From fcf8ab5dd6d3a767113d77f96fbe23201e3cb377 Mon Sep 17 00:00:00 2001 From: Gabriel Stechschulte <63432018+GStechschulte@users.noreply.github.com> Date: Fri, 11 Aug 2023 01:41:49 +0200 Subject: [PATCH] Add documentation notebook for `slopes` (#701) --- bambi/plots/effects.py | 3 +- bambi/plots/plot_types.py | 2 +- bambi/plots/plotting.py | 1 - bambi/plots/utils.py | 1 - docs/examples.rst | 3 +- docs/notebooks/plot_comparisons.ipynb | 38 +- docs/notebooks/plot_slopes.ipynb | 2498 +++++++++++++++++++++++++ 7 files changed, 2522 insertions(+), 24 deletions(-) create mode 100644 docs/notebooks/plot_slopes.ipynb diff --git a/bambi/plots/effects.py b/bambi/plots/effects.py index e821ba348..f1c7b1d44 100644 --- a/bambi/plots/effects.py +++ b/bambi/plots/effects.py @@ -1,3 +1,4 @@ +# pylint: disable=ungrouped-imports from dataclasses import dataclass, field import itertools from typing import Dict, Union @@ -5,8 +6,8 @@ import arviz as az import numpy as np import pandas as pd -import xarray as xr from pandas.api.types import is_categorical_dtype, is_string_dtype +import xarray as xr from bambi.models import Model from bambi.plots.create_data import create_cap_data, create_differences_data diff --git a/bambi/plots/plot_types.py b/bambi/plots/plot_types.py index 2db74e8ec..e7bb53c23 100644 --- a/bambi/plots/plot_types.py +++ b/bambi/plots/plot_types.py @@ -46,7 +46,7 @@ def plot_numeric( ax = axes[0] values_main = transform_main(plot_data[main]) ax.plot(values_main, y_hat_mean, solid_capstyle="butt", color="C0") - ax.fill_between(values_main, y_hat_bounds[0], y_hat_bounds[1], color="C0", alpha=0.4) + ax.fill_between(values_main, y_hat_bounds[0], y_hat_bounds[1], alpha=0.4) elif "group" in covariates and not "panel" in covariates: ax = axes[0] colors = get_unique_levels(plot_data[color]) diff --git a/bambi/plots/plotting.py b/bambi/plots/plotting.py index 243bcfcee..455298231 100644 --- a/bambi/plots/plotting.py +++ b/bambi/plots/plotting.py @@ -455,7 +455,6 @@ def plot_slopes( if conditional is None and average_by is None: raise ValueError("Must specify at least one of 'conditional' or 'average_by'.") - if conditional is not None: if not isinstance(conditional, str): if len(conditional) > 3 and average_by is None: diff --git a/bambi/plots/utils.py b/bambi/plots/utils.py index 3f7ab436d..5e05213f9 100644 --- a/bambi/plots/utils.py +++ b/bambi/plots/utils.py @@ -356,7 +356,6 @@ def set_default_values(model: Model, data_dict: dict, kind: str) -> dict: if not isinstance(value, (list, np.ndarray)): data_dict[key] = [value] return data_dict - return data_dict diff --git a/docs/examples.rst b/docs/examples.rst index a1ad3b24e..67244cbec 100644 --- a/docs/examples.rst +++ b/docs/examples.rst @@ -27,4 +27,5 @@ Examples notebooks/hsgp_1d notebooks/hsgp_2d notebooks/plot_cap - notebooks/plot_comparisons \ No newline at end of file + notebooks/plot_comparisons + notebooks/plot_slopes \ No newline at end of file diff --git a/docs/notebooks/plot_comparisons.ipynb b/docs/notebooks/plot_comparisons.ipynb index 3e785c075..d1dec6bb1 100644 --- a/docs/notebooks/plot_comparisons.ipynb +++ b/docs/notebooks/plot_comparisons.ipynb @@ -29,15 +29,15 @@ "\n", "Here, we adopt the notation from Chapter 14.4 of [Regression and Other Stories](https://avehtari.github.io/ROS-Examples/) to describe average predictive differences. Assume we have fit a Bambi model predicting an outcome $Y$ based on inputs $X$ and parameters $\\theta$. Consider the following scalar inputs:\n", "\n", - "$$u: \\text{the input of interest}$$\n", - "$$v: \\text{all the other inputs}$$\n", - "$$X = (u, v)$$\n", + "$$w: \\text{the input of interest}$$\n", + "$$c: \\text{all the other inputs}$$\n", + "$$X = (w, c)$$\n", "\n", - "Suppose for the input of interest, we are interested in comparing $u^{\\text{high}}$ to $u^{\\text{low}}$ (perhaps age = $60$ and $40$ respectively) with all other inputs $v$ held constant. The _predictive difference_ in the outcome changing **only** $u$ is:\n", + "Suppose for the input of interest, we are interested in comparing $w^{\\text{high}}$ to $w^{\\text{low}}$ (perhaps age = $60$ and $40$ respectively) with all other inputs $c$ held constant. The _predictive difference_ in the outcome changing **only** $w$ is:\n", "\n", - "$$\\text{average predictive difference} = \\mathbb{E}(y|u^{\\text{high}}, v, \\theta) - \\mathbb{E}(y|u^{\\text{low}}, v, \\theta)$$\n", + "$$\\text{average predictive difference} = \\mathbb{E}(y|w^{\\text{high}}, c, \\theta) - \\mathbb{E}(y|w^{\\text{low}}, c, \\theta)$$\n", "\n", - "Selecting the maximum and minimum values of $u$ and averaging over all other inputs $v$ in the data gives you a new \"hypothetical\" dataset and corresponds to counting all pairs of transitions of $(u^\\text{low})$ to $(u^\\text{high})$, i.e., differences in $u$ with $v$ held constant. The difference between these two terms is the average predictive difference." + "Selecting the maximum and minimum values of $w$ and averaging over all other inputs $c$ in the data gives you a new \"hypothetical\" dataset and corresponds to counting all pairs of transitions of $(w^\\text{low})$ to $(w^\\text{high})$, i.e., differences in $w$ with $c$ held constant. The difference between these two terms is the average predictive difference." ] }, { @@ -47,13 +47,13 @@ "source": [ "### Computing Average Predictive Differences\n", "\n", - "The objective of `comparisons` and `plot_comparisons` is to compute the expected difference in the outcome corresponding to three different scenarios for $u$ and $v$ where $u$ is either provided by the user, else a default value is computed by Bambi (described in the default values section). The three scenarios are:\n", + "The objective of `comparisons` and `plot_comparisons` is to compute the expected difference in the outcome corresponding to three different scenarios for $w$ and $c$ where $w$ is either provided by the user, else a default value is computed by Bambi (described in the default values section). The three scenarios are:\n", "\n", - "1. user provided values for $v$.\n", - "2. a grid of equally spaced and central values for $v$.\n", - "3. empirical distribution (original data used to fit the model) for $v$. \n", + "1. user provided values for $c$.\n", + "2. a grid of equally spaced and central values for $c$.\n", + "3. empirical distribution (original data used to fit the model) for $c$. \n", "\n", - "In the case of (1) and (2) above, Bambi assembles all pairwise combinations (transitions) of $u$ and $v$ into a new \"hypothetical\" dataset. In (3), Bambi uses the original $v$, but replaces $u$ with the user provided value or the default value computed by Bambi. In each scenario, predictions are made on the data using the fitted model. Once the predictions are made, comparisons are computed using the posterior samples by taking the difference in the predicted outcome for each pair of transitions. The average of these differences is the average predictive difference. \n", + "In the case of (1) and (2) above, Bambi assembles all pairwise combinations (transitions) of $w$ and $c$ into a new \"hypothetical\" dataset. In (3), Bambi uses the original $c$, but replaces $w$ with the user provided value or the default value computed by Bambi. In each scenario, predictions are made on the data using the fitted model. Once the predictions are made, comparisons are computed using the posterior samples by taking the difference in the predicted outcome for each pair of transitions. The average of these differences is the average predictive difference. \n", "\n", "Thus, the goal of `comparisons` and `plot_comparisons` is to provide the modeler with a summary and visualization of the average predictive difference. Below, we demonstrate how to compute and plot average predictive differences with `comparisons` and `plot_comparions` using several examples." ] @@ -86,7 +86,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We model and predict how many fish are caught by visitors at a state park using survey [data](\"https://stats.idre.ucla.edu/stat/data/fish.csv\"). Many visitors catch zero fish, either because they did not fish at all, or because they were unlucky. We would like to explicitly model this bimodal behavior (zero versus non-zero) using a Zero Inflated Poisson model, and to compare how different inputs of interest $u$ and other covariate values $v$ are associated with the number of fish caught. The dataset contains data on 250 groups that went to a state park to fish. Each group was questioned about how many fish they caught (`count`), how many children were in the group (`child`), how many people were in the group (`persons`), if they used a live bait and whether or not they brought a camper to the park (`camper`)." + "We model and predict how many fish are caught by visitors at a state park using survey [data](\"https://stats.idre.ucla.edu/stat/data/fish.csv\"). Many visitors catch zero fish, either because they did not fish at all, or because they were unlucky. We would like to explicitly model this bimodal behavior (zero versus non-zero) using a Zero Inflated Poisson model, and to compare how different inputs of interest $w$ and other covariate values $c$ are associated with the number of fish caught. The dataset contains data on 250 groups that went to a state park to fish. Each group was questioned about how many fish they caught (`count`), how many children were in the group (`child`), how many people were in the group (`persons`), if they used a live bait and whether or not they brought a camper to the park (`camper`)." ] }, { @@ -191,7 +191,7 @@ "source": [ "### User Provided Values\n", "\n", - "First, an example of scenario 1 (user provided values) is given below. In both `plot_comparisons` and `comparisons`, $u$ and $v$ are represented by `contrast` and `conditional`, respectively. The modeler has the ability to pass their own values for `contrast` and `conditional` by using a dictionary where the key-value pairs are the covariate and value(s) of interest. For example, if we wanted to compare the number of fish caught for $4$ versus $1$ `persons` conditional on a range of `child` and `livebait` values, we would pass the following dictionary in the code block below. By default, for $u$, Bambi compares $u^\\text{high}$ to $u^\\text{low}$. Thus, in this example, $u^\\text{high}$ = 4 and $u^\\text{low}$ = 1. The user is not limited to passing a list for the values. A `np.array` can also be used. Furthermore, Bambi by default, maps the order of the dict keys to the main, group, and panel of the matplotlib figure. Below, since `child` is the first key, this is used for the x-axis, and `livebait` is used for the group (color). If a third key was passed, it would be used for the panel (facet)." + "First, an example of scenario 1 (user provided values) is given below. In both `plot_comparisons` and `comparisons`, $w$ and $c$ are represented by `contrast` and `conditional`, respectively. The modeler has the ability to pass their own values for `contrast` and `conditional` by using a dictionary where the key-value pairs are the covariate and value(s) of interest. For example, if we wanted to compare the number of fish caught for $4$ versus $1$ `persons` conditional on a range of `child` and `livebait` values, we would pass the following dictionary in the code block below. By default, for $w$, Bambi compares $w^\\text{high}$ to $w^\\text{low}$. Thus, in this example, $w^\\text{high}$ = 4 and $w^\\text{low}$ = 1. The user is not limited to passing a list for the values. A `np.array` can also be used. Furthermore, Bambi by default, maps the order of the dict keys to the main, group, and panel of the matplotlib figure. Below, since `child` is the first key, this is used for the x-axis, and `livebait` is used for the group (color). If a third key was passed, it would be used for the panel (facet)." ] }, { @@ -227,7 +227,7 @@ "source": [ "The plot above shows that, comparing $4$ to $1$ persons given $0$ children and using livebait, the expected difference is about $26$ fish. When not using livebait, the expected difference decreases substantially to about $5$ fish. Using livebait with a group of people is associated with a much larger expected difference in the number of fish caught. \n", "\n", - "`comparisons` can be called to view a summary dataframe that includes the term $u$ and its contrast, the specified `conditional` covariate, and the expected difference in the outcome with the uncertainty interval (by default the 94% highest density interval is computed). " + "`comparisons` can be called to view a summary dataframe that includes the term $w$ and its contrast, the specified `conditional` covariate, and the expected difference in the outcome with the uncertainty interval (by default the 94% highest density interval is computed). " ] }, { @@ -366,7 +366,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "But why is `camper` also in the summary dataframe? This is because in order to peform predictions, Bambi is expecting a value for each covariate used to fit the model. Additionally, with GLM models, average predictive comparisons are conditional in the sense that the estimate depends on the values of all the covariates in the model. Thus, for unspecified covariates, `comparisons` and `plot_comparisons` computes a default value (mean or mode based on the data type of the covariate). Thus, $v$ = `child`, `livebait`, `camper`. Each row in the summary dataframe is read as \"comparing $4$ to $1$ persons conditional on $v$, the expected difference in the outcome is $y$.\"" + "But why is `camper` also in the summary dataframe? This is because in order to peform predictions, Bambi is expecting a value for each covariate used to fit the model. Additionally, with GLM models, average predictive comparisons are conditional in the sense that the estimate depends on the values of all the covariates in the model. Thus, for unspecified covariates, `comparisons` and `plot_comparisons` computes a default value (mean or mode based on the data type of the covariate). Thus, $c$ = `child`, `livebait`, `camper`. Each row in the summary dataframe is read as \"comparing $4$ to $1$ persons conditional on $c$, the expected difference in the outcome is $y$.\"" ] }, { @@ -681,7 +681,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Notice how the contrast $u$ varies while the covariates $v$ are held constant. Currently, however, plotting multiple contrast values can be difficult to interpret since the contrast is \"abstracted\" away onto the y-axis. Thus, it would be difficult to interpret which portion of the plot corresponds to which contrast value. Therefore, it is currently recommended that if you want to plot multiple contrast values, call `comparisons` directly to obtain the summary dataframe and plot the results yourself." + "Notice how the contrast $w$ varies while the covariates $c$ are held constant. Currently, however, plotting multiple contrast values can be difficult to interpret since the contrast is \"abstracted\" away onto the y-axis. Thus, it would be difficult to interpret which portion of the plot corresponds to which contrast value. Therefore, it is currently recommended that if you want to plot multiple contrast values, call `comparisons` directly to obtain the summary dataframe and plot the results yourself." ] }, { @@ -693,7 +693,7 @@ "\n", "Now, we move onto scenario 2 described above (grid of equally spaced and central values) in computing average predictive comparisons. You are not required to pass values for `contrast` and `conditional`. If you do not pass values, Bambi will compute default values for you. Below, it is described how these default values are computed.\n", "\n", - "The default value for `contrast` is a _centered difference_ at the mean for a contrast variable with a numeric dtype, and _unique levels_ for a contrast varaible with a categorical dtype. For example, if the modeler is interested in the comparison of a $5$ unit increase in $u$ where $u$ is a numeric variable, Bambi computes the mean and then subtracts and adds $2.5$ units to the mean to obtain a _centered difference_. By default, if no value is passed for the contrast covariate, Bambi computes a one unit centered difference at the mean. For example, if only `contrast=\"persons\"` is passed, then $\\pm$ $0.5$ is applied to the mean of persons. If $u$ is a categorical variable, Bambi computes and returns the unique levels. For example, if $u$ has levels [\"high scool\", \"vocational\", \"university\"], Bambi computes and returns the unique values of this variable.\n", + "The default value for `contrast` is a _centered difference_ at the mean for a contrast variable with a numeric dtype, and _unique levels_ for a contrast varaible with a categorical dtype. For example, if the modeler is interested in the comparison of a $5$ unit increase in $w$ where $w$ is a numeric variable, Bambi computes the mean and then subtracts and adds $2.5$ units to the mean to obtain a _centered difference_. By default, if no value is passed for the contrast covariate, Bambi computes a one unit centered difference at the mean. For example, if only `contrast=\"persons\"` is passed, then $\\pm$ $0.5$ is applied to the mean of persons. If $w$ is a categorical variable, Bambi computes and returns the unique levels. For example, if $w$ has levels [\"high scool\", \"vocational\", \"university\"], Bambi computes and returns the unique values of this variable.\n", "\n", "The default values for `conditional` are more involved. Currently, by default, if a dict or list is passed to `conditional`, Bambi uses the ordering (keys if dict and elements if list) to determine which covariate to use as the main, group (color), and panel (facet) variable. This is the same logic used in `plot_comparisons` described above. Subsequently, the default values used for the `conditional` covariates depend on their ordering **and** dtype. Below, the psuedocode used for computing default values covariates passed to `conditional` is outlined:\n", "\n", @@ -993,7 +993,7 @@ "source": [ "### Unit level contrasts\n", "\n", - "Evaluating average predictive comparisons at central values for the conditional covariates $v$ can be problematic when the inputs have a large variance since no single central value (mean, median, etc.) is representative of the covariate. This is especially true when $v$ exhibits bi or multimodality. Thus, it may be desireable to use the empirical distribution of $v$ to compute the predictive comparisons, and then average over a specific or set of covariates to obtain the average predictive comparisons. To achieve unit level contrasts, do not pass a parameter into `conditional` and or specify `None`." + "Evaluating average predictive comparisons at central values for the conditional covariates $c$ can be problematic when the inputs have a large variance since no single central value (mean, median, etc.) is representative of the covariate. This is especially true when $c$ exhibits bi or multimodality. Thus, it may be desireable to use the empirical distribution of $c$ to compute the predictive comparisons, and then average over a specific or set of covariates to obtain the average predictive comparisons. To achieve unit level contrasts, do not pass a parameter into `conditional` and or specify `None`." ] }, { @@ -1345,7 +1345,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Above, `unit_level` is the comparisons summary dataframe and `fish_model.data` is the empirical data. Notice how the values for $v$ are identical in both dataframes. However, for $u$, the values are different. However, these unit level contrasts are difficult to interpret as each row corresponds to _that_ unit's contrast. Therefore, it is useful to average over (marginalize) the estimates to summarize the unit level predictive comparisons." + "Above, `unit_level` is the comparisons summary dataframe and `fish_model.data` is the empirical data. Notice how the values for $c$ are identical in both dataframes. However, for $w$, the values are different. However, these unit level contrasts are difficult to interpret as each row corresponds to _that_ unit's contrast. Therefore, it is useful to average over (marginalize) the estimates to summarize the unit level predictive comparisons." ] }, { diff --git a/docs/notebooks/plot_slopes.ipynb b/docs/notebooks/plot_slopes.ipynb new file mode 100644 index 000000000..d44e39d49 --- /dev/null +++ b/docs/notebooks/plot_slopes.ipynb @@ -0,0 +1,2498 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot Slopes\n", + "\n", + "The Bambi's sub-package `plots` features a set of functions to help interpret complex regression models. This sub-package is inspired by the R package [marginaleffects](https://vincentarelbundock.github.io/marginaleffects/articles/predictions.html#conditional-adjusted-predictions-plot). In this notebook we will discuss two functions `slopes` and `plot_slopes`. These two functions allow the modeler to easier interpret slopes, either by a inspecting a summary output or plotting them.\n", + "\n", + "Below, it is described why estimating the slope of the prediction function is useful in interpreting generalized linear models (GLMs), how this methodology is implemented in Bambi, and how to use `slopes` and `plot_slopes`. It is assumed that the reader is familiar with the basics of GLMs. If not, refer to the Bambi [Basic Building Blocks](https://bambinos.github.io/bambi/notebooks/how_bambi_works.html#Link-functions) example." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Interpretation of Regression Coefficients\n", + "\n", + "Assuming we have fit a linear regression model of the form\n", + "\n", + "$$y = \\beta_0 + \\beta_1 x_1 + \\beta_2 x_2 + \\dots + \\beta_k x_k + \\epsilon$$\n", + "\n", + "the \"safest\" interpretation of the regression coefficients $\\beta$ is as a comparison between two groups of items that differ by $1$ in the relevant predictor variable $x_i$ while being identical in all the other predictors. Formally, the predicted difference between two items $i$ and $j$ that differ by an amount $n$ on predictor $k$, but are identical on all other predictors, the predicted difference is $y_i - y_j$ is $\\beta_kx$, on average.\n", + "\n", + "However, once we move away from a regression model with a Gaussian response, the identity function, and no interaction terms, the interpretation of the coefficients are not as straightforward. For example, in a logistic regression model, the coefficients are on a different scale and are measured in logits (log odds), not probabilities or percentage points. Thus, you cannot interpret the coefficents as a \"one unit increase in $x_k$ is associated with an $n$ percentage point decrease in $y$\". First, the logits must be converted to the probability scale. Secondly, a one unit change in $x_k$ may produce a larger or smaller change in the outcome, depending upon how far away from zero the logits are. \n", + "\n", + "`slopes` and `plot_slopes`, by default, computes quantities of interest on the response scale for GLMs. For example, for a logistic regression model, this is the probability scale, and for a Poisson regression model, this is the count scale." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Interpreting interaction effects\n", + "\n", + "Specifying interactions in a regression model is a way of allowing parameters to be conditional on certain aspects of the data. By contrast, for a model with no interactions, the parameters are **not** conditional and thus, the value of one parameter is not dependent on the value of another covariate. However, once interactions exist, multiple parameters are always in play at the same time. Additionally, interactions can be specified for either categorical, continuous, or both types of covariates. Thus, making the interpretation of the parameters more difficult.\n", + "\n", + "With GLMs, every covariate essentially interacts with itself because of the link function. To demonstrate parameters interacting with themselves, consider the mean of a Gaussian linear model with an identity link function\n", + "\n", + "$$\\mu = \\alpha + \\beta x$$\n", + "\n", + "where the rate of change in $\\mu$ with respect to $x$ is just $\\beta$, i.e., the rate of change is constant no matter what the value of $x$ is. But when we consider GLMs with link functions used to map outputs to exponential family distribution parameters, calculating the derivative of the mean output $\\mu$ with respect to the predictor is not as straightforward as in the Gaussian linear model. For example, computing the rate of change in a binomial probability $p$ with respect to $x$\n", + "\n", + "$$p = \\frac{exp(\\alpha + \\beta x)}{1 + exp(\\alpha + \\beta x)}$$\n", + "\n", + "And taking the derivative of $p$ with respect to $x$ yields\n", + "\n", + "$$\\frac{\\partial p}{\\partial x} = \\frac{\\beta}{2(1 + cosh(\\alpha + \\beta x))}$$\n", + "\n", + "Since $x$ appears in the derivative, the impact of a change in $x$ depends upon $x$, i.e., an interaction with itself even though no interaction term was specified in the model.Thus, visualizing the rate of change in the mean response with respect to a covariate $x$ becomes a useful tool in interpreting GLMs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Average Predictive Slopes\n", + "\n", + "Here, we adopt the notation from Chapter 14.4 of [Regression and Other Stories](https://avehtari.github.io/ROS-Examples/) to first describe average predictive differences which is essential to computing `slopes`, and then secondly, average predictive slopes. Assume we have fit a Bambi model predicting an outcome $Y$ based on inputs $X$ and parameters $\\theta$. Consider the following scalar inputs:\n", + "\n", + "$$w: \\text{the input of interest}$$\n", + "$$c: \\text{all the other inputs}$$\n", + "$$X = (w, c)$$\n", + "\n", + "In contrast to `comparisons`, for `slopes` we are interested in comparing $w^{\\text{value}}$ to $w^{\\text{value}+\\epsilon}$ (perhaps age = 60 and 60.0001 respectively) with all other inputs $c$ held constant. The _predictive difference_ in the outcome changing **only** $w$ is:\n", + "\n", + "$$\\text{average predictive difference} = \\mathbb{E}(y|w^{\\text{value}}, c, \\theta) - \\mathbb{E}(y|w^{\\text{value}+\\epsilon}, c, \\theta)$$\n", + "\n", + "Selecting $w$ and $w^{\\text{value}+\\epsilon}$ and averaging over all other inputs $c$ in the data gives you a new \"hypothetical\" dataset and corresponds to counting all pairs of transitions of $(w^\\text{value})$ to $(w^{\\text{value}+\\epsilon})$, i.e., differences in $w$ with $c$ held constant. The difference between these two terms is the average predictive difference.\n", + "\n", + "However, to obtain the slope estimate, we need to take the above formula and divide by $\\epsilon$ to obtain the _average predictive slope_:\n", + "\n", + "$$\\text{average predictive slope} = \\frac{\\mathbb{E}(y|w^{\\text{value}}, c, \\theta) - \\mathbb{E}(y|w^{\\text{value}+\\epsilon}, c, \\theta)}{\\epsilon}$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Computing Slopes\n", + "\n", + "The objective of `slopes` and `plot_slopes` is to compute the rate of change (slope) in the mean of the response $y$ with respect to a small change $\\epsilon$ in the predictor $x$ conditional on other covariates $c$ specified in the model. $w$ is specified by the user and the original value is either provided by the user, else a default value (the mean) is computed by Bambi. The values for the other covariates $c$ specified in the model can be determined under the following three scenarios:\n", + "\n", + "1. user provided values \n", + "2. a grid of equally spaced and central values\n", + "3. empirical distribution (original data used to fit the model)\n", + "\n", + "In the case of (1) and (2) above, Bambi assembles all pairwise combinations (transitions) of $w$ and $c$ into a new \"hypothetical\" dataset. In (3), Bambi uses the original $c$, and adds a small amount $\\epsilon$ to each unit of observation's $w$. In each scenario, predictions are made on the data using the fitted model. Once the predictions are made, comparisons are computed using the posterior samples by taking the difference in the predicted outcome for each pair of transitions and dividing by $\\epsilon$. The average of these slopes is the average predictive slopes.\n", + "\n", + "For variables $w$ with a string or categorical data type, the `comparisons` function is called to compute the expected difference in group means. Please refer to the [comparisons](https://bambinos.github.io/bambi/notebooks/plot_comparisons.html) documentation for more details.\n", + "\n", + "Below, we present several examples showing how to use Bambi to perform these computations for us, and to return either a summary dataframe, or a visualization of the results." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import arviz as az\n", + "import pandas as pd\n", + "\n", + "import bambi as bmb\n", + "from bambi.plots import slopes, plot_slopes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Logistic Regression\n", + "\n", + "To demonstrate `slopes` and `plot_slopes`, we will use the [well switching dataset](https://vincentarelbundock.github.io/Rdatasets/doc/carData/Wells.html) to model the probability a household in Bangladesh switches water wells. The data are for an area of Arahazar Upazila, Bangladesh. The researchers labelled each well with its level of arsenic and an indication of whether the well was “safe” or “unsafe”. Those using unsafe wells were encouraged to switch. After several years, it was determined whether each household using an unsafe well had changed its well. The data contains $3020$ observations on the following five variables:\n", + "\n", + "- `switch`: a factor with levels `no` and `yes` indicating whether the household switched to a new well\n", + "- `arsenic`: the level of arsenic in the old well (measured in micrograms per liter)\n", + "- `dist`: the distance to the nearest safe well (measured in meters)\n", + "- `assoc`: a factor with levels `no` and `yes` indicating whether the household is a member of an arsenic education group\n", + "- `educ`: years of education of the household head\n", + "\n", + "First, a logistic regression model with no interactions is fit to the data. Subsequently, to demonstrate the benefits of `plot_slopes` in interpreting interactions, we will fit a logistic regression model with an interaction term." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " switch arsenic dist assoc educ dist100 educ4\n", + "1 1 2.36 16.826000 0 0 0.16826 0.0\n", + "2 1 0.71 47.321999 0 0 0.47322 0.0\n", + "3 0 2.07 20.966999 0 10 0.20967 2.5\n", + "4 1 1.15 21.486000 0 12 0.21486 3.0\n", + "5 1 1.10 40.874001 1 14 0.40874 3.5" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv(\"http://www.stat.columbia.edu/~gelman/arm/examples/arsenic/wells.dat\", sep=\" \")\n", + "data[\"switch\"] = pd.Categorical(data[\"switch\"])\n", + "data[\"dist100\"] = data[\"dist\"] / 100\n", + "data[\"educ4\"] = data[\"educ\"] / 4\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Modeling the probability that switch==0\n", + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [Intercept, dist100, arsenic, educ4]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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The modeler has the ability to pass their own values for `wrt` and `conditional` by using a dictionary where the key-value pairs are the covariate and value(s) of interest.\n", + "\n", + "For example, if we wanted to compute the slope of the probability of switching wells for a typical `arsenic` value of $1.3$ conditional on a range of `dist` and `educ` values, we would pass the following dictionary in the code block below. By default, for $w$, Bambi compares $w^\\text{value}$ to $w^{\\text{value} + \\epsilon}$ where $\\epsilon =$ `1e-4`. However, the value for $\\epsilon$ can be changed by passing a value to the argument `eps`. \n", + "\n", + "Thus, in this example, $w^\\text{value} = 1.3$ and $w^{\\text{value} + \\epsilon} = 1.3001$. The user is not limited to passing a list for the values. A `np.array` can also be used. Furthermore, Bambi by default, maps the order of the dict keys to the main, group, and panel of the matplotlib figure. Below, since `dist100` is the first key, this is used for the x-axis, and `educ4` is used for the group (color). If a third key was passed, it would be used for the panel (facet)." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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8arsenicdydx(1.5, 1.5001)0.82.0-0.116957-0.135079-0.096476
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" + ], + "text/plain": [ + " term estimate_type value dist100 educ4 estimate lower_3.0% \n", + "0 arsenic dydx (1.5, 1.5001) 0.2 1.0 -0.110797 -0.128775 \\\n", + "1 arsenic dydx (1.5, 1.5001) 0.2 1.2 -0.109867 -0.126725 \n", + "2 arsenic dydx (1.5, 1.5001) 0.2 2.0 -0.105618 -0.122685 \n", + "3 arsenic dydx (1.5, 1.5001) 0.5 1.0 -0.116087 -0.134965 \n", + "4 arsenic dydx (1.5, 1.5001) 0.5 1.2 -0.115632 -0.134562 \n", + "5 arsenic dydx (1.5, 1.5001) 0.5 2.0 -0.113140 -0.130448 \n", + "6 arsenic dydx (1.5, 1.5001) 0.8 1.0 -0.117262 -0.136850 \n", + "7 arsenic dydx (1.5, 1.5001) 0.8 1.2 -0.117347 -0.136475 \n", + "8 arsenic dydx (1.5, 1.5001) 0.8 2.0 -0.116957 -0.135079 \n", + "\n", + " upper_97.0% \n", + "0 -0.092806 \n", + "1 -0.091065 \n", + "2 -0.088383 \n", + "3 -0.096843 \n", + "4 -0.096543 \n", + "5 -0.093209 \n", + "6 -0.098549 \n", + "7 -0.098044 \n", + "8 -0.096476 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "slopes(\n", + " well_model,\n", + " well_idata,\n", + " wrt={\"arsenic\": 1.5},\n", + " conditional={\n", + " \"dist100\": [0.20, 0.50, 0.80], \n", + " \"educ4\": [1.00, 1.20, 2.00]\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since all covariates used to fit the model were also specified to compute the slopes, no default value is used for unspecified covariates. A default value is computed for the unspecified covariates because in order to peform predictions, Bambi is expecting a value for each covariate used to fit the model. Additionally, with GLM models, average predictive slopes are conditional in the sense that the estimate depends on the values of **all** the covariates in the model. Thus, for unspecified covariates, `slopes` and `plot_slopes` computes a default value (mean or mode based on the data type of the covariate). Each row in the summary dataframe is read as \"the slope (or rate of change) of the probability of switching wells with respect to a small change in $w$ conditional on $c$ is $y$\"." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Multiple slope values\n", + "\n", + "Users can also compute slopes on multiple values for `wrt`. For example, if we want to compute the slope of $y$ with respect to `arsenic` $= 1.5$, $2.0$, and $2.5$, simply pass a list or numpy array as the dictionary values for `wrt`. Keeping the conditional covariate and values the same, the following slope estimates are computed below." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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termestimate_typevaluedist100educ4estimatelower_3.0%upper_97.0%
0arsenicdydx(1.5, 1.5001)0.21.0-0.110797-0.128775-0.092806
1arsenicdydx(2.0, 2.0001)0.21.0-0.109867-0.126725-0.091065
2arsenicdydx(2.5, 2.5001)0.21.0-0.105618-0.122685-0.088383
3arsenicdydx(1.5, 1.5001)0.21.2-0.116087-0.134965-0.096843
4arsenicdydx(2.0, 2.0001)0.21.2-0.115632-0.134562-0.096543
5arsenicdydx(2.5, 2.5001)0.21.2-0.113140-0.130448-0.093209
\n", + "
" + ], + "text/plain": [ + " term estimate_type value dist100 educ4 estimate lower_3.0% \n", + "0 arsenic dydx (1.5, 1.5001) 0.2 1.0 -0.110797 -0.128775 \\\n", + "1 arsenic dydx (2.0, 2.0001) 0.2 1.0 -0.109867 -0.126725 \n", + "2 arsenic dydx (2.5, 2.5001) 0.2 1.0 -0.105618 -0.122685 \n", + "3 arsenic dydx (1.5, 1.5001) 0.2 1.2 -0.116087 -0.134965 \n", + "4 arsenic dydx (2.0, 2.0001) 0.2 1.2 -0.115632 -0.134562 \n", + "5 arsenic dydx (2.5, 2.5001) 0.2 1.2 -0.113140 -0.130448 \n", + "\n", + " upper_97.0% \n", + "0 -0.092806 \n", + "1 -0.091065 \n", + "2 -0.088383 \n", + "3 -0.096843 \n", + "4 -0.096543 \n", + "5 -0.093209 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "multiple_values = slopes(\n", + " well_model,\n", + " well_idata,\n", + " wrt={\"arsenic\": [1.5, 2.0, 2.5]},\n", + " conditional={\n", + " \"dist100\": [0.20, 0.50, 0.80], \n", + " \"educ4\": [1.00, 1.20, 2.00]\n", + " }\n", + ")\n", + "\n", + "multiple_values.head(6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output above is essentially the same as the summary dataframe when we only passed one value to `wrt`. However, now each element (value) in the list gets a small amount $\\epsilon$ added to it, and the slope is calculated for each of these values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conditional slopes\n", + "\n", + "As stated in the _interpreting interaction effects_ section, interpreting coefficients of multiple interaction terms can be difficult and cumbersome. Thus, `plot_slopes` provides an effective way to visualize the conditional slopes of the interaction effects. Below, we will use the same well switching dataset, but with interaction terms. Specifically, one interaction is added between `dist100` and `educ4`, and another between `arsenic` and `educ4`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Modeling the probability that switch==0\n", + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [Intercept, dist100, arsenic, educ4, dist100:educ4, arsenic:educ4]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
Intercept-0.0970.122-0.3220.1370.0030.0022259.02203.01.0
dist1001.3200.1750.9821.6400.0040.0032085.02457.01.0
arsenic-0.3980.061-0.521-0.2910.0010.0012141.02558.01.0
educ40.1020.080-0.0530.2460.0020.0011935.02184.01.0
dist100:educ4-0.3300.106-0.528-0.1360.0020.0022070.02331.01.0
arsenic:educ4-0.0790.043-0.161-0.0000.0010.0012006.02348.01.0
\n", + "
" + ], + "text/plain": [ + " mean sd hdi_3% hdi_97% mcse_mean mcse_sd ess_bulk \n", + "Intercept -0.097 0.122 -0.322 0.137 0.003 0.002 2259.0 \\\n", + "dist100 1.320 0.175 0.982 1.640 0.004 0.003 2085.0 \n", + "arsenic -0.398 0.061 -0.521 -0.291 0.001 0.001 2141.0 \n", + "educ4 0.102 0.080 -0.053 0.246 0.002 0.001 1935.0 \n", + "dist100:educ4 -0.330 0.106 -0.528 -0.136 0.002 0.002 2070.0 \n", + "arsenic:educ4 -0.079 0.043 -0.161 -0.000 0.001 0.001 2006.0 \n", + "\n", + " ess_tail r_hat \n", + "Intercept 2203.0 1.0 \n", + "dist100 2457.0 1.0 \n", + "arsenic 2558.0 1.0 \n", + "educ4 2184.0 1.0 \n", + "dist100:educ4 2331.0 1.0 \n", + "arsenic:educ4 2348.0 1.0 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# summary of coefficients\n", + "az.summary(well_idata_interact)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The coefficients of the linear model are shown in the table above. The interaction coefficents indicate the slope varies in a continuous fashion with the continuous variable.\n", + "\n", + "A negative value for `arsenic:dist100` indicates that the \"effect\" of arsenic on the outcome is less negative as distance from the well increases. Similarly, a negative value for `arsenic:educ4` indicates that the \"effect\" of arsenic on the outcome is more negative as education increases. Remember, these coefficients are still on the logit scale. Furthermore, as more variables and interaction terms are added to the model, interpreting these coefficients becomes more difficult. \n", + "\n", + "Thus, lets use `plot_slopes` to visually see how the slope changes with respect to `arsenic` conditional on `dist100` and `educ4` changing. Notice in the code block below how parameters are passed to the `subplot_kwargs` and `fig_kwargs` arguments. At times, it can be useful to pass specific `group` and `panel` arguments to aid in the interpretation of the plot. Therefore, `subplot_kwargs` allows the user to manipulate the plotting by passing a dictionary where the keys are `{\"main\": ..., \"group\": ..., \"panel\": ...}` and the values are the names of the covariates to be plotted. `fig_kwargs` are figure level key word arguments such as `figsize` and `sharey`." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plot_slopes(\n", + " well_model_interact,\n", + " well_idata_interact,\n", + " wrt=\"arsenic\",\n", + " conditional=[\"dist100\", \"educ4\"],\n", + " subplot_kwargs={\"main\": \"dist100\", \"group\": \"educ4\", \"panel\": \"educ4\"},\n", + " fig_kwargs={\"figsize\": (16, 4), \"sharey\": True},\n", + " legend=False\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With interaction terms now defined, it can be seen how the slope of the outcome with respect to `arsenic` differ depending on the value of `educ4`. Especially in the case of `educ4` $= 4.25$, the slope is more \"constant\", but with greater uncertainty. Lets compare this with the model that does not include any interaction terms." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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SpJgphBBCCCGEEEIIIYQYEqSYKYQQQgghhBBCCCGEGBKkmCmEEEIIIYQQQgghhBgSpJgphBBCCCGEEEIIIYQYEoZMMbO7u5ulS5eSkpJCSkoKS5cupaen57LHKKVYtmwZ+fn5xMXFMW/ePI4fP973866uLv7mb/6G8ePHEx8fT1FREc899xwOh2OQ340QYjhRSlHX6SEQ0vQORQghhBBCCCGEGNaGTDHzySefpLy8nNWrV7N69WrKy8tZunTpZY/51a9+xQsvvMBLL73Evn37yM3NZcGCBfT29gLQ1NREU1MT//t//2+OHj3KK6+8wurVq/nLv/zLaLwlIcQw4AmEWHO8hffKG6jrcusdjhBCCCGEEEIIMaxZ9Q6gP06ePMnq1avZvXs3M2bMAOB3v/sds2bNoqKigvHjx19wjFKKF198kR/+8Ic8+OCDALz66qvk5OTw+uuv88wzz3D99dezfPnyvmNGjx7Nz372M7785S8TCoWwWofEH48QQif1XR42nmrjTLsLkwmU0jsiIYQQQgghhBBieBsS1bpdu3aRkpLSV8gEmDlzJikpKezcufOixczq6mpaWlpYuHBh33N2u525c+eyc+dOnnnmmYuey+FwkJycfNlCpt/vx+/3933vdDqv5W0JIYaoUFjjUH0PWyvbcftDjMlK5GznyFuVKblQCCEiJB8KIYTkQiFE9AyJbeYtLS1kZ2df8Hx2djYtLS2XPAYgJyfnvOdzcnIueUxnZyf/83/+z0sWOs/5xS9+0de7MyUlhcLCwv68DSHEMODwBPnoSDMrjzRjMZkYk52E1TIkUumAk1wohBARkg+FEEJyoRAienT9BL5s2TJMJtNlv/bv3w+AyWS64Hil1EWf/7TP/vxSxzidTu655x4mTZrET37yk8v+zh/84Ac4HI6+r/r6+iu9VSHEMHCm3cVb++s5WNfNqLR4spNj9Q5JV5ILhRAiQvKhEEJILhRCRI+u28yfffZZHn/88cu+pqSkhCNHjtDa2nrBz9rb2y9YeXlObm4uEFmhmZeX1/d8W1vbBcf09vayaNEiEhMTWbFiBTab7bIx2e127Hb7ZV8jhBg+gmGNfdVdbD/dQUhTjM1OwmK+/I2UkUByoRBCREg+FEIIyYVCiOjRtZiZmZlJZmbmFV83a9YsHA4He/fuZfr06QDs2bMHh8PB7NmzL3pMaWkpubm5rFu3jqlTpwIQCATYsmULv/zlL/te53Q6ueuuu7Db7XzwwQfExo7slVZCiPN1uwNsPNXK0QYnGYkxZCTKBZoQQgghhBBCCKGXIdHobeLEiSxatIinn36a3bt3s3v3bp5++mmWLFly3vCfCRMmsGLFCiCyvfzb3/42P//5z1mxYgXHjh3jqaeeIj4+nieffBKIrMhcuHAhbreb//qv/8LpdNLS0kJLSwvhcFiX9yqEMAalFBUtvby5v54jDQ6KMuKlkCmEEEIIIYQQQuhsSEwzB3jttdd47rnn+qaT33fffbz00kvnvaaiogKHw9H3/Xe/+128Xi/f/OY36e7uZsaMGaxdu5akpCQADhw4wJ49ewAYM2bMeb+rurqakpKSQXxHQgij8gbC7Djdzt6abkzA2JwkzFfozyuEEEIIIYQQQojBN2SKmenp6fzxj3+87GuUUud9bzKZWLZsGcuWLbvo6+fNm3fBMUKIka2+y8PGU22caXeRlxJHStzle+gKIYQQQgghhBAieoZMMVMIIQaTpin21kSG/Hj9IUZnJWKzDIlOHEIIYRz+XvC7IDnvyq8VQgghhBDiGsgndSHEiBfWFFsr21l7vAWr2cTo7CQpZAohxNVyNkH563B6nd6RCCGEEEKIYUw+rQshRrRQWGPTqVa2VLWTmWgnOylW75CEEGLoaT0Bh16D5nIIB/WORgghhBBCDGOyzVwIMWIFwxqbTrWx80wnucmxJEt/TCGEuDpaGGp3wpkNoBQk5uodkRBCCCGEGOakmCmEGJECIY31J1vZc7aT/NQ4kmKlkCmEEFcl6IXKtVC3C+LSIDEbeur1jkoIIYQQQgxzss1cCDHi+IJh1hxvZvfZTgrS4qWQKYQQVyvohePvQ802SM6PFDKFEEIAoJSizllHIBzQOxQhhBiWpJgphBhRHN4gHxxuYl9NN0Vp8STaZYG6EEJclaAPTn4EjfshvQzsSXpHJIQQhnKo7RBra9bS7G7WOxQhhBiW5FO8EGLEaHX6WHm0meoON6WZCditFr1DEkKIoSUUgIqPoX4PpJWCLU7viIQQwlAOth5kU/0mgloQpZTe4QghxLAkxUwhxIhQ3eFm1dFm2nv9jMlOxGqWhelCCHFVwkGoWAV1uyGtBGLi9Y5ICCEM5VDbITbVbyLOGkcwENQ7HCGEGLakmCmEGNaUUhxrdLLuZAveQJjR2YmYTSa9wxJCiKElHILK1VC7A1KKICZB74iEEMJQytvK2Vi3kThrHJlxmTgDTr1DEkKIYUuKmUKIYcsXDLO9qp091V3YrRZKMxP1DkkIIYaecAiq1kL1dkgZBXbJpUII8WmfLmRmxWfJ9nIhhBhkUswUQgxLzQ4vG062UdnaS15KHClxMrFcCCGu2rlC5tktkJwnw36EEOIzKroq2Fy/mVhrLFnxWXqHI4QQI4IUM4UQw4qmKY40Othc0YbDE2R0ViI2i/THFEKIq/bZQmZsit4RCSGEodT31rOxbiMmk0kKmUIIEUVSzBRCDBv+UJiNJ9vYX9tNfIyFMdmJmKQ/phBCXD0pZAohxGV1ejtZX7seV9BFSUqJ3uEIIcSIIsVMIcSw4AuGWXO8hYO13RSkxZNol/QmhBDXRAqZQghxWe6gm3W162hxt1CWWqZ3OEIIMeLIp30hxJDnDYRZfayFQ/XdFKcnEBdj0TskIYQYmoI+qFwDtTulkCmEEBcRCAfYULeBsz1nKU0txWySdkZCCBFtUswUQgxpnkCIVUebOdzgkEKmEEJ8Hj4nnPoIGg9CapEM+xFCiM8IaSG2N27nWMcxCpMLsZrl47QQQuhBsq8QYshy+0OsPNrM0UYHJRkJxNqkkCmEENfE1QYn3oeOSkgfDbZYvSMSQghDCYQDbKnfwoHWA+Qm5BJrlTwphBB6kWKmEGJIcvqCrDzSzMlmJ6WZCditUsgUQohr0l0DJz4ARwNkjAWLTe+IhBDCUDxBDxvrNnKk4wj5ifkk2BL0DkkIIUY0KWYKIYacbneAj482UdnqoiwzkRir9CoSQohr0nIssrXc54Ss8SC934QQ4jzOgJN1Neuo6K6gMKlQVmQKIYQBSDFTCDGktPX6+PhIMzWdHkZnJWKzyAdvIYS4apoGdTvh9HpQQMYYMJn0jkoIIQyly9fF2uq1nHGcoSSlhBhLjN4hCSGEQIqZQoghpLHHy8ojzTT2eBmdlYDVLIVMIYS4aiE/VK2Hmm0QlwaJ2XpHJIQQhtPh7WBV9Srqe+spSy2TYT9CCGEgkpGFEENCbaebj4800+HyMyY7EbOsIBJCiKvnc8DJj6C5HJJHQWyy3hEJIYThdHo7WV29mobeBkanjsYsLTiEEMJQpJgphDC8hm4PHx5uoscbpCxLCplCCHFNnE2RQT+dZyC9TCaWCyHERXT7ullds5r63npKU0qlkCmEEAYkxUwhhKF1uPysOtpCtztAWVYiJgMWMoNhTe8QhBDi8tpORQb9uDsgaxzIdkkhhLiAw+9gVfUqap21lKWUYTFb9A5JCCHERciVrBDCsHp9QVYdbaahx8OYrCRDFjJPNDn54HAjt0+UnnNCCAPSNGjYC5VrQAtD5liZWC6EEBfx6UJmaUqpFDKFEMLApJgphDAkXzDM6mMtVLW5GJ2ViMVsrEKmUoqtle2sPdGKAo43OvUOSQghzhfyw+mNkUE/9iRIzdU7IiGEMCRP0MPamrWcdZylNKVUhv0IIYTBSZYWQhhOKKyx4WQbRxsdlGYkYLMYaxVRMKyx4lAj5fU9AMwoTWd8bpK+QQkhxKf5nJFt5U2HILkAYlP0jkgIIQwpqAXZVL+Jqu4qSlJKpJAphBBDgGRqIYShaJpie1UH+2q6GJUWj91mrC0+vb4gf9xdS323F7MJltyQz8yyDCrbevUOTQghIoLeyKCflsOQJoN+hBDiUpRS7GrcxZH2I4xKHoXNYtM7JCGEGFa85eWEXW4S59w6oL9XiplCCMPQNMX20x1srWonO8lOot1YKarF6eP3O2vo8QaJs1l4YnoRY7IT9Q5LCCH+LByCitWRQmb6aLDa9Y5ICCEM63D7Yfa07CE7Pps4a5ze4QghxLDiPXIE57r12EeXDfjvNlalQAgxYp0rZG6uaCMj0U5qfIzeIZ3ndJuL1/bU4g9pZCTE8JXZJWQmSpFACGEgSsGZTVC3G1KLpJAphBCXcbbnLFsbthJviyfZnqx3OEIIMaz4Tp6kd/0Gwj09g/L7pZgphNDduULmlso2MhLspBmskHmgtosVhxrRFJRkxPPlGcXEG2zVqBBCUL8Hzm6CpByIkVXjQghxKa3uVjbUbiCoBclLzNM7HCGEGFb8VVU4164DkwlLWtqgnMNYUzWEECPOpwuZ6fF20hKMU8hUSrHuRAvLD0YKmTeOSuFrt5ZKIVMIYTwtx6ByTWRqedzgXDQKIcRw4Aq4WFe7jk5/J4VJhXqHI4QQw4q/uhrnmjWoYBBbfv6gnUc+kQshdLXDoIXMkKbx7sE/TyyfPz6LOyfmYDKZ9A1MCCE+q7s2MrlcKUjK1TsaIYQwrJAWYkvDFmqdtYxOHS3XdUIIMYD81dX0rl6N5vYQU1IyqOeSYqYQQjen21zsONNBmsEKmf5gmNf21nG6zYXZBA9MKeDmknS9wxJCiAv5nFCxEnwOyBirdzRCCGFoB1oPcLT9KIVJhVjMFr3DEUKIYUGFQngOHsK9YwcqEMBWXDzo55RiphBCF55AiG1V7YQ1RbqBCpm9viC/31VLY4+XGIuZJ2cUMS4nSe+whBDiQpoGp9dD51nIGgeywkgIIS7pdPdpdjXtIi02jTibTC4XQoiBEHY6cW3divfwESypqYO6tfzThkzPzO7ubpYuXUpKSgopKSksXbqUnitMRVJKsWzZMvLz84mLi2PevHkcP378kq9dvHgxJpOJ9957b+DfgBDiPLvPdlHd4aYwLV7vUPp0uvz8v61naezxEh9j4a9uK5VCphDCuBr3Q/3eyORys9yfFkKIS+nwdrC5fjMaGulxsttGCCEGQqC2lp7ly/EeOoQtPx9rZmbUzj1kiplPPvkk5eXlrF69mtWrV1NeXs7SpUsve8yvfvUrXnjhBV566SX27dtHbm4uCxYsoLe394LXvvjii9IzRYgoqelws7+mi5ykWKwWY6Shxh4v/3frWbrcAdLibXxj7mhGGajQKoQQ5+mpi6zKtCeDXSaXCyHEpfhCPjbVbaLd286oxFF6hyOEEMOC9+gxet57j2BrGzGjx2COj+5n5yFxG//kyZOsXr2a3bt3M2PGDAB+97vfMWvWLCoqKhg/fvwFxyilePHFF/nhD3/Igw8+CMCrr75KTk4Or7/+Os8880zfaw8fPswLL7zAvn37yMvLi86bEmKE8gXDbKlsJxDSGJVmjO3lZztc/GFXLf6QRl5KLE/NLiEp1qZ3WEIIcXF+F1SsjvTLzLrwGkgIIUSEN+RlR+MOqnqqKEkukcUrQggxAPxnq+nduBEU2EtLdYlhSBQzd+3aRUpKSl8hE2DmzJmkpKSwc+fOixYzq6uraWlpYeHChX3P2e125s6dy86dO/uKmR6PhyeeeIKXXnqJ3Nz+TQD1+/34/f6+751O57W+NSFGnL3VXZxtd1GWZYyVRKeanby+t46QpijNTGDpzGJibdIQvj8kFwqhA02D0xugoxIypZBpFJIPhTAWTWmc7jnNnuY91PfWk5eQh80iN6oHm+RCIYa/YGsbvevXofx+YqIw6OdSjLG/8wpaWlrIzs6+4Pns7GxaWloueQxATk7Oec/n5OScd8x3vvMdZs+ezf3339/veH7xi1/09e5MSUmhsLCw38cKMZLVd3nYW91FZqIdmwG2l5fX9/DHPbWENMXE3CSeml0ihcyrILlQiChztUPlaqjfFemTaTHYPemmcuip1TsKXUg+FMI42jxtfHz2Y94//T5tnjZKU0pJjDHGTfThTnKhEMNb2Omkd+0aQh0d2HT++61rNWHZsmWYTKbLfu3fvx/golsClFJX3Crw2Z9/+pgPPviAjRs38uKLL15V3D/4wQ9wOBx9X/X19Vd1vBAjkcMbZEtlG55AiIxEu97hsPtsJ2/vr0dTMKUwlSdnFBuiwDqUSC4UIkrcHVC5Bvb9Ds5ugoRssBtsOFn9Hjj4Kpx4D3pb9Y4m6iQfCqE/pRS7m3fzVsVbHOs4RnZ8NkXJRVhlQFrUSC4UYvjS/H56168nUFtLTHEJJrO+n511zezPPvssjz/++GVfU1JSwpEjR2htvfDCuL29/YKVl+ec2zLe0tJyXh/Mtra2vmM2btzImTNnSE1NPe/Yhx56iNtuu43Nmzdf9Hfb7Xbsdv2LMUIMFQ5vkI+ONFHV5qIsU/8741sq2lhzIpJTZpals+SGfMzSQ+mqSS4UYpD5XVC7KzK13NsVKWJmTQSj5avqrXD83cjjrImQmKVvPDqQfCiE/qqd1exo3EG8LZ4xaWP0DmdEklwoxPCkwmFcW7bgO3YcW0kJJqv+N4l0jSAzM5PMfoxunzVrFg6Hg7179zJ9+nQA9uzZg8PhYPbs2Rc9prS0lNzcXNatW8fUqVMBCAQCbNmyhV/+8pcAfP/73+ev/uqvzjtu8uTJ/PrXv+bee+/9PG9NCPEJhyfIh0eaqGrtpSwrUdfVj0opNpxqY+OpNgDmj8/izok50gxeCGFMtbugao1xi5gAVeug4uPI47J5kHcTmGSVuxAiuoLhIHub9qIpjcy4K3++FEII0X/ew0fw7D+AtaAAs0FuWOhfTu2HiRMnsmjRIp5++mn+3//7fwB8/etfZ8mSJecN/5kwYQK/+MUv+OIXv4jJZOLb3/42P//5zxk7dixjx47l5z//OfHx8Tz55JNAZPXmxYb+FBUVUarTRCYhhpMeT4APD0dWZI42QCFzzfFWtla1A3DXdbnMHTfyVg8JIYYInwOaDkFCpjFXOioFpz6CMxsi34+9C8YtAkeDvnEJIUakE10nqHHWUJgsPRqFEGIghXt68OzZgzkhAUui/rsszxkSxUyA1157jeeee65vOvl9993HSy+9dN5rKioqcDgcfd9/97vfxev18s1vfpPu7m5mzJjB2rVrSUoyWJ8pIYahbnekkHmm3RiFzI+PNrPzTCcA90zO49YxctdeCGFgrSfA0w5ZE/SO5EJKwYkVke3lABPvh9Hz9Y1JCDFiOQNO9jbvJT4mnhhLjN7hCCHEsOI5cJBQRzsxY8bqHcp5hkwxMz09nT/+8Y+XfY1S6rzvTSYTy5YtY9myZf0+z2d/hxDi6rn8IT468udCplXHQqamFB8cbmJvdRcA90/JZ0Zphm7xCCHEFYX8kT6ZMUnG27KtNDj2LtRuj3w/+REovlXfmIQQI1p5azntnnZGp43WOxQhhBhWAg2NeA8fxpqdo/vAn88yVjRCiCEvFNbYcLI1MuzHAIXMFYca2VvdhQl46KYCKWQKIYyvvQIcjZB08SGHulEaHH3nk0KmCW54XAqZQghdtbhbONx+mMz4TMxGu/kjhBBDmAqH8ezdg+bzYvnM0GwjkIwvhBhQe6o7Ka/voSg9Xtet5ZpSrDjYyIHabkzAIzcXMq04Xbd4hBCiXzQtsirTbAUjbZdUGhx5C+p2Aia48Qkomql3VEKIEUxTGntb9uIOukmLTdM7HCGEGFb8lZX4Kyux5RfoHcpFDZlt5kII4zvV4mRbVQfp8THEx+iXXjSleO9QIwfqIoXMx24p5IZRqbrFI4QQ/dZdDV1nISlP70j+TGlw+A1o2AuYYMqXYNTNekclhBjhTvecpqKrgvzEfL1DEUKIYUXzenHv2g1WG+a4OL3DuShZmSmEGBCtTh/rT7QCJjIS7brFca6Quf+TFZmPSiFTCDGUNJVDOAQx8XpHEqE0OPynSCHTZIablkohUwihO1fAxd7mvZhNZuJsxvqgrZTibM9ZvcMQQohr5jl8mGBDA7Y8A91c/wxZmSmE+Nzc/hBrj7fQ6QowOjtRtzg0pXi//FOFzJsLuVEKmUKIocLZDG3HIdEgvTLPbS1v2BcpZE79C8ifondUQogRLhgOsrF+I3W9dZSmlOodznmUUqyuWc2+ln1MyZoC4/WOSAghrk6osxPv/v1Y0tMxWY1bMpSVmUKIzyWsKTZVtFHV5qIkMwGzyaRLHEopPihvYl/Nn3tk3liYqkssQghxTVqOgr8X4lL1jgSUigz7qd8NmGDql6WQKYTQnVKKXU27ONZxjMKkQqxm43zQVkqxtnYt+1r2ARBnNdaKUSGEuBItEMC1ZQvhHgeWzEy9w7ks42R/IcSQVF7fw4HabgrT9Bv4o5Ti46PN7K3p+qSQOYopUsgUQgwlPgc0HYL4DL0jiRQyjy3/87CfKV+C/Jv0jkoIITjacZQ9LXvIic8h1hqrdzh9lFKsr13PnuY9ACwpW0KKPUXnqIQQov+UUrh37sR7/AQxxcWYdFqk1F+yMlMIcc0ae7xsrWwnKdZGgl2feyNKKdaeaGXnmU4AHrxpFFMKZaKlEGIIcXfCqVXgboeELH1jUQpOrIDa7fRNLZcemUIIA6hx1LClYQtx1jiS7cl6h9NHKcXG+o3sat4FwD1l9zA1e6rOUQkhxNXxHTuGZ89ebDk5mO36zcDoL1mZKYS4Jt5AmA0nWun1BRmTnaRbHJsq2thS2Q7AfTfmM61YCplCiCFCKWg9DlVrI/0y00sjvSn1dOojqN4aeXzDY1A4Xd94hBAC6PB2sLFuI/6Qn+KUYr3DOc+Whi3saNwBwOLSxUzLmYZSSueohBCi/wINDbg2b8YUG4slZWisKpdiphDiqiml2FbVzul2F6Oz9Bv4s62qnfUn2wC4e3IeM8sMsD1TCCH6I+CGM5uhbheYLZA9Qf9CZtU6OLMh8njyI1A0U994hBCCyOTyDbUbaPW0UpZapnc459nRuIOtDZEbQAtLFnJL7i06RySEEFcn7HDQu34DYbcbe6mxcuzlSDFTCHHVjjc52VvTRX5KnG59Mnef7WTVsRYAFkzKYc4YYzcoFkKIPs5mOPkhdFZBUr4xBv6c3QIVH0ceT7ofim/VNx4hhAC8IS/ra9dzxnGG0pRSzHrf9PmUfS372FAXuQF0e9HtzMyTG0BCiKFFBQL0btxEsL6OmNFj9A7nqkgxUwhxVdp7/WyuaCPGYiY5zqZLDIfquvngcBMA88ZnMX98ti5xCCHEVfO7PilknoaMsWDRJ4+ep253pE8mwLhFUDZf33iEEAIIhANsrNvIic4TFKcUG2py+eG2w6yqXgXAnII5zCmYo3NEQghxdVQwSO+WLfiOH8NWVIzJYtE7pKtinFtbQgjDC4Q0Np5qpd3lJz81TpcYTjY7WX6wAYBZZRksmJijSxxCCHHVNA1Or4eOSsgYY4xCZuMBOPJm5HHZfBh7l77xCCEEENJCbG3YyuH2w4xKHkWMJUbvkPqc6DzBB2c+AGB67nTmF8oNICHE0KIFAvRu2IBnzx6sefmYY2P1DumqGef2lhDC8Haf7eREs5OS9ATMJlPUz3+23cWf9tahKZhamMo9N+Rh0iEOIYS4Jo37I6sgU4uMUchsPQ7lrwEKimfDxPtAcqoQQmea0tjZuJP9LfvJT8wnzqrPDfSLqequ4t2qd1EopmZP5a6Su+RaVAgxpGg+H70bNuA9eAhbQQHmhAS9Q7omUswUQvTLmXYXu852kpUYi90W/SXoDd0efr+7lpCmmJiXzIM3jdKloCqEENekuwZOrwN7MtiT9I4GOs/AgVdAaVBwM1z/sBQyhRCGsK9lH7ubd5MVn0WCzTgfsuucdbxd8Taa0rgu4zruKbtHCplCiCFF83hwrluH78gRbIVFmOOMc7Poask2cyHEFTm8QTaeaiOsKdITor/Np9Xp45WdNQRCGmWZCTx+SyEWs1w8CiGGCJ8TKlZH+mUm5+kdDTgaYN/vQAtCznVw4xP6T1IXQgjgrOMsu5p2kRKbQrI9We9w+rS4W/jTqT8RUiHGpo7lgTEPGGoYkRBCXEnY5caxchXeI0ewFRUP6UImSDFTCHEFYU2xuaKN+i4PhWnxUT9/jyfAKztr8ATCjEqLY+nMYt0mqF+K0xvEajJJgVUIcSEtDFXrIish08v0jgbc7bD3/0HIF4nnpq+AeWg1fBdCDE+ugIvtDdsJqzDpsel6h9Ony9fFaydfwx/2U5hUyMPjHsYieVMIMYSocBjXls34T54kprhkSPbI/CxjVQSEEIZTXt9DeX0PRWnxUS/WeQIhXt5Zg8MbJCvJzlOzSnTZ4n45Tm+Q1l4fM8vSKUqPfrFXCGFwdbuhYR+kFYPek3h9Dtj9H+DvheR8uOVpMNBQDSHEyKWUYnfzbhp6GyhILNA7nD69gV7+eOKPuINucuJzeGLCE9iM0PNYCCGugu/oUbyHj2AbNQqz3a53OANCiplCiEtq7PGytbKdpFgb8fbofggPhDR+v6uW9l4/ybFWvjq7JOoxXMm5QuatozOYNz4bq8FWjAohdNZeGZleHpcKMTr3fQt4YM//BW8XxGfCjL8G29DeXiSEGD4quys51HaI3MRcw6x69Ia8vHbyNXr8PaTZ0/jSxC8Rax36q5mEECNLsLUV1/btmBMSMMcPn8U38slbCHFR3kCYjSdb6fUFyU2O7oVbWFO8sa+Oui4PsTYzX721lNR4Y60eckghUwhxOa52qPgYwkFIzNE3lnAQ9v8n9DaDPQVm/rUxhhAJIQTg8DvY3rgdq9lKUowxclMwHOSNU2/Q5mkj0ZbIlyd9mcSYRL3DEkKIq6L5/bg2bybsdGLN0fl6dIDJp28hxAWUUmyraqeqzUVxRnRXEymleO9QI6daerGaTXxlVgk5US6mXonDG6TN6WPOmEzmT8iRQqYQ4nxBL1SsBGcTpJfqG4vS4NAfoOssWGNhxjMQn6FvTEII8QlNaexo2kGrp5X8xHy9wwEiMb1b9S71vfXEWmL50sQvkRabpndYQghx1Tx79+KvrCKmsAiTaXjNd5BP4EKICxxrdLK3pov81LioD9tZe6KVA3XdmIAnphdFvZh6JX2FzLGZzBufLUN/hBDn0zQ4vRFaj0H6aH2nhCsFx96FliORIT+3/FWkV6YQQhjEic4THGs/RkFigSGmgyulWFW9ioruCiwmC49NeIychOG1mkkIMTL4z5zBs2cvlqwsTDHG2uU4EPT/F0MIYSitTh+bKtqwWy0kx0a3wfme6k62VLYD8MWpBUzMS47q+a/E6Q3S1iuFTCHEZTQdhNodkFwAVp0brJ9eD7XbARNMWQoZY/SNRwghPqXF3cL2xu3E2mKJtxmjj9u2xm0caD0AwINjH6Q4uVjniIQQ4uqFnU5cW7aiNA1r2vBcWS7FTCFEH18wzPqTrXS5A+SnRHdr98lmJx+UNwFwx8Rsbi5Jj+r5r6TXF6TF+ecemVLIFEJcoOssVK2JDPuJTdE3lvo9kZ6dANd9EfKn6BqOEEJ8mivgYn3tehx+B7nxuXqHA8DB1oNsrt8MwOLSxUzMmKhrPEIIcS1UIEDvpk0EGxuwjRqldziDRoqZQgggsq1mx+kOKlp6KclIiGpPjYZuD2/sq0MBNxencfv47Kiduz96fUGae3zMlkKmEOJSelvhxAcQcOu/lbvtJBx5M/J49B1Q+gV94xFCiE8JhoNsrN9IXW8dxcnFhujjVtldycdnIzeA5hTM4ZbcW3SOSAghrp7SNFzbt+M9ehRbUTEmi0XvkAaNFDOFEACcbO5lz9ku8lLiiLFGLzV0uQO8uquWYFgxNjuR+6cUGOKi9pxeX5Bmh4/ZYzK4fYJMLRdCXIS3B06898nAn9H6xuJogAOvRAb/FNwME5boG48QQnyKUordzbs53nGcwqRCLGb9P2g3uhpZXrkchWJK1hTmF87XOyQhhLgm3kOH8Ozdiy0nF3OssYboDjT5VC6EoMsdYHNFGxaziZS46PXJ9PhDvLKzGrc/RH5KLE9OLzLUqsdzhcyZZelSyBRCXFzAAyfeh87TkDlW34E/3m7Y+zsI+yFjLNz4OBjo5pAQQpzoPMHu5t1kxWcRa9X/g3a3r5s3Tr1BUAsyOnU095TdY6ib6kII0V/+qipcW7ZiTkzCkmys2RODQT6ZCzHChTXF1sp2Wp0+CtLionbeYFjjD7tr6XAFSI2z8RezS7Db9L87f47DG+mROXt0BndMzJFCphDiQuEgVKyClqORFZlmq36xBL2w97fgd0BSHtz8NX3jEUKIz2jobWBL/RZirbGk2HXuKwx4Q17+dOpPuINucuNzeXjcw4ZYKSqEEFcr2NJC7/oNkYE/WVl6hxMV8ulciBHuSEMPhxt6KEyPxxylO9FKKZYfbKC2y0OszcxXZpdEfXL65fR4ApGp5WMyuX1CNjYpZAohPkvT4PQGqN8NaSX6Ti7XQnDgZehtBnsyTP862KJ3c0oIIa6kN9DLxrqNuIIuchP0H/gT0kK8eepNOrwdJMck88TEJ7BbdMzjQghxjcJOJ71r1xLq7h7WA38+Sz6hCzGCtfX62FbVTkKMlfiY6K3gWX+yjSMNDswm+NKMYnKS9d9mdE6XO0Cn28+8cVnMGy9by4UQl9B0CKq3QlJ+ZHq5XpSKDPvpqASLPVLIjEvTLx4hhPgMTWnsaNxBg6uBouQivcNBKcUHpz+grrcOu8XOExOeICkmSe+whBDimngPHyZQU0tMsTEGqkWLfEoXYoQKhTW2VLTT6Q6QlxK9YuLB2m42VbQB8MWpBYzOSozaua+ko9dPjzfA7ROyuW1slqH6dwohDMTbDWc3R1Y/xqXqG0vVWmjYF+nVOe0rkDJy7sgLIYaGox1HOdJ+hILEAkNs495Uv4ljnccwm8w8Mv4RchJy9A5JCCGuiebz4Tt5EnNaGibryGovJMVMIUaoQ/U9nGh2UpyeELU7OGfbXaw41AjA3HFZTCtOj8p5+6PHE8DpD3LnxBxmj87ELIVMIcSl1GwHVwsk5+sbR9NBqFwVeXz9Q5A9Sd94hBDiM1rcLexo3EG8LZ54W7ze4VDeVs72xu0ALClbQllKmc4RCSHEtQvU1hLq6MSabpzP1dEixUwhRqBmh5ftVR0kx9qIjdLQnfZeP6/tqSOsFJMLUlgwyTh3wb2BMK1OH7eOzmRGafqIWp4vhLhKnWegYX9ke7mek8u7q6H89cjjsnlQfKt+sQghxEX4w362NWyjN9BLdny23uFQ46jho7MfAXBbwW1MyZ6ib0BCCPE5+U6eBJNpxK3KBClmCjHi+ENhNle04/QGyU6KTqNzjz/E73fV4A2GKUyL4+Fpo6I2bOhKgmGN2i43U4vSmDM2UwqZQohLCwUi28vDQX23l3s6Yd9/RQb/5FwPE+/TLxYhhLiEPU17qOqpojCpUPfrq05vJ29VvIWmNK7LuI55hfN0jUcIIT6vUEcHgZoarJmZeoeiCylmCjHC7D7TyclmJ0UZ8VG5sAxpGq/traPTHSAt3sbSWSWGmQ6uKUV1h5ux2YncOSnHMHEJIQyq6WBk0E6qjgMsgl7Y9zsIuCC5AKYu1XeFqBBCXMTp7tPsb91Pdnw2NotN11g8QQ9/OvUnfGEfBYkF3Df6Pt2Lq0II8XkFqqsJ9/ZiThqZA8zk6leIEeR0m4vd1V1kJ8Vitw7+9nKlFB8ebqK6w02M1czSWSUk2o2zBL6uy0NOsp27rs81VFxCCANyd0L1NrAngzU6q9ovoIXh4KvQ2wL2FLjlaf1iEUKIS3D4HWxr3IZCkWJP0TWWsBbmrYq36PJ1kWJP4bHxj+leXBVCiM9LBYN4jx/Hkpg0Ym/OSDFTiBHC4Q2y6VQbobAiPSEmKufceaaTfTXdmIDHbykkNzl6U9OvpMXhI8Zq5q7rcslOMk5cQggDUgpqtoG7DZJy9YvjxHvQfgosMXDLX+k/SV0IIT4jpIXY0rCFZnczo5JG6RqLUoqPzn5EXW8ddoudJyY8QWJMoq4xCSHEQAjUNxBqacWSkaF3KLoZMsXM7u5uli5dSkpKCikpKSxdupSenp7LHqOUYtmyZeTn5xMXF8e8efM4fvz4Ba/btWsXt99+OwkJCaSmpjJv3jy8Xu8gvRMhoi+sKTZXtFHf7aEoPTqTJCtaell5tBmAxdfnMiE3OSrn7Y+OXj/eYIjbJ2RTliUXtUKIK+iohMaDkW3dem3prt0RKagCTPkypBbqE8eVhP1gjs5gOSGE8RxsPciJjhMUJhVi1rkFxq6mXRxuP4wJEw+Ne8gQQ4iEEGIg+KuqUFoYs33k7tAZMsXMJ598kvLyclavXs3q1aspLy9n6dKllz3mV7/6FS+88AIvvfQS+/btIzc3lwULFtDb29v3ml27drFo0SIWLlzI3r172bdvH88++yxm85D5oxHiisrreyiv76EoLR6LefCXobc6fbyxrw4F3Fycxq1jjNOUuMPlx+kPcsfEHKYWpuodjhDC6HwOqFoHaBCr03bJjio4tjzyePw9kHeDPnFcibM5smo0/ya9IxFC6KDWWcvu5t2kxqYSa9V310tFVwXr69YDcFfJXYxJHaNrPEIIMVDCvb34q6qwpKXrHcoV+c+cIdTVNSi/e0g0iTt58iSrV69m9+7dzJgxA4Df/e53zJo1i4qKCsaPH3/BMUopXnzxRX74wx/y4IMPAvDqq6+Sk5PD66+/zjPPPAPAd77zHZ577jm+//3v9x07duzYKLwrIaKjqcfL1sp2kuw24qPQF9LjD/GH3bX4QxolGQncNyXfMH08Ol1+nL4gd0zIZkZpumHiEkIYlKbB6fXQUwuZF15rRIW7HQ68DEqD/Gkw5k594rgSVzuEvJHJ6kYttgohBk1voJct9Vvwh/3kJebpGkuru5UVVSsAmJYzjVtyb9E1HiGEGEiB6mrCPT3EjB6tdyiXFWxtpeeNN1BKEZM/8P8uDInlh7t27SIlJaWvkAkwc+ZMUlJS2Llz50WPqa6upqWlhYULF/Y9Z7fbmTt3bt8xbW1t7Nmzh+zsbGbPnk1OTg5z585l+/btl43H7/fjdDrP+xLCiFz+EBtOtuL0BclNGfw75GFN8fq+Oro+mVz+pRlFWA2yyrnT5cfxSSFzZlmGFDIHgORCMew1HYSG/ZHp5XpsnQ56Yd9/QtATieHGx8CIucvTBb4eGLsQCkdm0UDyoRjJwlqYbY3baHA1UJikbwsMd9DNmxVvEtAClCSXsKhkkVzzRZHkQiEGl9I0fCdOYLLbMRnkc/bFhF0uul9/HRUIYM3MxJqTM+DnMO67/5SWlhaysy/scZKdnU1LS8sljwHI+cwfWk5OTt/Pzp49C8CyZct4+umnWb16NTfddBN33HEHVVVVl4znF7/4RV/vzpSUFAoLDdq3SoxogZDGuuOtnG5zUZKREJVzrjzazNl2NzEWM0tnlpBgkAnhXe4ADq8UMgea5EIxrDmbI6syYxJAj4ERSoODvwdXa2R7+81/GdnCbTQ+R2T16Og7oPhWvaPRjeRDMZId6TjCkfYjFCQWYNGxZ25IC/FWxVv0+HtIs6fx8LiHdY1nJJJcKMTgCjU3E2howJppnDZun6VCIXreeAOtpwdLejqJd9yByTLwuVjXYuayZcswmUyX/dq/fz/ARYsPSqkrFiU++/NPH6NpGgDPPPMMX/3qV5k6dSq//vWvGT9+PP/93/99yd/5gx/8AIfD0fdVX19/Ve9biMGmaYotFW0cqu+mKD0Bm2Xw/6rvq+li19lOAB69eVRUVoL2R1uvjx5vgNulkDngJBeKYSvog8rVkRWHSfn6xHDyQ2g/CWZbZHK5Xv06LyfgAmcTlM6F0fPAwCsEBpvkQzFSne05y/bG7STFJBFvi86QyYtRSrHy7Erqe+uxW+w8PuFxXeMZqSQXCjF4lKbhKS9H+fyY442Z35RSOD74gGB9PabYWNK+9CXMsYNTF9B12dSzzz7L448/ftnXlJSUcOTIEVpbWy/4WXt7+wUrL8/Jzc0FIis08/L+vD+/ra2t75hzz0+aNOm8YydOnEhdXd0lY7Lb7dhH8NQoYXx7Pyks5qfEERcz+HekazrcfFDeBMCdE7OZlG+MD91NPV5CmsZd1+Vyc3GaFDIHmORCMWzVbIe2E5A+Wp9t3Q374eymyOMpT0KKAVe2hAORXqJFt8LYO0f8BHPJh2IkanI1sa52HYFwgKLkIl1j2duyl/L28sjk8rEPkRWfpWs8I5XkQiEGj+/kSXxHj2LL1+lGez+4t2/Hd/gwmM2kPvoo1sxMAoN0U0PXYmZmZiaZ/VgeO2vWLBwOB3v37mX69OkA7NmzB4fDwezZsy96TGlpKbm5uaxbt46pU6cCEAgE2LJlC7/85S+BSKE0Pz+fioqK846trKxk8eLFn+etCaGb400ONle0kRoXQ3KcbdDP1+MJ8NreOsJKcX1BCvPHX9gSItqUUtR1eYixmllyQz7XFxijuCqEGAJajkHtdkjMAasOH8h66uDIG5HHYxZA/tTox3AlSoPOM5A9CcbdBZbB/7dGCGEsnd5O1tasxeF3UJJSomssZx1nWVuzFoA7i+9kTJpMLhdCDC/hnh7cO3ZgirFjTohOC7mr5Tt1Ctf69QAkL16MfZAHFA2J/UATJ05k0aJFPP300+zevZvdu3fz9NNPs2TJkvMmmU+YMIEVKyKT60wmE9/+9rf5+c9/zooVKzh27BhPPfUU8fHxPPnkk32v+R//43/wb//2b7zzzjucPn2af/zHf+TUqVP85V/+pS7vVYjPo67Tw7rjrZhNJjKTBv9DeCCk8cc9tbj9IfJSYnn4plG6r37UlKK6w02C3cr9U6SQKYS4Cs1H4MR7oBTEZ0T//D4H7P8v0EKQcx2MN+iN1e4aSMqFCUsgxpjbnIQQg6c30Mu6mnU0u5spTinW9dqv29fN8srlKBQ3ZN7AzLyZusUihBCDQWkarh07CbW2Ys0b+KngAyHY2opj+XIA4qdPJ/6TRYiDyRjTOfrhtdde47nnnuubTn7ffffx0ksvnfeaiooKHA5H3/ff/e538Xq9fPOb36S7u5sZM2awdu1akpKS+l7z7W9/G5/Px3e+8x26urq48cYbWbduHaMNPuZeiM9qdfpYeawZlz9EWdbgD6tQSvFeeSNNPT7iYyx8eWYxMVZ9749oSnGmzUV2SixLJudRmC4fsoUQ/dRwAE59BJggrST65w+H4MDLkYJmYg5MWQomA95zdrWC2RopZCbqvxJfCBFd3pCX9bXrOeM4Q1lqGWYd85Q/7OeNU2/gDXnJT8hnyeglut9UF0KIgeY/dSqyvbxglCEnmGtuNz2fTC6PKSsjadGiqJx3yBQz09PT+eMf/3jZ1yilzvveZDKxbNkyli1bdtnjvv/97/P973//84YohG46XH4+PNxEq8PH6OzoTN3dcaaT8voezCZ4cnoRafH6TtnVlOJsu4vc1FgemFJATrIxBhAJIQxOKajbDRWrItvKk3XoQ6QUHHsnsuLRFgc3/xXYDJjD/L3g7YFJ90PWOL2jEUJEmaY0ttRv4UTXCUpSSrCa9fsoqZTivar3aPe2k2hL5NHxj+oajxBCDIaww4Fr+w6w2Qy5vVyFw3S/9RbhTyaXpz7yyKBMLr8Y45V1hRBXpccT4KPDTdR3eyjLSsQchTvSp9tcrD7WDMDdk/OishL0Suq6PGQk2FlyQ74UMoUQ/aNpUL01siIzJl6fQiZEenTW7wZMMPUvINGAgyvCgUg/z+LZUDhD72iEEDqoc9ZxvPM4BYkFxFj0vYm9pWELFd0VWEwWHh3/KMn2ZF3jEUKIgaY0DdfOnYRaWww79Me5ahXBmhpMdjupTzwR1SnrcvtKiCHM6Qvy0ZFmqjvcjM5OxGIe/EJmtzvAG/vq0BTcVJTKrDId+sp9Rn2XhzibhcWTcylIjdM7HCHEUNF0EKrWQGwaJFx5IOGg6DwDxyP9vpl4L2RP1CeOy9HCkThzroMxd4IBtzgJIQaXUorD7YcJaSESbPquDjrVdYqtDVsBuKfsHkYljdI1notRqCu/SAghLsN/6hS+I0ex5hcYcnu5Z+9evPv2gclEykMPYcuObvsh4/2JCCH6xe0PsfJIM5WtvZRlJWKNQoI7N/DHEwhTkBrH/VMKdO9N1OLwYTLBoutzDbFCVAgxRATcULsDLLH6FTK9PZE+mUqLTC0vm69PHJejFHSehtQimHifDPwRYoSq763nTM8ZchNydY2j3dPOe1XvATA9dzpTsqfoGs+lNLmayLBnkGKXQZRXK9jSgu/UKb3DEEJXwdY2XFu2YLLZsCQa7zOuv7oa56pVACTecQexnxrMHS1SzBRiCAqFNdYcb+F4k5OyzERslsH/q6yUYsWhBpodPhLsVr40oygq572cDpcfXyjMwutymZgn24uEEFeh+TA4GiBZp6mQ4SAc+G8IuCApH254HIw4uKK7JjLZfdL9kKD/SnwhRPQppShvLyeoBYm36XdDwxfy8VbFWwS0AMXJxSwoXqBbLJfT5esCYG7RXNJi03SOZugJ1tfjXLUaz/79F8zEEGIk0LxeXBs3EOrqwmrA7eXhnh563noLNI3YyZNJmDNHlzikmCnEEHS4wcHRBgclGQlRmyC+80wnhxscfQN/UnUe+NPtCeDwBpk/Pospham6xiKEGGJ8DqjdCbGpkcnc0XZu4E9PHdji4Za/jAwfMhpnM1iske3vqYV6RyOE0EmDq4HT3afJScjRLQalFCtOr6DT10lyTDIPj3sYizk6Qyauhjfopdvbzay8WYxLk0Fp1yrc2Unvho24d+9GaZre4QgRNUrTcG3bhr/qNDHFJbrvgvwsFQjQ/ac/oTwerHl5pNx3n24xSjFTiCGmw+Vnx+kOEmOtxMVE5yLubIeLVZ8a+FOaqW+vJIc3SIfLz9xxWcwolZVCQoir1HAQXC2QqNMH89odUL8HMMFNX4msfDQaTxcE3TBuMWRP0DsaIYROlFIcaT9CUAvq2itzc/1mqrqr+gb+6N2382JCWoiG3gZuyL6Bm3Nv1jucIc0UH4c5ORn3li24d+xAhcN6hyREVPiOHMF78CDW/HxMNpve4ZxHKYXj/fcJtbRgTkgg7fHHMcXot8BJiplCDCGapthe1UGnyx+1id0Ob5A/7a1HUzClUP+BP72+IK1OH3PGZDJnTCbmKAw9EkIMI54uaNgD8Zmgx6qerrNw/N3I44n3Qlb0ewxdkd8FrjYoux1GyQdyIUayJncTVd1VZMdHd7DDp53qOsW2xm0ALBm9hPxE4227VEpR66ylJKWEuaPmYtVj1f8wY01Px5KWjmvbdlxbt6JCIb1DEmJQBRoa6N26DVN8ApakJL3DuYB7xw58x46B2Uzqo49iSU3VNR4pZgoxhJxodnK00UFhWjzmKCznDoU1Xt9Ti9sfIi8llgd0Hvjj9odocniZWZbO3HFZUsgUQly9hn3g6YSErOif2+eAA69EBv7kTTHmwJ+ABxx1UDwLyuYas4+nECJqjrQfwRfykRijzwCKDm8H751+D4gM/Lkx60Zd4riSJlcTafY0bi+63ZCrRocqS2oq1qws3Dt30rthI5rPp3dIQgyKcG8vro0b0TxubDn6tfS4FH9VFa716wFIXryYmJISfQMC5JaREEOEwxNkW1U7MRYz8fbo/NX96Ggz9d1eYm1mvjSjOGr9OS/GGwjT0O1hemkGd0zMwarz8CEhxBDU2woN+yEhG0xRziFaGA6+Cn4nJOXCjU8Yr1AY9EF3NRTNhPGLI/0yhRAjVrOrmcruSrIT9FmV6Q/7IwN/wgGKkooMO/DH4XcQVmHmFc7Tfdr7cGRJTgazGc/evYR7e0m+8w7dV4QJMZDO9ckM1NYSUzZa73AuEOrspOedd0Ap4m66ibhbbtE7JEBWZgoxJCil2HGmg2aHj4K0uKic80BtF3uruzABj91cRHqCfv0wAiGN2i43U4vSuHNStu5T1IUQQ1T9HvD26NOj8sT7kS3m1liYZsCBPyE/dJ2Bgpthwj3Gi08IEXVHO47iCXpIion+dkelFB+c/oAObwdJtiTDDvwJhoO0edq4OedmxqcbsG3IMGFJTMRWXIzv5El63nufYGOj3iEJMWB8x4/jO3IEW8EoTFZj3UjW/H56/vQnlM+HrbCQ5HvuMcxQIqkICDEEVLa6KK/rIT81Lirbyxu7vbxf3gTAHRNzGJ+rX88OTSmqO1xMzEvmrutysVuNdyErhBgCeuqhuRyS86K/IrLxANRsjTye8mVI1GGL++WEA5FCZt6NMOlesEXnppkQwrgaXY2c7DqpW6/MnU07Odl1ErPJzCPjH9Ftm/vlKKWo661jdMpoZuTN0DucYc9st2MfPZpgcxM9772P79QpvUMS4nMLdXTg3r4dkz0Wc4KxWlQopXC89x6h9nbMSUmkPvaYoYqtUswUwuB6PAG2VrUDkBw7+BPNPIEQr++tJaQpJuQmMW+8vh+6azvd5KfGsXBSTtSmtwshhhmfEypXR/pBxqVF99zOJjj8RuTxmAWQe310z38l4SB0nobsiTDpfogx1oW0ECL6NKVxoOUA3qCXZHty1M9/1nGWjXUbAVhUsohRSaOiHkN/tHnaSI5JZm7hXGKt0RnMOdKZLBZiSsvQfD6cH32Me/9+vUMS4pqpYBDX1q2EOruw5hqvRYV7+3b8J06AxULqY48ZbiiRFDOFMDBfMMzaE63Ud3koTI8f9PNpSvHW/nq6PUHSE2J4ZFphVFaCXkqL04fdZmHhdblkJMqWRyHENQgF4NRKaK+A9LLonjvogf3/DVoQsiZE+lAaiRaCzirIHAfXPQix0S9aCCGMp9pRTUV3BXmJeVE/t8Pv4N3Kd1EopmRNYVrOtKjH0B+eoAdP0MOtBbdKn8woM5lMxIwahSk2FveWLfgqK/UOSYhr4jl4CN/JU9gKCzGZjVWa858+jWvDBuCTgT+FhTpHdCFj/YkJIfpommJzRRvHGh2UZiZgicLk7k0VbVS2urCaTTw5vUjXlZAObxC3P8TtE7IpzZSVQkKIa6BpcHoDNB2A9FKwDP7q9j5Kg0OvgacD4tJh6tLoDx26HC0EHZWQMRaufwjiUvWOSAhhAMFwkH0t+zBhIt42+DfSPy2khXi74m08IQ95CXksLl1smN5sn6YpjUZXI5OzJnN9hsFW248g1qwslALXxo0EW9v0DkeIqxJoaMS9ezeWlBTMscZa2R3q6jp/4M/NN+sd0kUZ6KpaCPFpB+q62VvdRUFqXFT6RFa29rLxZORC4IEpBeSn6tczzR8M0+LwMqssnSmjUnWLQwgxxNXvgZptkFwAUf5QzukN0HYczFa4+avG2r6thaGjCtJHRwqZ8el6RySEMIiK7gpqnDW6rMpcXb2aJncTcdY4Hhn3CLZo3oC6Cg29DRQkFnBrwa2GHEo0ktgKCgh1duLasB7N7dY7HCH6RfN6cW3ZguZxY80yVh91LRCg5403UF4vtlGjDDXw57M+V/fOQCBAW1sbmqad93xRUdHnCkqIka6qtZdNp9pIjrORFIU+md3uAG/uq0cB00vSuak4yj3lPiWkaVR3uplckMJt47IwR2FF6kgnuVwMS60noGptZOt0bEp0z91eARUrI4+vfxhSDLQ1RwtHVmSmlUQKmQk6THY3KMmFYqTzBD3sb9lPrCWWGEtMVM9d3lbOwbaDADw49kFSY1Ojev7+anW3EmOO4QujvkByzPBtzTFU8qHJbCamuATf6TOYt2wheeFCQw0oEeKzlKbh2r6dwNkzxJRGuf3RFSilcH7wAaHWVsyJiYYb+PNZ1xRZVVUVX/va19i5c+d5zyulMJlMhMPhAQlOiJGo1elj7fFWwpoiO2nwl5yHwhqv763DGwwzKi2OJTdE/078OZpSVLe7Kc1MYOEkmVw+2CSXi2HL0QinPo5s9U7Mie65vd1w6PeAgsIZUDQzuue/HC0c6ZGZWhQpZBptqrpOJBcKEXG84zhN7ibKUqL7AbvF3cLKs5EbQPMK5zE6dXRUz99fnd5OAuEAC0sWUppSqnc4g2Io5kOTzUbMqFF4y8uxpKeTONNA/+4K8Rnuffvw7NuHNS8fk81Yq889u3fjO3oUzGZSH3kES7Kxb9hcUzHzqaeewmq18tFHH5GXl2fYZadCDDW9viCrj7XQ4fIzOjsxKuf86GgzjT1e4mwWnphehNWiX/eJ2k43WUl27p6cR0q8sZL7cCS5XAxLoQBUrYn0qswcH91zh0Nw4GUIuCF5VKRgaBRaKLK1PLUwEldSlIu8Bia5UIjI4J0DbQdIsadEdeu0N+TlrYq3CKkQY1PHclvBbVE799Vw+B04/U7mF87n+szh2ydzqOZDc0ICltQ0PDt2YE1LJ3b8OL1DEuICvpMncW/dhiU1zXCTwQO1tfSuXQtA0sKFxJSU6BtQP1xTMbO8vJwDBw4wYcKEgY5HiBErENJYd6KVM+0uxmQlRmWK+KFP+nKagMduKSQtPrpbij6tsdtLfIyVxZPzyEk2VhPk4UpyuRiWGvZC26nI5PJofwg78R701IEtLtInM8rbNC8p5IeuM5FhP5Pul0LmZ0guFAIOtR2iy9fFmNQxUTunUor3qt6jx99Dqj2VB8Y+YMjimTvopsPTwez82UzLNeZ09YEylPOhNSODQL0H18aNWFKSseXKlHlhHIH6eno3bMBktWLNMFaLn7DTSc9bb4GmETt5MvFDZHXzNS3BmjRpEh0dHQMdixAjllKK7ac7ONzQQ3F6QlRWR7Y4fbxX3gjA/AnZjMvR7+5Qe6+fsNJYMClHJpdHkeRyMew4m6B6K8SlgdUe3XM37Ifa7ZHHU74M8Qa5UA24ofM05E6GGx6VQuZFSC4UI93p7tOUt5WTFZcV1WLitsZtVPVUYTVZeWT8I8RZ9Rs+eSm+kI8mVxPTcqYxu2A2ZtPwnp871POhbdQoQt1d9K5dR9jp1DscIQAIdXbSu3YtYbcbW36+3uGcR4VC9Lz1FprLhTUnh+T77jPkTaWL6Xc2djqdfV+//OUv+e53v8vmzZvp7Ow872dOSRpCXLVD9T3sPNNBbnIccTGDv7XHHwzz+p46gmHFmOxEbp+QPejnvBSHN4jTF+T2CTlcXxDlIR0jkORyMWyFg5EJ4t6e6PfJ7G2Go29FHo9dCDnXRff8l+LtgZ5aKJoVGUQUl6p3RIYhuVCIiDZPG5vqN6GhRXXozpmeM2yu3wzA3WV3k5egX8/2SwmGg9Q565icOZm5hXOxmo07COPzGE750GQyEVNcQqC2ht4NG9ACAb1DEiOc5nbTu3YdweYWYoqK9Q7nAr1r1hCsr8cUG0vqY49hjjHIrqJ+6HdGTk1NPa9Cq5TijjvuOO81Rm4MLIRRnW13sfFUG/E2Kylxg98nUinF8kONdLj8pMTZePTmwqhsab+YQEijxell7rhsbtZxgvpIIrlcDFuNB6H1GKSVRnd7ecgH+1+GcAAyx8G4RdE79+V4OsHdAaPvgDF3gEX6EH+a5EIhItPLN9ZtpNPbSVlq9Ib+OPwOVlStAGBq9lSmZE+J2rn7K6yFqXHWMD59PHcU3RH16e7RNNzyoclqxVZUjPfYcSxJySTePh+TeXivqBXGpDSN3q1b8Z0+jb201HD/H3oPH8azdy8AKQ8+aLjt71fS72Lmpk2bBjMOIUak9l4/a463EAhplGREZ3v1rrOdHGt0YDbBE7cUkmjX7y5zXZebcTlJzBmTidk8NJazD3WSy8Ww1NsKZzeBPRlsUey5qxQcfgPcbRCbAlP/AoywBTHgAnd7pLBaOhcMdvFsBJILxUgX0kJsadjC2Z6zlKaWRm1bYVgL807lO3hCHnITcllcujgq570amtKocdRQlFTEguIFxNvi9Q5pUA3HfGiOjcWWm4t7314saanETxvevU6FMfmOHsVbXk5MQYHhJpcHm5txfPABAAlz5xI7PspDMwdAv6sYc+fOHcw4hBhxen1B1hxvpsXpY2x2dPpV1nV5WHm0GYC7J+dRFKUC6sV0uwPE2izcNiaLGKt80I4WyeVi2NHCcGZTZCVi1sTonrtmKzSXRwqYNz0F9sTonv9iwgHoroXi2VBymxQyL0FyoRjp9rfs53DbYQqSCqK6fXpd7ToaXY3EWmJ5ZNwjhtu6rZSi1llLdnw2d5XcRYp9+LdAGq750JKcjPL7cW3ZiiU5GfvYsXqHJEaQYEsLrm3bMccnYE4w1kwIzeul5803IRQiZuxYEufN0zuka3JNV7gvv/wyb7/99gXPv/3227z66qufOyghhjtfMMza4y1Utrooy4zO5HK3P8Sf9tahKbi+IIVZZfotIw9pGq29Pm4uSacoY3jf7TYyyeViWGg8ECkoppZEd3t5dzWceD/yeOJ9kF4avXNfitKg8wzkTIJxd4HFWEUCo5JcKEaaiq4KdjbtJC0uLaqrDo91HGNvS2RL4wNjHyAt1ngthhpdjSTFJLGgZAFZ8Vl6hxN1wy0fWrOyIlt9168n2NSkdzhihNB8PlybNhN2OrDmGGvwotI0HO++S7i7G0tqKqkPPmi47e/9dU1R//M//zOZmZkXPJ+dnc3Pf/7zzx2UEMNZKKyx4WQbRxoclGQkYIvC5HJNKd7aX4/DGyQjIYYHpxboOqWssdtLYXo8M0rTdYtBSC4Xw0DnGahaG9leHhPFGyN+Fxx4NVI8zJsS2cptBF3VkJwPE+6FGGOtAjAyyYVipPCFfOxp3sPa2rWYTWbSY6N3HdbuaefDMx8CcGvBrYxLGxe1c/dXq7sVs8nMnUV3UphUqHc4uhiO+dA2ahShnh6cq9cQ6u7WOxwxzCmlcO/ejf/0aWIKiww3Gdy9dSv+ykqwWkl9/HHM8UN3YdE1VVFqa2spLb1wBUJxcTF1dXWfOyghhitNU2yt6mBfdRcFafHE2gZ/cjnA5op2qtpcWM0mnpxRFLXzXozLHyKsKeaMySRBx36dQnK5GOI8XVCxEoIeSI7iFFylQfkfwNcDCVlww+PRXRF6Kc5msMbChCWQOPJWE30ekgvFcKeU4kzPGd6pfIcNdRuwmq3kJ+ZH7fyBcIB3Kt8hqAUpSS5hfuH8qJ27v5x+J96Ql/mF8xmbNnK3Iw/HfHhuwnmwqYneNWsIu9x6hySGMX9lFZ59+7Dm5GAy2GRwf1UVrs2bAUhesgRbXhSvnwfBNRUzs7OzOXLkyAXPHz58mIwhNgFJiGjaV9PFjtMdZCfbozZ450y7iw0nWwG4f0o+eSlxUTnvxWhK0djt4YZRKYzPiU6fUHFpksvFkBXyQ8Uq6K6DtOhN4AUiK0HbK8Bsg2lfje7AoUvxOSJDf8bdBZlj9I5myJFcKIazLl8Xa2rWsKJqBS2eFkpTSsmMy4zaaiGlFB+f/Zh2bzuJtkQeHPsgZiMMSvuUQDhAq6eVW3JvYXLmZL3D0dVwzYcmi4WYkhJ8lVX0bliPFgjoHZIYhkLd3bi2bAGTGUuKsfrthrq76Vm+HJQi7uabiZ86Ve+QPrdrqqY8/vjjPPfccyQlJfGFL3wBgC1btvC3f/u3PP744wMaoBDDxbFGBxsr2kiJtZEaH527NE5vkDf21aOAaUVpTCvWd1t3q9NHZpKdOWOzZHq5AUguF0OSUpGBP83lkFEG5iiuNG+vgMo1kceTH4ls6dZbOAiOBhh9O4y6We9ohiTJhWK46vR28v6Z92lxt5CXkEdiTPSHlB1oPcDRjqOYMPHQuId0ieFylFLUOesYlzaOWfmzDLclNNqGcz402WzEFBXhPXIUS0IiifPnYbLot1tNDC/ntpcHW1uxjzHWjWUVDNLz1lsorxdbQQHJixfrHdKAuKZi5k9/+lNqa2u54447sFojv0LTNP7iL/6Cn/3sZwMaoBDDQXuvn/UnW7GazWQm2aNyzrCmeGNfPW5/iNzkWO69Ud8P3Q5vEHcgzLzx2aQnGGvJ/UgluVwMSU2HoGYbJOVHtlVHi7cHDv0BUFA4EwqnR+/cl9NdA5njoGyeMba7D0GSC8VwFAwH2dqwlRZ3C2NSx+hSpGtyNbGmJnID6I7iOyhOLo56DFfS6GokMy6TeYXzsFuic41uZMM9H5rj4rDl5eHeuxdTUiKJM2boHZIYJoJ1dfiOH8eWl2e4gTrOlSsJNTVhio8n9dFHMVmHR6u3a3oXMTExvPnmm/z0pz+lvLycuLg4Jk+eTHGx8f6BEkJvSil2nemg2x1gXBS3Vq8/2UpNpxu71cyT04uIseqXVB3eIG1OH3PGZjKlMFW3OMT5JJeLIae7NrIy0hoLcanRO68WhoOvRrZyJxfA9Q9G79yX42oDWxyMXRDdAUjDjORCMRztb93Pya6TFCXrM4DCG/TyduXbhFWY8WnjmZU3K+oxXEmPrwelFF8Y9QUy4y4cejMSjYR8aElKQgUCeHbuIiY3l5hh9N6EPlQohHvfPlQwiCXJWK3UPAcP4j14EEwmUh96CEtqqt4hDZhrqm780z/9Ex6Ph7Fjx/LII4+wZMkSiouL8Xq9/NM//dNAxyjEkHam3cWxRicFafFRu5g81eJkS2U7AF+cWhC11aAX4/AGaeuNFDLnjc/GItvLDUNyuRhSPF1w8kPwOyFlVHTPfeoj6K6OFFGnPQUWA6wuD/nA0wllcyH9wmENov8kF4rh5qzjLHua95AZl6nLakOlFO+deQ+H30GaPY37x9xvuO3b/rCfdm870/OmMz59vN7hGMZIyYfWjAw0vw/Xli0yEEh8bv6qKgKnT2PLL9A7lPMEm5txfvwxAInz5xtu+/vndU3FzOeffx6Xy3XB8x6Ph+eff/5zByXEcOELhtlxuhOFitrAn25PgLf3NwAwsyyDG0alRuW8F+P8pJB56xgpZBqR5HIxZAS9kUJmTx2kj47uuVuOwtlNkcc3PhGZYK43paCrGnKui2x5F5+L5EIxnDgDTrbWbyWswqTFpukSw46mHVR1V2ExWXh4/MPERrMlSD9oSqPOWcek9EnMyJVtxp82kvJhTGER/tpa3Dt3oJTSOxwxRGl+P569+8BixRxrnFyneb30vPkmhELYx40j4bbb9A5pwF1TMVMpddG7a4cPHyY9Xd8BI0IYydFGB9UdLkalRWf7X0jT+NPeOrzBMKPS4rj7+tyonPdinN4grb0+bh2dwXwpZBqS5HIxJGhhqFoHrUchPcoDf9wdUP5a5HHpXMi7MXrnvpzeJojPiEwvtxpglegQJ7lQDBchLcS2hm00uhoZlRTlFeyfqHHUsKkucgNoceli8hLydInjchp6G8iNz2Vu4VxsFpve4RjKSMqHJqsVW14+3kPl+E+e1DscMUT5jh0jUF+HLc84uU5pGo7lywl3d2NJSyPlwQcN18dzIFzVUrG0tDRMJhMmk4lx48adl+jC4TAul4tvfOMbAx6kEENRlzvArjMdJMfFYLNEJ3msPtZCQ7eXWJuZJ24pwhql836W2x+ixeljzpgM5k/IkUKmwUguF0NK7Q6o3QkpRWCN4nbJcBAOvhLZzp1WAhPvi965L8fnBL8LJj8MSfrdsBoOJBeK4eZI+xGOdhylMKkQsyn614C9gV6WVy1Hobgh6wamZk+NegxX0uXrwmKyMK9wnm4rV41opOZDS1ISmtOJa9t2rDk5WDMy9A5JDCFhpxPPvn2Yk5Ix2YxzY8S9bRv+qiqwWkl97DHMcXF6hzQorqqY+eKLL6KU4mtf+xrPP/88KSkpfT+LiYmhpKSEWbOM19xZiGhTSrH7bCedrgBjozT051ijg51nOgF4ZFohaTpNDA+ENOq7PUwvTZet5QYluVwMGS3H4PQGiE8He5Qbqp9YAY4GsCXATV+J7orQiwkHwFEf2WJeNAPyjVckGGokF4rhpM5Zx86mnSTHJBNni/4HV01pvFv1Lu6gm+y4bO4pvcdwfTJ9IR9d3i7mF86nLLVM73AMZSTnQ2teHoEzZ3Bt2ULKvfcaqigljM1z8BDB9g5D9aL0nz6Na1NkdXzykiWGWjE60K6qmPmVr3wFgNLSUmbPno0tin/Ru7u7ee655/jggw8AuO+++/jNb35D6mWmMSmleP755/ntb39Ld3c3M2bM4N///d+57rrr+l7T0tLC//gf/4N169bR29vL+PHj+Yd/+AcefvjhwX5LYhir7nBzuKGHvJQ4zFG4kOt0+Vl+MNIn87axmUzMSx70c15MWFNUd7iYlJfMHRNydFsZKi5Pz1wuRL85GuHUx4Ap+n0qGw9EVoNigqlfhjgdV+8oDXpbwOeAjDFQehtkjodhuF0o2iQXiuGix9fDxrqNeENeSlJKdIlhU/0map21xJhjeHj8w4bbvq0pjfreeq7PvJ5pudP0DsdwRnI+NJnN2AoL8Z08ibWggMQZ0kdVXFmwtRXv4cNYMzMNs4U73NNDz/LloBRx06YRP3V43/judzHT6XSSnBwpkEydOhWv14vX673oa8+9biA9+eSTNDQ0sHr1agC+/vWvs3TpUj788MNLHvOrX/2KF154gVdeeYVx48bx05/+lAULFlBRUUFSUmSFx9KlS3E4HHzwwQdkZmby+uuv89hjj7F//36mDvP/+GJw+IJhdp7pIBxWJMcN/oVAMKzx+t46/CGN4ox4Fk7SZ9uhUpFCZlF6PHddl0tcjM6rmMRF6Z3LhegXTYOzm8HbCZkTonvu3hY48mbk8dgFkD0xuuf/tIArMvQoIRuufxDypoDNOM3lhzLJhWK48If9bKrfRLO7WbfVhpXdlexo3AHAvaPvJTMuU5c4LqfeWU9BYgFzR83FZh45hbr+MGI+VMFgVM5zjjk2FktaOu6t27DExRF3ww1RPb8YWkKdnfSuXYvmchlmVaYKheh56y2Ux4M1P5/kxYv1DmnQ9buYmZaWRnNzM9nZ2aSmpl5028C5hsHhcHhAgzx58iSrV69m9+7dzPjkTsnvfvc7Zs2aRUVFBePHj79oLC+++CI//OEPefDBBwF49dVXycnJ4fXXX+eZZ54BYNeuXfzHf/wH06dPB+BHP/oRv/71rzl48KAUM8U12XO2k6pWF6WZiVE530dHmml2+IiPsfD4LUW6betu6PaSGm9j0eQ83ba4iyvTM5cL0W8dFdB2ItInM5rbFEP+SJ/McAAyxsK4RdE798X0NMCo6TDmjshWezFgJBeK4UApxc6mnZzsOklxcrEufTJ7fD28V/UeALfk3sJ1mddd/gAddHg7sFvszCucR4o95coHjDBGyocqHKZn+XJ6168npqgYc3x0hqgCWDMyCIZC9K7fgMlmI3aijjczhWGF2ttxrFxJsL6BmNJSvcPp41y1imBjI6a4OFIffXREtEvodzFz48aNfRPMNm7cGNUeKLt27SIlJaWvkAkwc+ZMUlJS2Llz50WLmdXV1bS0tLBw4cK+5+x2O3PnzmXnzp19xcw5c+bw5ptvcs8995Camspbb72F3+9n3rx5l4zH7/fj9/v7vnc6nQPwLsVwcKbdxe7qLrKSYomxDv4F5aG6bvbVdGECHru5kJQorAS9mPZeP5hg4XW5FKQOzwbDw8VA5nLJhWJQhINQsz3yOCYheudVCo6+HVmZaU+GqUtBh8JAH58TYuIi/TGlkDngBvq6VvKh0MOxjmPsb9lPTnwOMZbo30gOaSHeqXwHX9hHfkI+C4oXRD2Gy9GURrOrmZAWYn7RfIqTi/UOyZCMdG2oeX24d+1Gc7lxrFhB6hNPRHULry0nh2BTE8516zDZbIZZdSeMIdjahvPjjwk2NxNTVobJYoydiN7ycrz794PJROpDD2FNGxnDzfpdzJw7d27f48sV+gZDS0sL2dnZFzyfnZ1NS0vLJY8ByMnJOe/5nJwcamtr+75/8803eeyxx8jIyMBqtRIfH8+KFSsYPXr0JeP5xS9+wfPPP38tb0UMY72+IJtOtREKa6RHYWViq9PHe+WNAMyfkB21QUOf5fKHcHgDLLo+jwm5shXP6AYyl0suFIOi5Sh0nolMEI+mut3QuB8wwU1/AbE657Pe5si28pRCfeMYpgb6ulbyoYi2+t56tjRsIc4aR7Jdn3y1rnYdTe4mYi2xPDz+YazmqxrHMKh6fD20e9vJS8hjRt4MJqRHuWXJEGKka0NLYgJZf/MszT/+Cf7KSjw7d5IwZ87niulq2fLzCdTV4VyzhhSbjZhiKYILCLa0RAqZra3ElJYappAZbGnB8UnrxYS5c7GPHatzRNFzTbc5br31Vv7hH/6BtWvX4na7r/nky5Ytw2QyXfZr//79AJdd7n45n/35Z4/50Y9+RHd3N+vXr2f//v383d/9HY888ghHjx695O/8wQ9+gMPh6Puqr6+/mrcthiFNU2yt7KC+y0NR+uCvJAqENP60t45gWFGWlcDtEy4s9kdDSNNo6PYwrTidm4tHxh2g4eTz5nLJhWLABTyRwTtWe+QrWhwNcHx55PGEeyKDdvQU8IDZCqOmRXeb/Qg1ENe1kg9FNDn8DjbVbcIb8pKTkHPlAwbB8Y7j7GvZB8ADYx8g1Z6qSxyf5Q/5OdtzFk/Iw5z8OTwy7hEmZUzSZQv+UGSEa8OY4mLip98CQO+GDQTq6q76d3xetsJCwi4XztVrCDY1Rf38wliCTU04PvyQYGsbMaXGWZGpeb30vPkmhELEjBlD4qduTIwE13T7bMmSJWzZsoWXXnoJn8/HtGnTmDt3LvPmzWPOnDkkJvavV+Czzz7L448/ftnXlJSUcOTIEVpbWy/4WXt7+wUrL8/JzY0MQWlpaSHvU+Po29ra+o45c+YML730EseOHeubcH7jjTeybds2/v3f/53/+3//70V/t91ux26P4ocsYXjHm5wcquumIC1+0HtWKqV4v7yRtl4/SbFWHru5MCoT0y+mttNDaWYCc8dnYdapV6e4dp83l0suFAOuuRx6aiFjXPTOGfTCgVdAC0H2dTD69uid+1J6myBjNKRfepeIGDgDcV07EPmwPzfphQiGg2xp2EKDq4HRqfrkiA5vBx+eiawEmlMwh3FpUczZl+EP+6lz1jE+fTwz82dSkFigd0hDjlGuDWPGjkVze/AdPUrP22+T+Y1vYE6IXusZk8lETFExgZpqHB99TPLddxMzSv5/GokCDQ04V64k1NkZWZFpkMnlStNwrFhBuKsLc2oqqQ89ZJjYouWa3u0PfvADVq9eTXd3N1u3buX++++nvLyc++67j4yMjH7/nszMTCZMmHDZr9jYWGbNmoXD4WDv3r19x+7ZsweHw8Hs2bMv+rtLS0vJzc1l3bp1fc8FAgG2bNnSd4zH44n8IXzmP7rFYkHTtH6/DzGytff62VLZht1qIdE++Ntr9td2c6i+J9In85ZCkmL16ZPZ6owMHbpzYk5U3rcYeAOVy4UYEN6eyKrM2FSwRCmnKAWH/wSeDohLgylP6tsnEyJDiLRwZPDPCLso1YsRcmFIC/Fu1bsc7zgelfOJoWtvy16OdxynMKlQl9WGgXCAtyveJqAFKEkuYV7hvKjHcCmNvY2MSx/HfaPvk0LmNTJCPoRIMTH53nuxZGaiOZ30vPsuKsqfz01mMzElpYS6OnF+/LEuK0SFvgK1tTg//IhQVxcxJcYpZAK4t2/HX1EBFgtpjz4a1WFZRvG5/mtUVVVx+PBhDh8+zJEjR0hOTubuu+8eqNj6TJw4kUWLFvH000+ze/dudu/ezdNPP82SJUvOG/4zYcIEVqxYAUQS4Le//W1+/vOfs2LFCo4dO8ZTTz1FfHw8Tz75ZN/rx4wZwzPPPMPevXs5c+YM//Iv/8K6det44IEHBvx9iOHH5Q+xqaKNdpefvNTYQT9fU4+XDw9HtjosnJRDWZQmpn+Wxx/C6Qsyd1wWhekjL3EON9HK5UJcVsP+yPCdpNzonbN6C7QcAZMFbnoqugOHLqW3OdIvNNMYK51GEj1zYViFafO2saZmDXub96IpuakuLlTRVcGe5j1kxmUSax38687PUkqx8uxK2r3tJNoSeXDsg4bZvt3j6yHWGsvMvJnYLMN/iu9gM8K1odluJ/XRR8FmI3D6NO7t26N6fvikoFlcQqi7G8dHH+Ovro56DEIf/rORVbkhp8NwhUz/2bO4Nm4EIPnuu7EVjMybN9e09OGxxx5j69ataJrGF77wBb7whS/wgx/8gBtuuGGg4+vz2muv8dxzz/VNJ7/vvvt46aWXzntNRUUFDoej7/vvfve7eL1evvnNb9Ld3c2MGTNYu3YtSUmRQSk2m42VK1fy/e9/n3vvvReXy8WYMWN49dVX5YO8uKL2Xj+rjzVzus1FSUbCoG/19gXD/GlvHSFNMT4nidvGZQ3q+S4lrCnquj3cXJzG1CLpkzmU6ZHLhbio3lZo2AsJ2dFbGdldAyc/iDye9ACkGaDBvxaKbHsfdQtYoz+ZeKQyUi4MqRCb6zfjDrmZUzAHm1mKMiKi3dPOlvotmEwmUmNTdYnhUNshjnQcwYSJh8Y9RGKMPjfVPyushWn3tjMnfw6jkkbpHc6QZqR8CJHp4sl3343z/fdxbdyIrbAQe2lpVGOIrNAsIVhbi/PjlSQvukumnA9z/tOnca5eg+bxEFNcYqgWMGGHg5633waliJs6lbhp0/QOSTfXVMx8++23yczM5KmnnmL+/Pncdttt/e6Tea3S09P54x//eNnXKKXO+95kMrFs2TKWLVt2yWPGjh3L8uXLByJEMYLUdrpZfayFJoeXsqxEbJbB/fCtlGL5wQY63QFS42w8Mm2Ujn0y3RRnxDN/Qvag9wcVg0uPXC7EBdwdcOL9yDbzrChNmw24In0ylRaZGF4S3Umpl9TbAsl5kD1R70hGFCPlwjR7GhazhV1Nu/CGvMwvnE+cNU6XWIRxeENeNtZtpMvXRVlqmS4xNLuaWVW9CoDbi26nONkAN4A+0exuJi8hj5tybtI7lCHPSPnwnLipUwnU1uIrL8fxzjtkfOMbWD5ZnBQtJpOJmJISAnV1OFauIvmuhcR+aoeoGD781dU4Vq5C+f2Gm2SvQiF63noL5fFgzcsj+Z57DFVojbZrqsB0dXXxn//5n4RCIX70ox+RmZnJjBkz+N73vseqVasGOkYhDOVYo4MVBxtp7/UzNjtp0AuZALvOdnK8yYnFZOKJ6UXE69SjstXpI+6TPpl69eoUA0dyudBdbwsceQs6qyBjbHQmdysNDv0BfD2RlaA3Pm6MieFKA78z0iszRtp3RJPRcmFSTBKjEkdR3lrOyrMrcfgdVz5IDFua0tjRsIPTPacpSi7S5YOrN+Tl7cq3Casw49LGMTv/4jML9OANegmGg8zMm2mYlaJDmdHyIXzSP/Oee7BmZ6O5XDiWL496/8xzYoqKUMEgzpWr8B45oksMYvBogQDuHTsjKzKLivQO5wK9q1cTbGjAFBtL6qOPYrKN7M/j11SFSU1N5b777uOFF17gwIEDHD9+nEmTJvHCCy+wZMmSgY5RCEPQNMWuMx18eKSJYFhRlpUYldWR9V0eVh1tAWDx5FzdelT2+oK4/EHmjc+iOMMAfeXE5ya5XOiqpx6OvBmZXp45PnpDf6rWQnsFmG0w7augQ9+5CygNeuogIQtyr9c7mhHHiLkwzhZHcUoxFd0VvHf6Peqd9brEIfRX2V3JofZD5Cfm69ILUinF+6ffp8ffQ6o9lfvH3G+YlUBKKRpdjUzImGCYiepDnRHzIYA5JiZSvImJIVBdjWvzZt1iiRk1CsxmnGvW4t67T7fCqhh4/hMnCNTWEmPAHpTe8nI8+/YBkPLQQ1jT03WOSH/X9Mmhq6uLLVu2sHnzZjZv3szx48dJT0/n/vvvZ/78+QMdoxC6U0qx62wHG0+2kxJvIzPRHpXzevwh/rS3jrBSXJ+fzKwyfSZMB0IajT1eZo/OYGqh9MkcLiSXC910nYVjK8DdHhl0E60+me0VULkm8viGRyJbuvUWcP+5kDluIcSm6B3RiGPUXBhjiWF06mjqnHW8f+Z9bht1G5MzJxtm4IoYfA6/g+2N27GZbbqtOtzZtJPK7kosJguPjHvEUG0POn2dJMckMzNvJhazRe9whgWj5kMAa1YWyffei2P5ctxbtxJTVKRb70pbbi6hjg5cGzeg/D4SZs3CZNVn55wYGGGXC/fefZjj4zHFGKtvebC5GceHHwKQMG8eseOG2M2bz7SDHCjX9DcuKyuLzMxMbrvtNp5++mnmzZvH9dfLSgIxPCml2HWmk02n2klLiCE9ITrJTVOKtw7U0+MNkpEQw4M3jdLlTrimFNWdLsbnJjF3XDZm6ZM5bEguF7poOxXpkel3QubY6BUyvT1w6PeAgqJZke3celIaOBog5IdRN8Po2yEhU9+YRigj50KzyUxJSgmt7lbW1qyly9vF7ILZ2C3Ruakq9KMpjV1Nu2jztDE6dbQuMdQ4athYF5mYu7h0MXmJBrgB9AlfyEePr4fbi24nOz5b73CGDSPnQ4C4G24gUFuLd/9+epYvJ/Mb38CSos9NQGtmJmGrFde2bWg+H0lz5xquCCb6z1t+mFBrCzGjjTXcSfN66XnzTQiFiBk7lsS5c/UO6aqEXS5UMDAoE9evqZh56NAhysrK+poB19bW8uKLLzJp0qS+aeNCDAdKKXaf7WTjqTZS4mxRK2QCbK5op7LVhdVs4skZRcTa9LnjXN/lIScploWTcomLkbvew4nkchFVSkHjAahYCeEQpI+OXq9KLQwHX42sgkweBdc9GJ3zXkrAHdlen5QHk+6D3BvBLKvt9DIUcmFOQg69gV52Ne2iy9fFncV3kmKXVbzD2amuUxztOEp+Yr4uq3F7A70sr1qOQnFj1o1MzZ4a9RguJRAOUOesY3LWZKZkT9E7nGFlKOTD5EWLCDY2Empupuftt0n/6lcxWfT5jGJJTQWrFffuPSivj8R5c7EkJ+sSi7h2ofZ2PAcPYknP0O3/pYtRmoZj+XLC3d1Y0tJIffBBTEPoelHzegk1NxE/fTrxN9884L//mv4k/v7v/75vsnhPTw8zZszgX/7lX7j//vv5j//4jwENUAi9KKXYU93Fhk8KmRlR2loOcLrNxYaTrQDcP6WAvBR9tvR0uvyYzSYWTMohK0lWgQw3hsnlg7T1QBiIFoYzm+D4CjBZIb00ukN3Tn4A3dWR/pjTngId+s71CfkihcxR0yOx5E+VQqbODJMLryApJqmvj+bq6tUyGGgYc/gd7Gzcic1iI8EW/T7lYS3M8srluINusuOzubv0bsP0yQxpIWqdtUxIn8CdRXfKKuUBNhTyoclmi/TPjI0lWF9P77p1usZjSUwkprAQ7+Fyet59l0Bdna7xiKvnOXCAsNOBJUOflm6X4t66FX9VFVitpD72GOb4oTMgUgUCBOrribvhBhLnzRuUIvE1XT0fPHiQ2267DYB33nmHnJwcamtr+f3vf8+//du/DWiAQuhlX00360+2khwb3UKmwxvkzX11KODm4jSmFevTo9ITCNHlCfCFsZmMzUnSJQYxuAyRy1uOwr7/hKZyCAWic04RXUEfnPwoMngnLi36fSqbyqF6S+TxlC/pu5U7HITOs5A3BSYugXhp3m4EhsiF/RRjiaE0pZQzjjOsql4lBc1hSFMaO5p20OZtIy9Bn23dG+o2UNdbR4wlhkfGPaLL4KGLCWthqh3VlKWUsaBkAfG2ofPBfqgYKvnQmp5Oyhe/CIBn1y58J07oGo85Lo6Y0WMItrTQs2IFngMHUOGwrjGJ/gnU1eE7dhxbTq5hbtoA+Kuq+gZdpdx7L7Y847T5uBIVDuOvrSF23FiS7rgD8yC1X7imYqbH4yEpKVLcWLt2LQ8++CBms5mZM2dSW1s7oAEKoYdTLU42nmolyR69YT8AYU3xxt463IEweSmx3HtjftTO/dk46ro83DgqlVtK5MP2cGWIXB70QssxOPwGHHgFmo9EtiCL4cHnhOPvQs02SM6H+Cjf8Xa1wuE/RR6Pvh1yJ0f3/J+mhaHzNGSNg4n3gs04QzRGOkPkwqtgNVspTSml2lHNqrOr6PH16B2SGEAnu05yrP0YBYkFumwvP9F5gt3NuwG4f/T9ZMQZY6WSpjRqnbUUJhVyV8ldJMfIVt7BMJTyYeyECSTceisAjvfeI9TZqWs8JosFe2kZJosV55o1ONesIexy6xqTuDwVDkcm0gcDhmoPEOrqouedd0Ap4m6+mbgpU/QOqd+UphGoqcZeVETyXXdhThi83QXX9C/kmDFjeO+996ivr2fNmjV9/TPa2tpINtD/BEJci1anj/UnWgFT1LdWrzneQm2Xh1ibmSenF2Gz6LP1sLbTTXFGPPMnZGPVKQYx+AyTy22xkFYCjnoofx0OvhKZOi2GtqAPTn4ITYci/THtUV7hHfLD/pch7I+cf/w90T3/pykVKWSmFsKkByBWrpWMxDC58CpYzVZKUko46zzLyuqVdPu69Q5JDIBaZy3bG7Zjt9p1WXXY4e3ggzMfADArfxYTMyZGPYaLUUpR66glOz6bu0ruIi1Wn11LI8FQy4eJd9yBrbgY5ffT8+abqID+u3ysWVnYcvPwHjiI4/33CbW36x2SuATvkSMETldhzdNnAdHFaIEAPW+8gfL5sI0aRfLixXqH1G8qHCZQU401M4ukuxZFesoOomuqUvz4xz/m7//+7ykpKWHGjBnMmjULiNy9mTrVOM2hhbhabn+Itcdb6HQFGJUW3VUzRxsdbD/dAcBDN42K6tb2T2t1+oiLsXDnxBxS4oyxrUgMDkPlcqsd0ssgrTiyDffwn6BmO2hadOMQA0MLQ+UaaC6P/He1xUb3/ErBkTfB1QL2ZLjpK2DWsaF7d3VkVeqk+yExS784xEUZKhdeBavZSllKGTXOGlZWr6TR1ah3SOJzqOyu5OMzH9Mb6NVle3kgHODtircJhAMUJRVxR9EdUY/hYpRS1DhqSItNY2HJQplcPsiGWj40WSykPvII5sREQq2tOD7+GGWAXuzmhARiysoIVFfjeP996aNpMErTcO3ZQ+/6DZji4jHHGWO3jFIK5wcfEGptxZyYSOpjj2GyXtPM7qjTAgH8Z89gy80lZck92HIGP1df05/Mww8/zJw5c2hububGG2/se/6OO+7gi5/0rhBiqAlrik0VbVS1uRidlYg5ij0z2nv9LD/YAMBtYzO5Ll+fCaVufwinL8g9k/Mozoh+w3kRXYbM5dZYyBwLrrZIn0VvD4y5M/rFMHHtlIKzW6FuJ6QURv6bRlvtdmg6CCZzpJCp50pIZxNY7JGp5alF+sUhLsmQubCfLGYLZSll1DpreafyHaZmTeWmnJtIjEnUOzRxFY62H2Vj/UaUUhSnFEf9/EopPjrzEe3edhJtiTw87mFdtrhfLK4aRw3pceksLl1MQWKB3iENe0MxH1qSkkh5+GG6X30VX3k5MUVFxE+bpndYmKxWYkaPJlBbi+ODD0i64w5iJxpjtfNIpgIBXNu2496zB0tqKlYDDf3x7NqF7+hRMJtJffRRQ219vxzN7SbY2EDsxIkk3XEH1rTorJ6/5jJvbm4uubm55z03ffr0zx2QEHrZW93JgdpuCtPio7q92x8K89qeWgIhjdLMBBZOyr3yQYMgFNao7/YwvTSdqUWyfWekMGwuT8yOFMGqt4DPARPugbhUvaMS/dF0EM5sgISs6G8tB+iugePvRR5PvBcyRkc/hnM8nZG+sNd9EbLG6xeHuCLD5sJ+sJgtlKWW0ePrYXvTdk73nGZG/gzGp43Hah4aKzpGKk1pHGg5wNbGrdgtdnISc3SJY1/LPo51HsOEiYfHPWyIYrimNGocNWTGZXJ36d3kJQ6d4RdD3VDMh/bSUhJvvx3Xhg04V67ElpeHLV//rcMmsxl7aSnBxkacH69Ec7uJu+kmTGb9bxaMRJrHQ++GDXgPH8aak2uoYqG/upredesASLrrLmKKo39j61qEursJd3YSf8stJM6dG9VVrvK3SAigqrWXbVUdpMXFkGCP3oW/UooVhxpp6/WTFGvl8VsKsZj1maJW0+mmNDOBeeOzdYtBiPPEJkNaWWSr8uE3wCFbKA2vowpOrYoMt4n2sB8Afy8ceBlUGHJvhNJ50Y/hnIALXO1QNh8KbtIvDjFipMamMjp1NM6gk4/PfMyHZz6kw9uhd1jiEjxBDzsad7CpfhMJtgRyEvQpZNY561hbuxaABcULKErWfwX5uUJmVlwW95TdI4VM0S8Jc+ZgHz8eQiF63nwTzePRO6Q+toICTHFx9K5fj2vzZjQD9PYcaULd3Tg++hhPeTm2UYWGKmSGHQ4cb78NmkbsDTcQP2OG3iH1S6itDc3pIHHuXJLuvDPq2/WlmClGNE1THG1wsOpYC2FNkRnlgT+7znZypMGB2QRP3FJEUqw+PSrbnD4S7FbunJhDYhSLuUJckS0WMsdFeg4e+iM0HoxsYxbG01UNJz6IDNxJ1mE1hBaGg7+PrORNyIYbn4Aotgs5TzgAPbVQNB3K5uoXhxhxzCYzBYkF5CXmcarrFO9WvsuprlOG6CEnIvxhP4fbD/PGqTfY1riN9Lh03SaGuwIu3ql8B01pTMqYxIw8/T9An9tanh2fzd1ld5OboM+OJTH0mMxmUr74RSzp6YR7euhZvhxloN7r1sxMLBmZuHbsxPnRR4QdDr1DGjH81dU4VqzAV1mBvaTUMD0yAVQwSPcbb6C53Vhzc0m5915MQ+C6MexwoLndJN15Jwm3ztalt6dULcSI1esLsq2qg4O13ditFgrTozs1sq7TzcqjzQAsvj6Pkkx9elT6gmF6vEHunpwX9T8DIfrFbIXM8eBsgGPLwdkMo+dDjPz/agh+F9TuhPrdEPJFJofroeJj6KwCSwzc/DX9+qxqYeg8AznXw7jFYJFLLRF9sdZYRqeOpsndxEdnPqI1t5WZ+TOxW/QZLiggGA5S2VPJwdaDNPQ2EG+LpyylDItOw8nCWpi3K9/GFXSRFZfFfaPvM8QH6AZXA+mx6dxderduq1XF0GWOiyP1scfo/M//JHD6NK4tW0iaP1/vsPpYkpMxxcTgO36CsMNB0oIFxIwapXdYw5YKh/GWl+Patg3N78deNhqTRceBkJ+hlMLx0UeEmpowxcWR+vjjmGJi9A7rijSfj1BbKwlz5hA3dapu/3bIFbYYkao73Gw81Uptp4eC1Lior4js9QV5fW8dmoLJBSnMHq3PHXlNKWr+f/b+Mzqu80rwvf/nVM6FnDNAMOecg8SgbFuWZLU0Vjt093vH3atn+q53Tc/90H3XvLPca81d0zO3g9vtdmgnyZaVZYvKWWIUSVFiJoicgUJVoXLVOe+HQ9KSJZGUSOBUAfunVSJCoWrXAfCgap/97D0WY3FtgGX1QVNiEOKaKIoxTCYZho5XIdoP7bdAQIYBmEbTYPhD6HgNQl3gqzS+R2YYOAbnXzHeXnK/EYsZdB3GzxuDfubeLgl3YSpFUajx1hBJRXi7722G48Nsq99GqavU7NBmnWg6yt4LezkfPo/D4qAx0Gh6P9MXu16kJ9qDw+LgnvZ7sFvMfwEdSoZQUNhat1USmeILs12sbgs//jix11/HXluLo63N7LAuU51OYzBQdxfhJ57Au3UbzoUL8uJkwkyixWJMvvkm8feOYPH7cVSZ30P1D8X37yd59CgoCsF77pm2wTnXQ89myXR34Vy8GO/69ab+3EoyU8wq2ZzGvo4x3jk/Rjqn0VruxTrNDZhzms7DB3qIJLOUeR18eVmNaYtA/0SCcp+Dre3l0zr0SIgvzBkAmxvGzsGRn0HLDqhaAlbzX4TNKrExY8hP/1Gj8rBsLphUXcTkEBz9pfF28zaoXmpOHAATncbP6LzbwZM/0zHF7OZ3+HFanZybOMd4cpy11WuZXzIfm2pOa5vZJplN8lLXS5wJnaEx0JgXScPjI8c5MHgAgLta7zJtm/tHJbNJxhJjbK3bSltR/iSeRGFyLVlCuqeHxMGDTDz2GCV/+qd5lShSLBYcTc1kBgaI/O63ZMfG8Kxbi+qQ6vkbITsyQvTFF0meP4+9phbVY84OyCtJXbhA9PnnAfDt3ImjudnkiK5O1zTSXZ3Ym5rwbd9uehWpZC/ErJHM5Hj+wyFeOjmMy2ahuXT6E5kAez8YoHMshsOq8kdr63HYzEkARJMZ0lmNLXPKKfXKH05RQCw2Y9u5loUPfgOHfwKDHxjbe8XUC3XBsV9C70HwV0FRk3mJzGwSDv3I6NNZ3AJzbzMnDjCmqFudMP9OKCqMCZRi9rBb7LQEW0hpKZ678BxPnn2S7ki32WHNeFkty2s9r3Fi/ETeJDKHYkM80/EMABtrNtJe3G5yRMaW9+5IN4tKF7GycqXZ4YgZwr97N7aaGvREgolHHkHPw6E7tqoqLEXFxN56k/BTT5MdGTE7pIKXC4eJPLeXVMcFHE3NeZnIzE1MMPHrX/9+4M+6dWaHdE0yfX1Yi4rx3XwzFp/P7HAkmWmWbC5/mhHPBpFkhmeO9bP/whjVQRclJiXvjvVM8Pb5MQDuXlFLuc+cnm45Tad/IsGy+iIWVOfPJDchrpmigL8GipthohuO/sIYEDR6VgYETaXBD+DYw0bf0tJ2sHvNi0XXjYrMySFwBGDFQ+YlVUNdRq/O+XdB+TxzYhDiKhRFocpTRZ2vjgvhCzx+9nFe6nqJcEqGUEwFTdd4p+8d3ht+j1pvbV4kMhOZBL8+/WuyWpaWQAtb67aaHRJgTFSv99ezpW6LVAyLG0axWgneey+qx0N2cJDwM8/k5TA0SyCAraGR1JkzTPzmMRIffpiXcRYCLZEg8uKLpC5WDyq2/FtPLg380eNxrFVVBTPwJzs2hqKq+G7aga0iP9qASDLTBJqm86tDPRzrmTA7lFlhJJriyff6ON4XpqnEY9q07oFwgseP9AKwdU4ZC6oDpsQB0BOKU1vsZvOcUlQ1/xdPIT6TxQ4lLUavxpHTxjTr44/C5LDZkc0smgZd7xoDmDJJKGk1L3F4yfmXYPB9UCyw8iFwmHSGeKLb2Gq/4C6omG9ODEJ8DnaLnaZgEz67jwODB/jVqV9xaPAQ8Uzc7NBmlKPDR9k3uI9ydzlum/n9czVd47GzjxFKhQg6gnyp7UuoivkvBYdiQ3hsHrbXb8dnN7/SR8wslkCA4D33gKqSfP994vv3mx3Sp1LtduwtLUYy7tlnmXzlVbREwuywCoqeyRB99VVSJ09ib2wyZbr21ei6Tvipp8gODKC43RQVyMAfPZMhOz6Ge/26vOo/m3/f4VlA03XGY2lePDGEx2GhtVz+cE+VrrEYez8YpD+coLXMi9WkvpCJdI5f7O8mk9NpK/dy03zzzmaMTaawqArb2sunffCREFPG5oLSNmOydu8hGO+Aps1QswKs0kbhuuQycO5luPAGOP3gzYOzscMn4dTvjLcX3m1sdTfDRLeRTJ1/F1QsMCcGIb4gv8OPz+5jOD7MC50vcHTkKMvLlzO3eG5eJN8K2cmxk7ze+zo+u4+Aw7yT1x/1SvcrdIQ7sKpW7mm/Jy++x6FkiGQ2yc7GndR4ZaCfmBr2xkZ8O3cS3buX6PPPY6usxN7YaHZYn6CoKraaGnKRCLF33iZ1/jyO5mZsdXXYaqqxeE3cDZPndE1j8u13SBw5gq22DjVPE4Sxt94iefw4qCrBe+7BEgyaHdI1Sff14mhswr1smdmhfIwkM000OpniuQ8G+fJyKzVBl9nhzBjhRIausRinBqN0jsZIZzXayn2oJpVva7rOrw/1MB5LU+S2ce/KOtNiSaRzjMXS3Dy/nNZy+YMoZiCH1xhGMzkIHz4BwyeMoTAlLWZHVpjSMTj9HPQcAF8VuIJmRwSxETjyU0CH+nXQYFKfociA0e5gwZ1QudCcGIS4ToqiUOGpoNRVykhihL2dezk6fJTlFUZS02k1px1OIdJ1nf5YP8dHjnNy/CSqoubN5PgPRz/knf53ALij5Q4qPZUmRwThVJiJ5ASbazezqHSR2eGIGc69di2Zvj6Sx48z8etfU/Knf4olkB8nGv6Qxe9HdbnIhkLE9u+HA/ux+P3YamtxtrfjaG9HMWHuQz5LHDlCfN8+rGXlqG7zT9R8muTp00y+/DIA/j17cDSZdCL+c8pFIiiqBc/6dXk3oEqSmSaqDroIxdL87vgAX15WY1ofx5lA03TODk9yajBCx0iMiUQau8VCiceO32Vu9eFLJ4Y4PRTFqir80ZoG3CZtc89pOt3jMZbUBVndZP7USiGmjKIYiTd3CYx1QLgPaldD0yYj2SmuTSIEJ56GweNQ1Aj2PGignk0ZA38yCSOmBV8xJ45MAlJhWPBlqJQX4aLwWVQLlZ5KylxlDMeH+d2F3/H+yPusqFzBnKI50sfwCnRdp3eyl/dH3uds6CyJbIJydzl+R370JB+MDfL0+acBWFe9joWl5p98iaQijCZG2VizkdVVqwuiX5wobIqiELjjDrIjI2QHB5n41a8o/sY38nIrMoBis2ErL4fycvRcjlwkQvLUaVKnTuFcvBjvhg15m4ydTnouR/L4cSZffx3V683bY5IdHib82GOg67hWrsS9erXZIV0TXdPIDg7gXrMGex4mX/Pzt3eWUICmUi/nRqL87vgAdy2rubztN5HOMTqZIpzIUFvkIujOz1LpfDAeS/PGmRE+7AujAcVuu6mVmB91vC/Ma2eMqXRfXl5LtYkVuN3jMWqL3GyfW47NpO32Qkwri93Yep4IwfmXIdQBLTugrN1IeIrPFhmAE0/B2DmjP2Y+bNXXdWP4UHQAHH5Y8cdGr0oz4pjogsrFRhsDIWYQi2qhyltFVssyGBvk2fPP0uBvYGXFSpqDzXnRYzGfDMYGOTB4gHOhc2S0DOXucmp8+bNdOp6J8+vTvyajZWgONLOjfofZIRFNRxlJjLC+aj3rq9fLz5SYNordTvC++xj7/vfJ9PURefZZ/HfemffJdMViwVpUhLWoCC0WI3H4MNn+fjybNuGYMyfv458q2VCI2FtvkfjgA1S3B2tZmdkhfSotHif08MPoqRS2xkb8e/aYHdI1yw4OYi2vwLNmTV7+nEky02QWVaG51MvZ4Ume/3CQqoCL7rE4Q9EksVSWVFaj0u9kY1spC6oDWGRYy2U5Ted4X5i3zo4wEk1RV+Q2rerx0wyEE/zmcA8Am1pLWVoXNC2W0WgKu8XCjnnlkhgXs4+ryBgOE+oypp7XrYamLUb/R/FJY+eNRObksJH4VfNkXT3/MgwcNXpUrvhjcJp09n1yCJxBaNluTjJViGlgVa3U+mpJ59L0RnvpifbQFmxjQekC6v31OCx5cILDRPFMnPeG3+Po8FEm05NUeivx2PKgev0jNF3j8bOPM5GaIOgI8pW2r5ieOIxlYgzHhllTtYYNNRtMj0fMPtaiIoJ3303o5z8nceQI1spKPGvXmh3WNVM9HuytbWT6+gg/+SSupcvwbFg/q/pp6ppG8sMTxN5+i8zIKPba2rzdWq7nckw8+ii58XEswSBF99yTt9XAf0hLJtESCXw7tudtxWthHMkZzmZRaSzx8GF/mON9YVw2K16HlZqgG6tFoX8iwZNH+jg3PMnGtlLKfbO3f1FO00lnNSLJDO+cG+N43wQuu5W2ivyoxLwknsry831dZHI6reVedi4wrzdRPJ0llEiza0ElzWWz5w+dEB+jWo2+mckwdLwO4xegdQeUz5cqzUuyaRg4BudegvSkUdWaLy80hz6AU7813l74ZSg2aatLNmVU+i64C/xV5sQgxDSyW+w0BBpIZBKcCZ3hTOgMpa5S5pXMoznQTLm7PC+rNaZKTstxJnSG/QP76Yv1UeoqpaUoP3syv9j5Ih3hDmyqjXvb78VlM7c/fyqboj/az+qq1Wyq3YRFtZgaj5i9HK2t+G6+megLLxB9/nms5eU4mpvNDuuaKaqKva6OXDRK/MABMn19eDdvwtHaanZoU0bPZslFo2jhMIkPPiBx/Diq04WjtTWv+4dG9+4l3dFhVAV/7Wuonvw66XUlmb5eHHPacc6fb3Yon0mSmXnCabPQVv7pVUK1RW7i6SzHeiboHo+z8WKVn1mTuaeLrut0j8f5oD9MKJYhkc6RympkNY1MVmcylaGu2I3bnl8/xjlN5+GD3YTiGYo9du5bVWdaRW0mp9E9Hmd5fRErG4pMiUGIvOIMgN1rTKE+9jDUrYPmLbO7l6auw+gZY1r56Dlw+oyt5fkiOghHfgbo0LDBuJgldAEq5kPNSvNiEMIELpuLpmATmVyG8eQ4r3S/wgH7Aeq8dbQXt1Pvr8dn95kd5pSKpCO82v0qp8dPY7PYaAm05G1C7sjQEfYP7gfgrta7qPBUmBqPpmt0R7tZULqALbVbsOZLxb+Ytdzr15MZGiJ57JgxEOhP/gRrcbHZYX0uFp8P1e0m09vLxBNP4l6xHM/atXlbpfh56LpO+sIF0l3dZIeHyIVCaIkkeiqJns1hq6nJ+8cZ27+f+IEDoCgEvvxlbJXmD167VtnRUVSPB+/6dXldSZq/kYmPcdutzKnwMRRN8dvjAwyEE9w0rxKXPT+fRF0PTdPpGo9zpDvE6aEo6YyGy27BalGxKgp2i4rLplAVdOZVNeYlez8Y4PxIDLtF5YE1DaYlW9NZjY7RSdorfeyYVz7jk99CXDPVYlT2JcPQ8SqEu6H1ZijNowTedIkMQOdbMHjMSGoWN+VHf8xL0jE4+G9GRWRJqzFwxyyTw2D3GX1XrdKuQ8xONouNCk8FFZ4KJtOTdEQ6OBM6Q8ARoCXYQkuwhVpf7Yzbhj4UG+LFrhfpjnZT66vFZTW3yvFKuiPd/PaCUcm+pXYL80rmmRwR9ER6qPZUs7VuKzaLDJMS5lMUhcDtt5MbHSXT18fEww9T/K1v5d205qtRLBbsDQ3kwmFib79Npteo0rQ3Npod2heWGR4mfvAgyRMn0NNpFKcL1elE9ftRneV5nVy7JHX2LNHnngPAe9NNOOeZvw5fq2wohBaN4Nm2DVt1tdnhXFH+/ySIyxRFodLvxO+wcrAzRCyVY/fCyhnTA1HTdDrHYhzuCnF2eJJsTqMy4MKbR30wr+ZQ5zhvnx8D4O4VtVQGzGkJcCmROa/Kz62Lqy4PlhJCfIQzYEzoDnUavTQbNkLjBrDN8FYeyYhRmTp2DoY+hOQEBOqMvqL5RMvBe/8O8VFwFcOKh4xEtBlyaSOOubdCsM6cGITIM167F6/di6ZrTKQmODJ8hCPDRyh1ldJe3E6jv5EqT1XeVi9eq45wBy93vcx4cpzmQHNeP55wKsyvT/8aTdeYVzyPzbWbzQ6J0cQodoudbfXbCDjys++ayDOqip7LoedyKJap+31TbDZjINC//qsxbfrxxwnee29eb1v+LJZAANXjId3TzcTjT+BctBBbdTXW0jKsJcUFkQDUYjHiR44Sf+89tEgEa1VVQfYCzQ4PM/Hoo8bk8mXL8GwwcUfR55QNhdAmQng2bcazapXZ4VxV/v9Ui09wO6w0l3o5ORAhns5xy6Iq05JmN0pvKM7BCyFODkbI5jSqAi48BZTEBLgwGuOpo/0AbJ9bzsIac56wpbI5OkdjzJdEphBXp1qNir/YKJzdCyMnoXKh8TFfNRTgE9pPlZgwkrZjZ40BP4kQoIC7xOgbmo9OPm1sf7fYYdW3jPYAZtB1GO+A0jlQVzhDAoSYLqqiUuwspthZTCaXIZQK8WbvmxywHKDCU8G84nnU++spdZWaHernous6H4x+wGu9r5HOpWkKNOV1f9B0Ls2vTv2KeDZOpbuSO1vNn9Icz8SJpCLc3HAzDf4GU2MRhcPR0oK9uppMby/2hqn9ubH4/QTvu4/xH/+Y1KlTTL76Kr4dO6b0PqeKYrXiaGomGwoRP3gQNN2oaPT5sNXVYS0tQbXbwWpDsRkX1ePBWlZqagJXSyZJnTlD/OBBMv39WIpLsLe2mr5+fRFaLEbol780Jpc3NOC/7baCeRzZUIhcaBzvps141q0tiKR+YWWLxGV2q0pruY+O0Ukef6+XXQsraSnA4S6jkykOdY7zfm+YeDpHdbCwKjEvCcXS/GJ/FzldZ2FNgO1zy02JI5XJcWEsxsKaALcsqirIYymEKTylxnTzyWFj0IzdA4FaqFhkDA7y5OmLcC1nbJe3uT9ZUZqMGD0eR04bCczkhDHQx1UMJW3mVTlei+59cOF14+2lfwR+E7e5hHuMY9Z+y8yv2hWm0nQNCuM1z2eyWWyUu8spd5eTyCYYiY/QFenCa/NS6amkJdhCtaeacnd5Xlc4TqYnOTpylHf738VldVHvrzc7pCvSdZ2nzj3FYHwQj83DvXPvxW4xd+dWVsvSF+1jecVylpQtMTUWUVgswSDebdsIP/kk2bExrCUlU3p/9tpaArffTviJJ4i98QbWsjJcixdP6X1OJWtREdaiInRdR08m0SYnSRw7BrpmnKC9TEF1OrCWlmFvbcFWXY2tunrattpr8TjJU6dJHD1CZmAA1eXG3tI6pdW4U0nPZgk98gi5UAhLURFF995bEBWxALmJCXLj43g3b8azfl1BJDJBkpkFzaIqtJR56RqL88R7fSypDTCn0kdtkdu0gTNXk81phOIZxmIpBiaSHO2ZYCKepjLgorYov5v4fpZUJsdP93VeTMY6uXt5rSm9PJOZHJ1jMRZdTGQWWmWrEKaz2I0EJhjTvCd6jESgMwAlc6ByARS35E9CK5eFU8/C4HGwOMDuBleRUW2ZjhlVmPGQUV3qLjEqC/NlOvmVjJ6F47823p6zG6pMfBGcCEEuA/PvhECNeXGIGS+SivDs+WdpDbayuc78rcE3gsvqosZXg67rTGYm6Z3s5fzEeRwWByWuElqCLZS5ygg6ggSdwbzosxlKhjg1forjI8cZTYxS5jHiy3ev9rzKyfGTqIrKV+d81fTt3Lqu0x3ppjHQKJPLxRfiaG7GvW4dk6+8gurxoDqn9rmXa+lSsiMjxN56i/CTT2IJBrHX5/dJjKtRFAXF5UJ1ubCWlX3i87qmoSeTZMfHSb/ag2K1YAkEsVyq1FRVQEFRFdRAEEdTI7bq6utO0OUmY6ROniB+5CiZ4SEsHi/2xqaCSfx9Gl3TCD/5JJnubhSnk6I/+qOCmVyeDYUuJjI3FVQiEySZWfBURaGp1MPYZIq3z49ysCtEbdDFwtoAreVe/HmwxTiVzfFhf4Te8Th9EwmiySyJdA4NnRKPgzkVvoIpv/5Dmq7zq0M9DEVS+BxWHlzbiN06/QvApUTmktoAexZV5d2EdyEKjt1rXHTdqHwcOAr974G3HCoXQ2kb+GvAYtLvmqbBuZeg+13wXHyCmgxDbBiyaVAUo5qwNM8rMP/Q5DAc/rFRPVC9HNp2mRdLJgnRAWi9ydyEqpgVXuh6gd7JXvom+6jx1dASbDE7pBtGURR8dt/laeeJbIJwKsybvW+ioGC32PHYPBQ7i6n2VtMSbKHSM31TX3VdZyg+xMnxk5wcPclEaoKgM0hLUQtqAZwAOjp8lLf63gLg9pbb86KKtG+yj6AjyPb67XhshfGCXuQfz8qVZAeHSHzwAY7W1ilPsnh37CA7Okrq1CkmHnmE4m9/G2tR0ZTep5kUVUVxuy9PBdfTaXKRCOmu7t9XcOo66Dp6Ok183z5slZU458/D3tj4qQnSK9HTaZKnThE/cIDMwCCq34+juaVgKzE/avLVV0kePw6qSvCeez73sTGDrutkBwbQM+mCq8i8RDIeM0SJ10GJ10EinaN/IsH5kRhFHhtt5V6ay7zUF7tNqdQbCCd47fQIpwejWFUFj8NKkdtOddCSl5PIP68XPhzk1MXH9sDaBgKu6U8eJ9I5usZjLK0Lsmdh1YyccC+EaRQFXEHjkstc7K35Alx4w9h6XjYPihqMoTC2aZpuq+vQ+SZ0vAbeSiO2mSAdg4M/gEwcgg2w5GvG8TeDljP6ZFYvheZt5sUhZo2vtH2FJ84+wenQaX5z5jd8c9E3C67H5LVyWV3GNHCP8WIqlUsRz8Tpnezl7MRZDg8dZmHpQpaULaHENXXbS8eT4/REezgTOkP/ZD+xTIwSVwmtRYXTq60z3MmzHc8CsLFmY15s5x5NjKIqKtvrt09rUlrMPIrNhm/bVnJjY2R6eqa8f6aiqgS+8hXGf/QjsgMDTPziF8aE8ymuCs0Xit2OtfSz/+5oySTZsTHCe/di8Rp9OJ1z2rDX12MJBj/z63RdJ33hAvH9+0lduIDqdGFvmRlJTID44cPE3ngDAP8dd+Boyf+TkXouR7qrE4vPh2/nzTjnzy+Yv3sfJcnMGcZlt1Bf4kHTdUKxNIe7QhzqChF022gr99Fc5qHK7yLgntqkWyancbRngrfOjhJJZGgs8ZhSsTiVDl4Y542zowB8eXktdcXTv00+ns7SMx5neX0RuxdW4rTNjD8KQuQliw38VcYlEze2IJ97ydiG4yqCokbwVYIzaLzvChrVnTf6yUHvITj7orF1fKYkMrWsUZEZGzGO3apvGcfbLKFOI0Hdvges5vadE7ODoihsrdtKJB1hIDbAwycf5puLvonbVpgteK6Voig4rU6cVifFFAMwkZpg38A+To2fYmnZUhaVLbpc1Xk9clqOseQYw/FhzoXO0TPZw2R6ErvFfrkqtJCMJcZ49MyjaLrG/JL5bKvbZnZIRFIRoqkoNzXcRFtRm9nhiBnA6J+5lfBTT01L/0zVbqfo/vuNCecjI0w8+ihF998/YxJv10N1OlFrarDqOtrkJOmODpKnTmLx+7HX1+Noa8NaUoKezaFn0pDNomezpM6fJ3niBLqmYautm7aenNMhde4ckWeNE0qeLVtwL1tmckRXpyWTZLq7sdXV4bvpJuy1hdtGSZKZM5SqKJerNbOaRjie4VDnOAc7x/E6rJR67TSWeCj3OynzObCqCllNJ/eRi6IYt2NRFRQFrKqK06bislk+M3OfyuYYj6V56+woH/ZHCLhstFVc/xPQfHN2OMpTx/oAY3L50rrgtMcQT2XpCcVZJolMIaafzW1c/BgVm4kQDLxvJBoBrA7j8w4feCuM7emuoNF/0+41eleqFuNfxWL067yWLeuDH8Dp3xm3na9DiT4vXYfjj8LYOeO4rfoT47iZFUu410hgtt8C7mJz4hCzkkW1cGvTrfzm7G8IpUI8euZRHpj3wKzrNxh0BAnYA4wnx3mt9zWOjx6nzF1GqasUv92Px+bBa/PisXtwW91Y1U+unZqukcgmmExPMpocZWBygJ5oD+FUmEQ2gVW1UuwspsJdUZDVKIlMgkdOPUIim6DaW82dLeZPLk9mkwzFh9hQvYGl5UtNjUXMLI6WFtxr1zL56msoDgcW79QOvbX4/UZC80c/In3uHNHnnsN3662m/47lC0VRsPh8WHw+dE1Di0RInjpN8sMTKHa7sbslp6HrOqCDomKtqJjy79t0ywwOMvGrX4Gm4VyyBO82808oXU0uGiUzMIBrwXx8O3ZgCZjbX/l6STJzFrCq6uXEZk7TmUxlGYqkuDAaB3TcdiuqoqDp+sWL0QtS4WLjYAXUi2/bLSoOm0rAaSPgseF32khlNELxNBOJDIl0jmQmRyydpaHYMyMTbIORJL/c342mw9K6IDtMmFyezmp0h+Ksaizi5vmSyBTCVBabkaz0fmQtyKaM6s1k2Oi7mMsYFZqq1UhcKurFi3IxsWkFX5UxZMZVbCTRrC5jEFEmbmzBTkWge79x+/4qcx7rVDj/MvTsBxRY9nXzHls2CeMXjIrXObugtNWcOMSs5ra5uW/uffzogx/RFenidxd+x23Nt826F9GKolDiKqHIWUQoGaIr0sWZ0BnQAQVsqg2HxYHdYsdr81LkLCJgDxDPxAmlQoTTYVLZFKmccVEVFa/duF61tbqgj2dWy/LrM79mLDlGwB7gvvb7sJlZyX4xpu5IN0vLl7K+en1B9BoVhcWzZg1aLEZs/36U2jpU19S29rFVVxP88peZ+PWviR88iCUYxLNx45TeZyFSVBVLMIglGETP5dAzGaOK1WIpuP6Ln0cuHCb0i1+gp9PYm5oI3HFH3v9d0ZJJMoMDeNatxbt5M6q98HceFUwyMxQK8Rd/8Rc8/fTTANxxxx38wz/8A8Er9Gd4/PHH+f73v8/hw4cZGxvjyJEjLF269GPXSaVS/J//5//Jww8/TCKRYMeOHfzzP/8ztbW1U/hozGNRFQIu2+XejpquE0/nAFAvVmKqFxOYlz5/se8vmq6TyWkkMxrRZIKO0RhZTUNVVOwWBZtVxWG1EHTbqQm68v4X+ouIJjP89J1OUlmNxhI3X15WM+2PU9d1OsdizKvySyJTiHxldRgX1x80js+ljculhVXXAN342Mhpo7oTHaxOo1Izm76YCMW4vsMLQfOHO9wwfYeNiewAC74EFfPNiWNyGOJjULnQGDrkqzAnDiGAcnc5d7fdzcOnHubI8BGKnEVsrJmdL6JVRf1E30xd18loGdK5NKlcitHEKP2xfrK5LKqqYrfYcVgcOK1O/A4/Dotjxjwn1XWdp889TVekC7vFzn1z78NrN7faSdM1OsOdtAZb2Va3zfTEqpiZFIsF35Yt6KkUiSNHsDU0Tvl2Zef8+fh27iT6/PNEX3wRNRjEtXDhlN5nIVMsllmxHV+Lxwn97GdokQjWsjKC996b95PY9WyWTHcXrkWLZkwiEwoomXn//ffT29vL3r17AfiTP/kTHnzwQZ555pnP/JpYLMaGDRv46le/yre//e1Pvc5f/uVf8swzz/DII49QUlLCX/3VX3Hbbbdx+PBhLLPgl1FVFLwmDAYqROmsxk/f7WIikaHEY+eBNQ1YLdN/xmkgnCTotrF9brkkMoUoNBa7cbkSXTcqO7Wssd1Ztc3MATSjZ+HoL423m7dC0+bpjyGXhdAFY3jTvNugbq30yBR5obWold1Nu3nuwnO80v0KAXuARWWLzA4rLxg7hexGVSYza9vi1bzc/TIfjH2Aqqh8dc5XqfCYe+JF0zU6Jjqo9dVyU8NNM77HqzCXYrfj27EDLZkidfIE9qZmFNvUJs/d69aRm5ggvn8/4ccfx+L1Ym9snNL7FPlLT6cJ/fKXZEdGUP1+ih54YMqrhK+XruukuzqxNzbi27FjxiQyoUCSmSdPnmTv3r3s27ePNWvWAPCDH/yAdevWcfr0adrb2z/16x588EEAOjs7P/Xz4XCYH/7wh/zsZz/jpptuAuDnP/85dXV1vPTSS+zatevGPxhRkDRd51eHeuibSOC2W3hofSNuE5LAk6ksiUyW2+fXUOGfHZP1hJh1FAVsM/z3OzoAh34Ieg6qlsC8O8yJY/y8Mbhpzi4objInBiE+w6rKVUwkJ3h34F2eOv8UXruXpoD8nM5WBwcP8k7/OwDc3nI7LUFzJ+bmtBwXwheo9dWyp2nPlE6eF+IS1eXCv2snkXSKVMcF7M3NU1oNqCgKvt27yUUipE6eJPTww5R861tYy8qm7D5FftJzOSZ+8xsyPT0oTidFDzxwxSnu+SLT24u1qBjfzTdj8c2sWSYF0cjg3XffJRAIXE5kAqxdu5ZAIMA777zzhW/38OHDZDIZdu7ceflj1dXVLFy48Iq3m0qliEQiH7uImUvXdZ462s/JgQhWVeHBtQ2UeKd/CltO0+m9OPBncU1hN+sVM4OsheILSYbhwL8aPSqLmmDpA0bfUDPisDqg7WZJZIrrNlXr4U0NNzG/ZD6arvHr079mOD58Q25XFJZTY6d47sJzAGyr28aSsiWmxvOHicxS1wwZSCeu23Q8N7T4fPj27MFWW0umq+uG3/4fUlSV4Fe+gq22Fj2ZZPznPycXjU75/Yr8oes6kWeeIXX6NFitFN1/P7aK/G9JlB0ZQVFVfDftwFZZaXY4N1xBJDMHBwcpL//kkJXy8nIGBwev63btdjtFRR/vaVZRUXHF2/3ud79LIBC4fKmrq/vCMYj898rpYQ52jqMA96yso6HEY0oc3eMx6ovdbJ5ThqrOwC2nouDIWig+t2wSDvzAmP7uKYdV3zIGKE23S1PLq5dDibnVTWJmmKr1UFEU7mq9izpfHalcil+e/CXRtLyInk16oj08fvZxAJaXLze9f6okMsWVTNdzQ2tREb6bbkJxOsmOj0/JfXyUYrNRdP/9WIqL0SYmCP3852iJxJTfr8gPky+/TOLIEVAUgl/9KvaGBrNDuqpcNEouEsG7ZTOOtjazw5kSpiYz//Zv//bitOzPvhw6dAjgUxt367o+JQ29r3a7f/3Xf004HL586enpueExiPxw8MI4L580qiBuX1LNQpMqIsdjaawWlW1zy/E7pbG6yA83bC3U9RsbmMhPuSwc+hFEesHuhdV/AnZzTg4xOQSeUmjcYM79ixlnKp8bWlUr97bfS4mzhEg6wsMnHyaVTd2w2xf5ayQ+wiOnHiGrZ2kLtnFL8y2mDjPSdV0SmeKKpvN1sr22BveqleRGR9Cz2Sm7n0tUj4eiBx9E9XrJDg4Sevhh9Exmyu9XmCv2zjvE3nwTAP/tt+OcO9fkiK5OSySMyeWrV+NatszscKaMqcnM73znO5w8efKKl4ULF1JZWcnQ0NAnvn5kZISK6yjvraysJJ1OEwqFPvbx4eHhK96uw+HA7/d/7CJmnpMDEZ482gfAtvYy1jab0wsokc4xMplkXXMJLWWzq9G9yG83ZC3sOQAf/AZSUmk0o+kaHPkZjJ4Bi8NIZHpMehGsZSExDg3rzYtBzDhT/dzQbXNz/7z78dg8DMYH+dXpX5HVpv7FuzDPRGqCn5/8OYlsgmpvNV+Z8xVUM1pyfMRoYpSgMyiJTPGZpvt1snv5cmx19WT6+qb0fi6xFhdT9MADKA4Hma4uJh59FD2Xm5b7FtMvfugQ0eefB8C7fTvuFStMjujq9HSadHc37sVL8G7ehKIWxGbsL8TUR1ZaWsrcuXOveHE6naxbt45wOMyBAwcuf+3+/fsJh8OsX7/+C9//ihUrsNlsvPjii5c/NjAwwAcffHBdtysKX9dYjIcPdKMDKxqKuGmeOT0xMjmN7vEYS2qDpiVThZgyuQzs+2eIj8H+70NGtuvMSLoOxx+FwWOgWmDVNyFYb1484V4INkDNSvNiEOILKHIW8bW5X8NusdMZ6eTxs4+j6ZrZYYkpEMvE+PmJnxNNRylzlXH/3PuxW8ydQJvTckykJlhevlwSmSJvqG433o0bAH3a+ljaqqoouv9+sFpJnT5N+Omn0TVZi2eaxPvvE3n2WQA8Gzbg2bzZ5IiuTs/lSHV24myfg2/HdpQZNLn80xREmnbevHns3r2bb3/72+zbt499+/bx7W9/m9tuu+1jk8znzp3LE088cfn98fFxjh49yokTJwA4ffo0R48evdwPMxAI8M1vfpO/+qu/4uWXX+bIkSM88MADLFq06PJ0czH7DIQT/Pu7nWQ1nbmVPu5aWmPKlh5N17kwGqOlzMvN8yuwWwvi11WIa2exwc7/H1idxtbjg/8GubTZUYkb7fTvoPtdQIFl/wFK55gXSyZh/Iw1bQK727w4hPiCqr3V3Nt+LxbFwqnxU/y247fo0qpjRklmk/zi5C8YT44TcAT4o3l/hNtm/no1FB+iylPFotJFZocixMfYW1pwLV5MdqB/2pKK9sZGgl/9KqgqyaNHib7wgqzFM0jy5EnCTzwBuo571Sq8N99saouPa6FrGunOC9jr6vDt3InqMamV0zQqmOzIL37xCxYtWsTOnTvZuXMnixcv5mc/+9nHrnP69GnC4fDl959++mmWLVvGrbfeCsB9993HsmXL+Jd/+ZfL1/n7v/977rrrLu655x42bNiA2+3mmWeewWKxTM8DE3llJJriR293ksxo1Be7uW9VPRaThu10jcWo8DvYvagSn/TJFDNVoBbm3mYkNMfPw3s/BU2268wYHa/BuYu7HxbdA1XmTuAl3A3l86B8gblxCHEdmgJNfKXtKygoHBk+wivdr5gdkrhBMrkMj5x6hMHYIB6bhwfmPYDfYX47q0wuQyKbYGXFyrxIrArxUYqi4Fm7Fmt5BdnrGA78eTnnziVw550AxN99l9jrr0/bfYupkzp3jolHHwVNw7lkCb5bzO1VfK0yPd1YS0rx796F9Q8GXM9UVrMDuFbFxcX8/Oc/v+J1/vBsyEMPPcRDDz10xa9xOp38wz/8A//wD/9wvSGKAheKp/nR2xeIpbJUB5x8fV2jadWQA+EETpuF3QurKPc5TYlBiGnjKTWmWu//Fxj6AN5/BJZ8DUzuDSauU89+OPGk8Xb7rdCwztRwSITA6oLGTWApmKc/QnyquSVzua35Np7peIa3+9/GbXOzrtrk3zFxXXJajsfOPkZ3tBuHxcH98+6nxJUfLYb6Y/00+BqYW5z/gy/E7GQJBvGsX0f4mWfQEglUl2ta7te1dClaPE70+eeZfPVVsFrxbtw4Lfctbrx0ZyehRx6BXA7H/PkE7ryzIHpOZoaHUZxOfDtvxlZZaXY40yb/vzNCTINIMsMP37pAOJGhzOvgoQ1NuOzmVOeGYmlSGY2b5lXQVDrzy8OFAKCkFZY/ZCQwew8aSTDZrlO4eg/BsUeMt5u2QKvJrVtSUYgOQt0aKG4yNxYhbpBlFcvYUb8DgBe7XuTw0GGTIxJf1KVE5pnQGayKlfvm3keVp8rssABIZBLous7KypXYLLJTSOQv57x5OOfOJdPbO63361m/Hu/27QBMvvgisXffndb7FzdG6sIFQj//OWQy2NvaCH7lKygFsFtX1zS0iRCetWtxNM2u57iSzBSzXjyV5cdvX2A8lqbIbeMbG5vwOsyp2plMZRmNpdjYVsri2oApMQhhmsqFsPg+4+0Lb8DJZyShWYj6j8LRXwC6MTF8/l1g5vacVNQY+tO40fykqhA32IaaDayvNoZW/rbjtxwZPmJyROLz0nSNJ889yanxU1gUC/fOvZcGf4PZYV02EBugLdhGS7DF7FCEuCLFasWzYQOq30d2dHRa79u7ZQueLVsAiO7dS/wjg4tF/kt1dBD6xS/QMxnsra0U3XsvirUwdvHkxsexFBfjnDv7KuclmSlmtUQ6x4/f6WQoksLntPLNjc0EXOacdU5lc/SF4qxsKGZ9S0lB9OYQ4oarWw0L7zbe7ngFzjxnbjzi8xk8Dkd+Cui//16auZalJyHcAw0boH2PbC8XM9KO+h2sqVwDwDPnn+H9kfdNjkhcK03XeOrcU3w49iGqovLV9q/mVdIwkorgsDhYUbkCVVq/iAJgq6jAvXo12fFx9ExmWu/bu20bnotbzCO//S3xw1ItXwhSHR2EfvnLyxWZRffdh2IrnCr03Pg4zgULsfjN76883eSvkpi14uksP3y7g76JBG67hW9saKLYYzcllqym0TkaY0FNgB3zyrFa5FdTzGKNG2H+l4y3z74AZ543Nx5xbYZPwOGfgK5BzQqjytbMF7/pGEx0fySRWThPTIX4PBRFYWfjTlZWrAQwkmOjH5oclbgaXdd55vwzHB89biQy53yVOUVzzA7rMl3XGY4Ps6BkAXW+OrPDEeKauZctw9HUSLpverebK4qC96abcK8z+hdHnnmG+HvvTWsM4vNJnT9P6Be/gEwGR1ubUZFZSInMcBjV68U5b57ZoZhCMiZiVoqns/zorQv0TyTx2C18a1MzFX5zBu1ous6FkRiNpR52za/Eacv/3hxCTLnmLTDvDuPtM8/BuZfMjUdc2fBJOPQj0HNQtRSW3G9yIjMOE11QvxbabwGrOSeqhJguiqKwp2kPy8qXoaPz+NnHOTV2yuywxGfQdZ1nO57l2MgxFBS+3PZl2ovbzQ7rY0YTowQdQZZVLDM7FCE+F9XhwLN+A4pqIReJTOt9K4qCb9cu3GvWgK4TeeopYvv3T2sM4tqkzp41KjKzWRxz5hAssIpMgOzoCI45bdgqys0OxRSSzBSzTjyV5YdvXaA/nMTjsPKtTc1UmpTIBOgai1Pmc3DLoioC7sJaQIWYUi3bYe6txtunnoXzr5gbj/h0g8fh0L+BloWKhbDsQVBNPCmjZWGiE2pXw9zbJJEpZg1FUbit+TYWly5GR+c3Z3/DibETZocl/sClHplHho+goHBX613ML5lvdlgfk8llCKfCrKpcRamr1OxwhPjcHM1NuJYuITs4gK5p03rfiqLg27MH99q1AER/9ztib701rTGIK0scP/77RGZ7O8EC6pF5iRaPo1ituBYsMDsU00gyU8wqsVSWH759gYFwEq/Dyrc2NplWkQkwEE7gtKnsWlhpahxC5K3Wm2HObuPtk0/Dmb0yFCif9B+Bwz8GLQeVS2DFQ+YmMgHGL0BRE8zZCVaHubEIMc0UReGO1jtYWLoQTdd47Mxj0kMzj+S0HI+deYzjo8dRUPhS25dYVLbI7LA+oW+yj6ZAE4vLFpsdihBfmGfNGqwVlWQHBqb9vhVFwbd7N57NmwGIvvgi0VdeQZfnsKaLHzxI+LHHQNNwLlpE8J57Ci6RCZAdHsbe2Iitvt7sUEwjyUwxa0SSGX74Vv4kMscmU6SyGjfNq6ClzGtaHELkvTm7of1iheaZvTLlPF/0HoD3fvr7HpnL/wOoJj8ZjA6CzQ3tu8EZMDcWIUyiKip3td7F0rKl6Og8ee5JDg/JIAqzZXIZfnX6V5wcP4lFsXBP+z0sLF1odlifEElFsKk21lWvw26RynZRuCx+P55169CSSbREYtrvX1EUfDt24N2xA4DY668TfeEFSWiaRNd1Jt94g8izz4Ku41q1isCXv1yQiUw9nUbP5XAtXoyizt6U3ux95GJWGZtM8f3XzzMYSeJzGonMchMTmeFEhlA8zbb2MpbUBU2LQ4iC0XYzzL/LeLvjFfjgMSOJJszR9Q4c/SXG1PK1sPSPzK/ITMcgGYbWHVDcbG4sQphMVVRub7mdVZWrAPhtx2/Z17/P5Khmr3QuzcOnHubcxDmsqpX75t6Xdz0ywdgCPxQbYknZEhr8DWaHI8R1c86fh3NuO5neXtOSiN7Nm/Ht2QNA/J13iDz9NHouZ0oss5Wu60RfeIHJl18GwLN5M/5bby3YRGB2ZARbdRWO5tn9fLcwv3tCfA4D4QTff6ODUDxDscfOn25uMTWROZnKMhRJsrGtjDVNJabFIUTBad4Ki+8FFOh6C449bGxvFtNH1+Hcy3D818b7jZtg8T3mDvuBi30yu6B2FdStNjcWIfKEoijsbtzN+ur1ALzQ9QJv9L4hVUHTLJ6J87MTP6Mz0oldtfNH8/6IlmCL2WF9qoHYAJWeSlZWrjQ7FCFuCMViwbNxI5ZgkOzwsGlxeNauxX/HHaAoJN57j4mHH0ZLpUyLZzbRMxnCjz1G/J13APDt2oVvxw4URTE5si9Gz+XIxWO4lixBsc/u6nlJZooZrXM0xg/e7GAylaUq4ORPNzdT7DHvlz6ZydEbirOqsZjNbaWoamEuokKYpn4dLHvASJ71HoTDP4Fc2uyoZgddgw8fh1PPGO+37IAFXzY/kQm/75PZdrP5FaJC5BFFUdhRv4OtdVsBeK3nNfZ27kWTyvZpEUqG+NEHP6Jvsg+nxckD8x/I24rHRDZBOptmTfUafHaf2eEIccPYysvxbFiPNhk1Zbv5Je4VKwjedx/YbKTOnmX8Jz8hNzlpWjyzgRaLMf7Tn5I8fhxUFf9dd+FZv97ssK5LdmQEW2kpjrY5Zodiujx4BTI7yVnxqXdqMMKP37lAMqPRUOLmWxub8TnNmxaezmp0jsVYWhdkx7xyrBb59RPiC6lZ8ftBM0PH4d1/grQ8GZxSuQwc/nfofBNQjC3/826HfDirHRkAuxva94DTb3Y0QuQdRVHYXLuZXY27ADg4eJBHTz9KJpcxObKZrX+ynx998CPGk+ME7AH+eOEfU+urNTusT6XrOn3RPtqL25lbNNfscIS44VyLFuFcsMDYbj7N080/yjl3LsVf/zqK2022v5/xf/s3sqOjpsUzk2VHRxn7wQ/IdHejOJ0UPfgg7mXLzA7ruuQiEfREHPfqNVi8HrPDMZ1kU0yg6zovnRzmUFdIkppT5N3zo/zs3S4yOZ32Ch9/vL4Jl928ah1N17kwOsncSj+7FlTitEnlkBDXpXIxrPk/jGEvE13w9v+GmDwZnBLpOOz/HgweMxLIy/+DseU/H0wOQzYObbuguMnsaITIa2uq1nD3nLuxKBZOh07zsxM/I56Jmx3WjHQ2dJZ///DfiWViVLor+caib1DmLjM7rE+l6zqd4U7KXGWsr16PRarbxQykWK14N27EWlJCdnDQ1FjsdXWUfOtbWIqLyYVCjP3bv5Hu6jI1ppkmdeECY//2b+RCISzBICXf/GbB95fUEgmyQ4O4167FtXSJ2eHkBUlmmuCtc6O83xvm9TMjPHm0j5wmCc0bJafpPH2sn2feH0AHltcX8cDaBuxWc3/UB8JJKvxO9iysxOMovIlpQuSlkhZY/xfgKoLYCLz9vyAkTwZvqPgYvPP/wngHWJ2w+s+gOk/OasfHjIE/c3ZD3SqzoxGiIMwvmc8D8x/AaXHSO9nLjz74EaFkyOywZpT3ht7jkVOPkNEyNAea+fqCr+fttu1LicxiZzF7mvfkbcJViBvBWlKCZ+NG9FQSLRYzPZbib34TW00NeiLB+E9+QvzAASl0uk66rhM/eJDQz36Gnkhgq62l+NvfxlpebnZo10VPp0n3dONauhTv+vUFO7joRpOjYIKNraVsbS9DAQ52hvjxOxdIpGWIxfVKZnL8bF8n+zrGANi1oJKvLK/BYnJfymQmRzydZX1rKUUm9usUYkbyVcKG/wSBWmOr+bv/CAPvmx3VzDB6Ft78nzA5CM6AkTgubTM7KkMiZFTitu2Ehg1mRyNEQWnwN/DHC/+YgCPAeHKcHx7/IV0RORF0vXJajuc6nuPZjmfR0VlStoSvzf0aDqvD7NA+la7rdEW6KHIWsad5DzXeGrNDEmLKORfMx7loEZm+PlO3mwNYvF6KH3oI58KFoGlEfvtbY9J5NmtqXIVKT6cJP/EEkWefhVwO54IFFD/0EBav1+zQrouey5Hu6sI5Zw6+bdtm/dCfj5JkpgkURWFFQxF3La3GblXpGInxvdfPMTopE82+qFA8zfffOM+ZoUlsFoX7V9ezZU5ZXkwp6x6PM7/Kz8Jq6eUmxJRw+mHdn0PZPNAycPhHcPp3xsAa8fnputEbc//3IBODQJ2RMPZXmx2ZIRk2tpe3bIemzfnRt1OIAlPmLuMbC79BlaeKeNaYtn1gQKqCvqhYJsbPTvyMg0MHAdhat5U7Wu7I2y3blxKZQUeQPU2SyBSzh6KqeDdswFpRQaa/3+xwUOx2Anffjffmmy9POh//8Y/JRSJmh1ZQsuPjjP3whySPHQNFwXvzzQS++lUUm3nzMm4EXddJd3Viq67Gt3Mnqkf6ZH6UJDNN1Fzm5U83NxN02RidTPO9185zalAWrs/rzFCUf3r1HEORFD6nlW9vamZhTcDssAAYnTRi2thWKgN/hJhKVges+hY0bTHeP/sCHPw3kH5wn08uC+//Cj54zEgG16yA9X8OrqDZkRnSkxDph8ZN0LoDZJuNEF+Yz+7joQUPsbBkIZqusbdzL8+cf4asJlVBn0f/ZD8/eP8HdEe7sVvs3Nt+L5trN+fFCfVPk8ll6Ax3Xk5k5utQIiGmiiUYxLtxA2TSaHHznycqioJ340aKHngAxeUi09vL2Pe/T7qz0+zQCkLy9GnGvv99soODqB4PRf/hP+DduDFv1+Brpes6me5uLIEg/t27sBYVmR1S3pFXASarCrj4/2xtoa7IRSKT46fvdvH8h4PSR/MaaLrOiyeG+Pd3Oomnc1QHnfwfW1upLXKbHRoAmZzG2GSK1Y3FVAVcZocjxMynWmDBl2DpA6DaYPiEsU06OmB2ZIUhMQH7/gl69gEKzLvDOJaWPNnOkk0ZPVEb1sOcncb3WwhxXWwWG19q+xI3N9yMgsLRkaP85IOfEEnJyfVr8f7I+8bxSkcocZbwrUXfor243eywPlUym6Qr0kVPtIcqb5UkMsWs5pg7F8e8eWT6+swO5TJHayslf/InWCsq0CYnGf/JT4i+/DJ6TtrRfRo9kyGydy8Tv/wlejKJrbaWkj/904If9AMXE5ldXageD/7du7BV58nuqDwjk0jygM9p49ubmvndB4Ps6xjj9TMjdI/HuXdVHX5nYZdGT5XJVJZfHezm/IjRvHl1UzG3LqrClkfVj30TCRpLPaxsLDY7FCFml9qV4KuAQz+C+Ci89few+F6jylB8uqEP4OjDxrZyqxOWfx3K55kd1e9pORg7D5WLYM4usMjfRiFuFEVRWFe9jgp3BY+dfYz+WD//+v6/cmfrnbQV5Umf3DyTyqZ47sJzvD9q9GhuC7bxpbYv4bQ6TY7sk+KZOEOxIRRFod5Xz+KyxbQEW7Dny4kqIUygqCqetWtJd3WRHRvDWlJidkgAWIuLKf7mN4n+7nckjh4l9sYbpDs6CHzlK1iL5TXlJZmBAcKPP052eBgA9+rV+HbtQrEWfnpL1zTSXZ1YA0H8e3Zjb2w0O6S8Vfjf7RnCalG5Y0k1jSVuHj/Sx4XRGP/4yjnuXVVHS1lhN6290S6MxvjVwW4iySw2i8JdS2tYVp9fZdeRZAaATW2luOxSPSTEtAvUwaa/gsP/DmNn4cjPYPgkLLwbbPn3YtM0uQycfNrokQnGIKVlXwdvHk201XUYOwfFTTDvdrBJpbuYeRQUVFQyWsa0GJqDzXxr0bf49elfMxQf4uFTD7O6cjU3NdyEVZWXDJf0Rft4/OzjhFIhFBQ2125mU+0mVCV/TqhfEk6FGUuM0V7UzuKyxTT4G/K2j6cQ081WUYF7+QomX30VSyCQN4kw1eEg8KUvYW9rI/LMM8a28+99D/+tt+JcsqTgt09fD13TiL39NpOvvgq5HKrXi//OO3HOmWN2aDeErmmkOy9gLSnBf8st2Gulev5K8uM3Vly2uDZIVcDFwwe6GYwk+dFbF9jQWsrN8yvyqurQDJmcxosnhnj73Cg6UOZ1cP+aeir8+ZWY0HSd/okEa5tLJBEthJnsXljzZ0b/zLMvQN8hCF0wtk4XN5kdnfmig/DeTyF6sQF+81Zovw0sefbUYKIL3MVGItMtVQliZrJb7CwuW8xrPa/hs/tMq/ArchbxzUXf5OWul9k/uJ8DgwfoDHfypbYvUeGpMCWmfKHpGu/0v8NrPa+h6RoBe4AvtX2Jen+92aF9qngmzmh8lI21G1lfvT4vk61CmM29fBmps2fI9Pdjr8+v32XXwoXYa2uZePxxMl1dhJ94guTJk/hvuQVLID/mQ0yn7Ogo4aeeItPdDYBj3jwCt98+Y4bi6Lkc6c4L2MrL8d9yi2wtvwZ59opFAJT5HPzZlhaefb+fQ10h3jo3yqnBKHevqKW+OD/6QU63nvE4vzncy8jFie/L64PcvqQahzX/zi73jsepDrpY31Iyq8+cCZEXVAu074Gydjjyc4iPwTv/L7TtNC6zsUJF1+DCG3Dqt8b0d7sXlt4P5fPNjuyTJodAUWHurRCsMzsaIabUyoqVDMeH+XDsQ1qCLaYln6yqlV1Nu2gONvP0+acZTgzzb8f/jZsabmJ15epZ+dxmNDHKs+efpTtqvIieXzKf25pvy8tt5QDpXJreyV5Wlq9kbdVaSWQK8RlUtxvP+vWEn3gSLR5HdefXa21LMEjxQw8Re/NNJl97jdSpU4x2dODdvh33mjUos2AQopZOE3vjDWLvvAO5HIrDgW/PHlxLl86Yv0eXKjJtlVX4b70FW8XsPnl4rSSZmafsVpUvL69lfrWfJ470MTqZ4vuvn2dTWyk75s2eKs2spvHKqWHeODOCpoPPYeWuZTXMq/KbHdqnCicyaMDW9jKCbulFJETeKG6Gzf9f+PAx6D0IZ583+kQuugeKGsyObvpE+uDYIxDuMd4vbYelfwTOPFxTkxFjKNH8O6BigdnRCDHlbBYbW2q3MJ4cpyfaQ4Pf3LWpraiNP1vyZzx17inOTZzj+c7n+WD0A25tvpVKT6WpsU2XrJbl7b63eavvLXJ6DptqY3fTbpaW5e+L6JyWoyvSxbzieWyp2yItAoS4CkdbG87580m8/z721ta8+91WVBXvli045s41tp339BDdu5fEsWME7rhjxlbw6bpO6tQpIs89hxYOA+CYMwffLbfMqMneuq6T7urCWlomiczPSf665bm5lX7+coeHZ9/v50jPBG+cHeXkQJTbllTRVu4zO7wpdXY4yrPvDzASNaoxF9cGuGNxNW5Hfv7YZnIaA+EEm9tKaa+Y2d8bIQqSzWkk7srnwfHfGIm9t/+XMR177q1gy6+z8TdULg1nnoeOV43KTKsT5t0J9WuMysd8o2sQ6YXGTVC31uxohJg2QWeQLXVbeOb8M4wnxil2mdtawWPz8LW5X+Pw0GFe6n6Jvsk+fvD+D1hTtYatdVtn9BCZ7kg3z3Y8y2hiFICWYAu3NN1CkTN/X0Truk5XpIt6Xz076nfkbeWoEPlEUVXca9aQ6uwkNzaGtbTU7JA+la2iguJvfIPE4cNEX3qJ7MAAY//6r7iWL8e7ZcuM2nqeGR5m8oUXSJ09C4AaDOLfswdHe3veJZuvV6a3F4vPi3/Pbklkfk75mRUSH+OyW/jqyjoW1gR48kgfI5Mpfvx2J/MqfdyyqIoSr8PsEG+osckUvzs+wMnBKAAeu4U7l9awsCa/F+jusRitZV42tJbNuEVWiBmlejmUtBmDb3oPQtfbMPg+zL/L+NxM+v3VdRg6DieeNia7A1QtgQVfBmcer6mRfvBWQONGmAVbqIT4qOZAM2ur1vJq96u4bW7TE1KKorCyciXtxe083/k8J8ZOsG9gHyfGTrCrcRdzi+fOqOc9kVSE13pe4+jIUcBI6O5q3MWCkgV5/zh7oj2UOEvY2biTgCOP13gh8oytohz36lVMvvQSqs+H6sjP19eKquJetQrH3LlEn3+e5PHjJA4fJnHsGO7Vq/Fu2pR3W+U/j+zoKJOvvUbygw+M57AWC5716/Fu3oxin3knzzKDgyhWC/6dO7HXSTulz0uSmQVkXpWfxhIPr5wa4t2OMU4ORjkzNMmG1hK2tpfjtBV277dUNsdrp0d469woOU1HVWBdcwnb51bk/UTwkWgKl8PKtrnleR+rEAJw+IwqzdrV8MGjMDlsTDzvfAvab4HSNrMjvH7jHXDyGWPoERjJy4V3Q+Uic+O6mmwKUpMwZ5cM/BGz1oqKFQzHhzk+etzU/pkf5bP7uHvO3ZwNneW5C88xkZrg0TOPUuutZXv9dhoDjWaHeF0SmQRv9b/FwYGDZPUsAMvKl3FT/U24bC6To7u6wdggDouDmxpuotxdbnY4QhQcz/LlZPv6SJ48hb2lJa/7UVp8PoJ330161SqiL71Eprub+DvvkDh8GM/69bjXrkV1Fk5ldjYUIvbaaySOHTOSmIBj/nx8O3bkbaXs9cqOjqKn0/h378bRNgNed5hAkpkFxmW3cOvialY1FvPb4wOcHZ7kjbOjHO4KsbG1lDXNJQWX1ExmcuzrGOOtc6PE0zkA2sq93LqoivI8m1T+aZKZHKFYmt2LKqmbpQOahChYpW1GL83zrxoTz0MXYN8/QekcI6lZ1Gh2hJ9fdBBOPWv0BAVQbcak8pYdxlb7fDfRDeXtULXU7EiEMI1VtbK5djOhZIjuSHdeJQrbitpo9DfyZt+b7BvYR+9kLz898VOaA81sq99GjbfG7BA/l0wuw4HBA7zd9zbJXBLA2KbdsIM6X2FUyownxsnkMuxq2pVXPytCFBLFbse7bRvZsXEyvb15N93809gbGij+xjdInz1L9OWXyQ4OMvnqq8TefhvX0qW4V6/GWlZmdpifStd1Ml1dxA8cIHnyJGgaAI72drzbtmGrqjI5wqmTHR9Hi0bw7diBa9FCs8MpWJLMLFDlficPrW/k9FCU3x0fYHQyzfMnhnj97Ahrm0tY31KKN097S14SS2V55/wo73aMkcwYi1eJx84ti6qYW+nL+608ADlNp2ssxsKaACsa8reHkhDiClQrtN0Mdavh7IvQ/S6MnjEuFQug9WYINuT39nNdNxKxHa/B4HFAN3ph1q2BObvze0v5RyUmwGKDpi1gnXnbiYT4PAKOANvrt/P0uacZjA3m1dAdm8XG9vrtrKpcxZu9b/Le8Ht0hDvoON7BnKI5rKlaQ6O/Ma+fy0XTUQ4NHuLw0GHi2TgA5e5yttdvpy3Yltexf9RkepJQKsS2um0sKJFhaUJcD2txMb5tWwk/9TTZ8XGsxfm/Q0RRFBxz5mBvbSX54YdMvv46uZER4gcOED9wAHtrK+41a3C0tuZFtamWTJI4dozEwYNkR0Yuf9ze0oJ3+3bstbUmRje19FyOTG8PisWCZ/NmXCtWmB1SQcvvbJe4IkVRmFvpp63cx/u9E7x2ZoSRaIrXTo/w9rlRVjQUs6qxiKpAfm2N6Z9IcKgrxHtdIdI5I4lZ7nOwtb2cRTUBLGphPHnMahrnRyapL3azfW75rJkwL8SM5QzAoruhZZsx7bznIAx9aFwCtdCwEWqWQz4NvNByMHDUSGJemlAOULEI5t1m9J0sFLpm9Mps2gQlLWZHI0ReqPXVsqVuy+Vt3UFH0OyQPsZn93FL8y2sq17HG71v8P7I+5wJneFM6AxlrjJWVa5iUdkiHJb86T/XP9nP/oH9fDj2IZpuPA8NOoJsrdvKwtKFebGl/1ols0kGYgOsrVrLqspVZocjxIzgaGvDvXYtk6+9iup2F8x2bUVVcS1ahHPhQtIdHcT37yd15gzpc+dInzuH6vHgmDcP5/z52BsbUSzTt5tTSyRInT1L8uRJ0mfPomcyRsx2O87Fi3GvWoWtMn9O2E0FLRYj09eLraYW79YtOJqbzQ6p4EkycwawqArL6otYUhfk5ECE106P0DeRYF/HGPs6xqgOOFneUMSS2iAek6o146ksR3snONwVYiCcvPzx6oCTre3lzK/2oxbIGXCAbM5IZDaWerh9SfWMG8IkxKzmLoEl9xvbss+9BP1HINwL7z9iDA2qWwO1q8BXZU61pq4bicv+I9D3HqTCxsdVK9SshOYtRmyFJjoA3nJj6I8Q4rL5JfMJpUK82fsmDosDlzW/TlIDFDmLuLP1TjbUbODAwAGOjRxjJDHC7y78jpe6X2Jx6WLmlcyjwd9gSrIwlAxxYuwEJ8ZOMBAbuPzxOl8da6vW0l7cXlBJTICslqUn0sPCsoVsrNlYcPELkc88q1eRHRkh8cEHeVPReK0URcHR0oKjpYXs+DjxgwdJHDmCFouROHSIxKFDKC4XzvZ27M3N2GprsRQX39BqdD2XIzs0RLq7m9Tp06Q7Oy9vIwewlJXhXrUK15IlBZMs/qJ0XSc7NIQWj+FasQLvpk1YfD6zw5oRFF2/2GFVfGGRSIRAIEA4HMbv91/1+tmcxvdePw86U5IE03Wdc8OTHOgc59RAlNzFb7FFUWir8NJW4aOtzEuJ1z5lW2h0XWd0Ms3Z4ShnhyY5NzJJTrsYh6owr8rPqoYiWsu9BbON55JMTqNjZJKWMi+3Lamm2JNHVVqiIHzeNaNQfKHH1XMAPngMyuZObXDXIz0J3fuNqeeJ8d9/3FVsDNOpXARFTaBO4RluXYNwn1GFOXAU4mO//5zDZ1SNNmwAh3fqYphK2ZQxsGjhV6B+jdnRiGkk6+G1yWgZnr/wPO+PvE9TsAmrmt/1CMlskmMjxzg0eIix5O/XK5fVxZyiOcwtnktzoBmbxTYl96/rOiOJEc6Gzn4igakqKgtLFrK6ajXV3uopuf+ppukaHRMdNAeaua3lNjw2j9khieska2H+yYZChB97nOzYGPbGRrPDuS56Nku6s5PkiROkTp1Ci8U+9nnF7cZeW4utpgZLaSkWvx+L34/q812xglNLpdCiUXLRKFo4TKa/n0xfH5nBQchmP3Zda3k5jrlzcc6di7W6uuByAF+Ermmkuzqx+Hx4N27EuWhRQSXGzXKt60Z+PxMSX4iiKEbCssJHPJXlWO8E73VP0DeR4NRglFODUQCCLhut5V6aSj2U+52UeR3YrV/slyud1RiOJhmKpOgej3F2eJKJeOZj17lUIbq0Nog7z/t5fpZ0VqNjdJI5FT5uW1xF0C2JTCFmPLsXWncY28+HTxo9NUdOG4nNC68bF5sHipsgWA+BOuNf+3W8uMylIdRl9MEc74BQJ2R/X9WOxW7086xaBuXzwVKYayoAuSyMnzcS2tXLzI5GiLxkU21srdtKJB2hO9JNU6Apr18IOq1O1lStYXXlajrCHXw4+iGnQ6dJZBMcGznGsZFjqIpKpbuSam/15Uupq/RzVxjquk48G2c4PkxPtIeeaA+90V5SudTl6ygoNPgbmF8yn3kl8wo++dcT6aHSU8lNDTcV/GMRIl9Zi4rwbt9m9M8cGcnbQTrXQrFacbS24mhtRb/tNtJdXaROnybT20umvx89Hid15gypM2f+4AsVVK/XSGgqysd2JGmxGHoqxWdRnE5s1dXG/c6di7WkZKoeXl7SdZ1MdzfWYBD/bbfN6F6gZingVz/iWrgdVta1lLKupZTBSJJTAxHODk/SPRZnIpHhUFeIQ12hy9cPum2U+xwUue04bRYcVhW7VcVhtaAqkMxqpDI5kpkcyYxGJJlhKJIk9AeJSzAqMBtL3LSV+5hT4aMyUNgl5JmckcicV+XnlkVVBFxTU00ghMhTimokECsWGJWEo6eNYTtDH0ImZkwPvzRBHIzt6q5icPrB4TcqKB0+43Z07fcXLQfJMMTHjQRpYhySEeAPNk5Y7FA2z0j4lc8D6wxob5FLw9g5Y3r8/Ltk6I8QV+C1e9lRv4Mnzz3JYGyQKm/+t5NQFIWWYAstwRY0XaM70s2p8VOcHj9NOB2mP9ZPf6wfhozrq4qKz+bDa/fis/vw2Xw4rA50dHT94gWdVC5FOBU2LukwWS37ifu2qTZqfbXML5nP3OK5MybpNxQbwml1sqN+ByWu2ZUcEGK6OZqb8W7eROT5F1BcLizeAt0B8xGKquJoasLR1AQYVZuZwUEyPT1k+vvJhcNo4TC5aBRyObRo9Mq353Cg+nxYfD6sFRXYamqMCs8bvHW90GR6elC9Hvy7d0sic4pIMnMWqfQ7qfQbPSrTWY3OsRjnhifpDcUZjqaIp3NMxDOfqKi8Vh6HlQqfg6qAk9ZyH02lni9c6Zlvspqxtby90qjI9DklkSnEjaEYybxctrCqC60OqFxsXLQcTHTBRLdxCfdAbMTYCv7R7eCflyNgVHsWN0FxM/iqp3Yr+3TLpoyKzPJ5sOBL4CoyOyIh8l6Fp4KNNRv5bcdviWViBZWgUxWVxkAjjYFGdjXuYiI1Qf9kP32TffRP9jMQGyCjZQinjQTl5xVwBKj11lLnq6PWV0ulp3LG9ZEMp8Iks0l2Ne6i3l9vdjhCzAquZcvIjo4SP3AAtbEJxT6zTrwqViv22tpPJNx0TUOLxdCiUfRc7uIHdeOiKMZwJJ8P1TEDTq7fYJm+PhSHA/+uXQXfoiCfFdArR3Ej2a0qcyqMislLYqksw9EUI9EU4USaVFa7fElnc2gaOGwqTpsFp9X41+OwUu53UOFzmjZcaKrlNJ3zI5M0lXq4dZEkMoW4oUpaoKwdRs9ASWt+TQq/VqrFSDYWf2QqYToOkT6j4jIVgVTUqLZMR40ngaoKyqUtOxajctNdbCT0XMXG23avOQOGpkMmaSQyq5bA/DuN6lUhxDWZVzKPnmgPh4cO01rUWpAJO0VRKHIWUeQsYkHpAsDoAxlJR5hMTzKZniSaiRJNR0nn0iiKwuX/FAWbaiPgCFy++O3+vO8jer2S2STD8WE2Vm9kYelCs8MRYtZQVBXv5s3kJiZInTuPvbl5VvQ9VFQVy8WKS3HtMoODoCj4d96Mo7XV7HBmtJn9V198Lh6HlSaHlabSwjnLP9U0XefC6CS1RW5uXVwtPTKFuNHcxbDoq3DiSRj80EgI2gq7JQUAdjeUtpkdRX5Kx4xK1pqVMP/26+stKsQspCoq66rXMRAboG+yjzpfndkh3RCqohJ0BAk6gmaHkneyWpaeaA8LSxeyrnrdrN66KYQZVJcL344d5MIRMj092BsazA5J5KHs6Ch6Jo1/1y6cc/N4uOkMUTCnFEKhEA8++CCBQIBAIMCDDz7IxMTEFb/m8ccfZ9euXZSWlqIoCkePHv3Y58fHx/nzP/9z2tvbcbvd1NfX8xd/8ReEw59/a4uYeXRdp3M0RqnXwW2LqyjzSQm9EFPCFYSFd0PNMqNaLx276peIApWeNLbi162BBXdKIlOILyjgCLChZgO6rhNJRcwOR0whXdfpjnTT6G9ke932KZsAL4S4MmtpKb6bdqBYrWRHRswOR+QZLZEgF57Au3kzrkWLzA5nViiYZOb999/P0aNH2bt3L3v37uXo0aM8+OCDV/yaWCzGhg0b+Lu/+7tP/Xx/fz/9/f38P//P/8Px48f5yU9+wt69e/nmN785FQ9BFJju8Tg+l5XbFldTFXCZHY4QM5vDa/RNrF9rVO2lrtxsXBSgVNRIZDash3m3g03WVSGuR1uwjWXlyxiMDZLTcmaHI6bIYGyQgCPAjvodeO2FP3xEiEJ2aSBQLhIhF5ETScKgaxrpnm6cCxbgXr7c7HBmjYLYZn7y5En27t3Lvn37WLNmDQA/+MEPWLduHadPn6a9vf1Tv+5SsrOzs/NTP79w4UIee+yxy++3tLTw3//7f+eBBx4gm81itRbE4RFTYGwyhUVVuGVhFfUlbrPDEWJ2sLlg3h1G38zON6GoEWzy+zcjJMMQ6YfGTTBnt0wtF+IGUBSFtVVr6Z/spyfaQ2Og0eyQxA02mZ4klUuxo34HFZ4Ks8MRQgCu5cvJRSLE3nkHxWZDdcnJ2dku09uLrbIS7+bNKJJDmjYFUZn57rvvEggELicyAdauXUsgEOCdd965ofcVDofx+/1XTGSmUikikcjHLmLmSGVyjMVSbGgtoa1CGh4L8VmmZC202o1kV+1KGO+EXOb6b1OYKzEB0QFo3grtt0giU8xIZj03dNvcbKzZiN1iZzQxOi33KaZHVssyMDnAsvJlzCuZZ3Y4QlyT2fA6WVFVvBs34lqyhHRPD3pGnqvOZrmLrQ+9W7ZgLSoyN5hZpiCSmYODg5SXl3/i4+Xl5QwODt6w+xkbG+O//bf/xp/+6Z9e8Xrf/e53L/fuDAQC1NXNjMbr4mKfzPEYcyv9rGosMTscIfLalK2FVruR9Cpvh7FzoGs35nbF9EtMwOQQtGyHObvAImerxcxk5nPDxkAjm2o2EUvHCCVD03a/Ymp1R7ppDDSyvnp9QU6sF7PTbHmdrNjt+LZvxzmnjXRnJ3pOWn3MRno6TXZoCPeqVTjnzDE7nFnH1L+Mf/u3f4uiKFe8HDp0COBTp/bpun7DpvlFIhFuvfVW5s+fz9/8zd9c8bp//dd/TTgcvnzp6em5ITEI8w2Ek5R4HGyfV47dKk8chbiSKV0LHT5jy3mgBsY7btztiumTSUK030hktt4MqsXsiISYMmY/N1xWvowNNRsIJUMyEGgGGI4P47F52Fq3Fbe0WxEFxOy1cDqpHg++nTuxVVeT7upE13WzQxLTSNd10j3dONra8Kxdc/UvEDecqSUS3/nOd7jvvvuueJ3Gxkbef/99hoaGPvG5kZERKiquv39MNBpl9+7deL1ennjiCWy2K08JdDgcOBwy2XqmmUxlSWRy3Dy/gnKf0+xwhMh7U74Westh7m3w/q8g3AOBmXl2f0bSchDqgOplxvZyVU4OiZnN7OeGiqKwpmoN6Vyad/rfwaJa8Ng8psUjvrh4Js5kepJdjbuo9labHY4Qn4vZa+F0sxYV4d+1k4mnnibT04O9vt7skMQ00DMZMn19WPwBvNu2ojold2AGU5OZpaWllJaWXvV669atIxwOc+DAAVavXg3A/v37CYfDrF+//rpiiEQi7Nq1C4fDwdNPP41TfhBnpZym0xeKs6qpmEU1AbPDEUJcUtIC7Xvgg8chNgqeq//NEHkg1AHBBuN7Jz0yhZgWqqKyoWYD6Vyag0MHqfXV4rLKYIpCksll6I32srxiOYtKF5kdjhDiGthqavDvvJnw754j3dODfYZurReg53Jkh4bQ4jHsdXV41q/HVllpdlizVkGUSsybN4/du3fz7W9/m3379rFv3z6+/e1vc9ttt31skvncuXN54oknLr8/Pj7O0aNHOXHiBACnT5/m6NGjl/tsRqNRdu7cSSwW44c//CGRSITBwUEGBwfJSd+LWaV7PEZtsZvNc8pQ1RvTukAIcYNUL4PWHRAfhVTU7GjE1UQGwOaBubeCSxqhCzGdrKqVLXVbWFK2hJ5ID+lc2uyQxDVKZBN0RjppL25nU80mLNKaQ4iC4WhtJbBnN4rdTnoGb62frXRNIzs8TPr8eVSvh8CttxK8914cra1mhzarFUQyE+AXv/gFixYtYufOnezcuZPFixfzs5/97GPXOX36NOFw+PL7Tz/9NMuWLePWW28F4L777mPZsmX8y7/8CwCHDx9m//79HD9+nNbWVqqqqi5fZnJ/D/F7mq5zYXQSt93Kjrnl+J1XbjEghDBJ4yaoXw/hbsimzI5GfJZkBDKT0LYTipvMjkaIWclusbO9fjvzS+bTFekip8kJ+nwXTUfpjfSypGwJtzTdgtfuNTskIcTn5GhrI3DLHlSHnXR3t9nhiBskNzlJ+vw5UFW8O2+m+P77cS1dijqL2inkq4IZK1pcXMzPf/7zK17nD5vuPvTQQzz00EOfef2tW7dKo95ZLJPTuDAao8LvYPfCKppKpbeUEHlLtRjTsFMRGHwfSueAWjB/wmaHbAoiPdC0DWpWmB2NELOay+piR8MO4tk4neFOmoPNN2xoprixQskQ48lx1lavZVPNJmwWObEuRKFytLbiv+UWIs89R7q7W3poFjA9myXT1we6jnvlStxr1mAtkh1H+aRgKjOFuJGSmRznRyZpLvXwlRW1ksgUohDYnDDvNihugbFzoGtmRyQuSYaN70nFImjdLgN/hMgDfrufmxtuptRdSndEqoTyja7rDMWGiKajbK3dyta6rZLIFGIGcLS04L/lFlSXSyo0C1Q2FCLdcR5reTmBL92Fb9cuSWTmIXm1IWadSCJD11icpXVBvrS8RiaXC1FIXEUw73bwlEGo0+xohK5BqMsYztS8DRbdDTYZOCJEvih3l3NT/U04rU4GYgNmhyMuimfinJ84j4rKzoadrK1ei6rIyzIhZgpHczP+W/agOBxGdZ8oGOneXvR4HM+WLRR99W6cc+bIzoY8JX81xawSSWQYiibZ2FrCbYur8UmPTCEKT6DGSGhabMawGWGOTBJGToHDB4vvgbm3gF2q3IXIN42BRrbVbyObyzKeGDc7nFktq2XpjnQzEh9hUeki7m6/m0VlMrVciJnI0dyM/+abAMgMDZkcjbgWuUgEcln8e3bj27wZ1SPPa/OZNBwTs0YslWUwYiQyt8+tkKnlQhSysnaYswdOPgORfvBXmx3R7BIfh8khqFpqDPvxlpkdkRDiChaULGAyPcmrPa9iVa34HX6zQ5p1xhJjjCfHqfPVsaZqDa3BVqnGFGKGc86bh55KEXnhBbJjY1hLSswOSXwGPZslOziAZ916HHPnmh2OuAaSzBSzQjKToycUZ3VTMVvayyWRKcRMULcKFBVOPQsT3RCUJuvTIjEBsREjidm8xaiQFULkvVWVq0hmk7w78C46OgFHwOyQZo2R+AjpXJrtddtZUr4El1XacQgxWziXLCGXSjH5yisoViuWgKy9+Sjd24O9oRHPurWyrbxASDJTzHiZnEbXWIwltQFumleBzSJnwYWYMWpXGMm0k8/A+AUoagR5AjJ1khGYHISW7cZFBv0IUTBURWVjzUZUReXd/nfRdZ2gM2h2WDNeNB1lMj3JzY03s6x8mdnhCCGmmaIoeFatQk8kib39FlgsWLxes8MSH5EdH0e12fFuka3lhURehYgZLafpdIxMMqfSx66FVThtFrNDEkLcaFWLjcEzdg+Mn5cp51MlPQmRPmjcDC07JJEpRAGyqBY21GxgY81GQskQ40npoTmVUtkUg7FBVlWuYknZErPDEUKYRFFVvBs34F61iuxAP1osZnZI4iItnSY3Nop7zRrsDQ1mhyM+B3klYhIFhZymmx3GjJbJaZwfmaSx1MOti6rwOqQQWYgZq6zdSGi6S2D0rCQ0b7RM3NjK37Ae5uwEi6ynQhQqVVFZW72WLXVbiKaijCXGzA5pRsppOboiXSwoWcCGmg3SH1OIWU6xWvFt3Yp75Uoy/X2S0MwDuq6T6e7GMacd96qVZocjPif5q2oCq0WlvcLLeDxNNicvuG80XdcZjiY5PzJJ88VEZtBtNzssIcRUK2mBRV8Ff5WR0NRyZkc0M2STELoAtaugfY/0yBRiBlAVldWVq9lat5VYJsZQTCbt3ki6rtMV6aIx0Mj2+u3YLfI8VAgBit2Ob/t23KtWkenvIzc5aXZIs5aeTpPp6sIS8OPdvAnVLut0oZFkpknWt5bSVOqhazxudigzSiKd48xwFF2HPQsruWdVHeV+p9lhCSGmS7DOSGgGamFMEprXLZeGsQ6oWgZzbwOrw+yIhBA3iKIorKhYwc6GnQB0RbrQddk1dL10Xacn2kOxo5ib6m/CZ/eZHZIQIo9cTmiuXk12YEASmtNMT6dJ9/SQ7unGVlWJb+dObJWVZoclvgDZJ2YSj8PK9rnlPHqol9FoilKfvEC8Hrqu0x9OEk9nWVgdYPOcMiokiSnE7OSvNhKaHz4Oo6ehdA6o8ufuc9OyMHoOKubB/DvA7jY7IiHEDaYoCovKFuGz+3ip+yU6JjpoCDRglTXzc9N1nVAqxGh8lBJXCdsbtlPhqTA7LCFEHlJsNnzbtgEQP3gQKqtkKNAU0zMZMoOD6OkU9tpaXMtX4JzThiIVmQVLnqmYqKHEw/qWEl44MYTPacUhw2m+EF3X6RyL43FYuGtpDQuq/VhlYrkQs5uvwkhofvC4seW8pFW2R38eWg7GzkFpC8y/CxxSWSTETNYYaOTOljt5setFLoQvUOerw2mVk8LXaiI5wUhiBL/dz8aajSwqW0Sxs9jssIQQeexyQlNRiB84AGXlWAIBs8OacXRNIzs8jBaNYK+rx7V8OY72ObKtfAaQZKbJVjYW0zuR4ER/mNZyH6qimB1SQTESmTF8Tiu3La6muUzOaAkhLvKUGkOBPnwCRk5BSZskNK+FrhmJzEAtLPgyuOUFuRCzQZm7jDta7uCVnlf4cPRDKjwVskX6KlK5FD2RHrx2L2ur1rK4bDGlrlKzwxJCFIhLCU3F4SD+9tvomQzWUllDbpRsKERuZARrWSneTbfgXLAA1SE7YmcKSWaazG5V2dpexlAkyUA4SU3QZXZIBUPXdS6MxQi4bNy+uJrGUo/ZIQkh8o27GBZ+BT54DIZPGVvOZRL3Z9N1GO8Abzks+JLxrxBi1vDavexp3IPP5uPg4EFSuZQk5z5DNB1lMDbI/OL5rKteJ1vKhRBfiGK14t24EYvLRfS118kMDGCrqjI7rIKmJRJk+npR3R48G9bjXrFCql5nIHlFlwfKfU62zCnj6aP9nBmKAAqKAqqioCoKlX4nLrtsQf8oTdfpHI0RdNu4fUk1DSWSyBRCfAZX0EjMaY8ZQ4FK26SH5qfRNRi/YGwpn3+nUZkphJh1bBYbW+q24LP7eKvvLfqifVR7q1Fk99BlI/ERJtOTrK1ay4aaDTgsUukjhPjiFFXFvWoVitNF9JWXSXd3Y6urk3X3C8hFImSHBnEuWIBnzRps1dVmhySmiLyayxMLqwNoOkwms2i6TjankdV0QvE0JwYiVPpdBFyyPRIgns7SP5Gg1Ovg9iXV1BXLUAohxFW4i2Hhl+H4o8ZQm9I2UOUk0WXZFIyfB38NzLsdSlrMjkgIYSJVUVlZuRKf3ccrPa/QGe6kIdCAqszunuSXJpXbVBs7G3eyuGzxrD8mQogbx7VoIarLSfTFF0mfP4etphbVJTs3r1V2fJxcKIRn3Xq8mzbKcJ8ZTpKZeUJVFZbWBT/x8XRWo8QzzLsdY2Sy2qydep7JaYxNpgkn0jjtFppKPWxtL5dEphDi2nlKjC3nxx+F0TMXp5xLQpNkBCI9ULEI5t5q9BoVQgigvbgdr93Li50vcn7iPA3+BuyW2fniMKfl6Ax3UuouZXv9dpoDzWaHJISYgRytrahuN5PvvEv63Dl0wFZVheqUoWxXcmlSuXfHdjyrVqGocqJpppNkZp6zW1W2z63A67Tx6ulhUhParOqrmc5q9IzHQIFSr4MVDeW0lHupDrhQVSm7F0J8Tt6y3yc0x84ZU85nc0IzOgCpSWjaCq07wDZ7/r4IIa5NjbeGO1rv4JXuVzgbOkvQGZx1fTQ1XaMz3Emtr5bdTbtn3eMXQkwvW3U1wS9/iXRHB/HDh0l3dIBqwVpVJQNs/oCu62S6u1Ecdvy79+BatNDskMQ0kWRmAVBVhbXNJXgdVl48McSF0UkaSjwzfvJ5KJZmOJqkvdLPysYi6ovdOG2zOOkghLgxfBUfSWieNSo0Z9s2wUwcJnrA4YUFd0HNSpAz2EKIz1DsLObOljs5OnyU/YP76ZjooNZXOyuqNC8lMqu8VZLIFEJMG0VVcbS2Ym9sJHX+PPFDh0l3XsASLJKJ54Cey5G7uK3cWlqKb9dOHM1SMT+bSDKzgCysCeBxWHnu+ACdozGaSj0zsilwTtPpGY+jqrB9bjlrmkskiSmEuLH8VbDoqx/fcj4bEpq5DIR7QMtCxXxo2gxFjWZHJYQoADaLjVVVq6jz1/FW31ucDZ0l4AhQ6iqdkc9Hwaj46Qp3UeYqY3ejJDKFENNPsVpxtrfjaGoiduQI8XffJXWhA3tdPYp19qVztESC7MgIejqFtbgE79YtOOfOxVpWZnZoYprNvp/+AtdU6uH2JdU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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plot_slopes(\n", + " well_model,\n", + " well_idata,\n", + " wrt=\"arsenic\",\n", + " conditional=[\"dist100\", \"educ4\"],\n", + " subplot_kwargs={\"main\": \"dist100\", \"group\": \"educ4\", \"panel\": \"educ4\"},\n", + " fig_kwargs={\"figsize\": (16, 4), \"sharey\": True},\n", + " legend=False\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the non-interaction model, conditional on a range of values for `educ4` and `dist100`, the slopes of the outcome are nearly identical." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Unit level slopes\n", + "\n", + "Evaluating average predictive slopes at central values for the conditional covariates $c$ can be problematic when the inputs have a large variance since no single central value (mean, median, etc.) is representative of the covariate. This is especially true when $c$ exhibits bi or multimodality. Thus, it may be desireable to use the empirical distribution of $c$ to compute the predictive slopes, and then average over a specific or set of covariates to obtain average slopes. To achieve unit level slopes, do not pass a parameter into `conditional` and or specify `None`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + }, + { + "data": { + "text/html": [ + "
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termestimate_typevaluedist100educ4estimatelower_3.0%upper_97.0%
0arsenicdydx(2.36, 2.3601)0.168260.00-0.084280-0.105566-0.063403
1arsenicdydx(0.71, 0.7101)0.473220.00-0.097837-0.125057-0.070959
2arsenicdydx(2.07, 2.0701)0.209672.50-0.118093-0.139848-0.093442
3arsenicdydx(1.15, 1.1501)0.214863.00-0.150638-0.194765-0.108946
4arsenicdydx(1.1, 1.1001)0.408743.50-0.161272-0.214761-0.108663
5arsenicdydx(3.9, 3.9001)0.695182.25-0.073908-0.080525-0.067493
6arsenicdydx(2.97, 2.9701000000000004)0.807111.00-0.108482-0.123517-0.093042
7arsenicdydx(3.24, 3.2401000000000004)0.551462.50-0.088049-0.097939-0.078020
8arsenicdydx(3.28, 3.2801)0.526470.00-0.087388-0.107331-0.068076
9arsenicdydx(2.52, 2.5201000000000002)0.750720.00-0.099035-0.129517-0.073222
\n", + "
" + ], + "text/plain": [ + " term estimate_type value dist100 educ4 \n", + "0 arsenic dydx (2.36, 2.3601) 0.16826 0.00 \\\n", + "1 arsenic dydx (0.71, 0.7101) 0.47322 0.00 \n", + "2 arsenic dydx (2.07, 2.0701) 0.20967 2.50 \n", + "3 arsenic dydx (1.15, 1.1501) 0.21486 3.00 \n", + "4 arsenic dydx (1.1, 1.1001) 0.40874 3.50 \n", + "5 arsenic dydx (3.9, 3.9001) 0.69518 2.25 \n", + "6 arsenic dydx (2.97, 2.9701000000000004) 0.80711 1.00 \n", + "7 arsenic dydx (3.24, 3.2401000000000004) 0.55146 2.50 \n", + "8 arsenic dydx (3.28, 3.2801) 0.52647 0.00 \n", + "9 arsenic dydx (2.52, 2.5201000000000002) 0.75072 0.00 \n", + "\n", + " estimate lower_3.0% upper_97.0% \n", + "0 -0.084280 -0.105566 -0.063403 \n", + "1 -0.097837 -0.125057 -0.070959 \n", + "2 -0.118093 -0.139848 -0.093442 \n", + "3 -0.150638 -0.194765 -0.108946 \n", + "4 -0.161272 -0.214761 -0.108663 \n", + "5 -0.073908 -0.080525 -0.067493 \n", + "6 -0.108482 -0.123517 -0.093042 \n", + "7 -0.088049 -0.097939 -0.078020 \n", + "8 -0.087388 -0.107331 -0.068076 \n", + "9 -0.099035 -0.129517 -0.073222 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unit_level = slopes(\n", + " well_model_interact,\n", + " well_idata_interact,\n", + " wrt=\"arsenic\",\n", + " conditional=None\n", + ")\n", + "\n", + "# empirical distribution\n", + "print(unit_level.shape[0] == well_model_interact.data.shape[0])\n", + "unit_level.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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switcharsenicdistassoceducdist100educ4
112.3616.826000000.168260.00
210.7147.321999000.473220.00
302.0720.9669990100.209672.50
411.1521.4860000120.214863.00
511.1040.8740011140.408743.50
613.9069.517998190.695182.25
712.9780.710999140.807111.00
813.2455.1460000100.551462.50
913.2852.646999100.526470.00
1012.5275.071999100.750720.00
\n", + "
" + ], + "text/plain": [ + " switch arsenic dist assoc educ dist100 educ4\n", + "1 1 2.36 16.826000 0 0 0.16826 0.00\n", + "2 1 0.71 47.321999 0 0 0.47322 0.00\n", + "3 0 2.07 20.966999 0 10 0.20967 2.50\n", + "4 1 1.15 21.486000 0 12 0.21486 3.00\n", + "5 1 1.10 40.874001 1 14 0.40874 3.50\n", + "6 1 3.90 69.517998 1 9 0.69518 2.25\n", + "7 1 2.97 80.710999 1 4 0.80711 1.00\n", + "8 1 3.24 55.146000 0 10 0.55146 2.50\n", + "9 1 3.28 52.646999 1 0 0.52647 0.00\n", + "10 1 2.52 75.071999 1 0 0.75072 0.00" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "well_model_interact.data.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Above, `unit_level` is the slopes summary dataframe and `well_model_interact.data` is the empirical data used to fit the model. Notice how the values for $c$ are identical in both dataframes. However, for $w$, the values are the original $w$ value plus $\\epsilon$. Thus, the `estimate` value represents the instantaneous rate of change for that unit of observation. However, these unit level slopes are difficult to interpret since each row may have a different slope estimate. Therefore, it is useful to average over (marginalize) the estimates to summarize the unit level predictive slopes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Marginalizing over covariates\n", + "\n", + "Since the empirical distrubution is used for computing the average predictive slopes, the same number of rows ($3020$) is returned as the data used to fit the model. To average over a covariate, use the `average_by` argument. If `True` is passed, then `slopes` averages over all covariates. Else, if a single or list of covariates are passed, then `slopes` averages by the covariates passed." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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termestimate_typeestimatelower_3.0%upper_97.0%
0arsenicdydx-0.111342-0.134846-0.088171
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" + ], + "text/plain": [ + " term estimate_type estimate lower_3.0% upper_97.0%\n", + "0 arsenic dydx -0.111342 -0.134846 -0.088171" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "slopes(\n", + " well_model_interact,\n", + " well_idata_interact,\n", + " wrt=\"arsenic\",\n", + " conditional=None,\n", + " average_by=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The code block above is equivalent to taking the mean of the `estimate` and uncertainty columns. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "estimate -0.111342\n", + "lower_3.0% -0.134846\n", + "upper_97.0% -0.088171\n", + "dtype: float64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unit_level[[\"estimate\", \"lower_3.0%\", \"upper_97.0%\"]].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Average by subgroups\n", + "\n", + "Averaging over all covariates may not be desired, and you would rather average by a group or specific covariate. To perform averaging by subgroups, users can pass a single or list of covariates to `average_by` to average over specific covariates. For example, if we wanted to average by `educ4`:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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termestimate_typeeduc4estimatelower_3.0%upper_97.0%
0arsenicdydx0.00-0.092389-0.119320-0.068167
1arsenicdydx0.25-0.101704-0.126096-0.076910
2arsenicdydx0.50-0.102112-0.122443-0.082142
3arsenicdydx0.75-0.106004-0.124247-0.088132
4arsenicdydx1.00-0.110580-0.127803-0.093221
5arsenicdydx1.25-0.112334-0.128771-0.094870
6arsenicdydx1.50-0.114875-0.132652-0.096790
7arsenicdydx1.75-0.122557-0.142921-0.101423
8arsenicdydx2.00-0.125187-0.148096-0.101350
9arsenicdydx2.25-0.125367-0.150676-0.099852
10arsenicdydx2.50-0.130748-0.159912-0.101058
11arsenicdydx2.75-0.137422-0.170662-0.102995
12arsenicdydx3.00-0.136103-0.172119-0.099548
13arsenicdydx3.25-0.156941-0.202215-0.107625
14arsenicdydx3.50-0.142571-0.186079-0.098362
15arsenicdydx3.75-0.138336-0.181042-0.093120
16arsenicdydx4.00-0.138152-0.185974-0.089611
17arsenicdydx4.25-0.176623-0.244273-0.107141
\n", + "
" + ], + "text/plain": [ + " term estimate_type educ4 estimate lower_3.0% upper_97.0%\n", + "0 arsenic dydx 0.00 -0.092389 -0.119320 -0.068167\n", + "1 arsenic dydx 0.25 -0.101704 -0.126096 -0.076910\n", + "2 arsenic dydx 0.50 -0.102112 -0.122443 -0.082142\n", + "3 arsenic dydx 0.75 -0.106004 -0.124247 -0.088132\n", + "4 arsenic dydx 1.00 -0.110580 -0.127803 -0.093221\n", + "5 arsenic dydx 1.25 -0.112334 -0.128771 -0.094870\n", + "6 arsenic dydx 1.50 -0.114875 -0.132652 -0.096790\n", + "7 arsenic dydx 1.75 -0.122557 -0.142921 -0.101423\n", + "8 arsenic dydx 2.00 -0.125187 -0.148096 -0.101350\n", + "9 arsenic dydx 2.25 -0.125367 -0.150676 -0.099852\n", + "10 arsenic dydx 2.50 -0.130748 -0.159912 -0.101058\n", + "11 arsenic dydx 2.75 -0.137422 -0.170662 -0.102995\n", + "12 arsenic dydx 3.00 -0.136103 -0.172119 -0.099548\n", + "13 arsenic dydx 3.25 -0.156941 -0.202215 -0.107625\n", + "14 arsenic dydx 3.50 -0.142571 -0.186079 -0.098362\n", + "15 arsenic dydx 3.75 -0.138336 -0.181042 -0.093120\n", + "16 arsenic dydx 4.00 -0.138152 -0.185974 -0.089611\n", + "17 arsenic dydx 4.25 -0.176623 -0.244273 -0.107141" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# average by educ4\n", + "slopes(\n", + " well_model_interact,\n", + " well_idata_interact,\n", + " wrt=\"arsenic\",\n", + " conditional=None,\n", + " average_by=\"educ4\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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termestimate_typeeduc4dist100estimatelower_3.0%upper_97.0%
0arsenicdydx0.000.00591-0.085861-0.109133-0.061614
1arsenicdydx0.000.02409-0.096272-0.127518-0.069670
2arsenicdydx0.000.02454-0.056617-0.065433-0.046970
3arsenicdydx0.000.02791-0.097646-0.128131-0.069660
4arsenicdydx0.000.03252-0.076300-0.095832-0.057900
........................
2992arsenicdydx4.001.13727-0.070078-0.094698-0.046623
2993arsenicdydx4.001.14418-0.125547-0.172943-0.075368
2994arsenicdydx4.001.25308-0.156780-0.218836-0.088258
2995arsenicdydx4.001.67025-0.161465-0.227211-0.085394
2996arsenicdydx4.250.29633-0.176623-0.244273-0.107141
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2997 rows × 7 columns

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" + ], + "text/plain": [ + " term estimate_type educ4 dist100 estimate lower_3.0% upper_97.0%\n", + "0 arsenic dydx 0.00 0.00591 -0.085861 -0.109133 -0.061614\n", + "1 arsenic dydx 0.00 0.02409 -0.096272 -0.127518 -0.069670\n", + "2 arsenic dydx 0.00 0.02454 -0.056617 -0.065433 -0.046970\n", + "3 arsenic dydx 0.00 0.02791 -0.097646 -0.128131 -0.069660\n", + "4 arsenic dydx 0.00 0.03252 -0.076300 -0.095832 -0.057900\n", + "... ... ... ... ... ... ... ...\n", + "2992 arsenic dydx 4.00 1.13727 -0.070078 -0.094698 -0.046623\n", + "2993 arsenic dydx 4.00 1.14418 -0.125547 -0.172943 -0.075368\n", + "2994 arsenic dydx 4.00 1.25308 -0.156780 -0.218836 -0.088258\n", + "2995 arsenic dydx 4.00 1.67025 -0.161465 -0.227211 -0.085394\n", + "2996 arsenic dydx 4.25 0.29633 -0.176623 -0.244273 -0.107141\n", + "\n", + "[2997 rows x 7 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# average by both educ4 and dist100\n", + "slopes(\n", + " well_model_interact,\n", + " well_idata_interact,\n", + " wrt=\"arsenic\",\n", + " conditional=None,\n", + " average_by=[\"educ4\", \"dist100\"]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is still possible to use `plot_slopes` when passing an argument to `average_by`. In the plot below, the empirical distribution is used to compute unit level slopes with respect to `arsenic` and then averaged over `educ4` to obtain the average predictive slopes." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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termestimate_typeestimatelower_3.0%upper_97.0%
0arsenicdyex-0.167616-0.201147-0.134605
\n", + "
" + ], + "text/plain": [ + " term estimate_type estimate lower_3.0% upper_97.0%\n", + "0 arsenic dyex -0.167616 -0.201147 -0.134605" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "slopes(\n", + " well_model_interact,\n", + " well_idata_interact,\n", + " wrt=\"arsenic\",\n", + " slope=\"dyex\",\n", + " conditional=None,\n", + " average_by=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`slope` is also an argument for `plot_slopes`. Below, we visualize the elasticity with respect to `arsenic` conditional on a range of `dist100` and `educ4` values (notice this is the same plot as in the _conditional slopes_ section)." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plot_slopes(\n", + " well_model_interact,\n", + " well_idata_interact,\n", + " wrt=\"arsenic\",\n", + " conditional=[\"dist100\", \"educ4\"],\n", + " slope=\"eyex\",\n", + " subplot_kwargs={\"main\": \"dist100\", \"group\": \"educ4\", \"panel\": \"educ4\"},\n", + " fig_kwargs={\"figsize\": (16, 4), \"sharey\": True},\n", + " legend=False\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Categorical covariates\n", + "\n", + "As mentioned in the _computing slopes_ section, if you pass a variable with a string or categorical data type, the `comparisons` function will be called to compute the expected difference in group means. Here, we fit the same interaction model as above, albeit, by specifying `educ4` as an ordinal data type." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv(\"http://www.stat.columbia.edu/~gelman/arm/examples/arsenic/wells.dat\", sep=\" \")\n", + "data[\"switch\"] = pd.Categorical(data[\"switch\"])\n", + "data[\"dist100\"] = data[\"dist\"] / 100\n", + "data[\"educ4\"] = pd.Categorical(data[\"educ\"] / 4, ordered=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Modeling the probability that switch==0\n", + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [Intercept, dist100, arsenic, educ4, dist100:educ4, arsenic:educ4]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " 100.00% [8000/8000 04:30<00:00 Sampling 4 chains, 0 divergences]\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 271 seconds.\n" + ] + } + ], + "source": [ + "well_model_interact = bmb.Model(\n", + " \"switch ~ dist100 + arsenic + educ4 + dist100:educ4 + arsenic:educ4\",\n", + " data,\n", + " family=\"bernoulli\"\n", + ")\n", + "\n", + "well_idata_interact = well_model_interact.fit(\n", + " draws=1000, \n", + " target_accept=0.95, \n", + " random_seed=1234, \n", + " chains=4\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plot_slopes(\n", + " well_model_interact,\n", + " well_idata_interact,\n", + " wrt=\"educ4\",\n", + " conditional=\"dist100\",\n", + " average_by=\"dist100\"\n", + ")\n", + "fig.set_size_inches(7, 3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As the model was fit with `educ4` as a categorical data type, Bambi recognized this, and calls `comparisons` to compute the differences between each level of `educ4`. As `educ4` contains many category levels, a covariate must be passed to `average_by` in order to perform plotting. Below, we can see this plot is equivalent to `plot_comparisons`." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from bambi.plots import plot_comparison\n", + "\n", + "fig, ax = plot_comparison(\n", + " well_model_interact,\n", + " well_idata_interact,\n", + " contrast=\"educ4\",\n", + " conditional=\"dist100\",\n", + " average_by=\"dist100\"\n", + ")\n", + "fig.set_size_inches(7, 3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "However, computing the predictive difference between each `educ4` level may not be desired. Thus, in `plot_slopes`, as in `plot_comparisons`, if `wrt` is a categorical or string data type, it is possible to specify the `wrt` values. For example, if we wanted to compute the expected difference in probability of switching wells for when `educ4` is $4$ versus $1$ conditional on a range of `dist100` and `arsenic` values, we would pass the following dictionary in the code block below. Please refer to the [comparisons](https://bambinos.github.io/bambi/notebooks/plot_comparisons.html) documentation for more details." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plot_slopes(\n", + " well_model_interact,\n", + " well_idata_interact,\n", + " wrt={\"educ4\": [1, 4]},\n", + " conditional=\"dist100\",\n", + " average_by=\"dist100\"\n", + ")\n", + "fig.set_size_inches(7, 3)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Last updated: Tue Aug 01 2023\n", + "\n", + "Python implementation: CPython\n", + "Python version : 3.11.0\n", + "IPython version : 8.13.2\n", + "\n", + "pandas: 2.0.1\n", + "arviz : 0.15.1\n", + "bambi : 0.10.0.dev0\n", + "\n", + "Watermark: 2.3.1\n", + "\n" + ] + } + ], + "source": [ + "%load_ext watermark\n", + "%watermark -n -u -v -iv -w" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "bambinos", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +}