diff --git a/README.rst b/README.rst
index 0e10efaa..896af0b0 100644
--- a/README.rst
+++ b/README.rst
@@ -27,7 +27,7 @@
.. image:: https://mybinder.org/badge_logo.svg
:target: https://mybinder.org/v2/gh/tomasstolker/species/HEAD
-*species* is a toolkit for atmospheric characterization of directly imaged exoplanets. It provides a coherent framework for spectral and photometric analysis which builds on publicly-available data and models from various resources. There are tools available for both grid retrievals and free retrievals with Bayesian inference, color-magnitude and color-color diagrams, empirical spectral analysis, spectral and photometric calibration, and synthetic photometry. The package has been released on `PyPI `_ and is actively developed and maintained on Github.
+*species* is a toolkit for atmospheric characterization of directly imaged exoplanets. It provides a coherent framework for spectral and photometric analysis which builds on publicly-available data and models from various resources. There are tools available for both grid retrievals and free retrievals with Bayesian inference, color-magnitude and color-color diagrams, empirical spectral analysis, spectral and photometric calibration, analysis of emission lines, and synthetic photometry. The package has been released on `PyPI `_ and is actively developed and maintained on Github.
Documentation
-------------
diff --git a/docs/about.rst b/docs/about.rst
index 5d7917cb..48c568af 100644
--- a/docs/about.rst
+++ b/docs/about.rst
@@ -6,7 +6,7 @@ About
Questions & feedback
--------------------
-*species* is developed by Tomas Stolker (stolker@strw.leidenuniv.nl). Feel free to send an email for questions, comments, or suggestions.
+*species* has been developed and is maintained by Tomas Stolker (stolker@strw.leidenuniv.nl). Feel free to send an email for questions, comments, or suggestions.
Attribution
-----------
diff --git a/docs/configuration.rst b/docs/configuration.rst
index dc498ef1..abe8d412 100644
--- a/docs/configuration.rst
+++ b/docs/configuration.rst
@@ -28,7 +28,7 @@ The workflow with *species* can now be initiated with the :class:`~species.core.
>>> import species
>>> species.SpeciesInit()
-Alternatively, a configuration file with default values is automatically created when `species` is initiated and the configuration file is not present in the working folder.
+A configuration file with default values is automatically created when `species` is initiated and the file is not present in the working folder.
.. tip::
The same `data_folder` can be used in multiple configuration files. In this way, the data is only downloaded once and easily reused by a new instance of :class:`~species.core.setup.SpeciesInit`. Also the HDF5 database can be reused by simply including the same `database` in the configuration file.
diff --git a/docs/contributing.rst b/docs/contributing.rst
index 3fdd614e..0e7132c7 100644
--- a/docs/contributing.rst
+++ b/docs/contributing.rst
@@ -3,4 +3,4 @@
Contributing
============
-Contributions are very welcome, please consider `forking `_ the repository and creating a `pull request `_. Bug reports and feature requests can be provided by creating an `issue `_ on the Github page.
+Contributions are very welcome so please consider `forking `_ the repository and creating a `pull request `_. Bug reports and feature requests can be provided by creating an `issue `_ on the Github page.
diff --git a/docs/index.rst b/docs/index.rst
index e3628298..8127d9bb 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -3,11 +3,7 @@
Documentation for *species*
===========================
-*species* is a toolkit for atmospheric characterization of directly imaged exoplanets. The package has been developed and is maintained by |stolker|.
-
-.. |stolker| raw:: html
-
- Tomas Stolker
+*species* is a toolkit for atmospheric characterization of directly imaged exoplanets.
.. toctree::
:maxdepth: 2
diff --git a/docs/installation.rst b/docs/installation.rst
index cb25c436..751f8671 100644
--- a/docs/installation.rst
+++ b/docs/installation.rst
@@ -3,18 +3,18 @@
Installation
============
-*species* is compatible with Python 3.6/3.7/3.8 and is available in the |pypi| and on |github|.
+*species* is compatible with Python 3.6/3.7/3.8 and is available in the `PyPI repository `_ and on `Github `_.
Installation from PyPI
----------------------
-*species* can be installed with the |pip|:
+The Python package can be installed with the `pip package manager `_:
.. code-block:: console
$ pip install species
-And to update to the most recent version:
+Or, to update to the most recent version:
.. code-block:: console
@@ -39,7 +39,7 @@ And running the setup script to install the package and its dependencies:
$ python setup.py install
.. important::
- If an error occurs when running ``setup.py`` then possibly ``pip`` needs to be updated the latest version:
+ If an error occurs when running ``setup.py`` then possibly ``pip`` needs to be updated to the latest version:
.. code-block:: console
@@ -62,21 +62,9 @@ Do you want to make changes to the code? Please fork the `species` repository on
Testing `species`
-----------------
-The installation can be tested by starting Python in interactive mode and printing the `species` version:
+The installation can now be tested, for example by starting Python in interactive mode and printing the version number of the installed package:
.. code-block:: python
>>> import species
>>> species.__version__
-
-.. |pypi| raw:: html
-
- PyPI repository
-
-.. |github| raw:: html
-
- Github
-
-.. |pip| raw:: html
-
- pip package manager
diff --git a/docs/overview.rst b/docs/overview.rst
index 92ed2561..434ae5f8 100644
--- a/docs/overview.rst
+++ b/docs/overview.rst
@@ -6,12 +6,12 @@ Overview
Introduction
------------
-*species* provides a coherent framework for spectral and photometric analysis of directly imaged planets and brown dwarfs. This page contains a short overview of the various data that are supported and some of the tools and features that have been implemented.
+*species* provides a coherent framework for spectral and photometric analysis of directly imaged planets and brown dwarfs. This page contains a short overview of the various data that are supported and some of the tools and features that are provided.
Supported data
--------------
-The *species* toolkit benefits from publicly available data resources such as photometric and spectral libraries, atmospheric model spectra, evolutionary tracks, and photometry of directly imaged planets and brown dwarfs. The relevant data are automatically downloaded and added to the HDF5 database, which acts as the central data storage for a workflow. All data are stored in a fixed format such that the analysis and plotting tools can easily access and manipulate the data.
+The toolkit benefits from publicly available data resources such as photometric and spectral libraries, atmospheric model spectra, evolutionary tracks, and photometry of directly imaged, low-mass objects. The relevant data are automatically downloaded and added to the HDF5 database, which acts as the central data storage for a workflow. All data are stored in a fixed format such that the analysis and plotting tools can easily access and process the data.
The following data and models are currently supported:
@@ -55,7 +55,7 @@ The following data and models are currently supported:
- Extinction cross sections computed with `PyMieScatt `_
- Optical constants compiled by `Mollière et al. (2019) `_
-Please give credit to the relevant authors when using any of the external data in a publication. More information is available on the respective websites. Support for other datasets can be requested by creating an `issue `_ on the Github page.
+Please give credit to the relevant references when using any of the external data in a publication. More information is available on the respective websites. Support for other datasets can be requested by creating an `issue `_ on the Github page.
Analysis tools
--------------
@@ -72,3 +72,4 @@ After adding the relevant data to the database, the user can take advantage of t
- Computing synthetic fluxes from isochrones and model spectra (see :class:`~species.read.read_isochrone.ReadIsochrone`)
- Flux calibration of photometric and spectroscopic data (see :class:`~species.read.read_calibration.ReadCalibration`, :class:`~species.analysis.fit_model.FitModel`, and :class:`~species.analysis.fit_spectrum.FitSpectrum`).
- Empirical comparison of spectra to infer the spectral type (see :class:`~species.analysis.empirical.CompareSpectra`).
+- Analyzing emission lines from accreting planets (see :class:`~species.analysis.emission_line.EmissionLine`).
diff --git a/docs/species.analysis.rst b/docs/species.analysis.rst
index 73523e20..6c622670 100644
--- a/docs/species.analysis.rst
+++ b/docs/species.analysis.rst
@@ -4,6 +4,14 @@ species.analysis package
Submodules
----------
+species.analysis.emission\_line module
+--------------------------------------
+
+.. automodule:: species.analysis.emission_line
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
species.analysis.empirical module
---------------------------------
diff --git a/docs/tutorials.rst b/docs/tutorials.rst
index 6f9aa739..831e6f76 100644
--- a/docs/tutorials.rst
+++ b/docs/tutorials.rst
@@ -20,6 +20,7 @@ This page contains a list of tutorials which highlight some of the functionaliti
tutorials/spectral_library.ipynb
tutorials/fitting_model_spectra.ipynb
tutorials/flux_calibration.ipynb
+ tutorials/emission_line.ipynb
.. important::
A flux calibrated spectrum of Vega is used for the conversion between a flux density and magnitude. The magnitude of Vega is set to 0.03 for all filters. If needed, the magnitude of Vega can be changed with the ``vega_mag`` attribute of a ``SyntheticPhotometry`` object:
diff --git a/docs/tutorials/emission_line.ipynb b/docs/tutorials/emission_line.ipynb
new file mode 100644
index 00000000..9f2bd836
--- /dev/null
+++ b/docs/tutorials/emission_line.ipynb
@@ -0,0 +1,6503 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Emission line analysis"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this tutorial we will analyze the Pa$\\beta$ emission line in the $J$ band spectrum of GQ Lup B. This hydrogen emission line is related to the accretion processes in the circumsubstellar disk around this young, directly imaged brown dwarf."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Getting started"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We start by importing the required Python modules."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import urllib.request\n",
+ "import species\n",
+ "from IPython.display import Image"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Next, we initiate the *species* workflow by running the [SpeciesInit](https://species.readthedocs.io/en/latest/species.core.html?highlight=speciesinit#species.core.setup.SpeciesInit) class."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Initiating species v0.3.5... [DONE]\n",
+ "Creating species_config.ini... [DONE]\n",
+ "Database: /Users/tomasstolker/applications/species/docs/tutorials/species_database.hdf5\n",
+ "Data folder: /Users/tomasstolker/applications/species/docs/tutorials/data\n",
+ "Working folder: /Users/tomasstolker/applications/species/docs/tutorials\n",
+ "Creating species_database.hdf5... [DONE]\n",
+ "Creating data folder... [DONE]\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "species.SpeciesInit()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will also download the VLT/SINFONI $J$ band spectrum of GQ Lup B that has been published by [Seifahrt et al. (2007)](https://ui.adsabs.harvard.edu/abs/2007A%26A...463..309S/abstract)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "('gqlupb_sinfoni_j.dat', )"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "urllib.request.urlretrieve('https://home.strw.leidenuniv.nl/~stolker/species/gqlupb_sinfoni_j.dat',\n",
+ " 'gqlupb_sinfoni_j.dat')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Adding data of GQ Lup B to the database"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To add data to the HDF5 database, we first create an instance of [Database](https://species.readthedocs.io/en/latest/species.data.html?highlight=database#species.data.database.Database). This object provides read and write access to the HDF5 file."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "database = species.Database()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Next, we will add the available [magnitudes and distance](https://github.com/tomasstolker/species/blob/master/species/data/companions.py) of GQ Lup B to the database (only the distance is required later on for calculating the line luminosity) by using the [add_companion](https://species.readthedocs.io/en/latest/species.data.html?highlight=database#species.data.database.Database.add_companion) method of `Database`. This method will add the magnitudes and filter profiles, but it also downloads a flux-calibrated spectrum of Vega and converts the magnitudes into fluxes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Downloading Vega spectrum (270 kB)... [DONE]\n",
+ "Adding Vega spectrum... [DONE]\n",
+ "Adding filter: HST/WFPC2-PC.F606W... [DONE]\n",
+ "Adding filter: HST/WFPC2-PC.F814W... [DONE]\n",
+ "Adding filter: HST/NICMOS2.F171M... [DONE]\n",
+ "Adding filter: HST/NICMOS2.F190N... [DONE]\n",
+ "Adding filter: HST/NICMOS2.F215N... [DONE]\n",
+ "Adding filter: Magellan/VisAO.ip... [DONE]\n",
+ "Adding filter: Magellan/VisAO.zp... [DONE]\n",
+ "Adding filter: Magellan/VisAO.Ys... [DONE]\n",
+ "Adding filter: Paranal/NACO.Ks... [DONE]\n",
+ "Adding filter: Subaru/CIAO.CH4s... [DONE]\n",
+ "Adding filter: Subaru/CIAO.K... [DONE]\n",
+ "Adding filter: Subaru/CIAO.Lp... [DONE]\n",
+ "Adding object: GQ Lup B\n",
+ " - Distance (pc) = 151.82 +/- 1.10\n",
+ " - HST/WFPC2-PC.F606W:\n",
+ " - Apparent magnitude = 19.19 +/- 0.07\n",
+ " - Flux (W m-2 um-1) = 5.94e-16 +/- 3.83e-17\n",
+ " - HST/WFPC2-PC.F814W:\n",
+ " - Apparent magnitude = 17.67 +/- 0.05\n",
+ " - Flux (W m-2 um-1) = 1.02e-15 +/- 4.68e-17\n",
+ " - HST/NICMOS2.F171M:\n",
+ " - Apparent magnitude = 13.84 +/- 0.13\n",
+ " - Flux (W m-2 um-1) = 2.91e-15 +/- 3.49e-16\n",
+ " - HST/NICMOS2.F190N:\n",
+ " - Apparent magnitude = 14.08 +/- 0.20\n",
+ " - Flux (W m-2 um-1) = 1.65e-15 +/- 3.06e-16\n",
+ " - HST/NICMOS2.F215N:\n",
+ " - Apparent magnitude = 13.40 +/- 0.15\n",
+ " - Flux (W m-2 um-1) = 1.94e-15 +/- 2.69e-16\n",
+ " - Magellan/VisAO.ip:\n",
+ " - Apparent magnitude = 18.89 +/- 0.24\n",
+ " - Flux (W m-2 um-1) = 3.77e-16 +/- 8.40e-17\n",
+ " - Magellan/VisAO.zp:\n",
+ " - Apparent magnitude = 16.40 +/- 0.10\n",
+ " - Flux (W m-2 um-1) = 2.33e-15 +/- 2.15e-16\n",
+ " - Magellan/VisAO.Ys:\n",
+ " - Apparent magnitude = 15.88 +/- 0.10\n",
+ " - Flux (W m-2 um-1) = 3.09e-15 +/- 2.85e-16\n",
+ " - Paranal/NACO.Ks (4 values):\n",
+ " - Apparent magnitude = 13.47 +/- 0.03\n",
+ " - Flux (W m-2 um-1) = 1.88e-15 +/- 5.36e-17\n",
+ " - Apparent magnitude = 13.39 +/- 0.03\n",
+ " - Flux (W m-2 um-1) = 2.03e-15 +/- 6.00e-17\n",
+ " - Apparent magnitude = 13.50 +/- 0.05\n",
+ " - Flux (W m-2 um-1) = 1.84e-15 +/- 8.47e-17\n",
+ " - Apparent magnitude = 13.50 +/- 0.03\n",
+ " - Flux (W m-2 um-1) = 1.83e-15 +/- 4.72e-17\n",
+ " - Subaru/CIAO.CH4s:\n",
+ " - Apparent magnitude = 13.76 +/- 0.26\n",
+ " - Flux (W m-2 um-1) = 3.96e-15 +/- 9.58e-16\n",
+ " - Subaru/CIAO.K:\n",
+ " - Apparent magnitude = 13.37 +/- 0.12\n",
+ " - Flux (W m-2 um-1) = 1.87e-15 +/- 2.07e-16\n",
+ " - Subaru/CIAO.Lp:\n",
+ " - Apparent magnitude = 12.44 +/- 0.22\n",
+ " - Flux (W m-2 um-1) = 5.78e-16 +/- 1.18e-16\n"
+ ]
+ }
+ ],
+ "source": [
+ "database.add_companion('GQ Lup B')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that the photometric data of GQ Lup B are stored in the database, we will add the $J$ band spectrum by using the [add_object](https://species.readthedocs.io/en/latest/species.data.html?highlight=database#species.data.database.Database.add_object) method. Here, it is important that we use the same argument for `object_name` as was used as `name` with `add_companion` (i.e. *GQ Lup B*). The argument of `spectrum` contains a dictionary with the spectra. Each value is a tuple with the spectrum, covariance matrix (set to `None` in this case), and the spectral resolution."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Adding object: GQ Lup B\n",
+ " - Spectrum:\n",
+ " - Database tag: SINFONI\n",
+ " - Filename: gqlupb_sinfoni_j.dat\n",
+ " - Data shape: (1014, 3)\n",
+ " - Wavelength range (um): 1.20 - 1.35\n",
+ " - Mean flux (W m-2 um-1): 3.27e-15\n",
+ " - Mean error (W m-2 um-1): 2.79e-17\n",
+ " - Spectral resolution:\n",
+ " - SINFONI: 2500.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "database.add_object('GQ Lup B',\n",
+ " app_mag=None,\n",
+ " flux_density=None,\n",
+ " spectrum={'SINFONI': ('gqlupb_sinfoni_j.dat', None, 2500.)},\n",
+ " deredden=None)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Initiating the line analysis"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will now analyze the emission line by first creating an instance of [EmissionLine](https://species.readthedocs.io/en/latest/species.data.html?highlight=database#species.analysis.emission_line.EmissionLine). The spectrum is read from the database by providing the same object name and spectrum name as were used with `add_object`. We will select a limited wavelength range around the Pa$\\beta$ line by setting the argument of `wavel_range`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "line = species.EmissionLine(object_name='GQ Lup B',\n",
+ " spec_name='SINFONI',\n",
+ " wavel_range=(1.26, 1.32))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's have a look at the data of the cropped $J$ spectrum that is extracted."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[1.26000500e+00, 3.28487860e-15, 1.58013906e-17],\n",
+ " [1.26014996e+00, 3.30313622e-15, 1.06466256e-17],\n",
+ " [1.26029503e+00, 3.32310600e-15, 1.08635306e-17],\n",
+ " ...,\n",
+ " [1.31959999e+00, 3.36931744e-15, 8.78280340e-18],\n",
+ " [1.31974494e+00, 3.40047958e-15, 6.23790556e-17],\n",
+ " [1.31989002e+00, 3.39223842e-15, 3.46253392e-17]])"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "line.spectrum"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Subtracting the continuum flux"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the $J$ band, we need to subtract the pseudo-continuum of the atmospheric emission. To do so, we make use of the [subtract_continuum](https://species.readthedocs.io/en/latest/species.data.html?highlight=database#species.analysis.emission_line.EmissionLine) method of `EmissionLine`. The continuum is modeled with a 3rd order polynomial (after smoothing the spectrum) and we use a linear least square fitting algorithm to optimize the parameters. A plot is created with the result which can be inspected before continuing with the further analysis."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Fitting continuum... [DONE]\n",
+ "Plotting continuum fit: continuum.png... [DONE]\n"
+ ]
+ }
+ ],
+ "source": [
+ "line.subtract_continuum(poly_degree=3,\n",
+ " plot_filename='continuum.png')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's have a look at the best-fit polynomial model and the continuum-subtracted spectrum."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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DpW3btrJnzx659tprBZDOnTvLsWPHRETk5ptvlpYtW8qIESPkvvvuk4iICHn++edFROTIkSPSpUsX6dy5s0yYMEF69eplL/v111+XiIgIqV+/vvz0009yzz332I+1Y8cOe9379++f75z8/PPP0rx5c6lUqZL07t1bEhISpG/fvva6HThwQI4fPy7t27eXbt26yQMPPODy+c51/XJ6nTTWfc4/xpivgYHAVSKyxMn2UcA+Z9vy7GeNAs7T86TKBtvd/4svvshjjz3Gs88+yzPPPENWVhb+/v6lXT2l1DnINpJARJw14bs/CsAY07WYdfI5Y8wlwFkgsbTropQ3pKWlAf91XLJ9zd0mqZRS7vBkGODPXq+F903OWYpylzFmqTFmiTHmXl9XSilPFRQA2NYrpZS7PAkAnKYSzhXGmCuArSJyoIhdjwILgGisjze+zxhT4BRLxhiHJfdkE0r5Wt4AwNYZUAMApZRNTEyMw3WqKJ7MBHjONoYbY/yAicB1Re0rIr/meplgjHkbeAB4u4D9vVJHpTxRUAZARwIopWxiYmIcbk6LCgLOt2cB3AT8JiLHPXhvPFDfy/VRyiu0CUAp5W3nWwDQE7g6p01/CVATeNMYk6/fgjHm0TyrIgB9roE6J9ku9LbUvwYASqniKtbDgM41InJX7tfGmH3AQyKyxBhzETANGCAi2cAVxpiVIrLUGBMG3AV8UeKVVsoFBfUB0CYApZSnPAkANnq9Fl5mjOkEvMJ/GYCfsHb4awEEAtnA68DTOf0GygMLgZdKp8ZKFU6bAJRS3uZ2ACAi/X1REW8SkVVYe/fnVSfXPnOAOSVVJ6WKw3anrwGAUspbfNIHwBijqXSlvEgzAEopb/O4D4AxpgnQG2uaPe9cpP2KUymllCMdBqiU8jaPAoCcWfPepuBJgXTQvFJepBMBKaW8zdMmgEeBsUB1EfHLuwCbvFdFpZQ2ASilvM3TJoBEEXm/kO0jPSxXKeWEBgBKKW/zNAMw0xgTVcj2uwrZppRyk+1CHxwcDGgfAKVU8XmUARCRF40xU4wxjYFdQEqeXYYABT5YRynlnrS0NEJCQuxze/v7+xMYGKgZAKWUx4rTCXBCIbtoJ0ClvMgWAOQWEhKiAYBSymOeNgE8jjXNX007ASrle6mpqRoAKKW8ytNOgAki8lEh27UToFJeVFAGQPsAKKU85WkG4HftBKhUydEmAKWUt3maAUgHZhlj1qOdAJXyOWcBQGhoqAYASimPeRoAPJnztWEB27UToFJepBkApZS3edoEsNFZ5z/tBKiUb2gfAKWUt3kaADxdxHZN/yvlRWlpafb5/200A6CUKg6PAgARmV3E9r88q45SyhntA6CU8jZPMwCFMsbM8kW5Sl2otAlAKeVtns4EWFQTQCdPylVKOVfQREAaACilPOXpKICYIrbrKAClvMhZBiAsLEwDAKWUx7w2CgAoD3QEpgHdvFZDpZQGAEopr/M0A3BT3hUikgKsy5kcaBnQszgVU0r9p6AAICUlBRGxPyVQKaVc5ekogO1FlBmZe4Ux5nVPjqOUgqysLLKyspwGACJCenp6KdVMKVWWedoJ8BZnq4HKwDVApjFmZM46sE4NPN6jGip1gbNd4PPOA2B7nZKSki84UEqponjaBDDdhX1m5PpeOwUq5SHbWH9nGQCwBgBVqlQp8Xoppco2TwOA7cCVLu5rgLkeHkepC54rAYBSSrnL0wBgsojEubqzMeYtD4+j1AVPAwCllC942gnwOzf3/8CT4yilsA/10wBAKeVNPpkKWCnlPUVlAHQuAKWUJzQAUOocp00ASilf0ABAqXNcQQFA7mGASinlLg0AlDrHaQZAKeULGgAodY7TToBKKV8oMgAwxjQ2xiw2xuwxxrxhjAnJtW2Vb6unlLJd4MuVK+ewXgMApVRxuJIBeAf4CRgKVAf+MMaUz9kW6KuKKaWskpOTgfwBgPYBUEoVhysBQISIvC0ia0VkJPA78LsxpgI6xa9SPmcLAGx3/Db+/v4EBwdrAKCU8ogrMwE6PIFERCYbY7KBBUB5529RSnlLQQGAbZ3OA6CU8oQrGYBYY0zf3CtE5HlgPtDEJ7VSStklJycTEhKCv79/vm2hoaGaAVBKecSVDMBInKT6czIB33u/Skqp3FJSUvK1/9uEhYVpAKCU8kiRAYCInCxk2zav1kYplU9ycrIGAEopr3N7HgBjTFdfVEQp5ZwGAEopX/BkIqCfvV4LHzDGNDXGZBpjogvZZ7wxZm3OMqHkaqeU6zQAUEr5git9APIyXq+FbzwLZBS00RhzOXAn0C5n1QZjzDYRmVsCdVPKZcnJyU5HAIA1ADhx4kQJ10gpdT7wJANwzo/9N8ZcApwFEgvZ7S7gKxFJE5E04Evg7pKon1LuKCwDUL58ec6cOVPCNVJKnQ/O12cBTM5ZCnMJsCPX621AR5/VSCkPFTYKIDw8nJMnT5ZshZRS54XzLgAwxlwBbBWRA0XsGgGcyvX6JFCjkHIdlpiYmGLXVSlXFJYBqFy5MidOnEDknE/MKaV8LCYmxuE6VRRP+gCcs4wxfsBE4DoX3+Lyf039B6tKS1EBQHZ2NsnJyZQvrxNzKnUhi4mJcbg5LSoI8CQDsNGD95SUm4DfROS4C/smAOG5XodTeJ8BpUpFYQFAeHg4gHYEVEq5ze0AQET6+6IiXtITuNoYs8QYswSoCbxpjHE2dHE10DzX65Y565Q6Z2RnZ5OWllZoBgA0AFBKuc8nfQCMMV/4otyiiMhdItJdRKJFJBo4AjwkIoOMMRcZYxYaY2wTqr8P3GiMCTHGhGDNHrxfGvVWqiC2Mf4FDQO0BQDaEVAp5S6P+wAYY5oAvbHeZed9Skm/4lSquIwxnYBX+C8D8BPWpxe2AAKBbBGZb4xpBfyd87ZPdQ4Ada6xBQDaBKCU8jaPAgBjzL3A2xQ8KVCp9pgTkVVAtJNNdfLs9zrweknUSSlP2B4FrE0ASilv87QJ4FFgLFBdRPzyLsAm71VRqQuXqwGANgEopdzlaRNAoogU1l4+0sNylVK5FBUAVKxYEdAMgFLKfZ5mAGYaY6IK2X6Xh+UqpXIpKgDw9/enUqVKGgAopdzmUQZARF40xkwxxjQGdgF5H0c2BLi/uJVT6kJnu7BXqlSpwH0qV66sTQBKKbcVpxNgYY/P1WnzlPKCo0ePAhAREVHgPuHh4ZoBUEq5zdMmgMexpvmraSdApXwnISEBYwzVqlUrcJ/KlStz/Lgrk18qpdR/PA0AEkTko0Km3NVOgEp5wdGjR6lWrRoBAQUn62rVqsXhw4dLsFZKqfOBpwHA79oJUCnfO3r0aKHpf4C6dety8OBBLBZLCdVKKXU+8HQYYDowyxizHu0EqJTPuBoAZGRkkJiYWOS+Sill42kA8GTO14YFbNdOgEp5wdGjR+nSpUuh+0RGRgJw4MABDQCUUi7ztAlgo7POf9oJUCnvOnr0KDVq1Ch0n7p16wKwf//+kqiSUuo84WkA8HQR2zX9r1QxJScnk5ycXORdfe4MgFJKucqjAEBEZhex/S/PqqOUsklISAAKnwMAoHr16gQGBmoAoJRyi6cZAKWUj9mG9hUVAPj5+VGnTh1tAlBKuUUDAKXOUdu3bwegefPmRe5br1494uPjfV0lpdR5RAMApc5RW7ZsITQ0lIYNCxps858GDRqwd+/eEqiVUup8oQGAUueozZs306pVK/z8iv4zbdiwIYcOHSI9Pb0EaqaUOh+4HAAYY67zZUWUUo62bNlCVFRhE27+p2HDhogIcXFxPq6VUup84U4G4EVjTMHPJFVKeU1iYiJHjx6ldevWLu1vaybQZgCllKvcmQmwOjDfGGMBFgILgH9EJNsnNVPqArZlyxYAtzIAoAGAUsp17mQAdohIV+AaYCswCthkjPnZGDPWGNPUFxVU6kJkCwBczQDUrl2bwMBADQCUUi5zOQMgIt1zvh4Hvs1ZMMa0APoDbxhjGgD/AL+JyI9er61SF4jNmzdTpUoVatas6dL+/v7+NGjQgD179vi4Zkqp84U3RgHEYX0i4B4gBLgD+M4L5Sp13pg3bx59+vRh0qRJbN68ucje+rYOgMYYl4/RtGlTdu7cWdyqKqUuEB4FAMaYdsaYR40xC4HjwBzgHuAw1ucEdPVeFZUq+/73v/+xZMkSXnvtNdq0aUOfPn0K3FdE2LJli8vpf5sWLVqwc+dOLBZLcaurlLoAuDMMcKQx5gtjzGFgLfAyUBf4GLgWqCIivUTkeRFZ5ZvqKlU2bdu2jSuuuIJdu3Zx11138c8//7Br1y6n++7fv58zZ864HQA0b96ctLQ0nRFQKeUSdzIAM4CbgATgLqChiDQXkftF5BcROeuTGipVxmVnZ7Njxw5atWpFgwYNePzxxwH4/vvvne6/e/duwJrSd0eLFi0A+Pfff4tRW6XUhcKdAKA1MB44CEwAnjbGDDfGVPVJzZQ6T+zZs4f09HRatmwJWOft79Kliz0AOHLkCKmpqQ77AzRq1Mit49ieGaABgFLKFS4HACKyTUTeFJErgSjgS6A98JsxZpUx5kVjTLQxxp25BZQ6JyUnJ7Np0yavlLV161YAWrVqZV83bNgw1q9fzy+//EKzZs3o06cPGRkZgHUsv7+/P5GRkW4dp0aNGoSHh9uPp5RShfGoE6CIZIjIIhGZJCIdgauAncAHwAljzGxvVlKpkiQiDB8+nHbt2rF48eJil2d7qt9FF11kX3f99dcDMGjQIPz8/Fi5ciVvvPEGYM0A1K9fn4AA92JpYww9e/bk119/9agj4FNPPcUVV1zh9vuUUmWTO50A+ztZ18IYcz/wKfAW0BQoB7iXu1TqHDJ79mzmzp1LWFgYo0aNQkSKVd7BgwepXLkyFSpUsK+LjIzkiiuu4KKLLuLPP/+kQ4cO/P7774A1AHDlCYDODBs2jP3797Ny5Uq33ztv3jzmz5/PgQMHPDq2UqpscScDMMUYUyWn3f9jY0wc1hkBp2Id9vcrcCdQX0RaFVaQUueyhQsXUq5cOd58803i4+OLnVI/cuSI0wl95syZw7Zt24iKiqJz586sXr0ai8XCnj173G7/txk4cCDBwcE88MADBT4YSEQ4cuSIw7rs7Gy2bdsGwNy5cz06tlKqbHG3E2AC8DUwEtiHdcx/Z6C6iAwXkU9EZL/Xa6lUCdqxYwctWrSgf39r0qu4zQAFBQC5H/PbqVMnzpw5w5o1a0hMTPQ4A1CxYkW++OILtm/fzsSJE53uM336dCIjIx2mDd6zZw9paWmABgBKXSjcCQDSgPeAQUBVEektIi+IyGopbo5UqXPIjh07uOiii6hfvz6NGjVi0aJFxSqvoAAgt06dOgEwdepUANq3b+/x8YYOHcqoUaP4+eefOXXqlMM2EeGNN94gKyuLpUuX2tdv3rwZsI482LFjh8fHVkqVHe4EAGtyxvzP1jH/6nx19uxZ4uPj7WPqo6OjWbZsmdN+AJmZmS71D3AlAGjevDnVq1fnm2++wd/fn+7du3v2AXKMHDmStLQ0fvzR8ZEcf//9t/1BQ3///bd9/ZYtWzDG0KdPH44ePVqsYyulygaXAgBjTJiI9HW3cGMV7H61lCodtrn0bQFAp06dOH78OPv27XPYLzMzk6ZNm/LKK68UWt7Zs2dJTk6mVq1ahe7n5+fHLbfcgsVioWPHjg4dBj3RqVMn6tSpw2+//eawfv78+fj7+9OzZ898AUCjRo1o2LAhp0+ftjcH+IqI8Oqrr9onPVJKlTxXMwDjjTFvuFNwzoV/DtDP7VopVUps6W/bkL2OHTsCsGbNGof9/vzzT+Li4vJdYG1j+W1sne1cearfHXfcAVDocwJcZYyhb9++LF682CFLsXDhQjp16sTll1/O9u3bOXnyJGBtAoiKiiIiIgKAhISEYtehMBs2bODRRx+lSZMmnD592qfHUko552oA8CLQxBiz0Rhza85jf/MxxvgbYy4yxjwMxALrRWSel+qqlM/Z7khtvfBbt25NUFBQvgBg1qxZgDUwsI25nz9/PsHBwSxfvty+3+HDhwHXAoAWLVrwxx9/8Oijjxb7c4A1kEhMTGTChAnExMSwZ88eVq9eTd++fWnbti1gnaMgLS2N2NhYWrdubQ8AfN0MsHDhQvv3BU2JrJTyLZdmGhGRbGPMYOAx4A0g3BiTBZzA2jnQAKFAZaxBRTwwSUS+8kWllfKVvXv3Urt2bUJCQgAIDg6mTZs2rF69GhEhJSOb02mZzF66mvKRF5Fhga8Xr6dy1RqMfmYa5aIu5f/mrGVrVk2yLMLGTUlU6nYDCxNC2Pjbv2SLYLEI2RYhO+fO3N8Y/PwMfsbg71eHjasT8PNLtK8P8DMEB/gREuhPcKAfIQH+9u+DA/wJCbRuCwvyp3xwAOWCAvDzs2YAAF5//XUAPv74Y7Kzs+nfvz9169YFrBmPsLAwsrOzad26NTVq1ABKJgBo2rQpu3fvzte8opQqGcbdDvw5U/32A6KBmkANIBs4ChwAfj2fngZojBGg2JPBqHNLtkU4npxB0tl0jp21fk06m87bH80g0y+IXv0u53RaJqfTMtmz/zBn07MJLFeJbIvnvwd+BvztF3qDvzHWuohgEcFisX5fnGMAGAPlgwIoHxKAyUqnQmggZ44lErt9Mw3qRDDw8v5UCg3kmUnjuaJfL1o0jOT5pyax6NdfiKhcnmZNGvPJJ58wevToYtWjIJmZmYSHhzN69GhmzpzJpZdeyvTp031yLFcdPnyYXbt2cfHFF1OuXLlSrYtS3mJy/seIiHG6XS9shdMAoOzJzLZw9HQah06mcehkKodOpVq/5rxOPJPO8ZQMnP5Is7MIkgwa1KlBxZBAKoYGcuLoQZb+8Ru3jRhO80aRJByM56XnYnhswnheffkFBl9zFS2bNeHJxyfSpmULjhzcz46tmwnw82NyzDO89uorpKel4u/v7/JnsORkCLJzsgXpWRbSMrNJy8zO9b2F9Kz/vqZkZHM2LYsz6VmcScvkbFoWZ9Oty+nUDA4lnsAvKJQz6dZ9Czx2egqVQgJoWKc64WFBVCsfRPUKwVQvH0yNiiE5X4OpXiGYCsEB9n8yrtqwYQPt27fn66+/ZurUqYSFhTk0CZQ0EaFBgwbEx8dzxx138NFHH5VaXZTypqICgPPuwT05UxMPxtosUR34UETedrJfNPA+kHtKtGki8oPva6mKKyUji31JKcQdS2bvsWT2JSWzLymF+OMpJJxJI+9NdHhYILUrhVK3cigX169MtXJBVKsQTLXywVTN+T48xJ+aVSry6KOP8sLDL9jfe+hQHepMupY6VzRlzKhoPvpoESnbl3FT78/45vVkTu1ax9nQTCwnDnHDNQ/w6KOPcuZ4IrVr1ybx6GEialR36+IPWJsEMATmvK2cl8fSpGdlM3TEKLbtjifLP5jaDZpy78OPcjIlg+deeZPwqHZUKVeHE8kZ7E44S+KZdDKy8z9fICTQ77/goEIINSoGU6tSKLUqhVCrUgi1w0OJqBhCUMB/3Y3Wrl0LQIcOHYiMjGTDhg3e/XBuiouLIz4+nnLlyvHVV1/x+uuvU7FixRI59v79+1m8eDG33HJLiRxPqdzOuwAAuAPoLyIJxpimwHZjzMoCmiVeFpHpJVs978nOzmbRokV07dqV8uXLl3Z1fOJUSiY7E87w75Ez7DxqXfYmJXP0dLrDftXKB9OwWhjdm1SjTuVQaudcfKxLCGFBRf+qx8XFkZWVRYMGDRzW165dm8aNG7Ns2TLGjRvH1q1bCQsLo0GDBjRp0oRdu3YRHBxMw4YN6dy5MwCbNm2idu3aLs0BUBqCA/xp07Q+s7/7HwCT7x3BzV3qA/DmmN+IrHKC6a/ead9fRDidmkXCmTQSz6STcCY95+t/r3cnnmX57iROp2U5HMsY68+ndqUQalUKZceWVKr3vIEdyWGE1WvFwcV/k5VtIcDfo2eTFds///wDwGuvvcY999zD999/z+233+7z42ZlZdG0aVPS09Pp1q0bTZo08fkxlcrtfAwARopIAoCIxBpjTgANgPOmXwKAxWKhd+/e/P333zz99NNMnjy5tKtULNkWYXfiWTYfOMWOI6f59+hZdh45w5HT/41HLx8cQNOI8vRoUp2G1cJoUK0cDaqWo0G1cpQPLv6vsq0zWt4AAKBv37588803ZGRksHXrVlq2bImfnx9NmjTht99+IyAggGbNmtG0aVPgv9EE52oAADBmzBg+//xzTp48yeDBg+3rIyIi8nUCNMZQKSyQSmGBNKoWxgcffMCOHTt49tlnCQ8Pd9g3OT2Lw6esTS65vx4+lUZswhni/OsQ1u1m7v9mAwR0pPrtHWnx1HzqVg4lskoYdSuHEVkllMjKYURWCSOycihVygW53dTgqhUrVhAWFsYdd9xBTEwMf/31V4kEAJ9++inp6dZA9tdff+X+++936/3r1q2jffv2PjsvyneuuuoqunTpwlNPPVWq9TjvAgARsT/E3RgzBDgLLChg98HGmFuxnoffsGYEsgrY95wSGxtrn8jl999/L1MBQO6L/eaD1mXbodOkZlrbpYMD/GgaUZ5uTarSPKICzWpWoHlEBWpVCvHpPzvbRdvZPPwDBw7ko48+YsGCBaxevZqhQ4cC0KRJE9LS0ti4cSO9e/emZs2ahIWFOQQAxZnW15fq1avHrl27OHXqFJUqVbKvr1GjBrGxsQW+7+OPP2bs2LGA9fM/8MADDtvLBQfQpEYFmtTIP5lRWloa4eHhjLnvIe4Z/zg/zlvIlGkfcceDE8kIrMD+EylsOXiYEymZjmUG+dsDg7q5AoMG1cpRr0oYIYHuNbHktmLFCjp27EhAQABRUVH2aZF97f3336dt27YkJye7HQAsX76c7t27M2vWLAYNGuTVep09e5YlS5Zw1VVXaXDhA0ePHmXevHnMmzePBg0aMHLkyFKry3kXAAAYY6KAb7E+mniYiJx0stsp4B/gdSAE66RFlYHxBZTp8PqZZ54hJibGa3V2ly1tOXjwYGbPns3p06dLrN3SXadSMlkXf4K1cSdYE3ecjftP2S/2oYH+tK5TkRs6RRJVpxJRdSrRqHp5/P1K/h/Pli1bCA0NdRoA9OvXj9DQUO68805OnTrFkCFDABzSti1atMAYQ6NGjdi9ezfZ2dkcPXr0nM0AAAQFBVG9enWHdREREQ6zBOaWnp7Oc889R9euXUlOTuabb77JFwAUZvny5aSnpzMgugcX1arIZW3q8tSGX+lT5W4GDuxt3+9sehb7j6ew/3gK2/cnEnvoOGn+oew/nsry3cccOjEaA7UqhlA/JxvUoKo1O9TQheDAYrGwZcsW7rzT2twRFRXFe++9R3Z2ttv9Ntyxdu1a1q9fz7Rp09ixYweffPIJWVlZBAS49i953jzr9Cp//vmnVwMAEeGOO+7g22+/5c8//6RHjx5eK1tZrV+/3v79119/7dUAICYmxq2bQY8CAGPMaBH5tJDt34nIME/K9gYR2Qy0NMa0B341xgzM2wdARNYDtp/EWWPMFOAHY8wjzh5udC6MAsjIyMAYQ2BgICtWrKBSpUqMHTuWWbNmsXTpUq655hqfHl9EirwjEBH2H09l9b7jrIk7wdq44+w8an10hL+foWWtigy/JJI2dT2/2B87doz333+f++67z+HOtbg2b95My5Ytnf7jDw0N5cYbb+TTT62/9v36WSe4zB0AjBgxAoDGjRuza9cujh07RnZ29jkdADgTERFBUlKS0wvSX3/9xcGDB3n33XfZunUrjz/+OHv37nX56YULFy7E39+fXr16ARAZGQlYO8PlVj44gItqVeSiWhX5/KVH+PLLL3nyySf5+LnnELEO4dx/IpW4Y8n8e+gEh05nEnc8hflb8mcPalcqODhIPHKIlJQU+9TPUVFRpKWlsXv3bpo1a1boZ8nKykJECAwMLPJzJyYmMnz4cO68805uvPFGPv74Y0JCQhgxYgSzZs1i2rRp7N27196EVJTff/8d+O9GwFtmzpzJt99+C8Bnn33m1QAgJSWF2NhY2rRpg8ViwWKxuHTuzjfr1q0DrM/r+PHHH0lPTyc42Du9fGNiYhxuTIvM4IiI2wuwGwgtYNtVwAlPyvXFAnwC/M+F/VoBAtTIs16sp6lkZGZmyubNmyU5OTnftl69eknjxo0lMzNT2rZtKwMGDJC0tDSpVKmS3HzzzT6t19NPPy2NGjWSkydP5tt29HSqzFp/QCZ8v0G6vbRQ6k+cI/UnzpHWz8yXWz5ZKW/9sVP+3pUoZ9Myi12PzMxM6dWrlwAyefJkh22bNm2SDz74QCwWi0dlR0REyKhRowrcnpaWJvfcc4+8/fbb9nXZ2dly//33y6pVq+zrxo0bJ6GhobJhwwYB5Pvvv/eoPqXlnXfeEUAOHz6cb9tTTz0lfn5+curUKYmPj5eAgAC5//77XS67U6dO0rVrV/vr7OxsCQwMlIkTJxb4nvr16wsgVatWzbdt8eLFAsjzzz9vX3cyOUM2xJ+QWesPyJu/75SHv1kvg9/5S9pN/s3+u2lb2j89R2oMf0FGTZsvn/y5Rz6c/ZcEVIqQ777/odDP8dlnn0lgYKA0atRI0tPTi/zcb731ltj+l/z0009SoUIFGTlypIiIrFy5UgCZNWtWkeWIiBw/flz8/PwkNDRUgoODXTp+YVJTU+Whhx6S0aNHS8OGDaVVq1Zyyy23SPny5SU1NbVYZef26KOPCiCtW7eWunXrSuvWrYtd97JoyJAh0rhxY5k1a5YAsmTJEp8dK9f1y/l1r6ANhS3AcWAD0C/XusrAF4AFyPak3OIuQFVgSJ51bwEznez7ABCS63VfIB3wz7NfiQYAd911lwASEREhaWlp9vWZmZn2H+bYsWPFGCPPPfec/T2hoaFy6tQpn9Rp9uzZ9mPPnDlTTqdmyG9bDsszP2+RS19fYv9nGvXMfBnz+WqZsXyvbD98SrKzPbsQF2bhwoUCSPXq1R3O0ZEjR6RWrVoCyDfffON2uQkJCQLI66+/Xuw62i6gn332mQDy559/FrvMkvT9998LIBs3bsy3rVevXtKxY0f761GjRkloaKjTYCGvP/74QwCZMmWKw/qGDRvKjTfe6PQ9Z86cEWOMVK5cWQBJSEiwb8vKypJq1aoJIGFhYZKRkVHgsePi4uTrr7+WE8npDsHBFZO/kZojX5NWT/3qEBg0mviLDHhjqdz9xRp5Zf52+WHNflkXd1xOpliPcdlll0lQUJAA8vnnn8utt94q9957b4HH79atmzRv3lzq1asnYWFhDr8Xp0+fFkBefPHFIs+hiMiyZcsEkNtvv10Ah+DTEz///LMAUrFiRQFk7ty59ovT33//Xayyc7vooosEkEsvvVS6desmgLz55pteK7+saNy4sVx//fVy8uRJ8fPzk6eeespnx/JVADANqA8szrnDvhnrePoTwBhggyflFnfB2tt/AxCW87o61mmJ7wOqAX8CVXK2TQduyfneH5gJfOakzBILAGx/dLY/jvnz59u3bd682f7DtC3btm0TEZF//vlHAHnsscc8Om5mZqZcd9118vnnnzvdPmz4cKnS4CKp3PV6ufiRGdL4sblSf+IcafHkrzLyk5Xy3pJdsmn/ScnywgX/+PHjcuzYsQK3P/fcc/a7attFVkTk/vvvl8DAQGnVqpXUqFHDIXhyxe+//y6ALFiwoDjVF5H/LnQjRowQQHbu3FnsMkuS7QKT91ykpaVJcHCwjBs3zr5u586dEhgYKLfcckuhZWZnZ0vLli2lcePGkpKS4rCtV69e0qNHD6fv+/vvv+1BLyBLly61b9u2bZsAMnToUAHkf//7n6xfv16WL1/ukAVKSUmR1q1bCyBvvfWWQ/n33XefVKxYUbKzsyXhdJqs2J0kdXoNkx73vyGjP1sl0a8ulkY5v++25eJnF0jtka9K94fekcZX3SUVW3SXgMq1BeMnSUlJ9rL37dsn6enpEh8fb89STJ06VQC5++67HeoRGRnpchbvk08+EUB++eUXAeSrr75y6X0FeeCBByQ0NFRSUlIkLi5OREQOHTokgPzf//1fscq2iYuLcwiwLRaLREdHS6NGjbxSflmRnp7ucNFv37699O3b12fH80kAIP9dHBsCCVinAl4J1CpOecVdsHbmew5r576lwEYgBuvzCSKBRKBuzr7dgXk5Qcxq4EOgopMySyQAOHLkiFSrVk3at28vp06dkrCwMBk7dqx9+xdffCGAfPzxx/Y0mo3FYrHfDaxYsaLIYy1evFi++eYb+z/Jzz//XAAJCQmR7du3i4hIakaWLPk3QZ74cYPUvfsT+z+/Bvd8JC/N2y7/7E6StMwsr54Di8UiHTt2lF69ehW4zxVXXCEtW7YUi8UirVu3lrZt20paWppUqVJFhg0bJgsWLBBAvvzyy0KPtXjxYoeMyQMPPCDBwcFOmzjcderUKTHGSFBQkJQvX77QO9Nz0b///iuAfPHFFw7rt27d6vTcTpw4UQDZu3dvgWX+9NNPAsjXX3+db9uIESOkfv36Tt83bdo0+50oIO+//759m+33duPGjdK+fXsJDg52SLPbPPvsswJI+/btJTAwUPbt22ff1q9fP7nkkkscjtmtWzeJjo62v07PzJbYo2fkty2H5b0lu2T0+4skYsQUuejxXxwCg3qPzJKLn5wld8xYLRP+97eEt79Crhg5Vp575f8EkNjYWMnMzJRffvklX+r7sssuk4svvrjA85fbY489JgEBAXLy5EkB5KWXXnLpfQVp2bKlDBgwIN/6unXryg033OD0PQsXLpT169e7fAzb/60tW7bY17399tsCyO7du92uc1kVGxvrcOPywAMPFJm9Kg5fZQAWAKOBk8AxrOn0ZcDLQDBwjyflnotLSQUADz74oAQEBNjv6gcNGiT169e3X6THjRsnISEhkpmZKQ888EC+NHdSUpIA8uqrrxZ5rPDwcAGkX79+8tVXX0njxo2lRYsWUqlqDel76yNy75dr5aKclGiTx+ZI9SFPy6SP58pjz78qgFcuks78+eefAoifn5/TLEB2drZUrlxZ7rjjDhH5707oyiuvFEDmzZsn2dnZ0rRp0wLvKEVE3n//fQGka9eukpycLFlZWVKzZk257rrrvPZZoqKiBJCrrrrKa2WWlBMnTgggr732msP6uXPnCiDLly93WL9p0yYBZMaMGU7Ls1gs0rlzZ2nUqJFkZubvBzJp0iQJCAiQrKz8AeVNN90kNWrUkOzsbAkLC5MHH3zQvu3++++XsLAwycrKkm3btknNmjVl/PjxUr16dXuTwtmzZ6Vq1apyzTXXSHx8vAQHB0vXrl3l5ZdftmcvHnroIYdjjhgxQurVq1fg+bG158fFxcmJ5HRZs++4fLc6XhoMfFCi7nlbLn19iTSY8LNDcNDg4e9k8Dt/ybhvN8i0RbEyb9Mh2XH4tKRmWD/zI488IsHBwXL27NkCj2tz/fXXS7NmzUREpFq1anLXXXcV+Z6CHD161GmzjIi1rdrZHfrOnTslKChI6tev7/KFy/b5cmdmtm/fLoB88MEHHtffVUlJSdKpUyf5559/fH6swsyfP18AWbZsmYiIfPfdd15pximIrwKA7JzlRyAiZ50BHgLWAQc9KfdcXEoiAEhKSpKwsDCHNKrtzsd2t+LsTiWvOnXqOKQRd+/eLVOmTHH4ozt27JgAUqlSJalfv76YoDAJb9tfrnvjV2k00fpPq/H47+XWt+fLou1H5dbRd0jFihUlPT1dvvnmGwFk8+bNXj4DViNGjBB/f397ij+vLVu2CCCffPKJiFgDgquvvloAufzyy+0Xl5deesnh3OWWnJwswcHBEhUVJcYYue2222T69OkCyHfffee1zzJmzBiv9SkoaRaLRYKCguTRRx91WG/r23Do0CGH9bbA7Pbbb3da3pIlSwSQd9991+l2W7kHDx4UEZHffvtNqlevLk2bNpXw8HC59dZbRUTk4osvdrhT7dq1q0OgZ/s9v/322+2/s7kzCCLWzqzlypVzaErbtGmTQ32efPJJ8fPzK/DiNnToUKcBwk033ST16tWTs2fPip9/gNzx0CS5ZOAoqdBxkFwd86UM/2C5dHrhd8fAYNIc6f7yQrnylblSud8YeeSDX+TPnYly8ERKgX1o2rVrJ1deeaWIiHTo0EEuu+wyp/sVZP/+/fYMxKJFiwSQP/74I99+//d//+e0L8iwYcPsf6d5s0QFGT58uDRp0sRhncVikbp168rQoUPdqr8nXnnlFQFk9OjRPj9WYd59912H3/WDBw96taklL18FAKeA6wvY1hg45Em55+JSEgHAm2++me8f0fr16+3tmiIitWvXLrSHuojIlVdeKW3atLG/rlGjRr5yZ8+eLSYoVF748g8Z9ekKaTRxttSfOEc6vfC7TPh6ldRs01MqVqokAQEBMn/+fKlatarcdNNNIiKyfPlyIaeTkLdZLBapVauWDBs2TCpWrCh33nlnvn1sKcM9e/bY1509e1Z+/fVXyc7Otq/bvXu3APLKK6/kK2P16tUCyI8//ihPPfWU/Q+kV69eXk3DffXVV/lSnmVJZGSk/cJrM2HCBAkODnY41zYDBw6Upk2b5lufmZkpPXv2lBo1auRr+7exdTK1NV8NHTpUKlSoYP/Z/PCDtUf+iBEjJDIy0l5uSEhIvrt3kf8yFb/88otERUVJhw4d8u2zevVqCQoKchiRYPPpp58KILt27cq3zfZ7avubyO3FF1+0992B/3r179271yG7cSYtUzbtPymz1h+QNxb8K/d9tU6ufHOp1Bv3g0Nw0OLJX+XyN5fJvV+uldd+2yE/rdsv6+OOS/nK1eyZkCFDhkiLFi0c6hETEyM1a9aUV199VZ577jkZPny4fPHFF5KWlibR0dEC2IM7WzYsPj4+3+c5duyYlCtXTkaMGOGw3naj0bhxY4cM18mTJ6VNmzZOe7V37drVaVv34MGDpVWrVvnWe1N2drY0btxYwDqSxFkWyl1nzpyRJ598Ul5++eUCf6+dGT9+vISEhDj8DdWpUyffOfYWXwUAhQ6rA573pNxzcSmJAKBr164OF24Raw/nChUqyD333COnTp0SQF5++eVCy5k0aZIEBgZKenq6bNy40f7Dnzp1qmRmZcuiHUel18RPJXLcj1J/4hzp9tJCeW72Vlmz77jD3cbJkyfloosusvdWtv0DtkWr7733ntc++759+2TDhg32i/Z7770n/fr1k86dO9v3Wbp0qSQmJsp1110nDRo0cKncSy65RDp16pRvve2f+86dOyUzM1Pef/99eeqppxx6l3tDdna2vTmnLLrkkkvytQsPHTrUnnrO67XXXhPIP3TwiSeecGjzdMbWLvree+9JWlqaVKhQQe68804ZOXKkhIWFyenTp0VE5PnnnxdAzpw5Y//9dtbXIyMjQ2rUqCHly5cXQD788EOnxz1z5ozTkTO2oYW///57vm27du0q8G/A1pv+pptuEkD2799f4Gd25rohQ6R6vSayYMNe+eKffTL5l60y6tOV0uuVRdJwkuPwxYse+1mGvr9cek/4UKp0GyYLth6WXQlnZOPmLRIQECARERH2v//atWsLIM2aNbOvs/U3sA1ZdRbUiVgvWH5+fvaUte1/0YsvviijR4+WqlWr2jMvtn4ew4cPz1dOnTp18gWUIiIPPfSQlCtXzuOhu644cOCAANKnTx+B4g+7s1gscv3119vP5RNPPOHyewcPHiwtW7bMt65Zs2YSFxcn3bt39+oNlk8CgKIW4F1flFsai68DAFvvWGdDgC677DJp3bq1rFq1yuGOoiC2u85NmzbZ70YqN24rHce8LB2eWyD1J86RhuN/kKY3PiVr9h0r9I9u1apVUqdOHZkwYYL97sU2Zrug0QZ79uwptAd/Xt988434+flJUFCQvXf/5s2b5c4775QaNWqIyH9DpACpXLmyyym8xx9/XAICAvLNp/Dwww9LaGio0/Zm9Z9rr7023z8qZ0GBjW0se+5mlGPHjklYWJjTu+XccqeC582bJ4DMmTNHUlJSJDY21r7fjz/+KICsXr3a3v/j33//dVrmww8/LIDUr19fzpw54+rHFpH//iadXeRtQzudNYPZggNAatSo4fZFbe3ateLn5yfDhg2Ta665Ri677DJ7FiItM0tij56WFz+fKxU7D5GRb/8mQ979W1o8lqevwaO/SJ0xH8qID/6Sh2f8Ka/+tFz+jk2Qxya/JK2jomTSpEnyxBNPiL+/v5w5c0auvvpqiYqKKrBOp0+flkaNGknz5s3FYrHYf84zZ86Ujz76yB5Mi1g7tAFSrlw5h7vijIyMAoe72bKfiYmJbp0rd9jm45g+fboEBATIpEmTilWerTPs5MmT5YYbbpDQ0FA5cuSIS++NioqSq6++2mGdLbCdNGmS/fdn0aJFxaqjTVEBgEeP3zLG3FLYAlzpSbkXohkzZgBwww035NsWHR3Nli1bWLJkCYB9trKCtGnTBoDlazczPy6bevd8TMXrXyChYnM61q/MByM7UGHhi7Q4u4EO9asUOkvUJZdcwoEDB3jllVfsM+P5+flRt25d4uPj8+2fnZ1Njx49uOyyy8jOLvhZ87kfMvPee+/ZZ4KbPHky4eHhtGzZkoYNG5KQkMDZs2dZvXq1ff/k5GSGDXNtgsmuXbuSlZVlf/SszebNm2nVqpVPp3k9H9StW5cDBw44rNu3b5/TByUBtG/fnnLlyrFs2TL7ug8//JCUlBQmTZpU6LGMMfTp04clS5YwdepUatSoYZ96OfdMixdddBEAO3bsYM2aNVSsWLHAJ+iNGzeO0aNHs2zZMreflBkZGUnlypXtM7bl9vvvv1O9enVatmyZb1vu2RA7dOjg9jz6F198Mc888wzfffcdv/32GytWrLDPLhkc4G99tsKBjZxe+SNvj+zMD/d047Xu/uyfegNPdw3j1eujyNg0j+pBmSSlZDN35xmmrTzOTR+v4quUKLIGvcK6GlewuVJXKvYaxcs/LGfHSUO9Vh3IdPKoZ4AKFSrw8MMP8++//xIfH8+OHTsA68+iS5cuwH+zES5evJgqVaqQnJzMwoUL7WUcOnQIi8Vi/1vPzfb7ZHsQly8kJSUB1p9Ply5d7LMoOrNx40ZmzZpVaHm2z3vDDTcwceJEUlNT+e2334qsR0ZGBjt27KBVq1YO6y+55BIAXn75ZZo2bcpTTz1F9+7diyzPKwqKDApbsE72U9CSTSlNBOSLBR9mALKysqRevXrSv39/p9vXrl0rgNStW1cCAgIKbaPOzrbIwq2HpMZ1T0iDR63Dky667yMZM2WG+AWXs6cjq1evLmPGjPG4zr1795aePXvmW28bO04hnb1snQi/+eYbSUxMFD8/P3nyySdl4sSJUqdOHXtTQ+7OhrZMRlxcnFttbbaJffL2Ayhqtj9lZes0ZUuRnz17tsBMlU3//v3tTVnp6elSu3ZtufTSS106nq1pBrBPcJVXenq6+Pv7y+OPPy4dOnSQPn36uPmpXHfZZZfla5bLzs6WatWqFTpe/4YbbpCoqKhi9Tbftm2bbN26Vd544418/UiGDRvm0DPfNvrn+eeft4+isY0Qys62SPyxZFnyb4LMWL5Xnp29VW6fvlr6vrrI3gxon/josbnSc8oiufnjFfL4T5vkg6W75NfNh2XboVOyfNUae3OLbcRGRkaGZGdnS8WKFeWee+6R48ePCyCPPPKIgOMIElu9cs9rYlMSs2Xa/p9s3bpVJk+eLMYYh/kachs0aJCEhYUVOkPhHXfcIZUrVxaLxWI/B678T7U1W+Wdt+HMmTNStWpVAeTJJ59078MVgSIyAJ4+DGg7+e/yywEtgJuAdzws94KyYMEC4uPjee2115xub9euHaGhoRw4cICLLrrI6bzZR06l8d2a/Xy7ej8HT6YSVr8NFY+uI3b+DB68bTh92/flw/Rkdu3aRUREBImJidSuXdvjOterV8/hLs/mxx9/JDg4mAYNGvDtt99yzz335Nvn//7v/wC45557uO+++7BYLAwePJgOHTrw8ssv2/ez3Unt2bOHFStW0Lx5c+rVq+dWPatXr07jxo0d5kpPTEzk6NGjREVFuVXWhSj3HP2tWrUiLi4OcP6oZJs+ffrYnw2wfPlyDh06xMcff+zS8a6//no2bdrE/v377U8azCsoKIgmTZowe/ZsNm/ezEsvveTeh3JDp06deOGFF0hOTqZcuXKA9SEuSUlJXHbZZQW+7+uvvy72sW2ZjurVq/Poo4/ywQcf8NZbb9nr0K5dO/u+VatWJSoqiqVLlxIbG0tYWBhXXmn91+znZ6xPTawShnVOtP9c3KEj+xNPcUaCuf/xZ4lo1Iq44ynEH0tmzqbDnEp1fJ5C5H1f8Or6bORsTeoPfIBv1x6iTuVQWnXtx9qNW9iwYQMA/fv356OPPmLv3r3299oyhs4yAPXr1wfyZwBOnjxJxYoV8fPzKEntwJYBqFatGldeeSXPPPMM48ePp2LFirzwwgtUqPDfUys3btxISkoKa9eupWvXrk7LW7FiBV26dMEYgzGGrl27snz58iLrsXHjRgCHnx9A+fLlWbFiBU8++aT9oVQlpqDIoLCFXFMAO9kWCvzsSbnn4oIPMwDXXnutVK9evdBo8/7775cqVao4zMpmsVhkxe4kufuLNfZZykZ8tEJmbzwoI2+9zR71ffnll7J3714B5KOPPrLPSFaccbdPPfWU+Pv755sj3NYjePz48RIUFJTvbt02Vvy+++6T0NBQAes4fGftpImJiULO0JiIiIgiZ5kryNChQ6Vx48b217ZphL0x29/57q+//hJAfv31VxEpeA6A3Pbv3y/+/v4yevRoqVOnjrRu3brAzmWeevLJJwUQf3//fMMRvWnOnDkCSM+ePe3t07ZZ/GxDuErC7bffLgEBAbJ582ZJSkpymALc5r777pOgoCAJCAiQBx54wKVybe3NgYGBTuf1OJmcIRv3n5BfNhyUaYtipfXol6XRHVMl8t5P7RnG3EvzSbOk5i3/J7d+9Jc0G/6YdB45SX7bcli2HDwpk1+0ZpNsnTnzqlSpksOkZwcOHJBy5crlm7XRU5MnTxbA3vvf1kmTPJkK28RKUPDkSrapqWNiYuzrnn32WTHGyIkTJwqtR+65XEoKRWQAPAqvRGRhIdtSsWYCVCEOHjzIL7/8wqhRowgKCipwvzfffJOkpCT69+9PakY236yK54qpfzL8wxX8s+cYd/RsyLIJffjfHZ25uk1t2kb9177Uvn17IiMjCQwMJDY2lsOHDwMUKwPQtm1bsrOz2bJli33d4cOH2b17N3369KFv375kZGTki4htr8eNG8fUqVOpWbMmn332mdN20qpVq1KhQgX+/vtvjh49SseOHT2q60UXXcTevXtJS0sDsD/nXTMARatbty6AvR+A7Q6tsAxA3bp1GTx4MJ9++imJiYl8/vnnXrmDy+2JJ56gVatWDBkyhFq1anm17Nx69OhB27Zt+fPPP5k+fTpg7XsQHh7u0+Pm9fLLL1OpUiXuvvtuvvnmG0Qk31M/r776ajIyMggICODhhx92qdz+/fsD0LdvX6dP1KwUFkibuuFc07Y2Y/s0YWRzw56PH2T/u6OZ1PgI/zzWlx/u7sr1dc5yYukMOLCRAEsa+09lklGvM0dq92DMF2u56q2/+PRUS+qN+5Gr31/DsA/+4YGv1/PC3G18/Oce5mw6RN12vYg9dJz0LGvfoalTp5KcnGx/KmFup0+fZv78+babM5ckJSURHh5uf7Ll22+/zbPPPkuLFi2YOnUqmZnWbMemTZsAa5+UpUuXOi1r+/btiAht27a1r+vWrRsiwooVKwqtx8aNG2ndurXLj3wuEQVFBoUtQC8nS29gMNY59jd6Uu65uOCjDMADDzwg/v7+DmPaC3LgRIq8OG+btM15mtll/7dUvlkVJynp+Xuyb9iwQcLDwx0moGnevLlcd911MnPmTAFkzZo1HtfbllHIPSXrt99+K4CsXLlSTp8+Lf7+/vl62t59991SqVIl+x1/UXeGl1xyiYSEhAh4/jAd26gIW4/t22+/XapVq+bTIUfni4yMDDHGyNNPPy0ihc8BkNvBgwfl448/dmuaWHelpaWV2F1U+/btpVu3biIi0qdPH+nSpUuJHDc328gDsE4B7uz39+TJkwXeYTuTlpYmHTt2lB9//NGl/XOPcMg9vNU2ORdgHyHy8LhxElYlQtbHHZe5mw5Jl1sfl6bDJsrYL9fK0PeWS88pi6TpE/PyZRHqT5wj7Sf/JnVvf0dqDn9Oql09Xh77bo28u3iXfLc6XhZsOShdrxou/hWqy4+zZrv8WW+88cZ8kxCJ/Nc3wNZfwzbPSHR0dIHDjW2Thu3YscO+7vTp00U+1MdisUjVqlXts5iWFIrIAHgaiizJKTgvAxzA+nAgVYDDhw/z4YcfcssttxT6HPVth07z4bLdzN5kvXO/rFUEt3ZtQKeGBffgb9u2LSdOnHBY17RpU69lAOrXr5+vh/Rff/1FWFgY7du3JzAwkN69ezNr1iyHNtoNGzbQrl07e72LujPs16+ffQSAbXSDu2yjJnbs2EHr1q3ZvHkzUVFRbvfOvhAFBgZSq1Yte/vtvn37qF+/fpE/t9q1a3P77bf7tG7eena6KwYPHkxMTAxHjhzh33//ZcCAASV2bJtbb72VtWvXMmPGDMaOHev099fZXXxhgoODHUbYFKVx48a0bduWgwcPOoxGat68OeHh4Zw8eZL27dsD0LBBA1KOH6VWcAbtomrx+Ia5NKtbl2k3/dfPR0Q4mZLJ0TNpTJr8Mmu27mLC0y+w+1Ai32w6Sq0GzTh2No3v1h0mS478V5HWI6nbeiTj/oGn1/1G1fJBVA4LolJoIJVCA6kYGmD/3rbEp4VQqUEr9h9PoXxwAKFB/gQH+Nl72q9evZouXbqwadMmqlatSps2bfKNHrLZvn07gYGBNG7c2L6uQoUKtG3bttB+AIcOHeLYsWMOmYNzgacBwG7gjjzrsrE+GGi3iBQ8Dkzx2muvkZmZyRNPPJFvm4jw965jfLBsN3/GJlEuyJ/bujXgth4NqRMe6tHxmjRpwsKFCzlw4AB+fn7UqFHD47obY+jQoYP9D0REWLBgAd26dbN3Urzuuuu477772L59OxdddBHZ2dls2rSJMWPGuHycAQMG8PLLL9OkSRMqVqzoUV2bNWsGwL///mtvtrjjjry/tqogLVq0YOvWrUDhQwDPZ7ZOY7Nnz+bQoUM0b968xOtgjOHtt9/m7bffLvFj5/bhhx9y/PhxhwAkICCADRs2sGjRInvnQ9tNzd69e4mIiGD//v1069bNoSxjDJXLBVG5XBBR1fyZ/feP3Nfna5Yu3c/bPz7L13/8wahRo4jq2JEHxz/K0Jtv43SGMOyW26lUoy5ffD+Lq26/G4LLkXA6lZMpQtyxZE6lZnI6LYtsS67702ZDAOj5ymL7Kn8/Q1igP/Xu/4J34/yZ/9af7PRrT/Xr27CxfDhBvQJ45Lv1BAcGEOjvR1CAH4H+hsWJYTS48i4+XR5HgJ8ftlNRq/eNrFy5kk/+3J0vSM62CJu3bqdS9xvZGdKCF+ZuIyPLQka25Hy1kJGVTWqmhdSMLFIysnlzeDuaRlTA1zwNAN4REeeNJKpQCQkJvPfee4wYMcIhiszKtjBvyxE+WLqbrYdOU71CMI9e3pwRnetTKTR/7393REVFkZqayrJly6hRo0axx8D36NGDyZMn8++//5KSksK///7LuHHj7NsHDx7Mfffdx0cffcQbb7zB9u3bSUlJcSv67datG+XKlePiiy/2uJ7lypWjXr16bN++nR07dpCSkuJxf4ILUfv27Zk2bRqZmZns27fPfod3IWnTpg1BQUH23v1FzcVxPuvUqZPT9fXr1+e2226zv7YF3jt27CAqKorjx487HQFgU6tWLSwWCwkJCezevRuw3rQMHDiQd999l7///psK5csz9bXnGT58OKdOneL9R28hpUU5vvrqKzIyMkhLS7NnhkSEs+lZnErN5FRqJlcMHEJUh87cesfdJOdcYFMyskhOz+aXeVs4eTyViFaNWX/mNBF1IjnjF0pI3ZYs3ZmIRQyZ2RYys4XMbAtZ1TtAdXhx3g7HDxHckvK9WvLc3B15P55deI8RzNuTQWBcfE5A4UdwwH/BRWigP6FB/kRUDCm5LGVBbQPFWdCZAAs0YcIE8fPzs7chZWZly/dr9kv0q4ul/sQ50ue1xfLNqjivPmp3zZo1+drpiiMhIcE+w9u4ceMkICAg37jaO+64Q/z8/OSdd96RMWPGSEBAgBw4cMCt4yxevNjpfOzuuOqqq6RVq1b2truyPD1vSfvyyy8FsI8AeOONN0q7SqWiY8eO9r+f3DMTKueysrIkNDRUHn74YfsT/2zPNHEmd9+kiRMnSmBgoGRlZdmfqWDblluvXr3s2yhgjgGbsLAwGT9+vNNttln4bHMVfPbZZ/Y+TXlne0xJSRE/P3954qln5GxappxITrcvm3bsEr+Q8vLq1Hcd1p9ITpeTKRly/fAbpWGjxk7r4Et4ow9Azux+7tCZAJ1ISkri3Xff5YYbbqBRk6Z8uzqedxbvJv54Ci1rVeT9my9mQMua+Pl5N/qzzXyXnZ1Nr169il1e9erVefjhh3nhhRfw9/dn2LBhVK1a1WGfV199lTVr1tjHdN90003UqVPHreNER0cXu66dOnVi3rx5LFq0iPLly9vvTlTRbHf8zz33HACXX355aVan1HTs2JE1a9bQrl27AmceVP/x9/cnKiqKjRs3FjoHgI1tVIVtNFHDhg3x9/fn0ksvZdq0aVxxxRU0atTI4T0TJkzAYrHw2muvcemllzJr1iyn8zOkpqaSkpKS7/+TzZVXXsmTTz7J5MmTAWsfquPHjwNw7Ngxh303bdqExZJNx4vbUS7Y8dJZqVkjalapyLoVfxL+QP45UNauWlGsbKavuNoEMN3Ncl0fo3EBCQ8P56133uVkeAv6vLaEAydSiapTiY9v6Ui/i2r4LO0TEhJC8+bN2bZtGz179vRKmU8//TTLli1j//79TJs2Ld/28PBw1q1bx+LFi3n77bd56qmnvHJcd3Xu3BkR4fPPP6dXr146BbAbmjVrRrly5VixYgW1a9e+YNPftslqrrjiilKuSdnRpk0bZs6caZ8gqLDAydYp+dChQ+zatcveNOrv71/gpFBXX301V199NWAd0rhgwQKn+9ku5lWqVHG6vV27dtSrV48//viD4OBgWrZsaZ/u2DaBkI2t35OzC7kxhu7duzt0BLRYLPj5+ZGQkMDevXu59957nZ+AUuTqIN3tQEMXl0ZAwQ0hF7DjqVl8fKgOb/2TSNXywXw26hJ+ua87l7aM8HmbT7t27QgKCiqwHc9dQUFBLFq0iK1btxb4x2WMoW/fvsycObPULh65P+/AgQNLpQ5llb+/v332xpEjR16woyfuvPNO7r33XiZOnFjaVSkz2rZty7Fjx5g2bRoXX3xxoSOPIiIiAGsAsHv3boe+Ua5o0aIF8fHxTp9BYhsRVblyZafvNcbYOyd/+OGHBAcH27MFeTMA69ato2rVqgVmM7p3705cXByxsbGMGjWKFi1akJyczMqVKwHrzcg5p6C2AXFsB7/Llf083f9cXvBiHwCLxSJPzdosS/5NKPGx6P/++6/MmTOnRI95rqhSpYoEBgYW+iwFVbBjx47puVNuWb9+vb39+YUXXihy/2rVqkm/fv3ss5a647333hPAaR8j2zNKnD3a2SY7O9thdseUlBSn9W7fvn2hfaj27dsngDRs2ND+2V9++WX70xfzPpm0JOCNPgAi8oEx5umcl9+IyM6i9nczDrkgGGN4dlDrUjl2s2bNLtj2761btxISEuL0WQqqaAVleJQqSLt27fjggw947rnnuPHGG4vcv0WLFixebB2m5+5oE9tzQuLj4/P1MyoqAwDWOUlyZyhCQ0MJCwtzyACkp6ezZcsWxo8fX2A59evXp0ePHvz111/cfPPNnDhxghdeeIEqVarQoUMHwsLC3PpcJcGdeTrz92xQqgyoWbMm4eHhpV0NpS4oY8aMIT4+vtDJzmz69++PxWIhICCA1q3du0nKHQDkZesDUFgA4EzVqlUdAoAtW7aQmZlZZEe++++/n+bNm/P6668zbdo0/Pz8Cn3gW2lzJwA4IiLPishOY8xeY8we2+Kz2imllCqzXO03YuvB36pVK7dneiwsALBlANzNYlWrVs2hE6CtA2CHDh0Kfd+wYcPYsWMHNWrUoEGDBvz222/88MMPXut87W3uTASUu2d/NNZpf+eiQ/6UUkoVQ8eOHalZs2aBj+AtTMWKFalUqRL79+/Pt+3EiRMYY9yeTbRq1aoOAcC6deuoVKmSS9mM3M7Jjn+5eDQToIjEARhj0m3fK6WUUp7w9/dn1apVbj/TwKZevXoFZgDCw8PdfiplRESEfVZCsAYAF1988Xk3Esa7z+rMYYz52hflKqWUOj9FRkZ6/NyPyMjIAvsAuNv+D9YA4OjRo4gImZmZbNq0qcj0f1nkkwAAKPknZiillLog1a5dm0OHDuVbf+LECY9GsURERJCSksLZs2fZtm0b6enp5+RMfsXlThNAO2NM3pkWjJN1SimlVImpVasWCQkJZGVlERDw32XtxIkTHmcAAI4ePWp/9Pn5mAFwJwA4Afziwn4GuNqz6iillFLuqVWrFiJCQkKCw5j+EydO2EcJuCN3ALB27VrKly9/Xj4Hwp0AIF5Ebit6NzDGrPewPkoppZRbcj9QKHcAcPz4cY+bAOC/DED79u3d7khYFrjziQb4aF+llFLKY7kDABuLxeJxE0DNmjUB6/MJNmzYcF6m/8GNAEBEEn2xr1JKKVUczgKApKQksrKyCn0QUUGqV6+OMYZly5aRmpp6XnYABN+NAlBKKaVKhC1lnzsAOHDgAAB169Z1u7yAgACqVq3KvHnzAOePAD4faACglFKqTAsODqZKlSoOAcDBgwcB8j0gyFUREREkJycTGhpaao8z9zUNAJRSSpV5tWrV8loGAODSSy8FrP0B/P39i1/Bc5BHUwErpZRS55I6derYL/pgDQD8/f2pUaOGR+W98cYbtGjR4rx+jLoGAEoppcq8Jk2asHLlSkQEYwwHDx6kdu3aHt+9+/n5cffdd3u5lucWbQJQSilV5jVp0oRTp05x7NgxwJoB8DT9f6HQAEAppVSZ17RpUwBiY2MBaydATzsAXig0AFBKKVXm2abq3bVrFxaLhfj4eCIjI0u5Vuc2DQCUUkqVeQ0bNsTPz4/Y2Fj27t1LSkoKrVq1Ku1qndM0AFBKKVXmBQcHU69ePWJjY9myZQsArVu3LuVandvOywDAGHO/MWahMWaRMWazMeb+QvYdYYxZa4xZY4x53RhjSrKuSimlvKNdu3asWLGCzZs3A2gGoAjnZQAA3AHcKCJ9geuA/zPGdMq7kzGmNfA6cBnQCbgYuLckK6qUUso7Lr30Uvbt28cvv/xCw4YNKV++fGlX6Zx2vgYAI0UkAUBEYoETQAMn+90OzBORJBGxAJ8C5/fAT6WUOk/ZZu9bvXq1pv9dcF4GACKyyfa9MWYIcBZY4GTXS4AduV5vA1oZY0J9W0OllFLe1qxZM6pVqwbAgw8+WMq1OfedlwEAgDEmyhizDXgDuEFETjrZLQI4lev1ScAA1ZyU57DExMR4v9JKKaU8Zoxh5cqVHDhwgH79+pV2dUpcTEyMw3WqKEZESqBapccY0x74FRgoIqvybIsFXhWRD3NeNwZ2AfVFJD5nnQCc7+dJKaXU+cUWBIiI02jgvM0A2IjIemAu8ICTzQlAeK7X4YAAiT6vmFJKKVWKzrsAwBhTNafdP7dkoJyT3VcDzXO9bglsFZFUX9VPKaWUOhecdwEAUAF4yhgTBmCMqQ4MBhYaY6oZY/40xlTJ2fdj4MqcoMEPGAW87+sKav8BPQc2eh70HNjoebDS81By5+C86wNgjAkBngAuBTKwpvVnAs8CdYB1QHsROZCz/whgHGABlgGPSK6T4os+AMaYC75PgZ4DKz0Peg5s9DxY6Xnw3jkoqg/AeRcAeJsGAL6h58BKz4OeAxs9D1Z6HkouADgfmwCUUkopVQTNABTBlgFQSimlyiLNACillFLKTjMASiml1AVIMwBKKaXUBUgDAKWUUuoCpAGAUkopdQHSAEAppZS6AGkAoJRSSl2ANABQSimlLkAaACillFIXIA0AlFJKqQuQBgBKKaXUBUgDABcZY2oaYz42xqz2UnlNjDGzjDE/5FkfY4xZkmvp743jKaWUUrkFlHYFypAewM9AOy+V1xmYBwzIu0FEor10DKWUUsopzQC4SER+AM7kXmeMaWWM+dwYM8EY84kxppEb5X0JZDjbZox5whjziDFmojEmrHg1V0oppfLTDEDxfAyMF5Hlxpho4HXgWmPMtcAzTvY/JCJXFlHm98A+EUk2xtwLvA3c7sU6K6WUUhoAFFMbYIAxphcQCpwFEJGZwExPChSRrbleLgImFLeSSimlVF4aABTPRuAnEdlkjAkGri1ugcaYV0XEdtFvCuwubplKKaVUXhoAuMgY0xsYCdQyxjyJNd1/OzDeGLMLqIU1fe9qeYOAa4DmxphHReSVnE1ZxpipQAIQBdzrxY+hlFJKAWBEpLTroJRSSqkSpqMAlFJKqQuQNgEUwRijKRKllFJllogYZ+s1A6CUUkpdgDQD4CJv9pUwxni1vLJIz4GVngc9BzZ6Hqz0PHjvHBjj9MbfTjMASiml1AVIAwCllFLqAqQBgFJKKXUB0gCgFDzzjLPHBFxY9BxY6XnQc2Cj58FKz0PJnQOdCKgItmGAep6UUkqVJbZOgDoMUCmllFJ2GgAoVYb8+uuvdO/enezs7NKuilKqjCuTTQDGmEuAr4HnRWR6Afv0ACZhfUxvODAfeEpELDnbpwMN8rztahE5m6ccbQJQ54xq1apx7Ngx4uPjiYyMLO3qKKXOYUU1AZS5iYCMMdcCQ4FTRez6PPCGiPxijCkPrMH6hL2pth1EJNpX9VTKF8LDwzl27Bj79u3TAEApVSxlsQlgtYjcBJwpYr9ZwGyAnLv6OcAA31ZNKd8KDw8HYN++faVaD1Uyli5dSv/+/YmOjqZnz57079+fGTNmkJGR4ZPjtWnThl27dvmk7NL27LPPUrNmTWJiYkq7Kh65//77CQ8PZ/r06V4rs8xlAETkgIv7vZlnVQiQmHuFMeZ9oBVwFnhFRBZ7o45K+UrFihUBDQAuBPPnz+euu+5i4cKFNGnSBLD2ARk4cCBt27alXbt2xSp/1KhRNGjQwOGCuGzZMnuQeb55+umn2bNnT6H7GGPYu3cvDRo08EkdnJ1zV7399tts3rzZq/UpixkAtxlj/IHLgPdyrd4OfCIiPYGngdnGmHaFlOGwlNUoUpVtaWlpAOzdu7eUa6J8yWKxcO+99/LEE0/YL/4AV1xxBddff73Pjnu+XvwvFDExMQ7XqaJcEAEA8AgwS0RW2laIyBQRWZ3z/WpgLnBXQQWIiMOiAYAqDSdOnAA0A3C+W7duHXv37mXAgPytlp9++ilRUVEAHD16lCFDhtCrVy+6dOnCjBkzAIiPj6dLly4YY5gxYwaXXnopzZs3Z/ny5QBMnTqV+fPnM336dKKjo/nkk08YP368Q4r5uuuuIyQkhClTpjB48GCaNGnCu+++C8CiRYto0aIF0dHRAMycOZMGDRowatQoAN544w2HdPszzzzjULYtHX/fffdx880306xZMyZOnMiqVasYPHgwjRo1YtasWQWen8mTJ9O1a1f69OnD8OHDOXz4MF999ZVDHd577z2nKf+EhASuv/56OnXqxFVXXUVSUhJgDa4AbrjhBqKjo4mLiyM6OhpjDO+88w5XXXUVFSpUYMmSJcycOZN+/fpx6aWX0qtXL/7++297+VlZWUyaNIlu3brRq1cvhg0bxp49e5yec4C1a9fSq1cvevfuTb9+/dixY4e9rDVr1tCxY0d69OjBww8/XGRn9JiYGIfrVFHKXBOAu4wxVwIdgRuK2DUea3OAUueskydPApoB8IWHHnqIDRs2+PQY7dq148033yxyv927dwNQp06dfNtCQ0Pt348YMYIePXoQExNDUlISUVFRNGrUiJ49e/LNN9/QsGFDQkND+eOPP3jllVeYPHkyv/32Gw8++CDr16/Pl45eu3at/fuffvqJBg0aEBcXx6xZs1i1ahV9+/ZlzJgx9O3bl0mTJtkv6Ndeey0bN260B6bjxo1j06ZN9rImT57M0qVL7a9t6fgVK1awfPlyTp06RZ06dahQoQKzZs3ip59+YtKkSQwePDjf59+2bRvffvstW7duxRjDww8/zL///stNN93Ezp077XW45557WLlyZb73L1q0iNWrV1OhQgXGjBnDAw88wFdffcWvv/6KMYZvvvnG3gSwZMkSjDEcO3aMuXPn8vXXX1OxYkUOHDjA999/T5UqVdi3bx+9evUiPj4egFdeeYW1a9fy559/4u/vz3333ceyZcucnvNTp05x+eWX8+2339K3b1/mzp3LoEGD2L59O1lZWVx77bW88sor3HjjjWzYsIH33nuP2267zfkvjQfO6wyAMaYL8CAwUkSyjTFNc217NM/uEcChkqyfUu6yBQAHDrjUFUadxw4ePMjChQsZPXo0YB0ievXVV/PZZ5857Hf55ZcD1g5+ngSOud+fnJxMQkJCMWv+n969exMUFET16tWpUaMGbdu2tR+roPb6ChUqcOTIEX766ScyMzOZMmUKPXr0cPmYl112GRUqVABg5MiR/PDDD0XOqzFo0CAAbrzxRi6++GLatm3LbbfdRo8ePRg1ahT79++3n5fPPvuMkSNH4u/vD8Djjz9O7969nZY7Z84cypcvT9++fQG46qqrOHLkCCtXruSff/4hISGBYcOGAdbgsVmzZi5/TlecNxkAY0w1YCYwSESOG2Muwjrk73ogIGco4NPAyJy3jDPGTBeRBGNMQ2AQMLA06q6UK9LS0khLSyM0NJTU1FQyMzMJDAws7WqdN1y5My8ptnb/gwcPFtghzRYEVq9e3b6uevXqrFmzxmE/W8fRkJAQj0YP5H4/4NURCLYLMUBAQID9dUBAAJmZmU7fExkZydy5c5kyZQpjx45lxIgRPPfccwQEuHY5q1y5sv37qlWrkpmZSVJSEhEREQW+p1KlSg6vBw4cyNixY3nkkUcAax+xlJQUwPpzyf0zqV27doHlHjhwgOPHj9ubUsD6Mzx27Bhnz54lPDzcHkgAVKlSxaXP6KoylwEwxnQwxiwB2gGTjDE/5WwKBVoAYTmvPwY6YU3tn8lZeuYq6jVgpjFmKfA9cJ+ILEWpc5Tt7r9WrVoApKamlmJtlC+1b9+epk2bsmDBgnzbbr/9dtatW2efByIx8b/BTYmJidStW7dE6hgUFER6err9te3309XtnkpJSaFly5bMmjWLDRs28M8//zBlyhSXj3n8+HH790lJSQQGBlKtWjWXj5+QkMC+ffvsmZG8gUpkZKTDz8Q2b4czkZGR1K1blyVLltiXdevWMWDAAGrVqsXJkyfJyspyKMubylwAICJrRSRaRMJFpIWIXJezfr+IVLcNExSR7iJi8iwNcpXzWs4+vUWko4h8UUofSSmX2P6Z2e4oNAA4f/n5+fHhhx/y0ksvOYzL/+yzz9i8eTPt2rWjdu3a9O/f394Ob2undrWNuEKFCqSkpJCcnMyIESPcrmPDhg2JjY0lPT2dtLQ0Fi9enG/7li1bANi1axexsbFuH8OZVatW2Z+WV7NmTZo3b25P4Tds2JBt27YhIhw/ftxpH4B58+Zx5ox1GpnPP/+c66+/3n6XXb58eVJSUvjf//7HDz/84PT4VatWJTw83F72/PnzHbaPGjWKL774wl6nSZMmsXHjRiD/Ob/66qtJSkpi9erVACQnJ9OnTx9OnTpF165dqVGjBt9++y0AGzZsYPv27Z6fOGfy9m7XxXEBxHqalCpdy5cvF0CGDRsmgOzZs6e0q6R8bNmyZdKnTx/p2bOndO/eXUaPHi2JiYn27UePHpUhQ4ZIz549pXPnzjJ9+nQRETl27Jh07txZABk4cKDExcVJ27ZtJTg4WEaOHCki1t+n5s2byyWXXCJffvmljBs3TipVqiTNmzeXOXPmyMiRIyU4OFjatm0re/bskWuvvVYA6dy5sxw7dkxERG6++WZp2bKljBgxQu677z6JiIiQ559/XkREjhw5Il26dJHOnTvLhAkTpFevXvayX3/9dYmIiJD69evLTz/9JPfcc4/9WDt27LDXvX///vnOyeHDh2Xo0KHSq1cv6datm1x77bVy4sQJERFJSUmRyy+/XNq3by9jxoyR4cOHS/369eXjjz+WyZMnS0REhIwePVquueYa6dixo1xxxRUO53PixIkSFRUl3bt3l8OHD0v//v3tn3nhwoX2/WbOnCkNGzaUAQMGyBNPPOFwXjIyMmTixInSpUsX6d69uzz++OP29+U95yIia9askd69e0uvXr2kZ8+eMnv2bPv+q1atkosvvli6desmd955p3Tv3l2aN28uP//8s0u/P7muX06vb2XyWQAlSZ8FoM4Vv/76K1deeSUPPvggU6dOZevWrbRs2bK0q6WUOkfp44CVOk9oE4BSyps0AFCqjNBOgEopb9IAQKkywjbMyNZj2fZaKaU8oQGAUmWEbXiTbb52zQAopYpDAwClyghbAGCblEQDAKVUcWgAoFQZkZ6eTlBQEGFh1rmutAlAKVUcbk8FbIxpCQwD+gG1sM6hnwUcBfYDc4AfROSgF+up1AUvLS2N4OBgewCgGQClVHG4nAEwxlQ2xswCtgBjsF7012Gdf38OsBXrNLxPA/uMMf9njDlvnjWgVGlLT08nODjY/jQ4DQCUUsXh0gXaGFMJ+BNYBbQSkULnIzTG9ACeAH5CH7CjlFfkDQC0CUApVRyuZgAeAl4TkdFFXfwBROQvEbkC2GaMua44FVRKWdkCgICAAAIDAzUDoJQqFpcyACIy2ZPCRWSSJ+9TSuVnCwAA+yOBlVLKUzoKQKkyIj093f5Mdg0AlFLFVawAwBhT21sVUUoVLm8GQPsAKKWKo7gZgBVeqYVSqki5A4CwsDDNACiliqXIPgDGmMJ68Yd4sS5KqUKkp6fbZwHUJgClVHG50glwJrAUcPY84QrerY5SqiDaBKCU8iZXAoBdwGgR2Zd3gzFmv9dr5CJjzCXA18DzIjK9kP1GAOMAwRrIPCIikrOtCjAdqIL1XNwrIut8W3OlPJO3CeDUqVOlXCOlVFnmSh+AL4AaBWz72It1cZkx5lrgYaDQ/4DGmNbA68BlQCfgYuDeXLu8C6wXkR7AY8DPxphgn1RaqWLSYYBKKW8qMgAQkedFZFUB2zyaH8ALVovITcCZIva7HZgnIkkiYgE+Be4G+93/UOATABFZDGQAV/us1koVgzYBKKW8ye1RAMaYrr6oiDtE5ICLu14C7Mj1ehvQyhgTijUbkC4i8Xm2d/ROLZXyLh0FoJTyJk+GAf7s9Vr4TgSOzQQnsXZmrOZkm2270+YOY4zDEhMT4/XKKlUY29MAQZsAlFL5xcTEOFyniuLJ0/qKLvXcIk7WGRe2ORYiznZVquTkzgAEBweTnp5eyjVSSp1LYmJiHG5OiwoCPMkAlKUrYQIQnut1ONb6J+Zsq5Rn//Cc9UqdUywWC5mZmQ4BQFpaWinXSilVlp3vzwJYDTTP9bolsFVEUoF1QIgxJjLP9tUlWD+lXJKRkQFgDwBCQkLIzs4mOzu7NKullCrDzqsAwBhTzRjzZ04Pf7AOU7zSGFPVGOMHjALeBxCRY8D3wOic9/YGgoC5JV5xpYpgS/fnzgDkXq+UUu4qkwGAMaaDMWYJ0A6YZIz5KWdTKNACCAMQkS3AI8ACYCWwAevYf5t7gQ7GmL+Al4FBIqJ5VXXOsV3obU8D1ABAKVVcnnQC3Oj1WrhJRNYC0U7W7weq51n3JfBlAeUcBwp71oFS5wTNACilvM3tDICI9PdFRZRSBSsoANCOgEopT/mkCcAY84UvylXqQpU3ALA1BWgGQCnlKU+aAAAwxjQBegM1Af88m/sVp1JKKUfaBKCU8jaPAgBjzL3A2xQ8KVBZmitAqXOeBgBKKW/ztAngUWAsUF1E/PIuwCbvVVEppQGAUsrbPG0CSBSR9wvZPtLDcpVSThTUB0A7ASqlPOVpBmCmMSaqkO13eViuUsoJzQAopbzNowyAiLxojJlijGkM7ALyPph8CHB/cSunlLKy3elrAKCU8pbidAKcUMgu2glQKS/SDIBSyts8bQJ4HGuav5p2AlTK93QiIKWUt3naCTBBRD4qZLt2AlTKi3QiIKWUt3maAfhdOwEqVXK0CUAp5W2eZgDSgVnGmPVoJ0ClfE6fBqiU8jZPA4Anc742LGC7dgJUyotsF/qgoCBAAwClVPF52gSw0VnnP+0EqJRvpKenExgYiJ+f9U82ICAAPz8/7QSolPKYpwHA00Vs1/S/Ul6Unp5uv+u3CQkJ0QyAUspjHgUAIjK7iO1/eVYdpZQzzgKA4OBgDQCUUh7zNANQKGPMLF+Uq9SFSgMApZS3eToTYFFNAJ08KdeN44cA7wMtsH6Gx0VkgZP95gMhuVYFAlEiUrGA7WdF5GqfVVwpD2kAoJTyNk9HAcQUsd3XowBiACMiXYwxzYAVxpiLRORonv32iMi9thfGmGuBgbm2HxGRUT6uq1LFVlAfAO0EqJTylNdGAQDlgY7ANKCb12qYhzHGD7gD+ARARHYC64Gb8+6b++KfYwTwP1/VTSlf0QyAUsrbPM0A3JR3hYikAOtyJgdaBvQsTsUK0QioCuzItW4b1uCjQMaYSkA7YHGu1eWMMV8C9YBjwDMistGrtVXKC9LS0jQAUEp5laejALYXUWZk7hXGmNc9OU4BInK+nsq17iRQo4j3DQF+FBFLrnW7sV70e2LNKPxljKnl7M3GGIclJibGo8or5QnNACilihITE+NwnSqKp50Ab3G2GqgMXANkGmNG5qwD68V3vCfHKkTefgZFfdoRwIMOBYhMyvX9bGPMNuAWYEq+g4lObqhKT3p6OuXLl3dYFxwcTEpK3lm4lVIXqpiYGIeb06KCAE+bAKa7sM+MXN978+qZkPM1vIDv8zHG1AHCRWRLEWXHA/WLWT+lvC49PZ2qVas6rAsJCeHEiROlVCOlVFnnaQCwHbjSxX0NMNfD4zizGzgONOe/i35LYF4h77kR+MqhUsbUAK4Ukem5VkcA2gdAnXPS09PtDwKy0SYApVRxeBoATBaROFd3Nsa85eFx8hERizHmI2A08KcxpinWzn0jjDEXYR2FMEBEsnO97QasTRO5hQHjjTHfi0iyMeYS4BLgdm/VVSlv0T4ASilv8ygAEJHv3Nz/A0+OU4gY4H1jzAqsn+FGETlijGmAdXKgQCAbwBjTEjgmIofzlHEEmAX8bozJAoKAa0Uk1st1VarYdB4ApZS3eZoBKFUikgaMcrJ+BVAnz7ptwGUFlPFUzqLUOU0DAKWUt/nkWQBKKe9yFgCEhoaSmppaSjVSSpV1GgAoVQYUlAFITU3VIapKKY9oAKDUOU5ECswAWCwWsrKySqlmSqmyTAMApc5xmZmZAE4DAECbAZRSHikyADDGNDbGLDbG7DHGvJHzKF7btlW+rZ5SyjbUz1kTAKAdAZVSHnElA/AO8BMwFKgO/GGMsc1JGuiriimlrAoKADQDoJQqDlcCgAgReVtE1orISOB3rGPnK+DdKX6VUk7Y7vA1AFBKeZMr8wCE5n4hIpONMdnAAqC887copbxFmwCUUr7gSgYg1hjTN/cKEXkemA808UmtlFJ22gSglPIFVzIAI3GS6s/JBHzv/SoppXIrKgOgAYBSyhNFBgAicrKQbdu8WhulVD62ACDv0wBtGQBtAlBKecLteQCMMV19URGllHPaBKCU8gVPJgL62eu1UEoVSDsBKqV8wZMAwHi9FkqpAmkGQCnlC54EADr2X6kSpJ0AlVK+oM8CUOocZ7vA2+74bbQToFKqODQAUOocl5KSAuQPADQDoJQqDg0AlDrH2S7wYWFhDusDAgIICAjQDIBSyiOeBAAbvV4LNxljQowx040xK4wxa4wxAwrYb5QxZoMxZkmupUeu7VWMMb8YY/7KKevikvsUSrmmoCYA2zrNACilPOHKTIAORKS/LyriphjAiEgXY0wzYIUx5iIROepk34dEZEkB5bwLrBeRZ4wxfYCfjTFNRCTdN9VWyn0pKSkYY/J1AgQNAJRSnvNJE4Ax5gtflJtTth9wB/AJgIjsBNYDN7tZThWsjzi2lbMYyACu9mZ9lSqu1NRUQkNDMSb/CNyQkBBtAlBKecTtDICNMaYJ0BuoCfjn2dyvOJUqQiOgKrAj17ptQMcC9r/LGDMZ6/DF70Tk3Zz1FwPpIhLvpJwfvVtlpTyXkpKSr/3fRjMASilPeZQBMMbcC/wLfAQ8hzUln3uJ8EblCmAr+1SudSeBGk72PYr1scXRwDDgPmPM/bnKOZVn/4LKwRjjsMTExHhSd6XcZssAOBMSEqIBgFIKgJiYGIfrVFE8bQJ4FBgLVBcRv7wLsMnDct2Rd0KifJ9WRH4Vkc/EKgF4G7i3kDKclpNTlsOiAYAqKSkpKQUGAKGhodoEoJQCrAFA7utUUTwNABJF5H0ROVbA9pEeluuKhJyv4bnWhedaX5h4oH6ucirl2e5qOUqVmNTUVG0CUEp5nacBwExjTFQh2+/ysFxX7AaOA81zrWsJrM67ozHm0TyrIoBDOd+vA0KMMZFFlaNUaSqqCUAzAEopT3jUCVBEXjTGTDHGNAZ2ASl5dhkC3J//ncUnIhZjzEfAaOBPY0xToB0wwhhzETANGCAi2cAVxpiVIrLUGBOGNTD5IqecY8aY73PKmWyM6Q0EAXN9UW+lPFVYJ8CwsDD7TIFKKeUOjwKAnE6AEwrZxdcPDIoB3jfGrMD6GW4UkSPGmAZACyAQyAZeB57OGTpYHlgIvJSrnHuB6caYv7COZBgkIno7pc4pqampVKlSxem2ChUqcPr06RKukVLqfODpMMDHsd5N/ygix/NuNMasL1atipBzkR7lZP0KoE6u13OAOYWUcxwY6IMqKuU1hXUCrFSpkgYASimPeBoAJIjIR4Vs92UnQKUuKIV1AqxYsSJnzpzBYrHg56eP9lBKuc7T/xi/l2InQKUuKEVlAESEs2fPlnCtlFJlnacZgHRgVk6qv0Q7ASp1oSksA1CpknUk66lTp6hYsWJJVkspVcZ5GgA8mfO1YQHbfd0JUKkLgogUOgzQdtE/deoUkZGRTvdRSilnPG0C2OhsBsASnglQqfNeRkYGFoulyAyAdgRUSrnL0wDg6SK2a/pfKS+wzfLnSgZAKaXc4VEAICKzi9j+l2fVUUrlZpvkp7BOgKAZAKWU+3TckFLnMFsGoLBhgKAZAKWU+zQAUOocphkApZSvaACg1DmsqAxA+fLlMcZoBkAp5TYNAJQ6hxXVCdAYQ8WKFTUAUEq5zeUAwBhznS8ropTKzzbDX7ly5QrcR58HoJTyhDsZgBeNMZV8VhOlVD7Hjh0DoGrVqgXuoxkApZQn3JkJsDow3xhjwfpY3QXAPyKS7ZOaKaVISkoCoFq1agXuoxkApZQn3MkA7BCRrsA1wFasj+PdZIz52Rgz1hjT1BcVVOpClpSUhL+/v723vzNVqlSxZwqUUspVLgcAItI95+txEflWRO4QkVbAxJxy3jDGbDbGfGiMGeKj+ip1QUlKSqJatWoYYwrcp2bNmhw5cqQEa6WUOh94YxRAHNYnAu4BQoA7gO+8UK5SFzxbAFCYWrVqkZCQQFZWVgnVSil1PvDoaYDGmHbAAOAyoBsQBGQDK4AZWPsHKKWKyZUAoGbNmogICQkJ1K5du4RqppQq61wOAIwxI7Fe9C8FagAGiAU+Bn4HFonIWV9UUqkLVVJSEi1btix0n1q1agFw+PBhDQCUUi5zJwMwAxBgC/AU8LuIxPmkVkUwxoQA7wMtsH6Gx0UkX9bBGHMN1icTBgDhwHQReSvX9vlYmy1szorI1T6sulJucbUJAKwBgFJKucqdAKA11gzAAGAC0NUYswD4Q0RKugtyDGBEpIsxphmwwhhzkYgczbPf/wFDRWS9MaYmsNUYEyciP+dsPyIio0qu2kq5zmKxcOzYMQ0AlFI+4c4ogG0i8qaIXAlEAV8C7YHfjDGrjDEvGmOijTEe9StwlTHGD2tHw09y6rUTWA/c7GT3d0Rkfc5+R4DFWAMYpc55p06dwmKxuNQHADQAUEq5x6OLtYhkAItyFowx1YGrgA+A2saYJSJyjddq6agRUBXYkWvdNqCjk3r+X55VIUBirtfljDFfAvWAY8AzIrLRu9VVyjOuTAIEEBQURNWqVTUAUEq5xZ1nAfR3sq6FMeZ+4FPgLaApUA7rRdpXInK+5p779CTWjokFMsZUBC4BPsu1ejfWi35PrBmFv4wxtQp4v8MSExPjYfWVck1CQgJQdAAA1mYADQCUurDFxMQ4XKeK4k4GYIoxZi3QP9dSF+togOPAr1iH/y0Qkf1u19x9kud1UZ92CvBs7o6LIjIp1/ezjTHbgFty9nU8mOQ9nFK+tWvXLgAaNSo6nq5duzYHDx70dZWUUuewmJgYh5vTooIAdzsBJmDNGmRiHfP/IdaL/hopuStkQs7X8AK+z8cYMwbIFJF3iig7HqhfzPop5RX//vsvAQEBNGzYsMh9IyMj2bhRW6+UUq5zJwBI479JfhaX4pj/3VgzDs3576LfEpjnbGdjzLVAH+CmnNdNRSTWGFMDuFJEpufaPQLQ/6LqnLBz504aNWpEYGBgkftGRkZy9OhR0tPTCQ4OLoHaKaXKOnemAl4jIveLyOzSnPBHRCzAR8BosF7QgXbAl8aYi4wxC40x/jnbegEPAPdh7fBXHngyp6gwYLwxplzOvpdg7SPwbQl+HKUK9O+//9K8eXOX9q1Xrx6ANgMoVQbs27eP1NTU0q6GawGAMSZMRPq6W7ix8sXtSExO8SuAr4Ebc4b5VcI6OZDtlulrIBpIAs7kLLYU/xFgFvC7MWYZ8DZwrYjE+qC+SrnFYrEQGxtLs2bNXNo/MjISgP37S6L7jVLKUxaLhYYNG9KgQQMyMzNLtS6uZgDGG2PecKfgnAv/HKCf27UqgoikicgoEekiIh1tswCKyAoRqSMiaTmv64iIybNE5yrjKRHpJiK9csqa7+26KuWJ+Ph40tPTXc4AaACgVNkQF2fth56QkMDbb79dqnVxNQB4EWhijNlojLnVGNPA2U7GGP+cNPzDWJ8TsF5EnLbNK6UKtnPnTgDNACh1ntmyZYv9+99//70Ua+JiJ0ARyTbGDAYeA94Awo0xWcAJrJ0DDRAKVMYaVMQDk0TkK19UWqnz3b///gvgcgYgLCyMKlWqaACg1Dlu69atANxwww3MmzeP7Oxs/P39S6Uu7kwFbBGRF7D2lL8SayDwK7AVa8/5n4Hnga4i0lAv/p45ffo0S5YscWnegdmzZ/PMM88QHx9fAjVTJWnnzp1UqFCBiIiIonfOUa9ePXt6USl1btqyZQuRkZFcddVVnD592h4QlAa3pwIWkSzgt5xFeVF6ejpXXXUVf/31FzfeeCNffvllgRM5nDp1ikGDBiEipKWlMWVKvrmL1Dnk6NGjLF26lK5du9rT9YWxjQBwZTYvm0aNGrFt27biVFMp5WNbt26lVatWdO/eHYC//vqLNm3alEpd3BkGqHwoIyODYcOG8ddff9GvXz++/vrrQu/s16xZY88SrFmzpqSqqTz00ksvMXz4cBo1asTQoUP57rvvCt1/586dLrf/2zRq1Ii9e/disViKU1WllI+ICLGxsTRv3pwGDRpQo0aNUv3/rQHAOeKVV17hl19+Ydq0abz66qsA/P333w77jB49mjFjxgCwatUqAIYNG8aaNWv0n/45bvv27YSGhnLnnXeyePFi7r77brKzs53um5qaSnx8vMvt/zaNGzcmPT2dQ4cOeaPKSnlVQkICP/zwAykpKcybN4+ZM2eWdpVK3IkTJ0hOTqZ+/foYY+jYsaMGABe648eP8+qrrzJo0CDGjh1LVFQU5cuXdwgARITPPvuMjz76iF27drFq1SqaNm3KgAEDOH36NLGxOn3BuSw2NpZBgwbx7rvv8u6773LixAlWrFjhdN+9e/ciIjRp0sStYzRu3BiA3bt3F7u+6sK1YcMGhg0bRnJyslfLff755xk6dCjly5fnqquuYvjw4ezbt8+rxzjX2bK6tom7OnbsyNatW0lJSSmV+mgAcA6YMWMGp0+fZvLkyQAEBATQpUsXhwAgd+eu119/nRUrVnDJJZdwySWXAP9lBNS5JyMjg7i4OJo2bQrAgAED8Pf3Z+7cuU73t/2TqF/fvcdS2B4atGfPnmLUVl3ILBYLd911F99//z0///yzV8teunQpgYGBPPXUU7zyyiv4+/vz3HPPefUYBRERfvnll1K70Nrk/dvu2LEjFouFDRs2lEp9NAA4B3z55Zd06NCBtm3b2td16dKFzZs326eL3LRpEwANGzbkk08+4ciRIwwYMIBWrVpRpUoVFi5cWCp1P595q1llz549WCwWewAQHh5Ojx49mDfPOkVGcnKyw7FsQ/lc6SyYW7169fD39/c4AxAbG1tgVkL5hq0T77li1qxZrFq1Cn9/f77++muvlXvy5Ek2b97ME088weTJk5kwYQI33HADM2fOLJHmy7///ptBgwbx3nvv+fxYhcmbAejQoQMAa9euLZX6eBQAGGNGF7G98B5Oym7Hjh2sXbuWESNGOKxv27YtFovFPkRk48aNGGOYPHkymZmZBAUFMXjwYPz9/RkwYADz58/XfgBe9PLLL9O4cWMSExOLXZatecYWAABceeWVbNy4kXXr1tGsWTOGDBli79QZHx+Pn58ftWvXdus4gYGBNG7c2OOnAj766KNcc801+ntUgp588knKlSvH2bPuPV5l9+7dlCtXzmuZv8zMTOLi4njttddo2LAhDzzwAPPnz+fMmTNeKX/58uWICD179rSvi46O5sSJEyUycuWrr6yj0ufP985kr6mpqTzxxBO89dZbZGRkuPy++Ph4goODqV69OgC1atWiatWq9hu8Eicibi9Yn8gXWsC2q4ATnpR7Li6AWE+Tb9x///0SGBgohw8fdli/c+dOAeTjjz8WEZHrr79emjRpIikpKVKhQgUZNGiQfd/p06cLIGvXrvVZPS8ke/bskeDgYAFk1KhRxS7v9ddfF0CSkpLs67Zs2SK23y3b8tVXX4mIyK233ip169b16Fhjx46VsLAwSUlJcfu9zZs3F0DWr1/v0bGVexITE+0/+19//dWt97733nsCyAsvvFDseuzfv18qVqxor8ubb74pc+fOFUCWLVtW7PJFRJ5//nkB5MyZM/Z1u3fvFkDeeecdrxyjIJmZmVKtWjUxxkhQUJCcPXu22GU++OCD9vM1ZcoUl983bNgwadq0qcO6Pn36SOfOncVisciSJUskIyOj2PWzyXX9cnp987QJoDLwjzHGPs+/MaayMeYLYDZQ0cNyLyinT5/ms88+Y/jw4dSsWdNhW+PGjSlXrpz9bm7btm20bNmS0NBQlixZ4pDKuuKKKwgODmbatGklWv/z1Zdffkl6ejpDhw7lf//7X7HbDQ8ePEhoaChVq1a1r2vZsiUhISEATJ8+nXr16tl7RcfHx7ud/re5+uqrSUlJYcmSJW69Lysry9508Mcff3h0bOWejz76yP69u014y5cvB7yTOl68eDGnT5/mueee45lnnuHOO++kXbt2AKxfv97pe/bu3cvRo0ddPsahQ4eoXLky5cuXt69r2LAhtWvX5s8//yxW/Yty5MgRkpKSuP7668nIyHD7byOvgwcPMnXqVO6991769+/P//3f/7ncjBMfH29P/9tERUWxefNm5syZQ3R0NFdeeWWBI4S8zdMA4CtgEPCkMeYTY8zNwHbgauBuYLOX6ndemz59OmfPnuWBBx7It83Pz4+oqCg2btyIxWJh9+7d9nHhF198MbVq1bLvW6NGDcaOHcuMGTPsU8gqz8XGxhIZGcnIkSPJysoq9j/ZxMREe8rPxhjDihUrWL9+PbfeeisdOnSwB3v79+/3OACIjo6mfPnyjB8/vsCRIbt37+b22293+Ke1b98+srKyAPcvRp7au3dvif2jK8rTTz9N06ZN+fDDD0vsmD/88ANdunShd+/eLFq0yK33ejMA+Oeff6hQoQKPPfYYMTExhIWFUatWLWrUqOG0c1pqairdunXjuuuuc/kYhw8fztekZYyhffv2Pv+fdfz4cQCuueYaAgMDWbZsWbHKsw3bGzlyJOPGjePIkSMu/83s3r2bhg0bOqxr06YNKSkpvP/++4A1AF+8eHGx6ugqjwIAEblPROKA0cA1wAwgDmgpIh+KSDvvVfH8ZLFYePvtt+nSpYu9J39e7dq1Y8OGDcTFxZGenu7QhpzX2LFjsVgsPo+mLwS7du2icePGdOnSBaDYHeOcBQBg7edhu9Nq164dsbGxnD17lv379+e7S3BVSEgIP/30E4cOHeKpp55yus8bb7zBp59+yj///GNfZ3v4UJ06dUpkFMHRo0dp1KgRAwcO9PmxipKRkcGUKVPYtWsXr732mkvTcBfXvn37WLduHUOGDKFXr16sX7/e5bvIhIQEdu/eTe3atYmLi+PYsWPFqsuKFSvo3Lmzw3z0touzswyArRPy8uXLWblypcO2U6dOOT1/hw4dcrhpsalbt67Pn19x4sQJwPq73bFjx0L/R37//fc89thjhZa3fv16/Pz8aNOmDT169MAY41IglpiYSGJiIi1btnRYHxUVBcC8efMYMGAAa9eu5dJLLy2yPG/wtBPggpyOgOsBf+BhIB140BgTbIy5x4t1PC999dVX7Nq1iwcffLDAfTp37szp06eZPXs2UPiT4erXr09wcLD9H7ny3K5du2jSpAnVq1encePGPgsAcmvbti0iwoIFC0hPT/c4AADo378/w4cPZ+7cufkuKhkZGXz77beA4wyStt+b7t27k5SU5PGxXWW7g503b16pPxFtx44dZGRkcOmllxIbG8vq1at9fsyffvoJ+P/2zjysqmp9/J8XEBVQBhWUwlAUcMoBE4c00zJLzbTBNJtsvtnw6zZYdstGK68NNtnXvHUrs8wGzW4OpZmplUMOgDjggOKsiIIDCuv3xx4653DOYTqAyPo8z37g7L322muvM6x3vyMMHTqUxMRElFIljt6w3qvhw4cDlMuBLC8vj3Xr1tnCriMdO3YkNTWVnJwcp/2ffvopF154IfXq1ePDDz+09+/Zs4fGjRvzxRdfFOnLnQYAjEiXgwcPVmgkhCUAhIeH06tXL1auXGlHV7kydepUJkyY4NXs99dffxEfH09QUBAhISEkJCSUSADYsGEDAK1atXLa36lTJ1sI6N27N506dSrRffmCspoA+gJTgJ8xnvonAZcAe4HlwNO+Gd65ybFjx3j88cfp0qULN9xwg8d2ycnJAHz22WcAXjUA/v7+xMXFaQGgnBw7doz9+/fbSXW6du1a5CmntJREALA0AZYK2goPKitDhw4lNzeX+fPnO+1fuHAhhw4dQkSKCADh4eEkJCSQnZ1tmwMqCkehqqxRC77CUnO/8MILBAQE+Dz+HQx/n+HDh9uhn19//TUdOnSgefPmtmBf0u+ulRPkkksuAcpXAnrjxo0UFBTQsWPHIseuu+46Tp8+zUcffeS0f/PmzfTo0YMePXo4fTeWLl3KyZMniwgASin27NnjUQMAhl29onAUAHr27Mnp06eZNWsWixYtKqKtSE9Pp6CggNWrV3vsb82aNU7zlZSUVCoBwFUDEBAQwIIFCxg1ahS33HJLie/LF5RVAMgFhimlrlVK7QPbTf5N4HqM8sAaD7z66qvs2bOHSZMm4efn+S1ISEggNDSUFStWEBQUVGxYWHx8vM4IWE6spzArC1+HDh3Iysqy7YiOrF271u1+Vw4cOEBkZKTXNk2bNqVJkybMmzePgIAAtz/IpaFPnz6EhIQwb55zza4lS5bg7+9P//79iwgA8fHxNGrUCKWU/aNZUSxfvpzk5GSCgoLYs2dPhV6rONasWUPdunW56KKLaNmyZYWEpc2ZM4cvvviCAQMGMH36dJYtW2bb0C3BvqQCgJU9r3v37gDs2rWrzOOy+nK1S4OxsHXv3p1JkybZT+jZ2dlkZ2cTFxdHUlISaWlp9tO0FZK4YMECpyfsQ4cOcfr0abe/X5YAUJFmAOs7Gh4ezuWXX07Lli0ZPnw4ffr0sQUyMLQhlnDlSeg/cuQImZmZtsAOxhN8VlZWsU6RaWlpBAcHu/XviYqKYurUqZx33nmlvb1yUVYB4Hul1Ex3B5RSGcB/yj6kc5vMzEwmTpzIiBEj7Cd8T/j5+dn+Aa1atSq2MlzLli3ZsmWL7Vg1e/ZsunXrxqlTp5zaTZgwgUcffdRJ+v322291KVn+FgAsDUDbtm0Bo4SnI3l5eXTr1o3nn3/ea395eXmcOHGiWA2AiHD99dcDhtBRt27dMo3folatWnTp0sXJzg/Gk3f79u3p1asXW7dutRf6jRs3Eh8fT8OGDQEq1Axw5swZVq5cSbdu3YiOjq7y2gVr1qyhbdu2+Pv7k5iYSHp6us+vMXfuXIKCgmjUqBG333670/sdGhpKZGRkiYX3HTt2EBkZSYMGDYiIiCiVAPDhhx9y3nnn8fbbbwOGIya4FwAAnn32WbZt28azzz4L/J1lsnnz5iQlJVFQUGCbIP744w/q1q3LiRMnWLx4sd2H9f660wBYi2F5hJjiyM7Oxs/Pj3r16hEYGMg777xjR+A4On06CmCeBADrPXKs01HSZD4bNmwo0e94ZVJWJ8CRxTSJKEu/JUVE6ojIxyLyu4isFJF+Xtr+U0RWmdtjLsdiRWSRiCwRkV9ExP23wIdYDibjx48vUfsJEybw1ltv8dVXXxXbNj4+nvz8fDIzM8nPz2fw4MH8/vvvRVSsjz/+OBMnTmTChAkopVizZg1Dhw61Cw25opRi4sSJ9OjRo8pTaVY01g+ilVbXss2tX+8c2LJ48WJOnDjhVVUI2ImEihMAAG688UYAt/bYstC9e3fWrVtnJ5kpKCjgzz//pFu3brYdcvPmzeTl5bFr165KEwC2bNnCiRMn6NSp01khAGzYsMEW9BITE9myZQunT5/2ek5mZibXX389I0eORClFYWGhR+fBwsJC5s2bxzXXXMPo0aM5deoU9957L4mJiXab+Pj4UpkArFSyMTExRRbP1NRUJk6cyL59+5gyZQpDhgzhlVdeYfXq1dx9993s3r3bDjndvn07oaGhhIWFub1Wv379GDlyJJMmTeL06dNOArJlq161apUt1F1zzTUATsKMpeFxJwBYT7yO91BQUMD999/vMQSxtGRnZxMeHm5rW/v160dubi5jxoxhzpw59nfUUtHHx8cX+b5buGoIAVtbV5wAYIVyn02U1QnwFm8bcJWPx+nKOECUUl2BEcAXIhLlZpz9gbuAHuZ2h4gMcGgyHfhEKdUTI5Lhy4oc9J49e/j222959NFHS+zk1aFDBx588EGPErojli1x8+bNtpMRODt7OdrannjiCUaPHs3YsWMBmD9/vluHN2vMy5YtqxQHqapk586d1KtXj9DQUACio6MJDw8v8oNgqdbXrVtn//Dv2LGD3r17s2XLFrtdaQSArl278vTTT3PPPff45F66d+9OQUEBYWFhNGzYkFdffZXc3Fy6detm/4BlZGTY43UUAMrrWe4NS5vStm1bmjRpUqUCwPHjx9m7d6+t8WnVqpVTTgRPvPjii8ycOZNp06axdOlSevfu7TGiYc2aNezfv5/+/fvz0EMP8cwzz/DKK684tYmPjyc9Pb1EEQjbt2+3BYDzzz/fafFUSjFq1CgeffRRGjduzN13382qVat48sknSU5OJiIigmHDhrF69WoKCwvZtm1bsb8tV111FSdPniQ1NdWel+bNm9O0aVMaNGjA6tWr2bp1K8ePH6dfv37Url3bqZS59f66MwEEBwcTHh7udA/z58/nvffec5vXJDc3lwULFpQqUsMSAByxzGCFhYX2wr1hwwb8/Pzo3r27R9Oe9V2xHhAA6tevT3x8vFcB4OjRo2RlZRVxAKxqymoC+NjL9hFQtiDmEiAifsCdwFQApdQmjGgEd1qJe4DPlVInlVIngWkYeQoQkfZAB3Mf5t92IlI+7ysvNGnShPT0dJ544okK6d/Rlrh69WoCAwOJiIhw+mBaC/yyZct45JFHeO+99/jf//7Hc889R2hoqB2L6sgXX3xBQEAAcO4XHdq1a5dtlwRDNd+2bdsiAsCCBQvw8/MjJyfH/rG78cYbWbx4sVOZ09IIACLCCy+8YD+Nlpdu3bpRr149GjVqRJ06dRg7dixhYWFcccUV9g9YRkaG/eRZWRqAlJQU/Pz8SExMJDo6mj179lRK6J07XG3g1lN5cWaAVatW0a1bN+rXr8+IESNYsmQJc+bMseft5MmTtG/fntdff50ff/wRMJ48Q0NDee6556hf3zlXWnJyMgcOHCj2ukopMjMziY2NBYoKAAsXLuTPP//k4Ycf5sUXX2Ty5Mls376dL7/8kjvvvJNPP/2Uyy+/nJycHLZs2cK2bdvsvjxhmSFXrFhBRkYGkZGRhISEICK0a9eOlJQUe9ytW7cuEtrnTQNg3YNj+//8x7Agz58/3+lzoZTi9ttvp1+/fqWKHHEnAIARfw9/O6Gmp6fTvHlzoqKiyM7OdvuZ3LJlC+eff34RE11SUpJXbaAnB8CqpqwCwAagmcvWFrgO+BaoyCDG5kADwPGbkgZ0dtP2Ii/tLgK2KaXyAcy/Wzz04zOaNm3qlA3LlzRu3JiQkBA2b95MWloaCQkJJCcnO2kAfv/9d2rXrk1SUhL//ve/GT9+PJ999hnPPPMM1157LV9//bWTmv/UqVP8+OOPjBo1imbNmpXbI/5sx10Sng4dOrB27Vrbt+LUqVNs3LiRvn2NRJjr1q0jIyPDFq7KqgHwNWFhYezevZusrCzef/99RITXXnuNhg0bUrduXaKjo50EgJYtW9rZCitSAEhNTSUuLs4eQ15ens9yzpcWR5s2/G3b9Zac5tSpU6xfv55evXrx2GOPceTIEXsRtSo8fvzxx6xbt45//etfvPHGG3Tq1ImoqCJKSpvLL78coNiFbd++fZw8edJJA3DgwAHbSe/zzz8nLCyMV155hbFjx3LPPffg5+fHDTfcwPvvv8+VV17ptKBv3769WA1AXFwc4eHhrFixgo0bNzpFI7Vr147U1FTbcTIhIYGYmJgiAkBYWJhHv5bGjRuzf/9+wKhJMHv2bKKioti1a5eTQDRjxgxmzpyJn59fEQ2KNw4fPuxWAAgPD+eCCy6wo0AsG314eDinT592a+60QoRdSUpKYufOnR5rh3gKAaxqyioAPKiU2uGypSmlvgFuBh723RCLYH2LHINTjwDu3KyjvLRzPeatH0TEaRs3blypBl0ZiIhtS9ywYQOtW7emU6dOpKam2gUrUlJSaNOmDYGBgYgIY8aMsQsR3XTTTeTm5tKxY0fbieeXX34hNzeXq6++muTk5BqnAQDjCSgvL8/+ElvV/Swv7vXr19uLaEBAgJO2oCoFAICQkBD8/PwYNGgQe/fu5a677rKPxcXFsWXLFpYsWUJ8fDzBwcEEBQURFBRU4RqANm3aAH+rhavKDOAqANSvX5+wsDAnFbYrKSkpnD59mqSkJJ5++mmOHj3K1q1bOe+885gzZw4Ab7zxBq1ataKwsJBDhw7Rv39/r+No1qwZcXFxxQoA1oJomfusz+ru3btRSrFgwQL69u1L7dq1PfbRunVrgoODmTlzJsePH3dSZ7tDROjcuTPLly/nr7/+copTb9u2Lbm5ucydO5cmTZoQGhpaRADwlATIIjIy0vag3717N/n5+bY/0rx581BK8e2333L//ffTpUsXXnzxRRYtWkRGRgb5+fnFmquys7OJiHDvlta+fXvWrFnDmTNn2Lx5M4mJibaw4C4SxpsAAJ79ANLS0qhdu3aJTLnlYdy4cU7rVHGU1QnQY95DpdQJINHTcR/iqp/xdLfedIvujrntx7WIwtkoAIDxFLd27Vq2bdtG69atiYuLo6CgwLb979u3z+OXsXfv3owZM4bTp09zww03sG/fPmbNmkVwcDB9+/alY8eO7Ny5s0hikHOF/Px89u7dW0QD4PjEBH8/HSYlJREZGcm2bdts58Grr76alJQUW3144MABAgMDqVevXmXdhkdcQxFbtGjBunXrWLRoEQMG/O0a07BhwxIJAJ6SqXgjLy+PzZs3286V1mfRWyjghAkTiI+Pd1uj4Pjx44wcOdLJ69wdBw4csFXxjmzbto3g4GAnAa1p06Zew9KsH3nHXA0iQnJyMuvXr+fIkSNs2rSJW2+9lbS0ND788EMeeeQRr+MDI1XtvHnzSE9P58cff2T69OlFUiW7Pklai1FqaiqbN29m586dtjbBEwEBAXTv3p3vvvsOMJI/FUevXr1ISUkhLy/P6b4tc9XixYvtMcXExLB792577J6SAFlERUXZGgArEqlnz54kJCQwf/58PvjgA4YOHUrt2rX573//y3XXXQcYoZVJSUnEx8d7rWDpyQQAhnZv06ZNpKSkkJ+fb2sArPMcOXLkCPv27XMrABTnCJiWlkZ8fLxtSq0oxo0b51rMzitldQLs5Wa7RESuEZGPgYoscL3f/BvmsC/MYb9rW9d2Bzwc89ZPtSE+Pt62qbZq1cp2NrS+WPv27fOoivTz82P8+PHMmTOHw4cP8/LLLzN79myuuOIK6tSpY3vsVnXcdkVhzZurBiA+Pp769evbAoCjyjw2NpYdO3awbds2ateuTb9+/Th27Jg931YSoLMp9MciMTGRo0ePkp+fz8CBA+39xQkABw4coGPHjmUqR7ty5UoKCgrsENiSaAC+/vprNm/e7NY58p///CfTpk3jmWee8Xi+UoqbbrqJq666qkhSm61bt9K8eXOn9ycmJsarBmDVqlWEh4cXeZqLj48nIyPDtim3adOGZs2acccddzgVgvLEk08+SVBQEIMGDWLQoEGMGDGiyD1v2LCBkJAQ+zOalJREQEAAy5cvt0vdWqYpb1hJhCIiImjfvn2x7R0/H507/20ldfRXsfwnYmJiKCgosH8nSqIByMvLIy8vz573pk2b0q9fP3788UceeeQRLr30UjIzM0lMTKRly5Y0a9aMhx9+mJSUFA4fPuzR/m7ltPAkAPTs2ZPCwkLeffdd+x48CQBWaXZ3PjqhoaG0bNnSowCwdu1aW+g9myirCeAXYJHLthD4BugDPOCLwXkgAzgMJDjsaw24c09f4aXdCqCZiAQCmH/jPPRTbXC0z3Xq1Mm2FWZmZlJYWMj+/fu92iLBUBHefPPNvPPOO2RlZdnezdaP9bkqAFhPfa4aAD8/Pzp37mz7P2zatInIyEjCwsK44IILbAEgNjbWVm1bT2olyQJYVdx3331ce+21JCQkcPHFF9v7ixMA3n33XdasWYNSym3aV29YeQmsUMeSCACWmn779u1OOS1OnjzJlClTiIiI4Ndff2Xr1q0opYqklZ03bx4LFiywi2ZZ6malFKtXr3aK6YaSaQA6depURKhLSEjgzJkzthmgtM6ckZGRfPrpp/j5+ZGcnMzIkSOZNm0aR48etdts2LCBxMRE+9p169alQ4cOLFu2jM8++4z27du7fUJ1xRIALr30Uq/JyCzat2/PeeedR1BQkFP4Yv369Rk3bhw9e/a0n8yt78/OnTvtLIDeNACWZmr//v1FBAAwNE3vv/++U60CS8i54447EBFb+HElNzfXjoRxR69evahfv76d0risAgB4zgh48OBBdu3aVe7kXhVBWQWADIyF3nG7BEP130wpVb5yS15QShVipCEeBSAiLTG9+UWklYj8LCLWJ2UyMNzMG1AHI2RwstnPGmAtMNxsOxxIVUqVv7xWFTJw4EBGjx7NokWLiIuLs58UMjMz7RSvxQkAAGPHjqVdu3bcfffddrpiS4qv6rjttLQ0hgwZ4vPsYVZ/rhoAwC7Ykp2dbWfNA2wBYOvWrTRr1sx2BrN+yEqSBbCqqFevHjNnziQ9PZ3AwEB7vzcB4NSpU0yePJkBAwZw5ZVXMmvWrFJ58C9fvpz4+Hj7ibhevXqEhIR4/EwdPHiQAwcO0KFDBztszWLDhg0UFBQwduxY/P39uf/++20P7d9++81u98477xAdHc3ChQs5deoUt9xyCy+//DLfffcdu3btcjJ/gLGAHT58mLy8vCLjyc/PZ/369W7ztVuCxDfffENwcHCZ6jkMGjSIjRs3snTpUv7xj39w8uRJW1Vv3bOrI1n37t1ZvHgxK1as4LbbbivRdbp06UKXLl24+eabS9ReRHjssce47777nBZiMJIF/frrr1x66aXA3xEVGRkZHD58mPz8fK8aAOv3yBIAGjVqRN26dbn88st54oknWLt2bREhbcKECcydO5cpU6aQlJTkUQCwzJWeBIDAwEA7o+JVV11FeHi4RwEgJSWFkJAQj+9r586dyczMZM+ePUycOJGBAwdSUFBgOxmejQJAEdt2STbg4bKc56sNqIMRcvg7sBLoZ+7vCmQBdRza/hNYZW6PufQTi6G5WIKh1Wjm5loKO9Nx9SQqKkrdddddKjU1VQFq+vTpZeonJydHAWrChAk+HmHJOXr0qGrYsKEC1FNPPeXTvp977jklIiovL6/IsSVLlthzFxISou677z6llFJvv/22sj4j9957rzpz5owKCAiwx9asWTN10003+XScFc2DDz6oQkND3R779ddfFaC++eYbNXnyZAWov/76q0T9Hj9+XEVERKhbb73VaX98fLwaNmyY1+s9++yzClDff/+9feyTTz5RgEpLS1NPPfWUAuzPxoMPPqiUUmrXrl3Kz8/Pfj9uu+02BSgRsd+3gwcPOl3zs88+U4DasGFDkfGsWrVKAeqLL74ocuzQoUN2nxdddFGJ5sQbhYWFqkWLFqpTp06qoKBAHTx4UAFq/PjxTu1mz56tABUVFaX27dtX7uuWl1OnTtnfgfXr13ucL4sVK1YoQM2aNUv1799fJSUllep69913n2rQoIHbYykpKQpQX375pdfr33HHHero0aNKKaUOHz6sAPX66687tbv00ktVcnKyx37Wrl2rADV48GD7czBz5kz12muvuf2cVQYO65fbtbSsToBvejsuIu+Vpd9SXP+kUuo2pVRXpVRnpdR8c//vSqnzlBHzb7WdqJRKMrcJLv1sV0r1UUr1VEr1Vkptc73WuUDTpk3JzMy0VZ8l0QC4o169ehWeu/2tt95i0qRJHo//+uuv9tPp559/7uT8s3DhQsaOHVvmmPL09HQuuOACgoKCihzr0qULwcHBPP300+Tm5tpPjZaJBYwnH39/f84///wiPgDViYYNG5KTk+M2G57lB9G9e3euv/566taty3vvlezrPmXKFA4fPsztt9/utN9bMiArvMwyQzlmmFu/fj2BgYG0aNGCZ599lhdeeIFly5Zx1VVX2Tnerc+I9WT8wQcfcPDgQTZt2kRCQgJDhw4tYp+3VNju/ADcOQBaOHqaF5fmuyRY0UarV6/ms88+s59y+/Tp49Ru4MCB7Nmzh507d54V2ibrPUlLS/OaBMjC1QRQWs1JdHQ0hw4dKpLyHP7WAFiJvdzRuXNnPvzwQ9tRNzQ0FBFxqwHwZtZp164dsbGxzJo1i/bt29O8eXNeeeUVli1bRkxMTIn8QCqbErkkmtn9SkNFZwLUlIKmTZuSmppabgFARCo0c1t2djZPPvkk4eHhPPDAA24d51auXImI8N5773HfffexdOlSevbsybZt2xg6dCg5OTkkJSXZIXqlIT093cm+6UhgYCADBw60S+laP8KOAsCVV14J/C1wnTx5ktzc3GopAICRDbBx48ZOx1asWEFMTIz9GRo5ciSffvopL730ktf7LCgoYMKECVxyySW2/dkiOjraozPh+vXrCQkJoWPHjoSFhTnlWEhJSSExMZFatWoB8PTTRhFSSwDYtGkTM2fOpHPnzrZvTGBgIA0aNKBBgwZs2LDBrfe4q+OsI8uWLaNhw4Z25kBXXn31VbKzs+2xlJfhw4fz9ttv8+ijj9KmTRuioqKcnPDA+F66vk9VTevWrZ0yB7orgGNhCQD79u1jx44dtu2/pFjOybt37y7imFkSAcAVPz8/QkNDnQSA/fv3c+DAAa8CgIgwePBg3nrrLV577TV2797N7bffzsqVK3nsscc8nleleFINKGc1eGEpt4KS9FsdNs4BE8Ajjzyi6tatq954441yq6J69uypLrnkErfHsrKyVE5OTpn7fuutt2yV1bZt29y2GThwoGrdurU6duyYCgoKUnfffbdSSql7771X1a1bV7Vo0UK1bt1aFRYWluraBQUFKigoSD388MMe2xw6dEjFxcWpO+64w9537NgxBahrr73W3jdy5Eh1wQUXqMzMTAWo//u//yvVWKqaGTNmKECtX7++yLHmzZs73Wt6erry9/dXo0eP9trnDz/8oAD19ddfFzn2z3/+UwUFBTm9Z5mZmSo9PV21bdtW9evXTymlVOfOne3/lVIqJibGrXllx44dClB33XWXAtQrr7xS/E07UFBQoIKDg9UDDzxQ5FiLFi3U4MGDS9VfeVm3bp0KCAhQgBo1alSlXrusPP3008rf318NGjRInX/++cV+H+vXr6+uueYaBajJkyeX6lpz585VgPrtt9+KHJs+fboCVGpqaqn6dDXdLVy4UAFqwYIFXs/bu3ev+uKLL1RhYaEqLCxUl19+uYqNjVXHjh0r1fV9BT4yAbjL/Odpa45z9j1NFdOiRQtOnDjBX3/9RUBAgMeQmJLgSQNQWFhI9+7dGTZsWJn7/vrrr+2xLVu2rMhxpRQrV66kc+fOhISEMHToUGbMmEFubi5fffUVgwcPZsyYMaSlpRVbpMeVrKwsjh8/7lEDAIaKd/PmzXzwwQf2PsuBbcaMGfa+pk2bsmvXLvbu3QtUXRKgsuIpHfCRI0fYunWr0xNoQkICd999N++//74dy+2OqVOn0qhRI6dwMovo6GiOHz9ue7t/+eWXNG3alMTERFJSUujduzdgfI4tE8CRI0fYuXOn2yeypk2b0rNnT6ZMmYK/v79dda+k+Pn50b59+yLFaPbt28eWLVucIiYqg3bt2rFs2TLGjx/vM81CRdOqVSsKCgr4/vvv6devX7FhsNHR0SxatAgofbpcb5EkZdEAgJEl0FEDYNWvsKJ8PBEVFcWwYcPsRDz/+9//bC3W2UhJBYBJqmjmP0/bdsCzEVdT6Vge64sXLyYyMrJEYT+esAQA5WJnX7VqFTt27GDu3LnFVsVyh1VNbMSIEYSEhLB06dIibbKysti7d6+9AN15550cOXKEQYMGcejQIYYPH86QIUMICAgodXiaYypTb4hIES/oJk2aOM3pBRdcQEFBgR0Pfq4IAFbefMdQUzDMAAUFBUVKD1vs37+f2bNnc/PNNztFG1i4Rpd89913TvZSy7u8ZcuW7Nixg/z8/GJDskaMGAEYHurFZbpzR6dOnVizZo2TicASSkuSOMfXXHTRRYwZM6bCM8n5CsdcBMUlJgIjLNRarMsqADgWOrPwpQAQERFRalNLQEDAWbv4QwkFAKXUByLyjLnFl6R9+Yem8RXWorZjx45yF5qJi4sjLy/P9iewmDVrFn5+foSEhDjV2PbG8uXLadmyJe3atWPNmjUcP36cHj160L59e1vidsQSCrp16wYYscw33ngjv/zyCx07duSKK64gIiKCfv368dVXX3l0Bjxx4gSPPPKIHZ4DRuGRwMBAt+FdpcX6kbYWjHNFALB+YF3DJDt27EhAQIDbSpIAn376KWfOnOGOO+5we9yy4WZmZqKUYuHChVx55ZU8+OCD1KtXz3a4a9GihR0K6FhR0B2jRo3i22+/5amnnirJLRehY8eO5Obm8sMPP9ifIyu3g1VERuOZqKgoUlJSGD16tFutjyuWlicqKqrUznIRERHUrl3bowbA39+f4ODgUvXZsGFDpxTDqamptG3b9qxM6FUeSvMoeF+FjUJToURHR9ue7eV9erGkc+uJ2eKHH37g4osvpkePHh4Xgp07d3LrrbfapTYnTJjAli1bSElJ4V//+hdgeE9bKnSLrVu3curUKX777TeCg4Pp0KGDfWzy5MlMmzaN5cuX2/nPr776anbs2OGxstoDDzzAG2+8wZAhQ8jJyUEpxXfffUffvn2LVGkrC9b4vvnmG2rVquXVAepsxPoBds2xbr0n1oJtYSWjcVcoKjc3l0mTJtG1a1ePT3YXXnghIsKKFStITU1l//799OnThzfffJODBw/aTn5WgpstW7bYalVHJ0xHAgMDueaaa4poa0qKJXRcffXVzJ49GzDSBkdFRZV6MamptGnThrfffrtET8CWY2hZquWJCNHR0R4FgPr165d64XbMhaGUcqpfcS5RGgFgr1LqeaXUJhHZJiJbra3CRqfxCX5+frbatrwCgJWExHoaAjh27Bjr1q2jd+/edOnSxc4Z7srrr7/OJ598wvjx48nPz2fBggXceeedREdHM3fuXBo1akSzZs3sEqeFhYWcPHmSuLg4unTpwm+//UbXrl2d8mmHhoYyYsQIp+Inlje+u9zv2dnZTJ06lf79+5OZmcmTTz7JihUr2Lp1K4MHDy7X3Fg0atSImJgYcnJy6NSpk8cqaGcrgYGB1K9fv4gGYNeuXfj5+blVgyYnJ7NixYoiuevHjRvHzp07+fe//+3xemFhYbRp04alS5fy/fffA4YKWUScTAbWZ3jz5s12SFZFPZFdeOGFfPbZZ4Ch3YK/0wZrfE9sbCwXXXRRidIYuyM6OtqjCaC06n8wBIDDhw9z5swZsrKyyMnJ8VmZ7rOJ0ggAjvrU3hjZ/04Al/pyQJqKIT4+Hn9//3LHJ1sVvxw1ACtXrqSwsJCuXbvSpUsXCgsLizjhnThxgv/+97/4+/vzzjvv2M57V199NS+++CJ9+/Zl2rRpiAgxMTHk5+dz8OBBu7LeunXrWLNmTYkEmKZNm9K6dWu32cEsrcD999/P6NGjmTx5MkOHDqVx48Z2KlNfYJkSrCxj1Q132QCzsrJo3Lix24ImycnJ5ObmOn0uTpw4wZQpUxgxYkSx71uPHj1YtmwZU6dOpVevXm5jwRs2bEj9+vVZv349K1eu9Im5xhMiwk033cQNN9zAjz/+iFJKCwAVzJ9//snYsWPLdG6TJk3c5icpqwBgme0OHz5crLmpOlPWRECWs98ppVTRYFnNWcfo0aN57bXXyu2QIiK0bt3a6YfeUvknJyfblfNc47oXL15MdnY2b7/9NmfOnGHUqFGEhobSp08fbr/9dn766SfbWciyMe/cudMWJEaNGsVDDz1klwktjt69e/PHH38U8QOwKvklJibywgsvcP3119tOg75M1FHdBYBGjRoV8ep3VyrZwhIsHc0As2bN4ujRo4waNarY6/Xo0YOjR4+SkZFRJFGQhVXu+osvviAvL49rrrmmhHdTdq688kr27t3LypUryczM1ALAWUpkZKRdetuR8mgAwPCDKWkEQHWk7O7gXhCR6RXRr6bs9OrVq0QlSUtC69atWbduHWfOnAEMZ7eEhAQiIiKIiooiLi6OJUuWOJ1jPckPGzaMO++8k9OnT/Pmm2+6tadaNvNdu3axevVqwsPD+fDDD3nzzTeL2J890b59e44ePWp7rltYee9jY2OpX78+X375Jdu3by+SnKa8DBo0iE6dOtke7NWNxo0bF3miysrK8igAtGzZkvDwcCcB4JNPPiEmJsZ28PLGkCFDGDNmDA888IDXUNJ//OMfHD9+nPDw8BL1W1569uwJGP4chYWF1cYLv6YRGRnJ4cOHi2Sv9IUAkJqaSuPGjc/KTH7lpUIEAJwr8GnOMQYMGMDhw4eZP38+ubm5/Pzzz1x22WX28T59+vDLL7/YAgIYYTTR0dFEREQwceJE5s2bx6233uq2f0cNgKfKa8VhOeI5evqDIQC0aNGiwutyd+zYkVWrVlXbHw13+R527drlUQATEZKTk20BYO/evcybN4+bb765RGGnISEhjB8/nkmTJnn1mbjtttu47rrrGD16tO0cWJE0a9aM4OBgvv32WwCtAThLsbIJupqtfKUBOBfV/1A6AaCDiBQ4bu72mfuLLzCtqbYMGDCARo0aMXXqVObMmcOJEyecntr69OlDTk6OUyIVxy9RUFCQ1+QgkZGR1KpVyw73KksVrbZt2+Ln5+dWAPCW7EdjYOVXz8/PBwxHz6NHj3rUAIARnpmSksKePXuYNm0ahYWFJa42V1JEhK+++ornn3/ep/16ws/Pj3bt2rFx40b8/PzO2YWguuNYT8CR8voA7Nu3zw4BPBcpjQCQDXzisv3Xzb5Pzbaac5TAwEBGjRrFd999x0svvUSTJk2cnLwstbdVF72goIC0tLQSf4n8/PyIiYnhp59+4tSpU7Rr167UYwwKCiIhIcFJCDl9+jQZGRlaACgBVnIeK5uh5WHtzQQzbNgwlFK8++67vPbaa/Ts2fOcmOv27Y3nmYsvvrjaanTOddwJAIWFheTk5HgsBewN631esWIFJ06cOCft/1DCYkAmmUop9945LojIX8W30lRnHn/8cT744ANSUlL46KOPnNS8UVFRDBgwgHfffZfHHnuMrKwsTp48WaovUceOHfn666+Bsnvftm7d2vY9AKM++ZkzZ86JRamicczO55iXwZsGICEhge7du/PSSy8hIvzwww+VMtaKxkr846swUY3vcScAHDp0iMLCwjIVSqpduzb16tVj8eLFwLkZAQCl0wCUpkRT6co5aaodERERfP7557z//vt2qVVH/vWvf3Ho0CGGDBnC66+/joiUymnL8p4XETv3QGmJi4tj+/btdmy6FQKoBYDisQQAyxGwJAIAwMsvv8yIESOYPn16kap11ZX+/fvTrVs3brzxxqoeisYD7gQAS3tV1kqJDRs2ZOtWI81NWRIUVQdKrAFQShWNsfBBW031xUq4447k5GQmT57MQw89xE8//cSVV15ZKgcqSwBo0aJFmRPpNG/enPz8fHbv3k1MTIwdAlhcvn9NUQHAMgF4q+sOuC33W91p3ry52+JUmrOHsLAwAgICfCoAREVFsW3bNurWreuTDKFnIxUVBaDRcM899/Dzzz/Trl07O9VvSenYsSO1a9cul+rNqtluSfHp6elER0efs19mX2IVjXLUADRo0KDaZTXU1AxEhMjISJ8KAM8++yxgODWfq1RsLJSmxtOjRw/WrVtX6vNq167Nf/7zH7uSYVmwNA4ZGRlccsklOgKgFPj7+xMVFWU/+WdlZZU4B4NGUxX4WgDo378/O3fupE6dOj4Z39lItRIARKQOMBlIxBj7U0qp+R7aDgIeMNuFAR8rpSY5HJ8LOL6zuUqp4stWaSoNq6RrWYmJicHf35+tW7eilGLDhg3cdNNNPhrduU/z5s3ZsmUL4D0LoEZzNtC4cWOn3BV79+4lODi4XNlPz/XPfLUSAIBxgCiluppliX8XkVZKqX1u2r4BXK+U+ktEGgOpIrJDKTXLPL5XKXVb5QxbUxXUqlWLCy64gIyMDDIzM8nJySlTSGFNpW3btsyYMQOlFLt27bLTPGs0ZyOxsbGsWLHCfr13794yP/3XFKqND4CI+AF3AlMBlFKbgL+AkR5OeVcp9ZfZdi+wCB2dUOOIj48nPT3dNkNYMd2a4mnbti3Z2dmkpqZy4MABYmNjq3pIGo1HYmNjOXToEMeOHQO0AFASqo0AADQHGgCORd7TALexRkqpN1x21QEcoxOCRWSaiCwRke9ERK8M5yAXXnghaWlprFy5Ejh343krAmuu3nvvPcBIhKPRnK1YAqpV/0MLAMVTnQSAKPNvjsO+I0BkcSeKSH3gIuAjh90ZwLNKqZ4YWoXfRKSJlz6ctnHjxpVy+JqqoH379uTn5zNjxgzi4uKoV69eVQ+p2mAlbnr//fepU6cOXbp0qeIRaTSecRUAdu/eXeMEgHHjxjmtU8VRnQQAC+XyuiRVYl4FnncsXayUGqOU2mL+/z2GNuEWjxdVymnTAkD1wFL5p6en2xndNCWjUaNGdvW7pKQkateuXcUj0mg8Y31Wt2/fzqFDhzhy5IgdClxTGDdunNM6VRxnjQAgIj+KSK6HbT5gxXeEOZwW5rDfU793A6eVUu8WM4RM4IIyDl9zluKY9Oeuu+6qwpFUT77//nv69u3L6NGjq3ooGo1XGjVqRN26ddm+fTubNm0CKFcYcU3grIkCUEp5TiuH7QR4GKPUsLXotwb+5+WcIcClwAjzdUul1GYRiQSuUkp97NA8Clhb5hvQnJUEBAQwcOBAAgICvGYu1LinTZs2/PTTT1U9DI2mWESE2NhYtm7damf91AKAd84aDUBxKKUKgSnAKDAWc6ADMM183UpEfhYRf/N1L+BBYDSGw18I8LTZXRDwTxEJNttehOEj8GWl3ZCm0vj+++/55ptvqnoYGo2mgmnfvj0rVqxg48aNBAQE6MiVYqg2AoDJOEBE5HdgOjDcDPEDCMVIEFTLfD0d6A0cBI6Zm6Xi3wt8BywQkV+Bt4EhSqnNFX8LmqqgJA4xGo2menPxxReTlZXFggULiIuLo1atWsWfVIORkjgK1GRERAElcqjQaDQaTdWxbt062/F30KBBzJ49u4pHVLVYDz5KKbdPQNVNA6DRaDQajVus0FVAp/0uAWeNE6BGo9FoNOXB39+fL7/8krp16zJo0KCqHs5ZjzYBFIM2AWg0Go2mOqJNABqNRqPRaIqgBQCNRqPRaGogWgCoAnQaYT0HFnoe9BxY6Hkw0PNQeXOgfQCKoSJ8AESkxvsU6Dkw0POg58BCz4OBngffzYH2AdBoNBqNRlMELQBoNBqNRlMD0SaAYrBMABqNRqPRVEe0CUCj0Wg0Go2N1gBoNBqNRlMD0RoAjUaj0WhqIFoA0Gg0Go2mBqIFAI1Go9FoaiBaAPARInKRiGwRkdu8tLlYROaIyM8iskpEXhIRP5c2d4rIUhH5TUTWicglFT54H+GLORCRvSLyi8OWKiLVqqi3j+ahlYgsMj8Hf4nIE5UyeB/io3mIEpGZIrJERNaLyO2VMngfUcI5uEhEvhWR+eZ7/aWIRDgcryMiH4vI7yKyUkT6VcrgfYgv5qGk/ZytlHcORKSWiDxs/i4uFpHlItK3PGPS5YB9gIgMAa4Hcopp+iLwulJqtoiEACuB/cBbZj/XA32BXkqpAvPHrnHFjdx3+GoOgG+UUv9w6PctYHUFDLlC8OE8/BdYoJQaKyINgM0islYpNbeixu5LfDgP04HFSqnrRKQpkCIiG5RSv1fU2H1FKeZgMPCDUupDEfEHFmDMi/U9GIfhsN1VROKB30WklVJqXwUN3af4ah5K0c9Zh4/m4DzgIaCDUipHRC4HZolIglIqqyzj0hoA37BCKTUCOFZMu++A7wGUUrnAHMBRmn8GeF4pVWC2+Ugp9aXvh1sh+GQOXBZ/f2AQ8I2vB1uB+Oqz0AZYbh4/BGwGOvp6sBVIuedBRM4HLgX+Yx7PxPhBvKNihuxzSjoH/wWmAZjf/VVALICpDbkTmGoe3wT8BYysmCFXCOWeh1L2czbiizk4BjyjlMoxjy8ATgLdyzooLQD4AKXUrhK2e1M5x13WAQ4AiEgk0AroYKp+l4jIPb4fbcXgizlww2XA70qpavOF9+E8/IAh/CAizTEEgj98Nc6KxkfzcJ7596DD8X1Ah3IPsBIoxRxsVkqdABCRJsAAzAUfaA40ANIdTkkDOvtwqBWKj+ahxP2cjfhiDpRSh5RSn1ptRUSAQDz/fhaLFgCqCPPp9grgfXNXLCDAEIyF7wbgaRG5sUoGWAm4mQNXbgI+9XDsnMHDPNwBxItIBsZTwCNKqYVVMb7Kws08WD+a5zk0awyEV+a4KgsR+QrYBExXSn1t7o4y/zqqjo8AkZU4tErFwzzUKEo4B5cAO4Bfy3odLQBUHY8C3ymlrKe62hjvx9tKqQKl1B6MxW9UVQ2wEnCdAxsRCcJQbc2v9FFVPu7m4RtguVIqDrgQeEREOlXJ6CoPp3kw7Zo/AQ+LQSsMH5mTVTjGCkMpdT3QBOgmIv92Pezy2m1q13OBYuahRlDcHIhIHeBl4DalVGFZr6MFgCpARK7CUOGNcdidbf51dOzZBZxfWeOqTDzMgSODgTmWP8S5irt5MBe6y4A3AZRSOzFs349VwRArBS+fhxuBIGAp8CzwIcZTzzmJ6QcxHkPoqYPhEAkQ5tAszGH/OYmbeahxeJoDU/X/f8AbSqlV5bmGFgAqGRHpiuHJebPp6d/SPLQZOI6zaq8RsLuSh1jheJkDR8559b+XeQg0/552aH4aqF+Z46ssvH0eTLvnKKVUd6XUjRiL3zllChGRu1125QH+GP4QGcBhIMHheGtgReWMrvIoZh5qBCWcg4nAn0qpr0SkthkdUya0AFCBiEhD05nPiuNshRHedCcQYIY9PQOglDoFfILp4SwiwcAwDK/Qaktp5sDxHCCmvNLt2UQp5yEdyAKGm23rAVdzDix8pf08iMin5ucBEWkNXIyhBai2uM4BMFxEepjHBGMuVimljpjq3SmYpkBTOOqA6SlenSnNPFTVGCua0s6BGPlAAoCPze9KHOUxEyul9FbODUgCfsFwzknHiGUHiMHw0DzffL0Uw5bnuG136CcY46l3FYbH9+OYBZvO9s1Xc2C2+QfwVFXfUxV/FjpjOPcswQj7mgAEVPX9VcE8jAdSgd+AuUDrqr63CpiDgRjC3UKMJ/vvgOYO/dQBPgZ+x8iT0K+q762K5sFtP9Vh88UcAPFuvisKGFfWcelqgBqNRqPR1EC0CUCj0Wg0mhqIFgA0Go1Go6mBaAFAo9FoNJoaiBYANBqNRqOpgWgBQKPRaDSaGogWADQajUajqYFoAUCj0bhFRK4WkTUiokRkXFWPxxO+HKeIdBCRW300tJJe8zIRGViZ19RoQAsAGk2lYpZ6PiYip81F6wqHY23MRexWl3OWichhEdkkItdV1liVUrOVUh0q63olQURuE5HbHPf5apwi0hv4lsovu7wKeE5ERlfydTU1HC0AaDSViFLqUoz0zv7A5UqpeQ6H+7n8tc7pjlEroqtSamalDPTs5TZz8ykiEgbMAB5XSqX7un9vKKWygeuBiSLSuTKvranZaAFAo6l85mOUc73MZX8/IAW4zMwDDoCINAAKlVKHK2+INY47MIpxVUn9eaXUVmAWnqtjajQ+RwsAGk3lswijut/l1g4RqY1R2ONtjIqQHR3aXw78bLbrICJfmuaDNSKy2tFkICLDRGSXaUpItUwGInKviGSISLaIPG/uqysi/xaRbSKyUUTWicjNJbkBEWkqIjNEZIfZ7yIR6eJw/F4RSTPHcb+I/J+IrBWR7e5U3SJylTneTBH53byP7abpY42I+IvIGowaCZ0d7v8Wl678ReRVEVllzsNLJbkf4DrgV+VQW11EJpjjUSISa+67RUS2mPt6m/uizbHkisgvppniNxHZJyJTRKSWiAw252i3iHwgIoFuxvAzcJVZ5EWjqXiqukiC3vRWEzeMQj87HV73BT4CYjEKfIxxODYV6GX+PwajamSA+ToeOAgMdWifbPZxncs1nwcedHj9A7AFiDZf9wBOAre4nOdUcARoAGRiFCqpZe57FKN0aaJDO+te1gHNzH13A4VAgkO7dhgC0XPma38MM8lJ4GOXsfwC/OJhThWwHUgyX/cz93ktngPUNcf0jJtjt5l9xDrs623u6+1mbPuAu8zXrYACYDJwu7mvjbnvTjfXsvq9vKo/n3qrGZvWAGg0VcN84HwxStyCsVjNV0ptx7D3X+HQthuw3Pz/Y4xF/AyAUmoT8BNwl9VYKfUHRsWx26x9pknhRuBz8/VlwFXAK0qp3eZ5SzEW9eeKGfv/w6hi9qhS6rS57w0gG/cq7IVKqW3m/99gmD8ucTj+JIbw8LI5jgLgKaB2MeNwxxpllpFWSs0HcjEWVm9EmWM6WIbrueIH/Me8/gYgDbgWQ2hDKZUKbAAudXPuAfNvEx+MQ6MpFi0AaDRVw3zzr2UGuAxjIbeOdReRYBFpBWQ4LLRHgQdF5A8RWW+qxfsBzV36/y9whYhEma8vBVKVUtYiZ/kfLHU5LwWItVTeHrgM2K+U2mLtMBftdNwvtpsc/rf8GKIc9nUzx3bKob8sjNKppWWTy+tsl2u5I9L8e7IM13NlmzkXFofd7DsENHZzrnX94sar0fiEgKoegEZTQ1mJsTj0E5HpgFJKWU+AC4D7MRbTOP4WFsB4uuyNoX5OBxCRjym68H4GvATcBLwO3IohFFg0NP/OEBHHxSkIQ43dAEOd7o6GQH1T+HAkDONJ2pXj1j9KqULTv9Hf4XhjjCdlV3I8XN8bx11eF7pcyx3W/bsbe3mvrzzsczcm6/pnfDAOjaZYtACg0VQB5kL4M4YafgB/P/0DLMRYBK7AeLL/JxhOexjOau+pYkLVlFK7zP5vFZH/A3oBdzo0sTQBA5RSmaUc/kHAT/kuR8AeINzN/jAf9V8c+8y/db20cRQOgitoHEHm331eW2k0PkKbADSaqmM+xmLyFA5P+UqpY8DvGMJBM6XURvNQLYwnR+XSjzt1Mhj+AhdiaAJmO5gRwNAyALR3PEFEzjOjDNx5qTuee76IRLice4WIvODlPE8sB1qbkRBWX+cDoW7ansZcjEWkkenLUF72mf16U707CigJPrimO6z3cWcF9a/ROKEFAI2m6rAW/WjgNzfH4vjb+Q+l1FFgCXCDuUAiIt0xIgjc8S2mzwDO6n+UUj8D3wPPi0hjs69g4E1gn1Iq38u43wB2A29agoKIxABvAWu9nOeJlzCefp8y+/IHnsVw4HNlG3Ce6dR4jXVOeTAFo5+BRC/Nhppja2hetyJojWEWWl5cQ43GJ1R1GILe9FaTNwyP8P+52W+F8t3gsj8Gw1N/L0Yo4VSMcL58YA0O4Wpm+w+B9R6uXRt4BWNRXQ/8BTwD+JvHrzb7VOb1Zjucez5GRMFO87zljmMFhmHY9RVGyOBYjAXOsb9PHNr3x3BAzMQQhq7E8EH4yGXM8Rj+ExvM63YFerr0+y6G+WCNOS+HgZ+KeR9GYXjh13HZf5vZ70YMJ8c55r0pjBDKZzBMB2swBJZc4A/z3D+K2bcGqOtwrZ+A/6vqz6Teas4mSrlqEzUajabqEZGjwPtKqScq4VqBGALFB0qpSQ77b8PIz9BMGSGaFXX9rhhan3ZKqR0VdR2NxhFtAtBoNFWKiLQQkedc9jUF6gGplTEGZZg8BgP3i0ifyrimhYg0x8gTcJ1e/DWViRYANBpNVRMCPCQiiQAiUgt4FcgAvqisQSgjr0H3yrqeA6EY0Rjzi22p0fgQbQLQaDRVihjFjsZjhCqewshB8CdGpsGtVTiuDzGSLMVg+By8pJSaVlXj0Wh8jRYANBqNRqOpgWgTgEaj0Wg0NRAtAGg0Go1GUwPRAoBGo9FoNDUQLQBoNBqNRlMD0QKARqPRaDQ1kP8PzWAXUNp19dkAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Image('continuum.png')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Modeling the Pa$\\beta$ emission line"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that the continuum is subtracted, we use the [fit_gaussian](https://species.readthedocs.io/en/latest/species.data.html?highlight=database#species.analysis.emission_line.EmissionLine) method of `EmissionLine` to fit the spectrum with a Gaussian function while mapping the posterior distributions of the 3 free parameters (amplitude, mean, and standard deviation) with the nested sampling algorithm of [UltraNest](https://johannesbuchner.github.io/UltraNest/index.html). \n",
+ "\n",
+ "The argument of `bounds` is a dictionary with the prior boundaries of the three parameters (`gauss_amplitude`, `gauss_mean`, and `gauss_sigma`). If any parameter is missing or `bounds=None` then conservative boundaries will be estimated from the spectrum. The posterior samples will be stored in the database with the `tag` name and a plot is created with a comparison of the data and best-fit model (i.e. the median parameter values)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Creating directory for new run ultranest/run1\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "DEBUG:ultranest:ReactiveNestedSampler: dims=3+0, resume=False, log_dir=ultranest/run1, backend=hdf5, vectorized=False, nbootstraps=30, ndraw=128..65536\n",
+ "/Users/tomasstolker/.pyenv/versions/3.8.7/envs/general3.8/lib/python3.8/site-packages/ultranest/store.py:194: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+ "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+ " 'points', dtype=np.float,\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ultranest] Sampling 400 live points from prior ...\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:ultranest:Sampling 400 live points from prior ...\n",
+ "DEBUG:ultranest:run_iter dlogz=0.5, dKL=0.5, frac_remain=0.01, Lepsilon=0.0010, min_ess=400\n",
+ "DEBUG:ultranest:max_iters=-1, max_ncalls=-1, max_num_improvement_loops=-1, min_num_live_points=400, cluster_num_live_points=40\n",
+ "DEBUG:ultranest:minimal_widths_sequence: [(-inf, 400.0), (inf, 400.0)]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Z=-inf(0.00%) | Like=-1.4e+10..-1.3e+07 [-1.447e+10..-1.327e+07] | it/evals=0/401 eff=0.0000% N=400 \r"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "DEBUG:ultranest:iteration=0, ncalls=401, logz=-inf, remainder_fraction=100.0000%, Lmin=-14465509254.64, Lmax=-13269104.43\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Z=-1e+10(0.00%) | Like=-9.7e+09..-1.3e+07 [-1.447e+10..-1.327e+07] | it/evals=40/444 eff=90.9091% N=400 \r"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "DEBUG:ultranest:iteration=40, ncalls=444, logz=-9740523003.48, remainder_fraction=100.0000%, Lmin=-9716123149.09, Lmax=-13269104.43\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Z=-7e+09(0.00%) | Like=-7.1e+09..-1.3e+07 [-1.447e+10..-1.327e+07] | it/evals=80/490 eff=88.8889% N=400 \r"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "DEBUG:ultranest:iteration=80, ncalls=490, logz=-7112557374.27, remainder_fraction=100.0000%, Lmin=-7104229468.53, Lmax=-13269104.43\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Z=-6e+09(0.00%) | Like=-5.6e+09..-1.3e+07 [-1.447e+10..-1.327e+07] | it/evals=120/538 eff=86.9565% N=400 \r"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "DEBUG:ultranest:iteration=120, ncalls=538, logz=-5664918658.24, remainder_fraction=100.0000%, Lmin=-5608921639.03, Lmax=-13269104.43\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Z=-4e+09(0.00%) | Like=-4.2e+09..-1.3e+07 [-1.447e+10..-1.327e+07] | it/evals=160/599 eff=80.4020% N=400 \r"
+ ]
+ },
+ {
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+ "output_type": "stream",
+ "text": [
+ "DEBUG:ultranest:iteration=160, ncalls=599, logz=-4223717597.11, remainder_fraction=100.0000%, Lmin=-4220324423.21, Lmax=-13269104.43\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Z=-3e+09(0.00%) | Like=-2.9e+09..-1.3e+07 [-1.447e+10..-1.327e+07] | it/evals=200/647 eff=80.9717% N=400 \r"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "DEBUG:ultranest:iteration=200, ncalls=647, logz=-2952581118.24, remainder_fraction=100.0000%, Lmin=-2923450223.80, Lmax=-13269104.43\n"
+ ]
+ },
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+ "output_type": "stream",
+ "text": [
+ "Z=-2e+09(0.00%) | Like=-2.2e+09..-1.3e+07 [-1.447e+10..-1.327e+07] | it/evals=240/704 eff=78.9474% N=400 \r"
+ ]
+ },
+ {
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+ "DEBUG:ultranest:iteration=240, ncalls=704, logz=-2269350023.45, remainder_fraction=100.0000%, Lmin=-2249523402.42, Lmax=-13269104.43\n"
+ ]
+ },
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+ ]
+ },
+ {
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+ "output_type": "stream",
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+ "DEBUG:ultranest:iteration=270, ncalls=761, logz=-1899318723.23, remainder_fraction=100.0000%, Lmin=-1871003501.39, Lmax=-13269104.43\n"
+ ]
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+ ]
+ },
+ {
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+ "text": [
+ "DEBUG:ultranest:iteration=280, ncalls=779, logz=-1748103348.53, remainder_fraction=100.0000%, Lmin=-1711515393.65, Lmax=-13269104.43\n"
+ ]
+ },
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+ "output_type": "stream",
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+ "Z=-1e+09(0.00%) | Like=-1.3e+09..-1.3e+07 [-1.447e+10..-1.327e+07] | it/evals=320/838 eff=73.0594% N=400 \r"
+ ]
+ },
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+ "output_type": "stream",
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+ "DEBUG:ultranest:iteration=320, ncalls=838, logz=-1306760249.51, remainder_fraction=100.0000%, Lmin=-1304783306.80, Lmax=-13269104.43\n"
+ ]
+ },
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+ ]
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Z=-13250634.2(98.79%) | Like=-13250612.90..-13250612.84 [-13250612.9046..-13250612.9045]*| it/evals=10040/73952 eff=13.6325% N=387 \r"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "DEBUG:ultranest:iteration=10040, ncalls=73952, logz=-13250634.20, remainder_fraction=1.2122%, Lmin=-13250612.90, Lmax=-13250612.84\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Z=-13250634.2(98.90%) | Like=-13250612.90..-13250612.84 [-13250612.9009..-13250612.9008]*| it/evals=10080/74017 eff=13.6748% N=387 \r"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "DEBUG:ultranest:iteration=10080, ncalls=74017, logz=-13250634.20, remainder_fraction=1.0960%, Lmin=-13250612.90, Lmax=-13250612.84\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ultranest] Explored until L=-1e+07 \n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:ultranest:Explored until L=-1e+07 \n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ultranest] Likelihood function evaluations: 74097\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:ultranest:Likelihood function evaluations: 74097\n",
+ "/Users/tomasstolker/.pyenv/versions/3.8.7/envs/general3.8/lib/python3.8/site-packages/ultranest/utils.py:170: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
+ "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+ " idx = np.zeros(N, dtype=np.int)\n",
+ "/Users/tomasstolker/.pyenv/versions/3.8.7/envs/general3.8/lib/python3.8/site-packages/ultranest/utils.py:170: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
+ "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+ " idx = np.zeros(N, dtype=np.int)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ultranest] Writing samples and results to disk ...\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:ultranest:Writing samples and results to disk ...\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ultranest] Writing samples and results to disk ... done\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:ultranest:Writing samples and results to disk ... done\n",
+ "DEBUG:ultranest:did a run_iter pass!\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ultranest] logZ = -1.325e+07 +- 0.1748\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:ultranest: logZ = -1.325e+07 +- 0.1748\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ultranest] Effective samples strategy satisfied (ESS = 1800.0, need >400)\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:ultranest:Effective samples strategy satisfied (ESS = 1800.0, need >400)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.45+-0.09 nat, need <0.50 nat)\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:ultranest:Posterior uncertainty strategy is satisfied (KL: 0.45+-0.09 nat, need <0.50 nat)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.39, need <0.5)\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:ultranest:Evidency uncertainty strategy is satisfied (dlogz=0.39, need <0.5)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ultranest] logZ error budget: single: 0.23 bs:0.17 tail:0.01 total:0.18 required:<0.50\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:ultranest: logZ error budget: single: 0.23 bs:0.17 tail:0.01 total:0.18 required:<0.50\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ultranest] done iterating.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:ultranest:done iterating.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Log-evidence = -13250634.19 +/- 0.24\n",
+ "Best-fit parameters (mean +/- std):\n",
+ " - gauss_amplitude = 1.23e-15 +/- 1.07e-17\n",
+ " - gauss_mean = 1.28e+00 +/- 5.90e-06\n",
+ " - gauss_sigma = 4.30e-04 +/- 5.81e-06\n",
+ "Maximum likelihood sample:\n",
+ " - Log-likelihood = -1.33e+07\n",
+ " - gauss_amplitude = 1.23e-15\n",
+ " - gauss_mean = 1.28e+00\n",
+ " - gauss_sigma = 4.30e-04\n",
+ "Calculating line fluxes... [DONE]\n",
+ "Log-evidence = -13250634.19 +/- 0.24\n",
+ "Integrated autocorrelation time = [0.98513013]\n",
+ "Plotting best-fit line profile: fit.png... [DONE]\n"
+ ]
+ }
+ ],
+ "source": [
+ "line.fit_gaussian(tag='p-beta',\n",
+ " min_num_live_points=400,\n",
+ " bounds={'gauss_amplitude': (0., 2e-15)},\n",
+ " output='ultranest',\n",
+ " plot_filename='fit.png')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's have a look at the result!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Image('fit.png')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plotting the posterior distributions"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that the posterior samples are available in the database, we can use the [plot_posterior](https://species.readthedocs.io/en/latest/species.plot.html?highlight=plot_posterior#species.plot.plot_mcmc.plot_posterior) function for visualizing the 1D and 2D projected distributions with [corner.py](https://corner.readthedocs.io). The argument of `tag` is set to the same database tag that was used with `fit_gaussian`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Median sample:\n",
+ " - gauss_amplitude = 1.23e-15\n",
+ " - gauss_mean = 1.28e+00\n",
+ " - gauss_sigma = 4.30e-04\n",
+ " - gauss_fwhm = 2.37e+02\n",
+ " - line_flux = 1.32e-18\n",
+ " - line_luminosity = 9.52e-07\n",
+ " - line_eq_width = -3.72e+00\n",
+ " - distance = 1.52e+02\n",
+ "Plotting the posterior: posterior.png... [DONE]\n"
+ ]
+ }
+ ],
+ "source": [
+ "species.plot_posterior(tag='p-beta',\n",
+ " burnin=None,\n",
+ " title=None,\n",
+ " title_fmt=['.2e', '.3f', '.3f', '.1f', '.2e', '.2e', '.2f'],\n",
+ " offset=(-0.4, -0.35),\n",
+ " output='posterior.png')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's have a look at the result! The first three parameters (from left to right) are the free parameters while the FWHM velocity, line flux, line luminosity, and equivalent width have been calculated from the posterior distributions of these three Gaussian model parameters."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Image('posterior.png')"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/requirements.txt b/requirements.txt
index 14a0bd74..88e38957 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -11,6 +11,7 @@ PyMieScatt ~= 1.8.0
pymultinest ~= 2.9.0
scipy ~= 1.5.0
spectres ~= 2.1.0
+specutils ~= 1.2
tqdm ~= 4.56.0
typeguard ~= 2.10.0
ultranest ~= 3.2.0
diff --git a/species/__init__.py b/species/__init__.py
index 0a4866b3..021b0750 100644
--- a/species/__init__.py
+++ b/species/__init__.py
@@ -1,3 +1,5 @@
+from species.analysis.emission_line import EmissionLine
+
from species.analysis.empirical import CompareSpectra
from species.analysis.fit_model import FitModel
@@ -63,6 +65,7 @@
from species.util.read_util import add_luminosity, \
get_mass, \
powerlaw_spectrum, \
+ gaussian_spectrum, \
update_spectra
diff --git a/species/analysis/emission_line.py b/species/analysis/emission_line.py
new file mode 100644
index 00000000..aa634b94
--- /dev/null
+++ b/species/analysis/emission_line.py
@@ -0,0 +1,544 @@
+"""
+Module with functionalities for the analysis of emission lines.
+"""
+
+import configparser
+import os
+
+from typing import Dict, Optional, Tuple, Union
+
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import numpy as np
+import ultranest
+
+from astropy import units as u
+from astropy.modeling.fitting import LinearLSQFitter
+from astropy.modeling.polynomial import Polynomial1D
+from astropy.nddata import StdDevUncertainty
+from scipy.interpolate import interp1d
+from specutils import Spectrum1D
+from specutils.fitting import fit_generic_continuum
+from typeguard import typechecked
+
+from species.core import constants
+from species.data import database
+from species.read import read_object
+from species.util import read_util
+
+
+class EmissionLine:
+ """
+ Class for the analysis of emission lines.
+ """
+
+ @typechecked
+ def __init__(self,
+ object_name: str,
+ spec_name: str,
+ wavel_range: Optional[Tuple[float, float]] = None) -> None:
+ """
+ Parameters
+ ----------
+ object_name : str
+ Object name as stored in the database with
+ :func:`~species.data.database.Database.add_object` or
+ :func:`~species.data.database.Database.add_companion`.
+ spec_name : str
+ Name of the spectrum that is stored at the object data of ``object_name``.
+ wavel_range : tuple(float, float), None
+ Wavelength range (um) that is cropped from the spectrum. The full spectrum is used if
+ the argument is set to ``None``.
+
+ Returns
+ -------
+ NoneType
+ None
+ """
+
+ self.object_name = object_name
+ self.spec_name = spec_name
+
+ self.object = read_object.ReadObject(object_name)
+
+ self.spectrum = self.object.get_spectrum()[spec_name][0]
+
+ if wavel_range is None:
+ self.wavel_range = (self.spectrum[0, 0], self.spectrum[-1, 0])
+
+ else:
+ self.wavel_range = wavel_range
+
+ indices = np.where((self.spectrum[:, 0] >= wavel_range[0]) &
+ (self.spectrum[:, 0] <= wavel_range[1]))[0]
+
+ self.spectrum = self.spectrum[indices, ]
+
+ self.continuum_flux = np.full(self.spectrum.shape[0], 0.)
+ self.continuum_check = False
+
+ config_file = os.path.join(os.getcwd(), 'species_config.ini')
+
+ config = configparser.ConfigParser()
+ config.read_file(open(config_file))
+
+ self.database = config['species']['database']
+
+ @typechecked
+ def subtract_continuum(self,
+ poly_degree: int = 3,
+ plot_filename: str = 'continuum.pdf') -> None:
+ """
+ Method for fitting the continuum with a polynomial function of the following form:
+ :math:`P = \\sum_{i=0}^{i=n}C_{i} * x^{i}`. The spectrum is first smoothed with a median
+ filter and then fitted with a linear least squares algorithm.
+
+ Parameters
+ ----------
+ poly_degree : int
+ Degree of the polynomial series.
+ plot_filename : str
+ Filename for the plots with the continuum fit and the continuum-subtracted spectrum.
+
+ Returns
+ -------
+ NoneType
+ None
+ """
+
+ # Fit continuum
+
+ print('Fitting continuum...', end='', flush=True)
+
+ spec_extract = Spectrum1D(flux=self.spectrum[:, 1]*u.W,
+ spectral_axis=self.spectrum[:, 0]*u.um,
+ uncertainty=StdDevUncertainty(self.spectrum[:, 2]*u.W))
+
+ g1_fit = fit_generic_continuum(spec_extract,
+ median_window=3,
+ model=Polynomial1D(poly_degree),
+ fitter=LinearLSQFitter())
+
+ continuum_fit = g1_fit(spec_extract.spectral_axis)
+
+ print(' [DONE]')
+
+ # Subtract continuum
+
+ spec_cont_sub = spec_extract - continuum_fit
+
+ self.continuum_flux = continuum_fit/u.W
+
+ # Create plot
+
+ print(f'Plotting continuum fit: {plot_filename}...', end='', flush=True)
+
+ mpl.rcParams['font.serif'] = ['Bitstream Vera Serif']
+ mpl.rcParams['font.family'] = 'serif'
+
+ plt.rc('axes', edgecolor='black', linewidth=2)
+ plt.rcParams['axes.axisbelow'] = False
+
+ plt.figure(1, figsize=(6, 6))
+ gs = mpl.gridspec.GridSpec(2, 1)
+ gs.update(wspace=0, hspace=0.1, left=0, right=1, bottom=0, top=1)
+
+ ax1 = plt.subplot(gs[0, 0])
+ ax2 = plt.subplot(gs[1, 0])
+
+ ax1.tick_params(axis='both', which='major', colors='black', labelcolor='black',
+ direction='in', width=1, length=5, labelsize=12, top=True, bottom=True,
+ left=True, right=True, labelbottom=False)
+
+ ax1.tick_params(axis='both', which='minor', colors='black', labelcolor='black',
+ direction='in', width=1, length=3, labelsize=12, top=True, bottom=True,
+ left=True, right=True, labelbottom=False)
+
+ ax2.tick_params(axis='both', which='major', colors='black', labelcolor='black',
+ direction='in', width=1, length=5, labelsize=12, top=True, bottom=True,
+ left=True, right=True)
+
+ ax2.tick_params(axis='both', which='minor', colors='black', labelcolor='black',
+ direction='in', width=1, length=3, labelsize=12, top=True, bottom=True,
+ left=True, right=True)
+
+ ax2.set_xlabel('Wavelength (µm)', fontsize=16)
+ ax1.set_ylabel('Flux (W m$^{-2}$ µm$^{-1}$)', fontsize=16)
+ ax2.set_ylabel('Flux (W m$^{-2}$ µm$^{-1}$)', fontsize=16)
+
+ ax2.get_xaxis().set_label_coords(0.5, -0.1)
+ ax1.get_yaxis().set_label_coords(-0.1, 0.5)
+ ax2.get_yaxis().set_label_coords(-0.1, 0.5)
+
+ ax1.plot(spec_extract.spectral_axis, spec_extract.flux, color='black', label=self.spec_name)
+ ax1.plot(spec_extract.spectral_axis, continuum_fit, color='tab:blue', label='Continuum fit')
+
+ ax2.plot(spec_cont_sub.spectral_axis, spec_cont_sub.flux,
+ color='black', label='Continuum subtracted')
+
+ ax1.legend(loc='upper right', frameon=False, fontsize=12.)
+ ax2.legend(loc='upper right', frameon=False, fontsize=12.)
+
+ plt.savefig(plot_filename, bbox_inches='tight')
+ plt.clf()
+ plt.close()
+
+ print(' [DONE]')
+
+ # Overwrite original spectrum with continuum-subtracted spectrum
+ self.spectrum[:, 1] = spec_cont_sub.flux
+
+ self.continuum_check = True
+
+ @typechecked
+ def fit_gaussian(self,
+ tag: str,
+ min_num_live_points: float = 400,
+ bounds: Dict[str, Union[Tuple[float, float]]] = None,
+ output: str = 'ultranest/',
+ plot_filename: str = 'line_fit.pdf') -> None:
+ """
+ Method for fitting a Gaussian profile to an emission line and using ``UltraNest`` for
+ sampling the posterior distributions and estimating the evidence.
+
+ Parameters
+ ----------
+ tag : str
+ Database tag where the posterior samples will be stored.
+ min_num_live_points : int
+ Minimum number of live points (see
+ https://johannesbuchner.github.io/UltraNest/issues.html).
+ bounds : dict(str, tuple(float, float)), None
+ The boundaries that are used for the uniform priors. Conservative prior boundaries will
+ be estimated from the spectrum if the argument is set to ``None`` or if any of the
+ required parameters is missing in the ``bounds`` dictionary.
+ output : str
+ Path that is used for the output files from ``UltraNest``.
+ plot_filename : str
+ Filename for the plot with the best-fit line profile.
+
+ Returns
+ -------
+ NoneType
+ None
+ """
+
+ high_spec_res = 1e5
+
+ @typechecked
+ def gaussian_function(amplitude: float,
+ mean: float,
+ sigma: float,
+ wavel: np.ndarray):
+
+ return amplitude * np.exp(-0.5*(wavel-mean)**2/sigma**2)
+
+ # Model parameters
+
+ modelpar = ['gauss_amplitude', 'gauss_mean', 'gauss_sigma']
+
+ # Create a dictionary with the cube indices of the parameters
+
+ cube_index = {}
+ for i, item in enumerate(modelpar):
+ cube_index[item] = i
+
+ # Check if all prior boundaries are present
+
+ if bounds is None:
+ bounds = {}
+
+ if 'gauss_amplitude' not in bounds:
+ bounds['gauss_amplitude'] = (0., 2.*np.amax(self.spectrum[:, 1]))
+
+ if 'gauss_mean' not in bounds:
+ bounds['gauss_mean'] = (self.spectrum[0, 0], self.spectrum[-1, 0])
+
+ if 'gauss_sigma' not in bounds:
+ bounds['gauss_sigma'] = (0., self.spectrum[-1, 0]-self.spectrum[0, 0])
+
+ # Get the MPI rank of the process
+
+ try:
+ from mpi4py import MPI
+ mpi_rank = MPI.COMM_WORLD.Get_rank()
+
+ except ModuleNotFoundError:
+ mpi_rank = 0
+
+ # Create the output folder if required
+
+ if mpi_rank == 0 and not os.path.exists(output):
+ os.mkdir(output)
+
+ @typechecked
+ def lnprior_ultranest(cube: np.ndarray) -> np.ndarray:
+ """
+ Function for transforming the unit cube into the parameter cube.
+
+ Parameters
+ ----------
+ cube : np.ndarray
+ Array with unit parameters.
+
+ Returns
+ -------
+ np.ndarray
+ Array with physical parameters.
+ """
+
+ params = cube.copy()
+
+ for item in cube_index:
+ # Uniform priors for all parameters
+ params[cube_index[item]] = bounds[item][0] + \
+ (bounds[item][1]-bounds[item][0])*params[cube_index[item]]
+
+ return params
+
+ @typechecked
+ def lnlike_ultranest(params: np.ndarray) -> np.float64:
+ """
+ Function for calculating the log-likelihood for the sampled parameter cube.
+
+ Parameters
+ ----------
+ params : np.ndarray
+ Cube with physical parameters.
+
+ Returns
+ -------
+ float
+ Log-likelihood.
+ """
+
+ data_flux = self.spectrum[:, 1]
+ data_var = self.spectrum[:, 2]**2
+
+ model_flux = gaussian_function(params[cube_index['gauss_amplitude']],
+ params[cube_index['gauss_mean']],
+ params[cube_index['gauss_sigma']],
+ self.spectrum[:, 0])
+
+ chi_sq = -0.5 * (data_flux-model_flux)**2 / data_var
+
+ return np.nansum(chi_sq)
+
+ sampler = ultranest.ReactiveNestedSampler(modelpar,
+ lnlike_ultranest,
+ transform=lnprior_ultranest,
+ resume='subfolder',
+ log_dir=output)
+
+ result = sampler.run(show_status=True,
+ viz_callback=False,
+ min_num_live_points=min_num_live_points)
+
+ # Log-evidence
+
+ ln_z = result['logz']
+ ln_z_error = result['logzerr']
+ print(f'Log-evidence = {ln_z:.2f} +/- {ln_z_error:.2f}')
+
+ # Best-fit parameters
+
+ print('Best-fit parameters (mean +/- std):')
+
+ for i, item in enumerate(modelpar):
+ mean = np.mean(result['samples'][:, i])
+ std = np.std(result['samples'][:, i])
+
+ print(f' - {item} = {mean:.2e} +/- {std:.2e}')
+
+ # Maximum likelihood sample
+
+ print('Maximum likelihood sample:')
+
+ max_lnlike = result['maximum_likelihood']['logl']
+ print(f' - Log-likelihood = {max_lnlike:.2e}')
+
+ for i, item in enumerate(result['maximum_likelihood']['point']):
+ print(f' - {modelpar[i]} = {item:.2e}')
+
+ # Posterior samples
+
+ samples = result['samples']
+
+ # Best-fit model parameters
+
+ model_param = {'gauss_amplitude': np.median(samples[:, 0]),
+ 'gauss_mean': np.median(samples[:, 1]),
+ 'gauss_sigma': np.median(samples[:, 2])}
+
+ best_model = read_util.gaussian_spectrum(
+ self.wavel_range, model_param, spec_res=high_spec_res)
+
+ # Interpolate high-resolution continuum
+
+ if self.continuum_check:
+ cont_interp = interp1d(self.spectrum[:, 0], self.continuum_flux, bounds_error=False)
+ cont_high_res = cont_interp(best_model.wavelength)
+
+ else:
+ cont_high_res = np.full(best_model.wavelength.shape[0], 0.)
+
+ # Add FWHM velocity
+
+ modelpar.append('gauss_fwhm')
+
+ gauss_mean = samples[:, 1] # (um)
+ gauss_fwhm = 2. * np.sqrt(2.*np.log(2.)) * samples[:, 2] # (um)
+
+ vel_fwhm = 1e-3*constants.LIGHT*gauss_fwhm/gauss_mean # (km s-1)
+ vel_fwhm = vel_fwhm[..., np.newaxis]
+
+ samples = np.append(samples, vel_fwhm, axis=1)
+
+ # Add line flux and luminosity
+
+ print('Calculating line fluxes...', end='', flush=True)
+
+ distance = self.object.get_distance()[0]
+
+ modelpar.append('line_flux')
+ modelpar.append('line_luminosity')
+
+ line_flux = np.zeros(samples.shape[0])
+ line_lum = np.zeros(samples.shape[0])
+
+ if self.continuum_check:
+ modelpar.append('line_eq_width')
+ eq_width = np.zeros(samples.shape[0])
+
+ for i in range(samples.shape[0]):
+ model_param = {'gauss_amplitude': samples[i, 0],
+ 'gauss_mean': samples[i, 1],
+ 'gauss_sigma': samples[i, 2]}
+
+ model_box = read_util.gaussian_spectrum(
+ self.wavel_range, model_param, spec_res=high_spec_res)
+
+ line_flux[i] = np.trapz(model_box.flux, model_box.wavelength) # (W m-2)
+
+ line_lum[i] = 4. * np.pi * (distance*constants.PARSEC)**2 * line_flux[i] # (W)
+ line_lum[i] /= constants.L_SUN # (Lsun)
+
+ if self.continuum_check:
+ # Normalize the spectrum to the continuum
+ spec_norm = (model_box.flux+cont_high_res) / cont_high_res
+
+ # Check if the flux is NaN (due to interpolation errors at the spectrum edge)
+ indices = ~np.isnan(spec_norm)
+
+ eq_width[i] = np.trapz(1.-spec_norm[indices], model_box.wavelength[indices]) # (um)
+ eq_width[i] *= 1e4 # (A)
+
+ line_flux = line_flux[..., np.newaxis]
+ samples = np.append(samples, line_flux, axis=1)
+
+ line_lum = line_lum[..., np.newaxis]
+ samples = np.append(samples, line_lum, axis=1)
+
+ if self.continuum_check:
+ eq_width = eq_width[..., np.newaxis]
+ samples = np.append(samples, eq_width, axis=1)
+
+ print(' [DONE]')
+
+ # Log-likelihood
+
+ ln_prob = result['weighted_samples']['logl']
+
+ # Log-evidence
+
+ ln_z = result['logz']
+ ln_z_error = result['logzerr']
+ print(f'Log-evidence = {ln_z:.2f} +/- {ln_z_error:.2f}')
+
+ # Get the MPI rank of the process
+
+ try:
+ from mpi4py import MPI
+ mpi_rank = MPI.COMM_WORLD.Get_rank()
+
+ except ModuleNotFoundError:
+ mpi_rank = 0
+
+ # Add samples to the database
+
+ if mpi_rank == 0:
+ # Writing the samples to the database is only possible when using a single process
+
+ species_db = database.Database()
+
+ species_db.add_samples(sampler='ultranest',
+ samples=samples,
+ ln_prob=ln_prob,
+ ln_evidence=(ln_z, ln_z_error),
+ mean_accept=None,
+ spectrum=('model', 'gaussian'),
+ tag=tag,
+ modelpar=modelpar,
+ distance=distance,
+ spec_labels=None)
+
+ # Create plot
+
+ print(f'Plotting best-fit line profile: {plot_filename}...', end='', flush=True)
+
+ mpl.rcParams['font.serif'] = ['Bitstream Vera Serif']
+ mpl.rcParams['font.family'] = 'serif'
+
+ plt.rc('axes', edgecolor='black', linewidth=2)
+ plt.rcParams['axes.axisbelow'] = False
+
+ plt.figure(1, figsize=(6, 6))
+ gs = mpl.gridspec.GridSpec(2, 1)
+ gs.update(wspace=0, hspace=0.1, left=0, right=1, bottom=0, top=1)
+
+ ax1 = plt.subplot(gs[0, 0])
+ ax2 = plt.subplot(gs[1, 0])
+
+ ax1.tick_params(axis='both', which='major', colors='black', labelcolor='black',
+ direction='in', width=1, length=5, labelsize=12, top=True, bottom=True,
+ left=True, right=True, labelbottom=False)
+
+ ax1.tick_params(axis='both', which='minor', colors='black', labelcolor='black',
+ direction='in', width=1, length=3, labelsize=12, top=True, bottom=True,
+ left=True, right=True, labelbottom=False)
+
+ ax2.tick_params(axis='both', which='major', colors='black', labelcolor='black',
+ direction='in', width=1, length=5, labelsize=12, top=True, bottom=True,
+ left=True, right=True)
+
+ ax2.tick_params(axis='both', which='minor', colors='black', labelcolor='black',
+ direction='in', width=1, length=3, labelsize=12, top=True, bottom=True,
+ left=True, right=True)
+
+ ax2.set_xlabel('Wavelength (µm)', fontsize=16)
+ ax1.set_ylabel('Flux (W m$^{-2}$ µm$^{-1}$)', fontsize=16)
+ ax2.set_ylabel('Flux (W m$^{-2}$ µm$^{-1}$)', fontsize=16)
+
+ ax2.get_xaxis().set_label_coords(0.5, -0.1)
+ ax1.get_yaxis().set_label_coords(-0.1, 0.5)
+ ax2.get_yaxis().set_label_coords(-0.1, 0.5)
+
+ ax1.plot(self.spectrum[:, 0], self.spectrum[:, 1]+self.continuum_flux,
+ color='black', label=self.spec_name)
+
+ ax1.plot(best_model.wavelength, best_model.flux+cont_high_res,
+ color='tab:blue', label='Best-fit model (continuum + line)')
+
+ ax2.plot(self.spectrum[:, 0], self.spectrum[:, 1], color='black', label=self.spec_name)
+
+ ax2.plot(best_model.wavelength, best_model.flux, color='tab:blue',
+ label='Best-fit line profile')
+
+ ax1.legend(loc='upper left', frameon=False, fontsize=12.)
+ ax2.legend(loc='upper left', frameon=False, fontsize=12.)
+
+ plt.savefig(plot_filename, bbox_inches='tight')
+ plt.clf()
+ plt.close()
+
+ print(' [DONE]')
diff --git a/species/analysis/fit_model.py b/species/analysis/fit_model.py
index 40df9059..64da55a2 100644
--- a/species/analysis/fit_model.py
+++ b/species/analysis/fit_model.py
@@ -1093,6 +1093,7 @@ def run_mcmc(self,
species_db.add_samples(sampler='emcee',
samples=ens_sampler.chain,
ln_prob=ens_sampler.lnprobability,
+ ln_evidence=None,
mean_accept=np.mean(ens_sampler.acceptance_fraction),
spectrum=('model', self.model),
tag=tag,
@@ -1663,6 +1664,7 @@ def lnlike_multinest(params,
species_db.add_samples(sampler='multinest',
samples=samples,
ln_prob=ln_prob,
+ ln_evidence=(ln_z, ln_z_error),
mean_accept=None,
spectrum=('model', self.model),
tag=tag,
@@ -1853,6 +1855,7 @@ def lnlike_ultranest(params: np.ndarray) -> np.float64:
species_db.add_samples(sampler='ultranest',
samples=samples,
ln_prob=ln_prob,
+ ln_evidence=(ln_z, ln_z_error),
mean_accept=None,
spectrum=('model', self.model),
tag=tag,
diff --git a/species/analysis/fit_spectrum.py b/species/analysis/fit_spectrum.py
index e51f5357..011384a3 100644
--- a/species/analysis/fit_spectrum.py
+++ b/species/analysis/fit_spectrum.py
@@ -194,6 +194,7 @@ def run_mcmc(self,
species_db.add_samples(sampler='emcee',
samples=ens_sampler.chain,
ln_prob=ens_sampler.lnprobability,
+ ln_evidence=None,
mean_accept=np.mean(ens_sampler.acceptance_fraction),
spectrum=('calibration', self.spectrum),
tag=tag,
diff --git a/species/core/box.py b/species/core/box.py
index 36e786f5..3b917054 100644
--- a/species/core/box.py
+++ b/species/core/box.py
@@ -97,6 +97,7 @@ def create_box(boxtype,
box.parameters = kwargs['parameters']
box.samples = kwargs['samples']
box.ln_prob = kwargs['ln_prob']
+ box.ln_evidence = kwargs['ln_evidence']
box.prob_sample = kwargs['prob_sample']
box.median_sample = kwargs['median_sample']
@@ -365,6 +366,7 @@ def __init__(self):
self.parameters = None
self.samples = None
self.ln_prob = None
+ self.ln_evidence = None
self.prob_sample = None
self.median_sample = None
diff --git a/species/data/database.py b/species/data/database.py
index ec9235da..57e52282 100644
--- a/species/data/database.py
+++ b/species/data/database.py
@@ -1155,6 +1155,7 @@ def add_samples(self,
sampler: str,
samples: np.ndarray,
ln_prob: np.ndarray,
+ ln_evidence: Optional[Tuple[float, float]],
mean_accept: Optional[float],
spectrum: Tuple[str, str],
tag: str,
@@ -1170,6 +1171,8 @@ def add_samples(self,
Samples of the posterior.
ln_prob : np.ndarray
Log posterior for each sample.
+ ln_evidence : tuple(float, float)
+ Log evidence and uncertainty. Set to ``None`` when ``sampler`` is 'emcee'.
mean_accept : float, None
Mean acceptance fraction. Set to ``None`` when ``sampler`` is 'multinest' or
'ultranest'.
@@ -1221,6 +1224,9 @@ def add_samples(self,
if distance is not None:
dset.attrs['distance'] = float(distance)
+ if ln_evidence is not None:
+ dset.attrs['ln_evidence'] = ln_evidence
+
count_scaling = 0
for i, item in enumerate(modelpar):
@@ -1778,6 +1784,10 @@ def get_samples(self,
elif 'nparam' in dset.attrs:
n_param = dset.attrs['nparam']
+ if 'ln_evidence' in dset.attrs:
+ # Use if condition for backward compatibility
+ ln_evidence = dset.attrs['ln_evidence']
+
samples = np.asarray(dset)
if samples.ndim == 3:
@@ -1810,6 +1820,7 @@ def get_samples(self,
parameters=param,
samples=samples,
ln_prob=ln_prob,
+ ln_evidence=ln_evidence,
prob_sample=prob_sample,
median_sample=median_sample)
diff --git a/species/data/dust.py b/species/data/dust.py
index 998cb94c..a8079ec7 100644
--- a/species/data/dust.py
+++ b/species/data/dust.py
@@ -58,24 +58,28 @@ def add_optical_constants(input_path: str,
'mgsio3_jaeger_98_scott_96_axis1.dat')
data = np.loadtxt(nk_file)
- database.create_dataset('dust/mgsio3/crystalline/axis_1/', data=data)
+
+ database.create_dataset('dust/mgsio3/crystalline/axis_1', data=data)
nk_file = os.path.join(input_path, 'optical_constants/mgsio3/crystalline/'
'mgsio3_jaeger_98_scott_96_axis2.dat')
data = np.loadtxt(nk_file)
- database.create_dataset('dust/mgsio3/crystalline/axis_2/', data=data)
+
+ database.create_dataset('dust/mgsio3/crystalline/axis_2', data=data)
nk_file = os.path.join(input_path, 'optical_constants/mgsio3/crystalline/'
'mgsio3_jaeger_98_scott_96_axis3.dat')
data = np.loadtxt(nk_file)
- database.create_dataset('dust/mgsio3/crystalline/axis_3/', data=data)
+
+ database.create_dataset('dust/mgsio3/crystalline/axis_3', data=data)
nk_file = os.path.join(input_path, 'optical_constants/mgsio3/amorphous/'
'mgsio3_jaeger_2003_reformat.dat')
data = np.loadtxt(nk_file)
+
database.create_dataset('dust/mgsio3/amorphous', data=data)
print(' [DONE]')
@@ -127,24 +131,24 @@ def add_cross_sections(input_path: str,
print('Adding log-normal dust cross sections:')
with fits.open(os.path.join(input_path, 'lognorm_mgsio3_c_ext.fits')) as hdu_list:
- database.create_dataset('dust/lognorm/mgsio3/crystalline/cross_section/',
+ database.create_dataset('dust/lognorm/mgsio3/crystalline/cross_section',
data=hdu_list[0].data)
print(f' - Data shape (n_wavelength, n_radius, n_sigma): {hdu_list[0].data.shape}')
- database.create_dataset('dust/lognorm/mgsio3/crystalline/wavelength/',
+ database.create_dataset('dust/lognorm/mgsio3/crystalline/wavelength',
data=hdu_list[1].data)
data_range = f'{np.amin(hdu_list[1].data)} - {np.amax(hdu_list[1].data)}'
print(f' - Wavelength range: {data_range} um')
- database.create_dataset('dust/lognorm/mgsio3/crystalline/radius_g/',
+ database.create_dataset('dust/lognorm/mgsio3/crystalline/radius_g',
data=hdu_list[2].data)
data_range = f'{np.amin(hdu_list[2].data)} - {np.amax(hdu_list[2].data)}'
print(f' - Mean geometric radius range: {data_range} um')
- database.create_dataset('dust/lognorm/mgsio3/crystalline/sigma_g/',
+ database.create_dataset('dust/lognorm/mgsio3/crystalline/sigma_g',
data=hdu_list[3].data)
data_range = f'{np.amin(hdu_list[3].data)} - {np.amax(hdu_list[3].data)}'
@@ -161,24 +165,24 @@ def add_cross_sections(input_path: str,
print('Adding power-law dust cross sections')
with fits.open(os.path.join(input_path, 'powerlaw_mgsio3_c_ext.fits')) as hdu_list:
- database.create_dataset('dust/powerlaw/mgsio3/crystalline/cross_section/',
+ database.create_dataset('dust/powerlaw/mgsio3/crystalline/cross_section',
data=hdu_list[0].data)
print(f' - Data shape (n_wavelength, n_radius, n_exponent): {hdu_list[0].data.shape}')
- database.create_dataset('dust/powerlaw/mgsio3/crystalline/wavelength/',
+ database.create_dataset('dust/powerlaw/mgsio3/crystalline/wavelength',
data=hdu_list[1].data)
data_range = f'{np.amin(hdu_list[1].data)} - {np.amax(hdu_list[1].data)}'
print(f' - Wavelength range: {data_range} um')
- database.create_dataset('dust/powerlaw/mgsio3/crystalline/radius_max/',
+ database.create_dataset('dust/powerlaw/mgsio3/crystalline/radius_max',
data=hdu_list[2].data)
data_range = f'{np.amin(hdu_list[2].data)} - {np.amax(hdu_list[2].data)}'
print(f' - Maximum grain radius range: {data_range} um')
- database.create_dataset('dust/powerlaw/mgsio3/crystalline/exponent/',
+ database.create_dataset('dust/powerlaw/mgsio3/crystalline/exponent',
data=hdu_list[3].data)
data_range = f'{np.amin(hdu_list[3].data)} - {np.amax(hdu_list[3].data)}'
diff --git a/species/plot/plot_mcmc.py b/species/plot/plot_mcmc.py
index 4e0b1caa..e2c7eefb 100644
--- a/species/plot/plot_mcmc.py
+++ b/species/plot/plot_mcmc.py
@@ -180,17 +180,25 @@ def plot_posterior(tag: str,
print('Median sample:')
for key, value in box.median_sample.items():
- print(f' - {key} = {value:.2f}')
+ print(f' - {key} = {value:.2e}')
samples = box.samples
ndim = samples.shape[-1]
+ if 'gauss_mean' in box.parameters:
+ param_index = np.argwhere(np.array(box.parameters) == 'gauss_mean')[0]
+ samples[:, param_index] *= 1e3 # (um) -> (nm)
+
+ if 'gauss_sigma' in box.parameters:
+ param_index = np.argwhere(np.array(box.parameters) == 'gauss_sigma')[0]
+ samples[:, param_index] *= 1e3 # (um) -> (nm)
+
if box.prob_sample is not None:
par_val = tuple(box.prob_sample.values())
print('Maximum posterior sample:')
for key, value in box.prob_sample.items():
- print(f' - {key} = {value:.2f}')
+ print(f' - {key} = {value:.2e}')
print(f'Plotting the posterior: {output}...', end='', flush=True)
diff --git a/species/read/read_color.py b/species/read/read_color.py
index 3b3ee1d6..87e4bcf4 100644
--- a/species/read/read_color.py
+++ b/species/read/read_color.py
@@ -95,6 +95,16 @@ def get_color_magnitude(self,
flag = np.asarray(h5_file[f'photometry/{self.library}/flag'])
obj_names = np.asarray(h5_file[f'photometry/{self.library}/name'])
+ for i in range(sptype.shape[0]):
+ if isinstance(sptype[i], bytes):
+ sptype[i] = sptype[i].decode('utf-8')
+
+ if isinstance(flag[i], bytes):
+ flag[i] = flag[i].decode('utf-8')
+
+ if isinstance(obj_names[i], bytes):
+ obj_names[i] = obj_names[i].decode('utf-8')
+
if object_type is None:
indices = np.arange(0, np.size(sptype), 1)
diff --git a/species/util/plot_util.py b/species/util/plot_util.py
index 835bd94a..23ec7bb8 100644
--- a/species/util/plot_util.py
+++ b/species/util/plot_util.py
@@ -304,6 +304,34 @@ def update_labels(param: List[str]) -> List[str]:
index = param.index('veil_ref')
param[index] = r'$F_\mathregular{ref, veil}$'
+ if 'gauss_amplitude' in param:
+ index = param.index('gauss_amplitude')
+ param[index] = r'$a$ (W m$^{-2}$ µm$^{-1}$)'
+
+ if 'gauss_mean' in param:
+ index = param.index('gauss_mean')
+ param[index] = r'$\mu$ (nm)'
+
+ if 'gauss_sigma' in param:
+ index = param.index('gauss_sigma')
+ param[index] = r'$\sigma$ (nm)'
+
+ if 'gauss_fwhm' in param:
+ index = param.index('gauss_fwhm')
+ param[index] = r'$v$ (km s$^{-1}$)'
+
+ if 'line_flux' in param:
+ index = param.index('line_flux')
+ param[index] = r'$F_\mathregular{line}$ (W m$^{-2}$)'
+
+ if 'line_luminosity' in param:
+ index = param.index('line_luminosity')
+ param[index] = r'$L_\mathregular{line}$ (L$_\mathregular{\odot}$)'
+
+ if 'line_eq_width' in param:
+ index = param.index('line_eq_width')
+ param[index] = r'EW ($\AA$)'
+
return param
diff --git a/species/util/read_util.py b/species/util/read_util.py
index 988eb667..3f484528 100644
--- a/species/util/read_util.py
+++ b/species/util/read_util.py
@@ -339,3 +339,46 @@ def powerlaw_spectrum(wavel_range: Union[Tuple[float, float],
quantity='flux')
return model_box
+
+
+@typechecked
+def gaussian_spectrum(wavel_range: Union[Tuple[float, float],
+ Tuple[np.float32, np.float32]],
+ model_param: Dict[str, float],
+ spec_res: float = 100.) -> box.ModelBox:
+ """
+ Function for calculating a Gaussian spectrum (i.e. for an emission line).
+
+ Parameters
+ ----------
+ wavel_range : tuple(float, float)
+ Tuple with the minimum and maximum wavelength (um).
+ model_param : dict
+ Dictionary with the model parameters. Should contain `'gauss_amplitude'`, `'gauss_mean'`,
+ `'gauss_sigma'`, and optionally `'gauss_offset'`.
+ spec_res : float
+ Spectral resolution (default: 100).
+
+ Returns
+ -------
+ species.core.box.ModelBox
+ Box with the Gaussian spectrum.
+ """
+
+ wavel = create_wavelengths((wavel_range[0], wavel_range[1]), spec_res)
+
+ gauss_exp = np.exp(-0.5*(wavel-model_param['gauss_mean'])**2/model_param['gauss_sigma']**2)
+
+ flux = model_param['gauss_amplitude'] * gauss_exp
+
+ if 'gauss_offset' in model_param:
+ flux += model_param['gauss_offset']
+
+ model_box = box.create_box(boxtype='model',
+ model='gaussian',
+ wavelength=wavel,
+ flux=flux,
+ parameters=model_param,
+ quantity='flux')
+
+ return model_box