_dict[cm_type] = []
# Loop over all cmaps and add their Colormap objects
for cmap in maps:
if isinstance(cmap, str):
cmaps_dict[cm_type].append(mpl.colormaps[cmap])
else:
cmaps_dict[cm_type].append(cmap)
# Else, it is a list with no cm_types
else:
# If cm_types are requested
if use_types:
# Define empty dict with the base cm_types
cm_types = ["sequential", "diverging", "cyclic", "qualitative", "misc"]
cmaps_dict.update({cm_type: [] for cm_type in cm_types})
# Loop over all cmaps and add their Colormap objects
for cm in cmaps:
cm_type = get_cmap_type(cm)
if isinstance(cm, str):
cmaps_dict[cm_type].append(mpl.colormaps[cm])
else:
cmaps_dict[cm_type].append(cm)
else:
# Loop over all cmaps and add their Colormap objects
for cm in cmaps:
if isinstance(cm, str):
cmaps_list.append(mpl.colormaps[cm])
else:
cmaps_list.append(cm)
# If use_types is True, a dict is currently used
if use_types:
# Convert entire cmaps_dict into a list again
for key, value in cmaps_dict.items():
# If this cm_type has at least 1 colormap, sort and add them
if value:
# Obtain the names of all colormaps
names = [x.name for x in value]
# Remove all reversed colormaps that also have their original
off_dex = len(names) - 1
for i, name in enumerate(reversed(names)):
if name.endswith("_r") and name[:-2] in names:
value.pop(off_dex - i)
# Sort the colormaps if requested
if sort is not None:
value.sort(key=sort_key)
# Add to list
cmaps_list.append((key, False))
cmaps_list.extend(value)
# Else, a list is used
else:
# Obtain the names of all colormaps
names = [x.name for x in cmaps_list if isinstance(x, Colormap)]
# Remove all reversed colormaps that also have their original
off_dex = len(names) - 1
for i, name in enumerate(reversed(names)):
if name.endswith("_r") and name[:-2] in names:
cmaps_list.pop(off_dex - i)
# Sort the colormaps if requested
if sort is not None:
cmaps_list.sort(key=sort_key)
# Add title to cmaps_list if requested
if title:
cmaps_list.insert(0, (title, True))
# Check value of show_grayscale
if show_grayscale:
# If True, the overview will have two columns
ncols = 2
else:
# If False, the overview will have one column
ncols = 1
wscale *= 0.5
# Determine text/element positions
wscale = 0.2 + 0.8 * wscale
left_pos = 0.2 / wscale
spacing = 0.01 / wscale
title_pos = left_pos + (1 - spacing - left_pos) / 2
# If plot_profile is True, set it to its default value
if plot_profile is True:
plot_profile = 0.25
# Check if dark mode is requested
if dark_mode:
# If so, use dark gray for the background and light gray for the text
edge_color = "#24292E"
face_color = "#24292E"
text_color = "#9DA5B4"
else:
# If not, use white for the background and black for the text
edge_color = "#FFFFFF"
face_color = "#FFFFFF"
text_color = "#000000"
# Create figure instance
height = (0.4 * len(cmaps_list) + 0.1) * hscale
fig, axs = plt.subplots(
figsize=(6.4 * wscale, height),
nrows=len(cmaps_list),
ncols=ncols,
edgecolor=edge_color,
facecolor=face_color,
)
# Adjust subplot positioning
fig.subplots_adjust(
top=(1 - 0.05 / height),
bottom=0.05 / height,
left=left_pos,
right=1.0 - spacing,
wspace=0.05,
)
# Narrow axs' type
if len(cmaps_list) == 1 or isinstance(axs, Axes):
axs = np.array([axs])
# Loop over all cmaps defined in cmaps list
for ax, _cm in zip(axs, cmaps_list, strict=True):
# Obtain axes objects and turn them off
if show_grayscale:
# Obtain Axes objects
ax0, ax1 = ax
# Turn axes off
ax0.set_axis_off()
ax1.set_axis_off()
else:
# Obtain Axes object
ax0 = ax
# Turn axis off
ax0.set_axis_off()
# Obtain position bbox of ax0
pos0 = ax0.get_position()
# If cmap is a tuple, it defines a title or cm_type
if isinstance(_cm, tuple):
# If it is a title
if _cm[1]:
# Write the title as text in the correct position
fig.text(
title_pos,
pos0.y0 + pos0.height / 2,
_cm[0],
va="center",
ha="center",
fontsize=18,
c=text_color,
)
# If it is a cm_type
else:
# Write the cm_type as text in the correct position
fig.text(
title_pos,
pos0.y0,
_cm[0],
va="bottom",
ha="center",
fontsize=14,
c=text_color,
)
# Else, this is a colormap
elif isinstance(_cm, Colormap):
# Obtain the colormap type
cm_type = get_cmap_type(_cm)
# Get array of all values for which a colormap value is requested
x = np.arange(_cm.N)
# Get RGB values for colormap
rgb = _cm(x)[:, :3]
# Add colormap subplot
ax0.imshow(rgb[np.newaxis, ...], aspect="auto")
# Add gray-scale colormap subplot if requested
if show_grayscale:
# Get lightness values of colormap
lab = cspace_convert(rgb)
L = lab[:, 0]
# Normalize lightness values
L /= 99.99871678
# Get RGB values for lightness values using neutral
rgb_L = cmrcm.neutral(L)[:, :3]
# Add gray-scale colormap subplot
ax1.imshow(rgb_L[np.newaxis, ...], aspect="auto")
# Check if the lightness profile was requested
if plot_profile and (cm_type != "qualitative"):
# Determine the points that need to be plotted
plot_L = -(L - 0.5)
points = np.stack([x, plot_L], axis=1)
# Determine the colors that each point must have
# Use black for L >= 0.5 and white for L <= 0.5.
colors = np.zeros_like(plot_L, dtype=int)
colors[plot_L >= 0] = 1
# Split points up into segments with the same color
s_idx = np.nonzero(np.diff(colors))[0] + 1
segments = np.split(points, s_idx)
# Loop over all pairs of adjacent segments
for i, (seg1, seg2) in enumerate(
zip(segments[:-1], segments[1:], strict=True)
):
# Determine the point in the center of these segments
central_point = (seg1[-1] + seg2[0]) / 2
# Add this point to the ends of these segments
# This ensures that color changes in between segments
segments[i] = np.r_[segments[i], [central_point]]
segments[i + 1] = np.r_[[central_point], segments[i + 1]]
from matplotlib.collections import LineCollection
# Create an MPL LineCollection object with these segments
lc = LineCollection(
segments,
cmap=cmrcm.neutral,
alpha=plot_profile,
)
lc.set_linewidth(1)
# Determine the colors of each segment
s_colors = [colors[0]]
s_colors.extend(colors[s_idx])
# Set the values of the line-collection to be these colors
lc.set_array(np.array(s_colors))
# Add line-collection to this subplot
ax1.add_collection(lc)
# Determine positions of colormap name
x_text = pos0.x0 - spacing
y_text = pos0.y0 + pos0.height / 2
# Check if lightness information was requested for valid cm_type
if show_info and cm_type in ("sequential", "diverging", "cyclic"):
# If so, obtain lightness/perceptual profile information
rank = _get_cmap_perceptual_rank(_cm)[0:6]
# Write name of colormap in the correct position
fig.text(
x_text,
y_text,
_cm.name,
va="bottom",
ha="right",
fontsize=10,
c=text_color,
)
# Write lightness profile information in the correct position
fig.text(
x_text,
y_text,
f"({rank[2]:.3g}, {rank[2]-rank[0]*rank[3]:.3g}, {rank[5]:.3g})",
va="top",
ha="right",
fontsize=10,
c=text_color,
)
else:
# If not, just write the name of the colormap
fig.text(
x_text,
y_text,
_cm.name,
va="center",
ha="right",
fontsize=10,
c=text_color,
)
else: # pragma: no cover
raise RuntimeError
# If savefig is not None, save the figure
if savefig is not None:
savefig = Path(savefig)
dpi = 100 if (savefig.suffix == ".svg") else 250
fig.savefig(savefig, dpi=dpi, facecolor=face_color, edgecolor=edge_color)
plt.close(fig)
# Else, simply show it
else:
plt.show()
# Define function that prints a string with the BibTeX entry to CMasher's paper
def get_bibtex() -> None:
"""
Prints a string that gives the BibTeX entry for citing the *CMasher* paper
(Van der Velden 2020, JOSS, 5, 2004).
"""
# Create string with BibTeX entry
bibtex = dedent(
r"""
@ARTICLE{2020JOSS....5.2004V,
author = {{van der Velden}, Ellert},
title = "{CMasher: Scientific colormaps for making accessible,
informative and 'cmashing' plots}",
journal = {The Journal of Open Source Software},
keywords = {Python, science, colormaps, data visualization,
plotting, Electrical Engineering and Systems Science - Image
and Video Processing, Physics - Data Analysis, Statistics and
Probability},
year = 2020,
month = feb,
volume = {5},
number = {46},
eid = {2004},
pages = {2004},
doi = {10.21105/joss.02004},
archivePrefix = {arXiv},
eprint = {2003.01069},
primaryClass = {eess.IV},
adsurl = {https://ui.adsabs.harvard.edu/abs/2020JOSS....5.2004V},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
"""
)
# Print the string
print(bibtex.strip())
# This function returns a list of all colormaps available in CMasher
def get_cmap_list(cmap_type: str = "all") -> list[str]:
"""
Returns a list with the names of all colormaps available in *CMasher* of
the given `cmap_type`.
Note that *CMasher* colormaps registered in *MPL* have an added 'cmr.'
prefix.
Optional
--------
cmap_type : {'a'/'all'; 's'/'seq'/'sequential'; 'd'/'div'/'diverging'; \
'c'/'cyc'/'cyclic'}. Default: 'all'
The colormap type that should be in the returned list.
Returns
-------
cmap_list : list of str
List containing the names of all colormaps available in *CMasher*.
"""
# Convert cmap_type to lowercase
cmap_type = cmap_type.lower()
# Obtain proper list
if cmap_type in ("a", "all"):
cmaps = list(cmrcm.cmap_d)
elif cmap_type in ("s", "seq", "sequential"):
cmaps = list(cmrcm.cmap_cd["sequential"])
elif cmap_type in ("d", "div", "diverging"):
cmaps = list(cmrcm.cmap_cd["diverging"])
elif cmap_type in ("c", "cyc", "cyclic"):
cmaps = list(cmrcm.cmap_cd["cyclic"])
# Return cmaps
return cmaps
# This function determines the colormap type of a given colormap
def get_cmap_type(cmap: CMAP) -> str:
"""
Checks what the colormap type (sequential; diverging; cyclic; qualitative;
misc) of the provided `cmap` is and returns it.
Parameters
----------
cmap : str or :obj:`~matplotlib.colors.Colormap` object
The registered name of the colormap in :mod:`matplotlib.cm` or its
corresponding :obj:`~matplotlib.colors.Colormap` object.
Returns
-------
cm_type : {'sequential'; 'diverging'; 'cyclic'; 'qualitative'; 'misc'}
A string stating which of the defined colormap types the provided
`cmap` has.
"""
# Obtain the colormap
if isinstance(cmap, str):
if cmap in _CMASHER_BUILTIN_MAP_TYPES:
# fast track for known results
return _CMASHER_BUILTIN_MAP_TYPES[cmap]
cmap = mpl.colormaps[cmap]
# Get RGB values for colormap
rgb = cmap(np.arange(cmap.N))[:, :3]
# Get lightness values of colormap
lab = cspace_converter("sRGB1", "CAM02-UCS")(rgb)
L = lab[:, 0]
diff_L = np.diff(L)
# Obtain central values of lightness
N = cmap.N - 1
central_i = [int(np.floor(N / 2)), int(np.ceil(N / 2))]
diff_L0 = np.diff(L[: central_i[0] + 1])
diff_L1 = np.diff(L[central_i[1] :])
# Obtain perceptual differences of last two and first two values
lab_red = lab[[-2, -1, 0, 1]]
deltas = np.sqrt(np.sum(np.diff(lab_red, axis=0) ** 2, axis=-1))
# Check the statistics of cmap and determine the colormap type
# QUALITATIVE
# If the colormap has less than 40 values, assume it is qualitative
if cmap.N < 40:
return "qualitative"
# MISC 1
# If the colormap has only a single lightness, it is misc
elif np.allclose(diff_L, 0): # pragma: no cover
return "misc"
# SEQUENTIAL
# If the lightness values always increase or decrease, it is sequential
elif np.isclose(np.abs(np.sum(diff_L)), np.sum(np.abs(diff_L))):
return "sequential"
# DIVERGING
# If the lightness values have a central extreme and sequential sides
# Then it is diverging
elif np.isclose(np.abs(np.sum(diff_L0)), np.sum(np.abs(diff_L0))) and np.isclose(
np.abs(np.sum(diff_L1)), np.sum(np.abs(diff_L1))
):
# If the perceptual difference between the last and first value is
# comparable to the other perceptual differences, it is cyclic
if np.all(np.abs(np.diff(deltas)) < deltas[::2]) and np.diff(deltas[::2]):
return "cyclic"
# Otherwise, it is a normal diverging colormap
else:
return "diverging"
# MISC 2
# If none of the criteria above apply, it is misc
else:
return "misc"
# Function create a colormap using a subset of the colors in an existing one
def get_sub_cmap(cmap: CMAP, start: float, stop: float, *, N: int | None = None) -> LC:
"""
Creates a :obj:`~matplotlib.cm.ListedColormap` object using the colors in
the range `[start, stop]` of the provided `cmap` and returns it.
This function can be used to create a colormap that only uses a portion of
an existing colormap.
If `N` is not set to *None*, this function creates a qualitative colormap
from `cmap` instead.
Parameters
----------
cmap : str or :obj:`~matplotlib.colors.Colormap` object
The registered name of the colormap in :mod:`matplotlib.cm` or its
corresponding :obj:`~matplotlib.colors.Colormap` object.
start, stop : float
The normalized range of the colors in `cmap` that must be in the
sub-colormap.
Optional
--------
N : int or None. Default: None
The number of color segments to take from the provided `cmap` within
the range given by the provided `start` and `stop`.
If *None*, take all colors in `cmap` within this range.
Returns
-------
sub_cmap : :obj:`~matplotlib.colors.ListedColormap`
The created colormap that uses a subset of the colors in `cmap`.
If `N` is not *None*, this will be a qualitative colormap.
Example
-------
Creating a colormap using the first 80% of the 'rainforest' colormap::
>>> get_sub_cmap('cmr.rainforest', 0, 0.8)
Creating a qualitative colormap containing five colors from the middle 60%
of the 'lilac' colormap:
>>> get_sub_cmap('cmr.lilac', 0.2, 0.8, N=5)
Notes
-----
As it can create artifacts, this function does not interpolate between the
colors in `cmap` to fill up the space. Therefore, using values for `start`
and `stop` that are too close to each other, may result in a colormap that
contains too few different colors to be smooth.
It is recommended to use at least 128 different colors in a colormap for
optimal results (*CMasher* colormaps have 256 or 511/510 different colors,
for sequential or diverging/cyclic colormaps respectively).
One can check the number of colors in a colormap with
:attr:`matplotlib.colors.Colormap.N`.
Any colormaps created using this function are not registered in either
*CMasher* or *MPL*.
"""
if isinstance(cmap, str):
# Obtain the colormap
cmap = mpl.colormaps[cmap]
# Check value of N to determine suffix for the name
suffix = "_sub" if N is None else "_qual"
# Obtain colors
colors = take_cmap_colors(cmap, N, cmap_range=(start, stop))
# Create new colormap
sub_cmap = LC(colors, cmap.name + suffix, N=len(colors))
# Return sub_cmap
return sub_cmap
# Function to import all custom colormaps in a file or directory
def import_cmaps(
cmap_path: str | os.PathLike[str],
*,
_skip_registration: bool = False,
) -> None:
"""
Reads in custom colormaps from a provided file or directory `cmap_path`;
transforms them into :obj:`~matplotlib.colors.ListedColormap` objects; and
makes them available in the :mod:`cmasher.cm` module, in addition to
registering them in the :mod:`matplotlib.cm` module.
Both the imported colormap and its reversed version will be registered.
If a provided colormap is a 'cyclic' colormap, its shifted version will
also be registered with the `_s` suffix.
Parameters
----------
cmap_path : str or os.PathLike[str]
Relative or absolute path to a custom colormap file; or directory that
contains custom colormap files. A colormap file can be a *NumPy* binary
file ('.npy'); a *viscm* source file ('.jscm'); or any text file.
If the file is not a JSCM-file, it must contain the normalized; 8-bit;
or hexadecimal string RGB values that define the colormap.
Notes
-----
All colormap files must have names starting with the 'cm\\_' prefix. The
resulting colormaps will have the name of their file without the prefix and
extension.
In *MPL*, the colormaps will have the added 'cmr.' prefix to avoid name
clashes.
Example
-------
Importing a colormap named 'test' can be done by saving its normalized RGB
values in a file called 'cm_test.txt' and executing
>>> import_cmaps('/path/to/dir/cm_test.txt')
The 'test' colormap is now available in *CMasher* and *MPL* using
>>> cmr.cm.test # CMasher
>>> plt.get_cmap('cmr.test') # MPL
"""
# Obtain path to file or directory with colormaps
cmap_path_input = cmap_path
cmap_path = Path(cmap_path).resolve()
# Check if provided file or directory exists
if not cmap_path.exists():
raise FileNotFoundError(
"Input argument 'cmap_path' is a non-existing path "
f"({cmap_path_input!r})!"
)
cm_files: list[Path]
# Check if cmap_path is a file or directory and act accordingly
if cmap_path.is_file():
# If file, split cmap_path up into dir and file components
cmap_dir = cmap_path.parent
cmap_file = cmap_path
# Check if its name starts with 'cm_' and raise error if not
if not cmap_file.stem.startswith("cm_"):
raise OSError(
"Input argument 'cmap_path' does not lead to a file "
f"with the 'cm_' prefix ({cmap_path_input!r})!"
)
# Set cm_files to be the sole read-in file
cm_files = [cmap_file]
else:
# If directory, obtain the names of all colormap files in cmap_path
cmap_dir = cmap_path
cm_files = sorted(cmap_dir.glob("cm_*"))
def sort_key(name):
# prioritize binary files over text files because binary loads faster
if (ext := name.suffix) == ".npy":
return 0
if ext == ".txt":
return 1
return 10
cm_files.sort(key=sort_key)
del sort_key
if any(file.suffix == ".jscm" for file in cm_files) and not _HAS_VISCM:
raise ValueError("The 'viscm' package is required to read '.jscm' files!")
# Read in all the defined colormaps, transform and register them
seen: set[str] = set()
for cm_file in cm_files:
# Split basename and extension
base_str = cm_file.stem
ext_str = cm_file.suffix
if base_str in seen:
continue
else:
seen.add(base_str)
cm_name = base_str[3:]
# Process colormap files
try:
# If file is a NumPy binary file
if ext_str == ".npy":
rgb = np.load(cm_file)
# If file is viscm source file
elif ext_str == ".jscm":
import viscm
# Load colormap
cmap = viscm.gui.Colormap(None, None, None)
cmap.load(cm_file)
# Create editor and obtain RGB values
v = viscm.viscm_editor(
uniform_space=cmap.uniform_space,
cmtype=cmap.cmtype,
method=cmap.method,
**cmap.params,
)
rgb, _ = v.cmap_model.get_sRGB()
# If file is anything else
else:
rgb = np.genfromtxt(cm_file, dtype=None, comments="//", encoding=None) # type: ignore [call-overload]
if not _skip_registration:
# Register colormap
register_cmap(cm_name, rgb)
# Check if provided cmap is a cyclic colormap
# If so, obtain its shifted (reversed) versions as well
if get_cmap_type("cmr." + cm_name) == "cyclic":
# Determine the central value index of the colormap
idx = len(rgb) // 2
# Shift the entire colormap by this index
rgb_s = np.r_[rgb[idx:], rgb[:idx]]
if not _skip_registration:
# Register this colormap as well
register_cmap(cm_name + "_s", rgb_s)
# If any error is raised, reraise it
except Exception as error:
raise ValueError(f"Failed to import colormap {cm_name!r}") from error
# Function to register a custom colormap in MPL and CMasher
def register_cmap(name: str, data: RGB) -> None:
"""
Creates a :obj:`~matplotlib.colors.ListedColormap` object using the
provided `name` and `data`, and registers the colormap in the
:mod:`cmasher.cm` and :mod:`matplotlib.cm` modules.
A reversed version of the colormap will be registered as well.
Parameters
----------
name : str
The name that this colormap must have.
data : 2D array_like of {float; int} with shape `(N, 3)` or 1D array_like \
of str with shape `(N, )`
An array containing the RGB values of all segments in the colormap.
If float, the array contains normalized RGB values.
If int, the array contains 8-bit RGB values.
If str, the array contains hexadecimal string RGB values.
Note
----
In *MPL*, the colormap will have the added 'cmr.' prefix to avoid name
clashes.
"""
# Convert provided data to a NumPy array
cm_data_arr = np.array(data)
# Check the type of the data
if issubclass(cm_data_arr.dtype.type, str):
# If the values are strings, make sure they start with a '#'
if cm_data_arr.ndim == 0:
cm_data = [cm_data_arr.item()]
else:
cm_data = [f"#{x.removeprefix('#')}" for x in cm_data_arr]
try:
# Convert all values to floats
colorlist = [to_rgb(_) for _ in cm_data]
except ValueError:
raise ValueError(
f"Input data isn't valid hexadecimal RGB values: {data=}"
) from None
else:
# Make sure that cm_data is 2D
cm_data_arr = np.atleast_2d(cm_data_arr)
# If the values are integers, divide them by 255
if issubclass(cm_data_arr.dtype.type, np.integer):
cm_data_arr = cm_data_arr / 255
# Convert cm_data to a list
# see https://github.com/numpy/numpy/issues/27944
colorlist = cm_data_arr.tolist() # type: ignore[assignment]
# Transform colorlist into a Colormap
cmap_N = len(colorlist)
cmap_mpl = LC(colorlist, "cmr." + name, N=cmap_N)
cmap_cmr = LC(colorlist, name, N=cmap_N)
cmap_mpl_r = cmap_mpl.reversed()
cmap_cmr_r = cmap_cmr.reversed()
# Determine the cm_type of the colormap
if name in _CMASHER_BUILTIN_MAP_TYPES:
cm_type = _CMASHER_BUILTIN_MAP_TYPES[name]
else:
cm_type = get_cmap_type(cmap_mpl)
# Test that the colormaps can be called
cmap_mpl(1)
cmap_mpl_r(1)
# Add cmap to matplotlib's cmap list
mpl.colormaps.register(cmap=cmap_mpl)
setattr(cmrcm, cmap_cmr.name, cmap_cmr)
cmrcm.__all__.append(cmap_cmr.name)
cmrcm.cmap_d[cmap_cmr.name] = cmap_cmr
cmrcm.cmap_cd[cm_type][cmap_cmr.name] = cmap_cmr
# Add reversed cmap to matplotlib's cmap list
mpl.colormaps.register(cmap=cmap_mpl_r)
setattr(cmrcm, cmap_cmr_r.name, cmap_cmr_r)
cmrcm.__all__.append(cmap_cmr_r.name)
cmrcm.cmap_d[cmap_cmr_r.name] = cmap_cmr_r
cmrcm.cmap_cd[cm_type][cmap_cmr_r.name] = cmap_cmr_r
# Function to set the legend label of an artist that uses a colormap
def set_cmap_legend_entry(artist: "Artist", label: str) -> None:
"""
Sets the label of the provided `artist` to `label`, and creates a legend
entry using a miniature version of the colormap of `artist` as the legend
icon.
This function can be used to add legend entries for *MPL* artists that use
a colormap, like those made with :func:`~matplotlib.pyplot.hexbin`;
:func:`~matplotlib.pyplot.hist2d`; :func:`~matplotlib.pyplot.scatter`; or
any :mod:`~matplotlib.pyplot` function that takes `cmap` as an input
argument.
Keep in mind that using this function will override any legend entry that
already exists for `artist`.
Parameters
----------
artist : :obj:`~matplotlib.artist.Artist` object
Any artist object that has the `cmap` attribute, for which a legend
entry must be made using its colormap as the icon.
label : str
The string that must be set as the label of `artist`.
"""
from matplotlib.legend import Legend
# Obtain the colormap of the provided artist
cmap = getattr(artist, "cmap", None)
# If cmap is None, raise error
if cmap is None:
raise ValueError("Input argument 'artist' does not have attribute 'cmap'!")
# Set the label of this artist
artist.set_label(label)
# Add the HandlerColorPolyCollection to the default handler map for artist
from ._handlercolorpolycollection import _HandlerColorPolyCollection
Legend.get_default_handler_map()[artist] = _HandlerColorPolyCollection() # type: ignore [index]
# Function to take N equally spaced colors from a colormap
def take_cmap_colors(
cmap: CMAP,
N: int | None,
*,
cmap_range: tuple[float, float] = (0, 1),
return_fmt: str = "float",
) -> RGB:
"""
Takes `N` equally spaced colors from the provided colormap `cmap` and
returns them.
Parameters
----------
cmap : str or :obj:`~matplotlib.colors.Colormap` object
The registered name of the colormap in :mod:`matplotlib.cm` or its
corresponding :obj:`~matplotlib.colors.Colormap` object.
N : int or None
The number of colors to take from the provided `cmap` within the given
`cmap_range`.
If *None*, take all colors in `cmap` within this range.
Optional
--------
cmap_range : tuple of float. Default: (0, 1)
The normalized value range in the colormap from which colors should be
taken.
By default, colors are taken from the entire colormap.
return_fmt : {'float'/'norm'; 'int'/'8bit'; 'str'/'hex'}. Default: 'float'
The format of the requested colors.
If 'float'/'norm', the colors are returned as normalized RGB tuples.
If 'int'/'8bit', the colors are returned as 8-bit RGB tuples.
If 'str'/'hex', the colors are returned using their hexadecimal string
representations.
Returns
-------
colors : list of {tuple; str}
The colors that were taken from the provided `cmap`.
Examples
--------
Taking five equally spaced colors from the 'rainforest' colormap::
>>> take_cmap_colors('cmr.rainforest', 5)
[(0.0, 0.0, 0.0),
(0.226123592, 0.124584033, 0.562997277),
(0.0548210513, 0.515835251, 0.45667819),
(0.709615979, 0.722863985, 0.0834727592),
(1.0, 1.0, 1.0)]
Requesting their 8-bit RGB values instead::
>>> take_cmap_colors('cmr.rainforest', 5, return_fmt='int')
[(0, 0, 0),
(58, 32, 144),
(14, 132, 116),
(181, 184, 21),
(255, 255, 255)]
Requesting HEX-code values instead::
>>> take_cmap_colors('cmr.rainforest', 5, return_fmt='hex')
['#000000', '#3A2090', '#0E8474', '#B5B815', '#FFFFFF']
Requesting colors in a specific range::
>>> take_cmap_colors('cmr.rainforest', 5, cmap_range=(0.2, 0.8),
return_fmt='hex')
['#3E0374', '#10528A', '#0E8474', '#5CAD3C', '#D6BF4A']
Note
----
Using this function on a perceptually uniform sequential colormap, like
those in *CMasher*, allows one to pick a number of line colors that are
different but still sequential. This is useful when plotting a set of lines
that describe the same property, but have a different initial state.
"""
# Convert provided fmt to lowercase
return_fmt = return_fmt.lower()
# Obtain the colormap
if isinstance(cmap, str):
cmap = mpl.colormaps[cmap]
# Check if provided cmap_range is valid
if not ((0 <= cmap_range[0] <= 1) and (0 <= cmap_range[1] <= 1)):
raise ValueError(
"Input argument 'cmap_range' does not contain normalized values!"
)
# Extract and convert start and stop to their integer indices (inclusive)
start = int(np.floor(cmap_range[0] * cmap.N))
stop = int(np.ceil(cmap_range[1] * cmap.N)) - 1
# Pick colors
index: NDArray
if N is None:
index = np.arange(start, stop + 1, dtype=int)
else:
index = np.array(np.rint(np.linspace(start, stop, num=N)), dtype=int)
colors = cmap(index)
# Convert colors to proper format
if return_fmt in ("float", "norm", "int", "8bit"):
colors = np.apply_along_axis(to_rgb, 1, colors) # type: ignore [arg-type]
if return_fmt in ("int", "8bit"):
colors = np.array(np.rint(colors * 255), dtype=int)
colors = list(map(tuple, colors))
else:
colors = [to_hex(x).upper() for x in colors]
# Return colors
return colors
# Function to view what a colormap looks like
def view_cmap(
cmap: CMAP,
*,
savefig: str | None = None,
show_test: bool = False,
show_grayscale: bool = False,
) -> None:
"""
Shows a simple plot of the provided `cmap`.
Parameters
----------
cmap : str or :obj:`~matplotlib.colors.Colormap` object
The registered name of the colormap in :mod:`matplotlib.cm` or its
corresponding :obj:`~matplotlib.colors.Colormap` object.
Optional
--------
savefig : str or None. Default: None
If not *None*, the path where the plot must be saved to.
Else, the plot will simply be shown.
show_test : bool. Default: False
If *True*, show a colormap test in the plot instead.
show_grayscale : bool. Default: False
If *True*, also show the grayscale version of `cmap`.
"""
import matplotlib.pyplot as plt
from matplotlib.axes import Axes
if isinstance(cmap, str):
# Obtain cmap
cmap = mpl.colormaps[cmap]
# Check if show_grayscale is True
if show_grayscale:
# If so, create a colormap of cmap in grayscale
rgb = cmap(np.arange(cmap.N))[:, :3]
L = cspace_convert(rgb)[:, 0]
L /= 99.99871678
rgb_L = cmrcm.neutral(L)[:, :3]
cmap_L = LC(rgb_L)
# Set that there are two plots to create
nplots = 2
else:
# Else, there is only one plot
nplots = 1
# Create figure
fig, ax = plt.subplots(ncols=nplots, figsize=(12.8 * nplots, 3.2))
# Check if show_test is True
if show_test:
# If so, use a colormap test data file
data = np.load(Path(__file__).parent / "data" / "colormaptest.npy")
else:
# If not, just plot the colormap
data = [np.linspace(0, 1, cmap.N)]
# If show_grayscale is True, show both plots instead of just one
if show_grayscale:
if isinstance(ax, Axes): # pragma: no cover
raise RuntimeError
ax[0].imshow(data, cmap=cmap, aspect="auto")
ax[0].set_axis_off()
ax[1].imshow(data, cmap=cmap_L, aspect="auto")
ax[1].set_axis_off()
else:
if not isinstance(ax, Axes): # pragma: no cover
raise RuntimeError
ax.imshow(data, cmap=cmap, aspect="auto")
ax.set_axis_off()
# Use tight layout
fig.tight_layout(pad=0, h_pad=0, w_pad=0)
# If savefig is not None, save the figure
if savefig is not None:
fig.savefig(savefig, dpi=100, bbox_inches="tight", pad_inches=0)
plt.close(fig)
# Else, simply show it
else:
plt.show()
# %% IMPORT SCRIPT
# Import all colormaps defined in './colormaps'
import_cmaps(Path(__file__).parent / "colormaps")
|
1313eREPO_NAMECMasherPATH_START.@CMasher_extracted@CMasher-master@src@cmasher@utils.py@.PATH_END.py
|
{
"filename": "_sunburst.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/graph_objs/layout/template/data/_sunburst.py",
"type": "Python"
}
|
from plotly.graph_objs import Sunburst
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@graph_objs@layout@template@data@_sunburst.py@.PATH_END.py
|
{
"filename": "setup_package.py",
"repo_name": "astropy/astropy",
"repo_path": "astropy_extracted/astropy-main/astropy/timeseries/periodograms/lombscargle/setup_package.py",
"type": "Python"
}
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
from pathlib import Path
from numpy import get_include as get_numpy_include
from setuptools import Extension
ROOT = Path(__file__).parent.resolve().relative_to(Path.cwd())
def get_extensions():
ext = Extension(
"astropy.timeseries.periodograms.lombscargle.implementations.cython_impl",
define_macros=[("NPY_NO_DEPRECATED_API", "NPY_1_7_API_VERSION")],
sources=[str(ROOT / "implementations" / "cython_impl.pyx")],
include_dirs=[get_numpy_include()],
)
return [ext]
|
astropyREPO_NAMEastropyPATH_START.@astropy_extracted@astropy-main@astropy@timeseries@periodograms@lombscargle@setup_package.py@.PATH_END.py
|
{
"filename": "_outsidetextfont.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/graph_objs/funnel/_outsidetextfont.py",
"type": "Python"
}
|
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Outsidetextfont(_BaseTraceHierarchyType):
# class properties
# --------------------
_parent_path_str = "funnel"
_path_str = "funnel.outsidetextfont"
_valid_props = {
"color",
"colorsrc",
"family",
"familysrc",
"lineposition",
"linepositionsrc",
"shadow",
"shadowsrc",
"size",
"sizesrc",
"style",
"stylesrc",
"textcase",
"textcasesrc",
"variant",
"variantsrc",
"weight",
"weightsrc",
}
# color
# -----
@property
def color(self):
"""
The 'color' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
- A list or array of any of the above
Returns
-------
str|numpy.ndarray
"""
return self["color"]
@color.setter
def color(self, val):
self["color"] = val
# colorsrc
# --------
@property
def colorsrc(self):
"""
Sets the source reference on Chart Studio Cloud for `color`.
The 'colorsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["colorsrc"]
@colorsrc.setter
def colorsrc(self, val):
self["colorsrc"] = val
# family
# ------
@property
def family(self):
"""
HTML font family - the typeface that will be applied by the web
browser. The web browser will only be able to apply a font if
it is available on the system which it operates. Provide
multiple font families, separated by commas, to indicate the
preference in which to apply fonts if they aren't available on
the system. The Chart Studio Cloud (at https://chart-
studio.plotly.com or on-premise) generates images on a server,
where only a select number of fonts are installed and
supported. These include "Arial", "Balto", "Courier New",
"Droid Sans", "Droid Serif", "Droid Sans Mono", "Gravitas One",
"Old Standard TT", "Open Sans", "Overpass", "PT Sans Narrow",
"Raleway", "Times New Roman".
The 'family' property is a string and must be specified as:
- A non-empty string
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
str|numpy.ndarray
"""
return self["family"]
@family.setter
def family(self, val):
self["family"] = val
# familysrc
# ---------
@property
def familysrc(self):
"""
Sets the source reference on Chart Studio Cloud for `family`.
The 'familysrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["familysrc"]
@familysrc.setter
def familysrc(self, val):
self["familysrc"] = val
# lineposition
# ------------
@property
def lineposition(self):
"""
Sets the kind of decoration line(s) with text, such as an
"under", "over" or "through" as well as combinations e.g.
"under+over", etc.
The 'lineposition' property is a flaglist and may be specified
as a string containing:
- Any combination of ['under', 'over', 'through'] joined with '+' characters
(e.g. 'under+over')
OR exactly one of ['none'] (e.g. 'none')
- A list or array of the above
Returns
-------
Any|numpy.ndarray
"""
return self["lineposition"]
@lineposition.setter
def lineposition(self, val):
self["lineposition"] = val
# linepositionsrc
# ---------------
@property
def linepositionsrc(self):
"""
Sets the source reference on Chart Studio Cloud for
`lineposition`.
The 'linepositionsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["linepositionsrc"]
@linepositionsrc.setter
def linepositionsrc(self, val):
self["linepositionsrc"] = val
# shadow
# ------
@property
def shadow(self):
"""
Sets the shape and color of the shadow behind text. "auto"
places minimal shadow and applies contrast text font color. See
https://developer.mozilla.org/en-US/docs/Web/CSS/text-shadow
for additional options.
The 'shadow' property is a string and must be specified as:
- A string
- A number that will be converted to a string
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
str|numpy.ndarray
"""
return self["shadow"]
@shadow.setter
def shadow(self, val):
self["shadow"] = val
# shadowsrc
# ---------
@property
def shadowsrc(self):
"""
Sets the source reference on Chart Studio Cloud for `shadow`.
The 'shadowsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["shadowsrc"]
@shadowsrc.setter
def shadowsrc(self, val):
self["shadowsrc"] = val
# size
# ----
@property
def size(self):
"""
The 'size' property is a number and may be specified as:
- An int or float in the interval [1, inf]
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
int|float|numpy.ndarray
"""
return self["size"]
@size.setter
def size(self, val):
self["size"] = val
# sizesrc
# -------
@property
def sizesrc(self):
"""
Sets the source reference on Chart Studio Cloud for `size`.
The 'sizesrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["sizesrc"]
@sizesrc.setter
def sizesrc(self, val):
self["sizesrc"] = val
# style
# -----
@property
def style(self):
"""
Sets whether a font should be styled with a normal or italic
face from its family.
The 'style' property is an enumeration that may be specified as:
- One of the following enumeration values:
['normal', 'italic']
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
Any|numpy.ndarray
"""
return self["style"]
@style.setter
def style(self, val):
self["style"] = val
# stylesrc
# --------
@property
def stylesrc(self):
"""
Sets the source reference on Chart Studio Cloud for `style`.
The 'stylesrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["stylesrc"]
@stylesrc.setter
def stylesrc(self, val):
self["stylesrc"] = val
# textcase
# --------
@property
def textcase(self):
"""
Sets capitalization of text. It can be used to make text appear
in all-uppercase or all-lowercase, or with each word
capitalized.
The 'textcase' property is an enumeration that may be specified as:
- One of the following enumeration values:
['normal', 'word caps', 'upper', 'lower']
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
Any|numpy.ndarray
"""
return self["textcase"]
@textcase.setter
def textcase(self, val):
self["textcase"] = val
# textcasesrc
# -----------
@property
def textcasesrc(self):
"""
Sets the source reference on Chart Studio Cloud for `textcase`.
The 'textcasesrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["textcasesrc"]
@textcasesrc.setter
def textcasesrc(self, val):
self["textcasesrc"] = val
# variant
# -------
@property
def variant(self):
"""
Sets the variant of the font.
The 'variant' property is an enumeration that may be specified as:
- One of the following enumeration values:
['normal', 'small-caps', 'all-small-caps',
'all-petite-caps', 'petite-caps', 'unicase']
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
Any|numpy.ndarray
"""
return self["variant"]
@variant.setter
def variant(self, val):
self["variant"] = val
# variantsrc
# ----------
@property
def variantsrc(self):
"""
Sets the source reference on Chart Studio Cloud for `variant`.
The 'variantsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["variantsrc"]
@variantsrc.setter
def variantsrc(self, val):
self["variantsrc"] = val
# weight
# ------
@property
def weight(self):
"""
Sets the weight (or boldness) of the font.
The 'weight' property is a integer and may be specified as:
- An int (or float that will be cast to an int)
in the interval [1, 1000]
OR exactly one of ['normal', 'bold'] (e.g. 'bold')
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
int|numpy.ndarray
"""
return self["weight"]
@weight.setter
def weight(self, val):
self["weight"] = val
# weightsrc
# ---------
@property
def weightsrc(self):
"""
Sets the source reference on Chart Studio Cloud for `weight`.
The 'weightsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["weightsrc"]
@weightsrc.setter
def weightsrc(self, val):
self["weightsrc"] = val
# Self properties description
# ---------------------------
@property
def _prop_descriptions(self):
return """\
color
colorsrc
Sets the source reference on Chart Studio Cloud for
`color`.
family
HTML font family - the typeface that will be applied by
the web browser. The web browser will only be able to
apply a font if it is available on the system which it
operates. Provide multiple font families, separated by
commas, to indicate the preference in which to apply
fonts if they aren't available on the system. The Chart
Studio Cloud (at https://chart-studio.plotly.com or on-
premise) generates images on a server, where only a
select number of fonts are installed and supported.
These include "Arial", "Balto", "Courier New", "Droid
Sans", "Droid Serif", "Droid Sans Mono", "Gravitas
One", "Old Standard TT", "Open Sans", "Overpass", "PT
Sans Narrow", "Raleway", "Times New Roman".
familysrc
Sets the source reference on Chart Studio Cloud for
`family`.
lineposition
Sets the kind of decoration line(s) with text, such as
an "under", "over" or "through" as well as combinations
e.g. "under+over", etc.
linepositionsrc
Sets the source reference on Chart Studio Cloud for
`lineposition`.
shadow
Sets the shape and color of the shadow behind text.
"auto" places minimal shadow and applies contrast text
font color. See https://developer.mozilla.org/en-
US/docs/Web/CSS/text-shadow for additional options.
shadowsrc
Sets the source reference on Chart Studio Cloud for
`shadow`.
size
sizesrc
Sets the source reference on Chart Studio Cloud for
`size`.
style
Sets whether a font should be styled with a normal or
italic face from its family.
stylesrc
Sets the source reference on Chart Studio Cloud for
`style`.
textcase
Sets capitalization of text. It can be used to make
text appear in all-uppercase or all-lowercase, or with
each word capitalized.
textcasesrc
Sets the source reference on Chart Studio Cloud for
`textcase`.
variant
Sets the variant of the font.
variantsrc
Sets the source reference on Chart Studio Cloud for
`variant`.
weight
Sets the weight (or boldness) of the font.
weightsrc
Sets the source reference on Chart Studio Cloud for
`weight`.
"""
def __init__(
self,
arg=None,
color=None,
colorsrc=None,
family=None,
familysrc=None,
lineposition=None,
linepositionsrc=None,
shadow=None,
shadowsrc=None,
size=None,
sizesrc=None,
style=None,
stylesrc=None,
textcase=None,
textcasesrc=None,
variant=None,
variantsrc=None,
weight=None,
weightsrc=None,
**kwargs,
):
"""
Construct a new Outsidetextfont object
Sets the font used for `text` lying outside the bar.
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of
:class:`plotly.graph_objs.funnel.Outsidetextfont`
color
colorsrc
Sets the source reference on Chart Studio Cloud for
`color`.
family
HTML font family - the typeface that will be applied by
the web browser. The web browser will only be able to
apply a font if it is available on the system which it
operates. Provide multiple font families, separated by
commas, to indicate the preference in which to apply
fonts if they aren't available on the system. The Chart
Studio Cloud (at https://chart-studio.plotly.com or on-
premise) generates images on a server, where only a
select number of fonts are installed and supported.
These include "Arial", "Balto", "Courier New", "Droid
Sans", "Droid Serif", "Droid Sans Mono", "Gravitas
One", "Old Standard TT", "Open Sans", "Overpass", "PT
Sans Narrow", "Raleway", "Times New Roman".
familysrc
Sets the source reference on Chart Studio Cloud for
`family`.
lineposition
Sets the kind of decoration line(s) with text, such as
an "under", "over" or "through" as well as combinations
e.g. "under+over", etc.
linepositionsrc
Sets the source reference on Chart Studio Cloud for
`lineposition`.
shadow
Sets the shape and color of the shadow behind text.
"auto" places minimal shadow and applies contrast text
font color. See https://developer.mozilla.org/en-
US/docs/Web/CSS/text-shadow for additional options.
shadowsrc
Sets the source reference on Chart Studio Cloud for
`shadow`.
size
sizesrc
Sets the source reference on Chart Studio Cloud for
`size`.
style
Sets whether a font should be styled with a normal or
italic face from its family.
stylesrc
Sets the source reference on Chart Studio Cloud for
`style`.
textcase
Sets capitalization of text. It can be used to make
text appear in all-uppercase or all-lowercase, or with
each word capitalized.
textcasesrc
Sets the source reference on Chart Studio Cloud for
`textcase`.
variant
Sets the variant of the font.
variantsrc
Sets the source reference on Chart Studio Cloud for
`variant`.
weight
Sets the weight (or boldness) of the font.
weightsrc
Sets the source reference on Chart Studio Cloud for
`weight`.
Returns
-------
Outsidetextfont
"""
super(Outsidetextfont, self).__init__("outsidetextfont")
if "_parent" in kwargs:
self._parent = kwargs["_parent"]
return
# Validate arg
# ------------
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError(
"""\
The first argument to the plotly.graph_objs.funnel.Outsidetextfont
constructor must be a dict or
an instance of :class:`plotly.graph_objs.funnel.Outsidetextfont`"""
)
# Handle skip_invalid
# -------------------
self._skip_invalid = kwargs.pop("skip_invalid", False)
self._validate = kwargs.pop("_validate", True)
# Populate data dict with properties
# ----------------------------------
_v = arg.pop("color", None)
_v = color if color is not None else _v
if _v is not None:
self["color"] = _v
_v = arg.pop("colorsrc", None)
_v = colorsrc if colorsrc is not None else _v
if _v is not None:
self["colorsrc"] = _v
_v = arg.pop("family", None)
_v = family if family is not None else _v
if _v is not None:
self["family"] = _v
_v = arg.pop("familysrc", None)
_v = familysrc if familysrc is not None else _v
if _v is not None:
self["familysrc"] = _v
_v = arg.pop("lineposition", None)
_v = lineposition if lineposition is not None else _v
if _v is not None:
self["lineposition"] = _v
_v = arg.pop("linepositionsrc", None)
_v = linepositionsrc if linepositionsrc is not None else _v
if _v is not None:
self["linepositionsrc"] = _v
_v = arg.pop("shadow", None)
_v = shadow if shadow is not None else _v
if _v is not None:
self["shadow"] = _v
_v = arg.pop("shadowsrc", None)
_v = shadowsrc if shadowsrc is not None else _v
if _v is not None:
self["shadowsrc"] = _v
_v = arg.pop("size", None)
_v = size if size is not None else _v
if _v is not None:
self["size"] = _v
_v = arg.pop("sizesrc", None)
_v = sizesrc if sizesrc is not None else _v
if _v is not None:
self["sizesrc"] = _v
_v = arg.pop("style", None)
_v = style if style is not None else _v
if _v is not None:
self["style"] = _v
_v = arg.pop("stylesrc", None)
_v = stylesrc if stylesrc is not None else _v
if _v is not None:
self["stylesrc"] = _v
_v = arg.pop("textcase", None)
_v = textcase if textcase is not None else _v
if _v is not None:
self["textcase"] = _v
_v = arg.pop("textcasesrc", None)
_v = textcasesrc if textcasesrc is not None else _v
if _v is not None:
self["textcasesrc"] = _v
_v = arg.pop("variant", None)
_v = variant if variant is not None else _v
if _v is not None:
self["variant"] = _v
_v = arg.pop("variantsrc", None)
_v = variantsrc if variantsrc is not None else _v
if _v is not None:
self["variantsrc"] = _v
_v = arg.pop("weight", None)
_v = weight if weight is not None else _v
if _v is not None:
self["weight"] = _v
_v = arg.pop("weightsrc", None)
_v = weightsrc if weightsrc is not None else _v
if _v is not None:
self["weightsrc"] = _v
# Process unknown kwargs
# ----------------------
self._process_kwargs(**dict(arg, **kwargs))
# Reset skip_invalid
# ------------------
self._skip_invalid = False
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@graph_objs@funnel@_outsidetextfont.py@.PATH_END.py
|
{
"filename": "test_spectral_models.py",
"repo_name": "lucabaldini/ixpeobssim",
"repo_path": "ixpeobssim_extracted/ixpeobssim-main/tests/test_spectral_models.py",
"type": "Python"
}
|
#!/usr/bin/env python
#
# Copyright (C) 2015, the ixpeobssim team.
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
"""Unit tests for srcmodel.spectrum
"""
from __future__ import print_function, division
import sys
import unittest
import numpy
from ixpeobssim.srcmodel.spectrum import powerlaw, cutoffpl
from ixpeobssim.core.spline import xInterpolatedUnivariateSpline
from ixpeobssim.utils.matplotlib_ import plt
if sys.flags.interactive:
plt.ion()
class TestModels(unittest.TestCase):
"""Unit tests for the spectral models.
"""
@staticmethod
def basic_test(model, figure_name):
"""
"""
E = numpy.linspace(1., 10., 250)
t = numpy.linspace(0., 1000., 3)
fmt = dict(xlabel='Energy [keV]',
ylabel='dN/dE [cm$^{-2}$ s$^{-1}$ keV$^{-1}$]')
plt.figure(figure_name)
for _t in t:
s = xInterpolatedUnivariateSpline(E, model(E, _t), **fmt)
s.plot(logx=True, logy=True, label='t = %.1f' % _t)
plt.legend()
@staticmethod
def norm(t):
"""Time-dependent normalization.
"""
return 1. + t * 0.001
@staticmethod
def index(t):
"""Time-dependent index.
"""
return 2. - t * 0.001
def test_powerlaw_stationary(self):
"""
"""
model = powerlaw(self.norm(0), self.index(0))
self.basic_test(model, 'Power law (stationary)')
# Make sure in the stationary case we can call the model without passing
# the time explicitly,
E = numpy.linspace(1., 10., 10)
self.assertTrue((model(E) == model(E, 0.)).all())
def test_powerlaw_norm(self):
"""
"""
model = powerlaw(self.norm, self.index(0))
self.basic_test(model, 'Power law (time-dependent normalization)')
def test_powerlaw_index(self):
"""
"""
model = powerlaw(self.norm(0), self.index)
self.basic_test(model, 'Power law (time-dependent index)')
def test_powerlaw(self):
"""
"""
model = powerlaw(self.norm, self.index)
self.basic_test(model, 'Power law (time-dependent)')
def test_cutoffpl(self):
model = cutoffpl(self.norm, self.index, 5.)
self.basic_test(model, 'Power law with exponential cutoff')
if __name__ == '__main__':
unittest.main(exit=not sys.flags.interactive)
|
lucabaldiniREPO_NAMEixpeobssimPATH_START.@ixpeobssim_extracted@ixpeobssim-main@tests@test_spectral_models.py@.PATH_END.py
|
{
"filename": "seq.py",
"repo_name": "simonsobs/sorunlib",
"repo_path": "sorunlib_extracted/sorunlib-main/src/sorunlib/seq.py",
"type": "Python"
}
|
import datetime as dt
import sorunlib as run
from sorunlib.commands import _timestamp_to_utc_datetime
from sorunlib._internal import check_response, check_started, monitor_process
OP_TIMEOUT = 60
def scan(description, stop_time, width, az_drift=0, tag=None, subtype=None,
min_duration=None):
"""Run a constant elevation scan, collecting detector data.
Args:
description (str): Description of the field/object being scanned.
stop_time (str): Time in ISO format to scan until, i.e.
"2022-06-21T15:58:00"
width (float): Scan width in azimuth. The scan will start at the
current position and move in the positive azimuth direction.
az_drift (float): Drift velocity in deg/s, causing scan extrema to move
accordingly.
tag (str, optional): Tag or comma-separated listed of tags to attach to
the operation. Passed through to the smurf stream command.
subtype (str, optional): Operation subtype used to tag the stream.
min_duration (float, optional): Minimum duration required to scan,
specified in seconds. If not enough time exists between now and the
``stop_time`` the scan is not executed. Defaults to None.
"""
now = dt.datetime.now(dt.timezone.utc)
scan_stop = _timestamp_to_utc_datetime(stop_time)
# Check stop time has not already passed
if now > scan_stop:
return
# Check there is enough time to perform scan
if min_duration is not None:
start_by_time = scan_stop - dt.timedelta(seconds=min_duration)
if now > start_by_time:
return
acu = run.CLIENTS['acu']
# Enable SMuRF streams
run.smurf.stream('on', subtype=subtype, tag=tag)
try:
# Grab current telescope position
resp = acu.monitor.status()
az = resp.session['data']['StatusDetailed']['Azimuth current position']
el = resp.session['data']['StatusDetailed']['Elevation current position']
# Start telescope motion
# az_speed and az_accel assumed from ACU defaults
# Can be modified by acu.set_scan_params()
resp = acu.generate_scan.start(az_endpoint1=az,
az_endpoint2=az + width,
el_endpoint1=el,
el_endpoint2=el,
el_speed=0,
az_drift=az_drift)
check_started(acu, resp)
# Wait until stop time
monitor_process(acu, 'generate_scan', stop_time)
finally:
print("Stopping scan.")
# Stop SMuRF streams
run.smurf.stream('off')
# Stop motion
acu.generate_scan.stop()
resp = acu.generate_scan.wait(timeout=OP_TIMEOUT)
check_response(acu, resp)
print("Scan finished.")
|
simonsobsREPO_NAMEsorunlibPATH_START.@sorunlib_extracted@sorunlib-main@src@sorunlib@seq.py@.PATH_END.py
|
{
"filename": "check_sph.py",
"repo_name": "LSSTDESC/NaMaster",
"repo_path": "NaMaster_extracted/NaMaster-master/sandbox_validation/check_sph.py",
"type": "Python"
}
|
from __future__ import print_function
from optparse import OptionParser
import numpy as np
import healpy as hp
import matplotlib.pyplot as plt
import pymaster as nmt
import os
import sys
DTOR=np.pi/180
def opt_callback(option, opt, value, parser):
setattr(parser.values, option.dest, value.split(','))
parser = OptionParser()
parser.add_option('--nside', dest='nside_out', default=512, type=int,
help='Resolution parameter')
parser.add_option('--isim-ini', dest='isim_ini', default=1, type=int,
help='Index of first simulation')
parser.add_option('--isim-end', dest='isim_end', default=100, type=int,
help='Index of last simulation')
parser.add_option('--wo-mask', dest='wo_mask', default=False, action='store_true',
help='Set if you don\'t want to use a mask')
parser.add_option('--wo-contaminants', dest='wo_cont', default=False, action='store_true',
help='Set if you don\'t want to use contaminants')
parser.add_option('--wo-varnoise', dest='wo_nvar', default=False, action='store_true',
help='Set if you don\'t want to use inhomogeneous noise')
parser.add_option('--plot', dest='plot_stuff', default=False, action='store_true',
help='Set if you want to produce plots')
parser.add_option('--aposize', dest='aposize', default=0.0, type=float,
help='Mask apodization (in degrees)')
(o, args) = parser.parse_args()
nsims=o.isim_end-o.isim_ini+1
w_mask=not o.wo_mask
w_cont=not o.wo_cont
w_nvar=not o.wo_nvar
#Switch off contaminants and inhomogeneous noiseif there's no mask
if not w_mask :
w_cont=False
w_nvar=False
#Create output directory
predir="tests_sph"
os.system("mkdir -p "+predir)
prefix=predir+"/run_ns%d_mask%d_cont%d_nvar%d_apo%.2lf"%(o.nside_out,w_mask,w_cont,w_nvar,o.aposize)
fname_mask=prefix+"_mask"
#Read theory power spectra
l,cltt,clee,clbb,clte,nltt,nlee,nlbb,nlte=np.loadtxt("data/cls_lss.txt",unpack=True)
cltt=cltt[:3*o.nside_out]; clee=clee[:3*o.nside_out]; clbb=clbb[:3*o.nside_out];
clte=clte[:3*o.nside_out];
nltt=nltt[:3*o.nside_out]; nlee=nlee[:3*o.nside_out]; nlbb=nlbb[:3*o.nside_out];
nlte=nlte[:3*o.nside_out];
#Read noise variance map
if w_nvar :
depth_nvar=hp.read_map("data/cont_lss_nvar_ns%d.fits"%o.nside_out,verbose=False)
depth_nvar[depth_nvar<0.8]=0
else :
depth_nvar=np.ones(hp.nside2npix(o.nside_out))
pixel_area=4*np.pi/hp.nside2npix(o.nside_out)
#Transform variance in sterad to variance in pix
depth_nvar_t=nltt[-1]*depth_nvar/pixel_area
depth_nvar_p=nlee[-1]*depth_nvar/pixel_area
#Generate mask
if not os.path.isfile(fname_mask+'.fits') :
if w_mask :
depth_ivar=np.zeros_like(depth_nvar); depth_ivar[depth_nvar>0.1]=1./depth_nvar[depth_nvar>0.1]
mask_raw=hp.read_map("data/mask_lss_ns%d.fits"%o.nside_out,verbose=False)
if o.aposize>0 :
mask=nmt.mask_apodization(mask_raw,o.aposize,apotype='C1')
else :
mask=mask_raw
mask*=depth_ivar
else :
mask=np.ones(hp.nside2npix(o.nside_out))
hp.write_map(fname_mask+".fits",mask,overwrite=True)
mask=hp.read_map(fname_mask+".fits",verbose=False)
if o.plot_stuff :
hp.mollview(mask)
fsky=np.mean(mask/np.amax(mask));
#Read contaminant maps
if w_cont :
fgt=np.zeros([2,1,hp.nside2npix(o.nside_out)])
fgt[0,0,:]=hp.read_map("data/cont_lss_star_ns%d.fits"%o.nside_out,verbose=False); #Stars
fgt[1,0,:]=hp.read_map("data/cont_lss_dust_ns%d.fits"%o.nside_out,verbose=False); #Dust
fgp=np.zeros([2,2,hp.nside2npix(o.nside_out)]);
fgp[0,0,:],fgp[0,1,:]=hp.read_map("data/cont_wl_psf_ns%d.fits"%o.nside_out,
field=[0,1],verbose=False); #PSF
fgp[1,0,:],fgp[1,1,:]=hp.read_map("data/cont_wl_ss_ns%d.fits"%o.nside_out,
field=[0,1],verbose=False); #Small-scales
if o.plot_stuff :
hp.mollview(np.sum(fgt,axis=0)[0,:]*mask)
hp.mollview(np.sum(fgp,axis=0)[0,:]*mask)
hp.mollview(np.sum(fgp,axis=0)[1,:]*mask)
#Binning scheme
d_ell=int(1./fsky)
b=nmt.NmtBin(o.nside_out,nlb=d_ell)
#Generate some initial fields
print(" - Res: %.3lf arcmin. "%(np.sqrt(4*np.pi*(180*60/np.pi)**2/hp.nside2npix(o.nside_out))))
def get_fields() :
#Signal
st,sq,su=hp.synfast([cltt,clee,clbb,clte],o.nside_out,new=True,verbose=False,pol=True)
#Inhomogeneous white noise
nt=np.sqrt(depth_nvar_t)*np.random.randn(hp.nside2npix(o.nside_out))
nq=np.sqrt(depth_nvar_p)*np.random.randn(hp.nside2npix(o.nside_out))
nu=np.sqrt(depth_nvar_p)*np.random.randn(hp.nside2npix(o.nside_out))
st+=nt; sq+=nq; su+=nu;
#Contaminants
if w_cont :
st+=np.sum(fgt,axis=0)[0,:]; sq+=np.sum(fgp,axis=0)[0,:]; su+=np.sum(fgp,axis=0)[1,:]
ff0=nmt.NmtField(mask,[st],templates=fgt)
ff2=nmt.NmtField(mask,[sq,su],templates=fgp)
else :
ff0=nmt.NmtField(mask,[st])
ff2=nmt.NmtField(mask,[sq,su])
return ff0,ff2
np.random.seed(1000)
f0,f2=get_fields()
if o.plot_stuff :
hp.mollview(f0.get_maps()[0]*mask,title='$\\delta_g$')
hp.mollview(f2.get_maps()[0]*mask,title='$\\gamma_1$')
hp.mollview(f2.get_maps()[1]*mask,title='$\\gamma_2$')
#Use initial fields to generate coupling matrix
w00=nmt.NmtWorkspace();
if not os.path.isfile(prefix+"_w00.dat") :
print("Computing 00")
w00.compute_coupling_matrix(f0,f0,b)
w00.write_to(prefix+"_w00.dat");
else :
w00.read_from(prefix+"_w00.dat")
w02=nmt.NmtWorkspace();
if not os.path.isfile(prefix+"_w02.dat") :
print("Computing 02")
w02.compute_coupling_matrix(f0,f2,b)
w02.write_to(prefix+"_w02.dat");
else :
w02.read_from(prefix+"_w02.dat")
w22=nmt.NmtWorkspace();
if not os.path.isfile(prefix+"_w22.dat") :
print("Computing 22")
w22.compute_coupling_matrix(f2,f2,b)
w22.write_to(prefix+"_w22.dat");
else :
w22.read_from(prefix+"_w22.dat")
#Generate theory prediction
cl00_th=w00.decouple_cell(w00.couple_cell([cltt]))
cl02_th=w02.decouple_cell(w02.couple_cell([clte,0*clte]))
cl22_th=w22.decouple_cell(w22.couple_cell([clee,0*clee,0*clbb,clbb]))
np.savetxt(prefix+"_cl_th.txt",
np.transpose([b.get_effective_ells(),cl00_th[0],cl02_th[0],cl02_th[1],
cl22_th[0],cl22_th[1],cl22_th[2],cl22_th[3]]))
#Compute noise and deprojection bias
if not os.path.isfile(prefix+"_clb00.npy") :
print("Computing deprojection and noise bias 00")
#Compute noise bias
clb00=np.sum(depth_nvar_t*mask*mask*pixel_area*pixel_area)/(4*np.pi)*np.ones([1,3*o.nside_out])
#Compute deprojection bias
if w_cont :
#Signal contribution
clb00+=nmt.deprojection_bias(f0,f0,[cltt])
#Noise contribution
clb00+=nmt.uncorr_noise_deprojection_bias(f0,depth_nvar_t*pixel_area)
np.save(prefix+"_clb00",clb00)
else :
clb00=np.load(prefix+"_clb00.npy")
if not os.path.isfile(prefix+"_clb02.npy") :
print("Computing deprojection and noise bias 02")
clb02=np.zeros([2,3*o.nside_out])
if w_cont :
clb02+=nmt.deprojection_bias(f0,f2,[clte,0*clte])
np.save(prefix+"_clb02",clb02)
else :
clb02=np.load(prefix+"_clb02.npy")
if not os.path.isfile(prefix+"_clb22.npy") :
print("Computing deprojection and noise bias 22")
clb22=np.sum(depth_nvar_p*mask*mask*pixel_area*pixel_area)/(4*np.pi)*np.array([1,0,0,1])[:,None]*np.ones(3*o.nside_out)[None,:]
if w_cont :
clb22+=nmt.deprojection_bias(f2,f2,[clee,0*clee,0*clbb,clbb])
clb22+=nmt.uncorr_noise_deprojection_bias(f2,depth_nvar_p*pixel_area)
np.save(prefix+"_clb22",clb22)
else :
clb22=np.load(prefix+"_clb22.npy")
#Compute mean and variance over nsims simulations
cl00_all=[]
cl02_all=[]
cl22_all=[]
for i in np.arange(nsims) :
#if i%100==0 :
print("%d-th sim"%(i+o.isim_ini))
if not os.path.isfile(prefix+"_cl_%04d.txt"%(o.isim_ini+i)) :
np.random.seed(1000+o.isim_ini+i)
f0,f2=get_fields()
cl00=w00.decouple_cell(nmt.compute_coupled_cell(f0,f0),cl_bias=clb00)
cl02=w02.decouple_cell(nmt.compute_coupled_cell(f0,f2),cl_bias=clb02)
cl22=w22.decouple_cell(nmt.compute_coupled_cell(f2,f2),cl_bias=clb22)
np.savetxt(prefix+"_cl_%04d.txt"%(o.isim_ini+i),
np.transpose([b.get_effective_ells(),cl00[0],cl02[0],cl02[1],
cl22[0],cl22[1],cl22[2],cl22[3]]))
cld=np.loadtxt(prefix+"_cl_%04d.txt"%(o.isim_ini+i),unpack=True)
cl00_all.append([cld[1]])
cl02_all.append([cld[2],cld[3]])
cl22_all.append([cld[4],cld[5],cld[6],cld[7]])
cl00_all=np.array(cl00_all)
cl02_all=np.array(cl02_all)
cl22_all=np.array(cl22_all)
#Plot results
if o.plot_stuff :
l_eff=b.get_effective_ells()
cols=plt.cm.rainbow(np.linspace(0,1,6))
plt.figure()
plt.errorbar(l_eff,np.mean(cl00_all,axis=0)[0]/cl00_th[0]-1,
yerr=np.std(cl00_all,axis=0)[0]/cl00_th[0]/np.sqrt(nsims+0.),
label='$\\delta\\times\\delta$',fmt='ro')
plt.errorbar(l_eff,np.mean(cl02_all,axis=0)[0]/cl02_th[0]-1,
yerr=np.std(cl02_all,axis=0)[0]/cl02_th[0]/np.sqrt(nsims+0.),
label='$\\delta\\times\\gamma_E$',fmt='go')
plt.errorbar(l_eff,np.mean(cl22_all,axis=0)[0]/cl22_th[0]-1,
yerr=np.std(cl22_all,axis=0)[0]/cl22_th[0]/np.sqrt(nsims+0.),
label='$\\gamma_E\\times\\gamma_E$',fmt='bo')
plt.xlabel('$\\ell$',fontsize=16)
plt.ylabel('$\\Delta C_\\ell/C_\\ell$',fontsize=16)
plt.xlim([2,2*o.nside_out])
plt.legend(loc='lower right',frameon=False,fontsize=16)
plt.xscale('log')
plt.savefig(prefix+'_celldiff.png',bbox_inches='tight')
plt.savefig(prefix+'_celldiff.pdf',bbox_inches='tight')
import scipy.stats as st
bins_use=np.where(l_eff<2*o.nside_out)[0]; ndof=len(bins_use)
res=(cl00_all[:,:,:]-cl00_th[None,:,:])/np.std(cl00_all,axis=0)[None,:,:]
chi2_00=np.sum(res[:,:,bins_use]**2,axis=2)
res=(cl02_all[:,:,:]-cl02_th[None,:,:])/np.std(cl02_all,axis=0)[None,:,:]
chi2_02=np.sum(res[:,:,bins_use]**2,axis=2)
res=(cl22_all[:,:,:]-cl22_th[None,:,:])/np.std(cl22_all,axis=0)[None,:,:]
chi2_22=np.sum(res[:,:,bins_use]**2,axis=2)
x=np.linspace(ndof-5*np.sqrt(2.*ndof),ndof+5*np.sqrt(2*ndof),256)
pdf=st.chi2.pdf(x,ndof)
plt.figure(figsize=(10,7))
ax=[plt.subplot(2,3,i+1) for i in range(6)]
plt.subplots_adjust(wspace=0, hspace=0)
h,b,p=ax[0].hist(chi2_00[:,0],bins=40,density=True)
ax[0].text(0.75,0.9,'$\\delta\\times\\delta$' ,transform=ax[0].transAxes)
ax[0].set_ylabel('$P(\\chi^2)$')
h,b,p=ax[1].hist(chi2_02[:,0],bins=40,density=True)
ax[1].text(0.75,0.9,'$\\delta\\times\\gamma_E$' ,transform=ax[1].transAxes)
h,b,p=ax[2].hist(chi2_02[:,1],bins=40,density=True)
ax[2].text(0.75,0.9,'$\\delta\\times\\gamma_B$' ,transform=ax[2].transAxes)
h,b,p=ax[3].hist(chi2_22[:,0],bins=40,density=True)
ax[3].text(0.75,0.9,'$\\gamma_E\\times\\gamma_E$',transform=ax[3].transAxes)
ax[3].set_xlabel('$\\chi^2$')
ax[3].set_ylabel('$P(\\chi^2)$')
h,b,p=ax[4].hist(chi2_22[:,1],bins=40,density=True)
ax[4].text(0.75,0.9,'$\\gamma_E\\times\\gamma_B$',transform=ax[4].transAxes)
h,b,p=ax[5].hist(chi2_22[:,3],bins=40,density=True)
ax[5].text(0.75,0.9,'$\\gamma_B\\times\\gamma_B$',transform=ax[5].transAxes)
for a in ax[:3] :
a.set_xticklabels([])
for a in ax[3:] :
a.set_xlabel('$\\chi^2$')
ax[1].set_yticklabels([])
ax[2].set_yticklabels([])
ax[4].set_yticklabels([])
ax[5].set_yticklabels([])
for a in ax :
a.set_xlim([ndof-5*np.sqrt(2.*ndof),ndof+5*np.sqrt(2.*ndof)])
a.set_ylim([0,1.4*np.amax(pdf)])
a.plot([ndof,ndof],[0,1.4*np.amax(pdf)],'k--',label='$N_{\\rm dof}$')
a.plot(x,pdf,'k-',label='$P(\\chi^2,N_{\\rm dof})$')
ax[3].legend(loc='upper left',frameon=False)
plt.savefig(prefix+'_distributions.png',bbox_inches='tight')
plt.savefig(prefix+'_distributions.pdf',bbox_inches='tight')
ic=0
plt.figure()
plt.plot(l_eff,np.mean(cl00_all,axis=0)[0],
label='$\\delta\\times\\delta$',c=cols[ic])
plt.plot(l_eff,cl00_th[0],'--',c=cols[ic]); ic+=1
plt.plot(l_eff,np.mean(cl02_all,axis=0)[0],
label='$\\delta\\times\\gamma_E$',c=cols[ic]);
plt.plot(l_eff,cl02_th[0],'--',c=cols[ic]); ic+=1
plt.plot(l_eff,np.mean(cl02_all,axis=0)[1],
label='$\\delta\\times\\gamma_B$',c=cols[ic]); ic+=1
plt.plot(l_eff,np.mean(cl22_all,axis=0)[0],
label='$\\gamma\\times\\gamma_E$',c=cols[ic]);
plt.plot(l_eff,cl22_th[0],'--',c=cols[ic]); ic+=1
plt.plot(l_eff,np.mean(cl22_all,axis=0)[1],
label='$\\gamma_E\\times\\gamma_B$',c=cols[ic]); ic+=1
plt.plot(l_eff,np.mean(cl22_all,axis=0)[3],
label='$\\gamma_B\\times\\gamma_B$',c=cols[ic]); ic+=1
plt.loglog()
plt.xlim([2,2*o.nside_out])
plt.xlabel('$\\ell$',fontsize=16)
plt.ylabel('$C_\\ell$',fontsize=16)
plt.legend(loc='lower left',frameon=False,fontsize=14,ncol=2)
plt.savefig(prefix+'_cellfull.png',bbox_inches='tight')
plt.savefig(prefix+'_cellfull.pdf',bbox_inches='tight')
plt.show()
|
LSSTDESCREPO_NAMENaMasterPATH_START.@NaMaster_extracted@NaMaster-master@sandbox_validation@check_sph.py@.PATH_END.py
|
{
"filename": "_textcase.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/scattercarpet/marker/colorbar/tickfont/_textcase.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TextcaseValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(
self,
plotly_name="textcase",
parent_name="scattercarpet.marker.colorbar.tickfont",
**kwargs,
):
super(TextcaseValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "colorbars"),
values=kwargs.pop("values", ["normal", "word caps", "upper", "lower"]),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@scattercarpet@marker@colorbar@tickfont@_textcase.py@.PATH_END.py
|
{
"filename": "_coloraxis.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/parcats/line/_coloraxis.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ColoraxisValidator(_plotly_utils.basevalidators.SubplotidValidator):
def __init__(self, plotly_name="coloraxis", parent_name="parcats.line", **kwargs):
super(ColoraxisValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
dflt=kwargs.pop("dflt", None),
edit_type=kwargs.pop("edit_type", "calc"),
regex=kwargs.pop("regex", "/^coloraxis([2-9]|[1-9][0-9]+)?$/"),
role=kwargs.pop("role", "info"),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@parcats@line@_coloraxis.py@.PATH_END.py
|
{
"filename": "field_plugin_registry.py",
"repo_name": "yt-project/yt",
"repo_path": "yt_extracted/yt-main/yt/fields/field_plugin_registry.py",
"type": "Python"
}
|
from collections.abc import Callable
FunctionName = str
FieldPluginMap = dict[FunctionName, Callable]
field_plugins: FieldPluginMap = {}
def register_field_plugin(func: Callable) -> Callable:
name = func.__name__
if name.startswith("setup_"):
name = name[len("setup_") :]
if name.endswith("_fields"):
name = name[: -len("_fields")]
field_plugins[name] = func
# And, we return it, too
return func
|
yt-projectREPO_NAMEytPATH_START.@yt_extracted@yt-main@yt@fields@field_plugin_registry.py@.PATH_END.py
|
{
"filename": "normalizers.py",
"repo_name": "neuraloperator/neuraloperator",
"repo_path": "neuraloperator_extracted/neuraloperator-main/neuralop/data/transforms/normalizers.py",
"type": "Python"
}
|
from typing import Dict
from ...utils import count_tensor_params
from .base_transforms import Transform, DictTransform
import torch
class Normalizer(Transform):
def __init__(self, mean, std, eps=1e-6):
self.mean = mean
self.std = std
self.eps = eps
def transform(self, data):
return (data - self.mean)/(self.std + self.eps)
def inverse_transform(self, data):
return (data * (self.std + self.eps)) + self.mean
def to(self, device):
self.mean = self.mean.to(device)
self.std = self.std.to(device)
def cuda(self):
self.mean = self.mean.cuda()
self.std = self.std.cuda()
def cpu(self):
self.mean = self.mean.cpu()
self.std = self.std.cpu()
class UnitGaussianNormalizer(Transform):
"""
UnitGaussianNormalizer normalizes data to be zero mean and unit std.
"""
def __init__(self, mean=None, std=None, eps=1e-7, dim=None, mask=None):
"""
mean : torch.tensor or None
has to include batch-size as a dim of 1
e.g. for tensors of shape ``(batch_size, channels, height, width)``,
the mean over height and width should have shape ``(1, channels, 1, 1)``
std : torch.tensor or None
eps : float, default is 0
for safe division by the std
dim : int list, default is None
if not None, dimensions of the data to reduce over to compute the mean and std.
.. important::
Has to include the batch-size (typically 0).
For instance, to normalize data of shape ``(batch_size, channels, height, width)``
along batch-size, height and width, pass ``dim=[0, 2, 3]``
mask : torch.Tensor or None, default is None
If not None, a tensor with the same size as a sample,
with value 0 where the data should be ignored and 1 everywhere else
Notes
-----
The resulting mean will have the same size as the input MINUS the specified dims.
If you do not specify any dims, the mean and std will both be scalars.
Returns
-------
UnitGaussianNormalizer instance
"""
super().__init__()
self.register_buffer("mean", mean)
self.register_buffer("std", std)
self.register_buffer("mask", mask)
self.eps = eps
if mean is not None:
self.ndim = mean.ndim
if isinstance(dim, int):
dim = [dim]
self.dim = dim
self.n_elements = 0
def fit(self, data_batch):
self.update_mean_std(data_batch)
def partial_fit(self, data_batch, batch_size=1):
if 0 in list(data_batch.shape):
return
count = 0
n_samples = len(data_batch)
while count < n_samples:
samples = data_batch[count : count + batch_size]
# print(samples.shape)
# if batch_size == 1:
# samples = samples.unsqueeze(0)
if self.n_elements:
self.incremental_update_mean_std(samples)
else:
self.update_mean_std(samples)
count += batch_size
def update_mean_std(self, data_batch):
self.ndim = data_batch.ndim # Note this includes batch-size
if self.mask is None:
self.n_elements = count_tensor_params(data_batch, self.dim)
self.mean = torch.mean(data_batch, dim=self.dim, keepdim=True)
self.squared_mean = torch.mean(data_batch**2, dim=self.dim, keepdim=True)
self.std = torch.std(data_batch, dim=self.dim, keepdim=True)
else:
batch_size = data_batch.shape[0]
dim = [i - 1 for i in self.dim if i]
shape = [s for i, s in enumerate(self.mask.shape) if i not in dim]
self.n_elements = torch.count_nonzero(self.mask, dim=dim) * batch_size
self.mean = torch.zeros(shape)
self.std = torch.zeros(shape)
self.squared_mean = torch.zeros(shape)
data_batch[:, self.mask == 1] = 0
self.mean[self.mask == 1] = (
torch.sum(data_batch, dim=dim, keepdim=True) / self.n_elements
)
self.squared_mean = (
torch.sum(data_batch**2, dim=dim, keepdim=True) / self.n_elements
)
self.std = torch.std(data_batch, dim=self.dim, keepdim=True)
def incremental_update_mean_std(self, data_batch):
if self.mask is None:
n_elements = count_tensor_params(data_batch, self.dim)
dim = self.dim
else:
dim = [i - 1 for i in self.dim if i]
n_elements = torch.count_nonzero(self.mask, dim=dim) * data_batch.shape[0]
data_batch[:, self.mask == 1] = 0
self.mean = (1.0 / (self.n_elements + n_elements)) * (
self.n_elements * self.mean + torch.sum(data_batch, dim=dim, keepdim=True)
)
self.squared_mean = (1.0 / (self.n_elements + n_elements)) * (
self.n_elements * self.squared_mean
+ torch.sum(data_batch**2, dim=dim, keepdim=True)
)
self.n_elements += n_elements
# 1/(n_i + n_j) * (n_i * sum(x_i^2)/n_i + sum(x_j^2) - (n_i*sum(x_i)/n_i + sum(x_j))^2)
# = 1/(n_i + n_j) * (sum(x_i^2) + sum(x_j^2) - sum(x_i)^2 - 2sum(x_i)sum(x_j) - sum(x_j)^2))
# multiply by (n_i + n_j) / (n_i + n_j + 1) for unbiased estimator
self.std = torch.sqrt(self.squared_mean - self.mean**2) * self.n_elements / (self.n_elements - 1)
def transform(self, x):
return (x - self.mean) / (self.std + self.eps)
def inverse_transform(self, x):
return x * (self.std + self.eps) + self.mean
def forward(self, x):
return self.transform(x)
def cuda(self):
self.mean = self.mean.cuda()
self.std = self.std.cuda()
return self
def cpu(self):
self.mean = self.mean.cpu()
self.std = self.std.cpu()
return self
def to(self, device):
self.mean = self.mean.to(device)
self.std = self.std.to(device)
return self
@classmethod
def from_dataset(cls, dataset, dim=None, keys=None, mask=None):
"""Return a dictionary of normalizer instances, fitted on the given dataset
Parameters
----------
dataset : pytorch dataset
each element must be a dict {key: sample}
e.g. {'x': input_samples, 'y': target_labels}
dim : int list, default is None
* If None, reduce over all dims (scalar mean and std)
* Otherwise, must include batch-dimensions and all over dims to reduce over
keys : str list or None
if not None, a normalizer is instanciated only for the given keys
"""
for i, data_dict in enumerate(dataset):
if not i:
if not keys:
keys = data_dict.keys()
instances = {key: cls(dim=dim, mask=mask) for key in keys}
for i, data_dict in enumerate(dataset):
for key, sample in data_dict.items():
if key in keys:
instances[key].partial_fit(sample.unsqueeze(0))
return instances
class DictUnitGaussianNormalizer(DictTransform):
"""DictUnitGaussianNormalizer composes
DictTransform and UnitGaussianNormalizer to normalize different
fields of a model output tensor to Gaussian distributions w/
mean 0 and unit variance.
Parameters
----------
normalizer_dict : Dict[str, UnitGaussianNormalizer]
dictionary of normalizers, keyed to fields
input_mappings : Dict[slice]
slices of input tensor to grab per field, must share keys with above
return_mappings : Dict[slice]
_description_
"""
def __init__(self,
normalizer_dict: Dict[str, UnitGaussianNormalizer],
input_mappings: Dict[str, slice],
return_mappings: Dict[str, slice]):
assert set(normalizer_dict.keys()) == set(input_mappings.keys()), \
"Error: normalizers and model input fields must be keyed identically"
assert set(normalizer_dict.keys()) == set(return_mappings.keys()), \
"Error: normalizers and model output fields must be keyed identically"
super().__init__(transform_dict=normalizer_dict,
input_mappings=input_mappings,
return_mappings=return_mappings)
@classmethod
def from_dataset(cls, dataset, dim=None, keys=None, mask=None):
"""Return a dictionary of normalizer instances, fitted on the given dataset
Parameters
----------
dataset : pytorch dataset
each element must be a dict {key: sample}
e.g. {'x': input_samples, 'y': target_labels}
dim : int list, default is None
* If None, reduce over all dims (scalar mean and std)
* Otherwise, must include batch-dimensions and all over dims to reduce over
keys : str list or None
if not None, a normalizer is instanciated only for the given keys
"""
for i, data_dict in enumerate(dataset):
if not i:
if not keys:
keys = data_dict.keys()
instances = {key: cls(dim=dim, mask=mask) for key in keys}
for i, data_dict in enumerate(dataset):
for key, sample in data_dict.items():
if key in keys:
instances[key].partial_fit(sample.unsqueeze(0))
return instances
|
neuraloperatorREPO_NAMEneuraloperatorPATH_START.@neuraloperator_extracted@neuraloperator-main@neuralop@data@transforms@normalizers.py@.PATH_END.py
|
{
"filename": "_tickformat.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/layout/ternary/aaxis/_tickformat.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TickformatValidator(_plotly_utils.basevalidators.StringValidator):
def __init__(
self, plotly_name="tickformat", parent_name="layout.ternary.aaxis", **kwargs
):
super(TickformatValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "plot"),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@layout@ternary@aaxis@_tickformat.py@.PATH_END.py
|
{
"filename": "test_return_logical.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/numpy/py3/numpy/f2py/tests/test_return_logical.py",
"type": "Python"
}
|
import pytest
from numpy import array
from . import util
class TestReturnLogical(util.F2PyTest):
def check_function(self, t):
assert t(True) == 1
assert t(False) == 0
assert t(0) == 0
assert t(None) == 0
assert t(0.0) == 0
assert t(0j) == 0
assert t(1j) == 1
assert t(234) == 1
assert t(234.6) == 1
assert t(234.6 + 3j) == 1
assert t("234") == 1
assert t("aaa") == 1
assert t("") == 0
assert t([]) == 0
assert t(()) == 0
assert t({}) == 0
assert t(t) == 1
assert t(-234) == 1
assert t(10**100) == 1
assert t([234]) == 1
assert t((234, )) == 1
assert t(array(234)) == 1
assert t(array([234])) == 1
assert t(array([[234]])) == 1
assert t(array([127], "b")) == 1
assert t(array([234], "h")) == 1
assert t(array([234], "i")) == 1
assert t(array([234], "l")) == 1
assert t(array([234], "f")) == 1
assert t(array([234], "d")) == 1
assert t(array([234 + 3j], "F")) == 1
assert t(array([234], "D")) == 1
assert t(array(0)) == 0
assert t(array([0])) == 0
assert t(array([[0]])) == 0
assert t(array([0j])) == 0
assert t(array([1])) == 1
pytest.raises(ValueError, t, array([0, 0]))
class TestFReturnLogical(TestReturnLogical):
sources = [
util.getpath("tests", "src", "return_logical", "foo77.f"),
util.getpath("tests", "src", "return_logical", "foo90.f90"),
]
@pytest.mark.slow
@pytest.mark.parametrize("name", "t0,t1,t2,t4,s0,s1,s2,s4".split(","))
def test_all_f77(self, name):
self.check_function(getattr(self.module, name))
@pytest.mark.slow
@pytest.mark.parametrize("name",
"t0,t1,t2,t4,t8,s0,s1,s2,s4,s8".split(","))
def test_all_f90(self, name):
self.check_function(getattr(self.module.f90_return_logical, name))
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@numpy@py3@numpy@f2py@tests@test_return_logical.py@.PATH_END.py
|
{
"filename": "README.md",
"repo_name": "California-Planet-Search/planet-pi",
"repo_path": "planet-pi_extracted/planet-pi-master/README.md",
"type": "Markdown"
}
|
# planet-pi
|
California-Planet-SearchREPO_NAMEplanet-piPATH_START.@planet-pi_extracted@planet-pi-master@README.md@.PATH_END.py
|
{
"filename": "minres.py",
"repo_name": "scipy/scipy",
"repo_path": "scipy_extracted/scipy-main/scipy/sparse/linalg/_isolve/minres.py",
"type": "Python"
}
|
from numpy import inner, zeros, inf, finfo
from numpy.linalg import norm
from math import sqrt
from .utils import make_system
__all__ = ['minres']
def minres(A, b, x0=None, *, rtol=1e-5, shift=0.0, maxiter=None,
M=None, callback=None, show=False, check=False):
"""
Use MINimum RESidual iteration to solve Ax=b
MINRES minimizes norm(Ax - b) for a real symmetric matrix A. Unlike
the Conjugate Gradient method, A can be indefinite or singular.
If shift != 0 then the method solves (A - shift*I)x = b
Parameters
----------
A : {sparse array, ndarray, LinearOperator}
The real symmetric N-by-N matrix of the linear system
Alternatively, ``A`` can be a linear operator which can
produce ``Ax`` using, e.g.,
``scipy.sparse.linalg.LinearOperator``.
b : ndarray
Right hand side of the linear system. Has shape (N,) or (N,1).
Returns
-------
x : ndarray
The converged solution.
info : integer
Provides convergence information:
0 : successful exit
>0 : convergence to tolerance not achieved, number of iterations
<0 : illegal input or breakdown
Other Parameters
----------------
x0 : ndarray
Starting guess for the solution.
shift : float
Value to apply to the system ``(A - shift * I)x = b``. Default is 0.
rtol : float
Tolerance to achieve. The algorithm terminates when the relative
residual is below ``rtol``.
maxiter : integer
Maximum number of iterations. Iteration will stop after maxiter
steps even if the specified tolerance has not been achieved.
M : {sparse array, ndarray, LinearOperator}
Preconditioner for A. The preconditioner should approximate the
inverse of A. Effective preconditioning dramatically improves the
rate of convergence, which implies that fewer iterations are needed
to reach a given error tolerance.
callback : function
User-supplied function to call after each iteration. It is called
as callback(xk), where xk is the current solution vector.
show : bool
If ``True``, print out a summary and metrics related to the solution
during iterations. Default is ``False``.
check : bool
If ``True``, run additional input validation to check that `A` and
`M` (if specified) are symmetric. Default is ``False``.
Examples
--------
>>> import numpy as np
>>> from scipy.sparse import csc_array
>>> from scipy.sparse.linalg import minres
>>> A = csc_array([[3, 2, 0], [1, -1, 0], [0, 5, 1]], dtype=float)
>>> A = A + A.T
>>> b = np.array([2, 4, -1], dtype=float)
>>> x, exitCode = minres(A, b)
>>> print(exitCode) # 0 indicates successful convergence
0
>>> np.allclose(A.dot(x), b)
True
References
----------
Solution of sparse indefinite systems of linear equations,
C. C. Paige and M. A. Saunders (1975),
SIAM J. Numer. Anal. 12(4), pp. 617-629.
https://web.stanford.edu/group/SOL/software/minres/
This file is a translation of the following MATLAB implementation:
https://web.stanford.edu/group/SOL/software/minres/minres-matlab.zip
"""
A, M, x, b, postprocess = make_system(A, M, x0, b)
matvec = A.matvec
psolve = M.matvec
first = 'Enter minres. '
last = 'Exit minres. '
n = A.shape[0]
if maxiter is None:
maxiter = 5 * n
msg = [' beta2 = 0. If M = I, b and x are eigenvectors ', # -1
' beta1 = 0. The exact solution is x0 ', # 0
' A solution to Ax = b was found, given rtol ', # 1
' A least-squares solution was found, given rtol ', # 2
' Reasonable accuracy achieved, given eps ', # 3
' x has converged to an eigenvector ', # 4
' acond has exceeded 0.1/eps ', # 5
' The iteration limit was reached ', # 6
' A does not define a symmetric matrix ', # 7
' M does not define a symmetric matrix ', # 8
' M does not define a pos-def preconditioner '] # 9
if show:
print(first + 'Solution of symmetric Ax = b')
print(first + f'n = {n:3g} shift = {shift:23.14e}')
print(first + f'itnlim = {maxiter:3g} rtol = {rtol:11.2e}')
print()
istop = 0
itn = 0
Anorm = 0
Acond = 0
rnorm = 0
ynorm = 0
xtype = x.dtype
eps = finfo(xtype).eps
# Set up y and v for the first Lanczos vector v1.
# y = beta1 P' v1, where P = C**(-1).
# v is really P' v1.
if x0 is None:
r1 = b.copy()
else:
r1 = b - A@x
y = psolve(r1)
beta1 = inner(r1, y)
if beta1 < 0:
raise ValueError('indefinite preconditioner')
elif beta1 == 0:
return (postprocess(x), 0)
bnorm = norm(b)
if bnorm == 0:
x = b
return (postprocess(x), 0)
beta1 = sqrt(beta1)
if check:
# are these too strict?
# see if A is symmetric
w = matvec(y)
r2 = matvec(w)
s = inner(w,w)
t = inner(y,r2)
z = abs(s - t)
epsa = (s + eps) * eps**(1.0/3.0)
if z > epsa:
raise ValueError('non-symmetric matrix')
# see if M is symmetric
r2 = psolve(y)
s = inner(y,y)
t = inner(r1,r2)
z = abs(s - t)
epsa = (s + eps) * eps**(1.0/3.0)
if z > epsa:
raise ValueError('non-symmetric preconditioner')
# Initialize other quantities
oldb = 0
beta = beta1
dbar = 0
epsln = 0
qrnorm = beta1
phibar = beta1
rhs1 = beta1
rhs2 = 0
tnorm2 = 0
gmax = 0
gmin = finfo(xtype).max
cs = -1
sn = 0
w = zeros(n, dtype=xtype)
w2 = zeros(n, dtype=xtype)
r2 = r1
if show:
print()
print()
print(' Itn x(1) Compatible LS norm(A) cond(A) gbar/|A|')
while itn < maxiter:
itn += 1
s = 1.0/beta
v = s*y
y = matvec(v)
y = y - shift * v
if itn >= 2:
y = y - (beta/oldb)*r1
alfa = inner(v,y)
y = y - (alfa/beta)*r2
r1 = r2
r2 = y
y = psolve(r2)
oldb = beta
beta = inner(r2,y)
if beta < 0:
raise ValueError('non-symmetric matrix')
beta = sqrt(beta)
tnorm2 += alfa**2 + oldb**2 + beta**2
if itn == 1:
if beta/beta1 <= 10*eps:
istop = -1 # Terminate later
# Apply previous rotation Qk-1 to get
# [deltak epslnk+1] = [cs sn][dbark 0 ]
# [gbar k dbar k+1] [sn -cs][alfak betak+1].
oldeps = epsln
delta = cs * dbar + sn * alfa # delta1 = 0 deltak
gbar = sn * dbar - cs * alfa # gbar 1 = alfa1 gbar k
epsln = sn * beta # epsln2 = 0 epslnk+1
dbar = - cs * beta # dbar 2 = beta2 dbar k+1
root = norm([gbar, dbar])
Arnorm = phibar * root
# Compute the next plane rotation Qk
gamma = norm([gbar, beta]) # gammak
gamma = max(gamma, eps)
cs = gbar / gamma # ck
sn = beta / gamma # sk
phi = cs * phibar # phik
phibar = sn * phibar # phibark+1
# Update x.
denom = 1.0/gamma
w1 = w2
w2 = w
w = (v - oldeps*w1 - delta*w2) * denom
x = x + phi*w
# Go round again.
gmax = max(gmax, gamma)
gmin = min(gmin, gamma)
z = rhs1 / gamma
rhs1 = rhs2 - delta*z
rhs2 = - epsln*z
# Estimate various norms and test for convergence.
Anorm = sqrt(tnorm2)
ynorm = norm(x)
epsa = Anorm * eps
epsx = Anorm * ynorm * eps
epsr = Anorm * ynorm * rtol
diag = gbar
if diag == 0:
diag = epsa
qrnorm = phibar
rnorm = qrnorm
if ynorm == 0 or Anorm == 0:
test1 = inf
else:
test1 = rnorm / (Anorm*ynorm) # ||r|| / (||A|| ||x||)
if Anorm == 0:
test2 = inf
else:
test2 = root / Anorm # ||Ar|| / (||A|| ||r||)
# Estimate cond(A).
# In this version we look at the diagonals of R in the
# factorization of the lower Hessenberg matrix, Q @ H = R,
# where H is the tridiagonal matrix from Lanczos with one
# extra row, beta(k+1) e_k^T.
Acond = gmax/gmin
# See if any of the stopping criteria are satisfied.
# In rare cases, istop is already -1 from above (Abar = const*I).
if istop == 0:
t1 = 1 + test1 # These tests work if rtol < eps
t2 = 1 + test2
if t2 <= 1:
istop = 2
if t1 <= 1:
istop = 1
if itn >= maxiter:
istop = 6
if Acond >= 0.1/eps:
istop = 4
if epsx >= beta1:
istop = 3
# if rnorm <= epsx : istop = 2
# if rnorm <= epsr : istop = 1
if test2 <= rtol:
istop = 2
if test1 <= rtol:
istop = 1
# See if it is time to print something.
prnt = False
if n <= 40:
prnt = True
if itn <= 10:
prnt = True
if itn >= maxiter-10:
prnt = True
if itn % 10 == 0:
prnt = True
if qrnorm <= 10*epsx:
prnt = True
if qrnorm <= 10*epsr:
prnt = True
if Acond <= 1e-2/eps:
prnt = True
if istop != 0:
prnt = True
if show and prnt:
str1 = f'{itn:6g} {x[0]:12.5e} {test1:10.3e}'
str2 = f' {test2:10.3e}'
str3 = f' {Anorm:8.1e} {Acond:8.1e} {gbar/Anorm:8.1e}'
print(str1 + str2 + str3)
if itn % 10 == 0:
print()
if callback is not None:
callback(x)
if istop != 0:
break # TODO check this
if show:
print()
print(last + f' istop = {istop:3g} itn ={itn:5g}')
print(last + f' Anorm = {Anorm:12.4e} Acond = {Acond:12.4e}')
print(last + f' rnorm = {rnorm:12.4e} ynorm = {ynorm:12.4e}')
print(last + f' Arnorm = {Arnorm:12.4e}')
print(last + msg[istop+1])
if istop == 6:
info = maxiter
else:
info = 0
return (postprocess(x),info)
|
scipyREPO_NAMEscipyPATH_START.@scipy_extracted@scipy-main@scipy@sparse@linalg@_isolve@minres.py@.PATH_END.py
|
{
"filename": "model_export_as_cpp_code_tutorial.md",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/catboost/tutorials/apply_model/model_export_as_cpp_code_tutorial.md",
"type": "Markdown"
}
|
Export of CatBoost model as standalone C++ code
===============================================
Catboost model could be saved as standalone C++ code. This can ease an integration of a generated model into an application built from C++ sources, simplify porting the model to an architecture not direcly supported by CatBoost (eq. ARM), or allow manual exploration and editing of the model parameters by advanced users.
The exported model code contains complete data for the current trained model and *apply_catboost_model()* function which applies the model to a given dataset. The only current dependency for the code is [CityHash library](https://github.com/google/cityhash/tree/00b9287e8c1255b5922ef90e304d5287361b2c2a) (NOTE: The exact revision under the link is required).
### Exporting from Catboost application via command line interface:
```bash
catboost fit --model-format CPP <other_fit_parameters>
```
By default model is saved into *model.cpp* file. One could alter the output name using *-m* key. If there is more that one model-format specified, then the *.cpp* extention will be added to the name provided after *-m* key.
### Exporting from Catboost python library interface:
```python
model = CatBoost(<train_params>)
model.fit(train_pool)
model.save_model(OUTPUT_CPP_MODEL_PATH, format="CPP")
```
## Models trained with only Float features
If the model was trained using only numerical features (no cat features), then the application function in generated code will have the following interface:
```cpp
double ApplyCatboostModel(const std::vector<float>& features);
```
### Parameters
| parameter | description |
|-----------|--------------------------------------------------|
| features | features of a single document to make prediction |
### Return value
Prediction of the model for the document with given features.
The result is equivalent to the code below except it won't require linking of libcatboostmodel.<so|dll|dylib>.
```cpp
#include <catboost/libs/model_interface/wrapped_calcer.h>
double ApplyCatboostModel(const std::vector<float>& features) {
ModelCalcerWrapper calcer("model.cbm");
return calcer.Calc(features, {});
}
```
### Compiler requirements
C++11 support of non-static data member initializers and extended initializer lists
## Models trained with Categorical features
If the model was trained with categorical features present, then the application function in output code will be generated with the following interface:
```cpp
double ApplyCatboostModel(const std::vector<float>& floatFeatures, const std::vector<std::string>& catFeatures);
```
### Parameters
| parameter | description |
|---------------|-------------------------------------------|
| floatFeatures | numerical features of a single document |
| catFeatures | categorical features of a single document |
NOTE: You need to pass float and categorical features separately in the same order they appeared in the train dataset. For example if you had features f1,f2,f3,f4, where f2 and f4 were considered categorical, you need to pass here floatFeatures = {f1, f3}, catFeatures = {f2, f4}.
### Return value
Prediction of the model for the document with given features.
The result is equivalent to the code below except it won't require linking of libcatboostmodel.<so|dll|dylib>.
```cpp
#include <catboost/libs/model_interface/wrapped_calcer.h>
double ApplyCatboostModel(const std::vector<float>& floatFeatures, const std::vector<std::string>& catFeatures) {
ModelCalcerWrapper calcer("model.cbm");
return calcer.Calc(floatFeatures, catFeatures);
}
```
### Compiler requiremens
C++14 compiler with aggregate member initialization support. Tested compilers: g++ 5(5.4.1 20160904), clang++ 3.8.
## Current limitations
- MultiClassification models are not supported.
- applyCatboostModel() function has reference implementation and may lack of performance comparing to native applicator of CatBoost, especially on large models and multiple of documents.
## Troubleshooting
Q: Generated model results differ from native model when categorical features present
A: Please check that CityHash version 1 is used. Exact required revision of [C++ Google CityHash library](https://github.com/Amper/cityhash/tree/4f02fe0ba78d4a6d1735950a9c25809b11786a56%29). There is also proper CityHash implementation in [Catboost repository](https://github.com/catboost/catboost/blob/master/util/digest/city.h). This is due other versions of CityHash may produce different hash code for the same string.
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@catboost@tutorials@apply_model@model_export_as_cpp_code_tutorial.md@.PATH_END.py
|
{
"filename": "spherical_geometry.py",
"repo_name": "astropy/halotools",
"repo_path": "halotools_extracted/halotools-master/halotools/utils/spherical_geometry.py",
"type": "Python"
}
|
"""
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np
from astropy.utils.misc import NumpyRNGContext
__all__ = ['spherical_to_cartesian', 'chord_to_cartesian', 'sample_spherical_surface']
__author__ = ('Duncan Campbell', )
def spherical_to_cartesian(ra, dec):
"""
Calculate cartesian coordinates on a unit sphere given two angular coordinates.
parameters
Parameters
-----------
ra : array
Angular coordinate in degrees
dec : array
Angular coordinate in degrees
Returns
--------
x,y,z : sequence of arrays
Cartesian coordinates.
Examples
---------
>>> ra, dec = 0.1, 1.5
>>> x, y, z = spherical_to_cartesian(ra, dec)
"""
rar = np.radians(ra)
decr = np.radians(dec)
x = np.cos(rar) * np.cos(decr)
y = np.sin(rar) * np.cos(decr)
z = np.sin(decr)
return x, y, z
def chord_to_cartesian(theta, radians=True):
"""
Calculate chord distance on a unit sphere given an angular distance between two
points.
Parameters
-----------
theta : array
angular distance
radians : bool, optional
If True, input is interpreted as radians.
If False, input in degrees. Default is True.
Returns
--------
C : array
chord distance
Examples
--------
>>> theta = np.linspace(0, 1, 100)
>>> chord_distance = chord_to_cartesian(theta)
"""
theta = np.atleast_1d(theta)
if radians is False:
theta = np.radians(theta)
C = 2.0*np.sin(theta/2.0)
return C
def sample_spherical_surface(N_points, seed=None):
"""
Randomly sample the sky.
Parameters
----------
N_points : int
number of points to sample.
seed : int, optional
Random number seed permitting deterministic behavior.
Default is None for stochastic results.
Returns
----------
coords : list
(ra,dec) coordinate pairs in degrees.
Examples
---------
>>> angular_coords_in_degrees = sample_spherical_surface(100, seed=43)
"""
with NumpyRNGContext(seed):
ran1 = np.random.rand(N_points) # oversample, to account for box sample
ran2 = np.random.rand(N_points) # oversample, to account for box sample
ran1 = ran1 * 2.0 * np.pi # convert to radians
ran2 = np.arccos(2.0 * ran2 - 1.0) - 0.5*np.pi # convert to radians
ran1 = ran1 * 360.0 / (2.0 * np.pi) # convert to degrees
ran2 = ran2 * 360.0 / (2.0 * np.pi) # convert to degrees
ran_ra = ran1
ran_dec = ran2
coords = list(zip(ran_ra, ran_dec))
return coords
|
astropyREPO_NAMEhalotoolsPATH_START.@halotools_extracted@halotools-master@halotools@utils@spherical_geometry.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/community/tests/integration_tests/chat_models/__init__.py",
"type": "Python"
}
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@community@tests@integration_tests@chat_models@__init__.py@.PATH_END.py
|
|
{
"filename": "jaxemulator.py",
"repo_name": "gully/blase",
"repo_path": "blase_extracted/blase-main/src/blase/jaxemulator.py",
"type": "Python"
}
|
r"""
emulator
--------------
Precomputed synthetic spectral models are awesome but imperfect and rigid. Here we clone the most prominent spectral lines and continuum appearance of synthetic spectral models to turn them into tunable, flexible, semi-empirical models. We can ultimately learn the properties of the pre-computed models with a neural network training loop, and then transfer those weights to real data, where a second transfer-learning training step can take place. The spectrum has :math:`N_{\rm pix} \sim 300,000` pixels and :math:`N_{\rm lines} \sim 5000` spectral lines. The number of lines is set by the `prominence=` kwarg: lower produces more lines and higher (up to about 0.3) produces fewer lines.
"""
from dataclasses import dataclass
import math
import jax
import jax.numpy as jnp
import numpy as np
from scipy.signal import find_peaks, peak_prominences, peak_widths
from tqdm import trange
from exojax.spec import voigt, vvoigt
# jax.config.update("jax_enable_x64", True)
class SparseLinearEmulator(object):
r"""
A sparse implementation of the LinearEmulator
Parameters
----------
wl_native : float vector
The input wavelength at native sampling
flux_native : float vector
The continuum-flattened flux at native sampling
prominence : int
The threshold for detecting lines
wing_cut_pixels : int
The number of pixels centered on the line center to evaluate in the
sparse implementation, default: 1000 pixels
init_state_dict : dict
A dictionary of model parameters to initialize the model with
"""
def __init__(
self,
wl_native,
flux_native=None,
prominence=None,
wing_cut_pixels=None,
init_state_dict=None,
):
# Read in the synthetic spectra at native resolution
self.wl_native = jnp.array(wl_native)
self.wl_min = wl_native.min()
self.wl_max = wl_native.max()
self.n_pix = len(wl_native)
## Set up "active area", where the region-of-interest is:
## Restrict the lines to the active region plus 30 A buffer region
## These are hardcoded, and care should be taken if changing them
line_buffer = 30 # Angstroms
active_buffer = 60 # Angstroms
active_lower, active_upper = (
self.wl_min + active_buffer,
self.wl_max - active_buffer,
)
active_mask = (wl_native > active_lower) & (wl_native < active_upper)
self.active_mask = jnp.array(active_mask)
self.wl_active = self.wl_native[active_mask]
if flux_native is not None:
self.flux_native = jnp.array(flux_native)
self.flux_active = self.flux_native[active_mask]
else:
self.flux_native = None
self.flux_active = None
# Set up line threshold, where lines are computed outside the active area
line_threshold_lower, line_threshold_upper = (
self.wl_min + line_buffer,
self.wl_max - line_buffer,
)
if init_state_dict is not None:
if prominence is not None:
print(
"You have entered both an initial state dict and a prominence kwarg. Discarding prominence kwarg in favor of state dict."
)
lam_centers = init_state_dict["lam_centers"]
log_amps = init_state_dict["amplitudes"]
log_sigma_widths = init_state_dict["sigma_widths"]
log_gamma_widths = init_state_dict["gamma_widths"]
elif init_state_dict is None and self.flux_native is not None:
if prominence is None:
prominence = 0.03
(lam_centers, amplitudes, widths_angstroms,) = self.detect_lines(
self.wl_native, self.flux_native, prominence=prominence
)
# Experimentally determined scale factor tweaks
amp_tweak = 0.14
sigma_width_tweak = 1.28
gamma_width_tweak = 1.52
mask = (lam_centers > line_threshold_lower) & (
lam_centers < line_threshold_upper
)
lam_centers = lam_centers[mask]
log_amps = jnp.log(amplitudes[mask] * amp_tweak)
log_sigma_widths = jnp.log(
widths_angstroms[mask] / math.sqrt(2) * sigma_width_tweak
)
log_gamma_widths = jnp.log(
widths_angstroms[mask] / math.sqrt(2) * gamma_width_tweak
)
elif init_state_dict is None and self.flux_native is None:
raise ValueError(
"Either flux_native or init_state_dict must be provided to specify the spectral lines"
)
# Fix the wavelength centers as gospel for now.
self.lam_centers = jnp.array(lam_centers)
self.amplitudes = jnp.array(log_amps)
self.sigma_widths = jnp.array(log_sigma_widths)
self.gamma_widths = jnp.array(log_gamma_widths)
self.n_lines = len(lam_centers)
self.a_coeff = jnp.array(1.0)
self.b_coeff = jnp.array(0.0)
self.c_coeff = jnp.array(0.0)
self.wl_normed = (self.wl_native - 10_500.0) / 2500.0
if self.flux_native is not None:
self.target = jnp.array(self.flux_active)
else:
self.target = None
## Define the wing cut
# Currently defined in *pixels*
if wing_cut_pixels is None:
wing_cut_pixels = 1000
else:
wing_cut_pixels = int(wing_cut_pixels)
lines = self.lam_centers
wl_native = self.wl_native
print("Initializing a sparse model with {:} spectral lines".format(len(lines)))
# Find the index position of each spectral line
center_indices = np.searchsorted(wl_native, lines)
# From that, determine the beginning and ending indices
zero_indices = center_indices - (wing_cut_pixels // 2)
too_low = zero_indices < 0
## The next lines are a JAX workaround for item assignment:
# zero_indices[too_low] = 0 # can't do this in JAX
zero_indices = zero_indices.at[too_low].set(0)
end_indices = zero_indices + wing_cut_pixels
too_high = end_indices > self.n_pix
## The next lines are a JAX workaround for item assignment:
# zero_indices[too_low] = 0 # can't do this in JAX
zero_indices = zero_indices.at[too_high].set(self.n_pix - wing_cut_pixels - 1)
end_indices = end_indices.at[too_high].set(self.n_pix - 1)
# Make a 2D array of the indices
indices_2D = np.linspace(
zero_indices, end_indices, num=wing_cut_pixels, endpoint=True
)
self.indices_2D = jnp.array(indices_2D.T, dtype=jnp.int32)
self.indices_1D = self.indices_2D.reshape(-1)
self.indices = np.expand_dims(self.indices_1D, axis=0)
self.wl_2D = self.wl_native[self.indices_2D]
self.wl_1D = self.wl_2D.reshape(-1)
self.active_mask = self.active_mask
self.radial_velocity = jnp.array(0.0)
def detect_lines(self, wl_native, flux_native, prominence=0.03):
"""Identify the spectral lines in the native model
Parameters
----------
wl_native : torch.tensor
The 1D vector of native model wavelengths (Angstroms)
flux_native: torch.tensor
The 1D vector of continuum-flattened model fluxes
Returns
-------
tuple of tensors
The wavelength centers, prominences, and widths for all ID'ed
spectral lines
"""
peaks, _ = find_peaks(-flux_native, distance=4, prominence=prominence)
prominence_data = peak_prominences(-flux_native, peaks)
width_data = peak_widths(-flux_native, peaks, prominence_data=prominence_data)
lam_centers = wl_native[peaks]
prominences = jnp.array(prominence_data[0])
widths = width_data[0]
d_lam = np.diff(wl_native)[peaks]
# Convert FWHM in pixels to Gaussian sigma in Angstroms
widths_angs = jnp.array(widths * d_lam / 2.355)
return (lam_centers, prominences, widths_angs)
def _lorentzian_line(self, lam_center, width, wavelengths):
"""Return a Lorentzian line, given properties"""
return 1 / 3.141592654 * width / (width**2 + (wavelengths - lam_center) ** 2)
def _gaussian_line(self, lam_center, width, wavelengths):
"""Return a normalized Gaussian line, given properties"""
return (
1.0
/ (width * 2.5066)
* jnp.exp(-0.5 * ((wavelengths - lam_center) / width) ** 2)
)
def _compute_eta(self, fwhm_L, fwhm):
"""Compute the eta mixture ratio for pseudo-Voigt weighting"""
f_ratio = fwhm_L / fwhm
return 1.36603 * f_ratio - 0.47719 * f_ratio**2 + 0.11116 * f_ratio**3
def _compute_fwhm(self, fwhm_L, fwhm_G):
"""Compute the fwhm for pseudo Voigt using the approximation"""
return (
fwhm_G**5
+ 2.69269 * fwhm_G**4 * fwhm_L**1
+ 2.42843 * fwhm_G**3 * fwhm_L**2
+ 4.47163 * fwhm_G**2 * fwhm_L**3
+ 0.07842 * fwhm_G**1 * fwhm_L**4
+ fwhm_L**5
) ** (1 / 5)
def forward(self, ln_amplitudes, ln_sigma_widths, ln_gamma_widths):
r"""The forward pass of the sparse implementation--- no wavelengths needed!
Returns
-------
torch.tensor
The 1D generative spectral model destined for backpropagation
"""
return self.sparse_pseudo_Voigt_model(
ln_amplitudes, ln_sigma_widths, ln_gamma_widths
)
def sparse_pseudo_Voigt_model(self, ln_amplitudes, ln_sigma_widths, ln_gamma_widths):
r"""A sparse pseudo-Voigt model
The sparse matrix :math:`\hat{F}` is composed of the log flux
values. Instead of a dense matrix :math:`\bar{F}`, the log fluxes
are stored as trios of coordinate values and fluxes.
:math:`(i, j, \ln{F_{ji}})`. The computation proceeds as follows:
.. math::
\mathsf{S}_{\rm clone} = \exp{\Big(\sum_{j=1}^{N_{lines}} \ln{F_{ji}} \Big)}
Returns
-------
torch.tensor
The 1D generative sparse spectral model
"""
fwhm_G = 2.3548 * jnp.exp(ln_sigma_widths)[:, None]
fwhm_L = 2.0 * jnp.exp(ln_gamma_widths)[:, None]
fwhm = self._compute_fwhm(fwhm_L, fwhm_G)
eta = self._compute_eta(fwhm_L, fwhm)
rv_shifted_centers = self.lam_centers * (
1.0 + self.radial_velocity / 299_792.458
)
flux_2D = jnp.exp(ln_amplitudes)[:, None] * (
eta
* self._lorentzian_line(
rv_shifted_centers[:, None],
jnp.exp(ln_gamma_widths)[:, None],
self.wl_2D,
)
+ (1 - eta)
* self._gaussian_line(
rv_shifted_centers[:, None],
jnp.exp(ln_sigma_widths)[:, None],
self.wl_2D,
)
)
# Enforce that you cannot have negative flux or emission lines
flux_2D = jnp.clip(flux_2D, 1e-6, 1 - 1e-6)
flux_1D = flux_2D.reshape(-1)
ln_term = jnp.log(1 - flux_1D)
## This operation applies a sparse COALESCE operation:
# Repeated indices get added together.
flux_out = jnp.zeros_like(self.flux_native)
flux_out = flux_out.at[self.indices_1D].add(ln_term)
return jnp.exp(flux_out)[self.active_mask]
class SparseLinearEmissionEmulator(SparseLinearEmulator):
"""An emission line version of the sparse emulator"""
def forward(self, ln_amplitudes, ln_sigma_widths, ln_gamma_widths):
r"""The forward pass of the sparse implementation--- no wavelengths needed!
Returns
-------
torch.tensor
The 1D generative spectral model destined for backpropagation
"""
return self.sparse_Voigt_model(
ln_amplitudes, ln_sigma_widths, ln_gamma_widths
)
def sparse_Voigt_model(
self, ln_amplitudes, ln_sigma_widths, ln_gamma_widths
):
r"""A sparse pseudo-Voigt model
The sparse matrix :math:`\hat{F}` is composed of the log flux
values. Instead of a dense matrix :math:`\bar{F}`, the log fluxes
are stored as trios of coordinate values and fluxes.
:math:`(i, j, \ln{F_{ji}})`. The computation proceeds as follows:
.. math::
\mathsf{S}_{\rm clone} = \exp{\Big(\sum_{j=1}^{N_{lines}} \ln{F_{ji}} \Big)}
Returns
-------
torch.tensor
The 1D generative sparse spectral model
"""
#fwhm_G = 2.3548 * jnp.exp(ln_sigma_widths)[:, None]
#fwhm_L = 2.0 * jnp.exp(ln_gamma_widths)[:, None]
#fwhm = self._compute_fwhm(fwhm_L, fwhm_G)
rv_shifted_centers = self.lam_centers * (
1.0 + self.radial_velocity / 299_792.458
)
flux_2D = jnp.exp(ln_amplitudes)[:, None] * vvoigt(
self.wl_2D - rv_shifted_centers[:, None],
jnp.exp(ln_sigma_widths)[:, None],
jnp.exp(ln_gamma_widths)[:, None]).squeeze()
flux_1D = flux_2D.reshape(-1)
## This operation applies a sparse COALESCE operation:
# Repeated indices get added together.
flux_out = jnp.zeros_like(self.flux_native)
flux_out = flux_out.at[self.indices_1D].add(flux_1D)
return flux_out
|
gullyREPO_NAMEblasePATH_START.@blase_extracted@blase-main@src@blase@jaxemulator.py@.PATH_END.py
|
{
"filename": "fast_bilinar_interpolation.py",
"repo_name": "threeML/hawc_hal",
"repo_path": "hawc_hal_extracted/hawc_hal-master/hawc_hal/interpolation/fast_bilinar_interpolation.py",
"type": "Python"
}
|
from __future__ import division
from builtins import object
from past.utils import old_div
import numpy as np
from numba import jit
class FastBilinearInterpolation(object):
"""
A super fast bilinar interpolation implementation which exploits the fact that we are always interpolating in the
same grid. For example, if we always go from the same flat sky projection to the same Healpix map, we can precompute
the weights for the interpolation and then apply them to any new data instead of recomputing them every time.
"""
def __init__(self, input_shape, new_points):
self._gridx = np.arange(input_shape[0])
self._gridy = np.arange(input_shape[1])
self._x_bounds = [self._gridx.min(), self._gridx.max()]
self._y_bounds = [self._gridy.min(), self._gridy.max()]
self._data_shape = (self._gridx.shape[0], self._gridy.shape[0])
self._bs, self._flat_points = self.compute_coefficients(new_points)
@staticmethod
def _find_bounding_box(xaxis, yaxis, xs, ys):
# Find lower left corner of bounding box
xidx = np.searchsorted(xaxis, xs, 'left') - 1
yidx = np.searchsorted(yaxis, ys, 'left') - 1
lower_left_x = xaxis[xidx]
lower_left_y = yaxis[yidx]
upper_left_x = xaxis[xidx]
upper_left_y = yaxis[yidx + 1]
upper_right_x = xaxis[xidx + 1]
upper_right_y = yaxis[yidx + 1]
lower_right_x = xaxis[xidx + 1]
lower_right_y = yaxis[yidx]
return (lower_left_x, lower_left_y,
upper_left_x, upper_left_y,
upper_right_x, upper_right_y,
lower_right_x, lower_right_y)
def compute_coefficients(self, p):
xx = p[0]
yy = p[1]
# Find bounding boxes
# We need a situation like this
# x1 x2
# y1 q11 q21
#
# y2 q12 q22
x1, y2, xx1, y1, x2, yy1, xx2, yy2 = self._find_bounding_box(self._gridx, self._gridy, xx, yy)
bs = np.zeros((xx.shape[0], 4), np.float64)
bs[:, 0] = old_div((x2 - xx) * (y2 - yy), (x2 - x1)) * (y2 - y1)
bs[:, 1] = old_div((xx - x1) * (y2 - yy), (x2 - x1)) * (y2 - y1)
bs[:, 2] = old_div((x2 - xx) * (yy - y1), (x2 - x1)) * (y2 - y1)
bs[:, 3] = old_div((xx - x1) * (yy - y1), (x2 - x1)) * (y2 - y1)
# Get the flat indexing for all the corners of the bounding boxes
flat_upper_left = np.ravel_multi_index((x1, y1), self._data_shape)
flat_upper_right = np.ravel_multi_index((x2, y1), self._data_shape)
flat_lower_left = np.ravel_multi_index((x1, y2), self._data_shape)
flat_lower_right = np.ravel_multi_index((x2, y2), self._data_shape)
# Stack them so that they are in the right order, i.e.:
# ul1, ur1, ll1, lr1, ul2, ur2, ll2, lr2 ... uln, urn, lln, lrn
flat_points = np.vstack([flat_upper_left,
flat_upper_right,
flat_lower_left,
flat_lower_right]).T.flatten()
return bs, flat_points.astype(np.int64)
def __call__(self, data):
res = _apply_bilinar_interpolation(self._bs, self._flat_points, data)
return res
@jit("float64[:](float64[:, :], int64[:], float64[:, :])", nopython=True)
def _apply_bilinar_interpolation(bs, points, data): # pragma: no cover
vs = data.ravel()[points]
return np.sum(bs * vs.reshape(bs.shape), axis=1).flatten()
|
threeMLREPO_NAMEhawc_halPATH_START.@hawc_hal_extracted@hawc_hal-master@hawc_hal@interpolation@fast_bilinar_interpolation.py@.PATH_END.py
|
{
"filename": "test_with_scipy.py",
"repo_name": "dmlc/xgboost",
"repo_path": "xgboost_extracted/xgboost-master/tests/python/test_with_scipy.py",
"type": "Python"
}
|
import itertools
import warnings
from typing import Type
import numpy as np
import pytest
import scipy.sparse
import xgboost as xgb
from xgboost import testing as tm
@pytest.mark.filterwarnings("error")
@pytest.mark.parametrize(
"DMatrixT,CSR",
[
(m, n)
for m, n in itertools.product(
(xgb.DMatrix, xgb.QuantileDMatrix),
(scipy.sparse.csr_matrix, scipy.sparse.csr_array),
)
],
)
def test_csr(DMatrixT: Type[xgb.DMatrix], CSR: Type) -> None:
with warnings.catch_warnings():
indptr = np.array([0, 2, 3, 6])
indices = np.array([0, 2, 2, 0, 1, 2])
data = np.array([1, 2, 3, 4, 5, 6])
X = CSR((data, indices, indptr), shape=(3, 3))
dtrain = DMatrixT(X)
assert dtrain.num_row() == 3
assert dtrain.num_col() == 3
assert dtrain.num_nonmissing() == data.size
@pytest.mark.filterwarnings("error")
@pytest.mark.parametrize(
"DMatrixT,CSC",
[
(m, n)
for m, n in itertools.product(
(xgb.DMatrix, xgb.QuantileDMatrix),
(scipy.sparse.csc_matrix, scipy.sparse.csc_array),
)
],
)
def test_csc(DMatrixT: Type[xgb.DMatrix], CSC: Type) -> None:
with warnings.catch_warnings():
row = np.array([0, 2, 2, 0, 1, 2])
col = np.array([0, 0, 1, 2, 2, 2])
data = np.array([1, 2, 3, 4, 5, 6])
X = CSC((data, (row, col)), shape=(3, 3))
dtrain = DMatrixT(X)
assert dtrain.num_row() == 3
assert dtrain.num_col() == 3
assert dtrain.num_nonmissing() == data.size
indptr = np.array([0, 3, 5])
data = np.array([0, 1, 2, 3, 4])
row_idx = np.array([0, 1, 2, 0, 2])
X = CSC((data, row_idx, indptr), shape=(3, 2))
assert tm.predictor_equal(DMatrixT(X.tocsr()), DMatrixT(X))
@pytest.mark.filterwarnings("error")
@pytest.mark.parametrize(
"DMatrixT,COO",
[
(m, n)
for m, n in itertools.product(
(xgb.DMatrix, xgb.QuantileDMatrix),
(scipy.sparse.coo_matrix, scipy.sparse.coo_array),
)
],
)
def test_coo(DMatrixT: Type[xgb.DMatrix], COO: Type) -> None:
with warnings.catch_warnings():
row = np.array([0, 2, 2, 0, 1, 2])
col = np.array([0, 0, 1, 2, 2, 2])
data = np.array([1, 2, 3, 4, 5, 6])
X = COO((data, (row, col)), shape=(3, 3))
dtrain = DMatrixT(X)
assert dtrain.num_row() == 3
assert dtrain.num_col() == 3
assert dtrain.num_nonmissing() == data.size
assert tm.predictor_equal(DMatrixT(X.tocsr()), DMatrixT(X))
|
dmlcREPO_NAMExgboostPATH_START.@xgboost_extracted@xgboost-master@tests@python@test_with_scipy.py@.PATH_END.py
|
{
"filename": "test_imaging_data.py",
"repo_name": "sibirrer/lenstronomy",
"repo_path": "lenstronomy_extracted/lenstronomy-main/test/test_Data/test_imaging_data.py",
"type": "Python"
}
|
import pytest
import numpy as np
import numpy.testing as npt
import copy
import unittest
from lenstronomy.Data.imaging_data import ImageData
import lenstronomy.Util.util as util
class TestData(object):
def setup_method(self):
self.numPix = 10
kwargs_data = {
"image_data": np.zeros((self.numPix, self.numPix)),
"noise_map": np.ones((self.numPix, self.numPix)),
}
self.Data = ImageData(**kwargs_data)
def test_numData(self):
assert self.Data.num_pixel == self.numPix**2
def test_shift_coords(self):
numPix = 10
deltaPix = 0.05
(
x_grid,
y_grid,
ra_at_xy_0,
dec_at_xy_0,
x_at_radec_0,
y_at_radec_0,
Mpix2coord,
Mcoord2pix,
) = util.make_grid_with_coordtransform(
numPix=numPix, deltapix=deltaPix, subgrid_res=1, inverse=True
)
# mask (1= model this pixel, 0= leave blanck)
kwargs_data = {
"ra_at_xy_0": ra_at_xy_0,
"dec_at_xy_0": dec_at_xy_0,
"transform_pix2angle": Mpix2coord,
"image_data": np.ones((numPix, numPix)),
}
data = ImageData(**kwargs_data)
ra_shift = 0.05
dec_shift = 0.0
kwargs_data["ra_shift"] = ra_shift
kwargs_data["dec_shift"] = dec_shift
data_shift = ImageData(**kwargs_data)
ra, dec = data.map_pix2coord(1, 1)
ra_new, dec_new = data_shift.map_pix2coord(1, 1)
npt.assert_almost_equal(ra_new - ra, ra_shift, decimal=10)
npt.assert_almost_equal(dec_new - dec, dec_shift, decimal=10)
ra_2, dec_2 = data_shift.map_pix2coord(2, 1)
npt.assert_almost_equal(ra, ra_2, decimal=10)
npt.assert_almost_equal(dec, dec_2, decimal=10)
x, y = data.map_coord2pix(0, 0)
x_new, y_new = data_shift.map_coord2pix(ra_shift, dec_shift)
npt.assert_almost_equal(x, x_new, decimal=10)
npt.assert_almost_equal(y, y_new, decimal=10)
def test_shift_coordinate_system(self):
x_shift = 0.05
y_shift = 0
numPix = 10
deltaPix = 0.05
(
x_grid,
y_grid,
ra_at_xy_0,
dec_at_xy_0,
x_at_radec_0,
y_at_radec_0,
Mpix2coord,
Mcoord2pix,
) = util.make_grid_with_coordtransform(
numPix=numPix, deltapix=deltaPix, subgrid_res=1, inverse=True
)
kwargs_data = {
"ra_at_xy_0": ra_at_xy_0,
"dec_at_xy_0": dec_at_xy_0,
"transform_pix2angle": Mpix2coord,
"image_data": np.ones((numPix, numPix)),
}
data = ImageData(**kwargs_data)
data_new = copy.deepcopy(data)
data_new.shift_coordinate_system(x_shift, y_shift, pixel_unit=False)
ra, dec = 0, 0
x, y = data.map_coord2pix(ra, dec)
x_new, y_new = data_new.map_coord2pix(ra + x_shift, dec + y_shift)
npt.assert_almost_equal(x, x_new, decimal=10)
npt.assert_almost_equal(y, y_new, decimal=10)
ra, dec = data.map_pix2coord(x, y)
ra_new, dec_new = data_new.map_pix2coord(x, y)
npt.assert_almost_equal(ra, ra_new - x_shift, decimal=10)
npt.assert_almost_equal(dec, dec_new - y_shift, decimal=10)
x_coords, y_coords = data.pixel_coordinates
x_coords_new, y_coords_new = data_new.pixel_coordinates
npt.assert_almost_equal(x_coords[0], x_coords_new[0] - x_shift, decimal=10)
npt.assert_almost_equal(y_coords[0], y_coords_new[0] - y_shift, decimal=10)
def test_update_data(self):
kwargs_data = {
"image_data": np.zeros((self.numPix, self.numPix)),
"noise_map": None,
"exposure_time": 1,
"background_rms": 1,
}
data = ImageData(**kwargs_data)
C_D = data.C_D
data.update_data(image_data=np.ones((self.numPix, self.numPix)))
C_D_new = data.C_D
assert C_D_new[0, 0] > C_D[0, 0]
data_new = data.data
npt.assert_almost_equal(data_new, np.ones((self.numPix, self.numPix)))
class TestRaise(unittest.TestCase):
def test_raise(self):
kwargs_data = {"image_data": np.zeros((10, 10))}
Data = ImageData(**kwargs_data)
image_data_new = np.zeros((5, 5))
with self.assertRaises(ValueError):
out = Data.update_data(image_data_new)
with self.assertRaises(ValueError):
ImageData(**kwargs_data, likelihood_method="WRONG")
if __name__ == "__main__":
pytest.main()
|
sibirrerREPO_NAMElenstronomyPATH_START.@lenstronomy_extracted@lenstronomy-main@test@test_Data@test_imaging_data.py@.PATH_END.py
|
{
"filename": "ps_roi_align.py",
"repo_name": "pytorch/vision",
"repo_path": "vision_extracted/vision-main/torchvision/ops/ps_roi_align.py",
"type": "Python"
}
|
import torch
import torch.fx
from torch import nn, Tensor
from torch.nn.modules.utils import _pair
from torchvision.extension import _assert_has_ops
from ..utils import _log_api_usage_once
from ._utils import check_roi_boxes_shape, convert_boxes_to_roi_format
@torch.fx.wrap
def ps_roi_align(
input: Tensor,
boxes: Tensor,
output_size: int,
spatial_scale: float = 1.0,
sampling_ratio: int = -1,
) -> Tensor:
"""
Performs Position-Sensitive Region of Interest (RoI) Align operator
mentioned in Light-Head R-CNN.
Args:
input (Tensor[N, C, H, W]): The input tensor, i.e. a batch with ``N`` elements. Each element
contains ``C`` feature maps of dimensions ``H x W``.
boxes (Tensor[K, 5] or List[Tensor[L, 4]]): the box coordinates in (x1, y1, x2, y2)
format where the regions will be taken from.
The coordinate must satisfy ``0 <= x1 < x2`` and ``0 <= y1 < y2``.
If a single Tensor is passed, then the first column should
contain the index of the corresponding element in the batch, i.e. a number in ``[0, N - 1]``.
If a list of Tensors is passed, then each Tensor will correspond to the boxes for an element i
in the batch.
output_size (int or Tuple[int, int]): the size of the output (in bins or pixels) after the pooling
is performed, as (height, width).
spatial_scale (float): a scaling factor that maps the box coordinates to
the input coordinates. For example, if your boxes are defined on the scale
of a 224x224 image and your input is a 112x112 feature map (resulting from a 0.5x scaling of
the original image), you'll want to set this to 0.5. Default: 1.0
sampling_ratio (int): number of sampling points in the interpolation grid
used to compute the output value of each pooled output bin. If > 0,
then exactly ``sampling_ratio x sampling_ratio`` sampling points per bin are used. If
<= 0, then an adaptive number of grid points are used (computed as
``ceil(roi_width / output_width)``, and likewise for height). Default: -1
Returns:
Tensor[K, C / (output_size[0] * output_size[1]), output_size[0], output_size[1]]: The pooled RoIs
"""
if not torch.jit.is_scripting() and not torch.jit.is_tracing():
_log_api_usage_once(ps_roi_align)
_assert_has_ops()
check_roi_boxes_shape(boxes)
rois = boxes
output_size = _pair(output_size)
if not isinstance(rois, torch.Tensor):
rois = convert_boxes_to_roi_format(rois)
output, _ = torch.ops.torchvision.ps_roi_align(
input, rois, spatial_scale, output_size[0], output_size[1], sampling_ratio
)
return output
class PSRoIAlign(nn.Module):
"""
See :func:`ps_roi_align`.
"""
def __init__(
self,
output_size: int,
spatial_scale: float,
sampling_ratio: int,
):
super().__init__()
_log_api_usage_once(self)
self.output_size = output_size
self.spatial_scale = spatial_scale
self.sampling_ratio = sampling_ratio
def forward(self, input: Tensor, rois: Tensor) -> Tensor:
return ps_roi_align(input, rois, self.output_size, self.spatial_scale, self.sampling_ratio)
def __repr__(self) -> str:
s = (
f"{self.__class__.__name__}("
f"output_size={self.output_size}"
f", spatial_scale={self.spatial_scale}"
f", sampling_ratio={self.sampling_ratio}"
f")"
)
return s
|
pytorchREPO_NAMEvisionPATH_START.@vision_extracted@vision-main@torchvision@ops@ps_roi_align.py@.PATH_END.py
|
{
"filename": "cc_calc_cbass_plot.py",
"repo_name": "mpeel/fastcc",
"repo_path": "fastcc_extracted/fastcc-master/cc_calc_cbass_plot.py",
"type": "Python"
}
|
import matplotlib.pyplot as plt
import numpy as np
outdir = 'plots_2022_11_08/'
alphas = [-3.0, -2.5, -2.0, -1.5, -1.0, -0.5, 0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0]
alphas = np.asarray(alphas)
old_I = [1.0001032131966172, -0.0007238230723749948, -0.00133638092466769, 4.76]
old_P = [0.9985614382180937, -0.005905286967874863, -0.001680320871506988, 4.76]
new_I = [1.0017152397219213, 0.004681376835425419, -0.0011099406898534073, 4.76]
new_P = [1.00067251064749, 0.0010118648563905157, -0.0014314759990750453, 4.76]
test_I = [1.0003317241376664, 3.5727991808456454e-05, -0.0013071225208285435, 4.76]
test_P = [0.9988609218460283, -0.00493454594155687, -0.0016504828405324431, 4.76]
plt.plot(alphas,old_I[0]+old_I[1]*alphas+old_I[2]*alphas**2,'--',c='r',label='I_eq1')
plt.plot(alphas,old_P[0]+old_P[1]*alphas+old_P[2]*alphas**2,'--',c='b',label='P_eq1')
plt.plot(alphas,new_I[0]+new_I[1]*alphas+new_I[2]*alphas**2,'-',c='r',label='I_eq2')
plt.plot(alphas,new_P[0]+new_P[1]*alphas+new_P[2]*alphas**2,'-',c='b',label='P_eq2')
plt.plot(alphas,test_I[0]+test_I[1]*alphas+test_I[2]*alphas**2,linestyle='dotted',c='r',label='I_eq3')
plt.plot(alphas,test_P[0]+test_P[1]*alphas+test_P[2]*alphas**2,linestyle='dotted',c='b',label='P_eq3')
l = plt.legend(prop={'size':11})
l.set_zorder(20)
plt.savefig(outdir+'cbass_comparison.png')
|
mpeelREPO_NAMEfastccPATH_START.@fastcc_extracted@fastcc-master@cc_calc_cbass_plot.py@.PATH_END.py
|
{
"filename": "conf.py",
"repo_name": "PeterKamphuis/pyFAT-astro",
"repo_path": "pyFAT-astro_extracted/pyFAT-astro-main/docs/source/conf.py",
"type": "Python"
}
|
# Configuration file for the Sphinx documentation builder.
# -- Project information
project = 'pyFAT-astro'
copyright = '2021, P. Kamphuis'
author = 'P. Kamphuis'
release = '0.1'
version = '0.1.6'
# -- General configuration
extensions = [
'sphinx.ext.duration',
'sphinx.ext.doctest',
'sphinx.ext.autodoc',
'sphinx.ext.autosummary',
'sphinx.ext.intersphinx',
'myst_parser',
]
source_suffix = {
'.rst': 'restructuredtext',
'.md': 'markdown',
}
intersphinx_mapping = {
'python': ('https://docs.python.org/3/', None),
'sphinx': ('https://www.sphinx-doc.org/en/master/', None),
}
intersphinx_disabled_domains = ['std']
templates_path = ['_templates']
# -- Options for HTML output
html_theme = 'sphinx_rtd_theme'
# -- Options for EPUB output
epub_show_urls = 'footnote'
|
PeterKamphuisREPO_NAMEpyFAT-astroPATH_START.@pyFAT-astro_extracted@pyFAT-astro-main@docs@source@conf.py@.PATH_END.py
|
{
"filename": "_font.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/heatmapgl/colorbar/title/_font.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class FontValidator(_plotly_utils.basevalidators.CompoundValidator):
def __init__(
self, plotly_name="font", parent_name="heatmapgl.colorbar.title", **kwargs
):
super(FontValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
data_class_str=kwargs.pop("data_class_str", "Font"),
data_docs=kwargs.pop(
"data_docs",
"""
color
family
HTML font family - the typeface that will be
applied by the web browser. The web browser
will only be able to apply a font if it is
available on the system which it operates.
Provide multiple font families, separated by
commas, to indicate the preference in which to
apply fonts if they aren't available on the
system. The Chart Studio Cloud (at
https://chart-studio.plotly.com or on-premise)
generates images on a server, where only a
select number of fonts are installed and
supported. These include "Arial", "Balto",
"Courier New", "Droid Sans",, "Droid Serif",
"Droid Sans Mono", "Gravitas One", "Old
Standard TT", "Open Sans", "Overpass", "PT Sans
Narrow", "Raleway", "Times New Roman".
size
""",
),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@heatmapgl@colorbar@title@_font.py@.PATH_END.py
|
{
"filename": "_weightsrc.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/scattersmith/hoverlabel/font/_weightsrc.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class WeightsrcValidator(_plotly_utils.basevalidators.SrcValidator):
def __init__(
self,
plotly_name="weightsrc",
parent_name="scattersmith.hoverlabel.font",
**kwargs,
):
super(WeightsrcValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "none"),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@scattersmith@hoverlabel@font@_weightsrc.py@.PATH_END.py
|
{
"filename": "milvus.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/community/langchain_community/query_constructors/milvus.py",
"type": "Python"
}
|
"""Logic for converting internal query language to a valid Milvus query."""
from typing import Tuple, Union
from langchain_core.structured_query import (
Comparator,
Comparison,
Operation,
Operator,
StructuredQuery,
Visitor,
)
COMPARATOR_TO_BER = {
Comparator.EQ: "==",
Comparator.GT: ">",
Comparator.GTE: ">=",
Comparator.LT: "<",
Comparator.LTE: "<=",
Comparator.IN: "in",
Comparator.LIKE: "like",
}
UNARY_OPERATORS = [Operator.NOT]
def process_value(value: Union[int, float, str], comparator: Comparator) -> str:
"""Convert a value to a string and add double quotes if it is a string.
It required for comparators involving strings.
Args:
value: The value to convert.
comparator: The comparator.
Returns:
The converted value as a string.
"""
#
if isinstance(value, str):
if comparator is Comparator.LIKE:
# If the comparator is LIKE, add a percent sign after it for prefix matching
# and add double quotes
return f'"{value}%"'
else:
# If the value is already a string, add double quotes
return f'"{value}"'
else:
# If the value is not a string, convert it to a string without double quotes
return str(value)
class MilvusTranslator(Visitor):
"""Translate Milvus internal query language elements to valid filters."""
"""Subset of allowed logical operators."""
allowed_operators = [Operator.AND, Operator.NOT, Operator.OR]
"""Subset of allowed logical comparators."""
allowed_comparators = [
Comparator.EQ,
Comparator.GT,
Comparator.GTE,
Comparator.LT,
Comparator.LTE,
Comparator.IN,
Comparator.LIKE,
]
def _format_func(self, func: Union[Operator, Comparator]) -> str:
self._validate_func(func)
value = func.value
if isinstance(func, Comparator):
value = COMPARATOR_TO_BER[func]
return f"{value}"
def visit_operation(self, operation: Operation) -> str:
if operation.operator in UNARY_OPERATORS and len(operation.arguments) == 1:
operator = self._format_func(operation.operator)
return operator + "(" + operation.arguments[0].accept(self) + ")"
elif operation.operator in UNARY_OPERATORS:
raise ValueError(
f'"{operation.operator.value}" can have only one argument in Milvus'
)
else:
args = [arg.accept(self) for arg in operation.arguments]
operator = self._format_func(operation.operator)
return "(" + (" " + operator + " ").join(args) + ")"
def visit_comparison(self, comparison: Comparison) -> str:
comparator = self._format_func(comparison.comparator)
processed_value = process_value(comparison.value, comparison.comparator)
attribute = comparison.attribute
return "( " + attribute + " " + comparator + " " + processed_value + " )"
def visit_structured_query(
self, structured_query: StructuredQuery
) -> Tuple[str, dict]:
if structured_query.filter is None:
kwargs = {}
else:
kwargs = {"expr": structured_query.filter.accept(self)}
return structured_query.query, kwargs
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@community@langchain_community@query_constructors@milvus.py@.PATH_END.py
|
{
"filename": "LHCDMCosmology.py",
"repo_name": "igomezv/simplemc_tests",
"repo_path": "simplemc_tests_extracted/simplemc_tests-main/simplemc/models/LHCDMCosmology.py",
"type": "Python"
}
|
from simplemc.models.LCDMCosmology import LCDMCosmology
from simplemc.cosmo.paramDefs import alpha_1_par
import math as N
import numpy as np
#TODO Add more DE EoS for comparison
class LHCDMCosmology(LCDMCosmology):
"""
This is CDM cosmology with w, wa and Ok.
CPL parameterization with curvature.
This class inherits LCDMCosmology class as the rest of the cosmological
models already included in SimpleMC.
:param varyw: variable w0 parameter
:type varyw: Boolean
:param varywa: variable wa parameter
:type varywa: Boolean
:param varyOk: variable Ok parameter
:type varyOk: Boolean
"""
def __init__(self):
self.alpha_1 = alpha_1_par.value
LCDMCosmology.__init__(self)
# my free parameters. We add Ok on top of LCDM ones (we inherit LCDM)
def freeParameters(self):
l = LCDMCosmology.freeParameters(self)
l.append(alpha_1_par)
return l
def updateParams(self, pars):
ok = LCDMCosmology.updateParams(self, pars)
if not ok:
return False
for p in pars:
if p.name == "alpha_1":
self.alpha_1 = p.value
return True
# this is relative hsquared as a function of a
## i.e. H(z)^2/H(z=0)^2
# '1.0 +Omrad/a**4 +(1.0 +alpha_1)*Om*(a**(-3/(1.0+alpha_1)) -1.0)'
def RHSquared_a(self, a):
return 1.0 +self.Omrad/a**4 + (1.0 +self.alpha_1)*self.Om*(a**(-3/(1.0+self.alpha_1)) -1.0)
|
igomezvREPO_NAMEsimplemc_testsPATH_START.@simplemc_tests_extracted@simplemc_tests-main@simplemc@models@LHCDMCosmology.py@.PATH_END.py
|
{
"filename": "TensorStrainPolicyInst.cc.py",
"repo_name": "LLNL/spheral",
"repo_path": "spheral_extracted/spheral-main/src/Damage/TensorStrainPolicyInst.cc.py",
"type": "Python"
}
|
text = """
//------------------------------------------------------------------------------
// Explicit instantiation.
//------------------------------------------------------------------------------
#include "Geometry/Dimension.hh"
#include "Damage/TensorStrainPolicy.cc"
namespace Spheral {
template class Spheral::TensorStrainPolicy<Dim< %(ndim)s > >;
}
"""
|
LLNLREPO_NAMEspheralPATH_START.@spheral_extracted@spheral-main@src@Damage@TensorStrainPolicyInst.cc.py@.PATH_END.py
|
{
"filename": "python-reference_pool_set_feature_names.md",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/catboost/docs/en/concepts/python-reference_pool_set_feature_names.md",
"type": "Markdown"
}
|
# set_feature_names
{% include [set_feature_names-set_feature_names__desc](../_includes/work_src/reusage-python/set_feature_names__desc.md) %}
## {{ dl--invoke-format }} {#call-format}
```python
set_feature_names(feature_names)
```
## {{ dl--parameters }} {#parameters}
### feature_names
#### Description
A list of names for each feature in the dataset.
**Possible types**
{{ python-type--list-of-strings }}
**Default value**
{{ python--required }}
## {{ input_data__title__example }} {#example}
```python
import numpy as np
from catboost import Pool
train_data = [[76, 'blvd', 41, 50, 7],
[75, 'today', 57, 0, 48],
[70, 'letters', 33, 17, 7],
[72, 'now', 43, 29, 12],
[60, 'back', 2, 0, 1]]
label_values = [1, 0, 0, 1, 4]
input_pool = Pool(data = train_data,
label = label_values,
cat_features = [1])
input_pool.set_feature_names(['year', 'name', 'BLBRD', 'CAC', 'OAC'])
```
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@catboost@docs@en@concepts@python-reference_pool_set_feature_names.md@.PATH_END.py
|
{
"filename": "_shadow.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/layout/scene/zaxis/tickfont/_shadow.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ShadowValidator(_plotly_utils.basevalidators.StringValidator):
def __init__(
self, plotly_name="shadow", parent_name="layout.scene.zaxis.tickfont", **kwargs
):
super(ShadowValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "plot"),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@layout@scene@zaxis@tickfont@_shadow.py@.PATH_END.py
|
{
"filename": "_streamtube.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/graph_objs/layout/template/data/_streamtube.py",
"type": "Python"
}
|
from plotly.graph_objs import Streamtube
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@graph_objs@layout@template@data@_streamtube.py@.PATH_END.py
|
{
"filename": "_token.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/indicator/stream/_token.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TokenValidator(_plotly_utils.basevalidators.StringValidator):
def __init__(self, plotly_name="token", parent_name="indicator.stream", **kwargs):
super(TokenValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
no_blank=kwargs.pop("no_blank", True),
strict=kwargs.pop("strict", True),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@indicator@stream@_token.py@.PATH_END.py
|
{
"filename": "_labelformat.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/contour/contours/_labelformat.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class LabelformatValidator(_plotly_utils.basevalidators.StringValidator):
def __init__(
self, plotly_name="labelformat", parent_name="contour.contours", **kwargs
):
super(LabelformatValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "plot"),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@contour@contours@_labelformat.py@.PATH_END.py
|
{
"filename": "_x.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/graph_objs/surface/contours/_x.py",
"type": "Python"
}
|
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class X(_BaseTraceHierarchyType):
# class properties
# --------------------
_parent_path_str = "surface.contours"
_path_str = "surface.contours.x"
_valid_props = {
"color",
"end",
"highlight",
"highlightcolor",
"highlightwidth",
"project",
"show",
"size",
"start",
"usecolormap",
"width",
}
# color
# -----
@property
def color(self):
"""
Sets the color of the contour lines.
The 'color' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
Returns
-------
str
"""
return self["color"]
@color.setter
def color(self, val):
self["color"] = val
# end
# ---
@property
def end(self):
"""
Sets the end contour level value. Must be more than
`contours.start`
The 'end' property is a number and may be specified as:
- An int or float
Returns
-------
int|float
"""
return self["end"]
@end.setter
def end(self, val):
self["end"] = val
# highlight
# ---------
@property
def highlight(self):
"""
Determines whether or not contour lines about the x dimension
are highlighted on hover.
The 'highlight' property must be specified as a bool
(either True, or False)
Returns
-------
bool
"""
return self["highlight"]
@highlight.setter
def highlight(self, val):
self["highlight"] = val
# highlightcolor
# --------------
@property
def highlightcolor(self):
"""
Sets the color of the highlighted contour lines.
The 'highlightcolor' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
Returns
-------
str
"""
return self["highlightcolor"]
@highlightcolor.setter
def highlightcolor(self, val):
self["highlightcolor"] = val
# highlightwidth
# --------------
@property
def highlightwidth(self):
"""
Sets the width of the highlighted contour lines.
The 'highlightwidth' property is a number and may be specified as:
- An int or float in the interval [1, 16]
Returns
-------
int|float
"""
return self["highlightwidth"]
@highlightwidth.setter
def highlightwidth(self, val):
self["highlightwidth"] = val
# project
# -------
@property
def project(self):
"""
The 'project' property is an instance of Project
that may be specified as:
- An instance of :class:`plotly.graph_objs.surface.contours.x.Project`
- A dict of string/value properties that will be passed
to the Project constructor
Supported dict properties:
x
Determines whether or not these contour lines
are projected on the x plane. If `highlight` is
set to True (the default), the projected lines
are shown on hover. If `show` is set to True,
the projected lines are shown in permanence.
y
Determines whether or not these contour lines
are projected on the y plane. If `highlight` is
set to True (the default), the projected lines
are shown on hover. If `show` is set to True,
the projected lines are shown in permanence.
z
Determines whether or not these contour lines
are projected on the z plane. If `highlight` is
set to True (the default), the projected lines
are shown on hover. If `show` is set to True,
the projected lines are shown in permanence.
Returns
-------
plotly.graph_objs.surface.contours.x.Project
"""
return self["project"]
@project.setter
def project(self, val):
self["project"] = val
# show
# ----
@property
def show(self):
"""
Determines whether or not contour lines about the x dimension
are drawn.
The 'show' property must be specified as a bool
(either True, or False)
Returns
-------
bool
"""
return self["show"]
@show.setter
def show(self, val):
self["show"] = val
# size
# ----
@property
def size(self):
"""
Sets the step between each contour level. Must be positive.
The 'size' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
"""
return self["size"]
@size.setter
def size(self, val):
self["size"] = val
# start
# -----
@property
def start(self):
"""
Sets the starting contour level value. Must be less than
`contours.end`
The 'start' property is a number and may be specified as:
- An int or float
Returns
-------
int|float
"""
return self["start"]
@start.setter
def start(self, val):
self["start"] = val
# usecolormap
# -----------
@property
def usecolormap(self):
"""
An alternate to "color". Determines whether or not the contour
lines are colored using the trace "colorscale".
The 'usecolormap' property must be specified as a bool
(either True, or False)
Returns
-------
bool
"""
return self["usecolormap"]
@usecolormap.setter
def usecolormap(self, val):
self["usecolormap"] = val
# width
# -----
@property
def width(self):
"""
Sets the width of the contour lines.
The 'width' property is a number and may be specified as:
- An int or float in the interval [1, 16]
Returns
-------
int|float
"""
return self["width"]
@width.setter
def width(self, val):
self["width"] = val
# Self properties description
# ---------------------------
@property
def _prop_descriptions(self):
return """\
color
Sets the color of the contour lines.
end
Sets the end contour level value. Must be more than
`contours.start`
highlight
Determines whether or not contour lines about the x
dimension are highlighted on hover.
highlightcolor
Sets the color of the highlighted contour lines.
highlightwidth
Sets the width of the highlighted contour lines.
project
:class:`plotly.graph_objects.surface.contours.x.Project
` instance or dict with compatible properties
show
Determines whether or not contour lines about the x
dimension are drawn.
size
Sets the step between each contour level. Must be
positive.
start
Sets the starting contour level value. Must be less
than `contours.end`
usecolormap
An alternate to "color". Determines whether or not the
contour lines are colored using the trace "colorscale".
width
Sets the width of the contour lines.
"""
def __init__(
self,
arg=None,
color=None,
end=None,
highlight=None,
highlightcolor=None,
highlightwidth=None,
project=None,
show=None,
size=None,
start=None,
usecolormap=None,
width=None,
**kwargs,
):
"""
Construct a new X object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of
:class:`plotly.graph_objs.surface.contours.X`
color
Sets the color of the contour lines.
end
Sets the end contour level value. Must be more than
`contours.start`
highlight
Determines whether or not contour lines about the x
dimension are highlighted on hover.
highlightcolor
Sets the color of the highlighted contour lines.
highlightwidth
Sets the width of the highlighted contour lines.
project
:class:`plotly.graph_objects.surface.contours.x.Project
` instance or dict with compatible properties
show
Determines whether or not contour lines about the x
dimension are drawn.
size
Sets the step between each contour level. Must be
positive.
start
Sets the starting contour level value. Must be less
than `contours.end`
usecolormap
An alternate to "color". Determines whether or not the
contour lines are colored using the trace "colorscale".
width
Sets the width of the contour lines.
Returns
-------
X
"""
super(X, self).__init__("x")
if "_parent" in kwargs:
self._parent = kwargs["_parent"]
return
# Validate arg
# ------------
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError(
"""\
The first argument to the plotly.graph_objs.surface.contours.X
constructor must be a dict or
an instance of :class:`plotly.graph_objs.surface.contours.X`"""
)
# Handle skip_invalid
# -------------------
self._skip_invalid = kwargs.pop("skip_invalid", False)
self._validate = kwargs.pop("_validate", True)
# Populate data dict with properties
# ----------------------------------
_v = arg.pop("color", None)
_v = color if color is not None else _v
if _v is not None:
self["color"] = _v
_v = arg.pop("end", None)
_v = end if end is not None else _v
if _v is not None:
self["end"] = _v
_v = arg.pop("highlight", None)
_v = highlight if highlight is not None else _v
if _v is not None:
self["highlight"] = _v
_v = arg.pop("highlightcolor", None)
_v = highlightcolor if highlightcolor is not None else _v
if _v is not None:
self["highlightcolor"] = _v
_v = arg.pop("highlightwidth", None)
_v = highlightwidth if highlightwidth is not None else _v
if _v is not None:
self["highlightwidth"] = _v
_v = arg.pop("project", None)
_v = project if project is not None else _v
if _v is not None:
self["project"] = _v
_v = arg.pop("show", None)
_v = show if show is not None else _v
if _v is not None:
self["show"] = _v
_v = arg.pop("size", None)
_v = size if size is not None else _v
if _v is not None:
self["size"] = _v
_v = arg.pop("start", None)
_v = start if start is not None else _v
if _v is not None:
self["start"] = _v
_v = arg.pop("usecolormap", None)
_v = usecolormap if usecolormap is not None else _v
if _v is not None:
self["usecolormap"] = _v
_v = arg.pop("width", None)
_v = width if width is not None else _v
if _v is not None:
self["width"] = _v
# Process unknown kwargs
# ----------------------
self._process_kwargs(**dict(arg, **kwargs))
# Reset skip_invalid
# ------------------
self._skip_invalid = False
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@graph_objs@surface@contours@_x.py@.PATH_END.py
|
{
"filename": "_sizemin.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/scatter3d/marker/_sizemin.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class SizeminValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(self, plotly_name="sizemin", parent_name="scatter3d.marker", **kwargs):
super(SizeminValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
min=kwargs.pop("min", 0),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@scatter3d@marker@_sizemin.py@.PATH_END.py
|
{
"filename": "run_lsd.ipynb",
"repo_name": "florian-lienhard/MM-LSD",
"repo_path": "MM-LSD_extracted/MM-LSD-main/notebooks/run_lsd.ipynb",
"type": "Jupyter Notebook"
}
|
```python
import numpy as np
from pandas import read_csv, DataFrame, concat
import matplotlib.pyplot as plt
from matplotlib import rc,rcParams
from matplotlib.cm import rainbow
from matplotlib.colors import rgb2hex
from scipy.sparse import csc_matrix
from scipy.optimize import curve_fit
from scipy.stats import median_abs_deviation
from time import time
from sys import path as syspath
from sys import argv
import os
from multiprocessing import Pool,get_context
from functools import partial
import pickle
t00 = time()
try:
rc('text', usetex = True)
except:
pass
rc('xtick',labelsize=15)
rc('ytick',labelsize=15)
rcParams['font.family'] = 'STIXGeneral'
rcParams['mathtext.fontset'] = 'stix'
rcParams['hatch.linewidth'] = 2.0
plt.rcParams.update({
"font.size":15,
"figure.facecolor": "white",
"axes.facecolor": "white",
"savefig.facecolor": "white",
})
try:
get_ipython().__class__.__name__
injupyternotebook = True
except:
injupyternotebook = False
```
```python
if not injupyternotebook:
#star name
star = argv[1]
output_intermediate_results = False
else:
star = "Sun"
#star = "teststar"
indic = "1"
from IPython import display
from IPython.core.display import display, HTML
from bokeh.io import output_file
from bokeh.plotting import Figure, output_notebook, show, save, ColumnDataSource
display(HTML("<style>.container { width:90% !important; }</style>"))
output_notebook()
output_intermediate_results = True
```
<style>.container { width:90% !important; }</style>
<div class="bk-root">
<a href="https://bokeh.org" target="_blank" class="bk-logo bk-logo-small bk-logo-notebook"></a>
<span id="1001">Loading BokehJS ...</span>
</div>
```python
stardir = './stars/'+star+'/'
inf_name = stardir + 'input.py'
exec(open(inf_name).read())
```
```python
syspath.insert(0, "./helper_functions/")
from classes import stellar_parameters,analyse,extract_rv_from_common_profiles,Gaussian,runLSD_inv,prep_spec3,RVerror
```
### Set parameters for computation (velcity grid width etc.)
```python
if injupyternotebook:
paramnr = 0
#results so far
else:
paramnr = int(argv[2])
#results so far
resfile = f"{resdir}results_{star}_{indic}.csv"
results = read_csv(resfile)
print(paramnr)
#load parameter combination
prms = read_csv(stardir+'params.csv')
maxdepthparam= prms["maxdepthparam"][paramnr]
mindepthparam = prms["mindepthparam"][paramnr]
telluric_cut = prms["telluric_cut"][paramnr]
velgridwidth = prms["velgridwidth"][paramnr]
modelspecdeviationcut = prms["modelspecdeviationcut"][paramnr]
max_nr_of_specs = prms["max_nr_of_specs"][paramnr]
exclwidelinesparam = prms["exclwidelinesparam"][paramnr]
rassoption = prms["rassoption"][paramnr]
erroption = prms["erroption"][paramnr]
telloption = prms["telloption"][paramnr]
#here we save the results of this parameter combination
newres = DataFrame()
#copy entries of parameter file
for key in prms.keys():
newres[key] = [prms[key][paramnr]]
```
0
### Information about spectra
```python
info_file = read_csv(dirdir+"Info.csv")
#set system RV. i.e. RV that is used to convert absorption line wavelengths from rest frame to stellar frame
systemrv = (info_file["rv_ccf"][0])
```
### Load data from VALD3
```python
valddir = "./VALD_files/"
sp = stellar_parameters(star,valddir,dirdir,pipname,c)
sp.VALD_data()
```
loaded ./VALD_files/Sun.txt
### Load data from 1_preprocess notebook
```python
an = analyse(c,sp.VALDlambdas,sp.VALDdepths)
```
```python
with open(dirdir+"data_dict.pkl","rb") as f:
prov = pickle.load(f)
an.alldata = {}
if rassoption==1:
an.alldata["spectrum"] = prov["spectrum_overlap_corrected"]
an.alldata["err"] = prov["err_overlap_corrected"]
an.alldata["err_envelope"] = prov["err_envelope_overlap_corrected"]
an.alldata["wavelengths"] = prov["wavelengths"]
del prov
```
```python
# save these for later.
iis = list(an.alldata["spectrum"].keys())
#index numbers of spectra
an.iis = iis
#see input.py
an.excllower = excllower
an.exclupper = exclupper
an.telluric_cut = telluric_cut
an.modelspecdeviationcut = modelspecdeviationcut
an.mindepthparam = mindepthparam
an.maxdepthparam = maxdepthparam
an.exclwidelinesparam = exclwidelinesparam
an.telloption = telloption
nr_of_orders,nr_of_pixels = an.alldata["spectrum"][0].shape
#shift fluxes to between -1 and 0 for lsd procedure
for key in iis:
an.alldata["spectrum"][key] = an.alldata["spectrum"][key]-1
an.tapas_tellurics = {}
an.resdir = resdir
```
```python
an.barycentric_to_stellar_restframe = {}
an.observatory_to_barycentric_restframe = {}
an.observatory_to_stellar_restframe = {}
for ii in iis:
an.barycentric_to_stellar_restframe[ii] = 1.0 / (1.0 + systemrv / c)
an.observatory_to_barycentric_restframe[ii] = 1.0 + info_file["berv"][0] / c
an.observatory_to_stellar_restframe[ii] = (
an.observatory_to_barycentric_restframe[ii]
* an.barycentric_to_stellar_restframe[ii]
)
```
```python
if output_intermediate_results:
sp.inspect_data(0,an.alldata["spectrum"][0]+1,an.alldata["wavelengths"][0],5000,20)
```
### Get tapas telluric information
```python
compute_tellurics = True
if os.path.exists("./tellurics/tellurics"+star+".pkl"):
with open("./tellurics/tellurics"+star+".pkl","rb") as f:
an.tapas_tellurics = pickle.load(f)
if len(iis) == len(an.tapas_tellurics.keys()):
compute_tellurics = False
if output_intermediate_results:
print(f"loaded tellurics from ./tellurics/tellurics"+star+".pkl")
else:
compute_tellurics = True
if compute_tellurics:
print("produce tellurics")
transmittance_file = None
an.get_tapas_transmittance(pipname , transmittance_file, info_file)
print("save tellurics in ","./tellurics/tellurics"+star+".pkl")
with open("./tellurics/tellurics"+star+".pkl","wb") as f:
pickle.dump(an.tapas_tellurics,f)
```
loaded tellurics from ./tellurics/telluricsSun.pkl
### Set preliminary velocity grid
```python
#set velocity grid
#roughly centre velocity grid around the rv of the star of the first measurement. add +- dvel km/s
dvel = 20
vel_inital = np.arange(systemrv-dvel, systemrv+dvel, vStep)
#set upper limit to number of absorption lines of depth min_depth_required within a region (other regions excluded)
an.alldata["vel_inital"] = vel_inital
```
### FIRST RUN OF LSD (TO GET FWHM OF SPECTRA, FIRST COMMON PROFILE, AND TO CHECK DEVIATION OF SPECTRA FROM CONVOLUTION MODEL))
```python
#choose test spectrum for the first LSD run
test_ii = 0
an.test_ii = test_ii
#get fluxes, wavelengths, and weights for first spectrum
an.prep_spec(iis[test_ii],an.alldata,erroption)
#choose echelle orders to run code on (all here)
testorders = np.arange(nr_of_orders)
#get rough common profile (equal weight for each order). this is only used to get an idea about the common profile shape.
zlast = np.zeros((len(vel_inital)))
model_h = np.zeros((nr_of_orders,nr_of_pixels))
count = 0
for order in testorders:
output = an.worker(order,vel_inital)
if not np.isnan(output[0]).any():
model_h[order,:] = output[1]
zlast += output[0]
count +=1
zlast/=count
an.model_h = model_h
an.div = np.abs(model_h - an.spectrum)
```
```python
#first common profile
#fit gaussian to common profile and extraxt hwhm
popt, pcov = curve_fit(Gaussian, vel_inital, zlast, [-1, systemrv, 3, 0])
fit = Gaussian(vel_inital,*popt)
vel_hwhm = np.abs(popt[2])*np.sqrt(np.log(2.)*2.)
if output_intermediate_results:
plt.figure(figsize=(5,3))
plt.plot(vel_inital,zlast,".",label="Common Profile")
plt.plot(vel_inital,fit,label="Fit (Gaussian)")
plt.xlabel("Vel")
plt.title(np.round(vel_hwhm,2))
plt.legend()
#estimate typical half-width of an absorption line as 5 times the hwhm
#will be multiplied by wvl when used
an.alldata["initial_v_halfwidth"]=vel_hwhm
```
### Set velocity grid
```python
#new velocity grid based on first run.
dvel = np.round(vel_hwhm)*velgridwidth
vel = np.arange(systemrv-dvel, systemrv+dvel, vStep)
#set upper limit to number of absorption lines of depth min_depth_required within a region (other regions excluded)
an.alldata["vel"] = vel
#how much should we exclude near data points with high model-spectrum deviation?
an.alldata["absline_halfwidth_include"] = (vel.max()-vel.min()+1.)/2./c
```
### EXCLUDE SPECTRAL REGIONS WITH HIGH MODEL-SPECTRUM DEVIATION
```python
an.get_wide_lines()
an.get_q_map(info_file)
#get telluric map.
an.get_t_map()
```
```python
if output_intermediate_results and False:
an.show_map()
```
### RUN LSD ON ALL SPECTRA WITH QUALITY/TELLURIC MAP
```python
def worker3(order,weights,spectrum,wavelengths,vlambda,vdepth,vel):
"""
For given order: perform LSD and extract the common profile, common profile uncertainties. Compute convolution model
Parameters
----------
order : int
First array: periods [d]
weights : array orders x pixels
Weights of the individual fluxes
spectrum : array orders x pixels
Fluxes
wavelengths : array orders x pixels
Wavelength corresponding to the fluxes
vlambda : array
Central wavelength of absorption lines (VALD3)
vdepth : array
Depth of absorption lines (VALD3)
vel : array
Velocity grid to run LSD on (velocity grid for common profile)
Output:
----------
Z : array
common profile
Zerr : array
uncertainty estimates of common profile
M.dot(Z) : array
convolution model
selection : array
indices of included pixels
"""
#Get data of given order
#Only include data with weight > 0
selection = np.where(weights[order,:]>0)[0]
spectrum_o = spectrum[order,:][selection]
#Don't run if only 2% of order included. Bad order.
if len(selection)<0.02*len(spectrum_o):
return 0
wavelengths_o = wavelengths[order,:][selection]
weights_o = weights[order,:][selection]
#CREATE CONVOLUTION MATRIX
#-----------------------------------------------------
value, row, column = an.cvmt(wavelengths_o, vel, vlambda,vdepth)
M = csc_matrix((value,(row,column)), shape=(len(wavelengths_o), len(vel)))
#-----------------------------------------------------
Z,Zerr = runLSD_inv(value, row, column, len(wavelengths_o), len(vel), weights_o, spectrum_o)
return Z,Zerr,M.dot(Z),selection
```
```python
#multiprocessing
num_processors = 4
#on which orders to run
testorders = np.arange(nr_of_orders)
#save results here
LSD_results = {}
vel = an.alldata["vel"]
```
```python
t_start = time()
for ii in iis:
if output_intermediate_results:
print(ii,np.round(time()-t_start,2))
#get weights, spectrum, wavelengths after excluding some data according to parameters.
weights,spectrum,wavelengths = prep_spec3(an.alldata,ii,an.tapas_tellurics,erroption=erroption,usetapas=usetapas)
#empty containers
LSD_results[ii] = {}
common_profile_all_orders = np.zeros((np.shape(wavelengths)[0],len(vel)))
common_profile_all_orders_err = np.zeros((np.shape(wavelengths)[0],len(vel)))
MZ = np.zeros((np.shape(wavelengths)[0],len(an.alldata["spectrum"][ii][20,:])))
incl_map = np.zeros((np.shape(MZ)))
#partial function for multiprocessing
worker_partial3 = partial(worker3,
weights=weights,
spectrum=spectrum,
wavelengths=wavelengths,
vlambda=sp.VALDlambdas,
vdepth=sp.VALDdepths,
vel=vel)
#initialise multiprocessing
with get_context("fork").Pool(processes = num_processors) as p:
output = p.map(worker_partial3,[order for order in testorders])
#save output into containers
for order in testorders:
if output[order]!=0:
common_profile_all_orders[order,:] = output[order][0]
common_profile_all_orders_err[order,:] = output[order][1]
selection = output[order][3]
MZ[order,:][selection] = output[order][2]
incl_map[order,:][selection] = np.ones((len(selection)))
#save results in dict
LSD_results[ii]["common_profile"] = common_profile_all_orders
LSD_results[ii]["common_profile_err"] = common_profile_all_orders_err
LSD_results[ii]["LSD_spectrum_model"] = MZ # LSD_spectrum
LSD_results[ii]["incl_map"] = incl_map
if injupyternotebook:
print("Computation time:", np.round((time()-t_start)/60.,1),"minutes")
```
0 0.0
1 0.47
2 0.9
3 1.45
4 1.78
Computation time: 0.0 minutes
```python
# inspect the individual common profiles
if output_intermediate_results:
plt.figure(figsize=(7,5))
for order in range(len(LSD_results[test_ii]["common_profile"])):
color = rgb2hex(rainbow(order/nr_of_orders))
plt.plot(vel,LSD_results[ii]["common_profile"][order],color=color)
if order//10 == order/10:
plt.plot(vel,LSD_results[ii]["common_profile"][order],color=color,label="Order "+str(order))
else:
plt.plot(vel,LSD_results[ii]["common_profile"][order],color=color)
plt.legend()
plt.ylim(-1.3,0.2)
plt.xlabel("Velocity grid [km/s]")
plt.title("Common profiles of individual orders")
plt.savefig(an.resdir+f"Common_profiles.pdf")
```
```python
#define weight matrix to compute order weights (to combine common profiles to master common profile)
wmat = np.ones((len(iis),nr_of_orders))
for count1,ii in enumerate(iis):
if erroption == 0:
pre_weights = 1./(an.alldata["err"][ii]**2)
if erroption ==1:
pre_weights = 1./(an.alldata["err_envelope"][ii]**2)
if erroption ==2:
err = np.transpose(np.tile(np.median(an.alldata["err"][ii],axis=1),(np.shape(an.alldata["err"][0])[1],1)))
pre_weights = 1./(err**2)
pre_weights[LSD_results[ii]["incl_map"]==0]=0
for count,order in enumerate(testorders):
wmat[count1,order] = np.nanmean(pre_weights[order,:])
an.alldata["order_weight"] = np.mean(wmat,axis=0)
```
```python
#check excluding outer parts of common profile. see paper.
testsigma = np.arange(1.0,5,step=0.25)*an.alldata["initial_v_halfwidth"]
std_dep_on_sigma = np.zeros((len(testsigma)))
an.alldata["fitfunction"] = "Gaussian"
order_choice = np.arange(nr_of_orders)
for count,sigma in enumerate(testsigma):
an.alldata["sigmafit"] = sigma
lsd_rv_orig, Zs,Z,Zerrs = extract_rv_from_common_profiles(LSD_results,an.alldata,iis,order_choice,weight_orders=weight_schemes[0],use_uncertainties=True)
lsd_norm_t = lsd_rv_orig - np.median(lsd_rv_orig)
no_outliers = np.where(np.abs(lsd_norm_t-np.median(lsd_norm_t))<delta_rv_outlier)[0]
lsd_norm_t = lsd_norm_t[no_outliers]
std_dep_on_sigma[count] = np.std(lsd_norm_t)
an.alldata["sigmafit"] = testsigma[np.argmin(std_dep_on_sigma)]
an.alldata["sigmafit_used"] = np.copy(testsigma[np.argmin(std_dep_on_sigma)])
```
```python
if len(iis)!=len(no_outliers):
print(f"Removed {len(iis)-len(no_outliers)} out of {len(iis)} spectra due to |RV-med(RV)| >= delta_rv_outlier (= {delta_rv_outlier} m/s)")
```
```python
#compare LSD results to DRS CCF method
drs_rv_orig = info_file["rv_ccf"].values*1000.
t = info_file["mjd"].values
for weight_scheme in weight_schemes:
for use_uncertainties in [True]:
#only second one is used for plots later on
#"flux weight2b": weight of order o same for all spectra (=weight of order o in first spectrum)
#"flux weight2c": weight of order o varies (depending on weight of fluxes in order o in spectrum ii)
#choose an LSD container
#this extracts the RV information
lsd_rv_orig, Zs,Z,Zerrs = extract_rv_from_common_profiles(LSD_results,an.alldata,iis,order_choice,weight_orders=weight_scheme,use_uncertainties=use_uncertainties)
if pipname == "DRS_3.7":
#drift correction
lsd_rv_orig -= info_file["drift"].values
#drs_rv_orig -= info_file["drift"].values
# subtract median radial velocity to analyse rv change
drs_norm = drs_rv_orig - np.median(drs_rv_orig)
lsd_norm = lsd_rv_orig - np.median(lsd_rv_orig)
#------------------------
#remove outliers
no_outliers = np.where(np.abs(drs_norm-np.median(drs_norm))<200)[0]
if len(no_outliers)<len(drs_norm):
print(f"Removed {len(drs_norm)-len(no_outliers)} outliers.")
drs_norm = drs_norm[no_outliers]
lsd_norm = lsd_norm[no_outliers]
t = t[no_outliers]
#yerr = np.asarray(list(an.alldata["ccfrvs_err"].values()))[no_outliers]*1000.
yerr = info_file["rv_ccf_error"].values[no_outliers]*1000.
#------------------------
difference = drs_norm -lsd_norm
print("LSD RMS: ",np.std(lsd_norm).round(2),"m/s")
```
LSD RMS: 1.0 m/s
```python
if output_intermediate_results:
fig,ax = plt.subplots(2,1,figsize=(14,3.8),gridspec_kw={'height_ratios': [3, 1]},sharex=True)
ax[0].set_title("RVs in barycentric frame")
#ax[0].set_ylim(-10,20)
ax[0].plot(t ,drs_norm,"D",label=f"CCF technique. RMS: {np.std(drs_norm):.2f} m/s")
ax[0].plot(t ,lsd_norm,".",label=f"LSD technique. RMS: {np.std(lsd_norm):.2f} m/s")
#ax[0].plot(t-2450000 ,lsd_norm_avg,".",label="LSD\_avg")
ax[0].set_ylabel("RV [m/s]",fontsize=15)
ax[0].legend(fontsize=15)
ax[1].errorbar(t,difference,yerr=yerr, fmt='o',color="black")
plt.xlabel(r"MJD",fontsize=15)
print(f"STD:\t LSD: {np.std(lsd_norm):.2f} m/s \t \t DRS: {np.std(drs_norm):.2f} m/s")
print(f"MAD:\t LSD: {median_abs_deviation(lsd_norm):.2f} m/s \t \t DRS: {median_abs_deviation(drs_norm):.2f} m/s")
plt.savefig(an.resdir+f"RVs.pdf")
```
STD: LSD: 1.00 m/s DRS: 2.41 m/s
MAD: LSD: 0.81 m/s DRS: 2.76 m/s
```python
#save results
if not injupyternotebook:
yerr_wls = np.zeros((len(lsd_norm)))
for count,ii in enumerate(np.asarray(iis)[no_outliers]):
rverrc = RVerror(vel, Zs[ii], Zerrs[ii])
yerr_wls[count] = rverrc*1000.
newres["LSD RV std"] = [np.std(lsd_norm).round(3)]
newres["LSD RV MAD"] = [median_abs_deviation(lsd_norm).round(3)]
newres["DRS RV std"] = [np.std(drs_norm).round(3)]
newres["DRS RV MAD"] = [median_abs_deviation(drs_norm).round(3)]
newres["sigmafit_used"] = [an.alldata["sigmafit_used"].round(3)]
newres["comp time"] = [np.round(time()-t00,1)]
nn = concat([results,newres])
nn.to_csv(resfile,index=False)
if os.path.exists(rvresfile):
f = open(rvresfile,"rb")
dth = pickle.load(f)
dth[paramnr]= lsd_norm
f.close()
else:
f = open(rvresfile,"wb")
dth = {}
dth["mjd"] = t
dth["rv_ccf"] = drs_norm
dth["rv_ccf_err"] = yerr
dth[paramnr]=lsd_norm
f = open(rvresfile,"wb")
pickle.dump(dth,f)
f.close()
if os.path.exists(rverrresfile):
f = open(rverrresfile,"rb")
dth = pickle.load(f)
dth[paramnr] = yerr_wls
f.close()
else:
f = open(rverrresfile,"wb")
dth = {}
dth["mjd"] = t
dth["rv_ccf"] = drs_norm
dth["rv_ccf_err"] = yerr
dth[paramnr] = yerr_wls
f = open(rverrresfile,"wb")
pickle.dump(dth,f)
f.close()
if os.path.exists(commonprofilefile):
f = open(commonprofilefile,"rb")
dth = pickle.load(f)
dth[f"vel_{paramnr}"]= vel
dth[f"Z_{paramnr}"]= Zs
f.close()
else:
f = open(commonprofilefile,"wb")
dth = {}
dth["mjd"] = t
dth[f"vel_{paramnr}"] = vel
dth[f"Z_{paramnr}"] = Zs
f = open(commonprofilefile,"wb")
pickle.dump(dth,f)
f.close()
```
```python
```
|
florian-lienhardREPO_NAMEMM-LSDPATH_START.@MM-LSD_extracted@MM-LSD-main@notebooks@run_lsd.ipynb@.PATH_END.py
|
{
"filename": "tfsa-2021-156.md",
"repo_name": "tensorflow/tensorflow",
"repo_path": "tensorflow_extracted/tensorflow-master/tensorflow/security/advisory/tfsa-2021-156.md",
"type": "Markdown"
}
|
## TFSA-2021-156: Use of unitialized value in TFLite
### CVE Number
CVE-2021-37682
### Impact
All TFLite operations that use quantization can be made to use unitialized
values. [For
example](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/depthwise_conv.cc#L198-L200):
```cc
const auto* affine_quantization =
reinterpret_cast<TfLiteAffineQuantization*>(
filter->quantization.params);
```
The issue stems from the fact that `quantization.params` is only valid if
`quantization.type` is different that `kTfLiteNoQuantization`. However, these
checks are missing in large parts of the code.
### Patches
We have patched the issue in GitHub commits
[537bc7c723439b9194a358f64d871dd326c18887](https://github.com/tensorflow/tensorflow/commit/537bc7c723439b9194a358f64d871dd326c18887),
[4a91f2069f7145aab6ba2d8cfe41be8a110c18a5](https://github.com/tensorflow/tensorflow/commit/4a91f2069f7145aab6ba2d8cfe41be8a110c18a5)
and
[8933b8a21280696ab119b63263babdb54c298538](https://github.com/tensorflow/tensorflow/commit/8933b8a21280696ab119b63263babdb54c298538).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this
commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are
also affected and still in supported range.
### For more information
Please consult [our security
guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for
more information regarding the security model and how to contact us with issues
and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo
360.
|
tensorflowREPO_NAMEtensorflowPATH_START.@tensorflow_extracted@tensorflow-master@tensorflow@security@advisory@tfsa-2021-156.md@.PATH_END.py
|
{
"filename": "words.md",
"repo_name": "youngjookim/sdr",
"repo_path": "sdr_extracted/sdr-master/Code/packages/tapkee-master/examples/words/words.md",
"type": "Markdown"
}
|
This example shows how Tapkee can be used with Python, thanks to the language
bindings provided by Shogun. The data consists of words from the English
vocabulary. These words belong to different grammar groups: there are several
common nouns (e.g. cowboy, dragon), adjectives (e.g. harmful), proper nouns such
as Rivera or America, verbs in different forms (e.g. in gerund such as finishing
or in past participle like disrupted), among other classes. The method used for
embedding is Kernel Locally Linear Embedding (KLLE) and the callback required
in this case is a kernel callback. The kernel callback is defined in Python, in
the function word_kernel. Note the power of using language bindings
and how code written in target interfaces (Python in this case) is able to interact
with the underlying C++ code. The word kernel used is rather simple and it is
based on a measure of sequence similarity implemented in the difflib module of
Python (import the difflib module and issue help(difflib.SequenceMatcher)
from a Python console for more information, tested in both Python 2.7.3 and
Python 3.3.0). The target dimension of the embedding is two and, as it can be
seen in the figure, the different word classes form clusters in the two dimensional
plane.
|
youngjookimREPO_NAMEsdrPATH_START.@sdr_extracted@sdr-master@Code@packages@tapkee-master@examples@words@words.md@.PATH_END.py
|
{
"filename": "_shadow.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/scattersmith/legendgrouptitle/font/_shadow.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ShadowValidator(_plotly_utils.basevalidators.StringValidator):
def __init__(
self,
plotly_name="shadow",
parent_name="scattersmith.legendgrouptitle.font",
**kwargs,
):
super(ShadowValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "style"),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@scattersmith@legendgrouptitle@font@_shadow.py@.PATH_END.py
|
{
"filename": "open_city_data.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/langchain/langchain/document_loaders/open_city_data.py",
"type": "Python"
}
|
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders import OpenCityDataLoader
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {"OpenCityDataLoader": "langchain_community.document_loaders"}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"OpenCityDataLoader",
]
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@langchain@langchain@document_loaders@open_city_data.py@.PATH_END.py
|
{
"filename": "toasim_seed.py",
"repo_name": "mattpitkin/tempo2",
"repo_path": "tempo2_extracted/tempo2-master/python/toasim/bin/toasim_seed.py",
"type": "Python"
}
|
#!/usr/bin/python
import sys
import hashlib
str="".join(sys.argv[1:])
m=hashlib.md5()
m.update(str)
hex=m.hexdigest()
print(int(hex,16)%(2**30))
|
mattpitkinREPO_NAMEtempo2PATH_START.@tempo2_extracted@tempo2-master@python@toasim@bin@toasim_seed.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/scatter/marker/colorbar/tickformatstop/__init__.py",
"type": "Python"
}
|
import sys
if sys.version_info < (3, 7):
from ._value import ValueValidator
from ._templateitemname import TemplateitemnameValidator
from ._name import NameValidator
from ._enabled import EnabledValidator
from ._dtickrange import DtickrangeValidator
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(
__name__,
[],
[
"._value.ValueValidator",
"._templateitemname.TemplateitemnameValidator",
"._name.NameValidator",
"._enabled.EnabledValidator",
"._dtickrange.DtickrangeValidator",
],
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@scatter@marker@colorbar@tickformatstop@__init__.py@.PATH_END.py
|
{
"filename": "_optimize.py",
"repo_name": "scipy/scipy",
"repo_path": "scipy_extracted/scipy-main/scipy/optimize/_optimize.py",
"type": "Python"
}
|
#__docformat__ = "restructuredtext en"
# ******NOTICE***************
# optimize.py module by Travis E. Oliphant
#
# You may copy and use this module as you see fit with no
# guarantee implied provided you keep this notice in all copies.
# *****END NOTICE************
# A collection of optimization algorithms. Version 0.5
# CHANGES
# Added fminbound (July 2001)
# Added brute (Aug. 2002)
# Finished line search satisfying strong Wolfe conditions (Mar. 2004)
# Updated strong Wolfe conditions line search to use
# cubic-interpolation (Mar. 2004)
# Minimization routines
__all__ = ['fmin', 'fmin_powell', 'fmin_bfgs', 'fmin_ncg', 'fmin_cg',
'fminbound', 'brent', 'golden', 'bracket', 'rosen', 'rosen_der',
'rosen_hess', 'rosen_hess_prod', 'brute', 'approx_fprime',
'line_search', 'check_grad', 'OptimizeResult', 'show_options',
'OptimizeWarning']
__docformat__ = "restructuredtext en"
import math
import warnings
import sys
import inspect
from numpy import eye, argmin, zeros, shape, asarray, sqrt
import numpy as np
from scipy.linalg import cholesky, issymmetric, LinAlgError
from scipy.sparse.linalg import LinearOperator
from ._linesearch import (line_search_wolfe1, line_search_wolfe2,
line_search_wolfe2 as line_search,
LineSearchWarning)
from ._numdiff import approx_derivative
from scipy._lib._util import getfullargspec_no_self as _getfullargspec
from scipy._lib._util import (MapWrapper, check_random_state, _RichResult,
_call_callback_maybe_halt, _transition_to_rng)
from scipy.optimize._differentiable_functions import ScalarFunction, FD_METHODS
from scipy._lib._array_api import array_namespace
from scipy._lib import array_api_extra as xpx
# standard status messages of optimizers
_status_message = {'success': 'Optimization terminated successfully.',
'maxfev': 'Maximum number of function evaluations has '
'been exceeded.',
'maxiter': 'Maximum number of iterations has been '
'exceeded.',
'pr_loss': 'Desired error not necessarily achieved due '
'to precision loss.',
'nan': 'NaN result encountered.',
'out_of_bounds': 'The result is outside of the provided '
'bounds.'}
class MemoizeJac:
"""Decorator that caches the return values of a function returning ``(fun, grad)``
each time it is called."""
def __init__(self, fun):
self.fun = fun
self.jac = None
self._value = None
self.x = None
def _compute_if_needed(self, x, *args):
if not np.all(x == self.x) or self._value is None or self.jac is None:
self.x = np.asarray(x).copy()
fg = self.fun(x, *args)
self.jac = fg[1]
self._value = fg[0]
def __call__(self, x, *args):
""" returns the function value """
self._compute_if_needed(x, *args)
return self._value
def derivative(self, x, *args):
self._compute_if_needed(x, *args)
return self.jac
def _wrap_callback(callback, method=None):
"""Wrap a user-provided callback so that attributes can be attached."""
if callback is None or method in {'tnc', 'slsqp', 'cobyla', 'cobyqa'}:
return callback # don't wrap
sig = inspect.signature(callback)
if set(sig.parameters) == {'intermediate_result'}:
def wrapped_callback(res):
return callback(intermediate_result=res)
elif method == 'trust-constr':
def wrapped_callback(res):
return callback(np.copy(res.x), res)
elif method == 'differential_evolution':
def wrapped_callback(res):
return callback(np.copy(res.x), res.convergence)
else:
def wrapped_callback(res):
return callback(np.copy(res.x))
wrapped_callback.stop_iteration = False
return wrapped_callback
class OptimizeResult(_RichResult):
"""
Represents the optimization result.
Attributes
----------
x : ndarray
The solution of the optimization.
success : bool
Whether or not the optimizer exited successfully.
status : int
Termination status of the optimizer. Its value depends on the
underlying solver. Refer to `message` for details.
message : str
Description of the cause of the termination.
fun : float
Value of objective function at `x`.
jac, hess : ndarray
Values of objective function's Jacobian and its Hessian at `x` (if
available). The Hessian may be an approximation, see the documentation
of the function in question.
hess_inv : object
Inverse of the objective function's Hessian; may be an approximation.
Not available for all solvers. The type of this attribute may be
either np.ndarray or scipy.sparse.linalg.LinearOperator.
nfev, njev, nhev : int
Number of evaluations of the objective functions and of its
Jacobian and Hessian.
nit : int
Number of iterations performed by the optimizer.
maxcv : float
The maximum constraint violation.
Notes
-----
Depending on the specific solver being used, `OptimizeResult` may
not have all attributes listed here, and they may have additional
attributes not listed here. Since this class is essentially a
subclass of dict with attribute accessors, one can see which
attributes are available using the `OptimizeResult.keys` method.
"""
pass
class OptimizeWarning(UserWarning):
pass
def _check_positive_definite(Hk):
def is_pos_def(A):
if issymmetric(A):
try:
cholesky(A)
return True
except LinAlgError:
return False
else:
return False
if Hk is not None:
if not is_pos_def(Hk):
raise ValueError("'hess_inv0' matrix isn't positive definite.")
def _check_unknown_options(unknown_options):
if unknown_options:
msg = ", ".join(map(str, unknown_options.keys()))
# Stack level 4: this is called from _minimize_*, which is
# called from another function in SciPy. Level 4 is the first
# level in user code.
warnings.warn(f"Unknown solver options: {msg}", OptimizeWarning, stacklevel=4)
def is_finite_scalar(x):
"""Test whether `x` is either a finite scalar or a finite array scalar.
"""
return np.size(x) == 1 and np.isfinite(x)
_epsilon = sqrt(np.finfo(float).eps)
def vecnorm(x, ord=2):
if ord == np.inf:
return np.amax(np.abs(x))
elif ord == -np.inf:
return np.amin(np.abs(x))
else:
return np.sum(np.abs(x)**ord, axis=0)**(1.0 / ord)
def _prepare_scalar_function(fun, x0, jac=None, args=(), bounds=None,
epsilon=None, finite_diff_rel_step=None,
hess=None):
"""
Creates a ScalarFunction object for use with scalar minimizers
(BFGS/LBFGSB/SLSQP/TNC/CG/etc).
Parameters
----------
fun : callable
The objective function to be minimized.
``fun(x, *args) -> float``
where ``x`` is an 1-D array with shape (n,) and ``args``
is a tuple of the fixed parameters needed to completely
specify the function.
x0 : ndarray, shape (n,)
Initial guess. Array of real elements of size (n,),
where 'n' is the number of independent variables.
jac : {callable, '2-point', '3-point', 'cs', None}, optional
Method for computing the gradient vector. If it is a callable, it
should be a function that returns the gradient vector:
``jac(x, *args) -> array_like, shape (n,)``
If one of `{'2-point', '3-point', 'cs'}` is selected then the gradient
is calculated with a relative step for finite differences. If `None`,
then two-point finite differences with an absolute step is used.
args : tuple, optional
Extra arguments passed to the objective function and its
derivatives (`fun`, `jac` functions).
bounds : sequence, optional
Bounds on variables. 'new-style' bounds are required.
eps : float or ndarray
If ``jac is None`` the absolute step size used for numerical
approximation of the jacobian via forward differences.
finite_diff_rel_step : None or array_like, optional
If ``jac in ['2-point', '3-point', 'cs']`` the relative step size to
use for numerical approximation of the jacobian. The absolute step
size is computed as ``h = rel_step * sign(x0) * max(1, abs(x0))``,
possibly adjusted to fit into the bounds. For ``jac='3-point'``
the sign of `h` is ignored. If None (default) then step is selected
automatically.
hess : {callable, '2-point', '3-point', 'cs', None}
Computes the Hessian matrix. If it is callable, it should return the
Hessian matrix:
``hess(x, *args) -> {LinearOperator, spmatrix, array}, (n, n)``
Alternatively, the keywords {'2-point', '3-point', 'cs'} select a
finite difference scheme for numerical estimation.
Whenever the gradient is estimated via finite-differences, the Hessian
cannot be estimated with options {'2-point', '3-point', 'cs'} and needs
to be estimated using one of the quasi-Newton strategies.
Returns
-------
sf : ScalarFunction
"""
if callable(jac):
grad = jac
elif jac in FD_METHODS:
# epsilon is set to None so that ScalarFunction is made to use
# rel_step
epsilon = None
grad = jac
else:
# default (jac is None) is to do 2-point finite differences with
# absolute step size. ScalarFunction has to be provided an
# epsilon value that is not None to use absolute steps. This is
# normally the case from most _minimize* methods.
grad = '2-point'
epsilon = epsilon
if hess is None:
# ScalarFunction requires something for hess, so we give a dummy
# implementation here if nothing is provided, return a value of None
# so that downstream minimisers halt. The results of `fun.hess`
# should not be used.
def hess(x, *args):
return None
if bounds is None:
bounds = (-np.inf, np.inf)
# ScalarFunction caches. Reuse of fun(x) during grad
# calculation reduces overall function evaluations.
sf = ScalarFunction(fun, x0, args, grad, hess,
finite_diff_rel_step, bounds, epsilon=epsilon)
return sf
def _clip_x_for_func(func, bounds):
# ensures that x values sent to func are clipped to bounds
# this is used as a mitigation for gh11403, slsqp/tnc sometimes
# suggest a move that is outside the limits by 1 or 2 ULP. This
# unclean fix makes sure x is strictly within bounds.
def eval(x):
x = _check_clip_x(x, bounds)
return func(x)
return eval
def _check_clip_x(x, bounds):
if (x < bounds[0]).any() or (x > bounds[1]).any():
warnings.warn("Values in x were outside bounds during a "
"minimize step, clipping to bounds",
RuntimeWarning, stacklevel=3)
x = np.clip(x, bounds[0], bounds[1])
return x
return x
def rosen(x):
"""
The Rosenbrock function.
The function computed is::
sum(100.0*(x[1:] - x[:-1]**2.0)**2.0 + (1 - x[:-1])**2.0)
Parameters
----------
x : array_like
1-D array of points at which the Rosenbrock function is to be computed.
Returns
-------
f : float
The value of the Rosenbrock function.
See Also
--------
rosen_der, rosen_hess, rosen_hess_prod
Examples
--------
>>> import numpy as np
>>> from scipy.optimize import rosen
>>> X = 0.1 * np.arange(10)
>>> rosen(X)
76.56
For higher-dimensional input ``rosen`` broadcasts.
In the following example, we use this to plot a 2D landscape.
Note that ``rosen_hess`` does not broadcast in this manner.
>>> import matplotlib.pyplot as plt
>>> from mpl_toolkits.mplot3d import Axes3D
>>> x = np.linspace(-1, 1, 50)
>>> X, Y = np.meshgrid(x, x)
>>> ax = plt.subplot(111, projection='3d')
>>> ax.plot_surface(X, Y, rosen([X, Y]))
>>> plt.show()
"""
xp = array_namespace(x)
x = xp.asarray(x)
if xp.isdtype(x.dtype, 'integral'):
x = xp.astype(x, xp.asarray(1.).dtype)
r = xp.sum(100.0 * (x[1:] - x[:-1]**2.0)**2.0 + (1 - x[:-1])**2.0,
axis=0, dtype=x.dtype)
return r
def rosen_der(x):
"""
The derivative (i.e. gradient) of the Rosenbrock function.
Parameters
----------
x : array_like
1-D array of points at which the derivative is to be computed.
Returns
-------
rosen_der : (N,) ndarray
The gradient of the Rosenbrock function at `x`.
See Also
--------
rosen, rosen_hess, rosen_hess_prod
Examples
--------
>>> import numpy as np
>>> from scipy.optimize import rosen_der
>>> X = 0.1 * np.arange(9)
>>> rosen_der(X)
array([ -2. , 10.6, 15.6, 13.4, 6.4, -3. , -12.4, -19.4, 62. ])
"""
xp = array_namespace(x)
x = xp.asarray(x)
if xp.isdtype(x.dtype, 'integral'):
x = xp.astype(x, xp.asarray(1.).dtype)
xm = x[1:-1]
xm_m1 = x[:-2]
xm_p1 = x[2:]
der = xp.zeros_like(x)
der[1:-1] = (200 * (xm - xm_m1**2) -
400 * (xm_p1 - xm**2) * xm - 2 * (1 - xm))
der[0] = -400 * x[0] * (x[1] - x[0]**2) - 2 * (1 - x[0])
der[-1] = 200 * (x[-1] - x[-2]**2)
return der
def rosen_hess(x):
"""
The Hessian matrix of the Rosenbrock function.
Parameters
----------
x : array_like
1-D array of points at which the Hessian matrix is to be computed.
Returns
-------
rosen_hess : ndarray
The Hessian matrix of the Rosenbrock function at `x`.
See Also
--------
rosen, rosen_der, rosen_hess_prod
Examples
--------
>>> import numpy as np
>>> from scipy.optimize import rosen_hess
>>> X = 0.1 * np.arange(4)
>>> rosen_hess(X)
array([[-38., 0., 0., 0.],
[ 0., 134., -40., 0.],
[ 0., -40., 130., -80.],
[ 0., 0., -80., 200.]])
"""
xp = array_namespace(x)
x = xpx.atleast_nd(x, ndim=1, xp=xp)
if xp.isdtype(x.dtype, 'integral'):
x = xp.astype(x, xp.asarray(1.).dtype)
H = (xpx.create_diagonal(-400 * x[:-1], offset=1, xp=xp)
- xpx.create_diagonal(400 * x[:-1], offset=-1, xp=xp))
diagonal = xp.zeros(x.shape[0], dtype=x.dtype)
diagonal[0] = 1200 * x[0]**2 - 400 * x[1] + 2
diagonal[-1] = 200
diagonal[1:-1] = 202 + 1200 * x[1:-1]**2 - 400 * x[2:]
return H + xpx.create_diagonal(diagonal, xp=xp)
def rosen_hess_prod(x, p):
"""
Product of the Hessian matrix of the Rosenbrock function with a vector.
Parameters
----------
x : array_like
1-D array of points at which the Hessian matrix is to be computed.
p : array_like
1-D array, the vector to be multiplied by the Hessian matrix.
Returns
-------
rosen_hess_prod : ndarray
The Hessian matrix of the Rosenbrock function at `x` multiplied
by the vector `p`.
See Also
--------
rosen, rosen_der, rosen_hess
Examples
--------
>>> import numpy as np
>>> from scipy.optimize import rosen_hess_prod
>>> X = 0.1 * np.arange(9)
>>> p = 0.5 * np.arange(9)
>>> rosen_hess_prod(X, p)
array([ -0., 27., -10., -95., -192., -265., -278., -195., -180.])
"""
xp = array_namespace(x, p)
x = xpx.atleast_nd(x, ndim=1, xp=xp)
if xp.isdtype(x.dtype, 'integral'):
x = xp.astype(x, xp.asarray(1.).dtype)
p = xp.asarray(p, dtype=x.dtype)
Hp = xp.zeros(x.shape[0], dtype=x.dtype)
Hp[0] = (1200 * x[0]**2 - 400 * x[1] + 2) * p[0] - 400 * x[0] * p[1]
Hp[1:-1] = (-400 * x[:-2] * p[:-2] +
(202 + 1200 * x[1:-1]**2 - 400 * x[2:]) * p[1:-1] -
400 * x[1:-1] * p[2:])
Hp[-1] = -400 * x[-2] * p[-2] + 200*p[-1]
return Hp
def _wrap_scalar_function(function, args):
# wraps a minimizer function to count number of evaluations
# and to easily provide an args kwd.
ncalls = [0]
if function is None:
return ncalls, None
def function_wrapper(x, *wrapper_args):
ncalls[0] += 1
# A copy of x is sent to the user function (gh13740)
fx = function(np.copy(x), *(wrapper_args + args))
# Ideally, we'd like to a have a true scalar returned from f(x). For
# backwards-compatibility, also allow np.array([1.3]), np.array([[1.3]]) etc.
if not np.isscalar(fx):
try:
fx = np.asarray(fx).item()
except (TypeError, ValueError) as e:
raise ValueError("The user-provided objective function "
"must return a scalar value.") from e
return fx
return ncalls, function_wrapper
class _MaxFuncCallError(RuntimeError):
pass
def _wrap_scalar_function_maxfun_validation(function, args, maxfun):
# wraps a minimizer function to count number of evaluations
# and to easily provide an args kwd.
ncalls = [0]
if function is None:
return ncalls, None
def function_wrapper(x, *wrapper_args):
if ncalls[0] >= maxfun:
raise _MaxFuncCallError("Too many function calls")
ncalls[0] += 1
# A copy of x is sent to the user function (gh13740)
fx = function(np.copy(x), *(wrapper_args + args))
# Ideally, we'd like to a have a true scalar returned from f(x). For
# backwards-compatibility, also allow np.array([1.3]),
# np.array([[1.3]]) etc.
if not np.isscalar(fx):
try:
fx = np.asarray(fx).item()
except (TypeError, ValueError) as e:
raise ValueError("The user-provided objective function "
"must return a scalar value.") from e
return fx
return ncalls, function_wrapper
def fmin(func, x0, args=(), xtol=1e-4, ftol=1e-4, maxiter=None, maxfun=None,
full_output=0, disp=1, retall=0, callback=None, initial_simplex=None):
"""
Minimize a function using the downhill simplex algorithm.
This algorithm only uses function values, not derivatives or second
derivatives.
Parameters
----------
func : callable func(x,*args)
The objective function to be minimized.
x0 : ndarray
Initial guess.
args : tuple, optional
Extra arguments passed to func, i.e., ``f(x,*args)``.
xtol : float, optional
Absolute error in xopt between iterations that is acceptable for
convergence.
ftol : number, optional
Absolute error in func(xopt) between iterations that is acceptable for
convergence.
maxiter : int, optional
Maximum number of iterations to perform.
maxfun : number, optional
Maximum number of function evaluations to make.
full_output : bool, optional
Set to True if fopt and warnflag outputs are desired.
disp : bool, optional
Set to True to print convergence messages.
retall : bool, optional
Set to True to return list of solutions at each iteration.
callback : callable, optional
Called after each iteration, as callback(xk), where xk is the
current parameter vector.
initial_simplex : array_like of shape (N + 1, N), optional
Initial simplex. If given, overrides `x0`.
``initial_simplex[j,:]`` should contain the coordinates of
the jth vertex of the ``N+1`` vertices in the simplex, where
``N`` is the dimension.
Returns
-------
xopt : ndarray
Parameter that minimizes function.
fopt : float
Value of function at minimum: ``fopt = func(xopt)``.
iter : int
Number of iterations performed.
funcalls : int
Number of function calls made.
warnflag : int
1 : Maximum number of function evaluations made.
2 : Maximum number of iterations reached.
allvecs : list
Solution at each iteration.
See also
--------
minimize: Interface to minimization algorithms for multivariate
functions. See the 'Nelder-Mead' `method` in particular.
Notes
-----
Uses a Nelder-Mead simplex algorithm to find the minimum of function of
one or more variables.
This algorithm has a long history of successful use in applications.
But it will usually be slower than an algorithm that uses first or
second derivative information. In practice, it can have poor
performance in high-dimensional problems and is not robust to
minimizing complicated functions. Additionally, there currently is no
complete theory describing when the algorithm will successfully
converge to the minimum, or how fast it will if it does. Both the ftol and
xtol criteria must be met for convergence.
Examples
--------
>>> def f(x):
... return x**2
>>> from scipy import optimize
>>> minimum = optimize.fmin(f, 1)
Optimization terminated successfully.
Current function value: 0.000000
Iterations: 17
Function evaluations: 34
>>> minimum[0]
-8.8817841970012523e-16
References
----------
.. [1] Nelder, J.A. and Mead, R. (1965), "A simplex method for function
minimization", The Computer Journal, 7, pp. 308-313
.. [2] Wright, M.H. (1996), "Direct Search Methods: Once Scorned, Now
Respectable", in Numerical Analysis 1995, Proceedings of the
1995 Dundee Biennial Conference in Numerical Analysis, D.F.
Griffiths and G.A. Watson (Eds.), Addison Wesley Longman,
Harlow, UK, pp. 191-208.
"""
opts = {'xatol': xtol,
'fatol': ftol,
'maxiter': maxiter,
'maxfev': maxfun,
'disp': disp,
'return_all': retall,
'initial_simplex': initial_simplex}
callback = _wrap_callback(callback)
res = _minimize_neldermead(func, x0, args, callback=callback, **opts)
if full_output:
retlist = res['x'], res['fun'], res['nit'], res['nfev'], res['status']
if retall:
retlist += (res['allvecs'], )
return retlist
else:
if retall:
return res['x'], res['allvecs']
else:
return res['x']
def _minimize_neldermead(func, x0, args=(), callback=None,
maxiter=None, maxfev=None, disp=False,
return_all=False, initial_simplex=None,
xatol=1e-4, fatol=1e-4, adaptive=False, bounds=None,
**unknown_options):
"""
Minimization of scalar function of one or more variables using the
Nelder-Mead algorithm.
Options
-------
disp : bool
Set to True to print convergence messages.
maxiter, maxfev : int
Maximum allowed number of iterations and function evaluations.
Will default to ``N*200``, where ``N`` is the number of
variables, if neither `maxiter` or `maxfev` is set. If both
`maxiter` and `maxfev` are set, minimization will stop at the
first reached.
return_all : bool, optional
Set to True to return a list of the best solution at each of the
iterations.
initial_simplex : array_like of shape (N + 1, N)
Initial simplex. If given, overrides `x0`.
``initial_simplex[j,:]`` should contain the coordinates of
the jth vertex of the ``N+1`` vertices in the simplex, where
``N`` is the dimension.
xatol : float, optional
Absolute error in xopt between iterations that is acceptable for
convergence.
fatol : number, optional
Absolute error in func(xopt) between iterations that is acceptable for
convergence.
adaptive : bool, optional
Adapt algorithm parameters to dimensionality of problem. Useful for
high-dimensional minimization [1]_.
bounds : sequence or `Bounds`, optional
Bounds on variables. There are two ways to specify the bounds:
1. Instance of `Bounds` class.
2. Sequence of ``(min, max)`` pairs for each element in `x`. None
is used to specify no bound.
Note that this just clips all vertices in simplex based on
the bounds.
References
----------
.. [1] Gao, F. and Han, L.
Implementing the Nelder-Mead simplex algorithm with adaptive
parameters. 2012. Computational Optimization and Applications.
51:1, pp. 259-277
"""
_check_unknown_options(unknown_options)
maxfun = maxfev
retall = return_all
x0 = np.atleast_1d(x0).flatten()
dtype = x0.dtype if np.issubdtype(x0.dtype, np.inexact) else np.float64
x0 = np.asarray(x0, dtype=dtype)
if adaptive:
dim = float(len(x0))
rho = 1
chi = 1 + 2/dim
psi = 0.75 - 1/(2*dim)
sigma = 1 - 1/dim
else:
rho = 1
chi = 2
psi = 0.5
sigma = 0.5
nonzdelt = 0.05
zdelt = 0.00025
if bounds is not None:
lower_bound, upper_bound = bounds.lb, bounds.ub
# check bounds
if (lower_bound > upper_bound).any():
raise ValueError("Nelder Mead - one of the lower bounds "
"is greater than an upper bound.")
if np.any(lower_bound > x0) or np.any(x0 > upper_bound):
warnings.warn("Initial guess is not within the specified bounds",
OptimizeWarning, stacklevel=3)
if bounds is not None:
x0 = np.clip(x0, lower_bound, upper_bound)
if initial_simplex is None:
N = len(x0)
sim = np.empty((N + 1, N), dtype=x0.dtype)
sim[0] = x0
for k in range(N):
y = np.array(x0, copy=True)
if y[k] != 0:
y[k] = (1 + nonzdelt)*y[k]
else:
y[k] = zdelt
sim[k + 1] = y
else:
sim = np.atleast_2d(initial_simplex).copy()
dtype = sim.dtype if np.issubdtype(sim.dtype, np.inexact) else np.float64
sim = np.asarray(sim, dtype=dtype)
if sim.ndim != 2 or sim.shape[0] != sim.shape[1] + 1:
raise ValueError("`initial_simplex` should be an array of shape (N+1,N)")
if len(x0) != sim.shape[1]:
raise ValueError("Size of `initial_simplex` is not consistent with `x0`")
N = sim.shape[1]
if retall:
allvecs = [sim[0]]
# If neither are set, then set both to default
if maxiter is None and maxfun is None:
maxiter = N * 200
maxfun = N * 200
elif maxiter is None:
# Convert remaining Nones, to np.inf, unless the other is np.inf, in
# which case use the default to avoid unbounded iteration
if maxfun == np.inf:
maxiter = N * 200
else:
maxiter = np.inf
elif maxfun is None:
if maxiter == np.inf:
maxfun = N * 200
else:
maxfun = np.inf
if bounds is not None:
# The default simplex construction may make all entries (for a given
# parameter) greater than an upper bound if x0 is very close to the
# upper bound. If one simply clips the simplex to the bounds this could
# make the simplex entries degenerate. If that occurs reflect into the
# interior.
msk = sim > upper_bound
# reflect into the interior
sim = np.where(msk, 2*upper_bound - sim, sim)
# but make sure the reflection is no less than the lower_bound
sim = np.clip(sim, lower_bound, upper_bound)
one2np1 = list(range(1, N + 1))
fsim = np.full((N + 1,), np.inf, dtype=float)
fcalls, func = _wrap_scalar_function_maxfun_validation(func, args, maxfun)
try:
for k in range(N + 1):
fsim[k] = func(sim[k])
except _MaxFuncCallError:
pass
finally:
ind = np.argsort(fsim)
sim = np.take(sim, ind, 0)
fsim = np.take(fsim, ind, 0)
ind = np.argsort(fsim)
fsim = np.take(fsim, ind, 0)
# sort so sim[0,:] has the lowest function value
sim = np.take(sim, ind, 0)
iterations = 1
while (fcalls[0] < maxfun and iterations < maxiter):
try:
if (np.max(np.ravel(np.abs(sim[1:] - sim[0]))) <= xatol and
np.max(np.abs(fsim[0] - fsim[1:])) <= fatol):
break
xbar = np.add.reduce(sim[:-1], 0) / N
xr = (1 + rho) * xbar - rho * sim[-1]
if bounds is not None:
xr = np.clip(xr, lower_bound, upper_bound)
fxr = func(xr)
doshrink = 0
if fxr < fsim[0]:
xe = (1 + rho * chi) * xbar - rho * chi * sim[-1]
if bounds is not None:
xe = np.clip(xe, lower_bound, upper_bound)
fxe = func(xe)
if fxe < fxr:
sim[-1] = xe
fsim[-1] = fxe
else:
sim[-1] = xr
fsim[-1] = fxr
else: # fsim[0] <= fxr
if fxr < fsim[-2]:
sim[-1] = xr
fsim[-1] = fxr
else: # fxr >= fsim[-2]
# Perform contraction
if fxr < fsim[-1]:
xc = (1 + psi * rho) * xbar - psi * rho * sim[-1]
if bounds is not None:
xc = np.clip(xc, lower_bound, upper_bound)
fxc = func(xc)
if fxc <= fxr:
sim[-1] = xc
fsim[-1] = fxc
else:
doshrink = 1
else:
# Perform an inside contraction
xcc = (1 - psi) * xbar + psi * sim[-1]
if bounds is not None:
xcc = np.clip(xcc, lower_bound, upper_bound)
fxcc = func(xcc)
if fxcc < fsim[-1]:
sim[-1] = xcc
fsim[-1] = fxcc
else:
doshrink = 1
if doshrink:
for j in one2np1:
sim[j] = sim[0] + sigma * (sim[j] - sim[0])
if bounds is not None:
sim[j] = np.clip(
sim[j], lower_bound, upper_bound)
fsim[j] = func(sim[j])
iterations += 1
except _MaxFuncCallError:
pass
finally:
ind = np.argsort(fsim)
sim = np.take(sim, ind, 0)
fsim = np.take(fsim, ind, 0)
if retall:
allvecs.append(sim[0])
intermediate_result = OptimizeResult(x=sim[0], fun=fsim[0])
if _call_callback_maybe_halt(callback, intermediate_result):
break
x = sim[0]
fval = np.min(fsim)
warnflag = 0
if fcalls[0] >= maxfun:
warnflag = 1
msg = _status_message['maxfev']
if disp:
warnings.warn(msg, RuntimeWarning, stacklevel=3)
elif iterations >= maxiter:
warnflag = 2
msg = _status_message['maxiter']
if disp:
warnings.warn(msg, RuntimeWarning, stacklevel=3)
else:
msg = _status_message['success']
if disp:
print(msg)
print(f" Current function value: {fval:f}")
print(" Iterations: %d" % iterations)
print(" Function evaluations: %d" % fcalls[0])
result = OptimizeResult(fun=fval, nit=iterations, nfev=fcalls[0],
status=warnflag, success=(warnflag == 0),
message=msg, x=x, final_simplex=(sim, fsim))
if retall:
result['allvecs'] = allvecs
return result
def approx_fprime(xk, f, epsilon=_epsilon, *args):
"""Finite difference approximation of the derivatives of a
scalar or vector-valued function.
If a function maps from :math:`R^n` to :math:`R^m`, its derivatives form
an m-by-n matrix
called the Jacobian, where an element :math:`(i, j)` is a partial
derivative of f[i] with respect to ``xk[j]``.
Parameters
----------
xk : array_like
The coordinate vector at which to determine the gradient of `f`.
f : callable
Function of which to estimate the derivatives of. Has the signature
``f(xk, *args)`` where `xk` is the argument in the form of a 1-D array
and `args` is a tuple of any additional fixed parameters needed to
completely specify the function. The argument `xk` passed to this
function is an ndarray of shape (n,) (never a scalar even if n=1).
It must return a 1-D array_like of shape (m,) or a scalar.
Suppose the callable has signature ``f0(x, *my_args, **my_kwargs)``, where
``my_args`` and ``my_kwargs`` are required positional and keyword arguments.
Rather than passing ``f0`` as the callable, wrap it to accept
only ``x``; e.g., pass ``fun=lambda x: f0(x, *my_args, **my_kwargs)`` as the
callable, where ``my_args`` (tuple) and ``my_kwargs`` (dict) have been
gathered before invoking this function.
.. versionchanged:: 1.9.0
`f` is now able to return a 1-D array-like, with the :math:`(m, n)`
Jacobian being estimated.
epsilon : {float, array_like}, optional
Increment to `xk` to use for determining the function gradient.
If a scalar, uses the same finite difference delta for all partial
derivatives. If an array, should contain one value per element of
`xk`. Defaults to ``sqrt(np.finfo(float).eps)``, which is approximately
1.49e-08.
\\*args : args, optional
Any other arguments that are to be passed to `f`.
Returns
-------
jac : ndarray
The partial derivatives of `f` to `xk`.
See Also
--------
check_grad : Check correctness of gradient function against approx_fprime.
Notes
-----
The function gradient is determined by the forward finite difference
formula::
f(xk[i] + epsilon[i]) - f(xk[i])
f'[i] = ---------------------------------
epsilon[i]
Examples
--------
>>> import numpy as np
>>> from scipy import optimize
>>> def func(x, c0, c1):
... "Coordinate vector `x` should be an array of size two."
... return c0 * x[0]**2 + c1*x[1]**2
>>> x = np.ones(2)
>>> c0, c1 = (1, 200)
>>> eps = np.sqrt(np.finfo(float).eps)
>>> optimize.approx_fprime(x, func, [eps, np.sqrt(200) * eps], c0, c1)
array([ 2. , 400.00004208])
"""
xk = np.asarray(xk, float)
f0 = f(xk, *args)
return approx_derivative(f, xk, method='2-point', abs_step=epsilon,
args=args, f0=f0)
@_transition_to_rng("seed", position_num=6)
def check_grad(func, grad, x0, *args, epsilon=_epsilon,
direction='all', rng=None):
r"""Check the correctness of a gradient function by comparing it against a
(forward) finite-difference approximation of the gradient.
Parameters
----------
func : callable ``func(x0, *args)``
Function whose derivative is to be checked.
grad : callable ``grad(x0, *args)``
Jacobian of `func`.
x0 : ndarray
Points to check `grad` against forward difference approximation of grad
using `func`.
args : \\*args, optional
Extra arguments passed to `func` and `grad`.
epsilon : float, optional
Step size used for the finite difference approximation. It defaults to
``sqrt(np.finfo(float).eps)``, which is approximately 1.49e-08.
direction : str, optional
If set to ``'random'``, then gradients along a random vector
are used to check `grad` against forward difference approximation
using `func`. By default it is ``'all'``, in which case, all
the one hot direction vectors are considered to check `grad`.
If `func` is a vector valued function then only ``'all'`` can be used.
rng : `numpy.random.Generator`, optional
Pseudorandom number generator state. When `rng` is None, a new
`numpy.random.Generator` is created using entropy from the
operating system. Types other than `numpy.random.Generator` are
passed to `numpy.random.default_rng` to instantiate a ``Generator``.
The random numbers generated affect the random vector along which gradients
are computed to check ``grad``. Note that `rng` is only used when `direction`
argument is set to `'random'`.
Returns
-------
err : float
The square root of the sum of squares (i.e., the 2-norm) of the
difference between ``grad(x0, *args)`` and the finite difference
approximation of `grad` using func at the points `x0`.
See Also
--------
approx_fprime
Examples
--------
>>> import numpy as np
>>> def func(x):
... return x[0]**2 - 0.5 * x[1]**3
>>> def grad(x):
... return [2 * x[0], -1.5 * x[1]**2]
>>> from scipy.optimize import check_grad
>>> check_grad(func, grad, [1.5, -1.5])
2.9802322387695312e-08 # may vary
>>> rng = np.random.default_rng()
>>> check_grad(func, grad, [1.5, -1.5],
... direction='random', seed=rng)
2.9802322387695312e-08
"""
step = epsilon
x0 = np.asarray(x0)
def g(w, func, x0, v, *args):
return func(x0 + w*v, *args)
if direction == 'random':
_grad = np.asanyarray(grad(x0, *args))
if _grad.ndim > 1:
raise ValueError("'random' can only be used with scalar valued"
" func")
rng_gen = check_random_state(rng)
v = rng_gen.standard_normal(size=(x0.shape))
_args = (func, x0, v) + args
_func = g
vars = np.zeros((1,))
analytical_grad = np.dot(_grad, v)
elif direction == 'all':
_args = args
_func = func
vars = x0
analytical_grad = grad(x0, *args)
else:
raise ValueError(f"{direction} is not a valid string for "
"``direction`` argument")
return np.sqrt(np.sum(np.abs(
(analytical_grad - approx_fprime(vars, _func, step, *_args))**2
)))
def approx_fhess_p(x0, p, fprime, epsilon, *args):
# calculate fprime(x0) first, as this may be cached by ScalarFunction
f1 = fprime(*((x0,) + args))
f2 = fprime(*((x0 + epsilon*p,) + args))
return (f2 - f1) / epsilon
class _LineSearchError(RuntimeError):
pass
def _line_search_wolfe12(f, fprime, xk, pk, gfk, old_fval, old_old_fval,
**kwargs):
"""
Same as line_search_wolfe1, but fall back to line_search_wolfe2 if
suitable step length is not found, and raise an exception if a
suitable step length is not found.
Raises
------
_LineSearchError
If no suitable step size is found
"""
extra_condition = kwargs.pop('extra_condition', None)
ret = line_search_wolfe1(f, fprime, xk, pk, gfk,
old_fval, old_old_fval,
**kwargs)
if ret[0] is not None and extra_condition is not None:
xp1 = xk + ret[0] * pk
if not extra_condition(ret[0], xp1, ret[3], ret[5]):
# Reject step if extra_condition fails
ret = (None,)
if ret[0] is None:
# line search failed: try different one.
with warnings.catch_warnings():
warnings.simplefilter('ignore', LineSearchWarning)
kwargs2 = {}
for key in ('c1', 'c2', 'amax'):
if key in kwargs:
kwargs2[key] = kwargs[key]
ret = line_search_wolfe2(f, fprime, xk, pk, gfk,
old_fval, old_old_fval,
extra_condition=extra_condition,
**kwargs2)
if ret[0] is None:
raise _LineSearchError()
return ret
def fmin_bfgs(f, x0, fprime=None, args=(), gtol=1e-5, norm=np.inf,
epsilon=_epsilon, maxiter=None, full_output=0, disp=1,
retall=0, callback=None, xrtol=0, c1=1e-4, c2=0.9,
hess_inv0=None):
"""
Minimize a function using the BFGS algorithm.
Parameters
----------
f : callable ``f(x,*args)``
Objective function to be minimized.
x0 : ndarray
Initial guess, shape (n,)
fprime : callable ``f'(x,*args)``, optional
Gradient of f.
args : tuple, optional
Extra arguments passed to f and fprime.
gtol : float, optional
Terminate successfully if gradient norm is less than `gtol`
norm : float, optional
Order of norm (Inf is max, -Inf is min)
epsilon : int or ndarray, optional
If `fprime` is approximated, use this value for the step size.
callback : callable, optional
An optional user-supplied function to call after each
iteration. Called as ``callback(xk)``, where ``xk`` is the
current parameter vector.
maxiter : int, optional
Maximum number of iterations to perform.
full_output : bool, optional
If True, return ``fopt``, ``func_calls``, ``grad_calls``, and
``warnflag`` in addition to ``xopt``.
disp : bool, optional
Print convergence message if True.
retall : bool, optional
Return a list of results at each iteration if True.
xrtol : float, default: 0
Relative tolerance for `x`. Terminate successfully if step
size is less than ``xk * xrtol`` where ``xk`` is the current
parameter vector.
c1 : float, default: 1e-4
Parameter for Armijo condition rule.
c2 : float, default: 0.9
Parameter for curvature condition rule.
hess_inv0 : None or ndarray, optional``
Initial inverse hessian estimate, shape (n, n). If None (default) then
the identity matrix is used.
Returns
-------
xopt : ndarray
Parameters which minimize f, i.e., ``f(xopt) == fopt``.
fopt : float
Minimum value.
gopt : ndarray
Value of gradient at minimum, f'(xopt), which should be near 0.
Bopt : ndarray
Value of 1/f''(xopt), i.e., the inverse Hessian matrix.
func_calls : int
Number of function_calls made.
grad_calls : int
Number of gradient calls made.
warnflag : integer
1 : Maximum number of iterations exceeded.
2 : Gradient and/or function calls not changing.
3 : NaN result encountered.
allvecs : list
The value of `xopt` at each iteration. Only returned if `retall` is
True.
Notes
-----
Optimize the function, `f`, whose gradient is given by `fprime`
using the quasi-Newton method of Broyden, Fletcher, Goldfarb,
and Shanno (BFGS).
Parameters `c1` and `c2` must satisfy ``0 < c1 < c2 < 1``.
See Also
--------
minimize: Interface to minimization algorithms for multivariate
functions. See ``method='BFGS'`` in particular.
References
----------
Wright, and Nocedal 'Numerical Optimization', 1999, p. 198.
Examples
--------
>>> import numpy as np
>>> from scipy.optimize import fmin_bfgs
>>> def quadratic_cost(x, Q):
... return x @ Q @ x
...
>>> x0 = np.array([-3, -4])
>>> cost_weight = np.diag([1., 10.])
>>> # Note that a trailing comma is necessary for a tuple with single element
>>> fmin_bfgs(quadratic_cost, x0, args=(cost_weight,))
Optimization terminated successfully.
Current function value: 0.000000
Iterations: 7 # may vary
Function evaluations: 24 # may vary
Gradient evaluations: 8 # may vary
array([ 2.85169950e-06, -4.61820139e-07])
>>> def quadratic_cost_grad(x, Q):
... return 2 * Q @ x
...
>>> fmin_bfgs(quadratic_cost, x0, quadratic_cost_grad, args=(cost_weight,))
Optimization terminated successfully.
Current function value: 0.000000
Iterations: 7
Function evaluations: 8
Gradient evaluations: 8
array([ 2.85916637e-06, -4.54371951e-07])
"""
opts = {'gtol': gtol,
'norm': norm,
'eps': epsilon,
'disp': disp,
'maxiter': maxiter,
'return_all': retall,
'xrtol': xrtol,
'c1': c1,
'c2': c2,
'hess_inv0': hess_inv0}
callback = _wrap_callback(callback)
res = _minimize_bfgs(f, x0, args, fprime, callback=callback, **opts)
if full_output:
retlist = (res['x'], res['fun'], res['jac'], res['hess_inv'],
res['nfev'], res['njev'], res['status'])
if retall:
retlist += (res['allvecs'], )
return retlist
else:
if retall:
return res['x'], res['allvecs']
else:
return res['x']
def _minimize_bfgs(fun, x0, args=(), jac=None, callback=None,
gtol=1e-5, norm=np.inf, eps=_epsilon, maxiter=None,
disp=False, return_all=False, finite_diff_rel_step=None,
xrtol=0, c1=1e-4, c2=0.9,
hess_inv0=None, **unknown_options):
"""
Minimization of scalar function of one or more variables using the
BFGS algorithm.
Options
-------
disp : bool
Set to True to print convergence messages.
maxiter : int
Maximum number of iterations to perform.
gtol : float
Terminate successfully if gradient norm is less than `gtol`.
norm : float
Order of norm (Inf is max, -Inf is min).
eps : float or ndarray
If `jac is None` the absolute step size used for numerical
approximation of the jacobian via forward differences.
return_all : bool, optional
Set to True to return a list of the best solution at each of the
iterations.
finite_diff_rel_step : None or array_like, optional
If ``jac in ['2-point', '3-point', 'cs']`` the relative step size to
use for numerical approximation of the jacobian. The absolute step
size is computed as ``h = rel_step * sign(x) * max(1, abs(x))``,
possibly adjusted to fit into the bounds. For ``jac='3-point'``
the sign of `h` is ignored. If None (default) then step is selected
automatically.
xrtol : float, default: 0
Relative tolerance for `x`. Terminate successfully if step size is
less than ``xk * xrtol`` where ``xk`` is the current parameter vector.
c1 : float, default: 1e-4
Parameter for Armijo condition rule.
c2 : float, default: 0.9
Parameter for curvature condition rule.
hess_inv0 : None or ndarray, optional
Initial inverse hessian estimate, shape (n, n). If None (default) then
the identity matrix is used.
Notes
-----
Parameters `c1` and `c2` must satisfy ``0 < c1 < c2 < 1``.
If minimization doesn't complete successfully, with an error message of
``Desired error not necessarily achieved due to precision loss``, then
consider setting `gtol` to a higher value. This precision loss typically
occurs when the (finite difference) numerical differentiation cannot provide
sufficient precision to satisfy the `gtol` termination criterion.
This can happen when working in single precision and a callable jac is not
provided. For single precision problems a `gtol` of 1e-3 seems to work.
"""
_check_unknown_options(unknown_options)
_check_positive_definite(hess_inv0)
retall = return_all
x0 = asarray(x0).flatten()
if x0.ndim == 0:
x0.shape = (1,)
if maxiter is None:
maxiter = len(x0) * 200
sf = _prepare_scalar_function(fun, x0, jac, args=args, epsilon=eps,
finite_diff_rel_step=finite_diff_rel_step)
f = sf.fun
myfprime = sf.grad
old_fval = f(x0)
gfk = myfprime(x0)
k = 0
N = len(x0)
I = np.eye(N, dtype=int)
Hk = I if hess_inv0 is None else hess_inv0
# Sets the initial step guess to dx ~ 1
old_old_fval = old_fval + np.linalg.norm(gfk) / 2
xk = x0
if retall:
allvecs = [x0]
warnflag = 0
gnorm = vecnorm(gfk, ord=norm)
while (gnorm > gtol) and (k < maxiter):
pk = -np.dot(Hk, gfk)
try:
alpha_k, fc, gc, old_fval, old_old_fval, gfkp1 = \
_line_search_wolfe12(f, myfprime, xk, pk, gfk,
old_fval, old_old_fval, amin=1e-100,
amax=1e100, c1=c1, c2=c2)
except _LineSearchError:
# Line search failed to find a better solution.
warnflag = 2
break
sk = alpha_k * pk
xkp1 = xk + sk
if retall:
allvecs.append(xkp1)
xk = xkp1
if gfkp1 is None:
gfkp1 = myfprime(xkp1)
yk = gfkp1 - gfk
gfk = gfkp1
k += 1
intermediate_result = OptimizeResult(x=xk, fun=old_fval)
if _call_callback_maybe_halt(callback, intermediate_result):
break
gnorm = vecnorm(gfk, ord=norm)
if (gnorm <= gtol):
break
# See Chapter 5 in P.E. Frandsen, K. Jonasson, H.B. Nielsen,
# O. Tingleff: "Unconstrained Optimization", IMM, DTU. 1999.
# These notes are available here:
# http://www2.imm.dtu.dk/documents/ftp/publlec.html
if (alpha_k*vecnorm(pk) <= xrtol*(xrtol + vecnorm(xk))):
break
if not np.isfinite(old_fval):
# We correctly found +-Inf as optimal value, or something went
# wrong.
warnflag = 2
break
rhok_inv = np.dot(yk, sk)
# this was handled in numeric, let it remains for more safety
# Cryptic comment above is preserved for posterity. Future reader:
# consider change to condition below proposed in gh-1261/gh-17345.
if rhok_inv == 0.:
rhok = 1000.0
if disp:
msg = "Divide-by-zero encountered: rhok assumed large"
_print_success_message_or_warn(True, msg)
else:
rhok = 1. / rhok_inv
A1 = I - sk[:, np.newaxis] * yk[np.newaxis, :] * rhok
A2 = I - yk[:, np.newaxis] * sk[np.newaxis, :] * rhok
Hk = np.dot(A1, np.dot(Hk, A2)) + (rhok * sk[:, np.newaxis] *
sk[np.newaxis, :])
fval = old_fval
if warnflag == 2:
msg = _status_message['pr_loss']
elif k >= maxiter:
warnflag = 1
msg = _status_message['maxiter']
elif np.isnan(gnorm) or np.isnan(fval) or np.isnan(xk).any():
warnflag = 3
msg = _status_message['nan']
else:
msg = _status_message['success']
if disp:
_print_success_message_or_warn(warnflag, msg)
print(f" Current function value: {fval:f}")
print(" Iterations: %d" % k)
print(" Function evaluations: %d" % sf.nfev)
print(" Gradient evaluations: %d" % sf.ngev)
result = OptimizeResult(fun=fval, jac=gfk, hess_inv=Hk, nfev=sf.nfev,
njev=sf.ngev, status=warnflag,
success=(warnflag == 0), message=msg, x=xk,
nit=k)
if retall:
result['allvecs'] = allvecs
return result
def _print_success_message_or_warn(warnflag, message, warntype=None):
if not warnflag:
print(message)
else:
warnings.warn(message, warntype or OptimizeWarning, stacklevel=3)
def fmin_cg(f, x0, fprime=None, args=(), gtol=1e-5, norm=np.inf,
epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0,
callback=None, c1=1e-4, c2=0.4):
"""
Minimize a function using a nonlinear conjugate gradient algorithm.
Parameters
----------
f : callable, ``f(x, *args)``
Objective function to be minimized. Here `x` must be a 1-D array of
the variables that are to be changed in the search for a minimum, and
`args` are the other (fixed) parameters of `f`.
x0 : ndarray
A user-supplied initial estimate of `xopt`, the optimal value of `x`.
It must be a 1-D array of values.
fprime : callable, ``fprime(x, *args)``, optional
A function that returns the gradient of `f` at `x`. Here `x` and `args`
are as described above for `f`. The returned value must be a 1-D array.
Defaults to None, in which case the gradient is approximated
numerically (see `epsilon`, below).
args : tuple, optional
Parameter values passed to `f` and `fprime`. Must be supplied whenever
additional fixed parameters are needed to completely specify the
functions `f` and `fprime`.
gtol : float, optional
Stop when the norm of the gradient is less than `gtol`.
norm : float, optional
Order to use for the norm of the gradient
(``-np.inf`` is min, ``np.inf`` is max).
epsilon : float or ndarray, optional
Step size(s) to use when `fprime` is approximated numerically. Can be a
scalar or a 1-D array. Defaults to ``sqrt(eps)``, with eps the
floating point machine precision. Usually ``sqrt(eps)`` is about
1.5e-8.
maxiter : int, optional
Maximum number of iterations to perform. Default is ``200 * len(x0)``.
full_output : bool, optional
If True, return `fopt`, `func_calls`, `grad_calls`, and `warnflag` in
addition to `xopt`. See the Returns section below for additional
information on optional return values.
disp : bool, optional
If True, return a convergence message, followed by `xopt`.
retall : bool, optional
If True, add to the returned values the results of each iteration.
callback : callable, optional
An optional user-supplied function, called after each iteration.
Called as ``callback(xk)``, where ``xk`` is the current value of `x0`.
c1 : float, default: 1e-4
Parameter for Armijo condition rule.
c2 : float, default: 0.4
Parameter for curvature condition rule.
Returns
-------
xopt : ndarray
Parameters which minimize f, i.e., ``f(xopt) == fopt``.
fopt : float, optional
Minimum value found, f(xopt). Only returned if `full_output` is True.
func_calls : int, optional
The number of function_calls made. Only returned if `full_output`
is True.
grad_calls : int, optional
The number of gradient calls made. Only returned if `full_output` is
True.
warnflag : int, optional
Integer value with warning status, only returned if `full_output` is
True.
0 : Success.
1 : The maximum number of iterations was exceeded.
2 : Gradient and/or function calls were not changing. May indicate
that precision was lost, i.e., the routine did not converge.
3 : NaN result encountered.
allvecs : list of ndarray, optional
List of arrays, containing the results at each iteration.
Only returned if `retall` is True.
See Also
--------
minimize : common interface to all `scipy.optimize` algorithms for
unconstrained and constrained minimization of multivariate
functions. It provides an alternative way to call
``fmin_cg``, by specifying ``method='CG'``.
Notes
-----
This conjugate gradient algorithm is based on that of Polak and Ribiere
[1]_.
Conjugate gradient methods tend to work better when:
1. `f` has a unique global minimizing point, and no local minima or
other stationary points,
2. `f` is, at least locally, reasonably well approximated by a
quadratic function of the variables,
3. `f` is continuous and has a continuous gradient,
4. `fprime` is not too large, e.g., has a norm less than 1000,
5. The initial guess, `x0`, is reasonably close to `f` 's global
minimizing point, `xopt`.
Parameters `c1` and `c2` must satisfy ``0 < c1 < c2 < 1``.
References
----------
.. [1] Wright & Nocedal, "Numerical Optimization", 1999, pp. 120-122.
Examples
--------
Example 1: seek the minimum value of the expression
``a*u**2 + b*u*v + c*v**2 + d*u + e*v + f`` for given values
of the parameters and an initial guess ``(u, v) = (0, 0)``.
>>> import numpy as np
>>> args = (2, 3, 7, 8, 9, 10) # parameter values
>>> def f(x, *args):
... u, v = x
... a, b, c, d, e, f = args
... return a*u**2 + b*u*v + c*v**2 + d*u + e*v + f
>>> def gradf(x, *args):
... u, v = x
... a, b, c, d, e, f = args
... gu = 2*a*u + b*v + d # u-component of the gradient
... gv = b*u + 2*c*v + e # v-component of the gradient
... return np.asarray((gu, gv))
>>> x0 = np.asarray((0, 0)) # Initial guess.
>>> from scipy import optimize
>>> res1 = optimize.fmin_cg(f, x0, fprime=gradf, args=args)
Optimization terminated successfully.
Current function value: 1.617021
Iterations: 4
Function evaluations: 8
Gradient evaluations: 8
>>> res1
array([-1.80851064, -0.25531915])
Example 2: solve the same problem using the `minimize` function.
(This `myopts` dictionary shows all of the available options,
although in practice only non-default values would be needed.
The returned value will be a dictionary.)
>>> opts = {'maxiter' : None, # default value.
... 'disp' : True, # non-default value.
... 'gtol' : 1e-5, # default value.
... 'norm' : np.inf, # default value.
... 'eps' : 1.4901161193847656e-08} # default value.
>>> res2 = optimize.minimize(f, x0, jac=gradf, args=args,
... method='CG', options=opts)
Optimization terminated successfully.
Current function value: 1.617021
Iterations: 4
Function evaluations: 8
Gradient evaluations: 8
>>> res2.x # minimum found
array([-1.80851064, -0.25531915])
"""
opts = {'gtol': gtol,
'norm': norm,
'eps': epsilon,
'disp': disp,
'maxiter': maxiter,
'return_all': retall}
callback = _wrap_callback(callback)
res = _minimize_cg(f, x0, args, fprime, callback=callback, c1=c1, c2=c2,
**opts)
if full_output:
retlist = res['x'], res['fun'], res['nfev'], res['njev'], res['status']
if retall:
retlist += (res['allvecs'], )
return retlist
else:
if retall:
return res['x'], res['allvecs']
else:
return res['x']
def _minimize_cg(fun, x0, args=(), jac=None, callback=None,
gtol=1e-5, norm=np.inf, eps=_epsilon, maxiter=None,
disp=False, return_all=False, finite_diff_rel_step=None,
c1=1e-4, c2=0.4, **unknown_options):
"""
Minimization of scalar function of one or more variables using the
conjugate gradient algorithm.
Options
-------
disp : bool
Set to True to print convergence messages.
maxiter : int
Maximum number of iterations to perform.
gtol : float
Gradient norm must be less than `gtol` before successful
termination.
norm : float
Order of norm (Inf is max, -Inf is min).
eps : float or ndarray
If `jac is None` the absolute step size used for numerical
approximation of the jacobian via forward differences.
return_all : bool, optional
Set to True to return a list of the best solution at each of the
iterations.
finite_diff_rel_step : None or array_like, optional
If ``jac in ['2-point', '3-point', 'cs']`` the relative step size to
use for numerical approximation of the jacobian. The absolute step
size is computed as ``h = rel_step * sign(x) * max(1, abs(x))``,
possibly adjusted to fit into the bounds. For ``jac='3-point'``
the sign of `h` is ignored. If None (default) then step is selected
automatically.
c1 : float, default: 1e-4
Parameter for Armijo condition rule.
c2 : float, default: 0.4
Parameter for curvature condition rule.
Notes
-----
Parameters `c1` and `c2` must satisfy ``0 < c1 < c2 < 1``.
"""
_check_unknown_options(unknown_options)
retall = return_all
x0 = asarray(x0).flatten()
if maxiter is None:
maxiter = len(x0) * 200
sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps,
finite_diff_rel_step=finite_diff_rel_step)
f = sf.fun
myfprime = sf.grad
old_fval = f(x0)
gfk = myfprime(x0)
k = 0
xk = x0
# Sets the initial step guess to dx ~ 1
old_old_fval = old_fval + np.linalg.norm(gfk) / 2
if retall:
allvecs = [xk]
warnflag = 0
pk = -gfk
gnorm = vecnorm(gfk, ord=norm)
sigma_3 = 0.01
while (gnorm > gtol) and (k < maxiter):
deltak = np.dot(gfk, gfk)
cached_step = [None]
def polak_ribiere_powell_step(alpha, gfkp1=None):
xkp1 = xk + alpha * pk
if gfkp1 is None:
gfkp1 = myfprime(xkp1)
yk = gfkp1 - gfk
beta_k = max(0, np.dot(yk, gfkp1) / deltak)
pkp1 = -gfkp1 + beta_k * pk
gnorm = vecnorm(gfkp1, ord=norm)
return (alpha, xkp1, pkp1, gfkp1, gnorm)
def descent_condition(alpha, xkp1, fp1, gfkp1):
# Polak-Ribiere+ needs an explicit check of a sufficient
# descent condition, which is not guaranteed by strong Wolfe.
#
# See Gilbert & Nocedal, "Global convergence properties of
# conjugate gradient methods for optimization",
# SIAM J. Optimization 2, 21 (1992).
cached_step[:] = polak_ribiere_powell_step(alpha, gfkp1)
alpha, xk, pk, gfk, gnorm = cached_step
# Accept step if it leads to convergence.
if gnorm <= gtol:
return True
# Accept step if sufficient descent condition applies.
return np.dot(pk, gfk) <= -sigma_3 * np.dot(gfk, gfk)
try:
alpha_k, fc, gc, old_fval, old_old_fval, gfkp1 = \
_line_search_wolfe12(f, myfprime, xk, pk, gfk, old_fval,
old_old_fval, c1=c1, c2=c2, amin=1e-100,
amax=1e100, extra_condition=descent_condition)
except _LineSearchError:
# Line search failed to find a better solution.
warnflag = 2
break
# Reuse already computed results if possible
if alpha_k == cached_step[0]:
alpha_k, xk, pk, gfk, gnorm = cached_step
else:
alpha_k, xk, pk, gfk, gnorm = polak_ribiere_powell_step(alpha_k, gfkp1)
if retall:
allvecs.append(xk)
k += 1
intermediate_result = OptimizeResult(x=xk, fun=old_fval)
if _call_callback_maybe_halt(callback, intermediate_result):
break
fval = old_fval
if warnflag == 2:
msg = _status_message['pr_loss']
elif k >= maxiter:
warnflag = 1
msg = _status_message['maxiter']
elif np.isnan(gnorm) or np.isnan(fval) or np.isnan(xk).any():
warnflag = 3
msg = _status_message['nan']
else:
msg = _status_message['success']
if disp:
_print_success_message_or_warn(warnflag, msg)
print(f" Current function value: {fval:f}")
print(" Iterations: %d" % k)
print(" Function evaluations: %d" % sf.nfev)
print(" Gradient evaluations: %d" % sf.ngev)
result = OptimizeResult(fun=fval, jac=gfk, nfev=sf.nfev,
njev=sf.ngev, status=warnflag,
success=(warnflag == 0), message=msg, x=xk,
nit=k)
if retall:
result['allvecs'] = allvecs
return result
def fmin_ncg(f, x0, fprime, fhess_p=None, fhess=None, args=(), avextol=1e-5,
epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0,
callback=None, c1=1e-4, c2=0.9):
"""
Unconstrained minimization of a function using the Newton-CG method.
Parameters
----------
f : callable ``f(x, *args)``
Objective function to be minimized.
x0 : ndarray
Initial guess.
fprime : callable ``f'(x, *args)``
Gradient of f.
fhess_p : callable ``fhess_p(x, p, *args)``, optional
Function which computes the Hessian of f times an
arbitrary vector, p.
fhess : callable ``fhess(x, *args)``, optional
Function to compute the Hessian matrix of f.
args : tuple, optional
Extra arguments passed to f, fprime, fhess_p, and fhess
(the same set of extra arguments is supplied to all of
these functions).
epsilon : float or ndarray, optional
If fhess is approximated, use this value for the step size.
callback : callable, optional
An optional user-supplied function which is called after
each iteration. Called as callback(xk), where xk is the
current parameter vector.
avextol : float, optional
Convergence is assumed when the average relative error in
the minimizer falls below this amount.
maxiter : int, optional
Maximum number of iterations to perform.
full_output : bool, optional
If True, return the optional outputs.
disp : bool, optional
If True, print convergence message.
retall : bool, optional
If True, return a list of results at each iteration.
c1 : float, default: 1e-4
Parameter for Armijo condition rule.
c2 : float, default: 0.9
Parameter for curvature condition rule
Returns
-------
xopt : ndarray
Parameters which minimize f, i.e., ``f(xopt) == fopt``.
fopt : float
Value of the function at xopt, i.e., ``fopt = f(xopt)``.
fcalls : int
Number of function calls made.
gcalls : int
Number of gradient calls made.
hcalls : int
Number of Hessian calls made.
warnflag : int
Warnings generated by the algorithm.
1 : Maximum number of iterations exceeded.
2 : Line search failure (precision loss).
3 : NaN result encountered.
allvecs : list
The result at each iteration, if retall is True (see below).
See also
--------
minimize: Interface to minimization algorithms for multivariate
functions. See the 'Newton-CG' `method` in particular.
Notes
-----
Only one of `fhess_p` or `fhess` need to be given. If `fhess`
is provided, then `fhess_p` will be ignored. If neither `fhess`
nor `fhess_p` is provided, then the hessian product will be
approximated using finite differences on `fprime`. `fhess_p`
must compute the hessian times an arbitrary vector. If it is not
given, finite-differences on `fprime` are used to compute
it.
Newton-CG methods are also called truncated Newton methods. This
function differs from scipy.optimize.fmin_tnc because
1. scipy.optimize.fmin_ncg is written purely in Python using NumPy
and scipy while scipy.optimize.fmin_tnc calls a C function.
2. scipy.optimize.fmin_ncg is only for unconstrained minimization
while scipy.optimize.fmin_tnc is for unconstrained minimization
or box constrained minimization. (Box constraints give
lower and upper bounds for each variable separately.)
Parameters `c1` and `c2` must satisfy ``0 < c1 < c2 < 1``.
References
----------
Wright & Nocedal, 'Numerical Optimization', 1999, p. 140.
"""
opts = {'xtol': avextol,
'eps': epsilon,
'maxiter': maxiter,
'disp': disp,
'return_all': retall}
callback = _wrap_callback(callback)
res = _minimize_newtoncg(f, x0, args, fprime, fhess, fhess_p,
callback=callback, c1=c1, c2=c2, **opts)
if full_output:
retlist = (res['x'], res['fun'], res['nfev'], res['njev'],
res['nhev'], res['status'])
if retall:
retlist += (res['allvecs'], )
return retlist
else:
if retall:
return res['x'], res['allvecs']
else:
return res['x']
def _minimize_newtoncg(fun, x0, args=(), jac=None, hess=None, hessp=None,
callback=None, xtol=1e-5, eps=_epsilon, maxiter=None,
disp=False, return_all=False, c1=1e-4, c2=0.9,
**unknown_options):
"""
Minimization of scalar function of one or more variables using the
Newton-CG algorithm.
Note that the `jac` parameter (Jacobian) is required.
Options
-------
disp : bool
Set to True to print convergence messages.
xtol : float
Average relative error in solution `xopt` acceptable for
convergence.
maxiter : int
Maximum number of iterations to perform.
eps : float or ndarray
If `hessp` is approximated, use this value for the step size.
return_all : bool, optional
Set to True to return a list of the best solution at each of the
iterations.
c1 : float, default: 1e-4
Parameter for Armijo condition rule.
c2 : float, default: 0.9
Parameter for curvature condition rule.
Notes
-----
Parameters `c1` and `c2` must satisfy ``0 < c1 < c2 < 1``.
"""
_check_unknown_options(unknown_options)
if jac is None:
raise ValueError('Jacobian is required for Newton-CG method')
fhess_p = hessp
fhess = hess
avextol = xtol
epsilon = eps
retall = return_all
x0 = asarray(x0).flatten()
# TODO: add hessp (callable or FD) to ScalarFunction?
sf = _prepare_scalar_function(
fun, x0, jac, args=args, epsilon=eps, hess=hess
)
f = sf.fun
fprime = sf.grad
_h = sf.hess(x0)
# Logic for hess/hessp
# - If a callable(hess) is provided, then use that
# - If hess is a FD_METHOD, or the output from hess(x) is a LinearOperator
# then create a hessp function using those.
# - If hess is None but you have callable(hessp) then use the hessp.
# - If hess and hessp are None then approximate hessp using the grad/jac.
if (hess in FD_METHODS or isinstance(_h, LinearOperator)):
fhess = None
def _hessp(x, p, *args):
return sf.hess(x).dot(p)
fhess_p = _hessp
def terminate(warnflag, msg):
if disp:
_print_success_message_or_warn(warnflag, msg)
print(f" Current function value: {old_fval:f}")
print(" Iterations: %d" % k)
print(" Function evaluations: %d" % sf.nfev)
print(" Gradient evaluations: %d" % sf.ngev)
print(" Hessian evaluations: %d" % hcalls)
fval = old_fval
result = OptimizeResult(fun=fval, jac=gfk, nfev=sf.nfev,
njev=sf.ngev, nhev=hcalls, status=warnflag,
success=(warnflag == 0), message=msg, x=xk,
nit=k)
if retall:
result['allvecs'] = allvecs
return result
hcalls = 0
if maxiter is None:
maxiter = len(x0)*200
cg_maxiter = 20*len(x0)
xtol = len(x0) * avextol
# Make sure we enter the while loop.
update_l1norm = np.finfo(float).max
xk = np.copy(x0)
if retall:
allvecs = [xk]
k = 0
gfk = None
old_fval = f(x0)
old_old_fval = None
float64eps = np.finfo(np.float64).eps
while update_l1norm > xtol:
if k >= maxiter:
msg = "Warning: " + _status_message['maxiter']
return terminate(1, msg)
# Compute a search direction pk by applying the CG method to
# del2 f(xk) p = - grad f(xk) starting from 0.
b = -fprime(xk)
maggrad = np.linalg.norm(b, ord=1)
eta = min(0.5, math.sqrt(maggrad))
termcond = eta * maggrad
xsupi = zeros(len(x0), dtype=x0.dtype)
ri = -b
psupi = -ri
i = 0
dri0 = np.dot(ri, ri)
if fhess is not None: # you want to compute hessian once.
A = sf.hess(xk)
hcalls += 1
for k2 in range(cg_maxiter):
if np.add.reduce(np.abs(ri)) <= termcond:
break
if fhess is None:
if fhess_p is None:
Ap = approx_fhess_p(xk, psupi, fprime, epsilon)
else:
Ap = fhess_p(xk, psupi, *args)
hcalls += 1
else:
# hess was supplied as a callable or hessian update strategy, so
# A is a dense numpy array or sparse matrix
Ap = A.dot(psupi)
# check curvature
Ap = asarray(Ap).squeeze() # get rid of matrices...
curv = np.dot(psupi, Ap)
if 0 <= curv <= 3 * float64eps:
break
elif curv < 0:
if (i > 0):
break
else:
# fall back to steepest descent direction
xsupi = dri0 / (-curv) * b
break
alphai = dri0 / curv
xsupi += alphai * psupi
ri += alphai * Ap
dri1 = np.dot(ri, ri)
betai = dri1 / dri0
psupi = -ri + betai * psupi
i += 1
dri0 = dri1 # update np.dot(ri,ri) for next time.
else:
# curvature keeps increasing, bail out
msg = ("Warning: CG iterations didn't converge. The Hessian is not "
"positive definite.")
return terminate(3, msg)
pk = xsupi # search direction is solution to system.
gfk = -b # gradient at xk
try:
alphak, fc, gc, old_fval, old_old_fval, gfkp1 = \
_line_search_wolfe12(f, fprime, xk, pk, gfk,
old_fval, old_old_fval, c1=c1, c2=c2)
except _LineSearchError:
# Line search failed to find a better solution.
msg = "Warning: " + _status_message['pr_loss']
return terminate(2, msg)
update = alphak * pk
xk += update # upcast if necessary
if retall:
allvecs.append(xk)
k += 1
intermediate_result = OptimizeResult(x=xk, fun=old_fval)
if _call_callback_maybe_halt(callback, intermediate_result):
return terminate(5, "")
update_l1norm = np.linalg.norm(update, ord=1)
else:
if np.isnan(old_fval) or np.isnan(update_l1norm):
return terminate(3, _status_message['nan'])
msg = _status_message['success']
return terminate(0, msg)
def fminbound(func, x1, x2, args=(), xtol=1e-5, maxfun=500,
full_output=0, disp=1):
"""Bounded minimization for scalar functions.
Parameters
----------
func : callable f(x,*args)
Objective function to be minimized (must accept and return scalars).
x1, x2 : float or array scalar
Finite optimization bounds.
args : tuple, optional
Extra arguments passed to function.
xtol : float, optional
The convergence tolerance.
maxfun : int, optional
Maximum number of function evaluations allowed.
full_output : bool, optional
If True, return optional outputs.
disp: int, optional
If non-zero, print messages.
``0`` : no message printing.
``1`` : non-convergence notification messages only.
``2`` : print a message on convergence too.
``3`` : print iteration results.
Returns
-------
xopt : ndarray
Parameters (over given interval) which minimize the
objective function.
fval : number
(Optional output) The function value evaluated at the minimizer.
ierr : int
(Optional output) An error flag (0 if converged, 1 if maximum number of
function calls reached).
numfunc : int
(Optional output) The number of function calls made.
See also
--------
minimize_scalar: Interface to minimization algorithms for scalar
univariate functions. See the 'Bounded' `method` in particular.
Notes
-----
Finds a local minimizer of the scalar function `func` in the
interval x1 < xopt < x2 using Brent's method. (See `brent`
for auto-bracketing.)
References
----------
.. [1] Forsythe, G.E., M. A. Malcolm, and C. B. Moler. "Computer Methods
for Mathematical Computations." Prentice-Hall Series in Automatic
Computation 259 (1977).
.. [2] Brent, Richard P. Algorithms for Minimization Without Derivatives.
Courier Corporation, 2013.
Examples
--------
`fminbound` finds the minimizer of the function in the given range.
The following examples illustrate this.
>>> from scipy import optimize
>>> def f(x):
... return (x-1)**2
>>> minimizer = optimize.fminbound(f, -4, 4)
>>> minimizer
1.0
>>> minimum = f(minimizer)
>>> minimum
0.0
>>> res = optimize.fminbound(f, 3, 4, full_output=True)
>>> minimizer, fval, ierr, numfunc = res
>>> minimizer
3.000005960860986
>>> minimum = f(minimizer)
>>> minimum, fval
(4.000023843479476, 4.000023843479476)
"""
options = {'xatol': xtol,
'maxiter': maxfun,
'disp': disp}
res = _minimize_scalar_bounded(func, (x1, x2), args, **options)
if full_output:
return res['x'], res['fun'], res['status'], res['nfev']
else:
return res['x']
def _minimize_scalar_bounded(func, bounds, args=(),
xatol=1e-5, maxiter=500, disp=0,
**unknown_options):
"""
Options
-------
maxiter : int
Maximum number of iterations to perform.
disp: int, optional
If non-zero, print messages.
``0`` : no message printing.
``1`` : non-convergence notification messages only.
``2`` : print a message on convergence too.
``3`` : print iteration results.
xatol : float
Absolute error in solution `xopt` acceptable for convergence.
"""
_check_unknown_options(unknown_options)
maxfun = maxiter
# Test bounds are of correct form
if len(bounds) != 2:
raise ValueError('bounds must have two elements.')
x1, x2 = bounds
if not (is_finite_scalar(x1) and is_finite_scalar(x2)):
raise ValueError("Optimization bounds must be finite scalars.")
if x1 > x2:
raise ValueError("The lower bound exceeds the upper bound.")
flag = 0
header = ' Func-count x f(x) Procedure'
step = ' initial'
sqrt_eps = sqrt(2.2e-16)
golden_mean = 0.5 * (3.0 - sqrt(5.0))
a, b = x1, x2
fulc = a + golden_mean * (b - a)
nfc, xf = fulc, fulc
rat = e = 0.0
x = xf
fx = func(x, *args)
num = 1
fmin_data = (1, xf, fx)
fu = np.inf
ffulc = fnfc = fx
xm = 0.5 * (a + b)
tol1 = sqrt_eps * np.abs(xf) + xatol / 3.0
tol2 = 2.0 * tol1
if disp > 2:
print(" ")
print(header)
print("%5.0f %12.6g %12.6g %s" % (fmin_data + (step,)))
while (np.abs(xf - xm) > (tol2 - 0.5 * (b - a))):
golden = 1
# Check for parabolic fit
if np.abs(e) > tol1:
golden = 0
r = (xf - nfc) * (fx - ffulc)
q = (xf - fulc) * (fx - fnfc)
p = (xf - fulc) * q - (xf - nfc) * r
q = 2.0 * (q - r)
if q > 0.0:
p = -p
q = np.abs(q)
r = e
e = rat
# Check for acceptability of parabola
if ((np.abs(p) < np.abs(0.5*q*r)) and (p > q*(a - xf)) and
(p < q * (b - xf))):
rat = (p + 0.0) / q
x = xf + rat
step = ' parabolic'
if ((x - a) < tol2) or ((b - x) < tol2):
si = np.sign(xm - xf) + ((xm - xf) == 0)
rat = tol1 * si
else: # do a golden-section step
golden = 1
if golden: # do a golden-section step
if xf >= xm:
e = a - xf
else:
e = b - xf
rat = golden_mean*e
step = ' golden'
si = np.sign(rat) + (rat == 0)
x = xf + si * np.maximum(np.abs(rat), tol1)
fu = func(x, *args)
num += 1
fmin_data = (num, x, fu)
if disp > 2:
print("%5.0f %12.6g %12.6g %s" % (fmin_data + (step,)))
if fu <= fx:
if x >= xf:
a = xf
else:
b = xf
fulc, ffulc = nfc, fnfc
nfc, fnfc = xf, fx
xf, fx = x, fu
else:
if x < xf:
a = x
else:
b = x
if (fu <= fnfc) or (nfc == xf):
fulc, ffulc = nfc, fnfc
nfc, fnfc = x, fu
elif (fu <= ffulc) or (fulc == xf) or (fulc == nfc):
fulc, ffulc = x, fu
xm = 0.5 * (a + b)
tol1 = sqrt_eps * np.abs(xf) + xatol / 3.0
tol2 = 2.0 * tol1
if num >= maxfun:
flag = 1
break
if np.isnan(xf) or np.isnan(fx) or np.isnan(fu):
flag = 2
fval = fx
if disp > 0:
_endprint(x, flag, fval, maxfun, xatol, disp)
result = OptimizeResult(fun=fval, status=flag, success=(flag == 0),
message={0: 'Solution found.',
1: 'Maximum number of function calls '
'reached.',
2: _status_message['nan']}.get(flag, ''),
x=xf, nfev=num, nit=num)
return result
class Brent:
#need to rethink design of __init__
def __init__(self, func, args=(), tol=1.48e-8, maxiter=500,
full_output=0, disp=0):
self.func = func
self.args = args
self.tol = tol
self.maxiter = maxiter
self._mintol = 1.0e-11
self._cg = 0.3819660
self.xmin = None
self.fval = None
self.iter = 0
self.funcalls = 0
self.disp = disp
# need to rethink design of set_bracket (new options, etc.)
def set_bracket(self, brack=None):
self.brack = brack
def get_bracket_info(self):
#set up
func = self.func
args = self.args
brack = self.brack
### BEGIN core bracket_info code ###
### carefully DOCUMENT any CHANGES in core ##
if brack is None:
xa, xb, xc, fa, fb, fc, funcalls = bracket(func, args=args)
elif len(brack) == 2:
xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0],
xb=brack[1], args=args)
elif len(brack) == 3:
xa, xb, xc = brack
if (xa > xc): # swap so xa < xc can be assumed
xc, xa = xa, xc
if not ((xa < xb) and (xb < xc)):
raise ValueError(
"Bracketing values (xa, xb, xc) do not"
" fulfill this requirement: (xa < xb) and (xb < xc)"
)
fa = func(*((xa,) + args))
fb = func(*((xb,) + args))
fc = func(*((xc,) + args))
if not ((fb < fa) and (fb < fc)):
raise ValueError(
"Bracketing values (xa, xb, xc) do not fulfill"
" this requirement: (f(xb) < f(xa)) and (f(xb) < f(xc))"
)
funcalls = 3
else:
raise ValueError("Bracketing interval must be "
"length 2 or 3 sequence.")
### END core bracket_info code ###
return xa, xb, xc, fa, fb, fc, funcalls
def optimize(self):
# set up for optimization
func = self.func
xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info()
_mintol = self._mintol
_cg = self._cg
#################################
#BEGIN CORE ALGORITHM
#################################
x = w = v = xb
fw = fv = fx = fb
if (xa < xc):
a = xa
b = xc
else:
a = xc
b = xa
deltax = 0.0
iter = 0
if self.disp > 2:
print(" ")
print(f"{'Func-count':^12} {'x':^12} {'f(x)': ^12}")
print(f"{funcalls:^12g} {x:^12.6g} {fx:^12.6g}")
while (iter < self.maxiter):
tol1 = self.tol * np.abs(x) + _mintol
tol2 = 2.0 * tol1
xmid = 0.5 * (a + b)
# check for convergence
if np.abs(x - xmid) < (tol2 - 0.5 * (b - a)):
break
# XXX In the first iteration, rat is only bound in the true case
# of this conditional. This used to cause an UnboundLocalError
# (gh-4140). It should be set before the if (but to what?).
if (np.abs(deltax) <= tol1):
if (x >= xmid):
deltax = a - x # do a golden section step
else:
deltax = b - x
rat = _cg * deltax
else: # do a parabolic step
tmp1 = (x - w) * (fx - fv)
tmp2 = (x - v) * (fx - fw)
p = (x - v) * tmp2 - (x - w) * tmp1
tmp2 = 2.0 * (tmp2 - tmp1)
if (tmp2 > 0.0):
p = -p
tmp2 = np.abs(tmp2)
dx_temp = deltax
deltax = rat
# check parabolic fit
if ((p > tmp2 * (a - x)) and (p < tmp2 * (b - x)) and
(np.abs(p) < np.abs(0.5 * tmp2 * dx_temp))):
rat = p * 1.0 / tmp2 # if parabolic step is useful.
u = x + rat
if ((u - a) < tol2 or (b - u) < tol2):
if xmid - x >= 0:
rat = tol1
else:
rat = -tol1
else:
if (x >= xmid):
deltax = a - x # if it's not do a golden section step
else:
deltax = b - x
rat = _cg * deltax
if (np.abs(rat) < tol1): # update by at least tol1
if rat >= 0:
u = x + tol1
else:
u = x - tol1
else:
u = x + rat
fu = func(*((u,) + self.args)) # calculate new output value
funcalls += 1
if (fu > fx): # if it's bigger than current
if (u < x):
a = u
else:
b = u
if (fu <= fw) or (w == x):
v = w
w = u
fv = fw
fw = fu
elif (fu <= fv) or (v == x) or (v == w):
v = u
fv = fu
else:
if (u >= x):
a = x
else:
b = x
v = w
w = x
x = u
fv = fw
fw = fx
fx = fu
if self.disp > 2:
print(f"{funcalls:^12g} {x:^12.6g} {fx:^12.6g}")
iter += 1
#################################
#END CORE ALGORITHM
#################################
self.xmin = x
self.fval = fx
self.iter = iter
self.funcalls = funcalls
def get_result(self, full_output=False):
if full_output:
return self.xmin, self.fval, self.iter, self.funcalls
else:
return self.xmin
def brent(func, args=(), brack=None, tol=1.48e-8, full_output=0, maxiter=500):
"""
Given a function of one variable and a possible bracket, return
a local minimizer of the function isolated to a fractional precision
of tol.
Parameters
----------
func : callable f(x,*args)
Objective function.
args : tuple, optional
Additional arguments (if present).
brack : tuple, optional
Either a triple ``(xa, xb, xc)`` satisfying ``xa < xb < xc`` and
``func(xb) < func(xa) and func(xb) < func(xc)``, or a pair
``(xa, xb)`` to be used as initial points for a downhill bracket search
(see `scipy.optimize.bracket`).
The minimizer ``x`` will not necessarily satisfy ``xa <= x <= xb``.
tol : float, optional
Relative error in solution `xopt` acceptable for convergence.
full_output : bool, optional
If True, return all output args (xmin, fval, iter,
funcalls).
maxiter : int, optional
Maximum number of iterations in solution.
Returns
-------
xmin : ndarray
Optimum point.
fval : float
(Optional output) Optimum function value.
iter : int
(Optional output) Number of iterations.
funcalls : int
(Optional output) Number of objective function evaluations made.
See also
--------
minimize_scalar: Interface to minimization algorithms for scalar
univariate functions. See the 'Brent' `method` in particular.
Notes
-----
Uses inverse parabolic interpolation when possible to speed up
convergence of golden section method.
Does not ensure that the minimum lies in the range specified by
`brack`. See `scipy.optimize.fminbound`.
Examples
--------
We illustrate the behaviour of the function when `brack` is of
size 2 and 3 respectively. In the case where `brack` is of the
form ``(xa, xb)``, we can see for the given values, the output does
not necessarily lie in the range ``(xa, xb)``.
>>> def f(x):
... return (x-1)**2
>>> from scipy import optimize
>>> minimizer = optimize.brent(f, brack=(1, 2))
>>> minimizer
1
>>> res = optimize.brent(f, brack=(-1, 0.5, 2), full_output=True)
>>> xmin, fval, iter, funcalls = res
>>> f(xmin), fval
(0.0, 0.0)
"""
options = {'xtol': tol,
'maxiter': maxiter}
res = _minimize_scalar_brent(func, brack, args, **options)
if full_output:
return res['x'], res['fun'], res['nit'], res['nfev']
else:
return res['x']
def _minimize_scalar_brent(func, brack=None, args=(), xtol=1.48e-8,
maxiter=500, disp=0,
**unknown_options):
"""
Options
-------
maxiter : int
Maximum number of iterations to perform.
xtol : float
Relative error in solution `xopt` acceptable for convergence.
disp : int, optional
If non-zero, print messages.
``0`` : no message printing.
``1`` : non-convergence notification messages only.
``2`` : print a message on convergence too.
``3`` : print iteration results.
Notes
-----
Uses inverse parabolic interpolation when possible to speed up
convergence of golden section method.
"""
_check_unknown_options(unknown_options)
tol = xtol
if tol < 0:
raise ValueError(f'tolerance should be >= 0, got {tol!r}')
brent = Brent(func=func, args=args, tol=tol,
full_output=True, maxiter=maxiter, disp=disp)
brent.set_bracket(brack)
brent.optimize()
x, fval, nit, nfev = brent.get_result(full_output=True)
success = nit < maxiter and not (np.isnan(x) or np.isnan(fval))
if success:
message = ("\nOptimization terminated successfully;\n"
"The returned value satisfies the termination criteria\n"
f"(using xtol = {xtol} )")
else:
if nit >= maxiter:
message = "\nMaximum number of iterations exceeded"
if np.isnan(x) or np.isnan(fval):
message = f"{_status_message['nan']}"
if disp:
_print_success_message_or_warn(not success, message)
return OptimizeResult(fun=fval, x=x, nit=nit, nfev=nfev,
success=success, message=message)
def golden(func, args=(), brack=None, tol=_epsilon,
full_output=0, maxiter=5000):
"""
Return the minimizer of a function of one variable using the golden section
method.
Given a function of one variable and a possible bracketing interval,
return a minimizer of the function isolated to a fractional precision of
tol.
Parameters
----------
func : callable func(x,*args)
Objective function to minimize.
args : tuple, optional
Additional arguments (if present), passed to func.
brack : tuple, optional
Either a triple ``(xa, xb, xc)`` where ``xa < xb < xc`` and
``func(xb) < func(xa) and func(xb) < func(xc)``, or a pair (xa, xb)
to be used as initial points for a downhill bracket search (see
`scipy.optimize.bracket`).
The minimizer ``x`` will not necessarily satisfy ``xa <= x <= xb``.
tol : float, optional
x tolerance stop criterion
full_output : bool, optional
If True, return optional outputs.
maxiter : int
Maximum number of iterations to perform.
Returns
-------
xmin : ndarray
Optimum point.
fval : float
(Optional output) Optimum function value.
funcalls : int
(Optional output) Number of objective function evaluations made.
See also
--------
minimize_scalar: Interface to minimization algorithms for scalar
univariate functions. See the 'Golden' `method` in particular.
Notes
-----
Uses analog of bisection method to decrease the bracketed
interval.
Examples
--------
We illustrate the behaviour of the function when `brack` is of
size 2 and 3, respectively. In the case where `brack` is of the
form (xa,xb), we can see for the given values, the output need
not necessarily lie in the range ``(xa, xb)``.
>>> def f(x):
... return (x-1)**2
>>> from scipy import optimize
>>> minimizer = optimize.golden(f, brack=(1, 2))
>>> minimizer
1
>>> res = optimize.golden(f, brack=(-1, 0.5, 2), full_output=True)
>>> xmin, fval, funcalls = res
>>> f(xmin), fval
(9.925165290385052e-18, 9.925165290385052e-18)
"""
options = {'xtol': tol, 'maxiter': maxiter}
res = _minimize_scalar_golden(func, brack, args, **options)
if full_output:
return res['x'], res['fun'], res['nfev']
else:
return res['x']
def _minimize_scalar_golden(func, brack=None, args=(),
xtol=_epsilon, maxiter=5000, disp=0,
**unknown_options):
"""
Options
-------
xtol : float
Relative error in solution `xopt` acceptable for convergence.
maxiter : int
Maximum number of iterations to perform.
disp: int, optional
If non-zero, print messages.
``0`` : no message printing.
``1`` : non-convergence notification messages only.
``2`` : print a message on convergence too.
``3`` : print iteration results.
"""
_check_unknown_options(unknown_options)
tol = xtol
if brack is None:
xa, xb, xc, fa, fb, fc, funcalls = bracket(func, args=args)
elif len(brack) == 2:
xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0],
xb=brack[1], args=args)
elif len(brack) == 3:
xa, xb, xc = brack
if (xa > xc): # swap so xa < xc can be assumed
xc, xa = xa, xc
if not ((xa < xb) and (xb < xc)):
raise ValueError(
"Bracketing values (xa, xb, xc) do not"
" fulfill this requirement: (xa < xb) and (xb < xc)"
)
fa = func(*((xa,) + args))
fb = func(*((xb,) + args))
fc = func(*((xc,) + args))
if not ((fb < fa) and (fb < fc)):
raise ValueError(
"Bracketing values (xa, xb, xc) do not fulfill"
" this requirement: (f(xb) < f(xa)) and (f(xb) < f(xc))"
)
funcalls = 3
else:
raise ValueError("Bracketing interval must be length 2 or 3 sequence.")
_gR = 0.61803399 # golden ratio conjugate: 2.0/(1.0+sqrt(5.0))
_gC = 1.0 - _gR
x3 = xc
x0 = xa
if (np.abs(xc - xb) > np.abs(xb - xa)):
x1 = xb
x2 = xb + _gC * (xc - xb)
else:
x2 = xb
x1 = xb - _gC * (xb - xa)
f1 = func(*((x1,) + args))
f2 = func(*((x2,) + args))
funcalls += 2
nit = 0
if disp > 2:
print(" ")
print(f"{'Func-count':^12} {'x':^12} {'f(x)': ^12}")
for i in range(maxiter):
if np.abs(x3 - x0) <= tol * (np.abs(x1) + np.abs(x2)):
break
if (f2 < f1):
x0 = x1
x1 = x2
x2 = _gR * x1 + _gC * x3
f1 = f2
f2 = func(*((x2,) + args))
else:
x3 = x2
x2 = x1
x1 = _gR * x2 + _gC * x0
f2 = f1
f1 = func(*((x1,) + args))
funcalls += 1
if disp > 2:
if (f1 < f2):
xmin, fval = x1, f1
else:
xmin, fval = x2, f2
print(f"{funcalls:^12g} {xmin:^12.6g} {fval:^12.6g}")
nit += 1
# end of iteration loop
if (f1 < f2):
xmin = x1
fval = f1
else:
xmin = x2
fval = f2
success = nit < maxiter and not (np.isnan(fval) or np.isnan(xmin))
if success:
message = ("\nOptimization terminated successfully;\n"
"The returned value satisfies the termination criteria\n"
f"(using xtol = {xtol} )")
else:
if nit >= maxiter:
message = "\nMaximum number of iterations exceeded"
if np.isnan(xmin) or np.isnan(fval):
message = f"{_status_message['nan']}"
if disp:
_print_success_message_or_warn(not success, message)
return OptimizeResult(fun=fval, nfev=funcalls, x=xmin, nit=nit,
success=success, message=message)
def bracket(func, xa=0.0, xb=1.0, args=(), grow_limit=110.0, maxiter=1000):
"""
Bracket the minimum of a function.
Given a function and distinct initial points, search in the
downhill direction (as defined by the initial points) and return
three points that bracket the minimum of the function.
Parameters
----------
func : callable f(x,*args)
Objective function to minimize.
xa, xb : float, optional
Initial points. Defaults `xa` to 0.0, and `xb` to 1.0.
A local minimum need not be contained within this interval.
args : tuple, optional
Additional arguments (if present), passed to `func`.
grow_limit : float, optional
Maximum grow limit. Defaults to 110.0
maxiter : int, optional
Maximum number of iterations to perform. Defaults to 1000.
Returns
-------
xa, xb, xc : float
Final points of the bracket.
fa, fb, fc : float
Objective function values at the bracket points.
funcalls : int
Number of function evaluations made.
Raises
------
BracketError
If no valid bracket is found before the algorithm terminates.
See notes for conditions of a valid bracket.
Notes
-----
The algorithm attempts to find three strictly ordered points (i.e.
:math:`x_a < x_b < x_c` or :math:`x_c < x_b < x_a`) satisfying
:math:`f(x_b) ≤ f(x_a)` and :math:`f(x_b) ≤ f(x_c)`, where one of the
inequalities must be satisfied strictly and all :math:`x_i` must be
finite.
Examples
--------
This function can find a downward convex region of a function:
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from scipy.optimize import bracket
>>> def f(x):
... return 10*x**2 + 3*x + 5
>>> x = np.linspace(-2, 2)
>>> y = f(x)
>>> init_xa, init_xb = 0.1, 1
>>> xa, xb, xc, fa, fb, fc, funcalls = bracket(f, xa=init_xa, xb=init_xb)
>>> plt.axvline(x=init_xa, color="k", linestyle="--")
>>> plt.axvline(x=init_xb, color="k", linestyle="--")
>>> plt.plot(x, y, "-k")
>>> plt.plot(xa, fa, "bx")
>>> plt.plot(xb, fb, "rx")
>>> plt.plot(xc, fc, "bx")
>>> plt.show()
Note that both initial points were to the right of the minimum, and the
third point was found in the "downhill" direction: the direction
in which the function appeared to be decreasing (to the left).
The final points are strictly ordered, and the function value
at the middle point is less than the function values at the endpoints;
it follows that a minimum must lie within the bracket.
"""
_gold = 1.618034 # golden ratio: (1.0+sqrt(5.0))/2.0
_verysmall_num = 1e-21
# convert to numpy floats if not already
xa, xb = np.asarray([xa, xb])
fa = func(*(xa,) + args)
fb = func(*(xb,) + args)
if (fa < fb): # Switch so fa > fb
xa, xb = xb, xa
fa, fb = fb, fa
xc = xb + _gold * (xb - xa)
fc = func(*((xc,) + args))
funcalls = 3
iter = 0
while (fc < fb):
tmp1 = (xb - xa) * (fb - fc)
tmp2 = (xb - xc) * (fb - fa)
val = tmp2 - tmp1
if np.abs(val) < _verysmall_num:
denom = 2.0 * _verysmall_num
else:
denom = 2.0 * val
w = xb - ((xb - xc) * tmp2 - (xb - xa) * tmp1) / denom
wlim = xb + grow_limit * (xc - xb)
msg = ("No valid bracket was found before the iteration limit was "
"reached. Consider trying different initial points or "
"increasing `maxiter`.")
if iter > maxiter:
raise RuntimeError(msg)
iter += 1
if (w - xc) * (xb - w) > 0.0:
fw = func(*((w,) + args))
funcalls += 1
if (fw < fc):
xa = xb
xb = w
fa = fb
fb = fw
break
elif (fw > fb):
xc = w
fc = fw
break
w = xc + _gold * (xc - xb)
fw = func(*((w,) + args))
funcalls += 1
elif (w - wlim)*(wlim - xc) >= 0.0:
w = wlim
fw = func(*((w,) + args))
funcalls += 1
elif (w - wlim)*(xc - w) > 0.0:
fw = func(*((w,) + args))
funcalls += 1
if (fw < fc):
xb = xc
xc = w
w = xc + _gold * (xc - xb)
fb = fc
fc = fw
fw = func(*((w,) + args))
funcalls += 1
else:
w = xc + _gold * (xc - xb)
fw = func(*((w,) + args))
funcalls += 1
xa = xb
xb = xc
xc = w
fa = fb
fb = fc
fc = fw
# three conditions for a valid bracket
cond1 = (fb < fc and fb <= fa) or (fb < fa and fb <= fc)
cond2 = (xa < xb < xc or xc < xb < xa)
cond3 = np.isfinite(xa) and np.isfinite(xb) and np.isfinite(xc)
msg = ("The algorithm terminated without finding a valid bracket. "
"Consider trying different initial points.")
if not (cond1 and cond2 and cond3):
e = BracketError(msg)
e.data = (xa, xb, xc, fa, fb, fc, funcalls)
raise e
return xa, xb, xc, fa, fb, fc, funcalls
class BracketError(RuntimeError):
pass
def _recover_from_bracket_error(solver, fun, bracket, args, **options):
# `bracket` was originally written without checking whether the resulting
# bracket is valid. `brent` and `golden` built on top of it without
# checking the returned bracket for validity, and their output can be
# incorrect without warning/error if the original bracket is invalid.
# gh-14858 noticed the problem, and the following is the desired
# behavior:
# - `scipy.optimize.bracket`, `scipy.optimize.brent`, and
# `scipy.optimize.golden` should raise an error if the bracket is
# invalid, as opposed to silently returning garbage
# - `scipy.optimize.minimize_scalar` should return with `success=False`
# and other information
# The changes that would be required to achieve this the traditional
# way (`return`ing all the required information from bracket all the way
# up to `minimizer_scalar`) are extensive and invasive. (See a6aa40d.)
# We can achieve the same thing by raising the error in `bracket`, but
# storing the information needed by `minimize_scalar` in the error object,
# and intercepting it here.
try:
res = solver(fun, bracket, args, **options)
except BracketError as e:
msg = str(e)
xa, xb, xc, fa, fb, fc, funcalls = e.data
xs, fs = [xa, xb, xc], [fa, fb, fc]
if np.any(np.isnan([xs, fs])):
x, fun = np.nan, np.nan
else:
imin = np.argmin(fs)
x, fun = xs[imin], fs[imin]
return OptimizeResult(fun=fun, nfev=funcalls, x=x,
nit=0, success=False, message=msg)
return res
def _line_for_search(x0, alpha, lower_bound, upper_bound):
"""
Given a parameter vector ``x0`` with length ``n`` and a direction
vector ``alpha`` with length ``n``, and lower and upper bounds on
each of the ``n`` parameters, what are the bounds on a scalar
``l`` such that ``lower_bound <= x0 + alpha * l <= upper_bound``.
Parameters
----------
x0 : np.array.
The vector representing the current location.
Note ``np.shape(x0) == (n,)``.
alpha : np.array.
The vector representing the direction.
Note ``np.shape(alpha) == (n,)``.
lower_bound : np.array.
The lower bounds for each parameter in ``x0``. If the ``i``th
parameter in ``x0`` is unbounded below, then ``lower_bound[i]``
should be ``-np.inf``.
Note ``np.shape(lower_bound) == (n,)``.
upper_bound : np.array.
The upper bounds for each parameter in ``x0``. If the ``i``th
parameter in ``x0`` is unbounded above, then ``upper_bound[i]``
should be ``np.inf``.
Note ``np.shape(upper_bound) == (n,)``.
Returns
-------
res : tuple ``(lmin, lmax)``
The bounds for ``l`` such that
``lower_bound[i] <= x0[i] + alpha[i] * l <= upper_bound[i]``
for all ``i``.
"""
# get nonzero indices of alpha so we don't get any zero division errors.
# alpha will not be all zero, since it is called from _linesearch_powell
# where we have a check for this.
nonzero, = alpha.nonzero()
lower_bound, upper_bound = lower_bound[nonzero], upper_bound[nonzero]
x0, alpha = x0[nonzero], alpha[nonzero]
low = (lower_bound - x0) / alpha
high = (upper_bound - x0) / alpha
# positive and negative indices
pos = alpha > 0
lmin_pos = np.where(pos, low, 0)
lmin_neg = np.where(pos, 0, high)
lmax_pos = np.where(pos, high, 0)
lmax_neg = np.where(pos, 0, low)
lmin = np.max(lmin_pos + lmin_neg)
lmax = np.min(lmax_pos + lmax_neg)
# if x0 is outside the bounds, then it is possible that there is
# no way to get back in the bounds for the parameters being updated
# with the current direction alpha.
# when this happens, lmax < lmin.
# If this is the case, then we can just return (0, 0)
return (lmin, lmax) if lmax >= lmin else (0, 0)
def _linesearch_powell(func, p, xi, tol=1e-3,
lower_bound=None, upper_bound=None, fval=None):
"""Line-search algorithm using fminbound.
Find the minimum of the function ``func(x0 + alpha*direc)``.
lower_bound : np.array.
The lower bounds for each parameter in ``x0``. If the ``i``th
parameter in ``x0`` is unbounded below, then ``lower_bound[i]``
should be ``-np.inf``.
Note ``np.shape(lower_bound) == (n,)``.
upper_bound : np.array.
The upper bounds for each parameter in ``x0``. If the ``i``th
parameter in ``x0`` is unbounded above, then ``upper_bound[i]``
should be ``np.inf``.
Note ``np.shape(upper_bound) == (n,)``.
fval : number.
``fval`` is equal to ``func(p)``, the idea is just to avoid
recomputing it so we can limit the ``fevals``.
"""
def myfunc(alpha):
return func(p + alpha*xi)
# if xi is zero, then don't optimize
if not np.any(xi):
return ((fval, p, xi) if fval is not None else (func(p), p, xi))
elif lower_bound is None and upper_bound is None:
# non-bounded minimization
res = _recover_from_bracket_error(_minimize_scalar_brent,
myfunc, None, tuple(), xtol=tol)
alpha_min, fret = res.x, res.fun
xi = alpha_min * xi
return fret, p + xi, xi
else:
bound = _line_for_search(p, xi, lower_bound, upper_bound)
if np.isneginf(bound[0]) and np.isposinf(bound[1]):
# equivalent to unbounded
return _linesearch_powell(func, p, xi, fval=fval, tol=tol)
elif not np.isneginf(bound[0]) and not np.isposinf(bound[1]):
# we can use a bounded scalar minimization
res = _minimize_scalar_bounded(myfunc, bound, xatol=tol / 100)
xi = res.x * xi
return res.fun, p + xi, xi
else:
# only bounded on one side. use the tangent function to convert
# the infinity bound to a finite bound. The new bounded region
# is a subregion of the region bounded by -np.pi/2 and np.pi/2.
bound = np.arctan(bound[0]), np.arctan(bound[1])
res = _minimize_scalar_bounded(
lambda x: myfunc(np.tan(x)),
bound,
xatol=tol / 100)
xi = np.tan(res.x) * xi
return res.fun, p + xi, xi
def fmin_powell(func, x0, args=(), xtol=1e-4, ftol=1e-4, maxiter=None,
maxfun=None, full_output=0, disp=1, retall=0, callback=None,
direc=None):
"""
Minimize a function using modified Powell's method.
This method only uses function values, not derivatives.
Parameters
----------
func : callable f(x,*args)
Objective function to be minimized.
x0 : ndarray
Initial guess.
args : tuple, optional
Extra arguments passed to func.
xtol : float, optional
Line-search error tolerance.
ftol : float, optional
Relative error in ``func(xopt)`` acceptable for convergence.
maxiter : int, optional
Maximum number of iterations to perform.
maxfun : int, optional
Maximum number of function evaluations to make.
full_output : bool, optional
If True, ``fopt``, ``xi``, ``direc``, ``iter``, ``funcalls``, and
``warnflag`` are returned.
disp : bool, optional
If True, print convergence messages.
retall : bool, optional
If True, return a list of the solution at each iteration.
callback : callable, optional
An optional user-supplied function, called after each
iteration. Called as ``callback(xk)``, where ``xk`` is the
current parameter vector.
direc : ndarray, optional
Initial fitting step and parameter order set as an (N, N) array, where N
is the number of fitting parameters in `x0`. Defaults to step size 1.0
fitting all parameters simultaneously (``np.eye((N, N))``). To
prevent initial consideration of values in a step or to change initial
step size, set to 0 or desired step size in the Jth position in the Mth
block, where J is the position in `x0` and M is the desired evaluation
step, with steps being evaluated in index order. Step size and ordering
will change freely as minimization proceeds.
Returns
-------
xopt : ndarray
Parameter which minimizes `func`.
fopt : number
Value of function at minimum: ``fopt = func(xopt)``.
direc : ndarray
Current direction set.
iter : int
Number of iterations.
funcalls : int
Number of function calls made.
warnflag : int
Integer warning flag:
1 : Maximum number of function evaluations.
2 : Maximum number of iterations.
3 : NaN result encountered.
4 : The result is out of the provided bounds.
allvecs : list
List of solutions at each iteration.
See also
--------
minimize: Interface to unconstrained minimization algorithms for
multivariate functions. See the 'Powell' method in particular.
Notes
-----
Uses a modification of Powell's method to find the minimum of
a function of N variables. Powell's method is a conjugate
direction method.
The algorithm has two loops. The outer loop merely iterates over the inner
loop. The inner loop minimizes over each current direction in the direction
set. At the end of the inner loop, if certain conditions are met, the
direction that gave the largest decrease is dropped and replaced with the
difference between the current estimated x and the estimated x from the
beginning of the inner-loop.
The technical conditions for replacing the direction of greatest
increase amount to checking that
1. No further gain can be made along the direction of greatest increase
from that iteration.
2. The direction of greatest increase accounted for a large sufficient
fraction of the decrease in the function value from that iteration of
the inner loop.
References
----------
Powell M.J.D. (1964) An efficient method for finding the minimum of a
function of several variables without calculating derivatives,
Computer Journal, 7 (2):155-162.
Press W., Teukolsky S.A., Vetterling W.T., and Flannery B.P.:
Numerical Recipes (any edition), Cambridge University Press
Examples
--------
>>> def f(x):
... return x**2
>>> from scipy import optimize
>>> minimum = optimize.fmin_powell(f, -1)
Optimization terminated successfully.
Current function value: 0.000000
Iterations: 2
Function evaluations: 16
>>> minimum
array(0.0)
"""
opts = {'xtol': xtol,
'ftol': ftol,
'maxiter': maxiter,
'maxfev': maxfun,
'disp': disp,
'direc': direc,
'return_all': retall}
callback = _wrap_callback(callback)
res = _minimize_powell(func, x0, args, callback=callback, **opts)
if full_output:
retlist = (res['x'], res['fun'], res['direc'], res['nit'],
res['nfev'], res['status'])
if retall:
retlist += (res['allvecs'], )
return retlist
else:
if retall:
return res['x'], res['allvecs']
else:
return res['x']
def _minimize_powell(func, x0, args=(), callback=None, bounds=None,
xtol=1e-4, ftol=1e-4, maxiter=None, maxfev=None,
disp=False, direc=None, return_all=False,
**unknown_options):
"""
Minimization of scalar function of one or more variables using the
modified Powell algorithm.
Parameters
----------
fun : callable
The objective function to be minimized::
fun(x, *args) -> float
where ``x`` is a 1-D array with shape (n,) and ``args``
is a tuple of the fixed parameters needed to completely
specify the function.
x0 : ndarray, shape (n,)
Initial guess. Array of real elements of size (n,),
where ``n`` is the number of independent variables.
args : tuple, optional
Extra arguments passed to the objective function and its
derivatives (`fun`, `jac` and `hess` functions).
method : str or callable, optional
The present documentation is specific to ``method='powell'``, but other
options are available. See documentation for `scipy.optimize.minimize`.
bounds : sequence or `Bounds`, optional
Bounds on decision variables. There are two ways to specify the bounds:
1. Instance of `Bounds` class.
2. Sequence of ``(min, max)`` pairs for each element in `x`. None
is used to specify no bound.
If bounds are not provided, then an unbounded line search will be used.
If bounds are provided and the initial guess is within the bounds, then
every function evaluation throughout the minimization procedure will be
within the bounds. If bounds are provided, the initial guess is outside
the bounds, and `direc` is full rank (or left to default), then some
function evaluations during the first iteration may be outside the
bounds, but every function evaluation after the first iteration will be
within the bounds. If `direc` is not full rank, then some parameters
may not be optimized and the solution is not guaranteed to be within
the bounds.
options : dict, optional
A dictionary of solver options. All methods accept the following
generic options:
maxiter : int
Maximum number of iterations to perform. Depending on the
method each iteration may use several function evaluations.
disp : bool
Set to True to print convergence messages.
See method-specific options for ``method='powell'`` below.
callback : callable, optional
Called after each iteration. The signature is::
callback(xk)
where ``xk`` is the current parameter vector.
Returns
-------
res : OptimizeResult
The optimization result represented as a ``OptimizeResult`` object.
Important attributes are: ``x`` the solution array, ``success`` a
Boolean flag indicating if the optimizer exited successfully and
``message`` which describes the cause of the termination. See
`OptimizeResult` for a description of other attributes.
Options
-------
disp : bool
Set to True to print convergence messages.
xtol : float
Relative error in solution `xopt` acceptable for convergence.
ftol : float
Relative error in ``fun(xopt)`` acceptable for convergence.
maxiter, maxfev : int
Maximum allowed number of iterations and function evaluations.
Will default to ``N*1000``, where ``N`` is the number of
variables, if neither `maxiter` or `maxfev` is set. If both
`maxiter` and `maxfev` are set, minimization will stop at the
first reached.
direc : ndarray
Initial set of direction vectors for the Powell method.
return_all : bool, optional
Set to True to return a list of the best solution at each of the
iterations.
"""
_check_unknown_options(unknown_options)
maxfun = maxfev
retall = return_all
x = asarray(x0).flatten()
if retall:
allvecs = [x]
N = len(x)
# If neither are set, then set both to default
if maxiter is None and maxfun is None:
maxiter = N * 1000
maxfun = N * 1000
elif maxiter is None:
# Convert remaining Nones, to np.inf, unless the other is np.inf, in
# which case use the default to avoid unbounded iteration
if maxfun == np.inf:
maxiter = N * 1000
else:
maxiter = np.inf
elif maxfun is None:
if maxiter == np.inf:
maxfun = N * 1000
else:
maxfun = np.inf
# we need to use a mutable object here that we can update in the
# wrapper function
fcalls, func = _wrap_scalar_function_maxfun_validation(func, args, maxfun)
if direc is None:
direc = eye(N, dtype=float)
else:
direc = asarray(direc, dtype=float)
if np.linalg.matrix_rank(direc) != direc.shape[0]:
warnings.warn("direc input is not full rank, some parameters may "
"not be optimized",
OptimizeWarning, stacklevel=3)
if bounds is None:
# don't make these arrays of all +/- inf. because
# _linesearch_powell will do an unnecessary check of all the elements.
# just keep them None, _linesearch_powell will not have to check
# all the elements.
lower_bound, upper_bound = None, None
else:
# bounds is standardized in _minimize.py.
lower_bound, upper_bound = bounds.lb, bounds.ub
if np.any(lower_bound > x0) or np.any(x0 > upper_bound):
warnings.warn("Initial guess is not within the specified bounds",
OptimizeWarning, stacklevel=3)
fval = func(x)
x1 = x.copy()
iter = 0
while True:
try:
fx = fval
bigind = 0
delta = 0.0
for i in range(N):
direc1 = direc[i]
fx2 = fval
fval, x, direc1 = _linesearch_powell(func, x, direc1,
tol=xtol * 100,
lower_bound=lower_bound,
upper_bound=upper_bound,
fval=fval)
if (fx2 - fval) > delta:
delta = fx2 - fval
bigind = i
iter += 1
if retall:
allvecs.append(x)
intermediate_result = OptimizeResult(x=x, fun=fval)
if _call_callback_maybe_halt(callback, intermediate_result):
break
bnd = ftol * (np.abs(fx) + np.abs(fval)) + 1e-20
if 2.0 * (fx - fval) <= bnd:
break
if fcalls[0] >= maxfun:
break
if iter >= maxiter:
break
if np.isnan(fx) and np.isnan(fval):
# Ended up in a nan-region: bail out
break
# Construct the extrapolated point
direc1 = x - x1
x1 = x.copy()
# make sure that we don't go outside the bounds when extrapolating
if lower_bound is None and upper_bound is None:
lmax = 1
else:
_, lmax = _line_for_search(x, direc1, lower_bound, upper_bound)
x2 = x + min(lmax, 1) * direc1
fx2 = func(x2)
if (fx > fx2):
t = 2.0*(fx + fx2 - 2.0*fval)
temp = (fx - fval - delta)
t *= temp*temp
temp = fx - fx2
t -= delta*temp*temp
if t < 0.0:
fval, x, direc1 = _linesearch_powell(
func, x, direc1,
tol=xtol * 100,
lower_bound=lower_bound,
upper_bound=upper_bound,
fval=fval
)
if np.any(direc1):
direc[bigind] = direc[-1]
direc[-1] = direc1
except _MaxFuncCallError:
break
warnflag = 0
msg = _status_message['success']
# out of bounds is more urgent than exceeding function evals or iters,
# but I don't want to cause inconsistencies by changing the
# established warning flags for maxfev and maxiter, so the out of bounds
# warning flag becomes 3, but is checked for first.
if bounds and (np.any(lower_bound > x) or np.any(x > upper_bound)):
warnflag = 4
msg = _status_message['out_of_bounds']
elif fcalls[0] >= maxfun:
warnflag = 1
msg = _status_message['maxfev']
elif iter >= maxiter:
warnflag = 2
msg = _status_message['maxiter']
elif np.isnan(fval) or np.isnan(x).any():
warnflag = 3
msg = _status_message['nan']
if disp:
_print_success_message_or_warn(warnflag, msg, RuntimeWarning)
print(f" Current function value: {fval:f}")
print(" Iterations: %d" % iter)
print(" Function evaluations: %d" % fcalls[0])
result = OptimizeResult(fun=fval, direc=direc, nit=iter, nfev=fcalls[0],
status=warnflag, success=(warnflag == 0),
message=msg, x=x)
if retall:
result['allvecs'] = allvecs
return result
def _endprint(x, flag, fval, maxfun, xtol, disp):
if flag == 0:
if disp > 1:
print("\nOptimization terminated successfully;\n"
"The returned value satisfies the termination criteria\n"
"(using xtol = ", xtol, ")")
return
if flag == 1:
msg = ("\nMaximum number of function evaluations exceeded --- "
"increase maxfun argument.\n")
elif flag == 2:
msg = f"\n{_status_message['nan']}"
_print_success_message_or_warn(flag, msg)
return
def brute(func, ranges, args=(), Ns=20, full_output=0, finish=fmin,
disp=False, workers=1):
"""Minimize a function over a given range by brute force.
Uses the "brute force" method, i.e., computes the function's value
at each point of a multidimensional grid of points, to find the global
minimum of the function.
The function is evaluated everywhere in the range with the datatype of the
first call to the function, as enforced by the ``vectorize`` NumPy
function. The value and type of the function evaluation returned when
``full_output=True`` are affected in addition by the ``finish`` argument
(see Notes).
The brute force approach is inefficient because the number of grid points
increases exponentially - the number of grid points to evaluate is
``Ns ** len(x)``. Consequently, even with coarse grid spacing, even
moderately sized problems can take a long time to run, and/or run into
memory limitations.
Parameters
----------
func : callable
The objective function to be minimized. Must be in the
form ``f(x, *args)``, where ``x`` is the argument in
the form of a 1-D array and ``args`` is a tuple of any
additional fixed parameters needed to completely specify
the function.
ranges : tuple
Each component of the `ranges` tuple must be either a
"slice object" or a range tuple of the form ``(low, high)``.
The program uses these to create the grid of points on which
the objective function will be computed. See `Note 2` for
more detail.
args : tuple, optional
Any additional fixed parameters needed to completely specify
the function.
Ns : int, optional
Number of grid points along the axes, if not otherwise
specified. See `Note2`.
full_output : bool, optional
If True, return the evaluation grid and the objective function's
values on it.
finish : callable, optional
An optimization function that is called with the result of brute force
minimization as initial guess. `finish` should take `func` and
the initial guess as positional arguments, and take `args` as
keyword arguments. It may additionally take `full_output`
and/or `disp` as keyword arguments. Use None if no "polishing"
function is to be used. See Notes for more details.
disp : bool, optional
Set to True to print convergence messages from the `finish` callable.
workers : int or map-like callable, optional
If `workers` is an int the grid is subdivided into `workers`
sections and evaluated in parallel (uses
`multiprocessing.Pool <multiprocessing>`).
Supply `-1` to use all cores available to the Process.
Alternatively supply a map-like callable, such as
`multiprocessing.Pool.map` for evaluating the grid in parallel.
This evaluation is carried out as ``workers(func, iterable)``.
Requires that `func` be pickleable.
.. versionadded:: 1.3.0
Returns
-------
x0 : ndarray
A 1-D array containing the coordinates of a point at which the
objective function had its minimum value. (See `Note 1` for
which point is returned.)
fval : float
Function value at the point `x0`. (Returned when `full_output` is
True.)
grid : tuple
Representation of the evaluation grid. It has the same
length as `x0`. (Returned when `full_output` is True.)
Jout : ndarray
Function values at each point of the evaluation
grid, i.e., ``Jout = func(*grid)``. (Returned
when `full_output` is True.)
See Also
--------
basinhopping, differential_evolution
Notes
-----
*Note 1*: The program finds the gridpoint at which the lowest value
of the objective function occurs. If `finish` is None, that is the
point returned. When the global minimum occurs within (or not very far
outside) the grid's boundaries, and the grid is fine enough, that
point will be in the neighborhood of the global minimum.
However, users often employ some other optimization program to
"polish" the gridpoint values, i.e., to seek a more precise
(local) minimum near `brute's` best gridpoint.
The `brute` function's `finish` option provides a convenient way to do
that. Any polishing program used must take `brute's` output as its
initial guess as a positional argument, and take `brute's` input values
for `args` as keyword arguments, otherwise an error will be raised.
It may additionally take `full_output` and/or `disp` as keyword arguments.
`brute` assumes that the `finish` function returns either an
`OptimizeResult` object or a tuple in the form:
``(xmin, Jmin, ... , statuscode)``, where ``xmin`` is the minimizing
value of the argument, ``Jmin`` is the minimum value of the objective
function, "..." may be some other returned values (which are not used
by `brute`), and ``statuscode`` is the status code of the `finish` program.
Note that when `finish` is not None, the values returned are those
of the `finish` program, *not* the gridpoint ones. Consequently,
while `brute` confines its search to the input grid points,
the `finish` program's results usually will not coincide with any
gridpoint, and may fall outside the grid's boundary. Thus, if a
minimum only needs to be found over the provided grid points, make
sure to pass in ``finish=None``.
*Note 2*: The grid of points is a `numpy.mgrid` object.
For `brute` the `ranges` and `Ns` inputs have the following effect.
Each component of the `ranges` tuple can be either a slice object or a
two-tuple giving a range of values, such as (0, 5). If the component is a
slice object, `brute` uses it directly. If the component is a two-tuple
range, `brute` internally converts it to a slice object that interpolates
`Ns` points from its low-value to its high-value, inclusive.
Examples
--------
We illustrate the use of `brute` to seek the global minimum of a function
of two variables that is given as the sum of a positive-definite
quadratic and two deep "Gaussian-shaped" craters. Specifically, define
the objective function `f` as the sum of three other functions,
``f = f1 + f2 + f3``. We suppose each of these has a signature
``(z, *params)``, where ``z = (x, y)``, and ``params`` and the functions
are as defined below.
>>> import numpy as np
>>> params = (2, 3, 7, 8, 9, 10, 44, -1, 2, 26, 1, -2, 0.5)
>>> def f1(z, *params):
... x, y = z
... a, b, c, d, e, f, g, h, i, j, k, l, scale = params
... return (a * x**2 + b * x * y + c * y**2 + d*x + e*y + f)
>>> def f2(z, *params):
... x, y = z
... a, b, c, d, e, f, g, h, i, j, k, l, scale = params
... return (-g*np.exp(-((x-h)**2 + (y-i)**2) / scale))
>>> def f3(z, *params):
... x, y = z
... a, b, c, d, e, f, g, h, i, j, k, l, scale = params
... return (-j*np.exp(-((x-k)**2 + (y-l)**2) / scale))
>>> def f(z, *params):
... return f1(z, *params) + f2(z, *params) + f3(z, *params)
Thus, the objective function may have local minima near the minimum
of each of the three functions of which it is composed. To
use `fmin` to polish its gridpoint result, we may then continue as
follows:
>>> rranges = (slice(-4, 4, 0.25), slice(-4, 4, 0.25))
>>> from scipy import optimize
>>> resbrute = optimize.brute(f, rranges, args=params, full_output=True,
... finish=optimize.fmin)
>>> resbrute[0] # global minimum
array([-1.05665192, 1.80834843])
>>> resbrute[1] # function value at global minimum
-3.4085818767
Note that if `finish` had been set to None, we would have gotten the
gridpoint [-1.0 1.75] where the rounded function value is -2.892.
"""
N = len(ranges)
if N > 40:
raise ValueError("Brute Force not possible with more "
"than 40 variables.")
lrange = list(ranges)
for k in range(N):
if not isinstance(lrange[k], slice):
if len(lrange[k]) < 3:
lrange[k] = tuple(lrange[k]) + (complex(Ns),)
lrange[k] = slice(*lrange[k])
if (N == 1):
lrange = lrange[0]
grid = np.mgrid[lrange]
# obtain an array of parameters that is iterable by a map-like callable
inpt_shape = grid.shape
if (N > 1):
grid = np.reshape(grid, (inpt_shape[0], np.prod(inpt_shape[1:]))).T
if not np.iterable(args):
args = (args,)
wrapped_func = _Brute_Wrapper(func, args)
# iterate over input arrays, possibly in parallel
with MapWrapper(pool=workers) as mapper:
Jout = np.array(list(mapper(wrapped_func, grid)))
if (N == 1):
grid = (grid,)
Jout = np.squeeze(Jout)
elif (N > 1):
Jout = np.reshape(Jout, inpt_shape[1:])
grid = np.reshape(grid.T, inpt_shape)
Nshape = shape(Jout)
indx = argmin(Jout.ravel(), axis=-1)
Nindx = np.empty(N, int)
xmin = np.empty(N, float)
for k in range(N - 1, -1, -1):
thisN = Nshape[k]
Nindx[k] = indx % Nshape[k]
indx = indx // thisN
for k in range(N):
xmin[k] = grid[k][tuple(Nindx)]
Jmin = Jout[tuple(Nindx)]
if (N == 1):
grid = grid[0]
xmin = xmin[0]
if callable(finish):
# set up kwargs for `finish` function
finish_args = _getfullargspec(finish).args
finish_kwargs = dict()
if 'full_output' in finish_args:
finish_kwargs['full_output'] = 1
if 'disp' in finish_args:
finish_kwargs['disp'] = disp
elif 'options' in finish_args:
# pass 'disp' as `options`
# (e.g., if `finish` is `minimize`)
finish_kwargs['options'] = {'disp': disp}
# run minimizer
res = finish(func, xmin, args=args, **finish_kwargs)
if isinstance(res, OptimizeResult):
xmin = res.x
Jmin = res.fun
success = res.success
else:
xmin = res[0]
Jmin = res[1]
success = res[-1] == 0
if not success:
if disp:
warnings.warn("Either final optimization did not succeed or `finish` "
"does not return `statuscode` as its last argument.",
RuntimeWarning, stacklevel=2)
if full_output:
return xmin, Jmin, grid, Jout
else:
return xmin
class _Brute_Wrapper:
"""
Object to wrap user cost function for optimize.brute, allowing picklability
"""
def __init__(self, f, args):
self.f = f
self.args = [] if args is None else args
def __call__(self, x):
# flatten needed for one dimensional case.
return self.f(np.asarray(x).flatten(), *self.args)
def show_options(solver=None, method=None, disp=True):
"""
Show documentation for additional options of optimization solvers.
These are method-specific options that can be supplied through the
``options`` dict.
Parameters
----------
solver : str
Type of optimization solver. One of 'minimize', 'minimize_scalar',
'root', 'root_scalar', 'linprog', or 'quadratic_assignment'.
method : str, optional
If not given, shows all methods of the specified solver. Otherwise,
show only the options for the specified method. Valid values
corresponds to methods' names of respective solver (e.g., 'BFGS' for
'minimize').
disp : bool, optional
Whether to print the result rather than returning it.
Returns
-------
text
Either None (for disp=True) or the text string (disp=False)
Notes
-----
The solver-specific methods are:
`scipy.optimize.minimize`
- :ref:`Nelder-Mead <optimize.minimize-neldermead>`
- :ref:`Powell <optimize.minimize-powell>`
- :ref:`CG <optimize.minimize-cg>`
- :ref:`BFGS <optimize.minimize-bfgs>`
- :ref:`Newton-CG <optimize.minimize-newtoncg>`
- :ref:`L-BFGS-B <optimize.minimize-lbfgsb>`
- :ref:`TNC <optimize.minimize-tnc>`
- :ref:`COBYLA <optimize.minimize-cobyla>`
- :ref:`COBYQA <optimize.minimize-cobyqa>`
- :ref:`SLSQP <optimize.minimize-slsqp>`
- :ref:`dogleg <optimize.minimize-dogleg>`
- :ref:`trust-ncg <optimize.minimize-trustncg>`
`scipy.optimize.root`
- :ref:`hybr <optimize.root-hybr>`
- :ref:`lm <optimize.root-lm>`
- :ref:`broyden1 <optimize.root-broyden1>`
- :ref:`broyden2 <optimize.root-broyden2>`
- :ref:`anderson <optimize.root-anderson>`
- :ref:`linearmixing <optimize.root-linearmixing>`
- :ref:`diagbroyden <optimize.root-diagbroyden>`
- :ref:`excitingmixing <optimize.root-excitingmixing>`
- :ref:`krylov <optimize.root-krylov>`
- :ref:`df-sane <optimize.root-dfsane>`
`scipy.optimize.minimize_scalar`
- :ref:`brent <optimize.minimize_scalar-brent>`
- :ref:`golden <optimize.minimize_scalar-golden>`
- :ref:`bounded <optimize.minimize_scalar-bounded>`
`scipy.optimize.root_scalar`
- :ref:`bisect <optimize.root_scalar-bisect>`
- :ref:`brentq <optimize.root_scalar-brentq>`
- :ref:`brenth <optimize.root_scalar-brenth>`
- :ref:`ridder <optimize.root_scalar-ridder>`
- :ref:`toms748 <optimize.root_scalar-toms748>`
- :ref:`newton <optimize.root_scalar-newton>`
- :ref:`secant <optimize.root_scalar-secant>`
- :ref:`halley <optimize.root_scalar-halley>`
`scipy.optimize.linprog`
- :ref:`simplex <optimize.linprog-simplex>`
- :ref:`interior-point <optimize.linprog-interior-point>`
- :ref:`revised simplex <optimize.linprog-revised_simplex>`
- :ref:`highs <optimize.linprog-highs>`
- :ref:`highs-ds <optimize.linprog-highs-ds>`
- :ref:`highs-ipm <optimize.linprog-highs-ipm>`
`scipy.optimize.quadratic_assignment`
- :ref:`faq <optimize.qap-faq>`
- :ref:`2opt <optimize.qap-2opt>`
Examples
--------
We can print documentations of a solver in stdout:
>>> from scipy.optimize import show_options
>>> show_options(solver="minimize")
...
Specifying a method is possible:
>>> show_options(solver="minimize", method="Nelder-Mead")
...
We can also get the documentations as a string:
>>> show_options(solver="minimize", method="Nelder-Mead", disp=False)
Minimization of scalar function of one or more variables using the ...
"""
import textwrap
doc_routines = {
'minimize': (
('bfgs', 'scipy.optimize._optimize._minimize_bfgs'),
('cg', 'scipy.optimize._optimize._minimize_cg'),
('cobyla', 'scipy.optimize._cobyla_py._minimize_cobyla'),
('cobyqa', 'scipy.optimize._cobyqa_py._minimize_cobyqa'),
('dogleg', 'scipy.optimize._trustregion_dogleg._minimize_dogleg'),
('l-bfgs-b', 'scipy.optimize._lbfgsb_py._minimize_lbfgsb'),
('nelder-mead', 'scipy.optimize._optimize._minimize_neldermead'),
('newton-cg', 'scipy.optimize._optimize._minimize_newtoncg'),
('powell', 'scipy.optimize._optimize._minimize_powell'),
('slsqp', 'scipy.optimize._slsqp_py._minimize_slsqp'),
('tnc', 'scipy.optimize._tnc._minimize_tnc'),
('trust-ncg',
'scipy.optimize._trustregion_ncg._minimize_trust_ncg'),
('trust-constr',
'scipy.optimize._trustregion_constr.'
'_minimize_trustregion_constr'),
('trust-exact',
'scipy.optimize._trustregion_exact._minimize_trustregion_exact'),
('trust-krylov',
'scipy.optimize._trustregion_krylov._minimize_trust_krylov'),
),
'root': (
('hybr', 'scipy.optimize._minpack_py._root_hybr'),
('lm', 'scipy.optimize._root._root_leastsq'),
('broyden1', 'scipy.optimize._root._root_broyden1_doc'),
('broyden2', 'scipy.optimize._root._root_broyden2_doc'),
('anderson', 'scipy.optimize._root._root_anderson_doc'),
('diagbroyden', 'scipy.optimize._root._root_diagbroyden_doc'),
('excitingmixing', 'scipy.optimize._root._root_excitingmixing_doc'),
('linearmixing', 'scipy.optimize._root._root_linearmixing_doc'),
('krylov', 'scipy.optimize._root._root_krylov_doc'),
('df-sane', 'scipy.optimize._spectral._root_df_sane'),
),
'root_scalar': (
('bisect', 'scipy.optimize._root_scalar._root_scalar_bisect_doc'),
('brentq', 'scipy.optimize._root_scalar._root_scalar_brentq_doc'),
('brenth', 'scipy.optimize._root_scalar._root_scalar_brenth_doc'),
('ridder', 'scipy.optimize._root_scalar._root_scalar_ridder_doc'),
('toms748', 'scipy.optimize._root_scalar._root_scalar_toms748_doc'),
('secant', 'scipy.optimize._root_scalar._root_scalar_secant_doc'),
('newton', 'scipy.optimize._root_scalar._root_scalar_newton_doc'),
('halley', 'scipy.optimize._root_scalar._root_scalar_halley_doc'),
),
'linprog': (
('simplex', 'scipy.optimize._linprog._linprog_simplex_doc'),
('interior-point', 'scipy.optimize._linprog._linprog_ip_doc'),
('revised simplex', 'scipy.optimize._linprog._linprog_rs_doc'),
('highs-ipm', 'scipy.optimize._linprog._linprog_highs_ipm_doc'),
('highs-ds', 'scipy.optimize._linprog._linprog_highs_ds_doc'),
('highs', 'scipy.optimize._linprog._linprog_highs_doc'),
),
'quadratic_assignment': (
('faq', 'scipy.optimize._qap._quadratic_assignment_faq'),
('2opt', 'scipy.optimize._qap._quadratic_assignment_2opt'),
),
'minimize_scalar': (
('brent', 'scipy.optimize._optimize._minimize_scalar_brent'),
('bounded', 'scipy.optimize._optimize._minimize_scalar_bounded'),
('golden', 'scipy.optimize._optimize._minimize_scalar_golden'),
),
}
if solver is None:
text = ["\n\n\n========\n", "minimize\n", "========\n"]
text.append(show_options('minimize', disp=False))
text.extend(["\n\n===============\n", "minimize_scalar\n",
"===============\n"])
text.append(show_options('minimize_scalar', disp=False))
text.extend(["\n\n\n====\n", "root\n",
"====\n"])
text.append(show_options('root', disp=False))
text.extend(['\n\n\n=======\n', 'linprog\n',
'=======\n'])
text.append(show_options('linprog', disp=False))
text = "".join(text)
else:
solver = solver.lower()
if solver not in doc_routines:
raise ValueError(f'Unknown solver {solver!r}')
if method is None:
text = []
for name, _ in doc_routines[solver]:
text.extend(["\n\n" + name, "\n" + "="*len(name) + "\n\n"])
text.append(show_options(solver, name, disp=False))
text = "".join(text)
else:
method = method.lower()
methods = dict(doc_routines[solver])
if method not in methods:
raise ValueError(f"Unknown method {method!r}")
name = methods[method]
# Import function object
parts = name.split('.')
mod_name = ".".join(parts[:-1])
__import__(mod_name)
obj = getattr(sys.modules[mod_name], parts[-1])
# Get doc
doc = obj.__doc__
if doc is not None:
text = textwrap.dedent(doc).strip()
else:
text = ""
if disp:
print(text)
return
else:
return text
|
scipyREPO_NAMEscipyPATH_START.@scipy_extracted@scipy-main@scipy@optimize@_optimize.py@.PATH_END.py
|
{
"filename": "setup.py",
"repo_name": "franciscovillaescusa/Pylians3",
"repo_path": "Pylians3_extracted/Pylians3-master/library/integration_library/setup.py",
"type": "Python"
}
|
from setuptools import setup
from setuptools.extension import Extension
from Cython.Distutils import build_ext
from Cython.Build import cythonize
import numpy
ext_modules = cythonize([
Extension("integration_library",
["integration_library.pyx",'integration.c'],
include_dirs=[numpy.get_include()],
extra_compile_args=["-O3","-ffast-math","-march=native"]
)])
setup(ext_modules = ext_modules)
|
franciscovillaescusaREPO_NAMEPylians3PATH_START.@Pylians3_extracted@Pylians3-master@library@integration_library@setup.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "scikit-learn/scikit-learn",
"repo_path": "scikit-learn_extracted/scikit-learn-main/sklearn/mixture/tests/__init__.py",
"type": "Python"
}
|
scikit-learnREPO_NAMEscikit-learnPATH_START.@scikit-learn_extracted@scikit-learn-main@sklearn@mixture@tests@__init__.py@.PATH_END.py
|
|
{
"filename": "managerhelper.py",
"repo_name": "astroufsc/chimera",
"repo_path": "chimera_extracted/chimera-master/src/chimera/core/tests/managerhelper.py",
"type": "Python"
}
|
from chimera.core.chimeraobject import ChimeraObject
class ManagerHelper(ChimeraObject):
def __init__(self):
ChimeraObject.__init__(self)
def foo(self):
return 42
|
astroufscREPO_NAMEchimeraPATH_START.@chimera_extracted@chimera-master@src@chimera@core@tests@managerhelper.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/waterfall/insidetextfont/__init__.py",
"type": "Python"
}
|
import sys
from typing import TYPE_CHECKING
if sys.version_info < (3, 7) or TYPE_CHECKING:
from ._weightsrc import WeightsrcValidator
from ._weight import WeightValidator
from ._variantsrc import VariantsrcValidator
from ._variant import VariantValidator
from ._textcasesrc import TextcasesrcValidator
from ._textcase import TextcaseValidator
from ._stylesrc import StylesrcValidator
from ._style import StyleValidator
from ._sizesrc import SizesrcValidator
from ._size import SizeValidator
from ._shadowsrc import ShadowsrcValidator
from ._shadow import ShadowValidator
from ._linepositionsrc import LinepositionsrcValidator
from ._lineposition import LinepositionValidator
from ._familysrc import FamilysrcValidator
from ._family import FamilyValidator
from ._colorsrc import ColorsrcValidator
from ._color import ColorValidator
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(
__name__,
[],
[
"._weightsrc.WeightsrcValidator",
"._weight.WeightValidator",
"._variantsrc.VariantsrcValidator",
"._variant.VariantValidator",
"._textcasesrc.TextcasesrcValidator",
"._textcase.TextcaseValidator",
"._stylesrc.StylesrcValidator",
"._style.StyleValidator",
"._sizesrc.SizesrcValidator",
"._size.SizeValidator",
"._shadowsrc.ShadowsrcValidator",
"._shadow.ShadowValidator",
"._linepositionsrc.LinepositionsrcValidator",
"._lineposition.LinepositionValidator",
"._familysrc.FamilysrcValidator",
"._family.FamilyValidator",
"._colorsrc.ColorsrcValidator",
"._color.ColorValidator",
],
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@waterfall@insidetextfont@__init__.py@.PATH_END.py
|
{
"filename": "data_structures.py",
"repo_name": "rennehan/yt-swift",
"repo_path": "yt-swift_extracted/yt-swift-main/yt/frontends/halo_catalog/data_structures.py",
"type": "Python"
}
|
import glob
import weakref
from collections import defaultdict
from functools import cached_property, partial
import numpy as np
from yt.data_objects.selection_objects.data_selection_objects import (
YTSelectionContainer,
)
from yt.data_objects.static_output import (
ParticleDataset,
ParticleFile,
)
from yt.frontends.ytdata.data_structures import SavedDataset
from yt.funcs import parse_h5_attr
from yt.geometry.particle_geometry_handler import ParticleIndex
from yt.utilities.on_demand_imports import _h5py as h5py
from .fields import YTHaloCatalogFieldInfo, YTHaloCatalogHaloFieldInfo
class HaloCatalogFile(ParticleFile):
"""
Base class for data files of halo catalog datasets.
This is mainly here to correct for periodicity when
reading particle positions.
"""
def __init__(self, ds, io, filename, file_id, frange):
super().__init__(ds, io, filename, file_id, frange)
def _read_particle_positions(self, ptype, f=None):
raise NotImplementedError
def _get_particle_positions(self, ptype, f=None):
pcount = self.total_particles[ptype]
if pcount == 0:
return None
# Correct for periodicity.
dle = self.ds.domain_left_edge.to("code_length").v
dw = self.ds.domain_width.to("code_length").v
pos = self._read_particle_positions(ptype, f=f)
si, ei = self.start, self.end
if None not in (si, ei):
pos = pos[si:ei]
np.subtract(pos, dle, out=pos)
np.mod(pos, dw, out=pos)
np.add(pos, dle, out=pos)
return pos
class YTHaloCatalogFile(HaloCatalogFile):
"""
Data file class for the YTHaloCatalogDataset.
"""
def __init__(self, ds, io, filename, file_id, frange):
with h5py.File(filename, mode="r") as f:
self.header = {field: parse_h5_attr(f, field) for field in f.attrs.keys()}
pids = f.get("particles/ids")
self.total_ids = 0 if pids is None else pids.size
self.group_length_sum = self.total_ids
super().__init__(ds, io, filename, file_id, frange)
def _read_particle_positions(self, ptype, f=None):
"""
Read all particle positions in this file.
"""
if f is None:
close = True
f = h5py.File(self.filename, mode="r")
else:
close = False
pcount = self.header["num_halos"]
pos = np.empty((pcount, 3), dtype="float64")
for i, ax in enumerate("xyz"):
pos[:, i] = f[f"particle_position_{ax}"][()]
if close:
f.close()
return pos
class YTHaloCatalogDataset(SavedDataset):
"""
Dataset class for halo catalogs made with yt.
This covers yt FoF/HoP halo finders and the halo analysis
in yt_astro_analysis.
"""
_load_requirements = ["h5py"]
_index_class = ParticleIndex
_file_class = YTHaloCatalogFile
_field_info_class = YTHaloCatalogFieldInfo
_suffix = ".h5"
_con_attrs = (
"cosmological_simulation",
"current_time",
"current_redshift",
"hubble_constant",
"omega_matter",
"omega_lambda",
"domain_left_edge",
"domain_right_edge",
)
def __init__(
self,
filename,
dataset_type="ythalocatalog",
index_order=None,
units_override=None,
unit_system="cgs",
):
self.index_order = index_order
super().__init__(
filename,
dataset_type,
units_override=units_override,
unit_system=unit_system,
)
def add_field(self, *args, **kwargs):
super().add_field(*args, **kwargs)
self._halos_ds.add_field(*args, **kwargs)
@property
def halos_field_list(self):
return self._halos_ds.field_list
@property
def halos_derived_field_list(self):
return self._halos_ds.derived_field_list
@cached_property
def _halos_ds(self):
return YTHaloDataset(self)
def _setup_classes(self):
super()._setup_classes()
self.halo = partial(YTHaloCatalogHaloContainer, ds=self._halos_ds)
self.halo.__doc__ = YTHaloCatalogHaloContainer.__doc__
def _parse_parameter_file(self):
self.refine_by = 2
self.dimensionality = 3
self.domain_dimensions = np.ones(self.dimensionality, "int32")
self._periodicity = (True, True, True)
prefix = ".".join(self.parameter_filename.rsplit(".", 2)[:-2])
self.filename_template = f"{prefix}.%(num)s{self._suffix}"
self.file_count = len(glob.glob(prefix + "*" + self._suffix))
self.particle_types = ("halos",)
self.particle_types_raw = ("halos",)
super()._parse_parameter_file()
@classmethod
def _is_valid(cls, filename: str, *args, **kwargs) -> bool:
if not filename.endswith(".h5"):
return False
if cls._missing_load_requirements():
return False
with h5py.File(filename, mode="r") as f:
if (
"data_type" in f.attrs
and parse_h5_attr(f, "data_type") == "halo_catalog"
):
return True
return False
class YTHaloParticleIndex(ParticleIndex):
"""
Particle index for getting halo particles from YTHaloCatalogDatasets.
"""
def __init__(self, ds, dataset_type):
self.real_ds = weakref.proxy(ds.real_ds)
super().__init__(ds, dataset_type)
def _calculate_particle_index_starts(self):
"""
Create a dict of halo id offsets for each file.
"""
particle_count = defaultdict(int)
offset_count = 0
for data_file in self.data_files:
data_file.index_start = {
ptype: particle_count[ptype] for ptype in data_file.total_particles
}
data_file.offset_start = offset_count
for ptype in data_file.total_particles:
particle_count[ptype] += data_file.total_particles[ptype]
offset_count += getattr(data_file, "total_offset", 0)
self._halo_index_start = {}
for ptype in self.ds.particle_types_raw:
d = [data_file.index_start[ptype] for data_file in self.data_files]
self._halo_index_start.update({ptype: np.array(d)})
def _detect_output_fields(self):
field_list = []
scalar_field_list = []
units = {}
pc = {}
for ptype in self.ds.particle_types_raw:
d = [df.total_particles[ptype] for df in self.data_files]
pc.update({ptype: sum(d)})
found_fields = {ptype: False for ptype, pnum in pc.items() if pnum > 0}
has_ids = False
for data_file in self.data_files:
fl, sl, idl, _units = self.io._identify_fields(data_file)
units.update(_units)
field_list.extend([f for f in fl if f not in field_list])
scalar_field_list.extend([f for f in sl if f not in scalar_field_list])
for ptype in found_fields:
found_fields[ptype] |= data_file.total_particles[ptype]
has_ids |= len(idl) > 0
if all(found_fields.values()) and has_ids:
break
self.field_list = field_list
self.scalar_field_list = scalar_field_list
ds = self.dataset
ds.scalar_field_list = scalar_field_list
ds.particle_types = tuple({pt for pt, ds in field_list})
ds.field_units.update(units)
ds.particle_types_raw = ds.particle_types
def _get_halo_file_indices(self, ptype, identifiers):
"""
Get the index of the data file list where this halo lives.
Digitize returns i such that bins[i-1] <= x < bins[i], so we subtract
one because we will open data file i.
"""
return np.digitize(identifiers, self._halo_index_start[ptype], right=False) - 1
def _get_halo_scalar_index(self, ptype, identifier):
i_scalar = self._get_halo_file_indices(ptype, [identifier])[0]
scalar_index = identifier - self._halo_index_start[ptype][i_scalar]
return scalar_index
def _get_halo_values(self, ptype, identifiers, fields, f=None):
"""
Get field values for halo data containers.
"""
# if a file is already open, don't open it again
filename = None if f is None else f.filename
data = defaultdict(lambda: np.empty(identifiers.size))
i_scalars = self._get_halo_file_indices(ptype, identifiers)
for i_scalar in np.unique(i_scalars):
# mask array to get field data for this halo
target = i_scalars == i_scalar
scalar_indices = identifiers - self._halo_index_start[ptype][i_scalar]
# only open file if it's not already open
my_f = (
f
if self.data_files[i_scalar].filename == filename
else h5py.File(self.data_files[i_scalar].filename, mode="r")
)
for field in fields:
data[field][target] = self._read_halo_particle_field(
my_f, ptype, field, scalar_indices[target]
)
if self.data_files[i_scalar].filename != filename:
my_f.close()
return data
def _identify_base_chunk(self, dobj):
pass
def _read_halo_particle_field(self, fh, ptype, field, indices):
return fh[field][indices]
def _read_particle_fields(self, fields, dobj, chunk=None):
if not fields:
return {}, []
fields_to_read, fields_to_generate = self._split_fields(fields)
if not fields_to_read:
return {}, fields_to_generate
fields_to_return = self.io._read_particle_selection(dobj, fields_to_read)
return fields_to_return, fields_to_generate
def _setup_data_io(self):
super()._setup_data_io()
if self.real_ds._instantiated_index is None:
self.real_ds.index
self.real_ds.index
# inherit some things from parent index
self._data_files = self.real_ds.index.data_files
self._total_particles = self.real_ds.index.total_particles
self._calculate_particle_index_starts()
class HaloDataset(ParticleDataset):
"""
Base class for dataset accessing particles from halo catalogs.
"""
def __init__(self, ds, dataset_type):
self.real_ds = ds
for attr in [
"filename_template",
"file_count",
"particle_types_raw",
"particle_types",
"_periodicity",
]:
setattr(self, attr, getattr(self.real_ds, attr))
super().__init__(self.real_ds.parameter_filename, dataset_type)
def print_key_parameters(self):
pass
def _set_derived_attrs(self):
pass
def _parse_parameter_file(self):
for attr in [
"cosmological_simulation",
"cosmology",
"current_redshift",
"current_time",
"dimensionality",
"domain_dimensions",
"domain_left_edge",
"domain_right_edge",
"domain_width",
"hubble_constant",
"omega_lambda",
"omega_matter",
"unique_identifier",
]:
setattr(self, attr, getattr(self.real_ds, attr))
def set_code_units(self):
self._set_code_unit_attributes()
self.unit_registry = self.real_ds.unit_registry
def _set_code_unit_attributes(self):
for unit in ["length", "time", "mass", "velocity", "magnetic", "temperature"]:
my_unit = f"{unit}_unit"
setattr(self, my_unit, getattr(self.real_ds, my_unit, None))
def __str__(self):
return f"{self.real_ds}"
def _setup_classes(self):
self.objects = []
class YTHaloDataset(HaloDataset):
"""
Dataset used for accessing member particles from YTHaloCatalogDatasets.
"""
_index_class = YTHaloParticleIndex
_file_class = YTHaloCatalogFile
_field_info_class = YTHaloCatalogHaloFieldInfo
def __init__(self, ds, dataset_type="ythalo"):
super().__init__(ds, dataset_type)
def _set_code_unit_attributes(self):
pass
@classmethod
def _is_valid(cls, filename: str, *args, **kwargs) -> bool:
# We don't ever want this to be loaded by yt.load.
return False
class HaloContainer(YTSelectionContainer):
"""
Base class for data containers providing halo particles.
"""
_type_name = "halo"
_con_args = ("ptype", "particle_identifier")
_skip_add = True
_spatial = False
def __init__(self, ptype, particle_identifier, ds=None):
if ptype not in ds.particle_types_raw:
raise RuntimeError(
f'Possible halo types are {ds.particle_types_raw}, supplied "{ptype}".'
)
self.ptype = ptype
self._current_particle_type = ptype
super().__init__(ds, {})
self._set_identifiers(particle_identifier)
# Find the file that has the scalar values for this halo.
i_scalar = self.index._get_halo_file_indices(ptype, [self.particle_identifier])[
0
]
self.i_scalar = i_scalar
self.scalar_data_file = self.index.data_files[i_scalar]
# Data files containing particles belonging to this halo.
self.field_data_files = [self.index.data_files[i_scalar]]
# index within halo arrays that corresponds to this halo
self.scalar_index = self.index._get_halo_scalar_index(
ptype, self.particle_identifier
)
self._set_io_data()
self.particle_number = self._get_particle_number()
# starting and ending indices for each file containing particles
self._set_field_indices()
@cached_property
def mass(self):
return self[self.ptype, "particle_mass"][0]
@cached_property
def radius(self):
return self[self.ptype, "virial_radius"][0]
@cached_property
def position(self):
return self[self.ptype, "particle_position"][0]
@cached_property
def velocity(self):
return self[self.ptype, "particle_velocity"][0]
def _set_io_data(self):
halo_fields = self._get_member_fieldnames()
my_data = self.index._get_halo_values(
self.ptype, np.array([self.particle_identifier]), halo_fields
)
self._io_data = {field: np.int64(val[0]) for field, val in my_data.items()}
def __repr__(self):
return f"{self.ds}_{self.ptype}_{self.particle_identifier:09d}"
class YTHaloCatalogHaloContainer(HaloContainer):
"""
Data container for accessing particles from a halo.
Create a data container to get member particles and individual
values from halos and subhalos. Halo mass, radius, position, and
velocity are set as attributes. Halo IDs are accessible
through the field, "member_ids". Other fields that are one
value per halo are accessible as normal. The field list for
halo objects can be seen in `ds.halos_field_list`.
Parameters
----------
ptype : string
The type of halo. Possible options can be found by
inspecting the value of ds.particle_types_raw.
particle_identifier : int
The halo id.
Examples
--------
>>> import yt
>>> ds = yt.load("tiny_fof_halos/DD0046/DD0046.0.h5")
>>> halo = ds.halo("halos", 0)
>>> print(halo.particle_identifier)
0
>>> print(halo.mass)
8724990744704.453 Msun
>>> print(halo.radius)
658.8140635766607 kpc
>>> print(halo.position)
[0.05496909 0.19451951 0.04056824] code_length
>>> print(halo.velocity)
[7034181.07118151 5323471.09102874 3234522.50495914] cm/s
>>> # particle ids for this halo
>>> print(halo["member_ids"])
[ 1248. 129. 128. 31999. 31969. 31933. 31934. 159. 31903. 31841. ...
2241. 2240. 2239. 2177. 2209. 2207. 2208.] dimensionless
"""
def _get_member_fieldnames(self):
return ["particle_number", "particle_index_start"]
def _get_particle_number(self):
return self._io_data["particle_number"]
def _set_field_indices(self):
self.field_data_start = [self._io_data["particle_index_start"]]
self.field_data_end = [self.field_data_start[0] + self.particle_number]
def _set_identifiers(self, particle_identifier):
self.particle_identifier = particle_identifier
self.group_identifier = self.particle_identifier
|
rennehanREPO_NAMEyt-swiftPATH_START.@yt-swift_extracted@yt-swift-main@yt@frontends@halo_catalog@data_structures.py@.PATH_END.py
|
{
"filename": "recipes_FLAT_IMAGE.py",
"repo_name": "GeminiDRSoftware/DRAGONS",
"repo_path": "DRAGONS_extracted/DRAGONS-master/geminidr/gmos/recipes/qa/recipes_FLAT_IMAGE.py",
"type": "Python"
}
|
"""
Recipes available to data with tags ['GMOS', 'IMAGE', 'CAL', 'FLAT']
Default is "makeProcessedFlat".
"""
recipe_tags = {'GMOS', 'IMAGE', 'CAL', 'FLAT'}
def makeProcessedFlat(p):
"""
This recipe performs the standardization and corrections needed to
convert the raw input flat images into a single stacked and normalized
flat image. This output processed flat is stored on disk using
storeProcessedFlat and has a name equal to the name of the first input
flat image with "_flat.fits" appended.
Parameters
----------
p : PrimitivesBASE object
A primitive set matching the recipe_tags.
"""
p.prepare()
p.addDQ()
p.addVAR(read_noise=True)
p.overscanCorrect()
p.biasCorrect()
p.ADUToElectrons()
p.addVAR(poisson_noise=True)
p.addToList(purpose="forStack")
p.getList(purpose="forStack")
p.stackFlats()
p.normalizeFlat()
p.storeProcessedFlat()
return
_default = makeProcessedFlat
|
GeminiDRSoftwareREPO_NAMEDRAGONSPATH_START.@DRAGONS_extracted@DRAGONS-master@geminidr@gmos@recipes@qa@recipes_FLAT_IMAGE.py@.PATH_END.py
|
{
"filename": "sigmas.ipynb",
"repo_name": "dfm/corner.py",
"repo_path": "corner.py_extracted/corner.py-main/docs/pages/sigmas.ipynb",
"type": "Jupyter Notebook"
}
|
# A note about sigmas
We are regularly asked about the "sigma" levels in the 2D histograms. These are not the 68%, *etc.* values that we're used to for 1D distributions. In two dimensions, a Gaussian density is given by:
pdf(r) = exp(-(r/s)^2/2) / (2*pi*s^2)
The integral under this density (using polar coordinates and implicitly integrating out the angle) is:
cdf(x) = Integral(r * exp(-(r/s)^2/2) / s^2, {r, 0, x})
= 1 - exp(-(x/s)^2/2)
This means that within "1-sigma", the Gaussian contains `1-exp(-0.5) ~ 0.393` or 39.3% of the volume. Therefore the relevant 1-sigma levels for a 2D histogram of samples is 39% not 68%. If you must use 68% of the mass, use the `levels` keyword argument when you call `corner.corner`.
We can visualize the difference between sigma definitions:
```python
import corner
import numpy as np
# Generate some fake data from a Gaussian
np.random.seed(42)
x = np.random.randn(50000, 2)
```
First, plot this using the correct (default) 1-sigma level:
```python
fig = corner.corner(x, quantiles=(0.16, 0.84), levels=(1 - np.exp(-0.5),))
_ = fig.suptitle("default 'one-sigma' level")
```
Compare this to the 68% mass level and specifically compare to how the contour compares to the marginalized 68% quantile:
```python
fig = corner.corner(x, quantiles=(0.16, 0.84), levels=(0.68,))
_ = fig.suptitle("alternative 'one-sigma' level")
```
|
dfmREPO_NAMEcorner.pyPATH_START.@corner.py_extracted@corner.py-main@docs@pages@sigmas.ipynb@.PATH_END.py
|
{
"filename": "test_embeddings.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/partners/fireworks/tests/unit_tests/test_embeddings.py",
"type": "Python"
}
|
"""Test embedding model integration."""
from langchain_fireworks.embeddings import FireworksEmbeddings
def test_initialization() -> None:
"""Test embedding model initialization."""
FireworksEmbeddings(model="nomic-ai/nomic-embed-text-v1.5")
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@partners@fireworks@tests@unit_tests@test_embeddings.py@.PATH_END.py
|
{
"filename": "_color.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/scatterternary/textfont/_color.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ColorValidator(_plotly_utils.basevalidators.ColorValidator):
def __init__(
self, plotly_name="color", parent_name="scatterternary.textfont", **kwargs
):
super(ColorValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
array_ok=kwargs.pop("array_ok", True),
edit_type=kwargs.pop("edit_type", "style"),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@scatterternary@textfont@_color.py@.PATH_END.py
|
{
"filename": "_mesh3d.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/_mesh3d.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class Mesh3DValidator(_plotly_utils.basevalidators.CompoundValidator):
def __init__(self, plotly_name="mesh3d", parent_name="", **kwargs):
super(Mesh3DValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
data_class_str=kwargs.pop("data_class_str", "Mesh3d"),
data_docs=kwargs.pop(
"data_docs",
"""
alphahull
Determines how the mesh surface triangles are
derived from the set of vertices (points)
represented by the `x`, `y` and `z` arrays, if
the `i`, `j`, `k` arrays are not supplied. For
general use of `mesh3d` it is preferred that
`i`, `j`, `k` are supplied. If "-1", Delaunay
triangulation is used, which is mainly suitable
if the mesh is a single, more or less layer
surface that is perpendicular to
`delaunayaxis`. In case the `delaunayaxis`
intersects the mesh surface at more than one
point it will result triangles that are very
long in the dimension of `delaunayaxis`. If
">0", the alpha-shape algorithm is used. In
this case, the positive `alphahull` value
signals the use of the alpha-shape algorithm,
_and_ its value acts as the parameter for the
mesh fitting. If 0, the convex-hull algorithm
is used. It is suitable for convex bodies or if
the intention is to enclose the `x`, `y` and
`z` point set into a convex hull.
autocolorscale
Determines whether the colorscale is a default
palette (`autocolorscale: true`) or the palette
determined by `colorscale`. In case
`colorscale` is unspecified or `autocolorscale`
is true, the default palette will be chosen
according to whether numbers in the `color`
array are all positive, all negative or mixed.
cauto
Determines whether or not the color domain is
computed with respect to the input data (here
`intensity`) or the bounds set in `cmin` and
`cmax` Defaults to `false` when `cmin` and
`cmax` are set by the user.
cmax
Sets the upper bound of the color domain. Value
should have the same units as `intensity` and
if set, `cmin` must be set as well.
cmid
Sets the mid-point of the color domain by
scaling `cmin` and/or `cmax` to be equidistant
to this point. Value should have the same units
as `intensity`. Has no effect when `cauto` is
`false`.
cmin
Sets the lower bound of the color domain. Value
should have the same units as `intensity` and
if set, `cmax` must be set as well.
color
Sets the color of the whole mesh
coloraxis
Sets a reference to a shared color axis.
References to these shared color axes are
"coloraxis", "coloraxis2", "coloraxis3", etc.
Settings for these shared color axes are set in
the layout, under `layout.coloraxis`,
`layout.coloraxis2`, etc. Note that multiple
color scales can be linked to the same color
axis.
colorbar
:class:`plotly.graph_objects.mesh3d.ColorBar`
instance or dict with compatible properties
colorscale
Sets the colorscale. The colorscale must be an
array containing arrays mapping a normalized
value to an rgb, rgba, hex, hsl, hsv, or named
color string. At minimum, a mapping for the
lowest (0) and highest (1) values are required.
For example, `[[0, 'rgb(0,0,255)'], [1,
'rgb(255,0,0)']]`. To control the bounds of the
colorscale in color space, use `cmin` and
`cmax`. Alternatively, `colorscale` may be a
palette name string of the following list: Blac
kbody,Bluered,Blues,Cividis,Earth,Electric,Gree
ns,Greys,Hot,Jet,Picnic,Portland,Rainbow,RdBu,R
eds,Viridis,YlGnBu,YlOrRd.
contour
:class:`plotly.graph_objects.mesh3d.Contour`
instance or dict with compatible properties
customdata
Assigns extra data each datum. This may be
useful when listening to hover, click and
selection events. Note that, "scatter" traces
also appends customdata items in the markers
DOM elements
customdatasrc
Sets the source reference on Chart Studio Cloud
for `customdata`.
delaunayaxis
Sets the Delaunay axis, which is the axis that
is perpendicular to the surface of the Delaunay
triangulation. It has an effect if `i`, `j`,
`k` are not provided and `alphahull` is set to
indicate Delaunay triangulation.
facecolor
Sets the color of each face Overrides "color"
and "vertexcolor".
facecolorsrc
Sets the source reference on Chart Studio Cloud
for `facecolor`.
flatshading
Determines whether or not normal smoothing is
applied to the meshes, creating meshes with an
angular, low-poly look via flat reflections.
hoverinfo
Determines which trace information appear on
hover. If `none` or `skip` are set, no
information is displayed upon hovering. But, if
`none` is set, click and hover events are still
fired.
hoverinfosrc
Sets the source reference on Chart Studio Cloud
for `hoverinfo`.
hoverlabel
:class:`plotly.graph_objects.mesh3d.Hoverlabel`
instance or dict with compatible properties
hovertemplate
Template string used for rendering the
information that appear on hover box. Note that
this will override `hoverinfo`. Variables are
inserted using %{variable}, for example "y:
%{y}" as well as %{xother}, {%_xother},
{%_xother_}, {%xother_}. When showing info for
several points, "xother" will be added to those
with different x positions from the first
point. An underscore before or after
"(x|y)other" will add a space on that side,
only when this field is shown. Numbers are
formatted using d3-format's syntax
%{variable:d3-format}, for example "Price:
%{y:$.2f}". https://github.com/d3/d3-
format/tree/v1.4.5#d3-format for details on the
formatting syntax. Dates are formatted using
d3-time-format's syntax %{variable|d3-time-
format}, for example "Day: %{2019-01-01|%A}".
https://github.com/d3/d3-time-
format/tree/v2.2.3#locale_format for details on
the date formatting syntax. The variables
available in `hovertemplate` are the ones
emitted as event data described at this link
https://plotly.com/javascript/plotlyjs-
events/#event-data. Additionally, every
attributes that can be specified per-point (the
ones that are `arrayOk: true`) are available.
Anything contained in tag `<extra>` is
displayed in the secondary box, for example
"<extra>{fullData.name}</extra>". To hide the
secondary box completely, use an empty tag
`<extra></extra>`.
hovertemplatesrc
Sets the source reference on Chart Studio Cloud
for `hovertemplate`.
hovertext
Same as `text`.
hovertextsrc
Sets the source reference on Chart Studio Cloud
for `hovertext`.
i
A vector of vertex indices, i.e. integer values
between 0 and the length of the vertex vectors,
representing the "first" vertex of a triangle.
For example, `{i[m], j[m], k[m]}` together
represent face m (triangle m) in the mesh,
where `i[m] = n` points to the triplet `{x[n],
y[n], z[n]}` in the vertex arrays. Therefore,
each element in `i` represents a point in
space, which is the first vertex of a triangle.
ids
Assigns id labels to each datum. These ids for
object constancy of data points during
animation. Should be an array of strings, not
numbers or any other type.
idssrc
Sets the source reference on Chart Studio Cloud
for `ids`.
intensity
Sets the intensity values for vertices or cells
as defined by `intensitymode`. It can be used
for plotting fields on meshes.
intensitymode
Determines the source of `intensity` values.
intensitysrc
Sets the source reference on Chart Studio Cloud
for `intensity`.
isrc
Sets the source reference on Chart Studio Cloud
for `i`.
j
A vector of vertex indices, i.e. integer values
between 0 and the length of the vertex vectors,
representing the "second" vertex of a triangle.
For example, `{i[m], j[m], k[m]}` together
represent face m (triangle m) in the mesh,
where `j[m] = n` points to the triplet `{x[n],
y[n], z[n]}` in the vertex arrays. Therefore,
each element in `j` represents a point in
space, which is the second vertex of a
triangle.
jsrc
Sets the source reference on Chart Studio Cloud
for `j`.
k
A vector of vertex indices, i.e. integer values
between 0 and the length of the vertex vectors,
representing the "third" vertex of a triangle.
For example, `{i[m], j[m], k[m]}` together
represent face m (triangle m) in the mesh,
where `k[m] = n` points to the triplet `{x[n],
y[n], z[n]}` in the vertex arrays. Therefore,
each element in `k` represents a point in
space, which is the third vertex of a triangle.
ksrc
Sets the source reference on Chart Studio Cloud
for `k`.
legend
Sets the reference to a legend to show this
trace in. References to these legends are
"legend", "legend2", "legend3", etc. Settings
for these legends are set in the layout, under
`layout.legend`, `layout.legend2`, etc.
legendgroup
Sets the legend group for this trace. Traces
and shapes part of the same legend group
hide/show at the same time when toggling legend
items.
legendgrouptitle
:class:`plotly.graph_objects.mesh3d.Legendgroup
title` instance or dict with compatible
properties
legendrank
Sets the legend rank for this trace. Items and
groups with smaller ranks are presented on
top/left side while with "reversed"
`legend.traceorder` they are on bottom/right
side. The default legendrank is 1000, so that
you can use ranks less than 1000 to place
certain items before all unranked items, and
ranks greater than 1000 to go after all
unranked items. When having unranked or equal
rank items shapes would be displayed after
traces i.e. according to their order in data
and layout.
legendwidth
Sets the width (in px or fraction) of the
legend for this trace.
lighting
:class:`plotly.graph_objects.mesh3d.Lighting`
instance or dict with compatible properties
lightposition
:class:`plotly.graph_objects.mesh3d.Lightpositi
on` instance or dict with compatible properties
meta
Assigns extra meta information associated with
this trace that can be used in various text
attributes. Attributes such as trace `name`,
graph, axis and colorbar `title.text`,
annotation `text` `rangeselector`,
`updatemenues` and `sliders` `label` text all
support `meta`. To access the trace `meta`
values in an attribute in the same trace,
simply use `%{meta[i]}` where `i` is the index
or key of the `meta` item in question. To
access trace `meta` in layout attributes, use
`%{data[n[.meta[i]}` where `i` is the index or
key of the `meta` and `n` is the trace index.
metasrc
Sets the source reference on Chart Studio Cloud
for `meta`.
name
Sets the trace name. The trace name appears as
the legend item and on hover.
opacity
Sets the opacity of the surface. Please note
that in the case of using high `opacity` values
for example a value greater than or equal to
0.5 on two surfaces (and 0.25 with four
surfaces), an overlay of multiple transparent
surfaces may not perfectly be sorted in depth
by the webgl API. This behavior may be improved
in the near future and is subject to change.
reversescale
Reverses the color mapping if true. If true,
`cmin` will correspond to the last color in the
array and `cmax` will correspond to the first
color.
scene
Sets a reference between this trace's 3D
coordinate system and a 3D scene. If "scene"
(the default value), the (x,y,z) coordinates
refer to `layout.scene`. If "scene2", the
(x,y,z) coordinates refer to `layout.scene2`,
and so on.
showlegend
Determines whether or not an item corresponding
to this trace is shown in the legend.
showscale
Determines whether or not a colorbar is
displayed for this trace.
stream
:class:`plotly.graph_objects.mesh3d.Stream`
instance or dict with compatible properties
text
Sets the text elements associated with the
vertices. If trace `hoverinfo` contains a
"text" flag and "hovertext" is not set, these
elements will be seen in the hover labels.
textsrc
Sets the source reference on Chart Studio Cloud
for `text`.
uid
Assign an id to this trace, Use this to provide
object constancy between traces during
animations and transitions.
uirevision
Controls persistence of some user-driven
changes to the trace: `constraintrange` in
`parcoords` traces, as well as some `editable:
true` modifications such as `name` and
`colorbar.title`. Defaults to
`layout.uirevision`. Note that other user-
driven trace attribute changes are controlled
by `layout` attributes: `trace.visible` is
controlled by `layout.legend.uirevision`,
`selectedpoints` is controlled by
`layout.selectionrevision`, and
`colorbar.(x|y)` (accessible with `config:
{editable: true}`) is controlled by
`layout.editrevision`. Trace changes are
tracked by `uid`, which only falls back on
trace index if no `uid` is provided. So if your
app can add/remove traces before the end of the
`data` array, such that the same trace has a
different index, you can still preserve user-
driven changes if you give each trace a `uid`
that stays with it as it moves.
vertexcolor
Sets the color of each vertex Overrides
"color". While Red, green and blue colors are
in the range of 0 and 255; in the case of
having vertex color data in RGBA format, the
alpha color should be normalized to be between
0 and 1.
vertexcolorsrc
Sets the source reference on Chart Studio Cloud
for `vertexcolor`.
visible
Determines whether or not this trace is
visible. If "legendonly", the trace is not
drawn, but can appear as a legend item
(provided that the legend itself is visible).
x
Sets the X coordinates of the vertices. The nth
element of vectors `x`, `y` and `z` jointly
represent the X, Y and Z coordinates of the nth
vertex.
xcalendar
Sets the calendar system to use with `x` date
data.
xhoverformat
Sets the hover text formatting rulefor `x`
using d3 formatting mini-languages which are
very similar to those in Python. For numbers,
see: https://github.com/d3/d3-
format/tree/v1.4.5#d3-format. And for dates
see: https://github.com/d3/d3-time-
format/tree/v2.2.3#locale_format. We add two
items to d3's date formatter: "%h" for half of
the year as a decimal number as well as "%{n}f"
for fractional seconds with n digits. For
example, *2016-10-13 09:15:23.456* with
tickformat "%H~%M~%S.%2f" would display
*09~15~23.46*By default the values are
formatted using `xaxis.hoverformat`.
xsrc
Sets the source reference on Chart Studio Cloud
for `x`.
y
Sets the Y coordinates of the vertices. The nth
element of vectors `x`, `y` and `z` jointly
represent the X, Y and Z coordinates of the nth
vertex.
ycalendar
Sets the calendar system to use with `y` date
data.
yhoverformat
Sets the hover text formatting rulefor `y`
using d3 formatting mini-languages which are
very similar to those in Python. For numbers,
see: https://github.com/d3/d3-
format/tree/v1.4.5#d3-format. And for dates
see: https://github.com/d3/d3-time-
format/tree/v2.2.3#locale_format. We add two
items to d3's date formatter: "%h" for half of
the year as a decimal number as well as "%{n}f"
for fractional seconds with n digits. For
example, *2016-10-13 09:15:23.456* with
tickformat "%H~%M~%S.%2f" would display
*09~15~23.46*By default the values are
formatted using `yaxis.hoverformat`.
ysrc
Sets the source reference on Chart Studio Cloud
for `y`.
z
Sets the Z coordinates of the vertices. The nth
element of vectors `x`, `y` and `z` jointly
represent the X, Y and Z coordinates of the nth
vertex.
zcalendar
Sets the calendar system to use with `z` date
data.
zhoverformat
Sets the hover text formatting rulefor `z`
using d3 formatting mini-languages which are
very similar to those in Python. For numbers,
see: https://github.com/d3/d3-
format/tree/v1.4.5#d3-format. And for dates
see: https://github.com/d3/d3-time-
format/tree/v2.2.3#locale_format. We add two
items to d3's date formatter: "%h" for half of
the year as a decimal number as well as "%{n}f"
for fractional seconds with n digits. For
example, *2016-10-13 09:15:23.456* with
tickformat "%H~%M~%S.%2f" would display
*09~15~23.46*By default the values are
formatted using `zaxis.hoverformat`.
zsrc
Sets the source reference on Chart Studio Cloud
for `z`.
""",
),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@_mesh3d.py@.PATH_END.py
|
{
"filename": "_symbolsrc.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/scattergeo/marker/_symbolsrc.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class SymbolsrcValidator(_plotly_utils.basevalidators.SrcValidator):
def __init__(
self, plotly_name="symbolsrc", parent_name="scattergeo.marker", **kwargs
):
super(SymbolsrcValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "none"),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@scattergeo@marker@_symbolsrc.py@.PATH_END.py
|
{
"filename": "test_schechter.py",
"repo_name": "skypyproject/skypy",
"repo_path": "skypy_extracted/skypy-main/skypy/galaxies/tests/test_schechter.py",
"type": "Python"
}
|
import numpy as np
from astropy.cosmology import default_cosmology
def test_schechter_lf():
from pytest import raises
from skypy.galaxies import schechter_lf
from astropy import units
# redshift and magnitude distributions are tested separately
# only test that output is consistent here
# parameters for the sampling
z = np.linspace(0., 1., 100)
M_star = -20
phi_star = 1e-3
alpha = -0.5
m_lim = 30.
sky_area = 1.0 * units.deg**2
cosmo = default_cosmology.get()
# sample redshifts and magnitudes
z_gal, M_gal = schechter_lf(z, M_star, phi_star, alpha, m_lim, sky_area, cosmo)
# check length
assert len(z_gal) == len(M_gal)
# turn M_star, phi_star, alpha into arrays
z, M_star, phi_star, alpha = np.broadcast_arrays(z, M_star, phi_star, alpha)
# sample s.t. arrays need to be interpolated
# alpha array not yet supported
with raises(NotImplementedError):
z_gal, M_gal = schechter_lf(z, M_star, phi_star, alpha, m_lim, sky_area, cosmo)
def test_schechter_smf():
from pytest import raises
from skypy.galaxies import schechter_smf
from astropy import units
# redshift and magnitude distributions are tested separately
# only test that output is consistent here
# parameters for the sampling
z = np.linspace(0., 1., 100)
m_star = 10 ** 10.67
phi_star = 1e-3
alpha = -1.5
m_min = 1.e7
m_max = 1.e13
sky_area = 1.0 * units.deg**2
cosmo = default_cosmology.get()
# sample redshifts and magnitudes
z_gal, m_gal = schechter_smf(z, m_star, phi_star, alpha, m_min, m_max, sky_area, cosmo)
# check length
assert len(z_gal) == len(m_gal)
# turn m_star, phi_star, alpha into arrays
z, m_star, phi_star, alpha = np.broadcast_arrays(z, m_star, phi_star, alpha)
# sample s.t. arrays need to be interpolated
# alpha array not yet supported
with raises(NotImplementedError):
z_gal, m_gal = schechter_smf(z, m_star, phi_star, alpha, m_min, m_max, sky_area, cosmo)
|
skypyprojectREPO_NAMEskypyPATH_START.@skypy_extracted@skypy-main@skypy@galaxies@tests@test_schechter.py@.PATH_END.py
|
{
"filename": "_locations.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/isosurface/slices/y/_locations.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class LocationsValidator(_plotly_utils.basevalidators.DataArrayValidator):
def __init__(
self, plotly_name="locations", parent_name="isosurface.slices.y", **kwargs
):
super(LocationsValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@isosurface@slices@y@_locations.py@.PATH_END.py
|
{
"filename": "test_vonkarman.py",
"repo_name": "GalSim-developers/GalSim",
"repo_path": "GalSim_extracted/GalSim-main/tests/test_vonkarman.py",
"type": "Python"
}
|
# Copyright (c) 2012-2023 by the GalSim developers team on GitHub
# https://github.com/GalSim-developers
#
# This file is part of GalSim: The modular galaxy image simulation toolkit.
# https://github.com/GalSim-developers/GalSim
#
# GalSim is free software: redistribution and use in source and binary forms,
# with or without modification, are permitted provided that the following
# conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions, and the disclaimer given in the accompanying LICENSE
# file.
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions, and the disclaimer given in the documentation
# and/or other materials provided with the distribution.
#
import numpy as np
import os
import sys
import galsim
from galsim_test_helpers import *
@timer
def test_vk(run_slow):
"""Test the generation of VonKarman profiles
"""
if run_slow:
lams = [300.0, 500.0, 1100.0]
r0_500s = [0.05, 0.15, 0.3]
L0s = [1e10, 25.0, 10.0]
do_deltas = [False, True]
else:
lams = [500.0]
r0_500s = [0.2]
L0s = [25.0]
do_deltas = [False]
for lam in lams:
for r0_500 in r0_500s:
r0 = r0_500*(lam/500)**(6./5)
for L0 in L0s:
for do_delta in do_deltas:
kwargs = {'lam':lam, 'r0':r0, 'L0':L0, 'do_delta':do_delta}
print(kwargs)
delta_amp = np.exp(-0.5*0.172629*(r0/L0)**(-5./3.))
if delta_amp > 1.e-3:
print("Skip this combination, since delta > maxk_threshold")
continue
vk = galsim.VonKarman(flux=2.2, **kwargs)
np.testing.assert_almost_equal(vk.flux, 2.2)
gsp = galsim.GSParams(xvalue_accuracy=1.e-8, kvalue_accuracy=1.e-8)
vk2 = galsim.VonKarman(flux=2.2, gsparams=gsp, **kwargs)
assert vk2 != vk
assert vk2 == vk.withGSParams(gsp)
assert vk2 == vk.withGSParams(xvalue_accuracy=1.e-8, kvalue_accuracy=1.e-8)
check_basic(vk, "VonKarman")
check_pickle(vk)
img = galsim.Image(16, 16, scale=0.25)
if not do_delta:
do_shoot(vk, img, "VonKarman")
do_kvalue(vk, img, "VonKarman")
with np.testing.assert_raises(galsim.GalSimIncompatibleValuesError):
vk = galsim.VonKarman(lam=500, r0=0.1, r0_500=0.2)
with np.testing.assert_raises(galsim.GalSimIncompatibleValuesError):
vk = galsim.VonKarman(lam=500)
@timer
def test_vk_delta():
"""Test a VonKarman with a significant delta-function amplitude"""
kwargs = {'lam':1100.0, 'r0':0.8, 'L0':5.0, 'flux':2.2}
# Try to see if we can catch the warning first
with assert_warns(galsim.GalSimWarning):
vk = galsim.VonKarman(**kwargs)
kwargs['suppress_warning'] = True
vk = galsim.VonKarman(**kwargs)
check_pickle(vk)
# This profile has more than 15% of its flux in the delta-function component.
assert vk.delta_amplitude > 0.15 * vk.flux
# If do_delta is False (the default), then the asymptotic kValue should still be zero.
np.testing.assert_almost_equal(vk.kValue(1e10, 0).real, 0.0)
# But if we use do_delta=True, then the asymptotic kValue should be that of the delta function.
vkd = galsim.VonKarman(do_delta=True, **kwargs)
check_pickle(vkd)
np.testing.assert_almost_equal(vkd.kValue(1e10, 0).real, vkd.delta_amplitude)
# Either way, the fluxes should be the same.
np.testing.assert_almost_equal(vk.flux, vkd.flux)
assert vk != vkd
# The half-light-radius of the profile with do_delta=True should be smaller though, as we're
# accounting for the 15% flux at r=0 in this case
assert vkd.half_light_radius < vk.half_light_radius
@timer
def test_vk_scale():
"""Test vk scale argument"""
kwargs = {'lam':500, 'r0':0.2, 'L0':25.0, 'flux':2.2}
vk_arcsec = galsim.VonKarman(scale_unit=galsim.arcsec, **kwargs)
vk_arcmin = galsim.VonKarman(scale_unit='arcmin', **kwargs)
check_pickle(vk_arcmin)
np.testing.assert_almost_equal(vk_arcsec.flux, vk_arcmin.flux)
np.testing.assert_almost_equal(vk_arcsec.kValue(0.0, 0.0), vk_arcmin.kValue(0.0, 0.0))
np.testing.assert_almost_equal(vk_arcsec.kValue(0.0, 10.0), vk_arcmin.kValue(0.0, 600.0))
np.testing.assert_almost_equal(vk_arcsec.xValue(0.0, 6.0), vk_arcmin.xValue(0.0, 0.1))
img1 = vk_arcsec.drawImage(nx=32, ny=32, scale=0.2)
img2 = vk_arcmin.drawImage(nx=32, ny=32, scale=0.2/60.0)
np.testing.assert_almost_equal(img1.array, img2.array)
@timer
def test_vk_shoot():
"""Test VonKarman with photon shooting. Particularly the flux of the final image.
"""
rng = galsim.BaseDeviate(1234)
obj = galsim.VonKarman(lam=500, r0=0.2, flux=1.e4)
im = galsim.Image(100,100, scale=1)
im.setCenter(0,0)
added_flux, photons = obj.drawPhot(im, poisson_flux=False, rng=rng.duplicate())
print('obj.flux = ',obj.flux)
print('added_flux = ',added_flux)
print('photon fluxes = ',photons.flux.min(),'..',photons.flux.max())
print('image flux = ',im.array.sum())
assert np.isclose(added_flux, obj.flux)
assert np.isclose(im.array.sum(), obj.flux)
photons2 = obj.makePhot(poisson_flux=False, rng=rng)
assert photons2 == photons, "VonKarman makePhot not equivalent to drawPhot"
obj = galsim.VonKarman(lam=500, r0=0.2, L0=10., flux=1.e4)
added_flux, photons = obj.drawPhot(im, poisson_flux=False, rng=rng.duplicate())
print('obj.flux = ',obj.flux)
print('added_flux = ',added_flux)
print('photon fluxes = ',photons.flux.min(),'..',photons.flux.max())
print('image flux = ',im.array.sum())
assert np.isclose(added_flux, obj.flux)
assert np.isclose(im.array.sum(), obj.flux)
photons2 = obj.makePhot(poisson_flux=False, rng=rng)
assert photons2 == photons, "VonKarman makePhot not equivalent to drawPhot"
obj = galsim.VonKarman(lam=700, r0=0.02, L0=10., flux=1.e4)
im = galsim.Image(500,500, scale=1)
im.setCenter(0,0)
added_flux, photons = obj.drawPhot(im, poisson_flux=False, rng=rng.duplicate())
print('obj.flux = ',obj.flux)
print('added_flux = ',added_flux)
print('photon fluxes = ',photons.flux.min(),'..',photons.flux.max())
print('image flux = ',im.array.sum())
assert np.isclose(added_flux, obj.flux)
assert np.isclose(im.array.sum(), obj.flux)
photons2 = obj.makePhot(poisson_flux=False, rng=rng.duplicate())
assert photons2 == photons, "VonKarman makePhot not equivalent to drawPhot"
# Can treat the profile as a convolution of a delta function and put it in a photon_ops list.
delta = galsim.DeltaFunction(flux=1.e4)
psf = galsim.VonKarman(lam=700, r0=0.02, L0=10.)
photons3 = delta.makePhot(poisson_flux=False, rng=rng.duplicate(), photon_ops=[psf])
assert photons3 == photons, "Using VonKarman in photon_ops not equivalent to drawPhot"
@timer
def test_vk_ne():
gsp = galsim.GSParams(maxk_threshold=1.1e-3, folding_threshold=5.1e-3)
objs = [galsim.VonKarman(lam=500.0, r0=0.2),
galsim.VonKarman(lam=500.0, r0=0.2, L0=20.0),
galsim.VonKarman(lam=500.0, r0=0.2, L0=20.0, flux=2.2),
galsim.VonKarman(lam=500.0, r0=0.2, L0=1e11),
galsim.VonKarman(lam=550.0, r0=0.1, L0=20.0),
galsim.VonKarman(lam=550.0, r0=0.1, L0=20.0, do_delta=True),
galsim.VonKarman(lam=550.0, r0=0.1, L0=20.0, scale_unit=galsim.arcmin),
galsim.VonKarman(lam=550.0, r0=0.1, L0=20.0, gsparams=gsp),
galsim.VonKarman(lam=550.0, r0=0.1, L0=20.0, gsparams=gsp, force_stepk=1.0)]
check_all_diff(objs)
@timer
def test_vk_eq_kolm():
lam = 500.0
r0 = 0.2
L0 = 1e10 # Need to make this surprisingly large to make vk -> kolm.
flux = 3.3
kolm = galsim.Kolmogorov(lam=lam, r0=r0, flux=flux)
vk = galsim.VonKarman(lam=lam, r0=r0, L0=L0, flux=flux)
np.testing.assert_allclose(kolm.xValue(0,0), vk.xValue(0,0), rtol=5e-4, atol=0)
kolm_img = kolm.drawImage(nx=24, ny=24, scale=0.2)
vk_img = vk.drawImage(nx=24, ny=24, scale=0.2)
np.testing.assert_allclose(kolm_img.array, vk_img.array, atol=flux*4e-5, rtol=0)
@timer
def test_vk_fitting_formulae():
# lam, r0_500, L0
params = [(650, 0.15, 10.0),
(450, 0.12, 25.0),
(900, 0.18, 100.0)]
def predicted_FWHM_ratio(r0, L0):
"""Fitting formula for VonKarman FWHM / Kolmogorov FWHM
from Martinez++2014
"""
return np.sqrt(1 - 2.183*(r0/L0)**0.356)
def predicted_HLR_ratio(r0, L0):
"""Fitting formula for VonKarman HLR / Kolmogorov HLR
from Martinez++2014
"""
return np.sqrt(1 - 1.534*(r0/L0)**0.347)
for lam, r0_500, L0 in params:
print(lam, r0_500, L0)
r0 = r0_500*(lam/500.0)**(6./5)
kolm = galsim.Kolmogorov(lam=lam, r0=r0)
vk = galsim.VonKarman(lam=lam, r0=r0, L0=L0)
vk2 = galsim.VonKarman(lam=lam, r0_500=r0_500, L0=L0)
np.testing.assert_allclose(vk.r0, vk2.r0)
np.testing.assert_allclose(vk.r0_500, vk2.r0_500)
for prof in [vk, vk2]:
HLR_ratio = prof.calculateHLR() / kolm.calculateHLR()
FWHM_ratio = prof.calculateFWHM() / kolm.calculateFWHM()
print(HLR_ratio)
print(FWHM_ratio)
np.testing.assert_allclose(HLR_ratio, predicted_HLR_ratio(r0, L0), rtol=0.015)
np.testing.assert_allclose(FWHM_ratio, predicted_FWHM_ratio(r0, L0), rtol=0.015)
@timer
def test_vk_gsp():
"""Test that we can construct a vK with non-standard folding_threshold.
"""
# default folding_threshold is 5e-3.
# We can't go too much smaller than this for such a flat asymptotic profile, but check a little
# bit further works.
gsp1 = galsim.GSParams(folding_threshold=1e-2)
gsp2 = galsim.GSParams(folding_threshold=2e-3)
# Just testing that these construct successfully
galsim.VonKarman(lam=700, r0=0.1, L0=24.3, gsparams=gsp1)
galsim.VonKarman(lam=700, r0=0.1, L0=24.3, gsparams=gsp2)
def vk_benchmark():
import time
t0 = time.time()
vk = galsim.VonKarman(lam=700, r0=0.1, L0=24.3)
vk.drawImage(nx=16, ny=16, scale=0.2)
t1 = time.time()
print("Time to create/draw first time: {:6.3f}s".format(t1-t0)) # ~0.7s
for i in range(10):
vk.drawImage(nx=16, ny=16, scale=0.2)
t2 = time.time()
print("Time to draw 10 more: {:6.3f}s".format(t2-t1)) # ~0.07s
for i in range(100):
vk.drawImage(nx=16, ny=16, scale=0.2, method='phot', n_photons=50000)
t3 = time.time()
print("Time to photon-shoot 100 more with 50000 photons each: {:6.3f}s".format(t3-t2)) # ~0.9s
@timer
def test_vk_r0(run_slow):
"""Test a special r0 value that resulted in an error, reported in issue #957.
Note: the resolution of the bug was to add explicit split points for the first several
j0 zeros. Without that, the integral in rawXValue can spuriously fail badly, leading to
an invalid estimate of the total integrated flux within R=pi/stepk.
Update: With the new Ogata method for doing the Hankel transform, this seems no longer to
be necessary. However, we continue to test these r values anyway.
"""
# The first one was issue #957.
# Aaron Roodman ran across another, which is now included here as well.
r0_list = [0.146068884, 0.16879518207956518]
for r0 in r0_list:
vk = galsim.VonKarman(L0=25.,lam=700.,r0=r0)
check_basic(vk, "VonKarman, r0=%s"%r0)
if run_slow:
# Josh then tried a bunch more random triples of r0_500, lam, L0 to find more failures,
# which are given in input/vk_fail.txt.
r0_500_list, lam_list, L0_list = np.loadtxt('input/vk_fail.txt').T
for r0_500, lam, L0 in zip(r0_500_list, lam_list, L0_list):
print(r0_500,lam,L0)
vk = galsim.VonKarman(L0=L0,lam=lam,r0_500=r0_500)
#check_basic(vk, "VonKarman, r0_500=%s"%r0_500)
@timer
def test_vk_force_stepk():
"""Check that manually forcing stepk works"""
vk1 = galsim.VonKarman(r0_500=0.1, L0=25.0, lam=750.0)
vk2 = galsim.VonKarman(r0_500=0.1, L0=25.0, lam=750.0, force_stepk=10.0)
# Make sure we get expected stepk
assert vk1.stepk != vk2.stepk
assert vk2.stepk == 10.0
# Many products will actually be the same for both
# Asking for the half_light_radius or xValue will trigger the table build,
# which is identical for each.
assert vk1.half_light_radius == vk2.half_light_radius
assert vk1.xValue(0, 1) == vk2.xValue(0, 1)
# Images will be the same if you assert specific bounds
img1 = vk1.drawImage(nx=50, ny=50, scale=0.2, method='fft')
img2 = vk2.drawImage(nx=50, ny=50, scale=0.2, method='fft')
np.testing.assert_equal(img1.array, img2.array)
# Though "goodImageSize" will differ.
assert vk1.getGoodImageSize(0.2) != vk2.getGoodImageSize(0.2)
# Can we pickle?
check_pickle(vk2)
check_pickle(vk2, lambda obj:obj.stepk)
check_basic(vk2, 'vk2', do_x=False) # x fails b/c stamp size is bad
img = galsim.Image(50, 50, scale=0.2)
do_shoot(vk2, img, "VonKarman")
# Check works with scale
vk3 = galsim.VonKarman(
r0_500=0.1, L0=25.0, lam=750.0, force_stepk=10.0,
scale_unit=galsim.radians
)
assert vk3.stepk == 10.0
assert vk3.scale_unit == galsim.radians
# force_stepk is retained through a reflux
vk4 = vk3.withFlux(11.0)
assert vk4.flux == 11.0
assert vk3.force_stepk == vk4.force_stepk
@timer
def test_low_folding_threshold():
"""Test VonKarman with a very low folding_threshold.
"""
folding_threshold = 1e-4
pixel_scale = 0.2
kwargs = {'lam':500, 'r0':0.2, 'L0':25.0, 'flux':2.2}
gsparams = galsim.GSParams(folding_threshold=folding_threshold)
psf = galsim.VonKarman(gsparams=gsparams, **kwargs)
image_size = psf.getGoodImageSize(pixel_scale)
print('ft = 1.e-4: psf.getGoodImageSize:', image_size)
assert image_size == 298
folding_threshold = 1e-6
gsparams = galsim.GSParams(folding_threshold=folding_threshold)
psf = galsim.VonKarman(gsparams=gsparams, **kwargs)
image_size = psf.getGoodImageSize(pixel_scale)
print('ft = 1.e-6: psf.getGoodImageSize:', image_size)
assert image_size == 600
# Check an extremely small L0
kwargs['L0'] = 1.0
with assert_warns(galsim.GalSimWarning):
# This low an L0 has a non-negligible delta function, hence a warning.
psf = galsim.VonKarman(gsparams=gsparams, **kwargs)
image_size = psf.getGoodImageSize(pixel_scale)
print('L0=1.0, ft = 1.e-6: psf.getGoodImageSize:', image_size)
assert image_size == 600
if __name__ == "__main__":
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument("--benchmark", action='store_true', help="Run timing benchmark")
runtests(__file__, parser=parser)
args, unknown_args = parser.parse_known_args()
if args.benchmark:
vk_benchmark()
|
GalSim-developersREPO_NAMEGalSimPATH_START.@GalSim_extracted@GalSim-main@tests@test_vonkarman.py@.PATH_END.py
|
{
"filename": "test_doc.py",
"repo_name": "pandas-dev/pandas",
"repo_path": "pandas_extracted/pandas-main/pandas/tests/util/test_doc.py",
"type": "Python"
}
|
from textwrap import dedent
from pandas.util._decorators import doc
@doc(method="cumsum", operation="sum")
def cumsum(whatever):
"""
This is the {method} method.
It computes the cumulative {operation}.
"""
@doc(
cumsum,
dedent(
"""
Examples
--------
>>> cumavg([1, 2, 3])
2
"""
),
method="cumavg",
operation="average",
)
def cumavg(whatever):
pass
@doc(cumsum, method="cummax", operation="maximum")
def cummax(whatever):
pass
@doc(cummax, method="cummin", operation="minimum")
def cummin(whatever):
pass
def test_docstring_formatting():
docstr = dedent(
"""
This is the cumsum method.
It computes the cumulative sum.
"""
)
assert cumsum.__doc__ == docstr
def test_docstring_appending():
docstr = dedent(
"""
This is the cumavg method.
It computes the cumulative average.
Examples
--------
>>> cumavg([1, 2, 3])
2
"""
)
assert cumavg.__doc__ == docstr
def test_doc_template_from_func():
docstr = dedent(
"""
This is the cummax method.
It computes the cumulative maximum.
"""
)
assert cummax.__doc__ == docstr
def test_inherit_doc_template():
docstr = dedent(
"""
This is the cummin method.
It computes the cumulative minimum.
"""
)
assert cummin.__doc__ == docstr
|
pandas-devREPO_NAMEpandasPATH_START.@pandas_extracted@pandas-main@pandas@tests@util@test_doc.py@.PATH_END.py
|
{
"filename": "_uirevision.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/layout/map/_uirevision.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class UirevisionValidator(_plotly_utils.basevalidators.AnyValidator):
def __init__(self, plotly_name="uirevision", parent_name="layout.map", **kwargs):
super(UirevisionValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "none"),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@layout@map@_uirevision.py@.PATH_END.py
|
{
"filename": "metricWrapper.py",
"repo_name": "lsstdesc/sn_pipe",
"repo_path": "sn_pipe_extracted/sn_pipe-master/run_scripts/metrics/metricWrapper.py",
"type": "Python"
}
|
import numpy as np
from sn_metrics.sn_snr_metric import SNSNRMetric
from sn_metrics.sn_cadence_metric import SNCadenceMetric
from sn_metrics.sn_obsrate_metric import SNObsRateMetric
from sn_metrics.sn_nsn_metric import SNNSNMetric
from sn_metrics.sn_sl_metric import SLSNMetric
from sn_tools.sn_cadence_tools import ReferenceData
from sn_tools.sn_utils import GetReference, LoadDust
from sn_tools.sn_telescope import Telescope
from sn_tools.sn_io import check_get_file
import os
import multiprocessing
import healpy as hp
import yaml
class MetricWrapper:
def __init__(self, name='Cadence', season=-1,
coadd=True, fieldType='DD', nside=64,
RAmin=0., RAmax=360.,
Decmin=-1.0, Decmax=-1.0,
npixels=0, metadata={}, outDir='', ebvofMW=-1.0):
self.name = '{}Metric_{}_nside_{}_coadd_{}_{}_{}_{}_{}_npixels_{}_ebvofMW_{}'.format(name,
fieldType, nside, coadd, RAmin, RAmax, Decmin, Decmax, npixels, ebvofMW)
self.metric = None
self.metadata = vars(metadata)
# select values to dump
self.metaout = ['name', 'seasons', 'coadd', 'fieldType',
'nside', 'RAmin', 'RAmax', 'Decmin', 'Decmax', 'metric', 'Output dir', 'remove_dithering', 'ebvofMW']
self.metadata['name'] = self.name
self.metadata['metric'] = name
self.metadata['Output dir'] = outDir
self.outDir = outDir
def run(self, obs):
return self.metric.run(obs)
def saveConfig(self):
ti = dict(zip(self.metaout, [self.metadata[k] for k in self.metaout]))
nameOut = '{}/{}_conf.yaml'.format(self.outDir, self.name)
print('Saving configuration file', nameOut)
with open(nameOut, 'w') as file:
yaml.dump(ti, file)
class CadenceMetricWrapper(MetricWrapper):
def __init__(self, name='Cadence', season=-1,
coadd=True, fieldType='DD', nside=64,
RAmin=0., RAmax=360.,
Decmin=-1.0, Decmax=-1.0,
npixels=0,
metadata={}, outDir='', ebvofMW=-1.0, bluecutoff=380.0, redcutoff=800.0):
super(CadenceMetricWrapper, self).__init__(
name=name, season=season, coadd=coadd, fieldType=fieldType,
nside=nside, RAmin=RAmin, RAmax=RAmax,
Decmin=Decmin, Decmax=Decmax, npixels=npixels, metadata=metadata, outDir=outDir, ebvofMW=ebvofMW)
self.metric = SNCadenceMetric(
coadd=coadd, nside=nside, verbose=metadata.verbose)
self.saveConfig()
class SNRMetricWrapper(MetricWrapper):
def __init__(self, name='SNR', season=-1,
coadd=True, fieldType='DD', nside=64,
RAmin=0., RAmax=360.,
Decmin=-1.0, Decmax=-1.0,
npixels=0,
metadata={}, outDir='', ebvofMW=-1.0, bluecutoff=380.0, redcutoff=800.0):
super(SNRMetricWrapper, self).__init__(
name=name, season=season, coadd=coadd, fieldType=fieldType,
nside=nside, RAmin=RAmin, RAmax=RAmax,
Decmin=Decmin, Decmax=Decmax, npixels=npixels,
metadata=metadata, outDir=outDir, ebvofMW=ebvofMW)
self.metaout += ['x1', 'color', 'dirFake', 'dirRefs', 'band', 'z']
shift = 10.
x1 = metadata.x1
color = metadata.color
print(metadata)
fake_file = '{}/{}.yaml'.format(metadata.dirFake,
'Fake_cadence_snrmetric')
Li_files = []
mag_to_flux_files = []
# names_ref = list(metadata.names_ref)
for name in [metadata.names_ref]:
Li_files.append(
'{}/Li_{}_{}_{}.npy'.format(metadata.dirRefs, name, x1, color))
mag_to_flux_files.append(
'{}/Mag_to_Flux_{}.npy'.format(metadata.dirRefs, name))
lim_sn = ReferenceData(
Li_files, mag_to_flux_files, metadata.band, metadata.z)
self.metric = SNSNRMetric(lim_sn=lim_sn, fake_file=fake_file, coadd=coadd,
names_ref=[metadata.names_ref], shift=shift, season=season, z=metadata.z, verbose=metadata.verbose)
self.saveConfig()
class ObsRateMetricWrapper(MetricWrapper):
def __init__(self, name='ObsRate', season=-1,
coadd=True, fieldType='DD', nside=64,
RAmin=0., RAmax=360.,
Decmin=-1.0, Decmax=-1.0,
npixels=0,
metadata={}, outDir='', ebvofMW=-1.0, bluecutoff=380.0, redcutoff=800.0):
super(ObsRateMetricWrapper, self).__init__(
name=name, season=season, coadd=coadd, fieldType=fieldType,
nside=nside, RAmin=RAmin, RAmax=RAmax,
Decmin=Decmin, Decmax=Decmax,
npixels=npixels,
metadata=metadata, outDir=outDir, ebvofMW=ebvofMW)
self.metaout += ['x1', 'color', 'dirRefs', 'z', 'bands', 'SNR']
x1 = metadata.x1
color = metadata.color
bands = 'gri'
SNR = [30., 40., 30.] # WFD SNR cut to estimate sum(Li**2)
self.metadata['bands'] = bands
self.metadata['SNR'] = SNR
Li_files = []
mag_to_flux_files = []
for name in [metadata.names_ref]:
Li_files.append(
'{}/Li_{}_{}_{}.npy'.format(metadata.dirRefs, name, x1, color))
mag_to_flux_files.append(
'{}/Mag_to_Flux_{}.npy'.format(metadata.dirRefs, name))
# self.bands = bands
lim_sn = {}
for band in bands:
lim_sn[band] = ReferenceData(
Li_files, mag_to_flux_files, band, metadata.z)
snr_ref = dict(zip(bands, SNR))
self.metric = SNObsRateMetric(lim_sn=lim_sn, names_ref=[metadata.names_ref],
coadd=coadd, season=season, z=metadata.z, bands=bands, snr_ref=snr_ref, verbose=metadata.verbose)
self.saveConfig()
class NSNMetricWrapper(MetricWrapper):
def __init__(self, name='NSN', season=-1, coadd=True, fieldType='DD',
nside=64, RAmin=0., RAmax=360.,
Decmin=-1.0, Decmax=-1.0,
npixels=0,
metadata={}, outDir='', ebvofMW=-1.0, bluecutoff=380.0, redcutoff=800.0):
super(NSNMetricWrapper, self).__init__(
name=name, season=season, coadd=coadd, fieldType=fieldType,
nside=nside, RAmin=RAmin, RAmax=RAmax,
Decmin=Decmin, Decmax=Decmax,
npixels=npixels,
metadata=metadata, outDir=outDir, ebvofMW=ebvofMW)
zmin = 0.
zmax = 1.1
if fieldType == 'WFD':
zmax = 0.6
tel_par = {}
tel_par['name'] = 'LSST' # name of the telescope (internal)
# dir of throughput
tel_par['throughput_dir'] = 'LSST_THROUGHPUTS_BASELINE'
tel_par['atmos_dir'] = 'THROUGHPUTS_DIR' # dir of atmos
tel_par['airmass'] = 1.2 # airmass value
tel_par['atmos'] = True # atmos
tel_par['aerosol'] = False # aerosol
self.telescope = Telescope(name=tel_par['name'],
throughput_dir=tel_par['throughput_dir'],
atmos_dir=tel_par['atmos_dir'],
atmos=tel_par['atmos'],
aerosol=tel_par['aerosol'],
airmass=tel_par['airmass'])
lc_reference = {}
templateDir = 'Template_LC'
gammaDir = 'reference_files'
gammaName = 'gamma.hdf5'
web_path = 'https://me.lsst.eu/gris/DESC_SN_pipeline'
# loading dust file
dustDir = 'Template_Dust'
dustcorr = {}
x1_colors = [(-2.0, -0.2), (-2.0, 0.0), (-2.0, 0.2),
(0.0, -0.2), (0.0, 0.0), (0.0, 0.2),
(2.0, -0.2), (2.0, 0.0), (2.0, 0.2)]
# (2.0, -0.2)] #(2.0, 0.0), (2.0, 0.2)]
if metadata.proxy_level == 2:
x1_colors = [(-2.0, 0.2), (0.0, 0.0)]
print('Loading reference files')
result_queue = multiprocessing.Queue()
for j in range(len(x1_colors)):
x1 = x1_colors[j][0]
color = x1_colors[j][1]
fname = 'LC_{}_{}_{}_{}_ebvofMW_0.0_vstack.hdf5'.format(
x1, color, bluecutoff, redcutoff)
if np.abs(ebvofMW) > 0.:
dustFile = 'Dust_{}_{}_{}_{}.hdf5'.format(
x1, color, bluecutoff, redcutoff)
dustcorr[x1_colors[j]] = LoadDust(
dustDir, dustFile, web_path).dustcorr
else:
dustcorr[x1_colors[j]] = None
p = multiprocessing.Process(
name='Subprocess_main-'+str(j), target=self.load, args=(templateDir, fname, gammaDir, gammaName, web_path, j, result_queue))
p.start()
resultdict = {}
for j in range(len(x1_colors)):
resultdict.update(result_queue.get())
for p in multiprocessing.active_children():
p.join()
for j in range(len(x1_colors)):
if resultdict[j] is not None:
lc_reference[x1_colors[j]] = resultdict[j]
print('Reference data loaded', lc_reference.keys(), fieldType)
# LC selection criteria
if fieldType == 'DD':
n_bef = 4
n_aft = 10
snr_min = 5.
n_phase_min = 1
n_phase_max = 1
zlim_coeff = 0.95
if fieldType == 'WFD':
n_bef = 4
n_aft = 10
snr_min = 0.
n_phase_min = 1
n_phase_max = 1
zlim_coeff = 0.85
if fieldType == 'Fake':
n_bef = 0
n_aft = 0
snr_min = 0.
n_phase_min = 0
n_phase_max = 0
zlim_coeff = 0.95
# load x1_color_dist
fName = 'Dist_X1_Color_JLA_high_z.txt'
fDir = 'reference_files'
check_get_file(web_path, fDir, fName)
x1_color_dist = np.genfromtxt('{}/{}'.format(fDir, fName), dtype=None,
names=('x1', 'color', 'weight_x1', 'weight_c', 'weight_tot'))
# print(x1_color_dist)
if metadata.proxy_level == 1:
x1vals = np.arange(-3., 5., 2.)
cvals = np.arange(-0.3, 0.5, 0.2)
r = []
for ix in range(len(x1vals)-1):
ii = x1_color_dist['x1'] >= x1vals[ix]
ii &= x1_color_dist['x1'] < x1vals[ix+1]
x1med = np.median([x1vals[ix], x1vals[ix+1]])
for ic in range(len(cvals)-1):
iib = x1_color_dist['color'] >= cvals[ic]
iib &= x1_color_dist['color'] < cvals[ic+1]
cmed = np.median([cvals[ic], cvals[ic+1]])
print(x1med, np.round(cmed, 1), np.sum(
x1_color_dist[ii & iib]['weight_tot']))
r.append((np.round(x1med, 1), np.round(cmed, 1),
np.sum(x1_color_dist[ii & iib]['weight_tot'])))
x1_color_dist = np.rec.fromrecords(
r, names=['x1', 'color', 'weight_tot'])
pixArea = hp.nside2pixarea(nside, degrees=True)
# metric instance
self.metric = SNNSNMetric(
lc_reference, dustcorr, season=season, zmin=zmin,
zmax=zmax, pixArea=pixArea,
verbose=metadata.verbose, timer=metadata.timer,
ploteffi=metadata.ploteffi,
n_bef=n_bef, n_aft=n_aft,
snr_min=snr_min,
n_phase_min=n_phase_min,
n_phase_max=n_phase_max,
outputType=metadata.outputType,
proxy_level=metadata.proxy_level,
x1_color_dist=x1_color_dist,
coadd=coadd, lightOutput=metadata.lightOutput,
T0s=metadata.T0s, zlim_coeff=zlim_coeff, ebvofMW=ebvofMW)
self.metadata['n_bef'] = n_bef
self.metadata['n_aft'] = n_aft
self.metadata['snr_min'] = snr_min
self.metadata['n_phase_min'] = n_phase_min
self.metadata['n_phase_max'] = n_phase_max
self.metadata['zlim_coeff'] = zlim_coeff
self.metaout += ['ploteffi', 'outputType',
'proxy_level', 'lightOutput', 'T0s',
'n_bef', 'n_aft', 'snr_min', 'n_phase_min', 'n_phase_max', 'zlim_coeff']
self.saveConfig()
def load(self, templateDir, fname, gammaDir, gammaName, web_path, j=-1, output_q=None):
lc_ref = GetReference(templateDir,
fname, gammaDir, gammaName, web_path, self.telescope)
if output_q is not None:
output_q.put({j: lc_ref})
else:
return tab_tot
class SLMetricWrapper(MetricWrapper):
def __init__(self, name='SL', season=-1,
coadd=0, nside=64, fieldType='WFD',
RAmin=0., RAmax=360.,
Decmin=-1.0, Decmax=-1.0,
npixels=0,
metadata={}, outDir=''):
super(SLMetricWrapper, self).__init__(
name=name, season=season, coadd=coadd, fieldType=fieldType,
nside=nside, RAmin=RAmin, RAmax=RAmax,
Decmin=Decmin, Decmax=Decmax,
npixels=npixels,
metadata=metadata, outDir=outDir)
self.metric = SLSNMetric(
season=season, nside=nside, coadd=coadd, verbose=metadata.verbose)
self.saveConfig()
|
lsstdescREPO_NAMEsn_pipePATH_START.@sn_pipe_extracted@sn_pipe-master@run_scripts@metrics@metricWrapper.py@.PATH_END.py
|
{
"filename": "_bgcolor.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/isosurface/hoverlabel/_bgcolor.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class BgcolorValidator(_plotly_utils.basevalidators.ColorValidator):
def __init__(
self, plotly_name="bgcolor", parent_name="isosurface.hoverlabel", **kwargs
):
super(BgcolorValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
array_ok=kwargs.pop("array_ok", True),
edit_type=kwargs.pop("edit_type", "none"),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@isosurface@hoverlabel@_bgcolor.py@.PATH_END.py
|
{
"filename": "README.md",
"repo_name": "exo-cesm/CESM2.1.3",
"repo_path": "CESM2.1.3_extracted/CESM2.1.3-main/Tidally_locked_exoplanets/cases/TP_1_e_SSPO_W21_10pc/README.md",
"type": "Markdown"
}
|
# TRAPPIST-1 e (W21 spectrum, 10% PAL of oxygen, with the substellar point over ocean) case setup instructions
## If setting the case up on ARC4:
run buildcase_TP_1_e_SSPO_W21_10pc. A case will be created with the name b.e21.BWma1850.f19_g17.TRAPPIST1_e.SSPO..W21.10pc_o2.my_case.001. Change the name in buildcase_TP_1_e_SSPO_W21_10pc if you would like a different name.
The restart file for this case is currently at /nobackup/Alternative_Earths/restarts/TP1_e/W21_SSPO_10pc/0357-08-25/
A case will be created and the project will be planet with the job queue planet.q. If switching to the main queue, n the case directory, ./xmlchange the project to be blank (no input) and the job queue to be 40core-192G.q.
A user_nl_cam example will be copied in to the case directory. This contains a changed solar file, based on the [Wilson et al. (2021)](https://zenodo.org/record/4556130#.Y_82yezP39E) TRAPPIST-1 spectrum. It also specifies a different rotation rate, gravity, and radius.
There is 10 times less oxygen in this case compared to the modern level of oxygen (21% by volume)
Included source mods fix the solar zenith angle so the planet is considered to be tidally locked.
|
exo-cesmREPO_NAMECESM2.1.3PATH_START.@CESM2.1.3_extracted@CESM2.1.3-main@Tidally_locked_exoplanets@cases@TP_1_e_SSPO_W21_10pc@README.md@.PATH_END.py
|
{
"filename": "_thicknessmode.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/histogram2d/colorbar/_thicknessmode.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ThicknessmodeValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(
self, plotly_name="thicknessmode", parent_name="histogram2d.colorbar", **kwargs
):
super(ThicknessmodeValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "colorbars"),
values=kwargs.pop("values", ["fraction", "pixels"]),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@histogram2d@colorbar@_thicknessmode.py@.PATH_END.py
|
{
"filename": "_minexponent.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/scattergl/marker/colorbar/_minexponent.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class MinexponentValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(
self,
plotly_name="minexponent",
parent_name="scattergl.marker.colorbar",
**kwargs
):
super(MinexponentValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
min=kwargs.pop("min", 0),
role=kwargs.pop("role", "style"),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@scattergl@marker@colorbar@_minexponent.py@.PATH_END.py
|
{
"filename": "test_invertible_resnet.py",
"repo_name": "vislearn/FrEIA",
"repo_path": "FrEIA_extracted/FrEIA-master/tests/test_invertible_resnet.py",
"type": "Python"
}
|
import unittest
import torch.optim
import sys
sys.path.append('../')
from FrEIA.modules import *
from FrEIA.framework import *
class ActNormTest(unittest.TestCase):
batch_size = 256
def test_linear(self):
torch.manual_seed(0)
inp_size_linear = (20,)
act_norm = ActNorm([inp_size_linear])
x = torch.randn(self.batch_size, *inp_size_linear)
x = x * torch.rand_like(x) + torch.randn_like(x)
y, = act_norm([x], jac=False)[0]
self.assertStandardMoments(y)
def test_conv(self):
torch.manual_seed(0)
inp_size_conv = (3, 10, 10)
act_norm = ActNorm([inp_size_conv])
x = torch.randn(self.batch_size, *inp_size_conv)
x = x * torch.rand_like(x) + torch.randn_like(x)
y, = act_norm([x], jac=False)[0]
y_ = y.reshape(self.batch_size, inp_size_conv[0], -1)
self.assertStandardMoments(y_)
def assertStandardMoments(self, data):
dims = [0] + list(range(2, data.ndim))
mean = torch.mean(data, dim=dims)
std = torch.std(data, dim=dims)
self.assertTrue(torch.allclose(mean, torch.zeros_like(mean), atol=1e-7))
self.assertTrue(torch.allclose(std, torch.ones_like(std)))
class IResNetTest(unittest.TestCase):
def __init__(self, *args):
super().__init__(*args)
self.batch_size = 7
self.inp_size_linear = (20,)
self.inp_size_conv = (3, 10, 10)
self.tol = 1e-6
torch.manual_seed(0)
nodes = [InputNode(*self.inp_size_linear, name='input')]
cond = ConditionNode(*self.inp_size_linear, name='cond')
for i in range(5):
nodes.append(Node(nodes[-1], ActNorm, {},
name=f'actnorm_{i}'))
nodes.append(Node(nodes[-1], IResNetLayer,
{'hutchinson_samples': 20,
'internal_size': 100,
'n_internal_layers': 3},
conditions=[cond],
name=f'i_resnet_{i}'))
nodes.append(OutputNode(nodes[-1], name='output'))
self.i_resnet_linear = GraphINN(nodes + [cond,], verbose=False)
for node in self.i_resnet_linear.node_list:
if isinstance(node.module, IResNetLayer):
node.module.lipschitz_correction()
nodes = [InputNode(*self.inp_size_conv, name='input')]
for i in range(5):
nodes.append(Node(nodes[-1], ActNorm, {},
name=f'actnorm_{i}'))
nodes.append(Node(nodes[-1], IResNetLayer, {'hutchinson_samples': 20},
name=f'i_resnet_{i}'))
nodes.append(OutputNode(nodes[-1], name='output'))
self.i_resnet_conv = GraphINN(nodes, verbose=False)
for node in self.i_resnet_conv.node_list:
if isinstance(node.module, IResNetLayer):
node.module.lipschitz_correction()
def test_inverse(self):
x = torch.randn(self.batch_size, *self.inp_size_linear)
x = x * torch.randn_like(x)
x = x + torch.randn_like(x)
c = torch.randn(self.batch_size, *self.inp_size_linear)
y = self.i_resnet_linear(x, c, jac=False)[0]
x_hat = self.i_resnet_linear(y, c, rev=True, jac=False)[0]
# Check that inverse is close to input
self.assertTrue(torch.allclose(x, x_hat, atol=self.tol))
x = torch.randn(self.batch_size, *self.inp_size_conv)
x = x * torch.randn_like(x)
x = x + torch.randn_like(x)
y = self.i_resnet_conv(x, jac=False)[0]
x_hat = self.i_resnet_conv(y, rev=True, jac=False)[0]
# Check that inverse is close to input
self.assertTrue(torch.allclose(x, x_hat, atol=self.tol))
def test_jacobian(self):
x = torch.randn(self.batch_size, *self.inp_size_linear)
x = x * torch.randn(self.batch_size, *[1 for i in range(len(self.inp_size_linear))])
x = x + torch.randn(self.batch_size, *[1 for i in range(len(self.inp_size_linear))])
c = torch.randn(self.batch_size, *self.inp_size_linear)
# Estimate log det of Jacobian via power series
z, logdet = self.i_resnet_linear(x, c=c)
# Approximate log det of Jacobian numerically
logdet_num = self.i_resnet_linear.log_jacobian_numerical(x, c=c)
# Check that they are the same (with huge tolerance)
# print(f'\n{logdet}\n{logdet_num}')
self.assertTrue(torch.allclose(logdet, logdet_num, atol=1.5, rtol=0.15))
x = torch.randn(self.batch_size, *self.inp_size_conv)
x = x * torch.randn(self.batch_size, *[1 for i in range(len(self.inp_size_conv))])
x = x + torch.randn(self.batch_size, *[1 for i in range(len(self.inp_size_conv))])
# Estimate log det of Jacobian via power series
logdet = self.i_resnet_conv(x)[1]
# Approximate log det of Jacobian numerically
logdet_num = self.i_resnet_conv.log_jacobian_numerical(x)
# Check that they are the same (with huge tolerance)
# print(f'\n{logdet}\n{logdet_num}')
self.assertTrue(torch.allclose(logdet, logdet_num, atol=1.5, rtol=0.1))
if __name__ == '__main__':
unittest.main()
|
vislearnREPO_NAMEFrEIAPATH_START.@FrEIA_extracted@FrEIA-master@tests@test_invertible_resnet.py@.PATH_END.py
|
{
"filename": "getting_started.ipynb",
"repo_name": "icecube/skyllh",
"repo_path": "skyllh_extracted/skyllh-master/doc/sphinx/tutorials/getting_started.ipynb",
"type": "Jupyter Notebook"
}
|
# Getting started
SkyLLH is a Python based framework to develop and to perform maximum likelihood ratio hypothesis testing.
The idea of SkyLLH is to provide a framework with a class structure that is tied to the mathematical objects of the likelihood functions.
Slack channel: [#skyllh](https://icecube-spno.slack.com/channels/skyllh)
An IceCube member can find pre-defined IceCube log-likelihood analyses in the (privat) [i3skyllh](https://github.com/icecube/i3skyllh) project.
## SkyLLH's analysis workflow
To set-up and run an analysis the following procedure applies:
1. Create a (local) configuration for the analysis.
```python
from skyllh.core.config import Config
cfg = Config()
```
An updated configuration from the default configuration can also be loaded from
a *yaml* file or form a Python dictionary.
```python
cfg = Config.from_dict({
'project': {
'working_directory': '/home/mwolf/projects/publicdata_ps',
}})
```
|
icecubeREPO_NAMEskyllhPATH_START.@skyllh_extracted@skyllh-master@doc@sphinx@tutorials@getting_started.ipynb@.PATH_END.py
|
{
"filename": "_legendwidth.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/heatmap/_legendwidth.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class LegendwidthValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(self, plotly_name="legendwidth", parent_name="heatmap", **kwargs):
super(LegendwidthValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "style"),
min=kwargs.pop("min", 0),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@heatmap@_legendwidth.py@.PATH_END.py
|
{
"filename": "_legendgroup.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/pie/_legendgroup.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class LegendgroupValidator(_plotly_utils.basevalidators.StringValidator):
def __init__(self, plotly_name="legendgroup", parent_name="pie", **kwargs):
super(LegendgroupValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "style"),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@pie@_legendgroup.py@.PATH_END.py
|
{
"filename": "adadelta.py",
"repo_name": "BRML/climin",
"repo_path": "climin_extracted/climin-master/climin/adadelta.py",
"type": "Python"
}
|
# -*- coding: utf-8 -*-
"""This module provides an implementation of adadelta."""
from __future__ import absolute_import
from .base import Minimizer
from .mathadapt import sqrt, ones_like, clip
class Adadelta(Minimizer):
"""Adadelta optimizer.
Adadelta [zeiler2013adadelta]_ is a method that uses the magnitude of recent
gradients and steps to obtain an adaptive step rate. An exponential moving
average over the gradients and steps is kept; a scale of the learning rate
is then obtained by their ration.
Let :math:`f'(\\theta_t)` be the derivative of the loss with respect to the
parameters at time step :math:`t`. In its
basic form, given a step rate :math:`\\alpha`, a decay term
:math:`\\gamma` and an offset :math:`\\epsilon` we perform the following
updates:
.. math::
g_t &=& (1 - \\gamma)~f'(\\theta_t)^2 + \\gamma g_{t-1}
where :math:`g_0 = 0`. Let :math:`s_0 = 0` for updating the parameters:
.. math::
\\Delta \\theta_t &=& \\alpha {\sqrt{s_{t-1} + \\epsilon} \over \sqrt{g_t + \\epsilon}}~f'(\\theta_t), \\\\
\\theta_{t+1} &=& \\theta_t - \\Delta \\theta_t.
Subsequently we adapt the moving average of the steps:
.. math::
s_t &=& (1 - \\gamma)~\\Delta\\theta_t^2 + \\gamma s_{t-1}.
To extend this with Nesterov's accelerated gradient, we need a momentum
coefficient :math:`\\beta` and incorporate it by using slightly different
formulas:
.. math::
\\theta_{t + {1 \over 2}} &=& \\theta_t - \\beta \\Delta \\theta_{t-1}, \\\\
g_t &=& (1 - \\gamma)~f'(\\theta_{t + {1 \over 2}})^2 + \\gamma g_{t-1}, \\\\
\\Delta \\theta_t &=& \\alpha {\sqrt{s_{t-1} + \\epsilon} \over \sqrt{g_t + \\epsilon}}~f'(\\theta_{t + {1 \over 2}}).
In its original formulation, the case :math:`\\alpha = 1, \\beta = 0` was
considered only.
.. [zeiler2013adadelta] Zeiler, Matthew D.
"ADADELTA: An adaptive learning rate method."
arXiv preprint arXiv:1212.5701 (2012).
"""
state_fields = 'n_iter gms sms step step_rate decay offset momentum'.split()
def __init__(self, wrt, fprime, step_rate=1, decay=0.9, momentum=0,
offset=1e-4, args=None):
"""Create an Adadelta object.
Parameters
----------
wrt : array_like
Array that represents the solution. Will be operated upon in
place. ``fprime`` should accept this array as a first argument.
fprime : callable
Callable that given a solution vector as first parameter and *args
and **kwargs drawn from the iterations ``args`` returns a
search direction, such as a gradient.
step_rate : scalar or array_like, optional [default: 1]
Value to multiply steps with before they are applied to the
parameter vector.
decay : float, optional [default: 0.9]
Decay parameter for the moving average. Must lie in [0, 1) where
lower numbers means a shorter "memory".
momentum : float or array_like, optional [default: 0]
Momentum to use during optimization. Can be specified analoguously
(but independent of) step rate.
offset : float, optional, [default: 1e-4]
Before taking the square root of the running averages, this offset
is added.
args : iterable
Iterator over arguments which ``fprime`` will be called with.
"""
super(Adadelta, self).__init__(wrt, args=args)
self.fprime = fprime
self.step_rate = step_rate
self.decay = decay
self.offset = offset
self.momentum = momentum
self.gms = 0
self.sms = 0
self.step = 0
def _iterate(self):
for args, kwargs in self.args:
step_m1 = self.step
d = self.decay
o = self.offset
m = self.momentum
step1 = step_m1 * m
self.wrt -= step1
gradient = self.fprime(self.wrt, *args, **kwargs)
self.gms = (d * self.gms) + (1 - d) * gradient ** 2
step2 = sqrt(self.sms + o) / sqrt(self.gms + o) * gradient * self.step_rate
self.wrt -= step2
self.step = step1 + step2
self.sms = (d * self.sms) + (1 - d) * self.step ** 2
self.n_iter += 1
yield {
'n_iter': self.n_iter,
'gradient': gradient,
'args': args,
'kwargs': kwargs,
}
|
BRMLREPO_NAMEcliminPATH_START.@climin_extracted@climin-master@climin@adadelta.py@.PATH_END.py
|
{
"filename": "5_wavelength_binning_individual_wvl_solutions.ipynb",
"repo_name": "JamesKirk11/Tiberius",
"repo_path": "Tiberius_extracted/Tiberius-main/src/reduction_utils/ACAM_utils/example_notebooks/5_wavelength_binning_individual_wvl_solutions.ipynb",
"type": "Jupyter Notebook"
}
|
Now we can make the spectroscopic light curves given all spectra are aligned and the wavelength solution has been calculated.
```python
%matplotlib nbagg
```
```python
import numpy as np
import reduction_utils.wavelength_binning as wb
import reduction_utils.wavelength_calibration as wc
import matplotlib.pyplot as plt
import pickle
```
First of all, I check what airmasses the data were taken at. If I have plenty of out of transit data, I will ignore data taken at airmass > 2. If not, I keep all data regardless of airmass.
I also check the sky background. If observations were taken at the end of the night, we sometimes observe into morning twilight and the sky background increases quickly and suddenly, which can introduce unwanted noise in the light curves.
Load in the airmass array:
```python
parent_direc = '/storage/astro2/phrgmk/Data/EFOSC/WASP-94A/reduction_26/'
am = pickle.load(open(parent_direc+'pickled_objects/airmass.pickle','rb'))
print(am[0],min(am),am[-1])
print(np.mean(am))
```
1.699 1.004 2.391
1.2810461215932913
Also, now load in the input data:
```python
wvl_solution_1 = pickle.load(open(parent_direc+'pickled_objects/improved_resampling/wavelength_solution_individual_1_3lines_gauss.pickle','rb'))
wvl_solution_2 = pickle.load(open(parent_direc+'pickled_objects/improved_resampling/wavelength_solution_individual_2_3lines_gauss.pickle','rb'))
f1 = pickle.load(open(parent_direc+'pickled_objects/improved_resampling/star1_flux_resampled.pickle','rb'))
f2 = pickle.load(open(parent_direc+'pickled_objects/improved_resampling/star2_flux_resampled.pickle','rb'))
e1 = pickle.load(open(parent_direc+'pickled_objects/improved_resampling/star1_error_resampled.pickle','rb'))
e2 = pickle.load(open(parent_direc+'pickled_objects/improved_resampling/star2_error_resampled.pickle','rb'))
mjd = pickle.load(open(parent_direc+'pickled_objects/mjd_time.pickle','rb'))
exp_times = pickle.load(open(parent_direc+'pickled_objects/exposure_times.pickle','rb'))
sky1 = pickle.load(open(parent_direc+'pickled_objects/improved_resampling/sky1_resampled.pickle','rb'))
sky2 = pickle.load(open(parent_direc+'pickled_objects/improved_resampling/sky2_resampled.pickle','rb'))
print(sky1.shape)
xpos1 = pickle.load(open(parent_direc+'pickled_objects/improved_resampling/xpos1_resampled.pickle','rb'))
xpos2 = pickle.load(open(parent_direc+'pickled_objects/improved_resampling/xpos2_resampled.pickle','rb'))
nframes = len(f1)
```
(477, 800)
First, we need to rescale the sky background to account for the differing exposure times.
We also only want to deal with a single sky array, so we take the mean of the sky arrays recorded at the locations of the 2 stars.
```python
sky1 = np.array([i/j for i,j in zip(sky1,exp_times)])
sky2 = np.array([i/j for i,j in zip(sky2,exp_times)])
sky1_norm = (sky1-sky1.mean())/sky1.std()
sky2_norm = (sky2-sky2.mean())/sky2.std()
sky = np.mean((sky1_norm,sky2_norm),axis=0)
```
Now I also standardise (subtract the mean and divide by the standard deviation) the x positions, and again combine into a single array for both stars. The standardisation is neccessary to help with the fitting process later on.
```python
xpos1_norm = (xpos1-xpos1.mean())/xpos1.std()
xpos2_norm = (xpos2-xpos2.mean())/xpos2.std()
xpos = np.mean((xpos1_norm,xpos2_norm),axis=0)
```
Now plot the white light curve to determine the positions of the contact points in frame number, and to check the noise in the light curve and whether I should cut any data out (due e.g. to clouds).
```python
plt.figure()
plt.plot(f1.sum(axis=1)/f2.sum(axis=1),'bo')
plt.ylabel('Flux')
plt.xlabel('Frame number')
plt.show()
x = f1.sum(axis=1)/f2.sum(axis=1)
for i in range(len(x)):
if x[i] > 1.420:
print(i)
print(x[i])
print(len(f1))
```
<IPython.core.display.Javascript object>
<img 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PiJiTstu4DjbgHeDwGRUkti3HvL3e++KDhhOqOgmqw8q9F/J6vGzUuDAbRooWoJyJ0S1mlhffr1a/ivVaudhdc03bubreNTQ1ZbK6I7fvv3twCQ4VBGADPcuFQtFQT0O0qXwZR7orqxJy7TmYpGyVAhq9V/JxkZBtCidZImIJMp48KRw7QcPr3rriLbtlk0lI9QNcc6wsBNID6gkRQCIROYP7/hHE79YVvu0R+w8+aJbNggsmmTSPv2Iromuk+f5j+OTaeQ3ZMY6uubvlW/F/RRUxrlyGPIGMmuAoGk9d9xNBYG0IJ6VALymn6o9PdyU8Z77imiu1uvu27nl+OTT4qceqoFgIhD3S94fU01yxnVyELEuMgeAqkkUPx9pvehVxr586rkfvuJnHuuyA9+0HC3+ogRTZe56PfKFVc0/S7UPN2zWMttqOPHoRf5dP09qv47TRQwgBatFYWAvKYfyv198uSGX8B1dQ3Hurhfqlq9UlPGms/PfmY2ZWyBKFBo8a/tah6SXby2MlAFCIIABBwCXj9mS32f6dKWSiN/YaEt/qyb3sbE8pCwWiDefKLov+Otkf+3YwD9M2uMCFtAXtMPV10l8stfihRPT5SqQvFC6cJ1hK++2jCVEeTRfP/1r+abUoLk5caocdUpHPfRDSpTpjSdail3SLa7wSSsg6yZ6rFpSWIhsJOAyY/ZwYPNvs+i5Dp2bIPhfPllkQULvN9UuEHMy+B650aKuAiE3X/HVQ+b92IALeiFKSCv6Qctpt9fxu5mD40ttZPYtOotW4oMGyYycGDD+pqFC8M5OsadUtHdyMuXN4xedu7cfA2PW85SHYprFnW65447GqZ7bB+memwJEp93Al4/ZufOFfn5z6M/gSCKdnBHAL0MbhTvJs/wCITZf4dXqurmhAG04B2mgEynH/wWN4yRMV1oXbwmp9xaQz/lcw2qn0XVlX5xh72phakeP61JWgg0ENDPqN4w9O67pYno536vvfQg/fQR0xmQmTNF/vCHhlmKco+ObA4d2vy0hvTVOLslDrP/TislDKBFy4UpoLDNi0W1GkNLTcUW5qtf9DffLDJ1asOB0n6eKM7yCttEDx8uotfl8UAAAuYE/J5Jap5z/Cl1M93WreblCLqmmKllc8ZBU4bZfwctQ9xxGECLFghTQGGbF4tqOaFqfHTat/CswXJ5Fn5ZrVolomtqKj3jxzffgWdbXo2PYrMIO4LDaBnyyAsB/Qzus48/k5QHNn6+R5haro4iwuy/q1Pi8N+CAbRgGqaATMxLtW7UUNM3Z46ITmMEeUxvLwmSt1dMuc0iXnGl/s5awCDUiMkzgaQfLRVX2+iMx9q13j+ovdZOcg5heC0YZv8dXqmqmxMG0IJ32AKqtNNVi3nmmSK6eLoaT5D1eYXlMrm9JKp6lPoFrVMx+pRbl1SpLKwFjKqlyDdrBG64QWTChKzVKpz66PeIbqIrdyi110ZAfpCG0w5uLmH33+GWrjq5YQAtOEchIK+drpUWV5eqih566nd9nuaT9i+bUmtotF6FX756pM5jj3kLgHuBvRmRAgJKAANYXgenny7yl780PfKqcC206TKgJP4gTeOaxSj677R9C2AALVosKgF53fQxaJBZoc86q2HU0DR9qVyT+GVjVvvKqZRxp04NNwV4PVll4FVv/g4BvwSYAvZLrCG9ros++OCGG0y8nko/SP0YMT9pK5UprWsWo+q/vdovSX/HAFq0RlwCMr0n8+23G9ac2OzKy+rol+mvbfd2FZPNMBZSIhQCmSDgdQRMJioZUSX0CjubH6R+jJiftF7mr9Rh3uUO1A/LdIbRBHH132GUPaw8MIAWJOMUkC4GrnRfZuGuM5sjZrI6+mXKhKNgLD4ghOaSgJoLm1mHXEIzqHSlZTl+No+US+sWQb/z9GB9Xa9Y6Yev3zWLYZlOA1RGSeLsv40KWIVEGEALyHELqNwH6sc/FunRY+etGrrurX9/fxVN+xpAr9qajgBm1QB78eHvELAhYDPrYPPerMZWuqLSy4gpEz3Tdc2aBjoHHmh2A4vXWa1+vkN1HbqfkcJqtGPc/Xc16uj1DgygF6EKf0+CgIrP4LvvvqYfbv0QT57ccO3S+vVm927m4T5ck2N33C9Npn8tPiSE5o6AfrbGjfO/G/iLXxT59NPc4TKqsC5F0asudRNg8ZWZpkZMz3bVUxBMd2l79QOmsyi/+Y3INdeUN51xDTYkof82avwIE2EALeAmSUBeUwBXXSWiu171qa+vXGmvG0AskCUq1OvMwFJX4CWqAhQGAgkjUGpWwrSIahT0IHndEMHTnIDeyb5jx85/d0fotm0TOeecaIhVMmd+jKfJHe3Fsy1RrxdMUv8dTet554oB9GZUNkVSBOQ1BeB+iHUkUD+I69Y1/RK56KKGWzT06dcvX/dXVuqwvKZALKRDKAQyR8BrbZlXhV0DUOozqSNgmzZ55ZDPv+vNS1Gb5lJLYUw2/Gi76Vr1adO826Zww2E11gsmpf/2JhNdCgygBdukCMj0l1jxQaT6a7vUlLHe7VtTYwEmZaHlNtR4TYGkrJoUFwKREfD6EVrpxaVGmYpHf048seGYlMIfr5FVJoUZ77KLyCefRFfwUqdBhL3Zp/AHQDXWCyal/46u1bxzxgB6M0r8CKDpWoziX1jV+JBZ4K1KqFfHFdf6lKpUnpdAICQCpj9Ci1/n50eW3xFG9+pMPQ+1WjcohYQzcdmUmp413UziVZnC71hNWynfML+PMYAiGEAvdVb4e1IEZPrl636IMT07G9UvOwu5EAqBzBIw/RFaDMDvemM/awwL8x41SmTSpMr41VzoWmmtCyONDazKGS7T700vwRf/ANCpbN1A5PWEcTpDUvpvr7pG+XcMoAXdpAjIa0dr8YfY9MMbxofMAm9VQk07rqweiF0VyLwk8wRMv1MUxPXXixx55M5jqvzusi+cHu7QoQGtHnpf+P937rzzHDuvH7war2WYM6fhqBL3HnO9RcnPNZr6PdulS0N5smAgK43Omn5vegnfr0l38wvj+zgp/bcXoyj/jgG0oJskAZXb0VrqQ2z64Q3jQ2aBtyqhph1XHsxwVYDzkkwSSPLVikE+46YxbmMWfs/qv2XhIOxKo7N++RSL/thjRU47beemQ+2/1HCbPmF8Hyep/zatd9jpMIAWRJMmoFLTI6U+xKYf3jA+ZBZ4qxLqNXqqheA8wKo0BS9JOQG9otKrE4/jsxTkB69pjNtkxd+zJtd1JrW5dYR2wICmI6h6mUDh+YNadj2TUM8VtH101HTLFpEPPzTLKSwNJa3/Nqt9uKkwgBY8kyggk7OTvExPmAttLfBWLdRrcfnIkSITJ1atOLwIAqklUGmtnX6v6I77ap8wEOQHr2lMsVkqbDjdeDJkSLqaUu8j1pkfvZNYp9H1/5Y6Ouzss73XVEZV88JrTm3ekcT+26Y+QWIxgEGofR6TZgH5mTK2QJSa0CR2XKmBR0Eh8DkB/XF5000Ntw/pqE65EbJqAgvygzdITHGdTE1kNVm473J3SMfxbpt36iaRMWNsctgZm+b+OxwC7AK24ph2AZlOGVtBSkGw1yLxvI2IpqDJKGICCZT6PmnXTuSKK0Suu65ho0VcT5AfvEFiCuvndxq5Wmz23Vdk993Tt1FFRyc3bAhPR2nvv8PQCyOAFhSzICCTKWMLRKkINf2lnoc1kaloMAqZOAJeV1HGMfVbDCnID94gMe57Tb9X4mhMPRLn1lsbpnjT8ui6St2lHdaThf7blgUG0IIgArKAl6BQ01/qedgVnaBmoSgpIZCmEfQgP3iDxGjTeU0jp6R5E1HMKNZh038zBWwlbgRkhS8xwaa/1BkBTEyTUZAEEeDzU74xyk0jJ6j5El2UvfYS+fnPRW64IbypX7fC9N8YQCvxIyArfIkJ9vqlzhrAxDQVBUkgAUbQKzeKn9tLyuWU1k0bYcm1a1eRsO+op//GAFrpEwFZ4UtUsO2C70RVhsJAoIoEbrxRZOxY7xfmeQS9eBq51PEqxQTdw6VLXU/n9+w879ZJdgo/d0ab1oT+GwNoqpWS6RCQFb7EBdss+E5cZSgQBKpAQI1N9+4i69dXfpmO4KxdG/40XhWqGNkrCk3hqlUi993XdGdu4eHSpdYhLlzYsCmivj6yIoaesWvkgpQ57JkY+m8MoJXAEZAVvkQGB13wncjKUCgIREzAdP1fmOe3RVyl2LIP8t1T6kdrmzbmt2pUu7LHHy/y/PN2bw1rJJn+OwEGcOnSpTJp0iRZsWKF1NXVyaOPPioDBw40Usizzz4rffv2laOOOkpWrlzZGGOS5wUXXCAPPvhgk/ecdtpp8sQTTxi9WxMhIGNUJIQABDJIgPV/8TdqsXE88cSGWzzeey/+skVRgrBOY6D/ToABXLRokaiR69Wrl9TU1BgbwA8++MCJOeSQQ2Tjxo1NDKBJnmoANW7GjBmNGt11112lbdu2xppFQMaoSAgBCGSQgOkIYFijNhlEGEmVTNdlRvLyiDMNS0v03wkwgIVaadGihbEBHDJkiPTo0UNatWolCxYsaGIATfJUA6gmUmODPggoKDniIACBLBBgB30yW1HbpWNHkXffTWb5gpSKNYBBqFWOSdRB0KYGUEftpk+fLsuXL5cJEyYENoBq/nbZZRdn1O+UU05x8tpX78kp82zbtk30P/dRA9itWzfZvHmztNGFFzyZIRBkPU5mKk9FIOCDADvofcCqYtJyt7NUsQihvYpdwKGhbJJR6gzgqlWr5KSTTpJly5bJoYceKuPGjQtkAOfMmSN77LGHHHTQQfL666/LtddeK3vuuac899xzzqhiqUffNV5XMxc9GMBoxBlXrqUWVkdxDlVc9eO9EAibADvowyYaTn6l2qVlS5EdO8LJP6pcdBymcPSycEd0WO9kBi9lU8Dbt2+X3r17y0UXXSRDhw51dBDUABaL6I033pCDDz5Y/vSnP8mAAQNKaowRwLA+esnNJw13miaXHiXLMwFGzZPZ+qU2iSxfLvLkkyITJiSzzPvtJ6KbPfS8RN3Q0qdP+EcIYQBTZgB1zZ5O1xaO0O3YsUPq6+udf1u8eLEzlVv4mE4ra0z79u2daeCf/OQnRp8KBGSEKTWJ0nSnaWqgUlAIQCCRBEzPcIyr8O3bi9x9t0hNTTQloP9OmQFUs/fKK680UcO0adNkyZIlUltb60zntm7dOpABXLdunRxwwAHOdPL3v/99I8UhICNMqUnEjsbUNBUFhQAEQiCQ9N3CuvavtjYaE0j/nQADuHXrVlm9erUj5Z49e8rkyZOlf//+0q5dO8eQjR49WtavXy+zZs0qKfdSU8BeeerfdS3foEGDpFOnTs4awFGjRsmWLVvkpZdeEj0OxuRBQCaU0pNmxAiRKVO8yxvWOVTebyIFBNJBgOnfdLRTcSlNz3GMq3Zh7/wtrAf9dwIM4NNPP+0YvuLn/PPPl5kzZ4oe17J27VrRdKWeUgbQK89//etfzmHTL7zwgnMUzP777y/f/OY35aabbpKOunfe8EFAhqBSkEw7sE6dGtaceD1hnUPl9R7+DoE0EGDTVBpaqXQZTWc99Mo5HYmL64niO5f+OwEGMC5BhfFeBBQGxWTkYfpFqOtS6urCX5CcDAqUAgL+CLBpyh+vpKU2PcdR70s49VTv0uv346ZNO9Pp6Qn/+lfDrSRB7v91c4pi1oX+GwPoregKKRCQFb5EBZtOhQwfLnLbbYkqOoWBQCwE2DQVC/bQX2pyjuMPfiBy4IEi69eXNnLuVK2u5tIdxvoj2d29u3ChiI4g6hPUBDICGHqzOxkm6hzAaKoYXa4YwOjYVjtn0xHAKL6Iql1X3geBMAjwmQmDYjLyMDnH0cQoltuxWyr/du3M7ivWMwE3bgx/1oX+GwNo9elDQFb4EhXsNRWihdXDSNesCf+LKFEgKAwEDAmYjppHMX1nWESS+SBgspHHxCiWe2Vx/vq/TaaV9e6FMWN8VMQwKf03BtBQKqWTISArfIkLLvcL1y3ovHkiZ5y/jr4eAAAgAElEQVSRuGJTIAjEQoARwFiwx/5SE6NoUkiTH91Rjf5p+ei/MYAmOi2bBgFZ4UtkcKlfuG5BuQ4ukU1GoWIi4NWBR3mER0xV5rUhE/D60f3ww9GcAYgBbGhI1gBaCBoDaAEvwaF63EGpkb4oLiRPMAaKBgFPAjbrwjwzJ0EuCNhMK9sAov/GANrohyFkK3rJDGZnYzLbhVIll0BcHXhyiVAyvwTCmlb2814MIAbQj16apUVAVvgSGcy6pkQ2C4VKOIE4OvCEI6F4CSdA/40BtJIoArLCl8hgdjYmslkoFAQgAIFQCdB/YwCtBIWArPAlMpgRwEQ2C4VKKAFG/hLaMBTLkwD9NwbQUySVEiAgK3yJDPZaA+gWev78nafbJ7IiFAoCERMotfZvv/1Epk3juKSI0ZN9CATovzGAVjJCQFb4Ehus5u7MMysXj0OhE9t8FKwKBMrdAey+euRIkYkTq1AQXpFZAlGPLtN/YwCtPjwIyApfYoOZBk5s01CwBBBglDwBjZDxIpQaXQ77HFb6bwyg1ccIAVnhS2wwG0ES2zQULAEETH8gtW8vUlfH1YkJaLJUFaHc6HLY57DSf2MArT4YCMgKX2KDTTu4p54S6dcvsdWgYBCIhIDpDyR9OZ+RSJogs5l6jS6HebsM/TcG0OqDhICs8CU2mCuuEts0FCwBBEx/IGlRZ88WOfvsBBSaIqSCgKm2wvhhQf+NAbT6UCAgK3yJDuaKq0Q3D4WLkYD+QGrXTuTDD70LEUZH7f0WUmSFgOnochg/LOi/MYBWnxsEZIUv8cFccZX4JqKAMRDQz8WgQd4vZqe8NyNSNCXACGB1FdGivr6+vrqvzM7bMIDZactyNYn6KILsE6SGWSLgtUbLrauu1aqtFampyVLtqUvUBKq5/Ib+mxFAKz0jICt8BEMAAikjYDpCM368yJgxKascxU0EgWotv6H/xgBaCR4BWeEjGAIQSBmBaq7RShkaihsigWosv6H/xgBaSRYBWeEjGAIQSBkB0xFANn+krGETWNyol9/Qf2MArWSPgKzwEQwBCKSMQDXXaKUMDcVNGQH6bwyglWQRkBW+1ARH/Us0NSAoKAREpFprtIANgSgJ0H9jAK30hYCs8KUiuBp3UqYCBIWEQAGBaqzRAjgEoiRA/40BtNIXArLCl/jgat1JmXgQFBACJQgwMo4s0kyA/hsDaKVfBGSFL9HBXuedhXknZaJBUDgIQAACGSRA/40BtJI1ArLCl+hgdjsmunkoHAQgAAErAvTfGEAEZEUgu8Gcd5bdtqVmEIAABDCAGECrTwECssKX6GBGABPdPBQOAhCAgBUB+m8MIAKyIpDdYM47y27bUjMIQAACGEAMoNWnAAFZ4Ut8MOedJb6JKCAEIACBQATovzGAgYTjBiEgK3ypCOa8s1Q0E4WEAAQg4IsA/TcG0JdgihMjICt8qQnmvLPUNBUFhQAEIGBEgP4bA2gklHKJEJAVPoIhAAEIQAACsRCg/8YAWgkPAVnhIxgCEIAABCAQCwH6bwyglfAQkBU+giEAAQhAAAKxEKD/xgBaCQ8BWeEjGAIQSCkB1sWmtOEodiMB+m8MoNXHAQFZ4UtVMB1eqpqLwkZIoNTO+K5dRaZOFampifDFZA2BEAnQf2MAreSEgKzwpSaYDi81TUVBIybgno1ZX9/0RS1aNPzv2lpMYMRNQPYhEaD/xgBaSQkBWeFLRTAdXiqaiUJWgYB7O866daVfpiZQRwLXrBFp1aoKBeIVELAgQP+NAbSQjwgCssKX+GCvDk8r0K0bHV7iG5IChkKA+7FDwUgmCSFA/40BtJIiArLCl/hgOrzENxEFrCKBhx4SOecc7xfOni1y9tne6UgBgTgJ0H9jAK30h4Cs8CU+2LTDGz5c5LbbEl8dCggBKwL8ILLCR3DCCNB/YwCtJImArPAlPti0w2vfXqSujnVPiW9QCmhFwF0SsX69SPEmEM2YNYBWeAmuMgH6bwygleQQkBW+xAdrh9epk8g773gX9amnRPr1805HCgikmcCoUSKTJpWugRpAdgGnuXXzVXb6bwygleIRkBW+VASPGCEyZYp3UVn35M2IFOkmUG5HvFurkSNFJk5Mdx0pfX4I0H9jAK3UjoCs8KUi2HQamBHAVDQnhQxIgB3xAcERllgC9N8YQCtxIiArfKkIZt1TKpqJQkZMgB9CEQMm+6oToP/GAFqJDgFZ4UtNsDv1pQUuXPzO7QepaUIKaknAdEc8SyEsQRNeNQL03xhAK7EhICt8qQoudR2c7v696y6RM85IVVUoLAR8E2AE0DcyAhJOgP4bA2glUQRkhS91wbrD8bLLRDZt2ll0vfpq6lTuP01dY1JgXwRYCuELF4lTQID+GwNoJVMEZIUvVcHcCZyq5qKwERDgCJgIoJJlbATovzGAVuJDQFb4UhPstQOSA3BT05QUNCABjoAJCI6wxBKg/8YAWokTAVnhS00w659S01QUNAICXj+A9JXduomsWcNtOBHgJ8uICNB/YwCtpIWArPClJpgdkKlpKgoaAQF+AEUAlSxjJ0D/jQG0EiECssKXmmA6wNQ0FQWNgAA/gCKASpaxE6D/xgBaiRABWeFLTTA7IFPTVBQ0AgL8AIoAKlnGToD+GwNoJUIEZIUvVcEcBp2q5qKwIRLgB1CIMMkqMQTovzGAVmJEQFb4Uhdc6jBoXfw+ZQrnAKauMSmwLwL8APKFi8QpIED/jQG0kikCssKXymAdDVm2TKSuTqRzZ5E+fdj5mMqGpNC+CZQ6CJ0fQL4xEpAQAvTfGEArKSIgK3wEQwACKSFQavR7v/1Epk3jKsSUNCHFLCJA/40BtPpQICArfARDAAIpIMAtOCloJIromwD9dwIM4NKlS2XSpEmyYsUKqaurk0cffVQGDhxo1JjPPvus9O3bV4466ihZuXJlY4xJnvX19TJ27Fi577775IMPPpCvf/3rMn36dOnRo4fRuzURAjJGRUIIQCCFBLwOgeYWnBQ2KkV2CNB/J8AALlq0SNTI9erVS2pqaowNoJo2jTnkkENk48aNTQygSZ633nqr3HLLLfLggw/KQQcdJDfccIO89NJL8sorr8huu+1m9BFBQEaYSAQBCKSUAEfApLThKLYnAfrvBBjAwlZq0aKFsQEcMmSIM1rXqlUrWbBgQRMD6JWnjv7tv//+cuWVV8pVV13lJN+8ebN07NhRZs6cKZq3yYOATCiRBgIQSCsBDoFOa8tRbi8C9N8pNYAzZsxwpmuXL18uEyZM8G0A33jjDTn44IPlhRdekGOOOaZRJzqdrP976tSpXtphCNmIEIkgAIE0E2AEMM2tR9krEcAAptAArlq1Sk466SRZtmyZHHrooTJu3DjfBlCNo675e+utt6SznuXx+XPmmWeKjkLOnTu3pG62bdsm+p/7qIC6devmjB62adOGTxsEIACBTBHgEOhMNSeVKSCAAUyZAdy+fbv07t1bLrroIhk6dKjTlNU0gPqu8ePHN/sQYQD5XoEABLJKYNQokUmTStdON4Ho+YA1NVmtPfXKKgEMYMoMoG78aNu2rbPuz3127NghuqZP/23x4sVyyimnNNFrqXWFQaeAGQHM6lcB9YIABEoRKHcEjJt25EiRiRNhB4H0EcAApswAqtnTXbqFz7Rp02TJkiVSW1vr7OZt3bq1pwF0N4HoBhDdCKKPiqFDhw5sAknf57jqJeY2kKoj54UxEPA6AkaLpDeBrFnDbTgxNA+vtCSAAUyAAdy6dausXr3aacqePXvK5MmTpX///tKuXTs54IADZPTo0bJ+/XqZNWtWyeYuNQXsladmpMfA/OIXv2hyDMyLL77IMTCWH6qsh5e6EaFrVxHdN8Q0WNZbP1/1YwNIvto7b7XFACbAAD799NOO4St+zj//fGc07oILLpC1a9eKpiv1lDKAXnlqPu5B0Pfee69zELRuLNHRRN1YYvogIFNS2UjHjQjZaEdqYUaAI2DMOJEqnQTovxNgANMpnYZSI6A0t56/sntNh3Ejgj+epE4+AUYAk99GlDA4AfpvDGBw9WAArdilLZjOMG0tRnltCXAEjC1B4pNMAAOIAbTSJwKywpeqYKbDUtVcFDYkAu6yB82uvn5npjrirQ9HwIQEmmyqToD+GwNoJToEZIUvVcGMAKaquShsiARKbXzS3b9TprDxKUTMZFVlAvTfGEArySEgK3ypCmY6LFXNRWFDJsDRRyEDJbvYCdB/YwCtRIiArPClLpjpsNQ1GQWGAAQgUJIA/TcG0OqjgYCs8KUyuNR0WPv2InfdJXLGGamsEoWGQEUCjP4hkCwSoP/GAFrpGgFZ4UttsC58v+wykU2bdlaBw6BT25wUvAIBDj5HHlklQP+NAbTSNgKywpfKYA6DTmWzUegABNB6AGiEpIYA/TcG0EqsCMgKX+qCOQw6dU1GgQMSQOsBwRGWGgL03xhAK7EiICt8qQvmKJjUNRkFDkgArQcER1hqCNB/YwCtxIqArPClLpjDoFPXZBQ4IAG0HhAcYakhQP+NAbQSKwKywpe6YEZFUtdkFDggAbQeEBxhqSFA/40BtBIrArLCl7pgDoNOXZNR4IAE0HpAcISlhgD9NwbQSqwIyApfKoM5DDqVzUahAxBA6wGgEZIaAvTfFgbw448/lt12261kY9fV1Unnzp1TI4SgBUVAQcmlO47DoNPdfpTenAD3AJuzImW6CNB/WxjAI488UmbPni3HHHNMk1Z/+OGHZejQobKp8JTcdOnCuLQIyBhV5hJyGHTmmpQKlSHATSBII4sE6L8tDOBll10mDzzwgIwfP16uvvpq+eijj+SnP/2pzJs3T26++WYZMWJEFjXTpE4IKPNNXLKCHJCbz3an1hCAQHYI0H9bGECVwWOPPSYXX3yxHHLIIaLTvnvuuaf85je/kaOOOio7KqlQEwSUi2ZuUkkOyM1fm1NjCEAgewTovy0N4I4dO2TYsGEyffp0+cIXviC///3v5bTTTsueUsrUCAHlpqkbK8rxGPlr8zzXmOnfPLd+tutO/21hAF9//XU555xzZMOGDXL//ffLM888I5MmTZIrrrjCmQL+4he/mG31iAgCynwTN6sgB+Tmr83zWuNSG0C6dhWZOlWkpiavVKh3VgjQf1sYwL322ku+853vyN133y377LOPo4nly5fLeeedJ/q3F154ISs6KVsPBJT5Jm5WQUYA89fmeawx61zz2Or5qjP9t4UB/M///E/50Y9+1EwxW7ZskeHDh8uvf/3rzKsJAWW+iZtVkANy89fmeasx61zz1uL5rC/9t4UBzKdkmtYaAeVTBRyQm892z0utGeXOS0vnu5703xYGcNasWWXV06JFi5Kjg1mTGwLKWoua14cDcs1ZkTJdBFjnmq72orTBCNB/WxjAtm3bNqH+6aefyj//+U/ZZZddZI899pD33nsvWKukKAoBpaixIigqOyQjgEqWsRNgBDD2JqAAVSBA/21hAEu1z6pVq+TSSy+VkSNH5uI4GARUhU8pr4AABKpKgHWuVcXNy2IiQP8dsgHUdvzrX/8q5557rrz66qsxNWv1XouAqseaN0EAAtUjwDrX6rHmTfEQoP+OwACuXLlSTj75ZOeMvKw/CCjrLUz9IJBfAqxzzW/b56Hm9N8WBvB3v/tdE43U19c718Hdeeed0q1bN1m0aFHmNYSAMt/EnhVkHaAnIhKkmAD6TnHjUfSKBOi/LQxgy5Ytm8DVnb/t27eXU045RX71q19J586dMy8/BJT5Jq5YQW5KyHf7Z732mL+st3C+60f/bWEA8y2dhtojoPyqgJsS8tv2eag5P27y0Mr5riP9NwbQ6hOAgKzwpTaYmxJS23QU3IAAP24MIJEk9QTov30awJ///OfGjT558mTjtGlNiIDS2nJ25eacNDt+RCeXAD9ukts2lCxcAvTfPg1g//79jVpA1wMuWbLEKG2aEyGgNLde8LJzU0JwdkQmmwA/bpLdPpQuPAL03z4NoKJ/44035MADD5TiTSDhNUt6ckJA6WmrMEtKJxkmTfJKEgF+3CSpNShLlATovwMYwFatWjnHvXTo0MFpm7POOktuv/126dixY5Rtlci8EVAimyXyQnFTQuSIeUFMBPhxExN4Xlt1AvTfAQygjvxt2LCh0QDutdde8re//U2+9KUvVb0B434hAoq7BeJ7PzclxMeeN0dHgB830bEl52QRoP/GAFopEgFZ4Ut9MDclpL4JqUAJAvy4QRZ5IED/HcAA6hSwjgDqoc/66Ajgiy++KAcddFAeNNOkjggod03erMI6YqLTZvqfPv36NfzXqhVsIJBeAvy4SW/bUXIzAvTfAQygTgF/+9vfll133dWh/Pvf/965/aN169ZNqD+i3yAZfxBQxhvYoHocmGsAiSSpJMBNIKlsNgptSID+O4ABvPDCC43wzpgxwyhdmhMhoDS3nn3ZOTDXniE5JJMA5i+Z7UKpwiNA/x3AAIaHP/05IaD0t2HQGnBgblByxCWdAKPaSW8hyhcGAfpvDKCVjhCQFb5UB3NcRqqbj8KXIcCoNtLICwH6bwygldYRkBW+VAdzYG6qm4/ClyDAqDayyBMB+m8MoJXeEZAVvlQHMwKY6uaj8CUIoGlkkScC9N8YQCu9IyArfKkO5sDcVDcfhS9BgFFtZJEnAvTfGEArvSMgK3ypD+bA3NQ3IRUoIMAIIHLIEwH6bwygld4RkBW+TARzYG4mmpFKiAij2sggTwTovzGAVnpHQFb4MhPMmWmZacrcV4RdwLmXQG4A0H9jAK3EjoCs8BEMAQgkjIAawEsuEXn33aYF23dfkXvvFampSViBKQ4EAhKg/8YABpROQxgCssKXmWBGADPTlLmuSLnRPxfKww9jAHMtkIxVnv4bA2glaQRkhS8TwdyakIlmzH0lOAMw9xLIHQD6bwyglegRkBW+1AezXir1TUgFPifADmCkkDcC9N8YQCvNIyArfKkOZsQk1c1H4YsIcAYgksgbAfpvDKCV5hGQFb5UBzNikurmo/BFBNAzksgbAfpvDKCV5hGQFb5UBzNikurmo/BFBDgDEEnkjQD9NwbQSvMIyApfqoMZMUl181H4EgS42QZZ5IkA/TcG0ErvCMgKX6qDGTFJdfNR+DIEuNkGaeSFAP03BtBK6wjICl/qgxkxSX0TUoESBDjXElnkgQD9NwbQSucIyApfJoIZMclEM1IJCEAgZwTovzGAVpJHQFb4MhPMiElmmpKKQAACOSFA/40BtJI6ArLCRzAEIAABCEAgFgL03xhAK+EhICt8BEMAAgkkwIh2AhuFIoVOgP4bA2glKgRkhS9zwXScmWvS3FWIu61z1+S5rTD9dwIM4NKlS2XSpEmyYsUKqaurk0cffVQGDhxoJMpnn31W+vbtK0cddZSsXLmyScxdd93l5LthwwY5+uij5Y477pDjjjuuMU2/fv3kmWeeaRLzk5/8RO6++26jd2siBGSMKvMJa2tFLrtMZNOmnVXt2lVk6lSRmprMV58KZoAAd1tnoBGpgjEB+u8EGMBFixaJGrlevXpJTU2NsQH84IMPnJhDDjlENm7c2MQAzp07V8477zzHzB1//PEyZcoUmT9/vrz22mvSoUMHRyBqAA899FC58cYbGwWzxx57SJs2bRCQMQESKoFRo0QmTSrNokULETWHmEC0kmQC3G2d5NahbFEQwAAmwAAWNmyLFi2MDeCQIUOkR48e0qpVK1mwYEETA6im79hjj5U777zTyX7Hjh3SrVs3GTZsmFxzzTWNBvCYY45xzGHQBwEFJZeduPnzRc48s3J9unUTWbNGpFWr7NSbmmSLADfbZKs9qY03AfrvlBrAGTNmyPTp02X58uUyYcKEJgbwk08+ER3Jq62tbTKVfP7554uOGi5cuLDRAP7973+X+vp66dSpk3zve9+TG264wYkt92zbtk30P/dRAamx3Lx5s6+RQ29pkiINBHTUpFMnkXfe8S7tU0/pqLN3OlJAIA4C3G0dB3XeGScBDGAKDeCqVavkpJNOkmXLljlTuOPGjWtiAN966y3p0qWLYw5POOGERn2NGjXKWfP3/PPPO/927733Svfu3WX//feXF198Ua6++mpnjeAjuhCmzKPvGj9+fLO/YgDj/BjH927TURMt4ezZImefHV9ZeTMEKhEw1TI/ZNBRVghgAFNmALdv3y69e/eWiy66SIYOHeroMKgBLBbxkiVLZMCAAbJ69Wo5+OCDS2qcEcCsfPTDqYfpqIm+jY4zHObkEg0B7raOhiu5JpcABjBlBlCncNu2beus+3MfXd+n07j6b4sXL3ZGB02mgItl+dFHH8mee+4pTzzxhJx22mlGqkVARpgym8h01KR9e5G6OtYAZlYIGakYd1tnpCGphhEB+u+UGUA1e6+88kqTxp02bZro6J2u+TvooIOkdevWzs5fnc7Vo1/00bgDDjhALr/88sZNIMUK0Z3Iah7/9re/yVe+8hUEZEQg34m8dk66dHSjyODB+WZF7dNBgLut09FOlNKeAAYwAQZw69atzrSrPj179pTJkydL//79pV27do5pGz16tKxfv15mzZpVssWLp4A1kR4Do5s+7rnnHscI6k7fefPmyauvviodO3aU119/XWbPni2nn3667Lvvvs4awBEjRkjXrl2bnQ1YSWYIyP5DmPYcyp2d5tZr5EiRiRPTXkvKnycCHGiep9bOb13pvxNgAJ9++mnH8BU/auBmzpwpF1xwgaxdu1Y0XamnlAHUdHoEjHsQtB73cvvttzsjg/q8+eabcu6558rLL78sOvWrO3l/+MMfyvXXX+9rNy8Cyu+XR2HNS42a6LTvXXeJnHEGjCCQHgKYv/S0FSW1I0D/nQADaNeE8UYjoHj5J+ntdJxJag3KEoQA18AFoUZMWgnQf2MArbSLgKzwEQwBCCSEANfAJaQhKEbVCNB/YwCtxIaArPARDAEIJICA12Ymvc5Q77XmNpsENBZFCI0A/TcG0EpMCMgKXyaDmQrOZLNmulKmxxlxlmWmZZC7ytF/YwCtRI+ArPBlLrjUGqr99hOZNo3NIJlr7AxVyPRAc26zyVCjUxWh/8YAWn0MEJAVvkwFex0Hc+WVIr/8ZaaqTGUyQuDGG0XGjvWuDCOA3oxIkR4C9N8YQCu1IiArfJkJ9lpD5Vb0qqtEJk3KTLWpSAYIqHa7dxdZv75yZXQN4Nq13GaTgSanCp8ToP/GAFp9GBCQFb7MBJuuodIKcytIZpo9ExUx1e748SJjxmSiylQCAg4B+m8MoNVHAQFZ4ctMsOkaKq0w9wJnptkzURFT7bL+LxPNTSUKCNB/YwCtPhAIyApfZoJNR1HcCrOWKjNNn/qKmGoXzaa+qalAEQH6bwyg1YcCAVnhy0ywrqPq1EnknXfMqsRoihknUkVPwF2/qmsA6+ubv48zAKNvA94QDwH6bwyglfIQkBW+TAXr2r4zzzSrEqMpZpxIVR0C7g52fVuhCVTzp09trUhNTXXKwlsgUC0C9N8YQCutISArfJkL1l2+v/qVd7XmzBE56yzvdKSAQLUIlDrDsls3kSlTMH/VagPeU10C9N8YQCvFISArfJkM1vP+Jk+uXLVWrUTUBA4enEkEVCqlBLjFJqUNR7EDEaD/xgAGEo4bhICs8GUy2HRRvVb+4YcZXcmkCFJYKcxfChuNIlsRoP/GACIgKwIEFxMwPVZD43SKbc0aDtdFRfESKDX9qwc/T53KD5R4W4a3R0kAA4gBtNIXArLCl8lgPyOACoANIZmUQWoqVe4KQzaApKYJKWhAAvTfGMCA0mkIQ0BW+DIZbHotnFt5joTJpAxSUSkvrXIETCqakUIGJED/jQEMKB0MoBW4jAfrqMqgQWaVZATQjBOpwidgOlqNRsNnT47xE8AAYgCtVIiArPBlOtjkSBjWAGZaAomunI7+jRsnMmGCdzEZpfZmRIr0EaD/xgBaqRYBWeHLbLDX1JpbcT04WjtXPRaGBwLVIlBq00eldzMCWK2W4T3VJED/jQG00hsCssKX2WDTqTUFsO++Ivfey27LzIohYRUrt+mjVDFZA5iwxqM4oRKg/8YAWgkKAVnhy2ywn6NgFIJ2tFy3lVk5JKZipiPTboHRZWKajoJEQID+GwNoJSsEZIUvs8F+RgBdCKwHzKwcElMxv7rUdayTJiWm+BQEAqESoP/GAFoJCgFZ4ctssN+RFhcEa60yK4lEVMzvyHT79iJ1daxRTUTjUYjQCdB/YwCtRIWArPBlOtjPUTAuCHZbZloSsVfO7wigFpgfJbE3GwWIiAD9NwbQSloIyApf5oPnzxcZMkRkxw6zqtLZmnEiVTACfvWob+FHSTDWRCWfAP03BtBKpQjICl8ugocPb7hT1etp00Zk0yaRXXbxSsnfIeCfgJ/dv4W586PEP2si0kGA/hsDaKVUBGSFLxfBfqbdunZtMIs1NblAQyWrRCDomlQ2JlWpgXhNLATovzGAVsJDQFb4chHsdr7r14vU11eush67oQ9HwuRCGlWr5JNPipx6qr/XcQSMP16kTh8B+m8MoJVqEZAVvtwE+5l+4/Dd3MiiKhXVHxP/7/+JbNli/joOJzdnRcr0EqD/xgBaqRcBWeHLVbAuwD/7bBEdETR5WHtlQok0lQiMGuX/HL9Bg0TmzuXoF5SVfQL03xhAK5UjICt8uQr2sxZQwbD7MlfyCL2y+oND75r2+/DDwy8x0qeVAP03BtBKuwjICl+ugv0ewktHnCt5hFpZHWXu1EnknXf8ZcumD3+8SJ1uAvTfGEArBSMgK3y5CvYzAqhrsDZuZBouVwIJsbJ+tOa+lk0fITYAWaWCAP03BtBKqAjICl+ugv2MyowfLzJmTK7wUNmQCKjOxo0TmTDBPMNWrUTmzBEZPNg8hpQQSDsB+m8MoJWGEZAVvtwFm6zL0pGYsWNFrr+eEWm0epwAACAASURBVMDcCcSywrrb/IorRNat858RSw78MyMi3QTovzGAVgpGQFb4chlsujOTozhyKY/AlfZz1FCpl7DpKDB6AlNKgP4bA2glXQRkhS+3wXrMxjnneN8RzLqs3ErEV8WD3vRR+BJGAH0hJ3EGCNB/YwCtZIyArPDlNtjPIn12ZuZWJsYV96OnUpm2by9SV8eSA2PgJMwEAfpvDKCVkBGQFb7cBnMkTG6bPpKK+9VTcSGGDxe57bZIikamEEgsAfpvDKCVOBGQFb7cBvsdsWF9Vm6lYlRxv3oqzpTpXyPMJMoYAfpvDKCVpBGQFb7cBvtds0UHnVupGFXc1dP69SL19UYhjYlYYuCPF6mzQ4D+GwNopWYEZIUv18G6a1PvXfV66KC9CPF3JeDuAtb/38QE6gYjfWprRWpqYAiB/BGg/8YAWqkeAVnhy3Wwjtp07Cjy7ruVMejZgRzQm2upGFdezdxll4ls2uQdoj8spkzB/HmTIkVWCdB/YwCttI2ArPDlOth03RbTv7mWiXHlvcyfnit5+eUihx0m0rmzSJ8+7Po1hkvCTBKg/8YAWgkbAVnhy3Ww6c5NNoDkWiZGlTc5XJwpXyOUJMoRAfpvDKCV3BGQFb5cB5uOAF57rYie06b/denCyE2uRVOi8ibXC7phagK7dhVZs4bRP3QEAfpvDKDVpwABWeHLdXDQnZvagU+dytqtXIvn88qrhjp1EnnnHX80WFbgjxeps0mA/hsDaKVsBGSFL/fBfnduFo7ksHsz9/IR01HkYlIsK0A7EBCh/8YAWn0OEJAVPoI/P77jiitE1q3zh0NHAteuZSrPH7VspTZdR1pca0YAs6UDahOMAP03BjCYcj6PQkBW+Aj+nICO5p1xhn8c48eLjBnjP46IbBDwOwLIGsBstDu1CIcA/TcG0EpJCMgKH8EiYnoeYDlYDz/MesC8CsnvjTJqAFk6kFe1UO9iAvTfGECrTwUCssJHsIg8+aTIqacGR8FNIcHZpTVSjZ+O/k2bJvKHP4h88olZTUaOFJk40SwtqSCQdQL03xhAK40jICt8BIvIDTeITJhgh4I1XXb80hStG4cuucT7BplSdeLHQppamrJGTYD+GwNopTEEZIWP4JAMILs68yEl0/ujK9Hgx0I+tEItvQnQf2MAvVVSIQUCssJHcAhTwAqRTj37UtJp3+7dRdavt6srPxbs+BGdHQL03xhAKzUjICt8BH++CWSffUS2bg2GY7/9RDZs4DiYYPTSE+V3x2+5mvFjIT1tTkmjJUD/jQG0UhgCssJH8OcEbrxRZOzYYDj23FPkwQfZCRyMXnqigp75V1hD1gCmp70pafQE6L8xgFYqQ0BW+Aj+nIBO79mMAmo2HAeTbTmFMQKIRrKtEWrnjwD9NwbQn2KKUiMgK3wEFxCwGQXUbLgZJNtymj9f5Mwzg9WxVSuROXNEBg8OFk8UBLJIgP4bA2ilawRkhY/gAgK2B0JrVqzvyqak/B74XExh3rxgN81kkya1gkADAfpvDKDVZwEBWeEjuIiA7TEf7PDMpqSCTv/uu6/IvfeyPjSbqqBWtgTovzGAVhpCQFb4CC5BwOagX0YAsykpvxtA2rUTueIKkeuuY3d4NhVBrcIgQP+dAAO4dOlSmTRpkqxYsULq6urk0UcflYEDBxq177PPPit9+/aVo446SlauXNkk5q677nLy3bBhgxx99NFyxx13yHHHHdeY5uOPP5Yrr7xS5syZI9u2bZPTTjtNpk2bJh07djR6N0PIxphI6JOATvndfLPI1Kki771nFswaQDNOaUxlOgJ4/fUiAwaI9OmD8UtjO1Pm6hLAACbAAC5atEjUyPXq1UtqamqMDeAHH3zgxBxyyCGycePGJgZw7ty5ct5558ndd98txx9/vEyZMkXmz58vr732mnTo0MFR2aWXXiqPPfaYzJw5U/bee2+5/PLLpWXLlk5ZTB8EZEqKdEEIqBFctkxk4UKRKVMq58AOzyCE0xHjrgHUQ6Dr65uXuUWLhk1Aa9Zg/NLRopQyCQTovxNgAAuF0KJFC2MDOGTIEOnRo4e0atVKFixY0MQAquk79thj5c4773Sy37Fjh3Tr1k2GDRsm11xzjWzevFnat28vs2fPlsGfb4179dVX5YgjjpDnnntOevfubaRPBGSEiUQhECg3Nbz77iJXXSXSt6/I22+LdO7MCFAIuBOXhba/flUVG0A1f/rU1rLWL3GNRoESTYD+O6UGcMaMGTJ9+nRZvny5TJgwoYkB/OSTT2SPPfaQ2traJlPJ559/vuio4cKFC2XJkiUyYMAAef/992UfPYDt86d79+4yfPhwGTFihJFwEZARJhKFREBHgm66SeTWW0U+/rh8pl26iFxyiUiPHhjCkNDHnk25HwBs9Ii9aShASgnQf6fQAK5atUpOOukkWbZsmRx66KEybty4Jgbwrbfeki5dujjm8IQTTmiU5qhRo+SZZ56R559/3hn5u/DCC521f4WPrhHs37+/3Ko9bIlH0xfGqIB0ZFFHFNu0aZPSjwHFTguBoLuEdXpQ1xPW1KSlppSzkEC50T83DdP/6AUC/glgAFNmALdv3+5Mz1500UUydOhQp8WraQD1XePHj2+mNAyg/w8fEf4I6Ohf9+4iug7M78M0oV9iyUivba4bQPQA6HKbgVj/l4y2ohTpI4ABTJkB1Cnctm3bOuv+3EfX99XX1zv/tnjxYmd0MKopYEYA0/chz0qJTXeClqsvRiFdStBRPz3KZd06s3JzBJAZJ1JBwCWAAUyZAVSz98orrzRRsB7domv6dM3fQQcdJK1bt3Z2/up0rh79oo/GHXDAAc5O38JNIA899JAMGjTISaM7hA8//HA2gfD9kEgCfs+CK1cJjEIim7dJobymfEvVgEPAk9+ulDBZBDCACTCAW7duldWrVzvK6Nmzp0yePNlZh9euXTvHtI0ePVrWr18vs2bNKqme4ilgTaTHwOimj3vuuccxgnoMzLx580R3+rrn/OkxMI8//rhzDIyu39Mdwvro2kHTBwGZkiKdLQHbEUD3/RgF25aINj7otW8Y+2jbhdyzR4D+OwEG8Omnn3YMX/GjBk7N2QUXXCBr164VTVfqKWUANZ0eAeMeBH3MMcfI7bff7owMuo97ELSOAhYeBN2pUydjpSMgY1QktCRgswaw8NUYBcuGiDg8iNHv1o0zACNuFrLPIAH67wQYwDTrCgGlufXSV/agu4DdmnJbSPLbPMhU//z5DWcE8kAAAuYE6L8xgOZqKZESAVnhIzgAATWB//7vlc8BLJetnlR0//0iZ5wR4MWEVIXAkCG6hMXfqxjV9ceL1BBQAvTfGECrTwICssJHcEACn3wi0r69foEFy2DkSJGJE81i3evo6uo4VNqMWPBUOpKnR774fVjX6ZcY6SGAAVQNtKjXM1R4AhHAAAbCRlAIBILsFC18rcm0YamjSNq1azie5LrruHfWthkLzbXe6KEjs0FMPSOAti1BfB4J0H9jAK10j4Cs8BFsSUBNnE4Z7tjhP6O99xa56y4RvTauT5/mZs7LYHIFmX/mboQav5tvbridpdwBzya5c7ajCSXSQKA0AfpvDKDVZwMBWeEj2JJAkB2jpV5ZfFWcn6NIuIbMXyOWu9PXXy4NqdUA1tZyxV8QdsRAgP4bA2j1KUBAVvgItiQQZMdoqVcWXxXnx1jqSODGjUwHmzSl7S7uwnfo0S9TpmD+TLiTBgKlCNB/YwCtPhkIyAofwZYE/Bg1r1cVTifOmydyzjleETv/rtdjjxljnj6PKf2Mqlbio+fV19SUnrbPI1fqDIGgBOi/MYBBtePEISArfARbEnBNxfr1ImFt5dINBfqUOJu9bGkZBfRuyDDMup/d294lIgUE8k2A/hsDaPUJQEBW+AgOgYDXZg2/r9AjRfQokg4d/G1QYCdqZdK20/Xf+Y7IH/7gtzVJDwEIlCNA/40BtPp0ICArfASHREBN4M9+JqIjgbaPGjndmTpokL+cfvObhh3FnBfYnJuO1N5xh8iIEf6YFqbWcx+VbatWwfMgEgIQ2EmA/hsDaPV5QEBW+AgOkYCajJtuEtH1eEEfNRn/+78ihx4qsm6dv1z0lpHCM+yKdxYX5panw6VLnaXoj+zO1IyyBiVHHASaE6D/xgBafS4QkBU+giMgMGqUyKRJwTPebz+Rd94JHu9GujuL9Vozd/Sqc+eGvHUkrNBgVjKL9iWJL4copufPPju++vBmCGSJAP03BtBKzwjICh/BERHQs+EuvTQcI2dbxJYtvQ+qLj6GxvadSYgPa9dvYV0YAUxCy1KGrBCg/8YAWmkZAVnhIzhCAmpALr5YZObMCF8SYtZZu9UijF2/haOpOkq6Zg1rAEOUHFnlnAD9NwbQ6iOAgKzwERwxATWBHTuKvPtuxC8KMfvbbhPRs+7Svtlh+PCGq97CeLjxIwyK5AGBpgTovzGAVp8JBGSFj+AqEAjz9okqFNd5RdrXBNquwyzkzJ3L1VId78kbAfpvDKCV5hGQFT6Cq0QgzPtnq1Tk1N5zO39+wzmKYT1/+pPIgAFh5UY+EICAS4D+GwNo9WlAQFb4CK4iAfeYmBtvDO/WkKiLr/fdpmndmzLu1CmczTdZWxMZtVbIHwJ+CdB/YwD9aqZJegRkhY/gGAjoDuEzzojhxQFfGXQELI6zBsPa+JHFXdEBm58wCERGgP4bA2glLgRkhY/gmAiEeThx1FXYbTeR3/5WpKbG/E2l6leNdYV6vuGUKeblLJdSRz41Hz91tn8rOUAgXwTovzGAVopHQFb4CI6RgI6Q3XyzyNixMRbCx6sfftjMEJU7fDnqUbWgI6tqcK++WqRPH5G33xbRw7L1/0/7LmgfTUtSCMRCgP4bA2glPARkhY/gGAmoAezePZz7g6tRDR3BW7u2sjEyOXzZJB+3PqbTyLrxY8gQ7wOvS3HyU55qcOYdEMgLAfpvDKCV1hGQFT6CYyQQ1nq1alZB1wPqyFhdXemRMtM66X3JY8ZULrnpNHIYx+xww0c1VcS7INBAgP4bA2j1WUBAVvgIjpFAWOvVqlmF1q1FPvpo5xu7dBG5/fadU8MPPSRyzjlmJao0pex1h68ayB49RDp0EDnrLPuDtmfPFuGOX7N2IxUEwiJA/40BtNISArLCR3BMBGxGrUaOFFGjtW5dTIUv8VrXzJmOAGoW5Y6YMZlGDrvmjACGTZT8IOBNgP4bA+itkgopEJAVPoJjIGBjcPR6M72qzV0bt3ChyAMP6FRKDBUpeGW7dg2m9JlnGsr3r3+ZlaeU8fJjIs3eUjlV2s46DKPO5AGBJBCg/8YAWukQAVnhIzgGAjYGp9gwqRHU/JYsEfm//2u4wu3++xumROvrY6icz1eWmnqt5tQ4d/z6bDCSQyBEAvTfGEArOSEgK3wEx0DAzzo5t3ilbqUot0lC17L98pcNkUk3gaUMbVg3eXg1LXf8ehHi7xCIlgD9NwbQSmEIyAofwTEQ8DsCWOr8PK+z9q66SkRH19av919BPZfw009FJk8W+fhj//GmEW3aiEybJqIbSdxz9558UuTUU01zCJZOp6uvuELkuus46y8YQaIgEA4B+m8MoJWSEJAVPoJjIOCuAVRzZjJCV3wrhcZ37Fh+56s7WvjrX4t885vmFSx8j1+Tav6W0il16lpHLrXM771nm1vzeHfXMIc8h8+WHCEQlAD9NwYwqHacOARkhY/gmAi4I3j6+kITqOZN/3clw3LjjWa3h1x/vciECd4VvPxykUGDmt5+EWSa2vtN1U+ho4wzZpjdYFL90vFGCOSbAP03BtDqE4CArPARHCOBUmv4vO6g1dE/PfvOZJTM1ACW2olbjanYqNGr+du0SWSXXaJ+E/lDAAJBCNB/YwCD6KYxBgFZ4SM4ZgKmV525xfQzNau3dlxwQcM6wFJTzaU2luh71Jj+7GfB1g/GjLPJ603vLk5SmSkLBPJEgP4bA2ildwRkhY/glBEwnZrVHa4bN4roOYGDBzdUsniqWf+ttrbp9KjXDRxpwaWjfzpKqtfW8UAAAskkQP+NAbRSJgKywkdwygiYjgAW3rVrOtVsc0B1EjFyu0cSW4UyQWAnAfpvDKDV5wEBWeEjOGUETHYQu6N/haNfJlPNpuYyLci43zctLUU580qA/hsDaKV9BGSFj+AUEii3g9itStC1b6bTy3Eic3dJm5SBEUATSqSBQHwE6L8xgFbqQ0BW+AhOKQHTaV0/1UvDCKAe4uy1A7rc5hY/LEgLAQhET4D+GwNopTIEZIWP4BQTMJnW9VM9P0fM+Mk3rLRnnSUyd65ZbkFHQc1yJxUEIBAGAfpvDKCVjhCQFT6CIdCEgOkh09XEtt9+DVfGffaZyDnneL95+HCR227zTkcKCEAgXgL03xhAKwUiICt8BEOgCQGva+aqjUvPMbz//objXEynqFn7V+1W4n0QCEaA/hsDGEw5n0chICt8BEOgGQGvswCvvLJhhG3HjsrwdL3e0KEi//EfwSEX7uT12gHN2r/gnImEQBwE6L8xgFa6Q0BW+AiGQEkCXptM9ADpM86oDE/X4akJ7N8/OOTi0bxKdyjrW4oPtg7+ZiIhAIGoCdB/YwCtNIaArPARDIGyBLw2magZu+QSkXffbZqFnkN4770NN4zYbCxp316krq75bR5e5pQmhQAE0kGA/hsDaKVUBGSFj2AIWBFQg6dr8/Q/ffr1a/iv8BDqoBtL5s/feY1dcSG9zKlVpQiGAASqQoD+GwNoJTQEZIWPYAhETiDIxhI98mXOnMiLxgsgAIEYCdB/YwCt5IeArPARDIGqEPDaWFJYCF03+Pbbzad+q1JQXgIBCFSNAP03BtBKbAjICh/BEKgagVJr90q9nEOcq9YkvAgCsRKg/8YAWgkQAVnhIxgCVSXgrt1buFDkt78V2bRp5+u7dROZMqVh8wgPBCCQfQL03xhAK5UjICt8BEMgNgJs5IgNPS+GQCII0H9jAK2EiICs8BEMAQhAAAIQiIUA/TcG0Ep4CMgKH8EQgAAEIACBWAjQf2MArYSHgKzwEQwBCEAAAhCIhQD9NwbQSngIyAofwRCAAAQgAIFYCNB/YwCthIeArPARDAEIQAACEIiFAP03BtBKeAjICh/BEIAABCAAgVgI0H9jAK2Eh4Cs8BEMAQhAAAIQiIUA/TcG0Ep4CMgKH8EQgAAEIACBWAjQf2MArYSHgKzwEQwBCEAAAhCIhQD9NwbQSngIyAofwRCAAAQgAIFYCNB/YwCthLd582bZZ5995M0335Q2bdpY5UUwBCAAAQhAAALVIaAGsFu3bvLBBx/I3nvvXZ2XJuwtLerr6+sTVqbUFGfdunWOgHggAAEIQAACEEgfAR3A6dq1a/oKHkKJMYAWEHfs2CFvvfWW7LXXXtKiRQuLnJqHur9OGF0MFatnZnD3RBRZAthHhtYzY9h7IookAdwjwWqUqY59bdmyRfbff39p2bKlUUzWEmEAE9qirE+Ip2HgHg93fSvsYR8fgXjejObj4c5bGwhgABOqBL4Y4mkYuMfDHQMYH3fYx8ee75v42PNmDGBiNcAXQzxNA/d4uGNC4uMO+/jY830TH3vejAFMrAa2bdsmt9xyi4wePVp23XXXxJYzawWDe3wtCnvYx0cgnjej+Xi481amgNEABCAAAQhAAAIQyCUB1gDmstmpNAQgAAEIQAACeSaAAcxz61N3CEAAAhCAAARySQADmMtmp9IQgAAEIAABCOSZAAYwz61P3SEAAQhAAAIQyCUBDGACm/2uu+6SSZMmyYYNG+Too4+WO+64Q4477rgEljQ9RVq6dKnDdMWKFVJXVyePPvqoDBw4sLECeir82LFj5b777nPuhvz6178u06dPlx49ejSmee+992TYsGHy+9//3jk5ftCgQTJ16lTZc8890wOiyiXVneyPPPKIvPrqq7L77rvLiSeeKLfeeqscdthhjSX5+OOP5corr5Q5c+aI7oo87bTTZNq0adKxY8fGNP/3f/8nl156qTz11FMO7/PPP9/ZJf+FL3yhyjVKz+tUv/rf2rVrnUJ/+ctfljFjxsi3v/1t53/DvTpt+Ytf/MI5zeGKK66QKVOmwL462HmLAQEMoAGkaiaZO3eunHfeeXL33XfL8ccf73xhzJ8/X1577TXp0KFDNYuSqXctWrRInn32WenVq5fU1NQ0M4BqStRQPPjgg3LQQQfJDTfcIC+99JK88sorsttuuzkstONU83jPPffIp59+KhdeeKEce+yxMnv27EyxCrMy3/rWt2TIkCEOp88++0yuvfZaefnllx2urVu3dl6lxu6xxx6TmTNnOpeyX3755Y7B1vbSZ/v27XLMMcdIp06dHBOvbaCfkR//+MfyH//xH2EWN1N56Q+VVq1aOT9i9AeOalv5vfDCC44ZhHv0zf2Xv/xFzjzzTGnTpo3079+/0QDCPnr2vMGbAAbQm1FVU6jp087yzjvvdN6r9w1369bNGXm65pprqlqWrL5M720uHAHUzlHvg9RRqKuuusqp9ubNm50RKDUlamD+8Y9/yJFHHin6hf61r33NSfPEE0/I6aefLuvWrXPiebwJbNq0yfkh88wzz8jJJ5/scG7fvr1jogcPHuxkoKOFRxxxhDz33HPSu3dvUfP+3e9+17l32x0V1B9IV199tWh+u+yyi/eLSeEQaNeunWMClTXcoxXF1q1b5atf/aozmj1hwgTnR4z+oEfz0XInd3MCGEBzVpGn/OSTT2SPPfaQ2traJtOTOt2l05ILFy6MvAx5eEGxAXzjjTfk4IMPdkZG9Evaffr27ev8b53mfeCBBxyD+P777zf+XUe0dHRQR2h/+MMf5gGddR1Xr17tjEjp6OpRRx0lS5YskQEDBjhc99lnn8b8u3fvLsOHD5cRI0Y405a/+93vZOXKlY1/X7NmjXzpS1+S//mf/5GePXtalyvrGegoqupUv0tU57q8BO7RtrqyVsN92223Sb9+/RoNIJqPlju5mxPAAJqzijyljnB06dJFli9fLieccELj+0aNGuWMmDz//PORlyEPLyg2gMpb1/wp/86dOzci0KkbTavT8jrVqFNoOhVf+Oho1vjx453pNJ7KBHQ0+/vf/77zY+bPf/6zk1hH/nQqXdf+FT665lWnzHRq/pJLLpH//d//lf/6r/9qTPLPf/7TmUJ+/PHHG9e0wb85ATXa+l2i6/107aTy1lFruEerFl3PevPNNzszBvojsdAAwj5a9uRuTgADaM4q8pQYwMgROy/AAFaHc/Fb1CTrdK6av65du2IAq9AMOqugG2h02lFnFu6//37nx6SOpmK8o2mAN99801km8sc//lG+8pWvOC/BAEbDmlztCGAA7fiFGs0UcKg4y2bGFHB1OBe+RTd26BIG3Y2tm2zch+mw6rbFqaee6ix3OOuss5gCjgj9ggULnCUhugHHfXQKXr93dHOTjmRrO7DsIaIGIFtjAhhAY1TVSaibQHT6S49+0UenzQ444ABnZySbQMJpg3KbQHQDiK7z0+fDDz90NisUbwL561//6uwk1mfx4sWiu1zZBFK+XXSDjW5g0k03Tz/9dJNjdTTKXRD/0EMPOcfq6KPT7IcffnizTSC6+9fdCX/vvffKyJEj5e2335Zdd901HGHkIJdTTjnF+T7Rda26CQTu4Tf6li1bnCULhY+OtqqmdeOSbuqDffjcydE/AQygf2aRRuh6M108rEeNqBHUXWPz5s1zdkYWnosWaSEymLnuyNMNCPropoHJkyc7a8x0kbZ2iLrWTM/rKjwG5sUXX2x2DMzGjRudI3rcY2B0qodjYMoL5rLLLnP46Ohf4dl/etyLnguoj04N61o+Ndt6XIYaRn10baY+7jEwutN64sSJzgaGH/3oR3LxxRdzDEyFz6qePadHF6m+1ZRoO6jOdQTqG9/4Btyr+D1XOAWM5qsInldVJIABTKBA9AgY9yBo3YV6++23O2cC8gQnoKNPaviKHzXbajzcg6B1ZEk3KZx00knO8Q2HHnpoY4geBK0jsYUHQWvbcBB0+XbR0dZSz4wZM+SCCy5w/uQeSKyjUYUHQeu5f+6jIypqFLUddfOHtpsadg6CLs/+oosukieffNI5N1ENt65H0xEoNX9wD/5dEiSy2ACi+SAUiQmbAAYwbKLkBwEIQAACEIAABBJOAAOY8AaieBCAAAQgAAEIQCBsAhjAsImSHwQgAAEIQAACEEg4AQxgwhuI4kEAAhCAAAQgAIGwCWAAwyZKfhCAAAQgAAEIQCDhBDCACW8gigcBCEAAAhCAAATCJoABDJso+UEAAhCAAAQgAIGEE8AAJryBKB4EIAABCEAAAhAImwAGMGyi5AcBCEDAgsBnn30mX/ziF50Dx7/73e9a5EQoBCAAgfIEMICoAwIQCJ2A3vKh1+oVP6tWrZJDDjkk9PdlKUMMYJZak7pAILkEMIDJbRtKBoHUElADqPcm65VvhU/79u2lVatWzer1ySefyC677JLa+oZZ8KgMIIzDbCXygkD6CWAA09+G1AACiSOgBlDvVF6wYEHJsuldy7169XLuYP7tb38rX/3qV+WPf/yjcwe2jhy+8cYbsu+++8oPfvADufXWW537f/W5//775ZprrpEHHnhArrrqKlm3bp1873vfc+5z1ruEx48fL1u2bHHuCv7Vr34lLVu2dOL07tVrr71W5syZI5s3b5Z/+7d/k4kTJ8rJJ59csnyuCfv1r3/t1OFPf/qTdO3aVW677Tb5zne+06Qs77zzTmMetbW1MmTIENF4fa6//np54okn5Cc/+YnceOONDhNlM2XKFKeu+n937NghV155pXNPrz7uu++++255+OGHZdmyZdKlSxcn/Q9/+MPGd+n9yBqnZVNT3adPH+fe8AMOOMBJc+655zr11juAp0+f7txZrSOwPBCAAASUAAYQryHvRQAABdZJREFUHUAAAqETMDGAf/vb3+Tyyy+XCy+80DGChx12mGOwevbsKQceeKC8/vrrcumll8q3vvUtx9i4BvCnP/2p9O/fX37xi184Zq6mpka+9rWvSbt27eSGG26Q1atXy+DBgx1DOGjQICdO36H/fsstt0jnzp1Fjdq4cePk73//u3zpS19qVn/XhHXr1s0xXmpWtWy/+c1vRI3XPvvs02hGvQzg1KlTHdM4ZswYx4CdccYZcsopp8gRRxwhQ4cOlaVLl8oll1wif/3rX533uO/eb7/9HPP79a9/3THFWo5XXnlFevToITqapya2b9++MmzYMMcAqsF86aWXRLl+4QtfcAzgwoULHRYjR450GH/5y18Ova3JEAIQSCcBDGA6241SQyDRBNQAqlnabbfdGsv57W9/W+bPn+/8bx0B3LZtm/zlL3+pWA8dsRs+fLhs2LCh0QD++Mc/lrVr10r37t2df7v44otl7ty5Thp3pPDUU0+Vww8/XO68805Zs2aNY5rWr18vHTt2bHxfv379nBFANU7Fj2vC1CSOHTvW+fOHH34oe++9tzNSqfm7o5EmBrC4bFp+NYMtWrRw8tZ1kWoGdVTTfbea4zvuuKOxaGpyTzzxRMcM64jnL3/5S3n55Zcb/648tXyPP/64YzDVAD711FMOK91UwgMBCECgkAAGED1AAAKhE1ADqIZLpx7dR82Zjr65BlBHsAr/rv++ePFiZ2Tv1VdfdQzX9u3bnWlM/W/XXXd1TJdOe+rIn/tcd9118oc//MEZ+XKff//3f5dPP/1U5s2b54yCDRw4sNEcumnUMJ155pnOFHQ5A/jII480mXbVOtx3331yzjnnGBtA3c1bXLatW7c65XIfHeXT/3Ra2jWAWi59j/voSJ9yUQM6YsQIxxwWGmxN989//lPuueceUZOsBvDdd9+VRYsWhd6+ZAgBCKSfAAYw/W1IDSCQOAImU8C9e/d2RrHcR6d8jzzySGdaWI1Z27Zt5ZlnnnGmR3Vdn65hKzXq5q6z0ylU93HXv+lUrxopnQLW6V53xM1Np3l26tSprAEsPopF0+vaPM1f1yHq1KqaLPfRaecf/ehHzdYAliubG6cjoi4PEwOoBk+ng0vttNaNNjoSWMggcQKhQBCAQOwEMICxNwEFgED2CAQxgDqNq5s3dLTPfXQK1t3YEdQA/uMf/3CM5fLly+WEE04wgl1uJ26hAVRzqCOLH330UeNInG7k0M0nxZtAghhAHfFz1z5qoY899lin/PpvOnKqawp1s8xee+1Vsk4YQKOmJhEEcksAA5jbpqfiEIiOQBADuGLFCmczh05tnn766c7u19GjR0tdXZ3VCKDWUnfm/vd//7dMnjxZjj76aNm0aZOze1Z3H+smk+LHxABqHrpZRXf46saU5557TkaNGiVvv/12KAZQR/J0SljX/c2aNcuZGtdRTN0so1PIxxxzjLMOUg2y7hLWtX66a1inxHWqHQMYnb7JGQJZIIABzEIrUgcIJIxAEAOoVdApYR1B0zV+uknjrLPOco5NsZkC1nx11+xNN93kbEzRtYlqrnTKVTeAlNoZa2IANV81XDrq99Zbb8k3vvEN0Y0uOoUdxgigjvLpFPaf//xn2X///Z1dwO6uZn23GmN9t67xUz56TI1uTtF0OiqIAUzYh4LiQCBhBDCACWsQigMBCEAAAhCAAASiJoABjJow+UMAAhCAAAQgAIGEEcAAJqxBKA4EIAABCEAAAhCImgAGMGrC5A8BCEAAAhCAAAQSRgADmLAGoTgQgAAEIAABCEAgagIYwKgJkz8EIAABCEAAAhBIGAEMYMIahOJAAAIQgAAEIACBqAlgAKMmTP4QgAAEIAABCEAgYQQwgAlrEIoDAQhAAAIQgAAEoiaAAYyaMPlDAAIQgAAEIACBhBHAACasQSgOBCAAAQhAAAIQiJoABjBqwuQPAQhAAAIQgAAEEkYAA5iwBqE4EIAABCAAAQhAIGoC/x9W5Xb0vZoUnAAAAABJRU5ErkJggg==" width="640">
477
Also plot the sky flux to check for clouds.
```python
plt.figure()
plt.plot(sky1.mean(axis=1))
plt.plot(sky2.mean(axis=1))
plt.ylabel('Sky backround flux')
plt.xlabel('Frame number')
plt.show()
print(sky1.mean(axis=1)[50:75]>6.0)
```
<IPython.core.display.Javascript object>
<img 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6THveS2exy/sjOGvOjpPAUHh9VQzWqZv1kAygpGBpAsPjamAAUoQIGUIHD3OLCkurSSe0JGDIrpijWO4+NX1lE3EG6+9TG7RQnVrJb5mwWgrGBkAMniY2MKUIACFEgJAn8NAI4vllbSTDsSIUIeXHbuEL8y7+jlGPGlP1oH5FLNapm/WQDKCkYGkCw+NqYABShAAWsXEIS4Gz/C72JaxrH48V4+aUWhzt9I/xsr2KGEZi2CB1VR7LVviREzf7MAlPWrxwCSxcfGFKAABShg7QKPzgMLykMLRxSPXgiDvQsG1iqI1zsmYECq9eisG4AytVuhS8W4wlAtB/M3C0BZscgAksXHxhSgAAUoYO0Cfw8DjvyEnfqS+DZmAIbXK4w6xbLhi0n/Ij1e4xk8cGRoNWT1cFbVSpm/WQDKCkgGkCw+NqYABShAAWsWiHkDTC8k3fnbQTcIFeu1QofyeSAIAvIM3Ra/stBJ9VS3SubvFFIAzp8/H+Kf0NBQKch8fHwwcuRI1KlT56NBt27dOowYMUJqU6BAAUyePBl169ZNUpAygJLExZMpQAEKUCAlCVzaCvzWWrrzt0rMbJwYXgserqmkFeYe8pf0v/5eabGxR3nVrZr5O4UUgFu3boW9vb1UyIn/5bFixQpMnToVp0+florB949Dhw6hYsWKmDhxIurXr4/Vq1dLBeCpU6dQtGhRowOVAWQ0FU+kAAUoQIGUJrDte+DYQqyMrYHtXgOxpktA/AqP3HyOBcE3MLZRUXimV8fDn9/lZ/5OIQVgYr9T6dOnl4rATp06ffDj5s2bIzIyEn/++Wf8zwICAuDn54cFCxYY/SvKADKaiidSgAIUoEBKEji/AVjfUVpRD913qP5VVzTxz2k1K2T+ToEFoF6vh/jxbrt27aQrgEWKFPkgIL28vNC/f3/07ds3/mejRo3C5s2bcfbs2Y8GsFarhfjn7SEGkKenJ8LDw+Hu7m41gc+JUoACFKAABZIt8PgiMD8wvnklLMaOH5rAOZV9sru0dEMWgCmoAAwJCUFgYCCio6ORJk0a6WPdj32nz9HRUfqYuGXLlvExN2/ePIwePRqPH//3+pr3AzIoKEg65/2DBaClf3U5HgUoQAEKKCZwdBGwfZA0/HFDQfxaZBFmqegtH8a4sABMQQWgTqfDnTt3pKtx69evx+LFixEcHJzoFcDkFoC8AmjMrxXPoQAFKECBFC2wrj1wYRN+11dBUEwbjGpaGs1Le1nVklkApqAC8P3Iq169OvLly4eFCxd+EJTJ/Qj4/Y4YQFb1+87JUoACFKCAXAFBQOw0bzhEPkZz7QgcFQpj36Aq8Mqgvhs9PrVU5u8UXABWrVoVYqG3fPnyD2JAvAkkKioK4t3Db49y5crB19eXN4HI/T8HtqcABShAgZQrcOcIsLQWtIIDimt/RpYM6bB3YGVoNBqrWjMLwBRSAA4dOlR65p9Y8L1+/Tr+sS47duxAjRo10LZtW+TIkUN67It4iI+BqVSpEiZNmoR69eph7dq1mDBhAh8DY1W/vpwsBShAAQpYVEB87++yOsCdw1gdWwXLM/TDj9/4o2AWN4tOwxSDsQBMIQWg+KiXXbt24eHDh/Dw8JCu5A0ePFgq/sSjcuXKyJ07d4KrgeKdwsOHD49/EPSUKVP4IGhT/FaxDwpQgAIUSJkCx34Gtg2U3vtbMXoGpnasjYoFM1nlWlkAppACUKnoYwApJc9xKUABClDAogJ7JgLBk6Qhg2LaYqWhNs6Oqgk357g3f1jbwfzNAlBWzDKAZPGxMQUoQAEKWIOA9jUwyQsQDNjmWAs9X7VBI7+cVvfol3epmb9ZAMr61WMAyeJjYwpQgAIUsAaBG7uBVY3xyikrfMNnIH1qR+zsVxEZ0jhZw+wTnSPzNwtAWcHLAJLFx8YUoAAFKGANAv//+HeTvjz6xfTE7BZ+aOSXwxpm/tE5Mn+zAJQVwAwgWXxsTAEKUIACViAQu7wRHEL3YnhMBzgHdsEP9Qpb3WNf3mdm/mYBKOtXjwEki4+NKUABClBA7QLPb8DwYxnYCbFo4zQbq4a2V/uMjZof8zcLQKMC5WMnMYBk8bExBShAAQqoXWBdB+DCRuzRF8c237mY2qy42mds1PyYv1kAGhUoLABlMbExBShAAQpYo0B0ODA1P6DXoZ52Ajp+1QhNS+a0xpV8MGcWgCwAZQUyA0gWHxtTgAIUoICaBULWAxs64bohO2rHTsfBIVWRxd1ZzTM2em7M3ywAjQ6WxE5kAMniY2MKUIACFFCzwG9tgEt/4KfYhnhQ8nuMb1xMzbNN0tyYv1kAJilg3j+ZASSLj40pQAEKUECtAhFPIcwoDI0hRvr4d+H3HZAznataZ5vkeTF/swBMctC824ABJIuPjSlAAQpQQK0CwVOBPeNwxpAPY7LOxcYe5dU602TNi/mbBWCyAudtIwaQLD42pgAFKEABNQrcOQKsaCDd/NFP1x0+dbqg8xd51TjTZM+J+ZsFYLKDR2zIAJLFx8YUoAAFKKA2AYMBWFAeeHIRO/Sl0EvfDweGVE8xN3/wAs5/AacRBEFQW/xZy3xYAFrLTnGeFKAABSjwWYHI58DplcC/QdDauaB01GyU88mPBW1KfraptZ3A/M0rgLJilgEki4+NKUABClBALQLiI182dQUMsdKMFsQ2wKTYlljzbQAC82VQyyxNNg/mbxaAsoKJASSLj40pQAEKUEANAq8fAT+WAbThMGQqgkXPi2F6VD10q1oIA2p6q2GGJp8D8zcLQFlBxQCSxcfGFKAABSigBoFdY4H903DPpRDa203A9efRyJXBFf/2r4RU9nZqmKHJ58D8zQJQVlAxgGTxsTEFKEABCqhAQFjwBTSPzkl3/G4yfAFHezv81MofNYpkUcHszDMF5m8WgLIiiwEki4+NKUABClBAaYHXj4HpBaVZlNcvwojmFeHvlQ6ZU8gr3z7Gy/zNAlDWrx4DSBYfG1OAAhSggNICZ9YAm7vhnCEP/in/GwbWSpnf+XufmfmbBaCsXz0GkCw+NqYABShAAYUFDH98B7tTK6S7fst2mYsSXukUnpFlhmf+ZgEoK9IYQLL42JgCFKAABRQWiJpdFq4vL6MvBmH6yB9gb6dReEaWGZ75mwWgrEhjAMniY2MKUIACFFBSIPoVhEle0EDAkDzrMaldDSVnY9Gxmb9ZAMoKOAaQLD42pgAFKEABpQTC7wHL6wEvQ3HXkAn/1NyJThXyKDUbi4/L/M0CUFbQMYBk8bExBShAAQooIfD4IvB7G+D5dWn0ZbG1ULzLQunuX1s5mL9ZAMqKdQaQLD42pgAFKEABSwuE3QV+KgvERCLaNRs6hHXEKY0Pzo2uDScHe0vPRrHxmL9ZAMoKPgaQLD42pgAFKEABSwnoY4EHp4ELG4Ej8/AyTQHUftYHj5Eefp5psblneUvNRBXjMH+zAJQViAwgWXxsTAEKUIAClhDQxwALKwJPLsaPNjamNZbo6yJfptQY2cAHlQpmssRMVDMG8zcLQFnByACSxcfGFKAABShgCYFrO4Ffv0owUlXtNNSs+AUG1/aGRmMbj355F4D5mwWgrF89BpAsPjamAAUoQAFLCGzqDpxdjfOOvvDSXsNNITs2l1yJoEZFLTG6Ksdg/mYBKCswGUCy+NiYAhSgAAXMLaCPhWFKHthpX6GZdiSuCDmhhSMWdiiPyt6ZzT26avtn/mYBKCs4GUCy+NiYAhSgAAXMLXD/JPBzVYQLrvDTLoIAO2nEi2NqwdXRwdyjq7Z/5m8WgLKCkwEki4+NKUABClDA3AKH5gL/DMdOvT/u1V6K608iUDCLG9qVy23ukVXdP/M3C0BZAcoAksXHxhSgAAUoYGaBNyu/hsvNHZgY2wo9hs2Gh2sqM49oHd0zf7MAlBWpDCBZfGxMAQpQgALmEBAE4NV94PxGGP4dDTshFoPSzsTUvh3NMZpV9sn8zQJQVuAygGTxsTEFKEABCphD4I/vgFMr4nv+U18WIYGzMLRuEXOMZpV9Mn+zAJQVuAwgWXxsTAEKUIACphYQr/5Nzg1Eh0k9j9N0xeI3FbGsQxlUseG7ft9nZv5mASjrV48BJIuPjSlAAQpQwNQCL28Ds30RA3v4RC+FDqngld4VO/pWhIuj7bzr93OszN8sAD8XI5/8OQNIFh8bU4ACFKCAqQUubgF+b4sQQ2400E3AN2W9MKimN9KldjT1SFbdH/M3C0BZAcwAksXHxhSgAAUoYGKBqO0j4Xp0NtbEVkGdYb8jrSsLv8SImb9ZAMr61WMAyeJjYwpQgAIUMKVA6EHo1raFY/QzzHDqhv5DJ5uy9xTVF/M3C0BZAc0AksXHxhSgAAUoYCqB8xuBDZ0AwYBrhhz4ueACTGld0VS9p7h+mL9ZAMoKagaQLD42pgAFKEABUwgYDMBcf+DlLfwllMMgbWf0reuHLhXzmaL3FNkH8zcLQFmBzQCSxcfGFKAABShgCoHr/wK/NMUrwRVltT/C0cUNf/auAM/0rqboPUX2wfzNAlBWYDOAZPGxMQUoQAEKmEBAWNkImpt7sSy2Fo54f48JjYshQxonE/Sccrtg/lZxASgIAjQajaqjjwGk6u3h5ChAAQqkfIHQg8DyutAJ9qgrzMamYS3g5sz3/X5u45m/FS4A27dvj59++gmpU6dOsFehoaFo06YN9u/f/7k9VPTnDCBF+Tk4BShAAdsW0EUCCysCz6/j19hquFBytHT1j8fnBZi/FS4AS5QoAXETfvnlFwQGBko7tmLFCnz33XeoWrUqNm3a9PldVPAMBpCC+ByaAhSggK0LHJgJ/BuER0I61NFNxob+9ZA3UxpbVzFq/czfCheAMTExGDZsGObMmYMBAwbg+vXr2L59O2bMmIFvv/3WqE1U8iQGkJL6HJsCFKCADQqE35fe9AHxK1L3jksAQ2I6I7pYa8xqUcIGQZK3ZOZvhQvAt9s2atQojB07Fg4ODggODo6/Gmjstk6cOBEbN27E5cuX4eLignLlymHy5Mnw9vb+aBfLly9Hhw4dEvzcyckJ0dHRxg4rXb308PBAeHg43N3djW7HEylAAQpQgALJEvhrAHB8cYKmlTRL8Vvfesjq4ZysLm2xEfO3wgWgeAVwyJAh0vcAxSuABw4cwNWrV7FkyRLUrVvX6JisXbs2WrRogdKlSyM2Nla6qnj+/HlcvHjxg+8Xvu1ULAD79OmDK1euxI8j3nSSJUsWo8dlABlNxRMpQAEKUECuQORzGGYUgZ3+vwsVu/Ql8Kj+CrQqm0tu7zbVnvlb4QKwePHiiIqKwqpVqxAQEADxzt8pU6ZAvCLYsWNHzJs3L1kB+fTpU2TOnFm6mlixYuJPQhcLwL59+yIsLCxZY4iNGEDJpmNDClCAAhRIqsDeycDeCQgx5EZz3Ug0sw/GTn1J/B30Ddx552+SNJm/FS4AO3XqJH3/7/27gE+fPi3dBSxexUvOIX6XsECBAggJCUHRokUT7UIsADt37owcOXLAYDDA398fEyZMgI+Pj9FDMoCMpuKJFKAABSiQXIFYLbClJxCyTuqht64XTrlXw/2wN2jinwMzvvZLbs822475W+EC8FORp9VqIX4nL6mHWMw1bNhQurInfqT8sePw4cO4du0afH19pe/wTZs2Dfv27cOFCxeQM2fORJuJcxL/vD3EAPL09OR3AJO6STyfAhSgAAWMF9g/A9g1Wjr/tiEz+mZajPU9K+LorefwzZkWaZwcjO+LZ0oCLAAVLgDv3LnzyVD08vJKcqh2795dupNYLP4+Vsgl1qn4fcTChQujZcuW0g0piR1BQUEYPTrul/DdgzeBJHmb2IACFKAABYwRiA6HMKMINLoI/KEPxMSYbzC6TU3U9MlqTGue8xEBFoAKF4B2dnaffNuHXq9PUvD26tULW7Zska7k5cmTJ0ltxZObNWsm3Ym8Zs2aRNvyCmCSSdmAAhSgAAWSK3DnKHDmV+DUCtwwZEN13VT4eaXHxu7lVP+mrOQu2VLtWAAqXACePXs2wV7dyI1kAAAgAElEQVSLV+HE7/+JzwEcP348mjRpYlQsiDeP9O7dW3pw9N69e6Xv/yX1EItN8ft/4t3H4vjGHAwgY5R4DgUoQAEKJFkg7C4w1x/Q66Sm82MbwFAtCJ0q5IFzKvskd8cGCQWYvxUuAD8WkH/99RemTp0qFXPGHD169MDq1aulq3/vPvtPfEaf+FxA8Wjbtq10w4f4zEDxGDNmjHTncf78+aXvC4rjbd68GSdPnkSRIkWMGZbfITBKiSdRgAIUoECSBf7sD5xYEt9soNsUTO3fhVf+kgyZeAMWgCotAMW7eMVHxERGRhq11eLz+xI7li1bBvF9w+JRuXJl5M6dG+Ldv+LRr18/6eHRjx49Qrp06VCyZEmMGzcO4uvpjD0YQMZK8TwKUIACFDBKQPzYN3Q/hODJ0Pz/6t8pQ34crPgrelcvZFQXPOnzAszfCheA4ga8e4gf5T58+BDizRbiWz3OnDnz+V1U8AwGkIL4HJoCFKBAShN4cBpYWgeIfSOt7JLBE3V0kwBosL3PFyicjW+cMtWWM38rXAAmdhOIWASKj1ZZu3Ztkl8JZ6rAMLYfBpCxUjyPAhSgAAU+K7CwEvDwvwsfY2LaYFfapgjIkwGTmhbjx7+fBTT+BOZvhQtA8U0d7x5iQZgpUybpe3ni3bhqPxhAat8hzo8CFKCAlQg8uwb8WAqxgh2OGgojoyYck7LOxLIeNa1kAdY1TeZvhQtA6wqXD2fLALL2HeT8KUABCqhEYNdYYP807NEXR4eYwdKkZnxdHE38E38xgUpmbbXTYP5WoAD8448/jA4Y8Y0eaj4YQGreHc6NAhSggBUIGAxS4Yc946XJ9ovpgbQBrRGpjcXYL4vCyYGPfDHHLjJ/K1AAih/zGnOId/Ym9UHQxvRrynMYQKbUZF8UoAAFbFBgz0QgWLzRA1gRWwN78w7Eso4BNghh2SUzfytQAFp2i807GgPIvL7snQIUoECKFoh+9f/XvL1GUExbLNfXxqI2JfmaNwtsOvO3AgVg+vTpce3aNWTIkAEdO3bE7Nmz4ebmZoHtNv0QDCDTm7JHClCAAjYjcGQ+8PcQXDPkQAPDVPxQzwetA3Lxbl8LBADztwIFYJo0aXDu3DnkzZsX9vb20oOYxTt/rfFgAFnjrnHOFKAABdQhELGkEdLc3YsxsW3QsOs4+HmmVcfEbGAWzN8KFIA1atTA48ePpTdvrFixAs2bN49/Xdv7Mbd06VJVhyEDSNXbw8lRgAIUUK+AwYA34z3hoo/AeM+F+KFTC/XONQXOjPlbgQJQLP5mzpyJGzduSK9iq1WrFpycnBINr02bNqk67BhAqt4eTo4CFKCAegWeXALmBSBScML+JidRu7ineueaAmfG/K1AAfhuHOXJkwcnTpyQvg9ojQcDyBp3jXOmAAUooLxA9NGlcN7eD4f0RZBv0B5kcXdWflI2NAPmb4ULQGuPNQaQte8g508BClBAGYHHKzsiy80NWOHwFdoNX6LMJGx4VOZvFoCywp8BJIuPjSlAAQrYrMDLycWQ7s0dLMw5CV07d7dZB6UWzvzNAlBW7DGAZPGxMQUoQAHbFIh8DkzNK619bZV9aFGpuG06KLhq5m8WgLLCjwEki4+NKUABCtiewP1TwM9VpHWLz//Tdj2Mojk8bM9B4RUzf7MAlBWCDCBZfGxMAQpQwLYEXj2AsL4TNHcOSev+VaiF5iPXwsHeuFek2haWeVfL/K1AASiiG3u4u7sbe6oi5zGAFGHnoBSgAAWsT+DGbmBV4/h5T435Gpc8W2Bpt2rWt5YUMGPmbwUKQDs7O6Nfc6PX61UdZgwgVW8PJ0cBClBAHQKxOugXfAH7Z5el+WzSl0e/mJ6Y3cIPjfxyqGOONjYL5m8FCsDg4OD4MAsNDcWQIUPQvn17BAYGSn9/+PBh6Q0hEydORLt27VQdkgwgVW8PJ0cBClBAeYHw+8C69sC9YwgTUqOD7nu8dC+Mnd/XQCp+9KvY/jB/K1AAvrvb1apVQ+fOndGyZcsEQbB69WosWrQIe/fuVSw4jBmYAWSMEs+hAAUoYGMCL24BhljANQOEeYHQRDzCK8EVPWO+ww23MpjR3A8Bea3zBQgpZSeZvxUuAF1dXXH27FkUKFAgQUxdvXoVfn5+iIqKUnWsMYBUvT2cHAUoQAHLC+hjgRmFgcgnMHjXg92Vv3DLkAVtY4bAu5AvFrcrZfk5ccQPBJi/FS4Avb290ahRI0yZMiXB5nz//ffYsmULrly5ouqwZQCpens4OQpQgAIWFxCeXoHmpzIJxu0b2xu5K7dFj8r54ejAO34tvimJDMj8rXABuG3bNjRt2hT58+dH2bJlpS06duwYrl27hg0bNqBu3bpqiJOPzoEBpOrt4eQoQAEKWFwg/MQ6ePzZOX7cq4Yc+Kv8OvSr5WPxuXDAjwswfytcAIpbc+/ePcybNw+XL8fdHVW4cGF069YNnp6eqo9dBpDqt4gTpAAFKGBRgTsbhsMrZC626MthWmwzPNNkxN4hNZHF3dmi8+BgnxZg/lZBAWjNQcoAsubd49wpQAEKmF4gdF4T5H6yC2Ni2kAT2ANl86RHTZ+sph+IPcoSYP5WQQEYFhYmfez75MkTGAyGBBvatm1bWRts7sYMIHMLs38KUIAC1iXwbIIPMuru4ffCc/F1c3XnMOuSNe1smb8VLgC3bt2KVq1aISIiAuJbPzQaTfwOi//84sUL0+64iXtjAJkYlN1RgAIUsGaBp1eBn0ojVrDDzrr7UadsUWteTYqeO/O3wgVgwYIFpRs9JkyYAPGRMNZ2MICsbcc4XwpQgALmE9Bv7gX7M6uwU++PHN23oEh2db/O1HwS6u+Z+VvhAjB16tQICQlB3rx51R8ticyQAWSV28ZJU4ACFDCNgCAA+6YCp1cBrx7EPfwZwCCHIZg4dDAc+KYP0ziboRfmb4ULwCZNmqBFixb4+uuvzbC95u+SAWR+Y45AAQpQQLUCB2YC/wbFT08rOGCVvgZeVQxC/5qFVDttTgxg/la4AFyyZAnGjBmDDh06oFixYkiVKlWCuGzYsKGq45QBpOrt4eQoQAEKmE8g9CCEFfWhEQyIEexxRsiHoJh2uO1YADv7V0Q2Dxfzjc2eZQswfytcANrZffyJ6OJNIHq9XvYmm7MDBpA5ddk3BShAAfUKxCyqhlQPTmC9viIGxnSDm5MDhtQthPq+2eHhkvBihnpXYbszY/5WuAC09tBjAFn7DnL+FKAABZIh8PAssLAidII9WrktxcrvGkiveLO3++9JFsnolU0sKMD8zQJQVrgxgGTxsTEFKEABqxTQ/jUYTscX4A99ILJ0+BVl82awynXY8qSZvxUuAMXv/33qGDlypKrjkwGk6u3h5ChAAQqYReDhnBrI9uIYpjr3wcDBoxM8w9YsA7JTkwswfytcAJYoUSLBpsbExODWrVtwcHBAvnz5cOrUKZNvuik7ZACZUpN9UYACFLAOgVdjc8Nd/xLrSqxEs0aNrGPSnGUCAeZvhQvAxOJR3JT27dujcePGaNOmjapDlgGk6u3h5ChAAQqYXiDqBTAlj9Tv/mZn8YVPbtOPwR7NLsD8rcICUNx18eHQDRo0QGhoqNmDQM4ADCA5emxLAQpQwPoEtDcOwGlVPdwTMsKh/wVk9XC2vkVwxnwOIFRaAB44cEAqAF++fKnqMGUBqOrt4eQoQAEKmFzgwb8/IvuBH7AfJVBh1B5+/8/kwpbpkPlb4QJwzpw5CXZaEAQ8fPgQq1atQqVKlbB69WrLREIyR2EAJROOzShAAQpYqUDo0o7IfWcDtqRuhkaDFlvpKjht5m+FC8A8eeK+R/H2EB8MnSlTJlStWhVDhw6Fm5ubqqOUAaTq7eHkKEABCphWQBAQNrEQ0uoeYWXeaWjb9lvT9s/eLCbA/K1wAWixnTbTQAwgM8GyWwpQgAJqFHhxE5hTQnoA9O5Gx1DbP78aZ8k5GSHA/K2iAvDevXvSluXMmdOIrVPHKQwgdewDZ0EBClDAEgJh+39G2l0DccxQCN5DDsDDla98s4S7OcZg/la4ADQYDBg3bhymT5+OiIgIaY/Fj30HDBiAH374AZ96V7A5AiKpfTKAkirG8ylAAQpYmYD2NbBtEFCoHq4c+wfet1Zii0tjNBq83MoWwum+K8D8rXABKH7Pb8mSJRg9ejTKly8v7Y14B3BQUBC+/fZbjB8/XtURywBS9fZwchSgAAWSL3D2N8A9G4RH56HZMVTq535qH+SIvIC/Pfuhdqeg5PfNlooLMH8rXABmz54dCxYsQMOGDRMEw5YtW9CjRw/cv39f8SD51AQYQKreHk6OAhSgQPIE7hwBltaS2r5OWwhuYZcT9POv70xUb9IxeX2zlSoEmL8VLgCdnZ1x7tw5FCxYMEFAXLlyBX5+fnjz5o0qAuVjk2AAqXp7ODkKUIACyRKI+L0r0lxc+9G2B6ttRPkvqiWrbzZShwDzt8IFYNmyZSH+ef95gL1798bx48dx5MgRdUTKR2bBAFL19nByFKAABZIuoIuCblJeOBo+fgHiQuuz8MnPV8AlHVc9LZi/FS4Ag4ODUa9ePXh5eSEwMFCKjMOHD+Pu3bvYtm0bvvjiC/VESyIzYQCpens4OQpQgAJJFtBf2wX7X5vgvpABXXT9MSrVSvyjL4XhqX6N7+vZgMfI6MZXwCUZV0UNmL8VLgDFWHjw4AF++uknXL4c9x2LwoULS9//E78faOwxceJEbNy4UerDxcUF5cqVw+TJk+Ht7f3JLtatW4cRI0ZI7xwuUKCA1KZu3brGDst3CRotxRMpQAEKWIfAk41DkPncfGxBJVwsOxkLg28iIG96rLhfH06aWGkRwqgwvgLOOrbzo7NkAahgARgbG4sJEyagY8eOsp/9V7t2bbRo0QKlS5eG2O+wYcNw/vx5XLx4EalTp040AA4dOoSKFStCLB7r168vvXZOLABPnTqFokWLGhXaDCCjmHgSBShAAasReDy9PLK8Po8lGQejU69huPLoNbJ6OOPGxAD4212PW0dQuNWshxNNXID5W8ECUNySNGnSSIVa7tym/S7F06dPkTlzZogfMYtFXmJH8+bNERkZiT///DP+xwEBAdLNJ+KdycYcDCBjlHgOBShAASsRiHwOw9T8sIMBa8pvR8sa5eIn/vX45RiunYWfYhth4YTRVrIgTvNjAszfCheAjRo1QpMmTdCuXTuTRun169elj3RDQkI+ejVP/N5h//790bdv3/ixR40ahc2bN+Ps2bNGzYcBZBQTT6IABShgFQL6Iwtg//dghBhyA132oVhOj/h5H77xHK2XHMWAmgXRozJfAWcVG/qJSTJ/K1wAilfaxIdAt2rVCiVLlvzg49r3nw9oTMCJbxcR24WFhUkPlf7Y4ejoiBUrVqBly5bxp8ybN0+az+PHjxNtptVqIf55e4gB5OnpifDwcLi7uxszPZ5DAQpQgAJqFLi1H1hRX5rZFE0HDBgxE/Z2mgQzfaPTw8XRXo2z55ySKMACUOEC8FOvetNoNNDr9UncUqB79+7Yvn27VPx96r3CySkAxTeUiAXi+wcLwCRvExtQgAIUUI9A+H0Ic0pAo9fijeCIWUU3YGizxL8+pJ5JcyZyBFgAKlwAytm8xNr26tUL4ltE9u3bhzx58nyy++R8BMwrgKbeMfZHAQpQQHmBmC19kOr0ctw1ZELHmEEY36UZyuRJr/zEOAOzCbAAVLgAvHfv3kev0okPgRZvyjDmEAQB4sOjN23ahL1790rf//vcId4EEhUVha1bt8afKj4+xtfXlzeBfA6PP6cABSiQUgSeXIJhXnnYQY+vtSOQr3RNjP+yGOze+/g3pSyX64gTYAGocAFYpEgR6aPa9OkT/pfWwYMHpQdEi9/jM+YQnxsoPsZFvPr37rP/PDw8pOcCikfbtm2RI0cO6bEv4iE+BqZSpUqYNGmSNNbatWulx9LwMTDGiPMcClCAAilA4E0YYld8CYdHp7FDXwpvmqzElyVypICFcQmfE2ABqHABKD4DUHwX8J49e+Dm5ibtl/jxbYMGDSB+365fv36f20Pp5+L3BRM7li1bhvbt20s/qly5svS4meXLl8efKj4Ievjw4fEPgp4yZQofBG2UOE+iAAUoYP0ChrWtYXd5K8IFV/TxmIOlfb/ilT/r31ajVsACUOECULxj96uvvsKLFy+wY8cO6aqceAfvuHHj0KdPH6M2UcmTGEBK6nNsClCAAjIEdFGInegFByEGTbRB6NOhNSoVzCSjQza1JgHmb4ULQDFYdDqd9BGs+H088Wqg+BGteDOHNRwMIGvYJc6RAhSgwIcCMZd3INXar3FPyIj9dXejZdlcZLIhAeZvBQpAsch7/3j9+rX0PD6xEBQf4/L2EG/IUPPBAFLz7nBuFKAABT4u8GR1N2S+ugYbNdXx5Yj1/OjXxoKF+VuBAlB89p/4nT3xzt23x7v//vafk/scQEvGMAPIktociwIUoIAJBM6sAf4aAMRESp0tyDEB3b7taYKO2YU1CTB/K1AA3r592+gYyZVL3ZfkGUBGbyVPpAAFKKC8QMwb6KYXhWP0M2kuM2OaIkejIHxdxkv5uXEGFhVg/lagALToDpt5MAaQmYHZPQUoQAETCuj3ToH93vF4JbigmnYaihXyxvzW/nBy4OvdTMhsFV0xfytcAIo3fGTJkgXi42DePZYuXYqnT59i8ODBqg4kBpCqt4eTowAFKPCfQMh6YEMn6d8Hx3yLKi0HokaRLB+875dktiHA/K1wASg+l098gLP4Bo53j6NHj6JFixa4deuWqiORAaTq7eHkKEABCsQJPDiN2OUN4aB7hZ9j6wI1xuHbSvmoY8MCzN8KF4DOzs64dOnSB+/tvXnzJsS3hERHR6s6PBlAqt4eTo4CFLB1AW0EsG0gcHaNJHHNkAM/ei/DzJZleNevjccG87fCBaD4zt5Ro0ahdevWCUJx1apV0t+LhaCaDwaQmneHc6MABWxaIFYLw8rGsLtzEAZBg30GXyxN0xkL+n0DV0cHm6bh4vkuYDEGNMK7z2OxcFSIr14T/0ydOhVVq1aVRt+1axe+//57DBgwAEOHDrXwjJI2HAvApHnxbApQgAKWEog5ship/h4g3fDRSTcIdrnLYdyXRVEgS9xrR3nYtgDzt8IFoFh7DhkyBHPmzJHeCCIe4sfC4s0fI0eOVH10MoBUv0WcIAUoYIsCgoDwqX7wiArFZKEtSrccjqqFstiiBNf8EQHmb4ULwLf7EhERIX0X0MXFBeLHwk5OTlYRtAwgq9gmTpICFLAlgbvHIfzWCpqIx9AKDthUZRdaVPazJQGu1QgB5m+VFIBG7JUqT2EAqXJbOCkKUMAWBWKigd1jYTiyAHZCLHSCPRYKjdF26AJ4uKSyRRGu+RMCzN8qKABPnDiB33//HXfu3In/GPjtnm3cuFHVAcwAUvX2cHIUoICtCBj0iF1SCw73j0sr/lMfgDGaruhbrxS+Kcu3fNhKGCRlnczfCheAa9euRdu2bVGrVi38888/qFmzJq5evYrHjx+jcePGWLZsWVL20+LnMoAsTs4BKUABCnwgoLu2G46/NkaE4Iw+sb2Qs0xjDKzlDTdnXvljuCQuwPytcAHo6+uLrl27omfPnnBzc8PZs2elZwKKf5ctWzaMHj1a1bHLAFL19nByFKBAChaI3jMVuL4Lzs2X4t7G4cgZugEbNDWQu/0ilMyVPgWvnEszhQDzt8IFYOrUqXHhwgWIbwTJkCED9u7di2LFikk3hIiPhXn48KEp9tlsfTCAzEbLjilAAQp8XCDmDTA+6wc//6XQPLRu0YpyFPisAPO3wgVgzpw5sX37dqnoE68Gis/9a9myJQ4fPozatWsjPDz8s5uo5AkMICX1OTYFKGCrAuFntsJjc8IXCBzU+yBtt7/gkyOdrbJw3UkQYP5WuAD85ptvUKpUKfTv3x9jx47F3Llz0ahRI+zcuRP+/v7gTSBJiGaeSgEKUMAWBF4/xuuFteEWcRMnDAVxW8gsvtMA53yHY3SzAFsQ4BpNIMACUOEC8MWLF9L7frNnzw6DwSC9FeTQoUPSswCHDx+OdOnU/V9yDCAT/BayCwpQgALGCuiioJv/BRxfXpde7zbUYzJc8ldAvkyp0apsLr7f11hHngfmb4ULQGuPQQaQte8g508BCliNQMRTxKzvjFShe/FccEP/mB7o3aUbSuXmDR9Ws4cqmijztwoKQL1ej02bNkk3fohHkSJFpI+BHRzU/7JuBpCKfps5FQpQwLoEdJGAXge4GPFJjz4Wb36uBZdHJ6AVUmGg43C0btkGZfNmsK41c7aqEWD+VrgAFO8AbtiwIR49egRvb28pMMTnAGbKlAlbt25F0aJFVRMsiU2EAaTq7eHkKEABtQoIAqJ+LA+H1w/g2Pso4Pbp9/RG75sN590j8UpwwbcO4zCyUzP4ZPdQ6+o4LysQYP5WuAAMDAyUir0VK1bEf9/v5cuXaN++PZ4+fSp9H1DNBwNIzbvDuVGAAmoV0N49DacllaXpPSwzDNnqDv74VGOiETG5MNLEvsAkh+74tu9oZEhjHe+LV6s/5wV+B1C8dUoQBEGpYHBxcYH4KjgfH58EUzh//jxKly6NN2/eKDU1o8ZlAWgUE0+iAAUokEDgxroRyHdhjvR3D1wKIvvguFe4vX/oL/8N+7XNpb++L2TAteb7UblIDmpSQLYA87fCBWDx4sUxc+ZM6aHP7x67d+9Gnz59EBISInuTzdkBA8icuuybAhRIkQJRL/B0ZgVkirkfvzxdz9NwzJQ3wXJjH56HflE1OAnR0t/PdP0OfQeNgUajSZEsXJRlBZi/FSgARfS3x4EDB/D9998jKCgIAQFxz286cuQIxowZg0mTJqFu3bqWjYgkjsYASiIYT6cABWxPQPyQ6Z2i7eGPdZHt2UE8E9zxQnBDQbv7uBkwAXlr9wSu/gOkywVk8sbteY2R68luHDUUwiSPERjcOBABvOnD9uLHTCtm/lagALSzs0vwX3BvP4F++1917/67eIewmg8GkJp3h3OjAAWUFri442fkPzwE1zPXQsH282FniAGm5YcdBMzzXo7cz/ai7vPluJKhOvJWaYNU69tJUzZk9IbdsyvSs/42l9uAJrWqKb0Ujp/CBJi/FSgAg4ODjQ6jSpUqGX2uEicygJRQ55gUoIC1CNwcWwJ59Tel6Z7PVA/uPjXhtbcPrgi5kHfkGez7dyuqHW770eVsE8qh4rCtSOOk/seCWcuecJ5xAszfChSAKSn4GEApaTe5FgpQwBQC0VotTvz5MzJ6eqPQtq8SdKnTOMJR0GG7RwvU6bcQ95+/gnZ2aeS1e5TgvDAhNQbFdIV/tWboXq2IKabFPiiQQID5mwWgrF8JBpAsPjamAAWsVEAfowUEAfaOzh+s4ND87ij3eHX8359zLIEnLnlRPXxD/N9tC1yNurXqSf/+4x8HUPD4SETBCRvSd0GaFxdgn6UQpnZpDBdHeysV4rTVLsD8zQJQVowygGTxsTEFKGCFAvrYGNyZWArOhkicytwU9mk9UbtlL2klt+/dQ67FCR/rdajwcOSr0QVPZldBMc0NLLD/Bi0GzEZaV8f41QdffQonBzvpJo9X0TFwTWUPB3s7K9ThlK1FgPmbBaCsWGUAyeJjYwpQwAoFLuxZA5/gbglmHt73Njw8PHB8+pcoHbE3wc+edAlB5uxeOHzlPo6fPI5m9Wohm4eLFa6cU05JAszfLABlxTMDSBYfG1OAAlYkoH1yHdrH13BnxxwUjUj4lqaQmmvhqH0B7+AeiBHsodfYwxk6aAUHOI1+bkWr5FRtRYD5W+ECcNSoUejYsSNy5cpllTHHALLKbeOkKUCBZAjcH++LHDG3E215MG8f5Ly9Cbn0d7A3Szvkq9UdT37vB+eqg+BTho9wSQY3m5hZgPlb4QLQz88P4mvfxMe9dOrUCU2bNoWTk/W845EBZObfUHZPAQqoQiDy+T2knvvfd/tC7ArhVc5K8L+9DC4aHSIEF6TRvMErwRWxvc8ifcbMqpg3J0GBjwkwfytcAIobc/r0aSxbtgxr1qxBbGwsWrRoIV0VFN8FrPaDAaT2HeL8KEABUwhc2rkchQ/2ie9qR95hqNV2MC4e2IIi//73HL/jWb5G6e4/m2JI9kEBswowf6ugAHy7wzExMdi6datUDO7YsQOFChWSrgq2b99e+nKxGg8GkBp3hXOiAAVMLXBmfgf4Pd4odfuXUB7Fe65CzswZoH3zGm8mF0ZavJZ+dq/ZNuT0KW/q4dkfBUwuwPytogJQp9Nh06ZNWLp0KXbv3o1y5crhwYMHePz4MX7++Wc0b97c5AEgt0MGkFxBtqcABdQucPPA7/D6txscoMeu4rPgX7MV0qX+7xEu90/9jSx/fIMHzvnhNfhogvf+qn1tnJ/tCjB/q6AAPHnyZPxHwOL3/9q2bYvOnTsjf/78UmTOnTsX48aNkwpBtR0MILXtCOdDAQqYUuDV49vQzA+AG6KwJ9UXKNV/A9xcPvyetvDyNjQuaQFndX5aY0oT9pUyBJi/FS4AixUrhsuXL6NmzZr49ttv0aBBA9jbJ3zy+7Nnz5A5c2YYDAbVRR0DSHVbwglRgAImFDj9YxuUePYHLtkVgNegA0jt8uGbP0w4HLuigMUEmL8VLgDHjh0r3fCRI0cOi226KQdiAJlSk31RgAJqE7g5rgTyxt7E0bJzUbbOfzd7qG2enA8FkirA/K1wASg+AqZo0aKJ7tvmzZvx5ZdfJnVPLXo+A8ii3ByMAhSwoIBg0CN6TFa4QIebLfYhb6HiFhydQ1HAvALM3woXgOKVvwMHDiBPnjwJdnrDhg3SdwEjIyPNGwEye2cAyQRkcwpQQLUCz+9fR4afS0In2MMw7CGcregZrapF5cRUI8D8rXABKL4J5JdffsHBg6eBR5kAACAASURBVAeRNWtWKTB+++036WPh5cuXo1mzZqoJlsQmwgBS9fZwchSggAyBSwc2o/C/7RCqyYncoy7I6IlNKaA+AeZvhQtAMSR69+6NPXv2YN++ffj777+lO4BXrVolvRVE7QcDSO07xPlRgALJFTj52wSUvDQZJ1zKo9Tgbcnthu0ooEoB5m8VFIBiZLRq1QrHjx/H/fv3sXr1ajRq1EiVAfP+pBhAVrFNnCQFKJBEgRdXD8N9dT3p2X8HsrZBhW4/JrEHnk4BdQswfytQAP7xxx8fRIX4FpB+/fpJj4Np2LBh/M/f/Wc1hhIDSI27wjlRgAJyBF49vAHDospIK7xCmJAal2qsRGCF6nK6ZFsKqE6A+VuBAtDOzs6oQNBoNNDr9Uadq9RJDCCl5DkuBShgLoETP/dCqfurcFmTF7pvNsK3QMKb9Mw1LvulgCUFmL8VKAAtucHmHosBZG5h9k8BClhC4Nad28iUISPSpE6N85Oro+ib4zhYeATKNx9oieE5BgUsLsD8nYIKQPEmkqlTp0J8tdzDhw+l9wp/6jmCe/fuRZUqVT4IOrHt2zuSPxeRDKDPCfHnFKCA2gUunD6EApvr40iaGqg46Dc8HZ0HmYQXOFdrHXwDa6p9+pwfBZIlwPytUAF4+PBhPH/+HPXr14/fuJUrV0J8LIz47D+xcBPfASy+G9jYY/v27dLjZEqWLIkmTZoYXQBeuXIF7u7u8cOIr50z9mNqBpCxu8PzKEABtQqcmPYlSkXskab3pt8NuMzMJ/3zs143kDFjRrVOm/OigCwB5m+FCsA6deqgcuXKGDx4sLSBISEh8Pf3R/v27VG4cGHpSl7Xrl0RFBSUrA0Wvz9o7BXAly9fIm3atMkahwGULDY2ogAFVCRwcmp9lIzcL83oQoUf4XOgFx4iI7KOug7x/0t5UCAlCjB/K1QAZsuWDVu3bkWpUqWkuPrhhx8QHBwsvRVEPNatWyddDbx48WKy4i4pBWCuXLmg1WqlV9KJBWf58uU/OqZ4nvjn7SEGkKenJ8LDwxNcRUzWpNmIAhSggAICN8b4IZ/hljTyebcvUPT1fpxyKg3/of8qMBsOSQHLCLAAVKgAdHZ2xrVr16TiSTwqVKgA8aqgWAiKR2hoKIoVK4bXr18nKxKMKQDFj37F7wGKRahY1C1evFh6APXRo0elq5GJHWKBOHr06A9+xAIwWdvERhSggMICWm00NBOyw1GT8IkLu7J3Q7UukxWeHYengPkEWAAqVACKV93EYqtixYrQ6XTSR7DiFcFq1apJuy1+JFypUiW8ePEiWbtvTAGYWMfimF5eXtLcEjt4BTBZ28FGFKCASgVunD+GfOtrJJidQdDgcotDKFK4iEpnzWlRQL4AC0CFCsDu3bvj7NmzmDx5MjZv3owVK1bgwYMHcHR0lHb1119/xaxZs6S3gyTnSG4BOGjQIOljaPEmFWMOBpAxSjyHAhRQq8DxPxag9Km472K/PY47+KPUD7v5/T+1bhrnZRIB5m+FCsBnz55Jd+qKxVaaNGmkArBx48bxmypeCQwICMD48eOTtdHJLQBr1KgBNzc3bNy40ahxGUBGMfEkClBApQKHF/ZE4MNfcDTDl3hRuDXCLu9DwUotULKYj0pnzGlRwDQCzN8KFYBvt0/87pxYANrb2yfYUfGjX/Hv314RNGa7IyIicP36denUEiVKYMaMGdJz/tKnTy99rDt06FDpXcPi42bEQ7zCmCdPHvj4+CA6Olr6DqD46Jl//vkn/qPoz43LAPqcEH9OAQqoWeDspGooHn0Cx4qOQJmv+NBnNe8V52ZaAeZvhQtAU27nxx7s3K5dOyxfvlx6xIx4c4l4nnhMmTIFixYtkopCV1dX+Pr6YuTIkYk+HPpj82QAmXIH2RcFKGBpgadBuZEJL3Gl/gZ4l+L7fi3tz/GUE2D+TkEFoBJhxABSQp1jUoACSRUYvWIrHt04h0LlG+K7mkWl7/c9f3IfGebF3egR2T8Uqd3TJbVbnk8BqxVg/mYBKCt4GUCy+NiYAhQws0CENhZ3nkch1YIAFLC7j7uGTHj0zU6ULpQHJ7YtR6ljfXBXkx2eoy6ZeSbsngLqEmD+ZgEoKyIZQLL42JgCFDCjwMnQ59i1aiICdEdQ0T4kfqQt2fqgUdcxODatMcpE7MaxbK1Qpus8M86EXVNAfQLM3ywAZUUlA0gWHxtTgAJmEngRqcO66T3R1fD7ByPcErLBsXswPOYXRxrNG9xqtAV5SlQ200zYLQXUKcD8zQJQVmQygGTxsTEFKGAmgZmrNqD39c5w0BjiR7jpUgxZ3lxHarzBdXghP+7goV1WZB1+ERq7hE9iMNO02C0FVCPA/M0CUFYwMoBk8bExBShgBoGH4W9wa3o1lLO7AJ1DGjjGRkijnMrRGtDr4P8o7qqgVkiFx003wMu3khlmwS4poG4B5m8WgLIilAEki4+NKUABMwis+v03tLnYBbFwgH33/dDMD5RGCfH+Dh4lm8JrdVzBd8xvAsp82dMMM2CXFFC/APM3C0BZUcoAksXHxhSggBkEdo1rgGqx+3AndzN4tV+MRwsbI8OjA0DPo0iVMS8OblkEQa9HhSbdzTA6u6SAdQgwf7MAlBWpDCBZfGxMAQqYWCD0STjS/lQYaTWRiGy9Danzlwdi3gC6SCB1RhOPxu4oYL0CzN8sAGVFLwNIFh8bU4ACJhR4EPYGk+YtxBzdKLzWuMFtxG2AN3eYUJhdpSQB5m8WgLLimQEki4+NKUABEwr8svZXtL7cQ+oxNHtd5O6yxoS9sysKpCwB5m8WgLIimgEki4+NKUABEwrsnvYNqkb8Fddjp52AZxkT9s6uKJCyBJi/WQDKimgGkCw+NqYABUwocGZ0APyES7j5xQzkrdbJhD2zKwqkPAHmbxaAsqJayQAKi9LBzk4Dd+dUstbAxhSggPULvHqjg2FSbunmj9cd9sItVwnrXxRXQAEzCiiZv824rCR1rREEQUhSC54cL6BUAI3ZdBqRJ36Fo6MzBgwaibSujtwVClDAhgXOXrqC4r+VgR52sP/hAZDKxYY1uHQKfF5Aqfz9+ZlZ7gwWgDKslQigw9efQrOyAQLsLkkzbxQ7EZUqVUf/GgVlrIRNKUABaxV4+lqLMTPnYK5hHB45ZEfW4XH/38CDAhT4uIAS+Vtt+8ECUMaOWDqAzt4Nw46Vk/B9zPwEsz5qKITsfXbBM0MaGathUwpQwBoFft+8CV+faS9NPTRjZeTutcUal8E5U8CiApbO3xZdnJGDsQA0Eiqx0ywdQIsn9kZn7UppKlHpi8D1xcX4aW0p8ysa1a0vYzVsSgEKWKPA39Pao3bEpript1gNFKpnjcvgnClgUQFL52+LLs7IwVgAGgmldAH4OCwSLjPzw10ThehcVeDcfCmiZ5WEs+6FNLUNab5B04EJrwzKWBqbUoACViAQozfgxhg/FNLcxv3q85CjQisrmDWnSAHlBVgA8i5gWVFoyQAK3rMDlYK/RqTGFalH3It7wv+rhwg7tBxpj0zCVUMO/F1xE44dOYB8BYsgqFkANBqNrPWxMQUooG6B4xdvoORvJWGnEWAYcA12bpnVPWHOjgIqEbBk/lbJkj+YBq8AytgZSwXQ0ZvPcWZpb3R1+AtX01ZAwb7/f9irOPeoF9BNKwxHQzQO6YugnP1FqRg8XOU3tKtSTMbq2JQCFFCzwItIHX6Z3h/fGVbhoWNuZBt2Vs3T5dwooCoBS+VvVS36vcmwAJSxO5YKoLXT+6DF6+XSTG+V/AF5GnyfYNb6Y4thv21Agr/bgGqoO2wdXBztjV7hvZdRCIuKQaGsbnCwtzO6HU+kAAUsL7A7eA8q724sXf2LqjYRrl/EvQaOBwUo8HkBS+Xvz89EuTNYAMqwt1QAXQoqgcK4CYPGAXb9LwBuWRPOWhCg39oX9qfiikTx0Aqp8GeNPWhawbirgKHPIhE0+ye468OhzVMFC7+tLkOGTSlAAXML7Fk9BVWujsd1Vz/kH7QX4Fc+zE3O/lOQgKXyt5rJWADK2B1LBFBYZDQcp3jBVaNFVJcjcM1eOPEZi8/zvvYPkKkQni/5ChkirmKJWzd0GjDZqBVOW7wSA+/1ls4NEfKi2OjTRrXjSRSggDICu+f1RtUnK3Ema1P4dVuqzCQ4KgWsVMAS+VvtNCwAZeyQJQLo3IXz8F1XHrGwh8OIx4D951/9Fh78Izz2/IBzhjzI/v1RZEzj9MlV3noagRdzK6Ok3TXpvGghFQzDHsLV6fNjyeBjUwpQQIbAwSlNUD5qF84W6oviLUbL6IlNKWB7ApbI32pXZQEoY4csEUB7t61F5WNdcd/BEzmGnzduthFPoZ9WEPYwYFvlbahbufwn263esB7fhCR8efzdrlfhmS2LcePxLApQwOIC58YGwld/EZfLz0KhGh0sPj4HpIA1C1gif6vdhwWgjB2yRADtWDYGtW5PxyWPiijcb6vRs709qyZyhR3Flgyd0Kj3jI+2u/08Esd/6oCvDDtwz7MB0t/dCVdE43yTPSjq62/0eDyRAhSwnID4CveHQfmRXfMMD5r+gezFKllucI5EgRQgYIn8rXYmFoAydsgSAbRnRltUebUFIXk6oli7mUbP9ub2Och7dASOa4qh4KA98HD98ONcvUFA18k/Y0b0SLhr3kDXcgOe/9YL2QwPcbTKapStxDcKGA3OEylgQYGXr6PgNi0HHDQGRH93Ac7pc1pwdA5FAesXsET+VrsSC0AZO2SJADo+oTpK647jQsmx8GnwndGzjb4XAufFFRAhOMNXuxgN/HJCfCz0D/WKIJNb3HcCT9x8DK8VZZBZEwZd1hJw7LIL1ydXQH7tRewrMQMVGyX8WNjowXkiBShgVoErly/Ae2056OAAx5FPATs+tsms4Ow8xQlYIn+rHY0FoIwdskQAXRntB2/hFm7WXIa85ZoYP1uDHlFjsksf54rHj7GNkBYReFRqEL6rXxbiQ2R3bV+HVpd7IcLeA2kGnQec3XFhRn34vNqP7bkGYY9bA+RM54p2gbkTvYJo/GR4JgUoYEqBAzs3ocLB9nhonw3ZRlw2ZdfsiwI2IWCJ/K12SBaAMnbI3AEkfkT7crQXMmpe4VmrXchYoFSSZvtoYnFk1YYmaLPKoSl+dfkGz58+Rg+HLejgsAN3czWBZ4dl0nln5reH3+NN+Dm2LpwQg/tCRvyqr4acWbNgZIMiKJcvY5LmwJMpQAHTC+xYOhq17szAZffyKNR/m+kHYI8USOEC5s7f1sDHAlDGLpk7gJ6EvUbmWXHf7YntfxUO7km7Kzfi77FIc2TaByu8a8gET7un8X+v/eoXOBVtEFcArhgEv1uLErQRP0Y+aiiMf7N2xnetmyKbh4sMNTalAAXkCuyZ0hxVov7Ghfxd4dN6itzu2J4CNidg7vxtDaAsAGXskrkD6MqVS/BeExD3DMCRz5L+PZ/oV8DpXwDX9MC/QcDrhx+sNjaDNxy67QNSOUs/C9k0FcXOjktUZZe+BDrFDMKMr4ujiT+/dC4jdNiUAskWEO8AvjjaHz64iTvVF8CrQstk98WGFLBVAXPnb2twZQEoY5fMHUDH9v+DMrua4aldJmQaeV3GTP/fNMhD+gfBzgGaMl2Al6FA/ZkJXi13I/hX5NsT905RrcYZTr2PAAdmAqdWSH93zOCNWa69sXpIG/nzYQ8UoIDRAmfuhuFlpA6eaR3hOT8/nDQx0PY4CafM+Y3ugydSgAJxAubO39bgzAJQxi6ZO4CCtyxFpdP9cMOpMPINPSJjpv9vemoVcGwR8OV8IGvRRPsz3DsFu8VVpJ/FVhwKh6pDAEFA5MT8SK17Jv39Nn0ZeHVbj6I54gpKHhSggHkFtoc8xPq1S+CNO7grZMJcxx8RBWe4jnyY9E8GzDtV9k4BqxAwd/62BgQWgDJ2ydwBtGvFOFS7NRXnPSqhaL8/ZMw0CU3FdwqLHxunyw3k+SK+oWHPJNgFT5T+3SBo0CrtKizrVRfOqeyT0DlPpQAFkioQfPUpTq0ain726xI0vWufE54jLiS1O55PAQrwCqAUAywAZfwqmLsA3DOvF6o8WYUz2b6GX9efZczUBE31McCDM9D9OQCOj8/iT30AxjsPROtyedC1Yl442PM5ZCZQZhcUSCAgPq5p1tQRGIMFH8hcT+WN/D8coxgFKJAMAXPn72RMyeJNWADKIDd3AB2e2RKB4dtwIk93lGo3ScZMTdj09iEYVjSEnSEGI2PaYZW+BiZ/5YevS3macBB2RQEKiAKz1/2Drue/gbMmBvoKA4H902GvESSciy7+KDJ4D6EoQIFkCJg7fydjShZvwgJQBrm5A+jYtMYoE7EbJwoNQqkWw2XM1MRNjy4Etn8vdRopOGFopnmY0+srEw/C7ihg2wIGg4DdY2qiOo7hZZZApOu2Ha9G54A7IiWYc24V4TvA+PeD27YmV0+BhALmzt/W4M0CUMYumTuATk6ui5JvDuJksREo2XSgjJmauKlBDyyuDjw4JXX8S2w15G2/COXy8yHRJpZmdzYscOPRc+ScXxBOmljEdtkPh+y+eDw6P7IIcc/wPJW+Lvy/W2PDQlw6BZIvYO78nfyZWa4lC0AZ1uYOoDMTq8JPexKn/SeiRMO4R7Oo5ngTFndH8Z7xiBKc0FgzHQ0qBuDorRfS6+P6VS+AzO5xzxbkQQEKJF1gz67tqLK/BV5p3OE+8g6g0SB0bHHk1se93edE1q9RqpvC3w1O+rLYggKqEDB3/lbFIj8zCRaAMnbJ3AF0fnx5FI05j3OBs+Bbq4OMmZqpqSDAsLAy7B6dwX0hAybHtEQBu3u4YciOfzSB6FKlEPpWL2imwdktBVK2wF+LR6PevRm45h6IAv3/lhZ7ZWJ5eGvPxxWAuTqjVIfpKRuBq6OAmQTMnb/NNG2TdssCUAanuQPoythS8NZfw8VKi1CkSnMZMzVj01cPEL2oFpwj7iQY5IGQHuNiWqNHz4FI4+SAOy+iUDJXOohfXxf/nQcFKPBpgT2TmqBK9C5c9u6BQi3jHsEU8r/2zgOs6eON49+EMAREAUVAAQcobgH9u+q27loU96ijjqp11b1BrVpr3dZZbavWWcW9F666ByqiCKIoioqyV0j+z11MmArkR0iI7z1Pnwq58d7nXrgvN977tTWqxyligt6oNBYePb0JIxEgAmoQ0PT8rYZJBV6EBKAA5Jp2oGCf6igvf4bAVltQqYHirV6dTHe2A3uHctPkxkUhMjQDYl8hSS5Bk6QleAVrGCMZSTBkkYd4PpuixljaoxYaVKBzgzo5pmSU1gkE+dSAszwUIa02olwDL27P7aVeqPXhpOLfNWehVqeftG4nGUAECiMBTc/fhYEJCUABo6RpBwrzrogyeI0nHfeggnsLAZZquCiLEbiuKRD3Fhh6DihiicQ/OsAk/CreyIshBQawF0XiocwBr+RWCJLbI1xuhZslPbFn9NdIlckpjqCGh4iqL1wE2Hu/kd6OsBZF43WvUyhVsTbvwM3f+8M9Yi//t3+9xaje5vvC1TGylgjoCAFNz9860s3PmkECUMAoadqBIrzLwgbv8azrUThWrS/A0gIoKk0G5KmAYRFFY8/+g3xTW4jksk82/o+0GaZKB6OoiQRbB9VFjTLFC8BQaoII6DaB7VefYd3ZQJyI68pj/sWPug9TqzLc6BsbRsEjTPEu98Pmf8C1MYVf0u3RJOt0lYCm529d7Xd6u0gAChglTTtQ1Cx7FBPFIbyPH+ycawqwVEtFn18DHvgCJSoCMeHAWcU5JmVKkRtgubQTzERJWCXrjOX9vkKzSjZaMpaaJQK6QWDp9AEYI9mTZsz0N4DESCEAt0yHR9AK/u+QjrtRzv1r3TCarCAChYyApufvwoCDBKCAUdKkA7EgsCk+JWEsSkHk4BuwKu0swFIdKcq2iE2teTgLbO4EPDmtMmyF1BO/Sbthx5B6qFveWkcMJjOIQMEReB2diNMPI9DzcHVVozEwRVHvcNXXt/79FW7+c/nX4T1Pwa6SYmuYEhEgAnkjoMn5O2+WaC83CUAB7DXlQFuPX4DfpctYC8Uv+rhRD2FmZSfAUh0sGvMa2D0QCL2gMi5QVgZ/VFgOU0tbJElT8bNndYjFiksjlIiAPhMIfBWDMWv2oH7KNcw03Kzq6kuxHexnPlR9fefQWtS8pniF58OQGyhurwd/GOrzwFLfdJaApuZvne1wNoaRABQwWppyoOsbx6L2s40qy6STnkNSxEKApTpaVC4HkmKALZ2BsGvcyDXSDtif2gBiyDB7WB+4O1rqqPFkFhHIHwJstd972UrMjsr63ONjo8pwmaoI+8LSreOb4XbpR/7v5HHBMCpKq+X5MwpUy5dGQFPzd2HiqDcC0M/PD7/++itu3LiB8PBw7N27F56enp8di7Nnz+Knn37C/fv34eDggOnTp6N///65Hj9NOdCNbT7wCFycZsfMSEBskGu7Cl3GxGjg8irg3IIMpi91P4rh7eqCLQJKDMSFrltkMBHIDYE9N0JRc19rVBCnbfUqy90zb4Bq44+kE4Bb4HZphOLrGW8BAxZaiRIRIAJ5JaCp+Tuvdmgzv94IwCNHjuDixYvw8PBA586dcxSAISEhqFatGn744QcMGjQIp06dwpgxY3Do0CG0bt06V2OiKQe6vW8Fat1SrAawixKGPpG5sqdQZ5LL8XZ+NZRIDlN1Y1Tyjzht2JgLwCaVbDC0cXlUK13sk92MSUzBiw8JcLXVw9XSQj24ZPynCETGJWPJIh/Mka/MNsst6w5wG7lV9Vncnf0w29tX8bV3FIElAkRATQKamr/VNEcrxfRGAKanJxKJchSAkyZN4mLv3j3Fs0os9ejRAx8+fMDRo4pnl3JKmnKgB6e3ooqf4u3fWJjCPN0h8JxsKsyfh67vDacXB1Vd+De1EcalDOPbwTKIUdOhOPaNaJhtF+OSpGi88AzexSXj5E+N4WxTtDCjINu/EAKrDlxEr+vdYCmKhbTZDEjOzMnQ88u2vVD/h9Vp30tJgGxVPaBUNYh7pgnDLwQXdZMI5BsBTc3f+WZgAVT0xQrAxo0bw93dHUuXLlVh3rRpE18FjIrK/i/rpKQksP+UiTkQ2zpm+S0s8m/VKfjqUZQ/rHj67R2Kw9o7tABcQftNvD25DCUuzMxgSJLckN+EviFzwXT5cBzyGZDlYsj9l1GYuvce6r3cjIbie0jy+gtBz8MRExeHCd1bIT45Fb/uPIly9nbo16IQhtPR/tCQBflM4GxgBCrbWeD48mHoK92DqGKVUWykH+R3dwDnfoEo6jlv8WLJHmg4Ym3G1mWpgEisuE1PiQgQAbUIkAAEvlgBWLFiRQwYMABTpkxROc/hw4fRvn17xMfHo0iRjwGN07mWt7c3fHx8sjhbfgvAV4HXYLutJW8nXGQDu1mP1XLwwlZI/uwKRBtbfdLsKzJXOPx0FvbF08bG79Eb/LD5KrxkxzHH8E9e9nC1xWjtP44H0X0y8D6OnLuAwUEjECy3x+Riv6BrA1f0qV++sOEhe/WEwKUnb+GzYRdeyEvgkNFUOIkjkOz5B4xqpQvq7K046nDPviuqDdmgJz2nbhAB3SFAApAEYJ4EYEGtAMZEPEXR3xUrVaFiBzjNTNum1p0fHw1YkpIA/GyrqPj7k8D1PxRxA0u785Ax8XJj+FQ7holtq8Da3BjsuaxvfzuMiVE/4yuD+yqDDhTxxDcJvvzr2aZT0Dv2zwwH7A+m1kXdiQdQsqixBjpBVRKBzxPYuscXve/2Q6pcxP9ISRYZw2hKCGBkpiqY+IsLTBIikNR9B4wrtyGkRIAI5DMBEoBfsABUZws4s/9pyoFkiTEQL1A8/fRUVAZlZ6WJm3z+GdC96gKPAokfgJo90mxLlSJlji0MkYKvkpbCurQL9g5viItP3uLd39/B0+AS5IZmEKXE8TJh8hIoI3r72b5trroBfbp0QUB4DCqWMqdbxrrnCXpr0fm1Y9AofJOqf1Hl2qNYv38y9jc2AogIAMo1pq1evfUE6pg2CWhq/tZmn/La9he7BcwugbAtX39/fxWzXr16ITIyUuuXQMDi4/ko3sV9CRvYe38ZW8Cfc95Xv3jANiGIZ5mWMhA1PMfiv5u3sOhlP76KgoHHcefgatSM2JulmliYwaznH0jdNRASaTz//LxhA1xyHgdT/80wqTsQg79pktefHcpPBNQicHzJYLSK2plWtv9hoGz2l5vUaoAKEQEikCMBEoB6tAIYGxuLoCCFQHBzc8PixYvRrFkzWFlZwdHRkW/1vnjxAn///TfPowwDM2LECAwcOBCnT5/GqFGjdCIMDDfw4xmgNyiOkl/IJZDP/cSGrusBp5dp8dA8EldjhGQfBkqOIsGxCYoM3I9bm8bALTRtZUVZX6CpOypNPAMkxyPi4SXY7PFCstwAiTCChSgBh2X10Mb7KGKTpbgXFgV3J0t+vj4hORXFTY34VnPI2ziYGUtQysIkx18s2WVgwX7pVRO10OldoYvz2qJh8iXeL1nxchCPvkWrfHo3ytQhXSdAAlCPBCAL6swEX+bUr18//PnnnzzA89OnT8HyKRP799ixY/HgwQOUKVMGM2bM0IlA0OkFYJTcDMV8Xur6z5LG7Us9PBkGV9PCYeySNkY7gyswEyUBffcCFZrjznZv1Hy4JIstt0v3Rq3Bv/Pvy+MjIVpYLkuelgYbUdHoHWrHnMJ5g/8hPlmOauJgBNl1QGR8ClyjLiABJnBq2A0T2qW91ZpTx8OjEuC9+RiSY99h1qAeKFvCjAvK9eeD4WJTFM1cbXKqgj4v5ATYs4ZikQiGH4OZP/CuhSoIQUzZ1ijquQgo7ljIe0jmE4HCR4AEoB4JQG24n0Yd6OMKYKLcECY+nz/Ppo2+F3ibz68Cf3wNGBUFkmNUzaeWrAqD4Rf5Cor//uWoippCzAAAIABJREFUfnNGFtPu11mAqu2Hqb7/bnZ5WMveqdWFC6lVMcF4JjrXdsKENpVzrGPWstWYGDmLC9WZJpPx06hxuP44DCa7++C23Bkj5vwFFreSkn4SSJbK0GaZHxeAR0c3QqJUBuk8RxQXxSFuoB/MHCkskX6OPPVK1wlodP7W9c5/tE8vzwAWFHuNOtBHAahYDaSI/5xDZDBgURrY3BkIvaAY5p7bgUpt+T8fnt4CV7+Pz2Slc4LwbkdgV6WB6jv3f26IqimKm9XxNfrB9O5fObpMiqkNDOMjVPn2p9ZH02kHYG4kwc7rz3H07jN861EWnrVKI+x9Ai4Hv0PbarZ4O68qyolf83KxchN42yxH8yKP0e7Zr/x7b8aGo2Qx0xzbpwyFk8D5wFdI2tIDcojhOHwv4mM/wG1rDUVnprwAjM0LZ8fIaiJQyAlodP4uJGxIAAoYKI06EAnAT4/Muyc8WC6/KVyhuSpfyLUjKHdIcXv4iZErSsrewFCWBJMpQRAZpsUOfLO4AUpGf7xZPfM98D4EcZs6wSw2FB/qjIGFsQGkIRdh5LUasptbIUuMhqSVN0LWdEe5yPOq9twT1yAVYswx3IT24v+wX9YAgbXn4sPt/agjvY57ZQdg1vOBPH+idVWYvLuPJzI7XJW5oqfkDP/+zY4n4O7+PwFeSEV1mcCG7Tsx6OFgbuKWOntx7votrJfPRrS4GCxmPtNl08k2IqDXBDQ6fxcSciQABQyUJh1INrskxLJkSIuUgGTSEwFWfjlFXwVehe22r3mH71o0QY1hf4HfqDa1ygAh9b4vDHb1Q5JzOxj32ab4LCoMCDgIePQHDLO/6BF5fj2sTo3/LNCbMme4ixWXkV7JLWEreo9XEnvYjvFD7NL/wVya8V3nszUXoWknhUCgpF8E2LvUh1eMweDU7bxji6TdMMTgAL94lFihDUz67tCvDlNviEAhIqDJ+buwYCABKGCkNOpAL24AJ2YBreYA9m4CrPxyika/CoHFmlq8w9dtuqD28D+y7zwThS9uAjauGYLv5kgqMRopf3nCMPxGhqzJkqKIMSwB64SQbKt4aN0CriP34P3JxbC8kPElmTOl+iOl8RRcCHoLY4kY39UvCwcr2hLOcSxymSFVJseK04/h5miJJhVL5rKU8GyxSVK0W3waqxImoLr4aYYKE0u5waT/XqCIpfCGqAYiQATUIqDR+Vstiwq+EAlAAczJgQTA00BRWUI0xL848Jr/KzMQ9QZlvRGcL81GhUH+Z3uIzG0BuxpAjR64dmo36oQobhpnToFVxqBSNx8gOQ6Ja1vA6EMwxKmKN6UvplZFOKxRVxSA5/KSuOE6HiN7e+WLmVQJcOLBa6ze/A9C5aXgO9GzwMT1vIN30eLqYNQVP8wwDKkiQxiM+A8o4UzDQwSIgBYJ0PxNt4AFuR85kCB8+V84XQDtK+WGo26/+fnfxidqvL5tDmoHLlJ9GugxE5VuzOZfJ/faA6OKLdJKsriC7Lzi4Z5Zarts0Qb1f6KtQXUGjoXXYSn9reoNO/ZgUMAAvJRbYX75v7GiXyN1qs5Q5t6LKEQlpMDDyRImhgZZ6otPlmL53LGYLP4bUokZRHWHwuDiYp5P2nQaJE0nCraBKiACREAYAZq/SQAK8iByIEH4NFP44+WZ57WnwqHDJM20kU2tN/5dDA//tO3dgOabULmCk2Kruc6gLIF+k6Jew3hJxSw1XTdrgtoT9oMFjv79bBDOBL7Byl5usCuWdoklvzsVEB4Nc2NJtqtjh/3DseTEI/h8WxUNKpTI76bVqo8JvYevYsC2d+2LF8GN0PewL26CeYcD8PJDIo6MbqQSZpsXjkLfeMUt77XS9nAftBLujpYwEH869E5Kqgz7b7+EqZEB2la3y2DjqYDX+P6v6yyiJFpWLoX139XOEsbnwK1QNPRtACtRLOQdlkHk3hfY+R1gaAp4rgYMJGr1mwoRASKQfwRo/iYBKMibyIEE4dNI4ZjNvWHy/DwMR98EzApOsNw+uBa1rqet7DxuvwsudVp9to+RPk6wkn/geUKKVEW5hPu4WaQ+3CcdxaozQQg5uQ7NxbeQ3PY3eDb4GDpEILUzDyMQFBGL778qx187eR6ZgJaLz8GiiCHOTWiK19GJsC1mAlMjCS4/eYfvNl6BSWocrKxL4MTYJjCSiAVaoF5xtqrGbGJp2cnHWHLyUZaKqouCUUIUhamjR8PZxhxzDgagwdXhaGlwi+eNlxujftIKGBe1xsjmzuhd1ynL6ywsbl/P9f/hZWgQDEVSbJvUE6WLK8Q3E54T1vli2IspcBRFYLm0E4q1mYZBjcqrbLkS/A4b/1yHteIFiDO0gtmUIECcdZVQPQpUiggQgfwiQPM3CUBBvkQOJAifZgqzbUBp0idv8mqmUcD/5BZUv5AWgzC0yxE4VUuLPZhduwELm6NyvOJCyQOHnqjyfBtuG3ug1pTTWLVoOkbEruCfXag2F191GcmFG7vQIBGLMa9zNVx68g7FixjC1dYCYrHiwvOuG2FoUMEaFUoq4suFvovD8fuv0bOuI/zDojB5gy/sRe/Q7ptu2PLfM4S9jsA+oxn8/KFvlaXYfzccXzmXQN96Thi/6zYmSNejl8Ep+Ei/w6minvhncF04WZtpCiNf1Zux7x4cLE0xrGkF3s75x2/Qb+NVNKtkg3GtKsFnhx9c3p7EW3kxvJCXQBeDczCEFL0+htZ52OsqpGa2GLVyJ04bZ721fT61GpZIu0BSth7aV7fD2cAISGVy/OJVA9uvPUfq2V8xRvIvkiHB+Q7n0Lq2K1afe4KFRwMxV/IH+khOcbtS5SJ0T54B6ypN8Fu3WohJTIHnkmNYJfsZtcWPEF29Pyy8lmmMFVVMBIiA+gRo/iYBqL73ACAHEoRPrwo/OL8XVU71V/UpvO952FX4/Krd9SVdUTvqOC/jX3seql+fCn/DGqgyxQ93fP4Hd5Fileu8yyTU6TYJ3X/Zju6Ju5ECA+wq8SNsI/zwXm6OB3InQGKCBKkcTcR38UTijIX9mqN+eWv0XnkMxuHX8dK6AWTvHmO3oTeKieLRJ3kKWopvoJPBBf41S+NThmK8ZCf2pTbAfGkvzJRs5m8ts5QgN8LAlAlo3qYLBjdOW/HKr0FkQjUhJRWRscnoteEKr/bXLjXwlUsJ/PjPLcQ8u4tguR2kkGCeZL1K7GXX/v2Oh/Ak3gwdTzblH8sMTCD+Zing+4MqOxNvf6e2wp+prVFXHAA5RHhUqgPKvjmFZQZpou37lIkItmwIy3e3MEqyF00N7vA65BITiKSJfFVxmbQzLJqOwtXQDxj1bBQ8xI95faJBp4AyHvmFiOohAkQgHwnQ/E0CUJA7kQMJwqdXhYOunYDzoS6qPr0fehuWdlnfHE7f6fcHpsPyhmKVz7/RalQ/PwwPJZVgNOQkSq1yVrxzDMCv7Ci8qToQdQ+2RBmR4lnA46keaGWQFo7muawkIlEUNcXBPP7gaINp8GrXBjb7enPRckdWHk6i1/wJstykCHlx2IgU29OwKANEh/F/bnP+DT37DMpNFbnOw7ZW6847hbexSWhY1hyDw6bhudwG06Tf8zq+FV/AMqPfESqz4aJthMSXn69TJeNiim3WBEWMxTtt9uDBo0foGTxZkaX5DKDxeCD0EpCaAtzeCtzNetFmnbQ9vAz8YC1Ke2pwh7QpjslqY53hYkhEMl5dqlUFGAw5A+zqDzw5zb83L6UnJJBhouEOpBoVhYHXBqBSm1wzoIxEgAgULAGav0kACvI4ciBB+PSqcOi9y3DanTbhJ4wNRpFi1p/vY2IUpLu+h6jKtwiMM0WV0wPxWFweL1qsQtMTiuftWPIrPQhXYmwwIXperpmFyUtgYUp3LDdalaFMUrFyMIx/DXGKYtXvs0liArSZD1RsA/niKhBBjhXybgirPhIT2lRCCXNjVfHAVzH8fGC5EnnfHo6KT0GHOZthjBQ4iN5gk5HimTzf1AbYkdoMCyXr4CB+k8FUGcQQd1oNhPgpBJ5JMcTPLw9TeQJuNt+KoKtH0S12M56W6YiygzZn7WbwWcj3jYCIBQDPlBKKOiHWbQhK+k3L8Im8TF2IKrUGKrYFSlUBZKmIWNMBNhGXwHjb4R0MRHLg298Bt9450aXPiQAR0CIBmr9JAApyP3IgQfj0qnD4k7uw25wWYkQ2LQJiwzSBlFNnA/87hEpHeyFE5IB7Lj/gm0dp4uOkVU8Yv72HRmJ/xP5vNMwTXwN3Fa9LwGOAIlB4+B3g+h+Q2bsjNf4DDD8Eq5rkK1bFHYBUKfDtSiAmHAg4oHghJfAovywT+fQurJJfpplpWRYYcASwsOffe7RtMioGrsYWaQvMlA6AlXkRXJ7SHIYGYjABV3O2Yis7cG4bGEvydukh6HU0zH+vwV9NuS9zQlVxaPa4jMyBZMXKn9S4GCRTMj6lFjLXDeWkwbj61QZEX1iHlriKV/VmwLbNJ15viY9UrAo6twB8hwH39yra9VwNmW1NiNekO8PJVhlHXgfMbTLYFnJqA8qdH6f63tsKXijR548st75zGn/6nAgQgYIlQPM3CUBBHkcOJAifXhV+Hx4Cy7WKV0hS5AYw9Mn45FtOnX1y4zQqHOiEMJTCfcsWaP3+H1WRfWiKdvLzMBSlAiNvAlblgcDDQBErwKm+Ih+7AfImUPHZ+6eQ/dMN4vchSHVoAIM+OwHjop81IW7bAJgF7lHk6biCr/qlFzshR1ei3H8KUfpBbobByePw06D+qF/BmodhubB+LGLkpmgxcA7/Xl7S1QdB+N/Oz5yVY+FTOiwFanYHFlYA4t8C1byALhszNPN4Xj24JAfgSLXFqHJ3AZzEEUju7Qsjl2Y5myNLBR4dA+LfAW59FDw3ewLSRKD+j0BJV6Bk1rA97+6fgvWuzqr6o7vvhUXltPepc26YchABIqANAjR/kwAU5HfkQILw6VXh+Ki3MF2iuLXKLgaY+kTkqX9P/S+h7L9t8RpWCDZyRf3kS3gvtoKlLFL1pnCkkR2spmZ8WeKTjSTHAc+vAE4NAUkuViLv7gT2fHyT2DsqS7UR131hc7Bfhu8f73gdrdxdcPzCf2h1sjX/bFXtI/Bq5I7eG/5DZ/cyGNEs5xcvTvudQ/PTHdPVLQJ+OA+wVcjol4B5KaBIccXnkSHA5VVAo3GARcYYfQ8XNIZr4h1sKzUePV9/DMo9MSTLW9B5GpgcMqdGPoXB8pqqXPKfAiD6uGqan+1QXUSACOQvAZq/SQAK8ihyIEH49KqwLDkR4nmleJ/i5MYwy6MAfBF4E6W3NUOkvCheiW1QRf4ET8w9UCE27aJHqGNnOA3cpBlubMXr2gbArhbgUCdLG4nPbsJkY8aVtLN11qJp+x7Ys3srOt8bzsvMLzoV0eXaYtvV5/zrkPntsgRKzlz54f070O7mEERKSsGqfl+FsGPBs/OY7i/8GlXjr2KfYTt8m3IYUQaWKDYj4zu8eawy5+zsUsmcdPEmZ32g7d+cqVEOIqB1AjR/kwAU5ITkQILw6VfhdM/QqbMC+PppAEr9WQ+xchMkwAglRdF46NAdrs/TbquGNlkCp2YDtcMtNgJY5JKh7RNVF+DrrsOwc+1cdAtXXNzYJG2Nv0UdsUk0G76yhug+4Xekf8WEvbLBzg2mT/s3L0PHJzMRUtQD5cYpbtWqk/wXtUf12Au4KquE/4kDEVrUDU7jzqpTVd7KfHx9hhfKZvU0b5VRbiJABAqCAM3fJAAF+Rk5kCB8+lf4oxBQRwBGhofA6uMZQiWYh24z4XpL8Z4wSy+6HkLpql9ph5tMBsy2zND2sfKT0bLPZPguGgqveIVQDZA54K6sArpLFMLrqFcAXGwt8POhAP5c287rYVjUtSY61lRcLmFp3+qp+Pb1KjyyaY2Kw3eq3b/bS71Q68NJLqLNRYl47NAVLt9vULu+XBf8OO4sRqHE+12ui1FGIkAEtEeA5m8SgIK8jxxIED79K/xRCMTJTWDm8zpP/YuNDIf5cldVmQSYgK34uZ4bpvre+4GXYelYJU/15mvm9CtdAI7ZDccG+Tf47oUPvjH4L9umfq+1Dxv8UxAZlwwLxKG++AFOydxwcWprlLIw4WUO/TYY7WN2IrB8P1T6brnaJt9a0Qtu7w6pyj+tPQ1lO6Q9z6d2xTkUTJ3vBIOkD0gp1xyG/T7eJNZUY1QvESAC+UKA5m8SgIIciRxIED79KyxgBTAp7gOMf3VSMQmTOCGhxVy4HOur+l7KT49gaKE4Z6iVlEkAHi7eEz+9aoWDRtPgLH4JmWlJiOMzxuv7rfg0XH0txhKjVUiQG6OCOBwnU90Q1mYT+jdUBMo+Pc8TzZPP4EmtiajgmTH2Xl76efP3AXCP+HiTGcB7z62wrNUhL1Wol/eVP3B1HdB0apaLKepVSKWIABHQNAGav0kACvIxciBB+PSvsAABKJcmQTQ3LcZcoHldmLScDCffTmmcpkfk7kavpsheXA5cXIZnxWvD8eVRHDBsA9ckf7iIXyha7LEN2MFCqKSqLFgrbY+hkrRVOeUHmyutQt+effiXV3waoa78LsKaLkGZpuqfcbyxbhg8XqaFz0kdeRsG1p9/jUVTqKheIkAEdJsAzd8kAAV5KDmQIHz6V1jAFjCPO+fzMdQJe86sVGeUbPYD7Le34pwSYQQT74yra1oBKJcjYN9CVL49D6dk7mghvplmxuTnQFI0v00sjwqDyH8XbsvKo5Y4LSi1MvN+6+/RceRiJEtlCJ5dA67i54jushMW1RThZNRJ1zeORe1n6WIDzngLGBiqUxWVIQJEQM8J0PxNAlCQi5MDCcKnf4WFCEAm8maVgIkohXO5WW4onJr2g/UmxWsUb0VWKDErRCeYBR1fB+dLExAss0V58SuFTd02A1XSxfJ7Hwosq/FJe4+ZtEXrydvx7F08zJe78Ld95T9chMi2mtp9vP73ZNQOXq1bglnt3lBBIkAENEmA5m8SgIL8ixxIED79KyxQAMZ426EoFG/03qo8AS7Nv4P5qur862cGjnCc4a8TzJ5d2gXH42lx+iIktrCZHpjFtrhFNWAWm/2zbpfE7pD32oV1J/3x1ytPRdlJoWkBn9Xo6c1t3nAPXMJLRoqKw2rWJ56UU6NuKkIEiIB+EaD5mwSgII8mBxKET/8KK88AwgSm3nm7BcxFi7cjrKB4heOux8+o0qwXJIsUZ9geSyrCZfo1nWAW4X8SNv96qWx5WqQqyk66lMU2+b+D+DZwdomFi2mXPB/Oopc4YTwRcSIzmM1K9xaxGj29tWsB3O7P5yVfGJRG6RkP1KiFihABIvAlEKD5mwSgID8nBxKET+8Kv13TASVenceb6kNR0mthnvv32qcCSsnf8nIPvlqJKk27A3NL8q8DDaug0rTLea5TEwViQ2/BfFNTVdWPLBuj4ugDWZt6cQP4qyPQcAxwZm6Gz6Pkpjgp84CXwXn+/ZcmFWA/Od15QjUMv+27FLVuz+Ilg40qovxU3RDManSFihABIqBhAjR/kwAU5GLkQILw6V/hpBgg9BJQvhkgMcpz/8JmV0YZmWIVLKj1FjjX66C6GPLAuCaqTPHLc52aKCCLDIV4edr5vgB7L1Qeku7yRfpG2eUWkQjIFEIms13BVo1QftRBQebePbwWNa4q4v4FFnFDpUkF8AqIIIupMBEgAtoiQPM3CUBBvkcOJAgfFc5E4OmcGiibqji39tzrEByqf6USTveKeKDaJPWfSctX2IlRwAJHVZUPKw6Fa68cVjxZeJiAA4BbH8Tf3gNTueKsozI9LtsLLv0VFzjUTfdP/IWqF0fx4vcsGqPaT9msSqpbOZUjAkRArwjQ/E0CUJBDkwMJwkeFMxEImusBZ2kQ/+67/hdhXbaaSgDeNa2HGhOP6QYzmQyy2VYQQ87teewxEy7fjPu8bQkfgEdHAdf2iF3ZGOYxGUPDPKg+EVW81A8CzRp/6LcTrqcHczvuWLdDzZHbdIMXWUEEiIDOEaD5mwSgIKckBxKEjwpnIvBwbj24SgP4dxNGBaCIlb1KAN42b4xa43VnRSvlZwcYpkRzW5+1+B2OjXrnfjx39gMe+GbIn/TNKhh7KAJDq5uCLh+A8zFFHbftuqPW0HXqVkXliAAR0HMCNH+TABTk4uRAgvBR4UwEAufURqXUx/y78qnhEBmZqgTgi9JtUXrwdp1hJl/dEKLX97g9KX0PwrBCo9zbFvsGeHYZcPka+NlWUW7wGaC0e+7ryCZnyM1TKLe/s0IAlh2EWv1/E1QfFSYCREB/CdD8TQJQkHeTAwnCR4UzEXgyuwYqyD7Grpv1IcPlCXmj8RC1mKE7zELOA399fGd3zD2guIN6toXfBd4FAdUUwk1Ien7/Ehx2teVV3Kn8E2p2V9wIpkQEiAARyEyA5m8SgIJ+KsiBBOGjwpkIPPOpDEf5x1h43op4gHh0DLjvC7RfBBiZ6RazZ1eA2NcZXwDRooWvgm7BdosiPM09N29U+3asFq2hpokAEdBlAjR/kwAU5J/kQILwUeFMBF56O8MeH9/7VQpAopRrApFhD2G1oS7P/7DhErh+PTDXZSkjESACXxYBmr9JAAryeHIgQfiocCYCb70dUeLjSyAgAZhn/4h980z1dF5gi42o1CjttZI8V0YFiAAR0GsCNH+TABTk4ORAgvBR4UwEomfZwUL0MT4eCcA8+0dyzDsY/Vael3vcdhtc6rbLcx1UgAgQgS+DAM3fJAAFeTo5kCB8VDgTgcRZJWAiSlF8lwRgnv1DnhwH0Tx7Xu5t9wMoUblxnuugAkSACHwZBGj+JgEoyNPJgQTho8KZCaR/Lo0EYN79Q5YKzLZSlBtyFrB3y3sdVIIIEIEvggDN3yQABTk6OZAgfFQ4E4Gkf4fD2H8rkpyawXhAxkDJBCuXBLZ4KW4mDz4LGEhyWYiyEQEi8KURoPmbBKAgnycHEoSPCmcmkBwHBB5RBEg2KUZ81CEgVzxPB5FIndJUhggQgS+EAM3fJAAFuTo5kCB8VJgIEAEiQASIgFYI0PxNAlCQ45EDCcJHhYkAESACRIAIaIUAzd8kAAU5HjmQIHxUmAgQASJABIiAVgjQ/E0CUJDjkQMJwkeFiQARIAJEgAhohQDN3yQABTkeOZAgfFSYCBABIkAEiIBWCND8TQJQkOORAwnCR4WJABEgAkSACGiFAM3fJAAFOR45kCB8VJgIEAEiQASIgFYI0PxNAlCQ45EDCcJHhYkAESACRIAIaIUAzd8kAAU5HjmQIHxUmAgQASJABIiAVgjQ/K1nAnDVqlX49ddf8erVK9SsWRMrVqzA//73v2yd688//8SAAQMyfGZsbIzExMRcOyM5UK5RUUYiQASIABEgAjpDgOZvPRKAO3bswHfffYc1a9agbt26WLp0KXbt2oXAwEDY2NhkcTomAEePHs0/VyaRSIRSpUrl2kHJgXKNijISASJABIgAEdAZAjR/65EAZKKvTp06WLlyJXcwmUwGBwcHjBw5EpMnT85WAI4ZMwYfPnxQ2yHJgdRGRwWJABEgAkSACGiNAM3feiIAk5OTYWpqit27d8PT01PlUP369eMCb9++fdkKwEGDBqF06dJcLLq7u2PevHmoWrXqJx0yKSkJ7D9lYg7ERGZUVBQsLCy05sjUMBEgAkSACBABIpB7AiQA9UQAvnz5kgu5S5cuoX79+ioPmDhxIs6dO4crV65k8YrLly/j8ePHqFGjBhdwixYtgp+fH+7fv48yZcpk60Xe3t7w8fHJ8hkJwNz/0FFOIkAEiAARIALaJkAC8AsWgJmdLyUlBZUrV0bPnj0xZ86cbH0z8wogE36Ojo54/vw5rQBq+6eZ2icCRIAIEAEikEsCyh08tktYrFixXJbSr2wiuVwuL+xdUmcLOLs+d+3aFRKJBNu2bcsVkrCwML4FTIkIEAEiQASIABEofATYAs6ndv0KX2/yZrFeCEDWZXYJhIV8YaFfWGLn+tjq3I8//pjtJZDMmFJTU/n5v3bt2mHx4sW5osjaYNvPRYsWBbtBnJ9J+dcJrS7mJ9Wc6yLuOTPSVA5irymyOddL7HNmpIkcxF0TVHNXJ1v7iomJgb29PcRice4K6VkuvRGALAwMu/Sxdu1aLgRZGJidO3fi4cOHPLQLCxHDzgnOnz+fD+Hs2bNRr149ODs784siLH6gr68vbty4gSpVqmh9mOl8gnaGgLhrhztrldgTe+0R0E7L5PPa4U6tKgjojQBknWEhYJSBoGvVqoXly5fzlUGWmjZtirJly4LF/2Np7Nix2LNnDw8abWlpCQ8PD8ydOxdubm464Rv0i0E7w0DctcOdBKD2uBN77bGn3zfaY08t65kA1KcBpV8M2hlN4q4d7iRCtMed2GuPPf2+0R57apkEoM76ALtxzLarp0yZAvZEHaWCIUDcC4Zzdq0Qe2KvPQLaaZl8XjvcqVUFAb3aAqZBJQJEgAgQASJABIgAEciZAAnAnBlRDiJABIgAESACRIAI6BUBEoB6NZzUGSJABIgAESACRIAI5EyABGDOjCgHESACRIAIEAEiQAT0igAJQL0aTuoMESACRIAIEAEiQARyJkACMGdGBZ5j1apVqniGNWvW5K+bsODWlNQn4Ofnx5myQN/h4eHYu3cvPD09VRWyqPCzZs3C+vXreWDwhg0bYvXq1XBxcVHliYyMxMiRI3HgwAEeOd7LywvLli2Dubm5+obpeUl2k53F22QB2YsUKYIGDRrgl19+QaVKlVQ9T0xMxLhx47B9+3awW5GtW7fG77//zgO4K9OzZ88wbNgwnDlzhvNmQd9Z3ezpRkrZE2D+y/57+vQpz8BeOpo5cybatm3LvybuBeM5CxYs4NEcRo8ezR8oIPYFw51ayZkACcCcGRVoDvaiCXu1ZM2aNTyINfuFsWvXLgQGBsLGxqZAbdGnxo4cOYKLFy/ygN+VZ8HVAAAN0ElEQVSdO3fOIgCZKGGC4q+//kK5cuUwY8YM+Pv748GDBzAxMeEo2MTJxCN7bSYlJQUDBgxAnTp18M8//+gTqnztS5s2bdCjRw/OSSqVYurUqbh37x7namZmxttiwu7QoUM8SDt7lJ0938gENhsvltgzjSywu62tLRfxbAzYz8jgwYMxb968fLVXnypjf6gYGBjwP2LYHzjMtxm/W7ducTFI3DU/2teuXUO3bt1gYWGBZs2aqQQgsdc8e2ohZwIkAHNmVKA5mOhjkyV71YQl9t6wg4MDX3maPHlygdqir42xd5vTrwCyyZG9B8lWocaPH8+7HRUVxVegmChhAiYgIIA/Ech+odeuXZvnOXr0KH87OiwsjJenlDOBN2/e8D9kzp07h8aNG3POJUuW5CK6S5cuvAK2Wli5cmVcvnyZP9fIxHuHDh34u9vKVUH2B9KkSZPA6jMyMsq5YcrBCVhZWXERyFgTd806RWxsLNzd3flqNntliv0Rw/6gJ5/XLHeqPfcESADmnpXGcyYnJ8PU1BS7d+/OsD3JtrvYtuS+ffs0bsOX0EBmARgcHIwKFSrwlRH2S1qZmjRpwr9m27wbN27kAvH9+/eqz9mKFlsdZCu0nTp1+hLQCe5jUFAQX5Fiq6vVqlXD6dOn0aJFC861ePHiqvqdnJwwZswY/mQj27bcv38/bt++rfo8JCQE5cuXx82bN3Xm+UbBcDRYAVtFZX7KfpcwP2dPYBJ3DQIHOGsmuJcsWcKfIlUKQPJ5zXKn2nNPgARg7llpPCdb4ShdujQuXbqE+vXrq9qbOHEiXzG5cuWKxm34EhrILAAZb3bmj/G3s7NTIWBbNywv25ZnW41sC41txadPbDXLx8eHb6dR+jwBtprdsWNH/sfMhQsXeGa28se20tnZv/SJnXllW2Zsa37IkCEIDQ3FsWPHVFni4+P5FvLhw4dVZ9qIf1YCTGiz3yXsvB87O8l4s1Vr4q5Zb2HnWX/++We+Y8D+SEwvAIm9ZtlT7bknQAIw96w0npMEoMYR8wZIABYM58ytMJHMtnOZ+CtTpgwJwAIYBrarwC7QsG1HtrOwYcMG/sckW00l4a2ZAXj+/Dk/JnLixAnUqFGDN0ICUDOsqVZhBEgACuOXr6VpCzhfcX6yMtoCLhjO6VthFzvYEQZ2G5tdslEm2g4r2LFo2bIlP+7QvXt32gLWEHpfX19+JIRdwFEmtgXPfu+wy01sJZuNAx170NAAULW5JkACMNeoCiYjuwTCtr9Y6BeW2LaZo6MjvxlJl0DyZww+dQmEXQBh5/xYio6O5pcVMl8CuX79Or9JzNLx48fBbrnSJZBPjwu7YMMuMLFLN2fPns0QVoeVUh6I37ZtGw+rwxLbZnd1dc1yCYTd/lXehF+3bh0mTJiAiIgIGBsb549jfAG1NG/enP8+Yeda2SUQ4p7/gx4TE8OPLKRPbLWV+TS7uMQu9RH7/OdONeadAAnAvDPTaAl23owdHmahRpgQZLfGdu7cyW9Gpo+LplEj9LBydiOPXUBgyc3NDYsXL+ZnzNghbTYhsrNmLF5X+jAwd+/ezRIG5vXr1zxEjzIMDNvqoTAwn3aY4cOHcz5s9S997D8W7oXFBWSJbQ2zs3xMbLNwGUwwssTOZrKkDAPDblovXLiQX2Do27cvBg0aRGFgPvOzymLPsdBFzL+ZKGHjwPycrUB9/fXXxL0Af8+l3wImny9A8NTUZwmQANRBB2EhYFioBjbRsZtjy5cv5zEBKalPgK0+McGXOTGxzYSHMhA0W1lilxS++uorHr6hYsWKqiIsEDRbiU0fCJqNDQWC/vS4sNXW7NKmTZvQv39//pEyIDFbjUofCJrF/VMmtqLChCIbR3b5g40bE+wUCPrT7L///nucOnWKx01kgpudR2MrUEz8EXf1f5eoUzKzACSfV4cilclvAiQA85so1UcEiAARIAJEgAgQAR0nQAJQxweIzCMCRIAIEAEiQASIQH4TIAGY30SpPiJABIgAESACRIAI6DgBEoA6PkBkHhEgAkSACBABIkAE8psACcD8Jkr1EQEiQASIABEgAkRAxwmQANTxASLziAARIAJEgAgQASKQ3wRIAOY3UaqPCBABIkAEiAARIAI6ToAEoI4PEJlHBIgAESACRIAIEIH8JkACML+JUn1EgAgQAQEEpFIpDA0NecDxDh06CKiJihIBIkAEPk2ABCB5BxEgAvlOgL3ywZ7Vy5weP34MZ2fnfG9PnyokAahPo0l9IQK6S4AEoO6ODVlGBAotASYA2bvJ7Mm39KlkyZIwMDDI0q/k5GQYGRkV2v7mp+GaEoDEOD9HieoiAoWfAAnAwj+G1AMioHMEmABkbyr7+vpmaxt7a9nDw4O/wbx161a4u7vjxIkT/A1stnIYHBwMa2trfPvtt/jll1/4+78sbdiwAZMnT8bGjRsxfvx4hIWF4ZtvvuHvObO3hH18fBATE8PfCv7tt98gFot5Ofb26tSpU7F9+3ZERUWhevXqWLhwIRo3bpytfUoR9scff/A+nDx5EmXKlMGSJUvQvn37DLa8fftWVcfu3bvRo0cPsPIsTZ8+HUePHsXQoUMxe/ZszoSxWbp0Ke8r+79MJsO4ceP4O70sKdtes2YN/v33X5w/fx6lS5fm+Tt16qRqi72PzMox25iobtSoEX833NHRkefp06cP7zd7A3j16tX8zWq2AkuJCBABIsAIkAAkPyACRCDfCeRGAN65cwc//vgjBgwYwIVgpUqVuMByc3ND2bJl8eTJEwwbNgxt2rThwkYpAEeMGIFmzZphwYIFXMx17twZtWvXhpWVFWbMmIGgoCB06dKFC0IvLy9ejrXBvj9//nzY2dmBCTVvb2/cv38f5cuXz9J/pQhzcHDgwouJVWbbli1bwIRX8eLFVWI0JwG4bNkyLhpnzpzJBVjXrl3RvHlzVK5cGT/88AP8/PwwZMgQXL9+nbejbLtEiRJc/DZs2JCLYmbHgwcP4OLiAraax0RskyZNMHLkSC4AmcD09/cH4yqRSLgA3LdvH2cxYcIEzrhq1ar5PtZUIREgAoWTAAnAwjluZDUR0GkCTAAysWRiYqKys23btti1axf/mq0AJiUl4dq1a5/tB1uxGzNmDF69eqUSgIMHD8bTp0/h5OTEvzdo0CDs2LGD51GuFLZs2RKurq5YuXIlQkJCuGh68eIFSpUqpWqvadOmfAWQCafMSSnCmEicNWsW/zg6OhrFihXjK5WsfuVqZG4EYGbbmP1MDIpEIl43OxfJxCBb1VS2zcTxihUrVKYxkdugQQMuhtmK56JFi3Dv3j3V54wns+/w4cNcYDIBeObMGc6KXSqhRASIABFIT4AEIPkDESAC+U6ACUAmuNjWozIxccZW35QCkK1gpf+cff/48eN8Ze/hw4dccKWmpvJtTPafsbExF11s25Ot/CnTtGnTcPDgQb7ypUy9e/dGSkoKdu7cyVfBPD09VeJQmYcJpm7duvEt6E8JwD179mTYdmV9WL9+PXr16pVrAchu82a2LTY2ltulTGyVj/3HtqWVApDZxdpRJrbSx7gwATp27FguDtMLbJYvPj4ea9euBRPJTAC+e/cOR44cyffxpQqJABEo/ARIABb+MaQeEAGdI5CbLeB69erxVSxlYlu+VapU4dvCTJhZWlri3LlzfHuUnetjZ9iyW3VTnrNjW6jKpDz/xrZ6mZBiW8Bsu1e54qbMx+q0tbX9pADMHIqF5Wdn81j97Bwi21plIkuZ2LZz3759s5wB/JRtynJsRVTJIzcCkAk8th2c3U1rdtGGrQSmZ6BzDkIGEQEioHUCJAC1PgRkABHQPwLqCEC2jcsub7DVPmViW7DKix3qCsCAgAAuLC9duoT69evnCvanbuKmF4BMHLKVxbi4ONVKHLvIwS6fZL4Eoo4AZCt+yrOPzOg6depw+9n32MopO1PILssULVo02z6RAMzVUFMmIvDFEiAB+MUOPXWcCGiOgDoC8MaNG/wyB9vabNeuHb/9OmXKFISHhwtaAWS9ZDdzr169isWLF6NmzZp48+YNvz3Lbh+zSyaZU24EIKuDXVZhN3zZxZTLly9j4sSJiIiIyBcByFby2JYwO/f3999/861xtorJLsuwLeRatWrxc5BMILNbwuysH7s1zLbE2VY7CUDN+TfVTAT0gQAJQH0YReoDEdAxAuoIQNYFtiXMVtDYGT92SaN79+48bIqQLWBWL7s1O2fOHH4xhZ1NZOKKbbmyCyDZ3YzNjQBk9TLBxVb9Xr58ia+//hrsogvbws6PFUC2yse2sC9cuAB7e3t+C1h5q5m1zYQxa5ud8WN8WJgadjmF5WOrgiQAdeyHgswhAjpGgASgjg0ImUMEiAARIAJEgAgQAU0TIAGoacJUPxEgAkSACBABIkAEdIwACUAdGxAyhwgQASJABIgAESACmiZAAlDThKl+IkAEiAARIAJEgAjoGAESgDo2IGQOESACRIAIEAEiQAQ0TYAEoKYJU/1EgAgQASJABIgAEdAxAiQAdWxAyBwiQASIABEgAkSACGiaAAlATROm+okAESACRIAIEAEioGMESADq2ICQOUSACBABIkAEiAAR0DQBEoCaJkz1EwEiQASIABEgAkRAxwiQANSxASFziAARIAJEgAgQASKgaQIkADVNmOonAkSACBABIkAEiICOESABqGMDQuYQASJABIgAESACREDTBP4PZURLP2eDERQAAAAASUVORK5CYII=" width="640">
[False False False False False False False False False False False False
False False False False False False False False False False False False
False]
Remove that one frame.
```python
print(len(f2))
print(len(f1))
frame_beginning = 70
frame_ending = 69
f1 = np.vstack((f1[:frame_beginning],f1[frame_ending+1:]))
f2 = np.vstack((f2[:frame_beginning],f2[frame_ending+1:]))
e1 = np.vstack((e1[:frame_beginning],e1[frame_ending+1:]))
e2 = np.vstack((e2[:frame_beginning],e2[frame_ending+1:]))
mjd = np.hstack((mjd[:frame_beginning],mjd[frame_ending+1:]))
exp_times = np.hstack((exp_times[:frame_beginning],exp_times[frame_ending+1:]))
sky1 = np.vstack((sky1[:frame_beginning],sky1[frame_ending+1:]))
sky2 = np.vstack((sky2[:frame_beginning],sky2[frame_ending+1:]))
xpos1 = np.vstack((xpos1[:frame_beginning],xpos1[frame_ending+1:]))
xpos2 = np.vstack((xpos2[:frame_beginning],xpos2[frame_ending+1:]))
nframes = len(f1)
print(nframes)
print(len(f2))
plt.figure()
plt.plot(f1.sum(axis=1)/f2.sum(axis=1),'b.')
plt.ylabel('Flux')
plt.xlabel('Frame number')
plt.show()
```
477
477
477
477
<IPython.core.display.Javascript object>
<img 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" width="640">
And the contact points from the white light curve:
```python
contact1 = 97
contact2 = 119
contact3 = 301
contact4 = 324
```
Now we can plot the spectra to begin defining where the bins should be located.
```python
wb.plot_spectra(f1[nframes//2],f2[nframes//2],wvl_solution_1[nframes//2],telluric=False)
wb.plot_spectra(f1[nframes//2],f2[nframes//2],wvl_solution_2[nframes//2],telluric=False)
```
<IPython.core.display.Javascript object>
<img 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sCmJEACCUWAAjChSHOcpECAAtD6XfQJAbhy5UqMHDkSYWFhOHPmDGbNmoU2bdq4pLN8+XKEhIREe1/a5s6d2/76999/r/o9e/YsKlSogDFjxqB69epuU7daAPaZuUNZ/D5uVhzvNyqm5rX28EW8/NMG9XPK5MlwaHgrt+fLiiRAAolHgAIw8dhzZN8jQAFo/Z75hAAMDQ3FmjVrUKVKFbRt29ZtAbh//34EBgbaKebMmRPJk9tCp0ybNg3t27fHuHHjUKNGDYwePRp//fUXpI3Uc6dYLQB7/rUdf4WdxCctSuKdhkXVlHadisCTY1arn5MlA46OeMKdqbIOCZBAIhOgAEzkDeDwPkWAAtD67fIJAajHkCxZMrcF4JUrV5A5c2ZDiiL6qlWrhrFjx6r3Hz16hAIFCqBr167o3bu3W+StFoDdpm3DrK2n0LdVKbxRv4iaU/jFm2j41XL7/MI/pwB0a7NYiQQSmQAFYCJvAIf3KQIUgNZvV5IWgIUKFcLdu3dRtmxZDBo0CHXq1FFE7927h/Tp02PGjBkOR8kdOnTA1atXMWfOHLfIWy0A3/t9C+btPINBrUujY50gNaebdx+gzMCF6udUKZLh4DAeAbu1WaxEAolMgAIwkTeAw/sUAQpA67crSQpAOcaVe4BVq1ZVAnDixImYOnUqNmzYgMqVK+P06dPIly8f1q5di1q1omLp9erVCytWrFD1jIr0Jf+0IgJQrIYREREOR83xtW1dftmEJXvP4/O25dCuekF7t1uOX0HbH9aq348Mb4XkyZPF15DshwRIwCICFIAWgWW3SZIABaD125okBaARtgYNGqBgwYJKCHoqAMWKOHjw4GjdWyUAX524AasPXYRz1o9b9x6g9ACbFXDPkOZInzql9U8KRyABEjBFgALQFD429jMCFIDWb7jfCMCePXti9erVWLduncdHwAltAXzux7XYfOwKxr1aGS3K5rE/DY8ePUaRT+er37f0b4qsGVJb/6RwBBIgAVMEKABN4WNjPyNAAWj9hvuNAGzatCkCAgIwc+ZMRVWcQCTki4R+kSJOIGIhfP/9973GCaT1mNXYeSoCkztWQ0hJR8/k4v1Cce/BI6zp3Qj5Mqez/knhCCRAAqYIUACawsfGfkaAAtD6DfcJAXjjxg0cOnRI0ahUqRJGjRql4vxlzZpVibY+ffrg1KlT+PXXX1UdCekSFBSEMmXK4M6dO+oOoAi9RYsWoXHjxqqOhIERp4/x48crIShtpk+fjn379iFXrlxukbfaCaTpqBU4eP4G/nijBmoXze4wpwqDFyHi9n0s6d4AwTkzujVfViIBEkg8AhSAiceeI/seAQpA6/fMJwSgq8DOIuCmTJmCjh07Ijw8XDl+SPnyyy8xYcIEJQrF27d8+fIYMGBAtODQEgJGCwRdsWJFfPfdd8oy6G6xWgDW+3IZTly+jZnv1kblglkcplVz+FKcvXYH/3ati7L5Mrk7ZdYjARJIJAIUgIkEnsP6JAEKQOu3zScEoPUYPBvBagFYbdgSXLh+F/M+qIsyeR1FXshXy3H04k3MeLsWqhbO6tkC2IoESCDBCFAAJhhqDpQECFAAWr+JFIAmGFspANccuohXJtrC0Szt0QBFczge87b8dhX2nrmGqZ2ro16xHCZWwaYkQAIJQYACMCEoc4ykQoAC0PqdpAA0wdhKAVi49zz7zIwcPZ75YQ22Hr+KCa9VQbMyUfmNTSyHTUmABCwkQAFoIVx2neQIUABav6UUgCYYJ5QA3NyvCbJnTOMw05d/Wo+1hy/h23YV8XTFfCZWwaYkQAIJQYACMCEoc4ykQoAC0PqdtFQASlq1SZMmYe/evWol4pX7+uuvI1OmpOG0kFACcNfg5siYxjHYc+cpm7B033l88Ww5vFgtKkuIcN51KgL/bD+Nro2CEZA2lfVPEUcgARKIlQAFYKyIWIEE7AQoAK1/GCwTgJs3b0bz5s2RLl06FWZFyqZNm3D79m0VjkVSsvl6SSgBeGhYS6RMkdwB1zu/hSF011l89nQZvFarsMN72vHxKzUKYtgz5XwdM+dPAkmCAAVgkthGLiKBCFAAWg/aMgFYr149BAcH46effkLKlDbr1YMHD9ClSxccOXIEK1eutH51Fo+QUAIw/PMnoq2k659bMXf7aQx4sjRerxtkKAArFMiMOe/VsZgCuycBEnCHAAWgO5RYhwRsBCgArX8SLBOAYvnbunUrSpYs6bCKPXv2oGrVqrh165b1q7N4BCsFYPlBC3HtzgO8XicIA1qXjraS7tO2YebWU/i0VUm8Wb+ooQAsly8T5natazEFdk8CJOAOAQpAdyixDglQACbUM2CZAJRsGlOnTkWzZs0c1rJw4UK0b98e586dS6g1WjaOVQLw4aPHKBqZ63dT3ybIEeDoACIL6jVjO6ZvPomezUvgvZBgQwFYOk8g5n9Yz7L1s2MSIAH3CVAAus+KNUmAFkDrnwHLBOAHH3yAWbNm4auvvkLt2rXVStasWYOePXvi2WefVanXfL1YJQBv3n2AMgMXKjx7hjRH+tSODiDy+qezduKPDcfRrUlxfNikmKEALJk7AAs+qu/rmDl/EkgSBCgAk8Q2chEJRIAC0HrQlgnAe/fuKbE3btw4dfdPSqpUqfDOO+/g888/R5o00a1a1i83fkewSgBevXUPFYcsVpM1cgCR1wfO2YVf1h1Tnr49mpUwFIDFcmbE4u4N4nfR7I0ESMAjAhSAHmFjIz8lQAFo/cZbJgC1qctdv8OHD6tfixYtqnLzJpVilQA8f/0Oqg9bimTJgCPDWyGZ/OBUPvt3DyatPorGJXNiUsdqhgKwSI4MWNajYVLBzXWQgE8ToAD06e3j5BOYAAWg9cAtF4DWLyHxRrBKAJ66eht1Pl+G1CmT48DQloYLHBG6F+NXHFHvOccC1MLAFM6WHst7hiQeII5MAiRgJ0AByIeBBNwnQAHoPitPa8arAGzbti2mTJmCwMBAyM8xlZkzZ3o6Z69pZ5UADL94Ew2/Wo6ANCmxc3Bzw/V+tXA/xv53yP6ePlSMJgALZE2HVb0aeQ0vToQE/JkABaA/7z7XHlcCFIBxJRb3+vEqADt16oTvvvsOAQEB6Nixo+HRpTbFyZMnx322XtbCKgF48Nx1NP1mJbJmSI0t/Zsarnr0kgMYveRgjAIwX+Z0kDzCLCRAAolPgAIw8feAM/AdAhSA1u9VvApA66frXSNYJQB3n47AE9+tRq7ANNjwaRPDRX//3yGMXLg/RgGYOzAt1n/a2LugcTYk4KcEKAD9dOO5bI8IUAB6hC1OjSwTgI0aNYIc82bOnNlhQiKa2rRpg2XLlsVpot5Y2SoBuPX4FTzzw1rkz5IOqz8xtuCNX3EYI0L3xSgAcwakwca+xgLSG3lyTiSQlAlQACbl3eXa4psABWB8E43en2UCMHny5Dh79ixy5szpMOr58+eRL18+3L9/3/rVWTyCVQJw49HLeGH8OhTJngHLPjb24hUPYPEE1sq+z1ogbaoU6lftDmD2jKmxuZ/xEbLFaNg9CZCAEwEKQD4SJOA+AQpA91l5WjPeBeCOHTvUXCpWrKisfFmzZrXP7eHDh1iwYAHGjx+P8PBwT+fsNe2sEoBrDl3EKxM3oESuACzsZhzI+dd14RgwZ7edhT5jiCYAs6RPha0DHDOxeA08ToQE/IwABaCfbTiXa4oABaApfG41jncBKJY/LW7d48ePo01CcgSPGTMGr7/+ulsT9OZKVgnA//afR6fJm1A2XyD+7Wqcyk2ygEg2EK0s7dEARXNkdLAABqZNiR2DjL2IvZkr50YCSZEABWBS3FWuySoCFIBWkY3qN94F4LFjxyDCr0iRIti4cSNy5MhhHy116tTqSDhFCttRpa8XqwTgot1n8ebUMFQqmBmz3q1jiGn65hPoNcNmbZUy+706qFjAdt9SswBmTJMSuyLDyOw5fQ2P8Rhl8mbydeycPwn4JAEKQJ/cNk46kQhQAFoPPt4FoPVT9p4RrBKA83acwXt/bEGNoKyY9lYtwwXP3HIS3advt7/3wyuV0apcHgcBmC5VCuz9rAXuPniIEv0WqPf2DmmBdKmThgD3nieBMyGB2AlQAMbOiDVIQCNAAWj9s2C5ANyzZw+OHz8OyQ2sL0899ZT1q7N4BKsE4Oytp/DRtG2oVyw7pnauYbiKv8NOosdfUQKwY+3CGPRUGQcBmDpFchwY1hL63MKrPwlB/ixJJx2fxVvM7kkg3ghQAMYbSnbkBwQoAK3fZMsE4JEjR/DMM89g586d6k6gdh9Qux8oDiG+XqwSgNrxbqOSOfGzU55fjZnzEXDtotnwxxs1HQRgiuTJcHh4K5y7dgc1hi9V7y38qD5K5A7wdfScPwn4HAEKQJ/bMk44EQlQAFoP3zIB2Lp1a3XXb+LEiQgKClL3AS9duoQePXrgq6++Qr16xs4N1i85/kawSgD+vuEY+s7aheZlcmH8a1UNJzxt03F88neUE4jc/5N7gFK0O4Dys6SIO37pFuqP/E+9N/2tWqgeFOWZHX802BMJkEBMBCgA+XyQgPsEKADdZ+VpTcsEYPbs2VUYmPLlyyNTpkxKAJYoUUK9JiJw69atns7Za9pZJQAnrzmKwXP34MnyeTD25cqG6/1z43H0mRklAPUhY/QC8OiIVjh4/gaafbNS9fNT+6poWjqX1zDkREjAXwhQAPrLTnOd8UGAAjA+KMbch2UCMEuWLNiyZYuy/hUtWlRZAkNCQnD48GGUK1cOt27dsn51Fo9glQCcsPIwhs/fh7aV8mHUixUNV/Hb+mPoN3uX/b2CWdNjZa8Q9XuxvvNx/6EtBM+Kng1x7fYDtB67Wv3+1fMV8FyV/BaTYfckQALOBCgA+UyQgPsEKADdZ+VpTcsEoBzxiqVP0r69/PLLuHLlCvr164cJEyYgLCwMu3ZFiRdPJ5/Y7awSgFqe3xerFsAXz5U3XObUdeHorwsEnT1jGmzuZ0v7Vqr/Aty+b7tjOe7VKsiWMTWeH7dO/d7/ydLoXDcosdFxfBLwOwIUgH635VywCQIUgCbgudnUMgG4cOFC3Lx5E23btsWhQ4fw5JNP4sCBA8iWLRumTZsGyRXs68UqAfjN4gP4dulBvFqzIIa2KWeIyTkQdIbUKbB7SAtVt3i/UNx78Ej9/G27isiWIQ1enbRB/d61UTB6NCvh6+g5fxLwOQIUgD63ZZxwIhKgALQevmUC0Gjqly9fhhwNa57A1i/P2hGsEoBhx65gy7ErKJUnEHWLZTdcxK17D1S+4JK5AzEj7CSSJ4Py+BW2wZ/Ox4NHtiPgL54tB7EOdv5ls/r9+Sr5MfL5CtaCYe8kQALRCFAA8qEgAfcJUAC6z8rTmpYIwPv370NSvm3btg1ly5b1dG5e384qARiXhV+/cx/lBi1STfZ91gJpU6VAUJ950LLwDXm6jLIASmBpKXWDs+O3LsaxBeMyLuuSAAnEjQAFYNx4sbZ/E6AAtH7/LRGAMm1JBTdr1ixUqJB0rU3eIAAfPHyE4L6h6knZ2r8pMqdPhaA+8+1PzqetSioLoJY1pEiODFjWo6H1TxZHIAEScCBAAcgHggTcJ0AB6D4rT2taJgAnTZqEmTNnYurUqciaNWnGnfMGASgbX7xvKO49fIS1vRshV2BaFP00SgD2aFoc2TKmwaezbCFjtPRwnj4wbEcCJOAZAQpAz7ixlX8SoAC0ft8tE4CVKlVSzh9yHFyoUCFkyJDBYTUSIsbXi7cIwAqDFyHi9n0s6d4AEg5GnEC08n5IsPIClriCWjkwtCVSp0zu6/g5fxLwKQIUgD61XZxsIhOgALR+AywTgIMHD45x9gMHDnR7dStXrsTIkSNV+JgzZ86oo2UJL+NOWbNmDRo0aKDuIsqdRK0MGjQIznOUQNX79u1zp1tVx1sEYJ3Pl+HU1dv4+53aKJM3ECX7L7CvoUvdIGUB/GJB1LrkqDhLhtRur5MVSYAEzBOgADTPkD34DwEKQOv32jIBGJ9TDw0NhQi5KlW6VHbHAAAgAElEQVSqqLAy7grAq1evqjbBwcE4d+5cNAE4Y8YMLFmyxD7VlClTQjKYuFu8RQCKN/DGo5dVyJcmpXKhzMCF9iW8VrOQsgCOXnLQ/tqqXiEokDW9u8tkPRIggXggQAEYDxDZhd8QoAC0fqt9QgDqMUiYE3cFYLt27VCsWDGVk3j27NnRBKDza3HF7S0CsOdf2/FX2El0b1ocHesURvlIr2BZj4R9EQvguBWH7csL/bCeCjHDQgIkkHAEKAATjjVH8n0CFIDW72GSFYCTJ0/Gjz/+iLVr12Lo0KGGAlCOlSVPcdq0aVGrVi2MGDECBQsWdEn97t27kH9aEQFYoEABREREIDAw8QTVmKUH8fXiAyrFW78nSqHikMX2OT5VIa+yAE5eE25/bcbbtVC1cNJ0zLH+I8MRSMAzAhSAnnFjK/8kQAFo/b4nSQF48OBB1K1bF6tWrULx4sUh9/2crX1yrHzjxg3IvT+5Vyj3AU+dOqVS1AUEBBiSN7o3KBUTWwBqaeFals2NoW3KosrQqGPtZqVzKQvgnxuP29c0uVM1hJTIaf3TFYcRzl27gxX7L6BNpXx0UIkDN1b1HQIUgL6zV5xp4hOgALR+D5KcAHz48CFq1qyJzp074+2331YEjQSgM1q5LyjeyqNGjVJtjYq3WgD/t/E4es/ciSalcmJ423KoPmypffoNiudQFsCZW07ZXxv7ciU8WT6v9U9XHEZ4euxqbD8ZoVpI/uIWZXPHoTWrkoD3E6AA9P494gy9hwAFoPV7YZkAvHPnjjpaNSpiccuTJ49Hq4vtDqAIOUk3J/f+tPLo0SM8fvxYvbZo0SKXeYirVauGJk2aqKNgd4q33AGUVHAf/7UdIva+eLY8ao6IEoA1i2RVmUDm7TxjX5Kkh3uxmuujbnfWHt91Cvee59Bl+OdPxPcQ7I8EEpUABWCi4ufgPkaAAtD6DbNMAJYuXRp//PEHKlas6LCKv//+W1nmLly44NHqYhOAIvb27ImKeSeD/PDDD1i2bBnE6zcoKChaTEKpI8fBcv9PrIUffPCBW3PzFgE4Z9spfPi/bagTnA1fPlcBEhZGK2XzBSJ3YFos2Xve/prcE+xSr4hba7S60p37D1X6OgpAq0mz/8QmQAGY2DvA8X2JAAWg9btlmQB899138fPPP6u7dZ988glu3ryJ9957D9OnT8ewYcPQrVs3t1cn4kyCSkuRANNyTBsSEqIyjIho69Onj7q/9+uvvxr2aXQE/PHHH6N169bq2Pf06dOQuIQSJ1DEY44cOdyam7cIwPk7z+Dd37egeuGs+PqFCqj35X/2+Uvmj0oFM2Pt4UtInzoFbt17iG5NiuPDJsXcWqOVlRbuPou3fwvD523LYfzKIzhy4aZ9OFoArSTPvhODAAVgYlDnmL5KgALQ+p2zTADK1OfNm4cuXbqoOHxy7JsxY0b89ttvKihzXMry5cuV4HMuHTp0wJQpU9CxY0eEh4dD6hkVIwEoIWIkwPSlS5eU4BOnERGmRYsWdXtq3iIAF+0+izenhimh980LFdHwq+VImyo5Hj8G7j54pHIBX7xxFwWypsOJy7fxRr0g9H2itNvrtKqi3upXsUBmbDtx1T7U0RGtINZeFhJIKgQoAJPKTnIdCUGAAtB6ypYKQDmO7dq1qwrHIkGW586di+bNm1u/qgQawVsE4H/7zqPTlE0oly8TRreriMZfr0BA2pQqL/Ch8zfsNDSR9VL1ghjRtlwCUQJu33uIdKmj7mRqA+sFYOk8gdhz5pp9TvuHtkCalNHbJNikORAJxDMBCsB4BsrukjQBCkDrt9cyAXj48GG8/PLLOHv2LCZOnIgVK1aodG4ffvihsrSlSpXK+tVZPIK3CMDVBy/i1UkbUDJ3AMa8VAlNv1mJzOlTIV/mdNh9OkpUiZew3AVsXSEvmpfJBWn3WZuySJUi7nmBHzx8pKyLGdKkjJGyFqJG5iXjSrlw/S7ORtxB67Gr7W2Dc2Z0EKtMV2fxw8vuE5wABWCCI+eAPkyAAtD6zbNMAEosvSeeeALjxo1D5syZ1UokKHP79u1VnL2tW7davzqLR/AWAbj+yCW0m7AeRXNkwGdPl8XLEzegcLb0yJw+tcOx6otVC2Da5hNoVDInlu2zOYVI3MBXaxaKM6lW367CvrPXsHVAM2RK51rM66182r2+sgMX4sbdBw5jFsqWHscu3bK/tvqTEOTPwnR1cd4YNvBaAhSAXrs1nJgXEqAAtH5TLBOAU6dOxWuvvRZtBdevX8dHH32ESZMmWb86i0fwFgEYduwynv1xnVrtWw2KYPyKI5AA0Fdv3cfG8Mt2Cu82LIoflh9WziLa6+80LIpPWpSMRkosfCljsAxqwm7Ca1XQrIwtZl/E7fuQrCTyX7Es6r175Trf0RG20C7OHr/yWp5MaXEm4o59Hou61UfxXMYBuS3eVnZPApYQoAC0BCs7TaIEKACt31jLBKD1U0/8EbxFAO44eRVPjV3jAOT9kGBsPXEFaw5dsr/e/8nS+OzfPdDft3u9ThAGtLY5hPy6LhwFs6ZH3szp8Mz3a/Bm/aKG3sL3HjxC8X6hqs3UztVRr5jNa7rJqBX2Y1zJS/xB42J2sZcqRTIs69EQG49eRo+/tkfbvGwZUuPSzXv212e+WxuVC2ZJ/E3mDEggnghQAMYTSHbjFwQoAK3fZssEoKuQLLIk8e40sg5av9z4HcFbBOCe09fQ6rtVDosTsbfq4AUs3x8Vb/HbdhVVvEAReccv245bX6tZSFnrwo5dwbM/rlWvNSyRw97OKByLeBRXjUw399fbtVAtMq+w3rInVsbO9YLw1tQw1ad4JT96BNx7+MhwEzKmSamOhdOkTK7uFuqFZfzuGnsjgcQhQAGYONw5qm8SoAC0ft8sE4CSjUNf7t+/j1u3biF16tRInz49Ll+OOpq0fpnWjOAtAvDguevK8UNfPnu6DFYevIjFe87ZX/6tcw3lLCJ39uSYVorcC/ziufKYueUkuk+3WebqBmfH6kMX1c9GAvDIhRto9PUK9b4cIdcIyoqGJXIaHu3GlXzxXBlx4NwNvBdSFD2bRz+ajmt/rE8C3kKAAtBbdoLz8AUCFIDW75JlAtBo6gcPHsQ777yDnj17JolwMN4iAMMv3lSx//RFgiuvOnjRIQVc6If10PJbR0thm4p5MbpdJWj5hKWPWkWyYd0R29GxkQCUeH1tvnc8ct49uDnKDFxo+okd8GRpDIk8pp7/YT3T/bEDEvAWAhSA3rITnIcvEKAAtH6XElQAynI2b96MV199Ffv27bN+dRaP4C0C8OSVW6j7RVT2D1n2189XUEfAs7edtlPY2Lcxqg+LyhMsb4izyIT2VTFlzVEMmmtLoVetcBZsCr9iKAAlp/JPq45g+HzH/VvZMwT1RzrOwRP8M96uhefGrUNg2pTYMSjpxIz0hAXbJC0CFIBJaz+5GmsJUABay1d6T3ABKOnW6tevDxFPvl68RQCeu3YHNYY7CrvvXqqEVQcu4K+wkwqzeOEeGNoSxfranDe0IvmDf+9SE+NXHMaI0Oii3DkjhxZ02nnv/n6nlt0T2cy+hvVrgiqR9wu3D4w5xIyZcdiWBBKaAAVgQhPneL5MgALQ+t2zTAD+888/DrMXy5Gkgxs7diwKFCiA0FBHIWL9UuN/BG8RgJdv3kPlzxY7LHDcq5XVHcA/NhxXr4sTxr7PWqJk/1DcuR/liCHZQWa/V0eFb/l68YFokHYNbg5x0NDKu7+HYf7Os9HqffNiBXSbFt27N67UDw9vherDliiP4H+71kXZfJni2gXrk4BXEqAA9Mpt4aS8lAAFoPUbY5kATJ7cMbuEeP5Kzt1GjRrh66+/Rp48eaxfncUjeIsAvHbnPsoPWuSw2kkdqqo7gFPWhqvXxfFDLGpVhy7GxRtR4VZK5ArAwm71MXLhPnz/3+FoxNb1aYQ8mdLZX3/j180OjiXaGx81KYbRSw6aIp4ieTKIAJR7invPXMMvr1dHg+K2EDPOReIU3rr/EIFpfT+jjClobOwzBCgAfWarOFEvIEABaP0mWCYArZ964o/gLQLwzv2HKNl/gQOQX1+vru4A/rTqqHo9V2AabPi0CRqO/A/huowb+bOkw+pPGmHQP7vtYlHfkXNA5k6TN+I/XWgZrW7LsrkRuiu6ZTAuu6RZKV8Yv07FC/z+5cp4orzjHwoPHz1GzxnbMXPLKdX1ql4hKJCVGUPiwpl1E4cABWDicOeovkmAAtD6faMANMHYWwSgWMOCne72/flGTSUAJfOHFIn9t7JXCJ74bpVDfmAJwBzWvyl6TN+Ov7fY7gvqixwPyzGxVl6btEFZFmMqktZNLHM7T0UYVvugUTC+W3Yo2nvFcmbE4u4N0HnKJizddx7iydyuekGHes5H0K4ymZjYVjYlAUsIUABagpWdJlECFIDWb2y8CsDu3bu7PeNRo0a5XddbK3qLAJT7lUF95jtgEqeMlQcu4tultmNZia+3qFsDvDh+HTYcdYzB2K1JcZXX18iC9783a6JmkWz2vl+asN4eIsZoXyQH8fKeIeoto5Rvg1qXRofahaPNV+oPe6YsXqlRCB/9b6vyXu7bqhTeqF/EPsycbadUIGvn8keXGqgdnN1bHxPOiwQUAQpAPggk4D4BCkD3WXlaM14FYEiI7Ys/tiL3AZctWxZbNa9/31sEoJHYmvNeHRXMeeTC/YpjuXyZMLdrXXT5ZROW7D0fja2WicP5Ded7eE+OWYVdp1x7cAfnzIgl3Ru4FIDbBzRDpvSpDMXhfx83RFD2DOg/exemrj8GsRR2b1ZC9bXrVASeHLPa8JloWjoXfmpf1eufF07QvwlQAPr3/nP1cSNAARg3Xp7UjlcB6MkEfLmNNwvA+R/Uw+pDF+zx+gLSpsTOQc0xYM4u/LrumNvYx79WBc3L5Fb1T129jTqfxyzcS+YOwIKP6rsUgPuHtkCalCkg1ryD525g4e6zOHj+hqp/ZHgrJE+eDF8u2KeOrjvWLoxBT5VR74XuPIN3ft9iOO8mpXJiYodqbq+JFb2fQMSt+3hxwjq0LJvHMB+1968g+gwpAH1x1zjnxCJAAWg9+XgXgEeOHEFQUJDK95vUizcJwM3hl1UAZa0s6V4fi/acw5cLbBZAKZLV4/TV28rLVvL9ztEFiXa1V5I/+OmK+dTb3y09iFEGoWL0bTVLo7xmdATsHFdQQr6cv37XPj/54cflh/HFgn14tnJ+fP1CBfXegl1n8PZvxgKwXrHsmNq5RlJ/3PxqfRNXHcHQeXsdngtfB0AB6Os7yPknJAEKQOtpx7sATJEihYr3lzNnTjX7F198Ed999x1y5cpl/WoSeARvEoD7z15H89FR+YBX9GyIn1cfxS86a5+W1u3+w0dImTyZ4T08I4STO1VDSImcqj9J0xZTqVQwM2a9W0dVMRKAzqnl9HEJtfd+W38M/Wbvsmcpkb7aTViH9UeM80dXD8qK6W/VSuDd53BWEpi6Lhz95+xWQzj/0WDluFb2TQFoJV32ndQIUABav6PxLgAl/t/Zs2ftAjAgIADbt29HkSJRl/mtX1bCjOBNAvDwhRto/PUK+8LX9m6EYfP2OuQCdhZfEjxagkgbFRGIDx49Vm+JtXBKp+rQW2X0bfJmSovTEXfUS9ULZ8X0t21izFkAGlnq9HW0+c3aelIFlS6SI4MKBZMmZXI00q3NaL7a0XLC7DxHsZrA3O2n0fXPrWoY7d6o1WNa3T8FoNWE2X9SIkABaP1uUgCaYOxNAvDE5Vuo92VULt5NfZtAUsRpjhOSG/jZKvkdViuOFeIoMnPLSRw4Z7uHJ6VsvkDkzZROHSFLCSmRA5M7VcfXi/ZjjEH4lvL5M2HHSVvIl9pFs+GPN2pGE4BvNyiKXs1LqDt++mIkAJ3v+8kxtJH3r76fpyvmxbftKrm9m+I5HXH7PjKnT+12G1ZMOALzdpzBe3/YjvwXd6uPYrkCEm5wi0aiALQILLtNkgQoAK3f1ngXgHIELBZAyfohRSyAO3bsUPcCk1rxJgF4NuIOao6IygesWU0kSHTaVCliRN99+jZ7YGWpWCF/JmTJkBrLIwM+axZAVw4ktYpks4eGqV88ByQItZTP/t2DSauP4sWqBfDFc+UN5yDevuL1q4WAkUqu8g07dyDZTjr/stn+suQ6Tp3SMQONq4Vrnsa/da6BusUYQiamB0T247N5ezDyuQqoUihLgnyM9SF/fu9SA3WSQJgfCsAEeXQ4SBIhQAFo/UbGuwCUI+CWLVsiTZo0avZz585V6d8yZMjgsJqZM2davzqLR/AmAXjpxl1UGbrEvuI9Q5ojfeqoHL4xodC8brU68iUvWTnWHLqkXpI7dvWCsxvmCpb3xQtXCy3TuGROTOpo88i99+ARwo5dQeVCmZXnr6ty5eY9JTi1svbQRbw8cUOsuydBqtt8v8ZeT6yMvVuWjLWdVNAsj1ouZLca+WkljVWR7Bmw7OOGCUJhRthJfPyXLbf0D69URqtyvp86kgIwQR4dDpJECFAAWr+R8S4AO3Xq5NasJ0+e7FY9b67kTQLQOR/wwWEtkSqFe9YwzQonrMV5O/TDehgwezc2htucLtKnToFb9x663IqnKuTFP9tPq/ebl8mF8a+Zi8knovHZH9fGuPVTOlVDzoC0aPXdKod6zvcc5c1Hjx5j1+kIlMwdaLcQaqKmQoHMkJiJ7hbhfOH6XRTNkdHdJj5fT2NVIGs6rOrVKEHW87+Nx9F75k41lt4TPUEGt2gQCkCLwLLbJEmAAtD6bY13AWj9lL1nBG8SgM75gOPiOakPsbLvsxbqyPjp79dg+4mrscIW66DE/tNiC0ruXnHcMFNiCvqs9StCz9nxRZxF9g9tGW1oOYaW4+hXaxbE0Dbl1Pt2AZg/E+a8X9ft6dYYvgTnrt2Fc45ktzvwsYrOWWZalcuNH16pYvkq9F7AXz1fAc853V+1fAIWDEABaAFUdplkCVAAWr+1FIAmGHuTAHz46DGKfhqVDs7IEuZqqXtOX7Nb0jTh2OrbVdhzxnXGD+mrTnA2jHu1irrnN3qJLeVcm4p5MToOzhhGczp0/gaajIryaHauI1bKoyOegLPji9STI93RL1aE5CMWq2WGNCkdvJE1LpoAFAeWf+IgALV2ctQsR85JoUhYIIkJWbNIVuTPkt5hSWcibqPWCMfg33GxLnvKRx9yaETbcnjJKSe0p/0mZjsKwMSkz7F9jQAFoPU7RgFogrE3CUC9VUt+josAlPoS4DlL+lToVMfmrPP02NXYHunZq0ckX8Z9Io/mPm1VEm/WLwq9tUYsNWKxMVNOXrmFul9EeTQ796VlNTl//Q6qD4tyfNHqSWq4nAFpMG3TCZWVRC8m40sAftKiJN5pmDQE4PgVhzEidB80rnreaw5dxCtO9zHX9WmEPJnSmdniWNtOWHnYnsVmyNNl0L5W4VjbeHsFCkBv3yHOz5sIUABavxsUgCYYJyUB6Ixh9+kIPPGdY+7dJqVyYWKHqnaLmtzDa1giJ+bvPIN3I9O0vVS9AEa0Nfb4dRe13LGrNizKocW5XZ5MabGuT2NIurAKQxZF67ZZ6Vz2EDZikZwdmfFEItCEflgfxXJmRJFIa2muwDTqXps73sPi1FK8X6gar1eLEni3YbC7S/Lqes+PW4tN4VfUHJ3/cNDfD9UWIXcm5e6kleX7/w7Z81j3e6IUutTz/TiiFIBWPjHsO6kRoAC0fkcpAE0wTsoCULBInLwKg6MEVtvK+TDqhYrYeTICe89cw/NV86uUf+uPXEK7CesVyddqFsJnbcqaoApcv3Mf5QZFF3ZapyLgFndvAOd7j0aDNiqZE8v2nXd4672Qovj+v8P212JLJSf34K7cug/5r+Zp/XGz4ni/UTHIEanEy86X2dgi5k4YHlOw4qFx6zGrsfOULY6jCMAHkikm0oFo2Lw9+GnVUYdR+rYqhTfqWyvIvl1yEN8sOaDGTSrWVgrAeHhY2YXfEKAAtH6rKQBNME7qAlCEQHBfm8VLSsfahTHoqTLRiB08dx1Nv7GloetUpzAGto5eJy6Y9ZY2abeyZwjqj4w6EtbuhIl3r2bJi0v/RnWNjszljuFPq46oGIk37j7Al8+VR68ZO1Rzuf8nAliyr8jR+fpPG0cLdSN3KyVMzb2Hj1Raux9frYIUToGw3Zm3HIc+fgy8ZdGdw6ajVuDgeVsg8D/fqImOkzei35OllZjvM3MH/tx4Ito043rFwJ116ut8tXA/xv53SL3Uo2lxdG1cLK5deF19CkCv2xJOyIsJUABavzkUgCYYJ3UBKGhqDl+Ks9dsad4+aBSM7s1KRCOmD0L9Zv0i+LRVKRNUoSxtQX0cHVr0GUOODG9lzyhilG/Yk8F3DW6ummVMExU7scf07fh7y0nD7iRNXbPSuTFuhc2SuKZ3o2hWwPY/b8TKAxfs7bWcynGZn6Tqk5R9UmSO+vm56keskrkD0yrrrDul3pfLcOLybVU1a4bU9vSAIvI++HOrPcSPvi+rHUFGhO7F+BVHYnzu3FmbN9WhAPSm3eBcvJ0ABaD1O0QBaIKxPwjAF8avw8ajtniAcrQrViHnoj+y7Vw3CP2fLG2Cqq2pc4o4zVHhl9ero0FxW5YZ53pmBs2WITUu3byHL58tr6x0kjbvvd+3OORSjqn/f7vWRdl8mexVjI6nJ7xWBc3K5I7TNPefvY7mo23WVUnvlyPAFmDdVfl3x2m8/8dWB0useIiL4dGVIJT7lnLvUkqG1ClwMzLmo2RWeff3MHuQb/2Y2jF8nBYTh8paFhlpEpcA33EYIkGqylUJsQTL9Ylb928h44io+JE3+txAhtSOAfITZFIcxGMCRy/exKwtJ9G5bhFkSp/K437YMHYCFICxMzJbgwLQBEF/EIA9/9qOv8JsVrBpb9ZEjSLZohHTH8W+XKMghj9ji7VnphjlCDa6T3f+2h1cv/tAHcXGZ1nWo4HyjF0cmQ85tr4l/Z2kwRPrpQitbxYfwLdLbaFxtNK+ViF1n01C07hbxIIolkQpq3qFoEBWxzAt+n6cRadY8OQ4veW3K5EtQxpMaF9FZWYRAa3d8ZP25QYtxPU7D1RXKZMnwwO51AggVYpkuP/Q9rNRsfIYeOCcXfhl3TE1bHz9UeEu8/ispz3H8odL1cLpKQDjE24i9FVmwAL1B1LrCnkx5iX3c48nwlR9fkgKQOu30CcE4MqVKzFy5EiEhYXhzJkzmDVrFtq0aeMWnTVr1qBBgwYoW7Ystm3b5tDm+++/V/1K7uIKFSpgzJgxqF7dlsfWneIPAvCvzSfQM/Le25b+TdURoVHRvuierZwfX79gLgyM9G8kAGPaE6Oj4IJZ0+Of9+tgxPx9mLY5+j22mPqb2L4qfttwzJ4PObbnQbJVhF+8BfGaFTE4eO5ubIi0nOrbaoGUxWoakDZ2C4Kev3PwaQngXTxXAIpEZiUZuXCfg3OLCDRx2Gk91ubNXThbeoRfuqViJA5+qozy4JZSvG+ouqcYU+nTsiRalM2NBiOX26vFJfdybPyc35dQQ39uPK5eFuE85GlzjkVxHT++6mvPpXgyv1QjFwVgfIFNpH60/ZQrFnLvl8U6AhSA1rHVevYJARgaGgoRclWqVEHbtm3dFoBXr15VbYKDg3Hu3DkHATht2jS0b98e48aNQ40aNTB69Gj89ddf2L9/P3LmtH0xxlb8QQCKI0j36duVo8PgGL6Etf8xxkcmkPgSgJULZsbMd21p3sQZY5sbmU20PReHl4Pnr9vzIcf2LLxRL8juLSttVx28gMMXbho2G9S6NAbN3aOCaIuoMiqSB1eOD+sWy26/C9ehViEl2kJK5sR/+8+j0+RNqqkIvS8W7MOPy6M8m+X13zrXUO+/Osk4r7JzTMSY1vi/N2uiZpFsDsLc1ZH08Uu3cOrqbRVY2t17iM5j6y3P8RFaKLb9s+p97XMxtE1ZPFM5OwWgVaATqF9tPyV81IZPmyTQqP45DAWg9fvuEwJQj0G+UNy1ALZr1w7FihVDihQpMHv2bAcBKKKvWrVqGDt2rOr+0aNHKFCgALp27YrevXu7Rd4fBKBbIHQWuyalcmJih2ruNnNZLz4sgBIC5ueOtrm8NmkDVh28aHperjqoUiiLOl6NS8mULhW2D2xm2CQm5xaxvNX9YhnOR97bEyHnqr5emDoPNKlDVWVBrPel66DbWpu579dFufyZHMZZ0r0BgnNG3WkT4ffST+uV+NPK7sHN43TkrbXrNm0bZm09pX41Y1WWUEbCWcS/HG9fu30faVOnQOWCWeKyVR7V1R/JS3D0luWyUAB6RNJ7GmmfMwk0v7EvBaCVO0MBaCVdW99JVgBOnjwZP/74I9auXYuhQ4c6CMB79+4hffr0mDFjhsNRcocOHSBWwzlz5rhFngIwCpP2P8bYYuq5BTaejoAblsiBKZ1sR/qNvlqOIxeNLXLuzimmemlTJced+zEfozq3d3WMFFt8Q7E+SD5irUj6Pr3XdHysx7mPpT0aoGiOjA4C8O93akOEr1Y++t9We9Bt7bXvXqqEpyrkjfOU3v9jC/7dcUa1k/bST1zLHxuO49NZOyEZa75ZfBC37z90YOapddLdeegz1cgVgSalM1MAugvPS+tRACbcxlAAWs86SQrAgwcPom7duli1ahWKFy+OQYMGOQjA06dPI1++fEoc1qpVy065V69eWLFiBTZsMD4yu3v3LuSfVkQAitUwIiICgYGB1u9WLCPE1WoWnxPWxq5WOAv+eru26a6L9JmnAixLccfZQPIgi7erds9N2unT0jmHZDEzwRerFkDRnBnsqco87at4roxY1K1BtOauUtx5Ok58tFvfpzFyZ0rrIADlqPvqrXtoV72gsiR+8veOaE4zkpe5TaV8agriQSlXCorlCjCc0uqDF/HHxmMY/FRZ9J+9Cwt2n1X1tIxPzMcAACAASURBVHuT+kZytzEoRwaHsDhhxy7jvd+3ot+TpfBk+bwuraLSz77PWiBtqhTxgcZlH/qc1l8/XwEt4mABXL7/PPadvY636hfx+Bjd0sX5aecUgAm38RSA1rNOcgLw4cOHqFmzJjp37oy3335bEYwvASj9DB48ONquUAACr07cgNWHLkL/hW/m8S3Vf4HdYuOOANx+4qryuhVrz7YTESo/8bjXqkDLWXvkwg11l9H5HuCHjYtF89aNbd6fPV0GdYKzo1EsnscSviQwXUp8uWC/YZeuQqkcOn8dTUbZQr94S5GjajlKjeloumKBzNH4aqGD9MG99wxpjvSpo3tCa32Lh+Wtuw+wNDKDi6Qg/OGVypi85ijqFcuhMs8M+XePQrPwo/rqrmbtotlVuBwtnE1Mx+LSLqxfE2TLGHNInbiwl8wlc7afwoy3a9sdpbYcv4K2P6xV3Ujw8qcqZnPbAqixmNq5ulozi3cQ0PZFwjHJHVgW6whQAFrHVus5yQlAOcLNkiWLuvenFbnfJ+E55LVFixYp66AnR8C0ALp+IO8+eKgsPCVyBcSLxaLSkEUq/ZoUdwSgOx8VfbxC8cosnTcQu09dw7D5ex2a582UFqcjbMGvjYqIzDYV86H68KUxDjvk6TJ4tUYhFOsXqqyTRkXLqyuiL1dgWuUZLHcJn/3RJhzio0jeYlci1J3+qxYSq24tta9xDbwtAlsssXKsrWWLWdGzIQplc4x/t+PkVTw1do2ajlhGc2dK5xBEu2fzEvbcwEZzLpcvEyRmoubNHJsAdA6ps+tUhBJueV2k9BOr7I4TEWhcKqfh861xkfV2a1pcTVHvqCPPwrNVcjgIwOu9r2P/2fsokTsgWoBvrT/JPvNC1QLubBPrJAABCsAEgBw5BAWg9ayTnAAUsbdnj806oJUffvgBy5YtU3f+goKCkCFDBuX5KyFfJPSLFGlXsGBBvP/++3QCsf65i3WEWiOW4kykCIsvASiDLt17DulSpUDt4OxqDhNXHcHQeY4C8MnyedTYrpw6xAIogaJLD1josA5xgJGjx27Tt6nUbZrjxNNjV2P7SVuuXaMi9SRUS4UCmSGCUC8cXLXJnjENLt6Iuo4QE1A5fnz4+LE9jV2s8HUVJPSM3P3TUtj9s/20yg7ibhHvcU3Ia21mv1cHYi3UyrFLNx3Cy8gxvgixtYcvuTuMqidz1IS2WAa1ANpGncj7WTKkUsfA4hhS9wubI4yrZ63tD2uw5fhVZcl7qXrBaF1qwuCjJsXwURObAJy+6QR6/W1LHWgUBua3FvvRd9ZBVMifCXPer+vQp9af7J08a85FYm+K2LX6GDtOG+AHlbV9kc/f5n60AFq55RSAVtK19e0TAvDGjRs4dMiWF7RSpUoYNWoUQkJCkDVrViXa+vTpg1OnTuHXX381JOZ8BCyVJAyMOH2MHz9eCUEJAzN9+nTs27cPuXLlcos8nUDcwuRRJX0GkvgUgM6T0Wec+ObFClh98BLealAEX4Tusx9BjnyuvAqevO7IJXXHbeRzFSBOH86OFx83K473GxVT99zOXb9rTw0nMQEnrwl3yeGFqvkxfbMt2Lb0MXH1UVyNtH66aiRx+SRQtTtFwsFIOBmZ+xu/bnaniaojHPq0jJ7WT5/72agzEXgSdsdV0afEW3v4Il7+yfjOrdsTNahYIGs6e3o7o34kK8ubU8PUnVEJb9QhMtj26k9CkD9L9GDb2he/9LuqVyN7l3K0LUxXRKb8692ypMpcIqXvrJ34fYMtlqFYMDvWyeNgAXw211JsDrd5TMszrgURFxErd1q1cmhYS4fA3fK65GsWgSx3M13F5jTDj20dCZy7dgfyrHabtl29kT1jamzu15SYLCRAAWgh3MiufUIALl++XAk+5yICbsqUKejYsSPCw8Mh9YyKkQCUehICRgsEXbFiRXz33XfKMuhuoQB0l1Tc6524fAu9Z+7AG/WK2AMWx72X2FtsPX4Fz/ywVlmkRLhoRZ8HWPOAde5t9tZTygonX/KSak28YjOnjx4oW46ef1tvEwISr8+5lM4TiD1nrsU+WV0N8Sr98H+Ogc1tX0xpIDH75I6clod4cbf6yvFi7vbT6BoH692Pr1RGy3J5DOfV+OvlhnEOn6+SHyOfr4DQnWfwzu9bDNt+8Ww5lMgdCDm21QudOAHwoLLEhRQrnhSx8mpexnK8/fy4dfYe9X9wHDh3Hcv2ncfnOrGtf995nRLjsWOdINXXU2NXY0ek5Vcsg2/Uz+cgAAvcnoHkSKvqtqtWQN2hDf2wHpInS4YyA6Osy0ae1JogdZWe0QM8sTYRYSrPu1xVkCJrlyDkcoSd1Ev1YUvsYZdsnzP3BKA4ScndWU88zsXKK/dg5f9NsaWATIr8KQCt31WfEIDWY/BsBApAz7h5W6vDF24gb6Z0SJc66t6o3jIYWwo2+WKUf6lTJo9xaUv2nEOXSAtc9cJZsTHclmM5tiLetvKlK6nZtONqEXm37z1Epym2YNBSJPOJCAhJNXfz7gO7iNg+oJnKWyr33J4cY8sKohURri6uJ+LnjlXRqKSxNXz4/L2YsPKI6kbLoyw/v1KjIIY9Uw6bwi87iCqjNXoifGNjFdP7Lcrkxrnrd7D1+FWVDeXYpVuq+pRO1dAxMqi2/C680qZOjjQpUyCozzx1nK8vmgA0OqpvVjoXhrctp4R4nc+XOcRE3DmoAQK/iBJLegGo9S9pFJuVyYWqQ5fYh5QrB6/WLOQgIvQZRrrUK4L7Dx/hn22nUTs4GzTHJ60DSZco80kum+1hEeukWGrFCj7j7VpK1Gh3OuPqUS2frZQpkhlalz2cnuXNnO++yjMf1j9mC6B2r/Xpinnxbbu4hzH6bf0x9Ju9S1mpl/eMbgCxfNGJPAAFoPUbQAFogjEFoAl4Xt507LKD+GrRATXLDZ82tls9zExbwpxoWTkkvZmECYnpntvyjxuqe21a/l+5v9j5F9sRrrxXOHsGSLiQpXvPK2taraLZHHIFy329h48e4ZlKUXfI5u88A/li0cbNnyUdTl6JCtysX5/kr5Wjb6MyatF+fLfMdi1DPIQrDF6kfpa7biJIwi/eRMOvjC3ycWEoglI7Ro1LO6O6ElBajvLE0qYv4rn+0bQoa6rEspQ0fiJ0NMcUfX3tSDYmh5iujYLxy9pwXIvMsSztH+EOTqR7zt6VkQCUjCHC3Dk4d5HsGTC3a10l7kWMadcP8mVOpyy+S/aew+C5e+CcoUJ75sTiOfblyi4RioBMlcL1HzCD/tmNKWtt1xgkLuOL1QrglYm2o/uYMto4DyhiVHOecuUNbnafrWjviQB8748tmBcZy9KTayziCKbdQ/akvRUcErJPCkDraVMAmmDsbQJQvtwlhdaYlyu5tNyYWK5fNZUv74H/7FZr3tq/KbK4yIEcFyibwy/jucijRhEIYoESkSZFvsj1GTTktSPDWzlYbeSemXZXbe+QFg4Wy7jM4+tF+zEmUryJh+6MMNv9QykSnPrsNZsHdEwhSEYvOYDRSw6qegeHtVQi9L995zGkTRllObtx9wHK6o4x4zI/fd1dg5sb9hOT5dLVWJJKT5x7Fu0551BlwJOl7WFl3Jnnxr6NkSV9ahTrGxpjdec5uiMA5Ui3ZlBWu3VNP8D416qgeZnc0IfUkffFulc2X6A9b7VeLLwycb09naErESF5l+ftOI3F3Ru4/ENHL4Dkzqo4O0lsTSmuHGO0uYvoTpMyuboeIdli6o+0OdxooYXcYe5pHRnvf5uOo1OdIFPHqM4CUO5dSm70mEr36dswc4stm40ze7GQixVRy+Nt1E+L0StVLEij9p7y8KV2FIDW7xYFoAnG3iYAZSlyFKl5bJpYmt83FVEk+XiliAjJmCZ63Lq4QtIfwQ5+qoyKX6fdDaxdNBtOXLlld1zIkDoFdg9p4TCExDLUYg+asQg4W++GzN2j7iDKAeG0t2qi3CCbNe/3LjVUvEOjMmbpQXy92GYhNZqL3koVV076+vGZ5eTdhkWVANRSzGnjiPf2kr3n3Z7m/A/qqeN47QjU3YbuCkDxCjayPGpH8pLeTrO4amO3rZzPLja09HvOIlzbJwnZJCJdK5q4+aBxMXSPDGEjMRfljqQ47GRIndLhrqZYr+sXy2G/ziDPcofahQ0xXLtzH+UjnycZXx8cW4S03Hecuu4YXqhWwO405S5Pd+ppd/f0aSHdaedcx8jau6R7fQTndH3/UYT1nxttd3/1n5HTV2+j9ufL1OvyfMv9QPm8yFWUItkz2v/ok3SPmnXezOfdk/V6QxsKQOt3gQLQBGNvFIAmlsOmOgL6y/2Seze2+33uwNMHeB7zUiXsPXMNPyw/rJo2LpkTF2/egwS0luIq2fyyfeeQMyAtyubL5M6QhnVGLT6A75barHdGXyzal90fb9RQAZaNyvf/HbLH5XP15TRh5WHsP3sDc7adwgNXFw1jWYWreH5yMV7i7YkoFqHiTh5mOZ4+fvkWfl13zGN20vDX16urO46aFVVec+eo2h0BKE4khy7csP9hoJ+oOP5ULZwVe05fi+bN3aZiXnsaPi38kLP3+cT2VZUgkZzYneoURp9WNg9vbb81L3Z9KsLP25ZDveI51H1GrbxZvwgq5M8MOeKU0rdVKbxRv4ghU3men470CJfPkcRqlJBHUuRubd/Zu5SzkuSUltzS8VHECz9l5HG2trbAtCmxY1Bzt7oXMbb+yGX1Gbx176H6rLk67j88vJX9D275A+/d37fgkxYl8UT5PNAfm+vraY5nMhnN61z7PHWpG4R+T5ZW87QiFqpbALykEgWg9RtBAWiCMQWgCXhe3lR/3Kr9lW52yuLZrN3tEnEl6cy0UC5yR0vui2meu0VzZMDSHg3NDmnY/pvFB+zZT2ISgDEdff+8+qj92DQ264Ss6c2pmyFOGLO32Y685WK8ZEKRe4zv/xEVV1C8HZuXyaUcNeRotkaRbIZfvtodSOlL720bEzC56yfHke6Gz3HVl4QL+v6/w8qapRURT9qdUVft3BGAqVMktwezdu5HePy06og9Pqb+/VpFsikHDSk/ta+q7hG2m7DO7vVsNCeJOblw91n7HyGakFt76CJejrzfJx7IuQLS4MUJ6+1diCW1WK6M9pAoEuLmvZBghyF+WnkEOQPTqBiSmuORZF8RZlpf6VOnUAJLK7E9R+58GCSu59eLDuDPN2sq71lNuLn6g8qoT/1dW3lf9lsL/+Jcf1mPBuoYV0SjxJLUrnHIWkaE7sX4FTZHqW0DmtojBOgFoGS4aVUuj8MzrnEo1nc+7j+0eSDFBxt3+HlTHQpA63eDAtAEYwpAE/C8vKk+1l18/c9X0pRVG2bz7pRAxJIqTI6JpMi9qjv3H9nvBGpBoa3ANG3TcXzyt21co7VF3Lqv0vBJ7l9XRULbPPfjOjQsmcMtb06xysh9Jk0MiLdyqTyB0TyTJRzK58+WdxjWyPoi9w41pwX9XSlpKKIl4tY9PFUxn8NRqdxpvH7ngbLSxKUUyZEB7zYMVrH+JJSOCB4R0XqrplgXnQOKO4/hjgCMaV7yTGgWYud6+oDbYo38e8tJ9TzFVALSplQ8tKId5erDBUke5hpB2ez3YaVu57pBKluL9gzJ/dWxL1dCpYJZbM+UzgFIPJj7z7Hdpf3v44aQoN96j2v9/Mx+ziRsSpHI+IlamkXt2QnKnkGNH1uRI+suv2zGxqPueeiLgJNUhXLXclP4FXv3spah/+5RMT2lSPabIxduKkF8OuI22kUKahGpZyJu49y1qKDu+4e2wL/bz6BH5BUUV5/T2Nbi6+9TAFq/gxSAJhhTAJqA5wNNxZogF+zbVMoXL7PV34da27uRsnJpx2gS6kXub05dbzuerBOcDb93qRkv4zp3Ih6fcjxYp2h2l3H+rBhYrCPaUaKsX7J9yH22up8vw/W7D/BGvSB0bVwMgWlTOQwvx7uS/3f36QjM33nWJjI+f8Jep9HXy9WXqxS9Q4JYZSSenmZlmvdBXTx6BPsRpPMaXTmWiFfw1M41oIUGCimRA//tv6DuhYrwq1Ioi/Kq1pyGXLEzKwDd3ZPM6VPFGkjcqC8R3+LdK8e0vSP/MDGqJ5lQSucJsAs7rY78USNxE/WxJvXOTf+8Xwenrtx2GR9S21M51hdhKoLXnSLxOEXwynOi7bV4TS8TT/ne81QXEnJo/of1Yu2u9oilMaaBdO6gR9PiytKpiWHtfVmLPhC4HAtrMUBjsvJKe7GwaldDtP70R8ixLiKJVKAAtH4jKQBNMKYANAHPD5uKIJHjzsd4jO9frqzCkbw2yeZJ+U7DokiRLBnG/mcLrdK6Ql7IPcGkVCRuYakBC9SS9CFAxDIqdywltlxM5d3fwwwFYL0vl9mdZ5ytSCFfLVc5qqVIOB85dtScXJzHkvFFkDoXuS83sHUZ/LD8kMqpLFlgxLpWqWBmzHrXFjxc7lTK3cqYSvWg9PjrbFQWEaMwMHHdbxHNP62yWZniqzxTKV80RxnnvuVIVW+1cmdsyUgzYdURhxzP+nZi1U2ZPJk9xM3f79RC5YJZYgyirHeM0vclIktydg+aa0sLKvmsZ7xTO8Zp6i2I7qxH6ki2HHmune+VynOo9wIWS9+2yPu9sfUtcf/CI2NUanXjyxEttrG96X0KQOt3gwLQBGMKQBPw2FQd52kX5MWSIIGotWPE90OC8XHzEkmOks1Z4zGqFMoa57W9+etmewgXvdDTvmjFIidflPqiD8asHRu7utAvmULki/fyzXv2LsQqJs4SIg6nbz7hkE/5xaoF8MVztuNqvcOFXhy9Vb8I9p69DqnbsGSgy0wg+jnrM5bEBkliFWqhhWKr6+77zkfD7raLrV5sYlWshXJMr3m6S3/yh5FYz4yKeDP/uvYYhs13zOVtVFez4ool+cHDxyo1or7sO3tNBS7XH4nHth55X0ILSdikhbsdQwvJNQE5StfCuMgRtPaHSGz9GqUxFI9pcf76a/MJTFp9FD93rKYs6Em5UABav7sUgCYYUwCagMemkLt2FYbYQq5IXEAJ+Nxrxg71+5fPlccLVQuQko6A3M2SgMdS9AJQ0m3Jl6JYrpzjqunFntam85RN9jzPesASTFuccTQnkZK5A7Dgo/r2KhLnUJ95RQt6LRXky73Vt6vUEapkgNBSzunnqf9CkzauLIASaPlsxB23MsWIo06lzxbH+TkRr1h9kGp3OpDwUnJNwdMioY1u6pw+jPoRwf2/TScc3nJ1N/D1KZtUmj53ihzbF8qWwR7MWtIjnrx6GxLOSD53cyKdk9zpS19H7u7uP3fD5d1Mra7+jmZsYxjFBF3ZMwT5sqSzh+OJSRjH1r+vvE8BaP1OUQCaYEwBaAIemyoCmkCRsBFiBdSsH3L85YmVLClj3XDkkvIgFW/ica9VcWupU9eFq7tqEt9O4txJkSNnCXwswb3lSFfz3BTHBsm20W/WLiUK5HhPHy9PwqNU/myx/Z7Z5I7VEFIyp30eWny9NYcuqiwZIuTEIUUrzgLw2VxLsTn8NsQ6JaFZtPJ6nSAlIjcraykw7JmykNSBRnEHncPkSMDluw9idv6QPkXcatYpt0A6pfxzt0181HMVZzCmTCzO4+rTFcbHnMQxSO6dylWNdYcvqRzJ8VX0wdi1Phd8VE9ZLjUnqphC78TXPBK7HwpA63eAAtAEYwpAE/DYVBF4e2oYFuw+q9J51SySTd2PkqDMcnToSQL5pI5VwriIY467wc7FYiUBdsUr1BVPzYtYLxJdcdTf6xKnhxK5jQMBi0Uwb+a0DgLSWQBGfHIdyZOlVc4kEhJIi48nR56L95x1sCLqrcWaN7Dm2FCq/wLltS2lRlBWlcYupiKWy7k7zsRqtXLuIy5WLHEUEecYLZi62edSswIu3nNOCa5PWpZAiX62+6QJXQLSpMSnT5RSHvzJkiFarmiz8zESqzPfra3uGmrp9/q0LIm3GhQ1O5RXt6cAtH57KABNMKYANAGPTRUB8ciV4z4t3y+xJDyBKzfvKS/jxqVyxRrwW/MEllnGNZWZswC80ecGMqTOoBZ8/vodVB+2VP0swZdzZUqLTpM3QfIXf/1CBYf8v3I9QESCOEiIFVMfMFgcViavseXsdS5ybNireQklhF8cv85QKHZrUhzfLDF2ZpEYjWI9dafs+6wF0qZK4TKAsjt96OuIANRnl+ndsiQ+D90X127ipb5Y/z5oVMwhf7R07IlV1WhCro7av3i2nN3b2J0/VuJlsYnYCQWg9fApAE0wpgA0AY9NScAHCehju8U1QHhMAlDvgaqFshGxlT1jarvlUjvyXNqjgYonpxV96BIJdfPsj2sNYwDKvbdiuWwWS8kpLXENncukDlXR+ZfNhjvzUZNikJArIpTlzmVMRbPYxeWYNqb+dgxqBkmh1mL0KlWtelBWt2P16fstkSsA+8/Z8ut6WiR14PNVC+CtqWH2LsRq26JsbojXuVVFsqVowcffCymKns2NnWOsGj+h+6UAtJ44BaAJxhSAJuCxKQn4IAGJDal5asc1cHFMAlBQaGLp3651DVP9yfH3+Wt3US6/YxrARl8tx5HIUDcyJzn2Fo/ppZEOEhL+RuLjiSeqVmZuOYnu0225rvVFghC7Olr98tnyKmevfq5GWygOF5M7VY+1nlSQjDeHI2M46vtyzqwiTCQMjxYHUl83JsvktDdrOmQxeaJcHszbecbUkyeOH0+Wz4v2P9tCOEkZ2qYsJN+wluPX1AC6xnrRp+9TgnH3j0wZF19jeVs/FIDW7wgFoAnGFIAm4LEpCfggAXEEkXttkv0hrgHCYxOAEtPuxJXbKo1bXIo+E4omSl+btMHuWGIkVOU4VSyAzlk5pK7kGhYPWwlvoi8Sl1KcHmITgHrLaJ+ZO/DnRkevXn2fUjeoz/xoy9Ufd8qbRsGRtUbiJGIUhFu8ace/VsXuOCH1P2xczJ4GMS6M9XUl+4eITgkboxVx9qlTNBuqDLVl+tFKulQp7Pcz5TXNeURfJ6awO2LpE+9lLTqAvp2kqHumUn5Pl+H17SgArd8iCkATjCkATcBjUxLwMwKxCUBPcQycswu/rLNlkNHE3vgVh1U4Gwm9sntIC5ddOx/Rau0lqLUEt9YXyeRRPr8tO0dMR7t6wSnWSBG2Rh7M2nyN+pLUcvoc0TGxGdS6tD3gs1ZPYukNf6YccgWmRbNvVjoItQ/+jMo9HRfmEtuvZpGs6u7l7tPXHITl5E7VlKe2ZJ7RF/FYFycvKSJie7UoGY1dmbyBqj+jIpbQdxoGo/yghdFC6Mj6Xq5RMC5L8Km6FIDWbxcFoAnGFIAm4LEpCfgZAasE4I27D/D9f4dUDMMyeW3Hw/cePMKsrSdRu2j2GB2MNPGVMyANfutSA8Uj7wiOmL8X41cecdghvWWvRL9Qw3Azcmdxc7+m0XbWlWAUsahlUdGHw5EQO1L0cRddPS4DniyNIf/aMn5oRQv6LbmHG4y03csTi5l4kGvZd+L6+OnTDMpdvCajVti7+Pud2qiQPxOC+4Y6dPtazUL29I5Dni6D9rUKRxOAYvE1uo8pHWnhXtr+sMbuFa4NoLfIxnUtvlCfAtD6XaIANMGYAtAEPDYlAT8jYJUANINx3IrDCN11Fr91ro4AXQ5m/R1BsXxVK5xVOV5o5eSVW+j9906VzlBfXIUnEYE6cuF+yHHttdv38fXiA5B+3wsJVncWd52KQJ5MaVF9uM0T+o83aijx2mP6dpXn11WR8CiSU1u8s/VFE6vhF2+iYaRjxs5BzXDw/A20/WGt28hkTmcibEfh2pzkZ31ea/ldc7BxFrpi9asWlBXL9p5H3ydKRfOMXtStPn747xBmuwhErYnGtYcv4uWfNjjMe0qnamhYIioOpduL8pGKFIDWbxQFoAnGFIAm4LEpCfgZAW8UgK62QETZb+uPoWrhLHaronNdzbomcQzXfNIIYccvo0HxnIYxGuXO4YnLtyFHs5qAkjt6+tiMYrUs3s9mQfujSw3UDs6OT2ftxB8bjhtO89WaBTG0TTnoHXO0itoxtATnltzPEiB7x0CbANQfCbvK/yz9iJgVJwzNK/rA0Jb2MEESOkifgUUcbeS42VkASriat53i9Wl1tFzSQ+buwc9rjL2q5S7ki9UK4tKNu9HuFyb1YPEUgNb/D5IC0ARjCkAT8NiUBPyMgC8JQHe3Ru73ZcuQBpnSp3K3SYz1NHEkmS9K5g7Ee79viea1Kyn7dp6KwPwP6qF03kCcuHwL9b78z6Ff/T3E63fuI1WK5Mr6JqFkNE/dtpXzod8TpZVlUlLQSVDvK7fuodaIZaovuX8n4q3vrF2oUjiLQ2pGfUxCqbt3SAuVy9tZAIZ+WA+l8gQ6zE1ySn+75KDK5ytjitDuN3uXIRc5WpaA2vowQVpFsR5qR/bxAt/LOqEAtH5DKABNMKYANAGPTUnAzwgkRQEY31s4b8cZnIm4jS71iqiuu/yyCUv2Oub7lZiAt+4+RO5Mae3DSyq2qjoPXFcheiJu30eFwbb822/WL4JPW5WKtgRNxL0fEoyPm5dwuUSjPNPlBi7E9bsPVBu900xMnDaFX3bwKJa6f71dC3J8LfEGteIsLtf1aYQ8mWwW1aRYKACt31UKQBOMKQBNwGNTEvAzAhSAcd9wuRv4wvh19vzL0sOR4a2QPHkyl8JN3nAlAB88fGR31NDSLzp39MrE9Vhz6BKW9WiAIrqA2871Kg5ZhKu37qtUfrsGN1dvbzl+BR0mbUSvFiXwWq3Cbi1YLJRyTK0vRvN3FoByp1F/b9OtwXyoEgWg9ZtFAWiCMQWgCXhsSgJ+RoAC0LMNF9Em3sCrDtocTlyJO00gxRb65vcNx5RwEwcNo/zQcv9RHFUkzV5MJezYZQyeuwfD2pRzCM4tx8NxzeMdduyKyuCiFaM1vjpxg4PTjSsh7Bll72tFAWj90M3eSAAAIABJREFUnlAAmmBMAWgCHpuSgJ8RoAD0fMNjC2wtPWsCsGGJHJgSmYnE8xETvqXko37/jy0Y2LqMPeC2fhbiJCPZUEYvscVnjGsmmoRfkbkRKQDN8XOnNQWgO5Rc1KEANAGPTUnAzwhQAHq+4ZJ2bWVk7mJXwkdi6YlDxbBnyiJnQNT9QM9HTfiWsVkP5X6kOKlIPMPN/Zok/AQTcEQKQOthUwCaYEwBaAIem5KAnxGgAPR8wzv8vNEeLDmpW75ioyQ5oSV9XPrUKWOr6tPvUwBav30UgCYYUwCagMemJOBnBCgAPd/wjpM3Yvn+C6oDfxeAnlP0rZYUgNbvFwWgCcYUgCbgsSkJ+BkBCkDPN5wC0HN2vtqSAtD6naMANMGYAtAEPDYlAT8jQAHo+YZTAHrOzldbUgBav3MUgCYYUwCagMemJOBnBCgAPd9wfUBoHgF7ztGXWlIAWr9bFIAmGFMAmoDHpiTgZwQoAD3fcEk5J3HyJEPIeyHBnnfElj5DgALQ+q2iADTBmALQBDw2JQE/I0ABaG7DJR+uUQYQc72ytbcSoAC0fmd8QgCuXLkSI0eORFhYGM6cOYNZs2ahTZs2LumsXr0an3zyCfbt24dbt26hUKFCeOutt9CtWzd7m0GDBmHw4MEOfZQoUUK1cbdQALpLivVIgAQoAPkMkID7BCgA3WflaU2fEIChoaFYs2YNqlSpgrZt28YqALdu3aqEXPny5ZEhQwaIIBQB+M033+DNN99UrEQAzpgxA0uWLLGzS5kyJbJnz+42SwpAt1GxIgn4PQEKQL9/BAggDgQoAOMAy8OqPiEA9WuTHIuxWQCNWIhwFDE4depUuwCcPXs2tm3b5iE6gALQY3RsSAJ+R4AC0O+2nAs2QYAC0AQ8N5v6hQAUi2DLli0xdOhQdOnSxS4A5Vg5U6ZMSJs2LWrVqoURI0agYMGCLtHdvXsX8k8rIgALFCiAiIgIBAYGuomc1UiABPyRAAWgP+461+wpAQpAT8m53y5JC8D8+fPjwoULePDggTry7d+/v52MHCvfuHEDcu9P7hXKfcBTp05h165dCAgIMCRodG9QKlIAuv/AsSYJ+CsBCkB/3Xmu2xMCFICeUItbmyQtAI8ePapE3vr169G7d2+MHTsWL730kiGhq1evKmeRUaNGoXPnzoZ1nC2AV65cQeHChbF7925aAOP23LE2CfgdgVv3b6HEmBL2de/vuh/pU6X3Ow5cMAm4Q0D/eZHPihTt8xMfnx05wStTpgwuX/4/e+cBXuPZxvFfEmLE3iv2bO09apXiQ1Gq9i6qVlGjqoIWVWrErtZs7VIUpYrUrr1XbGKT2Nnf9bxHQgg557wn78nJuZ/ryiU551n3735P/POM+75H6tSpzZlSvKsTrwXgy95S27/q/N/p06YHKbpSpkwZatasqW0Fm1P27t1L2bJlzakqdYSAEBACQkAICIE4RuC///5D/d/vjMVpBOCIESOYPXs2Fy9ejNbPaqVQnf9T27y9evUy61lQK4Bp0qThypUrsgJoFjGpJAScj8DtR7fJO9kUvNi3py/pk6V3PghisRAwg4Da9s3yY5bIz0rE58avnx8e7h5m9GB+lYgz/LICaD4zu9RU4szX11cbu0SJEto2bfXq1TXxpUTbV199pZ3fmz9/vlZn6tSp2usFCxbUflZxBFUMQCXs1EqgKl9++SUffvihtu3r5+eHl5eXdiP4xIkTpE9v3i9ouQVsl8dBBhUCDkXg1qNbZPwxozbnm/1ukiFZBoeav0xWCBhF4OVzf+qzEvG5efTVo1gRgOoSqDOf4XeIFcCtW7dqgu/V0q5dO+bOnUv79u21lT1VT5XJkyczc+ZM1BlAFdsvT548dO7cWYsF6OrqqtVp3ry5Jgzv3r2rCb733nuPkSNHanXNLSIAzSUl9YSA8xIQAei8vhfLLSMgAtAyXnprO4QA1GtkbLUXARhbZKVfIRB/CIgAjD++FEtil4AIwNjl+2rvIgB18BYBqAOeNBUCTkJABKCTOFrM1E1ABKBuhBZ1IALQIlxRK4sA1AFPmgoBJyEgAtBJHC1m6iYgAlA3Qos6EAFoES4RgDpwSVMh4JQERAA6pdvFaCsIiAC0ApqOJiIAdcCTFUAd8KSpEHASAiIAncTRYqZuAiIAdSO0qAMRgBbhkhVAHbikqRBwSgIiAJ3S7WK0FQREAFoBTUcTEYA64MkKoA540lQIOAkBEYBO4mgxUzcBEYC6EVrUgQhAi3DJCqAOXNJUCDglARGATul2MdoKAiIArYCmo4kIQB3wZAVQBzxpKgSchIAIQCdxtJipm4AIQN0ILeog3ghAldVj7Nix7N+/n+vXr7Ny5UoaNWr0Vhgqc0jfvn05fvw4np6eDBkyRMsqYm4RAWguKaknBJyXgAhA5/W9WG4ZARGAlvHSWzveCMD169ezY8cOSpUqRePGjWMUgCpNXOHChfnss8/49NNP+eeff/jiiy9Yu3YttWvXNourCECzMEklIeDUBEQAOrX7xXgLCIgAtACWDarGGwH4MgsXF5cYBeDAgQM1sXfs2LHIpio/sL+/P3/99ZdZaEUAmoVJKgkBpyYgAtCp3S/GW0BABKAFsGxQ1WkFYJUqVShZsiQTJ06MxDhnzhxtFTAgICBatIGBgaiviKIEoNo6VvVTpEhhA3dIF3GJwNOnsHgxJE4MTZqAu7v+2c2YAQsXQuWcV+h5theZmlWFL77Q37H0EGcJiACMs66RicUxAiIAjXWI0wrA/Pnz06FDB7766qtI4uvWraNevXo8efKEJEmSvOaJYcOGMXz48NdeFwFo7ENr7WgHD0KuXJAqlXk9dO8OP08LJAEhfD3Sg8GDzWsXXa09e2DzuAO4LF9KDi7RkFUk5amp6s6dUKGC9Z1LyzhNQARgnHaPTC4OERABaKwzRABaIABlBdDYh9OWow0bBkq7f/ABbNwYc8+7d0OnCsfZQnUekpwmWXaz/3J63NxibhtRIyQEEiSAy5dhXJ5peId0j7bx+Sb9yb38B/M7lpoORUAEoEO5SyZrRwIiAI2F77QC0Jot4FddI2cAjX1YrR3tyRP42GOdtuo2mFGcvp2WdOne3tugQVB6zMd8zO9axT2U5ehPu/m0s0uM0/D3h3Ll4MwZ6NcP0oXdYtCEjJHtrtdoRaavP2VYFz+G+7bipHtRCj47jEvMXcc4tlSIewREAMY9n8iM4iYBEYDG+sVpBaC6BKK2fI8ePRpJvGXLlty7d08ugRj7DMb6aEvmPaNu+/Qk5xEBpGCj92ma9swUOe7Zs6Zzea1aQd68ppdHVN/C0K3vR5lb17IHmbmn+BvnGx4OR47Ahx9C0JUb5OQieyiHV6afGHbjM62dzy++VO2YR/v+xrE7ZCiSAVfC8fW5Rt4qWWKdhQxgPAERgMYzlxEdk4AIQGP9Fm8E4KNHj/D19dXolShRgvHjx1O9enXSpElD9uzZtbN+165dY/78+VqdiDAw3bt3p2PHjmzevJlevXpJGBhjnz9DRhtRfh1D99SLHGtWyel03m8SZKpUKvWM1gf6EIQ7rsO8aNUzDbvT1qUu67lUpQ1pEwaQ7J/VLHNpSvVbS9+4ejhoYDjHflhLSxbSkkVa32uoT2auU5r93O3/PWl/GBjF5lMpy1HwwX/89cls6izpYBGP27dh6lTo1Ak8PS1qKpUNJCAC0EDYMpRDExABaKz74o0AVEGdleB7tbRr1465c+dqAZ4vXryIqhdR1Pd9+vThxIkTZMuWjW+++UYCQRv7/MX6aEFB4J3sK74M/j5yrD+TfEz9J8u0n9euCeN2g460Z57281/UZjrdWIUpiPixZScpnOsxYaXLaCt1BTjFuNUFtFW+06fh5k2oXBkePYIR6bwZG9T7zTapP1DymFb/Isp/dYZSdsO3bMnQjOo3F5vFQ600rlkZwqgm++jKTHzTlGPota4kSix7yGYBNLiSCECDgctwDktABKCxros3AtBYbKbR5AygPahbNuaWLRD2fg1qsJngj1uQcPkizpAPj6tnyJoVphfyptup6EXbL6n70fLaONSFcL8S9chyaB1DGY53yqFs3w5Vq8K9e/DDD5DW9T5Nv8yubTOH16mDS/36kD8/1KqlTdi/VXdS/Trltcmf/3UnudtU4h6pSf70NgkTx3zLZMK4UOr1L0R+zkb2t7rd7zSY29gyOFLbEAIiAA3BLIPEAwIiAI11oghAHbxFAOqAZ1BTdZmj95jMZOYGrFwJH31ECG5M++Exnbq5czVFIQqEn+ZGz5Fk3L4cFxUrBgjO7EmCo4dwSZvGNNNZs6BLF+3bFXzESL6mEX9QkZ34uuRnd3hZ5tCRexkKkOb6CXB1NbVr2NB0MFDFgcmQ4TWrw4JCeJA4PanC/Tkxby/vtC0dpU5oKPj4QL58pm1ebaUx01R+eNwjSr0/PFrR8OGvcpHEoOfKkmFEAFpCS+o6MwERgMZ6XwSgDt4iAHXAM6hp1SL38DmW1jTagwcEZciK+7OHFOMQj/HAl3za2b+E/rdxuXYVGjSAa9dgxw4oWfLFLG/cMN0Qefz4rTN/9nlfEk/90SLr9meoQ6nbG/in6QxqLO0ape3M+qsptHYca9J3YrRfO+ZOfUzzLzKSjMeE581L0Dffkahdc3zJw7OjvhQuHP3Qm/4OZ0HvfdxKW4il65KRPLmp3vr18PPPoAJUp09v0bSlspkERACaCUqqOT0BEYDGPgIiAHXwFgGoA54BTVetgtWNfuEXPiUkdz4SnDvDsxp1Sbx5PT/Qn4vkZBrduVeoImlO7DDNSGV6efAgejW0bRtUqfL2me/fH1U4mmHnzmpfUdHnezbl7kLNczMjW6jzhZcylaUse7XXvuwcQNItaxnh2xL/1DlJdeccBAQQnDYjCcODqcEmFt+q8ZqQu3MHRmWezPiQXtr299JeO+gxPD0qeY2Ka5iEJ7h5JCHggUvkwqUZ05YqZhIQAWgmKKnm9AREABr7CIgA1MFbBKAOeAY0rVIplBk7i/AOJ2HcOFNQvl9/hTZtoo7ev7/pIJ855WURmDQpbN4M5cu/aKluaFhY9vdfTKlxLTjkUYnij7ZHtl79Rxjvf5RCW+1TZSSDeY/tVOVf/Ht+QyrvEdrrR6v1oIjPVO3G8cJmq1m0OOplkKneodTqXZB8mG7Jq9KFmXhyhW/4Tvt5F+W5NnsjH3d4vjRooQ1S/c0ERADK0yEEzCMgAtA8TraqJQJQB0kRgDrgxXLTq1dhkudYxjKAsOQpcL1yGVKmhLAwU3qOCKGWJg2oc3/Zs5s/I3WwUAnBNWsgTRqeNG5N0pW/8XjAcDzGDDW/n+c1fZcdJO8nJbnjko50Ybcj20/pfZYe3vlf6y/UNQFup06YDgYCAduOkLJKMe37BbQm08YFWsaTiDKpwmJ6724R47yWvTuMpse8YqwnFSwjIALQMl5S23kJiAA01vciAHXwFgGoA14sN500MZzafQpRkNOmYHmff/5ixHnzoH17aNECJk3Sf/jt6VP480/T+cFEiSy27OGNxyTPnExr9+DCXVLkNF08mVxqLj0PdMAvZwUC8xUm19+ztNdve00h/bCoaeWCJk7DrU9P3Ajj62a+jFxsCjejLpH8maw5DZ8t4WKrwaSoXpo0n0Z/W/gW6Tm+4RrVayW02AZp8GYCIgDl6RAC5hEQAWgeJ1vVEgGog6QIQB3wYrlp+xKHmXuoOCEJEpHg7i20A28RRa3+HT4M774LCeOG2LnumpXM4X74LtpL3uamm8DLknek6aM5XGo+gByLxnByzm4yBF0hbZePie667+0StUh/6G8mpR1B7zvfsHcvlC8bym3Sk4b7BG7eQaLqFUEJVnUwMFUq1G2Q8OAQHqTMRsqnN/HKPodhF9vb5Dbx7JnB3D54lc9/yEnyFM4bo1AEYCx/2KX7eENABKCxrhQBqIO3CEAd8GzYVO3q/vsvdO8OrVtD27YwL9tgBjOap3U+Isn6FTYcLXa6OpakDIWf7ePwt6spNuRD/O+GEpguCxm5xYPlG0nR5KU93TdM4fH0eXh83p7T5Cfs+CnaNH5Mv9OdacFinrinJOnjO6bt72jK46Hf4/HtV5wlL3d3nqF8BX2C7dh2f1wqV+JdTvBXkf7UOWLmGcvYwWvXXkUA2hW/DO5ABEQAGussEYA6eIsA1AHPRk39/EwXc8+de9HhtyPCaT40H3k5B4sWQfPmNhot9rrZmfZDKt77k72fzqTMrC7sn32YUp2K89AlOckD75q3UvngAc9SZSRx+DM+YCOjGEwZ9mmTDmvRCteFv77ZABX6Jls27f09hdpR7sRcXcZuzd2RahfmmMbGhafnb+CRKwPP/tmB/3dTSNe1CQmaf6xrDEdpLALQUTwl87Q3ARGAxnpABKAO3iIAdcCzUdPevcHbG8qyh+YsZiO1OEVBLpCbULeEuPnfg2Sm83VxuWghYC7MYnedYZRf78XfrebywcIOHE1blSJ3XqQvjMmGy5Vbkn27KQ+xKoEp0pHou6GmZVF1CeZtxeXFql/zSlf4+a9sb0V3/TrMGXqB075ujP7VkyxZTe13fLuZSkNraMJPpc9TZUuvlVSf1AjflCXJ+8AUbPvkiGUUGNwEVzd9q40xMbH3+yIA7e0BGd9RCIgANNZTIgB18BYBqAOeDZqqkH2ZM0Pt+4v4jVaa2AjFlal0pxeTuf9ORVIffx7fzwbjxWYX68p4UXffCP4r+Rll909n07u9qXnCm13lvqDC7gnmD71zJ1SqFFk/yGcX7lVeClPzlp7CFi3BtaVptVTFC1z5/RkGDozaQB2ffPjQdLlkeO55/OjfUbt48le+ntQ5480533Ce5i9K4fBjrMnRnawZQii5dybrPD7mcfpcNL04NkqHAQnTkmz1ItzqxLzFbT6EuFVTBGDc8ofMJu4SEAForG9EAOrgLQJQBzwbNF2+HOY3Xc1qGkbbW+CAb0g0xhQrL66XP+vPoP7abhzK2ZDiF/7gQPIqlHy0jX0951Hau61F07/ZaTAZZ4/mWM56FL7wp0Vtw7Nlw0VtBwPDCixi2KkX2+fq7siXlfdQd/8ISrNPO58YUe6ShoT3b/PbwCN0+6kET12S4H/cj+R3LpC0SqnIlUBVf3e6euS+u5cM4ab2fsnykfnBmdcunhw9Ck+2/kcu10tk+NS6G9YWGR9LlUUAxhJY6TbeERABaKxLRQDq4C0CUAc8GzStXy+ckeuKU4wj0KwZqIDOpU03aMNdXHA5eRIKFLDBSLHfxbquq6j7UyPOpC5Lruu7eJo4FSl4yNX1R8lW5w353d4yrfBrfrikSQ1Jklg2+UuXeNKgGUmP7GGXayXKBW/XsoOolb+Bn95lyOxc2rwiil+GYqS7dQJ3gtnkfYJLE1bQ6cIQLhRrRK5DK7Vq9xt3IvXK2TwmKcMYxuAbvQm9/4BVvf6h098mgXmy0VcUWjlK+16t7HZrcosP13blI/7QXttReRCV/h1tmS1xpLYIwDjiCJlGnCcgAtBYF4kA1MFbBKAOeDqb+vvDwDSzmBnehfAECXBRt0FUMtuvvzYFaVbBmuvW1TmKcc03jf6PmoPLcTORJ9cX+VC8cW4Cccc96DEuCaO/uRtbsws55UuCQvm08a8cDSBv4cTs8gmiQrXXYxz6/riK0HHjKXDdhyU1Z5F28zJqhm3k+tdTyPzd81iFz55xZvo/LDhSjO6js5Ep04uZX8r+Hjmu7OBOwkykC/TTwtuMHhVO7a9LURLTWcGI4tvKi4vth2nnEl9OvhJbHGzVrwhAW5GUfuI7ARGAxnpYBKAO3iIAdcDT2VQl4Qhp8JFphahbN5g2TWeP9m2+a+kVKjTLThAJWdN+BU3mfsjFFEXIGXDE+ImFh3PPPSNpQm6zadQean5Vlv6VdzN2ewXTXDp00M4ZhiRNQYIWTTn84dcU+3MUC11b0yBspZa6LuzQEVyLFYlx7jdOB5CpYCqtnu+6M1ybuYbAdZupFbxWe+3H91by6e5OpAy5p/2sglWvc6mPZ043HqTOweM0ntT4ujzjJ7lRLnw3H/QvTspKlq+YxjhRHRVEAOqAJ02dioAIQGPdbVcBuGXLFqpXrx6txTNnzqRr167G0rBwNBGAFgKzsnpQEHz1FVy8CHPmmGI6q0ej50+FKcxx+OsvqF3byt7jRrMTh4J4p4RphW1VoUE0PPk9J4t+QqHDS+wywRPZavHOtb9Z8b9ZFPX+lEn5JjOZXqa5+PpCHlOmEVWuzfyTrJ99GPnzw4SpSf7sDtresRnlWOrKFPZ/kQM5oknou0VwO3YEv12XeFaxOrm58MbeXr5xfP8PH1I3rGLGyMZUEQFoDGcZxfEJiAA01od2FYCJEiWiV69ejBo1ioTPMzLcuXOHDh06sH37du7fv28sDQtHEwFoITArqw8eDKOfH/+aPRsaNgTPrGHcfZaUxASaggDmzm1l73Gj2e3b4JIhHem4y9EkZSjydC+nm3tRYNEwu0zwSO3+FN04jqUZeuDby5vaQ0pTigMwYgR8802UOYVfvYaLpymGoCpnCjYg/8lVZs/7RKPBvLMqmvN9aqVROVyFkfn1GvTqRfX7MQf1Pp6oJDkubyNZhqRmzyE2K4oAjE260nd8IiAC0Fhv2lUA7ty5k7Zt25IsWTIWLlzIhQsX6NSpEwUKFGD+/PnkyJHDWBoWjiYC0EJgVlav/O49vE/UYDPvc3vgj9oZsp/7HOMYRQhPmhSXgIA3ZriwckjDm6mwKicSFKEIxyLH9puwhCxffGL4XNSA/pMXkKpXW3yoQnemaqxDEiYmwdVLkCFD1DmFh/PIIwPJnt7RXj/3+TjyTO1n9rzDdv8HFcrz1MWDv5v/TKNFz28eT5liSu/ycnn2jKd9v+bms5Rk5Rp/H8/Cxv9S4l6iMF1H5yRDnRIk5xHz0/ah1c3xuLmZPY1YqygCMNbQSsfxjIAIQGMdalcBqEx99OgRn332GcuXLycsLIxvv/2WAQMG4PJSUFpjkZg/mghA81lZW1Nt/w5IMpmJYabtx/IF7uOSOhVldnvjTW+oWRP+/tva7uNUO59Etaga9MKW4P1HSFgy5nN0sWKEypVcvDj+pGQWnenPOIKrfUDCLRujHS504mTc+ph8FH7gIC4lils2rStXtLzEWn7iMmXgzBk4dcoU6DGG8ugRJE5s+htgdYeVNJjbWGsxsfoqvtjc4EVrdb14/XooVQo8PWPq1mbviwC0GUrpKJ4TEAForIPtLgAPHDhAy5YtCQkJwc/Pj+bNmzN58mQ8PDyMJWHFaCIArYBmYROlQw4Wb0975mktW/Ibi2nOaQqQD1/48Ufo29fCXuNm9XUZ2lH39nxtcoEuiUj0xN+kbOxRgoIIT5YMl+DgF6NPmqRtw76xHDoE6ja23tvXakX3yROzxF90c9mY41NqXf5Feyto/T+czVad5e3W0PbCMHLdN90sfvRRG5KNG2bI0QERgPZ4gGVMRyQgAtBYr9lVAH7//fd4eXnRpUsXxo4di6+vL23atEEJq19//ZUKFZ7fOjSWidmjiQA0G5XVFX+b9YQPu2SKjD33O40Zx5fsoqJpxUgJDgdI9WYOgNXvDqLBiTFa1YO5G1Pi3O/mNIu9Ok2awIrnZ+6SJoV79yDR66FgYm8C1vUc7nedkznq8E7IEfw9sjDnWUv6hI57rTP/mk1I9ffyNw4SMGcFxwct4M8EjWi+phVFS8YQjkcFS1Rfr1x+EQFonR+llfMREAForM/tKgAzZ87M7Nmz+d///hdpdXBwMIMHD8bb25tAtWUTh4sIwNh3zqQWu+m9+MUfAirV20Vykofz0LIl/PZb7E/CoBGWNFlKsxXNtNGODV1K4eFNDRr5DcMcO/Yi4J66aJMxo33nY8HoY4Y9penwd1+7ObyET2jG0sielg45AnfuUHfUeyRLnTDy9X3T/qN494okIFR77YR7MQqM/wy3Vs1N29SvlIC2PUm5YArBKdKQcNXvUK1aZA0RgBY4Tqo6NQERgMa6364CUN34TZcuXbQW+/j4ULVqVYtoTJ06VVtJvHHjBsWKFdO2ksuWLfvGPiZOnMj06dO5fPmyNo+PP/6Y0aNHk9jMbTcRgBa5x6rK40ot4ssDLfHLV4Us6jjYv/9q/YS7ueFy4gTkz29Vv3Gx0fnTwYSWq0BKlwekv3oIF484cItVxd5RBzEdjLPaQZ5QcCZfX/lMc7WW3WSWN2Gp07L687+o8HPHKKnsVJ3A92pwo0QdPEd9zrm0ZcgXdEJrq2IzqkwnESVsyFBcvx2u/fj0SThnu46l6K8vkiYHJUxKwq2bcKlo+sNFBGBc/LTJnOIiARGAxnrFrgLQlqYuWbJEu1E8Y8YMypUrhxJ3y5Yt4/Tp02R49dYiaLeOO3bsqK1AVqxYkTNnztC+fXvtDOL48ePNmpoIQLMw6ao0Octoel4fzNUabclWOjOMMW2RxqfLH1EAhYVp2TBeS4yri6JzNg64G8Kdqo3xfHAC9/27TJliAIW4U6Gd/HLmvSg5il+ldJOMuJ89zvKlYZz/+me8GK6FHVIxB6986c2dK085tOkOne7+oDVV+ZAT8wwPnnAlQ0myXt+v7QaLAHTO50+stpyACEDLmelpYVcBqIJAv+227+bNm822TYm+MmXKMEWFjtB+yYfh6elJz549GaTSgr1SevTowcmTJ/nnn38i3+nXrx979uzRYhCaU0QAmkNJX515ibvQLnAWt7p5kaFJZZPwU+fQ1LNRsaK+zqW10xJQ90zGNtiGz79Q79MssHQJTx8EM5wXcRenfbCSzzc2IiQE+vSBhVPucoJ3Xls5VBB/dP+KZr4jmT38CkN/eRG+6omrB3e8viVHuOmi0s1+N8mQ7JUwOk7rBTFcCEQlIALQ2CfCrgKwj/qt+lJR5/8OHTrEsWPHaNeuHZPUrUMzSlBQEElnkqZ3AAAgAElEQVSTJtVCyTRq1CiyherD39+fVateD0qrVgA///xzNm7cqG0Tnz9/nnr16mmXUNQZRHOKCMA3U1K5er/80rSQpTS50mzqfPzPP5sicdSrB506vZ3y48ewI1ktavE3T6bOIenn7WHfPlMcuuzZzXGR1BECbyWgnlN1pO/pU1i2DHzbjWAYw5id+ktqHfohymO2cydsX3iZDlNLkR5TzENVJiYfQvm/hlO+oinzybnK7cmz3XRrXZXbSbKRqf9VwlyfC8AQd3j40PQcO8ClGnmEhIBRBEQAGkXaNI5dBeCbTB02bJgWH3DcuNdv7kXXRoWPyZo1Kyqw9Ms3h1U8QXWWUK3qRVfURZMvv/yS8PBwLQyNikeozgS+qahLKS9fTFECUK0yBgQEkELlJ5MSSaBjR1PaNlXq1AGlwdW5+F27XkBSUUOKFXszNBUGzq1QPlO4l61bwcIzoeIOIWApgUuXYOtfz2jSKvEbL5ef2XSZtSP2U6JYGJXb5sKtTMmow6hUefnyRXmt1kfp+bvYbW52u0Da7O/gFvhUez88Y0ZcihQx7UvPmPFaO0vnL/WFgCMTEAForPfipABU4WDUqtw9FXbCjGKNANy6dat23u+7777TzgyqMXv37k3nzp355pVUVxFTUMJ0+HDT4e+XiwjAqDxUVouUKYHHj0hIMP6kjtaLSiSqzGJZs0bv5L//CqXq/5KYDuCr/5ll1c+MT4NUiRMEBg8m3NeXP3xS89Gtn5iXpjzte+1m752JlJ7yxRunGPzXPySs/X6cMEEmIQSMJiAC0FjicVIALliwgIEDB2qBoc0p1mwBV65cmfLly2u3hiOKij2oYhKq1UfXaBLZywrg695Q27onT0LBgi/Cnx09CqWLBnLItSSZuEH+sFPcwXQAX5X+/SECu9p+u3zZFNLv1bJ47BWaD8hOiEsCEgQ9dfh0b+Y8y1InfhHYPmIz73nV4IZLWj76MB2rVt8jA7ffauSF75eQa6B9UgDGL/pijaMREAForMfsKgAbNzalbIooaiv2+vXr7Nu3T1uFU0GizS1qFU+tGqrQL6qoSyDZs2dHXfaI7hJIqVKlqFmzJmMibpUCixYt0nIRP3z4EDczkog6+xlAJfxq1waVxattW5g3D65ehQYNoOjBucylg+aLrszgJ7riPT6Enhf6Er50GSuqedN1U1Pu3oU1a6B+/dc9Pa/dZtrNr8GNlPnJ5H/a3EdB6gmBOEPg1pVAkmVPTVJMW76qnE9WhCxX/uPE+cQsqPMbeW7vYjI9+YNGFOKUVmdbqS+oPLO1KW1dRNm9m7uHr+LWuCGp0r+IWRhnjJWJCAGdBEQA6gRoYXO7CsAOHUwCIaKoVbf06dPz/vvvU6tWLYtMUWFg1KWPmTNnakJQhYFZunQpp06dImPGjFqIGHVOUMX5U0Vt56pwLz/99FPkFnC3bt1QwlD1ZU5xZgGojizlzGkSfxFFXcxt2NB0vn0FH/ERf2hvnctVkyQDe5Jl8QTTWb7n5YcG2xi4+j1tRfAHUyQNrRw4AJkywYaPZtDhv26cLVCffKfWmOMSqSME4hyBfYkrUDpwtzav8655SH12L6lzm45FXLsG06ZBz55wYdtVSn6Sh0QEae8FJUqG+/XL2vnAoDYdcV+/Wnv9llsmUlYsTKKfpzpcfMY45xyZUJwiIALQWHfYVQDa2lQVAiYiEHTx4sW1bCJqZVCVatWqkTNnTubOnav9rC59jBw5ErXdfO3aNU14fvjhh9prqaKJ9B/dXJ1VAKpVuzJl4MIFE5UcXMSVMC6QW/s5Ec94kCAt7iFP3uriMFc30ofd5L0GabVLIqqo1L7q9nCSJDDepS+fPZmAb4O+5F31o60fF+lPCBhCYG2ez6h3fqY21oraE2j815vPAH5R9SDZ/11AXyZo9U96fkD2QF88bj3/sL0649at4aefTB8YKULAwQmIADTWgfFKABqLDi1nccqUKZ3uFvDUqdCjh0noLa4zjw83dOdRuAf5OMv9BBk48vUSCg1vDlmyQPXqUdO1qTgw6jqwyiwRFobK7Tuk4O/aOUJVPD1N28iqrKE+9VnLzeEzyDi0q9HulfGEgE0ILB/8D/VH1yMUN/z27SVfqXfe2O/Nm6A2KZ6t3sjECw20wNMRZR5t+ZJxDHSfyJdBo170MWoUfPWVTeYqnQgBexIQAWgsfcMFYOrUqd8a/Pll8829BWwsshejOasAVGf8gtb8xV+8yOGsqHjTkxobBvJu+zJw/ToMGQJDh5r+c1K3PL7++sVFDnX918tLy6pQLMEJDjwpSHAweHhAeXbRjnl8hmnVJPTvzbjVrG4vN8u4QkAXgZsPb1G2RzkSu99h24RzZgeC7ltyK6MP1saNUJqzmBHHP+ad59rxq09v894v7anHOgI8MpNo4VwSF84LuU2r8FKEgCMSEAForNcMF4Dz1E0BM4s60xeXizMKQHXrV2XUOnjXE0+eL9VF56QCBeDgwbduTYU3bITL6lVMpDfvH56opc36oMh1zpEnyqF57aBhtmxx+VGQuQmBNxKwNhWceuwHNL/M7Ruh9PgxFy/FuNeCqndr4Y/XkkJk5saLsdW5alXxww8lnaA8kw5HQASgsS4zXAD27duXb7/9Fg8PD/79918tD2+CBAmMtdpGozmjANywAdrUucUtMpooqlQfW7aYAvpFpO5TgW3Vob5cud5OeuVKaNyYM+Rjx+wzpE4Ntz7qQhdmRW0XkR/XRn6TboSAkQSsFYAxzVGlqJvYZj8dF39AGu5Hrf7rr9CqVfRdqKX23383RWEvVCimYeR9IWAYARGAhqE2/fcdrmKvGFgSJkzI1atXtZu5KtSKCvuSQaVEcsDijAJQhX2puNELL0ZA4cKggv6polJ8VKkCGTOaAgNGF9jvVR8/ePA8YjQM73Ebj+xp6TQgDanxB7WCePo0qGjRv/zigE+HTFkImAjElgCM4Pv7oiDad05I9cdrWE3DF9jV50dlJFF/pD0vYVv/xbV6Ve2n8Jw5cTl7VuJryoMaZwiIADTWFYYLwHz58vHJJ59oYV6qV6/OypUrUecCoytVlKCIw8XZBKBacSib8jT7nhTClXBTrrf27V946MwZk/DLnNlsrwWkzUXKexcZWtWHsMxZ+W5xXkLc3Elw/w6sWKGtEJolJs0eUSoKAWMJxLYAVNYEBJg+jmP7XNOOUERcHgmtWRu3jesJPn6G4O/HkWjhHNzCQ6MCiIjkbiwWGU0IvEZABKCxD4XhAvCPP/7Qcu7eunVLuwzypgVI9V6oyikWh4uzCcBNm2DJB7OYRRfCS5XCZe9e3eeMbpapR8Z96xjtOY1nKTMy/FgT7uYoSdqL++Ow52VqQsB8AkYIwIjZqBv0M4tN49t73SMnGFL9A+7vPUv6Rxe119ZThwSE8AGbXhjx+DEkTWq+UVJTCMQCARGAsQD1LV0aLgAj5qLSraVIkYLTp0+/cQtYhViJy8XZBGD37lBhWmta85vpZq8KP6Gz3Gnfj3TzxjM1UR/uJMmOl38f7rzflHT/LNXZszQXAnGDgJECUFmsLuBXznmFnUGlXks79xErOF3oIzo1uE2/MS+O3twf9D2pRw+EO3fA1xfKl48b8GQWTkVABKCx7rabAFRm+vj4UKlSJbkEYqzPrR7to/LXWbbHkwSEwo4dULGi1X1FNAyaMBX3vj34g4ba1lU/xvPks74knS6Bn3XDlQ7iBAGjBWCE0fMnB9C2V6pIBu0K7Obno+VI+DyL3MoV4aRsUoP32cKThClIumQufP89/Pef6SaxOvDbqRORDeIETZlEfCYgAtBY79pVABprqu1Hc7YVwD7Jf2bCo848frcMHsf+sw3Q9euhbl2OUIRTFOQTlhE+fgIufd6cLcE2A0svQsAYAvYSgMq6JXOf8rhLH06mfY+u21qTN29Um5ctDCZzq+q8x45oYQS3aEPC+bNfXBRRdwZfulRiDEEZxVkIiAA01tMiAHXwdiYBqHaG/kpv2v4N+soL91HDdJB7qam6qViwII/w4BiFKc8eWL4cmjSxTf/SixCwMwF7CkBlelCQaRHvTbrts5q+DPqnJjm5FC2pqyU/JNv2JXDxoimLT+nS0LataZVwwADw9jYl71ZhnWTr2M5Pm2MPLwLQWP+JANTB25kE4LZtkLJKUYpyFNasgfr1dZB7qWlgIGGJk2i3ilVWEO128b59UKqUbfqXXoSAnQnYWwDGZL7SdRXLhZL8li8NM+3hQY3GLPjNhQ7MYQo9Y2oe5f3wLl1xmTnDojZSWQhEEBABaOyzIAJQB+/4LgC/+Qbmz4clS+DYgSDadfcgISFw6RJkz66DXNSmj9Nmx+PelRcv3r0LadLYrH/pSAjYk0BcF4CKjVrhP3YMKlUyrRaeOgXek8IJnzGDCfSJkpM4Jpb+rXuQakRfuHULVGioRIlMK/pubjE1lfednIAIQGMfABGAOng7ugBUCTaGD4fZs0GFj1C7N9OnQ/HioJLSq10dCKdgQRc6lDrCgN+K8TRRSpI8vW/Tc0BBlarhvtNH80RQkhS4P/a3af86XCxNhYBuAo4gAN9kpDry98n/HnJgwy3Sc5useZKQ4NwpdlKRBqzGkysMx4v8RZPQ4tjXDAp7Q2SANm1MQ5QoYQruHscjPOh2unRgFQERgFZhs7qR4QKwsQrsa2ZZoQIBx+Hi6ALw+f2LKITVCsD27aajPfvH/sMSmjGXDvjnKMZ3l9pwPV9lMp/517ZeUf8hqCi2QFjR4rgePmjb/qU3IWBHAo4sABW2wEDTCmHixJA2remPRZWcZ9Ik+PhjmDzZtMinNgbGVP6TgVe6k4PLbyc+Zgz07//iDz25XGLHJzTuDC0C0FhfGC4AO6hk5c+LCgKtMoGoeH+l1cFiYP/+/fj7+6OE4pznosBYJOaP5ugCsEsXmDULPmUWDVnFYEZxlKJaitDTh59yn9SRWz/T6MbnTMevcQ+y/D7ZfEjm1PzuO1D7zap89JEpA4gUIRBPCDi6ALTEDSqEYNGiUOjpfmqyiUn0pgWLmEPH17tZvNikHNVnXhUfH1M6SSlOS0AEoLGuN1wAvmzewIEDuXfvHjNmzNDyAquisn98/vnnWpDosWPHGkvDwtEcWQDeuweFMvtzJKgAGbmlWR7slogCoSe4QG4m0YtevC70Hk/4CY8vOltIKobqCxe+SFzfty/8KDEAbQtYerMnAWcSgBGc1XlCT09Ql8fUcZInX3xFx0feNOF3avAP/Rn3mkvCUqbC9aeZ8Mkn9nSXjG1HAiIAjYVvVwGYPn16tm/fToECBaJYrbKDVKxYkbvqMkAcLo4sAFWklf1NRzOawa8Rzurix6lkpUj+8Prr9HfvhnLlbOuV/ftNoSVUmTIFVMoRKUIgnhBwRgH4quvCQk3xA1VImq+73aPD3CoU5nj0Hla3zt4mAtXFEg8PyJoV1NbxlSsmtSnxCR3+EyMC0FgX2lUApk6dmrlz59KwYcMoVq9atYr27dtz//59Y2lYOJojC8AOrYMZ+VsOsnDdtA0zbZop6v/LJVEino6aQJJ+n2uvhrq44fYwwPTL15ZF/RLv3RvWrYO//uK1aLW2HEv6EgIGExAB+DrwZk1CCFzxJ5fIwY1MJUgVdIvv7nWjCSu4n6cUqc9Gk2d87VoYPBiOHCE8WTJcFi2CnTth9Gho3hwWLHgRsNpgH8twtiEgAtA2HM3txa4CsG/fvsyfP5/BgwdTtmxZbc579uzh+++/p02bNowfP95cO+xSz1EFoMr7/n6qA+wJKUVIitQkuH0D3N1Nq289X4r71aKFKchr+vQa3/CChXA5ecIurGVQIeCoBEQAvu45tS2s7gOqS2c//GBayPPqfofB07Np547Dd+zEpWKFFw2XLyesfQdcHz96+2OgdhJUpyqkQZYscqbQwT40IgCNdZhdBWBYWBjjxo1j0qRJXFcZzIHMmTPTu3dv+vXrF3ku0Fgk5o/mqAJQ7eLOqDBXu91L9eqwebPJaBUXRv0VvWoVuLqazuLlyGG6oKEuaowcafoLXIoQEAJmExABaB6qZ89grccnNAlbxs0vRpNxwiBTw6NHCS9WDBelEoG6rKUX3tRhA6G44kbYmweYN8+UtUSKQxAQAWism+wqAF82VYkpVdTlD0cpjioAZ86Ep599wRdMgl69TPEcYipqOz5VKjlnExMneV8IvEJABKD5j8S8PMNpd34YJyt2otCOn7WGAW26k/LXaaynDo34g/frJOL+1cdkPfYXeymDP6loyjIekpysXKMLP1GIUy8GVRfLDh8GddZchZ9Jlsz8CUlNQwmIADQUt/qj6vmfVcaOGzlaSEgIW7du5dy5c7Rs2ZLkyZPj5+enCcFkcfyD6qgCsNtn4fSbmY+8nIOlS6FpUzt5X4YVAvGfgAhA8328qP5vtFjbmrNZqpDvmg88e8aTVJlJGuhPLTbQdXktLanI06dQu7bpb1IVk1ClFP/tN1OswsBLN+i/sgK5uBj9wEWKmNKdqEYNGoCKNqEulEixOwERgMa6wK4C8NKlS9SpU4fLly8TGBjImTNnyJ07t7YFrH5W4WHicnFUAdi62FF+PVKU0ISJcLt3R/4ijssPmczN4QmIADTfhWu9/qPeiHLcds9C+sBrhPy2hAStm3MZT46tvkDdD81LJzdhfDh7+i1hJl05T262UZnGrCAb1948mbp1TauESmGqc4RSDCcgAtBY5HYVgI0aNdJW/H755RfSpk3L4cOHNQGoVgQ7d+7M2bNnLaIxdepULXbgjRs3KFasGJMnT468XBJdRyrg9Ndff43KOKLiEebIkYOJEydSV/0iMKM4ogBUx/yGJf6eEcFf8bBafZJvWWOGpVJFCAgBawmIADSf3MF/7lGiZlpTg0ePuFehHmmO+vBj0iH0DviWBAnM60vta6kg9yNGQJ488O+/4EqotkVcgV2UZh/l2U1ltr/WYbinJy6XY8hkYt40pJaFBEQAWghMZ3W7CkAl+nbu3KnFAVRCMEIAXrx4kXfeeYcnT56Ybd6SJUto27attmpYrlw5TcgtW7YMFVMwQ4YMr/UTFBREpUqVtPfULeSsWbOiViRTpUqliUdziiMKQKWp9+dvTnOWEDp6DG6DBphjqtQRAkLASgIiAM0Hp46CB6dMS1ru8XjecjzafUwQCRne7gIj51q/TXvhAowaZUpXt2eP6b7bo0eQiGdausuGrI4yydAUqXAb+a0pJqnEFzTfgTprigDUCdDC5nYVgCoO4I4dOzSx97IAVMGhmzRpwk0VK8DMokRfmTJlmKJCmWgXWsPw9PSkZ8+eDBr0/DbZS30poahWC0+dOkVCdR7EiuKIAlAFgC7QtAhFOAZ//gn16llhuTQRAkLAXAIiAM0lZap3wL0cJYP/4171xqTZsoIdVOT+mh3Ur29ZP+bUVplKVGCDE9vvMoUevM/myMxIWnuVljIiVZ05HUodXQREAOrCZ3FjuwrAZs2aaXmAf/rpJ00AHjlyBJUdRAWGzp49u9m5gNVqXtKkSVm+fDlqWzmitGvXTssrrAJLv1rUNm+aNGm0dup9Na66hKLS00WkpXu1jTqXqL4iihKASmQGBAQ4zO3l0V7P6DciBe4Ew8WLpjAvUoSAEIg1AiIALUP7V8Z21Lk1P7KRNz1pdsObjBkt68eS2ipDiVoZPH4snL39lzLjcWsSEsL14v8j04F1sghoCUwddUUA6oBnRVO7CsCrV69Su3Zt1EVkdd6vdOnS2r/p0qXj33//jXbrNjob1a1htYWrtpMrVHgRPHTAgAH4+PhowaVfLQULFkRtNbdq1UrLPezr66v926tXL7y8vKJFOWzYMIYPH/7ae44kAH/4cBsD/qzCw2SZSP7AT7Y3rPjQSBMhYAkBEYCW0IJFFSfTYlevyEb9089l7K12lnWio7ZKRjTkkzPseVhIizF4pv0o8s/5SkeP0tRcAiIAzSVlm3p2FYDKBBUGRp3fU+f/Hj16RMmSJTVRliRJErMttEYA5s+fn2fPnnHhwoXIFT+VeURtC0cEpX51AvFhBXB6gYl0O9OHSyU/Isf+FWYzlopCQAhYR0AEoGXcfu2xm9ZTX/whP+nTo/SeVdiyTnTWVtmSlmXvS/t7E3iaMDlJHt81hY6REqsERADGKt7XOre7ALSFudZsAVetWlU7+7dp06bIKaxfv167AayEnrtKjRZDccQzgHPT9KX9/Qlcbtaf7It/iMlEeV8ICAGdBEQAWgZw9YoQ6jdxxxVT5g+fTcFUrWHm9V/Lhnpr7W0+YbxbLR1puE/Yv9txrfyG0DAqAOGAAfDwIeEZMhCc/13cVy0XwWiFL0QAWgFNRxO7CsB58+Zp2731nl9EUFu26jyguhSyaNEiLSyLuUVdAlH5hFXoF1XUJRB1jrBHjx7RXgJRN38XLlzI+fPncVVpz1AJMSYxZswYLRC1OcXRBKAKjbAi4Sc0CV3GnW8mkW7Ei20Wc+yVOkJACFhOQASgZcxUpIIF+UcwlBF8wURGP+hB8uSW9WGL2sHB8HeSBtQNXcOVQVPxHP35691eu0Z43ry4qDx2rxYPD6hc2ZRa04wFBVvM2dH7EAForAftKgBV+Jfp06fz/vvvs2vXLmrUqKGFb/nzzz9JkCCBFp/P3KK2kdWlj5kzZ2pCUPWzdOlS7ZZvxowZtRAx6pzg6NGjtS6vXLnCu+++q7VRN4XV2cOOHTtqZwBVbEBziqMJwFu3wDdjRSqyi+Alv5Pwk8bmmCl1hIAQ0EFABKDl8JRu2rf9KdX/l4R16yxvb6sWy3P15+OL4zhSvTdFN0+M2u3du/gXKk+q277a6+fITR7ORz/01q1QtWrU90JDTTnXJcxMJBcRgLZ6cs3rx64CUN3AVQJNrdSp27fq7N38+fM5fvw41apV4/bt2+ZZ8byWCgETEQi6ePHieHt7azEBVVH95cyZk7lz50b2qURnnz59OHTokCYOO3Xq9NZbwK9OxtEE4N69kLFsdrJzxXTlrWxZi/hKZSEgBCwnIALQcmYqbt8ff0CnTio/vOXtbdXi9zqzaLKhC8ez/493L0VVov4N25Jq9QL8yExNNnE73TtaUIXENy+R4upx2rCAFizWphLqnhi3HdvgnXfg+nX45htYtMg0zerVQf1yXrgQPvzQVlN3yH5EABrrNrsKQBWEecOGDZQoUUL76tu3L23atNHyAqtgzOpSSFwujiYAf18aSsNmiUhAKFy7BlmyxGW8MjchEC8IiAB0XDdu+NqH2qOqcTVxHrI9Na30qeK/yodUjaoRhouWWWSsTzmqVDG9pxb2vL1hy+ZwkvmsZeFDC0RdnTrw889Om5tYBKCxnxW7CkB121etACrxp878qZzAKjvI6tWrtewcx44dM5aGhaM5mgCcM9KPDkOyEurihltwILiZl1fTQixSXQgIgZcIiAB03Mfh9NbrFKiehVBcCQ54SuIU7rB6NUGNm+Ee+oylyTtR8cTPZMsWvY0ql8GMSgvwOtc2SoWz5OUUBUnNfd5jx2uNn75biiQFc8L+/ZA5M6hjSSpFaTzfLhYBaOxnxa4CUAVpHjJkiHYer1u3btRRf/2AFodP3cI19yyeschejOZoAvCnT/+jyy/luJfMkzQPJdelvZ4bGde5CIgAdFx/h4eF8zhBCpKFP2LX7JNUaJCeZzkLkPjRXf6iNinWLaHi/1K+1cA7d6Bpuct0PP81N8nI+lzdea9NLg4ehDXPU7F35idG85WWAu+NpV07mDMnXotAEYDGflbsKgCNNdX2ozmcAKyzgi4bmnA5WwWyX9lpeyDSoxAQAq8REAHo2A/F+dQlye1/kNXtV9AgwTpti/YY7zKl40Fm/GJebEB1SViltlcnb/Llg8SJTUx8feG//9QZdVA5Bi6ffEzVbd9qK4MBpKQoR6jDhhcAd++G5+faHZtq9LMXAWisVw0XgCrdW+HChbXQK+r7t5WiRYsaS8PC0RxNAM4qOZ3OBz/nXNFG5Dm80kJrpboQEALWEBABaA21uNNmb6mulDnwU5QJVXfzYeWdKqRKZft5Ll4MKhLZhg2wbx8kuXeVDszhCEX57WFDkiWz/ZhxpUcRgMZ6wnABqITfjRs3tDRv6nsXFxctFVxEifhZ/RuqTtPG4eJoAvCXXN/S6eJQzr3fmTz/RP2FFocxy9SEgEMTEAHo0O5j65d/Uu3HFxc5RjOI461G8+uvxtil4iKOGwcZMkC/fsSK6DTGkphHEQEYMyNb1jBcAF66dEkL+6IEnvr+bcWSQNC2hGJuX44mABek7U2be95cbPEVOReOMtdMqScEhIAOAiIAdcCLA03PngzhxjvVqcx2lvAJA7Mv5r+9Lpogk2JbAiIAbcszpt4MF4AxTciR3nc0Abg8cSs+DlyIX78fyTKuryOhlrkKAYclIALQYV0XOfFfpgexe/5pOowrTJmyLpIWOJZcKgIwlsC+oVvDBaAK8WJuadCggblV7VLPkQSg2k3flKA2tdmI/6R5pOoVNSyBXQDKoELACQiIAHQCJ4uJNiEgAtAmGM3uxHABGJF3N6YZyhnAmAhZ9r5KqnI5QylKcYCQVWtJ0KCuZR1IbSEgBKwiIALQKmzSyAkJiAA01umGC0BjzYvd0RxpBfDECUj27vM0cCruQJkysQtHehcCQkAjIAJQHgQhYB4BEYDmcbJVLRGAOkg6kgD08YEy1ZKSlKdw/jzkyqXDcmkqBISAuQREAJpLSuo5OwERgMY+AXYXgI8fP8bHx0dLAxcUFBTF+l69ehlLw8LRHEkArlr0hIYtPUwWPngAyZNbaK1UFwJCwBoCIgCtoSZtnJGACEBjvW5XAXjw4EHq1q3LkydPUEIwTZo03Llzh6RJk2pxAs+rlao4XBxJAC4ac5kWg3IQ7OpOwpBn8TqdUBx+ZGRqTkhABKATOl1MtoqACECrsFndyMLRZfIAACAASURBVK4CsFq1auTPn58ZM2aQMmVKDh8+TMKECWndujW9e/emcePGVhtmRENHEoDzeh+gnXcp7ifJQuon14zAI2MIASEgZwDlGRACZhMQAWg2KptUtKsATJUqFXv27KFAgQKo73ft2kWhQoW019q1a8epU6dsYmRsdeJIAnB28410XFIbv7RFyHLn7Sn4YouX9CsEnJGArAA6o9fFZmsIiAC0hpr1bewqANOnT8/OnTvJly+fthI4efJkateurQm/UqVKadvCcbk4kgCcVWMxnTe34GLOauS8sCUuY5W5CYF4RUAEYLxypxgTiwREAMYi3Gi6tqsArFWrFu3bt6dly5Z07tyZI0eOoC5+LFiwgPv372srgXG5OJQALDmNzge7c65YY/Ic+j0uY5W5CYF4RUAEYLxypxgTiwREAMYi3LgmAPft28fDhw+pXr06t27dom3btpErgrNnz6ZYsWLG0rBwNEcSgLPzjKTj+SH4Vu1E3q0/W2ipVBcCQsBaAiIArSUn7ZyNgAhAYz1u1xVAY021/WiOJAAXZOhHm9vjOd+kP7mX/2B7GNKjEBAC0RIQASgPhhAwj4AIQPM42aqWCEAdJOOSAHz0CJIle7Mxy5J1oOnjuVz+fDTZpw7SYbU0FQJCwBICIgAtoSV1nZmACEBjvW9XAXj37l2GDh3Kli1btC3gsLCwKNbfu3fPWBoWjhZXBOD8+dCuHcyaBZ9+Gr0RfyZsRP2QVdwcPoOMQ7taaKlUFwJCwFoCIgCtJSftnI2ACEBjPW5XAaiCQPv6+tKpUycyZsyIi4tLFOtVKJi4XOKCAAwPB1fXF5TUz6+W4GDY5V6FKmzjwc9LSdGpaVzGKnMTAvGKgAjAeOVOMSYWCYgAjEW40XRtVwGYPHlytm/fHucve7zJJXFBAO7dC2XLhpOdy9wkIxf8EpM5c9QZ374NNzMUpjDHCd2wCbdaNYx9ymQ0IeDEBEQAOrHzxXSLCIgAtAiX7sp2FYBlypTRYv+VL19etyGqg6lTpzJ27Fhu3LihiUrVd9myZWPse/HixbRo0YKGDRvyxx9/xFg/okJcEIBDhsCNkT/zM505TX7OrjpJ/QYvLQkCZ86AR4GsZMUP9u+HkiXNtlEqCgEhoI+ACEB9/KS18xAQAWisr+0qAPfu3cugQYO0c4CFCxfW0sC9XFKkSGE2jSVLlmhhZFRauXLlyjFx4kSWLVvG6dOntbzCbyoXL17kvffeI3fu3FouYkcTgNWqgZdPdaqzVTNxSt/z9PgxVxRzd++GYhWSkIRncOEC5MxpNlepKASEgD4CIgD18ZPWzkNABKCxvrarADx79qwWBPrAgQNRrA4PD9fOA4aGhppNQ4k+taI4ZcoUrY26UOLp6UnPnj01kRldUf1XqVKFjh07sm3bNvz9/R1KAD58CNnTPeFGUGoSEaSZ6F1nHb3W/0/7PiAAFMLdW55S9+OkJgTqRQuEtdkOkIpCQAhES0AEoDwYQsA8AiIAzeNkq1p2FYBqezZBggT07t072ksgVatWNcvOoKAgkiZNyvLly2nUqFFkG3WJRIm6VatWRduPl5eXln1k5cqVWkaSmARgYGAg6iuiqC1gJTIDAgKwZLXSLKPMqPT77/DTxxvYQJ3I2j8V+JEup/py9y6UKAFPn8L3vfzoNDQroS5uuIUGwyuXbcwYSqoIASFgJQERgFaCk2ZOR0AEoLEut6sAVKLt4MGDFChQQJfVfn5+ZM2aVcsiUqFChci+BgwYgI+PT7Qp5dTlk+bNm3Po0CHSpUtnlgAcNmwYw4cPf22u9hKAQ4dCim+/5Et+jJzT8lSf8vH9WUyaBF98YXq5XaljzN1fhAeJ0pHi2W1drKWxEBAClhEQAWgZL6ntvAREABrre7sKQLX9qs7/1axZU5fVlgpAlX6uaNGiTJs2jf/9z7Rd6ogrgE2bwpDlxSjGER5Wb0DyLavZ5VaJCiHb6dkjHM+pA0nLXRbTnL+pxc3UBch475Qu1tJYCAgBywiIALSMl9R2XgIiAI31vV0FoLqkoVbV+vfvT5EiRV67BKJEmjnF0i1gtepXokQJ3NzcIruPCELt6uqqXRzJkydPjEPb+xbwe/lust03kzbPB8s3kuLjWtzGtMo3qtpGhu+urb23jfeozHYu5axCjgs+MdolFYSAELAdARGAtmMpPcVvAiIAjfWvXQWgEluvFnX5w9pLIOpMoQr9oooSdNmzZ6dHjx6vXQJ59uyZFoD65TJkyBDUyuCkSZPInz8/7u7uMXrCXgIwJARU6reuqZewhOYEFy6O25Z/cE2fVpvztXPPWFtxJF1ufhvFhnMlm5Jn/9IY7ZIKQkAI2I6ACEDbsZSe4jcBEYDG+teuAvDSpUtvtTZHjhxm01BhYNSlj5kzZ2qx/1QYmKVLl3Lq1CntgokKEaPOCY4ePTraPs3ZAn61odEC8No16N4d1q+HJElgQMBgBjMaunaF6dMJdE2s3QY+vu4SBxsNp3XQ7ChTvli/BznXmASyFCEgBIwhIALQGM4yiuMTEAForA/tJgCDg4Pp2rUr33zzDblyRY1bZy0CFQImIhB08eLF8fb21mICqlKtWjVy5szJ3LlzHVYAdusGM2a8mP4fNKQhq8HbG3r2xM89B1mCL+MzZjfPBg6lNhuj2Hqjx7dkmjzEWrzSTggIASsIiAC0Apo0cUoCIgCNdbvdBKAyM2XKlNotXFsJQGPRgZErgPfuQb58oP6tVw9u+wWz7nAW0obdga1boWpVTqQozzsP97Cw6UqKLhuipX57uTyZ/AtJe3Q0GpOMJwScmoAIQKd2vxhvAQERgBbAskFVuwpAtWWrVur69OljA1OM78JIAfjtt6DCvuTPD8eOQcItG6F2bciYEa5ehQQJ2Ju1EWX8VjH5nem0OTGIVAQQ6JaERKFPTXC2bYP33jMelIwoBJyYgAhAJ3a+mG4RARGAFuHSXdmuAvC7777jxx9/pEaNGpQqVQoPD48oBvXq1Uu3gbHZgZECsFQpUAlTfpkVRsfKZ2HOHBgzBlq3hgULNDN3vtOJiidnMynJIHo//V57LbBaLRJtfb4VfOsWpE8fm0ikbyEgBF4hIAJQHgkhYB4BEYDmcbJVLbsKwLdt/arbwOfPn7eVnbHSj1ECUN34TZkSEoQF8qDcByTas+2FPSri83OhvKPSACrtHMuf1KM+a3nqnoIk61dCjRqgzlmeOydZQGLlSZBOhcCbCYgAlKdDCJhHQASgeZxsVcuuAtBWRtirH6MEoNq5rVIFxif3os/DES/MTZ4cjhyBnDm113Y1GkOFVYO4jCfZucKdDIVId/OEqY5SkBbcqrYXUxlXCMQ3AiIA45tHxZ7YIiACMLbIRt9vnBGAKvafKmrlz1GKUQJw/Hjw6veQO64ZSBT2DBYuhMSJoWBBKFQoEte+z36m9MzOkT9fL/IBmY9EvQnsKGxlnkIgvhAQARhfPCl2xDYBEYCxTThq/3YXgPPnz9dCt5w9e1abmQrCrDKDtGnTxlgSVowWWwLwzh34919QyUiKFYOWLSF40TKW8YnpKvDp09Fu5R4bsYLCXk0iLblZtwMZ10aNBWiFmdJECAgBHQREAOqAJ02dioAIQGPdbVcBOH78eC0OoMrWUalSJc3y7du3M3XqVNQFkbh+Ozi2BOBnn8HMmfDFFzBhgmmRr/epz/iMmaBuTKslwWiK3yIfsrSsFvlOQK8hpJwUNRuIsY+XjCYEhIAIQHkGhIB5BEQAmsfJVrXsKgDVJZDhw4drWTpeLvPmzdNyBF+4cMFWdsZKP7ElABcvhhYtoHhx+O8/8EgSxvHQAuTDF1auhEaNorUn5JQvCQrli3wvbNoMXLt1jRXbpVMhIATMIyAC0DxOUksIiAA09hmwqwBMnDgxx44dI2/evFGsVtvBRYoUQeXsjcsltgRgwITZHOk7h6U0o/2+HgwtvZa11Cc8RQpcLl82XeiIriheKkdcRFmzBurXj8sIZW5CIN4TEAEY710sBtqIgAhAG4E0sxu7CsDChQvTsmVLBg8eHGW6avtX5fY9evSomWbYp1psCUBGjoQhQ1hAa84MWUDq7/rSlwmg9oanT3+rsU8SpyZpoL+pjq+v6SChFCEgBOxGQASg3dDLwA5GQASgsQ6zqwD8/fffadasGTVr1ow8A7hjxw7++ecfli5dykcffWQsDQtHizUBuHo1NGzIQYrT+p2D/HKiPOXZA7/+Cq1avXWW4dmy4XLtmqlOWJjE/bPQp1JdCNiagAhAWxOV/uIrARGAxnrWrgJQmbp//34mTJjAyZMnNcsLFSpEv379KFGihLEkrBgt1gSgCoCdJw/PSEQyHnGHdFpaNy0H3Lvvvn2mW7ZA+/bQoQMMG2aFVdJECAgBWxIQAWhLmtJXfCYgAtBY79pdABprrm1HizUBGBZGaNLkuAU+oTL/so0qhLu44PLkiSn+nxQhIAQchoAIQIdxlUzUzgREABrrABGAOnjHmgBUu7dlyuK6by/j6aOd/wvNlh23K5d0zFaaCgEhYA8CIgDtQV3GdEQCIgCN9ZpdBKCrq2uMGT9URpCQkBBjaVg4WmwKQDp1gtmzeeaahMRhT6FOHVi/3sIZSnUhIATsTUAEoL09IOM7CgERgMZ6yi4CcNWqVW+0cteuXXh7exMWFua0YWA0OM8vgkSCUjel1e1gKUJACDgUARGADuUumawdCYgANBa+XQRgdCaePn2aQYMGsWbNGlq1asWIESPIkSOHsTQsHC1WVwDV6mfOnBBxo/f336FxYwtnKNWFgBCwNwERgPb2gIzvKAREABrrKbsLQD8/P7y8vFDZP2rXrs3o0aNR8QEdocSqAFQA+vY15YJTRSUITpvWEbDIHIWAEHiJgAhAeRyEgHkERACax8lWtewmAAMCAhg1ahSTJ0+mePHijBkzhsqVK9vKLkP6iXUBqFb/SpWCihVhxQpDbJJBhIAQsC0BEYC25Sm9xV8CIgCN9a1dBOAPP/ygCb5MmTJpIrBhw4bGWm2j0WJdAKp5hoaCm5uNZizdCAEhYDQBEYBGE5fxHJWACEBjPWcXAahuASdJkkTLAOL2FnGzIo6vehkiAI19HmQ0ISAEbExABKCNgUp38ZaACEBjXWsXAdi+ffsYw8AoDHPmzDGWhoWjiQC0EJhUFwJOSEAEoBM6XUy2ioAIQKuwWd3ILgLQ6tnG0HDq1KmMHTuWGzduUKxYMe18YdmyZaNtNWvWLObPn88xlV4NddSulLYd/ab60XUiAjC2PCn9CoH4Q0AEYPzxpVgSuwREAMYu31d7jzcCcMmSJbRt25YZM2ZQrlw5Jk6cyLJly1DhZTJkyPAaVRVqplKlSlSsWJHEiRNrZxJXrlzJ8ePHyZo1q1leEAFoFiapJAScmoAIQKd2vxhvAQERgBbAskHVeCMAlegrU6YMU6ZM0bCoQNKenp707NlTiy8YUwkNDSV16tRaeyUkzSkiAM2hJHWEgHMTEAHo3P4X680nIALQfFa2qBkvBGBQUBBJkyZl+fLlNGrUKJJLu3bt8Pf3522ZRyIqP3z4UFspVKuG9evXN4utCECzMEklIeDUBEQAOrX7xXgLCIgAtACWDarGCwGogkmrbdudO3dSoUKFSCwDBgzAx8eHPXv2xIjq888/Z8OGDdoWsNoSjq4EBgaiviKKEoBqlVHFNEyRIkWMY0gFISAEnI+ACEDn87lYbB0BEYDWcbO2lQhA4Pvvv0fFJty6dStFixZ9I8thw4YxfPjw194XAWjt4yfthED8JyACMP77WCy0DQERgLbhaG4v8UIA6tkCHjduHN999x2bNm2idOnSb+UmK4DmPlZSTwgIgQgCIgDlWRAC5hEQAWgeJ1vVihcCUMFQl0BUCBcV+kUVdQkke/bs9OjR442XQNSq38iRI7Wt3/Lly1vMVM4AWoxMGggBpyMgAtDpXC4GW0lABKCV4KxsFm8EoAoDoy59zJw5UxOCKgzM0qVLOXXqFBkzZtRu9qpzgqNHj9ZQqbAvQ4cOZeHChVo4mIiSLFky1Jc5RQSgOZSkjhBwbgIiAJ3b/2K9+QREAJrPyhY1440AVDBUCJeIQNDFixfH29tbWxlUpVq1auTMmZO5c+dqP6vvL1269BpDLy8v1Fk/c4oIQHMoSR0h4NwERAA6t//FevMJiAA0n5UtasYrAWgLIJb0IQLQElpSVwg4JwERgM7pd7HacgIiAC1npqeFCEAd9EQA6oAnTYWAkxAQAegkjhYzdRMQAagboUUdiAC0CFfUyiIAdcCTpkLASQiIAHQSR4uZugmIANSN0KIORABahEsEoA5c0lQIOCUBEYBO6XYx2goCIgCtgKajiQhAHfBkBVAHPGkqBJyEgAhAJ3G0mKmbgAhA3Qgt6kAEoEW4ZAVQBy5pKgSckoAIQKd0uxhtBQERgFZA09FEBKAOeLICqAOeNBUCTkJABKCTOFrM1E1ABKBuhBZ1IALQIlyyAqgDlzQVAk5JQASgU7pdjLaCgAhAK6DpaCICUAc8WQHUAU+aCgEnISAC0EkcLWbqJiACUDdCizoQAWgRLlkB1IFLmgoBpyQgAtAp3S5GW0FABKAV0HQ0EQGoA56sAOqAJ02FgJMQEAHoJI4WM3UTEAGoG6FFHYgAtAiXrADqwCVNhYBTEhAB6JRuF6OtICAC0ApoOpqIANQBT1YAdcCTpkLASQiIAHQSR4uZugmIANSN0KIORABahEtWAHXgkqZCwCkJiAB0SreL0VYQEAFoBTQdTUQA6oAnK4A64ElTIeAkBEQAOomjxUzdBEQA6kZoUQciAC3CJSuAOnBJUyHglAREADql28VoKwiIALQCmo4mIgB1wJMVQB3wpKkQcBICIgCdxNFipm4CIgB1I7SoAxGAFuGSFUAduKSpEHBKAiIAndLtYrQVBEQAWgFNRxMRgDrgyQqgDnjSVAg4CQERgE7iaDFTNwERgLoRWtSBCECLcMkKoA5c0lQIOCUBEYBO6XYx2goCIgCtgKajiQhAHfBkBVAHPGkqBJyEgAhAJ3G0mKmbgAhA3Qgt6kAEoEW4ZAVQBy5pKgSckoAIQKd0uxhtBQERgFZA09FEBKAOeLICqAOeNBUCTkJABKCTOFrM1E1ABKBuhBZ1IALQIlyyAqgDlzQVAk5JQASgU7pdjLaCgAhAK6DpaBKvBODUqVMZO3YsN27coFixYkyePJmyZcu+Ec+yZcv45ptvuHjxIvny5WPMmDHUrVvXbJyyAmg2KqkoBJyWgAhAp3W9GG4hARGAFgLTWT3eCMAlS5bQtm1bZsyYQbly5Zg4cSJK4J0+fZoMGTK8hmnnzp1UqVKF0aNHU79+fRYuXKgJwAMHDlC4cGGzsIoANAuTVBICTk1ABKBTu1+Mt4CACEALYNmgarwRgEr0lSlThilTpmhYwsLC8PT0pGfPngwaNOg1VM2aNePx48f8+eefke+VL1+e4sWLayLSnCIC0BxKUkcIODcBEYDO7X+x3nwCIgDNZ2WLmvFCAAYFBZE0aVKWL19Oo0aNIrm0a9cOf39/Vq1a9Rqr7Nmz07dvX7744ovI97y8vPjjjz84fPhwtGwDAwNRXxElICAA1c+VK1dIkSKFLfwhfQgBIRDPCNx+dJu8k/NqVvn29CV9svTxzEIxRwjYhoASgFl+zBL5WYn43Pj188PD3cM2gzzvRS3gqEUipRFSpkxp074dpbN4IQD9/PzImjUralu3QoUKkewHDBiAj48Pe/bsec0f7u7uzJs3jxYtWkS+N23aNIYPH87Nmzej9d+wYcO096UIASEgBISAEBACjk9ALeBky5bN8Q2xwgIRgBYIwFdXANU2871790ibNi0uLi5W4I87TSL+GorPq5liY9x53vTMRPyoh17caesMflS0ncFOR7QxPDychw8fkiVLFlxdXePOB8PAmcQLAWjUFrCBfjF8KGc4zyg2Gv5YxcqA4sdYwWp4p87gxwgBqLYY1ZGh+HpUyFl8afiHJJYHjBcCUDFSl0BUyBcV+kUVtTqnzuf16NHjjZdAnjz5f3tnAqxT/cbxpwhFZctSyr5Fyla27EtENGRkKftaKImEIoQpomQNNYWRSsq+RjKWGfuWLUuIpEKUpf98nvmfd859ve573fu+t/ec+/xmmq57z3vO7/n8zjnv9zzb+Uu++eabAOIKFSpIiRIlElwEEuW1Sdbdp4QL2GxM1lMqagezdYwa2mTdcUpYRxOAyXpK2cFukoBvBCBtYCj6mDhxogpB2sDMnj1b9uzZI9mzZ9cWMeQJ0vaFQb5glSpVZPjw4fLkk0/KrFmzZNiwYTfVBuYmWcf05inhZmw2xvQpmODJ2TomGFVMb5gS1tEEYEyfgil+cr4RgKwkLWCcRtC0cxk7dqx6BhlVq1aVPHnyyPTp0wOLTp/A/v37BxpBjxw58qYaQfvp7CG/EXH82muvSdq0af1kWsAWs9Efy2rraOvoJQJ2vnpptVLWXH0lAFPW0pm1RsAIGAEjYASMgBFIHAETgInjZp8yAkbACBgBI2AEjIBnCZgA9OzS2cSNgBEwAkbACBgBI5A4AiYAE8fNPmUEjIARMAJGwAgYAc8SMAHo2aWziRsBI2AEjIARMAJGIHEETAAmjlvMf4r2NlT09ujRQ1viMC5duiS9evXSljdUptWpU0d4/R1tcpxx5MgR6dKli6xcuVIyZMigrXWoDk6dOnVgm1WrVul7lHfu3KnvUqSSunXr1snOJJSNVHvz+j/36NSpU5zejrFuY6hXDhYuXFhbGvllHcPZ6Id1ZK1+/vln6dOnjyxcuFDoO1qgQAGZNm2alClTRteStxHwDvLJkyfrO0krVqwo48ePl4IFCwZOYd429OKLL2rPUt5Y0LhxYxkzZoxen87Ytm2bdOvWTTZu3Cj33HOPbs+rMJNjhLORewOv3XQP7j2LFi3yhI10jzh8+PB1KLt27Srjxo3zxX01nI1+uR6T43rw0jFMAHpptRI4V74EmjZtql3nq1WrFhCACLv58+drKxw609Mkmy+UtWvX6p6vXr0qtM/JkSOHttM5ceKE9k/s0KGD9khkHDp0SIoXLy6dO3eW9u3by/Lly6Vnz566X27qyTVuZCM3qkKFCsngwYMDU7njjjsCHfi9YCPiaM6cObJs2bKADQjwrFmz6r/9sI7hbPTDOp49e1ZKliyp1yBrhjDbt2+f5M+fX/9jjBgxQh+wEEh58+aVAQMGyPbt22XXrl2SLl063aZu3bp6LdLj9PLly9KmTRspW7aszJgxQ/9OPz3O+Zo1a+pDH59v27atXvcdO3aM6iWZEBsRgLxfHeHrDFpNZcqUKfDvWLbx9OnTem90xo4dO6RWrVr6kMx56ofrMZyNfrgeo3oheHTnJgA9unA3mvb58+elVKlS6tkbMmSICjq+CHgNEV9AfGk0adJEP45HqWjRorJu3TopV66ceinq168vx48fD3gFJ0yYoB4MbhBp0qTRnxF73ASd0axZM/VeuJ/oo4n1RjZyTG5Ujs2h5uAFGxFHc+fOlS1btlxngl/WMT4b/bKOffv21YerNWvWhLwc8P7xHlK88q+88opuw/rikechjetq9+7d8uCDD6pnz/Eacp3Vq1dPjh07pp/HY/j666/LyZMn9RplcGzOIcdrHK3rMZyNHBcByP2B+YQasW5j8Jx54P32229VzCO+/XJfddvptpH33Pvhvhqta8DL+zUB6OXVCzF3QraZM2eW0aNHx7loV6xYITVq1BCe2DNmzBj4ZO7cudWD99JLL8nAgQNl3rx5cYQHHr98+fLpG1LwZlSuXFkFphNWZkc82bMPvrySY9zIRkc4EJrmyxVPZoMGDdSrgheQ4QUbEUd4YPHS4gUqX768eol4taFf1jE+G/2yjgg3vOIINdISeBMRYUM86oyDBw+qJ3Dz5s360OIM3lDEvwnzTp06VQUi160zrly5oucFjeyffvpp9dIjRNwCC+9U9erVhfCx29MW6esznI2OAGRuiFPmwrx4OM2SJYtOJ9ZtdDPjvfOIblJg+vXr55vrMT4b/XI9Rvrc98P+TAD6YRX/bwO5fUOHDlVvAV8Q7qc2PH+Ejsj9cw9em0eIilAU4SJyXRYvXhzYhLyl9OnTy4IFCzQURaiJ/RBqcgZ/43V6bHv77bdHlWh8NnLgSZMmCaKWmzR5UXgssfHLL7/UeXnBRryUeDnJ+yP0N2jQIM0lw+tKHpgf1jE+G++8805frKMTwkUsPPPMM3pdkpOLV52HGF5HSc4fHvecOXMGrhvSN/C68HpLUi8ID+/duzfOdZUtWzY9Lwg/1q5dW8PHhIidQQi5WLFiGkrGyx+tEc5Gjss1ywMYczxw4IAKJ/IXiTykSpUq5m10s+P1os2bNxfyiLnH+OW+Gp+NfrmvRusa8PJ+TQB6efVccz969KiGiJYuXSolSpTQv/hNAIazMdRSOh6z/fv3q7fFCwIw2A7CZ4jaUaNGqcD2gwCMz8Z27dpdt5ReXEc8XlyTCD1ndO/eXYUg4scPAjCcjaGuScfzSY4rUYlYF7luG/DoYjMPYgw/CsBgG/16X/XJV3+SzDABmCR8sfNhQiyEg3iidgaJy3gSKPTAq0eSuJdDwOFsxLvpth8OFy5cUG8DeVPc2LwQAg51VpH0z/qRfO6XUH6wnY6NhLuDhxfXEdHOek2ZMiVgDvl6hD/x6PohBBzOxhvdIcmbgwMV+l4JARMdIR2GaELDhg3VNL+kZDjrFMrGUGvoxesxdr6tY2cmJgBjZy2SNJNz585d16oAT1GRIkU0DEq7Fm66M2fO1DYSDMJK/D24CISwIyEmBiHV3r17y6lTp4TKPfZFyJdKQ2cQEiHXKNpFIOFspDo5eJCEX6lSJdm6dat6Rp0ikFi1MdRJQDiY/D/y5ggden0dw9mIl8wP68h1gdfaXQRCru369evV0lxZaAAAC8dJREFU++cUgVAAQp4fg1w+rr3gIpBNmzZJ6dKldZslS5bIE088cV0RCJW2t912m25DmBWhEu0ikHA2hlprciI5n3mge+qppwKFLrFqo2MD1x9hdtbUaYvlFGV5+b7qXqNQNoZaQz/cV5P0heuTD5sA9MlChjIjuHKLfCHEG18utIihVxjDCVE5LVLIbRk5cqRWFbZq1UrbvQS3gaHnGK0meALmCzu528A49rptJL+IkAwVkiSYkwPIF26uXLkCvQG9YCOCgOIVvCvkh9Enjopg8rkQf35Yx/hsRAT5YR0J9VaoUEFz9cjr27BhgxaA8FDVokULPYXJvaWfpbsNDOdtcBsYxB25g04bGELLThsYRAj5ouQC8oBGrijXJoVg0W4DE85GHl6wn4dOirK4RulPyMMcD5E8VDLIL45VG5nftWvXNIfx2Wef1fVyDz9cj/HZ6Jf7qo+/6hNtmgnARKOL/Q8GC0CnETRPq+5G0NyYnUEIgBsazZ4p/sDjxA0vuBE0woovKcQVVbb/RSNo5uy2kSfzli1b6hcgIQq8noTFaVSN4PWKjbT/WL16tZw5c0YFHx5Minuc3nF+WMf4bPTLOnK+0S6EgilahiAgKAhxqoD5u9MIGlFIridrTQsniq2cgXednp3uRtBjx469YSNo+kXycIcYTI4Rn40XL16URo0aaaUz9vFwiVB966234jSgj3Ub8bqSQkLUxL028PXD9YgdN7LRT9djclwPXjqGCUAvrZbN1QgYASNgBIyAETACESBgAjACEG0XRsAIGAEjYASMgBHwEgETgF5aLZurETACRsAIGAEjYAQiQMAEYAQg2i6MgBEwAkbACBgBI+AlAiYAvbRaNlcjYASMgBEwAkbACESAgAnACEC0XRgBI2AEjIARMAJGwEsETAB6abVsrkbACBgBI2AEjIARiAABE4ARgGi7MAJGwAgYASNgBIyAlwiYAPTSatlcjYARMAJGwAgYASMQAQImACMA0XZhBIyAETACRsAIGAEvETAB6KXVsrkaASNgBOIhwGvZeA0crxD87LPPpGDBgsbLCBgBIxCSgAlAOzGMgBEwAj4hULhwYRk3bpzs3LlT1q1bJ7NmzfKJZWaGETACkSZgAjDSRG1/RsAIxByBqlWryiOPPCLvvfdeTMwtsfM5c+aMFC1aVDZs2CB58uS5zpYKFSrIjBkzZOvWrfLFF1/IJ598ots0a9ZMypYtK7169YoJ+20SRsAI/PcETAD+92tgMzACviAwYcIE6d27t5w9e1ZSp06tNp0/f14yZcokFStWlFWrVgXs5Odq1arJ/v37JX/+/FG3P7GCK6kTu9FxEzufl19+Wc6dOyeTJ08OOTV+37lzZ8mePbvs2LFDMmfOrNvxc+XKleXQoUNy9913J9Us+7wRMAI+IGAC0AeLaCYYgVggsHfvXilSpIiGHsuVK6dTWrhwoXTs2FF+/fVXFYbp0qXT37/xxhsyffp0OXz4cLJMPbGCK6mTi6QA/OuvvyRnzpyyePHiAF/3/K5cuaJezgYNGmgY+I8//pBbbrklsAkewNatW0u3bt2SapZ93ggYAR8QMAHog0U0E4xArBC49957pXv37tK3b1+dUp8+feTChQuyYsUK+fDDDwVBxKhSpYrkzZtXReCiRYtkyJAh6qVKlSqVlC9fXsaMGRPwDE6aNEnefPNNOXbsmNx6660BUxs2bChZsmSRqVOnyrVr12TEiBHCtidPnpRChQrJgAEDpEmTJrp9sBALt73zmRIlSqhonTJliqRJk0a9a8zFGXjj+N3cuXPlrrvukldffVW+/vprFWK///67fPzxx3GWBg8coVvmE27fwWs6Z84c6dq1q5w6dSrkcn/wwQeyZMkS5Xz//fdf510dPHiwLF26VNasWRMrp4vNwwgYgf+QgAnA/xC+HdoI+I1AixYt1NuHl4rx6KOPqihavny5ZMuWTQYNGiQXL17UsPDEiRPl+eef11w1PFUIIkLGAwcOlJ9++km2bNmigg/PYY4cOWTBggVSo0YN3e9vv/2m3jDnd0OHDpVPP/1Uc/yofF29erUKM+aB2AwWgOG2dwTg5s2bhbBr8+bN1bOJB4191qpVS+fRoUMHFVUfffSRhl2Z+7Jly6Rt27Zqa926daV48eKC+GJQnYvIZT7h9h18bvTo0UN+/PFH9aoGD3iQG0honf9nzJhR59S4cePApghtRPOff/4padOm9dupZ/YYASNwkwRMAN4kMNvcCBiBGxPAU9azZ0/1fiH0yEE7fvy4iiJyBL/77jv1BiLkCP8+8MAD1+0MAYlQ2r59u4onRqNGjdTbh6hh4OlDYB09elQuX76sx+EYeA+d0b59eyFsSlGEWwD+/fffYbd3BODVq1fjeMwQtNWrV5fhw4drLh5zYv+Op5GwK15QhCFiNL4QcHz7DkU4mIF7G1q/wAHGDIpBatasGRCe/G7btm3y8MMPq7jOnTu3ncZGwAikcAImAFP4CWDmG4FIEqCoAw/cDz/8oJ47ikJoSYIIzJcvnwrDYcOGaY+6AwcO6KH37dunnrP169er95DwLGHj+fPnS7169XSbzz//XEXVL7/8ot4rvHplypSRd999V/ePUEyfPn0cU/755x8pWbKk7tctxBKyvSMAixUrpvl0znCHnam0JdQbLGRLlSqlBRfhBGB8+w61JnXq1JECBQrEmQ/b7dq1S4tsdu/erZ5SBnmXhMLnzZsX2BWcCY2zPV5CG0bACKRsAiYAU/b6m/VGIOIEyD/r0qWLCkCEHDlpDIQhYV8KQCgWcSpZ+RmPFKFivGcIQATdV199pZ4/xqVLlzTEOm3aNG1nwvabNm0SxBYCj6ITwp/33XdfHHsQi8zHLQATsr0jAINbxzAfwqvkLiZVAMa371CLQnj933//VY+jeyAMyf0jtOwMGGK3u8jGsfv06dOSNWvWiK+77dAIGAFvETAB6K31stkagZgn8Nxzz6n3yfEANm3aVOfcrl07Db2OHTtWhRx5dfS1Q4yQs/f444/rdt9//73+7BaA/L5Nmzaav/bYY4/p5/F4MQjFEjJGULZq1SokH7cATMj2CRGATgh45syZgVw7QsCIUMLPeABr164tNGd+//3348wrVGjYLS5DGfHOO+9oniO5kc7gzR/kJRL+dlrv8LeNGzdqHiK5geRbMgifU8BC2NyGETACRsAEoJ0DRsAIRJQA4oxWI+SkUbmL545BU+IXXnhBBRshYYo48FRRHEKxBJ7BI0eOaAUxAiZYACJy6tevr1W0LVu2lP79+wfmzc/kvxESrlSpkrZAWbt2rVbmUmgSLLjCbZ8QAcg2hKUpcEFcYQc24I1D7I4ePVpDsQi22bNnS4YMGVQAU9iSGAFITiQeT6qAEXXwxVOK0KPa2j3giJd05cqVgcprhCJeQiePMqKLbjszAkbAcwRMAHpuyWzCRiC2CVBkQIsXQruOl44ZE45EvOER27NnT8AIhB2tYw4ePKh/w0OIQAoWgIjFXLlyyYkTJzR/kJxCZxAa5XPjx4/X/RCmRSz169dP8/GCBVe47RMqAEO1geH1axSKvP3221q1iwAlXExRjLsNzM2GgJkT3k8EX6dOnWTUqFHa+gZ7g/MfsQ/BSbUzRTmE0MkPpBLY6dEY22eRzc4IGIFoEzABGG3Ctn8jYARSDAFyHgkB44nECxjpQWEMhTX0THT3RAx3HIQxghrvpA0jYASMAARMANp5YASMgBFIJAF6+eHNpD0MYWf6/VGMQjV0tAotyC2kvx9FHgkdtOchrxIPqw0jYASMgAlAOweMgBEwAkkggACk4IPX4PGmkNKlS2to9qGHHkrCXu2jRsAIGIHoEzAPYPQZ2xGMgBEwAkbACBgBIxBTBEwAxtRy2GSMgBEwAkbACBgBIxB9AiYAo8/YjmAEjIARMAJGwAgYgZgiYAIwppbDJmMEjIARMAJGwAgYgegTMAEYfcZ2BCNgBIyAETACRsAIxBQBE4AxtRw2GSNgBIyAETACRsAIRJ/A/wBU13ZLIKc5eAAAAABJRU5ErkJggg==" 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" width="640">
### Spectroscopic bins
Now we begin the iterative process of defining bin edges. We want to go as narrow as possible (100-250A) and cover as much wavelength range (4000-9250A for ACAM, 3700-7200A for EFOSC) while avoiding bin edges falling on strong stellar/telluric absorption lines and making sure the noise in the resulting light curves is not too severe.
Using bins I made earlier (with wider bins for the blue and red edges)...
```python
print(wb.sodium_centre)
no_bins_na = 35
bin_width_na = 14
na_centre = wb.sodium_centre
print(na_centre)
# Now make the wavelength bins for sodium
bin_edges = np.arange(na_centre-(bin_width_na/2)*no_bins_na,na_centre+(bin_width_na/2)*(no_bins_na+1),bin_width_na)
#bin_edges = [ wb.sodium_centre-70,wb.sodium_centre-50,wb.sodium_centre-30, wb.sodium_centre-10,wb.sodium_centre+10,wb.sodium_centre+30,wb.sodium_centre+50, wb.sodium_centre+70]
#bin_edges = [ wb.sodium_centre-75,wb.sodium_centre-45, wb.sodium_centre-15,wb.sodium_centre+15,wb.sodium_centre+45,wb.sodium_centre+75]
#bin_edges = [ 3810,4020, 4140, 4240,4440, 4570, 4680, 4800, 4910, 5010, 5120, 5230, 5350, 5470, 5560, 5670, 5783, 5853, 5923, 5993, 6110, 6240, 6330, 6420, 6510, 6620, 6720, 6820, 6940, 7040, 7150]
#bin_edges = [ 3810, 4140, 4440, 4680, 4910, 5120, 5350, 5560, 5783, 5853, 5923, 5993, 6110, 6330, 6510, 6720, 6940, 7150]
#bin_edges = [5493., 5523., 5558., 5583., 5608., 5630., 5665., 5693., 5733., 5763., 5793., 5827.,5853., 5882., 5915., 5948., 5973., 6008., 6033., 6063., 6090., 6123., 6153., 6188.,
#6218., 6243., 6273., 6300.]
nbins = len(bin_edges) - 1
bin_centres = np.array([(bin_edges[i+1]+bin_edges[i])/2 for i in range(nbins)])
bin_widths = np.array([(bin_edges[i+1]-bin_edges[i]) for i in range(nbins)])
print(bin_centres)
print(bin_edges)
print(nbins)
print(bin_widths)
print(na_centre-(bin_width_na/2)*no_bins_na)
```
5893.0
5893.0
[5655. 5669. 5683. 5697. 5711. 5725. 5739. 5753. 5767. 5781. 5795. 5809.
5823. 5837. 5851. 5865. 5879. 5893. 5907. 5921. 5935. 5949. 5963. 5977.
5991. 6005. 6019. 6033. 6047. 6061. 6075. 6089. 6103. 6117. 6131.]
[5648. 5662. 5676. 5690. 5704. 5718. 5732. 5746. 5760. 5774. 5788. 5802.
5816. 5830. 5844. 5858. 5872. 5886. 5900. 5914. 5928. 5942. 5956. 5970.
5984. 5998. 6012. 6026. 6040. 6054. 6068. 6082. 6096. 6110. 6124. 6138.]
35
[14. 14. 14. 14. 14. 14. 14. 14. 14. 14. 14. 14. 14. 14. 14. 14. 14. 14.
14. 14. 14. 14. 14. 14. 14. 14. 14. 14. 14. 14. 14. 14. 14. 14. 14.]
5648.0
```python
frame_no = nframes//2
frame_no = nframes//4
wb.plot_spectra(f1[frame_no],f2[frame_no],wvl_solution_1[frame_no],\
bin_edges=bin_edges,telluric=False,ratio=True)
wb.plot_spectra(f1[frame_no],f2[frame_no],wvl_solution_2[frame_no],\
bin_edges=bin_edges,telluric=False,ratio=True)
```
<IPython.core.display.Javascript object>
<img 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aj+3LAAVMBYTQH43/XH6LX8NKqWsMG2QY1EL2tP3Iunb6KxbVBDVC1RWEHPuSgTYAIsAHkNMAHjJcACUP25YQGogLGaAnDftVB8/vsZVCtVGFsGNhS9bPnrQdwIfYPlPevAt5KDgp5zUSbABFgA8hpgAsZLgAWg+nPDAlABYzUF4O6rIei76ixqOBfBpgGayB9f/3keWy8GY0SriujfxE1Bz7koE2ACLAB5DTAB4yXAAlD9uWEBqICxmgJw55VH+OqPc6hdpijW96sverngQCCm7ryO9tVLYO4nngp6zkWZABN4+/YtZs+ejdjYWPF14sQJeHl5wdzcXMDRfkbfK3lPdb5NeIvxMeNl8GPyjMG5k+dUbVNqjNr/5ptvkD9/fp54JpAjCLAAVH+aWAAqYKymAPz3UjAGrjmPui62WPelt+jl5gtB+OavC/B2tcOffb0U9JyLMgEmkNUEOBJIVhPn9nIyARaA6s8eC0AFjNUUgPrEnmQYUqW4DbZ/ozEM4cQEmEDOIMACMGfME/fSOAiwAFR/HlgAKmCspgDceP4hBq+9iIZuxfBHn3qil1J4OI4HrGDSuCgTeEeAQsGdO3dODgUXGBgINzc3nVBw0jMqouS9CAUXG4mm+xLjeO/x3YOgO0GqtilNNrVfs2ZNDgXHqz/HEGABqP5UsQBUwFhNAbj+7EMM+/sifCrY4/fedUUvbz15g2YzDsK6gDkuT/BT0HMuygSYABuB8BpgAsZLgAWg+nPDAlABYzUF4NrT9/HdP5fRtJIDfutZR/SSfACSL0BKp0Y1g4ONhYLec1EmYNoEWACa9vzz6I2bAAtA9eeHBaACxmoKwDUn72PUxstoXtkRS3vUFr2MiYtH+dE75B5fneAHqwIai0VOTIAJpI8AC8D08eLcTCArCbAAVJ82C0AFjNUUgKtO3MMPm67Ar6ojFnfTCEBKZb/fJv9/7xAfuDkUUjACLsoETJcAC0DTnXseufETYAGo/hyxAFTAWE0B+Puxuxi35SraehTH/K419QrAwyN84WzLMYEVTCEXNWECLABNePJ56EZPgAWg+lPEAlABYzUF4LIjd/DTv/7JnD6XH70dMXEJotcHhzdBGTsrBSPgokzAdAmwADTdueeRGz8BFoDqzxELQAWM1RSAdPxLx8Ada5TArC6JUT8q/7ATkTFxotf7hvqgnD0fASuYQi5qwgRYAJrw5PPQjZ4AC0D1p4gFoALGaglAbWOPNh5OWNC1ltxLj/G78DoqVny/Z3BjlHe0VjACLsoETJcAhYKbPHmy7Afw2LFjqF+/vo4fQOkZUVLynvzwvcVbTDWfKgMfGj0UZ46dUbVNqTFqf9SoURwKznSXe44bOQtA9aeMBaACxmoJwNdRMfAYv1v0TDsUHH0/8V9/LD1yR7zb+W0jVHKyUTACLsoEmEBWEeBIIFlFmtvJDQRYAKo/iywAFTBWSwA+exONWu/8/dUsXQQb+jeQexkVE4dKP+wU3//7dUO4lyysYARclAkwgawiwAIwq0hzO7mBAAtA9WcxRwjAQ4cOYdq0aTh79iwePXqEjRs3omPHjinSOXDgAHx9fZO9p7JOTk7y8/nz54t6Q0JCUL16dcydOxd162qibhiS1BKAj15GwnvKftGFSk7W2PltY53uNPh5P4LCIrFlYANUK1XEkK5yHibABJIQiI+Px7Vr10D/0tfdu3dRtmxZmJmZiZzaz+h7Je+pzojYCHht8ZJ7cbzdcYQ+DFW1Takxar9y5cry2HgxMAFjJ8ACUP0ZyhECcMeOHTh69Chq1aqFTp06GSwAr1+/DhubxCNSBwcH+Rfg2rVr0b17dyxatAj16tXDrFmz8Pfff4PKUD5DkloC8N6zcPhMOyC6UNrWEodG6IrZRlP348HzSGzoXx81Sxc1pKuchwkwgSQE2AiElwQTMF4CLADVn5scIQC1MeTJk8dgAfjixQsUKaJ/h4xEX506dTBv3jz5r31nZ2d8/fXX+P777w0ir5YAvBn6Gi1+PST6UKxQfpwZ00KnP77TD+DO03Cs/8obtcvaGtRXzsQEmIAuARaAvCKYgPESYAGo/tzkagFYpkwZREdHw93dHePHj0eDBpq7dGT9Z2lpifXr1+scJffo0QNhYWHYvHmzXvJUF31JiQQgicaXL1/q7DQqnbYrQS/Rbu4RUU3BfHlx7adWOlU2m3EAt56E46++XvBytVPaHJdnAiZJgAWgSU47DzqHEGABqP5E5UoBSMe4dA+wdu3aQrAtXboUq1atwsmTJ1GzZk0EBwejZMmSwq2Dt7e3THnEiBE4ePCgyKcvkYicMGFCsleZLQDP3X+BTguOye3c/bmtTpstfz2IG6FvsKZPPdR3K6b+KuEWmEAuJMACMBdOKg8p1xBgAaj+VOZKAagPm4+PD0qXLi2EYEYFYFbtAJ64/QxdlpwQw2hfvQTmfpLoCJqetZ59GNcevcLK3nXRuIK9+quEW2ACuZAAC8BcOKk8pFxDgAWg+lNpMgJw+PDhOHLkCI4fP57hI+Ck06HWHcBDN56g+2+nRHOXx7eEtUU+nabbzT2MK0GvsLxXHfhWNMxgRf2lxC0wgZxFgAVgzpov7q1pEWABqP58qyoA6T7dsmXLhKsFSlWrVkXv3r1RuHDGfdcZYgSiD1uLFi1gbW2NDRs2iNdkBEIuX8j1CyVy+UA7hAMHDsx2I5C9/qHos/IMqjsXweYBiT4ApXG9N/8oLj4Iw7IetdGssqP6q4RbYAK5kAALwFw4qTykXEOABaD6U6maADxz5gz8/PxQsGBB2bfe6dOnERkZid27d4u7eIamN2/eIDAwUGT39PTEzJkzhZ8/W1tbIdpGjhyJoKAgrFy5UuQhly4uLi5CcEZFRYk7gCT0qN1mzZqJPOQGhow+Fi9eLPpHZdatW4eAgAA4OhomqtTaAdx++RH6rz6HumVtse6rxDuKEq9OC47i3P0wLO5WC35VE/0aGsqT8zEBJqAxBhs9enSWhYKLQQzmFdZ4HaDU73k/nD52OstCwU2aNIlDwfHCzzEEWACqP1WqCcBGjRrBzc0N//vf/2Bubi5GEhsbiz59+uD27dsg586GppQcO5OAW7FiBXr27CmctFI+SlOnTsWSJUuEKCRr32rVqmHs2LHJnEOTCxjJEXSNGjUwZ84csTNoaFJLAG46H4Rv115AQ7di+KNP8v58tOg4Tt19joVda6K1R3FDu8v5mAATyEYCHAkkG+Fz0zmOAAtA9adMNQFIO3/nz59HpUqVdEbh7+8vrHMjIiLUH53KLaglANedfoAR/1xC00oO+K1nnWSj6LLkOE7cfi6MQ8hIhBMTYALGT4AFoPHPEffQeAiwAFR/LlQTgHSMSha3LVu21BnFrl27RASO0NBQ9UencgtqCcBVJ+7hh01X0KqqExZ1q5VsFJ8tPYkjgU8xu0sNvFejpMqj5OqZQO4kQPd+79+/L4eCo5CQFCpSOxSc9IwIKHkvQsHFRMDjLw8Z5sWPL+LV01eqtik1Ru3TdRlpbLlzRnlUuYkAC0D1Z1M1ATho0CARsWP69OnijgslCudG1ridO3cWd+5yelJLAC47cgc//euPDtVLYE4SFzDEjCyEyVJ4xofV0blWqZyOkfvPBLKFABuBZAt2bpQJGESABaBBmBRlUk0A0gVrEnsUa5fu/lHKly8f+vXrh59//hkFChRQ1HFjKKyWAFx44BZ+2RmAD2qVwvQPqycbau8Vp7E/4DGmflANH9V2NgYU3AcmkOMIsADMcVPGHTYhAiwA1Z9s1QSg1HW663fr1i3xbbly5YRRRm5JagnA2Xtv4te9N/BpvdKY/H7ikZHErc/vZ7D3WiimdPLAJ3VL5xacPA4mkKUEWABmKW5ujAmkiwALwHThylBm1QVghnqVQwqpJQDJCOTvsw+Ei5c+jVyT0fhy1RnsuhqKiR3d8ZlXmRxCi7vJBIyLAAtA45oP7g0T0CbAAlD99ZCpArBTp07CLYuNjQ3o/6klySGz+kNUrwW1BGBaPR6w+hy2XX6ECR2qokf9smll5/dMgAnoIcACkJcFEzBeAiwA1Z+bTBWAvXr1Er70KOIG+eajqB0ppeXLl6s/OpVbyC4B+PWf57H1YjDGtquC3g1dVB4lV88EcicBFoC5c155VLmDAAtA9ecxUwWg+t01rhaySwAOXnsBG88HwdrCHJfH+xkE5e8zD8Su4bxPa6JQAY1jbk5MwJQJsAA05dnnsRs7ARaA6s+QagKwadOmIu5ukSJFdEZBoqljx47Yv3+/+qNTuYXsEoBfrDyDPf4aP4oXxrZAEcv8aY607PfbRJ4hLSpgULPyaebnDEwgtxOIjo7GkCFD5FBwJ06cgJeXF/LmzSuGHhcXB+kZfa/kPdUZkycGvzn9JmPtEdQDZ4+fVbVNqTFqn0Jo5gbvC7l9XfL4NARYAKq/ElQTgORwlBynOjg46Izi8ePHKFmyJGJiYtQfncotZJcA7LbsJA7ffCpGd2i4L0rbpW1ZLQnAL31cMbJ1ZZXJcPVMgAkkJcCRQHhNMAHDCbAANJxVRnNmugC8dOmS6AvF1qVdPltbW7lv9Bf1zp07sXjxYhG7N6en7BKAHyw8hjP3Xgh8jcoXw5wunihqpdkFfPYmGi8jY+BqX0gHryQAafePdgE5MQEmkLUEWABmLW9uLWcTYAGo/vxlugCknT/J+CMhISHZCChG8Ny5c9G7d2/1R6dyC9klANvMPgz/R6/k0b1XowRmd/EU30tC7/jIpiheuKCcR3o+3K8iBvi6qUyGq2cCxk+Afj89ffoU9C99PX/+XPzBqv37S3pGo1HynuoMjwmH69JEt063Pr+F6NfRqrYpzQK1X6xYsVQN84x/xriHpkSABaD6s53pAvDevXvil6mrqytOnToFe3t7eRT58+cXR8LSHRv1h6duC9klAH2nH8Cdp+Hy4ErbWuLQCF/ExyfAddR28ZxCyFEoOUoxcfEoP3qH+P+YtpX1+hZUlxTXzgSMjwAbgRjfnHCPmIBEgAWg+msh0wWg+l02nhaySwDWm7wXoa+iZRAVHa2xa3BjvAh/C8+f9sjPA35qBYt8eREW8RY1ftQ8/6mjO7qx82jjWUTck2wjwAIw29Bzw0wgTQIsANNEpDiD6gLQ398f9+/fB8UG1k4dOnRQ3PnsriC7BKDH+F14HaWJr0ypunMRbB7QAIGP36D5zIPy8+2DGqFKCRs8eB6BRlP/0wjA96qimzc7j87utcPtZz8BFoDZPwfcAyaQEgEWgOqvDdUE4O3bt/H+++/j8uXL4t6JdB9Qul9DBiE5PWWXAHQbtR2x8Yn3K71cbfFXX2+cuvMcHy0+LmPdO8QHbg6FEBDyCq1mHRbP2Xl0Tl913P/MIsACMLNIcj1MIPMJsADMfKZJa1RNALZv317c9Vu6dClcXFzEfcBnz55h6NChmD59Oho1aqT+6FRuIbsEoLYVMA2xaSUH/NazDnZcfoR+q8/Jo97xTSNULm6Ds/eeo/NCjTAc1aYS+jYupzIZrp4JGD8BFoDGP0fcQ9MlwAJQ/blXTQCSxRm5galWrRoKFy4sBGDFihXFMxKB58+fV390KreQXQLw0ctITNt1HRvOBYkRSgLwz1P3MXLDZXnUmwY0QA3nIjrCkK2AVV4UXH2OIcACMMdMFXfUBAmwAFR/0lUTgEWLFsW5c+fE7l+5cuXETqCvry9u3boFDw8PREREqD86lVvILgEoDWv+f4FCCNYvZ4c1X3jh92N3MW7LVXnU6770Rl0XW/y8IwCLDt4Szwc3r4BvmnMkEJWXBlefAwiwAMwBk8RdNFkCLADVn3rVBCAd8dJOH4V9+/TTT/HixQuMGTMGS5YswdmzZ3HlyhX1R6dyC9ktACkcHIWFo10+2u1bevg2Jm67Jo961ed10ai8PbQjh3zd1A1DW1ZUmQxXzwSMnwCFgvvyyy/lUHB0SlG3bl2dUHDSMxqNkvd0HSY2Tyz+KveXDObDGx/i3MlzqrYpNUbtkwN+DgVn/OuSe6jpRRCuAAAgAElEQVQhwAJQ/ZWgmgDctWsX6C/sTp06ITAwEO3atcONGzdgZ2eHtWvXgmIF5/SU3QLwyM2n+GzZSVRyssbObxtj4YFb+GVngIx1affaaF7FEU1nHMDtJxq/gf2alMN3rSrldPTcfyaQ4whwJJAcN2Xc4WwkwAJQffiqCUB9XSdP+nQ0LFkCqz88dVvIbgEoGXeUsbPEweG+mLvvJmbsuSEPekHXmmjt7oSq43Yh4q3G6vqLRi4Y3baKumC4dibABJIRYAHIi4IJGE6ABaDhrDKaUxUBGBMTAwr5duHCBbi7u2e0b0ZfLrsFoH/wK7SZcxj21gVwenRzzNxzA3P23ZS5/fpxdTSt5IjqE3bLz3rWL4vxHaoaPVvuIBNQmwC5pqK7yFIouMjISPF7SzsUnPSM+qLkvRQKzmmOkzysR18/glmsmaptSo1R+5aWlrnmj2+11wbXn/0EWACqPweqCEDqNoWC27hxI6pXr67+KLKphewWgBQOjsLCWRcwx+UJfuL4l46BpfRzJw/UKF1E9gFIzz/zKo2JHT2yiRg3ywSMhwAbgRjPXHBPmEBSAiwA1V8TqgnAZcuWYcOGDVi1apUIdp4bU3YLwJCXUfCasg/mZnkQOLkNJm3zx/8O35FR//heVRQrVAD9tXwDdqnjjJ87V8uN08FjYgLpIsACMF24ODMTyFICLADVx62aAPT09BTGH3QcXKZMGVhZWemMhlzE5PSU3QLwZWSMfLx7Y2JrTN5+DSuO3ZWxjm5TGeFvYzFrb+KxcOeapTDjo9y7K5vT1xT3P+sIsADMOtbcEhNILwEWgOkllv78qgnACRMmpNqbcePGpb+3RlYiuwXg29h4VBizQ1C5OK4lpu4MwOqT92VKw1pWQEDIa/x76RGcbCwQ8ioK79UogdldPI2MZPLuLD54C2XsrNDKPfHOlNF3mjuYowiwAMxR08WdNTECLADVn3DVBKD6Xc/+FrJbANLldbfROxAXn4ATI5vh1z03sPbMAxnMoKZuOHv/BY4GPkPN0kVw7n4Y2noUx/yuNbMfXpIePHsTjZXH7+HD2qUQHBYlxzS++3Nbo+srdyh3EGABmDvmkUeROwmwAFR/XlkAKmCc3QKQuu4xbhdeR8fiv2FNhBuYDec14eEoFcyXF24OhXA56CWaV3bE3muhaFnFEUu611YwanWK9vjtFA7eeIIKjoXQq4GLHNKOBaA6vLlWCD+lhQoVyjoU+QCM1mpuEoCYrGv+zZs3ya7iZF3r3BITSB8BFoDp45WR3CwAM0LtXRljEIB1Ju3Fk9fR2D6oERYevIWtF4NhlT8vwt/5/XO0KYDQV9H4qHYprDvzUI4brGDYqhQt+/02uV5yVC05tGYBqApurpQiDbAA5HXABIyWAAtA9acmRwjAQ4cOYdq0aSKE3KNHj4R7GQoxZ0g6evQofHx8hD9C8ksopfHjxyPpPcWKFSsiICAxkkZa9RuDAGw89T/cfx6Bf/p543+H7mDn1RDh7FnbGpjG8ZVPOREPuFH5Ylj1eb20hqba+w3nHqJEkYLwcrXTaUNbAH7p44rFB2+L9ywAVZsKk684KioK3bp1k0PB0e+XWrVq6YSCk54RLCXvRSg4s1hs9dgqc29zoQ0unr6oaptSY9Q+eWSwsLAw+XlnADmDAAtA9ecpRwjAHTt2gIQc/XKm0HKGCsCwsDBRxs3NDaGhockE4Pr167F3716Zsrm5OYoVK2YwdWMQgI2m7seD55GoUtwGxQtbYF/AY5D/v7FbroKMRKQ0qk0lTN4eAG9XO/zZ18vgMWZmRilyiT5hpy0AyVXNX6c1dxlZAGbmDHBd2UmAI4FkJ31uO6cRYAGo/oypJgDpr+uU/tqkXbzixYtnaHTk0d5QAdilSxeUL19e/EW/adOmZAIw6bP0dsgYBKC2cLLMn1eEfJvxYXWM33oVr6Ni5SHRs6F/X0TtMkWxvl/99A5VcX4paolU0cSO7vi4jjPy5TUTj7TH0aqqk9jJZAGoGDtXYEQEWAAa0WRwV4yeAAtA9adINQFYpUoVrFmzBjVq1NAZxT///IOvvvoKT548ydDoDBWAy5cvx8KFC3Hs2DFMnDhRrwCkY+XChQsLoert7Y0pU6agdOnSBvfL2ASg1PHZXWpg4rZr4m4gJbM8wNIetdF7xRlUK1UYWwY2NHiMmZVRW+BJdY5pWxl9GrmKUFwuI7fLfW3gVgyHbz4V39+e3AZmNABOTCCHE2ABmMMnkLufpQRYAKqPWzUB2L9/f/z222/int13330nLlwPGDAA69atw6RJkzB48OAMjc4QAXjz5k00bNgQhw8fRoUKFUD3/ZLu9tGxMlnF0b0/2pGkfgYFBeHKlSuwtrbW27fo6GjQl5RIADo7O+Ply5ewsbHJ0HiUFtInrBZ2rYnJO66Jo2FKNhbmWPhZLXRdehIVHa2xa3Bjpc2mu7y+frao4oj/da+NqJg4VPphp6iToprUc7UVrmsokYPr/OaaXUJOTCAzCbARSGbS5LqYQOYSYAGYuTz11aaaAKTGtm3bhj59+og7eCSyyOXCH3/8IQwyMprSEoBxcXHw8vLC559/LnYaKekTgEnbp/uCFLFk5syZoqy+pM9whPJlpwA8cfsZuiw5odNdElXkFPrm4zfieYnCFpjziSc+WHQcZe0scWC4r9h1++qPs7C2yIfpH1bH7Sdv4GBjgUIFzDM6NSmW03ZYrZ3Jt6I9Jr7vgXxmeVB38j695QN+agWLfHkzvU9cIRNgAchrgAkYLwEWgOrPjaoCMD4+Hl9//bU4iiUDi61bt8LPz0/RqNISgCTkihYtKlvyUWPUDxI8dBdw9+7daNq0qd4+1KlTB82bNxdHwfqSMe4AUj/JZcrCA7fkLi/vVQczd98Q/v8okS/AmR9VR4d5R2FdwBw7BzdGXFwCGk/7T7zfPKAB3pt/VMQNPjOmebrn50X4WxS1yp9iuefhb1Hzpz0pvidxOujP83rfX5ngp4ooTfcguUCuI8ACMNdNKQ8oFxFgAaj+ZKomAG/duoVPP/0UISEhWLp0KQ4ePChcuXzzzTfiCDhfPvKKmv6UlgAksefv769T8YIFC7B//36Q1a+Li4teZ6h0HEz3/2iXb9CgQQZ1zBjuAFJHSfxJfvPo+9V96mHW3hs4ffeFGAfd+5v2QXX4zTokj2v9V95iR5BS38auWHLIcLcrr6NihJuZ9tWK42jgU4zf6o9J77uja70yor6XETH4fsMlcXdv1sc1QAIxpR2+tEBTiLvCBTO2VtKqm9+bNgEWgKY9/zx64ybAAlD9+VFNANI9urZt22LRokUoUqSIGAkZZHTv3l3csTt/Xv+Oj74hkzgLDAwUrzw9PcUxra+vL2xtbYVoGzlypLi/t3LlSr3E9B0BDxs2DO3btxfHvsHBwaDYxOQnkMSjvb29QeSNRQD+ceIexmy6Ivd5bV8vzPsvUDak8HK1xc+dqqHJ9ANyHjLAIEMRSt29y4gwbJQMcbsyZtNl/HEiMeawVKlUdsvFYHlH759+9RHxNhbdlp0yiGnSTOd/aJHq7mKGKuVCTIAdQfMaYAJGTYAFoPrTo5oAJKej5GQ1aXr9+jW+/fZbLFu2zODRHThwQAi+pKlHjx5YsWIFevbsibt374Ly6Uv6BCC5iCEH08+ePROCj4xGaGeyXLlyBvfLWASgtuCizlNUkJl7bojQb5SaVXLATx3dUf/n/fLYxrevInbukiZDBGDbOYdxNfhVimVXn7yH0Rs1gnT+pzUxYM05g5kmzXh6dHPYWxfIcHkuyARSIsA7gLw2mIDxEmABqP7cqCYA1e969rdgLALwwPXH6Ln8tAzkzpQ2+PrP8/j30iPxrF214pjQoSpqTUx0ej24eQX8uvdGhgQg3Re8+CAsxbL/O3Qbk7ZrdhebV3bA3muPMzxZJ0c1g6MNRy/IMEAumCIBFoC8OJiA8RJgAaj+3KgmAFM6jqUh0T0+fbuD6g83c1swFgF47dErtJ59WAyOwsCNblsFw/6+iPVnH4pnH9d2xph2leExfrcMoHcDF/x29E4yIMe+bypCtaWWPlh4DGfuae4Xaidp9/DXPTcwe99N8cqlmBXuPA03CPznDV2w7Ihun45+3xQl9fQnNk4T5cT8nSNpgxrgTExAiwA5q+/cubMIBUd3h+kKCPktJWMxMhrTfkbFlLynOmPyxGBfvURr96bHm+LyucuqtknjoN+3ZmZmIB+sHAqOfwRyCgEWgOrPlGoCkCxxtVNMTAwiIiKQP39+WFpa4vnz5+qPTuUWjEUAhkW8RY0fNVa23zYvj2+bV4D2Pb1eDcpiZOvKqDBmh0ykk2dJbDgfpJfQ5fEthXuYlFKXJcdx4nby+Ts1qpkwPDl774UsLsmA42VkjE5VS7vXRp+VZ3Se5TXLg0vjWsJj/C7EJyS+OjzCF862ljp54+MT0GzmQfFs3xAfdhSt8jrn6jOHADuCzhyOXItpEGABqP48qyYA9XWdHDT369cPw4cPV+wORn00abdgLAJQO5LGAN9yGO5XCdN2BWD+fxrXMAN93TC0ZQU52gY9a1rJAfsD9B/NktUwiUbfig4oYpncvctnS0/iSKAmUod2onjE/o+S3w1Mmm9j//p4f8ExncckFMnit/tvp3DoRmKUmP+GNRG7iFKinb/d/qHov1pzr5Dc1pD7Gk5MwNgJsAA09hni/hkTARaA6s9GlgpA8YF95gw+++wzBAQEqD86lVswFgFIw5QibXzXqhL6NSmHVSfu4Yd3lsHD/SpigK+bTrzdWmWKip261FIRy3wgIwwpXq+Ut+fyUzhwPWOh/Ia2qIDWHk5oPjPRJQ3VW7ywBY6PbIbJ26/JLmno+d4hPkIAvoqMEdbA8/bfxPTdiXcXadeRHFhzYgLGToAFoLHPEPfPmAiwAFR/NrJcANI9msaNG4PEU05PxiQA/zx1H9svPxIh3yiaxx7/UHzx7piVYgO/V6OkjgCkXbOnbxLD2qU0Fz+0qwK6m0cpLj4BdFTb5/fTGTbsoHuCIS+j4DVFN/KHq70V9g9tgjn7bgoLZint+rYxfvrXX+w47hvqg/fnH8WrqFj5/aHhvihtp3tEnNPXFfc/awiQEYiDg4O470cpMjISBQsWFHfmkj5T+p7qjDePR9SQKHlwFjMtEPUqStU2pTuA1Ojjx4/1+kDNGtrcChNIHwEWgOnjlZHcqgnALVu26PSHfhFROLh58+aJ+LkUizenJ2MSgElZXnoYJiJ/UNo9uDEqOFrrCEBD2VtbmOPyeD88fh0Fv18PobVHcTx+FS27mDG0HikfCUByJK1tkELvyjsUwp4hPlh+9A4maLmnIZc2beZoDFz6NymHv88+xJPXicJVGlt6+8H5mQBbAfMaYALGS4AFoPpzo5oAJKsz7UR/AZO/PQrDNmPGDBQvXlz90ancgjELwGdvomW3LzcntRbHuNIxcXqwtPFwwoKutTDxX38sfWeh26h8MdnJdHrqorwkAMmIw3XUdp2i5eytsG9oE6w78wAj1l+S320d2BDt5x0R3w9qVh4bzz/Eg+eROu89ShVObzc4PxMAC0BeBEzAeAmwAFR/blQTgOp3PftbMGYBSHRO3H4mjoPdS2oEUkr++/SRrOtii1N3ngsfgvM+rYnBay9g4zur4bplbXHq7nPYWeXHs/C36ZoIyVVMUjHqWswK+4c1wdaLwcKHoZQokkjnhRqDEbo/uPliMAIfv5Hfr/miHrxc7NgSOF2zwJmJAAtAXgdMwHgJsABUf25YACpgbOwCMOnQ3sbG499LwRiy7mKqo746wQ8UXWTkhstoXtkRS3vURo/fTuGglnUuVUDOpcdtuZougikJQIpWsqxnHey+GoK+q87KdTapaC8bnIxoVRGbzwfjeuhrnTbrl7PDmi+80tUPzswEWADyGmACxkuABaD6c5OpAnDIkCEG95ji+eb0lNMEoMSbRODANfpjMd+e3Ebspm0491AIRTruXfV5Pbw37wguPnypM2WrPq+L6buuC799l4N036U0t/oEYEO3YpjziSdsrfILkUliM73JkBB26a2T8+duAiwAc/f88uhyNgEWgOrPX6YKQH3xevUNge4D7t+fGJdW/WGq00JOFYBEQ999wMYV7LGyd10Ba9ulRyKGLx0Fr/vSG61mHUJAiO7O2z/9vFGzdFGQEWXSO32GCkDJcbWUn46dP1p8PN0TJt1zTHdBLmCyBFgAmuzU88BzAAEWgOpPUqYKQPW7a1wt5GQB6DZqO2LjE0SYtaAwjVGFtNtH/9/rHyqidVR3LoLNAxqg2YwDuPVEN6Qb+Qi0t9Y4YTbUwETaqTt884lwVTOqTWVY5MsrTywdU7ebexg3QhPv+Rky60mjl0THxon665crJnYWOTGBpATI7Uvr1q3lUHBXr15F1apVdULBSc+orJL3FAouNk8sjjdN/OOm3r56CLgUoGqb2qHgyPMCubnhxARyAgEWgOrPUqYLwNu3b8PFxUX40srtKScLwMsPX2LqrgCQ4+h2czVWtnQU+0efeuL/JNC6LTuFSk7W2PltY9Sfsg/BLxN9mP2ve220qOIoT3FSATi4eQUsO3IblYrbCGMSKRlyVEtWwmT5ezXYcF+Rp0Y3g4N1okNoKRKK1P/cvhZ5fMZPgB1BG/8ccQ+NhwALQPXnItMFIP2lS/7+yMEqpY8//hhz5syBo2OiWFB/WFnTQk4WgNqEJPHWwM0Oq/tojCmko1iKwvFRbWf8slM3cktSISfVYZU/L2Z38USzyg7yHwHa4tAQAUjta985TC1snTSOpA6hG03dL7uLMbTNrFk13IqpEmABaKozz+POCAEWgBmhlr4ymS4Ayf9fSEiILACtra1x8eJFuLq6pq9nOSB3bhOAbT2KY37XmoL8xQdhwm2MvmRulgeBk9vovJJ2CPs2dhXHuvoEJj0zVIxpHzkv7V5bHEenlihiSEUnazlLw1/24+ELzdG2oW3qq592IydtvwaPkoXR0bNkDliV3EVjJcAC0FhnhvtljARYAKo/KywAFTDOLQKQfO/9fuwu5n7qieKFNXeEAkJeodUsTQSOpEny2af9PDgsEvsCHuPDWqV07vRRnozsAJYfvR0xcZoQXSTgDL1jOOl9d3StVwYNft4v322k8lJor/ReTdC2SlYiJBUsMy6qEgEyAilbtqxYG/T14sULFC1aVN651n5GXVDyXoSXM0/A836J1yGKLiiKsCdhqrYpoaP27969y6HgVFpLXG3mE2ABmPlMk9aY6QKQjoBpB5CiflCiHcBLly6Je4G5LeUWAahvXu48DYfv9AN6p6xVVScs6lbL4OnMiABMWqbJtP9w91mEQW2SUNMWgLcmt8H7C47CxiIfyHVNekTgjsuP0G/1OdGu5CLHoE5wpmwjsPPKIxEvmq4upJay2wrY6t+WCD+zO8s4vXnzhgVgltHmhpQSYAGolGDa5TNdANIRMFnWFSigsQ7dunWrCP9mZWWl05sNGzak3Tsjz5GbBSDt6NX/ObmrHkebAtjQv4GwHjY0ZYYAbPnrQdkymI5jye8g7URaFTBP5oPwl84emLMvUN4B3DfUB81mHBTdvfZjKxTMn2h1nNYYJGMYyndxbEsUtsyXVhF+n40EtMMMnhzVDI42iYZBSbuV3QLQOXI9HvzyQZbRYgGYZai5oUwgwAIwEyCmUUWmC8BevXoZ1Ovly5cblM+YM+VmAfg8/C1q/rRHB3+vBmUxtl2VdO2gUQVSfF8SZh/XKW3QlCYVje3nHpGF3vGRTbHs8B109y6LYX9fFGHpUkt0P9Bv1iGR5cTIZnAqnLIoSFrP0cCn6Lr0pHj837AmIKMYfYlcztCRYcuqTgaNz5gzHQt8iln7bmLy++5wc0i8V2nMfZb6FvE2FlXG7hLf7h3iAzeHQil2mwVgTphR7qOpEmABqP7MZ7oAVL/LxtNCbhaA2h+kRLxqCRtsG9Qow/Aj38ala+dNEoA2Fua4NN4PdKz31R/n0LZaccz/VGOoQumTJSdw/PazVPvVp6ELlh65I/JIxiJn7j6Hs62l3h2i6yGv8eWqM+Iu4yd1S8vh7iguca0yRZO19SY6Fu7jNKKDwujRrmROThJ7bRc6JG7D38aJ2NJppRfhbxEZE4cS6dglTqtOQ98/exONWhP3iuypCXZ6zwLQUKqcjwlkPQEWgOozZwGogHFuFoCEpcO8I7j0LvxbVvvTk0SIs21BHB7RVMzS3afhKFW0IMzzmsmz1mnBUZy7H2bwLFJUk7xmedB54TFNnT+3TVa26fQDuP1U1+k1ZZrauRo+qpP8Xtm9Z+Hwmaa5L5neHUaDO56FGbV3X+9MaSN2fMdtvoI/Tt4XTsHdSxZOtTdS+fM/tEDRLHbC/fBFBBr+8p/o357BjVHeMeUdTBaAWbiouCkmkE4CLADTCSwD2VkAZgCaVCS3C8BXUTGoNl5zSb2cvRX2DW2igFb6ijafeRCBj9/gm2blMbhFhRQLz9p7A7P23jS4cnIpcz30Nabtui7K/NazNk7eeY4RfpWEMKSUmsVx/XJ2WN6rDgqYJ94jPHvvOTov1ER4+LyhC35oV8Xg/qSU8cHzCJy4/Qzve5bUEbza+S89DANFTqld1lZxe9oVaI9fsqqWnrV2dxJH72QdPbRlBeTTEuNUB+0UuozcLqr7+ytv1MnkvqU10MDHr9F8pua4f9ughqhaImWxahQCcPonQFxMWsPKlPd8BzBTMHIlWUSABaD6oFkAKmCc2wWgthgqa2eJA8N9FdBKX9Enr6PF0S4JjqQiQ7smEhxP37wVbms2nQ+GVYG8WHn8XoqNzfiwOp68icbPO3QdWy/6rBZauWvu70lh8lKqZFCz8hiiJUp3XgnBV3+clbMHTmoti7bdV0NQwdEaZVO4O5hSG5IbnPHtq6BnA10L+tBXUShWqADKjdIIrQtjW6CIpbJwd1ExcUJwernaodIPO+VukdHPyVHNZVH8Ue1SWHfmoXivr29Uj1R+dZ96eB0Vg4bl7Q06Ok7fCtGfm0Rxh3ka/5Ub+9eHZ+nkR/ZSSQoF17hxY8THx4uvGzduoEKFCiBDNkraz+h7Je+pzri8cbjY9qLccTICeb7sa5Qv5aham1Jj1P6hQ4c4FFxmLDKuI0sIsABUHzMLQAWMTUkA0tHrke80R7HGnJLeXUzaVzJiiXvn3Fn73ewuNfBeDY2jZ+8p+/BIK+ydvvGSMBruVwkf1CqFP07cw5hNV+Rsl8a3hHUBc+y79lg4sKZ7c1cm+KULm7Tj5lPBHr/3riuXJZ+NX/95Xvhb/PusRojtHtxYiMzUElnHmr3b4dSXb/TGy1h98j4+ru2MtWceyFko1jPdpZPuOGrfp+xZvyzGd6iqU11YxFvU+FFjPFTBsZCw3G5ZxRFLutdO1/gzmvnk7Wf4eMkJUXxtXy/Uc7XLaFWZXi6pI2gSgPuH+KVqqJLpneAKmUAOIcACUP2JYgGogLEpCUAnGwucGNVMAa2sK9p35Rns9g/V2+Cgpm4obJkfP/3rr/N+SbdasgWvx7hdeB0da1CHKQbxN39e0DFE2T/UBz2Xn8b954l+C9PrRFr7GLaHdxlMeM9d9Kfe5L0IfRWt07dNAxqghnMRvf2lHblf997AhnNB+L1XXVQpYaM3X2rH3hTXmeqgREfcy94Z1PRu4IKx7XWPu1NyH5Te8RsEX0+mA9cfC/aU/vi8HhqWL5bRqkDsCpibpdvqPaUG9QnATf2bomYqu5QZ7jwXZAI5nAALQPUnkAWgAsamJADpyPHMmOYKaGVd0VEbL2PNyft6G6Qdu/Xvds60M8z71BN+VZ3E7qD2EWhavaZdsBXH7upkG9O2MiZuu6bzLL0CKKkgo/L+wa/QZk7y6CwretXBjsshwsp6XPtENz2PXkaiybQDiI6Nl/tCO3xNKtpj66Vg9GrgIt/RMzTSivagvmjkgtFtdQXgrSdvZJ+L2nml8dORsLWFer4UJWtxapvudzatlLEY5LSTWW/yPtR1scWqz+ultQwMeq9PAK753EeRSDWoYc7EBHIgARaA6k8aC0AFjE1JAJLj56PfG/8RME1nagIwpemmI9XNF4JBsYzn/ReIPHmA0W2SC7mMLheypp3/X6DYmSSDk7R2ppIKsv91r40vUoiH/H3rSvKdxuU968C3koPoJuUn/4SppUPDfVHazhKuI7chXhN5z+D0ZWNXjEwS9/lK0Eu0m3skWR3rv/LG6bsv8MvOAMz6uIZqcZU3nn+IwWs19+y073XqG1RERASqVKki7vrRXdJHjx6hePHi4j5egosXzLy6i2Jxq78S/+q8fxc+TnqW1nvpDmBQjyC5K+IO4IapKBoZrGkznXUa0ibVSVbc9OXv7w9LS0uD55czMoHsJMACUH36LAAVMDYFAUh3ziZu88fCz2rlmKOqCVuvYvlR3V259E4z3eG7MK6lbGiR3vJJ85NlsHTsXLtMUazvVz/FKrUtaaVMZISTUii8T+o6489Tmnt7dMQ9pGVFpCTEkjaaP68Zjo1sCp+p/wk/f+lJ/ZqUw3etKukUIf+KHyzSWESnlEhc35mS3P2Ovvzz9t/ErquhIoSfIYYuq0/ew+iNmvuYcz7xRIfqJVLsR2pWwIWqtYRd60Gi7L1f2qUHS8p5aeNzdOJrEoBPN85A5I3UeWVO4wBbAWcWSa4nKwiwAFSfMgtABYxNQQAqwJNtRckR8Yh/LqF5ZQd898/lDPWjeGELHB/ZLFWXMNoVN63kgP0Bjw1qq1qpwtgysGGKebUtaQ2qUCtTd+8yICvlz1ecxsV3PhzTqoNczWw8n7gzlVZ+6f1AXzcM86uok107dF5GBSBFjvnn7EOQCxrJpQsdbdORdVpp6eHb8vE7WXx3rlUqYwKwuh/sWn2duQKwQF5gZKLIJgH4bMscRFzTuK1RO7EAVJsw15+ZBFgAZi8vfXoAACAASURBVCZN/XWxAFTAmAWgAnhZUFTbIjW9zZEF6+7BPgYLwIkd3XUsgVNrr7StJQ6N0LjUIbFHiaKOSEk7mkV6++3laosTt1MPjZdWnSR+07KCpjokAUg7luRLsXJxG0zYchUbDBCT+u5E/n7sLn47egf3niUaz0h9JUOUb5qXT7XrG849xLjNV2UDnimdPEQkF0rSUah2BdIOoHWdjoh79QQR1zXuYygVqtEadn4DxP/T2gEsULISYl6EID4iZYfk5kVLwLHnLwgq3E1ugwRgTNBdPN02E7EvgtOaFsXvWQAqRsgVZCEBFoDqw2YBqIAxC0AF8LKgaHRsHCqOSfRp99N7VUV4ss9/P5Nm6zVLF8GG/g0MFoDkRuabvy6kWq8krOjYlQQj3b3rsuQEyKUMRTvJb67xP0dOoBtN1USzUDN1rVdauH7RTju/bYQX4TH45H8aVypSoj42KFdMR9zRfclRbSqDfB32XZXoB9GQPpMFMcWWfvw6Cp7ORYWLmjazD8P/0Su9xdt6FBfuUlpUcZQjkVAIvlXH76G8QyHM3X8z2Y7nj+9VFU6r910LxXf/XML0D6ujSUXN/UhKJACLurijRO954nttoWeoACxQqiqcuv6ChNi3uD+jU4pDt2s3FJZVvfGg4AdyHhKAZtDEpU5LZBrCVMqTx7wAEmJ1LcXpHQvA9FDkvNlNgAWg+jOQIwQgOTCdNm0azp49Ky5ib9y4ER07djSIztGjR+Hj4wN3d3dcuKD7AT1//nxRb0hICKpXr465c+eibt1En2tpNcACMC1C2fuedn1qT9yLZ+Fv5RjA1CNDLF4l/3v68pJl6Kk7urtsZHzRa4XG/UhKie6jbbmof6dnQ//68h3La49eofXs5Na+mU2TYip/v+ESXkclurwhQ5+gF5H4aLHuvTTXYlbYP6wJhqy7IFzKUJLc0wxddxH/nNP4JMxIqu5cRBjcjN18BQEhr9OsQto9pDuVkksafYXIGpuipHScr9nZo0gvl8e3FI7F6YsEoF0Vbzh9MkW8vzetIxCvYVHIsy3sWvZ79/w9IF7//cjCDbuiSINPNPlSuStYrMMIFKxcV3UBmM/BBSV6zcWrM1vwYt8SHSymKgBj4uLx1aqzqFW2KPo3cUtzfXEG4yDAAlD9ecgRAnDHjh0gIVerVi106tTJYAEYFhYmyri5uSE0NFRHAK5duxbdu3fHokWLUK9ePcyaNQt///03rl+/DgeHxF2C1KaABaD6C1RpC8/D34I+ABxtNDstlMgad9HBW8JKlMRLyKuoZM20rVYcJJD0CcCbk1oLkdRkuib+LyWyck1q/PBPv/pC1FwN1uxq0d28Ofv0h62jHcHPvMqIfNqh5eZ+4ikcP6uRyLCi/x/ndHweXhzXEiRAaWdSO0lH4t+tvyQ7iqadNzJmGf73xRT9LqrRbxKAtHNYd9K+VKun3UJ9VtA0ll3fNgZZAdtVqid28Cg9mNsV8REvxf+ta7aDbQuN9e/9GZ317qjROxvvj1C0scZaOFUB2HEkClaspboAtO/0AyzLa9zWJO2PoQIwNi5e/Hx4l7NDrTIZDzNI9ZDhEoWRJCvk7Er0R9egdz9D6XXHlF195nYBFoDqr4IcIQC1MdAvEkN3ALt06YLy5csjb9682LRpk44AJNFXp04dzJunOf4hVxDOzs74+uuv8f333xtEngWgQZiMMhP5+6MdoW//Oo9NF5LvynWp44yfO1fTKwDpQ4TKS6HYaIAk9h6+iMAPm65g5kc1hCsWqr/LkuPynbyfOrpj2s4AvNLacZPgaMcQpji7PX47Je7U7fimUYpHo7RrNmm7rr/BlGCToO3uVUaOkkH5Ng9ogE//d0K2/pUMLY7fepbsCJgMRX79uAakiCFSO62qOuHG49e4/SQ8y+aZXOpQjGpDnXXr6xhFazGPj4Fnm66I9uojsoTt+BUOecNhHvUSMS4NEOOhOWUosG008sRE4e7duyjj4oo49/Ywe3YbZsGX8di2Gmwa9xD5QmZ9gFK+n8D8xT3keRks7h1SmbJlyyKmXi/EFHdLUQBabhkuu4GRylCd9P/S7nWRNypMxAzWrlN6T/VLbmSi6/ZCfHFNdBapTskNzOnTpw1yA0N+Mof9rXGlo0QwST9bZAFPVuyNytvL1xyybLEA+OvUfXy/QWMMpmQ8WdlnbosFYFasgVwrAJcvX46FCxfi2LFjmDhxoo4AfPv2rfhFuH79ep2j5B49eoB2DTdv3qyXfXR0NOhLSiQASTS+fPkSNjb6IyxkxSRyGxknMGDNOWy79ChZBV/6uGJk68qyAKxawgYkguhIUYq6ob07eOz7puJ+YVJjAzp62nk1RNRPO4qz990Q4dGSJoqxO/WD6uLxjsuP0G/1OdQpWxR/f1Uf780/iosPkhsYUAQQ6XiTol7M3HMd5+7rN0Tw/9EPlvnNxW4iufahROHp6kzci8h3hijSh+OrqBghsOys8osQbn+feYARrSrB1iq/OPpOejyccfoZK7l1YEO0n5fc12B6ajswrImIz0yCfdUJ3djRdDfRKr+58AdJiRygkyP0pNbZxGvF0TsYv1UTVeaXzh6y1bm20JBCBcYjKkUBONyvIr5o5JpMIP0X8FhcLaBrB+u+9E5ziGT9ve+dNXpGxc7MPTfkneqM1kEdTbp73rFGCYxtX1Wso6xMLACzknbmtcU7gJnHMqWacqUAvHnzJho2bIjDhw+L4O7jx4/XEYDBwcEoWbKkEIfe3om/VEeMGIGDBw/i5MmTenlRPRMmTEj2jgWg+gtVrRa+XHVG+JmjRCJs3RnNXTaKGdy7oYv8ISbtxmn3g3ze7boaInY2Glew19vFkRsuyT76/vzCC+SmRPqA1i5AO2mLutUSj0hwDV9/CdI9xA8WHsOZey/EOxKfFH83IjpWHNENXHNe7EYu/KymOGbTd2QtHWdTeSlWrnR/r+KYHXKkEO0P+5eRMSIMmrZ1stRffaJJrfnRVy+5ull5XFe0pbd92rEtUcQC3lP2p1mU5qGVuxMs8pnJTqapEPHS9jtIAkfaTdZmKc1JagKQ6tMXWUU7rKEhYiwzBOBvR+7gx3ehEq9O8INVAfM0GenLkNJdW0PGkaEGUyjEAjAzaWZdXSwA1Wed6wRgXFwcvLy88Pnnn+OrrzR3eDJLAPIOoPoLMqtb0P7APDzCV7a+XdC1Jtp4FEenBUfFrpokCNPbv593BIj7VJT+G9YE03ddx7bLyXcc6f2aPvVQ360YyB3KuC1X0cbDCQu61tI5Rk7rw1OvAPQojvlda8pdD30VJXa06Ii6wpgdePsuVFxadUsV0D1G2iXKrlSqaEE8fBGpqHmKrEKWzR3mJbp+SavCpJbexIt2D0kQU2pZxVG+C5kRASi5B6Jd5COBT1G1RGGxDqQdW33zQ7uSSw7dRrPKDiJ/ZghA7V3NvUMaw83BOi00et+nJACvT2yFAuaJbo8yVHk6CrEATAcsI8rKAlD9ych1ApCOcIsWLSru/UlJCvVEz3bv3i12BzNyBJx0OvgOoPoLVO0Weq84LTtwvjC2BWr8uEc0+VdfL3i52oFcjVx++FIcwZFgSm+aufs65uzXHCXSMSxFKJm263qK1QzwLYcHzyOFtTCFp5v2YXV0W3YSh28+FWXSEml/nrqPWXtvYFmPOnJItmEtK2BgU/0+9MqP3o6YOE0MuLTqljpNIlY6HpWekQsbSUiml1F681vlz5vuqCVJ2/i5kwcqFLNApyWpW25rl4u+fwkFSleTHxXYNBRPilaFjU9v8Sz63gUUKFND/L/g5mHiOsC9oBA4DlgtnqW1A5gn7CEKHPgVIQXLoIjfIOR5FYLwB/6wrKoJwWixbTTi81nivv85ca+Q0uMiVWHTRNM+tRldrxfinXTvACZYFkWCtQPOb19j0B1AMpKS1ujK3nVT3N1Oa95SEoCnRjWDg5ZRVlr1KH2/9vR9+Wj+9uQ2wuUQJ+MnwAJQ/TnKdQKQxB7FvNROCxYswP79+8WdPxcXF1hZWQnLX3L5Qq5fxC/n+HiULl0aAwcOZCMQ9ded0bRwM/Q1Oi08Bopr+6VPOZQfvUP0bc/gxijvmLGdD+3BTdl+DYsP3RaPSGBFvo3D4kO3MGuvfmtg7bI965cVx73Lj97BhK3+KFTAXNzbMzRdehiGfdceg0K26TvKpXrcRm1H7LsgwIYKwMevovDp0pOo6Ggt72ZqH59L/aMdsyrFbUQMYIrPnDSl5TybrIxvPk5+X9LQ8aeWj+7c1SxphU9+O5fh6p5smYZCHs1Q0EWzuxoVdA0WJSuL/0sWuOa2JVHyi8Wa3zGp3AGk92+f3sOjZQPg8PEkFCyruQ/6+vx2WHu2Ef+Pff0U5tbFELSkr3AcbVbACvbvj4JFGU3ee1M7wL7TGFi6aVxZ3fulPSxca8LxQ821ld+714BPlZJpjnfargDM/0+za72wa0209iieahnyt0m+LeluK+1uk39IWqspCUCywK7opPnZoogv+czNUg3Zl2aHU8hAf5AMX39R7BaffXeFIqt3HzPady7HRiBZsQZyhAAk9wWBgZpdFE9PT8ycORO+vr6wtbUVom3kyJEICgrCypUr9TJLegRMmcgNDBl9LF68WAhBcgOzbt06BAQEwNHR0SD2vANoECajzyRZBFNHf91zA3T/jSxiM8N1BcWynb5bc1yqLbDIPUxa99hoN3C4XyWQOw26W1bPxRbOtpaZypOMDL5cdRYT33fHR7Wd01V34OPXcqg2+tDv5lVGPkInFzt0b45SSnGJ533qKe4wppTIUvdRWJSwVCZfjknT5Pc9dIQlRf2gHVBKWwY2ED4CN18IFiEBQ19F43KQxsULJbrPFxUTn67xppU55sUj5CuqEUskxpAQjwKlPWQ/g2kJwNiwEAQt7gOnbtNRoIQmxjL587Op3UGn6We7F+LN+W0o9fVq5LUsLL8LXjYARXx6yALw6dbpKNZ+mPz+26Yu+LZllbSGgfFbrmLFMU0sbRLx79XQiMbgsEhhwEFRXyjqyo8d3BEbH49mMw8iLCJGrpecfI9tXyVFASjtrtPPWfUJu0U5yUgpzc6lI8PG8w917mxSUXIGv+DALTSpaC/u035YyznH7QjGxyeA9uwzciKRDnzZnpV3ANWfghwhAA8cOCAEX9JEAm7FihXo2bOncJdA+fQlfQKQ8pELGMkRdI0aNTBnzhyxM2hoYgFoKCnTzUcWteRrjwwxpLBkRIPubo3eeCVVB8q0SzXAV33HtbRTIkUhSc9MkSCo/7PGiGJIiwqir5JrHLpjR374KGkLRe369w/1QdMZB/U2qb3bWfOnPSB/jkkTHed1XHAUl97FPF7SrZYckYR2cJ0KW2DnlRC0rOoE2qUiB9YkYEasv6S3zcjbZ1HQVWOIkzSFXzsEq8qNDcYTunYM8juVR9zrpyjWbqgol5YAjAt/gYcLeqHM8E1yO6/PbYN1zbY67dLOY8S1gyjz3b86zyPvXpR3DulF5K0zKFiutpznu5bl0K+pRlhSIndDR24+QTevsihexEI4x6Y0Yv1F2Rhqaudq+KiOszyHZJlOO7qUaIe6qGV+/LpX9z5o7TJF8WdfL3k3PSk06Y+DRy8jZSOcXz+ujvc9U47bbDB4rYyS9XVqZekqQJd34QIz0oaSMvTzQ+52SDA3f/ezklZ9JP7IAp7cKm4Z0DDHide0xqf9ngVgemhlLG+OEIAZG5r6pVgAqs84t7eQ1LWI9ngndKiKHvU1d72MMWnv4HzfuhK+8ikn7/pQZBTyhUhJX2g7+gAjAXcl6BWeR7zFqTvPxHG1X1Un+FS0BxlEkKEKpeYzDyIwyVGwZf688P+xFZrNOIBb73wQahvxkMGNSzGrZNi0RYf2y7Bjf8EsnwVs6uiPMPT4nx/h0Hlsuqch6uFVWJTS3MlLSwDGR0fgxcEVsGvZX24nIvCUvKMnPXyyZSoib51G6cF/p6s/FR2t4GpvLe6VJj2iJQvmWV08RX3arpHojxCyNt/rHyp2zrRT88qOsLfOL1u5S+8auNkJl0fSfdqknZRE1+0nb3T+ACAn5IUL5kvXmOi4eseVEKzp4yUEv3ai4+Wh7/wZplRp/yblhIsjtRM5LqexaRu/fLHyjOyo3NDrF2TAVW+yxgH6+R9aoGgWu9RRm5N2/SwA1afNAlABYxaACuBxUZlASnelJEtkY0VFR9Nu7+5MSgKwz+9ncPPxaxFpQ7p3GBQWiQbvdgqlsRSxzIcLY1saNDRtIxipAIlD8s83bvMV/H78nhCLp0c3g+dPe4RbnNOjm6d477Hp9AO4/VTXcfW96R1RuN4HKNLoM719evT7YBTv8atB/dXOFB8dLu7qUUpLACbEx+Hl8XVyaLmUGnu+ZxEsynrKET/S3SlA3M1MGndZEiHahlFS3RQ9Jan/SmLuXtIGB64/0elCs0oO4u5qSvGsBzevgG+al8fV4JdoOyfRn+PyXnXgW9FB/MEwe99N9GnkgkpOqftX1f7ZOTGymY4I3HwhKM343J/WKw26SpAZ6embaJBrKBLG5u92U1+Ev8W/lx8JS3G6drBnsI98jeP9BUdx/p3fTok97QoSV3078nSaEBYeg8bTNHHCT41uBgdrXdGbGeMwljpYAKo/EywAFTBmAagAHhdNUwDemNg6Q0ezWYlW+gAe2bqSMKIhy1eyKdG+n/TkdTTqTNqr061KTtbY+a1hR6p0hEzuWiLe6sbjpQ/N11ExWH3yPtp6FBcfrBFvYxETm4DClinvJEmiUepQ5N3zeLz2B1jXag/b5l+Kx0//nYli7YbIfSbDi5J9dWPrpocz7TBGh15F1KeJdx6dI9fDDLof4K/ObIZN7ffSU3Wm5ZVEyMeLj4t7fkqSvnjZ2vX93rsunr6O1tmhG9TUDUNaVhTOzS88CENRy3w4n8YfCdoCcPqH1fFBrcRjZG3r35TGQutG20VSWmM+dOMJTtx+hm7eZVC8cEGd7C1mHhRGS9+1qoTIt7GwKZgPk7dfEz8P2km67/jhomPycTqxJ2fv5PSd7iaSk3fttOBAIKbu1PUeQDvemX0nOK3xZ+V7FoDq02YBqIAxC0AF8LioTICsdZP6o6PjSzrGNPYkfQCPalMJfRuXS7G7A1af0/F/SJfwV/TSWKsakmi3MSo2Hu7jdsnZDT02S1o/WTE3m3FQDiWXEHId8ft+xSv7aij67vg1fu0g5Gn6LfLYu4ricRu+Q95OmpjBGUkJx1cg7sFxBPUIkouX2NEU+ZokisyM1JuZZaQQh15T9oFEe1anj2s745cPdMMvpjXH2gJw2gfV8GFtZ3G/lpyYkz9NKUpLSmNp6FYMf/TRvfdNf8TQbifFxKajW+113WjqfuGmiVLSI1ipLwXz5ZWj6+hr99BwX5S2s0zm31PbwXrSces7JVDiozGr5zYj7bEAzAi19JVhAZg+Xjq5WQAqgMdFdQjsvhoiGzDQC7JirVaqiNFTkj6YtI0+9HX6eshr+M06JL9qVL4YVn1uuMGVVLDe5L3CopdSWuIgNXhSuDvKU6tMURHLeR2FvHtnIELxhsk9kHRER/fTJIvVjEwKHddZFYhDoSmF5OJvRr5B1bH6DdfS2wbF26UYznuvaaLaZCTR3Tz6w+PjJScyUlxxGbLWLudQCIsPatwmpTXHSeNx0x8hrd2Lo+Wvh9DRs6S4R/rLzoBU+0VH2P9+3Ugnj+SIXXq4/itvEQKSkrYQI0fiJ0c1l8umdJUjaQcODm+CMnZW+GzpSeHwWxpnUgFI914p0hAZiVQeuzPZOP79uiHcSyZagSueACOrgAWg+hPCAlABYxaACuBxUR0Ch8kac9kp8Uw7LrCxYyLRcTkoTMSxTc1tzq0nb8Sum5SkUHTpHd+YTZfxx4n7Gr9zk1qnt7hOfukDu1qpwtgysCG074yRuOww74hsYXxzUmvZqtXawhyvo2INbvv4yKag40LtDzQqnJkCkI4DFx68hTUnNW5wcmKieNtXg1/pdJ1iNpOQ0+e8mY7/PcZr3MhQIn+X5I5GcgU0qFl5OaZxajwk34APX0QIQ5ekDMe3r4KG5TWGSRQ5Rzvpi/iSFvu5n3gK6/9jt57pROFJKgC1wzTqq/Offt6oVUYjTHNjYgGo/qyyAFTAmAWgAnhcVIfA2XvP0XnhcfFMcgCdmxDRsVqvFaeFj0VP56IY2NRNuGRJb6I7fvP2Bwq3OhT6TEmSBKB0H5FczZDLGen71rMPi2NASvRBL13aJ+fI/VYb7kD61uQ24k6koQKQxG3nWqVwI/Q1ytpZpegqiHagpN3QwEmtMXXXdREWTjuRP0syLPjf4TviMRlz0G5WWkIxvSJXyTyQQRAJN+sC5vKxvHZ9JOyaVLDHzqshwq3PN83KC8tkn2m6u6ef1HXGq6hYbLukCbVIzt0lJ+yp9e/H96qiu3dZHf+H2vlJ+N1/HoF21Yrj33d1S+8zIgD19YUs4insH4UWpETReyT/oSn1fXWfemjgVkwJeqMuywJQ/elhAaiAMQtABfC4qA4B/+BXaDPnsHhGH3h0kZyTugQkAWgW/gSFj8zGjRs3UL6KB8wS4pEnIQ4va/ZErJ3mDmDRXWNw4/Y9uFb3Qr7wUMRbFMbbImUR4dE5zU5SWTMzM8TljcPFthfl/B5bPfCq+RTd8vFxeLaiP9xK2ovn1KcSH09AjIMmwoiULK9sRIydK2KKa6KA2O0Zi/DyfogqqzEeKLJ3AswS4kBBzxLymOGFz3dIyFcQrao6iftnSYVi0kHQfby1Zx6kObb0ZtB3P66snSXuPoswuCqypiVL4ik7dI93aWzhb2PlsImGVtjduwx+fM8dg/48L0IwpifRzjAJN7pL2D8dfxQkbePY900xcM05EXfc0LSsR200q2xY0AJD6zSmfCwA1Z8NFoAKGLMAVACPi+oQuPcsXN7RIH965FaFk7oEJAEYExaC4MV9kjVmXasDbJv3Fc+l0G5JMyV1xpz0ffj1o3i66Z3II8Pk0Vo5JgFlhmicOce8CEaefBZ4snES3gbrWnvadx6bzBcg9cfKvSmKtR2CuDcv8HB+N9j6DYR1jVZ6+2tZoT4GTpyHUW3d8fvxu5i5R9d5s3a/ySdgpeI2+DmJwFI6GyUKW2BJ99og/3dWBcxl344lixQEuQoyNNGOIR2pS7uzUjk3h0LCCl3yC2lofdVLFRZ3H2k39fjtZ4YWy/Z85GuRdsJza2IBqP7MsgBUwJgFoAJ4XFSHgPaFdskdBiNSl4AkACnGbtCCnskbMzOHnd8ARAdfx5uLyS/hU4FSA/9AXiv9xjoPZn+C+CiKZfzOD4geAWhuUwr57V0QEaDZ/dWX7Dv9kMznnxCkecxQ0K2uEIwURaRYhxFytBJ9gpVCalIcdAqR99O/uvHSpXbL2VthzRdeWH/2Iabt0hWihswGuVWho2sphrP27t7JUc3gaGMhrHQTEiAbNtBVAO1IL/bWBYTT5KTOv1NrPy3LW6lsvrx5EBOXxC+LIQMzwjwzP6qOTjUzN3qKMQ2TBaD6s8ECUAFjFoAK4HHRZATI9xlZII5qUxn0IchJXQKSAIyLeImHc7tmqDGzgjbI7+CCt6G3kM+uFBJiY1C852xEBV1D6B/DdevUIwCRGEI3xfbz2TnDqdsMhF/9D+a2JfDm4i5EBCQ6UJYKFnSrB4fOPyA6JBAhv3+brD5JAFK8a3K0LCUK3UZ3ycgyW7J2nbX3Bmbt1eQh69rJ21O3ppXqojtx2y8/ko9DfSrYi5BzlM790ELn3qfryG3CRx5Z4lJEGClRlBe6+5jUUXVqE0R9P3xTY1GbWpLuG6aVLzPfr+lTD58uPZmZVYq6yIE1ObLOrYkFoPozywJQAWMWgArgcVEmkM0Epu+4inkH7+LJxsmIuHEs03pjbltSxABOiEniSy+DAlB0LI8ZkBCfZh8p/nDM84dIeJv8SFUSgNoxco985yuiSSSNPDFj93XM3R8o2iMDhSWHb4OiWqRmVLHuS2+QA+gD1x+j5/LTomw3rzKyYcPl8S1hbZHooPtK0EshREf4VRSOp8dsuiKPj/4ASuqLkHb5YuPj9e7gdfIsiQ3nE30spgRK23AmTZjvMlC/v/3rAvYFPDa0iE4+8td35u4LfL/hcobKJy3U2t1JhL8b264Kejd0yZQ6jbESFoDqzwoLQAWMWQAqgMdFmUA2EwgPD4e1nSMSonXDwqnWLSUCMBM6JQlAOoIlS2qK1Uw+EPUluv+36KAm9q9k6Xr67nN8uEhjqU7JKn9ehL+LziJFt6Dn+wNC0XvFGZGH7rJKdwkDfmqVYng+urvnMnK7XPdAXzfM+08jQKVEojCfWR4Ev4zSeT71g2oiOseGc8kFYPvqJbBVy7DD2bag7MjZUEtnyUF2TFy8sGKXYvEaOiVSjOMKo3fgbVzaIl67Xld7K9x+F+uanu/8thGWHr4jjujJb+LSHnUM7UaOy8cCUP0pYwGogDELQAXwuCgTyGYCJAALFUp0zKx6d4xEABoyzmm7AjD/P10BSH7yGv6iiUP7Sd3SIgyf5BZF2x3KydvPZGfS5Fxa2vmS3OGk1L62I+VrP7bCbv8QIX6k42q6T1jAPC+uh76Wq5jQoSp61C+L6buuJxOMkkDSrpfuOEpGImT48X/2zgM6yqoJw08aJPTQe5HeO4iA0kSqqHSkIyKoIB0EBBSkilQVfhUEUbqIBREBUaqU0AXpvfdASCP/mW/JJoGEbLLZb7PJzDk5wO6tz/2yvHvvnZmTj+WEfnxs4qCyeUidKC/bGvA5vJIEFZcYmRJiKPyuY9FsaaPMIzomki1nzCulrMzl/uLRsY0Ij4UpdSTUjYjsp8XgtGW9E2MZFYCOXxUVgHYwVgFoBzytqgScTEAEYP78+Q3PUfm5efMmvr6+1v9MI78mQ7XnffkPOswzjBs9I3Ls+n7my62rqhy3qwAAIABJREFUtxzaZzhi6f/UqVOGE4gtdt0/0Njtk4waElBZTHbACg+zBEJuUzkPnh5uRlBuscgCULhJDtwi2dIaR8t9Fu15okx0Y6gz+U9OXLtHjvTebB1a1yiy6J8zVgEpgaLFWerwpQgBKLt/rSrlMXbm3lvkR7NyuXhvsaW/l0pmY3aHSlGyd8hraw5asqXI7tqr5XLxydr/cHfjiZy94tDSv34RnskS9UuCrQJQuIlHdXj90qPWWAOIS4DrWpMtcQzrl8iGl6e7NX5hOJuXy+ZEBG75j9YaL6VJ6cmB0S8xdMV+a7Dr9+oV5r16RWxZUpcrowLQ8UumAtAOxioA7YCnVZVAMiMQXSDo1ClsE2SJBVW4+BFxki9TKus9wZjS8kVOcRhb6j7ZjZu+7qgRB1PEo1jk7Cxyv1AysEQO/xIuACPzCR+jhEiRUCnzt57igx8PUjxHOuZ3rULlsX8YxWX8IsQkt+9vBy9GcXQRL2TxWvb28ngCfWwCsELeDOT2TcW0NuWi7MwVH/GbNUew7AqGH3mv7lOTTUevMfbXf6P0JcfXsoNa8lH+azmy3j/qJYOJiNwuzxVgRJPiSXL3T0CoAHT8b70KQDsYqwC0A55WVQLJjEBSEoC9ahU07g92+8Zy1y8mcSfx/aqPX//UMk97DNYeumzEDRSrXTSLEasvsnfwuNdKG8fR0QlAEanT25Y33pJdyfBj0nABF/l4VzKMvPXtLqPsL71rkDtDKtKninBYia796MadPZ0329637F4+boXe/5UQcXt+xEvSKIroFU9eSV8nO3uRTQTstNblKPRo1zWdtyf7Rr1kFJHj98gONUnxV0kFoONXVQWgHYxVANoBT6sqgWRGICkIQMlZ/eOeC0iKOTmSlNRlJXKks4aPiW5Jxds3nbeXkYEkrrb52DVefxRCRVKxVcjry4eRYhh+9Eopw9M4srX4fAs7T9/ku+5Vea7gk6nSwgWg3Cn8c2Bto2rkTDzh8QpjGuvTdgCjE6Th7XSZ+w8bjlw1dh43Puo3/L2f913gne/8onQZnhIyvD/ZlRSHkuRiKgAdv9IqAO1grALQDnhaVQk4mUBAQAANGzYkNDSUhw8fcvDgQUqWLImHh4exYxT5NRmqPe9LmyFuIWytE+FFW3VdVQ7vO+zQPsN3viQV3erVq/Hx8XEy9bh1v/vMTV77zBKi5/WqeY07cSKi+i3ZYxwH/z2oNnkyRhWWgSGhXLz1gPyZoz9elyNU8Uye3aEiZXJbgnjfeRBMmVG/G3/fN6q+IVhjsuW7ztF/qSWln+z4Xbpj8UqWO4q/9K4ZYz25VymCuWWlPMjuY2Tbcvwa7f4XNVbgng9eJEOqFNY7jBLDcM8HKgCFm/9Qf+y9PqH/f4MKwLh9HkUprQ+QHfC0qhJwMgH1AnbyAtjQ/eFLd2gw1ZIl5e3aBRn4kiVFooSyEccPySySUPbd9jOGo4t4Fcdm4nCycs8FFr/5rNXjWVLK/fhOjdiqRvv+sSt3qTflL+t7r5bPxaetyxn/Dt8B9E3lhZ8KQIOJCsB4PWZPVFIBaAdHFYB2wNOqSsDJBFQAOnkBbOj+zPX7PD/JEnpmeOPivFHzGRtqOb5IUMhDbgUEGUG0wwWaOH+s6FU9Xp3fvh9M2Q8tO5Bir1XIxZRWUQWgpMyTbCrJxfQI2PErrQLQDsYqAO2Ap1WVgJMJqAB08gLY0L1kAwn32p3UooxxfJrYLFwAVsmfkSVvVYvX8B4PhN2yYm4mtSxrtBXefqbUKdilAtBgojuA8XrMnqikAtAOjioA7YCnVZWAkwmoAHTyAtjQ/b3AEGsYlBltyyOhURKbhQu0as9k4vs3n4338CI7l/yvYyVeLJEtigDMnCYFO4frDqAKwHg/YioAEw4dqABMSJralhIwl4AKQHN5x6e3hw/DeOZ9S4q4OR0qUr9k9vg049A64cKtZuHMLOhWNd59NZ2xif3nb/Nhs5KGZ/PjYWsyp0nJzuH14t2+q1XUI2DHr5juANrBWAWgHfC0qhJwMgEVgE5eABu7DxdYK3o9Z4SBSWwWPj5J3TavS5V4D0+8l6/7B5HzMQ/h8PYlF/KOYSoAdQcw3o+Y7gAmHDrdAUxIltqWEjCbgAjArFmzGiFfxCQsjIRJMdK2Pfaave9Lmw89H/KgnyVkiJj3FG8e3Hng0D4jB0C+cuWKzangzF6Lp/Un3rlnbtxncIOiiTLrRfPPt7Dr9E0jrMxLDtihDBeAWdOm5B8VgMajoncAE+Y3VHcA7eCoO4B2wNOqSiCZEUgKgaCT2ZLZNF0JSXPq+j2KZkvrEIEaLgCflmXEpoG6WCE9Anb8gqkAtIOxCkA74GlVJZDMCKgATGYLnkDTDReAOdJ7s3Vo9GnmEqirRNWMCkDHL4cKQDsYqwC0A55WVQLJjIAKwGS24Ak03XABmNvXh02D6yRQq4m/GRWAjl8jFYB2MFYBaAc8raoEnEzgwYMHNG/e3JoKbs+ePZQrVy5KKrjw12So9rwvqeCC3YJZV3WdddZ1ttZh/+79Du0zciq45cuX4+2dcJkznLx8yab7xTvOMPaXf5nbpQoV8yU+JxhHLYQKQEeRjWhXBaAdjFUA2gFPqyoBJxNQL2AnL4B2bzMBCYfj7u5mc/mkUFAFoONXUQWgHYxVANoBT6sqAScTUAHo5AXQ7pXAUwioAHT84+ESAvCvv/5i0qRJ7Nq1i4sXL/LDDz/wyiuvxEhn06ZNDB48mMOHD3P//n3y5ctHjx496Nu3r7XOqFGjGD16dJQ2ihYtatSx1VQA2kpKyymBxEdABWDiWxMdkRIIJ6AC0PHPgksIwNWrV7N582YqVqzIa6+9FqsA9PPzM4RcmTJljLhXIghFAH766ae8+eabBlURgMuWLeOPP/6wUvb09CRz5sw2U1cBaDMqLagEEh0BFYCJbkl0QErASkAFoOMfBpcQgJExSEDV2HYAo8MmwlHE4IIFC6wCcOXKlcbF7viaCsD4ktN6SsD5BFQAOn8NdARKICYCKgAd/2wkCwEoO4INGzZkzJgxvPHGG1YBKMfK6dOnNzzjqlWrxrhx48ibN2+M1AMDA5GfcBMBmCdPHm7fvk26dOkcv1ragxJQAglGQAVggqHUhpRAghNQAZjgSJ9oMEkLwNy5c3P16lVCQkKMI98RI0ZYAcixsr+/P3LvT+4Vyn3A8+fPc+DAAdKmTRst+ejuDUrBgwcPqgB0/LOqPSiBBCUg94Pl99808wB6R+ptOhBqWu8cOXKEVKlSmdeh9qQE7CBwP/g+RWdYfj+PvHvE+DPyv1N52fcsywZOyZIluXHjBr6+ySe8TuQlSdIC8OTJk4bI27ZtG0OGDGHmzJm0bds22kfy1q1bhrPIlClT6NatW7RlHt8BFKeUOnWST2BOO36XtaoSUAJKQAkogURH4J9//qFy5cqJblxmDChJC8DIAOX4V+7/ybfgmEwegnr16hlHwbbYzZs3yZgxI2fPntUdQFuAaRklkAgIyNFvzpw5zRuJJzAgUneTgRBzur9w4YJx91lNCbgCATn2zfmJ5Xfz2LvHKDSjkPH3C/0vkDpFwj7H4Ve4dAfQFZ6MR2OMrxPIhx9+yNdff82pU6eina3sFMr9Pznm7d078jlNzHDUCcSFHhwdqhJ4RCA53f2TzzUVgProuwqByPf+Lve/TLZPshlD9x/q7xABKD4AyfkOv0vsAMqH2LFjx4wHoXz58sYxbe3atY3dNxFtQ4cONe7vzZ8/3ygza9Ys4/VixYoZ/5Y4ghIDUISd7ASKDRgwgKZNmxrHvvIteeTIkYZH8KFDh8iSJYtNvy8qAG3CpIWUQKIioAIwUS2HDkYJWAmoADT3YXAJAfjnn38agu9x69SpE/PmzaNz587Gzp6UE5sxYwazZ89G7gBKbL+CBQvSvXt3Ixagu7u7UaZNmzaGMLx+/boh+GrUqMHYsWONsraaCkBbSWk5JZB4CKgATDxroSNRApEJqAA093lwCQFoLhLbe1MBaDsrLakEEgsBFYCJZSV0HEogKgEVgOY+ESoA7eCtAtAOeFpVCTiJgApAJ4HXbpVALARUAJr7iKgAtIO3CkA74GlVJeAkAioAnQReu1UCKgAT1TOgAtCO5VABaAc8raoEnERAAsNLOsnQ0FDjR+J5Sp5xDw+J1EyU1+Tf9r6/bdc2ZqafaZ3tl898yUG/g0afCdF+TOOrUqUKLVq0MO5BqykBVyCgO4DmrpIKQDt4qwC0A55WVQLJhEDk/9Rkyo4IaZFMUOo0kzgBFYDmLrAKQDt4qwC0A55WVQLJhIAKwGSy0DpNuwmoALQbYZwaUAEYJ1xRC6sAtAOeVlUCTiKgR8BOAq/dKoFYCKgANPcRUQFoB28VgHbA06pKwEkE1AnESeC1WyWgAjBRPQNJRgBKUOdJkyYZF7YvXrxoXPJ+5ZVXngpbAkf369ePgwcPkidPHoYPH24ElbbVVADaSkrLKYHEQ0AFYOJZCx2JEohMQHcAzX0ekowAXL16NZs3bzY861577bVYBaBkCSlVqhRvvfUWb7zxBuvWreO9997jl19+4aWXXrJpFVQA2oRJCymBREVABWCiWg4djBKwElABaO7DkGQEYGRsbm5usQrAwYMHG2LvwIED1qqSHu7WrVv89ttvNq2CCkCbMGkhJZCoCKgATFTLoYNRAioAnfQMJFsB+Pzzz1OhQgWmTp1qRT937lxjF/D27dvRLkdgYCDyE24iAOXoWMqnS5fOSUuo3TqSwK+/wrp1MHAgZM8e/57+/hvWfryDNpvepoT/DhbQnoCRE3hzVM74N6o140VABWC8sGklJeBwAroD6HDEUTpItgKwSJEidOnShaFDh1qB/PrrrzRu3Jj79+/j4+PzxEqMGjWK0aNHP/G6CkBzH1ozepPvAI3LX6DNyY+ZzACa9c7PtGnx6zkoCIpkv8Opm+mfaGBRm5W0+b5Z/BrWWvEioAIwXti0khJwOAEVgA5HrAJQCMRHAOoOoLkPpzN7E53feFQlKrHLGEaJfPfYdywVtiZVePhQMkqAlxdMmhhG68H5yMvZJ6a0nSpkP7mNfPndnDndZNW3CsBktdw6WRcioALQ3MVKtjuA8TkCfnxp9A6guQ9rfHvbswfat4cbN0Cud5Yp8/SWtm6FVs+d5Sx5rQXHM5gMn4/nrbdirvvgASxdCnLcu+WHywRc86eSz0F8Am4wjy6WipMnw4svci0gNZmfLWS8tKTzr7Sa2zC+09N6cSQQHBzMwoULkXiAkgpux44dVK5c2UiZFhYWFuU1adre97fs3ML83POto5yWdRr7du0z+kyI9mMaX7Vq1ejYsSNe8i1ETQm4AAEVgOYuUrIVgOIEIke++/fvtxJv164dN27cUCcQc59Bh/dWtiz03deZF9jIOyU28NP+/Li7R3R75gxMGh9K8Tz+9BiUnvLlodf+t3iL2VHGVvu5QDZsThHjeCd3+5c3vq5GBqK/Qxo2aDBuE8Zb6+96oR8V//qUXVkbUPHyaodz0A6cQ0AzgTiHu/bqegRUAJq7ZklGAPr7+3Ps2DGDXvny5ZkyZQq1a9cmY8aM5M2b17jrd/78eebPt3wTDw8D8/bbb9O1a1fWr19P7969NQyMuc+fw3s7fhw6FNrCFqobfa2kGWnWrqRePUvXU6bA4P7BrOA1XmINzbNu4r8r6dlFRdJwD7p0gblzjbJf0wXfFV/z6qtPDjssDH5M255X7i2Mdk4PipTB++AuIp8h71x0jEptC/MQN9xOnsQtfz6beIgfUu/e4Otxh+IVfGjexos0aWyqqoWcQEAFoBOga5cuSUAFoLnLlmQEoAR1FsH3uHXq1Il58+YZAZ5PnTqFlAs3+Xvfvn05dOgQuXPnZsSIERoI2tznz+G9ffwx5BrWiU5EHMG939CPj38tZ/T9wTPf0vfku/hy68mxVKsGmzYRumIlHi2bG+9n4xLrD2SjZElL8YsX4fRp8PeHMi9mJStXo7Rzp1IdQt4bQMb6lSBLlijvBQTAP2lq88LDP7kw8FNyTnzPJh7z5sHnXbbzJ7Xw4QGDiv/E+ANNouxq2tRQMi0kR79r1qwxjnrlZ+/evZQtWxYPDw+DSOTX5N/2vr9j7w7GuY2z0v6u1Hcc2X/E6DMh2o9pfBLloFGjRsbRtpoScAUCKgDNXaUkIwDNxWbpTe8AOoO67X3Krlyn/BuZf6aWUSk4fSa8bl/nE4+BdL8xke2bginROD+5uBB9o6dOQT7LrtyDkhXwPuTHd7TlnQwLGTnKjfvnbzJv2m3+C8rPc2xmMzUIcfPEc/cOyJnTstsnW3MpYj42XlTkA9oc/Yi9ld+g7D//i3Vy9+/D4DzfMePG69ay90jFh+2PMmGBhpSJFSCgTiC2UNIySsB8AioAzWWuAtAO3ioA7YBnQlU/PzhXoSlN+ZmgNh3xatEMtxbNOUMe/Fac4qtuW1h1syb38cH7zlVunbqFZ9MGpDt9AJo0gZ9+ihjl4sU8bNsO97CHnCIfa3mR7nxpvP8nL/As2/AmkMtNu5FtleV1W+yX9t/TeGE7DvlWp8SNTU9UERE7pE8Aew5788kUN44cDqNUy2IU5b8oZUfwIW0PjqBEieh7lVA048eL9zu0bg1uydjpWAWgLU+mllEC5hNQAWgucxWAdvBWAWgHPAdXlTAsr1c6wkK/4rgTBkeOQN683E+XjVTBd6jHWvrzCQ35jWu1W5J5/RLLiMSVd/lyqFULcuWKMsqHI0biPubDp49cokbXqWPz7PYt2EuZjuW46eaLb+j1KMpMxN/4kgsY/G8n1lGXzmmW09N/IsMZa2lfvFc2boQOHbhGJpZ+eJieIzJH2/eH793gtWnPE4oHywsPZbBfG1KntnmYSaqgCsAktZw6mSREQAWguYupAtAO3ioA7YDn4Kq//w7nXupKV+ZyrfrLZN70o0Uz1elM3g3fRO190SLLtlhsJtGhM2SIWkqOesUr4/p1KF0adu586pHv413cuxaAT5bUhki9fvASmUpksxbZuDaIkvVzkpnrxmu7qEBFdht/D6haC59tGyA4mCv5KpH14j4W5BtOh1MfPTEL0bRjMk9lzL2+xnu3SUedUle5fjcFp0+H0cJ3vXhO8c1PGUmVKjYIrv++CkDXX0OdQdIkoALQ3HVVAWgHbxWAdsBzcNW+vUMZNiObRTxt2GDZ0RNbswYaNLD2/nDGTNzf7mX7meiVK5b6coH/iy8gZUq4e9cS9flxcWjjHE97FSRfyAn2T99A6XcfjROY1ehn3l7dNPpWVq6EZpYMIpdnLSPbOy25SHbjaLvRqymtdUJCoGHVGyzeXYiM3LS+vpZ6lGEf2bhivPYjL/MKPxpTSeoexSoAbXwwtZgSMJmACkBzgasAtIO3CkA74Dmw6r170CvLUr4JaEVw6vR43bxqSckhJsF/CxfF49QJHjRohvfqlQ4ciW1Nb836MtWu/sT2lpOoumSAUUmOf1emac+r9xdy4uU+ZHa/QbqVC3iYJi3uixdBo0YRjQcF4e+bmzT3r+LnVYUCp/8kQw5LKsOuXSHb3HGM431u5y5J+vZNLZcBo7HBjKfQ7EF0fzNpXxBUAWjbc6mllIDZBFQAmktcBaAdvFUA2gHPgVUlE8fN51/mZX4ibPAQ3MZHhOAwuj1/3nLPr0MH8PV14Ehsa3pZrRm02Nib47lfoOBZS5iiQwcekrN0RiOodMAfm/GpURFWr4bnn4eMGZ9oOODH3wl6rTXpH97if8/NpfvmzmxY95AN9cbwISMtonLuPNw6drAEP5w7l2s5SrMra0Ne+r6ztb2eJTby+cHnbRu4i5ZSAeiiC6fDTvIEVACau8QqAO3grQLQDngOrDpzJjR69xme4WTU418H9mlP04tGHKTNmFLc90xLqqDbxnH0gtEn6DCqIEHuKUnx4G7EDuZTOjrXfgi5F04w7vht/uY4N/uM5PVbn1lqtGwJixdHf9R97x6hRYrhceEc8+hElYPzYvQmtmWecuz88vO3qHZsARVndKZhq7SJyutYUsHNmTPHGgdw69atSNq0yHEAw1+T+dr7/t/b/uaHEj9Y0Y1LO47d23YbfSZE+zGNr0aNGvTs2VNTwdny0GqZREFABaC5y6AC0A7eKgDtgJeAVeVan6TYlVy/kqXj9P47/G9JeksPcmfvsQDMCdh1gjS1cc0DajZIZfFWfjTe2fWX02NtC85nr0iuiztt6+fqVe7nKUKqwFvMohdvYxF/xpH3/r2W+4ox2ZYtUL06ElOwxbPn+GWzb7wDS2/aBOdqtqENi9lLGVaWGcmw7S/j6e1pXJXcMGYzN3cep/789qT3jZSTz7ZZulwpzQTickumA3YSARWA5oJXAWgHbxWAdsBLoKoSBSXcvyO8yQFMYhKDCMhVEJ9zlvSAidkkm0hIzjzk4RxBG7eS4vlnWZB7KB3Oj+dY7TcotD72ANHh87v4/gxyjOttnW6AZxp8gu/GPv2wMO4XKUuqY/v5gNG8uvsDIydybCbxBSU4dWT/ly+areatVZHuKD5qJOzIf0xuv4eBO1oZr8yrMJ3Ou96NrQuXf18FoMsvoU7AJAIqAE0C/agbFYB28FYBaAe8BKgqcZpbtAARIZ4E8y4z8OYBbzKH/Jzm4f++wv2NrgnQk2ObEIePTV61qBm6kXPjvyXXoNf5K0VdXghZz7mR/yP3qDdsHkDYlau4ZctqLR/m7o6bbLvZYt9+a9yLvExWejQ4w8rV0e8YBgdDyP0gTn4wl93/28XKhy8zZFMTKlWyrMWWVHWpFbo+1h6D8OL4l39SvNtzsZZNyAKS6u3vv/+2HgFLKsgSJUpEOQIOf036tfd9v4N+DLkzxDqFH6v8yIkjJ4w+E6L9mMZXunRpatWqZZ1XQjLUtpSAIwioAHQE1ZjbVAFoB28VgHbAS4Cq8v+n97+7mZt5IGWvRRUcYZky4Xb2LPhYvGETu63K0o2Xr33NwdajyfzpcHxyZiAddwn8Zy8pK5eJ0/BDho7Ac/wYS50vv4Ru3Wyrf+cOQZlzkCL4PhMZSIO9EykTqWvJXTyl+nKe85tJIY4ZO5bhNjfXMDqf+Qi/1Zco1ySXcZwdduIkp895kP/5vFH6v54mL6fD8lHh3t/G69++8D9e3/BGlHuCco/wzzWB1Li8DO/G9SBbRHxE2yYTcyl1ArGXoNZXAo4hoALQMVxjalUFoB28VQDaAc/OqhcuwIu5DuJHeVIQ/GRr33wDHTva2Yt51ZdW+JiWfsPYV64jge8NpnLnktxzS03q4Nvg4RG3gciW4n//We4+ipdzHPK+BX08mRTDBhr9zX5+IT02tjP+Lk2KA/K8TQUpyIlox3N45Pdc2H+dOive4XCGZyl2c6tR7ujs9RR+q67x9+7MYcjWV7gTmALfWmWNnVqxtR/8zYuja1jbHdJoH01W96IGm7lCFv7o/RPtplWNG4cYSqsATBCM2ogSSHACKgATHOlTG1QBaAdvFYB2wLOz6tix4D/cEt8OT094800YMgQkFZvkOBOvVxeylW0X88qiNhzJUp3zTXtS5+v2HPCtQakbll0y0+zOHUhvcaDZ4Fab4hfWkz07/PILLGsyl7lYjtR3vTiEMVd70HZofjKMHUD9fZ/gl7keD7wzUO3cMv6s/zG11gy1DDswkEvPvcrmy4Up/Os0667i1Z2nOV+5GeXYaxQLu36DkNTp2VOoOZXPPRmf8UbXAWT8fKzFI1oiVqdLFy8sKgDjhU0rKQGHE1AB6HDEUTpQAWgHbxWAdsCzs2rx4jDjcD3qsQ6mTYPeEY4PdjbtlOobJu2k9qDKXPPKzvYqvWm8+X3+KdGZKgfnmj8e2T3t3Jn7+PDZh9cZMMKHgQ32M25NeTwJ5Wbj1/H9+VvruE6t2kf+ZmW5SxrukZrsXMZv6kbK94k9nuDtNdtI38ASDuXn1gu4edafDlt6Wts+kLcR3meOUIjjT3A4k7Mq6T/sz39lWnJk/nYalL3I5WebUbLU0wNZqwA0/5HSHpWALQRUANpCKeHKqAC0g6UKQDvg2VH18GFoVXwf+yiL4eQgx50FC9rRovOrHtl2k6LVLAGef8/Vmfrn57G74TAq/ProLp+ZQwwL43amAqS/eZqJVZczcOtrLErdlbYBc7letRGZJK+y7LqGW2god1NmIm3obeOVYHHJuXqbVJltSyx8sGJHSu5e8MQMA09dJGW+7Gz5O5Sv6i7kq+BOsVJ4nYV0W9eOOnViLqoCMFaMWkAJOIWACkBzsTtVAG7YsIHatWtHO+PZs2fTo0cPc2nEsTcVgHEEFs/iBw7A8ePQuLFFd/TtC2WndqYz30Dr1rBoUTxbTjzVxHvWP2VGI1/vCSSI9QmO9v2MwlMidsPMHO2ZVgPIu/QTfkrdhmzrvydr1fzGfb3An34nZZMXnxjKL8+NofHWEcbr1wpUJvOJf2webti27YRVq2aJgygC0iMlXmdOQM6c1jYk1uPsJj+xIuTlWNvdkeFFyp9Yjqdv2mjLqgCMFaEWUAJOIaAC0FzsThWAKVOmpHfv3nz88cfWaPXXrl2jS5cubNq0iZs3I5LXm4vFtt5UANrGyZ5Sktc3b15LkGe55jd7NpQqEsSmo1mNNGlI1OHq1e3pItHU3edThTIPdljHc3P+Knw7NHXK+O788Q/pXqyKP6l5lR9YS30eurnjfvsWpH1SWB388ypFa+cwjogDBozAZ9KHcRp36E+/cq1VL+6HeJFxwXTSt2n4RH3J4CexrDNnhoATFzkzezXeATdx37eHwMo1KDT5LWudrSXfoNqB6OMnqgCM09JoYSVgGgEVgKahNjpyqgDcsmULHTt2JE2aNHz33XecPHmSbt26UbRoUebPn0++fPnMpRHH3lQAxhFYPIovXAjt20dUlDS2S/ptZSvP8TBzFtwvXyLeKSviMR5HVlmXtwt1z85AqOdrAAAgAElEQVSzdhF28BBuJYo7ssuY2w4L42aG/PjeOWMtE1i/KSnXrIqxzv0vF8LJU6QaORBSpIjzuMXT2PhQevoVvpjbFa/vBZaj5EBScPLXwxRrWCBq+aAgggICmDZnDiEhIcbPtm3bePbZZ/F8dKwd+TWpbO/7m/7ZxO+VfreOY7jbcHZv3230mRDtxzQ+SQXXv39/UsRjLeK8eFpBCSQAARWACQAxDk04VQDKOP39/XnrrbdYtmwZDx8+5KOPPmLQoEG4xft/gTjM3s6iKgDtBGhD9bfegrDZsynLXqbQj+MUMkKJzKEHvPQS/PabDa24RpGf635Kk/X9jMHe8chAuqDrThW3IQOH4Dl5QgS8gwexK0mwo5dBAl7fv8/xvLUpeGsX/6WtSM6z20mT3oMVMy/wzMy+lD79M6ROg8fff4J4EplgmgnEBMjaRZIgoALQ3GV0ugDcvXs37dq1M76JX7hwgTZt2jBjxgxSSyiPRG4qAB2/QJXKBLF5f1pSEsRD3MjNOS6Qy9LxgAEwaZLjB2FSDz9/8A9NPrLEuttbuDll/1tmUs8xdHP9OmGlSuF26RKULQt+fnZsz5k3lQOrTpC7WQXjisBVj2zs9K1Pw2tRnUwCUmYgZNWvpK1v8UCOycL27uPBP/vwLFYIr5qWHbsY7fRpy/F4RoszT7ipADRv7bUn1yagAtDc9XOqABw/fjwjR47kzTffZNKkSRw7dowOHTogwurbb7+lWrWnfzibi+rJ3lQAOnYFZEOnps9OtgRXtnY0l8504dEx6e+/w4tPOiQ4dlSOa10Sl4zN+wU1+Zuc331C7bbZHdeZrS0fPQrLlhlhYciRw9ZaTi93fODnFJzcK8o4LpCDwd7T6f1gApXZabz3R6uRnGtYgCIZM+Pl7UVounRGirjDfocpOnIZNW5Ydpjly8e/M2Zwt1JF4335rCpUqJDx3vHDh2mwbBlZf/uNh56eHO8/gC3Fi1nfP/jfQXqciXBoW1t7LedPnre+H96Wx6OA34+3H9/35SpN5cqVNRWc059GHYCtBFQA2koqYco5VQDmyJGDr7/+moYNIy58BwcH8/777zN9+nQCAwMTZpYOakUFoIPAPmr25EmY9cxkJmPJTBHFGjSA1asdOwAntC6nrLt3W+49usAtCCcQsrHLsDDudXiL1AvnGBWOFHiJfF+NxLt2NTK63eA/ipCZ68Z7uyhLxUfBqLvhztc8ZA7l6Y4fobjjwUNrp0PwYQ0B7Hn0Sk0RkcDjNx7lif1EgltLOS9gWKRxjzVi5ZhicsXGFU5TTIGhnSR6AioAzV0ipwpA8fjNLC590djGjRt54YUX4kRj1qxZxk7ipUuXKFu2rHGUXKVKlRjbmDp1Kp9//jlnzpwxxtGiRQvGjRuHt7e3Tf2qALQJU7wLSeiPjA0qW3ZrJNDz9OkRbS1eDK1axbttrZhMCBw6RFievLilTWOd8JQpgYzpf4+L5DCuFjzNXqQfAXiziY+jFPuQTGwlhNXiif7IRCyepIA1aPUXeNCTUBWAyeRR02naT0AFoP0M49KCUwVgXAYaW9nFixcbHsVffPEFVatWRcTd0qVLOXLkCFmzZn2iungdd+3a1diBfO655/jvv//o3LmzcQdxiria2mAqAG2AZEeRmVNDeLNvKkuuX9kO7NnT4vTh4wMSIkhigqgpgTgS8Pe/R9q0tWhDCmZwhNukZy9leY0forT0GT15G9llPsVb1GIM+8jEjWh7+5sajKE9+6nNt7xFHTYQjAcVCWW/xMweHqma7gDGccW0eHIhoALQ3JV2qgCUINBP8/Zdv369zTRE9Ml9l5kzZxp1xKM4T548vPvuuwyRHLGP2TvvvMO///7LOskd+8gkZML27duNGIS2mApAWyjFv8yo9scYtbAwQZ4+pAj0B7mP9umn8O67ULJk/BvWmsmaQNQ4gOIJ/AC4QB4yMYECtGWzwSc1+7lPaSsrT54lhLl8yhe8xzTj9dukIz+nuMUWoAmQBVjFWoZb0hQCE9zSMWTknQjmY6FgMMjXl0MOXgk9AnYwYG0+QQmoAExQnLE25lQB2FdSOkQyuf+3Z88eDhw4QKdOnZgmOV5tsKCgIFKlSmWEknnllVesNaSNW7du8eOPPz7RiuwA9urVi99//904Jj5x4gSNGzc2nFDkDqItpgLQFkoRZTZuhM8+g3btQK59xhaebET5n/loT1Ou5ylLpjPht67i1qeWVgKPE4gtEPTL+HCdqmzmKHD+sepupKAsg3iNBvzGR7zIGuOOoBwRh1rL1mQUfzHK+u9cL7/IhSx3SLfkSz66+z69+cl47wC+nOUmXwJbgYsJvFwqABMYqDbnUAIqAB2K94nGnSoAY5rqqFGjjPiAkydPtomGhI/JlSsXElg6suewxBOUu4SyqxediaPJgAEDCAsLM8LQSDxCuRMYk4lTSmTHFBGAsst4+/Zt0qVLZ9NYk0Oha9egVy/46y+QaD4//wz9+0f12ZDrnX/++XQaY30nM+zWQK7WaU2Wda6f7i05rL0rzDE2AWjbHCSFZX3ggxg8Otx4m8rMxJISb03KqjQq0YWxficZQqTYio91Jn7Fx20bgE2lVADahEkLJRICKgDNXYhEKQAl7IHsyt2Q/F82WHwE4J9//mnc9xszZoxxZ1D67NOnD927d2fECEtO08dNhOno0aOfeF0FYFQkkRIyPHX1du2CChWiLxIQAAtTvcEbfMW9ASNJPSliN8WGR0KLKIEYCSSMALQN8HMUYzOHjcId04zjS/8PLHdan2Jzgd4SJN+2Lp5aSgVgAkDUJkwjoALQNNRGR4lSAC5YsIDBgwcbgaFtsfgcAdesWdNIxSRew+EmsQclJqF8aLq7uz/Rte4Axr4a585BwYIQFAQ1Kj3gzNFAztxOb1QcORLkOmbbtrByJUyYAIMGRd/m/v1wq0xNarKJsIXf4daubeydawklYAMB+byQ/OMSb09+5ORAHMEix+ELf02as/f9YpOP0DF4hXVkt7zS0bBoW6rWzcN/v5Vkz5EqfMQI2vI9qQiwllvRsCE7y5Wz9l/49Gk8Q0N54OHBwnPneO5RDuyYxiefcfJlVlPB2fBQaJFEQUAFoLnL4FQB+Nprr0WZrRzFXrx4kZ07dxofXBIk2laTXTzZNZTQL2LiBJI3b17E2SM6J5CKFStSr149JogKeWTff/+9kYv47t27NgVPTc53AK9fBzktF+fcbdtg7FiQ65fyI1cuB+dfzLjbPQkLCqb0vW0cphhnf9pLzpoF+WLiHXp+nIdXX4UVEf8vGqsgAnL5csvRcbPuWcjCNUtgvPLlbX0UtJwSSFQEfhuymgYTGlnHFPDVl/h07Wb995kzIIEHQg79R5O1vWnAmojxS25jCXck35zEASqy/fsvFCuWqOaqg1EC9hBQAWgPvbjXdaoA7NKlS5QRy65blixZqFOnDvXry/0a203CwIjTx+zZsw0hKGFglixZwuHDh8mWLZsRIkbuCUqcPzE5zpVwL3PmzLEeAffs2RMRhtKWLZZcBWBYGDz/PER2lpZIO8OHW8L1eRHE/XTZ8bxz08AYks6XB5lykebkASvW1ixiZYrWHDpk2TEMtxIlQP5fy8h1rvMoRqS/v0URqikBFyRw4fJdcmaPuCN879JZUmfLHe1MRONt77eIRcS+4x3gmwOv/X545srmglR0yErgSQIqAM19KpwqABN6qhICJjwQdLly5YxsIrIzKFarVi3y58/PvHmWNGLi9DF27FjkuPn8+fOG8GzatKnxWoYMGWwaWnIVgPPnQ6dOFkRdu8LXX0fgcieUnwv1peExy05sTBbq5kGlsB2UaFeehQstpWQnJF8+y9+fYzObqUFA1rz4XD5t03poISVgCwE5HZAQUPKn/Jw6dcr4bAi/9hH5NWnP3vcPHz/MXx9NYNYeP+aU9aLMV39x+dxlo8/o2t+4MTUZ3h9L+7uRfrGAL+nGRl5gAR2t0zyfviin+rUjTbNmT4z/mWeeoWTJktFeZ7GFk5ZRAmYTUAFoLvEkJQDNRYeRszh9+vTJygtYdv8KFADJez9q0H1GFvyWtQdy0HBGQ0LxZGbaobx9d7xlKWbNAnGauXIlYmnE60Pudl66xELa8Xb6hchxsqRBXboUPm7lR08+Jy9njKOwsGbNcJMLg2pKIIEImOkEYgxZUsG9D8+dhV05IHBi7KngMuJOS6YRgrdxP3AVL/MWkttYcl/XoRDD2EQ9shHxuyWZ07c9xkidQBLoodFmTCGgAtAUzNZOTBeAvr6+Tw3+HHn6tnoBm4ssorfkKAAlG1ufPlDO5wg7S3XCY4clxM4KXmUWb7OOehZAn3wC/frBkSOWChImZ+pUyJkTdu6EypUJJAW5OM/vuzIb3sCDB8OEiW5Rl/PLL6FbxH0pZ6219pt0CDhFAMYrF7CksZSK/5MzC+B3ud1sXYgszGQ5iwxHqXAbhTujI5VRAZh0ntvkMBMVgOausukC8JtvvrF5hnKnLzFbchOAslMnp1YZ/M9y1Ls03g8i8qBGWafu3WHOnKcvXblysHcvHZhPg2878Prr8HKjEFatlu2SSCbxYGzMzZyYnxUdW+Ih4DoCMDZmnngzkFE8YDARDiLylazroywj0QpAcdH38gK3x75sxdadvq8EHExABaCDAT/WvOkCsF+/fnz00UekTp2av/76ywi/4OkpyTJdz5KbABRPxeH973PSqwjZgs/DM89YLgOK57VEfw63gwdBvDmeZgMHwuTJzKE75z+YY5wUN867n1/Olomo9cYb8D/Z/VBTAglHIOkIwHAmzzGGWgwzspFEmNx+Xu/vb3zWGibeVfL7umOH5d/Nmll252tLUGs1JeB8AioAzV0D0wWgl5cX586dMzxzJe6WhH3JKi6kLmjJTQA2aABt1nSmM492ccOPZx8+BPHoXrsWJLTPo3zMT11SSQ/StCnnyMXAlqeZt8CDTj5LWBTWOqLa+PGWc2E1JZCABJKeABQ43qRgEsWoyTrqkpnrBrE7739Iut5vQrZsULGiJaTS46a77An4dGlT9hBQAWgPvbjXNV0AFi5cmFatWhlhXmrXrs0PP/yA3AuMzp6XWCOJ2JKTADRCv/juZ8Pt8nhKztP33gMJoh3f3dvAQIIzZ8fL/xavZdvMmPXPsbjkaEaH508Vr5DNm+GRF3cifgx0aC5GIGkKwPBFKE87WrJQvE4eWZj8Lr3WHLelS4xXDlCSUhyMeL//ANwmTdQjYRd7jpPicFUAmruqpgvAlStXGjl3r1y5YjiDSPDn6Ezekyj9idmSkwA8cAD2lW5HO74ntFETPH6xJLO3x0KavornzysZyERyfDKQnP3b0IbFFmHZowekTWtP81pXCURLIGkLQJmyNwX4mq4cZDhjozCYQl8+zjSFdPcvsSjgZarw6DhYkneL176aEnAiARWA5sI3XQCGT08uJ6dLl44jR47EeAQsIVYSsyUnAShJWTp/WIACnLIc9dZ75O1rzwJNnGgc8YoH8ZDCK1h2tAxl2A9yPNy4sT0ta10lECMBSQU3bNgw01LB/b39b/wa+lnH0/NGT3Zs2WHcfxazN9VcdPU3b95OqlTDCPk7E1uobnjcD2UcGUf1YdgHHob/R5/Xr9Hpu/pU4NHY5MpF8eIg2Udq1IC3347/Dr8+f0ogHgRUAMYDmh1VnCYAZcwbN26kevXq6gRixwKaVbVL4yvM/TUbYW5uuN28CQkhziWVSM2aXCQ7uTnHPVLjTSAcP25xMFFTAkmAQOT/1GQ6/kP9SZ3CnMw2e/fCtIa/EZAtP82HFaN584iTXgnHKeGXVlx+jufYGj3pjh0NZy3knq/cI1RTAg4koALQgXCjadqpAtDcqSZ8b8lpB/CdAr8w81QT7uYpTtozhxIGZkAAD9Omwz00hDqsYz11eZjSG/d7/pbI0GpKIAkQcKYAjA1fSAi8UOAMzc9NpV+kUDJP1JMtQ0mjKfey9+2Df/6x5H2UHcNLlyxxP8XJpE4dyB19mrvYxqLvKwEVgOY+AyoA7eCdXASgXNMcn3IkQ4M/5PYrnUj/gyWdXkJYaMnSeBw6wCx68TafQdmysGdPQjStbSiBaAlIqrczZ85YU8FdunSJ7NmzR0mlFv6aNGDv+yfPn6T+nxG5zfe23suda3eMPhOi/ZjGlzNnzigp7mJ6HLZtg9mz4c3va1EtcCP7KM1IRlObDfTm6Skdo23zzz/hhRf06VMCcSagAjDOyOyqoALQDnzJRQBKJre/srWgBcsJmTgFz4F97aD2WNUWLWD5cq545iBryEVo2xa++y7h2teWlMBjBJK+E0jEhOOSCSTs4iVOLtvJ6SL1yZQjhbHBV2jjl0ynN6kIsPk5Cqv5PG6DBhI2cCBBZauQcsZkyJLF5vpaMPkSUAFo7tqrALSDd3IRgFu3QurnHjlo/PILNGpkB7XHqg4bBh9HCmA7diy8HxHCIuE60paUgIWACkDbn4SjR6FIkTBScZ9m/EgAPuyiImXYR3U2U4zDRt5ucTC5QlbjPY9IqeisPR06BGvWQLVqGtrJdvzJrqQKQHOXXAWgHbxdWQBK7Ff5hp8nj8WhN3NmKFw44oL4ypXw++/w0Uew+peHNO+UGh8ewLFjULCgHdQeq7p6dVRB+ccfULduwrWvLSkB3QG06xmQKyASIWbXLmjXDiST3L17ID5c338PixZZrv7JZ8aNNj3pFvTF0/uT2J5vvWXJSqLp6Oxam6RWWQWguStqugB8TTJF2GgrVqywsaRzirmyAJw3z5K8I7JJVqipUy1RWCRLlDj+VaoEdfMeZfyKIgR5eJPiwd2EDQ0hsR4jB5O+fRvSpXPOgmqvyYKA7gA6bpkv7z7PrZpNWXv/OdJxh/ycojx+pMX/iU4D6zQkZc+u8OmnFmcS+VNjfzpucVygZRWA5i6S6QKwSyTVIUGgJROIxPurJEoD+Za5i1u3biFCce7cuebSiGNvriwAu3aFp+FNQSD9mMIf1KMAJ1lCa24UqkLGo5JqPoFNthfeeQcGDLAEgVZTAg4koALQgXCxfHGUjfyrV6FlS8vu4ef9jlKIozThZ3rxebQDCClUFM9d/+gXQMcuT6JuXQWguctjugCMPL3Bgwdz48YNvvjiCyMvsJhk/+jVq5cRJHpSIhcDrioAr12DAgWgmP8O5jRcSbF7u9i8JxWN7iwimBTGOkzNMoY+V0dwHx/8KE91thD65lt4zI7+w9uux1bOmCRRfbFi4O5uV1NaWQnERkAFYGyEEv79U6dgwwa4fh38vj3Iwr2lou0k+ONJeA0dEP0A5HTg4EFIndryp4SdKVo04QerLTqNgApAc9E7VQBmyZKFTZs2UfSxX2LJDiJR8q/Lp0UiNlcVgF99BePfOMpu90qkfXjHSvhw+zG88Psw+lTcxNC1tXGTIGGRbdUqaNo0Ea+IDk0JxE5ABWDsjBxd4uthx9k97jdWhjXjLmlpzWLm0AP/1FlJ888GKFEi6hDkC2LDhnD6dNTXZZPgr7+gVi3LKUIKyxdYNdckoALQ3HVzqgD09fVl3rx5NJMLZ5Hsxx9/pHPnztyUjBOJ2FxVAEo2gH4rqhu7ek9YvnwgcV/ES6RhQ8KuXcdtxz9QujTs2AEpUybiFdGhKYHYCQQGBtKvXz9rKrht27bx7LPPRjmFCH9NWrP3/S07tnDo1Yjg6Z3Od2LX1l1GnwnRfkzjkyxL06ZNI2Ui/Z2Vj/f16y1xpDP53KdUtyqU4iChpcrisT9SLNC7dwkrWxa3kydjX1zZFZQUexJJ4FGcxdgraYnEQkAFoLkr4VQBKB/C8+fP5/3336dKlSrGzLdv38748ePp0KEDU6ZMMZdGHHtzRQEYHAzP+h5h171ihHl44CbfrOVspl8/i2tfuFWuDBLQ1csLJA6MfLDK0YuaElACcSKQmDOBxGkiDiwst0AaFT3O6qOFLL2cPw85c1r+/sEHRjiCk+RnIJPYT2mqsZX3mGo4mdwgI88QVRwGe3rjWaoYbpLXuFT0x80OnI42HU8CKgDjCS6e1ZwqACUi/+TJk41vqRcvXjSmkCNHDvr06UP//v2t38jjOTeHV3NFAbh9O6x9djjDGUtYw0a4/fqLhZPc3N640RKrS745S5gGb2+HM9QOlEBSJ6AC0LYV/uknyPFyJSqxi9BvvsWj4+sQFkZI/oJ4njlJaxYZzmhDh1oiR73+Opw5Y2n7eTaSgiDqso5BTMSdsIhOf/sNZIPhhx9AolBkyGDbgLSU6QRUAJqL3KkCMPJURUyJifOHq5grCkDxyKv2TgUq4Afffmv5FFVTAsmIgEQfuHbtGvKn/IgjWsaMGXF7FJMu8muCxd73z105R6UVligHYse7HSfwbqDRZ0K0H9P4MmXKhNyzDp9XYl9iiQg1K9VAegdN5mrTrmRZ9RUcOWI4hwWSgmcL32DX4dRWP7G7d+HLL6FkScthhaQqFkvJA8YyjP5Ef4IU5uaGW6tWcP8+yD1nCWIoEQjUnE5ABaC5S+B0ARgSEsKff/7J8ePHadeuHWnTpuXChQuGEEyTJo25NOLYmysKwO5t7vLF4gyWaP3nzkGuXHGctRZXAq5NQJ1AEu/6jam+muFbGnEjfX4y3jrJzQ8+xfejfqylHpl3r6V8edvGPnf6XRr2KUx2LttWQUpNnAhyQfqZZ2yvoyUTlIAKwATFGWtjThWAp0+fpkGDBkZidrmY/d9///HMM88YR8DybwkPk5jN1QSgfMNukXE9P9ypS0C2fPhcOpWY8erYlIBDCKgAdAjWBGn0hwX+NOnoixchcOIEp15+l/wHfmFG/k9492S/OPXx1ZxQ3urxkJr8bcQflFR1HoTSgN+4RmZeZlX0aes2b7Y4kqiZTkAFoLnInSoAX3nlFWPH76uvvkKOK/bu3WsIQNkR7N69O0clEWUcbNasWUbswEuXLlG2bFlmzJhhdS6JrhkJOD1s2DAk44gco+TLl4+pU6fSyMZct64mAMXfY16JCUxgCA+bt8R92ZI40NWiSiBpEFABmHjXUTyDD2asQQ02c3fK/3gw7EOyBJzlm+6b6DSnepwHLs4lEjlGkkodPoyxgyinvXL660EIafBnAoNpyVIyEinqxNtvw7Rp8Cg+bZw71grxIqACMF7Y4l3JqQJQRN+WLVuMOIAiBMMF4KlTpyhRogT35bfURlu8eDEdO3Y0dg2rVq1qCLmlS5ciMQWzZs36RCtBQUFImAR5T7yQc+XKhexIZsiQwRCPtpirCUC5Ax36WgtasNxy3DFwoC3T1DJKIEkRUAGYuJdzVuYPePv6R1yp1IisO381Bvv3qpvUbJowzhuSoeTGDZCoMhLg4PPPLVlLfLnBTzSNCI81dqwlnIyaaQRUAJqG2ujIqQJQ4gBu3rzZEHuRBaAEh27evDmXL9t+f0NEX+XKlZk5c6YxMfEwzpMnD++++y5Dhgx5gqoIRdktPHz4MF4S6iQe5moCUC5Jt3s/H/k4Ywn9IsFT1ZRAMiOgAjBxL/i4BhsZuibis+kcufC6dI5s2Rw3bnEi2bIFRgwPo0fY53zG2zx098D9+DHIn99xHWvLUQioADT3gXCqAGzdurWRB3jOnDmGANy3b5/htSaBofPmzWtzLmDZzUuVKhXLli1DjpXDrVOnTkZeYQks/bjJMa944Uk9eV/6FScUSU8Xnpbu8TpyL1F+wk0EoIjM27dvu4T38jutrjBzaTYML7hbtzTnprm/a9pbIiGgAjCRLEQMw5g9PZAOfXxJRYBRYmmK12nx4FseOWk7dPByZNzjzTBaffki9VhHcJNX8Vq1HFM6d+jMXKNxFYDmrpNTBeC5c+d46aWXjFAMct+vUqVKxp+ZM2fmr7/+ivboNjo84jUsR7hynFytWjVrkUGDBrFx40YjuPTjVqxYMeSo+fXXXzdyDx87dsz4s3fv3owcOTLaVRg1ahSjR49+4j1XEYC9i/3O9CMvcTdHEdJeOGLuk6a9KYFEQkAFYCJZiBiGsWsX3KxUzxBgYhPKL2Lw7tamDfrBA+hSZDMLzr6AJ6Hwxx9Qt+7T+5eLhnJnvXZtvTdox0qpALQDXjyqOlUAynglDIzc35P7f/7+/lSoUMEQZT4+PjZPJz4CsEiRIjx48ICTJ09ad/wk84gcC4cHpX58AK68AyjfbEf6TOTDwMHcfqkV6X9bbDNfLagEkhIB+T3u0aOHNRXcP//8YziLhe/8h4aGEv6azNve97ft3sbJthGZKlr+15Ld23dbHdTsbT+m+vJl+Msvv0y0qeBieqbkkKVtqh9Z8fAVrpCF5Z+eped75qaglBsyh+q8zdt8hn/TtqRZ9V3MvwLff28JnP8oli2yCSGZlV59VcVgHD84VADGEZidxZ0uAO0cv1E9PkfAL7zwgnH37w/5dvfIVq9ebXgAy38QKWxIKu5KdwCvX4c1mdvRju8J/vBjvEYMTQj02oYSUAKxENBMIHF/RPLnC6PSmeUcoSg/Hi/tlNB8fSpvYdrO6gSkTI/P3auWtJiP20cfWVLVRWNhzV7BbeQHRjYTKlSIO4RkWEMFoLmL7lQB+M033xjHvY0bNzZmLUe2ch9QnEK+//57IyyLrSZOIPItXkK/iIkTiNwjfOedd6J1AhHP3++++44TJ07g7u5u1JGUdBMmTDACUdtiriQA9+0Dr7LFKc5hWL0aGjSwZYpaRgkoATsJqACMO8Dp06FPHyhTBvbujXv9hKixdFEotdpmJwvXCP19HR4v1oloVoKqfvIJDB5svLaOOhygFEU5QgPWPNn9zz/Do//nEmJsSbUNFYDmrqxTBaCEf/n888+pU6cOW7dupW7dukb4lp9//hlPT08jPp+tJsfI4vQxe/ZsQwhKO0uWLDG8fLNly2aEiJF7guMe5Qs6e/YsJUuWNOqIp7DcPezatatxB1BiA9piriQA16wKpF6zVBgNIR8AACAASURBVJbApyJwc+SwZYpaRgkkOQJy51hCTIWnggsICDCunEROBRf+mkze3vev3bnGM19GZJe4+O5F3EPcrddc7G0/pvri4JY6dWqXSQX3+IMmqcklKUeePM55BOUoenm6zrQL+oYLrfuSc1Gk1HJNmsAvljzqs+jFQK9ptGzrSd688MfaMHJuX8FyWkQd+NKlsGwZ5M4Nu3dbghK2bg1nz4JsdlSsmOydTVQAmvusO1UAygeUCDTZqRPvW7l7N3/+fA4ePEitWrW4KsGZ4mASAiY8EHS5cuWYPn26ERNQTNrLnz8/8+bNs7YoorNv377s2bPHEIfdunV7qhfw40NxJQG4aPI52gzMQ4ibJ56hQcn+gyYOj5UWTWIE1AkkiS2oA6czvdJ8eu/qxJn8z5P35EZLT1euEB6T5iea8EHZVfjtcYsyCokvOKr1vww925NaPKpnyziPH0/WqehUANrykCRcGacKQAnCvGbNGsqXL2/89OvXjw4dOhh5gSUYsziFJGZzJQH4v15+dP+8Ard8spPh/sXEjFXHpgQcSkAFoEPxJqnGFw3aTZtJFbnjlZF0gdeML85h7w/DbdzHnCIfJVKe4I/17tFmjpOrf7NmwZF3ZzCOoaThXhQ2QXhxmWzk4Vz0zLp3hwkTwNfXco/QjDg4Tl49FYDmLoBTBaB4+8oOoIg/ufMnOYElO8iqVauM7BwHDhwwl0Yce3MlATi96Vp6/1yfy1lKke3K/jjOVIsrgaRDQAVg0llLR8/k4M4AildOjTthBJ86j9eZ4/D880a37VlAk+/b06ZNzKMQ3TZmDPzvs2BSXzrGQ9ypz+/MpQv3SGNUzM9J1vASRXhK6tPChWHHDkif3tFTdmr7KgDNxe9UAShBmocPH47cx+vZsycNHjkmSBw+8cK19S6eucgienMlATi54vcM2N2O80VqkevIBmch036VgNMJqAB0+hK4zAAePoRDKcpRKnQvx4Z8ScFP38YtMJBDFOfztw8yY2bUo9+YJiZCcNMmSJvWct0vTRqQ7CMiHiUtXalSILna84Uepwz76MZXNMFyxzDcgipWI8XiBVCwoMvwi+tAVQDGlZh95Z0qAO0buvNru5IAnJR3BgPP9uZC9Zbk3LTE+fB0BErASQRUADoJvIt2+0PRIbz634Qoo2+Uch1fnqhDzpz2TUrS3Xt6gkQdk1Bdfn4giasko2lDfqUK/9CVr8nLWUtH0uG2bc7zjLFvurHWVgEYK6IELWC6AJR0b6VKlTJCr8jfn2ZlJAZAIjZXEoCfpv2Avv4fcaVFT7Iu/SwRU9WhKQHHElAB6Fi+Sa317wfvoe3E8tZpDWQi5b4dyOuvO2amISFQrx7s3AlyDTBPzlAuDfqEiQwm2CMlnqt+wK1RQ8d07uRWVQCauwCmC0ARfpcuXTLSvMnfJfSChGMIt/B/y58SkT8xm6sIQMH7hUcveoZ9zp0+I0g39cPEjFXHpgQcSkAFoEPxJrnGJWJL1oq5yc15Y24tal9n2fqMps1TPr8lYsyNvWfIw1m6fVWdrl1N697UjlQAmoob0wXg6dOnjbAvIvDk70+zuASCNhebpTdXEYCSoei39K1oxVKCJk8jRf/ezsClfSqBREFAUkBKtAH5gik/u3btomLFilFSwYW/JgO29/0de3dwsUuE532jPY3Yu2Ov0WdCtB/T+CQeqgS79/b2ThTcXXUQsiPXMs1qlgc2ZiKDKLxsPM2bmzubmzctcadPnIBvvok+KYm5I3JMbyoAHcM1plZNF4DmTs+xvbmKADx1Ck4UqEMdNsB330Hbto4Fo60rASVgJaCZQFz/YVi4EL6ZepOCFdIzY5a7cW/PGZbUo8GoADT3qTJdAEqIF1vt5ZdftrWoU8q5igCUi8WeFUpTmgPw++/w4otO4aWdKoHkSEAFYHJcdZ1zfAioAIwPtfjXMV0AhufdjW3IegcwNkK2v79+PRSvm4McXIpIQWR7dS2pBJSAHQRUANoBT6smKwIqAM1dbtMFoLnTc2xvrrIDuHxZGC+3TIEXIXDmTJINIeDY1dbWkwoBdQJJKiup80hqBFQAmruiKgDt4O0qAvCb6bfp1CeDZaYSeMrHx45Za1Ul4NoEVAC69vrp6JMuARWA5q6t0wWgfBhv3LjRSAMXFBQUZfa9eydub1VXEYBzBh3jzUmFeeCZGu/gxJ1f2dzHX3tLjgRUACbHVdc5uwIBFYDmrpJTBaCfnx+NGjXi/v37yIdyxowZuXbtGqlSpTLiBJ4Qn/dEbK4iAGd12Mbb31bjRtp8ZLxzKhET1aEpAccTUAHoeMbagxKIDwEVgPGhFv86ThWAtWrVokiRInzxxRekT5+evXv34uXlRfv27enTpw+vvfZa/GdmQk1XEYDT6/9M77VNuZCrEjnP7TCBjHahBBIvARWAiXdtdGTJm4AKQHPX36kCMEOGDGzfvp2iRYsif9+6dSvFixc3XuvUqROHDx82l0Yce3MZAVhhHr39unCmRAPyHlwdx1lqcSWQtAioAExa66mzSToEVACau5ZOFYBZsmRhy5YtFC5c2NgJnDFjBi+99JIh/CRKvnxQJ2ZzFQH4+TOT6HlyEKdqtif/XwsSM1IdmxJwOAEVgA5HrB0ogXgRUAEYL2zxruRUAVi/fn06d+5Mu3bt6N69O/v27UMcPxYsWMDNmzeNncDEbK4iAL/KPJhu1ydypnlf8i6bkpiR6tiUgMMJSCq45s2bG2ngHj58yJ49eyhXrpyRCk7ykkd+TQZj7/t+B/y41uOadV51ttZh/+79Rp8J0X5M45Mv0T/88IOmgnP4E6UdJBQBFYAJRdK2dpwqAHfu3Mndu3epXbs2V65coWPHjtYdwa+//pqyZcvaNgsnlXIVAfh9qq60DZjLhXfGknPG+06ipd0qgeRJQANBJ89111nHnYAKwLgzs6eGUwWgPQNPDHVdQQBK7shfPF6mSdhPXB83m0xD3kwM6HQMSiDZEFABmGyWWidqJwEVgHYCjGN1FYBxBBa5uCsIwLt34UC6alRjGw++W4F321ftmLFWVQJKIK4EVADGlZiWT64EVACau/JOFYDXr1/ngw8+YMOGDcYRsNy9iWw3btwwl0Yce3MFAXjqFAQXKExhjhG28S/cnq8Zx1lqcSWQtAiIE4jEGZX7fmIBAQH4+Pgg+ccffy1B3g8JgGERDL2nePPgzgOjzwRpP4bxSzxV+VxNnTp10lpAnU2SJaAC0NyldaoAlCDQx44do1u3bmTLls34AI5sEgomMZsrCMDdu6FARV98uQX//gvFiiVmpDo2JeBwAuoF7HDE2oESiBcBFYDxwhbvSk4VgGnTpmXTpk2J3tkjJrqJQQCuXAmHDsGgQeDp+eRI/1gdTL1GKSxvXL0KmTPH+2HRikogKRBQAZgUVlHnkBQJqAA0d1WdKgArV65sxP579tlnE2TWs2bNYtKkSVy6dMkQldJ2lSpVYm170aJFtG3blmbNmrFSFJWN5mwB+Ntv0LChZbBffw1dujw58JVfXOKVnjl4iBvuIcHg4WHj7LSYEkiaBFQAJs111Vm5PgEVgOauoVMF4I4dOxgyZIhxD7BUqVJGGrjIli5dOptpLF682AgjI2nlqlatytSpU1m6dClHjhwx7vvEZKdOnaJGjRo888wzRi5iVxKAtWvDxj8f4kUwLzVNyapVT85y/sD9dJxchjspM5PuwVWbeWpBJZBUCagATKorq/NydQIqAM1dQacKwKNHjxpBoHfLRbVIJhex5T6gBGq11UT0yY7izJkzjSriUJInTx7effddQ2RGZ9L+888/T9euXfn777+5deuWywjA27ctp7n/C+lMK5bwnNdONt0oQZo0UWf6WcsN9FpWh8sZi5Ht+r+24tRySiDJElABmGSXVifm4gRUAJq7gE4VgHI86+npSZ8+faJ1AnnhhRdsohEUFIR4vC1btoxXXnnFWkecSETU/fjjj9G2M3LkSCP7iETLl4wksQnAwMBA5Cfc5AhYRObt27eJy26lTZOKpdDy5TCixSEOUdIoOYN3KLluBnXqRK34ybNL6b+9FecL1iTXsb8SomttQwm4NAEVgC69fDr4JExABaC5i+tUASiizc/Pj6JFi9o16wsXLpArVy4ji0i1atWsbQ0aNIiNGzdGm1JOnE/atGljpHnKnDmzTQJw1KhRjB49+omxOkMAvvEG5P9qOMMZa4znCEXY+MVh3uzhhmjUli3hyhWoun0a03iP89VakGvLUrs4a2UlkBQISNiXhg0bWlPBHTx4kJIlS0ZJBRf+mszX3vcPHDnAnXfvWNFVXVeVw/sOG30mRPsxja9MmTKsWbPGGm4mKaydziFpE1ABaO76OlUAyvGr3P+rV6+eXbOOqwCU9HPy4fjZZ58Z/xGIudIOoIQvy50rjA0Xi1KEo1Z2k7r+y8CvirF+PdSta3l5Cn3py1Sudx1Ipq8m2sVZKysBJRB3AhoIOu7MtEbyJKAC0Nx1d6oAFCcN2VUbOHAgpUuXfsIJRESaLRbXI2DZ9StfvrzxjT/cwoNQu7u7G44jBQsWjLVrZ3kB790Lr5c7wAFKE5YyJdeyFCfLuT1MrTCf93Z1YOpUONj3f0xmAOmx7DyEzZyF29u9Yp2TFlACSiBhCagATFie2lrSJaAC0Ny1daoAFLH1uIVH44+PE4jcKZTQL2Ii6PLmzcs777zzhBPIgwcPjADUkW348OHIzuC0adMoUqQIKVI8ip33lPVwhgCUZCkFCkCLM5/wCQOgQQNOexUi308zmZepP52vTWZA67NMXJIPdyyZDgz7+Wdo3Njcp0t7UwJKABWA+hAoAdsIqAC0jVNClXKqADx9+vRT55EvXz6b5ylhYMTpY/bs2UbsPwkDs2TJEg4fPmw4mEiIGLknOG7cuGjbtOUI+PGKZgrAn34Ccfw4dw7WrYPVNKABa+CTT7gSkJasw99krXt96gavYWSJpXx0pFXU4V6+DE8Jh2MzaC2oBFycgDiB5M+f30j7Jj83b97E19fXmoko8msyVXvfv+F/A96PgOb7mS+3rt4y+kyI9mMan4S1ks9YTQXn4g9sMhq+CkBzF9tpAjA4OJgePXowYsQICsiWVgKYhIAJDwRdrlw5pk+fbsQEFKtVq5bxoT9v3jyXE4APHkCOHHDrlmXo3gRwyyMTKUMDYN8+Qq7exLPuC5ygACnOnmBp0WH0vf8x/uWqk+bkAejYEaZPTwDC2oQScH0C6gXs+muoM0iaBFQAmruuThOAMs306dMbXrgJJQDNRQdm7QDOnw+SFjl7dmjXDhpdnkvdhV0hb144dQpkdy9HDkJxZ82K+wS+1oZXWcmDidPxHviu2Vi0PyWQqAmoAEzUy6ODS8YEVACau/hOFYByZCs7dX379jV31gnUm1kCsHJl2LkT5PTaiGndpAn88guMGQPDhkFYGPe90pEq1J+P2x+i8bdtKMs++PXXiFxxCTRnbUYJuDoBFYCuvoI6/qRKQAWguSvrVAE4ZswYPvnkE+rWrUvFihWfuKvSu3dvc2nEsTczBODZs5aNPvGXuXgRsvrctZwH37sHfn5Qrpwx6tOZK5Dvuh+9cv3IhPOvkxZ/OHwY7IyxGEckWlwJJHoCKgAT/RLpAJMpARWA5i68UwXg045+xQv4xIkT5tKIY29mCMCFC6F9e5CrjNtm7YLu3S3Cr1AhOHLEogwlWG3pNpQ8sJjxDGYIEywzCQgAb+84zkqLK4GkTUAFYNJeX52d6xJQAWju2jlVAJo71YTvzQwB+N57MG0azHr1D3r9+JLEt8FI+LtiBbz4onVSh5qPoMSKMRyiOCX4l9sZ85P++smEn7S2qARcnIAKQBdfQB1+kiWgAtDcpU00AlBCL4jJzp+rmBkCsGZNOLHpPEcyVyfNtdNQvz7MnQs5c0bBdHHifHIM7mR97VrVRmTe9ouroNRxKgHTCEgqOMlCJLFC5ee///4zYn+GxyWN/JoMyt73j5w8QkDfAOv8Sv9UmuP/Hjf6TIj2YxpfsWLFkJSXPj4+prHVjpSAPQRUANpDL+51nS4A58+fb4RuOXrUktJMPhQlM0iHDh3iPhuTazhaAIomllBhC243pSk/W459d+yADBmemGno5m141IjIgxzwzgB8ZkwymYh2pwSUwOMENBC0PhNKwDYCKgBt45RQpZwqAKdMmWLEAZRsHdWrVzfmJN9YZ82ahTiIJHbvYEcLQHEAKZg3iFtkIBUBsHs3lC8f/dpfvw6ZM0e8J7uEnTsn1HOi7SgBJRBPAioA4wlOqyU7AioAzV1ypwpAcQIZPXq0kaUjsn3zzTdGjuCTJxP3HTZHC0CJ4vJh421soxpkygRXr8oZeYxPyMNs2XG/ctny/vbtUKWKuU+T9qYElMATBFQA6kOhBGwjoALQNk4JVcqpAtDb25sDBw5QSI42I5kcB5cuXRrJ2ZuYzdECcMIEuDhkKlPpC02bwqpVT8dRrRps22Ypc+cOpE2bmPHp2JSAUwjcv3+fEiVKGPf/5O7xxYsXyZEjh3EHMDw9XPhrMkB7379w7QIPhzy0zjXnvJxcPnvZ6DMh2o9pfJL68t9//yVVqlRO4aydKoG4ElABGFdi9pV3qgAsVaoU7dq14/33IyXKROIbj0Fy++7fv9++2Tm4tqMFoGT9ePn7NrRhMYwdC49xemJ6X38N3brBgAEwSe//OXj5tXkXJaBewC66cDrsJE9ABaC5S+xUAbh8+XJat25NvXr1rHcAN2/ezLp161iyZAmvvvqquTTi2JsjBaCE8CtZEtafzE9+TsO6dVCnztNHKF4j585B7txPPSqO4zS1uBJIUgRUACap5dTJJCECKgDNXUynCkCZ6q5du/j000+Nowqx4sWL079/f8rH5OxgLp+n9uYoASinuHKam52LXCQnYW5uuN2+rUe6iWjtdSiuS0AFoOuunY48aRNQAWju+jpdAJo73f+3dy7QNlX7H/+Vrk5x5dD1fiWkcChvCukhri4N3W5RSA8lSim5yjPvhI5HHhU1isIN6aUoejCUkXfOKfKq46A8Qjhe//H9+a/dPvvsc/beZ+2911pzf+cYhnPOXmuu+fv85lr7u35zzt+M7tViJQAx9Dtnjkg7WSgL5Q6RWrVENmyIbuNZGwkkKAEKwAR1PM12PQEKwPi6iALQBu9YCcBTp0RmvJApRV8bKx0zXjq//dv06TZaylNJgAQsAhSA7Ask4E4CFIDx9YsjAhCr7ULt+IHPT58+HV8aEV4tVgJQxo8XGTBA5Nix8y167TWRbt0ibB0PJwESCEaAApD9ggTcSYACML5+cUQALlq0KFcrV61aJampqZqiIWHTwEyYIPLkk38x2rTp/IoQFhIgAdsEkAamfv36vjQwO3bskEqVKmVLA2P9DRez+/n2X7dL1tNZvnZXnVtVdm3bpdeMRv25ta9y5cqyZs0apoGx3WNYQbwIUADGi/T56zgiAIOZmJ6eLv369ZPFixdLp06dZOjQoVKxYsX40ojwajGLAGZliTRvfj6nH3IkpqWJFCgQYet4OAmQgBsIMBG0G7zANniBAAVgfL3kuADMyMiQQYMGCXb/aNWqlYwcOVKQH9ALJWYC0DIee8FhI3f/Ld68AIZtJAES8BGgAGRnIIHwCFAAhscpWkc5JgAPHz4sI0aMkIkTJ0qdOnVk9OjRcsMNN0TLrrjUE3MBGBcreBESIIFYEqAAjCVd1m0SAQrA+HrTEQE4ZswYFXylSpVSEdiuXbv4Wh2lq1EARgkkqyGBOBLgHMA4wualSCACAhSAEcCKwqGOCECsAr7kkkt0B5ACecxte++996JgYuyqoACMHVvWTAKxIsBVwLEiy3pJwB4BCkB7/CI92xEB2LVr15BpYGDIzJkzI7UnrsdTAMYVNy9GAlEhQAEYFYyshASiToACMOpI86zQEQEYKxMnT54sL774omRmZkrt2rV1fmGDBg2CXm7GjBny5ptvyiakWBGRunXr6nB0bscHq4QCMFaeZL0kEDsCFICxY8uaScAOAQpAO/QiP9cYAfjuu+9K586dZerUqdKwYUOZMGGCzJs3T5BepkSJEjnIINVM06ZNpUmTJpKUlKRzEhcsWCCbN2+WsmXLhkWSAjAsTDyIBFxFgALQVe5gY0jAR4ACML6dwRgBCNGH5K6TJk1SgkgkXb58eenVq5fmFwxVzpw5I8nJyXo+hGQ4hQIwHEo8hgTcRYAC0F3+YGtIwCJAARjfvmCEAMzKytJs9/Pnz5f27dv7CHbp0kUOHTokee08Yh185MgRjRQiati2bduwvEABGBYmHkQCriJAAegqd7AxJMAIoEN9wAgBiGTSGLZduXKlNG7c2Ieyb9++smLFClm9enVIvD169JAlS5boEDCGhIOVkydPCv5ZBQIQUUbkNCxSpEjIa/AAEiAB5wkgDcw111zj2wpuz549Urp06WxbwVl/Q2vtfp7xW4ac7XfWZ3iZWWVk7+69es1o1J9b+/BM3LJlC7eCc77LsQVhEmAEMExQUTqMAlBERo0aJchNuHz5cklJSckV7eDBg2XIkCE5PqcAjFJvZDUkYCABJoI20Kk0KSYEKABjgjXXSo0QgHaGgMeOHSvDhg2TpUuXSr169fKkzwhgfDsnr0YCJhCgADTBi7QhHgQoAONB+a9rGCEAYQ4WgSCFC1K/oGARSIUKFaRnz565LgJB1G/48OE69NuoUaOIyXMOYMTIeAIJJBwBCsCEczkNzicBCsB8gsvnacYIQKSBwaKPadOmqRBEGpi5c+dKWlqalCxZUlf2Yk7MyJEjFRXSvgwcOFBmz56t6WCsUrhwYcG/cAoFYDiUeAwJuIvA8ePHpVmzZvqSiH8//vijVKtWTecAWi+P1t/wu93P07eny/Enj/sg1FpcS7Zt2abXjEb9ubWvevXq8vXXX+uuSywk4AUCFIDx9ZIxAhDYkMLFSgRdp04dSU1N1cggSosWLaRSpUoya9Ys/R0/79y5MwftQYMGCeb6hVMoAMOhxGNIwF0EuArYXf5ga0jAIkABGN++YJQAjC86EQrAeBPn9UjAPgEKQPsMWQMJxIIABWAsqOZeJwWgDd4UgDbg8VQScIgABaBD4HlZEghBgAIwvl2EAtAGbwpAG/B4Kgk4RIAC0CHwvCwJUAC6qg9QANpwBwWgDXg8lQQcIkAB6BB4XpYEKABd1QcoAG24gwLQBjyeSgIOEaAAdAg8L0sCFICu6gMUgDbcQQFoAx5PJQGHCEAAIgvAuXPn9N/BgwclOTlZLrjgAm2R/9/wu93PDxw9INL/L2OTpyTLof2H9JrRqD+39hUrVkwzHRQqVMgh0rwsCURGgHMAI+Nl92gKQBsEKQBtwOOpJJAgBJgIOkEcTTNtE6AAtI0wogooACPClf1gCkAb8HgqCSQIAQrABHE0zbRNgALQNsKIKqAAjAgXBaANXDyVBBKSAAVgQrqdRueDAAVgPqDZOIUC0AY8RgBtwOOpJOAQAWwF17p1azlz5oxuBbd582apUaOGFChQQOf/+f8NTbT7+ab0TfJHrz981jZc1lDSNqTpNaNRf27tS0lJ0X3OuRWcQx2Nl42YAAVgxMhsnUABaAMfBaANeDyVBBwiwFXADoHnZUkgBAEKwPh2EQpAG7wpAG3A46kk4BABCkCHwPOyJEAB6Ko+QAFowx0UgDbg8VQScIgABaBD4HlZEqAAdFUfoAC04Q4KQBvweCoJOESAAtAh8LwsCVAAuqoPUADacAcFoA14PJUEHCJAAegQeF6WBCgAXdUHKABtuIMC0AY8nkoCDhGgAHQIPC9LAhSAruoDFIA23EEBaAMeTyUBhwhAAJYoUUJTvqAgLQxSpWAruMC/ReXz08dFnvvL2KRxSXLijxO+9Cyxuv6ll14q+/bt41ZwDvUzXjZyAlwFHDkzO2dQANqgRwFoAx5PJYEEIcBE0AniaJppmwAFoG2EEVVAARgRruwHUwDagMdTSSBBCFAAJoijaaZtAhSAthFGVAEFYES4KABt4OKpJJCQBCgAE9LtNDofBCgA8wHNxikUgDbgMQJoAx5PJQGHCJw4cUI6dOjg2wpu3bp1UqdOnWxbwVl/QxPtfr5201r5rftvPmtbrmopG7/fqNeMRv25ta9u3bqyYMECSUpKcog0L0sCkRGgAIyMl92jKQBtEKQAtAGPp5KAQwS4Ctgh8LwsCYQgQAEY3y5CAWiDNwWgDXg8lQQcIkAB6BB4XpYEKABd1QcoAG24gwLQBjyeSgIOEaAAdAg8L0sCFICu6gNGCcDJkyfLiy++KJmZmVK7dm2ZOHGiNGjQIFfg8+bNkwEDBsiOHTukatWqMnr0aGnTpk3YDqIADBsVDyQB1xCgAHSNK9gQEshGgEPA8e0QxgjAd999Vzp37ixTp06Vhg0byoQJEwQCLz09XZO+BpaVK1dKs2bNZOTIkdK2bVuZPXu2CsDvv/9eatasGZYXKADDwsSDSMBVBCgAXeUONoYEfAQoAOPbGYwRgBB99evXl0mTJinBs2fPSvny5aVXr17Sr1+/HFT/85//CL4IPvjgA99njRo10pV5EJHhFArAcCjxGBJwFwEKQHf5g60hAYsABWB8+4IRAjArK0uw7dH8+fOlffv2PoJdunSRQ4cOyaJFi3JQrVChgjz11FPSu3dv32eDBg2ShQsXyvr164N64eTJk4J/Vjl8+LCgnt27d0uRIkXi6zlejQRIIF8EIADLlCmTr3PzddJFIvK035ljReR0vmqK+KSMjAxuBRcxNZ7gFAEIwDIvnb83t/baKlUmVtGfM/pkSKGChaLaLARwECSCRrjsssuiWrdXKjNCAOIhV7ZsWcGwbuPGjX3s+/btKytWrJDVq1fn8EfBggXljTfekHvuucf32ZQpU2TIkCGyd+/eoP4bPHiwfs5CAiRAAiRAAiTgfQII4JQrV877rJySMgAAFJZJREFUhuTDAgrACARgYAQQw8wHDhyQ4sWL60byXi7W25DJ0Uza6OUe+lfb6Ucz/Agr6EszfOlFP547d06OHDmiowEXXnihGY6I0AojBGC8hoAjZOupwxNhPiNt9FSXzLWx9KMZfrQEIIbfMJ3G1Gk07K/m9FfTLDFCAMIpWASClC9I/YKC6Bzm5/Xs2TPXRSB//vmnLF682OfTJk2aSEpKStiLQEzqDHxImeFN+pF+9BIB9lcveSv3tiaCH83wVHYrjBGASAODRR/Tpk1TIYg0MHPnzpW0tDQpWbKkpojBPEGkfUHBfMHmzZvLqFGj5J///Ke88847MmLEiIjSwJjUIRLhBqaNZvRY+tEMPzICSD+aQ8CblhgjAIEfKWCsRNBI55KamqqRQZQWLVpIpUqVZNasWT5PIU/g888/70sEPWbMmIgSQXvT5cFbjfmNEMf//e9/5eKLLzbJNJ8ttNEMt9KPZvgRVtCXZvgyEfxohqcMjQCa6BzaRAIkQAIkQAIkQAKxIGBUBDAWgFgnCZAACZAACZAACZhGgALQNI/SHhIgARIgARIgARIIQYACkF2EBEiABEiABEiABBKMAAVggjmc5pIACZAACZAACZAABaChfQDpbbCi94knntCUOCgnTpyQPn36aMobrNpq1aqVYPs7pMmxyq5du+TRRx+VL774QgoXLqypdbA6+KKLsKHp+bJ8+XLdR3nz5s26lyJWUnft2jXuJIPZiNXe2P7Pv3Tv3j1bbke32xhsy8GrrrpKUxqZ4sdQNprgR/jq119/lWeffVY+/vhjQd7RKlWqyMyZM6VevXrqS+xGgD3IZ8yYoXuSNm3aVF555RWpWrWqrwtjt6FevXppzlLsWNChQwd5+eWX9f60yoYNG+Sxxx6T7777Tv7xj3/o8dgKMx4llI14NmDbTf+CZ88nn3ziCRuRPWLnzp05UPbo0UMmT55sxHM1lI2m3I/xuB+8dA0KQC95K8y24kvgrrvu0sz6N954o08AQth9+OGHmgoH2feRJBtfKN98843WfObMGUH6nFKlSmk6nT179mj+xIceekhzJKJs375datasKY888og8+OCDsmzZMundu7fWi4d6vEpuNuJBVa1aNRk6dKivKZdeeqlvlwEv2AhxNH/+fFm6dKnPBgjwyy+/XH83wY+hbDTBjwcPHpRrr71W70H4DMLsp59+kiuvvFL/oYwePVpfsCCQrrjiChkwYIBs3LhRfvjhB0lKStJjWrdurfcicpyeOnVK7r//fqlfv77Mnj1bP0deRPT5m2++WV/6cH63bt30vn/44YdjekuGYyMEIPZXh/C1ClJNJScn+353s4379+/XZ6NVNm3aJLfccou+JKOfmnA/hrLRhPsxpjeCRyunAPSo43Jr9tGjR+W6667TyN6wYcNU0OGLAFst4QsIXxp33nmnno6I0tVXXy2rVq2SRo0aaZSibdu2kpGR4YsKTp06VSMYeEAULFhQf4bYw0PQKnfffbdGL/zf6GOJNTcbcU08qCybg7XBCzZCHC1cuFDWrVuXwwRT/JiXjab4sV+/fvpy9dVXXwW9HRD9wz6kiMo//fTTegz8i4g8XtJwX23ZskWuueYajexZUUPcZ23atJFffvlFz0fE8LnnnpPMzEy9R1FwbfQhK2ocq/sxlI24LgQgng9oT7DidhsD24wX3g8++EDFPMS3Kc9Vfzv9bcQ+9yY8V2N1D3i5XgpAL3svSNsxZFusWDEZP358tpv2888/l5tuuknwxl60aFHfmRUrVtQI3pNPPikDBw6U999/P5vwQMSvcuXKukMKohnNmjVTgWkNK6MivNmjDnx5xaPkZqMlHDA0jS9XRDJvv/12jaogCojiBRshjhCBRZQWUaDGjRtrlAhbG5rix7xsNMWPEG6IikOoYVoCdiLCsCEi6ig///yzRgLXrl2rLy1WwQ5F+B3DvK+//roKRNy3Vjl9+rT2CySyv+OOOzRKDyHiL7AQnWrZsqVg+Ng/0hbt+zOUjZYARNsgTtEWtAsvp8WLF9fmuN1Gf2bYdx6iG1Ng+vfvb8z9mJeNptyP0e77JtRHAWiCF//fBsztGz58uEYL8AXh/9aGyB+GjjD3z79g2zwMUWEoCsNFmOuyZMkS3yGYt1SoUCH56KOPdCgKQ02oB0NNVsFn2E4Px15yySUxJZqXjbjw9OnTBaIWD2nMi0LEEja+99572i4v2IgoJaKcmPeHob8hQ4boXDJEXTEPzAQ/5mXj3//+dyP8aA3hQiz8+9//1vsSc3IRVcdLDLajxJw/RNxLly7tu28wfQNRF2xviakXGB5OT0/Pdl+VKFFC+wWGH2+99VYdPsYQsVUwhFyjRg0dSkaUP1YllI24Lu5ZvIChjdu2bVPhhPmLGHkoUKCA6230Z4ftRTt27CiYR4xnjCnP1bxsNOW5Gqt7wMv1UgB62Xt+bd+9e7cOEX322WeSkpKin5gmAEPZGMyVVsRs69atGm3xggAMtAPDZxC148aNU4FtggDMy8YHHngghyu96EdEvHBPQuhZ5fHHH1chCPFjggAMZWOwe9KKfGKOK0Yl3C5y/W1ARBc240UMxUQBGGijqc9VQ776bZlBAWgLn3tOxhALhoPwRm0VTFxGJAELPRDVwyRxLw8Bh7IR0U1/+8Hh2LFjGm3AvCk82LwwBBysV2HSP/yHyeemDOUH2mnZiOHuwOJFP0K0w1+vvvqqzxzM18PwJyK6JgwBh7Ixtyck5s2BA1boe2UIGKMjmA6D0YR27dqpaaZMybD8FMzGYD704v3onm9r97SEAtA9vrDVkiNHjuRIVYBIUfXq1XUYFOla8NCdM2eOppFAwbASPg9cBIJhRwwxoWBI9ZlnnpF9+/YJVu6hLgz5YqWhVTAkgrlGsV4EEspGrE4OLJiEf/3118v69es1MmotAnGrjcE6AYaDMf8P8+YwdOh1P4ayEVEyE/yI+wJRa/9FIJhru3r1ao3+WYtAsAAE8/xQMJcP917gIpA1a9ZI3bp19ZhPP/1UbrvtthyLQLDS9m9/+5seg2FWCJVYLwIJZWMwX2NOJPozXuj+9a9/+Ra6uNVGywbcfxhmh0+ttFjWoiwvP1f9fRTMxmA+NOG5ausL15CTKQANcWQwMwJXbmG+EMQbvlyQIga5wlCsISorRQrmtowZM0ZXFd53332a7iUwDQxyjiHVBN6A8YUd7zQwlr3+NmJ+EYZksEISE8wxBxBfuOXKlfPlBvSCjRAEWLyC6ArmhyFPHFYEYz4XxJ8JfszLRoggE/yIod4mTZroXD3M6/v22291AQheqjp16qRdGHNvkc/SPw0M+m1gGhiIO8wdtNLAYGjZSgMDEYL5opgLiBc0zBXFvYmFYLFOAxPKRry8wH68dGJRFu5R5CfEyxxeIvFSiYL5xW61Ee07e/aszmG855571F/+xYT7MS8bTXmuGvxVn2/TKADzjc79JwYKQCsRNN5W/RNB48FsFQwB4IGGZM9Y/IGIEx54gYmgIazwJQVxhVW2TiSCRpv9bcSb+b333qtfgBiiQNQTw+JIVA3B6xUbkf7jyy+/lN9//10FHyKYWNxj5Y4zwY952WiKH9HfkC4EC6aQMgQCAgtCrFXA+NxKBA1RiLme8DVSOGGxlVUQXUfOTv9E0Kmpqbkmgka+SLzcQQzGo+Rl4/Hjx6V9+/a60hn24eUSQvWFF17IloDe7TYi6oopJBg18fcN+JpwP8KO3Gw06X6Mx/3gpWtQAHrJW2wrCZAACZAACZAACUSBAAVgFCCyChIgARIgARIgARLwEgEKQC95i20lARIgARIgARIggSgQoACMAkRWQQIkQAIkQAIkQAJeIkAB6CVvsa0kQAIkQAIkQAIkEAUCFIBRgMgqSIAESIAESIAESMBLBCgAveQttpUESIAESIAESIAEokCAAjAKEFkFCZAACZAACZAACXiJAAWgl7zFtpIACZAACZAACZBAFAhQAEYBIqsgARIgARIgARIgAS8RoAD0krfYVhIgARLIgwC2ZcM2cNhC8O2335aqVauSFwmQAAkEJUAByI5BAiRAAoYQuOqqq2Ty5MmyefNmWbVqlbzzzjuGWEYzSIAEok2AAjDaRFkfCZCA6wi0aNFC6tSpIxMmTHBF2/Lbnt9//12uvvpq+fbbb6VSpUo5bGnSpInMnj1b1q9fL//73//kzTff1GPuvvtuqV+/vvTp08cV9rMRJEACzhOgAHTeB2wBCRhBYOrUqfLMM8/IwYMH5aKLLlKbjh49KsnJydK0aVNZvny5z078fOONN8rWrVvlyiuvjLn9+RVcdhuW23Xz256nnnpKjhw5IjNmzAjaNPz9kUcekZIlS8qmTZukWLFiehx+btasmWzfvl0uu+wyu2bxfBIgAQMIUAAa4ESaQAJuIJCeni7Vq1fXocdGjRppkz7++GN5+OGH5bffflNhmJSUpH8fNGiQzJo1S3bu3BmXpudXcNltXDQF4J9//imlS5eWJUuW+Pj6t+/06dMa5bz99tt1GPjw4cNywQUX+A5BBLBr167y2GOP2TWL55MACRhAgALQACfSBBJwC4EyZcrI448/Lv369dMmPfvss3Ls2DH5/PPPZcqUKQJBhNK8eXO54oorVAR+8sknMmzYMI1SFShQQBo3biwvv/yyLzI4ffp0GTx4sPzyyy9y4YUX+kxt166dFC9eXF5//XU5e/asjB49WnBsZmamVKtWTQYMGCB33nmnHh8oxEIdb52TkpKiovXVV1+VggULanQNbbEKonH428KFC6VIkSLSt29fWbRokQqxQ4cOyRtvvJHNNYjAYegW7QlVd6BP58+fLz169JB9+/YFdfekSZPk008/Vc7ly5fPEV0dOnSofPbZZ/LVV1+5pbuwHSRAAg4SoAB0ED4vTQKmEejUqZNG+xClQmnQoIGKomXLlkmJEiVkyJAhcvz4cR0WnjZtmnTp0kXnqiFSBUGEIeOBAwfKjh07ZN26dSr4EDksVaqUfPTRR3LTTTdpvQcOHNBomPW34cOHy1tvvaVz/LDy9csvv1RhhnZAbAYKwFDHWwJw7dq1gmHXjh07amQTETTUecstt2g7HnroIRVVr732mg67ou1Lly6Vbt26qa2tW7eWmjVrCsQXClbnQuSiPaHqDuwbTzzxhPz4448aVQ0s4IG5gRhax/9FixbVNnXo0MF3KIQ2RPMff/whF198sWldj/aQAAlESIACMEJgPJwESCB3AoiU9e7dW6NfEHqYg5aRkaGiCHMEV6xYodFACDkM/1aoUCFHZRCQEEobN25U8YTSvn17jfZB1KAg0geBtXv3bjl16pReB9dA9NAqDz74oGDYFIsi/AXgyZMnQx5vCcAzZ85ki5hB0LZs2VJGjRqlc/HQJtRvRRox7IooKIQhxGheQ8B51R2McCAD/2OQ+gUcwBgFi0Fuvvlmn/DE3zZs2CC1a9dWcV2xYkV2YxIggQQnQAGY4B2A5pNANAlgUQcicCtXrtTIHRaFICUJRGDlypVVGI4YMUJz1G3btk0v/dNPP2nkbPXq1Ro9xPAsho0//PBDadOmjR4zb948FVV79+7V6BWievXq1ZOXXnpJ64dQLFSoUDZTsrKy5Nprr9V6/YVYOMdbArBGjRo6n84q/sPOWGmLod5AIXvdddfpgotQAjCvuoP5pFWrVlKlSpVs7cFxP/zwgy6y2bJli0ZKUTDvEkPh77//vq8qcMbQOI5HlJCFBEggsQlQACa2/2k9CUSdAOafPfrooyoAIeQwJw0FwhDDvlgAgsUi1kpW/IyIFIaKET2DAISgW7BggUb+UE6cOKFDrDNnztR0Jjh+zZo1ArEFgYdFJxj+LFu2bDZ7IBbRHn8BGM7xlgAMTB2D9mB4FXMX7QrAvOoO5hQMr587d04jjv4FwhBz/zC0bBUwhN3+i2wsu/fv3y+XX3551P3OCkmABLxFgALQW/5ia0nA9QQ6d+6s0ScrAnjXXXdpmx944AEdek1NTVUhh3l1yGsHMYI5ezfccIMe9/XXX+vP/gIQf7///vt1/lrDhg31fES8UDAUiyFjCMr77rsvKB9/ARjO8eEIQGsIeM6cOb65dhgChgjF8DMigLfeeqsgOfPEiROztSvY0LC/uAxmxNixY3WeI+ZGWgU7f2BeIoa/rdQ7+Oy7777TeYiYG4j5ligYPscCFgybs5AACZAABSD7AAmQQFQJQJwh1QjmpGHlLiJ3KEhK3LNnTxVsGBLGIg5EqrA4BIslEBnctWuXriCGgAkUgBA5bdu21VW09957rzz//PO+duNnzH/DkPD111+vKVC++eYbXZmLhSaBgivU8eEIQByDYWkscIG4gh2wAdE4iN3x48frUCwE29y5c6Vw4cIqgLGwJT8CEHMiEfHEKmCIOvBFpBRCD6ut/Qs4Ikr6xRdf+FZeQygiSmjNo4yq01kZCZCA5whQAHrOZWwwCbibABYZIMULhnatKB1ajOFIiDdExNLS0nxGQNghdczPP/+snyFCCIEUKAAhFsuVKyd79uzR+YOYU2gVDI3ivFdeeUXrwTAtxFL//v11Pl6g4Ap1fLgCMFgaGGy/hoUiI0eO1FW7EKAYLsaiGP80MJEOAaNNiH5C8HXv3l3GjRunqW9gb+D8R9gHwYnVzliUgyF0zA/ESmArR6O7exFbRwIkEGsCFICxJsz6SYAEEoYA5jxiCBiRSEQBo12wMAYLa5Az0T8nYqjrQBhDUCM6yUICJEACIEAByH5AAiRAAvkkgFx+iGYiPQyGnZHvD4tRsBo6VgstMLcQ+f2wyCPcgvQ8mFeJCCsLCZAACVAAsg+QAAmQgA0CEIBY8IFt8LBTSN26dXVotlatWjZq5akkQAIkEHsCjADGnjGvQAIkQAIkQAIkQAKuIkAB6Cp3sDEkQAIkQAIkQAIkEHsCFICxZ8wrkAAJkAAJkAAJkICrCFAAusodbAwJkAAJkAAJkAAJxJ4ABWDsGfMKJEACJEACJEACJOAqAhSArnIHG0MCJEACJEACJEACsSfwf6vesizz6WeaAAAAAElFTkSuQmCC" 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" width="640">
So this is perhaps overly zealous in removing the red and blue edges.
Now make the spectroscopic light curves by portioning the spectra into the above bins and making light curves for each bin. We also need to portion out the ancillary data (sky flux and x position) into the same bins for use in the fitting process later on.
```python
help(wb.wvl_bin_data_different_wvl_solutions)
```
Help on function wvl_bin_data_different_wvl_solutions in module reduction_utils.wavelength_binning:
wvl_bin_data_different_wvl_solutions(flux1, err1, flux2, err2, wvl_solutions, bins, xpos, sky, weighted=False, n_tukey_points=0)
A function to bin the spectra of the target and comparison to make spectroscopic light curves for each by summing the flux within the defined wavelength bins.
The target's light curves are divided by the comparison's light curves to correct for telluric extinction.
Inputs:
flux1 - ndarray of spectra of the target
err1 - ndarray of errors of the target
flux2 - ndarray of spectra of the comparison
err2 - ndarray of errors of the comparison
wvl_solutions - the wavelength solution which is used to bin the data for both star1 & star2, expects individual wvl solution for each frame.
My tests show that this provides much better light curves than using separate wavelength solutions
bins - the list of bin edges of the light curves, in Angstroms
xpos - the ndarray of the average (**non-standarized**) x positions of the target and comparison, so that they can be binned to the same wavelength bins
sky - the ndarray of the average (**non-standarized**) sky background of the target and comparison, so that they can be binned to the same wavelength bins
weighted - True/False - define whether we want to perform a weighted (by the flux errors) sum or not. Default=False as I have found this often produce better light curves (less noise)
n_tukey_points - If wanting to use Tukey bins, set this parameter to the number of points to fall within the Tukey smoothed edges per bin.
This downweights those points falling at the edges of bins. I often find this to lead to noisier light curves. Default=0 (no Tukey window is used)
Returns:
flux_ratio - the differential (target/comparison) spectroscopic light curves
err_ratio - the flux errors in the differential light curves
binned_flux1 - the target's spectroscopic light curves
binned_err1 - the flux errors in the target's spectroscopic light curves
binned_flux2 - the comparison's spectroscopic light curves
binned_err2 - the flux errors in the comparison's spectroscopic light curves
XPOS_norm - the **standarized**, combined, wavelength-binned x positions
SKY_norm - the **standarized**, combined, wavelength-binned sky background
photon_noise_star1 - the photon noise for each bin for the target
photon_noise_star2 - the photon noise for each bin for the comparison
```python
#for i in wvl_solution_2:
# i += -5
bin_fluxes,bin_errors,bin_fluxes_target,bin_errors_target,bin_fluxes_comp,bin_errors_comp,\
bin_xpos,bin_sky,photon_noise_star1,photon_noise_star2 = \
wb.wvl_bin_data_different_wvl_solutions(f1,e1,f2,e2,wvl_solution_2,bin_edges,xpos=xpos,sky=sky,n_tukey_points=0,weighted=False,)
```
```python
bin_fluxes,bin_errors,bin_fluxes_target,bin_errors_target,bin_fluxes_comp,bin_errors_comp,\
bin_xpos,bin_sky,photon_noise_star1,photon_noise_star2 = \
wb.wvl_bin_data_indivdual_wvl_solutions(f1,e1,f2,e2,wvl_solution_1,wvl_solution_2,bin_edges,xpos=xpos,sky=sky,n_tukey_points=0,weighted=False)
```
Now normalise the light curves to the out of transit data and plot:
```python
nf, ne = wb.normalise_flux(bin_fluxes,bin_errors,contact1,contact4)
wb.plot_all_bins(mjd,nf,ne)
#plt.savefig("lightcurves(wavelength).png")
#plt.plot(nf[0])
```
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```python
plt.close('all')
font = {'family' : 'serif',
'weight' : 'normal',
'size' : 28}
plt.rc('font', **font)
fig = plt.figure(figsize = (20,10))
average_flux_in = np.zeros(nbins)
flux_in_err = np.zeros(nbins)
average_flux_out = np.zeros(nbins)
flux_out_err = np.zeros(nbins)
for j in range(nbins):
out_flux = np.hstack((nf[j,:contact1],nf[j,contact4:]))
average_flux_out[j] = np.average(out_flux)
flux_out_err[j] = np.std(out_flux)/np.sqrt(len(out_flux))
in_flux = nf[j,contact1:contact4]
average_flux_in[j] = np.average(in_flux)
flux_in_err[j] = np.std(in_flux)/np.sqrt(len(in_flux))
#print(nf[j,contact1:contact4])
#print(len(nf))
plt.plot(bin_centres,average_flux_out/np.average(average_flux_out))
plt.plot(bin_centres,average_flux_in/np.average(average_flux_in),'k')
#print(average_flux_in)
plt.errorbar(bin_centres, average_flux_in/np.average(average_flux_in), yerr=flux_in_err, xerr=bin_widths/2,fmt='o',\
mfc='white',mec='k',ecolor='k',zorder=-1,capsize=2, label='In transit')
plt.errorbar(bin_centres, average_flux_out/np.average(average_flux_out), yerr=flux_out_err, xerr=bin_widths/2,fmt='o',\
mfc='white',mec='tab:blue',ecolor='tab:blue',zorder=-1,capsize=2, label='Out of transit')
plt.xlabel('Wavelength ($\AA$)')
plt.vlines(wb.sodium_centre, 0.998,1.002,'r','dashed')
plt.ylim(0.9984,1.0016)
plt.ylabel('Normalised Flux Averages')
plt.legend()
plt.savefig('Flux_ratio_vs_wave.pdf',bbox_inches='tight')
```
<IPython.core.display.Javascript object>
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" width="2000">
```python
plt.close('all')
font = {'family' : 'serif',
'weight' : 'normal',
'size' : 30}
plt.rc('font', **font)
fig = plt.figure(figsize = (20,10))
average_flux_in = np.zeros(nbins)
flux_in_err = np.zeros(nbins)
average_flux_out = np.zeros(nbins)
flux_out_err = np.zeros(nbins)
for j in range(nbins):
out_flux = np.hstack((nf[j,:contact1],nf[j,contact4:]))
average_flux_out[j] = np.average(out_flux)
flux_out_err[j] = np.std(out_flux)/np.sqrt(len(out_flux))
in_flux = nf[j,contact2:contact3]
average_flux_in[j] = np.average(in_flux)
flux_in_err[j] = np.std(in_flux)/np.sqrt(len(in_flux))
#print(nf[j,contact1:contact4])
#print(len(nf))
flux_in_err /= np.average(average_flux_in)
flux_out_err /= np.average(average_flux_out)
average_flux_out /= np.average(average_flux_out)
average_flux_in /= np.average(average_flux_in)
plt.plot(bin_centres,average_flux_out)
plt.plot(bin_centres,average_flux_in,'k')
#print(average_flux_in)
plt.errorbar(bin_centres, average_flux_in, yerr=flux_in_err, xerr=bin_widths/2,fmt='o',\
mfc='white',mec='k',ecolor='k',zorder=-1,capsize=2, label='In transit')
plt.errorbar(bin_centres, average_flux_out, yerr=flux_out_err, xerr=bin_widths/2,fmt='o',\
mfc='white',mec='tab:blue',ecolor='tab:blue',zorder=-1,capsize=2, label='Out of transit')
plt.xlabel('Wavelength ($\AA$)')
plt.vlines(wb.sodium_centre, 0.998,1.002,'r','dashed')
plt.ylim(0.9984,1.0016)
plt.ylabel('Normalised Flux Averages')
plt.legend()
plt.savefig('Flux_ratio_vs_wave_contact2_contact3.pdf',bbox_inches='tight')
pickle.dump(average_flux_in, open('/storage/astro2/phrgmk/Fitting Na feature/flux_in_average.pickle','wb'))
pickle.dump(flux_in_err, open('/storage/astro2/phrgmk/Fitting Na feature/flux_in_average_error.pickle','wb'))
pickle.dump(bin_centres, open('/storage/astro2/phrgmk/Fitting Na feature/bin_centres.pickle','wb'))
pickle.dump(bin_widths, open('/storage/astro2/phrgmk/Fitting Na feature/bin_widths.pickle','wb'))
plt.hlines(average_flux_in[18],5800,6100,'red')
average_out_transit = np.average(average_flux_out)
plt.hlines(average_out_transit,5800,6100)
signal_Na_diff = abs(average_flux_in[18]-average_out_transit)
#print(signal_Na_diff)
#print(flux_in_err[18])
#print(average_out_transit)
print(signal_Na_diff/flux_in_err[18])
```
<IPython.core.display.Javascript object>
<img 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" width="2000">
4.788534685944593
```python
plt.close('all')
font = {'family' : 'serif',
'weight' : 'normal',
'size' : 28}
plt.rc('font', **font)
fig = plt.figure(figsize = (20,10))
average_flux_target = np.zeros(nbins)
flux_target_err = np.zeros(nbins)
average_flux_comparison = np.zeros(nbins)
flux_comparison_err = np.zeros(nbins)
for j in range(nbins):
average_flux_target[j] = np.average(bin_fluxes_target[j,:])
flux_target_err[j] = np.std(bin_fluxes_target[j,:])/np.sqrt(len(bin_fluxes_target[j,:]))
average_flux_comparison[j] = np.average(bin_fluxes_comp[j,:])
flux_comparison_err[j] = np.std(bin_fluxes_comp[j,:])/np.sqrt(len(bin_fluxes_comp[j,:]))
flux_target_err /= np.max(average_flux_target)
flux_comparison_err /= np.max(average_flux_comparison)
average_flux_target /= np.max(average_flux_target)
average_flux_comparison /= np.max(average_flux_comparison)
plt.plot(bin_centres,average_flux_target, label='WASP-94A')
plt.plot(bin_centres,average_flux_comparison, 'k', label='WASP-94B')
plt.errorbar(bin_centres, average_flux_target, yerr=flux_target_err, xerr=bin_widths/2,fmt='o',\
mfc='white',mec='tab:blue',ecolor='tab:blue',zorder=-2,capsize=2)
plt.errorbar(bin_centres, average_flux_comparison, yerr=flux_comparison_err, xerr=bin_widths/2,fmt='o',\
mfc='white',mec='k',ecolor='k',zorder=-2,capsize=2)
plt.xlabel('Wavelength ($\AA$)')
plt.vlines(wb.sodium_centre, 0,1.1,'r','dashed')
plt.ylim(0.75,1.05)
plt.ylabel('Normalised Star Flux Average')
plt.legend()
plt.savefig('Flux_stars_vs_wave.pdf',bbox_inches='tight')
```
<IPython.core.display.Javascript object>
<img 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" width="2000">
So these are our spectroscopic (wavelength-binned) light curves going from blue (top) to red (bottom). Ultimately what we are trying to determine is whether the depth of the transit varies between these light curves.
If we're happy, save the output:
```python
import os
path = parent_direc + '/pickled_objects/wvl_bins'
try:
os.mkdir(path)
except OSError:
print("Creation of directory %s failed" %path)
else:
print("successfully created directory %s" %path)
```
Creation of directory /storage/astro2/phrgmk/Data/EFOSC/WASP-94A/reduction_26//pickled_objects/wvl_bins failed
```python
add_text = '_111_20A_1305'
pickle.dump(nf,open(parent_direc + '/pickled_objects/wvl_bins/fluxes_individual_wvl_solutions' + add_text + '.pickle','wb'))
pickle.dump(ne,open(parent_direc + '/pickled_objects/wvl_bins/errors_individual_wvl_solutions' + add_text + '.pickle','wb'))
pickle.dump(bin_centres,open(parent_direc + '/pickled_objects/wvl_bins/wvl_bin_centres_individual_wvl_solutions' + add_text + '.pickle','wb'))
pickle.dump(bin_widths,open(parent_direc + '/pickled_objects/wvl_bins/wvl_bin_full_widths_individual_wvl_solutions' + add_text + '.pickle','wb'))
print(bin_centres)
pickle.dump(bin_xpos,open(parent_direc + '/pickled_objects/wvl_bins/xpos_individual_wvl_solutions' + add_text + '.pickle','wb'))
pickle.dump(bin_sky,open(parent_direc + '/pickled_objects/wvl_bins/sky_individual_wvl_solutions' + add_text + '.pickle','wb'))
pickle.dump(photon_noise_star1,open(parent_direc + '/pickled_objects/wvl_bins/photon_noise_star1_individual_wvl_solutions' + add_text + '.pickle','wb'))
pickle.dump(photon_noise_star2,open(parent_direc + '/pickled_objects/wvl_bins/photon_noise_star2_individual_wvl_solutions' + add_text + '.pickle','wb'))
```
[4793. 4813. 4833. 4853. 4873. 4893. 4913. 4933. 4953. 4973. 4993. 5013.
5033. 5053. 5073. 5093. 5113. 5133. 5153. 5173. 5193. 5213. 5233. 5253.
5273. 5293. 5313. 5333. 5353. 5373. 5393. 5413. 5433. 5453. 5473. 5493.
5513. 5533. 5553. 5573. 5593. 5613. 5633. 5653. 5673. 5693. 5713. 5733.
5753. 5773. 5793. 5813. 5833. 5853. 5873. 5893. 5913. 5933. 5953. 5973.
5993. 6013. 6033. 6053. 6073. 6093. 6113. 6133. 6153. 6173. 6193. 6213.
6233. 6253. 6273. 6293. 6313. 6333. 6353. 6373. 6393. 6413. 6433. 6453.
6473. 6493. 6513. 6533. 6553. 6573. 6593. 6613. 6633. 6653. 6673. 6693.
6713. 6733. 6753. 6773. 6793. 6813. 6833. 6853. 6873. 6893. 6913. 6933.
6953. 6973. 6993.]
### Sodium bins
We also want to make narrower bins centred on the sodium and potassium lines, which are expected to be strong absorption lines in the atmospheres of the planets we consider.
We do this in 2 ways, firstly by making a handful of narrow bins of uniform width centred on each feature and covering ~200A. And secondly by having a single bin centred on each feature and making a single light curve. We then incrementally increase this bin width by 10A, until the bin centred on the feature is ~100A wide. I call this 'incrementally increasing bins'.
First start off with 5 bins of 30A width centred on sodium:
```python
no_bins_na = 5
bin_width_na = 15
na_centre = wb.sodium_centre
print(na_centre)
# Now make the wavelength bins for sodium
na_bins = np.arange(na_centre-(bin_width_na/2)*no_bins_na,na_centre+(bin_width_na/2)*(no_bins_na+1),bin_width_na)
na_bins_centres = np.array([(na_bins[i+1]+na_bins[i])/2 for i in range(no_bins_na)])
na_bins_widths = np.array([(na_bins[i+1]-na_bins[i]) for i in range(no_bins_na)])
# And plot these to make sure everything looks OK
wb.plot_spectra(f1[nframes//2],f2[nframes//2],wvl_solution_1[0],bin_edges=na_bins,alkali=True)
```
5893.0
<IPython.core.display.Javascript object>
<img 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" width="639.9999861283738">
Ok, these look to be well centred on sodium, now make the light curves as before:
(I'm neglecting the flux of the target and comparison from the returned variables, hence defining them with the underscore)
```python
na_bins_fluxes,na_bins_errors,_,_,_,_,na_bins_xpos,na_bins_sky,_,_ = \
wb.wvl_bin_data(f1,e1,f2,e2,wvl_solution_1,na_bins,n_tukey_points=0,xpos=xpos,sky=sky,weighted=False)
nf_na, ne_na = wb.normalise_flux(na_bins_fluxes,na_bins_errors,contact1,contact4)
wb.plot_all_bins(mjd,nf_na,ne_na)
```
<IPython.core.display.Javascript object>
<img 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ZUxl0qZEObsR2GiVWA9BPWrgvYXaBo+AQJAAAh0IVDnRV1j4FZ77u+JaB8ieqFiK5jG9DVHQ4LOq4zyisgDWfUWKTNHt3v+atPl1J4DUkOFIHA1gFp3kU1bGHwY3nHHHVG3jzrqKLIat6yNqyheoRtenvp9ZeYpw9mfCghM2TYU/T71tjFHv4rWX3R+4DsgAASAQJ7LrNAzpSyqoRqc0Pec7TFWD5yJdcn2TuBynFdpY5z7LAuotw5taan5U3KSN01OLdmdRn4OAtfIYUlvVJMWBm8Q8+fPj4KU8MN+bvzEPm8rV9LQtGk0QERDRDTw1FNEU6eWRp3rve688+ig4WHKMg/Jcyg7N+aAzTezQ4EmhGk3w4JzIV/CEn1IxS+wX4yPlMPBbDiwTb+C2mSOFV4AAkBgRCPgE8BDzooqhOKQevSeafd82wZnm8ye/0UiWjZ5Mq1at25Ej62vc4zRnvfdR8ffcsu2VxYvJjqNqa3/6cK2yFmadU7WpC0NnWd1TIgmyal19K8JZYLANWEUcrahSQtDNgjpQheBW7iQaNasXBtmCBx8AC8fHKQdiSjLPCR4EzMb8+I5c2j9lCl09s47J/pw6wkn0IN7752PgARu+lkmnEKScweDyTpAUkBPvW0M7JcWRkaNGkVbt26tPKhNyLzBO0AACGznCKTsWSGaleDzxAGzkAG2UuFLz6zLOKlrwoQJ9IUvfCGK4qwvTeX7NMuRowcH6fNEtEPFl6hZs6gKoptVR57fGaPXrl1LjxBFckPohXIXtkXOUkPQrp87l2Zfcsm25qdoS8vgWGau5sHW9W6T5NSyfWnq9yBwTR2ZlHY1aWG4CNzuW7bQOwYH6Yd827dqVS0auDk77ECL1q+PUUozDwnexHybqNEiHjJ5Mj08OJh5+NohLCMcuA7yXFM3B9FKlJvxndaERuOdcrubV3jJ1T+8DASAABDIQCBVCxOwR7rIUx7QZR9nG5R3E71yRqo90wrrcmbco/zX9JkrxM4XKIzfPWHtWvqqbmSG1qkMYdDVBJ+7eQAs8a5g+TQR7eHA3ld0Vz8c8+TiG2/MTJXkGsu4zhRtaRkcQ2SOEpCmftokObWuPva7XBC4fo9AgfqbtDDkVmtmpx+PDwzQDUNDCZPJ/fbbbxuhq8h8Q2vg+CYtzTwk+EBKMWPQGyF3NSaoOfqjSdhhhx1G06dP79Li2Q1X2v7vF10UmYtmHfh6OvmEgSyilZiSGbeNWpjw3SZX0o4C6wSfAAEgAAQsqfBqYTx7nd6/mCjx94flIAC6ft7f0zRiCWFdXRyyg8Lx48bR4eefH/mYSxuE2CUuTQ0hnLRmDc1atChxJqcRjjKEwfa1yDnJZVQeZMZztofkqHWRIPs3H2Z27rBrCT98Vtq6fXPjNfvvXyyKd8CFRJ27Q5Pk1Dr72c+yQeD6iX7Bupu0MGbsuCPdNDREEzp92UgU/3/0p8WL6TVz5xbbgFLwkc3utUT0yQrNQ3y3dLJB7zt5Mq0YHIwPw9jMMiBBub69dZoROjZc3rzTzD7SNHP2UCl0MGccAiEEzieUhJqwFFwm+AwIAAEgECFgtf9ODZhnr9P7FxMlMd2X/StE+xI1QpWfGJbFi+ni55+PNDiJi7pLL02Y7vPZOn3yZNr15ZfjM5eJ3ZxJk+iy9eu9fuYuK4m0syBLaxN0IVqSPGS5EmSZnnZNe491Db/HpEqbqCa+DZgT7M7gaq/V1nK5mvz/bMyYhFzk05bud+aZ0YVxlq9/Vp9ZFsvy96tyu2iSnFplv5pUFghck0YjsC2NWRirV9PGadOShI2IhMTJAVf4BsmHh9pU+QCbqN8ruUn57OSlD5+cNCnYdNPVfDkcv8fneecmLvZnc9wA8+Z9+uAgfdTTxzQCFXpLmDXt0g50HTnr1PnzIx8N++jvL3zPe+jYJUu2vVJyvLLajt+BABAAAtZ00bn/EpFrr9PC+afWr6fzNvIJ13kcF5RegmP3d+WH5byou/NO2njkkYnz9dwxY+hzW7Yk/raJiMalnIGuM8JL4AKIV9BFYA4/McHrgQceiK1SvrlggdPKJYtcarKug2RdddZZ9OGFC7suXsX0lL9zkkJPPywGvnmjCdvHP/7xRBvEFUOCenFbpmzYkPDRu/HUU2nmdddF7c7y9e9a5QFjWefO0Bg5tc5O9rnsEU/grr/+elq+fDmtXLmSfvSjH9Hw8DB961vfoj/7sz/LBf3jjz9On/3sZ+n73/8+bdy4kfbaay/69Kc/TSeffLKznGeeeYbmzZtHy5Yto8HBQXrLW95Cs2fPpk984hPE2pcyT2MWhuNAeomI/nzcONp106bY3C9ow88DiKnXEkZflMuQm0Ofj4TckL5haCixCYdG9uJyrSlL17d2w73zTho68sho895KRDxrrNZKNJEMXyLCWEE7fdcwyPh1RY8MOSDMO6cMDNC/DQ3lciLPMzXwLhAAAkDAdYmkTRe1MKxN2Vy+ZCKc/3LcOPrWpk1RMBB++Ky7Zs4cuvrqqxPm7Wnk6KVp0+Lv+dw6aWCAbvn97yMNjstXbcYZZyS0bYuI6CzH8MoZyP2yATJcl2xeIhRgRmrJVVYETFebNNGSgC5ileK1cpk5M/an95XJ5brwl4tZa/rq8ktL9EfVqc9e/c5Ov/1tQiaQSNvWzWNgwQKiefPi0Zs/YQL9YuNGksuEMWPGELtWrFm+PJpP/Hf2k9T+i3lTQYQQ3rp2i8bIqXV1sAHljngCt/vuu9PPf/5z2nnnnSN/IyZWeQncww8/TIceeiht3ryZTjnlFHrd615HS5YsoZ/+9Kd06aWX0qc+9anEUHId73znO+k3v/kNnXTSScRtYCL3yCOP0BlnnEGXXXZZqaFvzMJQwjkfIJcT0dUdzZLcaPHhqHPEuTQ0ucEw9uzHGadkH1ELJpIZxCTNjyGt7q4D2kPIYr8BY0JzLWOsnd5XryYtELBAsYOkachxA5qFv+AmB6wQuQMfe8yrTWMceNyPWbOG5j7xROLQWt+5wdaEM4RcZ7UTvwMBIAAEuhDwmC5+e8YMeuJd70rkLe3yiUqx9jiHiP6dqCuqYZrFyfyJExMaPDZ/XPTSS5Hmz+Ubx2Xtu3Yt3dZxTeA9fkzn//WlHl+MvX5oiHSwkwgHx1l21VVXOQmHfZ9J0icOOoj2Ou64GCNLrpis+PqbGrRDES0xZ+W2/5RdInxWLiai9eyxY+mpdzPFeSX/rMgWrnPe2RYztjMHBujgc8+N0yLJOefzP5d5ZgOqiamiHWtasICG5s2LLmR5HPnhywC5TOC+6yjefN7usXVrYn7luTBmk9zQaKd17BqNkVPr6FxDyhzxBI6FyLe+9a202267RWF4OZdWXgJ30EEH0Q9+8AP67ne/G20U/LBWjUna008/TT/5yU9o1113jYf0gx/8IN1www301a9+lf7qr/4q+vumTZvo6KOPprvvvpseeugh2n///QtPgSYtDL0x8gakn66UAi++WLjP8qEN6sF24U+PHp0IyVyF/XzazZXvtpTty3Xd9mZX+7IlgFBmhPr7i2bNig9ap/mEQwPKhwdrC12O60Xz71n/Ed9Nqc7xJ/14sxJw7KF1qCKjweS69AxCAUAACIxEBFyXQE6LCnNxJnuP+EExNiz8RiZ4JoWMmCvyfryvQ0PCKWY+e/vtkfnf8tGjabfDD4/JhfiiPTg8HKe/ibRls2cnIjVrIZ2/+dnnPkdXbd6cGDJ2HWBtDLsPcFAq9qcScz0+E2NzdnNGMGn9zne+kyzPmLHbM51x4T2fMbHkis8b7aOlzeiz/KNdpJX7rgOhybm3ZuJEuvhjH4uDsQj+7IbAjzZ/7Dq7zYVvfO44LHnYx5AjTPMj51xW2hurafv63LmRldbHFi1KaGv5clUCuu0yZgxdsGVLPKZ8OXtBJ9Io/5H98oTYslz1TqKgVBC+szq3z2AFG0ST5NQKutPIIkY8gdOoFyFw//M//xOZSzJxu/322xODyOaZH/7whyMHaRbW+fnd734XaftY6/bkk08m3l+xYkWkyeMN+8orryw8IZq0MPQmbTtUB4Gz2iBXHUUInD380wiFz0+OCZI2NxRBgNsoUcI4hPGBKZuxL+KZ02fDo4GTG1FfqOqiE8/lv+eLfqZNOx/saEh3mzAhcfvMws6xN98cNccKUZVoaot2FN8BASDQOgTSTOd07i+rxbDaGRGgmcBp0iAaL4kI+cyoUXTc8HAsWDOp+KCKwmwv3fSlVsKUzxAJ9iK+eqedSHyjNUHQg8IE7msd8qKDq3C9MUlxRF98ExF9p6PFcwVisVEumUDIWfKc6d/SWbPo+MWLYx8teynnjdjpC+jSIdeiUeS6+RJQzj9ux0kdEPhc4bOUn9iaw2U9c889XXlco3PH4cPPGtFb16+PooxKnSEEjrWn0g4m09bahs0lz9uwIT7n9MWmnleiBdQXsDyeTO4uVoMv2mN7TvqsZbQ/YK8WdpPk1F71udf1gMBlIM7mBuy3xpva3/3d3yXeZhPJN7zhDfTe976Xbr311ui32267Lfo3f/OVr3wl8f6WLVvo1a9+daQNZGJY9GnSwpg0aVJ0MycPb0y8+cktoC8/TdG+5yFwNrKUvZkTMz9uy7333pvQ4qWZ9El0KXY4lr5+4Jxzohtb8Q8QcxAuW5udyKHO/mxs/JG4LTWgaMGCbyD5ksBuxC4fuNRAKUWB9+SpsZG0ouIdxHJvInqVieCpNXZZ5jZFm43vgAAQ2D4QSPP1ktxfXRdhDjO6X44eHWnQNGn4LFEikBQL5HM3boyDYjApvJGIjk0JcmUvO0UrwgE2Tl24MKGt2X/8eJIzhUfvuvPO6yKLB44fT08MD0fap3894oiEObsQBv5WnwdMFoTM8qnNhEF88FjrI5eNEmmTXSOuI6IPqUjP2lzT9lfXawOH8H4vETu7zO/NFGUi9aP162PTUX1uPtxJxC3Ehz+N3QcMWYvMGQ87zJmLls/xuz//eVq6aVMUFIbJ7KcmTaIr1q+P/83julSNhSZM1q9diLNoQwVnTaj1HGAyaudV5MvIbhCmH0zqz+302xfMREe9rPrytsgO0iQ5tUj72/ANCFzGKM2dO5f+4R/+gW6++WY64YQTut6ePHky7bLLLrG27YorrqA5c+ZE3/zt3/5t1/t77703/fjHP46CqRR9mrQwhMDxLdVbRo+m/96yJRGgouoccC4CZ/3sNIl6QW4yPSH6JS+LbHguQuUic5pwPD1qFG3dupX2HD+eHhoejm8jZ3RuD63miW9Or+18w3PAad7gM/swk0YfCPbGWDb6ZydMiMyHS2m1HOaaXP6cQw+lNx1xREwsfSZL4nwtGrtESGTlKK6FiqLrA98BASCwnSHg2i9XrUoEvdiHiLouwhxmdGzYpn2TeH9/99atCTKx8uij6bBly2KQWcB+nog40MmSTZuiM0D8wq/tEADxpbIkkvfwj69dm9CwsH/X1Zs3x2eD7PNaMD953rxtpp4zZ3r9ofW3ljCID57WXloyaGcSf8Mkhx8+81YOD3eZCjJR2/O+++j4W27Z9rmK2KkvNbVfn5AxDoT27U2bEhGm2cyQbZou8k3tFLKWFl1ULp1Zu6hTIkk1vnQRLlcKHrfvjx4dkW1OcfRCh9iLHCJmtBKkhEno465I2op0ao2vfNcVwbpjxcIaT87J+/kKUysV3UmaJKcW7UPTvwOByxihv/7rv6ZrrrkmMp8U/zf9yRvf+MYouAlr4/hZsGBBFK2Sv5k1a1ZX6QcffDDdd9999NJLLxGTn7RHFoB959FHH6W3v/3txCaZ/X70xt8VYlk57VaV80vfssoNJRMTaYfVdvns3SU/nd3wmJTwzaarbHFq1pEotS0+k5TFakDYa2GscVhmHDh33HeefJK+973vJbR+ibE0goU2N9Tv6Vs3JtEf3bo1EbXKCgKF54vH5EUEAClXzEsfJYqEGDmQP26c4RNmKcY5Pb6FnMriCh4gAASAQAYCZr+M9qVFixKmc8G/hzMAACAASURBVM4Ifp5UOFIbk4YbFCmLL5geeSQmh7xfvUxEfJrzeTC7EzlQcqMyQXkfETGV2bGj6dH5Q10m+aLlYv+2VevWdeWxc136dQXN6Pi22fJFc6UjOepzlcPdz160qCs9EGMi5+PG4eHYzJBNGrV5nz5btemqDXgil3mvN6SMyfDasWMTfnrSZu1H7dTATZ2amQpCm6ayptUXPCUx4xzpIiyujM2Jaq7I2cfa1Ng30Fw08CXve4noDGPSyu4YOscuyw2SdkAIp8wNaafP1LbX+d+kPSBw9e/aIHAZGH/sYx+jxYsXRxH1jjzyyK63LYG78MIL6dxzz42+Oe00PjKSz0gjcLLx8+3VN4eGkjdxJmxu6Y0kJTqkEDjrZB0TDMe3M/bZx3njxrSbQ/fKIWm1frYOzs3zqy1bohtX/k4Obj3yckOrN10fGeXvXOYnWekR+IbX+mRoM5s431zBfcU6nbu0ZdIn8fXjqti0SG4gXRrUF1eu7M4niPxwBUcJnwGB7RABhynkLYpkCfHQQTbEsuL+BQtiMzoXQZAQ/YKqnClawNaI/zMR/aUZAr5qPUT/Te1vsmeKv5f2M7MXn2m+2TaYhpiou6wiJKIxa/GsvxVrKrWfHDeb9W3zO3v5BGVpwu37MkeONBofnx+21oSJ+T1f04lZpFiNaHN7m+dOwu/rM0ZHNPb5QoqJaHQOOnwDdZ085uzmMF4FvLHWRPqs26MTTOaQzZsTeWIZu8QlZ0oKJOubaaNbin+eJcUiF3QReEeU617uDCBw9aMNApeBMUwoUwAyaQQ0cYls4R9/3Gl7Xnhap4TGl01bmzFagpEwo1i1ypmEnL85lojuVgTO+pRZ8w/uD5vc6BtB3Uc+DP6oEyI5vjl1HCDazIKjpYqPnb1pc+FnCZ/4ZIivXUgZWeMiGB/ucIKPDhFHnyQyqfTbRhGVdmlhKC3HT1Yb8TsQAALbJwJWY8FaLsnR1hVeXwVOYrS0VoP/ffrgYMLnTedZE6uOrvDxHL2SNXBjx9JXNm+OLzPlXHDm8nTsmdavTFtfpPlmi0bI5jqLZoPnvOGfbMANMfHnEPZsjsfPTZ3zjcu2LgH8OxM5TUCcftgrVybkAZ3Ieo+XX45ynwmJ5XOBH9aQ8d+09lK+07NcayRdBK4LN0d0Tk4pwe4XOp2DS+MakWqVo1X7pB2yaVPiUpgvBK7jSKOXXPJKc1NkJqshdkW3fPiyy7oiiHLU6ceXLqWr7r8/tnoRwp3IDdvjbQEErn7AQeAyMEYQkxSAPLdJ2mnXZXteeFqbQ0gnLNUHxv/DwWQcJgmJjd1htsftEmfm+DBSfhT6kJLDxXWYWfLGGzOXmxAiDHb8zr92fNVsFLDYjy8FOCtMcHlcn++2rsgYyCH46wsvdIehNn1iQeb2zZsjU5sEgfT4I0rEMpewVaS9+AYIAIHtBwHXHsi+xkxEXKTGBhVhpCSqsU75wvu+mDRqH2l9aab9uOT9vYaGokBVbCInT1cuT8c5wHvlisFBZ2THtNHU5vQcwIuTQk+fPj32T3aRKi7P9pXPPiZsEqKf39FaMu4fXwxKQnNpk5xhl82Z405/Y85cJqYP7r131D458/R5pYmaJtisNeQLTh08TRO4oJyiHmsel1+byxyXPvIRon/5l3g4uO963MT/8RvGBFYHOrMRPVnzqc967S/nuujkceCUBWdecQX9xYYNCdcJzlHIZq02kFsvdwMQuPrRBoHLwDgkjcBFF10UB4gISSPANuZMDIs+jVkYZhO0yUQ5uIhstFXlIXFGKzTEzhdyNxERU90G8i3bIiI6ywwIb5APGWd1fkXbo2uNn75hZbMSdmD+gbo9TGiWHFEdxdTQGxLatE8fVHxzKJHDuB62rT/VRE7z+dHlnYde807Tp2+rENMJUyCHJpVvEfWhXNV8yds3vA8EgEA7EdAaC1fUQvE/Xj9lSnRe6wi+Qlh0WhodMMQZbVdFeLR+XGLuZn2BORpv4kLO7JniGyeaIB6JPFoUTUpt+Hv5TXy5NZlw5RqbfdBBdMNDD0U+/h83PmlMEPi8k2AZOhm1fTcOea+CVdmx8BEnDpYi5E1f7ElfxowZExFVncg7ZPa6yBF/x39nbdaV998f5+qLNK7mIldr4KQvb3n22UQkUK1R05fHXI+O+MlaxkRQr7PZ4eKVJ3HpbGQWjiC6Yty4qCydliAtR14INlW90xg5taoONbAcEDg1KKtXr44Sbk+dOpXGjRsX/1JVIu9jjjmG7rrrLnrwwQfpgAMOKDwdmrQwXDbt3DF9EMq/y/pg6Q0tUaYjj44r5K7eOLktmgzyjaLY4euBYVPQT2/cGG3m+pHbQyaFYuoooap1CgF7mGk/QK7/g4ODsQOzaC7/bHjYmy9Nt0Fv7kz6JBfNKuVIrR3WdY6evJPP3mr6NKuJv196aSKIQNz3lNtPb96gvA3G+0AACGxXCGihXPZimzA5FqpXruyK2njNnDnE5I4f1u7w3s7/5bOMLyPj5N4OAVtf5Ilm5Pf/9V8094kn4jFgX+mx557rTAXDQrz1j5YLuTwBwFwY2EAocSqajItP3q/5Yo377QvcpaM3c0fZDJKxX6oCtrCW6NcDA4k0OzYSpw3JLxhyQuxZixZ1aSPLWvZoouu6LExLSSF4dr2jCKoN9e+Sk9Ly5NqE3FEbjQaTLwmunzAh0qCyqatEveS0B5xAPjVIWg92hibJqT3obl+qGPEEjoOJSLTGhx9+mB555BE6/PDDo1xs/HCkSJlonHz75z//OT399NNRIm55+Dt+h/O4nXLKKfS6172OvvWtbxETvksvvZQ+9alPJQbvmWeeoQMPPJCee+45Oumkk2iPPfaI8sNx3WeccQZddtllpQa7nwsjLeG1ECRR24vGqyoCp01EEmU6tFms+dIJQF2HsA3Bz3b4OgiINoXhzdE6auvE3dweufGU/zIO2pzEHsS8qd8/OJgIlXz5+PE0yxGW2RXAxBd58x9Mwmy+XV6Z8ybXTlDrV+BzpvfdGLr6LkKLhMP++V130RXLl8e5lXTeoH4kIi21SPExEAACPUfARgbUQUkSQrURhqOGqsAiOkdomnbHWkEkiFjOVDCaSHT516UEdUqcyYpEOH3u9IiYi8+/J6K/UcRL5+nkz1waSznrtF+4BDZhCxTWHmrtpcgIvjxlXcTMFY357/++y7de8svJOZFlRum05FHYhARCcZFAZ2Ayo3nUhNaXJ9elLT3bEES+kN315Ze7fO4O7phtliW5ZRdvP+XUsm1vy/cjnsD95V/+Jf2LslW2A3PttdcSv8OPj8Dxbz/60Y+i6JJ8q8G3QnvttRd9+tOfjgid6/nFL35B8+bNo2XLltHg4CBNmzYtSu79N3/zN5GgX+bp58JIE+Rlc7bRG6uyw5a6XWF0bYREOawlR5vrls0e0rKZyuFibdSPIyKJNsWhneVQExOIp0ePjm9pddJt30Z664knJkwuuDwb8UwnRrVzRsq1fng2yaz4K+S5yfXVJbePQQROaTltEBWXSYmN7inhqBPRw8osHHwLBIDAiEbAFXFRRy2M9+9Vq7x50xigtGiPPgBd32QRBS7LRTbyRCFO8+2O2+oigKtXd2HA+dd23bQp6bPcKcQG1WBzz7OvuSY687r27k7KASYZQuCYXHEU7l1eeikOuJEZZdNlreFI1v2auXNjs0S2rkkdvxRfesGrKIFz1psSfM1HNH3t1wTx9NNPp48uXNgV9Try2XvssWoDyBXYNfoppxZobis/GfEErpWjktHofi6MNAJnzUyqvgGyt6tiZsHmGUy+rCMvm8uwqQY7kouN+QMPPJBw7NZQS9/kVtEeSja9gI4opdtitUXew8STW80V8axrSqhv7S0zB3fh6GtMLK0JUaFUDo5D1N54SvvSNLTahFa/J8TZ3uLyDXDVieBH4n6APgEBIKAQMPsVB8NYNzjYFdCJ9xYxO7d+ZkXOLpcwLnu/7xIzTVMU2gaf1UOmBo5TA0ycmDDXlyTdrgtPl1bQZWKp5yKXt2Ls2OgsErNJ10UddVIuufDowsETBIsv+sS/L9X81UOodN3cB2syay98+Z3rzjsvMhtNvdhMSX/kW7e+sc8i63HUy7e9ze2+0MONop9yag+72deqQOD6Cn+xyvu5MCwZ8R5ABTatLDR4U7PhnSVRNZMujjYmUawkkIeEHw7VyGnfB5sUfM6hh9Kbjjhimw9Dys2a7kvWIa3DFuuIZ6nRGE3dcstsDxIXKfLlk/PiH9hP1/dpfZffOH+dHIJcRmyGZB3Hn3qKcrc9a1LhdyAABFqLgG9/0QIwm2jved99dPwtfBp0HpWYmf+SICwVnl1ZpnxpmqJQLaAV9uXf4geYlkLGatW0eR8HetFPllbQZQWz1ASy4giXjw8M0A1DQwlT+bR9PU2zaf3RxP88NQBNig+2z9rD6ZNm0iKkmfyHkvEI75T55yPrNuplWhm9Wuz9lFN71cd+1wMC1+8RKFB/PxdG8EZUQuh3QqI2NR2Yg0PvcgQt0ZqJeSVr3C46+ujEoS35bbZu3Rrf1Ll8q1L9GnTjKjjoXWahYnKSanrquGV+eHAwFkSC+xAy/zz9zBJOsop2aVQldLTLabuQ9jCrEfgdCACB1iIQasrtEmjZisAZJbnqsysF3dIELkUbZaNQupohe7Azd5zjg7TzX/qS8G+zgaw6UZwlYnVIftJMc0ZFpOzFLV+IHjB+PD0xPJwg6a5+hIxFAlPrR5nipxhKxiPIc5hcahNdloP0RUSwrFbT6u+nnFpTlxpXLAhc44Yku0F9Wxh5CEued7O73LWpcT6dC1VyUSZs4oMmN6oXzZrVlY9GfOJCDjcuJ2vjLbtJSvkCQVrEMwuTvWV2RUkL6UMI/HkPvJAyue82iSxH9/zH3/0u+vyk/faLx6+M/15IW/AOEAACzUZALoy0GbwvCESQKZ7H/6yX2ou08yPogswh7Is/WMgZpwODhaRuyU1yFLnSs0ssZ0LqdOGQ+NvOOyfMBVcQ0SGqMtYjPm9ykYZo9XR7neQ0h4VIlhyRWHk5ZCeX/CCuCrnqrGHp901OraEvTS0SBK6pI5PSrr4tjJw3k2XJjW9T8+X4sXno+HsJSy/mJPJfG8bYB3fWJpj1e9b0cgkl1nSlaNvku7Jt9JHAsuVakxshaa/Zf//IIZ21oxwZVPsYaCyCBJysAcDvQAAItAIB66PMwj+nT7Eh+PMGhnB1vtKzy4duDkHdO0Ap/slp/t5F99EQk3jtfybperSbgM4ZF0LgMienwzpH+/+5gnh1nV0ZY+E7q0J9tPOeVaHzT/rhyodX9nzOxD3jhb7JqWUb3qLvQeBaNFjS1L4tjJwHTtUbiGxqQr74Zu0iNX42+IeYIuokl9b8MpFU1TEXsjbeqvuYZzqG1p3Vh5A6Q24sc9XjSP0ggQTsOHP7xDldhBL+b7/z3ITghneAABCoBgG7L9jcaxL23rcv5tmfQvfWUj3LeSHqqytU2C/V1hwf+wJZiWmlnN9VRafmps3YcUdaMjRE27L3ErGVzv8R0Vm67R0zx665kDYWnmBjdUZJDp1/IYS6Xyl4+ian5pirbX8VBK6FI9jPhZHnsAjdhEKHQN/AcsCSR4kiR2h+bPh9NsX77O23R7ezO7OpjKMS3uAv7+RMCW2DfS+PUFC0Dt93vay7q64U34ugW1VzYOoEu0PTpsWJW62Po861x3Mg1HejauxRHhAAAvUg4NzXHBc+XHtiX+8I53nOKF8Pqj67nPXkvBDta1tLDLXVnPpywJWogmyETJYHjiGi/1QyAkdo3MEXCCttLCy56/jx8YWBWIsEnXk5OtiT+ZejPUVe7aecWqS9bfwGBK6Fo9bPhZFnY6maYEjdfKN0w3vekwhQwgmwTxse3hbZ6s47aejII+N/ixmFaOBCQiy3cGr0rskpvhdBh5nJxcNELdKGGsdwJtkXjR4dBak5ZMuWOBQ1CwEPpySd7R0QqAkIAIEqEXCeMQ4hmoX0l4loUifnGKdPmT17diX5r6o+u3z4VEE2e9XWomOsIzhO2bChK51DFZGFdTTNDUR0/Lhx9DfHHJOQEdJyqnLfvGORYi1SF/Z1lVt0DIt81085tUh72/gNCFwLR62fCyMPgasa2kTdJvIUJwzlJ/aHMNGvdDLXDxPRR3XjUqJHVd2HEVOe48Yy1B9AMJADU3L0RaYeM2fGApjPj4FDUf+BxzSmV/iOhAO2V1ihHiCQBwF7xvBam7RmDc1atCi2uJDymMSdR0Q3ei6Amh65tp/naZ4xqeJdHkdXOgfJAVemDi5bcrLF6XccMsKqdeu81aSNhTXfDbqkLNOhEfBtP+XUEQBfUBdA4IJgatZL/VwY/TxwrNCsQ+iy9oafOI+LydEi4eltvjhENyw+txM3ljkickmNvrnE5dp8f7aVQu7476mmMcW7l/plP9dBTV1CsUCgEQhYTYisNda6n8TJp4logmopm19/TUKoO3JzVaHhqQuY7e4iqCKzUdd4yDzh34Rg6UvCU+fPp7QAYWlj4QqgI9Ee65obbS+3n3Jq27ELbT8IXChSDXqvnwujSQeO3bATBO7FFxMmERLQRHyo6rDDb9AU6UlTEiQmR04caVxatDiOHipJ2ZmgjTFCm+5glmlMHWCMNALXpHVdx3ihzJYgkKLZl+AXhxPRdzr7AV/AnTBuHO26aRPZpM4hOcZagsqIamYVZqNpBE4HR6lqn9buG5/5zGeioFqh0aJH1ODl6Ew/5dQczWz1qyBwLRw+LIxXBk02Vdmw+W86F5revIXc6SAYnNAbphDFF0BC6Femj2W1mvbG/fPsfN4JVCM+LzqaKJvPnjxvXmLsi/cq7Mu6hJCw2qt/qypBp/qWocSmIVAr2be+bgsW0NC8eYmgRpzLUwIYPTcwQDcMDW3zfa4xsETTxqGt7alrr3HNy6rqqnXOt3UgM9oNObX+gQWBqx/jymvAwkgSOB8J05s335oxuZMw9DZXTeWDtB0WWBWp0U7vf7FhA31VYXkJEZ3euX1nZ/X3cjATZT5bZWhq7xDWaAbUr2lT1dj1q/2ot3cI1CkUX3XWWfThhQu3EbIFC4jmzYs79+0ZM+jK4eE4hcgnJ02iRevXb+v84sUkiaxxOde7OZGnpl6SoV7WlQeD7eFdyKn1jzIIXP0YV14DFsYrkGZtzmmCRlVCSOWD2+ICq8bUBi/gBLAXmtx/s8eOpd2/+EViQs7+j/xYwS1rnuSGvKL8TbnrLfBBUN9HICEtABU+CUSgKrLv2i/4b2w+HacHUb61vP6jSJOXXBJbX+w7eTKtGBxMaOAuvvHGnmrjA2HDa0Bgu0IAcmr9ww0CVz/GldeAhREGaZOTXIb1oF1vBZGFAl3SjujvfOc76Yrly2OTKhHobEAb7WDuEhRLtbVFhCeIVLeIkBaYPvikSgQqmPtaw85WEfrCxfo1s+VEV3TBF19MXN59c8GCbdGHU6IMVgkDygICQCAdAcip9c8QELj6Ma68BiyMcpCWEt7LVY2vCyBgSUiXBkAJlZID6PDzz4+dzF0agyBik9LWqrQQBeDI9UlQOysQynM1Ci+3F4EKyL72cX03URx8hEGxUQNFs/5mom1aOUPSyq7l9g4GWg4EmosA5NT6xwYErn6MK68BC6McpDjwy+HX668t4e4aPyNUcm6o6ZMnU5Tzx0NOgohNSkdbMYdyELO8eOASpNeroCH1eeZUnvkgZpISZTYOekTUlYSbc0sePThIEsjIFSApT90NQRHNAAIjHgHIqfUPMQhc/RhXXgMWRjlIWyF8l+viiP66a/xWr6aN06YlckPFqQVcGoPDDusSFPPmimqF0JhDW5J3TeR9f0RPyO2sc3k12q78nV15HhcvfgXFWbO2oakiUCYg5ndPOy3TB3o7GxZ0Fwg0CgHIqfUPBwhc/RhXXgMWRjlIIXyWw6/fX7vI04wdd6SbhoZiEhcn93bc6tM99yQFxY5AWLRfjSVzOTRweddE3veLYlv1d40dq6o7WmN5rrFP0+Am3leJtiUViE8D99DRR9Nhy5YleqI1cG2dgzUODYoGAo1BAHJq/UMBAlc/xpXXgIVRDtK85mLlasPXdSKgAyKcsWEDXawr6xCzNJ+5sjnruLomC5Khc51xvOOOOyL0jjrqqMwktaHl2rHvN4Fq8ljVuU6qLNulAR+aNq0rEqTMp3vvvTeKChkFK1m4MHF5ci0RXS7mzsoHjpNw87N8cJB25EsYIvoiES1T72IsqxxVlAUEqkUAcmq1eLpKA4GrH+PKa8DCKAFpDq1EiVrwaY8QECFu4sSJtMfLL9ODw8OxwLd4zhxaP2VKnGJAR7srSkBc3Uory0dYekVk8gi5we+WWEO+OnqFR5lx71Ube7R0ClfThYPDVFdysXElU4mIg5XcP348nX766XGeN04LsA8RPTthAn3hC1+ILg30/OBvJaXA94hotUkRUmYsC3ceHwIBIBCEAOTUIJhKvQQCVwq+/nyMhVEC9xx+QSVqwac9QkALcSfPmxeHHNcCHzeFc8Rxku/DDjuMpk+fHpE6G8K8UJMdZEbnoZIoejY3XTBZKtSobR/lqSdYIC6xhmx70kLKl+x69+cliCcXlgfLytteY4GliakD19fsv39EvmYSxQFImLAd2tGsvWNwMCJ0T48enViHui2SHuAeInp61CjaunXrtpQDJceyRjhRNBAAAkQEObX+aQACVz/GldeAhVECUhz8JcBr2KcewZHJ2igl8HEuqfnz50eCovxd/65zxuXuYYr2gUnb7lu2OHNUhZCBUMG6knyHedZFnnc7gEob//2ii+ig4eE4dLzgUNl4pA1gCeI5kglcyFzMWheW/FufVPmegwtdNmlSfKHC4+68SFFzjInfgePH0wfOOYdY086aOio5lln9we9AAAiUQwByajn8Qr4GgQtBqWHvYGGUG5BgTUO5avB13QikkCcx22JfGk4nIEKq5JPi238WCB944IFIIxcLhnnb7CAzHPqcNQzPDQzQDUNDCd8giXYZMgddgrWLrJURwKW8Ax97jI5dsmRb7zMCu4S0X0PpCx0vWD1NRHuYnGB5hyLz/QLEU5eZt8+Z7cl4oVfmt6HzLO2iwAYqsVFhpascXOi6uXPpzCuuiIibXacxJGZt33rCCXTszTdvQ6zkWJYdG3wPBIBAOgKQU+ufISBw9WNceQ1YGOUgLSPwlqsZX1eKgMd8cdKaNTRr0aIEcfLlk2JTL9bYaRPHUM2X9CUh2K9aFaco4Hx0E3SHhRQFCp+ueZo3AmAW3lLevpMn04rBQSfZdJWRdw3x+yesXUtf1YWpMPFdEQmnsmhf/VOYhAWOWZUt1hizFpkJD180+Mxyi9YdmhbA1x7ru2YDlWwionGqcd+eMYPOWbaMjhse9ud3C8C78FgWBQrfAQEgEIwA5NRgqAq/CAJXGLr+fYiFUQ77vMJnudrwdZ0IOIU4q5lLySclwRbYP84VSCHEvDIxn0yUPSFxiWiXDu3CP/7udxFMOgJkkGAdIOim4a/r4PdYcyhay7TvLMnNIr1cT1dC5gULiObN664mR1qHrHpt4TrozcEHH9yFubfPASZ7oW3Jes/lF8jtkosGn1muq+1ZdfkS3WucmDxa4qjbw2skUc/MmfElxgZO7UZEVxFFwYXYHHIGEd3S+XeizWbcswga9vE6d1aUDQTKIQA5tRx+IV+DwIWg1LB3sDDKDQgO/nL4Nelr51gaUnP/EUfQUXfdlWi25IkTEz4OlPAChzl/8UXKEhx9pCDS4qk8VyKsstlmghSZ9h0yeTI9PDgYFRtrAj3ErKttAcTCO16edtiAKyHjLeOgiXD8nalHwsF//OMfjyMSFtXA5V3L1ucugXlaRwOIcmhbfIFcxIxXftfmhUKu08xypfmaTPm0dZI24pg1a2juE0/EPWft2BPvehdJABEdDEgTR2kPmyKfOn9+nHbC+jryulrTiRDLvo9sKvthIvqowdqVziMLz0xyGjJx8Q4QAAK1IAA5tRZYE4WCwNWPceU1YGGUgxQHfzn8mvS1T8gTosMCKJODx4loomo4B1M47/HHY02BRMhbpUwgQ3PE2fkkdbPw+tNOnUyKtBmcCMiS74q1U/zEea5cxOyww+L2+pIfDzz1FImfXeY4mTrmTJpEV6xfnzAnzSyj84KMA/+TiYhobVwBJ04joiU77RR9KWHixQdOfBNDfRKzhHzbfq3dmrJhAx2Ww+9OxtXXxtC2WLJrSZbVVvJlAz87EJHXLFd1VLeD/2xNhPlv8g5fLjyitGMc1v9VxpyWCffjyp9TtydeN+vWRS3wEWQZa6lLE3ab3026gn06dPXhPSDQPAQgp9Y/JiBw9WNceQ1YGJVDigJbioBPyLOC5OFE9J2OT1osdF56aSKpcETqvvzlxN8ohzmfQMh1Swh1/tuNHS0E/79E3BPBmn3P7h0cjIRzfkQzyP9vkyPTPfc426aJhdaG+MiLkKOrzjor1oAxIfz63Ll006pV0Wchybx1+UI6+G832ZxdSnvFJnXHjxtHh59/fqzl0URXolF2kUCipJne2WdH/77uvPMSUS1Dp7GOksjz4fq5c2n2JZdEZXIC6p/97Ge0++6709ixY+MgN0K0fBEzQzS3us2i9RXNFhND1kqK/6avL9osl8fs1wMDcRAeLl8irtooqJxmQyfXFgLLlwxM5ETb9slJk2jR+vWJ6ruIo/o1WjcbeGQp1l5zX6Ycemg8lxg76wPJSbwvdOR3Cx1DvJcPARDifHjh7XIIQE4th1/I1yBwISg17B0sjIYNCJrTKARECOdGrVm+PBLwRRPHGpfYXHLlSnpp2rQEefqPWbPo+MWLg4N5uDrOxOjUhQsT5e4/fjxtHB6OND5auP3bV786Gf2RiCTintX4vGFoKJEEWUiH1n5ZTV+kAes8VkMkRFNr6JeZ8wAAIABJREFUoUK1SFykCITcLt1fFv3na20i+z3tuCPdNDQUBXWJtId33klDRx4Z4awTOr9569Z4vGziZts2xmf54GCcuD1Y+7h6Ndkoiax9XPTSS7EGSTCz6SiYgLtyCFoy7GuLbjNTHq73svXrYxwuYFzNpGK8RnU0cPz/pwwM0OuHhhJ51ITwctuEmFlzS22qay80WPPGdXDCbf2dboqQOL5k4HcndX5MvXToBKPhuaKDC8mYs3llQlvbqJ1kZDUmz9oeWT1Hb/qBAOTU+lEHgasf48prwMKoHFIUOIIQEEHFRlY8YPx4emJ4OErozQFL+Pn9uefSBVu2xL1ngfrW9etTTeuEuHhTEFjzRyK6nLUrXLciLJHPnYNEHmxSH2gSIdoa7bNnCRx3Js1sTkieaGr4ffF7C9EiWULo0tjwO9oEdc4OOyS1Oh/5CNG//EuMO2tjlpi0C9a0zrZt/sSJdN5GphWdJ1RbasaHS5jewVzqsFop7Rfowqirf522WK2Hfc9GaGS6fW7HpJHxYwwe5DxonS6yie3PxoyJxpcfm9eQyfhtHU0zE0RtNnzumDH0qy1bIrNe1kbb387ZsiW+uGCSuNfQUCJKpBBHNvv9s+Hhbuy5QbM4ZIl/PLRpsyXoI2gLamRXQOAaOSwjtlGQU+sfWhC4+jGuvAYsjMohRYEjCAEREndj0y4l4FsfL9FAiV8OC8zOgCIGG2ue2RX0Y/XqhGaPNVKjTUoBJiyXK9LAPnCv5UAqSnNlA0Kw4KyFd6nXmhLKO7ZdmnjwO+J/pjWSXWabKeH8pTzW2HxzaCjWOCbg6hAZq6H6ttJ0ij+Uy0yPNTVzDj2U9t1332RqiDvvpJeOPDKh5bxmzhxaP2VKFFAj1VxMmXRynZcT0bXGfNHro+VJXcGmnA8ODye0gRffeGPCnJED5LAmcsnQUCKsvjaJ/HIHPJ4HbIrK7dA+aodOnkxsCimmo+I76PP11GWLFs+SRv73lePH0+nDw4mLDPaH1IFUZF7xBQhrSm1/I9I+bVqq9toV3TJzvEbQ3tTPruS5nOlnO1H3yEAAcmr94wgCVz/GldfQhoUBe/vKhx0FhiCgBGwdbEF8vLS/kA7kIGaEXAWH0tcaLlttJoHr+AJJYJJJhkjaiIuci47JlCaSsQmeIQzs83T55Zcn/b7MO1pTwknMo8e886lOwBLRCEbmmG97W5cG5eLnn4/zj2lzTFueaGyYhH5SmftpU0IXgWRtk45I6CJxrNNhzBI55IwG75yO6aGQ1ixtgw0UwkTRZb4oYx9rEx1+iJyKQvs8SiCaLhNVFSBHzwHB7jxjlshmtxcdfTQdfwsH3X/licxr//7vY6Kky2Gt2klqsjKW7+0kSN/ZmGbKd/Jf31rR5qJs6ihaWzbZ3OPll7v8Dy1JsOeAb1yyxitk6eOdFAQCoqgCPyBQJQJtkFOr7G8/ygKB6wfqJetsw8Jo6oEMYlly8jX9c2MexwEWfrFxozO3mTXn0iaXOrCF7rLWdon2w5U3TZs16nKt9oODpDABsAEeWFB/cO+96cDHHkv6yKmcdtzGTxx0EH1wl10S74TknnO1gxxRLl2JziM8DM6zx46lqzdvjn7SWhsbOEOCuIgpKJvzSU4w8e/Spnvcx32NJsrlQ8fvaJM8l7bBhte3mLPGi3OUJZKv68FnbaLCSObIf115Zezfp6Mydvn9mbx3ooVlzVZXkvOOf9yixx5LaLWYwO/y4x8nSJ1uotZmsmbx6g52Mzt+ibpvK1jjrD52rRWX36SY5/Kn/P864IyNqGm/9+2/0A7VvLGWSTdSc9NQ/MhEoA1yatuRB4Fr4Qi2YWE0lcA1tV0tnIbNbHJobjPz3uI5c+gtzz6bJEsOnyodhCItd5kOpMIRHSVtgCUsEvnx5Z/8hJb+4hdxsuMDO/561o9vwJAADsnPBHLF4GBkuuYNM28iQWr/p+ibTvoBK0jbkPdi7mYDUnB7nx49OtLWaZ89EfL5v/J3S5SP62iJtIkoR0Vk0sGP+IBZE1Md5IXf42A1EZn2pILQa5+1SY8vXUpX3n9/hLnWRI3pkDjG5eWOVkz78+k0ERxy/4HBwQTpEy2ZDZRChnxz4BD2g+Rn37VrY981/jdrxK7rRMZ0aS5tOH69GJmcM5XmICNas8b/L32zCbW1+bA2vfWlyNBjpSOHcjuEpLPJaNB+C+1Q/XspMK4fY9SQQKANcmrbhwwEroUj2IaFEXRw9wH7rHZBQ9eHQam4Si3wsgaIBcquvGKhedaMD1hX4Axpe0YADa2Rc/kV6ZDvbL4pEQElTxybdVpywkEq2ETu4Z12ItFosVZwqQqC4TJh1O9wGTMHBuiW37NIvy2Plw1q4gqaIv2Q3yTPnQ7uwsRVTEmXjh9PHzjnnC6tomjvuE4hfDo/mZArITY2yEsWyZVUEC4tD//t9MHBhBknBxJ5vhOtlOu0ueK0aeQuY8YkguDEZNiYWsrfJXG8TpB90axZcXRR8cn7Rsc/0o6JjDO/xyalrFHj/+f5Mj5gHUnfJBLqM/fcQ4ds2RLNLe9akXId5rxskvzvF12UiByqk7kHadagHQoYufKvBI1F+WpQAhCIEGiDnNr2oQKBa+EItmFhNPWwyGpXFsHj6QKS1+xFEzKG1odLiE7q/DDBSdI0cBYhadOYMWMiTdTmzZujyI8ikFvtkyuwipAqbZonBGzFuHGRORuTKvGDipOCdxojbdDvcI66KBrmiy92E7iVK2PzPU30tA/gHlu3JghOYm3MnNmVpmEH1vSZYBcSOEZHx/yLDRuSPm8KUCZ8u3/xi1E4f+7zx8eOpas65pvRa0rTlZXw3AaykfD2EoUykXaig5ENyCLBQTRG+h0XdnyhcPDBB0fNtakkJNE5j4nO66aJqo0weQkRnd4hdDrtgE26zVo/7luor6De77rMeR0mpaJVjOZTqNZHveczXW72jtOO1gXti+3oynbVyrbKG22QU9s+kUDgWjiCjV8YoQd3r7EPaJfNvdWluXFoKXrdDdSXjkCooOIia6nfGk0BC83/2zFhjIOFeJomh7AEheDXrOaIicy6wcEojLwO7KE1VCwY21D0rIG5du5cuvfee2OTQO2LJU1y5eJi00ed/DshLOy8cyKoCddz1Zw5dPXVV0caF5e2L+Ezt3BhMigKN6SjqUzTktoAI1YDt3eHdIrGjzVANiKiaLpi/0SPlkcIkuROE83ULi+9RFfdf3+cn01y7kVYmrK0xk7IsNbSae2l9cNjAmpNSjUJ0iasS973PqevI481j6PkGbRaW26yaEGXjB5Nux1+eJyoPUQ4lDZ0aTqZjBtNo46uanGSsXctEW2Wqi8UsNdVh0DovlhdjSipCgTaOm6Nl1OrGJw+lwEC1+cBKFJ94xdGU01istplboITt8lqoNqwoYYIZkXmXhu+Ce27axxDw89LcI28uaz0BcHC978/IZDf86d/Sgd897sRaRDSopNcc+469kFj7c5HFy5MBttYvJjmzJmTyLUmCcETY2bWgCSwdo6rI+G1zlPm8rfb78wzoyieYu5pE6VHGripU/2+UeaSxeZB40AjrgTfkhJBTAElWmLsz5VyeeMkDwYnCSoTXejMnJkILMJtZE3msyq/oM6xp00KNc6axPLfGTcmkGxmKhdHiUsG49t3wrhxtOumTbH5o+4zlyfBRljLWyThub10sBrj6NJC4dqlkQ5IKyB4dEXslOipbdhwWtLG0H2xV93pZXtC6wp9LxSjKsoLkTeqqCe0T6HvNV5ODe1Ig98DgWvw4Pia1viFEaDp6gvsWe0yQps2ZfIJXlmal770E1rCINiLHHqagLEmY/r06d3+db7a7fy7804aOvLIbXmzHFEKv64CfOi51hXh8KmnaMY++8TREHXQDR1Q5aT99ov9rbLM1fi7uz//eVq6aVOcgNyG2k9EvLT9eeopYk2YaH+0SadXKHGswWtHjaKtW5keJCNcCh6WgAl54f9mJeDWBCShtcwIhuNKQ8A52rhNmowwIdMaznhqhATbcexXWrNo8/3p+cz1iO/n2x96KDM4j2vKapPbd3c0za4cgy4/wjS/Q1ddOjiQnrtBC3k7eKnIXtV0WEKISVYfQnEJrSv0vax26YsJuUgRM3X9bUj7s9w+uLyq2x3av7T3Gi+nVtHJPpcBAtfnAShSfRsWRlAgiSKdL/lNlo+TTkTLN+vWj8jl18EJe52BMkq2tcznTdzQy/SnKd+WwtWhAbYaK5l/riiBOiBJl8ZCaWd8gUmE0Gi/u7QLCOkrBxRhXzD2nXonEX1e5XlL5Jy79NKuPHKcIoEFGEumvIKLIzrod558kr73ve8Rmzl25cpTWh4hYFyXaLM0eXKOnUPTduzNN0ftdWnIxCSTy3KlfuBvLbnmSKMc7EMHdznbmKfaJPNRAxzzRfDUAV+6EsnbxZJ1ceVZXNY/0JVLUYRHXx7D0PVy64knFiKZTdkX8rQjRGi35YXimKcd/X43T598mIWWUfV7odhl1Zv1e6gfaWY5oQ2u8L02yKkVdrcvRYHA9QX2cpW2YWFo3w0O2qBDS5frfbmvszY6e7PedRucIVS5btnKtbjY1yG3dnlLLiJ4uOqoqpy87a/i/az5k1qHQ5C2edZk3DhKIWs9dPJqbcbH9STMBI2/mTaNtGTPpUVxtVv6yr8xgbtNBcmQy41EWSroiawb6R+XkUk0Oo1wzV2eM3ved18y/xn70/Ezi1N9v/JoM1S7dp1jl0Ju5H2X7xdf2uhUCjpYik0fIORMp1d40WAlJC/hc+tom74s4v6GXhwV2Q/SNGt6vrg0dXIxELzWC5LMvGs6uD15C87xfpE9pMg3OZpU+atpOMtvEr3UlUfTNsjXf/m7zkXIZub2Cb1QLrJO0sDz7WWybiUQk8/MOtSPtOp2VzEh2iCnVtHPfpYBAtdP9AvW3YaFoYW/hODSieRWsOuFPnMFDvAJk66b9YTzvUNDIEEdQg6iQh3I+1FNwlBVQkRV5eSFJev9EOEu5J08B7rFQgskF3/sYzRr0aLYxFJHa5QIhbEAr/yybAATn0bIFaBHt12EAg5WcisR6dxxYl4sgUScPlvr1kVRFO+4446oWM6H5xKuQoU1a+4YBRaZPTvhj2bz5Om16xs7n/Aj7zujL57GCCS1dBFpMRc8bGI6ffJkenhwMJEfj8cvROgKeSdrXvPvuddcmm+bSa3h8pUrcpFVVV/T8MiNgyms7Pr3jUVWudp0W/tIhox9yDtZ9XMZIe9IXWk4uzS72rrA1V5fefrCy+uPHGKuzJVWeG4yVs4LHuP/K9YQOtBRov8hbWpoFNc2yKkha6PJ74DANXl0PG1r2sJwbex1E7jQw4Tf8zn3u4SMkMNFHxrsmdNl1mUEnJ5PsaxgLQUbVFb4kWp7IagV6WJV/csjQNp5bP+dZsZn69FCXmw66AhEkhYNMC5TCQU2WIlEo1w/ZUoXIasCw7S1bf3duoiQJ4l3njHpejdAS+cKliKmrL8cPToy6WQivIeKWhqCVcg7IXM9dzlmD0lEl/RUGLon+9qbu40hHTfvlK2j7PdO0p9FsAODa0lXi4yDr195Lj811FKeS7OUeUnqGFfnmeEIeNTl8sBlOQI3XbF+fbdFQEqk2lBNtyawLhPrB/feO0p/wuXZ6LPiQ6u7z/hfd955UdRf7wVxnsBUBdZM0U+aJqcW7UeTvwOBa/LotITAuTZ/vfFYwaUKyEMPUk0kJc9U2mYshE/Cirs2TSmT82l91oR8zxKO5UDU/jCMR14tRSqGIbd2BQYhS1ORpdGJqqypbQW60/VJL4hl6LzVgoANNe8L8mDLdpkdxommsy4ZHJokThpt/essiHn7l3fc5IJFJ9e2deYdxxCBN8/c1yTzVZMn04rBwURkUWtemmZaWhWeIX1MjEVO0pB3HF3vV9XXtLbknRu2rCLfa+x3+u1v4yBCeh2n9j0nmS6Co+8b/fdQ31lNUl2apTQ3Bec89Z0ZlnCxPywRsTYv4ZdurBNOVNFbEz7Annp88k2aHOG6CBYLCpYb2Dx+twkT6LyNvCO/8sznf2/gHXbbY+UXp2bbrFUhgrnXfBWLWJUBAlcxoI7iQODqx7jyGpq2MJybfw4TnDwA5TXX0QKfRITj+uzmq0291ixfHt14+cwarHlEVkJn1y2mNivl9rgCPVhc8mzIVZvc2OTFNqBGWqStRD9q0g7mmUPOd3tELPOMIbdT1tYnJ01KpAhwXRTYdWiFiCzy5RPgWSiSYCVeU5/Ox64LihCzydDxc81DG0SoiACbVX+eMrX2weZui+tZvJgufv75TB+2vPMlqx95fq96D8mqu/a+ll3jBb/Xc+f/3bjRuY5TiWHAWeo6v/KY9Pvql7Y7cwD6LoE8hCIa/wytmcZKzLNffdNNdObjj2+bPp1ckrosPbe+PWMGnXL77Qm/e+kfB2K6hYh2VGSP05rI48LBtQ7Ed813+aLNz0Xrzv7COtjPS0Q0iogmdSrnf0uaFWlPEIHrmHLbaL959qystVnk96bJqUX60PRvQOCaPkKO9jVtYchGkXAkNlHWtOBCHf+RItBr7ZeEtj553ryEIKQPszHf/34iDDrfTrmCOEi5HHFPTCIlzPraP/iDbkErx62o3kiFrMktnCTddYVa53dDTFhcZnhiNlqV/6FNHq0DanxzwYJtub8c+ZsS7TM5tLJ8H4rMkULfNJRYCnZvGBpy3tzrvtp5YNdKFvmyuGlhRtaM19nefFyb8BAwTnXUnafMxLsqWEnWRU+heVvjR3n6XGMzqis6YO6kVZYVKdNHQPX5uMfLL3clnec6dfRj157Ia/H0wUH6qGqg3oOFUOjzKzgdQwoxlT3AaotSLU0MzgnNUkaqHteew2flwx7SZbV5kjeTiZHIB5Lagy8Z/8oEhrL96JrzHk20nONOAuf5hjWYdgyXjh1Lx23e3E1OO39xmsU7Jim/Z3M92gBZ1S2ksJKaJqeGtbpdb4HAtWu8otY2bWE4HYlrElzSTBNkM9VkjO/tdPAFjgh3/YQJcZJbMUmQcs91mETq0N2xCUMOEyPrx8RkTUKxu27hrpkzh8S/KMSExaV54cOKhe0qIoCyYKLzgbFgIOYge44fTw8ND2/LZdZJ1KyXlW1fETOk2pdpwdv12tulKsiLm12XXFRoJEh+13XxEPp9bcJ/wDjVocnJU6Z913Ub39T8kWkXAr2c67XUFTB3fPWKS8CDw8Ne7Y1vzut1yCR+ZqeS2F8rgFhmnXti3rgzX/rpToi2Kg1QUz9rsJ5417sSCev5nOJnh47marE6o/jv9pLupWnTonf5SWiWHEHAYl9aT7ARPsP22Lo1ioRrtYquSyquU/ulc4TXM6+4IkpDwtjrNCiaLIv7RCJitodwppqTOr7hdrtMqdmy4d+GhhJzii0K2KVil5deoqvuvz/1bJVhtResTEwTKWocF6u1rDFVaNPk1Lr724/ytxsCt2LFiiiYxYMPPkgvv/wy/cmf/Amde+65UWS00Oe6666jr3zlK/TYY4/R6NGj6e1vfzt9+tOfphNOOMFZBNe1YMECWrVqFT333HM0ZcoUOuiggyLH/z/+4z8OrbbrvX4ujC5BJoXIaLMFvhnMY9LhA4c3bOscbHMoyaZub9skIpxLA6cj9dmbct9NlpO42iib5lDim0E52Hx91AnEpY7nBgbohqEh52auTba+8IUvRE7STOBYI8o3vqkO0AGzUN/uiRneinHjojq4rZ1g7q+U5BAYLPGoTbgP6EvaK3kJUsnqcn+eCzcz72YfdBD9aocdgiNBWqGM/53Hkb9OLH1l5yFZucHvfFCkDhm3qrThRduO7xxRQwNB0ZeCLiLBxdj1aU3tmciM6aTiSEsab8mRLltbbsg5ps0b+Yx7uWOWV0QDJxqsF3baiV40qUk4OP/zRMQpTjjio9bG277Pnzgx4d+lzwXf+rUaTtbc/WLjxjj4j8uCwK4tljPsJSzLB7euX58gda4cr/YcjUy/HSafj6ecxTxW1sybx/Mtzz6byHMoQYHErNLl02vlF61xtWbpXabld95JQ0ceGUT+ApdA7tf6KafmbmxLP9guCNyyZcvofe97H+244470wQ9+MNp4vvnNb9JvfvMbuummm7wETI/pGWecQZdffjm98Y1vpBkzZkQ/LV26lNasWUP/+I//SGeeeWZiCnC5H/jAB2hgYIBOPPFEesMb3kBPPvlk9M3YsWPpzjvvpIMPPrjQtOnnwugSIlNMCeVd6WTo7X2WkK01WFobJEFKJGqTtnfng+1yIvrG5MlR8RwVLiaUjkh9Ouqa68DRt3WpOWgcjta6fy4N3N5EFB2gSotpIwHKgSg3w9pnT24HOXjM0hR7/9DJZ2/3+EBcMXZsHFlP19GVz8px6+0ixEUE49D2h76XiyCFFlrhe7naF3CrX2HTkkWV0HSEtMmHQy58QipyvFOkjrr9Agt2Zbv8rMj4CYFi/yUWtH2myK6LKnvZqEFngfzRAw6ILkYkJ5qQI3tWuny/ZZ+35o18VpxHRM6IjJ50AK7LyItmzYrNtoXY8ZkqJFLjYPvuMukTX7M0TaWYAWqNn1yo2vQojKXGZfqGDXGuSn0Jy9YifN7rnJquNCjiCsC+8jpVgzXTtGcxj+OxN98cD61oSxNE33PRzX5+iXyeL74YXQTwXLPaQptGxi5gbW658P3vTxDGrOBqdWwG/ZRT6+hPE8sc8QRueHiY3vrWt0YasIceeoj22muvaBx+9atf0b777huZmP30pz+lHXbw60X4uwMPPDAq5wc/+EFkhsTPCy+8EP39mWeeof/93/+lPfZgd9VXnre97W20evVqevTRR2nPPfeM/87E8ZRTTqHjjz+e/uM//qPQnOjnwujafFMcrHUSX+5oaQLnuA1bOn58hKFomfj/5RBgbRGn+N1VmU1YU5DIhOKeexLJgHmD/qMOidKHmCtqWGa/PBo4LotvAW8iIm1W86AONX7ppV3t4kiAuh36oOQD7oPqdpD7r81Hi27iWjtp6+C2fJmx6ggMcisspNaVQ8sVvKGoYFVoAXk+akIb0vqTi+TWTKJSca+ZPGaZqlWh6ff1r+lzpMr10OayfGvF9XeXD7HVNts98MDx4ylO1cFAeS6qdNAKjadEg5UzUrSzWVpamX9a66Y1e1KHL08j/+7SNFlTRF5DWjskkaT1pWhMqBypO9gM0Bf+Ps1XUEhyl89dp2OaLOlyOLrnRxcuJD4f5eFL2Es68sFxw8Ox6aSQ0WeVi4F2BZDf10ycSEyw2KLlLzZsSBBAIXEuUqkvrrWWUggWE1/GUeQhi4fN2ynaQpflkJ5Tut5cQWdqWuj9lFNr6lLjih3xBO7WW2+NtG+zZs2ia665JjEAF1xwAX3uc5+jG264gU4++WTv4PA7/C6bT37iE59IvLdo0SI6/fTTI3PML36Rl9orDwuwu+66Kz3FBEE9g4OD9KpXvSrSvrFZZ5GnnwvDJcC4HKyZLLDvWNatZa7+O2zLeTPU9u4LTWheuY1M+AXoStnk77DDYidyJj3HEtHdnQ02K2rY8tGjabfDD081TZMbPK72B0TEZh5y0yoHNv/W5YBuzCAkEqAWUK12zDpFbyKicZ6IW0HYK8GEsTl+3Dg6Y8aM5O0eEclBJoeM9Cv0IKnT5C6onzkT1YaW2c/3+oZpzeTRKQDWXKeMY98w7edEamHdeYi2fdf+m83TLDlggvSNiRNjK5q/ffWruzQe7IPE1iKv7eD3yY75vI4GK3XJhZdo4nyXEBKg65g1a2juE0/EI3P5+PH08eHhiMBoM0h+wUYI1kQisvR48cVIk2UTT8t5o0mbvRSMApR8+cuJi0a5KEwbAxdpFsLHZ/oE5VttXRok6qzkU4uIkDH3lEtYdlnQAVBYQuNL36dHj440n3JOWXNFHt9rR42irVu3Ru+wD56WMxibvYb4+rJb0+kjaj5il1heDmsgIeOicbUaQvne+vDlSftQxxLvp5xaR3+aWOaIJ3BsK8wLnTVfbNKoH9amTZ8+PSJlTM58z8c+9jFavHgxMRl873vfm3jtlltuoeOOOy4KLLJ8+fL4N/axY+2bTwPHpPD8888vNCf6uTBcAgxvTPuuXRubL4iG6KqrrkqYYFw/dy7NvuSSQn2OPnKYIbCZgjaN4APlMxs3JjRPbLvPdvEcOtipgZs6lfSmKzdvfKjee8EFdNPQUHQwxpovFTUs4TPQOQjt7a3Vku1DRE93DgdtjmL7Yh2Rs3z3GKL1HY0e65Pl4MsVPt6OjitJ6GOPxYRXv25NKyV8Mr+TMFk1dVj7/S4zzOIzZrv+Mo8QWzVQPSc6NWv97P4T7F9UNbAoLwgBO/fTtNf2XTt37SWZXAqy9QT7gvFjL6puPPVU+sB11yUCeby/Y13hNT0MTEIvJnaaUBwwfjxtHB6OTTxZu8T+0NYsUEez7dIepeR+1JYc2oQwClDi8bcKxZy1XEzeJEAMt2vfzvnFZ6LNIyvjldBWKncDPu9YSmOc+ey/SM0YIUP8Jya2cv7a9AJM9MQaRgiTkGv+Nz+ugDb6LHPJBnmTcgsR1fkkbbl6Qei5m6WxC1pIJV7qp5xaotmt+nTEE7iTTjqJbr75Zvrv//5vesc73pEYnN/+9rf0ute9jt7znvfQd7/7Xe/AnXPOOdEN1ZVXXkmzZ89OvCcauNe//vWRT50899xzD73//e+Pbm/YB26XXXahH//4x/Sf//mfNHPmzIgQTpokGUDcVcsCsL8yKeQAKkU1eIVnqOeW2xXk4pbf/56oBqFK2+qL6aGOKsU3Y4cODdEZylmcCRM/YpPO/28Jhb4di009Hdqng+bNI2sWqP3lXEKzFQDOHTOGVh51FL33rW+lWYsWRY7GvCmft99+dP6qVbHjMQeeuOGhh2jz5s2RRpefRIQs1T49pkxid9u4MRHM5ql0AAAgAElEQVRyevbYsbT7F78YBdDJ9Xhy+li/AG7/0lmz6PjFi7uSFmfZ7rt87K5Yv768yW2ujo68l3OZXFbc/Z6Tx15o4GrYzyqGHcV1EMgKnKTXRiINioNEzdhnn/gSTwcJ4T2PveHZckKEejblZ+sKDmGvLxa5WbwHX71587bgHybB9Mqjj6bDli3bNoadgFB2HVtzR7Hm0IMfnWGK1IjQf6zJJ5kI8W/OOxdp43KuJKKzdGUpEQ9FY8ivc8A4OX8s0bC+gnxG/mrLloiEyWWnjTKtA7pwVFer+WKzyJXDwwkSLf7lrJ3SvvQudwut+ZNIlnz+8mODd0nkzmfPPz+R50/LBi4zW52DLipYjYH47V87fjxZnzYd6CweCrMHMql/Yni4b+coCFz92/GIJ3B/+qd/Srfffjv95Cc/oWnTpiUQ3bRpE40fPz7yY2NtnO/53ve+R4cddhj94R/+YfTeq1/96uhVvsE54IADIjNJLmfjRl5y2x4mjUzefv7zn8d/ZOJ1ySWXELcr62kcgfMIMC4BfNFLLzn9Aro2rCwQzO9yeNnw9XxjtmrcOFqyaVNMgMRcgh2StbmFjJ32yXMeNKa/7E/Hh7K+bfVFrNSBTdg+/9SFC7vDKhvfO61xs4ey3DYm8nA5AqRIUJd1g4OJfHZMYsVcJifk8cEoidD5EHb5TKwyPntSjwguPsd8vtnUt5lZhC9v+/F+7xHoB3msXevXC5LY+6EaUTW6TAHZz3m//faLLu1kD5NIvV0m3gsWEM2btw2TBQtoaN686Exh0X2RIS9iom6DfJzUCSSikzQfPHkyPTw4uE2gduRLm7txY1fkQJ+GUM4IbcXBDZeLygvf856uyIdLBgbom0ND7hD/nYTQjBNLMnzWieWJmFNynXzmae0f4+uL1KwvRuVMtDkteb/faXAwtuDRVjKM6zuMm4K9PBRtuLSBCTVjYP3omBSOPfdc0vXryZ/mbiGRruV9l88cn69MDMUHvyvvo+O81zlxZc/k81D77EX9M1pOVxRNe2HOJO9fO5rY3Be3FewKIHAVgJhRxIgncKxd45waTLKmTuWtbtvDmo1x48ZlEjj+4kMf+hB94xvfiPza2GSSbaM5ouRrX/vaKK0Aa9NeYtLSefg3/oY1gPPmzYuiVz7xxBNR2gE2tbz++uujiJhFnr4tDI8AY80GOFm25DmqUqjSNt6fNDeJEuLYFWmKbfz1ISgELjV6JL9ktE9y2yrJt21SThcBE9LiDKusfO/sIaR947z53BxBXTjymCQ2z4psFjr3XOZIvKb4uffee7dpBR25//gAZod/HdVL6tUkkAUefrj9/KSZXIa2G+9tXwj0QutX5X62fY1Ob3rLc6Ar8qMiYWI2KHvMLmPG0AVbtmxrnHo32pMNoWMScM6WLQkrA/mYBWYmiDovpg7wJPsynzs2RL2Yuf9y9OiYaMqe6dMQWqsN6x6gc4xpMvHV8ePp9OHhbX1WqV+seaZNG6NNDnVqIN/a0/7fYnJqz27tv8f1/X+mfQltk8fqhM9eDo71+NKldOX993e5SyTMRVMiQ2t3CxvJksm3kEORAfic0kFJRLO3PxHJmRYBbXztXSbY2jT0o1u3JjW4HS2njcCdSPqektKpN6svWUvf5NR+dLZPdY54AleFCSWPzZYtW+if/umf6Gtf+1qkzeNAJBxJku23OTrlbrvtFmva/u///i8ii6KxY2Fcnt/97nf05je/OSJ8v/zlLwsNez8Xhk+AcTlGc+eqFKqkLCY0l82Z06XVeh8R3dLxdUvzTZPb16yIX9x+6Ze1kedbQ63lsrdktn5fWGWLp72FyyI1Podpjb01Nck76XwmPJK2Qfv8SXvYb+HATkXsNM4R2/jR71phgTE7ftQo+vbWrd5kuXnbjve3HwSq3Gt8qPWiju1nxKrvqcs/7CFjmsiE4bTh4VjTJbk5RagWbV0USESZVPL+xH7cV199NdnLO9HeTDJBtJh8fC0lArONOCjWB3I2WUsTSyj/ddw4+tAm1gN2P2weuX7jRrJkgv/+6Y0b4z1W553jvX7P++6j42/hk/SVh7VPb/z85xNBTkJ8v/kCVCf0Zv9sPgV0HjVXBE1un2giu/y9PFYnMZFxaDU5l1wi9YMxU+TxZ682Hn/GgseXTWG17x3j4EtfJKTO5mrV80r8utOC1GgCZ4OmiJaz63LC5F3V56/4oMtlevWrLb3Efsqpve5rv+ob8QSuiiAmaYPz/e9/n9797nfTn//5n9OSJUuiVyWwCWvbFnJ0JPMceeSRdNddd9Hzzz8f+eDlffq5MHwCjCZX7DwtKvsqBR4pSzRnmy+4IHF7yvbm13V8EuRWLGFy2AHalVOHtXSuR+q0ztBiUpEgR8aEUNu/c50uB2YnPo6Dj28DOTCJvblzOnR3+uIzJyprxpqmhdBaNYn+ZSNUilaSy7GCEGP2UT0QjsTgedcL3t8+EOiF2WYv6tg+RqueXuoLN9EQcU0rBgcjwsaPzePFmqGVKpqgz2RRIkNakqj94qz5H5uu65DxttfW/UCSNYvJv/W1ImPSyefQZevXR33TdVttnCYTcvnIZoaupNzW8iS2qHFEgV7SiWLpHE2PpovPMDHJdOWwu7ZDklm7ZQOY2IBXXaaExipFCLGVA3yh+pns2DNVm9nqfjL2P1q/Pjb9tPPqHCJ6jijKN2ujM7tSKsnctUFTfPPOp8XTqSv6GWypn3JqPbtL80od8QQuJI3Av/3bv0W52Yo8HNSEb2x0KgJO4s2BSj7ykY/QP//zP3cVu88++0TRKVkbx5q8vE8/F0YWgWNyJQnK2WlZtF2lc8ApbZi2/ReSoJN98qHJkST5SQT9MECHmEPJYf0om9N0vufD8bq5c+nXAwMJ3zrtNK7bI9E3Xdi5BMJbTzyxK0x/oumK1Mj3DzzwQBRRNTbPkQ+qDryQ4Qck7fn1hRfSVZs3x83mw+76CRO2hbT2mFuymapoUeVGdP2UKfmDr+RdVHgfCACBdiPgMSFjs7bPDg4mTNpE2HYlibYXhYlw9cocXy7vduYUJAo50Xzxn9j6wGU+Lvvkz++6i65YvrzL703OJuuLxpoYHVRFkyFt1meJkSYTbMopyaO1qb4+ozWZiPPdmb2ftVXfefLJKMgWP2PHjk2eQeYiUk8u8YvW5MimV3AREfZzk9REvqTnIZEYLXHWZpr2gteafHI/GPdvz5pFJy5enMg9p/PDseuABBHhbzhmgtfqx5NHUKeAEBeSKRs2xH6OVrvmNCHu0yVoP+XUdm9k4a0f8QSOE3m/5S1vibRdrkTeDBUn8h4YeEU85+TbHNyETSDZP06edevWdZEtTsTNJpqcEJwFaN7A+OHE3rvvvntkJsl16kTe3/rWt+iEE06InKp/+MMfho+UerOfCyOLwNkDgU1MbVj9Qp12HM4cnYpNDThFgNbasLbs6k6ydZsDJ647MCCBy6RER+5y3dZarRKTFw7q4vIlc2HDB5C+MdapAXLfqDkO3VKEKJAQ6ptS69/BgobLwf7rHbNLzpvEicHFZ6IK8l9ozuEjIAAE2oOA2Zv4HGCfWtlLtU8Ta4BOGBpKnBuyT1sB3grd9nebv1P7nvn2a30W2PxeOsiGKxVMWtReIQqaGLEFxJ+MGUNPbtkSB1DRlhIcMTNhYpji+uCL7sn1usiJ+MDxni558CRNAKdg4L1d+q/b4CMinFuWz3R+fD7sGluf/KFdGlxh+XVQs5P22y9Oh8TjGWv9jMUNk7eTBgbo9UNDCa2b7qMvh5srYrf01UWs7XjJIq3L6qbIJtBPObVIe9v4zYgncDwot912WxTSf8cdd4wCh7A6nfPCcdh/1pYxoZKHiRdHjXz66acjEiYPR43kKJMcRZLJ3sqVK+nOO++kN73pTXT33XfTHnuwxfG256yzzorMJ3mT4UiUEsSE0wgw0Vu2bBkdfvjhheZMPxeGz4TIHmrcsUoFb4f5hiTa5APUasg44pfNg5Jo+847O5OPOgfEE0qf39Vl8r/5ZnP6hg2xWYV2nk7zJdMmnPrQYyJTNrCHTr0gh6bPZDRzQgYSXy5Hm6mkOdhLOO7/NFpOyZvkS2qb2Va8AASAwPaDgNH48J7zoPF/E7N2Phu0hkf823SeUquJs9YNIuRzlOrdNm+OtSKHbN6cCCUvia31QGiSIaRLCJBL48PvCMG0ZoQ6aq8mLBL8REc0ljPZCvo2V2jWRa02g2ffMH5cF25SDv+uo2XqXKtipaPNHLOiifoIjD2TfdEXtUuDi1T5zGgTWj+Te27mwACtGDcutjKRsbBRN8VfLyEPqJQSQvolYbkr6I3IFaxNlYBirjQNXsLYg12hn3JqD7rXiCq2CwLHSHPONPbN4jQAHEGSE21zMm2e9PrxEThO9H3ttddGAUxYa8LEjYkfL1LezFzP17/+dbrmmmvokUceoaGhocjf7dBDDyXOK2dz0uWZDU1eGHrDrpTAOSIusnnK06NHR+NhfdTYl+Czt9+eiGaY2JTV5hui1fIFabGHshYKJI/LN1RUTt8hHhMqjxmQHPJFME3zk8sz7+Rdn+mpJaj2pljXZc2MLjr66ITjPL8r5igh41OkH/gGCACBkYVAV7RfE4FS0qnIfqqjCmpzNJ/PsgstezZcNGtWQmOTiBTYKcBl5icEzpWmxibd1t93Rbfs1GG1bDoNTPSKK3H3U08R+0hnXdRGpGLmTGeQEr5wFCy1PMD9Y4sZSXNg30nzC/MFlNGEO+9MTvPPt7/5zlA79s5z2mO1YjWFeZN8s4zzobvuijWSGr+qz/y82PL7TZZTi/Snid9sNwSuieAXbVOTFobd6HXYYL2RF+2r/k7K1om75faRb/fEH86VN8XmqmHiLRtm18HmaKxsiK6gKPK60+zDEXgklQiZzX7hnnvS5lNPjT4paooqY5QWASvP+OS5nZWcOD4H+1gwWbUqIQxYh3A+rI69+eY8zcS7QAAIbGcIuKL9SlRJrT3R+79Lm+OLGtwFp7lwE3/nVP9qcxnpilDI9eggHvaMSiMf+jxKDWixejVtnDYt4cPl0hZ6p1BKkBKtZRIN0ZrlyxP5Pp2aqLP5Knbbk+gnB4SbxdlYO09J3640DH2motZs08oFznPaY7Xi0sLy995L2hQ/OfudJe/9sGJpkpw6UrdBELgWjmyTFobVaumwwSy079C50asCZhdB0lG7Es69Nqn04sWkbcq5PWJLn7ppdhoecmBagpnosz1sfKaIOUwULaapEfJKlGvrCSVw9hZbhBLrYC+mQZr861DT9va5irmEMoAAEBh5CNg9RwfsSAjfymTNtb90WQ/4yEKAdqXLXN31jckJKheTMkJWqA85j1z+25ag2WiMLm2hd5akBClxEcGu4FwBBCzNakanPygyk73npeOs7DJn7FSYGUSs856L0Lu0sFmyiC3Hd2HeBF+4JsmpReZHG74BgWvDKJk2NmlhpN6QcbsDNunQIeC6fDeKXaabDhNJnd9H/ON8B2QucsQvm02fE7iK07bTBDAlGEhIdEyuMtSnLupLYPCRkLHwHXyudrtuGa2DvY3SKYeYy7k9pH14BwgAge0XgSDNhtkPrYY/mNgEaFe6CJzrm3vuSWiXuD0n3nprZHXBj7X8SL2s00OfcXEnZ6o2awwuuxMZmn3gdJASr8l7gUtEl4UPayZ9ESgrmfUFzsosQt31u8FC0ipkWQPlujAv0I9K8OsU0iQ5tcp+NaksELgmjUZgW5q0MHJtKIH9873msumXEM38jYRG1g7WvNlb+3kdetnmmSncREeQFT5kOMKX03wh5TDLOgykjfa9PGY7uW5aQ0Dx9McGeREzUHGw19joyF+SgkJCRvfDBCSk23gHCAABNwJ5iEAVGNpAWjb6XxwOnytLIxPqNxvcw9VO176b1feubzLM4+RSq0jwqbRzgdtpz83Q84fbpC9OJUhJWuCM0MvJUBmglnOhANHMwqxrPrj8D1PcLZxnvjUp5Zf0hXmBflSxDqWMJsmpVfarSWWBwDVpNALb0qSFEarSD+xa6mv2gLZhi+0mmqYdtHmASgfKcITrP/uaawrlocs6DJybeUBQlrKHpx2cxKGUJ6pnSphqXUewH0oVkwtlAAEgUCkCoftYSKVZhEiTCTGV9EX/k/p8xGvP++5LBFQSE+8sUpEnwJQLG9dZWoW2KWscfFquEHKkCZycx95cZ4H7ftp8SLPCCZlHoe/kPSvzvq8vEKy/d5rVko1cmeWykrtdoQAFvNckOTWgua18BQSuhcPWmIWRw6m2Spjl0PDl55Gwuz77+a4NUxpX0tzTbpZZQofvYM36zimAOHz+6DROT7rtyTrI845Rmn9CloYvpC3WZwKBTPKOEN4HAv1DoErhMWS/6Honw4TMVaYlCK70AhbRkLalXn51Anek7adx7rF163IPaK725dTaaKsJSeSdFiwr9GzzdTLEry83QI4P6sTMnt/PDQzQDUNDXcncQ/qRFTQuVz9CKszxTmPk1BxtbturIHBtG7EmhWd1HJA60WaeG8k8w2BNKX03hT4TQ71h6uSuWaQjq415N8u87yfqtwftnXfS0JFHph4CZQ/PLMElj8AW1JaU/HtZY4HfgQAQ6CMCOYlAVktlr7S5wnRk3hCzRA6RL4/efyVnF+ck4zLFHDBECxW0l2V10Po0W4uGAPO6NNITHMG4rN9UxePu7FMP6sh1NhfETNch/t4h801jojWgLnkrVz8C5mieV0Dg8qBV7F0QuGK49fWrxiyMFA1cWrj9suCFRlhKC/IhG2ZlPnCOoCJZ/Sx18DsOjf3OPDOR9y6r/rK/+0ItV0ncs24Yy/YB3wMBIFADAo4gIQ/uvTfZZNihNcteo0P+i3A6ZswY+uCBB9JV99/fdYGV5f8lxEaSSQtxqzVIRkin1dmaeL2klUhm1WXJUUEyk9ku80Key8K8ZfP7uc7mgphpcsV1clTsvGeny4dR9zdXP4oAlfJNY+TUivvVpOJA4Jo0GoFtadLCyGs2GNjF7NcKHBSuDTPNXj+7EX18I4U85z0ECvUiR6jlMgcK/OAKjQ4+AgL9RcDsDxIW37c3pQqaJqjI8ePG0UHz5pFoy7ijf8WBdnWPO0QnVAMhF0U6x2jZMPVlB8DVprJWIiFtKkWOCpKZkHbpd0LHNW+5Rd8vgpme83KBUOTs7idJS8OrSXJq0XFt+ncgcE0fIUf7mrQw+raRFjgoXATOJuZs03TohQbMi0cBAs1l5Z0vRXIHtWkM0VYgMFIR0PuTpG3xCaip+4IjYt8fdUCbSRSFsOfHlbYlaL/pl7YrY+BDXQWqnj9BmKVUWoTM5O1D2TbmrS/r/bLtaSoJy+o3CFwZhMp/CwJXHsOelwAC9wrkeQ+Kqm68ej7gngrtodHTQ6AAgS5C4HS0LgkosPYP/iDyU3GZY/UUg6ZMBLQDCDQQgTw+Pql7+erVtHHaNJqg+ng2EX2OKDKZlGc9p5IhomWTJ9OqTrCPoP3AXkaV8DerchhcaRGKpBHI26ayZKTs9yHtDRrXkIIqeqcXfa6oqT0rpklyas863eOKQOB6DHgV1TVpYRxzzDGxMH3bbbdV0b3gMspsmk07AII7rV4s0/8i9dlv8hLoIqRbfyP+L/w3n79AX0ltFaCiDCAwQhCQtbjv5Mm0YnDQH2DJXAZ9fe5c+vXAAD3wwAM0ffr06KLm3gsuoJuGhiISxylfvkNEJzlw4ri7S3baiXIRHVN/mYiPdQxdr/f5smdjr9tbB+Z5yyyLWd762vB+k+TUNuBVpI0gcEVQ6/M3TVoY/dys+1l3n6dAVH2/D43c+JfU2nGfWZjb4+WX6aDhYWeC9L6alTZhUqANQKAhCMj+dOBjj9GxS5Zsa5UNxGE0YJx37Yr160n7J/PHr127lthkUvzUJIKwFPwSEV0zZw6tnzKFzu6E5g+FQvaNvgcvcTS43/t8KIbyXtvam7d/eD8MgSbJqWEtbt9bIHDtGzNq0sLILcRXiHc/666wG60tKvdBXcJvjoW3w4iIo4YuJaIdXWZODoK433779TQyZ2sHEw0HAnUhkHVxY34/ZWCAXj80RJpMcZj/+fPn019s2JAIVrKJiP6ZiFYb08m8XZGzpM0+0Xn7jPeBQJ0INElOrbOf/SwbBK6f6Besu0kLo58kKjeBKIg3PqsIgSxBzlPNVWedRR9euDAywdrAWjj9nr7NtwRxwQIamjevUILUinpcSTGY55XAiEL6iIDWjJ88b16XD6v8/stx42jJpk3xmtXmjHzW8EXO42YPYLPJrxHlDsGu4ejnOdbHYUHVQKA2BJokp9bWyT4XDALX5wEoUn2TFkYRP6gifcY3IwOBQvPFEDO+dR/X0cAlQn1bgrhgAdG8eduAqzuHUk1DBOGyJmBRbBACoRcIae+5IgDriJTyO5OxxaZVNnjR/QsW0NJNmyJ/OP7tT8aMoSe3bClF4EL7GAQYXgICQKBRlmIjdThA4Fo4so0hcAU1Ki2EHE2uCIFCZETNM/F7YU3csUR0d+fmnU2s7rjjDnrmnnvokC1b6P7x4+n000+PNXcc+KAXOZQqgilRTCHSW0dDUOZ2hUDeKIh6bfN61JFisyIA65D5D3dMpDXY7Be36CX2cnslFYmYVP9w8mRyafS2q4FCZ4FAAxFojJzaQGyqahIIXFVI9rCcxiyMgj5NPYQKVTUMgaI33UxiTh8cpI+q/pxDRM8RRcFMJM+U7i7f8O++ZUtrfeAYq0lr1tCsRYtabwbasGmI5gQgkDcPWWjeN9eFhOwL/37RRXTc8HCU203yuvFlzcyBAbrl96xveyV4E/vDMUEskvg4oOt4BQgAgZIINEZOLdmPJn8OAtfk0fG0rTELAxq4Fs6edjZZbt0fUQFMRhHRDh1TykMmT6aHBwcTnWPhjh9fyoGmI8F9PmHt2kTQhm/PmEFPvOtduaPsNb2vaF/zEGCidfTgYBz1UWuxuy5izFkg67GLYKn3mI7NOfRQetMRR7yS03HmTBqaNi26rODfZhNFc19SB2gNetGLoOahjBYBgZGJQGPk1JEJb9QrELgWDm6TFgbMu1o4gVrYZH1DzykEdpswgc7buDHuyfwJE2h9598/IKI3G81cG2/qLWlloXYfInohb56rhow3hO76B6IyjBXRSrS640faZQptrDF4Pf5i48buVB/mvVkdkhatz4ULiWbxX155OLrkX+rKW+rDWv+oowYg0DwEmiSnNg+daloEAlcNjj0tpUkLo5BPU0/RQmUjCQFfcuDRRDSp01Hxk2ONgSQFjm74c+aG6jducjnCqRP2IEqEVfclKvYJ8JUJ9iVAwV5RArzAT8tgnJgjO++cIFNcvdbAddWjCN8rnmrbtOMJ31P1HptGMl1jDRubQa9atSqhgTt///3pvJUrYT4cOPZ4DQg0CYEmyalNwqXKtoDAVYlmj8pq0sIoIzD0CC5UM4IQ0ELmNxcsiPzbdma/GF8fc97aZxEd+f2BBx6g6dOnR6Zf/OiADZXAbUzNWPPGJqPvFmF33TpnNb71GLpO0/qfhU1Wv5umrS/bn6z+Fv29TLvKYJyYIytXxmSKiZsO58/9ctUjf7PacTJrcMaOO9JNQ0MRcdOXLUz0bN7GMv0pij++AwJAoDwCTZJTy/emmSWAwDVzXFJb1aSFESoYthBmNLnhCMjcY3PJRznKZKe9ViikqVOjwAeWZLn+ljWf5fdRo0bR1q1boyAK/JTxs3MK7MbU7PYjjqB33XVXpjaiLIFL638WNqnTpYH+sqX6U+PaKNyukhhbsiT/1gm1WfOr8zK6tHL7Tp5MKwYH47lqteBzdtiBFq1f343g4sX0mrlzE2upMBY1jg+KBgJAIBuBJsmp2a1t5xsgcC0ctyYtjDK3xS2EHk1uEAI6nDibGR7YaduDHZNDTiVw6vz5kemkSxAM/ZvusovAFYl0mRVWnQrmtPNpLLI0GSEh40sJ0w2MWJuFSb+meuF2lcHYQf5es//+EZmSywrWNnN6gF9feCFdtXnzNngWL6aLn38+SuPBz1FHHUWiHdcRYsUPVRNAe9ly8Y03Ji5aSs25fg0g6gUCQAB54HowB0DgegBy1VU0icBV3TeUBwRCEdCCoAT4+CmbGna0Y1yOCI0iFDOp+8A550SmjyJkLh89mnY7/PAuwXOVw0zR+qX9ctw4WrJpU6ZmzPZJC6b8m9XgMaG67rzziAO2WP8g1nokEphL4Q4hnAXikFQEISHjixKLRqZDKKitqv3CqmC7oilQ5lsH+RNtmBC3L33pSxG5mkpEkqtNNHBC9mS9Zc1vmUu8XiXgkGu9Md6aGLbNjzV0L8N7QGCkIQA5tf4RBYGrH+PKa8DCqBxSFNhGBIzQuXDPPem2KVNo7Nix9Iu773aSHyF6r1JmXvK3CePH00PDw34y5kko/ooXXOfx+NxZwV8LuKLB0+SShWUmdS4Seg8RseDbFVnTI4TbVATWJ4nrsBEvuxKflyAH2tT1sAwfPoHRJ7hXJdDfeuKJdOySJZnjlka8fYFkSi2lMlo0j29aUHs85F+bHevLC9F2L+PgI+vWdfnEuXxVo4uIzqWIzAlpW1qUWGjhgkYQLwGBRiEAObX+4QCBqx/jymvAwqgcUhTYRgR8pCLD/PBaIvqFSUNwNhH9H2u20siYFa4773IyA1euKl2UFUJFGH5uYIBuGBqKc19xsJJnJ0yItIgsPE+YMIG+8IUvJMxAWWPhJEIOPDgohM7lxWT1+rlzafYllyRG3JUonYkem8ZxOw587LFChEfIoZBRTUjTppwW8LVwr/8umiFXhNEsTRn3d/ngIO1ooitmLQOXJpe1QiHRP7lsxlIHwLHf7vTb39KHFy4M0ugW8eF09c+m6GCidfK8eUmfUcflBc+lQ030yFDi7yJwbJ7pCgZUVPObNZb4HQgAgfoQgJxaH7ZSMghc/Sd+OxIAACAASURBVBhXXgMWxv/P3ptA61EVa8OV6YTkEPGKoAIyaEQREZDhEgYJg3pFCBqIglxANAh8n2H4DPdCCGAYgr/kipAoILkgERQkoASMzAkEiAJeUBaCSATlEhQEQw4nwzlJzr+ena5OdfXeu3f3O/V7TvdaLsl5u/dQe6pnV9VTdRdpVWCbSsCq3GmgNX06dU+ZYpRijrkB1TlYHTn1AEDY14joKpEoPEF/DvlYlFj8GXQM04iIrRE2USbaKejSGfzxNwCXFzkYJ9mt8rGeHifwcNXDTIK32PLIWfqlXeM0MYWWjQ8wcZtclkMXEHnXsmUpoMrJpSGvuRgSmyUysij6iGVkOb5xS4ylhRkUOfkAPKZNm2bAh7Yk2VwJJQEOrHj6HVu/bXPKZpnKAq6+crzEPI7Li/njx9MhhxySTDkgrdAOi6J0EeZYVbY6J2RYg+W3Tbe0qtmVBPqFBCo9tfHDWAG4xsu47jVUC6PuIq0KzCmBIopiziqCXre6VzksUed0ddGRotR5Q4fSOEHGADB1OQCOB4wxGPkIEZ0pygL4u00k2JaufkfuumvCqrL4wAPp4AceiL9mECcJHQAuR1qsQ9M22iiRwFy7QybkoRIjo43XBrheAkReoVzjoGTjQUyedPXkmCSuV1oMzQcq79fhw4bRARdckMjJZxtDSTXPMX8o+7gZM4xcJHCeJ8hq8PdU/GDktsdz9r3d3fF4xFYkR1qGxCRUYITl9NKQISl3V/5OAmr8DWkvNKujfMdXll4QRVwLbevWxjaJuhIA2APyr7rqKrfVMNRKbkkhYPpbo0tp0CZSvVRJoJJA3SVQ6al1F2mqwArANV7Gda+hWhh1F2lVYE4JFFEec1bhfZ0VUe2OZlOc4Q4GQhCOb8M7sMB9qbOTbunupkQMGxH5lHpJ9sFEDhxDB2sMLCpoG1tkUNc3RoxI0KbDXXOqsPSNHzaMjurtpRNcPWaLxpIltGL06BjAoA8jX3iBJHMfiojd0CZMSOTyQj1b9fauJ0WRgMVhWfqnyAVmI4mRlhJpZWM52BRwA1ZfeIGQ2sEJtpYsodWjRxu3VH4APkcqOfJverxc7pE8dno8JqHcFZx+2jPtHCDmqM5O2rEbMFNZYT3JreO8asIaixJQ1ubd3QYkM4OqbpHN5dFGAGLrSWrdKoDF5DhM8GMj/eHE8jyPJBusBvcaTLNrZsolV1jJE26YlQWunttmVVYlgaZJoNJTGy/qCsA1XsZ1r6FaGBtEWhZLUN0HueQFthrAZdWv3dI0kcfUIUPov4YNo71WraK7ohg2KXLjGnbrralRkMCRiVK00qrje7T7IZR3TnXA1piQXHbaGjENcXyrVqVSJNgIJGSsHSvIEvjZFPazNtss4RoHi9OlHR304uDBSXdBZWWb0NlJd7wNWLXeAqfBmLQaWsHWwoWJegH6zieiZzo76ebu7hjAysFhWeBvqTxjEQB2xR3aYgJdyw9laEuudINlUG0AqsPtkMs24zB9OtGUKXF1ofGUcLP8ncfd17Uv6hg+a2zj2LEx8JeXE2ikTCugGSfxu8s9FL/JnIkpl1wlBz1HYLlMXTyUfI+smldJYCBLoNJTGz/6FYBrvIzrXkO1MDaIVCvyFaCr+3SzFthqYoE8AA4sj5rIY5cofgqdA3iaQETnOdwWXRJ1tYHjq94l3DFRhm4DCEtejFIewD1w8po1dNHatXF1DJg47cFZwqImrU7afVHGErHSra1OUJBl0mR+zxV/JN072UoUK9QKqEgrG9bjgvPOo3m9vSmiF/z29tSpiT4bxV0ACJS1LopVZAsVrF2Q7SQRwyiBk3a/NPF6cEcdPTomB0n1IXDZYGw5STU+0TGMphhhMZV1slusrGrhpz9Ne9xzj2lXqiwHo6mNcEZeOEgLMMaTyUG06yjmn5V5VQFo7SqqyWOk9RV93F8wjUrACIuizA8HOcTATFkiZYxl1loPHLrqtUoCbSeBdtZnKj218dOtAnCNl3Hda6gWhhvAVYd93adbusASuDVlAUhbvJMk8mDgJHPGwalPKp8uSXLZnBdLgx7p5ogyAC72GTXKKKv/LQpF2oNrlyyJ0x3gJ2ZGZMsHt1PnswMhCLsqavdFCcb4v20kJGCoZAU6xToYtdMGFqSVCC53AJ8nzJiRcHlkQKFTCGiXPAmGJAjjPm2GmDYhM7g7zloJ2hgiuKJeIgdJgb9VAOZsDVQgk8tx0de7FCdt3buio4Mm9vSk3FphgZPugxgvgBvEYPJFAYNwBjI2Kym7msbddLBB7inyG2JeSiIUjqubOGgQXdMHKL7+cRLmOFxFfzx5Mv2ts9PkUIzzsam9IBG7ef/91H3QQRsYNdW/9x01ip7q6krlatSWtmpPb8KeXlVRSgm089yv9NTGT6kKwDVexnWvoawLo9m3RfqmWTO6NSRPU91Hsw0LbDWxQB4AqdqKWKobhw+nD/T1xYQcW+y3nxmERx55JI4fg9XCRk+P9/hQtdLYe9j6fvrqq3Tl4sWGQRLK+9EihQDAJRTa5V1dhnmRgY4GiVz3kCFDaOzYsSQJUgBYQBLy8qBBMSiUwExaP0466aQgunqOb2J3PW0lYvKWfXt7aW53twFxNusgz3LpYnf6ypUJQhbpBimBn3QVZKUfoBFjmGLkVNajOL5NzRkux5WKwKU46eTxAE54xvX0mP+XhCoavLK7LF8UMAhnkI22bLdu3Yb8hcuXGxCYSGRtcWsFa6kE+rA4yxQJe3R00LM9PcbSzLJkiyrmzCFEtECR27jSStDXMOLi8bmJHn880fXXb3hZ/Rsg/7GddorXmUvmzT5X2nBHrprcTyVQAbh+OrB16lYF4OokyGYWU1YA1+zNRsYa6XgM/nd1+DdgZuYBUA2oPhcznYWgQybxloQJXsua6IfX+qeIRvAZW5b+ZffdjWUE7pp4RqhcdFBoj5g/PxFfpuvSbdSukQBYcMK0MVjK9fnvq1cniFVcMX8M4LjNiEPjvHVs0QQNP6w8sm86wTMD0p9dcokBKLA2yXg2tlIyGYcELksXLYrB9tYHHEB77bWXUfq1pcl8q8bb5CmLCFukLBnYWq2oAqQzUD744INjq5O0eDJwktOcQSHK3mLVqhg0Savqdn19cYoEyTzJ5djiy8zfBLEMwBfgFEh4JLOlBsawVOKB2ykeADnJxoo581GVXkID91R+N26oh6ilM8MClycVRSO2karMSgJll0CWp0mZ219WPbXMMsvbtgrA5ZVYCd4v68JoBYDTOZP0hudrUwXuwiezllVLD5acADKIuEFYFrIAmoxrSuWKIyJbDBxABP+dXeiY91CCLUksImPetAItCTnY8mUdTZGMmxk78f8gYGHrlY91U1ucAIjwyHg+aT2U1rJbjjuOJsyZY3Wh05Y8gIyrRRoG2RfbRQ1+lxZ4mb5Ax13pVAe2xOC+uK4/R9YpjiVj0MdWQABSWNPwwHoqLW3nECXYRW8/7DD6xbJlNGvRolgucE08Y9YsA/bY+op4MTxgT2XrHkAxgKeMKZSxiSDH0UQvAHlwa5SMnnBAHaz+hnY9u/feMUjNw3Qp1wvaLAlH9Fpq6b4Rvt316zerc6/8w4sxGrF0KU2cOXPD/inYe8vfA6Ky6qntILvQNlYALlRSJXqvrAvD61rWAPmlGOz0je8LLxCsHq6Evs0GnA0QQVOKLKOrah5FMDXOPgCYBQ4D3Ec1C6W2nMhBg9vgX1evtjPsuepSVqZvDB1KV69ZY5R0TfqBGLWzrrkmYdWTVjVJtGKlorfEjt0wfDidtGxZIjYNFrx58+bRVSKvHmKsEqkRlAsdgzgASLgiumjzub0AN674KMjUZYVnedvWu7SmMVC7ZOLE2L0UbbyCiH4SWRklC6OOK5TxX99H+ojIEipBFsDa+/74Rzr8jjs2TIPZs2nXM85IuD3iYgAxijpG0BZLKeeTBsZriGioZZe4lIhOjUCc07pmSYhuU/6lXFGV3G+1zKs9tylbtreSagyS4mkGoM1bB8ZIMyfrnJ+tn0n+FpRVTy273PK0rwJweaRVknfLujD4YNBU0o0SWyqpsY65UEx7OiauOsjCRibEVTWsJP9beQ65PGPnU9xT1ORZAC0L4EWWIcQtvfTSS7TtttuScb9TsUuQBFuuQORgjcVy1aXaODFi1OR0COzaODf6uyZnkRcfElzYXNrkLTCAFij38ciE2uwiKhM6493DiAgwBTF/BiSoCxaAHCRNv0W57+l5IK2AGmxIohAeS9c8SsWTKVZPsDKCGEa7l6K/7OIpxypFwe+Z3pLJ0ZYCYNqOO6YStE+aNCnh5oriEdM3f+XKVCybDcTB+qZzHHJfdu/ooNU9PQmLX0xMIgrzATC2SMpcjPg0zkN41lnGSir/nWfd1mNPqcpIS6Aag/XWe56XkrW3UXHzeWUe7MJc4gleVj21xCLL3bQKwOUWWes/KNPCkBshkyTIeIxGbYiIdUklNbZY4CTTnrYwhFpx8gALOTuKftf6GZZsgc0C0oi+5Tnk8tSfZTVIzNEAgBY6bxJSVOVyIuesWCxrXcoCB+vVJ9etS1i/EBuFdShZDxn8pMACN1TR1vN4pNg5FYCU5CPcXlizEGulkz5rN1Kb9S01D3ygOmC85Dgkyp4xI5FvDoD3vI4Oen9fH93Z20vD1EJMWUsd8V96/cpcaiAYgYthQi5PPmlN0H7YzjvHxDAMvJB8Hom+pXz3VMyW44hoOyLzDgNoWObmRICeYxZhKeMnlI1TzkeO3XN9a9vH8qzbeu2Dsk4NMOtVR73KaYZ8Cu1f9epgA8vJIzuf1djXxDx1OPedf/4zUwo8Rnr/zPwweqFoO0PLD3mvTHpqSHvb8Z0KwLXhqJVpYfBGKN2JWGFZutFGJgeRi82vJtE7lMjE4eTJLSTJDnwuRGhjHmBRy6Zdkzwa+LE1rxYSFXueIgdIMxULX/uy2lF0PnC5tgsOV52uuhgIYQigkOORzIMy/iwxTNOn04opU2Lae58FzmlRtwBIdn/UN8cSvDBLrHYNipkio4amZOEDaWofcJGxsAz0/qBTPiA+DDJBXCLLBt/Kv8v9Qsd/ITYQScdHRBUCOI0bNozGTJlCtjxsJhWEApIGED/zTJy3jt04HxIEMjrNhGS2lLkFmW0VMXoAdHg4N9y0adOMFYL/lnnZpsahaC69Bm5V1qKLKuuudhbZ27L6zGW6SHWyvg/+PceFRyP6mdXOWurMsy/Ld/lSJSRRvK4jtL152qaJmNgzwLU+c11QZg1AHX8vk55ax26VqqgKwJVqOMIaU6aFwRuTZsODy9DF0Y1vnhvaMAmg4CWxgmOjLTd1KsUo4UOe5SonGpJr8835HW++0g3J5soULJd6v7hkCa0ePTpBguDzxS+siORQLOrdRV1e1nhn/e5qXwggsrkIShc0CUQkYENs1RVXXLGBgv6yyxLWJXxnyp4+nWjKlLiJiIV6Loot07ngQgAnAwMZf6bj4yRBBuaHzS3z5EvRkuS6dgGlhCXdAyZT42CZY05rZPQxrHJPWBhDeQ3YwIHOTxcDVLXnxHKxJWhX4weL6rChQxNWVnx/ZU+PYeVkxkuZgoKZOqWlTYI1m/upd23ZErbzvCoxwUI9ARxkxsC3nueac2+o92bXhHMvq8kpV+azmCe1+GUp6nTtyzaAI0mgJKuuNdeh50wPPQs0kZbvUnv+EUfQIbfdFtfKqVpcAM7WhtB2ZY1VLb+XSU+tpR9l/rYCcGUeHUfbyrQwRowYYW5xd+jooMd7egxjEt9cN9oSJwkIOKlxYuMSlNspK1sOwOCzyIRaclyJkptxcIfeElqnm01p8yhrhfuTQ7Fo9JLNOvyKypPLTcW7Fei7TigNQPEvkydvIJBQc59dNp8UVmmsz13EJQvkGhNQqO9BhrJyiy1ilkLuC9wkJbkI4tVs8XE6IbVeUyzTVFxZ5NbpGxMbEYlV2bHIGeQhkihEsjby/gXLlS0ROvok28UxYUiVIBk+ETdoAKpnz0ntZUL+3A6Mj2T5lDGLrjhBeUG0JiKYiVMi5NgDNbjWZCllJljwsWTm3Ut4vCUQzluGfF9feKXclVXhrr0neE/KMeZZ+yA3Lbju6AOfDEPrtMk82ItByEDPY1y2zFq5MiZE0vXo9gW115LKhvUVVz/4ci6L4MkFXLO8SGqZs6HflklPDW1zu71XAbh2GzEk/N13X9Pqhx9+uOmt15v14MGDqa+vj0BcsvPGG9OpXV0J5jncHl03aJB5p543lnLjkoep3lB9G1nQJuc58Ly3sY7EwVoGhQFPRJZhs85kHTq5Jo3oB5TbCZ2ddMfbOFbsD/cnSxFJfZ1DscjV/gIv51VIQqtwllug7zaFPZGC4KyzTNoCxFtpl015G/zi4MExQ2XClUhb8CzxcTYyDlKJtGV8nJSTSxHKAkoyXQCXh7JsOehC5hhkxlT9YORk5kiZ4Jr3F5urlct9Sac34bbIPUde6jCRgisdAo+Tc10VuATIlU8x6oDLcmFLpxG6Lhr6ni9PXQGrIc81GyNqaD/knOFxx7ja4lX1xYcLMPiAhJ6jrjmoPT9CLy7zkoD4AFzQmWwTtGcPTcnGY0ned9QocpJKRSliZJoM56WcbKOqL8SixvsHPByyQlFyuZyHTtI6vNdKPbUOzW+LIioA1xbDlGxkKxeG3gxHDxpE+0eECW+8851GkZK3xLj5Zza8egE4fWsplR59WPkONtvtecq1weX2dNZZ8e27BJDxSFno1203e3mClfP0zacs553yNkunqwyOzeJcZ7B8Zrml2JRbK6V93oa30ftFFJesb1yXA7Z5L+OzbIyRNobKHR59NEWHT2PHWl2b9VD4brJdQMnlugY5aLr9kQ7l3CYzbstXiei/RUNPHjqUXtgfuxsZJtFQRdV3sSP7jXLZ4snWO7n/yHdZJk6CqAKXAHnigCVY5jbniR1q2VLUwFY2RF1KhLSxSDywb+7jN3lmJt4VORx5XhQBcL61xnMw5RWgrEaxFTlqoGsew/KddQGm43d5r/exzWaOjecCIyUz1Tew5cKTADFweGyXXuYHyxpjkjQvcZuDwMp1xkmAi2q9rN62da8u0VplHW+lnpo5X/rJCxWAa8OBbOXCSGyGTzxBb48ebWjCOQ4Nt1f6NlETGdQq8jxWq1AAJ5UpFyuhdgmVcSepAOiQgH/xjo9MwqY8MSGEK8edlHGWop81Hr4b08S3oj/y79otxXXAh7ijZCkHWX1p9u+h7Q3pexYIcv2ulTNrmxzuhfK2OSU7B2iQFj5XbjeXVWBxRwdJq6Bci655rGNGTDsdyrlNzhyT874VK+iqxYuNG7hObu6L27HNKdd4yn4za6+LPIHf1eBau7Ny/UXWed5vfIp7s9dWUH1qjspcfbmthjnigX3rXgNz7bWCfrHLv85j6lpbvnEMAXApkGC5gJy5Ask01j+2ywWex969zMYeHV222NzC6WuwVwU8Ae7Jcp1pt2vUYCNiS7g66j1y+nTqnjIl3i98ZCO2i03X/OO2MdOsFxzagKu4RMsiaAuQbOFXWqmnFm50m31YAbg2GzA0t5ULI7E5K5IQVtRxGJzQ15e4zeY8SPWwrAS5LVgOGh0XY3NlsVkJtSsaH3YAqk/JPFfq1j/T3ch1OxyofOqDUisN+LckjCi6mXM9Nve1RJ0Zuc6KJFrOC1rKtpxDgVnoe7J/WeAw6/eErCwKkFYebbL1WbSCLe6WGBFOqh2vWZ+FyaMU5p0/LgU57/hkyj6PxSzQPTJvG7UinslCqdy2Qy2SrV6TUlkHgINNNYRxMNVui+vddZMn0986O1NMy76xsJ0L8gIvjlddvjxJzKHiIvcbNYrMeZrh5g8X4TE9PXGfbZcIKZCg1mRcVyQU39nm3TdsZ1503qFMScyUF2BbQaxDNhgf6aaI/UYTsaVcHXVZihDKp+OgvqDE3JZLXd8FuLZasrcL4nDlmLdiDbZST21Ff1tRZwXgWiH1Guts5cJIHEy33kp9Bx1EOBRxAP104kR6fsstY0Y0dqUMsS7lEUkeRSVTkVIHEiwAXzz7bJKskDJOgd1FlxCRZpvTt/7cTn0wxO8FWOB8N/ZS2dxiv/3owQcfJBAVSKAUdGhkCN8lw5SrmIM4A8Xb4ga0gh8yVnnGPs+catS7oRaOkL43qo1crm5rSJtcFq2Q2My4P0qpgyJ0aUcHJax3GSDG5ZalZZbVJ9f8Ch3H4DEKBGWmvECwl9U3W9uKfMPl1PJtsJzq8KL0IPC6o2XVZYkHfnjYsA3EPyK/l2seSYVbE2hg3l8UpXvgvTEx71RcKlx833vOOeQi/kF3vKAow7XPuabEdzYyGxADOS33nsuW0DXsmn9WmVvW2bdff93EvTLREOJdDx82jL5w2ml07IwZxqKGy04JpK17pCCEytJxGDC6SIhceyH/nfdE6CbazVrG23JuxprmedY6CPy9lXpqYBPb/rUBAeBA9oH4iccee4zWrVtHu+22G02dOtXENYQ+c+bMoR/84Af09NNPE4g7Pv7xj9M3v/lNGj9+vLOI559/nqZPn073338/vfbaa/Sud72Ldt55Z5Mb7YADDgitOvVeKxeG70DR1NqwUJ1DlCA1qcUfmxWGut8uWSwAnEsJBykeHdv3fSKaJPI9wblkDrPNRSPGB4qLmAH9kQQKb0Y5vaSV0lmGSFou2fJQtQRwut0u96sik1EqRlrh4BtdW999ydWz2lF3RTqrwlp+D1S8a6mint8WAcdFvkm1OeAiIwvE2OZiEdlYQUkjxjFnmW0174sIvoHfSGuLtDTZ4g6zmiGteXCvw2NzY3eNl3YTZAAEEAGLz0ZEBOIK47qnWZTFni9JdlznC9rmdUu0WMN06g5pEYvPDuVpwX1wxc7ayJWQLxEPclhqKyK7L7uYm3mMXK6hCZdxkaJDuqXqi03ThxdeIJxNaJuM4ZaWQF0nxlm7wCIX5WM77ZS4BEabcakldRdr/yx7oWb1lm6det/j+VgBuKyV3D9+7/cA7u6776bPfe5ztPHGG9PRRx9NcAO7+eab6e9//zvNnTvXC8B4iE877TSTY2nLLbekww5DyCvRvHnzaOnSpfTd736XzjjjjNRsuPPOO+nII4+kYcOG0bhx42ibbbahN954g5544glT5znnANoUe1oG4LTScf/99PZBB8UxcBtHLoQcLwKL0NZr1jipr/P23neLlXUb7P3dwhJ1bdQ4BnApS5ZqPKxxV+PQFbewWfEuuj+2uBsmbkhZ8Y4/nuj66+NW4PDHwS/Z0ViJeJGItouIZmA5DHZtyxggqRjp+Ac+QHS7F37607THPffEt5y53GRyKr1551fd389jZal75fkLLALGstZdaCtsipC+7PGBmJQ1WKzD0DY432vQOOYBZUXGpuZ+95MCtJsZAxHpAhoK5rTCbCVyUfuUvDTTFrHTI9r64Sr9DkhDtv7znxP5wLAeDjvtNJrb3W3yckorEYYKVi/2IGErjZd0xRGzzPkJX7ngApq5Eins1z/sUnjJxImxpQpnFpOAoO5LPvOZFLFRIr3JP/9pJ/+yxNzdMHy4AccpgpXIlVeSGmH8ZGJ6tJdd/nWsKZ9bz0SAOe5glIpF57EEIDvk1lvNazbQqC9JmcnSBqLk9yjPB/5xbh+rLsC1W6drPnrj5pq0rlumpzapf2Wopl8DuJ6eHtp+++2N9evxxx+nHXfc0cj81VdfpV122cWw+/z5z3+mkSNHOscC3+25556mnN/85jdG+cXz5ptvmr+//PLL9Nxzz9F220FFXv+89NJLtNNOOxnAB+sb/l8+cHMbOnRo4fFv2cKwKDKjJ06MWShf6MNd0fpHbiw6V1TRjvv8yLMUHO/vFleS3whmKtyUyVgy3f6QfHe2+m394aS/rJBusWqVAWWvdXaSTDjaKW5jcZDfSETH49DiAHiLhU5aFUPiXbLGyaYccPwDH7qagOGSIUPoorVrNxSdhwmuQYp0Vj8L/95mgLNeYKyIvEJcjLLWeMPa36BxzOqPlGPD+lZksNrgG29sriW3ILrkI4Ti8gD6YEkBOPjepEkJlzsGah9//HFnImbpJjivo4M+uW5dIjl7LNrZswmuiDombNJOOyVAFd7H/m8FDcKCx26CY6ZMSbjgaXINPsv+MmQIfXLLLWneX/8aX9CCpOOV4cON3sTnEl8O4vIQ58t2fX2pC1sdE2e1lisvGADYs2fPNmPiAkI210E9NVlfk2PLFyewuoF1Nj4zIwucZrTdJ7IS8sU06uBcitwXmd7D58ZoA3A6tpzfQb+1PLVbpwyjOPXUU63zUaeHaNbybZme2qwOlqCefg3g5s+fb6xvEydOpGuuuSYh7osuuojOPfdcuummm+hLX/qScyjwDt6F++Qpp5ySeG/mzJmERQN3zAsvxNJa/3z961839S1cuJD2j2io6znWLVsYFkVm8Ic+FOeBg3sqP3KT5r/VavnxKXlaGdIKT5aypA8ym0+7fodvQBns+W69bPXbDk8cXMs23dTcJuKQlD7zR3V20ubd3XEwOn9/vnDljAfAYqFD+woH8OsJbGFkw03ld996y7wZJwsWuXNQP+SaGQfgWiwNUqTruTZ1WXmsLI1sR9nLDnGPzlrDjexjI8axlf1ppKzKUHZCttoVMfIUkWPKSrfrjOLyEtYg5UrI1hFY5WQi5pid0JKcHfHjTISlzxwAH51PUFrwpJwlgVicc1WRjE2MAAv6yBZHBqR8ycpnGJfNwEQDNdSBb3gvB/DbBeGaRIaFGucMLHKIY9UWMBsQQn16jfG/uU06R6IEtxLAAvTg0pNdUVE2W/K+feKJNHHmzJg5UqYQgCunZrRlzxpp4ZPyQ6w8LuRl/Lkv5QfPI/TlA319hmhE53pjy7AkS7PmCAR4Hz069mZZfOCBdPADD2yYFnkuRxuwaFumpzagL2Utsl8DyHC/EgAAIABJREFUONw8YDHAZfKLX/xiYgxgTdtrr70MKAM4cz0nnngizZ49mwAGP/vZzyZeu+OOO4x7JCbqokWLzG/Y2DbddFMaMmQIvf7668by99BDD5lbpN13350++clP1jwXWrkw9CY7YsQIcyOJg22lcLdoBIDjuplel932bO5TNlcH3w2r1brHI6VubPmA0oDExxalAaV267Exj/0nXEXEbNF0/K42G39+ZaG75bjjaMKcOcVcF20z1sLItmtHBz3b05Nw0ZRjwzeKE6Ly4viHHCuiEYp0jupzv1op6flE5pNXK61QjRjHVvYn36i039t6vFL7hroM4ssx7YLIPbeNv9zD5d6Pbxh4Jc4qRUICwPeTjTai7datM4o8gyQ+12S8HSdzBmD464IFNK6nJxGnBfbBuU8+SS8vXEj7rl27/pJPkGzIOGlplWKQoAEbQAXarq1AKGdc5JK/mTqfpg4ZQq+uXUsSBPri8/SskjLWbpoAwZKZVsf2TRs+nP66ejVJqxqsjhM6O2mfqVONjgJd8MurViXYsTEGCJeIgbslHl7GOqLN2qrHABH/r/srz3VXaIkEvxocQke1tdu4l+OZCFi+/gHYnOphxW72Km6lntrsvraqvn4N4BCDduutt9Jvf/tb+sQnPpGQMeLR3v3ud9OnPvUpuueee5zyP/vss01iyiuvvJJOPvnkxHtsgdt8881NTB2eJUuW0OjRo2mPPfYwbpra8jd27Fi67bbbjIth0aeVCyMUGOnbM974CrvuOTZWzofGBybf+mmK6yzFX1v3bBY46doAoF4kXQIrbX+7+OKE64z0bUc9uyxbRndF7h2QHUhSdlcAyfYeDrAjOzvpjrffjm80IZMZhx6aiqcIzrGjJir6IF1KcVDiauPBQYPMBYa8xZZAHrQ9v4r6pOP9QtdCIxTp0LqLvNdu7S3Sx3p+U1Z5VWCrnqNce1lZ4yFdy8Dcx5ameG+yXEDBlc7mDo/Waip+/I3PDFh7JFDDb4hHw74IcGBza5fxa1aXO2WtY+uWBA/SbQ/WIx1fB1CH2H228qBdHCPNdeLilQEk+nBHBABwjlyBuO7ImiYT3PcQUUfkusl59ThDHIJRJFh0MjBbpoBc+/++enXCVZQZOpnAQ7vvczv43ObimZyEPvjBeLykB8ge4kxlqyQTjcgxhbxsLptyPFBnqr+WnH42veHsCAzbLMA2zyO468KKx6yZLHM5xvVI2VTLSm2lnlpLu9vp234N4D796U/TvffeS3/6058MqJJPb28vdXR0mDg2WONcD0zjAF0f/vCHzXubbLKJeRXWHIC0F154wZSzejW2CjLMQ2PGjDEWOMTWAfgdeuihJg4PFkGANwDLW265JXOe8ALQL/7+9783LJhg12z2k+WqaNpjiSmDv78rqW9QH9SBK0lD5MHFwCDhEuNwoUG9rAiwKwQtWEC/XLfOHLp8gwcwJN9lZQCHn3TJkKBPBq5LZUOmJGDXGXl7i0NkwXnn0Z29vTRMCIY3eOlGoimpceBeFTGY6aDuPDehWePBc4AVCL6t1QoFKzlYKzt0dNCTPT1GrvyABnvbCy806yL0KauC72p/lqIZ2u+B8l4lr4Ey0rX107sPWC772ILF5B4+On+2yqCFUKilS6RkaeQ2SFdCCWQ0mICL+Tn33ptiOeTcXQmQqdwf2boFyxqfbSxBduvUl4LssYH3dM5StjjK2Gqcd2DBlA/687PjjqMvzplDNqYAtnxtTkSXiA+5vanYbZUrVdalSb/YRZLlGJ/Hv/td7DqIv80kojN900nkmQOwlpZOyQQpXRd17Jkkm5Exhzw/mEBGxqElmhQRpOAsxFjAJVKGPoAqZpqFhVrqHQwsZagGu2tKF9dUDs3allrhrysAV1h0wR/2awAH69p9991nQNYHP4hls+GB3zIYIrMAHL445phj6Cc/+QlttdVWxmUSVgawUCItANIKwI1wxYr1W/ejjz5K++yzj/nvyy+/3MTI8QNSFQDBv/zlL4boZOutt/YOVDsAOGsHMuiJg2enfNGSP4aDi7U7BQ6uh4cOjVm5tPWJaX5x8ElLHarTbJNMKuI6aAA+bAx62qLG7psoR27i+DfALed3QbzA4q6uxEGKqwHQ76QYJB3smfpWEHXIg6ZQElshAG2JhAIDt0hNvQz6aL653nr4cDo/uuRAUejTRwXYxDrFI2PnbPOkUvALrZ7qo0oC/UoCEsClGCQteQWvYLr6SArSeqZBBtzecL3KirC2BjE7Klv5tCuhTdDY7/aKwJcrN6fc2975xhuxdUWCQoAHAL4zZs0yboF42A1SejhIkIF3dM5Spv7Xedx6iRIXh6aC2bNp2v/9v4n9G3+WdUiAqNurY7dDJiLHd+8egR3+xrRXJdCWroMM9lIeNCJezBXqIM+1E/r6Eq6WWi4MXP932DC6rbc3EZqgUxGgPsS2X3311QnwnsqhJ6x1sBjqR4el2FIcsTfMU4oRO0Tm9X6nAnD1lmi6vH4N4OrhQgmRrV271oCxa6+91ljz3vGOd9Dhhx9uAoHBTgkgBlCG55lnnqGPfexj5r/BcCnZKfE3Jjj5+c9/Tp///OcLjXArF4ZLgU78XeReSXSwxqDaaRttlDhEGFxJdwpslkcLxkb9b317hvZJcCUpgUPd/LSLgz4gpLKBGzOZZ0bXocEoDo5PE9HLNgISy00zWMK+9a1vxcBUMoBmBepnTUZNNKFjAPVYSxY1eajLHEYabNZKdJPVh+r3SgKVBNpfAl4CkoC8glIZ5sstuS+zC+FPRo2irdatS1D3m9QnAhBgbxsSuUraLHBSqcZ3mnbelkpFAkx9+QVAxdT88MhgVkh2DWRL1f8OHmwuMQEyft7bG1vQNHCQedykpQltRX9GRuyMkjSEY7v4TGGrEr4ZoS7rdOx25uxTHjzg65aeGzR9OnVPmZJIuP1YFJfHligdJ6/JSTQdP9ok3W5POumkmOzEJi/wKMMiqa2WuBw+Yv58A66lh8o7Ro0iyW6ZAKRaIA49yQXgbDntcFlQuVBmzrS2f6FfA7h6kJj4RhjkJGCZ/MIXvmBcI/Fg4SLnHEAf4uxgpZPPmWeeSTNmzDAWPeSlK/K0EsC52subC7tzwNyvA61z5fyyVKRdJXGrhbhEZmLiQ3eF5QBhi5x0QdFWKVSJA483f46ny3Lx0y6FfIhoFi55Wye7J618Eozywf/3jg56vKfHSkDCh86iwYNp6wMOiJPTc4JwaRVjpq6iIMnmi59ivhK3iJrymm8t4XKJmAVmxJQuQbJtlbWtyO5QfVNJoJ9LQCn4tn3N5hUh8wpKghCmxtcEF5AiXNtgzWFlHaQYxqXe4mWigZMNTLDLnkzobFO0pbJuc3+HlQdnFc4zPDqZNFwYz1671pwZGoBc0dFBXxPnibSQcayXJpqSgFInQ5fx0FkXpq48qYkzVsn2UvTPQvevL0JBdMKpcngFSH3ksZ4ekxIBl7gyxMG8a2E4ZhlrN9MfdnTQqT2IBFz/SADMcxF/lwQxttg4zqH3r8J7ha11J1+KXicfnVoIY88pjk6eOTMBcmXuulbtBmXUU1sli0bV268BXEgagZ/+9Kd01FFHFZIvwAPM4joVASbuI488YmLU2J2SK0A83C9/+Uvz+957712o3jIuDBvBB1xNbXFRhTqtgsZjN0DLQaoDqvcTcQPS4oO4LAZGtiBsDpjOaq/MESSp//VhZosxSFjgxCHC4A1gxxcIbosFcVFNS9fRLFBq6zPqSrn/WG5DmV1SB5rz4SZjRnCYQoHYsRv/RYRvWcmROZdgVSzS5qyxq36vJFBJoM0koPZ8GR/GYMh22cSxZjI3JXtLcA6zVHJnJZr4ss2RBBu/cwJqWx4vvsTMiuWVABPnUCJhuGCXtFn80KeLFEMkgww+b9AtBoDSMwN/h0cKP3yhptmEE2RVliTcMoQBF4tgU+T4Qy7bKQPlWbInOAZ6emICFoyx7SyCVe26iEiL62AQxWPBJC4p0GzJMcpWTkn0gvNYWtNwToMHEhZCeTksLaO2PK6wYM6NwiLQVmmt8+kdrsTs3oTtLVreZdRTWySKhlXbrwEcYs4+9KEPxXT+OpE3pAo3x85O3FOtZ5AEuQni5RAfx8/y5cuN26R8fvGLXxgyEjBNggBDJua+4YYb6NhjjzUMl3feeachOcED0IY0AoilQ11Fk3mXcWG4AJwtCWfR2azdXsxtnuMglbQYiDF7cfDgON0Bs275YhdsLhZZ7bYdKjcOG0bH9MIRcv0jb+uwiSfIXSwxbTdaEsZKS6btENRumKhXBt9n9cP1uy0PHtrCN5WJPHgqiSzTOSPW7d+WLqXJzz6bkgn+AIUEsY1PdXUlbjCLWg2L9rX6rpJAJYGSSkBZS5iB0EYwYkuwrC/EmM4fIRGLp0+neb29sTVDWuAAfk4ZM4Z2HDfOXCbZ8njKy0Ib2+Dthx1Gz0YXt5x+J3Ux5UlqbfZBRXAiR4nZGvE3Ww5RnRtNU+Jr8isbo3AiFx4IqDztRTuycuvZfpfujHx2y7JczIy/ev55AxahkzEFP+QsrWHW9lgscJyAXKdawPf79vbGbrUyZEICbbhhMkskX1Qijyufk5AjHo5l5HF0nnWW3KtsVZYWUhnj3kovljLqqSXd0Qo3q18DOEjlrrvuMiyQcGuEyyJuRpAXDrT/c+fOpfHjx8fC23bbbU0s24svvkj4b37AZgmWSTA/Auw98cQTdP/999M222xDCxYsSMW54XYKbpW33347ATQCyCEnHOrDb8gfhzKLPmVcGDptgLz50tTyRfttBXCR77p2p0CCTqZElrTGmo3LRoNsdbEIaLSNXh83st9buTJ2f/w+iDvE7Vtis7bcPDJzpysNgm2D1rdxMhdNQDfsr1iYRdnSpt1nzU2rSnKbIINRlkbNfMYxE6G3koX7VH1YSaCSQFtKwEkgEsUPSa8IKMgSqPhydbEizC6EIDNhd+/EBdU//2msQExIJRNXs1IOEPHZ7bdPJI6Ok3rj8tH1eKxB5rwQl2M2zxFur2RbZCsbsxhL8i5NAuNKtO0DA/p8cp3Vssu+1D76QliCG7SXSa+WLlpk0iS4iLm4DsnS6LJw6fboOYQ2sPxeueCCRJoDXAKgDRznBh1ChxfYkq1LwCzj1a3xa7bUFxGrp0veWZbeRi7+MuqpjexvK8puGYADIMJiyHr++te/ZrI1ZpUBV0a4YCENAMDEbrvtRueee24cL8TfuwAcEn1fd911hsAEhwGAG4AfFh8fDLoNsORddtll9KMf/chY+cBUud9++9F5551nEnrX8pRtYdgSmrIvej0tcLaE3ZAjb1K8AeKgxYEqc9dgg71+yBD6al8f/XDdulj8oOd/Ldp88Ud2K+ENtMgNlgZQ7CLI8Wg6caeMC5A3jzLtQvBGrMDRIUS0wHMLGjwPLdbB2yKmK2vbFBjFzbQ8lGRS9juJDMDFA4XkS5FLJcsLbiqHDxtGB1xwQeVGGTxg1YuVBPqvBCTQknG+J48ZQ6+OHBmz2cq9SVpHIBl56cfWO7a64Hd5noABWJ9lGmS4LCf6cjPTm8BiDQKjr7TYSbDB55YmldLJxZmsC+dNkXPNN5v0GYDybbnz4jJUH9m9ld0stWyl1U8zR8t+JdpoIbPxEZO5zljbmaxz7iGm7kOvvJLItaoTa3MIgbZg2qyJVr4AdbbH8ZhROqRp06aZOWKzmmbOuQZsFWXTUxvQxZYX2TIAB8sUiDx23hl3UvYHroiTJk2iwsmfWy7exjSgbAtDu+zBjePSiBo/lUC1RpHITZZvDrkOLpoZsSRg4gBnmd/FxYYYsgH6DkBbWoMVK1cmACVyoL33nHOC4wKCAZwCWvDPB5CteQNXB64rz510B3KBUQbdnBgVt6N7RoP3pKBlllOFabhbzaxV4/StPq8kUEmgDhLQCrSOKZKxW1BqZdwbMxHjsk5e8sEd7duvv05QhHUsM84PSbyEfchm5bPpKtpSF5LGxWed4v1Tp6YBMLjpU5+iw++A78n6h0MBUqkW6jAGsojU+eQAoWw5+3+bbJIAO5qp0idbWRfa4ARwlvh4vO8i+Mi6iJQuulaAamGFlnNG5pzT56QmoZGEO1LOOjZSzjdb+7PmUZ2nQaK4sumpjexrq8puGYBj/98LL7yQwMwon7feesuwC/7sZz+j97///SZnWvVskEDZFoY8THX+FX1zWOs42pJi60Bl6fuPg50phvmG8iQiOi0KPPbdUuqDMmuz5L5p5QI3b8u7uuKYBB/TlAuoBd+YWtwwOcdcrSQgeQ8DH+jUjKJQkMD+CYVjtmOS2HLy1Tqfqu8rCVQSKJcEQvY6Gy28JLBIXVhZiDbmr1xp3ZNR/9A5cxJxuvceeCDt/cADVibgrPYGW1jEMGRd2DlBjHCvLBoKUGQ2pGTgcQNF+dqN1cWQbHNrlJY9FygyfQhIJyH7ah1HSz/oazilNnj+yLmmXTa1221Ktjnb6HNNTbXfAqJt+eWKjHfIN2XTU0Pa3G7vtAzAIQH2l7/8ZfrDH/5gqPh//OMf05ZbbkkLFy6k448/nl5++WUTswb3xU022aTd5NrQ9pZtYfABNZWITpA9rzHvW5YQdewVLHGaClq7MXDMmyzbd0sprUgSCPkOWH07F0odjTZlKQNZMsHvPstXyPeud7KUCv2d732bAnZtxMb1FJGhe5b5lZii21B4e556yK8WGVXfVhKoJFCbBIL2GYe148ioao7PjVvicNljOn8m94gv6bTyq5JHuywktp5jT9rh0UcTlrGs77NkIPc51Olyr2yZx4KHFATtlSl8kG4HTyqWzUHtL/PRZaUm0oAqxPqZGEMPCLKNkQbrrqThcR0BSedle7hOjsXzXcrqMzZrztW2atNfl01PrXf/ylBeywAcOg+WyP/4j/+gmTNnGpAGshG4VY4aNYq+//3vG4BXPeVfGDKeiYlD6sF6mDX2egO1bZ6aTlmX6b2ltCgJHACdZY1ybe5Od4+szub4Pevwz1FU4tW85XrBlDoYOXYFQenIH4gHNMvvBxGRygHks+rmbWNRWVTfVRKoJNAYCYSuYXlRBca/E2fOjJNVcwJqaXGw7dk+i0bifUHdX+hsy2kNqeUiKlR+jRm9DaXqi8Sbp083cYd4ALBlEnC+tEvI1mL9QkjNzJXgBo2ejEtilkUtsfh5CMRQn06142W09ugYtvHJMy+0J1AW2K33fKgAXL0lmi6vpQCOm3PFFVfQ6aefboKEN910U3r88ccNUUj12CVQqoWhNqBxRLRdRArS6Ns/F2sU36rqoG5274RUcQRMiw4SZztdxB3KTcW2MfZHAJfn8AhZu6mDUc2lXSxkNLhF5Pw8tri+sigvIf2v3qkkUEkgLQGX14N+M7HWLdT6OtbJZzGx5QfT72dd2mXtj1nf12suNHIPzOqj7IMEx3CZfKSrKwGwOV1Mym2eQZkF9B62884xfX8IkK4HgMsjT8hHJjYPcWPNTXITOFH4QtuZ+y6wnKKvlUpPLdqJkn/XcgAH6xvMwGvWrDH515577jnD1giXyq233rrk4mtN80q1MBpFmpEl2gDXBr51A6nJOcq9E66V/18WuYeLOv+yy4gmgh4kfQvIBxznoZHJS/Mcflnd9/3erHpqaSO+TR2Mai6B6OWhwYPpsZ4e41KpmeJ8+YNyu8rU2pnq+0oClQRql0AOi0Rin5swgVaMHp0CCPJyzqaISwZlDQhsHh4+D4osRT/r99qFt76ERu7/efogAZzOvYd2AmA/ttNOidx7egxcVtC8LvW28zhU3nn6zGXmAetcfiq/XmgDHe/5CE9qLDro81LpqUEtbr+XWgbgkIftK1/5Ct1zzz304Q9/mG688UbaaaedDM3+d77zHZO3DeAOCbGrJymBUi0MC2nGsz09tbMeZg16QHAxb4iIjdOsYrDugBo6i50Rm2CKbvr++6n7oIOsAe1FNvusrvbX31OyUnPphsmTadmmm8Z01IiVQGoFuOLodA9GRjndlPqrXKt+VRJoWwl40pVk9Yn3aryH/G0631dekgr9ftbeXuvvWf0rw+9ZfZRtlLHgr3V20s3d3WkXV1zMjR5tzlIbKPNZWZtFapWnz9z/PN/keTd4DmSk8Qkup4YXS6Wn1tCPMn/aMgC32Wab0RtvvGHYJv/rv/7L5EnjZ9GiRQa4gcjkiCOOMGyU1bNBAmVbGPK2if3as4BRzeMZYIHjNvAGKRNDc566rHbafNrhxrfrGWdYQURDNuOahVXOAmwKVVacimYvk+kMyAPqyymBqlWVBCoJJCTg8npYvjxTULz3ZiZEliXluPTJsmxlWV2yvs/sYAleyOqjbyw5HyreiUlmLOygM1dwenLlpSFCF3z53OotpiLjlkcPKFJ+Zh+VXF2pEzLLqeGFsumpNXSltJ+2DMC95z3vMcmxDzkEqYbTz/Lly+mUU06hm266idauXVtaAbaiYWVbGHKzanS+GSnv0OBi7QePMkLdFdiPXKYmQMwbJ3zVADDXAdeKyVPyOm0Hn88VJxEgnkMZK7kYquZVEhiwErB6PbzwAtko0KXyC08L5JbUe3UWdXpd9uyBsPfk7aMFnM2KCEjiczPDUqTBTaMYluu92PIAuHrXbcrLO1YNaETZ9NQGdLHlRbYMwL322mu0+eabZwoAAO6oo47KfG8gvVC2hdGqzSq0Xj4EOOF3CAUvzyf+lolROLbKWncJNs12Xwc2uWpXnJu6u43LjY2iuS7KWLsLsc3a35Ab6DaTQTs2t1Hj5vJ64PxbUlb68lDnbwuhTg89R7xjNBCs/3n7qM5DzvUGOdpyp7GLvI8avy5j1YTF1qi1kafprT4Ly6an5pFdu7zbMgDXLgIqYzvLtjBatanmrTfv+y5FAbmC8sZTlHEelbFNVrkqRQCuOJt3d9OLFsbTWsa4qDzKcFgXbXsZvmvFmJWh3+3ehkaNm8vrwWZJS7WhwCVaXfpRoN62G/8CfZQgAvlQ77vvPtPtgw8+2JDX4ckj/zzvtp1869zgVsuqbHpqncVbiuIqAFeKYcjXiLItjFYpsHk3qFraGVRXgQMu38gP0LctrjjzV660ukoFjVOdmdpC6xygo5fZ7Up+mSIq5QuNGjeX14NNCDYrQ17LQy3ngmxT3npLOagZjcrbx5A5EvION6teY9WOss/b5jxyzVt2yPtl01ND2txu77QMwA0ePNjkfct68A5SDFTPBglUC2O9LJq5mYfWlfeAq+Z1gAQsrjhgofxv8SkHaYeOUz0Pt3qWFSCNfvdKtWbac0gbPW68rpzxyo4Ls1atx1bV28zZk7ePIe+H7tnN7Gd/qCtE9o3sZ6WnNlK668tuGYAbO3asFcCBvOT555+n7u5u2nnnnY2v9IIFCxoviTaqoVoY5R2sVm+a5ZVMbS3TrjgyWWoRRrJ6jpMrobzMAVhb7/vx15XVOmhwS6fkNmHceI1yPs8UY7AjJqueaztocKKXWlVvnjbW+m7ePpZu3tYqgDb6vtWyr/TUxk+WlgE4X9dWrFhB55xzDv3yl7+khx9+OIjspPGiKk8N1cJorvUtz8jnPeDylD2Q37XJtUiiUu2eFRI475W7RZF1MZTayqnlkK3l29LMpbzECKVpeHMbUrp9pQnjpsmnUpY4B4hs1bpoVb3NnIkDoY/NlGd/rqvSUxs/uqUEcNztMWPG0Pbbb0/XX3994yXRRjVUCyNf4HMzh7Y64Bojba3AQs7Tpk0zCdrHEhGzg2bVjnIk1XgR611ijDfbjGjixA3VenIE8kvyezCjLlu2LDOhPL7Vc6t0Sn2W8G2/N8GSU6RZZfumdGMdMG712gv5ouZBsKM7GAxD13/ZxrVqTyWB/iqBSk9t/MiWGsBNnjyZfvSjH9E//vGPxkuijWqoFkZ5AVwbTaO2aqoNvLhyPsl30clVq1aZvH9gPYMiPH7ZskT83KQRI0gmj80STEKZFsllu4mo8/77qfugg0yaA/NvS/4q+T3qAoALyUvI33EajDzgL6tPtt/rpYBn1d3oWKqs+tvh9ywZ+eZ8o/qX1aa6gE6VJ2xnInrzne8kMAHjqUsdjRJQVW4lgQEsgUpPbfzglxrATZgwgebPn2/i4apngwSqhVEd3AN9PdiAGOd8sgEkxM8gyfyc88+ncT09dB4RjQS4I6IJnZ10x9uwxYU9WmlMKLKXXZawyIFc5bGddooBpFY6t127lkDIYrMu6NZwvfg7+sPfNsr60EjlmAHHr3/9a3rwwQcNyNYxTs0CkGGj3sK3AqxdrjnPQKcRrc+aH1m/yzbJ+bDXXnttWC/KVfM6Irpi1Ch6cvnyCsA1YlCrMisJ1EkClZ5aJ0F6iikdgFu3bh0tXbqU5syZQ+eddx7tv//+dP/99zdeEm1UQ7UwiLJuf9toOKumFpAAxv8zXV0xEMMVz+xJk2jlFlvQzdOnG1AEYPPSkCGxiyIAz6KuLtqYiFYQ0RAiGu6xlLmapeeezyK3R0cHPdvTY01c+1pnJ/mSkuv6ZczfO0aNooe7uryWvgJiTXySRwHPW5cmqGBQKgEHvxNincxbf9H3WwIqA+LNWg3gcDkiLd2Qr2uPtsnQRVhy1Zln0rEzZph53gfWtWi98lpHPbreomNbfVdJoJJA/SRQ6an1k6WrpJYBuKw0An19fbTJJpvQAw88QLvuumvjJdFGNQz4hRFwI91Gw1k1Na8E1PhfSERzo/iYXRSw4aTfAHP7rllDM1eutNc2ezbR176W3RLl0nXD5Ml09uzZiTg2HbOTACfi+9URgORKtXUh0RjxHayGP+zooFN7eja8Etr+7B7Gb9TzksQVv8cMg+wWysl90YhMFsIcfanXq40Etc42Bux3PFYg5cEzpqcnOC60qGy8sZyeNttkyH/7IBHtL2JaOWZ1KhGdIBqKlXqbcKUs2ofqu0oClQQaI4EBr6c2Rqy2tCVmAAAgAElEQVSJUlsG4FxpBADs4Eqz++670wknnEDvfe97myCG9qpiwC+MgBvp9hrRqrW5JKDGH8rcdYMGES59voF4NgHSGCTBQnf6iBE0a+XK2OqG23y4Ubpi1axtUnWfPHQovXTQQSTdviTw2K6vL0myor7n9knrgoybYyV5z6efpkNuuy1uEr5bG9D+whYjpYD/ePJk+ltnZ8IVNM+YudxOXySi7RwkND7yijx11/PdlgA4jzXL9E2NVaF5bSHJyTN3UmBfzfPbDzuMnt177zgOVRP32CzqWAfM6AqioruExRyXNncLV8p6jnFVViWBSgK1S2DA66m1izCzhJYBuMyWVS84JTDgF0bAjXQ1ffqxBNT4w53q6quvNlYH6ZaoLVz8b1iwDh82jF4eNChhqQhRWKVLF8r5LBE9pSwBrOQfQES/0m6aAIyjR8euj7AQju/uTlgXOJYPI8hlwbL4664uAz75mTZ8OP119WqvpaUw4FAKOIheAH5TubgCp5nLzdQFXDUoKYvCXk+rpBRd1tzzjqO+0JIF57DM6jqC545tPxbzHNGlknwkJUPxfWI6RYyu0lUaa3gdEY3Ie/ESOE+r1yoJVBKojwQGvJ5aHzF6S6kAXBOEXO8qqoVRxcDVe061W3kJJfDJJ1OgaPPu7gSYA9jaSHQSgOSG4cMTro+a5VG68/GneGeXZctia4AtDQGUcSQaP3nmzATgYmAm244YPRub5rdvucXE9oBpkgk+9u3tpbnd3bEFcd9Ro+ipri4vqApWwvUEUEp5SF2+OeQjeom/k2DDAkpyWUobMaHrfHFkc0G0uZLivfvuu8/06OCDDzZWrMSj3HKLApzCAM7hESFjNv/M9P+atRUsrQsXJlNxMDgTjK7O4cwBUBsxJaoyKwlUErBLoNJTGz8zKgDXeBnXvYZqYVQslHWfVG1WYELZnDEjoQCytQjK8Af6+oyVDQrkHUSGwIRBlyQ4AXlGSGJw1KvTEMASdv4qQETx2Fwlo5QCNsIJW+wP3Mzk3xnswZ0MsU4vDh5sZW+UzZDA6UtTpsSED3gni/yhbnFVGvgIxVxb4Bi4vre7OyavkP2RrnhNn7Iet0C0JcuKpttrmwd4R1s5bSBcszYunj6d5vX2xuA+xGKp26stY8HWRgew5XZ/gGiDG7FiaZ2xww70u3e+k65avDi2SsdtV+9CNgDxRV1Emz5fqgorCQxgCVR6auMHv2kA7gMfwDae/0GQ+5IlSN9ZPSyBamFUAG6grwYf8yNbiyAj7B+IQ5tARO9CDikimtfRQcdNm2ZEGIOYCRNiK54vuTfH6pwfuXGhDDBajtT53hTpiExVIC19iAVG/Jy0tDGY1Cybul+I+bNZbOK54bGi4Z2sBOKSWOKpCPxqK1gQaLFYaHY94wzDFKpj4OS4gjVUykC74tW6BoLaLiupc04yW19RnY7tsgEp/pabhzjQ2aKtISQfvjWUlc8wC/yhKUxA8js5dwR45/HE5QpfVCwkMpctBsQqax2AHdYunmaQtNQ6v6rvKwkMZAlUemrjR79pAG7bbbc1ylSR58UXccxXTzsCuNxKUuAwF3YNCyy/eq3cEvApkLA0Id8blDzsHLC8gYacwdY+NvIDBTKsVjUPg6SMW2PJ2Sx67A6H3Gdr1qyJrS0S1H1v0iSr9Qk55Y6YP9+ATm2xs46WJ44tD4DT4MAWo+eNjbNYaJicglko+XufdVLnyiuyt3iZEzOmPL7leWXL25d3T+L5gbjNm7u7DaEOz9FropQYZ4mLBQmeNYDDfJAg++QxY+jVkSPtLpeRtXDatGkbLLjKik3HH090/fUbJKJcFUPcLSGvt6dOpYvWgm4neqK4Ngbve0Z/ZhZZfo3ng43IJmTulnv3qlpXSaD/S6ACcI0f46YBuMZ3ZeDU0E4LI69SEzqKRZS30LKr99pPAi5rgiYyQc/gYjlzBexm6x+OWTtx5syEEp2yqjkYJF2xWVLJ5lxmbGmTwAU5tBDjxKDupKFD6ao1axKDwHXAxRDxdRNnzszOAaeAk0ypgMKhRMMV84tnn21ll+Q1pt3zJEtm6PqW7piwfkIOsABqAKfjA+U7LjfTPMQqNoAY+r0cTxuADnY5hPB9lwFEFFvQNLCKgBRbgqVVGcWyZYrdg1059Ng6BndckwhexZFmWeCyAJxvTQG8I+7z9+pi5XNEBD8dzEnMEcT6cT1ynlQArv3256rFA08C7aSntuvoNA3AHXjggSYtwLHHHmtkhUTdu+yyC3384x9vV9m1rN3ttDBCFbyWCbOquF9IwBcTJ0EcYNucyZPp5EsvjfvN34Ia4hIpDU2QoFzoDosUTqMAL1+ekqO0qkEBheWMFVEd2ybj3WS8HoAbu46xUksWl8Rvv/66NaZNWnk4aThDV1h82I1t6UYbEYAkFP4ESYbHFRQdzgItDAIZuOIbACZO/PzrX//auJDi/z+7/fYJYMqpC/DtFqtWUcIV74UXTH5QTthuk79tYmu3xTzfy5gu3RbUJdlFJci1LjDHZQDexfjsBHdfixuhKRfvjB4dXzbwN7AsM6kNgxwNkLktGDdOas+XA1qetrFlC/LLCxfSvmvXxgyoOiYPY/blVavov0Xnpw4ZQhtfdJEB7zqOFK/JlB8sP5a5BKJ4Nyt+s19salUnKgm0sQTaSU9tVzE3DcAhv9u3vvUtOu+884ys9L/bVYCtaHc7LYwKwLVihgy8OhMWWeV2BsvTjt1QU+25o0JivaTiy5arLBIR2aabp0+PY77gNoZdkHPQIbZteVdXDFAAqsZF+dHYVY+BD+LjNMW+zJflIsDQ+fHkDJF59FLWKIsbZmy9DGBl1BaURD9EI/BeSqmPADTkOHTOHJr87LMbvpg+nbqnTMm2QqqlYAO0oeyW/O37hgxJuQXOnz8/kafP5lIrmyLTUaD+8cOG0a69veaV36iLgawca1yuTPWA+EHMU+nqyaAZYGj1t75F568GZIqe2bPpXyZPtrKyyjlhi2uTOQKlZRWxpwx0Zfwi2gErsrR4pyzlYuwrsDbw9vOqx+0vgXbSU9tV2k0DcO95z3to/PjxdOWVV1YArsbZ0k4LI+uGvkZRVJ9XErBKwOaK5wIPMs4GrIixW5nHqgalFk8WEYh5SQAdZl2UjUa83darVydywZ0N107VM6lI63UlL0qkos4KNfLIPdzVZQCPtMCxhY9jkFIATlkdb5DWSwcrI5rNtPdLFy0ybn1wi8MjySd0PJotnQJ9ELbK9Za+hMVo+nSiKVMSAIS+BijqeXLGMCZKEt9q+QFAw3qlLVrcdi5HWqngLgurIuaaBOlwIdTWPY4XjMdmyRKrBQ6WZSRblwyePL4gRmG3SsyFR7q6Uu7CzADKVljb5ZsNaDNwxHfbrVsXx54iQTuT1CTISXAJEY0pSGrwPNPZSWwhDgXU1dZXSaCSQHkl0E56anml6G9Z0wDcoYceSg888AB95Stfofe+973GGgcGNvzP98AF5Nxzz21X+Tak3W2zMHzKX0MkUxU6kCXgIqiATAC08PgAChTHPTo66NmeHmduNRdQsuWMi8fCk2hZggEJ7tiNDu6UCRr2CFRq5doGWKWrIpRrtgICMKAuMHNKS6CLet55CeNgZWR5SzBiAz0SmLDFSLNSsgwzY7Y0C6htIXhiGJEMfuUWW6RzrHE5FpIbmURdts8VVyitkbBO4eQDsHlx0CACoyie/9TA3WIZYzKVcT09MbNqgrnSkUOPWUy1NdZK2ONIi4C5cHRXF50mEtRzuZJIhecyW97QN3kxgj4wiQqvS54DLpfkgby3VX2vJNBuEmgbPbXdBCva2zQA9/zzz9PnP/95eu6550z17JufJTu8t1ayWGV9MAB+b5uF4XO/GgDjVHWxuRJwEVQwSQhak0qE7GBqdNHzFyLPsVjg2DIyAjnkpCubEBlihtasXZsAWTo2yEa37kq6rd0ZU2CBEygrMGSLQ2LA6mMJ/CpRIgYqMRsEMJHWQVb4/zJkiLnc4/GyMXrmtu4rl0+4LW7V25twMzQuqgK8cIyetmppGcv2MSBzubMeQES/igCQdJd9adAgmtfXl2BMBZGOyzIm5ZmoS/RTvgOgBtAJ1ku2dqH+PQVpCL9vnefK8ge3xyM7O+l/Bw827pqb6xjSqDDElk5VaSgYvHN9uSzazd1WqtoqCVQSKCCBttFTC/StLJ80DcChw+vWrSOkBHjllVfM4Qxr3PGgK8549t9//6xXBtTvbbMw1A39fg6yhwE1eFVnGyYBqdDLpNVe65hS6hHPc8asWZkJsrM64UpzwBYmdpuT4EUmKZYWKw184CqYKH+zzRKJzFlRl5YMafGApeRIIpK57Gx18N9sTIAMdGT8IHZp1IkHrnFgSPxGFOvncjuE4r+1ArEnDx1KV0csnBqgyhx9ReJrNeBEW22MkrY+SwtRIgm8yFfGgIituNKdFRZQyAUZCIcLgXP8l44Dc1nGJBumBEA8Jvgbs1SyhVXLnxlJba6N+N5mad7z6aeTcX5EhDZOXr06ds0dEvVNJmhffOCBdPADD2zosQDvQwRQZ3ffUFbQrDVY/V5JoB0kUOhSsA061jZ6ahvI0tXEpgI42YjtttuOzjjjDDr11FPbWHytaXo7LQzbzXlrpFbV2q8lEECo4eq/L55MKsV55OeiWWfPA8kOKAknOAZPgxrU7YwNyojPQiwWt2eHjg56vKcntvJwnxg8AIAk4tyENepnl1ySimFDrNv7VqygqxYvjpV4ZPscERW8Ekp+RB6DPzEBDPb9Y2fMsMbkyYTlRplXVPrIh3fIrbfGfZIgwAvWBTBh0KYZP7WFUwIk/DfHPEpgdpYHQMu4M45BlPMItCXDLGAuK5k8y3GL/fYzX7OlUrN+cj818QrHrfkAoCbrwdx5oqcnETv32Gc+Q2PvvjuxNFYR0cQIyFlTFDgYRPurIptn36jebY4EWjXXbPUWuYhqjpRqq6Wd9NTaetq6r1sG4FrX5favuZ0WRip2xUIMEToirdp0Q9tXvddCCVio9TNJLaLm+uLJQunpdc+zAJykRWfLA/6GB6x72jLnik/jen0MiZAD/24DhtKNDxYZQ18fuRLG/fIk4/YxXOJ7zmnGAAj/b4vD4pgy/M7kFia2S+UoY9ZDuDeCDATywuPKeSbHRrMo4sv1Uo8ekWcNAIZjEAFEElY3KR8PgHbFnZlxjnISfm/lyphNU1rGQsdBkqVoyyFfFGhyFBm3xpbTeK5bYhshB5SF2D3ETuLRYyPFCACHFAI6ITfiA0866aSwPIYt3E6qqvu3BFoFmlzEQEFkWG02JO2kp7aZaOPmVgCuDUeunRaGLd+QZmcLHYK8m25/Anz9qS+h453rvYIWOM5rhbqMFUOlIMjM5+VopLbq6TxZMt+aK3bPRgXvsghKV8anVLwRmsg5ymwMlLcoIg2rC5sFIDPtvGa4lBY4mdMM7QDTJMgsZBwWLIsMKFwMiTJHmQRSEhS6cp7JIcI47PDoo3T4HXfEf5b5xwAOV69eHQMM6Qoo6fK1pc8FkG1xZxz/CLkDpNlcMzkG0wC9Vas25OfLuKjQ8YoS5EpXUbgYOxPCqzoQw/a6YstEuyQ4A+Bmd012I5UJ4iWwPKGvLxkbqfMt5lr41cvV2ZB/DuTVJWw1FJG7rd7csbz5u9uSL9pJT22JgOpQaQXg6iDEZhfRTgtj0siRNHMlHKmip4bDOu+mm/f9Zo9jnvr6U1/y9DvPu0UOwpRca7DkxW3NCSalIiAVdplTCyyFrtggCUIfeeSRmJ6eY+DmH3FEInaJQQXH4QEsAPwwrb2OnQOA0CQeTJ3PibDRbna5w3+DIRHPvI4OYje/9/f00KxFi1LWJmndslrzRMyUpKqX8Xao67Eod14mi6EaH7Z6cTtsSaZNZzy5yXge+dI16LgzBkH4f76Bl/8NsJ6anxlzy7YGbEQz5iLANdeVRZHj2iTwhEWO56OOg4SVLU46Hy0KCeBkfrgqZUCeHc7+bnU25JdhkbNC11JE7ql6c54V+Xvaui/aSU9tnZRqq7kCcLXJryVfl31hSIX0nW+8Ece61HpY6w0z6wasyAbbkgENqLQeB05ANW39SpHxzqsgBwmoHiBQxGtluQZKqwvn4ZKukNoKztYuabG6ZOJE6zqV8knQvCu3Rhm3JhmGJbugBmcy8TTHlAEoHjdjRipHGbMWSqp6WPektU9ay2yWU1tide0aifGFlfD3RIk4QdQFRkgZT6gTXDMIYzltplIC6LgztrJJ8g4ZW4e2MNW+Lweg9+JAWF9lMm1XQniZd89GuIK6UM4pY8bQjuPGmbQL2m3TdtHAsuc4Sle6iKD1Vb2UkECRfS+vCLPO2rzlhbzfsDoVaNIpRELrzS13G1hbuDBBQMWXRCHyKfs7ZddTyy6/kPZVAC5ESiV7p+wLoxExRRiCvGQTuTfYBo9z6MGQakY/vqWrp8iLjLfPYpFpyXE1vk7jFTpfbO6Tmq2RXRclYEkAQ0XEwYqEy71TE4tIcOICcDquz+WSyAyKEC/nN2NZ/O3ii+mqiKHSO3csln5XX1AOwBcA1Qf6+gxRy0eI6ExRgWSEtM0zOVYMyOAqepdIFwC6/tU9PcaFVFqpXOOMeuS4cYyac57bLg7Qh4mISFv/zNhhB1pz3HFxvjvXxRDqcFoiUZCQr889ODFGjryBRYmC6rl3yLJC11096q9HXc243Cuyt9Yqnzx15pKjWicco8uXJ1i/8DrIYkPNLXfb+hw7NnZvr/WCu1Z51/v7suup9e5vK8qrAFwrpF5jnWVfGHrjzbMRO0XjIVFwbbR5N9hgRaTg+IXIwdoGh3JdsBn99rNchzik4ABaIeOUJcS8cy+rPN/vXJfOw8VsjZoJNsGgeBYinJKykEyUsh8y9kxb7ADGrrjiCgN+AE4kOyInEGeXR3a7dBHEsPzRLL22D9t4Y5rb3W1o+PNa4FwADvKApQsupL8T8YOw7o2MLE4yBUpWugoGXlwWyEo+S0R/FwygIcqaLX4YOeFsVjk9hnH5ygIHEGmLTbPlrAN45D4gTnBdxC4a0nbrfHUozmUDcPVY/1nrmfeqULCgy2O3ackAW3hcshpruTwN+KTmV/KMQ5535b4vrdJoMC5yQmJpXWeHt9OO86aZZ0XNg5KjgLLrqTm6UtpXKwBX2qFxN6zsC6MhAM5DogDlI6WUFrCChLgC1TJdQg4ZaxtEnqlGHtK19K0tv3W4OoaMU1Z/61GGq44EUBWkKwA0Ml5JE3L4qOlRVyrlh5p30mIGxTNhHVIulbELo1qHriTjsq9OACfKAiiaECWRZsbKrBg4H/CyWZyYvCPBzBnQH4zP21On0kVr18bdwi3/yBEjcsUDT9too2SSdxEL6Eo077Moox86ybhrnmqXR3wLQJtirMxaCPL3DNe1PEU18t1mKNR6n89yk9b95e+/GrF9xr8XiDG3XXy59phmnj95xiHvfstlS1devqCy5YVMzbcAN3mbXG19ytv2Rs79epZddj21nn1tVVkVgGuV5Guot+wLI6+rY5AoLIDMxUznDdD3VNZoABdyILnaEPJtkByrlzZIwAHyc1vyLDLNcrPLylfmG6ZE2SpHGr5jcPPwsGEpNzi2ytnKTwEnXbYg8WArEMrx5WrTRBky7s1leZGkLJzfzLRXKU0o64bhw82tOR7vzXkA+YdmUtyZyKQRSIAlSxtmrVyZtBQuWUIrRo+O4/g4F94znZ10U3d3TOLiZThVZXAMHscCurwObPOOLYJw3WTiGv7eKetogsg5EWSZyNhfSr+PFbj4K7KlagChxyWrTB5TpHRg9k+AKx3PlVUOfnfNmZja3rEPhJRd+J2c45B3XmkrOWQHkqEdu7sT8nSu0YD2Zco1SkVSAbjCs2TAf9g0APeBDyATTf4Hh8aSJUvyf9iPvyg1gLNsbHD7SVBhFxybxCbtIFGIFZuADVY3w3YrVyQPmBUABLbH1Yb+uskXnAp1+yzvwR9asW0O1GsMXetAtg3g5uGhQ00+Nang+UCDVPRdCZiZxIOtb7F1SljrEpY+Ne99VPyZslUxVHBr5FtzJPPGWbFmzRp77Irvxly18fug948YNKW7oWmfh8Ey3itUXTJNgWS89O4tqgyOwcuar7Y5dtWZZyYIanROQd+85N+YqTQkNsg3jvVaA5lzpegLGZaVvJc7WWcBE++kCGYy2i9diZkddC6mp3I7DmmvbUxce4x0r+YmhtSRdzg0c66PAbaIO6PN4s7rNNFWj0XTtRZt7rG++LpGyC+vvBvxfqn11EZ0uAVlNg3AbbvttuaAlU9PTw+9+uqr5k9Dhw6ld7/73fSPf/zDHMJ43ve+91FHRwe9+CIM3dXDEij1wghwLSg6kj7LA1OjS+KJLGUn0Q6hmGWx2WW136qkhMjF04asm/esNlW/2yXQTIWyLnU5LNHaenS0svZkJQKHdLSir1MFMOCQsVm2eDmdDDzXOsyYqFwWk4DgdYAKjiVyuRZ6lTwP4LIBXm6DzmVncxvVicJDLJBGBBawqhOaZzFtxlZevfcgLg4smxGrpm98pHLJspY5DPPuK6VXVjMu2iSg/da3vhWTwbjkEHQWRB9fR0SXWlIwpMpesoRWjx5t4kD5gYvutdE/pDsmE+o414Utvs3j7cJrW4YsSBbVesUz2uI/XQywznQYnsmJeSjzIOp1ik997qL4nr0QtGx5zKUrJl80YWz22WefWOfda6+9NuR4dLS39GvG0e5S66l5N66Svt80AKf7j4UO9xhM9gsvvJAwkdlFY/HixXTuuefSW2+9Rffdd5+5Ua2eDRIo9cIItDQVGc/EYShu/HVyYlZMcinMFiXHtNFzA+faWK0KUYi7niYrYSHNnk3ffv31QlbMdt38i8yPIt80Uz51ATKeWFBWGABuZhx6aCL3GzOteRWsjDhT/tYWm0Vf+5rZy3OxJhYYMNealn+3krQ4iBgyFTkPm2UqZ514l8caLphIGb5xpBCGxACyWFIxiUVd2cTekxA52tuPWfBs0ytkvfvWacrNOHKDc03lrLOALwulJU4S5ljLtVw4fDR6UbvJ4s/sYmx1u7WdSxZq+3+ZPDmxtiUg4dQlhVl7LZ0M3Uv0+s0bo+dap/rCS88bOQ/QfJ1SRBIAoU287qUlO9QlOZceU2BPbdQnpdZTG9XpJpfbMgB34okn0m9+8xt66qmnaPDgwalur127lnbZZRcD7K655pomi6Xc1ZV9YdRFUXVs6rFf/j//GZMubD18eCLYn+N8cm18BSxw1vI9AFZbD2QOJdzMffvEE2nizJkmRqZWKyCLL5cMHNM+ROkp94opQevqdbFhKYetswmFQLk0Ip7rzXe+k7wAzlO2dE2W8V0yP5rr1rye88c1n+Wew8ql62bclr+Nwe//DhtGt/X2euPUuA06LYK0iElLDacmgIL7pSlTgi9iUtYeQVhTRFG1utP24zxUtlUfsh/63kmB6n/+k5zzO+AsgG/R/yGiI0VjYaWduYKvJS29UGQ+hw8bRi8PGkSP9fSYiwLpjsk5CWE5W7rRRobsC2cNHliu93z66cRFj7mwtIB6xJsv6upKXUTIvIw8J+sRMmGTM9qsdQubtStP2IMEihiLPSNx36L2S70W2erIo6PzNJ7a1UUniKGbOmQIvbp2LUkioQrAleBcbvMmtAzAvec976GvfvWrdMkllzhFCAX3uuuuo7///e9tLub6Nr9sAM51O5WVRyWvVFz1SEVK57+SgC+rPlf8WS5Lm8dVUt7asZsLJ7blIHZWJJkdC9aUVByO6EiWchyisGTJJauMrDZklT8gfg9xoQ0UhFZibDEXAGrywsA3h2S1rrJjtzlHbBbKcMWtBHYr6LWsmCImI9i8u9soSxK02uYx91eSSCQSli9fnmqXbINMkSAVR/lOURczX3vzWjucim69LhaCRq/1L2XtZWih8x0FnMCCesfbb7vfDzgLsN8jVQMu7fAgDuvIqFzMIXgg4WEyH55XfG7wHD995crEJSbcMW8SlxFwETyEiBZE1iKUibNxB0dqC70P6LXNIQs6dQkAIKx1ec7d1KxQ7sOT9tuPtjnwQHpvd3cilhMXJtbLqwyrKNcHWc45//wY+GLvcKUO0ZZX3iNSCekdF8Gy3HFEtB0YYaP/t63lkD1Gy802X1q54sqmp7ZSFo2qu2UArrOzk4455hj64Q9/6OzbxIkT6ac//Sl1d2NpFX8efvhh46/82GOP0bp162i33XajqVOnmk0x9JkzZw794Ac/oKefftpYDD/+8Y/TN7/5TRo/fnxmEb/+9a+N3zPqvuyyy+j000/P/Mb3QtkWhj7wQg7JmgQQfSzrkTeNrLTlBRZcHt+M8Q2+VQFTik/M/uW5JZeHAOrYrq8vkXcKbhsciM5t8N3SST/8rFx4MnFwXtlnjWfW73nr65fv11FRdsm7HhcpmWNpi81ikFPHPuaaA44YNp02IbUfqL6wlZKV25ALqJSVjPPqiQ7k3YekgqnJnzLHxyE4vbdpi0FWbr5c41Hil7OIJ6R1ii8tWDH+t6VLafKzz8a9M6QXAkTo+WKLKUUcFx4ej5OGDk0lpmdLFoMTvM9l63HEb7jAfKSrK2Y9Ze8NHdeF9p5PRHcL8h8+h+B6KYFEap6JtSJDFnTqEgmqfOtHrgn0ITHPHUyvNpflXc84gzB3+QImTzoG9NGXrF4y9kqL4DtGjaKHu7oSnjJxLPBHPkI0cWI8RwCk/0hE3xZrooeIOixeNtJyyTqHtvL7WEY1yKxXPGLR5Vw2PbVoP8r8XcsAHADN73//ewK42nlnHJ3JB66VmABwo8Q7RZ+7776bPve5z9HGG29MRx99tKGEvvnmm41Vb+7cuUEA7LTTTjNJarfccks67LDDTFPmzZtHS5cupe9+97t0xhlnOJuHjQnuBy+//LIBov0ZwPHmWTQ5aVEYXsAAACAASURBVN4xlocMK13ysMtbHh/UDz74YEykw/GXqRtFR1JauKjgVg8JjfXNGt/44TccOMip9N+qkUwFfdY115hDzQfgpPuHrIsPR3ljmZUDzCerLJdYm1LpPaDzDkw/eT9LjqHdDFXiQ9+T9YZ8Y3MF5jLq1cdQWZj3hHKpmeSYvdFanlrDtx92GD27997mVQ2cXCAs5BIlV18yXq4VDNr25pAxr2cfWlaW54LBJwP+DTzazxDRegfE9Q+U/Ml33hknsGdLN37j5PAaHOE32x4t5YJyz7n3XsMiiwegCxZebTGGPnPb5z6XdIMUBfUS0TDLGcNsqAx88IrNtdgG9HXIglkvUV5GWCRDQLK8FMX3fL7i/JTkIjJ+LOWyfP/91H3QQbG7M8ANn5shly9op3QrdsXSy/0F/fxhRwed2gMYlnyM6+vTT1P36NGmTThzoS3+awScR/gmvrBcyksEDfB9scwVgGvZztKyilsG4BYsWECf+cxnjIJ65JFHGgvVZpttRq+//roBbLfeeuv6jevuu2nsWGyB+R+wXG6//fb02muv0eOPP0477rijKQTMlwCGqPvPf/4zjRw50lk4vttzzz1NOYjZY4X+zTffNH8HMHvuuedou+1gFE8///Ef/0FXX301nXnmmYaYpT8DOO49KL0xZokcTvmHL/ML7aZkI1DILES9IDdBPtSsLlUZt/f6QDTVWOjKv4EEv7qRgrSEXWVsbhZZTF36xjIztsImrACriu3AtoFrBviwSIewb+Udu7K/Xy9FOVSJD31Pyi3kmxBlN0SBqud48RwEM+TN3d3xmpIxeqn6AuY2f1PGPheRn2186zUvi7Snqd8owA6Q9NhOO5mYMJ+bqzwTDiCiX8HyFpHSAAj9tLs7FXuGfjF5iDwLbPLXQAKKP1hk9Tz+xcSJdPjs2QnAsnKLLegs5fXB7noAPzcS0fFRe6WsZWoLeH7omC/fPJFASpapAYyO07SdCdgn5PlqS6aNOthCLP/7ycsuS1i7crlvqrXPpCWJ8tmzwGLhXxud2zbyGd6LZDytLUWBjHNHepUzZs2KL23hnQOtV5IgZaWa4HmEPjDgb+r6UpVVFrjGS79lAA5du+eee+ikk06iv/zlL3GKgb4+TGsipB246qqr6NOf/nRhKcyfP99Y3+CKqYlQLrroIgOobrrpJvrSl77krAPv4F24T55yyimJ92bOnEmnnnqqcccEk6Z+4LK5995706xZs8whccIJJ/RLACfdC7DhyAOr8ODl/NAFZnzF+A4pjkVzUoWLgGrpy84HkFUGDjpvecgmDr0MBXPSyJE0cyXSBEdPxILHB6WMb5A3pLlEm2GlsMUlwE1IWmlQH1sdkaeIrYoDDdD1F0W5jGBGtknHA/mYZEMthr4+h5aRa9018eUQ0N7E5jSuKrWfMjMg9iEfk6JUjOHiDg2FrWr7rlmT2IM5FQA6MS6y0gAcvTJ8OCHtgA0o8tzCmcNkJiMUMRfKu3HYMDqmFza15H6Pf8kzGP+WCb6lmyN+s4EJgINTxoyhHceNc6ZG4HayvNA/zjEZe3goUCXdEOUaYtCmSYN4TPiM0FY3yeb4vUmTUjFx8HYKcgd2eNBAPilvG0vaBo4BlMQnDJq4n/oCVYPmx0QMHPQGvoBGmfMi5lrINSRmDu66x82Ykbi4mjN5Mv2tszMzTUGjFlwF4Bol2Q3lthTAoRkAbLC4/e53vzNpAzbZZBPjUgmLnI2dMo9IQIKCDRMuk1/84hcTn8KaBisAQBnAmesBW+bs2bMJYPCzn/1s4rU77riDxo0bZ1w9Fy1alPht9erV9IlPfMLktlu4cCFdf/31/RPAWSxRAHHNvoEvQqBgU8r0bWjqsBNU4ezCiCB0pgln1xRrzJmDzpvrSIGsDOILncxV53VCgPs26sbYl8jZugYyLI0TBw2ia6JLF/O9hcVMBnDDWRr/hvsou+9IN1EXBXyedV/Wd/uLouzrR6v6qIP+JWNeVvLyEMIFJ0jLYcUr67wcKO2SLuzYn18cPNhYPGxMihyrBq8Jzbq6U2QZwRm3b28vze3uNhY5aVEBrza7zAFA4RvEZtsAggRwT0VnCb7BPinL+IKHIVV7jnzV4p7P43xFRwd9racnJk7hv8M97yeCqTLOIxi9YAsxkKlLjps2LWEN1G770kp+U3e3qV/H6TFbI58NGgQBEB4xf74ZN229gzHg2BkzvAyy8VzPGf+qz1oGkra5w7GLmlwN7pRww7WdewC0vGdpneNsIvpZRHbDeobe01KXuUTEOSd9OQAbufYrANdI6a4vu+UArpFdhGsmXDF/+9vfGjAlnzfeeMOAq0996lPGEuh6zj77bOOvfuWVV9LJJ5+ceI0tcJtvvnmKKROb3+WXX27i/D70oQ/Rj370o34B4FIKmgIZuIG8KISyvIaB123Av7XffAhQSSllGQDL5hKig6DlLRu3QSuX0u/elug3ppD2KYcORjSdAPhiIkrwvHry2rmGxMbOiXdx06nzXJk+a2pyUTBusMGGBrdRdgl5cdAgc5HjjDesYa5Unw48CbhiQ22SCLKK+tZhHdlFB95I1afHofG2EuRI8o6PENGZsinChT1Fs09EM3bYge7aYgt6f08PzVq0yAAGW6yZLJJjl2zu+Nz+v118cYLQ5KyogHfhPI3cHPnyS18Qct/4MgxAQV4sanZFFC3PIdtebCPBkDKUOc0keQgzs+ICEZZHhFN8dvvt4xQ5GqDIM1O6fyKmDXVoUMYACaEa6O+aNWvM2fHvq1cnrKEyplWzeaL/NpZe/F3Hv+Jvek9hTxuM62wx0ACYv99jj7gMloW8MLARlKW8BkSZAPMXKCIU9ipgUP3ywoX0P2vXxqAc3+wzahQ9FcVQNvtCHc2vAFx99jdfKS0HcLBUYXH98Y9/NCQfcFnkRbR8+XIDsopa4uB+ee+999Kf/vQnGj16dEIOvb291NHRYeLYYI1zPSC0wAb04Q9/2LwHCyEe3Nruscce9MILL5hy0A9+EDc3ZswYmj59OiEGDk8RAMcLQLcNoBAsmLWQuxSdWimFx0KbW9hdL7BR3AbNEsm3gTbykNSmbFPKLMADoO370QE6T1D6o6mYt+9bsYKuWrzYbJwua53N99+Wd8pGNOK8+bcwdRngp/7+nSjPEN/c+VisXOLXlkYd7M3ygQsJ8lxJMA2ZrItukvmGWtYDwP9j5UoSYhEJnCrVawNQAnmIRYIshj6QVlngWj7DXLFVGoBI8CEtJ3Jf4phJaUWRDI8yplJbPRjEaZdFOLlPAwFKZyex5cmWx0+yVjKggnAZiOFvuyCUOpK4VMp5HnPMNAADHibTwn/jwo3TDrAOo9PW+Kjt8Y0LyEgvCrzH7oCc9+yEvr4EYZf0OgFnIyyYLiCtScF0yAbrAQBLbMWS8WLcV/x/FlGL7wyUCdHZU+QvDzwQg3jb+W2bmzp0AGEI0v3R1oZrPvhB+vKSJSnrIpcPwA63WQb7kmlU97tZC7YCcI2XdEsB3M9//nMTAwdrGG7gsQkggTcexI8BBIG+H+kGijywrkHJBsj6YETfy+Xg1mbYsGGZAA7vo/6f/OQntNVWWxmXSbQVLJTvete7TFqBESNG0Ioo8SaIU2DtwyIF4MMtEZ7+CuBwcCw47zy6s7c3wXbFFMuxK0qRAXR847oFdDE2Wm/ZbUqZSmAKYMIkI/LwlCxR0o3DlQTYtonLtvKtLN+qotsAnCD7wAUCu4skFBLlArJnBC71YQBl4nPKdSPvbRza76NbRnv58OKbSR3sj8B/mawW37DiJF2P2AU1b56rOk6vqqh+IIEgy1poPzNAWrvHwIWKoazvhQI4aXEZv3YtXRTpGrJfcOEbOnWqCb2QbnqaDRLfSLc63sPkhRX+ew70h8jjwOeOz23Q3g6bKcsL3OmYkj61jzti/KRnA4MdPmPQzy1WrYpBotyLbV4s2pUQ5BuIs5LyumTixNhqxvnn/qIsgqePGEGzVq40wI3ZGu8U+fA0kI5ByBNPxCyPkOcVcPsUDJ08TpyOR/YdZUh3Qt8eYfPyAauo7SyWgFLmnUR9sg5beADq2eHRR+nwO+6IpyHPE02Qghd0fJ++XNVJ3G3ztlnruAJwjZd0ywDcQw89RAcddJABRZMnT6bFixebnG8M4ND1j3zkI/TRj36UbrvttkKSqIcLJSpGm+AOee211xpr3jve8Q46/PDDCQsS7JRbb721IWLBg0Dliy++mJ544olEeoQiAM7V6VYuDK2s+JR7yfKl/ekLDWj0kfb357J0/jau07pRO5QySdOsaf61fz4fKvh/3M65cupImcFChUNAHnj8vS/Xjw10SYWE4znwnnbHwA0n0hX4UhL4xoNjRx7r6Ylj/WSMB3/L1Nc4ZHSCV7gDTRWxgjZAh3JsLqi1zJXq24EpgSDLWg7R+EBaXcFijjZVr66XgBwbVnJxGQZKf2a8lZYSHV8mFWW2cElvDisxlTg/fO6T8DA4QQxU1v5mi6vW8XSIhduqtzeVpkZ7X3AMlHZNl0AC55Dzck6721vIPNiVL7E+FJEJ559j0g6MzSfXrUu4i2o5cfoPXlvMbP3/NtkklTLBgD2RUkASf+AiEA8DGcm26dsjbGua3RVRnmTY9rlsZ+1DNgDGpCUIT4BlTVtNdWqHc7q6EpejcOuEjJ/o6Qlj423QRtJKPbVBXSpdsS0DcLCOwXr1zDPP0KabbmpyplxwwQUJAAfLF6xYsKAVeepBYuKrFyB0//33py984QsxyPz85z9Pt99+e2Zzzz//fAP2ijwtWxgW0ANXE7hLsJuHvMHbo6ODnu3pqTuhiXahwCYHVjDpZx7iKuGjv9eJtqXbIN8Yol4cEHzAM0BiYGcsZg6gKDd2zUzGh0cqQFwlCXbdPMuAaL4BXUBkgvUBSotYtzQTG6xpMjmpUUxUXh4feQnmiZ4zthvqb7/+ujUmoci6qb6pJFBUAj6QVgG4olKtw3eOBNM6pkvHKsma4WaOZMvs7gcKdxk/Jine43PFkTwerpFy3ztCEY9keRjYLkTnDR1K49asiZvsBIHqrGHrmE5OLs8bnXfNa4Gz9TnSzWTus3kTJ9IRs2cb65p8eEwATDT5xy3HHUcT5syJ86ft58h7B2KQX3d1pcqm448nuv76lIxsZDCIDUNOPd/jWtO2vxdhwOa69VnPc5DZ2Fm3kXpGbEWcMCFBsIMy0d+TxoyhzscfTyWI97Hx1mElpopomZ7aiM6UtMyWAThshKDvR440PDYABwAGohDExhV5QtIIwOp31FFHFSnekJqg/TIVwfe//3168sknU+XBcgfAB+ZL5KODKyb+V+Rp2cKwuB3uesYZMZkFNhu+NXIlCC3S38Q3irwDBzNmEMd4MTuULQHposGDaesDDohvz2ybMf+N/dQ5pkAnL+VbV8xMPij5Xb4xMwAugORA39JpJkxbvARkItuvc+nssmwZ3SXyFckD0lWec2yUuyZm7Z5EMYV0HPN4+eWJvDxsseTbcLY6ykB7AG+Ou7C5oHIsSl63z5rnWVVAJQEhgTzuVpXgmigBS2oWrl3G10pqdh3Dy3nQQLjxgb4+OrOnJ2E1g+KLi6QEEYbIvQZgAqAGq5iMPWOXeBuRhWs/sxFyyVxzWW6YIe68LhACzwlvDJyLOMsSj/3S4MExO6ecDfDEeD26bMXfOUZPJimPz0/hLildAyXzJ4MWaYHTxGB6NrKbLM54l2eQS4422dkYsEMvHqXHD2IbJTGMTAPEegbnijPzZ8aMZB486NFIP7FqlbFKP9zVlSA1GQmwrUKJGrlSW6anNrJTJSu7ZQCus7PTxL9997vfdQK4r3/96/Szn/0skRAzj/wQjwYGSCQHtyXyRllI5I224FmyZAmB3ATxcoiP4wdkKnCblM8vfvELk4AcCcHhpjF06FBv0/qFC6W2Jim3BVDEcx44qzUqz+C53lWHBe4lpeSl24i2gOlEmDb/dJ1kmt+Rm2mKjENR56OeGyZPppMvvdRpgQsBS4l3LOyRDPx0LjYAyqe+973kDZy6ocx1G+e5bU4kgRUHLg4cDaYlOOa8S/rGUVsJK+tGPRZNVUatEshyhaq1/Or7/BLQYEcCM1t8rSRbQm2ahRHJs5lkxGaJ0nuRVPLZC8PGpMvfuVz8bT3XZTMpiC8vKcoJ2S+zPE+YEdjGQmmN91IXfLCecV4znY8ObQQDsT4fYAnkGLPYwrTZZgmAAkB+hSob5XH+NVuaAm2BY8DHaR1sfXR5zeBbK7BTehGIws665hp77LocbEcqA3ZxTTGgzp5N/zJ58oY8dU88kUpxwdZFvgSWpCZZVsf8K9D/RQXg6i3RdHktA3AAPgBOjzzyiBXArVu3jj72sY8Z97tHH320sCTuuusuOvTQQ2njjTemo48+2txwIC/c3//+d5o7dy6NHz8+LhvJwxHL9uKLL5pE4vyAzRIsk2B+RJsR33b//ffTNttsQwsWLKDtttsus339AsDpDUz5usMadm0kCZmUlDfYTCGFvGDxw/eSYDgSdmLjlj7tmIe2AGV5+MK1BnFcqZgGC3U+4sG++9ZbpkdLFy1K3DR6u+lI9u1Ni2Cx8l313HNJ+mXl3hiSZiFup7p53Uh0gMecb5RdSo2Ut2TuBNidtN9+xlVaJvu2ldfsAyhkOlbvVBKoJNAaCdhczdGSlAVNNE+SLenYr99/4hP08f/5n/htTspt8puddVZKeXe5sGuyCgb/Mu7ZChxEO/XloiTQsKUi4E8zLxocLv0aZLoshPyezi0mXeyZjZjbzJdy2ovFFqOHfsR1WxiuJfDjPuvYcZ2YHe/Z4sRugxULXjL6Uecpx/Lri1J5hmpLWlC8uaqH0x7EVkHLWHGicmndTcX3OSyXWXOu3qu4AnD1lmi6vJYBuEsvvdRsipdddhmdeuqpCRdK3ACdc845huhh1qxZJtl2LQ/o9hFvBiURZe+2224mXQECUeXjAnBI9H3dddcZAhMo+QBuAH7YqPnGLat9/QXAJW74nniC3h492rgv4mA8ZcwYuunxx2nrNWvo9xZGqXqZ78GEdVt3d4L1Ei6N/2fECPreypUx1a7OI6MtcBizrINLH74yv04iXYKDAQx1BG3mPIFUObBwZQFgST8t3SP1bWGIe41rHttuN1HXyWPG0KsjR1rdUm2sW7YYD5o+nbqnTEmM28ottqB3vvFGeGLWrAVY/V5JoJJAv5KAS2mWe7QtdQmAww3Dhydit82+6fAoMQBBeRdAeZfu3ba9Tgs7z/6rLWm+mOlcg+pw6efypQcKytXpd1wWPlscGMuHzz+d2BoXd9sceKBh7Oa4PBswPLWrK3FpKpN5S8Cnz2r+Df8v4/Qx1t5z1cLwjFh+nVRcerFoPSIo3jwgBUlizjz5ZMzAqVmf0UeX5ZJzEFYALtdKaYuXWwbg4N4IAAXLByxbyPX21FNPGcp+uDsiL9yBBx5okmwXzQPXFiNQoJGtvNnQG/iBgwbRsVEurwf6+oxVS9Pimi4WSCDtEg3agBgvpF/f4OhKdOOwYXRML6Dc+gcb10822ojgrjeupyfOkYJ8bnyrqm9xXQQf0nWEk6kmYt0cbGh4Z+lGGxmwr33uXbelWjHhG0bX++gDu9jI9uuxCnGv8cmc87P5boB9igcubLitkiSgc/p0oilTNlQdzRWdZ6mec6jAsqs+qSRQSaAsErC4n704aJC5oMUj85vJeF12b1+26aYGnHDcMu+bLmbfVLyRSPbti6WKxWVxs8MllSsGy2dJy7Sy+cYoADT4LIauun1xYDolg8xHx6DCF3OmwRfA87dvuSUZkygIvrSVkMEh50o7T7hxujxRbOe9z31V6hEg+AqpA8OUBeoT56mKeeOLCJmfLitspJnLt5V6ajP72cq6Wgbg0Gm4JcIyBiIQnoT4+6hRowxBCFgpcatQPUkJtHJh+CxwG3OQ7JIlKd/segbQchtAgMFEHTiYZQyDZCGD66O2CO4e0UzjAAe48264jhs5jIqN7VLeNPqSmrsAlcsqmPW+qz3axaQIIYjLXUjf6ukD3hU3kgiWVzeLfDO7ePp0mtfbaxjHchOvVJtGJYFKAv1XAsqSdPLQofTQ4MHGDVuyEWvCJH1B57vkSlxUiT2q0F7kceVv9iCFgoYgKxI33gMMbdbDxFnl+Tb0glXKUPYPaXtAPvPSSy+Z8Jjj1qxJJBR3XQpyvdKai3F3sYjqeMzEmHour7MuVbW+xWyfrFfsM3WqFcjKiwik0wi6ZKjzRGylnlrnrpS2uJYCOJYKbs1gcXvzzTcNWcgOO+wQJ8AureRa2LBWLoyEgq6CjOVmqH3i6xm/xJsahoBpdtkaxocus2DiAD+hry+5aUfWOXzDliDvhmth2TJJSIcPp7FjxxpmUekGol0tXEnNswAZM2Hy5utzX7HdatqSkWqXmNBpLMvCN6HluA4g7c4qbzxxky6pvH0gOLT91XuVBCoJ9CMJqEs1eXkn46QkmZa+4MK/XR4Dtngnjj8qkoZFEmPYXPmbOTIhoEFavRKkXJ6GZgFDlnfKW8TD1Jw7fjAjmbnNmmcL7eDcpzqeMkWSJuVRIH49ayz0Ga4TqbssiFnlNmO+tVJPbUb/ylBHKQBcGQTRTm0ozcJYsiQRAxdb4MTBaDs0a5U1NjUOkOay2P+b3SX49hBADk412gIHFipY8JDgWj7W21VPfh2dUwfABof/CTNmJHPVWG7hXAdelmtlQoFQikzMflmrkOv0faKPivRGBm1LUG4D3Tg4Z65gnrk6Na4qppJAJYG2lAAr2PCewDMC9OmrcVW2/mHiCcR0/XXBgtgyx67z/J7THdECKhIMgDbyiwxJ8l7opepvwmhkKfdop447C9l/s8pF16zx2vCwGD06joG2gZKQso3oHJet8DrhOEXtNmsVuYNARebWS7m/Fohfz+sOGxpWECyvBs630uipDexjq4tuGYB79dVXDdsjbrVGjBgRy+Hyyy+nefPmGYvG6aefTkj4XT1JCZRpYXx0+PD4cPyDOEClQl7EZS9rzDWIk26CtoDl3xDRvyKYmYjeJKJbogpkLJYvsNkFtlx/z7wpC4hFSMjA9X4Wk1WWIBv5uyfthAbKHBvHeeFscSsmLUP02ILuXfEkjexiVXYlgUoCzZeAjqXVud1iEJB3n+WuWL6rNSelNf63ybm50D0vaLAAFybMyPKiCQIjDmtblvUuqGx0To2bzeUxCNyodnIKgywZuOLX67VCbGQxLgtiqJdMvdqmyymTntqoPra63JYBuBNOOIGQSw10/h0dHUYOl1xyiWGf5Ae51UBysscee7RaTqWqv0wLI8sNEIJrBIDjg0jSKyMeC+3R9NB4F4fQYUR0h0j6DcD2GBEhCQS7XGbRJyd+z/DdtxGLxBMpIMF3YtK53nfkkmk245R1gXgSv2s3JHkzK9NC2EC1BO+5GD5LtYqrxlQSqCQQIgGb8m6zEiGJ8V9Xr6bE3pJ3nxUN0qAiGEQ4OmUl+KojuVeILDPfUfKaS0RPBDAhZ5brAcYAIEGgKrASF2Myg/qgugqeq6749cCmZ77GlmeZAD3zoxa9UCY9tUUiaHi1LQNwSLCNXHC33LLeFoK8b+973/tos802o7vvvpv+9re/GZZKWOCQzLt6NkigTAvDtRlio0HwMB6MY72sI1lxXbzBPdbTY9IbyAe3aDKHG37jeAQXUyR/bz28PcpBpgUy782w5/2yuOak1mieW2xL/ADKs1Ega3dLnXi2VkWr2msqCVQSKI8EUmdMRn6w0Eu2rB4GKfpZhejf8+77ecuv9X3VPjAuc+LtXLlDPe2wWdvqKWsuy0X7H3o+FLGmcd06fr3WYZHf11NW9WyXLqtMemoj+9nKslsG4EBWAqbJ73znO6b/v/3tb42l7corr6STTjrJ/O2rX/0qPfDAA4ZBqHo2SKBMC6PZm0lIfeyqMkHQ+bIF7k6Rn44lWjhPSgbzlrYO6jmc5TYS+n5ZXHNsazT4FtvhurMzXF5F/ALHtHDsC1tQfekTqr2jkkAlgfaVQGqfdLi3gXHQ5jaWd59lSYUq+nklW7Q9eesp+j63b3N4RclCClgLbTK0neH1lDWXL9mgizCHFgFj9eyHa/xCdKCiY1/P78qkp9azX2Uqq6UADkANCb3xfO9736NvfvObho1y9OjR5m9Tpkwxib5XrlxZJpm1vC1lWhjN2LCkwEM2L1sOF1BLb7HffvSvDzxAF0e5glAuwt6vmjSJfDl5fAPuO4yzZBPSl5C+W91GCxy2tU7s0MPaVY+2JGLMmAJZktMgbrEzKkS6W/INcR4FKWuMapVJ9X0lgUoCNUjAdkkmSC9CGB3z7rM1tDbo07K1Rzea27dDRwc93tPjJRfJ6rCtr43uv9zTb54+ncDQXIQ5tNHtzJKd6/d2ObPKpKcWlXXZv2sZgNtpp52M3/NDDz1kZHTAAQcYSxuITfhBnBwSeb/yyitll2NT2zeQF0bmpqp813dBXLOIw5NBwPWgp89sj2dm5N2Ife6qI5YupYkzZ9Z02NY6iWu9WfXFDzhvhWWjAVrHjs1kNAsBxbXKovq+kkAlgdok4IsZ8+W5ynKzr61VtX+dd9+vvcZ8Jch9PJELb/nyfAU5ElU3s//NPJ9zC6effzCQ9dRmDW3LABwTlowfP55GjhxJN954o4mTuvjii+O+I0YO/uwLFyJjV/WwBAbywsi0rij3molRqgDNUgm2Q84dVwvhR+gBUY9DK6uMTNk0eAnVWr+LWVISnIBtbghRnKIhZYHDXjERox49GZbIWtvcYJFWxVcSGLAS8LmG+/bd0D15wAo2o+PynJFpcnKfkyWI96vmQutm+UDWU5sl9ZYBOLhFHnPMMXT77bcTSAj+7d/+jW699dY4pQDHxCGO6Nxzz22WPNqingG7MEIOBGWB27Ojg57t6YmZMHlD54Gu4tOAxwAAIABJREFUlSEzC1RxPc04SJpRh3OBhIxNwdWVyn0Tub5eQUS/ilhEYxcZSzu+fcst9sTjDWxzwa5Wn1USqCQQSYD3M87pGRrn2tJ9sJ+NXuj5Zu12DQyg9RJjTe2vVyMGaDkDVk9t4ni3DMBxH9966y0aPHgwjRo1KtHtf/zjH8Z1ctttt6VNNtmkiSIpf1UDdmEEHgjSqqID27GhyyTgtQK40NnSDEtPSxWXwLEJlZd8T7q9yr+DfOa6QYPMBZAcRy1rlgsne48ZURvY5iL9rL6pJFBJYIMENAugZPbDW648V83Ya6txCpBAdUEWIKT++8qA1VObOKQtB3BN7Gu/qWrALozAAyELyDgV+kbNkMB211p9S28bG9hHKGSf6eoySdi/EdFag7wA1tWtDziA9tprL4Jyx8BMj7+0utaLYrzWsaq+ryRQdgk0ez9J1DdhQhzLykQlf44E5qVob+A+VPbxKmP7KjBdxlFpTpsGrJ7aHPGaWioA10Rh16uqgbwwQg6ELADXbMWEBoilJ2Rscq8B5RI7LnKZBEPlcdOmWfML6vFnAIi67x41ip4UwfgNaXPuTlYfVBIonwSy9lHZ4nrsqYn6ZsxIxLKePHQo3btmDSF2+UFFTJWQ3ADZa8s3W+wtyjOH2qVPVTvDJDCQ9dQwCdX+VtMAHNwk8b8//OEPtP3225v/HjQIaSL9D95Zs2ZN1msD6veBvDBCDoSQd5o6YQbIrXBD5O4hpfnP//xPqxuVvslfMXq0SUaLByQoI194geiDiKwhw4S7bNmyhAtmU+dGVVklgZJKIM/ayPOuq7uJMp54ImGBO2LYMLq1t5c2RgoBIrrQchljyh0ge21Jp0yqWfWYF+3S16qdSQkMZD21WXOhaQBu7NixBrD9+Mc/pq222or43yEdXbBgQchrA+adgbwwQg6EetwG13syDQRLT8jY/P/svQm4HVWVL75Icm/IhEgLrZFBIOrjYQQEkTA8QgBpowmYEIFmkjYR+EsYNPAnIWEyBvolrUBQQCI0CApNiJggiggBAwYBGySPhlbCKHHggTHXy+XeDPd9v51alVXr7F21q07VOXXurfo+P8M9VXtYe1q/vdb6rdRyVRa46YccQruMG2dcJr0Y0vSNPBogmCkLaXPqTlYfVBIonwTS7Fl5rCNdn8zn+QWdVDoAcpz7UUovTbvLJ/W+1aIynsV9S8Ll7U1/1lMbNSoNA3CN6lB/qKc/L4w8FIVmzJFWbXcaWRV1WLsUMi+Zrl5NcRa4otqcRm7Vu5UESieBlJasekGTTBUCCxuA2Xv324+2W7uWphDRJUQ0xCYkS5oQr32hdAKvGlRJoG9JoD/rqY0ayQrANUrSOdbTnxdGqyrclVKRfQG4ZBenNMp5cue8eYYEBY+OgcveqmK+bNX5XYw0qlKzSqDueaQs1/dNmkTj777b3pyUYM9WiE4V8uMJE+ietWvp2hUraJj6oDvIA8lAj92h+bW6+55V6NV3lQQqCYQS6M96aqOmQQXgGiXpHOupFkaOwmxQUZVSkV3QVtklKI0M+uBqiQeU43iYhbKe8ajn2yQpVEA/SULV7z4SqHseKdflQxT5j2zDfZMn0/glS7b8SVjF4taKvmRZ0dFhYtyYdXL8kCG0sKsr0l38xkRGMi+cj0yqdyoJVBJonAQqPbV4WTcMwI0bNy5TbxA39+CDD2b6tq9+VC2MvjqyVb+8JWBhm7vyzTfpF7/4hSniscceM6AN+wfyxPHDFOSIn8PvWfIA+ijHWUGeT9neMqpe7HcS4HlXz/yG0FDOrZdcQmN6euhhInp7223pr3/9q1WeMk+jtorFzWf524c2bgyt5E8ETLN/GTaM7ujsNBY4Ji65i4hetuR+7HcDXXW4kkDJJVDpqcUPUMMAHFgnszxQwDZu3Jjl0z77TbUw+uzQVh3zlYCywH1/xgw679prQ0sbOCYPFZTjAG4HHXQQPfLII4bVloFdUQAuKxCrN5bIV3yu97ICz3rrbdT3fb1/PO94/me1UnE5uxEZ6v64crKm6eA69h4xgh7t6AiBGripwRoL0Hb8sGG0Q2dnmDpg8ODBZu1mvXxp1Dyr6qkk0N8lUOmpxc+AhgG44rvSf2qoFkb/Geuqp24JSLDzysCBhvAAyiYS/i4jqqEcxztIGYCnHgXXB2T5vFPTszpjifIAJ1mBZ6vM01bsX5pxRf+wDn4r5r+NqZGtbABCuNy48MILI0OYVA636f2dnXTyggUGfMG9Ubpa2ohJOF5NWvh2HjyYLulGZFvtM33IELq2q8u0EWlDpEu0rd2tMg+rdlYS6OsSqPTU4ke4AnDFyzj3GqqFkbtIqwJbUALaBYtjaNYTUZvqD9/m79nZSdsR0Vnilt+l4FpF4gOyfN6xFV5nEuI8wEkm4Bkzd9KAj0ZMwTxk1Ih2yjrStBnjd3ZHB50mC7AwNeLnuHKTyuFvz1JxapLsRBOTyPQd0u0SuRnxwOom/401e/CIEfRMR0cmV+dGj1NVXyWBSgJbJFDpqcXPhgrAFS/j3GuoFkbuIq0KbEEJSHDQfemlkVt8RL3BFUs+zF6nuxrLsKdf9gFZPu8E5eqk452jRoWuZIumT6eukSNrrCOuoUqj6GcGpynnSd1tSllf0ut5A9Sk+tL87gK73m0WFwc8/11MjbEAzqMcbpOOU5NzdsLw4bS4s5MGq5xt6Ocrc+bQ9Rs2hOK5bPBgeq2727hq4vlER4f593GzZhl3ycralmYmVe9WEmi+BCo9tfgxaDqAe/311wmJuv/4xz9St8WNAv7uc+bMKV4SLVRDtTBaaLCqphYvAZXrjStcTETjg5t9F3jTbl+JFiMP61qc6xjaJuvQychZMX6EiFYL1kwfIXor+qKwSH+3355o6tQtvzosN7ItSfKSbKDsAqfd9Xz6lss7lrjJPw0blgocJPW3nnZawa7HfAvrVBcHNxPRNTEgyDlfLOXMb2+nnQ87jA444ACSbpO2ODUTV/rUU8SXEeB/nTJsGC37+9/N3L/sssvogHffpZ+JdABsaZMuklniU+uRf/VtJYFKAvlJoNJT85Olq6SmArivfvWrtHDhQtq0aVPYPjDGAbTh4X9XJCbR4asWRvELo6qhhSSgLV6Cihw7CchMYCm4s7PTuGnhATn5QrDrqdxwPhajJKDE8UMu8gdZB9qCuDxWVjOTUFgU/SvvuivRehHpr1C64yw3cmYkySsrIK0HKOFbZiM94ogjtlgw1Tzh+Ko0QCGpv/WsGmvZKay5pKj/9wrYI/UcM22MA4aWchBXysQ/2m2S5RiJK/3WtyKXAWcMGkTvv+giwoWFBG8M7g6aPdvMVWbPRBPTjEs9cq++rSRQSSB/CVR6av4y1SU2DcB95zvfobPOOouOOuoomjZtGh177LH0xS9+kf7pn/6JHn30UbrxxhtpwoQJ9JWvfIUOPRQqWPWwBKqFUc2FSgJCAkoZ/ToRwfoGC5Zkm9RU5UstRA8+CnrSO/y7SwnVsXvsLvb0unUmLikTCYXFevbeGTMi4FDOGRfdfBI41fMuVhYOQOGio3cBQ1juXG50NqDnkr+2jGaJr0oa+3rWpVX2aSxwRMRlrGxvpy/MnGmsi0hkL+eYaWMCMJTlvDxgQCQlh2aNhBzXdXREiVMefJA6Dz88JDfZO1iPAHn/h4g2Z2fc/AAALnxnc/SbrPeUyy7zdh+uR+7Vt5UEKgnkL4FKT81fpqUBcPvuuy+tW7eO/vu//5uQYgD/u/TSS+niiy82bXz88ccNcLvlllvo+OOPL14SLVRDtTBaaLCqpjZEAqz4IWcVrAW2fG9oCLNQnj5oUCQGhwkWkgCM07ojeinbYsuf5YofApkKrGZ7/OpXdPQy8GgGjw8JhcV69t799nMCOJelLy1AiZWXw6UPQDXpibNSym9t7XUBOG0ZRTka2CRZ/pLmR1K/bL+jziFr1tDUhQvD+EdJrJOmzhp5OABgkpuv7ZLh5SA/G8AhHuSI4zg1PWcRVzrj3nvNOysGDDAXKQdv3Ej/g4jOF0KAa/PNM2bQGfPnx1sFswi2+qaSQBMlkLSXNLFpDam60lOLF3PTLHDDhw+nU089lb797W+bXgLAzZ49my6//PKw11OmTKFXXnmFnnzyyeIl0UI1VAujhQarampDJCCVdlTI7le2eDPkkrpq+vSQ/jx0FwTZgiARsbFT2gCDi4gE8XW3sXLKUhAKdU1cHgM1T6uLbotW9OMUf87dhesyzrmF/saBvpqBTGqnBxmGa3LIttuAFn9n6yP3De/cP2IEMWCUc0RakTBOZ44ZQ3tOnGhc+KRLa6R9Sf3NONPRrklr19L35PcCtOtxjlMMa+anw9Lm6+Yr5cTEKJAX3DNxURK6OSrZsHUTXYLV7ZnA2i3JhTD/jw1i40zX07iLZpR19VklgUZJIO1lWKPa1ah6Kj21eEk3DcC95z3voTPPPNMENePZZptt6MQTT6Trrrsu7DVcZwDw/v53HBnVwxIo+8Lo7zdP1UxtvAR4zsFyD6IFG2udnpc1yr+HAmkDDJGDesGCSOwPLBFPjB69pT2qDgZxAJGSwc9WT1L7XfFsNUBSKNuRkVq0iK58883EuLnwG095+dLah+UqMCATO0dAtYWUBERY0xYuDGMd4Zg39MUXCfnHpHvegs99jsYvWRJW+SUiWrLttgQ3W1jlpAsik67cN3ly5JsfT5hAzx94YN1ufklus1oRjFMMa/ZeB+iUVuI1Isca95XL2X/VqkifWWAgSJkbxNixS6ycszLnImS7yLIlIPPc1bLuKVMSL1Aav7NUNba6BJqlj1QA7mAzdRASVT3FSKBpAO5jH/sY7b333nTbbbeZnu23337U1dVFzz33XNjTcePG0e9+9zv6wx/+UEzvW7TUsgO4/r5xtei06nfN9nU3cwELBhMRsPX00xEltCbOSinUxw8bRjt0dhK7fmoyE5nAmK1D+NuV06bVuNxFSEtUTJyMM6qxdCiad9dE0IpQkhseykkCJ9a6LKQ04XvSndRCSvJOV1fUkoUP8c3YsVFwIGK0AJ4RN/l0WxstWb8+jNtiEhAJUDjXIFuhbC6yvvLj93j+sIsi3BKlm6mWe9r9teYyQMUm7t/eTs/39ESSZYdMoRYrqrTESQsztwsW7t16e437JIDwTr29tHT9epNOQH6Lejm+jud9GnfRfrdhVR3OJIG06wWV5AH68p7LebQpkwAzflR2PTVjt0r1WdMAHMhJli1bRq+99poRCJSTmTNn0vjx483/gNrvvPNO42Z50003lUpozW5M2RdGlg2z2TKt6s8ugVY7WLintnmaJqYL1rXff/CDNUBqn332CWOr2BIhGfVslordiEiyVnLbJAkL2g33PvzttN7eCFCpsfQpJf0QCQospC/S1dA1E6S84B0BOviR774babf+lufGf1xxRRgzpWPgkqxG0gIXyY1ncdsDmcazRAaE4QktcA8/XJMiYZ/zzqMTOjronIDOHoyIklyDrXIM4KTboU7z4FoDcSkjTAMzEL34WGflOEhghTjzCxW4nz1wIP1x40Zypa7g+ky8GhG1ycItrp6Yy78NXCZlYm7IF0kqAORwYQHwyxZPBq3V2ZF9H66+tEsgy5zK8k2k9gLcretuU4MnSNn11AaLo5Dqmgbg/vM//9MwTV500UW04447mhxwxxxzDN1///1hR2GV+8lPfkLb48CpnlACZV8YrbbRVFOrPgm06njHMRhaKcwtgAHudjp2SbI/agUVktZAaPnFF4cWCo7HY2sa06qjPVwWYo8+RUQyfs3GqOiKBUMbWCn3zTfH+buYDRJlMHiDMs5ueHBh3RAkaJYU/nFzxAWkj+roMBPziYA8wwaabr3kkhAY4l2MByxZ+wdTOgSmFoUKQHtlR0cEtEmXVljlJLCVboecniIJeMi+MQCXVlUNpqRbphUUKsDnE7vHbUD9Okcb5hsDZJdVkb/XrpBGVoF7KspGezEe5/f00GmOLQUADuvFFYeaKgazvm2r+rqfSCCLJazuM83DvTyt+OtuU9oK63y/7Hpqnd0rxedNA3Cu3oOw5KWXXqKdd96ZPvWpTxlyk+qJSqDsCyPLhtmXxrhVLVJZx6CvjLcEKa4cVJpcAyBDE4Ew+NIJj9nlUpOedI8aZawS4SOsGjbGSnZDY9e/pRZXNAZp7PKn87rZLHxx9P7a+rQTUZiIGYo/08SzxTAEC3/9q+lWFgCn3RVdoAnla1KSGpINAVoZdOmYNpSD/IDfJ6JTRZJpWP0AOE5esCBkidRxedLqKi2Mst8MwNldduDAgXTC/vvT9StXhuV+f8YM4uTiVkIVpRjq2D3tfsnACtZSBt+wnrJFVLNCcvJvWx8kGQmsaUe3tdGYWbO2xHda3C2lBU7OE7O+VLwoXF1TxWBm3bAK/q6/7f8Fi7O+4jNawuo+0zLWG9fZuttUnyRTf112PTV1h0r4QekAXAllVLomlXphFLBx1TsARR2ornJb7aasLvmWcLzT9ofHka1dUNaNq9mFoFmIPjy2OheWttbgqxqAYEsHYCM1CawaMsashrEyaBZa+L+32op27e2tcWWMq5/7EbEGWfrLvYfywIAKSjnc6KQrHRI137Bhg3HvtLUFMrYm17YAK5vs0M/ZKm8fgyYAoiNVWghujwTicTGPcpTXq74BJA1FrrIuwDvLM28edc6aZQVhnIMNieTv6OwM38F8uStgcpQJsKXLLWqqYcQU6w0g6jN4Z8QIerSjw1o/z2FtceVxku6m8mJAksVIgqDXli83Fk/I/OWttqLe3t4tTJSOtBHoBxPD7HzYYVtIhlqEtCTt+dGv9v+0m20D3o+MlyU/Jn0JKzrmyelMcwGutPPJtDSnNjVA/GEVpdZTGymIAuuqAFyBwi2q6FIvjAJcB+qVY1EHqqvctPVl2tCVUHzK8HkntaxLON5p+8DjJRVpV64yfvcspdDreCm0QYIebQEL26gU8imCVn360KER0GADcQBTX2a3tICMhK04Lgsg6rbNhbj5cdnWW9Ml3WhB7cN08CBjwb+Ni5yFGEW7jsIi5GyjUlhWjhtHRzz00JbKBWiCZWcCESFz3nAlA8lGausfu5myFVXHwaE/lxDRcwKASauSGdd584hmzQrbxuBRgnzb2LHLogRC0uWWgY+0qqEP0uUW7f3OwIH01Y0bw/pBWHNtV5cBVrC26RxznOj+0GCsrIOakHsQ38j4TGO9dcTz4V1XaoZWsCqk3c/L1qdC9v20m2wD34+Ml8qPGYmhdbUppzMtL/3ANDOnNjVwGKjUemojBVFgXU0FcKtXr6arr76aVq1aRWvWrKH163H3GX1wSOC96tkigVIvjBLeFKU9gH3nmqvctAd4Hu3zKcPnHd++2wCIE6SkLrSxH2glPq4frAy9+tBDdO2KFU7GQvRAxqDBzfELM2da0xvI2CrJaigBIJR9JFiRSZBZStpiJBV4WwyetKjIlAvO+bF6Nb0zalRIzS9HB3X/f0OG0FVdXUYWMs+Xec/iDgpAwpYmDYRd7z+t2D01aAKARowcQInNjdA1o7jPTCLzB8FECdC1iYiGBKCQGUN1ImvZNozTxCBmb+fBgyOg1wbiFhMRrIs44STgqwGJgVUW7dU541Au4Bvn85PxkOh3TY65oD93nXIKTbn1VqdbKFIv6IflJZkmpbx5Lss0DCiD3Te1VTvvPakIsJKqjU0+/1LH9ToWRhFybNSurscrbbxvXtauXAFck+dVlrErtZ6apUMl/KZpAO7ee++lyZMnG9DW3t5uiEoGDRpkFdHLLyMsvXpYAmVfGGkBTNEjm+oATtEYaz8zbLR5tM9H5nnUI8XDh3wcw2AKcTbn1ZicaHGuNpDldmvXGrdFW84wbY3gGDGbeyaPCwSg2SoRY7cdGPsCMo+lKiGyBgX47wNGjKBnOjpMWXi09YPrkxYUZpWEol0T/+fIXccufB9zuBdGSC4ciZ73aG+nJ3t6QiBso6XXFPOQ99lnnx2JSWOrEoCQK37RNneZdOWRRx4x5CuSLAbUWZuzlG5+GBjb4vwYrGOszgrAlAZh5waWsUi8Y8CUeeP06fThN96w5lxD3WAZHX/33eGlwGWBlZPbNpOI/hKAV/wNLosMlDFPmRUyssgWLSIwceJduETqGEPbguQ177Kcpt1j0r6ftEnkXR7qs4FSm3u1aVuTLSW2/vucDVquRcgxaezy+l3317bfxcX7yjH3uQxygV3X37OMh56Hp1x2WejiX1awXXY9Na/51sxymgbg9txzT3r11Vfp1ltvpc9//vPGHaN6/CRQ5oXhQwTh18v83sq6Yca2wAXUMhzgdbdPuS5JJVj2oe56RGFynGtcqfIbuuJLsuQc00m1bY1wWc3Cd1W5zL6H3zWIYwUDpBZjx44lw94o4oNkbNKktjbacf16AhMlrEYytgqACi6YfxgwIFTgj5s1q8b6YXMZhUWMAWmN0qLmF1uYmIxDWo64rUxycdjll29WNJQ8JHU9vgEQZmp5Vq6kYgK5MREH/s1AS5PIeLlIWUhVpMKK8gF6JbjEnGDXVFuspLSWyvly2eDB9Fp3twFTB2/Y4I6jU7nqNPmHSQMhrJD4faBIfzCeiJYrKx7azFZDZubUhDvM+og5iXVsBfC2BeDY59Iq/nkrn2nrT9xg0qZ5yHCBl9iGFC/U9D9je3KXY4o+1PWqpb88x31c5LnuNP33eTfp4sOnz66LPp/6fcrP+50y66l597VZ5TUNwA0ZMsTkeLv++uub1feWrbfMC8O1yTRN2BkPMG6vU8FwAbW09aV930OZiiRt5vfzqEfULce5pQGckouXJUcpdZEcaxZ5w61ucgC8GPRI8hB8AsVZpgyoYegLymUrEFcjLUYoextFaCGZL5lEZM2KFTSxpyfCnimtQzqVAaxUTF4B6xcnX5ZglMk6OBm1Tkyu3ZIki6Mk9IBcDjroINM9WxoCmS8PoLPGPdARu6WXjVZ6NFhklz/ul81NUP5NxyuiPsiR3RkBkMA4yW630tU0zFW3++6htQdl4+FUCnDBveKoo+joZYj02/z8b6Iwhx3H02kyF7bQ8u/4zpZvkOPlXK6ONduOYz/JG5ClPTfyvKQydTtYP+MsOLm3IYUQaurOcKGI6prZhxTdrX3V0l8wm+o4UEnSY6svbn9I4wasCbLiXMaT+l0BuCQJ9b/fmwbgPvKRj5gD+jvf+U7/k3qdPa4AnL8AvZgAY4pz3m7FACLb4ZcaCAZt8lKIYogwwq5lPMhdokl0IfQfoqa/qWMkEm9qlSzZvU13hMvVVjIwBz6iGfy0VUgE30sLHAMCW4JvgIQrRo2i8557bktTAkDD81gmWZbt1XF0iEXjfHbaZRDfwUIlLYnaTUknJpdKoXZNxG+cEgExYRzxrBkkpSsgAMn+7e3U3dMTugfiby7rs2tsklykfC0akjGU+4IccrCAsuXQlssP7ZK55mR9iF+UqRQkUQv6Ole5eII85YENG0J5aPdaxNq9qSydXvuLY4WWTsnP+ZLKdNtywZOU9L5pFhFb/7G2Ro0K4xyTgIutz17fNH0XDxqQk2eMXhdxYxp33vOlHO+hmr04Sbb6YknmvWSirdKtw2AoyqynlmW61tuOpgG4+fPn08KFC+nZZ58NYzXq7Ux/+b7MC0PGC0g/7UaNjd54pWtTFpKNtBs3+mn7Ji0Q1Dd3SXE9E4YPp8WdnTXsf3m4btjGzlWf7d16lMRGzBsJPkCBz/FCPsyRcXOKy9W3rgBLSHSMWKg4yw6vJbZq4V12iYRVjAlIZL4wqfTLtnGM1n5EdKwSqi2ODgmamaZfWtVAsoJ4OW2pkcQo269bR9etXBkyQrKSwqB/ikhCrscXbf52EPOnk2if3dERSRANwPL+iy4Kc5ppF0zn3PFU9K3u4I4bfsiDY0E54TgDXGnFY/IW2TZXqoPbx42LxMRJohZYQvfdd9+InBnc8wWEvDiwumSuW1fX8moaUHG1OudLKq6G1452P7WRvLj2f/y98H3Q0f/UCn5BckyabHnJJ21suk+9ac9vfaHFa7KGAClhDUbqVYyaZl/NAtCTBiKn38usp+bUxaYX0zQAh55/5StfIQSPz5o1iz7+8Y/TNttsYxUIknpXzxYJlHZheCpGRY6l3milpUjftvts3D63axpcoVyZ8wr9lbfwOsbHRgKiD4AkF6caFy5leZG3/0lWh8TxWb2aXMmn82JBS2xDji+wrOG+d8eRR0bc1CQroqzSRynicnHr+nhHRyRZd03OLctBzLEbSS6qUuYAC+x2FwIgxSKpE4FLinyOo1v229+GN/f8fpKFi9ux/6pVUTKOYC7qy5TbRcJs23CGroVCNjLGjlMupHbb9lRQudxIzGJMjA2Pk7RY6jg6BlkuV1G5dxy7zz6JRC1yHkKGICRhACnXvGbEdFmN0ywrn/0zTXl1v1vQ+YN5oF114+TnkkuRgBd1utwEU9dbpxx95kUe50RaObv2bLn/43JKXo7pPIo1Z2cCaNaxp+zKjBjmpEvZmssAS+J7s2amIsI6eDxdyOteax4FlFZP9Wh7q7zSVAB33XXX0cyZM6mjo8MpLxyGYAernhYAcJ6KUZFjaQNwcPfCozdM28EWORQciWY1uLIlQ5Zlo24bQYQNpDG4499cSX51DIZWjpnMQVpn0A5XPqZUY2JhJbx5xgw6Y/78dNbHVJUW97LPmOvafZQiWe5jc+fSks7OSPLryKFrOYgRu1ETG4d8W67HFZtnIWq5mYi+Ebgr6jg6k8pAKwseCoIErLbE0u9ZvDji3smWP0nXX9M1KCRKNuzuKWP1bK5FPnKKs6C6FD75d1hEuW4GTtgPdt20ySS81u6iUPyfGD3amk6C26vdKCVRCyuAnG7CJXO9/tO6bhW32oot2ediJW0L+BKQLfMYA2vca0LBPnuGqwgNVlwufjYd5O2hAAAgAElEQVT37yz11iNHn/ps7/h8J+Xjej/r3/UFTKgvxAHaBLfVmrQqRIS9F+7PMmWMa9wj46BTqbz4ouGQOHnBgnQusmkXQMb3KwCXUXApPmsagPu3f/s3uuCCC0wKAQz0Bz7wAQILm+25+WZM+ephCZR2YdR5c5fHCOvNO44t0HZIRb633Xh96UshSGHgFiGfCBRsrYRxLItUGPXNHycdxuEMpVATTcj8ThrA8TdQGm038NL9TiY3ziRzFXOHmK5noPT/9a9WAFePMpCpfXV+5NPeLMrG3mvX0s8C10kfCxy7aPncaJsuu2LzlAUOlq3RRCQTSOPzSFybgwUzLmZDz3lJZY+y4xJbgwYfMkHSbORdw2OzwOlE28ZCOnZs6jifxDGO2cvi1jaAEyx2rFTpXHIu+ckx1pcu2vrz4wkT6PkDDzTsnklWT22d5TFxJaqvc+k0/fO069IHMOEddpHFnownKQ6Oy81L/vqs0GdO3GWg9/4hhJFGjtJ9Gm7dNpZWLWdb+XpNJrVbliHdulGX9IBhK5qrTzoGusbjIeFiumYvsVyYcf99vRnM+5Y9iN3a2RLIlwvOdDZNXJGl1VObKJO8q24agPvQhz5k+rJy5UoD3qrHXwJlXhipCDz8u+z9puvGqubW1KGg8SaPywSwxl2/cmXN7ZbuY9JhdExPTyShL7vlxd3wM+CTHZeU5DYFTFvtZAxMlvi/OKFLYCzdQWr6FKMIJx3Q3oOe84s+ykvatuN9uNGOfPddwwIo49pyC0ZPINaRjIZIKu6Ko+PLAVsMXpziL8eeY7206y6GCiBCzk2sTc6TBwsCx+jpGDh8B7lxLrxwTj/8cGo3osQxjlHa5Ni/cfnl0dQAFoshQByAaZzSHwd+JXlLPXGZiX3OeR01o7i8+uja020XcXH95HJ8LaCufSUJZOjffdzzfNrtU44Gj2wFREzqG4MH06WXXhq5bMAFYg27q9q7kLLjvGuvrUlr4QLEvN/EXapaZWshqXmCiHaV8ckJF9M1c0W8Ly/qfqrjj5PcHS17EBNL8bhw3RjLJFf7Rq/HMuupjZZFUfU1DcANGzaMTj/9dPrmN79ZVN/6bLllXhh5uEbEDZzchPFehExBb7Tz5hHNmhUWJ+MWXOyUGpjI4GNDymJxq9S3YvLmDFYEUKbHWRW0UmajJNdJgW03+Xwbx64+mpzCFc+VZaHwOEfYBi0uHvddcIE1Hgp15qVsZWl/XspLmroZxGHO2g7bPOSRFOeB9tqUsriYFO5jrDKn1h5bi7XiKq3IIFwBKENg/86HHWZIWfj2XrdTWiDYPTGMR8lg+U+UtWeZVjIfopoYURCQLAms1LY541L0OfecLU5Wl5NkVUx76ZBmbpfl3bz6yLLE3GQyLlecMffdVjeXo2MQXXuxdV4mgQy17/rmQpRjZnPRtFmxbOMswSMAi3Q13UuAOLYsW8GsuoSR6VJsJD+6DM53yOlLksAM+ov+/dOaNTTj+ecj3arxjlDpPfQlVtwFLl/UwStGykbmlXR6xMTE27JMpL4iPSqSEpU3Yr2WWU9tRP8bUUfTANwnP/lJ2mOPPUwi76KfRx991Nx+P/HEE7Rp0ybD3jV79myTxsD3QTuR8mDVqlU0YMAAQ7ryta99jSZNmhQpYv369bR06VL68Y9/bOp77bXXqK2tjUaPHk1f/vKX6ZRTTvGt0vlemReG3syk0upzm5ckHC7fmnhW31jNm0eds2ZFLGhX3nVX6A7zRE9PlClPkCXAVUvSvYcKpXartNRB2iIgOgUr2iXvvlvj7iYPdK0UIk/WEHzXDUgWPOL2TrqwnNneHiHhYBCXtwUOddYEzSvADFevmfffTzVy3h2wuLwALi8l0DaXuWyb220e9bqAibyp9V2H8hsX8Av7aHHfRKyXJjRhxQxtwKNjMl3tdLkYamuhL0GPj6yTAJG8qAnJX/4OeyKRXMM6bi1OCXYp+omAs8TrKWlPL93vjlhSnSpC51gEIRvi9W2xU0kXcDwfrUDd4pIXARlq3+WzxGeOs+wzX7wqkKGtTEhd8a/BpRGvd2suNOUGjYuedR0dkZyF+N4FiHW+Q1yaHqpYfuU8kxeQDDit89BCBmYLX9CsvNoqyYDSN6ad2xLrViouk2WuUd89sOh1V2Y9tei+N6r8pgE4AJyTTjqJHnvsMQOGinruv/9++uxnP0vDhw+nE044wcQn3HnnnfTnP/+ZFi9eXAPAbO0455xz6JprrqEPfvCDNGECsvGQAWlr1qwxFsTzzjsv/OyFF14wwBSMmocffjgh391bb71FS5YsobfffpumTZtG3/3ud+vqbpkWhraIabZFtgrJ5LH1dN7mMhCyxMVYx3CTCrcxmZcFlPGRdqmDEmQJRxHR8qDBqOeq6dOjQcO2w1McRk76btcNv4ovGx/Ur9nspAUucvgqqmG+mSxkU48BzKy04iBllzgkI8YY8I1jooJcz0Qp+bdplKs0XXEp+nzjjLJkguy4svENE3RIS4T1G4/5rG+dUY4tHUHSzX9mZTONIBUgsqVO0DGHAKcL39m84uW+x3uPz027S9H3AXBFzamUYmv919W+Fl66ORJcW70RQA9vKee17m5jcWarnrzQgXu1NYWJWFtW4apLRLba2i6J+Hs9V+KsSLFnRwy4RF0AVtcQ0Q+CxPTafVqnOuEYzR03bYqkxdGxpXKdaE8T5JLEuTk0yC/p8lZhcjN22+Y0EdoCh0vfpD1Jj4sGcDbXTnyTRCoWu+4tZGJITZP3ZW3WBV0mPTVrH8r+XdMAHCxad999N/385z+nk08+OTaNQFarVU9PjwFQf/nLX+jJJ5+kPffc04zHH//4R9p7772NG9NLL71EQ4diqdsffLf//vubcn7961+HOesAxvD3119/nQDadt0VXtNEb7zxhgF3p556aqTcN99807z/yiuvmLg/uAtlfcq0MIYMGRIqYdikdIJfKOlp4wbi5GIDcHjflnMJrg6umzB2/ZO5o3DDetqCBRG6dxwOE9vaaMf164l9+nfr7TUMc+Zgs7gNgnxCghO0DweTzlNlBTBqUwZB8PcC//YawBkISm/ytlikCBV61omnv4sJsrbRJ3Ock7mhtuW0CSxzeTWvP5aTqxLv6UbIco6L6cyTPMPWx1z7bVlXVmXLxfopwB++S7R4OhR9qTgX0b/+uD58+oxz4JQFCwwAwBOS6ey+u5Nl1xqnqFKDsPuwnBPyfDqtt9fs9eGzaBExEy0sc5LUSoMM6cbP8WBxboSuM4MvG97f2enHbpgELoPOdAUuhAyqJgVnqgSz0o1Yp12BC/JQXJB0oaTND8eEy3haG+ujzV0VexXHBAPwrSYiBnLbqTyUNhCVdCEmvWI4XyfIVGRZ0oVcXmwmeRuEAhCyLzJcwmfN2N4pk56atQ9l/65pAA5uiDJfDgSF/5ZPb2+v+dvGjRszyfG+++4z1repU6fSjTfeGClj7ty5NGfOHLrjjjvouOOOc5aPd/Au3CfPPPPMyHtIRH722Wcbd8yvfx2ObvHPFVdcYXLeIYn5jBkzkl53/l6mhYFx5HF6z3veE7lRwkb0ypw5dL1MA5EUuJsgFenzzW4S7Pduc6u0ATiZrJnJTUAHDuvhAe++Sz8nitC98+ao3Sq1H7pkepS3n+gSgC2IUcaOHRtaQJLiHdC2/ZXlUB7+LCoXqQrqA1nQq6++GnXtyTjz4mI8NCuWzS0FiZdvYBcjB8NnxqZVnxUhgZRpQZLcNxOBTBF9qKNMH0XKFiuFKlO5jjviB1tNXnWIulSfYh6fvnYtXSlb5XClYyv15T09oaeB+UzkPuTLCz5j2OoMqy6fE9oKxFYUnQuSXfBAHIVLSLnvsjWHgYEmSnLOZ3GZxp4T4xVYinPLZDAkE50PCth2XQMLQHZT8CPnOGXvHe1iifP3fxLRNiNGkExPIuNpD16/3pqmBXKsiQl0sPJCn3iGyIRVSNKzOIu/7TyOu2zh3yRAZpkz4YtkobVa/oVQef8pkrAs6+Isk56atQ9l/65pAO7f//3fawCbS1iwZmV5cOOBxQCXyS984QuRImBNw80IQBnAmeuBy+OiRYsIYPAzn0FU1JZn2bJlNHHiRJMGYcWKFYlNXLBgAZ1//vn0rW99i84999zE910vlGlhjNpqK+NrjiDdt7bdNgLgsPFJ6vS6TfvqxmlTQA7CwApAjh+2OPFmKH3QLyKi08S7IDc58aGHTNvxwK2S6d71zRb+G3Zc3NhpJigZ1yNzw+k28Hfa/ZQPdmbokreTLkXRRjXMhz530ZYbKO3kcymk+nDDe3v86lfRZNiBWwe7c6JfIDCaunBhKfPXpJVN2d/PbL1JaYHrawBOjmsSOYwvMYxrrrgYPMtARlD2+Z13+2yARMa7SbdfvMuggtths9hpDxFO9aKBG7sc3mxx+eeLUvbEkCyPqBtufh94552QOVlb6fhc0Myw3zjyyAjRFMAV9ugne3pq9uc4MMNeLfh2p95eWrp+fejNIi1wMpk12o2zEg/kqsMUcLbDCwWugQCreHR6EnzzxY0bIxfFzPzK1rVIrtbtt4+y1hIR4rXhjXXec8+F04lJz2xrX14k63xuPu7O2r2WAa0rNtg1x20WPQb1SVbCvNeNLq9MemrRfW1W+U0DcI3o8LHHHmvcNH/zm9/QJz7xiUiViEt73/veR0ceeaRx43Q9SDSOhYCk42eccUbkNbbA7bDDDiamLu6BFXG//fajZ555xhChfOxjH0sUAS8A/eKzzz5rXE5BztLoJ6IMTplCfx81KryxOmHYMNqhs9NstJzglgksdJB/lnbXMEeKQviWDjeT/PDNnszh4/JzhwsKu1TcFRQAIIebrcWdnRG3ytkDB9Kg2bPDWC4pkxp65OA2ntsQul4iRkI8ciPGnyO+8XGKtMVKArcbvs3EoWqNq0g5ANJ1VR6GfPBG4nssrGnPDRtGd3R2GoVAxsfFBZqnbGL1ukMCXgqF49s0cYouoJgZQJZlRBuQCkOOUdKte1nE0grtSK3EWvauuPQPl229dZRcKnDtM0RVypVWMidiD0Q0/bLA4iNlKS8JJSiCmx1cKflMtSV11zkDw3IXLSKmoLcxOP6wszMk9IIvEc5ASQSC8xxnWFzOU3nu4t9wK52C/w9cEpmen62HmqFRxqPzvoN3WUa4AJZWN6wTPucAhNl6Bslz7LgtOfcVU6faXWSVy6sB7Ql/s+k1XvutmmffDmSEOHE8Lj1Br7k4vUGe2c2w5lcArvgdsmkAbty4ccZydfnllxfWy09/+tP0wAMP0O9//3saNWpUpB6wRSKJOOLSYI1zPWCWgtvbRz/6UfMe3ARZwQaT5osvvmjK6ZYMgZbCLrnkEtNXWBNhffR5ygjgIpuTcoOTjIfYaHFTJn36ZZC/T//1O9jUV3Z00Ob7utoHt23/PxExiLOxgcmv8P7FgTVo2sKFkZgHJDrm/GZwz5AgTt6wRloRB7RUvMxtM2bQGfPnh4l4bS6XDJQ0k5/06Xcxo/Et8n5pc884ZItxx4GMQ1YmCndZCCS1NKyV2i3mZiL6hrJkZpkT1TfJEvBSKGLGPSnQPrkFLf5GSlfSLL1teZCbpdMN+CZJia0BeMo6Y0v/IN3gZKwcuoOz4SDERgcXdNJzQe+BuH492CEDTmzPF44Aazgzdn7ppRprGaen4D2aL+ykBQ7EJiBiAzDQTKdwbX9gwwYDtmR9HLMsQxNs3hyyj9rzRV4gatAq2wewKElH2Fr4L0EMOItJstji7OHxxVnJ6UVgmVyz9dYEgMesnvLMQlkMLvFvgCYmlam5sLKtfXw0FXbBzY/RewD2ghhu16WXXuN8RiNHJKcZ0rGCNgIWOWXi3LyT5n7Ry68CcEVLmKhpAA6skIgfmwcWv4IeWNfgVgCQtbsiSADdL+j9kwAcmnbiiSfSD37wA9pxxx2NyyRcGUBUst122xlrGog83gnYx2xdAWHLF7/4RZNKAFazEYErQNZuN3NhRJTBu++m3sMPNzd1OngYG+3/6eoKXRFrEmln6Lx2ycRhOVD52mMzhaMrmCNDAGdhyuLDFvynsAJFgseJSMZr4V0dEwH3CtCkRyxPcYqe+o3BLMtTHoz6tjMuCawEVjIv1jujRoWA1JbXJo34cUgsv/ji0CXGhxZdxx7KPsj2ZMlZlKbt1btbqLezMJHWA/76jOxTupL2mX63cEdYsWUFngmktHWzRsm1xIPBRU5+xy7xGpDxBSK71Ok22Mg2eC+UZxksTQxo5BCYM2PVKuocNSp0bYS1jC2EDBzYsiVj4FAOk4lpZmRYAgG0toe3iKgQIJL/G5YsGTuuwyFsdPfQvXZbvpy+uwnBDpsfyIhZiZ3TS1gLtQcJM2zamIyZvMXFSs2y4vdk/bZwCHM5aUkwjsv6MxYujHjlhJeqMXuF3kvx306LKTcuBWeADskAgzCIb/DEWZGLWubN1FOL6lPZym0agBszZgztvPPOJj6tqCcPF0q0De6PV199Nd10003GmocUAUcffbTZ1MFOiX6AKML2oH8AgHjv4YcfJrhb1vs0c2H4WuDOHTKEru3qMpscx6g9E5PI1kcmXLdkkMStYSTYPLgRw60W09bLgGEd08Y3nfIWDAfcrTNm0J+GDTMADQe2ZhljV46Ib71KYxA5bFTOlkOCW9q4eIs4/3aWl/WWzQJYYfG6RtwM+8ib37FZPpPKkzejWCeQE9xLz+7oiMQfSmtiZYVIMyqe79YJPqox2SznNK6kniNTvVagBLQ1CmfQ0W1t9MuBA40roFbY0RRNSiXTP0hiCSYJkYBME1Ew46A+NxAHPKmzM7IHsjcCwBw8HGpyASpSDeuZ8eCD1Hn44REXdZnY+aTubiuDI0De0sCNE6BsgLAG4Rzcr72dnu/pMQyNOnbcB7RIDxGUB2DK1iYGr/Lyl71bZL5WGwjBvqTzkEr3SgO+LOcg+qiTfsuxTyLqYvB3GBEh510NbX/MJa4NwGF+PEtkxg0P9BOO7a+HM0DHZjq9hgpcg83UUwvsVqmKbhqA+9GPfmTysj300EN04IEHFiKUPEhM4hr2y1/+kg499FD6/Oc/b/K86Qd55tBHpBiAK+YHPvCBXPrZzIURUWSefjoSAzeRiJBMwRY3BovWh77+dcKYZH1k4DDKwGEHN8d7xQYoy5aWIj50tU+9dN1gP3R9W8UuIsjTw376LqrmODICSV1s6hApCGS7EVD9/IEHGsCjA7a1L7s1mNrBsqWDrX3HYfrQoZHD38eiZ4s90UqVPqAqa4/viKR4rwHufyla07KvVnOztYYO+6K+LLIRUNXsySL1jNxrefxt8WOI+2YmZElQBQvI+T09EbAGK9p9XV1hXDKfUXD5wwNwiToe6+gIPShkLjVOjaMtN0/vtRft81s4T25+4Pr5g623poMOOsj898Bf/jL0oJCxZDiNrxBD+6OttqLP92KH3/yAqr+ruzt0reS9n106EQZQA5SU1YjPKA1M5xPR+xShWCS+3HHpyefxP7/7bsRzpsYrxpHiQCb9nhykM4jzTrDFmbFFsia/bcyFmc1KybHxX+zpoXMEIIS1ULp2pl19+sw236ew5qWtz/Z+M/XUPNrfCmU0DcDBrRAU/jCzT5o0ifbdd19jndKpBCDErHngfNII/PCHP6Tjjz8+01iB1OSGG26wpiK45557DPPlTjvtRAB6SAKe19O0hWHZnBBbCBdEHGB842ejAT4go/UnlJmqm4EX+9XDbZI3QClnjmHA35hlki14Hxg4kOaKFBWSCUrHdmkWRgaEOh4sLqgYG7jMibdy3Dg64qGHItOCD/Q4yuSwbY64OhQoFRNm4soayDxh+PAwBhDJzdtki8WhoG8vtdKL311uHfybb/B2Xmupz5dTpwWuz8vHs4OVJdJTUGV4Tcx57doP0PAC0+8//TRJV3O2Uki2Rt5rXSCEXe0jnhgXXmj2X97rdSwaPDvYtVNa+SA6ZrjsvvTSGnIUvvCCdUpan3RsmaTO194jTLpx0OzZpgwd+33csGHEhCbS1VIOKyyGc0G6EbiWakuYjtuygV+0GTF2kA3H7PHfQoZnFWOvww58CLq0tdLGKp2U/NoWZ8fnPuTiulSNgMIE10oNyON0EZ8lJs9svF9Z4Hyk1nrvNA3A2fLAQXwSwNWbBw6JvD/84Q8TkmjbEnmjPlDHDhu22YC9evVqArkJ4uUQH8fPunXrjNukfADQ4KKJhOCPP/44DRqEzCebn3vvvZcmT55sLG4Ab3CxzPNpGoCz3OYPmDbNxATy2OHf8pYyL/dJmzsEy5Q3O2zoOhB7r+CgwbtMwsG3pZoVDO8ycYmNnEMGr5+8YIE1DiHO7VHfiuH2c7ZwX8GtGzN16dvKtDF30rVSKxep5qI4eDCWkPUNos3ysNaArcbtLCbxsQa3LtruVG2vXjYSqNz/qonQrySgzil56RTxHkD8/axZEdFgn/3m3/5m/nbEEUds9hgR+5aOH3OdGXqv1y7ncq+0sY7K/TDSQHFhpomibAQjtjirkEzMkkD+qiFDwnPS5crJMXMAKOyJEpeqxsbSLC98+Vv5NwOKVDwiE3/JuG+d607Pcx1jLt1efZNfy1hGZgAFSP5k4F6qAZzVWu/whJDePRLIJukisetZpVu6BtbYei/QM2wgTdNTM7S1VT9pGoC75ZZbvGWWNQ8cKvjZz35Gn/vc5wikKXBnRIAr4tJA+w8XR1j/+OGkxy+//LJJgMwP2CwRuArqfoC9p556ih588EHaZZddaPny5cZFkp8XXnjBgDq8D8uh/I3fwe/HHHOMd//1i01bGJZbpCEf+xixa6G8TWT3P5mrph73SdchKjdSlhMfCK4gbptrJdo+8pBDoge3a4QcvvUAHey7L+Pu+Ob0+uuvJwZ+mkofN3ryNlIqCk5f+AR6c1i3I4pIlhmn+oob518OGGClOdbutTLY3gCyhx+OsHexIoFDzJXwvXJdyzJo0W8qGdYvw6qE1pGAjLvCPnsdEZ1vaf4D48bRQQ89FGEfBoPkMx0dUauKBei81t0dsvGGpBkiPEBaQDTpk3Yvl/F1fHHIIEVeSLpczvm8s7EDawsU2rJ/kGNOJ5NGXLZMNq7dRXHB+ORWW9GPe3vD1EHsgujDTIwh4L0IMgO7N3LxMgMz/rZbb2/kbLFdPsXJ1gXgOJ2AzIGXNvm1BuXWS1WRNigutQ5fULI8Bg4caOIMD9640eTgw5PGGyXiIeDBpNqI1dw0PbURnStJHU0DcI3sP5gfL730UpMGABYiuGvOmTPH3LDJxwXgkOj75ptvNgQmcHEAcAPww80ZJ17kckBUcthhCHF1P2lSCdhKaebC0Buqy3JiJdeoc9Bl3ShKx4dx8TbGrCePOorG3n9/2ALtWpnKvdDhWx/xMXfcuGmmMD740ObTensjPv2IP4CiEOefX7h1xWI141vXiMw0mNS327g5Hjs2ZFCTMRSYK66E7xX4qHPRuBSK+outSqgkUEoJSAuNvhiTMVywcuABSMKDuGR5rnAqAM1EyGQZTquT8loAecqYWbNCxmIbmYVO1SFd9l312Ig8Iqy+lpx2iKt6ecCAkMiFQwEAGphK3+ZFwueUpvWX8WTs7RKXfN62n7viCwFyatxZV6+m7lGjIgyQtlQPPDEZ2NjS9OiE5uF4O2a1BI5ZCEZsZ7Ut5CKOddq14CJydTCpxo1LEQu5mXpqEf0pY5n9AsCVUfD1tKmZC0NvwDq5Nm6lxt99d3jThn6mAkcxgnHFlwFIw9oEohikh7CBIZo3jzpnzQrdHvmgs91++oyNjQnMxTopN3tWLrS7BILYr5w2jaYuXBi2sYZVSzVMx+UVtUHzwcOHPJrBsRqhVVUDViVvvnG0HWLyEkAnRi0coPoMdvVOJYFKAqWSgC0m0ZY6APsqiDxef/hhY93gJNIyhok7Fqc4a3IqzS4ZiftypIzhemyXoHwZKUGEDXzU7PEpUtcw0GHQJl0g5Rltyy3GFiwZd6bjoZl8K87TJm7cbPlOOfm4KyUQAOT106dT18iRsQRpmmKfz6+48z/SVkGoYkve7RMjawOvci5w+iAth2/tuSf97dhjoymL1Gp0hS+sGDCAdj7ssC3uwA1cxc3UUxvYzaZW1XQA9+yzzxIITX77298Sx5rBxfDkk082LovVUyuBZi6Mmk3IEdckXSVg/azLfTIQgQvA8aEmDzy4dTJIYgC1zz77hFY7mWi1Box4TDrtW287DG3gA23c41e/oqOXLQtrkQefVhTikigXIWNb172sYBZ3Tpu8bWVdtvXWkYB9uGm+/6KLyOaGyslSPYaoeqWSQCWBPioBlyVHAiuZe1Ra5eDWDxIszkd2VxD7rMm3pEeFrA8XhnoPj3hfxMT6amveMEH/77LqxO6/cSRF6jdcWsJNUcZv+1wS4nxEnP9ry5cbtz6AJuRNBfmHDyOx9xS09IVDElA/3C3lmWADUt51WXQKDY4jcncQqnB9PmdknO4UGXs1fzihumQ41TqLvGT9wsyZoVtqXpfnaeWK95upp2Zpbyt+01QABzfGK664gjaJRI8sRJCczJo1iy6//PJWlGuhbW7mwoi7ReJkqdgIfW6k0gopCcBxeTbaf6ZfjgNEadqT5JoBGTg3dc9kn7bgdtlGn0MjTZ9c7/qOpQastvZxWfJAPmXBgkgcCq5twEQWp1Dl0a+qjEoClQRaUwIuAKfZ/NgjRLv663xnp48ZYwRx/cqVoQeEtKrV7IEJ7K7aa8HpqXDqqUSSD8BC9Z60/8Z5KdT8pix2rjiumlmh3EI3Jz3Y/CTlBE0zw1x9sbl1Sn0jTR3y3TjZxrklch5XLovfjSMMq6krxnrKHj4oX7NIJ12iw5VV5gAsyjMnSebN1FOT2tZXfm8agLvtttsMyQcSXF900UUmn9o//uM/GnIRMDfOnTvXxJx9//vfp3/+54u+CUMAACAASURBVH/uK/LOpR/NXBi2DU/ebsbFa9XbeZcbhM26xweB3OSLADy2g4VlEFefz0GVtPGWzb1Q9zeu//ybBmnghJs/aJBxhc0SC1DvHKu+ryRQSaD8EuC9T7qIgTjrqI6OMGeZtGpoV3/dQxAqXdvVZYgkkBbH5xyL23+9L+88LHBJo+Gzz4aWmKxpRRTYSBv7ltQHDYTimB3xLqcEKtLC5HJ1lfGC3G5+15fMxXyXkDNOphwCgQznp00C5ZwDEBfAESIV30HI6b1m6qk5daH0xTQNwI0ZM4bWrFlDq1atqqHoh9T+9re/GRdKUPHjtr56tkigbAvDRWTStDFLcOvMc9N3pRaQjJS2TdR16CbdtoYyzXoQFzgouk8+t5s6lQPnBuIEqWiuLT6kwG5URVcSqCRQEglY9xC1v8PioPNmSoVXK8tg+IUL5ZCgj/hv5D9DQu40CngccIrb+3xj4HyHIBWAy5pWRJ03k4IE2Gnk5dMfV19soQU29k+fOrzesZyvtvyAem5pxtGkulyXALZE3OZCwgb4iUJyMLxTM/eTGlHQ72XTUwvqZlOLbRqAGzFiBE2bNo2++c1vOgXw1a9+lW688Ubq6OhoqpDKVnnZFkbNZmNxA2moDB1B5N7gKEtj44LJLeXVbQ1MWV+WLqX9Jo185cHFII1jU54ReeaSYjTStrF6v5JAJYHWkYCOPUPsjyZ5gIvZc4MHR+JobcyEes+BtQ7Pc8OG0R2dncZ1Mo0Cnma/kxJP46ngM1Jx7XB5zGQJJbCBqLpyjFo6lwTYAVAiZGEidYOPrLzfsZyvNYQqAcOvjodM41Lq0gNkKoxImx0ut+xyeTGRCUfIwpLpLRvPF8ump3o2u6VeaxqAQ2Lsf/mXf6GrrrrKKbBzzz2Xvve971UATkmobAtDbjaN3DicB5e6oeUEoIWuzJQWsayHf9iHlPUV2ve0hau2M0jDjeqMDRto7saNYYkL9tiDfjZyZFNYtNJ2q3q/kkAlgXwlYIt7htX+sY6OMG4WNXYFhBpQXl0gzFYWvtVu3D5sivX0Mo2nQj31uL7NennoG4Oee5ubcVkZQ6giPWo4fIQJ09KSuiSFWdS4BDtcblGOjv+MEOvkPijJBZZNT01uceu90TQAB2rfV155xbhQbrfddjWSe+utt2j06NG02267EfK4Vc8WCZRxYTQjHitp82u0612jZdDo+nJbgxYLKeJPtGIG16bRRPT2tttSUjxgbm2rCqokUEmgNBJwsfKe29UVsbihwZw3EzFKYOLTrus6hvqyyy4z6VAaHWtb9+VdnaOTtX6bNbQhMVZNuqz0OV9ZJoidvIiITpNj4+GJ5BNmoOMybe3SQLKRF+mu6VhGPbXOpVO6z5sG4O666y467rjjCMmzL7jgAkM5KklM5s+fT6+99hrdeeeddOyxTPhbOvk1pUFlXBhZb/XqEWBa3/966vL5ttEyaHR9PjLwekcdyOwSo12j5hPRC56EAl71Vi9VEqgk0HQJeAMItU8cH8SpgWAEz6MdHcbtEQ8uew4aMYKe6ejwzjsq2wEClEZf+DV9IFI2wHvcUpbr87oPmPIpJ807PuerJC+B9U2nLqon7Q3Xzzn4OHbf1i4em/+44gqT6sGHhCeNLLK8W0Y9NUs/yvxN0wAchIIUAhdffHFNGoHe3l4aOHCgSSEwc+bMMsuvKW0r48Lw2ezyFlbZAFyjD7hmyDyvMbQeyEphA9EA+/PXE/fQ6HHJS0ZVOZUE+qIEvPctB/Mhx0Fdc801NLGnx4gIDH2cCDmLVci7TX1xQFqgT80Yn8Q6LWQ6OLN8WUyTxG5LVRSbnojInbooqbICfi+jnlpAN5taZFMBHHr+4osv0u23305I6M2JvPfaay+TOmDUqFFNFU5ZKy/jwkjc7AoQZiYa5wLa0awiWxmYJLGNfWDgwEgsHJJ737Bhg/ftuhyTZszNZs2Jqt5KAmWXgLc1JSb3GOJ7mFQC/a2XkbDaI8o9a5oxPonnq7pgQOzk8Q88YNxyi2C65osJH7fLPOvPOjPKqKdm7UtZv2s6gCurYMrcrjIujMTNLm+BJvjFN7w9efevj5fnOpD57zq9wAQi2k24U2J8f/GLXxgpHXHEEeb/cXCmSdnQx0Vcda+SQPkkkDKeicHey0S0VLDTIk0L07pzJ+tRWqvzonxTRbaolOOTJtVAg8RbJjmVUU9t0DA0rJoKwDVM1PlVVC0MImoGM1V+Q9jvS3IdNLa4lCTlDYobHqbGRgJTCea8b/z7/ahUAqgkkK8EatZ5yn1bkkRwJDwnNOZLnNcffpgO3rixFHE/+UqvKq3sEtBnS5kAVLNlV+mpxY9AQwHcrbfemqlHp5xySqbv+upH1cIgopQ3uX11LvTlfrHydiHiZUVH75s0iSbfd58BaXgkgIMrFYK+QzeWp56KJDnFzX09geV9Wd5V3yoJ5C2BGkt7yn0bCvGQNWto6sKFYZ62Q0aMoKfXrdvc1JTl5d2/qrz+LYFmuHa2isQrPbX4kWoogBswYIBRruIeEJjg4ffw/xs2bCheEi1UQ7UwNg9WZVlpoUmboalMjfwsUQ3b3LqODpoSlIkbeTxgkeNk4AgkB534tA9/mM577rkttXtQO2doas0n1U1sHlKsymh1CdgU3NT7trLa4QJn/N13bxZNSoteq8uzan+5JFABOPd4VHpq8XO1oQDu6quv9uoRbs9vvPFGeumllwyQ2ygS+3oV0MdfqhbG5gGuNs++PdEBgvb41a/o6GXLwo4i19NTgwbR4s5OGhz8VSbxBUMds1cioe9EFTtTD5tlGmn317lZAdc0s6Rx7zZrXFw5q+DujHhVuDsnkjM40o6Y76ZMqSzsjZtGVU1KAs1aV60wEJWeWvwoNRTAJXVn06ZNdPPNN5v0Aa+//jqNHDnSpBn48pe/nPRpv/q9WhgVgOs3E15RNU9ua6Ol69eH4C1JDsgl91bw0tL2dnp5wIDcWcJsbUhtZUjqSIv8XnbgmpfClVc5jRrWRo1LRC4OcMVt4b7DBRpAjhNrMxmJK0/bKwMHhvGuoFXvr2utUXOnqqeSQBYJVHpqFqml+6Y0AG7x4sU0Z84c+t3vfmcsKxdeeCGdddZZ5naueqISqBbGZnm0mhJVzeNsEmAFDe6RhxHRIlUMLHB4hgT/DydsdtTmf8MatzdCZoJ36mGsS+xFP47LKbsyzeBBW38Sx1S90ChAlLZdrvcb1d5IPQsWEE2dGjYJNOvPH3ig+W8Ga/g3x7But3YtjRVMs7IsvMckRfLfSXmx8pJfVU4lgUoC6SRQ6anp5JXl7aYDuPvvv58uuugievrpp2nYsGF07rnn0owZM2ibbbbJ0p9+8U21MPrFMFedDCTAihwISnbr7aUnenpoOBF1ExFokQDK/oGIzk+QGFTJ5UShklhP4t/YqixxOVe++aYzzUEzBrqQy48WAK4MMB8J5k1WIF92oKrnVKPaGwFwgkAIFyh7EdHb225LDLokYEN7V3R0mHWNd2+bMYNuuOEGE9f6nypJ953z5oV/B5lJo8BpM9ZpVWclgVaVQKWnFj9yTQNwK1eupJkzZ9KKFSuovb2dzjjjDJo1axZtv/32xfe6xWuoFkaLD2DV/FQS0K5UR3V0mO9/TUSIjoPSJ2Pf5L+lBe6sIBk4YufwzsEjRtAzHR01sTipGmd7uYT5gXQzXUpvXcCu7IQSyh1XAopUY14gUK1L/q5OFNjeJKAo87jtKqxrE4YPD+NYsRa/195OZ/f0hMUh1nVGd7chL8Lvi6ZPp66RI60xb1fedVepLkdSzaXq5UoCfVQClZ5a/MA2HMCtWrXKWNx+8pOfEFgpkSLg0ksvpZ122qn43vaRGqqF0UcGsupGOgkoBXwu3GhFCbMHDqQNAeERwB0SfyOHHBTHvwwbFiE+wWdQEl/r7qZ6rTG2TmiLR9msBEmJ1DNZphoIFNJNnOBtBTBvJqJrJCV98FoiiCoQqBYyT3Jur1M+FrKR8669lka++y79VibhfvBB6j788EgcK6zpTEqEf9+gAN2XiGgJrHfKLZMaxCqbab5VH1US6McSqPTU4ge/oQDupJNOojvuuIOQKmDy5Mk0d+5c+shHPlJ8L/tYDWVcGIlKTx8bg6o7TZCAUkQB2GZu3Bje0h8/bBj9sLMzdMOCheXlrbYy+81ZQ4bQwi6OltvsfrmRiIYGLluZrTEOMWhFvFEubL6j4mpPve2s93vf9md6TwAMtszCuqNzAyaCqAKBamLdWTqu2htasy5EhsX0j7ONan1OHzKEru3qotMHDaLrZSqgU08luuWWsOL1RNSmmqEt6l8nIpPA++mnK9bJ9ENWfVFJoOESKKOe2nAhFFxhQwEc54Hbdddd6eMf/7hX15BG4G7O+eL1Rd9/qYwLoxDFo+8PZdXDNBJQFjgdJ/ONI4+k8UuWhCXi1v7mAMDtPWIEPdrRYcAe0n9/V93wu6wxaZon342LBZp+yCG0y7hxIX26bx25XZK4AEgMMPGtu+z7AADm2R0ddJoUurLi+PShKKBaRLkYu1svuYTG9PRYrc22sXWNN/6u2SJDUar5wy7Kcu0ZwPzgg9R5+OHhWsQ6vSGw0MlhmUlEANoXBxctDLb32WefSAyc7/qp3qskUEmgcRIoo57auN43pqaGA7i03arywNVKrIwLw0fpSTv21fuVBLQEYl0TBWkCx8389He/owMOOMCAJSY/AJvlNgLQxVljso5ARAFGXK9g4wOZyvcC9j3QpyPvJefCiqsvaY2hzl/84hemiCOOOMIw+Vofl0tdjKtdUt1cj+97WeVa73doH8gzIi59L75ItPvuYdE+IKqQfmaw7MUBLZ5X//qv/2oYHPHgPIVFWrrI2voS52Kr2SLlmEjZ4e9MQiL/DeIRfu8l4eq8vwBrzBp7aLBWwjoWLaL3zpgRSSNQ75yovq8kUEkgfwmUUU/Nv5fNLbGhAO7VV1/N1Ntddtkl03d99aMyLoxCFJq+OoBVvzJLQM8zX1dFaTkAmyXibgHokqwxmRsqP1SWQ05nwPTptqTGtnolGyfaj0eCP/4df4+NYctggfNd376WulzkmqEQ7gfgGsABGA4BKMLHE0T5ysOniSyz/VetiliQ4+K7+BuAM8wBPd6yfR/auNEAqZXt7aY5sMTJftv6Eudiy2yRce6n2uqmCYPQbhkbxyyVaN+UQGiLAwucBtvv3W+/CsD5TKzqnUoCTZRAGfXUJoqjkKobCuAK6UE/LLSMC8Pn1rofDlXV5ZwloAGC/u8kcg4Gb7BO2awx358xg/40bJiXRSwNWMH6YPbMJwJiFSjReKBc+xCpyHx4oGPHw7mxYMljNzlYGJmu3SX+tDFwtvfT9D/NuzlPmUhxScCHPAk/8uwPz9kaV0NlGZQd4W9sFjW8x+MF8p47OjuNu+I7QQGI+5Tgq2ZsHSAWff777Nk0NyAKMsUp91MXGD0jYIDl9sLifMeRR9LRy8Aju/lBnriZ998fpglhKzrSCUjQmafsi5xrVdmVBPqzBMqop/a18agAXAuOaOkWhuetdQuKumpyi0nApdzFuYmxNQYWipcHDLBaNGxiSGOFgZLMlgt22dQKdUjUIC1CXLGy4h0iwB8DQS4fMX5Thg2jZX+HXWPz4wK6OqG1tU+O9e3qvw1kO+OmcphfWRT6NEyKDOjRVC8X1ZR9slnLpIXM1labJZHzGr760EN07YoVBrRJdseaZi1aRNe/8AKdvGBBSARkCF0efjji8ssgDXOY40hRFubvUBfItKRseCPI4xiCMQshyfTRoyNkQ6i7cplMOaGq1ysJlEACpdNTSyCTvJtQAbi8JdqA8kq3MDxvrRsgmqqKSgJWCcRZkNgNDdaBXXt7w0TfEdc6S6lpANx9kydH3eMc42RzSzOvqjV2TXs7famnJ1S8bxg4kL4qLCNGcRfKtW6ryxJl7ZNjfbuseDY317i4qXqnbJpx8KlL9uuVgQNDKye+5ViyTGkWHJW76kPCazwuF0dYdCXBB7spghRkkaiLQZy+MIC1+ZmrrooyRMKiNnZshOmRrdIb5s6NWN/AAjto9myntVrngJPWQBchyfXnn18DKCuXSZ9ZW71TSaBcEiidnlou8eTSmgrA5SLGxhZSuoXRYha4LDf2jR3hqrZcJZAwP3k+/McVV0TctzS9vG5TKrdhC4W9VKgjZdtyW61eTe+MGmXSHuDRlhWrpUWU4wI5ug/WtWGTH9zwRo2KWm4CIhBdprQ+OgFqzIDLNuE1TfqSahw8JpaUFYM2jlfMHYg6mBslQIxzcZTd4byGyH24VORdQ3qNHTo7TdwbHiYWATjde+1a+lmQg83lVon30O9nwSAZVIi5Cx7p1THxltpFuWaOOqxrXnPSYxyrVyoJVBJongRKp6c2TxSF1VwBuMJEW1zBZVwYeStRxUnPfqNdZH1V2U2WgKeF+LKtt6ZLuqFmBk9ckuAMubU08x4U6h03bYokGI9zS9PtY4UYLpNbKxFroGRbn9raEZcfrOZ7l0wtYO+yPfd0ytXnMsUFqIyFKofLo7i4SmYu1WOVBYhaV4GS432TJtETo0dvsWpZ+nffBRfUWHO1de3biIMMcqfZrIgAiAevXx/OPcyho9vaaMysWaZuZq7Ev3fdtInO7+mJpF6A9e3f2tpi3Y0h1z1+9atInBvPWZafzbqWt0W1ybtPVX0lgVgJ+OyBrSjCMuqprSjHuDZXAK4FR7SMC6OVDt1WamsLTs/yNdlHyVcWrjggZVNM4ba2ZNttid3ebEKQLJH4HUr0Sd3dkZgfWFEueRfqdO2jLVlsWYFSjLQEgwNyippYOkf/pw8dGqk7rg96zdhc3QwVvwZ28+bRO7NmhZZDLVeftRgbI+YJzuMmpbMNQm5o98BAxlwWwNb4hBylicpZ0ty09G+f884L4ykBhDDeQzBv5OVDkKAesZISwHHaivd3dtJpCxaYOcOPTG+Bv8FdFPGhzwTWPI7dRGSljsFE/J2MD8T3sJSiHhljJ62BcFG2ySdRZuXbYaoWVRLILAGfPTBz4U38sIx6ahPFUUjVFYArRKzFFlrGhdFKmxC3VRM4FDtqVenNlECihVgpynFAysZeaSMggSKqlVpmilwxYADtfNhhtGbFitBtkxVjV+wdypPfg3Bl5w0bwpxmgH2fISKwUEZitBwgZ8Lw4aEFhmncbeyVuh+SwXMskaGnP+WyyzbnndOAZN48olmzwqFnuUq3VU1rj5elEs9WsD+0tdGS9etDt03EZnV3d9PUhQutrpy+8825d2m5iQIBmg/QKQjE70lsl1KmmAOQQUSOXJYF4CGRNTOa3kVEyKUmGSxlv6cPGUIfvPjiGrdTHZOJ/kxsa6Md1683rpZMiKLj3pDwfn57O5199tkRYMbxd6hbpsfAvzmNAcqVYDLussN37Kr3Kgm0ugRaSXdKI+sy6qlp2t8K71YArhVGSbWxjAujlTYhVuZ9qNtbcHpUTbZIIHF+WlgeXUBKkjPI5MParU5a3AygEonGGTCtCVzUoMADeOG/YSV5/PHHwwTkMiG3tuL9i0p0DCvaTQrASWsZ6r1txgw644wzwhg2AD9YX2CNkSAiDoTIlAYAEK6YrdNPPz1U9CVAtYFgGXPI/ZTARLuKApxc29VlrETI6WYFQB6rwQXupdykiyLAzjVE9IMYABfHEildFNE8ptZn8KOBTaR9grmR5xDYHceOHUuvLV9OE3t6QmKTcKznz6+RgrTmQq6Q5VVdXVEgDKZJEXcJGYzmFBULFkSYKjn+jlNYoEJOceH6dwXgPCZn9Uqfl0Di5WKLSqCMemqLitLZ7ArAteCIlnFhtIzbi4XeOilnVgtOkarJSgI+81PnWbMqmGr+zIW1SNZlIQ4JFXOl9LLLIiu4yFEHZR6uZzJfFgAdlH4AObbCQVHH87SwSkFh37+9nUYecoj57YgjjjDfMFiCtSzMEafawrFJGmRBCddtkTT17MJ3vwAzOmaNiT9kfjq8M2ntWuP6GT5CdjwWOyvXQBlDxe54fBGDcmSeP69FEOPCKOWGOuBCiCTTkvlRgk5tNXSxRGpCFJ1gm61fPOaRywc1brCIXSMsW+izK0m5rX14H1a88UOG1ND3G/lNBbTf/MwM5rq+jJDgllNY/GHAgAhZisxXqElovMapeqmSQEYJ+Oz9GYuu/7MkF+r6a2haCWXUU5smjIIqrgBcQYItsthqYdQhXeUWxQpQEmV8HTVWn5ZYAvJwZ+KGWBCg5g/IHGZu3OhkY2RXNwNwVN4rdru0kUwwaOL/D9s0ZUqNVeS4gGWQLVDcD7aK2chTZFtslq2F77wT0tczIGCgpGnqMbzS+miLWQN4g6Xo0ksvNaASbdIAJwRDKvZsK8R4BXOoi4guI6LnRIJqgM4JRLSbBKgBBX/S1KtJ72ABkWj7y1ttRb29vaQtnjKRtc1qKOtni6EmENEEJOyOyOMn5XnF1KmhRZNj0iB76cLoSu5tax9b8bYZMSLM8RaOpWAa5cuBL8ycGV4mxIHsjcgRF8wLTkPAgDRpTKrfKwnkKYFE74s8K0tbljpPkMz++QMP3OyOXsdTBtBa6al1DKDnpxWA8xRUmV4r+8Iow+bhHC8LnXtujHIFT5JSy7XgvuddvM09kAkeXO6Lpg0JCbXDiwAHKQrHL0krjlRwOd4LVPC7EpG0LhmFXllg0CQJDGA1lDGeV06bFsaISYUfYAltAaW8jC2TScA1yGJlfxci+qki9EA7mEGR5ScJLCLufOrWWccPalC1dNAgmrhhQzgFACCHKouRZOREEvODZs+uifvSc4itmU/09NBwBUL1OE8O4sNe2Wor+nFvb/i+HDsGzmdpa1ZQNlsMkQ9tcWdnhEBEto3dETmZt75kgEVzNlGEFZK/AYhHbOUBBxxQk5/NBbgW7LEHbTjlFJKMmzyPXVZptImTs8PN9fGODmd/JMjNex1X5VUSSJJAqQFcQR5BZehz2fXUpHnTCr9XAK4VRkm1sewLowybR9ywQik5u6MjogCVXcmQClOeSYRbcPrn0mSeozZrRdL8ZWCDhsCypokZbCyVptFB3iun66DjcgEAB25uxtX3qadqLHC3zphBfxo2rMYyAvCH2LCIm2IgPdz0Hv/AAyENvKSUNxcaDz5InYcfHub9YqEvhrtdYF0BaNoUWMekFQjzE2BYU8iHa0zdOmv2Sxvb5g87Ow1oYqKWtcJiZMuJB4KRZzo6omQuaubwOMNyB/dSSd6x/6pVEap+m+umJOVgun3EMmqyFYyfdHW1tZctVrZk27axRdzhsoAdUn9jc+uUYNr1vm3e89+0VVr+HfF3vQ89REvXrzcgLq49uSzeqpDCJNAXLwnLHmNWREx+0hlW2AQSBZddT22EDIquowJwRUu4gPLLvjBch34BoshUZBKBQqZCC/6ohhDD00Ws4Ga1bPGSiASWLrZ2oENJB74GGDaXN1vi46EvvkjIe4XffisSLYcKdwzroc0yxgAy4v6rbnThWsiKvqSB30u4BdrSGdCppxLdcktkfPl7+UfERf0lIBDB35lRkpM/y36ytUqCCQZkjwfkLQBCsARJ11PEhC2/+OIQILA174YbbjAWRJtFS1uxbBNVAnEGyHgP8VqSPEUDLmnx5Pcl3b526QTY0q6X2mLIMWM65i9iXVUkOBMDK63+Rrph8b6hrYI2+cQBOH1pJNcIX2BIIAy5cMLwyj29dbbJohX/hgPEFogxsxEe1btmks6wRszIsuupjZBB0XVUAK5oCRdQfpkWhm1DLjvYKGLDLGCYI0VKQgUJNlz1NvygLFoAeZYf50Yr4n5srrWQ6ytz5tD1wqVPJ1+W8+vYoN1M8hFLn29pl+y2UfoDEAiQgafGGqtAICxmABBI6vyEcsuUaTS2feutCC28tMBBDnCZ5L5wmwBU9oZXaUCcwfnC8D67C7IrqKaQlxY/jq96vqeH9mhvpyd7eozlj/+OFAVvXH55DcnGe2fMCJkOZXleFiCLiyssmTMXLTJlSosaAOIdnZ1hnKME7EyRvwMRXSEGC7G1ILiB7PEA3DCYtcXs2YCjtGqiPXcceWQkKfYZgwbRDRs2RMCmTgdhi3vT1lIm6/HOyaaUYh5rxAoyIyn3p697C/S1fTZPABenGxQ5LyL1br99hIinjJ42uV8olwS0lklPzVN9KFNZFYAr02h4tqVMC0Nu+BxD9B9XXGFu4iXrnGfXMr2mD4qkQzXOfS7pWzQwbX2ZOqU+kjm7fGL28jyI622/j0zrrSPV9y5LFwgs8AjmPX3g+xy22iXGBrRc46NTFICwQyZbZjdMJ4BzgECAmo8LsAXXSn0RoG9tZfwT+iCtaXALXNreTshFB1ZBhNxLACNZLWHtg/unVOp1AnO2NGmCFHavBFBa0dERiVWDNRNyYPbOke++a1whNZiyKm22ObBoESFJNixHOvZQ5jJjlkjtlsiJvmWsIeT06wDAMZhloMNz1pU3TbJTMgumHAMbkJSAGbf4ckxRH1vF6srHpmQnx/rMMWNoz4mwDW5O5N3XiUuS9tmy7H2+7Yiz3PiWwfM6jUU31f5tedlGRqXZUn3OTS46bV+ztJ/rQMzwme3tkcuZuoCmI+9nljbW802Z9NR6+lHmbysAV+bRcbStTAtDbtJornQPk25fRYpZHxS+hypIB6BkyNtA/jYuyXfa+uru++rV1D1qVI0iT1+Cemt/kmRQd5tSFFCmtphm12GBS7TeqtvPM8aMoT8OHRpS+scpCDLOkWPzAEh+FoA4VkCuvOuuSIJwzVhmjfEMcr0tV0DMlntNsh9yCgANJmB141i3IWvW0LSFCw3rIB7tcihBGAMImR9OWuzwLWL2AFqlNQmXQ5zEXJJ7MJGGzKUm3RmdSpuNZEbE/ckca2DO1MyepqMWELieiNrU2pCuq3Bp/VTw+5IgmTvSPeDhpO+PYtIf5wAAIABJREFUPfaY2Zd0PwAG2Yoq4/U4r1yNa67ojy1GMTPAEnNcj3VdimeKPaWZrzrBgnJrzyNuOS8g4bUHK/drky9S5BD0KkMMjH6fSYPYzVqny5BjmrbfWg/htBWwLie5EzbLUhiRj3CPTgM0reugssA1c3toaN0VgGuouPOprMwAThM0sNsX7Q51pJgnK6CybdxxCYq59Vnry9x72433iy+Slqnsj41RLnP9dX6Y9uCvszqvz7PGwCUGnNdx+8lygkUJxBCPPPKIUeRlbJFPbARbCZ8FGUkgDVjgvnLIIXTMtts6b3r1epCJrCUAqCF+UX2+pr2dvhS4QeK7RdOnGyvZyQsWRNItMAumdFFksCOTizNgs12u8DwHwGRrIHYa7boKMej+aTKap7/1rYj1VaYYsSqUQlGSk64G1Igf5W8YE8RF8jrWngHaqolitIskF4321ZDGqDhGzVbqtVAcL/E60O6l8kKgnvLL/K3cz6RlVq9Nfg99yeoymNfe6VWOWseYL0gn4jr3ksbIZtGXVnQdOyzL82qvAyzqMZH7KqcxSarLBj59Lzx8wadul+3M9i3Ltrc1Owa1THpq0lxt1d8rANeCI1emhaGD2XEL/BwRbS3lKnIr5S1u2y1n2s0/bJOF0neNIFdgS4fL1cwnNi1N/3lT1qQPoElf9neoctGH+y0JGOq+zUvT4ARlL2/51NM0rSzbrLA1Spe62QQw6Ro5Mpqzp47bTz2vpAKYRgmUVsIaIJOmfUqh02kCQvc4n9QKFmDLMWynDxoUiSnkcZXMjwAG7DLJAFLPc5CkdHd3h2kTZFJylOm6eAllK27BdcoF1wUUxuyEjg46R1gNkXJgn/Xrw4TfXJYV2FkSv0OuSP8grZpyrmvWTtv+pZlE8d955mPzBTH1rNGyfuu7z/JFCjOc+ly+6D4nWY98ZeTjGqnTfhyC3JXr1oVVcBm4LJH5APmFCIiYMoU6R42KXNhMHz06EscaRzSUtt9xFwq8b9j2UJeV1LUX+wBxX/1DXhZrF3OOS/UtS19u2byLfOdKXu+VSU/Nq09lK6cCcGUbEY/2lGZhOILZpRtU0QBCHpKcyDir9Unnn8IN/DeCuKFw49YKsHJT4hto35uzuOG2KUlxcYX8fk0eqgIAtHf/PAGDd3ke68PnFZs1hwG689D0tK6lVT5Mey1yYguVS2Fy9ZNlyYe4Vly82+c5dig/URmxlMWKlXQVlBY4fQl05ZtvGoskx9hq9kXjvodHxDBKK4ILwEmKfKv7acz6wftsVWCX8Ufb2kxsHidA57g0WKvu7OwMXU21BS4yLsoaKMGs9eIgmAyNulySskTV0mXNZ/214ju8rmLnn3Br13HLqQF0ivVnBVGcDDqhHH3OSAZYmdeSAZnLChxZXzpf5aJFNOGcc8L8h7FEQ2n7neDSi32DXa01ALOCbIsrKZMb5QbgLJdeHJcqwzek6zZzDNjiSrVniC09TqPXXGn01EZ3vIH1VQCugcLOq6pmLoyIoq0YnmQwO2I94PpVtNVFKlA4WE5QjHFpXHpkWZpy3eTgQoyDVuI13Xqg7PnenMUBF+ligQ0ZyqvOx4Q5pRWLRrg1yf7FHSw18nIow3GAKq91I8uJk7vzN0/FwnfsI/3S82rePOqcNStyg53WDdl1u5ymfb5gT7sD2WLGdFkSaDIBCYMdnUstXMdiDKxKoGARjSQPt6SHcMW+ONM8WCaivvQBYLxt8GADaFiJksoU3LuSFGRtPUM/OGVA0n7qAqk+imeadeYbB5amzLK/y8o+E8kkgRAdt5zahdXzwkjKzbYv65yGOk5RrkuUxRcSkfWTkLsR30Xmno7pEheduOj4bns7nd3Ts6Xp8lxI228HqY68PHbtY/LMD99/+OGaS6BHBw3yTovhtWeqNsMaOfjSS80ZL61n0h3USTzk6TnU6PXVTD210X1tVn0VgGuW5Ouot5kLw7VJ24gLbqrD999XPNOHDo24Zdze1kYnrgeVQPAkWJ+0IrL32rX0c0VEAEvc/PZ2Ap35hdo1xGGB89rELW5dst/SqvHyVltRb2+vNZZCKxaSFEIqfHlaubTrrPMGPiXoKcPNYdzc8xnXTHLWcpo3j2jWLO957GpzvQH6vmBPv5emXtc8t8YXWRSf17q7k/P4ec5DfRmSBJik5ZQV3rX/8A9GEQO73AEHHBD+P5ONWBlELUorM2LK+L4kIOYzDr57q+889n3Pt96yvgfZ6hhvpxugBVQcO2wY7dDZ6X+p6TlnXQAOf9c5DfWlhrb8Lxg8mC7pxmm++Qkt2KotINTh1Cj8bqz1V1mUIbcZ3d32C6q0/XZ4AoVr11IeiKCwRjfMnUtzN24M+7tgjz3ot9tuS9evXBmmMkm6FE5yHZUXbzIs4pQFC6zWeJeLLscM1lziqrkmY3ebuZaaqac2s9+NrLsCcI2Udk51NXNhaAWBN215Y84uFpwTiP25c+p+pBjppoKbPRw4V3V11RwMLiVDu4+c3dFBp4kapCVu/wDEaRdNfXBp8oc4K6BTQVa3aqjbFneAptoUC9ttr68ynjhO6kCUweg2a5wP6NEuIEmKamIbC3ohNxla2heR09NP18SQpLXApQF19b7ro8RbZWeZ58gHp6n1wz0kZu7Fxo2kvNVPM84+85vlCzkx2yTYJ0MG0RgXU6csLIPmMw6+SyONDHzLbOX3MM6wnl4M8hlYeonIScQhxjPuXEqSR5q5hbJcF2t8GVLjgq/WxcOf/jR98uc/D8GLjIOz9d9mGWdLFrtIazp/jk1Fe11EG1n7bb3ssMTynvjQQ4YxWxM9jQ5yGUoipCEK1NpSy4QXmBbXUckWLT0VZmzYEAGPP54wgZ4/8EBi/UK7iPNlQc0YirnmG7ubNO/y+L2Zemoe7W+FMvoFgHv00UeND/QTTzxBmzZton333Zdmz55tqL19n1tvvZW+853v0KpVq2jAgAH08Y9/nL72ta/RpEmTrEW8/fbbdPHFF9M999xDb775Ju2888506qmnEhTctjZNNO3bis3vNXNhuFx00C6O+VgxYIBhhNuwYUNm9q0kiUBRAX351IULzWEjN65JbW204/r1kZtOlzJiu+3isjQlOFjhblD5rKA06rK1VRAbPsfvsP964k2/xdJwybtQBWofX8UiN4Usxgpic/NIrNfjdjdpPjTq9zwVZN1m1+VIohWoUZ2vsx6r7BxEKVgneGzMb0nW3ySg6BOXm2acE+e3p9y00mpzh2tkXrW0SrRnN0v/mnXsLXuUzINou3CSluXxYHTs6trSd4tniKwXLwLog4kW52hcWpuw0BhLFO/LeDfSVkfsrc3N13ZRCGKj8XffXRtasGgRMUkR18fy4Fh1a2qOoDNp1xS/j89rZOWIN9PW1PlE9FZQP9J1LCUyeSdjXWWTXEcVW7Rsp4z95UvvbUaMoEc7OoxOo+vdKrg40ORMaDJkqy+fm53So5l6auk3mZwa2OcB3P3330+f/exnafjw4XTCCSeYGKI777yT/vznP9PixYudAEzK95xzzqFrrrmGPvjBD9KECYjuIlq6dCmtWbOGvvnNb9J5550XGY5169bRmDFj6L/+679o/PjxNHr0aEJuHwDJY445hn70ox/VNXzNXBguJQMdkrmYvA6cOqRgO0y4OLC0afdN14HA/dG3XXBDuJWI7lVU7AeNGEEapOgD3+ZXz7eRLBccXoj7kYl5I5Y6W54qS+oA7QLDri22HDu5KWQxcUjHW9yEXMow/70mRiO43U4Tv1jHVCrNp1pOaZWY0nQkTUPSuksphclm8XXNt9zmv+pfXuOUp/tjmiGwvpthXOqus6ACbPMhDqBbx9MSA3Zz4NZui0tGV6SyrhlTbUQ0sl58z6628mx1eSagPzqNhCS+gaVwXUeHSXSv3ep1fkVrXNjuu5tY6+eWLqXrVq40wIaBBM4aeZkq81XKCxjX5a+tT641leRJUwNQgzklwTTi2TkWVVpTBxDRkOB9HRKShjFT7jPyHEbRLGtY0RAWsWtvrxkTjv11Wd22R6y7WB9oj7zQhbzSxO4WtNQixTZTT21E/8pQR58GcD09PfSRj3yE/vKXv9CTTz5Je+65p5H5H//4R9p7770N4HjppZdo6FBOQVs7JPhu//33N+X8+te/Dl17YGHD319//XV64YUXaNdddw0/njlzptns5syZQ5dffrn5O+KXYIH7/ve/b4Dj5MmTM49/0xZGjJuPBnBFuMDpeDW5YWnSEe2+mXQrL2+7ZBJf7ebAwcZWN6iAUASb9MQgQBtxAtjEeeN+JGC1xNw7rbfXJC0OH3Ure9nWW0fiEaw3ah7B5dpa6WOBsE1OKX+Xm4emfo9z+7NZPyP1FsCemXnRNeHDNFagJjQvtyrTAqusgCnrd0kdzWuc8ionqb1ev6d0OfUqs0kv2cY9bi4knRV8PsTFJaOrXA4DPN4z+QzgfI+wsL3yyiv06quvhl4r0uXxjcGDja4CMOQ6V7UCD18NyeIqXSPl/i9BJpdt8yBhN0CuZ0owljjf+EKTPXBcHgNJFxTaAmmzvrvKQLzpa8uXEycJt+Xkg74AsKRJvnDpiUcCJPy3BsDPdHTUyj+F6zPGVDLWHt3WRr8cODBykautbuyiC0I47e7JOSQhN2bZTBqDRi7Bpumpjexkk+vq0wDuvvvuM9a3qVOn0o033hgR9dy5cw3AuuOOO+i4445zDgPewbtwnzzzzDMj7y1cuJDOPvts44759a9v3gbgoglL3TvvvGOAogSHAHu77LILHXXUUfTTn/4089A3bWFYDnW4Bmpwgo4VAeBsN5S8YQGwYZOTh6NM2mlVEh2ugJK2HYcuuzSgX9hgb50xg1y0wvL2Uvr6ywSmmClw0difKBJPoS1OrpvQyMSxMFCFjJnBi1ZrZQZwxPKX7m3yZrkmz1VCHXI8bTei/c0Cl3lDaPEP0wKrrEAn63ctLt5sze9DFri0AM51ocB/R4jAzocdRmtWrHACBgMIrrwy4gLM7ZCeKvLfOL80wODUFH8YMCCMF4vzroAlB+fKdkR0lnC5u2O33WjqSzglg0exJcszW8dwS2uhzV2fvS747MX5cNBBB5mKBg0aZEAp/xukPi43YJ99wOUBJM8hSdQiQxWe6OkxlkMb4RrO/Kd6eiKkIscJbxItc6f3iHAdBUCXwPuk7u6IGy3accCIEWZc5UUuW/s4no8Zev8HEZ0vV3MQmsHgTddXJPeAz6bSND3Vp3F95J0+DeAQIA5XNbhMfuELX4gMGaxp2EwAygDOXM+0adNo0aJFBDD4mc98JvLasmXLaOLEiSYmbcWKFeY3WOP22GMP8y6+0Q9+A5Dr6OgwizvL07SFYTnUr7/+ejp5wYKQNMQWf5alj7ZvtMKPjY8PDX7fegPlUkY8CRFOX7s2cjsnqcI1UNVWM7z7vk2bopY0FbMnGb30LaR2b7HJRcYW2IhO8nKvsCkg6D/fFqdNX2CLZSrTDWJe87YqJ14CFbAq5wxJaxktZy+ixB5smXH1LY6AKs4l0ueyib/nPY7d6OCdA/DG7vQaYOC/wZPIxCmfbG8nJvoxSro6xzhWCpY2nC13BQNjc9fXcWlMqsN/5/NVE5HIsdY5Ci+88UYDXPFIgCrPStuaT5xvMdYunfuU2TNZ5qcPGkTXB0AS7ZIpj84cM4b2nDjRkIfI2L+4UAmO39OusZgHYLi0WcR23LSJlnR2kmRAQAzh7z/4wTCWXxLjQHZwsZTjJscWdekk5ehbWXIyNk1PLetGVEC7+jSAO/bYY+nuu++m3/zmN/SJT3wiIr633nqL3ve+99GRRx5JP/85iOPtD7tDXnfddXTGGWdEXmIL3A477GBi6vDce++9Jk5u+vTpJm5OP/gN77zxxhs0cuTITEPazIWhN1ntbmFzoctLQeO6JVCQ7ix6s+PNVMcGSFfEJEIEtB0gim/vNINWJMnylCn0zqhRkVu8e6ZOpWMWLQr/ZhtwxOzdPngwwWLIB4OMlYtznZHuExpMyttHuJawn31WUgx9qLNrkAxG19TvrvgTHTPBSW5lDpxm3yBmWpzVR7ESyGsvqMRcvAR8LCLFt6LOGmyXdyJXoI7X+tM3vhFR9OVZIfdaDRh8CCO0KzsgzlQi2pGIDiUieT2sCbSkFECm9aaMZdOeMeJljgfHn2oux2KsrPqyzpwtmmHRYs0CIGGGR1gTAQAR3qBj72rmlqMtkf1C5Z2FzDnVhtYJmD3T5qYv0+xIRkftTqrBkO0CGWcpHs2mqS9NpWukjSnSpofYwix47CURDLuGoi0262ydKyjz583UUzM3usU+7NMA7tOf/jQ98MAD9Pvf/55GjRoVGZr169dTe3u7iWODNc71gAVq7Nix9NGPftS89573vMe8iluOT37yk/Tiiy+acrqD3Ck/+MEP6MQTT6SLLrrIuF7qB7/hHRCcwBoX9/AC0O88++yzhgUTpCiNfvTGK9389M0hNvMnRo+OJKbMrJSLDd7mAoGAcltMGZiwZKyczoMj3QLZ7SNC7x3EMqAM9vt/TiQLZwBp3BbVAWcCjb/97UhSUG67K2aP5xaPa1JeNFsMA3+rD5FIW3Fz6/nYYuhcVkPNLOZyX9IMYKwAoS5XfKFnc6vXSiyBPgEKSizfPJvWJ8bKFssHIU0FdAoe4fYmmQFtscK2S0S8xxdQiSyhqj18Dshxg7J/zpAhdG1XFw2OYSMM2ycAqY6hgkfMPkFe1MVB/HV40RcT56gv/8yln0ptwqEAzNbIl5uwNC3u7AzbPpDI/Ds2sbYjPEO6B141fXrE22eYysEKV849O1HLlhx1Ehjh7wBaMtUA/sby0GepJkhygUFbGp3/uOIKOr+nJ5KSSI6xztUm1xrXizIQSy+JVtj6yu0+eP36UNZZY9vz3DNkWRWAK0qyW8rt0wAO1jUogwBZu++OrXnLA79s0PknATh8waBrxx13NC6TcHkAC+V2221n0goMGTLExLzhuf322+mkk06KxMXJevsagGOQYAsO5o0tCYR4TXNLYlQ+FOCj/9Pf/Y4+85GPRFwRcKied+21xp1Duq0gKJzj4/igirP8aCucBpDhZiwOOBxmyN12+umnh23C3yYSEehu2Bqm89awBYtj+iIWvgtx/xZ9eOO3MaHZqIU510yYf8pD+NYYOgdTpD6I+ACW1sEkl84+oTh6yLU/vlKNbeuMep+wlnpY4PQ5cWwwRDpRtXZVlIy70t3Oxowajvrq1dQ9apQBNK5n9sCB9G9tbYbcgi1XDD5scVAgGLEBFVil7uzsjHiE3Dh9OnWNHLk5/2BSnKMjxYAMXZD50uCqCRr8xzs63P1D7N3YsTU5LnUohnYPZMAS8fCwJAf/Wne3iXMzYEYBPK2PcMwhe6QkXR66GKtlzlW000auZiNJk7HqSRZJvjCV6SDgvlkj6wyx7UXtSBWAK0qy/QTA5eFCCVFt3LiRrr76arrpppuMNW+bbbaho48+2uR0AzslcryBQQpPX3ehtFngsKGzK4LcYH3ZqbymeUyOG45rYKAFd0EdAKxj5bS7YZL/vctVVLpDQBGA26ysX4NHZi6ToNYWQ+abkNPZ7hyTe2rAJcdLAkJ5ACJ4HdZrqYRgnOQYuVw6k8bCa75UL5VSAtXYlnJY+nSjbHNOu6yxhwVbO2y5tkhdIrLiDqvbrps2hYQmtlyYLGDsf8svvpiWrl9vQI62wEHRRzJpPicYuDAw0EyEpwfxW9LzAe/CQ6jGzRM/BAq+1cK2bl10HlisYpzbzRafBZBRk/RalGguPpEK5+GHIxZQeOpMvu++mrNCXg5Dv6hh43zqqQgQfPKoo2js/fdvqfHUU4luuSX8bx4vW9tlDKP14iImdY60vmIctHcJX/AyCEdfNEt2TZ0OdmnonBwXr1MOQL7XS4De5FVdAbjiB6BPW+DyIDGJG4Jf/vKXdOihh9LnP/95WrJkiXm1T5OYaGatKVPCDZQPPHl46duoek38MuAaicJ1bJh0gdCbNLsPMg9XJE+d6IerjTq4Hbevp3Z2Et/Woq98QMhDFwcpgzXJEsX1v7+zM3QLqWFx5MnnuFWLC7jXyoZ22UiztbDcIbtPCeZM7ZIpwb1r7PfZZ5+QSpm/X7P11uYyxLgfeYxFmrZX75ZIAkk3/iVqatWUviMB6SbP+4yO22WWYNnrb+25J/3t2GO3sCaq+cuKO1zdZIw0PC8iJCOiUG4LW38ka+TbRIRgDvyGC8iRhxxivoRLPx4dyw1/jH8NXAAZtOFikv8NC81jHR0RCxxTz+sYN2ve1hjSELx/x5FH0tHLlkUmirQ04TzbFORWYzbNZX//e43lT+aoY6AGD5nH5s4N3QPxPWIEHwly73F7IfswdYBy8dQWODleNe6N4oy1egk4GKt1PLlm6ZRnJI+LV45cF7u0AK1S1pDPeCJarhjAm2lFrwBc8XtonwZwPmkEfvjDH9Lxxx+fSdIgNbnhhhsiqQiQRgDkJF1dXfSnP/3JuFfyw2kEEJv3s5/9LFOd+Kg0C8NyI3nb4MEhCxIsS2d3dET9wOsw8UuAxsLTrnmc/DQuAFiyf1mDsx1t1G4qOr0AknwjVwwDOLas2eiVwzg7JUMdvB7eWioXYAMYhw6N0BJHAuk90gt4TUBRDh+irwdB99qCpgGcZu9E+0Dus7CrK6x6wR570JyXX94CxnWgfB3zxat/1UuNk0CJc4s1U9Fp3AD0z5o0WMGeL90cuy+9tIYlWLIByjPGZs3TzMMzkWtMkIzIucW54NgKo9MI2Jgiw1FTe/qEGLDHOdQ0syJ7q2hQ6woh0P2NrBNx2SZnFtPgo49gTQRBiyQLkV4YTALCAJoBj80VE2fj59raaOP/+l/GuwNhMDo8Q7e3Zrw8PFOsAM6DsVq7pH6biADK4VoKd0kJsn24AKzs0orIRcralpewmS7rpdFT+/C216cBHBJ5f/jDH6Y333zTmsgb44pE3sOGDTNDvHr1agK5CeLlEB/Hz7p164zbpHzuuecegosmEoIjiSRcxvixJfLGb6eccopJ5H3XXXeZb7M+pVkY6kC5bcYMWvsP/2CUcWlZ8nUHTJKHL4BjS9cB775LgMkcK8cUvDW3VcoVw0UJLTfD28eNo/GB1RXtxkZ6T3t7GCSN/IAyvQLi9JheOeK+qQ6Gc0XweuTW0iIcSSAjLYfaPSZCBZ2CvMRUaYk93AekPT09EXpjyAzWNWbjkoHsKAa3hbj9nbDXXuGtqs1q+1fPsUiaK9XvJZRAiS1wzVR0SjhSLd0knYqFSZE4bxsz+MFDB14MpyxYEFqpcLV0WUCCYXOFtMUqSeZhfI/zjin/ZSyXzlW2l3KV1FT3NmZLVupxNtwUnG1x5FSamIOBgwY2rvmftC60xUkCX0wiWxoB3aZzu7oiABrMmUOHDIleTgYzknOn8UWpBnC6vTXtV+eZzTPFBVqltc+WYkBbSGWqAqkbJRLdBH1NAyTxiS0FT9L4FbnQS6OnFtnJJpfdpwEcZAtL1+c+9zkaPnw4nXDCCQTlHnnhQPu/ePFimjRpUjgEH/rQh0ws28svv0z4Nz+wmIFlEsyPAHtPPfUUPfjggyYp9/Lly2nXXUFLseUB4EOOueeff94kEh89erRhjMT/jjnmGPrRj35U17CXaWE4Y1o8Nsq0QtDuJ5xQla1Zsi0SQOCwW6iSYGJz/Z/BzRgONZ/YnMhmKIAGDtATBDOlAVPz5hHNmrWli4Ly2OZ2wcAHBwPTAstbS5us+PDEbzLg3ss9xlf4lqB7xCzgoiPiPjNvHnXOmmXyAQKsMfMYV2MYOZ97LnS5ZWveMyKPHMvFZyx8m1+9Vy4JlHVsy9quco1ea7RGewLAK2OP9nZ6sqfH7E8yvk17McB6dmXghibjuZmeHZe1kWTU6px7ioj2E2JiWn1bTBrih6/r6QkTXR+7zz5RlkXEiynPC44Le46Ithb1uFzkrQAuhpxEn01JlmkuX4IHBjfQtcDgzYm8+ZyWbdJungygJdOzjhPEWdIVsH4vbW8nmftUt9cGuDtHjQrnAUC0JBOxEbtwrjUZCoF+yXnA4+LK5ceM3L7gDUPrAl86flPPb5kawyePbFGrukx6alF9bHa5fR7AQcAATvCpRhoAMEjuu+++NGfOnNC3nAfBBeCQ6Pvmm282BCawLgG4AfjBDYN9zvVAvv3226YOWOr+7//9v7TTTjvRF7/4RbrgggtM2oF6njItDOcNT14ufEJQtpxs4QasfMM1gACFMlxOJPuXPPR8bqr04cAbKYDWeH1jKACNjRFLWvlk3ZhTzNyIrmuylVAcSr6c9wa/c7vqsrwJuU8YPryWqljnUtKAVXzPt6ZPK9YwyH+JAr42S2WSElHPWqq+bawEfNZZY1tkXC9qWPG04tzwNlUVeknAtjfYABysOotEiZzoWe5tAHZ7B1T7OkFzTfJsLsvikidTxGBfxoMLOpmrzBVrnXSRgL5pkgzp4YL9M2SZDGLWa9KyaFdmfVZZgKNrMPRloXZPtbEdS9fABZ/7XMSTpYeIoB3JXG3S2ojLQXjScGAKe3a41qttfsSejzHELQBfeDj2XrrhckwlA1k91jLVgI/7ZBwjptYX0J79V62KyJFz5LFrar38A16LUb1UJj01S/tb4Zt+AeBaYSDStLFMCyNOIcsbSEBGOt4AB/MSR5JRlikfcNIypN06+ZbNCZgsA8R9x01jTY4a5VKowQs22CvffNMcBjr+QJbL6Q5qqlcHzRmDBtH7L7oo4rqKTVsf6GnmGb/Lt4s6Gas8iKXLqA6uPrqtjQ67/PIIQQnL35XTDxctOj9PmrHJ0s/qm+IlUEowXuLYvOJHpHVqSAJrNvdABk+I1+V8Zeya/tkzzwwtXvgb9qmtxo0zlpX3LF5M5z0HO9fmhxNi2/Yg7IM61huXU8gCi1gujpWWoERbuuReC4uKrEfu+e6OAAAgAElEQVS7hA5ZsyZMTaNjpsPzMM5VXl9YWLxFkJrAZ636pOGJzDBdt6D6xxhIq6IkBWPSF828iLJh3Rp/993eE5nPV1/iFhkaoEnaeKx0vDvGT1pweR66xl03Pk6v8nGtxAXx9NGj3THy3tLK/mKZ9NTsvSj3lxWAK/f4WFtXpoXhs9FwJ+pWwFevJhlvwJTLxgXCwc6kDzgwd32UqIZYhcFUGhcHl0WON2mXyyXfhjFo1AeJz8EprQaccw7MZzXU0TmQgKA9rnxuTBrjOrC0G6hN2ZGxArCoRlgpL7zQ6UrSgku3anIZJVBZ4Mo4KjVtsp012hohwQ32WY57xr9vJ6JTRUz0Arh1B654qIytcvi3ji9mQoqa3HCBq5vM/YX9mAlGNOCQOcNslpikPmqQCmZg8EBy7jPOF8ZkJa6BjVj6NHtjYIFLYy33OrPQGMtlyT7nnWcslLovbLWSfbAxa4I8TPc3rj1J/YqTjS33ny22UZKWaGuuK8Ze9jOuja6+aeut1XPGQoZW1OIvk55aVB+bXW4F4Jo9AhnqL9PC8Nko0UVbounUXVeb/3ykbRBsX5olUh8KHOyNemPZvlI3bPMHetNNAniyvRxjkQZA2nzh9WEhc9SkSd7tcyOoLXO220mUk5TEmw9FzWrplaso41hVn1USkBJIcl2rpNV8CWiwBtfA1x9+mA7euJFwaYb9R7sXxrUasVSc+BlnA4gmzpiPU2UzgDuqo4O2A7BLcNnTcWB/aGujJevXh/HAKI9JTZJc6XytjDKOjJNpcw47H3c5fVbZ5n8S0Mk0I2JSE0BH2K23N0wLIOMOwTrJrouwbPHYgOXRBaq1JZPbmwQ2I/1WrMh82SitqRqkc25UdqvMcqmaZT9yXRgnkaFlGkePj8qkp3o0tyVfqQBcCw5bqywMl+Ums8jV5i9vVyXbl8yFg7rYL10m87YxNmVuV/Bh0oEXZ5FLOthtbZPludw14pLKpumv7dDTeegkWETZNbEXAuTqoHd5gy0tlLa/x8UnJR3Oafpcvdt/JJC0dvuPJMrXU9tFDpM9yYs4zicmiSQ4D5nuFSvge3biXwoEqNQp0q3PvKy8GrQboVbYmeodILPeizq2NMkYqccee4z++d136XuykwmeF3q+2+Z/FhDhM3viGDDxvQ14yX0dwCjpXKhnPbvOae3uz/8tQwZsbNCSkdsHXGeNyY0DntK67DNGebzTKnpqHn1tVhkVgGuW5Ouot5UWRj0bqU1EvPlvH7CFhe8sWkTvRRqDIH6ADwKdPBt/x02eph+uYzjCT5PAQ9wGKw/5JPcXrpDL01Y3CQZdB2Ie/bW5wyB2Iu6xxUwgGFxTMENJwnjW3KinVExy6WdVSJ+XQN77VJ8XWAM7yJZ+DdbgYfH/2PsWcKuqav8hcA6PIzfz0U18YWJqXEzNCEsLFbNUtFB8XG+miVf9F6ZFBogiamBJ+Tb7xCzTNEUqLdN8h4qKBmpe71XwVWlqKnE8HDmHx//7TfZYjjX3XGvNtfZae6+9Gev7+pKz55qP33ys8ZvjJUkLn6Ey6AUEZpg/fkNowWBmKKMcyqiUZliO1Ckc/CouaIbPeewTwCIErYd5rwufJDO9NNEavUhHivUQZ6nC3+c4SxS8n/RdqIV8RlnOyMAkNpmb1rcvvVbRBLu+36n6k9EnN9RvkaOvan2nmKtaijaTnFrLOBv5rhK4RqKfse1m2hipDi4PPPjwh1OzbQYpA5FI4iL/myNHRSUu9ehC5iJRB2zUDV5SQ4yFfeMr/SykZs6XGCa1G/zuIVzIumxnfE42a0w7I0Jbw1SmFtMg77FowfUagaTLl/UanAYP3iWwg6wNXLmSvlnxaZNnqCuwB6wvkFCarTBcZ+alKyq1WGcRTLydmjoLF7mGOGF3Vq1bULWHMJ+HqXnV+vdoN+uyqPWyBH195tZb6ccLFgS+fyHCmvK7lDQOl6WLJHMyemmUn3+qMefUfxlk7JgZM6gWF4okjFy/N5OcmmV8ZXhHCVwZZiFlH5pmY+R0EEl4+CC07eVBTqIIAtui275YUTbyKacjc3E+YKsia3kGHuHx2iYabMqY2nQjw0jSEPSkj1ic83gm5/zlyzOMSF9RBBSBMiHA/mh8kWOHkoc25Cft7XRKD4LQr3tkKH+ZMJsDRsmz0fZ/w/su/2L83TcQV9JZ541vim9obm2ic1a7eflSo+q8Lksivz05k0/bfNOOPIlAXXbgLXt+085Nmu9q1FpK26b3mvQs2DRyqud4ylhMCVwZZyWhT02zMXI+SAGLPJTwbx8S5vpg5PURqWX5RJncJJm/uNp0HvgF4F/LhynpoxRnYuoTHtv2ycuCYy3zqe8qAopAOgQSz2GLSOAiZ6AVPRItQiM3eeXKqjyfF1QSPVcl4LZImm2d4PrOpAnElTiuFDAlnZtcVd4Cu4vE+hLYFMPLXDRyvClIb9rG7XUBXzw7vY6rzrTrIY+5zKOOtPjI8k0jp9YyyAa/qwSuwROQpfmm2RgFHKRRt2GpfQuyAJ/zOy7TDN88MV5EqgD8vdp14eTRlygTU998dhMHDWpo3pucl4dWpwi0PAKJQqZ1CYWLHJyRD3V2msiOeKCRu3bSJPr9j39Mc7u6DInjiMMmxUxETrS4tuVvMmFzvc3QML5EjCo4+JIE33JZLkvruWDjcPElvWn7a5vJFpUo23fO4/rvO89pMfAt3zRyqu+ASlhOCVwJJyWpS820MYo6SIHRwIEDTUASmMV0d3cnwVa63/Mko1GHdZH4A1Dvj0QGbWDavqN8UR/U0i0e7ZAi0AIIJO5xcfEjSRmHkgcEHEaeg3lAI8K+bnEao7izy/tcq8Mc5N0XX3KQ5wVjETDF4eI7xlr6dfuhh9IB8+a9X4Wn64NPm3nPuU+beZdpJjk177HXqz4lcPVCOsd2mmljFHmQNjuBk0uiKJyKqjf1cvbQwNl1Sn/Hs88+O9EJm31lpFCXup/6giKgCNQHAc8zQQZjOHzKFHNhx37N6CiTNAi9M2bMCC71oDlLk1ezPoNufCuJpLnSxSgf62YwTa8LAfJcv42f8cb0oJnk1MYgVHurSuBqx7DuNTTTxiiSQBRZd70ntagPTpkw8hUcGHvuuxTSIufFuqnfa/Bgyj3qZr0XhbanCLQyAp5aefsMw1mJHJMvvfQSDR06lMaMGRNc7hR1jrbMNGQhHZ7z1DIYpRhI2m9aiqqbvmgzyanNCrYSuCacuWbaGEUSCD08kxdvmQSatGsh1Y26JWTcPm4cHXDLLckAaQlFQBFoDAKeZMI+w9KeI40ZXElbzULGPOeppCMutFu6FqPhbSY5tdBFUmDlSuAKBLeoqptpYxRGIPSjUtTyKqzeLB87JumJPi26HgqbN61YESgKgSyXcPY7hX1jihp0I+vNeE5mmadGDrNebWf5ptWrb41up5nk1EZjlbV9JXBZkWvge7oxiCjLTWID50ybThHwhMGKCGAQFRWu3kKGCo66qhWBdAhk1abF+WN9cPfdvdLJpOtp65bOck4qUXGvB/0GqAaukSeFErhGop+xbSVw1YlGm8GxOuN0r7+vWST9GiJCXqcX+/RxBiqoVchI+zGutb31d2J15GVHIO1e8B2PvWd89xCX+8bAgVWpQnY97TTarbPTpBdQv9fkmfDFXNaU5Z3knmiJVkZA5dTiZ1cJXPEY596Cbox1kGa5Scx9MrTC4hAQGri1RLRBJb/TWCL6iBUqHFo5jkCXNtksC6uIbIe0FL7vq1BT3NRrzY1FoKi1nWT+mJQO5Y2ODrqxq4s6iAj5ITvuuYe69t33/X8vWUK03XaNBa/krWeZ26IIfcmh0u7VgIDKqTWA5/mqEjhPoMpUTDfGutnI8iEq0zxqX5IRgMB3SmcnHSeKriSqStaLn5ctW0b9+/enqJQDUUIIr6MNNtiA1q5d603g6nWBoMJT8jrREvkiIEP3HzNjRmIKD6/WPfyvnGe69d6RHR30oa6udRq3Cy8kmjDh/eZzzMXlNaYmLKTnSRNOWhN2WeXU4idNCVzxGOfegm4MJXC5L6qSVgiBbuNly+hJItqQiJi8cXdhVnnJ4MHmn0lmVFGEn/+Oe/vPEfmZYnkIo3lB2ioXFSo45rUiCq6nqJQcHn7LrpQBOz38MB1y223BoCcOHEiXdXevu2h5/HHqGjZMNXAFLwmtXhFIi4DKqWkRS19eCVx6zBr+hm4MJXANX4R16gAL/TfNmkUH9/TQdCIaWGmbzSpXVP49iM2qIsyooogQJwA/i4iS6giG7SGM5gVRqxC4VhlHXvNa2nqstT2jf3+a/t57Xt2NJekxlx5yn+/R0xNcotgXODCb3HPwYFrc2RloyuulCfcCQAspAoqAQUDl1OIXghK44jHOvQXdGOsg1Rv93JdWaSuEIDdu2TK62qeHwoxKrpFfzZxZraUTQqWs+rdjx9Kzn/50tOlYHTVwZRVQ0+6/so7DZ0lFlUmLQS1t1e3dpUtpxbBh5jIDDy5IBjkuRVxjTyLpUWvARdQQmIojTErt+BFTpwZBjCZPnqym9HVbGNqQIuCPgMqp/lhlLakELityDXxPN0YDwdemG4IAhMWBr75KEy69NDCXQlATCJkuDdz5N99shDwOTLJTezst7OmpNrWyNWmVQCkfJ6K3YaL1zjuR460LIfHQWgwYMCAfH6WUM5skrIeqqyPhTTmMmi6DUmGQpWMNemfGgAE0fSUMliuPw7eMx471hyBC+H/7kgT79u677zaVjBkzxuxH+KrKd5iEVV3QzJlDHGEyLg9kq85Bg6Zem1UEckFA5dRcYIytRAlc8Rjn3oJujNwh1QqbBAEmTfdXolKyzxq6zz5wuKHniJR8c/8hIpolx8gCqUUsziWiRytRLpPCkicJjrloZyyCKTWDSe3bU5pLf0SlLn8lkGYnoayjyWmWpZwWS24j63tZ+pj0jmt+s8459tn8zk7jd2qiPTo0cLwXmVztMngwPdjZGbokYQ0a+g6fNTwgcHbAINuM+V0iunXCBDpkzhxTH/4ddamSdYxJeOrvioAikB0BlVOzY+f7phI4X6RKVE43RokmQ7tSVwRYYO7bt68RAletWhUSDKWQiFQDHPwEWrq+leiVtkAqBVH41fE7UYIrDzhJcMxFuI9IZg7NYFoNYC79EbNtRypk7YozDUPJNXBZsUk7B0VuFtcYUpFsi5wjeNDoqKA+jnV5QESOtv07O03Nt7a309Z7702jRo0i+LQGvm6LFoUCkeAS5WYisutLNGsuElytWxFQBFIhoHJqKrgyFVYClwm2xr6kG6Ox+GvrjUPAztkGIjd69Gh66KGHghxu6B2Ez2lEofQD+DtCMYzv6KDb3n038KFkM0sQwtPXrqXz5fBqCEvuI9zbJNBFCm1NR9boez798Z5ZR6TCl/r2NdqVqDx6ubbv3VG/gpn6VjJS6hqD/Tdfoop1GJtX0dKoIhrsPEeOthX77hvypfsMQv9bhK1j5kyiqVODiTqeiH5KRJFmz35TqqUUAUWggQionFo8+Ergisc49xZ0Y+QOqVbYZAjYudukT81D551Hc7u6TK44jlQph4cw5FucdVYgoPK7L997L10+f35i8AYvqBKE+6jk4XFaFDYHXdDeTrMPOogOmDfv/a4kEc28yYYlwAPTB/v1i03lwGOLy9XnhW3ehVJiw3M38umnU81BlMY2SZPrNVzXGGD+KELsz5k4kSZfdZV3svpY7d348UHdvMegseYcbVijs/bfPxT+H+MwES0vvzycu23mTOqaOjUwlRxbMWGGaSbqjtQCegGjhRQBRaARCKicWjzqSuCKxzj3FnRj5A6pVthkCLBm4UUi2laaeS1dSiuHDTPkjZ+5MMeqBDyBL811kybRlDlzjJYuJBx6hk/3ErgTfL6ics+5CJwdwAVjOMrWdkSkTghAyNsHTRAGaDVB4C7q7g6EcGB80gUXhFYVjw1/tLV0XpgWFXk2JTY8DpfPF20Hmu1+orRfUX/3xcS05vCVbGtrCxFMaLZAinxzHXL7jzzyiDF7ZE015g5BS66dPp2+09MT0nJzjjZos7ddu5aegv+cgMNEtLznHurad9+Qr9yuu+5qyP8LRISMb/C9Y783/C1Kq9tkx5Z2VxFYbxBQObX4qVYCVzzGubegGyN3SLXCZkJAkAd5+28CLdx/f+h2H3H0PmYFPFm0fLnxH6sK0iA0FhAe94K51/LlVch4+RUlaHWiwqazIMsBVKK0PT4ar1DHHf3hSJ1Zo1iO3XDDQNNpJ1hH/y5dsSKU6gP9iTLL8zXt8y2Xajlb2EBT1T1kiInsmRQqf+jq1YkJ5Lkv0mfw8ClTgmAvaYmda2xXfuc79JXZs0MBP/5NBBXBeoZmi8mR9O+0iZq9Hmxtd5SfqczRxkFK4If6fSI6THZaRJfkdc77wTZ7hmnmBe3tdMyMGQ2JtJpqHWlhRUARCBBQObX4xaAErniMc29BN0bukGqFzYSAI/S/6T7MCEePDky7oBmC5u0+MTa+yY8Kky6jXEalEbBNAaOCd0T5VUFgdmkvrtpuOzpq6dIg8t8vJk2i0y67zJi8SW0P+/E92NYW63NmT2kWf6g4LdDEQYPo0u7uoBkmcZL82uQkLg+YHV4eFdtRLTP5qnmsbaef4TvvOHOMyT4k+f2haZcGVUZUlPXJHGfOvIURY2ECBI2y1EqjODRb+NtXLJ9QBAX5cU8PPfDAAwZnOzIkN+XSFnO9MhoszCaH7LWXee3V+fNNkBL8jscODMTRKXk/SgLKlzJVlzMx2k2PKdYiioAiUEcEVE4tHmwlcMVjnHsLujFyhzSoMJXZUnHd0JrjEIjTwG23XRCdEcLjqwMGmJogoOLhACBRiYp9/LRskhelhZHkBSZnTEYgmLP2j4VUkJ7zIOyLcUOLdXt3d2DmueWaNYHGS2o77JxaXIW9lm0yFdU/aJ5s4d1lwiaFbvZ/Gt6F/yK6s6K9lFqnE088MZTHT4amdxEo1BMKipLSVy1pE0l8mITLhNGsqeX0FEYba/UBJPsfHR3u1AmVDgBnO8cZa5ZsTPYcPJgWd3aGCDsQtduJmlsZeZXXyPLOzoBAyfUGEvniBhvQ2rX4K5E9dsbPDvEv8y5i3Y5sb6dne3oCAmhfNnyRiF6xfNmqtI7WpcyDRLSnnMAkH8+kydbfFYEmQaBVZBCVU4tfcErgisc49xZ0Y+QOqZfAWlyrWnNaBCJ94IgCrQkHy2BNBjQExhRrs81CZpYmsEKF4CV+PK3oi3G+aFJIZTKCPk1atYrOW706GDKE+e8R0VZEdIdIdXDqwIF0WXe38eeDMD4b/RTJlW8fN44eGzGiyjeJEyfLyJxIO2ATNmgBOZR7lCYpyWRRao8woJBZqvB1chFUozE9/vgqUoQw8iCAeELkKcZXLXHeKmi7SBv7dMlE8XYessDk0DLRDcYQs4BtAhRH2k/q149+smoVfcMKyc++ZUykozSbmzkuAVZ0d9PVon+83pZWSBvMGzcmom9U/ESleaUk6VFDxDr80b/+RX+9/37ac/Vq2tpap9DM7ty3Lz23enXgy1Y1X9a+ijL3THtOaHlFoNkQSDpzm2U8KqcWP1NK4IrHOPcWyrQxfAQnnzK5g5SxwlY5PDMOv2lec/nlgKTgCc3h44+HIvEZrY+nr5sTDItE3NqvHx28atX7RefMofPffNNo2zj4AzRkrOGBhkQGdoA2YwQRbUNEf6iQNw4KwuSNK7//85+nT/7xj6HgDwia4SKKeMfWqMh9KLWAICuHtrXRlr29FJDcihYuyWSR28YYz912W5r07LPvY/HVrxL9/OfBv2UwGalVqormSERSyxMQCiuqotTgSc0p0kog6IbLt8+FVaBdtOb2+rY2Orq3N+g/TA5f22GHwNcsKU+gedHS2GGOpT+YHWCHIzC+YQWpYc2cTeAwxvNPOCGk2dxAEDFo7kDkf7xgQSgwCMqgH9OJaKBroVfItW0ma/ZOxad0kEjyDd9NJu8y5yJXbRNQV5O2j2AoV5zDF7VpDivtqCKQAoFWkUHKJKemgL+piiqBa6rpWtfZMm0Mn8PGp0xZpiFJYC1LP9f3fkSF4QcuoQsDS9vGWisWDmFmGeXr5sR46VKS5pfwAINGRQqz7N8jw+WzFmZ3S4CfQkQ3EdEzRLTO2HPdY5MH41+2ZAnZQU5Q1taC2dH8XETDx3/NJh+SMHE/JZFG1MGQr5PQwEl/JtawsdZPmtxFrusKoXDtT5mzDO9H+XJFYRWQVhEaHyQa2rDLV60KBQaBSe62a9a8n4Q6hligXzs9/HAolD6MUxGoA1EWbUz+hmCSFRIvSTUCfeCR2khpcoqoklLDBo3yKytXkh0gBP5xWO/929tpYU9PKDqkxF2uFxmoBn/nJNsoLyO42j6ll7S304k9PYH22GVqmsbEly9n1vdzT8ff+gi0igxSJjm1VVeNErgmnNkybQwfcuZTphTT4Jm7K2vUviLHWG8tZ73bi8IusR/WnLImg4X8LDnJbGHVJTDDdwuPy+dOmtDBf8jOlwWydsuECXTInDlGyJbJx6v2khif1FpBb9QmQbN8iKRgbpdlk9LbDz00FIaeya9c/4w/E2I7rQOEkVM6O0Oh5hHOft5GG5nesY8b/Aj37+ykswQZlpokjgwpA8awXyH+NuS99wyhAEFhvy7bb0+aA9p+XBxxVOLCkRuhNUX4fZgc8roJ5rai9XWtTw4swqSWw+KjrB1tEcTubIvET6gQs6p0BVYY/sst88cobR330TbP5L/jMmKG8F+UBF4GBAIejLW5/Hj88dClBrBFwm743sXlcLPXchKhK/L81LoVgVIgkLOfbyPHVCY5tZE4FNm2Ergi0S2o7jJtDB9y5lOmIKjSVevwsWFzOGkGV8acRPXGuN7tpZvIcGlX1MCowB8+7dgpCGyBuSqS5ezZ4cTFRBQKj25pfsZ3dNBt774bCsbCWsIqwhoVkVNoeEAcTt5jDxp+8MEmFHtcxD+M3+Tqqmj7pE+bPc4AK8t/SUZYtEkMtDhMxmSURdbGsdmnjG7I5AlkGwQK5qnyv/HO4opWC0QDxAd+g6yB4n66zAH5NyatdhnME4LLgExC08UE1Udza/tpQtP3Yp8+pv92sJEF++xDY+69N4AUJH54hTRWES7LNBUvsXYMvoO2PyP3A/nU0K5tngkC+DYRwZwTJDXKpBQaSZDjW3t7Db5BtNELL6zyKb1o4EBDzl1E1yb99jwxCImXMz6b1bNMPdvy7JIWS4lAS8xhypyUKSGqa/Eyyal1HXgdG1MCV0ew82qqTBvDR93vUyYvbLLWY4f6ZjMiGe4adYei4mVtrID36k2o6t1eLZDJvqbJ2xXVJqcBcAYAET53gebsySdDGgoI518A6YD2oqLBcaUv4H67hGAXecKa7VchL/w7ExCZDDmOyAT1zplDH5w0KZTsHL+xGZ8Md28HhWENGwdOQcL08ZWKOTqlHcFSakZhignNDRM3SepkVFHWhkF7NUtMltRwyoTikniDpIKUsf+Xi7RKE0fWBkbVLYVHdMUOiMKkltcf5uNTEpNFiwJfTayPS0Dy29sJ+eI+3NUV9rsTGji5Rhl3GfF0srgckOM5sqODPtTVFWCMeqpMTy1ifuuECXTonDmGvPEDrewBP/hB0HcmdVgfHExnzJgxoRxuLlLvMs+tZc+nfbeZzrO0Y1tfyrvmsOlIXY0auDKNt0xyaqvuASVwTTizpdkYPoeNT5k6zEHSwcaHvx34IW/hP+tQk/pfb5Jc7/ay4ob3eA6rTNEQ0CRjbim5LkICs+VzJ33XjurspG8KHyeZKNxF1mRgjrPPPjsQgu21IMnf1iKSpTTZk6Zsksi4SApfXrC/HbRNMik0fv9kJXQ8m4h2DRsWBFeRibDtBNM8ZptEzt5pJ7pjyBCTP+yxnp4g4IaMRsjkgIkktGEj4wJxEBEnFMc77IeI/761vZ3GrV4digaKiJLQuLty9DnXnzBLtYOj2GkDQK6gaXqws9PgJIkgY2JHq5SBXuyAHq6yrgTkrgAxGAsHFeFxQaPpCv4S2ueWpo3XNvaQ6wIias+6zGp9onnWcgYkvasELgmhdb8nfYf8aimmlGsOm3Fea/m2lmm8pZFTi1lupahVCVwppiFdJ0qzMXzU/T5l0g0/U+mkg00GY0BeJDsEvW12VO8b49j+15skx7RXxg8896lKmM2QW4rrkhEmZd40+A2tHDYspKWAcAptli3UG+3FLbcYoWjGjBmBaSCTtSgsbWIHHzCp5Xq0YioHDZadmgDrFsQM/mZ4HiOibSvaLpCKgOgJjRCI003bb09fe/75YO9B6/WmMFOMFDqs/c+ESpJIJpowE71+n31Cfnd3bL45feG114J2ERzj+EoADiZB9oHAf5e+g9KfC+3tUiFR0j+NtXW2hkj6y0nCKwmTbaaL+ZABXVB2+7//PTQ27jdjgnbtNWITLTb9xZyzKabLz8x1aSH7LpPEx/mBhs4dl3b5XaBZnbojtCfkBCXkcMx0uOfwUi1Ccw7Nl7aKZvJLdM1h0ne/jMDX0uda3s0bi9LIqXkPrET1KYEr0WT4dqU0G8OHOPiU8R14DeWSDjYZ1Q1+IHikxsbcNsv2Mwj/vt13Ce6x/a83SY5pLwlnXwwKKZfDWvQZnx25D8L7H557jl65775AuyQj/UVp2qIwsLUdsyZMoGNmzzaRMPFwagJEG1xUiQQY1DVzJnVNnRpEVgSRwXoHMcDD/mXzDjwwRDbgI/btlSuNZkyGiXeZGsN0ktewNP+DqM8kSWrDQr5XgiSgPPLs/aqrKxhb1T4UILEfmIzmGJW/jQOEVAXkEPnypDZeJlGXAT1cGkg2T7QDukgS6TLFdJkWcl3QgsLsks0c5XwBAvZbc0VnZbNNV4CZUJ49x4Kz13uUps1nX5jqrbMDJr6XVJK+F+kMInwAACAASURBVLLnfSrN4VzwaaYZy9jz6j3P9R6sZe4rz5mkNZ53V2u9xIz7HiTVXaaLiNLIqXlPcInqUwJXosnw7UqZNobPgeFTxnfsrnJJhxrecX14XEKmNA+zgwew8OiV/6mGAbn6GothvQWQmPaKnusaYDWv1to/n/dZEJd+XJwsWibPXlQJQe9TZzBugX2gYbr44qogKTBJ/NhOO4VImFm/M2cSTZ0aVMeJo9E/PBx4wjY3hY8aogrClw2RCu3LDGgYpX+oXMNVfoeOpM0gUhzMwiYJp3Z3hxKY8z60w/DfXCE5MFecIxcKLltGjw75aXHeO2gpQ5rH5csD8slaLmDzXytX0qXdiNO47mECaIiTFaSG+8eaxb/370+sVeWxVZE7YZ7G5pJS64+6ELGUg6BAa/aRtWtNOgMZUIVxl+STg5qEfNxc+REd5sRRGhhba+dzBhvgYgLe1Lq3M79f7wuwzB2t74u2ry/Oq1RnVaW73mujluG5NP1PP12dAzSjybzdtbgx1UpypUWGHTCtVJY4CfNVJjm1lqVV5neVwJV5diL6VqaN4XNY+ZSpZRqiyJl0opcR71hw5vdsosZh4f/W1kbzensD3x6+DY+KmlbLGOS7VR9JD4KW5cNaS3+d7Xn0s5Y283jXXiuphAvP8ckPsB0YomqtetYZjN0SVAxZuOceWrHvviENHKJI4mHfNBlQRfqryfxcKM+mnBAcJPHCb7jJ3gxEQ0wE+0Gdf/PNRnvHKQbkOFE8FPzHNQaRFBqBg2CCyKaBeJ+jYYLIHFwx+2QSxMFO2Lzw5Xvvpcvmz69KeM5r1jaHlukKXMnBgQW0nF+ZPTvQXHIfcBaceOKJwW/AWebzA5n8qdCQMS5xuerizqXftLebeYA27rZK5E2XZlOmY+A5Dpn9Wr6avj5oqfZLxIa1rR0aHtU37R7M4yBqgjqkmbO5tLTSV/i6EdTy/fdebz4XWzlazcSNKeo377GIC2c7WnKpLHGUwDV8FyuBa/gUpO+AErgwZq5Djf+GklHBK7iM/N3ODyUFNTs8d/qZ83jDJUzcf39Yw+L4EMUd7Gk+HB49NEWc7TXBTXZNPh0pxsft2MEnqnCLqDNyziJ87HY97bTAr40jPWKeXCZvcWTf9uUC8bL3DxOekI+ZtXBkPaGIlZMnh7QwLtNkjMVOX8A5xWSeN2lOiEAyMj9dnD9MVB40MwSxt6KCxdgXO1I7ubGVky0U7EWYlkb6UApNHMxPpWmsTBrvzPMntIyh6XAJrg0kLbwHaknl4XtO+ZaL2xM+56dPGd++1KOcT3+rotXa6Ss8CVEtBC7Nu7bpuiSc8pIjC75pvhtRaynNWKIuOdJY4kgf3SxjrvWdMsmptY6lrO8rgSvrzMT0q0wbw+dQ8vlYpJkGn8NUErgqga3y4ZGHIdrHzfbW/fuHzLWmVDQO0sSsyBtjO3myESiFYBZlvpnlRjAN5igrcce/pcbF1NVAoTDtWLh8Ks1l2vE5ytuaqijM4uazSlBZsoQ43QXGJden3Ads9iaTYXMaA9f8cWh/e/9M69uXXlu9OmS6F8LfAyf2gZvuCOV/++mnVyUQP/T224Nk3bbGTRI37keUVh5rVvrlyQubJNPoKM0YAo3c3t0dClpybiUhNp8rWbX2dtJ4ibM0ITXaEPuiR2g1XdFWU639rBvM8Z7PNyPH5ryqqvX8lO+HotLiwqKEj88cxBGipL0ih1zLOvPpJ7dVRTjnzCFcBuG7Ls2M8R2LSnHBdSXJGJFjijn7vMcSZWZs+QfDz2/ZJpuEvsNl0m6XSU4t4RbMpUtK4HKBsb6VlGlj5E3OfJC0D8K423bU59TACfMyfIxYWEXZhzo7qwJCIEJeHh/mJLyqzFYqoe6TPoJF2uTHCcX2fLn6mTRmnzkvpIwH0fAZX2TfPDV2Lszi5hvrX5oYwiQ4ym+ChZUHHniAVq1aZchd5Dq2+osomY+NGFGVh4z3ik0WAxw8xo0x2BEXOZm2i9ReeeWVoVxoSbfLScKS6/ImiWS5NPY4O2BaPa6ri44TCwGmk0jcvbASMTONwGsLv6yNlMFjuEwoCIi1nplEssm4vU6TMCpkz5U0FH0cAfPBSZYBbr75Qht1NiZ9T2T6D6lpT3oPYw+NSeQhzLIHotpz4eb6droutuSlVtRlrD3noX/H+Y/GnH1x30Zo5HE+4/nWBz4QusAK9riVxgMXR9f17x+sNZzrbAIfZ6Jd1L626y2TnFqvMde7HSVw9UY8h/bW940hTXDOP+EEmnDppU5/Fw6VjpxPeDjxshFoHE7Pl3V3GwHXDprw27Fj6dlPfzqUjDbrNCYJAy7hHG0lvRfXH5+Pbtz7/LGUQR1CmhvxsquftfQ9K85e73kQjZqEXk+CWIVPwntRAkicMOi1BiLaRb0y+AqbEkeFn7eTdLt8ZVCnnexa5sWr6m9KU9MkwTiLwC3rZJ9a6U8nNWIwncR5c7VcQJ4mZ/IV+zyQ0TBl+gU7ITxrKeOsBUq7L702b76F5NzaGmp7LUaRB452mMbUnucgLpVDviNNtpTA+F4680y6skIo0L5MdZFETkPrygrw4/K1jDMXl/66fI7IyyqJm31O4Tvv+p6y9h/jkubm9sWJjF4ZWgMWkQqNKersjvg7Y8WEC32QF84hLbvwQeScmHKt8cUB/t/OZ5v7GvKocH2XUz0gqrmIEriaIax/Bev7xpCH3nFr11YLSaNH04phw6q0aHD8DwQa60CVwRwgnEkfHF9nbZ+VkCQ08YdCmnzUROA8SURc37nPHHI9TlNhjy9KM+SDVeFlMmCTNH92n32IU1WdCcQyNaYpxpmkzY7V4FUGH3UJ4ZpP7/WeIAClNWuOE9h91h3PmW1eyrflEKzsXHBZzhF7rtE31r5Cw3f4lCkh3z95uWX7BdrjSiK5PjiUtUwtY0u6IGStDq85+8IiTbArH20QMM46Hud7CecL71+ZI5EvV3zOvyT8bHNeWWfIOiAi0I7EDNi4TMaj5gZ+cXbAp6smTqTuIUPev6C1zxo7eIv1b9sawHnmR2Buj4XHgwBSp3R2hrT6IIq2rzPK2xcHO+Wg+c9jX6/vcmoeGCbVoQQuCaES/l7mjZH1Q5MGZmnnjRsqmTA3yheEo8FJx3lXZEomS7aJWlz/0ozZ1wmZb9jY/ClNG6G+ZtAyuUgItJkysl2UCZvdzzTCfJo1kFdZH4Il20o7Dz4CT1WdHoQrVT9SrIE4DaotLEXNQRQpc5WP0kAkaTnsSLJpCVwtcyoFajvP3ceJCObWUbnXsqxbiQXel5FCbU24z3rL0odme6cWHOT35XNIXScHP2cO2SkzbN8rmW5GXgy6kpv77pVIkpPgY+fEIcE3F99FnPcIyPM2EcGC5ZgZSB5CxnfspZdeoqFDh9KYMWMM6bH3qu2HlaQNitSGR5gqym9K4kWnffbZgViICLLBPKQDeeeddTOd9I7wrbM13ZGXaxFnOmOFy+VPVdYZtIJRF0C77rprcLls++/yxYHtx+8bYTbvPV5mOTXvsTaqPiVwjUK+hnbLvDFq+XB6QeJw8EVUPHxoA83Q0qWRGjhpG472XOYgSRoOF0lJMisxY0sSzB0+SAfccosXLJGFktpMql28L4vyRy/JL9AVcMMVUCGpG0X9XvR6TUW0xCDTEstYfFKsgTjiBI0PBDmXIBq0b+1PTqgb1b80+LvK5opTxkVmC6zSBMpl5uhqJs06icMsTT0Zh9sUr7kw8sLGWr9jRboG9uGCEC3N6+QZZ0dVhekhm+ZXmZ27Qt+/ixVT/USSHCYdEbMStVbsfcPlXD7gn6kkWucysalRrEAbuNB4cYMNaO3atZH+t9wXnC+c55AvZFz7W+JtR8JNvAxzaOBGVC5dgvmxzsvfTphAh8yZE3LTYC1sFBaYDvtSqWosoh02lcR7IGbAfHFnJ7HVC5+9fz/nnFA+SjntvPbSBGYqcjOXWU4tctz1rHu9IXAPPvigubl87LHHaM2aNfSJT3yCpk2bZm6RfB4cQDfddBNddtll9Nxzz1FXVxdts802NHbsWPr2t79Nm0HdL5605X36wGXKvDHsD4bXRzPN4C2SA5Ol74EbIR+WlTCXfeAeJTIH4fw+fcwHgoM5oNko4hUnJNm/xTknhyLkJWlCUgjaDJkPvjUJuXafiUgKpXEYgrCuHDasKukzHQ/6V44nDYGoZ4/z7lfmNZB2TSatcQtEn/UbudbT9q2gCZQmY7jQGPn006EgBD4+tGnmOw1mBQ259NW6/Nak5tI3kA++L7+o5B00F4SLFoWTQwtzOpAJELaLuruDfIFf6tOHtlmz5v3LRYmcKx9iJWiVDbAcTxofu6h9b683/rcrxQb7v9kXFUxQQnVZPm/y++yM5OwgzPhW82Ws3U+Yqx43e3bom8K5KKMuBm0MpA/cY3Ju4RtfeWy8bYsc7petXeS/9+3b12gp8bC2sir6r+Pbyu0jgNSk3/3O+NFCy8juHzIfpSRqdv5JzmWbFJipyI1cZjm1yHHXs+71gsDdeeeddOCBB9KGG25IRx11lBH0f/WrX9Hrr79Oc+fOpXHjxiViPnHiREPett56a0PaBg4cSAsWLKCHHnrI/A0H+8Ybw+hg3ZO2fGIHRIGybYw4f5I0gkkSBmjnmVtvpR8vWEAbEhHfWjGhwCHHJpIccAPJgO8AuRPEgw/DOO1RnJAkzb4umjgxFB2P/Vx8TVfsj478cFTlz3IA5IOvLWC6wq5HYm9pM/GxBP0Cnvg4xAoTMQJKWYTQsvTDxt9nXpP2i/w9c30pCVmiljlNp5PKpu1bUn3i9zTrIvHWP0Ioz2V+UoxpvSnqIPZ2NEJg4by8c2hFQlpUOyiHZZIHsvNgv35VydbtCIxYMwjic9KllyZfcFnj8faxs96TZu9Rl5AIyvOrrq6Q//i1kybRSSedFCKuMsJpiCAJgisDcHDb7LYQaPMtPzfb/HTuokVm2bK5ZlWqAF7UMQGC5Pf67LPPJiZSIIpV7hfbgZKFg4bxWsH/M2nl+ZMB1DDGn/zkJ4Z0sWkldy+KvEpffS4LYjbICljCptnQEkq88Q60wS/C1JXIyEUyqnZdg+NYB0zZ5NRWPP9ansD19PTQRz/6UXrjjTdo4cKFNHz4cDOPr732Gu2yyy4EFfgLL7xAgwYNipxflN1iiy1o2LBhhqh1dHQEZU8++WRCiOsLL7yQTj311KDuNOXTLqyybQxJELZdsyYU7TGz4BhDVnDwTiOqCtv9UyIzn9B+4v+3XbuWngGpE3UhK8+b0twyg9Am/RYOGDgwZNLAodftBM7cTJImJK2pTFJ98mOUNbSwnYtKfmRjhYkYE6E810Xa/dMM5dMQCJ/xZMY7g5bLZ0369DmxTIa+JdZZKZAZr8r7aTFIW953HOtlOQext4M/xF08YS7sABKBn5Ttl2UJ2jAZnjJnjiGH363kEA3mQJAMXl97E9EfKhdikWH2XRdhCTn+TJvWe9LXS54vG731VugSEmf68C70RkRpdGiLTH/tAB9LlhBMTF0BOGQ+08DHWxC+KPNT22+dA4xV5UGskC97zTPW+LskUl9z+DeydYgkfR9ZuzbQhL3cty+NHj16HaG0yCdkC8glIFEYH8xvIauwn559YYw5eHfaNDpv9eqgy3NhUQRNrxXpUqYLcckIoJ2HVWphHzqsQR5zVNToIs+HssmpRY61UXW3PIG7/fbbjfZtwoQJdNVVV4VwPu+88+jMM8+kG2+8kY444ojIOUCOjj322IOOP/54moNDWDy33XYbHXzwwXTGGWcQ6sOTtnzayS/bxpDmBIvFLRAcuE+77DKTaLKWIAOMjzyI5e0ZDsuR7e30bE9PCEr7gMYHAkclqLrrY+kltFmkRJrM8M3X8s7O0M2edGS3zShsQV3+2w6ykvWmn9/zSQPgWosQaPijaZtqSDLnirLnctJP8jFMux+0fDICtRDCtOTCax8ld9mrRNq+eVVKFLrljsqjFldXKgwKJKK+422pco4ognbkwSM6OuhDXV1Vpo18Ng15773QGc4aJOAkU2pgbUgfLkQFxTk76r33AssPvGO0KkITy+uDLxphKQKLBqfVhRiPTXIQoOL8N98MkjmjLXxvjZWFyMEW5YuJ8bpSBsBvLyT8R/hCkx0UpBLkxRmB1Uo0f1K/fjT03HOJg6ZMJ6KBlYUo/cDkxSOCA9muEUlmgvb3BkQKwVg2X7GCrlywoCoFEbrgWgfS0sfIM5a/H6S/88VG6iWiNswHEY3v6KDbLP9GrAGJU2iORN22JnPyVVcFMhWas+tAxFCso7hgR/XY72WTU+sx5nq30fIEDg73OFBhMnn44YeH8H300Udp1KhRBC3aFVdcEYn9W2+9RUOGDDE+b4sXLw5p61gDd88999A+++xj6khbPu2kl21j8McIN3yS3v53nz50z5o1xB+nLIKQxIYPPNSHWy08/N/9K3mX2F4cv23fpw/9ec0acyOGQxS+DCfICi2zCy+hy3EbepgQBnCzaycolo7s9i1cZJsxZkABGU5pQpZV2JW4/62tjeb19pqPnkuY4BtMmzRKYUDW5wqMUQvZSLuXtHwyAl77QlSTtnxyD6JLFNJWDoQq1RpOuY9rwWt9eTd01tl5u8TZZV/kyUtCV/REW5sDzYYkY7D+QBj3RT09IdPIIEl9ZQKiAnfgZ2hOpNYJMgyXl+cv+g5iaQv0eJ9N5/gSEG4D0reM14FNIqT5XYjAVS417GjELg0cm6tW4Wddfn6RiBZvtJHpiv3NBF6/aW8PzAO3JSI59ioylRDMJerbF/dNBDZ2vziaNadTYTIPE8aRREGkZhmUBONz+enxuuFAJdLXjU0lXZrgXw4YQJwiBHXv9PDDdMhttwVb2/ZZTCK4RZ0JZZNTixpnI+tteQJ32GGH0S233EJPPPEE7bbbbiGsQbQ23XRT2m+//eiPf/xj7DxccMEFdPrpp1f5wP35z38m2FXjN/mkLe9qnDeA/dtTTz1FO++8MyEwSxkePgSlHTabEOBYYbvsLHmQ5Phk9ClpooCPk2wH9vk3W+G8UeZ3MPmoVGjfiOLPXgTHEZhDEjS+IZQh9/esRJRyaSEjBVCHUGeHr07lb1SDUGrfYHKIdJnEGB9+l6aRb09ZoADO9511Ft3a2xv4JcoEzvi9EKG8DBulSfuQiozUkLMqCzxp++bVRr0JVQ1702s862Gh0Bny+OOhqMRxF09RxAoCtTwHAantb81nnR0IBO3tKiK42nnj5PeBCVxUdEOpsWM/K0mU2DSUyRf+f5dlywJtoE1YOaAHpwyA+R20N9BQ4WHfM3ku28TMFTAGWkC2+JB+WHYEyUPa2uhPffuS1Hji235URwfd2NVlvtdMhmzrjwkVE8hE656Y/SXXiX25ytjIb/nlldQK0kzR9qWDSSybM4a2nnVhLK1TIJ8g8Elgnjl5cpCEXObjk75wQd2uyL+Wz2KtsleWI0QJXBbU0r3T8gTu85//PN111130/PPPGx82+fT29lJ7ezuNHDmSoI1Leq699lrjzNtdMS9A+UMOOcQcVDvssEPV62nL2xU0BYGzDkc+4ECgoB2zc+hkjkDoIE7SREH6uQFHtrHnD5ptTmnfiCaRISkoPnTeeTS3qysgIGy+KcOH46N9CRH9cvBgM60y7LSc50jS6Pjo2OGrvUknCtYolLrs7vHhZDJnBzThv9vJhjHeBZ2dIb/EqNvpRt0cJp0D+nuLI9AAQuV1edTisNc6vLhgWiyMo41nBDkIERpr3iWxgnAPkzQQDXzXZMh7JnLs9ywvtkA6oGmCdoWJhh2Ig/2mUQ9rzFz5xUCKuA3Uh/Q5IAqSYEj/NeRvQ7TtqxYsCJ23QV4wR7odThngmgtb0+iMQlnRhNlkN2rsTG5hespjA7azDzooFMmV+8PkG9/3iXvtRdvss48h0ngC01E7N17Mty/quybNI7FGINdMjDDxtGUL6QvnCuRi0rDEBJjhsUrXFKRJwgU5NJGu72LV+VHj977WvYj3lcDlgWJ8HS1P4KBdw23SkiVLaDvLyRXh5Nva2rwI3FlnnWWI2qxZs+jII480ES3nz59PX//612n58uX08MMP04477hignbZ8mqku1caICDMPB158uL6Z5KDtO3CrHaeJglUXiMFFAwcakxTbZ87W+iQRHPug57DC+JC+WjFpsMOH42MD80q+Sazyu0s4xKOie8l6vLVVNQqlUjjijyV8PR944IGQPT6wlkTWHrMrilhIG1pjP32Xk5ZTBOIQqDeh8t7HOm2RCMRpU2zTR3nBtPXeextXCvv8lsQK337brytkWWEFN+HgTtKkn83upAVCHIGEJQkn0XYJ/SBwdug1qV3EuXqO5ZcVMuVzfLvhl/bhM85Y50NnEaEoX2rX2pV4Y8JkXjf7Ag8WLFucdVbYZ8vhAwYyDK0bR0CWLhmx+yfmmxJJ+h3pEI5zyBevrFzpNO3k3LRsusqEnMm+vdZcybYl3rZPpq1Rqxp/Cb6jpZJTW/TcbHkCl4cJJTR40ORNmjSJYBopH5C4z372s3T00UfTddddZ35KWz7t2irVxohwbmbtGMbGRMZ24k01btGOrBt14N+I+DWrcrhzvdIRGmYcMprU23BClnbzCQee66aO2wlMOBxawuvb2ujoXvRw3QOhAMm5cTjbtusyShjKhg5lO3x1xRyDy/iEC85bKI0Sirbu35+mr8Ssh8eMf0kTmtD8V8Zz+6GHhm9eY8JDp1o/WlgRSIFAvQlVIaagKcbbCkXj5sw+JzmYlBx33MUTzs4zOjtDpnGca8ulMWKzetQvTQ7xbxkQioNbuL4HKAtN0259+hBI5rc+8AGnVorHUGUaSkSIanhAheiBAMFkce9zzllHzhwauN0rwcDizBLtyzxckL/00ktBvjPps2drjfCuJLAc4OPBtjZz0WrncoXlCsYFSx6ZCoi/374Buny+fdJ89sQTTyROEcDuGtIFAyFecInMAdGYsLPFCZuP8tzI6Njwk4xba/ZedPniScIXFRTMZ8xF7vtSyalFDrSBdbc8gcsjiAkSdf/oRz8iRJw86KCDQtO1evVqkxMOqQr+8pe/mN/Slk87/2XbGLatuK0dw/jYsTbtWGV5PpCkz1uUfbwkD4+NGGFuFOVH2/WBijrwcEDKyGMc4Uma0xwzY4b5KNp+erdOmECHzJkTJHZlzZ/tPO6KEmb7cXQNG1YVMct1eEcJg3kLpfKDd8oppwShqGWC0SBcNJKkWtHU1lRMUuQtNOpkAScyrHYti0jfVQQ8EMh7r3g0qUVqRCBOYOUzEVYD0LZJ6wFuVpqEh0y/x48P+dChvBTgbZ8tGQgEAU0W9vQE5/Zs63KLv4v290BCwYEzpNAvz1i0B23dorY2+nVvb6CVk+Z77BfOOVD58lKalkq/rkS/skoHbbNKEDCZG9UV6h/v2H55rnQ0XLcdHO23Y8fSs5/+tPne2maGUZE8Ey86LT8y+JqxFo3XAkw8D65Euh7omEdE7rTNSnkemcRL01jpfhAX4A1rFzkDZc45qYGLGlujz7Cyyak1Hi+lfL3lCZxPGoEbbrjBmEVGPd/4xjfo8ssvp2uuuYaOPfbYULG3336bNtlkExMgBYFS8KQtn3ZllG1j8EHhys+GsbEmLI8olJzbBAfiGVYuOL6BjMoP4zro4vwmeF5cpAK5/74ye3aImNnEjm3VpbMy3xxKMgrcXPbt6Jt0JrfTCnD/7IM66uDO9UC3NZYzZxJNnRosZdxOd1e0cBAMMPe2do1z8uEDOWSvvcy7Dz30UOBnoj5waU8GLZ8XAqoRywvJOtXjaTImz0AWoGWERvaZDgURsa0fYNrYrx8dvGpVMDiZwFuSCNtU7kd9+tDX16wJtEnIG3fSBRcEAbTQl08JvzZ8064loqWVnGxokP2p+b+ZFHCAk/FEtLsVSINJIN6RES7tS800pEIGKmG8jPxj5UYNQKpYU7hSF7jS0UhyJtMTSfLiCjTiChpmW4tU5UWzzEmRd+17FdxdpMzWoNltyguDL370oyHyxaaxL/bpkyrFUtQFRV2+9xm2cdnk1AxDKP0rLU/gkMh7++23pzfffNOZyBszhETenJx76dKlhOAm8JeDfxyeX/7yl8ZEEpEfIWDC/40fJO+++OKL6bTTTjNauizl066Ssm0Mvj2Ec7fUjnEgjysrESFrTSYpD2H+WHGEJtxCjmtroy17e00fokImgwDK20WXaaR9+1jlt8W5ACfAIn/dgw/4df37GzMQfuTBzz5zhpSI5KVJ0Tp9+mcf7JKoIqoVbpwTHb3TLkLrg3fXPvvQp++9N7hpxo3qDV1doQikCMLC2jUZBlp+/PmmkpOf1rpm0g5LyysCikATIuAZtIHPShloRF74sSAeirS4aFFIA4cLSeSRY99m2www9P2wiCX7rYVygznC66McJ/jm2ZAuAfI8t83zUN72+ZYaJakFQlk2XZSaM9v6wb7QsDVfHEYf9ckgMVGXqfJSNCoqqCRBr9x3n0mmHbrUi3DfYNNWWTbKzDBY6aIu7rNtFWNfAMsLVWf+Pq48Ik7ALhZBTNp1UUQtznIoMrBLUmM5/F42OTWHIZWuipYncED8jjvuMKaPIF5HHXWUsbNGXrjXX3+d5s6dS+PGjQsmZujQofTyyy/Tiy++aGy68SDYCfzcFixYYPLBcV3wf1u4cCFtueWW9Pjjj9O///u/ZyqfdlWUbWPIwxiH3sGVaEnswO3jn+WDgdRIgUjjcOIcKjIKok2g+N+uQBwyubUdRpjfk2aRwYcNycArJo38Mf5bnz7mdnR+xWeBQzDb+Dy+//40+s47gyHjti/kIC38vuThzLfDcQICbic5WiXwf3GDDQg2974mMT7zYMrEmJxgLvZctYouFdFaYbOPNAhMZGUUN3lzzBHVQqaX3p3SgoqAIrBeIuCjgYtLiF0B9ka90wAAIABJREFUDT7Kz2+xRZW5Gs5UTiDNgUVkNGI7jYoMAMJn+Icqfto8P4FbgSO36NlWWfkOTPXkpZcdBZO/u0xCcdZKny42ueSUAYGQv9lmROJSUvpZ2eQhzm2CCS4SpUdFTpRuCXY6GlfIeyd5cRAj23yfNZzAzyY6Nil1+TlKc80oEpv4bY0gmnEBY1x72ImBz7pv0IFQNjm1QTAU2ux6QeCAIHKmIV8b0gVAoEV43TPPPNPkOZGPi8Dh9xUrVtDs2bMN4UNKAvi+bbXVVnTAAQfQtGnTAvLGdaUtn2aWy7YxbNM4zs8SRaTSjDWqrNTGoQwOUTwuDVjcYehK1hoqb92OstM5ykhiZ/skyI+QjQ9uCCetXGm0VRxZC1rKqnx51uEs86wFAoL9EZs5k7qmTg3qtkNY54E918EfRGnCw2Rx1oQJgYkpk15JLGFKyoR5wcyZQV442T8O+JJnn7UuRUARaC0EWLCWYejZJzk0UuustINhBREa77+/ishwDk7+1sAywGmZcTyMFcMPf6v2Flo12zd45bBhocTf0/r2pSmrVwd5S40MAnPKSZPoHx0d1TnWxo8PLhRR98l77EFzFy1630TPYQaaFBXZ5WdlyIqIEBm1kjiIi53PTpZ3mbNGmc37kBeYJrp80y5dsaIqbD/GxgnHJQFmf0XGe9CSJURW9HIeQxoza5vwhjSwntvR2Z6n5tmziVyLlU1OzXVwJalsvSFwJcE7l240cmM4DxFLG8N50XiwiTdUGVBxRZ9iAhf30eCmbKfxyIAZ1gEpg7G4QuIHQ5HREy18EMhkyzVrQrnkkHahyuzT53C2SN6CffahMffeG3QDQsn0iv9ErT6IUYKJy0EbAk5UGgQIGMHNqCNyJ9rRACYZNoW+ogishwjYgTQkyQp9rwTJwfly6sCBBG0WohtGmTTyOcTCvrQmsRNyRyVLtoNQVEWDtC4DWZt3ySWXmKAZMsk2n+G2NskVvZdJZyzpikgubROpUHsXXhgmuCLZNhMf5JNb3NkZaApdMkCc/7ktZ0SRJfsSUfqmhUiy41u662mnGYsZvoC0g6VU5YqtYW+5SHwu3zjVwNUwK83/qhK4JpzDRhI4HztsNvXr27cvwQeLTQmLgNplFsmRxly5bLgPOPhP6eyMNF0M+uogX/Ijyj5dcRo41GVH+7JNDNnJPMk80nUbKD9isN9/Bo7qFti5fCysOl0keM7EidQ9ZEgoQpgZU0QaBDv/Hm7FL6okQM+bcBax/rRORUARaCwCthAvCVyc3xK+UzKfp0wtk2RuJ78jHFQk9ryKuQhEXdKPnMmTy8rE+AQ7hHbpX8xn/fk33xxKbu0K+JGYTwyds9u75x7q2nffwN8Zmq/HiGhkBRRpmunzLebxS/9034BbNnnnSKJs7soBtOLGwL5u/UXE0LxN+LmfNkl05X9Lu5t8oq/GyUJp2/Mt30g51bePzV5OCVwTzmAjN4Z9sLo0YVG+ZKWB2uGwHEdw+ICEL4E0zbHTC2B8UR9zO5KlHeSjKmlrBay4w5nxtB3KZd6cEOY551TD+O1cdpF+E1ai20BwiHAe53QLacxUSrO+tCOKgCJQHwQscsGBrFwkSJrZc1LtGTNmGJJj+2mnJRCJViYxF4GSwLjIJwMZtOHQJkn/4iQzxDi3AS9fszlzyNZeSZ+8RCwcK8Plj+ZDjLm/MrImgou4UtHEaRHhh37J4MGmZ2g37yBa0sz3sZ6eaneJGnZL3Fr1Xcc1NB/5aiPl1CLGU8Y6lcCVcVYS+tTIjWEfCK4bsNJHDnSEDMbhHXWD6ntAYtrsKJc8lba5Jfy7Jv3ud8ZEhgN3uEikzwFsa8Jwe3giEf1UJDaHhjDOnj/zNogx4fD9KLu0oTI/UhSmmfusLyoCikBrIOAIAAKTyCi/W/uMjrog8jl3JfHyIS1RF4GoxxUlkYN22UmyozRwScTDddlqf6uTXCRsk1ImTjKvng8WsYsvhVmgq79RvomhORUXiq4opHkFXrPHaV/65mFl4iuf1Fsua6Sc2hqHW/IolMAlY1S6Eo3cGFEh6338zkoDpCOCojSfsfvpe0DGETiXvwT8KsYtW0ZXywYtLZmPIOHShF3bpw8dswapstc9edrz2/j4aAnjhB0XAWUfOZ/xl2ZdaUcUAUWgvgjERZa0AjohANVnpk0LmRVGddZX8+9bzofspakrzr+YrReixpblTI367kuyltT/pN+D/vr4fsessijfRHvcrotDXKw+NmKESbsjI4nmuaiz4B/Xfhyuvt/mPMfHdTVSTi1iPGWsUwlcGWcloU8N2xiOmzE7klORB1+eUyVvQw+fMiX2wI47IF0+eFEYuPwqBr76alXIaunn5v3Rs+ZGmmjmbc+fhuDKslEfLv67TAkR5ayf5xrQuhQBRaA5EPARUqvC0c+cSTR1ajDAIMpkRFTBopHwPss9OhI6Sy3/4qTovVn6EeU6keZ7701cUmjgoqByERd73PbFYVY/8bR4euPgsQ5ii+SAYy1daJicWkunm+xdJXBNNmHobsM2Rkwkpyi7+zLC6zJXqUc/ow5un4+Nq39xJopILHrt9Okm+antu5f3WH0/SFEfukjTngZ/gPLGSetTBBSBbAj4WkEMXb36fT/kRYvIDs+fR9CIbCPI963QWWpF14yKhllLD3zP+Lg20tRRq+bIpy0uk5hKKAE4n7ZkFWkJX+Z5q1GTmbndyosNk1Nr7XgTva8Erokmi7vasI2REI2qiA9HEdPDBy7qrtleP6aDrhs/lz+X6wPg81GwyyT9uwgsUWdeH6Sqehr8ASoKL61XEVAE0iHgS+BQqzxjZa7OrBqWdD1tTOlaCU9Sr/M449P00ef7F9dnn/76+AQm4YLfa+2rTxuZyjT4ArRhcmomsJrzJSVwTThvjdwYsflgco5yWNTU1IvA+ZqduD4APh+7JMJW2g+L78Q2+APk200tpwgoAsUi4BsqHb1AVEk27WMzudFE1ExWImnRLP1Zn/Isr+d4fMhe3Hz4fKvTzmde5RvZt0bKqXnhV/Z6lMCVfYYc/WvkxggdrFGh4UuOKY+hqEhTPHzfw7PqA+L5sUvKVVTrh6nR0ygjdhVtBtrosWr7ikAZESjFGeI4D+0cZ1HYNcpcvt5zWYp5iht0SmuK0o+Hx+r5ra73euD26kmE7TE2Uk5tFN71blcJXL0Rz6G9Rm4M+0DwJSk5DDu3KurS51oOdp+PXS3154ZksRVJTSlaKtLctdiRaO2KQHMiUE8BMFJoj8h75ptepGnIQHMuEb9et+r3yudb7YdQIaXquX+VwBUyhbGVKoGrP+Y1t1gmAtfIAyITkPX6kNRysPv0sZb6MwFX/5eUwNUfc21REZAI1PN8j2zLcR7uuuuu7wcrWb5cJ60JEKjLxWm9cfD5Vte7T6K9Rl5eNFJObSDkdW1aCVxd4c6nsUZuDN/AHPmMtIBa6kV8ajzYEz92NdZfALK5V8kYvEhE21o+LI38MOU+UK1QESgpAonnUI79jkp0LU2pjR/bokXUNWwYdYiE3TL1So5d0qpyRKCelwE5djuxqnrukcTOlKhAI+XUEsFQaFeUwBUKbzGVl2ljNJ0gXUfiU8vB7vOxq6X+YlZmjrWKeVpLRBsQEfLZIcH3sk02oRkzZphgBWpWmSPmWlXhCDTVeVnHs5JEW5y3EqlQsMe///3vm//HA7/leQceSAfMm/f+XDVJ8KzCF1fJG2iqtZ8CS59vdYrqWqZomeTUlgHVGogSuCacWd0YtU1avYhPLQe7z7s+ZWpDqoFv25rSSldO6tePft6vXyDQKYFr4Bxp06kRaKo9Wy9rBaBotTVx4EC6rn9/kxJggw02oLVrcY2z7tll8GB6sLNTNXCpV5++UAQCTbWniwAgok6VU4sHWwlc8Rjn3oJujNogrdeBW0s7PreVtdRfG4J1eDtCA7cLEUGUG1/pwq3t7XT4lClB2HBXz3ywrMOItAlFoLw5o1xz0yANHHRt4zs66MG2NkPgONGyNKVGd3fr7CSNTqubqtEI6PfFPQMqpxa/MpXAFY9x7i3oxqgN0noRn6IP9qLrrw3l2t+WPnAjK9U9RkS3EZnbdzwriGgEEb06YAB95jOfMX8bM2YMTZ48OehAvea79hFrDa2OQL20/3nh6OpvXueOXY+ddPvIjg4a3tVFZxHRoMrFDUypkZB7z8GDaXFnpxmmauHzmm2tRxHIDwGVU/PDMqomJXDFY5x7C7oxaoNUBfra8KvX2zxPuIFfTEQbEtFK+MFYHTieiK4RZla2QNdsQnO98NV26oxAPTVaOQ3NdVbaf8tK6Ox6Jg4aRJd2dwc9d+11/hEmlpdVyhadzzMnKLUaRWC9QkDl1OKnWwlc8Rjn3oJujNogzSpw1Naqvp0WARbwQNDmRLwMcW8GEUEzx1q6OxGpjkOLN6HQnBanosrrPskZ2Xr6lOXUddcasIlX1gsx+2IF/57f2Rl5UcPBjKCB+8WkSXTJJZfQHj09dD8Rvb3RRvTOO+/kNGqtRhFQBGpFQOXUWhFMfl8JXDJGpSuhG6N0U6IdKgABFh5fvvdeumz+/MBskpv6AxF9zjKvwm8wqxy0ZAmZ0OKW0Hz7uHF0wC23FNDb5qwyjqRlFcybE4k69DrmMqGZyLJNvDKtEwcW5998M107fbohZW90dNCNXV1BoJJzK5c0QToRkUqAo1YGlzZ1mEptQhFQBOIRUDm1+BWiBK54jHNvQTdG7pBqhSVHAELj/p2dgT8MhLbziOj8qH5XQotf+Z3v0FdmzzaCYN6CXlqhO235oqZE9gMh2hEowuVHlEkwL6rTLVJvlDlv02CdV1LtCG2kxGHo6tUmUMkDRLQUqUQqZtJmrc6eTTRhQrAqZvTvT9MrqQZaZKnoMBSBpkZA5dTip08JXPEY596CbozcIdUKS46A9IeD1g1mU3ierPjGsXkV/iY1cHhv42XLaHTlnThTq7QEy0fodpGlon12ksbB/UY/IBRH5dOrxXcwqQ8lX26FdS9qzfispcI6laZim3jNnEldU6emD+kfoY107ZcBAwbQd7/73SAfHP59/gkn0AmXXmqCm9h7Ps1wtKwioAgUg4DKqcXgKmtVAlc8xrm3oBsjd0i1wpIjwAIu38L37duXhg4dSn1feon2XL2aEGKcfeBkagHWMH2EyJC4P0v/OGvM3AYLjPh/Gc3ShihK6I7ScLFGQfrsFEF0ksgA/47xcIj2Klxq9B1M6kPRy60IXPPocxQutZDlPPrlXYe9LmbOJJo6NXj9t2PH0rOf/nTsvuHCSWO255D/zYm9EWd2luy4JvT2nkYtqAgUjYDKqUUjTKQErniMc29BN0bukGqFJUeAhbdHHnmERo0aZfK+MTlD12WyX2lqBdIkTS8RAKGD/eNggnn++UYDhfp+NXNmyGQrKTy5jzkc+gYTRZl8mPNc3fbuu7nmBeOx3DRrlvEjiiKr3O8XKikZEN2TcYEfEvAY+fTTdMC8ee+vipTCcZJwXvRyazSBjBqfk1jWSJaLwtK159AW+6mZ9WX5on1cBBSJItE2EcM+g4aN96G8NIkLoiKj09r7uihMtF5FQBHwQ0DlVD+caimlBK4W9Br0rm6MBgGvzZYKASYJ0Gi9KNIIsFZJBkIIdVyQERb0JcGCMIigCaFolvbIY4Ru7heSDOMBmdoaPjorERh93WNCpC9ZQrvuuqshjXGaQV/Q2VyUzUqdQq3ody8RtcnK58yhD06aVEU4UwvHHoQkjYYsTVkeTlkJnHMuM0anjNJQ2ZpjF34+mNpabxAtPLiQwMMXHDJfYxBkZPny0OWEJGh88SIvWrhe+9LENY+u/ZXH/vHdZ1pOEVAEkhFQOTUZo1pLKIGrFcEGvK8bowGga5PlQkCQBAQnmbjXXvTX9nbafMUKunLBAuOT48ojBTIyZ+JE6h4yxJh5sTBoEywMNpa4RAndFnlB4mH46cAvr6+dw87Tf8hH2EZ/IeyOW7aMrrZIGR2PRAyVx+q3DM0OzeQHd989CGrCJp+phWMPQpKGYKUpy8NstAbQtVki59GD8Lrq8w3n78IvCVP0dcaMGUYrxhciuJB4sU8f8zfpxxl1cSDXkiRo/N/SVDkqmE5VPy2sOKF3kra8XIeX9kYRaH0EVE4tfo6VwBWPce4t6MbIHVKtsNkQiCIJ1t+ZxLFWbW4lop0R+B5/nLqGDTNkDwQLDwdFCOCIMh2MErrtfglcL2lvpxN7ekwickMOLf+hqBQHPr52TEbjzEVNV5YupZXDhoWSoUMTN66jg2DSKYnPS337RkaoRFW+hAQ5u/7R0WHMVNk8LolAyOVok7FEQpuREPlugcT2IyqKG3NawikJlq0Jk4Qb5UImj5X8iEn4MykbTxSK/DqWiOBParex08MP0yG33RaMHGv5jLvuCrTLci25Lgbk+I+YOrXKrDloz5EW5LERI0Jry3cetZwioAgUh4DKqcVhyzUrgSse49xb0I2RO6RaYbMhECWkW38/sqODPtTVFYQil0E7vrfffiE/L4Qi7165MhBYozRwsb5mon07MuZnBg+m5Z2d7wdT8cxl5eNrJ8lokgno2A03pHldXSHzyYkDB9KlTz8dEFpOlmwTL7lMXCTAhY2LCHoTFsc8s2YnMtiMhwYwKwnD+JPIT9RWinvPl6Rz3UywgsA81lq6btIkOumCCwwh5+TYcj1LM8TDp0ypIkDyPTkeNrut2huWRvwoK48b70M7v5vxR8WFRuUiBfWyVs02a3aVlf6s3M9a5rbZjkHtryJQVgRUTi1+ZpTAFY9x7i3oxsgdUq2wCRGIIgEuLRL8bbZduzZIOwBBEULlDV1dxEE8WHCMjMxYwcjlG/TOO+8ECKL9Uzo76TiBKcjhRQMHRvoPcWTKqmAOMdqkkNBv5cWC0eQ8aBlFv2TwiFHvvUd3VEw6g/x4F14Yyq1FCYFL4kzz7EAyIT+/NBoyBxljP71QXjAxTmgZJSFwCflZSVgtBC6ONEWRjriolUzMMH+P778/jb7zzmDFGUK+YgXNGDAg5Htp5nT06AAfvCsDj3AFVe8RkbyQMOWs9cG5GvFTv7596bzVq4P+sCa8yqwZdeCxcrq9snJlld8ot5dE/muZ2yY8BrXLikApEVA5tfhpUQJXPMa5t6AbI3dItcImRCBKUJN/x7AQdAHamhv32y9k5gUh9/bu7kAjxpqiSGJQwShJe2H7BDFBgmkY+xWx2ZutSanSVkVok6pM42KiAfLUMi7AAs+Q994zY4dv0zEzZtDk8eMTiY9cJi5B2ia3UosCHIxmaMcd/YliTOJopI5A0Iyg/5MRWH7dEyXk2xEQfVNGxI3bK0iIpaFykSbXFmQ87dyBNsHCBcGklSuNOXAQ5fTJJ2nFsGGhXGnXTppEm//f/4X2Acj+9f3709lnn73OxHXp0tB73UT0eyI6THQQROxK4UvKmEttH/t/2qRNmjXbWjVpyiz/W2r8kgha0u9NeNRplxWBpkNA5dTip0wJXPEY596CbozcIdUKmxABHwIX0mhZBIXNumztF+eZcgZG8NTuIHk4/IfwIC+dIUiTJ1f5jUlTNRCcke3t9GxPTxDh78rvfIe+Mnt2KFEyQv3Dr+mxnp5AewhBmCNazu/Th7bee28aM2ZMKB+X7Wdkk8k44lO1PCJwsMmtnY4gylRz7qJFpgm7z1V9EkSVNUKBBrHi34V3ktYGa1kfkD6RUosXtR+sccO/77TLLqtKhl7VvkXE4zSk0ldQRlrlJPRYE8fMnh0iZru3t9NWa9fSrb29xr/RpZWb1rcv/bCtzRB3GakUEVdvFuH/yerrFCK6iSh4BwTxACK6DxpcQfwmDhpEl3aD7q174G/6OBE9I8wp0S+XH11UMCEQU2jjbJ87V8oBbjdJQ9eER512WRFoOgRUTi1+ypTAFY9x7i3oxsgdUq2wCRGIyzN19913OwkBC3fSF4eJUxTJCkHj4V8lSZeLXMj6bj/00JAfHnRIb1a0SvBNAplkTRmEWNbi/ed771VFm2TTQtRfRT5jfMlk2TjtRQjvzTar1qJZpnlG23bSSSFzvb0qidRdZq7Ofltk7Pp99gnnp6uAeVK/fjT03HMTg6S4tKO+2jDTlDX/UosriXNVVEULf1eairiIiyBNh7S10d7nnEN/P+ecKqKEdfM5dE8sLqmVY1NJ/AytKye+P6sSuCe0Ti1t4S4Vksuk107bwetHrnubXKNdmNGy1lQms5eE2/Z7Sx1l0uOCpQmPOu2yItB0CKicWvyUKYErHuPcW9CNkTukWmGLIZCkgTmxXz+6ctWqYNQujYgTEh8B0SHkwx/Jpz42O2OBG/+GYM4CL+qASSiEcPZhk0m4XREHXcQD/kTnv/lmVfJk70AbIoInBzyxTfPYD0v6RnFuPWkaCJNVaFR8CBwiGLKZHpMEvPlFIlosfP6izBqBz3d6ekL+idcQ0SUVYpm4DRxBctiPUvqTRUVajIsSmqS1g+nhqMGDacs1a2huV5fRtEmiBM0W4kDCp5MJGRMnrB88rHnD7+chkqhF+KZX5kFqUqHNHTVqlLlM4HkCmWOTSpkv0eX/iYiUR997L0ErLdsHwV+2ySamzkceecS0Ae3jr2bODKJXyoiUMsF35Dx5XLAkzrEWUAQUgZoRUDm1ZggTK1AClwhR+QroxijfnGiPyoGA7eMUlRgYAuhiIeyyFqYqiIg1LFdY9iqyYGkwOCIgV4XyUkMIgRWC/e6WnxFM0GCqhtQGLJDDR26XZcsC8hb4O737rvFdigze4UE87X7ZArNNMGwtmhTQZb9ckRBhBirzjLnIgMSLhfxX7ruPDu7pMT/9raJxApEJ4RCxFGU/mPjYefBoO6yM+EeO+0s9PaEgISCD86wIjJxGAQTIpTnlPH1Vpn+OlA9Sq2YnYkfb1xLRpyrdB7Fin0qE/v++tb6gtTsT6Swq5XHFMAhRIbfbzmmCyvO/U3s7Pd7TEzLhlO/JdSAvF+xUA7hEYK2xrxY4aW58Atgk1qEFFAFFoGYEVE6tGcLECpTAJUJUvgK6Mco3J9qjciDAQmZUJEkZ5p6JwGOVYBgwUUzKfcb1Y7Ts/+NKQhznhyPrgODq0ipVRfwjoguHD6c7nnuOftfbW50CABq+BO1Dkm9QUij7m2bNoj16eow/kgzIAhw+snat+Q1aIpjxBbnuliyhiSNGhEz+OJogmzM+FUEi7BVl+wt+j4hmiUImOEaFgLhWo+2j9SBC1ouCvx07lp799KcDM0xJzFGM/a54vmHu91BnZ0BkeM7soB3QRF7W3U2uSKjwXQSZHfjqqzTh0kurfB3vO+uswK8NJPWKvn3pW47ojpKIshaXQ/IjdYXEGGMBWftyWxud0NsbCk7CGLjWNONh+zUaCCsRKWP3n3WJMGfiRPrJT34SrKlFnjnqkk6apHWe9L7+rggoArUjoHJq7Rgm1aAELgmhEv6uG6OEk6JdKgUCbK7Hvj3OXG6WICkFXg5sIoMmyIFJ8oW/MwELhclPyBVmB6aw/bpg2NnPQpPTHtzc1UXrYkiue0AWrqnk/ErSPiRF54sSfG2/MZmrC32AdodN40AyZP8g2F/5v/9bFYgFWh6XuZ0kA/aCsiMvIrjGdEd7rNGy30f+OzY9hEbzRCL6qUilYPvCuaKZYr5ZS+skMmJemMSyHxdHvGQizNEzQZbGLVsW8l9js0OXuaw0sz20rY2O7O0NmYTKcaOe3t7eUNRJ/I6k8sf39BjCKM0wvTTRVpRKqblL0oDzGkPwGLQrg6lwuoda87glrfNSHFTaCUWgxRFQObX4CVYCVzzGubegGyN3SLXCVkBAELPQcCragVgNgkPwduUPYwH0hQpxcSYmdpigcdsf7uoKyIwM975y2DCjtZLEjAkAB7ywzfVgQjeuo4M+M21aYJL56vz5VRoNrjNWsI0xsXQRLdYqQdN5BlGIQFSFia+QNZBckBYEZ5E42AnPr7LC05v+O0jD1/fai1595JFQ5MWT99iDhh98cCj6Jr/P5qXAHUFPLl+1Kgi7P6FC5CRxl3i5fNokYZbrDeM/rJJAHvXhkQTfpYGVvnG2/9pmlq8aR3dkE0lpsugMvQ+Nm5VO4IL+/atMP38hNNGsDYs6Fmy/RttXLckHFdrI49aurQrEE0W+0xxPtRLANG1pWUVAEXAjoHJq8StDCVzxGOfegm6M3CHVClsBAduEkIhc+aNkpDsp8DqTDB+P8CaVR5CcKHM5NiWzhUgWaL+BBMsi1Dqb/Y39+MdD2iFoVrbs7TXBS0AWbXM9vPeFSuAO9A6aGjxROeyS/NsizS8dY2Zt4PCuLmJNpzThc2kx7fxwNg5MSvD/SyuazZA/ohX1Er5gCLjB2kEEdWGsnOkfrLVxfVsbHd0LCrzukaSTfdZYUxZF0vGerfHlaJF/6tvXmFzaURXZZJKDzdh1c0j//u3ttLCiIQPerHWzozsycZLmrTZhlP3EfyNlwL8NHkwPdnYaAsuh/TkAilNrbZ0PNgnlNcjYR5mfohxwQX9hwhyrKW+FM0nHoAispwionFr8xCuBKx7j3FvQjZE7pFphKyCQEKo9SqPCAm+coG7gcRBEW/h3ae1QhtuGQP9IZ2dI28bBHKBJARFhXzz+N4jJARbxQwCKiyo5uDhqnwzvHhW8xUlu0MEoDZw1ZgTKuLGtjeb19gbBL3jpcDRHlx+hbVpnJ/iG6R6IqiSgkhTMmjAh0FzKkPeol4OhwJST8avSIFmBZW6dMIEOmTMn0MBJs09o5+5atSow77OJPcwSD7jlFjNsnlduW84BfAPnHXhgOO0BtMEi3YJtcsppJGytG3K4bbd6dUjTyVE+ZT8wvzaZkr/jv9l3k9cNtKKz9t8/bGZZ0VpHHQsS9ygzYn43Lj0CyKIrpUIrHEc6BkVgfUZA5dTiZ18JXPEY596CbozcIdUKWwQB6WOKZqUCAAAgAElEQVTDmpx3KgmaXbnHbIHXaSrH2MRooz7U1RVKNmzDKduWYeBllD6ZWFsGNgFhkYQjlLdOJCdn7c8eU6eacOwyimRSYAdXdE2MQeb24nD9MJucYw1QhtB3EQguLvuBv8G0kJNpy2AotukhSKEktJzUmsmJjMwJTBEgo3vIkFgMonICgkyCCMqcakzi7Lx+NjFFf0IBdETi8UCzdf/9oRx6XLetDV5DRAMrWmT40SEYiR2G/6QLLjDQRmm8eA3ItA1nn322wSVErKy0EFEXEXLaXWbBtvbOJnpmL3okNW+R40iHoQistwionFr81CuBKx7j3FvQjZE7pFphiyDAQikHjAiIjKVhkgmCJeGIIx/4jYV+TkjsE7nS1m6xMG5ri6QQvvPChVWam11PO80QHg5+YYRzSxh2RmL0SCFgm8Qx6cXfJTkCgYF55y29vSbfGGtQokwfgRlHb5wsyKYMhMJzJf3ibF8uNsuEhuvvFc0jkxOQzONmzw5pNV15/WxNkFwr265ZY3wHObjGeKKQeR/ah8konlvb24mTvvO2YRPVzVesoCsXLAiiSTojLVrawIMrvmdbW35pmEsEaYGvm8tUMslPzUW2JLGPSwuRpu646KfSxDXwL7TGnyqReoucUzoMRaDVEVA5tfgZVgJXPMa5t6AbI3dItcIWQSAyUIdFdGAG99iIEUZTJUmGK3y6hMb25YoyWQvB6UjsjbDyeNKYNH5w992Nr1voHUeuMNsPrypaosM8Lo7A2RES4X/WvRL0goiJWxVhrgAg58OOtsmBUILxRJiooqooDZhpxkFimfhIImL7Jbq0Z3sT0R9EGgQ270MznECcNaAIxmJrOl3apWs22IDWrl0bmjcZBIQxdJnX2kTUN8KiTyCPKD/NyDUZdUYkBMBh3KR2TiYKd+LYIueRDkMRWF8RUDm1+JlXAlc8xrm3oBsjd0i1whZBIFJw9dBCAYIkwddF4JKSf9saOATJOO2yy4xmKk5Yts0eo4R3GR7fFbQFiZdlQIy46Joh7Z4jSIfUjIHI7BIVdATaQaGxZJNIKcxzsBCXljQuuIz0QTONiLkF0ZOmhz6mgDKyKMib9IdjAvVfK1eGgs8ghcH5LgLu8MOExs4mKXZOO9ZC7dnbGwpmE5fagLWkrq3rS/RclxOpCZw1z5I026kf7HxxWdpqkaNKh6EItDQCKqcWP71K4IrHOPcWdGPkDqlWuB4gkOQH5gMBE7xHHnmERo0aVa2BiajEbjuJKKIaWwiPesdppiaEapgd4vEJ8BHyYXKQkYGWmR8Cfnz4jDMMDlXaS4fZKvy4Ivsh+gwiiQdmjYnBZcR7dvAPJgyRcyv6aAcUQYzKg9raaO9zzqGN3nqLjpk9O0jaDYI5goikLx63YUemBNG9jnP1VQrZScVlAm329eOUC1LL57NuXGsnzdqu0ip6vOwkjB754rK05dEdLaIIKAINRkDl1OInQAlc8Rjn3oJujNwh1QrXAwSyaCXygiVL277vOM0fHX5GiPLoE2I/ID2OYBPQpMnw85KYVBFk632YXr6ycqUJ9+8iPpJ44L9lRMrY4DJWNEhXcujIeXT5EIrk1pLQ2tokjrpp+4thPmyzUxkxEn2RwWFkG86gHxkWYR6XFWmada5Vx/wj9YM+ioAi0PoIqJxa/BwrgSse49xb0I2RO6Ra4XqAgK/2oggosrTt+44zx5sj/P95UcQpyrzU+jtHduTw8zLptTMNAYKcDBtmAnrYppd7DR5MrkAZksBEpRRwmQ7KKIsfWbs2Mpl51dxaY0SwknFdXaFw/UxopdmjzHtnm2myRlRGjHSNN4pk+RL3yHXqaS6c5zp3rlXrEiFqzvPsh9alCCgC5UBA5dTi50EJXPEY596CbozcIdUKFYHWQsCR8sBlyseDjiITrr/7aFsk6UHkTDvCYpUfm0Cf25zfpw9tvffeNGbMmGrzTGu2JIFICkRjT7SdXgImjC4tHhOzaUROgsf1cl84WTX+jkiSLk1dVVAaDz/MxIVqB4NJyOmWWF8NBWSwEjtyZw3V6quKgCJQcgRUTi1+gtYbAvfggw+ahK+PPfYYrVmzhj7xiU/QtGnTjHDg8yCK2E033USXXXYZPffcc9TV1UXbbLMNjR07lr797W/TZpvB+6L6+eMf/0gXX3wxPfroo/Tuu+/S5ptvTp/61KfoggsuoK222sqn6aoyujEywaYvKQLrFQIIbjKvq4va5KgjhPkorY/r70naFlcgFZm42zYZDNIMIPBJhNbGVxuJoabVYMnyHJAG5Av+d1LLyNrBIe+95yR4LmLoisDI5dL203vxNkADF9W3wsboDYYWVAQUgUYgoHJq8aivFwTuzjvvpAMPPJA23HBDOuqoowgJY3/1q1/R66+/TnPnzqVx48YlIj1x4kRD3rbeemtD2gYOHEgLFiyghx56yPxt0aJFtPHGG4fqAUH83ve+R1tuuaVpHx+zV199le6//366/vrriRd4YuNWAd0YaRHT8orA+oeAHSjDmSOuAkuUoJ2GOCVp61x+bFXt5uA3labPUYQvidCGknUvX+5cXDb+dkCVtP1Ms4Lr7QMX1bcix5gGDy2rCCgC9UVA5dTi8W55AtfT00Mf/ehH6Y033qCFCxfS8OHDDaqvvfYa7bLLLsZR/oUXXqBBgwZFoo2yW2yxBQ0bNswQtY4OeHWse04++WS68sor6cILL6RTTz01+DuI4fjx4+nQQw81ZA2kUT6rVq2ifv36ZZph3RiZYNOXFIH1CgHps4XQEeM7Oui2d2FIWf3kIWgnaetcpo1V78RELixq8lxjj4o2aueOiwuDHxWopKhxyHpV81UPlLUNRUARiEJA5dTi10bLE7jbb7/daL8mTJhAV111VQjR8847j84880y68cYb6YgjjohEGyHD99hjDzr++ONpDkyQxHPbbbfRwQcfTGeccQahPjwwt9xxxx3pH//4B/31r3+lf/u3f8t1JnVj5AqnVqYItCQCIBvXTp9uTAHjIj/mNfgk0uAiSi5NUVTusLz6maaeWjWTjdKE5UHI0+CkZRUBRUARkAionFr8emh5Ajd58mTjAA+TycMPPzyEKPzSkMsJWrQrrrgiEu233nqLhgwZYnzeFi9eHNLWsQbunnvuoX322cfUgTK77rqr0b6h3TvuuIOefvppQ+RQBuSulkc3Ri3o6buKwPqDgIzOePbZZxPOw6Ke1KQhwldLag5DeemK6nhMvUmkNKlLtb6fVL/+rggoAopAGRFQObX4WWl5AnfYYYfRLbfcQk888QTttttuIURBzDbddFPab7/9CMFG4h4EHTn99NOrfOD+/Oc/EwQj/MbP1VdfbTR+J5xwAj355JMmcAo/MNk85ZRTjMkl/jvLoxsjC2r6jiKw/iGQmlTVE6KIaIlScxhKVVDPvlXaqlWDVmr8G4CnNqkIKALrBwIqpxY/zy1P4D7/+c/TXXfdRc8//7zxYZNPb28vtbe308iRI02UyKTn2muvpZNOOom6u7uDoocccojR8O2www7B3/DRnjJlCvXt29e0CdNNaOSghQOpe+aZZ0xAlK9//euxTUYFOXnqqado5513JkTW1EcRUAQUgaZEICZaYik0VyWK5tiU86udVgQUgfUWASVwxU99yxM4aNfuvvtuWrJkCW23HWKHvf8gkEhbW5sXgTvrrLMMUZs1axYdeeSRJqLl/PnzDQlbvnw5Pfzww4Fp5MyZM41PHDRsCHry8Y9/PGj02Wefpf/4j/+goUOH0tKlS5XAFb/GtQVFQBEoKQKFJbPOY7wlyqeWx3C0DkVAEVAE6oWAErjikW55ApeHCSU0eNDkTZo0yeRvkw9I3Gc/+1k6+uij6brrrjM/XX755fSNb3zDmFu+/PLLVbOIqJjQCL7zzjuEKGZpH90YaRHT8oqAIlBGBGoNElLomFQDVyi8WrkioAi0LgIqpxY/ty1P4PIIYoJE3T/60Y8IEScPOuig0KysXr3a5IQDKfvLX/5ifmPCN2LECIK5o/188pOfpMcff9zkhENi77SPboy0iGl5RUARKCMCpTCVjAGmVh+4MmKufVIEFAFFoGgEVE4tGmGilidwPmkEbrjhBmMWGfVAmwat2jXXXEPHHntsqNjbb79Nm2yyiQmQgkApeN59913abLPNjA8cAqXIHHDwu/v3f/93WrlyJf3rX//KlAtON0bxG0NbUAQUgeIRKHuQj7ITzOJnSFtQBBQBRSA9Aiqnpscs7RstT+CQyHv77benN99805nIG4AhkTcn54ZfGkgW/OXgH4fnl7/8pTGRROCQhx56yPi/8YPk3RdffDGddtppRkvHD6JQIhrl9OnTTZRKfuBDN3XqVDrqqKNMvVke3RhZUNN3FAFFQBFIh0DZCWa60WhpRUARUATqg4DKqcXj3PIEDhAiDxtMH0G8QJygEUN+ttdff53mzp1L48aNC5BGcBH4rb344osm0AgeBDuBn9uCBQtMPjiuC/5vCxcupC233NKYREKzxg8II5J/gxDuu+++tMsuu5golEhXsMUWW5iol/j/LI9ujCyo6TuKgCKgCCgCioAioAgoAkUjoHJq0QivByaUDCFC7kMTBuK0du1a+sQnPkFnnnkmjRkzJoSyi8ChwIoVK2j27NmG8CEACXzfttpqKzrggANo2rRpIfLGFf7zn/80Grjf/va39MYbbxizSpA/9COL7xvXqxuj+I2hLSgCioAioAgoAoqAIqAIpEdA5dT0mKV9Y73QwKUFpezldWOUfYa0f4qAIqAIKAKKgCKgCKyfCKicWvy8K4ErHuPcW9CNkTukWqEioAgoAoqAIqAIKAKKQA4IqJyaA4gJVSiBKx7j3FvQjZE7pFqhIqAIKAKKgCKgCCgCikAOCKicmgOISuCKB7HeLejGqDfi2p4ioAgoAoqAIqAIKAKKgA8CKqf6oFRbGdXA1YZfQ97WjdEQ2LVRRUARUAQUAUVAEVAEFIEEBFROLX6JKIErHuPcW9CNkTukWqEioAgoAoqAIqAIKAKKQA4IqJyaA4gJVSiBKx7j3FvQjZE7pFqhIqAIKAKKgCKgCCgCikAOCKicmgOISuCKB7HeLejGqDfi2p4ioAgoAoqAIqAIKAKKgA8CKqf6oFRbGdXA1YZfQ97WjdEQ2LVRRUARUAQUAUVAEVAEFIEEBFROLX6JKIErHuPcW9CNkTukWqEioAgoAoqAIqAIKAKKQA4IqJyaA4gJVSiBKx7j3FvQjZE7pFqhIqAIKAKKgCKgCCgCikAOCKicmgOISuCKB7HeLejGqDfi2p4ioAgoAoqAIqAIKAKKgA8CKqf6oFRbGdXA1YZfQ97WjdEQ2LVRRUARUAQUAUVAEVAEFIEEBFROLX6JKIErHuPcW9CNkTukWqEioAgoAoqAIqAIKAKKQA4IqJyaA4gJVSiBKx7j3FvQjZE7pFqhIqAIKAKKgCKgCCgCikAOCKicmgOISuCKB7HeLejGqDfi2p4ioAgoAoqAIqAIKAKKgA8CKqf6oFRbGdXA1YZfQ97WjdEQ2LVRRUARUAQUAUVAEVAEFIEEBFROLX6JKIErHuPcW9CNkTukWqEioAgoAoqAIqAIKAKKQA4IqJyaA4gJVSiBKx7j3FvQjZE7pFqhIqAIKAKKgCKgCCgCikAOCKicmgOISuCKB7HeLWy++ebU1dVFO++8c72b1vYUAUVAEVAEFAFFQBFQBBSBSASeeuop6ujooNdee01RKggB1cAVBGyR1Q4fPpzefvtt2m677YpsJre6sZHxKOFMD6lilx4zvKG4KW7ZEMj2lq63bLjpXlXcsiOQ7U3dq/XBbenSpbTxxhvTM888k61BfSsRASVwiRBpgVoRUFV6dgQVu2zYKW6KWzYEsr2l6y0bbnhLscuGneKmuGVDINtbut6y4VbkW0rgikRX6zYI6MbPvhAUu2zYKW6KWzYEsr2l6y0bbvp9UNyyI5DtTd2rils2BMr3lhK48s1Jy/VID8zsU6rYZcNOcVPcsiGQ7S1db9lwUwKnuGVHINubulcVt2wIlO8tJXDlm5OW65EemNmnVLHLhp3iprhlQyDbW7resuGmBE5xy45Atjd1rypu2RAo31tK4Mo3Jy3XIz0ws0+pYpcNO8VNccuGQLa3dL1lw00JnOKWHYFsb+peVdyyIVC+t5TAlW9OWq5HemBmn1LFLht2ipvilg2BbG/pesuGmxI4xS07Atne1L2quGVDoHxvKYEr35xojxQBRUARUAQUAUVAEVAEFAFFQBFwIqAETheGIqAIKAKKgCKgCCgCioAioAgoAk2CgBK4Jpko7aYioAgoAoqAIqAIKAKKgCKgCCgCSuB0DSgCioAioAgoAoqAIqAIKAKKgCLQJAgogWuSidJuKgKKgCKgCCgCioAioAgoAoqAIqAETteAIqAIKAKKgCKgCCgCioAioAgoAk2CgBK4Jpko7aYioAgoAoqAIqAIKAKKgCKgCCgCSuB0DSgCioAioAgoAoqAIqAIKAKKgCLQJAgogWuSiWpEN6+77jqaP38+Pf744/SXv/yFenp66Ne//jV96UtfStWdZ555hs444wz605/+RCtXrqThw4fTt7/9bTriiCOc9fz1r3+lqVOn0p133kmdnZ20/fbb00knnUQnn3wybbDBBqnablThRmA3evRoeuCBB5xDPvroowl9KvuTB2633XYb/fGPf6QnnniCFi9eTN3d3XThhRfSqaeeGjn8Zl9zjcBN1xvRW2+9RXPnziWsOZxzr776Kn3gAx+gPfbYg04//XT6zGc+o2dcxK7Lgp2uOaK1a9eatfXoo4/SkiVLzBrcZJNNaIcddqCvf/3rdOihhzq/k+v7GZcFN11v7s37gx/8gL773e+aHxctWkS77LJLVcG3336bzjrrLPrNb35Db775Jm299db01a9+1bzX1tZWdlGkKfqnBK4ppqkxnRw6dCi9/PLLtNlmm9GAAQMIH4C0BA4C9F577UWrVq2iI488kjbddFOaN28evfDCC06hGm186lOfotdff50OO+wwQh9A5J588kn65je/SRdddFFjwEjZaiOw44/N9OnTq3q7884707hx41KOov7F88CNcYAgDcEmaq3x6FphzTUCN11vRFdeeaW5WNpyyy1p3333pc0335yWLl1qzsnVq1fTtddeS//1X/8V2kitsN4woFrXXBbsdM2R+ZZuuOGGNHLkSNpxxx3NN/Wf//ynuUT4xz/+QSeeeKJZl/JphTVX63rLgpuut2oZ4P/+7/8MYevbty91dXU5Cdzy5cvNJdb//M//0AEHHEAjRoyghx56iB588EGjAMD5qE/tCCiBqx3Dlq3h7rvvpo9+9KPm5uTss8+mGTNmpCZw2MS4KYRGZMyYMQYraNVA0l588UV6/vnnjfDDz1FHHUU33ngjXX311fS1r33N/Lm3t5f2339/uu+++2jhwoW0++67lx7zRmDHHxvcNDbrkwdu+Eh8+MMfpu22245+/vOf03HHHRergWuFNdcI3HS9Ed17771Gw/vFL36R+vTpE2y7hx9+mIDPoEGDjFCNCzA948KnUhbsdM2tw/C9994LrSn87d133zXfVQjNzz33nLFcaaU1l8cZlxY3XW/hPbtmzRrac889jUyGywNYfrg0cFOmTKHzzz+fzjzzTDrnnHNMJZBLoIH7xS9+YawWoCnWpzYElMDVht9683YWAocPCcwlQdzuuuuuEFbY+F/5ylfMJmdV/L/+9S+j7cNNGz5A8oFQDk0eTCl//OMfNxXu9cAOgLTCx0ZObBbc7IXxs5/9LJbAteKaqwduut6SjyBcOuHiSl46teJ6AxJ5rDmJqAs7XXPJa+5b3/qWuay64447zKUnnlZcc3mvNxduut6q19vs2bONewvcE374wx+aC1KbwIHkbbHFFrRixQp67bXXzCUWP9AEb7PNNmZt/uEPf0he0FoiFgElcLpAvBDIcmCyicz3v/99Y7MvH5hIQkuCm+vbb7/d/ISPDv4Nk6QrrrgiVB7mSDCJgzYQxLCZnnpgJz82N9xwg9FuspkNbmWb8cmCW1oC14prrh646XpL3lEHHXQQ/f73vw8JOK243oogcC7sdM3Frzn4l48aNYqefvppYzaOb2WrflfzOOMYzSjcdL2F1xsu1WE6OWnSJKNVO/bYY50E7n//939pp512Csl2sib8BiIHS6xmiWmQfNo3poQSuMbg3nStZjkwsdFxS3PLLbc4/a8GDx5sfEZY23bZZZfRxIkTzTu4EbMf2FHD/hrBVJrpqQd28mNjY4NACjBLlaaqzYBfFtzSErhWXHP1wE3XW/wO+vvf/25M2HDp9Le//c34i+BpxfWWN4GLwk7XXHjN4Ts4c+ZMY5qGIBG4CIXPOv4GEzZ+WnHN1XLG+eKm6+399QatGiygli1bZi6k2tvbIwnc7373Oxo7dqyR5S655JKqgxK/oQz2+ZAhQ5pBFCltH5XAlXZqytWxLAfmf//3f9NVV11lzCfZ/02OCmp2OBZDG4cHHx5Eq8Q7EyZMqAIARAS+JVDNDxw4sFwAxfSmHtiheZjOIBIZbsk22mgjQ3aBKezNQX7//Oc/U79+/Voat7QErhXXXJb1lhY3XW/R2whn2he+8AW655576JprrjGCDj+tuN7yJHBx2OmaC685+LzhEpQfRPabNWuWifAsn1Zcc7Wccb646Xp7fxX96Ec/ou985zsmEAm0vHiiNHC//OUvCVGvIcudd955VQclfkMZWFJBG6dPdgSUwGXHbr16M8uBecIJJ9CcOXMIzseI0GY/NoH73ve+R9OmTTPvHH/88es1gUuLXdxiPPDAA83t7E033UTjx49vmnWbZc2lJSKtuObqgZuuNzcC0IZg7yIIE4Ln/PSnPw0VbMX1lheBS8JO15wbAWhHoOWFlQXCth9yyCEEM3oOqtOKay6PMy4JN11v6xBAoLmPf/zjhAt5GQU8isBdf/31JvIuZLlzzz1XCVyBEpcSuALBbaWqsxyYakK5bgXUA7u4tYa0DYj4dNpppxFu0prlyYJbWgKn5kXu1ZAU/EXXmxsBmA1hTR1++OHmlplNJ7l0K663rGecjWASdrrmkk9uuB/gu4uLA1wg4GnFNZfHt0Gi6cJN19s6BBAcDaa5yAXc0dERwBJF4NSEMnmf5lVCCVxeSLZ4PVkOTA1ikp3ApcUubvkhGftnP/tZZ36gMi/bLGsuLYFrxaAS9cBN11s1ArggwQ31l7/8ZaPtdpkrt+J6y4PA+WCnay75tH7qqaeMtuSYY44xASbwtOKay+OMk2i6cNP1tg4BuGMgkmnSgzRPIHsaxCQJqfx+VwKXH5YtXVOWA9MnjQBs9idPnmyw8wl37EpSWnbg64FdHAbwjUNQGNu5vRVxS0vgWnHNZVlvaXHT9RZGAP4hCLGN6InQeMMfyfW04nqrlcD5YqdrLvnEZrKGHKow423V72oeZ5xE04Wbrrd1CJxyyikm7oD9/OlPfzLmlbiw2njjjY2PHHzwYZqK4CTIj4kcmDJeAacR+PznP28uFvSpDQElcLXht968nXRgLl261CR3RPJkKbzklcgbQQGQ+PWxxx6jT37yk02Fez2ww8GIkLx2pEmQaESPguCI/0Zi9mZ5suImx+djChiVyLtZ11w9cNP19j5BQ14kXERhvfzmN7+h/v37x26xVltvPgQu6vuQBjtdc+vWHDQcEJg/9KEPhdbZO++8Y4KFIViV7e/camsuyxmXFjddb+5LKF50USaU+N2VyBt/h2YYibxvvvlmOuyww5pFFCltP5XAlXZqGt8xBBNBAm08ixcvpieffJL23nvvIL8MIkXuueee5nck34adNPKP4b/5wXsogzxuRx55JG266ab061//mvBBh2bo1FNPDQ0Uh+bIkSPpjTfeMBt82223NTc1aPub3/xmyIm28QhF96De2EFwBF4wYUD4ckQnw+0Y7NGBPWz8YaZU9icP3IAF/odnyZIlJnIWSP/HPvYx87cvfelL5n/8tMKaqzduut7WnXGIMgltB0gbzrIBAwZUbTGsNUSGbaX1hrHUuubSYqdrbt2ag5nud7/7Xdpnn33M9xFnPc4wnPXIrXXEEUeYICYyx5aecelx0/X2vhznkhviCNzy5ctNtMpnn32WEEQNUbAhS+J/OA8hA+pTOwJK4GrHsGVr4A0aNUAZIjuKwOFdOL8iItEDDzxASJo5fPhwE+oYhM71vPLKK4Sb2TvvvNN8kIYNG2aSe/+///f/mibxY72xA1lDtLGFCxea/CpdXV2GLEMDCuL7uc99rinWaR648e1s1ICnT59uAsvIp9nXXL1x0/W2TrhJWmtM8mQqAfyt2dcbxlDrmkuLna65dWsOibqvuOIKIwwj+iTC4n/wgx+k3Xbbjb761a+a76orQXKzr7la11ta3HS9ZSdwWKdvv/02nXnmmeYy9Z///CdttdVW5sw4/fTTTR45fWpHQAlc7RhqDYqAIqAIKAKKgCKgCCgCioAioAjUBQElcHWBWRtRBBQBRUARUAQUAUVAEVAEFAFFoHYElMDVjqHWoAgoAoqAIqAIKAKKgCKgCCgCikBdEFACVxeYtRFFQBFQBBQBRUARUAQUAUVAEVAEakdACVztGGoNioAioAgoAoqAIqAIKAKKgCKgCNQFASVwdYFZG1EEFAFFQBFQBBQBRUARUAQUAUWgdgSUwNWOodagCCgCioAioAgoAoqAIqAIKAKKQF0QUAJXF5i1EUVAEVAEFAFFQBFQBBQBRUARUARqR0AJXO0Yag2KgCKgCCgCioAioAgoAoqAIqAI1AUBJXB1gVkbUQQUAUVAEVAEFAFFQBFQBBQBRaB2BJTA1Y6h1qAIKAKKgCKgCCgCioAioAgoAopAXRBQAlcXmLURRUARUAQUAUVAEVAEFAFFQBFQBGpHQAlc7RhqDYqAIqAItDaVtxIAAAsnSURBVAwCL730Em277bZmPNtvvz0999xzzrHdcMMN9J//+Z/mt69+9av0s5/9LCh37LHH0s9//nNatGgR7bLLLubvsl4u2NHRQRtttBF97GMfo9GjR5t6tthii1JgucEGG0T246qrrqIJEyYEv2Psxx13XGy/v/a1r9HVV18dKvP73/+efvjDH9ITTzxBvb29tOOOO9LJJ59s6rbbHzp0KL388suxbbzyyiu01VZbBWXefvttOvfcc+k3v/kNvfrqqwbr/fbbj8477zxCffooAoqAIqAINCcCSuCac96014qAIqAIFIIAE61+/frRqlWraP78+bTnnntWtbX//vvTvffea8qkIXA77LADHXnkkaa+7u5uev311+nhhx+m559/ngYMGEA/+MEPaOLEiYWMLU2lIFDbbLMNgYzaz8EHH0y77bZb8OfFixcbkuR6QO5AvG688UY64ogjgiIXXnghfetb36IPfvCD9OUvf5lAZu+44w6DwymnnEIXX3xxqLqLLrqIli1bVtUE5gtkGeTv2WefDX7/5z//SaNGjaKlS5fSXnvtRSNHjjQk+te//jVtvPHGBnMQdH0UAUVAEVAEmg8BJXDNN2faY0VAEVAECkOACdw+++xDjzzyCB111FH0/9u7sxAbwziO4/+UNXFBtqQMuRESLlAksibClWwlwrig7FuIQZQs2aNwhcierYbIhSjrhcQFIpKyZaffv97pzPGe4ZzzvDNnzPepiea87/Oe8zku/Hr+z//Zs2dPuec9e/bMw83QoUPt1KlTWQW4ESNGxIYdzaNVqtevX/tKlf6e61D4Sg+V2c6lOfr27WulpaXZ3lp2/du3b61ly5YezrQCVrduXX/t+fPnVlRUZA0bNjSFv2jVTIF2wIABHq6uXr1qvXv3/uuz582bZ+vXr/fgO3fu3LLri4uLbdu2bf47vRaNEydOmL4DPefChQt/nZ8LEEAAAQQKT4AAV3jfCe8IAQQQqDKBKMDpP/kKGCdPnrQXL15YgwYNyt5TSUmJLV682I4cOWJjxowJEuA0uVb7+vTpY02bNvXVIgWfXEahBLitW7f6auLMmTNty5YtZR9FAVVlkgpf69atK/cR5a0VvnHjxtmBAwcq/Pha/VT402qbQnXz5s3LrtfvFRrfvXv3h2O3bt3s1q1b9ujRI2vXrl0uxNyDAAIIIFCFAgS4KsTn0QgggEChCaQGOIUPrdQoSChQRKNDhw7WokUL27x5s3Xt2jVYgNP8WvW6cuWKaaVo+PDhOfGECnBdunTx8PXq1SsPR9qnl03gUZml9gEqLMkpGqtXr7YlS5b4Cpn2vKWOu3fvWufOna1169b29OnTCj9/tJqmwHf8+PFy19auXdtLJVWimj5Gjx5tR48e9ZXVyZMn52TMTQgggAACVSdAgKs6e56MAAIIFJxAaoDTfik1u9BeqYsXL/p7vXbtmu+J0yqSAkroALds2TJvvKE/V6xYkZNPqACX/nDNq9JOBa86depU+N5u377tDVzkowCXOnbu3GnTpk2z+fPn29q1a8u9Fq3A6ZcfP34st/KZ/sCRI0d6cNOPQlzqUOmmgmdFK3BxK4A5gXMTAggggEClChDgKpWbhyGAAAKFLZAa4NSYY+nSpaaSySdPnlibNm289E8dKF++fOkNMkIHuB07dviqlH4UlHIZIQLcnDlzTCtVarqi+W7cuOGBS3vW/uW9qRGJyib1o1W81PH48WMPxWpgoqAXdd78/Pmzd4nU/jcNlUAqiMUNhTPd16RJEy+fVNOZ1KGumGqgsmDBAluzZk3ZS9prGK1sTp061RQmGQgggAAC1UuAAFe9vi/eLQIIIJCoQHqAU0hr3769r4qpa6IChfbH7d+/38NM6AC3fft2mzFjxj+FpLijCSrCUeMV3ZPrUFOSTp06eXhVaFIZadz4+vWrtWrVyj58+OD7BxXU0seiRYs8WKnMcdSoUb7Spi6UamTy/v177zip8sdmzZrFPmPDhg3eoCS9SUl0scove/To4XNoX6G6UKobpkondWyDSjX/JYjmasV9CCCAAALJCRDgkrNlZgQQQKDaCaQHOH0AtaFXaFFZ44QJE+zSpUumLpVJBLiohFIrfytXrqzQTyFH7fXTh0ovtX9NJYapQ+egzZo1K6/vJFpZiytbjCY+dOiQHxmgHx0fkGlohUyNTu7fv+8dKrX6pmCmkPjp0ycPc9rLFjc6duxoDx488KMDdIRA3NC5cPJUMNSZcNq/N3v2bA+IWmHMp0w1L0RuRgABBBDIS4AAlxcfNyOAAAL/l0BcgIu6JqpkT6V6KqdUWWESAa5Qmphk+laj/XkqI43Os0u/dvDgwXbu3Dk7f/68h7JshkKXVgrj9s5F8+h4h549e1qvXr18T2K2Q/v49u3bF7t3Ltu5uB4BBBBAoPIFCHCVb84TEUAAgYIViAtwWrFRuaBWhVJXxkIHuEI6RiDTF6TyUXV/1FltClHpIzojT10kFXRr1aqV1XetpiYLFy705ibacxc3tHdt9+7dOXWRVFmnGtP8/PnTz6OrX79+Vu+PixFAAAEEql6AAFf13wHvAAEEECgYgbgApzencPXmzRvvQKlz2jRCBrgzZ87YpEmT/CDvXbt22ZQpU3I2ybeJyZ07d8oO2k59E1p1Gzt2rLVt29YePnz4R+MQXbtq1SoPuX8rT1R3yEaNGpX7jNevX7eBAwf6nrl79+798bouVojWPsQfP354WavO6osb375982vq1atX9rL25slYn2Pjxo15l5Pm/AVxIwIIIIBAXgIEuLz4uBkBBBD4vwQyBbi4T5lLgFNXx6j08MuXLx5CtJqlQKR9YGrsoX1a+Yx8A5z2ye3du9f69+/v5YwaN2/e9O6QCkxnz571IJs+fv365d0l1WVSP1rpyjQUUOXXvXt3D2zaB3f69Glr3Lixl17qsO24oeYxEydO9CCmMshMQyuB2ic3aNAgD5w6kkAhWauCuldlsdmuDubznXAvAggggEA4AQJcOEtmQgABBKq9QIgAN378eDt48KCHEnU81IjrGKnyPYUXXaNDshVMVHqY78g3wKnph1YBFbDUxfH79+/+vnSouc5Oy3SY9+XLl/1zqMGLGr1UNA4fPmybNm3yRiQKV+paOWzYMFN3Sv090+jXr5+Vlpb6YedqLpNpqOy1uLjYQ6eOI1A41r46nT+Xae9evu7cjwACCCBQOQIEuMpx5ikIIIBAjREYMmSIdz6sqA1+jcHggyKAAAIIIBBYgAAXGJTpEEAAgZosoH1yRUVFppb9OneMgQACCCCAAAJhBQhwYT2ZDQEEEKiRAirrO3bsmLemV3CjSUaN/GfAh0YAAQQQqAQBAlwlIPMIBBBA4H8XWL58uZWUlPj+sOnTp5sOvGYggAACCCCAQHgBAlx4U2ZEAAEEEEAAAQQQQAABBBIRIMAlwsqkCCCAAAIIIIAAAggggEB4AQJceFNmRAABBBBAAAEEEEAAAQQSESDAJcLKpAgggAACCCCAAAIIIIBAeAECXHhTZkQAAQQQQAABBBBAAAEEEhEgwCXCyqQIIIAAAggggAACCCCAQHgBAlx4U2ZEAAEEEEAAAQQQQAABBBIRIMAlwsqkCCCAAAIIIIAAAggggEB4AQJceFNmRAABBBBAAAEEEEAAAQQSESDAJcLKpAgggAACCCCAAAIIIIBAeAECXHhTZkQAAQQQQAABBBBAAAEEEhEgwCXCyqQIIIAAAggggAACCCCAQHgBAlx4U2ZEAAEEEEAAAQQQQAABBBIRIMAlwsqkCCCAAAIIIIAAAggggEB4gd+KMvyEUw++PQAAAABJRU5ErkJggg==" width="640">
These are noisier than the wider bins above but that's OK. Now save the output:
```python
path = parent_direc + '/pickled_objects/Na'
try:
os.mkdir(path)
except OSError:
print("Creation of directory %s failed" %path)
else:
print("successfully created directory %s" %path)
#Note that it prints fail once it has been created once.
```
Creation of directory /storage/astro2/phrgmk/Data/EFOSC/WASP-94A/reduction_13//pickled_objects/Na failed
```python
pickle.dump(nf_na,open(parent_direc + '/pickled_objects/Na/fluxes.pickle','wb'))
pickle.dump(ne_na,open(parent_direc + '/pickled_objects/Na/errors.pickle','wb'))
pickle.dump(na_bins_xpos,open(parent_direc + '/pickled_objects/Na/xpos.pickle','wb'))
pickle.dump(na_bins_sky,open(parent_direc + '/pickled_objects/Na/sky.pickle','wb'))
pickle.dump(na_bins_centres,open(parent_direc + '/pickled_objects/Na/bin_centres.pickle','wb'))
pickle.dump(na_bins_widths,open(parent_direc + '/pickled_objects/Na/bin_widths.pickle','wb'))
print(na_bins_centres)
print(na_bins_widths)
```
[5803. 5833. 5863. 5893. 5923. 5953. 5983.]
[30. 30. 30. 30. 30. 30. 30.]
And make the 'incrementally increasing bins' (iib), starting from a width of 10A and increasing to 100A, in steps of 10A.
```python
bin_widths_iib = np.arange(30,210,10)
iib_fluxes_Na = []
iib_errors_Na = []
iib_xpos_Na = []
iib_sky_Na = []
iib_centres_Na = []
iib_widths_Na = []
for i in bin_widths_iib:
bin_left = na_centre - i/2
bin_right = na_centre + i/2
iib_centres_Na.append(na_centre)
iib_widths_Na.append(i)
curr_flux,curr_error,_,_,_,_,curr_xpos,curr_sky,_,_ = wb.wvl_bin_data(f1,e1,f2,e2, wvl_solution_1,np.array([bin_left,bin_right]),n_tukey_points=0,xpos=xpos,sky=sky,weighted=False)
iib_fluxes_Na.append(curr_flux[0])
iib_errors_Na.append(curr_error[0])
iib_xpos_Na.append(curr_xpos[0])
iib_sky_Na.append(curr_sky[0])
iib_fluxes_Na = np.array(iib_fluxes_Na)
iib_errors_Na = np.array(iib_errors_Na)
iib_centres_Na = np.array(iib_centres_Na)
iib_widths_Na = np.array(iib_widths_Na)
iib_xpos_Na = np.array(iib_xpos_Na)
iib_sky_Na = np.array(iib_sky_Na)
nf_na_iib, ne_na_iib = wb.normalise_flux(iib_fluxes_Na,iib_errors_Na,contact1,contact4)
wb.plot_all_bins(mjd,nf_na_iib, ne_na_iib)
```
<IPython.core.display.Javascript object>
<img 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" width="640">
And save:
```python
path = parent_direc + '/pickled_objects/Na/iib'
try:
os.mkdir(path)
except OSError:
print("Creation of directory %s failed" %path)
else:
print("successfully created directory %s" %path)
```
Creation of directory /storage/astro2/phrgmk/Data/EFOSC/WASP-94A/reduction_13//pickled_objects/Na/iib failed
```python
pickle.dump(nf_na_iib,open(parent_direc + '/pickled_objects/Na/iib/fluxes.pickle','wb'))
pickle.dump(ne_na_iib,open(parent_direc + '/pickled_objects/Na/iib/errors.pickle','wb'))
pickle.dump(iib_centres_Na,open(parent_direc + '/pickled_objects/Na/iib/centres.pickle','wb'))
pickle.dump(iib_widths_Na,open(parent_direc + '/pickled_objects/Na/iib/widths.pickle','wb'))
pickle.dump(iib_xpos_Na,open(parent_direc + '/pickled_objects/Na/iib/xpos.pickle','wb'))
pickle.dump(iib_sky_Na,open(parent_direc + '/pickled_objects/Na/iib/sky.pickle','wb'))
print(ne_na_iib)
```
[[0.00190862 0.00189754 0.0019052 ... 0.00162496 0.00164408 0.00163849]
[0.00159612 0.00158316 0.00159327 ... 0.00135703 0.00137138 0.00136507]
[0.00145481 0.00144668 0.00145186 ... 0.00123626 0.00124802 0.00124458]
...
[0.00077081 0.00076846 0.00077167 ... 0.00065622 0.00066279 0.00065957]
[0.00075335 0.00075126 0.00075385 ... 0.00064121 0.00064771 0.00064435]
[0.000737 0.00073533 0.0007373 ... 0.00062746 0.00063366 0.00063029]]
### Potassium bins
Now do the same for the potassium doublet. The difference here, however, is that the doublet has a larger separation (30A) than sodium (5A). It is also right next to the big telluric O2-A band absorption line. Therefore, I choose to centre on the redder of the two potassium lines.
```python
no_bins_K = 5
bin_width_K = 30
K_bins = np.arange(wb.potassium_d2-(bin_width_K/2)*no_bins_K,wb.potassium_d2+(bin_width_K/2)*(no_bins_K+1),bin_width_K)
K_bins_centres = np.array([(K_bins[i+1]+K_bins[i])/2 for i in range(no_bins_K)])
K_bins_widths = np.array([(K_bins[i+1]-K_bins[i]) for i in range(no_bins_K)])
wb.plot_spectra(f1[nframes//2],f2[nframes//2],wvl_solution_1,bin_edges=K_bins,alkali=True)
```
<IPython.core.display.Javascript object>
<div id='c8f63a07-1703-4b07-88f7-d1179df0fd39'></div>
Again looks OK, but it's hard to tell as there's no stellar feature to guide the eye.
Now make the light curves.
```python
K_bins_fluxes,K_bins_errors,_,_,_,_,K_bins_xpos,K_bins_sky,_,_ = wb.wvl_bin_data(f1,e1,f2,e2,wvl_solution_1,\
K_bins,n_tukey_points=0,\
xpos=xpos,sky=sky,weighted=False)
nf_K, ne_K = wb.normalise_flux(K_bins_fluxes,K_bins_errors,contact1,contact4)
wb.plot_all_bins(mjd,nf_K, ne_K)
```
/home/astro/phrgmk/python-path/reduction_utils/wavelength_binning.py:299: RuntimeWarning: Mean of empty slice.
current_xpos.append(bin_xpos.mean())
/warwick/desktop/2018/software/Core/Anaconda3/2019.03/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars
ret = ret.dtype.type(ret / rcount)
/home/astro/phrgmk/python-path/reduction_utils/wavelength_binning.py:300: RuntimeWarning: Mean of empty slice.
current_sky.append(bin_sky.mean())
/warwick/desktop/2018/software/Core/Anaconda3/2019.03/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.
out=out, **kwargs)
/home/astro/phrgmk/python-path/reduction_utils/wavelength_binning.py:346: RuntimeWarning: invalid value encountered in true_divide
flux_ratio = (binned_flux1/binned_flux2)
/home/astro/phrgmk/python-path/reduction_utils/wavelength_binning.py:347: RuntimeWarning: invalid value encountered in true_divide
err_ratio = np.sqrt((binned_err1/binned_flux1)**2 + (binned_err2/binned_flux2)**2)*flux_ratio
/warwick/desktop/2018/software/Core/Anaconda3/2019.03/lib/python3.7/site-packages/numpy/lib/function_base.py:3405: RuntimeWarning: Invalid value encountered in median for 5 results
r = func(a, **kwargs)
<IPython.core.display.Javascript object>
<div id='f155240d-c93e-4b45-aab7-72ba8de5c028'></div>
Light curves look pretty good. Now save:
```python
# pickle.dump(nf_K,open('../pickled_objects/K/fluxes.pickle','wb'))
# pickle.dump(ne_K,open('../pickled_objects/K/errors.pickle','wb'))
# pickle.dump(K_bins_centres,open('../pickled_objects/K/centres.pickle','wb'))
# pickle.dump(K_bins_widths,open('../pickled_objects/K/widths.pickle','wb'))
# pickle.dump(K_bins_xpos,open('../pickled_objects/K/xpos.pickle','wb'))
# pickle.dump(K_bins_sky,open('../pickled_objects/K/sky.pickle','wb'))
```
For potassium iib, we centre the bin on the first line and step outwards to the right, to make sure we don't include too much of the telluric O2 line.
First find where the lines are in relation to the tellurics.
```python
wb.plot_spectra(f1[nframes//2],f2[nframes//2],wvl_solution_1,telluric=True)
```
<IPython.core.display.Javascript object>
<div id='c6ba8b21-e16f-4de7-8d1b-06af73929b3e'></div>
OK, so K D1 occurs where telluric O2 is at approx 90% transmission. Make left hand edge where it reaches 70% transmission.
```python
telluric_wvl,telluric_flux = np.loadtxt('../line_lists/tellurics_halpha.dat',unpack=True)
# First only consider wavelength region of interest
telluric_wvl_cut = ((telluric_wvl > 7630) & (telluric_wvl < 7700))
# Now find the index where the transmission is greater than 70%
cut_index = min(np.where(telluric_flux[telluric_wvl_cut] >= 0.7)[0])
# And now find the wavelength where this occurs
print("Left hand K bin should be cut at %dA"%(telluric_wvl[telluric_wvl_cut][cut_index]))
```
---------------------------------------------------------------------------
OSError Traceback (most recent call last)
<ipython-input-112-b80280581659> in <module>
----> 1 telluric_wvl,telluric_flux = np.loadtxt('../line_lists/tellurics_halpha.dat',unpack=True)
2
3 # First only consider wavelength region of interest
4 telluric_wvl_cut = ((telluric_wvl > 7630) & (telluric_wvl < 7700))
5
/warwick/desktop/2018/software/Core/Anaconda3/2019.03/lib/python3.7/site-packages/numpy/lib/npyio.py in loadtxt(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin, encoding, max_rows)
953 fname = os_fspath(fname)
954 if _is_string_like(fname):
--> 955 fh = np.lib._datasource.open(fname, 'rt', encoding=encoding)
956 fencoding = getattr(fh, 'encoding', 'latin1')
957 fh = iter(fh)
/warwick/desktop/2018/software/Core/Anaconda3/2019.03/lib/python3.7/site-packages/numpy/lib/_datasource.py in open(path, mode, destpath, encoding, newline)
264
265 ds = DataSource(destpath)
--> 266 return ds.open(path, mode, encoding=encoding, newline=newline)
267
268
/warwick/desktop/2018/software/Core/Anaconda3/2019.03/lib/python3.7/site-packages/numpy/lib/_datasource.py in open(self, path, mode, encoding, newline)
622 encoding=encoding, newline=newline)
623 else:
--> 624 raise IOError("%s not found." % path)
625
626
OSError: ../line_lists/tellurics_halpha.dat not found.
```python
iib_fluxes_K = []
iib_errors_K = []
iib_xpos_K = []
iib_sky_K = []
iib_centres_K = []
iib_widths_K = []
for i in bin_widths_iib:
# centering on potassium D1
# left hand boundary is 7645A
if wb.potassium_d1 - i/2 >= 7645:
bin_left = wb.potassium_d1 - i/2
bin_right = wb.potassium_d1 + i/2
iib_centres_K.append(wb.potassium_d1)
iib_widths_K.append(i)
else: # bin expands towards the red
bin_left = 7645
bin_right = bin_left + i
iib_centres_K.append((bin_left+bin_right)//2)
iib_widths_K.append(i)
curr_flux,curr_error,_,_,_,_,curr_xpos,curr_sky,_,_ = \
wb.wvl_bin_data(f1,e1,f2,e2,wvl_solution_1,np.array([bin_left,bin_right]),\
n_tukey_points=0,xpos=xpos,sky=sky,weighted=False)
iib_fluxes_K.append(curr_flux[0])
iib_errors_K.append(curr_error[0])
iib_xpos_K.append(curr_xpos[0])
iib_sky_K.append(curr_sky[0])
iib_fluxes_K = np.array(iib_fluxes_K)
iib_errors_K = np.array(iib_errors_K)
iib_centres_K = np.array(iib_centres_K)
iib_widths_K = np.array(iib_widths_K)
iib_xpos_K = np.array(iib_xpos_K)
iib_sky_K = np.array(iib_sky_K)
nf_K_iib, ne_K_iib = wb.normalise_flux(iib_fluxes_K,iib_errors_K,contact1,contact4)
wb.plot_all_bins(mjd,nf_K_iib, ne_K_iib)
```
And save:
```python
# pickle.dump(nf_K_iib,open('../pickled_objects/K/iib/fluxes.pickle','wb'))
# pickle.dump(ne_K_iib,open('../pickled_objects/K/iib/errors.pickle','wb'))
# pickle.dump(iib_centres_K,open('../pickled_objects/K/iib/centres.pickle','wb'))
# pickle.dump(iib_widths_K,open('../pickled_objects/K/iib/widths.pickle','wb'))
# pickle.dump(iib_xpos_K,open('../pickled_objects/K/iib/xpos.pickle','wb'))
# pickle.dump(iib_sky_K,open('../pickled_objects/K/iib/sky.pickle','wb'))
```
Now we can move to notebook 6 - preparing the white light curve for fitting.
```python
```
|
JamesKirk11REPO_NAMETiberiusPATH_START.@Tiberius_extracted@Tiberius-main@src@reduction_utils@ACAM_utils@example_notebooks@5_wavelength_binning_individual_wvl_solutions.ipynb@.PATH_END.py
|
{
"filename": "CHANGELOG.md",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/chart-studio/CHANGELOG.md",
"type": "Markdown"
}
|
# Change Log
All notable changes to this project will be documented in this file.
This project adheres to [Semantic Versioning](http://semver.org/).
## [1.1.0] - 2020-01-4-01
### Updated
- The default URLs have been changed from `plot.ly` to `plotly.com` to match the changes to Chart Studio Cloud.
## [1.0.0] - 2019-07-16
The initial release of the stand-alone `chart-studio` package. This package contains utilities for interfacing with Plotly's Chart Studio service (both Chart Studio cloud and Chart Studio On-Prem). Prior to plotly.py version 4, This functionality was included in the `plotly` package under the `plotly.plotly` module. As part of plotly.py version 4, the Chart Studio functionality was removed from the `plotly` package and released in this `chart-studio` package.
### Updated
- The `chart_studio.plotly.plot`/`iplot` functions have been ported to the Chart Studio [v2 API](https://api.plot.ly/v2/).
- The `chart_studio.plotly.plot`/`iplot` functions now support uploading figures that contain frames. This makes the legacy `chart_studio.plotly.create_animations`/`icreate_animations` functions unnecessary, though they are still included for backward compatibility.
### Fixed
- Fixed iframe warning resulting from `chart_studio.plotly.iplot`
### Removed
- The `fileopt` argument to `chart_studio.plotly.plot`/`iplot` was deprecated in plotly.py version 3.9.0 and has been removed in this initial release of the `chart-studio` package.
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@chart-studio@CHANGELOG.md@.PATH_END.py
|
{
"filename": "afino_series.py",
"repo_name": "aringlis/afino_release_version",
"repo_path": "afino_release_version_extracted/afino_release_version-master/afino/afino_series.py",
"type": "Python"
}
|
"""
A simple time series object
"""
import numpy as np
from matplotlib import pyplot as plt
class SampleTimes:
def __init__(self, time, label='time', units='seconds'):
"""A class holding time series sample times."""
# ensure that the initial time is zero
self.time = time - time[0]
# Number of sample times
self.nt = self.time.size
# Average cadence
self.dt = self.time[-1] / (self.nt - 1)
# Information on the units for the time
self.label = label
self.units = units
# Differences between consecutive sample times
self.tdiff = self.time[1:] - self.time[0:-1]
# Include base time for input series
self.basetime = time[0]
class Frequencies:
def __init__(self, frequencies, label='frequency', units='Hz'):
self.frequencies = frequencies
self.posindex = self.frequencies > 0
self.positive = self.frequencies[self.posindex]
self.label = label
self.units = units
class PowerSpectrum:
def __init__(self, frequencies, power, label='Fourier power'):
self.frequencies = Frequencies(frequencies)
self.power = power
self.ppower = self.power[self.frequencies.posindex]
self.label = label
# Mean power
self.vaughan_mean = np.mean(self.ppower)
# Power spectrum normalized by its mean
self.normed_by_mean = self.ppower / self.vaughan_mean
# Standard deviation of the normalized power
self.vaughan_std = np.std(self.normed_by_mean)
# Normalized power expressed in units of its standard deviation
self.Npower = self.normed_by_mean / self.vaughan_std
def peek(self, **kwargs):
"""
Generates a quick plot of the positive frequency part of the power
spectrum.
"""
plt.plot(self.frequencies.positive, self.ppower, **kwargs)
class AfinoSeries:
def __init__(self, time, data, label='data', units=None, name=None):
self.SampleTimes = SampleTimes(time)
if self.SampleTimes.nt != data.size:
raise ValueError('length of sample times not the same as the data')
self.data = data
self.PowerSpectrum = PowerSpectrum(np.fft.fftfreq(self.SampleTimes.nt, self.SampleTimes.dt),
np.abs(np.fft.fft(self.data)) ** 2)
self.label = label
self.units = units
self.name = name
def peek(self, **kwargs):
"""
Generates a quick plot of the data
"""
plt.plot(self.SampleTimes.time, self.data, **kwargs)
xunits = prepend_space(bracketize(self.SampleTimes.units))
plt.xlabel(self.SampleTimes.label + xunits)
nsamples = ' [%i samples]' % self.SampleTimes.nt
if self.units is not None:
yunits = prepend_space(bracketize(self.units))
plt.ylabel(self.label + yunits + nsamples)
else:
plt.ylabel(self.label + nsamples)
def prepend_left_bracket(s, bracket='(', force_replace=False,
test_set=('(', '{', '[')):
"""Prepend a left bracket if possible"""
if s[0] not in test_set:
s = bracket + s
else:
if force_replace:
s[0] = bracket
return s
def append_right_bracket(s, bracket=')', force_replace=False,
test_set=(')', '}', ']')):
"""Append a left bracket if possible"""
if s[-1] not in test_set:
s = s + bracket
else:
if force_replace:
s[-1] = bracket
return s
def bracketize(s, bracket='()', force_replace=False):
"""Add enclosing brackets if need be"""
s = prepend_left_bracket(s, bracket=bracket[0],
force_replace=force_replace)
s = append_right_bracket(s, bracket=bracket[1],
force_replace=force_replace)
return s
def prepend_space(s, force_replace=False):
s = prepend_left_bracket(s, bracket=' ', force_replace=force_replace,
test_set=(' '))
return s
def prep_series(ts):
"""
put the data series into the form (I - <I>) / <I>.
multiply the series by a Hanning window to aid the FFT process
"""
data_ave = np.mean(ts.data)
newdata = ((ts.data - data_ave) / data_ave) * np.hanning(len(ts.data))
ts_hann = AfinoSeries(ts.SampleTimes.time + ts.SampleTimes.basetime, newdata)
return ts_hann
|
aringlisREPO_NAMEafino_release_versionPATH_START.@afino_release_version_extracted@afino_release_version-master@afino@afino_series.py@.PATH_END.py
|
{
"filename": "README.md",
"repo_name": "QEF/q-e",
"repo_path": "q-e_extracted/q-e-master/EPW/irobjs/README.md",
"type": "Markdown"
}
|
# How to Install IR Object Files
To use `sparse-ir` sampling for anisotropic Migdal-Eliashberg calculations, set the input value `gridsamp` to 2 and specify the file containing the IR functions in `filirobj`.
By running the following command in this directory, you can download several files that contain precomputed IR functions:
```
make irobjs
```
The files are organized by the parameters Λ and ε, so make sure to set the corresponding file in `filirobj` according to the parameters you intend to use.
If you want to modify the parameters and calculate the IR functions yourself, you will need to install the `sparse-ir` Python package. After installation, you can run the following command:
```
python dump.py 1e+4 1e-10 ir_nlambda4_ndigit10.dat
```
For more details, please refer to the [sparse-ir-fortran GitHub repository](https://github.com/SpM-lab/sparse-ir-fortran).
|
QEFREPO_NAMEq-ePATH_START.@q-e_extracted@q-e-master@EPW@irobjs@README.md@.PATH_END.py
|
{
"filename": "gaindrifts.py",
"repo_name": "litebird/litebird_sim",
"repo_path": "litebird_sim_extracted/litebird_sim-master/litebird_sim/gaindrifts.py",
"type": "Python"
}
|
import numpy as np
import hashlib
from enum import IntEnum
from typing import Union, List
from dataclasses import dataclass
from .observations import Observation
class GainDriftType(IntEnum):
"""An enumeration class to specify the type of gain drift injection.
The gain drift type can be:
- ``LINEAR_GAIN``: To inject linear gain drift in time with the
possibility to calibrate the detectors at periodic interval
- ``THERMAL_GAIN``: To inject a gain drift with :math:`1/f` psd
mimicking the fluctuations in the focalplane temperature
"""
LINEAR_GAIN = 0
THERMAL_GAIN = 1
# SLOW_GAIN = 2 # Remains to be implemented
class SamplingDist(IntEnum):
"""An enumeration class to specify the distribution for the random
scaling factor applied on the gain drift. For linear gain drift, it
specifies the distribution of the slope of the gain drift. In case of
thermal gain drift, it specifies the distribution of the detector
mismatch.
The implemented distributions are:
- ``UNIFORM``: Uniform distribution. The lower and upper bound of the
uniform distribution can be specified by the attributes
:attr:`.GainDriftParams.sampling_uniform_low` and
:attr:`.GainDriftParams.sampling_uniform_high`.
- ``GAUSSIAN``: Normal (Gaussian) distribution. The mean and standard
deviation of the Gaussian distribution can be specified by the
attributes :attr:`.GainDriftParams.sampling_gaussian_loc` and
:attr:`.GainDriftParams.sampling_gaussian_scale`.
"""
UNIFORM = 0
GAUSSIAN = 1
@dataclass
class GainDriftParams:
"""
A class to store the gain drift injection parameters.
The gain drift type can be one of the following:
- Linear: It simulates gain drift that increases linearly in time. The
gain factor resets to one periodically with time interval specified by the
attribute :attr:`.GainDriftParams.calibration_period_sec`.
- Thermal: It simulates the gain drift as the fluctuation in the
focalplane temperature. It offers the possibility to inject common mode drift
to the TODs of detectors belonging to the same group of detectors identified
by the attribute :attr:`.GainDriftParams.focalplane_group`. This is
enabled by setting the attribute :attr:`.GainDriftParams.detector_mismatch` to 0.
The complete list of parameters is provided here:
- Parameters common for the simulation of all types of gain drifts:
- ``drift_type`` (:class:`.GainDriftType`):
Enumeration to determine the type of gain drift to be simulated.
See :class:`.GainDriftType`.
- ``sigma_drift`` (`float`): A dimensionless parameter that
determines the slope of gain drift in case of linear gain drift, and
amplitude of thermal fluctuation in case of thermal gain drift.
- ``sampling_dist`` (:class:`.SamplingDist`): Enumeration
to specify the distribution of the random scaling/mismatch
factor applied on the gain drift. See :class:`.SamplingDist`.
- Parameters that are specific to the simulation of linear gain drift:
- ``calibration_period_sec`` (`int`): This is the time
period in seconds after which the linear gain drift resets periodically.
- Parameters that are specific to the simulation of thermal gain drift:
- ``focalplane_group`` (`str`): Detector attribute to
group the detectors. It is used to simulate same noise timestream
for all the detectors belonging to a given group. It can be any of the
detector attributes like `"wafer"`, `"pixtype"` or `"channel"`.
- ``oversample`` (`int`): The factor by which to oversample
thermal noise FFT beyond the TOD size.
- ``fknee_drift_mHz`` (`float`): :math:`f_{knee}` of the thermal drift
power spectral density given in mHz.
- ``alpha_drift`` (`float`): The spectral index of thermal
drift power spectral density.
- ``detector_mismatch`` (`float`): The factor that determines
the degree of mismatch in thermal fluctuation of detectors belonging
to same focalplane group. A value other than 0 implies no common
gain. Whereas a value 0 sets the thermal gain to be same for all
detectors in a focalplane group.
- ``thermal_fluctuation_amplitude_K`` (`float`): Amplitude of
thermal gain fluctuation in Kelvin.
- ``focalplane_Tbath_K`` (`float`): Temperature of the
focalplane in Kelvin.
- Parameters for the sampling distributions:
- ``sampling_uniform_low`` (`float`): Lower boundary of the output for uniform
distribution.
- ``sampling_uniform_high`` (`float`): Upper boundary of the output for uniform
distribution.
- ``sampling_gaussian_loc`` (`float`): Mean of the Gaussian distribution.
- ``sampling_gaussian_scale`` (`float`): Standard deviation of the Gaussian
distribution.
"""
# Parameters for sampling distribution
sampling_uniform_low: float = 0.0
sampling_uniform_high: float = 1.0
sampling_gaussian_loc: float = 0.7
sampling_gaussian_scale: float = 0.5
# Common parameters
drift_type: GainDriftType = GainDriftType.LINEAR_GAIN
sigma_drift: float = 1.0e-2
sampling_dist: SamplingDist = SamplingDist.UNIFORM
# Linear gain parameters
calibration_period_sec: int = 86400
# Thermal gain parameters
focalplane_group: str = "wafer"
oversample: int = 2
fknee_drift_mHz: float = 20.0
alpha_drift: float = 1.0
detector_mismatch: float = 1.0
thermal_fluctuation_amplitude_K: float = 1.0
focalplane_Tbath_K: float = 0.1
# Slow gain parameters
# To be added
def _responsivity_function(dT):
"""A function to specify the response of the detector electronics to the
temperature"""
# Appropriate function to be implemented later
return dT
def _hash_function(
input_str: str,
user_seed: int = 12345,
) -> int:
"""This functions generates a unique and reproducible hash for a given pair of
`input_str` and `user_seed`. This hash is used to generate the common noise time
stream for a group of detectors, and to introduce randomness in the noise time
streams.
Args:
input_str (str): A string, for example, the detector name.
user_seed (int, optional): A seed provided by the user. Defaults to 12345.
Returns:
int: An `md5` hash from generated from `input_str` and `user_seed`
"""
bytesobj = (str(input_str) + str(user_seed)).encode("utf-8")
hashobj = hashlib.md5()
hashobj.update(bytesobj)
digest = hashobj.digest()
return int.from_bytes(bytes=digest, byteorder="little")
def _get_psd(
freq: np.ndarray,
sigma_drift: float = GainDriftParams.sigma_drift,
fknee_drift_mHz: float = GainDriftParams.fknee_drift_mHz,
alpha_drift: float = GainDriftParams.alpha_drift,
) -> np.ndarray:
"""The function to generate the :math:`1/f` noise power spectral density for the
thermal fluctuation.
Args:
freq (np.ndarray): The frequency array
sigma_drift (float, optional): A dimensionless parameter that
determines the amplitude of thermal fluctuation. Defaults to
:attr:`GainDriftParams.sigma_drift`.
fknee_drift_mHz (float, optional): f_knee of the thermal drift
power spectral density given in mHz. Defaults to
:attr:`GainDriftParams.fknee_drift_mHz`.
alpha_drift (float, optional): The spectral index of thermal drift
power spectral density. Defaults to :attr:`GainDriftParams.alpha_drift`.
Returns:
np.ndarray: :math:`1/f` noise power spectral density
"""
return (sigma_drift**2) * (fknee_drift_mHz * 1.0e-3 / freq) ** alpha_drift
def _noise_timestream(
tod_size: int,
sampling_freq_hz: float,
focalplane_attr: str,
drift_params: GainDriftParams = None,
user_seed: int = 12345,
) -> np.ndarray:
"""The function to generate the thermal noise time stream with
:math:`1/f` power spectral density.
Args:
tod_size (int): The length of time ordered data array.
sampling_freq_hz (float): The sampling frequency of the detector in Hz.
focalplane_attr (str): The name of the focalplane attribute
corresponding the focalplane group attribute.
See :attr:`.GainDriftParams.focalplane_group`.
drift_params (GainDriftParams, optional): The class object for
gain drift simulation parameters. Defaults to None.
user_seed (int, optional): The user provided seed for random number
generation. Defaults to 12345.
Returns:
np.ndarray: Thermal noise time stream with :math:`1/f` PSD.
"""
if drift_params is None:
drift_params = GainDriftParams()
fftlen = 2
while fftlen <= (drift_params.oversample * tod_size):
fftlen *= 2
npsd = fftlen // 2 + 1
norm = sampling_freq_hz * fftlen / 2.0
freq = np.fft.rfftfreq(fftlen, 1.0 / sampling_freq_hz)
assert (
freq.size == npsd
), f"The size of frequency array is {freq.size} that is not same as the expected"
" value {npsd}"
psd = np.zeros_like(freq)
# Starting from 1st element to keep the dc term zero
psd[1:] = _get_psd(
freq[1:],
drift_params.sigma_drift,
drift_params.fknee_drift_mHz,
drift_params.alpha_drift,
)
rng = np.random.default_rng(seed=_hash_function(focalplane_attr, user_seed))
randarr = rng.standard_normal(size=fftlen)
fnoise_stream = np.zeros(npsd, dtype=np.complex128)
fnoise_stream[1:-1] = randarr[1 : npsd - 1] + 1j * randarr[-1 : npsd - 1 : -1]
fnoise_stream[0] = randarr[0] + 1j * 0.0
fnoise_stream[-1] = randarr[npsd - 1] + 1j * 0.0
fnoise_stream *= np.sqrt(psd * norm)
noise_stream = np.fft.irfft(fnoise_stream)
offset = (fftlen - tod_size) // 2
noise_avg = np.mean(noise_stream[offset : offset + tod_size])
return noise_stream[offset : offset + tod_size] - noise_avg
def apply_gaindrift_for_one_detector(
det_tod: np.ndarray,
sampling_freq_hz: float,
det_name: str,
drift_params: GainDriftParams = None,
focalplane_attr: str = None,
noise_timestream: np.ndarray = None,
user_seed: int = 12345,
):
"""This function applies the gain drift on the TOD corresponding to only one
detector.
The linear drift is applied on the TODs in a periodic way with the period size
specified in seconds by ``drift_params.callibration_period_sec``. This is by
assuming that the detectors are calibrated for linear gain drift periodically.
The slope of the linear gain is determined randomly based on the detector name
and the user-provided seed.
The thermal gain drift, on the other hand, is based on the fluctuation of the
focalplane temperature modeled after :math:`1/f` power spectral
density (PSD). This :math:`1/f`
PSD is common to all the detectors belonging to the focalplane group identified
by ``drift_params.focalplane_group``. The function provides an option to introduce a
mismatch between the individual detectors within a focalplane group with the
parameter ``drift_params.detector_mismatch``. This mismatch parameter along with a
random number determines the extent of the mismatch of the thermal fluctuation
within the focalplane group. Finally the thermal fluctuation is applied to the TODs
according to the responsivity function of the detectors.
Args:
det_tod (np.ndarray): The TOD array corresponding to only one
detector.
sampling_freq_hz (float): The sampling frequency of the detector in Hz.
det_name (str): The name of the detector to which the TOD belongs.
This name is used with ``user_seed`` to generate hash. This hash is used to
set random slope in case of linear drift, and randomized detector mismatch
in case of thermal gain drift.
drift_params (:class:`.GainDriftParams`, optional): The gain drift
injection parameters object. Defaults to None.
focalplane_attr (str, optional): This is the parameter
corresponding to the ``drift_params.focalplane_group`` attribute.
For example, if ``drift_params.focalplane_group = 'wafer'``, the
``focalplane_attr`` will be the name of the detector wafer. Defaults to None.
noise_timestream (np.ndarray, optional): The thermal noise time
stream. Defaults to None.
user_seed (int, optional): A seed provided by the user. Defaults
to 12345.
"""
if drift_params is None:
drift_params = GainDriftParams()
tod_size = len(
det_tod
) # must be equal to sampling_freq_hz * mission_duration_seconds
assert isinstance(det_name, str), "The parameter `det_name` must be a string"
rng = np.random.default_rng(seed=_hash_function(det_name, user_seed))
if drift_params.sampling_dist == SamplingDist.UNIFORM:
rand = rng.uniform(
low=drift_params.sampling_uniform_low,
high=drift_params.sampling_uniform_high,
)
elif drift_params.sampling_dist == SamplingDist.GAUSSIAN:
rand = rng.normal(
loc=drift_params.sampling_gaussian_loc,
scale=drift_params.sampling_gaussian_scale,
)
gain_arr_size = int(sampling_freq_hz * drift_params.calibration_period_sec)
if drift_params.drift_type == GainDriftType.LINEAR_GAIN:
gain_arr = 1.0 + rand * drift_params.sigma_drift * np.linspace(
0, 1, gain_arr_size
)
div, mod = (
tod_size // gain_arr_size,
tod_size % gain_arr_size,
)
for i in np.arange(div):
det_tod[i * gain_arr_size : (i + 1) * gain_arr_size] *= gain_arr
det_tod[div * gain_arr_size :] *= gain_arr[:mod]
elif drift_params.drift_type == GainDriftType.THERMAL_GAIN:
if focalplane_attr is not None and noise_timestream is not None:
raise ValueError(
"`focalplane_attr` and `noise_timestream` cannot be used at the same"
" time. Internally, `focalplane_attr` is hashed, and it is used to"
" generate the `noise_timestream`."
)
if noise_timestream is None:
assert isinstance(
focalplane_attr, str
), "The parameter `focalplane_attr` must be a string"
noise_timestream = _noise_timestream(
tod_size=tod_size,
sampling_freq_hz=sampling_freq_hz,
focalplane_attr=focalplane_attr,
drift_params=drift_params,
user_seed=user_seed,
)
thermal_factor = drift_params.thermal_fluctuation_amplitude_K
if drift_params.detector_mismatch != 0:
thermal_factor *= 1.0 + rand * drift_params.detector_mismatch
Tdrift = thermal_factor * noise_timestream # Thermal factor has kelvin unit
dT = 1.0 + Tdrift / drift_params.focalplane_Tbath_K # dT is scaler (no units)
det_tod *= _responsivity_function(dT)
elif drift_params.drift_type == GainDriftType.SLOW_GAIN:
# !!! Remains to be implemented
pass
else:
raise ValueError(
"`drift_params.drift_type` can only be one of GainDriftType.LINEAR_GAIN,"
" GainDriftType.THERMAL_GAIN or GainDriftType.SLOW_GAIN."
)
def apply_gaindrift_to_tod(
tod: np.ndarray,
sampling_freq_hz: float,
det_name: Union[List, np.ndarray],
drift_params: GainDriftParams = None,
focalplane_attr: Union[List, np.ndarray] = None,
user_seed: int = 12345,
):
"""The function to apply the gain drift to all the detectors of a given TOD object.
This function is a wrapper around :func:`.apply_gaindrift_for_one_detector()`
that applies the gain drift on each detector TODs of the TOD object. In case of
thermal gain drift injection, this function computes the thermal noise
fluctuations at once for all the detectors belonging to the focalplane group
specified by ``drift_params.focalplane_group`` and passes them to
:func:`.apply_gaindrift_for_one_detector()` with individual TOD arrays to inject
thermal gain drift.
Args:
tod (np.ndarray): The TOD object consisting TOD arrays for
multiple detectors.
sampling_freq_hz (float): The sampling frequency of the detector in Hz.
det_name (Union[List, np.ndarray]): The list of the name of the
detectors to which the TOD arrays correspond. The detector names
are used to generate unique and reproducible random numbers for
each detector.
drift_params (:class:`.GainDriftParams`, optional): The gain drift
injection parameters object. Defaults to None.
focalplane_attr (Union[List, np.ndarray], optional): This is the
parameter corresponding to the ``drift_params.focalplane_group``
attribute. For example, if
``drift_params.focalplane_group = 'wafer'``, the
``focalplane_attr`` will be the list of the names of detector
wafer. Defaults to None.
user_seed (int, optional): A seed provided by the user. Defaults
to 12345.
"""
if drift_params is None:
drift_params = GainDriftParams()
if tod.shape[0] != len(det_name):
raise AssertionError(
"The number of elements in `det_name` must be same as the number of"
" detectors included in tod object"
)
tod_size = len(tod[0])
if drift_params.drift_type == GainDriftType.LINEAR_GAIN:
for detidx in np.arange(tod.shape[0]):
apply_gaindrift_for_one_detector(
det_tod=tod[detidx],
sampling_freq_hz=sampling_freq_hz,
det_name=det_name[detidx],
drift_params=drift_params,
noise_timestream=None,
user_seed=user_seed,
)
elif drift_params.drift_type == GainDriftType.THERMAL_GAIN:
if focalplane_attr is None:
raise ValueError(
"The argument `focalplane_attr` is required to simulate thermal"
" gaindrift."
)
if tod.shape[0] != len(focalplane_attr):
raise AssertionError(
"The number of elements in `focalplane_attr` must be same as the"
" number of detectors included in tod object"
)
det_group = np.unique(focalplane_attr)
noise_timestream = np.zeros((len(det_group), tod_size))
for detidx, det_elem in enumerate(det_group):
noise_timestream[detidx][:] = _noise_timestream(
tod_size=tod_size,
sampling_freq_hz=sampling_freq_hz,
focalplane_attr=det_elem,
drift_params=drift_params,
user_seed=user_seed,
)
for detidx in np.arange(tod.shape[0]):
det_mask = focalplane_attr[detidx] == det_group
apply_gaindrift_for_one_detector(
det_tod=tod[detidx],
sampling_freq_hz=sampling_freq_hz,
det_name=det_name[detidx],
drift_params=drift_params,
noise_timestream=noise_timestream[det_mask][
0
], # array[mask] returns an array of shape (1, len(array)).
# Therefore [0] indexing is necessary
user_seed=user_seed,
)
def apply_gaindrift_to_observations(
observations: Union[Observation, List[Observation]],
drift_params: GainDriftParams = None,
user_seed: int = 12345,
component: str = "tod",
):
"""The function to apply gain drift to the TOD of a :class:`.Observation`
instance or a list of observations.
This function is a wrapper around :func:`.apply_gaindrift_to_tod()`
that injects gain drift to the TOD object.
Args:
observations (Union[Observation, List[Observation]]): An instance or a list
of instances of :class:`.Observation`.
drift_params (:class:`.GainDriftParams`, optional): The gain drift
injection parameters object. Defaults to None.
user_seed (int, optional): A seed provided by the user. Defaults
to 12345.
component (str, optional): The name of the TOD on which the gain
drift has to be injected. Defaults to "tod".
"""
if drift_params is None:
drift_params = GainDriftParams()
if isinstance(observations, Observation):
obs_list = [observations]
elif isinstance(observations, list):
obs_list = observations
else:
raise TypeError(
"The parameter `observations` must be an `Observation` or a list of `Observation`."
)
for cur_obs in obs_list:
tod = getattr(cur_obs, component)
det_name = cur_obs.name
sampling_freq_hz = cur_obs.sampling_rate_hz
focalplane_attr = getattr(cur_obs, drift_params.focalplane_group)
apply_gaindrift_to_tod(
tod=tod,
sampling_freq_hz=sampling_freq_hz,
det_name=det_name,
drift_params=drift_params,
focalplane_attr=focalplane_attr,
user_seed=user_seed,
)
|
litebirdREPO_NAMElitebird_simPATH_START.@litebird_sim_extracted@litebird_sim-master@litebird_sim@gaindrifts.py@.PATH_END.py
|
{
"filename": "test_spectral_model_creation.py",
"repo_name": "andycasey/smhr",
"repo_path": "smhr_extracted/smhr-master/smh/tests/test_spectral_model_creation.py",
"type": "Python"
}
|
from __future__ import (division, print_function, absolute_import,
unicode_literals)
import os
from nose.tools import assert_equals, assert_almost_equals, ok_
from smh import Session, LineList
import smh.spectral_models as sm
datadir = os.path.dirname(os.path.abspath(__file__))+'/test_data'
synth_ll_filenames = [datadir+'/linelists/'+fname for fname in ['masseron_linch.txt','lin4077new','lin4554new']]
synth_lls = [LineList.read(filename) for filename in synth_ll_filenames]
synth_elems = ["C", "Sr", "Ba"]
eqw_ll_fname = datadir+'/linelists/complete.list'
eqw_ll = LineList.read(eqw_ll_fname)
def test_create_profile():
session = Session([datadir+'/spectra/hd122563.fits'])
model = sm.ProfileFittingModel(session, eqw_ll[0])
model = sm.ProfileFittingModel(session, eqw_ll[0])
print("Created profile model!")
def test_create_synthesis():
session = Session([datadir+'/spectra/hd122563.fits'])
for ll,elem in zip(synth_lls,synth_elems):
model = sm.SpectralSynthesisModel(session, ll, elem)
print("Created synthesis models!")
def test_import_models_into_session():
session = Session([datadir+'/spectra/hd122563.fits'])
session.import_linelist_as_profile_models(eqw_ll_fname)
print ("Loaded profiles into session")
for fname, elem in zip(synth_ll_filenames, synth_elems):
session.import_linelist_as_synthesis_model(fname, elem)
print ("Loaded syntheses into session")
def test_import_eqw_into_session():
session = Session([datadir+'/spectra/hd122563.fits'])
session.import_linelist_as_profile_models(datadir+"/linelists/frebel13_HD122563.moog2",
import_equivalent_widths=True)
print ("Loaded eqws into session")
if __name__=="__main__":
test_create_profile()
test_create_synthesis()
test_import_models_into_session()
test_import_eqw_into_session()
|
andycaseyREPO_NAMEsmhrPATH_START.@smhr_extracted@smhr-master@smh@tests@test_spectral_model_creation.py@.PATH_END.py
|
{
"filename": "README.md",
"repo_name": "CosmoStatGW/WF4Py",
"repo_path": "WF4Py_extracted/WF4Py-master/docs/README.md",
"type": "Markdown"
}
|
# WF4Py documentation
## Documentation requirements
In order to build the documentation, the following packages have to be installed
* [```sphinx```](<https://www.sphinx-doc.org/en/master>)
* [```sphinx_rtd_theme```](<https://sphinx-rtd-theme.readthedocs.io/en/stable/>)
* [```nbsphinx```](<https://nbsphinx.readthedocs.io/en/0.8.11/>)
* [```myst-parser```](<https://myst-parser.readthedocs.io/en/latest/>)
* [```sphinx-argparse```](<https://sphinx-argparse.readthedocs.io/en/stable/install.html>)
* [```sphinx-copybutton```](<https://sphinx-copybutton.readthedocs.io/en/latest/?badge=latest>)
* [```readthedocs-sphinx-search```](<https://readthedocs-sphinx-search.readthedocs.io/en/latest/>)
* [```docutils```](<https://docutils.sourceforge.io>)
To install them just run in the terminal
```
pip install --upgrade pip
pip install -r docs/docs_requirements.txt
```
## Build the documentation
The HTML documentation can easily be built from the ```docs``` folder, running in the terminal
```
cd docs/
make html
```
The produced ```.html``` files will be stored in the directory ```./build/html```.
It is also possible to build a LaTex version, running in the terminal
```
make latexpdf
```
the output pdf of this command will be ```./build/latex/wf4py.pdf```.
|
CosmoStatGWREPO_NAMEWF4PyPATH_START.@WF4Py_extracted@WF4Py-master@docs@README.md@.PATH_END.py
|
{
"filename": "cirs.py",
"repo_name": "astropy/astropy",
"repo_path": "astropy_extracted/astropy-main/astropy/coordinates/builtin_frames/cirs.py",
"type": "Python"
}
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
from astropy.coordinates.attributes import EarthLocationAttribute, TimeAttribute
from astropy.coordinates.baseframe import base_doc
from astropy.utils.decorators import format_doc
from .baseradec import BaseRADecFrame, doc_components
from .utils import DEFAULT_OBSTIME, EARTH_CENTER
__all__ = ["CIRS"]
doc_footer = """
Other parameters
----------------
obstime : `~astropy.time.Time`
The time at which the observation is taken. Used for determining the
position of the Earth and its precession.
location : `~astropy.coordinates.EarthLocation`
The location on the Earth. This can be specified either as an
`~astropy.coordinates.EarthLocation` object or as anything that can be
transformed to an `~astropy.coordinates.ITRS` frame. The default is the
centre of the Earth.
"""
@format_doc(base_doc, components=doc_components, footer=doc_footer)
class CIRS(BaseRADecFrame):
"""
A coordinate or frame in the Celestial Intermediate Reference System (CIRS).
The frame attributes are listed under **Other Parameters**.
"""
obstime = TimeAttribute(
default=DEFAULT_OBSTIME, doc="The reference time (e.g., time of observation"
)
location = EarthLocationAttribute(
default=EARTH_CENTER, doc="The location on Earth of the observer"
)
# The "self-transform" is defined in icrs_cirs_transformations.py, because in
# the current implementation it goes through ICRS (like GCRS)
|
astropyREPO_NAMEastropyPATH_START.@astropy_extracted@astropy-main@astropy@coordinates@builtin_frames@cirs.py@.PATH_END.py
|
{
"filename": "_shadow.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/layout/ternary/aaxis/tickfont/_shadow.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ShadowValidator(_plotly_utils.basevalidators.StringValidator):
def __init__(
self,
plotly_name="shadow",
parent_name="layout.ternary.aaxis.tickfont",
**kwargs,
):
super(ShadowValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "plot"),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@layout@ternary@aaxis@tickfont@_shadow.py@.PATH_END.py
|
{
"filename": "ModelHParams.md",
"repo_name": "tensorflow/tensorflow",
"repo_path": "tensorflow_extracted/tensorflow-master/tensorflow/lite/g3doc/api_docs/python/tflite_model_maker/recommendation/spec/ModelHParams.md",
"type": "Markdown"
}
|
page_type: reference
description: Class to hold parameters for model architecture configuration.
<link rel="stylesheet" href="/site-assets/css/style.css">
<!-- DO NOT EDIT! Automatically generated file. -->
<div itemscope itemtype="http://developers.google.com/ReferenceObject">
<meta itemprop="name" content="tflite_model_maker.recommendation.spec.ModelHParams" />
<meta itemprop="path" content="Stable" />
<meta itemprop="property" content="__eq__"/>
<meta itemprop="property" content="__ge__"/>
<meta itemprop="property" content="__gt__"/>
<meta itemprop="property" content="__init__"/>
<meta itemprop="property" content="__le__"/>
<meta itemprop="property" content="__lt__"/>
<meta itemprop="property" content="__ne__"/>
</div>
# tflite_model_maker.recommendation.spec.ModelHParams
<!-- Insert buttons and diff -->
<table class="tfo-notebook-buttons tfo-api nocontent" align="left">
<td>
<a target="_blank" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/third_party/recommendation/ml/configs/model_config.py#L19-L37">
<img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />
View source on GitHub
</a>
</td>
</table>
Class to hold parameters for model architecture configuration.
<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link">
<code>tflite_model_maker.recommendation.spec.ModelHParams(
hidden_layer_dims,
eval_top_k,
conv_num_filter_ratios,
conv_kernel_size,
lstm_num_units,
num_predictions=attr_dict['num_predictions'].default
)
</code></pre>
<!-- Placeholder for "Used in" -->
<!-- Tabular view -->
<table class="responsive fixed orange">
<colgroup><col width="214px"><col></colgroup>
<tr><th colspan="2"><h2 class="add-link">Attributes</h2></th></tr>
<tr>
<td>
`hidden_layer_dims`<a id="hidden_layer_dims"></a>
</td>
<td>
List of hidden layer dimensions.
</td>
</tr><tr>
<td>
`eval_top_k`<a id="eval_top_k"></a>
</td>
<td>
Top k to evaluate.
</td>
</tr><tr>
<td>
`conv_num_filter_ratios`<a id="conv_num_filter_ratios"></a>
</td>
<td>
Number of filter ratios for the Conv1D layer.
</td>
</tr><tr>
<td>
`conv_kernel_size`<a id="conv_kernel_size"></a>
</td>
<td>
Size of the Conv1D layer kernel size.
</td>
</tr><tr>
<td>
`lstm_num_units`<a id="lstm_num_units"></a>
</td>
<td>
Number of units for the LSTM layer.
</td>
</tr><tr>
<td>
`num_predictions`<a id="num_predictions"></a>
</td>
<td>
Number of predictions to return with serving mode, which
has default value 10.
</td>
</tr>
</table>
## Methods
<h3 id="__eq__"><code>__eq__</code></h3>
<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link">
<code>__eq__(
other
)
</code></pre>
Method generated by attrs for class ModelConfig.
<h3 id="__ge__"><code>__ge__</code></h3>
<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link">
<code>__ge__(
other
)
</code></pre>
Method generated by attrs for class ModelConfig.
<h3 id="__gt__"><code>__gt__</code></h3>
<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link">
<code>__gt__(
other
)
</code></pre>
Method generated by attrs for class ModelConfig.
<h3 id="__le__"><code>__le__</code></h3>
<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link">
<code>__le__(
other
)
</code></pre>
Method generated by attrs for class ModelConfig.
<h3 id="__lt__"><code>__lt__</code></h3>
<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link">
<code>__lt__(
other
)
</code></pre>
Method generated by attrs for class ModelConfig.
<h3 id="__ne__"><code>__ne__</code></h3>
<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link">
<code>__ne__(
other
)
</code></pre>
Method generated by attrs for class ModelConfig.
|
tensorflowREPO_NAMEtensorflowPATH_START.@tensorflow_extracted@tensorflow-master@tensorflow@lite@g3doc@api_docs@python@tflite_model_maker@recommendation@spec@ModelHParams.md@.PATH_END.py
|
{
"filename": "decorators.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/numpy/py2/numpy/testing/_private/decorators.py",
"type": "Python"
}
|
"""
Decorators for labeling and modifying behavior of test objects.
Decorators that merely return a modified version of the original
function object are straightforward. Decorators that return a new
function object need to use
::
nose.tools.make_decorator(original_function)(decorator)
in returning the decorator, in order to preserve meta-data such as
function name, setup and teardown functions and so on - see
``nose.tools`` for more information.
"""
from __future__ import division, absolute_import, print_function
try:
# Accessing collections abstract classes from collections
# has been deprecated since Python 3.3
import collections.abc as collections_abc
except ImportError:
import collections as collections_abc
from .utils import SkipTest, assert_warns, HAS_REFCOUNT
__all__ = ['slow', 'setastest', 'skipif', 'knownfailureif', 'deprecated',
'parametrize', '_needs_refcount',]
def slow(t):
"""
Label a test as 'slow'.
The exact definition of a slow test is obviously both subjective and
hardware-dependent, but in general any individual test that requires more
than a second or two should be labeled as slow (the whole suite consists of
thousands of tests, so even a second is significant).
Parameters
----------
t : callable
The test to label as slow.
Returns
-------
t : callable
The decorated test `t`.
Examples
--------
The `numpy.testing` module includes ``import decorators as dec``.
A test can be decorated as slow like this::
from numpy.testing import *
@dec.slow
def test_big(self):
print('Big, slow test')
"""
t.slow = True
return t
def setastest(tf=True):
"""
Signals to nose that this function is or is not a test.
Parameters
----------
tf : bool
If True, specifies that the decorated callable is a test.
If False, specifies that the decorated callable is not a test.
Default is True.
Notes
-----
This decorator can't use the nose namespace, because it can be
called from a non-test module. See also ``istest`` and ``nottest`` in
``nose.tools``.
Examples
--------
`setastest` can be used in the following way::
from numpy.testing import dec
@dec.setastest(False)
def func_with_test_in_name(arg1, arg2):
pass
"""
def set_test(t):
t.__test__ = tf
return t
return set_test
def skipif(skip_condition, msg=None):
"""
Make function raise SkipTest exception if a given condition is true.
If the condition is a callable, it is used at runtime to dynamically
make the decision. This is useful for tests that may require costly
imports, to delay the cost until the test suite is actually executed.
Parameters
----------
skip_condition : bool or callable
Flag to determine whether to skip the decorated test.
msg : str, optional
Message to give on raising a SkipTest exception. Default is None.
Returns
-------
decorator : function
Decorator which, when applied to a function, causes SkipTest
to be raised when `skip_condition` is True, and the function
to be called normally otherwise.
Notes
-----
The decorator itself is decorated with the ``nose.tools.make_decorator``
function in order to transmit function name, and various other metadata.
"""
def skip_decorator(f):
# Local import to avoid a hard nose dependency and only incur the
# import time overhead at actual test-time.
import nose
# Allow for both boolean or callable skip conditions.
if isinstance(skip_condition, collections_abc.Callable):
skip_val = lambda: skip_condition()
else:
skip_val = lambda: skip_condition
def get_msg(func,msg=None):
"""Skip message with information about function being skipped."""
if msg is None:
out = 'Test skipped due to test condition'
else:
out = msg
return "Skipping test: %s: %s" % (func.__name__, out)
# We need to define *two* skippers because Python doesn't allow both
# return with value and yield inside the same function.
def skipper_func(*args, **kwargs):
"""Skipper for normal test functions."""
if skip_val():
raise SkipTest(get_msg(f, msg))
else:
return f(*args, **kwargs)
def skipper_gen(*args, **kwargs):
"""Skipper for test generators."""
if skip_val():
raise SkipTest(get_msg(f, msg))
else:
for x in f(*args, **kwargs):
yield x
# Choose the right skipper to use when building the actual decorator.
if nose.util.isgenerator(f):
skipper = skipper_gen
else:
skipper = skipper_func
return nose.tools.make_decorator(f)(skipper)
return skip_decorator
def knownfailureif(fail_condition, msg=None):
"""
Make function raise KnownFailureException exception if given condition is true.
If the condition is a callable, it is used at runtime to dynamically
make the decision. This is useful for tests that may require costly
imports, to delay the cost until the test suite is actually executed.
Parameters
----------
fail_condition : bool or callable
Flag to determine whether to mark the decorated test as a known
failure (if True) or not (if False).
msg : str, optional
Message to give on raising a KnownFailureException exception.
Default is None.
Returns
-------
decorator : function
Decorator, which, when applied to a function, causes
KnownFailureException to be raised when `fail_condition` is True,
and the function to be called normally otherwise.
Notes
-----
The decorator itself is decorated with the ``nose.tools.make_decorator``
function in order to transmit function name, and various other metadata.
"""
if msg is None:
msg = 'Test skipped due to known failure'
# Allow for both boolean or callable known failure conditions.
if isinstance(fail_condition, collections_abc.Callable):
fail_val = lambda: fail_condition()
else:
fail_val = lambda: fail_condition
def knownfail_decorator(f):
# Local import to avoid a hard nose dependency and only incur the
# import time overhead at actual test-time.
import nose
from .noseclasses import KnownFailureException
def knownfailer(*args, **kwargs):
if fail_val():
raise KnownFailureException(msg)
else:
return f(*args, **kwargs)
return nose.tools.make_decorator(f)(knownfailer)
return knownfail_decorator
def deprecated(conditional=True):
"""
Filter deprecation warnings while running the test suite.
This decorator can be used to filter DeprecationWarning's, to avoid
printing them during the test suite run, while checking that the test
actually raises a DeprecationWarning.
Parameters
----------
conditional : bool or callable, optional
Flag to determine whether to mark test as deprecated or not. If the
condition is a callable, it is used at runtime to dynamically make the
decision. Default is True.
Returns
-------
decorator : function
The `deprecated` decorator itself.
Notes
-----
.. versionadded:: 1.4.0
"""
def deprecate_decorator(f):
# Local import to avoid a hard nose dependency and only incur the
# import time overhead at actual test-time.
import nose
def _deprecated_imp(*args, **kwargs):
# Poor man's replacement for the with statement
with assert_warns(DeprecationWarning):
f(*args, **kwargs)
if isinstance(conditional, collections_abc.Callable):
cond = conditional()
else:
cond = conditional
if cond:
return nose.tools.make_decorator(f)(_deprecated_imp)
else:
return f
return deprecate_decorator
def parametrize(vars, input):
"""
Pytest compatibility class. This implements the simplest level of
pytest.mark.parametrize for use in nose as an aid in making the transition
to pytest. It achieves that by adding a dummy var parameter and ignoring
the doc_func parameter of the base class. It does not support variable
substitution by name, nor does it support nesting or classes. See the
pytest documentation for usage.
.. versionadded:: 1.14.0
"""
from .parameterized import parameterized
return parameterized(input)
_needs_refcount = skipif(not HAS_REFCOUNT, "python has no sys.getrefcount")
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@numpy@py2@numpy@testing@_private@decorators.py@.PATH_END.py
|
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