diff --git a/.gitignore b/.gitignore index bfeeffe..c6ebd6a 100644 --- a/.gitignore +++ b/.gitignore @@ -158,3 +158,12 @@ slopes.ipynb structurefunction.ipynb example/outdir example/.DS_Store +outdir/*.json +old_test.ipynb +triple_dev.ipynb +triple_test.ipynb +lmc.ipynb +uncle_timmy.pickle +example/lmc_data_table.csv +lmc/broken_power_law_2_point_result.json +lmc/broken_power_law_3_point_result.json diff --git a/docs/example.rst b/docs/example.rst index 58b7fc8..907aa68 100644 --- a/docs/example.rst +++ b/docs/example.rst @@ -1,5 +1,3 @@ -Example usage -============= .. code:: ipython3 %matplotlib inline @@ -10,6 +8,8 @@ Example usage from tqdm.auto import tqdm import matplotlib.pyplot as plt import astropy.units as u + import os + import shutil The second-order structure function of rotation measure (RM) is given by: @@ -20,7 +20,7 @@ That is, the ensemble average of the squared-difference in RM for sources with angular seperation :math:`\delta\theta`. We also need to correct for the impact of errors by: -.. math:: SF_{\text{RM}}(\delta\theta) = SF_{\text{RM},\text{obs}}(\delta\theta) - SF_{\sigma_\text{RM}}(\delta\theta) +.. math:: SF_{\text{RM}}(\delta\theta) = SF_{\text{RM},\text{obs}}(\delta\theta) - SF_{\sigma_\text{RM}}(\delta\theta) See Haverkorn et al. 2004 (2004ApJ…609..776H) for details. @@ -41,15 +41,15 @@ the paper’s plots using a web plot digitiser. 2.2444782900152482, 2.2476963207124476, 2.2837806390213578,]) * (u.rad / u.m**2)**2 - mao_sep = 10**np.array([-0.7729091483767441, - -0.5386163683663935, - -0.2730532911440767, - -0.02550632317850443, - 0.21819567988496358, - 0.47213008276920787, - 0.7173429798998987, - 0.9643533199726302, - 1.18882007856649, + mao_sep = 10**np.array([-0.7729091483767441, + -0.5386163683663935, + -0.2730532911440767, + -0.02550632317850443, + 0.21819567988496358, + 0.47213008276920787, + 0.7173429798998987, + 0.9643533199726302, + 1.18882007856649, 1.3453070240944185,]) * u.deg .. code:: ipython3 @@ -104,14 +104,14 @@ astropy table for convenience rms.append(rm) e_rms.append(e_rm) flags.append(flag) - + mao_rm_tab = Table() mao_rm_tab.add_column(coords, name='coordinates') mao_rm_tab.add_column(rms, name='RM') mao_rm_tab.add_column(e_rms, name='e_RM') mao_rm_tab.add_column(incs, name='included') mao_rm_tab.add_column(flags, name='flag') - + mao_rm_tab @@ -132,7 +132,7 @@ astropy table for convenience .. raw:: html
Table length=472 - +
@@ -160,8 +160,6 @@ astropy table for convenience -f - Now we’ll define our own bin edges to compute a structure function .. code:: ipython3 @@ -190,7 +188,13 @@ broken power law. Here we’re using ``nestle`` to do the sampling. All .. code:: ipython3 - cbins, medians, err, count, result = structure_function( + # Clean up if a previous run was done + if os.path.exists("outdir"): + shutil.rmtree("outdir") + +.. code:: ipython3 + + sf_result, result = structure_function( data=np.array(mao_rm_tab['RM'][mao_rm_tab['included']]) * u.rad / u.m**2, errors=np.array(mao_rm_tab['e_RM'][mao_rm_tab['included']]) * u.rad / u.m**2, coords=mao_rm_tab['coordinates'][mao_rm_tab['included']], @@ -207,50 +211,46 @@ broken power law. Here we’re using ``nestle`` to do the sampling. All .. parsed-literal:: - 2022-09-16 09:14:10.461 INFO structurefunction - structure_function: Sampling errors... - 2022-09-16 09:14:12.527 INFO structurefunction - structure_function: Getting data differences... - 2022-09-16 09:14:13.876 INFO structurefunction - structure_function: Getting data error differences... - 2022-09-16 09:14:15.241 INFO structurefunction - structure_function: Getting angular separations... - 2022-09-16 09:14:15.269 INFO structurefunction - structure_function: Computing SF... - 2022-09-16 09:14:16.936 INFO structurefunction - structure_function: Fitting SF with a broken power law... - 09:14 bilby INFO : Running for label 'broken_power_law', output will be saved to 'outdir' - 09:14 bilby INFO : Search parameters: - 09:14 bilby INFO : amplitude = Uniform(minimum=-63.26544230534412, maximum=470.26399685658464, name='amplitude', latex_label='$a$', unit=None, boundary=None) - 09:14 bilby INFO : x_break = Uniform(minimum=0.17782794100389226, maximum=28.18382931264449, name='x_break', latex_label='$\\theta_\\mathrm{break}$', unit=None, boundary=None) - 09:14 bilby INFO : alpha_1 = Uniform(minimum=-2, maximum=2, name='alpha_1', latex_label='$\\alpha_1$', unit=None, boundary=None) - 09:14 bilby INFO : alpha_2 = Uniform(minimum=-2, maximum=2, name='alpha_2', latex_label='$\\alpha_2$', unit=None, boundary=None) - 09:14 bilby INFO : Single likelihood evaluation took 9.987e-05 s - 09:14 bilby WARNING : Supplied argument 'npool' not an argument of 'Nestle', removing. - 09:14 bilby WARNING : Supplied argument 'sample' not an argument of 'Nestle', removing. - 09:14 bilby INFO : Using sampler Nestle with kwargs {'method': 'multi', 'npoints': 400, 'update_interval': None, 'npdim': None, 'maxiter': None, 'maxcall': None, 'dlogz': None, 'decline_factor': None, 'rstate': None, 'callback': , 'steps': 20, 'enlarge': 1.2} + 2022-11-07 14:03:13.640 INFO structurefunction - structure_function: Sampling errors... + 2022-11-07 14:03:17.396 INFO structurefunction - structure_function: Getting angular separations... + 2022-11-07 14:03:17.434 INFO structurefunction - structure_function: Computing SF... + 2022-11-07 14:03:31.302 INFO structurefunction - fit_data: Fitting SF with a broken power law... + 14:03 bilby INFO : Running for label 'broken_power_law_2_point', output will be saved to 'outdir' + 14:03 bilby INFO : Search parameters: + 14:03 bilby INFO : amplitude = Uniform(minimum=-55.43796027883391, maximum=471.5431939956728, name='amplitude', latex_label='$a$', unit=None, boundary=None) + 14:03 bilby INFO : x_break = Uniform(minimum=0.179, maximum=28.371000000000002, name='x_break', latex_label='$\\theta_\\mathrm{break}$', unit=None, boundary=None) + 14:03 bilby INFO : alpha_1 = Uniform(minimum=-2, maximum=2, name='alpha_1', latex_label='$\\alpha_1$', unit=None, boundary=None) + 14:03 bilby INFO : alpha_2 = Uniform(minimum=-2, maximum=2, name='alpha_2', latex_label='$\\alpha_2$', unit=None, boundary=None) + 14:03 bilby INFO : Single likelihood evaluation took 1.555e-04 s + 14:03 bilby WARNING : Supplied argument 'npool' not an argument of 'Nestle', removing. + 14:03 bilby WARNING : Supplied argument 'sample' not an argument of 'Nestle', removing. + 14:03 bilby INFO : Using sampler Nestle with kwargs {'method': 'multi', 'npoints': 400, 'update_interval': None, 'npdim': None, 'maxiter': None, 'maxcall': None, 'dlogz': None, 'decline_factor': None, 'rstate': None, 'callback': , 'steps': 20, 'enlarge': 1.2} .. parsed-literal:: - it= 3530 logz=-115.1097827102492 + it= 3592 logz=-114.816709542922 .. parsed-literal:: - 09:14 bilby INFO : Sampling time: 0:00:10.811501 - 09:14 bilby INFO : Summary of results: - nsamples: 3931 + 14:03 bilby INFO : Sampling time: 0:00:27.098431 + 14:03 bilby INFO : Summary of results: + nsamples: 3993 ln_noise_evidence: nan - ln_evidence: -114.815 +/- 0.127 - ln_bayes_factor: nan +/- 0.127 - - 2022-09-16 09:14:29.269 INFO structurefunction - structure_function: Fitting results: - 2022-09-16 09:14:29.269 INFO structurefunction - structure_function: amplitude: 180 ± 20 - 2022-09-16 09:14:29.270 INFO structurefunction - structure_function: x_break: 22 ± 4 - 2022-09-16 09:14:29.271 INFO structurefunction - structure_function: alpha_1: 0.10 ± 0.04 - 2022-09-16 09:14:29.272 INFO structurefunction - structure_function: alpha_2: 0 ± 1 - 2022-09-16 09:14:29.272 INFO structurefunction - structure_function: Fit log evidence: -114.8152379924337 ± 0.12701652449377673 + ln_evidence: -114.499 +/- 0.130 + ln_bayes_factor: nan +/- 0.130 + + 2022-11-07 14:04:01.465 INFO structurefunction - fit_data: Fitting results: + 2022-11-07 14:04:01.467 INFO structurefunction - fit_data: amplitude: 180 ± 10 + 2022-11-07 14:04:01.468 INFO structurefunction - fit_data: x_break: 22 ± 4 + 2022-11-07 14:04:01.469 INFO structurefunction - fit_data: alpha_1: 0.11 ± 0.04 + 2022-11-07 14:04:01.470 INFO structurefunction - fit_data: alpha_2: 0 ± 1 + 2022-11-07 14:04:01.471 INFO structurefunction - fit_data: Fit log evidence: -114.49868298129567 ± 0.12984044020094218 -.. parsed-literal:: - - Making hist plot: 0%| | 0/24 [00:00 - - - -.. image:: example_files/example_11_9.png - - .. code:: ipython3 - cbins, medians, err, count, result = structure_function( + sf_result, result = structure_function( data=np.array(mao_rm_tab['RM'][mao_rm_tab['included']]) * u.rad / u.m**2, errors=np.array(mao_rm_tab['e_RM'][mao_rm_tab['included']]) * u.rad / u.m**2, coords=mao_rm_tab['coordinates'][mao_rm_tab['included']], @@ -298,48 +284,44 @@ broken power law. Here we’re using ``nestle`` to do the sampling. All .. parsed-literal:: - 2022-09-16 09:14:35.282 INFO structurefunction - structure_function: Sampling errors... - 2022-09-16 09:14:35.323 INFO structurefunction - structure_function: Getting data differences... - 2022-09-16 09:14:36.885 INFO structurefunction - structure_function: Getting data error differences... - 2022-09-16 09:14:38.499 INFO structurefunction - structure_function: Getting angular separations... - 2022-09-16 09:14:38.529 INFO structurefunction - structure_function: Computing SF... - 2022-09-16 09:14:40.027 INFO structurefunction - structure_function: Fitting SF with a power law... - 09:14 bilby INFO : Running for label 'power_law', output will be saved to 'outdir' - 09:14 bilby INFO : Search parameters: - 09:14 bilby INFO : amplitude = Uniform(minimum=-63.81717705255777, maximum=468.5890166911098, name='amplitude', latex_label='$a$', unit=None, boundary=None) - 09:14 bilby INFO : x_break = Uniform(minimum=0.17782794100389226, maximum=28.18382931264449, name='x_break', latex_label='$\\theta_\\mathrm{break}$', unit=None, boundary=None) - 09:14 bilby INFO : alpha = Uniform(minimum=-2, maximum=2, name='alpha', latex_label='$\\alpha$', unit=None, boundary=None) - 09:14 bilby INFO : Single likelihood evaluation took 1.050e-04 s - 09:14 bilby WARNING : Supplied argument 'npool' not an argument of 'Nestle', removing. - 09:14 bilby WARNING : Supplied argument 'sample' not an argument of 'Nestle', removing. - 09:14 bilby INFO : Using sampler Nestle with kwargs {'method': 'multi', 'npoints': 400, 'update_interval': None, 'npdim': None, 'maxiter': None, 'maxcall': None, 'dlogz': None, 'decline_factor': None, 'rstate': None, 'callback': , 'steps': 20, 'enlarge': 1.2} + 2022-11-07 14:04:07.099 INFO structurefunction - structure_function: Sampling errors... + 2022-11-07 14:04:09.998 INFO structurefunction - structure_function: Getting angular separations... + 2022-11-07 14:04:10.063 INFO structurefunction - structure_function: Computing SF... + 2022-11-07 14:04:32.609 INFO structurefunction - fit_data: Fitting SF with a power law... + 14:04 bilby INFO : Running for label 'power_law_2_point', output will be saved to 'outdir' + 14:04 bilby INFO : Search parameters: + 14:04 bilby INFO : amplitude = Uniform(minimum=-52.035011894175796, maximum=464.9494929780924, name='amplitude', latex_label='$a$', unit=None, boundary=None) + 14:04 bilby INFO : x_break = Uniform(minimum=0.179, maximum=28.371000000000002, name='x_break', latex_label='$\\theta_\\mathrm{break}$', unit=None, boundary=None) + 14:04 bilby INFO : alpha = Uniform(minimum=-2, maximum=2, name='alpha', latex_label='$\\alpha$', unit=None, boundary=None) + 14:04 bilby INFO : Single likelihood evaluation took 1.576e-04 s + 14:04 bilby WARNING : Supplied argument 'npool' not an argument of 'Nestle', removing. + 14:04 bilby WARNING : Supplied argument 'sample' not an argument of 'Nestle', removing. + 14:04 bilby INFO : Using sampler Nestle with kwargs {'method': 'multi', 'npoints': 400, 'update_interval': None, 'npdim': None, 'maxiter': None, 'maxcall': None, 'dlogz': None, 'decline_factor': None, 'rstate': None, 'callback': , 'steps': 20, 'enlarge': 1.2} .. parsed-literal:: - it= 3254 logz=-114.3524352848590 + it= 3219 logz=-114.1911086409369 .. parsed-literal:: - 09:14 bilby INFO : Sampling time: 0:00:09.337694 - 09:14 bilby INFO : Summary of results: - nsamples: 3655 + 14:04 bilby INFO : Sampling time: 0:00:13.769841 + 14:04 bilby INFO : Summary of results: + nsamples: 3620 ln_noise_evidence: nan - ln_evidence: -113.927 +/- 0.125 - ln_bayes_factor: nan +/- 0.125 - - 2022-09-16 09:14:50.400 INFO structurefunction - structure_function: Fitting results: - 2022-09-16 09:14:50.401 INFO structurefunction - structure_function: amplitude: 180 ± 20 - 2022-09-16 09:14:50.402 INFO structurefunction - structure_function: x_break: 16 ± 8 - 2022-09-16 09:14:50.403 INFO structurefunction - structure_function: alpha: 0.10 ± 0.04 - 2022-09-16 09:14:50.403 INFO structurefunction - structure_function: Fit log evidence: -113.9273916449165 ± 0.1252873443614207 + ln_evidence: -113.771 +/- 0.124 + ln_bayes_factor: nan +/- 0.124 + + 2022-11-07 14:04:47.401 INFO structurefunction - fit_data: Fitting results: + 2022-11-07 14:04:47.402 INFO structurefunction - fit_data: amplitude: 180 ± 20 + 2022-11-07 14:04:47.403 INFO structurefunction - fit_data: x_break: 15 ± 9 + 2022-11-07 14:04:47.404 INFO structurefunction - fit_data: alpha: 0.10 ± 0.04 + 2022-11-07 14:04:47.406 INFO structurefunction - fit_data: Fit log evidence: -113.77116114103981 ± 0.1240670676073616 -.. parsed-literal:: - - Making hist plot: 0%| | 0/24 [00:00 - - - -.. image:: example_files/example_12_9.png - - -Finally, we can compare our results to those from the original paper. +We can compare our results to those from the original paper. .. code:: ipython3 plt.figure(figsize=(6,6), facecolor='w') - plt.plot(cbins, medians, '.', label='Median from MC') - plt.errorbar(cbins.value, medians, yerr=err, color='tab:blue', marker=None, fmt=' ', )#label = '16th to 84th percentile range') + plt.plot(sf_result.c_bins, sf_result.med, '.', label='Median from MC') + plt.errorbar(sf_result.c_bins, sf_result.med, yerr=[sf_result.err_low, sf_result.err_high], color='tab:blue', marker=None, fmt=' ', )#label = '16th to 84th percentile range') plt.plot(mao_sep, mao_sf, 'X', label='Paper SF') plt.xscale('log') plt.yscale('log') @@ -397,3 +365,99 @@ Finally, we can compare our results to those from the original paper. .. image:: example_files/example_14_1.png +Finally, we extend to using mutli-point structure functions, as +described by Seta et al. 2022 (10.1093/mnras/stac2972). Currently, only +the triple-point structure function is implemented. + +.. code:: ipython3 + + sf_result, result = structure_function( + data=np.array(mao_rm_tab['RM'][mao_rm_tab['included']]) * u.rad / u.m**2, + errors=np.array(mao_rm_tab['e_RM'][mao_rm_tab['included']]) * u.rad / u.m**2, + coords=mao_rm_tab['coordinates'][mao_rm_tab['included']], + samples=1000, + bins=bins, + show_plots=True, + verbose=True, + fit='bilby', + nlive=400, + sampler='nestle', + model_name='power_law', + n_point=3 + ) + + +.. parsed-literal:: + + 2022-11-07 14:04:53.006 INFO structurefunction - structure_function: Sampling errors... + 2022-11-07 14:04:55.827 INFO structurefunction - structure_function: Getting angular separations... + 2022-11-07 14:04:55.888 INFO structurefunction - structure_function: Computing SF... + 2022-11-07 14:05:06.528 INFO structurefunction - flush: Grouping triplets: 0%| | 0/23 [00:00, 'steps': 20, 'enlarge': 1.2} + + +.. parsed-literal:: + + it= 2984 logz=-108.29771321747 + + +.. parsed-literal:: + + 14:15 bilby INFO : Sampling time: 0:00:07.572824 + 14:15 bilby INFO : Summary of results: + nsamples: 3385 + ln_noise_evidence: nan + ln_evidence: -107.875 +/- 0.118 + ln_bayes_factor: nan +/- 0.118 + + 2022-11-07 14:15:22.691 INFO structurefunction - fit_data: Fitting results: + 2022-11-07 14:15:22.692 INFO structurefunction - fit_data: amplitude: 590 ± 70 + 2022-11-07 14:15:22.693 INFO structurefunction - fit_data: x_break: 16 ± 9 + 2022-11-07 14:15:22.694 INFO structurefunction - fit_data: alpha: 0.16 ± 0.05 + 2022-11-07 14:15:22.694 INFO structurefunction - fit_data: Fit log evidence: -107.874896434051 ± 0.11839303208862947 + + + +.. image:: example_files/example_16_3.png + + + +.. image:: example_files/example_16_4.png + + + +.. image:: example_files/example_16_5.png + + + +.. image:: example_files/example_16_6.png + + diff --git a/docs/example_files/example_11_3.png b/docs/example_files/example_11_3.png new file mode 100644 index 0000000..9a708a2 Binary files /dev/null and b/docs/example_files/example_11_3.png differ diff --git a/docs/example_files/example_11_4.png b/docs/example_files/example_11_4.png index f43394d..895f4d3 100644 Binary files a/docs/example_files/example_11_4.png and b/docs/example_files/example_11_4.png differ diff --git a/docs/example_files/example_11_5.png b/docs/example_files/example_11_5.png index f8a21d3..0a976c8 100644 Binary files a/docs/example_files/example_11_5.png and b/docs/example_files/example_11_5.png differ diff --git a/docs/example_files/example_11_6.png b/docs/example_files/example_11_6.png index 3a9e429..1b33a7e 100644 Binary files a/docs/example_files/example_11_6.png and b/docs/example_files/example_11_6.png differ diff --git a/docs/example_files/example_11_7.png b/docs/example_files/example_11_7.png deleted file mode 100644 index e32c45f..0000000 Binary files a/docs/example_files/example_11_7.png and /dev/null differ diff --git a/docs/example_files/example_11_9.png b/docs/example_files/example_11_9.png deleted file mode 100644 index ea77a77..0000000 Binary files a/docs/example_files/example_11_9.png and /dev/null differ diff --git a/docs/example_files/example_12_3.png b/docs/example_files/example_12_3.png new file mode 100644 index 0000000..4f42d15 Binary files /dev/null and b/docs/example_files/example_12_3.png differ diff --git a/docs/example_files/example_12_4.png b/docs/example_files/example_12_4.png index 1d0f77d..493a6ac 100644 Binary files a/docs/example_files/example_12_4.png and b/docs/example_files/example_12_4.png differ diff --git a/docs/example_files/example_12_5.png b/docs/example_files/example_12_5.png index d512b34..5f95859 100644 Binary files a/docs/example_files/example_12_5.png and b/docs/example_files/example_12_5.png differ diff --git a/docs/example_files/example_12_6.png b/docs/example_files/example_12_6.png index 36fd63f..1b33a7e 100644 Binary files a/docs/example_files/example_12_6.png and b/docs/example_files/example_12_6.png differ diff --git a/docs/example_files/example_12_7.png b/docs/example_files/example_12_7.png deleted file mode 100644 index e32c45f..0000000 Binary files a/docs/example_files/example_12_7.png and /dev/null differ diff --git a/docs/example_files/example_12_9.png b/docs/example_files/example_12_9.png deleted file mode 100644 index 1309f5c..0000000 Binary files a/docs/example_files/example_12_9.png and /dev/null differ diff --git a/docs/example_files/example_14_1.png b/docs/example_files/example_14_1.png index 4502227..c35a203 100644 Binary files a/docs/example_files/example_14_1.png and b/docs/example_files/example_14_1.png differ diff --git a/docs/example_files/example_16_3.png b/docs/example_files/example_16_3.png new file mode 100644 index 0000000..53ee61f Binary files /dev/null and b/docs/example_files/example_16_3.png differ diff --git a/docs/example_files/example_16_4.png b/docs/example_files/example_16_4.png new file mode 100644 index 0000000..782226a Binary files /dev/null and b/docs/example_files/example_16_4.png differ diff --git a/docs/example_files/example_16_5.png b/docs/example_files/example_16_5.png new file mode 100644 index 0000000..828e3be Binary files /dev/null and b/docs/example_files/example_16_5.png differ diff --git a/docs/example_files/example_16_6.png b/docs/example_files/example_16_6.png new file mode 100644 index 0000000..48ac464 Binary files /dev/null and b/docs/example_files/example_16_6.png differ diff --git a/example/example.ipynb b/example/example.ipynb index f9a17a1..8a0aebf 100644 --- a/example/example.ipynb +++ b/example/example.ipynb @@ -13,7 +13,9 @@ "from astropy.coordinates import SkyCoord\n", "from tqdm.auto import tqdm\n", "import matplotlib.pyplot as plt\n", - "import astropy.units as u" + "import astropy.units as u\n", + "import os\n", + "import shutil" ] }, { @@ -123,7 +125,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "77a51b6d1bb04146af9b37f1c72a79c5", + "model_id": "63a1254194004bd581c8c1615bf9c763", "version_major": 2, "version_minor": 0 }, @@ -137,7 +139,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7db591c4f9754b79a33a2ea9087fd791", + "model_id": "672a1d8848f6455da16ecda5c0d6efc5", "version_major": 2, "version_minor": 0 }, @@ -152,7 +154,7 @@ "data": { "text/html": [ "
Table length=472\n", - "
coordinatesRMe_RMincludedflag
deg,degrad / m2rad / m2
SkyCoordfloat64float64boolobject
\n", + "
\n", "\n", "\n", "\n", @@ -245,13 +247,6 @@ "mao_rm_tab" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "f" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -290,6 +285,17 @@ { "cell_type": "code", "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Clean up if a previous run was done\n", + "if os.path.exists(\"outdir\"):\n", + " shutil.rmtree(\"outdir\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": { "scrolled": false }, @@ -298,67 +304,51 @@ "name": "stderr", "output_type": "stream", "text": [ - "2022-09-16 09:14:10.461 INFO structurefunction - structure_function: Sampling errors...\n", - "2022-09-16 09:14:12.527 INFO structurefunction - structure_function: Getting data differences...\n", - "2022-09-16 09:14:13.876 INFO structurefunction - structure_function: Getting data error differences...\n", - "2022-09-16 09:14:15.241 INFO structurefunction - structure_function: Getting angular separations...\n", - "2022-09-16 09:14:15.269 INFO structurefunction - structure_function: Computing SF...\n", - "2022-09-16 09:14:16.936 INFO structurefunction - structure_function: Fitting SF with a broken power law...\n", - "09:14 bilby INFO : Running for label 'broken_power_law', output will be saved to 'outdir'\n", - "09:14 bilby INFO : Search parameters:\n", - "09:14 bilby INFO : amplitude = Uniform(minimum=-63.26544230534412, maximum=470.26399685658464, name='amplitude', latex_label='$a$', unit=None, boundary=None)\n", - "09:14 bilby INFO : x_break = Uniform(minimum=0.17782794100389226, maximum=28.18382931264449, name='x_break', latex_label='$\\\\theta_\\\\mathrm{break}$', unit=None, boundary=None)\n", - "09:14 bilby INFO : alpha_1 = Uniform(minimum=-2, maximum=2, name='alpha_1', latex_label='$\\\\alpha_1$', unit=None, boundary=None)\n", - "09:14 bilby INFO : alpha_2 = Uniform(minimum=-2, maximum=2, name='alpha_2', latex_label='$\\\\alpha_2$', unit=None, boundary=None)\n", - "09:14 bilby INFO : Single likelihood evaluation took 9.987e-05 s\n", - "09:14 bilby WARNING : Supplied argument 'npool' not an argument of 'Nestle', removing.\n", - "09:14 bilby WARNING : Supplied argument 'sample' not an argument of 'Nestle', removing.\n", - "09:14 bilby INFO : Using sampler Nestle with kwargs {'method': 'multi', 'npoints': 400, 'update_interval': None, 'npdim': None, 'maxiter': None, 'maxcall': None, 'dlogz': None, 'decline_factor': None, 'rstate': None, 'callback': , 'steps': 20, 'enlarge': 1.2}\n" + "2022-11-07 14:03:13.640 INFO structurefunction - structure_function: Sampling errors...\n", + "2022-11-07 14:03:17.396 INFO structurefunction - structure_function: Getting angular separations...\n", + "2022-11-07 14:03:17.434 INFO structurefunction - structure_function: Computing SF...\n", + "2022-11-07 14:03:31.302 INFO structurefunction - fit_data: Fitting SF with a broken power law...\n", + "14:03 bilby INFO : Running for label 'broken_power_law_2_point', output will be saved to 'outdir'\n", + "14:03 bilby INFO : Search parameters:\n", + "14:03 bilby INFO : amplitude = Uniform(minimum=-55.43796027883391, maximum=471.5431939956728, name='amplitude', latex_label='$a$', unit=None, boundary=None)\n", + "14:03 bilby INFO : x_break = Uniform(minimum=0.179, maximum=28.371000000000002, name='x_break', latex_label='$\\\\theta_\\\\mathrm{break}$', unit=None, boundary=None)\n", + "14:03 bilby INFO : alpha_1 = Uniform(minimum=-2, maximum=2, name='alpha_1', latex_label='$\\\\alpha_1$', unit=None, boundary=None)\n", + "14:03 bilby INFO : alpha_2 = Uniform(minimum=-2, maximum=2, name='alpha_2', latex_label='$\\\\alpha_2$', unit=None, boundary=None)\n", + "14:03 bilby INFO : Single likelihood evaluation took 1.555e-04 s\n", + "14:03 bilby WARNING : Supplied argument 'npool' not an argument of 'Nestle', removing.\n", + "14:03 bilby WARNING : Supplied argument 'sample' not an argument of 'Nestle', removing.\n", + "14:03 bilby INFO : Using sampler Nestle with kwargs {'method': 'multi', 'npoints': 400, 'update_interval': None, 'npdim': None, 'maxiter': None, 'maxcall': None, 'dlogz': None, 'decline_factor': None, 'rstate': None, 'callback': , 'steps': 20, 'enlarge': 1.2}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[Kit= 3530 logz=-115.1097827102492\n" + "\u001b[Kit= 3592 logz=-114.816709542922\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "09:14 bilby INFO : Sampling time: 0:00:10.811501\n", - "09:14 bilby INFO : Summary of results:\n", - "nsamples: 3931\n", + "14:03 bilby INFO : Sampling time: 0:00:27.098431\n", + "14:03 bilby INFO : Summary of results:\n", + "nsamples: 3993\n", "ln_noise_evidence: nan\n", - "ln_evidence: -114.815 +/- 0.127\n", - "ln_bayes_factor: nan +/- 0.127\n", + "ln_evidence: -114.499 +/- 0.130\n", + "ln_bayes_factor: nan +/- 0.130\n", "\n", - "2022-09-16 09:14:29.269 INFO structurefunction - structure_function: Fitting results:\n", - "2022-09-16 09:14:29.269 INFO structurefunction - structure_function: amplitude: 180 ± 20\n", - "2022-09-16 09:14:29.270 INFO structurefunction - structure_function: x_break: 22 ± 4\n", - "2022-09-16 09:14:29.271 INFO structurefunction - structure_function: alpha_1: 0.10 ± 0.04\n", - "2022-09-16 09:14:29.272 INFO structurefunction - structure_function: alpha_2: 0 ± 1\n", - "2022-09-16 09:14:29.272 INFO structurefunction - structure_function: Fit log evidence: -114.8152379924337 ± 0.12701652449377673\n" + "2022-11-07 14:04:01.465 INFO structurefunction - fit_data: Fitting results:\n", + "2022-11-07 14:04:01.467 INFO structurefunction - fit_data: amplitude: 180 ± 10\n", + "2022-11-07 14:04:01.468 INFO structurefunction - fit_data: x_break: 22 ± 4\n", + "2022-11-07 14:04:01.469 INFO structurefunction - fit_data: alpha_1: 0.11 ± 0.04\n", + "2022-11-07 14:04:01.470 INFO structurefunction - fit_data: alpha_2: 0 ± 1\n", + "2022-11-07 14:04:01.471 INFO structurefunction - fit_data: Fit log evidence: -114.49868298129567 ± 0.12984044020094218\n" ] }, { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "26315913f1fb4ac09024bf66105d8939", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Making hist plot: 0%| | 0/24 [00:00" ] @@ -370,7 +360,7 @@ }, { "data": { - "image/png": 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", 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", 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", 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ - "cbins, medians, err, count, result = structure_function(\n", + "sf_result, result = structure_function(\n", " data=np.array(mao_rm_tab['RM'][mao_rm_tab['included']]) * u.rad / u.m**2,\n", " errors=np.array(mao_rm_tab['e_RM'][mao_rm_tab['included']]) * u.rad / u.m**2,\n", " coords=mao_rm_tab['coordinates'][mao_rm_tab['included']],\n", @@ -436,72 +407,56 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2022-09-16 09:14:35.282 INFO structurefunction - structure_function: Sampling errors...\n", - "2022-09-16 09:14:35.323 INFO structurefunction - structure_function: Getting data differences...\n", - "2022-09-16 09:14:36.885 INFO structurefunction - structure_function: Getting data error differences...\n", - "2022-09-16 09:14:38.499 INFO structurefunction - structure_function: Getting angular separations...\n", - "2022-09-16 09:14:38.529 INFO structurefunction - structure_function: Computing SF...\n", - "2022-09-16 09:14:40.027 INFO structurefunction - structure_function: Fitting SF with a power law...\n", - "09:14 bilby INFO : Running for label 'power_law', output will be saved to 'outdir'\n", - "09:14 bilby INFO : Search parameters:\n", - "09:14 bilby INFO : amplitude = Uniform(minimum=-63.81717705255777, maximum=468.5890166911098, name='amplitude', latex_label='$a$', unit=None, boundary=None)\n", - "09:14 bilby INFO : x_break = Uniform(minimum=0.17782794100389226, maximum=28.18382931264449, name='x_break', latex_label='$\\\\theta_\\\\mathrm{break}$', unit=None, boundary=None)\n", - "09:14 bilby INFO : alpha = Uniform(minimum=-2, maximum=2, name='alpha', latex_label='$\\\\alpha$', unit=None, boundary=None)\n", - "09:14 bilby INFO : Single likelihood evaluation took 1.050e-04 s\n", - "09:14 bilby WARNING : Supplied argument 'npool' not an argument of 'Nestle', removing.\n", - "09:14 bilby WARNING : Supplied argument 'sample' not an argument of 'Nestle', removing.\n", - "09:14 bilby INFO : Using sampler Nestle with kwargs {'method': 'multi', 'npoints': 400, 'update_interval': None, 'npdim': None, 'maxiter': None, 'maxcall': None, 'dlogz': None, 'decline_factor': None, 'rstate': None, 'callback': , 'steps': 20, 'enlarge': 1.2}\n" + "2022-11-07 14:04:07.099 INFO structurefunction - structure_function: Sampling errors...\n", + "2022-11-07 14:04:09.998 INFO structurefunction - structure_function: Getting angular separations...\n", + "2022-11-07 14:04:10.063 INFO structurefunction - structure_function: Computing SF...\n", + "2022-11-07 14:04:32.609 INFO structurefunction - fit_data: Fitting SF with a power law...\n", + "14:04 bilby INFO : Running for label 'power_law_2_point', output will be saved to 'outdir'\n", + "14:04 bilby INFO : Search parameters:\n", + "14:04 bilby INFO : amplitude = Uniform(minimum=-52.035011894175796, maximum=464.9494929780924, name='amplitude', latex_label='$a$', unit=None, boundary=None)\n", + "14:04 bilby INFO : x_break = Uniform(minimum=0.179, maximum=28.371000000000002, name='x_break', latex_label='$\\\\theta_\\\\mathrm{break}$', unit=None, boundary=None)\n", + "14:04 bilby INFO : alpha = Uniform(minimum=-2, maximum=2, name='alpha', latex_label='$\\\\alpha$', unit=None, boundary=None)\n", + "14:04 bilby INFO : Single likelihood evaluation took 1.576e-04 s\n", + "14:04 bilby WARNING : Supplied argument 'npool' not an argument of 'Nestle', removing.\n", + "14:04 bilby WARNING : Supplied argument 'sample' not an argument of 'Nestle', removing.\n", + "14:04 bilby INFO : Using sampler Nestle with kwargs {'method': 'multi', 'npoints': 400, 'update_interval': None, 'npdim': None, 'maxiter': None, 'maxcall': None, 'dlogz': None, 'decline_factor': None, 'rstate': None, 'callback': , 'steps': 20, 'enlarge': 1.2}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[Kit= 3254 logz=-114.3524352848590\n" + "\u001b[Kit= 3219 logz=-114.1911086409369\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "09:14 bilby INFO : Sampling time: 0:00:09.337694\n", - "09:14 bilby INFO : Summary of results:\n", - "nsamples: 3655\n", + "14:04 bilby INFO : Sampling time: 0:00:13.769841\n", + "14:04 bilby INFO : Summary of results:\n", + "nsamples: 3620\n", "ln_noise_evidence: nan\n", - "ln_evidence: -113.927 +/- 0.125\n", - "ln_bayes_factor: nan +/- 0.125\n", + "ln_evidence: -113.771 +/- 0.124\n", + "ln_bayes_factor: nan +/- 0.124\n", "\n", - "2022-09-16 09:14:50.400 INFO structurefunction - structure_function: Fitting results:\n", - "2022-09-16 09:14:50.401 INFO structurefunction - structure_function: amplitude: 180 ± 20\n", - "2022-09-16 09:14:50.402 INFO structurefunction - structure_function: x_break: 16 ± 8\n", - "2022-09-16 09:14:50.403 INFO structurefunction - structure_function: alpha: 0.10 ± 0.04\n", - "2022-09-16 09:14:50.403 INFO structurefunction - structure_function: Fit log evidence: -113.9273916449165 ± 0.1252873443614207\n" + "2022-11-07 14:04:47.401 INFO structurefunction - fit_data: Fitting results:\n", + "2022-11-07 14:04:47.402 INFO structurefunction - fit_data: amplitude: 180 ± 20\n", + "2022-11-07 14:04:47.403 INFO structurefunction - fit_data: x_break: 15 ± 9\n", + "2022-11-07 14:04:47.404 INFO structurefunction - fit_data: alpha: 0.10 ± 0.04\n", + "2022-11-07 14:04:47.406 INFO structurefunction - fit_data: Fit log evidence: -113.77116114103981 ± 0.1240670676073616\n" ] }, { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "55f7f37c25a94005be5aacda377b7e22", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Making hist plot: 0%| | 0/24 [00:00" ] @@ -513,7 +468,7 @@ }, { "data": { - "image/png": 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Gg6OjI7m5uWg0GjIzMzl69Chz585l165djB49muXLl2O1WnnrrbfIyckhNjYWe3t7xdTl6OhIWloaQ4YMYfHixezcuZPmzZvj6OhIcnIy9erVw2KxkJycjNFoLFMDLT09nXfffZfNmzfTunVrli9fToMGDXBwcKCoqIi8vDyysrIwGAx4eXlds1DnvVJXraYiwCpb4G/HOJWZ2oQ2I/g31JrA2b9/P//73//47bffaNu2La+88gqff/45s2fPZu7cuUyZMoU//viDfv36MWzYMJKTkwkICKit6dYa8s5V1gxsbGxYsmQJCxYsIDY2ls8++4wFCxaQmZnJ559/ToMGDQCIiopi48aNpKSkKNWcfXx8mDp1Kj4+PqxevZp3330XOzs7XF1dadKkCbt27cLV1RV7e3tcXV2Bkk3Ba6+9RnZ2Ng8++CBDhgyhfv36+Pv7Exsbi0ajwWAw4ODggIeHh6gKfQ2utWjfCVrBnTBHwZ1BrWo4b7/9Nm3btgXggw8+4LnnnsNgMDBnzhwcHBy47777AFi5cmVtTvO2U7riQPlFSU4AlRf15ORk/vjjD6Kjo1m+fDn29vZERETQrl07GjVqpHxu27ZtREVFKX9nZGSQkZHBhg0bGDNmDPfffz+AUtW5WbNmrF69msjISOrVqwfA5s2bGTlyJK1atWLatGl0794dGxsbAgMDSU9PJzMzExsbG9zc3HBycqpQp03wf9S2NiKEhqAuUCUfzk8//VThubfffvuWBu7UqROPP/44AMXFxRQWFhIfH09aWhoPPfQQbdq0uWdCo/Pz80lMTCQ3N7dCxYGCggJyc3PJzc0lOTkZZ2dnXnvtNQB27dpFp06dePLJJzl27BgfffSR0i566NChTJgwgX79+tGyZUseffRRxo4dy9NPP13pHOTw6aVLlwIlCaZTpkyhadOmvPLKK9x33320atWKJk2aoNPpcHd3JyAggCZNmuDi4nJP+GbuBCpz/Ffm9BcIaoMqaTirV69Gr9cri9Urr7xCYWHhLQ0sO72hZBfv4uKCq6srHh4eLFu2jH379vHFF1/cVX6bG1FQUIDVaiUvLw8vLy/g/6oR5ObmkpKSAsD999/P448/zg8//EC/fv0YOHAgdnZ2/PLLL7z11lt06tSJrKwsAgMD6dChAxqNBpVKRXZ2NpGRkQA4OjpiMBjKPNezZ09+//13hg4dyvbt27l8+TJLliyhbdu22NjYUFRUhNlsVmqjubu7lwmBlgWlMKEJBILKqJLAWbNmDY8++ihqtZrNmzfj5ubGd999V32T+P+1turVq8fkyZPZunUrv/zyy13tZC69KMsRXXq9nrS0NCwWixJqLCeA6vV65f+XL19m/PjxrFmzhkmTJrFlyxa6d++Ol5cXM2fO5OzZs1WaQ0REBBEREYrgb9iwIdu3b+fPP//kzz//pEOHDgwfPpzU1FRSUlKIj49XCo1aLBYKCwvR6XTY2Njg6ekp8mqqgcpK/INw0gvuDq4rcDIyMpT/L1iwgMcee4xu3brx3nvvkZGRgZubW7VMQpIkzGazkly4Y8cOGjZsWC3HvhOQBYkkSXh5eZVJkJSRWwucOHGCs2fPsnnzZgBeffVVAE6ePMmiRYuUiDaVSoVOp0Oj0SgaTnFxseJn6dChgyL0JEliwYIFLFy4ED8/PxYuXEhISAiffPIJKpUKR0dHEhISlNYGtra2pKenY7VaMZvNqNXqCpFtgn9HZRFuAsHdwnUFTvv27VGpVEq4riRJbNy4kY0bN6JSqbh8+XK1TEKlUmFjY8PUqVPp0KHDPSVsSlO6tL/8d/nFx8nJiaioKH777TdGjBjBE088wZw5c5g7dy4uLi7MmTOHkJCQMmHRmZmZSoFQWZsxGAwcOnSInJwcfvjhB/7880/8/f1JSEigW7dujB07lo4dOyJJEikpKeTn55OcnExQUBAmkwk3NzeKiooU81p5s1rpygIi0VMgEMANBM6VK1dqah4AjBkz5p5emEp/d3nBLh25ZrVaKSwsZOHChdSrV48PPviAVatW8eWXX9K6dWs++OADXFxcgBKfT1RUFDNnzqx0LBsbG6ZNm8auXbvYunUrwcHBREdH89RTTzFs2DBycnJITEykYcOG+Pv7AxAQEIDJZFIEl729vaJJlad0gU6h+QgEAriJsOgzZ85w7tw5TCaT8ty1stL/LfeysClPZQu2XI0hJiaGyZMnY29vz8GDB3Fycqo0wKK0TyUgIICwsDC8vLwwm80sWbKETZs2KdpU6ZpnUFJmSK/XK2azwMBApZmak5OTotFci9I10wQCgQCqKHA++OADdu3axblz53jooYfYvHkz3bt3r3aBc69T2nwmL9SlF3Y7OzulvI2cB+Pq6qoU8SwtYDIzM/Hx8eHTTz/lt99+4+TJk3h5edG8eXNUKhWtWrXi4MGDTJ48mezsbMLDw2nWrBnLly9HkiTeeOMN3NzcSE1NxWKxoNVqsVqtODk5odfrMRqN6PV6tNrKb6F7qbLAv6WmMvhFpQBBXaHKYdEnT56kbdu2LFy4kOTkZF544YXbPbd7jvKLgrxgy8/LAQHOzs4UFRXh6upKcHAwxcXFuLm5lWklbWtrq7Sbvu+++/jyyy/5/fffsbOzY/To0fTv359jx44RERHB7NmzeeaZZzh37hw9e/ZkxYoVFBYWMnToUEJCQggICMDV1VUJiZYj6VxcXCr0sxFaatWpqWRMkfQpqCtUKY5VDoXVarXk5OTg5eVVbQEDgoqUbi9dnqKiIoKCgti5cydGo1HJ1ykdUVgejUbDiBEjsLW1JTw8XDmGu7s7kZGRODg4MGzYMKBEs3Jzc2PDhg0cOHCA8+fPo1ar0Wg02NnZkZ6eTlFREVqt9oZmNYFAIChNlTScsLAwsrKyGDt2LO3bt8fBwYGOHTve1ondCy2mr0Vp/41ery/zWlxcHE8++SSTJ0/m66+/VqpCHzt2jKSkJP755x8cHR2xt7enUaNG2NvbExcXx5w5cwAYMGAAu3btIiYmhqKiIgoLC0lLS+Obb74hJCSEo0ePYjKZ+Oyzz2jWrBmNGjXC3d0dSZIwmUxKszVPT8+79vpcKxemMkTZGIGg6lRJ4MhJni+99BL9+/cnJyeHVq1a3ZYJJSUl4ePjc9cuZtejfHtpg8GA1WoFSsr/yJUG+vfvz4kTJ5g3bx4//vgjUCKgt27dyrZt28oc08HBAbPZjIODA7169cLe3p7Vq1dTWFiIwWBAq9WyYMECcnJysFgsGAwGRfg4OjoCKBpO6XndzdfnZnJh7ubzIBBUN1UyqUmSxNKlS5k+fTrBwcG4uLhw+PDhap/M5s2bmThxolJq5V6lspbTGRkZnDt3jr1792IymRg6dChFRUX8888/QElfm127dtG+fXumTZvGsGHDePrpp+nUqRMdO3Zk2rRpODs7s2PHDqUskclkIi8vj2XLllG/fn20Wq1SQFWn06FWq8nIyCA9Pb1WzoNAILi7qJKG88orr6BWq/n777957733cHR05IknnrhhJ86b4dChQ7z44ov88ssvSol9GavVet2yKXdzi2kZJycniouLsbGxITMzk/nz56PVapXzEhwcjKurKxaLBV9fXywWC82aNatwnAcffJADBw4QFRWFTqcjJiaGjh074uXlRVRUFD179qRevXp4e3uTl5eHxWLB3d0dELk1AoHg1qiSwDl06BDHjh1TWgm4uroqVYmri0uXLjFq1Ch69+7N1atXOX78OOnp6YwePRq1Wn1doTNu3DjGjRsHlPib7iZkk5ocrOHl5cV3333Htm3b+OSTT/jjjz/w8fFRwqAvXbpETEwMWVlZZTTFjIwMjhw5Qnx8PHl5eUpIs0ajYeHChXz55Zf8888/tG7dGpPJhL29PTqdrkzVA6vVioODg8itKYUIORYIqk6VTGo6nY7i4mLlh5WamlrthRoDAgLIysoiLi6ORx55hL179zJnzhyGDx9eMtF7tDCkVqtFq9UqXVHDw8NZuHAhzz77LEOHDuXYsWOEhYXRrl07unbtSkFBAX5+fvj7+9OgQQMaNGjAgQMHmD17NhcvXqRt27Z88skntGnTBpPJxPfff09QUBBWq5XMzEyuXLlCREQEqampillPrpWWl5en/C3K1ZRQWTsAEUQgEFROlVbxiRMnMnjwYFJSUnjnnXfo3r07U6ZMueXBIyIilP+7uroSFxfH4sWLGTlyJJ9++inh4eHExMQoEVb3MsXFxZw/f56tW7eiUql4//33KSgowGKxKN05k5KS0Gg0ZXJjkpOT+euvv+jSpQtfffUV8+fPZ9++fRw+fJjp06fTtm1bPv30U3755Rcef/xxrl69Sn5+foXxDQYDTk5Od512ExwcrAhP+SE0FIHg9nBDk5rVaqV+/frMnDmTHTt2IEkSa9eupWnTprc08IYNGxg2bBiDBg3it99+o3Xr1jz00EPMnDmThx9+mOzsbJydnRk8eLASLXU3UVkUVGWh4EVFRVitVo4ePapk/Ts6OmIymRSzZlJSEidPnmT37t0EBwdz5coVsrKyUKlU/Pjjj+h0Onr16oVKpWLZsmVs3LiR8ePH07dvXz7++GN+++03evbsybRp0ygsLMTJyUmpXl2a8kmedxqVhTsHBQWJ6swCQQ1xQ4GjVquZNGkSBw4coEmTJtUyaH5+Pt988w2zZ89m//79jBgxgl9//ZWXXnoJjUbD6tWrWbhwIZmZmaxcuZI//vijWsatS5Svpnyt13U6Henp6aSnp6PVapXGZ+7u7jg7OwMliZ1FRUVkZmbSr18/vLy8sLGx4dy5c1y5coU33niD++67j9jYWL744gt69+7NBx98wOTJk/ntt9948MEHmTt3Lra2tiQkJKBWqykoKECr1d5VwQGi9L9AULtUyaTWr18/fv/992r7sdrb2/Pzzz8zYsQIvvjiC4qKinjqqacAGDt2LFOnTiU0NJTi4mLWrl1bbYKuLiFHfBUUFFz3daPRiMViwWq14ujoyIkTJ5T2DWlpaUBJGZuNGzcqwvrAgQMAhIeH07hxY3r37g3Ajh07sFqtzJs3D7VazZo1a2jfvj0ffvghsbGxpKamYmdnh6ur6x1lPqvMLBYcHFzb0xIIBOWoksD56quvGDp0KDY2Njg6OuLo6Kj0Vfm3+Pn54eDggIeHB/Pnzy8jdJycnGjXrh0ffvghjRs3vqVx6io38okYDAYcHBwoLCzEarXi7u7Ovn37SElJYcSIEWRkZLB9+3YAoqKiOH36NFarFavVytKlS9m1axcODg7k5uaSlJREUlISFy9eJCQkBJ1OR3Z2NsHBwWg0GlxdXUlKSqK4uBgvLy+l+2hBQUEZZ3hdRdZcys9V+GYEgrpFlQRObm6uUqY+NzeX3NxcJR+jOnB3d2f+/Pno9XoaN27M448/XqcXuOqgdHKnvCCW/79arSY/Px+j0YgkSSxbtowuXbrQs2dPRdsxGAxlWko7ODig0WjYvn07Dg4OZGZm0qJFC1q2bElSUhItWrTAwcEBBwcHmjdvzqVLl8jKylJaRRsMBoxGI2lpaYoGdidGpInoMYGg7lHlfjhr1qxh3759qFQqevTowWOPPVatE/Hw8KBVq1Zs3ryZbdu2ERAQUK3HvxMxGAxYLBaSkpI4dOgQcXFxfP7556hUKtLT09m0aRM+Pj5KIVWDwYBGo8He3p7i4mJOnz6N0WgkPz+fgoICkpOTad68OVarlV9//ZW//vqL3NxcMjMzadSoEY0bN1aSSQ0GAyaT6Y4xqwkEgrpPlTScV155hXnz5tGyZUtatGjBvHnzGD9+fLVOJDMzk02bNrF161ZatmxZrce+U1Gr1fj5+dGgQQMlD6lt27ZkZWUxfPhwsrOziY2NBUrydeQGbGq1Gjc3N6VhW35+vlJj7aGHHmLq1Km8/vrrBAUF8cknn6DRaBSNSq1WY29vrwiuO02zEQgEdZcqaTi7d+/mzJkzyuIzZsyYahcKrq6urF+/vkJ15HsJOZvfaDRiZ2eH1WolPT0dZ2dnsrOzUalUaLVannjiCaKjo/H39ycxMREo8VfIAgbA2dlZ6c4aFRXF+vXrCQ0NJTw8nB9++IFHH32UV199FTs7O7KysrCzs+PSpUs0aNBAEUACgUBQnVRpVWncuLGyk4aSEvm3o1r0vSxsZIxGoxKdJnf1LCgoICEhAR8fH9544w2io6Pp3bs3sbGx2NjYAGA2myksLFQeeXl5Slmcy5cvExkZSfPmzfnf//5Hr169mDlzJj179sTZ2RlXV1eysrIoKCggKysLjUZTweEuNB2BQHCrXFfDGThwICqViuzsbJo2bUrHjh1RqVQcOnSIrl271tQc7ynkpmZ2dnbY2tpSXFyMi4sLu3fvpl27dmzatIkHHniAlJQUvL29efDBB1m8eHGF48gVnhs3bszXX3+Nv78/GzZsoFWrVnz00UdKhFqjRo2IjY1VWkfLhToFAoGgurmuwPnf//5XU/MQQJmQXkmS0Gg02NjYsGfPHuLi4hgwYAAmkwkXFxd27dpF8+bN8ff3v+bxWrVqxalTpwgKCiImJob27dszZcoUOnbsiNFoRKVSYTKZcHd3Jz09HU9PTzQaTU19XYFAcI9xXYHTs2fPKh2kS5cuSrKh4N8jC4CcnBylOrZWq+XTTz/F09MTtVqNTqdTqnX7+/vj6enJBx98QMOGDZk+fToREREYDAZCQ0M5efIkHTt25PDhwzz++OM899xz9OjRA5PJRG5uLvn5+UoFaB8fHwwGg/DdCASC20aVw6Kvh+ycrg4OHjzIpUuXaNiwIe3atVN8FPcKsknNarWSk5ODWq0mPj6esLAwLly4QIsWLThx4gQajYYNGzYQExPDoEGDSEtL48KFCxgMBr766ivGjRvHiy++yIoVK+jWrRs//fQTer0eW1tbxbdja2urdP4UgkYgENxuqmWVqS6H8p9//sm4cePYvn07X3zxRZX7yt8tyMU7DQaDUonAYrHQvHlzDh8+zJUrV/D29ubw4cNYrVb0ej2nT5/mp59+Ii8vD51OR4cOHZQER19fX7KysnjmmWeUFhOlx5DbRgthIxAIaoI6s9Kkp6fz7bff8uuvv7Jo0SKcnJw4ceIEKSkp1apB1WVKR4TJeTD5+fk0aNCA9PR04uPjyc7OVnw9slBKSkrip59+IiQkhKSkJGxtbYGSpmsADzzwgKLdyLXbRPSZQCCoaaokcM6dO1fhuV27din/r44yNFqtFqPRyIULF8jJyWHXrl0sXryY//znP3z44YeV9miR+eGHHwgLCyMsLOyuazFta2vLyZMngZKeOOHh4cD/NWbT6/X4+PgQFRVFUlISRqOR7du3o9FoOH78OCEhIdjZ2VFcXKyEXF+rYKhAIBDcTqokcIYNG8Znn32GJEkYjUZeffVVJk+erLy+ZMmSW56Is7MzEydO5JNPPqFfv348++yzrF+/nhdeeIH4+Pgy7ZLLM27cOMLDwwkPD8fT0/OW51ITlK/zJUkSFotFEQwnT55k27ZtdO7cmZ07dwIlQsbV1RVbW1ssFotSUToxMREHBweMRiOdOnVi7969PPTQQ+zdu5cuXbqQkpJCRkYGer3+jqoCLRAI7i6qJHDkOl5du3alQ4cO+Pn58c8//yivt2jRolomM2TIELZv306PHj1o27YtAL179yY3N/ee8OeoVCrMZjN79+7lp59+YtiwYaSnpyNJEnZ2duh0OsaPH8/TTz+tNKUzmUw4OzuTn5/PRx99xJYtW2jfvj3+/v6o1WqeeOIJnJyccHd3V7qBli4SKkxqAoGgpqiSwNHpdNjZ2WE0GjGZTNSvX/+2OZpdXV3p3bs3v//+O1u3buXPP//kypUrt6WyQV3k6tWrxMbGMm/ePDQaDcXFxXh7e2M0Gnn55Zfx8fFBq9XSu3dv8vLy8PHxITs7mw8//JBly5YB8MQTT7B06VIefvhhpcK3yK8RCAS1TZXCojt06MCgQYM4cuQI6enpvPjii6xevZrVq1fflkl16dKFS5cu8dFHH6HX61m4cOFd31BL7vDp6+vLkSNHAHBxcSEgIIC8vDylmrNMWFgY7u7u7N69Gx8fHxwcHDhx4gS9e/dmypQptGjRgvfffx+z2UxISEhtfS2BQCBQqJLA+emnnwgLCwPAx8eHdevWVYvf5lq4uLgwceJEnn32WSRJuuVmb3cCOTk5XL58GScnJ9auXUvv3r3ZunUrPXv2JCcnh9zcXKUNQWZmpuLviYmJoVWrVnz22Wf4+Pjw999/8+CDD/LRRx8pLajlvBuBQCCoTaokcGRhU5pRo0ZV+2TKI/sp7kbK+04KCgqIiori22+/JT09nUaNGrF161Y6duyoFO+UfWUmk4l69epx5MgRLBYLJpOJq1evotfreeihh1i3bp0S4AElLb1Fro1AIKhtqqXSgODWKCoq4pdffuHDDz8EYMCAAXzzzTd06NCB0NBQTpw4wenTp5WkzbS0NLZv387Zs2extbXl3LlzqFQq/P39+eabbxRhIwcICAQCQV1ACJxq5lo5SeU1Gjkpc8+ePbz55ptcunSJli1bkpeXx8aNG3nggQf4z3/+w4EDB9DpdJhMJvbs2cPhw4c5deqU0nCtsLAQgMcee4wFCxYoEWt5eXlKG2uBQCCoC4jtby1RXFzMpEmTGDx4MEVFRQwYMIAzZ86QlZXFW2+9xaRJk9BqtXh4eCgh4gsWLODcuXM4ODhgsViUigTTp0/n999/x83NTQl9Fvk2AoGgriEETi2xZMkSfvnlF1566SUmT57M5s2b6du3L9999x0pKSlKPxuA0NBQfH19adSoERaLBVtbWzQaDQEBAXz11VcMHz68jAYlazbCnCYQCOoSwqRWzZQ3qUmSRF5eHnZ2dooASE1N5cMPPyQsLIwJEyYwZMgQ6tevT48ePfjpp5/Yvn07R48eZejQoRQUFODh4UHjxo3ZtWsX9evXJzo6ms6dO/PZZ59ha2uLl5dXbXxVgUAguCnEFriaKd+WuaCggLy8PEwmE1arlYKCAqZMmUJWVhbTpk3jyJEjXLhwgZdffpnc3Fx2796NjY0Np0+fpqioCG9vbxYsWMCuXbvo2bMnV65c4f7772fRokWEhobi7OwsqgUIBII7AqHh3GYMBgPFxcVYrValIsDixYsZOnQoLVq0UGrSrVmzhvz8fMxmMw4ODhQVFbF69WpiYmIwm8307NmT3bt3M2jQICZMmKA0ZLteUVOBQCCoSwiBcxu4evUqX375JWfPnlVaDKjVajIzMzl06BBPPvkkU6dOBUraePv4+LBq1Sri4+PRaksuiVar5fz587i4uFBUVMTu3bt55plneO+997CxsVFylHx9fZWmbQKBQFCXuacETmUhy3Juy795zmKxlPk7LS2NqVOnsnTpUoqLiwkICFD8NjqdTokoe+6554iKilISM0eMGMHw4cP55ptvOHr0KBEREcpYZrOZhx56iDFjxuDs7ExRURG2trZIkoRarRaRaAKB4I6hzgmcixcvkpGRQVhYGGq1utaLTlqtVoxGI3q9Ho1Go/xdXquIiYmhffv25OXlMWjQIF599VXq1asHlJSt8fHxKfN+R0dHXFxcyjx3/PhxJYnzgQceoEuXLgwYMAB/f3+sVitRUVGKIDKZTNjb2yvzEZ07BQJBXadOCZw1a9YwZcoU/P398ff3JywsjGeeeabaaqnJBTINBkOljnb5dTs7OyVb32q1kpWVhcViITAwkMLCQnJycrBarUrbAIvFwoEDB8jJyWHWrFkMHDjwX83v7Nmz9OnThw8//JAWLVpQXFxMbm4uqampeHh40Lx5c1xdXSkqKlIEntxUDRBJngKBoE5TZ7bEZrOZFStW8NNPP7Fjxw4GDRpEXFwcM2fOVBbUW0VuWHatjpfy60ajscxCbrFYyMzMJD09HTs7O0UA5uXlYTQaSUxM5Pz58wAVtJabJSwsjPbt22Nra4vBYMDd3R0fHx+8vb3x8vJCp9OV0Wbk+QjTmkAgqOvUKQ0nJyeHS5cu0a1bNwYPHoyHhwcbN27k119/5cUXX7xm+O8PP/zADz/8AHDdFtPyolxawyl9zNKvl35Ofnh4eCiZ/FarVfGh2NnZ0bRpU+U7hISE3DBU2dfXt8zfFy9eRJIkdDqdEjgAJcED5c1xpZHnIxAIBHWdOiNwdDod//3vf5k7dy6hoaH06NGD7t27k5CQwKZNm3jxxRev+dlx48Yxbtw4oPLK1jI3qi1W/nX5/2q1usKiX/q9NjY29O7dG39/f1577TXmzZvHa6+9RuPGjcnMzCQjI4OMjAwyMzPR6XQ4OzsrD5VKxaJFi1i/fj16vf668xcIBII7mTojcAB69OjBxYsXWbJkCZIkcd999zFixAgWLFjAyZMnadOmTW1P8Zr4+/tz+fJlVq5cyezZs3nppZcqvEfu4FkeDw8Ppk2bxiuvvIKnp2dNTFcgEAhqnDolcPR6PU8//TQqlYpPPvmECxcuYGtrS3JycgUTVF3ExsaGkSNH8vTTT3Ps2DFycnJwc3PD1dUVNzc3JaosLy+P7OxssrOzyc/Pp3Xr1iKXRiAQ3PWopGvV069FioqK+Oeff5g/fz56vZ7XXntNqZh8Izw8PCq0o05NTb3tmsPdMEZd+g7R0dGkpaVVeL789a2JOZenNsa8m8a91rUV3P3USYEjU1xcjEqluuX8krCwMMLDw6tpVnfvGHfid6iJOdeFMe/FcQV3H3XKpFae2k76FAgEAkH1UWfycAQCgUBwd1OnTWr/hsp8OII7j6r6cAR3HuLa3t1cz0dXp01q/4bg4GBhb74LuFY+kri+dz7i2t7dXDcXsgbnIRAIBIJ7GCFwBAKBQFAjCIEjuC3ICa5Wq7W2p3JPIs6/oC4iBI6gWpEkSWnzIFfmvsviUuos8rkvf/4FgrpCnQ0akKsxC+5MKqu8Lag55FJJomSSoC5R51b0K1euACXVmIU54M5FpVJhb29/wzYNgtuDWq3G3t5ebNrqGMHBwahUqjKPeykUvE7djUeOHKFHjx688847wN0tdEqbP270uN1j3srxSvsK7gTT2e0+x3fCXORxiouLycvLo7i4+I64dncDMTExFa53TExMbU+rxqhTAsfT05NmzZoRFxfHa6+9BqDs0K73g/jhhx8ICwsjLCzsug3YBNVPZb6C8ju4W9VyxPWtGpWd9+ude7mrrdForMFZCu5l6ozAsVqt2NnZ4erqyvPPP4/JZOL999/nzJkzREVFXfeHM27cOMLDwwkPD78j+8lIkkR+fn4ZoVrZc7dr3FvRIg0Gw21vcV2d19dqtSrfufT/6wI1PR+5Pbnw8whqijojcNRqNd7e3jRv3hydTscHH3zAwYMH6d27NykpKQB1ZmG4WW5kNqlMS6iJKKNrjVHZfGWTWWlTjNVqLeOrkRdMi8VSxlRTl0xWpXf1dW2HL8+nKte8qua4672vtJ+n9LXNzc3FbDYrPZuudx3rkolSUPep1Si1Y8eOUVRUhFqtpmPHjgAUFhaSlJSETqfj3LlztGnTht9//50uXbrcdQ5QWWsrHdFV2XPVPZ7MjcawWq0YjUb0ej0mk0n5t7TfRq1WY2dnh1qtVhbM0vkf9vb21Tb/6kDezZf+zrUVSXez1+N6yNfKYDBU+juRX5evVemx5Y2CwWAgLy+P1NRUDAYDRqMRSZJu2JpdIKgqtbaCb968mZEjR7Jy5Uoee+wxfvvtNwBGjhzJ4sWLGThwILNnz2bBggWYTCaSk5Nra6q3zI3MY/IPuvRCUdlz1c2NxpAFSFpaWpl/AZycnADKaAiyicbDw6POmGrKm6lKf+eaOMc3w63M50ba0fW0uYKCAnJzc8t81mAw4OHhgaenpwhtF1QbtaLhnDp1ikmTJvHjjz/SrVs3+vbty/z58xk0aBAODg4EBwfz6quv8sADD2CxWPjiiy/Q6/U1Oker1UpBQcE1d4w3g2y6grq3478essCQNRsbGxsyMjIwGAxoNJoyGg6ULJgGg6HWTCqVXTN5oYU769zfLJVpbpW9XtkmQL5m8mfl61g66EA+t+VDrctrTgLB9agVgVNYWMi0adPo1q0bVquVRo0akZGRQXFxMYGBgcyYMQNHR0csFgtarRattnqmea2FsLxpo3SmNvzfQvVvo62uZSopnRVe2pxW2Vyu9fq1kCSp0u9V+rni4mLS0tLw8PAo0+xO9s2oVCplznq9noKCAvLy8tDr9Wi1WvR6PQaDgeLiYlJTU3F3dy9jWpO/s2yWKX3865l//g2VXTNJkiostOXvgdILJnBNs9PNzqWy50ov3rKpsvx5z8/Pr7Coy58tLVCh7P0oX6vy51Oei0qlQq/XK+OW/k3Jm4aCggLs7OyU+91isSipCWlpaeTn5+Pp6VnGvJaXl6dUkyh9f4vmiYLKqBWB06FDB+rVqweU3OyhoaE4ODgoZo/MzEwcHR2rTdDI3MziXZ0+lMps4PJCcCPt599qR/J3lQWFrI2UXszS0tK4evUqAF5eXmUWs4KCAkWjcXd3R6VSkZeXR1FRETExMbi6uuLm5oa9vT3p6ekkJCSQn59PUFCQsnjb2toqi1hpSguk6vINyFFnDg4Oynhye/LrjVF6LkC1akMWi4X09HTc3d3LLMDymMXFxYo2oVarycvLIy0tTRH4sqCxt7dHo9GQm5tLTEwMQUFBODk5VUkgln6PyWS65nk3mUzk5uaiUqmws7PDaDRiY2ODSqXCZDKRl5envAdQ5mxra4vJZMLW1lYk+QpuSI0LHPlH5O3trfxdVFREQkICZrOZX375hTlz5rB7924cHByq9Sa+mcVb/qHfbkoLtsoEYlUEX2WfK+3Az83NVXbOvr6+yvfy8PBQ/i19buD/fDOlFyg5+sxsNpOenk6rVq2ws7PD3d2dvLw88vPzOXnyJAaDgdDQUAoLC8nJyakQXHAj88+/wWg0kpeXh5OT001pTba2tsrCKX+uunxP6enpXL16lYKCAurVq6cIHfn4ckQYlBUABQUFFBQUKNdMpVLh6OioPFdQUICDgwMmk6mCZlaZZiFrVLa2ttf0rZXWBOXr7uDggL29vXKNExISSEpKIj09neDgYPR6PQkJCRQVFWG1WnFzcyMrKws3NzdsbGyq5RwK7i5qROBcvHiRjIwMwsLCUKvVZez/KpUKW1tbmjRpwueff84///zD4sWLcXR0rPZ5XM+0dSNuxhxXlfeVfl4WAPn5+ZWa8fR6PSkpKRVMXzLlBWlxcTE5OTkYDAYcHBwwGAxKWLNarSYxMRFbW1vF7FTe9GS1WnFycsLV1RVA+ddiseDp6UlMTAy5ublcvnyZZs2aKTvhpKQkoqKi0Ov12NjYKCU7rFZrGRPbtcw/t4KtrS2Ojo7Y2tqWCZ8vf75KXx+LxUJ8fDxarZbCwkLs7e1vepNR2fWWNRQnJyfi4uJQqVSK8JDR6/VKuHFubq5yPcxmMwUFBTg5OeHi4oJKpVJ+L7LJ0tXVlfz8fCX0HFA2AhqNpow/xWq1KpqJo6NjmWte/nuU1kwdHR3RaDTk5eVhZ2dHUVERhYWF5OfnK/dDZmYmaWlppKam0qBBAwoLC8nOzsZoNBIUFHTP+3SCg4MrVBEICgqqpdnUDW67wFmzZg1TpkzB398ff39/wsLCeOaZZ3BycipToDM/P59Vq1axceNGmjZtelvmcj3T1rWQtQc7OzvFjl46VPhGC2dp7aP0+yoTQJWFR0uSVMb0JWuGpT8vCwn5kZaWRlJSEn5+fjg7OyvC22KxkJaWxmuvvcbq1at5/fXX6du3Lz4+PjRs2JArV64QGhqqJOHKu2aj0Uh6ejoajQa9Xk/nzp2Ji4vD3d0dOzs7UlNTycrKwt3dXVk4AwMDFROeLOhu1g91M8gLrRyRJo9dntLXIyMjg6ysLFxcXKplbqXvDbVaTU5OjuIDke8fGYvFovgtU1NTlTy0goICIiMjCQ4OxtbWFldXV8xmM+fPnyc0NBQvLy/l8yaTCTc3N0WoyuHqUHbDUtl9VXoe6enpuLq6otPplHNpb2+vaK1qtRoPDw8kSUKr1ZKTk6P4etLS0igqKkKn0+Hr64skSWg0GkULu5eRy9gI/o/bKnDMZjMrVqzgp59+olu3bvz+++8cPHiQmTNn8sYbb+Ds7Ky8d9iwYXTt2pUGDRrczildl8pMU+W1h8pyTa73wyr9+Rv9AK/lbyht+rrW50ovcOXfLzuji4uLcXZ25uzZswCcPXsWLy8vMjIySE9PJy4ujqtXr+Lt7Y1WqyUvLw9PT08sFguXL1+mXr16iiBp0KABxcXFGI1GnJ2dycnJwcbGRvHHmUwmtFptmeCDfxP8cDPk5+cTExOj+HLUanUFTVkOAQZwd3dX/q2O3Xj5iLjSx1epVJjNZhISEvD39yczM5OUlBTc3d3x9fVV3iuTkZFBTk4ODRo0ICcnh/PnzwPQrFkz5fWYmBh0Oh3Ozs7Y2dkpQkir1ZKSkqIII7VajV6vJzk5WdF6ZSEl+98KCgrw8/NTBLDZbFb8MrKGrFarKSoqIjc3l8zMTLy9vWnUqBGZmZm4ubmh1WoJCAggIyOjxqNKBXcGt13DycnJ4dKlS3Tr1o3Bgwfj4eHBxo0b+e2333jppZc4cOAAJpOJ0aNH3+6p3JDKfDzlo5zKhwpfyw9RVFREfHw8fn5+VS79Iu+GykdOFRcXU1BQgNFoJDU1FV9fX2xsbCgoKODYsWO0a9euzLxkoaNSqSgsLCQyMlLxU7i5uSmLgZ2dHQaDgQ0bNnDq1CnOnTtXYU4qlYquXbsSFhbGyJEjFeElaz55eXlKZFpaWpoSAVX+PN7u0PDi4mJFk5B335IkERUVhZ+fHxqNBqPRiE6nUwIL1Go1Li4uHD16lJSUFL744guOHz9O3759GTZsGG3atCEoKEjZ+cs7/NKYzeYyAjQ9PR1HR0dlgZZL8WRmZhIfH8/s2bOxtbVlypQpODg4KH6PvLw87O3tcXJyomHDhhQVFSnRXx4eHgQEBACQm5uLTqfDbDaj1WrJzs4mOTmZgIAARQNPTExU8mp8fHzQ6/XKhsJgMODl5VUm+lCj0XD58mUOHz5MdnY2Xbt2xd7eXnkvQGpqKpcvX8bd3Z309HSWLFlCq1at6NKlC4GBgVgsFiwWCwUFBRQVFWEyme55DaeqBAUFVdiEBQUFER0dXTsTuo3cVoGj0+n473//y9y5cwkNDaVHjx50796dhIQENm7cyLPPPktcXBzdu3e/ndO4LqUvtPwjLO1ULW2ake3p8g/pej+o+Ph4Ll++jNlsRqfTERgYeENHqmze0Ov15OTkYDabCQwMJCEhgcuXLxMbG4vFYgHAz8+PTZs2ceXKFSwWC8HBwQQEBCg7V7PZTGJiIkVFRSQmJuLq6qosgk899RRpaWnEx8crRVK7dOnC9OnTle8kC47Y2Fj++OMPvv76a+bMmUO3bt0YMGAA48aNw87OjrS0NBo0aKBEw5WOyLpW8MPt0HBk7bC0DyMyMpKjR4/Srl07vLy8iI+PJyAgQPFBAFy4cIH333+f7du34+DgQNeuXdmwYQPr1q3j8ccf5/XXX1eqYMjIQS7+/v7KdzGbzRw7doz8/HxUKhVNmjQpozWdPHmSd955h0OHDgGwY8cO5s2bh8FgICYmBnt7e8URb2NjQ1xcHEFBQXh5eZGbm4vFYuHIkSPk5uYSGBhIcXExrq6upKWlcfbsWWxsbAgJCVGc91evXlUEk52dHW5ubmVMe5s2beKjjz4iJiamTLAIQL9+/RgxYoRy3lQqFTY2NkRFRbFu3Tq+//578vPzgRLzW8eOHenduzdNmzZFq9VSVFSkREP6+vrSqVOnar/edxOVCZa7NeLvtms4PXr04OLFiyxZsgRJkrjvvvsYMWIEP/74IzExMQwbNqzM+/fv349Wq6Vdu3bVHhZ9I+RFq7zdtXS+SlXnJO9IzWYzly9fBiA0NPS6n0lPTycxMRFvb2/MZjNZWVnY29vj7+8PlFTTvnLlCjExMeTn5+Pg4ED9+vVxc3Nj3759hIWF4e7ujru7O4mJiVy+fJmAgACCgoLYt28fmzZt4ujRo8oN7ubmxowZMxg6dGgZZ2ZhYSG2trbK3++88w5nz55l/fr1LFmyhGnTptGrVy9sbGxITU3F3t6ekJAQxUSqUqkqCOPbXR5F9om4ublVah4zGo3k5+djNBoVgbNnzx5Gjx5NQkICI0eOZPz48bi6upKYmMiTTz7J6tWr6datG97e3vj5+SnXXt4AwP9d58TERIxGo7IhkaPC/vjjD7799lv27t2Lg4MDP/zwAx4eHkycOJFHH32URYsW0alTJyWxFmDnzp38/PPPSgRYSkoKaWlpFBYWVvrd69evz9GjR5kwYQIuLi54eHhgtVoxGAyKNqvVavHx8QFKAgzmzp3L6dOneemll/D29sbZ2ZnAwEAuXLjA9OnT2bp1K1ASMBISEsL58+eVSgRdu3bl66+/JjExkR07drB3714+/vjja/orZBO6QHDbV3S9Xs/TTz+NSqXik08+4cKFC9ja2pKSklLGhwMl5W6ef/55pcwNVJ7AWNOUdtrLP1q4foSSTqejfv36ZTQc+f1ms5m4uDj8/PwoLi5WduTOzs5IkoSrqysmk0lx4ut0OiXiKzMzU9Gctm7dio+PD1euXCErK4ucnBy8vb1JTk4mPz+fM2fOEBkZSXR0NMnJyWi1Wrp27cqzzz5Ls2bN0Gg0JCYmsnPnTsXZXr9+fTw9PZU8KRlXV1fefvttOnTowCOPPMLatWvp27cvjo6OuLq6KjklUDu7s4yMDKV1gbzgOjo6EhgYiI+PjxJJl5+fz/Hjx5k9ezZLly6lXr16fPfdd3Tt2hUo8QPNnDmTpKQkRo8eTf/+/YmJiSErK0txnC9atIjTp08zaNAgunfvjpeXF25uboSEhODu7s7JkyeZPn06f/31F4mJiQQGBjJy5Ejs7OyYOXMmLi4uvPPOO8yePZvhw4czY8YMunfvzsqVK1mxYgVnzpxBp9PRpEkT3N3d8ff3x8/PDzc3N2UjIPvDzGYzu3fvZtGiRfzyyy+0b9+eVq1aKe9LTEwkOTmZ3NxcwsLCGDBgAG3btmXIkCH8888/PPTQQ3Tp0oWjR4+yZ88e5s2bh1qtRqfTUVxcTGZmJkePHsXNzY2+ffvyxhtvKOY7Wfvp06cPW7duZcuWLUrUnPzbTk1N5c0338RsNjNlypSavi0EdQ2phigsLJT+/vtv6cknn5TGjBkjHTt2THnNarVK6enpUu/evaXNmzdLkiRJ+fn5Zf61Wq3XPPb8+fOl9u3bS+3bt5fq1atX7XM3m81SYmKiZDabyzxvtVorPIqLi2/4XGRkpLRt2zbp1KlTUkJCgpSbmytZrVbJbDZLZrNZysvLk1asWCF9++230pEjR8p8Njc3V9q+fbv06quvSsB1Hw4ODlLbtm2lp556Slq8eLG0b98+ae3atVKnTp2u+zmVSiUFBQVJffv2ld555x0pKipKio6OloqKiqTs7GzJ1tZW6tevn/TNN99I+/btk44dOyZlZmYqc6wO2rdvr/y/Kte3sLBQSkxMlAoLC6Xi4mIpJydHOnz4sLRhwwYpMjJSslgskslkkt59913J2dlZUqvV0qhRo6RTp05JFy9elPLy8qSMjAxp0KBBEiCNGTNGiomJkYxGo3T48GHp0KFD0ldffSUFBwdLgGRnZycBkru7u/Tiiy9Ky5cvlzZs2CCNHDlSsrGxkQCpa9eu0rvvvis99dRTkl6vlwApNDRU0uv1krOzs/Thhx9Kffv2lQDl9YYNG0rvvvuu9Ndff0nbt2+XTp06JR08eFCKiYkp8zh69GiZvw8dOiS9+eabUosWLcpcS09PT6l169ZSjx49lHm1atVKmjZtmgRIM2bMkHJzc6UnnnhCAqQGDRpI3bt3l5o1aybZ2dlJarVaOZZGo5FeeuklKSIiQsrJyZFycnKkmTNnSs2aNZMAyd/fX2rRooXUokULqVevXlKvXr0kvV4vubi4SID0wQcfVLi217rmdwO3srzW4NJc7VzvOqokqWbj9oqLi5UM8PK8+uqrzJkzh5SUFJ5//nklWmrcuHG0b9++SscPCwsjPDy8uqddKVI5jSUwMFCJzCr/vtLPXUvDkXeH58+fJzw8nKysLEaNGoW7uzuFhYVERETQqFEjfv31V/773//SokUL1qxZQ2JiIpmZmaSmppKbm0tQUBDBwcEEBgaWCa9+9913mTVrFgaDgeeee46goCBMJhP16tXD3t4eq9XKlStXOHfuHMnJyWzYsAGABQsW0KdPH/z8/IiIiKB79+60adOGF154ATs7O8UEKpv+qkPDudZ1vNbzVquVoqIirl69qpi/srKyyMrKIiAgAKvVyvDhw1m/fj1du3bl3XffpXHjxkCJOc7f35+1a9cycuRIDAYDTz31FEOGDKFXr14kJSUxceJE1q9fT+PGjZk6dSrdunVj586d/P7772zfvl0xdxkMBh599FE6duzIkiVLOH78OLa2tnTu3JnHH3+cxo0bExcXx4wZM5QggsjISPLy8hg1ahQFBQUsWrSIP/74A7PZDJQUSvXx8cHLywtbW1slOlCj0SBJEs888wz9+/fHZDLh7e3NlStXFP9Jdna24j/LyMjgjz/+4Ndff+XChQsAvPLKK/j5+fHuu+/y4IMP0qpVKz7//PNKr4n8m/Xw8GDt2rWsWLGCuXPnYm9vz8iRI3nggQeU9wwePBgo8VUNGDCAkJAQLl68yOnTp3nmmWdu6treqcjVImr6s7XN9a5jlQXOpUuXmDx5MufOncNkMinPy7bsW0EOL37sscdo0KABFouFli1b0qpVKw4dOsSRI0eYO3cuzs7ON1zMakPgXL58maioKEJDQ6lfvz4qlaqM36eyulyyEJKj2by9vYmLi8NoNOLv78+ff/6JXq+nTZs2tGjRgtOnT3PmzBkOHTrE3LlzadmyJWvWrCEoKEiJWCqNbMqDEpPgCy+8wKZNm+jduzcffPCB4sdITEzE19e3zGezs7Px9PSkT58+6HQ6NmzYgJ2dHc7OznTu3Jn09HT++OMPvL290Wg0ZGdnU79+/TI+nFvl3wic6OhoLl++TEhICMHBwUrkWnx8PC+//DLbtm1j6tSpjBkzpkJOjL+/P2azmSVLlrBp0yZ27txJYWGh8p0KCwt54403eOGFF8qca4vFgo2NDatWrSI/P5/777+f6dOns2nTJry8vHjppZcYNGgQkZGRuLi4KJ8zGo3MmDGDo0ePMm3aNB5++GG+/PJLVq1ahUajYejQobRs2ZKUlBRiY2PJzc0lJSWFoqIitFotVqtVSQhOSEjghx9+UPxNixYtomfPngQHByutBkpjNpuJjo5m6dKljBkzhkceeYSGDRsybdo0zGYzO3bs4MCBAxw7dky5x7VaLX379uX999/n4YcfJj09HYDevXszZsyYCuHnssCRJAl7e3v69OnD+vXrOX78OC+88IIQOLfxs7XN9a5jlX04zz77LB988AGvv/46O3fuZOHChbd8QkoXfAT47rvvGD9+PKmpqcyZMwcocWwfO3YMvV5f676caxEYGKj8K5+TtLQ04uLiSElJoVmzZhU0OjliLCEhgZiYGBISEoiPjycnJ4fQ0FBlV67VajGZTHh4eLBu3TpWrlzJ4MGD+eabb5T+JcnJyej1eqxWK+fOnSMhIYHIyEglZHb//v1kZWUxduxYhg4dislkIjExEYCoqCilwZ2MRqNh3bp1xMbG8vPPP6NWqyksLOR///sfERER/Pnnn3Tq1ElJ8isdwl1bSJKEt7c36enpeHt7I0kSmZmZXLlyhfHjx3Ps2DFmz57Nfffdx8GDB1m4cCHHjx+nT58+PPzww/Tp0weVSsWQIUMYMmQIiYmJnDhxgm3btpGTk8Nbb72FjY1NhYiujIwMPDw8aNu2LYsWLeL9999HkiS6detGZmYmixYt4uLFizRu3LhClvnYsWPx8PBg2rRpTJs2Db1eT9++fXn00UeVDUFQUBChoaEYDAalIVpOTo4SgSgnho4bN4533nmHRx99VIk8vHr1Kunp6UpEmUxubi5+fn68+eabfP755+Tk5NC3b1/lnmjdujXNmzdnwYIFSlJvSEgI3t7eJCYmMnnyZFatWsXAgQMBlHlVRmpqKkajUQkYKS10BfceVRY4RqORBx54AEmSCAoKYtq0afTo0YMPPvjgXw28du1aZs+ezdixYxk+fDgajQZfX19ef/11nnrqKd544w0+//xzjh49yuXLl8nPz69zyWSyAJQTHuH/tB4PDw9SUlIwmUykpaUpFQJkZK3H09OT2NhYWrRoodQ0a9SoEampqRQVFREbG8vJkyf54osvOHbsGB9//DEvvfRSBY3m0KFDvPXWW2VCLLVaLV5eXjRo0IC33noLJyenCvOIjY2tkBdjNpuZP38+/fr1Y+jQoUDJ9frpp58YNmwYnTt3xmg0KkUl60K+hVqtJjc3F61WS25uLgaDAZPJxJgxY4iJiWHhwoU8/PDDvPrqqyxfvhx3d3fatm3Lhg0bWLlyJf3792fZsmXK8RwcHOjfvz+dOnUiPT2dBg0aKFFwpcnNzeWDDz5g5cqVAAwcOBCNRsOff/6JjY0Nzs7OrFixAjs7O5YsWVIm6CQpKYkPP/yQkJAQzGYzw4cPJzIyUsl9gZINwfTp05Vk1cqQc48++eQTTp8+rTy/Zs0a+vfvr2i6MkVFRWRlZTFz5kz+/PNPBg0aRI8ePZTk0/Pnz/Of//xHEVR6vZ4TJ05w6dIlQkJCOHToEAkJCezevZumTZvSuXPna1aHjo2NBVCCGITAubepssCRd9ANGzbkm2++wd/fv8LOuKrExMTwzjvv0KJFC86dO8eqVasYOnQoGo2GXr16sW/fPl544QVeeeUV9u3bx6+//lohE7uuo9FoaNasmWJWK01xcTFXr15VumdaLBZSUlLQ6/U0aNAAnU6Ho6MjWVlZvP/++6xfvx6tVsuaNWsYOHBgmcWnqKiIL7/8kgULFhAUFMSsWbNo2LAhUBKGXVqzSkhIqNLcjxw5QnZ2Nv/9738BFC2hY8eODB48mFOnTlGvXj18fHzqhLCRKZ3ZDzBnzhwuXrzIihUr6N27NwDt2rVj9erV6HQ66tWrx6FDh7CxsamQaxMdHc2vv/7KypUrMRqN9OnTRzkH5ZHPwYQJE3jllVf4/PPPsVqtdOnShV69evHll18qpqXyqNVqnnjiCYAy+UEyrq6uGAwGpRbaiBEj8PHxISEhgWbNmpGfn8/SpUu5ePEiGo2GNWvWoNFoqF+/Pi1atKj0PEVERPDWW2+RlpbGgAEDePvtt8nIyChzHjt06MDhw4cpKChQTOi2trZMmzYNgMaNG7Nnzx62bdvGkiVL6NGjB507d66g6cqbMvmelU1xgnuTKvtwjhw5QtOmTcnKymLq1KlkZ2fz5ptv0rlz55seNC8vj3PnztGgQQN+++03IiIi6Ny5M0888YSSiyBX0pUT2apKbduBSxeOlJHK9YNJTk7mxIkTGI1GGjZsSGZmJg4ODkRFReHq6kr79u1ZtWoVX3zxBRERETz++ON89tln+Pn5ASW7Rq1WS2RkJJMmTeLcuXM8+OCDjB07VvnBR0VFYTKZOHr0KBcuXKC4uFgp7ihJEra2trRv3x5vb+8KPpwlS5Zw5coV5XMPPvggUVFRbN++HavVqpgQPTw8bltV4Jv14cgOdpnIyEjatGnD/fffz6RJk9i4cSOPPPIIDg4O5OfnM3HiRM6dO0ffvn0ZO3YsDRs2RJIkjh8/zpIlS9i9ezcajYYOHTrg7OzM7t27MZlMPPLII0ruCkBcXBwODg6MHz+ezMxMVq1axfLly9m2bRuxsbEEBgZy5coVnnzySR5//PEyczx79iyHDx9Wzmu7du1o3749TZo0qfBdTp8+zd69e7G3t+fRRx9Fr9eX+V0kJCSwd+9epViofI379+/Pu+++q2gY27ZtY/Lkydjb2zN27Fh8fHxQqVRkZGTg6OiIJElkZ2eTnp5OTEyMUu4oMzMTnU5HWFgYnTp1wsXFhaKiIs6cOaPcK+WvAZRsvBo2bIjBYODYsWNs3ryZd999V/hwbuNna5tq8eF06NABKNnNLVy48JYm5ODgQKtWrdDr9Tz//PP89NNPHDx4EICnnnpKcfzeieq32WxWMtrlxbh050QoWaibNWumaDiFhYWK6S05OZkFCxawfPlyQkNDWbNmDQMGDCgzho2NDTt27GDixInY2dnx9ttv07dvX6DETHPo0CE2bNjAlStXkCQJLy8v7OzsKCwsVDLNk5KSOHHiBHZ2dgwZMoTnnnsOKHGOnz59mhEjRmAwGHj11Vc5fvw4X3zxBYGBgcTHxxMXF4evry9FRUW1XoZerk4sV2Awm82cPXuWmTNnotVqeeKJJ5gyZQrh4eEsW7aMoUOH8uCDD7Ju3TqSk5Px9fVl27ZtrFy5kr///ptDhw7h4uLC/fffj7+/P+vWrSMnJ4e2bdtib2/PX3/9xdatW3nkkUfw8/NTqmKHhoayefNmCgoKaNeuHY0aNWLSpElcvnyZYcOG0bdv3zKJvyaTiddee43ExEQaNGiAo6Mjx44dIzw8nD59+vDaa6+VMYXdf//9BAYGsnTpUpYtW0ZYWBgTJ05U7qtWrVpx8uRJ4P/MuoWFhaxbt47Dhw8zefJkLly4wHfffUe9evVo1qwZX375pRIZeS20Wi3Ozs7cd999hISE0KNHjzKv5+fn07t3bwoLC4mPj1c2XEOGDCE7O5vHHnsMd3d3jhw5ApRoV4J7lyoLnIiICD7//HNiYmKUHzfA33///a8G1uv1SJKEXq/nmWee4ZdffuH8+fM8/fTT7Ny5k7Nnz1ZqYqjryCVtAEJCQip9j1zkECArK4sTJ07Qq1cvkpOTmTx5MsnJyUydOpU333yzwmetVqtSZqZNmzb8+OOPxMfHk5+fz/z589m4cSNQEmwxYMAAwsLCFA3m0qVLNGrUSDnOxYsX+f3331m2bBnPPvssKpWKBQsWYDQaGTlyJDNmzGDBggWMHj2al156SaksUFBQoJRMqS0kSeLjjz/mvffeq1SrlKsvjxs3Dp1Ox4svvsj+/fv5/vvv+f7779FoNHh5eSmOcihJVvTx8eH3339n7969zJo1Czs7Ozp06MCePXsAGDFiBGlpaezYsYOsrKwyY5bWznfs2FFm4S+/W83JySEtLQ2VSkVoaChOTk7Ex8eTmprK8ePHlQrMUBKYsHTpUmXXqNPpSEpKqhBuX34+ct2zgoICJk6cCJQU/5TLJPn7+9OhQwccHR1JS0tT6r7J1QpOnjxZps6XXIm6MmxtbcsI1F69egElpjez2UxRUREqlepfm+HrMpW1IQDRiqAyqixwhg4dyksvvcTYsWNvun1sZf1w5B+LbNseP348gwYN4tSpU2zevPmOEDaVqbx+fn5KIcPCwkJsbGzKZOHLyO17z58/T3JyMm+//TZLliyhfv36/P3337Rt2xaAlJQUZeHJzc3ltddeY9u2bYpPITU1lWXLlvHXX3+Rn59P69atlR41dnZ2pKSkKD/yrKysMgsslCwucm+VEydOsHr1agYOHEjr1q0ZPHgwgwcP5r333kOn06HVamnSpInSxbK2+p0UFBTw3HPPsWLFCsVUVHqnLodDS5LE448/TqNGjbBarTz44IO0b99eKSGUn59Pu3btCAkJoX///rz99tucPn2af/75R7ku7dq1o169ejz22GOcPHmSRYsW4eLiwkMPPUTLli2VytTZ2dl4eHiQnp7Ohx9+SEJCglJpe926dcTHxzNlypQy5+zRRx/l8OHDbNmyBQBHR0d69uzJI488QmxsLMXFxezZs4eNGzcq/XCcnJzw9/enfv36HD58uMx5CQwMJCAgQBEm7u7uJCcnc/LkSUXDPXfunCIYExISSExMpH79+nTt2lWpZgH/1/5AdvpDyUbp1KlTZcZMS0ur1JRW+jOywKqrUaa3imhDUHWqLHC0Wi0vv/zyTQ9QlX44xcXFREZGcuDAAbZv306rVq1uepy6gq2tLVqtlsuXL6PVavH09GT//v2Ehobi7e1NREQEKpWKxo0bK7ktq1evZt++fTz99NPMmjWrTE6DRqNRjjdmzBiuXLnC2LFjefLJJzGZTHz77bds3LgRR0dHevXqpYTJ5ubm0q5duzJzO3PmTJkoKShZvH19falfvz5jx46lQYMGfPXVVyxZsoSsrCz69OlDQEAAOp1OKdlT/hg1idlspnv37hw/fhwoMc+6uLhUCFeW2zWsWbPmusdTq9V06dIFFxcXJcJLdpZDyflfvnw5bdq0oX379vTq1Yv169fz66+/YmNjQ9OmTXn22Wfp1KkT0dHRvPHGGyQlJeHo6KiU2jEYDBw9epRffvmFt99+W4kwHDlyJJMnT1Z8OL179yY8PBxPT0+io6P5/vvviY6OVprKZWRkKKHe//zzD0FBQYSEhNC8eXNsbGwICwvj2LFjpKSkYLVaefrppzl69Cj16tVj586dWCwWJUFWxmq1EhUVRWZmJh9++GGZey89PV3x10GJlaO8v89kMilas6D6uFsrSN9Q4MjRKwMHDuS7775j8ODBZQo7Xs+hX9V+OBqNhsaNG3P27FlFrb/TMJlMREZGKjk0UFLY8e+//2b37t0cOnSITp06ceDAAZycnNDr9YSEhDBjxgz27dvHV199xbhx466pPc6dO5fIyEhmz56tJJfu2LGDjRs3EhQURLt27ZSEzJCQkCr1FUpISCA2NpYHHniAcePGkZGRwS+//IJarearr76ia9eujBgxotb9NKVJSkpS7snQ0FCioqIICAgoU9hSpVLh7OyMp6cnQUFB5Ofnc/HiRVQqFSaTSWnhkJqaytmzZwkPD1c0ant7e06fPq0srKdPn8ZkMnHgwAE8PT1p3bo1r776KleuXOHw4cOcPHmSGTNm8Ouvv7J69WqSkpKws7NTQrRVKhUFBQU0btyY9evXc+XKFd577z3F3KJSqRT/W2m+//57MjMzFR+nXq+nadOmSi5QZmYm0dHRREdHI0kS7dq148CBAxw9ehQoCUz56KOPlAoEcpTYtRy6GRkZ7Nu3r4K/8FYpKChQhFjphouC63O3VpC+ocBp3759mYiJ0mUvVCrVDSsN3KgfzqFDh0hPT+ehhx66Y4UNlEQSHT9+nMuXL9OvXz9CQkIoKirC19eX4OBgioqKMBqN+Pj4KBWcr169ygMPPMDatWs5dOgQ48aNu+bxX3vtNbZs2cLcuXOVNgKypuHl5YVGo+HChQuo1WqioqKIj4/HwcGBZs2aVVrl4MKFC5w9exZvb28KCws5cOCAUsFg0aJFxMTE8Oabb96WVt+3gmyCzc7OpmHDhly9epVjx47xww8/VHjvgQMHOHPmDMeOHUOtVqPVaikuLiY7O1uJ4nrggQfo0qUL/fv3x9fXl8TERJ566ini4+ORJElpJ+Dp6UlWVpayaAYFBbFz505UKhWvvPIKUJLIaW9vz9q1a8v8Zpo2bUqXLl0YOXIkM2fOZPjw4dx///08+OCDFYqkyphMJlq0aEG/fv1YvXo1OTk5WCwWJRctMzMTlUpFixYtlA65PXv2pHPnzkqI9MiRI9myZQshISE0a9aMiIiIa0YPeXp60rlzZ5YtW0bXrl2pX7/+rV0oSnw+0dHRZXw75Qv2Cu4tbihwrly58q8PXpV+ODExMRUiX6BuVIm+GRo0aEBkZCQFBQWcP38eZ2dnpb/N/fffj9lsxt/fn6SkJAoLCyksLFT62PTr148VK1YwZMgQ+vfvX+a4RqOR4uJivL29+f777xk1ahQfffQRU6dOVXblV69eJTk5WekgaTAYyMzM5Pfff2fPnj20aNFCSYTMysri1KlTpKSkKM3hdu3axXvvvccjjzxCTk4OX3/9NT169ODhhx8us3DWheshty8GlCZwpQM1oMRXtX//fqVPzAMPPEBYWBhJSUlKjpHcTuDEiRNYrVb8/f353//+x4YNG+jUqRP79u0jIyND6ZiZlZXFnj17OHXqFG3btiUmJobLly/z4IMP0rx5czIzMwGU5M2kpCTS0tLKCLlLly4pOVm7d+9mx44dNGrUiP79+9OyZUvUajVxcXFkZ2djNpvJy8sjMjISDw8PRUuzs7MjOzsbOzs7goKCOH36NElJSQwYMEDZOMhh8zExMfj6+uLh4UHz5s2VNuLl/TBQ4txftmwZhw4dYuvWrfTo0aOC+SwnJ6fCepCTk0NcXByA4jeDkvyw48ePK03rXFxclG6ignuXKvtwTCYT3333Hfv27UOlUtGjRw9eeumlG2b/32w/nLi4OLy8vCguLq5Sl8zapPQCbGdnx4ABA7h06ZLSQTEwMJDg4GD8/f0VM2R8fDxnz55VStBLkkTjxo25dOkS//nPf/jrr7/K5GH4+PgoZraHH36YWbNmMWHCBE6cOMHo0aPR6/VlEjrT09NJT08nODgYW1tbUlNT2bNnDw8++CCNGjVSggtee+01IiIi2Lx5M2+99RaTJ08mISGBN998k5SUFD799NMyDcbqCmq1GrPZrLQ9hsqjgWxsbHjkkUd45513+O233/jiiy8oLi5WckmGDx+Oj48P69evZ9euXezcuZMxY8bw5ptvMnbsWOLj49Hr9TzwwAM4OjqyatUqpc/Pzp07ARgzZgx9+/at4Cv78ssv8fDw4Ny5c3z88cecP3+epKQkzGYzRqNReZ/BYCAiIoKIiAgGDhzIyJEjcXJyokGDBkrDs507dxIQEEDLli1Zu3YtZrOZgIAAMjMziYmJoXfv3orJNiYmBpPJhJubG0ajkQsXLhAVFcXzzz/P/v376d27N0OGDCE8PJz8/HzOnj1Lhw4dCAkJIS4ujlmzZtGzZ0/UajU7d+5UWnS7ubkxcOBAJfy7NFevXqVFixb8/fffzJ8/X3l+9uzZyv9NJhNeXl5kZWUJDecep8oCZ/To0Tg6OvLqq68C8NtvvzFq1ChWrVp13c/dTD+cjRs3KqU+5KKAarX6jrH76vV6WrRoQVFREba2tkoujiRJSpfIevXqkZCQoISrduvWjcDAQDp06MDEiRPp2bMna9asoVu3bpWOIfsvmjVrxq+//lqmkGpp5MgZg8GgFPiUy63MmDGDL774gvT0dMaPH897773HggULmDJlCmazmbfffpvBgwfXOWED/1cipVWrVly4cIFZs2aRm5vLmTNnlPfY2NjQo0cP1Go17733Htu2beORRx5h+PDhyk5fRi7quWDBAhYtWsSOHTtITU3lww8/ZNy4cRgMBn7++WdWrlxJSEgIU6dOJSoqivz8fDp16nTdCK1mzZrxyy+/KGWcSgsb+D//RuvWrenSpUuZ10aPHs2OHTvYt28fJpMJtVqNm5sbOTk5xMfHExwczJtvvkmrVq3Yu3cvn376KSaTCZVKRWZmZhkLwbx587CxseH06dOMHj0aNzc3WrRoUaYSgY2NDVqtFoPBwNixY+nRowc7d+5Eo9EQGRnJnDlzCA0N5bnnnqvUb9umTRtcXV2V4A15YwAlAUey5iOiue5tqixwLl68qCSWQUkiWuvWrav0WVdXV8aOHUuzZs2YP38+er2epUuXlqnrdeLECV5//XWWLFlCREQE27Zt4/nnn6/S8X/44QfFhi9HBtUWhYWFXL16FX9/f0WDkSSpTJfI+++/n7i4OGxsbJRQVy8vL7755humTJlC//792b59O2FhYRiNRiWqKT09nc8//5zWrVuTlJTEV199RfPmzTl79myFeTg6OipdOC0WC//88w9ms5n333+fjz76CIPBwKJFi/D09GTw4MFs3bqV3r178/nnnxMUFERBQQFarbbSciw1TenrKwvYZs2a8fPPP9O/f3+aNGnCt99+W+YzV69e5ccffyQ5OZmHH36Y6Ohodu/ejV6vr1BPTqvVMmHCBGJjYzl9+jRvvfUWoaGhLFq0iA0bNrB7927Fz5OTk4Onpyeenp4kJycr1ahLEx8fr5j9ADp27KgEdcTFxSntoRs1asSwYcMUDTYzM5P8/HxSU1OV6syPPPII27Zto6CggISEBOrXr0+bNm0ICAjAzc2N+Ph46tevz8yZM5k0aRJ5eXnKBq10pebs7GxiY2NZtGgRLVq0oHPnzmU2FBqNhtDQUA4ePEjPnj2xt7enY8eOpKamEhUVRVhYGCdOnFBqKLZv316JBDx//jyAUjQUUNp29+3bF51OpwQtlBe6gnuLKguctm3bcvDgQaWUzaFDh665C68MGxsb7r//fu67775K++FERUXRtWtXOnXqRL169Zg7dy4TJkwgJCSEhx9+WOldUhnjxo1THO5hYWFVntPtIDo6moMHD9KgQQM6deqkRCoFBAQgSZKi9chBBUePHiUtLY1GjRrh6+vL2rVradGiBb/99hudO3dWesMDfP311+Tn5/Poo4/y/fffI0kSw4cPJy4ujqCgIGXx8/X1JT8/X/FbfP3115jNZp577jnmzp2LTqdj69atnD9/nqeeegqj0cjcuXMZM2YMubm56HQ6bG1tMRgMdULLKX1927VrR1JSklJYctasWQwdOrTM/RQfH88333yDSqVi9OjRLFy4kAYNGvDzzz/z448/0qVLF5YsWaL4wBo1aoSDgwOrV6/m/Pnz5ObmMmHCBNLT03FxcWHUqFE8//zzJCcnl8lVgZL2DgaDgYKCAhwcHNDr9eTm5pYJHf/44485efJkmcTJxMREEhMTOXPmDK1atSI0NJSIiAguXrxIcnJyhXOgVqtxcXHBzs6Ohg0b0qlTpwrh6QsWLODixYv88MMPBAQE0Lp1a3bt2sXq1auBkk1I7969+fvvv1GpVDzyyCPKfCMjI7GxsSEzMxOtVovZbGbjxo1cuHABLy8vwsPDCQkJQa/X8/fffxMZGUnLli2BklwuPz+/CiZw2QekUqkUzedaGrngxtwNodJVFjiHDh1i8eLFSlRNbGwsTZs2pWXLlqhUqkodkZVxrbDfjh07MmHCBJ577jnWrFnDW2+9RXBwMBERESxZsoSpU6cqbYLrOnLvErlUPpStKC33wCkqKiIqKoq8vDzFAS6X+JFDq2WuXr3KTz/9xOjRo/H29iYpKYm2bdsqfXSgZFGSr4/VamXDhg2sWLECtVpNkyZNmD9/Pr6+vqxZs4ZFixbx+eef065dO5YtW0bjxo0VM4zBYKizZky1Ws3TTz+taMiltZ/StGjRgk8++USphNGiRQskSSIqKorU1NRKS7o4ODjQoUMHNm3apAi0evXq4eHhwZIlS4iOjqagoICUlBQyMjIwGo1lFlBXV1emT59eJvJM1m7z8vJwdnZm2bJltG3bljNnzrB9+3YuXbrEzp072bVrF6GhobRu3ZrWrVvTsGFDbG1tyczMJDY2lqKiIjIyMjh//jwff/wxPj4+TJgwQWmrINO5c2dcXV35+eef+eGHH/Dz8+PDDz+kS5cuTJw4kb///htfX1/OnDlDTEwMKpVKaS2gVqsJDg5m1apVHDp0CCcnJ+bMmcNTTz3FsmXLFJNrz549iY2NZevWrYpgghKBJv++p06dqrRlT09Px9XVVRHKgn/H3RAqXeXinZWVbijNvynjcPjwYcxms5J8Fx0dzbFjx/jjjz9YsmQJAFu3bmXx4sUsXbq0Sses7QKAhYWFxMTEoNfr8fX1VezXpW+My5cvc+XKlTJCxcbGhujoaN5++22uXr3K6dOn0ev1SsTRjBkzWLZsGdu3b2f79u38/vvvxMXF8frrr3Pp0qUy1bTT09P5+++/SUlJoV69eqSlpVFYWEj//v3p3r078+bNIyYmhkcffZR58+ZVmshZ2zfy9Yp3HjlyhIULF/Kf//xHaZNQWoDY2Njw+OOP89hjj9GyZUteffVV/vzzTxo3bszjjz9Oy5YtWb58OefPn2f48OF069atwv17+PBh9u3bx9q1axUzrZOTE35+fnh6euLi4oKDgwPFxcX4+Phga2vLunXriIiIoE+fPowdO1apEGG1Wtm0aROrVq3CaDTy9NNPM2bMGPLy8ggICMBkMmGxWHBwcCA1NbVCdFhKSkqZa7R//34+/PBDYmNjadKkCf7+/ly9epX4+HjFdOXi4sLIkSPp0aOH4rMqKiri22+/ZcmSJTg6OmJvb4+9vT22traYzWaKi4uVygL9+/dn0KBB9OnTRxl3+fLlLF++nJMnT9KwYUMGDhxIZmYmxcXFJCcnK74jQDG3u7m5MWvWLPr168fGjRs5ceIEzz///F1VvLM2C23WxSKft9Txs3TZ8sq4mUrOpdmyZQtjxozh2WefZfny5bz++uu8+OKLaLVahgwZwtChQxkxYgSrVq3ixx9/ZNWqVVWKcKmLN235U1y6XbQc5WexWFi6dCnPPvss3377reK/io+P59KlSzz88MMMGTKEzz//nH/++YcjR44wa9YsWrVqhbu7Ow0aNFAE1MaNG9Hr9QQHB3PmzBm6dOlChw4dWLFiBcnJyXTu3JlRo0bx4osvYjQa66RGU5Vq0XFxccydO1fRRoqLi8nLyyMzM5NNmzZhb2/PmDFjmDBhAtHR0fj7+zNp0iQlAsvX15fY2Fjatm3Lzz//XGYDUFBQgLOzM2azmcTERHQ6HQsXLuT06dMcOXKEjIwM/P39CQgIIDg4mHr16tGqVSv27dvH999/T+PGjZk2bZqShR8VFYWdnR3ffPONkhszY8YMBg4cqPg17OzsSE9Pr1CjrqCgoMLvLD4+ns2bN/P1119jtVoJCgoiICCAZs2aERISQs+ePXF0dCQmJqaCVvH333/zwQcflClzZDAYaNeuHR06dOD5558nODiYzMzMMlGoeXl5ODo6snz5cj744ANSU1Px9/dn6NChDB06tEyPJrmNw/Lly3nqqad45plnWLx4Mfn5+XTv3v2OFTiV1U2rTbPWnSZwbjrxU975yrv2m20xLUds/fbbb8yZM4dhw4YxbNgw3nzzTYxGI//5z38YOnQo33zzDWvWrOHMmTOsXr36rgqntFgseHh4lCmCqtVqFedrbGxsmazstWvXYrVayxQD7dq1K+fPnyciIoKzZ88qoboADRs2JC4ujujoaN59913WrVvHnDlz6NOnDy+++CJPPvkkoaGhGI1GxbZ+J5o6AgMDmTlzZqWvnT59mvfee4/vv/+ef/75h8WLF7N37162b98OlCywss/h+PHjbNiwgRdeeIHCwsIygRJy35xx48axatUqmjVrRs+ePfH29ubSpUvk5+ezd+9erl69itVqpXnz5owePZo///yTp556iiZNmuDk5IQkSbi5uWFnZ4eTkxOXL19m7dq17Nmzh2XLllFYWEi7du1o164d3bt3p0OHDjg5OV3zu2s0GuW3I1NQUFCmedu1CAsLY/v27SxZsgQPDw/atGlD/fr1KwRTVIZKpeKpp57iiSeeYP369cyZM4fZs2ezY8cOZsyYUUY7s1qtfPXVVwQEBHDhwgWaN29e55oo3iyibtqtUWWTGpRoO5cuXSpjt+7Zs+e/Gvizzz4jNzeXt99+GwcHB86ePcurr77K008/zfPPP8/Fixc5e/Ys7du3vylzXV3cJZU/xVarVWnLLAsVi8VCXFwckyZNYu3atYwePZrvvvuO6OhoCgsLeeWVVzh8+DBTp06t4KC9fPkyvr6+XLlyhc2bN3Py5EmaNWvG+PHjmTx5Mjqdjueff57evXvTuHFjPDw8sLe3V+Zxp2o4pamsxXFKSgq7d+/m1VdfxdnZmTZt2iitDDIzM8nNzcXV1VVJJpWTGp944glGjx5NmzZtgJKFvFWrVvTo0YPvv/8erVaLo6Mj6enpSuvnixcvsm/fPv744w+ioqKoX78+oaGh5OXlUVBQQFZWFmazWdGcbGxsiIyMRKfTMXDgQBwcHDh//jzHjx9XCru6ublha2uLjY0NBoMBW1tbvLy8CAoKwtXVlQYNGlCvXj2CgoLQ6XRKBF1poqOjiYiI4OjRo4SFhdG5c2dSU1MrNAWUq2CUJiMjo8xGLyMjg/DwcH777TdOnTrFW2+9RZMmTTh06BBffvklRqNRKYOkVquxWq2YTCamT5/Oe++9x6xZs/jPf/5z09e2LlHXNIq6Nh+opn44CxYs4OuvvyY+Pp42bdpw8OBBunbtyo4dO/7VpFq1asWff/5JVFQUzZs3p3nz5sycOZMnn3ySsLAwWrdufd3ItDuZ4uJiUlJS8Pf3V36g6enp5OTkMHv2bLy8vBRH+OzZs9Fqtaxfv55nnnmG6dOnM378eCZMmIBKpSI3N5fLly/z448/Eh4ejo2NDR9++CF5eXmMHz+e9u3bM2HCBNzd3RVhI0ef1ZX20P+W06dP8/nnn5Ofn4/ZbFZ6C7m4uDBs2DD69OnDc889R6NGjfjoo4+Ij48nOTmZtLQ05RhyP5r69eszePBgMjMzWbJkCcuXL+fhhx/m9ddfJzY2loKCAqXW3enTp2nRogXBwcGKCc/W1pamTZsycuRIwsPDmTlzJtu3b8fW1haVSoXValWqWGdnZ+Pt7c2bb77JuHHjyM3N5dy5c0yaNAmTycTGjRtJTU0lKyuL9PR0bGxsUKvVZGRkEB8fz969exUzIpTkJrVs2ZJWrVrRrl07JVR+y5Yt7Ny5s0x0XNu2bXnxxRcZNGhQGRNYXl5emRqJUGIud3Bw4PTp0yxbtozly5crpsTg4GAmTZrEkCFD+OKLLxg4cCDLli1Tcm9ks2BISAhHjhzBYDDwzDPP3I7bQHAHUWWB8/XXX3PkyBE6d+7Mzp07uXDhAu+///5NDyib4gYMGMDff//N119/zcSJE2nQoAFhYWH079+/zkns6sRqtXLmzBkiIyOxWCyKQ9fR0ZG4uDgcHR157rnnMBgMzJ49G4PBwMyZM9Hr9SxZsoRXXnmFb7/9ltjYWJ599lnGjx9PYmIiLVq04PPPP2fo0KG89dZbrFixgs6dOzN48GDatWuHl5cX7u7uFfrb36lYrVZ69uxJZmYmDRs2LNMTJyoqitWrV/Ptt9/y3HPP0b17dzZv3gz8X1CH7Cj39/cnNzcXNzc3tFot586do2PHjhw8eJDVq1crEVh+fn506dJFafgma/kpKSkkJiYqSZM6nY4nn3ySkSNHsnz5cvbu3Yuzs7NSy02tVuPn50ebNm2wsbEhMDBQMZ82atQISZKwsbHB0dFRSZ6Us/Rzc3OVUjFWq5XLly+TmJjI+fPnOXnyJKtXr+bnn39WzoO/vz9du3ZlwIABdO7cmb/++ovly5fz0ksv8eOPPzJ//nzFelBcXExGRoZS0PTEiRMcP36cM2fOkJOTg42NDV27dmX06NGMGDECgBkzZvDxxx9z8eJFfvvtN9555x1lbFmDunLlCv/9738ZNWqUKGsjqLrA0ev1iv21sLCQJk2acPHixSp9trJ+OFBSCPTNN99UkkEDAwNZu3Ytb7zxxr/4KncGsklHrVaXsWcnJyeTnZ3NwYMHycrKYvjw4VitVubMmUOHDh0YOnQoOp2O+fPn4+7uzvz581m/fj0uLi4sXbqUIUOGYLFYGDVqFH/88Qfvvfceb731FomJiWW6j94tqNVqhg8fzvfff8+lS5cqvC5X7b569SrBwcGK+TAlJYW4uDhCQkIUYa/X6xXfpBy63qpVK3r27Mlff/3FmTNnmD59OiaTCU9PT9q0aYO/vz+A4jNxc3NT8mcsFgsajYbu3bszbNgwdDodxcXFikZhsVg4deoUmZmZxMXFERoaqrTksFqt+Pj4KMm8zZs3Jz8/n/Pnz2M0GmnVqhWBgYFKiRutVquExVssFo4ePUpOTg7t27fH0dERi8VC06ZN0ev1dOrUialTp/Ljjz/y1ltvcd999+Hh4UFWVlaF9g6yxjZw4EC6du3KQw89RFFREYGBgcrv99lnn8VisTB37lweeughVqxYUaZKuSRJSiDQu+++e6uXXHAXUGWBExAQQFZWFo899hh9+/bF1dUVPz+/G37uev1wAGbOnMnOnTs5deqUUmGgfHLd3YSdnR2NGjUiICAAT09PRZuTFzAPDw9iYmJo2LAhLVu25PDhw7z66qs0bdpUqQo8evRoOnbsyOLFi3nxxReVHJopU6bwxx9/8PLLLzNmzJgKgQZ3G3KUXmZmJgUFBVitVtLS0lCr1Tz55JO4u7srSYxFRUXk5ubi4uJCSEiIkogLJYu81WolMzMTV1dXmjZtqpiyHnzwQWxsbLBYLKSlpaHX6/Hx8VEilTw8PPDx8SEzM1PJldHpdKjVaiVyqbwPUqvV0rJlS65evYqfnx9WqxWLxUJGRgYuLi64ubkp5mS5LlqTJk0oLCzE1dWVlJQURUPLz8/n3LlzADRp0oQuXbrg4eFBUlIS58+fp169eqhUqjJh44MGDSIsLIzp06eTmppK06ZNCQoKIjAwEEmSaNq0qVLPsLi4GE9PT1xdXSkuLlYKcGo0Gnx8fBg4cCAGg4E5c+Zw3333KQEXshkxOzubb7755ppVsQX3FjcVNCCze/dusrOz6d+//3V3zmazmZEjRzJx4sQy/XBsbW3L9MORsVgsZezK/4Y7wfEIFQMJJEmioKCgTCBBcnIyJ06cUAT04sWLad++PXl5eWUSaHNzc5k/fz4zZszgkUce4emnnyYkJETRKO9EbtaxLEf8WSwWLl26RGFhobIoy7kzRqMRW1tbCgsLy5xnSZIU05ivry/e3t6YzWZMJpPyPvl1uQqDPIfOnTtjMBi4fPkyNjY22Nra0qhRI1QqFXFxcQQGBl7zN1LaDJiSkkJSUhI+Pj4VnPdms1kJz4aSSCkPDw8KCwspKirC2dkZrVarCC87OzsuX77Mnj17FH9Lw4YN0Wq1imBzd3fHarWWEYrZ2dlcvXoVFxcX/P39SU1NJTU1FYvFojRik+coa3YFBQWkpaVx6dIlNmzYQG5urpIbZTAYCA4O5j//+U+Z+1AEDVQfdW0+UE1BA6W5mci0G/XDOXjwIOnp6Tz88MM33br6bqKgoEAxa8i7RA8PD1q3bs2iRYsYPHgwXbp0oWHDhgwaNIihQ4cSGhrK4sWL+fTTT0lJSWHo0KFMnz6doqIiGjZseMcKm1tBq9XSsGFDMjMzcXJyIjc3V2mHbW9vT35+PllZWeTl5eHp6amcIzlxVv7XZDIpznZ7e3vc3NzIyMjAZDLh7OysVIj28fFBp9NhtVoxGAyK70WlUlWorFweefF3c3NT8myuldeWmJhIdHS0Ep3o6OhIYWEheXl5+Pj4EBwcTE5OjhJMEBQURK9evRR/U0ZGBl5eXiQnJ3PhwgWaNGlCQEAAISEhZGRkoFary5wDWYB5e3srLcVlAeni4qIkmGo0GpydnenRowf33XcfxcXFZGZm4u3tfcubR8Hdx229I6rSDyc2Nlbph1M6u/1O64dzq8gLiV6vR5IkiouLlVpeDRs2ZOnSpRw5coTDhw/z1Vdf8cUXX+Dk5EROTg6dO3dm1qxZdOzYERsbG1xcXO65H3vpXZ5Go8HDwwNJkpSduPy63ALcYrEoO3FJktBoNGXeK0dsyddDo9FQv359srOzlVBq+b1arbZCdYDyyOY2Z2dnsrOzFQEmt4N2cHDA3d0dlUqF2WxWFnk5QEA+vqenJ3l5eTg5OeHr60tCQgL+/v5l5izPNyQkhMLCQiUoQpIkjEaj8pAkSZkDgLe3d5lcHPk36ODgoEQ1enl5KQVGocRELAsig8GgnPt77f4TVI3bflfcbD+cvLw85Qa/VyhtToOSc5CYmEhKSgqhoaH4+/tjtVrp1asX0dHRrF27lnPnzpGSksLEiRPp27cvSUlJSutki8VCYWHhXRORVhUqW+Aq27RotVq8vLwoKChQwsNLa4JWq1XRbBwcHJR8EqPRqPhvylOVezUjI0Npyiab/+TioXq9nry8PCRJQq1Wk5ubqwQgeHt7o9PplIKvUKLdyN8tODgYo9GITqdTFv+cnBx0Oh0ODg5lKn7L5jZPT0/s7OywWCxYLBbc3NwUYVcatVpNfn5+Gc1bpVJhb2+vCGe5sV9dzOUS1D1uu8C5mX44f/75J8uWLVOq/LZo0eKecDaWNqdBie8mLy9PCbWVf8gpKSlKgc5evXrRoEED3Nzc0Ov1ShsCeZEsXx7lXqN8cm3pv6+XfyS3RZbfZ29vX6Yig1xktbQPqCrIprLSGo5Go8Hb2xur1YpKpSI/P1/Rdnx9fcvUx7sWpecm+02ACpWb5feq1Wp8fHxQq9UkJCSQk5ODj4/PNTUS+T4qfT+p1eoyVRDu5FwuQc1SI3pvVfrhRERE8PLLL7Ny5UrCw8P5559/2LJlC6+88spdmwAqI/+YZZOIvb29Yk7R6/Wkp6eTmJhIcXExHh4e+Pv7ExISomSim0wmPDw8yji57zVKR5yZTKYymorBYLipMj52dnY4ODhUWGz1en2FBb4yh21pzUoWdHKVAPi/UGqLxVImUMRqtSpVDK5Vyqm0tqXRaCrMUa1WX/P7ye/R6XQUFRWh0+kwGAyKua28hlO6erg8dmX31r1kjRDcGjVmaL1RPxyTyUT37t3p1q0b3bp149ixY2zatIn58+fz3//+t0K5/tLUpQZs/wbZoS1JEnl5eeTn5+Pg4KDkUci116BkkfP19cXFxUVxgt/J9dCqQlWur7zomUwmcnJycHBwwMnJCTs7O1QqFba2tqjV6grZ9KU/C/9XYr+0ZllaIyqtQVS20JbXrGQB5eTkVOH6yKHa8qKuUqkUzepa8ysvOG9ULaJ8kzUHBwcluRS4bv00ObRZ/j5CsAhulRrfCms0mkp3SU2aNCEyMpLvvvsOKGm0NWDAALRaLREREcC129OOGzeO8PBwwsPDK9SSuhOwWq2KOaU86enppKam4uzsTGBgIL6+vjRo0EAxaRgMBpycnCo1odwt3Mz1lYtjyuYl+V4rLCxUSt9cD1lDuJaWeKPXZYFQugK0LPiuNVdZOJVutnctquN6X+9+K0/57yMQ3Aq1ans5fPgw//zzD/v27cPGxoaPP/6YI0eOsHz5cqCkUrWbm5vSC+du3WHl5+eTmJhIQUGBosEYDAby8/NxcXHByclJKbgpO4HlcyE7ce9FM1plyP4ro9FYZkG93sJfnZQf53qCpKpCpvxnrifwqoLRaCQrK4vU1NQbCp3y3+dmhNWdTnBwMCqVqszj3/T9up3IXUDLP+pq8nytrVJbtmzh0UcfZePGjYwaNYp58+bRpEkTHnjgAf766y/mzJkDoERo3WhneqchSZLyKI1sXpFNQ1lZWdjZ2VFUVFRLM73zkHflpaua/5vF/d+gVqvrfMSWXq8vExpe2X0oU/q8yWHVssZzvc/dDcitCEo/6lo75+jo6ApzlCTphg0za4saD5avrB/O0KFDeeONN7BYLDz22GP4+fkxadIk9uzZw5EjR/jzzz8rtb3fycgZwnJ4rq+vL3q9Xvm7dF6OHHZ6t2p41UHpc3MjP8vtGvd6z1X1szVB6UCA6/lmKguTlkOvrVarEsYtEFSVGhc4sgO3adOmnDp1ioceeoi2bdsye/ZsXn31VXQ6HS+++CKHDx8mNjYWR0fHKjWVuhMpX12gfM6DnD9xtwYD3C6uF6klKNEA5eTRmxUYarUatVpNTk6Oov0IBFWl1rYnrVq1Ij09naioKCwWCy1atOCLL75g5syZHD16FJ1OR2ho6F0rbKCiA/haDuHKVGbBv6Oyc3mvnc9b9WfVlD9McPdR4wJH/nEPGDAABwcHvv76a86cOUNeXh7t27enf//+90RNNTk0XHYAV/Z3+VI/+fn599zieLu4l5zf5ZHDozUaTRlH842Q31f+8wJBVakRgXPx4kUOHDigdGWU+fzzz/Hw8GD+/PlMnTqVr776irVr14pGTZUgm98KCgpqeyp3BSLcVyCoeW67D0f0w6mca2kq19oxXq9kieDmqaxkS01zs/eAQHCnc1sFjtlsZsWKFfz0009l+uHMnDmzTD+c+++/n/vvv79a+uHcbZTOtxGO8FtHPp83ytAX3FsEBwdXCCWuazk3dwO33aQm98MBGDx4MI888ogSFg1w8OBBpW/8veC7EQgEdY87IefmbuC2Chy5H86aNWvYu3cvarWa7t2706ZNG/bu3UthYSGxsbFKM6t7yZQgggAEMvdyAIPg9lBZBYK64Kqoc/1w7hUq6/ApuDcpX4FaILhVKtPO6sKGvk71wynN3d7xsyaz4QV1E/m6i3tBUBPIWk/552rSdFhn+uEAnD17FqvViqurKwEBAXe10BHZ8AIZcS/ULPdqgEBd0HrqTD+cLVu2MHHiRO6//36OHTvGunXrbtgnXuZO74cjuD7i+gqqEzlAQFDz1Il+OJGRkfz3v/9l3rx5zJs3j+7duyu93QGKi4uve8w7vR+O4PqI6yv4t9wJLQZqk5oOLlBJdUDUR0dH8+mnnzJv3jyio6MJCwtj4MCBhIeHs3DhQsLCwqpsXvPw8KhwwlJTU2/7QnU3jFGXvkN0dDRpaWkVnq/s+t7McauLmh6vNsa8XeNd69o6ODjQpEmTah+vPHXpPr8TxrnZMa51fQGQahGr1SpJkiRlZmZKPXr0kJ555hnJ19dX+uyzzyRJkqTvv/9eCgwMlBITE29pnPbt29/yXO+FMe7k71ATc6/N8WpjzLt1vDv5Pq+NcapzjFpL69+zZw/79++ncePGPPTQQ2zdupWMjAxcXV2ZMGECAC+99BIHDx4UzccEAoHgLqBW2hP8/fffPP7442i1WhYsWMCbb77JunXr8PPzIyMjg08//RSAZcuWcfz4cWxsbGpjmgKBQCCoRmpFw7ly5QrTpk1jwoQJDB06lD179rB9+3bc3Nz44osvCAsLIyIignPnzvHbb7/h4+NzS+ONGzeummZ+d49xJ3+Hmph7bY5XG2PerePdyfd5bYxTnWPUStDAL7/8wpw5c9ixYweurq6kpqayefNmjh07xuzZs8nOziYrKws7O7u7ugGbQCAQ3EvUikntmWee4YEHHuDjjz8mOzsbT09PevTowalTp9i0aRPOzs4EBQUJYSMQCAR3Ebdd4FxLgRo6dCgAM2bMICMjg/r169OhQweuXLlyu6ckEAgEglrgtvtwiouLy/S4KS4uRqPR0KFDB1QqFStXrqRnz5489thjLFy4kH/++ed2T0lwl3Dx4kUyMjIICwtDrVbf9vYWBw8e5NKlSzRs2JB27drVSjCLVIPlnmpyrNqkJr7n3XQurVZrheT9qnJbfTibN2/mp59+on379gQEBDBq1CiACo3WVqxYgUqlonXr1jRu3Ljaxo+Li8Pb2xuz2Yy9vf0tnagbIYdu385FqCbG2LZtGxEREVitVl599dVq/6FU1/Fv1Em2uvnzzz959913adu2Lfn5+XzyySc0bNjwtoxVGUlJSbccPFNVjh49Stu2bW/bb+XfUt33Yl5e3m2vYRcXF4eXlxfFxcW3tVvv/v370Wq1tGvX7rY1sbxy5Qr169cHbkHoVFtGTzkOHTokNWjQQFq6dKn066+/Sq1atZImT56svG42m2/X0JIkSdKGDRuk5s2bS+PGjZOGDh0qXbhwQZIkSSouLq72sVavXi098cQTUt++faUNGzZIGRkZd+QYe/fulTw8PKQFCxZI3bp1kyZMmCDt3bu32q5VdR2/qKhIGjZsmLRv3z5JkkrOzf/+9z/pnXfekbKzs6tlrqVJS0uT+vXrJ50+fVqSJEl69tlnpZUrV0rJycmS0Wis9vHKs2nTJmno0KHSpUuXbvtYiYmJkk6nk0aOHCkVFRXd9vFuxJkzZ6RTp05JcXFxkiT9X7L4rbJu3Tpp2LBh0pNPPilt3LhRiomJqZbjlmbDhg1S586dpREjRkgLFiyQzGbzbVl/Nm3aJPn6+kq7du1SfkvVdZ5kDh8+LPn7+0tTpkxRnvs33+W2CZy9e/dKL7zwgvJ3YmKiVL9+/TIT3r59u/T2229X67hWq1WKjY2VWrRoIe3cuVNKSkqSvvjiC8nX11c6c+aMJEnVK3QuXrwoNWnSRNq/f7/066+/SgMHDpRmz54tRUZG3lFjSJIkffnll9K0adMkSZIko9EovfPOO9Jrr70m7d+/v04dv6ioSOrfv7+0cOFCSZJKrueuXbukN954Q/r++++r/ceWlZUl9ejRQ1q1apWUnZ0t1a9fX3rkkUekp556SnrnnXekvLy8ah2vNAcPHpQCAwOlHTt2VHjtdixeGRkZ0oMPPig1bdpUGjp0qFRYWFjtY1SVv/76S2rUqJH04osvSh06dJCuXr1aLce9ePGi5OfnJ+3bt0+aPXu2NGXKFGnixInKprQ6OH78uNSwYUPp4MGD0uLFi6VRo0ZV27FlrFarlJ6eLvXu3VvavHmzJEmSlJ+fX+bf6votXLlyRerbt680atQoaeLEiRXmUVVum85sZ2dHcnIyGRkZAPj4+LB//342b97MypUrAWjXrh0vv/xytY6rUqnw9/enZ8+eNGjQAC8vLyZNmsTbb79Nv379iIiIqFZTQWZmJt7e3nTp0oWnnnqKyZMnc+bMGTZt2qQUH71V0tPTb/sYAC1btuTAgQNERESg1+uZOnUqBoOBZcuWVcvxW7duzcGDB2/5+NfrJLtv375qmWtpnJ2dmThxIp988gn9+vXj2WefZf369bzwwgvEx8cTGRlZ7WPKXLp0iVGjRtG7d2+uXr3Kxo0bWbx4MVDS1qC6u4S6urry6KOPsnnzZiRJYty4cezdu5cjR45U6zg34lYL+l4Pk8lE9+7d6datG6+99hpPPPEEnp6ezJ8/n/j4+GqZf1RUFF27dqVTp0706dOHCxcuMGHCBL766isuXrxYLWOoVCrc3Nxo1qwZDz74IMnJyQwbNoznn3+e119/naNHj1aLCdJqtWJnZ4erqyvPP/88JpOJ999/nzNnzhAVFXVTY9w2gSP7bfr376885+Pjw/jx40lOTgZKbu569epV25gHDx5k8eLF7Ny5k4SEBJYtW6acjIkTJ/Laa6/x8ccfYzKZqq08eadOnahXrx4rV67EYrHQpUsXnn32WTZt2sSBAwdu6diJiYkAdO7cmfr169+WMeLi4igsLCQvL49evXrRuHFj9u7dS2JiIra2trz33nscPnyYX3755V8d/+jRo8ri0KRJE5o1a8a+fftu+fg9evSgX79+LFmyhD179qDRaBgxYgRXr17l5MmT/2qu12PIkCFs376dHj160LZtWwB69+5Nbm5uhd4q1UlAQABZWVnExcXxyCOPsHfvXubMmcPw4cMBqmXzFBkZSXh4OEajESjZ4KxcuZJVq1Zx/vx5evbsSVJS0i2PczNotVp69OjB/fffT3R0NIsXL+btt9+ma9euhIeHo9Fo/vVvuEmTJkRGRvLdd98BJRvfAQMGoNVqiYiIAK4dXVtVOnbsyJYtW3juuedo2rQpgwcPplu3buTm5rJkyRIKCwtveQyr1YrVaiUmJoZJkybx0UcfMWjQIMaNG0fz5s2ZPXs2WVlZtzyOWq3G29ub5s2bo9Pp+OCDDzh48CC9e/cmJSVFmUuVjvX/2jvvsCivtI3fRkWi7hq72QQB3UukF4NGRaTEKAFFGARZpZgNRsFyGRGDBncvNdbEHlHjLoINlSIScI1B4gIKA4tYFgsmtJioEAxN2sD9/cE37wXGwjRkk/f3DzDzzrnPGeac553nec5zVOrJM5AHt/fu3YshQ4bAxsZG+MCWlZUhOzsbLS0taj2T4syZM5g/fz7Onz+PkydP4sMPP0R4eDi2bNkiXOPp6YlevXpBW1tbJcufm5uL9PR0ZGVlAWhd/DIzM5Geno6mpiZMmDABs2fPxsGDByGTyZTSOHv2LJYuXYq7d++CJMaMGQOpVKpWjaSkJDg5OWHx4sX44IMPcP/+ffj4+CAtLQ1JSUm4desWtLW1MX36dKUWtvv372P8+PHw9/dHc3Mz3njjDdjY2ODSpUtITk5WqX35SbLm5ubYuHEjDhw4gMjISDx48KDD5ygpSv/+/eHg4IDY2Fh8/fXXOHPmDAoLC2FmZqZWHfmiJ9csLS1FVFQU5s6di02bNiEnJwfFxcXYtWuXylpfffUV3N3dsWLFCvj7+6OgoACzZs2ClpYWSktLUVZWhnHjxiEyMhJNTU0q670I+Zrw2muvIT8/H/PmzcOECRMQEhKCiIgIBAUFwd3dHffv31doDkulUmRkZCA9PR1aWlrYsGEDsrOzER0dDaD1BnnAgAE4cuQIAOUOJpNrXL58GTo6Orh8+TJcXFwwffp0hIaGwtvbG+PHj0dRURF69eql9BpUVlYmBO1feeUV7N27F9999x1ycnIQEBCAcePGwcnJCd27d1d6rcvNzUVmZiakUqnwWENDA+7fv4/S0lLk5+fDwsICsbGxABS48VGLg68Nbf3KGzZs4IULF7ho0SK6ubnRy8uLo0ePFmIp6uLJoK6Pjw8zMjKYlpbGAQMGcOfOnbx9+zYjIiI4ZswYlQLuiYmJtLCwoI+PDyUSCY8cOcKGhgaGhYVx+fLljIqKIklGRkbSx8eHMplMYY2n+e1ramq4bt06Ll26VGWNp8W5Nm/ezDfffJPFxcXMzs7mRx99RFtbWy5ZsoRDhgxhfn6+wuNoGw+QSCRCPxMSEhgcHKxy+yTZ0NDACxcu0MvLi35+fszNzVWqnY7y6NEj7ty5k7a2tnz33XeZl5en1vYTExP56quvcvbs2cJj4eHh1NfX56JFi/jLL7+QJDdv3sx//vOfKmllZGTQwMBAeM8WLlzIgIAA1tbWcuTIkezduzfPnTtHkpw1a5YQuNcUFy9e5MaNGxkXF8f6+nrW1dXx3r17XLZsmRCTIEk/Pz+Fgvz/+te/OHToUIaGhlJPT4/h4eEsKiri4cOH6efnx507d5Iko6Ki6Ofnx/r6eoX7Ltf4+OOPqaenx507d7K+vp4ymYwzZ87k0aNHSZInT57klClThP+josTHx3Py5Mk8cuSIMJ9kMhlTU1M5bNgwBgcHkySPHz/OSZMmsby8XGGN5ORkGhoactmyZXz99dd57NgxkmR+fj5dXV05dOhQxsTEsLi4mEFBQbx//36H21arwWlrbFasWMEJEyYIf+fn5zMnJ4eFhYXqlCT566Cunp4ep02bxuDgYHp7e9PDw4NBQUF86623eO3aNaV1cnNzaWpqKiwyJ0+e5OLFi0mSVVVV3L9/P/38/Dh58mSamJgovfgdPnxYSK64d+8eExISGB8fz+bmZh46dIhz5sxRWUMmkzEgIIA//PCDEPTbtm0bdXR0hIXl3//+N7/88kuVsqO++OILFhUV0cPDgz4+Prx06RJv3LjBR48eMS0tTeX2245HE0H0Z1FVVaX2jLiamhpOnTpV+Bx5e3sLzx04cIDvvvsut2/fzjVr1nD06NG8efOmSnoZGRlC4gVJPnz4kC4uLiTJpKQkXrx4UaX2FSElJYUDBw7k1q1b+d5773HJkiWMjo4m2WpgwsLCSJJHjhyhmZlZh44saWlpYX19Pf38/HjixAmSrXPY0dGRu3fvZmlpKVNSUmhhYUGJRMLhw4crfAPxLI133nmHmzZtYn19PY8ePcrx48dTIpHQwMBAuDFWlKKiIhoZGdHT05OrVq3i8ePH291s3r17l3Z2dly4cCFNTU2V0rl69SoNDQ2FDNDk5GS6urqytraWJSUlXLp0Kb/55huSrZnGimZpqs3gtJ3sy5cv57Rp0zSe+tyWU6dO0crKiuPGjePatWtJkufOneOyZcuYmZlJkiqnEmdkZDA8PFz4u6CggNbW1oIRlS/cV65c4YMHD5TWSU1NZWBgIEtKSmhpacmVK1fSwsKC77//vnDN1atXldIoKCigVCpleXk5PT09hbOH5GzcuJH+/v58/PixUn0vKChgdna28Pq1a9dyy5YtJMmxY8eyW7duTExMVKrt3wP37t1jdXU1y8rKKJFI2n3TSUtL45kzZ7h69Wq1ZFTJZDLBaMpkMpaWltLCwoJlZWUkycrKyk5LjT548CB3795NsnVhjYqK4gcffMCvv/6aZWVl1NXVpZeXF01NTRX2kGzatImrV69mdXU1SfL69eu0s7Pjvn37SLZmPd69e1elOfukxo0bN2hvb8+DBw+SJG/dusXY2FgWFRUprVFdXc2srCz+/PPP3LNnD5csWcJjx461yySUyWR89OgRf/75Z6U0pFKpYDibm5t59+5dTpo0iVVVVSQp/FR2bVe7S+2jjz6ik5OT0CFlXErKUlFRweDg4HYL2syZMxkfH09S+RTB27dvC78/fPiQZOu4amtr6eLiIkzattepopGXl8fp06dz/fr1/Pzzz4XHx40bxx07diitkZiYSFNTU9ra2jIoKIgJCQnU1dXlhg0bhGsKCwsZEBCg1Hslb9/Ozo6enp68c+cOb968yR07drCkpIT6+vp8++23KZFIusQej65OeXk53d3dBaNz9epV/vDDDxrRampqYnV1Ne3t7Um2fpNYsGCBRlO+2xIREUFLS0vhpvDhw4eMjIzk0qVLSbZ6MYqKipQyCsnJyVywYAHz8vKEdSknJ4cjRoxgTk6OWvr/NI3s7GyOGDFCrW5X+TeKurq6dkaHJL/77ju1aDzpInNychJcgKruV1KrwSkuLqazs/NLMTZykpOT6e/vz3PnzjEhIYGWlpYqufHkPnUvLy/hMfm3uebmZjo5ObGyspJRUVGcPn26Ut+iOsNv/6S/PiAggKtXr+a9e/eoo6PDdevWsaCgQOk4V1eLB/xWKCsro7+/P0eNGsWRI0dq/H3z8/Pjxx9/TCsrK5Xcz8oQHBzM4OBg4fP+/fff097enklJSUq11/amKTg4mPPmzeOVK1eEbyGBgYG8cuWKSn3uDI1nadbU1HDPnj0MCwvjX/7yF77++usqeXGevMmUuwvNzMxYVlYm3BRUVVUpffOu9tI2/P/yE/KaaZ3NL7/8gqioKMTGxkJbWxtbtmyBubm5Um3V1tZCIpHA3d0dly5dgkwmE7JYmpubQRLe3t7o168f8vLyEBUVBSMjI5U0GhsbcezYMQDAl19+iZiYGDg5OeHRo0c4efIk4uPjlTr3/dKlS7hz5w78/f0BtGa6+Pv7IykpCd9//z3Wr18PbW1tSKVSREREwNTUVOX233//fSQmJiI5ORl9+/aFra2twv0WAbZv347Nmzfj/PnzCv9fOgpJNDU1wdDQEE1NTUhJSdFY6R4+o0SNVCrFqVOnQBKrVq3CgAEDsHLlSgwfPhxBQUEdavt59fVCQkJQXV0NbW1t6Ojo4PPPP0dGRgb09PQU6n9naDxLR/7etX0PXV1dce3aNZw+fVrhte5pGk+WrfHy8oKenh4yMjKwb98+mJiYKDwWAaXNYRdHXUHdJ33qc+bMafe8q6srDQ0NVfKpd4bf/ln+evnu7aKiIjY1NSmdPdOV4gG/JSoqKvjOO+/w6tWrnaIXERGh9izSJ3nS/y/3hLS0tFAqlTI4OJgmJib85JNPOHjwYN65c6dD7cbGxtLAwIAODg708fHhzp07f7UGXLhwgTt27GBgYCD/+9//Ktz3ztB4kY7cwyKTyXjr1i0OHjxYqc9HRzRI0tnZmfr6+kpnkrblN2twNIHcpy43Onfu3GFoaKjSH6rnaWjSby/31zs4OJBszYqbP3++0okCz2r/ZcUDfmt0Rr02OeouC/QkycnJlEgk3LBhg5DeT/7aCEVHR/PEiRMdvsl6Xn29p91EKRP07gwNZXTkcWVNaURGRqqtjl/XKgfbxRk4cCD279+Pnj17YtSoUXB2dsbSpUsVdqN1RENbWxsGBgZwd3cHSaGq7Y8//ggPDw8AQF5eHpKTkxXWWL9+Pfbt2wcdHR2EhoZi+/btWLRoEV599VW1jKFHjx7o27cvhg8fjtDQUGzbtg2BgYHo06ePWtr/vaGtrd1pWposoS+VSrFkyRK4ublBT08Pn332GVatWgWg9TPTdgOzl5cXPD09FaoeX1VVhYKCAgCAm5sbXFxc0NjYiOPHjwNorUSSlJQEAEq7+zuiMXToUJSXlz9XQ09PD+Xl5UrrZGVlCXN/8ODBGhnL5cuXkZqaCl9fX/z5z39WSuNJRIOjIIMGDYKZmRmqqqoQGxuLoUOHakyjsrIS8fHxePPNN4Xn/vSnPyEmJgaA8gaHJGQyGdLS0nD06FFER0erNTZAEo2NjRpr/3+Vb7/9Fi4uLhpp+0ULWFegsbERdnZ2mDNnDry9vXHu3DlER0dj9erVAFqNTkpKCkJDQxVu+3n19dLS0tDQ0ICSkhJYWVkBUM6wdlRDfnyIssa7IzrFxcVCiSVNaZSWlqr1uBgAv90YjqboiE/d1dWVVlZWNDIy4v79+0mSffr0YUhICK2srOjo6MisrCxOnjyZ+vr6TEhIINnqP58xYwYdHBzYu3dvLly4UGizT58+JFvTlo2NjdnQ0EAdHR0OGjSI5ubmjI6O5t/+9jdu3bpVeI2xsbGQobd+/XqOGjWKjo6OnD17Nrdu3cqIiAgmJydz6tSptLKyoo2NjcqbCdvSGfGA/yVSU1Pp7Oz8wuuUye7U1dUV4mVdlZycHE6fPr3dHpGffvqJlpaWwt6PiooKpVNv6+rquHv3bgYEBLTbtGpnZ6fSloXnacjneu/evYX9f/L/RWFhIQ0MDOjr60tTU1NKJBKhYoKuri7XrFlDS0tLmpiYCPMuKyuL48ePp7m5OfX19Tlr1qxOG4smNJ5ENDhK8CKfunxCPX78mMbGxiwvLycAJicnk2zdGzRlyhQ2NjYyLy+P5ubmJFsX6GHDhrG8vJwVFRU0NjZmdnY2yV8bHPn1QUFBgu6zDE5OTg5NTExYW1vLyspKjhw5klu3bmVLSwsdHByEoGxmZqYQd1EHmo4HdBWkUilNTU1ZV1fHmpoaGhkZPXWXd2pqKidNmsSZM2fS0NCQH374oRCc7dOnD8PCwjh27FimpaXx8OHDtLa2prm5OefPny8YoQULFnDMmDE0MjLimjVrhLbli9zjx485depUHjhwoHMGryALFy6ktbV1u8cOHjzIXbt2qaX9iooK7tmzh9OmTeP+/ft56NAhGhkZKVR+RRGNbdu28dChQxw9ejQNDAxYXl7ezuAAEOIk8+bNE+anrq6uMOYvvviCf/3rX0m2JtfIYz9xcXE0NzfvtLFoSqMtGj9i+rfIi3zqu3btQnx8PIDWaswFBQXQ0tISKmebmpqiV69e6NmzJ0xNTVFUVCS8dsqUKRg4cCAAwN3dHenp6XjrrbdU6m9aWhrc3NyEEwdnzJgBoDUl+9KlS5g1a5ZwbUNDg0pabfmtHKn7IqytrTFjxgx88sknqKurw9y5c5+ZOiqVSpGfnw9dXV1MmzYNcXFx8PDwQG1tLUxMTLB27VrcvHkTmzdvRkZGBnr27InAwEAcPXoUvr6++PTTTzFgwAA0NzfD0dER165dE4qH1tTUYPbs2fD19YWvr29nvgUvpLGxEVpaWti7dy9cXFxgY2ODmJgYDBs2DGVlZcjPz0dLSwu6deum0uemf//+CAgIgJGRkRALPXLkiFpd3201Fi9ejB9//BGDBg3CgwcPhJiIHB0dHUycOBEAMHfuXOzatQvBwcEAWuc30Fo4NC4uDgBQWVkJPz8/FBQUoFu3bmhsbERISEinjEVTGm0RDY6a+fbbb/HNN9/g8uXL6N27N+zs7FBfX4+ePXsKE+mVV15Br169hN/bBkufnGyKTL4ePXq0KxNeX1//3HZaWlrw2muvIS8vr8MaIk9nzZo1sLa2hra29nOrOI8dOxYjRowAAHh7eyM9PR0eHh7o3r07JBIJACAlJQX/+c9/YG1tDQCoq6vDkCFDAAAnT57EgQMHIJPJ8NNPPyE/P18wOK6urggJCcGcOXM0OVSFaWlpEeIaGzduxPLlyxEXF4fAwEBoaWnh6tWriImJUds5VVpaWrC3t4etrS26deumkaOytbS00K1bN/Tr1w+ZmZno27evMNfb8rz5LF8DunfvLqwBYWFhsLe3R3x8PIqKimBnZ9cpY9G0hhwxaUDNVFZWon///ujduzdu3bqFzMxMhV5//vx5VFRUoK6uDqdPnxbujp7GH/7wh3YHsOnp6SE3NxdAa3nxwsJCAICtrS3i4+NRV1eH6upqJCYmAgD++Mc/Ql9fH6dOnQLQGuzXxFkyvwcqKipQU1OD6urqXy06bXnWAqStrS1kNZGEn58f8vLykJeXh9u3b+Pvf/87CgsL8dlnnyElJQXXrl2Ds7NzO62JEycKB6d1FdpuIgwJCcFXX30Fe3t77N69G59++ilWrFiBs2fPwtjYWO3a3bt31+jiWVlZiQEDBqBv377PnOslJSXCmVXHjx+HjY3NC9t84403AKDdGVGaHktnaYgGR81MmzYNMpkMZmZmCAsLw9tvv63Q621sbODj4wMLCwtIJJLnutPs7e2FcylOnDgBiUSCiooKWFhYIDw8HKNGjQLQesCUl5eX0OakSZOENo4ePYp//OMfMDc3h7GxMRISEpQb+O+c+fPnY926dZgzZw5Wrlz5zOukUikKCwvR0tKCEydOPHUBcnR0RExMjHC4VUVFBYqLi1FVVYU+ffqgX79+ePDgAc6ePdvudWvXrsXAgQMRGBio3sEpSVtjExwcjOvXr+PixYvC84aGhhgzZoxSu/C7Ah2Z64aGhoiMjISZmRkqKipeeMJxSEgIQkNDMXHiRJVONe2yaCQyJKIUTyYBiPxvEBkZSTc3N5KtGWZjx45td5aRnNTUVNrb29PT0/OpSQNtiY6Oprm5OU1NTWllZcXLly+TbK11Nnr0aL733nt0c3MTjheQB6pbWlro7+/PFStWaHDEivEyC/q+TNom+Ii0ovZaaiLKc+jQIeTk5GDPnj0vuysiImqhpKQEgYGBOH36NHr06PHSaiy+DIqKiuDi4oIbN2687K50GUSDIyIiolH4kgv6inQdRIMjIqJmrl+/Dh8fn3aP9erVC1lZWS+pRyIiXQPR4IiIiIiIdApilpqIiIiISKcgGhwRERERkU5BNDgiIiIiIp2CaHBERERERDoF0eCIiIiIiHQKosEREREREekURIMjIiIiItIp/B+RmSwjjvrO8wAAAABJRU5ErkJggg==", + "image/png": 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", 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", 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ - "cbins, medians, err, count, result = structure_function(\n", + "sf_result, result = structure_function(\n", " data=np.array(mao_rm_tab['RM'][mao_rm_tab['included']]) * u.rad / u.m**2,\n", " errors=np.array(mao_rm_tab['e_RM'][mao_rm_tab['included']]) * u.rad / u.m**2,\n", " coords=mao_rm_tab['coordinates'][mao_rm_tab['included']],\n", @@ -581,12 +517,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Finally, we can compare our results to those from the original paper." + "We can compare our results to those from the original paper." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -595,13 +531,13 @@ "Text(0, 0.5, 'RM SF [$\\\\mathrm{rad^{2}\\\\,m^{-4}}$]')" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -612,8 +548,8 @@ ], "source": [ "plt.figure(figsize=(6,6), facecolor='w')\n", - "plt.plot(cbins, medians, '.', label='Median from MC')\n", - "plt.errorbar(cbins.value, medians, yerr=err, color='tab:blue', marker=None, fmt=' ', )#label = '16th to 84th percentile range')\n", + "plt.plot(sf_result.c_bins, sf_result.med, '.', label='Median from MC')\n", + "plt.errorbar(sf_result.c_bins, sf_result.med, yerr=[sf_result.err_low, sf_result.err_high], color='tab:blue', marker=None, fmt=' ', )#label = '16th to 84th percentile range')\n", "plt.plot(mao_sep, mao_sf, 'X', label='Paper SF')\n", "plt.xscale('log')\n", "plt.yscale('log')\n", @@ -624,6 +560,142 @@ "plt.ylabel(rf\"RM SF [{mao_sf.unit:latex_inline}]\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we extend to using mutli-point structure functions, as described by Seta et al. 2022 (10.1093/mnras/stac2972). Currently, only the triple-point structure function is implemented." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-11-07 14:04:53.006 INFO structurefunction - structure_function: Sampling errors...\n", + "2022-11-07 14:04:55.827 INFO structurefunction - structure_function: Getting angular separations...\n", + "2022-11-07 14:04:55.888 INFO structurefunction - structure_function: Computing SF...\n", + "2022-11-07 14:05:06.528 INFO structurefunction - flush: Grouping triplets: 0%| | 0/23 [00:00, 'steps': 20, 'enlarge': 1.2}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[Kit= 2984 logz=-108.29771321747\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "14:15 bilby INFO : Sampling time: 0:00:07.572824\n", + "14:15 bilby INFO : Summary of results:\n", + "nsamples: 3385\n", + "ln_noise_evidence: nan\n", + "ln_evidence: -107.875 +/- 0.118\n", + "ln_bayes_factor: nan +/- 0.118\n", + "\n", + "2022-11-07 14:15:22.691 INFO structurefunction - fit_data: Fitting results:\n", + "2022-11-07 14:15:22.692 INFO structurefunction - fit_data: amplitude: 590 ± 70\n", + "2022-11-07 14:15:22.693 INFO structurefunction - fit_data: x_break: 16 ± 9\n", + "2022-11-07 14:15:22.694 INFO structurefunction - fit_data: alpha: 0.16 ± 0.05\n", + "2022-11-07 14:15:22.694 INFO structurefunction - fit_data: Fit log evidence: -107.874896434051 ± 0.11839303208862947\n" + ] + }, + { + "data": { + "image/png": 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QQ4GMKAjg8JSDzPELIYRoTRIFMWg0FzbmPLgq0KEIIcSgIVMPYtBoHg2RokYhhOg9MqIghBBCCJ9kREF4+Ne5owIdgodgjEkIIYYCSRSEh2DcyjkYYxJCiKFAph6Eh693VfL1rspAh9FGMMYkhBBDgYwoCA8Pfn0AgJPGxgc4ksOCMSYhhBgKZERBCCGEED7JiMIQFqwbQAkhhAgekigMYcG6AZQQQojgIVMPQgghhPBJRhSEh6cuGhPoEDwEY0xCCDEUSKIwBLSuRWjNV13CuJSIvg6py4IxJiGEGAokURgCulqL8NHWcgDOnpTYVyF1WTDGJIQQQ4EkCsLDw4vzgeC6KAdjTEIIMRRIoiAGnebtppv/3LyrpBBCiK6TREEMOq0TA9lyWgghekaWRwohhBDCJ0kUhBBCCOGTTD0IDy9fNi7QIXgIxpiEEGIokERhkOrJPg7D48P6IqQeCcaYhBBiKJBEYZDqyT4Ob/xQAsClM1J6M6QeCcaYhBBiKJBEQXh4ckUhEFwX5WCMSQghhgJJFAa49lMM0jNACCFEb5JEYYBrPcUgPQOEEEL0NlkeKYQQQgifZERBDGrSzlkIIXpGEgXh4e2rJwY6BA/djUnaOQshRM9IoiA8JEWZAx2Ch2CMSQghhgJJFIJcIFY1vLC6CIBr5qT1+XP5KxhjEkKIoUAShSDXlVUN7efju+uFtcVAcF2UgzEmEXyUUmcAD+Aq1DYBD2mtX/JxbAjwd+BMwArkaa3PUEqFAa8D4wALUAz8VGu9rx9eghBBRxKFAchXe2Yp1POf9J8Y2JRS3wHXaK33t7rNALwKzNdab1NKZQO7lFLvaq3rvJzmz0AIME5r7VRKpbe670mt9Rfu8/4MeBY4oW9ejRDBTRKFAagn7ZmFy1DvP6GUygR+A8wCpgHhwIjWF173cQuAb72colprHefH8xwF/AGYDoQBe4DHtdb/a3fccOCfwMmAAr4GbtNaH/D/VaHc/09w/z8OKMc1WtA+rgjgRiBTa+0E0FoXuv/fBHzR6vCVwB1diEOIQUUSBSGGptHAJcA6YClwSifH/wJY0+rv9s6eQCk1FdcFfyVwA9AAXAQ8p5QK1Vo/6T4uAvgG1zD/jwENPAh8q5SaqrWu9+cFaa0dSqmLgfeVUvVAPHCB1tojUcD1+iuB3yqlTgZswN+01h94OfbngLfbhRgSJFEQQ0Zv1XAMEku01qkASqnr6TxR2K61XtnF5/gRYATObjX0/5VSahpwNfCk+7YbgJG4pgD2uGPaBOwGbgIecd+2EJjkfsxo4FOlVHMScCGQB/wOV3KwRCk1G/jAnWyUtYvNDGQBe7XWdyulxgNLlVJbtNZ7mw9SSt0FjAVO7OJrF2LQkERhAGm+0PX1Re7T6yf36fm7ozdikjqEw5qH2/tYCK5v6o3tbq/C9W2/2TnAyuYkAUBrnauU+h44F3eioLW+ovl+HzUKs4AMrfUS9/FrlFKHgBnAV+1iyMM1cvGy+9gdSqkN7mP3us93B64E5CStdUOXX70Qg4QkCkEo0MWKESHGfnmergjGmIaYhUqpJFwX+S+A3/pRP/AC8FPgMaXUn3BNPVyM69v5Va2Om4T3of2t7uP9dRDIUEpNdBczjsY18rCz/YFa6zKl1BfAacCH7kLGycBmAKXUr4HLcCUJVV2IQYhBRxKFIBToYsUnvi8A4JajMgIWQ3vBGNMQUQ08DCwGanB9474bWKGUmqG1LvH1QK31Fncx5HvALe6bbcDNWuvXWx2agKteoL0K2o48dEhrXayUugF4QynlxLVE8tbWCY1S6lngQ631h7iSmOfcSYwTuENrvdNd6PkwsA9XnQSAXWs9y99YhBhMJFEQHt7cWAoE10U5GGMaCrTWPwA/tLppsVJqCbAaV4HjPb4eq5QaA7yDa2TgZlxTEOcC/1VKNWmtF7Z+Km+n6CCuBT5ufw14rYPHXd/qz/vxUnugtc7v6LmFGGokURBCdInWer1Sahcwu5ND/4xrBOEsrbXNfdsipVQi8KhS6jV3rUQlh5c0thaP95EGIUQ/kkRBiFakEZPfFN5HAVqbAmxslSQ0Ww1cDqQARbhGHCbhaSKwrYdxCiF6yBDoAIaCnAdXoW5fgrp9CTkPrgp0OKKd5tUkzUsn9cPHoh8+tiVhgMM/Q/n5tawuGAt09mYUAdPdrZJbmws04apBAPgQmKeUGtnqOXKAo9z3dSW205RSa5VSm5RSK91LMX0de4ZSap1S6gel1Gal1NWt7rtbKbVTKeVUSp3XlRiEGGxkRKEftC5ObL7gdETW+PcvX6MG7fsu6IePbfPzG+gjDkqpi9x/PML9/9OVUqVAqdZ6sfuYhUAusB7XiocZwF3AIeDfrc51HLAIuLbV3gqPA28BHymlnsBVo3AOrtUE/2zVCOkZ4Ge4eh7cg2uk4gFcqxie6sLriQcWAkdrrbcrpea7/+6xttaPds+LgDeA5/x9fiEGK6V1Z6OHQ4/7wzIv0HG0kgS0bxgzUA2W19LR68jWWif3ZzDdoZTy9Y9/cXOxoLvh0GVANhCBa5TgM+APzS2P3cctwNXq+Sda6xda3X46rlbRk3C1cN4LPA08pbV2tDoui7YtnBfhauG8vwuvZxbwqtZ6bKvbaoAFWuv17Y414mrvfJbWepl75OEzIKd1J0d3v4Z/aa3f9zcOIQYbSRQGAKXU2sGyNGuwvJbB8joGE6VULK5E5Fyt9fdKqXNw9We4UGv9rpfjT8a1QqJ1u+ev2x3zHZIoiCFOph6EEAOCUmo9rrbL3szQWh9USl0A/EkpFQ0sw1UM2b6YEqWUCf/bPQsxpEmiIIQYELTWM/04ZgmwAEApFYprqmS7l0On43+7ZyGGNFn1MDA8HegAetFgeS2D5XUMKu5WzM3uBb5pvYdEKy3tnt2P89nuWYihTmoUhBCDhlLqGeAYXKOlK4Cft96roXULZ6XUZbjaUTe3e/6L1vpV93H34OommQzU4lrOOUtrXdSPL0eIoCCJghBCCCF8kqkHIYQQQvgkxYxeJCUl6ZycnECHEZQqGmzkVlgYkRCK2dj1PLOg2kKjzUlKdNtmfVa7k/IGu1/nSI9p3+jPu+ZzZsaFEGHu3W2qqxrtlNTZmJoeidnYdv+gdevWlfnqo5CUlKRzotxF+EljejWm9vaWNwEwKjGsT59HeNcX739Hv1tC9BVJFLzIyclh7dq1gQ4jKD38XT53fLSPf14zkdjwrv36lNfbuHzhDtKjzFw3N63NfflVFp5bXezXeW48xdcKubZK62w8sbyQi44ZxrmTE7sUa2f+8V0+i3ZXsvLB+YS3S0KUUj6bdeXk5LD21lTXX37ySa/GJAa/jn63hOgrMvUgumTtwVpSo8xdThK01tz2wV4cTs15Xi7a4Wb/fxXrrY7ODwKSIk3Ehhl57YcSv89td2gqGjyW3bfRaHPwzZ4qLpuR4pEkCCHEYCOJgvCL06mpszj4Prea7PiuD6V+sLWc/GorJ46JIzHS7HF/mMn/X8XCGmvnBwFKKaYPi6K4zkZ5fccX/2bPrS7iwhe3c6ja4vOYr3dX0Whzcn27UZFgc9cnudz1SW6gwxiy5P0Xg4UkCsIvDTYnNU12RiaFs+ZgLZ9sK/frcVprXlxbzKNLC8iICWFudrTX4w75efEH/xMFgMxYVz1DYa1/jymucx3ncHpfDbS9uIGnVhQyPiWcI3Ni/I4jEFbk1bAirybQYQxZ8v6LwUISBeGXCLOBmDATn1w3idnDo/nH4kP8cKiuw8c4nJpHlxbwwppipmVEcu2cVAxKeT32tR9K/Y6luNa/0YHWvD+rpzuOy+Tpi8aQ5WXUZHtxA3d+vI/YMBNf3jgF5eO1CCHEYCKJgvDQPM3gdGq2FdVTb3FgMCiiQo1EhppYfOs04sNNvLWx44v70ysL+WBrOfNzojl3UgJGg/cLa02Tf6sdmm0tbvD5jb+nIkKMjEkO97j9ULWlJUlY+YvpDO/G9IsQQgxEkigIDw02J9WNNv727UEmP7SO+f/egN1x+MIcZjZw/dw0Vh+spdbi+yKfEOGqRQg3Gzr89l3QhamEZl/tqvLruOaEwmTs/rd/rTVPLi/E4YTlP5ckQQgxtEiiIDyEGBR/+OIAd3+6n3Ep4WwqrOd3n7UtyrpwahIOJ6zYX+vzPJdMS2JEQiiLdlez+kAtlQ12vHUCrfSzf0Jrqw7U8vXuKq/na83mTnC6UizZ3qc7Kvl+fw33nZpNdsLASRIyY0PJjA0NdBhDlrz/YrCQPgrCwwNfH+C51UVcNiOZ6+em8eiSQ/z923wunJrEnCxXAd/s4dEkRZpYnlfDKePivZ5HKcWTF47hqld38tmOSj6jkhCj4pRx8RyRGdVyXFUXpx6afZ9bQ2KEiRnDonweY2seUfAx7dGZveWNPL7sEDOGRXHzkemdPyCIvHLF+ECHMKTJ+y8GCxlREB4qGmxEmA3cMDcNg1JcNcvVIGjtwcPFiwaDwmww0NmIfqjJwKtXjOc/F4zm9uOGkRodwlc7K9tMZaRF+9dp0Zvs+I6/saVEuaY/thY1dPncFQ027v50P1GhJt64ajxRodIzQQgx9EiiIDxMSoukweak1N17ICHChNmg2F/R1HKM3aEprrOSHtP50KrJqJiYGsFZExP55TEZWBya7SWHL9yT0yK6HOPp4+P5wylZLXUQvmTEhBAVauCZVYWdTlO099iyAmqa7Hxx42TGJEdg6OaoRKDc9v5ebnt/b6DDGLLk/ReDhSQKwsPU9EgAXv+hFIdTs6+8CYPhcI8BgIIaC04NqdG+L9R1Fgff7qniL4sO8pdFB9lYUMeU9EhMBsWu0saW48xGA5dMS+pSjLOH+55uaE0pxXEjYymrt7Oz1XN2ZltRPUv3VXPbsZnMzPTe+yHYbSioY0NBx0tYRd+R918MFlKjIDwcNSKGnx6ZzpMrCtlV1sjesiaiQ03cflymx7G++iJ8vqOCv3+Xj9YQalI4NXy5q5Iwk8Lu1ExMbTuKMCE1gjlZUaw+0PkH66+OzehSD4PmTaT8LZqsarTzhy8PkBYdwm9OGO7384j+kfPgKvIqXZ0zs+ND2X/P3ABHJMTgJomC8KCU4omLxjAzM4pb3tnDyMQwvvnpVDJaVXA3781gdTi9nuOplYXEhRk5b3ISmXEhOJyarUUNrMirZUZiGBNSPacbTh0X32mi8POj04kJ69qvrdk9ZWDxEWtrDqfmga8OUNNkZ9UvZxDXxT0tRN/Lq7SgHz4WAHX7kgBHI8TgJ5+Cwqfr56Vz1sREEiJMhLRbXljR/O3cy7R/ZYOdqkYHJ42JI8tdbGgwuvZdmN7BCgWDUtx9Yiafbq9kQ0G9x/2/ODqD+Iiu/8o22FwJgj8lCj8cqmP9oToeOC27w1hFx4byt/7Wrz3U5H3kayi/P2LgkRoF0aG0mBCPJAFc2yybjYpjRsZ63Nc8K2DuRpMjs9HAuZMTuXJmcpvbbz9uWLeSBK01n+9wTXnM87HPRGuT0yJJijSzcH1Jl4sfg83Y5HDGeuky2R+av/Xrh49tuSAOFc2v/YZ5aVjs3n+HhvL7IwYeGVEQXba1qJ4X1hRz1sQEkrzsBBnqTixsju5faEclhfPLYzLYUFDP3Kyobm/nvPZgHcV1Nn517DC/zhFmNnDtnFT+/m0+b20s45LpyZ0+pjWnU4MyBEUC/vTFY/vkvPJt2D9PXzyWZ1YWBToMIXosKD7QxMBhtTs569ktRIcauWJmitdjmr+Ja2/zEl0QF25iwajYbicJ24sb+GxnJcNiQjh7YoLfjztlbDw58aH84Yv9Hve13gfDmwabE5RhUDdckG/DQgwtkiiILvnHd/nsr7Rwx4JMr6MJQMvSx9So7jdS6qntxQ28tamMpAgTT108pkurJIwGxQlj4thR0khxu+2pm7fbbq57aC/CbADtdPQo+F5y41u7uPGtXYEOY8iS914MFpIoCL/tLWvkvi/zOHZkLPNzYnwe19xMaVhsYBKFWouDdze7koTnfzSOyJCuf8Gf525V/fmOija3N2+3HWH2/k/HYFCgnZ0vr+gHu0ob2/SrGIpyHlyFun0J6vYl5Dy4ql+fe6i/92LwkBoF4bcnlheggZ8fndHhcYeqrYSZFBHduED3hlV5tTic8M9zR3UrSQAYnRRGqEmxsd3qi+bttsXAIEspheg5SRSE397bXM7U9EifUw7NXG0LAtPu2OHUrMuvJTMuhGE93LnP5tBEhBhwOjUNNicRZsOAa+MsOhdMxZnBFIsQzSRREG34uih+vK2c3IomThvX+Q6KRoPqcSFjd20vaaDJrvnZUcN6dB6LXePUEB1qaqlLAJOMJgxCwTTqEEyxCNFMEgXRhreL4q7SBi59aTtjk8M5e1Lnqwfiwk1Y7ZoGq6Pfpx9W7K8lMsTALD/3gvBl0Z4qAMYlh7vrEXzXJQSr6RnSMKq17PjQlotvf3xbn54RxeK91X36HEL0B0kURBvtL4p1FgenPLUZs1Fx/6nZfi1VnJcdzQtritlV2tiv3Q1rmuwU1li58ogUn3tQ+KPWYueZlYVMSYvg3MmJKDUw6xL+dd6oQIcQVFonBv3xbf1f543i0aWH+vx5hOhrA+srkuhzzcV6zdMOF7ywlYNVFu49OYvUaP9WMYxNcn0LX3+of3fO21TYgMbVB6G7nFrzn+8LqbU4ePmK8V1aVimEEIORJArCp692VvLVrip+ND2ZI7qw1bJSipPHxlNQY8XhozFRb6tosLFkbzXp0WYy47pXxNhkc/LHL/L4YmcldyzIZEpaZC9H2b+uXLiDKxfuCHQYQ5a892KwkERBeFXZYOPyhTvIigvl6lmpPo9zak1pndXj9qkZkTicUFxn68swAbA7NAvXl6IUPHJu94bby+tt3PbBXpbl1vDXM0fwi6MzfDZVGijyqy3kV0vnxECR914MFpIoCK/u+nQ/FQ027j5xeMveDd7c+VEul7y8g9UHatvcPsm9jfTBqp59WPpqldza4n3VVDTYuffkLNL8nB5prcnm5Nb39pBXaeGDaydx54JMYsPNA654UQwuzcWX/d0oSoj2pJhReBUXZsSpW20n7UNGbAjrD0FMWNtiv5QoMwYFtU3d72ZcUGNt2VTnyOxoxiWHk50Q5nGcyV1P0d2Cw5I6K8W1Np65eAxnT0rs0bmE6C3NxZeyTFIEmnxlEl7defxwRieF8bdvD/LNniq+2lXJZzsqqGhoO5Vw+3GZfPvTqYxPiWhzu1IKs0FhdXR/+D6/1WjEirxaXlhbwkfbKrC325Vyfk40kSEG7vsiz+M+fxjdiUZHIydCCDFUySej8Co+3MTrV07AYtc88NUB/rzoIH//Np+rXt1JYY1nTUJ7jTYnGrD1oJjR22jG+vw6/rToYJvbzEYDZ01MoKrJtcdDV1ntrhjt/VR42V+OzI7hyGzfe3KIviXvvRgsZOpBeGUwKGYMi2Lb/x1BdaODyFAj3+2t4sa3dlPZaCc9xnctQJPNyXVv7MTm0C21Ct0xMjGMVe1qH5o5nLplJABcjZGGxYTw5IpCDAounJrk99LGL3dVYlBw7MjYbscajP5y5ohAhzCk/eXMEfz1m4OdHyhEkJMRBeFTg81JqMnA6ORwxiSHE+2et++oyK/R5uAnb+ykqNbGOZMSGJ0U3u3nH5sczrxs78syy+rbToEopbh6VgpZcaH8Z3kh/1pyyK9piCabk0+3V3D0iFhG9SBWIYQYrCRRED6131J5f0UTQIc7Mt7y7h6Ka22cPyWxV7oynjQmzuvtlY2e0xIhJgPXzE7hqJwYPtxWwcL1JZ2e/7u9VdRYHNx/WrbPY5xOTZ3F4dcKjGBy4QvbuPCFbYEOY8iS914MFpIoCJ9ad2ncW9bInxcdZHpGJEmR3mesDlVb2F9hYf6IGKak906zIqNBcfORaR63Z8d7rn4A18jClHTXdEeijzhb+25vNWnR5g6nHZr3vxhIfRVyHlzFu5vLeHdzmSyvC5Dyhr7vISJEf5AaBdGpwhoLxzy+EQX8+rhMn3P/r6wrwaBgbpb/XRz9kRLVdlvrG+elEd7B9Mf2kkYA5ud0XExWZ3GwLr+OXx4zrMN6hoG4KVRepYXjRrmSn2DdmKh5S2XZTlmI4DZwPvlEQGitOe3pLVQ22vnrmSMY7qM9cmmdlc93VjI3K7qllqG3KKU4cYzronf+5MQOCykBthbVkxxpJiHC3OFxW4rqsTs1Z0zoeG+I9vtfiN7RvKVyXqV0MBQimMmIgujQWxvL2FRYz6+OHcaEDlYwbCioB2BqL005tDc/J4bRSeGddl50ODUVDXYunprU6Tljwly//vXWgTOlIIQQ/U0SBeFTk83JL97bw+ikMM6ckNDhsRsL6gkxKlKiPb/F11oc5FU2YbVrbA7N8PhQMjoZFWjPoJRf7ZnL6m04NX6tthgW6zrfnrLGLsUyUJzoLgQN1qmHwe7EMXHy3otBQRIF4dOSfVUU19m4eX56m54F7Tm15ru9Ve62zZ7HPbL4kMdtvzg6g/iI3v/1K693rYbI8dLqub2YUCMRZgP7K5t6PY5gcO/JrpUcv/88L8CRDE33npwt770YFAZdjYJSarhSKkQpFen++6B7jf0lPcZVj+DsZGR+a1ED9VYns4d7FjE2+lgp8NiyArTuu+WGRj+aLSmliAgxUG+RqQcROM2bP6nbl5Ad370t0oXoS4NqREEpdSbwN+B7IF4pda/WeqdSyqC17vBqoJS6EbgRICsrq++DHQDGJIWj6HwHyK92VWIywPgUz+H+veW+h/VX5tVyZCcrE7rK4E4LHX4mIaEmAw227m9cFcxOf2ZzoEMY0vx9/2XFhwh2gyJRUK61bZnAX4GfAduBK4FvlVIna623dpYsaK2fBp4GmDVr1sDqrNNHwswG0qJDOrzYA3yzp4qxyRGEeNlUqbTO91ryL3dV8eWuKn5+dHqnKxT81Tz1YfOjK2N1o52KBjtmo8Lp1INuVYO30ZzmJYmALEvsY75G04QYaAbFsLx2jWEXACuA3UCJ1vphXInDl0qpsZ2NKAjvzpmcyNr8Op+7QNZa7NRbnT6LE5MjO08A/r2skC93VmKx9/xH1LzltD+JwovrirHYnfxkduqAaqbUE81LEmVZohDCXwM+UVBKjVZKzQbigFjgCnfigNb6MeBR4G6lVJjyd5cg0eLsiQk02pxsdC9/bC+33HWxad8UqVman6sbVuTV8tdv8j32cOiq5kShs+2tc8ub+HBrOdfNSWPW8JgB1UxJCCH604CeelBKnQX8GagENgMLgceUUkat9V/ch70J3KW1Hpyl7X1swahYwkwGluyr9lqsWOZuUxsb7r3JUmIXVzYs21fDeVMSux6o25YiV0LT0YqKbcUN3P1pLjGhJu4/LYeoXm4QJQKjt6dVZJpGCJcBmygopeYD/wAu01r/oJR6GpgDzAdWKqWMwOvA0cARSql4rXVl4CIemDSKMycm8MWOSn5+VIZHHUKo0fUN3tcXeKUU87KjWZnnfbvo9jYW1nPu5AS/t4gGV/fIOquT3aWNrDlYx0VTkxiV6L2Pwsq8Gu77Mo+ECDOLb53m94jHQHSWu/eFP2v5B8NFsXlaBUDdvqRb52hegdD8556c76wJCb3SR6F1TEIEwoBNFNz+qrX+wf3n3wEvaK0LlFILgHuAX+NKHn4iSUL3RJgNXHVECu9sKmNFXg3HjYprc3+oO3HoaKh/fEq434kCQGGtza+GTFprlubW8H1uDVZ3TUJChIkb5nluIgWu6YZ7Pt/PyIRwlv5sGqntGjg5nZoGm5MIs6HfCxv74kJ9x/HDAbjz49xOj+2Ni2xfav/+9JXeTJDuOH64X+99Z1rHpB7p8emE6LKBnCisArYAuEcPQoEMpVS61nqfUuo+4BAQqbWW9mjdZDAozpqYSGq0mTc2lHHsyNg23/YzY10f2nvKmnzu6OhrfwhfDlVbOk0U7A7Nh9vK2VzYwNT0SBaMiiU7PoyJqRGEGL3XG7ywtphQo4HlP59OkpeaiuZdIsHU79MRwX6hDrTW748Qon8N2ERBa+0Aatx/VUAVUKG1LlRKXQkcA9wmSYJ/Ovo2bTQoHjgthxvf2s26/DpmtapVSIsJISsulNUHajlmRIzXJZIGpTgyO5oVfo4qNHdX9KXO4uDFtcWU1du5bk4qV8xM6XSqYk9ZI0v2VfP7k7O8JgkwMHeJ7MiCJzYGOoRBo/2UhD8jD/L+i8Gix5+IzR0Q292W0dPzdoXW2q61rgMOKqX+AvwKeFxrPTib+PeB5m/TvpYJXj0rFbNBsTa/zuO+35wwHKtD84OPlREA0zL83yyqo5UPTTYnz6wsoqrRzn2nZnPlEamdJglaa55bXURkiIFfHZfp8zjZJdK3nAdXDenugfvvmSvLSsWQ1RsjCm8opc5uXpKolEoCPgOm9cK5/eJe9mjGNYpgBk7UWu/ur+cfDJq/TYebFA9+lceag3XurZpNVDTYWX+oDofWLcsPW5uYGkG42UBxrdXn+Zt3avTH3nLfC1QW76umxuLg0XNHMdXP5OP9reWszKvl4XNGEhc+YAfRAipYh/7bf9MPlP6qoRAiEHrjU/MH4D/ALUqpGOBT4OFeOK/f3EmKVSn1ALBGkoSua/42/fzqIu79PI+MmBAsdifVTQ6iQ42kx4Rw4pg4n7tImg2qwyZHRTW+kwhvtNYeIwWldTZWHajlrIkJficJ24rqeeL7QuZmRXPbMcO6FIMIfsGyOiNYEykhekOPEwWt9b1KqVeUUvcAJwMvaq1f6nlo3fJi88iG6LqyOhu3vLObqemRPHLOyA53jGzPaFBYO+ismNfJfhHtWeyaMHPb5/9kewVmg+K6Od5XNbRntTv545cHSIo08cn1k2VKwU/dmY8faIJlJEKIgaDbiYJ79KDZr4BPgEXAy0qpGK11jfdH9h1JEnomKtRIfLiZ8gYbVocm3M8La2WDnapGOxNTI3ze39X15N5+kBFmA2ajIjbMvxUJKw/UUlpv49PrJ5PoRyvpweaSacmAf30UWmuzHG+QrsDoj+TnkmnJvdJHQYhA60kxYxWujohVQBEwC/hNq9vFABNmNrDwinEcqrby/Joivx+3aE8VGpia4ZkoWO1OHltW0OVYQoyeScrIxDDqrU4OVfs3jfHN7iriw02cPDa+y88/GNxyVAa3HNWvdcWd8lYU2Xqb5ZwHVwU4wt4TbO+9EN3V7REFrfXgWEMm2jh+TDw3zEvjf6uLOHtiYqc9ELTWvLq+hIQIEylRnr0PPtvRvZzR22DGyERXn4b1h+rI7CSuRpuTFXk13DgvHZOXpGMoaLAG3/bZ3ubyB+sIRjC+/0J0h5SACw8PnJbDwnUl/Of7Av565ogOj11zsI7KRjtnT/QscqxqtLOhgyWTHalstHtsPR0fbsKg6HB1RbPNhfVYHZpzJnV/34iB7oxntwQ6hCGt/fsvKyPEQNXjREEplQLch2s5ZEtrPq31zJ6eWwRGanQI95+Wwx0f7WNFXg1HZsd4PU5rzUPfHSQyxOB1FcKqA/63bW6voNrqkSgopQgzGaho7LghE8D6/FrMBsW0jEicTi2FjKLXdLfYU1ZGiIGqN0YUngOWAScCtwM34VoyKQawnx+dwaNLD/Hk9wXMGR7tdQXE7rJGyurtnDE+3qO/gtOpu7S/Q3tVTZ7JgNXuRAP1Ft9Duo02Jx9sKeODrRVMTovA7u44GYw7RA62b5iD7fX4MlinSoTwpTcSheFa678ppa7QWn+klPoCV8MlMYCFmAw8dt4ozn9hG1/vruLUcZ4FgWHuds3e2jZ7u9B3xYSUtoWRWms+2V5Bo83JeZOTPI53ODXvbS7jlfUlVDc5mD08iv9dOpaYsOBtyTzYvmEOttcjhHDpjUSheYG8RSkVD1QDvvvkigHj3MmJpEeHsGRftddEIc29+2Kll6mA0jrfbZg785PZKR7LGTcU1LOpsIFrZqcyMzOqzX3bixt4eHE+e8ubOCIzin+fP5ojc7xPlwghhOia3kgUdimlEoBXgNVAHTL1ENT83U5ZKcUZExJ4c2Op106JISYDkSHeWzd7Sx78cfmMZLLa7UJ5sMrCJ9sqSIs2c+XMlDb3FdZYue2DvcSEmXjnxxM5f0pip3s/DBXXzEoFut5HQfSOa2alynsvBoUej8lqra/SWldorR8FrgHuBa7o6XlF3+lsA6jWJqZFUGtxUOOjLuDksfHsLm3E0q4ro7WDds6+XDo9iTHJ4W1uK6u3sXBdCREhRp68cIxHrcTza4owKPjh1zO5YGqSJAmtXDMnjWv87GIpep+892Kw6JXlkUqpdGCc1vo7pZQJMAKyiDhIdWU75VHu3gUF1VZivWzsdPSIGN7fUs7BKgujkw5f5O1+JgrHjYxhfGoEqVFmj4t8vcXBi2uKUQqevHC0x4ZO1U12vttTzU1HpnfaV2EoKuvB9I/oOXn/xWDRG8sjL8K1CZQGcoBJwF+AM3p6btE3mjeA8kdzwyVfWz+bDa5ko7bdiEO1H8WMPz863WMJZGtvbSqjwebkiQtGMyzWMxF4e2MZNqfmhnlD75tb8wqD1svz2q86uOilbYEMcci76KVthJqU7CkhBrzeGFG4C5gJfA2gtd6olMruhfOKINC8e4avUoZHluRjNqo2qxQcTs2mwoYOzzsmKazDJGF7cQN5lRaun5vGuBTP1tBFNVbe2FjK5TOSmZoR5eUMg1vzCoPWy/ParzpY8MTGDs8xmDdGan5tgX5d87Jj+O6WaQGNQYie6o1EwaG1Lm83bNy1PYVF0LI5XZlC+z4J4CoyzKu0cPSIGMJaTWPsr2jq9Ly7y3wf02Rz8tG2ChLCTVzq3tiovadWFmJU8LezRnb6XINZTy72g3FXyGaD+bUJ0d96I1GoVUql4d7wTyl1ElDRC+cVQWDFftcmoN52X3x5XTFGA8zNim657WCVhVfWl/p17j1ljW3qGpptKqyn0ebkH2eP9LpPQ5PNyeK91dy+IHPI1ybIBVEI0dd6oxPNb3E1WBqplFoGvIirQ6MYwJxOzQuri/j953nMyIj0uKDnVjTx1a4q5mZFt9Q7bC9u4H+ri/1+joXrS712WdxwqI7YMKPPbavzqy1oYPbwaK/3C9FfgmWKQ4i+1OMRBa31GqXUAmA+oIDlWuuqnp5XBE5xrZUFT2xkR0kjE1Mj+PVxnv2z/vhFHmaj4ih3Y6PNhfW8u7m8y8/1zd7qNhtKNdocFNXauLxdv4TW8qtdBXvjUjxHI8RhPz0yHZA+Cn2poxGd5vdfiIGuR4mCeynk37XWv0baNg8az64qYkdJI789YTgnj43D0G7ZotaaQ9UWZmZGERFipMnm7FaSALA+v65NotBk02ggJsz3qoz8KleiMDpREoWOXDrDlWz96JUdAY5kaGp+/4UY6HqUKGit7UqpGb0VjAgOb24oZUxSuNe2zQBVjQ4c+nDdwsoe7BKZ027INj7CREK4ibc2lnKJj0LGikY7USFGIoNwo6dgcrCy86LSnvKnmHIwr67oSPP7P7xdp1EhBpreKGb8VCn1O+B5XO2bAdBa1/TCuUUA5FdbfG4tDVDobtkcF2bEanf2aGjb22qKmZlRfL27iqIaK2kxIR7311kcRIYG50ZPweSq13b2+XP4U0zZlYLL7m7hHIya339ZHikGut5IFP7m/v8DrW7TuLozigHI5tCEeFlt0Ky0zp0ohJtw6K63am4tNtzzVzDU5Hpuq8N7i+n8KiupUZ4JxECwubCe7/ZWAf59u27fRKk7F86B9I2+9evLeXDVgIlbiMGsN4oZ5avdIBNiVB3u1VDnXqkQZjYQbu5ZPth+J0iAveVNRIYYWrpCtmZzONlT1sgvjx3Wo+cNFKtDs2BUHAD7f9L5Rb91E6XWzZW6YqB+Kx+ocQsx2PTKRV4pNUEpdatS6hal1PjeOKcIDJvDiVP7/jYPUNXkThRMrl+fU8fFdfv5MtpNLdgcTvaWNXH86DivGzztq2jC5tTMyZKlkUII0R96nCgopa4DvgFmAbOBb5RSP+npeUX/01pzyzt7qGy0+6xRcDg1b24oJT7c1DI9UdXYvf2/rpzpWay4t9yVCBwzItbrY3YUNwLSQ0EIIfpLb9Qo/B8wQ2tdBC07SX6Hq7hRDCAPfZvPs6uKuHJmCsePjvN6zOJ91dRYHFzs3tLZ7tCs6uaqh1FeujLuKm3EbFTMGBbp9TE7ShuIDTMOyTnrrtYa3O6l/4XoP/L+i8GiNxKF4uYkAUBrXaiUKumF84p+tDy3mt98ksvxo2P5yZxUn8c98X0BsWFGJqS6LvLbijve/MmXm4/03PFRa82OkkbmZ8dgNnoOdjm1ZnNhPeNTIrxOSwx2XZ2zP3tSYh9FIvwh778YLLo99aCUilFKxQCLlFL3KaWGu/+7H3i/1yIU/eKV9SWEmRR3Lhju0WCpWUWDjfIGO7OGR6OUwuHUvLel642WpqRHkBrtuWqhoMZKo83J/Bzv0x5L9lVzqNrK9XOH3rbS3bGzpIGdJd1L5ETPyfsvBouejChU4VoG2XxVubfVfRp4uAfnFv2ozuLgpbXFzM+JIdzsO3fcUFAPwIgE17D3D4fqfB7bkRnDvG8LvbOkEQXMy/asP3A4NS+sKSY7PpQfz/I94iEOu+nt3YCs4w8Uef/FYOF3oqCUCgEyADtQIMsiB49nVhZSb3Vy4ZSkDo/bWlSPyaBId48GfLK9slvP174bY7PNhfWkRJmJCTv8a6m1Zs3BOl77oYS8SgsvXTYOo5cmTYPJQOp7IIQY/DpNFNzTC48DFwPN48U2pdQG4HPgBa31/r4KUPStOouDB746wMxhUUxM815A2KzR5iTEqDD04EJ91sQEr/UFpXU2qpoc/OLow6MF5fU2/u/jXPZVNJEUaeLB03M4YXQsDTZny46Vg5H0DxBCBBN/RhSeBk4F/g4cAKKBR4BE4B7gbqXUU8CdWuu+by4vetXjyw5R2WjngQ4KGJtpoPU1fmJqONvcyxX9NdPHaoat7qLIY0ceXhb55a5K9lU08b9Lx3LFzBRMBkWDzUlEB9MjQgghepc/icIZwM1a61cBlFJGXInCpUA+cDXuJZJKqZO11l27coiA2V3ayINfH2BuVjSTOhlNANfUQOtCxzgv7Zc7csv8dJ+rFTYV1JEaZW7ZaApg7cE6RiWG8ZM5h4sXB/NIQjBqngaRKRAhhi5/PuktQKm3O7TWJcA/lFKvAN/iGmH4Xe+FJ/qKxebgzGe3YDQobvOjHfK6/FoKqq3MH3F4RUJXt3lIjjJ7vb2q0U5lo4Objzy8La/Wmt1ljVw2w/sOkqJz95yU1eNzyDRI9/XG+y9EMPAnUfgUuBn4ytcBWusipdTvca10kERhALj/qwPsLmvkvlOzSfOyVLG1A5VN3PXpfmLDjBzTKlGwO/3PFCamejZXarbdPe1wdKtzl9TZqLU4mO5jhYTo3EljvW8TLvqHvP9isPBnsve3wByl1AdKqVEdHNcEdFw2L4LGUysKmT08qk1NgDfVjXZufW8PRgU/np1KqHt/B7vDtRrBXyEm779qrlUNtSREmBgWe3h4e+1BV7fHWZnSqrm7NhyqY0M3l7CKnpP3XwwWnSYKWutC4FggFdgFLMZV1zZbKTXZvSHURbjqFlb3ZbCi9wyLDaXB5nvjJ3D1Lrjx7d00WJ1cMTOlTU3CV7u7tjRyw6F6r7fvKWuistHBDa2aKDm15s2NZYxOCmPWcBlR6K7bPtjLbR/sDXQYQ5a8/2Kw8Kt8XGudq7Weh2uJZBWu0YMngY3AFuBNwArc0Ddhit524pg49pQ14uhg+uC1H0opqbNx7qREMltt+byrtJHVB7r+Tal5e+pmWmsW7a4iwmzgxDFxLbev2F/DgSoL952aMyRbNQshRDDp0jozrfW7WuuzgDhgBnABcAkwW2s9SWu9u/dDFH1hXnYMFrtmewctZheuLyEzNoSpGW1XRKzL795wamWjvc3fl+bWUFxn47q5aS17O9Ra7Dy2rICMmBAuniYzWUIIEWjdauGstbbhGk3Y2Lvh9D6llNK6q/X5g9+p4+IxGRTLcmuY7GVpZJ3FQZPdyfiUCI/7ulLE2Fp6zOGiyW1FDXy7p5qTx8Zx3uTDm+e8tLaE0jobq345w+vGUEIIIfrXoPskVkrNVUodp5SaDaC11sqP8Wul1I1KqbVKqbWlpV5Xgw4qseEmZgyLZFlutdf7D1RZAEiK9Mwlnd1MFEzujo7FtVbe21JGcqSJOxZktvRmOFDZxHtbyrhubhqzs6SIUQghgkFvbDMdNJRSpwOP4erpkKKUKtdaX9ecLHQ0sqC1fhpXF0pmzZo1JEYgFoyK4+HF+Ti19tgxck+Zq29WSpTn0sn9lZYuP9eZE1xLxeqtDl5eV4LZaODJC8cQ4h41sDqc/GnRQcJNRh44LafL5xee/nx6TqBDGNLk/ReDxaBJFNwdI38M3K+1ftm9R8WnSqm3tdYX+ZMsDDX7KppIjwnxuq30xoJ6ws0G4sLbdkKstzo8ju3MrOFRHJEZhVNrXllXQpPNyeMXjG7ThfGpFYXsKm3kvWsmkhbTcV8H4Z/5Izpe+ir6lrz/YrAYNFMPWmsH8EOrv9dorY8GUt17USBJwmFaa77PrWZ0omcjJIdTszKvhpEJYR6rDnaVdr1D9xnj41FK8X1uDUW1Nu5YkNmm9mFVXg3vbi7nF8dkcF4nO1gK/y3PrWa5j6kl0ffk/ReDxYBPFJRSY1v99RDwG6VU696p5wOJSqmJ/RtZcHtxTTFFtTbmZnvWAry4tpgGm5NJaYcv5lprluyr5sOtFV16nntOGo5Sih0lDXyzp5qc+FBOHde2Y92Xu6pIiDDx97NGdu/FCK/u/mw/d3+2P9BhDFny/ovBYkAnCkqps4ANSqnXAbTWrwDvAd83Jwta6zLAjmvXS4GrmPAX7+9lSlqEx0V7yb5qXl5XwvSMSCakuhIFu1Nz/1cH+XZP174dXT83FaNBUVhj5Z1NZSRGmHjywjEeoxTbixs4YXRcS9dHIYQQwWPA1igopSKBnwG3AfOVUq9prS/TWt/rvhB9pJR6Aldb6WlAScCCDSJ2h+aC57fSZHO2WXEAcLDKwgNf5ZEcaeLMCQkAHKq28Oyq4m4917DYUOxOzWs/lBJqMvD0xWMIa7dF9KFqC4W1VuZlx/g4ixBCiEAasF/htNb1wLXAq8AdgFkp9Zr7vnuBP+J6fSOAi7XWuQEKNWg4nZpr39jJ8rxabp6fTlZ8WJv7X15XDCiumpWKyagorrV2O0m46gjXTpCrD9RSa3Hw+5OzSYhou3tkvdXBvZ/nERli4PzJidRZHN1eeimEEKJvDNhEAUBrXaC1rnNPL9wEhDQnC7j2pfhUa3291npL4KIMDlprbn5nNy+vK+G6Oalc0K5o8FC1ha93VTF7eBTRoUYcTs1/VxR1+/lGJobRYHXw3d5qhsWGMKddXwStNX/6+gAHqpr44CeTSIkOoabJ3un+E0IIIfrXgJ16aE9rXa6Uugl4SCm1EzACCwIbVfD4+7f5PLOyiCtnpnDlEake97+4thiDgvk5rimAFXk13X6u6+emorXmsx2V2B2aB730RViaW8OKvFoeOmsEJ46Nd48kmIgwD+jcNaj869yONnsVfU3efzFYDJpEAVyFi0qpTcDpwMla6/xAxxQMHE7Nw9/lMyszimvneCYJ3+6p4qtdVRw9IoaoUFffhEW7u7esa152NMNiQ/lqVyVbihq4bk4qOQltpzi2FTfw50UHGJ8Szs+OHgaAwaBanlv0junDZOfNQJL3XwwWg+rrm1IqHjgDOEVrvTnQ8QSLr3dVUlpv48yJCR4rDg5VW/jLooMkR5pYMMrVIKa83tbt5zp5TBzbixtYvr+WcyYlcsXMlDb351dZuPvTXBIjzCy+ZZpHcaPoPV/vquTrXV3bDlz0Hnn/xWAx2EYUKpVSZ2utmwIdSzB5YU0xsWHGlmmFZgU1Fm55Zw9KwRUzUzC692LYVFjfref5v+MzabI7eW9LOYkRJm49Kr1NYtJgdXDXp66a0sW3TiMlWjow9qUHvz4AwElj4zs5UvQFef/FYDHovs5JkuDp+9xqpqZHtuyrAFDdZOemt3djsTu5fGYyseGunHFPWSNL9nW9PuHyGcmEmw0sy63B7tA8dPbINs8H8NiyAgpqrHx47SRGJ3l2hBRCCBF8Bl2iINqy2J0cqrGS3WoppM3h5Ka3dtNgdXLlESkt9x2qtrBwffd2zhydFEZNk51VB2o5ZVw8I9rVJXyzu4ovdlZyz0lZHDsqrtuvRwghRP+SRGGQ21pUj1NDdnxoy20fb6+guM7GeZMTyYxz3X6wqvuNlS6amohSipV5tWgN18xqWzC5u7SRhxfnMyk1gntPzu7+ixFCCNHvJFEY5D7d7tqbYWbm4QrsLYX1RIYYmJIeCbhqB/63untJAsDE1AjsTs36Q3VkxYe22f0xt6KJOz/eR3Sokc9vnILJ6LlTpRBCiOA1qIoZhaeF60sYnxLepivimoN1ZMUdHmH4Ymf3K7N/e0ImSim2F9VjsWt+fnRGy30Hqyzc8dE+TAbFFzdNaRm9EP3jqYvGBDqEIU3efzFYSKIwiL20tpgdJY3cMDet5bbKBju1FgcZsa5v/eX1NjYVNnTr/LcelU6oyYDdoVm0u4roUCMz3GvH1x6s5Y9f5mE2KL66eSpT0iJ7/oJEl4xrtZW36H/y/ovBQhKFQeqNH0r4yes7mTksioumHm7X3NxxcVSia9XB/kpLt85//uREkiJdoxTf7KmiusnB384cgUEpai12/rzoIMmRZhb9dCojE2WFQyB8tLUcgLMnJQY4kqFJ3n8xWEiiMAi9tr6EK1/dweS0SB48PZsQ9/bNTTYnT64oIDbMSFq06yK/v6J7q0lHJLpWNeRXWViRV8vZExNa9nP47/JCqpvsLLpZkoRAenixqzGpXKgCQ95/MVhIMeMgk1fRxDWv72RyWiR/PXME4WZXW+TCGitXvLqDOouTcya5Vik4nZotRd2bdoh2t1v+eFsF4WYDNx+ZDsCy3Go+3VHJ7cdlMiNTWtgKIcRAJyMKg8yPX9uJUvC7k4YT7m6PvLmwnjs/2ocTuGxGMiPdowH7ujmacP5k1zekA5VNFNfZuPWoDCJCjBTVWvnbN/mMTQ7ngdNzeuPlCCGECDBJFAaRNzeUsnhfNdfOSSUlylWsuLGgjjs+2kdEiJEfz0pps/qhu82VJqe5irS+3FlFmElx1oQEAN7YUIrV4eTT6ycTapLBKiGEGAzk03yQKKyxcN0bu5iYGsGPpicD0Ghz8rvP9hMRYuSGuWltkoQtRd3bz+GiqUkYDIrdpY0cqrFy9axUwswGtNYs3VfN2ZMSGSXtmYUQYtCQEYVBQGvNpS9tx+Jw8pvjMzG791h4ZV0x9VYnP5md0mYL51qLg3c2lXf5eW6Yl0ZGTAhWu5MPtpYTG2ZsWVGxr6KJ8gY7Z7pHF0TgvXzZuECHMKTJ+y8GC0kUBoE3NpSyNLeGG+elkeXet+FAZROvbyhlWkZky23gSioeWXyoS+c/b3ICU9MjUUqhteaLXVXUW508dt6olqRkVV4tAKeOk53ygsXw+LDODxJ9Rt5/MVhIojDALdpVyU9e38nE1AgumeaacnBqzf99nIvJoDhpTFyb47/dU+33uS+amsSE1HAMrbaKXrKvhvX5dfxoenJLC2irwzXCMCktgoxY6b4YLN74oQSAS2ekBDiSoUnefzFYSKIwgH2yrZzzX9jG8NhQHjgtG6PBdUH/bEclxXU2zp6Y0GbKYVNhPUtz/dtC+jfHZxJmblvCsjKvhu/2VnPquHhumHe42+NHWysoqbPx6hXje+FVid7y5IpCQC5UgSLvvxgsJFEYoN7ZVMqlL29ndGI4fztrBLFhrh9lZYOdx5YeIjXKzIxhh9smF1RbeG+zf3UJPz86vU2SUF5v451NZRTW2siKC+XOBZktowwWu5OF60uYkRHJiWNl2kEIIQYbSRQGoBfXFHHtG7uYkBLBX88c0WbU4OmVhdidmoumJaHcF3Or3ckzfm4hfU2rJZRaa77fX8u3e6owGRS3zk/n3MmJLSMXAKsO1FLZaOfNqyf04isUQggRLCRRGGD+vfQQv3h/LzOHRfHg6dktnRcBluyr5vOdlczPiW7ZhwFg2X7/phuunpVCdoKrAEtrzWc7Klt2mvznuSPbLK9stvZgLRFmAwtGxfXshQkhhAhKkigMAKV1Vr7bW82HW8t5ZV0JR+XE8PuTs1r2cADYVtzA/V/lkRxpYsGo2JbbrXYnS/d1nig0L31stmh3NWsO1nHJtCRuPjK9ZXSitapGOyvyapieEYnJ6Hm/EEKIgU8ShSBUVGPltR9KWJdfx5J91Ryscu3wGGE2cP6URG6dn9Fm+D+vsolff7CXCLORq2eltixZBChvsHf6fLceld5mBKK41sry/TWcMT7eZ5JQWmfjjo/2UWtx8JczR/Tk5Yo+8vbVEwMdwpAm778YLCRRCDKfbCvnioU7qG5ykBRpZlxyOKeOi2fGsCjGJYe3SRDA1S/hlnf2YDAofjI7tU29AkB1U8eJwvVzU9skCVpr3t1cTohJcVO7JGFHSQM7Sho4UGVheW4NNRYHX944hWNl2iEoJUV5ThWJ/iPvvxgsJFEIEo02B7/5OJd/LytgZGIY/zx3FCMSfDdscTg1H24r57/LCzEoxTWzU4iP8Pxx1lkcPs9x+YxkhrXre7DqQC0ldTZ+c3wmMWGHz7cir4a7P90PQLjZwIiEMD68bhJzsmK6+EpFf3lhdREA18xJ6+RI0Rfk/R/YlFKjgBeBFKAeuEFrvbYrxyml9gMWoNF9+ONa62f7PvreJYlCEGi0OTjm8Y2sy6/jgimJ3DQvvU39QXt7yhr5zSe5VDTYSYs2c8m0ZK9JAtCyU2R750xKYExy2z0ZKhpsfL27iszYEI8Oi29uKCU50szaX81geFyo1+kIEVxeWOta6SIXqsCQ9z/4KaW+A67RWu/3cvd/gRe01s8qpU4GFiqlxmutdRePu1RrvaGPXkK/kE2hAqzJ5uS4/2xkfX4dvzk+k58fPcxnkmBzOHl+dRE3vr2bBquTi6YmceO8NJ9JAkBChLlNceO09EiumJnMjGFRbY7TWvPWxjIMSvHIOSPbJAK5FU1sKKjn9gWZZMWHSZIghOgypdRwpdTbSqlqpVSNUupdpVSWH4/LVEr9Wym1QinVoJTSSqmcDo4/Qym1RClV536etUqpE7oYazIwD9dIAVrrr9x3HdGd4wY6GVEIEK01FQ12Ln5pG2sO1nHngkxOG5/gccz24gZWHqhl0e4qyuptWB2aqekRnDY+vs3SyI4cOzKGiakRJEaa2rRjbm3twTqKam3ccdwwkqNC2tz3ybYKzAbFdfLNSAjRDUqpCOAbXMPwPwY08CDwrVJqqta6o+1sRwOXAOuApcApHTzPTcDj7v8ewPVleDoQ0cWQs4ACrbWt1W157tvXdvG4l5Tr29UPwF1a665tthMEJFHoR4U1Fl5eW8L/VhdxsMpCg80JwI3z0jij1a6LVoeTr3ZV8b/VRVQ02FFAXLiJSWkRTEyNYHQXt3FWSpHcQWHV7tJGPttZybCYkDZxgGvfiMX7qpiXHS3FWUKI7roBGAmM01rvAVBKbQJ2AzcBj3Tw2CVa61T3Y67HR6LgHmX4F3Cn1vpfre76ot1xC4FJ7r+OBj5VSlndf79Qa73XRxz+DqW2Pu44rXWeUsoE3A28DRzp53mChiQK/aCywcb5z29jaW41Tg2TUiM4dXw86dEhjEgI44jMw9MAVoeTa17bSWGtjbhwI2dOiGdKeiShPqYjtNbkVlhIjDS1tHHuigOVFt7YWEp8uImnLh7jMa2wtaiBsno7P52f0eVzCyGE2znAyuYkAUBrnauU+h44lw4SBa2108/nuBZw4qoZ8ElrfUXznzuoUTgAZCilzK1GC7Ldt/t9nNY6z/1/u1Lqn8Af2x07IEii0McabQ6Ofnwju0sbuWRaMqePj2+z7XN7b2907alw9sQEZgyL7LQeoKjWxsvrXLvU/eGUTqf7Wji1ZlluDYv3VhMVYuTZi8cSGdJ2KkNrzRsbSgk1Kc6amODjTCJYfXr95ECHMKTJ+9/GJOADL7dvBS7upec4GtgB/EgpdS+uC/Z+4J9a6/905URa61Kl1GrgGuAZd5GiwjX94ddxSqlIwKy1rnIffgWwZaAlCSCJQp9yOjWnPb2F7cUN/PHUbI4dGdvh8TaHk1fWl5AeY2ZmZlSHxzaLC3f9CHPi/d/euabJzms/lFJUayMnPpRHzhnltSDyu73VfL+/hr+fNYLoboxWiMCKCPGvhkX0DXn/20gAKr3cXgH01m5yGe7/HsI1zL8XVxLyuFLKpLV+tIvnuxl4USl1J9AAXNG8kkEp9Szwodb6Q1/HKaVSgXeUUkZcycNBei8p6lfy6d+HfvtJLkv2VXPL/PROkwRwXZgbbU7On5zo93OEmw1cN6dt06SO5FU08dqGUhxO11bSp46L9zpqUd3o2oVyfEo4vzo20+94RPB44vsCAG45SqaNAkHefw/tlxWC//P+/jAA0bimEt513/aNu3bhLqXUY+2XNmqtF/g6mdZ6NzDfx33Xd3ac1nofMKOLryEoyfLIPrKxoI6HvsvnnIkJXDQ1ya/HrDpQS5jJwOgk31MT3mTGhbbZFtqXOouD1zeUEmoy8L9Lx3La+ASvSUJBjYVffrCXequTN66aIPs4DFBvbizlzY2lgQ5jyJL3v41KXKMK7cXjfaShO8rd//+q3e1fAqlAei89z5AjiYIXmwvrGffXNSzdV93tc/zju3zCTAaun5fmd9+BmFAjDq37pE9Bc72B1aH517mjGB7nOVVRWmfj8WUFXPvGLiob7Hx50xSmZvg3BSKEEB3YyuGVBq1NBLb14nN40/yB6m9RpGhHph68GEU+zzb9GvvzmpphkcSEdu1tarI7uf5ADb+NMzNssf+1A3+ps/KLECtZW0Jb+h00WB002Z3EhZkwGLqfQNRaHBxvtTEsNZTktW2nKWxOJ4U1VlSjnZ8BDyaEkJMQRthSg2vVshBC9MyHwD+UUiPdQ/LNyxmPAn7bS8/xHnAdcCquZYjNTgXytdZFXTmZtHA+TBIFL0JNBsYkhbGjpJGCaisxKV17m6qb7GgNCR10TPSmecMnp4bmnKCkzlUgG2I0eGz41BU1TXZMRkVSpGdMVY0OKhrspEaHkJMQSrhJirCEEL3qGeBnwAdKqXtw1Ss8gKvA76nmg5RSxwGLgGu11i+1uv0i9x+bOx6erpQqBUq11ovdt30KfAs8pZRKAvYBF+Hqu/CTbsQsLZzdJFHwoiFmJFtPe4srFu7ghOFxvHz5+C49/qt1xVz16k5eOXqcx6ZLHflkWzn/WHyIX83OaNmQ6b4vXct2zx+VyNSMyC7F0dqTywvJjA3l4VNGetz39qYy/vN9ARV3H0l4hDRV6rFrpaZDiNa01vXuNsr/BF7GNR2wCLhNa13X6lAFGPGcFn+r3d+fcP9/MbDA/RxaKXUe8BfgPlz1DztwrUJ4tSvxtmrNfIb73F+5p4SPoFVnRn+PG+iUZ3IklDKUo1Qe2tm3c1oGkwmnveN9oAEMxjSUsRKH1dJymzEkBIfV2sGjglUSUBboIHpBR68jW2ud7O0O97egvD6Lqn8Mlp9hs4H0enz+boneo5Q6AnhVaz2u1W1fAv9ttaKi0+PcUw81uBIgaeE8mGjt9H99Yj9QSq3V2j4r0HH0Btdr0QP+tXT3dQyGD/nB8jNsNthej+iYUmo9rr0YvJmhtT7o66H+PkWrP0sLZyGEEGIg0VrP9OMwaeHciiyPFEIIIVrRWpcCza2Z6aiFs6/jlFKRSqm4VodLC2fRp54OdAC9aLC8lsHyOrpjsL32wfZ6RO+QFs5uUswohBBCCJ9k6kEIIYQQPsnUgxdJSUk6Jycn0GEMOfVWBztKGkmJMlNSZyMrLpTkqIHX12HdunVlvlY3JCUl6Zwo9xRl0pj+DKvf7SlrpNHmJCfBtXdJTZOdolobk9MiCDXJd5Tu6Oh3CwbOZ9fe8iYARiV2bV+bvhascfUXX79fkih4kZOTw9q1g6ZXxoCxv6KJUX9eTa3JADYnT10/mdMmeNtHJrgppXz2ScjJyWHtramuv/zkk/4KKSBufWc3Ty4vZP6sVOZlR3PflwcIbbSx9I9HtmyPLrqmo98tkM8u0TO+fr8krRdBIychjE+vn0xGTAjPXDxmQCYJ4rCHzh7JlUek8MLaYm5+Zw82p+bbn06TJEGIAUb+xYqgcur4BPbcPSfQYYheEBFi5MXLxnHsyFiW5lbzj7NHkhwVEuiwRBC465NcAP5y5ogAR9JWsMYVaJIoCCH6jFKK6+elc/289ECHIoLIiryaQIfgVbDGFWgy9SCEEEIInyRREEIIIYRPkigIIYQQwiepURCiF5TV2Tjz2S2U1Q+4Nu5C9LvM2NBAh+BVsMYVaJIoCNELzn9hKz8cqmNudjT7lEFG6kQLp1PTYHMSYTZgMPi7U/Hg9soV4wMdglfBGlegyQeaEG5Op6bO4sDp7Pr+JyaDQgNNdicog7H3oxPd1ZOfa29osDmpabLTYHMG5PmF6ClJFIRw68kH+rvXTOTI7GjqLU7QTkcfhCe6KdAX6gizgZgwExFm+bhtdtv7e7nt/b2BDsNDsMYVaDL1IISb64O8ex/o8RFmlvxsOgDqt0756hhEevJz7Q0GgyIqVAaZWttQUBfoELwK1rgCTRIFIdzkA31wkp/r0JPz4CryKi0AZMeHsv+euQGOaGCTREEIIcSgkldpQT98LADq9iUBjmbgk0kzIfpRdZOdikYbTh2YwjohhOgqGVHwotHmZF95IyMTwwMdihhk9pQ1samgnuhQI9OdGqMslxND0Njk4PxsDda4Ak1GFLzYVtzAqD+v4bGlhwIdihhksuJCSY8Jodbi4IGvvG793sa2onr+u7wAR4CW9gnRF56+eCxPXzw20GF4CNa4Ak0SBS9So8zkxIdy+4f7aBoAa58DvU5c+C/MbCAlykxUqJH7vzxASa21w+OP/c9GfvrOHp5bVSQ/XyFEQEii4EVxnY39lRauOiKFsAGw9jnQ68RF19RbndRZHRw9IobkKHOHx1Y22gEoqLHIz1cMGje+tYsb39oV6DA8BGtcgSY1Cl5kxoZw/6VjuWxGSqBD8Uug14kL/+0ubWRPWSOhJgMfXTcZpfyrUYgwG+XnKwaNXaWNgQ7Bq2CNK9AkUfAiNTqEn8xJC3QYfpN14gNHarSZ7Pgw0mLMhIf7/88vXPYJEEIEiHxFGcAGUm3CQIq1Lw2LDWVEQhjhpq4ldgOlmFF+zkIMPjKiMIA11yaAKehHFAZSrMGoyT4w6hMG+s953cFanl1VxENnjxyQ8Q8F0nWx/w3aREEppbQe3F1tBlJtQqBjbd7qN8yoWLSnige/PsCmgnruOnE4/3f88KAd1k+IMFFWbyctOiTQofgl0D/nnrrzo318u7easyYmcObExECHM2hNz4jq9mP7sutiT+IazAZdoqCUitJa1w32JAEGVm1CX8W6vbiB3IomThkbj8no+2LfYHOyq7SB857fxsEqC3FhRkYmhnHXp/t5b3M5H18/ieSo4LsYL7l1Oov3VnHVrNRAh+KX/vqd1Fqzs6SRD7eW02Bz8NsTsnplhdILl43jm91VHDMitheiFL7867xRXTq+/ShCX+lqXEPFoEoUlFLnAFcopTTwErBFa30gwGGJPvLKumKufX0XNqcmNcrM70/J5qfz072uJIgwG7jprd2U1Fm556ThHDMyFrNB8dG2Ch7/voCJf1/H0lunMT41IgCvxLcJqRFMCLKYAm3Zvmoue2U7+dWHe1B8uKWc5b+Y4Xey8J9lBfzsvT3Myoxi6c+mtzwuMzaUi6YlB2REZHtxA+f+byvFtVb+dtYIbp6f0XJfaZ2Volpbv8cULFqPIoj+NzDHB71QSo0FngQeA1YARwG3K6XG+fn4G5VSa5VSa0tLS/swUtEbLHYnN721m7Ep4fz+5CxSoszc+u4e/rzoIN4Gk+xOze6yRpwa6ixOzAaFUopzJiXyn/NHU1Zv45lVhX0Sq/xu9a6b39lNg83JL4/J4PUrx3P1ESn8UFDPtuJ6vx6vtebv3x4EYG1+HXvLDy+Jax4RCcRU1Ptbythd1kiNxcHvPtuPw6nJLW/kprd2kXHfKqb+Y12/x9RXrly4gysX7gh0GB6CNa5AGzSJAhAGLNNaf6+1fhR4BygFblJKZXb2YK3101rrWVrrWcnJyX0da9AaKFXr3+2posHm5IqZKRw/Oo5/nTeKBaNiueez/cx99AcKqi1tXkuIycC2/5vFzGFR/GvpIf7+XT5Wd4HgmORwYsKMWOx985qD9XdroPysWyuptbKtqIGzJyZw3uQkUqNDKG+wExliYGq69/nl9fm17C07nAwopVj6s2n86fRsvv/ZNCalRfZX+B26c8FwbpibxqzMKJbcOo37vsxj9F/W8PzqYs6YkMAfT8kKdIi9Jr/aQn61JdBheAjWuAJtME097ABGK6V+qrV+Umu9XrnGoC8FxgL5Q6HAsaf6u2q9yebkgy1lvLu5nOnDIrl+bppftQJvbSwjzKQ4Ypjr4mBQit+fnMW0jHL+u6KQCX9by7s/mciElAiaX0tGbCjLfz6d+77M4/6vDrC9uIGfHZXBrOHRGJXC6hgYKwt6S6BWKFjsTr7ZXcWbG0vZUljPjMwoUqNC+NGM5E4v2p9ur0ADR2bHAFBncbAyr4aTvdSoNNmcPPBVHn9ZdBCDAf5wSjb3npwNuKYYfnFMZsCKLj/cUs75L2zl3Wsmcu7kJABMRsV/LxpDg83JFzsqeOCrA5w4Jo6b5qV32sFTiL40oBMFpdRcIAbQWuuvlVK/A36klCrXWr+ptV6nlDoZuBL4RpKEzvVX1brN4eTuT/fz5PIC6q1ODAre3FjKH77I47IZyfz9rJGk+qj0f+jbgzy3uoizJiYQYjocp1KK8yYnMXNYFDe+vZu3N5bx0Nkj27wWg0Fx32k5zBoezTn/28ozq4qYNTyanPhQXltfyj0nZZEZG0qDzUmElyZHzasnvN030PT3CoUmm5M/fLGfx5cVuFagmAwMiw3h7Y1lVDfZeXTpIZb/fDqT030nC7HuJlXPri7CYnOyraQBhxOublfsuWRvFZcv3MGhaiunjotnW1E9T68obEkUAl0IvKmwDqeG1QdqOXtiYsvvksGgiDAb+NWH+xiZEMZvjx/eNgFShsE0CiwGiAGbKCilTgP+CSwE7lJKnQUsB1KAs5VSaVrrx4BDwHilVKjWWsaUOtEfH6C1TXZO+O8m1h6s44TRsZwxIYHpGVEcqLLwwZZyXv+hlG93V/H9z6czPD6s5XFaa37/eR4Pfn2A40fH8sujh3k9f5b7MVGhRp+v5exJidx3ajZ//CKP6kY7ty/I5Ia3dnP2c1tZcus06q0OWn/T3lpUT53FwcTUCGotbe8bqPrzYrnhUB0XvLCN3IomThgdx0lj4zhiWFRLoldYY+Xn7+1hwRMbWXPbDEb42OL9/ClJPHh6Dvd/mcfIxDDuXDCccyYlcmROTMsx3+6p4tSnN5MSZeYfZ49gekYU5z6/lctndr8lu9XupKbJQWKkyaNYVmvNtuIGlu+vYXpGFLOzojs93+9OyuLiaclEhxppsDnb/Bw+2FrOwSoL956c5bmSRxkG9i+dGJAGZKKglJoKPAL8VGv9nVLKCkQAYVrrl5RSh4B/KKWOBWYD50iSEBxqmuzM/tcP7Clr5I4FmZw5IaHlvhEJYdx27DBOHBPHXZ/mMvvRH1j1ixkMiw1l/aFanlxeyAtrijljfDy/Pi4To49v9A6nxmLXODsYQCqqsVLeYEMDPxTUsWBUHL86dhh/XnSQfy7O547jh7d80/7T1wf4/ef7cWqYkhbBJzdMGbB9AgLh5bXFXPvGLmLCjPz1jBzmZsd4HJMeE8JDZ4/ktvf3cvTjG9n529k+k5jfnZTFXSd4732xsaCOM5/dQkZMCI+eN4rYMBPbiuqptzo5aUx8l+LeXdrIi2uL+HRbBdtKGrDYNdGhRrLiQxmTFI7F7sRi1/xwqK5l8y6AE0bH8uJl48mM872MTynFmKTwltGpZsW1Vu78aB8ZMSEcN9LLEk3tdHTpRQSpI738DgSDYI0r0AZkogDYgcu11huUUhnAvcC7wJ+UUv/RWj+jlJoDZAG1WuuSQAY7kDicrorwjQX1mAyKEKPiurlpHNUL68qbbE5O/O8m9pY38uBpOW2+BbY2JT2Sh88ZyR0f5jLt4XVYHZpGm2t64vIZyVw/N63DzZQMCsYmh/PY0gJmDoviiiNSW17bJ9sq+Ns3B1h5oBanhuGxIaTHuKY4Th4bz6oDtdz31QHyqiz87cwRfLStnHs+288Jo+OYlBbBf74v4F9L8nn4nIG73npPWSNf7qzk692VbCls4PwpiZwzKZF52TE+k6/u2lRQx3Vv7mJyWgR/PCW7ZerAmxEJYfz2hOHc/dl+vttbxVkdNDzyliQ0WB2c+7+tRIYY+MfZI4kNcz1XjcV1bc3yc/19aZ2V+788wJMrCtAaRiWGc9aERFKizeRXWcirtLCpoB6zUWEyKmYNj2J6RhQTUyP4Zk8Vb2woZcxf1vDWjyd0+hqiQo00WB28/kMpT64oYH1+HQaluPfkLO8/C+0cFIU0fzlzRL89V3Z8aEtjps46OfZnXAPJgEwUtNbbAJRSRmABcJvW+jml1CxgkVJqh9Z6KbA3gGEOOE02J6c9vZnF+6rJcF88a5ocvLK+hLeuntBSdNXdcx/3n42sPVjHnQsyfSYJzcYlR/C3s0bw/JoiMmJCmZYRyfSMSBIiOi/qUkrx8Nkjuffz/Vz56k72VTQRG2bir4sOUlhrJSHCxKXTkzltXHzLNEWzO47LJDXKzCvrSnhrYylNdiezMqO4+8ThGA2KvWWNPLa0gFvmZzAqyfvweLCy2J38+oO9PLHctQw0NdpMalQIDy/O5+/f5hNhNpAVH8rcrBhOHR/PpdOSe1SHUW9xcM7/thIdauT3J3ecJDSbmRmF2aj4dHtFhxdZb/74RR55lRYeOmsESZGHf0/iI1zPW1xr9fq4miY7L64pZktRPasP1LKztBGL3clZExL48exUv37nml07J43Txyfwhy/y+NHL29l4+xEd/p7sr2jixCc3sa+iiYyYEH40PZlTvfxeiu5rnRj0difHoWJAJgrNtNYOpdT7WusGpZRBa71WKfUiINMMXVRvcXDU4xvYWFDPT+enc8k01zK+6iY7v/0klwtf3Maj547mlqO8NzTqiNaaE/+7iTUHa7ljQSZntJpu6MjE1AgeOmtkl18LuOoT/nbWCB76Np/ff54HwKS0CG46Mo2jR8T6/OYcZjZww7x0Th0Xz+PLCqhusvOHU7Jbjv/JnDQW7ani798e5KmLx3Yrtv7mdGpe31DKvZ/vZ195ExdNTeK8yYlkxISglKLO4mD1gVo2F9WTV2nhgy1lvLi2mIJqK7cv6HRlsU9//DKPA5UWHjp7RMvFujOhJgMLRsXy0tpiHjw9x++LdEmtlX8tPcRp4+KZNbxtjUCCO0HZWdJ2C2Gr3cmzq4q457P9VDbaiQkzMjw2lBPHxHHx1KRuX6zTY0K4/7RsbnxrN2c8u4Udv5nl9d/M17squfjFbdid8OczcpiXFd3lf1sD1YUvbAPgnWsm9uvzNo8u+BpZCFRcwW5AJArupkkJwFrA6U4QDFprp9a6AUBr7VRKXQYcC/wjgOEOON/sruSGt3azv6KJ3504nJPGHp7LjQ0z8fDZI7n/qwP87L09PLo0nz+cks0l05MxKMW+8ibK6m3MzYr2+e3z2VVFLN9fw63z09vUJPS1EKOBu08czuzh0WTGhTLRjw6HZfU23ttcxs7SRnaWNJIeYyYy5PAcclKkmQkpESzfX9OXofeaRpuD05/ZwuK91QyPDeH+U7M5pt3cd1SokRPGxHHCmDjAtSLlvi8P8H8f72NCaoTfiV176/JrmZgawRGZnRf3tXbp9GS+3VPNRS9u45ufTvPrMUtzq7E5NGdP8hyFSIo0Mzktgj98sZ9TxsUzLSOStzaW8asP9lJQY2VqeiR/PiOH8Sm91wEzLTqEm49M56Hv8lmWW+Pxnj+zspCb3tpNVnwo95+a3WlSUlRrpaDa+4jIQFTeEJguk83Jga+RhUDFFeyCPlFQSl0A/BnX6oVDwFql1Ata65rmZEEpFQOcB9wJ/EjaNvtnd0kDV7++k5V5taRGm33WDUSEGPnzGTl8sbOS138o5cpXd3LLO3totDuxOVwFg9nxoTxwWg6XzUhpU6n9Q34dP3t3DzOGRXHB1O5PXXSXUopTxvlfxPb2pjLe3FDK6KRw5mZHs2h3FbvKGhmXfPgikhYTwtqDtX0RbguHU7N8fw0fbCnnQFUTF01N5pxJiR4tip1OTVWjnZhQIyZT2/uKaqwc/+RGdpY0ctO8tJbkrjNmo4HfnZTFL9/fw8UvbuMvZ47gULWFPWVNTEyN4Lq5aeQkdP5tu7DGSkxo1z9iRiWG8+NZqTy3uojX1pdw5sQEFu+tZmtRPedPSWKclwv6mgN1mAyK0UmecSmluO/UbG5+ew+nPb2Z+AgTO0oaGZkQxl/OyGFuH32TP2F0HE8sL+DZVYVtEoW3N5Zy01u7mZMVzR9OySa8k8LYZbnVPPDVAayOwb+6W3aGDE5BnSgopcy4GiZdp7X+Xil1ITAP+D+l1ENa62oAd9JwENfqhtwAhtxvlu2rJiM2hJE+lpF1ZsOhOhY8sRG7U3PjvDQunJLUpidBewalOH18AqeOi2dVXi3f7q0iIcJMjrtA7M2NZVz92k5++f5ejhsVy9ysaBptTh5deojYMBP3npTl10Uq0PaVNzI6KYxdd82mosFGyh9WsGxfTdtEITqEsno7DVYHESHdX63WZHPy4dZyDlQ2UVpvo6zeRnGtjd1ljRTWWKm1ODC7C97e2lhGhNnA7OHRPHr+KKa5d7mrszg47/mtxIWb+PC6yS3nLq2zMvOR9VQ22rj/tGyO7mIxarjZwJ9Oz+GWd/fwy/f3YjYoUqLNvLe5jD99fYDM2BBOGhvPKePiOWlMPEntGgLVNNkpq7f57IXRmctmJLMyr4bLF+7AoKC5eeTdn+5nXnY0dywYTmZcCDGhJrYU1fO/1UWMSgwjxOj9dzghwsyDp2fzi/f34tTwm+MzOXlsfK8Xb7YWZjZw4ug4Xl1fypS0SE4eF8+haiuXv7KDSe7izs72pvh8RwUPfZfP2ORwnrhwDCc80mfhBoW+3BlSdF9QJwpuMcAY4HvgPaAMOBO4DPivUupIIEJrvShwIXauwerggy3lPLr0EBsK6rhyZip/P3tElwqlmj29opCb395NRIiBt388kdPGd21oeM2BWk787ybCzQYeP3tkh8u42jMoxZE5MR4jD6eMi+f73BqW5laz9mAt728pRwFzsqK5aV6633PUgWaxazJiXe9HQoSZmFCTu3vhYWPdxWmrD9SyYHRcl5/D7tQU1liYfv9KKhpc5zYbFLHhRuLDTcSHmxmTFM4RmVHMyYomzGRgQ0EdS/ZWs2hPFdMfXs/xo2N5+uKxjEwI47zJSS0rN8CVgBz/5CbKG2z885yRTOxmi+LkqBBeumwclY12UqNCMBoUxbVWvt5dxY6SBt7aWMbza4oByIkP5dTxCYxJCuetjaWsz6/D5tSc2oXRnNaMBsXvT8nipbUlJESYmDEsimGxIXy8rYKPt1Vw4Yvb2hw/PC6UW1ptouTN2OQIXr1iPFEhxg6T4t502YwUthQ1cOfHufCx6zvMmKRw/nxGTqdJwifbK/jHd/kckRnF4lumETnA+3b0tf7aYXIoCupPb621TSn1CPBzpdRerfVSpdQyYBhwplLqeWA4sDSggXZifX4tJzy5ieomB8mRZo7MjuH5NUW8s6mUx84fzZVHpPg99PnokkPc9sFeZg+PoqLBzpnPbuHhc0byy2OGdXgOrTW5FU18vK2Cuz/NJSbUxCPnjCQtpne2VjYoxTEjY1uGWGua7Fgduk31+UBgMijK6zuep5ySHolBwXd7q7qcKKzLr2NZrutDbERiGHefmMyElAjCzYYOf35HZEZzRGY0189L440NZbyzqYzpD6/jsxum8OtWBYdaa858djNbixr44ylZ3U4SmoWbjYSbD1+gUqNDuMLduMjh1OwsbWRdfi2bC+t5ZV0x9VYn6dEhnDs5keNHx/lVF+JLSlQId7Qrprx2ThpXHZHCztJG6iwOGqxOwswG5mZF+zU60J3EvCMWuxOnxuf0QVpMCM9dOpaSOivr8+sorrNx/uREojuZksktb+LRJYeYNTyKpbdO75UttIPJie56mN7UGztM9kVcg0FQJwpuS4FxwFXuvRqWAK8qpW4AsrXWbwY2vI412Zyc9N/NhJkM/P7sbKYPi8SgFHvKGnlk8SGufm0nd360j4umJXPimDhiw4yEmQwMjwtt05UQXCMBt32wl6NyYvjDKVnYnZo/LzrIrz7YR4TZyI1HpnuN4dmVhdzx0T6qm1zryccmh/Pgadl+7anQXTFhA+FXy5PZqNhVamHRrkqmD4ty9W9odwGKCjWSFh3C2oN1XT5/YoSJtJgQokON/OPMrq/oiA41cf3cNM6amMD/fZzLSf/dxMuXj+fiaUnsLmvkie8L+WZPNTfMTeO4UXFdPn9XGA2KiakRLcmAw6mpbLSTGOHZvbA3mY0GJgfBRk42h5OrX9uJw6l55JyRHRYkpkSFdGnk76V1xdicmqNyYnl5XTFJkeZOaxkGkuZW2sEmWOMKtKD/NNdaNymlFgIaV6vm8biWP6YA1QENzg+fbq+gstHO384cwczMw7vbjU4K59/nj+Lr3VUs2VfNs6sK+c/3BS33GxTcfGQ6952a0zL/OzIxDKMBMmNDMBsNmI1w36nZ/OqDffzfx/u4ZHoyca3WqtscTn71wT7+830B0zMiOW5ULLMyo7s01TDUnDIunjUHaznpqc0oXL90x7e74G4rqqegxsrtC+K8nKFjiZFm0nohQUuLDuHf543ins/3c+nL27nxLWNLInjmhAQum9H/u1QaDWrAjSD1xLLcGkrqXKNPt3+0j3+dO4phsb3zb+tH05M5WGXh0aWHeuV8QvRE0CcKAFrrSqXUM8A24CagCbhSa10c2Mg6968l+cSHmzgi03MLXKNBceq4eE4dF4/F7mRfeRMWhxOrXbMyr4b/rijkpbUl3H9aNrcelUFipJm5w6NZtKeaG+alYzQoDErxs6MyuOnt3dz/ZR6PnOvqGJhX0cR5z29lQ0E9l05P5oa5aX1auBUsGm0ODlZZKKmzMTY5nJQuXpRPGB3H3KxoNhfWs+FQHbHhJqZmHP72andonltdTEyokRvneR/B6S+x4a6lq/9bU0xVo50paRFMTo8kKy50yKzHD6SPtlWQGm3mixuncOx/NnLLO3t48qLRZMT0PFkYlxLBs5eMpcnmpMZip7rRgc3h5NZBUsx4+jObAfjshik9Ok/7rovBEtdgMyASBQCttRX4Vim1xPVXPSBamW4tbiArPpTOrtGhJgMTWs3nzsmK5uxJiTyxvIBff7iPkjobfzlzBL88NpNLX97O6oO1LX3JxySHc8aEBB5deois+FD2ljXx1IpCDAa4+8ThnDy2ewVlA4lTa657Yxf7Kw/32gozKf536bg2hX7+iAwxMi87hnle+r4v3lfF+kN1/OOsEb2yoVJueRPD40I9N//xU4jJwM0+ppz6itXh5OW1JZw8Nm7IdhC02J1sLKhjVGI4K/NqcDg19VYnb28q4xc+NivrjjCzgTBzCCme3zMGtEZb73x89/byyd6Ka7AZcJNeWmvHQEkSAO5YkMnGgno+2V7R5ceOSAjjobNGcvSIGB5fVkC9xcF5kxNJjTbz0tpiWu+afetR6czKjOZXH+zjyRUFnDIunpcuGxdUSYLWmp+8sZMznt3Cec9v5ZKXtvP2xlIczp6vD39jQyn7Ky3MGh7FxdOS+NH0JJwafvrObmot9s5P4KcmuyvWS6b3fGh/Z2kD1765i+vf2tXhBlbB5vFlBbyyvoTffba/y49tsjmpswyMfY1K6qxsK27wel+oycDtx2WSW9HEzW/vITHCzLVzUvnxEalejxdiIBswIwoD1W+OH867m8r497ICYsNMzM/p+sY7l05PZlluDS+tK+an8zN44LQcbnxrN6sP1LbsxBduNvKn03P4clcl04dF9srwZ29bl1/H/goLIxPDCDUqSups/Gd5IW9uLOOf547s9vzutqJ6nllVRHZ8KGeMj28Zdr98hoGX1pVw41u7eemycZh9rLHvihD3N39LLzS/+WpnFeCq1n5uVRE3BHgqwx+fbK/go20VxIebyK+2cqja4tfPrd7q4I0NpbyxoRS7U3PBlCR+PCu1z7a5vvnt3ZTU2RiXEs7NR6aT7cfIR02TnR0ljewoaeDDreWUu5eu3n7cMK/7TpwxIYGp6ZE02pyMTgqT6R4xaEmi0McMBsXH109m5iPr+f0XeaRGmzlrQiIXTEn0u1nPpNQI0qLNfL6jkp/Oz+DHs1L5/ef7eXjxIR47L6xliaPJqLrdbrc/LMt1tT0+dWwcKdEhaK3ZXNjAJ9sruOlt18Xc2/I1rTUfbavgxTXFjEkOZ1hsCAkRZqx2Jw02Jx9sKSfSbOBH05PbfFhnJ4Rx1sQEPtxawcq8Wo82ut1hbk4U7D0f1FqaW93STOitTWVcOyf460ieX10EQGZcCJWNdjYU1HeaKFgdTq57YxfFdTay4kKJCzfy9qYyEiJMXDYjpddjtNid7Cx17euwMq+WDYfqCDUZGJ8SQVp0CEmRZhIiTMSEGSmotrKrrJE1B2pbdpkESI40ceKYOHYUN/DI4kOMSAhjkpeVFv1eGGwwDcrP7OYeCNL/IDgNyl+6YJMaHULu7+bw/pZy/vrNQZ5bXcTH28q58/hMv/rgO5xQ3mBnbLKr0U+IycAXN07h6Mc38usP9/HoeaNIjgpMtXlNk50fDtV1uNFSs8tnJvP5jgpeXl/CtbPTiI9wFQomR5l5bnURN761m5cvH9dm3X6Tzck/lxziy12VJESY2FXayLr8Ouzu6QqjARIjzFw+I9nrWvPxKeF8uBUKfewc2FUm92u09cKIwkNnjeA3n+Ry7MhYzpyQ0CtJQlGtlcQIU6+Mnnjz2PmjuP3DfWwubCAlyszJY+M6fcx/lxdSXGfjgimJTEmPpKbJzqbCBiJ70NWyI6EmA786dhg7SxpIiQ7hhTXFxEcY2V3WyA+H6jxaIUeGGMiMDWVWbAiZsaGkxZhbfgdnZUbxn+8L+O0nubx+1YQOY65usmNUqs9GSQBw2ntvHi2Azmr3haY3eiD0hvZxCRdJFPpJiMnAJdOTuWR6Miv213Dpy9u546NczpqYwC+Ozujwg/1AVRM2h2bGsMMVTVMzolh081QWPLGR2z/ax2PnjWqzNLI/fJ9bzYNfH6TJ7uSkMXH89oThHV7sUqJC+Nd5o7jtg708s6qIH89KITU6hPSYEC6emsQbG8q44c3dvHjZOIwGxf6KJn794T4qG+0cNyqW40bGoJRCa43dqTEZVKfDvWEmA+FmA9uKGsC//YU6ZOzFRCErPozXrpzQ4/OU1Fn5ZncVb20qo6LBTmasq6NiXwyFZ8SEsvCK8aw9WMfE1AifLZObbThUx3tbypmbFc2UdNc38lr3N/fEPuzWec6kRJiUyOPLCjAouGJmcstF3u7Q1Fkd1FsdxIWZOux4GGZ2/bt9fnUxjyzO5+4Tszx+xysabDy7qojPd1aiNcSEGZmbFc2VM1P8KvY8UNlERYOdaRmRQ2b64o7jhwc6BK+CNa5AG3DFjIPBkTkx7PztLG47dhgfb6vgmz0dt4Moq3d9iciMbVu9Pzsrmi9unEJBjYXXfyjps3jbc2rNc6uKuOfzPCJCDMzLjubr3VX8+PWdNNo6LlQbnxLBUxeNQQEvri2h2t0eeVxKBKdPiOdQjZVv91Sxp6yRm9/eTYPNweUzklkwKrblQ1QphdnYcSfDZkopJqdFsHhfNf9edgibo/tTBlprvt1ThQISgqQl9Y6SBq58dSdPrSzCoGByWgT51Va2+yjCq2my8/amMt7bXMaXOyvZXFjf5ec0KMWcrGi/vjmvy3c1pcpJCKW60U5hjZX3Nrvae/uzsVRPfb6zgrHJ4W1GAkxGRVy4iWGxoX61RR4eF8qC0bF8s6eaH72ynbzKJsCVcKzLr+WqV3fyxc5KZg+P4vjRsaRHh/DNnip+/t5e6q3e/z1orVlzsJYrF+7gx6/v4lcf7uPq13ZS1klXUCECITg+7YagcLORR84ZyTubyvhwa3mHPfGHuROE3WWNHNuu+c/RI11NlL7bV81NR6b3+TcSp9Y8svgQn2yvYMawSM6YkIDJ3Wjnk20VXPnqTl740dgOW9Rmx4fx7/NHcfPbe/jv8kLOmZTIhNQIZmVGsSy3hn9/X4DF5sRkVFw3J63H+0ScOs61+c+7m8tZsq+aJy4Y062pmg+2lvPVriruOzWbUUnd24yrN+0oaeAX7+8l1KS4YW4ayVFmGqwOthY1sPpgrUf75ooGG9e/uYvKxrYXr+z4UB45Z6Rf7Y0dTs3nOyp4amURl89I5ked1Bj8aEYyH24r540NZS23GRU8eHpOl4tXXRfXOh5beoiyehuXzUjh0unep5zA9XrrrU6yemHe+5gRMSREmPh4awXXvrGLlCgzZfV27E5NZIiBG+amtWmHnlfZxItrSrj13T08f+nYNv8uy+pt3Pb+Xg7VWAkzGVgwKhazUfHNniquWLiDOxZkBtVqpb6w4ImNAHx3Sy8M8/WiYI0r0CRRCCClFLcfl8ltH+xld2kjY5K9X3zSokMwGxUbCzy//VU3umoXVh2oZWdpI+O9bMHbmT1ljbyyvoTECBO3HpXhc5dHh1PzyOJ8Pt1RyTEjYjh+9OFv+UdkRhEdauSNDaVc98Yunr1kbIdtnEcmhvPURWO44+N9vLmxjGnpkZwzKYGjR8Tw6fZKIsyGXkkS4HBjq+Fxoby3uZxrXt/JM5eM6dLKkA2H6vjP94XMzYrmnpOyehxTT+1slSRcPyeNWPe0U0SIkaRIEx9treDKmakt/RnK621c++Yu6q0OrpiZTHp0CE12J9uKG/hubzWXv7KDP52R02HNzLbiBu75bD+VjXbCzQaeWllEeYOdH01PJtFHR8bIECPPXjyGJftqCDMbiAoxMDopvMtJwsEqCz9/bw/VTQ7CzQYSI828sLaYtzaV8sBpOW2m5VrHC5DZC90SXSNTkeTEh/HO5jJqGh3MGBZJdnwYoxLDPJKV7PgwThwTx9e7q3hudTHzc2IINSl2lTbyryWHcGjNaePiOWJ4VEvdy7jkcN7cWMafFx1kV2kj181JG3R7PIiBSRKFADt/SiK3fbCX7SUNPhMFo0G1FFUV1Fhxak29xckWdythoM1WvF317uYyFu91TX+cMjaecV6SDYdTc+Nbu9hXYeHYkTFtpgKajU0O56KpSby9qYzLF+7gr2eO6LAn/4jEMN64cgLPryni1R9KyYgNYeawKBqsTqamR/b6jpMTUyNIiDDx1IoivtxZyTWz0zp9jNXh5MU1xby+oZRhMSF8dsNkj70f+pvdobnz41xCjG2ThGZzsqL5ZHslly/czqnjErA4nHy5s5IGq5OrjkhpWSoYGWrkmJGxTEiN4H+ri3l7U5nPRKGwxsov3YnJRVMTmZASwSfbK3h7k2uDqgunJnHtnNQ2hajNkqNCuHBqUo9e83d7q6hucnDupASmpEe21LC8t7mc33ySy+tXjvcYESmqdQ3j92YtRFSokR/P8q9XwvycaPaUNbJwfQkL1x+eGkyKNHlNrhIjzdw4L40vdlby9qYyPt9RwX2n5rRp/T6YrMyr6dWuiqLvSKIQYJ0VgzW79+RsHlmcz/Jc1zezUJNibHI4p49PYHRSGFPTI/1ebtnehkOHNzeqaPQsqtZa888lh9hXYeGE0bEdLjOckBrBdXNSeW1DKb94by9XzEzh6lkpPos1TUbF9XPT+Hp3Fd/trWbmsCiOG9XzZYy+pEWHEBliaEmwOrKpoJ5HluSTV2nh2jmpPHLOKI+LciB8vrOCWouDy2Yke41n1nDX1tQfbatg4foSjAZFqEnxo+lJXvsJJEWaiQoxYrV7zzS11vz6w70o4Ia5hxOTsyclMj8nhuX7a3h7Uxmf7ajg7hOzmJ/j2dGyp4zupHRyWmRLMWFOQhhXzUrhyeWFPLuqiP9rV4jW03xud2kjaw7WMjktkompEV3unqmU4upZKRTUWGmwOrE5NUbl2mbaV7JpNLiWOE9MjeC9LeX89pNcXrys691FBwKLXXe40qG32zOL7gv8p94Qp/FvGCDcbOB3fTTkXW91YlTg0FDZ4JkovLSuhE+2V3D0iBi/ehFkxIbys6My+HxHJa+sdz329uOGMSkt0mNlhsOpKa23cfr4eF5cW8KGgjpmDe98yWhPRIca2XDIdxFfWb2Nv35zkHX5dSRFmvn0+smc3sfLprTWbC9uoKTeRk2TA4OCk8fGE2pqm2DZHE6eXF5IcqSJMUm+iwEnp0cyKc01MuRP3UpkiIH8aovX+97fUk5RrY0zJ8R7JCaJkWbOnpTI9GFRvLupjN99tp/0aDN/P2tkr/YYaL5IO7TGxOHXkxRpZl52NJ/tqOScSYlep966M9BWXm/j1R9KAdhd1sR7W8o5b3Ii0zK6tmulUqpbjcRyEsL4yexUnlheyK8+2MvrV/V8dcxA09vtmUX3SaIQYKvyagFIDVAfBIDkKDM15Q6MCka3u/hUNNh4eW0xk1IjOGG0/9/0Q00Gzp2cyPiUcD7YWsE9n+cBEGE2EGY24HBqHE5Ng83ZMmViMqh+3ErX++XjQGUTd36cS63FwT/OGsFPj8ro9khNVzy3urjN8DTAUysL+f3J2cxulTjlVjTRYHNyWqsOlL50pbA1KtTI/koLr6wr5qSx8SRHmjlQaWH1wVqeWlHIsJgQrxubNRseF8rPjs5gzcFavt1TzTVv7OTW+RmcOznRZ81LVzRaXatVvC2/PXZkLOvz67j/yzxedS83dTg1C9eXEG42EGbq+u9U866QrfX3ioTYMCNRIUYabU601oNq6eQl05JbpjuDySXT+n/X1YFAEoUA+8d3+SREmPr8W3RHnr1kLLkVTZgMiuHtvgW+u7kch6ZN4WJXjEuJ4FeJYRyqtlJY4/rPYnetaDAZXM1pEiJcnfKat8/uazVNDo4e4Zn0WOxO/vBFHla7k+U/n850LwVyfeGdTWUsXF/CjGGRzM2KJsJsoLTezgdbyvm/j3PJiQ/lvlOzyYoPI7fC9a2/t4eijxkRQ2mdjedWF/Pc6mKMBlejL4C0aDM/np3S6c/faFDMy45hYmoEb24o47FlBby9qYx/nz/KrxUVHSmosRJhNrQU/rUWajJw7KhYvtxZxfr8OmLCjHy6o4KKBjsXTU3sViOr1GjPePtySsybHSWNVDbaueuE4YMqSQC45agMbn13T6DD8HDLURmBDiEoSaIQQFuL6lmRV8PF05ID3rp3hJc17Sv21/DmhlKy4kJ9VrX7w2w0kJMQ1i/r5jvTYHXQYHMyPM7zQvvk8kL2V1r47IbJ/ZYkrD1Yy+PfF5AVF8pZExNavn1Hh5n4+dEZLMutZtn+Gn78+i4umZaEzaExKEjo5VqJlOgQbp6fTlWjnc2F9dRbnWTEhJARG0JihKlLF6qYMBPXzU1lbX4dX+yo5PKFOzh3UiJhJgNKuVYEeCuG9cXmcLL6YA3RHfQ8mJUZxdJ9Ndz+0b6W23LiQ5mY2vVVQBa7k4NVbadhbjs2w2uS0leabE4+3V5BTKiRE8fE9dvz9pcGH/0lAq05rv4YRRxIJFEIkLI6G6c8tZnYMBMXTPHccCaQimqt/N/H+zhYZSU2zMh5k4Mrvp54e1MZSuGxhfSXOyv5YGs5vz5uGKeN7782ruvy6zAouOqIFI8hepNRsWB0HLOHR/P1nire3OjqRTAiIRSDwdWhstbioKTORmKEuVdWicSFm3plTwylFLOHR5MTH8a7m8t4a2NZm8mep1aY+euZIzpNHptsTq5/cxdVjQ7OmeQ7LrPRwPmTE9lZ2khWXCjZCaHEdrA81xuL3cnS3Bq+d+9J0uya2SldOpfWml2ljWggPtxEXLjJo9akM29uLKXe6uTf548K+JeIvnDGs1sCHYJXzXFJH4W2JFEIAIvdyQlPbqS03sa/zh1FSlTwVDSvOVjL7z7bj9aaE8fEcWR2dNB9UG0rbmBfeRNzhkeREu3/e7e5sJ7cCtcKhtGtGibtr2ji4cX5TMuI5K9njuiLkH0qb7C5htQ7qKiPDDVyzsQEKurtVDbaOWdSAh9tq2BrUT0W90oFg4J52dEcOzK2yxelvpQcZeamIw/viunUmg2H6vlyZ6Vri+05aVw2I9ljdMHmcLIuv45HFudTWm/n7IkJLb0SbA4n+yssjEoMa7N6YExyuM8lxh3RWrMuv45Ptld63DctPdKvnSebVTTY+PeyQo/bj8yO5sQxcX79W9rU6vfU20ZUonc1b0gFEGpSHl8ihCQKAfHAV3lsLmrgdycO79bQaF/5YEs5jy49RFy4iauOSOn1Pga9YUdJA2+5v1mvy6/j3EkJfk0TWO1OPtpWQXKkmcvbdRP8YGs5BgVf3DilX2okWttcWO9XAadSimtmp2B3aP6zvJCaJgcnjY1jQkoEWfGhLNpdxWc7KtlYUM+vjh0WdMldM4NSzMyMYlxKOJ9tr+SZVUXsKW/k+rlppEaFkFvRxLr8Op5fU4TFrgkxuvo2tL5gfrqjsmXVyh9O6flKoNUH6/h8h2eSADAz0/8LdX6VhedWF3u9b0VeLSvyarnnpI73Q3E6NR/7+D0Vvaf90svmZZrNt4m2gu9KMARMSnV9+BR7qazuC1praiwOYkKNXueFG21OnllZyHtbysmMDeGqI1IICaJvpeB6DY02J8ntaiXS/BxRqLc6sTk0185J9figrmmykxRpJrULoxO9xanxexdFpRQHqpuobnJw1wnDOaVV2+/RieF8s6eKMJOhx/0DqhvtFNVaabA5qbc6MCpFTJiRmDATqVHmXvndiAwxcuHURJL2mVmyr5rFe11bbttbFVAuGBXLqKTwNrUBNoezzdJWp9Y9XlWxr7zJ533ti3t9sTu1zyShta93V3XYrh1co0NT0iOCNtkbDGTpZddIohAAP5qRzFMrC3l+TRFzhkd3a7jUXxUNNn75/l7yq62EGBUJESaOGxXH5LQIpqRFsquskfu/zKPO6mRuVjSnjI0LeOdBb77cVcXKvFqumJnMHQuGUVpnc30r8PMi4dCuIXpfVfMWH82G+lpGTIhfzZ+abS1qwGRQLGhXgf/rD/dic2gunpbUssPmwSorESEGv4oRG6yO/2/vvMPbKs8+fL+atrz3iJ2dOHH2JIFAQggrZe8wWsreZRZKgY+2lNKySguUUUpK2XuPBEIG2YQssp048d7bsvb7/XFkx0OyZVu2JPvc15XLsXRsPa91dM7zPuP3sLWwkW8P1PhkxxnZ8UxJi+i2CFFrhFDWMSMjkvWH63BKSUaMkSExBuLCPducU9H2pl7T5Oh1R8X+8iaPj8/MiPT5/Fp9yLdWvw1H6tFrBAtGx3h0cDQawYTUCFYfqsPqcAVVGkll8KI6CgFACMG7l48j+/Et3P/VYR5YlMmUdP9X2Ttdkl+9tY8mu4vjhkdjcbg4XGXhve3lvLPt6HHRRi1XzkruVi62t1jsLv76fQGTUk1MHRLJiHjPN32L3cWKnBo25yvqkW/8VM5DJ2d2u4PC7Sdg8zAeOlyvodbioKTO1mawT39Q3A0nQUrJ7lIz80fFtNnV7y0zc6jKyvyR0S1RkfY591OzYpmZEeX1xv74ysJu2f357io+313VZSjdF6KM2jbRkc7IrWrrKFSZe+coNNm9TxMd66MDf6TKwppDdV0f6GZNbp176qrnXPiEFBM/FTRw3tLdhOs1uKTE6eqZcFSwcuXMlKDUUQDFNpW2qI5CgEiONrLs+kmc+cou7vjkEEumJXHlrBS/5si3FjbQYHN1yPE6nJKiOhv5NVa0Gjq9gfQFVofiJADsLDGzs0QZ3jMrM5JhcWGkRxsoa7BxqMrCpryGDj/fk57yBJOOCIOG134sZXE7lcWTx8bx5Z4qZjz9E6tvntJvkyGdLklVk4PJab7lwcsa7FgdktntNDead8TT3IJILik7FOZ9s6+Gb/bVcPNxaSS2S9/0RkjokW/zPToLFruLWouD5Ei9XzUAJqaaWpxG8D014I3cTtIOIxO6dkarzHaW/tj9Ee+dDW8bEW/krAnx5FZaaHK40GkEWo1AA+zs9isFJ1fOTuXX7+wPtBkeuXJ21zNgBhuqo9DHuFyS/20p5f++OcK8ETE8fOqwlor76RlR7LtvFrd/fJBXNpWwOb+B3y/K9NvOfuXBWnQa0WFnpNMKhsYZ/TJ+t7vYHC4eW1Hg8bnN+Q1tbgKeuH5Ozz7EGo3g2OHRLN9fw94yc5sL9bhkE0+eNYrffZnLrL9vZfn1k8hKNmHSa/o0DbMipwa7U3ZQw/RGc2V2e8eisNaKViiRIfAeSgd4bm0xi8fHMW3I0amF+8q8H+8Lj3ybz9WzUwjTa7A6XPxU2MhPBUffxzvnD+lUA6E7DI0Laxk8dstxab0OzedUel77ORO7FmoqqbPx4oaSbr3ehVMSGZ8c3qnzJIRg2pBIjxMxd2oNwdMi1Qsq+qk+qydUNNhJDKBSbjCiJsD6EJdLcv37B7jy7f1oheD9HeVMfPxHVh2saTkm0qjl3xeP5cMrsyltsHHD+zlsOOJ7GNMbZpuT5furGZ8c3u+V/J2x3i1Z3VN6kxqY7r7wfr67qsNz2Skm/nnOKDQClry+hzqLA3MnYene0mBztqhyZvkY4q4021t+thmrw8V3B2qIDjtaqLqz2PscC4Av91Tz7f6alu+9zXjoDq9sKuW5tcX8e2NpGycB4KlV3UtrdMWEVBP/d8rQXomAgVIYudXLzI9JaV13I3XHSZiREcm9J2aQnWLqXYTFafM9VxXEXPDa7kCb4JVgti1Q9OgOIoRQYzM+cPsnB/n3xhIunZbEyxeN4X9LxpEaZeC0l3Z2uJifOymRPb+dSWaskd9/dZjPdlf2+HVzKy1c+sZebE7JrKGBk4b2hKehU93B0oubd/ONNivZ8415aFwY8SY9UUYd0WE6TH0wd6Le4qC0wUZOZRPheg2XT+9aGrmZOUOjiTBouOXDHPaXm6ky27n8zb1Umh2c0EokyZcugHrr0ffh5LGxTOjjNl2X7F6G3Wxzsr+8iZU5NXy2u4p3t5XzxpYyvjtQw6FKC3Zn984Du9NFRaOd3EoLpfU2nC5FO8ETd5yQ7tPf8LLpvs8FOCM7njB/nE8anRoFVul3enrSHRRC/BP4q5SyTTJUCGEAtFLK3sUzQ5y1ubX884cizpuUwDXHpCKEIClSz1NnjeRXb+3npg8OsOaWqW1+Jj3GyE93TmfRCzv4++pCslNMjErofLdZb3Xw7w0l2FySTPeUuv9sKkGv1XDFjORe53C7g0tKqs0O4kw6rxfaRnvvpFsPV1s6ze92xvaiRrQCThwV6/F5i93Foaom7j0xk0g/hcrbU1xvp7jOhkEnuOGYNI83DyklVWYHtRYnNqeLaKOW9BgjcSYdV89O5ZVNJdz60UH0WoHF4eKiKYmMb3Wj90WXoXWXR7xJz/mTE9i13OyfRXqg2uzoEAE4WNnEvrImrA4XFofsNGXSTE6lhR/cyome6i3ak19j5T8+tC02c2Z2PNE+qjCOTgznzhPSeWp1UafHTe9kmJY3bA4Xe8rM2J2S5EgDqVHutlStTo2Jq/Q7PXUU5gMPAYeEEE8CT0spm7fIC4HPgEF9Qn/8sxIRWDKt7Y4x3qRnRkYk6w57Ti9EGrV8ee1EUv5vA9/sq+amYzt3FP75QxHL99dg1ImWi39alJ5Lpyf32c3OG98dqGHd4aOphYWjY5iZGUm4XrFjT6m5Q3tbd3lnWwUXT03strNgd7rYUdTICaNivP5dfiyox+mCucP7TpktKVLPkBgDOo3w6CRYvdRw3HRsGkmRikzzNcek8srGEpwSrpqVQnq7McbtR3l7on2bqBCCe0/MaCky9Tcl9bY2joLDJXl9S3mvfudza4u584R0ojzc2KWUrD1cx3cHuldZP21I95QQo8J03H9SBo9+5/3v1t0oWE5FE2/85OVv43QEb3JfZcDSU0ehFmi+4v8R+I0Q4iCgBSYCP/nBtpDm0mnJPLGygJc2FHPfwsyWHfbWwgZ+OFzLNcekef3ZSIMWh0t2KcRTVGfl2/01zBkWxalZcVgdLuqtzm4P8fEHFY32Nk4CwIqcWlbk1HLs8KgOz/WGd7Ypyozdac3bVWLG6pScme15boWUylji1Cg9p2X13awHnUZ4rRkpb7Dz/LqO8r8Aa3JrOW9SIqA4ArfOS0eCx2K+2ZlR1DQ5OhSGjkoIQyPApNdyooeR4WF6DTcdm+bVht6QX2Nr03nTfuhST3lqdVGbYkmL3cXuUjOfeahD8cb45HAyY41M74ZuQmv0Wg3zRkS3RDras7vUzB+W5QEwOc1Ek93FgVYOc3aKCb1W0GB1crCTLgwAXI7e5e5UVHpATx2F/wLpwDNADWAAfglkA+8DN/jDuFBmWkYkD58yjIeXHUGrEdx+/BC2FTbwf8uOkBlj5M+nD/f6syX1Sr1SYhcSym9tLUcIOHaYUodg1GkCJtDy3FrvNxd/OgmteeTbfB5clOlTZ8Law3XEhGmZmu55x/hTYQN7y5r41/mj+7RV1OWSOF2yg3piUa2Vlzd6D5HvLDYzf6S9ZVfemTqiTitYPD6e2UOj0GuFV0VOTyRF6vnVzGT+24OWv87YmFfPaeOOaiV0pobYXXpTLHn17BQy/JCeq/SxxXRHccf0zu7Svkv5BCs3zk0LSh2FxAgdqw7WIu5azbA4o6rg6KanjsJU4AIp5ZfNDwghngBuAv4KnAK83WvrQpyHThmKS0r+uDyPtbl11FudjIwPY/1tUzut2C6tVy46XQnJrM2tJS5c5zH02p8U+qFqvqfk1Vi7FF8y25xUNDq41l0r4omv91YTE6blyll9W6db2mAnv8baYY7GPh/y8/vLm6gvaMDqcGF1SnQaQWyYlthwHWOSwjtEoLrK33tjeHwYNx+X1qnz1xO+O1DDwtEx1FudXnff/Y0/5pmYbU729LLFdLBx8bRkLnl9b6DN6ED5H49t+b869+EoPf2UFANtJpZIKV3As+4L8eOojgJCCP5w2nAWjI7l/74+zOLx8dx2/JAuZ53XNCnRxa5qDC6cksRLG0oorrOR1s+Kgq35sQvtg76kztJ1cWRz7Ua8l5uClJLtRY2ckhXnn8r0TkiO1KPTCOqtbe2ekq7I9nrjoimJLWOmvXHfwgy/RZR66mR0xg+5ddRbnGzvon2zX/GD3OGKnODbGQc7+dX+iyj5k2a7MvtRpTYU6Kmj8CrwmBBij5RyY7vn8gHf+4YGASeOjuXEdh0OnVHtdhQiDJ1f9M/MTuDVTaV8s6+aK2cFRnbUbHOyrShwF/72N1xP2NytdM1Fle0pqbdT3mhv02LYV8SG60iO1FNUZ2vpEAElevR/pwxFSonNKbE4XEQatC01GMv3e55u2JrHVhT4ZZpiXxJUTgJHZ4B0ByklLglajaC03ua1zbJP0IcHz7jZXnDFW/sCbYJHmu1aedOUAFsSXPR0+/EY8DOwVgjxpRDiFiHEL4QQVwBPAMGpzRkifLa7Er1WaafsjEijlkumJXGk2sp728vJrbQge3Dh6w39NQHTG105U6BIHwMkRXj2i9/aWoZG4PO8gd7SvOuvtXSsSxNCYNRpiAnTtSnU7Ks6j8FOs9qlryipxHwe+TYfp0vS4IOj6lf6u0pZRYUeRhSklA4hxGnAzcD1wD9aPV0LXOAH2wYlWwsaeH1LGZdOSyLK2PXb86uZKThdkvd3VLC7tIloo5ZxKUoVd2askZg+rl8w9OOMiPbMHhrJFC/Fia3ZlFePSa9hnAdRoR1FjXy2u4o7ThjC2KT+2azVW50IQbeK6K6endLlGOOFHjoZesPU9IiARouOHR5FTJiOr/Z2HU3pKR/urCQ2XOez3kjr2opN+fXMHRbNvSdmkF9jJa/GisMlMWiVzpZwvfJvb5mZnR6KGHuG6iio9D89votIKR0oXQ/PCCFSgNGAC9gupRx8Zbx+4tr39hMTpmXJtOSuD0YJf147J41fuqexLd1cwk8FDS3DlCIMGqakR7BoTGyfbEYC1WUxfUgEp2XFdbmmRquTojobF0xO7CACVWdx8MSqAlKj9PzptOF9aO1RiutsVJntGLQajyOvvZERa2T20EiPQ7KamZXpXxXOkQlhAXMUfntiRotw1JT0CFYerGVDL+W/vfGfTaVcNTulS2chr9rC963qEZbtq2FKWgQmg5YxSeFex8WPSQzzn6Ngawyu3I3KoMAv200pZSngu/yZikdK6pR859WzU7otlmTUaTglK45TsuJwuiQHK5v4ucTM+sN1rDtcT1aSqU+GQPlr2E93+UV2fJdOgsMl+d8Wpc2vfVqhpM7GPZ8forTeztfXTSSin9bRaHOiEYKYsO6/3qlj47w6CiePjW1TiGl3usipsKDTCOJMOmLCtN2e+THKh+mJfcG0IRFt1CWNOg2nZsUxOjGs1yJN3mhWb1wyLYnRiWFtnEopJXvLmjwWk9ZanF0WJ+u1Gm45Lo1n/dFFEuJDoYY/srEl3WPUqcGRUEHVDQ8iVuTUAPR6PoNWIxibZGJskonTsuI48z+72FXa2CeOQiAiCpdNT+pSi19KyZs/lVHaYOf3J2W2kcIuqbdxx6cHabC6+P6myRw3ou+LGJtJiTL0uG9foxE8dHImPxY08GWrMdI3HptKcqRy/6i3KEJLa7y0H87IiGRUQhjjuphgCHR5A+wrFo6O9fj4yC7aYP3BW1sVR2RWZiRZScpAtVc3e98DJfs4ZbC3A6xaMJi6Jx0ZZByptiKfPIHPdvV8lk1fctf8jECbEJSojkIQseJADVFGLaO7mO/QHUwGLenRBnaVmDl9XN8oDk5INbGrpP+yTSN92Ol+f7CW3Cor1x6TyqKxR6MJTXYnd316iAari5U3TWaGn8P1fY0QglmZUQyPC8Phkm3aYi12V5dzB7YUNLCloIHzJiUwKa3re84pWbEs21fT6TE3HZtGgknHz6VmPtrZuxvA6ePivEbT+rOOz5eR54DPyqAAl09P4nVv0sy+4rAFTrTEj5w5wbNCaqAJVrsCTfDMH1bh+4M1ZKeYunXx6Yp95WaK6219miIY5+OYZH8wMiGsy2hCQY2VNYfqOG1cHEumte3UXXOojqI6Gx9emR1yToLN4WqZmpgUqe+gnVFc7/sEYl+mIwJkdzFTY/H4OJIi9Wg0gslpEfzm+HSfbfDEzEzvA5Sa+nDsd0+Y2s25EF0Jg/mGK7j+CD1kX5mZfWXBV8oWrHYFmgEbURBCaNwiUCGB3ekir9rKMZ2kHWotDrRC+Fy/UNZg445PDhGu03DZdN+KI3vChFQTH/RyJ+krXUn/Ol2SD3dWYNJruOW49A670BU5NaRE6lk0NrYPrfQfu0vNvNcuN35GdjzTh0R0WFt3Rnhnp/jm3MWE6/jF+Di+2OO582BGu8mIseE6Fo6O6ZEI0aIxsZ06MOu9DFILFBFedDlAqYGJDtO2Sd/4ZQMwQIZCXf/+ASD49AqC1a5AM+AiCkKI6dCiFBky7CtrwuGSjPCw67A5XfxnUwnnLd3Nktf3YHN0vbQGq5Mb38/B4ZJcMbNvJ0kKIbhhbt9KH/vKD7l1VDc5+d1JmR0kjRttTn4sqOeKmSkh0Y6+Nreug5MA8PnuKv64PJ9GW9se/hoPugyeOH9yQrfW394ZaOb6uakeb+ydObudMSHVe/Si2uzwWncRKDyN83a6JJ/8XMmLG0p4fGUhK3JqcLn8qG2iDoVSCQADylEQQpwCfCSEmNjqseC/IwA73Yp17fPvh6ssXPy/PfxvSxlGnYYGm6vLCXN7y8wseX0P1U0OLpyc2FLo1pekRAW+GDu3ysKqg7WMiDcyz0OBosXuwukKXDV/dzF1ISb1xMrCNgJbvsoud5VOaI8QgrvmD2n5/ozseB5clEmql/fcoNNw/qTu53q9jcd2uST/+KHz2otAUGluu7m3OVw88m1+m5bSNYfq+NO3+dS5nbjRiaFx7qmotGbApB6EEKejjLy+XEr5sxBCJ6V0SB+lCoUQ1wHXAQwd2v8yuC+sLyYmTMvQdhXxv/sylya7iyXTkkiO1PPMmiIOVDQx3oN4kJSS93ZU8OL6YsL1Gn7tQ2+4P1kwKoaVfTwR7hfjPasn1lkcvLutnOgwLc+eO9rjMc3tg/2tptf63ApLHkadJQ6dRlDT5CAmTBkpXlhro7rJQXaKqaWTZGp6BJ/u6nxc8p6yJrLd50J2somP6DwF9PuTfJu22Z5Io7Zb8tAT0yKYmBaBwyVpsrvIqWjqdC1jO7mBrg2ylEMzWwsbOdPdpltab+OF9SVej316dRFXzkz2y2wJFZX+ZkA4Cu6owS1ArZRyjRAiHbhFCBEJfAdsklJ22sQspXwJeAlg5syZ/fpxrrc4WHu4lvMmJbbpda82OyittzNvRDRjk8KRUlF9y6nwPKnug52V/GtdMZmxBpZMS/I626CviO6BNkB3mZDasYDMJSWvbynH4YIXzhnVJi/sdEm+3lfN/jIz+TVKwXi9rX8dhdbnlkgdJ6vctQTPrOm4S/50V1XLDVkIwT0LhvD4Su9jlN/bXtFyvE4rmJER6XX2wO9PyuzTEdqe0GkEUUYt04ZEMjktgu1uJcz2OFzKe9U+j19Yaw3qoUuNNhdNdlenTkIzS38sY2hc4CNvKirdZUA4ClJKKYS4EPhKCPE2kA58DNiAE4Fo4H9CCOFrhKE/sbskThcdUgRf76tCApPdMsVCCDrbDG4tqCfSqOHXs/o/B+90yS53v73ljOx4j3nh9YfrKW+0c/9JmQxrNfWttN7Go9/ls6O4kSijliExBi6ZmsQvZwRmgJYvTGqXpzcZtNy3MIPHVhT49PPzRkR7dBTumj+k352E9mg1gukZkYyID+uQSjhUZeGRb/M5f3IC2SkmHE7JrlJzn59TvcVk0PBToe9DodKjDeRV+96dMlB5YFFwDi8LVrsCTUg7CkKIJKBSSumSUprd6YcvgK+klE+5j7kJWAj8LxidBDiqbmhutdN1SclbW8tJidS3yT3bnNKrEM6O4kaGx4cFpFBv1aG+3/VN99COVtZgY0VODUNjjSwaE9vy+A+5tfx1RQFOKfnvkiyumJEcEgWM4zzUDxh1Gh5clMnX+6q77O2PDdfxm+PT20Qrbpyb2qfFrN0lNty7LR/sqOSDLtInwcKt89LQCNFG1rkrpqRHsuFI4MayBwuttU2CiWC1K9CErKMghDgHuB14WQjxtpTS6XYWTgZkq+hBvXK4MEopg1KsRK/VYNQJGlo5CjkVTdRbnW0G/didLlwSIj04Ck6XpMHmCoikclm9jTWH+jaPfO+JGR5v9J/tqkKvFfz97FEtz5c12PjDsjxGJYTx+TUTGZ3YfzoPvWWUl1y9RiNYPD6ekQlhvLOtbSfEzuJGJqaaWtYfG67jjhPS+WxXFXOGRZEcBIWmrelLh23JtCRGxBtbUnhmm5O8Giv1VicOp8Tukhh1GqIMWiwOl8c0iC9cOi2JeJO+W9Najx0e5fGzO9BpLds8zK0Ou80dhZk6xLtuRiAIVrsCTUg6CkKIYcCfUUZdZwMXCiHeczsLjlbH3QhcDVwZrE5CM8kResrqj1ZRC5SLaWv9/nL3uGRPBYpajSAlUs/O4kYWje28H92fSCn5lw/52d5w/uSENn+HZqwOF0V1Ns6blEic6eip/NWeahwuyVfXTmSEH1Uu+5pjhkZ1KYk9LtnE7Sek8/dWCowf7qzkw52VHDM0ijGJYZQ32vnGraaYHKVnVBA6StfPTeVFP583d80f0iFyYjJoPUZpQOlS6ImjcGpWbMsAKCGEz7oRU9MjeXKV93oTnwjBWQ/Nss2tuf2Tg0Dw6RW0tmtYnBFx12pAcXAOP3BMIE0LKCHpKACVwK+BHGAJMBdACPGhlNImhDAAicBJwK+llD8HzFIfmZgWQU7F0bbHKHdhYGs1uqI6JbfprcXqxmPTeHhZHjkVFsb2k1piblXf+1/ZHjo8AHIqLLikkpdvxumSfLm3ipkZkSHlJIDSNeILMWE6LpmayNvtIgsb8+rZmNd2wuK6w/UMjTWS1c2WyL4mNcrAA4syKay1kVtl6VW3zJT0CBaPi8PQzbkj+8s9FwV3xYR25+PxI2Owu2SnUbWLpyTy/Do/DIXSGdT+yn6itWPQ7DAMVkLSUZBSNgghdkgpLUKIV1CiBnMBAbwFpEop84QQlwV7JKGZUQnhrDpYi5QSIQSxYTo0An4qaCA7xcTm/Hq+z6klTCc6SPc2c9zwGDRCUS/sL0eheTpjX3HbvHSv0ZH1h+sI0wkmtuqEWJFTQ1mDnWfP89wiGaycmR3vMWrije68v29vq+iytbHB6qTK7GBIjMGvEuKdodUIhsYZGRpnZH4rJ8nmcLEip7aD0+OJm45NI8nHwUztKazrWVFhVFjHy+aJo2KICdNxsKKJPWVHHZCLpyYyIt7IYyt6GUloRmgHX+5CJeCEpKMA4HYShPvrUuBKYLwQ4k1ggRBigpTSs+5sEDIu2YTZ7qLK7CAhQk+YXsPNx6Xzzx+KeHxlAU4XZMQY+OOpw73eOF1IXNKzYlxfkFvVufBTb7lgctuUQmuOVFsorLNx3ZzUlhtbg9XJv9YVMy45nPMmJfapbf7GW22Cxe5CiI5TOptFkHwJZV/abt5Fa5wuyYYj9Xx7oKblsSlpEcweGkl6TP9pcLTGoNNw2rg4pg2JYHepmWqzg53uoWNT0iMYmRDGsDgjMR5u2J0hpWRjXj1RRh3jk8NZMCqGBJPOqzy1J7z9LYVQWlObVSybHX6AVf7UFtHpA/OmqAxqQsJREEJkAfHAj4BLSulsLlZ0f20EnhNCfAJMBk4PJScBICFCeSsabM6WkbTnTUqkpsnBigM13HdSZpudsycK3DoB/aFnIKXktR/7Lpowb0S0V0lfKSVf7qkmXK/h3IlHHYJXN5dQ0+Rg+fWT+m1X7C+iW+XWS+ptfLCjgjqLE5tTohXKuZDd7u8Raey6dfLE0TEt+fTWSCnZXtTIJx7aD7cXN7K9uJH7Fmb0eIz46kO1fJ9Ty4RUE7MyI9u0rfpKSpShRfHzvMk9MqMN3+yraROlOG1cHLMzI5mZGdXmxl5Wb/Nad+OrsmLz76pstPtXhMzW1Nj1QSoq/iXoHQUhxHnAo0Ch+9+PQoilUsq65sFPQggtMBol/bBISrkjgCb3iOYCxZ8KGtpcVK+ancpVszufo2B1uPhwZwVLN5eiETDSL1PqOsds69tRGp3l69cerqOswc4dJwxpCdfvKmnkk12VXH1MashNhWzPj/kNVDQ6OHtCAmnRBj7YUcH7Oyu4OjyFIe12+UadxmNaQUqJ3Sm95u2rzA6PTkJrjlRb26Q4dhY3YtAKxiaFd9q54HTJlpbBXSVmdpWYW2x0OCVVZjt6rQaDVmAyaPqlbdXudHVIZXy9t5qxieHEmXRtbEiOMnD/SRks31/D5vwG5g6LYlZmlNfoVme0TkP4BZdzQMx6ePT04YE2wSPBalegCWpHQQihBy4GrpZSrhVCnA/MAX4rhHhcSlkLIKV0Avvc6YZeDnwPDMcOj2ZGRiSvbCph/qgY4k2+5V3rrQ4ue2Mf9VYnGTEGzsiOJ8aLZn5rlCE7tdRbnVw6LanbF+u+3LHfvWCI19+/p9TMdweUeQ5nZscDUNvk4A/L8kiOMPD4mSP7zK6+pKTe3lJ74nBKIg0abj9Bma+weHwcS17fyzvbKrj5uDSfdvlCCAw67+9RnA/nSFir1ymps/Ghe0JoTJiW+aNimJwW4fF9av9QltvZqGy08+zajgV99ywY4lUbxF/otRrmj4ppkwb41cxkrzd/vVbD4vHxLB4f36vXnTMsiuomBz95UcvsPqExZtpTS2RrjvUwiyUYCFa7Ak1QOwpuooExwFrgI6AC+AVKt8MLQohjgAQp5Zeh6iSAcmF/8/JxTPzbFp5bW8wDizJ9unk/t7aYBquTy6Yn+aQXoHQFVLO1sIHmFvD95U3droo39IHK3+nj4piVGel13UW1Vj7cWUFShI4Xzh+DEAKXlDz6XT41TQ7W3zbV62ChYGdfmbnFUXBK2aYOJcqo49HTR3D7Jwf5em81Z0/s/sCl9vgy7yGi1VCqL/ZUYdAKbj9hCC9vKOHTXVV8u7+GU7PiWpRDm2n//mXGGimosfLKplKPr/P4ykLuXjCkw7RPf3P8iGjqLUoN0DFDo/olPaXTCM7Mjmd4nLHF0eoVDltIFGd7aolszbpcxWELthuzN7tat0q2f3wwtE0G9VVVSmkXQjwF3CqEOOie4/ADMAT4hRDiVWAYsCaghvqJsUkmfr9oKA8vOwJIrpyV2uVQp+X7q5k2JMJnUaHm3Y1RK3jxwjFc+fZ+8mts3XYUejJYqDNmZkYyu5PxxAfKm3hvRwVhOg0vXDCmJaT+6a5KNuXX8+y5o0M65VDddDSiLISizNmayekRnDspgY92VnJ6D1oBPXFGdjyfd6Ij0LrWpcrsIClCz+nj4jktK463t5Xz0oYSVh6s7eAotGd0YliXIfiSehuj+ridVasRnDmh905WT5iUFkFatIEtBQ2kROoZmRBGfo2V93d003kQmgEx8ff+rw4Dwaej4M0ub87AYGmbDGpHwc0aIAu4wl24uBp4UwhxLTBMSvluYM3zLw+dMhQh4E/L81iRU8uszEgunZbcqVJYeDd2YokRehaPi+PLvdXc8mEOAENieqbhctaEeL9p8bfWQmiNwyn59oBShBYXruOFC0a3pGUKa628uL6YmZmR3HRcml/sCBStHcLoMB1mu6vDkKTZmVF8uLOSojobw/1QhzIjI5IEk47/eihKvWRq2wFl0zMiWZtbx+e7K/mpsIHVh2qJMGi4fIbnLoAHFmVS0Wgn3qRDr9WQHKknJVLPu9srOhw7b0R0nzsJvmKxuzhUZcHlkiCU7wvrbORWWrDYXUQYtCRF6hkaZ2RWZmSbv1FXJEboOTXrqETwhFQdhbU21h/pug20BV1YcPyhVAYVQe8ouNsf30AZ0Po7IcQ4wAokA8E7Vq6HCCF46JRhXD83jZc2FPOPNYXc+ekhfntiBqeN85wvtTtdWB0u8qqtFNbZMOk1JEboSYrQdej5LmuwEWfStVGT85RD9IUpPoxB9hVPrW51FgdLN5dR3eTg/EmJXDcntWUnbXO6+MuKfLQawUdXTgiJOQ4AadEGMmONLZMsmxnR6sYfG6bFJZVdduvixXFuoZ+CWqtfHAWA4fFhPHRyJoeqLFQ0OshKCveYvpmVGcnaw3U8uaqQMJ1ganokx4+I9loPo9WIlo4FUM7r8SkmHjo5k7IGO7lVFo5UW0mPNnh1EvubH3JrWX2wDrurbTTHqBMkReiZPzKGskY7e0rN7CtvYt3hOi6ZmkRGL0a5Lxwd67OjcM7EBD7WaoP+mq0y8AiJk05KWS2EeBnYDVwPWIDLpZSek54DgJQoAw+ePIy75mcw5i+b+fZAjUdHITFCz+a8BjbnNXgcdZ9g0jEzM5KUSAPfH6whv0YRmbn52FQiDFocLu9DprpCIwS/mpnscUfaHX41M7nDY1JK3t1WQb3VwV8WD2fOsKM3E6vDxUNfH2FXiZm3Lh/Xqwt1INBqhOKctYrGx7cqqmt2DvaVN7VxFGLCdMSGa9lZbGaeH3O7QghGJYQzqpOofHSYjsunJyNQHMuepp6EEC1tj3OG9czevmJLfgNCwDNnj2pJuxh1GlKj9B0c0VUHa3h4WR65VZZenX86reCq2Sn8x0v9BsDZE+KZkh6BEIKP7VY/t1GoqHRNSDgKAFJKG/C9EGK18q0Mierf3mIyaHFK2eZG0ppXLx7L/7aUYdAKpqRHkp1iosHmJK/ayr5yM+/vqGjR/TdoBZdMTeKdbeVsLWrkZD9MSvPHztbT79hZYqawzsbNx6W3OAkOp2TZ/mr+t6WU0no7L184hkumdXQyQgEhBA+enMmeUjMjE9q2GzbfpGqbOnbCnT8piVc2KXoRnRVuWh0uCmqtpEUZ/NZRMDJhYKsHzx4WxbJ9NYTrNV2e1w1WZYCbJ42K7pIZa+Sy6Um88VPHWuxfzkxuE23CaVNnVKv0OyHjKDTjboUcNEgpqWp0EO/lpmAyaLl+btv8fJg79TA9I5Il05LJrbJwsKKJY4dHYzJoWXe4ji0FDSwcHeuXyu8b56b2aDDU6ePimOKhEK7B6uTLPVUkRug4113hv7O4kcdW5FNUZyMrKZzXlozj5KzeOzqBRCMEEzyIaIXrNQjaFjg2c9KYWF7ZVMKWggZOajVWu5naJgfvba+guN6GS4JOA4vHxzOtB9Pwqs0OciqbSDDpfXISNhypY21uHWF6DYvGxJKVbKKmycGza4twutwpFZT2ycQIPZPTIshOMQWNONbU9EhWHKjhs92V3Dk/w+txDqfktS1lRBo0pPRQPro9oxPD20QWpg+J4OSxcd2S9Q4l/n72qECb4JFgtSvQhJyjMBgZEmNgTW4dv56d2iOlvBHxYW12JZfPSObR7/KpaLS3ySP3lOQoA5PTTOwoNnd57LmTEhibGO71Aiil5M2tZTickr+dMRKtRrApr56HvjlMYoSeT6+awBnZ8SFTk9CeJruTRpuzjUZBe6wOiQSPx6RFGxgeZ+SH3DqkVKSf06MNVDTa2V7UyPaiRiRw0ZQkJqaaePCbI+TVWH12FFwuycb8ejYeqafWctQnn5ERyalZsV6L99YdrmP5/hompJjYXWbmQIWFrGQTLilxumN/SZEGMmIN2ByS7cUN5FRYWHWwlqtmp/S5joIv2Nxj3DubvOp0Sa5+dz9lDXZOHxfn1/MwM9bIbfPSabQ5Qy6d1l2CdYxzsNoVaFRHIcgRQvCfS7JY+K8dvPFTWZcqjb7Q7DT4y1EARTCqM0fh/MkJXUpQA2zOb6C4zs6d84cwIj6MVQdreeTbPIbHG1l7y1SS/WRvoKhpclLeYEerEVSbHR4Ff4rdw4qykj2HtV+8cAzXvLuftYfrWHv46MRCjYD5o2K4YkYKI+KV9jspIcPHmQ3FdTbe31FBldlBcqSem49LYVZmJF/tqeKd7RUcqGji8unJHYYwbcqrZ/n+GobHGfn72aM45aWdmNyOYLxJzx0npPP3NUVMTDNx2zxFRMolJSsP1vLot3m8tKGEq49JJcoYWGdhtVuM6RIv8xyklDyzppC8GiunZsV22s7bU+JMuh4pQIYa3+5XFPYX+SH96U+C1a5AM/DPyAHAiaNjuWx6Mm9vLWd4fBjHj4hus7NztRPo6YrMWCMCRQ1wQu/9DoBOHY7r5qR6nXjZmopGO8v315AWreeM8fHsKmnkj8uPMD7FxA+3hK6YUmuGxxsZmRBGbpWFlzaUsGRaIkNbSXbXW5384BZ9GeNFG8Og1fDaknHUWhzsK2tib5mZBJOO+aNiiWx1s91bpjhuvoTH95WZeWdbBWF6DQ+fMowTRka37JZvODad2UOjuf+rXN7aWs4Nc492n+wrM/PV3moyYw28fOFYqprsSCC8lVhTdJiOSakmPttVyfVzFGVJjRAsHB1LfLiOez7P5eUNJdx0bFrAQu01TQ62FjZyzqQEUr2cy1/ureaz3VUcOzyqTXGtP3G6FOlto06EbNTMFx75Ng8IvhtysNoVaEL/yjtIeOqskfyQW8uflucRZdRywsgY9FrB3jIzByssxITrGJccTnaKibMnJHQayjXqNKRG69mcX8/xI6L9It4DcP3cVH44VIfN6cLulEQatZySFefTTrHB6uS/m0vRaeCpM0chhGDlwVq0GsG6W6cS3c1JgcGKQash2qgjK8mEvkSwdHMZ8SYdmbFGdFrB1sIGXFKZUtjVmmPCdMweGuV1Zzs2KRytRhGlum5OGrpO1DQPV1vRCHj3ivFtnI1mpmdE8n8nD+P3Xx3m1c2lXD07lYOVTby7vYIEk46XLhiLRgN3fnIIjYDR7XQRKhsd6DQatO1uflOHRPLEmSO445NDPLmqEJNeg8mgYXyKianpEf32vu8vb0IC53cydTTBvdO3OTz1F/WcJruTvWVNHChvIqfCgt0l0WsFkQYtE1JNzB8Vgy5I6jhUBicD4+o7CEiOMnDgd7NYvr+GN38q44MdFQih6OjfMi+d0nobaw7V8kNuHQcqmnjo5M57zx5YNIybP8xhQ149J4z0T6tdapSBC6Z0f7xzTZOD134sw+Jw8c9zR5Pqjj78VNDA5LT+u1n0J2E6DW9eNo43t5axMqeGfeVNWOwuFo2N5VczOw5/6gnD4sJ4cNFQHl6Wx5tby7hiRrLXXap05+Y9OQnNzB0ezX0LM/nLinxe3FBMldlBXLiOpZdkYbY7uebdgxTW2Vg8Lq5NeqKswUZhnY2rZqd4dFampEfy59OHs6WggUqznZ3FjXyfU8vKnFrSYwxcMDmxy2iSlJJKs4OSOhvljXbKG+xUmh04XRKnlMwZFs0xnaQKDlVaiDRoOv27zxkWzcXurqHmP2O91UmV2YHN4cLmlKRE6TkzO6HL9IFLSgprbWwpaODn4kacEkx6DaeOiyMjxkhpg438ais/5Naxu9TMpdOSlKmyhgg1ia7S7wy8K/AApvWgmlcvcaERokPF+D2fHeKJlQWcmd3QaQFbdoqJjBgDaw7VkRFjDFjr2+EqC29tLUdK+PPpIxjnlpKuMts5VGXhL3OGB8Su/iDSqOW6OWlcN0fpWnE4Zae7/p4wf1Qs1xxj498bFbnlBaNivDsLyC7TWKdkxWFxuHh6dSFJETr+c3EWTXYXv3xrH3ani9PHxTEzs+15t/5wPVoNnNWJfPLc4dHMHX40nF9Ya+XrvdW8s72cpZtLuXRaksf6lIpGO9sKG9lSUI/FvdMXQJRRy6jEcAxaQZXZwTd7qxmdENYywr09uVUWFnroImnPtceksuZQLZvzG9BrBSa9hjCdhklpERh0GlYerOHZtUVcMDmR8W6BLCklxXU2DlYqIlNlDXYabU53V4rgF+PjWZwdz9jEjlM5f8it5ZHlebywvliRWQ9iCeeuBkGphC6qoxCieKo+33ikjlc2lpASqffpg/rkWSO5/r0DvL6ljFOyYjlmaFS/5kV3l5r5YEcFUUYtz503us1u7sd8ZdreKSHeAtkd/O0kNHPptCTyqq0s21+NzSk5ZWxsh/c5PcbAxjylDXVKeueb1rMmJDA+2URmrBGjTnD9+wdwuFxcMye1Q37f6ZLsKjVz8tg4j+qb3hgSY3SPDI/kd18c5sUNJZw0JpakCD0Ol6TW4uDH/AYqzQ4EkBFr4JKpyWQlh5MZa8TQ6vPxc0kjt350kOomh1dHIdKoZUt+1wqJWo3g9UuzsDgk4R7qKW6Ym8ZNHxzg/R0VJEXosThcmO1KKg4gLlzL9IxI0qIMLfVGnaUJ542I4X+XZnHHp4c4UmUBh9XSpZEBoqtBUCqhi+ooDBD+s7GEG94/QEKEjr/+YoRPY6qTIw28cdk4bvjgAN/sq6G4zsbi8fE9asHsLnnVVt7fUUGiSc+/LxrTIb3wY0E9sWFapnZx01LpGiEE9y7MIMKg4aOfK7E4XJyZHd8mcjAuKRydRrB8f02XjgIcFRraWdxIUZ2N07LiPBYB5lZZsDslJ/RQSXJqeiRvXjaO2z7OYfn+mjbPxYfruPHYNBaOjiXRiwMAYHQ7DQ6X99qCqekRrMippbTe1mUnkBCCcL1npy4xQs/LF47l1o9zsDkkU9IjiQvXMSHVxIyMSJ/Hx7cmKdLA65eOA+DExwaG4NKLF4wJtAkeCVa7Ao3qKIQ4Lpfkpg9zlOFIGZE8cPLQbu3cTAYtSy/J4vUtZSzdXEpxnY0bj03r08hCo83J29vKiTRo+c/FYzvkxXOrLKw7XMc5ExP9PqUy0FgdLpocneso9AUaIbh1XjpGnYa3t5UzIj6MyWlH21UNOg3ZKeEs31/NXfOH+Pz+7yppBCA71fP00T1lTeg0ghkZPXf44kw6XluSxf6KJpwuRWE00qj12p3QHqNOWUvzrt4T45JNrMipZVN+PWdm927CZEy4jteWjOvV7xjodHdabX8RrHYFGtVRCHGeXVvEi+uLuXhKItfOSeuRyp1GCH45M4WkSD1/+76AHT6En3uKlJK3firH6nDxjwvGtDgJTpdkV4mZtYdr+XpfNWE6DQ+fGmTDAPzAkWor+8qaMOgEZpuzX4WGhBAsGhvL29vK8XSapMcY2VFspqbJ6bEYb1NePZmxxjatrs2RhbxqKxPaOQvKe9rICSNjet1ZI4QgK6lnF/HmFIGtE0fB4lBUoeIGQAtuKPDZLmW8dqDGfnujN3Y112gMizN6HUsdqgRtYYxK1+wuaeSezw4xZ1gU18/tmZPQmlOz4kiM0PHNvhpsjr4ZpbH+SD2FdTZunZfOaLdOwOb8epa8vpfffHKQj3ZWMj7ZxLrbprY8P5CIC1cEdWwOyS/f2udxnkNf0jy10lOoPtY9Y6K4vmN0+3CVhXu/yOWWj3KQ8ugNd2p6JOF6DRuO1HX4mdwqC1aHZOHo7qUdLHYX64/U8fqWUh79Lo+Pf67AbOu5cnu4XllXZ+d0iVvkaqwfZjeodM2Tqwp4clVBoM3oQG/saq7RaC7oHEiojkII8+h3+Ri0Gu5ZkOGXVIFGCP5wyjCa7C6+2Vfd5obgDw5XWfh2fw3D4oyc7fbYl++v5t7PczEZNLx9+Tgq/jSXDb+Zxtge7h6DnTC9hpgwHcmReqrdcxD6kya7crPclFdPk/3ozbfO4mBniSLQVGvp6Lx8sLMCgCqzo82IbK1GcO7EBApqbXy7vwanuw7AYnexfH8NBq1glo8Khg6nZNXBGm744AD3f3mYVzaVsjGvnmfWFPHvTd2fJdJMuF6DVsCO4kaPdQpWh4sfCxoIc4+TVlFRaYsaZwthapocpEUbelQg5Y3s1AgunZbEm1vLSY4ydNp73h3qrU7e2VZBVJiW588bjRCC1YdqeWxFPlPTI/jh1qlBofffX5gMWianR7DqUC2/dbq8zlDwNwtGxbCrxMyXe6rYVWJmYqqJ3Cor1U1K98AVM5KZndnxPd9delSe29DO1stnpFBrcfLFniq2FymCUWa3Q3L/SZltjn/zpzLe31GB1eHC4nARYdAyJMZAWrSBncWNVDQ6SInS879LszhrQgL3fp7LSxuKWTSm590vWo3gzvkZPL6ygNd+LOXiKUloNQKb08WP+Q1szKvH5pT8bmGmXxxup0vSaHMSZdQOaHVFlcGD6iiEMA6X7BPFtquPSWVNbi3f7K0mwaTrdQrA5ZK8vqUMh8vF82ePxWTQYrY5eWxFPuOSTay+ZXA5Cc1kJYWzrbCRHcWNzMjw/9wAT4Trtdy9IIPzJiXw0NdH2FbUSHKknuvnpHLciBgyPQwjsjpcHK6yoNMIHC5JTLs8frhew90LMpg7LIpn1xYxMTWC4fFhTEg1tela2V1q5t8bSzhhVAzThygpi+omBxuP1LOn1MyI+DD+c/EwFo+PR6sR/GNNIS+sL+aiKYnYnS6eXFXA9XPSOhWF8sbi8fFKXcwPRTyxqrDNc8PijNy3MLNFw6O7lDfY+XpfFWsO1VFcZ6PBnSYZnRjGZdOTWTAqtke/N1hpr5cw0PLxKh1RHYUQJsKgpaTeRqPNSYQfb7QaIXjxgjFc9sY+PthRwW3Hp7fkebuD0yXJrbKwKa+esgY795+U2TKQ6ofcOprsLl66cEyPLvyhjsMl2V6kdAw058f7k5EJ4bx+2TiklF3uemstDqQEh5REGDQe9QMAjhsRw3GdtEH+e0MxCRE6Pr1qQqdqm1JKHv7mMH9YlsfxI6K5+phULvzvHuqsTmZlRvVYSfTcSYlkxBopqLHidCkTOo8ZGtVm1kZXuKTkqz1V7Co1o9UIpIQVOTU02V1MSDXx69kpJJj0GHSCF9YV84dleYhTFOGrgUJrvQRx1+oAW6PSH6iOQghz38JMPtpZwSubSlqm8vmLcL2Wv50xguveO8Crm8q4Ymayx5kNUkqqmxwU1dkoqrVRZXZgd0karE5qLQ6sDkW3/sqZKZzcatDK6kO1JEXomdtHw3WClQark2qzgzqrg/1NyjTGk72ISjVYnSzdXMraw7WY9FqiwrTEhutIjdSTEm1gYkpES9dBT/Eu6SwpqrOxraiR0YlhPHX2SP67uZSTfFAv9ER+jZWtRY38+fThXUpy3/XpIZ5eXchpWXHcvSADrUbw1Nkj+WJ3Va/aLAFmZUYxy0NqxRdyKy08uaqAXaVm4sJ1CKE4fNPSI3jt0nGMahd5u2dBJhMe/5G/ry5kanpkh0jMYOZ/S7ICbYJHgtWuQKOeuSHMrKFR3HxcOs+tLeLkMXEtkrH+YnRiOA+dMpQ/f5vPv9YVc+m0JKLCtOwoaqS6yUFBjZXqJmdLgZhGQEyYFp1GkBFjZHJaBPNHxTA7M6pNe1yT3cnm/Hqun5s24HQSuqLe7UAZdRr+e0kWGR5C/aA4Us+sKaTa7ODsiUrh56FKC4cqmlh3uK5FE2ByWgSXTE1izrDeq2ruKzez6mAtBTVW9pU3UdZgByApQqkZePrsUT3+3V/sqUIj4NezUzo9buOROp5eXcjZExK47fj0FlGoUQnh3Ha84gzbnC6qzA6fdRT8wVd7q3hyVQERBi3/XZLV6dyMZnRawbu/HM/UJ3/im33VXDTV8/jqwUhmN6I4/Umw2hVoVEchxHnk9OG8s62ch5cd4bnzRneqUNcTFoyKZVhcGLd9lMObW8txuSRWpyRMJ4gN13HmhHhGxYcxNsnE8HijT0V5zcVj53UyqW+gMiZRETrSCIHVi5Ow6mAtf1h2hDFJ4Sy7bhIz2u2AXS5JSb2Nd7aV89cV+dz/1WEmp0Xwu4WZLQO1esLHP1fy9d5qQGkTDNNryKu2UtFop6LR3qtBVZvz65k+JJK06M5/xye7KtFqlEmkrZUjnS7JtwdqWJtby48FDTTZXUxLj+CKmSlMTY/o06JBm8PFC+uKyU42seLGKSS2GnjVYHWi1eA1NTclPZIxieEs21/NhVMSB1xx47A4Y0v6oTvzHd7ZWgbAxdOS+8SuntJdu3q6/lBDdRRCnJhwHd9cN4lj/7mN+7/M5ZlzRnVaTyClxOGS3aqyHxEfxqOLR3DbxweJD9fx73NGed0J+8KaQ7XEhmk53k9TK0MJIUSnQ5f2lJp59Ls8slNMbL5jmsf3UqMRpMcYuWN+BrfMS2fp5lJu//gg17y3n98cP6RNiqc7/GbeEMJ1iszz/vImQCm4vGRaklcnocnuYmuhMuWzs1qTarODhaNju7Sh2uwg0qDtsO6XN5bwzrZykiL0/GpmChmxRp5aVcCdnx5iQqqJS6YmMXdYdK+1RDyx6lAtdVYnfz9nVIuTUNvk4G/f5/PUqgLCdBqeOnsUv5qZ4jFCds+JGVz33gF2FpuZnB7R4fnWbM6vp7jOxhntJLaDlZ4WMv5rfTEQfI5Cd+0aLIWcqqMwAJiWEckHV2Zz5is/84dlefzptGFtHAGzzcm3B2rYUtDAjqIGXMBz547u1s1+UloE/71kLKnRhg7tcd3B5nSx4Ug9l05P7pOLeihT3mDj918dJsGk57sbJvlUQKrXarh2ThqLxsRy1n928eh3+by3vZxTsuI5Z0KCT4OmpJQ8v66Y73NqePvy8ZwwMobCWiuzh0aRFNkxQuGSkpomB8v2VfPu9gpl2JJJx53zMzh2eMeaE5dUhji1Hj3tjeomR4fC3O9zanhnWzk3zE3j+fNHt+zK75qfwSsbS3jk2yM8+PURMmIM3DA3rdOCyu7idEk++bmSITEGTnQ7Oks3lfCbjw9SZ3WyYFQM5Y12rnpnP49/X8AHV2Z3SAFeNj2Zuz49xIc7Kzp1FJbtq+av3+fjkrA2t47fnZTZcbx2EE+PVBm4qI7CAGHx+Hj+df4Yrn//AFe+vZ8rZ6WwYFQMX+2tZunmUqqbHCRH6pk/KpYv9lRxz+eH+Oe53UtVdKc63BvrD9djtru4YPLgSzt0xUsbSmi0OfnhlimkdBGib8+IhHC23jmDF9YX8dxa5d8XuysZFh9GvcWJxeFiRHwYE1NNZKeYSIkyYNRpKKix8vc1hWwpUKZ17i83M3VIJFPbjSgvb7Dzp+VHyKm0YLG7aJYtmj4kkucWZPDg14f5/VeHOXdiAjfMTWtTk/Lxz5W4JG1mS3ji892VvL+jnHnDj97ocyst/O37AiakmnjmnFFtQvdheg03z0vn+rlpfLizgns+O8Sfv8vn/V9G+qXd1uZ08eh3+ewqNfPCBYqDsqOogavf3c/4ZBO3HZ/O2CQTUkqW76/h+XVFnPiv7ey6Z2abKZUmg5abj0vnryvyOVxlYXh8x89Rfo2VJ1cVMCUtgl/PTuWuzw5x7Xv7uWdBJrMyI4+uW2gGX4uQSsBRHYUBxHVz08iMNXLrRzk8+l0+T68upMnuYlKqiS+vndgyRnpzXj3zn9/Obz/P5e9nj+yyCt2fLNtfTWKEjkU9DI8PNCx2F5vz61mTW8t3B2q4d2Em2amd31C9odMKbpk3hFvmDeHjnRXc9ekhcistRIVpMWoFKw/W8MWeqpbjIwwarA6JUSe4Z4EiSLS/oqnD6zfLNzdYnVw7J5WYMB3RRi2njotnkvvmf97kRO77Ipe/ry5ke3EjDywayrA4I69sLOHNreXMzozioinei/m+21/NeUt3MzohnHtOzAAUtcgHvzmMyaBh2XWTvM6L0GkFF01NYni8kWOe2cby/TUtBaC94bkfilh1sJbHzxjB9XPTcbkkl76+lyijlkcXH+3eEEJwSlYcQ+OM3PbRQRa//DPrbpvaJmJ294IM/rGmkFc3l/KHdjNMXFLy+PeKyuoX104kLdrI8SNjOOuVn7n3i1wmpZr41awUpg+JBOnquZa1Sr/Qvm5hIKQnVEdhgHH6+Hj2Z83i3e3lvL+jgkunJXPupIQ2O7FZQ6P47KoJnP7yzzzw1eEOO7W+os7i4Ed3t8NgTTvUWhxUmO2YbU6ueXc/eTVW7E5JtFHL1cekcP9JmX55nXMmJXJOu2JRl0uyu9TMloJ6iupslNTb0GkEdy/IIDXKwIsbitlwpJ7F4+Ix6DQ4XZJNefX8ZUU+eq1g7a1TO0QamjHqNDx99ihOHhvH5W/s5Yb3DzAkxkhulYVrZqfy/AWjvXa47Cpp5IxXdpEZY+RvZ4xoST38YVkeZfV2vr9pMuk+FFLOyoxibFI4H/1cwalZcYR50XvwhXWH6/h0dxV3zc/g7hOV9+T1n8rYVWrm3hMzPDrX45JN3DIvnadXFypFpouGtjyXEKHn7gUZ/HF5HqsO1jJ/lBI1cUnJ0s2l7Cwx8+rFY1uKPacOiWT/72bzysYSHl52mLs/y0UASFffDGFR8RutHYOBojOhOgoDEI1GcMm0ZC7ppCDnpLFxPHjKUB76+gjVTQ6/ykB7os7i4O7PcpESfjmz8xa5gUxpvZ2CGitajWBsZjjnTUrk1Kw4jlRbuf2Tg8SG63no5KFE9UGUR6MRTEyLYKKXFMDNx6Xzl+/yuerd/UxJi2D9kXqqmxxkxhhYfctUjyHz9iweH8+ee2dy3tLdbCts4KmzRnL7CZ2Prf7zt3loNfC3M0a03IDtThc/FTZw38JMn2sOhBD87YwRnPvqbu79IpdHFw/vkRDZtqIGpeskMYxHTh/e8vjO4ka0GjotFj0zO57Vh2p5fl1RG0cB4J4TM3l3u9KhdNGURM6fnMgTKwvYnN/AwtGx/GpW289Fc2rl6mNSeX1LKUeqrTzyVLeXE5S8/8vsQJvgkWC1K9CojsIgJttddFVl7ltHodlJOFxl4ZOrJjCzh4I3A4FJaRHMHRaNQSf48teTAHj8+3x++3kuQ2IMPLGygFc3lfCPc0ezZFpSv7bTPbp4BAtGxXDNO/tZkVPL4vFxXDA5ibMmJBDRDfXMlCgDa2+d6tOx+dUW3t1ezrkTE9vk9ZtnN3Uo5uuCsycm8ubl47j8zb3c/GEOd83PaEmP+EJupYX7vzxMWrSyhtZRiaykcJwuKGuwtxm13RohBDMyInlpQwnlDbY2xaCRRi3b7prBXZ8e4rm1Rby/owKdRvD8+aO5YW6a1/c6TK/hmjlpADzi80r8T+sxyr0l0YfC1kAQrHYFGtVRGMSkuC9i1X006jinook3t5ax7nAdLhd8ctUETh8f3yevFSoYtAJjq1z7cz8U8dvPczlxdAz3Lcwkp8LCM2sKueyNvbywrohl10/uVQgdoLDWyvvbyzlnYiLDuogKnJIVz5EHj8HpwqeOid7y8oYSXC44d1LbmoJmR6EnFlwyLZnECD2Xv7GX2z4+yGXTk7l6dkqXTpeUksdXKu2O626d2qHjo3kWRF61xaujAEcd8I159ZyR3XZdRp2GZ88bzfxRMby1tYw/nz7C70JpfUVr6ebestQ9DfTK2al++X3+IljtCjSqozCIsTmVdGdfDJb674+lLN1cSoRBw9WzU/n17NRBHUnwxoa8OhJMOn5/0lC0GkF2ionnzxvN+zsqeGF9MSc8t41VN0/p0ayNZoxaDbHhOpwuicslu1TDFEKg66fa+pgwLRJotLZNvRt1AoNWtKhDdpdFY+PIuX82t398kFc2lSiqkLM6dxZ2l5rZU2bmX+eP9lgT0TwcraiL2RzN7cNODyOtm7lwShIXdlLcOdBZ+mMpEHw3ZH/bNVAKG1VHYRDTfMHzt5rjtsIG/ru5lIunJvHCBWO6HT4eTEQbtdhdsk1xp1YjuHhqElFGLU+sLOCEZ7ez7rapPR5FnRip5/zJSdRZHJjtrqAawnX1nDQe+PoI7+0o5/6Tjub0NUKQHm3gQEVTj393pFHLSxeOQQj498YSbA4X13cS4v98TxVhOg2XTfdc25MSpceoE106CgW1ymTFMb2cuqoS+gyUwkZVvGMQ0zy1MDbcfzeOequDv6zIZ0iMgX9fNFZ1ErogJlxHg9VJbqWlw3OLx8fzq5kp/FjQwF++y8fVyQ61K0x6DdFhOky9TGP4m9hwHdfNTWVFTg0/5Nbiksoaq80OXFIpIOwNGo0yCfXm49J5Z3sFz64tQsqOf0ebw8XKnBoun5HstZBUCEFalIG9ZeYWO9vjdEnWH6lDoEzo7C4u90C13rzXKir+Rr2KD2Kai7yaq657i5SSJ1cWUmm2s/7WaUG1cw1WfjkjhZfWF3PzRzncsyCDeJOOvBorOeVNbC1qJL9G2Z2OTw7vVTRAoxFB+37ctzCTD3ZU8ODXR0iJ0nP8iBiW76/GbHP1ahBVMxqN4J/njsKgFTy9uhCrQ/Kb49PbRGhsTonFIRmf3Hm9wF0LMrj1o4P8aXkexw2PZlyyibRoA1qNoMHq5JFv89iYV899CzN7VFtitruoszgAXdC+XyqDD9VRGIS4XBKz3cVJo2NJjdLz+e6qXjsKLil5dVMpqw7V8tdfjGDWULUewRfGpZjYfvcMTvrXDv64PK/l8XC9hslpEfzm+CEsHhdHZlxY0EUD/EVatJGD98/mk58r+dv3+by/o4KspHA+uDKbCW7xJ7PVyapDtSwaE4vei/BSZwghePKskZgMGv78bT75NVb+cOqwlohXc51Ova1zPaObj0snr9rKM2sKWXmwtuVxnUagEeCUkhcuGM31c9O7bSPgfo+DL/Kj4l+aO0ggNGoXVEdhENKyawnTceu8Ifz+q8Pk11jJ7OGgp0OVTTyzpogdxY38elYKdy/I8LPFA5shMUa23jWdD3ZUkBihZ3yyicxY46AawW3UabhoahIXTU2itslBlFHbZv1L3tjLp7sqee680dx0XM9uwkIIHjl9BBNSIrjy7X3c+MEBLp+ezNQhkTS4HQRdF39yIQR/O3Mkj5w2nC2FDewsbqS0wYbZ5qLJ7uL8yYm9GnYWDJGf1jex1vhzOuKX10z02+/yJ/1lV+sOklCoXVAdhUFI613LVbNT+b9vjvDxzxXcOm+ITz9f3mDn5Y3FFNTYKG+0U9loJypMyysXjeXXPrShqXQkXK/l8hmDV4iqNTEe6lruOGEItU0OfuGH9tol05MZnRjOma/8zBOrCgGlDTMlUs8VPoqBGfQa5g6PZq6HIVihjj/bIL3hj1kcfUFf2hXKI6lVR2EQ0nrXkhpt4OKpSXz8cwVXz07t8oNSXGfjrk8PUd3kYEKqiTOGxTM6MZwb5qa1EcxRCU6a004mvSakIhYLRsey0of0mK/rmzU0iuKH57Cn1MyqQ7UcKG/ituOH+GXwWSjSPhTe1zy/tgigx9GhvqIv7Qr29EJnqI6CCqePi+ONn8r4ucTM7E5qC+qtDu769BANNierb56i1iGEIAO9WK476xNCkJ0a0e0hXFaHC4dTdkutMtjpjyhCa97dXg4En6MQrHYFGtVRGOQ0WJ389vNc0qIMTEz1XvHtkpJHv8unvNHOmltUJyFU8Xex3Hvby7n1oxzuWZDJnfM7n+nQH/R1MeDBiiZOfH47jXYXy66bxIwQExELtSI6leBALa0d5Nz92SGK62z87qTMTtMOn++uYsORep4+eyRzhg28vOxgoTnt1Nu0g9MlufOTg1z02h5sDsndnx1i/nPbabAGdgqyv9bnifWH65jx9E/UWp3otYJ5z27j058r/f46fUlz5KB1IZ24a3XI5cxV+hc1ojCIqTbbeXF9MedOSuhycM4728qZlGriZjUkpwJ8u7+ap1cXclZ2PDfPS+ejnZW8sL6Yf28s5vYTBmbXyxVv7iVMr+GJM5Q2y/u+yOXSN/bQ8Jd5gTatR6jRhODDW8dJM4GKAqmOwiDG6lDU34b7UMBlc0pmD40OeGhZJTiod0cOzpmYiEGr4YLJibywvrjl8YFIk93FpNQIMtxtxMcMjebNrWUBtqotO4sbO223UyMHwUf7bojOakVav7e+pJG6e4zX1/UkZzrYEUKUA0cCbUcrEoGKPn0FodEgNFqky4l0uVoea/6//+j7tfQPna1jmJTS48SfIDy3eoKydk/nTGjSvXOybz4XvuL13IIBc351xUC5hvhKf67X4/mlOgohgBDiRynlzEDb4Q8GyloGyjp6wkBb+0Bbz0BnsL1fwbBetZhRRUVFRUVFxSuqo6CioqKioqLiFdVRCA1eCrQBfmSgrGWgrKMnDLS1D7T1DHQG2/sV8PWqNQoqKioqKioqXlEjCioqKioqKipeUR0FFRUVFRUVFa+ojoKKioqKSsgihBhU9zERANW7QfUHVlFR8Q+BuFj5GyFEphDCIISIcH+vXg9DCCHEdAApZSiLffmMECISQAagsFAtZgwBhBAnA2MBjZTyn0IIEYiTxR8MlLUMlHX4ihDiGCAMMEspN7sfC9k1CyF+AfwVWAvEAQ9KKfcJITSD5cYTygghTgFeBn4hpfzZ/VjIno9dIYQ4C7gMkMBrwM9Syrz+en3Vgw5yhBDzgDcBC3CxEOKfwHFCiJCb0zFQ1jJQ1uErQojTgddRLlS/F0K8AsrOJtQiC0IhE3gMuAV4CNgIfC+EmCCldKmRheDGfT7+GbhcSvlz8+duADsJY4F/Af8A1gPHAXcJIbL6ywb1AxH8zAaelVK+AiwCaoELgFkBtapnDJS1DJR1dIkQQgv8CvijlPI64JdAlhDifQg9Z8F9MylCueAeAMqklE+iOA7LhBBj1YhC8OI+124BaqWUa4QQ6cAfhRD/EEKcLYRIC7CJfUEY8IOUcq2U8hngA6AcuF4I0S+jWlVHIfjZCcx1X8AswJ8AM8ruLtQYKGsZKOvoEimlE9ja6vs6KeU8IEUI8aL7sZDYyQkhRgshZgGxQAxwWbPtUsp/AM8A9wshwkLJ+RlMuN+vCwG9EOJt4G2UgUlVwIkojvuAqKFpxV5gtBDiRgAp5U/AV4ADJf3Z5+tVHYUgxF1kZXQXr6wE9gHHCyHSpJRW4I/AbCHElQE00ycGyloGyjp8xR3ubKYQuFcIMbTVY+cCCUKI7P61rGcIIc4APgSeAP4AvAHcJIT4XavD3gWsUkpLqDg/gwUhRFJzSkhKaQZOB1KAr6SUT0kpHwb2Awvdx4T0+yeEOEYIcbIQYpGU0gb8HjhGCHERgJRyC4pzdLn7+z5dr+ooBBnuIquvgH8C/wZSgf8BxwO/EEKMc+9iPwOCOkQ6UNYyUNbhK+6b6jb3jg0p5evAR8DaZmdBSlmBsqOJCpihPiKEOBbFQfiVlHI+YEBJHx0L3CiEeEAIMRpYAMwQQsQFzFiVDgghzgHeA5a4U2HNzsLJwN9a7abrlcOFMSCG+gkhxGnAUuAY4BMhxInAOmAFcKYQ4jb3oYWApj/Wq3Y9BAnukz0D+BK4FdiDkhu+FaV4JRlYAswEtgGXAAuklHsCYW9XCCGGAN+g5BNDci0D7T3xBXer4Acou+9jAaOUcon7uT8BZwHPA4kou5nFUsrcAJnrE25HYayUcqn7+yRgqZTyF0KIkcADKIWps4FfSyl3BsxYlTYIIYahfP5+BnJQ0n7vuVNirY+7EbgauLK5CyIUEUJMRkmn3CSlXCmE+C2wC9gspSwTQpyE4vQeRKmJOktKub3P7VIdheDBfWN6GaUSu9hdKHYHcAdwrJSyQAhxPJAFrJRS5gTQXK8IIcJRdtZ/R8nfh/JaNCjVxo8BhaG6ju7gLhCrQymiegGwt3IWzkWJqMwA/h4KF2X3LjRCSlnn/n8aSvRnsZSy2H0zKnQfUxtIW1Xa4k71ZaM4CUtQcvLrgQ+llDYhhAHFaf0H8IdQd/LcqTyDlHKb+3O4D8VpnwI8J6V8WQihB4YC9VLKsn6xS3UUAo9Q2u1GSilfE0K8BhyQUv6p1fP3odyIbpJSNgXKTl8QQpwNnILi9T4G7JBS/rnV8yGxFiHEHJSLUiFKVGSDlPKvrZ4PiXX0FiFEAsr0OpuUcokQYgLQIKU8EmDTeoS7lS4M+ERKeZIQ4nKUFNLtA/l9DGWEEGFSSosQIgwlajAW5fP4lhBiqJQyTwhhdNcKDQjcDu3FQLiU8hUhxEzgO+AMKeWa/rZHrVEIIEIIjdtjfhGl2vpClJ3qVUKIB1od+jZgRwmPBi1CiPkoIjafusPR9wDXCSHubHVY0K9FKOImL6HkQC9CeX9udIcBmwn6dfgDKWUlcD1gEULsAz4BnJ3/VPAipXRIKRuAfCHEX1A+b8+qTkLw4nYShLsOaClK0eJ4IcSbwAYhRNxAchKgpdvoY7eToJFS/gj8FwjIOgekQEyo4O7XbhBC/Bfl4nsuIIDRwGEhRD1Kfu44YDpKW1d1YKz1iRnAv6WU37iL3iJR8r/PCyEsKB7xXIJ4Le4d9M3ApW4xl9dQwvCXoxQWWYCvUfL3QbsOfyKlrBBC7ECpND9ZSlkQaJt6iju9p0eJIuiBk6SUBwJrlUozQhERigd+BFxSSqfbSZDur43Ac0KIT4DJwOlSypD9/HlZr0ZK6XIXbCIVEbAlwAkokdp+R3UUggMHSs7pFeAGIBOlynUaSm5qEkqRVbB/IBwoFeWg7LiLUIpudqKkI7JQbrDBvBYHEA6ME0LkodxQklCKqb5xfz8WpSI5mNfhN9xdAIuBU0I9B+xuI7O5CzM3q05C8CCEOA94FCXdVwj8KIRY6q4t0bhvmFqUjdRcYJGUckcATe4VPq43GjgHJTp7iexH2eY2tqo1CoFHCDEKuFBK+ZgQ4i6U3P5jUsoH3c/HhcINSQgxEaVifivwjZTyVaH04/8aJaf4SSisRQhxAfA7lNTCF1LKPwlFW/404B0p5cZQWIc/ac4TB9oOf9G8Sw20HSoK7gK914F/SCnXCiHOB+aghNofb19kKoRIklKWB8BUv9Cd9brbIw8HsrtIrVEIDppQZHGvRYkoPAJMc7f8ANQEyrDu4K6Avxtltz3C/dh+lDbCGPdhNQExrhtIKd9HUXhbg1uVUEq5DGVNzRKxNQExLkAMJCcBQl+QZ4ASDYxx//8j4HOUCGVzx80xQojFAKHsJLSiq/XOFUKcJKX8PtAtyGrqIQiQUhYJIfKBB4GbpZSfub3IHPfzoXRR+wr4P+BhIURzZfwUlBBbyKxFSlkthFgBXCSEsKFUyg9D0UsImXWoqIQCUkq7EOIp4FYhxEGpzHH4ARiCImr2Ksrnr98r/vsCH9ebSZCsV009BAlCmWiXLBVpTkSIj7sVyqz4CwAjirhNyOW2hRCxKEOQzkfpbvhtf4ibqKgMRtztj9egFCm+LqVc7X78e+B6d3RywBBK61UdhSBDzZ0GH0KIKJTPSl2gbVFRGci4C2cvBc5ACcdbgd8CC6WUpYG0rS8IlfWqjoKKioqKStDgVls8Drd+B/CMlHJr5z8VuoTCelVHQUVFRUUl6HC3QspQTsF2h2Ber+ooqKioqKioqHhFbY9UUVFRUVFR8YrqKKioqKioqKh4RXUUVFRUVFRUVLyiOgoqKioqKioqXlEdBRUVFRUVFRWvqI6CioqKioqKildUR0FFRUVFRUXFK6qjoKKioqKiouIV1VFQUVFRUVFR8YrqKKioqKioqKh4RXUUVFRUVFRUVLyiOgoqKioqKioqXlEdBRUVFRUVFRWvqI6CioqKioqKildUR0FFRUVFRUXFK6qjoKKioqKiouIV1VFQUVHpNUKIdCHEf4UQFUKIeiHEO0KI2EDbpaKi0ntUR0HFZ4QQJwshPhdCFAohLEKIfCHEE0IIfaBtUwkcQogRwCYgGrgMuBE4FXg2kHapqAgh9EKIB4QQh4QQTUKI9UKILCHETCGEWQiRHmgbQwEhpQy0DSohghDibsAJ7AMagenAn4BHpZSPBtI2lcAghBDAeqACOFO6LyhCiD8C9wIxUkpLAE1UGaS4NzBfAZOB3wGlwPPARiAM2CulvCdwFoYOukAboBI6SCmfaP6/EEILrAUWAMcFyiaVgHMucAyQJdvuOvIAA5AOHAqEYSqDnpuAhcA8KeU6ACHELOA3gACuDJxpoYWaelDxCSGETghxpTt0VwE4ADtwFtAQWOtUAsjVKBGFQ+5zRCeE0AGR7ucdgTNNZZBzA7Cs2UlwUwPEAE9JKSsDYlUIokYUVHzlTeB0lLzzIyih5jDge+DnANqlEiCEEAbgRCAcxWlsjx0o6lejVFQAIUQqMA74V7unDEAV8FS/GxXCqI6CSpcIIaYCFwKXSSnfbPX4BSghvK0BMk0lsGSjOAk3oxQztuZtoFpK6RBCSCBOSlnTl8b01+uohASj3V9zmx9wp0t/CRyUUtYHxKoQRXUUVHxhqPvrvuYHhBARKJEFgJ/63SKVYGC4++sPUsodzQ8KIVKAEcBb3fllQgi9lNJTZEJFpbu43F/jWz12I4pzq16vuolao6DiC1sBG/C4u0XyCmADShivTEqphpcHJ80bDWe7x68AJLC01WO3CyHWCCEOCCGuaX5QCCGFEL8VQqwEHnc/NksIsUII8aMQYqsQ4sJWx78uhNgshNghhPjCHWLugBDiT0KIL4UQUb1fpkoIsh2oBR4SQpwjhLgF+BvwATBRCHG6ECIsoBaGEGpEQaVLpJT5QojLgL8Cn6J8CH8L3MFRz11l8HHY/XUCsAtacsP3Ai9JKQ+2OlYnpTxeCJEGbBVCrJdS7nI/FyalXOD++VjgZWCxlLJICJEI/CSEWCelLARul1JWuI+9D3gYpWitGYMQ4g2gHqVds70TozIIkFI2uh3Mp4F3gXLgWuAblAjp5yi6Hyo+oOooqKio9Ai3hsIuwIjiNBpRdDWqgZOklGb3cRIYIaU87P5+KbBFSvlP93OZUsoC93OLUVIWua1eKh64Ukq5QgjxG5SIhRGlPqJCSjmn1etsAT6VUv6xL9euojKYUFMPKioqPcKtm3AuUIiya3sa+BhY1OwktD68k+9bt9cKYJeUcmqrf0PdTsI84FbgNCnlJOBOlM6b1nwHnCyEiOnN2lRUVI6iOgoqKio9Rkq5T0p5gpQyTEqZIaW8T0rZ6OHQawHcqYfTUNpqPbEOGCGEOLn5ASHEVHcrZhyKImi1+/vrPfz8X4B3gO+EEEk9X5mKikozqqOgoqLSH9QKIX4AVgMPtKpPaIOUshr4BfA7IcR2IcRu4DGUa9VXwF73v2+AbV5+x7PAP4EVQogMfy9ERWWwodYoqKioqKioqHhFjSioqKioqKioeEV1FFRUVFRUVFS8ojoKKioqKioqKl5RHQUVFRUVFRUVr6iOgoqKioqKiopXVEdBRUVFRUVFxSuqo6CioqKioqLiFdVRUFFRUVFRUfHK/wO9m4EB8xbqBQAAAABJRU5ErkJggg==", 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sf_result, result = structure_function(\n", + " data=np.array(mao_rm_tab['RM'][mao_rm_tab['included']]) * u.rad / u.m**2,\n", + " errors=np.array(mao_rm_tab['e_RM'][mao_rm_tab['included']]) * u.rad / u.m**2,\n", + " coords=mao_rm_tab['coordinates'][mao_rm_tab['included']],\n", + " samples=1000,\n", + " bins=bins,\n", + " show_plots=True,\n", + " verbose=True,\n", + " fit='bilby',\n", + " nlive=400,\n", + " sampler='nestle',\n", + " model_name='power_law',\n", + " n_point=3\n", + ")" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/structurefunction.py b/structurefunction.py index 559da5d..bfcd62f 100644 --- a/structurefunction.py +++ b/structurefunction.py @@ -1,24 +1,66 @@ #!/usr/bin/env python3 -import os import inspect -from typing import Tuple, Union -import numpy as np -from astropy.coordinates import SkyCoord -import astropy.units as u -from tqdm.auto import tqdm -import matplotlib.pyplot as plt +import io import itertools +import logging as logger +import os import warnings +from typing import Callable, NamedTuple, Tuple, Union + +import astropy.units as u import bilby -from sigfig import round import corner -from astropy.visualization import quantity_support -from scipy.optimize import curve_fit -import pandas as pd +import matplotlib.pyplot as plt import numba as nb +import numpy as np +import pandas as pd import xarray as xr -import logging as logger +from astropy.coordinates import SkyCoord +from astropy.visualization import quantity_support +from scipy.optimize import curve_fit +from sigfig import round +from tqdm import tqdm +from tqdm.contrib.logging import logging_redirect_tqdm + +#################################### +### Helper functions and classes ### +#################################### + +SFResult = NamedTuple( + "SFResult", + [ + ("med", np.ndarray), + ("err_low", np.ndarray), + ("err_high", np.ndarray), + ("count", np.ndarray), + ("c_bins", np.ndarray), + ], +) + +class TqdmToLogger(io.StringIO): + """ + Output stream for TQDM which will output to logger module instead of + the StdOut. + """ + + logger = None + level = None + buf = "" + + def __init__(self, logger, level=None): + super(TqdmToLogger, self).__init__() + self.logger = logger + self.level = level or logger.INFO + + def write(self, buf): + self.buf = buf.strip("\r\n\t ") + + def flush(self): + self.logger.log(self.level, self.buf) + + +tqdm_out = TqdmToLogger(logger, level=logger.INFO) logger.basicConfig( format="%(asctime)s.%(msecs)03d %(levelname)s %(module)s - %(funcName)s: %(message)s", datefmt="%Y-%m-%d %H:%M:%S", @@ -193,7 +235,7 @@ def bilby_fit( outdir: str, label: str, model=broken_power_law, - **kwargs + **kwargs, ) -> bilby.core.result.Result: """Bilby fit @@ -289,6 +331,440 @@ def mc_sample(data: np.ndarray, errors: np.ndarray, samples: int = 1000) -> np.n data_dist[i] = np.random.normal(loc=data[i], scale=errors[i], size=samples) return data_dist +############################ +### SF compute functions ### +############################ + +def sf_two_point( + rm_1: np.ndarray, + rm_2: np.ndarray, + rm_err_1: np.ndarray, + rm_err_2: np.ndarray, + dtheta: u.Quantity, + bins: u.Quantity, +) -> SFResult: + """Compute the two-point structure function + + Args: + rm_1 (np.ndarray): RMs of source 1 in pair (n_samples, n_pairs) + rm_2 (np.ndarray): RMs of source 2 in pair (n_samples, n_pairs) + rm_err_1 (np.ndarray): RM errors of source 1 in pair (n_samples, n_pairs) + rm_err_2 (np.ndarray): RM errors of source 2 in pair (n_samples, n_pairs) + dtheta (u.Quantity): Separation between sources in pair (n_pairs) + bins (u.Quantity): Angular separation bins + + Returns: + SFResult: Structure function results + """ + samples = rm_1.shape[0] + + data_xr = xr.Dataset( + dict( + rm_1=(["sample", "source_pair"], rm_1), + rm_2=(["sample", "source_pair"], rm_2), + rm_err_1=(["sample", "source_pair"], rm_err_1), + rm_err_2=(["sample", "source_pair"], rm_err_2), + ), + coords=dict( + seps=("source_pair", dtheta.to(u.deg)), + sample=("sample", np.arange(samples)), + ), + ) + + # Groupby separation + grp = data_xr.groupby_bins("seps", bins.to(u.deg).value) + + # Compute Structure Function + sf_xr = grp.apply(lambda x: ((x.rm_1 - x.rm_2) ** 2).mean(dim="source_pair")) + # Correct for errors + sf_err_xr = grp.apply( + lambda x: ((x.rm_err_1 - x.rm_err_2) ** 2).mean(dim="source_pair") + ) + sf_corr_xr = sf_xr - sf_err_xr + + # Compute error + p1, med, p2 = sf_corr_xr.quantile([0.16, 0.5, 0.84], dim="sample") + + err_low = med - p1 + err_high = p2 - med + + # Get source pair count + count = grp.count(dim="source_pair").rm_1[:, 0] + + # Get bin centers + c_bins = np.array([i.mid for i in sf_corr_xr.seps_bins.values]) * u.deg + + return SFResult( + med.values, + err_low.values, + err_high.values, + count.values, + c_bins, + ) + + +def sf_three_point( + rm_1: np.ndarray, + rm_2: np.ndarray, + rm_err_1: np.ndarray, + rm_err_2: np.ndarray, + src_1: np.ndarray, + src_2: np.ndarray, + dtheta: u.Quantity, + bins: u.Quantity, +) -> SFResult: + """Compute the three-point structure function + + Args: + rm_1 (np.ndarray): RMs of source 1 in pair (n_samples, n_pairs) + rm_2 (np.ndarray): RMs of source 2 in pair (n_samples, n_pairs) + rm_err_1 (np.ndarray): RM errors of source 1 in pair (n_samples, n_pairs) + rm_err_2 (np.ndarray): RM errors of source 2 in pair (n_samples, n_pairs) + src_1 (np.ndarray): Source 1 in pair (n_pairs) + src_2 (np.ndarray): Source 2 in pair (n_pairs) + dtheta (u.Quantity): Separation between sources in pair (n_pairs) + bins (u.Quantity): Angular separation bins + + Returns: + SFResult: Structure function result + """ + samples = rm_1.shape[0] + + data_xr = xr.Dataset( + dict( + rm_1=(["sample", "source_pair"], rm_1), + rm_2=(["sample", "source_pair"], rm_2), + rm_err_1=(["sample", "source_pair"], rm_err_1), + rm_err_2=(["sample", "source_pair"], rm_err_2), + ), + coords=dict( + seps=("source_pair", dtheta.to(u.deg)), + sample=("sample", np.arange(samples)), + src_1=("source_pair", src_1), + src_2=("source_pair", src_2), + ), + ) + + # Groupby separation + grp = data_xr.groupby_bins("seps", bins.to(u.deg).value) + + rm_1s = [] + rm_2s = [] + rm_3s = [] + rm_err_1s = [] + rm_err_2s = [] + rm_err_3s = [] + centres = [] + + for i, g in tqdm(grp, desc="Grouping triplets", file=tqdm_out): + # Find repeats of source number in each pair + # If a source is repeated, then a triple is formed + src_1s, count_1s = np.unique(g.src_1.values, return_counts=True) + src_2s, count_2s = np.unique(g.src_2.values, return_counts=True) + + for s, (srcs, counts) in enumerate(zip([src_1s, src_2s], [count_1s, count_2s])): + # Loop over src 1 then src 2 + s1 = 1 if s == 0 else 2 + s2 = 2 if s == 0 else 1 + for _src_a, ca in zip(srcs[counts > 1], counts[counts > 1]): + t = g.where(g[f"src_{s1}"] == _src_a, drop=True) + if len(t[f"src_{s1}"]) < 1: + continue + _rm_1 = t[f"rm_{s1}"][:, 0].values + _rm_err_1 = t[f"rm_err_{s1}"][:, 0].values + for j in range(ca - 1): + _rm_2 = t[f"rm_{s2}"][:, j].values + _rm_3 = t[f"rm_{s2}"][:, j + 1].values + _rm_err_2 = t[f"rm_err_{s2}"][:, j].values + _rm_err_3 = t[f"rm_err_{s2}"][:, j + 1].values + + rm_1s.append(_rm_1) + rm_2s.append(_rm_2) + rm_3s.append(_rm_3) + rm_err_1s.append(_rm_err_1) + rm_err_2s.append(_rm_err_2) + rm_err_3s.append(_rm_err_3) + centres.append(i.mid) + + # Create triplets dataset + triple = xr.Dataset( + dict( + rm_1=(["source_triplet", "sample"], np.array(rm_1s)), + rm_2=(["source_triplet", "sample"], np.array(rm_2s)), + rm_3=(["source_triplet", "sample"], np.array(rm_3s)), + rm_err_1=(["source_triplet", "sample"], np.array(rm_err_1s)), + rm_err_2=(["source_triplet", "sample"], np.array(rm_err_2s)), + rm_err_3=(["source_triplet", "sample"], np.array(rm_err_3s)), + ), + coords=dict( + samples=("sample", np.arange(samples)), + seps=("source_triplet", np.array(centres)), + ), + ) + + # Groupby separtion 'bin' + triple_grp = triple.groupby("seps") + # Compute Structure Function + sf_t_xr = triple_grp.apply( + lambda x: ((x.rm_2 - 2 * x.rm_1 + x.rm_3) ** 2).mean(dim="source_triplet") + ) + # TODO: Check if this is correct + sf_err_t_xr = triple_grp.apply( + lambda x: ((x.rm_err_2 - 2 * x.rm_err_1 + x.rm_err_3) ** 2).mean( + dim="source_triplet" + ) + ) + logger.warning("Correcting for errors in three point SF") + sf_t_xr_corr = sf_t_xr - sf_err_t_xr + + p1, med, p2 = sf_t_xr_corr.quantile([0.16, 0.5, 0.84], dim="sample") + + err_low = med - p1 + err_high = p2 - med + + # Get source pair count + count = triple_grp.count(dim="source_triplet").rm_1[:, 0] + + # Get bin centers + c_bins = np.array([i for i in sf_t_xr_corr.seps.values]) * u.deg + + return SFResult( + med.values, + err_low.values, + err_high.values, + count.values, + c_bins, + ) + + +def fit_data( + sf_result: SFResult, + fit: str = "bilby", + outdir: str = None, + model_name: str = None, + n_point: int = 2, + show_plots: bool = False, + save_plots: bool = False, + **kwargs, +) -> Union[None, bilby.core.result.Result]: + """Fit the structure function data + + Args: + sf_result (SFResult): Structure function result + fit (str, optional): Fit type. Defaults to "bilby". + outdir (str, optional): Output directory for bilby. Defaults to None. + model_name (str, optional): Model to fit. Defaults to None. + n_point (int, optional): Number of points in SF. Defaults to 2. + show_plots (bool, optional): Show fitting plots. Defaults to False. + save_plots (bool, optional): Save fitting plots. Defaults to False. + + Raises: + NotImplementedError: If model_name is not implemented + ValueError: If fit is not implemented. + + Returns: + Union[None, bilby.core.result.Result]: _description_ + """ + if outdir is None: + outdir = "outdir" + bilby.utils.check_directory_exists_and_if_not_mkdir(outdir) + + medians, err_low, err_high, count, c_bins = sf_result + + if not fit: + return None, None, outdir + + if model_name is None: + model_name = "broken_power_law" + if model_name == "broken_power_law": + model = broken_power_law + elif model_name == "power_law": + model = power_law + else: + raise NotImplementedError("Only implemented for broken_power_law and power_law") + + logger.info(f"Fitting SF with a {model_name.replace('_',' ')}...") + # A few simple setup steps + label = f"{model_name}_{n_point}_point" + + # Only use bins with at least 10 sources + cut = ( + (count >= 10) + & np.isfinite(c_bins) + & np.isfinite(medians) + & np.isfinite(err_low) + & np.isfinite(err_high) + ) + x = np.array(c_bins[cut].value) + y = medians[cut] + per84 = err_high + medians + per16 = -err_low + medians + y_err = (per84 - per16)[cut] / 2 + + if fit == "lsq": + result = lsq_fit( + x=x, + y=y, + model=model, + outdir=outdir, + label=label, + ) + elif fit == "lsq_weight": + result = lsq_weight_fit( + x=x, + y=y, + yerr=y_err, + model=model, + outdir=outdir, + label=label, + ) + elif fit == "bilby": + result = bilby_fit( + x=x, y=y, y_err=y_err, model=model, outdir=outdir, label=label, **kwargs + ) + else: + raise ValueError("Invalid fit type") + + if show_plots: + try: + result.plot_corner(dpi=300, save=save_plots) + except: + pass + samps = result.samples + labels = result.parameter_labels + fig = plt.figure(facecolor="w") + fig = corner.corner(samps, labels=labels, fig=fig) + if save_plots: + plt.savefig( + os.path.join(outdir, f"{label}_corner.pdf"), + dpi=300, + bbox_inches="tight", + ) + perc_dict = { + key: np.nanpercentile(result.posterior[key], [16, 50, 84]) + for key in result.parameter_labels + } + + round_dict = { + key: round( + perc_dict[key][1].astype(float), + uncertainty=(perc_dict[key][2] - perc_dict[key][1]).astype(float), + ) + for key in result.parameter_labels + } + logger.info("Fitting results:") + for key in round_dict.keys(): + logger.info(f"{key}: {round_dict[key]}") + logger.info(f"Fit log evidence: {result.log_evidence} ± {result.log_evidence_err}") + + return result, model, outdir + + +def plot_sf( + data: u.Quantity, + bins: u.Quantity, + sf_result: SFResult, + saturate: float, + fit: str = None, + result: bilby.core.result.Result = None, + model: Callable = None, + outdir: str = ".", + save_plots: bool = False, + label: str = "", + n_point: int = 2, +): + medians, err_low, err_high, count, cbins = sf_result + word = "pairs" if n_point == 2 else "triplets" + good_idx = count >= 10 + plt.figure(facecolor="w") + plt.plot( + cbins[good_idx], + medians[good_idx], + ".", + c="tab:blue", + label=f"Reliable bins (>= 10 source {word})", + ) + plt.plot( + cbins[~good_idx], + medians[~good_idx], + ".", + c="tab:red", + label=f"Unreliable bins (< 10 source {word})", + ) + plt.errorbar( + cbins.value[good_idx], + medians[good_idx], + yerr=(err_low[good_idx], err_high[good_idx]), + color="tab:blue", + marker=None, + fmt=" ", + ) + plt.errorbar( + cbins.value[~good_idx], + medians[~good_idx], + yerr=(err_low[~good_idx], err_high[~good_idx]), + color="tab:red", + marker=None, + fmt=" ", + ) + if fit: + cbins_hi = np.logspace( + np.log10(cbins.value.min()), np.log10(cbins.value.max()), 1000 + ) + errmodel = [] + # Sample the posterior randomly 100 times + for i in range(1000): + idx = np.random.choice(np.arange(result.posterior.shape[0])) + s_dict = { + key: result.posterior[key][idx] for key in result.parameter_labels + } + _mod = model( + x=cbins_hi, + **s_dict, + ) + # errDict[name] = model_dict['posterior'][name][idx] + errmodel.append(_mod) + errmodel = np.array(errmodel) + low, med, high = np.percentile(errmodel, [16, 50, 84], axis=0) + # med = fitted_line(cbins_hi) + plt.plot(cbins_hi, med, "-", color="tab:orange", label="Best fit") + plt.fill_between(cbins_hi, low, high, color="tab:orange", alpha=0.5) + + plt.axhline( + saturate, + linestyle="--", + color="tab:red", + label="Expected saturation ($2\sigma^2$)" + if n_point == 2 + else "Expected saturation ($6\sigma^2$)", + ) + plt.xscale("log") + plt.yscale("log") + plt.xlabel(rf"$\Delta\theta$ [{cbins.unit:latex_inline}]") + plt.ylabel(rf"SF [{data.unit**2:latex_inline}]") + plt.xlim(bins[0].value, bins[-1].value) + plt.ylim(np.nanmin(medians) / 10, np.nanmax(medians) * 10) + plt.legend() + if save_plots: + plt.savefig( + os.path.join(outdir, f"{label}_errorbar.pdf"), dpi=300, bbox_inches="tight" + ) + + plt.figure(facecolor="w") + plt.plot(cbins, count, ".", color="tab:red", label="Median from MC") + plt.xscale("log") + plt.yscale("log") + plt.xlabel(rf"$\Delta\theta$ [{cbins.unit:latex_inline}]") + plt.ylabel(rf"Number of source {word}") + plt.xlim(bins[0].value, bins[-1].value) + if save_plots: + plt.savefig( + os.path.join(outdir, f"{label}_counts.pdf"), dpi=300, bbox_inches="tight" + ) + +##################### +### Main function ### +##################### def structure_function( data: u.Quantity, @@ -302,10 +778,11 @@ def structure_function( fit: str = None, outdir: str = None, model_name: str = None, + n_point: int = 2, **kwargs, -) -> Tuple[u.Quantity, u.Quantity, Tuple[u.Quantity, u.Quantity], np.ndarray]: +) -> Tuple[SFResult, bilby.core.result.Result]: - """Compute the second order structure function with Monte-Carlo error propagation. + """Compute the second or third order structure function with Monte-Carlo error propagation. Args: data (u.Quantity): 1D array of data values. @@ -314,6 +791,7 @@ def structure_function( samples (int): Number of samples to use for Monte-Carlo error propagation. bins (Union[u.Quantity, int]): Bin edges of the structure function, or number of bins. show_plots (bool, optional): Show plots. Defaults to False. + save_plots (bool, optional): Save plots. Defaults to False. verbose (bool, optional): Print progress. Defaults to False. fit (str, optional): How to fit the broken powerlaw. Can be 'astropy', 'astropy_mc' or 'bilby'. Defaults to None. outdir (str, optional): Output directory for bilby. Defaults to None. @@ -321,11 +799,7 @@ def structure_function( **kwargs: Additional keyword arguments to pass to the bilby.core.run_sampler function. Returns: - Tuple[u.Quantity, u.Quantity, Tuple[u.Quantity, u.Quantity], np.ndarray]: - cbins: center of bins. - medians: median of the structure function. - errors: upper and lower error of the structure function. - counts: number of source pairs in each bin. + Tuple[SFResult, bilby.core.result.Result]: The structure function result and the fitting result. """ if verbose: @@ -335,6 +809,8 @@ def structure_function( datefmt="%Y-%m-%d %H:%M:%S", force=True, ) + else: + logger.basicConfig(level=logger.ERROR) # Sample the errors assuming a Gaussian distribution @@ -346,20 +822,13 @@ def structure_function( ) d_rm_dist = mc_sample( data=errors.value.astype(np.float64), - errors=errors.value.astype(np.float64), + errors=errors.value.astype(np.float64), # Yo dawg... samples=samples, ) - # Get all combinations of sources and compute the difference - - logger.info("Getting data differences...") - diffs_dist = np.subtract(*combinate(rm_dist)).T ** 2 - - # Get all combinations of data_errs sources and compute the difference - - logger.info("Getting data error differences...") - d_diffs_dist = np.subtract(*combinate(d_rm_dist)).T ** 2 - + # Get all combinations of sources + rm_1, rm_2 = combinate(rm_dist) + d_rm_1, d_rm_2 = combinate(d_rm_dist) # Get the angular separation of the source pairs logger.info("Getting angular separations...") @@ -385,271 +854,59 @@ def structure_function( # Compute the SF logger.info("Computing SF...") - bins_idx = pd.cut( - dtheta, bins, include_lowest=True, labels=False, right=True - ).astype(int) - cbins = np.sqrt(bins[1:] * bins[:-1]) # Take geometric mean of bins - assuming log - - diffs_xr = xr.Dataset( - dict( - data=(["samples", "source pair"], diffs_dist), - error=(["samples", "source pair"], d_diffs_dist), - ), - coords=dict( - bins_idx=("source pair", bins_idx), - ), - ) - - # Compute SF - sf_xr = diffs_xr.groupby("bins_idx").mean(dim="source pair") - count_xr = diffs_xr["bins_idx"].groupby("bins_idx").count() - # Get the final SF correcting for the errors - sf_xr_cor = sf_xr.data - sf_xr.error - per16_xr, medians_xr, per84_xr = sf_xr_cor.quantile( - (0.16, 0.5, 0.84), dim="samples" - ) - # Return to numpy arrays for use later - count = np.zeros_like(cbins.value) - medians = np.zeros_like(cbins.value) - per16 = np.zeros_like(cbins.value) - per84 = np.zeros_like(cbins.value) - sf_dists_cor = np.zeros((len(cbins), samples)) - sf_dists = np.zeros((len(cbins), samples)) - d_sf_dists = np.zeros((len(cbins), samples)) - for arr, xarr in zip( - (count, medians, per16, per84, sf_dists_cor, sf_dists, d_sf_dists), - (count_xr, medians_xr, per16_xr, per84_xr, sf_xr_cor, sf_xr.data, sf_xr.error), - ): - idx = count_xr.coords.to_index() - nan_clip = idx >= 0 # Clip NaN indices - arr[idx[nan_clip]] = xarr[nan_clip] - err_low = medians - per16 - err_high = per84 - medians - err = np.array([err_low.astype(float), err_high.astype(float)]) - if outdir is None: - outdir = "outdir" - bilby.utils.check_directory_exists_and_if_not_mkdir(outdir) - if fit: - if model_name is None: - model_name = "broken_power_law" - if model_name == "broken_power_law": - model = broken_power_law - elif model_name == "power_law": - model = power_law - else: - raise NotImplementedError( - "Only implemented for broken_power_law and power_law" - ) - - logger.info(f"Fitting SF with a {model_name.replace('_',' ')}...") - # A few simple setup steps - label = model_name - - # Only use bins with at least 10 sources - cut = ( - (count >= 10) - & np.isfinite(cbins) - & np.isfinite(medians) - & np.isfinite(err[0]) - & np.isfinite(err[1]) - ) - x = np.array(cbins[cut].value) - y = medians[cut] - y_err = (per84 - per16)[cut] / 2 - y_dist = sf_dists_cor[cut] - - if fit == "lsq": - result = lsq_fit( - x=x, - y=y, - model=model, - outdir=outdir, - label=label, - ) - elif fit == "lsq_weight": - result = lsq_weight_fit( - x=x, - y=y, - yerr=y_err, - model=model, - outdir=outdir, - label=label, - ) - elif fit == "bilby": - result = bilby_fit( - x=x, y=y, y_err=y_err, model=model, outdir=outdir, label=label, **kwargs - ) - else: - raise ValueError("Invalid fit type") - - if show_plots: - try: - result.plot_corner(dpi=300, save=save_plots) - except: - pass - samps = result.samples - labels = result.parameter_labels - fig = plt.figure(facecolor="w") - fig = corner.corner(samps, labels=labels, fig=fig) - if save_plots: - plt.savefig( - os.path.join(outdir, f"{label}_corner.pdf"), dpi=300, bbox_inches="tight" - ) - perc_dict = { - key: np.nanpercentile(result.posterior[key], [16, 50, 84]) - for key in result.parameter_labels - } - - round_dict = { - key: round( - perc_dict[key][1].astype(float), - uncertainty=(perc_dict[key][2] - perc_dict[key][1]).astype(float), - ) - for key in result.parameter_labels - } - logger.info("Fitting results:") - for key in round_dict.keys(): - logger.info(f"{key}: {round_dict[key]}") - logger.info(f"Fit log evidence: {result.log_evidence} ± {result.log_evidence_err}") - else: - result = None - - ############################################################################## - ############################################################################## - if show_plots: - good_idx = count >= 10 - plt.figure(facecolor="w") - plt.plot( - cbins[good_idx], - medians[good_idx], - ".", - c="tab:blue", - label="Reliable bins (>= 10 source pairs)", - ) - plt.plot( - cbins[~good_idx], - medians[~good_idx], - ".", - c="tab:red", - label="Unreliable bins (< 10 source pairs)", - ) - plt.errorbar( - cbins.value[good_idx], - medians[good_idx], - yerr=err[:, good_idx], - color="tab:blue", - marker=None, - fmt=" ", + if n_point == 2: + sf_result = sf_two_point( + rm_1=rm_1.T, + rm_2=rm_2.T, + rm_err_1=d_rm_1.T, + rm_err_2=d_rm_2.T, + dtheta=dtheta, + bins=bins, ) - plt.errorbar( - cbins.value[~good_idx], - medians[~good_idx], - yerr=err[:, ~good_idx], - color="tab:red", - marker=None, - fmt=" ", - ) - if fit: - cbins_hi = np.logspace( - np.log10(cbins.value.min()), np.log10(cbins.value.max()), 1000 - ) - errmodel = [] - # Sample the posterior randomly 100 times - for i in range(1000): - idx = np.random.choice(np.arange(result.posterior.shape[0])) - s_dict = { - key: result.posterior[key][idx] for key in result.parameter_labels - } - _mod = model( - x=cbins_hi, - **s_dict, - ) - # errDict[name] = model_dict['posterior'][name][idx] - errmodel.append(_mod) - errmodel = np.array(errmodel) - low, med, high = np.percentile(errmodel, [16, 50, 84], axis=0) - # med = fitted_line(cbins_hi) - plt.plot(cbins_hi, med, "-", color="tab:orange", label="Best fit") - plt.fill_between(cbins_hi, low, high, color="tab:orange", alpha=0.5) - saturate = np.nanvar(data) * 2 - plt.hlines( - saturate, - cbins.value.min(), - cbins.value.max(), - linestyle="--", - color="tab:red", - label="Expected saturation ($2\sigma^2$)", + elif n_point == 3: + source_ids = np.arange(len(coords)) + src_1, src_2 = combinate(source_ids) + sf_result = sf_three_point( + rm_1=rm_1.T, + rm_2=rm_2.T, + rm_err_1=d_rm_1.T, + rm_err_2=d_rm_2.T, + src_1=src_1, + src_2=src_2, + dtheta=dtheta, + bins=bins, ) - plt.xscale("log") - plt.yscale("log") - plt.xlabel(rf"$\Delta\theta$ [{cbins.unit:latex_inline}]") - plt.ylabel(rf"SF [{data.unit**2:latex_inline}]") - plt.xlim(bins[0].value, bins[-1].value) - plt.ylim(np.nanmin(medians) / 10, np.nanmax(medians) * 10) - plt.legend() - if save_plots: - plt.savefig( - os.path.join(outdir, f"{label}_errorbar.pdf"), dpi=300, bbox_inches="tight" - ) + saturate = np.nanvar(data) * 6 + else: + raise NotImplementedError("Only 2 and 3 point SF are implemented.") - plt.figure(facecolor="w") - plt.plot(cbins, count, ".", color="tab:red", label="Median from MC") - plt.xscale("log") - plt.yscale("log") - plt.xlabel(rf"$\Delta\theta$ [{cbins.unit:latex_inline}]") - plt.ylabel(r"Number of source pairs") - plt.xlim(bins[0].value, bins[-1].value) - if save_plots: - plt.savefig( - os.path.join(outdir, f"{label}_counts.pdf"), dpi=300, bbox_inches="tight" - ) + # Fit the SF + result, model, outdir = fit_data( + sf_result=sf_result, + fit=fit, + outdir=outdir, + model_name=model_name, + n_point=n_point, + show_plots=show_plots, + save_plots=save_plots, + **kwargs, + ) - counts = [] - cor_dists = sf_dists - d_sf_dists - plt.figure(facecolor="w") - for dist in tqdm(cor_dists, disable=not verbose, desc="Making hist plot"): - n, hbins, _ = plt.hist( - dist, range=(np.nanmin(cor_dists), np.nanmax(cor_dists)), bins=100 - ) - plt.clf() - counts.append(n) - c_hbins = [] - for i in range(len(hbins) - 1): - c = (hbins[i] + hbins[i + 1]) / 2 - c_hbins.append(c) - counts = np.array(counts) - c_hbins = np.array(c_hbins) - - x = cbins - y = c_hbins - X, Y = np.meshgrid(x, y) - plt.figure(facecolor="w") - plt.pcolormesh(X, Y, counts.T, cmap=plt.cm.cubehelix_r) - plt.colorbar() - plt.xticks(x) - plt.yticks(y) - plt.xscale("log") - plt.yscale("log") - plt.xlabel(rf"$\Delta\theta$ [{cbins.unit:latex_inline}]") - plt.ylabel(rf"SF [{data.unit**2:latex_inline}]") - plt.xlim(bins[0].value, bins[-1].value) - plt.ylim(abs(np.nanmin(medians) / 10), np.nanmax(medians) * 10) - if fit: - plt.plot(cbins_hi, med, "-", color="tab:orange", label="Best fit") - plt.fill_between(cbins_hi, low, high, color="tab:orange", alpha=0.5) - plt.hlines( - saturate, - cbins.value.min(), - cbins.value.max(), - linestyle="--", - color="tab:red", - label="Expected saturation ($2\sigma^2$)", + if show_plots: + plot_sf( + data=data, + bins=bins, + sf_result=sf_result, + saturate=saturate, + fit=fit, + result=result, + model=model, + outdir=outdir, + save_plots=save_plots, + label=model_name, + n_point=n_point, ) - plt.legend() - if save_plots: - plt.savefig(os.path.join(outdir, f"{label}_PDF.pdf"), dpi=300, bbox_inches="tight") - ############################################################################## - return cbins, medians, err, count, result + return sf_result, result
coordinatesRMe_RMincludedflag
deg,degrad / m2rad / m2
SkyCoordfloat64float64boolobject