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Calculating fluxes in Sherpa CIAO (for X-ray spectra)
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Flux calculations for X-ray astronomy in Sherpa.html
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README.md
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acisf11309_000N022_r0002_arf3.fits.gz
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acisf11309_000N023_r0002_pha3.fits.gz
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environment.yml
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Flux calculations for X-ray astronomy in Sherpa.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# How can we calculate a flux in Sherpa\n",
"\n",
"This notebook is geared towards people who want to calculate the flux of a source for which they have an X-ray spectrum in PHA format. It is also aimed at people using Sherpa from CIAO - that is, they are used to the [Sherpa threads](https://cxc.cfa.harvard.edu/sherpa/threads/).\n",
"\n",
"The presentation is done using a Python (aka Jupyter) notebook, and is broken down into the following sections:\n",
"\n",
" - How do I ...\n",
" - calculate a flux?\n",
" - generate erros?\n",
" - 1. Introduction\n",
" - 1.1 How to load Sherpa\n",
" - 1.2 How to create a model\n",
" - 1.3 Getting help\n",
" - 1.4 What model instances have been created?\n",
" - 1.5 The many ways of specifying a model expression\n",
" - 1.6 Skip ahead\n",
" - 1.7 How do I delete a model instance\n",
" - 1.8 Evaluating a model\n",
" - 2. Manually calculating the flux\n",
" - 3. Setting up the data in Sherpa\n",
" - 4. calculate_energy_flux\n",
" - 4.1 Absorbed versus unabsorbed fluxes\n",
" - 5. How about parameter errors?\n",
" - 5.1 sample_photon_flux and sample_energy_flux\n",
" - 5.2 correlated=True\n",
" - 5.3 A note on clipping\n",
" - 5.4 What errors are used?\n",
" - 5.5 sample_flux\n",
" - 5.6 sample_flux and unabsorbed fluxes\n",
" - 5.7 sample_energy_flux versus sample_flux\n",
" - 6. XSPEC's cflux model\n",
" - 7. Calculating luminosities\n",
" - 7.1 The K correction\n",
" - 7.2 luminosity distance\n",
" - 7.3 Putting it all together\n",
"\n",
"---\n",
"\n",
"# History\n",
"\n",
"## CIAO 4.13\n",
"\n",
"Version 4.13 contained bug fixes and improvements to the `sample_energy_flux` and `sample_photon_flux` functions (the handling of the `samples` parameter was improved, the `model` and `clip` parameters were added, and the return value gained an extra column). It also adds support\n",
"for the XSPEC convolution models, which simplifies the `XSPEC's cflux model` section below compared to the CIAO 4.12.1 version.\n",
"\n",
"The notebook integration of Sherpa was also improved, with \"rich text\" output used for various objects, as shown below.\n",
"\n",
"## CIAO 4.12.1\n",
"\n",
"Version 4.12.1 - which was released July 14, 2020 - contained bug fixes and improvements to the `calc_energy_flux` and `calc_photon_flux` functions (the addition of the `model` parameter).\n",
"\n",
"---\n",
"\n",
"# How do I ...\n",
"\n",
"This section is for those people who have already read this document, or need an answer quickly. It is **strongly recomended** that you read the remainder of the document to explain the various\n",
"trade offs in the various approaches.\n",
"\n",
"The examples in this section assume the data and model use the default dataset (namely id=1).\n",
"\n",
"## ... calculate a flux?\n",
"\n",
"The [calc_energy_flux](https://cxc.harvard.edu/sherpa/ahelp/calc_energy_flux.html) function is used to calculate a model flux. In CIAO 4.13 the `ahelp` output has been updated to match the Python docstrings (in CIAO 4.12 they had not been updated).\n",
"\n",
"If you have a model expression like `xsphabs.gal * powlaw1d.pl` and want to calculate the unabsorbed flux of the power-law component, then use\n",
"\n",
" calc_energy_flux(lo, hi, model=pl)\n",
"\n",
"It is important to give the low and high limits for the calculation otherwise the response limits will be used.\n",
"\n",
"If the model argument is not used then the absorbed flux will be returned.\n",
"\n",
"The [calc_photon_flux](https://cxc.harvard.edu/sherpa/ahelp/calc_photon_flux.html) calculates fluxes in photon/cm$^2$/s rather than erg/cm$^2$/s.\n",
"\n",
"## ... generate errors?\n",
"\n",
"The [sample_flux](https://cxc.harvard.edu/sherpa/ahelp/sample_flux.html) function will create `n` samples of the parameter distribution (estimating errors using the `covar` routine) and return the flux and error values for both the full model and (if the `modelcomponent` argument is set), the unabsorbed model. It also returns the flux distribution, but only for the full (absorbed) moodel. \n",
"\n",
"The [sample_energy_flux](https://cxc.harvard.edu/sherpa/ahelp/sample_energy_flux.html) and\n",
"[sample_photon_flux](https://cxc.harvard.edu/sherpa/ahelp/sample_photon_flux.html) functions have more functionality and should be\n",
"used if you need something more complex than `sample_flux`.\n",
"\n",
"---\n",
"\n",
"First we start with some background material on using Sherpa:\n",
"\n",
"## 1. Introduction\n",
"\n",
"This document is a Jupyter notebook, a way of writing Python code along with documentation\n",
"that be read or even re-run, allowing you to change variables or calls. This is not a\n",
"guide on how to use Jupyter notebooks, for which there are many resources which start from the\n",
"[Jupyter home page](https://jupyter.org/). CIAO comes with a version of Jupyter, so all\n",
"you need to do is say **either** of\n",
"\n",
" jupyter notebook\n",
" jupyter lab\n",
"\n",
"and either a web page will open in your browser, or you can use the address printed to the\n",
"screen. Alternatives are to run Python directly, either with `ipython` or `sherpa` (which is\n",
"just sets up `ipython` to display the Sherpa prompt and loads in Sherpa).\n",
"\n",
"Note that the `lab` version requires you to have installed the `jupyterlab` package with `pip` or\n",
"`conda`.\n",
"\n",
"Other packages that are used by this notebook (you do **not** need them to calculate fluxes\n",
"with Sherpa, but they may come in handy) can be installed with either command:\n",
"\n",
" pip install scipy astropy\n",
" conda install scipy astropy\n",
"\n",
"and (this must be installed with `pip`:\n",
"\n",
" pip install corner"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.1 How to load Sherpa\n",
"\n",
"In this notebook I am going to be importing the `sherpa.astro.ui` module without qualification. This is essentially the environment provided by the Sherpa \"application\" (which is actually just IPython with some minor configuration changes):"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from sherpa.astro.ui import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As this is a Jupyter notebook, I want any plots to appear within the page, and not as a separate window:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For reproducability, let's check what version of the packages we have installed (the versions you get may differ from these, as they depend on how CIAO was installed and what version of Python was selected):"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Numpy: 1.19.1\n",
"Matplotlib: 3.3.1\n",
"Sherpa: 4.13.0+447.g37e3446a\n",
"XSPEC: 12.10.1s\n"
]
}
],
"source": [
"import matplotlib, sherpa\n",
"from sherpa.astro.xspec import get_xsversion\n",
"\n",
"print(\"Numpy: {}\".format(np.__version__))\n",
"print(\"Matplotlib: {}\".format(matplotlib.__version__))\n",
"print(\"Sherpa: {}\".format(sherpa.__version__))\n",
"\n",
"print(\"XSPEC: {}\".format(get_xsversion()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Although not essential, the\n",
"[Python corner package](https://corner.readthedocs.io/en/stable/) provides helpful visualization capabilities which I will use in this notebook."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"corner:: 2.1.0\n"
]
}
],
"source": [
"import corner\n",
"\n",
"print(\"corner:: {}\".format(corner.__version__))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I am also going to ensure we have a fixed set of abundance and cross-section values used in this notebook (this is only needed to make the notebook repeatable, since these settings default to the values in your `~/.xspec/Xspec.init` file):"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"set_xsabund('grsa')\n",
"set_xsxsect('vern')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These commands should create screen output, but due to the way XSPECs model library works the output gets directed to the terminal running the notebook application rather than the notebook itself. For this particular set of commands the output is:\n",
"\n",
"```\n",
" Solar Abundance Vector set to grsa: Grevesse, N. & Sauval, A. J. Space Science Reviews 85, 161 (1998)\n",
" Cross Section Table set to vern: Verner, Ferland, Korista, and Yakovlev 1996\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.2 How to create a model\n",
"\n",
"In Sherpa we have model classes (or types) and instances of those models, which we refer to with a user-supplied name. It is the model instance that we need here, and we can create in several different ways:\n",
"\n",
"- explicitly, with create_model_component\n",
"- implicitly, using the \"classname.instancename\" syntax\n",
"\n",
"Model types that come from the XSPEC model library - which in CIAO 4.13 is version `XSPEC 12.10.1s` - have names that begin with `xs`, and Sherpa directly supports additive, multiplicative, and convolution XSPEC models. That is, sources of emission (additive), simple changes to that emission (multiplicative), and models that can change the shape of the emission (convolution). These models are generally specific to X-ray astronomy, and are likely to be the ones you use to describe the emission from your source.\n",
"\n",
"Sherpa also contains a number of simple one-dimensional geometric models which can also be used. These names normally end in `1d`. There are also a number of other models which are intended for the optical pass band.\n",
"\n",
"The names of the model types can be found with the [list_models](https://cxc.harvard.edu/sherpa/ahelp/list_models.html) function, and the [list_model_components](https://cxc.harvard.edu/sherpa/ahelp/list_model_components.html) function to list the model instances that have been created. As I haven't done anything, there are no model instances yet:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list_model_components()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"However, there are plenty of model types to chose from:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"310"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(list_models())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First I'm going to use [create_model_component](https://cxc.harvard.edu/sherpa/ahelp/create_model_component.html) to create an instance called `spl` of the Sherpa power-law model type:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<style>/*\n",
"Copyright (C) 2020 Smithsonian Astrophysical Observatory\n",
"\n",
"\n",
" This program is free software; you can redistribute it and/or modify\n",
" it under the terms of the GNU General Public License as published by\n",
" the Free Software Foundation; either version 3 of the License, or\n",
" (at your option) any later version.\n",
"\n",
" This program is distributed in the hope that it will be useful,\n",
" but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
" MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n",
" GNU General Public License for more details.\n",
"\n",
" You should have received a copy of the GNU General Public License along\n",
" with this program; if not, write to the Free Software Foundation, Inc.,\n",
" 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
"\n",
"*/\n",
"\n",
":root {\n",
" --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
" --sherpa-background-color: var(--jp-layout-color0, white);\n",
" --sherpa-background-color-row-even: var(--jp-layout-color1, white);\n",
" --sherpa-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
"\n",
" /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
" --sherpa-background-color-dark1: #EBEFF2;\n",
" --sherpa-background-color-dark2: #D8E0E5;\n",
"}\n",
"\n",
"div.sherpa-text-fallback {\n",
" display: none;\n",
"}\n",
"\n",
"div.sherpa {\n",
" display: block;\n",
"}\n",
"\n",
"div.sherpa details summary {\n",
" display: list-item; /* needed for notebook, not lab */\n",
" font-size: larger;\n",
"}\n",
"\n",
"div.sherpa details div.datavals {\n",
" display: grid;\n",
" grid-template-columns: 1fr 3fr;\n",
" column-gap: 0.5em;\n",
"}\n",
"\n",
"div.sherpa div.dataname {\n",
" font-weight: bold;\n",
" border-right: 1px solid var(--sherpa-border-color);\n",
"}\n",
"\n",
"div.sherpa div.dataval { }\n",
"\n",
"div.sherpa div.datavals div:nth-child(4n + 1) ,\n",
"div.sherpa div.datavals div:nth-child(4n + 2) {\n",
" background: var(--sherpa-background-color-row-odd);\n",
"}\n",
"\n",
"div.sherpa table.model tbody {\n",
" border-bottom: 1px solid var(--sherpa-border-color);\n",
"}\n",
"\n",
"div.sherpa table.model tr.block {\n",
" border-top: 1px solid var(--sherpa-border-color);\n",
"}\n",
"\n",
"div.sherpa table.model th.model-odd ,\n",
"div.sherpa table.model th.model-even {\n",
" border-right: 1px solid var(--sherpa-border-color);\n",
"}\n",
"\n",
"div.sherpa table.model th.model-odd {\n",
" background: var(--sherpa-background-color-dark1);\n",
"}\n",
"\n",
"div.sherpa table.model th.model-even {\n",
" background: var(--sherpa-background-color-dark2);\n",
"}\n",
"\n",
"div.sherpa .failed {\n",
" background: orange;\n",
" font-size: large;\n",
" padding: 1em;\n",
"}\n",
"</style><div class=\"sherpa-text-fallback\">&lt;PowLaw1D model instance &#x27;powlaw1d.spl&#x27;&gt;</div><div hidden class=\"sherpa\"><details open><summary>Model</summary><table class=\"model\"><caption>Expression: powlaw1d.spl</caption><thead><tr><th>Component</th><th>Parameter</th><th>Thawed</th><th>Value</th><th>Min</th><th>Max</th><th>Units</th></tr></thead><tbody><tr><th class=\"model-odd\" scope=\"rowgroup\" rowspan=3>spl</th><td>gamma</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>-10.0</td><td>10.0</td><td></td></tr><tr><td>ref</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>ampl</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>0.0</td><td>MAX</td><td></td></tr></tbody></table></details></div>"
],
"text/plain": [
"<PowLaw1D model instance 'powlaw1d.spl'>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"create_model_component('powlaw1d', 'spl')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"New in CIAO 4.13 is the ability of certain Sherpa objects to take advantage of the Python notebook dispay capabilities,\n",
"as shown here with the model object (displayed as a HTML table). You can still display the data as you would\n",
"at the Sherpa or IPython prompt (with `print`, as shown below), but this notebook will rely\n",
"on the notebook output in most cases):"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"powlaw1d.spl\n",
" Param Type Value Min Max Units\n",
" ----- ---- ----- --- --- -----\n",
" spl.gamma thawed 1 -10 10 \n",
" spl.ref frozen 1 -3.40282e+38 3.40282e+38 \n",
" spl.ampl thawed 1 0 3.40282e+38 \n"
]
}
],
"source": [
"print(spl)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.3 Getting help\n",
"\n",
"You can find more information on the model by using the Python `help` function on the model instance: that is\n",
"\n",
" help(spl)\n",
" \n",
"Unfortunately you can't do this with the model name - that is `help(powlaw1d)` does not return anything useful (this is on our list of things to improve)."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on PowLaw1D in module sherpa.models.basic object:\n",
"\n",
"class PowLaw1D(sherpa.models.model.RegriddableModel1D)\n",
" | PowLaw1D(name='powlaw1d')\n",
" | \n",
" | One-dimensional power-law function.\n",
" | \n",
" | It is assumed that the independent axis is positive at all points.\n",
" | \n",
" | Attributes\n",
" | ----------\n",
" | gamma\n",
" | The photon index of the power law.\n",
" | ref\n",
" | As the reference point is degenerate with the amplitude, the\n",
" | ``alwaysfrozen`` attribute of the reference point is set so\n",
" | that it can not be varied during a fit.\n",
" | ampl\n",
" | The amplitude of the model.\n",
" | \n",
" | See Also\n",
" | --------\n",
" | Polynom1D\n",
" | \n",
" | Notes\n",
" | -----\n",
" | The functional form of the model for points is::\n",
" | \n",
" | f(x) = ampl * (x / ref)^(-gamma)\n",
" | \n",
" | and for an integrated grid it is the integral of this over\n",
" | the bin.\n",
" | \n",
" | Method resolution order:\n",
" | PowLaw1D\n",
" | sherpa.models.model.RegriddableModel1D\n",
" | sherpa.models.model.RegriddableModel\n",
" | sherpa.models.model.ArithmeticModel\n",
" | sherpa.models.model.Model\n",
" | sherpa.utils.NoNewAttributesAfterInit\n",
" | builtins.object\n",
" | \n",
" | Methods defined here:\n",
" | \n",
" | __init__(self, name='powlaw1d')\n",
" | Initialize self. See help(type(self)) for accurate signature.\n",
" | \n",
" | calc(cls, pars, xlo, *args, **kwargs)\n",
" | \n",
" | guess(self, dep, *args, **kwargs)\n",
" | Set an initial guess for the parameter values.\n",
" | \n",
" | Attempt to set the parameter values, and ranges, for\n",
" | the model to match the data values. This is intended\n",
" | as a rough guess, so it is expected that the model\n",
" | is only evaluated a small number of times, if at all.\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Methods inherited from sherpa.models.model.RegriddableModel1D:\n",
" | \n",
" | regrid(self, *args, **kwargs)\n",
" | The class RegriddableModel1D allows the user to evaluate in the\n",
" | requested space then interpolate onto the data space. An optional\n",
" | argument 'interp' enables the user to change the interpolation method.\n",
" | \n",
" | Examples\n",
" | --------\n",
" | >>> import numpy as np\n",
" | >>> from sherpa.models.basic import Box1D\n",
" | >>> mybox = Box1D()\n",
" | >>> request_space = np.arange(1, 10, 0.1)\n",
" | >>> regrid_model = mybox.regrid(request_space, interp=linear_interp)\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Data and other attributes inherited from sherpa.models.model.RegriddableModel1D:\n",
" | \n",
" | ndim = 1\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Methods inherited from sherpa.models.model.ArithmeticModel:\n",
" | \n",
" | __abs__ = func(self)\n",
" | \n",
" | __add__ = func(self, rhs)\n",
" | \n",
" | __div__ = func(self, rhs)\n",
" | \n",
" | __floordiv__ = func(self, rhs)\n",
" | \n",
" | __getitem__(self, filter)\n",
" | \n",
" | __mod__ = func(self, rhs)\n",
" | \n",
" | __mul__ = func(self, rhs)\n",
" | \n",
" | __neg__ = func(self)\n",
" | \n",
" | __pow__ = func(self, rhs)\n",
" | \n",
" | __radd__ = rfunc(self, lhs)\n",
" | \n",
" | __rdiv__ = rfunc(self, lhs)\n",
" | \n",
" | __rfloordiv__ = rfunc(self, lhs)\n",
" | \n",
" | __rmod__ = rfunc(self, lhs)\n",
" | \n",
" | __rmul__ = rfunc(self, lhs)\n",
" | \n",
" | __rpow__ = rfunc(self, lhs)\n",
" | \n",
" | __rsub__ = rfunc(self, lhs)\n",
" | \n",
" | __rtruediv__ = rfunc(self, lhs)\n",
" | \n",
" | __setstate__(self, state)\n",
" | \n",
" | __sub__ = func(self, rhs)\n",
" | \n",
" | __truediv__ = func(self, rhs)\n",
" | \n",
" | apply(self, outer, *otherargs, **otherkwargs)\n",
" | \n",
" | startup(self, cache=False)\n",
" | Called before a model may be evaluated multiple times.\n",
" | \n",
" | Parameters\n",
" | ----------\n",
" | cache : bool, optional\n",
" | Should a cache be used when evaluating the models.\n",
" | \n",
" | See Also\n",
" | --------\n",
" | teardown\n",
" | \n",
" | teardown(self)\n",
" | Called after a model may be evaluated multiple times.\n",
" | \n",
" | See Also\n",
" | --------\n",
" | setup\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Methods inherited from sherpa.models.model.Model:\n",
" | \n",
" | __call__(self, *args, **kwargs)\n",
" | Call self as a function.\n",
" | \n",
" | __getattr__(self, name)\n",
" | Access to parameters is case insensitive.\n",
" | \n",
" | __iter__(self)\n",
" | # This allows all models to be used in iteration contexts, whether or\n",
" | # not they're composite\n",
" | \n",
" | __repr__(self)\n",
" | Return repr(self).\n",
" | \n",
" | __setattr__(self, name, val)\n",
" | Implement setattr(self, name, value).\n",
" | \n",
" | __str__(self)\n",
" | Return str(self).\n",
" | \n",
" | get_center(self)\n",
" | \n",
" | reset(self)\n",
" | Reset the parameter values.\n",
" | \n",
" | set_center(self, *args, **kwargs)\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Readonly properties inherited from sherpa.models.model.Model:\n",
" | \n",
" | thawedparhardmaxes\n",
" | The hard maximum values for the thawed parameters.\n",
" | \n",
" | thawedparhardmins\n",
" | The hard minimum values for the thawed parameters.\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Data descriptors inherited from sherpa.models.model.Model:\n",
" | \n",
" | thawedparmaxes\n",
" | Access to the maximum limits for the thawed parameters\n",
" | \n",
" | thawedparmins\n",
" | Access to the minimum limits for the thawed parameters\n",
" | \n",
" | thawedpars\n",
" | Access to the thawed parameters of the model\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Methods inherited from sherpa.utils.NoNewAttributesAfterInit:\n",
" | \n",
" | __delattr__(self, name)\n",
" | Implement delattr(self, name).\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Data descriptors inherited from sherpa.utils.NoNewAttributesAfterInit:\n",
" | \n",
" | __dict__\n",
" | dictionary for instance variables (if defined)\n",
" | \n",
" | __weakref__\n",
" | list of weak references to the object (if defined)\n",
"\n"
]
}
],
"source": [
"help(spl)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I am also going to create an instance of the XSPEC powerlaw model, imaginatively called `xpl`, using the \"dotted\" syntax approach:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>/*\n",
"Copyright (C) 2020 Smithsonian Astrophysical Observatory\n",
"\n",
"\n",
" This program is free software; you can redistribute it and/or modify\n",
" it under the terms of the GNU General Public License as published by\n",
" the Free Software Foundation; either version 3 of the License, or\n",
" (at your option) any later version.\n",
"\n",
" This program is distributed in the hope that it will be useful,\n",
" but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
" MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n",
" GNU General Public License for more details.\n",
"\n",
" You should have received a copy of the GNU General Public License along\n",
" with this program; if not, write to the Free Software Foundation, Inc.,\n",
" 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
"\n",
"*/\n",
"\n",
":root {\n",
" --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
" --sherpa-background-color: var(--jp-layout-color0, white);\n",
" --sherpa-background-color-row-even: var(--jp-layout-color1, white);\n",
" --sherpa-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
"\n",
" /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
" --sherpa-background-color-dark1: #EBEFF2;\n",
" --sherpa-background-color-dark2: #D8E0E5;\n",
"}\n",
"\n",
"div.sherpa-text-fallback {\n",
" display: none;\n",
"}\n",
"\n",
"div.sherpa {\n",
" display: block;\n",
"}\n",
"\n",
"div.sherpa details summary {\n",
" display: list-item; /* needed for notebook, not lab */\n",
" font-size: larger;\n",
"}\n",
"\n",
"div.sherpa details div.datavals {\n",
" display: grid;\n",
" grid-template-columns: 1fr 3fr;\n",
" column-gap: 0.5em;\n",
"}\n",
"\n",
"div.sherpa div.dataname {\n",
" font-weight: bold;\n",
" border-right: 1px solid var(--sherpa-border-color);\n",
"}\n",
"\n",
"div.sherpa div.dataval { }\n",
"\n",
"div.sherpa div.datavals div:nth-child(4n + 1) ,\n",
"div.sherpa div.datavals div:nth-child(4n + 2) {\n",
" background: var(--sherpa-background-color-row-odd);\n",
"}\n",
"\n",
"div.sherpa table.model tbody {\n",
" border-bottom: 1px solid var(--sherpa-border-color);\n",
"}\n",
"\n",
"div.sherpa table.model tr.block {\n",
" border-top: 1px solid var(--sherpa-border-color);\n",
"}\n",
"\n",
"div.sherpa table.model th.model-odd ,\n",
"div.sherpa table.model th.model-even {\n",
" border-right: 1px solid var(--sherpa-border-color);\n",
"}\n",
"\n",
"div.sherpa table.model th.model-odd {\n",
" background: var(--sherpa-background-color-dark1);\n",
"}\n",
"\n",
"div.sherpa table.model th.model-even {\n",
" background: var(--sherpa-background-color-dark2);\n",
"}\n",
"\n",
"div.sherpa .failed {\n",
" background: orange;\n",
" font-size: large;\n",
" padding: 1em;\n",
"}\n",
"</style><div class=\"sherpa-text-fallback\">&lt;XSpowerlaw model instance &#x27;xspowerlaw.xpl&#x27;&gt;</div><div hidden class=\"sherpa\"><details open><summary>Model</summary><table class=\"model\"><caption>Expression: xspowerlaw.xpl</caption><thead><tr><th>Component</th><th>Parameter</th><th>Thawed</th><th>Value</th><th>Min</th><th>Max</th><th>Units</th></tr></thead><tbody><tr><th class=\"model-odd\" scope=\"rowgroup\" rowspan=2>xpl</th><td>PhoIndex</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>-2.0</td><td>9.0</td><td></td></tr><tr><td>norm</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>0.0</td><td>1e+24</td><td></td></tr></tbody></table></details></div>"
],
"text/plain": [
"<XSpowerlaw model instance 'xspowerlaw.xpl'>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xspowerlaw.xpl"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that the parameters have different names (e.g. `gamma` versus `PhoIndex`) and that Sherpa's powerlaw model lets you change the reference point (but that by default it is fixed to 1 which matches the XSPEC model).\n",
"\n",
"### 1.4 What model instances have been created?\n",
"\n",
"Since we now have some model instances, we now get a response from `list_model_components`:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['spl', 'xpl']"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list_model_components()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can retrieve models (if we have accidentally let a variable go out of scope or set it to something else) with the [get_model_component](https://cxc.harvard.edu/sherpa/ahelp/get_model_component.html) function:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"sillyName = get_model_component('spl')\n",
"sillyName.gamma = 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that as the `spl` variable still exists in this notebook, changing the `gamma` parameter of `sillyName` has also changed the `spl` variable (since they are actually referencing the same Python object):"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>/*\n",
"Copyright (C) 2020 Smithsonian Astrophysical Observatory\n",
"\n",
"\n",
" This program is free software; you can redistribute it and/or modify\n",
" it under the terms of the GNU General Public License as published by\n",
" the Free Software Foundation; either version 3 of the License, or\n",
" (at your option) any later version.\n",
"\n",
" This program is distributed in the hope that it will be useful,\n",
" but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
" MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n",
" GNU General Public License for more details.\n",
"\n",
" You should have received a copy of the GNU General Public License along\n",
" with this program; if not, write to the Free Software Foundation, Inc.,\n",
" 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
"\n",
"*/\n",
"\n",
":root {\n",
" --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
" --sherpa-background-color: var(--jp-layout-color0, white);\n",
" --sherpa-background-color-row-even: var(--jp-layout-color1, white);\n",
" --sherpa-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
"\n",
" /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
" --sherpa-background-color-dark1: #EBEFF2;\n",
" --sherpa-background-color-dark2: #D8E0E5;\n",
"}\n",
"\n",
"div.sherpa-text-fallback {\n",
" display: none;\n",
"}\n",
"\n",
"div.sherpa {\n",
" display: block;\n",
"}\n",
"\n",
"div.sherpa details summary {\n",
" display: list-item; /* needed for notebook, not lab */\n",
" font-size: larger;\n",
"}\n",
"\n",
"div.sherpa details div.datavals {\n",
" display: grid;\n",
" grid-template-columns: 1fr 3fr;\n",
" column-gap: 0.5em;\n",
"}\n",
"\n",
"div.sherpa div.dataname {\n",
" font-weight: bold;\n",
" border-right: 1px solid var(--sherpa-border-color);\n",
"}\n",
"\n",
"div.sherpa div.dataval { }\n",
"\n",
"div.sherpa div.datavals div:nth-child(4n + 1) ,\n",
"div.sherpa div.datavals div:nth-child(4n + 2) {\n",
" background: var(--sherpa-background-color-row-odd);\n",
"}\n",
"\n",
"div.sherpa table.model tbody {\n",
" border-bottom: 1px solid var(--sherpa-border-color);\n",
"}\n",
"\n",
"div.sherpa table.model tr.block {\n",
" border-top: 1px solid var(--sherpa-border-color);\n",
"}\n",
"\n",
"div.sherpa table.model th.model-odd ,\n",
"div.sherpa table.model th.model-even {\n",
" border-right: 1px solid var(--sherpa-border-color);\n",
"}\n",
"\n",
"div.sherpa table.model th.model-odd {\n",
" background: var(--sherpa-background-color-dark1);\n",
"}\n",
"\n",
"div.sherpa table.model th.model-even {\n",
" background: var(--sherpa-background-color-dark2);\n",
"}\n",
"\n",
"div.sherpa .failed {\n",
" background: orange;\n",
" font-size: large;\n",
" padding: 1em;\n",
"}\n",
"</style><div class=\"sherpa-text-fallback\">&lt;PowLaw1D model instance &#x27;powlaw1d.spl&#x27;&gt;</div><div hidden class=\"sherpa\"><details open><summary>Model</summary><table class=\"model\"><caption>Expression: powlaw1d.spl</caption><thead><tr><th>Component</th><th>Parameter</th><th>Thawed</th><th>Value</th><th>Min</th><th>Max</th><th>Units</th></tr></thead><tbody><tr><th class=\"model-odd\" scope=\"rowgroup\" rowspan=3>spl</th><td>gamma</td><td><input disabled type=\"checkbox\" checked></input></td><td>2.0</td><td>-10.0</td><td>10.0</td><td></td></tr><tr><td>ref</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>-MAX</td><td>MAX</td><td></td></tr><tr><td>ampl</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>0.0</td><td>MAX</td><td></td></tr></tbody></table></details></div>"
],
"text/plain": [
"<PowLaw1D model instance 'powlaw1d.spl'>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"spl"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This can be very useful, but can occasionally cause confusion, so I suggest trying to keep the Python variable names the same as the instance name you created. In other words, I should have said\n",
"\n",
" spl = get_model_compoenent('spl')\n",
" \n",
"earlier.\n",
"\n",
"### 1.5 The many ways of specifying a model expression\n",
"\n",
"It's rare that we have a situation where a single model type can describe the observed emission from a source, so Sherpa allows you to combine model instances with the normal mathematical operations (normally `+` and `*` but `-` and even `/` work too). You can even include numeric constants in the expression.\n",
"\n",
"So, if I wanted to model a power law that has been absorbed by the galactic medium (using the\n",
"[XSPEC phabs model](https://cxc.cfa.harvard.edu/sherpa/ahelp/xsphabs.html)) I could say\n",
"\n",
" set_source(xsphabs.gal * powlaw1d.pl)\n",
" \n",
"or\n",
"\n",
" mdl = xsphabs.gal * powlaw1d.pl)\n",
" set_source(mdl)\n",
" \n",
"or\n",
"\n",
" gal = create_model_component('xsphabs', 'gal')\n",
" pl = create_model_component('powlaw1d', 'pl')\n",
" set_source(gal * pl)\n",
"\n",
"There is even a variant where you specify the model combination as a string: `set_source(\"xsphabs.gal * powlaw1d.pl\")`.\n",
"\n",
"Once you have created a model instance, you do not need to specify the model type again, but it is not a problem if you do so. That is, the following two sets of commands are identical\n",
"\n",
" set_source(1, xsphabs.gal * powlaw1d.pl1)\n",
" set_source(2, xsphabs.gal * powlaw1d.pl2)\n",
" \n",
"and\n",
"\n",
" set_source(1, xsphabs.gal * powlaw1d.pl1)\n",
" set_source(2, gal * powlaw1d.pl2)\n",
"\n",
"### 1.6 Skip ahead\n",
"\n",
"Normally we use models associated with an actual dataset, but in the next few subsections (1.6 to 2) we are going to explore how much you can do in the purely theoretical model space where you have not loaded any observational data. This is particularly useful when you just want to evaluate a model for a simple grid (say 0.1 to 10 keV with a bin width of 10 eV) for a plot, or calculation, and do not want to bother with a PHA file and response.\n",
"\n",
"Skip ahead to section 3 to get back to our normal programming.\n",
"\n",
"### 1.7 How do I delete a model instance\n",
"\n",
"The [delete_model_component](https://cxc.cfa.harvard.edu/sherpa/ahelp/delete_model_component.html) function will remove the model instance from Sherpa. Fortunately Sherpa will stop you deleting any of the model components we used in `set_source`, so to illustrate this we have to make a new model component `'bngd'` purely\n",
"so we can delete it:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>/*\n",
"Copyright (C) 2020 Smithsonian Astrophysical Observatory\n",
"\n",
"\n",
" This program is free software; you can redistribute it and/or modify\n",
" it under the terms of the GNU General Public License as published by\n",
" the Free Software Foundation; either version 3 of the License, or\n",
" (at your option) any later version.\n",
"\n",
" This program is distributed in the hope that it will be useful,\n",
" but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
" MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n",
" GNU General Public License for more details.\n",
"\n",
" You should have received a copy of the GNU General Public License along\n",
" with this program; if not, write to the Free Software Foundation, Inc.,\n",
" 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
"\n",
"*/\n",
"\n",
":root {\n",
" --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
" --sherpa-background-color: var(--jp-layout-color0, white);\n",
" --sherpa-background-color-row-even: var(--jp-layout-color1, white);\n",
" --sherpa-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
"\n",
" /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
" --sherpa-background-color-dark1: #EBEFF2;\n",
" --sherpa-background-color-dark2: #D8E0E5;\n",
"}\n",
"\n",
"div.sherpa-text-fallback {\n",
" display: none;\n",
"}\n",
"\n",
"div.sherpa {\n",
" display: block;\n",
"}\n",
"\n",
"div.sherpa details summary {\n",
" display: list-item; /* needed for notebook, not lab */\n",
" font-size: larger;\n",
"}\n",
"\n",
"div.sherpa details div.datavals {\n",
" display: grid;\n",
" grid-template-columns: 1fr 3fr;\n",
" column-gap: 0.5em;\n",
"}\n",
"\n",
"div.sherpa div.dataname {\n",
" font-weight: bold;\n",
" border-right: 1px solid var(--sherpa-border-color);\n",
"}\n",
"\n",
"div.sherpa div.dataval { }\n",
"\n",
"div.sherpa div.datavals div:nth-child(4n + 1) ,\n",
"div.sherpa div.datavals div:nth-child(4n + 2) {\n",
" background: var(--sherpa-background-color-row-odd);\n",
"}\n",
"\n",
"div.sherpa table.model tbody {\n",
" border-bottom: 1px solid var(--sherpa-border-color);\n",
"}\n",
"\n",
"div.sherpa table.model tr.block {\n",
" border-top: 1px solid var(--sherpa-border-color);\n",
"}\n",
"\n",
"div.sherpa table.model th.model-odd ,\n",
"div.sherpa table.model th.model-even {\n",
" border-right: 1px solid var(--sherpa-border-color);\n",
"}\n",
"\n",
"div.sherpa table.model th.model-odd {\n",
" background: var(--sherpa-background-color-dark1);\n",
"}\n",
"\n",
"div.sherpa table.model th.model-even {\n",
" background: var(--sherpa-background-color-dark2);\n",
"}\n",
"\n",
"div.sherpa .failed {\n",
" background: orange;\n",
" font-size: large;\n",
" padding: 1em;\n",
"}\n",
"</style><div class=\"sherpa-text-fallback\">&lt;Const1D model instance &#x27;const1d.bgnd&#x27;&gt;</div><div hidden class=\"sherpa\"><details open><summary>Model</summary><table class=\"model\"><caption>Expression: const1d.bgnd</caption><thead><tr><th>Component</th><th>Parameter</th><th>Thawed</th><th>Value</th><th>Min</th><th>Max</th><th>Units</th></tr></thead><tbody><tr><th class=\"model-odd\" scope=\"rowgroup\" rowspan=1>bgnd</th><td>c0</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>-MAX</td><td>MAX</td><td></td></tr></tbody></table></details></div>"
],
"text/plain": [
"<Const1D model instance 'const1d.bgnd'>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"const1d.bgnd"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'bgnd' in list_model_components()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"delete_model_component('bgnd')\n",
"'bgnd' in list_model_components()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `delete_model_component` call will also remove the variable from the default namespace, which means that the variable `bgnd` is no-longer defined, and trying to use it will return an error. I don't show that here as it breaks attempts to re-run the notebook!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.8 Evaluating a model\n",
"\n",
"Normally you don't need to evaluate a model directly (that is, calculate the predicted values on a given energy grid), but it can be useful, for instance\n",
"\n",
" - to see how the model varies with parameter values\n",
" - for calculating things (such as the flux ;-)\n",
"\n",
"When evaluating the models directly, as I do below, it is simplest to always use an grid of values in keV (there are ways to use wavelengths, with bin values in Angstroms, but it depends on what combination of models are being used). With that out of the way, I create a \"typical\" (at least for Chandra imaging-mode data) energy grid:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"First bin: 0.10 - 0.11 keV\n",
"Last bin: 10.99 - 11.00 keV\n"
]
}
],
"source": [
"egrid = np.arange(0.1, 11.01, 0.01)\n",
"elo = egrid[:-1]\n",
"ehi = egrid[1:]\n",
"print(\"First bin: {:5.2f} - {:5.2f} keV\".format(elo[0], ehi[0]))\n",
"print(\"Last bin: {:5.2f} - {:5.2f} keV\".format(elo[-1], ehi[-1]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For this section I am going to use the\n",
"[XSPEC apec model](https://cxc.cfa.harvard.edu/sherpa/ahelp/xsapec.html), and see how varying the temperature and metallicity of a zero-redshift source changes the emission:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>/*\n",
"Copyright (C) 2020 Smithsonian Astrophysical Observatory\n",
"\n",
"\n",
" This program is free software; you can redistribute it and/or modify\n",
" it under the terms of the GNU General Public License as published by\n",
" the Free Software Foundation; either version 3 of the License, or\n",
" (at your option) any later version.\n",
"\n",
" This program is distributed in the hope that it will be useful,\n",
" but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
" MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n",
" GNU General Public License for more details.\n",
"\n",
" You should have received a copy of the GNU General Public License along\n",
" with this program; if not, write to the Free Software Foundation, Inc.,\n",
" 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
"\n",
"*/\n",
"\n",
":root {\n",
" --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
" --sherpa-background-color: var(--jp-layout-color0, white);\n",
" --sherpa-background-color-row-even: var(--jp-layout-color1, white);\n",
" --sherpa-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
"\n",
" /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
" --sherpa-background-color-dark1: #EBEFF2;\n",
" --sherpa-background-color-dark2: #D8E0E5;\n",
"}\n",
"\n",
"div.sherpa-text-fallback {\n",
" display: none;\n",
"}\n",
"\n",
"div.sherpa {\n",
" display: block;\n",
"}\n",
"\n",
"div.sherpa details summary {\n",
" display: list-item; /* needed for notebook, not lab */\n",
" font-size: larger;\n",
"}\n",
"\n",
"div.sherpa details div.datavals {\n",
" display: grid;\n",
" grid-template-columns: 1fr 3fr;\n",
" column-gap: 0.5em;\n",
"}\n",
"\n",
"div.sherpa div.dataname {\n",
" font-weight: bold;\n",
" border-right: 1px solid var(--sherpa-border-color);\n",
"}\n",
"\n",
"div.sherpa div.dataval { }\n",
"\n",
"div.sherpa div.datavals div:nth-child(4n + 1) ,\n",
"div.sherpa div.datavals div:nth-child(4n + 2) {\n",
" background: var(--sherpa-background-color-row-odd);\n",
"}\n",
"\n",
"div.sherpa table.model tbody {\n",
" border-bottom: 1px solid var(--sherpa-border-color);\n",
"}\n",
"\n",
"div.sherpa table.model tr.block {\n",
" border-top: 1px solid var(--sherpa-border-color);\n",
"}\n",
"\n",
"div.sherpa table.model th.model-odd ,\n",
"div.sherpa table.model th.model-even {\n",
" border-right: 1px solid var(--sherpa-border-color);\n",
"}\n",
"\n",
"div.sherpa table.model th.model-odd {\n",
" background: var(--sherpa-background-color-dark1);\n",
"}\n",
"\n",
"div.sherpa table.model th.model-even {\n",
" background: var(--sherpa-background-color-dark2);\n",
"}\n",
"\n",
"div.sherpa .failed {\n",
" background: orange;\n",
" font-size: large;\n",
" padding: 1em;\n",
"}\n",
"</style><div class=\"sherpa-text-fallback\">&lt;XSapec model instance &#x27;xsapec.testmdl&#x27;&gt;</div><div hidden class=\"sherpa\"><details open><summary>Model</summary><table class=\"model\"><caption>Expression: xsapec.testmdl</caption><thead><tr><th>Component</th><th>Parameter</th><th>Thawed</th><th>Value</th><th>Min</th><th>Max</th><th>Units</th></tr></thead><tbody><tr><th class=\"model-odd\" scope=\"rowgroup\" rowspan=4>testmdl</th><td>kT</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>0.008</td><td>64.0</td><td>keV</td></tr><tr><td>Abundanc</td><td><input disabled type=\"checkbox\"></input></td><td>1.0</td><td>0.0</td><td>5.0</td><td></td></tr><tr><td>redshift</td><td><input disabled type=\"checkbox\"></input></td><td>0.0</td><td>-0.999</td><td>10.0</td><td></td></tr><tr><td>norm</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.0</td><td>0.0</td><td>1e+24</td><td></td></tr></tbody></table></details></div>"
],
"text/plain": [
"<XSapec model instance 'xsapec.testmdl'>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xsapec.testmdl"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Evaluating the model is as simple as calling it with the grid you want to use. It uses the current parameter values, so the following evaluates the model for\n",
"\n",
"variable name | temperature (keV) | abundance (solar units)\n",
"--- | --- | ---\n",
"`y_1_1` | 1 | 1\n",
"`y_2_1` | 2 | 1\n",
"`y_5_1` | 5 | 1\n",
"`y_1_03` | 1 | 0.3\n",
"`y_1_01` | 1 | 0.1"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"y_1_1 = testmdl(elo, ehi)\n",
"\n",
"testmdl.kt = 2\n",
"y_2_1 = testmdl(elo, ehi)\n",
"\n",
"testmdl.kt = 5\n",
"y_5_1 = testmdl(elo, ehi)\n",
"\n",
"testmdl.kt = 1\n",
"testmdl.abundanc = 0.3\n",
"y_1_03 = testmdl(elo, ehi)\n",
"\n",
"testmdl.abundanc = 0.1\n",
"y_1_01 = testmdl(elo, ehi)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These can be displayed (the return value is a NumPy array the same size as the input grid). For XSPEC additive models, such as `xsapec`, the return value has units of photon/cm$^2$/s."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"emid = (elo + ehi) / 2\n",
"\n",
"fig = plt.figure(figsize=(8, 6))\n",
"\n",
"plt.plot(emid, y_1_1, label='kT=1 A=1')\n",
"plt.plot(emid, y_2_1, label='kT=2 A=1')\n",
"plt.plot(emid, y_5_1, label='kT=5 A=1')\n",
"plt.plot(emid, y_1_03, label='kT=1 A=0.3')\n",
"plt.plot(emid, y_1_01, label='kT=1 A=0.1')\n",
"\n",
"plt.xlabel('Energy (keV)')\n",
"plt.ylabel('photom/cm$^2$/s')\n",
"plt.legend()\n",
"\n",
"plt.xscale('log')\n",
"plt.yscale('log')\n",
"plt.title('XSPEC apec model');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Normally this is displayed \"per keV\", wihch we can get by dividing each bin by `ehi - elo`, but in this case each bin has the same width (0.01 keV), so all this would do would be to change the Y axis values, but not the shape.\n",
"\n",
"#### No instruments!\n",
"\n",
"The above has not used any instrument response (RMF or ARF), since we care here about the spectra before it has interacted with a telescope."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Manually calculating the flux\n",
"\n",
"Since we can evaluate the model, we can use this to directly calculate the flux from a given model, as I'll discuss in this section. The following sections are all ways that Sherpa provides that automate this process, but I feel it's useful to know how to do it manually.\n",
"\n",
"As mentioned when plotting the models, XSPEC additive models produce results with units of photon/cm$^2$/s. This means that summing up the values over over the desired energy range will give us the flux. As an example, I go back to the XSPEC power-law model (since this has an analytic form we can compare numerical results to):\n",
"\n",
"If we represent the model as $S(E)$ with $$S(E) = A E^{-\\gamma}$$ - where $A$ is the normalization and $\\gamma$ equal to the `PhoIndex` parameter - then the photon flux for the energy range $E_1$ to $E_2$ is \n",
"\n",
"$$\n",
"\\begin{eqnarray}\n",
"\\rm{pflux}(E_1, E_2) & = & A \\int_{E_1}^{E_2} E^{-\\gamma} dE \\\\\n",
" & = & \\frac{A}{1 - \\gamma} [E^{1 - \\gamma}]^{E_2}_{E_1}\n",
"\\end{eqnarray}\n",
"$$\n",
"\n",
"as long as $\\gamma \\ne 1$.\n",
"\n",
"We can compare this analytic expression to the model-calculated version. First I set up a power-law model with a slope of 1.7 and normaliation of $10^{-4}$ photon/cm$^2$/s/keV:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<style>/*\n",
"Copyright (C) 2020 Smithsonian Astrophysical Observatory\n",
"\n",
"\n",
" This program is free software; you can redistribute it and/or modify\n",
" it under the terms of the GNU General Public License as published by\n",
" the Free Software Foundation; either version 3 of the License, or\n",
" (at your option) any later version.\n",
"\n",
" This program is distributed in the hope that it will be useful,\n",
" but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
" MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n",
" GNU General Public License for more details.\n",
"\n",
" You should have received a copy of the GNU General Public License along\n",
" with this program; if not, write to the Free Software Foundation, Inc.,\n",
" 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
"\n",
"*/\n",
"\n",
":root {\n",
" --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
" --sherpa-background-color: var(--jp-layout-color0, white);\n",
" --sherpa-background-color-row-even: var(--jp-layout-color1, white);\n",
" --sherpa-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
"\n",
" /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
" --sherpa-background-color-dark1: #EBEFF2;\n",
" --sherpa-background-color-dark2: #D8E0E5;\n",
"}\n",
"\n",
"div.sherpa-text-fallback {\n",
" display: none;\n",
"}\n",
"\n",
"div.sherpa {\n",
" display: block;\n",
"}\n",
"\n",
"div.sherpa details summary {\n",
" display: list-item; /* needed for notebook, not lab */\n",
" font-size: larger;\n",
"}\n",
"\n",
"div.sherpa details div.datavals {\n",
" display: grid;\n",
" grid-template-columns: 1fr 3fr;\n",
" column-gap: 0.5em;\n",
"}\n",
"\n",
"div.sherpa div.dataname {\n",
" font-weight: bold;\n",
" border-right: 1px solid var(--sherpa-border-color);\n",
"}\n",
"\n",
"div.sherpa div.dataval { }\n",
"\n",
"div.sherpa div.datavals div:nth-child(4n + 1) ,\n",
"div.sherpa div.datavals div:nth-child(4n + 2) {\n",
" background: var(--sherpa-background-color-row-odd);\n",
"}\n",
"\n",
"div.sherpa table.model tbody {\n",
" border-bottom: 1px solid var(--sherpa-border-color);\n",
"}\n",
"\n",
"div.sherpa table.model tr.block {\n",
" border-top: 1px solid var(--sherpa-border-color);\n",
"}\n",
"\n",
"div.sherpa table.model th.model-odd ,\n",
"div.sherpa table.model th.model-even {\n",
" border-right: 1px solid var(--sherpa-border-color);\n",
"}\n",
"\n",
"div.sherpa table.model th.model-odd {\n",
" background: var(--sherpa-background-color-dark1);\n",
"}\n",
"\n",
"div.sherpa table.model th.model-even {\n",
" background: var(--sherpa-background-color-dark2);\n",
"}\n",
"\n",
"div.sherpa .failed {\n",
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" font-size: large;\n",
" padding: 1em;\n",
"}\n",
"</style><div class=\"sherpa-text-fallback\">&lt;XSpowerlaw model instance &#x27;xspowerlaw.xpl&#x27;&gt;</div><div hidden class=\"sherpa\"><details open><summary>Model</summary><table class=\"model\"><caption>Expression: xspowerlaw.xpl</caption><thead><tr><th>Component</th><th>Parameter</th><th>Thawed</th><th>Value</th><th>Min</th><th>Max</th><th>Units</th></tr></thead><tbody><tr><th class=\"model-odd\" scope=\"rowgroup\" rowspan=2>xpl</th><td>PhoIndex</td><td><input disabled type=\"checkbox\" checked></input></td><td>1.7</td><td>-2.0</td><td>9.0</td><td></td></tr><tr><td>norm</td><td><input disabled type=\"checkbox\" checked></input></td><td>0.0001</td><td>0.0</td><td>1e+24</td><td></td></tr></tbody></table></details></div>"
],
"text/plain": [
"<XSpowerlaw model instance 'xspowerlaw.xpl'>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xpl.phoindex = 1.7\n",
"xpl.norm = 1e-4\n",
"xpl"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So, the expected photon flux for the range 0.5-7 keV is:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Expected = 1.9548e-04 photon/cm^2/s\n"
]
}
],
"source": [
"gterm = 1.0 - xpl.phoindex.val\n",
"expected = xpl.norm.val * (7**gterm - 0.5**gterm) / gterm\n",
"print(\"Expected = {:.4e} photon/cm^2/s\".format(expected))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For this example I am going to use the 0.5 to 7.0 keV passband. \n",
"I could evaluate the model on `elo` and `ehi` (which are defined over 0.1 to 11 keV) and then filter the answer to the bins we want, but I've decided to just use the energy range I need. As I may just be re-using this a few times in the notebook, I have decided to make it into a small routine:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Power-law photon flux (0.5 - 7 keV)\n",
" nbins= 10 -> 1.9548e-04 photon/cm^2/s\n",
" nbins= 100 -> 1.9548e-04 photon/cm^2/s\n",
" nbins= 1000 -> 1.9548e-04 photon/cm^2/s\n"
]
}
],
"source": [
"def calc_pflux(model, elo, ehi, nbins):\n",
" \"\"\"Calculate the photon flux for the model over the energy range\n",
" \n",
" Parameters\n",
" ----------\n",
" model\n",
" The model instance to evaluate.\n",
" elo, ehi : number\n",
" The energy range, in keV. It is assumed that elo > 0 and ehi > elo.\n",
" nbins : int\n",
" The number of bins to use in the grid. It is assumed that nbins > 0.\n",
"\n",
" Returns\n",
" -------\n",
" pflux : number\n",
" The model flux for the energy range. It is assumed to be\n",
" in photon/cm^2/s, but this depends on the model instance.\n",
" \"\"\"\n",
" grid = np.linspace(elo, ehi, nbins)\n",
" y = model(grid[:-1], grid[1:])\n",
" return y.sum()\n",
"\n",
"print(\"Power-law photon flux (0.5 - 7 keV)\")\n",
"for n in [10, 100, 1000]:\n",
" pflux = calc_pflux(xpl, 0.5, 7, n)\n",
" print(\" nbins= {:4d} -> {:.4e} photon/cm^2/s\".format(n, pflux))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So, in this particular case the bin size doesn't matter, as even with only 10 bins the photon flux matches the expected value. This is because the power-law model is rather simple. It becomes more important when calculating the energy flux.\n",
"\n",
"The energy flux is given by $\\int E S(E) dE$ and so for the powerlaw model the analytic solution is\n",
"\n",
"$$\n",
"\\begin{eqnarray}\n",
"\\rm{eflux}(E_1, E_2) & = & A \\int_{E_1}^{E_2} E . E^{-\\gamma} dE \\\\\n",
" & = & \\frac{A}{2 - \\gamma} [E^{2 - \\gamma}]^{E_2}_{E_1}\n",
"\\end{eqnarray}\n",
"$$\n",
"\n",
"as long as $\\gamma \\ne 2$. Note that this gives a result in keV/cm$^2$/s, so we are going to have to convert this to erg/cm$^2$/s using the same constant that Sherpa does. \n",
"\n",
"The expected energy-flux for our power-law model is therefore:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Expected = 5.2366e-13 erg/cm^2/s\n"
]
}
],
"source": [
"KEV_TO_ERG = 1.60217653e-09\n",
"gterm = 2.0 - xpl.phoindex.val\n",
"expected = KEV_TO_ERG * xpl.norm.val * (7**gterm - 0.5**gterm) / gterm\n",
"print(\"Expected = {:.4e} erg/cm^2/s\".format(expected))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This time the bin size does make a difference when calculating the model flux, since we are approximating the integral by the trapezoidal rule, and weighting each bin with its mid-point: "
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Power-law energy flux (0.5 - 7 keV)\n",
" nbins= 10 -> 5.4375e-13 erg/cm^2/s\n",
" nbins= 100 -> 5.2385e-13 erg/cm^2/s\n",
" nbins= 1000 -> 5.2367e-13 erg/cm^2/s\n"
]
}
],
"source": [
"def calc_eflux(model, elo, ehi, nbins):\n",
" \"\"\"Calculate the photon flux for the model over the energy range\n",
" \n",
" Parameters\n",
" ----------\n",
" model\n",
" The model instance to evaluate.\n",
" elo, ehi : number\n",
" The energy range, in keV. It is assumed that elo > 0 and ehi > elo.\n",
" nbins : int\n",
" The number of bins to use in the grid. It is assumed that nbins > 0.\n",
"\n",
" Returns\n",
" -------\n",
" pflux : number\n",
" The model flux for the energy range. It is assumed to be\n",
" in erg/cm^2/s, but this depends on the model instance.\n",
" \"\"\"\n",
" grid = np.linspace(elo, ehi, nbins)\n",
" glo, ghi = (grid[:-1], grid[1:])\n",
" y = model(glo, ghi)\n",
" \n",
" # use the mid-point energy, convert from keV to erg\n",
" gmid = KEV_TO_ERG * (glo + ghi) / 2\n",
" return (gmid * y).sum()\n",
"\n",
"print(\"Power-law energy flux (0.5 - 7 keV)\")\n",
"for n in [10, 100, 1000]:\n",
" eflux = calc_eflux(xpl, 0.5, 7, n)\n",
" print(\" nbins= {:4d} -> {:.4e} erg/cm^2/s\".format(n, eflux))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Setting up the data in Sherpa\n",
"\n",
"Whilst you can calculate the flux manually, there are a number of routine provided by Sherpa for doing this for you. As they are\n",
"intended for use as part of a \"normal\" Sherpa session (or script)\n",
"they rely on having a PHA dataset loaded.\n",
"\n",
"For this, I'll use a spectrum from\n",
"[release 2 of the Chandra Source Catalog](https://cxc.harvard.edu/csc2/), as I happen to have it on disk and it is well described by an absorbed power-law model."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"read ARF file acisf11309_000N022_r0002_arf3.fits\n",
"read RMF file acisf11309_000N022_r0002_rmf3.fits\n",
"read background file acisf11309_000N023_r0002_pha3.fits\n"
]
}
],
"source": [
"load_pha('acisf11309_000N023_r0002_pha3.fits.gz')\n",
"subtract()\n",
"ignore(None, 0.5)\n",
"ignore(7, None)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For this example I want to use chi-square statistics, so I group the data into bins of at least 15 counts\n",
"with the `group_counts` call, using the `tabStops` argument to only apply the grouping over the\n",
"selected energy range (i.e. 0.5 to 7 keV):"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"# Ensure the grouping starts at 0.5 keV\n",
"tabs = ~get_data().mask\n",
"group_counts(15, tabStops=tabs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The data can be displayed in a notebook (for PHA data we get a plot of the data and tables summarizing the observation):"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
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"\n",
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" the Free Software Foundation; either version 3 of the License, or\n",
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"\n",
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"\n",
"\n",
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"\n",
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"\n",
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"</details></div><details open><summary>Summary (8)</summary><div class=\"datavals\"><div class=\"dataname\">Identifier</div><div class=\"dataval\">acisf11309_000N023_r0002_pha3.fits.gz</div><div class=\"dataname\">Exposure</div><div class=\"dataval\">58334.7 s</div><div class=\"dataname\">Number of bins</div><div class=\"dataval\">1024</div><div class=\"dataname\">Channel range</div><div class=\"dataval\">1 - 1024</div><div class=\"dataname\">Count range</div><div class=\"dataval\">0.0 - 37.0</div><div class=\"dataname\">Background</div><div class=\"dataval\">Subtracted</div><div class=\"dataname\">Grouping</div><div class=\"dataval\">Applied (716 groups)</div><div class=\"dataname\">Using</div><div class=\"dataval\">0.5256-6.8109 Energy (keV) with 136 groups</div></div></details><details><summary>Metadata (8)</summary><div class=\"datavals\"><div class=\"dataname\">Mission or Satellite</div><div class=\"dataval\">CHANDRA</div><div class=\"dataname\">Instrument or Detector</div><div class=\"dataval\">ACIS</div><div class=\"dataname\">Object</div><div class=\"dataval\">NGC 7814</div><div class=\"dataname\">Observation date</div><div class=\"dataval\">2009-09-01T19:34:56</div><div class=\"dataname\">Program that created the PHA</div><div class=\"dataval\">dmextract - Version CAT4.5</div><div class=\"dataname\">The channel type</div><div class=\"dataval\">PI</div><div class=\"dataname\">Data stored</div><div class=\"dataval\">TOTAL</div><div class=\"dataname\">Data format</div><div class=\"dataval\">COUNT</div></div></details></div>"
],
"text/plain": [
"<DataPHA data set instance 'acisf11309_000N023_r0002_pha3.fits.gz'>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_data()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For this example I shall use Chi-square statistics (errors set to the square-root of the number of counts in a bin):"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"set_stat('chi2datavar')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and for with an absorbed powerlaw model:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"set_source(xsphabs.gal * powlaw1d.pl)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For this example I am going to fix the absorption to use the column density from the\n",
"[colden tool](https://cxc.cfa.harvard.edu/ciao/ahelp/colden.html),\n",
"which requires converting the approximate position of this source\n",
"(00$^h$ 03$^m$ 39$^s$ +16$^\\circ$ 02$^{'}$ 20$^{''}$) to decimal and\n",
"then running colden, using Python modules provided with the CIAO contributed package:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"from coords.format import ra2deg, dec2deg\n",
"\n",
"ra = ra2deg('00 03 39')\n",
"dec = dec2deg('+16 02 20')"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"N_H = 3.69 * 1e20 cm^-2\n"
]
}
],
"source": [
"from ciao_contrib.proptools import colden\n",
"\n",
"nh = colden(ra, dec)\n",
"print(\"N_H = {} * 1e20 cm^-2\".format(nh))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The absorption models have units of"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'10^22 atoms / cm^2'"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gal.nh.units"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"whereas `colden` returns values in units of 10$^{20}$ atoms cm$^{-2}$, so let's scale the result before\n",
"[freezing](https://cxc.cfa.harvard.edu/sherpa/ahelp/freeze.html) \n",
"the parameter. "
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"gal.nh = nh / 100\n",
"freeze(gal.nh)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now I can fit:"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset = 1\n",
"Method = levmar\n",
"Statistic = chi2datavar\n",
"Initial fit statistic = 5.75307e+11\n",
"Final fit statistic = 115.737 at function evaluation 19\n",
"Data points = 136\n",
"Degrees of freedom = 134\n",
"Probability [Q-value] = 0.870659\n",
"Reduced statistic = 0.863709\n",
"Change in statistic = 5.75307e+11\n",
" pl.gamma 1.72494 +/- 0.0372655 \n",
" pl.ampl 0.000110411 +/- 2.99675e-06 \n"
]
}
],
"source": [
"fit()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_fit(xlog=True, ylog=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the model dislay for PHA datasets has changed in CIAO 4.13 from a line connecting the mid-points to a histogram style.\n",
"\n",
"The reason for fixing the absorption to the Galactic value is that it is large enough to make a difference in the predicted spectrum (the blue line in the plot below, compared to the unabsorbed model shown by the orange line). If the $n_H$ parameter were allowed to vary the best-fit is much closer to 0."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_source(xlog=True, ylog=True)\n",
"plot_source_component(pl, overplot=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. calculate_energy_flux\n",
"\n",
"The basic routines for PHA data are \n",
"[calc_photon_flux](https://cxc.cfa.harvard.edu/sherpa/ahelp/calc_photon_flux.html)\n",
"and\n",
"[calc_energy_flux](https://cxc.cfa.harvard.edu/sherpa/ahelp/calc_energy_flux.html).\n",
"You can call them without any arguments, but this is not advised as the energy range is\n",
"then set by the instrument response. I am going to always use them with a given pass band \n",
"(i.e. energy range), so that I know what I'm calculating:"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"p1 = calc_photon_flux(lo=0.5, hi=7)\n",
"e1 = calc_energy_flux(lo=0.5, hi=7)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"p1 = 1.9638e-04 photon/cm^2/s\n",
"e1 = 5.4455e-13 erg/cm^2/s\n"
]
}
],
"source": [
"print(\"p1 = {:.4e} photon/cm^2/s\".format(p1))\n",
"print(\"e1 = {:.4e} erg/cm^2/s\".format(e1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These results can be compared to the analytic answer (using the photon flux for demonstration purposes, and noting that the XSPEC and Sherpa power-law models are the same, they just have different parameter names):"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Expected = 2.1457e-04 photon/cm^2/s\n"
]
}
],
"source": [
"gterm = 1.0 - pl.gamma.val\n",
"expected = pl.ampl.val * (7**gterm - 0.5**gterm) / gterm\n",
"print(\"Expected = {:.4e} photon/cm^2/s\".format(expected))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.1 Absorbed versus unabsorbed fluxes\n",
"\n",
"The fact that the analytic solution is larger than the calculated value is because both `calc_photon_flux` and `calc_energy_flux` use the full model expression - that is an absorbed power-law - which is going to result in a smaller flux than the power-law only (i.e. unabsorbed) component.\n",
"\n",
"The CIAO 4.12.1 release added the model argument to `calc_energy_flux` which makes it easy to calculate unabsorbed fluxes too. Setting the model argument to `pl` in this example will calculate the unabsorbed flux."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"p2 = calc_photon_flux(lo=0.5, hi=7, model=pl)\n",
"e2 = calc_energy_flux(lo=0.5, hi=7, model=pl)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The photon-flux is now the same as the expected value (at least to the fourth decimal place in this particular example):"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"p2 = 2.1457e-04 photon/cm^2/s\n",
" vs 2.1457e-04\n",
"\n",
"e2 = 5.6688e-13 erg/cm^2/s\n"
]
}
],
"source": [
"print(\"p2 = {:.4e} photon/cm^2/s\".format(p2))\n",
"print(\" vs {:.4e}\\n\".format(expected))\n",
"print(\"e2 = {:.4e} erg/cm^2/s\".format(e2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is important to only calculate a flux for an XSPEC additive model component (or a combination of multiplicative * additive). The `calc_energy_flux` routine will allow you to specify a XSPEC multiplicative model - here this would be `model=gal` - but the number is meaningless."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. How about parameter errors?\n",
"\n",
"A simple solution for estimating the error on the flux is to just use the fractional error on the normalization (or amplitude) parameter, and apply that to the flux. However, this ignores the influence on any other model parameters on the flux calculation (so in our example the slope of the power law).\n",
"\n",
"Sherpa provides three routines that will simulate the flux distribution based on the parameter errors:\n",
"\n",
" - [`sample_photon_flux`](https://cxc.cfa.harvard.edu/sherpa/ahelp/sample_photon_flux.html)\n",
" - [`sample_energy_flux`](https://cxc.cfa.harvard.edu/sherpa/ahelp/sample_energy_flux.html)\n",
" - [`sample_flux`](https://cxc.cfa.harvard.edu/sherpa/ahelp/sample_flux.html)\n",
" \n",
"If you require photon fluxes then use `sample_photon_flux`, otherwise you can use either of `sample_energy_flux` or `sample_flux`.\n",
"The `sample_energy_flux` routins has more features, but `sample_flux` may be a bit easier to use.\n",
"\n",
"All three routines use the same technique: create a random set of the free parameters in the model using a Gaussian distribution centered on the best-fit parameter values and the widths from the errors on these parameters, and then use that set of parameters to calculate the flux. This step is repeated a given number of times, and the resulting flux distribution can be used to estimate the flux errors. Note that the Gaussian distribution can include correlations between the parameters, as we show below.\n",
"\n",
"### 5.1 sample_photon_flux and sample_energy_flux\n",
"\n",
"I am going to first look at `sample_energy_flux`. This will create `num` samples of the free parameters of the model (where `num` is an input argument and defaults to 1) assuming a normal distribution about the current parameter values. The default is to assume un-correlated errors, and to calculate these errors with the\n",
"[`covar` routine](https://cxc.cfa.harvard.edu/sherpa/ahelp/covariance). We show how to change these assumptions in sections 5.2 and 5.4. Each sample is then used to calculate a flux, using `calc_energy_flux`, resulting in a distribution of fluxes.\n",
"\n",
"New in CIAO 4.13 is the addition of the `model` and `clip` parameters, which mean that we can now generate unabsorbed fluxes with `sample_energy_flux` and `sample_photon_flux`, which was not possible in CIAO 4.12.1.\n",
"\n",
"Although not shown here, `sample_photon_flux` works the same way as `sample_energy_flux`, but returns fluxes in units of photon/cm$^2$/s rather than erg/cm$^2$/s.\n",
"\n",
"We can get a visual handle of `sample_energy_flux` by using the `plot_energy_flux` function to plot up 1000 samples of the 0.5 to 7 keV photon flux (the number of bins used for the histogram has been reduced from 75 to 30 as it works better for this dataset, and 1000 iterations have been used as it doesn't take too long for this notebook rather than providing any particular level of accuracy). We can take advantage of the `model` parameter, which is new to CIAO 4.13, to calculate the fluxes for an unabsorbed component. As expected, the unabsorbed fluxes are on average larger than the absorbed fluxes!\n",
"\n",
"Note that the process involves using random numbers, and so the results will change slightly each time this notebook is run."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_energy_flux(lo=0.5, hi=7, num=1000, bins=30)\n",
"plot_energy_flux(lo=0.5, hi=7, num=1000, bins=30, model=pl, overplot=True)\n",
"\n",
"plt.legend(['Absorbed', 'Unabsorbed']);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `sample_energy_flux` routine - which `plot_energy_flux` uses - returns the actual flux values per iteration (along with the parameter values), as a two-dimensional array (iteration number, and a combination of parameter values, clipping flag, and flux):"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1000, 4)\n",
"(1000, 4)\n"
]
}
],
"source": [
"sample1 = sample_energy_flux(lo=0.5, hi=7, num=1000)\n",
"sample2 = sample_energy_flux(lo=0.5, hi=7, num=1000, model=pl)\n",
"print(sample1.shape)\n",
"print(sample2.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that the fluxes - which are the first column - show the same behavior as seen in the `plot_energy_flux` plot\n",
"(note that the results will not exactly match because the binning is different *and* the results shown here are a\n",
"different realisation than in the `plot_energy_flux` call):"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_pdf(sample1[:, 0])\n",
"plot_pdf(sample2[:, 0], overplot=True)\n",
"\n",
"plt.legend(['Absorbed', 'Unabsorbed']);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the rest of this section I shall concentrate on the **unabsorbed**, rather than absorbed, fluxes, since this is new functionality in CIAO 4.13.\n",
"\n",
"Here I plot the ampl and $\\gamma$ parameter values used (there's no assumed correlation between these two parameters since the `correlated` argument to `sample_energy_flux` was left at its default setting of `False`), along with a histogram of the flux distribution:"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"eflux2 = sample2[:, 0] * 1e13\n",
"gamma2 = sample2[:, 1]\n",
"ampl2 = sample2[:, 2]\n",
"clip2 = sample2[:, 3]\n",
"\n",
"fig = plt.figure(figsize=(10, 8))\n",
"\n",
"plt.subplot(2, 1, 1)\n",
"plot_scatter(ampl2, gamma2, alpha=0.5, markersize=12, clearwindow=False,\n",
" xlabel='ampl', ylabel=r'$\\gamma$', name='parameters')\n",
"\n",
"plt.subplot(2, 1, 2)\n",
"plt.hist(eflux2, bins=20)\n",
"plt.xlabel(r'energy flux ($10^{-13}$ erg cm$^{-2}$ s$^{-1}$)')\n",
"plt.ylabel('number');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can compare the power-law normalization (i.e. the `ampl` parameter) to the calculated flux, to show that they are correlated, but that there is scatter (thanks to $\\gamma$ varying)."
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# We can use Matplotlib commands as well as the pre-canned routines like\n",
"# plot_scatter.\n",
"plt.scatter(ampl2 * 1e4, eflux2, alpha=0.25)\n",
"plt.xlabel('ampl * 10$^4$')\n",
"plt.ylabel('energy flux ($10^{-13}$ erg cm$^{-2}$ s$^{-1}$)');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The last column (here referred to as `clip1`) is a flag column, and indicates whether any of the parameter samples fell outside of the limits set by the `clip` parameter and were replaced bu the limit (this is a new feature in CIAO 4.13). The default setting for `clip` is `'hard'`, which means that only those rows where a parameter falls outside its hard limits will be flagged. For this model and dataset it is unlikely to happen, which we can check by looking if any value in `clip2` is not 0 (this column only has values of 0 and 1 but is stored as a floating-point value):"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(clip2 > 0).sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is the time for the `corner` module to shine, since it is designed to visualize the correlations for data like the return value from `sample_energy_flux`. There are a number of customization options for the plot, but let's see what the defaults look like, other than adding in the column labels (thanks to the way that the notebook works, I add a semi-colon so that the return value isn't displayed automatically, as this then leads to the plot being duplicated).\n",
"\n",
"Since the last column is all zeros I remove it from the call with `sample2[:, :-1]`:"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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dvhcUFISzszPvvvsuS5cuBeCFF15g/fr1ODk5kTdvXvbt20ezZs14+umnyZUrF8eOHTOaMdzc3HjmmWcoXbo0+/btM2ofmjVrRvv27WncuDEFChRIdn4JBOyTQEACgcwkgYDICtm1s+AB4Dfiqv5/01qP1loPAf4CRgNorcMfNAjILBaLhdjY2FRvoH5+fsb/bdUAJKW1NoKAAQMGMGPGDH744QemT59ubFO/fn1WrFjB9evXCQ8Pp2vXrixbtoyjR49y7949fvvtN9auXcuFCxfYv38/AwcO5I8//qBnz54UL16ctm3bcvTo0fS/eSGEEDlatukjoJRy0lqbAbTWF4ElSqmiwOtKqQJa6xvA34B7wm0flHV4XdKEPrYm8EmriIgIoqMzNibRWjNhwgRWrFjBgAEDeP/99wE4ceIEH330ERUrVqRXr14AdO3ala5duybbPyGTyUStWrWoVasWM2fO5MiRI2zevJlPP/2URo0asXv3bp577rkMfQ9CCCGyvywPBJRST2mtz2itzTZu8DFAWWCUUsoNqA90T28QABjD6RJ2ngsLCyMkJAStdbJZ/qwS3lgtFguRkZFGz3x3d3djQqGYmJhk+1qHByYtB8Dt27eTrdNa895777FixQo6d+5Mr169uHjxIgD9+/fnzz//ZMiQIZQpU4ann37aZnldXV3tfgahoaE8/fTTTJw4kX79+tG6dWuaNWvG559/TvXq1RPVXCQtV0q1HtKkIIQQOU+WNg3EDxH8M340ANZgIH6d0lpfAyYA/wD3gU5a65MPc04nJ6dkvf89PDyM5D/WNL5JF+uUwEopIiMjCQ4OJjIy0pgm2HrjdXFxSba4urri5OSU6IZv/TnpYjKZmDhxIsuXL6djx46MHTsWDw8P3N3dcXd3x9fXlzlz5uDp6Un37t25f/++zfLaO76Tk1OiG3bhwoXZvn07+fLlo127dhw+fPhhPl4hhBA5TJYFAvFpg4cAw4FopdR6MIIB5wRpg09rrZdrrd/VWv/zsOc1mUzJqv+TpuhNTWZl5UvaJ+CNN96w+ZSdP39+ZsyYwX///Uffvn2N2oX0ShoMHDx40OZ2mTF5khBCiKyVZYGA1joM6A1sBEYR1/ZvDQZiAZRSlYG+8RkGs02984MGDmn18ccfG0HA+++/n2JVe6VKlZg+fTo//PADU6dOfehzJwwGmjVrZnNOA2tNiMxPIIQQj48sbRrQWl/VWodqrYOAAYCHNRhQSj0DlAQ2a60js9vsgullMpmYMmWKzXV///03uXLlSjUIsHrttdfo2bMnc+fOZfv27Q9clps3b/Lxxx8bowaswUDhwoVtzmlgbZqQ+QlEZggICLDbNFesWLGsLp4Qj61sM3xQa32buGAgRin1D3FzC/yqtb6ZtSXLWNHR0bz11ls219WqVYt79+5x4cKFNB9v+vTpVKtWzWhK+Pnnn1NtKjh58iSDBw+mYsWKvPvuu7Ro0YJNmzYBccHAgQMHqFu3Lj179mTcuHFGB8jMqgkRAuJm2rQ3lwcgQYIQmSRbfaPH1wycAPyAV+I7CzqMOnXqAHDgwIE07+Pm5sb69etp27Yt27Zt4+WXX6Z8+fKMGjWKw4cPG1+isbGxfPnllzRu3JjGjRuzfft2evTowffff0/NmjUZNGgQkyZNwmKxkDt3br799lv69OnD9OnTqV+/PidPnpS+ASLLpBQkBAZm2jQjQjiEbJVZUCmVm7gpht/UWp/IjHNUq1ZNHzp0yO56e0+7qd0EhwwZwrp167h+/XqyddHR0bi7u9vc77///jOGHmqtqVWrFjVr1mTevHmcOHHCbjV8REQExYsXT/RaZGSkMYnR/v37iYmJoWjRotSrV4+ffvqJq1ev8uSTT9KyZUteeeUVY0rk2NhY5syZw1dffUXdunVZu3atMYzyq6++YtSoUZjNZiZPnsyQIUPsNltY34ctqX1+OaWWQTILZr8MgNmtPA9DMguKrJDleQQS0lrfVUq10lpHZuZ50nPTSa3NPuHwQVvs5fbPlStXon3q1avHr7/+Sq5cuShatKjdQOD69evJcgW4urrSsmVLWrZsSVRUFLt372b79u18+umn1KhRg/fee48XXniBs2fPJsoV4OrqytixYylRogQfffQRrVq1Yt26dTz55JO8/PLLVKtWjcGDBzNy5EgOHjzIwoULjfkOhBBC5GzZ7jEss4OA7K5u3bpcu3btgfoJ2OLr60uHDh1Yt24d//zzDxs3bqRp06Z2AxKlFB07dmTGjBn8+++/NG3a1Mgp8OSTT/Lll1/y1ltv8fnnn1O1alX279//UOUTQgiRPWS7QCAns1gsD11FWbduXQB++eWXjCgSYLs2IiwsjN9//521a9dy9uxZ4/UaNWrw7bff4u3tbfQ7sB5jxIgR7Nu3DxcXF1544QXGjx//QDkMJA+BEEJkPxIIZJCnnnqK8PBwRo8e/VDBQMmSJXFxcXnoGoGEtNZcuXKFbdu28d577zFkyBCaNGnCsGHDWLhwIQMGDOCvv/4yti9dujS7du2iUqVKDBs2jH///ddYV7NmTY4cOULXrl2ZNm0aI0aMSPP7jYiIICQkRPIQCCFENpKt+gjkRBaLhYiICHr37s2pU6dYsGABADNnzkxX7v3Y2FhiYmLsznnwoM6cOcOAAQOMntVeXl6ULl2a1157jWeeeYYCBQowatQohg4dyvz583nqqacAyJMnD4sXL6ZevXqMGzcuUU4BHx8fVq1aRZ48eZg/fz7Ozs7Mnj071bJY+ztIHgIhhMg+JBB4SBEREQQHB+Pp6cmsWbMAHioYCAsLA8iQQOC///6jZ8+eaK2ZOHEizz77LGXKlOHcuXPkyZPH2G7hwoUMHjyYoUOHMnv2bMqWLQtAkSJFGDNmDBMnTuSbb76hZcuWxj5KKePmP3/+fAA++ugju+/XGjCld4ZHIYQQmUMCgTSy167t5uaGj48PLi4uKKWYNWsWSqlEwUBsbKzd44aEhCTq/X/jxg0gbvTB1atXcXNzs7nfnTt37A5JBFixYgVbtmwhMjKS9u3bc/fuXfbs2cOePXuSTTwE0KJFCzZv3szw4cMBKF++PAD169enVKlSjBkzhiJFilCrVq1E+02dOpXY2FgjGJg9e7bNYCA8PJzg4GDCwsLImzdvsm0kOBBCiKwhgUAa2XvSdXJywsvLK9H6jz76CJPJxLx58zCZTEyfPt3u/tYgwsoacOTNm5e8efParUb38PCgSJEiNtfdv3+fLVu2EBoaSr169YiIiEjUzn/jxg2jCSChpk2b8t133/Hmm28yf/58KlSogKurK+PGjaNv374sW7bM6MyYkLUmxF4wYO1EaTKZiI2NJTIyMtE00EIIIbKOPIZlAqUUc+bMYdiwYcybN++BOhCGhoYC6W8aCA8Pp2fPngQHB/Pcc88lagJIjZeXFx07diRPnjwMHTrU6EBYvnx52rVrx5YtW7CVLEcpxfTp041+Bm+++Wai9xsREUFYWBheXl74+flJHwEhhMhGJBDIBBaLhfDwcGbNmsWwYcNYsGABY8aMSdO+DxMIxMTE0L9/f44dO0aNGjUoUKDAAx/Dx8eHhQsXJgsGBg4ciL+/P0OGDLHZ1GHtM2ArGPDw8DBqAKSPgBBCZC856htZKdVCKdUqq8uRGmsHwsjISObMmUOrVq2YP38+QUFBqe5rTdObnvzpR48eZe/evbzzzjsULlz4gfe3yp8/PwsXLsTPz48ZM2YAcYHJ8OHDOX78OF9++aXN/ZIGA0uWLDHWydBBIYTInnJMIKCUagLMBMKyqgy2EuLYes3Dw8OYrtdisXDy5Enq1auHv79/queoU6cOFStW5MMPPyQ6OvqBymetRbDXd+BB5M+fn7Zt23LmzBkjgGnYsCFPPvkkGzZssLuftcNk8+bNGTlyJH/88QcRERHExsbi7OwszQJCCJHNpCkQUEo9p5Q6rJQKVUpFK6XMSqngzC5cgvM3AJYBPbXWPyqlvJVSeZVSaS1/f6XUEaXUkVu3bqW7HLaeam29lnC63t27d3Pp0iUGDhyYpnOYTCbef/99Ll++zNatWx+ofPnz5wfg5s2MmbnZOkLg4MGDQFzHyG7duvH9999z9epVu/uZTCZWr15Nvnz56Ny5MzExMfj5+eHv7y/NAkIIkc2k9Vv5Y6ALcBbwAPoCCzKrUDb4xC9342co3ARsABYopdqktrPWeqnWuprWulq+fPnSXQgPDw98fHwSPdXaei2hhQsXUrBgQVq3bp3m8zRo0IAXXniB1atXc//+/TTvlzdvXpycnDIsEChVqhT58uXjt99+M1579dVXsVgsfPrppynu6+/vz4YNG7h06RKDBg3C09NTggAhhMiG0jx8UGt9TinlpLU2A6uUUr9mYrkAUErVAwK11l8rpd4AfgZCgdnALqAT0FwptUdrnalNBiaTCZPJZEzdm/B1Hx8fm/MMXLhwgW+//dboKGirqt/JyclmPoBp06ZRs2ZNNmzYwHvvvZdsvaura7LZBwHy5cvHrVu3KFq0qN33kitXLp599lmb6+7evcvt27eNnytXrsyvv/7KzZs3cXJyonr16tSoUYO1a9fSr18/Y5hgUFAQvr6+iY5lzT/w4Ycf0qBBAwYNGmS3TPY8LtMXCyFEdpXWb9FwpZQr8KdSaoZSagSQqQPBlVIvEHfjX6+UctFabwReB1ZorZdprf8FlgBPAak3vmeBJUuWYDKZ6NWr1wPvW6FCBdq3b8/q1au5fPlymvcrUKCAkZQoI1SrVo2wsDBOnTplvNa5c2fOnDnDH3/8kWz70NBQjh07xs8//4zWmkGDBtGwYUNGjBhhc3shhBBZK62BQHfACRhCXGe9IkC7zCqUUqoFMB3oARwDagBorbcBsxJs2gRwJ66WIFuJiIhg5cqVvPzyyxQqVChdxxgxYoSRkCitChYsmKGBQJUqVTCZTBw9etR4rXXr1nh4eLBw4UI2btzIhAkT6N+/PzVq1KBs2bK0bt2abt26sW7dOiOxUr58+ejYsSPBwQ/WtURmLBRCiMyVpkBAax2otY7QWgdrrSdprUdqrc9lRoGUUmWAN4HhWuv1QG6ga4Ky6PjtBgITgQFa69s2DpWl9u3bx507d8iXL1+6b2IFCxakf//+bNu2jYsXL6Zpnzx58iSq2n9YXl5eVKhQgW3btrFs2TKuXLmCr68vLVu2ZPv27QwfPpyVK1cSFBREjRo1GDt2LCtWrKBBgwZMmjSJu3fvkidPHjZt2sSlS5eYOHHiA51fhh0KIUTmSmuv+5eUUn8ope4opYKVUiGZOGrgLtBXa/1L/M/vAHXjawms5XEBQoAuWuu/bBwjyzVo0IAuXbqwePFievToQUhISLqO0759ewAOHDiQpu1v3rzJw3SItGX48OHUrl2b7du3U716dYYOHcprr73Ghg0bOHjwIJcuXeKLL77g448/5o033qBZs2aMHDmSqKgofvrpJyBuWOQrr7zC+vXrH2hYZGqdMYUQQjyctDYNfAT0BPJqrX211j5aa99U9kkXrfVNrfVFAKWUs9b6P+AzoHz8ayatdYzWeoPW+n+ZUYaM4Obmxrp165gxYwbffPMNjRo14ty5B69EKV68OAUKFEjUcz8lly5dIiAg4IHPkxLrVMXLly+nV69ebNu2jZYtW7J582by5MmDk5NTsn0qV65Mvnz5+P77743XevbsSVBQEDt37kzzua1DMQFpIhBCiEyQ1kDgX+AvnbRbfAZSSiW7m2itrblsjwCvK6XKaq1zxJ3AmmZ4xIgRfPXVV9y8eZMGDRqwe/fuBzqOUornnnuOgwcPpjpfgdls5t9//6VYsWIPUXL7ChQowAcffMCxY8cYOXIkO3fupGnTpok6ElqZTCYaN27MTz/9ZNQANG3alIIFC7JmzRpju7T2AZAmAiGEyBxpHT44BtiplPoZiLK+qLWe87AFUEo9pbU+o7U2JxiemIjW+jul1Hagn1JqdE4IBqw3LoDnn3+effv20a1bNzp06ECVKlXIlSsXuXLlwtfXF39/f3Lnzk3u3Llp3LixkR44JCQEZ2dnqlSpwrZt2zh58qRxk79582aynP/Xrl0jOjqa3Llzc/jwYbtl8/Ly4u+//7a5LiwsjBMnTthcV6hQoUSBTJs2bdi5cyeNGzemW7duyYYHPvvss3z66ad888039OvXD4CuXbsyf/58/v33XwoUKEBERAShoaForW3OSGg2m1FK4e7ujtYad3d3I2jQWtud1RHszxgpUlesWDG7aa4zusZJCJG10hoITCWuZ747kHzwejoppV4CNiulvtJad7UVDCT4eRNwIbsGAUnHs1unJvb09EQpxdNPP813333HxIkTOXv2LMHBwfz777/cvXuXu3fvYjbHveXixYtz6tQpXFxcyJ8/PyaTiWbNmjFx4kT+/vtvqlWrBsRNMJR0YiJrJ8HSpUvz33//2S1rWFiY3TwDK1asMHIlhIeHEx0djaenJy4uLpw+fZpSpUol2v7555/n999/Z/Xq1bi6ujJ06FCjqaBRo0a4ubnx+++/079/fwB69OjBvHnzaNCgAUuXLqVevXpore32AVBKGZ9t0vebiRVUDi8wMFA+XyEcRFoDgTxa66YZeWKllBdxwxGHA7WVUuu11q/GBwPOCZoFcgG3tdaZnsAoI5lMJuPGZf1CzZs3LzNmzEg0A5/16TYkJISdO3fSrVs31q5dS58+fYxjlShRggIFCvDrr7/So0cPu+e05htIKZlQWsTExHDlypVEow+s8wRER0eTK1cucufOjbu7Ox4eHtSrV49z586xdOlSTp06xcyZM43phmvXrs2PP/5oPL2XK1eO3bt3069fP5o0acLgwYOZPHlyokDKYrEQEREhHQSFEOIRSGsfgT1KqQwNBOIzAfYGNgKjAHel1Pr4dbEASqlKQBelVPLUezlQwjkIElJK4evrS6dOnahevTqzZs1Ktr5OnTrs3bs3xRz/V65cAeKGHaaX2Wzmf//7H3fu3KFAgQKUKVOGIkWK4OfnR0xMDKdPn+bgwYN8++23/Pzzz0BcdsQXXniBiRMncvDgQQYPHmwcr2HDhly9epUdO3YYr9WrV4+jR48yePBgFi5cSMOGDY0aEbDdH0DyCQghROZIayDwOrBLKRWRkcMHtdZXtdahWusgYADgYQ0GlFLPAKWAzVrryIc9V06glKJmzZo2b/aDBg0iOjqatm3b2g0GSpcuDZBi/4C0lAEgd+7cPPnkk3h7e5M/f36KFSvGU089RevWrXn++ed54oknuHPnTqLq406dOtG8eXOuXbtmvNaiRQvKlStHt27dEvUv8PLyYu7cuSxZsoQTJ06wZ88eY511yKC7uzvh4eFGDYGtzoISIAghxMNJa0IhH621SWvtkVnDB+OTAg0AYpRS/wCfA79qrTNmBp0cwtnZdmtNpUqV+Oyzz7h16xZt27a1mT2wSZMm+Pj48Pnnn6f7/CaTCT8/P+7fv2/z5urs7EzevHmNKZVjYmISrb99+3aiPAZeXl6sXLmScuXK0aFDh2SjJrp06ULevHlZuXJlojJ4eXkRGRlJaGio0UxgK59AREQEwcHBMprAgQUEBKCUsrlk1ggaIR4naZ6xRSn1jFKqtVLqFeuS0YWJrxk4AfgBr2itr6Wyy2MnpZ7u1atXN4KBvn37JnryhrjcBa1atWLXrl0PlLQnqdy5c2M2m1NMgmSd8ChpIHDr1q1kCY1y5crFt99+azMYcHNzo2vXrmzbto2kU0R7eHjg7e1t3PwvXbqULMOih4cHvr6+0p/AgV26dAmttc3F3sgHIcT/S2tmwZXASuLmF2gVv7yU0YWJn2K4BdBUa30yo4+fHVkslkSLtardYrFw//79ZMtTTz1lpPTt0qUL586dIyQkxFhefPFFIiMjjXZ+W0tUVBRBQUE2l+joaGJjY1FKcf36dW7fvm0sMTExXLhwgQsXLnDnzh0ALl68yIULF7h37x737t3jxo0b+Pn5GT8nXBYtWkSJEiVo3749n332mXGspk2bEhMTw8qVKzGbzcZnAeDi4oLZbObHH3+kTp061K9fnxs3bhATE0N0dLQxMkNrTVhYGGaz2bgJCCGESF1aRw08p7V+OlNLAmit7yqlWj1ufQJSespPmpXPuq21it7WNLsNGjRg9erV9O7dmwEDBvDll18aExsVLFiQ0qVLExkZyauvvmrznDdv3uTpp23/Ok+cOIGXlxdaa65du0blypWNMpw+fZq8efMa5bt48SJeXl7kyZMHNzc38uXLR3BwMCVLlqRIkSKJjhsSEkKuXLlYuXIlXbp0YeTIkXz++efkz5+f0qVLU7NmTdavX8/rr7+eaJigyWTi0KFDtG/fnjx58nDlyhWGDx/O2rVrMZlMxucVGRlpTGhkKx+BEEII29LaNPCbUirTAwGAxy0IyCxVqlRJ1GfA2oFQKUWnTp04ffp0sqaDB1G4cGFiYmK4edN2Fw0XFxcgcdOAtWq/QIECdo+bK1cu5s+fT1hYGCNGjDD27927N3///XeyZEYHDx6kZcuW5M+fnx9//JGxY8eyefPmZP0gpIlACCHSJ62BwBrigoF/lFInlFInlVK208+JhxYbG5umzm8J+wy0bdvWyCPQoUMHlFLs2LEj3VXkBQoUMKYfPnnyJBcuXCA0NJTIyEi01kanxoTZDa0dGK21BvaULl2ayZMnc+zYMT744AO01nTq1AkvLy/mzZvHpUuX2LhxI4MHD6ZVq1bkz5+f7777jieffJK33nqLihUr8uabbybqw2BvaKYQQoiUpfVbcyXQHWjO//cPaJVZhXJkDRs2JCoqijZt2qQ5GNi8eTN37tyhRYsWnDx5kieeeILGjRvz3XffsWLFikRj9NPK2tYeFRXF+fPn+fPPP7l8+TIHDhxg7969xmyICZ/An3zySTw8PFizZk2qAUjLli3p3bs3mzZtYtq0aXh7ezNixAi2bt1KyZIl6d69O+vXr6dWrVpGEABxnRTnz5/P9evX2bJlywO/LyGEEImltY/AZa319kwticBisdCgQQOWLVtGv3796NmzJ+vWrUu1urtatWrs2LGDzp0707p1a1atWkXfvn3x8PBg+/bt3Llzh2HDhuHm5pbmsly5csW4mZcuXdpIfezh4UF4eDgREREULlyY3LlzG/sULFiQcePG8d577/HZZ5/RuXPnFM8xatQoYmJiWLNmDbly5WLWrFkUL16ciIgIatWqRYUKFWzOJ1CrVi3KlSvH2rVrGThwYJrfk0hM5hMQQkDaA4HTSqmNwNcknnToi0wplYOyJs3p2LEjSin69u1L9+7d0xQMlClThp07d9KlSxe6du3KwIED6d69O/7+/qxatYrJkyczduxYYx6B1AQGBuLj44O3tzcXL16kTJkyeHl58cQTT6S4X69evdi5cyeTJk2iVKlSxtwItiilGDduHEop5s2bB8Ds2bMT3fiTDk+07tejRw/GjRvH6dOnKVu2bJrek0hM5hMQQkDaAwEP4gKAhGmGNSCBQLzUvlDtjRwwm81ERkbi7u6Ou3tcJmV3d3d69uxJcHAwI0eOpHv37qxZsyZRMBAdHZ0sV0DevHnZunUr/fr14+OPP+bmzZu0atUKb29vFi1axDvvvMOYMWMICwvj7NmzNstz48YN7ty5w+3bt8mbNy8mk4no6Gh+++03cuXKZTe3wP379/nqq68AePHFFzl37hzt2rWjQ4cO1K1bl/Lly9v9bDp06ADAvHnzuHfvHm+99Vai0QC2Oh+2bNmS8ePHs2rVKqZNm5ZsvdY6xf4CMjOhEELEUY72RFCtWjV95MiRDD9uegOB0NBQgoOD8fX1TTbszWKxsGbNGvr27UujRo3Ytm2bEQxERkbavdFFR0fTp08ftm7dSp8+fZg+fTq///47Xbt2xd3dndGjR1OuXDmb+7Zs2dLue6hTp44xi2BShw4dSjTZUXh4OJ9++ilnzpyhRo0aLFu2zEhClNT169cpUKAAH374IevWraNy5cq0bNmSpk2b4u3tnWwoolWPHj2MvgtJMzJm1hTFSqmjWmu71RyZdX1lBqXUY18jkNPeY2rXlxCZIa0JhdyVUq8rpRYqpVZal8wunCNIbdhbr169WL58OT/++CPNmzfn2LFjqR7T1dWVJUuW8MYbb7BixQp69uxJlSpV2LVrF87OzowfP54//vjjgcuacDbC1Hh6evLaa6/RsGFDDh06RI8ePewORYT/byYYN24cYWFhTJ06lQYNGtCnTx+WL1/O9evXk+3TpUsXrl+/zq5dux74vQghhIiT1lED64CCQDPgZ+BJwH7+WZHmyXDSMuytV69erFmzhr/++ovq1avz8ssvc+nSpVSP+/777zN9+nR27tzJyy+/TOHChfnuu+8oUKAAEyZMYO/evQ/0nqyzG6aVyWSiefPmdOvWjbNnz9KuXTvOnDljd3tr2//27dvZsWMHr7/+OiEhIbz99ttUqlSJVq1a8csvvxjbN27cmPz587N69eo0lUcmKBJCiOTSGgiU0lq/C4RprdcALYGKmVesnC+jJ8Pp1q0bFy5cYNKkSfz888/Url2bbdu2pbpf//79WblyJYcOHWLevHkUKlSIKVOmUK5cOebOncv58+fTXIaEOQMexDPPPEOHDh0ICgrizz//TNM+JUuW5PXXX2fz5s0cOHCAGjVq8Pvvv7Nz505jm7t376K15v79+2k6pkxQJIQQyaU1ELB23b6nlKpA3KRAxTKlRClQSgUopZ5K8lq27PWVGZnu/Pz8GD9+PMeOHaNUqVJ07tyZUaNGpTrB0Msvv8wrr7zCwoULuXbtGl5eXrz99tv4+fnx4YcfEhoamqbzV6pUKV3l/vPPP1mzZg0tWrSgffv2D7z/0aNHOXToEM8//zzvvvsuENcHYPjw4QQHBzN37tw0HUeyDwohRHJpDQSWxk8INB7YDvwNTM+0UtmglGoPfAWsVUrNUUq9CqC11qkFA0qp/kqpI0qpI0lnuMssmZnprnjx4uzZs4fXX3+dTz75hEaNGqXaVPDuu+8SGxvL9OlxvzY/Pz/Gjh3LvXv36NWrFwcOHEi1U5W9KZJTcuHCBTZv3ky1atX48MMPH/jz+Prrrxk2bBj169dn7dq1xk18xYoV7NmzhxkzZlChQgVj+5Sq/yX7oBBCJJfWb3Y/4LX4/38S/2+sUqqy1vrPDC9VEkopL2AQ0I+4IKQL8JxSyl9r/ZFO5Q6mtV4KLIW4Xt2ZXd6MYutmprUmIiICk8nE9OnTqVGjBkOHDqVWrVrs2bPHmEwoafV3gQIFjGGI5cqVo3jx4gQEBPD222+zZMkSPvjgA5566qlk50vKXofB8PDwZFX0QUFBrF27Fj8/P6ZMmUJ4eDjh4eHJ9r1//36iiYasduzYwfvvv0+9evVYtWoVrq6umM1mTp8+zaRJk3jhhRfo2bNnosyJ4eHhhISEYLFYbB7TKqXYMSf1MhdCiIeVpuGD8cmEqhGXUAji+ggcBsoCW7TWMzKthHHn94s/92it9e9KKR+gAdAE+EVrvTmtx8pJw7sS3uAsFgsRERHGE6+Pjw8mk4ng4GBu375tDLX7/fffyZ07N2FhYcmOd+vWLSpVqsRzzz3HqlWrjNdjY2PZvHkzs2fP5saNGzRu3JiRI0dSvHjxRPtfvnzZ7oRCV65coXDhwsbPQUFB9OjRg8jISNatW5dis0JwcDD+/v6JXtu0aZNRE7Bjx45Ewyafe+45bt26xXfffUeZMmUS1VRYPyd3d3djYiRb0hsImEwmGT6Yg+S09yjDB0VWSGuNQF6gqtY6FEApNQHYCtQHjgKZEggopeoBl7TW/yqltgKjlVJjtdbnlVI/A0WAmkCaA4Gcypp10MvLCx8fH1xdXYmOjsbb25sCBQqwdetWGjZsyKuvvsr27bazQefLl4/hw4czefJkjhw5YmT9c3Z2pmvXrrz88svMnDmTDRs28NNPP9GxY0eaNWvGvXv3uHPnDhcvXiQmJoa7d+9y584doqKi8Pb2Nqrb8+fPj7e3N97e3nzzzTfcuXOHFStWJAoQ0iJhEJA0kdK4ceP466+/+Pzzz8mbNy9RUVGJAgFr9X9O+vIXQoislNYagVNAJa11dPzPbsCfWutySqk/tNZVMrxgSr0AfA/8AjQCcgGDAX9gXnww4APsArpqrW0nTU8iJz2x2aoR8PDwwGQyERYWRmhoaKJEREuWLGHQoEG89dZbvPPOOzaPGRYWRsWKFSlWrBhffPFFsifjf//9l9jYWD755BO2bNmSbMIiHx8fcufOTe7cuXF3dycsLIywsDDu379PeHg4kZFxs0g7Ozsze/Zsnn/+eYBkT/wJJawR+Oyzzxg6dKjRJ8Dd3d2YzfDbb7+lVatWDBkyhDlz5hAWFma3zV9rjZOTk91zSo1AzntaTo+c9h6lRkBkhbTWCGwEDiqlrOPVWgGfxrfd/53RhVJKtQDeB3oA1YFn45sEdhA38+FcpdR0oCjgggPkNLA+6Vq5u7tjMpkSPS0PGDCAY8eOMW3aNEJCQpgyZUqyDn5eXl4MGzaM8ePHs3XrViO9b0L+/v5MmDCB3r178++//5InTx7y5MlDaGio3ad7a9NATEwM4eHhmEymNM9rkNDMmTMpXLiw0TEw4Zf4vHnzKF68ONOmTcNkMuHp6QnEBTfWACmjJQ3AhBDicZOmbzat9WTiOurdA+4DA7XW72utw7TW3TKyQEqpMsCbwHCt9XriagJ6xpfjGLAY+AYYB7waX5Y7GVmGnMBeD/hPPvmEYcOG8cknn9CxY0ebY+w7duxIjRo1mDhxos2MfVZFihShdu3alC1blvz586dp1ICLiwt+fn7pCgIgLlvgf//9x7lz55Kti46OpkiRIsacDPD/TSaZlRtAcg8IIR53aX7E0Vof1VrPi++ln5l1n3eBvlprawq5d4A6SqmX4stxU2u9BGgPtI0PDkQ8Z2dn5s6dy6xZs/jpp5944YUXuHDhQqJtTCYTs2bNIioqirFjxz7SqtOzZ8/SrFkzDh8+bHN9v3798PPzY+bMmXaPYe0wGRsbi8ViwcPDA4vFkmyURUZkEpTcA0KIx122q+uMv9FfBFBKOWut/wM+A8rFv+YSv12ktc+CSK5fv35s2bKFmzdv0qBBg2RzC5QoUYK33nqLH374wZg1MLNFREQwcOBA/ve//xnTDifl6+vLwIED2bVrFydPnrR7nJCQEO7du0dYWBhRUVGEhYUlemq3WCzcunWLe/fuPdTTvOQeyNkCAgJQStldihUrltVFFCLLZZtvN6VUsp5dWmtrTtsjwOtKqbJa6+QT1D+mnJyc7C4mkwmttd3F19eXl19+mUOHDuHr60vHjh25c+cO3t7e5M2bFy8vL9544w3KlCnDxo0b8fLywsvLi8jISO7fv29z8fPzo2TJkpQsWZLw8HB++OEHihQpQsmSJSlevDi+vr52Fz8/PyZNmsSZM2do2rQpe/fu5caNG/j5+REUFMSFCxeMpUmTJvj4+DBp0iQCA5P3AfXw8MDHx4c8efLg6+uLv7+/8dQeHh5u5FqIjY3FyckJNzc3o8bAuqT02eWkzmUiZZcuXUrx92zr+hLC0WR5IGBNGay1NtsKBuLXfUdcRsN+SqksL3NOUqpUKXbu3El4eDgvvfQSwcHBxjqTycQrr7zCwYMHU+wrkNSFCxdo1aoVb775Js8//zzHjx9PdZ8NGzawfv16Ro8ezaJFi3B1dWXZsmU2t/X29qZLly7s27ePf/75J9l661O6k5NTon8jIyON/gLu7u5GkCBP80IIYV+WfkPGt/v/GZ+wyGYwkODnTcBMrbVMHceDtX8//fTTbN26lb///ptOnTolmjyoTZs2aK3t5h5I6u7du7Rv3x6LxcJHH33ErVu3aNCgAQsWLLA758HZs2cZOXIkdevWZdy4ceTPn5927dqxfv36RIFJQh07dsTHxydZsJDS07q1psDaw1+q9IUQInVZ9i0ZP/RwCDAciFZKrQcjGEjYPT1X/Ou/aq3T/tj6mHvQ3vJNmjRh0aJF7Nq1i8mTJxuvlylThqeffpq5c+dy8ODBFI9x+/Zt2rRpw6VLl9i4cSN9+vTh0KFDtG/fnkWLFrFkyRKb+3344Yd4eHiwcuVKY+TBwIEDCQ0NZcqUKTb38fb2pnHjxuzbt8+YFMnNzY39+/dTunRpOnXqxIwZM/j++++5c8fhBo0IIUSGybJAQGsdBvQmLkfBKMA9QTAQC6CUqgR0UUq52z2Qg0r49JtQSjUF/fr1o1+/fixYsIB9+/YZry9ZsgQPDw9eeuklPv30U5v7Xrx4kb59+/LPP/+wYcMG6tatC0CePHlYtmwZZcqUSdYh0ercuXM0btyYJ554wnitatWqDBo0iEWLFvHFF18k2+fOnTvs2bOHGjVqGPkT5s+fzwcffMCzzz7LsWPHGD9+PM2bNydfvnyUKlWKYcOGcf/+fSM4etBRAwlHIzzsaIPsolixYnY7ygUEBGR18YQQ2cCDTyeXgbTWV+P/G6qUGkDcLIfrtdavKqWeAUoBm7XWkVlXyuwpaYIhK2tNgVLK5qQ7c+fOZe/evQwcOJBffvmFPHnyUKFCBfbu3cuwYcNYunQpJ0+eZMKECeTKlQuAP/74g7Fjx+Ls7My3335L1apVkx23VKlS/Pnnn8lej42N5dq1axQtWjTZug8//JBLly4xf/58ihYtSu3atY11CxcuJCIiglGjRhlZAEuXLs2YMWOMbYKCgjh+/DjHjh1j//79LFy4kMKFCzNmzBijw6B1AiJr8qWUmgqsOQNMJpMRBNj6jHOSwMBA6fwohEhRtmlA1VrfBgYAMUqpf4DPgV+11jeztmQ5i7WmwJp1LykvLy+WL19OUFAQw4YNM24Svr6+rFy5kuHDh3PkyBG6d+/O8ePH+f777xk6dCi5c+dm+fLlNoMAgJIlS3LlypVkkx3duHEDs9lsMxBwcnJi5cqVlCxZkvHjx3P27FkATp48yY4dO+jSpUuKw7vy5MlD48aNGTNmDNu3b+fll19m4sSJRudF62cBJGpGsVdTYM0ZYB2NILkDhBCOINsEAgBa6yDgBHHTHr+itb6WxUXKcaw1BdYc67aWChUqMH78eHbs2MGaNWsSDalr2bIlS5YswdXVlcGDB/Puu+9Svnx5Fi1aRP78+ZMNw7Mu1lkJDx48yNWrV43FWktQpEgRm/t5enoyadIkPD09GTlyJIGBgUyfPp18+fLRpUsXQkJC7A5nvHHjhvH/4OBgZs2ahb+/P126dOH69evGZ2GdqMl6Y0+YLTDh56KUwtPT0xiFIB0NhRCOIEubBpJSSuUGWgBNtda2s8kIgFRvUilVB/v5+fH222+zb98+3nnnHRo1akSFChUAqF27NiaTiZYtWzJ69GicnZ2ZM2cObm5uaK2Nf5ctW8bZs2eZMWMGSimqVImbd+rWrVtG/wHA6OhXtmxZu9XszZo144svvuDFF19k4MCB3L17l1WrVlGrVi1CQ0NTfK8JJw/KmzcvixYtom3btrz33nvGVMtOTk6JpiT29vY2mgpsTT5kbUMXQghHkK0eebTWd4FWWusTWV2Wx53JZGLhwoV4eXlRo0YN3n///UQjEHx9fVmyZAmffPIJbm5uxuv379+nU6dODBo0iDlz5rB//34g7onf1dU12RwBV65cQSlFkSJFUixPxYoVWbFiBffv36dBgwa0adMmXe/r+eefZ8iQIaxevdpuxkQZWiiEEP8v230TSsfAR6dEiRLs37+fNm3aMGnSJJ555hm+/fZbu9vv3LmTqlWr8tVXXzF58mT8/f0ZN24cwcHBODk5UaJECQ4cOGDUAoSFhXHgwAHy58+fKJiwp1mzZuzdu5c1a9Y81BP5+PHjKVu2LP369UuUM0EIIURy2S4QEI+OyWSidOnSfPrpp3z//fe4ubnRvn172rdvz8WLF43tLl68SLt27Wjfvj0eHh7s3buXt99+m/nz53PkyBEaNmzIrVu3GDhwIOfPn6dHjx4cPnyYTp06ceLECQYPHpzmMlWsWBFfX9+Hel+BgYFcvnyZKlWqpGnGRCGEcGQSCDzm0jKW3mKxULNmTY4cOcKUKVPYu3cvVapUYerUqUydOpUqVaqwb98+pkyZwh9//GEM8+vUqRPbtm3jzJkzdOvWjaeeeoqFCxdy8eJFevTowe3bt1m+fDkdO3Z8VG+X6Oho+vXrh7u7OwsWLHhk5xVCiJxKHpcec9Ye8mB/TLx1vL2Pjw8jRoygU6dOjBs3zsj616FDBz788EMKFSqUqNMdQPPmzfnhhx9o0aIF3bp1Y8mSJUbq4KJFi1K4cOHMfYNJTJkyhePHj7Ny5UpKly79SM8thBA5kQQCjznrkLmEY+KTjihwd3c3/o2OjubJJ59k3bp1DBkyhKioKOrWrWvMdmhLjRo12LBhA/3796dnz57MmzePevXqJdrG3jwE1nWurq5219nbNyYmJlFNx88//8yCBQvo1asXbdq0SbEzoNlsTnGds7MzFouFiIiIVBMRCSFETqYcLetYtWrV9JEjR7K6GFnK3u/ceuPz9PR84Buf1prr16/TokUL/vrrL5YuXUrPnj2NTn8xMfZnj9ZaGzUNt27dws/PzwgMIiMj7ZbFOpwR4uZBqFy5Mr6+vhw+fDjV95BaIODk5ERYWJhRU+Ll5YXFYsHV1fUPrbXtrEpkv+vLmk9C2JbdPh+l1FGtdbWsLodwLPKYIwzWZoTw8PB07V+wYEH27t1L/fr16d27N0WLFmXgwIHs2LEjxcmRoqOj2bJlC02bNqVgwYI8/fTT7NixI83n/frrr6lZsya3bt1i2bJluLm5PdRcAdZ+FW5ubskSEQE2p8oWQoicSgIBYbCm2LWXnjgtfH192blzJ6tXr+a5555j48aNtG7dmieeeIKePXsSFBRkbHv//n0++OADSpUqRefOnTl79ixvvfUW7u7utGnThs6dO6f65N65c2defvll3N3d2bZtGyVKlOD27duEhISkO6CJjIwkJCSEqKioRPkG4gMC+wUSQoicyF4a2sd1efbZZ7Wjs1gsKS4ZecyIiAi9a9cu3bdvX+3q6qoLFy6sv/rqKz1hwgSdK1cuDegWLVrodevW6bt372qz2azDwsL02LFjNaBnzJihIyIibC4zZszQgH733Xd1ZGSkjomJ0cHBwTo6OloHBwdrs9lst7yxsbE6Ojpa379/X0dHR+vY2FhjiYiIsPl6bGysBo7oHHR9xf2JC3uy2+eT2vUliyyZseSoPgJKqRaAk9b66/QeI7u14WaF1H7n6Unmk9oxIyMj2bdvH4MHD+bChQsAtGrVilGjRlGtWjXMZnOiTnlaa1q3bs1PP/3EoUOHKFWqVKLjnTt3jho1atCwYUO2b99us8yp9RFI2gcg4TonJ9stAM7Ozim24Wa36yu7tYFnN9nt85E+AiIr5JhRA0qpJsBM4I107Nsf6A/YnAVPZL6oqCjKly/Pjz/+yLVr1zCZTDz11FOEhoYSFRWFn59fou2VUixevJiKFSsyYMAAvv/+e+PGbjab6d+/P25ubixZsiRdgYt10iMvLy+jD4C1s6Srq6vdQEAIIR43OaKPgFKqAbAM6Km1/lEp5a2UyquUSlP5tdZLtdbVtNbV8uXLl5lFdVhmsznFxcPDA29vbwoWLEjVqlWpXLmy8ZqrqythYWGYzeZE1VWFChVi2rRp/Prrr3z88cfGsT755BN+++03pk2bRoECBTCbzcTGxhISEkJsbCxms5mYmBjjZ1uzHkZERBAWFobJZDICDGs+hchIyXIthHAcOaVGwBfwAe7Gz1C4jriyn1dKfae13palpcthMmNmPa11imPu3dzcbM434ObmRmhoKCEhISilkiU96tOnD19//TUTJ06kdevWAEycOJEWLVrQq1cv41xhYWGEhoYaxwgPDycsLAyI6+SXtGzWGQgTDjO09ZoQQjzusnUgoJR6Fvif1nq7UmoQ8DMQCswGdgGdgOZKqT1a67AsLKrDsz5NQ+IMhtanb+tN1hZbSY+slFIsWrSIZ555hr59+wJxwcPixYsTBTRJj2G96VuDgKRlM5lMeHt7JzqXrdfE4y0gIMBuYBwQEMClS5cebYGEyALZNhCI7xj4ETAA+ElrvVkpFQ2U1lovi99mCfAV4A9IIJCF7N3MrTfhlG6y1mmB7SlcuDBz586ld+/eAKxatYpChQoZQwttZQBMWLuQUqAhHFtKN/rMqDkTIjvKloGAUqooMAvop7X+WSnlrLWO1Vp/lWTTJoA7cbUEIgvZu5lbb74Pk5sAoHv37uzbtw+AV199NdG6sLAwgoKC8Pf3x8fHJ81lE0IIkU0DAeKyt/0dHwQUAobF9w3YCJzUWt9WSg0EBgNdtda3s7Kwwj7rTTi9be4Wi8VIDLR06VJpuxdCiAyWXb9VLwGuSql2wGrgMnAF6AVUj98mDOiitf4rC8on7EjLtMcPIjIykqCgIIKCguymKfby8iJ//vzy1C+EEOmQ7WoE4ocEauAnoC7wn9b6k/h1w4AewC6t9bqsK6Wwx16nwZSStlgsFrvtsa6urvj7+wNxsyMmPI71/xld9Z8wiJEZCIUQj7tsEwgopZy01mattSX+523AcKC8Uqq11no7cBWIVEq5aa2jsrC4IglrAh57Q/BsBQLWm6y7u7vdm6yrq6vNYYfWc9oLIDIqIZC9wCa7KFasGIGBgXbXBwQEPMLSCCFyoiwPBJRST2mtz2itzdZgAEBrfUEpNQvoAnRUSnUCqgKdJAjIvh5kCJ51tkPInjdZyP4jDgIDA7NVilwhRM6TpYGAUuolYLNS6iutddeEwYBSyqS1vqyUmge4ERcEnNFaX8nKMouMY725uru7Z3FJ7JMRB0KIx12WNXoqpbyAIcRV/0crpdYDxAcBztYmAsBLa31fa/2TBAGPl4cdUfCoxfcdyBmFFUKINMqyL7X4TIC9iRsSOApwTxAMxAIopSoBXZRS2feRUTiM+FELMhuREOKxkqVPN1rrq1rrUK11EHEZBD2swYBS6hmgFLBZay2zwDiAjB56mNHimzLMWV0OIYTISNmmmjM+KdAAIEYp9Q/wOfCr1vpm1pZMPCrWzoP28gVYZVXAEN+EkT2jFCGESKcsHzWQkNY6SCl1AngRaKK1vpbVZRIZI6W87dZ11jTEnp6exmu29ks42iAzJgnKKX0WhBAiI2Srb7z4NMItgKZa65NZXR7xaFmHHqZ2I/b09MTX1/eh5y8QIiXWmQltLcWKFcvq4gmRYbJbjcBdpVQr6RMgUuJo0wWnlDRIEgZlHpmZUDiKbBUIAEgQIERikjQo+7HWFthbl1IQIUR2k+0CASGEyO6ktkA8TrJVHwEhhBBCPFoSCAghhBAOTJoGRI6V3qmNIeUhgtIeL4RwJFIjIIQQQjgwCQTEYym7pytOqlixYnbHrMsQQSFEZpKmAfFYioyMJCQkBOCRTiN88uTJdA0rkyGCQoisIoGAeCxYLBYiIiLw8PDAZDLh7h43YWX8REGPTHR0tN0bugwrE0JkRzkqEFBKBQBuWuszCV5TWh6lHF7C+Qe8vLwwmUyJagKSBgpCZJaUkg0JkR3lmEBAKdUeeAeIUkr9ChzTWq/XWmsJBoT1yd9eDUBERMRDNxXE9zeQKEKk6GGyCkoAIbJCjggElFJewCCgH/A30AV4Tinlr7X+KLUgQCnVH+gf/2No/DTHD8IfCHrAfTJSVp7/cTq3ibRPI2zr3CYgtZ57KV5faZmFMZ2y4vck58x40jNUPHIqJzxIK6VyAduB0Vrr35VSPkADoAnwi9Z6cyaf/4jWulpmniO7nl/O/Wj2e1hZcV45pxCPh2xdzamUelYp5aa1vgdsAkYrpUpqrUOAn4HTQM2sLKMQQgiRk2XbQEAp1QL4FKgd/9KXwElgmFKqlNY6GFhHXBOBVKcJIYQQ6ZAtAwGlVFFgFtBPa/0TgNb6GnG1AjeAuUqpusBLgAsQkslFWprJx8/O55dzP5r9HlZWnFfOKcRjIFv2EVBKFQdmaq3bK6UKASMAL+ICgVtAHeBlQAHvaq2PZVVZhRBCiJwsuwYCCtgGrAEGxP8/H3E9ajdrrb9VSnkCsVrr6KwrqRBCCJGzZbtAQCllAjQwHCgK+Gmte8evGw48p7XunGUFFEIIIR4j2SaPgFLKSWtt1lpb4n/eRlwwUF4p1VprvR24AoTFjySISs95/P39dbFixTKq2OIxZ7FYMJvNODk5YTKZOHr0aJDWOp+97R+36yvp+xeZK6Xr63G7tsSjldK1leWBgFLqKa31Ga212RoMAGitLyilZhGXPKijUqoTUBXolN4gAOJmeTty5EjGFF5kqZRqs7TW6U7ek/C4SVMTm0ymwJTK9LhcX9bPwFZq5vR+tqnVPkpWPVBK2b2+HpdrS2SNlK6tLA0ElFIvAZuVUl9prbsmDAaUUiat9WWl1DzAjbgg4IzW+kpWllk4lqRzFjgaR3//QjiCLKvri08bPIS46v9opdR6gPggwNnaRAB4aa3va61/kiBACCGEyFhZFghorcOA3sBGYBTgniAYiAVQSlUCuiil3LOqnEIIIcTjLEt7/2itr2qtQ7XWQcQNE/SwBgNKqWeAUsQNF4zMynIKIYQQj6ss7yxopbW+rZQaAMyMn73NBNTXWt/M4qKJDJDejmLR0dHJOqtZWSwWTCYTkZGRuLq6Eh0djbu7OyaTyWaHtoQd35ycnNJd1sdBRr3HpJ0J7R3Xup27e1zlXmRkpPG7sq5P+Pu10Ukz3e9FOiEKkbJsEwgAaK2DlFIngBeBJvFphYUDi4iIIDQ01LixJwwIlFJERkYSHByMyWTCYonrVuLl5YVSKtkNICIiguDgYAC8vb3tnlNuHGmfLjmtn2nC7QDj/9aOiEqpRDf/iIgIQkJCEm0jhMgc2SoQUErlBloATbXWJ7O6PCJj2RqKlhoPDw8g7qkvNDQUSHxjsK53c3MjKirK+NkWT0/PRP+Kh5fWz9T6e0n4+0n6u0p487e1fVqk5xoTwtFlq0BAa31XKdVK+gQ8nhI+Fab1Kc86fM1isRg1ArbWAzg7p3w5m0ymFJ9axYNL62dqMpnw9PQ0ahNs/f4T3vzTO2wxPdeYEI4uWwUCABIEPL7S+5QHMp7dEWTE7/hhrjEhHFW2CwTE40tu5iKzyTUmxIOTRjQhkrBYLISFhRmdDx9njvRehRC2SSAgHgmLxUJISAghISHpuuk8yhuWtZ05IiIi08+V1bLqvWbU7/Nhryvx+ChWrJgxWijpIpM1pUyaBkSGSO1LODQ0lJs341JC5M+fP1H1rcViSbGjn8lkIiIigrCwsERVvw8zFj6lfa1j3XNCO/PDjqFPT5t6eid7MpvNxrqwsDBCQ0Mxm83G7zOl3A5RUbbnGQsPDycoKAilFAULFpRmAQcWGBho99qUIcEpkxoB8Uh4eHiQL18+8uXLl64brIeHBz4+Pri5uWVazYD1KRXiepzL8LOMYevp38PDA29v7we+Fn7//XcqVKjArFmzsFgsuLu7kydPHvLnz58jAjchsiP5phOPjFIKT0/Ph7rBWseaZ0ZVtrWaPDLScQauPIqmAes5wsPDjYDAWrPzINfC77//zksvvcT169cZP348bdu25c6dO3h5eeHj4yOBmxDpJH85ItNZLBZu3779UDechMlmfHx8MuXpz8PDA19fX6NpIKn4J9ps/zfzIO3v1vecmU/T1nNAXBNReq+Bfv36ERERwe+//06fPn3YvXs3o0ePlr4BQjykbP+lJnK+iIgIYmNjcXZ2TtMNx3oji42NJTw8HIvFgoeHB3fv3iUoKCjTqu1Te0qNv4HZb8jOJh7kKT89T+YPynoOT09PvL29jead2NjYB2rmGTNmDGazmfLly7NixQpKlCjBa6+9Zrf/gBAibaSzoMh0Hh4exs08LTcc69O/9SahlOLWrVs8//zzuLi4cPLkSfLkyWNz37ROLJTe9wGYM/SgmSCzk+qkN42vNSCwdhS0BnmQtiyAr776KlprPvroI8qVK8fHH3+Mq6srbm5u6X4vQggJBEQ2lHD+gMjISLTWtGvXjpiYGO7fv8/w4cNZu3atzX3TOglOesTf9LJ9PXRmJ9V52DS+DzI/RFLdunWjXbt2iWYudITZIoXITBIIiDSLjY1NdUpgW6wzCILtyWksFkuiL3Nrp0KAmJgYBg4cyPHjx9m0aRNHjx5lxowZtGjRghYtWuDn55foWAlvMultOzabzcZshkmny81u7L1Ha9ntdc601rSkh7OzM15eXri4uBAbG5toXXR0NE5OTlgsFg4fPkyePHkoXbo0EHf9uLm5Jfr9Wv9N6808MjIy2bWktba7v9Y6xemNZViZEBIIiAeQ0tSwKc0Zb50W2F4AERUVhYuLi839Fy1axNatWxk/fjzNmjWjYcOG7Ny5k5EjR1K7dm1y5cqVaHsnJye8vb2xWCwpVmGndAOw3iAiIyOz/VS49t6HtexKKZtltzVNc0IpfXZOTk42j2mxWDh48CBfffUVX375JVeuXMHHx4etW7dSr149nJ2d7eaLsFgshIaG2g1crNX/Li4uuLi4JNruQWoEZFIiIZLLno85IluyjuV/0LbnlDqkpdSx7YcffmDChAm0atWKN998EwBXV1cWLlzI7du3GTt2bIrnfdihhul9v9nBw5Y9rR0OLRYLv/76K2+++SYlSpSgcePGLF26lCpVqrBo0SLy589P+/bt2b9/f5rOFx4enuJ21tkO01tD8yhGSQiR00ggINIsM3qY2/tiDg4OpkuXLjz11FMsXLgw0dPrM888w8iRI9m8eTOfffZZisd+mJvho+hRn1ms0/5m1g0zJiaGFStWULZsWZ5//nkWL15M1apVWbFiBf/99x9bt27ltddeY/fu3Xh7e9OhQwejdiWl89lqOspIOfl3KjKHpCbOgYGAUup5pVT7rC6HyBj2blgbNmwgKCiI+fPn4+Pjk2y/UaNGUbNmTV577TWOHj1q99i2vvRlop3UPwN7n11MTAwrV67k6aefZuDAgfj7+7N69WquXbvGF198QadOnYycAQA//vgj169fp3r16ilWxT/sk754PKR0U86sG7M1NbGtJTAwMMPPlx3lqL86pVQb4GPg8Z8NJgc7e/YsM2bMSPEJMCVaa5YuXUrlypWpVq2azW1cXV1Zv349+fLlo3Xr1ly9ejXR+pRudKlVe+fkQCGtZY+MjLT7Gdg6RkxMDMuXL6dMmTIMHDiQfPny8fXXX3PgwAG6deuW6OZv9emnnzJgwAAaNmzI1q1b03yTj42N5e+//07TtuLxktJN2ZFuzI9ajgkElFKewGvAIK31N0opD6VUmsaHKaX6K6WOKKWO3Lp1K3ML6iBs3SzMZjNz587lmWeeYezYsVSvXj1dX+iHDx/m+PHj9O/fP8UObdab0f3792nTpk2i9uWU+gekVu1t7WiX1r4F2en6Smu/CHd3d7ufgTVQunfvHidOnGDhwoWUKVOG/v37kz9/frZv386BAwdo3ry53d/PvXv3GDFiBLVq1WLLli1pbp65evUqL7zwAuXLl6d3796p9hlIKicHcUJklZw2asAbCFJKFQBWA7FKqdvABK213VBRa70UWApQrVo1hx90rLW22ys8NjbW7pd7TEyMMTTs7t27xMbGYjab8fT0ZP/+/YwdO5ajR4/SsGFD2rVrx4QJE6hevTqTJk2iX79+dtusY2JijF767u7uLFmyBC8vLzp27Mjdu3ftJgaKjo6mZMmSrFy5ks6dO9OpUycWLVpE7ty50VobWexs3RRSGrZm7aGe1ptXVl5fSctvTY/s7u6eYm/6mJgYnJ2diYmJAeDUqVOcOHGC06dP8/fff/P3339z4cIF47OrVq0as2fPpkmTJty5c4fbt2/bPK7ZbCZv3rx88skn3Lt3j/Hjx6OUwmw2ExkZiZOTk81r4MqVK+zbt4/hw4cTHh5O27ZtWb16NQcOHOCTTz6hUaNGdt9LZGSkccxr164RHR2Nv78/Xl5exsRE9ti71q2fnb2/Exl2KB4n2bpGQMX/tSmlTFrrcGAHUAeYCnwOdAcUMDnLCplDPUga2oSz8plMJqKiooyUwW5ubsyfP5+WLVty5swZZsyYwbx58/D19WXbtm2ULVuW0aNHM2zYMGM4W9LFZDIZ48Nv3LjBZ599RqdOnfDz88PT09PuYr1Rt2zZkmnTprFz506qVKnCunXrgLibfVRUlPFlbi2/dXigvcXFxQUfHx+cnZ2N7VMaHpmVkpbP2dkZb29vnJ2dcXJysvseTSYTWmvCwsKYNm0aVatWpVevXsycOZPTp0/z9NNPM3r0aFatWsXvv//Ojz/+SNOmTRPtHxoayvLly/nnn3+M87u4uBAREcGCBQto06YN9erVw9fX18hrEBkZmawsZrOZGTNm0L17d/z9/Vm2bBlly5ZlyZIl3L59m9atW7NmzRq7T/pKKbTWfPzxx5QvX57XX3+diIgI42YdHh5uTI+cdEnNo5iUSYgsl1J7TFYvQO74f03x/7YBvgK2A2USbPcDUCgtx3z22We1o7NYLDo2NlaHhITo2NhYbbFYjCU6OlrHxMQkWu7du6f/++8/fefOHR0TE6OjoqL0vXv39F9//aVr166tAd2gQQN94MABvXHjRl20aFEN6MaNG+tDhw7pXr16aUDXqVNHBwYGarPZnGiJiooyjjl//nwN6F9//VXHxMTo69ev61u3btlc/vvvPx0aGmosv/76q37uuec0oGvUqKH379+vr1y5oq9du6avXLmig4ODjXMmfM+2FnuAIzobXV/pfR+RkZH6zp07esOGDdrJyUm3adNGHzlyRN+/f19HRkbqkJAQu8vJkyf1mDFjtJ+fnwZ0sWLF9IULF/SNGzf0jRs39MSJEzWgjx49apQjNjZWBwcH65iYmES/+3///VfXr19fA7pz5856wYIF2tfXVwM6ICBAr127VtesWVMDulOnTvr69evJrp8TJ04Y12HNmjW1Ukq/8MIL+u7du/rOnTv6ypUrOiQk5IE+n4TltvV3kllSur4c4bsr7paUvvUprQsICNCAzSUgICDd5clJUrq2svxmb7dg0BaIBNolef1V4BdgIFAcaA0cBPzSclxH+GNKTUo3jujoaB0dHa0PHDig+/Tpo+vXr6/r16+v69Spo+vVq2f8XK9ePe3h4aH9/Pz0ihUr9JkzZ/Tbb7+tAV20aFE9cOBA7ezsrAMCAvQff/yhN2zYoL28vHT+/Pn1sWPHjC/xe/fu6f379+ulS5fqESNG6MKFC+vKlSsbAcmDBAKhoaE6ODhYL168WPv7+2uTyaSnTZumo6Kikt2EJBCICwQKFy6sixcvrm/evKkjIyONxV4QsG3bNuNG3bx5cz1z5kwN6G7duunr16/rixcv6ly5cukWLVokK0tsbGyiG/jJkyd1vnz5tKenp547d65+5513NKArVaqkFy5cqAsWLKhdXV316tWr9dtvv62VUrpixYr66tWrxu9yzZo12sPDQ+fKlUsvX75cR0RE6KVLl2qllG7QoIEODAy0eSNPayDwoJ/rw5JAIHMCgfRylEAgW/YRUEoVB4YCC4GlSimltd4KoLVer5QKB8oC0wF/YIDW+n6WFfgxcf/+fdavX8/y5cs5fvw4Xl5eVKlSBZPJhJOTkzUQA+KqY9u1a8eUKVMoXLgwly5d4r///gNg+fLllCxZkps3b7Jjxw7MZjOdO3emYsWKvPjii7z00ktUrFiRU6dOcfnyZeOY7u7ulClThpkzZ6a7DdZkMvHqq6+SO3duOnXqxN27d1PMaOfIlFK4u7tz8+ZNTp06Rc2aNVPd59y5cwQHB7N8+XJatWoFwOXLl1mwYAFeXl689dZb+Pv7s3//fg4ePMhzzz1n91jvvPMOMTExHDp0CB8fH6ZNmwbEzTJYr149AgMDmT59OuHh4UyePJmKFSvSpUsXtm7dSq9evdiyZQt9+/alXr16rF69mkKFCgHQo0cPnJycGDRoELVr12bTpk3UqVMnAz4xIR5T9iKErFyI6xTYJv7/LwLBQHsb2/kDeR/k2I4QVacm4ZON2WzWBw8e1L1799aenp7GE9nHH3+sb9++naiJICIiIlmzgXU5e/as3r9/v3ZxcdHdunXTf/75p/by8tJt27bV58+fN54Cjx07psuWLasrVaqku3TpoqdMmaK3bNmiT506pSMjI5Md90FrBEJDQ/WNGzd08eLFdcmSJRM1ByRcpEYg7sn/3LlzumTJktrd3V2/+OKL+sMPP9QHDhzQ9+7ds1kjcObMGa2U0mPGjDGaAq5fv6779eunAd2/f38dGBioS5UqpX18fPSiRYv0+vXr9fr16/W6deuMxdoENGnSJG02m3VgYKA+ffq0Llq0qC5atKj+9ttvtbu7u27UqJHRnBQTE6MLFy6sW7VqpZcvX66VUrpx48b69u3biWozrMtvv/2mS5QooZ2cnPS0adNs1gpk9Of6sFK6vhzhuwupEcg0KV1bWX7Tt1swcE7w/+YJgwGgKvH9Bx50cYQ/ptRYv8wOHDigq1evrgHt5eWl+/Tpo3/99Veb/QTSEgicP39ed+3aVbu4uOg+ffpoQH/55ZeJAgFbS1RUlN3jpicQGDhwoAb0Tz/9ZPectr7cE7YH25PdAgF7bdhpDQQiIyP12bNnda9evXTp0qWNdlNfX1/dvHlzPXXqVP3LL78kCgaqV6+uy5YtawQCSYOBYcOG6cDAwETHs7X4+/vrO3fuGIHA5cuX9ebNmxOV4dChQ/ry5cvG723o0KHa2dnZCAJCQ0N1eHi4zUAgMjJSX79+XXfo0EED+sUXX9Q3b96UQCAbk0Ag86R0bWWb+lKlVG2gpNZ6XfxLlvjXnbTWu5RSHYH1SqlWxPUNeOyzC8b97uyvs1V9bonvIe/q6mq3l/v58+eZPHkyGzZsoFChQsyePZsOHTrg6+tLUFAQN2/etLmf2WzG39/f5rrQ0FBcXFx47bXX+Oyzz1ixYgWVKlWidOnSREREcP36dbvvJTQ0NNnkQVYhISHJZhhMWB6z2ZzotV9++YXFixfTv39/6tevb/ectnqfh4eHExISgsViyfApjFNjqzwJ2ftdWsustU6Wnjc2Ntbu0EvrqA+AvHnz8v777+Pt7c29e/fYv38/e/bs4bfffmPXrl0AzJo1iwEDBgDQuHFjPvzwQ44ePUrJkiWNY7755puEhYUxb948IiIi2LNnD1euXDHWR0dHJ8oSmTdvXpydnQkLCyMsLAx3d3eqVatG586d2bRpE++99x7+/v7ExsYSFRUFwCuvvML8+fN54YUX+OyzzzCZTNy7d89u009sbCwrVqygdu3ajB07lsqVK7N69Wpq1aqF1tputkPrDJT2WGentDW00N7fppUMPRTZjUrpZvPICqFUE2AbsBP4UWu9MP51pbXW8cMHLUqpxcQFAI201ifSc65q1arpI0eOZFjZM1N6AoGwsDCCg4Px8vJK9iVnNptZsmQJ7733HmFhYQwZMoRRo0YluundunXLbr53s9lM/vz5ba4LCQnBxcUFgE6dOrF7926WLVtGhw4djHLZExISQt68ee2uz5s3LxEREXTp0oVLly7xyiuv0KFDB0qXLp3oMwgLC6Nq1aoopTh69KjdAAJs33itX+xubm52byxOTk5Htda20x2S/usrvYFASlNDx8bGpngzs75Hi8VCeHh4ojwPZrOZiIgI7t69S//+/fn555/5888/KV26NCdOnKBKlSqMHj3amAzK6u7du8yePZtly5bRv39/3n//feN3FBUVRe7cuW2WJSYmJlFuB+uQQFvX+NGjR6lQoYKR7yE4ONi49pKKjIw0ru8///yTnj17EhgYyIQJExg6dCg+Pj42b+iWFKbVhribufVvzdfXN9Hf2sMEAkopu9dXTvruSi/r7z0961PbNzPKk5OkdG1ll4HRRYjLBbAMeEYpNRji6mQSBAHVgQo8RBDgCGxlzbNYLPz0008899xzDBs2jEqVKnHgwAEmTpxo88n3zp07LF68mIYNG9K5c+cUn+ZtWbduHXfv3uWVV15J9Pr169dZuXIlPXv25M0332TJkiXs3buXa9eupfjHFhERQZs2bfj6669xd3dn0qRJlC9fnipVqjBlyhROnToFxHU+u3DhAsuWLUvTFLOWJFnobOXXT7hN/HaZ+jeTtExJf07KWmYg3Rn1UsrzX6hQIZYtW4arqyt9+vTBYrFQsGBBatasyfbt25Ntr5Ri8uTJ9OvXj6VLl9K7d2++//57oqOj01we643S3g3z2WefNYKAB1G5cmX2799P69atee+99+jYsSNBQUE2szGm9rmDzGSYFQICAuzmxggICMjq4uVc9toMHvUCeMQvHYBFwJAk672BAg97npzUzmavfTK1dmGLxWK0sUdHR+vevXtrQBcuXFh/+umnOigoSN+7dy/RcvfuXb19+3bdsmVL7erqqgFdtWpV7enpqfPnz6+XL1+uL168aLct9tatW8mOaV3Wr1+va9SooZVSGtDFixfX/v7+idqLfXx8dLVq1XTXrl31zz//bPQDuHbtmm7SpIlWSulVq1Zpi8Wiz5w5o6dMmaLr1q1rHNO6vP7668Z7T4nZbNbBwcHJ8gtYO6VZ/59wm+DgYA0c15lwfdk6X8KfQ0JCHmi/pH0v/vrrL71s2bJE/T9SEhISkmj8/apVqzSg582bp69fv66nTp2qAb1///5EfQX++ecfo8/AqFGjjKGGvr6+un379nrLli369u3byToh3rlzx+61ldpy8+ZNffv2bb1nzx49YcIE/dtvv+m7d+/qu3fv6mvXriU7V3BwsJ47d652dXXVTz75pP7jjz+SDS+9f/++zWsjLZ1NY2Ji0tVvQ2vpI0A2a5PPbuV5GCldW1nWR0ApVYu4m3uo1vo3rXVE/OvfEpctsGF8vwBPIFJrvQkIzaryZifWbGdAqk++58+fZ+XKlXTr1o2PP/4Yb29vY1+A27dvs3HjRlavXs358+fx9fWle/fudOvWjTJlynD69Gn69+9P3759CQgIYPjw4XTv3j3NbegbNmxgyJAhFC9enDfffJNWrVpRqlQpIK7m4cyZM5w4cYJ///2Xf/75hy+++II7d+4Y2QGDg4PZs2cPffv2pWfPngCUKFGCYcOGMWrUKG7cuMHYsWPZtGkTAO3atSMsLCzVpzTrE76Xl1eK21rXJdjGbHfjDJD0fNZ/7TXXJGzOSFJOg9aanj17cvToUU6cOMH777+f6nS/Sc9ftGhRAE6cOEHHjh05fvw4gJGiOCmllJFRct++fXz99dfs3LmTrVu34ufnR4sWLXjllVdo1KgRrq6uKZbFnqCgIL7//nt27NjB3r17jbTH+/bt44svvrC7n1KKvn37EhERwdtvv82BAwd45plnEm3j7u6OUipdT/vWCZ0g9b9PIbIFexFCZi7EjQI4CywmLlXw+iTrvYHngd+B+8AzGXXunBRVp7dGIDY2Vt+7d09HRUXp//3vfxrQa9euNZ4Eb9++re/du6fPnz+v8+fPrwH93HPP6UWLFumTJ0/qK1euJFoCAwP14sWLddWqVTWgc+XKpd988039v//9T0dERNitEfjkk0+MxC7nzp1Ldlzrcvr0aaMGoH///trNzU1fuHDBeK1u3bq6QoUKNpMf7du3T/v4+OhSpUrpXLly6datW+vLly/rq1evarPZbPeztVcbkLRGIOlCJo0aSGlURXrfh7VGYNu2bRowRoj07dtX3717N8XyJPycz5w5o/PkyaPLlSun7969q8ePH6+BREMIk9YI2FrOnTunt27dqrt27WpkJfTz89Ndu3bVmzZt0sHBwSk++V+9elVv375dv/3227p69epGbZC/v7/u2LGjXrZsmR4yZIgG9B9//GG3RiAkJEQHBgbqAgUK6EqVKumwsLBkNQLW/8fExNjMiCg1ApmDbPYEnt3K8zBSurYeeY2AUspEXHbAD7TWq+JnFfxaKfW11roVcZ98qFKqGlAUqKW1ljlJEzCZTHh6etptQ42IiCA0NPXKk/Hjx3Pnzh12795tJJOxNXues7MzrVq1okWLFly4cIEFCxYwd+5cZs+ejY+PD+XKlaNUqVKUK1eOcuXKUbZsWX7++WeGDBnC888/z6effprmtus2bdqwdOlSdu/eTfv2cQND2rdvz/Dhwzl9+jRly5Y1tj148CAtW7akQIECfP/996xZs4aJEycyePBgihcvjpeXl83pccHmk36OlNr70Frz/vvvU6xYMfbu3cu4ceOYP38+Hh4ezJs3L9Ue7KdOnaJdu3YAbN++nZ9//pmpU6fy8ssvM3LkyAcqq6urK82aNaNZs2ZERUXx008/8eWXX/LNN9+wceNG/Pz8aNWqFe3ataNevXqcPn2aw4cPc+jQIQ4fPsy5c+eAuCf6GjVqMH78eJo1a0apUqWMGpHatWuzcOFC1qxZw4QJE+yWZezYsdy+fZudO3diNpuNKbOTPsFb+w/YWmdPwn4bQuQEjzwQ0HEd/44T3+lKx00m9IJSao9SarXWulf8ppFAi8chCIgLxuyz92Wc0s3TbDbbHRrm5uaGp6cnrq6uxvA6s9lMbGwsENdccOTIET799FN69epF7ty5OXPmDBBX3WqvB//Vq1fx9fVl8ODBvPLKKxw6dIhLly4RGBjIzp072bhxY6Lt69Spw8cff0xYWBj//PNPiu+lWLFiABQuXJj8+fPz2WefUadOHZRStGnThhEjRvDZZ5/xzjvvAP8fBOTLl4/t27eTJ08eevfuzdy5c1mwYAFz5swB7H/2OenL2hq122NvJsXw8HC+++47jh49yvz584mJiWHSpEnExMSwYMEClFLMmjXL5vV3/fp1Zs6cycKFC/Hy8mLDhg1cv36dbt26UbZsWUaOHJkoK6RVSgHof//9l6hJqWjRogwbNoxBgwbx66+/cujQIbZt28b69esT7ZcvXz7KlStH06ZNKV++POXKlUt0nNOnT/PEE08AcUFrgwYNWLduHX369MFkMiULknbu3MmmTZsYPXo0FSpUMEZMJJypMjo6GmdnZ1xcXPD09MTFxcX4+7Gexx6t7Y8aSO27QIis8MgCAaWUt9ba+i3xNzBHKbVfa30m/rX2xKUTrqS1Pq61/uRRlS27SulpzTqDXlKW+FnerL3frcGCk5OT8eUVHR3N9OnTKVy4MP369UsUUJhMpkRjvROKiIgwbjr+/v60aNHCWBcbG0vRokU5c+YM//zzDxEREfTs2dP4Ek7pizM2NjbRTblFixZs3LgRi8WCj48PTz75JLVr12br1q2MHz+e3377zQgCvv32WwoXLgxAnjx5eOONN5gyZQojR45MNMbd1ueXnnWZJaVzpnTzsHcdQNzvfMaMGQQEBPDqq68av+fp06cDGMHB3LlzjXVaa7Zu3cqoUaO4evUq3bt357333sNisfDCCy/g5+fHzJkz7Q4DBOzmhLCmoE7K1dWV6tWr06VLF6Kjo/nll1/4448/KFOmDFWqVKFQoULJRq4k/EwsFkuim32PHj3o1asXBw4c4MUXX0x0bd25c4eRI0fyzDPPGDVisbGx+Pn5JbpGTSaTMaTQVn+Y1IYHSq4AkZM8km88pVRrYIVS6jOlVAtgNzAL2K+UKgOgtb5HXEcs23chYVfCoU62hkIltWbNGgIDAxk3blyKc7U/qDx58vDcc8/Rs2dPBg4cmO5q9xYtWhAdHc2PP/5ovNa+fXv+97//sXr1aqM5YMeOHUYQYDVo0CBy5crF3Llzs+W0wY+StTZg9OjRicbZK6X44IMPGDp0KIsWLWLEiBForTl16hTNmjWja9eu+Pv7s3v3bubPn4+Pjw/du3fn9u3bbNiwgXz58j102Y4fP87WrVuT1SC4urrSqFEj3nzzTV566SUKFy6c6KYaERHBL7/8YnQMtOX555+ncOHCbNiwIdm6ESNGEBQUxIoVK7BYLEZipaTXqnVabOvfUVqGEwqRU2V6jYBS6inihgN2BKoB9YGmwNvEDfn6Uim1CMgFVAauZnaZHjcJ2zGtX2j2xlmfPn2a1atX07x5c2rVqpXuc968eZP79+9TunTpdB/DnipVqlCwYEF27txJmzZtgLiMciNHjqRfv36UKlWK77//njx58iTb18/PjyFDhjBlyhT++OMPqlSpkuHlywm01nz44YcEBATQtWvXZOutzQIQVzNw8uRJDh48iLe3NwsWLKBLly44OTkRFhbGyJEjOXToEKtXr6Zy5cpGMxLAyZMnyZMnT7KAzJ6wsDAWLVrEli1bAFi8eDFdunRJlnMiqbt377J27Vq+/vprI3lV48aN6d27d7KmLCcnJzp37szs2bO5fPmyMRnR119/zcaNG3n33XepXLmykXnRVjKmpKMGEva7ySlNSkKk1aNoGnAHftFaHwAOKKWqAq2AKcBo4DJQAniSuLkELjyCMmUL1if4hBnd0iNhhzFr27fFYkFrzeLFiwGML8vJkyfj6ur6wB29rG7evMmmTZvYvXs3sbGx1K1b13gKzygmk4mWLVuydu1arl69StGiRSlUqBDNmjXj/PnzfPfddzz55JOEh4fb3N/6Xr///nuHDQQuXrzI0aNHmTZtmt2sewn7CMybN4/XXnuNqVOn4u/vz3fffcenn37Ktm3bCA0N5e233zaCMqtt27bx7rvvAtCgQQO6du2abBielcVi4dtvv2XlypXcuXOHjh070qRJE9atW8fSpUsJCgrio48+srmv2WxmzJgx/O9//6Nhw4Y0b96cQ4cO8fXXX3PhwgUWLVqUbJ/27dsze/Zsvv/+e2MGxA0bNmAymejXrx9hYWG4ubnZvakn7UPyKDqXPoqEVULY8igCgdNAKaXUYK31Qq31MRVX19cVeF5r/d0jKEO2lDAfwMPktrfV8U1rzahRo1iwYAFDhw6lSZMm3Lhxgy+//JL27dunmNLXlqtXr7JixQp+/vlnAJo3b07evHn59NNPOXbsGD169KBixYp2OzA+qF69erF27VoWL15sfJFv3boVZ2fnFM+xfv16Ro4cSaNGjXj99dczpCw5iTW4tGbyS60a3xoMvP322wQHB7NkyRLWrVvHhQsX8Pb2pm3btnTp0iVZ7ZE1CKhZsyYVK1bk888/56effqJEiRIMGjSIdu3aGdfkb7/9xrvvvsvJkyepWLEis2bNonz58gDMnDmT2bNns3nzZrp06WJzKuRVq1Zx4sQJJkyYYPRLqVevHvny5eOTTz7h1q1bRm4Kq0KFCuHm5paob8HYsWPZtm0bo0aNYs6cOWit05zD4FF0Lo1vhsiYPyAhHkCmBAJKqRqAC3FzGfyilHob6KyU6qy13qS1Pho/v0AP4McUD/YYSy1ZDPz/F3vS6kt7r8P/BwHz589n6NChxlPf6tWriYmJMapho6Oj6devHyEhIZQoUYISJUrg7+/Pc889R7FixXBzc+Pq1assW7aMzz//HK01L774Ip06dTLmHGjYsCELFixg8eLFHDx4kOnTp1OhQoWH/mwKFSpEhw4d2Lx5M5MnT6ZIkSKpppVdv349gwYNomHDhixcuPChy5ATWYPLyMjINO9z7Ngxxo4dy969e4G43+no0aNp1aqVzZvfN998w+TJk6lZsyYLFizA3d2dAQMGsGvXLtatW8fo0aOZMmUKXbp04cqVK3z99dcULlyYd955h1atWiXrSDd48GD279/PqFGj2L17d6K/h4sXLzJ9+nTq1q3Liy++mGi/OnXq8Mknn3Do0KFkgYpSivz58yeaQKtKlSq89dZbTJkyhZdffpm2bdum+TOysv7d2UrJnPBvMj0Bcfz3QaYmrBLClgyvhlJKNQO2Ay2BdUqpgcTVCvwANFdKDY3f9Er89g+eNPwxYSu3fVL2Ov9FRkbazI8eGhrKyJEjkwUBZrOZZcuW0aBBA2Oo3pYtW/jrr78oVKgQFy5cYPXq1UyfPp22bdvy7LPP8uKLL9K8eXM+//xz2rdvz9y5c3njjTcSTTz0xBNPMHXqVMaMGcOVK1do0aIF77//foqTDKXVoEGDgP/v5Z6SzZs3G0HApk2byJs3rxFoPY4dvey9J2v++7R0AtVas2jRIurVq8c///zDxIkTOXfuHN999x2dO3e2GQR8+umnyYIAiOuT0qZNG6Md//nnn2fZsmXs2bOHsWPH8ssvv9CoUSObvek9PDwYM2YMgYGBiX7XZrOZN998Ezc3N956661k+5YoUYKCBQty8OBBm++vQIEC3LhxI9Fr48aNo2LFiowcOZJ79+4Zr6f1GrH2FbDVGdcahKXUUTcl8d8Dj89FKnKMDKsRiK/udwW6AEO11puVUluAmfHn+Yq4joCzlVL1gepAa611VEaVIbtKaShRWsaIWzstJTyOm5sbWutEY59DQ0MZPXo0y5cvZ9CgQUyaNMloR9+9ezeBgYFMmjSJXbt2ERsby+LFiyldurTR9hsbG8tff/2Fp6cn169f5+rVqxQvXpwmTZqQJ08e48Zsi3Wa123btrFkyRK2bNlC586dqVixIgA1atSwu+/58+e5du2azc+tRYsWrFy5kjfeeIMnn3zS5udz9OhRBg8eTP369dm0aRN58uRJNITrQVIy5xQP+55+/fVXZs2axY8//kjt2rWZMGECuXLl4vbt29y+fdtmLZW1OaBChQpMnDiR0NDQZL3+jx07RuHChenatSstW7Y0hqP+/vvvTJ8+3ShrTEwM0dHRxs+lSpWia9eurFq1ivr161OtWjXWrl3LkSNHmDp1KtevX7c5+VWZMmWMQCBpjdETTzzBqVOnktWOLFq0iAYNGjB8+HAj90XSxEHOzs42A3QfHx+cnJxwd3dP9ndrfS2lIEyGFYrsKMNqBOKzGEYBp4ibQdBba/0HMBxoB7TUWv8I1ADGAtW11scz6vyPK2tClIiIiERPK0lnnTObzbz33nssX76coUOH8sEHHyT60lm5ciUFChSgZcuWAOzdu5eIiAiaN29ubOPs7EzevHmpXr06rVq1YsCAAXTq1Mlm73xbvLy86Nq1K6NGjcLNzY2FCxeydOnSRE9eD6p79+4AzJ071+b6mzdv8sorr5A/f35WrlxJTEyM8VlZn/Aex1nirOPbE8yKCKStaeDPP/+kb9++RvZHa4bIlCTsEzBmzJg0zf7n5+dn87h37tzh2LFj/PHHH9y5c8d4fdiwYTz55JO89957nD59mvnz5/P888/TqlUru+d45plniIqKslkrYKtGAKBSpUqMGjWKzz77zJhB0cPDAx8fHzw8PFKsHUg4U2NaZq8UIifIjD4CJ4DWQEml1P+01n8ppUYBm5VSR7TWR4HzmXDex1ZKT3/WJ5lx48axaNEiozkgYY/6y5cv89133/Hmm2/i4uLC/fv3OXDgAJUrV07zsK/UJHw6KlmyJG+//TY//PAD33zzDadOneLu3bu0b9/+gdtOCxQoQPfu3Vm7di0jRoxIVCsQExNDp06duHXrFr/88gsBAQFGG23Sz+xxqQmwMplMmEwmgoODEwWF1mDn/v37yfbRWrN8+XLeeust/Pz8WLRoEU8//TQfffQRW7duxcfHhyeeeIInnniCokWL8sQTT1C4cGHOnTvHvHnzjOaACxfSN7DHbDZz7tw5rl+/bmS+PH36tNGnxNPTk/fff5/evXvTvXt3XF1dee+991J8ii5btiwuLi7s2bOH559/PtG6AgUKEBISQlhYWLLf/+jRo9mxYweDBg2ibt265MmTJ1FgHRISglIqxU686Uk/LER2lGGha3zTAFrrb4mbJXAYUCG+ZuAosAvpCJMuKT3Ruru788EHHyQKAhJ+cZrNZsaPH49Sip49exIdHc2XX36J1pqmTZtmWBmTVhE7OzvTrFkz3n33XQICApgxYwY7d+5M17GLFy9OTEyMMfbcavfu3fz888/MmTOHqlWrJnoic3d3f+xqAZKydV1YPwNrb/iEc0ccPHjQyLi4YsUKKlWqxBdffMFnn31GxYoVKV26NGfPnuXHH39k9erVfPjhhwwZMoSPPvqI4sWLJ+oTkB7W6v1ChQpRuXJlypcvj4uLC+fP//9zQfXq1enatSuRkZG89dZbifqj2OLm5ka5cuX44Ycfkq0rWLAggM1mJ1dXVxYtWkRQUBAffvhhonXW2oG0zNBorUUQIid7qEBAKVVGKVVLKeWS8Fha69FAEDAAmKyUGgm8DNx7mPPFn9NTJbjTKQdodLNX5ai1ZsyYMXaDAK01Y8eOZfv27UyaNInChQszePBgzp8/z8svv5ximtgHZe+JKF++fMZEQQ86miAyMpIPP/yQd999lzp16vDqq68mWm8dgmZroiRHqKa1Tj5l6z0WL16cZ599lkWLFhlTBVepUoVGjRpx+vRpfvvtNwAaNWrEk08+yZ9//snhw4eNfPqFChWibt269OzZk+LFi3Pnzh1jSGJ6Wa8RX19fTCYTrq6uKKWSNTOMGjWKjRs3ptgkkFD58uU5f/58sj4EBQoUALDZtwDimgiaN2/O5s2bbTa7pXbtOMI1JhxDuq9gpdQrwDbiEgOtAF5XShlTvWmtxwCbgUtASaCJ1vrSwxQ2fkTC58BCpdTY+PM8drN4pKUHs70hggnNnDmT5cuX88YbbzBkyBDGjRvH1q1badKkCdWrV8/QMtv7MrRYLOzfv5/q1atTvHjxNB/v4sWL9OvXj2+//ZbRo0ezbdu2ZGPiixcvTuPGjVmxYoUxuZKIo5Ri3LhxBAYGGh3i3N3d2bRpEw0bNmTatGns2LGDfPnysXTpUubOncu7777LypUr+fHHH9m9ezeLFi1i1KhRTJ8+nfv377N06dKHKpOfnx9ubm7GE3pERASRkZHJklG5uLhQsWLFNHesK1euHBDXATIhayBgq5+AVYcOHfjvv//4/fff0/o2knkcR6UIx5KuQCC+BqAT0Edr/QJxAUERYIxSys+6ndb6J631PGDYw84iGB8EzAVWEhcMNFBKvZjyXjlTah2+tNaMHj06xSBg1apVfPDBB3Tu3JlJkyYxd+5clixZwqBBg6hXr96jeBtAXApaayY5K7PZnOJIiW+//ZZ+/fpx9+5dZs+ezTvvvGN30qL+/fsbfSBEYk2bNuXZZ59l5syZRq2Ah4cHmzZt4tlnn2XKlCns2LGDvHnzUrt2bV566SXKly+frHanXLlytG3blg0bNhAYGJju8iileOKJJ7h//z5hYWFGJ9KHrZkKCAjAx8cnWSCQUtOAVatWrXB1dU3W7PQg0jK/hxDZ2cN0FvQFSgMHgC+JawpoSdzwwcVKqeeAvFrrb3jIvgFKKR+gGTBKa71TKeUENARSbkD8//37A/0hburTRy2lm57FYkl2E7e2wzo5OSWa+hTibqIjR45k0aJFDB48mPfffz/ZF9D27dsZOXIklSpVokmTJowaNYqVK1dSq1YtatSowY4dO4wbQ1Kurq42O5qlha1OZN9++y0+Pj7Url2b2NhYzp07x+uvv05wcDCenp7GUC0/Pz+8vLyIiYnhzz//pHLlyrz77rvkzp3bblnNZjMtWrQgf/78LF68mDp16uDu7o7JZEJrbTe1bmoetJIp6fVlb//0tmIlvQasLBaLMYWurRqZ6Ohoxo4dS8eOHVm/fj09evQA4n7HkyZNYsKECUyZMoXY2NhkyXqS6tu3L7t27WL69Om0b9/e7jVy7949/PzingVu3ryJyWTC398fiGvqcXNzQynFxYsXMZvNuLi4EB4eTlhYWLIbeUInTpywu65gwYJUqVKFffv2cfXq/09VYh1ee+XKFZvXkNlsxtfXlyZNmvD5558zbdo0AKOjba5cuezWdCX8HVv/Xq3DB1OahhhkCKHIftIVCGitY5RSc4A3lFLntdb7lVK/AIWBlkqpVUBRYH/89umqvldKqfhhiSFKqTXA9fjXzEqpy0ADYE0ayrsUWApQrVq1bNWUYGvKUicnJ7y8vIiNjU3W5p8wCJg+fXqyffft20efPn0oVqwYvXv35vDhw6xatYqnn37a6IR19epVu09hN27csDuR0LBhw+jQoYPNdb/99pvxBGZ17do1AgMDadeuHc8++yxms5khQ4bg4uLC6NGjCQ0NJTg4mODgYEJDQwkJCSEmJobx48fz1ltv4ezsTHR0tN00sGazGVdXV3r27MmcOXM4e/YspUuXxsvLK1OmgrWXCz7h9VW1alVtsVgytN3Y3vuIjIwkLCzMbvpbDw8PmjRpQrVq1ZgzZw59+/Y1gqM6deqwc+dOOnfuzLRp0yhUqBDdu3c3RnXYCj5Gjx7NpEmTaNiwod28EPnz56dq1ap8/fXXTJo0idy5c/PTTz/h5eXF0aNHcXNzw8XFhb///huLxUKFChVo3Lgx4eHhKY4oSSkltpubG7Vr12bfvn2EhYUZTQIQ10clKCjIZsc/s9mMyWSiQ4cOfPPNNxw+fJhnnnnGmNnQ1dXV7qiBhL9f65BCq8ewtVI85h7m22o/8B3QXSlVX2tt1lpvBAoBAVrrzVpr+3VyaZPL+h+t9XGt9Y0EQcV94qcsVkp1VUoln2LtMWLtE2DtGJg0TwDEPTV17tyZ4sWLM3jwYAIDA1mxYgUBAQH069fPbvV6epw6dYotW7Zw8uRJu53IfvjhB5ycnGjYsCEQN9Pc4cOHmTlzJpMmTWL27NksW7aMdevW8c0337Bv3z6OHj3K+PHjH6isffr0wWw289VXX2VqD+605II3m82PrIrYw8MDLy8vu+85KiqKsLAwxowZw6VLl1i3bl2y/a19Bt544w0KFChArVq16N27N7NmzWLHjh2cO3fO6H8xcOBAAgICWL16td0+GVpr5syZw+uvv07JkiW5fv068+bNS7RN5cqVjfb0gICAh/0YAIw5Cg4dOpTo9fz586fYNADw0ksv4erqytatW/Hw8MDf3x9/f/9URw0I8bhIdyCgtY4ENgDHgXFKqf5KqZ7EVdenr245AaVUW+CaUqpd/M+G+E3OAaeUUs2BkcAfD3vO7Oztt99OsU/AjRs3aNu2LT4+Pmzbto2IiAgWLVpEnjx5eP311x9q2FdSR44coV+/fsybN49BgwbRqFGjRFWyEHcT2r9/PzVq1MDPz4+QkBAmTJhAkyZN6NSpU4aVBeJSzZYvX57Nmzdnag/utOSCt05r+yhYk03Zes/WREOenp60bt2aokWL2kzK5OHhwWeffcbSpUsZMGAAhQoV4vfff2fq1Kl0796d6tWrU6xYMU6ePIm7uztvvfUWly9f5pdffrFZpq+//pqPPvoIX19fRo0aRdmyZVm+fLnxlA0YTQUARYoUyYBPIq4fg7e3d7JAoECBApw+fTrR+ZPy8/OjadOmbNmyhaioKHx8fPDx8ZHRAIKAgACjdtHWYk3XntM91JWutb4LLANmAI2Ia7d/VWttv5tuGiiligNDgYXAUqVUexvNC2bgHeADoKfW+tTDnDM7++ijj5g1axYDBgywGQTExMTQv39/goOD+fzzzylQoAArV64EYMiQIQ81s6EtAQEBiZoWypQpk6zn97FjxwgPD6dBgwYAXLlyhdDQUF599dUMrbK31pT873//o3Xr1hl2XFvSkgvemugnKyTsvR4REUFERARKKUaPHs3ly5eNrJJJubu706VLF6ZOncrnn3/Or7/+ypAhQ4z3Ub9+fYoUKYLWmi+++AIXFxdKlChh81jPPPMMjRo1Iioqit69e3P69GlatWpl9BuwGjp0KMOHD8+wANXJyYnSpUtz6dKlRK937dqVe/fu0aJFixSDgaFDh3Lt2jVmzZqVIeURj4dLly4Z/T5sLQ/TeTY7SbH+VSkVAiS8Aav4nxVxTf++Wuto4Cel1L741zJiDM0t4COt9Tal1PfAZ0optNZblVKm+DKEA38CnbTWZzPgnFnO1oyCmzZtYvTo0bRr14558+YRGRnJyZMn+f333zlx4gTHjx/nf//7H9HR0cyfP58KFSowYcIELl26RL9+/RI9fWWUfPnysW3bNsLCwvD29kYpZYxLt/rll1/ImzevkUPA2kM8acDwMBIOoRw4cCBTp07NsGNnVynNOmmdEAfinvStyaQ++eQT3njjjWSJc5K6f/8+ixYt4pNPPjFunmPHjuWZZ54BYOnSpXz//ff06dPH7pN8sWLFWL16NaGhoRw/fpwiRYrY7KCbGcFS4cKF+fPPPxO9VrNmTbZu3Ur79u1p0aIFO3futNnfoGHDhnTs2JEZM2bQpUuXZNMaJ2TtpGmvNkaInCbFQEBrnXIC8sTbZthAbq11qFLqm/j/f6uU6khcimJrMFBFa/2HUqqh1vqhmyEehZS+wK2SpsX94Ycf6N27N/Xr16dNmzZUq1aNU6dOGe2zefLkoVKlSgwePJjatWvz4osv8tNPPzFnzhzq1q1L1apVM+39WCeTseX+/fv89ddftGjRwniv1veV9MkwvazJkj755BMGDhzIiBEjjOlhH1cWi4Xbt28bHfkSdhC0NgVYc+VrrXnvvfeMIGD27Nk2a2K01pw9e5bPP/+chQsXcu/ePVq2bMmYMWOMAADgf//7H++99x5NmzZNdYQBgLe3N3Xq1MmAd512Tz75pDGhVsI+Jk2aNDGCgTp16jBnzhxatGiRbP/p06fzzTffMHr0aL788ku75wkPD3/gCZ8edopiITJTmntkKaWqAnWJexr/JX5CoQyjlKoNlNRaW3s0WeJfd9Ja74oPBtYrpVoBpZRSL2utk6eUy4a01olu8gk7IVl7LkNc72cfHx/c3Nw4cuQI7dq1o2jRojRt2pTXXnuNEiVK0KNHD5566in8/PwoXbp0oi/3Xbt2MWDAAIoUKcLTTz/NX3/9ZbM8UVFRieYiSLru9OnTNteVLFnS7uQ/+fLlM7589+3bh8Vi4emnnyYoKMg4rvU9Ju1MFxISYrdjVkxMTLIvzoRBwBtvvMGkSZOM8zyOrK1iERERxk3OOlQtNjYWi8XCvXv3jHVms5lx48axZMkS+vbty7hx44xqca0158+fZ8+ePfzxxx/89ttvRmbGpk2bMnz4cCpUqMB///3HqVNxrW1RUVH06dMHLy8vhg4dyj///GP393X48GFjGJ41D781YMyVKxeFChWyuV9MTEyKnfoSTk6UlNls5sqVK8ZImxMnThgjB6zXXZkyZVi1ahXvvvsur7zyCo0aNWLMmDGJEmv5+voyevRoJk6caMyeaYu1D4itGQgBm8MHE/79P87BqsiZ0hQIKKXeAzoAX8S/tFoptUVrPSUjCqGUakJcUqKdSikfrfVCrbUlwVBBU3wwsBVoDzTKKUEAYEwjDCSbTthkMhk/W4cNnj9/npdeegkfHx8GDBjAe++9R8mSJVmwYIHxBPLPP/8kymRmsViYMWMGISEhTJ06NcVcAIULF6Z+/fo21w0dOtTu+Pu9e/fardqvWLEi/fr1A2DNmjWULFkyUadAa+cyf3//ZMd3dXW1O0rAmorWytocYA0C5s2bZ+QMsDee/kE9bB+GjB62aP1svL29jc6I1tnvzGYzMTExxpj8XLlyMXLkSCMImDJlCkopjh8/zqJFi/j111+NTHsFChSgbt261K5dm3r16iXK/Jgw+Fq0aBEXLlzgo48+Il++fNy7d8/uZFUpTVX9/PPPM2DAAJvrzp49m+4JsO7cuUPevHl56qmngLgndmv1/+3bt43fR/Xq1dm+fTtr1qzh448/pnXr1owcOZKhQ4caf5+DBg1i48aNjBs3jjZt2tjs+GlvuKaVraGr1sBJRiLENR/Za1vPqFEk4sGk9VuzC3HTBk/QWk8AngO6ZWA5igCTiet4+IxSajDEdTiIDwIsSqnqQAXiggD72UWyKVt5ya1tjQlv6Ddv3qRFixaYzWZGjx7NBx98QO7cuZk5c2aKXz5fffUVhw8fpn///nY7ciUVERHBli1bOHs29S4WWmssFosxJC04ONhmrcKtW7c4fvw4TZo0SfS6NTDx9fVNtk9aJewTkLC6O+HUsI+zpNdQwj4Bvr6+5MmTh7Fjx7J48WKjpkQpxeHDh3nllVf4+eefqV27NjNnzmTXrl0cP36cxYsX06NHD5vpn81mMwsXLmTz5s107NiRWrVqPVT5MzsNtLW2IekIloRcXV3p168fu3btolGjRkybNo1atWqxa9cuY/2MGTMIDAxkxowZKZ7vQVILO8o1mhaBgYF2O98l7ewpHo20Ng1cAtwBa85bNzJwKmGt9UqllDX09gUaKaWGaK0/TtD58BTQ7mFHJGQnSacxtVgstGnThitXrvDNN9/QrVtcrDVnzpwUE6pcvnyZFStWUKtWrTRP1PLvv/8yZMgQbt++ja+vL2vXrk3WsfDmzZuJZoaD5BP8FC5cOFFV5+eff47WmsaNGyfa7t69ezg7O6f7ieju3buMHj2aNWvWMGTIELtt3o7E2iHQWkPwf+2dd3hU1dOA30lCSEICUkVQQGlWpCoooIgoghRp0lHAT6VLtcNPQUQURZoKUgVFqSogIAgCIoIFUBSQGloglBTSk/n+uHfXTdjdtN1sAvd9nn2SvW3OuXvuuXPmzJkZP348U6ZMsStJkZGRHD16lM6dO1O6dGmWLVtmf1leunQp0/v3xhtv8N1339GmTRsGDRqUaXncvYDBiHORmprqtTlym0PqyZMnMz32hhtu4N133+X5559n5MiRdOnShW7dujF16lQeeOABnnjiCSZMmMDzzz/vMgOiu/TgFhYFiawqAonAX6YHvwLNgK0i8iGAqmbeS2RARBoAoUCsqm5X1Xhz+xqMVQlNTL+AECBBVb/ASG981RAcHIyq2s2Pv/zyC7/88gszZsygSJEinD59mjFjxmRprbWqEhISkq2Xo23+NDo6OsfR0GzyVJUPP/yQefPm0aRJk3QjzOTkZJYuXUrt2rWz/fJOS0tj7ty5vPzyy5w/f54XX3yRN95445pXAuC/7IPeuhe2F/bRo0eJjY3NNCdAZm3I0R/Gkxw+fJgpU6awadMmwsLC0jk5Zkbjxo2ZNWsWjRs3dpucyBmO030WFgWZrCoCy82PjU25EWoGAZoCbABKi0i8qnYH+4qB1UAERnyCW4G8y5KTh9g6clXl8uXLfPPNN/j7+9OhQwf7nPqNN96Y6XUqVKhA586dWbhwIY0bN6Z+/fqZnnPTTTfx8ccf8+2333L33XdfkdkPjKhsjqOhQ4cOUbRoUVJTU0lNTaVw4cL2ueqff/6ZkydP0r59e0aOHJnuOt9++y3Hjh1j0qRJmZbLkd27dzN8+HB27txJgwYNWL16NTVr1szWNa4lXnzxRU6dOsWUKVPs3ytVqsTixYt58sknadeuXTqrQGa89tpr1K1bl7FjxzJnzhyGDh3q9vjM5vjvvPNOjyotBw4cYN68efz000+EhYXRv39/unXr5nI1izPi4uJ45plnKFmyJB9++CEAmzdvZvny5bz++uuUKlWKy5cvO13tk5mvgIVFQSFLioCqZhrPP6uYcQC6A2+p6hwRCQG+EZFvVLWVKS9WROpi5CtokNvMhfmdqKgoxo0bx5QpU3jooYcoUaKE3cya1TgAXbp04aeffmLy5MncfvvtWTqnYsWK9O/fP8vlFBH8/f3TmXZTUlI4efIkCQkJvPDCC3Tr1i1dZ5+cnMzs2bOpU6cOjzzySJbkXLp0iXHjxvHpp59SokQJZs2aRY8ePaz51UwQET744AMApkyZQnx8PGPHjqVu3bpXKANZmaIREVq0aMHOnTtZuXIlffr0ydXyT08FDzp48CBz587lp59+IjQ0lC5dujBw4MBsKQA2Ro0axf79+1myZAk33HADSUlJjBw5kkqVKjFgwADL/G9xTZDVVQOPYzjzVTTPsQcUyq5A0/FvN6ajoqrGAU1F5HsRmauqT5mHJgAtCooS4M4smpyc7HIktH37dvr378+ff/5Jly5dePvtt4mLiyM8PBw/Pz9SUlKumJcHuHz58hUZ1YYMGcLQoUOZPn26U+cvG0FBQaxfv97pvujoaJdOXTYvdRuJiYmcPn2a1NRU6tevT+vWre0+DzZWrVrFqVOn7KMt231KTk7m0qVLXLp0iVOnThEXF8elS5c4efIk06ZN48KFC/Tp04dRo0bZp0au1Xzv7uqdkpKSTjGzKQMBAQFMnToVgP/973/UqlWLzz//nC5dutCuXTumTZtG5cqVXV7XNm0E0LFjR1avXs3ixYvp1asX//zzD3v37s1RXWxLEjNiawOusC0H3LVrF+PGjSM4OJhu3brRsmVL+4oJZ2m7bYmtnPH111/z2WefMWzYMB544AG7c6TN0uA4FZJxqWBm0yCu/CCshEQW+ZGsTg18ALQD9uYik2Coqtrm+PcBk0Rki6oeMLd1wAgnfLeZYGhaTuT4CncBg5wtJ0pKSuKtt95iwoQJlCxZki+//DJdkJMzZ85QokQJl6P7qlWr2uNcx8bG8scff9C7d29Onz7N22+/bY8l4Izdu3e7XAZYqFAhp9MEANWqVeP1118HYNu2bfTr14/ixYvzySefULFixXRZ32x1XLx4MTVr1qRt27aICOHh4Tz88MP8+++/TmUA3HfffUydOtWenOZa9wdwV/9ChQo5tZTYLANTp04lODiY9957j+bNm7NmzRpatGhB//793U4TOG63KRErV65k/PjxfPXVVy5fdEOHDuWhhx4CjKkkf39/ezs9dOiQSwtXVFSU2ymLGjVqcOTIEbvF64svvrCvQElOTnbp0FemTBmnlogDBw7wzjvv0LhxY8aOHUtAQAAnTpzg3XffpVWrVnTp0sVeR09bArISXMzCIi/JqiIQDvyZCyWgNdDN7NDmAWuBd4EtZubC/ap6SURSMTMKFjSyY0L87bff6NOnD3/++SedO3fmnXfeoUSJEumOOX36tMsXsiOrV69m6NChnDhxgk8++YSRI0eyatUqli9fTqVKlbyybnnt2rUMGTKEihUrMnv2bMqVK+c0bsGSJUsIDw9n3Lhx9pfZ+PHjOXbsGC+++CLXX389JUuWpFixYhQpUoSyZctSsmRJypQpc82//HNCxheMLW6+zWfgvffeo379+qxevZrHHnssU5+B06dPs3LlSn788Uc6duzIunXrWLhwYZbL487ikF1iY2Pp27cvAQEBfPLJJ1csQ1VVli9fzptvvknJkiV5+umnadeundNrxcXF0bdvX0JCQliwYIE9TsOoUaNITU3lvffe81i5nWFNN1jkN7KqCIzECPazGWMFAQCqmqn3l4hUA2YAnYC6QGPgEeBljBUIy0VkBkbK4ZqA+zVI+ZSseBCrKm+99RZvvvkmpUuXZvny5Tz00ENOR1dnzpxx6x+QnJxMt27dWLlyJbfddhu1atVi5MiRNG3alI8//piGDRuyatUqOnbsmPvKOfDNN98wdOhQ7r77bj799FOXc8bJycl8+OGH1KxZ0z5CDA8PZ/bs2fTs2dM+p1ukSBEuX75MTEwMRYsWddoxWiOorIeodlyOKiK8+eabxMfHM2XKFIKCgnjrrbeoX79+ummCjMrA5s2bef/99/n555/tUfIKFSpE3bp1+fDDD2nRokWehslNTU3lhRde4PDhwyxcuPAKB9qLFy/Sp08fNm3axF133cX58+d57rnnePHFF2nfvj2vvfZauufylVde4cCBAyxatMhe702bNvHll18yevRobr75Zq+a8K3VBhb5jaz2quMwkvwEYYzYbZ+sEIQRknibqk4GvgQuAmOBOcAQIBm4EeigqoezXPp8hLOAQY6kpqYyaNAgxowZQ8eOHdm9ezePP/6402MjIyP5+++/3aa4vHDhAqtXr6ZChQps2bKFwYMHc+nSJQ4cOECNGjWoUKEC4eHhnqhaOmbNmkXVqlWZP3++W8ex8PBwwsPD0zkPbt26leTkZJ577jl7KOXLly8TGBhI0aJFnXaMaWlpREZGEhUVdUVo4muJy5cvExERweXLl10eExwcbI9HYfMrCAkJYcCAARQuXJjvv//efmydOnVYvHgx586do127duliAGzevJnt27ejqgQGBqKqnDlzhuLFi3P27FnvVdIJqampfPDBB6xdu5bXXnuN++6774pj9u3bx6ZNm+jXrx9btmzht99+Y82aNTRr1ozZs2fTq1cve9vZs2cPixYton///vbMmGBY1goXLszw4cOzVK7sBBPKSGZ9xdVApUqVXKbutaIH5j+yahEooapZc/m+kn8wcgP0M0MH/ybGm6Er8ICqrsvhdQsEtg7jpZde4qOPPmLQoEFOUwk7smjRIpKSktym1b3++uuZOHEiQ4YM4e233+b06dMUK1aMe++9lxMnTnDs2DGaNGni8fpERkbSsGHDTKccbCMqx5e7bVtoaGg6S0BYWJjL+OsJCQn2GPrWCMo9thTIMTEx+Pn5UbhwYaKioujcuTNFihThs88+S3d8xtUE33//PaGhobz++usMHjyYrVu3smXLFsqUKUPx4sV58cUXGTduHGfOnMmT+tiUgB9++IHhw4fTu3dvp8fVq1ePYsWKERUVZbdUNGzYkIYNG3LvvfcyYsQIevXqxbx583j33XcpVqwYgwcPTncNW5jqrK5suHz5MufOnaN06dI5Wq1wtWOLHmhRMMiqIvC9iDyS1Ze2iNwDFAJEVbeKyMtAZxHprKpfqOqvZn6BnsDGnBW9YBAXF8fw4cP59NNPs6QEqCpz5syhfv36blOhAvTp04c//vjDPhfcrVs3AgMDWbRoEapKnTp1PFoXVSUyMjJLvgtZwTF5iyts+67laQHAPoLMTBlyNDsnJCTQqVMnDh8+zNq1a+2x+B2pW7cuc+bMoWPHjixdupRevXoBRuS9jh070rFjR2JjY6lbty533nknzz//PKNHj/Z8BTPgqAR0797dbWTDwMBAWrRowapVq0hKSkqXm6Jjx44UKlSIIUOG0LZtW/744w9GjRpF0aJFnY7mbVMwQUFB1pSUxTVDVhWB/sBIEUnEMOO7XD4oIo9iOATOBrqIyARgDUbwoOYiUkZVPwROAreKSGFVTcx4nYKGLTVsWloaCQkJBAUFISK8/PLLfPrpp/Tr14833njjCvN2ZGQkhQsXtn//6aefOHjwIP379yc8PDxdp+ZIcnIyhQoVYtiwYaxcuZKLFy/SuHFjTpw4wdy5c7nuuuv47bffnJ4bGBjoMopaYmKiyxFfiRIlSElJISwszB7j3sbFixfTJROyOQ/GxsZy4cIFypQpYx8hOJquHdPmusJmfcjOCCMzk603O3ZXst3JdFc328oJZ/fB2VJPW8TKp556ik2bNvHuu++yYsUKhgwZwtSpU7nhhhuIjY21r/K47777uOOOO5g3b559Kmfbtm32682cOZOTJ08ybNgwfvnlF44cOeKyrNdff73LTJBHjx51mWymUKFCds//tLQ0PvroI7Zs2ULHjh1p2bKl21UmAC1atODzzz9nzZo1NG3a1L49Ojqatm3bkpyczIgRIyhatCjdu3cnISGB1NTUdM8tGCP9y5cv261Q0dHRxMTEUKJEiXS/X3BwsH1Fgu1cx2ff1XNrYZEfyWpAoTARKQFUxZjzvwLT3B+IkaBokKp+KSJfARNNOSswHAHfE5HGQD2g9dWgBMB/S7wSEhKIjY2154OfOnUq/fr1Y8KECU4tAYGBgekUgS+++IJixYrRrl07jh496tKr2Ba7PyAggA0bNtgTmuzcuZPjx49TtmxZTpw44fTcW2+9lZYtWzrdt2vXLpdmf1v5q1SpcoWnuW2e38bFixcBYxrA1onazreZsG0jrsDAQOLi4pyOvPz9/a+4b44jtaySV6M7W5TI7MixJZ9ydY7jvct4XlJSEoULFyYxMTHd+ePHj2fp0qUMHDiQhIQEPvzwQ0SEHj16sGDBAm644YZ0lp3nnnuOgQMHcuzYMerVq2d/Qe7bt4/ly5fz6KOPUq1aNVJSUihevLjL3BfFihVzaYnatm2by/YcGhrK/fffD8CcOXPYsmULw4cPt1sCbCmKIyIimDFjBr169bLHykhNTeWRRx6haNGirF69Ol3bLlWqFKGhoTz77LNUrlyZwMBAu+9NamqqfcWA7b7ZLC+FChUiICCAuLg4UlJSSEhISFd2f3//K6azbEmgRMSlImCthrHIj2SppxKRvsBm4DtgjPn3dcdj1CARIzlQDTNuwO8YzoDtgZaquhG4BxiFkc1wt4fqkW+wOWy99tprTJkyhUGDBvHWW29lqQM4f/483377LZ06dcrWS65YsWL2F+ZXX31FaGiofd5SVe256nOLzZrhas12Tq4XExPDxYsXiY6OzrIzoG35lbvjMzpz2WR52+FQVe1ysupQFhcXl63627DV6cKFC+nqtnjxYl599VVat25N7dq1GT16NI0bN2bRokVERkbSo0cP+4vVRteuXQkJCWHmzJn2bZGRkbz11ltcf/31PP3009kqW05JSEhg2rRpNGjQ4IrpgIiICPr168eaNWt466230t3XjNMDznj44Yddpt+24ejI5+fnR8mSJQkNDU1nxXJFcHAwYWFhli+LRYEjq0OjwRgj+GOq2gSoBTi3/8EeoCRQWUQCVPVPYDjG1EIdVU1W1UOqmrfux3mEn58fo0ePZurUqVnyCXBk8eLFJCUl0bNnzxzJjo2NZc2aNbRs2dI+womJiSEiIoKTJ0/m2nnHFrmtTJkynD9/nnXr1nHo0KEcp5e1dZzFixd3uWrA1XmZHZ/xxZ9XnbSI2OVkVfkICQnJVv1t2OpUokQJu8zt27fz9NNP07BhQ3r37s3AgQOpUqUKH3zwgd0fwKYMOK4qKVq0KE8++SSLFy8mOjqa5ORkxo8fT0JCAq+88opLZ053uJsKcMWiRYs4e/YsQ4YMSbf93Llz9OvXjwsXLvDkk0+yZ88eVq9ene6Ytm3bEhUVxaZNm7JdVkds1qO0tDS7QhAXF5fp73gtrAawuDrJaotNUNUEAHNO/x+guuMB5tQAqroGI0vgYOBO0zLwK4YVwbsJyfMBMTExzJgxg+DgYHs0vayyYcMG7rzzTm699dYcyT5x4gTx8fHplvXZXt4JCQm5CtGrqhw+fJjAwEDKli3Lc889R7du3ahfvz4VK1bkiSeeoH///kyePJnvvvuOOXPmANjDtMbHxzN37lz8/PzsUQ1tHWdAQEC2OtCsdLgZX/x51UmLSDrHvqwoHzktmy1ple3+AXz00UckJibywgsvsGrVKmJjY+nevbvdQlSrVi27MnDvvfemCzVdunRp4uLiOHToEIcPH2b//v00atTI7TJWV3z99de0b9+ejh078uOPP2bpnL179zJx4kTuu+8+GjRokG7f+vXrOXHiBB07dqRfv34EBQXZE3PZsE1J7N6dPUPjiRMn+Oabb+zTHfHx8Vy+fDmdEhkaGmqN9C2uWrLqLHhCRK7DmOdfLyIXgVMiUh0oAewC0jBf9Ko6QkTeAZ4FEkQkHGiL4S/gMUREchrt0NPYRhFFihRh7dq19O3bl4ceeogBAwbw8ssvXxEJzRkpKSlZOs4V1atXp23btsycOZNy5crZ5+dtc/65CQITHh7O6dOnee+999i+fTubNm1i0KBBVK5cmQMHDrBnzx527tzJihUr7Of07duXBx54gPj4eHr27MnGjRuZPn26x6YW3JEfMsPldRni4+MZOnQou3btomfPnrz//vvs2LGDN954g9KlS9ud6GrVqsWSJUsYPnw4LVu25MUXX6Ru3bpMnDiRrl27UrNmTSIiInjwwQf5/vvvue+++6hXr16WypCamsr48eNZtGgR99xzDzExMQwaNCjTlQYXLlygV69eXHfddfbwyI60a9eO7du3M3/+fNatW0dCQgIdOnRId4wtTsIDDzyQpbKCoQQ0a9aMs2fP2i0MNgfWjEqkK6zVBRYFnaw6Cz5h/jtGRH4AimE4Da7E8P4/CewykwZFm+eMFJEmQA2gGtBMVY/mtsAiUgYIUNVT+UUJgP8chQDuv/9+tmzZwrhx45g6dSqrVq1ixowZNGzY0KtlEBHGjh3L/v372b9/PxUrViQwMDBHZl1Hzp8/z8mTJ6lSpQqdO3emcePGVKlShRdffNG+UiAyMpKiRYsSGxvLwYMHiY+Pp0GDBiQkJPD000+zdetWpk+fTt++fT1RVQsnBAcHU6VKFdatW8cjjzzCkCFDmDx5MlOmTGHgwIFMmTLFrgxUqVKFn376iSFDhjB+/HgAatasyYwZM+yBXwYMGMDx48f53//+xwsvvMADDzxgd65zRlxcHMuXL+fYsWP06NGDoUOHkpiYyLBhw3j99depU6cODRs2vMJKFhcXx5IlS1BVFixYQNmyZa+4dlBQEBMnTmTEiBH88ssvNGnShLp166Y7ZuXKlZQrV+6K7a44efIkbdq0ISIigtWrV9vTd9usOc5e6o4Kv21/xoiOFhYFjWyrr6q6GWM5YHugj6o2xVAIbsLwAyjmcOwPZjTBwZ7IIigiLU3ZC0Xk49xez5M4mg/j4+Pt4YQ3bNiAqtK8eXOGDx/uNjKcp8oxbZqRr+nUqVO5ztgXFxfHv//+S2hoKPXq1WP27NkcOnSI//3vf+mWC9oIDQ2lVq1a3HfffemUgE8//ZT/+7//s0ZMXsQ2ci1fvjzff/89ZcuWZfDgwQwcOJDbbruNQYMG8eeff9qPDwkJ4ZNPPmHOnDk8+OCDLFmyJN2KkaCgIF555RVq1arF+++/T58+ffjiiy+czpVHRETw+eefc/LkSd566y1Gjhxpn7KYMmUKrVu35tdff2XDhg3p2mRSUhIrVqwgJiaGOXPmuI2dYVMG1q5dy5gxY9Lti4qKYuPGjbRp0yZLbezkyZO0atXKrgTcc889WXLstCn8jvfAchK0KOhITgbVIlII+BpYrKpzRcQPaAS0BA6r6kciUh8oqaqrPGHCF5F7gU+A54D9wCygu5nGOLNz/w/4P4AKFSrUya4DE2S+Lt1xlGPzFgfsEfReeeUVpk6dyi233MJXX31FzZo1AWMkbeu42rRpAxgjG4Dff//d5VK+xMREt74EixYtYuTIkTzyyCO8+uqr6cq3f//+K+K124iJibFnLYyKiqJTp07Ex8ezZMkSAgMDeeyxx7j33nv59ttvr6iz42gxPj6eNm3asHHjRj799FN69Ojh0nyqqm47b1d+Fpk1KW8s1RKRX1W1boZt6drX0aNHs12enMY9sHmzZ7y3aWlpHD58mDZt2hAeHs7ChQsZOHAg/v7+7Ny5k0OHDrktj2PmytTUVNatW8fHH3/Mhg0bKFSoEI8//jhPP/00NWvWZMmSJbz00kuUKlWKMWPG2EfWjqgqEydOZMGCBTRp0sSeKvnZZ59l+/btTJo0ic6dO7ssj2Na5IwkJyezceNGnnnmGb7//nvuvffedGXPOEq3TQdERESwbt066tevb09XXLRoUfsqAdt9dLy3tu8hISE5arOZ4ax92ahbt67u2rUrR9fNa0TkmogsWJDq6a5t5Wh4pqrJwCSgnYg0UtU0YCvwB9BIRAoDFYDfzOM9caeCgPWquh0oAtQGJoqIPV2xuHj6VPUTVa2rqnU9FRXPHTZPY9vIoUiRIkyePJkNGzaQkJBA9+7dSU5O9moZGjRoQO/evVm7di3Lli3L8nm2JYyjR4+mXbt2nD59msmTJ1OmTBmmTJlCbGwsEydOdNnRxcTEsGPHDrsSMH36dHusd28u38tN7PfckrF95XVZnN3b+Ph4QkNDWbFiBTfddBPdunXjpZde4uTJk/To0eOKsrkrq5+fH4899hgrVqzg119/5cknn2TdunW0bt2aBx98kKFDh1K7dm2+/fZbqlev7vQaIkKfPn1444032Lx5Mz169GDkyJFs3bqVsWPHZrqsLzOWL19O+fLlM/VlcFQCHKcDbCs3MireGe+ttTLA4mokq86CztiCsXKghzni/xFYJCLPABVV9UuPlPA/koG7TCfEdsBMYCEwT0Q+VdU++clnwFmGsQcffJAZM2bQpk0bPvzwQ4YNG+bVMvTq1Yu///6b999/n++//55y5cpRvHhxUlNTqVixIsWLF6dYsWKEh4ezZ88e9uzZw8mTJwEICwujbt26vP7669SqVYuDBw+yePFinnvuOW6//XZUlZ07d7Jv3z727dvHX3/9xT///MPx48cBo8N09Anwdsa1/JTaNa/L4uze2qILFilShEWLFtG1a1dGjRrFSy+9xBtvvMGNN97IM888A8BXX33F9OnT6d+/P+3bt7crearKrFmzGDNmDLfffjvdunWjQ4cOvP7667z00kssXbqU5cuX8+yzzzJq1CgCAgLsc+Wu6NKlC2XKlGHIkCHs2bOHoUOH0qFDhysiVWaHmJgYvv/+e5555hm3L2hXSgAY7dWZL42VKdDiWiBHUwP2k0WKYyQPehxYjpGieCTwkKo6j2GbvevXAW4BdqrqURGpDZQG+gJPqmqauZphMdBJVaMyu2ZOzWvuRky20KLuHIwcR9BNmjTh33//JTw8PN3UQKdOndi3bx/r1q2jXLlyuZoasIWBjYmJYe3ataxfv54LFy5w4cIFp2bWIkWKcNddd1G9enVatWpF9erV060yGDZsGNu2bePAgQOULFmS1157jbfffhuAwoULU716dW6//XaqVatGjRo1qFOnDhUqVHBZPkc8MTXgzDyeV1MDjtStW1d37NjhdBrEW1MDmV0zPj6eyMhIatasyaOPPmrPR/HNN99QpEgRmjZtir+/P8nJyUyfPp06depw++23s2nTJlq1akX9+vW5cOECBw4coFu3bowePdplfoijR4/al4xm5Pz585QvXx6Av/76i3379tGhQwdEhNjY2CuiVTriamrg4MGDvPLKK/z8889s2LCBe+65J91+x6mBTp06sX79etasWWNXAlw5P+bmeQdraqAgmcxzQ0Gqp7u2lRuLAKp6UURmAvswlwpizNt7QgloDbwH7ARai0gUMNHMXtgRwxrxN/AoxlRB7kPn5QBbmlxb5L7MlhlFRUXxzz//OJ1HHTVqFB06dKBt27bpluHlhrCwMDp06GBfaqWq7N69m5CQEC5evMilS5e4/vrrueWWW/D390/nI2AjKSmJzZs306pVK0qWLElSUhIzZ86kefPmTJo0iVtuuQURcetR7gzHBC+5NbXmh+WCNjxVFk8sS7OVpXDhwqSkpFC2bFkeeughFi5cSGRkJCVLlqRkyZKcO3eOokWLpjPtV6tWDRHh4YcfJiUlhbffftttRszscMcdd3DHHXfk+PzLly8zefJkZs2aRUhICJMnT75CCcjI+fPnqV27NvXr17ff27CwsGzfW1vmwTJlyliZB69xKlas6FLpq1ixIq78hfIbuVIEAFQ1CfhBRH40vqqnJkYfBkap6jIRqQm0xshT8AxG9MKFIrIbqAn0UFXvuuO7ID4+/oo0uX/99RfJycmUK1eOUqVKpTt2yZIlnD171m6WdaROnTp89dVXdOzYkbZt2zJ27FiP5+4WEYKDgylfvrx9dJYZv/zyC3Fxcfa0xqtXr+b8+fP079+fqlWrApmPaJ3huOzK5qB1ra7HdlZ3x/uT2xfOnj17iIuLo379+vZYFZcvX8bPz4+aNWuyfv16OnTokM48Xq5cOerXr8/YsWMJCAigS5cutGjR4orwxHmBqhIVFUVERAR79uxh4sSJnDlzhs6dOzN06FBuu+22bF3P5v3vLGeAhUVWcfeiL0h5JXKtCNhQVU9HDQzAiEGwTFX/EJFzGFMCL5sBi37HcHY8rqqu06F5GdvL3+ZF/Mknn/Dcc8/Z9wcEBFC2bFnKlSvHDTfcwL59+6hQoQKPPvqo0+vVrVvXrgyMGDGCqVOn5ioAT0pKCkuWLKF58+b2iH7Z5YcffiA4ONhuxZg/fz433HADDz/8cI7LBVemIc5P8/x5jbO16J6cn/75558BqF+/vj0hlG1ly4gRIxgwYIBTZeOJJ55g+/btdotAXhAZGcn06dM5fvw4ERERnDlzhoiIiHTTA3feeSczZsygTp06OXK8dXxus0uRIkXw9/e3/AYsrho8pgh4gcnA5yLyr6p+pqonRWQNMFREyqrqFl8XEP4zvcbExPDjjz8yYMAAmjZtSq9evThz5gxnzpzh1KlTREZGcujQIaKionjppZdISUkhJSWFmJiYK0zqt912G/Pnz6dbt24MGDCAiRMnknG1w6VLl1xmFwQjaxzAli1bWLBgAXv37qVbt26A0dG66sTi4uK4dOmS/buqsnHjRurVq0diYqLd0eqFF15IV25XSVls86nOllvZ7p1tju1adsxyVnfb/cl4bzNmX3RlPUlKSrLPYc6dO5dbbrmFsmXL2j3gz549a/+tg4KCSE5Otn+3KQvNmjVj/Pjx3HzzzYgIFy9e5OzZsy4VtTNnzrhcGRIVFeXyt01ISKBEiRKoKgMHDuTHH3+kYsWKlClThtq1a1OmTBn754YbbqB27dr4+/vb25crmampqfbsnrZ2ZpvGExGSk5NJSUnh0qVLFCtWjOTkZLti6pgV1JH8NA1lYeEJ8o0iICL3AZVVdQGAqu4XkVeB/uaqhAWq+ouIBGEsHVzt7nqeJjNT9eHDh+nVqxeVK1dm9uzZ6eL9p6SkuDQ/FitWzGlgHts8bs+ePXn11VdZsWJFOmeqI0eOuDVplipViuTkZNauXQsYKWC7detGqVKlKFKkiD2OQUZiYmLSxRj4888/iYiIYNSoUdx4440sXryY1NRUnn766XSKgKv7YzPBuvLKdiS7Ztr8anrLSbkCAgKybP7PaD1wde9tzp7fffcdv/76KzNmzMDf39/eNosWLerSpG57CZYrV47evXun23fddde5DIUdExPj0lkwNDSUG264wem+5ORkSpQowcKFC9m4cSMTJkygX79+9v2unBPBsGy4Cp/tmGrY9rvYvqempnLq1ClWr17Nhg0bCAoKYvTo0ZQpUybTOAEWFlcT+UIREJFmGNEJV4tImKpON3f9CCgw3sxrcAG4FfjT+ZV8Q1RUFF26dAHgiy++SKcE5IbatWun8xnIqAxkxqZNmzh79izPPvssn376KUuXLuXZZ5/NVhnWr1+PiNC0aVNUlTlz5nDPPfdc4VDoityYYC2ckx3LiarafU26d+8OYH+J52bJnjc4deoUo0aNokGDBumm1zxJYmIiX3zxBZs3b2bz5s0cPHgQMO6JLVPnV1995RXZFhb5lfyi8t4EvIkRG6CGiPQHUNVYM5thB0CAMhjLBI/7rKROGDZsGP/++y/z58/n5ptv9ui1bT4D586do0ePHtk6d9WqVfj7+3Pw4EGKFi3Khg0bshXQJy4ujmXLllG7dm1KlSrFpk2b2Lt3L0899VSWr2EFYPE82bmnW7duZdeuXQwYMMBueSpcuDCFCxdm586dbiP25SUJCQn07duXpKQkZsyY4fH2Eh8fz969e9mxYwc9evRg/vz5VK1alQkTJrB9+3ZOnz7NRx99xMaNGxk1apRHZVtY5HfyRe+sqrOBDzAsABsw0hcPcNj/r6q+oqovquoeHxXTJRUrViQ1NTVXmQPdUbduXW666aZsj6pbtmxJ+fLl2bFjByJCmzZtCAwMdHtOSkoKP//8M+PHj6d58+YcPXqUIUOG8Pfff/P8889z5513ZlshsfAd1apVo3Tp0sydO5e4uP+icQ8YMIAffviBJ598kjNnzlxxnqpy6tQp1qxZw+TJk1m2bBmHDh3ySrTEhIQE/u///o+tW7fy4YcfUrlyZY9ePz4+nieeeIKLFy9SvXp1RISkpCSOHDlCbGwswcHB+Pv7c+7cOQCX02YWFlcrPpsaEJEGQCgQq6rbVTXe3L4GY/TfREQ6ASFAnBciFXqMgQMH8t577zFhwgQWLVrk8esfOHCAv//+254lLqs0a9aMZs2akZiYSKFChVyOss6ePcuWLVvYsmULP/30k31ZVb169Rg1ahS33XYbrVq1okiRInz77bfWcqsCxPXXX8/s2bNp3bo1/fv35+OPPyYwMJD+/ftTpUoVhg0bRsuWLXnzzTcJCAhg79697N27lz179hAZGXnF9cLCwrj11lupVasWd911F3fddZfdkTAnJCQk0Lt3b3766SdmzJjhNt9AToiPj6d79+5s3LiRSZMm0bt3b6Kioli6dClLlixh7NixvPnmm3anyg4dOtCnTx+PlsHCIr/jE0VARJoDUzBG/6VFJF5Vu4MxHSAiq4EI4B0Mn4BGvihnVrnuuuvo168f48ePZ/fu3dx9990evf7XX3+NiPD444/n6Hyb49fly5c5deoUR44cYfPmzRw5coRDhw5x+PBhAMqUKcNDDz3E448/TqNGjezzpu3atSMmJoZFixa5jGtwLccA8AXZud/NmjVj1KhRvP322/z111/MnDmT0qVL07JlS6pUqULfvn3tc/J+fn5UrVqVpk2bcvfdd1OjRg2qV69OeHg4u3fvZs+ePezatYt58+bZpxXq1KnDm2++yZ133pmtOtiUgG3btjFhwgT7qhZPER8fT5cuXdi8eTOffPIJHTt2JDg4mODgYHr37s0zzzzDuXPnWLlyJUuWLKFp06YMGDAg3zqhWlh4i1yFGM6RQCNT4Xxgg6rOEZEQ4BuMUX8rh+OGAcOBpp5IYWwjp2E6M7tPx44do2bNmtx333189tln6fZdvnzZpUd4dHS0yxH2kSNHCAwMpF27dhQtWpS5c+fa950+fdoeX+Dff/9l4cKFJCQkkJKSQmpqKtHR0aSmppKWlkZSUhJnzpxJtywwICCAG2+8kUqVKlGzZk0aNmxItWrVuHz5MjfddBNgeHI/9dRT9jTCTZs2dZu1MCYmhrCwMKdTGK68ugsaWQkx7OkwsKmpV4boiIuLs99vV20rISHB/n9aWhorVqxg2LBhREZG8uyzzzJ48GACAwOJiori+PHjJCUlcfvttxMcHOzWSz8iIoKQkBAOHjzIjh07mDp1KhcvXqR79+60bdvWZaCq6OhouyIZHx/P888/z/bt2xk/fjytW7dO17ZsmQAdy++KuLg4p8mCbErAzJkz6dmzZ7rjo6OjERFKlCjhUpFydw+8hRVi+Oohv90Dt32Xqub5BxgB9Mmw7XtgrsP3/kAtT8uuU6eO5oS0tDS3n+TkZH355ZcV0F9++UWTk5Ptn6ioKI2NjXX6iYqKSnes4+fgwYO6du1aBXT06NF66NAh+2f//v0aGxurCxYs0CJFimhYWJhWqVJFq1evrnfeeafWrl1b77nnHr3//vv1wQcf1KeeekrHjx+vy5Yt0z179mhcXJxTmUlJSXro0CGdOnWqNmrUSAGdNWuWvZ6uSElJ0ZiYGE1JSXF6f64WgF3qhfblDmf30/F+Z+e8yMhI7d69uwJao0YN3blzp9N2kJqaqqmpqZqQkKD79u3TmJgY+7bExMR0x549e1b79eunfn5+WqpUKZ06daqeP39eL168mO5z7Ngx/eqrr7Rr165arFgxFRH96KOPNCYmRqOjozU1NVVTUlJ08ODB6ufnp7Nnz7bLjImJcfm5dOlSuvJER0dr06ZNVUTStV3He3f69GkNDw/XmJgYl8+0L3DXvrzRtryF8Wq5tslv98Bd28rLl3+ow/8tgf1ANYdt1wFfAnd7sxzeUARSUlL00qVLeubMGb3uuuu0devWHlMEhgwZoiKi27dvT6cI7Nu3T4cPH66A3nPPPXrw4MF017V1oOHh4bp06VJdtWqV/vLLL3r06NErZMbGxuqaNWt08ODBWr16dcVYsqk333yzTpw4Md3LPbP740ohuFrwhSKQUyXL3e+xdOlSLVu2rAYEBOirr76qly9f1ri4ON25c6d+/PHH+uyzz2q9evW0cOHCCqifn5/ecccd2qNHD500aZJu3rz5ina0c+dOrVevngJar1493bx5s0ZEROgXX3yhXbp00aJFiyqgxYoV065du+qaNWvsL/Po6GhNSUnRQYMGKaA33nijiohdGciqIpBRCUhKSnKrSCUlJbm8t77AUgSuHvLbPfC5IoCRJ2Cx+WmB4ZvwDIYfQHWH4z4HGnqzLN5QBGJiYvTEiRN66dIlHTNmjAL666+/ekQRuO2227RevXpXWAOaNGmigD711FN6/vz5K667YsUKrVatmv2lnvETFhamt9xyi9apU0dDQkIU0MKFC2uzZs100qRJ+vfff2t0dLSePHky3agps/sTExNzxTmWIpA7XN3TrPwmrs5NSkrSiIgI7dq1qwJatmxZ+0sf0KJFi2qTJk102LBhOmvWLH3ttde0RYsWev3119uPsSkHmzZtSmcdmDZtmpYqVUr9/Pw0LCzMfr1OnTrpkiVL9Pz581e8zKOjo3XcuHEK6LPPPqsxMTHaqFEjFRFdtWpVpopAQkKCrly5UuvXr68iojNnzrRbuFw9tzYFw9W99QWWInD1kN/ugbu25XVnQRGpBswAOgF1gcbAI8DLZoeyXERmYFgEagKnvF0mTxMcHExqairBwcG0a9eOMWPG8Ndff1GjRg2PXPv06dMkJyfb14FPnjyZH374gQkTJtC/f/8rzjl16hS9evWifPnyvPPOO9SuXZtChQpx/vx5zp07R0REBJGRkZw7d47IyEh69uzJY489xgMPPEBISIg98potfXJ2Qv5ey2GCvUVu7qm7c0uUKMG8efPo0KED8+bNo3LlytSuXZvatWtTtWpV+9y5o2OiiHD8+HE2bdrEgAED2LdvX7rYFH5+fnTt2pUWLVowadIkLl68SKtWrXjwwQdJS0tzu+IkNDQUPz8/li1bxs8//8w///xDSEiI08ibNk6dOsXcuXP57LPPCA8Pp0yZMkyePJmOHTvm+v5YWFwr5MWqgSBgq6puA7aJSG2gFTAWw1fgOHALcCPQQVUP50GZPIpj7HFPO8X169ePvn37snz5cjp16sSWLVuYMWMG7du3d6oEqBqx2pOSkli6dCnly5e3O5XZypiSkuLSQcpQHK+sV1ax4rB7ntzc06yc26pVK1q1apVum2P7yBjS2BYKOD4+nlmzZjlNPnXdddfxxhtvpNvm6LzojAEDBtCwYUMGDhzIP//8Q9u2bZk4cSLly5dPFwMhNTWV9evXM2fOHL777jvS0tJ4+OGHefPNN2nevDmBgYFZfrFb7dXCIm8UgX+AKiLST1Wnq+pvYqzP6Qo8oKrr8qAMBZYHH3yQu+66i2nTpnH//fczdOhQqlSpwmuvveb0+IULF7J27Vref/99qlWrZve2tnWM1jI/C2e4axeOo+b4+HjatWvHDz/8wKxZs9J543uCmjVrsnnzZlTVqVIdHh7O448/zuHDhyldujRDhgyhR48e3HzzzaSlpREXF0dQUJDVti18TsWKFV0uRa1YsaLbFMZ5jVcUARG5ByiEsTxxq4i8DHQWkc6q+oWq/mrmF+gJbPRGGXKC42g4IzYzuStsGc1sS71SU1Pt2+Lj46/IMGjD3SgpJiaGwMBAnnvuOfr370/jxo0JDg7m3XffTSfLhmOs9n79+jlVAmzLztLS0pyOmlTVZVkt8h/u2mxqaqrLNpuWlpbuRWtLDgXGqN/Wdm0ULlyYy5cv25WAjz76iM6dO5OUlJTuuMuXL7s05ScmJrocfV+8eNFt+OuQkBBSU1Pp27cv586dY/78+bRs2ZLAwEBSUlLs5QMoVKiQve2npqa6VAoy3oOMWPEELHKDuxd9fmtbHu/xReRRYB4wG+giIhOANRjBg5qLSBlV/RA4CdwqIoVVNX8EPHdBVkbRfn5+xMfH239gf39/+wvVFsLUFa72lStXjqCgILp27cpLL71EdHQ0kyZN4oEHHiAtLS3d2mlVZejQoSQlJTF79mzASDdcokSJdC/2kJAQVNVtXXLSSPNbw76ayOm9FRGX52aMNBkWFoa/v789615KSgppaWn2l6ufnx/t27e3KwG9evVyet2wsDCXikBoaKg97fQrr7zCTz/9xHfffUdQUFA607+rc8eOHctPP/3E/Pnz04W5tpXTljq5aNGi6WIAxMXFOW3vfn5+Tu+P7Xm3MhBaXCt4rJWLQWGgCzBIVV8G2mEkDGoFbMIIJPS0iCzB8BF4P78rAWCMlqKjo92OWGwjKm8kcRERDh48yNGjR+1ZDjOyYMEC1qxZw9ixY6lSpQoXLlzgzJkzXLhwId1xfn5+9k7R1oF6I368Rf7E1W9uSxOd0TcgMjKSEydO0L59ezZu3MisWbNyFQHQprBOmDCBLVu2MHHixCuOmT9/PnXr1uXvv/+2b9uxYwdjxoyhc+fO9iyKjgQHB1O6dGlKly6dztKVkJBATExMtpJt2Z73zJQTC4urBY9ZBMzlCYki8jdGBsHVqvq7iAzBCCecrKofm9MGFYAYVT3rKfneJKNnsTMLgW2fLZxvZo5R2cWWMc4Zx48fZ+jQodx///0MGGDkaipRokS6v86wKS+qal8dYI2Ari4ytlVHxz9XEQltBAcHU7JkSTp37syGDRsYN24cPXv2zLGym5KSwuDBg5k2bRr9+vXj3LlzvP3223Tu3JnrrrsOMNJ4jxgxAoDnn3+e9evXk5iYSLdu3bjxxhuZMWOG01G8n5+f0/oEBQU5XfnibtRvpc7OGpUqVeLYsWNO97kKRW6RP/FGr78HKAlUFpEAVf0TI1TwSBGpo6rJqnqooCgBYHQyjh2GMwuBzfu4fPnylC5dmsGDBzN58mSvj7ZPnDjBo48+SlpaGjNnzrSXMSAggDJlyrid7w8ODrYv54qNjSUuLs6yEFwFOI76bS9+W1sNDg4mLCwsS171fn5+FCpUiAMHDgDwyiuvcMcddzBw4EAWL17MqVNZX+l7+vRpWrVqxbRp0xgyZAgffPABb775JsHBwTz66KMcP25kFt+2bZv9nMOHDxMdHc3LL7/MkSNH+Pjjj+0KQ1ZxlbI5433JyjkW6Tl27JjLeBr5yRHOInM8OjUAoKprgFhgMEY64VBV/RX4DrgyaHoBJDg4mKJFizrtTMPCwti5cydNmjRh+PDhPPTQQxw6dMgr5Thx4gTNmjUjIiKC1atXU7Vq1Wydb+vwQkJCrlAILAouNkU1ISHhihd/dl9ywcHB7N+/nx07dvDOO+9QrVo1li1bRq9evbjlllu466676N+/v1vFYMOGDdx33338/vvvzJ07l/feew9/f3+qVq3KunXriI6Opl27dhw/fpy3337bHn9j9OjRbNmyhcWLFzNixAiaNWvmmRtE9hQiC4urnVwlHRKR6kAJYBeQpqqpDvveAcKABCAcGAbcr6pHc1VgkQrASUdZ2cFd4g5398JmPndFRg9+VWXBggV2B74xY8bw/PPPX9EBJyQkuExuEhsb63JfeHg4TzzxBBEREaxatYoGDRqk2++uo3cVR8A2ggwLC7NGQy7wRdIhdzhrs7bfsXDhwm6dVDNrI85IS0vj4sWLHDp0iK1bt7J582a2bdtGVFQUAJUrV6ZRo0Y0atSIBg0aMGfOHN59911uu+025s6dS506da645m+//UazZs0ICwtj2bJllClThsKFCxMeHk7Tpk2pWrUqP//8s0vrljsLVlpamtuYGe7ugS8cYAtS0qH8llSnIOGLe+eubeVYERCRdsBbGN7/JzGUgbmqGu1wTBOgBlANmKa5zCJoRin8B3gaWKqqsdm9Rk4VgawuH7Qdm5iYSOHChTl58iT9+vVj3bp1NGjQgOnTp1OlShViYmL4559/2LNnD4cPH+bvv//mn3/+oVy5cowcOZJHHnmE6Ohop4rAyZMnadOmDZGRkXz22Wf2iICOuHsBuAsoBFjLB91QEBQBG+7abGYvQVd+ADZrQ9GiRe1tLjU1lV9++YWNGzeyc+dOtm/fblcMAHr27MnEiRMJCgpyuXzw+++/p3PnzoSGhrJs2TLKly9P+/bt+fPPP1m/fj133nlnOidXR7+H7CyTzM49sBSBzP0ArCmAnJHfFIGc5g4ohJE34H7ze3tgIsZKgGJOjg/IiRwn17kZw7rwLdADKJLda3gr+6AtyU9qaqo9Rn90dLRGR0friRMndMaMGVqsWDEFtHjx4uni/gcFBWnNmjW1S5cuWqlSJXvSlpUrV2pSUlK6/ANHjhzRKlWqaFhYmG7dujVfJUy5FsAHuQa8QWbt2VX+i8TERL106ZKmpqamu54tbn9KSoomJyfrrl279L333tPly5ene0bcJe7auXOnFi9eXCtWrKgDBgxQQOfOnXtFzouM+QHcXbegPQvu2pcv2hb5LF7+1YIv7qu7tpUji4CIFAK+Bhar6lwR8QMaYWQVPKyqH4lIfaCkqq4SEdGcCHIu+3WMsMRPYygfJ4ALqnrczTn/B/wfQIUKFeq40nDd4ar4ttGJYzQzxxEL/Jcv/fTp0yxfvpwdO3Zw++232z+VK1e2j1qSk5OZP38+48aN4+jRo9StW5fXX3+d5s2bc/LkyXQ+Affff7+7+5TtOlpkjjOt2hPtK6/J7HHMONWVEVdWI3fXVTfTazbrxW+//cYjjzzCxYsXefLJJ1m4cCGqavd3cGYRsHVmrmJ9FKRnIb9ZBCzzv3fIbxaB3EwNNAMGAhNVdYuI+ANPYigDvYE2wBZVPZ2zYl8hT4BAYDowE4gElgC3AU1VdWtWrpPTh8nVfbp8+TLR0dHpYvlnJCfzlMnJycydO5fx48fbFYKLFy9y9uxZli1bxv33329PdOTs/ILU+RUk8tvUQE7JriLg6HeQmJjo0o8kt4oAwO+//86sWbMYN26cfZWAu/asqsTFxdmnLDI+hwXpWbAUgWuD/KYI5MYjbAuwDughIo1VNVVVFwHlgIqq+mVulQARqSMiHUTkFqCoGsGHVgBlgDSgNPA3UEZEfOL+624FQW4oVKgQTz/9NPv27ePjjz+2ZwtctmwZt912G5cuXco0yJGFhaewxZy4ePGi11eW1KpVi2nTpmVrqaC3nkMLi2uBHHuFqWqCiCzEmOd+SURuBRIxXtJRbk/OAiLSGngP2Ak8DsSLyHjgGIYyEAB0By4Dr2PkLMjzt6JtOZa7qQPb1ACQ7YQ/hQoVonfv3vTo0YO4uDjCwsKIj48nMDDQbhHIKM8Kj2qRVbKahMoxYFZiYqJHg+3YnhFXpv+sYGURtLDIOblyD1fViyIyE9gHPIuxVLC7qkZ4oGwPA6NUdZmI1ARaY/gEPAPMAA6q6g8AZjKjbK8gyAtsIymbedIxnWtG3HWAjrkLbIqHsxUFNq9uwG3udwsLSN9e3L1IbaGps/KCzu6LPGOa44zfLSwsvEuu14mpahLwg4j8aHxVT4WlC8BYerhMVf8QkXMYSsBo4DVVjTP9EtIwrAL5kuDgYHuSH8dtznDXKWfMDudOHljhUS2yRsbw2e5wbIOBgYFuj8uKcuFYBsdnJDtlsrCwyD2ezDXg6aiBk4HPReRfVf1MVU+KyGqMiIXFgbicynTnpJGbTG/O8Pf3JywszL4/Yzx0x7Jk7AADAgLSneeYHc5VHRxNpN6op0XBISuOe65M6s5WBdjaoO3F7QqbEhoSEpKunbl7RhytVxm/Z4a7LIu2FQXuzrWwuNbJN5PIInKfiNhzi6rqfuBVjFTGPcxtvwBFgLt9U0rv4i78q7PscFnByjB4bePJ3z+rbTCnbdXCwsI35IsQcuZSxJXAahEJU9Xp5q4fMZwRx5vhjC8AtwJ/+qakV5ITx6a8JLtmWouri6vp98/vz5qFRUElvzxNNwFvYsQHqCEi/QFUNVaNJEYdAMFYkdDJXfCgvMZZJkLw/kg8q9e3llVd22T19/e15Sgr8l09axYWFrkjX1gEVHW2QxyAosBDIjJAVaea+/8FXvGWfMeRhrsY/c5w5djk7ZFYdry9C/pI0CLnZPX397XlICvyPe1EaC21tbAw8JkiICINgFAgVlW3q2q8uX0Nxui/iYh0AkIwHAO/9FZZcrPkzlVHm9VOK6fmTsuz2sKTeKI95ebFmhX5nlZqraW2FhYGPlEERKQ5MAXYAJQWkXhV7Q7GdIC5OiACeAfDJ6CRN8vjjZeqt0di1kjfwpN4oj15Q6H2JtZSWwtfUbFiRZcrVnyR1THPFQEzQVF34C1VnSMiIcA3IvKNqrYCuzJQF6gANNBcpi/OSMblRCKSaWeQWVzonC5Dskb2FlcL3nyx5vT5y2zpoGUJsPAF7l70vljSmueKgKqmichuTEdFVY0DmorI9yIyV1WfMg9NAFp4WgmAnN3ozM7J6Y+X3TXTuZVnce3gro3kpv1kNR6AJ66ZW6znxMIic/LMQ0ZEHHuIfcBIEanmsK0DECIidwOo6jRV/T2vypcVfO1ZbWFxLWM9fxbXArZpA2efSpUqeUVmnlgEzARC3UztfB6wFngX2GJmLtyvqpdEJBUIc3Mpn+Jrz2oLi2sZ6/mzuBbwxbSB1xUBc9Q/A+gE1AUaA48AL2MEC1ouIjOA64CawClvlymnWPP5Fha+w3r+PE+lSpU4duyYy/0VK1bMw9JY+Iq8sAgEAVtVdRuwTURqA62AscAI4DhwC3Aj0EFVD3uzMGlpaaSlpeVo3bDlqW9h4Ts8/fxZcQTg2LFjmTpiWlz95IUi8A9QRUT6qep0Vf1NDPtGV+ABVV2XB2Wwk5qaSnx8vPVCt7C4xrHiCFhYGHhFDRaRe0TkfhFpaKYpfhmoJyKdAVT1V+Ac0NMb8t1hy57maWxZzlx9LCyuFq6Wtm4Lv2zFEbC41vG4IiAijwJfAy2BBSLyHIZVYAPQXEQGmYeeNI8v7OkyuMPPzw9/f3+XXpmucHV8ZudZWFjknpw+f+7OsS13vNqnBSpVquTyHlg+AAULdysKcrOqwGNTA6a5PxDoAgxS1S9F5CtgoilnBYYj4Hsi0hioB7RW1URPlcHCwsLCIj2WH8DVQ2YRB3M6KPWYIqBGS0sUkb8xMgiuVtXfRWQIRjjhZFX9WETuwYgYGKOqZz0l38LCwsLCwiL7eMMmtgcoCVQWkQBV/RMYjhFAqI6qJqvqIUsJsLCwsLCw8D0eUwTMqQFUdQ0QCwwG7hSRUNM58Dsg1VPyLCwsLCwsLP7DnQ+BO3KlCIhIdRFpICKFHK+lqiOASOBZ4E0RGQq0BS7lRp6FhYWFxZVYDoEWYPgQ5GQ1T459BESkHfAWhvf/SWCXmTQoGkBVR4pIE6AGUA1opqpHcyqvoGELVhIcHHzVeyVbXLtY7Tzv2Lt3r9vUtZZDoEVOkZw0HtMC8BnwoapuE5H2QH0gEZioqlEZjg9Q1RRPFDi3iMg5wHVMTeeUwrBwZAc/wB9jOiS3WVJyIt9TWLLTU1FVS7s6SURigP1eK5VrfHGvSgEX8Fw7z6pMX9Qzr2S6bF857Ltyw9V+r30tN69lumxbuVk1UBSoCmwDlmNUqCXG8sGPRKQ+UFJVV5GPfAPcdeKuEJFdqlrXG+XJ7/It2dlmvy/K7It7ZcnMW3LSd+WGa+leX0t1dUaObHmqmgxMAtqJSCNVTQO2An8AjcwgQRWA38zjLZuVhYWFhYVFPiQ3k3pbgHVADzOVcKqqLgLKYZggvlTV0x4ppYWFhYWFhYVXyPHUgKomiMhCjFTCL4nIrRg+AmWAKLcnFzw+uYblW7Lz5rzc4gu5lsyrm2vpXl9Ldb2CHDkLpruASCBwP8ZSwQRgsqr+7oGyWVhYWFhYWHiZXCsC9guJ+GO4A+SF57CFhYWFhYWFB/CYImBhYWFhYeELREQsp/ScY0UAyQbiKprHVSrXlH2jr2RbWFgUDESkgmkVzkuZZUSkHOTtyjQRCXHsk/OifxaRB0Skg7eubykCWUBESouIn6pqXr+URaQ50EtESuSlXFP2Y8BxEenkA9n3iMj9InKvD2TfKyLNROSBXFyjoohUy7DNq21HRFqISCtvynAi85qopyk3z+taEDDvyVGgu4iE5pHMlsAaYKGIfJwXMk25jwJLgekiMgq8r4SISBtgKhDvLRmWIpAJItIW+AroIiL+PlAGBgB9gIdFpFReCTUVkNeAj4AH81IRMR+2rzECVH0uIgPysINpDswF7gVWi8gjObhGB2AFMF9EJolIdzA6DG+1HRFpBkwELnvj+i5kXhP1NOXmeV0LEMkYYeY7Ak+ISBFvCjMHB29h9I3tgdIiEuJNmabcR4H3gdkYysCD5mDJmzJDgKeB51V1lYgEe6MvzE1kwaseEakIjAP+BG4HOorIV6qamodzUruBukAzoLCILMLw7fBayGYzKuQ7wPPAIWAOZjhZ0zLiFYdQs0MNxIhOOUhVvxSRLzE6/iARmaaqXtOKRaQGRqCs51V1k4gkAQEiUkFVj2fxGkUw7tszwD6MutQXkVKq+oE32oyIPAjMBDqo6i6zoygMXPTib3VN1NOUm+d1LUio6hERmQkcB/oCF0XkBHAhq89NNgkC1qvqdhG5CagNTBQRVLU/eN5nQETCgEeB4aq62pwGaYKxXN7bhAKRInI9xiAlRUTOA6NV1SMhpy2LgHvOY2pjwCmgAYYyEGiOBPJiTmw5Rl6HlUBj4H/AOBEJ8qLMEkBPVd2mqmeAf4EZIlLImx2uGiQCfwM1xEhh/QcwBGgB9PaWbJMUoKupBJTDsIh0Ar4WkeeyeI1C5sdfVeOAL4G1wC1enGIpCoRhdMDFgS+AhcAU06zoDQLI+3qGkff1BCOXQl7XtUAgBoWBisA/GNbLscAOjOiy3iAZuEtE3gF+wFAOJ2Kkvf8UPGeut1l7VDUGmAf8aioZqRiKz4OekONMpjnoigO+xViiPw7DEtEDEOBNT8m0FAE3qGossEdVLwCfAgcwlIH25iF54UjnBzylqt8Cp4ERGKNmj+dvcGj0q1X1DxGxWYzewGj095nHeaXdOJhY9wAlgcpiJKz6C6PeQ0Xkbm/IBlDVfWa9/TEe8CGq+hRG5zZBRBq4KXsdESmsqpcwXlAjRKSy2YFsxugkPervYMoMUtWvMZTVzcB2DKXxGeAI0NyTploRaSQiN5mJxZaQN/VsZFplvgEGkgf1NOUGmZ1xNEYH7PW6FgTMdtdBRG4BiprK+wqM0XEaUBpDmS8jIsEelNlRRCqp6k/AS8AG4HdgvBqZbdsAN4pIMU/INLnO9o+q7lbVCAclIwpDMUVEuopIV0/LNDkCtMK4v1tU9ZKq9gLKmwOW3KMuchdbn3R5nG3LLIsA/TFejAsxrATF80D+q2ZDOICh+S7EGKn651H9A4HpwBQvXb+Ik20TMebiagKh5rZpQE1vyza3h5h//cy/HwL3uDi2hfnbNDG/3wCMNs+pYm4Lw0jQVdFD5U4n09zWFhjh8D0Mo7P0lMymGB39jxgWgVLA62Y9K3upno4yC5nb2niznuY1HwfmA6swlNJgh9/UK3UtCB+gNXAQWIRhpp6BMfKvARzGGDA0Ae7BGMle52GZCzAc5yqa+2YBt5n/P4mR88bpM50DuU9gBMlrb34X28f8XhdjVN4c2GUrhydlOmzvbtbtOeBm8578DBTzSF193bDy0wfjZTvYxT5x+H8lhpZ2dx7Jnmc2jsfN7x2A8nlZbwwt/yjQ0sP33OYRW8b87uew7x2zo3kfGIrhkFTJW7LdHNcFI6FWBSf7KmDMGz+QYXt14BXgG6CheY1fgBIeKHc6mUCAi+PamS+qkh6Q2cLs7LoDk4F7ze21MZSBrzHMl56sp6PMD4D7M7ZLT9fTvF4TDN+cRhiWwM/N7VUxlAGP/6YF5YOhCLUz/69p/vaLMaanRgBtHY4N9aLMJUBxYBBGcrs5GNaBOz0k82aMaYdJGFPEHWztzqFPrIWhpP4G3OEtmQ772wEvY0xNbcST7x9fN6z88gEeMTv7Zk722X54f7ODPwvU8LbsDJ2dx370HNQ7wHwAngdu8KDsBzDMq1fIdjimCTAYwxpwe17I5j8rQFGgJ7DX1YNuPrxLzP/LYVgypmP4c9yG4Tz1LcbIsraHyp5R5gSMuOUPYr4MMUYOezzRMZptfgPQ0Pw+D5jusL8MRohxj9XThcxpTo7zWD0drjkWeN38/1YMy8BwoDKGtec5T/+mBeVjtu0xDt/LA2OA9/jPiuaPwwvTSzJHAxPN743M5/lmD9YzFGhj/v8YEM1/yoCtf6iOoQRU9bbMDMeVwkNKr/2avm5Y+eGDMfcdgWn6BYphOL8UwcloCyjtQ9keebhyKDvQw/d9KIYXLhgvtGYYc67XOTnW6ajX27IxFBGXHYzZ4X2N4TeyDmPqaAzGCOUx85gQT947FzJHY5hqm5vH9MADoxTzWmUc7wGGb8xuTAuVw/YgT9XTjcwWDtsKAd08VU+H63Y37+VgDCvYRAzL0SrMAYCnf9OC8uG/l193h233YExXesxKmUWZXwBlvVjXAIf/m5NeGahl/i2WhzJr46WpaGv5oMF5DE/UG0SkJIbZKR6IxQhaMUdE6mEoAKtV9Vwey65ryl6jZovIQ9mO9U7yoGwwvPQDzf+XAMfMbSIiTwN3YHQuq/C8c2Rmsu/E0Lq/d3UB02lSMcx5DYETqjrN3DcE6AWsUcPz1yNkInMwhgXjO1Vd4CmZqnrWQX6Aqp4QkcUYFo9vzdUkyaqakAcy78CI7+CnqskYLyBPsw1DGa4KbFfVEWY5JmCYv3t48jfNz4jIfRg+EQsAVHW/iLwK9De95xeo6i+mo+bdGNN3eSUzCOPluDq3Mp3JxTD7I0b8mO/MFSKfiRHMqoqItM3tuyCbMm/GmBb2PN7SpgraB6MRHwZOYHgi+2EsV/sCwxTVCQ+axS3ZCsbLdr8p62lz2y3AxxjOWr6WXc7Fuf4Zvt+CMY+5A2htbuuIMb9c2EPlzarM2d6SmWHfIxij5Vs9/LvkuUxXcjF8MSYA15vfe2H4R+SJk66vPxhWsjgMRbmfw/ZQDNP1HxjTKEMxptmu8KEpCDIzkWubHrVNB3wEROKBqWFfyHT1sZIOOSAit2N4YU9z2PYdhiPdfku2V2S3wnihLVDV181tszBG0kvzk2wRqaaqB8z//dVYS2zbVwHDeewuDNN9beBJVd2TyzLmK5kZjvsQw6I0QnMZX8IXMjOTKyI3YHTCBzACFzXGsAbsza3cgoCI9AauxzDLPwHszdBHVMGIs+IPLMptu/OVTBdy96jqdHOfn6qmmdbR9zFe2t6oq9dlusRbGsbV8MGYg/0VL85DXeuyMRwRe2JYJfqYn12Yy+7yi2wMK0EcRudj2+Zv/rVp7kEYfhZN8MB8aX6T6aQM93mijfhCZhbur22QVBPD4fNNoLq322R++2AsmwzGsDbNAAZcjTKzIhfDKnF9QZfp7GNZBJxgBrZ5GsNTuKMaAW0s2d6VXRtj/qswMFfzcNSVmWxz/nMpsAzjRRSgqt3NfQFqhnsWkZKqet5DZbJkeklmNuUGqQd9H/I7ZtCsUCBWVbc7bA/FWM7ZBMM/JQSIU9UvC6LMHMhNUNUvCqLMLJXLUgSuxHwhPgCcUdV/LNkWZgSvaIwR+EcYD2l3h/13YyxjmuWpF4cl03sysyi3JoZD5iwgUa/yzlKMhFtTMJZtlgbiM9yPUKAORnyPW4FGmvtpqDyX6Su5vqprlsp2lbdtCwuPY66w+ATzQRYjWVFVjPCfZ92fbcnMbzJ9KTe/YK5GmQ9sUNU5YmS9+wZjBN7K4bhhGBbDpqq6r6DJ9JVcX9U1q1i5Biwssolpon4WSBaR/Rgm5p+8+cKwZHr3ZewrufkFNRwvd2O+E1Q1TlWbAsEiMtfh0ASMWA65fkn5Qqav5PqqrlnFUgQsLHKAqkZiRLQrhhH+9LQls+DK9KVcXyLpc9vvA0aKSDWHbR2AEHN6BlWdpqq/FzSZvpLrq7pmFyugkIVFDhAjDW4L4JG8cmy0ZF6dcn2FiLQGuhmuQczDSK38LrBFRBqr6n5VvSQiqZhZ9gqiTF/J9VVdc4LlI2BRoBCR2RjLvs6q6p0euN53QH1gq6o+7rD9U4zsYoKxjvwpNdJSO56b5x7llsyrU25eY45Kf8AInFUXI4dCEEZSm64YAXtmYKTE7YqRbOxwQZPpK7m+qmtOsRQBiwKFiDTGCIE830OKQFOMpTrPZlAEiqqRhx4RmYSheLydW3kWFvkB0xnyFVV90vxeGyMLaTGMMMpNMaJX1gI+VA8sJfaFTF/J9VVdc4o1NWBRoFDVH0WkkuM2EamMkZ2wNEaAmGeyuvxRVTeIyINOttuUAMEI+GFpzBZXE/9gxMvvp6rTVfU3s613xUhvve4qkekrub6qa46wnAUtrgY+AQaqah2MpTfTPXFREZkDnMFY0zvFE9e0sPAVInKPiNwvIg3VSCD2MlBPRDoDqOqvwDmMaJsFVqav5Pqqrp7AUgQsCjSmV+59wFci8gdG0qAbzH3tRORPJ5+1Wbm2qj6NkaL4b+BJ79TAwsL7iMijGKmrWwILROQ5jFHrBqC5iAwyDz1pHl+4IMr0lVxf1dVTWFMDFgUdP+CSqtbMuENVl2GEkM0xqpoqRvrbEcCc3FzLwiKvMc3RgRjJqQap6pci8hUwEaP/XwGcAt4z/W/qYWSzTCxIMn0l11d19TSWImBRoFHVaBE5IiIdVfUr88Gsoaq7c3pN8xqVVfVf8/9WGNq9hUWBQg1v8EQR+RuoISKrVfV3ERmCMd2VrKofi8g9GGmXY3IbRMkXMn0l11d19TTW1IBFgUJEPge2A9VF5ISI9AG6AX1EZDfwF9AmG9fbAnwFNDWv9yjGksF5IrIX2Isx1fCGh6tiYZGX7AFKApXFSKr0J4Y/zUgRqaOqyap6yMMvKV/I9JVcX9XVI1jLBy0sLCyuUkREzFErIjIR42X1IfCvqsaKyDRgpqr+UZBl+kqur+rqaSxFwMLCwuIqQkSqAyWAXUCaqqY67HsHI4pdAhAODAPuV9WjBU2mr+T6qq7exFIELCwsLK4SRKQd8BaGd/pJjJfVXFtcDPOYJkANoBowTXOfWS/PZfpKrq/q6m0sRcDCwsLiKkBECgGfYUSq2yYi7THCZycCE1U1KsPxAaqaUtBk+kqur+qaF1jOghYWFhZXD0WBqub/y4Fv+W95GyJSX0RamvtTrzy9wMj0lVxf1dWrWIqAhYWFxVWAqiYDk4B2ItJIVdOArcAfQCMziE0F4Dfz+Fybg30h01dyfVXXvMCaGrCwsLC4ShCRIKAvxhz1Z6r6o7n9B4zEWgeuBpm+kuurunobK6DQNYYZ6vJ5DBPXclUd4OMiWVhYeAhVTRCRhRhJsl4SkVsx5rDLAFFuTy5AMnMjV0RiVTU0L2XmdyyLwDWGiPwDPAY8ANS1FAELi6sPEQkE7geexVjKNllVf7/aZOZEbm4UgZzKzO9YisA1hIh8BPQG9gOzMcLoDhCRucC3qrrEPC5WVUNF5AmgP9AMKAtsBhqr6hmfVMDCZ4jIaxgRHMOBSOBXjBHQ/2E4S/0L9FDVOLM9xWNkbawIPA30AhoAO1T1KfOasRjpox8GLmJka3sHY551iKp+LUbK6QVAEbMoA1T1Jy9X96pBRPwxpqvTCqJMEVkB3AQEYbxsP8mk3bygqitF5CngCaAwcDOwSFX/Z14z14qAQ/ny/P56A8tZ8BpCVZ/DSIDRBOMByuz45RhpePsDM4HRlhJw7SEidYH2QC2gHVDX3LVMVeup6t0YGRr7OJxWHHgIeAH4BngfuAO4S0RqmscUATapkT46BhiLoXQ+wX8hnc8CzVS1NkYGyA+9UcerFVVNzeuXlIdl9jbbR11gkIiUxH27+Z/DufdgKK81gY5mO/Yovri/3sDyEbDIjIHAn8DPqvq5rwtj4RMaAitVNR5ARL4xt98pImOB64BQwDG98zeqqma+hghV3Wue+xdQCcPTOgn4zjx+L5CoqsnmOZXM7YWAqabykIoRpMXi2mGQaZkEwzJQlay1G4D1qnoeQESWYbTjXXlS6gKGpQhYAKRgWodE7Gk1bZQH0oDrRcTvatB+LbKNuNg+F2irqrtNU+yDDvtsaVbTHP63fbf1O8kOS6zsx6lqmojYjnkBiADuxmijCTmuhUWBQkQexDD/NzCnnDZhTBFkpd2A4dCHm+8WJtbUgAXAUaCO+X8bjFEY5kM1B+iKYfod6ovCWficrUArEQkSkVDAFjAlDDhtRlzr5iXZxYDTpgLaA/D3khyL/Ecx4KKpBNyKEcUvOzQTkRIiEgy0BbZ5uoBXC5ZFwAKM+f+VIvILsAG4bG5/GdiiqltE5A9gp4isUtW/fVROCx+gqjtF5GtgN3AMw7waBbwG7DC37cVQDDzNdGCpiHQEfuC/tmlx9fMd8JyI7MFwcP45m+dvxXA0rYLhLGhNC7jAWjVgYWGRKSISqkZa1RDgR+D/VPU3X5fLwsIZ5lSVtTw6i1gWAQsLi6zwiYjcjjFHO89SAiwsrh4si4CFhYWFhcU1jOUsaGFhYWFhcQ1jKQIWFhYWFhbXMJYiYGFhYWFhcQ1jKQIWFhYWFhbXMJYiYGFhYWFhcQ1jKQIWFhYWFhbXMP8PsZfj7xSrRhMAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 547.2x547.2 with 9 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"corner.corner(sample2[:, :-1], labels=['flux', 'gamma', 'ampl']);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The diagonal elements show the histograms of the columns, and the other terms plot the distribution of the two column values. The binning used in these histograms is designed to show the structure of the data (and is more advanced than the simple scatter plots I showed above).\n",
"\n",
"We can even overlay the distributions we got for the unabsorbed (`orange`) on top of the absorbed (`black`)\n",
"results. We can clearly see the shift in the flux distribution:"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 547.2x547.2 with 9 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"c = corner.corner(sample1[:, :-1], labels=['flux', 'gamma', 'ampl']);\n",
"corner.corner(sample2[:, :-1], fig=c, color='orange');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use NumPy routines to extract the calculated energy flux values; in this case I chose to look at the median value along with the one-sigma range from the distribution."
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Median = 5.6766 * 1e-13 erg/cm^2/s\n",
" 5.4487 - 5.8954 * 1e-13 erg/cm^2/s\n"
]
}
],
"source": [
"efluxes2 = eflux2.copy()\n",
"efluxes2.sort()\n",
"\n",
"med2, lsig2, usig2 = np.percentile(efluxes2, [50, 15.87, 84.13])\n",
"\n",
"print(\"Median = {:.4f} * 1e-13 erg/cm^2/s\".format(med2))\n",
"print(\" {:.4f} - {:.4f} * 1e-13 erg/cm^2/s\".format(lsig2, usig2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use Sherpa's\n",
"[plot_cdf](https://cxc.cfa.harvard.edu/sherpa/ahelp/plot_cdf.html) function for displaying the cumulative distribution function:"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_cdf(efluxes2, xlabel='flux', name='energy flux')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.2 correlated=True\n",
"\n",
"As shown earlier, there was no assumed correlation between the amplitude and slope parameters when simulating the fluxes. However, we normally find there is some correlation: for this fit we can use the\n",
"[reg_proj function](https://cxc.cfa.harvard.edu/sherpa/ahelp/reg_proj.html)\n",
"to show that as the slope - i.e. $\\gamma$ - increases, so does the amplitude."
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"reg_proj(pl.ampl, pl.gamma, nloop=[21, 21])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we set the `correlated` parameter to `True` when calling `sample_energy_flux` then the covariance matrix will be used to define parameter correlations."
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"sample3 = sample_energy_flux(lo=0.5, hi=7, num=1000, correlated=True, model=pl)\n",
"eflux3 = sample3[:, 0] * 1e13\n",
"gamma3 = sample3[:, 1]\n",
"ampl3 = sample3[:, 2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First let's check whether we really do see a difference in the parameter values (i.e. were the correlations used in the samples?). I have overdrawn the contours from `reg_proj` to show that the correlated version matches, in shape at least."
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(ampl2 * 1e4, gamma2, alpha=0.5, label='No correlation')\n",
"plt.scatter(ampl3 * 1e4, gamma3, alpha=0.5, label='With correlation')\n",
"\n",
"# overplot the reg_proj contours\n",
"rproj = get_reg_proj()\n",
"x0 = rproj.x0.reshape(*rproj.nloop).copy()\n",
"x1 = rproj.x1.reshape(*rproj.nloop).copy()\n",
"y = rproj.y.reshape(*rproj.nloop).copy()\n",
"plt.contour(x0 * 1e4, x1, y, levels=rproj.levels)\n",
"\n",
"plt.xlabel('amplitude * 10$^4$')\n",
"plt.ylabel(r'$\\gamma$')\n",
"plt.legend();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So, the first run used uncorrelated samples, which results in an ellipse parallel to the horizontal, whereas the correlated version has the major axis rotated."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see if this makes a difference to the flux distribution:"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Median = 5.6749 * 1e-13 erg/cm^2/s\n",
" 5.5421 - 5.8077 * 1e-13 erg/cm^2/s\n"
]
}
],
"source": [
"efluxes3 = eflux3.copy()\n",
"efluxes3.sort()\n",
"\n",
"med3, lsig3, usig3 = np.percentile(efluxes3, [50, 15.87, 84.13])\n",
"\n",
"print(\"Median = {:.4f} * 1e-13 erg/cm^2/s\".format(med3))\n",
"print(\" {:.4f} - {:.4f} * 1e-13 erg/cm^2/s\".format(lsig3, usig3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see below, where I overlay the cumulative distributions, that the medians are essentially the same$^\\dagger$, but that the distribution of fluxes when the parameters are correlated is narrower (and so has a smaller error range).\n",
"\n",
"---\n",
"$^\\dagger$ although the exact difference depends on the random seed used, and so varies each time I re-run the notebook."
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_cdf(efluxes2, xlabel='flux', name='Unabsorbed fluxes')\n",
"plot_cdf(efluxes3, overplot=True)\n",
"\n",
"# Add a legend (please don't peak behind the curtains)\n",
"ax = plt.gca()\n",
"for i in [1, 2, 3]:\n",
" ax.lines[i].set_color(ax.lines[0].get_color())\n",
"for i in [5, 6, 7]:\n",
" ax.lines[i].set_color(ax.lines[4].get_color())\n",
"\n",
"plt.legend((ax.lines[0], ax.lines[4]), ('Uncorrelated', 'Correlated'));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This can also be seen by using the `corner` module to overplot the two distributions (the correlated version is in orange):"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 547.2x547.2 with 9 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"c = corner.corner(sample2[:, :-1], labels=['flux', r'$\\gamma$', 'ampl'])\n",
"corner.corner(sample3[:, :-1], fig=c, color='orange');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that the flux distribution is narrower in the correlated version but centered about the\n",
"same location, and the actual model parameters have similar one-dimensional distributions."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.3 A note on clipping\n",
"\n",
"If a sampled parameter exceeds the clip limit (by default the `hard` limit, but it can also be the soft limits) then that parameter will be\n",
"replaced by the value at the limit, and the clip flag for the row (the last column) will be set to 1. This can be useful when a source is\n",
"faint and so is not well detected, or a parameter is not well constrained (e.g. the n$_H$ parameter of an absorption model), and you want\n",
"to post-process the fluxes to include or exclude parameters at the limit.\n",
"\n",
"Note that the hard limits of a parameter are provided by the `hard_min` and `hard_max` attributes, as shown below for the power-law $\\gamma$ and $n_H$ column density:"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(-3.4028234663852886e+38, 3.4028234663852886e+38)"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pl.gamma.hard_min, pl.gamma.hard_max"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.0, 3.4028234663852886e+38)"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gal.nh.hard_min, gal.nh.hard_max"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and the soft limits by the `min` and `max` attributes:"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(-10.0, 10.0)"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pl.gamma.min, pl.gamma.max"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.0, 100000.0)"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gal.nh.min, gal.nh.max"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Section 5.7 contains an example when the clipping parameter makes a difference."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.4 What errors are used?\n",
"\n",
"The `sample_energy_flux` and `sample_photon_flux` routines require an error estimate to generate the parameter samples. The default is to run the \n",
"[covar routine](https://cxc.cfa.harvard.edu/sherpa/ahelp/covariance.html) to calculate the covariance matrix, and use that (just the diagonal elements when `correlated` is `False`).\n",
"\n",
"The `scales` parameter can be used to send in either a one- or two-dimensional matrix (size and ordering match the free parameters of the model) to define the errors used in the simulation. A normal (i.e. symmetric) distribution is used in the sampling."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.5 sample_flux\n",
"\n",
"The [`sample_flux`](https://cxc.cfa.harvard.edu/sherpa/ahelp/sample_flux.html) is similar to `sample_energy_flux` but\n",
"has several differences in what values are returned. It also automatically clips the flux results to exclude any\n",
"rows that are are equal to (or outside) the **soft** limits of the parameters, which can be problematic when\n",
"calculating fluxes for un-detected sources (where parameter values may hit the soft limits0.\n",
"\n",
"**NOTE**: In CIAO 4.13, the `sample_flux` routine is restricted to calculating the energy flux for PHA data sets (setting `Xrays=False` will result in an error).\n",
"\n",
"Unlike `sample_energy_flux`, `sample_flux` returns three items:\n",
"\n",
" - the errors for the \"full\" model (median and one-sigma limits);\n",
" - the errors for the \"component\" model (median and one-sigma limits);\n",
" - and the parameter values used for the simulations along with the fluxes for the \"full\" model.\n",
"\n",
"It will also display the calculated errors on the screen. In the following I have not set the `modelcomponent`\n",
"parameter - which is the same as the `model` argument for `calc_energy_flux` and `sample_energy_flux` - and\n",
"so the first two arguments will be the same."
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"original model flux = 5.42803e-13, + 2.54993e-14, - 2.13191e-14\n",
"model component flux = 5.42803e-13, + 2.54993e-14, - 2.13191e-14\n"
]
}
],
"source": [
"res1 = sample_flux(lo=0.5, hi=7, num=1000)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"fflux1 = res1[0]\n",
"vals1 = res1[2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Section 5.6 discusses using the `modelcomponent` to calculate the fluxes for a single component.\n",
"\n",
"The flux argument gives the median and one-sigma upper and lower limits (since I did not change the `confidence` parameter from its default value of `68`):"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([5.42803324e-13, 5.68302659e-13, 5.21484186e-13])"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fflux1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The third element is similar to the return value of `sample_energy_flux` - that is the flux, clip flag, and parameter values for each iteration - except that it has an extra value, the statistic value for the sample:"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1001, 5)"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vals1.shape"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"ef1 = vals1[:, 0] * 1e13 # flux\n",
"g1 = vals1[:, 1] # parameter: gamma\n",
"n1 = vals1[:, 2] # parameter: ampl\n",
"c1 = vals1[:, 3] # was the row clipped to match the hard limits (note: *not* the soft limits)\n",
"s1 = vals1[:, 4] # statistic"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use this extra information, for instance by coloring the points with the statistic value (top plot):"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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