From c4031f99f52037319468f24800545bea1f1b99b8 Mon Sep 17 00:00:00 2001 From: Nicholas Landry Date: Tue, 1 Oct 2024 13:52:35 -0400 Subject: [PATCH 01/17] Update definition of edit simpliciality --- sod/simpliciality/edit_simpliciality.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/sod/simpliciality/edit_simpliciality.py b/sod/simpliciality/edit_simpliciality.py index e9fc0db..8a29358 100644 --- a/sod/simpliciality/edit_simpliciality.py +++ b/sod/simpliciality/edit_simpliciality.py @@ -59,8 +59,9 @@ def edit_simpliciality(H, min_size=2, exclude_min_size=True): ms += nm - nf - rmf try: + mf = len(maxH.edges) s = len(edges) - return s / (ms + s) + return (s - mf) / (ms + s - mf) except ZeroDivisionError: return np.nan @@ -99,7 +100,8 @@ def edit_simpliciality_full_construction(H, min_size=2, exclude_min_size=True): try: s = len(edges) + mf = len(max_edges) m = len(ms) - return s / (m + s) + return (s - mf) / (m + s - mf) except ZeroDivisionError: return np.nan From 9c91c41945a52d506bb941986f9e12c846fa170b Mon Sep 17 00:00:00 2001 From: Nicholas Landry Date: Tue, 1 Oct 2024 13:52:46 -0400 Subject: [PATCH 02/17] Update local_simpliciality.ipynb --- local_simpliciality.ipynb | 93 +++++---------------------------------- 1 file changed, 12 insertions(+), 81 deletions(-) diff --git a/local_simpliciality.ipynb b/local_simpliciality.ipynb index 1f4db5d..e252aa5 100644 --- a/local_simpliciality.ipynb +++ b/local_simpliciality.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -77,7 +77,8 @@ "inset_node_size = 5\n", "nbins = 20\n", "\n", - "plt.figure(figsize=(8, 8), dpi=200)\n", + "plt.figure(figsize=(8, 8), dpi=50)\n", + "plt.subplots_adjust(hspace=0.5)\n", "\n", "plt.subplot(221)\n", "plt.title(\"Simplicial Fraction\\n\")\n", @@ -97,11 +98,14 @@ "sns.despine()\n", "\n", "plt.subplot(222)\n", + "plt.axis(\"off\")\n", + "inset_ax = plt.gca().inset_axes([-0.3, -0.25, 1.5, 1.75])\n", + "inset_ax.set_axis_off()\n", "xgi.draw_hyperedges(\n", - " H, pos, hyperedge_alpha=0.1, zorder=-1, dyad_lw=0.5, dyad_color=\"#333\"\n", + " H, ax=inset_ax, pos=pos, hyperedge_alpha=0.1, zorder=-1, dyad_lw=0.5, dyad_color=\"#333\"\n", ")\n", "xgi.draw_nodes(\n", - " H, pos, node_size=node_size, node_fc=c_sf, node_lw=0.5, alpha=0.5, zorder=10\n", + " H, ax=inset_ax, pos=pos, node_size=node_size, node_fc=c_sf, node_lw=0.5, alpha=0.5, zorder=10\n", ")\n", "\n", "\n", @@ -168,85 +172,12 @@ "plt.figtext(0.1, 0.4565, \"B\")\n", "plt.figtext(0.5625, 0.4565, \"C\")\n", "\n", - "plt.tight_layout()\n", + "# plt.tight_layout()\n", "plt.savefig(f\"Figures/Fig3/{dataset}_local_simpliciality.png\", dpi=1000)\n", "plt.savefig(f\"Figures/Fig3/{dataset}_local_simpliciality.pdf\", dpi=1000)\n", "plt.show()" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "node_size = 8\n", - "nbins = 20\n", - "\n", - "set_fonts()\n", - "\n", - "plt.figure(figsize=(12, 7))\n", - "\n", - "plt.subplot(231)\n", - "xgi.draw(H, pos, node_fc=c_sf, node_size=node_size)\n", - "plt.text(-1.1, 0.6, \"A\")\n", - "\n", - "plt.subplot(232)\n", - "xgi.draw(H, pos, node_fc=c_es, node_size=node_size)\n", - "plt.text(-1.1, 0.6, \"B\")\n", - "\n", - "plt.subplot(233)\n", - "xgi.draw(H, pos, node_fc=c_fes, node_size=node_size)\n", - "plt.text(-1.1, 0.6, \"C\")\n", - "\n", - "plt.subplot(234)\n", - "n, bins, patches = plt.hist(sf, bins=np.linspace(0, 1, nbins))\n", - "plt.xlim([0, 1])\n", - "plt.xticks([0, 0.25, 0.5, 0.75, 1], [0, 0.25, 0.5, 0.75, 1])\n", - "plt.ylim([0, 40])\n", - "plt.yticks([0, 20, 40])\n", - "plt.xlabel(\"Simplicial Fraction\")\n", - "plt.ylabel(\"Number of nodes\")\n", - "\n", - "bin_centers = 0.5 * (bins[:-1] + bins[1:])\n", - "\n", - "for c, p in zip(bin_centers, patches):\n", - " plt.setp(p, \"facecolor\", cmap(c))\n", - "sns.despine()\n", - "\n", - "plt.subplot(235)\n", - "n, bins, patches = plt.hist(es, bins=np.linspace(0, 1, nbins))\n", - "plt.xlim([0, 1])\n", - "plt.xticks([0, 0.25, 0.5, 0.75, 1], [0, 0.25, 0.5, 0.75, 1])\n", - "plt.ylim([0, 40])\n", - "plt.yticks([0, 20, 40])\n", - "plt.xlabel(\"Edit Simpliciality\")\n", - "\n", - "bin_centers = 0.5 * (bins[:-1] + bins[1:])\n", - "\n", - "for c, p in zip(bin_centers, patches):\n", - " plt.setp(p, \"facecolor\", cmap(c))\n", - "sns.despine()\n", - "\n", - "plt.subplot(236)\n", - "n, bins, patches = plt.hist(fes, bins=np.linspace(0, 1, nbins))\n", - "plt.xlim([0, 1])\n", - "plt.xticks([0, 0.25, 0.5, 0.75, 1], [0, 0.25, 0.5, 0.75, 1])\n", - "plt.ylim([0, 40])\n", - "plt.yticks([0, 20, 40])\n", - "plt.xlabel(\"Face Edit Simpliciality\")\n", - "\n", - "bin_centers = 0.5 * (bins[:-1] + bins[1:])\n", - "\n", - "for c, p in zip(bin_centers, patches):\n", - " plt.setp(p, \"facecolor\", cmap(c))\n", - "sns.despine()\n", - "\n", - "# plt.savefig(f\"Figures/Fig3/{dataset}_local_simpliciality.png\", dpi=1000)\n", - "# plt.savefig(f\"Figures/Fig3/{dataset}_local_simpliciality.pdf\", dpi=1000)\n", - "plt.show()" - ] - }, { "cell_type": "code", "execution_count": null, @@ -271,7 +202,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.0" + "version": "3.12.4" }, "orig_nbformat": 4 }, From d84e8c402a6586832a27b76914d524f370b8e072 Mon Sep 17 00:00:00 2001 From: Nicholas Landry Date: Tue, 1 Oct 2024 13:52:58 -0400 Subject: [PATCH 03/17] Update scripts --- empirical_simpliciality.py | 32 ++++---- model_fitting.py | 148 ++++++++++++++++++------------------- 2 files changed, 86 insertions(+), 94 deletions(-) diff --git a/empirical_simpliciality.py b/empirical_simpliciality.py index e86c4ef..bfd0765 100644 --- a/empirical_simpliciality.py +++ b/empirical_simpliciality.py @@ -25,26 +25,22 @@ min_size = 2 -try: - with open(f"Data/empirical_simpliciality.json", "r") as file: - data = json.loads(file.read()) -except: - data = dict() +data = dict() - # This argument is very important to the computational feasibility of this algorithm - max_order = 10 +# This argument is very important to the computational feasibility of this algorithm +max_order = 10 - for d in datasets: - data[d] = dict() - H = xgi.load_xgi_data(d, max_order=max_order) - H.cleanup(singletons=True) +for d in datasets: + data[d] = dict() + H = xgi.load_xgi_data(d, max_order=max_order) + H.cleanup(singletons=True) - data[d]["es"] = edit_simpliciality(H, min_size=min_size) - data[d]["fes"] = face_edit_simpliciality(H, min_size=min_size) - data[d]["sf"] = simplicial_fraction(H, min_size=min_size) + data[d]["es"] = edit_simpliciality(H, min_size=min_size) + data[d]["fes"] = face_edit_simpliciality(H, min_size=min_size) + data[d]["sf"] = simplicial_fraction(H, min_size=min_size) - print("Just finished ", d, flush=True) + print("Just finished ", d, flush=True) - with open(f"Data/empirical_simpliciality.json", "w") as file: - datastring = json.dumps(data, indent=2) - file.write(datastring) +with open(f"Data/empirical_simpliciality.json", "w") as file: + datastring = json.dumps(data, indent=2) + file.write(datastring) diff --git a/model_fitting.py b/model_fitting.py index 1f9438c..2d2356f 100644 --- a/model_fitting.py +++ b/model_fitting.py @@ -62,79 +62,75 @@ def dcsbm_in_parallel(d, s, g1, g2, omega, min_size): os.mkdir("Figures") -try: - with open(f"Data/model_simpliciality_{dataset}.json", "r") as file: - data = json.loads(file.read()) -except: - H = xgi.load_xgi_data(dataset, max_order=max_order) - H.cleanup(singletons=False) - - k = H.nodes.degree.asdict() - s = H.edges.size.asdict() - n = H.num_nodes - m = H.num_edges - - data = dict() - - # Initialize configuration model data - data["CM"] = dict() - - # Initialize Chung-Lu model data - data["CL"] = dict() - - # Initialize DCSBM data - data["DCSBM"] = dict() - - arglist = [] - - # configuration model - for i in range(realizations): - arglist.append((H, min_size)) - - cm_data = Parallel(n_jobs=num_processes)( - delayed(cm_in_parallel)(*arg) for arg in arglist - ) - - data["CM"]["sf"] = [d[0] for d in cm_data] - data["CM"]["es"] = [d[1] for d in cm_data] - data["CM"]["fes"] = [d[2] for d in cm_data] - - arglist = [] - # chung-lu model - for i in range(realizations): - arglist.append((k, s, min_size)) - - cl_data = Parallel(n_jobs=num_processes)( - delayed(cl_in_parallel)(*arg) for arg in arglist - ) - - data["CL"]["sf"] = [d[0] for d in cl_data] - data["CL"]["es"] = [d[1] for d in cl_data] - data["CL"]["fes"] = [d[2] for d in cl_data] - - # DCSBM - with open(f"Data/DCSBM_parameters_{dataset}.json", "r") as file: - j = json.loads(file.read()) - - # convert everything to int just in case - d = {int(i): int(deg) for i, deg in j["d"].items()} - s = {int(i): int(size) for i, size in j["s"].items()} - g1 = {int(i): int(g) for i, g in j["g1"].items()} - g2 = {int(i): int(g) for i, g in j["g2"].items()} - omega = np.array(j["omega"]) - - arglist = [] - for i in range(realizations): - arglist.append((d, s, g1, g2, omega, min_size)) - - dcsbm_data = Parallel(n_jobs=num_processes)( - delayed(dcsbm_in_parallel)(*arg) for arg in arglist - ) - - data["DCSBM"]["sf"] = [d[0] for d in dcsbm_data] - data["DCSBM"]["es"] = [d[1] for d in dcsbm_data] - data["DCSBM"]["fes"] = [d[2] for d in dcsbm_data] - - with open(f"Data/model_simpliciality_{dataset}.json", "w") as file: - datastring = json.dumps(data, indent=2) - file.write(datastring) +H = xgi.load_xgi_data(dataset, max_order=max_order) +H.cleanup(singletons=False) + +k = H.nodes.degree.asdict() +s = H.edges.size.asdict() +n = H.num_nodes +m = H.num_edges + +data = dict() + +# Initialize configuration model data +data["CM"] = dict() + +# Initialize Chung-Lu model data +data["CL"] = dict() + +# Initialize DCSBM data +data["DCSBM"] = dict() + +arglist = [] + +# configuration model +for i in range(realizations): + arglist.append((H, min_size)) + +cm_data = Parallel(n_jobs=num_processes)( + delayed(cm_in_parallel)(*arg) for arg in arglist +) + +data["CM"]["sf"] = [d[0] for d in cm_data] +data["CM"]["es"] = [d[1] for d in cm_data] +data["CM"]["fes"] = [d[2] for d in cm_data] + +arglist = [] +# chung-lu model +for i in range(realizations): + arglist.append((k, s, min_size)) + +cl_data = Parallel(n_jobs=num_processes)( + delayed(cl_in_parallel)(*arg) for arg in arglist +) + +data["CL"]["sf"] = [d[0] for d in cl_data] +data["CL"]["es"] = [d[1] for d in cl_data] +data["CL"]["fes"] = [d[2] for d in cl_data] + +# DCSBM +with open(f"Data/DCSBM_parameters_{dataset}.json", "r") as file: + j = json.loads(file.read()) + +# convert everything to int just in case +d = {int(i): int(deg) for i, deg in j["d"].items()} +s = {int(i): int(size) for i, size in j["s"].items()} +g1 = {int(i): int(g) for i, g in j["g1"].items()} +g2 = {int(i): int(g) for i, g in j["g2"].items()} +omega = np.array(j["omega"]) + +arglist = [] +for i in range(realizations): + arglist.append((d, s, g1, g2, omega, min_size)) + +dcsbm_data = Parallel(n_jobs=num_processes)( + delayed(dcsbm_in_parallel)(*arg) for arg in arglist +) + +data["DCSBM"]["sf"] = [d[0] for d in dcsbm_data] +data["DCSBM"]["es"] = [d[1] for d in dcsbm_data] +data["DCSBM"]["fes"] = [d[2] for d in dcsbm_data] + +with open(f"Data/model_simpliciality_{dataset}.json", "w") as file: + datastring = json.dumps(data, indent=2) + file.write(datastring) From d7816edeafb370b147aa6fb4f7f2b79526b29fc2 Mon Sep 17 00:00:00 2001 From: Nicholas Landry Date: Tue, 1 Oct 2024 13:53:09 -0400 Subject: [PATCH 04/17] update fig 3 --- .../Fig3/email-enron_local_simpliciality.pdf | Bin 65171 -> 59342 bytes 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zCl)76X#47cvsV=kw-<3*tTfxdz}_i($ayhvNYpX0Rb8#~zQjuE5l#a_!HD@5oYyGE z4TpE85;Py6ocN0S_skW@mPkV`{pvA_9_J~QjdCix62AWa^Y?XvVMTtAq2U+_35m-G zn(GMvqYWxR5{D?bjq6>O1YL%Y6s|{mmxX~Jf_K^zu+KJ;$IFD&LsHZ`@P>6hgqUyMLKAnHW(kT0S*y}*|0mcH`;^ZO$KJhzy4I8 zIaAzX`Bv6C7cC1xPae0}p)*VN(}a&1+!3(7S0dRV;SQ_?7Uw*$l}Qr=qG$z#NA@Rv z{m?qk+N6i;`cMow@w-t}%^x3m?_o&@4!3`wab4YBDz0Qz06kG!{pzAN&`oy{ssLhafmVAcI`QOmds7|!{R#p}b zX0ba2n+PKQ1p^mWgeNG-1JRxD<{sa(vL*`Ow7uIX(Ze78=kyc}o&K+%E2T0Uk>z&<`AU_Km(hasUHi1XxH+;`cWql3mr+hP4D^x? z(t&%9cn}ZbffW!BtcL7^cn}Y)fOudvWFN$Xcwhy@1FIqXARfd6D5RzvndJctKYKs>M-vJZcehf-AUpD&w?ccZyn#UJ!ZUSE1*OaVXL kFwjamNQb1?*q497!&R{QST4NZem|q>`P}x#<@(-P4-B4K0ssI2 From bd3430eabfa7ccf26088614eacba999933043565 Mon Sep 17 00:00:00 2001 From: Nicholas Landry Date: Tue, 1 Oct 2024 14:05:00 -0400 Subject: [PATCH 05/17] Update empirical_simpliciality.json --- Data/empirical_simpliciality.json | 32 +++++++++++++++---------------- 1 file changed, 16 insertions(+), 16 deletions(-) diff --git a/Data/empirical_simpliciality.json b/Data/empirical_simpliciality.json index 04534cb..a1fe63e 100644 --- a/Data/empirical_simpliciality.json +++ b/Data/empirical_simpliciality.json @@ -1,51 +1,51 @@ { "contact-primary-school": { - "es": 0.9175213057922866, - "fes": 0.9372632240822998, + "es": 0.8811035918792296, + "fes": 0.9372632240822959, "sf": 0.8470540758676351 }, "contact-high-school": { - "es": 0.9270722162931341, + "es": 0.9068746214415506, "fes": 0.9230435574995609, "sf": 0.8064655172413793 }, "hospital-lyon": { - "es": 0.9544740973312402, + "es": 0.9351230425055929, "fes": 0.9718947368421075, "sf": 0.9121338912133892 }, "email-enron": { - "es": 0.052244483895511035, - "fes": 0.49822878699507983, + "es": 0.043231776452946125, + "fes": 0.49822878699508, "sf": 0.3127962085308057 }, "email-eu": { - "es": 0.04924735333611438, - "fes": 0.521740030206559, + "es": 0.04001676445934619, + "fes": 0.5217400302065587, "sf": 0.3170553935860058 }, "ndc-substances": { - "es": 0.005277033139634914, - "fes": 0.07047994455353072, - "sf": 0.020143487858719646 + "es": 0.0018461291039516992, + "fes": 0.07028592483283039, + "sf": 0.02052291256676975 }, "diseasome": { - "es": 0.04538228067639832, + "es": 0.022928994082840236, "fes": 0.04065214786039886, "sf": 0.0 }, "disgenenet": { - "es": 0.004779934338796714, - "fes": 0.007864665990617565, + "es": 0.0009638126398955025, + "fes": 0.00820667884825538, "sf": 0.0 }, "congress-bills": { - "es": 0.005814213391956787, + "es": 0.002413013661041396, "fes": 0.10059626292667334, "sf": 0.02909811429970835 }, "tags-ask-ubuntu": { - "es": 0.24870932730705064, + "es": 0.11261561393098504, "fes": 0.45768523280329415, "sf": 0.15149467561903948 } From 65eeac752784810d249e95fd12bbab2df3feb96a Mon Sep 17 00:00:00 2001 From: Nicholas Landry Date: Thu, 3 Oct 2024 11:52:06 -0400 Subject: [PATCH 06/17] format with isort and black --- draw.py | 9 ++++- local_simpliciality.ipynb | 17 ++++++++-- model_fitting.py | 1 + plot_model_fitting.ipynb | 49 +++++++++++++++++++++++----- print_simplicial_assortativity.ipynb | 7 +++- simpliciality_correlation.ipynb | 36 ++++++++++++++++---- 6 files changed, 100 insertions(+), 19 deletions(-) diff --git a/draw.py b/draw.py index 9c41f0b..52d0d45 100644 --- a/draw.py +++ b/draw.py @@ -666,7 +666,14 @@ def draw_multilayer( ) ax.add_collection3d(between_lines) - (x, y, s, c, ec, lw,) = zip( + ( + x, + y, + s, + c, + ec, + lw, + ) = zip( *[ ( pos[i][0], diff --git a/local_simpliciality.ipynb b/local_simpliciality.ipynb index e252aa5..2adaaf2 100644 --- a/local_simpliciality.ipynb +++ b/local_simpliciality.ipynb @@ -102,10 +102,23 @@ "inset_ax = plt.gca().inset_axes([-0.3, -0.25, 1.5, 1.75])\n", "inset_ax.set_axis_off()\n", "xgi.draw_hyperedges(\n", - " H, ax=inset_ax, pos=pos, hyperedge_alpha=0.1, zorder=-1, dyad_lw=0.5, dyad_color=\"#333\"\n", + " H,\n", + " ax=inset_ax,\n", + " pos=pos,\n", + " hyperedge_alpha=0.1,\n", + " zorder=-1,\n", + " dyad_lw=0.5,\n", + " dyad_color=\"#333\",\n", ")\n", "xgi.draw_nodes(\n", - " H, ax=inset_ax, pos=pos, node_size=node_size, node_fc=c_sf, node_lw=0.5, alpha=0.5, zorder=10\n", + " H,\n", + " ax=inset_ax,\n", + " pos=pos,\n", + " node_size=node_size,\n", + " node_fc=c_sf,\n", + " node_lw=0.5,\n", + " alpha=0.5,\n", + " zorder=10,\n", ")\n", "\n", "\n", diff --git a/model_fitting.py b/model_fitting.py index 2d2356f..e09aaad 100644 --- a/model_fitting.py +++ b/model_fitting.py @@ -55,6 +55,7 @@ def dcsbm_in_parallel(d, s, g1, g2, omega, min_size): max_order = 10 min_size = 2 +print(f"{dataset} started", flush=True) if not os.path.exists("Data"): os.mkdir("Data") diff --git a/plot_model_fitting.ipynb b/plot_model_fitting.ipynb index f0c3734..2bae358 100644 --- a/plot_model_fitting.ipynb +++ b/plot_model_fitting.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -46,16 +46,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['tags-ask-ubuntu',\n", + " 'congress-bills',\n", + " 'ndc-substances',\n", + " 'disgenenet',\n", + " 'diseasome',\n", + " 'email-eu',\n", + " 'email-enron',\n", + " 'hospital-lyon',\n", + " 'contact-high-school',\n", + " 'contact-primary-school']" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "datasets" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -82,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -92,9 +112,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "

    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "def add_patch(legend, colors, titles):\n", " from matplotlib.patches import Patch\n", @@ -214,7 +245,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.0" + "version": "3.12.4" }, "orig_nbformat": 4 }, diff --git a/print_simplicial_assortativity.ipynb b/print_simplicial_assortativity.ipynb index 271c3f8..b7cf5c3 100644 --- a/print_simplicial_assortativity.ipynb +++ b/print_simplicial_assortativity.ipynb @@ -31,7 +31,12 @@ "for dataset, d in data.items():\n", " rows.append([f\"\\\\texttt{{{dataset}}}\", d[\"sf\"], d[\"es\"], d[\"fes\"]])\n", "\n", - "column_labels = [\"Dataset\", \"$\\\\rho_{\\mathrm{SF}}$\", \"$\\\\rho_{\\mathrm{SF}}$\", \"$\\\\rho_{\\mathrm{SF}}$\"]" + "column_labels = [\n", + " \"Dataset\",\n", + " \"$\\\\rho_{\\mathrm{SF}}$\",\n", + " \"$\\\\rho_{\\mathrm{SF}}$\",\n", + " \"$\\\\rho_{\\mathrm{SF}}$\",\n", + "]" ] }, { diff --git a/simpliciality_correlation.ipynb b/simpliciality_correlation.ipynb index f163d58..a2f6c4a 100644 --- a/simpliciality_correlation.ipynb +++ b/simpliciality_correlation.ipynb @@ -42,9 +42,9 @@ "output_type": "stream", "text": [ "Correlation (spearman) between es and fes: 0.9030, p-value: 0.0003\n", - "Correlation (pearson) between es and fes: 0.9027, p-value: 0.0003\n", + "Correlation (pearson) between es and fes: 0.8938, p-value: 0.0005\n", "Correlation (spearman) between es and sf: 0.8875, p-value: 0.0006\n", - "Correlation (pearson) between es and sf: 0.9500, p-value: 0.0000\n", + "Correlation (pearson) between es and sf: 0.9573, p-value: 0.0000\n", "Correlation (spearman) between fes and sf: 0.9970, p-value: 0.0000\n", "Correlation (pearson) between fes and sf: 0.9718, p-value: 0.0000\n" ] @@ -102,7 +102,18 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dataset = \"email-enron\"\n", "max_order = 2\n", @@ -124,9 +135,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correlation (spearman) between sf and es: 0.6710, p-value: 0.0000\n", + "Correlation (pearson) between sf and es: 0.6041, p-value: 0.0000\n", + "Correlation (spearman) between sf and fes: 0.9655, p-value: 0.0000\n", + "Correlation (pearson) between sf and fes: 0.8354, p-value: 0.0000\n", + "Correlation (spearman) between es and fes: 0.6844, p-value: 0.0000\n", + "Correlation (pearson) between es and fes: 0.6323, p-value: 0.0000\n" + ] + } + ], "source": [ "ls = {\"sf\": sf, \"es\": es, \"fes\": fes}\n", "results = {}\n", @@ -171,7 +195,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.0" + "version": "3.12.4" }, "orig_nbformat": 4 }, From 7a575fca689df5d79eb486da85e043293e5d87ff Mon Sep 17 00:00:00 2001 From: Nicholas Landry Date: Thu, 3 Oct 2024 11:59:57 -0400 Subject: [PATCH 07/17] remove notebook output --- dataset_characteristics.ipynb | 46 +++----------------------- local_simpliciality.ipynb | 7 ---- plot_model_fitting.ipynb | 39 +++------------------- print_simplicial_assortativity.ipynb | 29 ++-------------- simpliciality_correlation.ipynb | 49 ++++------------------------ 5 files changed, 16 insertions(+), 154 deletions(-) diff --git a/dataset_characteristics.ipynb b/dataset_characteristics.ipynb index 9daeebb..8d68891 100644 --- a/dataset_characteristics.ipynb +++ b/dataset_characteristics.ipynb @@ -39,28 +39,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Just finished contact-primary-school\n", - "Just finished contact-high-school\n", - "Just finished hospital-lyon\n", - "Just finished email-enron\n", - "Just finished email-eu\n", - "Just finished coauth-mag-geology\n", - "Just finished coauth-mag-history\n", - "Just finished ndc-substances\n", - "Just finished diseasome\n", - "Just finished disgenenet\n", - "Just finished congress-bills\n", - "Just finished tags-ask-ubuntu\n" - ] - } - ], + "outputs": [], "source": [ "try:\n", " with open(f\"Data/dataset_characteristics.json\", \"r\") as file:\n", @@ -87,28 +68,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "contact-primary-school & 242 & 12704 & 52.5 & 2.42\n", - "contact-high-school & 327 & 7818 & 23.91 & 2.33\n", - "hospital-lyon & 75 & 1824 & 24.32 & 2.43\n", - "email-enron & 143 & 1442 & 10.08 & 2.97\n", - "email-eu & 967 & 23729 & 24.54 & 3.12\n", - "coauth-mag-geology & 1061562 & 898649 & 0.85 & 3.72\n", - "coauth-mag-history & 448593 & 248633 & 0.55 & 2.72\n", - "ndc-substances & 2740 & 4754 & 1.74 & 5.16\n", - "diseasome & 516 & 314 & 0.61 & 3.0\n", - "disgenenet & 1982 & 760 & 0.38 & 5.14\n", - "congress-bills & 1715 & 58788 & 34.28 & 4.95\n", - "tags-ask-ubuntu & 3021 & 145053 & 48.01 & 3.43\n" - ] - } - ], + "outputs": [], "source": [ "for name in datasets:\n", " d = data[name]\n", diff --git a/local_simpliciality.ipynb b/local_simpliciality.ipynb index 2adaaf2..bc4cb14 100644 --- a/local_simpliciality.ipynb +++ b/local_simpliciality.ipynb @@ -190,13 +190,6 @@ "plt.savefig(f\"Figures/Fig3/{dataset}_local_simpliciality.pdf\", dpi=1000)\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/plot_model_fitting.ipynb b/plot_model_fitting.ipynb index 2bae358..852cdce 100644 --- a/plot_model_fitting.ipynb +++ b/plot_model_fitting.ipynb @@ -46,29 +46,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['tags-ask-ubuntu',\n", - " 'congress-bills',\n", - " 'ndc-substances',\n", - " 'disgenenet',\n", - " 'diseasome',\n", - " 'email-eu',\n", - " 'email-enron',\n", - " 'hospital-lyon',\n", - " 'contact-high-school',\n", - " 'contact-primary-school']" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "datasets" ] @@ -112,20 +92,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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89NFHFBQUcOutt/LSSy95BIPatm3LN998w4033uhVF9fnBBASEsI999wDeP79uc6Df/3rX3Ts2NG9XKfTMXv2bJo2bcpvv/3G1q1bASp9TpRm6tSpREREuH+io6P9lo3Qh7JnfXHwT6tS0CK0hcdnUlqgc9euXaxcudLv+unTp5Of7//JT2n7TkxM9BiaWpFts7OzmTFjht/1S5cuZf/+/ZXad2pqqs8eby4LFiwotcEubd/79+/3CDhfbsaMGWRn++9uXdq+N23a5B5qW9Ft8/PzmT59ut/1K1euLLWnW2n7TktLY86cOX7XL1y40O9QzrL2nZycXGrgfPbs2WRk+L4gLWvf27ZtY+3atX7XT5s2Dau1+MI0JyfHb9mScnJyWLt2rVcvxvLWKyMjg9mzZ/tdv2TJEp89Usuz73pJrYPQxsxfWeL7RRMGai0h6hBah7X2KK5RaTCoDR7LpJ3zVtPtXKi2+Dt92rR/+N1W2jlvNdnOlWS1Wpk2bZrPdRVpD32Rds63OXPmkJaW5nd9ua/ndJclhQ5vXq3t3NT3ptJI1wi9Wu+1LteUy+qvV2PUGn1sWXo7p1EqWfTFP72WNwvXo1Ao6lw7V959SzvnrS62c0Clr+fK20YeOnSowvuuCoWzAqHVksOVMjMzvXpfANhsNsLDwyksLOTEiRPExsZ6lXnzzTeZMmUKTz75JLNmzSIlJYW2bduiUqmw2byTisXHx7NkyRKmTJnChAkTgKIb7UmTJjF+/Hj+/e9/+6zv+fPnWbNmDXv37uX8+fNkZWVx5MgRkpOTGTJkCOvXryc9PZ2WLVsCcOnSpTJ7sLje86lTp2jduviCc9GiRSxatMir/ODBg90zjcXGxnLy5Ek2btzoc1haTEwMp06dYt26dQwdOrTM9+hr/bx583j44Yfd76+kN954g/fff5+///3vTJw40WPd/fffz6JFi5g8eTJ/+9vf3MsdDgebN29m8+bNHDt2jEuXLpGVlcWvv/6K2Wz2eC9lvb8JEyYwdepUj/ru2LGD3r1706NHD3bu3AkUBV02bNjgtf3o0aMZPXo0L7/8Mh999BEdOnRg5syZDBo0yGfwrHPnzhw+fJilS5d6DA/0pbS6z5w5k2eeeYabb76ZtWvXcvr0aaKjo9FoNOTm5rqDcCWNGTOGb7/9lvfff5+XX3454OcEFPUgMpuLk66aTCaio6PJfj0c46R00IZ6lF+0ZytT9j4GwBvXvse9XYejUWpK/VyEEBWTkpLC/Pnzyyw3duxYn21BbTGZTERERJCdne31cKUuKbOehTnw/p/fzY//Ci2LHnI4nU5+Of0Lz214DoCvR3xN92bda6jWorwu5pnp8W7RtcvBd27BoPX+bhf1R11rD4OmnQuU3Az4sH3R7/d9C3G3V9+xSjiefZwjl47wyi+vAPDhgA8J14TTp2UflIrKzdp0+FwOR87m8MzCogfdn91/LUOuugK9RhWwegsRaNXRRgai/ajQN++pU6cAaNeunc/gEBQNbSosLBoHGhMT47OMq6dDeRPzGgxFT/nKO/7Obrfz6quvMmPGDPfNtdFopFWrVtjtdo+yrvfUuHHjcg1v8ic5OZlly5Z5La/IPps3b86pU6f8JjiuKp1OV+a6kvHC7du3M27cOHdyLJVKRYsWLWjRooXf///SPProo7z//vssXryYTz75hNDQUPewrZK9h3bt2uXzs+zevTsAr776KitXruTQoUMMGzaMkJAQunfvzsCBA3n44YfdPXpc/7ft27evcF1Luvzv78yZM0DR/5ev4BB4/o1XxzkBRf9npf2fXs6oL35abgwJkeCQENUgJiYGo9FYapdho9Hoty0QVVRySlhtiPtXhUJBY31j9+tQjWcAXdQNamXFry1E3SXtYR1Xoo1EF15jh43URXota6RvVOngEEBUqOf1uFatlOCQqPPqahtZoTPRFTyoTHCgpKpuX5ZJkybx8ccfEx4ezsyZM0lPTyc7O5uDBw+6eyC5BOo9TZw4EafT6fUzb968cu/DFUTQ6727Xta0ixcvcsstt5CUlMSdd97J5s2bKSgo4NSpU2zfvt2d56ci2rVrx5AhQ8jJyWHJkiUUFhayaNEijEYjDzzwgLvcvHnzfH6Wrl5PzZs3Z+fOnXz++efccccdREREsGXLFqZOnepO5g2B+7/1p7T9VvSY1X1OAIRqioNJBm3t/40JEYyUSiXDhw8vtczw4cNRKit/ISzKSeXZzpUMCvka4iBqn1ol50UwkfawHtHWXNDcqDWiwPO6N1xbtQBVmM6zz4NBJ70PRd1XV9vICh3NNRTr8mnES2rcuLE7wHHy5EmfZVzjPFu1alWRw5ebKyjz1Vdf8dRTT9G8eXO/ZV3vKSMjo8wkUdXJ6XS6P5d27drVWj1cli5dSlZWFtdeey1Lly6lb9++aDRV73HiSlb91Vdf8Z///IesrCwefPBBQkMr9sUUGhrK448/zvLly0lPT+fgwYPcfvvtFBYW8vrrrwPF/7eljYOtDNffbXp6ut+k0iX/xuvCOQGgL/H/p1eWv+eREKJi4uLiiI+P9+raazQaiY+PJy4urpZq1sCoPL+ztKriJ8x6lQSIhKgJ0h7WEzUYNFcr1V454Kraq1OvUaEq0QMxXC8BIlE/1MU2skJnz3XXXUdISAgXL17kf//7H6NGjfIqo1Kp6N+/P+vXr2fp0qXuhMEuTqfTPXxowIABla64K1hx+ZAxKMo9BJTrA42OjiY2NpaUlBTmzJnjVV9XnV09PNRqNTabzedxq2LFihVkZ2fTsmVLrrrqqoDuuzJcn2Hnzp0D2rvlrrvuolmzZmzcuNGdZKy8yaldCgsLvXpZxcXFMW3aNFasWOEOCN10000cP36cOXPmcOedd/rcV8n/2/Jq3bo1HTp04OjRo6xcudLrPCgoKGDNmjVA0d+4Wq2ukXOiLAZVcVBIKzdHQlSruLg4OnfuTGpqKjk5OYSHhxMTEyNPymuS0n+AqOTvQojqJe1hPaCq2bQDIZoQj9cGjcFPyQrsU1s8pOzyHkVC1GV1rY2s0FFDQkJ46qmnAHjxxRfduWlc1q5dS3x8PC+//DJQNNRry5Yt7vV2u5033niDAwcO0KZNG+6+++5KV9yVqGnTpk3k5uZ6HMM11b1rOnaXZcuW8cYbb3jtyzW1/LvvvusxzTsUJVG++eabvY67evVqj2NWxOUzf508eZJXXilK1Pbaa6/ViS9M12f4888/c/bsWffyzMxMxowZ47cnTFk0Gg1jx44FICkpif79+7uPVV533HEHM2bM8OrxtW7dOgC6dOkCwPPPP49KpWL58uXMnDnTo2xaWhr333+/x2x2FeH6G//rX//qkVm+sLCQxx9/nIyMDPr27euega8mzomy6EsMMQsNwBexEKJ0SqWS2NhYrr76amJjY+tE296gKD3zT5TMu6ZSSG4KIWqStId1XA0HiEr2IFIpVOhUVe/ZHq4vfg+hOmnjRf1Sl9rICh95ypQpDBgwgJMnT3L11VfTt29fRowYQbt27bjllls4cuQIw4cPZ8KECWRnZ9OvXz969erFrbfeSmxsLNOmTSMqKoolS5b4TfBbHrfddhtRUVEcPXqUzp07c9NNN9G6dWv279/Phx9+iEKhYNKkSXTp0oURI0bQsWNH7rrrLp/5fZ599llGjx7NpUuXuPHGG+nRowe33norV111Fb179+b33393l33ooYfc21x//fVcd911HvlzymP8+PG0adOGYcOGMWDAAPdsW3fffTfPPfdcpT+TQLrtttsYPHgw586do2PHjgwZMoSBAwfSunVr1qxZQ1hYWNk78eOxxx5z99pxBRwr4sKFCzz//PM0a9aMXr16MXz4cDp37swLL7xASEiIe1rHa6+9lk8++QSlUskzzzxDu3btuO222+jTpw9t2rRh0aJFVGASPw9PPPEEf/nLXzh16hRdu3alf//+DB8+nOjoaL755htiYmL49ttv3eVr4pwoi1pdfLrrJP+GECLYKT2fIGsUxTcPVUmGKoQQQUdRwz2I1MU9iELVgcl/FFpi1kOZAVGIyqvwFZJer2f9+vXMnDmT3r17c/DgQXfPjccee4zZs2cDRYGk5cuXM3ToUI4dO8b69etRqVQ8+eST7N69m169elWp4pGRkfz666+MGDGCwsJCdu7cSbNmzYiMjGTgwIH8/PPP9OrViyNHjrBx40batm3Ljz/+yN///nevfSkUChYuXMi3337LoEGDSElJYc2aNZhMJsaMGcP333/vLjthwgSmTZtG+/bt2bt3L+np6RUeG/jGG2/QqVMn9uzZw7Zt2+jUqRMzZszg+++/rzNPVBQKBcuXL+ell14iJCSEX375hZMnT/L888+TlJRE48aNy96JHxEREWg0Gpo0acI999xT4e2XLFnCa6+9RlxcHElJSfz000/k5eUxfvx4Dh48SL9+/dxln3nmGX777TfuvfdeCgsLWbt2rXv2szlz5lR6OJ9CoeDrr79m/vz59OnTh3379pGYmEijRo149dVX2bVrF23btvXYprrPCbc3TvtMNnhFmJGnOv+DCT3+QfOwRoE5lhBC1CXaUHgpGR5YDHrPpKdhujA+G/IZn9/8eUCGM4jAM2jVLHmyL8feu1Vu8ISobtpQ+Os+uH8RhETU6KEb6RsxY/AMZgyeQVRIVGD2Garh3w9dx4JHenv0JhJCVIzCWdkuFKLCYmNjOXnyJBs3bqR///61XZ1a8/bbb/Puu+/y2muvMW3atNquTtAwmUxERESQnZ3tlegMwGJzMHfnBhqH27mz8yDJwSGEcCur/agrylXPvExI2w0dh3qt2pK2BZVCRe8Wvau5pqKydp68RM828hBDBF5QtXMBO1g6pO+BdgPhsrxA1clit7A5bTMAHRt1pFVY1Sdpyc63siPlIsYQDb3bBiboJER9E4j2Qx7PiBpVUFDArFmzUCgUPPHEE7VdnQZFo1KgVmhQqx2Sf0MIEbxUGq/8Qy5aldZremVRt2hU8v8jRI1R/5n7p4YfGmpVWpQKJQ6nw2O4WVVo1EVth05dN0ZjCFFfyRkkatT8+fPJyMhg2LBhtGvXrrar06AoFAp0ai1apQqVn5snIYSo91Qar/xDLhqlRtq/Ok6jkktTIWqMSgMKpd+genVyBYb0AZpZV6cueg86jbQhQlSFnEGixjidTj7++GOg4lPbi8AI1RjQqaXjoBAiiCn9B4i0Ki1qP+tE3SA9iISoQSpdjfcecnHNZKYP0MQpKqUClUrhDhQJISpHrpJqUEpKSm1XoVYpFAqPKeFFzTNoQtCr82u7GkIIUX1Uar9TNmuUGpROeTZWl0kPIiFqkEoDtTSzbagmlGxLdkBnlQzRqNBLDyIhqkQCREI0IJEhIag1kpxaCBHk/CRb1Sg1aBXSBtZlWskfIkTNUShAF152uWoQpg0jxBzYxNgGrQqDRm5vhagKOYOEaECah0eQ58yp7WoIIUT18nPDo1VpaaSXGbLqMrm5E6KG6Wt2inuXME2Ye5hZoBi0akJ1MsRMiKqQxzRCBBmb3QGA1W71WtcyIoxYY2wN10gIIWqYzntq10JbIY31jdGpdLVQIVEeNruDML0EiIQoN7sVHI6q7SPsijKL5BR6X1P6km/Nx+6wl6usXq0nOjy6XGXLq3WjENQyTFWIKpEzSIggU2At+mK+ZL7ktU6jUmLQBPZpjRBC1DkF3u3f2byzaPzkJhJ1Q57FjlJyVAtRfpZcsBVWbR/q0ofdWmwODp8rX+/z07mnMVlM5T50oK9J9RrpPSREVUmASIggY3c4ASiwFdRyTYQQopYUZnstyjJn1Xw9RIXY7A5sf36HCSHKwW4Fu6VaD3Exz0J2gRWns/Rz0+awcTbvLPk2mQxFiPpMAkRCBBnHnxfXhVV9oiSEEPWV2fsJdq41t8wbHFG77A4nNrv8HwlRbg5bUZCoGuVbbDgckG8pfehYljkLh9NBgVUeUApRn0mASIggY3dKDyIhRANnLfDIy2G1W7E5bFgd1XsjJarG7nRiq2o+FSEaEoet2nsQWf7MbVlWgMgVGJIeRELUbxIgEiLIuB6QS4BICNEg2czgdBTdOP2p0F7Uo9JSzTdSomoczqrn2xWiQamJAJGt6KQ028oIENmLrjtzrbnVWh8hRPWSAJEQQcbucMqTciFEw+UaXlsiQJRvLXqibXFIgKguczic7l6wQohycHgGw6uD1e4KEJUevXUF4C12i8+ZdIUQ9YMEiIQIMq4AkcPpKPdUo0IIETRcNyYlbppcPSqlB1Hd5nA6cUiASIjyc9hqIEBUdE5ayggQme1m9+951rxqrZMQovpIgEiIIOMEd+8h6UUkhGhwbH/epPgIEEmbWLcVDTGTAJEQ5eaw1lgPoooEiCTNgRD1lwSIhAgyoz5Zx4XcLJ7b8By9vu3lHlohhBBBz5IHH18FC0dDYdFMZvnWfOJXxPPchufIMefUcgWFP/kWG4M+TKT3ez+Rb6neG14hgoIlD2b3L/qxVE+PnTyzlQfnbOfRBb+TXeA/wO5wOsix5PDchud4bsNzXDJfqpb6CCGqnwSIKikxMZGJEyeyfPnyGj3uxIkTmThxIiaT9xS+ZYmNjUWhULBp06ZyH0uhUBAbG1vhY1XluDUtJSUFhUKBWq2u7aq4zZs3D4VCwdChQyu1vd0pQ8uEEA2cj95CkoNICCHKr2TeIbPV/7Xl5UPKCl254IQQ9Y4EiCopMTGRSZMm1XiAaNKkSUyaNKlSAaKKuvLKKxk5ciTDhg2r9mOJwLJJ7iEhREP3Zy4ih7P4BkduWoQQovxyC4uvJy02B3Y/Q0BzLZ4zl5ltZp/lhBB1X93pMiHqnNGjRzN69OjaroaoBGs1j0cXoqFyOBykpqaSk5NDeHg4MTExKJXyrKVO+jMhtUdeDLvkxRCirpF2te7KNXteT+ZbbITrNV7lTBbPB9fS1gpRurrc7kmASIggZJMAkRABl5SUREJCgkcPTqPRyPDhw4mLi6vFmgmfrEU3KCWHPljsFgpthejV+tqqlRCiBGlXA6SaZv/L8woQ2X0GiC4fYmZ1WLHarWhU3mWFaOjqertXN8JUFeR0Olm4cCFDhw4lKioKrVZLbGwsjzzyiEeem3Xr1nHrrbfSpEkTdDod7dq145lnnuH06dMe+yuZg8ZkMjFp0iTi4uLQ6/W0aNGC5557jry84oYvNjaWSZMmATBnzhwUCoX7JzExEYAXX3yRG264gZiYGPR6PSEhIXTq1Mnn8V0SEhK48847adasGVqtllatWjF69GhWrlzpzknjEh0d7T7mwIEDK/wZLly4kP79+xMaGkpERAR33XUXx44d8yhTWh6cn376iWHDhmE0GtHpdHTu3JkXXngBg8GAQqHw+x7Lc9zySEpK4sEHH6RDhw7odDoaN27MwIED+cc//uE1/K60z/Vy+fn5TJ06lS5duvj9/3cpKCjg/fffp3v37oSGhhIeHs7111/PzJkzsdm8AzR2u51Zs2bRp08fwsPDMRgMXHPNNUydOpX8/MAmkrbKVM5CBFRSUhKLFy/2al9MJhOLFy8mKSmplmomPJhLDHP4M0B0+dCHS4WSPFWIukDa1SpylJhVrJqGdF3egyin0Du3m9PpJNea67U8xyqTAghxufrQ7tW7AJHdbic+Pp4HHniAn3/+mY4dOzJgwAAsFgtz585lzJgxQFGunmHDhrFmzRratm3LkCFDsNlszJw5k+7du7Nr1y6vfTscDrp27co777xDREQEN910E7m5uXz66afEx8e7yw0bNozOnTsD0KZNG0aOHOn+adKkCQBffvklO3fupGnTpgwdOpQ+ffqQnp7OzJkz6dWrF+fOnfM49ssvv8yIESP48ccfadmyJYMGDUKr1fL9999z++23ExMTw8iRI93lb7nlFvcx+/fvX6HP8LHHHuOBBx4gMzOTAQMGEBYWxrJlyxgwYABZWVllbv+vf/2LoUOHsm7dOtq3b8+gQYNwOp188sknFBT471Ja1eO6rFmzhmuuuYZvvvkGtVrNkCFDiI2NZdOmTfy///f/2Lx5s7tsWZ9rSQ6Hg2uvvZY333yT8PBwv///AJcuXaJ///688cYbnDhxgn79+nHdddexf/9+nnnmGW677TYsluIgjcVi4bbbbuPpp59m3759XHfddfTr14+UlBQmTJhA//79uXQpcDctFqeM/RYiUBwOBwkJCaWWSUhIwOEofQpgUQPyLhT/bskBu40sc5ZHEZldR4jaJ+1qAFgLfP8eIBabg/zLAkQXcrwfQOZYczxyvblcLLgY8DoJUZ/Vl3av3gWI/vGPf/DDDz/QqlUrdu/ezbZt21i3bh2nT59mxYoVXHnllSQkJDBx4kSioqLYsmULO3bsYNWqVe6b8czMTO655x6PG3goioB36tSJpKQktm7dytq1a9mzZw86nY5Vq1a5g0pffPGFOzfP0KFDWbp0qfuna9euAHz77bdcuHCBnTt3smLFCn7++WfS09Pp2bMnZ8+e5dNPP3Ufd+HChXz00UcYjUY2bNjAnj17WLNmDSdOnGDjxo1cf/31DB48mKVLl7q3+fe//+0+5uTJkyv0GVosFtatW0dSUhKrVq3i6NGjdO3alTNnzvDVV1+Vuu3OnTt56aWXCAkJISEhgd27d5OQkMDhw4fZs2dPtR23pFdeeQWr1cr06dNJTk5m1apV7Ny5k7S0NF5//XV0Oh1Qvs+1JKfTSXR0dJn//wBPP/00u3btYsSIEZw6dYq1a9fy888/c+zYMbp3787atWvdvcwA/v73v7NmzRp69uzJ8ePH+fnnn1m3bh2pqakMGzaM3bt38+yzz5b7MwAwm82YTCaPH5eswuKnNjJrjxBVk5qaWubEACaTidTU1BqqkfDJZoFLKe6XToedixlJXk+xMwsysUgvSyFqlbSrAVAy74858JPXnM32TuqfZ7aRlV/cfuZZ8zieddzn9ml5aWQUZAS8XkLUV/Wl3atXASKr1coHH3wAwMcff0y3bt3c65RKJbfddhurVq3iww8/BIqmae/du7dHmcmTJ9OtWzdOnDjBf//7X4/9q1Qq1q5dS6dOndzL2rdvT9++fQHYvn17ues6cuRIjEajx7KwsDAeeeQRAPbt2+dePmXKFADefPNNBg0a5LFN//79PXrEBMLcuXM9ho2FhIRwzz33AGW/x2nTpmG323n11Ve55ZZbPNZdc8011Xbcko4ePQrAiBEjPJY3a9aMqVOnuj/Din6u5f3/T01NZfHixeh0OubPn+/x/9yiRQv+/e9/A/Dpp59isVgoLCx0BwTnzp1L8+bN3eUjIiKYP38+Go2GRYsWcebMmXJ/DlOnTiUiIsL9Ex0d7V6XZy7+8s6xFN0czZkzh7S0NL/7Ky3QuGvXLp9D8lymT59e6jC50vadmJjoMTS0IttmZ2czY8YMv+uXLl3K/v37K7Xv1NRUFixY4Hf9ggULSm3AS9v3/v37PQK+l5sxYwbZ2dmV2vemTZvcQ10rum1+fj7Tp0/3u37lypU+e1+WZ99paWnMmTPH7/qFCxeWOty0tH0nJyezZMkSv+tnz55NRob/i9TS9r1t27Zyt8E5OZ6BCKvVyrRp0/yWX7t2Ldu2batUvTIyMpg9e7bf9UuWLCE5OblS+653HI6i4RVZqcz/z2r34vMWE2cvu3FZOHMhdqedQrv3jY+0c95qsp2z2YufmDqdTmnnLlPd7dzatWv9rp82bRpWq/fQIqh8O3d5e+nP//73P7/rGlQ7V4L7es5Soj0yF3+egbqey7N4p0v4dvbHZOZZyCm0Ync4yTZnuxNU79u2z6Osw+ngfP75ctWrobRzFdm3tHPe6ls7d3m9ytvulSxXG+2cwumspqxm1WDLli3ccMMNNGrUiMzMTI+cPC42m43w8HAKCws5ceIEsbGxXmXefPNNpkyZwpNPPsmsWbNISUmhbdu2qFQqn7lj4uPjWbJkCVOmTGHChAlAUfBp0qRJjB8/3h0QuNz58+dZs2YNe/fu5fz582RlZXHkyBGSk5MZMmQI69evJz09nZYtWwJFw5YiIyNL/Qxc7/nUqVO0bt3avXzRokUsWrTIq/zgwYN5/vnngaLcSSdPnmTjxo1ew9JmzpzJM888w8033+w+eebNm8fDDz/srqvD4SAqKors7GwOHjzoM4mWr/pV9LiPP/4458+f53KTJ0+ma9eu9OzZ0917Z9q0aVx99dVeZSvyuVb0//+bb77hwQcfZMCAAX4b7tatW3PmzBm2bt2K1WrlxhtvpE2bNqSkpPgs379/f3777TcWLVrEfffd5/XZ+2I2mzGbi4eSmUwmoqOj6fjCAj5/oR3PJz4JwLYHtmHQGPy+fyFE6VJSUpg/f36Z5caOHevzO6c+MJlMREREkJ2d7fVwoy4ps54pv8G8W4t+v3cejvaDSTy/k7/+/FcAPhzwIU1CmtDzip41WGtRHvkWG1e9vQaAg+/cgkEr86gEs9poV4OmnXPJPgMfX1X0+/N7ISo2sPUotLLx0AWeWbgbgM/uvxaDXs1NHZuiUhbfg53LO0fSxSTMdjOv/PIKUNTWXmG4gu7Nuge0TkLUZzXR7gWinatX376nTp0CoF27dj6DQwCZmZkUFhY9GYyJifFZxtXTwl8i5csZDEU31+UdD+jqYTNjxgx3wMFoNNKqVSvsdrtHWdd7aty4cZnBodIkJyezbNkyr+Xl3Wd53uOlS5fcUfAOHTpUvJLlPO7atWs5efKkV9kXXngBgI8++og77riD1atXs3r1aqKioujVqxfDhw9n7NixNGrUKGCfq686unr5+Pv7gqK/sTNnznD69Gn330BZ5aH8f5MAOp3OPZzuciFKmaFHiECJiYnBaDSW2i3YaDSWeo6LGhJR3JMSdQhKfQSNdI08ijTWN67hSgkhLiftagCUfPhXDQ8CjXoNGrXnYJOmYTqP4BBAVEiUz+2bhzb3uVyIhqq+tHv1aoiZq7OTv+BQeVV1+7JMmjSJjz/+mPDwcGbOnEl6erq7142rB5JLoN7TxIkTcTqdXj/z5s2r0n5LKtlbpTqlpKT4fC+u2doGDBhAUlISkyZNYuDAgVitVtasWcOLL77INddcw6lTpwL2uZamtH37WlfR8lWhVmkDuj8hGjKlUsnw4cNLLTN8+HCUynr1lRqcQiKLf9eGARChi/Ao0kjvGTASQtQ8aVcDQF3iWk9TPQ8Gw/SefQkiDd7T1muUGkLUIV7Ljdq620tLiNpQX9q9etXquoYMHT/uOxkaFPUY0euLGklfvVAA9zjTVq1aBbiGRVxBma+++oqnnnrKI+fM5VzvKSMjo8ykVbWtUaNG7kDG5bOw1bRWrVrx9ttv8/PPP3Pp0iU2bNhA586dOXXqFNOnT6/Wz9X1d+NvuBh4/o1VtHwgaJTeX+BCiMqLi4sjPj7eq7uu0WgkPj7e55BbUQuUquLf/7xhKjnEVqlQyk2LEHWEtKsBVLLtC6BQnWeAKEzne/BJmCbMszoKpc+gkRANXX1o9+rVELPrrruOkJAQLl68yP/+9z9GjRrlVUalUtG/f3/Wr1/P0qVLefHFFz3WO51O91CsAQMGVLouGk3RDfjlQ8YAd/6c8vwHR0dHExsbS0pKCnPmzPGqr6vOrsCMWq3GZrP5PG51CwkJoXPnziQnJ/Pzzz/z4IMP1ngdHA4HNpsNrbb4qYlKpWLQoEE8//zzPPPMMxw7dqzCn2tFDBgwAKVSydatWzl37hxXXHGFx/rff/+dM2fOEB4ezrXXXovT6SQ8PJzU1FR2797Ntdde61E+PT2dbdu2oVQqufHGGytcH1/U1XShIERDFhcXR+fOnUlNTSUnJ4fw8HBiYmJq/UmP8EP9Z4BIXRwgMqgM1d6LWAhRftKu1m1hl+UCC9H6vr4M03oGiPRKvbS1QvhR19u9ulGLcgoJCeGpp54C4MUXXyQpKclj/dq1a4mPj+fll18GioZ6bdmyxb3ebrfzxhtvcODAAdq0acPdd99d6bq4Ekdt2rSJ3Nxcj2O4prpfuHChxzbLli3jjTfe8NrXSy+9BMC7777Lb7/95rFux44d3HzzzV7HXb26eKaWmgwW/eUvfwHg/fffJzMz0708MzOTJ598stqPbzKZ6NmzJ8uWLfNIKG232/n5558B6NKlC1Cxz7UioqOjuffee7FYLDz44IMesxOkpaUxfvx4AJ599ll0Oh16vZ6nn34agIcffpj09HR3+ezsbB566CFsNhv33XdfwHoQqRQSIBKiOiiVSmJjY7n66quJjY2tM1/mwgdV0Y2NWll8g6NXS342IeoaaVfrrjB98fWkSqlAp/Z9fRmuDfd4LW2tEKWry+1evepBBEVTl+/cuZNffvmFq6++ml69ehEZGcmhQ4c4ceIE11xzDcOHD2fChAm899579OvXj549e9K0aVP279/P6dOniYqKYsmSJR69UCrqtttuIyoqiqNHj9K5c2fat2/P8ePHWbFiBR9++CGDBw9m0qRJLFmyhJiYGI4ePcrRo0d9ZiR/9tln2bx5M4sWLeLGG2+ke/fuNG/enJSUFJKSkoiIKM6f8NBDD/H222/z7LPPMnfuXOx2O+3bt+f777+v9HupiJdeeoklS5awb98+2rVrR58+fSgoKGDHjh2oVMVfGq4eVtXhwIED3HXXXTRq1IiuXbui0+k4cOAAZ8+epV27drzyStEMChX5XCvqs88+4+DBg6xbt46YmBiuv/56rFYr27Zto6CggCFDhjBx4kR3+UmTJrFjxw42bNjg/tzUajXbt2/HZDJxzTXX8Omnn1b1o3GTHkRCiAbPRy42reRnE0KIcis5m6BW4/8G9vIhZhIgEqL+qjuhqnLS6/WsX7+emTNn0rt3b/dNOsBjjz3G7NmzgaJA0vLlyxk6dCjHjh1j/fr1qFQqnnzySXbv3k2vXr2qVI/IyEh+/fVXRowYQWFhITt37qRZs2ZERkYycOBAfv75Z3r16sWRI0fYuHEjbdu25ccff+Tvf/+7174UCgULFy7k22+/ZdCgQaSkpLBmzRpMJhNjxozxCP5MmDCBadOm0b59e/bu3Ut6enqNjlU0GAwkJiby3HPPERYWxoYNGzhy5Aj33Xcfe/bscb+fqs4c5k9kZCSJiYk88sgjNG7cmO3bt7Nx40aMRiOTJk1iz549NGnSxF2P8n6uFdW4cWM2b97MO++8Q0xMDBs3bmTHjh106dKFGTNmsHr1ao8ApE6nIyEhgU8++YQuXbqwY8cOfvvtN9q0aePu4RQV5XsWiMpQKutd7FcIIQJL4d0OSoBICCHKr+SMZXqN/4ePWpXWo/e6BIiEqL8UTtd0T0JU0fHjx2nfvj1XXnml1/A/Uf1MJhMRERH8cSKd2FZGtp/dTr+W/dCoJGG1EKJ0rvYjOzvbK3FiXVKueuacg7RdENPXPavZ0UtHOZ17ms6NOtMirEXNVVhUSEpGHgadimbhcnMpAi+o2jmXcwcBJ1zRpdrqs/loBvkWO80j9HRt5b/3/Y6zO8iz5gHQtUlXmoQ0qbY6CSF8C0Q7V+96EInat337do/8PwCFhYW8/vrrAIwZM6Y2qiX+pFQq0Cg1KFB45N4QQogGwRUUL9H+uWYyk4B53aZUKFBJYlshyk+p9mjrqoNGXXS7qFOXfttYsodmyckBhBD1i9w9igq76aabUCqVdO3alWbNmlFQUMCuXbvIysqib9++7hxAonaoFEWBIbVSLTNICCEaHrWu6N8SN02u6ZY1SgkQ1WVKpeeQFiFEGWog56RG5QoQlX4snaqo7ZUp7oWo3yRAJCps3LhxbN26lWPHjrF7924MBgNxcXHEx8fzzDPPoNPparuKDZpSUZR/ScZ/CyEaJNeNiY/ZyyQHUd2mVChQSoBIiPJTqkBRvQNCNKqic1JXSpJqKA4QhWpC5QGlEPWYBIhEhbkSgYu6yXVxrVdJgEgI0QAplUUzmJV4sq5X6VGgcN/AiLpJpVSglgCREOWnVEM194x0DS3Tl9GDyNVrKFQTWq31EUJUL8lBJESQcT21kR5EQogGS2eEEk+wFQoFBo0BZTU/aRdVo1QoZIiZEBWhVBfnXasmrqFlBl3pASKjtighruQfEqJ+kyslIYKM6+Jaxn8LIRosXbjXItfNi6i7NCoFGqVcmgpRbkpNtQeIIgwaQrQqdy4ifwwaAxG6CLn+FKKek29hIYKMqytwuNb7BsnpcNR0dQLK6XTWdhWEEPVBqPf0yo31jQFpR+oyvUZVKzmI5G9C1FsaPVRzj3GjXkPrRuUL+rQKa0WYNqxiB5DzL6CkPRNVJQEiIYKMXlPUBdgrQJR7nj/2zedifkYt1Krq8i02thzLZMuxTMw2e21XRwhRl10eICrMpkleJgD7MvaRbc6uhUqJsri+v2pSdr6VPaeyavy4QgSENrTaexABtGlcvrxCzQzNyt2D6Oiloxw6tweObYC8+nltWtfkmW38cvgCpkJrbVdF1GMSIBKigbBeOkFm4UWOnNuNw1m/ehKZbXZ2p2aRb7GTZ7axOzWLQqsEiYQQ5WC3wrk/IO8CdoedrMIsDl86LE9ZBQCns/K5lG/B4ZC/ByFqis1hIy33DOmnNmMyZ8OllNquUlA4diEXm93JyYz82q6KqMckQCREQ+B0cjH7FE6gIP8C5/PP13aNys3pdHLgTDYFluKAUG6hjS3HM0nPLqjFmgkh6oWcs1CYDeZccs3ZOHGSZ83jkvlSbddM1AE5hTYcDiiQhw5C1JiMggwcuefAVsBpcybkZxYF80WlFVrtXMgxA3AhtxCLrX49DBZ1hwSIhGgIzDlkWUxFv1tyySioP115T18q4FKe90WD3e7kYJqJrHxLLdRKCFFvmP9s+3CSl1ccHM8yZ9VKdUTd4XQ6ybfYAAkQCVGT0vPSIe8CABmWHBwOmwwzq6L07EJ3OieHA86ZCmu3QqLekgCREA2B2USO/c/eNuZcsgou1ovhFTa7g+MZeX7XO51w6GxODdZICFHvWIq72ueVCI7nWfy3LaJhKLQ6cM3dULKXqhCi+uRZ88jOPQu2ogCGAydZtnzIqz+92+sap9NJWpZnr/ozWdLLXlSOBIiEaACchSby7WbXK2wFmeRZ6/7N0cmL+VjL6CKbU2jjfI48JRFC+FGircsruFj8u63ut4GieuWYi3unyuQHQtSM0zmnodDksawoQHRBZjSrpMw8i1eQO7fQJr3sRaVIgEiIBqAgPxMHJb50C7IwWUz+N6gDrHYHqRfLl2TvxAW50RNC+OBwgLU4gJxbWJx3yGwzY3dIUKAhyym0uX8vtEq+DiGqm91hL8qDafa8Bs225RflICqQ3HCVccrP9fIxuT4WlSABIiEagLzCTM8F5pw6n3/j1MV87PbyPUnKKbSRmWsuu6AQomGx5sOfwfE8eyG2EsPKnDjJt8lMLw1ZVn5xDyKZGVOI6pdZmIndbgGzZ3qAHFsBdqejaFIBUSEFFjuZub57Cl3Ks3gNPROiLOraroAQoprZbeSZTVgt+bx0YjEAH7WNJyv/AjSu5br5YXc4OXXJ+wvNbLXzzMLdAHx2/7XoNCr3upTMPBqH6WqsjkKIesCcU5TnYsk4QgHr4Nd4acNzAHw44ENyLbmEa8Nrt46iVjidTi7kFPLkN7sA+PKhnrVcIyGCX0ZBBhRmY3ZYeOXItwBsSzmFwekkY+SnNFHpoEknUMktanmllZjR19d1cvJZE6FaNREGTW1VUdQz0oNIiGBnySHP7p2jx1KYTb61bj49T88uKDP30OUu5Vm5mCdjrYUQJZgvH0rr2a7U9Z6UovrkW+wevVStNgcOh+Q/EaK6OJ1OLhZeLJrS3ocMqwkcVrh0ooZrVr+dzS49D6fDAfvPZGOX9k2UkwSI6rGJEyeiUCh49NFHa7sqFTJw4EAUCgXjxo1zL9uzZw8TJ05kwYIFVd5/YmIiCoWCDh06VGg7hUKBQqHg9OnTVa5DnVJowuQjQIQ1r87eHJU399DlDp/LqRezswkhakhBVqmrLxbWjxkdReCVzD8ERblx8yw2P6WFEFV1yXwJmznHR+C+SIYlh3y7BS4e95h9Uvh3yUdyal8KrfZKX1uLhkcCRKLG9e/fn5EjR9KjRw/3sj179jBp0qSABIiEp8K885gdVu8V5ryiJzl1zPmcQvLNlcsFkVto4+j53ADXSAhRLzkcUJhdahGrw1rnE/aL6pFr9g4GmQolQCREdTmffx5yz/ld78DJgdxUrHYLnE+qwZrVX6culT/oc+pivvSSFOUiAzxFjZs8eXJtV6FBycrx0yPKbOJi7lmsUZ3RKOvGuGSn08mx81WbceFkZj4GnZpWkSEBqpUQtcvhcJCamkpOTg7h4eHExMSgVMrznTIVZoGz7GDzhfwLROgiqr8+ok7J8xEgupBjlu+OOkbav+BgsVs4n3MG8kt/MJnvsHAoP52uSjXknIPwK2qohvVPdoGV86byT9BisTk4k1VAdJShGmslqlNNtYcSIBIimFnyyci/4GelE8elE5xq1I52Ee1qtFr+HLuQ5/OivaKS003o1UpJWi3qvaSkJBISEjCZinu5GI1Ghg8fTlxcXC3WrB7I89f2eTpfcJ72ke1RKBTVXCFRl/jqQXQxz4zN7kCtkgBEXSDtX/BINaXiyDuPa1bJ0mRYc8iw5NDkQjKENgUJCHpxOJwkpVe89+uxC7k0DdehLzHJi6gfarI9lDMuSCQlJfHQQw9xxRVXoNPp6NatG4sWLfIqd+jQIcaNG0fr1q3RarVcccUV3H333Wzbts2rbF5eHtOmTePaa68lIiICg8FA165defrpp9m7d6+7nCt3z/Hjx/nnP/9J9+7dCQkJoXHjxjzwwAOcOnXKY7/jxo1DoVC4exINHDiQhx9+GICffvrJvT+FQsG8efMAeO+99xgwYACxsbEYDAb0ej2xsbGMHTuW5OTkQH2MbidPnkSlUqHRaDh71veUm3fffTcKhYJPPvnEvezMmTM8++yztGvXDp1OR+PGjRkxYgQJCQle27tyMX3zzTcsW7aMoUOHYjQaCQ0NZejQoezatavK78NqOs1FaylDrswmTmckkW0ufRhGdbM7nBw+l0NKRtV6D7k4nbDvTDamQh9D64SoJ5KSkli8eLHHxQCAyWRi8eLFJCVJF3y/HA4wnSlXUYvdwrl8/8MeRPAptNp95u1wOCBTJjuoE6T9Cx45lhzSso4X9Qgqp8P56ZjN2ZCVUn0Vq8cOppvIrcSQWJvdyZ5TWVjtFZsIRtSumm4PJUAUBDZs2ED37t1ZsWIF3bt3p1u3buzfv5/777+fxYsXu8utXr2aHj16MH/+fAwGAzfffDNXXHEF//3vf+nXrx9fffWVu2xubi7XX389r7/+OidPnqR379706dOHM2fOMGvWLKZOnepVj379+vHSSy+hUqkYOHAgKpWKhQsX0q9fP86fP++3/v379+faa68FoGnTpowcOdL9ExMTA8B3333Hpk2bMBqNDBo0iP79+5OTk8OCBQvo3bs3f/zxR6A+TgDatGnDzTffjM1m85kXKSMjgx9//JGQkBDGjh0LwM6dO+nevTufffYZNpuNIUOG0K5dO9asWcOIESOYNGmSz2O9+eab3HXXXRw7doz+/fvTokULfvrpJwYMGMDx48cr/yYcDo6l78BRxtMaR/ZpDlzYT66ldnL3nDMVsuVYJqmZgU2eZ7c72XXyksxsJuolh8PhM7BcUkJCAg6HXOR5cTjgQhLYzOTZCzmUl+67nL344vpo1lHyrIEJUIu6L72UWX9SMvIkT0ctk/YveORactl/aiOOC8nlGvLrYnHa2JeTiuV8EmSfLnryJ7A7nPyRll3mzGWlyS20se+0zGpWX9RGeygBoiBw6tQp/va3v3HmzBnWrFnDjh07ePfdd4HifD/nzp3j/vvvJz8/ny+++ILDhw+zcuVK9u3bx7Jly1AqlTz55JMcPHgQgM8//5w//viDPn36cPr0adatW8eGDRvIyMhg8eLFtG/f3qse7du3Z9++fezcuZPVq1dz/PhxbrjhBk6dOsXEiRP91n/y5Mk8//zzAHTr1o2lS5e6fwYPHgzABx98wLlz59i3bx8rV65k/fr1nDt3jttvv52cnBzef//9QH6kADz++OMAzJ0712vd119/jdVqZfTo0URGRmI2m7n77rvJyMjg7bffJiUlhVWrVrFjxw42b95MZGQkEydOZN26dV77ys/P5/vvv+f48eOsWrWKQ4cOccstt5Cbm8s///lPv/Uzm82YTCaPH5ccSw4HDi/jbJ7v3k8eLLlYz//BzrM72J6+nZOmk2VvU0WFVjtHz+ew+WgG+09nU2itXFLqstj+DBLtPZVFjvQmEvVIamqq15Oiy5lMJlJTU2uoRnWU0wl2a1Fei+wzcD6Z/MOrSD6zlb05J/nddJxzFj89JDOPga0of4PNYWPXuV2cNJ3E7qie9kjUDfkWGycy/D8QySm0sf9M7faqbeik/av/LuRf4I9zu9h5cBGWC0lF09dXUJ7DzO/ZRzl+Yj2n/lhcZv6iYJeWVcCmoxmkZ1U+OORyKc/CthOZnM8pJM9sk9k867DaaA8lQBQEHnzwQd5++21CQkI8lgEcOHCA/Px8vvzyS7Kzs/m///s/HnvsMY/t77zzTp544gmsViv/+te/ADh69CgAN910EwZDcTIzlUrFvffey5QpU7zqsWjRIrp27ep+HRYWxltvvQUU9QCqSmRzxIgRNGnSxGOZWq3mqaeeAmDfvn2V3rc/d955J82bNyc5OZnNmzd7rHMFjZ588kkAlixZwsmTJ+nZsyeTJk3ySBjWp08f3nzzTQCmT5/udZwPP/yQ+Ph4d/4LlUrF/fffD8D27dv91m/q1KlERES4f6Kjo93rwrXhdIzuj0FV/Dexbs1O3ztSqiAyhmVf/Ehc4zhiwmO8iuzatYuVK1f6rcv06dPJz/ffA+jyxOR6jYoOzcLp0jKClAM7OLDTe4ijy7ezP/a7Li/HxNJv/+13/W8/rSYn/RhxLYyE670TcZeWMD01NbXUWfUWLFhQamNc2r7379/P0qVL/a6fMWMG2dn+b1BK2/emTZtITEys1Lb5+fk+/0ZdVq5cWerQx9L2nZaWxpw5c/yuX7hwIceOHavUvpOTk1myZInf9bNnzyYjI6NS+962bRtr1671u37atGlYrb4vfK1WK9OmTfO77dq1a30O7wXIycnxu115yi1ZsqTU4bdBM1mAQgEqDRiiIKIVc5ZtJEvRiisbdSIutBXR+iaoFcW5FpYs+qV4W2NLUBfnKTMdN7H/1/2olL5zM1S0nSspMTGRTZs2VWrb7OxsZsyY4Xf90qVL2b9/f6X23RDbOafVzIpvv/S7fsfG9eSeOVypfUs75620ds5fvcrb/q1fv75htHOXmTNnDmlpaX7Xl/a+A309V1LJdq6poSmdml5NdOv+ENIIgK/nrve7LcDy/23xeK1EQbuQZrRr3IWdB8zsP+pn0pUy6hUs7Vxzo5645uHu/EGlXR8D/LeU6+MdG9djOXuMZuF6QnVqrxx80s55qo52ziUjI4PZs2f7Xb9+fennjUt5283yUDglZFhvTZw4kUmTJjF+/Hj+/W/PRiA/P5/Q0FCgKC/OQw89xE8//cTcuXMZN26c177Wr1/PzTffzJVXXklSUhIzZszg+eefp1mzZsyaNYvbbrsNnc53wl9Xo3Lq1Clat27tsc5ms6HT6XA4HBw5coQOHTowbtw45s+fz7vvvusOnMybN4+HH36YIUOG+D0RsrOzWbNmDbt37yY9PZ2srCxSU1PZvXs37du3dwe1EhMTGTRokMeyf/3rX2zYsMFrn6NHj2b06NF+38eECROYOnWqx2e8Y8cOevfuTY8ePdi5syjo8uijjzJnzhz+/ve/++wtdfToUTp27IjBYCAnJwelUsnAgQP55Zdf+PrrrxkzZoxH+VWrVnHbbbfRsWNHDh/2faFqNpsxm4tnLzCZTERHR5OdnY3RaCz6zE5vZ/fpjQBYLfm8dKJoyOFHbePRaP8M/EVEE9uyN7ERsT6PUxMycs0cOpvjMydESWarnWcW7gbgs/uvRVdGkr12TUNp1zQsYPUUoqakpKQwf/78MsuNHTuW2NjYKh/PZDIRERHh0X7UReWupyUPUjaB04Hdmo/qh0cAWDf4FXc7+OGAD9Gpir7XWoa1pFOjTtVef1E3ZBdY2XHios/vlPbNwmjbJLSWa9iwVVf7F3TtXD1xKucUx85sg6ziHupmh5VXjnwLwLaUUxicTn4d+joOtRYABdAlNJomjdpD6561Ue06q9BqZ9fJS+T7uGYuz3WyUgk9YhoRadBWe11F1VW0PQxE+yGzmAWpkr1+HA4HZ84UJet05fS5nKv3yenTRdH58ePHs3DhQrZs2cLdd9+NRqPh6quv5sYbb2Ts2LHunEFlUavVNG7cmAsXLnDxYuW7hk6bNo133nnH/VQjNDSUVq1aodWWr3HbtWsXy5Yt81revXv3Urd79NFHef/991m8eDGffPIJoaGh7lxNrt5DQLk/3/z8fC5dukTjxo1LPa7r/6+0Xlc6nc5v0M4lIqoDYek7yLX76Y6q1BAZ1aFWg0MATcJ0RLbVsCs1C1NBYIaCxTQ2SHBI1FsxMTEYjcZSuxUbjUa/bU6Dpw2FsCsgJx2VovTO0kqFkrbGtjVUMVEXRIRoUKsUmH183VxhlNkva5u0f8ElOjyazKhMsgqzoTCrfNvoG9NEFwFXXFW9lauH9BoV3aIj2XHiYqXyCHVtGSHBoXqkNtpDGWLWwPibxvfy5QaDgV9++YVvv/2We+65hxYtWrBr1y4++eQTevXq5ZHQuiyuXi56vb5Sdf7qq694/fXXcTqdTJkyhZSUFHJzczl06FCp3e5LmjdvHk6n0+untNxIAO3atWPIkCHk5OSwZMkSCgsLWbRoEUajkQceeMCrfHk/3xoT0oimukb+10fG0KGOPDVXq5R0ax2BSlX1z6ppuI6OzSQ4JOovpVLJ8OHDSy0zfPhwj+Gs4jJhV5SrWGN9YzQq7yGoIriF672fkYbq1Bi08uy0tkn7F3zaRrSF8OblKqtTamijbwqNYkETUmb5hihMp6ZDJa5zW0TqaWas3P2YqB210R5Ky9pAtGrVCijqpuaLa+ytqxyARqPhgQcecOfXOXHiBOPHj8dut/Pyyy+XK6HZpUuXMJlMKJXKSg+DKDnV/YQJE2jTpk2l9lNZrmTVX331Ff/5z3/IysriwQcfdA/hg/J/vgaDgUaNSgnYBJpCQaPw1r7XqfWEGVsTpq07gRS9RkWbKEPZBUsRrlfTtVVE7QXlhAiQuLg44uPjvboIG41G4uPjiYuLq6Wa1ROG0ntqujQJaVJ2IRF0QnXegaDmEXLjVFdI+xdcInQRRIS3hDKuOZUoiDO0RKUxQJT3hDiiWOtGIRh0padaKEmhgPbSs75equn2UB6TNBCDBg3ip59+YunSpTz88MNe610J1gYMGABAYWGhV4+f2NhY/vWvfzFnzhyysrK4ePFimUOlvvnmGwB69+5d6jhIjabo6a3d7j2e9vz58wC1djFw11130axZMzZu3OhOjFZyeBkUfb5z5sxh2bJlTJw40Ss44fp8+/fvX+NPvMLDWqA6p8SrJ73OSOOQ8t1A1aToKAMnL+Zjt1e826xKpeCa6EhUSgkOieAQFxdH586dSU1NJScnh/DwcGJiYuTJeXmotUVDzWz+Z3xRKpREhUTVYKVEXRHmI0AUGSI9yeoSaf+CS3NDc7LDmsFF/7MIdjK0IFITCs2uApXcppZGoVDQpnEoSWmlz3DlcoVR705wLeqfmmwPpYVtIB599FEiIiJYvnw5M2fO9Fi3dOlSPv/8czQajXu6+UcffZR33nmHCxcueJR1TdPeokULr54whw4d8ni9d+9ed9b2119/vdT6uXoX7d69m7Nni6dmt9vt7pnRFi1a5JGTZ9OmTe7ePdVJo9EwduxYAJKSkujfv7/HbG0A99xzD23atGHv3r387W9/8wh0bd68mXfffReAl156qdrrezlFSCThah9PRbWhROnr3o2RRqWkVWTluhS3axIqX34i6Lh6YF599dXExsbKzVFFhJTeY9OoNaJRSlCgIfIVIArzMexM1C5p/4JHk5AmKEIagcp3/hujKoTmukgIbQrh5Rsi3NBdEa4r10NRhQJJvh8Eaqo9lFa2gbjiiiv45ptv0Ol0PPPMM3Ts2JHbbruNbt26MWrUKBwOB5999hldunQBIDc3l7///e+0aNGCa6+9lhEjRtCtWzfuuusulEoln3zyidcf5dChQ+nUqRPDhw+nb9++9OzZk/Pnz/Piiy8ycuTIUuvXp08fOnbsSHZ2Nl27dmXAgAG0b9+eFStWMGXKFEJCQpg3bx4dO3ZkxIgR7oTZ/qYjDLTHHnvM3Svoqaee8lqv0+lYvHgxkZGRTJ06ldjYWG699VZ69epF//79MZlMvPnmm9xyyy01Ul/PyoUTpvIOECm1YYRrw2u+PuUQ3chARUeI6TRKohtVbXiaECLI6CMuW+DZsDTS1+CQX1GnhOs1Ht8zapUCjUoui4WoLhqVBqMuAkJ9D+u9QhcBKKBp55qtWD2mVilpGl52Yv12TcN8DqsVwhf5S2lAbr/9dn7//XemTp3Kzz//zLp164iMjGTUqFG8+uqr9O3b1132s88+o2fPnqxcuZKkpCT2799P48aNiY+P58033+Tqq6/22v97773HihUr2L59O4WFhfTs2ZO//vWvPpM5X06lUvHTTz/xyiuvkJiYyI4dO2jbti3Nmzenc+fObN26lRdffJHffvuNtLQ0evTowYIFC2jdujWDBw8O6OfkS0REBBqNBqPRyD333OOzTO/evdmzZw9Tp05l9erVrF+/nrCwMIYNG8bzzz/PrbfeWu319EmjJ0wThkZrYEbncUXLFCqMoc1QljG7T20J0apoFq7nnMlzaIhOo+LfD13nc5uYKANKGVomhChJFw5qPdy/iCxrHpqCs8wYXDy5gVFb/6eQFpWjUiqICtO5v1Ok95AQ1a+JvgnZIVHoTGnua9Ltf8aD+oQ0Lgoe6ermw8u6qmVkCGezi66XfV0nN4/QS+8hUSEKZ3kyDQtRClfPmlOnTtG6tZ+EyPXc22+/zbvvvstrr73GtGnTars6PplMJiIiIsjOzvbK92Q6+hO7MvYUL9AZiW13c61Pb1+anEIr245fLFdZjVpJ/w5NJPeQEJVUWvtRl1S4nnYrHF1f9KvTwabCdJyNO7hX39DyBrR+hjuI4JeUbuLMpQKgaPbLa6Ija7dColoFbTtXjxTYCtiWvg3O/eGRHy5EqeX6iA7Q4howtqzFGtZPm49mkG/xzuOq0yjp264xaukd2WAEov2QvxYhylBQUMCsWbNQKBQ88cQTtV2dSjFcPpuPzkiE7vKhF3VLuF5Trm6zUNR7SIJDQggvKk3RD6BSKDHoIt2rNEqNBIcauIgSSaklf50Q1S9EHUKkLhIuuwaNVBtAoSzKPyQqrLWfFAttm4RKcEhUmPzFCFGG+fPnk5GRwbBhw2jXrl1tV6dS1PoI9CUSsSoMjeps/qGS2jUNLTMXkVqloHWjyiW1FkI0AJrirvVhJZJWGzSSs6yhKzmsTK+RS2IhakLr8NZe+eEi1IaiSQVUMmlAZbSI1Hs9KNWolbSMkOtjUXHybShEKZxOJx9//DHgPbV9vaKPINyVqFqtJyykMWpl3c+3EK7X0DzCxwxsJbRrEiaJRYUQ/mmLA0GhIcXJUUM1kpOhoQvVlgwQSQ8iIWpCY31jtCFRoCg+5xppQqX3UBVoVEqaGT173beI0EtuTlEpdf8OUdR5wZzGSqFQcOjQodquRtXpjBjVoVyw5oA2jMb6xmVvU0d0aBZGRq4Fq83hta5xmJboKHk6IoQohTbM/ashpAlYLgGSoFoUJaoO0aoosNglQCREDVEoFDQPb0GqIQryLmBUhaBTaiCsWW1XrV5rFRlCelZxXqcWZTxgFcIfeewuREOgUtMorDkASr2R5qHNa7lC5adTq+ja0ugx1EyphM7Nw+keHelOki6EED6FNi3KbaHWEf7n9MoqhYrGIfUnUC6qj2vqZ4NWAkRC1JTmhuYQ1hwUKlrqGhUF8rXSq7MqIg1ad3sWplcTrpfheqJyJEAkRAMRFhlLiFJHdJOr0Kvr11OFxmE6rmxR9LRfoYBurSOJjjJIcEgIUTa9Ea7oCiGN0Kl06NV6YiNi0Sjl4llA41AtBq1KhioLUYMMGgORoc0IbdyJK7QRENGqtqsUFFzT2cdESY49UXkyxEyIIGN3FA35O5t3lqYhTVEp/3wq2iiWqzreRmhkm1qsXeW1igwhVKtCoVB4zDwjhGh4zHYzVoe1/EGe8BbupKhXNb6KcE3dT9IvakbrRiEY5TtF1Hf5F8EQValNMwoyCFGHEKoJpdBq52KehZaR1T98v1OjTtCoEwpzHlw+266olOYRRcmqyzsLsBC+yOMSIYJMrtkGwPHs45zPP1+8QqkivFFblIr6e9pHGrQSHBJCkFWYRY4lx/3a4SgjF55SCbqiXERGrVF6Hwo3eegg6r3MY3DpRKU3P5F9gsOXDgOQejGfQ2dzaiS/qEFjKJpNMqxpURstAkKCQ6Kq5GwUIshkF1iwOWxY7BYumi/WdnWEECLg8q35FNgKarsaQghR+y6dAGvl2kO7w06+NZ9sczaFtkIycszYHU73w0YhRMMjASIhgky+2Y7ZbgaKnrILIUSwsTqs7nYOIHjn0hRCiFLYLGC3grWw7LI+5Nvycf7Zgp7LzSDfYi9a/ue/QoiGRwJEQgQZh9NJoa3oQsHqsJJrya3lGgkhRGBZHVasdmttV0MIIWqXNa/oX4cVHI4Kb16yJ+bJ7LPFyyVAJESDJQEiIYKM3eH0eLJusphqsTZCCBF4dqcdm6N4CERN5MsQQog6p+TQshLXfuXleqAIcDY3E4u96HWBVQJEQjRUEiASIsjYHA6PJ0K5VulBJIQILlaHFatDehAJIRq4kgEiW8UDRCWvFwusNi5ZiiY3KZQAkRANlgSIhAgyeRaHxxMhSeQqhAg2DqfDI0Ak/YeEEA2SNb/49yoGiAqtDkzWoslNpAeREA2XBIiECDJWm4O8EhcMJYNFQggRDOwOu/QgEkKIksmpKzHELP/P60Wn04nZZsfsKMDqsFBotcvQXSEaKHVtV0AIEVivLtzCP55sxltbXwHgo4Ef4XQ6USgUtVwzIYQIjBcSX0AVomLbA9vAqeWqt9cAcPCdWzBo5dJGCNFAFFyChaOLfn/8V4iMKfemZrsZi8OC2W7mlV+Krhlzkt8B5z4+u/9aCqx2aU+FaICkB5GoVfPmzUOhUHgEL1JSUlAoFKjV8qVUWXlmi/t3h9Mhw8yEEEHJ4az4rD1CCBE0SvYSd81oVk4mc+mTmOQU2kpdL4QITnIHLmpVTEwMI0eOrO1qBB2z1fOmKc+ah0FjqKXaCCFE9bA5bWilc6QQoiGymcFZIleQuWKTklwyXyp1fVa+lSuM+srUTAhRj0mASNSqwYMHM3jw4NquRtCxXZay9WLhRZoamtZSbeonh8NBamoqOTk5hIeHExMTg1IpnS6FqEtsDhtale91cg4LIYKa5bIeQ9Z8sNtAVb7bu0uF/gNEFqeFzFwzEF6FCopAk+81URMkQCREELLYPANE5/PP0yGyAyqlnzsp4SEpKYmEhARMpuLu10ajkeHDhxMXF1eLNRNClGSxWzD4aNbkHBZCBL3LA0Q4wWwCQ1SZm+Zb893pB/It3kPJciyZhFtCyDXbCNPJ7WJdIN9roqZIyLEB+OGHHxg6dChRUVFotVratm3Ls88+y7lz59xlBg4ciEKh4JtvvuGrr76ie/fu6PV6YmJieOutt7BarVitVj744AOuvPJKDAYDV111FdOnT/c63nvvvceAAQOIjY3FYDCg1+uJjY1l7NixJCcne5RNTExEoVDQoUOHgLzX7Oxs3nrrLbp06UJISAhhYWH07t2bL774wmM2hpJ5jkwmE5MmTSIuLg69Xk+LFi147rnnyMvz/OIdN24cCoWCyZMns2PHDkaNGkVERATXXXedu8ylS5eYMGECcXFxhISEEBkZycCBA/nuu++86lpyf7/++isjR44kKiqKkJAQ+vTpw/r16yv9OVz+ZW932svsSiyKJCUlsXjxYo8vYACTycTixYtJSkqqpZoJIS5n9jFrj5zDQogGweJjSFlB+a71zuaddf9uyvcOEGVZLgBwziQz4dYF8r0mapIEiIKY0+nkkUce4d5772XLli107NiRoUOHYrPZ+Oyzz+jZsydpaWke27zxxhuMHz8ehULBjTfeyIULF5g8eTLPPvssgwYN4vXXX6dJkyb06tWLw4cP88orr/Dpp5967OO7775j06ZNGI1GBg0aRP/+/cnJyWHBggX07t2bP/74o1re74kTJ+jRoweTJ08mKyuLfv36cd1113HgwAGeeOIJxo8f77WNw+Gga9euvPPOO0RERHDTTTeRm5vLp59+Snx8vM/jzJs3jz59+vD777/Tv39/rr/+eqAo6NSzZ0+mTp1KZmYmAwYMoEuXLmzevJm//OUvPPLIIz739+WXXzJgwAC2b99O79696dChA9u2bWPEiBFs2bKlUp+Fze49NemF/AuV2ldD4nA4SEhIKLVMQkICDockxhWiLrg8yaqcw0KIBqMw23tZ9mkoY3p6i93CmdwzADgcTi4VWLzKmB2FmKwXScsqwGaX9rI2yfeaqGkSIApin3zyCXPnzuWOO+4gJSWFbdu2sWrVKo4fP85zzz3HmTNnmDBhgsc2DoeDn376id27d7Nu3TpWrlwJwBdffEFBQQEHDhxg06ZN/PLLL8yaNQuAf/7znx77+OCDDzh37hz79u1j5cqVrF+/nnPnznH77beTk5PD+++/H/D36nA4uPvuu0lJSWH27NmcOnWK9evXk5iYSHJyMp07d2bu3LkkJiZ6bOd0OunUqRNJSUls3bqVtWvXsmfPHnQ6HatWrWLXrl1ex7pw4QILFiwgNTWVlStX8tlnnwFw//33c+LECR555BFOnTpFQkICv/32GwcOHCAmJoa5c+fy5Zdfeu0vMzOTmTNnurfZv38/jz76KDabjalTp/p9z2azGZPJ5PFTmgsFF8gqzCr7w2zAUlNTy/wcTSYTqampNVQjIURpLpovesxkdurUaTmHhRDBz5IHBVney635kHnMb5DoUuEl/sj8A/ufya3P5RRi9fFQESAt/zgXC7LYnXqJQqvdZxlR/eTaVNQ0CRAFKVdwwWg0smjRIpo2LU5QrNFo+OCDD1Cr1fzvf//Dbi9u9KdOneqRNHrw4MEYDEWzXy1evNhjjKtr9rFjx46Rk5PjXj5ixAiaNGniUR+1Ws1TTz0FwL59+wL4TousWLGC3bt388gjj/DEE094JGyLiYnh1VdfBYqG25WkUqlYu3YtnTp1ci9r3749ffv2BWD79u1ex3r55Zf5y1/+gkJRPHXOpk2b2Lp1Ky1atGDmzJnodDr3uk6dOvHRRx8B+ByS99prr/HUU0+hVheP8R4zZozf47tMnTqViIgI9090dLTfsgA/LvyRIyeP+F0/efJkv+t27drlDhb6Mn36dPLz8yu178TERDZt2lSpbbOzs5kxY4bf9UuXLmX//v3l3nfJv+PSuHrElfZlXFq99+/fz9KlS/2unzFjBtnZPp4MlmPfmzZt8gqElnfb/Px8n3+jLitXrvQZNC3PvtPS0pgzZ47f9QsXLuTYsWOV2ndycjJLlizxu3727NlkZGRUat/btm1j7dq1ftdPmzYNq9Xqc53VamXatGl+t127di3btm2rVL0yMjKYPXu23/VLlizxGtJb3n3XJzaHDYu9+PNfvWpVubbzda43lHaupNTUVBYsWOB3vbRzFdu3tHPepJ0LvDlffkHaiUOA78DO5Gkfgt27VxDAr9t+ZV3COvfry2e9LcnutJFjvURGfjZmW1E5aec81UQ7V5FrUxdp57xJO1d+CqezjH6Iol7avn07119/PY0aNeKmm27yWSYhIQGz2cy5c+eIj4/nl19+4euvv3YHJ1xatGjB2bNnOXHiBLGxse7lZrMZvb5o+svTp0/TqlUr97rs7GzWrFnD7t27SU9PJysri9TUVHbv3k379u05evQoUPRlMmjQII9lKSkptG3bFpVKhc1WNC560aJFLFq0yOs9DB48mOeff56nn36aWbNmcc0113jU0eX8+fNs2bKFESNGsGrVKp/HKCk+Pp4lS5YwZcoUdy+rcePGMX/+fN59913efPNNj/KTJ0/mrbfeYuzYscybN89rf4WFhYSFhWG320lPT6d58+al7u/gwYN06dIFjUaDxeL7S95sNmM2F+ffMJlMREdH0/GFBYwbqWPhyXcB+HDAh+hUOiJ1kXRv1t3nvkSRlJQU5s+fX2a5sWPH+vw7E6K+MplMREREkJ2djdForO3q+OWqZ9ysOFQhKubeMperorpz1dtrAFg9Po7F331d5n7kHBai4alv7VyZ9Tz+CxRchCXjil7fOw+0YdB2AKi1fjfLNmez+/xuACw2B/vTMph3tOiaMSf5HXBqeeZWK61CW9M8NJpesVGE6zUBeneiouTaVFREINo5SUsfpFJSUoCipMnLli0rtWxpT0QBj94w/paXjDNOmzaNd955x73f0NBQWrVqhVbr/8uqLMnJyT7fR2RkJFD8fvfu3cvevXv97qes9+ri6jVV3vG8Z84UjeWOiYnxuV6v19O0aVPOnj3LmTNnaN68eZWPr9Pp/P7fqHz0DWxmaFbqMUXR/5/RaCy1K6/RaPT7/yyEqFmRukiP19HRreUcFkI0DCGNigJEJRlblRocAojQRdDU0JQL+RfQqpUYfQR/VAo1TfQtucKol+BQLZNrU1HTZIhZkHIFFvr164fT6Sz1J5DR5q+++orXX38dp9PJlClTSElJITc3l0OHDpXadbQsEydO9Fl3V28d1/v98ssvS32vpXVJD4SSw84qsi7Q9GrPeZ8VKGgc0rjGjl9fKZVKhg8fXmqZ4cOHewxhFELUHr1a7/FazmEhRIOhC/deZijftV7L0Jbu3yMN3gGgCG0TlAoVzY16r3WiZsn3mqhp8pcUpFq2LGr4Dx8+TE2OInQFbN577z0mTJhAmzZtauS4rvd76NChGjne5VzD61w9mS5XWFjI+fPnPcpWJ73W89RupG+ETuW7t5HwFBcXR3x8vFe3TKPRSHx8vEceLiFE7dKqvJ+UyzkshGgQfAWI9BHl2jRSF4laWTSQJEznPaAkQtMYtUpBVGjle/+LwJHvNVGTZIhZkOrduzdhYWFcuHCBpUuXMmrUKJ/l7HY7KpXK57rKcAVBarqhGjx4MHPnzuW7775j4sSJhIaG+iwX6PfrMmjQIADWrFlDYWGhOzeTy4oVK7Db7XTq1KnM4WWBoLqst1LTkKZ+Sgpf4uLi6Ny5M6mpqeTk5BAeHk5MTIw8nRGijtEofQ99kHNYCBH0NCGer1Va0JSvx49CoaCRvhEX8i+gVHr3cA9RhdI4VFejvd9F6eR7TdQU+YsKUnq9nhdeeAGAxx57zCtru9ls5ttvv/UbOKqsrl27AkVJpUvmz9m0aROPP/54QI9VUnx8PO3atSMtLY1Ro0aRnp7usf7MmTO8+uqr7inpA61fv35cf/31nDt3jieeeILCwkL3uuTkZF588UWgaAa0mqC5LAlRuNbHUyZRKqVSSWxsLFdffTWxsbHyBSxEHaRW+H/OJeewECKoaQxAiQCOrmIJaRvpGpW+PlRyD9U18r0maoL0IApif//730lOTuaHH37glltuoWPHjnTo0IGcnBz27duHyWSiS5cuAT3mlClTWLVqFfPmzePXX3+lU6dOnD59mgMHDlRrZn2tVsuyZcsYMWIE69atIzY2lp49exIZGUlaWhr79u3D6XTywQcfVFsdvvvuOwYOHMiCBQtYvXo1PXv2JCcnh23btmGz2XjwwQerNUhWUrjO80s9RB3ip6QQQtRfKqXK30zPQggR3BQKKJk+QOu797w/EbrSh6MZQyRAJERDJAGiIKZWq1myZAn/+c9/mDt3Lr///jvr1q1Do9Fw1VVXMWzYMB588MGAHrNz585s3bqVF198kd9++420tDR69OjBggULaN26NYMHDw7o8Urq2rUre/fu5bPPPmP58uUcOHCAvLw8GjduzIgRI7jrrru47777qu347dq1Y9euXUybNo1ly5axYcMG9Ho9ffv25fHHH2fMmDHVduzLhWiLuxhrVdqimyghhBBCCBE8Sg4zU1csobRBbUCp8N8DJVQrt4lCNEQKZ01mMBZCVBuTyURERATLdxwhpk0elwovAUVPiK5tdm0t104IUZe52o/s7GyvJJh1iaue65LXYTQa6d2iNwBWu8NraK0QQpRU39q5ctUzbQ/k/JlWoUV3MLao0LF+P/s7udZcAJLSTdjtSq6M6IVeo6J/xyYVr7wQolYFop2TqykhgkyYTu0xpEyGlwkhgo1KqUKjKh7+IGlUhRANksZQ/Lu64rPVGkpsr9eoCNcU5SUK0cotohANlfQdFCLIqJUKQlTFQSGD2lBKaSGEqH/USnWpCaqFEKJB8BhiVvEAkb7EsDS9WolBVTTrrU4tqQmEaKjk6kqIIKNWKj2+8I3autuNWgghKkOluKwHkUzFLIRoiEr2IFJVogdRiYeIUQYjKnNR4uoQrQSIhGiopP+gEEFGqQTdn0+RlAolxgpOeyqEEHWdWqlGo5QZdoQQDZyrB5FCBaqKP/cvmYagTUTz4uUaCRAJ0VBJgEiIIKNVK91DzIxaY6kzVAghRH2kUWjQqrTu19J/SAjRIGlCQKGs1PAy8OxB1NLYFI266JrRID2IhGiw5M5RiCDTKESLRqVBpVDRSN+otqsjhBABp1Pr0KsqNqWzEEIEHYUCQpt4DjWrAI2qKNiuU+kwao1EGbQoFEUTngghGiY5+4UIMqH6otO6maEZzQzNAHA6nZKjQwgRNIw6I2HaMPdrad6EEA1Wi+6QfbrSmzcPbe7uSRQdFYJCAWqV9CEQoqGSAJEQQUalUGC1O+gc1dm97JL5ElH6qFqslRBCBI5aofbInSEBcCFEg6VUgS680pu3i2jn/j3HkkPXVo0DUSshRD0l4WEhgozd6cRqd7hfO51O8qx5tVgjIYQILLPdXNtVEEKIusMSmOu887nZAdmPEKL+kgCREEHG4QC7w+l+bXPasDlstVgjIYQILGnThBCiBLslILvJNssDRSEaOgkQCRFkHE4nJeJD2B127A577VVICCECTNo0IYQoIUABolxLfkD2I4SovyRAJESQcTidOJ3OEq8d2J1yMyWECB42p/QgEkIItwD1qsyzFgRkP0KI+ksCREIEGYcTzx5ETrsEiIQQQUV6EAkhRAl2a0B2ky8BIiEaPAkQCRFsnEW9iFwcTgcOp6OUDYQQon6RoLcQQpQQgB5ETqcTi90qAXghGjgJEAkRZIpyEBUHiOxOuyR0FUIEFQl6CyFECQHoQWR32rE7nFhscs0oREMmASIhgszN/1hN7yk/kW+xkW/NZ8D3A3hw9YPkWyXxoBAiOIxZPYb8/AyPZfkWG7GvryT29ZXkW+QGRwjRAFjyYGIEzLu1ylPdn8/L5vNDb9J74XVk5ucEqIJCiPpGAkRCCCGEEEII0YDZ7BJYF0JIgEg0cPPmzUOhUKBQKNzLUlJSUCgUqNXqWqyZEEIIIYQQNcNiC0yiayFE/SZ3wKJBi4mJYeTIkbVdDSGEEEIIIWqNWQJEQggkQCQauMGDBzN48ODarkaNkFkpKs7hcJCamkpOTg7h4eHExMSgVErHSyHqOofDQUpKipy7QoiGw1G15P2FEiCqt+R6VQSSBIiEaCCsTvnir4ikpCQSEhIwmUzuZUajkeHDhxMXF1eLNRNClGXmzJkU5GS7X8u5K4QIeo6qXedZHJYAVUTUJLleFYEmoUVR7X744QeGDh1KVFQUWq2Wtm3b8uyzz3Lu3Dl3mYEDB6JQKPjmm2/46quv6N69O3q9npiYGN566y2sVitWq5UPPviAK6+8EoPBwFVXXcX06dO9jvfee+8xYMAAYmNjMRgM6PV6YmNjGTt2LMnJyR5lExMTUSgUdOjQISDvNTs7m7feeosuXboQEhJCWFgYvXv35osvvsBZYur5so5b8vMIlEJbYcD2FeySkpJYvHixx5ctgMlkYvHixSQlJdVSzYQQLo5SekWaTDmXvZZzVwgR5KrYUzzXWhCgioiaIterojpIgEhUG6fTySOPPMK9997Lli1b6NixI0OHDsVms/HZZ5/Rs2dP0tLSPLZ54403GD9+PAqFghtvvJELFy4wefJknn32WQYNGsTrr79OkyZN6NWrF4cPH+aVV17h008/9djHd999x6ZNmzAajQwaNIj+/fuTk5PDggUL6N27N3/88Ue1vN8TJ07Qo0cPJk+eTFZWFv369eO6667jwIEDPPHEE4wfP75ajlteedaqTX/aUDgcDhISEkotk5CQgKOKXbmFEFVjc3rOuFOec1LOXSFE0LJXrQdQbqE8SKxP5HpVVBcJEIlq88knnzB37lzuuOMOUlJS2LZtG6tWreL48eM899xznDlzhgkTJnhs43A4+Omnn9i9ezfr1q1j5cqVAHzxxRcUFBRw4MABNm3axC+//MKsWbMA+Oc//+mxjw8++IBz586xb98+Vq5cyfr16zl37hy33347OTk5vP/++wF/rw6Hg7vvvpuUlBRmz57NqVOnWL9+PYmJiSQnJ9O5c2fmzp1LYmJiwI5pNpsxmUweP171chZ/KVwqvBSwYwez1NRUn59lSSaTidTU1BqqkRDCF+tlUzKfOnW6zG3k3BVCBK3C7LLL+GF32LmQf9H92lzFYJOofnK9KqqLBIhEtbDZbEydOhWj0ciiRYto2rSpe51Go+GDDz5ArVbzv//9D7u9uEvs1KlTPZJGDx48GIPBAMDixYs9xtK6Zh87duwYOTnFwwlGjBhBkyZNPOqjVqt56qmnANi3b18A32mRFStWsHv3bh555BGeeOIJj8RwMTExvPrqq0DRcLtAmTp1KhEREe6f6OhorzJKRXE91v2wzqvHVkmTJ0/2u27Xrl3uYJ0v06dPJz8/v1L7TkxMZNOmTZXaNjs7mxkzZvhdv3TpUvbv31+hfZf8WyrNqlWrSv3SLa3e+/fvZ+nSpX7Xz5gxg+xs/xd6pe1706ZNpQYiS9s2Pz/f57BNl5UrV7Jr165K7TstLY05c+b4Xb9w4UKOHTtWqX0nJyezZMkSv+tnz55NRkZGpfa9bds21q5d63f9tGnTsFp9532wWq1MmzbN77Zr165l27ZtlapXRkYGs2fP9rt+yZIlXkNqy7vv+kJ1WQLO3Nzynbv+zvGG1M65pKamsmDBAr/rFyxYIO1cBfYt7Zw3aecCb86cOb6v53RGoHLXcyqlisg/twfQqbQ+t5d2zlNttnOlvaeSfH3nSTvnTdq5YgpnycQoQgTI9u3buf7662nUqBE33XSTzzIJCQmYzWbOnTtHfHw8v/zyC19//TVjxozxKNeiRQvOnj3LiRMniI2NdS83m83o9XoATp8+TatWrdzrsrOzWbNmDbt37yY9PZ2srCxSU1PZvXs37du35+jRo0DRl9mgQYM8lqWkpNC2bVtUKhU2W9ET6kWLFrFo0SKv9zB48GCef/55nn76aWbNmsU111zjUUeX8+fPs2XLFkaMGMGqVat8HrekgQMH+v08Sr5/s9nsfm0ymYiOjqbjCwuw6KI4+M4toLBw/XfXA7Dq/1YRHe4dRBKeUlJSmD9/fpnlxo4d6/P/Woj6yGQyERERQXZ2NkajsewNaomrnnGz4tg2ZgPhYc3d65KOHGfEnKJ8C2P0u9AofHerl3NXiIapvrVz5aqnJQ/ea1n0+8tHILxZpY+75sh2XtlclA4h8d7NNDaEV3pfovrJ9arwJRDtnMxiJqpFSkoKAJcuXWLZsmWlli3tiSyATqcrc3nJOOe0adN455133PsNDQ2lVatWaLW+n4aUR3Jyss/3ERkZCRS/371797J3716/+ynrvVaETqfz+9n4YlAbAnbsYBYTE4PRaCy1267RaCQmJqYGayWEuJxK6XkJEx3dGig9Iaecu0KIoKVUVWlzgzYkQBURNUGuV0V1kSFmolq4EqL169cPp9NZ6k8go9pfffUVr7/+Ok6nkylTppCSkkJubi6HDh0qtetqWSZOnOiz7vPmzQOK3++XX35Z6nsNZA6iitIoNbV27PpEqVQyfPjwUssMHz7cYxihEKL2leeclHNXCBG0VFW7ztP7GVYm6ia5XhXVRf5iRLVo2bKou+vhw4epyVGMroDNe++9x4QJE2jTpk2NHNf1fg8dOlSu8q7G2jWErSaoFFV7stSQxMXFER8f79U102g0Eh8f75ELSwhR9xiN4Ze9lnNXCBHkqtiDSK+WB4n1jVyviuogQ8xEtejduzdhYWFcuHCBpUuXMmrUKJ/l7HY7KlXgAhfnz58HqPEGcfDgwcydO5fvvvuOiRMnEhoa6rOc6/26hqZduHABi8XiNfytZOLuQFEoFAHfZzCLi4ujc+fOpKamkpOTQ3h4ODExMfIkRoh64OmnnybjbJqcu0IIUU66KvZAErVDrldFoMlfjqgWer2eF154AYDHHnvMK2u82Wzm22+/9Rs4qqyuXbsCRUmlXcO+oGgmgMcffzygxyopPj6edu3akZaWxqhRo0hPT/dYf+bMGV599VU+++wzAK688kqMRiP5+fn85z//cZe7dOkSDz/8cKmzQIiao1QqiY2N5eqrryY2Nla+bIWoJ+TcFUKIitFKD6J6S77zRCBJDyJRbf7+97+TnJzMDz/8wC233ELHjh3p0KEDOTk57Nu3D5PJRJcuXQJ6zClTprBq1SrmzZvHr7/+SqdOnTh9+jQHDhyo1gz+Wq2WZcuWMWLECNatW0dsbCw9e/YkMjKStLQ09u3bh9Pp5IMPPnCX/3//7//xt7/9jbFjx/L555+jUqnYunUrCoWCJk2alDqNoxBCCCGEEIGiUcltoRBCAkSiGqnVapYsWcJ//vMf5s6dy++//866devQaDRcddVVDBs2jAcffDCgx+zcuTNbt27lxRdf5LfffiMtLY0ePXqwYMECWrduzeDBgwN6vJK6du3K3r17+eyzz1i+fDkHDhwgLy+Pxo0bM2LECO666y7uu+8+d/k33ngDg8HAZ599xubNm2natCkPPvggb7zxBmPHjuWXX36pVD3WvTaCkLAwDFo1oOaX+34h1ZSKQSOzmAkhgsPXw7/GYGjiscygVZPy/m21VCMhhKgF2lCYmA0pm4p+r4JmYRE83vldHukxnFCtPkAVFELUNwpnTWYQFkJUG5PJREREBCfTL2AIC6dJmA6ArMIsTuWc4uqmV9dyDYUQdZWr/cjOzvZKdlmXuOq5K2UX17a5trarI4SoR+pbO1eheqZug5jrq3Rcp9PJrO0/8vh1t6KW3kRC1EuBaOdkgKIQQUahUFAyHbVSqURVxZkthBCiLpE2TQghSghAQEehUKBRqSU4JEQDJwEiIYKMUgHKEjOWqRQqmeJeCBFU1Aq5gRFCCDdlYNpEgyYkIPsRQtRfEiASIsgoFAqPAJFSoZQAkRAiqEgPIiGEKEEZmBnIQiVAJESDJwEiIYKMUgElZ7dUKVQy3aUQIqhI0FsIIUpQaQOymzCtBIiEaOjkrlGIIKO8rAeRWqlGowjMkyUhhKgLJEeGEEKUoArMdV64Tma8FaKhkwCREEFGpVSgURWf2kqFEp1aV4s1EkKIwNIqA/O0XAghgoImMIGdJobwgOxHCFF/SYBIiCCjVirQqj1P7Sh9VC3VRgghAs8QoJshIYQICobAXOdFRzQJyH6EEPWXBIiECDIKhcJrmTpAs1sIIURdoFTI5YsQQrgFKHG/Vi3Xi0I0dHKFJYQQQgghhBBCCNHASYBICCGEEEIIIYQQooGTAJEQQggh6hWn3VbbVRBCCCGECDoSIBJCCCFEvWKx5nktczqdtVATIYQQQojgIQEiIYQQQtQrFmtBbVdBCCGEECLoSIBICCGEEPWKzSYBIiGEEEKIQJMAkRBCCCHqFbstv7arIIQQQggRdCRAJESQuZh1rrarIIQQ1er2dePJz88AIN9iI/b1lbR9YxX5FkleLYQIDtdNXCJtmhCixkmASFRIYmIiCoWCDh06eLxWKBSkpKTUbuWEEEIIIYQQQghRKeraroCo35o0acLIkSMBMBgMtVwbIYQQQgghhBBCVIYEiESVdO3alaVLl9Z2NUQJp9PSiGndHqVSOggKIYKfw+Fw/37yZCqd28dK+yeECHoOh4PU1FRycnIIDw8nJiZG2j4hRJVJgEiIILN82TJ2bNnF8OHDiYuLq+3qCCFEtUlKSuLH1WuAjgB8++23NI4Ik/ZPCBHUkpKSSEhIwGQyuZcZjUZp+4QQVSZhZuGT3W5n1qxZ9OjRg5CQEJo3b86wYcNYsWKFR7mUlBQUCgVqtWesMTMzk9dff52uXbsSGhpKeHg4PXr04JVXXuH48eMeZY8ePcqjjz5KmzZt0Gq1REVFMWLECDZs2OBRLi0tjYceeohrr72W5s2bo9FoMBqN9OjRg/fee4+CAs9pj51OJ99//z033XQTV1xxBTqdjrZt2zJ69GiWL1/uUfbSpUtMmDCBuLg4QkJCiIyMZODAgXz33Xden824ceNQKBRMnjyZZcuW0bdvX/dn9Oyzz5Kbm4vT6eTf//433bt3x2Aw0LFjR9544w2sVqvX/rKzs3nrrbfo0qULISEhhIWF0bt3b7744gucTmfZ/1k+mEwmFi9eTFJSUqW2F0KIuu7w4SMsXrwYkynHY7m0f0KIYJaUlPRn22fyWC5tnxAiEKQHkfBit9v5v//7P5YvX45Wq6VPnz5oNBqSk5NZt25dmdufPn2a66+/nrS0NJo3b07//v0pKChg165d7N69G7Vazfvvvw/A6tWrueeee8jPz+fKK69kyJAhZGZmkpCQwJo1a5g7dy5jx44FigJEX3/9NUajkauvvprrrruOCxcusHPnTnbv3s1PP/3E+vXrUSgUQFEgZ8GCBWg0Gq6//noMBgPJycl8//33JCYmcueddwJFQa7Bgwdz4sQJmjZtyoABA8jJyWHz5s388ssvrF+/nq+++srrfX7++eecPn2azp07M2DAALZs2cJnn33GmTNnUCgU/O9//6Nnz57069ePTZs28f7772O1Wvnwww/d+zhx4gRDhw7l+PHjtGzZkn79+mGz2di+fTtPPPEEW7du9Xns8kpISKBz587S5VgIEXTWr19f6npp/4QQwcbhcJCQkFBqGWn7hBBVIQEi4eWjjz5i+fLldOrUibVr19KmTRv3uq+++orx48eXuv20adNIS0tj1KhRLF682N27qLCwkK+//prc3FwAUlNTue+++zAYDKxZs4b+/fu79/HLL79wyy238Ne//pW77rqLiIgIYmJi+Omnn7jppps8eiwlJyfTt29fNmzYwIYNGxgyZAg7d+5kwYIFREZGsnPnTtq1a+cuv3XrVv71r3+5X99///2cOHGCRx55hJkzZ6LT6QA4fPgwN998M3PnzqVv37489thjHu/TZDLx/fffEx8f7y5/1VVXsXTpUtq1a8eWLVvo06cPAOvWrWPYsGHMmjWLKVOmoNPpcDgc3H333aSkpDB79mwee+wx95d5amoqw4YNY+7cuTz00EMMHDjQ63M2m82YzWaP+lzOZDKRmppKbGxsqf9n9YndbvfZE0sI4U2j0aBSqWq7GtUiPy8PCPG7PhjbPyFEw5JvsWHQFl/zpqam+rzeK0naPiFEVUhoWXiw2WxMmzYNgDlz5ngEhwCPQIs/R48eBeDmm2/2COTo9Xoee+wxXnzxRQD++c9/kpOTwz//+U+P4BDAgAEDuPfee8nOznb3WmrWrBmDBw/2Gs525ZVXcvPNNwOwb98+jzp06tTJq859+vRxDx3btGkTW7dupUWLFh7BIde2H330EQDTp0/3ep8vv/yyOzjkKt+pUycAvvjiC3dwyPVZGAwG8vPzOXz4MAArVqxg9+7dPPLIIzzxxBMeT3piYmJ49dVXAfjhhx+8jg0wdepUIiIi3D/R0dE+y+XkFA2/mDNnDmlpaT7LAEyePNnvul27drFy5Uq/66dPn05+fn6l9p2YmMimTZvK3NbpdJKens6RI0c4ceIEJ06c4NixY+zdu9f9+vKfP/74g0OHDvldv2fPHr/rjhw5wv79+/2u379/v0ddKrLvQ4cO8ccff/hdv3fvXo4dO1apfSclJZGUlFSpbcv6PA8ePFjpz/Po0aOlfp4HDhzg8OHDldr34cOHOXDggN/1+/bt4+jRo5Xad3JyMgcPHix12+PHj/tcd/z48VL3ffDgQZKTkyv9ee7bt6/Mz/PIkSOkp6d7DVct7ZwMJq72z6Wut3O+ZGdnM2PGDL/rly5dyv79+yu179TUVBYsWOB3/YIFC0hNTa3Uvvfv31/qJBYzZswgOzu7UvvetGkTiYmJldo2Pz/f53e6y8qVK9m1a1el9p2WlsacOXP8rl+4cCHHjh2r1L6Tk5NZsmSJ3/WzZ88mIyOjUvvetm0ba9eu9bt+2rRpfh/MWK1W93WjL2vXrmXbtm2VqldGRgazZ8/2u37JkiUkJydXat/1xaVczzbs8jbNH3/lpJ3zJu1cxfYt7Zy3YGvnFM7KJjkRQWnbtm306dOHFi1a+AwmJCYmMmjQINq3b8/Ro0dJSUmhbdu2qFQqbDYbUBQ4+eijj+jQoQMzZ85k0KBBXkEdgKuuuoqkpCSGDBlCWFiY1/qDBw9y5MgRpk2bxmuvveZefujQIdavX09ycjKZmZlkZWWxa9cuzp07x7vvvsubb77J/v376datG0qlkn/84x+MGzeOxo0bex1j8uTJvPXWW4wdO5Z58+Z5rS8sLCQsLAy73U56ejrNmzdn3LhxzJ8/332skvr27cvWrVv5+eefvXr9tGjRgrNnz7Jp0yb69evH008/zaxZs7jmmmt8PuU5f/48W7ZsYcSIEaxatcprva8eRNHR0bz6+ouE6iPcy8eOHRsUT5GysrJIT0+nadOmhIaGuocSCiF8czqd5OXlceHCBVq0aEFkZKTfsiaTiYiICLKzszEajTVXyQpy1TNuVhyjz92JkxCsTiXfFPYAYIx+FxpF8axmwdL+CSGqrr61cx1f/Dd73h/r0YMoJSWF+fPnl7kPafuEaJgC0c7JEDPhwRVBL09PIX9effVVVq5cyaFDhxg2bBghISF0796dgQMH8vDDD9OxY9FsMykpKQD89NNPpe7P9STj3LlzPPzww6xevdq9rmnTprRs2dJrm6uvvprnn3+ef/3rX7zyyiu8+uqrdOjQgb59+3Lvvfdy++23A3DmzBmgqMeOL3q9nqZNm3L27FnOnDlD8+bNS61ryR5I/ta5YrKu979371727t3rdzt/T3J0Ol2px4OiGS38vbf6xOl0cv78eYxGI02aNKnt6ghRb4SEhGA2mzl//jwRERFBFVgNCwsjJ9fud32wtH9CCOESExOD0WgsdZiZtH1CiKqQIWbCg6sXUFU6ljVv3pydO3fy+eefc8cddxAREcGWLVuYOnUq3bp1cyfXcziKnvIeOXIEp9Pp92fixIkA3HvvvaxevZouXbqwdOlSTCYT58+fZ8+ePQwfPtyrHp988gmJiYk8+uijdOnShWPHjrFgwQLuuOMOxo0b51G2tJum6rqhcr3/L7/8stT3X1r30rIMHz48KJIU2u127HZ7nX7iJ0RdZTQa3edQMBk8ZEip64Ol/RNCCBelUunzmrckafuEEFUhrYfw0LRpUwDOnj1bpf2Ehoby+OOPs3z5ctLT0zl48CC33347hYWFvP766wDunj+HDh0qc3/Hjx9n48aNaLVaNmzYwMiRIwkPDy9zuwEDBvDll1+yf/9+MjMz+eSTT1AqlcyfP589e/bQqlUroLg3z+UKCws5f/48gLtsoFTk/VeU0WgkPj6euLi4gO+7NrgCl76GKgohSuc6b1znUbDo1Kkj8fHxGI2e3wXB1v4JIURJcXFxf7Z9ng/NpO0TQgSCBIiEhx49eqBSqdyJTiujsLDQa1lcXJw7uZcrcdngwYOBooTOpbHb7e4gTVRUFM2aNatUHSIjI3n++efp2bOnux6DBg0CYM2aNT63WbFiBXa7nU6dOpU5vKyiXO//u+++Iy8vz2+5ij71v3PkSP76178G5QVCMA2PEaKmBPN5ExcXx9NPP+1+/Ze//CVo2z8hhHCJi4vjr3/9K2PHjuX//u//GDt2rLR9QoiAkACR8BAVFcVtt92G0+nkb3/7m8cT523btvHCCy+UuY877riDGTNmeI2Pds1G1qVLFwBeeukltFoty5cv57XXXqOgoMCj/K5duxg1ahS7d++mc+fOaDQazp49y88//+wuY7PZmDp1KgsXLvTY9osvvmDcuHFeWd+PHz/uDlBdddVV9OvXj+uvv55z587xxBNPeASJkpOT3TOuvfzyy2W+74qKj4+nXbt2pKWlMWrUKNLT0z3WnzlzhldffZXPPvusQvtt3bKldC0WQjQYJdu7Nm1ipP0TQjQISqWS2NhYrr76amJjY6XtE0IEhLQkwsv06dNp3LgxCxcupG3bttx6661cc8019OnTp9RpIF0uXLjA888/T7NmzejVqxfDhw+nc+fOvPDCC4SEhLinXbzqqquYP38+Op2ODz74gBYtWjBkyBCGDRtGu3bt6Nmzp3vqyEaNGrlnDBs6dCg33HADt9xyCy1btuTtt992D41zcTgczJ8/n7i4ODp27Mjw4cPp27cvV111FRcvXuS5555zP2X57rvviI6OZsGCBcTExDBixAj69+/P1VdfzenTp3nwwQd5/PHHA/gJF9FqtSxbtozWrVuzbt06YmNjueGGG7j11lvp3r070dHRfPjhh1gsloAfWwghhBBCCCGEKEkCRMJLhw4d2Lp1K/feey95eXnuHjsffvihV08dX5YsWcJrr71GXFwcSUlJ/PTTT+Tl5TF+/HgOHjxIv3793GVHjx7N3r17eeqpp7jiiivYsmULGzZswOl0MmbMGJYtW8a1114LwFtvvcXs2bOJjY1l+/bt7Ny5k1tuuYXdu3czdOhQjzo8/vjjfP7559xyyy1kZ2fz008/cfDgQfr06cPy5cv517/+5S7brl07du3axSuvvEJkZCQbNmxg//799O3bl6+//poFCxYE4mP1qWvXruzdu5d33nmHbt26ceDAAdasWUNaWhojRozgiy++qHBwKiryimqqrRCBk5iYiEKh4IcffqjwtrfffrtM39vA/Tjk3xgMRTMaGrRqUt6/jRNTb/WYDloIIeqz3yfeK22aEKLGKZxVma5KCFFnmEwmIiIiyM7ODrrZvgoLCzlx4gRt27ZFr9fXdnUCauDAgfzyyy8+111zzTXs2bOnWo8/b948Hn74YebOnes1u19J48aNY/78+Zw4cSIgwZnExEQGDRrEkiVLuOeeeyq07e23386BAwf8Jpe/XFZWFv/85z8ZNmwYN9xwQyVqW3l79uxh6dKlPP744+7E9DWtPOdPfWk/XPU8cWIXsbHXeqxzOp1BnW9JCFE19a2dq+v1FELUPYFoPyQsLYRosBwOB6mpqeTk5BAeHk5MTO3kL2nZsiVvvfWW1/ImTZpU+7H79u3LrFmz6Nu3b6nlxo4dS58+fWjcuHG11ynQsrKymDRpEmFhYbUSIJo0aRK33357rQWIgpFaHVLbVRBCCCGECDoSIBJCNEhJSUkkJCR4JFM3Go0MHz68xmcBadSoEU8++WSNHtOlc+fOdO7cucxygwYNcs/6J0Rt02okQCSEEEIIEWiSg0gI0eAkJSWxePFir5n2TCYTixcvJikpqZZq5tvEiRNRKBQkJCTQr18/9Ho911xzDdu3b+fkyZPcfvvthIaG0q1bN4/haq7tEhMTufXWWzEYDLRs2ZKPP/7YXeaHH35wlym5zZYtWxg1ahQhISH8+OOPPPvss17Dd9auXcuNN96IwWCgRYsW3HXXXWzfvh2Av/71r3Tu3JmwsDB3fRcvXlzh915QUMBLL71E06ZNiYyMZPjw4Zw4ccKjzNSpU+nSpQsRERFotVo6d+7Mp59+CkBKSgpt27YF4NVXX0WhULjf744dO+jfvz/NmzdHo9HQtGlTHnroITIzM937TktLY8yYMTRv3hyj0ch1113HRx995F6fm5vLSy+9RKtWrdDpdFx99dX85z//AYqH7wH06tXLfWxRdVpNqNcy+WyFEEIIIapGehAJIRoUh8NBQkJCqWUSEhLo3LlzjQ03s9lsnD592mt5VFQUBoPB/XrUqFGMHz+eW265hQ8++ID4+HisVitDhgzhzTffZMaMGYwdO5YjR46g0Wjc2w0bNowxY8bwzjvv8N///peXXnqJdu3aMXLkSL91uvnmm7nvvvv48MMPiYuLY82aNR7rly5dyt13382VV17JhAkTKCgo4Ntvv+W7776jd+/e/P777/Tt25euXbtit9v59ttvGT16NK1bt67QMK9x48axZMkSHn74Ybp27cqBAwfYuHGjx8yFu3btomvXrjz44INoNBqWLl3Kc889R5MmTbjtttuYPHkyb775Jnfffbc7oX2nTp3YtGkTNpuNRx55hObNm3Po0CE+//xzzp8/T0JCAna7nVtvvZUzZ87w9NNPYzQa2bBhAx988AEvvfQSdrudESNGsGfPHh577DFatGjB6tWruffee1mzZg19+/bloYceYsGCBbzxxhvExMSU+32L0inV2tqughBCCCFE0JEAkRCiQUlNTfXqOXQ5k8lEampqjc2UdejQIaKjo72WX544esmSJdx+++0AmM1m3nvvPWbOnMlTTz0FQLNmzXj00UfZvn27x2yBq1evZsiQIQA888wzdOjQgenTp5caIPrvf//LsGHDfK6z2Ww899xzdOjQgW3bthEWFgbA22+/ze7duwHYtGmTR4+Ov/zlL0RHR7N06dJyB4i2bNny/9m777AorraBw7+ld6yIHSui2Bt2FHss0aiviRp7bIlJrNE3KpbEnmhMrFGJJcYSgx0LiiVRbNhbLIhdUQQFpCzz/cG387ouLEWU9ty59orMnDlzZnb32dlnzznDhg0bmDVrFqNHj1aXP3r0iAsXLqh/b9iwQW9fw4YNo0CBAmzevJnu3bvTo0cPvv32Wzw8PPSG8nXt2pVu3brp7TMmJoaVK1fy8uVLbt++zdmzZ1myZIl6N8GRI0dy6tQpAFatWsXRo0c5evQotWvXBmDUqFHUrFmT2bNns2fPHpo2bcqqVavo3LkztWrVStVxCyGEEEIIkRkkQSSEyFVevHiRoeUyQokSJfjll18MllerVk3vbw8PD71tADUxAVCsWDEAHj9+rLdd1apV1X9bW1vTrl07fHx8MHYTyxo1aiS77tSpU9y9e5cFCxaoySEAS0tLtY0ajYbIyEgCAwO5c+cOz58/x9zcnNDQ0GTrfdOaNWswMzNTE2DJ0Wg0xMbGEhgYSHBwMM+fP8fW1jbFfemSSteuXePs2bM8e/aMhw8fkpCQwLNnz9QeZCdOnKBfv36YmSV+ZNasWRNITKJVrFiRwoUL6/UAq1mzJps2bUr1cQohhBBCCJEVSIJICJGr2NvbZ2i5jGBvb6/2DEotS0vLZJdptVqj2xYrVozY2FiePHmSpn3q6G4vb2wy7zlz5uDt7U1kZCR2dnYUL14crVZLQkJCqvdz8+ZNChcurJeESsratWv58ssvefr0KdbW1hQvXpwXL16kuK+bN2/Ss2dPjh49ikajoWjRouo2CQkJuLm50bZtW3799Vf++usvGjduTNu2benZsydWVlZcv36dK1euJNn7S+bDEUIIIYQQ2Y1MUi2EyFVKlCiBg4OD0TIODg45er6YmJgYgHTPsaTreZRcEmTHjh2MHj2aRo0aceHCBV68eMGlS5ewtk7bnae0Wi0WFsbnmjl37hyffvopZcqU4dixY0RGRnL16lVKly6dYv3/+c9/uHjxIkuXLuXFixfcuXOHr7/+Wq+Mr68vq1atokmTJvz9998MHDiQli1boigKCQkJuLu7s23bNoPH1q1b03SsQgghhBBCZDbpQSSEyFVMTExo3bq10TtqtW7d+r1NUJ0Zrl+/Tp48eShQoEC6ttf1mLly5QrNmjUzWL97925MTEzYtGkTtraGd5tKLWdnZ/755x+0Wi2mpqZJltm3bx8JCQn89ttvVKhQIckySSWynj17xsmTJxk7diwDBw5Mtg3m5ub06tWLXr16odVqGTJkCMuWLePixYuULFmSK1euGO39JT2JhBBCCCFEdpFzvwEJIUQy3Nzc6Natm0FPIgcHB7p162Z06FR2FxISwpYtW2jfvn2666hVqxYFChTgl19+ISoqSl3+6tUrgoKCsLGxQVEUtacSwPnz54mNjU3Tfho0aEBkZKTeXeciIiL05vvR3eUtOjpaXRYSEqI3fE6XCHv27Jm6zMrKChMTE73ttFotZ86c0WvztWvX1L9NTU3VOZ/MzMz44IMPuHPnDitWrDBou24i66T2LYQQQgghRFYkPYiEELmSm5sbrq6uhISE8OLFC+zt7SlRokSm9BwKCwtj8eLFBssLFChAly5d3rr+jh070r59e169esXy5cuxtLTE29s73fVZWloyc+ZM+vfvT926denevbt6K/uPP/6YTp06MWvWLNq1a0fXrl0JDg5mxYoVxMfHp2k/PXr0YNq0afTq1Ythw4ZhYWHB8uXLCQkJUYcAtmnTBisrK3r06EG/fv14+vQpS5cu1btTna2tLVWrVuWXX37B1NSU+Ph4+vfvT6tWrVi6dCmWlpY4OTmxYcMGvQTRjRs3+M9//kOnTp2oU6cOYWFhLFmyBA8PD1xdXSlZsiQ+Pj4MGDCA3bt3U6dOHV6+fMnWrVsxNzfn2LFj1KxZE0tLS7766it69OhBaGgoP/74Y7rPvRBCCCGEEO+KJIiEELmWiYnJe7uVvTH3799P8k5dVatWzZAEkbu7Oz///DNPnz6ldu3azJ8/P1Vz9BjTr18/7O3tmTFjBlOnTsXa2ppmzZrRuXNnqlSpwooVK5g8eTLffPMNHh4e+Pn50apVqzTtw87Ojt27d/PZZ58xa9YsChUqxJAhQzh06BCXL18GoGTJkvz555+MGjWK8ePHU6VKFVauXMn48eP16vLx8eGzzz5j9uzZODk50bdvX1asWMHgwYP55ZdfsLe3p1+/frRv356JEycC0LBhQ7788kt8fX3ZvHkzdnZ2dOrUiRkzZqDRaLC2tubgwYNMmTKFzZs389dff2Fvb0+jRo0YOnQokDhMzsfHh//+979MmTKFypUrv9V5F0IIIYQQ4l3RKMbucyyEyDYiIiJwdHQkPDw8xUmYs5tXr15x69YtSpUqhZWVVWY3J9vw9vZm8uTJPHnyJN3zDYnsLzXvn+wSP7JLO4UQWU92iR/ZpZ1CiKwnI+KHzEEkhBBCCCGEEEIIkctJgkgIIYQQQgghhBAil5MEkRA5zLPnjzK7CUII8U7V8t5IVGzaJj0XQojsRK7nhBCZQRJEQgiRQ3l7e6Moisw/JIQQQgghhEiRJIiEUQEBAWg0GsqWLav3t0ajITg4OHMblwOdOXMGb29vVq1aldlNEUIIIYQQQgiRi8ht7kWaFChQgI4dOwJgY2OTya3Jec6cOcPkyZPx8vLi008/zezmCCGEEEIIIYTIJSRBJNLE3d0dX1/fzG6GMOLu/fuUKFYGExPpICiEyLkSEhIyuwlCCPHO3L1/nxfhr7C3t6dEiRJyXSeEeC8kQSREDrN1yxZOHD1N69atcXNzy+zmCCHEO7Fw4ULat2klcU4IkSNt3bIFWytHABwcHOS6TgjxXkgqWgCg1WpZtGgRNWrUwNraGmdnZ1q2bMn27dv1ygUHB6PRaDAz088tPn36lG+++QZ3d3dsbW2xt7enRo0ajBo1ips3b+qVPX36NJ07dyZfvnyYm5tTqlQpPvvsM4oVK4ZGo+HIkSN65V+9esWcOXOoWbMmdnZ2WFtbU6VKFWbOnElcXJxeWd38SDdu3GDevHlUr14da2trChQowKeffsqTJ0+SPP5NmzbRvHlz8uXLh4WFBaVKleLzzz/n0SP9O0h4enqi0WhYs2YNW7ZsoXnz5jg4OGBra0vz5s05ffp0kvWfPn2a7t27U6RIESwsLHBycqJLly565T09Penbty8A/v7+6rFoNBp8fHySrDc5ERERbNiwgcuXL6dpOyGEyC4iIl5InBNC5ApyXSeEeF+kB5FAq9XSuXNntm7dioWFBR4eHpibm3PlyhX27t2b4vZ3796lbt263L9/H2dnZxo2bEh0dDSnT58mKCgIMzMzZsyYAcCff/7Jxx9/TFxcHBUqVKBu3brcuXOHZcuWJVn306dPadWqFadOnaJAgQLUrl0bU1NTTpw4wTfffIO/vz+7du3C1NRUbzsvLy9u375NtWrV8PT05Pjx46xevZpz585x4sQJzM3NAVAUhf79+7Ny5UpsbGxwd3cnf/78nD9/nl9++QVfX1+OHz9OkSJF9Or/9ttvuX37Ni4uLjRs2JBr167h7+9PkyZNOHv2LKVLl1bLLl++nEGDBqEoCpUqVaJatWrcvXuXP//8k23btrFjxw6aN29Ow4YNiYiIICgoiIIFC1K/fn21jhIlSqTuyXyDn58frq6u0i1ZCJFjSZwTQuQWEu+EEO+aRBfBDz/8wNatWylfvjzXrl3j4MGD7Nu3j5CQEJYvX57i9jNnzuT+/ft06tSJO3fusHv3bg4dOkRoaChLly6lUKFCQGIiqU+fPsTHx7Ny5UouX77Mrl27uHDhAv/++2+St+Lu378/p06dYuLEidy7d48DBw6wb98+bt68ScOGDdm7dy+rV6822C5fvnycOHGCoKAgdu3axbVr1yhUqBBnz55l27Ztarn58+ezcuVK2rdvT3BwMIGBgezcuZObN2/yxRdfcO/ePcaPH29Qf1RUFOvXr+fmzZvs3LmTq1ev0qpVK16+fMm8efPUcidPnmTw4MGUKlWKs2fPcu7cOXbu3Mm5c+dYt24dsbGxDBo0iISEBKZNm8bw4cMBqFKlCr6+vuqjWbNmBm2IiYkhIiJC7/GmiIgIQkJCUnwOhRAiu5I4J4TILSTeCSHeNUkQ5XLx8fHMnDkTSOzpUrJkSb31r/eESc7169cBaNGihd7QMysrKwYOHMjXX38NwE8//cTLly/p1asXffr00aujbNmy2Nra6i07d+6cOoxr8uTJWFhYqOvy58+Pt7c3kDg87E2+vr7UqlVLr3ybNm0AOH78uHrs06dPx8HBgT/++IOCBQuq5c3NzZk9ezZmZmb89ddfaLVavfrnzJlDt27d0Gg0AJiamvLxxx/r1Q/w/fffEx8fj4+PD+7u7np1dO/enYYNG3Lz5s1kh6YZM336dBwdHdVH8eLFkyz34sULIPH5vX//frL1TZs2Ldl1p0+fZseOHcmunzt3LlFRUemqOyAgwGBYYWq3jY+PNxgG+LqwsDCj7TJ2PmJiYggNDU12fWhoKDExMemqOyoqirCwsGTXP3r0iPj4+HTV/eLFC/U5T27bS5cu0bRpU+zs7HB0dCQwMBBI7E348OHDZLd9/vw5kZGR6WpXbGxsskM8IbG34KtXr9JVd3R0NM+ePUt2/ePHjw2Go6a27pcvXxIeHp7s+gcPHiQ7WXJCQgIPHjxIdtvw8HBevnypt8zOzk6Nj8baFRcXx+PHjwkICECj0RjEwWfPnhEdHZ3s9sbeV9lRTo5z4eHhLFiwINn1vr6+nD9/Pl11h4SEsGrVqmTXr1q1yuiXUWN1nz9/3uhNLRYsWGD0vWWs7iNHjhAQEJCubaOiopg7d26y63fs2GH0M9lY3ffv3zf6w9q6deu4ceNGuuq+cuUKGzduTHb94sWLjX5mGas7MDCQPXv2JLs+qSH9OnFxcep1ZFL27NmjfsaktV2hoaEsXrw42fUbN27kypUr6ao7O9u2bZvEuTTULXHOkMQ5QxLn/kejKIqS4bWKbCMwMBAPDw8KFy6c5IdNQEAATZs2pUyZMly/fp3g4GBKlSqFqamp+gV25MiR/PDDD5QtW5aFCxfStGlTgzmKILFXzPnz59m5c6earHmdi4sLt2/f5vDhwzRs2JBZs2YxduxYypUrR8WKFQ3Kv3z5En9/f9zc3Lh06RKAmrC5c+cOxYoV0ys/ZswYZs+ezcCBA1m6dCnHjx+nbt265M2bl8aNGyd5fvz8/IiJieHRo0c4OTnh6enJwYMHWb16NT179tQru3PnTj744APKlSvHtWvXSEhIwNHRkZcvX9K+ffskuwOfPHmSe/fusX79erp164aPjw99+/bFy8uLffv2JdkmnZiYGL0ERUREBMWLF2f0N1+rkxoC9O7dGxcXF6N1ZXWvXr3i1q1blCpVCisrq8xuTobTarUsW7aMFStWqK/l8uXL0759e4YNG4aTk1OG7Ss+Pp4KFSqg1WoZPHgwsbGxFCxYkCFDhrBy5UqD5G1mCQgIICAggDFjxmBjY6Mu12g0NGnSxOhFU3ZmZ2dHly5dUj3vmC5Gb9y4kS5duiRZJjXvn4iICBwdHQkPD8fBwSG9zX/ndO0s9/WvdHO4j7kmMTmXE+KcEOLdym5x7s3rOR2Jd0KI5GREnJM5iHI5XcY8NT2FkjN69Gh27NjB1atXadmyJdbW1urcP3379qVcuXIA3Lp1C0D9OyXBwcEA/Pvvv/z777/JljP2S8frdF8ydb/26+oPCwtjy5YtRrdNzT7erD80NFTtHfD6sLb01v8mS0tLLC0tjZZxcHBI9/xF2cW+S8n3IHqfmlcslK7t4uLi6NixI7t27cLT0xNvb2+io6M5cuQIU6dO5dy5c/z1118Z1s7jx49z48YNduzYQdu2bQG4evUqixYtol69ehm2n7cVEBDA5MmT+fzzz/USRIsWLTKYE0zkbrkhzgkhBEi8E0K8e5IgyuV0vYDepiOZs7Mzp06dYu3atWzfvp0TJ05w9OhRjh49yo8//shff/1F69atjQ7HSYou0fLf//73nXSf09XfoEEDo11i37Z+SBxeo5sY+31q3bq1TGSYxU2bNo1du3Yxe/ZsRo0apbfu8OHD/PHHHxm6P11SuHz58uoyV1dXXF1dM3Q/78rgwYMzuwkii5E4J4TILSTeCSHeNYkwuZxu3h1j846khq2tLZ999hlbt27lwYMHXLp0iXbt2vHq1Su++eYbIHHi6LTsS9dL4OrVq2/VtpTqv3bt2lslyJKTP39+dd6ka9euZXj9xjg4ONCtWzfc3Nze635F2kRGRjJv3jwaN25skBwCaNSokd6Y/EWLFuHm5oalpSUuLi5MnjxZb64iT09P3N3d2bZtG7Vq1cLGxgYPDw/Onj0LgI+PjzpXVrly5dBoNPTp04dNmzah0Wj0hm1dvHiRFi1aYG1tTaFChfjoo4/QaDTq3F8+Pj5oNBpOnjyp12Z3d3c8PT3VvzUaDcOGDePnn3+mdOnS1KpVixcvXtC2bVtKliyJpaUlDg4OeHl5cebMGQC8vb2ZPHkykBijNBqN2p3+9Tl6ILH33ciRIylatChWVlZUrVqV9evX67VJo9EwZMgQZs+eTalSpXB0dKRLly5G5y3StUOj0eDn50eDBg3U+o8fP87t27dp164dtra2VKlShYMHD+pte/bsWVq3bq3O89S5c2du3rypV+b27dt07twZW1tbnJ2d6devn8HcU/Hx8UyZMoXSpUtjYWFB2bJlWbRokdF25xYODvYS54QQuYJc1wkh3hfpQZTL1ahRA1NTU27duqXOUZFWr169MpjXws3NjZkzZ7J9+3Z1orLq1avj5+fHgQMHaNiwYYr1NmvWjEmTJrFjxw7u37+f7LASrVZrcJv71KhTpw52dnY8efIEX19fOnXqlKH1m5ub06hRI/z9/Vm6dCnz589PspyiKCiKgomJidrL6M1JsdOiQ8eO1K/TSH5hygaOHDlCRESE0Xl/dM/j1KlTmThxIm3atKFfv36cOXMGb29vgoODWblypVr+6tWr9OjRg0GDBtGtWzdmzZpFp06duHbtGvXq1ePTTz9l1apVjBs3jhIlSuDq6srTp0/19hkSEkKjRo2wsbFhzJgxmJqavtUwt+XLl1O6dGkGDhxIqVKliIyM5Nq1a3z44YeULl2aJ0+esHTpUlq0aMHNmzdp164dJ0+eZMeOHcyZMwdbW1vs7e0N6lUUhY4dO3LgwAH69etHmTJl2L59O927d+fVq1f07t1brw1FixZl4MCBPH/+nLlz52JtbZ3kXRDf1KlTJ/r370+rVq2YPXs23bp1Iy4uDi8vL7799lsWLFhA7969+ffffzE3N+fSpUs0atSI/PnzM2rUKGJjY1m2bBkNGjTgzJkzFCpUiKioKDw9PXn+/DlffPEF+fPn5+DBg8TGxurtu1evXvz555/069ePcuXK8ffffzN06FDMzc0ZMGBAup+TnGDo0KHYWVmkXFAIIbKhDh074mibD3t7e0qUKCHXdUKI90ISRLlcvnz5+OCDD9i6dSv//e9/WbVqlTrBdGBgIF999VWKdbRv354OHTrQu3dvvcmw9u7dC0ClSpUA6NGjB35+fixcuJCePXuqyajIyEi+//577t27p1dvw4YNadiwIUeOHKFdu3asX79eb/6ip0+fsnjxYl6+fMn06dPTfOxWVlZ89dVXTJs2jYEDB2Jra0vLli3V9TExMWzatIn169ezdevWNNcP8M0337B//35+/vlnXFxc+OKLL9TzqygKhw4dYuLEifz5558UKFBA7SURFBTEw4cPcXZ2BtKWpCpWpIhcRGQTursSVK1a1Wi5J0+eMG3aNLp166bXO6Zo0aLMnj2bL7/8kmrVqgGQJ08ejhw5og4Zy58/PwMGDCAwMJAGDRrQtGlTVq1aRefOndU7/b15B6xJkyYRFRXFqVOn1Pfp0KFD9e70lxYeHh7s2bNH7VGnKIp690OdChUq0KtXLw4fPkzbtm2pVasWO3bsoHfv3hQoUCDJerdu3cq+fftYuHAhQ4YMARInzW/cuDFjxozhk08+UZOuderUYefOnWqMevDgARs2bGDVqlXq5PbJ2bhxI+3atQMS48L333+vt08nJycGDBjA8ePHadCgAf/9738BOHbsGIUKJc5N9cknn1C1alVmzJjBjz/+yPz58wkODub48ePUrl0bSJzPzc7OTt3vgQMH+OOPP9i8ebOawB45ciSdOnVi1qxZuT5BJHFOCJGTFStSBJcSqZu3UwghMopcXQnmzp1L/vz5WbduHaVKlaJt27ZUrVoVDw8Po7d91Hny5AnDhw/HycmJ2rVr07p1a1xdXfnqq6+wtrZWb7P4ySef0LJlSx4+fEjFihXx8vLCy8uLwoULM3v2bPVi//W5ev744w8qVqxIUFAQFSpUoFatWnzwwQfUrl0bZ2dnvv32W6O39E7JpEmT6NKlC0+fPqVVq1aUL1+etm3b0qhRI5ycnOjZs6fBsJC0aN68OXPmzAFgxIgRFClShJYtW+Ll5UWxYsXw9PTk0KFDankPDw/KlStHeHg47u7uNGnSRO0VIXIe3WvX0dHwLiWv27dvH7GxsQbz7+j+9vPzU5cVKlRIbz6hsmXLAol39ksNRVHYsmULHTp0SFePwqS4u7urySH4390GHz16xLZt21i+fDmnTp0CMHr70jft3LkTCwsL+vbtqy4zMzNjwIABPH78WK0ToFq1anoJ7LJlyxIbG8ujRylPcu7h4aH+Wzc5qC6pA6h3THz8+DFarZY9e/bQqVMnNTkEieegQYMG7Nq1C4A1a9bg4eGhV8+bNm/eTN68ealduzZ3795VH9WqVePff/81eutcIYQQQggh0koSRIKyZcty7NgxunbtSmRkJAcOHABgzpw5rFu3LsXtN27cyJgxY3Bzc+Py5cv4+/sTGRlJ//79uXTpEg0aNAASf+3dsmULEyZMoHDhwhw+fJhz587RsmVLTpw4oX6Zyps3r1p30aJFOXHiBD/++CP16tXjxo0b+Pn5cf36dRo3bsyPP/7IpEmT0n3sZmZmbNy4kU2bNvHBBx8QERHB3r17OXXqFOXKlWPcuHFs3Lgx3fVDYmIoMDCQ3r17Y2try6FDhzh8+DB2dnYMHDiQ/fv3qz0kTE1N8ff3p1u3bpiamnLixAmsrKzUnkSpkS9P+u6mJd4/XW+RlJKct2/fBjBI2JQsWRITExN1fVJsbW2BxKGgqREaGkpYWJjeJNYZLTo6mk8//ZQiRYrQoUMHvvnmGzUJ+vrk7im5ffs2hQsXNhjiqjtPGXledJK6c6BumVar5cmTJ0RFRSWZXCtVqpTapps3b6Z4R8fr168TFhZG8eLF9R66eaByc4LopHdXbCykE7QQIueS6zkhRGaQqysBJCaJNmzYkOS61ydwdnFxMZjQuVy5csycOZOZM2emuB8rKyumTJnClClT9Ja/evWKhw8fYmdnR5kyZfTW2djY8NVXX6VquJuxyaa9vb3VL1Zv+uijj/joo49SrP/1SXzf5Onpmez+a9WqhY+PT4r1AxQvXtxgkl2RM+kSBGfPnlWHiBnz5utL93dGTrKumwPnzaRLRpo4cSKrV69m1KhRjBo1ikKFCnHy5EmjvWmSY+zY38Xk86mV3L51y7VarV6vqqQkJCTg5OTE8uXLk1xfsGDBt+rhKIQQQgghxOskQSTeq1OnTlGpUiW9L59arZZx48YRFxdH79690zUhtBDZUcOGDbG2tmbFihV6Eyq/STes6ebNm3o9U27fvk1CQgIlS5bMsDblz58fExMTHjx4YLScbi6tuLi4NO9j9+7d1K1bl9mzZydbJqV5gSDxvOzfv5/o6Gisra3V5bqkSUael9QqWLAg1tbWSSZubt68qbbJ2dnZYN61N5UsWZL9+/fTvHnzd5qwE0IIIYQQAmSImXjPevTogaOjI9WrV+eDDz6gZcuWFClShHnz5lGuXDlmzJiR2U0U4r1xdHRk8ODBHDp0iFmzZhmsP3fuHDNmzKB58+aYm5sb3N584cKFALRu3TrD2mRlZUX16tXZsWMHMTEx6vLg4GC9crphj+fPn1eXXb16NVVz+tjY2BAdHa237PX5ggB12KWxW9G3bduWuLg4vR428fHxLF26lIIFC1KzZs0U25LRTE1NadmyJX/99RcPHz5Ul589e5a///5bfa4aNGjAwYMH9eZc+vfff/USbh988AHx8fF8//33Bvs5efLkOzwKIYQQQgiRG0kPIvFedevWDT8/P27dusWFCxewsrKibNmyDBkyhK+//jrFyXqFyGmmTZvGiRMnGDt2LLt376ZNmzbExMRw7Ngxdu7cyaeffoqTkxNjx45l2rRptGnThmbNmhEUFMS6devo1asX1atXz9A2jRw5kk8++QQvLy8+/PBDbt26ZTDMqVGjRpQsWZJvv/2WiIgIHj16xOLFi1M1p0/nzp0ZO3YsPXr0oHbt2vz9999s2bJFr0z9+vUB6N27Nx06dCA2NtZgvrEOHTrQuHFjvvrqK86dO0fZsmXZunUrx48fZ/ny5SkO4XpXpk6dyt69e/Hw8KBPnz7ExcWxZMkSChYsyLhx44DEc7xp0yaaNGlCr169ePz4MUuXLtW7zX2HDh1o06YNU6dO5cSJE3h6eqqTYF+9ejXFXl5CCCGEEEKkhSSIxHuV1PxDQryN5hWz9ySONjY27Nu3j59++ok1a9YwYcIEEhISqFKlCjNmzFBvZT516lTy58/PwoUL8ff3x9nZmYkTJzJhwoQMb9PHH3/M06dPmTlzJuPHj6dy5cqsWLGCHj16qEOdLC0t+f333/niiy+YMGECrq6urF27lvHjx6dY/9dff83Dhw9ZtWoVW7dupX379qxfv57OnTurZapXr84PP/zA3LlzmTx5Mm3btjWox8TEhO3btzN+/Hg2bdrEs2fPcHV15ffff+fjjz/OuBOSRpUrVyYgIIBx48Yxe/ZsTE1N8fLyYvbs2WrPq1q1arFhwwbGjh3LxIkTqVSpEqtXr6ZXr15qPRqNBl9fX2bNmsXatWuZOHEilpaWeHh4MG/evEw6OiGEEEIIkVNplMycxVMIkWEiIiJwdHQkPDxc73beOcGrV6+4desWpUqVkrlYMsm///5L+fLlWblyJX369Mns5og0SM37J7vEj+zSTiFE1pNd4kd2aacQIuvJiPghcxAJIYTQ8/owJ52dO3cCiXPnCCGEEEIIIXIeGWImRA6ToCRkdhNENvfPP/8wePBg2rVrR7Fixbh06RIrV66kW7dulCtXLrObJ4QQQuR8CQnEaxMwMzUhThuHual5ZrdICJELSIJIiBwmRhuTciEhjChcuDD58+dn5cqVREREUKRIEUaMGMHkyZMzu2lCCCFE7qCNJfb/E0Qv416S1zRvZrdICJELSIJIiBwmLiEu5UJCGOHq6srff/+d2c0QQgghcq+EOOITEqeKlR//hBDvi8xBJEQOE58Qn9lNEEIIIYQQb0Mbh1abmCCK08qPf0KI90MSRELkMHJjQiGEEEKIbE5R1B5E0jtcCPG+SIJIiBxGm6DN7CYIIYQQQoi3kaBFKwkiIcR7JgkiIXIYuYuZEEIIIUQ2p2iJT0i8potXZPoAIcT7IQkiIXIYuYgQQgghhMjmErT8f34IRVGkh7gQ4r2QBJEQOYz0IBJCCCGEyOaUBLUHkVbRyvWdEOK9kASREDlMt+3diIqLyuxmCCHEO1PLeyNRsdJbUgiRgy2uzwdzd+My7i967uxJjTU15PpOCPHOSYIoi/P09ESj0bBmzZrMbkqqBQcHo9FoMDMzy/C6fXx88Pb25ty5cxletxBCCCGEEEIIkVtJgkhkKz4+PkyePFkSRCLXcXFxoV27dpndjFxn06ZNaDQaAgICUr1Nnz59sLOze3eNEkIIIYQQ4h3I+C4eQogsIyEhgZCQEF68eIG9vT0lSpTAxCSH5YWv7srsFiRybZPuTbVaLcuWLWPFihVcunQJgPLly9O+fXuGDRuGk5NTRrXyvejTpw+bNm3i5cuXmd0UkYPdvh2CaxmXnBfThBAiA+SKa0AhRIaTBJEQOdTly5fx8/MjIiJCXebg4EDr1q1xc3PLxJaJ18XFxdGxY0d27dqFp6cn3t7eREdHc+TIEaZOncq5c+f466+/MruZQmQ5a9euJb+jncQ0IYR4g1wDCiHSS9LI74C3tzcajYYBAwZw9uxZPvnkEwoVKoSlpSVVqlThjz/+MNjm2rVr9OrVCycnJ2xsbKhQoQKjR4/m6dOnSe4jLi6OxYsX07BhQxwdHbG0tKR8+fJ88cUXnD17NtVt3b17Ny1btqRo0aJYWFhQrFgxPvzwQ1atWoWiKIDxeZACAgLQaDSULVs2yfqjo6OZPHkyrq6uWFlZUbhwYYYOHUpYWJjB8SxZsoS6deuSP39+rKysKF++PH379uXQoUPqvEYHDx4EoFevXmg0GvUBcP/+fT799FOqV6+Os7Mz5ubmODg4UKNGDb7//nuio6P19tmnTx80Gg3Tpk3j0KFDdOzYkXz58mFtbY2Hhwf79u1L8pguXbpE//79cXFxwcLCgnz58tG8eXOWL1+ut4/w8HAmTJhApUqVsLa2xs7Ojjp16rB06VL13OrcuXOHoUOHUqFCBaytrcmTJw/16tVj0qRJPHr0yNhTmKSrV6+yYcMGvQsDgIiICDZs2MDly5fTXKd4N6ZNm8auXbuYPXs2Bw4cYNSoUUyYMIHdu3dz8OBBihQpktlNFCLLkpgmhBD6Ll++LNeAQoh0kwTRO+Tn50fNmjXx8/OjWrVqVKlShfPnz/Pxxx+zYcMGtVxAQAA1atRgzZo12NnZ4enpiYODA/Pnz+fChQsG9UZGRuLl5cWQIUM4ceIEVapUoUGDBjx9+pSff/6ZL7/8MlXtmzRpEq1bt8bf359ixYrh5eWFjY0NW7ZsoXfv3sTExLzV8SckJODm5sbUqVPJmzcvTZo0ITo6mkWLFtGsWTNevXoFJA6vadWqFYMHD+bChQtUrVqVRo0aER4ejo+PD2PGjMHGxoaOHTuSP39+AGrWrEnHjh3VByQmiFavXs3NmzcpW7YsrVq1ws3NjXPnzvHf//6Xdu3aGSRmAJYtW0aTJk04fvw4derUoWzZsgQGBtKmTRuOHj2qV3bjxo3UqFGDFStWAODl5UXx4sXZv38/AwYMIDAwEIBbt25Ro0YNpk2bxvPnz2nQoAG1atXiwoULDBo0iP79+6t1njlzBnd3dxYtWsSrV6/w9PTEzc2N06dPM2XKlHT1Htm7d6/R9X5+fiQkyO1SM1tkZCTz5s2jcePGjBo1ymB9o0aNWLBggd6y1atXU7FiRezs7GjevDm3b99W13l6euLu7q5X/uTJk2g0Gnx8fIDEebw0Gg2bN2+mR48eODg4ULJkSX788Ue97V69esWoUaNwcnLCzs4ODw8PXF1dcXFxeevj1iXRQ0ND9Zbb2dnRp08fo9vq5mL68ccfKV68OHZ2dvTs2ZOoqCg2btxIhQoVcHBwoHPnzgYXx4sWLcLNzQ1LS0tcXFyYPHky8fH6d8LavHkzlStXxtramooVK/L7778btOHGjRt06dKFPHnyYGNjg5eXl8yLlskkpgkhROK1t5+fn9EyEi+FEMZIgugdevToERMnTuTevXvs3r2bEydOMHXqVCCx1wDAixcv+Pjjj4mMjGTGjBncuHGDnTt3cvz4cR4+fEiVKlUM6h0xYgSHDx/G3d2da9eucfjwYfbv38/Dhw9Zu3YtRYsWTVXbvvvuO0xMTAgMDCQwMJBdu3Zx7do1Lly4wEcffaT2zEkvRVGoXr06N2/e5NixY+zevZvr16/j6urKmTNn+OmnnwDw9fXlwIEDlC5dmtu3b7N//3727t3Lw4cP2b17N9WrV8fJyQlfX1/1y+9XX32Fr6+v+gAoUaIE/v7+PH36lCNHjrB9+3YCAwO5cOECefLkYf/+/ezfv9+gnU+fPmXhwoXcuXMHPz8/zp8/z4ABA4iPj2f69OlquZs3b6qJs7lz53Lr1i127drF2bNnuXHjBj169ECj0ZCQkMBHH31EcHAwixcv5s6dO+zbt4+AgACuXLmCq6srK1euVCe9nTBhAhEREXz55ZdqnUePHuXJkyfMnDkTBweHJM9vTEwMEREReg+dFy9eGH1uIiIiCAkJSfVzKd6NI0eOEBERYTQp8vp8Afv27WPkyJF06dKFMWPG8M8//9CzZ8907bt79+5EREQwZcoUypQpw4gRI/D391fX9+zZkx9++IF27drh7e1NyZIluXXrVrr2ldF2797N/PnzGTZsGO3atWPt2rV4eXnx+eef8/HHH9OzZ0/++usvZsyYoW4zdepUhg4dSqlSpZg2bRoNGjTA29ubgQMHqmV27NhBly5dsLGxwdvbm86dO3P+/Hm9fd+5c4d69eoRFBTEV199xfjx4wkJCaFZs2bp6u0nMobENCGEgJCQEIMfR94k8VIIYYwkiN6hXr16MXHiRKytrfWWAVy4cIGoqCh+/fVXHj58iJeXF2PHjtVLyuTLl4+8efPq1fnw4UO198qKFSsoWbKkus7c3JxPPvmE1atXp9i24OBgtFotBQoUoFatWnrrKlWqxKZNm7C0tEz7Qb/G1NSUv/76ixIlSqjLChQowMSJE4HE+SMArl+/DkCtWrUoUKCAWlaj0dCyZUsWLVqUqv05OTnRrFkzzMz0p9aqUKECLVq0AEjyV/4xY8YwZMgQve10X7qPHz+uLvvhhx+Ijo6mbdu2jBgxQu+5KlWqFGvWrKFRo0Zs376doKAg+vXrx6BBg/S+4JcoUYLRo0cDiXdHev34W7VqpVeng4MDY8aM4ZNPPknyeKdPn46jo6P6KF68eCrO0v+8mUTSJS2Tcvr0aXbs2JHs+rlz5xIVFZXsemN1BwQEcOTIkXRtGx8fT9jz58muf/nypdGecE+fPUt2XVxcnNGLrIiICOLi4pJdf//+/WTXRUVFERYWxpUrVwCoWrWq3vpHjx4Z9GwBKFmyJGfPnmXw4MFMnDiRkSNHcuTIEe7du6eWSUhIMJog1A3vnD59Otu2beOrr75i+/btWFpasm7dOh4+fMjBgwf5888/mTZtGitWrGDUqFGsX7+epk2bJtkLLzXHHBsby5MnT5Jdn9xw2qTqLlOmDCdPnuSbb75h3bp1FC1alH///ZeTJ08yadIkFi5cSMOGDdm4cSOQOCx02rRpdOvWjZ07dzJ69GjWrl3L6NGj8fHxYc+ePWi1WkaPHk21atU4fPgwY8eOZdq0aUyaNEmvHd9++y1WVlacPn0ab29v+vfvz6FDh3j58iXLly/XK5uQkMCDBw+SPabw8HCjE3kbO59xcXE8fvw42fXPnj0zGFb7OmPvq+zqxYsXLF++3Oh5y45xLjw83KAn4et8fX0NEpmprTskJIRVq1Ylu37VqlVGv0gaq/v8+fPqDzhJWbBgAeHh4emq+8iRI0bvLGhs26ioKObOnZvs+h07dnD69Ol01X3//n2DOPC6devWcePGjXTVfeXKFTWmJWXx4sUGvTJTW3dgYCB79uxJdv3MmTOT/byLi4tj5syZyW67Z88etXd1WtsVGhrK4sWLk12/ceNG9XM0rXVnZ8biXEo/EKZUTuJc2uqWOGdI4lza2pUV45xGMXa1L9LF29ubyZMn079/f3799Ve9dVFRUdja2gJw7949+vfvj5+fH0uWLOGzzz4zqMvT05ODBw+yevVqevbsybp16/jkk0+oVq0aQUFBybbh8ePHSdYHiUH26dOnFClShNjYWMaMGcOXX36Z7Fwnb7bhdQEBATRt2pQyZcqoiY7g4GBKlSqFqalpkl9y7927R7FixTA1NSUqKordu3fToUMHbGxs+Pnnn+natWuyt4g21hadq1evsm/fPq5cucLTp095/vw5p0+f5tGjR0ydOpVvv/0WSJyD6LffftNbpnPp0iUqVaqEubk5sbGxALi6unLt2jV8fX3VYW1JGTp0KIsWLaJq1apJDsd5/PgxR48epU2bNuzcuZOPPvqIzZs3U6dOHebNm4eHh0eqem/FxMToJT8iIiIoXrw4bovc+OjxR5gpxueg7927d4YMF3ofXr16xa1btyhVqhRWVlb6K7PxXcymTZvGhAkTuH79OmXKlDFa1sXFBXd3d7Zv364u++233+jTpw9Hjx7Fw8MDT09PQkND9Yamnjx5ktq1a7Ny5Ur69OmDj48Pffv25cSJE3rJ4VKlSuHq6oqfnx9fffUVP//8M8+ePdPrwdauXTsuXLhAcHAwMTExBgmffPnyYWNjk+JdzHQx8smTJ3pJYTs7O7p06aIOh0vteahXrx4vXrzQO+6PP/6YnTt3Eh4ersbN/fv307RpU7XMzZs3KVOmDNOnT8fLy4s6deqwePFiBg0apJbZtGkTXbt25cCBAzRu3Ji8efPSvXt3JkyYoNeu1q1bU65cOf76668sdxc3o++f/xcREYGjoyPh4eHJ9lrMCnTtLPf1r3RzuI+55n/DJLJTTBNCvH/ZLc6Ff2NPXWUl0RoT7Csk/rga+EkgNuY2yW4bHBzMb7/9luI+JF4KkTNlRJyTu5i9ZzY2/wvquttPApQuXTpV29+5cwcgxS+TUVFRbNmyJdn1+fPnZ/r06YwaNYpZs2Yxa9YsSpQoQd26denUqRPdunXD1NQ0VW1KK90tu7VaLc+fP6ddu3Z8+OGH+Pr60q9fPwYOHEiFChVo0KABPXr0oHHjxqmq99GjR/Tt25ddu/6XMChYsGC6JvnVPU+vj9FO7bkPDg4G4OzZs0YnDNf9QjN16lT++ecfjh8/Tv369bG3t6dmzZo0b96cvn37Jtt+S0vLZHt52dvbEx2RfO8BBwcHvZ5dInPoEqGp/cXvTbpks24+r7dha2ur1vPvv//i7Oxs9IPl6NGjeskWQE1CvW+WlpYG59DS0hKtVgugztNUqlQpvTIlS5bExMSE27dvc/PmTQDKlSuX7H6ePHlCREQES5cuZenSpQbrdbFNvH8S04QQIrGnuoODg9Ee0BIvhRDGyBCzTKbrYZPajly6cin1MHFxcUFRlCQfOiNGjODkyZN88cUX1KhRgwcPHrBx40Y++eQTWrVqpX65ymiv12tpaalOmLt161Z69uxJ6dKluXjxIkuXLqVJkyZ4e3unqt6uXbuya9cuKlWqhK+vLxERETx+/JgzZ87QunXrt253as+9Lqm0bNmyZJ8DRVHUbqMVK1bk4sWLzJ07l1atWmFubk5AQADffvst7u7uRnuKJUc3pC45rVu31hv6JjKHLhmRljsPvg+xsbHJ9jTRqVy5Mtu2bdN7NG/e/D21MH3ejLO6vxVFUeOShYVFstvr3tsff/yxwbFv27aN77777h21XKREYpoQQiTOW5jSNa/ESyGEMRIdMlnBggWBxLmFUkPXm8TY2M60qFGjBj/99BOnTp0iLCyM1atXY2Njg7+/P9u2bQP+N0luUsPF0kPXwyZfvnw4OjoCiUmX9u3bs3r1aq5du8aDBw8YP348kDgMx9h4U0gcKnL48GEsLCzYv38/HTt2xN7ePkPaq5Pac68rd/Xq1VTXnS9fPkaMGIGfnx+hoaGcOHECDw8PwsLCmDx5cprb6urqSrdu3Qx6gDg4ONCtWzfc3NzSXKfIeA0bNsTa2lqdV+xtmZmZGZ0XKbWcnJx49OiR0buc5M+fn3bt2uk9ihUrlup2AhnS1tTQ/VKq6yWkc/v2bRISEihZsiTOzs4AevM5valgwYJYW1tjampqcOzt2rWjXr167+4gRJIkpgkhhD43Nze5BhRCpJskiDJZ7dq1AfTuHmRMo0aN0Gg0nDlzJtnJxVLTGympISm2trb07NmTDz74APhfIiRPnjxA0l+c0tPLSDeZm66XS1JtcXZ25rvvvqNgwYJotVp1iIi5uXmS+9VN1povX753NsxDN9TN2MRsiqLQrFkzAH7//XciIyOTLas7hjePX6PRUKtWLXWOk/QmA93c3Pjyyy/p3bs3nTt3pnfv3nz55ZdyYZCFODo6MnjwYA4dOsSsWbMM1p87d07vTlwpcXZ2Jjg4WG/um4MHD6a5XfXq1ePly5fs3btXXfbq1asMu0uXLhnz+oSTx44dMzqh+Nto3rw55ubmBhPeL1y4EEj8NbVWrVpYWlqqk8frXLx4Uf23mZkZrVq1YuPGjVy+fFmvXGxsrNEJNEXG69Gjh8Q0IYRIglwDCiHSSxJEmUx3V7MNGzZw8uRJdfmrV6/49ttv+eeff/TKu7i40KlTJxRFYcCAAXpJG0VR+OOPPxg6dGiK+/Xz86N9+/acOHFCb3loaKg6pKlSpUoA1K1bF4D169erEzYDbN++nU8//TTZfSiKYvAl6uDBg3z//feYmpqqd/OaMGECI0aMMLiDwMmTJwkLC8PS0lKd90c3oZ6fn59aTqvV4urqirm5OQ8fPuTAgQPqOt2t6tetW5fiOUnJ8OHDMTU1ZevWreoXS5379+/z8ccfc+7cObp160bp0qW5f/8+nTp1MriL0b179xg9ejS//PILAB4eHqxatcogUaT7cq57HtLDxMQEFxcXKleujIuLi3QpzoKmTZtGw4YNGTt2LF5eXsyZM4fvvvuO9u3bU7169TT1ROvZsyexsbF06tSJ+fPn0759e8aNG5fmNvXs2RMnJyd69OjBxIkTmTZtGpUrV9aLUSmJjY3lm2++MXicOnVK7eE3dOhQ5s2bx9ChQ2natOk7G9bq5OTE2LFj+fPPP2nTpg2zZ8/mk08+4YcffqBXr15Ur14dBwcHhgwZwqZNm/j444/54YcfaNu2rUEPvhkzZmBlZUXt2rX5/PPPmTdvHiNHjqRMmTLqnRnF+1GyZAmJaUIIkQy5BhRCpIdMUp3JatSowfDhw/npp5+oV68eHh4eWFlZceLECV6+fImNjY3BMIwlS5Zw9epVgoKCKFOmDHXq1MHa2poLFy5w//59o3fYet327dvZvn07xYsXx9XVlVevXhEUFERkZCQdOnSgVatWAAwYMIB58+Zx/vx5ypcvT6VKlbhx4wZXr141egeEhIQEKlasSIUKFXBxceHx48cEBQWhKArz5s2jZs2aQOIwk/nz5zNv3jwqVqxI8eLFefLkCWfOnEGr1TJz5ky1F1OvXr1Yvnw5f/zxB2fPniVv3rzcv3+fW7du8e233zJp0iSaN29O3bp1sbe3JygoiLCwMAoVKmR06EhqVK9enfnz5zN8+HCGDRvGnDlzcHNz4+nTp5w6dYr4+HjGjh2LhYUFW7ZsoU2bNuzduxcXFxdq1qxJnjx5uH//PufOnUNRFGbPng0kDrnr3bs3w4YNo3Llytjb23P16lVu375N/vz5mTJlylu1O8dLx93DshIbGxv27dvHTz/9xJo1a5gwYQIJCQlUqVKFGTNmMGDAgFTX1bp1ayZPnsySJUs4duwYbdu25Y8//uCjjz5KU5vy5MnD3r17GTJkCDNnziR//vz079+f48ePq735UpLcLUErVKhAzZo1WbNmDWPHjmXcuHHUrFmTvXv3ZshcYcmZOnUq+fPnZ+HChfj7++Ps7MzEiRP17kY2c+ZM4uPjWbNmDdu2beODDz5g9uzZjBo1Si3j6urK8ePH8fb2ZsOGDYSFheHs7EybNm0yZYJuIYQQQgghMowiMtykSZMUQOnfv3+S6wEFUO7cuaMoiqIkJCQoP//8s1KxYkXFwsJCKViwoPKf//xHOX36tNKkSRMFUFavXq1Xx8uXL5Xp06crNWrUUGxtbRUzMzOlQoUKyldffaVcvHgxxTbGxsYqv//+u9KxY0elcOHCipmZmWJra6vUrl1bWbVqlaLVavXKX7t2TWnfvr1ib2+vWFtbK/Xq1VNWrVql7N+/XwGUMmXKqGVv3bqlAIqDg4MyZswYpUKFCoqFhYVia2urNG7cWNm5c6de3U+fPlXmzp2rNGnSRMmbN69iamqq5MuXT2nbtq1y+PBhg7Zv3bpVqVWrlmJjY6PkzZtXadOmjXoeFy9erJQuXVoxNTVV8ufPr/Ts2VM5f/680rt3bwVQpk6dqtaT1LI3j8HU1NRg3dGjR5WuXbuq5y1PnjxK27ZtleXLlysxMTF6xzVlyhSlVq1air29vWJiYqIULFhQadu2rbJ06VIlPDxcURRFOX36tPL5558rFStWVKytrRVzc3PFxcVFGTFihPLo0aMUn0ud8PBwBVDO3zmf6m2yi+joaOXSpUtKdHR0ZjclV6tfv77SpEmTzG6GSKPUvH908UMXl7Kq7NJOIUTWk13ih9rO4PPK9ccvFEVRlDOPzyix8bGZ3DIhRFaXEXFOoyipvH2WECJLi4iIwNHRkYt3L1KxaMXMbk6GevXqFbdu3aJUqVIp3l1LZIzY2Fi9O3o9evQIFxcXRowYIXfrymZS8/7RxY/w8HCDiU2zkuzSTiFE1pNd4ofaztsXCbMtScn8tpx/cp6K+StiamKa2c0TQmRhGRHnZIiZEDmMmUbe1uLt1a5dG3d3d2rWrElYWBhr1qzB2to6VXOcCSGEEOItmZhiaqJJ/KeJCSYamUNICPHuyTdJIXIYuYAQGaFOnTrs3r2bTZs2YW1tTaNGjZg+fTpFixbN7KYJIYQQOZ/GFLP/n1jaTGOGRqPJ5AYJIXIDSRAJkcNI92OREZYtW5bZTRBCCCFyL42J2oPIzES+sgkh3g/paiBEDiO/MAkhhBBCZHMm/0sQmZuYZ3JjhBC5hSSIhMhhTDXSg0gIIYQQIlszNVcTRBamFikUFkKIjCEJIiFyGPmVSQghhBAimzMxw9xUEkRCiPdLEkRC5DBWZnIbeCGEEEKIbM3UAgvTxK9qtua2mdwYIURuIQkiIXIYuYuZEEIIIUQ2Z2KK2f8niCw1/5ukOl6bgKIoAMQlxGVK04QQOZd8kxRCCCGEEEKIrEpJUP+ZoCQ+Ev+dkMwGQgiRPpIgEkIIIYQQQoisSi9BpKg9iLQJ2sxqkRAih5IEkRBCZCIXFxfatWuX2c0QQgghRFb1Rk+h/+9AhFaRBJEQImOZpVxECCGyroA7AZndBAA8i3u+m3o9PTl48KD6a2FCQgLz589n6dKl3Lx5kzx58uDi4kKHDh0YPHgw+fPnp0+fPvz2229qHebm5jg5OfHBBx8wffp08uXLByQmp27fvs2gQYNYvHix3n5jYmIoVqwYoaGhzJ49m1GjRr2T4xNCCCFECl5LEClK4gNkiJkQIuNJgkgIIbKwESNG0L17d/Xv/v3789tvv/Hhhx8yYMAAoqKi2L17N99++y1Vq1ZVeyNZWloyb948IDHZc+TIEZYuXcq9e/fYvn27Wp+JiQlr165l1qxZODg4qMs3b97Ms2fP3s9BCiGEECJ5ryeIUKQHkRDinZEEkRBCZGEdOnRQ/33u3Dl8fHwYPXo0s2bNUpdPmDCBXbt2kTdvXnWZmZkZgwcPVv/+8ssv8fLywt/fX6/+Bg0acPr0aVavXs2wYcPU5UuXLqVVq1bs2rXrXRyWEEIIIVLrzSFm0oNICPGOyBxEQgiRBfj4+ODq6oqVlRV169bl7NmzALRr1w4XFxcALl26BCQOO3tTmzZt8PDwMLoPCwsLihUrprfMwcGBjz/+mEWLFqnL/v33Xw4ePMiQIUPe4oiEEEIIkSHeGGKW+P//TVYthBAZRXoQCZHDePzuwcn+J7Ext8nspohU2r17N2fOnKFfv36YmJjwyy+/0LZtW27evKlXrnjx4gBs2bKFtm3bGq1TURTu3r0LJA4x8/X1xd/fnzVr1hiUHTx4ML/++isHDx6kSZMmLFu2jPr161O5cuUMOkIhMlYt743EWjhzaUorbCzkUkYIkUPFRsL3RRL/Pf4+UVhSadJu0MRiX2EiAIGfBMo1nxAiw8hVlRBCZDI3NzcOHTpEnjx5AKhbty5t27bl999/1ytXr1496taty9KlSzlz5gydO3emadOm1KpVCxMT/Q6hUVFRakJJp3Xr1rRo0cJg/zVr1qRWrVosWrSI+vXr89tvvzF37tyMPUghhBBCCCFEliZDzIRIgqenJxqNJsneFknx9vZGo9GoQ4EAAgIC0Gg0lC1bVq+sRqNBo9GovTuEKFGihJocAmjZsiW2trYcO3ZMr5yJiQm7du2iT58+XLhwgW+++Ya6devi4uKid9cyACsrK7Zt28a2bdvYtGkTY8aMISAggBYtWqDVGk5qOXjwYDZv3szixYtJSEiga9eu7+RYhRBCCCGEEFmTJIiEyAAVKlSgY8eOtGzZMrObInIAU1NTChUqlGQSMW/evKxcuZKnT5/i7+/P6NGjiYqKok+fPuzYsUOvjnbt2tGuXTs++ugjZs6cyfz58zl16hR+fn4G9X788cfY2NgwcuRI+vTpg6Wl5Ts9RiGEEEIIIUTWIgkiITJA9+7d8fX1ZenSpZndlCQlJCQQHBzM+fPnCQ4OJiFB7nqR1cXGxhpdb2VlRbNmzZg1axZHjhzBxMQkxR5vTZo0AeDixYsG62xsbOjVqxfx8fEMGjQo/Q0X4j2SWCaEEOkj14ZCiKTIHERC5HCXL1/Gz8+PiIgIdZmDgwOtW7fGzc0tE1smkhMdHc2jR48oW7YsN27cUJcrioJGozEoX6FCBfLly0d4eLjReqOiogCwtrZOcv2YMWNo0KCBwbBIIbKqhQsX0r5NK4llQgiRBnJtKIRIjvQgygYURWHdunU0b96cfPnyYWFhgYuLC/369ePIkSNqub1799K2bVsKFCiApaUlpUuXZtiwYQbDVIKDg9FoNJiZmREREcHkyZNxc3PDysqKwoUL88UXXxAZGWnQjpiYGGbOnEnFihWxtLTE0dGRJk2a8PHHH6PRaGjevLla9vX5d8LCwhg/fjylSpXCzMyMc+fOqeX2799P+/btKViwoHoL7t69e+t9Kdadg/Xr19O4cWMKFSqEpaUlpUqVonv37mzdulUtFxcXx5IlS6hbty758+fHysqK8uXL07dvXw4dOpSu879z5048PT1xcHDAzs6OZs2a8c8//+iV8fHxMTgHaREZGcnMmTOpXr06jo6O2NjY4O7uztChQ9XbnafH5cuX2bBhg94FAEBERAQbNmzg8uXL6a5bvDsrVqwgLi6ODh066C1fsmQJy5YtM7it7cmTJwkNDaVRo0ZG612yZAkajYZmzZolub548eJ079797RovxHsUEfFCYpkQQqSBXBsKIYyRHkRZnFarpXv37mzatAkTExNq1aqFg4MDFy9eZOXKlezfv5/g4GAmT56Mt7c3JiYm1KhRg4IFC3LhwgUWLlzI+vXr2bNnDzVq1NCrOyEhAXd3d+7du0ft2rVp3LgxR48e5eeff+bmzZt685lER0fTokUL/v77b2xtbWnUqBGxsbGcP3/eaOLlyZMnuLm5ER4ejoeHB+7u7tjYJN6KU9dmCwsL3N3dcXZ25tq1a6xatQpfX1+OHDmi3ma7T58+rFq1CnNzc+rWrYuNjQ1Xrlxh/fr1BAQE0KFDB7RaLa1ateLAgQPY2NhQt25dTE1NOXfuHD4+Ply+fNlg0t+UTJgwgeDgYEqVKkWDBg24desWBw4coGnTpuzdu5fGjRunqb6kvHz5Eg8PDy5evEjevHmpU6cOWq2WoKAgFi1axLNnz/jjjz/SXG9CQkKSc828zs/PD1dXV4M7YIn3y9/fnz59+lClShWuXr3K8uXLadeuHV5eXvz4449quVevXvH111/z448/0rFjR5ydnbl58yY+Pj6UL1+er776Si0bHx/P4sWLURSF8PBw9u7dy/79+/n888+pVKlSJhylEO+OxDIhhEiZXBsKIVIiCaIsbtasWWzatImiRYuyc+dOqlSpAiQG+F27drFgwQL8/Pzw9vYmX7587Nq1izp16qhlJkyYwPfff0+XLl24cuUKFhYWat2KolC+fHn27dtH+fLlAbhx4waVKlVi586dnD59Wk0qjRs3jr///pu6deuybds2ChYsCCTOkzJhwgRmzZqVZPsjIiLo2rUrs2fPJm/evOpyX19fvL298fDw4I8//qBkyZJqm2bPns3YsWP54osvCAgI4NSpU6xatYo8efJw6tQpSpcurdZz7NgxfvrpJ7XOAwcOULp0aQIDAylQoIBa5969e/nrr7/S9Rxs3bqV9u3bq3+PHj2aOXPmMGTIkCTnckmrJUuWcPHiRTw8PPD391cTaFqtls2bN3PmzJkkt4uJiSEmJkb9+81fgkJCQgyWvSkiIoKQkBC9u69lN57FPTO7CW+tTJky3Lhxg82bN2NqasrAgQOZM2eOQbm+fftiZmbGunXrWLRoES9fvqRgwYJ069aN7777Tm/oWExMDEOGDAESh5S5urqyZMkSPvvss/d2XEK8LzkhlgkhxLuWW64NhRDpJ6nhLCwuLo7Zs2cD8OOPP6rJIUi83fUHH3zAzp071S+S3t7eanJIV2batGlUqVKFW7dusXnzZr36TU1N2bNnj5ocgsQvqvXq1QPg+PHjADx79ozFixdjYmLC2rVr1eQQgIWFhdGxyqVKleLXX3/VSw4BTJkyBY1Gw4YNG9TkECTeAn7MmDEUL16cQ4cO8eTJE65fvw5A+fLl9ZJDAB4eHvz+++8AarlatWqpySFdnS1btmTRokXJtjM5U6dO1UsO6ZY5ODhw6dIlgoKC0lznm3Ttbty4sZocgsTnp2vXrnz33XdJbjd9+nQcHR3VR/HixfXWv3jxIlX715WbNm1asmVOnz6t16PsTXPnzlXnt0mKsboDAgL0hkqmZdv4+HgePXqU7PqwsDCj7bp//36y62JiYggNDU12fWhoqF6CLi11R0VFERYWBiQO+bxw4QKHDx8mIiKCsLAwvL291buIbd++neDgYAAcHR35/PPP2bhxI8+fPyc+Pp4HDx6wbNkynJycgMTnc8GCBSiKoj6ioqIICgris88+02tXcHAw27dvV//WarU8fPhQ/dvFxQVFURg1ahQAz58/T3L4aWqOOTY2lidPniS7/unTp7x69SpddUdHR/Ps2bNk1z9+/Ji4uLh01f3y5Uujczs9ePAg2Yk9ExISePDgQbLbhoeH8/Lly3S1Ky4ujsePHye7/tmzZ0RHRye73tj7Krt68eIFy5cvN3resmOcCw8PZ8GCBcmu9/X15fz58+mqOyQkhFWrViW7ftWqVYSEhKSr7vPnz+Pr65vs+gULFhh9bxmr+8iRIwQEBKRr26ioKObOnZvs+h07dnD69Ol01X3//n2WL1+e7Pp169YZDKFPbd1Xrlxh48aNya5fvHix0c8sY3UHBgayZ8+eZNfPnDkz2RgaFxfHzJkzk912z549BAYGpqtdoaGhLF68ONn1Gzdu5MqVK+mqOydL6X2ZGsldQ0qc0ydxzpDEubS1KyvGOY3y5mQWIss4evQo9evXJ2/evDx9+jTJyWnj4+Oxt7fn1atX3Lp1K8ls/7fffst3333H4MGDWbRokTpkytTUlPj4eIPy3bp1Y+PGjXz33XeMHz+ezZs389FHH1GnTp0kX/w+Pj707dsXLy8v9u3bByR+SDRt2pQyZcqoCRCdx48fq/MItW7dOsljP3ToEGFhYQQGBmJtbU2VKlUwMTFh1qxZ9OnTh/z58xtss23bNjp06ICNjQ0///wzXbt2xc7OzqDc/v371V5Hr3N3d1ffZJ6enhw8eJDVq1fTs2dPg7ItWrRg3759/Prrr/Tv3z9N50D3PN65c4dixYqxYMEChg8fjpOTE4sWLeKDDz5I1S3Gk+pBVLx4cdwWuXGy/0ke33vMb7/9lmI9vXv3zvK/Eule36VKlcLKyiqzmyNEtpKa909ERASOjo6Eh4fj4ODwnluYerp2lvv6V2ItnOlpdRpzTWKCLjvEMiFE5slucS48PBwHK1P4vkjiivH3icKSihN3gyYW+woTAQj8JBAbcxsjNf5PcHBwjrk2FEIYyog4J0PMsrA7d+4AULp06SSTQ6D/q3uJEiWSLKPrWfLmZNXJ0fVi0f0qfuvWLQDKlSuXypYbp+sNERMTw5YtW4yWjYqKok6dOgwfPpyffvqJUaNGMXr0aMqWLUu9evXo2rUr7dq1A6Bdu3Z8+OGH+Pr60q9fPwYOHEiFChVo0KABPXr0UOcLCgkJSXK/z58/T/Ux6HprPH36NNXbJKd///6sW7eOo0eP8tFHH2Fubk7lypVp1KgRvXv3pnr16kluZ2lpaTSRVKJECRwcHIx2JXZwcEj2dSOEENmFxDIhhEiZXBsKIVIiQ8yyMF3nruSSQ6n1ttsbG0aTHrrEU9GiRfWGwST18PT0BGD+/PkEBAQwYMAAKlWqxI0bN1i1ahXt27enT58+QOJxbt68ma1bt9KzZ09Kly7NxYsXWbp0KU2aNMHb2xtInPA6qX0Z68b5Jq1WC5Cqnj4psbGx4eDBg6xdu5YuXbpQuHBhTp8+zfz586lduzYrVqxIV70mJibJ9tDSad26tUxCKITI9iSWCSFEyuTaUAiREnn3Z2FFiiR2Kb1582ayZXS3cge4fft2kmV042qLFi2arnbky5cPQG9ukrehO66HDx+mOFHe65o0acKyZcs4f/48T58+Zf78+ZiYmPDbb7+pEzlrNBrat2/P6tWruXbtGg8ePGD8+PFA4hhNY+NW00LXC6pUqVIZUp+5uTmffPIJGzdu5Pbt29y6dYv+/fuj1WoZOXKkwW3NU8vNzY1u3boZdDF0cHCgW7duRuePEkKIrM7BwV5imRBCpIFcGwohjJEhZllYrVq1sLa25tmzZ/z111906tTJoIypqSkNGzZk3759+Pr68vXXX+utVxRFHU7VpEmTdLVDN8Tp+PHjREZGYmtrm656dEqUKKHetWn58uUGbdbRarWYmpry6tUrg3kz8uTJw/Dhw1mzZg0nTpzgxo0bVKhQwaCcs7Mz3333HcuWLePJkyfcvn1bbwLr9Lh79y4nT57EysqKRo0avVVdQJLH5+Liwk8//cTy5ct5/vw5z549S3LepdRwc3PD1dWVkJAQXrx4gb29PSVKlJBfh4QQ2d7QoUOxs7JIuaAQQgiVXBsKIZIjUSALs7a2Vm9T/fXXX3P58mW99Xv27KFbt26MHDkSgMmTJ3P06FF1vVarZdy4cVy4cIGSJUvy0UcfpasdderUoWzZsrx48cJgpvS///6befPmpbnOcePGATB+/HjWrl2r10NGq9Wybds2dc6gpUuX0qdPH4MZ3G/evKnOkl+xYkUmTJjAiBEjDO5EcPLkScLCwrC0tKRMmTJpaue1a9f0JvJ+9uwZvXr1QqvVMnjwYIO7s6XHgAEDmDJlisEdnvbu3QtA4cKF33o/JiYmuLi4ULlyZVxcXLLtBYDMqS9E2uXk9012jWVCCJHZcsq1oRAiY0kPoizuu+++49SpUxw8eJDKlStTu3Zt8uTJw9WrV7l16xZVq1aldevWjB8/nu+//54GDRpQs2ZNChYsyPnz57l79y758uVj48aNWFik71dWjUbDkiVLaNOmDTNmzODPP/+kXLlyBAcHc+nSJbVHkbm5earr7N+/P+fPn2f+/Pn07NmTcePGUalSJaKjo7l48SKhoaFqvQkJCfz222/89ttvlC1bljJlyhAeHk5QUBAxMTF88cUXuLm5ERcXx/z585k3bx4VK1akePHiPHnyhDNnzqDVapk5cyZ58uRJ07FPnTqVRYsW4e7ujqmpKYGBgbx8+ZIGDRoke/v5tHr58iWTJk1iypQpVK5cGWdnZ+7du8f58+cxMTFRh9LlZmZmiaEqqbvuCSGM071vdO8jIYQQQgghkiJXi1mclZUV+/btY9myZaxevZqLFy8SGRlJiRIlGDhwIP369QMSE0keHh4sWLCAkydPcvbsWYoUKcLgwYMZN27cW9+NoFmzZhw8eJCJEydy9OhRQkJCKFu2LLNnz8bOzo4hQ4akuZfLvHnzaN++PYsXL+bo0aP4+/tjYmJC+fLl6dGjBz169ADgs88+w8bGhs2bN3P69Gn8/f2xsbHBw8ODkSNH0r59ewAmTpxIiRIl2Lp1K+fOnePKlSs4OjrSqlUrxo0bR8OGDdN83P369ePWrVucOnWK6OhoSpYsSa9evRgzZkyG3W79l19+oWbNmuzYsYPLly9z/vx58ufPT7du3fj222+pXLlymuo79smxVN/uNLswNTXF1NSUiIgI7O3tM7s5QmQrERER6nsopzjp3TVL36ZaCCEyhIUteIdDdBhY2GIDXJzcClMTDZZmHxIaHZrjrvmEEJlLo+TkvufivZg4cSJTp05lxowZjB07NrObk2tFRETg6OhIeHh4jvzi9Pz5cx48eEDBggWxtbV967vzCZHTKYpCZGQkT548oXDhwkZ7UGaX+JFd2imEyHqyS/xIsp2vwsHKEYCo2HhMNBqszE0JjQ6lgPXbza0phMg5MiLOSQ8ikSr3798nNjYWFxcXveVXrlxh0aJFmJmZ0b1798xpnMgVHB0diY6OJjQ01GC+JiFE0jQaDXny5MHR0TGzmyKEECK9NPpTDeh+IzPR5O4pCIQQGU8SRCJVDh06xMcff4yzszNubm7Y2try8OFDTp8+jaIo/Pjjj5QsWTKzmylyMI1GQ+HChXFyciIuLi6zmyNEtmBubp6jhpYJIUSu9FoiSPP//wGYyP2GhBAZTBJEIlUqVKhAhw4dOHfuHCdOnCAmJoYCBQrQoUMHhg8fTtOmTTO7iSKXyGlzqQghhBBCGPV6gkjzvx5EpiZyPSSEyFiSIBKpUq1aNbZs2ZLZzRBCCCGEECKX0ST5l6lGEkRCiIwl/RKFEEIIIYQQIqt6raeQiUaj3qhDehAJITKaJIiEEEIIIYQQIqt6bYiZiSbxkfhv+SonhMhYElWEyG0StAaL4rUJen/HaWUSaCFENhMf+79/JsRnYkOEECKDvdZTyMzURO1BZG5inlktSpo2bbE3QUkwiNev4gyvU4UQ748kiITIbZJI/sS+kSCK1ka/r9YIIUTGiH+l/jNWG2uk4PunKEpmN0EIId69+LRdP2oVLXEJ+telkTGS4BciM0mCSIjcRjH8ZSZOq//lJat9uRJCiBS99iv0m184hBBCvAfxMWkqnqAkkKDo/0ipTZCEuhCZSRJEQuQ2SQwxS3jjw1iGZwghsp3/j22KomS5HjtZrDlCCPFupHGKgqQSRHGSIBIiU0mCSIjc5o0PYoD4Nz6M5dd3IUS28/+xLUFJIAHDOCeEEOIdS+MPjIqiGCSI3vzRUgjxfkmCSIjcJokEUYIiPYiEENmYovwvQURClutBJIQQuUIarx+T6kH05o+WQoj3SxJEQuQ2SXx4vzneO0GRL1hCiGxESQD+P2YpoCDxSwgh3rskfoQ0JgGZg0iIrEYSRELkNknMQfTmh7FW0aJNYjJrIYTIkhRFnegnKw4vk687QohcIY1zECU1xEwSREJkLkkQCZHbpKIHUVIf2EIIkXUp6NIwSQ1ZEEII8R6kcYhZfEK8JIiEyGLMMrsBQoj3KDYS5rkn/nv8faKwpOLE3QBcmtIKGwszouKi6LKtCwCBnwRiY26TWa0VQoiUxUbC90UAiBpzg7obmwJZJ35FxcYbxFkhhMhRXovDjL8PFrYpbhIVF4XnBk8gMV6jWEisFCILkB5E6XDmzBm8vb1ZtWpVZjclS/H29kaj0TBgwID3vm9PT080Gg1r1qx57/tOTnBwMN7e3vz000+Z3RQhhBBCCCGEEMIoSRClw5kzZ5g8ebIkiIRRwcHBTJ48WRJEQgghhBBCCCGyPOm7J4R4LxISEggJCeHFixfY29tTokQJTEwkRy2EyPokfgkhhHgX5PNFZDWSIBJCvHOXL1/Gz8+PiIgIdZmDgwOtW7fGzc0tE1smhBDGSfwSQgjxLsjni8iKJD2ZRp6envTt2xcAf39/NBqN+vDx8eH777+nSZMmuLi4YGNjg5WVFS4uLvTu3ZsrV64kWae/vz8tW7bEwcEBS0tLXF1d+eqrr7CxsUGj0XD37l21bGRkJDNnzqR69eo4OjpiY2ODu7s7Q4cO5ezZs6k+jq+//pr69etTokQJrKyssLa2pnz58gwbNkxvfzqBgYF06tSJkiVLYmFhQaFChWjdujULFy4kNjbW6L6uXr1KoUKF0Gg0zJkzJ8W2+fj4oNFoaN68eZLrXVxc0Gg0HDlyJMn1O3fuxNPTEwcHB+zs7GjWrBn//POPXpmU5kvSPaevnwvdfg8dOoSPjw/169fH1tYWR0dHPvzwQ27cuKGW7dOnD02bJk6UeuPGDb3Xibe3d7L7SG37spPLly+zYcMGvQ8/gIiICDZs2MDly5czqWVCCGGcxC8hhHj/FCXn38lMPl9EViU9iNKoYcOGREREEBQURMGCBalfv766rkSJEsyZM4fLly9TqVIlKlWqRExMDEFBQaxatYq//vqLo0ePUqlSJXWbn376iS+//BKAatWqUahQIW7evMn8+fMN9v3y5Us8PDy4ePEiefPmpU6dOmi1WoKCgli0aBHPnj3jjz/+SNVxLFu2jLi4ONzd3alSpQqRkZGcPHmShQsXsnnzZs6cOUOhQoUAWLFiBQMGDEBRFKpVq0alSpW4c+cOe/bsYffu3TRr1owKFSokuZ/bt2/TvHlzHj9+zMyZMxk1alSqz3V6TJgwgeDgYEqVKkWDBg24desWBw4coGnTpuzdu5fGjRu/9T4+/fRTbt++TYUKFWjSpAlnz55ly5YtnDx5kgsXLpAnTx5q1KjB9evX+fvvv7G1tdVLdiV3rnKihIQE/Pz8jJbx8/PD1dVVutMKIbIUiV9CCJEOCQkpl0lBnDZnJ4jk80VkZZIgSqNp06ZRtmxZ+vbtS5UqVfD19dVbP3v2bGrXrk2BAgXUZfHx8XTq1Int27czY8YMVq9eDcCpU6cYMWIE1tbW/PXXX7Rq1Urd5uzZs1SrVk2v7iVLlnDx4kU8PDzw9/fHxibx9r1arVZN6qTW2rVradq0KQ4ODuqyly9f4unpyalTp/j555+ZOnUq8fHxjBo1CkVR+PPPP+ncubNaPjg4mOnTp2NqaprkPh4+fEjz5s25e/cuM2bMYMyYMalu39vYunUr7du3V/8ePXo0c+bMYciQIVy8ePGt6zc1NWXv3r1q0ic6Opo6depw4cIFVqxYwYgRIxg+fDhVqlShadOmODs7G7xOMkJMTAwxMTHq32/+ApFWul9r4hPi36qe14WEhKTYroiICEJCQnBxccmw/QohcpHXvoxk5K/OEr+EECId4qLAyv6tqoiO1ZLHJoPakwXJ54vIyiQlmcHatGmjlxwCMDMzY8iQIQCcO3dOXT5z5ky0Wi2jR4/WSw4BVK1a1aDu69evA9C4cWM1OQSJCYuuXbvy3XffpbqdHTt21EsOAdjZ2dGvXz+9dj59+pSwsDD12F7n4uLCkiVLKFeunEH9z549o0WLFly/fp3vv/+esWPHprptb2Pq1Kl6ySHdMgcHBy5dukRQUNBb72PlypV6PYKsra3p0qULAMePH3/r+lNr+vTpODo6qo/ixYsbLb98+XIePHiQ7PqZM2cBoEFjsO706dPs2LEj2W3nzp1LVFSUwfIXL14YbZPOyZMnk103bdq0ZNeFh4ezYMGCZNf7+vpy/vz5dNUdEhJi9E6Fq1atIiQkJF11nz9/3mjScMGCBYSHh6er7iNHjhAQEJCubaOiopg7d26y63fs2MHp06fTVff9+/dZvnx5suvXrVunN0wzLXVfuXKFjRs3Jrt+8eLFhIaGpqvuwMBA9uzZk+z6mTNnEhcXl+S6uLg4Zs6cmey2e/bsITAwMF3tCg0NZfHixcmu37hxY7LDmlOqO9vR/C9m/b52rdGixo77zTiX2vilK/c251TiXNrqljhnSOJc2urOzpYvX879+/eTXZ+WOPem5K7nUlN3SoxtGxERweJFrz3Xb1yK5rQ4l9rPF2PfKyTOpa1uiXOpp1FywyDPDObj40Pfvn3x8vJi3759BuvDw8PZvXs3QUFBPHjwgOfPnxMSEkJQUBBlypTh+vXrJCQkkC9fPsLDw7l06VKSE5Fp/v+i986dOxQrVowFCxYwfPhwnJycWLRoER988AGWlpYG2/3xxx9JDjVr1qwZw4cPV/9+/Pgxu3fv5uzZszx+/Jjnz5/z77//cuXKFfXYFEXBycmJ0NBQPv30UyZOnEiZMmWSPC/e3t5MnjyZbt26cevWLU6cOMF3333H+PHj9cp99tlnPH782GD7adOm4e7unuL5dXFx4fbt2xw+fJiGDRsCiXNDHTx4kNWrV9OzZ0+DbVq0aMG+ffv49ddf6d+/v9rW/v378+uvvxqUf/PcJ7dfnYULFzJs2DBatGihBp+AgACaNm2qPuep2ceb5zK59kHSPYiKFy9OeHi4QfJPFRsJ3xdJ/Pf4+0RhScWJuwG4NKUVNhZmRMVFUff3ugAEfhKIjXn6f8IJDg7mt99+S7Fc79695RcSITJRREQEjo6OxuNHFpBkO1+La1FjblB3Y+L8b1klfkXFxhvEWSHE+5et41xW9/r15Td3U9WD6M3rTRQLNVaemdiCPDYW76y5mU2uj8W7khHxQ65SMtjMmTOZMmWKmn23tbWlaNGiWFjoB7mwsDA1o1y2bNlU1d2/f3/WrVvH0aNH+eijjzA3N6dy5co0atSI3r17U716dSAx+7plyxaD7fPkyQOg9lpasGAB8fGJw4kcHBwoWrQoWq1WbxuNRsPPP/9Mr169WLVqFatWrcLZ2Zk6derQrl07evToodebCWDDhg3qv0uUKGHQjj179nD79m2D5V999VWqzkN6ODk5AYk9ot4F3TlIyIBx16llaWmZZIIwKylRogQODg5Gu9E6ODgk+ToRQojMJPFLCCHSIQPmzDE3NezNnpPI54vIymSIWQZasWIF33zzDYqi8N133xEcHMzLly+5evWqQffw13t+pJaNjQ0HDx5k7dq1dOnShcKFC3P69Gnmz59P7dq1WbFiBZDY+0RRFIOHj48PAJMnT+bHH3/E3t6ehQsX8uDBA7Un05u9fQD+85//cPHiRcaOHUu9evV4/vw5W7du5bPPPsPDw8Ogm2SBAgWYMGECAAMHDuTUqVN664ODg5Nsn6enZ5rPSWrpEl9ZPaGS05iYmNC6dWujZVq3bi0T8AkhshyJX0IIkTk0mpydIJLPF5GVyasuA+kSMN9//z3jx4+nZMmSyZbNmzevGvwePXqU6n2Ym5vzySefsHHjRm7fvs2tW7fo378/Wq2WkSNHpmqCTl07V6xYwZAhQ3B2dk5xm3LlyjFjxgz++ecfwsPD2bJlC4UKFeL8+fMG41A7duzIlClTGDp0KK9eveLDDz9M0zHqgqGud1NGCA4OBqBUqVLvbB9ppXv+M7MN74ObmxvdunUz6Obo4OBAt27dkhxeKYQQWYHELyGEEO+CfL6IrEqGmKWDubk5gMFwLN28Oql5Q1tbW+Pq6sqVK1c4cOAAvXr1SnGbV69eYWVlpbfMxcWFn376ieXLl/P8+XOePXtG/vz5jdaTlnbGxsZibm6ul8m3sLCgQ4cOHDlyhNmzZyc7EdmPP/7I6dOnOXbsGF26dGH//v3quTNGNxTu3r17Sa5P6zCuu3fvcvLkSaysrGjUqFGK+3jzeU2v5F4nOnny5CEsLIx79+4ZjC/OqDZkFW5ubri6uhISEsKLFy+wt7enRIkS8suIECLLk/glhBDiXZDPF5EVyasvHXRf5oOCgnj48KG6XJdw+eOPP/SSGEeOHOGzzz4zqKdHjx4AzJgxQ29unKdPnzJ48GCD8gMGDGDKlCk8efJEb/nevXsBKFy4MHnz5k2x/e7u7kDiLPOv27JlC+PGjdNbdunSJRo1asT+/fv1eidFRUXx999/A1CpUqUk92NhYcGmTZtwcnLiyJEjfP755ym2DaBWrVqYmJhw/fp1jh07pi6/e/cuH3zwAXfu3El222vXrun1yHn27Bm9evVCq9UyePBg9fzUrZs4Kd6hQ4f05kO6fPmywQTU6aV7ndy+fZtLly6py3XJH10bVq9era6Lj49n9uzZzJkzJ0PakJWYmJjg4uJC5cqVcXFxkQ8/IUS2IfFLCCHEuyCfLyKrkVdgOnh4eFCuXDnCw8Nxd3enSZMmlClTBk9PT6ytrfHx8aFcuXK0adNGnUQ6qdvmjRgxgipVqnDp0iVKly5Nq1ataNy4McWKFWPNmjVqOV1PlJcvXzJp0iQKFy5M9erVadOmDVWqVOHDDz/ExMSE+fPnpyqozJkzB41Gw+TJk6lUqRJt2rShXLlyfPjhhwY9lAD+/vtvvLy8KFy4MM2aNaNp06YUK1aMf/75h9q1a9O3b99k91W0aFH++OMPTE1NWbp0qdHb+OkUKVKE/v37A+Dl5UWLFi1o3LgxpUuX5vjx49ja2ia77dSpUylcuDBNmzalefPmlCxZkoCAABo0aMB3332nlqtbty4tWrQgNjaWatWq0bp1azw8PHB3d9dL+r2NokWL0qxZMxRFoW7dujRu3Bg3Nzd+/vlnAMaPH4+JiQlLliyhRo0atGnThmLFijF27Fh1Um0hhBBCCCGEEOK9UES6hISEKN26dVOcnJwUa2trpWLFisqxY8eUs2fPKs2aNVMsLS0VKysrpX79+sqqVauU/fv3K4BSpkwZvXqePXumfPHFF0qRIkUUMzMzxdnZWendu7fy77//KoCi0WiUV69eKYqiKHfv3lWmTJmi1K1bV3FwcFBMTU0VJycnpVu3bsq5c+fS1P6AgACldu3airm5uWJra6u0aNFC2bZtm7Jy5UoFULy8vBRFURStVqts27ZN6d69u1KiRAnFwsJCsba2VipXrqzMnz9fbZuiKMqkSZMUQOnfv7/B/mbPnq0Airm5uXLo0KEU2xcbG6v897//VYoXL66Ym5srLi4uyqhRo5THjx8rJUuWVADl8OHDavkmTZoogNKvXz+ladOmioODg2Jubq6ULVtWmTx5shIdHW2wj4iICGXo0KFKoUKFFAsLC6VChQrK1KlTlRcvXiiAAih37txRyye1X503z5vO06dPlYEDBypFixZVLC0tlbJlyypbtmxR1/v5+Sl16tRRLC0tlTx58igdOnRQjhw5YvRcJic8PFwBlPDwcOMFw0IMFt168lLv7ytPrygx8TGp3rcQIntLdfzIZMm2Mz5WUZ4nxuuouCjlUeSjTGhd8uK1CZndBCFyvWwf57KDB+fTVDw0KlR5+PKh3rKL97LhcQuRRWRE/NAoSipmNRbv3c2bNylTpgwVKlTg8uXLmd0ckQ1ERETg6OhIeHi4wYR3+gUfgENhvUV3nkVRPJ+N+ve/Yf9SyrEUZiYyTZkQuUGq40cmS7ad2nh4+QgcixKjjeFF7AsKWBfIvIa+QZugYGqSs+/KI0RWl+3jXHbw5CoUdE118fCYcOIS4vTi9dWHL3B1tn8XrRMix8uI+CFDzDLZ8ePHDe5i9erVK7755hsAevbsmRnNEjmZianBoje/uJhoTDDVGJYTQogsSaNJfAAmWfDSRlJDQohcIY0/LGo0GjRvREhJpguRuaR7QCZr3LgxJiYmuLu74+TkRHR0NKdPn+b58+fUq1ePUaNGZXYTRU6TxIf3mx/GphpTvTvXCSFElqYxQU3DaDD4wiGEEOI90KQtQW+CCSZvbCMJIiEylySIMlmfPn04duwYN27cICgoCBsbG9zc3OjWrRvDhg3D0tIys5socpokPrwNEkRJ9DISQogsS6NRY5sJJpLgFkKIzJDGHkQmGsMEkZkkiITIVJIgymSpuauXEBkqqQTRG1+mzE3M31drhBAiY+gSRBqTLDnMTAghcrx0DDF7M0FkIgkiITKVXEEJkdsk0TvozQ9jmZxaCJHt/H9s02g0mJhkrcsb6dAkhMgVTNP2A2NSPYjMJUEkRKbKWldQQoh3L4nJp81N9T+MLUwt3ldrhBAiY7yW2DbTSJJbCCHeO7O0TY2R1E1RzEzl66kQmUmuoITIIRRFARJvb2hUghZMYvUWxWsTiH/1vw/kOG0cEa9SqEcIkWPo4oYujmRVRuNcfCzEJi6PT4gn3iTesIwQItfKEXEuq9PGQ0zq252gJJCgJBD72nVpfJyWCO2rd9E6IXK8jIhzkiASIod4+vQpAMWLF8/klgghsqunT5/i6OiY2c1IlsQ5IcTbkjgnhMjp3ibOSYJIiBwiX758AISEhGTpCx+ROSIiIihevDh37tzBwcEhs5sjspjw8HBKlCihxpGsSuKcMEbinDBG4pzICSTOCWMyIs5JgkiIHEI3Kaujo6N8YIhkOTg4yOtDJCurTe78JolzIjUkzgljJM6JnEDinDDmbeJc1o6QQgghhBBCCCGEEOKdkwSREEIIIYQQQgghRC4nCSIhcghLS0smTZqEpWXabjEqcgd5fQhjssvrI7u0U2QOeX0IY7LL6yO7tFNkDnl9CGMy4vWhUbL6vR6FEEIIIYQQQgghxDslPYiEEEIIIYQQQgghcjlJEAkhhBBCCCGEEELkcpIgEkIIIYQQQgghhMjlJEEkRA6wadMmGjVqhKOjI3Z2dtSqVYtly5ZldrPEO5YRz7tGozH6cHZ2fketF1nJ5s2b0Wg0eHt7Z3ZTkiVxLneSOCcyisQ5kVVJnBMZJSPinFnGNUcIkRm+++47vv32W0xMTKhXrx4WFhb8/ffffPbZZwQFBbFw4cLMbqJ4BzL6eW/VqhVWVlYGy/PmzZtRTRZZVEREBD/88ENmN8MoiXO5k8Q5kVEkzomsSuKcyCgZFucUIUS2deXKFcXExEQxMzNTDh06pC6/dOmS4ujoqADKwYMHM7GF4l3IyOcdUADlzp0776q5Iotau3atUr9+fcXa2lp9HUyaNCmzm2VA4lzuJHFOZASJcyIrkzgnMkJGxzkZYiZENrZs2TISEhLo3r07jRo1Upe7ubkxdOhQABYvXpxZzRPviDzvIiNcu3aNf/75h+jo6MxuilHyes+d5HkXGUHinMjK5HkXGSGj45wkiITIxvz9/QFo2bKlwbpWrVoBsHfv3vfaJvHuyfMuMoK3tzeKoqAoCv3798/s5iRLXu+5kzzvIiNInBNZmTzvIiNkdJyTBJEQ2ZSiKFy9ehWAsmXLGqzXLQsNDSU0NPS9tk28O/K8i9xEXu+5kzzvIjeR13vuJM+7yKokQSRENhUeHq52JUxq4rnXl927d++9tUu8W+/qeS9TpgwmJibY2dlRrVo1vv/++yzfJV/kfBLncieJcyI3kTiXO0mcE1mV3MVMiGwqMjJS/beFhYXB+tfvYPB6WZG9ZfTzbmVlRZkyZXBycsLMzIzbt29z9uxZzp49y59//sn+/ftxdHTMmMYLkUYS53IniXMiN5E4lztJnBNZlSSIhMgBNBqNwTJFUYyuF9lfRjzvL1++xNTUVG/ZsWPHaNGiBadPn2bq1KnMmTPn7RsrxFuSOJc7SZwTuYnEudxJ4pzISmSImRDZlK2trfrv2NhYg/UxMTHqv21sbN5Lm8S7l9HP+5sXEwAeHh5MmDABgM2bN6enmUJkCIlzuZPEOZGbSJzLnSTOiaxKEkRCZFOOjo7qB0ZERITB+rCwMPXfRYoUeW/tEu/W+3req1WrBsh8ByJzSZzLnSTOidxE4lzuJHFOZFWSIBIim9JoNFSoUAGA69evG6zXLcuXLx8FCxZ8r20T7877et4fPXoEgJ2dXbrrEOJtSZzLnSTOidxE4lzuJHFOZFWSIBIiG/Py8gJg165dBuv27t2rV0bkHO/jed+3bx+Q2D1ZiMwkcS53kjgnchOJc7mTxDmRFUmCSIhsbMCAAZiamvLHH39w5MgRdfnly5f55ZdfABg0aFBmNU+8I2l93r28vDAzM6Ns2bJ69fTv35/p06fz+PFjveV//vkna9asAWDEiBHv6jCESBWJc7mTxDmRm0icy50kzoksSRFCZGvTpk1TAMXExERp0KCB0rRpU8XCwkIBlAEDBmR288Q7kpbnvUmTJgqglCxZUm95x44d1TqqVq2qtGrVSilbtqwCKIAybdq093hE4n3btm2bMmjQIGXQoEFKhQoVFECpWbOmuuyff/7J7CaqJM7lThLnxNuSOCeyOolz4m1ldJyTBJEQOcD69euVBg0aKPb29oqNjY1So0YNZfHixUpCQkJmN028Q6l93pO7oDhx4oQyfPhwpU6dOkrRokUVCwsLpUCBAkqHDh0Uf3//93gkIjNMmjRJvXhM6rFy5crMbqIeiXO5k8Q58TYkzonsQOKceBsZHec0iqIoGdwpSQghhBBCCCGEEEJkIzIHkRBCCCGEEEIIIUQuJwkiIYQQQgghhBBCiFxOEkRCCCGEEEIIIYQQuZwkiIQQQgghhBBCCCFyOUkQCSGEEEIIIYQQQuRykiASQgghhBBCCCGEyOUkQSSEEEIIIYQQQgiRy0mCSAghhBBCCCGEECKXkwSREEIIIYQQQgghRC4nCSIhRJb0559/0qpVKwoUKICZmRkODg5UrlyZwYMH4+fnR1xcHAABAQFoNBo0Gg3BwcGZ2+j/5+Ligkaj4ciRIwbLvL2901yft7c3Go0GFxeXdLdJV8eAAQNSVf7182rsMW/evHS3KSN4e3vj7e1NRESEwTpPT080Gg19+vR5/w0T4i35+Pik6j3o6emZqvp074c1a9YYLHv9PXLmzBm8vb1ZtWpVutp9//59Ro4cSYUKFbC2tsbS0pKiRYvSsmVL5s2bx+3bt43uPzPpznnz5s0Nlmk0mnTVqdvWx8cn3e3S1XH37l2j9fr6+uLt7c2hQ4fSvS8hIPXxx9fXN7ObmiqpOZbUXkemJU4Yu0ZJSUJCAr/++iuNGzcmT548mJubkzdvXmrWrMmIESM4dOgQCQkJRvefmVIbt1KrT58+afrMM1bHtGnTjNYbHByMt7c3P/30U7r3lZ2ZZXYDhBDidfHx8Xz66aesW7cOgIoVK1K7dm2eP3/OmTNnuHDhAkuWLOHAgQN4enpSoEABOnbsCICNjU1mNt2oli1b8vjxYypUqJDmbStUqEDHjh1xcnJ6By0zztbWVu8i6E1lypR5j60xNHnyZAAGDBiAg4OD3rqGDRuSJ08eatSokRlNEyJDFCxYkPr16ye73t3dPd11J/UeOXPmDJMnT8bLy4tPP/00TfX9/ffftG/fnrCwMPLkyUOtWrWwsrLixo0b7N27l7179+Lr60tAQECy+89qSpQooX7GpIdu2xIlSmRUk5Kt19fXl99++w0zMzMaN26cofsTuVNK8adw4cLvsTVvr1WrVlhZWSW7Pr3XkcnFCWPXKMa8fPmSDh06cODAAUxMTKhSpQrOzs48efKEs2fPcvr0aX788Udu3bqFi4vLW8ep9+Vt4mGNGjV4/vz5W33mpbbe4OBgJk+eTJkyZRg+fHiG7i87kASRECJLmT17NuvWrcPJyYlt27ZRp04ddV1kZCRr1qxRP3Ah8ctRdvgFFJySbAAAH4tJREFUa+nSpenetnv37nTv3j0DW5N6zs7O2eL8JuX1X4iEyK6qVKnyzt6DGfkeCQ8Pp3PnzoSFhTF06FBmzZqFra2tuv7s2bN89913PHny5J3s/11p1qwZzZo1S/f27+q5y65xWWQv7zL+ZIZff/2VYsWKZXi9bxsn3jRy5EgOHDiAq6srW7ZswdXVVV0XFhbG0qVLmT59+jvb/7vyNq+l4cOHv5NkzbuqNzuTBJEQIkv59ddfAZgyZYpecggSe7MMGjSILl26qEPMhBBCZL6tW7fy+PFjihQpws8//2ww1KFq1aps2LCBCxcuZFILhRAi64uNjWX16tUAzJ8/Xy85BJA3b17Gjh1L9+7dyZ8/f2Y0UeRwMgeRECJLuXPnDmC823L+/PlxdnYGEruBajQazMz08926cc5Xr15lwoQJuLi4YGVlReXKlVm7di0AoaGhfP755xQpUgR7e3vq1avHrl279OrRzcVTtmxZnjx5wogRIyhVqhSWlpaULFmScePGER0dneJxJTX/B0BcXByLFy+mYcOGODo6YmlpSfny5fniiy84e/YskPR49+joaAYMGEDt2rUpWrQoFhYW2Nra4u7uzjfffENYWFiKbcpIrx/f3r17adGiBba2tnTp0gWAr7/+mvr161OiRAmsrKywtramfPnyDBs2TG98+uv8/Pzo0KEDTk5OWFhYULRoUbp3786OHTvUc6JTvHhxgzlZkhprDqDValm0aBEeHh7Y29tjY2ND1apVmT59OlFRUXplXz/3N2/eZNCgQRQrVkx9nhYsWJCBZ1GIt3ft2jV69eqFk5MTNjY2VKhQgdGjR/P06VODsm++Rzw9Penbty8A/v7+enNzpDRnhC52FypUyOg8GK9340/qPfr6fGmHDx9WY0n+/Pnp2bMnjx8/BuCvv/6ifv362NjY4OLiwuDBg3n58qXevl6PS1u3bqVp06Y4ODhgZ2dH06ZN9eaJS87rnwFvunTpEv3798fFxQULCwvy5ctH8+bNWb58ufq5kNScdL6+vnzwwQeUL18eR0dHzM3NKVSoEG3btjX4DErOm/VqNBp+++03ACZMmKD33Hl5eaHRaJgzZ06SdW3btg2NRkP16tVTtW8hXrds2TJatGhBmTJlsLOzw8LCgmLFitGlSxcCAwOT3CY17x1I7Jk4YcIEKlWqhLW1NXZ2dtSpU4elS5eiKMo7Pa5Xr14xY8YMKlasiJWVFUWLFuXDDz/k77//Nij7ZpxIzTVKckJDQ9VzYOxauGTJktjZ2SW5f9C/Pg4NDWX48OEULlwYa2tr6tSpw+7du9VyvXr1omDBguTNm5dmzZpx/PhxvX29fi0UHBzMgAEDKFq0qHotNGvWLLRardHjgqTjISSOEJg5cya1atXCzs4Oa2tr3N3dGTduHDdu3ACSnkvz/v37fPrpp1SvXh1nZ2fMzc1xcHCgRo0afP/996m6Pn+z3j59+tC0aVMAbty4oRdLy5Qpg0ajSXaYW3x8PM7Ozmg0GvUaPjuSHkRCiCylSJEi3L59Gx8fH9q1a4eJydvlsZs3b87Dhw9p0KABRYoU4ejRo/Ts2ZM7d+7w008/8eLFC+rVq8fjx485duwYHTp04MSJE1SrVk2vngcPHlC2bFliYmKoW7cupUuX5p9//mHGjBmcOXOGnTt3pnlywMjISNq0acPhw4exsLCgTp06mJubc/bsWX7++WfOnz+vztWR1LbLly/H2tqaKlWqUL16dZ4/f87JkyeZOXMm27Zt4+TJk1hbW6fzzKXPlClT+PfffylbtizNmjWjcuXKQOIFZFxcHO7u7lSpUoXIyEhOnjzJwoUL2bx5M2fOnKFQoUJqPSNHjuSHH34AEnseVK9enWvXrrF+/XrWr1+Pv78/HTt2ZMuWLYD+vALGxqfHxsbSoUMHdu/ejbW1NXXr1sXMzIzjx48zfvx4Nm7ciL+/P3nz5tXb7ty5c7i7u6PRaKhXrx4uLi4cPXqU4cOH8+rVK0aPHp2h51GI9AgICKBdu3ZERkZSqlQpatWqRWhoKPPnz09Vr8uGDRsSERFBUFCQwdwjKc0ZUaRIESDxvXLixAlq1679Vseyc+dOVq5cSbFixWjcuDGnT59m7dq1/Pvvv9SqVYuFCxdSqVIlGjduzNGjR1myZAmhoaFs2rTJoK7Ro0fz8OFDypQpQ4MGDbhx4wYBAQF4eXmxd+/edM3Xs3HjRnr16kVMTAwlS5bEy8uL+/fvs3//fvz9/SlTpkyyXwT37dvHzp07cXFxwcPDA41Gw7Vr19i1axe7du3Cx8eH3r17p6k9HTt25PTp09y5cwc3NzfKly+vruvcuTP79+9n5cqVjBo1ymDbFStWADB48OA07VMIgC1btrBv3z5cXV1p1KgRWq2WCxcu8Oeff7Jlyxb8/Pzw8vJSy6f2vXPr1i31x5kiRYrQoEED4uPjOX78OIMGDeLYsWPqazejRUZG4uXlRWBgILa2tjRs2JD4+HiOHj2qJqmN0c0JlNZrFEj8EdTS0pKYmBiWL1/O/Pnz3+pYEhISqFq1KmFhYTRo0IDQ0FBOnDhBu3btmD17NhMnTsTCwoJatWpx69YtDhw4QIsWLbh8+bIa13VOnz5NhQoV1GvWmJgYjh07xtixY7l58yaLFy9Oc/sePnyIl5cXly5dwsbGhrp16xIfH8+ZM2eYMWMGDx48SPYHivv377N69Wr1Rja1atXiyZMnnDp1iqCgIPz9/dm3b1+ars9r1KjB9evX+fvvvw3m4fzPf/7DsGHDuHjxIsePHzcY6bBjxw4ePXqEh4cHVatWTfO5yDIUIYTIQubOnasACqDUqFFDWbBggXL58uVky9+6dUsBFFNTU73lujrq16+vXL9+XV0+YcIEdV2PHj2UZ8+eqet69OihAEr//v3VZQcOHFAARaPRKH379lVCQ0PVddeuXVPy58+vAMr69evV5SVLllQA5fDhw+qyJk2aKICyevVqddlnn32mAIq7u7sSHBysLo+NjVXWrl2rfPLJJ4qiKMrKlSsVQPHy8lLLREVFKdu3b1eio6P1jvvBgwfq/n/99Vd1+aRJkwyOzRjdcZcpUyZV5XXHV7hwYcXPz89gva+vrxIeHq637MWLF0rNmjUVQPn222/V5b///rsCKA4ODsr+/fv1tjl8+LBSt25d9W/dc3nnzh2Dffbu3VsBlKlTp6rLvvnmGwVQatasqTx48EBd/vz5c6Vly5YKoJ53RfnfuQeUL774QgkLC1PXrV69WgGUvHnzKvHx8ak4S0Kknu61V7BgQaVjx47JPm7evKkoiqJEREQozs7OCqDMmDFDSUhIUOt6+vSpUqVKFYMYlNR7JKl4kxrh4eFKsWLFFECxtrZWhgwZouzcuVOJiIhIdpuk9q+LVRYWFsqCBQsUrVarKIqihIaGKnnz5lUApUCBAsr27dvVbS5fvqyYmJgoGo1GCQkJUZfr4lKJEiWULVu2qMsTEhKUL7/8UgEUNzc3o8eeVCy8ceOGYm1trQDK3Llz9c71zZs3lR49eigBAQGKoiT9eXD06FHl2rVrBudj+vTpCqAUL15cb3lScS6pepM6n4qiKDExMUqBAgUUQDl69KjeukePHinm5uaKvb298uLFC4M2idwpLXFg//79Bp/BCQkJypAhQxRAadiwobo8te8drVarVK9eXTExMVEWL16sxgFFUZTbt28rrq6uCqAcOHAgVcejew+1atUq2Vj6+jXT0KFDFUBp0KCB8uTJE3V5fHy8GqNSihOv7zepaxRjvvjiC3VbT09PZfny5WqsT0pS+9ddHwNKhw4dlIcPH6rrevXqpa4bMWKEEhUVpSiKomi1WqVRo0bJfi6YmZkpo0ePVl6+fKmuO3r0qGJhYaEAyrFjx4wee1JxS3ft1bRpU71zHRUVpfz888/Kl19+qShK0texjx49Uvz9/ZW4uDi983H58mUlT548CqDs27dPXW7sMyepa/+kroF1z82gQYMM1nXo0EEBlN9++81gXXYiCSIhRJai1WqVzz//XP1g0T0KFCigdOvWTdmzZ4/eBUVKCaLbt2/rLQ8MDFQApVixYgb7Xr9+vZqY0kkpUTJ27FgFUNq1a6cuS02C6MGDB4qZmZkCKMePH0/2XChK2r+wjR49WgGU4cOHq8vSmyBK6XHr1i2940vrh+Ivv/yiXrzoVKpUSQGUWbNmJbnN6xeKaUkQRUdHK3Z2dgqgnDt3zqD8gwcPFHNzc8XExES5e/euoij/O/dNmzY1KB8XF6eYm5srgNEkphDp8Xpy0tgjKChIURRF+eGHH4zGiaSS1BmZIFIURTl+/Lga/3QPU1NTpWbNmsq0adOUR48e6ZU3drHet29fg/rbtGljkPzWcXNzUwBl69atRo9ZJywsTLGyslIA5eTJk8kee1KfAcOGDVMApW3btsmeC12cSurzIDmRkZHqeXv9x4u3TRApiqKMHDlSAZSBAwfqLZ8zZ44CKEOGDEmxfSL3SG38MebixYsKJP7Yo5Pa986WLVsUQBkwYECSZX799VcFUIYNG5aq40nNsegSEbqkqampqd6PdzqpjROv7zetCaLIyEila9euBm0sWrSo0rdvX4NEr7EEkampqd51s6L873r39eSdzsyZMxVA6dy5s9Fjft1//vMfBVA+//xzo8f+Ztw6ceKEAiiWlpZ6yf3X6WJpWq9jdefvhx9+UJdlRILo/PnzCqA4OjqqiTVFUZSHDx8qZmZmSr58+Qx+vM1uZIiZECJLMTExYcGCBfTv35+1a9eyZ88eLly4QGhoKBs2bGDDhg107dqVNWvWYGFhkar6Xufo6AiAqampQVnduoiIiFS3t2XLlsycOZOgoKBUbwNw4MAB4uPjqVatWrJDMVIzvC4kJETvHD1//lydBDYyMjJNbUpKSre5f/OWsMba/PjxY3bv3s3Zs2d5/Pgxz58/599//9Vr64MHD7h48SIAAwcOTLKe9A47PHnyJC9fvqRkyZLq0LfXOTs7U6dOHf7++2+OHDnCf/7zH6P7NDMzI0+ePDx58oRnz56lq01CpMTLy4t9+/alWG7Pnj0AdOvW7V03KVm1a9dWh5Zs3ryZw4cPExYWxqlTpzh16hSzZ8/G19c3xTk4IOn3XEbG7zx58lC7dm0OHz5MUFAQNWvWTNV2AHv37gXgs88+S7ZMSnEqLi6OgIAAAgMDCQ4O5vnz5zx//lxdHxkZaTDU9W0MHDiQuXPnsn79eubNm6fG7pUrVwIwZMiQDNuXyDlSus29TnR0NHv37uXkyZPcvXuX58+f8+jRI0D/WiS17x0/Pz8ATpw4wYcffmhQRjfM6+bNm6k+FkicKy2lu5gdOHCAuLg46tWrR8mSJdNUf0axsbFhw4YNHDlyhHXr1rFv3z6uXbvGvXv3WLlyJStXruTLL79k3rx5qarvzSFW7+JaeP369Wm+Fta9Htq0aUPx4sWTLJOaa76rV6+yb98+rly5wtOnT3n+/DmnT58GMuZa+HXu7u7Uq1ePo0eP8ueff9KzZ08AVq1aRXx8PH369FGHE2ZXkiASQmRJ1apVo1q1asyePZvnz59z6NAhFi5cyO7du9m4cSP16tXj66+/TnO9lpaWKa5T0jDxoW6y7LQmCHQTupYpUyZN2+lERUXx2Wef8fvvv6vtzZcvH0WKFMnQiRsz4jb3Wq2W0aNHs2DBAuLj4wFwcHCgaNGiBpMa6s5L/vz5yZMnz1vt90337t0DjM+lortASW7i7DfpvmQlJCS8ZeuEeDshISEAlC5dOlPbYWdnR+/evenduzeKonDhwgW2b9/O7NmzCQsL45NPPuH69esGyeXUyCnxe+fOnQwaNEiNM+bm5hQpUsTohLRvy9XVlcaNG3Po0CE2bdrEp59+SmBgIBcvXqR+/fpJJs2FSM1t7n18fBg5cqT6PrK0tKRYsWJqouF1qX3vBAcHA3D27Fmjk/2+eWOJjJBVYikkzgvXsGFDIDEptn//fubNm0dgYCDz58+nSZMmdOrUKc315pRY+ujRI/r27as3uX/BggUN5k7KaAMHDuTo0aOsWLFCTRCtXLkSjUbDoEGD3um+3we5i5kQIsvLkycPHTp0YNeuXbRt2xZIvINNVvDq1SuANP9aoPvgTevE1jqDBw9m7dq1FCtWjDVr1vD06VOePn3K+fPn1bsQZRWTJ0/mxx9/xN7enoULF/LgwQPCw8O5dOkS48eP1yv7tuclNYzV/S73K8S7pEu+ZmSC+G1pNBoqV67MuHHj1Av4Bw8ecOzYsUxuWaLMiN9XrlyhU6dO3L17l379+hEUFER0dDTBwcEcPXo0zfWlha7Xhm5iX93/pfeQSC9/f3/69etHeHg4o0aN4sqVK0RHR3P9+nX+/PNPg/Kpfe/ofnRZtmwZSuKUKEk+kruRx9vIirEUwMnJie7du3P48GGqVKkCyLVw165d2bVrF5UqVcLX15eIiAgeP37MmTNnaN26dbrqTI3//Oc/ODo6EhAQwK1btzh69CiXL1+mWbNmejcIyK4kQSSEyDY0Go2aIHry5EkmtybR7du3gbT/0qT7dUN3+860iI2NZd26dQBs3bqVHj16kC9fvjTX877o7j6xYsUKhgwZov7SlBTdeQkNDU1T9+bUKFq0KPC/XyaTovvlUFdWiOyiYMGCQOIdYbKiunXrUqBAASB3x+/ff/+d2NhYOnbsyPLly6lWrVqSwzzehS5dupAvXz4OHTrEhQsX+OOPP8ifPz9du3Z9L/sXOc9vv/2Goih8/vnnzJ49G1dXV6Nf9lP73tGVu3r1asY1NpWyeiw1NzenZcuWQO6OpTdv3lTvArx//346duyIvb19mutJDxsbG3r06IGiKKxcuTLH3QlSEkRCiCzj0aNHrFmzxmiZ69evA1CpUqX30aQU6drbokWLNG3XqFEjNBoNZ86cUcdJvym5X6+ePXum/sJVoUKFNO03M+jmCnBzc0uxbPHixXFxcQFg+fLlSZZ5/byYmSWOlH5zqFpSatWqhb29PSEhIUmOk3/w4AGBgYGYmJjQqFGjFOsTIivRzWXm7+//VvWYm5sDqXtPvW716tVGv6w8e/ZMHX6QFeL3uXPnOHfuHNbW1uoQjtRq3LgxkHyMguTjd1riYVql9NxZWlry6aefoigK//nPf4iIiKBv375Gh5sIYUxaX8+pfe80a9YMSEyoGptDJq1xKjV0sTQwMJCXL1++VV1puUbROX/+vDqnXHJyyrWw7vXg5+fHgwcPkiyTUizNly8fTk5OadpvSlLzOajrkbly5UrWr19P4cKFk5wvKzuSBJEQIsuIjo6mV69edOzYkcDAQL11iqKwadMmFi5cCMCwYcPea9siIyMN5qVZu3bt/7V3/zFR138Ax5/cIWJl6UwxNDn5oQkXP6dgzB8gU7G2ci1ZzQiM7JejHxtzOguBTFNKdFeYs9Q5F6Ut0a2JARfCkdwJJxea4DWV4UUqMjVM1nl8/3D3mfcVicNfxL0eG//cfd6fz/v9OT6fz/vz+rw/rzd79uxh6NChbtdHo9Ewf/58urq6yMjIUPLjwPW2FhUV8dZbb3VbdtSoUcrF0DmSyFluy5YtrF+/3q263G1arRZwrStAcXExy5Ytu2n5999/H4C8vDwMBoPLdyaTyaUD4gwm3fj++a0u6L6+vso+TU9Pd+mMXLx4kdTUVOx2OykpKTKCSPznvPzyywB89913HD58WPn86tWrrFixgurq6l6tx3lMmc1mlyfo/3aDU1ZWhlarpaCgwCXZMlx/Er9w4UIcDgczZsxQzgn3itVqdan/+fPnldeqXn/9dbfznWVmZqJWq9m7d69yTXKy2Wy8+OKLWCyWbss6275v3z6Xm8/m5mbmzJnjVj3+n/O3Ky0tVR4iOBwOlxssZ/L/Y8eODZh8GeL+cf4/7969m3/++Uf5vKGhoduRab09dhYsWEBgYCA2m4358+ffFDw4c+YMWVlZfP7553e8TREREYSHh9PR0UFeXp7Ld/v37yc3N7fX63Knj+LU1tZGcnIyaWlpyqQdN5bV6XTs2bOHQYMG9Zjs+25wpjNw6urqYs2aNdTV1TF27FglH09vTZ8+nZiYGP7++2/S09Npb29XvrPb7eh0OlauXNlt2YkTJzJo0CBaW1vR6/Uu5VavXn1Tn9Mdzt/t9OnTHDt2TPn8xt8uIiKCyZMn09LSwuXLl3n11VeVgOB/3cBohRBiQPD29lY6Dnv37uWxxx5TnkpZrVaam5tRqVSsXbuWhISEe1q31tZWAgICiIyMxM/Pj1OnTvHbb7/h7e3N1q1b/3VWjO58+eWXNDY2YjabCQoKYsqUKQwZMoSGhgZsNhvPPvtst+VUKhXr1q3jlVdeYdGiReh0OkaOHMnRo0dpaWkhICDgjr+edTvy8/NJTEwkJyeHXbt2MW7cOKxWK1arVbkI32jJkiVUV1dTVFTEtGnTiIyMZPTo0co+vzHxZWpqKh9++CFLlixh69atXLt2jaCgIL799ttu65KTk4PJZKK8vJzAwEDi4uLw9vbGaDRy6dIlIiIi0Ol0d2tXCOE2i8XS41NJrVbLRx99RHR0NJmZmWzcuJGpU6cSFxeHr68vJpOJv/76iwceeMDlBu5W4uLiCAkJ4cSJE2i1WsLCwmhpaeGzzz675TkJrgdgz549y3vvvUdWVhbh4eH4+flx9uxZGhoa6Ozs5IknnmDnzp192Q23JScnh02bNhEaGopKpeLQoUN0dHQQHx/PqlWr3F5fVFQUGzZsIDMzk7fffpv8/HwmTZpEW1sbtbW12O12li5d2m3ZjIwMCgsLOXr0KBqNhujoaDo6OjAajX26jtwoJSWFvLw8qqqqmDBhAmPGjKGxsZGmpiYlCBYaGkp8fDwGg4GkpCSCg4Nva5vCsy1dupQdO3ZQWlqKRqPhySef5Pz589TV1XU7A1hvjx0fHx+Ki4tJTk7mp59+QqPREBMTw7Bhw7DZbFgsFrq6uli3bp1b9c3IyOgxT87mzZsZNWoUhYWFzJo1i7Vr1/LDDz8QHBzM77//TlNTk1uzC7rbR4HrI/0cDgfbt29n+/btaDQaQkJC6OzspLGxkT///JPBgwfz9ddf3/N8N0eOHMHf35/IyEhGjBjB8ePHOXnyJA899BBFRUU8+OCDbq3Py8uLb775hsTEREpKShg3bhxTpkxBpVJhNptpa2vjnXfe6bbs8OHDWbFiBdnZ2SQlJREbG8vQoUMxm820t7fj5+fn8vDVHWPGjCExMZHy8nJiY2OJiori3LlzvPHGGy71Wbx4MSaTCbVafc+DdXeTjCASQvQbY8eO5cyZMxQUFDB79my8vLyorq6msrKSa9eukZKSQnV1NVlZWfe8bv7+/mRnZ9PZ2cnBgwex2Ww8/fTTGAwGnn/++T6t89FHH6WmpobVq1cTFhZGXV0d5eXlPPzww7z77rt8/PHHtyybmprK999/T1hYGBaLhUOHDhETE0NVVRVpaWl9bOXdMXPmTPR6PZMnT+bEiRNUVlYyfvx49u3bR3Z29k3LOzsMO3fuJCEhgVOnTlFSUsKlS5dYuHChS8dq+fLlfPLJJwQFBVFfX88ff/zR41D3wYMHs3//fjZs2EBYWBgmkwmDwUBAQIAyYqk/53MSnufcuXMUFxff8q+qqkpZtqCgAJ1Ox4QJEzAajdTX1zN37lxMJhPR0dG92p5araasrIwFCxagVqsxmUz4+vr2mDsM4IsvvqCsrIzFixczadIkrFYrBw4coKmpCa1Wy5o1a6itrb0vo/PefPNNYmNjOX78OJWVlfj7+5Obm0tpaWmfZlOD66NYDQYDL7zwAlevXuXAgQM0NjYye/ZsvvrqK0JDQ7st5+vrS0VFBWlpadjtdvR6PRcuXGDlypX8+uuvt9NMgoODKSsrY9q0abS1tWGxWAgMDMTHx8dluREjRgADJ1+GuH9GjhxJTU0Nzz33HBcvXkSv1+NwONi4ceMtX5Pq7bGj1Wqpr68nNzeX8PBwGhoaKCkpwWazkZyczObNm92+KS8pKenxfOqcFe2pp56iqqqKuXPn0traSkVFBcOHD2fLli3k5+f3envu9lEApk6dysmTJ1m1ahUzZszgypUrVFRUUFNTw5AhQ0hPT8dsNvPSSy+51fY7ISoqiszMTNrb29Hr9Vy+fJmUlBQOHz5MfHx8n9YZEhLCkSNHWLZsGQEBAfzyyy8cPHiQxx9/nOXLl/c4Y/EHH3zApk2b0Gg0GI1GamtrmTNnDmazmaSkpL42E4Bdu3bx2muv8cgjj2A0GrHb7YwfP95lGee5dN68ecosuAOBV1d/S9EuhBD9yM8//0xCQgJBQUHKO99CCCH6v5kzZ1JRUcGOHTvcfvVhoLJarUycOJHRo0dz+vTpAfNKhBDi7tm2bRvp6enMmjWL0tLS+12dfmP69OlUVlby448/kpycfL+rc8fICCIhhBBCCCE8wPr163E4HGRkZEhwSAgh+shkMikj4m83f1x/IwEiIYQQQgghBrgLFy6wbds21Gq1kqxaCCGE+z799FPgeh4ilWpghVQGVmuEEEIIIYQQNyksLOTKlSs888wzt50QWwghPFVzczO7d+/Gx8eHRYsW3e/q3HGSg0gIIYQQQgghhBDCw8kIIiGEEEIIIYQQQggPJwEiIYQQQgghhBBCCA8nASIhhBBCCCGEEEIIDycBIiGEEEIIIYQQQggPJwEiIYQQQgghhBBCCA8nASIhhBBCCCGEEEIIDycBIiGEEEIIIYQQQggPJwEiIYQQQgghhBBCCA/3P5IQaaIfP4vTAAAAAElFTkSuQmCC", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "def add_patch(legend, colors, titles):\n", " from matplotlib.patches import Patch\n", diff --git a/print_simplicial_assortativity.ipynb b/print_simplicial_assortativity.ipynb index b7cf5c3..dc92706 100644 --- a/print_simplicial_assortativity.ipynb +++ b/print_simplicial_assortativity.ipynb @@ -41,34 +41,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\\begin{table}\n", - "\\begin{center}\n", - "\\begin{tabular}{cccc}\n", - "Dataset & $\\rho_{\\mathrm{SF}}$ & $\\rho_{\\mathrm{SF}}$ & $\\rho_{\\mathrm{SF}}$ \\\\ \n", - "\\hline\n", - "\\texttt{contact-primary-school} & 0.15 & 0.15 & 0.14 \\\\ \n", - "\\texttt{contact-high-school} & 0.22 & 0.34 & 0.24 \\\\ \n", - "\\texttt{hospital-lyon} & -0.02 & -0.02 & -0.01 \\\\ \n", - "\\texttt{email-enron} & 0.29 & 0.29 & 0.24 \\\\ \n", - "\\texttt{email-eu} & 0.19 & 0.16 & 0.16 \\\\ \n", - "\\texttt{ndc-substances} & 0.57 & 0.65 & 0.72 \\\\ \n", - "\\texttt{diseasome} & nan & 0.46 & 0.75 \\\\ \n", - "\\texttt{disgenenet} & nan & 0.55 & 0.89 \\\\ \n", - "\\texttt{congress-bills} & 0.78 & 0.48 & 0.75 \\\\ \n", - "\\texttt{tags-ask-ubuntu} & -0.03 & -0.08 & 0.04 \\\\ \n", - "\\end{tabular}\n", - "\\end{center}\n", - "\\end{table}\n" - ] - } - ], + "outputs": [], "source": [ "list_of_lists_to_latex_table(rows, column_labels)" ] diff --git a/simpliciality_correlation.ipynb b/simpliciality_correlation.ipynb index a2f6c4a..0c51487 100644 --- a/simpliciality_correlation.ipynb +++ b/simpliciality_correlation.ipynb @@ -34,22 +34,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Correlation (spearman) between es and fes: 0.9030, p-value: 0.0003\n", - "Correlation (pearson) between es and fes: 0.8938, p-value: 0.0005\n", - "Correlation (spearman) between es and sf: 0.8875, p-value: 0.0006\n", - "Correlation (pearson) between es and sf: 0.9573, p-value: 0.0000\n", - "Correlation (spearman) between fes and sf: 0.9970, p-value: 0.0000\n", - "Correlation (pearson) between fes and sf: 0.9718, p-value: 0.0000\n" - ] - } - ], + "outputs": [], "source": [ "def load_data(filename=\"Data/empirical_simpliciality.json\"):\n", " with open(filename, \"r\") as f:\n", @@ -100,20 +87,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "dataset = \"email-enron\"\n", "max_order = 2\n", @@ -135,22 +111,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Correlation (spearman) between sf and es: 0.6710, p-value: 0.0000\n", - "Correlation (pearson) between sf and es: 0.6041, p-value: 0.0000\n", - "Correlation (spearman) between sf and fes: 0.9655, p-value: 0.0000\n", - "Correlation (pearson) between sf and fes: 0.8354, p-value: 0.0000\n", - "Correlation (spearman) between es and fes: 0.6844, p-value: 0.0000\n", - "Correlation (pearson) between es and fes: 0.6323, p-value: 0.0000\n" - ] - } - ], + "outputs": [], "source": [ "ls = {\"sf\": sf, \"es\": es, \"fes\": fes}\n", "results = {}\n", From 16bf8a5a1fce0d5929a678c0166ff7707c4fe654 Mon Sep 17 00:00:00 2001 From: Nicholas Landry Date: Thu, 3 Oct 2024 12:42:32 -0400 Subject: [PATCH 08/17] update --- Data/empirical_simplicial_assortativity.json | 40 +- ...del_simpliciality_contact-high-school.json | 15722 +++++++------- ..._simpliciality_contact-primary-school.json | 17434 ++++++++-------- Data/model_simpliciality_diseasome.json | 11646 +++++------ Data/model_simpliciality_disgenenet.json | 11996 +++++------ Data/model_simpliciality_email-enron.json | 15202 +++++++------- Data/model_simpliciality_email-eu.json | 17354 +++++++-------- Data/model_simpliciality_hospital-lyon.json | 17368 +++++++-------- Data/model_simpliciality_ndc-substances.json | 14116 ++++++------- local_simpliciality.ipynb | 1 - print_simplicial_assortativity.ipynb | 14 +- 11 files changed, 60446 insertions(+), 60447 deletions(-) diff --git a/Data/empirical_simplicial_assortativity.json b/Data/empirical_simplicial_assortativity.json index c6d5cf3..28369c7 100644 --- a/Data/empirical_simplicial_assortativity.json +++ b/Data/empirical_simplicial_assortativity.json @@ -1,52 +1,52 @@ { "contact-primary-school": { "sf": 0.15315018199097935, - "es": 0.15412048075467288, - "fes": 0.1444169663363625 + "es": 0.17281886435380475, + "fes": 0.1444169663363616 }, "contact-high-school": { "sf": 0.2188437145146674, - "es": 0.3417641922613738, - "fes": 0.24450744143393663 + "es": 0.36939456266843834, + "fes": 0.24450744143393646 }, "hospital-lyon": { "sf": -0.023059134045750895, - "es": -0.015659496331685733, - "fes": -0.011268233164750082 + "es": -0.00966150249636985, + "fes": -0.011268233164750195 }, "email-enron": { "sf": 0.2937630328359225, - "es": 0.2855927428353681, + "es": 0.28782745071295157, "fes": 0.24027224883005704 }, "email-eu": { "sf": 0.19456098169279148, - "es": 0.15677631264150282, - "fes": 0.1582149160320125 + "es": 0.16309014701085017, + "fes": 0.15821491603201235 }, "ndc-substances": { - "sf": 0.5705945872846372, - "es": 0.6531808504197081, - "fes": 0.7221531698075448 + "sf": 0.558558520432066, + "es": 0.5442421964685049, + "fes": 0.6945237020099871 }, "diseasome": { "sf": NaN, - "es": 0.4627103675805388, - "fes": 0.7520629126720291 + "es": 0.28403564055609914, + "fes": 0.6752106539870169 }, "disgenenet": { "sf": NaN, - "es": 0.5548737446667837, - "fes": 0.8949664334475825 + "es": 0.28461644401597613, + "fes": 0.7817258883248732 }, "congress-bills": { "sf": 0.775717382167523, - "es": 0.4801728519804617, - "fes": 0.7499737912547585 + "es": 0.3259747684410255, + "fes": 0.7499737912547589 }, "tags-ask-ubuntu": { "sf": -0.030253949698663112, - "es": -0.0818609723204826, - "fes": 0.042905670257304636 + "es": -0.24169154469962634, + "fes": 0.04290567025731013 } } \ No newline at end of file diff --git a/Data/model_simpliciality_contact-high-school.json b/Data/model_simpliciality_contact-high-school.json index 77749d0..c228b92 100644 --- a/Data/model_simpliciality_contact-high-school.json +++ b/Data/model_simpliciality_contact-high-school.json @@ -1,3074 +1,3077 @@ { "CM": { "sf": [ - 0.001293103448275862, - 0.003017241379310345, 0.0034482758620689655, - 0.003879310344827586, - 0.005172413793103448, + 0.0047413793103448275, + 0.002586206896551724, + 0.002586206896551724, + 0.004310344827586207, 0.0034482758620689655, + 0.003879310344827586, 0.005172413793103448, + 0.003879310344827586, + 0.004310344827586207, + 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0.017095021993674745, + 0.015696146594939602, + 0.017964712525599936, + 0.019048820132275664, + 0.01548329199685512, + 0.01660763650074263, + 0.016947191076125467, + 0.014616452659623063, + 0.015722396006007704, + 0.016027719202913026 ] } } \ No newline at end of file diff --git a/local_simpliciality.ipynb b/local_simpliciality.ipynb index bc4cb14..005815c 100644 --- a/local_simpliciality.ipynb +++ b/local_simpliciality.ipynb @@ -185,7 +185,6 @@ "plt.figtext(0.1, 0.4565, \"B\")\n", "plt.figtext(0.5625, 0.4565, \"C\")\n", "\n", - "# plt.tight_layout()\n", "plt.savefig(f\"Figures/Fig3/{dataset}_local_simpliciality.png\", dpi=1000)\n", "plt.savefig(f\"Figures/Fig3/{dataset}_local_simpliciality.pdf\", dpi=1000)\n", "plt.show()" diff --git a/print_simplicial_assortativity.ipynb b/print_simplicial_assortativity.ipynb index dc92706..3dbb4e5 100644 --- a/print_simplicial_assortativity.ipynb +++ b/print_simplicial_assortativity.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -33,9 +33,9 @@ "\n", "column_labels = [\n", " \"Dataset\",\n", - " \"$\\\\rho_{\\mathrm{SF}}$\",\n", - " \"$\\\\rho_{\\mathrm{SF}}$\",\n", - " \"$\\\\rho_{\\mathrm{SF}}$\",\n", + " \"$\\\\rho_{\\\\mathrm{SF}}$\",\n", + " \"$\\\\rho_{\\\\mathrm{SF}}$\",\n", + " \"$\\\\rho_{\\\\mathrm{SF}}$\",\n", "]" ] }, @@ -65,7 +65,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.0" + "version": "3.12.4" } }, "nbformat": 4, From b1f2daa07345b058f95fe1ccbba322ceba48a4ce Mon Sep 17 00:00:00 2001 From: Nicholas Landry Date: Thu, 3 Oct 2024 14:44:14 -0400 Subject: [PATCH 09/17] refactor codebase --- sod/simpliciality/edit_simpliciality.py | 70 ++++++----------- sod/simpliciality/face_edit_simpliciality.py | 54 ++++++++----- sod/simpliciality/mean_face_edit_distance.py | 35 ++++++++- sod/simpliciality/simplicial_edit_distance.py | 71 ++++++++++++----- sod/simpliciality/simplicial_fraction.py | 24 ++++++ sod/simpliciality/utilities.py | 4 +- .../simpliciality/test_edit_simpliciality.py | 76 ++++++++++--------- .../test_simplicial_edit_distance.py | 73 ++++++++++++++++++ tests/simpliciality/test_utilities.py | 16 ++-- 9 files changed, 289 insertions(+), 134 deletions(-) create mode 100644 tests/simpliciality/test_simplicial_edit_distance.py diff --git a/sod/simpliciality/edit_simpliciality.py b/sod/simpliciality/edit_simpliciality.py index 8a29358..b3890a2 100644 --- a/sod/simpliciality/edit_simpliciality.py +++ b/sod/simpliciality/edit_simpliciality.py @@ -1,12 +1,12 @@ import numpy as np -import xgi from ..trie import Trie -from .utilities import count_subfaces, max_number_of_subfaces, missing_subfaces +from .simplicial_edit_distance import simplicial_edit_distance +from .utilities import missing_subfaces def edit_simpliciality(H, min_size=2, exclude_min_size=True): - """Computes the simplicial edit distance. + """Computes the edit simpliciality. The number of edges needed to be added to a hypergraph to make it a simplicial complex. @@ -15,55 +15,31 @@ def edit_simpliciality(H, min_size=2, exclude_min_size=True): ---------- H : xgi.Hypergraph The hypergraph of interest - min_size: int, default: 1 + min_size: int, default: 2 The minimum hyperedge size to include when calculating whether a hyperedge is a simplex by counting subfaces. + exclude_min_size : bool, optional + Whether to include minimal simplices when counting simplices, by default True Returns ------- - int + float The edit simpliciality - """ - edges = H.edges.filterby("size", min_size, "geq").members() - t = Trie() - t.build_trie(edges) + See Also + -------- + simplicial_edit_distance - maxH = xgi.Hypergraph( - H.edges.maximal() - .filterby("size", min_size + exclude_min_size, "geq") - .members(dtype=dict) + References + ---------- + "The simpliciality of higher-order order networks" + by Nicholas Landry, Jean-Gabriel Young, and Nicole Eikmeier, + *EPJ Data Science* **13**, 17 (2024). + """ + return 1 - simplicial_edit_distance( + H, min_size=min_size, exclude_min_size=exclude_min_size ) - if not maxH.edges: - return np.nan - - ms = 0 - for id1, e in maxH.edges.members(dtype=dict).items(): - redundant_missing_faces = set() - for id2 in maxH.edges.neighbors(id1): - if id2 < id1: - c = maxH._edge[id2].intersection(e) - if len(c) >= min_size: - redundant_missing_faces.update(missing_subfaces(t, c, min_size)) - - # we don't have to worry about the intersection being a max face - # because a) there are no multiedges and b) these are all maximal - # faces so no inclusions. - if not t.search(c): - redundant_missing_faces.add(frozenset(c)) - - nm = max_number_of_subfaces(min_size, len(e)) - nf = count_subfaces(t, e, min_size) - rmf = len(redundant_missing_faces) - ms += nm - nf - rmf - - try: - mf = len(maxH.edges) - s = len(edges) - return (s - mf) / (ms + s - mf) - except ZeroDivisionError: - return np.nan def edit_simpliciality_full_construction(H, min_size=2, exclude_min_size=True): @@ -98,10 +74,10 @@ def edit_simpliciality_full_construction(H, min_size=2, exclude_min_size=True): for e in max_edges: ms.update(missing_subfaces(t, e, min_size=min_size)) - try: - s = len(edges) - mf = len(max_edges) - m = len(ms) + s = len(edges) + mf = len(max_edges) + m = len(ms) + if m + s - mf > 0: return (s - mf) / (m + s - mf) - except ZeroDivisionError: + else: return np.nan diff --git a/sod/simpliciality/face_edit_simpliciality.py b/sod/simpliciality/face_edit_simpliciality.py index c1f1fab..166b406 100644 --- a/sod/simpliciality/face_edit_simpliciality.py +++ b/sod/simpliciality/face_edit_simpliciality.py @@ -1,26 +1,38 @@ -import numpy as np - -from ..trie import Trie -from .utilities import count_subfaces, max_number_of_subfaces +from .mean_face_edit_distance import mean_face_edit_distance def face_edit_simpliciality(H, min_size=2, exclude_min_size=True): - edges = ( - H.edges.maximal().filterby("size", min_size + exclude_min_size, "geq").members() - ) - t = Trie() - t.build_trie(H.edges.filterby("size", min_size, "geq").members()) + """Computes the face edit simpliciality. + + The average number of edges needed to be added + to a hyperedge to make it a simplex. - if not edges: - return np.nan + Parameters + ---------- + H : xgi.Hypergraph + The hypergraph of interest + min_size: int, default: 2 + The minimum hyperedge size to include when + calculating whether a hyperedge is a simplex + by counting subfaces. + exclude_min_size : bool, optional + Whether to include minimal simplices when counting simplices, by default True - fes = 0 - for e in edges: - n = count_subfaces(t, e, min_size=min_size) - d = max_number_of_subfaces(min_size, len(e)) - # happens when you include the minimal faces when counting simplices - try: - fes += float(n / (d * len(edges))) - except ZeroDivisionError: - fes += 1.0 / len(edges) - return fes + Returns + ------- + float + The face edit simpliciality + + See Also + -------- + mean_face_edit_distance + + References + ---------- + "The simpliciality of higher-order order networks" + by Nicholas Landry, Jean-Gabriel Young, and Nicole Eikmeier, + *EPJ Data Science* **13**, 17 (2024). + """ + return 1 - mean_face_edit_distance( + H, min_size=min_size, exclude_min_size=exclude_min_size + ) diff --git a/sod/simpliciality/mean_face_edit_distance.py b/sod/simpliciality/mean_face_edit_distance.py index c0a8df6..659140a 100644 --- a/sod/simpliciality/mean_face_edit_distance.py +++ b/sod/simpliciality/mean_face_edit_distance.py @@ -1,8 +1,36 @@ from ..trie import Trie -from .utilities import count_subfaces, max_number_of_subfaces +from .utilities import count_missing_subfaces, max_number_of_subfaces def mean_face_edit_distance(H, min_size=1, exclude_min_size=True, normalize=True): + """Computes the mean face edit distance + + Parameters + ---------- + H : Hypergraph + The hypergraph of interest + min_size : int, optional + The minimum size to be considered a simplex, by default 2 + exclude_min_size : bool, optional + Whether to include minimal simplices when counting simplices, by default True + normalize : bool, optional + Whether to normalize the face edit distance, by default True + + Returns + ------- + float + The mean face edit distance + + See Also + -------- + face_edit_simpliciality + + References + ---------- + "The simpliciality of higher-order order networks" + by Nicholas Landry, Jean-Gabriel Young, and Nicole Eikmeier, + *EPJ Data Science* **13**, 17 (2024). + """ t = Trie() t.build_trie(H.edges.filterby("size", min_size, "geq").members()) @@ -12,10 +40,9 @@ def mean_face_edit_distance(H, min_size=1, exclude_min_size=True, normalize=True avg_d = 0 for e in max_faces: if len(e) >= min_size: - s = count_subfaces(t, e, min_size=min_size) + d = count_missing_subfaces(t, e, min_size=min_size) # missing subfaces m = max_number_of_subfaces(min_size, len(e)) - d = m - s - if normalize: + if normalize and m != 0: d *= 1.0 / m avg_d += d / len(max_faces) return avg_d diff --git a/sod/simpliciality/simplicial_edit_distance.py b/sod/simpliciality/simplicial_edit_distance.py index 2085b20..6c8da49 100644 --- a/sod/simpliciality/simplicial_edit_distance.py +++ b/sod/simpliciality/simplicial_edit_distance.py @@ -1,7 +1,8 @@ import numpy as np +import xgi from ..trie import Trie -from .utilities import missing_subfaces +from .utilities import count_missing_subfaces, missing_subfaces def simplicial_edit_distance(H, min_size=2, exclude_min_size=True, normalize=True): @@ -14,34 +15,68 @@ def simplicial_edit_distance(H, min_size=2, exclude_min_size=True, normalize=Tru ---------- H : xgi.Hypergraph The hypergraph of interest - min_size: int, default: 1 + min_size: int, default: 2 The minimum hyperedge size to include when calculating whether a hyperedge is a simplex by counting subfaces. + exclude_min_size : bool, optional + Whether to include minimal simplices when counting simplices, by default True + normalize : bool, optional + Whether to normalize by the total number of edges Returns ------- - int + float The edit simpliciality + + See Also + -------- + edit_simpliciality + + References + ---------- + "The simpliciality of higher-order order networks" + by Nicholas Landry, Jean-Gabriel Young, and Nicole Eikmeier, + *EPJ Data Science* **13**, 17 (2024). """ + edges = H.edges.filterby("size", min_size, "geq").members() + + t = Trie() + t.build_trie(edges) - edges = ( - H.edges.maximal().filterby("size", min_size + exclude_min_size, "geq").members() + maxH = xgi.Hypergraph( + H.edges.maximal() + .filterby("size", min_size + exclude_min_size, "geq") + .members(dtype=dict) ) + if not maxH.edges: + return np.nan - t = Trie() - t.build_trie(H.edges.members()) + ms = 0 + for id1, e in maxH.edges.members(dtype=dict).items(): + redundant_missing_faces = set() + for id2 in maxH.edges.neighbors(id1): + if id2 < id1: + c = maxH._edge[id2].intersection(e) + if len(c) >= min_size: + redundant_missing_faces.update(missing_subfaces(t, c, min_size)) - ms = set() - for e in edges: - ms.update(missing_subfaces(H, e, min_size=min_size)) - try: - s = H.num_edges - m = len(ms) + # we don't have to worry about the intersection being a max face + # because a) there are no multiedges and b) these are all maximal + # faces so no inclusions. + if not t.search(c): + redundant_missing_faces.add(frozenset(c)) - if normalize: - return m / (m + s) + mf = count_missing_subfaces(t, e, min_size) + rmf = len(redundant_missing_faces) + ms += mf - rmf + + if normalize: + s = len(edges) + mf = maxH.num_edges + if s - mf + ms > 0: + return ms / (s - mf + ms) else: - return m - except ZeroDivisionError: - return np.nan + return np.nan + else: + return ms diff --git a/sod/simpliciality/simplicial_fraction.py b/sod/simpliciality/simplicial_fraction.py index a3c175d..bd212df 100644 --- a/sod/simpliciality/simplicial_fraction.py +++ b/sod/simpliciality/simplicial_fraction.py @@ -5,6 +5,30 @@ def simplicial_fraction(H, min_size=2, exclude_min_size=True): + """Computing the simplicial fraction for a hypergraph. + + What fraction of the hyperedges are simplices? + + Parameters + ---------- + H : Hypergraph + The hypergraph of interest + min_size : int, optional + The minimum edge size to consider a simplex, by default 2 + exclude_min_size : bool, optional + Whether to include minimal simplices when counting simplices, by default True + + Returns + ------- + float + The simplicial fraction + + References + ---------- + "The simpliciality of higher-order order networks" + by Nicholas Landry, Jean-Gabriel Young, and Nicole Eikmeier, + *EPJ Data Science* **13**, 17 (2024). + """ try: ns = count_simplices(H, min_size, exclude_min_size) ps = potential_simplices(H, min_size, exclude_min_size) diff --git a/sod/simpliciality/utilities.py b/sod/simpliciality/utilities.py index 561785a..6b8e6eb 100644 --- a/sod/simpliciality/utilities.py +++ b/sod/simpliciality/utilities.py @@ -39,7 +39,7 @@ def powerset(iterable, min_size=1, max_size=None): ) -def count_subfaces(t, face, min_size=1): +def count_missing_subfaces(t, face, min_size=1): """Computing the edit distance for a single face. Parameters @@ -61,7 +61,7 @@ def count_subfaces(t, face, min_size=1): sub_edges = list(powerset(face, min_size=min_size, max_size=len(face) - 1)) count = 0 for e in sub_edges: - if t.search(e): + if not t.search(e): count += 1 return count diff --git a/tests/simpliciality/test_edit_simpliciality.py b/tests/simpliciality/test_edit_simpliciality.py index c2986bb..ab05184 100644 --- a/tests/simpliciality/test_edit_simpliciality.py +++ b/tests/simpliciality/test_edit_simpliciality.py @@ -9,57 +9,62 @@ def test_edit_simpliciality( h1, ): # simplicial complex - es = edit_simpliciality(sc1_with_singletons) + es = xgi.edit_simpliciality(sc1_with_singletons) assert es == 1.0 - es = edit_simpliciality(sc1_with_singletons, min_size=1) + es = xgi.edit_simpliciality(sc1_with_singletons, min_size=1) assert es == 1.0 - es = edit_simpliciality(sc1_with_singletons, min_size=1, exclude_min_size=False) + es = xgi.edit_simpliciality(sc1_with_singletons, min_size=1, exclude_min_size=False) assert es == 1.0 # h1 - es = edit_simpliciality(h_missing_one_singleton) + es = xgi.edit_simpliciality(h_missing_one_singleton) assert es == 1.0 - es = edit_simpliciality(h_missing_one_singleton, min_size=1) - assert np.allclose(es, 6 / 7) + es = xgi.edit_simpliciality(h_missing_one_singleton, min_size=1) + assert np.allclose(es, 5 / 6) - es = edit_simpliciality(h_missing_one_singleton, min_size=1, exclude_min_size=False) - assert np.allclose(es, 6 / 7) + es = xgi.edit_simpliciality( + h_missing_one_singleton, min_size=1, exclude_min_size=False + ) + assert np.allclose(es, 5 / 6) # h2 - es = edit_simpliciality(h_missing_one_link) - assert np.allclose(es, 3 / 4) + es = xgi.edit_simpliciality(h_missing_one_link) + assert np.allclose(es, 2 / 3) - es = edit_simpliciality(h_missing_one_link, min_size=1) - assert np.allclose(es, 6 / 7) + es = xgi.edit_simpliciality(h_missing_one_link, min_size=1) + assert np.allclose(es, 5 / 6) # links and triangles 2 - es = edit_simpliciality(h_links_and_triangles2) - assert np.allclose(es, 3 / 4) + es = xgi.edit_simpliciality(h_links_and_triangles2) + assert np.allclose(es, 2 / 3) - es = edit_simpliciality(h_links_and_triangles2, min_size=1) - assert np.allclose(es, 1 / 2) + es = xgi.edit_simpliciality(h_links_and_triangles2, min_size=1) + assert np.allclose(es, 1 / 3) - es = edit_simpliciality(h_links_and_triangles2, exclude_min_size=False) - assert np.allclose(es, 3 / 4) + es = xgi.edit_simpliciality(h_links_and_triangles2, exclude_min_size=False) + assert np.allclose(es, 3 / 5) # test h1 - es = edit_simpliciality(h1) + es = xgi.edit_simpliciality(h1) s = 4 m = 4 + 10 - assert np.allclose(es, s / (m + s)) + mf = 3 + assert np.allclose(es, (s - mf) / (m + s - mf)) - es = edit_simpliciality(h1, min_size=1) + es = xgi.edit_simpliciality(h1, min_size=1) s = 4 m = 4 + 10 + 7 - assert np.allclose(es, s / (m + s)) + mf = 3 + assert np.allclose(es, (s - mf) / (m + s - mf)) - es = edit_simpliciality(h1, exclude_min_size=False) + es = xgi.edit_simpliciality(h1, exclude_min_size=False) s = 4 m = 4 + 10 - assert np.allclose(es, s / (m + s)) + mf = 3 + assert np.allclose(es, (s - mf) / (m + s - mf)) def test_edit_simpliciality_full_construction( @@ -86,44 +91,47 @@ def test_edit_simpliciality_full_construction( assert es == 1.0 es = edit_simpliciality_full_construction(h_missing_one_singleton, min_size=1) - assert np.allclose(es, 6 / 7) + assert np.allclose(es, 5 / 6) es = edit_simpliciality_full_construction( h_missing_one_singleton, min_size=1, exclude_min_size=False ) - assert np.allclose(es, 6 / 7) + assert np.allclose(es, 5 / 6) # h2 es = edit_simpliciality_full_construction(h_missing_one_link) - assert np.allclose(es, 3 / 4) + assert np.allclose(es, 2 / 3) es = edit_simpliciality_full_construction(h_missing_one_link, min_size=1) - assert np.allclose(es, 6 / 7) + assert np.allclose(es, 5 / 6) # links and triangles 2 es = edit_simpliciality_full_construction(h_links_and_triangles2) - assert np.allclose(es, 3 / 4) + assert np.allclose(es, 2 / 3) es = edit_simpliciality_full_construction(h_links_and_triangles2, min_size=1) - assert np.allclose(es, 1 / 2) + assert np.allclose(es, 1 / 3) es = edit_simpliciality_full_construction( h_links_and_triangles2, exclude_min_size=False ) - assert np.allclose(es, 3 / 4) + assert np.allclose(es, 3/ 5) # test h1 es = edit_simpliciality_full_construction(h1) s = 4 m = 4 + 10 - assert np.allclose(es, s / (m + s)) + mf = 3 + assert np.allclose(es, (s - mf) / (s - mf + m)) es = edit_simpliciality_full_construction(h1, min_size=1) s = 4 m = 4 + 10 + 7 - assert np.allclose(es, s / (m + s)) + mf = 3 + assert np.allclose(es, (s - mf) / (s - mf + m)) es = edit_simpliciality_full_construction(h1, exclude_min_size=False) s = 4 m = 4 + 10 - assert np.allclose(es, s / (m + s)) + mf = 3 + assert np.allclose(es, (s - mf) / (s - mf + m)) diff --git a/tests/simpliciality/test_simplicial_edit_distance.py b/tests/simpliciality/test_simplicial_edit_distance.py new file mode 100644 index 0000000..78d567b --- /dev/null +++ b/tests/simpliciality/test_simplicial_edit_distance.py @@ -0,0 +1,73 @@ +import numpy as np +import xgi + + +def test_simplicial_edit_distance( + sc1_with_singletons, + h_missing_one_singleton, + h_missing_one_link, + h_links_and_triangles2, + h1, +): + # simplicial complex + sed = xgi.simplicial_edit_distance(sc1_with_singletons) + assert sed == 0.0 + + sed = xgi.simplicial_edit_distance(sc1_with_singletons, min_size=1) + assert sed == 0.0 + + sed = xgi.simplicial_edit_distance( + sc1_with_singletons, min_size=1, exclude_min_size=False + ) + assert sed == 0.0 + + # h1 + sed = xgi.simplicial_edit_distance(h_missing_one_singleton) + assert sed == 0.0 + + sed = xgi.simplicial_edit_distance(h_missing_one_singleton, min_size=1) + assert np.allclose(sed, 1 / 6) + + sed = xgi.simplicial_edit_distance( + h_missing_one_singleton, min_size=1, exclude_min_size=False + ) + assert np.allclose(sed, 1 / 6) + + # h2 + sed = xgi.simplicial_edit_distance(h_missing_one_link) + assert np.allclose(sed, 1 / 3) + + sed = xgi.simplicial_edit_distance(h_missing_one_link, min_size=1) + assert np.allclose(sed, 1 / 6) + + # links and triangles 2 + sed = xgi.simplicial_edit_distance(h_links_and_triangles2) + assert np.allclose(sed, 1 / 3) + + sed = xgi.simplicial_edit_distance(h_links_and_triangles2, min_size=1) + assert np.allclose(sed, 2 / 3) + + sed = xgi.simplicial_edit_distance(h_links_and_triangles2, exclude_min_size=False) + assert np.allclose(sed, 2 / 5) + + # test h1 + sed = xgi.simplicial_edit_distance(h1) + s = 4 + m = 4 + 10 + mf = 3 + assert np.allclose(sed, m / (m + s - mf)) + + sed = xgi.simplicial_edit_distance(h1, min_size=1) + s = 4 + m = 4 + 10 + 7 + mf = 3 + assert np.allclose(sed, m / (m + s - mf)) + + sed = xgi.simplicial_edit_distance(h1, exclude_min_size=False) + s = 4 + m = 4 + 10 + mf = 3 + assert np.allclose(sed, m / (m + s - mf)) + + sed = xgi.simplicial_edit_distance(h1, exclude_min_size=False, normalize=False) + assert np.allclose(sed, m) diff --git a/tests/simpliciality/test_utilities.py b/tests/simpliciality/test_utilities.py index 562009a..3daf63f 100644 --- a/tests/simpliciality/test_utilities.py +++ b/tests/simpliciality/test_utilities.py @@ -1,6 +1,6 @@ import xgi -from sod import Trie, count_subfaces, max_number_of_subfaces, powerset +from sod import Trie, count_missing_subfaces, max_number_of_subfaces, powerset def test_powerset(): @@ -37,14 +37,14 @@ def test_powerset(): } -def test_count_subfaces(h_missing_one_link): - t = Trie() +def test_count_missing_subfaces(h_missing_one_link): + t = xgi.Trie() t.build_trie(h_missing_one_link.edges.members()) - assert count_subfaces(t, {1}, min_size=2) == 0 - assert count_subfaces(t, {2, 3}, min_size=2) == 0 - assert count_subfaces(t, {2, 3}) == 2 - assert count_subfaces(t, {1, 2, 3}) == 5 - assert count_subfaces(t, {1, 2, 3}, min_size=2) == 2 + assert count_missing_subfaces(t, {1}, min_size=2) == 0 + assert count_missing_subfaces(t, {2, 3}, min_size=2) == 0 + assert count_missing_subfaces(t, {2, 3}) == 0 + assert count_missing_subfaces(t, {1, 2, 3}) == 1 + assert count_missing_subfaces(t, {1, 2, 3}, min_size=2) == 1 def test_max_number_of_subfaces(): From 6cd07de1d8d04be99cc4342727d542d0b96fb80d Mon Sep 17 00:00:00 2001 From: Nicholas Landry Date: Sun, 6 Oct 2024 16:05:37 -0400 Subject: [PATCH 10/17] update tags-ask-ubuntu --- Data/model_simpliciality_tags-ask-ubuntu.json | 17796 ++++++++-------- 1 file changed, 8898 insertions(+), 8898 deletions(-) diff --git a/Data/model_simpliciality_tags-ask-ubuntu.json b/Data/model_simpliciality_tags-ask-ubuntu.json index 596b332..db7c131 100644 --- a/Data/model_simpliciality_tags-ask-ubuntu.json +++ b/Data/model_simpliciality_tags-ask-ubuntu.json @@ -1,9026 +1,9026 @@ { "CM": { "sf": [ - 0.05178120857032887, - 0.05144763289569345, + 0.05340632083137322, + 0.05105418466407219, + 0.050874566993114656, + 0.05346619338835906, + 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0.21431372084863345, + 0.2175651004451185, + 0.21746258326505583, + 0.21879566338450582 ] } } \ No newline at end of file From bb2c957f601e901704ee58d60cb7953e41426dfc Mon Sep 17 00:00:00 2001 From: Nicholas Landry Date: Mon, 7 Oct 2024 11:32:05 -0400 Subject: [PATCH 11/17] fix docstrings --- sod/simpliciality/edit_simpliciality.py | 35 ++++++++++++++--- sod/simpliciality/face_edit_simpliciality.py | 36 +++++++++++++++--- sod/simpliciality/mean_face_edit_distance.py | 37 ++++++++++++++++-- sod/simpliciality/simplicial_edit_distance.py | 38 ++++++++++++++++--- sod/simpliciality/simplicial_fraction.py | 34 +++++++++++++++-- .../simpliciality/test_edit_simpliciality.py | 28 +++++++------- .../test_simplicial_edit_distance.py | 32 ++++++++-------- tests/simpliciality/test_utilities.py | 4 +- 8 files changed, 190 insertions(+), 54 deletions(-) diff --git a/sod/simpliciality/edit_simpliciality.py b/sod/simpliciality/edit_simpliciality.py index b3890a2..8930c49 100644 --- a/sod/simpliciality/edit_simpliciality.py +++ b/sod/simpliciality/edit_simpliciality.py @@ -8,19 +8,20 @@ def edit_simpliciality(H, min_size=2, exclude_min_size=True): """Computes the edit simpliciality. - The number of edges needed to be added - to a hypergraph to make it a simplicial complex. + The fraction of sub-edges contained when compared to a simplicial complex. Parameters ---------- H : xgi.Hypergraph The hypergraph of interest - min_size: int, default: 2 + min_size: int, optional The minimum hyperedge size to include when calculating whether a hyperedge is a simplex - by counting subfaces. + by counting subfaces. For more details, see + the Notes below. By default, 2. exclude_min_size : bool, optional - Whether to include minimal simplices when counting simplices, by default True + Whether to exclude minimal simplices when counting simplices. + For more detailed information, see the Notes below. By default, True. Returns ------- @@ -31,6 +32,30 @@ def edit_simpliciality(H, min_size=2, exclude_min_size=True): -------- simplicial_edit_distance + Notes + ----- + 1. The formal definition of a simplicial complex can be unnecessarily + strict when used to represent perfect inclusion structures. + By definition, a simplex always contains singletons + (edges comprising a single node) and the empty set. + Several datasets will not include such interactions by construction. + To circumvent this issue, we use a relaxed definition of + downward closure that excludes edges of a certain size or smaller + wherever it makes sense. By default, we set the minimum size + to be 2 since some datasets do not contain singletons. + + 2. Hyperedges we call “minimal faces” may significantly skew the + simpliciality of a dataset. The minimal faces of a hypergraph :math:`H` + are the edges of the minimal size, i.e., :math:`|e| = min(K)`, where :math:`K` + is the set of sizes that we consider based on note 1. + (In a traditional simplicial complex, the minimal faces are singletons). + With the size restrictions in place, the minimal faces of a hypergraph + are always simplices because, by definition, there are no smaller edges + for these edges to include. When measuring the simpliciality of a dataset, + it is most meaningful to focus on the faces for which inclusion is possible, + and so, by default, we exclude these minimal faces when counting potential + simplices. + References ---------- "The simpliciality of higher-order order networks" diff --git a/sod/simpliciality/face_edit_simpliciality.py b/sod/simpliciality/face_edit_simpliciality.py index 166b406..d532875 100644 --- a/sod/simpliciality/face_edit_simpliciality.py +++ b/sod/simpliciality/face_edit_simpliciality.py @@ -4,19 +4,21 @@ def face_edit_simpliciality(H, min_size=2, exclude_min_size=True): """Computes the face edit simpliciality. - The average number of edges needed to be added - to a hyperedge to make it a simplex. + The average fraction of sub-edges contained in a hyperedge + relative to a simplex. Parameters ---------- H : xgi.Hypergraph The hypergraph of interest - min_size: int, default: 2 + min_size: int, optional The minimum hyperedge size to include when calculating whether a hyperedge is a simplex - by counting subfaces. + by counting subfaces. For more details, see + the Notes below. By default, 2. exclude_min_size : bool, optional - Whether to include minimal simplices when counting simplices, by default True + Whether to exclude minimal simplices when counting simplices. + For more detailed information, see the Notes below. By default, True. Returns ------- @@ -27,6 +29,30 @@ def face_edit_simpliciality(H, min_size=2, exclude_min_size=True): -------- mean_face_edit_distance + Notes + ----- + 1. The formal definition of a simplicial complex can be unnecessarily + strict when used to represent perfect inclusion structures. + By definition, a simplex always contains singletons + (edges comprising a single node) and the empty set. + Several datasets will not include such interactions by construction. + To circumvent this issue, we use a relaxed definition of + downward closure that excludes edges of a certain size or smaller + wherever it makes sense. By default, we set the minimum size + to be 2 since some datasets do not contain singletons. + + 2. Hyperedges we call “minimal faces” may significantly skew the + simpliciality of a dataset. The minimal faces of a hypergraph :math:`H` + are the edges of the minimal size, i.e., :math:`|e| = min(K)`, where :math:`K` + is the set of sizes that we consider based on note 1. + (In a traditional simplicial complex, the minimal faces are singletons). + With the size restrictions in place, the minimal faces of a hypergraph + are always simplices because, by definition, there are no smaller edges + for these edges to include. When measuring the simpliciality of a dataset, + it is most meaningful to focus on the faces for which inclusion is possible, + and so, by default, we exclude these minimal faces when counting potential + simplices. + References ---------- "The simpliciality of higher-order order networks" diff --git a/sod/simpliciality/mean_face_edit_distance.py b/sod/simpliciality/mean_face_edit_distance.py index 659140a..de5fd0a 100644 --- a/sod/simpliciality/mean_face_edit_distance.py +++ b/sod/simpliciality/mean_face_edit_distance.py @@ -5,14 +5,21 @@ def mean_face_edit_distance(H, min_size=1, exclude_min_size=True, normalize=True): """Computes the mean face edit distance + The average number (or fraction) of sub-edges needed to be added to make + a hyperedge a simplex. + Parameters ---------- H : Hypergraph The hypergraph of interest - min_size : int, optional - The minimum size to be considered a simplex, by default 2 + min_size: int, optional + The minimum hyperedge size to include when + calculating whether a hyperedge is a simplex + by counting subfaces. For more details, see + the Notes below. By default, 2. exclude_min_size : bool, optional - Whether to include minimal simplices when counting simplices, by default True + Whether to exclude minimal simplices when counting simplices. + For more detailed information, see the Notes below. By default, True. normalize : bool, optional Whether to normalize the face edit distance, by default True @@ -25,6 +32,30 @@ def mean_face_edit_distance(H, min_size=1, exclude_min_size=True, normalize=True -------- face_edit_simpliciality + Notes + ----- + 1. The formal definition of a simplicial complex can be unnecessarily + strict when used to represent perfect inclusion structures. + By definition, a simplex always contains singletons + (edges comprising a single node) and the empty set. + Several datasets will not include such interactions by construction. + To circumvent this issue, we use a relaxed definition of + downward closure that excludes edges of a certain size or smaller + wherever it makes sense. By default, we set the minimum size + to be 2 since some datasets do not contain singletons. + + 2. Hyperedges we call “minimal faces” may significantly skew the + simpliciality of a dataset. The minimal faces of a hypergraph :math:`H` + are the edges of the minimal size, i.e., :math:`|e| = min(K)`, where :math:`K` + is the set of sizes that we consider based on note 1. + (In a traditional simplicial complex, the minimal faces are singletons). + With the size restrictions in place, the minimal faces of a hypergraph + are always simplices because, by definition, there are no smaller edges + for these edges to include. When measuring the simpliciality of a dataset, + it is most meaningful to focus on the faces for which inclusion is possible, + and so, by default, we exclude these minimal faces when counting potential + simplices. + References ---------- "The simpliciality of higher-order order networks" diff --git a/sod/simpliciality/simplicial_edit_distance.py b/sod/simpliciality/simplicial_edit_distance.py index 6c8da49..11dc83a 100644 --- a/sod/simpliciality/simplicial_edit_distance.py +++ b/sod/simpliciality/simplicial_edit_distance.py @@ -8,19 +8,22 @@ def simplicial_edit_distance(H, min_size=2, exclude_min_size=True, normalize=True): """Computes the simplicial edit distance. - The number of edges needed to be added + The number (or fraction) of sub-edges needed to be added to a hypergraph to make it a simplicial complex. Parameters ---------- H : xgi.Hypergraph The hypergraph of interest - min_size: int, default: 2 + min_size: int, optional The minimum hyperedge size to include when calculating whether a hyperedge is a simplex - by counting subfaces. + by counting subfaces. For more details, see + the Notes below. By default, 2. exclude_min_size : bool, optional - Whether to include minimal simplices when counting simplices, by default True + Whether to exclude minimal simplices when counting simplices. + For more detailed information, see the Notes below. By default, True. + normalize : bool, optional Whether to normalize by the total number of edges @@ -33,6 +36,30 @@ def simplicial_edit_distance(H, min_size=2, exclude_min_size=True, normalize=Tru -------- edit_simpliciality + Notes + ----- + 1. The formal definition of a simplicial complex can be unnecessarily + strict when used to represent perfect inclusion structures. + By definition, a simplex always contains singletons + (edges comprising a single node) and the empty set. + Several datasets will not include such interactions by construction. + To circumvent this issue, we use a relaxed definition of + downward closure that excludes edges of a certain size or smaller + wherever it makes sense. By default, we set the minimum size + to be 2 since some datasets do not contain singletons. + + 2. Hyperedges we call “minimal faces” may significantly skew the + simpliciality of a dataset. The minimal faces of a hypergraph :math:`H` + are the edges of the minimal size, i.e., :math:`|e| = min(K)`, where :math:`K` + is the set of sizes that we consider based on note 1. + (In a traditional simplicial complex, the minimal faces are singletons). + With the size restrictions in place, the minimal faces of a hypergraph + are always simplices because, by definition, there are no smaller edges + for these edges to include. When measuring the simpliciality of a dataset, + it is most meaningful to focus on the faces for which inclusion is possible, + and so, by default, we exclude these minimal faces when counting potential + simplices. + References ---------- "The simpliciality of higher-order order networks" @@ -52,11 +79,12 @@ def simplicial_edit_distance(H, min_size=2, exclude_min_size=True, normalize=Tru if not maxH.edges: return np.nan + id_to_num = dict(zip(maxH.edges, range(maxH.num_edges))) ms = 0 for id1, e in maxH.edges.members(dtype=dict).items(): redundant_missing_faces = set() for id2 in maxH.edges.neighbors(id1): - if id2 < id1: + if id_to_num[id2] < id_to_num[id1]: c = maxH._edge[id2].intersection(e) if len(c) >= min_size: redundant_missing_faces.update(missing_subfaces(t, c, min_size)) diff --git a/sod/simpliciality/simplicial_fraction.py b/sod/simpliciality/simplicial_fraction.py index bd212df..f93795b 100644 --- a/sod/simpliciality/simplicial_fraction.py +++ b/sod/simpliciality/simplicial_fraction.py @@ -13,16 +13,44 @@ def simplicial_fraction(H, min_size=2, exclude_min_size=True): ---------- H : Hypergraph The hypergraph of interest - min_size : int, optional - The minimum edge size to consider a simplex, by default 2 + min_size: int, optional + The minimum hyperedge size to include when + calculating whether a hyperedge is a simplex + by counting subfaces. For more details, see + the Notes below. By default, 2. exclude_min_size : bool, optional - Whether to include minimal simplices when counting simplices, by default True + Whether to exclude minimal simplices when counting simplices. + For more detailed information, see the Notes below. By default, True. Returns ------- float The simplicial fraction + Notes + ----- + 1. The formal definition of a simplicial complex can be unnecessarily + strict when used to represent perfect inclusion structures. + By definition, a simplex always contains singletons + (edges comprising a single node) and the empty set. + Several datasets will not include such interactions by construction. + To circumvent this issue, we use a relaxed definition of + downward closure that excludes edges of a certain size or smaller + wherever it makes sense. By default, we set the minimum size + to be 2 since some datasets do not contain singletons. + + 2. Hyperedges we call “minimal faces” may significantly skew the + simpliciality of a dataset. The minimal faces of a hypergraph :math:`H` + are the edges of the minimal size, i.e., :math:`|e| = min(K)`, where :math:`K` + is the set of sizes that we consider based on note 1. + (In a traditional simplicial complex, the minimal faces are singletons). + With the size restrictions in place, the minimal faces of a hypergraph + are always simplices because, by definition, there are no smaller edges + for these edges to include. When measuring the simpliciality of a dataset, + it is most meaningful to focus on the faces for which inclusion is possible, + and so, by default, we exclude these minimal faces when counting potential + simplices. + References ---------- "The simpliciality of higher-order order networks" diff --git a/tests/simpliciality/test_edit_simpliciality.py b/tests/simpliciality/test_edit_simpliciality.py index ab05184..1740dee 100644 --- a/tests/simpliciality/test_edit_simpliciality.py +++ b/tests/simpliciality/test_edit_simpliciality.py @@ -9,58 +9,58 @@ def test_edit_simpliciality( h1, ): # simplicial complex - es = xgi.edit_simpliciality(sc1_with_singletons) + es = edit_simpliciality(sc1_with_singletons) assert es == 1.0 - es = xgi.edit_simpliciality(sc1_with_singletons, min_size=1) + es = edit_simpliciality(sc1_with_singletons, min_size=1) assert es == 1.0 - es = xgi.edit_simpliciality(sc1_with_singletons, min_size=1, exclude_min_size=False) + es = edit_simpliciality(sc1_with_singletons, min_size=1, exclude_min_size=False) assert es == 1.0 # h1 - es = xgi.edit_simpliciality(h_missing_one_singleton) + es = edit_simpliciality(h_missing_one_singleton) assert es == 1.0 - es = xgi.edit_simpliciality(h_missing_one_singleton, min_size=1) + es = edit_simpliciality(h_missing_one_singleton, min_size=1) assert np.allclose(es, 5 / 6) - es = xgi.edit_simpliciality( + es = edit_simpliciality( h_missing_one_singleton, min_size=1, exclude_min_size=False ) assert np.allclose(es, 5 / 6) # h2 - es = xgi.edit_simpliciality(h_missing_one_link) + es = edit_simpliciality(h_missing_one_link) assert np.allclose(es, 2 / 3) - es = xgi.edit_simpliciality(h_missing_one_link, min_size=1) + es = edit_simpliciality(h_missing_one_link, min_size=1) assert np.allclose(es, 5 / 6) # links and triangles 2 - es = xgi.edit_simpliciality(h_links_and_triangles2) + es = edit_simpliciality(h_links_and_triangles2) assert np.allclose(es, 2 / 3) - es = xgi.edit_simpliciality(h_links_and_triangles2, min_size=1) + es = edit_simpliciality(h_links_and_triangles2, min_size=1) assert np.allclose(es, 1 / 3) - es = xgi.edit_simpliciality(h_links_and_triangles2, exclude_min_size=False) + es = edit_simpliciality(h_links_and_triangles2, exclude_min_size=False) assert np.allclose(es, 3 / 5) # test h1 - es = xgi.edit_simpliciality(h1) + es = edit_simpliciality(h1) s = 4 m = 4 + 10 mf = 3 assert np.allclose(es, (s - mf) / (m + s - mf)) - es = xgi.edit_simpliciality(h1, min_size=1) + es = edit_simpliciality(h1, min_size=1) s = 4 m = 4 + 10 + 7 mf = 3 assert np.allclose(es, (s - mf) / (m + s - mf)) - es = xgi.edit_simpliciality(h1, exclude_min_size=False) + es = edit_simpliciality(h1, exclude_min_size=False) s = 4 m = 4 + 10 mf = 3 diff --git a/tests/simpliciality/test_simplicial_edit_distance.py b/tests/simpliciality/test_simplicial_edit_distance.py index 78d567b..9507abe 100644 --- a/tests/simpliciality/test_simplicial_edit_distance.py +++ b/tests/simpliciality/test_simplicial_edit_distance.py @@ -1,5 +1,5 @@ import numpy as np -import xgi +from sod import * def test_simplicial_edit_distance( @@ -10,64 +10,64 @@ def test_simplicial_edit_distance( h1, ): # simplicial complex - sed = xgi.simplicial_edit_distance(sc1_with_singletons) + sed = simplicial_edit_distance(sc1_with_singletons) assert sed == 0.0 - sed = xgi.simplicial_edit_distance(sc1_with_singletons, min_size=1) + sed = simplicial_edit_distance(sc1_with_singletons, min_size=1) assert sed == 0.0 - sed = xgi.simplicial_edit_distance( + sed = simplicial_edit_distance( sc1_with_singletons, min_size=1, exclude_min_size=False ) assert sed == 0.0 # h1 - sed = xgi.simplicial_edit_distance(h_missing_one_singleton) + sed = simplicial_edit_distance(h_missing_one_singleton) assert sed == 0.0 - sed = xgi.simplicial_edit_distance(h_missing_one_singleton, min_size=1) + sed = simplicial_edit_distance(h_missing_one_singleton, min_size=1) assert np.allclose(sed, 1 / 6) - sed = xgi.simplicial_edit_distance( + sed = simplicial_edit_distance( h_missing_one_singleton, min_size=1, exclude_min_size=False ) assert np.allclose(sed, 1 / 6) # h2 - sed = xgi.simplicial_edit_distance(h_missing_one_link) + sed = simplicial_edit_distance(h_missing_one_link) assert np.allclose(sed, 1 / 3) - sed = xgi.simplicial_edit_distance(h_missing_one_link, min_size=1) + sed = simplicial_edit_distance(h_missing_one_link, min_size=1) assert np.allclose(sed, 1 / 6) # links and triangles 2 - sed = xgi.simplicial_edit_distance(h_links_and_triangles2) + sed = simplicial_edit_distance(h_links_and_triangles2) assert np.allclose(sed, 1 / 3) - sed = xgi.simplicial_edit_distance(h_links_and_triangles2, min_size=1) + sed = simplicial_edit_distance(h_links_and_triangles2, min_size=1) assert np.allclose(sed, 2 / 3) - sed = xgi.simplicial_edit_distance(h_links_and_triangles2, exclude_min_size=False) + sed = simplicial_edit_distance(h_links_and_triangles2, exclude_min_size=False) assert np.allclose(sed, 2 / 5) # test h1 - sed = xgi.simplicial_edit_distance(h1) + sed = simplicial_edit_distance(h1) s = 4 m = 4 + 10 mf = 3 assert np.allclose(sed, m / (m + s - mf)) - sed = xgi.simplicial_edit_distance(h1, min_size=1) + sed = simplicial_edit_distance(h1, min_size=1) s = 4 m = 4 + 10 + 7 mf = 3 assert np.allclose(sed, m / (m + s - mf)) - sed = xgi.simplicial_edit_distance(h1, exclude_min_size=False) + sed = simplicial_edit_distance(h1, exclude_min_size=False) s = 4 m = 4 + 10 mf = 3 assert np.allclose(sed, m / (m + s - mf)) - sed = xgi.simplicial_edit_distance(h1, exclude_min_size=False, normalize=False) + sed = simplicial_edit_distance(h1, exclude_min_size=False, normalize=False) assert np.allclose(sed, m) diff --git a/tests/simpliciality/test_utilities.py b/tests/simpliciality/test_utilities.py index 3daf63f..c5e77c9 100644 --- a/tests/simpliciality/test_utilities.py +++ b/tests/simpliciality/test_utilities.py @@ -1,5 +1,3 @@ -import xgi - from sod import Trie, count_missing_subfaces, max_number_of_subfaces, powerset @@ -38,7 +36,7 @@ def test_powerset(): def test_count_missing_subfaces(h_missing_one_link): - t = xgi.Trie() + t = Trie() t.build_trie(h_missing_one_link.edges.members()) assert count_missing_subfaces(t, {1}, min_size=2) == 0 assert count_missing_subfaces(t, {2, 3}, min_size=2) == 0 From 2fa890304f5c9cb68b5868babe74fce384059555 Mon Sep 17 00:00:00 2001 From: Nicholas Landry Date: Tue, 8 Oct 2024 11:02:05 -0400 Subject: [PATCH 12/17] update plots --- Figures/Fig2/model_fitting.pdf | Bin 162900 -> 161281 bytes Figures/Fig2/model_fitting.png | Bin 1021546 -> 1005667 bytes plot_model_fitting.ipynb | 46 +++++++++++++++++++++++++-------- 3 files changed, 35 insertions(+), 11 deletions(-) diff --git a/Figures/Fig2/model_fitting.pdf b/Figures/Fig2/model_fitting.pdf index bd908aa5b09cdf3049cb3b68e3bfcbe7479e18df..31cb02fc2dcd16211a6c9cd64175458dbdc00b48 100644 GIT binary patch delta 148683 zcmV(|K+(U{_z8jR39yF&f7y;UyK)_Pzn`L>pu{=IOAmmqyZfRaK#hb|HvzS+7HY33KB7{{FB3 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