From ed7f79b3a4c7ef39f0af417ece1748e82799d5d7 Mon Sep 17 00:00:00 2001 From: pmaresnasarre <p.maresnasarre@tudelft.nl> Date: Mon, 6 Jan 2025 17:29:30 +0100 Subject: [PATCH 1/3] update friday 2.7 --- content/GA_2_7/rain_solution.ipynb | 381 +++++++++-------------------- 1 file changed, 120 insertions(+), 261 deletions(-) diff --git a/content/GA_2_7/rain_solution.ipynb b/content/GA_2_7/rain_solution.ipynb index 37f5b5df..1a4abbe3 100644 --- a/content/GA_2_7/rain_solution.ipynb +++ b/content/GA_2_7/rain_solution.ipynb @@ -30,19 +30,19 @@ "id": "1db6fea9-f3ad-44bc-a4c8-7b2b3008e945" }, "source": [ - "In this project, you will work on the uncertainty of precipitation in TurÃs, close to Valencia (Spain), where the past month of October an extreme flood occurred. TurÃs was the location where the highest rainfall was recorded. You have daily observations since 1999. The dataset was retrieved from the Ministry of Agriculture [here](https://servicio.mapa.gob.es/websiar/SeleccionParametrosMap.aspx?dst=1).\n", + "In this project, you will work on the uncertainty of precipitation in TurÃs, close to Valencia (Spain), where the past month of October an extreme flood occurred. TurÃs was the location where the highest rainfall was recorded, being also the record of that station with 771mm. You have cumulative daily observations since 1999. The dataset was retrieved from the Ministry of Agriculture [here](https://servicio.mapa.gob.es/websiar/SeleccionParametrosMap.aspx?dst=1).\n", "\n", "**The goal of this project is:**\n", "1. Perform Extreme Value Analysis using Yearly Maxima and GEV.\n", "2. Perform Extreme Value Analysis using POT and GPD.\n", - "3. Compare the results from both methods in terms of design return levels.\n", + "3. Compare the results from both methods in terms of return levels.\n", "\n", "_Read the instructions for this project in `README.md` before beginning the analysis._" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "id": "4fc6e87d-c66e-43df-a937-e969acc409f8", "metadata": { "id": "4fc6e87d-c66e-43df-a937-e969acc409f8", @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "id": "66e2b916-eb89-4d5e-a531-6ff0efa26018", "metadata": { "tags": [] @@ -114,22 +114,22 @@ " <td>0.0</td>\n", " </tr>\n", " <tr>\n", - " <th>30</th>\n", + " <th>1</th>\n", " <td>1999-01-12</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", - " <th>1</th>\n", + " <th>2</th>\n", " <td>1999-02-11</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", - " <th>31</th>\n", + " <th>3</th>\n", " <td>1999-02-12</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", - " <th>2</th>\n", + " <th>4</th>\n", " <td>1999-03-11</td>\n", " <td>0.0</td>\n", " </tr>\n", @@ -138,15 +138,15 @@ "</div>" ], "text/plain": [ - " Date Prec\n", - "0 1999-01-11 0.0\n", - "30 1999-01-12 0.0\n", - "1 1999-02-11 0.0\n", - "31 1999-02-12 0.0\n", - "2 1999-03-11 0.0" + " Date Prec\n", + "0 1999-01-11 0.0\n", + "1 1999-01-12 0.0\n", + "2 1999-02-11 0.0\n", + "3 1999-02-12 0.0\n", + "4 1999-03-11 0.0" ] }, - "execution_count": 4, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -157,13 +157,21 @@ "P.columns=['Date', 'Prec'] #rename columns\n", "P['Date'] = pd.to_datetime(P['Date'], format='mixed')\n", "P = P.sort_values(by='Date')\n", + "P=P.reset_index(drop=True)\n", "\n", "P.head()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To start the data exploration, we can compute some basic statistics." + ] + }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "id": "4dbeaa1a-84d4-43ad-bebe-cad9e7983c56", "metadata": { "tags": [] @@ -189,9 +197,16 @@ " sep=' | ')" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also plot the timeseries. Notice how the event of October 2024 stands out." + ] + }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "id": "30390de1-a79e-4d82-ab1b-8abafb91c5f8", "metadata": {}, "outputs": [ @@ -241,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "id": "da61b661-a22f-4adc-96f3-d6d494aabfc6", "metadata": {}, "outputs": [ @@ -301,13 +316,13 @@ "<br><code>params = ... .fit( ... )</code>\n", "<br>instead of like this:\n", "<br><code>param1, param2, param3 = ... .fit( ... )</code>\n", - "<br>(see WS15 solution for examples).\n", + "<br>(see WS 2.7 solution for examples).\n", "</p></div>" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "id": "3a47f881", "metadata": {}, "outputs": [ @@ -367,9 +382,20 @@ "# plt.savefig('./figures_solution/gev_rain.svg');" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + " <b>Solution:</b>\n", + " Since we are applying Block Maxima to select the extreme observations, we need to fit the Generalized Extreme Value distributinon (GEV). When assessing the goodness of fit, we can see that the performance is specially poor in the tail, where the event of October 2024 totally deviates from the fit. Also, the fitted distribution underestimates the higher observations, not being on the safe side.\n", + "</div>\n", + "</div>" + ] + }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "id": "646eb83a-3b6b-4eec-8e04-e5c5c5b4b8ad", "metadata": { "tags": [] @@ -432,7 +458,7 @@ "\n", "As such, we continue to perform our analysis in terms of $X$, while making sure to address the fact that our POT data and distributions are defined in terms of $E$. In practical terms, this means we need to <em>add and subtract the threshold value at certain points in the analysis.</em>\n", "\n", - "Note: with the exception of the check on the Poisson distribution, as done in the book and bonus, below, the threshold and declustering time evaluation is outside the scope of MUDE." + "Note: the threshold and declustering time evaluation is outside the scope of MUDE." ] }, { @@ -472,7 +498,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "id": "c200a7b5", "metadata": {}, "outputs": [ @@ -516,7 +542,7 @@ "id": "f0841e5c", "metadata": {}, "source": [ - "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n", + "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", "<p>\n", "<b>Task 3.2:</b> Compute the empirical cumulative distribution function of the sampled maxima. Fit the appropriate extreme value distribution function using <code>scipy.stats</code> and assess the fit via an exceedance plot with semi-log scale.\n", "</p>\n", @@ -530,24 +556,12 @@ "id": "0491cc69" }, "source": [ - "<div style=\"background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">Hint: you need to fit a GPD with a location parameter of 0 on the excesses. You can force the fitting of a distribution with a given value of the parameters using the keyword argument <code>floc</code>.</div>" - ] - }, - { - "cell_type": "markdown", - "id": "8ebabc18-cb85-44c8-b946-128e65622b1f", - "metadata": {}, - "source": [ - "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", - " <b>Solution:</b>\n", - " in this task the threshold is subtracted from the data in the argument of the GPD fitting method (thus fitting the distribution to the excesses). In preparing the plot, note the difference between the way the empirical and theoretical CDF are used: the empirical uses the random variable values directly (the DataFrame column at index 1), whereas the GPD \"adds the threshold back in\" for the random variable value, and uses the excess value as the argument for the CDF.\n", - "</div>\n", - "</div>" + "<div style=\"background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">Hint: you need to fit the appropriate distribution with a location parameter of 0 on the excesses. You can force the fitting of a distribution with a given value of the parameters using the keyword argument <code>floc</code>.</div>" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "id": "a8225377-2dc0-4629-8f28-c8fd7d6055bc", "metadata": {}, "outputs": [ @@ -597,9 +611,21 @@ "# plt.savefig('./figures_solution/gpd_rain.svg');" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", + " <b>Solution:</b>\n", + " in this task the threshold is subtracted from the data in the argument of the GPD fitting method (thus fitting the distribution to the excesses). In preparing the plot, note the difference between the way the empirical and theoretical CDF are used: the empirical uses the random variable values directly (the DataFrame column at index 1), whereas the GPD \"adds the threshold back in\" for the random variable value, and uses the excess value as the argument for the CDF.\n", + " Regarding the fitting, the event of October 2024 is not well captured by the distribution. However, the rest of the observations seem to be better described by the distribution function. Thus, POT+GPD seem to provide a better fit that YM+GEV. This could be due to the larger sample of extremes that are obtained when using POT.\n", + "</div>\n", + "</div>" + ] + }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "id": "cf9b1575-ce16-4309-b454-f0317fb62509", "metadata": { "tags": [] @@ -642,7 +668,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 19, "id": "d976adb0-b5d6-4959-ba0e-05f90a170597", "metadata": { "tags": [] @@ -650,7 +676,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 1000x600 with 1 Axes>" ] @@ -666,9 +692,9 @@ "plt.figure(figsize=(10, 6))\n", "plt.plot(P['Date'], P['Prec'],'k')\n", "plt.scatter(P.iloc[peaks, 0], P.iloc[peaks, 1],\n", - " 40, 'cornflowerblue', label='POT')\n", + " 50, 'cornflowerblue', label='POT')\n", "plt.scatter(YM['Date'], YM['Prec'],\n", - " 40, 'r', label='BM')\n", + " 60, 'r', marker='x', label='BM')\n", "plt.xlabel('Time')\n", "plt.ylabel('Precipitation [mm]')\n", "plt.grid()\n", @@ -685,17 +711,17 @@ "source": [ "## Task 4: Computing Return Levels\n", "\n", - "In this section, we are going to use the distributions found in the previous two Tasks to compute design return levels and compare the results that both EVA methods provide." + "In this section, we are going to use the distributions found in the previous two Tasks to compute the return periods that correspond to the flood of October 2024 and compare the results that both EVA methods provide." ] }, { "cell_type": "markdown", - "id": "44f448dd-d2af-43d4-81e9-819f1f270e48", + "id": "e41a1a60-c143-420e-b404-14c27e911c67", "metadata": {}, "source": [ "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", "<p>\n", - "<b>Task 4.1:</b> Assuming that we want to design our structure to survive a loading situation with a return period RT = 100 years, what are the design values predicted by both EVA approaches?\n", + "<b>Task 4.1:</b> create the return level plot for both EVA approaches (values of the random variable in the x-axis and return period on the y-axis; the y-axis in log scale). Consider return periods up to at least 800 years.\n", "</p>\n", "</div>" ] @@ -707,70 +733,7 @@ "id": "0491cc69" }, "source": [ - "<div style=\"background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"> <p>Remember you can use tuple unpacking as an argument for methods of <code>scipy.stats.rv_continuous</code>, like this: <code>*params</code> (see WS15 solution for examples).</p></div>" - ] - }, - { - "cell_type": "markdown", - "id": "13fed900-b7d1-468a-b141-8d6206ecefaa", - "metadata": {}, - "source": [ - "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", - " <b>Solution:</b>\n", - " here, again, note the addition of the threshold to the output of the inverse CDF method (<code>ppf</code>), thus \"converting\" the excess back to the original random variable.\n", - "</div>\n", - "</div>" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "75761235-51e8-4f14-acd0-0ddf598cd365", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The design value for a RT = 100 years computed using BM and GEV is: 472.684 mm\n", - "The design value for a RT = 100 years computed using POT and GPD is: 710.541 mm\n" - ] - } - ], - "source": [ - "# # YM & GEV\n", - "# YM_design_value = YOUR_CODE_HERE\n", - "\n", - "# # POT & GPD\n", - "# average_n_excesses = YOUR_CODE_HERE\n", - "# non_exc_prob = YOUR_CODE_HERE\n", - "# POT_design_value = YOUR_CODE_HERE\n", - "\n", - "# Solution:\n", - "# YM & GEV\n", - "YM_design_value = stats.genextreme.ppf(1 - 1/100, *params_YM)\n", - "\n", - "# POT & GPD\n", - "average_n_excesses = len(peaks)/YM.shape[0]\n", - "non_exc_prob = 1 - 1/(100*average_n_excesses)\n", - "POT_design_value = stats.genpareto.ppf(non_exc_prob, *params_POT) + threshold\n", - "\n", - "print('The design value for a RT = 100 years computed using',\n", - " 'BM and GEV is:', np.round(YM_design_value, 3), 'mm')\n", - "print('The design value for a RT = 100 years computed using',\n", - " 'POT and GPD is:', np.round(POT_design_value, 3), 'mm')" - ] - }, - { - "cell_type": "markdown", - "id": "e41a1a60-c143-420e-b404-14c27e911c67", - "metadata": {}, - "source": [ - "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", - "<p>\n", - "<b>Task 4.2:</b> create the return level plot for both EVA approaches (values of the random variable in the x-axis and return period on the y-axis; the y-axis in log scale). Consider return periods up to at least 500 years.\n", - "</p>\n", - "</div>" + "<div style=\"background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"> <p>Remember you can use tuple unpacking as an argument for methods of <code>scipy.stats.rv_continuous</code>, like this: <code>*params</code> (see WS 2.7 solution for examples).</p></div>" ] }, { @@ -780,14 +743,14 @@ "source": [ "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", " <b>Solution:</b>\n", - " the threshold is also added here, in an identical way as in Task 4.1.\n", + " here, again, note the addition of the threshold to the output of the inverse CDF method (<code>ppf</code>), thus \"converting\" the excess back to the original random variable.\n", "</div>\n", "</div>" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "id": "3b50e43c-d801-4779-ac0a-8cef194ceac7", "metadata": {}, "outputs": [ @@ -803,7 +766,7 @@ } ], "source": [ - "RT_range = np.linspace(1, 500, 500)\n", + "RT_range = np.linspace(1, 800, 500)\n", "\n", "# YOUR_CODE_HERE (more than one line!)\n", "\n", @@ -852,191 +815,87 @@ }, { "cell_type": "markdown", - "id": "b227f827-2523-4814-bf15-c8e2d499003a", - "metadata": {}, - "source": [ - "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", - "<p>\n", - "<b>Note:</b> At this point you will be able to answer all of the required questions in the Report. You are encouraged to add additional cells to make computations to further justify you explanations (e.g., to generate values for Markdown tables, or calculate a few more return levels, etc.).\n", - "</p>\n", - "</div>" - ] - }, - { - "cell_type": "markdown", - "id": "48506c98-73c0-4486-8aa1-0fbd66d670f0", - "metadata": {}, - "source": [ - "## Task 5 (optional): Influence of POT Parameters\n", - "\n", - "_This part is worth 2 extra credit points!_\n", - "\n", - "When deciding whether your POT distribution is suitable as a model for use in design, it is useful to analyze whether the selected parameters to perform the POT analysis (threshold and declustering time) are appropriate, in terms of the underlying assumptions of a Poisson distribution. Refer to Chapter 7.3 of the book, on page \"Parameters selection\" and apply the analysis presented there to your POT results. You can also apply a $\\chi^2$ (chi-squared) hypothesis test, as we did earlier in Q1. Perform the calculations in cells here, and include the following in your `Report.md`." - ] - }, - { - "cell_type": "markdown", - "id": "9a8883d4-58f9-4b71-9ebc-2764f6e6ec5a", "metadata": {}, "source": [ "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", "<p>\n", - "<b>Task 5 (optional):</b>\n", - " \n", - "There are three ways to do this, all of which use as \"data\" the _excesses_; the number of extremes sampled (observed) per year given our choice of threshold and declustering time:\n", - " \n", - "<ol>\n", - " <li>Compute and compare the mean and variance.</li>\n", - " <li>Fit a Poisson distribution to the number of excesses and compare theoretical and empirical distributions (see plot in book).</li>\n", - " <li>Use a $\\chi^2$ (chi-squared) hypothesis test for the mean number of excesses per year.</li>\n", - "</ol>\n", - "\n", - "The $\\chi^2$ value is found by taking the sum of the squared differences between the probability mass function (PMF) of the <em>observed</em> excesses and the PMF of the <em>predicted</em> excess, divided by the latter.\n", - " \n", - "$$\n", - "\\chi^2 = \\sum \\frac{\\bigl(p_O(o_i) - p_E(e_i)\\bigr)^2}{p_E(e_i)}\n", - "$$\n", - " \n", - "The degree of freedom is 1, and you can use a significance level of 0.05. The null hypothesis is: observations (number of excesses) come from a Poisson distribution.\n", - " \n", + "<b>Task 4.2:</b> What would be the return period associated with the event of October 2024? Compare the answer provided provided by both approaches and reflect on these differences. You may reflect on:\n", + "<li>The source of the differences between both methods.\n", + "<li>Which method would be the most reliable in this situation.\n", + "<li>If possible, how to improve the reliability of the obtained results.\n", + "<li>The meaningfulness of the obtained return periods.\n", + "<li>Which return period you would assign to the event if you must assign one.\n", + "</li>\n", "</p>\n", "</div>" ] }, - { - "cell_type": "markdown", - "id": "6650996f-2c1d-4ae6-bfac-519249cee8c2", - "metadata": { - "id": "0491cc69" - }, - "source": [ - "<div style=\"background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"> <p>Hint: while our primary objective in EVA is evaluating the distribution of the random variable $X$, the number of excesses observed in a given period (regardless of BM or POT) is also a random variable, which in the case of POT can be described using the Poisson distribution.\n", - " \n", - "Recall that the Poisson distribution is a <em>discrete probability distribution,</em> which is defined using a <em>probability mass function</em> (PMF) instead of a PDF. The PMF can be described using notation $p_X(X=x)$, which produces the probability of observing the discrete value $x$ of random variable $X$. The implementation in Python is slightly different, inheriting methods from the parent class <code>rv_discrete</code> instead of <code>rv_continuous</code>.</p></div>" - ] - }, { "cell_type": "code", - "execution_count": 12, - "id": "a7b593d6-c8aa-4f40-a583-004f1fbf3015", - "metadata": {}, - "outputs": [], - "source": [ - "# YOUR_CODE_HERE" - ] - }, - { - "cell_type": "markdown", - "id": "6aa4519a-640a-4106-98d7-6154ed4d885f", + "execution_count": 27, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9939170346188034" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", - " <b>Solution (bonus, part 1):</b>\n", - " here we create a new DataFrame with peaks and dates that <em>counts the number of excesses observed in each year of the data set.</em> Then the mean and variance are compared to see if they are equal (which would be expected for a Poisson distribution).\n", - " \n", - "<br><em>See report solution for explanation.</em>\n", - "</div>\n", - "</div>" + "POT_prob" ] }, { "cell_type": "code", - "execution_count": 23, - "id": "680ee15a-bde5-4b31-a3e8-866b7c54f8c7", + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Mean = 2.000 and variance = 1.778\n" + "The return period obtained with YM+GEV is 300.174\n", + "The return period obtained with POT+GPD is 164.394\n", + "\n" ] } ], "source": [ - "peaks_year = P.loc[peaks]\n", - "count = peaks_year.groupby(pd.DatetimeIndex(peaks_year['Date']).year)['Prec'].count()\n", - "mean_count = count.mean()\n", - "var_count = count.var()\n", - "print(f'Mean = {mean_count:.3f} and variance = {var_count:.3f}')" + "\n", + "#YM&GEV\n", + "YM_RT = 1/(1-stats.genextreme.cdf(771, *params_YM))\n", + "\n", + "#POT&GPD\n", + "average_n_excesses = len(peaks)/YM.shape[0]\n", + "POT_prob = stats.genpareto.cdf(771 - threshold, *params_POT)\n", + "RT_POT = 1/(1-POT_prob*average_n_excesses)\n", + "\n", + "#Print\n", + "print(f'The return period obtained with YM+GEV is {YM_RT:.3f}\\n'\n", + " f'The return period obtained with POT+GPD is {RT_POT:.3f}\\n')" ] }, { "cell_type": "markdown", - "id": "8508f086-2397-45c6-8549-55f5ee0bc36d", "metadata": {}, "source": [ "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", - " <b>Solution (bonus, part 2 and 3):</b>\n", - " here we use the DataFrame from the previous part to: find the range (min and max) of observed exceedences observed in each year of the data set; find the probability that the mean number of exceedances is observed, given a Poisson distribution; compute the chi-squared test; plot the empirical and theoretical PMF for comparison.\n", - " \n", - "<br>For convenience a second DataFrame is created to collect the number of excesses and counts for each year together in the same object. Since it is used for both parts 2 and 3 of the bonus, both solutions are presented together.\n", - " \n", - "<em>See report solution for explanation.</em>\n", + " <b>Solution:</b>\n", + " <li>The differences are mainly caused by the difference in the extremes selected by both methods: POT select much more observations and some observations selected by YM are not selected by POT as they are too low, although they are the biggest one in the time block. \n", + " <li>Based on the amount of information in which the method is applied, POT+GPD should be more realiable in this case, although we have not performed any analysis on the threshold and declustering time. Therefore, we would need to ensure that the selected extremes are independent.\n", + " <li>Some possibilities to improve the reliability of the method is gathering more data and performing a formal anaysis to assess the threshould and declustering time of POT. Also, different time blocks could be considered in the Block Maxima method accounting for instance with the hydrologic cycle.\n", + " <li>It is difficult to say which ret.\n", + " <li>If I would need to choose, I'd go for the return period of the distribution that has been informed with more data and provides a better fitting, thus the one obtained using POT+GPD, RT =.\n", + "</li>\n", "</div>\n", "</div>" ] }, - { - "cell_type": "code", - "execution_count": 24, - "id": "bd4f01be-34b2-4b7b-98d8-1567f603fbc3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accept the null hypothesis:\n", - " Poisson distribution can represent the excesses!\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 1000x600 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import math\n", - "\n", - "k = np.arange(count.min(), count.max()+1,1)\n", - "PMF_calc = stats.poisson.pmf(k, mean_count)\n", - "occur = count.groupby(count).size()\n", - "occur = pd.DataFrame(occur, columns=['Prec'])\n", - "\n", - "occur_df = pd.DataFrame(index=k, columns=['Count'])\n", - "occur_df = occur_df.join(occur)\n", - "occur_df = occur_df['Prec'].fillna(0)\n", - "\n", - "PMF_data = occur_df.values/len(peaks_year)\n", - "chi = np.sum((PMF_data - PMF_calc)**2/PMF_calc)\n", - "p_value = 1 - stats.chi2.cdf(x=chi, df=1)\n", - "if p_value > 0.05:\n", - " print('Accept the null hypothesis:\\n',\n", - " ' Poisson distribution can represent the excesses!')\n", - "else:\n", - " print('Reject the null hypothesis:\\n',\n", - " ' Poisson distribution does NOT represent the excesses!')\n", - "\n", - "plt.figure(figsize=(10, 6))\n", - "plt.hist(count, bins=25, label='Data', density=False)\n", - "plt.scatter(k, PMF_calc*len(peaks_year), label='Fit', color = 'k')\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.title(f'Rain Case: p-value = {p_value:.3f}')\n", - "plt.ylabel('Frequency, observed excesses per year')\n", - "plt.xlabel('Number of times the precipitation '\n", - " + f'{threshold} mm is exceeded in a year')\n", - "plt.tight_layout()\n", - "# plt.savefig('./figures_solution/bonus_rain.svg');" - ] - }, { "cell_type": "markdown", "id": "f54db065-1966-4cb6-a897-5e8502aa571b", -- GitLab From 4f9773e8694736e013736135a06cd825846769d7 Mon Sep 17 00:00:00 2001 From: pmaresnasarre <p.maresnasarre@tudelft.nl> Date: Tue, 7 Jan 2025 11:51:12 +0100 Subject: [PATCH 2/3] update solution Fri 2.7 --- content/GA_2_7/rain_solution.ipynb | 69 +++++++++++------------------- 1 file changed, 24 insertions(+), 45 deletions(-) diff --git a/content/GA_2_7/rain_solution.ipynb b/content/GA_2_7/rain_solution.ipynb index 0f09e13d..1387f870 100644 --- a/content/GA_2_7/rain_solution.ipynb +++ b/content/GA_2_7/rain_solution.ipynb @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "id": "4fc6e87d-c66e-43df-a937-e969acc409f8", "metadata": { "id": "4fc6e87d-c66e-43df-a937-e969acc409f8", @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "id": "66e2b916-eb89-4d5e-a531-6ff0efa26018", "metadata": { "tags": [] @@ -146,7 +146,7 @@ "4 1999-03-11 0.0" ] }, - "execution_count": 6, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -171,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "id": "4dbeaa1a-84d4-43ad-bebe-cad9e7983c56", "metadata": { "tags": [] @@ -206,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "id": "30390de1-a79e-4d82-ab1b-8abafb91c5f8", "metadata": {}, "outputs": [ @@ -256,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "id": "da61b661-a22f-4adc-96f3-d6d494aabfc6", "metadata": {}, "outputs": [ @@ -322,7 +322,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "id": "3a47f881", "metadata": {}, "outputs": [ @@ -395,7 +395,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "id": "646eb83a-3b6b-4eec-8e04-e5c5c5b4b8ad", "metadata": { "tags": [] @@ -498,7 +498,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "id": "c200a7b5", "metadata": {}, "outputs": [ @@ -561,7 +561,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 21, "id": "a8225377-2dc0-4629-8f28-c8fd7d6055bc", "metadata": {}, "outputs": [ @@ -625,7 +625,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 12, "id": "cf9b1575-ce16-4309-b454-f0317fb62509", "metadata": { "tags": [] @@ -668,7 +668,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 13, "id": "d976adb0-b5d6-4959-ba0e-05f90a170597", "metadata": { "tags": [] @@ -721,7 +721,7 @@ "source": [ "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", "<p>\n", - "<b>Task 4.1:</b> create the return level plot for both EVA approaches (values of the random variable in the x-axis and return period on the y-axis; the y-axis in log scale). Consider return periods up to at least 800 years.\n", + "<b>Task 4.1:</b> create the return level plot for both EVA approaches (values of the random variable in the x-axis and return period on the y-axis; the y-axis in log scale). Consider return periods up to at least 800 years. Plot a line to indicate the magnitude of the event of October 2024. \n", "</p>\n", "</div>" ] @@ -750,13 +750,13 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 17, "id": "3b50e43c-d801-4779-ac0a-8cef194ceac7", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ "<Figure size 1000x600 with 1 Axes>" ] @@ -766,8 +766,6 @@ } ], "source": [ - "RT_range = np.linspace(1, 800, 500)\n", - "\n", "# YOUR_CODE_HERE (more than one line!)\n", "\n", "# plt.figure(figsize=(10, 6))\n", @@ -777,7 +775,7 @@ "\n", "# Solution:\n", "#range of RT\n", - "RT_range = np.linspace(2, 500, 500)\n", + "RT_range = np.linspace(10, 800, 500)\n", "\n", "#YM&GEV\n", "YM_range = stats.genextreme.ppf(1 - 1/RT_range, *params_YM)\n", @@ -790,6 +788,7 @@ "plt.figure(figsize=(10, 6))\n", "plt.plot(YM_range, RT_range, '--r', label = 'YM&GEV', linewidth=5)\n", "plt.plot(POT_range, RT_range, 'cornflowerblue', label = 'POT&GPD', linewidth=5)\n", + "plt.plot([771, 771],[0, 1000], ':k', label = 'Event of October 2024')\n", "if params_YM[0]>0:\n", " bound_YM = params_YM[1] - params_YM[2]/(-params_YM[0])\n", " plt.axvline(x = bound_YM, color = 'black',\n", @@ -806,6 +805,7 @@ "plt.xlabel('Precipitation [mm]')\n", "plt.ylabel('RT [years]')\n", "plt.yscale('log') \n", + "plt.ylim([10, 1000])\n", "plt.grid()\n", "plt.legend()\n", "plt.title('Return Period and Design Values, Rain')\n", @@ -819,7 +819,7 @@ "source": [ "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", "<p>\n", - "<b>Task 4.2:</b> What would be the return period associated with the event of October 2024? Compare the answer provided provided by both approaches and reflect on these differences. You may reflect on:\n", + "<b>Task 4.2:</b> What would be the return period associated with the event of October 2024? Compare the answer provided by both approaches and reflect on these differences. You may reflect on:\n", "<li>The source of the differences between both methods.\n", "<li>Which method would be the most reliable in this situation.\n", "<li>If possible, how to improve the reliability of the obtained results.\n", @@ -832,27 +832,7 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9939170346188034" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "POT_prob" - ] - }, - { - "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -866,7 +846,6 @@ } ], "source": [ - "\n", "#YM&GEV\n", "YM_RT = 1/(1-stats.genextreme.cdf(771, *params_YM))\n", "\n", @@ -886,11 +865,11 @@ "source": [ "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", " <b>Solution:</b>\n", - " <li>The differences are mainly caused by the difference in the extremes selected by both methods: POT select much more observations and some observations selected by YM are not selected by POT as they are too low, although they are the biggest one in the time block. \n", + " <li>The differences are mainly caused by the different extremes selected by both methods: (1) POT selects much more observations, and (2) some observations selected by YM are not selected by POT as they are too low, although they are the biggest one in their time block. \n", " <li>Based on the amount of information in which the method is applied, POT+GPD should be more realiable in this case, although we have not performed any analysis on the threshold and declustering time. Therefore, we would need to ensure that the selected extremes are independent.\n", - " <li>Some possibilities to improve the reliability of the method is gathering more data and performing a formal anaysis to assess the threshould and declustering time of POT. Also, different time blocks could be considered in the Block Maxima method accounting for instance with the hydrologic cycle.\n", - " <li>It is difficult to say which ret.\n", - " <li>If I would need to choose, I'd go for the return period of the distribution that has been informed with more data and provides a better fitting, thus the one obtained using POT+GPD, RT =.\n", + " <li>Some possibilities to improve the reliability of the method is gathering more data and performing a formal anaysis to assess the threshold and declustering time of POT. Also, different time blocks could be considered in the Block Maxima method considering, for instance, the hydrologic cycle. We could also look at different ways of fitting the distribution; here, we fitted the coefficients by MLE but there are other approaches such as Bayesian Inference (makes use of previous knowledge to inform the fitting process) or L-moments method, between others. We could also consider weighting the observations to give more relevance to the larger ones to improve the fitting on those, although we sacrifice the fitting in the smaller ones.\n", + " <li>It is not possible to know as a ground truth what is the return period of the event. However, it gives us an idea of how extreme it is in comparison with other extremes that ocurred and helps us relativize the magnitud of the event.\n", + " <li>If I were to choose, I'd go for the return period of the distribution that has been informed with more data and provides a better fitting, thus the one obtained using POT+GPD, RT = 164 years.\n", "</li>\n", "</div>\n", "</div>" -- GitLab From 790ea4bcadd3f9179bc3686bf3796542d631ab93 Mon Sep 17 00:00:00 2001 From: pmaresnasarre <p.maresnasarre@tudelft.nl> Date: Tue, 7 Jan 2025 16:24:43 +0100 Subject: [PATCH 3/3] changes in Fri 2.7 --- content/GA_2_7/rain_solution.ipynb | 60 +++++++++++++++++------------- 1 file changed, 34 insertions(+), 26 deletions(-) diff --git a/content/GA_2_7/rain_solution.ipynb b/content/GA_2_7/rain_solution.ipynb index 1387f870..42193210 100644 --- a/content/GA_2_7/rain_solution.ipynb +++ b/content/GA_2_7/rain_solution.ipynb @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "id": "4fc6e87d-c66e-43df-a937-e969acc409f8", "metadata": { "id": "4fc6e87d-c66e-43df-a937-e969acc409f8", @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "id": "66e2b916-eb89-4d5e-a531-6ff0efa26018", "metadata": { "tags": [] @@ -146,7 +146,7 @@ "4 1999-03-11 0.0" ] }, - "execution_count": 4, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -171,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "id": "4dbeaa1a-84d4-43ad-bebe-cad9e7983c56", "metadata": { "tags": [] @@ -206,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "id": "30390de1-a79e-4d82-ab1b-8abafb91c5f8", "metadata": {}, "outputs": [ @@ -256,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "id": "da61b661-a22f-4adc-96f3-d6d494aabfc6", "metadata": {}, "outputs": [ @@ -322,7 +322,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "id": "3a47f881", "metadata": {}, "outputs": [ @@ -395,7 +395,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "id": "646eb83a-3b6b-4eec-8e04-e5c5c5b4b8ad", "metadata": { "tags": [] @@ -498,7 +498,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "id": "c200a7b5", "metadata": {}, "outputs": [ @@ -561,7 +561,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 9, "id": "a8225377-2dc0-4629-8f28-c8fd7d6055bc", "metadata": {}, "outputs": [ @@ -625,7 +625,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "id": "cf9b1575-ce16-4309-b454-f0317fb62509", "metadata": { "tags": [] @@ -668,7 +668,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "id": "d976adb0-b5d6-4959-ba0e-05f90a170597", "metadata": { "tags": [] @@ -721,7 +721,7 @@ "source": [ "<div style=\"background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", "<p>\n", - "<b>Task 4.1:</b> create the return level plot for both EVA approaches (values of the random variable in the x-axis and return period on the y-axis; the y-axis in log scale). Consider return periods up to at least 800 years. Plot a line to indicate the magnitude of the event of October 2024. \n", + "<b>Task 4.1:</b> create the return level plot for both EVA approaches (values of the random variable in the x-axis and return period on the y-axis; the y-axis in log scale). Consider return periods up to at least 800 years. Add also he return level plot generated using the empirical CDF for both sampling approaches. Plot a line to indicate the magnitude of the event of October 2024 (771mm). \n", "</p>\n", "</div>" ] @@ -750,13 +750,13 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 12, "id": "3b50e43c-d801-4779-ac0a-8cef194ceac7", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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XX+577/zJez9O5+/v02QyUadOHXbt2pXv7zTvtTRv3pyIiIhCr62s/b7zAnRsbKzvWHnuj4cffphbb72Vu+++m+eff57BgwfTq1cv+vfvT4sWLUpcV9590rBhQ999e7qSrIZdEf/WzjrrLLp06cLatWuZMWMGU6ZM8Z3LW4Qsb4XvPC6Xi5tuuqnYAOrvHgukUPj7La28faQNw2D//v38/fffgPffkL/vJfv27ePCCy/0u5BenrK85xX1PVxEKo+CtIhUWTfddFO+1WaPHTvGgw8+yLRp0xg8eDBr1qyhadOmvvNHjx4FIDs7u0Qr3GZlZQW85uJERUWVq13eD94ej6dMz3/DDTcUuZpwnkC8lyV9rafLC2716tXz26Z+/fr52p76ed4PqoUp6pw/aWlpXHbZZRw6dIhBgwbxr3/9i86dO5OQkIDNZgO8vXC7du3C6XQW+hhFvReF/Z1W1Gspi7xe31Ofrzz3xy233EJ8fDzPP/88K1as4I033uCNN94AoGfPnjz77LP079+/2MfMCyIxMTF+2xR1Lk9x/9aMMq4wfeONN3Lffffx3nvvMWnSJCwWi6+H2mQyFRit8dxzzzFz5kwiIiKYNGkSQ4YMoUmTJkRGRmIymZg2bRo333yz33sskELh77e0Pv7443w7KqxZs4bLLruMDz/8kJycHObMmVPgmhtvvJHVq1fTokUL/v3vf9O7d2/fLgkA119/PTNnzizTe15R95WIVB4N7RaRaiM+Pp633nqL3r17c+zYMe6888585/OGoJ555pkY3jUiivzwt+2SP3lbnRT1A1BmZmbpXlSIquj3sih54aeoLWL27duXr+2pnxe1t3hZ9h2fN28ehw4donHjxnz11Vecf/751KlTxxeiDcMo19Zahamo11JaLpeLn376CYBzzjnHd7y898dll13Gjz/+yJEjR5g3bx4PP/wwzZo145dffmHIkCEl2su8sCHnpyvqXEW79tprCQsLY+/evXzzzTfAyb2j+/XrR8uWLfO1z/sF13PPPcd9991H+/btiYqK8n3fKUuvaHHfs/x9vwqFv9/yOvPMM5kzZw42m43PPvvMNzQ+z/79+1mwYAHg3d7tqquuokmTJr4QDZXT+y8ioUtBWkSqFbPZ7Nvbdv78+SxcuNB3Lm/43vr16zl27FipHrew/UBPl9fDUNTw3tP3YK6qyvNelle7du0A+OOPP/y2yTvXvn37Atdt2LDB73Xr168vdT15+8/26NGD8PDwAud///33gP8CJe+1bNu2ze+e2WV5LaX1wQcfcOjQIQAuvvhi3/FA3R9xcXEMGzaMKVOm8Ndff9GrVy8cDgfTpk0r9tq2bdsCsGfPHlJTUwttkzdEORhq167NRRddBHhDck5ODh9++CFQ+NoBefdZYXPpAX788cdS15D3PcvfL138fb8Khb/fQOjUqRO33XYbAOPGjcPlcvnO5b3ftWrVKnQ6iMvlKtM0EBGpPhSkRaTa6d69OyNGjABgwoQJvuNdu3aldevWOJ3OfHMSSyJvUavs7Gy/bVq1aoXJZCInJ6fQOXXLly+vlJ6WylCe97K8LrzwQgBee+21Qv8+Fi5c6Fv8Z/jw4b7jw4YNA+CHH34oNGT+9NNPZfrBOO/e2Lt3b6Hn//Of/5T6MYvTp08fYmJiyMjI8PViniozM5O333474M97qvXr13PvvfcCcMkll+Sbb1wR94fNZuPss88G/L/Xp2rXrp1vKK+/YFZZgc2fvEXHPv/8c95++22OHz9OTEwMl112WYG2Rd1nGzZsyLewXkm1bt0agJ9//rnQ86+++mqhx0Ph7zdQxo0bh91uZ+vWrbz77ru+43nvd1paWqG/CJsxY4bvl0giUjMpSItItZQ3d3rp0qX88MMPgLdX+fnnn8dkMjFlyhTGjRvH8ePH812XmZnJ7NmzC6x43LJlS0wmEwcPHvTb0xcfH+/rLbr33nvzDftbvXo1119/vW+4b1VXnveyvO644w6SkpI4cOAA11xzTb5h06tXr/b15o0cOTJfuOvXrx+9e/fGMAyuvfZaX48TeBfvGj16dJn+fvLmc/7888+89tprvuMOh4Nx48bx/vvvExYWVurHLUpUVBR333034A0Cp468OHbsGNdcc02FDVvevn07TzzxBL169eLo0aO0atWKqVOn5mtT1vsjLS2Nyy+/nAULFpCbm5uv/apVq/joo48ASrS6uslk4uGHHwa8v1CbPXu275zD4eD+++/3GyAry5AhQ2jQoAEOh4OHHnoIgCuvvLLQ+bN599nYsWPZs2eP7/iaNWu46KKLsFgspX7+ESNGYDKZWLt2bb5f+Ljdbl5++WXee++9Qq+r7L/fGTNm+FbcDrQGDRpw6623AvDvf//b1yvdsWNHEhMTcblcJCcn5/ul3axZs7jnnnsKHYEiIjVIxeyqJSJScQrbR7oweXsq9+nTJ9/xt99+27eXqc1mM8444wyjV69eRuvWrX37txa2h+6IESMMwAgLCzPOOusso3///kb//v2N1atX+9qsWLHCCA8PNwAjPDzc6NKli9G6dWsDMAYPHmyMGjWqyH2ki9u7l0L2RD1V3h7A/fv3L/JxTlfUPtJFKct7WdLX6m8facMwjEWLFvn2Ubbb7cZZZ51ltGvXzvf+nHXWWUZqamqB67Zu3Wo0bNjQAAyLxWJ07tzZOOOMMwyTyWS0aNHCuPvuu8v0Plx33XW+527QoIHRvXt3Iy4uzgCMp59+2u8+vkW9xjz+rs3OzjYGDhzoe96WLVsa3bp1M8LDw42IiAhj8uTJ5dpH2m63G+eee67vo0uXLkadOnV8z2cymYwrr7yy0Pc5T2nvj6NHj/oePywszOjQoYPRs2dP337QgHH22WcbWVlZJXqPPB6PceWVV/qubdKkidGjRw8jLi7OsFgsxvPPP28AhtlsLlB73veZ6dOnF/raitoruDT+9a9/+R4HMJYvX15ou99//92Iiory/d107tzZaNu2rQEYjRs3NiZNmuT3376/98cwDOOBBx7wPXdSUpLRvXt3o1atWobZbPbdC/6+51TW3++pdZSGv32kT7d7927f65g6dWq+15d3fVxcnNGtWzejQYMGBmAMGTLEuPbaawv9fl6SfaQr+r4SkYpXI3ukX3rpJTp27Eh0dDTx8fEMGjSIFStWBLssEQmwvF7pZcuW8f333/uO33TTTb5hqa1bt2bbtm38/vvvuN1u+vfvz5QpU/j2228LPN67777LmDFjaNSoEX/88QeLFy9m8eLF+eYI9uzZk6VLl3LhhRcSHh7Opk2bsNlsPPvss8ydOxertXptllDW97K8+vfvz7p163x/H+vXr2f37t10796d5557juXLl1O7du0C17Vo0YJVq1Zx5513Uq9ePTZu3EhaWhrJycmsXLmSWrVqlame6dOnM2XKFNq2bcuhQ4fYunUrZ511FnPmzOHRRx8t78stVHh4OF9//TWTJ0+mXbt27N69m507dzJ8+HBWrFjhGyZbVg6Hg+XLl7N8+XJ++ukntm/fTlRUFBdeeCFPPvkkW7Zs4cMPPyz0fc5T2vsjJiaG//3vf9x88820adOGAwcOsGrVKo4fP07fvn15+eWXWbx4sd8tv05nMpl4//33efXVV+nSpQsHDx5ky5YtnH322SxcuNC3T/OpW3dVtlP3uG7bti29e/cutF2nTp346aefuOSSS4iIiGDTpk04nU7uueceVq9e7VupvrSee+45Xn75ZTp16uTbRrBHjx788MMPxS4SWFl/v3mLB+btDx9oDRs29PWcn9orfdNNNzFnzhzOOecccnNz2bhxI0lJSfznP//hq6++KtMoABGpPkyGUfPW158zZw6RkZG0atUKh8PBiy++yKxZs9i6dWuRPxCIiIhI9fHxxx9zxRVX0LVrV9+8egk9w4YN4+uvv2bWrFlcfvnlwS5HRASooUH6dGlpacTFxbFo0aIK2btQREREQs+QIUP45ptvuPfee3nxxReDXY4UwuPxkJCQQN26ddm4caNvn2URkWAL2e9G7733Hrfffjvdu3fHbrdjMpl8eyj6s3LlSoYPH05CQgJRUVH07NmT999/v8hrcnNzefPNN0lISPBt5yAiIiLVwzPPPMOaNWvyHTt+/Dh3330333zzDTabjdtvvz04xUmx1q5dS1paGg8//LBCtIiElJCdrDd+/Hh27NhBYmIi9evXZ8eOHUW2X7RoEUOGDCEsLIyrrrqKuLg4Zs+ezahRo9i+fTvjxo3L137p0qUMGzaM7Oxs6tWrx7ffflvmuXEiIiISmj788EPGjh1LbGwsLVq0wOPxsHHjRnJzczGbzfz3v//Nt9+4hJauXbuiwZMiEopCdmj3d999R+vWrWnatCnPPPMMY8eOZfr06YUufOFyuXwLrfz000++xSjS09M555xz2LRpExs2bPDtlwjevWD37NnD4cOHeeutt1i4cCErVqwgMTGxsl6iiIiIVLAPP/yQjz76iNWrV3P48GFyc3OpU6cOffr04b777qNXr17BLlFERKqgkB0jc/7559O0adMStf3hhx/YunUr11xzTb4VHWNiYnjsscdwuVxMnz493zURERG0atWKXr16MXXqVMxmc4E2IiIiUrVdddVVzJkzh+3bt5Oeno7D4WDXrl188MEHCtEiIlJmIRukS2PRokUADB48uMC5vGOLFy8u8jEMw8DhcAS8NhEREREREaleQnaOdGls3rwZIN/Q7TwJCQkkJib62gA88sgjXHzxxTRq1IgjR47w6quvsnv3bv75z3/6fQ6Hw5EvaHs8Ho4cOULt2rUxmUwBfDUiIiIiIiJS2QzDID09nQYNGhS7wGG1CNLHjx8HIC4urtDzsbGx7N692/f13r17ueqqqzh48CC1atWiR48eLF26tMjFRiZPnswTTzwR2MJFREREREQkpOzatYtGjRoV2aZaBOnSmjlzZqmvGTt2LA888IDv6+PHj9OkSRO2bdtGTExMIMurcE6nk4ULFzJw4EBsNluwy5Eg0X1QuMzMTN/6DDt27CAqKirIFVU83QuSR/eC5NG9IKD7QE6qKfdCeno6zZs3L1G+qxZBOq8nOq9n+nRpaWl+e6tLym63Y7fbCxyvVasWsbGx5XrsyuZ0OomMjKR27drV+h+CFE33QeHCw8N9n9euXbvGBGndCwK6F+Qk3QsCug/kpJpyL+S9tpJM3a0Wi43lzY0+dR50nqNHj5Kamlro/GkRERERERGR0qoWQbp///4AfPPNNwXO5R3LayMiIiIiIiJSHtUiSA8aNIgWLVrw/vvvs2bNGt/x9PR0nnrqKaxWK6NHjw5afSIiIiIiIlJ9hOwc6alTp7Js2TIA1q1b5zuWt2f0yJEjGTlyJABWq5WpU6cyZMgQ+vbty9VXX01sbCyzZ89m27ZtPP3007Rp0yYYL0NERERERESqmZAN0suWLeOdd97Jd2z58uUsX74cgGbNmvmCNMDAgQNZtmwZEyZMYNasWeTm5tKxY0eeeuopRo0aVZmli4iIiIiISDUWskF6xowZzJgxo1TX9OzZk/nz51dMQSekpKSQkpKC2+2u0OcRERERERGR0FQt5khXpuTkZDZs2MDKlSuDXYqIiIiIiIgEgYK0iIiIiIiISCkoSIuIiIiIiEilMQwDp8sIdhnlErJzpEVERERERKTqMQyD9GyDw+keUtPdHE73eD/SPBzO8HA4zU3HJjbuHBoT7FLLTEFaROQUUVFRGEbV/g2piIiISEXyGAZpWQapae4TwdhzMiynuzmS4SHXVfRjpKZ7KqfYCqIgLSIiIiIiIj4ej8GxvKCc7uHQMSdrj7Zmy7wsjmQYHEn34CpnDj6iIC0iIlLBDhyApCQwa2kPERGR8sobep2a7iE1zU1qmofUdO+Q69QTPcvuAjm3HmQFbgvgjByDnFyD8DBTwB6zMilIl5L2kRap3nJycrjuuusAmDlzJuHh4UGuqIY7dgyefRZeegmmToWrrw52RSIiIlVCZo43EHtD8smwnJrmHX5d3NDrynA43U3D2lUzklbNqoMoOTmZ5ORk0tLSiIuLC3Y5IhJgbrebTz75BIAZM2YEt5iaLCcHXnkFJk2Co0e9xyZMgMsvB6v+6xIREXE4vUOvU08Ly3nhOTs39Nd8OZzuoWHtYFdRNvppRETkFGFhYbzyyiu+z6WSuVzw7rve0Lx7d/5zmzfDO+/AzTcHpzYREZFK5HSfWPX6xDzl1LSTvcmp6R7Ss0M/KBclym4ix1l1X4OCtIjIKWw2G8nJycEuo+YxDPj8cxg3Dv7803+7J56Aa68Fu73yahMREakAhmFw/MSCXgePewPzobSTvcvHMw2qbsyEmAgTtWPM1I6xUDvGTGKM+cTX3mNVdW50HgVpEREJriVL4F//gp9+Kr7trl3wxhtwzz0VX5eIiEg55boMDqd5OHQiJB86sbDXweOhM0+5rOIiTSTGWKgda6Z2tNn754mQXCvajN1WtYNycRSkRURO4Xa7Wbp0KQB9+/bFYrEEuaJqbN06GDsW5s4t3XWTJsHtt6tXWkREgi5v9WtfUD5+MiwfSnNzLLPq9inHR50IyjFmakXDnm1/MKB3F+rEh1Er2ozNWr2DcnEUpEVETpGTk8PAgQMByMjIICoqKsgVVUM7dsBjj8F773mHdJdGv34webJCtIiIVBqn+2Svcmqah4N5QfnEcGxHFe1Vzht6nRhjIfFEb3JirJnEWAu1TwvKTqeTeQcP0K6hFZtNnQygIC0iIpXl+HFvCH7xRXA4Sndt587wzDMwdCiYavZvwEVEJLAMwyAjxzg59Pr4yaHYqWkejmZ4quRc5Ygw0ynh+GRgTowxUzvWQng1H3pd0RSkRUSkYjmd8NZb3pW4U1NLd22zZvDUU3DNNWA2V0h5IiJS/eUt7HXwuJtDxz3eP9O8fx48XjW2ijpdmBVqx1hIPC0s1z4RlqPC9f9mRVKQLqWUlBRSUlJwu93BLkVEJLQZBnz1FTz0EGzaVLprk5Jg/HjNhRYRkRLzGAbHMjwcPCUgHzrlz6o2BNtixrfKdWLsycBcO8ZCUqyZmAgTJo3SChoF6VJKTk4mOTmZtLQ04uLigl2OiEho+u03+L//g4ULS3dddDQ8+KD3IyamYmoTEZEqy+0xOJLh8fUqHzzlz0NpblxVrK8rJsJEUqyZpFgLSXHewJx0okc5PsqM2aygHKoUpEVEJHB274ZHH4WZM0u3kJjNBnfc4e2FrlOn4uoTEZGQ53IbpKbn700+ePzkIl9uT7ArLDmLGRLzgvKJ4denhmbNU666FKRFRCRw3n8f3n23dNdcc413HnSLFhVTk4iIhJy8sHzg2ImQfMzt7VlO83A43VPqTR2CKTr8RK9ynCVfaE6KVa9ydaYgLSIigXPPPZCSAjt3Ft+2Xz/4f/8Punev+LpERKTSeQyDoxkeDhzzDr8+cMzNgWMeDhz39ix7qkhYzpurnBhrpk7sybCcGOf9MyJMQbkmUpAWEZHACQ/3bnE1apT/Nm3awLPPwsUXaysrEZEqzjAM0rONE0HZ28N84MTnB4+7cVaROcuR9vxzlZNiT85XTog2Y1GvspxGQVpERALrqqu8e0WvXJn/eO3aMHGidyVumy0YlYmISBll5xocPOZm/ymBOS88V5Wto2IiTNQ5EZTrxFmoc8qf2ipKSktBWkREAsts9g7Z7tfP+3VYGNx7L4wbB/HxQS1NRET8c7oMDqadDMr7j7rYeKgzS97LIC27aoTluEiTLxwnnRKWk2LNRNoVliVwFKRFRKRohgHr18MZZ5T8mr594R//8IboyZOhWbMKK09ERErOMAyOZRrsP+b2fhz1/nngmIcj6R4KxuU4KORoMNWKNufvVY71/pkYp1WwpfIoSIuIiH+//+7tTV6+3BumW7cu+bUffqgh3CIiQeJ0GRw47mb/UU++wLz/mBuHM9jVFc1k8i7uVSf2RG+yLzR7F/oKsyosS/ApSIuInCIyMpKDBw/6Pq+xDh/27un85pvgObFh54MPwhdflPwxFKJFRCqUYRikZRsnQ/JRN/uPeYPz4bTCepdDhwmoFWOmbpyZuvHekFw3/sQWUjFmrBaFZQltCtKllJKSQkpKCm53FVmCUERKxWQykZSUFOwygsfjgWnT4F//8obpU335JSxYAEOGBKc2EZEayuk2OHTcw/6jbvYdc5+Yv+wNzaG+0FdshIm68ZYTYdl84nPvcGybepalClOQLqXk5GSSk5NJS0sjLi4u2OWIiATO6tXefaBXrPDf5v77Ye1a9TaLiFSA9GwP+47mBWWPbyj2oTQPRgjn5YgwE3XjzdSNs1DnRFCud+Jz7bEs1ZWCtIjIKRwOBw888AAAzz//PHa7PcgVVYKjR+n05ptYv/765DBuf/78E1591TtvWkRESs0wDI5meodj7z3iZt/Rkx8ZOaGblm0WfMOv68ZZfD3LdeMtRIebMJkUmKVmUZAWETmFy+Xi1VdfBeDZZ5+t3kHa44F338X68MO0OHSo5Ne99hqMGQMWS8XVJiJSxXk8BofTPew96h2GvTcvMB9xkxOii32ZTZAYe2L4dZyZxBjYvvFXRgw+m6T4MMwKyyI+CtIiIqew2WxMmDDB93m1tXYtJCfD8uWU+Mcim83bEz1+vEK0iMgJrhPzl/cezd+7vP+oG2eILqkTZTdRL8FCvXjziT+9H4mx+Rf5cjqdZGw/Rq1os0K0yGkUpEVEThEWFsbEiRODXUbFOX4cJkyAl18ufhj3qYYOhZdegjZtKq42EZEQluvy7r2877Th2AePe3CX4ttpZcnrXa6f4B2GXS/eQr0EM/XiLcREmINdnkiVpyAtIlITGAZ8/LG3R3n//pJf16SJN0Bfcol3Y08RkWrO4fTOX95zxDuHOW9odmqIbicVEWbyBWRvWLZQP96797K2kBKpOArSIiKn8Hg8/PnnnwC0b98es7ka/NZ++3bvMO5580p+TVgYPPQQjBsHNXk/bRGptpxugwNH3ew56mbvYW9g3nM4NAOziZNzl+slmKl/Yjup+gkWYiK00JdIMChIi4icIjs7mzPOOAOAjIwMoqKiglxRObhc8OKL3qHcWVklv+6CC+CVVzSMW0SqBbfH4OAxz4mg7PIF5oPHPXhCLDHbrXh7lBNOBuV68WbqxGnPZZFQoyAtIlId/fIL3Habd1GxEjIaNcL04ovwj39oGLeIVDkej0Fquoe9R7xBOS8wHzjmxhVic5gj7SbqJ1hokODtYW5QyxuaE7Sol0iVoSAtIlKdpKXBo49CSop3XnQJGFYrWy6+mGZTp2JLSKjgAkVEyscwDI5knAjMJ+Yx7znincec6wp2dfnFRXpXx25wope5/onAHKvh2CJVnoK0iEh1smMHvP56iUM0552H64UX2LBtG82ioyu2NhGRUspyeNh92BuUd6d6e5j3HHGF3D7MtWO8q2N7P05+HhVeDdbZEJFCKUiXUkpKCikpKbjdIboxoIjUbJ06wYMPwpQpRbdLSoIXXoBrrvHOpd62rXLqExEphNtjcOCYh92HXew+7PaG58NujmSEzphskwnqxJ0amL09zXUTLITb1LssUtMoSJdScnIyycnJpKWlERcXF+xyREQKevxxmDXLfzi+5RZv0K5Vq3LrEpEazzAM0rINdqe62X3Y5e1pPuzdmzlU5jHnBeYGCRYa1j4xLLuWd/Evm7aTEpETFKRFRKqbyEh47TUYOjT/8Xbt4I03oF+/4NQlIjVKrsvwLfyV19O854ib9OzQWSo7MfZEYK5loUEtCw1qe/dg1grZIlIcBWkRkepoyBC4+mr44APvntDjx8PDD4PdHuzKRKSaMQzvatl7TgzJ9g7LdnHguKfEyzVUtIQo78rYvsBcy9vLrCHZIlJWCtIiItXVCy9AdrZ3GLf2hBaRAHC6DfYdcbMr1c2uVBe7Dns/z84NjcQcG2EqEJgb1LIQadeiXyISWArSIiKhzumE//wHEhLgzjtLfl3dujBnTsXVJSLVWq7Hyqa9LvYdOxGaU93sO+rGHQJzmSPtJl9YPjU0x0QoMItI5VCQFhEJZatXw003wZo13rnPQ4dC8+bBrkpEqhHDMEhN83h7mQ+7fL3NRzLO4Zu52UGtzWKGevEWGtX2LvzVqLaFRrWtxEdpH2YRCS4FaRGRUJSbC//+t/cjb7u9rCy47Tb45hvvsrIiIqXkdHsXAPMNzU71zmkOhaHZ8VEmGtW2ngzMtSzUS7Bg1UrZIhKCFKRFRELNH3/A9dd7e6NP9913MG0a3Hxz5dclIlVKdq7BrlQXOw+52XHIG5r3Hwv+0OwwKzSsZckXmhvWthAdrmHZIlJ1KEiLiJwiIiKCP/74w/d5pXK74bnnvPtA5+b6b/fggzBsGDRoUHm1iUhIy8zxsDPVzc5DLnacCM4Hjwc3MZuApDjziaBspVEtC40SLSTGmjFrVI2IVHEK0iIipzCbzXTs2LHyn3jzZrjhBvjpp+LbHj8OyclaSEykhkrL8rAz1RuYdx5ysTPVTWpacENzRJiJxol5c5i9vc0Nalmwa3spEammFKRFRILJ44FXX/Xu8ZxdwkV92rf3theRas0wDI5nGew4dEpoPuTmaGZwQ3PtGDONEy00rm2hcaKVxokWaseYtfiXiNQoCtIiIqfIzc1l0qRJAIwbN46wsLCKe7IdO7wrcv/wQ8naWyzwr3/BY4+B3V5xdYlIpTMMgyMZHl9gzvszLTt4i4CZ8NCwtpXGiVaanAjMjWpbiNJcZhERBWkRkVM5nU6eeOIJAB566KGKCdKGAdOnw333QXp6ya7p0AHefRe6dQt8PSJS6Y5leth+0MWOQy62H3Sz/aCLjJzgheZIu+lED7O3l7l+vMGan79hxIXDsNlsQatLRCRUKUiLiJzCarVy1113+T4PuNRUuPVW+OyzkrU3meChh+CJJyA8PPD1iEiFy8zx+MLy9kMuth90cSwzeKHZNzQ70UqTRG94rhWdf2i20+nkd1Pwt8QSEQlVCtIiIqew2+2kpKRUzIN/8w2MHg379pWsfatWMGMGnHtuxdQjIgGXk5s3p/lkT/OhIC0EZgLqJZhpkmilaZ2ToTnSrqHZIiLlpSBdSikpKaSkpOB2u4NdiohUFTk5MHYsvPhiya8ZMwaeeQaioiqsLBEpH6fLYNfhEz3NB73Bef9RN8HoxzWboEEtC02SLN7gnGShUaKVcK2aLSJSIRSkSyk5OZnk5GTS0tKIi4sLdjkiEmCGYZCamgpAYmJi+VehXbcORo3y/lkSjRt7508PGlS+5xWRgPJ4DPYedbPtwMme5j1H3LiD0NlsNUPD2haaJHl7mZsmWWlY20KYVaFZRKSyKEiLiJwiKyuLOnXqAJCRkUFUWXuEPR54+WV45BFwOEp2zY03wgsvgH5JJxJ0xzI9bDvg4u8DLm94PuTC4az8OmwWaHQiLOf1NDeoZcFqUWgWEQkmBWkRkUBzuWDECFiwoGTt69SBqVPhoosqti4RKZTDabAz1XVKcHZzJKPyu5rDrPjCcpMk75/1EixYzArNIiKhRkFaRCTQrFZo06ZkQfrCC2HaNG+YFpEK5zEMDhzL39u8+7AbTyVPbLaavT3NzepYaZZkpVkdC/UTLJgVmkVEqgQFaRGRijBlCvzwA6xfX/j58HB4/nm44w7vFlciUiHSs/OH5m0H3WTnVm5qzlsIrGmSNzg3r+Od06zh2SIiVZeCtIhIRYiIgP/9D3r2hNzc/OfOPBPefx/atw9KaSLVldtjsOeIm637XWzd5w3Pwdh6qm68+UQvs7enuXGiFbtWzxYRqVYUpEVEKkqXLjB5Mjz4oPdrkwn+7//gqafAbg9ubSLVQJbDw98HXN7gvN8bnCt7QbDaMWaa1fEuBtasjndes/ZpFhGp/hSkRUQq0n33wfz58OefMHMmDBwY7IpEqiTDMDhw3OMLzVv3u9h7xF2pNUTZTTSva/V+nAjPsZEKzSIiNZGCtIhIRTKbvQE6LAxq1Qp2NSJVhsNpsOOQK19wzsipvLnNFjM0qm2hRT0rLep4w3OdOHP595YXEZFqQUFaRKSkDANmzIDhw6Fu3ZJfV69ehZUkUl0cyfCwdb/TF5p3pbpxV+L05toxZlrUtdKirpXmdS00SbRisyo0i4hI4RSkRURKIj0dbr0VPvoIBg/2Dtc2a0inSFkYhsH+ox4273OyeZ+LzftcHE6vvNQcbuPE8GwrLep5/9QQbRERKQ0FaRGR4qxfD//8J2za5P36m2/gP/+BRx4Jbl0iVYTLbbAr1e0Lzlv2Vd4wbZMJGtaynOhp9vY214/Xfs0iIlI+CtIiIkX53//gttsgKyv/8Ucfhb59oXfv4NQlEsIcToO/D7jYvNcbnP8+4CLXVTnPHWk30bKulZb1rLSs711JO1xbT4mISIApSIuI+HPffTB1auHn3G64+mpYvVqLiEmNl57tYcuJnubN+5zsrMT5zfXizd7QXM9Gq3pW6iaYMWtBMBERqWAK0iIipwgPD2fhBx/AxImE+wvReXbuhFtugdmzK6c4kRBxNMPDX3ud/LXXG5z3Ha2c1BxmheZ1TvQ21/POb44O19xmERGpfArSIiKnsHz7LQOSk+HIkeIb164NN99c8UWJBNmRDA9/7fEG5017nRw8XjnBuVa02ReaW9Wz0rC2BatFvc0iIhJ8CtIiIuAdqv3kk/DUU95tropz9tkwaxY0blzxtYlUsiMZHv4+6GDTifB8KK3ig7PZBI0TLbSqb6VVPRst6lmpFa3eZhERCU0K0iIiR47ANdfAggU4gTdPHL4NsBXW/p57vKt2h4VVWokiFelwuptNe1xs3O1g7f7ufPVBZoU/Z5jVuwVV6/pWWtf3BmctCiYiIlWFgrSI1Gzr1sHIkfD33wDkAmNOnBrNaUE6Otq7+NiVV1ZmhSIBl5rm9g3T3rTn9D2cIyrkOaPsJlrVPxGcG9hokqhh2iIiUnUpSJdSSkoKKSkpuN3uYJciIuX1yScwejRknux9swCXnfK5T4cO8Omn0K5d5dUnEiBpWR7+3ONk424Xf+52nhacK0ataPOJ0GylVX0r9RMsWk1bRESqDQXpUkpOTiY5OZm0tDTi4uKCXY6IlIXbDY8/DpMmFTgVDnx8+sGrr4Y33/T2SItUAVkOD3/tdbFxt5ONe1zsOVLxv/xtUMtC6/pWX69z7RhL8ReJiIhUUQrSIlKzHDvmnQ89f37xbW02eOEFuOsuUE+ahDCny2DLfm9w/nOPkx0H3XhKsGZeWZnwLgzWpqGVtg1stKqvbahERKRmUZAWkZpjwwbvfOjNm4tv27gxfPwx9OpV4WWJlJbbY7DjkJs/dzvZtMfJln0unBXY6Ww6saJ22wY22jb0bkUVpeAsIiI1mIK0iNQMn30G110HGRlFNssEogF27SLjjDOIqoTSRIpjGAZ7j7p9c5z/2usiO7fiupxNJmhyIji3aegdqh1pV3AWERHJoyAtItWbYcDTT3vnRJfEnXfCa69VbE0iJZCe7WHDbicbdnk/jmVWfHBuXd/M8T2ruWrE2cRGaXs3ERERfxSkRaR6W7y4ZCHabofXX4fLL1eQlqBwuQ227nex/kRw3nnITUVF57zg3K6hd6h2y3reHmen08m8eUeJCNOaACIiIkVRkBaR6m3AAHjkEZgyxX+bhg1hzhzo0SPfVlgiFckwDA4c8/iC86Y9Thyuinu++gkW2jey+sKzhmqLiIiUnYK0iFR/kybBxo3w+ecFz/Xp491Pum7dyq9LapzMHA8b95zsda7I/ZxrRZu9wbmRjXYNbcRHKTiLiIgEioK0iFR/ZjO89543NK9de/L4XXd5t7cK01xQqRgej8G2gy7+2OkNztsOujEqaLx2dLiJdg1tvvCcFGvGpG3bREREKoSCtIjUDNHR8MUX0LMnHDkCr74Kt9wS7KqkGkrP9rB+p5N1O52s3+kk01Exydlugzb1bbRrZKV9IxsNa1swKziLiIhUCgVpEak5mjTxhunsbOjfP9jVSDXhMQx2HHSzbqeTP3bksv1gxSwSZjFDi7re0Ny+kZVmdaxYLQrOIiIiwaAgLSI1S8+ewa5AqoHMHO8iYet2OFm/y0l6dsX0OteJM9OhsY2OjW20bWjTatoiIiIhQkFaRKqubdsgLg5q1Qp2JVLNGYbBrlQ363Y4+WOnk60HXBUy1zkizES7hlZfeE6KswT+SURERKTcFKRFpGr65RcYMQLat4dvvvHuAy0SQA6nwZ+7nazd7mTdjlyOZwU+OZtM0LyOxRecm9e1YjGr11lERCTUKUiLSNXz+edw9dXeuc6HDsHo0fC//3lX5xYph2OZHn7fnsua7U427nbidAf+OWpFm+nYxBuc2zW0EhWu+1ZERKSqUZAWkarllVfgnnvIN672ww+hWTOYPDloZUnVZBgGuw67WbvNydrtuew4FPjkbLVA2wY2zmji/agbr22pREREqjoFaRGpGjweePhh+H//r/DzzzzjDdO3316up7Hb7cyaNcv3uVQ/TrfBpj3eIdu/b3dyJMMT8OeoHWOmU1MbnZp4Fwmz2xScRUREqhMFaREJfdnZcP318MknRbe76y5o165cW1tZrVYuv/zyMl8voSk928O6Hd5e5/W7nDicgX18ixnaNLByRhMbnZqGUU+9ziIiItWagrSIhLZjx+Dii2Hp0uLbXnKJtrcSn0PH3azelsvqv51s3e8K+N7OCVEnep2b2mjX0Ea4tqYSERGpMRSkRSR07d0LQ4fCunXFt73vPnjuObCUb7sgl8vFnDlzALj00kuxWvVtsqowDIM9h938ts3J6r9z2X04sPOdzSZoVd9Kp6beuc4Na1nU6ywiIlJD6SdEEQlNf/0FQ4bA9u1FtzOZ4IUX4N57A/K0DoeDK664AoCMjAwF6RDnMQy2HXDx29/e8HwoLbDznSPtJs5oYqNLM294jrRrhW0RERFRkBaRULRqFQwb5t3aqijh4d5tr/7xj4A9tdlspv+JOdZmbacVklxug7/2ulj9dy6rtwV+f+ekWDNdmtvo0iyMVvWsWC3qdRYREZH8FKRFJLR8/z2MHAkZGUW3S0yEL76Ac84J6NNHRESwaNGigD6mlJ/DabBhl5Pf/s7l9x1OshyBC88moGU9K52becNz/QQtFCYiIiJFU5AWkdDx8cdw7bWQm1t0u2bN4JtvoHXrSilLgsPhNFi3w8mvW3NZtyOXXFfgHttuhY5NvMG5U1MbMREafSAiIiIlpyAtIqHh1VdhzBgwiulp7NwZvv4a6tevnLqkUjmcBn/sdLJyS+DDc0KUmS7NbHRpbqNtAxs2q3qdRUREpGwUpEUk+CZNgkcfLb5dv37w+ecQH19hpWRmZtKsWTMAtm/fTlRUVIU9l3jlhedft+Tye4DDc714M11bhHFWizCaJmmVbREREQkMBWkRCR7DgHHj4Jlnim87ciS8/z5ERFR4WampqRX+HDVdRYbnpkkWb3huHkb9WuXbDk1ERESkMArSpZSSkkJKSgpud2D3JxWpcTwe75ZVr7xSfNubb4bXXwdtRVWlOV0n5zz/vj0XR4DCs8kEretbOatFGGc2t1E7RuFZREREKpZ+Ki2l5ORkkpOTSUtLIy4uLtjliFRNbjfccgvMmFF823Hj4OmnvWlJqhy3x2DTHhcrNjtY/beT7NzArLZtNUP7xjbOahFGl2ZaLExEREQql4K0iFQup9O7MvesWcW3fekluOeeiq9JAsowDP4+4GblZgcrt+SSlh2Y8Gy3QqemYZzVwsYZTcOICNMvV0RERCQ4FKRFpHJZLBAZWXQbs9nbW33ddZVSkgTG3iNuVmx2sHJzLofSPAF5zDArdGkWRreWYZzRxIbdpvAsIiIiwacgLSKVy2yGt96CzEzvvtGns9ngww/hH/+o/Nqk1A6nu/llcy6/bM5l9+HArB0RZoXOTcPo3krhWUREREKTgrSIVD6rFd57D7KyYO7ck8fDw2HOHBg6NHi1SbEyczys3JLLis25bNkXmBXDFJ5FRESkKlGQFpHgCAvz9khfeCEsXAjR0fDVV9C/f7Ark0K43N7tqn7a5OD37U5cARi5HXZiznP3lmF0aqrwLCIiIlWHgrSIBE9EBHzxBVx5JUyYAD17BrsiOYVhGOw85ObHTQ5+2ZxLRk75Fw2zmKFTUxs9W4XRuVmYwrOIiIhUSQrSIhJc0dH5h3dL0B3J8LDiLwc/bcpl39Hyz3s2AW0bWunZOoyzWoQRFa6tqkRERKRqU5AWERFcHjMrNjtZuSWbP3e7CMSGVU2TLPRqY6dHqzDioxSeRUREpPpQkBaRwMrIgKgoMGnIbqgzDIOt+10sWZ/DL/t74d6XU+7HrBdvpmdrOz1bh1E33hKAKkVERERCj4K0iATO0aMweDD06gUvv1wlw3RYWBivvPKK7/PqKC3Lw0+bHCz708H+Y3mrhpX9v4P4KBM9W9np2SaMJokWTFXw711ERESkNBSkRSQwjh/3huhff/V+mM3w0ktVLkzbbDaSk5ODXUbAuT0G63c6Wfang993OHGXc9Vtuw26tQzj7DZ22ja0Yq5if88iIiIi5aEgLSLll5EBw4d7A3Sel1/2hukXXqhyYbo6OXjczfI/Hfy4ycGxzPLNfDYB7RtZOaetna4ttOK2iIiI1FwK0iJSPtnZcNFF8OOPBc/l9Ug//3yVCdNut5ulS5cC0LdvXyyWqjfPN9dl8NvWXJb96WDTXle5H69+gplz2trp1cZOrWgtGiYiIiKiIC0iZedwwKWXwqJF/tu8+CLUrQv/+ldlVVUuOTk5DBw4EICMjAyioqKCXFHJ7T3iZsn6HH76K5csR/l6n6PDTfRqHcY5be00SdK8ZxEREZFTKUiLSNk4nXDllbBgQdHtWraEa6+tnJoCwGQy0aFDB9/noc7p9vY+L17vYPO+8vU+m03QpZmN3u3snNHEhtUS+q9fREREJBgUpEWk9NxuuO46+Pzzots1b+7trW7UqFLKCoTIyEjWr18f7DKKdfC4myXrHSzf6CAjp3y9z/XizSR4tjD64k7UirUHqEIRERGR6ktBWkRKx+OBm2+Gjz4qul2jRvD991UqRIc6l9tg7XYnS9bnsGF3+Xqf7Tbo2crOue3tNK7lYf78PcREdAlQpSIiIiLVm4K0iJScYUByMrzzTtHt6tXzhujmzSunrmruSIaHJetzWPang+NZ5et9blXPSp/2drq1CiP8xKrbTqczEGWKiIiI1BgK0iJScuPHw+uvF90mMRG++w7atKmcmgIsKyuLHj16ALBy5UoiIyODUodhGGze5+KHdTms/tuJpxz5OSbCRO+23t7n+glVbxVyERERkVCjIC0iJfP88zBpUtFt4uPhm2+gY8dKKakiGIbBhg0bfJ9XtlyXwYq/cvlhXQ67D7vL/DgmoGMTG/062OnUVAuHiYiIiASSgrSIFO+dd+DBB4tuExMDX38NXbtWTk3VTGqam0XrHSzb4CCzHFtXxUaY6NPeTt8OdhJj1fssIiIiUhEUpEWkaF984V1crCgRETB3LvTqVTk1VROGYbBxj3f49trtTsrTAd6uoZX+HcM5s7l6n0VEREQqmoK0iPi3ZAlccYV3uyt/bDbvNlh9+1ZeXVVcrsvg500Ovl/nYO+Rsg/fjrKb6N3OTr+OdurFq/dZREREpLIoSItI4dasgYsuAofDfxuTCd5/Hy64oNLKqsrSsjws+iOHResdpGeXvfu5VT0r/Tra6dYyjDCrep9FREREKpuCtIgUtGULDBkCaWlFt3v9dbjsssqpqQrbd8TNt2tz+OkvB64ydkDbLNCrjZ3zOtlpnKhv3SIiIiLBpJ/GRCS/gwe9IfrgwaLb/fvfcNttlVNTFZQ3//mbNTn8sbPs+zTXjjEz4Aw7fdrbiQ43B7BCERERESkrBWkROSkzE0aMgL//Lrrd/ffD2LGVU1MV43IbrNySy7drc9iVWvb5z+0aWjmvUzhdmtkwmzV8W0RERCSUKEiLyEn33gsrVxbd5vrr4bnnvPOjxSfL4WHxegc/rMvhWGbZ5j+HWeGctnYGdrLTsJa+PYuIiIiEKv2kJiInPfYY/Pgj/Pln4edHjICpU8GsIcZ50rI8fLfWu4BYdm7ZAnRSrJmBncI5t10YkXa9tyIiIiKhTkFaRE5q2hSWL4dLL4XFi/Of69MHZs3ybnclHEpz883qHJZtLPsCYq3qWbngzHDO1PBtERERkSpFQVpE8ktIgAULYPRo+PBD77F27bx7RUdEBLW0ULD7sIv5v+Xw65ZcPGXogDaZoFuLMC44M5wWdfUtWERERKQq0k9xIlKQ3Q7/+x80bgwzZ8L8+VCrVrCrqhQ2m40JEyb4Ps+zZZ+Teb/lsG5H2Vbgttugb3s7gzqHkxhrCUitIiIiIhIcCtIiUjizGZ59Fh5+GBITg11NpQkLC2PixImAdwurP3c7+erXbP7a6yrT4yVEmRnU2U7fDnbNfxYRERGpJhSkRaRoNShE5zEMgw27XHz5azZb95ctQDdJsjC4SzjdWoZhtWj+s4iIiEh1oiAtInKCYRj8vt3B9M/XsDvVQ3y9NphKuUJ5+0ZWhnaNoH0jKyZtESYiIiJSLSlIi0iNZxgG63acGMK9K413HjgHgBue34nNHlWix+ja3MawsyJorgXERERERKo9/cQnUpO8+CK0bQvDhgW7kpBgGAZrt3sD9I5DJ/ewCo+uXaLrLWbo1SaMoWdGUL+WFhATERERqSlqZJCePHkyn376KZs2bSIyMpL+/fvz7LPP0qxZs2CXJlJxZs+G++/3LiL24otw++3BrihoDMPgj51OPvslm52H8m8CbbNHce2Uv4q8PswKfTvYGdwlnFoxCtAiIiIiNU2NDNKLFy/m7rvvpkePHjgcDh555BGGDRvGunXrsFpr5Fsi1d1vv8F113k/93jgnnswr1+P6YILgltXEPy118mcFdls2Vf6RcQiwkyc18m7hVVMhFbgFhEREampamRq/Prrr/N9/fbbb9OkSRM2bNhA586dg1SVSAXZswcuugiysvIdtrzxBmf//DP06wdJSUEqrvJsP+jisxXZrN9V+n2gI8JMnN/ZG6CjwhWgRURERGq6kP2J8L333uP222+ne/fu2O12TCYTM2bMKPKalStXMnz4cBISEoiKiqJnz568//77xT7X8ePHAahVq1YgShcJHTk5MHIk7N1b6Ok6a9diPe88cJY+XFYVe4+4ee3rdP79SVqJQrQrN5uvXryYr168mDBTDpf0jOCZ6+K4uGekQrSIiIiIACHcIz1+/Hh27NhBYmIi9evXZ8eOHUW2X7RoEUOGDCEsLIyrrrqKuLg4Zs+ezahRo9i+fTvjxo0r9DqPx8ODDz7I8OHDadSoUUW8FJHgMAy44w749dcim3nuvBOLzVZJRVWeQ8fdfLEymxV/5WKU4jrD8LB/83IAnrgqlsSEiIopUERERESqrJDtXpk6dSrbt2/n0KFD3HHHHUW2dblc3HLLLZhMJpYsWcJbb73Fc889x9q1a+nYsSMTJkxg8+bNBa4zDIPbb7+dbdu2FdvbLVLlvPIKvPNOkU22jhiB59ZbK6mgynE8y8P/Fmfy2AfH+bmUITo63MQlPU8G54gw7QMtIiIiIgWFbJA+//zzadq0aYna/vDDD2zdupVrrrmGrl27+o7HxMTw2GOP4XK5mD59er5rDMPgrrvu4rvvvuP7778nqQbMEZUaZNEi7wrdRfAMG8YfN95YOfVUghynwRcrs3n0vWMsWu/A7Sn5tZF2E/84O4LJ18Uz+Ez1QIuIiIhI0UJ2aHdpLFq0CIDBgwcXOJd3bPHixb5jhmGQnJzM3LlzWbx4MY0bN66UOkUqxc6dcPnl4Hb7b9OhA+6ZM2HZssqrq4K4PQbL/3TwxcpsjmeVpv8Z7Da4oEs4F3QJJ9Lu/b1iZm5FVCkiIiIi1Um1CNJ5w7Zbt25d4FxCQgKJiYn5hnbfddddfPjhh3z55ZdERESwf/9+wLvYWFhYWKHP4XA4cDgcvq/T0tIAcDqdOKvYQk159Va1uqUEsrOxjhyJKTXVbxMjPh7XJ5/gjPD2vFbV+8AwDNbtdPPZLw72HytF9zNgtUD/DjYGdwk7sY2VG6fT+4uHU9+Pqvjvuyz0PUHy6F6QPLoXBHQfyEk15V4ozeurFkE6b9XtuLi4Qs/Hxsaye/du39evv/46AH379s3XbuHChQwYMKDQx5g8eTJPPPFEgePffPMNkZGRZSk76L799ttglyCBZBic9eKLNF692n8Tk4mf77mHg3/9BX/9BVTN++BYbjQbjjfnSG58qa4z4aFx5AFax+4k4kguSxcWbJOTk+P7fMGCBYSHh5ez2qqjKt4LUjF0L0ge3QsCug/kpOp+L2Sdtl1sUapFkC4twyjd8E+AsWPH8sADD/i+TktLo3HjxgwePJjY2NhAllfhnE4n3377LRdccAG2arhac01l/u9/sZwyhaEwnqeeovvDDwNV8z44muHhs5UOVu5xleo6E9CjlZULu9lJio0D2vhtm5mZ6ft8yJAhREVFlbHaqqMq3gtSMXQvSB7dCwK6D+SkmnIv5I06LolqEaTzeqLzeqZPl5aW5re3uqTsdjt2u73AcZvNVmVvpqpcu5xm6VJ45JGi21x+OZZx47CY8q9EXRXug1yXwTdrcpj/Wza5pcvQnNncxsieETSsXbJvd6e+F1XhvQmkmvZ6xT/dC5JH94KA7gM5qbrfC6V5bdUiSOfNjd68eTPdunXLd+7o0aOkpqbSu3fvYJQmUvEOHIArryx6cbFOnWDaNDBVre2cDMPg1y25fPJTNkcySjcPukVdC5f1jqR1/er7zV5EREREgiNkt78qjf79+wPe+cqnyzuW10akWnG74eqrYd8+/20SEmDOHIiOrry6AmDHIRfPfpbOm99mlipEJ8WauWNINP/6R6xCtIiIiIhUiGrRIz1o0CBatGjB+++/zz333MOZZ54JQHp6Ok899RRWq5XRo0cHtUaRCjFhAiwsZMWsPGYzfPABtGxZeTWVU1qWhzkrsln+p4PSrGYQHW5iRPcI+ne0Y7VUrZ53EREREalaQjZIT506lWUn9rhdt26d71jentEjR45k5MiRAFitVqZOncqQIUPo27cvV199NbGxscyePZtt27bx9NNP06aN/8WFRKqkuXPh3/8uus2//w1DhlROPeXk9hgs+sPB579kk51b8ghts8D5XcIZ2vXkXtAiIiIiIhUpZIP0smXLeOedd/IdW758OcuXLwegWbNmviANMHDgQJYtW8aECROYNWsWubm5dOzYkaeeeopRo0YFrK6UlBRSUlJwFzUfVaSi7dgB111XdJuLLoITK3SHuq37XfxvSSa7Ukv376pn6zD+eXYEtWIsAavFarVy1113+T4XERERETldyP6UOGPGDGbMmFGqa3r27Mn8+fMrpqATkpOTSU5ODshK4CJl4nDAFVfA0aP+2zRrBu+84x3aHcIycjzM/imbpX86SnVd0yQLV/WJpFUFzIG22+2kpKQE/HFFREREpPoI2SAtIn489BD88ov/82Fh8Mkn3kXGQpTHMFj+p4PZP2eTkVPyYdxxkSYuPTuSc9qGYa5iK5CLiIiISPWhIC1SlRw4ADNnFt3mv/+F07aBCyU7U138b3Emfx8o+TBuqxku6BLO8G4RhIdVbIA2DIPU1FQAEhMTMSmwi4iIiMhpFKRFqpK6dWHNGrjmGvjxx4Lnr70Wbrut0ssqiRynwecrsvh+nQOjFMtxd21u47LekdSJC9w86KJkZWVRp04dADIyMoiKiqqU5xURERGRqkNBWqSqadoUFi+GJ57wrsqdl0o7doTXX4cQ7EH9Y2cuMxdllWo/6DpxZkb1i6JDY+0FLSIiIiKhRUFapCqyWuGpp+C887y90MePw8cfQ4j1nqZne5i1PIuf/8ot8TU2CwzvFsGQruHYgrAfdFRUFEZpusxFREREpMZRkBapygYOhLVrvcO927cPdjU+hmHwy+ZcPlyWVarFxDo3tXFV30iSYitnGLeIiIiISFkoSJeS9pGWkJOYCOefH+wqfA6nu/nf4izW7XSW+JraMWau6hPJmc3DKrAyEREREZHAUJAuJe0jLVI4j2Gw6A8Hs3/OwlHCDG0xw5Azvatx222hMbc7JyeH6667DoCZM2cSHh4e5IpEREREJNQoSItIuR087mb6D5ls2ecq8TWt61u5rn8U9WuF1jBut9vNJ598AsCMGTOCW4yIiIiIhCQFaREpM49hsPgPB5/8lEVuCTN0uA3+eU4k/TraMYfgCuMiIiIiIsVRkBYJNbm58Pvv0L17sCsp0uF0N+8szOTP3SXvhe7SzMY1/aKoFW2uwMpERERERCqWfpoVCTVPPgm9esGjj3pDdYgxDINlfzqY+OHxEofomAgTt10QRfKwaIVoEREREany1CMtEkp+/BEmTwaPByZNgvnz4b33oEOHYFcGwLFMD+8uymTdjpKvyH1O2zCuODeS6HAFaBERERGpHhSkRUJFRgZcf703ROdZvRrOOgueeQbuuQfMwQuja7bl8s7CzBLvC107xsx1/aPo2MRWwZWJiIiIiFQuBWmRUPHgg7B1a8HjDgfcfz/89Re8+mqll+VwGnz8YxaL1ztKfM2Ajnb+2TuS8BDZ0kpEREREJJAUpEspJSWFlJQU3G53sEuR6mTuXHjzTf/nbTa47bbKq+eEnYdcvPVtBvuPeYpvDCREmRl9XhQdGqsXWkRERESqLwXpUkpOTiY5OZm0tDTi4uKCXY5UB4cOwc03F93mySfhzDMrpRzwbmv17Zoc5qzIxl2yDM257bxzoSPtmgstIiIiItWbgrRIsI0ZAwcO+D9/7rnw0EOVVs6xTA/Tvs8o8YrcsREmrh8YRZdmYRVcmYiIiIhIaFCQFgmm2bNh1iz/56Oj4d13wWKplHLW73Ty9vcZpGeXbEGxbi3DuLa/VuQWERERkZpFQVokWA4fhjvvLLrNiy9CixYVXorbY/DFymzmr8qhJBHaboNr+kZxTtswTCYtKCYiIiIiNYuCtEiw3HsvHDzo//zFF8NNN1V4GccyPcxYlM5fe0s2lLt5HQu3XBBNnbjK6SWvbBaLhcsuu8z3uYiIiIjI6RSkRYLhyy/hf//zfz4hAd54Ayq4t/dQTjyTZmeVaG9okwku7BbOhd0isFqqby90eHg4H3/8cbDLEBEREZEQpiAtUtmOHYM77ii6zUsvQb16FVaC22Pw5a8OVhw+A0owmLtWtJmbz4+iTQNtayUiIiIiUqIgvWTJknI/UbNmzWjSpEm5H0ekynvgAdi71//5Cy+Ea6+tsKdPz/bwxjcZbNrjAorvWe7WMozrB2hbKxERERGRPCUK0gMGDCj3gkITJkzg8ccfL9djiFR5X38N06f7Px8XV6FDunccdPHq1xkcySh+c2irGa7sE0n/jvYataBYZmYm0dHRAGRkZBAVFRXkikREREQk1JR4aHf//v3p379/qZ/AMAyefPLJUl8nUu2kpcFttxXd5vnnoWHDCnn65RsdvLc4E5e7+LZJsWZuHxJN0yTN/hAREREROV2Jf0oeMGBAmXuUq1OQTklJISUlBbe7BGlE5FSPPgq7dvk/P3gw3HhjwJ/W5Tb4aHkWi/5wlKh9t5Y2rh8QVWOHckdGRnLwxGrqkZGRQa5GREREREJRiYL0ueeeW675zeW9PpQkJyeTnJxMWloacXFxwS5HqooVKyAlxf/56Gh4662AD+k+nuXh9a8z2LK/+K2trGa44txIBpxRs4Zyn85kMpGUlBTsMkREREQkhJUoSC9durRcT1Le60WqNKfTO6TbKGJ17P/8BwL8y6at+128viCdY5nFr8pdO8bMnUOiaVpHQ7lFRERERIqjn5pFKtpLL8Hvv/s/P2BA8XOnS+mnTQ7eXZiJq/g1xWjf0MLtQ2KICq+ZQ7lP53A4eOCBBwB4/vnnsdvtQa5IREREREJNQIP0tm3b+O6774iIiODSSy/Varci27fDhAn+z9vt3lW6zYEJsR7D4PNfspm3KqdE7VtG7yJ5aHvsNXQ+dGFcLhevvvoqAM8++6yCtIiIiIgUUKafnqdMmULr1q05evSo79iiRYvo1KkTd9xxBzfccAPdunXLd16kxjEMSE6GrCz/bR59FNq0CcjT5boM3vwms0Qh2m6FWwaF0z5uO2ZzzZ0PLSIiIiJSFmUK0p9//jkNGzYkISHBd+yhhx7C4/HwxBNPcOedd/LXX3/x0ksvBaxQkSrH44FevSAsrPDz7dvDww8H5KmOZ3l47rM0Vm3NLbZtnTgzYy+L5awWtoA8t4iIiIhITVOmIP3333/TsWNH39e7du1i1apVJCcnM378eF555RUGDRrEp59+GrBCRaociwUef9w7P3rAgILnX3/dO7S7nHalupj0SRrbDha/JVvnpjYevSyWhrW0PIKIiIiISFmVKUgfO3aM+Ph439fLli3DZDJx0UUX+Y6dddZZ7Ny5s9wFilR5bdvCDz/A9OlQq5b32M03Q79+5X7otdtzmTInjSMZxa8qNrxbOMnDo2vs/tAiIiIiIoFSpp+o69aty44dO3xff/vtt9jtdnr16uU7lpOTU6P3ohXJx2SC0aNh40bvvOlnny33Q/6wLoeU+Rk4nEW3s5rhpkFRXNorErP+TYqIiIiIlFuZxnf26NGDzz//nLlz5xIeHs6sWbMYMGBAvtVt//77bxo0aBCwQkWqhaQkeOWVcj2EYRjM/jmbr1cXv6hYdLiJu4ZF07q+5kOLiIiIiARKmYL0uHHjmDt3LhdffDEAJpOJsWPH+s6np6ezcOFCLr/88sBUKSIAuNwG7yzM5Oe/il9UrH6CmbuHx5AUZ6mEykREREREao4yBemzzjqLn3/+mZkzZwJw2WWXcfbZZ/vOr127lgsuuIBrrrkmMFWKCDm5Bq8tSGfDLlexbTs0snL7EM2HFhERERGpCGUK0jt37qRu3bo899xzhZ7v06cPffr0KVdhoSolJYWUlBTc7uJXSBYJlLQsD/+dm86OQ8Xfd/072rmqTyRWi+ZDi4iIiIhUhDJ1VzVv3pzx48cHupYqITk5mQ0bNrBy5cpglyI1xOF0N1PmpBUbok3AledGMqqfQrSIiIiISEUqU490rVq1SEhICHQtIlWTYcD+/VC/fsAfev9RNy98mV7s9lZWM9x8fjTdW4UFvAYREREREcmvTD3Sffv25eeffw50LSJV0yefQMuW8O9/Q07xK2mX1M5DLp79rPg9oiPCTNx3UYxCdICYzWb69+9P//79MZs1x1xERERECirTT4mTJ0/mjz/+4IknnsDlKn7hI5FqKzsbHnzQ++f48XDGGTB3brkfdss+J899nk56tlFku/goEw9fGkPbhtreKlAiIiJYtGgRixYtIiIiItjliIiIiEgIKtPQ7ilTpnDGGWfw5JNP8uabb9KlSxfq1q2LyZR/XqbJZOLtt98OSKEiIen552HXrpNfb90KI0Z4P1580dtTXUrrdzp59et0cov5HVX9BAv3jYimVoy2txIRERERqUxlCtIzZszwfb5v3z727dtXaDsFaanW9u2DyZMLP/fVV7BuHWzZAtaS/zP7fXsur32dgavo0dw0r2Ph3hExRIVr6LGIiIiISGUrU5Detm1boOsQqXrGj4fMTP/nJ04sVYheeyJEu4sJ0e0aWkkeFkN4mFbmrgiZmZk0a9YMgO3btxMVFRXcgkREREQk5JQpSDdt2jTQdYhULWvWwPTp/s936wbXX1/yh9uWy+sLig/RXZrZuH1wNDarQnRFSk1NDXYJIiIiIhLCyhSkRWo0w4AHHvD+6c8LL0AJV3z+7e9c3vym+BDdq3UYo8+L0h7RFSwiIoI//vjD97mIiIiIyOnKFaRzcnJYuXIle/fuxeFwFNrm+lL0yolUCV98AQsX+j//z39C374leqhVW3N569viQ/SAjnau7heJ2aQQXdHMZjMdO3YMdhkiIiIiEsLKHKRTUlJ47LHHOH78eKHnDcPAZDIpSEv1kpsLDz3k/3xYGEyZUqKHWn2iJ9pT9A5XnN/ZzhXnRhZYFV9ERERERIKjTEv+zp49m7vvvpvGjRvz3HPPYRgGl1xyCZMmTWLo0KEYhsE///lPpk2bFuh6RYLrrbdg82b/5++5p0RbXv2xs2QhenCXcIXoSpabm8vEiROZOHEiubm5wS5HREREREJQmYL0iy++SJ06dfjpp5+4//77ATjzzDN55JFHmDt3Lu+99x6fffaZFiWT6iU9HZ580v/5xETvSt7F2LTHyavzi9/iakjXcC7rHaEQXcmcTidPPPEETzzxBE6nM9jliIiIiEgIKlOQ/v3337n44ouJjIz0HXO73b7Pr7nmGgYNGsSTRYUOkarm+efh4EH/5598EuLiinyIrftdvDwvHae7yGYMPyucf56tEC0iIiIiEorKFKSdTidJSUm+ryMiIjh27Fi+Np07d+a3334rV3GhKCUlhQ4dOtCjR49glyKV6cABeO45/+fbtYNbby3yIXamuvjv3HQcxXRyXtgtnJG9FKJFREREREJVmYJ0gwYN2Ldvn+/rpk2bsnr16nxtduzYgdVa/XbXSk5OZsOGDaxcuTLYpUhlevppyMjwf37SJCjift93xM0LX6ST5Sh6UvTQruFc0lMhWkREREQklJUpSPfo0SNfb/PQoUNZvnw5zzzzDOvXr+eNN95g9uzZ6rWV6mHrVnj9df/nzz4bRo70e/pIhocXvkwnI6foED2wk51/aDi3iIiIiEjIK1OQvvzyy3E4HGzfvh2AsWPH0qhRIx599FE6d+7MnXfeSXR0NM8++2wgaxUJjsceA5fL//kpU8BP+M3M8fDSV+kczSx6ZbFz24VxVR+tzi0iIiIiUhWUaez1pZdeyqWXXur7OikpiTVr1jB16lT+/vtvmjZtynXXXUfDhg0DVqhIUPz2G3zwgf/zI0ZAv36Fnsp1GbwyP4O9R4peWaxHqzCuHxCFWSFaRERERKRKCNgk5oSEBB566KFAPZxIaBg71v85kwkmTy70lNtj8Na3GWzZV0RPNtClmY2bBkVhNitEi4iIiIhUFeUO0hkZGfz1119kZmbSt2/fQNQkEhqWLoVvvvF//oYb4IwzChw2DIP3l2SxZlvRy3O3b2Tl9sHRWC0K0SIiIiIiVUmZ5kgDbN++nUsuuYSEhAR69OjBwIEDfeeWL19Ohw4dWLRoUSBqFAmOxx/3f85uhyeeKPTUlyuzWbLBUeRDN0m0cOfQGGxWhWgRERERkaqmTEF6586dnH322cybN49LLrmEc845B8M4uSJxr169SE1N5YOi5paKhLKFC6GoXwQlJ0OTJgUO/7jRwZe/5hT50EmxZu4dEUNEmEK0iIiIiEhVVKYgPWHCBI4ePcrixYv55JNPuOCCC/Kdt1qt9O3bl+XLlwekSJFKZRjelbr9iYqCf/2rwOG/9jp5d1FmkQ8dE2HivotiiI0s82AQEREREREJsjLNkV6wYAGXXnopvXv39tumSZMm/PDDD2UuTCRoDh3yfvhz772QlJTv0MHjbl77OgN3Ebtc2a1wz4Ux1ImzBKhQqQgmk4kOHTr4PhcREREROV2ZusWOHDlCs2bNim3ncBQ9T1QkJNWpA+vXwzvvQIsW+c/FxsKDD+Y7lOXw8Mq8dDJyDPyxmOHOoTE0qxOwhfKlgkRGRrJ+/XrWr19PZGRksMsRERERkRBUpiBdt25dtmzZUmSbP/74gyaFzCEVqRKsVrj+eti4EaZOhaZNvcfvvx9q1fI1c3sM3vgmg31Hi+iKBkYPjKJjE1tFViwiIiIiIpWkTEH6ggsu4Msvv+SPP/4o9PzSpUv5/vvvGT58eLmKEwk6mw1uvhn++gtefx3uuy/f6Y+WZbFhV9F7RY/oHs7Zbe0VWKSIiIiIiFSmMgXp8ePHExERQZ8+fZg0aZKvd3r+/Pk89thjDB06lMTERB566KGAFisSNGFhcPvtEB/vO/TDuhwW/lH09IUercK4uEdEBRcngZSVlUXHjh3p2LEjWVlZwS5HREREREJQmSZsNmvWjAULFnDVVVcxfvx4TCYThmEwYsQIDMOgSZMmfPLJJ9SvXz/Q9YqEhL/2OvloWdEhq3kdC6PPi9KCVVWMYRhs2LDB97mIiIiIyOnKvPJRr1692Lx5M19++SUrVqzgyJEjxMbG0qtXLy655BLCwsICWadIyDiW6eGNBRl4ishYtaLNJA+PIcyqEF3VhIeHs3DhQt/nIiIiIiKnK9cSwlarlUsvvZRLL700UPWIhDSX2+D1BRmkZftP0XYb3D08mjjtFV0lWSwWBgwYEOwyRERERCSElekn/ZdeeomjR48GuhaRkDfrxyy27ve/uJgJuPWCaBolapsrEREREZHqqkxB+v7776dBgwaMGjWKRYsWBbgkkdD08yYHC9cVvbjYpWdH0KWZpjVUZU6nk5SUFFJSUnA6ncEuR0RERERCUJmC9CuvvEL79u354IMPGDRoEK1bt2bKlCns378/0PWFnJSUFDp06ECPHj2CXYoEwqZN3r2ii7Er1cXMxZlFtuna3MbQrppTW9Xl5uYyZswYxowZQ25ubrDLEREREZEQVKYgfdddd/Hbb7+xevVq7rjjDg4fPszYsWNp2rQp//jHP5g/f361Xe02OTmZDRs2sHLlymCXIoHwr39Bhw5w+eXw22+FNsnM8fDa1xnkFrFddL14MzcOitYK3SIiIiIiNUC5VkPq0qULKSkp7Nu3j3fffZdzzjmHzz//nBEjRtC0aVMmTpzIzp07A1WrSGCtXw+ffQaGAZ98At26wbBhsGyZr4lhGEz/IZNDaR6/D2O3wp1DY4gIU4gWEREREakJArKssN1u59prr2XRokUsXbqU+vXrs3v3bp588klatGjBRRddxC+//BKIpxIJnGeeKXjs66+hb18YPBgMg4V/OFi7veh5sjecF0WDWpYKKlJEREREREJNwPbn+frrr7nssss477zz2Lt3L40bN2bs2LEMHjyYefPm0bt3b959991APZ1I+fz9N3zwgf/zbdqw+7Cbj3/MKvJhLugSTo9W9gAXJyIiIiIioaxce/Ts3LmTadOmMX36dHbv3o3ZbGbYsGHcfvvtDB8+3DdfdP369Vx00UU8+eSTXH/99QEpXKRcnn0W3O7Cz1mtOB54iDe/zcTlpwlAm3pm/nlOxMkDWVneYeLh4WA50UPtcoHDAWYzRJzSNjsbPB6w28F64p+h2w05OaVrazJBZOTJtjk53nNhYWCzFdvWkpMDTufJth6P9/kAoqJOtnU4vK/FZvM+dmnbGob3/QFvDXlzyXNzTz5/Sdtard73Ik/miUXgIiK8711p2zqd3vYWi/fvTkRERESkGGXqkf70008ZOnQoLVq04Mknn8QwDB5//HG2b9/OF198wYUXXphv0aWOHTty3XXXsWPHjoAVLlJme/fC9Om+Lw3AERaJx3Tin8OoUczaWZt9R/2n6Njj+7l92i1YzCY8HgOH0yD37HMhOhqWLgUg12Xg+GIe7tg4OP98AF9bx8ALvG0XLADA6TJwfLcIV2w8nHNO/rYjLvG2nTPnZNvlK7xtu3TxtjVOtL3iGm/b//3P29Zt4FjzB664BGjdOl9b1023MeKqqzBPnXqy7V9bvW0bNvS+N3mPe+fdGNHR8NJLALjcBo5d+7xt4+Pzt33wEW/bSZNOtj18DGd8LW9tLu+qbQ6ngWP8RDwxMfDooyfbZjtPtj1+/GTbSc962z7wwMm2TgNnYl1v2337TrZ9KQVPTCzcfnv+tk2be9tu3Xqy7Ztve9tedx0Abk/1XChRRERERAKnTEH68ssv5/vvv2fYsGF88cUXbN++nQkTJtDwxA/fhWnXrh19+vQpc6EiAfPSS94eSLwhesoD8xjzwi42t/IG2N9uepwlG4re9mjgkreJdXhD3sY9Lsa8dZTJl72e/2m+SmfM/j6s7nKh79jfB7xtnxz5Wr62r32dwZitXVnR83Lfsd1H3Ix56yjjh6Xka/v29xmM+aMdS/vc4Dt26LiHMW8d5aFBL+VrO3NRJmNWNOb7gbf7jh3PNBjz1lHu65+/7azlWYz5oTbzhtzvO5ad6207pvszuM0nB7DMWZHNmLkRzLn4Ud8xtwdv2w6Pkx0R6zs+b1U2Y2YZzPrH0/me7963jzKm2f9xPLae79j3v+cwZkYGM69+Pl/bh945xpg6d3Eosbnv2NINDsa8dZS3r30lX9vx7x9jTNQN7G7YwXdsxeZcxrx1lNeuyf++PznrOGNMV/B38+6+Y2u3ae9oERERESlamYZ2P/7449xyyy00atSoxNdcffXVXH311WV5OpHASU+HN97wfZlriyArMu7E5+EcufJG3t2cgDdi+9fkxQnQxJb/YOs2kJFRcHjwzJnQ4rS2DRt529pPm1/9+uvQ5rS2ibULb/v889D+tH/CUVHetnnDpPM8/TR0mpL/mMXKVx9+yJCLLiLfUmnjxsGZj1PAsaNgP+1x770PZv5fwbZ79kDUaW1vuxWm3XVyeHqev/6C+NPajhoFr43OPxQdYO0aqHVa25Ej4cXL8w+HB/jxR6hzWttBg2ByRsG2330H9U9730VERERE/DAZ1XXD5wqWlpZGXFwcx48fJzY2tvgLQojT6WTevHkMHz4cm62GhYeXXoL77itw2BEWicWVywsztvNXekTB605onGjmwYtjCQ8zYTF7py94PAZOt3cqb5j15JSGXJeBYYDVQoG2AHbbybZOl4HHAIsZrJYytDUMnK5C2roNPJ7C2zqdTr7/dr7vPiisrWEYvv2zw6z4pmy43AbuUrY1m8FmOVmbw+n91mOzgrk8bU1gsxbS1gJmc+nbpqVnEBcbA0BGRgZRp87/rqZq9PcEyUf3guTRvSCg+0BOqin3QmkyXrkWGxOpUlwuePHFQk/Zc7OYe8d/iwzRdivcPjiGqPD8MyLMZhP2QiZJnBqqi2trK29bkwl7Id/TbBYTWApva8ZUbFuTn8e1WkxYy9EW8gf+UGqb90sPERERERF/SjRHukGDBjz//PPFN6yg60UCYs4c2L690FM7G3Xiy05FTz24pl8UdeO1X7SIiIiISE1Xoh7p/fv3k5GRUeYnKe/1IuVmGPD//l+Bw7m2cF6+8322Ne+Bu4jfK/VsHcY5bcP8npfqJTExMdgliIiIiEgIK/HQ7s8++4ztfnrzinPqVlgiQfHjj7BiRYHDhsnMxrb9i7y0doyZUf0idR/XEFFRURw6dCjYZYiIiIhICCtxkF6zZg1r1qypwFJEKlAhvdEAuxt0AM+J1a0KYTbBrRdEEVnYZGUREREREamRShSkt23bVu4nio+PL/djiJTJli3w2WcFDjutYbxz7X/9hmiAoWeF07Je9V2ZUERERERESq9EQbpp06YVXYdIxXnlFe8c6dPMG/IA++q39XtZo9oWLurufxVvqZ6ys7MZNmwYAPPnzyfi9D2nRURERKTG0/ZXUr1lZMD06QUO727QnvlD7vN7mcUMNw6K8u2RLDWHx+Nh8eLFvs9FRERERE6nIC3V27vvQlpavkMek5l3R72E2+J/yPaI7hE0SdQ/j5rIbrcza9Ys3+ciIiIiIqdTUpDqyzC8w7pP8/2A29jWrJvfy5okWhjaNbwiK5MQZrVaufzyy4NdhoiIiIiEMC1FLNXXDz/An3/mO3S4ViM+u2ic30vMJrhhoIZ0i4iIiIiIf+qRlurr5ZcLHPrwssnk2qP8XjL4zHCaJOmfRU3mcrmYM2cOAJdeeilWq+4HEREREcmvxD8h7ty5k/j4eGJjYyuyHpHA2L4dvvwy36G1ZwxhTZfhfi+pE2Pioh5aobmmczgcXHHFFQBkZGQoSIuIiIhIASUe2t28eXNeeumliqxFJHBeew1OWXHZYYvggyueKfKSa/pHEWbVkG4RERERESlaiYO0YRgYhezFKxKSLrsMrrkGbN6VuecNfYDDtZsUeUmr+v5X8RYREREREcmjxcakeurRA/73P9i5k31Pv8SCC+722zTMCpf3jsCifw0iIiIiIlICig5SrRl16/JR5xuL3DP6yr8/ZXDKPVhduZVYmYiIiIiIVFWlCtImk+aPStWyboeT9bucfs83TzTR59nbYepUcLkqsTIREREREamqShWkJ06ciMViKfFHdVztNiUlhQ4dOtCjR49glyLFcLkNPlqe5fe8yQSjzrWz79+vsGdSCp5qeL+KiIiIiEjglSo5xMbGEh8fX0GlVA3JyckkJyeTlpZGXFxcsMuRIvywLoeDxz1+zw/oaKdenQjGxF0FwCvmMOyVVZyIiIiIiFRZpQrS999/P48//nhF1SISMGlZHr76Ncfv+Si7iUt6eveMjg7XlAURERERESk5jWWVaunzX7LJzvW/XdslPSOICjeDYfDCRSfmRutfg4iIiIiIlICig1Q7e4+4Wfqnw+/5BrUs9Ot4YhB3VhbUqeP9PCMDoqIqoUIREREREanKtP2VVDuzf87C8N8ZzZXnRmIxazi3iIiIiIiUTYmDdNOmTUu10Ni2bdsYPXp0GUoSKaXMTPjoI3A42LzPydrt/re76tLMRofGJ/eUdtojeeubdN76Jh2nPbIyqhURERERkSquxEF627Zt3HPPPcW227lzJ7feeivt2rVj5syZ5SpOpERmzYKrrsJo1IhPP/jTbzOzCS47J39Y9hjwy+Zcftmci6eIXmypOaKiojAMA8MwiNJQfxEREREpRKmGdi9btoyBAwcSGxtLrVq1uOSSS9i0aRMAWVlZPPDAA7Rp04a3336bpKQk/vvf/1ZI0SL5vPUWAKsb9mJreCO/zfp2sFMvwZLvmMXsHertHe5doVWKiIiIiEg1UeLFxlatWsX5559Pbm6u79iXX37JypUrWbJkCSNHjmTDhg00aNCARx55hNtuuw27XbvySgVbtw5++gm32cKcix/z28xuhYt6RBQ4bnXlcv70f3m/mDIFLLpnRURERESkaCXug3v22WfJzc1l8uTJHDx4kIMHD/Lkk0+yf/9++vbty8aNGxk/fjxbtmzh7rvvVoiWyvH22wD83PMK9tdr7bfZBWeGExdZyO3ucsFLL3k/XK6KqlKqkJycHC6//HIuv/xycnL870UuIiIiIjVXiXukly9fznnnnccjjzziOzZ+/Hi+//57lixZwn/+8x8eeOCBCilSpFBOJ7z/Pi6zla+G/Z/fZjERJoacWbA3GsBjtXLksWcAqGW1ahl7we1288knnwAwY8aM4BYjIiIiIiGpxEH64MGDjBo1qsDxHj16sGTJEm644YaAFiZSrAUL4NAhfup9LamJzfw2u6h7BOFhhW935TSHMbberQC8Yg5D4ygkLCyMV155xfe5iIiIiMjpShykXS5XoSvY5h2rXbt24KoSKYl338VlsRXZG107xkzfDkXH47AS/yuQmsBms5GcnBzsMkREREQkhClCSNV09Ch8/jnLzhnFkVqN/TYb0T0Cq6Xw3mjwLkKWMupE0Na/BhERERERKYFSRYf33nuPn3/+Od+xLVu2ADB8+PAC7U0mE3Pnzi1HeSJ+zJqF02Ni3lD/8/KTYs2c3aaYoblZWRAd7f08IwO0b3CN53a7Wbp0KQB9+/bFYrEUc4WIiIiI1DSlCtJbtmzxBefTff311wWOmUz+ewJFyuXdd1l67nUcTWjot0lxvdEihcnJyWHgwIEAZGRkFDqlRURERERqthIH6W3btlVkHSIlt2ULrhUrWTDxNb9N6sab6VVcbzTgtEfwwTdHAbjaHoEtYEWKiIiIiEh1VeIg3bRp04qsQ6TkZs5kRY/LOFKrkd8mF3WPwGIuvjfaY5hYutkDwJUD1HstIiIiIiLF0/JKUrV4PHjencnX173vt0m9eDM9WpVs2yKLGUb2jPB9LiIiIiIiUhwFaalali1jTVwH9tdr47fJ8G4RmEvQGw1gdTu5cM7T3i86TwCL9g0WEREREZGiqQ9OqhTj3Xf5evC9fs/Xjil5bzQATidMmuT9cDoDUKGIiIiIiFR36pGWqiM7m42rdrHt1m5+mww+M7xUK3UbFgsZD4wFINpiQbOkRURERESkOArSUnV89hlf97nN7+kYu8G57eyleshci50HWv4fAK9Y7JTuahERERERqYk0tFuqjB2fL2FD+4F+zw/qEondpj5lERERERGpWOqRlqrh0CG+t3f2e9pucjHgjBP9yR4PZGd7P4+KOtnI4QCXC2w2CPPOo7Zb4a0b7PnbiYiIiIiIFEE90lIlpH3yFSvPGun3/IDcDUSFm3G6DRx/78QVlwB16gBgGAYOp4HjnvsxoqPhP/8BwOU2cGzdgTO+FphMkJlZGS9FRERERESqOAVpqRIWrcvGZSt8BrPF7eR8YxMAn/6UxZhvYvly+MO+87kuGPPWUcZ0eZrcsEjf8S9XZjPmm1g+vWQCnHsuREYWeGwREREREZHTaWi3hDznzj0sbuJ/bnS32OPEX3NT/oMPPQQfjC3Y+MABiDxte6zkZBgw1tsrLSIiIiIiUgwFaQl5v362irTYPn7Pnz+4qXfeM/DPcyK5tFckFjNwYhusMCu8cmuC7/O8wHxRjwiGd4vAbEYhWnwiIyM5ePCg73MRERERkdMpSEtIMwyD77MaQWzh51tk76Z5/ZOLkNksJrDkb2MymbDbCl5rtZiwWgoel5rNZDKRlJQU7DJEREREJIRpjrSEtK37XeyIbeb3/KD2SsIiIiIiIlK5FKQlpH33u8PvubjMQ5w1uH0lViM1gcPhIDk5meTkZBwO//efiIiIiNRcCtISso5lelj9d67f8wP7N8Rq1S0sgeVyuXj11Vd59dVXcblcwS5HREREREKQ5khLyFq+0YHHKPycLTebfi3DCj8pUg42m40JEyb4PhcREREROZ2CtIQkj2Gw7E//w2p7HVxJTNTQSqxIaoqwsDAmTpwY7DJEREREJIRpXKyEpI27XaSmefyeX9G0Hw6LvRIrEhERERER8VKPtISkpRuKXuTJ6a6kQqTG8Xg8/PnnnwC0b98es1m/bxQRERGR/BSkJeSkZ3tYvc3/ImMX9wznnDZ2bLp7pQJkZ2dzxhlnAJCRkUFUVFSQKxIRERGRUFMju1pmz57NBRdcQK1atTCZTGzfvj3YJckpftrkwO1nVHeYBQaN6kli55aYs7MrtzARERERERFqaJDOzMykb9++/Pvf/w52KXIawzCKHNbdrZmZyL/Ww44dYPhZ0ltERERERKQC1cjBsddddx0AGzduDHIlcrrN+1zsP+Z/kbHe7cP57otNAAyw2WvmDSwiIiIiIkEVsj3S7733Hrfffjvdu3fHbrdjMpmYMWNGkdesXLmS4cOHk5CQQFRUFD179uT999+vnIIlIIra8qp+gplm9cL4aFciH+1KxG2yVGJlIiIiIiIiXiHboTd+/Hh27NhBYmIi9evXZ8eOHUW2X7RoEUOGDCEsLIyrrrqKuLg4Zs+ezahRo9i+fTvjxo2rpMqlrHKcBqu2OABToef7LJ6OZcAoeraOBMBceDMREREREZEKFbI90lOnTmX79u0cOnSIO+64o8i2LpeLW265BZPJxJIlS3jrrbd47rnnWLt2LR07dmTChAls3ry5kiqXslqzLZdcd+Hp2Op0cM63b2GrHc+tBz/n1oOfY0N7YImIiIiISOUL2SB9/vnn07Rp0xK1/eGHH9i6dSvXXHMNXbt29R2PiYnhsccew+VyMX369IoqVQJkxV/+t7zqsu5rYoYMAKcTrr3W++Eoeq9pERERERGRihCyQ7tLY9GiRQAMHjy4wLm8Y4sXL67MkqSU0rM9bNjl9Hv+7JUfw7P3gtkM55/vPWgO2d8DiYiIiIhINVYtgnTesO3WrVsXOJeQkEBiYmK+od1Hjhxh586dvv2jN2zYwLFjx2jSpAm1atUq9DkcDgeOU3pA09LSAHA6nTid/gNgKMqrN5TqXrEpF4+f3awiM4/ScecKnOeei8Ow8NjVswB4yrBgD6HXUNWE4n0QCk59P6riv++y0L0geXQvSB7dCwK6D+SkmnIvlOb1VYsgffz4cQDi4uIKPR8bG8vu3bt9X3/xxRfceOONvq8vvPBCAKZPn87o0aMLfYzJkyfzxBNPFDj+zTffEBkZWdbSg+rbb78Ndgk+yw91Bgr/++u++nMOdmzPr99/j8tjJiPnXAAWLFiA1ex/qywpmVC6D0JBTk6O7/MFCxYQHh4exGoql+4FyaN7QfJUxL1gNpsxa1RZlWG1Wlm4cGGwy5AQUBXvBY/Hg8dT8ryQlZVV4rbVIkiX1ujRo/0GZn/Gjh3LAw884Ps6LS2Nxo0bM3jwYGJjYwNcYcVyOp18++23XHDBBdhstmCXQ2qah68+yvR7vufKT6k7/jaGDx+OxzDoftT7j6FewhDMJi3dXVahdh+EiszMk/fikCFDiIqKCmI1lUP3guTRvSB5An0vGIZBWloahw8fxu3WYqFVhWEY5OTkEB4ejkk/c9VoVflesFgs1K5dm9jY2GJrzxt1XBLVIkjn9UTn9UyfLi0tzW9vdUnZ7XbsdnuB4zabrcr+sBEqtf+2PdvvuYSje2i9/RfMF38FNhtkZdH0vB7ekytXQhUdDRBKQuU+CBWnvhc17b2paa9X/NO9IHkCdS/s27ePY8eOERsbS2xsLFartcr9MF4TeTweMjIyiI6O1iiCGq4q3guGYeByuUhLS+PQoUM4nU7q169f5DWl+X5XLYJ03tzozZs3061bt3znjh49SmpqKr179w5GaVIMwzCKXK2756+fYu7XDxIS8i6ADRtOfi4SYBEREfzxxx++z0VEpHzcbjfHjx8nKSmJxMTEYJcjpeDxeMjNzSU8PLzKhCepGFX5XoiJicFut5OamkqdOnWwWCwBedyq9S740b9/f8A7X/l0ecfy2kho2X3Yzb6j/od49Vr5CVx8se9rl83OktlrWTJ7LS5bwRECIuVlNpvp2LEjHTt2rHL/UYiIhCKn04lhGDViqoyIhKaoqCgMwwjoYmnV4qfEQYMG0aJFC95//33WrFnjO56ens5TTz2F1Wot9ZxoqRxF9UY32Psnjfash0su8R1zmyzM3NeImfsa4TYF5rdJIiIiUvE0lFtEgqUivv+E7NDuqVOnsmzZMgDWrVvnO5a3Z/TIkSMZOXIk4F1BburUqQwZMoS+ffty9dVXExsby+zZs9m2bRtPP/00bdq0CcbLkCJ4PB5+2ezwe77nr59iOuMMPE2a4nR6h3Gbc7I5s7EZrBbM+v9YKkBubi6TJk0CYNy4cYSFhQW5IhEREREJNSEbpJctW8Y777yT79jy5ctZvnw5AM2aNfMFaYCBAweybNkyJkyYwKxZs8jNzaVjx4489dRTjBo1qjJLlxIwDIOnPk7naKb/ec69Vn4Cdhc7DrmZ9GkatWPMPPPKEJItFnj4YWAEIXwLSxXldDp9W9099NBDCtIiIiIiUkDIppAZM2YwY8aMUl3Ts2dP5s+fXzEFnZCSkkJKSoq2biinXJd3frQ/dQ5uJfHILmjfPv8JqxV+/BGeey7fkG+RQLFardx1112+z0VERERETlct5khXpuTkZDZs2MDKlSuDXUqVZrNAYoz/26/v8nchKQlWrqRpkoVXbk3giavi4LvvICMDli4FzbWSCmC3232/MCtsyzsREZHSuOyyyzCZTEybNs1vm8ceewyTycRDDz3EokWLMJlMmEwmzj77bL/XfPHFF752Q4cOLbTN5s2bufbaa2ncuDERERE0b96c6667jt9++63Yug3D4IsvvuDGG2+kefPmREREEBERQYsWLbj88st5//33CyzcdGrt/j7yRpSOHTsWk8nEM888U2QdLpeLevXqYbPZOHDgQLF1i1QWdbdIUKQeyiI13eP3/Flr58KwYRAVhRmw52Vum7YjEhERkarj9ddfZ9myZdx///2cf/75NGnSJN/5VatW8cwzz9ChQweefvppfvrpJ8A7KmrFihVs2LCBDh06FHjcadOmYbVacblchT7v3r176dWrF0ePHuWCCy6ga9eu7N27lx9++IGWLVty1lln+a35yJEjXHnllXz33XfExMQwaNAgWrVqhdlsZteuXSxatIhPPvmEl19+2Vfvqbp168aIESMKfex27doBcNNNN/HMM88wffp0/vWvf/mtZe7cuRw4cICRI0dSt25dv+1EKpuCtATFmh2Ff9MH72rddQ5tg2GTKrEiES/DMEhNTQUgMTFRq8yKiEi5JCYm8uabb3LJJZdw00038e233/r+b3E4HNxwww0AzJw5M99IqCFDhjB//nymTZvGc889l+8xDx48yLx58xg+fDhffPFFoc/7ySefcPToUe644w5ee+0133GPx+P7f64wLpeLkSNHsnTpUm644QaeeOIJGjdunG9LSI/Hw5w5c3j99dcLfYzu3bszceLEIt+X1q1b069fP5YsWcLy5cs599xzC22X15N/8803F/l4IpVNQ7slKFbv8r/I2Jlr54HZDIMHV2JFIl5ZWVnUqVOHOnXqkJWVFexyRESkGrj44osZPXo033//PSkpKb7jEyZMYP369Tz22GMFeogbNWrE+eefz8yZMwv0Or/77rs4nU5uvPFGv88ZGRkJQJcuXfIdN5vN1KlTx+9177zzDkuXLmXQoEG8/fbbxMXFFWhjNpv55z//We61ifLC8fTp0ws9f+DAAebNm0f9+vUZNmxYuZ5LJNAUpKXSpWd72HLA/7DuM3+fD2efDbVqVWJVIiIiIhXnpZdeokmTJjzyyCNs2bKFFStW8Nxzz9G9e3fGjRtX6DU33XQTBw8eZO7cufmOT58+na5du3LmmWf6fb5//vOfNGjQgJdeeonjx4+XuM68HuBx48YVOyqrvItyXnbZZcTGxjJr1iwyMzMLnM/7JcLo0aOxWCzlei6RQNPQbql0v293+j0Xf3QvTXetgVufrLyCREREJHieegqefrrin+fnn6Fr15K1/cc/YO5cGD8eHnssIE8fGxvLjBkzGDRoENdffz1HjhzBZrMxc+ZMv4F05MiR1KpVi2nTpnHJid1KfvrpJzZs2MDLL79c5POlpqYSHh7Oxo0bGTx4MAsWLCA+Pr7Ia1wuFytXrsRms/kdal0Sv/76q9+h3UOHDvUtohYZGcnVV1/NG2+8wSeffOIb5p4nr6f6pptuKnMtIhVFQVoq3eptuX7Pnfn7fMyGAcOHV2JFIiIiEjRuN+T6/9kgYAz/08oKcLm8NQV4u9OBAwdy991389///heAF154wbf4VmHsdjvXXHMNr7/+OgcOHKBu3bpMmzbNdzwtLa3Q6/bu3Uu/fv2IiIhg1qxZ3HjjjQwcOJBvvvmGpKQkX7t58+Zx4YUXMmXKFB5++GGOHDmC0+mkXr162O12PJ78IwinTZvGzp078x275ZZbaNSoUb5jq1atYtWqVYXWFh8fn2818ptuuok33niDadOm5QvSP//8Mxs2bKB///60atXK73skEiwK0qWkfaTLx+E0+HO3/x7prr/Pg7p1oYihSiIiIiJV1TPPPMN///tf6tWrx7333lts+5tuuolXXnmFd999l+TkZGbNmuXrqfYXpB988EH279/P8uXL6d27N7Vr1+bCCy+kX79+fPfddzRs2BDwbo8F3lW2wbvgZlGmTZvG8uXL8x0bOnRogSB9++23+12I7HQ9e/bkjDPOYOnSpWzdupWWLVv6nivv9YuEIs2RLiXtI10+G3Y5yfWzYHdEdhptNi+HoUO9i42JiIiIVDMREd6tPO12e4l2hsibCz19+nQ+/vhj0tLSilxkzOl08umnn9KxY0d69+4NwHnnncfs2bP5+++/6du3L9u2bQO8e1EnJSXRv39/AGrXro3VaiU1NRWHw1HgsZctW4ZhGBiGUWAYdnncfPPNGIbBjBkzAO/Cnx999BGxsbFcdtllAXsekUBSWpFKVdSw7k5/fIPV7dSwbhEREZFT3Hjjjfz55588+uijNG7cmAsuuMBv29TUVJxOJzExMfmODxs2jI8++ohdu3bRt29f3nnnHRYuXMh9993nm6NttVrp0aMHLpeLZcuWVehrOtV1111HWFgY77zzDh6Ph08//ZS0tDSuvvpq3+rjIqFGQVoqjdtjsG6H/2HdZyY5oFkzKOI/BxEREZGa5tprr8Vut7Nnzx5uuOGGfHs6ny4xMZGoqCh+//13jh49mu/cyJEjmTlzJvv27WP06NG0bNmSBx54IF+bvN7uyZMnFzvUO1Bq167NJZdcwq5du/juu++0d7RUCQrSUmm2HXCRkVP4N2SLGc54egz8/TckJFRyZSIiIhI0FguEhVX8RwmGUftYrd5rQmTLpVq1arFgwQLmzJlT7Lxqm83GtddeS1ZWFtddd12BedTnnHMOzZs3B8DhcHDkyJF850ePHk3v3r35/vvvufnmmwudh20Yht/52WWVF5qfeOIJFi9eTKdOnejRo0dAn0MkkLTYmFSaDbv9TI4G2jeyERFWiv/gREREpHp47LGAbTEVMLNnB7uCAvLmMZfEs88+y5o1a5g7dy5t27Zl6NCh1KtXj02bNjFv3jyioqK46aabmDZtGsOGDWPp0qXExsYC3iD++eefc8UVV/DOO+8we/ZsBg0aROvWrTGZTOzfv5/FixezY8cOWrRoQYMGDQo8f1HbX8XHx3PfffcVOH7BBRfQuHFjfvzxR0C90RL6FKSl0hS1WvcZTWyVWImIiIhI9RUbG8uSJUt4+eWXef/99/noo48AaNGiBQ888AD33XcfderUwWKx8NZbb/GPf/yD+fPnY7N5fx5LTEzk+++/Z/bs2bzzzjusWrWK+fPnYzKZqFevHt26dWPSpElcdtllhIWFFXj+ora/atq0aaFB2mw2c+ONN/Lkk08SFhbGtddeG7g3RKQCKEhLpcjJNdh2wH+PdIdGCtIiIiJSMxQ193jAgAElnpvcrFkzv23DwsJ48MEHefDBB/1e/+abb/Lmm28Wes5kMnHppZcyaNAgYmNji5yXXZbaC/P/27vvsKauPg7g3xBC2EMZAiqoqHXWPeoARcW99wIHarV1tfq6ARVXtdVWWlcV1Kq4WlcdbQVxVGuLo84qilUQERSQHch9/6CJxiQYhszv53nyFM4599xzk+stv5zl5+cHPz+/fB9PVJQ4R5qKxD/RMmTLNedZGAOVrHgrEhERERFR6cAe6TwKCAhAQEAAsrOzi7spJU9KSs5/jYxe7wMtkwGZmbj9SPv7VdtRotM+ikRFwdDQECEhIcqfiYiIiIjexm7APJoyZQpu3bqFy5cvF3dTShRBECBzqgaYmgIREQCADJmAjE3fQ25mjtuXHmk9tkFlfp9DJYdYLIabmxvc3NwgLiGrtRIRERFRycJAmgpMEASs/PEVvhv+nUr64r2J+EQ0GNfqd0GUqaPW4z+oor5IBRERERERUUnFrkAqsMwsICImC/igAzJevoLU3Fgl/99v9wDXNQ/tdrDSg6Upe/2o5JDJZMqFVyZMmKBcwZSIiIiISIGBNBVMRgbEkz6GV8WmkI30hNjMBNDLme+8aLAFAGDnmWQAmgPpOlUYpFDJkpmZiU8++QQA4OXlxUCaiIiIiNQwkKaCycqCfuA2tME2wM8LEL9eNEwqEUEQBNyJ0r7tVZ1vfYAzVsDcua8XKCMqRmKxGAMHDlT+TERERET0NgbSVDASCbBq1euf3xKTIEdCiub9BPWys1D74Ebgb2dg/vz32Egi3RkaGmLfvn3F3QwiIiIiKsEYSFPBGBhA/tnnePIiG0gCKlcQoKf3ulf69hOZ1kOrR/4Jw4xkwN29KFpKRERERERUKDiWlgpMlg0s2ZuEJXuTIHtrKvStx9oD6Tp3zuT80KnTe2wdERERERFR4WIgTQUjlwPR0bA0FGBpLFLJypYLuBulPZCueycUEIuB9u3fcyOJdJeSkgKRSASRSISUlJTibg4RERERlUAc2k0Fk5YGqXNlfAEAycmA5HUwHRmbhXQtcbQ0PRnOkeFAq5aAuXmRNJWIiIiIiKgwsEc6jwICAlC3bl00b968uJtScujr57zecvuJ9tW6a987B315FudHExERERFRqcNAOo+mTJmCW7du4fLly8XdlJLBxASQyXJeJiYqWf9Ec340EREREZV8bm5uEIlE7y5YhgUGBkIkEiEwMLC4m1IqMJCmApNlCdhw8hU2nHwFWVbOVldyuYCHz3LZP/puGGBsDLRqVVTNJCIiIipRFGtyaHtZWlpqPO7ly5dYunQpWrdujYoVK0IikcDGxgadOnXCN998g+TkZGXZyMhItXqNjY3h4OAAd3d3LFq0CBERERrPExoaCisrK4jFYo3tc3Z2zvX6PvvsM4hEIixatEhrme+//x4ikQgDBw585/tFVJJwjjQVmFwA/orI6X0e0zEn7enLbK3zo41TE2Afcxfo2BEwMCiiVhIRERGVPBUrVsQnn3yiMc/Q0FAt7bfffsPgwYPx4sUL1KlTB4MGDULFihURHx+PsLAwTJ06FWvXrlULjmvUqIGRI0cCADIyMhAbG4s//vgDS5YswbJlyzB79mz4+/tr7JVt2rQpevbsqZauLdBX8Pf3x4kTJ7B8+XL07t0bzZo1U8l//PgxZs6cCTs7O2zYsCHXuuj969evH1q1agV7e/vibkqpwECaCiYjA+KZn2N4hWZA/wEQ/zfG4UEuvdHOkeHQEwSgXbsiaiQRERFRyWRtbQ1fX1+dyl67dg29evUCAOzcuRMjRoxQKxMaGoq5c+eqpbu4uGg8z9mzZzF69GgsX74cYrEYS5YsUSvTtGlTndv4JkNDQ2zfvh2tWrXC6NGjER4ervLlwLhx45CUlISdO3fC2to6z/VT4bKwsICFhUVxN6PU4NBuKpisLOh/ux4dlnqhQy0R9MU532I+fJat9ZDqkX/m/MBAmoiIiEhnU6dORVpaGr755huNQTSQM9c3NDRU5zrbtWuHkydPQiqVYtWqVXj8+HEhtTZH06ZNsWDBAty+fRsLFixQpm/YsAG//PILxowZo/xyIDY2FjNmzICLiwukUimsra0xYMAA3LhxQ63ekJAQjB07FrVr14apqSlMTU3RrFkzbNq0SWM7RCIR3NzcEBUVBS8vL1SqVAl6enpa36tt27ZBJBLhiy++0Jj/888/QyQSYdq0ae98DxTzrzMyMjBv3jxUrVoVRkZGaNq0KX799VcAwKtXrzB16lQ4OjrC0NAQrVu3xp9//lmg6166dClEIhE+/fRTtTxfX1+IRCLMmDFDmaZtjrRIJELHjh0RHR2NESNGwNraGmZmZujRowcePHgAALh79y769euHChUqwMzMDIMGDUJsbKzaebdu3Yo+ffrA2dkZhoaGqFChAjw8PBASEvLO97GkYSBNBSORQPDxQYbPEkAiUSbn1iNd/eGfOat8c340ERERkU7u37+PsLAwVK5cGWPGjMm1rFQqzVPdtWrVwpAhQ5CZmYmffvqpAK3UbP78+WjWrBm++uornDt3Dg8fPsSsWbPg5OSEtWvXAgAiIiLQtGlTrFu3Di4uLvj000/RvXt3nDhxAq1atcKlS5dU6ly5ciXCwsLQvHlzfPLJJxg5ciTi4uIwceJEfPbZZxrbER8fj9atW+Pq1asYMmQIJk6cCHMt27AOGTIEFhYW2LJli8Z8Rfr48eN1fh+GDBmC4OBg9O7dG8OHD8fNmzfRs2dPhIeHo2PHjggJCcHAgQPRv39//PHHH/Dw8EBSUlK+r3vevHlo37491q9fjyNHjijTz58/j6VLl6Jhw4ZYsWKFTm1/+fIlunXrhsjISHh6esLNzQ0///wzOnfujJs3b6J169Z49eoVxo4di2bNmmH//v0av+yZMmUKnj17hk6dOmHGjBno2bMnfv/9d3Tq1AmHDh3S+b0sCTi0mwpEkEiwssFMOFQQwyNVDBuJgAwZEP1Ce490tUfhQNOmOYuNERERUfmWkpLzX2NjQDE/NzMzZ0cQfX3gzaBQUdbICND7rz9IJsspLxYDb84pzkvZ1FRAEHLSxOKctKwsjdt7Fra4uDitw6Y/+OADDB06FEBO8APk9G7q6RV+X5irqyu2b9+ucWeav/76S2Mbhw4dig8++OCddevr62P79u1o0qQJvLy84OjoiJSUFBw+fFgZyI4ePRoxMTE4efIkOnfurDx2wYIFaNasGby9vXH9+nVl+nfffYdq1aqpnCcrKwvdu3fHunXrMG3aNFStWlUl/8aNGxgzZgw2b94MseJz1sLY2BgjR45EQEAAwsLC0L59e2VebGwsjh49ipYtW6JBgwbvvH6FuLg4XL9+HSb/7XTTpUsXDB06FO7u7ujcuTN27doF/f/uuUaNGuF///sfvv/+e5Ve47xct56eHnbu3IkPP/wQY8eOxfXr15XXZWBggN27d+v8pcv169cxefJkfPPNN8r77+OPP8aGDRvQtm1b+Pr6KnvnBUFAz5498fPPP+PKlSto3Lixsp5bt26ptf/p06do1qwZZs2ahT59+uj8fhY39khTgWRmAdEvs3H2dgY2nUqGCEBkbBYELeVtn92HacpLDusmIiKiHKamOa+4uNdpX3yRk/b2Ily2tjnp//77Oi0gICdt3DjVss7OOem3b79OCwzMSfsvOFWqWzcnPTz8dVpwcEGuSmfx8fHw8/PT+NqzZ4+yXExMDACgcuXK76UdDg4OAHKCvbf99ddfGtt3584dneuvU6cOli1bhoiICOWiaB06dAAAXLlyBRcuXICnp6dKEA3k9JZ7e3vj77//Vhni/XYwBuQE7JMmTUJ2drbGocIGBgZYtWrVO4NohYkTJwKAWq90UFAQZDIZvL29dapHwd/fXxlEA8DAgQMhkUiQkJCA1atXK4NoABg2bBiAnHnxb8rrdVepUgWbN29GXFwcRo8ejUmTJiEyMhJr1qxB3bp1dW67qakp5s2bp5I2fPhwADkL5k2dOlWZLhKJlF8A6dJ+e3t7DBgwAPfu3cOjR490blNxY480FYhUH/h6oAgZWYBBBTOIRKLch3VH/pXzAwNpIiIiItSuXTtPAen7IgjaukGACRMmYOPGjQU+x/Tp07Fq1SrExMRg+fLlyvSLFy8CyPmyQFPPt+L9uXPnDurXrw8gZ07x6tWr8dNPPyEiIgIpihEI/4mOjlarp1q1anla1KxBgwZo3bo19u/fj2+++Ua5ENfWrVthamqKIUOG6FwXAJWeWQAQi8WwtbVFSkqKWu+5YuXsqKgolfT8XPeAAQMwfvx45RcCffr0wccff5ynttesWVPlS4A329iwYUO11d61tf/BgwdYvnw5Tp8+jaioKGRkZKi138nJKU9tKy4MpKlgUlMBKytIASA5GTAxyXX/6OoP/1s0oU2bImkeERERlXCKPY/fnPI1axYwfbr60GrF4kVGRq/TpkwBvL1fD8lWiIxUL+vlBQwfrl721q3XQ7sV8hgkvW+VKlUCoB6YFJanT58CAGxsbN5L/UBOT6ViKLHRG5/LixcvAADHjh3DsWPHtB6vCBozMzPh5uaG8PBwNG7cGKNGjULFihWhr6+PyMhIBAUFqQVoAGBnZ5fnNk+YMAFjxozBDz/8gMmTJ+PcuXO4c+cOvL29YWpqmqe6NM3H1tfX17hStqJ3WiZ7vZ9sfq8bAPr3768MpKdMmZKndufW9nflvdn++/fvo0WLFkhKSkKHDh3Qq1cvmJubKxd9O3PmjNb2l0QMpKlQCYLwjh7pP4F69YCKFYuwVUS6k0ql2Lt3r/JnIiJ6z97q5QIAGBjkvHQpK5GoLHiar7Ka1m0pgvnRedHmv06I0NBQyOXyQp8nrVi9unnz5oVary4Ugdg333yjdU/tNx06dAjh4eEYP348Nm/erJK3Z88eBAUFaTxO0x7Z7zJkyBDMmDEDW7ZsweTJk5XBaF6HdReG/F73ixcvMGHCBJiamkImk+GTTz5BeHi4Wg/z+/bVV1/h5cuXGrdumzRpEs6cOVOk7SkozpHOo4CAANStW7dYHjIlkUxqhK2nErH1VCJkUiPEvZLjVZrmoUGSzDQ4Rt3isG4q0fT19TFo0CAMGjRIZa4SERFRcXJxcUH79u3x+PFjrQGTQl579f755x/s3bsXUqkU/fr1K0gz86Vly5YAgN9//12n8hEREQCA3r17q+WdPXu28BqGnJ7zkSNH4sqVKzhz5gz27duHhg0bFksskN/r9vb2xpMnT7B+/XqsWLEC//zzj07bdhU2be2Xy+XKxfRKEwbSeTRlyhTcunVL44qG5ZFcEOH3e1n4/V4W5ELu86Od/r0KfXkWA2kiIiKifPj6669hZGSETz75BMFaFkM7e/YsOnbsqHOd586dg4eHBzIyMjB37lw4OjoWVnN11qJFC7Rs2RK7d+/WeF1yuVylt1Ixh/bcuXMq5c6cOaPWU1sYFIuODR8+HKmpqcXSGw3k77o3b96MgwcPYsiQIfD09MS0adPg4eGB77//Hvv373/vbX6TtvavXLlS417hJR27W6hAxHrAwNZGyp8fxrxjWDfAQJpKtKysLPz4448AgH79+rFXmoiI3qvctr8CchbosrS0BAB8+OGHOHLkCAYPHoyhQ4di8eLFaN++PSpUqIAXL17g/Pnz+Pvvv+Hi4qJWz/3795XnyczMRGxsLC5duoQbN25ALBZjwYIFWLRo0Xu4Qt3s3r0bHTp0wNChQ7F27Vo0bdoUhoaG+Pfff/H777/j+fPnSE9PBwD06tULzs7OWLVqFW7cuIH69evj7t27OHr0KPr27YsDBw4Uatvq16+Pjz76CBcuXIChoSFGjhxZqPXrKq/XfffuXUyfPh1Vq1bFhg0bAOQMbw8MDETDhg0xYcIEtGzZElWqVCmS9k+aNAnbtm1D//79MWTIEFSsWBEXL15EeHg4evTokev8+JKIfyFSgehny+Cx67+Hbj3/3OdHP/wLcHICiugfK1F+ZGRkYPDgwQCA5ORkBtJERPReKba/0sbLy0sZSAOAu7s77t27h2+//RbHjh1DcHAwXr16BQsLCzRo0ADr1q3DuLe3AkPOsFrFeYyMjGBpaYkPPvgACxcuhKenJ2rUqFHo15YX1apVw5UrV/Dll1/ip59+wtatWyEWi2Fvb4/27dtj4MCByrKmpqY4ffo0Zs2ahbCwMISGhqJevXr44YcfYGdnV+iBNAB4enriwoULGDhwoMrnUZTyct2ZmZkYPnw40tPTsWPHDpU2V6pUCVu3bkWvXr0wcuRIhISEvJe9yd/WuHFjnDp1CgsWLMDBgwchFovx0Ucf4fz58zh8+HCpC6RFQm5r3ZNWSUlJsLCwQGJiosaV6koymUyGn3/+Gd27d4dE04IbeZGSkrPvIgBZ4itM3ZWJLLnmoivnN0CFnh2BHTsKdk4qFIV6H5QhaWlp6NatGwDg+PHjKquKllW8F0iB9wIpFOa9kJ6ejocPH6JatWowfHNVbCrx5HI5kpKSlCsrl2eTJ0/Gd999hzNnzqB9+/bF3ZwiV9rvBV2fQ3mJ8djVQgUi19dH4v98AQDxiXpag2jLl9GokBDNYd1U4hkZGSlXLiUiIiJ6/vw5tm/fjjp16pTLIJo0YyBNBSITSTC76qcAgAHPtW8pwPnRRERERFSaHDt2DOHh4di/fz9SUlLg4+NT3E2iEqT09ctTySEIQKdOEIv+W2gsNpf50X3bAQEBwAcfFGEDiYiIiIjyZ9++fVi0aBHi4uKwbNkyDBkypLibRCUIA2nKv9RUSM+GYMOL77BhlBSPnmdrLVqtcVVg8mRApL3XmqgkSElJgY2NDWxsbJCSklLczSEiIqJiEhgYCEEQEBUVhblz5xZ3c6iEYSBNBbdgARLTBMS/0jxBWk8EONlwFgGVHnFxcYiLiyvuZhARERFRCcXohvLP2Bh4+RIA8PCFBECmxmKVrcWQStgTTUREREREZQMDaco/kQgyMwvsPZ+KBzHpWotVt+NtRkREREREZQcjHCoQuRwIvZGRa5lqtrzNiIiIiIio7OAcacq/zEyI/ZegV8olGBtoL1bVRlx0bSIiIiIiInrPGEhT/slk0PfzQdf5/ZGmeXo0RCLAzpKBNBERERERlR0cc0v5p68PTJ6MGMNKELQUsTHXg0TMhcaIiIiIiKjsYCCdRwEBAQgICEB2tvY9k8sNqRTC+vWIvJUBnEnVWMShAnujiYiIiIiobOHQ7jyaMmUKbt26hcuXLxd3U0qEzCxgh5YgGgDsOaybiIiIiIjKGAbSVGD2VtpvI4d5U4B9+4qwNURERERERO8XA2nKv5QUSI0NIL9zV2sR+4hwoFKlImwUERERUckSGRkJkUik8jIwMECVKlUwfPhwXL9+Xe2Y9PR0rFu3Du3atUPFihUhlUpRuXJlDB48GKdPn1Yr/3b973q9KSMjA6tWrUK9evVgYmICW1tbuLm5YfPmzTpdX0REBGbOnIlGjRrB0tISEokENjY2cHV1hb+/P548eaJ2jLOzs0p7xGIxrK2t0aVLFxw6dEilbGhoqFr7TU1NUaVKFXTr1g0rVqxAdHS0Tm0lKiycI00FIoMeYq2racwTyeWoFBsBNGlSxK0iIiIiKnlq1KiBkSNHAgCSk5Nx8eJF7N69GwcPHsTp06fx0UcfAQDu37+PHj164J9//kH16tUxePBgWFpa4sGDBzh27Bj27duHCRMmICAgAPr6OX/O+/j4qJ3Pz88PFhYWmD59eq7t8vT0RHBwMJycnDBhwgRkZGQgPDwcK1aswJAhQ3I99ssvv8T//vc/ZGdno1WrVhg1ahTMzc3x4sUL/PHHH1i4cCEWL16My5cvo2HDhirHisViLFiwAACQmZmJO3fu4PDhw/jll1+wevVqfPbZZyrlmzZtip49ewIAUlNTERMTgwsXLuDEiRPw8/PDqlWr8Omnn+baXqLCwkCa8i3LwBA7Dz6G8FjzbVTxxb+Q1q4BmJgUccuI8s/AwADr169X/kxERFRYXFxc4Ovrq5K2YMEC+Pv7Y/78+QgJCUFSUhK6du2KiIgILFy4ED4+PhCLX685Ex0djb59+2LTpk2wsLDAqlWrAECtXiAnkLa0tNSYp5CQkIC9e/fCzs4O165dg4WFhTIvKioq1+vZuHEjPvvsM9SoUQN79+5FEw2dJ7dv38bChQuRlJSklqevr6/WtlOnTqFr165YtGgRPv74YxgbGyvzmjVrpvFaDh06hHHjxmHq1KkwNjbGuHHjcm03UWHg0G7Kt2zo4YKWIBoA7J/eBVq0KMIWERWcRCLBlClTMGXKFEgkkuJuDhERlXGKHlTFQrZffPEFIiIiMGLECCxevFgliAYABwcHHDlyBBUqVMCaNWtw//79Ap1fKpVCX18fzs7OKkE0ANjb22s97uXLl5g9ezYMDQ1x/PhxjUE0ANSpUwf79+9Hq1atdGpPly5dULt2baSmpuLWrVs6HdOnTx8cPHgQADBnzhykpKTodBxRQbBHmvJNrAfUsBMj4pnmrcAcYu4C3ZoXcauIiIiopPtk8wuUtp1ExWJgvXeFQq/37fnK27ZtAwAsXLhQ6zF2dnbw9vbGypUrERgYiKVLl+b7/EZGRhg3bhw2btyI48ePo1u3bjodt3//fiQlJWHUqFGoWbPmO8srhqC/L+3bt0f79u0RFhaG06dPo1evXu/1fETskaZ808+WwfLJPa359jH/sEeaSp3s7GyEhoYiNDSU+8UTEb0n2dlAlrx0vd7X/xK+/vprAEDz5s3x6NEjREVFwdHREbVr1871OHd3dwDA77//XqDzZ2VlISMjA4IgYMCAAfj11191Ok5x3g4dOhTo/G87deoU7t69C2NjY9StWzdPx7q6ugIAt6mlIsEeaco/mQxPn2cADpqzHV5EAvXqFWmTiAoqPT1d+UdBcnIyTDjHn4iICsn9+/eVc3wVi42dP38ehoaGWLZsGWJiYgAAVapUeWddijJPnz4tUJvGjRuHHTt2YNOmTQgKCkLPnj2xb98+tR5dW1tbWFtbK4dbK9rq4KD+h2B4eDgOHz6sktasWTPlQmEKWVlZyvdDJpPh9u3bOHz4MARBwNKlS1XmR+tC0Za4uLg8HUeUHwykKd9kIj3E2Llozbe3NwE4x5RKGZFIpPwG/O3hdkRERAUREREBPz8/ADlrctjZ2WH48OGYM2cOGjRogEuXLulclyAIAAr2/6qTJ09i+/bt8Pb2hre3N4YMGYJOnTqhf//+2LFjBwYPHgwgZz50XFwcPDw81M6vSXh4uPI6FSZOnKgWSGdnZyvL6enpwcrKCu7u7pgyZQp69+6d5+vJrU1EhY2BNOVbdIoEcrHmQLnCiycwbNKgiFtEVHDGxsa4efNmcTeDiIjKIA8PD5w4cUJrfqVKlQAAjx8/fmddir2ZFcfkx549ewAAEyZMAACYm5vj5MmTcHNzw4gRI/Dq1SsMGjQIR48ehSAIGDhwoPJYOzs7AJpX9h4/fjzGjx8PIGcPaG3Dv6VSKdLT0/Pd/rcpeudtbGwKrU4ibThHmvIt5oX2yUL2MXeB5lxojIiIiEhXTk5OcHBwQFRUFO7evZtr2d9++w0A0Lp163yfLzo6GgBgZmamTLOyssKvv/6K2rVrY+LEifj222+xZs0a1K5dW2W4t2LP65CQkHyfv7CFhoYCyJlvTvS+MZCmfItNlGnN49ZXREREpI1YDOjrla7XW7tQvTdeXl4AAH9/f61lnj9/ji1btkBPTw+enp75PpezszOA1wGogo2NDX777TfUqFED8+fPx99//43169dDT+916DBw4ECYmZlh3759uHdP++KzReXMmTM4e/YsbG1t0bFjx+JuDpUDHNpN+RbzIB6AmcY8h1dPgOrVi7ZBRIUgNTVV+U325cuX87zQCRERvdv72EaqrJg1axZ2796NHTt2wMXFBfPnz1fZSzomJgb9+vVDfHw8Zs2apdPWU9p4eXlh06ZNmDt3Lpo1a4amTZsq86ytreHu7q7cp/rtRc0qVKiA5cuX45NPPkG3bt2wb98+NG7cWO0cCQkJ+W6frg4fPoyxY8cCAFauXMn/d1ORYCBN+fb0RZbWPHtrA4ALNVEpJAiCckVSLlpCRERFzdLSEidOnECPHj3g4+OD7du3w8PDAxYWFnjw4AGOHTuG5ORkeHt7Y9myZQU6V+vWrbF8+XLMmzcPLVu2RLdu3VC3bl0kJCTg+PHjePz4MYYPH46TJ09i3LhxqFSpEjp37qw8fsqUKUhJScG8efPQtGlTtGrVCs2aNYOZmRni4+Nx+/ZtnD17FlKptFCGW//555/KVb7T09Px9OlTnD9/HhERETAyMkJAQICyR5/ofWMgTfkilwt4KjfVOjnAvlb+F74gIiIiKs9q1aqF69evY8OGDdi/fz927dqFlJQU2NjYoGvXrpg0aZJyH+mCmjNnDlq2bImvvvoKFy5cwMmTJ2FtbY127drhhx9+QIMGDRAeHo6uXbtiwIABCAsLQ6NGjZTHz549G3379kVAQABCQkIQFBSE1NRUWFpaok6dOvDz84OXl5dOW3q9y19//YW//voLQM7ioBUqVEC9evXg7e2N0aNHw97evsDnINIVA2nKl7hXcmTpaV6x2yIxBibNPyziFhERERGVTM7Oznke5WRkZIQZM2ZgxowZ+T6vrufs0KGDxpW15XI5kpKS4ObmhszMTK3H16pVC+vWrctT2yIjI3Uu6+bmxlFiVOJwsTHKl6cxaVrz7GP+4YrdRERERERUZjGQpnyJvq2+Z6CCfXI0YGtbhK0hIiIiIiIqOgyk8yggIAB169Yt9/vTPX2SpDXPwUxehC0hIiIiIiIqWgyk82jKlCm4desWLl++XNxNKXqpqYCjI+DoiOhk7Zsp2le1LLo2ERERERERFTEuNka6EwQgOhpykQgxhtqHbjs0qlaEjSIiIiIiIipaDKRJd4aGwJUrePk8FRn3NG90b/oqDmYtuGI3ERERERGVXRzaTboTi4FGjRCdrP22cXj1BDA1LcJGERERERERFS0G0pRnTyOea82zl2jfFouIiIiIiKgsYCBNupPJgMBARJs4aC1ib2tYhA0iIiIiIiIqepwjTbrLzATGjMHTz04A1TUvKObQum4RN4qocEkkEvj4+Ch/JiIiIiJ6GwNp0p1YDKF7dzytUk9rEfuadkXYIKLCZ2BgAF9f3+JuBhERERGVYBzaTbozNETy/iNIk2hesdvIQAQLY1ERN4qIiIiIiKhosUea8iQxVa41z8ZcDyIRA2kq3eRyOW7fvg0AqFOnDvT0+H0jEREREaniX4iUJwkp2gNpC2PeTlT6paWloX79+qhfvz7S0rgKPRERFUxkZCREIlGur0aNGmk8NioqCnPnzkWTJk1gaWkJAwMD2Nvbo0ePHggMDERmZqaybGhoqFq9pqamqFKlCrp164YVK1YgOjpa43kCAwM1tkssFsPKygodO3bM13UaGBigSpUqGD58OK5fv67xuPT0dKxbtw7t2rVDxYoVIZVKUblyZQwePBinT59WK/+u9/LtF9H7wh5p0l1qKhI/nQd4LNKYbWnCQJrKBmtr6+JuAhERlTE1atTAyJEjNeZVqlRJLW337t0YN24c0tLS0LRpU4wcORIWFhaIiYnB6dOnMWbMGOzYsQO//fabynFNmzZFz549AQCpqamIiYnBhQsXcOLECfj5+WHVqlX49NNPNbbD3d0dbdu2Vf4uCAIyMjJQq1atfF1ncnIyLl68iN27d+PgwYM4ffo0PvroI2XZ+/fvo0ePHvjnn39QvXp1DB48GJaWlnjw4AGOHTuGffv2YcKECQgICIC+fk7YolgQ9E1+fn6wsLDA9OnTdW4nUUExkCbdCQISMrQHyxYm/NaPSj8TExM8f659r3QiIqL8cHFx0XkxyxMnTmDkyJGwtLTEoUOH0LlzZ5V8QRDw008/YcuWLWrHNmvWTON5Dh06hHHjxmHq1KkwNjbGuHHj1Mp06tQJc+bMUf4ul8uRlJQEc3NzndoNaL7OBQsWwN/fH/Pnz0dISAgAICkpCV27dkVERAQWLlwIHx8fiMVi5THR0dHo27cvNm3aBAsLC6xatQoANF6bn58fLC0tuVgoFSl2IZLuDA2ROGay1mwO7SYiIiIqmOzsbEyZMgVyuRx79+5VC6KBnOHN/fr1w8GDB3Wut0+fPsryc+bMQUpKSqG1+V0UPeCXL19Wpn3xxReIiIjAiBEjsHjxYpUgGgAcHBxw5MgRVKhQAWvWrMH9+/eLrL1EumDkQ7oTi5Fgbqs1m0O7iYiIiAomJCQEDx48wEcffQR3d/dcy0ql0jzV3b59e7Rv3x5xcXEa5x+/L5rmKm/btg0AsHDhQq3H2dnZwdvbG3K5HIGBge+reUT5wqHd9G6RkUCvXsDo0UioPAbavn9hjzSVBWlpaejWrRsA4Pjx4zAyMirmFhERUVlw//59rUOPW7Vqha5duwIAzp8/DwA6LfCVH66urggLC8Ply5fRq1cvlbxff/0V6enpyt8Vc6SnTp0KBweHfJ/z66+/BgA0b94cAPDo0SNERUXB0dERtWvXzvVYd3d3rFy5Er///nu+z0/0PjCQpncLCgJu3ABmz0bi4s5Axaoai1mwR5rKALlcjjNnzih/JiKi9ytDJgAADPRf91xmZQvIlgN6eoBELFIrK9EH9N4uKwIk+gUsKwb09HLSs+UCxHqFt/5LREQE/Pz8NOZNmzZNGUjHxMQAACpXrlxo536TIiCOi4tTy/vtt9/UFi8DgMGDB+scSL/5hYFisbHz58/D0NAQy5YtA/D6GqtUqfLO+hRlnj59qtP5iYoKA2nKnVyeE0gDEAAkmdtpLCYCYG7ExcaIiIgobz7Z/BIA8OUYS5j997fEySvp+OmPNLSrI8XoDibKsjO3vURmFrB8pAWszXPm1IbeyEDw+VS0qGkA786myrJzdiQgOV2A71BzOFbI+ZP3wt0M7AhNRaNqEkzpZqYs67MnEfGv5Jg3wBzV7HLKXr6fiVa18jZ0OjceHh44ceJEodWXX4IgaM1bvnx5gRcbe/MLA4lEAjs7OwwfPhxz5sxBgwYN8t1ebmVFJQ0Dacrd2bPAw4cAgBQTK2RJNP8PxdRIBH0xH3BEREREBaHYCisqKuq91K/o2bWxsXkv9evyhYHiGh8/fvzO+p48eaJyDFFJwUCacvffQhAAkKilNxrgQmNERESUP+u9rQDkDO1W8GhsiE4fGkLvrT8vvhyTU1byRlm3+lK0qyvF26OwV4yyVCv7UW0pWtZUL+s31CKn7BsLRzd3McjztRSGNm3aAMgZZr148eJCrz80NBTA6/nKxcHJyQkODg6IiorC3bt3c50nrRhq3rp166JqHpFOGP2QdsnJwP79yl8TLLR/E8iFxoiIiCg/pBIRpBKRytBdfXFOmuSt0W6KsnqayuoXQtk3IuzCnB+dFx06dED16tVx4cIF5Z7L2mRkZOSp7jNnzuDs2bOwtbV9b4uZ6crLywsA4O/vr7XM8+fPsWXLFujp6cHT07OIWkakG0Y/pN2+fcAbewwm5hJIs0eaiIiIqODEYjECAgKgp6eHwYMHa92m6siRIxg4cKDO9R4+fBgDBgwAAKxcuRLGxsaF0t78mjVrFqpVq4YdO3Zg8eLFyM7OVsmPiYlB7969ER8fj88++ww1a9YsppYSacah3aTdW/v15d4jzfnRRERERNrktv0VAJW8rl27YseOHRg/fjzc3d3RrFkztG7dGmZmZnj27BlCQ0MRERGBTp06qdXz559/KutKT0/H06dPcf78eURERMDIyAgBAQHK3uDiZGlpiRMnTqBHjx7w8fHB9u3b4eHhAQsLCzx48ADHjh1DcnIyvL29lat9E5UkDKRJs4gIICxMJSnRgnOkiYiIiPIjt+2vAKgF2cOHD4erqyu++eYbnDp1CkFBQUhNTUXFihXRuHFjzJ8/HyNHjlSr56+//sJff/0FADA2NkaFChVQr149eHt7Y/To0bC3ty/U6yqIWrVq4fr169iwYQP279+PXbt2ISUlBTY2NujatSsmTZoEd3f34m4mkUYMpEmz/7a8etNLzpEmIiIiyhNnZ+dct5zKjaOjI1asWIEVK1a8s6ybm1u+z+Pl5VXgXur8XqeRkRFmzJiBGTNm5Pvc+b1uooJg9EPq3tg7+k0JFtq/wWSPNBERERERlReMfkhdaCjw779qyYnmtloP4RxpIiIiIiIqLxhI51FAQADq1q1brHvvvXdv7B2tIABIymWOtDmHdhMRERERUTnBOdJ5NGXKFEyZMgVJSUmwsLAo7uYUvqQk4MABteRUY0tkSQw1HmJmJIK+mD3SVDbo6+tj8uTJyp+JiIiIiN7GvxJJ1d69QFqaWnJCLr3RXGiMyhKpVIqAgIDibgYRERERlWCMgEjVW3tHKyRyoTEiIiIiIiIA7JGmN927B5w/rzErwTy3HmkO66ayQxAExMXFAQCsra0hEvH+JiIiIiJVDKTpNS290QCQWKWm1jwL9khTGZKamgpb25wV6pOTk2FiYlLMLSIiIiKikoYREOXIzga2b9eandikjdY8Du0mIiIiIqLyhD3SlOO334AnT7RmJ9T6EIjVnMfFxqgsMTExgSAIxd0MIiIiIirBGAFRjlyGdaN1ayTqaR/eyh5pIiIiIiIqTxgBEZCQAPz4o/b80aOREJesNZuLjRERERERUXnCQJqA4GAgPV1znqEhhF69kZiqfagrh3ZTWZKeno5BgwZh0KBBSNf274KIiIiIyjVGQJT7sO7+/ZFasRJkBkYas00NRdAXs0eayo7s7Gzs378f+/fvR3Z2dnE3h4iIqNyIjo7GyJEj4ejoCD09vTK7BaWbm1uZvbbyhIE0AV98AYwdC5iaqueNGZNrbzTnRxMRERFpFxkZCZFIlOurUaNGxd1Mrc6dOwexWAxfX9/3fi4vLy/s2rULHTp0wMKFC+Hj46PTcdnZ2di2bRs6d+4MGxsbGBgYoFKlSujVqxcOHDhQ4HYFBgZCJBIhMLfOpzIgPj4emzZtQu/evVG9enVIpVJYW1ujW7duOHnypNbjMjIysHjxYtSqVQuGhoawt7fH+PHjERMTo1b23r17WLZsGdq3bw8HBwcYGBigSpUqGD16NO7cuaNTOy9evAixWAyRSIQVK1bk+3oLiqt2E9C2bc7r66+BgweBbduAkBDA0RFYsQKJtseAtgs1Hsph3URERETvVqNGDYwcOVJjXqVKlYq4NSVPZmYmfvvtN3Tp0gU7d+7U+bjY2Fj06dMHFy9ehL29Pfr06QNbW1s8efIEx44dw9GjR9GrVy/s3r0bJibaF88lYN++ffj444/h6OiIjh07wtHREU+ePMGBAwdw4sQJLF68GPPnz1c5Ri6Xo0+fPjh58iRatmyJ/v37IyIiAtu2bcMvv/yCS5cuqdzfCxcuRHBwMOrXr48+ffrA3Nwcf//9N3bs2IH9+/fj5MmTaNeundY2pqWlwcvLC0ZGRkhJSXlv74UuGEjTayYmwKhROa/ISODmTaBnTyS0qAi01XyIpQmHpRARERG9i4uLS5H06pZWMTExkMvlefpSISsrC3379sXFixcxbtw4fPPNNzAyej0dMSEhASNHjsSRI0cwduxYBAcHv4+mlxm1atXC0aNH0a1bN+jpve4sW7BgAVq2bIklS5ZgzJgxqFy5sjIvKCgIJ0+exNChQ7Fr1y7lkPVt27Zh7Nix+N///oegoCBl+a5du2Lu3Ln48MMPVc69Z88eDBs2DJMmTcLNmze1tnH+/Pl4+vQp5syZg4ULNXf0FRV2J5Jmzs6AhwewcycSJ07TWow90kRERESFIzU1FWZmZnBxcdFaplatWjAzM0NqaqoyTRAEbN26FW3atIG5uTmMjY3RrFkzbN26Ve14X19fiEQihIaGYu/evWjSpAmMjIxgb2+PqVOnIi0tTVnWz88PvXr1Uv785nD0yMhIna7H19cXH3zwAQwNDVGhQgX06NEDFy5cUCnn5uYGJycnADmBmeIc7/riISgoCL///jvatWuHzZs3qwTRAGBpaYl9+/bBxcUFe/fuxenTp9XquH79OkaOHInKlStDKpXC3t4eXbt2xZEjRwDkDDcfM2YMAGDMmDEq78Gb/v33X4wbNw6Ojo4wMDBA5cqVMW7cODx+/Fhr+9PT0zF79mxUqVIFhoaGaNCggcbPTOHQoUNwd3eHlZUVDA0NUb9+faxevVptTZc3h6IfO3YM7dq1g5mZGZydnXN9Pzt27IgePXqoBNEAULt2bQwePBgymUzts9u8eTMAYMWKFSrvyZgxY1CnTh0EBwfj1atXynQvLy+1IBoAhg4dilq1auHWrVuIi4vT2L7z589j3bp1WL16tUowX1zYI03a6esDI0Yg4VwKcD1DYxHOkSYiIiIqHMbGxujfvz+2b9+O33//Ha1bt1bJv3TpEu7duwdPT08YGxsDyAmiR44ciV27dqFWrVoYPnw4DAwM8Msvv2DcuHG4desWVq9erXaugIAAHD9+HH369IGbmxtOnDiBb775BvHx8fjhhx8AAK6urhg2bBh2794NV1dXuLm5KY+3tLTM9VoyMjLg7u6OixcvokmTJpg+fTpiY2MRHByMU6dOITg4GP379weQE1w1atQI69atw4cffoi+ffsCgMr5NFEEnfPnz9e6eJeRkRE+++wzfPzxx9i6dSs6duyozPvxxx8xbNgwyOVy9OrVC7Vr10ZsbCwuXbqE77//Hr169ULfvn2RkJCAQ4cOoU+fPhrns9+7dw9t27ZFbGwsevXqhXr16uHmzZvYunUrjh49ivPnz2v8cmTQoEG4fv06Bg0aBJlMhr1792LcuHF49uwZ5s6dq1J23rx5WL58OSpXrowBAwbA3NwcYWFhmDVrFi5duoR9+/ap1b9v3z6cOnUKPXv2xOTJk1UC2rySSCQAAH391+Fjeno6Ll26hNq1ayu/CHlTly5dsG7dOly8eBGdO3fO1zkUUlNT4eXlBTc3N3h7e5eI+eoMpOmdElO49RURERG9H/mZ5yiVSpV/bGdlZSEjIwN6enoqPZL5qdfAwED5x3x2djbEYnGe69Dm/v37WntYW7Vqha5duwIARo0ahe3bt2Pnzp1qgbRi7vCoUaOUaVu2bMGuXbswbtw4bNiwQfm+ZGZmYuDAgVizZg2GDRuGpk2bqtT1yy+/4K+//kLt2rUBAP7+/mjUqBF2796NL774Ag4ODnBzc0Nqaip2794NNze3PA1NX7VqFS5evIgRI0Zgx44dykB3+vTpaNGiBcaPH4/OnTvDzMwMXl5eiIyMxLp169CoUSOdzpOVlYXLly9DX18frq6uuZZ1d3cHAPz+++/KtNjYWIwePRr6+vo4e/YsGjdurHLMkydPAEAlkO7bty+8vLzU6p80aRJiY2OxceNGTJgwQZm+adMmTJw4EZMmTcKvv/6qdtyDBw9w48YNmJmZAcj5QqBJkyZYtGgRhgwZgurVqwPI+ayWL1+Obt26Yf/+/SpfokyePBkbNmzAgQMHMGDAAJX6jx8/jlOnTqFTp065vj/v8urVKxw4cACGhoYq85cjIiIgl8tRs2ZNjccp0u/du/fOQPqPP/7AzZs30bx5c41f0syZMwdPnz7FqVOn8n8hhYxREGmXnQ2cOYOEZ4lai1iwR5qIiIgKwNTUNM+vH3/8UXn8jz/+CFNTU3Tr1k2lXmdn5zzXu2nTJuXxZ8+eLdTrjIiIgJ+fn8bXiRMnlOU6duwIBwcH7N27FzKZTJmelZWF4OBgODo6okOHDsr09evXw8TEBOvXr1fpyTMwMIC/vz8AYPfu3WrtmTZtmjKIBnJ6bocNGwZBEPDXX38V+HoDAwMhkUjUhvw2bNgQXl5eePnyJQ4dOpTv+uPj4yGTyWBtbQ1DQ8Ncy1apUgUA8PTpU2VaUFAQkpOT8dlnn6kF0QB0Hjr8+PFjnD59GnXr1oW3t7dKnre3N+rUqYPffvtN4xDv+fPnK4NoIGfRuZkzZyIrKwu7du1Spq9fvx4AsHHjRmUQDUC5arVIJNL4Gfft27fAQTSQ80XBs2fPMHPmTFSsWFGZnpiYEyNYWFhoPM7c3FylnDaJiYnw9PSEnp4eVq1apZZ/5swZrF+/HsuWLUO1atXyexmFjj3SpF16OuDmhkTfy4BNdY1FuNgYERER0bt5eHioBMza6OnpYfjw4Vi9ejVOnDihnKN84sQJPH/+HLNmzVLOYU1NTcXff/8NBwcHjdsAKQJxTdsKNWnSRC1NETwmJCTofF2aJCUl4cGDB6hTp47GgNTNzQ0bN27E1atXta5k/j68GdD/8ccfAHKGHxfElStXAOQMg397eLlIJEL79u1x+/ZtXLt2TRnQK2hanVqRdvXqVWXaxYsXYWJigu+//15jG4yMjDR+xi1atMjTtWgyb9487Nq1Cx4eHpg5c2aB63tbeno6+vfvjzt37sDf319tOH9KSgrGjh2L1q1b45NPPin08xcEA2nSzsQEgpMTEq0ctRYx59BuIiIiKoDk5OQ8HyOVSpU/9+vXD8nJyWoLJOmyGNbbDAwMlD/ntgXP+zZq1CisXr0aP/zwgzKQ1jSs++XLlxAEAVFRUfDz89Nan6Zh7pp6ERU92m8vXpVXSUlJAAA7OzuN+YqVud/VU5mbihUrQiKRIC4uDunp6bn2Sit6g99cEVzxZYGjo/a/c3VRkGu1tbVVS1PU82b5Fy9eICsrK8+fsbY26crPzw/Lly9Hx44dceDAAZUREsDre0jb56h4b7T1WGdkZKBfv344ffo05s6di3nz5qmVmT9/PqKjo/Hzzz+r/RsvbgykKVdpV28ic5fmhcZMDUWQiNkjTURERPlX0L199fX1NS5OVNB6C3N+dF41bNgQDRs2xOHDh5ULRB0+fBgffvghGjRooCynGDrbtGlT/Pnnn8XSVk0U7Xr27JnGfEW6olx+6Ovro3nz5rhw4QLOnDkDDw8PrWV/++03AFCZc66YhxsVFfXO1axzU5BrjY2NVeulVpR/M/g0NzeHSCTSupq1NtoWYNOFn58ffH194ebmhiNHjsDQ0FAtkK5Rowb09PRw7949jXUo0jXNoU5PT0ffvn1x8uRJzJ49G8uWLdNYx9WrV5Geno4PPvhAY/7cuXMxd+5cTJs2DWvXrs3DFRZcyQrr6f3LyoIoMBD6Oi7AkSjX/u0eFxojIiIiej9GjhyJtLQ0HDhwAAcOHEBaWpraMGgzMzPUqVMHt2/fLvBwbG0UvYB56aU2NzdH9erVcf/+fURFRanlnzlzBgA0roCdF4qFv5YvXw5B0Lw4bnp6Or788ksAwNixY5XpimHPuixepfhSRdN7oLiGsLAwtTYIgqCca6/pWjXNw9dUvmXLloiPj9casBY2X19f+Pr6wtXVFceOHVOZl/0mQ0NDtGjRAnfv3sWjR4/U8k+dOgWpVIqWLVuqpL8ZRH/++edYuXKl1rb06NED48aNU3u1b98eANC8eXOMGzdObWG+osBIqLw5cQL6Eyag65gxEI8aBfzyS86iYm9LTwf69kXC5/O1VmXB+dFUBonFYgwcOBADBw4s1t4IIiIq30aMGAE9PT3s3LkTO3bsUM6dftvUqVORmpoKb29vjcN7Hz58mK9h7gpWVlYAXq9irStPT0/IZDLMnTtXJcC8ceMGtm3bBgsLC+U2V/nl5eWFli1b4syZM5g0aRLS09NV8hMTEzFkyBDcu3cPgwYNUtn6ytPTE6amplizZo3KfGSFN78AqFChAgDN70HVqlXRoUMH5XZXb9q6dStu3ryJjh07qvU8Azkrpb+5JdWzZ8/w5ZdfQl9fX+Wznjp1KoCcLwLi4+PV6omJicHt27fV0vPDx8cHfn5+aNeuXa5BtIJilfI5c+aofM7btm3D7du3MWTIEJXe+PT0dPTp0wcnT57EzJkz8cUXX+Ra/6xZs7Blyxa1l2Jv7/79+2PLli0YMmRIfi853zi0u7z5b881cWYmEByc86pcGRg9Ghg/HlCshJedDRw6hMTmBkAzzVVxD2kqiwwNDTXuxUhERFQQuW1/BUAtz8HBAR07dsTp06cB5Gzh5ODgoHbcxIkTcfHiRQQFBeH8+fPo1KkTHBwc8OzZM9y5cweXLl3Crl278j18uVatWnBwcMCePXtgbGyMypUrQyQS4eOPP9Y69xUAZs+ejWPHjmHHjh24ffs23N3d8fz5cwQHB0Mmk2H79u0qK1bnh0QiwaFDh9C7d29s2rQJR48eRffu3WFra4uoqCgcPXoU8fHx6NmzJ7Zt26ZyrK2tLbZv346hQ4eiRYsW6N27N2rXro24uDhcunQJzs7O+OmnnwDkDAk3MjLC2rVrkZSUBBsbGwA5wSMAfPfdd2jbti28vb1x5MgR1K1bF7du3cLhw4dhY2OD7777TmP7q1evjvr162PAgAHKfaRjY2Ph7++v3PoKALp27YqFCxdiyZIlcHFxQdeuXeHk5IT4+Hjcv38fZ8+exdKlS1GnTp0CvZ+BgYFYvHgx9PX10aJFC5UgVxAEZGRkoEuXLipfSIwePRrBwcHYs2cPHj58CDc3Nzx48AAHDhxAlSpV1HqbJ02ahFOnTqFSpUowMzPT+G/Cy8urQMPti4xA+ZKYmCgAEBITE4u7KbqLixMEiUQQAM2vbdtel83MFIRNm4QT34UK4wPiNb4O/J5SbJdCBZeZmSn89NNPQmZmZnE3hYoZ7wVS4L1ACoV5L6SlpQm3bt0S0tLSCqFlpc/Dhw8FAO98aRIUFKTMDwoKyvU8wcHBQqdOnQQrKytBIpEIjo6Ogpubm7BmzRrh+fPnynI+Pj4CACEkJEStjm3btgkAhG3//U2YnZ0tvHz5Urhw4YLg6uoqmJmZKdvz8OHDd157cnKysHDhQqFWrVqCgYGBYGlpKXTr1k04e/as1vfJ09PznfW+TSaTCVu2bBE6duwoVKxYUZBIJIKtra3Qo0cPYd++fbkee+XKFWHw4MGCnZ2dIJFIBHt7e6Fbt27C0aNHVcodO3ZMaN68uWBkZKTxM4uMjBTGjBkj2NvbC/r6+oK9vb0wZswYITIyUu2crq6uAgAhNTVV+PzzzwVHR0fBwMBAqFevnrBlyxatbf3ll1+EXr16CTY2NoJEIhEqVaoktG7dWliyZInw77//Ksu9/TnqSnFv5PZatGiR2nHp6emCn5+f4OLiIhgYGAh2dnbC2LFjhejoaK3XnttL0735NsU1Ll++XKdr0/U5lJcYTyQIWiYUUK6SkpJgYWGBxMTEAi2UUKS++Qb4b2iIGhMTICYGMDVVSQ4+l4Jfr2tebGxYO2N0bJD7vn1UcslkMvz888/o3r07JBJJcTeHihHvBVLgvUAKhXkvpKen4+HDh6hWrdo79/ulkkUulyMpKQnm5uYlbsVkKlql/V7Q9TmUlxiv9L0LlH//DevWaNAgtSAaABJTtX/PwsXGqCxKSUmBSCSCSCTSONeMiIiIiIiRUHlx/ToQHq49/79VD5XkcuDyZSQ8f6WxOABYcrExIiIiIiIqhxhIlxe59UZXrw60a6ealpYGtGiBxHvaV2hkjzSVRcbGxoiNjUVsbOw7V6okIiIiovKJq3aXBzIZsHOn9nxPT+DtuQ4mJhCsrZFQwVHrYRZctZvKIJFIpFyNk4iIiIhIE0ZC5cHx48Dz59rzR4/WmJz+z0Nk6muejG8iFUEi5tBuIiIiIiIqfxhIlwdv7ZunomNHQMs+bQkw0noY95CmsiojIwNTpkzBlClTkJGhecV6IiIiIirfGA2Vdc+fA0ePas9/e5ExAEhPB0aMQOL8JVoPszBmbzSVTVlZWfj222/x7bffIisrq7ibQ0REREQlEAPpsm7XLkBLMCCYmQH9+6tnZGcDu3Yh4c+bWqvl/GgiIiLKC0HQvqUmEdH79D6eP1xsrKzLZVi3MHAgRCYm6hkGBsBXXyFRr67WYy25YjcRERHpQCwWAwBkMhmMjLRPGyMiel9kMhmA18+jwsBoqCy7ehW4dk1rtlzLImOQSIDp05HQtI3WY9kjTURERLqQSCSQSqVITExkrzQRFTlBEJCYmAipVAqJRFJo9bJHuizLZe/oZHt7SD/6SD1DLgf+/RcAkJhSUevxXGyMiIiIdGVtbY2oqCg8efIEFhYWkEgkEIm43kpJJ5fLkZmZifT0dOi9vVUqlSul8V4QBAEymQyJiYlITk6Go6P2bX3zg4F0WZWZCfzwg9bsxx07wkXT/8DS0oBq1QAAiftfaD2ei40RERGRrszNzQEAcXFxiIqKKubWkK4EQUBaWhqMjIz4xUc5V5rvBalUCkdHR+VzqLCU20D6q6++wpdffom4uDi0bt0aGzZsQK1atYq7WYXn2DEgLk5jliAS4V83N7hoO9bYGACQkKa9eg7tJiIiorwwNzeHubk5ZDIZsrOzi7s5pAOZTIawsDC0b9++UIfEUulTWu8FsVj83tpbLgPpXbt2Yd68efj+++/RsGFD+Pj4oGvXrrh9+zakUmlxN69w5LbImLs70m1sXidkZAATJuQsMrZ+PZCSAgBI3KS9R5qLjREREVF+SCSSUvWHeHkmFouRlZUFQ0NDfmblHO8FdeUyGvrqq68wefJkDB8+HPXr10dgYCCePn2KQ4cOFXfTCsezZ8DPP2vNlo8apZqQlQX89BOwZYtyq6z0TAEZWrbQNZaKINEvXUM6iIiIiIiICkuJDaR37tyJiRMnolmzZpBKpRCJRAjMZfEsALh8+TK6d+8OKysrmJiYoEWLFti1a5dKmczMTFy5cgUdO3ZUppmZmaFly5a4ePHi+7iUovfDDzl7QWtibg6hTx9AEIDnz3NexsbA7NlAmzavh3WnyLVWz4XGiIiIiIioPCuxQ7sXLFiAR48ewdraGvb29nj06FGu5UNDQ+Hh4QEDAwMMHToUFhYWOHjwIEaMGIHIyEjMmzcPQM4iF9nZ2bC1tVU53tbWFs+ePXtv11NkBCHXYd0YOhQwNoY4IwMSxcp1ycnArFnAvHnAf4sHJKZqD6S50BgREREREZVnJbZrccuWLYiMjMTz588xadKkXMtmZWVh/PjxEIlECAsLw+bNm7F69Wpcu3YN9erVg4+PD+7du1dELS9m4eHAjRva8728NKcbGCiDaIA90kRERERERNqU2IioU6dOcHJy0qns6dOnERERgeHDh6Nx48bKdDMzMyxcuBBZWVnY9l8vrbW1NcRiMWJjY1XqiI2NhZ2dXeFdQHHJbfh77dpAq1YAgGxDQ8gyM3N6sE1M1Irm3iNdYm8bogLT09ODq6srXF1dS80+iURERERUtMrEX4mhoaEAgC5duqjlKdLOnDkDADAwMEDjxo0REhKiLJOcnIxLly6h1X9BZqmWmZnTu6yJl5dKr3NuElIErXnskaayzMjICKGhoQgNDYWRkVFxN4eIiIiISqASO0c6LxTDtmvWrKmWZ2VlBWtra5Wh3dOnT4e3tzeaNWuG+vXrw8/PD/b29ujdu7fWc2RkZCAjI0P5e2JiIgDgxYsXkMlkhXUpBbdsGfD559A7eBCi3buhd+0agJy9o7N69ADi4yGTyZCamor4+Hity9dHP0tDZprmZbtFskzEx3PZ+9JOl/uAygfeC6TAe4EUeC8QwPuAXisv98KrV68AAIKgvVNRoUwE0oqg1sLCQmO+ubk5njx5ovx9xIgRiI2Nxeeff464uDi0bt0ax48fh6GhodZzLF++HH5+fmrp1apVK2Dri4ggAA0bFkpV2wulFiIiIiIiopLn1atXWmNLhTIRSOfHjBkzMGPGDJ3Lz507FzNnzlT+LpfL8eLFC1SsWBEiHYdLlxRJSUmoUqUKHj9+DHNz8+JuDhUT3gekwHuBFHgvkALvBQJ4H9Br5eVeEAQBr169goODwzvLlolAWvFtgaJn+m1JSUnv/EbhXaRSKaRSqUqapaVlgeosbubm5mX6HwLphvcBKfBeIAXeC6TAe4EA3gf0Wnm4F3SNG8vEqlGKudGatrh6+fIl4uLiNM6fJiIiIiIiIsqrMhFIu7q6AgBOnTqllqdIU5QhIiIiIiIiKogyEUi7u7ujevXq2LVrF65evapMf/XqFZYsWQJ9fX14eXkVW/tKGqlUCh8fH7Wh6lS+8D4gBd4LpMB7gRR4LxDA+4Be472gTiTosrZ3MdiyZQvOnTsHAPj7778RHh6ONm3awMXFBQDQt29f9O3bV1k+JCQEHh4ekEqlGDZsGMzNzXHw4EE8fPgQS5cuxfz584vjMoiIiIiIiKiMKbGBtJeXF4KCgrTm+/j4wNfXVyXtjz/+gI+PD37//XdkZmaiXr16mD59OkaMGPGeW0tERERERETlRYkNpImIiIiIiIhKojIxR5qIiIiIiIioqDCQLkcuX76M7t27w8rKCiYmJmjRogV27dpV3M2iAnB2doZIJNL4mjRpklr5pKQkzJw5E05OTpBKpXBycsLMmTORlJSk9Ry7du1CixYtYGJiAisrK3Tv3h1//vnn+7wsysXOnTsxceJENGvWDFKpFCKRCIGBgVrLF8Vnfu/ePQwePBg2NjYwMjJCw4YNsX79esjl8oJcKuUiL/eBr6+v1ueEoaGh1nPwPij5oqKisHbtWnTp0gVVq1aFgYEBKlWqhAEDBuDSpUsaj+EzoWzK673A50LZlZCQgKlTp6J169aoVKkSpFIpHB0d0bFjRxw4cACaBiPzuZBPApULISEhgoGBgWBqaiqMHz9e+Oyzz4Rq1aoJAAR/f//ibh7lk5OTk2BhYSH4+PiovY4cOaJSNjk5WWjUqJEAQOjcubPwv//9T+jatasAQGjUqJGQnJysVr+/v78AQKhataowc+ZMYcKECYK5ublgYGAghISEFNFV0pucnJwEAIK1tbXy523btmksWxSf+c2bNwULCwtBIpEII0aMEGbPni00aNBAACB4e3sX8tWTQl7uAx8fHwGA4OnpqfacWLJkicZjeB+UDv/73/8EAEKNGjWEsWPHCnPmzBEGDBggiMViQU9PTwgODlYpz2dC2ZXXe4HPhbLr3r17gomJieDu7i5MnDhRmDt3rjBu3DjB1tZW43vP50L+MZAuB2QymVCjRg1BKpUK4eHhyvSkpCShXr16gr6+vvDPP/8UYwspv5ycnAQnJyedyi5atEgAIMyePVtj+qJFi1TS//nnH0FfX1+oVauWkJCQoEy/ceOGYGxsLNSoUUOQyWQFvgbKm19++UWIjIwUBEEQli9fnmsAVRSfefv27QUAwrFjx5RpmZmZgru7uwBAOH36dEEul7TIy32g+INZ1y+/eB+UHgcOHBDCwsLU0sPCwgSJRCJUqFBBSE9PV6bzmVB25fVe4HOh7MrKytL491lSUpJQt25dAYBw48YNZTqfC/nHQLocOHnypABAGDNmjFrenj17BADC3Llzi6FlVFC6BtJyuVxwcHAQTE1N1b5ZTEtLE6ysrARHR0dBLpcr0+fOnSsAEIKCgtTqmzRpkgBAOHnyZIGvgfIvtwCqKD7zu3fvCgCEDh06qJW/ePGiAEAYNmxYAa6QdFHYgTTvg7KhS5cuAgDh8uXLgiDwmVCevX0vCAKfC+XVjBkzBADCTz/9JAgCnwsFxTnS5UBoaCgAoEuXLmp5irQzZ84UZZOoEGVkZCAoKAjLli3Dd999h2vXrqmVuXfvHqKjo9GmTRuYmJio5BkaGqJ9+/aIiorC/fv3lem53TceHh4AeN+UZEXxmedWvkWLFrC0tOQ9UoKcPXsWq1atwpo1a3Ds2DFkZGRoLMf7oGyQSCQAAH19fQB8JpRnb98Lb+JzofxIT0/H6dOnIRKJULduXQB8LhSU+r8oKnPu3bsHAKhZs6ZanpWVFaytrZVlqPSJiYmBl5eXSlrXrl2xY8cOWFtbA8j9Hngz/d69eyo/m5qaolKlSrmWp5KpKD7z3M4hEong4uKCP//8E6mpqTA2Ni7A1VBhWLRokcrv9vb2CAoKQufOnVXSeR+Ufv/++y9+/fVXVKpUCQ0aNADAZ0J5peleeBOfC2VXQkIC1q5dC7lcjtjYWPz88894/PgxfHx81D4zPhfyhz3S5UBiYiIAwMLCQmO+ubm5sgyVLmPHjkVoaCieP3+OpKQkXLx4Ed26dcOJEyfQu3dv5cqMutwDb5ZT/JyX8lSyFMVnnp9zUNFr1KgRgoKCEBkZibS0NNy7dw9LlixBQkICevfurTaKhfdB6SaTyTBq1ChkZGRg1apVEIvFAPhMKI+03QsAnwvlQUJCAvz8/LBkyRJs3LgRMTEx+OKLL+Dj46Msw+dCwbBHmqgUe/ub5JYtW+Lo0aNwdXXFuXPn8PPPP6NHjx7F1DoiKgn69u2r8ruLiwsWLFgAOzs7TJgwAUuXLsW+ffuKp3FUqORyOcaOHYuwsDB4e3tj1KhRxd0kKibvuhf4XCj7nJ2dIQgCsrOz8fjxY+zZswfz58/HhQsXsHfvXo1D/Slv2CNdDii+AdL2TU9SUpLWb4mo9NHT08OYMWMAAOfPnweg2z3wZjnFz3kpTyVLUXzmup5D8W0zlSyenp7Q19dXPicUeB+UToIgwNvbGzt37sTIkSOxYcMGlXw+E8qPd90LueFzoewRi8VwdnbGnDlzsHTpUvz444/YvHkzAD4XCoqBdDmQ23zWly9fIi4uTuvcCCqdFHOjU1NTAbx7TrOm+Ss1a9ZEcnIyYmJidCpPJUtRfOa5nUMQBNy/fx8ODg5qC5hQyWBgYAAzMzPlc0KB90HpI5fLMW7cOGzduhXDhg1DYGAg9PRU/8TjM6F80OVeyA2fC2WbYsEvxQJgfC4UDAPpcsDV1RUAcOrUKbU8RZqiDJUNly5dApAzrAfIeYg5ODjg/PnzSElJUSmbnp6OsLAwODg4wMXFRZme231z8uRJlTJU8hTFZ+7m5qa1/B9//IGEhATeIyXYvXv38PLlS+VzQoH3Qekil8sxfvx4bNu2DUOGDMGOHTtU5sIq8JlQ9ul6L+SGz4WyLTo6GsDrFdz5XCigYtt4i4qMTCYTqlevLkilUuHKlSvK9KSkJKFevXqCvr6+cPfu3eJrIOXLzZs3hZcvX6qlnz17VjA0NBSkUqnw6NEjZfqiRYsEAMLs2bNVyivSFy1apJJ+9+5dQV9fX6hVq5aQkJCgTL9x44ZgbGws1KhRQ5DJZIV7UZQn79o/uCg+8/bt2wsAhGPHjinTMjMzhU6dOgkAhNOnTxfwKuldcrsPkpKShGvXrqmlv3jxQmjXrp0AQFixYoVKHu+D0iM7O1vw8vISAAiDBg165zOZz4SyKy/3Ap8LZduVK1dUPiOF+Ph4oVGjRgIAYceOHcp0PhfyTyQI/y3rS2VaSEgIPDw8IJVKMWzYMJibm+PgwYN4+PAhli5divnz5xd3EymPfH19sWrVKri7u8PZ2RlSqRQ3btzAqVOnoKenhw0bNmD8+PHK8ikpKWjbti2uXr2Kzp07o2nTprh27RqOHz+ORo0a4dy5c2rDavz9/bFgwQJUrVoVAwcOREpKCnbv3o20tDScPHkSHTp0KOrLLve2bNmCc+fOAQD+/vtvhIeHo02bNspvi/v27atcRKYoPvNbt27ho48+QlpaGgYPHgwHBwecOHEC169fx/jx45XzsKhw6XofREZGolq1amjWrBkaNGgAW1tbREVF4fjx44iPj0fnzp1x9OhRGBgYqNTP+6B08PX1hZ+fH0xNTTFt2jSNiwf17dsXjRo1AsBnQlmWl3uBz4Wybfr06diyZQs6dOgAJycnmJiY4NGjRzh27BiSk5MxYMAA7N27Vznkn8+FAijuSJ6KzqVLl4SuXbsKFhYWgpGRkdCsWTNh586dxd0syqfQ0FBh8ODBgouLi2BmZiZIJBKhcuXKwtChQ4VLly5pPCYhIUGYMWOGUKVKFUEikQhVqlQRZsyYofGbS4WdO3cKzZo1E4yMjAQLCwuha9euwh9//PG+LovewdPTUwCg9eXj46NSvig+87t37woDBw4UKlasKEilUqFevXrC119/LWRnZxfWZdNbdL0PEhMThSlTpghNmzYVrK2tBX19fcHCwkJo27atsGHDBiErK0vrOXgflHzvug+gYaQCnwllU17uBT4XyrazZ88KXl5ewgcffCCYm5sL+vr6gq2trdC1a1dh165dglwuVzuGz4X8YY80ERERERERUR5wsTEiIiIiIiKiPGAgTURERERERJQHDKSJiIiIiIiI8oCBNBEREREREVEeMJAmIiIiIiIiygMG0kRERERERER5wECaiIiIiIiIKA8YSBMRERERERHlAQNpIiIiIiIiojxgIE1ERFQEAgMDIRKJEBgYWKB6fH19IRKJEBoaWijtyg9nZ2c4OzsX2/nfFBoaCpFIpHy1atWquJukk7Zt26q0uzg/TyIiyjsG0kREVKpFRkaqBCQikQgGBgaoUqUKhg8fjuvXrxd3E987xXvg5eVVKPV5eXlBJBIhMjKyUOorCq6urvDx8cH48eOLuyk6GTt2LHx8fODq6lrcTSEionzQL+4GEBERFYYaNWpg5MiRAIDk5GRcvHgRu3fvxsGDB3H69Gl89NFHxdq+fv36oVWrVrC3ty9QPZ988gmGDh2KqlWrFlLL8u63334rtnNr4+bmBl9f3+Juhs7Gjh0LIGeEwZkzZ4q5NURElFcMpImIqExwcXFRC6QWLFgAf39/zJ8/HyEhIcXTsP9YWFjAwsKiwPVYW1vD2tq6EFqUfzVq1CjW8xMRERU3Du0mIqIy69NPPwUAXL58WZkmEong5uaGqKgoeHl5oVKlStDT01OZoxoWFoZevXrB2toaUqkUNWvWxIIFC5CamqrxPGfPnkW/fv1gZ2cHqVSKKlWqoH///jh37pyyjLY50or2PH78GEOGDEHFihVhYmICNzc3XLhwQe1cb8+RDgwMRLVq1QAAQUFBGufdRkdHw8fHB61atYKtrS2kUimcnZ0xefJkxMbGqtTv7OyMoKAgAEC1atWUdbm5uamU0TRHOjU1Fb6+vvjggw9gaGiIChUqoEePHu+8jr1796JJkyYwMjKCvb09pk6dirS0NI3vdV4phqk/ePAAq1evRq1atWBkZIS6detiz549AACZTIZFixahWrVqMDQ0RMOGDXHy5Em1utzc3CASiZCRkYF58+ahatWqMDIyQtOmTfHrr78CAF69eoWpU6fC0dERhoaGaN26Nf78889CuRYiIio52CNNRERllkgk0pgeHx+P1q1bo0KFChgyZAgyMzNhbm4OANiwYQMmT54MKysr9OrVCzY2Nrh8+TL8/f0REhKCkJAQGBgYKOsKCAjAp59+CiMjI/Tr1w9Vq1ZFVFQUzp07h/3796Nt27bvbOfLly/Rpk0b2NvbY8KECYiKikJwcDA6dOiAkydPqgSxb2vUqBGmTZuGdevW4cMPP0Tfvn2VeYpgNywsDGvWrIG7uztatmwJiUSCK1eu4LvvvsPJkycRHh6u7C2fPn06AgMDce3aNUybNg2WlpYqdWmTkZEBd3d3XLx4EU2aNMH06dMRGxuL4OBgnDp1CsHBwejfv7/acQEBATh+/Dj69OkDNzc3nDhxAt988w3i4+Pxww8/vPO909XMmTNx6dIl9OrVC2KxGHv27MHw4cNhZWWFgIAA3LhxA927d0d6ejp27dqF3r17486dO8ovKd40ZMgQ/P333+jduzfS0tLwww8/oGfPnrhw4QImTpyI9PR0DBw4EM+fP0dwcDA8PDzw8OFD5T1GRERlgEBERFSKPXz4UAAgeHh4qOXNnz9fACC4ubkp0wAIAIQxY8YIWVlZKuVv3rwp6OvrC40bNxbi4+NV8pYvXy4AEFavXq1Mu379uiAWiwUHBwfh4cOHKuXlcrkQFRWl/H3btm0CAGHbtm0q5RTtGTVqlCCXy5XpoaGhgkgkElxcXITs7Gxluo+PjwBACAkJUXsPPD09Nb5Hz549E169eqWWHhQUJAAQli5dqpLu6ekpAFC7JgUnJyfByclJJW3x4sUCAGHEiBEq13Ht2jVBKpUKVlZWQlJSktp1WFhYCHfu3FGmp6amCrVq1RJEIpHK+6dNSEiIAEDw8fHRmK+4lpo1awqxsbHK9IsXLwoABEtLS6Ft27ZCcnKyMi84OFgAIEydOlWlLldXVwGA0KZNG5Xye/bsUdY1aNAgQSaTKfNWrlwpABC+/PJLje3T9HkSEVHJx6HdRERUJty/fx++vr7w9fXF559/jrZt28Lf3x+GhoZYtmyZSlkDAwOsWrUKYrFYJX3jxo3IysrC119/jQoVKqjkzZ49GzY2Nti9e7cybcOGDcjOzsbSpUvVemxFIhEcHBx0artYLIa/v79KD7qrqyu6d++O+/fvaxwanRe2trYwNTVVSx81ahTMzc2Vw5ILIjAwEBKJBCtWrFC5joYNG8LLywsvX77EoUOH1I6bNm0aateurfzdyMgIw4YNgyAI+OuvvwrcLoX58+fDxsZG+XvLli1RvXp1JCQkwN/fHyYmJsq8AQMGQCKR4Nq1axrrerv8wIEDIZFIkJCQgNWrV0Nf//WAv2HDhgGA1rqIiKh04tBuIiIqEyIiIuDn5wcAkEgksLOzw/DhwzFnzhw0aNBApWy1atU0Lth18eJFAMCJEyc0BpcSiQR37txR/v7HH38AALp06VKgtjs5OaFKlSpq6e3atcOxY8dw9epVnYaI5+bgwYPYuHEjwsPD8fLlS2RnZyvzoqOjC1R3UlISHjx4gDp16qBy5cpq+W5ubti4cSOuXr2qXFldoUmTJmrlFXUkJCQUqF1vaty4sVqavb09Hjx4gEaNGqmki8Vi2NraIioqSqe6FOVTUlLUVlNXrNKurS4iIiqdGEgTEVGZ4OHhgRMnTuhU1s7OTmP6ixcvAOT0OOoiISEBIpGowFta2draakxXtDMxMbFA9a9Zswaff/45bGxs0KVLF1SuXBlGRkYAgLVr1yIjI6NA9SclJam0922VKlUCoPk6NK1krujRfTPYLyhN85MV59GWJ5PJ8lRXbteirS4iIiqdGEgTEVG5o20RMkWAlJSUBDMzs3fWY2lpCUEQ8PTpUzg6Oua7PW+vnK3w7NkzAJqDTV1lZWVhyZIlcHBwwNWrV1WGNwuCgFWrVuW7bgXF+6Zo79sU6Vxsi4iIygrOkSYiIvpPy5YtAbwe4v0uLVq0AACcOnWqQOd99OgRHj9+rJZ+9uxZAFAbevw2xVxvTT24cXFxSExMRKtWrVSCaAD4888/NW4zlVt9mpibm6N69eq4f/++xiHMZ86c0ek6iIiISgsG0kRERP+ZPHky9PX18emnn2oMbBMSEnDlyhXl75MmTYJYLMaCBQvw6NEjlbKKnmpdZGdnY/78+RAEQZl25swZ/Pzzz3BxccFHH32U6/FWVlYQiUR48uSJWp6trS2MjIwQHh6usg/2y5cvlftsv02x0Jqm+rTx9PSETCbD3LlzVa7jxo0b2LZtGywsLFS25iIiIirNOLSbiIjoP/Xr18e3336Ljz/+GLVr10b37t1Ro0YN5WJaZ86cgZeXFzZs2AAAaNCgAdauXYupU6eiXr166Nu3L5ycnBATE4OwsDD06NEDa9eufed5GzZsiNDQULRq1QodO3ZEdHQ09uzZA4lEgs2bN0NPL/fvvU1NTdG8eXOEhYVhzJgxqFmzJvT09DB8+HBUrVoVkydPxpo1a/Dhhx+iV69eSEpKwvHjx+Hk5KRxZfGOHTti9erVmDhxIgYNGgQTExNUrVoVw4cP19qG2bNn49ixY9ixYwdu374Nd3d35T7KMpkM27dv12m4PBERUWnAQJqIiOgN3t7eaNSoEb788kuEhYXh8OHDsLCwQNWqVTFjxgx4enqqlP/kk09Qv359rFmzBsePH0dycjJsbW3RsmVLDB48WKdzWllZ4ciRI/j888+xceNGpKeno1WrVli2bBnatGmjUx07duzAjBkz8NNPPyExMRGCIKBVq1aoWrUqli9fjgoVKiAwMBDffvst7OzsMHToUPj5+aF+/fpqdXXr1g2rVq3C5s2bsXLlSshkMri6uuYaSBsaGuL06dNYuXIlgoOD8dVXX8HY2Bjt27fHvHnzCrzqOBERUUkiEt4cf0VERERFSiQSwdXVFaGhocXdlFIpNDQUHTp0gI+PD3x9fYu7OXnm6+sLPz8/hISEwM3NrbibQ0REOuIcaSIiIir1/Pz8IBKJ0KpVq+Juik7atm0LkUik3PuciIhKFw7tJiIiolLL2dkZPj4+yt8rV65cjK3R3dixY9GpUyfl787OzsXXGCIiyjMO7SYiIipGHNpNRERU+rBHmoiIqBjx+2wiIqLSh3OkiYiIiIiIiPKAgTQRERERERFRHjCQJiIiIiIiIsoDBtJEREREREREecBAmoiIiIiIiCgPGEgTERERERER5QEDaSIiIiIiIqI8YCBNRERERERElAcMpImIiIiIiIjy4P/mjP8OpIiraQAAAABJRU5ErkJggg==", "text/plain": [ "<Figure size 1000x600 with 1 Axes>" ] @@ -775,7 +775,7 @@ "\n", "# Solution:\n", "#range of RT\n", - "RT_range = np.linspace(10, 800, 500)\n", + "RT_range = np.linspace(1, 800, 500)\n", "\n", "#YM&GEV\n", "YM_range = stats.genextreme.ppf(1 - 1/RT_range, *params_YM)\n", @@ -787,8 +787,12 @@ "\n", "plt.figure(figsize=(10, 6))\n", "plt.plot(YM_range, RT_range, '--r', label = 'YM&GEV', linewidth=5)\n", + "plt.step(ecdf(YM['Prec'])[1], 1/(1 - ecdf(YM['Prec'])[0]),\n", + " ':r', label = ' ECDF Yearly maxima')\n", "plt.plot(POT_range, RT_range, 'cornflowerblue', label = 'POT&GPD', linewidth=5)\n", - "plt.plot([771, 771],[0, 1000], ':k', label = 'Event of October 2024')\n", + "plt.step(ecdf(P.iloc[peaks, 1])[1], 1/((1 - ecdf(P.iloc[peaks, 1])[0])*average_n_excesses),\n", + " linestyle=':', color = 'cornflowerblue', label = 'ECDF POT')\n", + "plt.plot([771, 771],[0, 1000], '-.k', label = 'Event of October 2024')\n", "if params_YM[0]>0:\n", " bound_YM = params_YM[1] - params_YM[2]/(-params_YM[0])\n", " plt.axvline(x = bound_YM, color = 'black',\n", @@ -805,7 +809,7 @@ "plt.xlabel('Precipitation [mm]')\n", "plt.ylabel('RT [years]')\n", "plt.yscale('log') \n", - "plt.ylim([10, 1000])\n", + "plt.ylim([1, 1000])\n", "plt.grid()\n", "plt.legend()\n", "plt.title('Return Period and Design Values, Rain')\n", @@ -832,7 +836,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -840,7 +844,7 @@ "output_type": "stream", "text": [ "The return period obtained with YM+GEV is 300.174\n", - "The return period obtained with POT+GPD is 164.394\n", + "The return period obtained with POT+GPD is 112.480\n", "\n" ] } @@ -850,9 +854,13 @@ "YM_RT = 1/(1-stats.genextreme.cdf(771, *params_YM))\n", "\n", "#POT&GPD\n", + "non_exc_prob_range = 1 - 1/(RT_range*average_n_excesses)\n", + "POT_range = stats.genpareto.ppf(non_exc_prob_range, *params_POT) + threshold\n", + "\n", + "#POT&GPD\n", "average_n_excesses = len(peaks)/YM.shape[0]\n", "POT_prob = stats.genpareto.cdf(771 - threshold, *params_POT)\n", - "RT_POT = 1/(1-POT_prob*average_n_excesses)\n", + "RT_POT = 1/((1-POT_prob)*average_n_excesses)\n", "\n", "#Print\n", "print(f'The return period obtained with YM+GEV is {YM_RT:.3f}\\n'\n", @@ -865,11 +873,11 @@ "source": [ "<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\">\n", " <b>Solution:</b>\n", - " <li>The differences are mainly caused by the different extremes selected by both methods: (1) POT selects much more observations, and (2) some observations selected by YM are not selected by POT as they are too low, although they are the biggest one in their time block. \n", - " <li>Based on the amount of information in which the method is applied, POT+GPD should be more realiable in this case, although we have not performed any analysis on the threshold and declustering time. Therefore, we would need to ensure that the selected extremes are independent.\n", - " <li>Some possibilities to improve the reliability of the method is gathering more data and performing a formal anaysis to assess the threshold and declustering time of POT. Also, different time blocks could be considered in the Block Maxima method considering, for instance, the hydrologic cycle. We could also look at different ways of fitting the distribution; here, we fitted the coefficients by MLE but there are other approaches such as Bayesian Inference (makes use of previous knowledge to inform the fitting process) or L-moments method, between others. We could also consider weighting the observations to give more relevance to the larger ones to improve the fitting on those, although we sacrifice the fitting in the smaller ones.\n", - " <li>It is not possible to know as a ground truth what is the return period of the event. However, it gives us an idea of how extreme it is in comparison with other extremes that ocurred and helps us relativize the magnitud of the event.\n", - " <li>If I were to choose, I'd go for the return period of the distribution that has been informed with more data and provides a better fitting, thus the one obtained using POT+GPD, RT = 164 years.\n", + " <li>The differences are mainly caused by the shape of the tail of the fitted distributions. As shown in the previous plot, the ECDFs computed from YM and from POT are similar, so no significant differences are observed in the sampled extremes. However, the tail of the fitted GEV and GPD are pretty different, being the GEV much more conservative. \n", + " <li>Based on the fitting of the GPD distribution to the observations, POT+GPD should be more realiable in this case, although we have not performed any analysis on the threshold and declustering time. Therefore, we would need to ensure that the selected extremes are independent.\n", + " <li>Some possibilities to improve the reliability of the method is gathering more data and performing a formal anaysis to assess the threshold and declustering time of POT. We could also look at different ways of fitting the distribution; here, we fitted the coefficients by MLE but there are other approaches such as Bayesian Inference (makes use of previous knowledge to inform the fitting process) or L-moments method, between others. We could also consider weighting the observations to give more relevance to the larger ones to improve the fitting on those, although we would sacrifice the fitting in the smaller ones.\n", + " <li>It is not possible to know as a ground truth what is the return period of the event. However, it gives us an idea of how extreme it is in comparison with other extremes that ocurred and helps us relativize the magnitude of the event. Also, the fitted distributions allow us to assign return periods to values of the random variable (precipitation) that have not occurred yet.\n", + " <li>If I were to choose, I'd go for the return period of the distribution that provides a better fitting to the observations, thus the one obtained using POT+GPD.\n", "</li>\n", "</div>\n", "</div>" -- GitLab