diff --git a/.gitignore b/.gitignore index ce12c01cb5b536c72191410ae6c37b7db342b0d7..6f6e521e7ed1f71067105cd31b658ff1bb6309fe 100644 --- a/.gitignore +++ b/.gitignore @@ -6,7 +6,7 @@ __pycache__ # Data -scratch/ +data/ artifacts/ artifacts_old/ lightning_logs/ diff --git a/LHCb_Pipeline/Embedding/embedding_base.py b/LHCb_Pipeline/Embedding/embedding_base.py index 27d4ecdce353aed095585dfde47ee7b9ed6272fc..b22e447510c184f5794b7d4c8fb8e80eeba3551d 100644 --- a/LHCb_Pipeline/Embedding/embedding_base.py +++ b/LHCb_Pipeline/Embedding/embedding_base.py @@ -186,7 +186,7 @@ class EmbeddingBase(ModelBase): e_spatial, y_cluster, new_weights, e_bidir ) if not self.bidir: # is it really what I want? - sort_edge_nodes(e_spatial, batch.z) + sort_edge_nodes(e_spatial, batch.un_z) included_hits = e_spatial.unique() spatial[included_hits] = self(input_data[included_hits]) diff --git a/LHCb_Pipeline/GNN/models/interaction_gnn.py b/LHCb_Pipeline/GNN/models/interaction_gnn.py index af6a3d7d9cdc7ccd46cbb5c844149816c0075647..676991e56d39736dfd6816492df72a8d15be91ea 100644 --- a/LHCb_Pipeline/GNN/models/interaction_gnn.py +++ b/LHCb_Pipeline/GNN/models/interaction_gnn.py @@ -105,8 +105,8 @@ class InteractionGNN(GNNBase): else: edge_messages = torch.cat( [ - scatter_max(e, end, dim=0, dim_size=x.shape[0])[0], - scatter_add(e, end, dim=0, dim_size=x.shape[0]), + scatter_max(e, end.reshape(-1,1), dim=0, dim_size=x.shape[0])[0], + scatter_add(e, end.reshape(-1,1), dim=0, dim_size=x.shape[0]), ], dim=-1, ) @@ -130,7 +130,8 @@ class InteractionGNN(GNNBase): return self.output_edge_classifier(classifier_inputs).squeeze(-1) def forward(self, x, edge_index): - start, end = edge_index + start = edge_index[0] + end = edge_index[1] # Encode the graph features into the hidden space x.requires_grad = True diff --git a/LHCb_Pipeline/GNN/models/interaction_gnn_export.py b/LHCb_Pipeline/GNN/models/interaction_gnn_export.py new file mode 100644 index 0000000000000000000000000000000000000000..5b796871a29d14eddca64d1a3a238ffca3e4ffe5 --- /dev/null +++ b/LHCb_Pipeline/GNN/models/interaction_gnn_export.py @@ -0,0 +1,147 @@ +from torch.nn import Linear +import torch +from torch_scatter import scatter_add, scatter_max +from torch.utils.checkpoint import checkpoint + +from utils.modelutils.mlp import make_mlp +from utils.commonutils.cfeatures import get_number_input_features + +from ..gnn_base import GNNBase + +class InteractionGNN(GNNBase): + """An interaction network class""" + + def __init__(self, hparams): + super().__init__(hparams) + """ + Initialise the Lightning Module that can scan over different GNN training + regimes + """ + + concatenation_factor = ( + 3 if (self.hparams["aggregation"] in ["sum_max", "mean_max"]) else 2 + ) + if not self.bidir: + concatenation_factor = (concatenation_factor - 1) * 2 + 1 + + nb_edge_layers: int = hparams["nb_edge_layers"] + nb_node_layers: int = hparams["nb_node_layers"] + nb_hidden: int = hparams["hidden"] + + # Setup input network + self.node_encoder = make_mlp( + get_number_input_features(hparams["feature_indices"]), + [nb_hidden] * hparams.get("nb_node_encoder_layers", nb_node_layers), + output_activation=None, + hidden_activation=hparams["hidden_activation"], + layer_norm=hparams["layernorm"], + ) + + # The edge network computes new edge features from connected nodes + self.edge_encoder = make_mlp( + 2 * (nb_hidden), + [nb_hidden] * hparams.get("nb_edge_encoder_layers", nb_edge_layers), + layer_norm=hparams["layernorm"], + output_activation=None, + hidden_activation=hparams["hidden_activation"], + ) + + # The edge network computes new edge features from connected nodes + self.edge_network = make_mlp( + 3 * nb_hidden, + [nb_hidden] * nb_edge_layers, + layer_norm=hparams["layernorm"], + output_activation=None, + hidden_activation=hparams["hidden_activation"], + ) + + # The node network computes new node features + self.node_network = make_mlp( + concatenation_factor * nb_hidden, + [nb_hidden] * nb_node_layers, + layer_norm=hparams["layernorm"], + output_activation=None, + hidden_activation=hparams["hidden_activation"], + ) + + # Final edge output classification network + self.output_edge_classifier = make_mlp( + 3 * nb_hidden, + [nb_hidden] + * hparams.get("nb_edge_classifier_layers", nb_edge_layers) + + [1], + layer_norm=hparams["layernorm"], + output_activation=None, + hidden_activation=hparams["hidden_activation"], + ) + + def reset_parameters(self): + for layer in self.modules(): + if type(layer) is Linear: + eval(self.hparams["initialization"])(layer.weight) + layer.bias.data.fill_(0) + + def message_step(self, x, start, end, e): + # Compute new node features + if self.hparams["aggregation"] == "sum": + assert not self.bidir + edge_messages = scatter_add(e, end, dim=0, dim_size=x.shape[0]) + + elif self.hparams["aggregation"] == "max": + assert not self.bidir + edge_messages = scatter_max(e, end, dim=0, dim_size=x.shape[0])[0] + + elif self.hparams["aggregation"] == "sum_max": + if not self.bidir: + edge_messages = torch.cat( + [ + scatter_max(e, end, dim=0, dim_size=x.shape[0])[0], + scatter_add(e, end, dim=0, dim_size=x.shape[0]), + scatter_max(e, start, dim=0, dim_size=x.shape[0])[0], + scatter_add(e, start, dim=0, dim_size=x.shape[0]), + ], + dim=-1, + ) + else: + edge_messages = torch.cat( + [ + scatter_max(e, end.reshape(-1,1), dim=0, dim_size=x.shape[0])[0], + scatter_add(e, end.reshape(-1,1), dim=0, dim_size=x.shape[0]), + ], + dim=-1, + ) + node_inputs = torch.cat([x, edge_messages], dim=-1) + + x_out = self.node_network(node_inputs) + + x_out += x + + # Compute new edge features + edge_inputs = torch.cat([x[start], x[end], e], dim=-1) + e_out = self.edge_network(edge_inputs) + + e_out += e + + return x_out, e_out + + def output_step(self, x, start, end, e): + classifier_inputs = torch.cat([x[start], x[end], e], dim=1) + + return self.output_edge_classifier(classifier_inputs).squeeze(-1) + + def forward(self, x, edge_index): + start = edge_index[0] + end = edge_index[1] + + # Encode the graph features into the hidden space + x.requires_grad = True + x = self.node_encoder(x) + e = self.edge_encoder(torch.cat([x[start], x[end]], dim=1)) + + # edge_outputs = [] + # Loop over iterations of edge and node networks + for i in range(self.hparams["n_graph_iters"]): + x, e = self.message_step(x, start, end, e) + + # Compute final edge scores; use original edge directions only + return self.output_step(x, start, end, e) diff --git a/LHCb_Pipeline/export_pipeline.ipynb b/LHCb_Pipeline/export_pipeline.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..a0a9aeefdb65eef18312e2fb9a6982ab79d338d7 --- /dev/null +++ b/LHCb_Pipeline/export_pipeline.ipynb @@ -0,0 +1,1293 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 0. Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div class=\"bk-root\">\n", + " <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n", + " <span id=\"1002\">Loading BokehJS ...</span>\n", + " </div>\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + "\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + "const JS_MIME_TYPE = 'application/javascript';\n", + " const HTML_MIME_TYPE = 'text/html';\n", + " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " const CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " const script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " const cell = handle.cell;\n", + "\n", + " const id = cell.output_area._bokeh_element_id;\n", + " const server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd_clean, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " const id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd_destroy);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " const output_area = handle.output_area;\n", + " const output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " const bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " const script_attrs = bk_div.children[0].attributes;\n", + " for (let i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " const toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " const events = require('base/js/events');\n", + " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " const NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"<div style='background-color: #fdd'>\\n\"+\n", + " \"<p>\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"</p>\\n\"+\n", + " \"<ul>\\n\"+\n", + " \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n", + " \"<li>use INLINE resources instead, as so:</li>\\n\"+\n", + " \"</ul>\\n\"+\n", + " \"<code>\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"</code>\\n\"+\n", + " \"</div>\"}};\n", + "\n", + " function display_loaded() {\n", + " const el = document.getElementById(\"1002\");\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error(url) {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (let i = 0; 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i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + "if (force === true) {\n", + " display_loaded();\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " const cell = $(document.getElementById(\"1002\")).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"<div style='background-color: #fdd'>\\n\"+\n \"<p>\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"</p>\\n\"+\n \"<ul>\\n\"+\n \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n \"<li>use INLINE resources instead, as so:</li>\\n\"+\n \"</ul>\\n\"+\n \"<code>\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"</code>\\n\"+\n \"</div>\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"1002\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"1002\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<div class=\"bk-root\">\n", + " <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n", + " <span id=\"1003\">Loading BokehJS ...</span>\n", + " </div>\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + "\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + "const JS_MIME_TYPE = 'application/javascript';\n", + " const HTML_MIME_TYPE = 'text/html';\n", + " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " const CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " const script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " const cell = handle.cell;\n", + "\n", + " const id = cell.output_area._bokeh_element_id;\n", + " const server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd_clean, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " const id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd_destroy);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " const output_area = handle.output_area;\n", + " const output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " const bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " const script_attrs = bk_div.children[0].attributes;\n", + " for (let i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " const toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " const events = require('base/js/events');\n", + " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " const NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"<div style='background-color: #fdd'>\\n\"+\n", + " \"<p>\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"</p>\\n\"+\n", + " \"<ul>\\n\"+\n", + " \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n", + " \"<li>use INLINE resources instead, as so:</li>\\n\"+\n", + " \"</ul>\\n\"+\n", + " \"<code>\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"</code>\\n\"+\n", + " \"</div>\"}};\n", + "\n", + " function display_loaded() {\n", + " const el = document.getElementById(\"1003\");\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error(url) {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n", + " const css_urls = [];\n", + "\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if (root.Bokeh !== undefined || force === true) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + "if (force === true) {\n", + " display_loaded();\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " const cell = $(document.getElementById(\"1003\")).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"<div style='background-color: #fdd'>\\n\"+\n \"<p>\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"</p>\\n\"+\n \"<ul>\\n\"+\n \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n \"<li>use INLINE resources instead, as so:</li>\\n\"+\n \"</ul>\\n\"+\n \"<code>\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"</code>\\n\"+\n \"</div>\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"1003\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"1003\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "from __future__ import annotations\n", + "import typing\n", + "import logging\n", + "import os\n", + "import sys\n", + "import warnings\n", + "\n", + "sys.path.append('../montetracko')\n", + "# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n", + "\n", + "import yaml\n", + "import numpy as np\n", + "import torch\n", + "\n", + "from Preprocessing.run_preprocessing import run_preprocessing_test_dataset\n", + "from Preprocessing.run_preprocessing import run_preprocessing\n", + "from Processing.run_processing import run_processing_from_config\n", + "\n", + "from Scripts.Step_1_Train_Metric_Learning import train as train_metric_learning\n", + "from Scripts.Step_2_Run_Metric_Learning import train as run_metric_learning_inference\n", + "from Scripts.Step_3_Train_GNN import train as train_gnn\n", + "from Scripts.Step_4_Run_GNN import train as run_gnn_inference\n", + "from Scripts.Step_5_Build_Track_Candidates import train as build_track_candidates\n", + "from Scripts.Step_6_Evaluate_Reconstruction_MonteTracko import (\n", + " evaluate as evaluate_candidates_montetracko\n", + ")\n", + "\n", + "from utils.plotutils import graph as graphplot\n", + "from utils.plotutils import performance as perfplot\n", + "from utils.plotutils import performance_mpl as perfplot_mpl\n", + "from utils.commonutils.ctests import get_required_test_dataset_names\n", + "from utils.commonutils.config import load_config\n", + "from utils.modelutils import checkpoint_utils\n", + "from utils.scriptutils.loghandler import headline\n", + "\n", + "from utils.plotutils.plotconfig import configure_matplotlib\n", + "\n", + "configure_matplotlib()\n", + "\n", + "warnings.filterwarnings(\n", + " \"ignore\", message=(\n", + " \"TypedStorage is deprecated. It will be removed in the future and \"\n", + " \"UntypedStorage will be the only storage class. This should only matter to you \"\n", + " \"if you are using storages directly.\"\n", + " )\n", + ")\n", + "\n", + "CONFIG = 'pipeline_configs/pipeline_config_export.yaml'\n", + "\n", + "run_training: bool = True\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Load models from artifacts\n", + "\n", + "## Metric learning model" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "embedding_metric_path='artifacts/metric_learning/export/version_0/metrics.csv'\n", + "embedding_artifact_path='artifacts/metric_learning/export/version_0/checkpoints/epoch=0-step=100.ckpt'\n" + ] + } + ], + "source": [ + "from Embedding.models.layerless_embedding import LayerlessEmbedding\n", + "\n", + "embedding_version_dir = checkpoint_utils.get_last_version_dir_from_config(\n", + " step=\"metric_learning\", path_or_config=CONFIG\n", + ")\n", + "embedding_metric_path = os.path.join(embedding_version_dir, \"metrics.csv\")\n", + "embedding_artifact_path = checkpoint_utils.get_last_artifact(\n", + " version_dir=embedding_version_dir\n", + ")\n", + "print(f\"{embedding_metric_path=}\")\n", + "print(f\"{embedding_artifact_path=}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/fgias/.conda/envs/etx4velo/lib/python3.10/site-packages/lightning_fabric/plugins/environments/slurm.py:165: PossibleUserWarning: The `srun` command is available on your system but is not used. HINT: If your intention is to run Lightning on SLURM, prepend your python command with `srun` like so: srun python /home/fgias/.conda/envs/etx4velo/lib/python3.10/site ...\n", + " rank_zero_warn(\n", + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "/home/fgias/.conda/envs/etx4velo/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py:67: UserWarning: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `pytorch_lightning` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n", + " warning_cache.warn(\n", + "/home/fgias/.conda/envs/etx4velo/lib/python3.10/site-packages/lightning_fabric/plugins/environments/slurm.py:165: PossibleUserWarning: The `srun` command is available on your system but is not used. HINT: If your intention is to run Lightning on SLURM, prepend your python command with `srun` like so: srun python /home/fgias/.conda/envs/etx4velo/lib/python3.10/site ...\n", + " rank_zero_warn(\n", + "You are using a CUDA device ('NVIDIA A100 80GB PCIe MIG 3g.40gb') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n", + "INFO:Load 100 files located in data/export/processed/train\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "65f5a67c1fdc45e6893f359a4b272c4c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Load 100 files located in data/export/processed/val\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "92fcc8995ae64cd6a5738c9d812ddfef", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", + "\n", + " | Name | Type | Params\n", + "---------------------------------------\n", + "0 | network | Sequential | 201 K \n", + "---------------------------------------\n", + "201 K Trainable params\n", + "0 Non-trainable params\n", + "201 K Total params\n", + "0.805 Total estimated model params size (MB)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/fgias/.conda/envs/etx4velo/lib/python3.10/site-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 8, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n", + " warnings.warn(_create_warning_msg(\n", + "`Trainer.fit` stopped: `max_epochs=0` reached.\n" + ] + } + ], + "source": [ + "import yaml\n", + "\n", + "with open(CONFIG, 'r') as f:\n", + " configs = yaml.load(f, Loader=yaml.FullLoader)\n", + "\n", + "from pytorch_lightning import Trainer\n", + "\n", + "metric_learning_model = LayerlessEmbedding.load_from_checkpoint(\n", + " embedding_artifact_path,\n", + ")\n", + "\n", + "save_directory = os.path.abspath(\n", + " os.path.join(configs[\"common\"][\"artifact_directory\"], \"metric_learning\")\n", + ")\n", + "\n", + "\n", + "metric_learning_trainer = Trainer(\n", + " accelerator=\"gpu\" if torch.cuda.is_available() else \"cpu\",\n", + " devices=1,\n", + " max_epochs=0, # you may increase the number of epochs\n", + ")\n", + "\n", + "metric_learning_trainer.fit(metric_learning_model)\n", + "# # return metric_learning_trainer, metric_learning_model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LayerlessEmbedding(\n", + " (network): Sequential(\n", + " (0): Linear(in_features=3, out_features=256, bias=True)\n", + " (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (2): Tanh()\n", + " (3): Linear(in_features=256, out_features=256, bias=True)\n", + " (4): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (5): Tanh()\n", + " (6): Linear(in_features=256, out_features=256, bias=True)\n", + " (7): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (8): Tanh()\n", + " (9): Linear(in_features=256, out_features=256, bias=True)\n", + " (10): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (11): Tanh()\n", + " (12): Linear(in_features=256, out_features=3, bias=True)\n", + " )\n", + ")" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Set the model to inference mode.\n", + "metric_learning_model.eval()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## GNN" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "gnn_metric_path='artifacts/gnn/export/version_0/metrics.csv'\n", + "gnn_artifact_path='artifacts/gnn/export/version_0/checkpoints/epoch=0-step=100.ckpt'\n" + ] + } + ], + "source": [ + "from utils.modelutils.checkpoint_utils import (\n", + " get_last_version_dir_from_config,\n", + " get_last_artifact,\n", + ")\n", + "from GNN.models.interaction_gnn_export import InteractionGNN\n", + "\n", + "gnn_version_dir = get_last_version_dir_from_config(step=\"gnn\", path_or_config=CONFIG)\n", + "gnn_metric_path = os.path.join(gnn_version_dir, \"metrics.csv\")\n", + "gnn_artifact_path = get_last_artifact(version_dir=gnn_version_dir)\n", + "print(f\"{gnn_metric_path=}\")\n", + "print(f\"{gnn_artifact_path=}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/fgias/.conda/envs/etx4velo/lib/python3.10/site-packages/lightning_fabric/plugins/environments/slurm.py:165: PossibleUserWarning: The `srun` command is available on your system but is not used. HINT: If your intention is to run Lightning on SLURM, prepend your python command with `srun` like so: srun python /home/fgias/.conda/envs/etx4velo/lib/python3.10/site ...\n", + " rank_zero_warn(\n", + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "You are using a CUDA device ('NVIDIA A100 80GB PCIe MIG 3g.40gb') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n", + "INFO:Load 100 files located in data/export/metric_learning_processed/train\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0e5d53b869d5443abb48e690b2c318d3", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Load 100 files located in data/export/metric_learning_processed/val\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d973fedc815941f99c309e53b821c5cb", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", + "\n", + " | Name | Type | Params\n", + "------------------------------------------------------\n", + "0 | node_encoder | Sequential | 332 K \n", + "1 | edge_encoder | Sequential | 462 K \n", + "2 | edge_network | Sequential | 793 K \n", + "3 | node_network | Sequential | 528 K \n", + "4 | output_edge_classifier | Sequential | 529 K \n", + "------------------------------------------------------\n", + "2.6 M Trainable params\n", + "0 Non-trainable params\n", + "2.6 M Total params\n", + "10.586 Total estimated model params size (MB)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/fgias/.conda/envs/etx4velo/lib/python3.10/site-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 8, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n", + " warnings.warn(_create_warning_msg(\n", + "/home/fgias/.conda/envs/etx4velo/lib/python3.10/site-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 8, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n", + " warnings.warn(_create_warning_msg(\n", + "`Trainer.fit` stopped: `max_epochs=0` reached.\n" + ] + } + ], + "source": [ + "from pytorch_lightning import Trainer\n", + "\n", + "gnn_model = InteractionGNN.load_from_checkpoint(\n", + " gnn_artifact_path,\n", + ")\n", + "\n", + "save_directory = os.path.abspath(\n", + " os.path.join(configs[\"common\"][\"artifact_directory\"], \"gnn\")\n", + ")\n", + "\n", + "gnn_trainer = Trainer(\n", + " accelerator=\"gpu\" if torch.cuda.is_available() else \"cpu\",\n", + " devices=1,\n", + " max_epochs=0, # you may increase the number of epochs\n", + ")\n", + "\n", + "gnn_trainer.fit(gnn_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "InteractionGNN(\n", + " (node_encoder): Sequential(\n", + " (0): Linear(in_features=3, out_features=256, bias=True)\n", + " (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (2): SiLU()\n", + " (3): Linear(in_features=256, out_features=256, bias=True)\n", + " (4): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (5): SiLU()\n", + " (6): Linear(in_features=256, out_features=256, bias=True)\n", + " (7): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (8): SiLU()\n", + " (9): Linear(in_features=256, out_features=256, bias=True)\n", + " (10): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (11): SiLU()\n", + " (12): Linear(in_features=256, out_features=256, bias=True)\n", + " (13): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (14): SiLU()\n", + " (15): Linear(in_features=256, out_features=256, bias=True)\n", + " )\n", + " (edge_encoder): Sequential(\n", + " (0): Linear(in_features=512, out_features=256, bias=True)\n", + " (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (2): SiLU()\n", + " (3): Linear(in_features=256, out_features=256, bias=True)\n", + " (4): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (5): SiLU()\n", + " (6): Linear(in_features=256, out_features=256, bias=True)\n", + " (7): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (8): SiLU()\n", + " (9): Linear(in_features=256, out_features=256, bias=True)\n", + " (10): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (11): SiLU()\n", + " (12): Linear(in_features=256, out_features=256, bias=True)\n", + " (13): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (14): SiLU()\n", + " (15): Linear(in_features=256, out_features=256, bias=True)\n", + " )\n", + " (edge_network): Sequential(\n", + " (0): Linear(in_features=768, out_features=256, bias=True)\n", + " (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (2): SiLU()\n", + " (3): Linear(in_features=256, out_features=256, bias=True)\n", + " (4): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (5): SiLU()\n", + " (6): Linear(in_features=256, out_features=256, bias=True)\n", + " (7): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (8): SiLU()\n", + " (9): Linear(in_features=256, out_features=256, bias=True)\n", + " (10): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (11): SiLU()\n", + " (12): Linear(in_features=256, out_features=256, bias=True)\n", + " (13): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (14): SiLU()\n", + " (15): Linear(in_features=256, out_features=256, bias=True)\n", + " (16): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (17): SiLU()\n", + " (18): Linear(in_features=256, out_features=256, bias=True)\n", + " (19): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (20): SiLU()\n", + " (21): Linear(in_features=256, out_features=256, bias=True)\n", + " (22): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (23): SiLU()\n", + " (24): Linear(in_features=256, out_features=256, bias=True)\n", + " (25): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (26): SiLU()\n", + " (27): Linear(in_features=256, out_features=256, bias=True)\n", + " )\n", + " (node_network): Sequential(\n", + " (0): Linear(in_features=768, out_features=256, bias=True)\n", + " (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (2): SiLU()\n", + " (3): Linear(in_features=256, out_features=256, bias=True)\n", + " (4): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (5): SiLU()\n", + " (6): Linear(in_features=256, out_features=256, bias=True)\n", + " (7): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (8): SiLU()\n", + " (9): Linear(in_features=256, out_features=256, bias=True)\n", + " (10): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (11): SiLU()\n", + " (12): Linear(in_features=256, out_features=256, bias=True)\n", + " (13): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (14): SiLU()\n", + " (15): Linear(in_features=256, out_features=256, bias=True)\n", + " )\n", + " (output_edge_classifier): Sequential(\n", + " (0): Linear(in_features=768, out_features=256, bias=True)\n", + " (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (2): SiLU()\n", + " (3): Linear(in_features=256, out_features=256, bias=True)\n", + " (4): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (5): SiLU()\n", + " (6): Linear(in_features=256, out_features=256, bias=True)\n", + " (7): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (8): SiLU()\n", + " (9): Linear(in_features=256, out_features=256, bias=True)\n", + " (10): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (11): SiLU()\n", + " (12): Linear(in_features=256, out_features=256, bias=True)\n", + " (13): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (14): SiLU()\n", + " (15): Linear(in_features=256, out_features=256, bias=True)\n", + " (16): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (17): SiLU()\n", + " (18): Linear(in_features=256, out_features=1, bias=True)\n", + " )\n", + ")" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gnn_model.eval()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Export models to ONNX format\n", + "\n", + "## Metric learning model" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Input to the model\n", + "batch_size = 1\n", + "x = torch.randn(batch_size, 3, requires_grad=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================ Diagnostic Run torch.onnx.export version 2.0.1 ================\n", + "verbose: False, log level: Level.ERROR\n", + "======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ========================\n", + "\n" + ] + } + ], + "source": [ + "# Export the model\n", + "torch.onnx.export(metric_learning_model, # model being run\n", + " x, # model input (or a tuple for multiple inputs)\n", + " \"ml_3dim.onnx\", # where to save the model (can be a file or file-like object)\n", + " export_params=True, # store the trained parameter weights inside the model file\n", + " opset_version=10, # the ONNX version to export the model to\n", + " do_constant_folding=True, # whether to execute constant folding for optimization\n", + " input_names = ['sp_features'], # the model's input names\n", + " output_names = ['embedding_output'], # the model's output names\n", + " dynamic_axes={'sp_features' : {0 : 'batch_size'}, # variable length axes\n", + " 'embedding_output' : {0 : 'batch_size'}},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## GNN" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Input to the model\n", + "gnn_model.eval()\n", + "\n", + "dynamic_axes={\"g_nodes\" : {0 : 'batch_size'}, \"g_edges\" : {1 : 'num_edges'}, \"gnn_edge_score\" : {0 : 'batch_size'}}\n", + "\n", + "from torch.onnx import symbolic_helper\n", + "@symbolic_helper.parse_args(\"v\", \"v\", \"i\", \"v\", \"i\", \"i\")\n", + "def symbolic_scatter_max(g, src, index, dim=-1, out=None, dim_size=None, fill_value=0):\n", + " return (index, g.op(\"ScatterElements\", out, index, src, axis_i=dim, reduction_s=\"max\"))\n", + "\n", + "@symbolic_helper.parse_args(\"v\", \"v\", \"i\", \"v\", \"i\", \"i\")\n", + "def symbolic_scatter_add(g, src, index, dim=-1, out=None, dim_size=None, fill_value=0):\n", + " return g.op(\"ScatterElements\", out, index, src, axis_i=dim, reduction_s=\"sum\")\n", + "\n", + "torch.onnx.register_custom_op_symbolic(\"torch_scatter::scatter_max\", symbolic_scatter_max, opset_version=18)\n", + "torch.onnx.register_custom_op_symbolic(\"torch_scatter::scatter_add\", symbolic_scatter_add, opset_version=18)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================ Diagnostic Run torch.onnx.export version 2.0.1 ================\n", + "verbose: False, log level: Level.ERROR\n", + "======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ========================\n", + "\n" + ] + } + ], + "source": [ + "# Export the model\n", + "batch_size = 1\n", + "num_edges = 10\n", + "x = torch.randn(batch_size, 3)\n", + "edge_index = torch.randint(batch_size, (2, num_edges))\n", + "input_data = (x, edge_index)\n", + "\n", + "torch.onnx.export(gnn_model, # model being run\n", + " input_data, # model input (or a tuple for multiple inputs)\n", + " \"gnn_3dim.onnx\", # where to save the model (can be a file or file-like object)\n", + " opset_version=18, # the ONNX version to export the model to\n", + " input_names=[\"g_nodes\", \"g_edges\"], # the model's input names\n", + " output_names=[\"gnn_edge_score\"], # the model's output names\n", + " dynamic_axes=dynamic_axes,\n", + " export_params=True,\n", + " # operator_export_type=torch.onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK,\n", + " # verbose=True,\n", + " # custom_opsets={\"torch_scatter\", 18},\n", + " # keep_initializers_as_inputs=False,\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/LHCb_Pipeline/full_pipeline.ipynb b/LHCb_Pipeline/full_pipeline.ipynb index 994a0a5fcbb85070bd3327c13954ea2bb27f2b8a..1be3ae2e7dd4c45b6f1e6a61269d1bf75aa3dafb 100644 --- a/LHCb_Pipeline/full_pipeline.ipynb +++ b/LHCb_Pipeline/full_pipeline.ipynb @@ -18,9 +18,608 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div class=\"bk-root\">\n", + " <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n", + " <span id=\"1002\">Loading BokehJS ...</span>\n", + " </div>\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + "\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + "const JS_MIME_TYPE = 'application/javascript';\n", + " const HTML_MIME_TYPE = 'text/html';\n", + " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " const CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " const script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " const cell = handle.cell;\n", + "\n", + " const id = cell.output_area._bokeh_element_id;\n", + " const server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd_clean, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " const id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd_destroy);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " const output_area = handle.output_area;\n", + " const output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " const bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " const script_attrs = bk_div.children[0].attributes;\n", + " for (let i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " const toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " const events = require('base/js/events');\n", + " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " const NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"<div style='background-color: #fdd'>\\n\"+\n", + " \"<p>\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"</p>\\n\"+\n", + " \"<ul>\\n\"+\n", + " \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n", + " \"<li>use INLINE resources instead, as so:</li>\\n\"+\n", + " \"</ul>\\n\"+\n", + " \"<code>\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"</code>\\n\"+\n", + " \"</div>\"}};\n", + "\n", + " function display_loaded() {\n", + " const el = document.getElementById(\"1002\");\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error(url) {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (let i = 0; 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If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"</p>\\n\"+\n \"<ul>\\n\"+\n \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n \"<li>use INLINE resources instead, as so:</li>\\n\"+\n \"</ul>\\n\"+\n \"<code>\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"</code>\\n\"+\n \"</div>\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"1002\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"1002\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<div class=\"bk-root\">\n", + " <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n", + " <span id=\"1003\">Loading BokehJS ...</span>\n", + " </div>\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + "\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + "const JS_MIME_TYPE = 'application/javascript';\n", + " const HTML_MIME_TYPE = 'text/html';\n", + " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " const CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " const script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " const cell = handle.cell;\n", + "\n", + " const id = cell.output_area._bokeh_element_id;\n", + " const server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd_clean, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " const id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd_destroy);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " const output_area = handle.output_area;\n", + " const output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " const bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " const script_attrs = bk_div.children[0].attributes;\n", + " for (let i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " const toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " const events = require('base/js/events');\n", + " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " const NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"<div style='background-color: #fdd'>\\n\"+\n", + " \"<p>\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"</p>\\n\"+\n", + " \"<ul>\\n\"+\n", + " \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n", + " \"<li>use INLINE resources instead, as so:</li>\\n\"+\n", + " \"</ul>\\n\"+\n", + " \"<code>\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"</code>\\n\"+\n", + " \"</div>\"}};\n", + "\n", + " function display_loaded() {\n", + " const el = document.getElementById(\"1003\");\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error(url) {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n", + " const css_urls = [];\n", + "\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if (root.Bokeh !== undefined || force === true) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + "if (force === true) {\n", + " display_loaded();\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " const cell = $(document.getElementById(\"1003\")).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"<div style='background-color: #fdd'>\\n\"+\n \"<p>\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"</p>\\n\"+\n \"<ul>\\n\"+\n \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n \"<li>use INLINE resources instead, as so:</li>\\n\"+\n \"</ul>\\n\"+\n \"<code>\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"</code>\\n\"+\n \"</div>\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"1003\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"1003\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", @@ -72,7 +671,7 @@ " )\n", ")\n", "\n", - "CONFIG = 'pipeline_config_default.yaml'\n", + "CONFIG = 'pipeline_configs/pipeline_config_export.yaml'\n", "\n", "run_training: bool = True\n" ] @@ -98,7 +697,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -117,7 +716,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -144,7 +743,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -166,9 +765,43 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Preprocessing: output will be written in data/__test__/velo-sim10b-nospillover\n", + "INFO:Input directories:\n", + "INFO:- /home/fgias/data/minbias-sim10b-xdigi-nospillover/500\n", + "INFO:Number of events to produce: 1000\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ee62b06057634c24adebf69ced40f9b1", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1000 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Load dataframes in /home/fgias/data/minbias-sim10b-xdigi-nospillover/500\n" + ] + } + ], "source": [ "for required_test_dataset_name in get_required_test_dataset_names(CONFIG):\n", " run_preprocessing_test_dataset(\n", @@ -189,9 +822,43 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Preprocessing: output will be written in data/export/preprocessed\n", + "INFO:Input directories:\n", + "INFO:- data/datasets/minbias-sim10b-xdigi-nospillover/0\n", + "INFO:Number of events to produce: 200\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4c0c6fa9674044a1982fbd61d54cd3ed", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/200 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Load dataframes in data/datasets/minbias-sim10b-xdigi-nospillover/0\n", + "INFO:Apply selection `triplets_first_selection`\n", + "INFO:Compute distance to line (that might take some time)\n" + ] + } + ], "source": [ "run_preprocessing(CONFIG, reproduce=False)" ] @@ -206,18 +873,92 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Input directory: data/export/preprocessed\n", + "INFO:Writing outputs to data/export/processed/train\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c12b4802eda14cbe872f26b47c68b1d8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Writing outputs to data/export/processed/val\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1fba69563c4043be91673c0ede44fec8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Splitting was saved in data/export/processed/splitting.json.\n" + ] + } + ], "source": [ "run_processing_from_config(CONFIG, reproduce=False)\n" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Input directory: data/__test__/velo-sim10b-nospillover\n", + "INFO:Writing outputs to data/export/processed/test/velo-sim10b-nospillover\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d63ea89e31ff43b09576a334ca565da1", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1000 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "for required_test_dataset_name in get_required_test_dataset_names(CONFIG):\n", " run_processing_from_config(\n", @@ -255,9 +996,150 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Load 100 files located in data/export/processed/val\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1bb30c5158f240a39a169b5dd9f86462", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>0</th>\n", + " <th>1</th>\n", + " <th>2</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>1.088615</td>\n", + " <td>-0.583434</td>\n", + " <td>-1.983070</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>1.803843</td>\n", + " <td>-0.889505</td>\n", + " <td>-1.978603</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>0.031438</td>\n", + " <td>-1.352497</td>\n", + " <td>-1.983070</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>-0.836741</td>\n", + " <td>-1.053595</td>\n", + " <td>-1.983070</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>-0.082678</td>\n", + " <td>-0.946665</td>\n", + " <td>-1.983070</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3595</th>\n", + " <td>-0.870953</td>\n", + " <td>-0.676339</td>\n", + " <td>1.636380</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3596</th>\n", + " <td>-0.281956</td>\n", + " <td>-0.031543</td>\n", + " <td>1.636380</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3597</th>\n", + " <td>1.153442</td>\n", + " <td>0.020097</td>\n", + " <td>1.631913</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3598</th>\n", + " <td>0.920227</td>\n", + " <td>-0.170272</td>\n", + " <td>1.631913</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3599</th>\n", + " <td>-0.354392</td>\n", + " <td>0.919738</td>\n", + " <td>1.631913</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>3600 rows × 3 columns</p>\n", + "</div>" + ], + "text/plain": [ + " 0 1 2\n", + "0 1.088615 -0.583434 -1.983070\n", + "1 1.803843 -0.889505 -1.978603\n", + "2 0.031438 -1.352497 -1.983070\n", + "3 -0.836741 -1.053595 -1.983070\n", + "4 -0.082678 -0.946665 -1.983070\n", + "... ... ... ...\n", + "3595 -0.870953 -0.676339 1.636380\n", + "3596 -0.281956 -0.031543 1.636380\n", + "3597 1.153442 0.020097 1.631913\n", + "3598 0.920227 -0.170272 1.631913\n", + "3599 -0.354392 0.919738 1.631913\n", + "\n", + "[3600 rows x 3 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from Embedding.embedding_base import get_example_data\n", "example_data_df, example_data_pyg = get_example_data(CONFIG)\n", @@ -266,9 +1148,28 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Applicable driver not found; attempting to install with Selenium Manager (Beta)\n", + "WARNING:Unable to obtain driver using Selenium Manager: /home/fgias/.conda/envs/etx4velo/lib/python3.10/site-packages/selenium/webdriver/common/linux/selenium-manager is missing. Please open an issue on https://github.com/SeleniumHQ/selenium/issues\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<IPython.core.display.Image object>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "graphplot.plot_true_graph(example_data_pyg, CONFIG, num_tracks=50)" ] @@ -285,31 +1186,212 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nvcc: NVIDIA (R) Cuda compiler driver\n", + "Copyright (c) 2005-2023 NVIDIA Corporation\n", + "Built on Mon_Apr__3_17:16:06_PDT_2023\n", + "Cuda compilation tools, release 12.1, V12.1.105\n", + "Build cuda_12.1.r12.1/compiler.32688072_0\n" + ] + } + ], "source": [ "! nvcc --version" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fri Oct 6 15:30:43 2023 \n", + "+---------------------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 535.104.12 Driver Version: 535.104.12 CUDA Version: 12.2 |\n", + "|-----------------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|=========================================+======================+======================|\n", + "| 0 NVIDIA A100 80GB PCIe On | 00000000:CA:00.0 Off | On |\n", + "| N/A 33C P0 67W / 300W | 693MiB / 81920MiB | N/A Default |\n", + "| | | Enabled |\n", + "+-----------------------------------------+----------------------+----------------------+\n", + "\n", + "+---------------------------------------------------------------------------------------+\n", + "| MIG devices: |\n", + "+------------------+--------------------------------+-----------+-----------------------+\n", + "| GPU GI CI MIG | Memory-Usage | Vol| Shared |\n", + "| ID ID Dev | BAR1-Usage | SM Unc| CE ENC DEC OFA JPG |\n", + "| | | ECC| |\n", + "|==================+================================+===========+=======================|\n", + "| 0 2 0 0 | 656MiB / 40192MiB | 42 0 | 3 0 2 0 0 |\n", + "| | 2MiB / 65535MiB | | |\n", + "+------------------+--------------------------------+-----------+-----------------------+\n", + " \n", + "+---------------------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=======================================================================================|\n", + "| 0 2 0 244922 C ...ias/.conda/envs/etx4velo/bin/python 610MiB |\n", + "+---------------------------------------------------------------------------------------+\n" + ] + } + ], "source": [ "! nvidia-smi" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:-------------------- Step 1: Running metric learning training --------------------\n", + "INFO:----------------------------- a) Initialising model -----------------------------\n", + "INFO:------------------------------ b) Running training ------------------------------\n", + "Missing logger folder: /home/fgias/etx4velo-2/LHCb_Pipeline/artifacts/metric_learning/export\n", + "INFO:Save hyperparameters, metrics and artifacts in /home/fgias/etx4velo-2/LHCb_Pipeline/artifacts/metric_learning/export/version_0\n", + "/home/fgias/.conda/envs/etx4velo/lib/python3.10/site-packages/lightning_fabric/plugins/environments/slurm.py:165: PossibleUserWarning: The `srun` command is available on your system but is not used. HINT: If your intention is to run Lightning on SLURM, prepend your python command with `srun` like so: srun python /home/fgias/.conda/envs/etx4velo/lib/python3.10/site ...\n", + " rank_zero_warn(\n", + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "/home/fgias/.conda/envs/etx4velo/lib/python3.10/site-packages/lightning_fabric/plugins/environments/slurm.py:165: PossibleUserWarning: The `srun` command is available on your system but is not used. HINT: If your intention is to run Lightning on SLURM, prepend your python command with `srun` like so: srun python /home/fgias/.conda/envs/etx4velo/lib/python3.10/site ...\n", + " rank_zero_warn(\n", + "You are using a CUDA device ('NVIDIA A100 80GB PCIe MIG 3g.40gb') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n", + "INFO:Load 100 files located in data/export/processed/train\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c4f12d715b0e4b0db853f5522d564fdb", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Load 100 files located in data/export/processed/val\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2beec128fd6d45b487540e73bc7a51c0", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", + "\n", + " | Name | Type | Params\n", + "---------------------------------------\n", + "0 | network | Sequential | 201 K \n", + "---------------------------------------\n", + "201 K Trainable params\n", + "0 Non-trainable params\n", + "201 K Total params\n", + "0.805 Total estimated model params size (MB)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/fgias/.conda/envs/etx4velo/lib/python3.10/site-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 8, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n", + " warnings.warn(_create_warning_msg(\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9e3d4909b9a64a99bda60f6fcad4d6bf", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "`Trainer.fit` stopped: `max_epochs=1` reached.\n", + "INFO:-------------------------------- c) Saving model --------------------------------\n" + ] + } + ], "source": [ "if run_training:\n", " send_telegram_message('Started metric learning training.', chat_id, api_key)\n", @@ -338,9 +1420,18 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "embedding_metric_path='artifacts/metric_learning/export/version_0/metrics.csv'\n", + "embedding_artifact_path='artifacts/metric_learning/export/version_0/checkpoints/epoch=0-step=100.ckpt'\n" + ] + } + ], "source": [ "from Embedding.models.layerless_embedding import LayerlessEmbedding\n", "\n", @@ -365,7 +1456,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -407,7 +1498,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -437,9 +1528,62 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>epoch</th>\n", + " <th>train_loss</th>\n", + " <th>val_loss</th>\n", + " <th>eff</th>\n", + " <th>pur</th>\n", + " <th>current_lr</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0</td>\n", + " <td>0.007796</td>\n", + " <td>0.008153</td>\n", + " <td>0.113685</td>\n", + " <td>0.184027</td>\n", + " <td>0.000125</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " epoch train_loss val_loss eff pur current_lr\n", + "0 0 0.007796 0.008153 0.113685 0.184027 0.000125" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# embedding_metrics = checkpoint_utils.get_training_metrics(metric_learning_trainer) \n", "\n", @@ -450,20 +1594,91 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Figure was saved in output/export/loss_metric_learning.pdf\n", + "Figure was saved in output/export/loss_metric_learning.png\n" + ] + }, + { + "data": { + "text/plain": [ + "(<Figure size 800x600 with 1 Axes>, <Axes: xlabel='Epoch', ylabel='Loss'>)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 800x600 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "perfplot_mpl.plot_loss(embedding_metrics, CONFIG, \"metric_learning\")\n" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Figure was saved in output/export/eff_metric_learning.pdf\n", + "Figure was saved in output/export/eff_metric_learning.png\n", + "Figure was saved in output/export/pur_metric_learning.pdf\n", + "Figure was saved in output/export/pur_metric_learning.png\n" + ] + }, + { + "data": { + "text/plain": [ + "(<Figure size 800x600 with 1 Axes>,\n", + " <Axes: xlabel='Epoch', ylabel='Edge Purity'>)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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eqeeff16BgYEqXbp0hn2UKlVKw4cP18CBA/XQQw/pv//9r3766Sfdc889+RU2AAAAUKhZXuD/9ttvatiwoQ4ePCgp7dl6T09PPfbYY3r++efVrl27HPdZvHhxjRo1Stu3b1doaCgFPgAAAJAJywv8iIgISWkL++rVq2vQoEF69tlnVb58eZf6dXO7frlAdHR03oMEAAAAbCpfpugYY+Tu7q6HH35Yzz//vLp27SqHw+FyfydPntS0adPkcDhUtmxZCyMFAAAA7MXyAr9MmTIaOnSoBg0apCpVquS5vxUrVugf//iHjDFyOBy64447LIgSAAAAsCfLC/wWLVrozTfftKy/KlWq6O9//7vz9b333mtZ3wAAAIDdWFrgBwQEqEKFClZ2qfvuu0/33XefpX0CAAAAdmVpgR8eHm5ldwAAAAByyfIpOpcvX1ZSUtL/BvDwUIkSJTJsu2PHDh09elRPPPGEfHx8rA4FAAAAKHLcrOzszJkzKleunMqWLev8eeSRRzJtf+DAAT377LOqXr265s6da2UoAAAAQJFkaYG/aNEi51NrU26VWb169UzbFy9eXMYYRUdHa8SIERo0aJCV4QAAAABFjqUF/v/93//J4XDIx8dHM2bM0F9//aUPPvgg0/Z9+/ZVeHi4RowYITc3N33wwQdasmSJlSEBAAAARYplBf6ZM2e0d+9eeXp6atOmTXr55ZdVpkyZbLcLCAjQO++8o08//VSS9Oabb6Z5Ci4AAACAnLOswP/ll18kSX369HHpXvWPP/64Hn/8cUVEROjHH3+0KiwAAACgSLGswD9+/LgcDocefPBBl/t47LHHZIzRnj17rAoLAAAAKFIsK/AvXrwoSapcubLLfVSpUkWSFB0dbUlMAAAAQFFjWYFfsmRJSdKFCxdc7iMmJkaSMr1vPgAAAICsWVbgV65cWcYYbd++3eU+tm3bJofDoUqVKlkVFgAAAFCkWFbg33///XI4HHr//fcVHh6e6+3Dw8P1/vvvy+FwqF27dlaFBQAAABQplhX4t912m9q3b69Lly6pbdu22rJlS4633bZtm9q1a6fY2Fh16NBBt912m1VhAQAAAEWKh5WdvfXWW2rdurVOnz6tzp07q2nTpnrkkUfUqFEjVatWTSVLlpTD4VBsbKwiIiK0d+9erV+/Xj/99JPzybdTp061MiQAAACgSLG0wL/33ns1Y8YMvfzyy3I4HPrll1+c98fPSsqDrWbMmKHmzZtbGRIAAABQpFg2RSfFyJEj9d5776lYsWLOwt0Yk+FPyjpvb2/NmzdPI0aMsDocAAAAoEixvMCXpCFDhujXX39VYGCgvLy8Mm3n5eWl/v3769dff9Xzzz+fH6EAAAAARYqlU3RSq1u3rpYsWaIFCxbohx9+0O+//+58gFW5cuV0xx136N5771WxYsXyKwQAAACgyMm3Aj9FsWLF1LZtW7Vt2za/hwIAAACKvHyZogMAAADg1ihQBf6YMWPUqVMnS/s8e/asXn/9dTVo0EA+Pj7y8/NTq1atNHv2bF29etXSscLCwtSuXTs5HA61b9/e5X5WrFghf39/ORwOBQcHu9RHSEiInn32WdWtW1e+vr7y9fVV3bp19fDDDysoKEj79+93OT4AAAAUXPk+RSc39u7dq61bt1rW3+7du9WzZ0/9+eef6ty5s4YMGaK4uDgtXbpUI0aMUHBwsL788ktVrlw5T+MYY/Tee+/p1Vdf1eXLl13u56+//tKQIUO0evVql/u4dOmSBg4cqJUrV+ruu+9W7969dfvtt+vcuXP66quv9PXXX+vrr7/Wjz/+qFWrVrk8DgAAAAqmAlXgWykiIkLdu3dXVFSURo4cqXfeece5bvjw4XrooYcUEhKiHj166Lvvvsvybj9ZCQsL04ABA7R9+3Z16dJFGzZscKmfFStWaOjQoYqNjVX79u1d+qATHx+vRx55RNu2bdO///1vvfTSS2nWjx49WuPGjeNhYgAAADaWLwX+vn37tHLlSoWGhur06dOKjY1VUlJSttudOXPGshhGjRqlqKgoBQQEaNq0aWnWeXl5adGiRbrjjjv0888/a+7cuXrllVdyPcbly5fVuHFjubu7a9GiRRo4cKAcDkeu+wkODlb//v3VvHlzBQcHa/fu3S4V+BMnTtS2bds0aNCgdMV9itdee00ffvihSpUqlev+AQAAUPBZWuBfu3ZNzz33nJYtW+bS9sYYlwrkGx05ckSfffaZJKlv374Znp2vVauWOnTooE2bNikoKEgjRoyQh0fu0nHt2jW1adNGixcvVpUqVVyONz4+XlOnTtXo0aPl7u6u3bt357qP8PBwvfPOO3I4HBo/fnym7Xx8fPTnn3+6HCsAAAAKNksL/BdffFEfffSRlV26ZNWqVc4n5T7wwAOZtuvcubM2bdqkqKgobd26Ncu2GSldurS++eabPMUqSYMHD87zB5ulS5fq6tWruvvuu1W1atU8xwQAAIDCybIC/48//tCiRYvkcDhkjNE999yjhx9+WHXr1pWfn5+8vb2zLWJHjx6tn376Kc+xbNmyxbncpEmTTNs1bdo0zTa5LfCt+LbBqn5SLpht3LhxmvcTExMVHx8vHx+fPI8BAACAgs+yAn/r1q1KTk6Ww+FQUFCQRo0ales+/Pz8LIkl5RaQvr6+Kl26dKbtUp/pPnDggCVj3wpxcXE6ePCgJCkgIEAxMTGaMWOGPv30Ux09elTGGPn4+Kht27bOC4wBAABgT5aewZekatWquVTcS9Jtt92mgICAPMWRkJDgvFi3QoUKWbZNvT48PDxP495KBw4cUHJysiTp5MmTatiwoS5evKgXX3xRzZs315UrV/T555/rk08+0ddff61BgwZp/vz5cnMrUI9BAAAAgAUsK/BLliwpKf0UkdxYunRpnuO4dOmSc9nb2zvLtsWLF89wu8Lmr7/+ci5/+OGHKlu2rHbv3q26des633/qqafUqlUrvfjii1q4cKFuv/12vfHGG7ciXAAAAOQjywr8O+64Q9L1O8vcSnFxcc7lYsWKZdk29forV67kW0z5LSYmJs3rV199NU1xn2L48OEKDg5WaGio3nrrLT3//PO67bbbMu03OjpaiYmJztdeXl4uPy+gIElMTFRycnKafYPryKe1yKe1yKe1yKe1yKe17J7P3OyXZQV+x44dVb16df3www9KTEzM9S0nJWnz5s36448/1LdvX5fjSH1W/urVq1m2Tb2+RIkSLo95q934jIEnn3wy07ZPP/20QkNDFR8fr1WrVumFF17ItG2NGjXSvA4MDFS/fv3yFGtBkJycrIiICElimpIFyKe1yKe1yKe1yKe1yKe17J7Py5cv57itZQW+m5ub3nvvPf3tb3/ThAkTNGXKlFz3MWPGDP33v//NU4Hv6+vrXI6Pj8+ybeqz/am3K2xSx+7j45OuME8t9RSq3bt3Z1ngHz9+PM0Dsex0Bl+SWrdu7dIHUaRFPq1FPq1FPq1FPq1FPq1l93zeOGMjK5bu/UMPPaRPPvlEgwcP1smTJzV+/PgMp4rkJy8vL1WsWFFnzpxRZGRklm1Tr69evXo+R5Z/ypUr51wuU6ZMlm39/f2dy6nn7mfEz8/Ptk+8dXNzk4eHhy3/ANwK5NNa5NNa5NNa5NNa5NNads5nbvbJ0r0fMGCAJKlFixZavny5/vOf/6hGjRq66667VKZMGXl6ema5/b59+yyJo0GDBjpz5owuXbqkixcvZnqrzFOnTjmX69evb8nYt0Lq2LO7BiLlAWCSPb++AgAAKOosLfCDg4OdD21KeeDVsWPHdPz48Rxtb4yx5KFPHTt21KZNmyRJe/bsUbt27TJsFxoammabwqpMmTKqU6eOjhw5onPnzunatWuZfpiKiopyLleuXPlmhQgAAICbxPJTuMYY509G72X1Y5UnnnjC+UFh8+bNmbZL+RDg7++v9u3bWzb+rfDEE09Iuj7/bM+ePZm2+/nnn53LmX3wAQAAQOFl+QSlBg0a6PHHH3dp2+XLl+vYsWN5jqFOnTp68skntXLlSi1btkzjx49Pd8vMY8eOacuWLZKkMWPGpJvXtH//fj322GO6cuWKPv74Y91///15jis/DRkyRLNmzVJcXJw++ugjNW/ePF2b5ORkffzxx5KkihUr6rHHHrvZYQIAACCf5UuBP2HCBJe23bVrlyUFviRNnz5dISEhCg8P19ixYzVjxgznuoSEBA0aNEhJSUlq1qyZhg0blm77KVOm6MiRI5Ku31f++++/tySu/FK1alW9+eabGjVqlBYsWKCePXuqU6dOadpMmjRJBw8elMPh0Pz589PcUhQAAAD2UKAuMbZymk5AQIDWrVunRx99VDNnztT+/fvVo0cPxcXFaenSpdq3b5+aNGmiL774IsMn3iYnJ+corr1792rv3r3p3o+MjNTy5cudrzt37qwKFSpk2s/atWsVGxsrSdq5c6fz/Z07dzq/XfDx8VHPnj0z7eOf//ynLly4oLfeektdu3ZVnz591KpVK8XHx2vt2rXasmWLvL29tWjRoiz7AQAAQOFlaYH/zjvvOJ9o64rp06crOjrasnhatGihvXv3atasWVqzZo1Gjx4tT09P1a1bV7NmzdKQIUMyfdrtuHHjFBoaqri4OAUFBWU6xurVqzVp0qR07x86dEh9+vRxvg4JCcmywB85cqROnDiR7v2FCxdq4cKFkqRq1aplW5hPnjxZjzzyiObPn6+QkBB9/PHHKlasmGrUqKFRo0bpxRdfVJUqVbLsAwAAAIWXpQX+iBEj8rR9w4YNLYrkf/z9/TV58mRNnjw5V9s1atTIOUUnKxMnTtTEiRNdjO5/wsPD89xHihYtWqhFixaW9QcAAIDCgxuhAwAAADaS73Pwo6Ki9MMPP+jkyZO6cOGCXnvtNee6M2fOqGLFivkdAgAAAFBk5NsZ/E8++UQtW7ZUxYoV9be//U3Dhg3T+PHj07QZNWqUAgICNHPmTF29ejW/QgEAAACKDMsL/IsXL+rhhx9W79699eOPPzrvQJPZnWhOnTql0aNH6957783wIlMAAAAAOWdpgX/t2jX16NFDGzZsSPN02syK+5EjR+q5556Tt7e39u7dq86dO+vSpUtWhgQAAAAUKZYW+HPmzNG3334rY4yqVaumCRMmaMOGDdq3b1+GT4Jt1qyZFixYoL1796pBgwYKCwtz+SFZAAAAACy8yNYYo7ffflsOh0P9+vXTvHnz5OXl5Vyf1VNTa9WqpQ0bNqhBgwb64IMPNHXq1AwfPgUAAAAga5adwQ8NDdVff/2lJk2a6IMPPkhT3OdEpUqV9Oyzzyo2Nla7du2yKiwAAACgSLGswN+7d68k6bnnnnO5j3bt2skYo4MHD1oVFgAAAFCkWFbgnz17Vg6HQ3feeafLfZQuXVrS9TvxAAAAAMg9ywp8D4/r0/kTExNd7iMyMlKSVLJkSUtiAgAAAIoaywr8ihUryhijH374weU+vvnmGzkcDlWqVMmqsAAAAIAixbICv02bNpKk2bNn6+zZs7ne/pdfftFHH30kh8Ohdu3aWRUWAAAAUKRYVuBXrVpVLVu21NmzZ9W+fXv9+uuvOd72888/V+fOnZWYmKgOHTrotttusyosAAAAoEix7D74khQUFKT27dvrt99+U7NmzdSxY0d1795dTZo00eXLlyVJR44c0aVLlxQREaFffvlFa9eu1f79+2WMkbu7u6ZOnWplSAAAAECRYmmB37ZtW02dOlWvvfaaJGnz5s3avHmzc70xJsO77BhjJEkzZsxQ8+bNrQwJAAAAKFIsm6KTYsyYMXr33XdVrFgxZ+Ge8l+HwyFjjPMnZZ23t7fmzZunESNGWB0OAAAAUKRYXuBL0vDhw7Vnzx4FBgY6n2ibuqhP4eXlpf79++vXX3/V888/nx+hAAAAAEWKpVN0Urvjjju0ZMkSLViwQD/88IN+//13RUdHS5LKlSunO+64Q/fee6+KFSuWXyEAAAAARU6+FfgpihUrprZt26pt27b5PRQAAABQ5OXLFB0AAAAAt0aBKvAfeugheXjk+5cKAAAAgG0VqAJfUroLcQEAAADknKWnyyMiIvK0fVxcnEWRAAAAAEVTrgr8jRs36rnnnlNycrLmzZun7t27p1lfvXp1ORwOl4MxxuRpewAAAKCoy1WB369fP505c0bGGA0ePFh//PFHhu1cmWZDYQ8AAADkXa7m4JcsWdJZvJcoUSLDNq7OoWfuPQAAAJB3uSrwP/74Y913331q1aqVli1blmGbrl27Kjk52aWfLl26WLJTAAAAQFGVqyk699xzj7799tv8igUAAABAHhWo22QaY5iqAwAAAOSBpbfJTE5OztP233zzjUWRAAAAAEVTgTqDDwAAACBvbmmBn5CQcCuHBwAAAGzH0ik6kjR27FjFx8c7X9euXVsvvPBChm2Dg4P11ltvadCgQXrxxRfl4+NjdTgAAABAkWJpgf/1119r2rRpaR5a1a5du0wLfEmKiIjQ66+/rg8++EBr1qxRo0aNrAwJAAAAKFIsnaKzZMkSSdfvhlOhQgW9/PLLmjhxYqbt27Ztq8DAQBUvXlzHjx/XAw88oFOnTlkZEgAAAFCkWFbgJyYmasOGDXI4HPrHP/6ho0ePavr06br//vsz3aZevXpasmSJjhw5orZt2+rs2bN6+eWXrQoJAAAAKHIsK/D37dunS5cu6a677tLSpUtVokSJHG9bqVIlrV+/XlWrVtXatWv1119/WRUWAAAAUKRYWuBL0rPPPit3d/dcb+/j46PBgwcrKSlJO3bssCosAAAAoEixrMA/e/asHA5Hni6Svffee2WM0YkTJ6wKCwAAAChSLCvwr169Kkny9PR0uY9ixYql6QsAAABA7lhW4JcvX16SdPToUZf7CAsLk8PhkL+/v1VhAQAAAEWKZQV+vXr1ZIzR0qVLXe4jODhYknTXXXdZFBUAAABQtFhW4Ldo0UJ+fn7asWOHxo0bl+vt33jjDX377bfy8/NTq1atrAoLAAAAKFIsK/Dd3Nz08ssvyxijadOmqV27dvrqq6+UkJCQ6TZXr17V119/rY4dO2rKlClyOBx66aWX0jwJFwAAAEDOeVjZ2UsvvaQVK1Zo37592rFjh3bs2CEvLy/VqVNH1apVU8mSJeVwOBQbG6uIiAgdPnzY+QHAGKNGjRrxoCsAAAAgDywt8IsXL67169erQ4cOzgtm4+PjtX//fu3fvz9de2OMc7l27dr66quv5O3tbWVIAAAAQJFi2RSdFFWqVFFoaKj69u0rN7fr3acu5FOkvOfu7q7+/fsrNDRUlStXtjocAAAAoEix9Ax+Cl9fXwUHB2vChAlatmyZtm3bpt9//13R0dGSpHLlyumOO+5Qu3bt1KdPH1WvXj0/wgAAAACKnHwp8FPUqFFDb7zxRn4OAQAAACAVy6foAAAAALh1ClSB//HHH+vNN9+81WEAAAAAhVaBKvCXLVumSZMm3eowAAAAgEKrQBX4+eHs2bN6/fXX1aBBA/n4+DiflDt79mxdvXrV0rHCwsLUrl07ORwOtW/f3uV+VqxYIX9/fzkcDgUHB+cppuTkZLVq1UoOh0MOh0Nbt27NU38AAAAo2HJ1kW1iYqKWL1+upKQk9enTR8WKFUuzfsCAAXkKZt++fXna/ka7d+9Wz5499eeff6pz584aMmSI4uLitHTpUo0YMULBwcH68ssv83x7TmOM3nvvPb366qu6fPmyy/389ddfGjJkiFavXp2neFKbM2eOdu3aZVl/AAAAKNhyVeA/88wzzuJz3bp1Wrt2bZr1wcHBcjgcLgdjjMnT9qlFRESoe/fuioqK0siRI/XOO+841w0fPlwPPfSQQkJC1KNHD3333Xfy8vJyaZywsDANGDBA27dvV5cuXbRhwwaX+lmxYoWGDh2q2NhYtW/f3pIz7RERERo/fnye+wEAAEDhkaspOv/9738lXS/EQ0JCMm1njHHpx0qjRo1SVFSUAgICNG3atDTrvLy8tGjRIrm7u+vnn3/W3LlzXRrj8uXLaty4sfbs2aNFixbpm2++camf4OBgPf3006pZs6ZCQ0MVGBjoUj83ev755xUXF6du3bpZ0h8AAAAKvlydwf/HP/6hBQsWSJL+/ve/Z9imVq1a6tWrl0vBLF++XMeOHXNp29SOHDmizz77TJLUt2/fDM/O16pVSx06dNCmTZsUFBSkESNGyMMjd48FuHbtmtq0aaPFixerSpUqLscbHx+vqVOnavTo0XJ3d9fu3btd7ivFf/7zH3399dd6+eWX5evrq/Xr1+e5TwAAABR8uapo58+fryeffFJJSUnq3Llzhm1q166tCRMmuBTMrl27LCnwV61a5fxG4IEHHsi0XefOnbVp0yZFRUVp69atWbbNSOnSpV0+a5/a4MGDLZuaJEnnzp3TSy+9pGrVqunNN9/U9OnTLesbAAAABVuOp+icP39eH330kU6dOqU///xTv/zyS37GlSdbtmxxLjdp0iTTdk2bNs1wm5yyqii3sriXpJEjRyoqKkrz589XyZIlLe0bAAAABVuOz+D/3//9nwYPHux8PWbMmDQFsiS98cYbqlOnjsvB9O7dWy1btnR5+xT79++XJPn6+qp06dKZtqtatapz+cCBA3ketyDYsGGDli9frn/84x966KGHbnU4AAAAuMlyXOCvX79exhhVq1ZN//znPzOcojNx4sQ8BePq3P3UEhISdObMGUlShQoVsmyben14eHiex77VLl++rOeff15+fn6aNWvWrQ4HAAAAt0COC/yDBw/K29tbW7duVbVq1TJsU7NmTbVv314ffvihZQHm1qVLl5zL3t7eWbYtXrx4htsVVuPGjVN4eLiCg4NVvnz5Wx0OAAAAboEcF/iRkZFq1KhRpsW9dP0seMrZc1f069dP3377rcLCwlzuIy4uzrl844O4bpR6/ZUrV1wesyDYvXu35syZo06dOll2m83o6GglJiY6X3t5ebn8vICCJDExUcnJyWn2Da4jn9Yin9Yin9Yin9Yin9ayez5zs185LvATEhKyLZjzKjIyMs9TZVKflb969WqWbVOvL1GiRJ7GvZWuXbumgQMHysvLy3kbUyvUqFEjzevAwED169fPsv5vleTkZEVEREiS3Nxy9SgIZIB8Wot8Wot8Wot8Wot8Wsvu+bx8+XKO2+a4wK9YsaJCQ0N1/vx5lS1b1qXAbgZfX1/ncnx8fJZtU5/tT71dYRMUFKR9+/Zp2rRpqlWrlmX9Hj9+XKVKlXK+ttMZfElq3bp1rp99gPTIp7XIp7XIp7XIp7XIp7Xsns+YmJgct83x3t933336+OOP9fDDDysoKKjAJs/Ly0sVK1bUmTNnFBkZmWXb1OurV6+ez5Hlj8OHD2vy5MmqW7euAgMDdfbs2XRtUk8/unjxYpo2/v7+mfbt5+eXpsC3Ezc3N3l4eBTIY7gwIp/WIp/WIp/WIp/WIp/WsnM+c7NPOW75wgsv6OOPP9bu3bvVoUMHubm5yd/fP82UGEnatm2batasmfNoU8nL/P3UGjRooDNnzujSpUu6ePFiprfKPHXqlHO5fv36lox9s33//fdKSEjQ4cOHValSpWzb9+zZM83rlAeCAQAAwB5yXOC3bt1akyZNcj6lNikpSX/99Ve6dvHx8Tpx4oRLwRhjLHnoU8eOHbVp0yZJ0p49e9SuXbsM24WGhqbZpjDq0qWLNm7cmGWbjz76SMuWLZMkzZgxQ40bN74ZoQEAAOAWyNX3F6+//rruvvtuTZs2TTt37lRycnK6NgXhjPATTzyhcePGyRijzZs3Z1rgp3wI8Pf3V/v27W9ihNapVKlStmfud+zY4Vxu1qxZod1XAAAAZC/XE5S6deumbt266fLlywoLC1NMTIySkpJkjFHHjh117733KigoyKVgRo8erZ9++smlbVOrU6eOnnzySa1cuVLLli3T+PHj090B6NixY9qyZYuk60/lvXFe0/79+/XYY4/pypUr+vjjj3X//ffnOS4AAAAgv7l8BULJkiXVqFGjdO/7+fllesY8O35+fq6Gk8706dMVEhKi8PBwjR07VjNmzHCuS0hI0KBBg5SUlKRmzZpp2LBh6bafMmWKjhw5Ikl69dVX9f3331sWGwAAAJBfclzgh4WFacqUKc7X3bp10+OPP25pMFZO7wkICNC6dev06KOPaubMmdq/f7969OihuLg4LV26VPv27VOTJk30xRdfZPjE29TTj7KKa+/evdq7d2+69yMjI7V8+XLn686dO6tChQqZ9rN27VrFxsZKknbu3Ol8f+fOnc5vF3x8fNJdJJuZjRs3Ou8SlDq+jRs3Oi8ubtSoUYYf0gDALmJOxej8L+cVUz1GftWtO4kEAAVZjgv8VatWKTg4WA6HQ8YYVaxYMV2BHxISkqez8NOnT1d0dLTL29+oRYsW2rt3r2bNmqU1a9Zo9OjR8vT0VN26dTVr1iwNGTIk04d3jRs3TqGhoYqLi8tyytHq1as1adKkdO8fOnRIffr0cb4OCQnJssAfOXJkhhcnL1y4UAsXLpQkVatWLccF/pQpU7Rt27Z070+dOtW5PGHCBAp8ALYV+kGovhz0pUyy0b5/7lP3hd1197N33+qwACDf5bjA37BhgyTpoYceUlBQkO644450bWrUqJHutpm50bBhQ5e3zYy/v78mT56syZMn52q7Ro0aOafoZGXixImaOHGii9H9T16f4HujrVu3WtofABQmMadinMW9JJlkoy8Hf6naXWqrVBV7Pt8DAFLk+Dm+4eHh8vPz08qVK1W/fv0Mb7Zfo0YN9evXz+VgoqKinI8YBgDAVeeOnHMW9ylMklH0Ueu+JQaAgirHBX5kZKTq16+vEiVKZNrGGJOnefR9+/Z1+SFZAACkKFennBxuaZ+r4nB3yK828/AB2F+OC3x3d3ddvnw5P2ORVDDuow8AKNxKVSml7gu7y+F+vch3uDvUfUF3pucAKBJyPAc/ICBAv/76q/bt25cvc+UBALDS3c/ereqdqmv72u26v+f93EUHQJGR4wL/gQce0MGDB9WhQwe9+OKLatu2rSpXrpzuotq4uDiX59HHxcW5tB0AABkpVaWUyjQpw5l7AEVKjgv8l19+WR9++KHOnz+f4W0hU2zfvl01atRwKRhjjBwOR/YNAQAAAGQox3PwAwICtHLlSpUqVcp5Me2NP5IyXZfdDwAAAIC8y3GBL0ldu3bVwYMHNW7cODVv3lxlypSRm1uuusgURT4AAACQd7muzitVqqR//etf+uGHHxQdHa3ExEQlJycrOTlZ0vUPASmvc/vTpUsXy3cQAAAAKEqsOf0OAAAAoEAoUAU+8/EBAACAvMnxXXRyImWajqu++eYbiyIBAAAAiqYCdQYfAAAAQN5Q4AMAAAA2QoEPAAAA2Eiu5uAPGDAg3XsOh0Pvv/++PD09cz34e++9pxdffDFdf4mJibnuCwAAAEAuC/zg4GA5HI40d7pxOByaM2eOSwW+xAOuAAAAACu5dBed4cOHy8/Pz/m6WLFiLg0eGBio7t27S5LCw8PVoUMHl/oBAAAAcJ1LBf6IESNUs2bNdO9/9NFH2W7bt29f57KPj498fHwkiWk5AAAAgAUsvQ9+v3795HA4smyTusAHAAAAYC1LC/z777/fWeBv27ZNZcuWVaNGjawcAgAAAEAWLC3wt27d6lx2c3NTixYt9NVXX1k5BAAAAIAscB98AAAAwEYo8AEAAAAbocAHAAAAbIQCHwAAALARCnwAAADARijwAQAAABtxqcDP7mFWAAAAAG4Nl+6DX7t27Ry127Bhg9zd3V0ZAgAAAIALXCrwjTHZtkk5y5+TtgAAAACsYemTbFOjsAcAAABuvlwX+GXLltXw4cPzIxbNnTtX0dHR+dI3AAAAUBTkusD38/PThAkT8iMWffrppxT4AAAAQB5wm0wAAADARnJ1Bv/+++9X1apV8ysWNW/eXBUrVsy3/gEAAAC7y1WBv3Xr1nwK47qPPvooX/sHAAAA7I4pOgAAAICNUOADAAAANkKBDwAAANgIBT4AAABgIxT4AAAAgI1Q4AMAAAA2QoEPAAAA2AgFPgAAAGAjFPgAAACAjVDgAwAAADZCgQ8AAADYCAU+AAAAYCMU+AAAAICNeORn58nJyfr555/1ww8/6OTJk7pw4YIWLFjgXL9v3z7VqFFDPj4++RkGAAAAUGTkS4F/7do1zZgxQ/PmzdPp06fTrEtd4L/99tv6v//7Pz377LOaOHGiypUrlx/hAAAAAEWG5VN0Tp06pXvvvVfjx4/X6dOnZYyRJOd/bxQfH6958+apSZMm2rNnj9XhAAAAAEWKpQX+pUuX1LVrV+3du1fGmGyL+ylTpmjKlCm67bbb9Mcff6hr166KjIy0MiSdPXtWr7/+uho0aCAfHx/5+fmpVatWmj17tq5evWrpWGFhYWrXrp0cDofat2/vcj8rVqyQv7+/HA6HgoODc7zd77//rjfffFOdOnVShQoVVKxYMZUuXVoNGjTQkCFDFBoa6nJMAAAAKBwsLfDffvttHTx4UMYYtWzZUkuWLNHvv/+uS5cuqUuXLunaBwQE6LXXXtNvv/2mDh066K+//tKrr75qWTy7d+9Wo0aNNHnyZFWuXFlBQUEaO3asYmNjNWLECLVs2TLdFCJXGGM0d+5cNW7cWNu3b3e5n7/++kuPP/64nn76aZ07dy7H250/f17du3fXnXfeqQkTJigqKkoDBw7UvHnzNH78ePn6+ur999/XPffco5dffjnTD1wAAAAo/Cwr8JOSkjRnzhw5HA5NmDBB33//vQIDA1WnTh2VLFkyy23LlCmjtWvXqnLlylqxYoViYmLyHE9ERIS6d++uP//8UyNHjtR///tfDR06VP/85z/1008/qUOHDvrll1/Uo0cPJSQkuDxOWFiY2rdvr+HDh6tNmzYu97NixQrVq1dP69evz/XZ/8jISK1fv16S9PLLL+uXX37RlClTNHDgQI0aNUo7d+7UmDFjZIzRO++8o7Fjx7ocJwAAAAo2ywr8Xbt2KSYmRu3atdOECRNyvb2vr6+ee+45JSQkaOfOnXmOZ9SoUYqKilJAQICmTZuWZp2Xl5cWLVokd3d3/fzzz5o7d65LY1y+fFmNGzfWnj17tGjRIn3zzTcu9RMcHKynn35aNWvWVGhoqAIDA13q56677lJQUJDc3d3TrZs6darq1asnSZoxY4ZOnjzp0hgAAAAo2Cwr8A8dOiSHw6E+ffq43EerVq1kjNGRI0fyFMuRI0f02WefSZL69u0rLy+vdG1q1aqlDh06SJKCgoKUmJiY63GuXbumNm3a6MCBAxo4cKDL8cbHx2vq1KnauXOnswh3Rbdu3eThkfGNkdzc3NSzZ09JUmJiossfRgAAAFCwWXabzLNnz0q6Xji7qnjx4pKuX6ybF6tWrXLOM3/ggQcybde5c2dt2rRJUVFR2rp1a5ZtM1K6dGlLCuXBgwfL4XC4vH3VqlX19ddfq0GDBlm2q1atmnM5IiLC5fEAAABQcFl2Bt/b21uSFBcX53Ifp06dknR9uk5ebNmyxbncpEmTTNs1bdo0w21yKi9FuZX9lCxZUl27dlWVKlWybHfhwoU02wAAAMB+LCvwK1euLGOMtm7d6nIfa9eulcPhUNWqVfMUy/79+yVd/6BQunTpTNulHufAgQN5GrMwOH78uHP5/vvvv4WRAAAAIL9YVuCn3P/9vffe09GjR3O9/YYNG7Rq1Sq5u7vn6R7yCQkJOnPmjCSpQoUKWbZNvT48PNzlMQuD5ORkrVu3TpLUqFEjtW7d+hZHBAAAgPxg2Rz82267TQ8++KA2bNig+++/X++//7569OiR7XaJiYl677339Nprr0mSevTokeVZ9+yknr+fMm0oMylz/m/czo6+/PJL/fHHH5KkmTNn5mib6OjoNBcfe3l5ZXjBcmGTmJio5ORkly6sRnrk01rk01rk01rk01rk01p2z2du9suyAl+6fjeaLVu2KDIyUo8++qhq166tbt26qUmTJs6LcDdu3KhLly4pIiJCv/zyi7766itFR0fLGCNvb29NnTo1TzGkvgagWLFiWbZNvf7KlSt5Grcgi4uL0+jRoyVJQ4cOzfHFxDVq1EjzOjAwUP369bM6vJsuOTnZeZGxm5ulz3orksintcintcintcintcinteyez8uXL+e4raUFfqNGjfThhx+qb9++kqSjR4/q3Xffda43xqhr165ptkm5243D4dBHH32kunXr5imG1Gflr169mmXb1OtLlCiRp3ELshdffFG///672rZtq3//+9853u748eMqVaqU87WdzuBLUuvWrTO9rShyjnxai3xai3xai3xai3xay+75zM2DYC3f+169esnX11fPPvuszp07J+l/d4lxOBxpCvqUZX9/fy1dulQPPfRQnsdPfQee+Pj4LNumPtuf1zv3FFTTp0/X4sWL1bBhQ33++efZfquRmp+fX5oC307c3Nzk4eFhyz8AtwL5tBb5tBb5tBb5tBb5tJad85mbfcqX7y969OihQ4cOacKECapevbqMMc6fFMYY1ahRQ5MmTdLvv/9uSXEvXT/LXLFiRUlSZGRklm1Tr69evbol4xck77//vkaPHq0GDRpo06ZNKlu27K0OCQAAAPks3z7elCtXThMmTNCECRN06tQpHTp0SNHR0c51d9xxR7b3bXdVgwYNdObMGV26dEkXL17M9KLdlPvuS1L9+vXzJZZbZfHixXrhhRdUv359bdmyReXLl7/VIQEAAOAmuCnfX1SpUiXfivmMdOzYUZs2bZIk7dmzR+3atcuwXWhoaJpt7GLJkiUaNGiQ7rzzTm3evJniHgAAoAix3yXGkp544gnnvP/Nmzdn2i7lQ4C/v3+e7r1fkCxbtkwDBw5U3bp1tWXLlgyfBdCjRw8NHjz4FkQHAACA/GZpgV+zZk0NGDDAyi5dUqdOHT355JOSrhe8Gd1N59ixY9qyZYskacyYMekuXNi/f7/q1q2rKlWqaPv27fkftAU+/vhj9evXT7Vq1dKWLVuc1yLcaO/evfr9999vcnQAAAC4GSydohMeHu58iuytNn36dIWEhCg8PFxjx47VjBkznOsSEhI0aNAgJSUlqVmzZho2bFi67adMmaIjR45Ikl599VV9//33Ny12V6xYsUJ9+/aVt7e3JkyYoMOHD+vw4cMZts3u7kIAAAAovOx3D6H/LyAgQOvWrdOjjz6qmTNnav/+/erRo4fi4uK0dOlS7du3T02aNNEXX3yR4RNvk5OTncup7/5zo71792rv3r3p3o+MjNTy5cudrzt37pzhdJkUa9euVWxsrCRp586dzvd37tzp/HbBx8dHPXv2TLftTz/9pN69eyspKUlXrlxR7969Mx0nxZ133pltGwAAABQ+lhf427ZtU82aNV3e3sPDQ2XLllX16tXVrl07PfPMMy7f3rFFixbau3evZs2apTVr1mj06NHy9PRU3bp1NWvWLA0ZMiTT+8KPGzdOoaGhiouLU1BQUKZjrF69WpMmTUr3/qFDh9SnTx/n65CQkCwL/JEjR+rEiRPp3l+4cKEWLlwoSapWrVqGBf6pU6ds+1hmAAAA5I7lBX58fLzCw8PTvJdyweuNbjwznrrdTz/9pFWrVum1117TpEmTNHLkSJfi8ff31+TJkzV58uRcbdeoUSPnFJ2sTJw4URMnTnQpttRuzFlu9OzZM8tvGQAAAFB0WH4XndRPqk39XkY/KVLaZtTm0qVLeuWVV/TSSy9ZHSoAAABgO5aewQ8JCVF8fLzGjh2rX375RS1bttSDDz6ou+66S2XKlHHOdU9ISND58+d16NAhbdy4UTt37lTt2rU1c+ZMlSpVSrGxsTp9+rR27dql//u//1NMTIxmz56tDh06qEePHlaGDAAAANiKpQX+vffeq7Zt2+ry5cvatWuX7r333my3mTBhgn788Uf17dtXo0eP1q5du5xPnn3uuef09ttv6x//+Ic2btyoSZMmUeADAAAAWbB0is6IESN08uRJbd++PUfFfYrmzZtr69atio6O1gsvvJBmXbly5bRmzRrdfvvt2rNnj44fP25lyAAAAICtWFbgR0dH66OPPtKwYcN022235Xr7ChUqaOjQofrss88UFRWVZl2JEiXUv39/SdIPP/xgSbwAAACAHVlW4IeEhOjatWtq0aKFy320atVKiYmJ2rp1a7p1LVq0kDFGkZGReYgSAAAAsDfLCvxTp05Jkjw9PV3uI+WBTn/88Ue6dSnz8i9fvuxy/wAAAIDdWVbgpzxoKaOnuubUr7/+KintU2RTnDt3TpJUvHhxl/sHAAAA7M6yAr9q1aoyxmjOnDkunWWPjY3VnDlz5HA4VLVq1XTrf//9dzkcDlWsWNGKcAEAAABbsqzA79y5szw9PXX8+HF17drVOWUnJ06ePKmuXbvq+PHj8vT0VOfOndOsT0pK0ocffihJqlu3rlUhAwAAALZjWYFftmxZ9e/fX8YYff/996pdu7Z69eql5cuX69dff1V0dLQSEhJ09epVRUdH69dff9Xy5cvVq1cv1a1bVzt37pTD4dDAgQNVpkwZZ79RUVF64okndPjwYZUoUUKNGze2KmQAAADAdix90NXbb7+tHTt26ODBg7p69ao+/fRTffrpp9luZ4yRJDVq1EjTpk1zvv+f//xH/fv3V1JSkhwOhzp27Oi8EBcAAABAepY+6KpUqVLaunWr7rvvPkn/K9yNMRn+pG7Trl07bd68WT4+Ps7+rl69qttvv10BAQEKCAjQwIEDrQwXAAAAsB1LC3xJ8vf317fffqv3339f9erVcxbwGTHGqH79+lq8eLFCQkJUrly5NOv79++v48ePO38eeeQRq8MFAAAAbCXf5rsMGjRIgwYN0oEDB7Rr1y4dPXpUFy5ckCSVKVNGtWvXVqtWrVSvXr38CgEAAAAocvJ9Qnv9+vVVv379/B4GAAAAgPJhig4AAACAW6dAFfj//ve/NWDAgFsdBgAAAFBoFagCf+PGjVq6dOmtDgMAAAAotApUgQ8AAAAgb3J8ke3NmDqzb9++fB8DAAAAsLMcF/jBwcFyOBz5GYuMMfk+BgAAAGBnub5NZlYPrkotpVDPrn1O2wEAAADIXq4K/Fq1aqlXr17Ztlu+fLnCwsLk5eWlFi1a6K677lKZMmXk7e0tSUpISND58+d16NAh/fDDD4qPj1ezZs3UvXt31/YCAAAAgKRcFvi1a9fWhAkTMl1vjFHv3r11+vRpBQUF6fnnn5evr2+WfcbGxur999/XpEmT1KpVK82ePTs3IQEAAABIxdIn2b711ltatWqVtmzZovvuuy9H2/j4+Oif//ynWrdurQ4dOqhGjRp66aWXrAwLAAAAKDJyfJvM6dOna+DAgZmuT0hI0Ntvv61nn302x8V9aq1bt9aAAQM0efJkXbt2LdfbAwAAAMhFgf/KK6/osccey3T95s2bFRMTo65du7oczEMPPaTz589r8+bNLvcBAAAAFGWWPejqxIkTcjgcKlu2rMt9lClTRpJ0/Phxi6ICAAAAihbLCvyYmBhJ0rFjx1zuIzw8XJJ06dIlK0ICAAAAihzLCvyKFSvKGKMlS5a43MfixYvlcDhUoUIFq8ICAAAAihTLCvy2bdtKkr799lu99NJLuXpwlTFGI0eO1I4dOyRJ999/v1VhAQAAAEWKZQV+zZo11bFjRxljNHv2bDVs2FCLFy/WqVOnMt3m1KlTWrRokRo1aqQ5c+bI4XDogQceUI0aNawKCwAAAChSLL0P/rx589S8eXPFxsbqt99+0+DBgyVJpUuXVpUqVVSyZEk5HA7Fxsbq5MmTznn70vWz+KVLl9a8efOsDAkAAAAoUiwt8OvWrasNGzaoe/fuio6OlsPhkDFGFy5c0IULF+RwOCQpzfSdlDb+/v5av369atWqZWVIAAAAQJFi2RSdFC1bttSBAwf09NNPy93dPdv27u7u6t27tw4cOKDmzZtbHQ4AAABQpFh6Bj9FhQoV9PHHH2vmzJlavXq1du3apaNHj+rChQuSrt/vvnbt2mrVqpUee+wxVaxYMT/CAAAAAIqcfCnwU1SqVElDhw7V0KFD83MYAAAAAP+f5VN0AAAAANw6FPgAAACAjVDgAwAAADaSqzn427dvz/D9tm3bOm+BmRtbtmzR8uXL07zncDj0wQcf5LovAAAAALks8Nu3b5+ukHc4HIqJiVGJEiVyPfhvv/2m4ODgdP1R4AMAAACucekuOpUrV5aHx/82dXNzbabPvffeqwkTJkiSoqOjNWfOHJf6AQAAAHCdSwX+tm3bVLNmzTwP3rx5c+fDrcLCwijwAQAAgDyy9D742T251uFwKDEx0cohAQAAAKRiaYFvjMnTegAAAAB5Y2mBf/z4cUnXC/maNWuqXbt26S6iBQAAAJB/LC3wq1WrluZ18eLF070HAAAAIP/woCsAAADARijwAQAAABuhwAcAAABshAIfAAAAsBHbF/hnz57V66+/rgYNGsjHx0d+fn5q1aqVZs+eratXr1o6VlhYmNq1ayeHw6H27du73M+KFSvk7+8vh8Ph0l2I9u/fr+eee041a9ZU8eLFValSJXXr1k1r1651OSYAAAAUDrYu8Hfv3q1GjRpp8uTJqly5soKCgjR27FjFxsZqxIgRatmypU6fPp3ncYwxmjt3rho3bqzt27e73M9ff/2lxx9/XE8//bTOnTvnUh8ffPCB7rnnHgUHB6tDhw5699139eyzz+rHH3/Uo48+ql69evGwMQAAABtz6TaZY8aMka+vb7bt9u3bpwEDBuSoz0uXLrkSSqYiIiLUvXt3RUVFaeTIkXrnnXec64YPH66HHnpIISEh6tGjh7777jt5eXm5NE5YWJgGDBig7du3q0uXLtqwYYNL/axYsUJDhw5VbGys2rdvr61bt+a6j/Xr12vQoEFKTk7WmjVr1LNnT+e65557Tq1atdLHH3+ssmXLau7cuS7FCQAAgILNpQJ/9erV2bZxOBw6ffq0li5d6soQeTZq1ChFRUUpICBA06ZNS7POy8tLixYt0h133KGff/5Zc+fO1SuvvJLrMS5fvqzGjRvL3d1dixYt0sCBA+VwOHLdT3BwsPr376/mzZsrODhYu3fvznWBn5CQoBdeeEHJycl66qmn0hT30vVnFEydOlX9+/fXvHnz1K9fP91zzz25jhUAAAAFm0tTdIwx2f7kpm3q9lY4cuSIPvvsM0lS3759Mzw7X6tWLXXo0EGSFBQU5NK0lWvXrqlNmzY6cOCABg4c6HK88fHxmjp1qnbu3Kl69eq51MeyZcsUEREh6frZ+ow8/fTT8vX1lTFGU6ZMcTleAAAAFFy5PoPv4eGhypUr50cs+uOPP5SUlJTnflatWuX80PDAAw9k2q5z587atGmToqKitHXr1izbZqR06dL65ptv8hSrJA0ePNilM/+ppXygKVasmNq2bZthG29vb7Vp00Zff/21vvnmG8XGxsrHxydP4wIAAKBgyXWBX61aNR0+fDg/YtFdd91lSd9btmxxLjdp0iTTdk2bNk2zTW4L/LwW5Vb1k5SU5Ly4t169eipWrFimbZs2baqvv/5a8fHx+v777/Xggw/maWwAAAAULLa8i87+/fslSb6+vipdunSm7apWrepcPnDgQL7HlV+OHj2q+Ph4SWn3KSN22WcAAABkrEAV+FbMxU9ISNCZM2ckSRUqVMiyber14eHheR77Vkkde1HZZwAAAGQsV1N0jh8/Lk9Pz/yKRVu2bNG1a9fy1Efq2216e3tn2bZ48eIZblfY5Nc+R0dHp7n42MvLy+XbiRYkiYmJSk5O5nkAFiGf1iKf1iKf1iKf1iKf1rJ7PnOzX7kq8KtVq5brYHLDiot34+LinMtZzUW/cf2VK1fyPPatkl/7XKNGjTSvAwMD1a9fv9wHWMAkJyc77zjk5lagvsQqlMintcintcintcintcinteyez8uXL+e4rUv3wS/IUp+hvnr1apZtU68vUaJEvsWU3/Jrn48fP65SpUo5X9vpDL4ktW7dWh4etvsVuOnIp7XIp7XIp7XIp7XIp7Xsns+YmJgct7Xd3qd+wm7KhaeZSX3mOydP5i2o8muf/fz80hT4duLm5iYPDw9b/gG4Fcintcintcintcintcinteycz9zsk+2+v/Dy8lLFihUlSZGRkVm2Tb2+evXq+RlWvkode1HZZwAAAGTMdgW+JDVo0EDS9YtIL168mGm7U6dOOZfr16+f73Hll9q1azsvrj158mSWbe2yzwAAAMiYLQv8jh07Opf37NmTabvQ0NAMtyls3N3ddf/990uSDh48mOU8/JR99vb2VuvWrW9KfAAAALh5bFngP/HEE86nw27evDnTdps2bZIk+fv7q3379jcjtHzz5JNPSrp+Ee2OHTsybBMfH6/vvvtOktS1a1f5+PjctPgAAABwc9iywK9Tp46z4F22bFmGZ7SPHTumLVu2SJLGjBmT7sKF/fv3q27duqpSpYq2b9+e/0HnUZ8+fRQQECBJWrx4cYZtVq5c6bwCe+zYsTctNgAAANw8tizwJWn69OkqX768wsPD0xWzCQkJGjRokJKSktSsWTMNGzYs3fZTpkzRkSNH9Mcff+jVV1+9WWG7zMvLS/PmzZObm5s+/fRTffHFF2nWR0RE6LXXXpMkDR06VM2bN78VYQIAACCf2e8eQv9fQECA1q1bp0cffVQzZ87U/v371aNHD8XFxWnp0qXat2+fmjRpoi+++CLDp78mJyc7l40xmY6zd+9e7d27N937kZGRWr58ufN1586dVaFChUz7Wbt2rWJjYyVJO3fudL6/c+dO57cLPj4+6tmzZ6Z9dOvWTQsWLNDQoUP1xBNPqG/fvmrRooUiIiK0YMECRUVF6ZlnntGsWbMy7QMAAACFm20LfElq0aKF9u7dq1mzZmnNmjUaPXq0PD09VbduXc2aNUtDhgzJ9Mmv48aNU2hoqOLi4hQUFJTpGKtXr9akSZPSvX/o0CH16dPH+TokJCTLAn/kyJE6ceJEuvcXLlyohQsXSrr+JOGsCnxJGjhwoFq2bKlZs2Zp8+bNWr58ucqUKaNmzZpp0KBBevTRR7PcHgAAAIWbrQt86foFtJMnT9bkyZNztV2jRo105MiRbNtNnDhREydOdDG6/wkPD89zHykaNGiQ6Tx8AAAA2Jtt5+ADAAAARREFPgAAAGAjFPgAAACAjVDgAwAAADZCgQ8AAADYCAU+AAAAYCMU+AAAAICNUOADAAAANkKBDwAAANgIBT4AAABgIxT4AAAAgI1Q4AMAAAA2QoEPAAAA2AgFPgAAAGAjFPgAAACAjVDgAwAAADZCgQ8AAADYCAU+AAAAYCMU+AAAAICNUOADAAAANkKBDwAAANgIBT4AAABgIxT4AAAAgI1Q4AMAAAA2QoEPAAAA2AgFPgAAAGAjFPgAAACAjVDgAwAAADZCgQ8AAADYCAU+AAAAYCMU+AAAAICNUOADAAAANkKBDwAAANgIBT4AAABgIxT4AAAAgI1Q4AMAAAA2QoEPAAAA2AgFPgAAAGAjFPgAAACAjVDgAwAAADZCgQ8AAADYCAU+AAAAYCMU+AAAAICNUOADAAAANkKBDwAAANgIBT4AAABgIxT4AAAAgI1Q4AMAAAA2QoEPAAAA2AgFPgAAAGAjFPgAAACAjVDgAwAAADZCgQ8AAADYiO0L/LNnz+r1119XgwYN5OPjIz8/P7Vq1UqzZ8/W1atXLR0rLCxM7dq1k8PhUPv27XO9/eXLl/X222+rWbNmKlOmjEqXLq2mTZtqypQpiomJyVEf586d05QpU3TffffJz89Pnp6eKlOmjJo3b67x48fr9OnTuY4LAAAAhYetC/zdu3erUaNGmjx5sipXrqygoCCNHTtWsbGxGjFihFq2bGlJwWuM0dy5c9W4cWNt377dpT6OHj2qZs2aacyYMXJ3d9fEiRP1r3/9SyVKlND48ePVpEkTHThwIMs+duzYoXr16mn8+PEKCwvTwIEDNXfuXA0bNkznz5/XlClTVK9ePX311VcuxQgAAICCz+NWB5BfIiIi1L17d0VFRWnkyJF65513nOuGDx+uhx56SCEhIerRo4e+++47eXl5uTROWFiYBgwYoO3bt6tLly7asGFDrvu4ePGiunXrpsOHD+vxxx/XihUr5O7u7oy1T58++s9//qNu3brpp59+kr+/f7o+IiMj9cgjj+jChQu666679O2336pcuXLO9WPHjlW3bt20detWPf7449q3b59q167t0j4DAACg4LLtGfxRo0YpKipKAQEBmjZtWpp1Xl5eWrRokdzd3fXzzz9r7ty5Lo1x+fJlNW7cWHv27NGiRYv0zTffuNTP1KlTdfjwYfn4+Gj+/PnO4l6SHA6H5syZozJlyujEiROaNGlShn0sXLhQFy5ckCRNmzYtTXEvSSVKlNC7774rSYqPj0/zgQcAAAD2YcsC/8iRI/rss88kSX379s3w7HytWrXUoUMHSVJQUJASExNzPc61a9fUpk0bHThwQAMHDnQp1kuXLmnOnDmSpJ49e6p8+fLp2pQtW1ZPPPGEJGnBggU6e/ZsujY//vijc7lFixYZjtWwYUMVL15ckrRz506X4gUAAEDBZssCf9WqVTLGSJIeeOCBTNt17txZkhQVFaWtW7fmepzSpUvrm2++UZUqVVyKU5K+/PJLxcXFScpZrNeuXdPatWvTrU99wXBKEX8jh8Mhb29vSde/fQAAAID92LLA37Jli3O5SZMmmbZr2rRphtvklMPhyPU2N7Iq1saNGzuXf/vttwz7iIyM1Pnz5yVJderUyW2oAAAAKARsWeDv379fkuTr66vSpUtn2q5q1arO5ezuUJNfUmKV0sZzo+xiHTJkiHx9fSVJb731VoZ9TJ061bk8ePDgXMcKAACAgs92BX5CQoLOnDkjSapQoUKWbVOvDw8Pz8+wMpUyroeHh/z8/DJt5+3trVKlSqXZJrXq1atr8+bNqlWrlj7//HM98sgjCgkJUUREhL777jsNGDBAs2fPloeHh4KCgvTII4/kx+4AAADgFrPdbTIvXbrkXE6Zb56Z1HPVU293M6WMm12s0vV4Y2JiFBsbK2NMuilCzZs314EDBzRz5ky9/fbb+vLLL53rvL299cILL+i5557LcipQatHR0WkuPvby8nL5dqIFSWJiopKTk126sBrpkU9rkU9rkU9rkU9rkU9r2T2fudkv2xX4KResSlKxYsWybJt6/ZUrV/ItpqykxJtdrKnbJCcnKyEhId2Hgh9++EHPP/+89uzZo+bNm6t///6qVq2aoqOjtWbNGi1ZskTnzp3Tm2++qbp162Y7Xo0aNdK8DgwMVL9+/XK4ZwVXcnKyIiIiJElubrb7EuumI5/WIp/WIp/WIp/WIp/Wsns+c3ODFNsV+KnPyqe+s0xGUq8vUaJEvsWUleLFi+vy5cvZxir9L143N7d0Z9J37typTp06KS4uTs8884yWL1+e5uDu3bu35syZoxdffFHr16/XunXr1L59+yzHO378uHNakGSvM/iS1Lp1a3l42O5X4KYjn9Yin9Yin9Yin9Yin9ayez5jYmJy3NZ2e59yoal0/YFOWUl9tj/1djeTr6+vLl++nG2s0v/i9fHxSTM9xxijgQMHKi4uTiVLltT8+fMz/OQ6fPhwffLJJ9q5c6f+/ve/KywsLMv99vPzS1Pg24mbm5s8PDxs+QfgViCf1iKf1iKf1iKf1iKf1rJzPnOzT7b7/sLLy0sVK1aUdP22kFlJvb569er5GVamUsZNTExUdHR0pu3i4+Odn9xujHXPnj06ePCgJKljx45Z3jnosccek3T93v8rV67MQ+QAAAAoiGxX4EtSgwYNJF2/gPXixYuZtjt16pRzuX79+vkeV0ZSYpWkkydPZtouq1gPHz7sXK5WrVqW46X+cLBnz54cRgkAAIDCwpYFfseOHZ3LWRWxoaGhGW5zM1kRa+rpOClP8M1McnKyczkpKSmnYQIAAKCQsGWB/8QTTzjnqG/evDnTdps2bZIk+fv7Z3vBaX7p3r2788LgnMTq6empv/3tb2nW1axZ07kcFhaW5Xip1wcEBOQ6XgAAABRstizw69SpoyeffFKStGzZsgzvUHPs2DFt2bJFkjRmzJh0Fy7s379fdevWVZUqVbR9+/Z8i9XX11fDhw+XJK1du1bnzp1L1+bixYtatWqVJGnQoEEqX758mvVNmzbV7bffLknaunWroqKiMhzLGJNm3v3DDz9syT4AAACg4LBlgS9J06dPV/ny5RUeHq6xY8emWZeQkKBBgwYpKSlJzZo107Bhw9JtP2XKFB05ckR//PGHXn311XyNdezYsapbt64uXbqkIUOGpJlGY4zR8OHDdf78eVWrVk0TJ05Mt72bm5uCgoIkXb8Yt3///kpISEjX7s0333ROA+rVq5caNWqUL/sDAACAW8d+9xD6/wICArRu3To9+uijmjlzpvbv368ePXooLi5OS5cu1b59+9SkSRN98cUXGT5F9sYiOzN79+7V3r17070fGRmp5cuXO1937txZFSpUyLCP0qVLa/369erWrZs+++wzRURE6JlnnpHD4dDKlSv13XffqUaNGlq3bp38/f0z7KNXr16KiorSmDFjtH79etWvX1+9e/d2Puhq7dq12rFjh6TrU5gWL16c6T4BAACg8LJtgS9JLVq00N69ezVr1iytWbNGo0ePlqenp+rWratZs2ZpyJAhmT5Bdty4cQoNDVVcXJzz7HhGVq9erUmTJqV7/9ChQ+rTp4/zdUhISKYFviTVrl1boaGhmjt3rlasWKHXX39d0vX59f/617/04osvZntP+pEjR6p79+5atGiRQkJCNHv2bMXExKh48eK6/fbbFRgYqL59+96yC4oBAACQ/2xd4EvXL6CdPHmyJk+enKvtGjVqpCNHjmTbbuLEiRlOm3FFyZIlNWbMGI0ZM8blPmrXrp3lBxIAAADYm23n4AMAAABFEQU+AAAAYCMU+AAAAICNUOADAAAANkKBDwAAANgIBT6KnISEBAUHB2f4MDDkHvm0Fvm0Fvm0Fvm0Fvm0Fvn8H4fJ6ilOKJJiYmJUunRpXbx4Mdt77xdG0dHRKleunM6dOyc/P79bHU6hRz6tRT6tRT6tRT6tRT6tZfd85qY+4ww+AAAAYCMU+AAAAICN2P5Jtsi9lFlbMTExtziS/JGyXzExMfLw4Fcgr8intcintcintcintcinteyez5T9y8nseubgI51Tp06patWqtzoMAAAA3ODkyZOqUqVKlm0o8JFOcnKyTp8+LV9fXzkcjlsdDgAAQJFnjNGlS5dUuXJlubllPcueAh8AAACwES6yBQAAAGyEAh8FVmJioubPn6/77rtP5cqVk4+Pj+rVq6fXXntNZ86csXSs/fv367nnnlPNmjVVvHhxVapUSd26ddPatWtd7vPXX3+Vp6enHA6Hqlevnm379u3by+FwZPvj4+PjUjyFJZ/Jycnavn27xo0bpw4dOqhSpUoqVqyYSpYsqVq1aukf//iH/vvf/+Y4lrNnz+r1119XgwYN5OPjIz8/P7Vq1UqzZ8/W1atXXd7HwpLPG4WGhqpx48ZyOBzq169fjrdz5fjMr9zf6KuvvlKPHj10++23y9vbWzVq1FD//v0VGhqa4z4uX76st99+W82aNVOZMmVUunRpNW3aVFOmTMnVDQfy429JisKUzxQrVqyQv7+/HA6HgoODc7xdTo41h8Oh7t275zqmFIUhn1euXNHq1av1wgsv6N5771W5cuXk6emp0qVLq3HjxnrxxRd18ODBHMfC8Zmx9957z3lM5eTv4s04PvPMAAVQVFSUadGihZFk7rzzThMUFGTee+898/DDDxtJpnz58mbbtm2WjLV48WLj5eVlPDw8zIABA8yCBQvMuHHjTPny5Y0k849//MNcu3YtV30mJiaae+65x0gykky1atWy3aZdu3bO9ln9lCxZMtf7WJjy2aBBA+e+3nXXXeb11183CxcuNG+++aa57777nOueeeYZc/Xq1Sxj+eGHH0ylSpWMJNO5c2czd+5cM336dOcYTZs2NX/88Ueu97Ew5TNFQkKCef31142Hh4czh4GBgTmOI7fHZ37lPrWkpCTz3HPPGUnGz8/PvPrqq2bBggXmueeeM8WKFTMeHh7m3XffzbafI0eOmDvuuMNIMs2bNzfvvPOOeffdd03r1q2NJFOjRg2zf//+bPvJj78lKQpTPo0xJjIy0jz22GNpjo0lS5bkOJacHGuSTLdu3Vza18KQz/DwcOPj4+Pc186dO5spU6aYhQsXmtdee81Ur17dSDIeHh7mnXfeyTYejs+MnTx50vj6+ubq72J+H59WoMBHgXPt2jXTpk0bI8m0bt3aXLlyJc361157zUgyZcqUMYcPH87TWF9++aVxc3MzksyaNWvSrAsPD3f+wRo6dGiu+p0xY0aaX/KcFvjNmzc3v/32W5Y/v//+e65iKWz5rFChgpFkBgwYYBITE9Otf/311515HT9+fKb9nDhxwvk/rpEjR6ZZFx8fbzp06GAkmWbNmpn4+Pgc72Nhy6cxxvz888+mUaNGRpJ58MEHXS7wc3p85lfubzRmzBgjyfj7+5sjR46kWff1118bNzc343A4zMqVKzPt48KFC6Zu3bpGknn88cfTHHPJycmmV69ezt/hqKioTPvJr78lxuTfsXwjK/JpjDGffvqpKVeunPHy8jLt27d3ucAfOnRotsfbyZMnc72fhSWfv/32mzN3H374Ybr1V65cMZ07d3a22bRpU6axcHxm7pFHHknz/+ucFvj5dXxahQIfBc68efOMJONwOMyBAwfSrb969aqpXbu2kWS6d+/u8jjx8fEmICDASDJPPfVUhm2WLFnijOXHH3/MUb/Hjh0zJUqUMHfffbepUqVKrgr8du3a5WIPcqaw5bNChQrG19c3XeGc4tq1a868+vv7m6SkpAzb/f3vfzeSTEBAQIb/Uzl69Khxd3c3ksyMGTNyuJeFL5+HDx82Hh4eplKlSubzzz83x48fd7nAz+nxmV+5T23//v3OgmX+/PkZtgkMDDSSTIUKFUxsbGyGbUaPHm0kGR8fH/PXX3+lWx8dHW3KlCljJJlhw4Zl2Ed+/S1JUZjymbKfzZs3NwcOHHC+dqXAnzBhggt7kr3Cks+UAr9Tp06ZjnPo0CFnfh977LEM23B8Zu7TTz81kszf/va3XBf4+XV8WoUCHwVKcnKyqVq1qpFk2rRpk2m7yZMnO38Z9+zZ49JYixYtyvbMR1xcnPOru549e+ao386dOxt3d3cTGhpqqlWrdksL/MKYz4cffjjTQipFt27dnGNFRkamW3/48GHjcDiyPcv/wAMPGOn6lJqcfDVdGPO5Z88e07t3bxMdHW2MMfle4OdX7m+Ucma9ePHiJiYmJsM2O3bscO7rrFmz0q2PiYkxxYsXN5JM7969Mx1r4MCBRpLx9PTM8Cx+fv0tMaZw5dMYY+bPn2+mTp3q/CakoBX4hSmf4eHhpkWLFhmevU8tZRrPXXfdleF6js+MRUdHmwoVKpiAgACzb98+2xX4XGSLAmXXrl06efKkJOmBBx7ItF3nzp2dy5999plLY6VsV6xYMbVt2zbDNt7e3mrTpo0k6ZtvvlFsbGyWfS5dulQbN27USy+9pKZNm7oUl5UKYz7Xr1+vOXPmZDlWiRIlnMteXl7p1q9atcr5pL+c7HdUVJS2bt2a5ZhS4cxno0aNtGzZMpUtW9alOHIrv3KfWkJCgtatWydJatGihXx9fTNs16pVK+dFvxn9O3z55ZeKi4vLcazXrl3L8GLE/PhbkqIw5VOSBg8erNdee03u7u65iuFmKUz5rFatmnbt2qX+/ftnOV7x4sUlZfy3MHXfHJ9pvfzyy4qMjNS8efNcvnlFQUaBjwJly5YtzuUmTZpk2q5x48bOhzyk3iankpKStH37dklSvXr1VKxYsUzbphTq8fHx+v777zNtFxUVpVdeeUU1atTQpEmTch1TRi5duqTk5GSXty/M+cyMMUY//fSTJOnuu+9W6dKl07XJ6X6n/hCWk/0ujPnMz4fVZXR85lfuU/vpp5+cd7bJagw3Nzc1atRIkrRz505nMW9lrPl97BemfEr5d7wlJyfnuOjMSmHLZ3bCw8MVFRUlSerYsWO69RyfGdu8ebOCg4P11FNPqVu3brmKJyNWHZ9WosBHgbJ//37nctWqVTNt5+XlpfLly0uSDhw4kOtxjh49qvj4+GzHuXF9VmO9+OKLOnfunN5///00Z5hz48qVK3rzzTfVqFEjlSxZUqVKlZKnp6fq1aunUaNG6fTp07nqrzDnMzNz5szR8ePH5eHhoRkzZmTYJmW/fX19M/wA4GosdsxnbuTk+Myv3KeW03+H1OuTk5N16NAhl/rJKtb8/rcqTPm0WkREhAYPHqyaNWvK29tbvr6+8vLyUuvWrfXuu++6VBDbKZ/JyckaNWqUJKly5coaPXp0ujYcn+lduXJFgwYNUtmyZfXuu+/mKpbU8uP4tBIFPgqU8PBw53KFChWybJuyPiYmRufPn8/3cW7cLrX169fr008/VZ8+ffTggw/mKpbUfvzxR7311ltq27atlixZoi+//FJvv/22kpKSNGPGDN1555364osvctxfYc1namfPnlV4eLi+/vpr9erVSyNGjFCNGjW0fft2dejQIV37hIQE533orY7FDvnMi+yOzzvuuCPfcp+aVflJee3h4SE/P79M+/D29lapUqWy7COvsWQkP4/l1Arq8bZkyRKtX79egYGBWrlypdasWaNXXnlFv/32m0aOHKkmTZro999/z3F/hT2f165d019//aXDhw/r448/1n333adVq1apY8eO+vHHHzMci+MzvTfeeEPHjh3TjBkzsu0vK1Yfn1bzuGUjAxm4dOmSc9nb2zvLtinzDlO2y80c47yMk1FfQ4YMkb+/v/7973/nOIaM3HbbbQoJCVG9evWc73Xr1k3Dhg3TI488oo0bN+rJJ59USEiIWrdunW1/hTGfN0o5Ey5Jnp6eevHFFzVp0iSVKVPmpsdih3zmRU6OzxT5GbdV+Ul5nV0fKf3ExMQoNjZWxhjnVJSCerzlRkE93u677z6tX78+zZnhnj17avDgwWrTpo0OHz6sLl266Oeff1a5cuWy7a+w5/O7775Lc1KjXLlymjNnjp5//nl5eGRcznF8pvXzzz9r1qxZ6tChgwYMGJCrOG5k9fFpNc7go0BJ/ZVWVnMFb1x/5cqVWzbOa6+9ppMnT+qdd96Rv79/ruJI7dNPP9WhQ4fSFE8pvLy8FBwcLC8vL129elXDhg3LUZ+FMZ832rhxo77++mt9+OGHeuKJJzRv3jxVrlxZr776aoZPR8zPWOyQT1fl5PhMHUthyE9KP9n1kbpNcnKyEhISLI8lI0X5eDt+/Lg2btyY4bSPatWqadasWZKkEydOaPLkyTnqs7Dns3Hjxtq4caPWrVun2bNnq2HDhho+fLgCAgL00Ucf3dRY8rvv/BgnMTFRAwcOlKenpxYsWJCrGG6UH8en1Sjw4ZLExMQcP6o5q58bH12e+tN3do+2Tr0+t3PerRpn586dmj9/vh588EH17t07VzGklpiYqEqVKsnPzy/TXN1+++3OwuKXX36xZT4z8sADD6hr167q37+/Pv74Y3377bdyd3dXUFCQHnnkESUlJWUaS2hoaJbHX+o7fURGRhaJfLqqYsWKWX4LUbly5TQX+aVcFJeZgpCflH6y6yN1Gzc3tzR3K8nPf6uifLxVr149TVw36tmzp/OsaHBwcI5uRlDY81m2bFk98MAD6t69u4YPH66QkBDNmTNHf/75pwIDA/Wvf/3rpsWS333nxzgzZszQnj179MYbb6hOnTq5iuFG+XF8Wo0CHwVK6ttfpVwYlJnUn+ozu21Wfo5z9epVDRw4UN7e3nr//fdzNf7NUpjymVMtW7bUlClTJEn//e9/NXfu3ExjsZod82mlFi1aOJcvXryYZduCkJ+U19n1kbofHx+fNHeKyc9/K463zLm7uzvvqHLhwgX99ttv2W5jx3wOGzZMjz76qCRpwoQJ2rNnz02LpTDl8+jRo5o0aZIaNmyof/7zn7ka3xWuHJ9WYw4+XOLh4WHJAVupUqU0r6tXr65du3ZJun5G9fbbb89028jISElSqVKlcn2P7+rVq6frJ7txbtxu2rRpOnjwoMaOHStfX1+dPXs23bYpn9qTk5PTrPf29k5z392c5nPlypWaMGGCJOn555/XiBEj0qwvzPnMjV69emnkyJEyxujDDz9MkwcvLy9VrFhRZ86cUYkSJfTzzz9n2s+xY8ect0jr0KGD5s2bl2Z9UcmnVSpXruxczq7Az0vcVuWnevXqOnPmjBITExUdHZ3phbbx8fHObyQy6sOKWDKS+ljOz+OgsB5vt912m3P5zJkzql+/fpbt7ZrP3r17a82aNTLGaMmSJWnuDMPxed2gQYMUHx+vt956K8O/TalvhJCQkJDm/9cpd8fJrdwen1ajwIfL7rzzTsv7bNCggXP55MmTuvvuuzNsl5CQ4Lz3ryu/NLVr15a3t7fi4+OdDy7KzKlTp5zLqcdKuZfv1KlTNXXq1Cz7OHnyZJqLRQMDA9NN/8hJPlP/wahQoUK22xSmfOZGuXLlVK5cOZ09e1YHDhxIc9GjdH2/z5w5oytXrqhSpUqZ3r4tdSwtW7Yssvm0Suqvoa9evaqLFy/mKPe5jfvGf4espIzj5uaW7t+3QYMGzg9sJ0+ezLTAzyrW/P63SjmWL126VODzebOlPt5y+mAtO+bzjjvucC7v27cvzTqOz+tCQkIkSd27d892vE8//VSffvqp8/WSJUvUr1+/3IQsybXj00pM0UGBknoO741fNab266+/On95Mnq4R3bc3d11//33S5IOHjyY5by+0NBQSdfPuqe+c83MmTO1cePGLH9SbsFVoUKFNO/feL/idevWafLkyc4nAmYm5ZZkUvqzyxkpTPlM2XbVqlW6fPlyjsaUrv8RvXEefk73OyWWG7fJTGHLp1VcOT4la3Of2j333OP8+j2rMZKTk/Xrr79Kuv6UyxvnzFpxnOT3v1V+HcupWZVPq8ydOzfTi0ZTy+3fQ6lw5XPbtm366quvsh0vdfGYmJiYbh3Hp7L9f/Xy5cudbR988ME067p06ZJmnPw8Pi1lgAIkOTnZVKlSxUgybdu2zbTdlClTjCQjyezZs8elsRYtWuTsY/PmzRm2iYuLM6VKlTKSTM+ePXM9RrVq1YwkU61atSzbBQYGGknmzz//zLJdly5dnDHv378/2/ELWz4nTJhgJJkff/wxy7EuXLhg3NzcjCRz++23p1t/+PBh43A4jCTz+uuvZ9pP586djSTj7+9vrl27ls0eFr58ZuT48ePOfgMDA3O0jSvHp9W5v1GvXr2MJFO8eHFz6dKlDNt89913zlhmzZqVbn1MTIwpXry4kWT69OmT6VjPPfeckWQ8PT3NX3/9lW59fv4tya9j+UZW5DMjS5YscW6zZMmSHG1TrVo1c8cdd2TZJj4+3pnP8uXLm+Tk5Bz1XZjy2a5dO+Pr65vtvq1bt87ZT69evdKt5/jMXm7+Lubn8WklCnwUOPPmzTOSjMPhML/99lu69deuXTN16tQxkky3bt0y7OPSpUumU6dOpnTp0pn+osfHx5uAgAAjyTzzzDMZtlm6dKnzl3737t253pfcFvgffvhhpm0OHz5s3N3djSTTunXrHMdQmPKZUuBPmjQpy32aM2eOs58hQ4Zk2Obvf/+7kWSqV69uEhIS0q0PCwtz5nP69OlZjpdaYcpnRvJS4Of0+CxXrlyecr9v3z5Tp04dc/vtt5tt27ZlON6+ffucH/IWLFiQYZv+/fsbSaZChQomNjY2wzajR482koyvr685e/ZsuvUXLlwwZcuWNZLM0KFDM+wjv/+W5PVYvpn5vJGrBb7D4TBhYWGZtvnwww+d/Y4dOzZH/aYoLPls166dkZTpGCkef/xxZy5WrFiRbj3HZ/ZyW+Dn5/FpFQp8FDjXrl0zbdq0MZJMmzZtzJUrV9KsHzdunJFkypQpYw4fPpxhH++//77zl8vDwyPTX/Yvv/zSuLm5GYfDYT7//PM0606cOGEqV66c5f/Ys5PbAr9ChQrm999/T7f+3LlzplmzZkaSKVGihAkNDc1xDIUpnykFfsmSJc0333yTYZtt27aZkiVLOgvJP/74I8N2J06cMOXLlzeSzCuvvJJmXXx8vOnUqZORZJo1a2bi4uIy7CMjhSmfGclLgZ/T43P9+vV5yv3TTz/tjLFVq1aZxjVmzBjnGbIb/2e7YcMGZ0GwcuXKTPu4cOGCqVu3rpFknnzySZOUlORcl5ycbPr06eP8HY6Kisq0n/z8W5LXY/lm5vNGrhb4ksz9999vYmJi0q3/5ZdfjJ+fn5Fk7rzzTnPx4sUcx2NM4clnSoF/5513miNHjmTYZubMmc5Y7rvvvjTHb2ocn1nLbYGfn8enVSjwUSBFRUWZFi1aGEnmrrvuMm+//bZ57733TLdu3Yx0/Wu+rVu3Zrp9ylnWlAIqs6/0jLn+9WWxYsWMp6enefbZZ83ChQvN+PHjnX+wnnnmmRx/pRgbG2uWLVvm/PH393fGm/r9Gwu6oKAg4+npaaTrX0H279/fzJs3zyxevNi88sorzlgqVKiQ5X5nprDkc/ny5aZEiRLOsTp27GjeeOMNs3jxYjNr1izzt7/9zflHvG7duubXX3/Ncr937dplKlWqZCSZLl26mPfee8/MmDHDNGzY0EgyTZo0yfQDQlYKSz5TfP/9985j75133nGO3bZt2yyPyxSuHJ95yX3KGUFJpmXLlpnuV1JSkhk4cKDzw97YsWPNwoULzeDBg02xYsWMh4dHjr6qP3LkiLPIb9GihZk1a5Z59913zX333WckmRo1auRoSpyVf0tuVJjyuWbNGucxNWjQIOfYgwYNcr6/Zs2aTLfv0aOHc5tKlSqZf/7zn2bx4sVm/vz5JjAw0BQrVsxIMs2bNzcRERHZxpORwpDPYcOGOae/eHh4mGeeecZMnTrVfPjhh2bKlCmmefPmzji6du1qzp8/n+U+c3ymFRYWlu3fxYyO05txfFqBAh8F1rVr18y8efNMq1atTNmyZU2JEiXMnXfeaUaPHm1Onz6d5bYxMTGmffv2plSpUjn6hd+3b5959tlnTfXq1Y2Xl5epUKGC6dq1q1m9enWuYk59FiCrn+PHj6fb9s8//zTvvfee6dmzp6lVq5YpUaKE8fT0NLfddpvp1KmTeeeddzI8W5BThSWf58+fN0uWLDFPP/20qV+/vilVqpRxd3c3JUuWNDVr1jSPPfaYWbZsmYmPj8/RfkdFRZlx48aZevXqmZIlS5oyZcqYe++918yaNSvDr5NzqrDk05j/nYF35bhM4crx6Wruf/31V1O7du0sv7JPbf369aZ79+6mYsWKxsvLy1SrVs307dvX/PzzzznKjzHXP5xPmzbNNG3a1Pj6+hpfX1/TuHFj869//StXZ+Cs+luSkcKSz5QznFn9ZPet5v79+82bb75pOnXqZCpXrmyKFStmvL29TdWqVc1jjz1mVq5caRITE3MUT2YKQz7DwsLM9OnTTbdu3UyNGjVMyZIljbu7uyldurRp2LChefbZZ82mTZtyvM8cn/+T+tul3B6nN+P4zCuHMdncFgEAAABAocFtMgEAAAAbocAHAAAAbIQCHwAAALARCnwAAADARijwAQAAABuhwAcAAABshAIfAAAAsBEKfAAAAMBGKPABAAAAG6HABwAAAGyEAh8AAACwEQp8AABugp49e8rhcKT7CQ4OvtWhAbAZj1sdAACg4AgPD1eNGjUs7TMkJETt27e3tM/C6O6771Z8fLwkaffu3Tp//vwtjgiAXVHgAwCcPD09VatWLefrEydOKDEx8RZGZB9vvPGGc7l9+/batm3bLYwGgJ1R4AMAnG6//XYdPXrU+bp69eo6ceKEJOmxxx5TUFBQjvtq3769/vjjD8tjBABkjQIfAJAjvr6+ql27do7be3jwvxgAuBW4yBYAAACwEQp8AAAAwEb4/hQAkC8OHjyo5ORkFS9e/FaHAgBFCgU+ACBflChRwqXtrl69qi1btuj48eO6ePGiKlasqLZt26a5u09uHDt2TDt37lRkZKQSExNVoUIF3X333WrYsKFL/UnSoUOH9OOPPyoyMlJJSUkqW7as7rzzTjVt2lS+vr4u9xsTE6NNmzbp2LFjcnNzU61atdShQweVKlXK5T4BFD0U+AAAS6S+h74xJsM2/fr109KlS9O9P2HCBE2cOFHvvPOOJk+erOjo6HRtOnXqpPnz56tOnTo5iuerr77SuHHjtGfPngzX16hRQxMnTlTv3r3l5pb9jFVjjFasWKEJEybo8OHDGbbx8PBQ27Zt9cwzz6hXr165+pDz9ttva8qUKYqJiUnzfqlSpfTWW2/phRdeyHFfAIo2CnwAwE3TsGFDdenSRZK0b98+nT592rlu2LBhWrBggQYMGKCHHnpIZcuW1fHjx/XBBx9ox44d2rx5s+655x5t2rRJzZs3z3SM5ORkvfTSS5o9e7YkqWLFinrhhRfUvHlzeXh4aN++fXr//fd1+PBhBQYGau3atfrPf/6T5VSihIQE9e/fX5988omk6x8Onn/+eTVp0kTFihXTsWPH9Omnn2rjxo0KCQlRSEiIPvjgA+3atStHeRk5cqTeffdd1axZU+3bt9fVq1f1ww8/6Pz584qJidHQoUPl7e2tAQMG5Kg/AEWcAQAgE9WqVTOSjCQTGBiYZdvjx4872+ZEYGCgs33Tpk1N8eLFzfbt29O1S05ONsOGDXO29ff3N5GRkZn2+8orrzjbNmrUyERFRaVrc+XKFdO1a1dnu549e2YZ61NPPeVs27FjR3Pp0qUM282ZM8fZrnHjxpn2165dO2e7Xr16meLFi5sVK1akaXP58mXzxBNPONuVL1/eXL16Ncs4AcAYYyjwAQCZSl3g5+YnJ1IX+JJMUFBQpm0TExPNXXfd5Wzbp0+fDNtt3rzZOBwOI8m4u7ub3377LdM+o6OjTZkyZZx9Llq0KMN2S5YscbYpVapUhh8YUuvevXuuCnxJZuHChRm2O3funPH29na2++9//5vl2ABgjDHcJhMAkCOVK1dWly5dMv25//77Xe7by8tLzz//fKbr3d3d9eKLLzpff/LJJ/rzzz/TtXvzzTed8///9re/6c4778y0z7Jly2rgwIHO11OmTFFSUlKaNsnJyZo8ebLz9cCBA+Xv75/lvgwePDjL9TeqXLmynn322QzX+fn5qWXLls7XoaGhueobQNHEHHwAQI507txZwcHBma5PfZFtbrVs2TLbO8U8/PDDzuXExEStXr1aQ4cOdb534sQJbdu2zfm6e/fu2Y77yCOPaMaMGZKux79161Z16tTJuf7bb79VWFhYrvps27at2rVrl+On/nbp0iXLi3zr1KmjrVu3SlKaaxYAIDOcwQcA3HJZnWlPERAQkOYWlDdewLp9+/Y0rxs1apRtnze2ubGP1B8Yctpn6dKltXXrVi1evDjbtpJ01113Zbnez8/PuRwbG5ujPgEUbRT4AIBbrmzZsjlqV7VqVedy6jPrkvT777+neR0QEJBtf2XKlEnzoeHG21+m7rNkyZIqV65cjuLMjTJlymS53tvb27l84xQiAMgIBT4A4Jbz8vLKUbuSJUs6l8+fP59m3blz59K89vHxyVGfqdvd2Efq+/GnHttKnp6e+dIvgKKLOfgAAEtUr1490wdc2YHD4bjVIQBAjnAGHwBwy129ejVH7S5fvuxcvnFaz43TZ3I6Xz11uxv7SP2a+e8ACgsKfADALXfjdJvMnDx50rlcq1atNOvuuOOONK8jIiKy7e/ChQu6dOmS83XdunUz7fPy5ctppuwAQEFFgQ8AyBe7d+/W3LlztXnz5mzbHjp0KNs2J06cSFOMt2rVKs36G+/Dv3fv3mz7vLFNu3btsnydkz7Pnz+v999/X++//366C4EB4GagwAcA5IuvvvpKw4cP17Jly7Jtu2vXrjTFe0a+/vpr57Knp6cee+yxNOurVaum9u3bO19/+eWX2Y67bt0653L16tXTFfRt2rRJcz/79evXZ9vnF198oSFDhmjIkCHM2wdwS1DgAwBuufj4eC1cuDDT9UlJSZo9e7bz9dNPP62KFSuma/fGG284i+rPP/883a0zUzt//nyae9WPGzdO7u7uadq4ubnp9ddfd75evHhxltN0kpOTNWfOHElSx44dVbNmzUzbAkB+ocAHAOTIpUuXdPTo0Rz/5Ga+eq1atfTGG2/ou+++S7fOGKOXXnpJv/32myTJ399fM2fOzLCfDh066OWXX5Z0/UPBk08+qbNnz6ZrFxcXp3/84x+6cOGCJKlnz54aOHBghn327dtXTz31lKTrc/affvrpNBf7po5z1KhR+vnnn+Xh4aGgoKDsdxwA8gG3yQQAOP3xxx/q1KlTmtcpVq9erdWrV+fLuL1799ahQ4fUoUMHDRgwQA899JDKlCmj8PBwLV68WDt27JAklSpVSuvXr1f58uUz7evtt9/WtWvXNHv2bO3bt08NGzbUCy+8oHvvvVceHh7at2+f5s+f73yoVc+ePfXxxx9nGd/SpUvlcDj06aefauPGjWrUqJGGDBmixo0by9PTU7///rs+/PBD7d69W25ublq4cKHuueeeNH0sX75cy5cvl5R2Lv/MmTP16aefOsepUKGCIiMjFRgYKEk6evSos+3GjRvVtWtXZ8569+6do/wCKGIMAAD/3/Hjx40kS38CAwMzHCswMNDZZsKECSYpKclMnDjR+Pj4ZNhPp06dzOHDh3O8L19++aVp0qRJpnHVqFHDLFmyxCQlJeWov+TkZPPxxx+bunXrZtrnPffcY7Zv357h9hMmTMg2V8ePH8/xv8OECRNynAsARYvDGBs/lQQAUGD169dPS5culSRNmDBBEydOlCRduXJFW7Zs0YkTJxQTE6OKFSuqbdu2aS52zY1jx47p+++/15kzZ5ScnKzbbrtNd999txo1auRy7AcPHtSPP/6ov/76S8nJyapYsaJatGihO++80+U+AcAqTNEBABQoJUqUUPfu3S3rr2bNmpZf7FqvXj3Vq1fP0j4BwCpcZAsAAADYCAU+AAAAYCMU+AAAAICNcJEtAOCmmTlzpjZu3ChJ2rdvn06fPi3p+n3wUy6i5faPAJA3XGQLALhp9u3bpw0bNqR7PywsTGFhYZKkli1b3uywAMBWOIMPAAAA2Ahz8AEAAAAbocAHAAAAbIQCHwAAALARCnwAAADARijwAQAAABuhwAcAAABshAIfAAAAsBEKfAAAAMBGKPABAAAAG/l/XNMVK8LK6/EAAAAASUVORK5CYII=", + "text/plain": [ + "<Figure size 800x600 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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AG5YH/IiICO3du1f/+Mc/VKJECX3yySfq1KmT6tWrp+joaJ07d87qVQIAAAD4P7lym8zw8HBNmzZNR48e1YIFC9S8eXPt3btXY8aMUaVKlfTII49o8+bNubFqAAAAoECzNOBHRUWpX79+rvdBQUEaMGCANm7cqK1bt2rw4MHy8/PTggUL1LJlSzVv3lzz58/X5cuXrSwDAAAAKLByNeCn16RJE82dO1fHjh1TdHS06tatqy1btmjo0KGqWLGiRo8erd27d1tZDgAAAFDg3PAn2RYvXlx///vf9dtvv2nt2rXq3bu3zp8/r5kzZ6phw4bq0KGDPvroI6WkpNzo0gAAAIB874YH/DTnz5/Xr7/+qu3bt7u+Z4zR+vXr1adPH1WpUkUTJkzQ4cOHb1aJAAAAQL5zwwP+1q1bNXToUFWqVElPPvmk9u7dK4fDIWOMJKly5cqKjIzUH3/8oSlTpig8PFwPPvigfv/99xtdKgAAAJDvWBrwa9asqcGDB2f6fmJiohYuXKgWLVq4Lqy9dOmSjDEyxsjhcOiee+7RZ599ppiYGK1evVoHDhzQc889p7Jly+qjjz5S06ZN9cknn1hZLgAAAGA7lgb82NhYnTx50vU+7X74abfG/Pnnn12hXpLKlSunZ599VgcOHNCKFSt03333yc/vaknVq1fXlClTdOjQIc2YMUOSNHToUCUkJFhZMgAAAGArAVYPmJycrE8++URz5szR2rVrJckV6NNERETo0UcfVa9evRQYGJjteIGBgXr88ceVlJSksWPHauXKlXrggQesLhsAAACwBcsD/po1a7RmzRpJGYN9yZIl1b9/fz366KOqV6+e1+PefvvtMsZw0S0AAACQDcsDvpQx2Ddv3lwjRoxQnz59FBwc7POYv/zyiyS5TuEBAAAAkJnlAd8Yo6JFi+pvf/ubHn30UTVt2vS6xtu9e7emTZum9957Tw6HQxUqVLCoUgAAAMB+LA/4zZs317fffquQkBBLxtu6dasWLFggSXI4HGrevLkl4wIAAAB2ZHnADw0NtSzcS1L37t21e/du1/vw8HDLxgYAAADsxtKAv2DBAlWuXNmrZU6cOKGffvpJDRs2VK1atTK1h4SEqG7dulaVCAAAANiapVesHjp0SKdOnfJqme3bt+svf/mL6tatq8jISB09etTKkgAAAIACxdKAP3HiRC1evNirZYKDg1W4cGEZY7Rhwwb16tXLypIAAACAAuWm33MyIiJCFy5c0ObNm1W7dm1t2bJFGzduvNllAQAAAPnSTQ/4aZo1a6bnn39exhht2bLlZpcDAAAA5Et5JuBLUvny5SVJ58+fv8mVAAAAAPlTngr4X3zxhRwOh4oVK3azSwEAAADyJZ9ukzl48OAs23bs2JFt+7WcTqcuXLig3bt3a8+ePZKkBg0a+FIWAAAAUOD5FPAXLlwoh8Phtu348eNatGiRT8UYY1StWjVFRkb6tDwAAABQ0Pn8oCtjjFff90TNmjX10Ucfyd/f3+cxAAAAgILMp4AfFRXl9vuTJk1SrVq11K9fP88LCAhQqVKldOutt+rOO++Un1+euiwAAAAAyFdyJeBn1Q4AAAAgd3G4HAAAALARn8/Bd2ft2rUqXbq0lUMCAAAA8IKlAT8iIsLK4QAAAAB4KU+donPPPfcoIMDSvzkAAACAAiVPBXzp+m6zCQAAABR0lh4uP3z48HUtf/nyZYsqAQAAAAomrwL+qlWrNHToUDmdTs2ePVvdunXL0F69evUsn3DrCWPMdS0PAAAAFHReBfyBAwfq5MmTMsZo+PDhOnbsmNt+vpxmQ7AHAAAArp9X5+AXLVrUFd6LFCnito+v59Bz7j0AAABw/bwK+O+//77uvPNOtWrVSu+++67bPl26dJHT6fTpq3PnzpZsFAAAAFBQeXWKzu23367//e9/uVULAAAAgOuUp26TaYzhVB0AAADgOlh6m0yn03ldy69cudKiSgAAAICCybKAf+bMGfXu3TvD955//nlFRkZatQoAAAAAObAs4G/dulXr1q2Tw+FwnWbz6KOPWjU8AAAAAA9Ydg7+/v37Xa/vuecerV27Vvfee69VwwMAAADwgGVH8M+dOydJqlSpkpYtW6ZChQpZNTQAAAAAD1l2BD80NFSS1LRpU8I9AAAAcJNYFvDr1KkjSUpMTPR5jLi4OB0+fNiqkgAAAIACx7KA365dO1WtWlU//fSTkpKSfBqjf//+qlmzplUlAQAAAAWOZQHf4XDoP//5jxISEvTPf/7T53F40BUAAADgO0ufZNurVy/Nnj1br7/+uoYNG6Y//vjDyuEBAAAA5MDSJ9m+8MILkqT7779f8+bN08KFC9WqVSvdeuutKlWqlAIDA7NdPv2tNgEAAAB4z9KAP3HiRDkcDtf7lJQUfffdd/ruu+88Wt4Yk2F5AAAAAN6xNOBLmc+h55x6AAAA4MaxPODXqFFD7dq182nZVatW6cSJExZXBAAAABQclgf8Fi1aaMGCBT4te8899xDwAQAAgOtg6V10AAAAANxclh7Bf+KJJ9SsWTOflx8zZoz69OljYUUAAABAwWJpwH/11Veva/mOHTtaVAkAAABQMHGKDgAAAGAjeSrgjxo16rpO8QEAAAAKujwV8A8cOKBt27bd7DIAAACAfCtPBXwAAAAA18fSi2xr1qx5XcufPHnSokoAAACAgsnSgB8bGyuHw+Fxf2OM67XD4ZAxxqvlAQAAAGRk+ZNspYzB3RNp4R4AAADA9bE84Ddv3lxTp07Ntk9SUpLOnj2r3377TcuXL9e2bds0bNgw9e3b1+pyAAAAgALF8oBfunRpRUREeNx/0qRJ+vTTTzVw4EBdvHhR7777rtUlAQAAAAWGpXfRKVSokAoVKuT1cj169NCiRYv03nvvafz48VaWpNOnT2vChAlq2LChihUrptKlS6tVq1Z67bXXdOXKFUvXdeDAAUVERMjhcCgyMtKrZY0x+uSTT9SzZ09VqVJFwcHBKlKkiMLDw/XQQw9p/fr1OY7hcDg8+urWrZuPWwgAAIC8ztKAn5iYqE8//dSnZe+//35VqVJF0dHROn36tCX1bNq0SY0aNdLkyZNVsWJFTZ06VePGjdOFCxf0xBNPqGXLljp+/Ph1r8cYo1mzZqlx48basGGD18tfuHBBXbp0Ue/evfXZZ5+pbdu2mjp1ql544QXVr19f7733niIjIzV8+HA5nc7rrhcAAAD2lSsX2fqqVq1aOnr0qNasWaO//vWv1zXW4cOH1a1bN8XFxWn06NF69dVXXW2PP/647rnnHq1du1bdu3fX999/r6CgIJ/Wc+DAAQ0ePFgbNmxQ586d9fXXX3s9xtChQ/XNN99IkpYvX657773X1fbUU09pzpw5euyxx/TWW2+pSpUq+uc//5nlWCNHjtSoUaOyXV+xYsW8rhEAAAD5Q5560FVCQoIk6dChQ9c91tNPP624uDhVrVpVL730Uoa2oKAgzZ07V/7+/tqyZYtmzZrl0zouXryoxo0ba9u2bZo7d65Wrlzp9RhHjhzRkiVLJEk9e/bMEO7TjBgxQk2bNpUkTZs2TcnJyVmOV6ZMGdWrVy/br8qVK3tdJwAAAPKHPBPwT5w4oV9//VWSfD6anmbfvn366KOPJEn9+/d3O154eLjat28vSZo6dapSUlK8Xk9ycrLatGmjXbt2aciQIT7VunnzZtfrFi1aZNnvjjvukCSdP39eu3fv9mldAAAAsL88EfD37Nmj3r17u45M33LLLdc13scff+y6r37Hjh2z7NepUydJUlxcnNatW+f1ekqUKKGVK1de1xHx9Bf6Fi5cOMt+RYoUcb2+ePGiz+sDAACAvVl6Dv7gwYM97puUlKQzZ87o999/z3BKTrly5dSuXbvrqmPNmjWu102aNMmyX9ppL2nLZPfHgDtWPHW3cePGrtfZHZlPa3M4HAoPD/dobKfTqUuXLnHOPQAAQAFiacBfuHChT6E37Wi7w+HQzJkzr/sUnZ07d0qSQkJCVKJEiSz7ValSxfV6165d17VOX91yyy2677779MUXX+jdd9/V+PHjM30isGXLFtdFuD179lS5cuWyHO/w4cMaPny4Vq1apaNHjyo5OVmFChVSs2bN9OCDD2rYsGHZflIAAACA/C1XTtExxnj1JUmNGjXSV199pd69e1/XupOSknTy5ElJUlhYWLZ907fHxsZe13qvx3vvvad+/frp4sWLatmypd555x3t3btXu3bt0uzZs9WxY0c5nU516NBBb731VrZjLViwQCtWrNCAAQP04YcfatmyZRozZox2796t0aNHq0mTJvr9999v0JYBAADgRrP8Npnh4eHq169fjv38/f1VtGhRVaxYUbfddptq165tyfrT7sQjScHBwdn2TX8kO/1yN1pISIgWL16sQYMG6amnntKAAQMytLdr106PPvqoHnzwQfn5Zf832Z133qkVK1Zk+OSiR48eGj58uNq0aaO9e/eqc+fO2rJli0JDQ7Md68yZMxkuPg4KCrruT1fygpSUFDmdTp8urEZmzKe1mE9rMZ/WYj6txXxay+7z6c12WR7wa9WqpaioKKuH9djly5ddr3N6qm769kuXLuVaTTmJj4/Xk08+qXfeeUchISEaN26c6645P/30k+bMmaNXXnlFDodDffr0yXKcmJgYhYWFuT0Fp1q1aoqOjlbv3r116NAhTZ48OcOzAdypUaNGhvcDBgzQwIEDvd/APMbpdOrw4cOSlOMfTMgZ82kt5tNazKe1mE9rMZ/Wsvt8enOTlTz1oCsrpA+36e9Q40769vR3qbmRkpKS1KlTJ23atEnly5fXDz/8kCFY33///XrkkUfUqlUr9e3bV999912W9+2vXr16tuvq0aOHQkND9eeff2rhwoWaPn16tj8AMTExKl68uOu9nY7gS1Lr1q0VEGC7H4Ebjvm0FvNpLebTWsyntZhPa9l9PuPj4z3ua+nWL1u2LMfz3nNbSEiI63ViYmK2fdMf7U+/3I00Y8YMbdq0SZI0efLkTEfNpaunPU2ePFnDhw/X66+/rrZt2+rBBx/0el3+/v5q0qSJVq9erXPnzmn37t1q0KBBlv1Lly6dIeDbiZ+fnwICAmz5C+BmYD6txXxai/m0FvNpLebTWnaeT2+2ydLPL+6//361bNnSyiG9FhQUpPLly0uSTp06lW3f9O05Hf3OLe+9957rdY8ePbLs16tXL9drX5+8KynDHXjSLkYGAACAfVzXnzdXrlzR+vXrtW/fPiUkJKhMmTJq1qxZtveevxEaNmyokydPKiEhQefPn8/yVplHjx51vc7uSHZu2rt3rySpWLFi2V70WqZMGRUtWlQXL17Utm3bfF6f0+l0vfb39/d5HAAAAORNPgV8p9Opl19+WVOnTnV7PlCdOnU0ffp03XvvvdddoC86dOigb7/9VpK0bds2RUREuO23devWDMvcDGnnwKfdLjQ7aeE8NTU1U9usWbNUvHhx9e/fP9sx0h+1r1ChgjelAgAAIB/w+hSd5ORk9ezZU+PHj9f58+cz3Ms+7fXvv/+u++67T9OnT7e8YE/07t3b9cCt1atXZ9kv7Y+AMmXKKDIy8kaUlknNmjUlXb0yOrtTZk6cOOG6ZqBq1aqZ2qdNm6YXX3wx23UlJSXpl19+kSSVLVtWderU8bVsAAAA5FFeB/znnntOX3zxhSRleGpt+iPQDodDxhiNHTvW9QTWG6l27dp64IEHJEnvvvuu27vpHDx4UGvWrJEkjR07NtOFCzt37lSdOnVUuXJlbdiwIddqve+++1yvP/rooyz7ffDBB67XXbt2ddtn7969OnjwYJZjvP/++65PXIYOHerTU4cBAACQt3kV8GNiYhQdHe0K8FWrVtVjjz2mGTNmaO7cuZo6dar69u3ruuWk0+nUk08+mSuF5+SVV15R2bJlFRsbq3HjxmVoS0pK0rBhw5SamqpmzZpp1KhRmZafMmWK9u3bp2PHjunZZ5/NtTrHjBnjuig4KipKO3fuzNRn+/btmjhxoiQpNDRUTz/9tNuxjDEaNGiQ24d2bdu2TU899ZQkqV69eho7dqxFWwAAAIC8xKtz8N9++205nU75+flp6tSp+sc//uH2Pupnz57VI488ok8//VR79uzRhg0b1K5dO8uK9kTVqlX1xRdfqGfPnpo+fbp27typ7t276/Lly1q0aJF27NihJk2a6PPPP3f7xNv0F6Nmd3789u3btX379kzfP3XqlBYvXux636lTJ7e3EA0NDdXXX3+tXr166cCBA2revLn69evnetDVpk2btHjxYiUlJalatWr65JNPXH8QpNe4cWMdOnRIGzZsUN26ddWvXz/Vq1dPycnJ2rhxo/773//qypUrat68uT755BPb3v4SAACgoPMq4K9fv14Oh0Njx451HQ12p1SpUvroo4/UqlUrbdmyRevXr7/hAV+SWrRooe3btys6OlrLli3TM888o8DAQNWpU0fR0dEaMWJElk+7HT9+vLZu3arLly9r6tSpWa5j6dKlmjRpUqbv79mzRw8//LDr/dq1a7N8RkCjRo20fft2LV68WJ999pm++uor1x8HpUuXVmRkpLp3767+/furWLFibsf47LPPtGvXLi1dulTr16/X+++/r9OnT8vPz09ly5ZVt27d1KdPH/Xq1Yu75wAAANiYVwF/3759CggI8OiUFX9/fz377LPq3bu39u/f73OB16tMmTKaPHmyJk+e7NVyjRo10r59+3LsN3HiRNfpM9ejSJEiGjZsmIYNG+bzGA0aNFCDBg00YcKE664HAAAA+ZNX5+CfPXtWDRs29Pipr23btpUknTt3zuvCAAAAAHjPq4CfnJyssmXLety/TJkyruUAAAAA5D6vb5Pp7qLarHh7G8aBAwcqPDzc25IAAAAA/B+vA35uOnXqlGJjY292GQAAAEC+lacCPgAAAIDrQ8AHAAAAbMSr22RK0tdff+31fdR9WQYAAACA93w6gm+M8fgr7UJbT/sDAGCV+KPxOvvLWcUfjb/ZpQDADZPrp+gQ3AEAN8PWt7dqVvgsbf/Hds0Kn6Wtb2+92SUBwA3h9Sk64eHh6tevX27UosWLF+vgwYO5MjYAoOCIPxqv5cOWyzivHmAyTqPlw5erVudaKl65+E2uDgByl9cBv1atWoqKisqNWrRx40YCPgDguv25709XuE9jUo3O7D9DwAdge9xFBwBgO6G1Q+Xwy/iwRYe/Q6Vrlb5JFQHAjeNVwG/Xrp0aNWqUW7Xo1ltvVbt27XJtfABAwVC8cnF1e6ubHP5XQ77D36Fub3bj6D2AAsGrU3TWrVuXS2Vc9fLLL+fq+ACAguO2R25T9buqa8OnG9SuRzuVrs7RewAFA6foAABsq3jl4irZpCRH7gEUKAR8AAAAwEYI+AAAAICNEPABAAAAGyHgAwAAADZCwAcAAABshIAPAAAA2AgBHwAAALARAj4AAABgIwR8AAAAwEYI+AAAAICNEPABAAAAGyHgAwAAADYSkJuDO51ObdmyRT/99JOOHDmic+fO6c0333S179ixQzVq1FCxYsVyswwAAACgwMiVgJ+cnKxp06Zp9uzZOn78eIa29AH/5Zdf1ieffKJHHnlEEydOVGhoaG6UAwAAABQYlp+ic/ToUd1xxx365z//qePHj8sYI0mu/14rMTFRs2fPVpMmTbRt2zarywEAAAAKFEsDfkJCgrp06aLt27fLGJNjuJ8yZYqmTJmicuXK6dixY+rSpYtOnTplZUkAAABAgWJpwH/55Zf122+/yRijli1basGCBfr999+VkJCgzp07Z+pftWpVPffcc9q9e7fat2+vP/74Q88++6yVJQEAAAAFimUBPzU1VTNnzpTD4VBUVJR++OEHDRgwQLVr11bRokWzXbZkyZL69NNPVbFiRX3wwQeKj4+3qiwAAACgQLEs4G/cuFHx8fGKiIhQVFSU18uHhIRo6NChSkpK0o8//mhVWQAAAECBYlnA37NnjxwOhx5++GGfx2jVqpWMMdq3b59VZQEAAAAFimUB//Tp05Kk8PBwn8coXLiwpKsX6wIAAADwnmUBPzg4WJJ0+fJln8c4evSopKun6wAAAADwnmUBv2LFijLGaN26dT6P8emnn8rhcKhKlSpWlQUAAAAUKJYF/IiICDkcDr3++uvav3+/18t//fXX+vjjj+Xv76/IyEirygIAAAAKFMsCfrly5XT33XfrwoULateunT7//HOPlktJSdGMGTPUs2dPSVL37t1VokQJq8oCAAAACpQAKwebOnWq1qxZo1OnTqlnz56qVauWunbtqiZNmrguwl21apUSEhJ0+PBh/fLLL/ryyy915swZGWMUHBysF1980cqSAAAAgALF0oDfqFEjzZ8/X/3795ck7d+/XzNmzHC1G2PUpUuXDMsYYyRJDodD77zzjurUqWNlSQAAAECBYtkpOmn69eunZcuWqXTp0jLGuAK8dDXEp/9e2n/LlCmj5cuXq3fv3laXAwAAABQolgd86ep59Hv27FFUVJSqV6/uCvXpw74xRjVq1NCkSZP0+++/65577smNUgAAAIACxdJTdNILDQ1VVFSUoqKidPToUe3Zs0dnzpxxtdWtW1eVK1fOrdUDAAAABVKuBfz0KleuTJgHAAAAboBcOUUHAAAAwM1hacCvWbOmBg8ebOWQAAAAALxgacCPjY3VyZMnrRwSAAAAgBc4RQcAAACwEcsvsl2/fr1q1qzp8/IBAQEqVaqUqlevroiICPXt21elSpWysEIAAADAviwP+ImJiYqNjc3wPYfD4bZv+vviX9vv559/1scff6znnntOkyZN0ujRo60uFQAAALAdy0/RSQvt6cN6+gdduXvoVVpfd30SEhI0ZswYPfnkk1aXCgAAANiOpUfw165dq8TERI0bN06//PKLWrZsqbvvvlu33HKLSpYsqeDgYElSUlKSzp49qz179mjVqlX68ccfVatWLU2fPl3FixfXhQsXdPz4cW3cuFGffPKJ4uPj9dprr6l9+/bq3r27lSUDAAAAtmJpwL/jjjvUtm1bXbx4URs3btQdd9yR4zJRUVHavHmz+vfvr2eeeUYbN25UiRIlJElDhw7Vyy+/rL/97W9atWqVJk2aRMAHAAAAsmHpKTpPPPGEjhw5og0bNngU7tM0b95c69at05kzZ/TYY49laAsNDdWyZctUqVIlbdu2TTExMVaWDAAAANiKZQH/zJkzeueddzRq1CiVK1fO6+XDwsI0cuRIffTRR4qLi8vQVqRIEQ0aNEiS9NNPP1lSLwAAAGBHlgX8tWvXKjk5WS1atPB5jFatWiklJUXr1q3L1NaiRQsZY3Tq1KnrqBIAAACwN8sC/tGjRyVJgYGBPo8REHD1koBjx45laks7L//ixYs+jw8AAADYnWUBPyUlRZK0fft2n8f49ddfJUlOpzNT259//ilJKly4sM/jAwAAAHZnWcCvUqWKjDGaOXOmT0fZL1y4oJkzZ8rhcKhKlSqZ2n///Xc5HA6VL1/einIBAAAAW7Is4Hfq1EmBgYGKiYlRly5dXKfseOLIkSPq0qWLYmJiFBgYqE6dOmVoT01N1fz58yVJderUsapkAAAAwHYsC/ilSpXSoEGDZIzRDz/8oFq1aqlfv35avHixfv31V505c0ZJSUm6cuWKzpw5o19//VWLFy9Wv379VKdOHf34449yOBwaMmSISpYs6Ro3Li5OvXv31t69e1WkSBE1btzYqpIBAAAA27H0QVcvv/yyvvvuO/3222+6cuWKlixZoiVLluS4nDFGktSoUSO99NJLru+/9957GjRokFJTU+VwONShQwfXhbgAAAAAMrP0QVfFixfXunXrdOedd0r6/8HdGOP2K32fiIgIrV69WsWKFXONd+XKFVWqVElVq1ZV1apVNWTIECvLBQAAAGzH0oAvSWXKlNH//vc/vfHGG6pfv74rwLtjjFGDBg00b948rV27VqGhoRnaBw0apJiYGNfXfffdZ3W5AAAAgK3k2vkuw4YN07Bhw7Rr1y5t3LhR+/fv17lz5yRJJUuWVK1atdSqVSvVr18/t0oAAAAACpxcP6G9QYMGatCgQW6vBgAAAIBy4RQdAAAAADdPngr4//nPfzR48OCbXQYAAACQb+WpgL9q1SotWrToZpcBAAAA5Ft5KuADAAAAuD4eX2R7I06d2bFjR66vAwAAALAzjwP+woUL5XA4crMWGWNyfR0AAACAnXl9m8zsHlyVXlpQz6m/p/0AAAAA5MyrgB8eHq5+/frl2G/x4sU6cOCAgoKC1KJFC91yyy0qWbKkgoODJUlJSUk6e/as9uzZo59++kmJiYlq1qyZunXr5ttWAAAAAJDkZcCvVauWoqKismw3xuihhx7S8ePHNXXqVD366KMKCQnJdswLFy7ojTfe0KRJk9SqVSu99tpr3pQEAAAAIB1Ln2T773//Wx9//LHWrFmjO++806NlihUrpqeeekqtW7dW+/btVaNGDT355JNWlgUAAAAUGB7fJvOVV17RkCFDsmxPSkrSyy+/rEceecTjcJ9e69atNXjwYE2ePFnJycleLw8AAADAi4A/ZswY9erVK8v21atXKz4+Xl26dPG5mHvuuUdnz57V6tWrfR4DAAAAKMgse9DVoUOH5HA4VKpUKZ/HKFmypCQpJibGoqoAAACAgsWygB8fHy9JOnjwoM9jxMbGSpISEhKsKAkAAAAocCwL+OXLl5cxRgsWLPB5jHnz5snhcCgsLMyqsgAAAIACxbKA37ZtW0nS//73Pz355JNePbjKGKPRo0fru+++kyS1a9fOqrIAAACAAsWygF+zZk116NBBxhi99tpruvXWWzVv3jwdPXo0y2WOHj2quXPnqlGjRpo5c6YcDoc6duyoGjVqWFUWAAAAUKBYeh/82bNnq3nz5rpw4YJ2796t4cOHS5JKlCihypUrq2jRonI4HLpw4YKOHDniOm9funoUv0SJEpo9e7aVJQEAAAAFiqUBv06dOvr666/VrVs3nTlzRg6HQ8YYnTt3TufOnZPD4ZCkDKfvpPUpU6aMVqxYofDwcCtLAgAAAAoUy07RSdOyZUvt2rVLffr0kb+/f479/f399dBDD2nXrl1q3ry51eUAAAAABYqlR/DThIWF6f3339f06dO1dOlSbdy4Ufv379e5c+ckXb3ffa1atdSqVSv16tVL5cuXz40yAAAAgAInVwJ+mgoVKmjkyJEaOXJkbq4GAAAAwP+x/BQdAAAAADcPAR8AAACwEQI+AAAAYCNenYO/YcMGt99v27at6xaY3lizZo0WL16c4XsOh0Nvv/2212MBAAAA8DLgR0ZGZgryDodD8fHxKlKkiNcr3717txYuXJhpPCsD/unTpzVjxgwtW7ZMsbGxKlSokOrWrau+ffvq0UcfVaFChSxb14EDBzR48GBt2LBBERERWrduncfLGmO0dOlSLV68WD///LPi4uLk5+enChUqqFWrVho6dKgiIiI8Gmvnzp2aMWOGVq9erRMnTqhkyZK67bbbNHToUPXo0cO3jQMAAEC+4NNddCpWrKiAgP+/qJ+fb2f63HHHHYqKipIknTlzRjNnzvRpnKxs2rRJPXr00IkTJ9SpUyeNGDFCly9f1qJFi/TEE09o4cKFWr58uSpWrHhd6zHG6PXXX9ezzz6rixcver38hQsX9Je//EXffPONHA6H+vTpoxYtWig5OVnr16/Xe++9p/fee0/Dhg3TnDlzsp3vt99+WyNHjlRqaqr69++vFi1a6PDhw3rrrbf05Zdf6m9/+5sWLVqU4d8PAAAANmK84HA4jJ+fnzlw4IA3i3lk//79rvGtcOjQIVO2bFkjyYwePTpDW2Jiomnfvr2RZJo1a2YSExN9Xs/+/ftNu3btjCTTuXNnI8lIMhERER6P0adPH9dyK1asyNQ+e/ZsV/u//vWvLMdZvny58fPzM5LMsmXLMrTFxsaaChUqGElm5MiR2dZz/vx5I8mcP3/e423IT5KTk83atWtNcnLyzS7FFphPazGf1mI+rcV8Wov5tJbd59ObfGbpRbb+/v7Zft3Io8ZPP/204uLiVLVqVb300ksZ2oKCgjR37lz5+/try5YtmjVrlk/ruHjxoho3bqxt27Zp7ty5WrlypddjHDlyREuWLJEk9ezZU/fee2+mPiNGjFDTpk0lSdOmTVNycnKmPklJSXrsscfkdDr14IMPZjoVp1q1anrxxRclSbNnz9bPP//sda0AAADI+ywN+MaYbL+cTqeVq8vSvn379NFHH0mS+vfvr6CgoEx9wsPD1b59e0nS1KlTlZKS4vV6kpOT1aZNG+3atUtDhgzxqdbNmze7Xrdo0SLLfnfccYck6fz589q9e3em9nfffVeHDx+WJA0dOtTtGH369FFISIiMMZoyZYpP9QIAACBvszTgx8TEKCYmRgcPHpQkRUREuL6X9nUjfPzxxzLGSJI6duyYZb9OnTpJkuLi4ry6IDZNiRIltHLlSlWuXNmnOiXpypUrrteFCxfOsl/6i5jdneef9gdNoUKF1LZtW7djBAcHq02bNpKklStX6sKFCz7VDAAAgLzL0oBfrVo1VatWTdWrV5d0NbCmfS/t60ZYs2aN63WTJk2y7Jd22su1y3jKl1uDXqtx48au1+6OzF/b5nA4FB4enqEtNTXVdQvT+vXrZ3tnoLRtTkxM1A8//OBz3QAAAMibbPmgq507d0qSQkJCVKJEiSz7ValSxfV6165duV6XO7fccovuu+8+SVdPszl69GimPlu2bNE333wj6ep5+uXKlcvQvn//fiUmJkrKuE3u5IVtBgAAQO6xXcBPSkrSyZMnJUlhYWHZ9k3fHhsbm5tlZeu9995Tv379dPHiRbVs2VLvvPOO9u7dq127dmn27Nnq2LGjnE6nOnTooLfeeivT8ulrzy/bDAAAgNxhu5uhJyQkuF4HBwdn2zf9Oe/pl7vRQkJCtHjxYg0aNEhPPfWUBgwYkKG9Xbt2evTRR/Xggw+6vQd+bm3zmTNnMlx8HBQU5PaC5fwmJSVFTqfTpwurkRnzaS3m01rMp7WYT2sxn9ay+3x6s122C/iXL192vc7pKbXp2y9dupRrNeUkPj5eTz75pN555x2FhIRo3Lhxrrvm/PTTT5ozZ45eeeUV10OwrpVb21yjRo0M7wcMGKCBAwfmtDl5ntPpdN1xyNeHtOH/Yz6txXxai/m0FvNpLebTWnafT28epmq7gJ/+CHX6O9S4k749/V1qbqSkpCR16tRJmzZtUvny5fXDDz9kCNb333+/HnnkEbVq1Up9+/bVd999l+m+/bm1zTExMSpevLjrvZ2O4EtS69ateaKvBZhPazGf1mI+rcV8Wov5tJbd5zM+Pt7jvrbb+pCQENfrtAtPs5L+yHf65W6kGTNmaNOmTZKkyZMnZzpqLl29Z//kyZM1fPhwvf7662rbtq0efPBBV3tubXPp0qUzBHw78fPzU0BAgC1/AdwMzKe1mE9rMZ/WYj6txXxay87z6c02+bT1Y8eO9SgQ79ixQ4MHD/ZoTKvOgQ8KClL58uV18uRJnTp1Ktu+6dvTbu15o7333nuu19c+fTa9Xr16afjw4ZKkWbNmZQj46WvPD9sMAACA3ONTwF+6dGmOfRwOh44fP65Fixb5sorr0rBhQ508eVIJCQk6f/58lrfKTH9LygYNGtyo8jLYu3evJKlYsWIKDQ3Nsl+ZMmVUtGhRXbx4Udu2bcvQVqtWLQUHBysxMVFHjhzJdn15YZsBAACQe3y6AsEYk+OXN33T97dChw4dXK+vDcPpbd261e0yN1LaRSCezIHT6ZR09cFW6fn7+6tdu3aSpN9++y3b8/DTtjk4OFitW7f2qWYAAADkXV4fwQ8ICFDFihVzoxYdO3YsU3j1Re/evTV+/HgZY7R69WpFRES47fftt99Kunp0PDIy8rrX64uaNWtq586dunjxok6ePKny5cu77XfixAnX+fNVq1bN1P7AAw/om2++0ZUrV/Tdd9+5/YMlMTFR33//vSSpS5cuKlasmIVbAgAAgLzA6yP41apVU0xMTK58hYeHW7JRtWvX1gMPPCDp6tNh3R3RPnjwoNasWSPp6jUF1164sHPnTtWpU0eVK1fWhg0bLKnLnbSn2ErSRx99lGW/Dz74wPW6a9eumdoffvhhV/CfN2+e2zE+/PBD1xXY48aN86leAAAA5G32u0no/3nllVdUtmxZxcbGZgqzSUlJGjZsmFJTU9WsWTONGjUq0/JTpkzRvn37dOzYMT377LO5VueYMWNcR+2joqK0c+fOTH22b9+uiRMnSpJCQ0P19NNPZ+oTFBSk2bNny8/PT0uWLNHnn3+eof3w4cN67rnnJEkjR45U8+bNLd4SAAAA5AV56h5CVp6LX7VqVX3xxRfq2bOnpk+frp07d6p79+66fPmyFi1apB07dqhJkyb6/PPP3T79Ne1895zq2r59u7Zv357p+6dOndLixYtd7zt16qSwsLBM/UJDQ/X111+rV69eOnDggJo3b65+/fq5HnS1adMmLV68WElJSapWrZo++eSTLE/j6dq1q958802NHDlSvXv3Vv/+/dWiRQsdPnxYb775puLi4tS3b19FR0dnuT0AAADI37wK+DExMQoMDMytWrRmzRolJydbNl6LFi20fft2RUdHa9myZXrmmWcUGBioOnXqKDo6WiNGjMjyya/jx4/X1q1bdfnyZU2dOjXLdSxdulSTJk3K9P09e/bo4Ycfdr1fu3at24AvSY0aNdL27du1ePFiffbZZ/rqq69cfxyULl1akZGR6t69u/r375/jefNDhgxRy5YtFR0drdWrV2vx4sUqWbKkmjVrpmHDhqlnz57ZLg8AAID8zWGsvoUN8r34+HiVKFFC58+ft+WDrlJSUvTdd9+pTZs2tnwQxo3GfFqL+bQW82kt5tNazKe17D6f3uQz256DDwAAABREBHwAAADARgj4AAAAgI0Q8AEAAAAbIeADAAAANkLABwAAAGyEgA8AAADYCAEfAAAAsBECPgAAAGAjBHwAAADARgj4AAAAgI0Q8AEAAAAbIeADAAAANkLABwAAAGyEgA8AAADYCAEfAAAAsBECPgAAAGAjBHwAAADARgj4AAAAgI0Q8AEAAAAbIeADAAAANkLABwAAAGyEgA8AAADYCAEfAAAAsBECPgAAAGAjBHwAAADARgj4AAAAgI0Q8AEAAAAbIeADAAAANkLABwAAAGyEgA8AAADYCAEfAAAAsBECPgAAAGAjBHwAAADARgj4AAAAgI0Q8AEAAAAbIeADAAAANkLABwAAAGyEgA8AAADYCAEfAAAAsBECPgAAAGAjBHwAAADARgj4AAAAgI0Q8AEAAAAbIeADAAAANkLABwAAAGyEgA8AAADYCAEfAAAAsBECPgAAAGAjBHwAAADARgj4AAAAgI0Q8AEAAAAbIeADAAAANkLABwAAAGyEgA8AAADYCAEfAAAAsBECPgAAAGAjBHwAAADARgj4AAAAgI0Q8AEAAAAbIeADAAAANkLABwAAAGyEgA8AAADYCAEfAAAAsBECPgAAAGAjBHwAAADARgj4AAAAgI0Q8AEAAAAbIeADAAAANkLABwAAAGyEgA8AAADYCAEfAAAAsBECPgAAAGAjBHwAAADARgj4AAAAgI0Q8AEAAAAbIeADAAAANkLABwAAAGyEgA8AAADYCAEfAAAAsBECPgAAAGAjBHwAAADARgj4AAAAgI0Q8AEAAAAbIeADAAAANkLABwAAAGyEgA8AAADYCAEfAAAAsBECPgAAAGAjBHwAAADARgj4AAAAgI0Q8AEAAAAbIeADAAAANkLABwAAAGzE9gH/9OnTmjBhgho2bKhixYqpdOnSatWqlV577TVduXLF0nUdOHBAERERcjgcioyM9GiZiRMnyuFwePXVpEkTt2N5uny3bt2s22gAAADkKbYO+Js2bVKjRo00efJkVaxYUVOnTtW4ceN04cIFPfHEE2rZsqWOHz9+3esxxmjWrFlq3LixNmzYYEHl2QsLC8v1dQAAACB/CrjZBeSWw4cPq1u3boqLi9Po0aP16quvutoef/xx3XPPPVq7dq26d++u77//XkFBQT6t58CBAxo8eLA2bNigzp076+uvv/ZpnG+//VaVKlXKts/gwYP1448/auDAgVn2GTlypEaNGpXtOMWKFfOlRAAAAOQDtg34Tz/9tOLi4lS1alW99NJLGdqCgoI0d+5c1a1bV1u2bNGsWbM0ZswYr9dx8eJFNW7cWP7+/po7d66GDBkih8PhU73h4eGqXr16lu1Hjx7Vpk2bVKZMGfXq1SvLfmXKlFG9evV8qgEAAAD5ny1P0dm3b58++ugjSVL//v3dHp0PDw9X+/btJUlTp05VSkqK1+tJTk5WmzZttGvXLg0ZMsSnWqtXr66IiAgFBwdn22/evHlKTU3VgAEDfP60AQAAAPZny4D/8ccfyxgjSerYsWOW/Tp16iRJiouL07p167xeT4kSJbRy5UpVrlzZpzolaeDAgVq3bp3Kly+fZR+n06n58+dLkoYNG+bzugAAAGB/tgz4a9ascb3O6o4zktS0aVO3y3jK19NxvPXll1/qyJEjat++verUqePxck6nUxcuXMjFygAAAJDX2DLg79y5U5IUEhKiEiVKZNmvSpUqrte7du3K9bp89dZbb0ny7Oj94cOHNXz4cNWsWVPBwcEKCQlRUFCQWrdurRkzZujy5cu5XS4AAABuIttdZJuUlKSTJ09Kyvl2kunbY2Njc7Msnx07dkxffvlljhfXplmwYIEqVaqkoUOHqnHjxpKu3i50zpw5Gj16tGbPnq3PP/9cdevWzXGsM2fOZLg2ISgoyBbn/6ekpMjpdPp03QUyYz6txXxai/m0FvNpLebTWnafT2+2y3YBPyEhwfU6pwtXCxcu7Ha5vOTtt99WamqqBg4cqEKFCuXY/84779SKFSsyfHLRo0cPDR8+XG3atNHevXvVuXNnbdmyRaGhodmOVaNGjQzvBwwYkO0tOvMLp9Opw4cPS5L8/Gz5IdYNxXxai/m0FvNpLebTWsyntew+nxcvXvS4r+0CfvpTUHIKxOnbL126lGs1+crpdOrtt9+Ww+Hw6PScmJgYhYWFZfjDJU21atUUHR2t3r1769ChQ5o8eXKGZwNkNV7x4sVd7+10BF+SWrdurYAA2/0I3HDMp7WYT2sxn9ZiPq3FfFrL7vMZHx/vcV/bbX36cHvlypVs+6ZvL1KkSK7V5KuVK1fq8OHDat++vWrXrp1j/+zuoy9dPZIfGhqqP//8UwsXLtT06dOz/Qu3dOnSGQK+nfj5+SkgIMCWvwBuBubTWsyntZhPazGf1mI+rWXn+fRmm2z3+UVISIjrdWJiYrZ90x/tT79cXvHmm29KkoYPH27JeP7+/q67Cp07d067d++2ZFwAAADkHbYL+EFBQa57yp86dSrbvunbczr6faMdP35cK1asUNmyZdWzZ0/Lxi1XrpzrddrFyAAAALAP2wV8SWrYsKGkqxfOnj9/Pst+R48edb1u0KBBrtflDW8vrvWU0+l0vfb397dsXAAAAOQNtgz4HTp0cL3etm1blv22bt3qdpmbLf3FtUOHDvVomVmzZumdd97JsV/6o/YVKlTwuUYAAADkTbYM+L1793Y9ZXb16tVZ9vv2228lSWXKlFFkZOSNKM0jX3/9tQ4dOuTxxbWSNG3aNL344ovZ9klKStIvv/wiSSpbtqxXT8UFAABA/mDLgF+7dm098MADkqR3333X7d10Dh48qDVr1kiSxo4dm+nK5J07d6pOnTqqXLmyNmzYkPtFp5P25FpvL67du3evDh48mGX7+++/77rF0tChQ11/BAEAAMA+bBnwJemVV15R2bJlFRsbq3HjxmVoS0pK0rBhw5SamqpmzZpp1KhRmZafMmWK9u3bp2PHjunZZ5+9UWXrxIkTWr58ucqWLasePXp4tawxRoMGDXL70K5t27bpqaeekiTVq1dPY8eOtaJcAAAA5DH2u0no/6lataq++OIL9ezZU9OnT9fOnTvVvXt3Xb58WYsWLdKOHTvUpEkTff75526feJv+YlRjTJbr2b59u7Zv357p+6dOndLixYtd7zt16qSwsLAc654/f75SUlI0aNAgry6ubdy4sQ4dOqQNGzaobt266tevn+rVq6fk5GRt3LhR//3vf3XlyhU1b95cn3zyiW3vbw8AAFDQ2TbgS1KLFi20fft2RUdHa9myZXrmmWcUGBioOnXqKDo6WiNGjMgyRI8fP15bt27V5cuXNXXq1CzXsXTpUk2aNCnT9/fs2aOHH37Y9X7t2rU5BnxjjObNm+fVxbVpPvvsM+3atUtLly7V+vXr9f777+v06dPy8/NT2bJl1a1bN/Xp00e9evXi7jkAAAA2ZuuAL129gHby5MmaPHmyV8s1atRI+/bty7HfxIkTNXHiRB+ry8jhcCgmJsbn5Rs0aKAGDRpowoQJltQDAACA/Me25+ADAAAABREBHwAAALARAj4AAABgIwR8AAAAwEYI+AAAAICNEPABAAAAGyHgAwAAADZCwAcAAABshICPAicpKUkLFy5UUlLSzS7FFphPazGf1mI+rcV8Wov5tBbz+f85jDHmZheBvCU+Pl4lSpTQ+fPnVbx48ZtdjuXOnDmj0NBQ/fnnnypduvTNLiffYz6txXxai/m0FvNpLebTWnafT2/yGUfwAQAAABsh4AMAAAA2EnCzC0Dek3bWVnx8/E2uJHekbVd8fLwCAvgRuF7Mp7WYT2sxn9ZiPq3FfFrL7vOZtn2enF3POfjI5OjRo6pSpcrNLgMAAADXOHLkiCpXrpxtHwI+MnE6nTp+/LhCQkLkcDhudjkAAAAFnjFGCQkJqlixovz8sj/LnoAPAAAA2AgX2QIAAAA2QsAHAAAAbISAjzwrJSVFc+bM0Z133qnQ0FAVK1ZM9evX13PPPaeTJ09auq6dO3dq6NChqlmzpgoXLqwKFSqoa9eu+vTTT30e89dff1VgYKAcDoeqV6+eY//IyEg5HI4cv4oVK+ZTPfllPp1OpzZs2KDx48erffv2qlChggoVKqSiRYsqPDxcf/vb3/TNN994XMvp06c1YcIENWzYUMWKFVPp0qXVqlUrvfbaa7py5YrP25hf5vNaW7duVePGjeVwODRw4ECPl/Nl/8ytub/Wl19+qe7du6tSpUoKDg5WjRo1NGjQIG3dutXjMS5evKiXX35ZzZo1U8mSJVWiRAk1bdpUU6ZM8eqOYrnxuyRNfprPNB988IHKlCkjh8OhhQsXerycJ/uaw+FQt27dvK4pTX6Yz0uXLmnp0qV67LHHdMcddyg0NFSBgYEqUaKEGjdurL///e/67bffPK6F/dO9119/3bVPefJ78Ubsn9fNAHlQXFycadGihZFk6tWrZ6ZOnWpef/11c++99xpJpmzZsmb9+vWWrGvevHkmKCjIBAQEmMGDB5s333zTjB8/3pQtW9ZIMn/7299McnKyV2OmpKSY22+/3Ugykky1atVyXCYiIsLVP7uvokWLer2N+Wk+GzZs6NrWW265xUyYMMG89dZb5oUXXjB33nmnq61v377mypUr2dby008/mQoVKhhJplOnTmbWrFnmlVdeca2jadOm5tixY15vY36azzRJSUlmwoQJJiAgwDWHAwYM8LgOb/fP3Jr79FJTU83QoUONJFO6dGnz7LPPmjfffNMMHTrUFCpUyAQEBJgZM2bkOM6+fftM3bp1jSTTvHlz8+qrr5oZM2aY1q1bG0mmRo0aZufOnTmOkxu/S9Lkp/k0xphTp06ZXr16Zdg3FixY4HEtnuxrkkzXrl192tb8MJ+xsbGmWLFirm3t1KmTmTJlinnrrbfMc889Z6pXr24kmYCAAPPqq6/mWA/7p3tHjhwxISEhXv1ezO390woEfOQ5ycnJpk2bNkaSad26tbl06VKG9ueee85IMiVLljR79+69rnUtX77c+Pn5GUlm2bJlGdpiY2Ndv7BGjhzp1bjTpk3L8EPuacBv3ry52b17d7Zfv//+u1e15Lf5DAsLM5LM4MGDTUpKSqb2CRMmuOb1n//8Z5bjHDp0yPU/rtGjR2doS0xMNO3btzeSTLNmzUxiYqLH25jf5tMYY7Zs2WIaNWpkJJm7777b54Dv6f6ZW3N/rbFjxxpJpkyZMmbfvn0Z2r766ivj5+dnHA6H+fDDD7Mc49y5c6ZOnTpGkvnLX/6SYZ9zOp2mX79+rp/huLi4LMfJrd8lxuTevnwtK+bTGGOWLFliQkNDTVBQkImMjPQ54I8cOTLH/e3IkSNeb2d+mc/du3e75m7+/PmZ2i9dumQ6derk6vPtt99mWQv7Z9buu+++DP+/9jTg59b+aRUCPvKc2bNnG0nG4XCYXbt2ZWq/cuWKqVWrlpFkunXr5vN6EhMTTdWqVY0k8+CDD7rts2DBAlctmzdv9mjcgwcPmiJFipjbbrvNVK5c2auAHxER4cUWeCa/zWdYWJgJCQnJFJzTJCcnu+a1TJkyJjU11W2/v/71r0aSqVq1qtv/qezfv9/4+/sbSWbatGkebmX+m8+9e/eagIAAU6FCBfPZZ5+ZmJgYnwO+p/tnbs19ejt37nQFljlz5rjtM2DAACPJhIWFmQsXLrjt88wzzxhJplixYuaPP/7I1H7mzBlTsmRJI8mMGjXK7Ri59bskTX6az7TtbN68udm1a5frvS8BPyoqyoctyVl+mc+0gH/XXXdluZ49e/a45rdXr15u+7B/Zm3JkiVGkrn//vu9Dvi5tX9ahYCPPMXpdJoqVaoYSaZNmzZZ9ps8ebLrh3Hbtm0+rWvu3Lk5Hvm4fPmy66O7Hj16eDRup06djL+/v9m6daupVq3aTQ34+XE+77333iyDVJquXbu61nXq1KlM7Xv37jUOhyPHo/wdO3Y00tVTajz5aDo/zue2bdvMQw89ZM6cOWOMMbke8HNr7q+VdmS9cOHCJj4+3m2f7777zrWt0dHRmdrj4+NN4cKFjSTz0EMPZbmuIUOGGEkmMDDQ7VH83PpdYkz+mk9jjJkzZ4558cUXXZ+E5LWAn5/mMzY21rRo0cLt0fv00k7jueWWW9y2s3+6d+bMGRMWFmaqVq1qduzYYbuAz0W2yFM2btyoI0eOSJI6duyYZb9OnTq5Xn/00Uc+rSttuUKFCqlt27Zu+wQHB6tNmzaSpJUrV+rChQvZjrlo0SKtWrVKTz75pJo2bepTXVbKj/O5YsUKzZw5M9t1FSlSxPU6KCgoU/vHH3/sepS3J9sdFxendevWZbtOKX/OZ6NGjfTuu++qVKlSPtXhrdya+/SSkpL0xRdfSJJatGihkJAQt/1atWrluujX3b/D8uXLdfnyZY9rTU5OdnsxYm78LkmTn+ZTkoYPH67nnntO/v7+XtVwo+Sn+axWrZo2btyoQYMGZbu+woULS3L/uzD92OyfGf3jH//QqVOnNHv2bJ9vXpGXEfCRp6xZs8b1ukmTJln2a9y4sespbumX8VRqaqo2bNggSapfv74KFSqUZd+0oJ6YmKgffvghy35xcXEaM2aMatSooUmTJnldkzsJCQlyOp0+L5+f5zMrxhj9/PPPkqTbbrtNJUqUyNTH0+1O/0eYJ9udH+czN59G7W7/zK25T+/nn3923dkmu3X4+fmpUaNGkqQff/zRFeatrDW39/38NJ9S7u1vTqfT49CZnfw2nzmJjY1VXFycJKlDhw6Z2tk/3Vu9erUWLlyoBx98UF27dvWqHnes2j+tRMBHnrJz507X6ypVqmTZLygoSGXLlpUk7dq1y+v17N+/X4mJiTmu59r27Nb197//XX/++afeeOONDEeYvXHp0iW98MILatSokYoWLarixYsrMDBQ9evX19NPP63jx497NV5+ns+szJw5UzExMQoICNC0adPc9knb7pCQELd/APhaix3n0xue7J+5NffpefrvkL7d6XRqz549Po2TXa25/W+Vn+bTaocPH9bw4cNVs2ZNBQcHKyQkREFBQWrdurVmzJjhUyC203w6nU49/fTTkqSKFSvqmWeeydSH/TOzS5cuadiwYSpVqpRmzJjhVS3p5cb+aSUCPvKU2NhY1+uwsLBs+6a1x8fH6+zZs7m+nmuXS2/FihVasmSJHn74Yd19991e1ZLe5s2b9e9//1tt27bVggULtHz5cr388stKTU3VtGnTVK9ePX3++ecej5df5zO906dPKzY2Vl999ZX69eunJ554QjVq1NCGDRvUvn37TP2TkpJc96G3uhY7zOf1yGn/rFu3bq7NfXpWzU/a+4CAAJUuXTrLMYKDg1W8ePFsx7jeWtzJzX05vby6vy1YsEArVqzQgAED9OGHH2rZsmUaM2aMdu/erdGjR6tJkyb6/fffPR4vv89ncnKy/vjjD+3du1fvv/++7rzzTn388cfq0KGDNm/e7HZd7J+ZPf/88zp48KCmTZuW43jZsXr/tFrATVsz4EZCQoLrdXBwcLZ90847TFvOm3OMr2c97sYaMWKEypQpo//85z8e1+BOuXLltHbtWtWvX9/1va5du2rUqFG67777tGrVKj3wwANau3atWrduneN4+XE+r5V2JFySAgMD9fe//12TJk1SyZIlb3gtdpjP6+HJ/pkmN+u2an7S3uc0Rto48fHxunDhgowxrlNR8ur+5o28ur/deeedWrFiRYYjwz169NDw4cPVpk0b7d27V507d9aWLVsUGhqa43j5fT6///77DAc1QkNDNXPmTD366KMKCHAf59g/M9qyZYuio6PVvn17DR482Ks6rmX1/mk1juAjT0n/kVZ25wpe237p0qWbtp7nnntOR44c0auvvqoyZcp4VUd6S5Ys0Z49ezKEpzRBQUFauHChgoKCdOXKFY0aNcqjMfPjfF5r1apV+uqrrzR//nz17t1bs2fPVsWKFfXss8+6fTpibtZih/n0lSf7Z/pa8sP8pI2T0xjp+zidTiUlJVleizsFeX+LiYnRqlWr3J72Ua1aNUVHR0uSDh06pMmTJ3s0Zn6fz8aNG2vVqlX64osv9Nprr+nWW2/V448/rqpVq+qdd965obXk9ti5sZ6UlBQNGTJEgYGBevPNN72q4Vq5sX9ajYAPn6SkpHj8qObsvq59dHn6v75zerR1+nZvz3m3aj0//vij5syZo7vvvlsPPfSQVzWkl5KSogoVKqh06dJZzlWlSpVcweKXX36x5Xy607FjR3Xp0kWDBg3S+++/r//973/y9/fX1KlTdd999yk1NTXLWrZu3Zrt/pf+Th+nTp0qEPPpq/Lly2f7KUTFihUzXOSXdlFcVvLC/KSNk9MY6fv4+flluFtJbv5bFeT9rXr16hnqulaPHj1cR0UXLlzo0c0I8vt8lipVSh07dlS3bt30+OOPa+3atZo5c6ZOnDihAQMG6F//+tcNqyW3x86N9UybNk3btm3T888/r9q1a3tVw7VyY/+0GgEfeUr621+lXRiUlfR/1Wd126zcXM+VK1c0ZMgQBQcH64033vBq/TdKfppPT7Vs2VJTpkyRJH3zzTeaNWtWlrVYzY7zaaUWLVq4Xp8/fz7bvnlhftLe5zRG+nGKFSuW4U4xuflvxf6WNX9/f9cdVc6dO6fdu3fnuIwd53PUqFHq2bOnJCkqKkrbtm27YbXkp/ncv3+/Jk2apFtvvVVPPfWUV+v3hS/7p9U4Bx8+CQgIsGSHrVChQob31atX18aNGyVdPaJaqVKlLJc9deqUJKl48eJe3+O7evXqmcbJaT3XLvfSSy/pt99+07hx4xQSEqLTp09nWjbtr3an05mhPTg4OMN9dz2dzw8//FBRUVGSpEcffVRPPPFEhvb8PJ/e6Nevn0aPHi1jjObPn59hHoKCglS+fHmdPHlSRYoU0ZYtW7Ic5+DBg65bpLVv316zZ8/O0F5Q5tMqFStWdL3OKeBfT91WzU/16tV18uRJpaSk6MyZM1leaJuYmOj6RMLdGFbU4k76fTk394P8ur+VK1fO9frkyZNq0KBBtv3tOp8PPfSQli1bJmOMFixYkOHOMOyfVw0bNkyJiYn697//7fZ3U/obISQlJWX4/3Xa3XG85e3+aTUCPnxWr149y8ds2LCh6/WRI0d02223ue2XlJTkuvevLz80tWrVUnBwsBITE10PLsrK0aNHXa/TryvtXr4vvviiXnzxxWzHOHLkSIaLRQcMGJDp9A9P5jP9L4ywsLAcl8lP8+mN0NBQhYaG6vTp09q1a1eGix6lq9t98uRJXbp0SRUqVMjy9m3pa2nZsmWBnU+rpP8Y+sqVKzp//rxHc+9t3df+O2QnbT1+fn6Z/n0bNmzo+oPtyJEjWQb87GrN7X+rtH05ISEhz8/njZZ+f/P0wVp2nM+6deu6Xu/YsSNDG/vnVWvXrpUkdevWLcf1LVmyREuWLHG9X7BggQYOHOhNyZJ82z+txCk6yFPSn8N77UeN6f3666+uHx53D/fIib+/v9q1aydJ+u2337I9r2/r1q2Srh51T3/nmunTp2vVqlXZfqXdgissLCzD96+9X/EXX3yhyZMnu54ImJW0W5JJmY8uu5Of5jNt2Y8//lgXL170aJ3S1V+i156H7+l2p9Vy7TJZyW/zaRVf9k/J2rlP7/bbb3d9/J7dOpxOp3799VdJV59yee05s1bsJ7n9b5Vb+3J6Vs2nVWbNmpXlRaPpefv7UMpf87l+/Xp9+eWXOa4vfXhMSUnJ1Mb+qRz/X7148WJX37vvvjtDW+fOnTOsJzf3T0sZIA9xOp2mcuXKRpJp27Ztlv2mTJliJBlJZtu2bT6ta+7cua4xVq9e7bbP5cuXTfHixY0k06NHD6/XUa1aNSPJVKtWLdt+AwYMMJLMiRMnsu3XuXNnV807d+7Mcf35bT6joqKMJLN58+Zs13Xu3Dnj5+dnJJlKlSplat+7d69xOBxGkpkwYUKW43Tq1MlIMmXKlDHJyck5bGH+m093YmJiXOMOGDDAo2V82T+tnvtr9evXz0gyhQsXNgkJCW77fP/9965aoqOjM7XHx8ebwoULG0nm4YcfznJdQ4cONZJMYGCg+eOPPzK15+bvktzal69lxXy6s2DBAtcyCxYs8GiZatWqmbp162bbJzEx0TWfZcuWNU6n06Ox89N8RkREmJCQkBy37YsvvnCN069fv0zt7J858+b3Ym7un1Yi4CPPmT17tpFkHA6H2b17d6b25ORkU7t2bSPJdO3a1e0YCQkJ5q677jIlSpTI8gc9MTHRVK1a1Ugyffv2ddtn0aJFrh/6TZs2eb0t3gb8+fPnZ9ln7969xt/f30gyrVu39riG/DSfaQF/0qRJ2W7TzJkzXeOMGDHCbZ+//vWvRpKpXr26SUpKytR+4MAB13y+8sor2a4vvfw0n+5cT8D3dP8MDQ29rrnfsWOHqV27tqlUqZJZv3692/Xt2LHD9Ufem2++6bbPoEGDjCQTFhZmLly44LbPM888YySZkJAQc/r06Uzt586dM6VKlTKSzMiRI92Okdu/S653X76R83ktXwO+w+EwBw4cyLLP/PnzXeOOGzfOo3HT5Jf5jIiIMJKyXEeav/zlL665+OCDDzK1s3/mzNuAn5v7p1UI+MhzkpOTTZs2bYwk06ZNG3Pp0qUM7ePHjzeSTMmSJc3evXvdjvHGG2+4frgCAgKy/GFfvny58fPzMw6Hw3z22WcZ2g4dOmQqVqyY7f/Yc+JtwA8LCzO///57pvY///zTNGvWzEgyRYoUMVu3bvW4hvw0n2kBv2jRomblypVu+6xfv94ULVrUFSSPHTvmtt+hQ4dM2bJljSQzZsyYDG2JiYnmrrvuMpJMs2bNzOXLl92O4U5+mk93rifge7p/rlix4rrmvk+fPq4aW7VqlWVdY8eOdR0hu/Z/tl9//bUrEHz44YdZjnHu3DlTp04dI8k88MADJjU11dXmdDrNww8/7PoZjouLy3Kc3Pxdcr378o2cz2v5GvAlmXbt2pn4+PhM7b/88ospXbq0kWTq1atnzp8/73E9xuSf+UwL+PXq1TP79u1z22f69OmuWu68884M+2967J/Z8zbg5+b+aRUCPvKkuLg406JFCyPJ3HLLLebll182r7/+uunatauRrn7Mt27duiyXTzvKmhagsvpIz5irH18WKlTIBAYGmkceecS89dZb5p///KfrF1bfvn09/kjxwoUL5t1333V9lSlTxlVv+u9fG+imTp1qAgMDjXT1I8hBgwaZ2bNnm3nz5pkxY8a4agkLC8t2u7OSX+Zz8eLFpkiRIq51dejQwTz//PNm3rx5Jjo62tx///2uX+J16tQxv/76a7bbvXHjRlOhQgUjyXTu3Nm8/vrrZtq0aebWW281kkyTJk2y/AMhO/llPtP88MMPrn3v1Vdfda27bdu22e6XaXzZP69n7tOOCEoyLVu2zHK7UlNTzZAhQ1x/7I0bN8689dZbZvjw4aZQoUImICDAo4/q9+3b5wr5LVq0MNHR0WbGjBnmzjvvNJJMjRo1PDolzsrfJdfKT/O5bNky1z41bNgw17qHDRvm+v6yZcuyXL579+6uZSpUqGCeeuopM2/ePDNnzhwzYMAAU6hQISPJNG/e3Bw+fDjHetzJD/M5atQo1+kvAQEBpm/fvubFF1808+fPN1OmTDHNmzd31dGlSxdz9uzZbLeZ/TOjAwcO5Ph70d1+eiP2TysQ8JFnJScnm9mzZ5tWrVqZUqVKmSJFiph69eqZZ555xhw/fjzbZePj401kZKQpXry4Rz/wO3bsMI888oipXr26CQoKMmFhYaZLly5m6dKlXtWc/ihAdl8xMTGZlj1x4oR5/fXXTY8ePUx4eLgpUqSICQwMNOXKlTN33XWXefXVV90eLfBUfpnPs2fPmgULFpg+ffqYBg0amOLFixt/f39TtGhRU7NmTdOrVy/z7rvvmsTERI+2Oy4uzowfP97Ur1/fFC1a1JQsWdLccccdJjo62u3HyZ7KL/NpzP8/Au/LfpnGl/3T17n/9ddfTa1atbL9yD69FStWmG7dupny5cuboKAgU61aNdO/f3+zZcsWj+bHmKt/nL/00kumadOmJiQkxISEhJjGjRubf/3rX14dgbPqd4k7+WU+045wZveV06eaO3fuNC+88IK56667TMWKFU2hQoVMcHCwqVKliunVq5f58MMPTUpKikf1ZCU/zOeBAwfMK6+8Yrp27Wpq1KhhihYtavz9/U2JEiXMrbfeah555BHz7bfferzN7J//X/pPl7zdT2/E/nm9HMbkcFsEAAAAAPkGt8kEAAAAbISADwAAANgIAR8AAACwEQI+AAAAYCMEfAAAAMBGCPgAAACAjRDwAQAAABsh4AMAAAA2QsAHAAAAbISADwAAANgIAR8AAACwEQI+AAA3QI8ePeRwODJ9LVy48GaXBsBmAm52AQCAvCM2NlY1atSwdMy1a9cqMjLS0jHzo9tuu02JiYmSpE2bNuns2bM3uSIAdkXABwC4BAYGKjw83PX+0KFDSklJuYkV2cfzzz/veh0ZGan169ffxGoA2BkBHwDgUqlSJe3fv9/1vnr16jp06JAkqVevXpo6darHY0VGRurYsWOW1wgAyB4BHwDgkZCQENWqVcvj/gEB/C8GAG4GLrIFAAAAbISADwAAANgIn58CAHLFb7/9JqfTqcKFC9/sUgCgQCHgAwByRZEiRXxa7sqVK1qzZo1iYmJ0/vx5lS9fXm3bts1wdx9vHDx4UD/++KNOnTqllJQUhYWF6bbbbtOtt97q03iStGfPHm3evFmnTp1SamqqSpUqpXr16qlp06YKCQnxedz4+Hh9++23OnjwoPz8/BQeHq727durePHiPo8JoOAh4AMALJH+HvrGGLd9Bg4cqEWLFmX6flRUlCZOnKhXX31VkydP1pkzZzL1ueuuuzRnzhzVrl3bo3q+/PJLjR8/Xtu2bXPbXqNGDU2cOFEPPfSQ/PxyPmPVGKMPPvhAUVFR2rt3r9s+AQEBatu2rfr27at+/fp59UfOyy+/rClTpig+Pj7D94sXL65///vfeuyxxzweC0DBRsAHANwwt956qzp37ixJ2rFjh44fP+5qGzVqlN58800NHjxY99xzj0qVKqWYmBi9/fbb+u6777R69Wrdfvvt+vbbb9W8efMs1+F0OvXkk0/qtddekySVL19ejz32mJo3b66AgADt2LFDb7zxhvbu3asBAwbo008/1XvvvZftqURJSUkaNGiQ/vvf/0q6+sfBo48+qiZNmqhQoUI6ePCglixZolWrVmnt2rVau3at3n77bW3cuNGjeRk9erRmzJihmjVrKjIyUleuXNFPP/2ks2fPKj4+XiNHjlRwcLAGDx7s0XgACjgDAEAWqlWrZiQZSWbAgAHZ9o2JiXH19cSAAQNc/Zs2bWoKFy5sNmzYkKmf0+k0o0aNcvUtU6aMOXXqVJbjjhkzxtW3UaNGJi4uLlOfS5cumS5durj69ejRI9taH3zwQVffDh06mISEBLf9Zs6c6erXuHHjLMeLiIhw9evXr58pXLiw+eCDDzL0uXjxoundu7erX9myZc2VK1eyrRMAjDGGgA8AyFL6gO/NlyfSB3xJZurUqVn2TUlJMbfccour78MPP+y23+rVq43D4TCSjL+/v9m9e3eWY545c8aULFnSNebcuXPd9luwYIGrT/Hixd3+wZBet27dvAr4ksxbb73ltt+ff/5pgoODXf2++eabbNcNAMYYw20yAQAeqVixojp37pzlV7t27XweOygoSI8++miW7f7+/vr73//uev/f//5XJ06cyNTvhRdecJ3/f//996tevXpZjlmqVCkNGTLE9X7KlClKTU3N0MfpdGry5Mmu90OGDFGZMmWy3Zbhw4dn236tihUr6pFHHnHbVrp0abVs2dL1fuvWrV6NDaBg4hx8AIBHOnXqpIULF2bZnv4iW2+1bNkyxzvF3Hvvva7XKSkpWrp0qUaOHOn63qFDh7R+/XrX+27duuW43vvuu0/Tpk2TdLX+devW6a677nK1/+9//9OBAwe8GrNt27aKiIjw+Km/nTt3zvYi39q1a2vdunWSlOGaBQDICkfwAQA3XXZH2tNUrVo1wy0or72AdcOGDRneN2rUKMcxr+1z7Rjp/2DwdMwSJUpo3bp1mjdvXo59JemWW27Jtr106dKu1xcuXPBoTAAFGwEfAHDTlSpVyqN+VapUcb1Of2Rdkn7//fcM76tWrZrjeCVLlszwR8O1t79MP2bRokUVGhrqUZ3eKFmyZLbtwcHBrtfXnkIEAO4Q8AEAN11QUJBH/YoWLep6ffbs2Qxtf/75Z4b3xYoV82jM9P2uHSP9/fjTr9tKgYGBuTIugIKLc/ABAJaoXr16lg+4sgOHw3GzSwAAj3AEHwBw0125csWjfhcvXnS9vva0nmtPn/H0fPX0/a4dI/17zn8HkF8Q8AEAN921p9tk5ciRI67X4eHhGdrq1q2b4f3hw4dzHO/cuXNKSEhwva9Tp06WY168eDHDKTsAkFcR8AEAuWLTpk2aNWuWVq9enWPfPXv25Njn0KFDGcJ4q1atMrRfex/+7du35zjmtX0iIiKyfe/JmGfPntUbb7yhN954I9OFwABwIxDwAQC54ssvv9Tjjz+ud999N8e+GzduzBDe3fnqq69crwMDA9WrV68M7dWqVVNkZKTr/fLly3Nc7xdffOF6Xb169UyBvk2bNhnuZ79ixYocx/z88881YsQIjRgxgvP2AdwUBHwAwE2XmJiot956K8v21NRUvfbaa673ffr0Ufny5TP1e/75512h+rPPPst068z0zp49m+Fe9ePHj5e/v3+GPn5+fpowYYLr/bx587I9TcfpdGrmzJmSpA4dOqhmzZpZ9gWA3ELABwB4JCEhQfv37/f4y5vz1cPDw/X888/r+++/z9RmjNGTTz6p3bt3S5LKlCmj6dOnux2nffv2+sc//iHp6h8FDzzwgE6fPp2p3+XLl/W3v/1N586dkyT16NFDQ4YMcTtm//799eCDD0q6es5+nz59Mlzsm77Op59+Wlu2bFFAQICmTp2a84YDQC7gNpkAAJdjx47prrvuyvA+zdKlS7V06dJcWe9DDz2kPXv2qH379ho8eLDuuecelSxZUrGxsZo3b56+++47SVLx4sW1YsUKlS1bNsuxXn75ZSUnJ+u1117Tjh07dOutt+qxxx7THXfcoYCAAO3YsUNz5sxxPdSqR48eev/997Otb9GiRXI4HFqyZIlWrVqlRo0aacSIEWrcuLECAwP1+++/a/78+dq0aZP8/Pz01ltv6fbbb88wxuLFi7V48WJJGc/lnz59upYsWeJaT1hYmE6dOqUBAwZIkvbv3+/qu2rVKnXp0sU1Zw899JBH8wuggDEAAPyfmJgYI8nSrwEDBrhd14ABA1x9oqKiTGpqqpk4caIpVqyY23Huuusus3fvXo+3Zfny5aZJkyZZ1lWjRg2zYMECk5qa6tF4TqfTvP/++6ZOnTpZjnn77bebDRs2uF0+Kioqx7mKiYnx+N8hKirK47kAULA4jLHxU0kAAHnWwIEDtWjRIklSVFSUJk6cKEm6dOmS1qxZo0OHDik+Pl7ly5dX27ZtM1zs6o2DBw/qhx9+0MmTJ+V0OlWuXDnddtttatSokc+1//bbb9q8ebP++OMPOZ1OlS9fXi1atFC9evV8HhMArMIpOgCAPKVIkSLq1q2bZePVrFnT8otd69evr/r161s6JgBYhYtsAQAAABsh4AMAAAA2QsAHAAAAbISLbAEAN8z06dO1atUqSdKOHTt0/PhxSVfvg592ES23fwSA68NFtgCAG2bHjh36+uuvM33/wIEDOnDggCSpZcuWN7osALAVjuADAAAANsI5+AAAAICNEPABAAAAGyHgAwAAADZCwAcAAABshIAPAAAA2AgBHwAAALARAj4AAABgIwR8AAAAwEb+H9bj7v/JcTmbAAAAAElFTkSuQmCC", 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"text/plain": [ + " 0%| | 0/10 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Compute edge performance metrics for val\n", + "INFO:Load 20 files located in data/export/processed/val\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5ccdff38d06f4713bdb6a204e7e4527d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/20 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e9c606530ce84851b4c38c2922b980bb", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/10 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Compute edge performance metrics for velo-sim10b-nospillover\n", + "INFO:Load 20 files located in data/export/processed/test/velo-sim10b-nospillover\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8da8fd5ca9cc4b408284318d03e0b265", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/20 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "fd55c0c4ab7b4f49831c3acdc8998b01", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/10 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Figure was saved in output/export/edge_efficiency.pdf\n", + "Figure was saved in output/export/edge_efficiency.png\n", + "Figure was saved in output/export/edge_purity.pdf\n", + "Figure was saved in output/export/edge_purity.png\n", + "Figure was saved in output/export/graph_size.pdf\n", + "Figure was saved in output/export/graph_size.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 800x600 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 800x600 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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XSiKi4oTvvkREZHGff/65zow6q1atQmZmJhYsWICmTZuiVKlSUlm7du2kbRUKBTZs2IBBgwahTp06cHFxgaOjI6pUqYLevXtjz549Bvfbvn17nf1GRkZq1alcubJOHQC4du0aBg8ejIoVK8LBwQG+vr744IMP8OzZM737yjlTUMCbAVrlt27d0tmPOs49e/agc+fO8PLygpOTExo0aIB58+ZBqVTm+txu27YNr776Kry8vODo6IiqVati2LBhuHr1KiIjI3X22bNnz1zbJCLrwTP3REQW8PAhULNGUfei+Ojbty/q1q2rNcNOWlqalHwPHjwY9vb2WLt2LU6cOKG1bdeuXXHo0CHY2dlhwIABePnll6FUKnHhwgWsW7cOv//+O0aOHIlly5bp7Hfq1KkYMWIEjh07prccAH788UekpKTg999/x9atWwEAR44cwcCBAzFw4EDMnj0bDx48wOLFi7F48WLs378f586dg4uLi1Y7a9asAQAs+3kZjoUd09lP+fLlpTr/+9//cP36dQDAN998g2XLlmHo0KHo378/zp8/j2XLluHTTz/FxYsXsW6d/guShRAIDg7G0qVLAQB9+vRBx44doVKpsG/fPjRp0gTffPONVH/UqFFo3bo1qlSporc9IrJOTO6JyKoIAaSmFs6+NHPHevWAH74HujTKPguckgLY2Bbcvp2dgf9OOBdLjRo1QqNGjbRm2Pnf//6Hnj174scff5TqDRkyRCf5zMjIgFwux969e9G+fXutsrFjx6JVq1YICQmBv78/goODtco7d+4MmUwGpVJpMLnv1asXgOwz6+rkfvTo0di/fz/q1q0r1evbty8aNmyIf//9F4sWLcLkyZO12lHPFrRv3z69yb2rq6tUZ/ny5bh+/TquX7+O1NRUnS8Lfn5+GDVqFNavX4+xY8ciICBAp72vv/5aSuxDQ0MxdOhQqSw4OBgrV67Ump60RYsWemc9IiLrxmE5RGRVUlMBV9fCeXz88fP9qlTAmA9tUKtNBdRqUwHupWwKdN+F9QXGkjIzMzF//nytda6urhg/fjxee+01rfWvvfaaTmIPAE2aNJGmt8zZVn707t1bK7EHgHr16iEwMBAAsH37dovsJyoqCnPnztX5FUD9S4ahfUVHR2PGjBkAgI4dO2ol9mrvvvuu1vAmIiqZeOaeiIgKRffu3eHo6KizPudNpjZv3qy3ntorr7wCIHve/Xv37sHHx8cifdOnfv36OHbsGK5du5bvfQDZX2b0JeCOjo6oVq0abty4oXdfa9euRVpaGgCgf//+Btvv378/Dhw4YJG+EtGLick9EVkVZ2cgObng9/PwYfZQHJXq+TpbW4FDG5+iQlkVyjUqV6Azxjg7F1jTBeall17KU73y5ctr/a1QKJCamgohBADAxub58/ro0SOLJPd16tTRu97T0xMAkJCQkO99AEDNmjVha6t/vJaxfR079nzYT9OmTQ227+fnl88eEtGLjsk9EVkVmQzIMeKhQNSuDfz4I/DBB9l/29oCS5YI1PDNApDdh4Icc/8icnd3z3Pd33//HStWrMCJEycQFxdnsJ76bHZ+ubm56V3v4OAAAMjKyirQ/WjuS9+MOXfu3JGWK1asaLAN9RcEIiq5mNwTEZkpKOh5cn/1avZsOU/OF22fijPNM+6GZGVlYeDAgdi4caM0jeMbb7yBypUrQy7P/sgKDw/HhAkTCr1vRbmfpKQkadnYkCX1c0REJRffBYiILKBSpaLugXVYtmwZNm7cCACYM2cOPv30U506JTGB1Tzjn5aWZvBXEEv9wkBELy7OlkNERMXGhg0bAGSf4VbPikNA9erVpeWHDx8arGdsCBMRlQxM7omIqNh48uQJAEh3btVHc4hKSdG6dWtp+ezZswbrXbp0qTC6Q0TFGJN7IiIqNipUqAAAePbsGWJjY/XWOXPmTGF2qVgYNGiQNNZe/euGPuohTURUcjG5JyKiYuOdd96RlidMmACV5lyjAK5du4bvvvuusLtV5Ly9vaWbWB08eBCrVq3SqbN27VqcP88ruolKupJ3VRIREZlk3759iIqKQnR0tLTuzp07WLt2LQCgV69ecHLUHkJz584dHD9+XGubEydOSBfDdu7cGeXKldPZ16hRo7Bnzx5s374doaGhuHTpEnr16oXSpUvj0qVLWLlyJapWrYrr169LfXvw4AEaNmyI6tWrY9++fXj69ClOnDghtfnHH3/A29sbfn5+8PPzk+K5ePGiVEczFhcXF5w4cQK3b9/WW0fdd/XfEXciAACpilSsXbcWNjY2OnWioqKkf9euXQtXV1f07NlT6l9ycrJOHSD7jL3apEmTEBERgZ9//hnDhg3Drl270KFDBwghcODAARw5cgShoaEGb8hFRCWEoBIlISFBABAJCQkFvq/05HQxAzPEDMwQKfEpBb6/4kQz9vTk9KLuTqGyROyZmZni0KFDIjMzUwghhEKhEFevXhUKhcKSXc235GQhgOxHcrIQWcos8fCfh+LhPw9FljKrqLtnMW3bthUADD4iIiJ0Yg8NDTW6zaFDhwzuLysrS4SEhIhWrVoJd3d3IZfLRdmyZcVrr70mfv31V3Ho0CGd9qZNmyYSExON9nX69Om5xhMRESGEECIoKCjXvuclPkPlvr6+Ury+vr4G6+nzxx9/iE6dOgkPDw/h6OgoqlevLoKDg8X9+/fF7du3pW3Xrl2bz1feOGs43lUqlUhMTBQqlcqk7awhdnMx9uzYlZlKk7Y193PMnLyNZ+6JiMiow4cP51pHlaU9fGbo0KEYOnSoWfuzsbHBiBEjMGLECIN1xH93q9X8Ozk5GYcOHYJMJjPafl7iWbVqld6hL/r6oMpS4cn57AuByzcur3Vn4pz91CcyMjLXOpp69OiBHj166C0LDw+XlkuXLm1Su0RkHTjmnoiI6AXw5MkTXL582WidGzduSMt+fn4F3SUiKoaY3BMREb0AVq1ahcaNGxucRQgAtm7dCgAICAhAJd5ZjahEYnJPRGQmFxf1iPvsZaKCplQq8dlnn+kt++OPP7Bx40bY2triq6++KuSeEVFxwTH3RERELwD1tQRLly7FmTNn0LNnT1SoUAGJiYk4ePAgduzYAXt7e/z8889o165d0XaWiIoMk3siIqIXwMcff4zatWtjx44duHjxIpYuXYqnT5/C3t4evr6+GDt2LD788EPUqFGjqLtKREWIyT0REdELwM7ODr169UKvXr2KuitEVIxxzD0RERERkZVgck9EREREZCWY3BMRERERWQkm90REREREVoLJPRERERGRlWByT0RERERkJZjcExERERFZCSb3RERERERWgsk9EREREZGV4B1qiYjMlJGSgbmucwEAU5KnQO7It1QiIipaPHNPRERERGQlmNwTEREREVkJJvdERERERFaCyT0REQEADh8+DJlMZvARGRlpcNuZM2eiUrNKqNSsEmzlttI2M2bMMLkfkZGROvtu166d3rrx8fFo3rw5ypQpg7/++svkfa1atUpnX+b0uSCMGDECbm5umDVrVlF3hYheILz6i4iIAAD16tXDmjVrEBMTg48++ggAYGdnh2XLlkEul6NMmTIGt+3Vqxe85d4IvxaOFb+uwLvvvov27dvDz8/P5H6UKVMGa9asAQD873//w/Xr1w3WPXDgAE6dOgUAWLFiBd566y2T9tWmTRtpX4MHDza5r6ZauHAh4uPj4e/vj549exqsFxMTgxUrVgAAFixYgGnTphV434jIOjC5JyIiAEC5cuUwaNAgAMDKlStx8eJFZGZmokyZMujatavRbf38/FA2qyyOnT4GGxsbfPnll6hYsaJZ/XBxcZH6sXz5cqPJfYcOHdCsWTPcvn0bQUFBJu+revXqqF69OoDCS+7v3r2LoKAgo8m9l5cXhg4dik2bNmHcuHEF3i8ish4clkNERDo0E131me3cKNIU2HVoFzp27Gh2Ym8qT09PnD59GtHR0XjjjTcKZZ+FJTQ0FMnJyfjyyy+LuitE9AJhck9ERDoGDhwIW1tbAMC2bduQkJCQ6za7Du5CSmoKhgwZUtDdIyIiA5jcE5FFpWZkIXTyywid/DJSM7KKujuFSiWA6PLOiC7vDJUo6t7kT4UKFdCpUycAQFpaGjZt2mS0vkoA6/dvhYurG958s2ch9LD4sKbX3RQlNW6AsTP24h07k3uiAlCSE1yyHppDc1avXm207qNHj3D67yPo3LUHnJ2dAQAJCQlYsWIF+vTpg+rVq8PJyQlOTk6oXr06Bg8ejJMnT+arf5oz3NjY2KBBgwZG6x89ehQ9e/ZE2bJl4ejoiKpVq2L48OG4ceNGnvanUCiwYcMGDBo0CHXq1IGLiwscHR1RrZovPh41BH8f2p9rX+/evQsA+OWXX3Rm6Vm1ahUAoF27dibNVJSZmYmff/4ZHTt2RNmyZWFvb4/y5cvjjTfewJo1a6BSqXS2uXXrlsEZifbs2YPOnTvDy8sLTk5OaNCgAebNmwelUpmn54mIipZVXlAbHx+PnTt34sCBAzh//jwiIiKQnJwMV1dX1KpVC506dcLo0aPh4+OTa1vHjx/HokWLEBYWhqdPn6JMmTJo0aIFgoODDU7NlpNSqURISAjWrl2L69evIz09HT4+PujRowfGjRuH8uXL56mdu3fvYuHChdi1axfu378PV1dXvPTSSwgKCsKQIUNgY8PvakRFJfFhIjyqly7qblhUr1694ObmhqSkJISFhSEiIgLVqlXTW3f9+nVQqVTo3ruftK5x48aIiIiAs7Mzhg4dioYNGyIpKQmnTp3CunXrsG7dOsyaNQuff/65Wf1TXwuwbNkyHDt2zGjd//3vf/jiiy8ghEDnzp3RvXt3ODg44O+//0ZAQAB+//33XPfXtWtXHDp0CHZ2dhgwYABefvllKJVKnDt3Hhs2rMeB3dtx9sgILF8eYrCv48ePR3R0NFq3bo1Ro0Zp1QkMDAQAfPbZZxgxYgSOHTuGZcuWGe1TZGQkunXrhitXrqB69eoYM2YMKlWqhIiICISGhmL37t346aefsG3bNpQrV07arnz58npnJPrmm2+wbNkyDB06FP3798f58+exbNkyfPrpp7h48SLWrVuX6/NEREVMWJm///5bODg4CABCJpOJt956S8ybN08sWbJEfPLJJ6JMmTICgHB2dhYbN2402tbMmTOFjY2NcHZ2FmPHjhXLli0T48ePF66urgKAmDhxYq79efbsmQgICBAARN26dcW8efPETz/9JN544w0BQJQpU0YcOXIk13Z27Ngh3N3dhUwmE3379hVLliwRs2fPFr6+vgKA6Nixo0hMTMy1nYSEBAFAJCQk5Fo3v9KT08UMzBAzMEOkxKcU+P6Kk7jYVOE7eYfwnbxDxMWmFnV3CpUlYs/MzBSHDh0SmZmZQgghFAqFuHr1qlAoFLluq1KpRHpyeqE8jn97XDrGZ9rMFCd+OCnO3ngqzt54KlLiFQW6b5VKZdZza6qhQ4cKAAKAmDlzpsF6L730kqhYxUdcuBcrMjOzhBBCVKpUSbi5uYkrV67o1N+5c6eQyWQCgNi5c6fBdtu2bSsAiLZt2xqsExQUJAAIHx8fvc/Lr7/+KsUwffp0nfK9e/eKihUrGq0jhBAtW7YUcrlcHDx4UGt9ZmaW+HXXYeHo6CQAiJ9++slgX9Xv2UFBQQbrqIWGhkp9ioiI0CmPi4sT1apVEwBEq1atREqK9vtsTEyMeOmllwQA4efnZ/D/j/o5LleunGjWrJlITk7WKl+2bJnUj5MnT2rFHX4/ToTfj5Ne8xeNSqUSiYmJJv9/sobYzcXYs2PPyFCatK0pn2OazMnbrO7MfWJiItLT02Fra4vdu3ejc+fOWuWff/452rRpg0uXLmHw4MFo2LAh6tWrp9PO0qVLMX36dDg6OuLgwYMICAiQygYPHozWrVtj/vz5KFu2LCZMmKC3L0qlEr169cKpU6cQGBiI/fv3w8nJCQAQHByMqVOnYu7cuejRowdOnz6NWrVq6W3n3Llz6Nu3LxQKBRYuXKg1LVpwcDBat26NAwcOYMCAAdi+fbvJzxmRNclMzcRc17mFvl+hEtg7do/0d0H/T5ySPAX2LvYFvJfs9zv1cJE1a9bonW/97NmzuHLlCkaNmwiZTKZVFhQUhPr16+ts88Ybb+Cdd97Br7/+ivnz5xfYTDeZmZkYP348AKBWrVr44osvdOq8+uqreP3116V55Y157bXX0L59e5319Ro2woDh72HlTwsxf/58BAcH57/zuZg6dSoiIiJgY2OD5cuXS8Oh1EqXLo2lS5eidevWuHjxIubOnYuZM2cabC8qKgrr1q2Di4uL1vrBgwdjzJgxyMjIwPbt27U+D4mo+LHacRxDhw7VSewBwMPDA/PnzwcAZGRkYPny5Tp1nj17hokTJwIAxo0bp/NG1rhxYymh/+KLL/DgwQO9fQgJCUFYWBhkMhlCQkKkxF5t5syZqFmzJuLj4/Hxxx8bjCU4OBgKhQIBAQE68x17enpi0aJFAIAdO3Zgy5YtBtspbBx3TvTia9++vTSE8datWzh+/LhOHfV4fM0hOQBw8uRJzJ1r+IvWK6+8AiB7+GNmZqaluqxl586dePz4MQDgnXfekWYAyql///65trV582aj04I2aNQUQPZQmXv37pnR27xLSkpCaGgogOznsU6dOnrrtWrVSprHf9GiRcjKMvxe7Orqqne4afZ1BdnDsa5du5bPnhNRQbO6M/ceHh4ICAhAnz59DNZp2rSptKzvjer7779HcnIygOzbf+szYsQIzJo1C2lpafjmm2/w3XffaZULIaQPtZYtW+o9c2VnZ4ehQ4fi888/x44dOxAeHo5GjRpp1dm/f79098WRI0fq7Uu7du1Qq1Yt3Lx5E7Nnz0bv3r0NhU5k9eyc7TAleUqB7yfxYSIW11sMoTFlgsxWho4HB8OpvCvqlnODXF5w50/snO0KrG1NMpkMAwcOlN7P1qxZI40NB7J/odywYQNatAiET7XqWttWrlxZ6++UlBSkpaVBiOznzM4uO4aMjAzExMTk+fojU2iOxdd8788pL3fSzdk/hUKB1NRUZGZmIS42Seu6p0ePHuXpui5zhYWFIS0tDQDQrFkzo3WbNWuGO3fuIDY2FufPn8fLL7+st17NmjUNfvnx9PQEgDxNiUpERcvqztw3b94cJ0+exGuvvWawjuZPlw4ODjrlmzdvBgBUrVoVNWvW1NtG5cqVUbduXam++sNK7eTJk7h//z4ASNPJ6aP564K+qeY01xlrR1124cIF3Lp1y2A9Imsnk8lg72Jf4A/v2t54/cfXn+/XVoY3FneFW3VPyJ3tCnz/OYe/FCTNWXM2btyI9PR06e/du3fj2bNneu/umpWVhdDQULRv3x5ubm5wdXWFt7c3ypQpgzJlyuDDDz+U6qoTVUu7c+eOtGzsxlrq5DU3v//+O7p27YrSpUvD2dkZ3t7eqFChHNo1qomPRgyU6hVUPGo3b96UlitVqmS0ruaXLGOfD25ubgbL1J+VnDGHqPizujP3efHPP/9Iyx06dNAqe/jwoTQtmr+/v9F2GjdujGvXruHBgwe4efMmateuLZUdPHhQWjbWTqNGjWBjYwOVSqW1Tc52PDw84Ovra7QvmtsY+lJCRJbTKKgRdn2wCwAQfDUYHtVL4+qTxCLuleXVq1cPL7/8Ms6cOYO4uDjs2LFD+oVw9erVcHBwQN++b+ORRj6bkpKCrl274siRI5DL5Rg0aBDatGmDSpUqSWe4//rrL2mYZEFJSkqSlh0dHQ3Wk8uNfxxmZWVh4MCB2LhxI2QyGXr27Ik33ngDlStXhkxmg7uxqbhx9TK+na07pr8gaMaVc8hnTprlxs68c8Y1IutQ4pL7tLQ0TJ06FQDQsGFDnWE3ly9flparVKlitC3N8itXrmgl93ltx8HBAWXKlEFUVBSuXLmiVaZQKKSzTqb2hYgKl3sl96LuQoEaMmQIzpw5AyA7oe/duzfi4+Oxfft29OjRAx4eHnik8cVm1qxZOHLkCIDsoTz9+vXTadPQ9UqWpHk22tjZdGNj0YHs6TY3btwIAJgzZw4+/fRTqUypVOHqk0TY5vIFwZLyGheQ/VmiVqpUqQLrExEVD1af3KenpyM+Ph4xMTE4fvw4vvvuO1y9ehVvv/02li1bpnPGQ/NGIZpzAuujWZ7zBiOmthMVFYXExETExcVJPw/fu3dPuvlIfvqiT2xsrNbPqw4ODnqHKOWHZvtKpbJE/ZzL2J8vmxO7UqmESqWStlUqlRBCSI/iQrMvOftW3PqaX/369cMnn3yCzMxMaSjOli1bkJ6ejsGDB+vEvmHDBgDZd7p955139D4Xpj5feXk+c9bRnJf/wYMHBsenx8bGGu2LOh4bGxuMHTtWp+/6+pHfeIw9PzVq1JCW79+/b7QtzS9RNWrUMFrXlOfYGo53c99XrCF2czF26Q+TYlc/V6Z+LprzGWr1yf2GDRswbNgw6W8fHx+sW7cO/fv31ztmNa8/4QLaP3VqbpffdtTJvaX6ok/OG9EEBQVh6NChuW5nitTk5wfkmX/+gat7wU/bV1xoxn761Ck4u1r9fzWJJWJXqVTSbCM2NjawsbGBm5sbUlNTi9UXpcyU5zO8pCSnwFb5PNbUlBTY2BbeuPiC5ujoiE6dOmH37t3IzMzEqlWrsGXLFpQtWxYtW7ZEakqKVDc1JQVPnjwBkJ3cqycoyCk6OlpaTklJ0VtPfUY9KyvLYDvqmXaEEEhOTtZ6b9dM5k+cOKF3FjUAOH36tLSckZGhs69Hjx4ByJ5eMmdfVFnZH/Apyc/fexUKhcH+qvtsrByA1rUNOZ8ff39/ODo6Ii0tDadPnzbaljo2T09P1KpVS6duXp5jfXXUcQMv7vEuhJBeb1OuY7GG2M3F2LOlpKbA1jbvQ9kyMjKQnp6Oc+fO6b1rtCEpGu+teWX1GUeXLl2wb98+pKSk4ObNm1i/fj0GDhyI6dOn4/vvv9eZW1nz50t7e+PJqGZ5amqqxduxVF/0iYiIgLv782EEBXHmPj5OAYQdBQC83KwZvMsYvljL2mjG/kpAADw8jY+JtSaWiF2dwAcGBkIulyMtLQ337t2Ds7Nzrl90C1OGLENadnF1gY2DHPgvwXN2cSnQ2XKKwrBhw7B7924AwOLFixEZGYmPPvoIHh4eUCpVWrFXqFAB9+7dQ0REBJycnPTOwqI5fNHFxQWurq46ddTb2dra6i0Hns+6I5PJ4OrqqpWk9e7dGxUqVMDjx4+xbds2zJ07V29f/vzzT2nZ3t5eZ1+VKlXC7du3ER0djYyMDJQu/fxuxOrYr168IK1zcnLS21/NOeQ1y5s3b47k5GSt50TzPTnn8+Pq6oqhQ4di6dKlOHPmDB4/fqz3Xil///03IiIiAABjxozROywnL8+xvjo5X/MX8XhXfyHMedzkxhpiNxdjz47dxdkFcjv9s0vpk5aWBgcHB9SqVcukz7HERNOv47L65L5ChQqoUKGC9Pcnn3yCyZMnY/78+ejWrRtCQ0MRFBQklWueAc/IyIAxmuU5bx5iiXYs1Rd9SpcurZXcFwTNC9TkcnmuF6xZE8b+fNnc2G1sbKTt5XI5ZDKZ9CguNPuSs2/Fra+W8Oabb8LDwwPx8fHS8L+hQ4fqjf2dd97B/PnzERcXh9mzZ2PGjBlabR05cgTr1q3T2ia35ysvz2fOduzt7fHdd9+hX79+0nTBOfty6NAhrfnr9fXlnXfewdGj2V9aJ06ciOXLl0sXoMpkMty5eQNrly/JNZ7atWvj6tWruHv3rlT+6NEj/PPPP6hTp47O82isvblz52Lv3r2IiIjAyJEjsXv3bq33//j4eOlmWg0bNsSUKVMs9hznpX8vCnPeW6wldnMwdukPk48ZmUxm8ueiOZ+hJSfj+I9MJsO8efNw4MABnDt3DqNHj0aXLl2k+YvNvUgp5xRilmjHUn0hIrIEBwcH6XolIHtu+Jz35lD74osvcOTIEZw+fRozZ85EWFgYunTpAmdnZ/zzzz9Yu3YtatasKc1O9scff8Db2xuBgYEoV64ctm7dCiD7rqnqf9euXQsA6NWrF1xcXKS/1RMPpKSkYO3atZDJZOjcubN0LdI777yDW7du4YsvvsDMmTNx4sQJdO/eHfb29jhx4gTWr1+PH3/8Ee+99x4A4OLFi1i7di1cXFzQq1cvAMCoUaOwZ88ebN++HaGhobh06RJ69eqF0qVLIzz8IkJDQ1Gxig8ibmWf1du3bx8ePHgAPz8/rTn0g4KC8Mcff+DEiROYMGECatSogVWrVkEIIe1/3759iIqKwokTJ6Tt1M+PZnseHh44ePAgunbtiqNHj8LPzw9BQUGoVKkSIiMjsXLlSjx8+BCvvPIK/vzzT51rzNTPX87n2NXVFT179pT2m5ycrPd16NdvQC5HDBEVCVFCffPNNwKAACC+/fZbaf2ePXuk9R9++KHRNiZPnizV/f3337XK+vXrJ5WdPXvWaDvlypUTAIS7u7vW+tTUVGFjYyMAiIYNGxptY/fu3dL+xo4da7BeQkKCACASEhKMtmcJcbGpwnfyDuE7eYd49jSxwPdXnGjGHhebWtTdKVSWiD0zM1McOnRIZGZmCiGEUCgU4urVq0KhUFiyq/mWnpwuZmCGmIEZIj05XWRmZonw+3Ei/H6cyMzMKuruFYiwsDDpvWbBggXSen2xp6Wlifnz54umTZsKFxcXYWdnJypWrCh69eol9u7dK0JDQ6W21I/Q0FARERGhs17zERERIYQQRuscOnRIp+9HjhwRb775pvD29hb29vaiYsWKok+fPuLvv//W216lSpW0ts/KyhIhISGiVatWwt3dXcjlclG2bFnRpUsXMe+nFWL5b9t12pg+fbpOP1auXCn8/f2Fo6OjcHd3F02aNBErV64UKpVKCCFE27ZtDcalr72MjAyxZMkS0b59e+Hl5SXs7Oykfv3yyy9CqVTqfS0N7cPX11eq4+vra7CeNRzvKpVKJCYmSs99XllD7OZi7NmxZ2To/39liLmfY+bkbSXuzL2a5q26L126JC03aNBAWlbfhMoQzRkIXnrpJa2ynO00adJEbxvp6el49uyZ3jacnJxQvXp13Lp1K199ISKylJYtW+Z5hggHBwdMmDABEyZMMFjH0IX8edmHyDFrR25jp9u0aYM2bdrkqT19bGxsMGLECJ0plNVTYQJAZmZWrmOQhw0bpjXRQ06HDx82un1OdnZ2eP/99/H++++btF1enmNjs68plXm/KJCICo/VXQWxe/duaW5lYzQvqNKcfaNSpUrSfPUXLlww2sa5c+cAZN/9L+eFTJo3xzLWTnh4uHTVdM4bammui4+Px927d3Pti6F2iIiIiMj6WV1yP3r0aHzwwQe51tO8BbePj49WWd++fQFkn7HQvHW5pocPH+L69esAgD59+uicKWrevLl0y+8DBw4Y7Mf+/ft19quvL7m1oy7z9/fn3WmJiIiISiirS+4B4Nq1a0Z/ShRCYPXq1dLf3bt31yofO3asNNXX8uXL9baxYsUKCCHg6OiITz75RKdcJpNJd8INCwuTvghoUiqVWLVqFQCga9euei9M69SpEwICAoz25ejRo/j3338BAJ999pneOkRkefYu9pgupmO6mA57l5JzHwciIiq+rDK5V6lUGDx4sHQTlZxl48ePxz///AMA6N+/v5Q8q5UtWxbz588HAHz33XdSXbXw8HB8/fXXAIAvv/xSOkOf08iRI9GqVSsIITBy5EitGW0AYMaMGbh58yY8PDzw3XffGYxn8eLFcHJywokTJ7Bo0SKtsvj4eOmXim7duqFPnz4G2yEiIiIi62Z1F9Q2atQId+/eRVhYGGrUqIF+/fqhbt268PLywr179/Dbb7/h2rVrAIAhQ4ZIU7rl9P777yMqKgqzZs1C+/btMWLECLz00ku4fv06QkJCkJKSkuuFYnK5HFu3bkW3bt0QFhaGpk2bYtiwYXBxccGuXbuwc+dOeHt7Y/PmzXpvPqLWpEkT/Pbbbxg4cCDGjh2LY8eOoUOHDoiJiUFISAgiIyPRoUMHrF+/Pn9PHhERERG90Kwuud+2bRsuX76MrVu34tixY9i3bx82bNiAzMxMuLm5oVq1ahgzZgyGDBmidVtyfaZPn47OnTvjhx9+wObNm7FkyRJ4e3vj1VdfxQcffID27dvn2h9vb2+EhYUhJCQEa9aswdy5c5Geng4fHx9MmjQJH330kdZNtgzp1q0bwsPDsXDhQuzcuRN//vknXFxc0KBBA0ybNg1BQUHSDVWIiIiIqGSyuuQeyJ6GUnMqyvwIDAxEYGBgvtqQy+UYPXo0Ro8ena92qlatioULF2LhwoX5aoeIiIiIrBNP9RIRERERWQkm90REREREVoLJPRERERGRlWByT0RERERkJZjcExERERFZCSb3RERERERWgsk9EREREZGVYHJPRERERGQlmNwTEZkpNUOJqp/uRNVPdyI1Q1nU3SEiImJyT0RERERkLZjcExERERFZCSb3RERERERWgsk9EREVC4cPH4ZMJtN6DB06tKi7ZbYRI0bAzc0Ns2bNKuqu6Dh37hwaNWpk9nMcHR2N6dOn4a2OLdC8TmWULeuNFi1a4IcffkBGRobebYYOHarz+h4+fDh/gVCezZgxA25ubhg1apROWc7XpWrVqlrlkZGRUpmdnS0aVfHE8L7dCqnnZCom90REVCzUq1cPa9aswZo1a+Dt7V3U3cmXmJgYrFixAsnJyViwYEFRd0eSkZGBadOmISAgABcvXjSrjdOnT8PPzw9z5vwPZcpVwEdTpuPTT6cgOTkZ48aNQ/PmzfHo0SOd7d577z2sWbMGU6dOzW8YZIZvvvkGycnJCAkJQWxsrFaZ+v9d69at9W5bpkwZqU7dunULo7uUD0zuiYioWChXrhwGDRqEQYMGwcXFpai7ky9eXl4YOnQoXFxcMG7cuKLuDoDss/XNmjXDl19+iQ4dOpjVxr1799CtWzc8fvwYY8eOw8/rf0e/oSPx8cef4MyZM2jfvj3Onz+PN998E+np6VrbtmjRAoMGDULnzp0tEQ6Z6KOPPoKLiwuCgoJQunRprTL1/7vq1avr3dbFxUWqU7ZsucLoLuUDk3siIqICEBoaiuTkZHz55ZdF3RXcvHkTAQEBePbsGbZt24aff/7ZrHYmTpyIZ8+ewcfHB3PmzNUqc3BwQEhICGxtbXH27FksWrTIEl0nC/nyyy+RnJyMVatWFXVXqIAxuSciIrJyqamp6NevH65cuYI333zTrDZu3ryJTZs2AQCGDBkCBwcHnTo1atRA+/btAQDz5s2DUsn7PxAVNib3REREVs7Pzw9r1qyBp6en2W1s3rwZQggAQKdOnQzWUw+7efbsGS+YJSoCTO6JiAiA9owYOR8zZszQqlu1alWtcjs7Wyz59iutOpcuXcLIkSNRo0YNODk5wd3dHfXr10dwcDCuXbtmkT7HxMRg+vTpePnll1GlShU4OTnB19cXgwYNwtGjRy2yj6tXr2LUqFGoXbs2nJ2d4ejoiGrVqqF37974+eefERMTo1W/Xbt2Os9fZGSkVp3KlSvr1FHvq3///ihfvjxcXV3RuHFjLFmyBFlZWdK2J0+exBtvvAFvb2+4uLigRYsW+P33343GoG4/Pw4ePCgt+/v7G6zXuHFjvdsYEh8fjylTpqBevXpwcXGBp6cn2rVrh19//dXsvt66dUvn+W3Xrh0AYM+ePejcuTO8vLzg7OyMgICAPP/KcO/ePXzyySfw9/dDi7pVEFCrIurVq4ORI0fmeoFyamoq5s+fjxYtWsDT0xN2dnbw9vZGmzZtMG3aNJw/f16rvqHZhe7fv48PPvgAtWrVgrOzMzw8PNCqVSuEhoZKX77y0k5BS0lJwYIFC9CyZUt4eXnBwcEBlSpVQu/evfHnn3/q1F+1apXB9x/N/hp6n9IX044dO/DWW2+hYsWKsLe3R+nSpREQEIAZM2bo/L8FgP379xucsWvLli147bVX0c6/Fvx9SsPeXm6R/1cFgck9EZEFPElIK+ou5Jt6Roz3339fWjd27FisWbMGb731llbdhQsXYs2aNShXrhxq166NVat+QcfXn0+NN3fuXPj7+2P16tXo0KEDfvjhB8ydOxf16tXD0qVL0aBBA3zzzTf56u++fftQs2ZNzJo1C87Ozpg8eTJ++OEHdO3aFVu2bEHbtm0xcuTIfA0N2bx5sxRHYGAg5s2bh4ULF6J79+7Yt28f3n//ffj4VMbTJ4+lbT777DOsWbNG75SDaj/++CPWrFmDXr16SeuOHj2KN954AzVq1MCsWbMQFBSEy5cvIzg4GCNHjgQA/P777wgODkaHDh0wffp0tG/fHidPnkTv3r0LfCz15cuXAQBubm4oVaqUwXpVqlSRlq9cuWK0zcePH6NJkyY4c+YMRo8ejW+//RbdunXDsWPH0L9/fwwePFjri01elS9fXu/sLt988w3GjRuHDh06YP78+Rg+fDhu376NKVOmICgoyGiba9asQZ06dfDtt9/C19cXYz/9AhOm/w8BAc0RGhqKxo0bY/r06Xq3jYqKQtOmTTFp0iQAwOTJk7F06VKMGzcO8fHx+PLLL9GkSROtmZX0zS509uxZNGjQANeuXcOYMWPw/fffo3fv3jh16hTeffdd9OjRQ2cq0qKYpej8+fOoW7cuJk6ciJSUFEyaNAmLFy9Gv379cPDgQfTo0QPdu3dHcnKytE2bNm2wZs0atG3bVlq3ePFirFmzBvXq1ZPWqd+nfvjhBwBAjx49dOooFAr07t0b3bt3x99//42hQ4di6dKlmDJlCgBg5syZqFWrFg4dOqTV7wYNGuidsWvChAmYNGkSWrdugw8nfY7Xe/ax7BNmaYJKlISEBAFAJCQkFPi+4mJThe/kHcJ38g7x7Glige+vONGMPS42tai7U6gsEXtmZqY4dOiQyMzMFEIIoVAoxNWrV4VCoch1W5VKJVLSMwvlEXL0thRrtU93iNCwO+LUnWhx6k60SEhJL9B9q1Qqs57bvIiJiREODg4CgBg4cKDBehcvXhQAxDfffCMyM7NE+P04EX4/TnzzzXcCgHB0dBQnTpzQ2W79+vUCgAAgNmzYoLdtX19fAUAEBQXpLT99+rSwt7cXAMTHH38sVCqVSExMlJ6Xs2fPCldXVwFAjBw50vQnQQgRFxcn3NzcBADx22+/6ZTfvXtXeHt7CwBi1/FwEX4/TmRmZknloaGhUpwRERF69zF9+nSpTsOGDcXt27e1yteuXSuV//777+LVV18VaWlpWnWCgoIEAOHl5SXS09PzFFtERITUrqHnWFNaWppUv2bNmkIIofWaa8YdGxsr1fXz89Np69ChQ1J52bJlxaxZs3Tq/Pbbb1KdL774Ik8xGdK2bVsBQJQrV040a9ZMJCcnS2UqlUr88MMP0r5Onjypt42tW7dKdX744Qed2Hfu3ClsbW0FAPG///1PZ/vBgwcLAKJr1646/3ezsrJEv379BAAxffp0nW01ny9bW1u9z8dff/0l5HK5ACBGjx6tNwbNdg4dOqS3jvpY8vX11VsuhBBt2mQ/ny83b6n1uqtFREQIT09PAUC8/fbb0vu4ZnmFChUEANGlSxed52PHjh1SP0NCQgz2Q/26nT17VqesV69eAoCoXbu2iIqK0ipTqVTi/fffFwCEq6ur+Pfff/W2r34Pqly5sggICBDJyclar3vHjh2FKWm0KZ9jmszJ2+QW/aZARFTEFJlZqD9tb6HvVyWAGduvFtr+rs7qAmf7gnkLL126NHr16oVff/0VW7ZswaJFi+Dh4aFTb8WKFbCzs8PgwYOldVGPH2LKlMkAIM15nlP//v3xyy+/YO/evZg4cSL69OkDuTzvsQghMGzYMGRkZKBq1ar46quvdOo0adIEkydPxhdffIGQkBD0799futAzr44ePYqkpCQAwOuvv65T7uPjg5EjR2Lu3Lk6Zebo3bu3zlSE/fr1w+jRo5GUlIT+/ftj7969Oheyvvfee/jll18QExODY8eOoWPHjhbpjyb18wAAjo6ORus6OTnp3U4fLy8vfPbZZzrr+/bti+7du2P79u346quvMGrUKFSuXNnEXmuLiorCunXrdKZZ7devHyZMmICMjAxs374dAQEBWuWpqanSLyeBgYH48MMPoVSqtOq88cYbGDp0KFasWIFp06bh7bffRs2aNaXyHTt2AABee+01naEcNjY2mDlzZp6GIdWsWVPvrwOdO3fG4MGDERoaiqVLlyI4OBgNGjTItb2CEBwcjLi4OLi5uWHJkiU6/7erVq2Kr7/+GoMHD8bevXuxatUqDBs2TCp/7bXXUKlSJTx8+BArVqzAiBEj9O5n5cqVaNSoEZo0aaK1ftOmTdi6dSsA4Pvvv0fZsmW1ymUyGb799lts3LgRcXFxmDZtGjZs2GAwngcPHuDPP/+Ei4uL1uv+7rsj8n1MFhQOyyEiIh3Dhw8HAKSlpWH9+vU65RkZGVi7di26d++OMmXKSOs3rQmVhgUMGDDAYPtvvPEGgOwPziNHjpjUt/3790vDPfr16wc7Ozu99TSHWXz//fcm7QMAVKrnH+SGxu8HBwdjx46d8PIuo7fcFF27dtVZZ2trixo1agAA7O3t9d5kSHM4gqWuZchJoVBIy/b29kbrapanpqYardu3b1/Y2OhPRdTHT2ZmJn755Ze8dtUgV1dXady9JvU1FID+52/dunWIjo4GkD1LkCHq4y0rK0tnGlD1sWToOKpVqxZ2796NQYMGGY3h7bffhq2trd4y9fMlhMCKFSuMtlNQbt68id27dwPIPp5zzqev1qdPHzg7OwPQ/b9pa2srjXM/efIkrl7VPWly9uxZXLhwQXqf0qRuz8vLC6+++qre/Ts5OUnHwpYtW7SO75xq1qypdR2JWt++fYvttKI8c09EVsXJzhZXZ3Up8P08SUhDp2+PQKVx/ZqNDPhpQGN4uTqgbjk3yOUFd/7EyU7/B7yldOzYEdWqVUNERARWrlyJ4OBgrfJt27YhJiZG58P1xLHDAAA7OzuUL19eSopycnd3l5ZPnTpl0tnm/fv3S8vNmjUzWK9KlSooV64coqKicOjQIahUKoOJpD6vvPIKHB0dkZaWhnfeeQcTJkzA8OHDtc7WVa5cGeXLV8TVJ4l5bteQWrVq6V3v5uYGIHuaSX3913wuExIS8t0PfTTPxucc052TZrk6gTOkYcOGBss0z8gePXpU7xl+U9SsWdNgYqyeRUjf85fX4+3ll1+GTCaDEEJrGwBo27Yt/vzzT2zatAk9e/bEhAkT0LJlS+ksvkwmw2uvvZZrDMaer6ZNm0rLlrqY3FR5fa4cHR3RsGFDnDp1CuHh4Xj27JnWSYJ3330Xc+bMgRACK1eu1LnL88qVK+Hg4ICBAwdqrU9OTsbJkycBAPXr19e5E68mLy8vANlfHs+fP4/AwEC99V566SWDbRRXTO6JyKrIZLICG66iqXoZV8x88yV8sS37DLKNDJjdowEqeWYnM8728gJN7guaTCbDsGHDMG3aNJw9exbh4eFo1KiRVL5ixQpUqlQJXbpof5G6H3kHQPYHZrlyebuT5ZMnT0zq282bN6XlSpUqGa1buXJlREVFITExEU+fPkX58uWhVCp1Zq9Rs7Ozg6+vLwCgYsWKWLhwIT744AMkJydjxowZmDlzJl555RX06NEDffr0MZiQm0OdxOekTuhzKweyn/eCoLnvtDTjF49rngU11Gc1Y1Nzar62ERER0nJSUhKioqL0buPu7q4zDCMvfVEPddJ38XVejzcnJyeULl0aMTExuHXrllbZwoULceHCBdy7dw/btm3Dtm3bUKlSJbz55pvo2bMnOnbsaPCLhyZDZ8KB7OdS/WVU8/kqTKb+3zx16hQA4Pbt21rJffXq1dG+fXscPHgQq1evxty5c6Vf6NS/Jvbq1Uvn+YiIiJAuwD527JhWm8YYew/S/PL8omByT0Rkpt5NK0vJ/f6P28LH09kiZ3CLi2HDhmHGjBlQqVRYsWKFNDvF/fv3sW/fPkyZMkUnIUlJzh5j7erqKo17zU1uSUBOmuO4Nc8o66NZnpCQgPLly+PBgwcGk3JfX1+txP+9995D8+bNMX/+fGzduhWpqak4deoUTp06halTp6JTp0745pvvgNL5H3ub268KpvzqYGkODg4oX748njx5YjCxVtMsr1q1qtG6xhJazbH9mq/5li1btMZoawoKCjI4VMLc58+c4y09PR3p6enSl4Zq1arh0qVL+OGHH7By5UpERETg4cOHWLJkCZYsWYLKlStj5syZePfdd422n9sXAHVyn9u1DgUlP/83cxo+fDgOHjyIZ8+eYfv27dKMXVu2bEF8fLzeITmJic/ff1u2bKkzha8hxq5PKMr/d+Zick9EZAHlSxm/yPBFVLlyZXTp0gW7d+/GunXrMH/+fDg4OEjzaetLsJxdXJGYEI+MjAyjNzrKD3PPIhubvtGYRo0aYe3atVAoFNi7dy82bdqEP/74A6mpqdi/fz9atmyB9bsOw7daDbPaf1E0aNAAT548QVJSEhISEuDiov9M+IMHD6Tl3IY0GJvmUvO1ze0XgIKU83gzdiZXfbw5ODjoXPjs7u6Ozz//HJ9//jnOnTuH33//HRs2bMCdO3fw4MEDDB8+HE+fPsWnn35qsP3cpgVVP2dF9XxZ8v/mW2+9BU9PT8TFxWHFihVScr9ixQr4+vrqHcqnuX8XF5cCew8q7l68ryNERFRo1GfHYmNjsXXrVgghEBoairZt20oXemry+S/BzcjIMHm4TV5pzkKimUjqoy53d3eXfqKvWrUqhBB6H4aG6wDZZxp79uyJdevW4eHDh/jggw8AZI/zXfqt7ow91qZDhw7S8oULFwzWO3funN5t9ImPjzdY9vDhQ2lZfcErkH1TJkOvX0Fc4JjX402hUEhjvDW30adJkyaYPXs2bt26hU2bNkmzUc2YMcPoWfe4uDijZeqEWvP5Kkzm/N8EoPe9xNHRURpTv3fvXjx8+BB37tzB4cOHMWzYML03kKpWrZr068bdu3fNisEaMLknIovSnBwjl4kyrI7G5Cpayy+yN998U0qKV6xYgQMHDiAyMlLnJ3F1vC1at5PWHT9+3GjbnTt3hlwux4EDB0zqU+fOnaXlM2fOGKx3//59aYhI+/bt8zSmWdP58+fx+eef4+nTpzplHh4eWLRokXQR442r2Td4spbXXZ8+ffpICdWBAwcMHu/qiyq9vb31zk6jSX1jLH00vyS0adPG9A5biL7jTV/sZ86cke4Qm/OM8eeff469e3Wn6JXJZOjTpw9mzZoFIHs4z40bNwz25dKlSwbLzp49Ky0X1vOV83jXjNvY/820tDTptW/UqJHBsfHqaTCzsrKwatUqrFy5UroeSB83Nzdp+t2bN28avKAfyJ7JydPTE66urlo30zJGM15RjP+vM7knKgAlOcEl62JnZydN/3fgwAHMnDkTpUqVQu/evfXW7zt4mDQV4vLlyw22e+PGDRw4cAAVK1bUuiNlXnTq1An169cHAPz6668G70C7evVqaXncuHEm7QMAwsPD8b///U+66E+fihUrAgBci3DYSGGpVasW+vbtCyD7bq36Zs25c+cODh48CCD7Lqy53b9g8+bNUkKck3oKVrlcnuvdYwvSwIEDpbuVrlmzxmA99fFma2sr/aqj9r///Q/Lli0zuK3mdSfGhv1s3rxZa4pWTernSyaT5Tp2v6DUrl1buifEzp07Df7SsGXLFqSkpAAw/n+zUaNG0hfolStX4pdffkGnTp3g4+NjcBt1eyqVCitXrjRYb926dYiPj0f37t3h6upqPLAXDJN7IiIySn2WXgiBsLAwDBgwwODFcuUqVMK8ednT1u3evRs//vijTp2YmBj07dsXQgjMmzfPpBtYAdnJS2hoKOzt7RERESHdUl7ThQsXMG/ePADAyJEjTb6BlaZZs2bpnQf79u3b0pSDXd7U/2XH2syfPx9lypRBZGQkvvhCe2rK9PR0jBo1CllZWWjatCnGjBmTa3vXr1/H119/rbN+8+bN2L59OwBgypQpRXqzIGdnZ4SEhAAAwsLCdOawByDdjAnIPl70XbC9fft2nDhxQme9EEKax79x48aoXbu2wb5ER0djzpw5Ouv37dsnffF4//33jU6ZWdAWL14MT09PJCYmYvTo0TrXCdy7dw+TJk0CALz66qvSnPaGqN9/NK9NMKZv377SyYdZs2ZJU2NqOnfuHD7++GO4uLjgyy+/zGtoLwxeUEtEREbVq1cPgYGB0jCb3D5cg4M/gFKZjk8//RRjx47Ftm3b0K1bNzg5OeHGjRtYtWoVEhISMGfOHPTv31/aLioqCvv27QMA6azenTt3sHbtWgDQusHPK6+8gu3bt+Ptt9/GggULcOrUKbzxxhvw8vLChQsXEBoaCoVCgeHDh+Onn34yK271xXlnzpxBjRo1MGjQIFSvXh1ZWVm4fv061q5di4SEBLz99jvoF/T8Lpr79u1DVFSUViL3xx9/wNvbG35+fvDz85PqXLx4UaqjjrNXr15wcXGR/lYPLYqKisLatWvh6uqKnj17aj1fahcvXsTatWtRo0YNtGjRQqvsxIkTuH37NgBoDVfQfI4196+Pj48Ptm/fjl69emHhwm9x+nw42nV+De5yYN261bh06RL8/f3x559/6tzJVr1/zRtFffXVV5g1axYOHTqEbt26wd7eHseOHZPOQg8cOFDvHVnzIrfnD8h+XaKjo3XqANrHW8+ePfHLL79g1KhR+PDDD7F79x40atEWcjs7RFw+hw0b1kOlUuGLL77A1KlTdfri5uaGpKQktGnTBn379kWTJk3g6emJR48eYcuWLQgPD0eVKlX03jBO08KFCzF16lQcPnwYb775JpycnHDq1Cn88ssvUCqV6NatG7777jutbfQ97/v27cODBw+k40Qd85072VPZpqSkSOs6d+6sNfuV+rmKiX6G9evXQi630TpmqlatigMHDqB79+7YuHEjrl+/jgEDBsDLywvXr1/HihUrEBcXh65du+LXX3/VO3Ze04ABA/DJJ59AoVDAy8sLPXr0MFofyH7t5XI5Nm7ciDZt2qB///5o0aIFMjIycObMGWzYsAHOzs7YtGmT1nUCKSkpUpz63oPq1WsAu3JVc91/kRNUoiQkJAgAIiEhocD3FRebKnwn7xC+k3eIZ08TC3x/xcnDB89jf/ggtai7U6gsEXtmZqY4dOiQyMzMFEIIoVAoxNWrV4VCobBkV/MtJT1TijUlPVOkp2eJ8PtxIvx+nEhPzyrq7lnUihUrBADh5+ent1xf7NeuXROjR48WtWvXFs7OzsLBwUFUr15dDB06VJw9e1anjUOHDgkABh/6PHv2THzxxReicePGwt3dXdjb24sqVaqI/v37iyNHjuQ77hs3bogvv/xSdOzYUVSoUEHY29sLe3t7UblyZfHWW2+JP//8Uyf2tm3bGoxh+vTpQghhtE5ERIQQQhgs9/X1zfX5CgoK0oklKCjI6PObc//GPHv2THz66VRRvXZd4eTsIjw8PMQrr7wiFi5cKNLT0/Vuo2//hw4dEnfv3hXvv/++qF69unBychLu7u6iTZs2Yv369Wa8Ys/l9vwJIYSvr69Jx9vdu3fF+PHjRb169YWTs4twcHAU1apVFyNGjBAXLlww2JekpCSxatUq0a9fP1GnTh3h4uIibG1thaenpwgMDBRfffWVSEzU/zmp+TofOnRIxMbGikmTJom6desKZ2dn4ebmJgIDA8XKlSuFSqXS2d7Y664+TowdD4cOHRIREREmHzPJycni66+/Fi1atBCenp7Czs5OVKhQQfTs2VP88ccfhl84PQYPHiwAiLFjx5q03V9//SXefvttUalSJWFvby9cXV2Fn5+fmDx5snj48KFO/dzi/Pzzac//r6cpTeqLuZ9j5uRtMiEMDHYjq5SYmIhSpUohISGhwG/M8OihAoE/Zo+9PDa6Dar4Wv+YVDXN2I9/2AEVKxmf79eaWCJ2pVKJsLAwtGrVCnK5XLopS7Vq1XTOBhal1Awl6k/Lvkju6qwukMMG159mz7Nct6w77O1LzsjHjAxVkcYuhEBycjJcXV1zPRNoaUUde1GxhrjNPW4KM/bDhw9Lw8oOHTqU60XKBc0aXndzacVexg32Dnm/SN/czzFz8raS84oQEREREVk5JvdERERERFaCF9QSEZnJ2V6OyK+6Sn9nZBTjiY+JiKhEYHJPREREVMzkZZYbIn2Y3BMREREVMz///LM0/72aeo77oKAgJvdkEJN7IiIiomJm1apV0o2xiEzBC2qJiIiIiKwEk3siIiIiIivB5J6IiIiIyEowuSeiFwZvqE1ERC+iwvz8YnJPRMWejU32W5VKxXnkiYjoxaP+/FJ/nhUkJvdEVOzZ2dnB1tYWKSkpRd0VIiIik6WlpcHGxgZyecFPVMnknoiKPZlMBjc3NyQmJnJoDhERvXCSk5Ph7OzMM/dERGqlSpVCZmYmHj16xASfiIheGHFxcUhNTYW7u3uh7I83sSKiF4KzszMqV66MBw8eQKFQwN3dHc7OzrC1tYVMJivq7gEAMjJUEMoMANk/wapUJef8SVHHLoRARkYG0tLSCv14KOrYi4o1xG3ucWMNsZuLsf8Xe3oaVMLWYF0hBJRKJRISEpCUlARPT0+UKlWqUPrJ5J6IXhhubm7w9fVFQkIC4uPjERMTU9Rd0pKlFHialAYAsE1yhK28eHzpKAxFHbsQAunp6XBwcCj05L6oYy8q1hC3uceNNcRuLsaeHbtNkgPk8ty/2Dg4OKBcuXLw9PQs6O5JmNwT0QvF2dkZzs7OKF++PDIzM4vVDDpPo9Lw3p8nAQAbBjVH2XKORdyjwlPUsSuVSpw7dw61atUqlAvWNBV17EXFGuI297ixhtjNxdizY1/brxkqVHIxWt/W1hZyubzQTzgwuSeiF5JMJoO9vX1Rd0OLXC7wMCnrv2UHODqWnA+9oo5dqVRCpVLB0dGx0JP7oo69qFhD3OYeN9YQu7kYuzp2+2Ibe8kZKEVEREREZOUK/PTGnj17sH37dpw6dQr3799HQkIC0tLSpPLFixfD3t4eAwcOhJOTU0F3h4iIiIjIahXYmfvjx4+jXr166Nq1K5YuXYrz58/j2bNnyMzM1Kp34sQJvPfee6hWrRo2bNhQUN0hIiIiIrJ6BZLcb9iwAe3atcO///4LIYT00KdKlSqQyWR4+vQpBg0ahNmzZ+d7/9HR0ViyZAl69uwJX19fODo6wtnZGdWqVcM777yD7du3G50nOzIyEjKZLE+PMWPG5NofpVKJJUuWoGXLlvDy8oKrqyvq16+PKVOm4MmTJ3mO6+7duxg/fjzq1KkDZ2dnlC1bFu3bt8eqVauK1UWFRERERFQ0LJ7cX7x4EcOGDUNWVhaEEHB2dkaLFi3Qu3dvVKxYUaf+nDlzcPfuXYwcORJCCEyfPh379+83e/8TJkxAlSpVEBwcjOPHj+Ott97Ct99+i/nz56Np06bYvHkz3nzzTXTq1AmxsbH5CTVPoqOj0apVKwQHByM2NhaTJ0/G119/jWrVquGrr76Cn58fjh49mms7O3fuhJ+fH77//ns0atQI3377LcaNG4eIiAgMGzYMr776KpKSkgo8HiIiIiIqviw+5n7q1KnIyMiAh4cH5s6diyFDhkhj6V9//XU8fvxYZ5tKlSrh559/RsOGDTF27FhMnToVnTp1Mmv/a9euRVpaGtq2bYtt27Zp3TDggw8+wI4dO9CzZ08cPHgQ3bt3x7FjxwzeCnjOnDno1auX0f0Zm7dUqVSiV69eOHXqFAIDA7F//37puQgODsbUqVMxd+5c9OjRA6dPn0atWrX0tnPu3Dn07dsXCoUCCxcuxLhx46Sy4OBgtG7dGgcOHMCAAQOwfft2o/0lIiIiIutl0TP3sbGx2LNnDxwdHXHw4EG89957Jl0kO2bMGLRt2xZnz57F7du3ze6HXC7H6tWr9d4JrFu3bhgxYgSA7OsCfvvtN4PtVKhQAXXr1jX6KFeunMHtQ0JCEBYWBplMhpCQEJ3nYubMmahZsybi4+Px8ccfG2wnODgYCoUCAQEBWok9kP3lYtGiRQCAHTt2YMuWLQbbISIiIiLrZtHk/ujRo1CpVBg+fDj8/f3NamPgwIEAgNOnT5vdD39/f/j4+Bgs7927t7RcUGe6hRCYO3cuAKBly5aoX7++Th07OzsMHToUQHZiHh4erlNn//79OHXqFABg5MiRevfVrl076ay/Ja5ZICIiIqIXk0WT+4cPH0Imk6Fz585mt1G7dm0IIfQO38mLDRs2YMWKFUbr+Pr6Ssv37t0zaz+5OXnyJO7fvw8ARocYaT5XmzZt0inXXGesHXXZhQsXcOvWLZP7S0REREQvPouOuU9MTAQAlC5d2uw2MjIyAEBnysy8at++fa514uPjpWUXF+O3DlZTKpVIT0/Pc/2DBw9Ky8Z+xWjUqBFsbGygUqm0tsnZjoeHh9aXkpwaN26stU3NmjXz1E8iIiIish4WPXPv5eUFANIZa3NcvnwZMpkM3t7eluqWjoiICGm5TZs2ButduXIFAwYMgI+PDxwdHeHq6goXFxd07NgRoaGhUCqVBre9fPmytFylShWD9RwcHFCmTBlpf5oUCgXu3LmTaxs5y3O2Q0REREQlg0WT+1q1akEIYfbNqLKysqQhNfXq1bNk17Rs3boVAODo6CiNeddnwYIFOHv2LMaMGYOtW7di06ZNGD58OI4fP453330XrVu3Njh8KDIyUlo2dtGtZnliYiLi4uKk9ffu3ZPmr89rGzn3TUREREQlh0WH5bRq1Qpubm7YuXMnFi1alKcbPGkKDg7GlStX4OXlhRYtWliya5KnT59i27ZtALLnxNc3975a7969sW7dOjg4OEjr+vTpg2HDhqFdu3Y4efIkunfvjr///lurDgCtOecdHR2N9klzFp2kpCRpes38tJGb2NhYrV8eHBwcdGLIryyN9pVKpdFfOqyNZuxZjN3kNpRKJVQq1Qv3vPF1f75c2LEX5TFT1LEXFWuI29zjxhpiNxdjz1ZYeY05+7Bocm9nZ4cPP/wQc+bMwbhx43Dw4EFMmDABgYGBBrdJS0vDrl27MGfOHJw/fx4ymQzjx4+HTCazZNckEydORFpaGpo0aYLPP/9cb53KlSsjIiIClSpVgp2dnU5548aNMW3aNEyYMAFnz57F0qVLdaaoVCgU0rK9vb3RPmmWp6amWrQNQ6pVq6b1d1BQkNFfMcwRE/38rrlnzp7F3fsWv61CsaUZ++l//oFXRIHcDLpYskTsKpVKutjd0H0oiiO+7tmKIvaiPGaKOvaiYg1xm3vcWEPs5mLs2Qorr0lJSTF5G4v3asqUKdiyZQtu3LiBbdu2Ydu2bfD09ISfnx+uX78OABgwYACSk5Nx79493LhxQ7qIVggBPz8/o3O+58fatWuxevVqlC1bFlu2bDF4ploul6Nq1apG2xo2bBgmTpwIIQRWrlypk9xrnklXx2eIZrmzs7NF2zAkIiIC7u7u0t8Fceb+8UMFcCb77rsvN22KKr5uFm2/ONOM/ZVmzVChUt7v9/Cis0Ts6jMVgYGBkMtfnC+FfN2LLvaiPGaKOvaiYg1xm3vcWEPs5mLshZvXqCerMYXF3wFdXFywd+9etG/fXrpwNTY2FkeOHJHqbNy4UVoWQkjLNWvWxK5du3IdgmKOo0ePYuTIkXB3d8euXbtyTd5zU7p0aVSvXh23b9/GpUuXkJycDFdXV6ncze35C56Wlma0Lc0z9JrbWaINY/3XTO4Lgq3GG6VcLn+hkrT80ozdlrGb1Y6Njc0Ld9zwdX++XBSxF9UxUxxiLwrWErc5x421xG4Oxp6tsN5rzNlHgfyW4uPjg/Pnz2Pw4MGwtbUFoJ3Eq6nX2draYtiwYTh37pzRMfDmOn36NLp37w57e3vs3bsXTZs2tUi7ZcuWBZAdR1RUlFaZ5peHnGU5qcvd3d2l8fZA9vOo/pkwr23k3DcRERERlRwFNlDK3d0dv/zyC/7991/MmDED7du3R4UKFaThHxUrVkSHDh0wc+ZM3Lx5EytWrNA6820pZ8+eRZcuXSCEwN69e9G8eXOLta2eyQaA9CVGrUGDBtKysalB09PT8ezZMwDASy+9pFXm5OSE6tWr59oGADx48EBaztkOEREREZUMBf57QrVq1TBt2rSC3o1e58+fR+fOnaFUKk1K7GfPno2GDRuiR48eRus9efIEQPZPeuqz+GodOnSQli9cuGCwrfDwcOlLguY2mu3cunUL8fHxuHv3rsEbWZ07d07vvomIiIio5LDaS5wvXryIzp07IyMjA7t379Y7Y8/MmTPx8ssv66z/4osvsGTJEqPtP378GHfv3gUANGnSROci1ubNm6Ny5coAgAMHDhhsZ//+/dJy3759dco11xlrR13m7+/Pu9MSERERlVBWmdxfvnwZHTt2hEKhwK5du9CqVSu99SIiInD27Fm9ZcePHzd6hfLSpUul5VGjRumUy2QyTJ06FQAQFhYmzRSkSalUYtWqVQCArl27olGjRjp1OnXqhICAAADA8uXL9fbl6NGj+PfffwEAn332mcE+ExEREZF1s3hy//bbb2PatGl5nnT/7t27sLW11Xrk5+rjq1evomPHjoiOjsaECROgUqlw+PBhvQ/1sBp9kpKSMGrUKL1TUP7111+YN28eAKBNmzYYNmyY3jZGjhyJVq1aQQiBkSNHas1oAwAzZszAzZs34eHhge+++85gXxYvXgwnJyecOHECixYt0iqLj4/HBx98AADo1q0b+vTpY7AdIiIiIrJuFh9zv3nzZshkMhw4cACbNm3KdfYbuVyOKlWqSDetevjwIbKysszad1paGjp27IinT58CAGbNmoVZs2aZ3E6jRo0QHh6OjRs34uzZs+jXrx+qV6+OlJQUHD58GL///juEEHj99dexbt06g19G5HI5tm7dim7duiEsLAxNmzbFsGHD4OLigl27dmHnzp3w9vbG5s2bUatWLYP9adKkCX777TcMHDgQY8eOxbFjx9ChQwfExMQgJCQEkZGR6NChA9avX29yrERERERkPQrsgtoTJ06gcePGWL9+PTp27GiwXqVKlRAZGSn9Xa9ePWmIianS0tKMno3PqwsXLuDUqVP4448/8Pfff2PZsmWIi4uDnZ0dypcvjwEDBmDw4MHo0qVLrm15e3sjLCwMISEhWLNmDebOnYv09HT4+Phg0qRJ+Oijj1ChQoVc2+nWrRvCw8OxcOFC7Ny5E3/++SdcXFzQoEEDTJs2DUFBQS/UnTyJiIiIyPIKLLmXyWR49uwZXnvtNcyYMaNQxoJ7eHjonU/fHAEBAdJY9/ySy+UYPXo0Ro8ena92qlatioULF2LhwoUW6RcRERERWZcCOdVbo0YNlCtXDgCQlZWFadOmoWvXroiLiyuI3REREREREQooua9ZsybOnz+PNm3aSOv27NmDpk2bGpydhoiIiIiI8qfABmmXK1cOBw8exKRJk6R1kZGRaNWqFX7++eeC2i0RERERUYlVoFdg2tjY4KuvvsK2bdvg4eEBmUyG9PR0BAcHY8iQITpTQxIRERERkfkKZXqVbt264dy5c2jatKm0bt26dQgICMDNmzcLowtERERERFav0OZO9PX1xd9//43Ro0dLM9pcvnwZL7/8MjZv3lxY3SAiIiIislqFOjG6nZ0dfvrpJ6xfvx4uLi6QyWRISkrCO++8g48//tjsm1cREREREVEhJ/dq/fr1w+nTp1GvXj0AgBAC33//Pdq0aYPExMSi6BIRERER0QuvyG5pWrduXfzzzz8YNGiQtO7kyZN4/PhxUXWJiIiIiOiFViB3qL106RLeffddAECVKlUwc+ZMvfWcnJywevVqtGrVCh999BHS09MLojtERERERCVCgZy5f/jwIVatWoVVq1Zh69atudYfNWoU/v77b1SrVq0gukNEREREVCJY/Mx9RESE1t/29vZ52q5x48Y4d+4cJk+ejCdPnli6W0REREREVs/iyb2vr6/Z27q7u2PJkiUW7A0RERERUclRZBfUGvLs2TPcu3evqLtBRERERPTCKXbJ/ZAhQ1C9evWi7gYRERER0Qun2CX3AKQ72BIRERERUd6ZNeY+PT0dKpUKTk5OOmVHjx7NV4diY2PztT0RERERUUllcnK/Zs0avPfee1CpVFi0aBFGjBihVd6uXTvIZDKzOySEyNf2REREREQllcnDcsaPH4+0tDRkZGTg008/NVhPCGHWg4iIiIiIzGPymXsfHx9p6EyVKlX01nF0dETZsmXN6lBUVBTvVEtEREREZAaTk/stW7Zg+vTpyMrKwowZM/TWadu2LXbt2mVWh15//XX89ddfZm1LRERERFSSmZzcV6tWDatXry6IvhARERERUT5YfCpMHx8flCtXzuzty5YtCx8fHwv2iIiIiIioZDBrKkxjIiMj87X9L7/8YpmOEBERERGVMMXyJlZERERERGS6Ik/uY2NjERcXV9TdICIiIiJ64RVIcn/v3j3p8fDhQ711Vq9ejRo1aqBMmTLw9vZG5cqVMWfOHCiVyoLoEhERERGR1bP4mPuwsDC0bdtW+rt8+fI6Cf7ChQvxySefAIB046pHjx7hiy++wKlTp7B161bY2BT5jwpERERERC8Ui2fQoaGh0t1mHR0d0ahRI63yO3fuYMqUKXrvRiuEwI4dO7B48WJLd4uIiIiIyOpZPLnfsWMHZDIZBg8ejCdPnujczOrbb79Feno6ZDIZ7Ozs8NVXX+HixYs4cuQIXn31VQghMH/+fEt3i4ioUDgnZqD83UQkP0oq6q4UOsZe8mIvqXEDjL2kx57yuPjGbtFhOVeuXMGzZ89Qs2ZNrFy5Era2tlrlmZmZ2LBhA2QyGQBg/vz5+PDDD6XyHTt2oE6dOoiMjMSFCxfg7+9vye4RUSFxTsyAe1xa9ht/Jaei7k6hub7hIvouvQgbAfz62010W9YNTYY3KepuFQrGXvJiL6lxA4ydsQO/FePYLZrcX7hwAQAQHBysk9gDwJEjRxAXFweZTIYKFSogODhYuzNyOUaOHInPPvsM4eHhTO7phVacE1xVlgoqpQqqzP/+VaqQlZmltU76O6/r/vv72u7bePv3q5AB2LDxX9TvWx+VAypnD8X7bzSeetnQOlWWCncj70IcEbCR2eRaX/oX0F2Xx32a1YbG+ozkDFzbck36OVSoBLaP3I6bO2/C3sX++fb6GFptYn2L7sOEbTJTM3Fr9y2d2K9tvgY7ZzvD7ZvSlzxsGx0djcdej2EjM/1HaXP3nanIxJ2/7ujEfmXjFdg55T32/MjP82auTEUmIvZH6MR9+dfLhRa3JQghEBsbi0elH0knHnNjLbGbg7Frx77jvR2o2aUm3Cu7F2nfcrJocv/kyRPIZDLUqVNHb/mff/4pLQcFBen9AlCvXj0IIfDs2TNLdo2owKmyVEiLT0NqdCpO/3gGby+5KCW4jQY3QuUWlU1PojWSb1PrG1tnLDm0BOkjUgBXf7uKq79dNaudSERaqktFQwDXt14v6l4UDQHc2nOr0Hcbg5hC36cOAdzZd6eoe1H4BBCxP6Koe2GWfB83L3Ds+VaCYxdZArG3Yq07uc/IyAAAODg46C3ftm2btNy/f3+9dby9vQFkD+EhKiqqLBXS4rIT9dSY1Ox/o1OhiFHoLv9XrohVaCXNmglu+OpwhK8OL4pQ8kxmI4ONnQ1s5NkPWzvb7GW7HH/rW/ff32nxaXhw4oFO29U6VYNbeTdAhudnx9TLMkhPlvpvIQSioqJQvkL57JmzNOpK20B3nb62Da4ztY1c6qclpCFsbpj2FycZ0ObzNnD0cNRap/f5N3TW0NT6FtxHXrdRxClw6PND2se/jQztZ7eHk6eJv1rl7eSpDlWWCrdu3ULNWjX1njgqqH0r4hQ48OkBndg7zu0Ip9KF+Iudmc+buRSxCuyfvF837q8KOe58UmWpcPPmTdSqVQs2tnn7xcdaYjcHY88Ru60MpWuWLrpOGWDR5N7T0xMAcP/+fZ2yo0eP4v79+5DJZKhduzYaNGigt42EhAQAgJubmyW7RiWYKksFRaxCKzHXl7BrJe5xCrPPbtu52CEzRffLaZVWVeBe0V0nQTYleTY36c51O7kNZDb5zw4SHyRioe9CCNXzJ09mK0PP0J4mndlQKpUICwtDq1atIJdbfMbeAiPzdMWRSXtgI7Lj7vZz8RyPWRCU9g5FGrtSqURqWCqatGpS6MdMho1diXzd02XyFz5upVKJ5LBk+LfyN+m4sYbYzcXYtWMvbmftAQsn9/Xr14cQAps3b0ZQUJBWmeYMOG+//bbBNs6cOQOZTIbKlStbsmtkJVRKFRRxes6e/5ewK6IVOol7fhJ1Rw9HOHk5wdnbGc5eznD2doaTt5O07Ozt/Lzc2xlOpZ2QEpWiN8Hts6FPsXwTsCT3yu5oNa/LC/HmVxDq9vfDmDuP4B6fhtXjO6J2s7JF3aVCw9hLXuwlNW6AsTP2NKwa2xZ1m1cs6i7pZdHkPiAgAB4eHti1axdmzJiB999/H7a2tvjmm2+wc+dOAJCmydQnNTUVK1euBAA0bNjQkl2jIqK+qDTlcRLgq/1rjEqZfUY9L2fS1eVpcWlm98XRw1F/Qp4jeVevcyrtBFs703/eZ4Jbct/4ASDV3R6p7vZwrVjyfn1k7CUv9pIaN8DYS3rsLhWKb+wWTe4dHBwwZswYzJ49G19++SW+/PJLrXKZTIZevXqhZs2aOtteunQJY8aMwb179+Dr64saNWpYsmtUBK5vuChdVLrx139RtmFZ2LvYSwl7Wnw+EnVPR70Jub5lZ6/sM+o28sK76zET3JL7xk9ERFSULD4w8bPPPsPRo0dx9OhRaZ36gqzy5cvj+++/16p/4cIFdOzYEfHx8VLdnj17WrpbVMgSHyQibPJerWu8nl56qltRBjh5OumcQXfy1n823dnbGU6ehZuom4sJLhERERU2iyf3Dg4O+Ouvv/DNN99g3bp1uH37NpydndGlSxd89dVXqFhRe3xSeno64uLitNaNHDnS0t2iQhZzM0ZrzLla+y/bo2q7qlLC7ujpmOcZCoiIiIjIuAKZUsDe3h5TpkzBlClTcq0bEBAAlUpVEN2gIuRVywsyG5nORaX+Q/1LzNhzIiIiosLGU6ZUINQXlarUc4OXsItKiYiIiIrCizOBNL1wXpQpo4iIiIisRbE7c//666+/UDetIeNS3e3xxMe9WE8ZRURERGQtil1yD2Tfep6IiIiIiExTKKfIExISkJycjKysrFzrKhSKQugREREREZH1KZDkPiMjAz///DN+++03XLhwAampqXneVgghzYtPRERERER5Z/Hk/tGjR+jYsSP+/fdfAKYNsWFST0RERERkPouPuR84cCBu3LgBwPSx8xxrT0RERERkPosm95cvX8aRI0cgk8ng5OSESZMm4fDhw3j48CEUCgVUKlWujy5duliyS0REREREJYZFh+WcOHECQPbwml27dqFNmzaWbJ6IiIiIiIywaHIfExMDAPD39zc7se/cuTPKly9vyW4REREREZUIFk3uy5YtCwCoWrWq2W18/PHHFuoNEREREVHJYtEx9/7+/gCA2NhYSzZLRERERER5YNHkvkmTJggMDMTJkyfNTvC//fZbvPvuu5bsFhERERFRiWDxqTB/+eUXuLm5YfDgwUhPTzd5+3379uGXX36xdLeIiIiIiKyexZP7GjVqICwsDMnJyWjYsCGWL1+OBw8eWHo3RkVHR2PJkiXo2bMnfH194ejoCGdnZ1SrVg3vvPMOtm/fnuc59Y8fP44BAwbAx8cHjo6OqFKlCt5++20cPnw4z/1RKpVYsmQJWrZsCS8vL7i6uqJ+/fqYMmUKnjx5kud27t69i/Hjx6NOnTpwdnZG2bJl0b59e6xatQoqlSrP7RARERGRdTLpgtrq1avnua4QAnfv3sV7770HAHBxcUGpUqVgZ2dndDtTkl19JkyYgJ9++glpaWkoU6YMBg4ciFq1akEIgUOHDmHz5s347bff0KFDB2zatAmlS5c22NasWbMwc+ZMODo6YsSIEWjQoAGuXbuGkJAQbNq0CRMnTsTXX39ttD/R0dHo1q0bTp06hbp162Ly5MlwdXXFzp078dVXX2HFihXYvHlzrrML7dy5EwMGDEBSUhL69OmD8ePHIyYmBiEhIRg2bBjWrl2LrVu3ws3NzaznjYiIiIhefCYl95GRkZDJZHmur64rhEBycjJSUlJy3UYIYdI+clq7di3S0tLQtm1bbNu2DaVKlZLKPvjgA+zYsQM9e/bEwYMH0b17dxw7dgw2Nro/YCxduhTTp0+Ho6MjDh48iICAAKls8ODBaN26NebPn4+yZctiwoQJevuiVCrRq1cvnDp1CoGBgdi/fz+cnJwAAMHBwZg6dSrmzp2LHj164PTp06hVq5beds6dO4e+fftCoVBg4cKFGDdunFQWHByM1q1b48CBAxgwYAC2b99u1vNGRERERC8+s4blCCFMepiynSXI5XKsXr1aK7FX69atG0aMGAEge8jNb7/9plPn2bNnmDhxIgBg3LhxWok9ADRu3FhK6L/44guDw45CQkIQFhYGmUyGkJAQKbFXmzlzJmrWrIn4+HijU4AGBwdDoVAgICBAK7EHAE9PTyxatAgAsGPHDmzZssVgO0RERERk3Uye575Zs2aYN29eQfQFADBp0iScOXMmX234+/vDx8fHYHnv3r3x888/AwC2b9+Ofv36aZV///33SE5OBgDpi0BOI0aMwKxZs5CWloZvvvkG3333nVa5EAJz584FALRs2RL169fXacPOzg5Dhw7F559/jh07diA8PByNGjXSqrN//36cOnUKADBy5Ei9fWnXrh1q1aqFmzdvYvbs2ejdu7fB2ImIiIjIepmc3JcuXRpt27YtiL5I7efHhg0b4OXlZbSOr6+vtHzv3j2d8s2bNwPIvhlXzZo19bZRuXJl1K1bF9euXcPmzZvx7bffag0nOnnyJO7fvw8A6NSpk8G+dO7cGZ9//jkAYNOmTTrJ/aZNm6RlY+106tQJN2/exIULF3Dr1i2D/SYiIiIi62Xx2XLyK7/Dc9q3bw8/Pz+jdeLj46VlFxcXrbKHDx/ixo0bAJ7flMuQxo0bAwAePHiAmzdvapUdPHhQWjbWTqNGjaQx/5rb5GzHw8ND60uJob4YaoeIiIiIrJ9JZ+7Pnz8Pd3f3guoLAGDPnj0F2j4ARERESMs5Z6m5fPmytFylShWj7WiWX7lyBbVr1za5HQcHB5QpUwZRUVG4cuWKVplCocCdO3fM6gsRERERlTwmJfc5h4y8qLZu3QoAcHR0xNChQ7XKIiMjpeVy5coZbUezXHM7c9qJiopCYmIi4uLi4OnpCSB7yJB6/vr89EWf2NhYKJVK6W8HBwc4ODjkup0psjTaVyqVWvuzdpqxZzF2k9tQKpVQqVQv3PPG1/35cmHHXpTHTFHHXlSsIW5zjxtriN1cjD1bYeU15uzD5DH3L7qnT59i27ZtALLnxK9YsaJWeVJSkrTs6OhotC3N2W80t8tvO+rk3lJ90adatWpafwcFBel80cmvmOjnN9Y6c/Ys7t4vOYebZuyn//kHXhHFbgRcgbFE7CqVSroeRt9UtcUVX/dsRRF7UR4zRR17UbGGuM09bqwhdnMx9myFldfkZRr5nAq8V5mZmdi/fz9OnjyJ27dvIy4uDkD2FI41atRAixYt0LFjx1xvbmUpEydORFpaGpo0aSJdyKpJoVBIy/b29kbb0ixPTU21eDuW6os+ERERWkOsCuLM/eOHCuDMUQDAy02boopvybnBlmbsrzRrhgqVnHLZwnpYInb1mYrAwEDI5S/Ol0K+7kUXe1EeM0Ude1GxhrjNPW6sIXZzMfbCzWsSExNN3qbA3gEzMjIwZ84cLF68GDExMUbrent7Izg4GFOmTMk1ic2PtWvXYvXq1Shbtiy2bNmiN5nVPAOekZFhtD3NcmdnZ4u3Y6m+6FO6dOkCv37CVuONUi6Xv1BJWn5pxm7L2M1qx8bG5oU7bvi6P18uitiL6pgpDrEXBWuJ25zjxlpiNwdjz1ZY7zXm7KNAfkuJjIyEv78/vvzyS0RHRxud/UYIgWfPnmHWrFnw9/fP03hxcxw9ehQjR46Eu7s7du3ahapVq+qt5+b2/FtYWlqa0TY1z6xrbmepdizVFyIiIiIqGSye3EdFRaFNmzbSdJJqQgg4OTnB29sb3t7ecHJy0kn6r1+/jrZt2yIqKsqifTp9+jS6d+8Oe3t77N27F02bNjVYVzPpz60fmuU5vyyY0467u7s03h4AfHx8pDGA+ekLEREREZUMFk/ux44diwcPHkAIgdKlS2Py5Mk4cuQIYmJikJycjKioKERFRSE5ORkxMTE4fPgwJk2aJN286sGDB/joo48s1p+zZ8+iS5cuEEJg7969aN68udH6DRo0kJbVN6Ey5MGDB9LySy+9ZFY76enpePbsmd42nJycUL169Xz3hYiIiIhKBosm948fP8aWLVsgk8nQq1cvREREYO7cuWjdurXWGWk1T09PtGnTBl999RUiIiLQs2dPCCGwefNmPHnyJN/9OX/+PDp37gylUok9e/bkmtgDQKVKlaT56i9cuGC07rlz5wBk3622Vq1aWmUdOnSQlo21Ex4eLk13qblNznbi4+Nx9+7dXPtiqB0iIiIisn4WTe4PHjwIlUoFPz8/bNy4Ea6urnne1tXVFRs3bkTDhg2hUqlw4MCBfPXl4sWL6Ny5MzIyMrB7924EBgbq1Jk5cyZefvllnfV9+/YFkH3tgPomUjk9fPgQ169fBwD06dMHMplMq7x58+aoXLkyABiNZf/+/Tr71deX3NpRl/n7+6NmzZoG6xERERGR9bJocv/w4UPIZDK8//77Zl3da2dnh+DgYAgh8OjRI7P7cfnyZXTs2BEKhQK7du1Cq1at9NaLiIjA2bNnddaPHTtW+mKyfPlyvduuWLECQgg4Ojrik08+0SmXyWSYOnUqACAsLEz6IqBJqVRi1apVAICuXbvqvUlYp06dEBAQYLQvR48exb///gsA+Oyzz/TWISIiIiLrZ9Hk3sXFBQBQt25ds9tQb6s5DaQprl69io4dOyI6OhoTJkyASqXC4cOH9T4MDf0pW7Ys5s+fDwD47rvv8M8//2iVh4eH4+uvvwYAfPnll9IZ+pxGjhyJVq1aQQiBkSNHas1oAwAzZszAzZs34eHhge+++85gTIsXL4aTkxNOnDiBRYsWaZXFx8fjgw8+AAB069YNffr0MfLsEBEREZE1s+gEnVWqVAFg3t201FJSUiCTyQwmzMakpaWhY8eOePr0KQBg1qxZmDVrlln9eP/99xEVFYVZs2ahffv2GDFiBF566SVcv34dISEhSElJwYQJEzBhwgSDbcjlcmzduhXdunVDWFgYmjZtimHDhsHFxQW7du3Czp074e3tjc2bN+uM2dfUpEkT/Pbbbxg4cCDGjh2LY8eOoUOHDoiJiUFISAgiIyPRoUMHrF+/3qxYiYiIiMg6WDS579ixI5ydnXHkyBG88cYbZrVx+PBhODg46L0o9MCBA3j48CGGDBmid9u0tDSLXIirNn36dHTu3Bk//PADNm/ejCVLlsDb2xuvvvoqPvjgA7Rv3z7XNry9vREWFoaQkBCsWbMGc+fORXp6Onx8fDBp0iR89NFHqFChQq7tdOvWDeHh4Vi4cCF27tyJP//8Ey4uLmjQoAGmTZuGoKCgQr/lOhEREREVLxZN7l1cXPDhhx/ixx9/xPDhw6VZZ/Lq33//xZIlSzBixAi9d09dsGAB/vrrL4PJvYeHh9EbZpkjMDBQ78W4ppDL5Rg9ejRGjx6dr3aqVq2KhQsXYuHChflqh6iwPE1OQ0WUnFuTa2LsjL0kKalxA4y95MaejioonjcNtfh9c2fPno3Lly+jTZs2+Omnn9C7d+88bbd161YEBwejSZMmWLBggaW7RVRkStqb3/Yrzy+Gf2vVccx9qyHeaeZThD3KvomeEIBQLwMQAlD9dzIgu0y7jkoA0LNe4L/tpHXP66w9+3y62l6rjmNSlzro5ldR2geQXfd5v9TrtPsqLeeol7O2/u0N7ycv5Xq3gdCzTrvPW08/lP7uFXoco9vVQKf65XQbNrJPAzXzVCsrKws347LgdDcOtra2Fmjxv7p5qLztH+3Y32tbHR3qGo7dWuSMe1Sb6uhQt6zJ7eTnlFx+z+cps5S4FpMF2zsxuR43mnaceSwt9wo9jhGtq6G9GbG/iPTGXqfwY7fsqdy82XHm+edb37WnisXnmz4yYeFT3atXr4YQAj/88AMuXLiAihUronPnzqhbty48PT3h4OAAAMjIyEBcXByuX7+O/fv348GDB6hRowY+/fRTgzPtLFiwAFeuXEFWVpYlu1yiJCYmolSpUkhISND764glPXqoQOCPBwEAx0a3QRXf4vkNtyD8/NdtzD2YPUOSjQwWewPIUglkZqmgVAkos1TIzBJQqlRQZj1fn5mV/bdS9V95lkDmf3WUWSpk/ret5npDbWbotJVze+22FBlZuP4kSafflTwcYWtj8zxRFtpJtr7EOjMjA3I7u+d1DCToWm3qScQt/GMeERERAMBWJkPYp+1RoVTBncAzJ2+zeHJvY2MjzfmubjrnHPA5mVJPJpMxuc8HJveWpVIJxCsyEZuSjpjkDMSmZCAiJgXz99zQOqsgA9C2dhnIbWXIzHqegOeWXD9fn12XiWrxIJNlv6Yymey/f7O/RChVui+Qg9wGtjbZ720yrTZ012n+IctRT71fPVX1tiUz0LB6vaG6slzr6r5PpyuzEJ2cobO+jJs9HO0Mnw2Vwfh7vr7+GSKEQFpaGpycnPLUam6fN1p1jZQpMrLwODFNZ30Fd0c42ef9TPCLxmDcpcyLO++vhp5tTXgtcxJCQKFIhbOzc56Px9QMJR7G68ZeycMRzvYWHxBRrBiKvbJn0cSe19fMElIzlLgfp9BZv2Fkc7So4VVg+zUnbyuQVyLn94W8fn+w9Hh5IlNlKFWITcmQHjEp6Vp/Z697vhyfmgE9+ZwOAeDwv88KpM92tjLIbWwgt5XBztYGcpv//rWV5Vi2yaXuf+Vayzaws8n+V24rg91/28ptbWCvp62kNCUmbArX+mJjIwOWDmoCL1dHyGSAjUZCLIPseQKpLpMBqqwsnD93Hk2aNoGd3BaADDYy7WRac1sbG931MgDQ2l+OOja6Cbq0bY52tOoYSCQeJyjQ8quDWseDrUyGwxPbFehZneLAUOx/jmlVaLErlUqEhYWhVatWZt1nxVyGYv/9g0Crft0Nxh38YsVtznFjKPbNo1+s2M1hKPZN75fc2Kt6OxddpwwokHfA0qVLo2HDhhZv9+LFi4iPj7d4u2SdhBBIzcjSSMjTEZvy31n2lAzE/nemPTb1v3+TM5CUrjRrX+6Ocni5OqC0iz2c7GwRditaq1wmAz55tQ68XeyzE2atxDgvCffzJNtOo56tjSxfZ60KQnRMOuYcuP5fsp49JOnVl3KfEUqTUqnEUzcb1CrrWqiJWn5UKOWEye3rasU+560GVv+BBzD2khh7SY0bYOyMvfjHXiCfmq+88gp27dpl8XZff/11/PXXXxZvl14MKpVAgiJTSsbVw2B0knaNR7pSZfJ+bG1k8HS2Q2kXe5R2sYeXS3bS7uliDy9pnT1Ku9qjtHP2ejtb7WlIf/7rtk6CWxwvuikIyRer4OGSCpB7KKBKcEJyTWegGaRx8UDuy0olkJ5ug9RUQH2NmynbG1rO7/a5tXVnvw8eLn0e+00XZ1x10X5+cv5AWZz+zs+2l7f74OEv2bFnxTshXOYMHxO/K+fnx1ulErh0yR0yGWDO98H87PvSNh88XPM89vPCGZXSzW/vRWENcWcfN6UAmHbcWEPs5mLsup9vxU2BjLl/7bXXCjS555h78xXVmPstgwPQ9CVvrfLMLBXicgxz0T7Lrr0+LjUTWXkZA5ODg9wGXv8l51Ji7uIAL9fsvz2d7aVlLxd7uDvawcYmf2fDv1mQgcmzlNIbwJJvnTF8uG49IQCVCsjMzH4olfqXjZXlZbmwts/IAFJSBPI3gpaIiKj4s7UFIiMBM+67mmfFYsx9mzZt4OfnZ+lmAQANGzZEWpruhRxUPGlOidh7zSnUq+AOJzsbKVlPTDNvCIybgzz7rPl/ybinc/ZZdClpz3GW3dne1qJDV1QqIC4OePYMiI7W/TcyEti61Q6APbKSssfijRgBTJnyPJHXTIitT/FO7KUx9TLtvw0t57WeUgkkJ+vuz90dsLfX7UNx/ducbRUK4PFj6KhYEXAy8Rdr8/+rCigUCjg7O8HcY9CcfaemAg8e6K6vXBlwLn5DcS3GeuLOPm6cnPJ+3FhP7KZj7NrrsrKAW7cKNrk3h8XP3FPxVlhn7vVdeKKPjQzwdM55Vt0+x5n27GExXq728HC2g4PcsjNQKBT6k3RD/8bEZCfpBcnWFrCzy37I5c+Xc/5d0MumbhMdDbRrJ6BSybRiOXMmO9HLa/KclaXEyZMn0KJFC9jZyS2SjBe0Bw8AX1/d2Av6rE5xUBxiL6oLaotD7EXBWuI257ixltjNwdgLP/ZiceY+vy5fvozY2Fi0adOmqLtC+RARnaI3sR/fqRaaV/f6bxiMA0o52UnTBFqCSgXEx+tPzA0l6ykp5u2rVCmgTBnA21v7X7kcmDtXQIjncdnYALt3Z78B5JYgy+WFk4wWlK/nZWLCJDkgbGBrK/DzzzL4+5vWhlIJODtnwc3NvPHTRaFyZf2xW/sHHsDYS2LsJTVugLEz9uIfe7H72Jw4cSLH1VuBat4usJFBZ8qot5tVMenK8rQ008+qm3Po2NllJ+c5E3V9ybu3N+DlpTvUQlNpT903gFdfNb1fL6L+/bPw3Z1jUMY7Y8d4f7zcrPjNJFBQGDtjL0mxl9S4AcbO2J3x59iGeKW5a1F3Sa9il9yTdahQygntXOrjQOJVyGwAoQJeK9cAjion/Ptv3s+q6xvDnBfu7oYTc33/urtb9mx5SX7zAwC5exrk7mmoWLGoe1L4GDtjL0lKatwAYy/psVeoUHxHtRdocq9SqXD27FmEh4cjJiYG6em5z5V069atguwSFZIHD4BfpleFzKU85B6pUMY7Y3GSExZPML0tuVz/WXVj/xo7q15YSvKbHxERERWNAkvuQ0JCMGvWLDx69Cj3yhqEEMXupjxkups3kX3RSZITspK0z1q7ueU+7EXz31KlXuwx6ERERESFpUCS++DgYPz8888AspP1vJDJZHmuS8VfrVqAjY3uVeXXrwM1axZhx4iIiIismMWT+zVr1mDp0qUAAC8vL7Rq1QoeHh7Yt28fHj9+jCFDhmjVT0tLw82bN3H+/HnIZDJ06tQJFTmO4YVn6KpyJvZEREREBcfiyf3s2bMhk8kwaNAgLFmyBM7/3dXg9ddfx+PHjxEaGqp3u0uXLmHw4MG4d+8eNmzYYOluURF4Ua4qJyIiIrIWNpZs7MqVK7h58ybq1KmDlStXSol9XjRs2BAHDhxAcnIyPv30U0t2i4qQ3D0Njj6xxfqqciIiIiJrYdHk/uzZswCAYcOGwdbW9LuIenl54cMPP8SqVasQHx9vya4REREREVk9iyb30dHRkMlkaNSokdltNGvWDEqlEgcPHrRgz4iIiIiIrJ9Fk3v1PPZOTro37HF0dAQAJCUl5amt+/fvW65jREREREQlgEWTe09PTwDAw4cPdco8PDwAAOHh4UbbUA/tUSgUluwaEREREZHVs2hyX6dOHQgh9A6pqVu3LoQQ+Omnnwxun5CQgB9//BEymQxlypSxZNeIiIiIiKyeRZP7Jk2awNbWFmvXrsU///yjVda6dWsAwG+//YZPPvkEiYmJWuUXL15Ep06dpOE4zZo1s2TXiIiIiIisnkWT+1KlSqF58+ZIS0tDx44dMXnyZERHRwMAAgMDUadOHQDAwoULUbZsWTRu3Bht2rRBjRo10LhxY5w7dw4ymQx+fn7w8/OzZNeIiIiIiKyeRZN7ABg4cCAAIDk5GQsWLMC5c+ekMvWQGwDIyMjAxYsX8ffffyMiIgJCCAghYGtra3ToDhERERER6WfxO9QOHjxYurAWAPz9/aXlTp06YdWqVRg9ejRSUlIghPaNjTw8PPDLL78gMDDQ0t0iIiIiIrJ6Fk/uXVxc8M477xgsHzRoEDp06IA1a9bg7NmzSEhIgLe3NwIDAzFo0CCUKlXK0l0iIiIiIioRLJ7c50XFihUxefLkotg1EREREZHVsmhyr1AoMH/+fK11ffr0Qf369S25GyIiIiIi0sOiyX14eDhmzJghXTQLZM9vz+SeiIiIiKjgWTS5v3r1qrRcsWJFjBgxgvPVExEREREVEosm9+o57d3c3PDPP/+gfPnylmyeiIiIiIiMsOg8987OzgCA5s2bM7EnIiIiIipkFk3ufX19AQB2dnaWbJaIiIiIiPLAosl9hw4d4O7ujvDwcLPbGDp0KGrUqGHBXhERERERlQwWTe5dXFwwfvx4PHjwAOvWrTOrjaioKERGRlqyW0REREREJYLFb2I1ffp0/Pvvvxg5ciRkMhkGDBhg6V0QEREREZEeFk/ujx49ivfeew/29vYYPHgw5s2bh549e6Jhw4bw9PTMdTx+bGyspbtERERERFQiWDy5b9eunXQTKyEELl26hMuXL+d5eyGE1k2wiIiIiIgobyye3APZCToArSSfiIiIiIgKVoEk9y4uLvD29jZr26ioKKSnp1u4R0RERERE1q9Akvvu3btj/fr1Zm37+uuv46+//rJwj4iIiIiIrJ9Fp8IkIiIiIqKiY/Ez935+fqhatarZ23fu3Bnly5e3XIeIiIiIiEoIiyf3Fy5cyNf2H3/8sWU6QkRERERUwnBYDhERERGRlWByT0RERERkJQpkthx9nj17BoVCYbSOTCZDlSpVCqlHRERERETWxazkXqlU4vTp03rLAgMD9a4fMmRInqa4PHToENq0aWNOt4iIiIiISjSzkvsdO3agd+/eOutlMhmUSqXB7fJyp9o5c+YwuSciIiIiMoNZY+43bdoEIYTOo169evnu0L59+/Dvv//mux0iIiIiopLG5DP3WVlZ2LFjB2QyGYQQcHFxwSeffIJRo0ahYsWKRreVyWRYuXKl3jKFQoGxY8ciKysLW7ZswZQpU0ztGhERERFRiWZycn/u3DkkJSVBJpOhdu3a2L17N6pVq5bn7YOCggyWXbhwAcuWLcMff/zB5J6IiIiIyEQmD8s5cuQIAMDNzQ379u0zKbHPzYQJEwBkJ/nGxu4TEREREZEuk5P7ixcvQiaTYdKkSRaftrJmzZp45ZVXoFQqcf36dYu2TURERERk7UxO7q9duwYAGDp0qEnb5WWmHABo27YtAODGjRsmtW9sv4sWLYKrqytkMhkOHz6c6zaRkZGQyWR5eowZMybX9pRKJZYsWYKWLVvCy8sLrq6uqF+/PqZMmYInT57kOZa7d+9i/PjxqFOnDpydnVG2bFm0b98eq1atgkqlynM7RERERGSdTE7uHzx4gMqVK+d68WxOq1atytMsOP7+/hBCIDY21tSu6bh9+zbatWuHDz/8ECkpKfluzxzR0dFo1aoVgoODERsbi8mTJ+Prr79GtWrV8NVXX8HPzw9Hjx7NtZ2dO3fCz88P33//PRo1aoRvv/0W48aNQ0REBIYNG4ZXX30VSUlJhRARERERERVXJl9Qm5iYCH9/f5N3VL58+TzV8/b2lvZjLiEEfvrpJ3z66aewtbVF8+bNcfLkSZPbmTNnDnr16mW0jqenp8EypVKJXr164dSpUwgMDMT+/fvh5OQEAAgODsbUqVMxd+5c9OjRA6dPn0atWrX0tnPu3Dn07dsXCoUCCxcuxLhx46Sy4OBgtG7dGgcOHMCAAQOwfft2k+MkIiIiIutg8pl7lUoFZ2fngugLAEjJb2ZmptltzJw5Ex9++CFatWqFy5cvo0uXLma1U6FCBdStW9foo1y5cga3DwkJQVhYGGQyGUJCQqTYNPtZs2ZNxMfH4+OPPzbYTnBwMBQKBQICArQSeyD7y8WiRYsAZN9cbMuWLWbFSkREREQvPpOTe29vb8TExBREXwBkD2MBgNKlS5vdhhACy5cvx549eyx+0a8pfZg7dy4AoGXLlqhfv75OHTs7O+nahR07diA8PFynzv79+3Hq1CkAwMiRI/Xuq127dtJZ/9mzZ1ui+0RERET0AjI5uS9TpgwePHhQEH0BAEREREAmk6FMmTJmtzFjxgwMHz7cgr0y3cmTJ3H//n0AQKdOnQzW69y5s7S8adMmnXLNdcbaUZdduHABt27dMrm/RERERPTiMzm59/PzQ0xMDM6cOVMQ/cHOnTsBAC+99JLZbchkMkt1R6JUKk26KPfgwYPSsrFrFBo1agQbGxudbXK24+HhAV9fX4PtNG7cWO++iYiIiKjkMDm579y5M4QQCA0NtXhnrl69iiNHjsDHxwe1a9e2ePumunLlCgYMGAAfHx84OjrC1dUVLi4u6NixI0JDQ43eaOvy5cvSsrGhQQ4ODtKvFFeuXNEqUygUuHPnTq5t5CzP2Q4RERERlQwmz5bz6quvwsHBAcuWLcPw4cPRpEkTi3QkKysLo0ePhkqlQo8ePSzSZn4tWLAAtWvXxpgxY1CvXj2kp6fj6NGjCAkJwcGDB7Fs2TL8/vvvqFChgs62kZGR0rKxi27V5VFRUUhMTERcXJw0A8+9e/ek+evz0oa+fRsSGxur9eXEwcEBDg4OuW5niiyN9pVKZYm667Bm7FmM3eQ2lEolVCrVC/e88XV/vlzYsRflMVPUsRcVa4jb3OPGGmI3F2PPVlh5jTn7MDm5L1OmDEaNGoUff/wRb7zxBvbt24eGDRuavGNNSqUS77zzDo4dOwZHR0dMnDgxX+1ZSu/evbFu3TqtpLdPnz4YNmwY2rVrh5MnT6J79+74+++/dRJjzTnnHR0dje5HcxadpKQkKbnPTxu5qVatmtbfQUFBJt+YLDcx0c9vrHXm7FncvW/y4fbC0oz99D//wCvC5B/JXliWiF2lUuHevXsAIA1bexHwdc9WFLEX5TFT1LEXFWuI29zjxhpiNxdjz1ZYeY0592kyq1efffYZVq9ejWfPnqFFixaYPHkyJk6cmGsCqs++ffswbtw43LhxAzKZDB9++CEqVapkTrcspnLlyoiIiEClSpVgZ2enU964cWNMmzYNEyZMwNmzZ7F06VKdKSoVCoW0bG9vb3R/muWpqakWbcOQiIgIuLu7S38XxJn7xw8VwJnsG3S93LQpqvi6WbT94kwz9leaNUOFSk65bGE9LBG7+kxFYGAg5PIX50shX/eii70oj5mijr2oWEPc5h431hC7uRh74eY15tz3yax3wLJly+LXX39Ft27doFAoMGPGDCxYsABvvvkmXn/9dTRo0AD16tXTmxg/evQIFy9exPHjx7Fx40ZpZhchBNq1a4c5c+aY0yWLksvlqFq1qtE6w4YNw8SJEyGEwMqVK3WSe80z6RkZGUbb0izXvIeAJdowpHTp0lrJfUGw1XijlMvlL1SSll+asdsydrPasbGxeeGOG77uz5eLIvaiOmaKQ+xFwVriNue4sZbYzcHYsxXWe405+zC7V126dMHixYsRHBwMlUqFpKQkrF+/HuvXr5fqODs7w9XVFfb29khOTkZSUhKysrKkciGEtOzv748tW7bA1tbW3C4VqtL/b+/Ow6oq1/+Pf0AQEHBEMUtA07TESD2KQ0aQZKbHToPVqYwwLbXUbNDToHYatG8jpmVl5ZDnaOUp09S+X8cMp2N5SWp5NAUcUlJxVhDk+f3hj3U2sveGDQjuxft1Xfu61mI963nuZ3OL9157DfXrq3nz5tq5c6c2b96skydPKiQkxNoeGvrfT3M5OTlu+3I8Qu+4X0X0AQAAgOqjXCdKDRo0SEuWLFGjRo2snxljrNepU6eUlZWlPXv26MiRI8rPz7e2OUpOTtaaNWusc829ReG8jTHKysoqss3xyP+F2y5UuL127dpF3oOIiAjrHMDS9nHh2AAAAKg+yn0VxI033qhffvlFL7zwgurWrVtkm4+PT7FXIWOM4uPjtXTpUn3yySdlOl+/qhXeyUZSsW8coqOjreXCh1k5k5ubq4MHD0oqfm//oKAgNW/evMQ+JBV5sFh5nhEAAAAA71UhlzjXq1dPL730knbv3q158+ZpxIgRio2NVVRUlEJDQ+Xn56dGjRrp6quv1m233aaUlBRt2bJFy5YtU0JCQkWEUKFeeeUVffPNNyW2O3DggKTz5+s5fnshqci8Nm3a5LKPtLQ060OCs/ei8GdHjx5VZmamy342btzodGwAAABUHxV6JUBwcLD69u2rvn37VmS3lW7MmDHq2bOn2/vt79+/3yq227dvX+wi1s6dO+uKK67Q3r17tWzZMo0bN85pP0uXLrWW+/XrV2x7v3799NFHH0mSli1bpgEDBjjtZ9myZZLOX7vQokULN7MDAACAXVWfm5N6aM2aNW5vP/TBBx9Yy4888kix7T4+PnruueckSampqdq2bVuxNvn5+Zo+fbokqXfv3oqJiSnWpkePHoqNjZUkffzxx05jWbVqlbZv3y7p/G1KAQAAUD1R3Ltw4sQJPfLII05vQfl///d/+p//+R9J0g033KDk5GSnfQwaNEjXX3+9jDEaNGhQkTvaSNKLL76oHTt2qG7dunrnnXdcxvL+++8rKChIa9eu1eTJk4tsO3r0qB577DFJUp8+fXTXXXd5NE8AAADYh21vTjpr1ixr+eeff7aWlyxZYl18Gh4ersTExGL7xsTEKC0tTZ9//rl++ukn3XvvvWrevLlOnTqllStX6quvvpIxRr169dI//vEPl/cg9fPz09dff60+ffooNTVVHTp0UHJysoKDg7Vo0SItXLhQYWFhmjt3rlq2bOlyLu3bt9cXX3yh+++/X8OHD9cPP/yghIQEHT58WFOnTlVGRoYSEhKK3IYUAAAA1Y9ti/v+/fs7/bnjQ7Li4uKcFvebNm3S+vXrNW/ePK1evVofffSRjhw5In9/fzVu3Fj33Xef+vfvr549e5YYR1hYmFJTUzV16lR99tlnmjBhgnJzcxUREaFRo0bpiSee0GWXXVZiP3369FFaWppSUlK0cOFCzZ8/X8HBwYqOjtbYsWOVlJRU6Y9cBwAAwKXFtsX9hffS91RsbKx1rnt5+fn5aciQIRoyZEi5+omKilJKSopSUlIqJC4AAADYC4d6AQAAAJuguAcAAABsguIeAAAAsAmKewAAAMAmKO4BAAAAm6C4BwAAAGyC4h4AAACwCYp7AAAAwCYo7gEAAACboLgHAAAAbILiHgAAALAJinsAAADAJijuAQAAAJuguAcAAABsguIeAAAAsAmKewAAAMAmKO4BAAAAm6C4BwAAAGyC4h4AAACwCYp7AAAAwCYo7gEAAACboLgHAAAAbILiHgAAALAJinsAAADAJijuAQAAAJuguAcAAABsguIeAAAAsAmKewAAAMAmKO4BAAAAm6C4BwAAAGyC4h4AAACwCYp7AAAAwCYo7gEAAACboLgHAAAAbILiHgAAALAJinsAAADAJijuAQAAAJuguAcAAABsguIeAAAAsAmKewAAAMAmKO4BAAAAm6C4BwAAAGyC4h4AAACwCYp7AAAAwCYo7gEAAACboLgHAAAAbILiHgAAALAJinsAAADAJijuAQAAAJuguAcAAABsguIeAAAAsAmKewAAAMAmKO4BAAAAm6C4BwAAAGyC4h4AAACwCdsX98YYTZ48WSEhIfLx8dHKlSs92n/NmjW67777FBERocDAQDVt2lR33323R/3k5+drypQp6tatmxo0aKCQkBBdc801evbZZ3XgwIFS95OZmamRI0eqVatWqlWrlho1aqT4+HhNnz5dBQUFHs0LAAAA9mPr4n7nzp268cYbNWzYMJ06dcrj/V966SV1795d33zzjW6//XZNmjRJ/fr10+LFixUfH69Ro0aV2MehQ4d0/fXXa+jQocrOztbo0aP1+uuvq1mzZnrttdd07bXXatWqVSX2s3DhQl177bWaOHGiYmJi9Pbbb2vEiBFKT09XcnKybr75Zp04ccLjOQIAAMA+/Ko6gIvBGKP33ntPf/vb31SjRg117txZ69at86iPDz74QOPGjVNgYKCWL1+u2NhYa1v//v3VvXt3vfHGG2rUqJGefvppp33k5+fr9ttv1/r169W1a1ctXbpUQUFBkqShQ4fqueee04QJE3Tbbbfp3//+t1q2bOm0n40bN6pfv346c+aMUlJSNGLECGvb0KFD1b17dy1btkz33XefFixY4NE8AQAAYB+2PHL/97//XcOGDdP111+vLVu2qGfPnh7tf/DgQT3zzDOSpBEjRhQp7CWpXbt2VkE/ZswY7d2712k/U6dOVWpqqnx8fDR16lSrsHeMs0WLFjp69KiefPJJl/EMHTpUZ86cUWxsbJHCXpLq1aunyZMnS5K+/fZb/etf//JorgAAALAPWxb3xhh9/PHH+u6779S0aVOP9584caJOnjwpSRo4cKDTNgMHDpSPj49ycnL01ltvOY1hwoQJkqRu3brpmmuuKdbG399fDz30kKTzhXlaWlqxNkuXLtX69eslSYMGDXIay4033mgd9X/llVdKmB0AAADsypbF/YsvvqiHH364zPvPnTtXkhQVFaUWLVo4bXPFFVeodevWVntjTJHt69at0549eyRJPXr0cDlWYmKitfzll18W2+74M3f9FG7btGmTfvvtN5ftAAAAYF+2LO59fHzKvO++ffv0n//8R5J03XXXuW3brl07SdLevXu1Y8eOItuWL19uLbvrJyYmRr6+vsX2ubCfunXrKjIyssRYXPUDVBb/Wv5Ol6sD5l58uTqornOvrvOWmLuz5erAcb5+l/DcbVncl8eWLVus5ZJO6XHcvnXr1jL1ExAQoIYNGzrt48yZM9q1a1e5YwFQOYKDnS9XB8y9+LLdVdd5S8zd2XJ14C1zp7i/QEZGhrUcHh7utq3jdsf9ytrP8ePHdeTIEevnu3fvtu5fX55YUPmq85ENb/njBwCAHdnyVpjl4Xiv+MDAQLdtHe9+c+E95svTT7169So0Fmeys7OVn59vrQcEBCggIKDE/TxxzqH//Pz8IuPZXUBAfpHlajT1Ir/n/Px85ZfhEEJ+fr4KCgq8LmcqYu7eqqrnXpU5U9Vzryp2mHdZ88YOcy8r5l64fK5S/t6UZQyK+wucOXPGWq5Zs6bbto7bT58+XeH9VFQszjRr1qzIelJSknXnnopy+NB/n5r7408/KXNP9Um33Pz/XmC9ds1aBfiV/ToQb1MRcy8oKNDu3bslybomxRvwez+vKuZelTlT1XOvKnaYd1nzxg5zLyvmft76desUVLPGRR+zLA9hrT7VVik5HgE/e/as27aO22vVqlXh/VRULM6kp6erdu3a1vrFOHK/f98Z6cfzT9/9U4cOahoZWqH9X8pOn82Xli6TJHXp2kW1alaff2oVMffCIxVdu3aVn5/3vHf83qtu7lWZM1U996pih3mXNW/sMPeyYu7n5x7bubNq16rYusmZ48ePe7xP9fmNlFJo6H8L0JycHLdtHY+sO+5XUf1UVCzO1K9fv0hxfzHUcPhD6efn51VFWnn5FTgsM/cy9ePr6+t17x2/9/+/XEVzr6qcuRTmXhXsMu+y5I1d5l4WzP3/L/vVqJS5l2UM7/m+u5JERUVZy1lZWW7bOm533K+s/dSuXds6316SIiIirK8JyxMLAAAAqgeK+wtER0dby4UPoXJl79691nKbNm3K1E9ubq4OHjzotI+goCA1b9683LFUFW+5HywAAIBdUNxf4PLLL9dVV10l6fzTXt3ZuHGjpPNPq23ZsmWRbQkJCdayu37S0tKs21067nNhP0ePHlVmZmaJsbjqBwAAAPZHce9Ev379JJ2/X3zhQ6QutG/fPm3btk2SdNdddxV7Km7nzp11xRVXSJKWLVvmcqylS5cWG9dZLCX1U7jtuuuuU4sWLVy2q0zc7xwAAKByUdw7MXz4cIWEhEiSPv74Y6dtPvnkExljFBgYqKeeeqrYdh8fHz333HOSpNTUVOuDgKP8/HxNnz5dktS7d2/FxMQUa9OjRw/Fxsa6jWXVqlXavn27JOn5558vYXYAAACwK4p7Jxo1aqQ33nhDkvTOO+9ow4YNRbanpaXp9ddflyS9/PLL1hH6Cw0aNEjXX3+9jDEaNGhQkTvaSNKLL76oHTt2qG7dunrnnXdcxvP+++8rKChIa9eu1eTJk4tsO3r0qB577DFJUp8+fXTXXXd5NlkAAADYhm3vXzRr1ixr+eeff7aWlyxZYl18Gh4ersTERKf7Dx48WFlZWXrppZcUHx+vgQMHqk2bNtq2bZumTp2qU6dO6emnn9bTTz/tMgY/Pz99/fXX6tOnj1JTU9WhQwclJycrODhYixYt0sKFCxUWFqa5c+cWO2ffUfv27fXFF1/o/vvv1/Dhw/XDDz8oISFBhw8f1tSpU5WRkaGEhAT985//9PRtAgAAgI3Ytrjv37+/05+PHz/eWo6Li3NZ3EvSuHHjlJiYqHfffVdz587VlClTFBYWpptvvlmPPfaY4uPjS4wjLCxMqampmjp1qj777DNNmDBBubm5ioiI0KhRo/TEE0/osssuK7GfPn36KC0tTSkpKVq4cKHmz5+v4OBgRUdHa+zYsUpKSvKqJ3kCAACg4tm2uDfGlNyoFLp27aquXbuWqw8/Pz8NGTJEQ4YMKVc/UVFRSklJUUpKSrn6AQAAgD1xqBcAAACwCYp7AAAAwCYo7gEAAACboLgHAAAAbILiHgAAALAJinsAAADAJijuAQAAAJuguAcAAABsguIeAAAAsAmKewAAAMAmKO4BAAAAm6C4BwAAAGyC4h4AAACwCYp7AAAAwCYo7gEAAACboLgHAAAAbILiHgAAALAJinsAAADAJijuAQAAAJuguAcAAABsguIeAAAAsAmKewAAAMAmKO4BAAAAm6C4BwAAAGyC4h4AAACwCYp7AAAAwCYo7gEAAACb8KvqAAAA3q9WTT9lvNa7qsOoEtV17tV13hJzr85z/+2VnkpNTVWtmpduCX3pRgbAK1X3P/zVde4AgEsDp+UAAAAANsGRe+Ai4AguAACoChy5BwAAAGyC4h4AAACwCYp7AAAAwCYo7gEAAACboLgHAAAAbILiHgAAALAJintcNIVPcpt+S/Al/SQ3AAAAu6C4BwAAAGyC4h4AAACwCYp7AAAAwCYo7gEAAACboLgHAAAAbILiHgAAALAJinsAAADAJijuAQAAAJuguAcAAABsguIeAAAAsAmKewAAAMAmKO4BAAAAm6C4BwAAAGyC4h4AAACwCYp7AAAAwCYo7nFR5ebmavr06crNza3qUOBFyBt4ipxBWZA38JQ35IyPMcZUdRCoPMePH1edOnV07Ngx1a5d+6KPl52drQYNGujw4cOqX7/+RR8P9kDewFPkDMqCvIGnKjtnylK3ceQeAAAAsAmKewAAAMAm/Ko6AFSuwrOwjh8/XinjFY5z/Phx+fmRbigd8gaeImdQFuQNPFXZOVM4nidn0XPOfTWzd+9eNW3atKrDAAAAQCnt2bNHV1xxRanaUtxXMwUFBfr9998VGhoqHx+fqg4HAAAALhhjdOLECTVp0kS+vqU7m57iHgAAALAJLqgFAAAAbILiHgAAALAJivtq7tChQxozZoyio6MVEhKi+vXrq0uXLnr33Xd19uzZChtn0aJF6tu3ry6//HIFBgaqWbNmSk5O1saNGz3u6/PPP1dYWJh8fHw0ffr0CosRpecNeXPixAnNnDlT99xzj1q0aKHg4GAFBgaqadOmuu222/SPf/xDeXl5FRYr3POGnDl9+rS++uorDR06VJ06dVKDBg3k7++vOnXqKCYmRsOHD9cvv/xSYbGiZN6QN66899578vHxkY+Pjx566KEKixXueUPOPPTQQ1ZulPQ6dOiQ58EZVFvr1683l112mZFkEhMTzeTJk80bb7xhoqOjjSTTrl07s2/fvnKNce7cOTNo0CAjydSvX9/87W9/Mx9++KEZNGiQqVmzpvHz8zMTJ04sVV9ZWVnmjjvuMJKs17Rp08oVHzznDXnz5ptvmtq1axtJJiQkxAwZMsRMnDjRfPDBByYpKcnUrFnTijU9Pb1csaJk3pAzGRkZJiQkxPrbkpiYaF599VXz0UcfmWeffdZERUUZScbPz8+888475YoVpeMNeePKnj17TGhoqJVPSUlJ5YoTpeMtOZOUlFSklnH3OnjwoMcxUtxXU5mZmaZhw4ZGknniiSeKbMvJyTHx8fFGkunQoYPJyckp8zijR482kkxYWJjZsWNHkW2LFy82vr6+xsfHx3zxxRdu+5kzZ45p0KCBCQgIMDfeeCPFfRXxlryJjY01kkzr1q3N/v37i23fsGGDVchdeeWV5uTJk2WOFe55S878+uuv1t+VTz/9tNj206dPm8TERKvN0qVLyxwrSuYteePKn//85yIFGsX9xedNOZOUlGSaNGlifv311xJf+fn5HsdIcV9N3X333UaSiYiIcJrkv/32m6lRo4aRZN58880yjbFlyxbj6+trJJkpU6Y4bVP46TU8PNxlgTVt2jQjyXTs2NFs3brVWqe4r3zekjeFxf3q1atdjjNhwgQrjyZMmFCmWFEyb8mZwuL+pptucjnOtm3brJy54447yhQrSsdb8saZOXPmGEnmtttuo7ivRN6UM0lJSSYyMrJMMZQGxX01tH37duPj42MkmRdeeMFlux49ehhJpmHDhiYvL8/jce6//34jyQQFBZnjx487bZOammr98UtJSXHaZsqUKWb8+PHWp1eK+6rhTXkTGxtrGjZs6Hac7du3W3107drV4zhRMm/KmYyMDBMbG+v0qL2jwm98rr76ao/jROl4U95cKDs724SHh5uIiAizefNmivtK4m05c7GLey6orYbmzp1rPca4R48eLtslJiZKkg4ePKiVK1d6NEZubq4WLFggSYqNjVVoaKjTdl26dFFISIgk6csvv3Ta5tFHH9Wzzz6rGjVqeBQDKpY35U1KSorVjyuRkZHW8u7duz2KE6XjTTkTGRmpdevWKTk52e14QUFBkqSAgACP4kTpeVPeXOjJJ59UVlaW3n//fWs/XHzenDMXA8V9NbR8+XJr+brrrnPZrl27dk73KY0ff/xRx48fL3EMX19fXXvttZKktWvX6syZM8Xa8CTdS4M35U3nzp0VGxvrdqyjR49ay8HBwR7FidLxppwpjYyMDB08eFCSlJCQ4PH+KB1vzZtly5Zp+vTpuueee9S7d2+P4kH5eGvOODp58qTOnTvnUUwuY6iQXuBVtmzZIkkKDQ1VnTp1XLZr2rSptbx169YyjXFhP+7GKSgo0LZt2zwaB5XHbnmTnp5uLd9www0e74+S2SlnCgoK9Mwzz0iSmjRpolGjRnm0P0rPG/Pm9OnTeuSRR1SvXj1NnDjRo1hQft6YM/n5+Zo4caI6duyoOnXqKDQ0VP7+/mrevLmGDBmiHTt2eBSfI4r7aiY3N1cHDhyQJIWHh7tt67g9IyPDo3Ec21/McVA57Jg3X3/9tbU8ePBgj/eHe96eM3l5efrjjz+0fft2/fOf/1S3bt00d+5cJSQkaMOGDSWOhbLx1rwZO3asdu3apTfffJPcqGTemjP79u3TU089pWuuuUbvv/++Fi5cqEmTJqlBgwb64IMP1KZNG3344YcexVjIr0x7wWudOHHCWg4MDHTbtvDc0gv3u5TGQeWwW97k5OTos88+kyQ98MADat++vUf7o2TenjOrV69WfHy8td6gQQNNmjRJgwcPlp8f/3VeLN6YNz/99JNSUlIUHx+vAQMGeBQHys8bc0aSatWqpcWLFxf75njIkCEaMGCAZsyYocGDB6t+/frq16+fR7Fy5L6acTz3q2bNmm7bOm4/ffr0JTkOKofd8mbChAn6/fff1bRpU75Cv0i8PWdiYmK0ZMkSLViwQO+++67atm2rYcOGKSIiQjNnzvQoRpSet+VNfn6+Bg4cKH9//zIfZUX5eFvOSNKbb76pjIwMp6eE+vr66r333rOO/o8YMUI5OTkexUpxX804fpos6THMjttr1ap1SY6DymGnvFm5cqXGjx+vWrVq6euvv1b9+vU9ihGl4+05U69ePfXo0UN9+vTRsGHDtGLFCk2aNEn79+9XUlKSXn75ZY/iROl4W968+eab2rRpk8aOHauWLVt6FAMqhrfljCSFhYWpYcOGLvcPDg7WvffeK0nav3+/Fi9e7FGsFPfVjOOtm0r6JOj4KdXVLZ+qehxUDrvkza+//qq77rpLPj4+mjt3rjp06OBRfCg9u+SMo8cff1y33367JGncuHHatGmTx33APW/Km99++01///vf1bZtWz399NMejY+K400544k//elP1vKaNWs82pfivpoJCAhQ48aNJUlZWVlu2zpuj4qK8mgcx/YXcxxUDjvkzc6dO5WYmKjjx4/ryy+/VK9evTyKDZ6xQ84488ADD0iSjDGaNm1amfqAa96UN4888ohycnI0YcIEHTt2TIcOHSryOnLkiNU2Nze3yLbc3FyP4oVr3pQznmjUqJG1XHjBcGlR3FdD0dHRks5f5HHs2DGX7fbu3Wstt2nTpkxjSNKePXvcti0cx9fXV61bt/ZoHFQeb86b9PR0xcfH648//tAXX3yh2267zaO4UDbenDOutGrVylrevHlzmfqAe96SNytWrJAk9enTRw0bNiz2crxQf86cOUW2zZ4926N44Z635IwnCgoKrGVPH+JJcV8NOT58xd3Xyhs3bnS6T2n86U9/sr6KcjdGQUGB0tLSJJ1/qpvjOW24tHhr3mRmZio+Pl779+/XnDlz9Je//MWjmFB23pQz33//vRYtWlTieI7/yebn53sUK0rHW/JmyZIlbl+zZs2y2t58881FtvXs2dOjeOGet+SMJK1atUqvvPJKkW92nHE8Wn/ZZZd5FKsMqp3t27cbHx8fI8mMGTPGZbvExEQjyYSFhZm8vDyPx7n//vuNJBMUFGROnDjhtM3q1auNJCPJpKSklKrfadOmWftMmzbN47hQNt6YN7t37zbNmjUzfn5+5osvvnDa5sMPPzQdOnQwv//+u8exwj1vypm4uDgTGhpqCgoK3I61YMECq5/777/f41hRMm/KG3fS09OtfZOSkjyOD6XnTTkzbtw4I8msXbvW7ViPPvqo1c+3337rUZwU99XU3XffbSSZqKgok5ubW2z7zp07TY0aNYwk88YbbxTbvnnzZtOyZUtz+eWXm++//97pGJs3bza+vr5Gkvnwww+dtklOTjaSTHh4uDl58mSpYqe4rzrelDd79+41V155palRo4aZM2eOyzkV/qFNT0932QZl5y05ExcXZyS5HKPQnXfeaf39+fzzz922Rdl5S964Q3FfubwlZwr/zxk7dqzLuRw+fNjUq1fPSDKRkZHm7NmzLts6Q3FfTWVmZpqGDRsaSeapp54qsi0nJ8fcdNNNRpLp0KGDOXPmTLH97733XuuPVpcuXVyOM3r0aCPJNGzY0OzcubPItv/93/+1/pG4OqrqDMV91fGWvPn999/NVVddZSSZRx991KxYscLlKykpieL+IvKWnCks7lu3bm127NjhtM1bb71lxdKtWzdz7ty5kqaPMvKWvHGH4r5yeUvOFBb3wcHBZvXq1cW2nz592tx6661GkvH19TWLFi0qzfSL4DF71VRERIQWLFig22+/XW+99Za2bNmivn376syZM5oxY4Y2b96s6667TvPnz3f6JDbHCz2MMS7HGT9+vA4fPqyPP/5YnTp10qOPPqqoqCj99NNPmjZtmnx9ffX222+X+PS1efPm6eTJk5KktWvXWj9fu3at9bTIkJAQzqe+yLwlb3r16qXt27dLkj788EMeLlOFvCVn2rZtq1WrVmnbtm26+uqr1a9fP7Vt21aNGzfW/v37NW/ePG3YsEGSdMstt2j27Nny9eWytYvFW/LmQrt27bJuW3jo0KEiPy88B5//qy4Ob8mZli1bqlatWjp16pTi4uJ0xx13qHv37goODtbOnTs1a9YsZWZmKjQ0VJ9++mnZ7uzm8ccB2MrBgwfN888/b6655hoTHBxs6tatazp16mRSUlKcfq1VKC0tzbRo0cLt11eOFi5caPr06WMaN25sAgICTGRkpHnwwQfNTz/9VKo4IyMjrU/Url6RkZGlnTbK6VLPmzp16pSYLxe+OHJ/cV3qOWPM+a/t33jjDdO7d2/TrFkzExwcbGrUqGHq1Klj2rZtax5++GGzdOlSj+aN8vGGvHHk+M0y/1dVDW/ImSNHjphp06aZe+65x7Ru3dqEhIQYPz8/ExYWZrp162Zefvll88cff3g0b0c+xrj5eAIAAADAa/CdIgAAAGATFPcAAACATVDcAwAAADZBcQ8AAADYBMU9AAAAYBMU9wAAAIBNUNwDAAAANkFxDwAAANgExT0AAABgExT3AAAAgE1Q3AMAAAA2QXEPAPBqP/74o3x8fIq9oqKiqjo0AKh0FPcAYGMZGRlOC9+SXnXq1FGrVq30wAMPaMGCBSooKKjqqbhUp04d9ezZUz179lTHjh1Lvd+uXbvUvn17NWjQQDNmzLiIEQJA5fExxpiqDgIAcHFkZWUpKSnJWv/++++Vk5MjSWrSpInatm1bpL0xRkePHtXWrVt16tQp6+edO3fW7NmzL/mj4StXrlR8fLwkKTIyUhkZGS7b9u/fX7NmzZIkBQQE6MiRIwoKCqqMMAHgovGr6gAAABdPeHi4vvvuO2s9KipKmZmZkqTExERNnz7d6X55eXmaPn26Ro4cqVOnTmndunVKSEjQ+vXr1bBhw8oI/aI7d+6ctVxQUHBJfzsBAKXFaTkAgGL8/f01aNCgIsV/enq6XnjhhaoLqoK9+OKLuvbaa1WvXj1NmjRJwcHBVR0SAJQbxT0AwKW77rpLrVq1stZnzpxpndbj7a666iqlpaUpOztbjz76aFWHAwAVguIeAOBWt27drOWcnBxt2LChCqMBALhDcQ8AcCs8PLzI+oEDB6ooEgBASbigFgDgVl5ensf7nDt3Tj///LO2bNmiAwcOKC8vT/Xq1VOrVq0UGxtb5vPbs7OztWTJEu3Zs0d+fn5q0qSJ4uPjvf4iX2OMNm3apE2bNungwYMqKChQgwYNFB0drXbt2ikwMLCqQwTgJSjuAQBuXXg7yYiICJdtV6xYoU8++USLFi3SkSNHnLapVauWkpOT9eqrr6pOnTqliuHYsWMaPXq0Pv3002IfNvz8/HTPPffovffeK1VfjncMujD2G2+80VrftGmT2rVrV6xdXFycVq5cWeznrVu31n/+859iPy/pjtOzZs3SuHHjtGvXLqfba9WqpYSEBPXv319/+ctfVLNmTbf9AajeKO4BAC7l5ORoxYoV1npYWJjat2/vsn2vXr2Um5srSYqNjVVSUpKaN2+ugIAAZWRkaP78+Zo3b57ee+89LV68WKtWrdLll1/uNoaDBw8qPj5eW7dulSQ1a9ZMjz/+uGJiYpSXl6f169dr8uTJio2N1euvv17inOLi4pSVlSWp6H3/L1S7dm317NlTkrRv3z5t2bLFbb/du3dXVFSUzpw5o1WrVpUYhyQ9++yzeu211yRJbdu21cMPP6wWLVooMDBQu3bt0uzZs7VixQp9++23+vbbbzVt2jQ99NBDpeobQDVlAADVRmRkpJFkJJmkpKQS248aNcpqL8mkpKS4bR8QEGAkmaeeesplm/nz5xs/Pz8jyXTp0sVtfwUFBeb666+3xu/SpYs5fvx4sXb79+83LVu2NFdeeaXVNjIyssT5Ob4fK1ascNlu2rRpVru4uDi3faanpxd5z1xZs2aN1SY+Pt7k5eU5bTdlyhSr3bRp00qcE4DqjQtqAQAWY4yOHDmi5cuX68477yxyJPzxxx/X8OHDS+wjPDzcOhrtzJ///Gc9/PDDkqS1a9dq8eLFLttOnTpVqampks7fe3/27NkKDQ0t1q5x48b67LPPtHPnzhLju1QUPh1XkgYOHCg/P+dfpg8ePFhdu3atrLAAeDmKewCopmbMmCEfH58iL19fX9WvX1833XSTvvrqKwUFBalv375avny5Jk2aJB8fH7d9LliwQIsXL3ZZqBa65ZZbrOX58+e7bDdx4kRr+c4771RkZKTLtrGxsercubPbcS8ljh9EatSo4bbtLbfcovDwcAUFBV3ssAB4Oc65B4BqqkmTJmrbtq21fvbsWWVmZloXdtatW1fjxo3T8OHD5etbumNBiYmJpWpXt25dazktLc1pmy1btuiXX36x1nv16lVivzfffLPWrVtXqhiqWq1atazlSZMm6Y477pC/v7/TtmPGjNGYMWMqKzQAXoziHgCqqcTERE2fPr3Yz1evXq0HHnhAGRkZGjlypJYsWaJvvvmmxKPxjvbt26eFCxcqLS1NBw8e1MmTJ1VQUGBtz87OtpYPHz7stI8Li3THDyKuXH311aWOsar17NlTX3/9taTz73m7du30/PPP64477lBAQEAVRwfAW1HcAwCK6NatmxYsWKAOHTro7NmzWrRokcaMGaMJEyaUuG9WVpZGjhypzz//vEgx707h3XUu9NtvvxVZb9q0aYl9OX4jcKkbMGCAZs+ere+//16StHXrVt13332qXbu2br31VvXt21e9evXyqjkBqHqccw8AKCY6OrrIxbPvvPOO03vDO9q3b5+6dOmi2bNnq6CgQC1atNDMmTO1d+9e5eXlyRhjvRxvr+nK0aNHi6yX5sFX3vSwJ39/f3333XcaPXp0kXPpjx8/rjlz5ui+++5TeHi4br/99lLfWhMAKO4BAE6NHj3aKqhzc3NLvIf8448/rvT0dEnnHxT173//W/3799fll1/u0Sk9rpR0Ma83CgwM1Guvvabdu3fr3XffVbdu3YrM8+zZs5o3b57i4uL04IMPurwnPwAUorgHADgVFhamgQMHWuuffvqp9fCnC+3fv1/ffPONtf7MM8+oXr165Rr/wtNRTp48WeI+rk7xqUxliSEsLEzDhg1Tamqq9u7dq7ffflsxMTFF2nz22WcaPHhwRYUJwKYo7gEALj355JPWUfecnBy9/fbbTttt3LhRxhhrvUuXLuUeu0WLFkXW9+zZU+I+R44cKfe4zjjeqrKk4r28MTRp0kQjR47Upk2btGjRoiJP8J05c6YyMjLK1T8Ae6O4BwC4FBERob/+9a/W+pQpU4qdCy9Jx44dK7Lu7EFTjkpzesmFHxA2b95c4j7btm0rsU1ZOJ7v73inH2ccb99ZUrt169a5/bDQq1cv/etf/7LWjTH68ccfS9U/gOqJ4h4A4NaoUaOs88BPnDihSZMmFWsTFhZWZH3fvn1u+9yxY0eJ47Zp00Zt2rSx1t09ybbQkiVLSmxTFk2aNLGWMzIy3Bbk3377ban6HDp0qLp06aJff/3VbbvY2FjVr1/fWrfjtQcAKg7FPQDArejoaN16663W+rvvvqvTp08XadOpU6ciF80W3r/dGWOMZs6cWaqxR44caS1/9dVXbu/Ys2HDBq1du7ZU/Xqqbdu21gOmzp49q2XLljltt3HjRrdP3HWm8FaYruTm5urMmTPWenR0tEf9A6heKO4BACUaPXq0tXzo0CF99NFHRbbXrVtXDzzwgLX+/vvvuyyAx44dW+pTSwYMGKC4uDhJ54vqv/71rzpx4kSxdllZWerfv7/at29fqn49FRwcXOQJuSNGjND27duLtPnhhx/Ut29fDRgwwKO+x48f7/abjNdff90q7hMSEtSqVSuP+gdQvfgYxyugAAC2kpWVpaSkJGv9+++/t853b9KkSZGnvs6YMUPh4eEu++ratat1ZLx+/frq2LGjJCkkJERz585Vdna2unXrZp337u/vr4ceeki9e/dW/fr1tXv3bs2YMUMrVqzQgAEDrA8IgYGBVgGfmJiop556qsi4hw4dUkJCgnXOfbNmzTRs2DDFxMQoPz9f69at0+TJk9W0aVO9+uqrVhHu2G94eLhmzJghSUpKSrLu+uP4fnTs2NE6/cXZe7F582Z17NjROiXH19dXbdu2VYMGDbR792799ttvuuGGG/Txxx/rqquusvbr2bNnkfepUHx8vFauXCnp/IeH5ORkxcXFqXHjxjpx4oQyMzM1a9YsrV69WpJ05ZVXavny5YqIiHD5OwIAGQCAbaWnpxtJpXqlp6e77WvevHlO96tTp47VJjs72zz44IPGz8/PaduYmBizatUqs2LFCqfbk5KSnI597NgxM2TIEOPv719sH39/f5OcnGxOnjzpst/IyEirr8jIyDK/F8uXLzfNmjUr1t7Pz88MGDDAnDlzxuV77vg+GWPMgQMHzPjx401MTIzbWEJDQ82wYcNMdnZ2yb9wANUeR+4BABUuKytLq1atUmZmpvLz89WoUSN16tSp3OeLZ2dna8mSJdqzZ49q1KihK664QvHx8cUu6L2Y8vLytHr1am3evFknT55U06ZN1b17d0VGRpa5zwMHDigtLU07d+7U0aNH5ePjo3r16unqq69Wp06dijzBFgDcobgHAAAAbIILagEAAACboLgHAAAAbILiHgAAALAJinsAAADAJijuAQAAAJuguAcAAABsguIeAAAAsAmKewAAAMAmKO4BAAAAm6C4BwAAAGyC4h4AAACwCYp7AAAAwCYo7gEAAACboLgHAAAAbOL/AfBlOHtzFgsrAAAAAElFTkSuQmCC", + "text/plain": [ + "<Figure size 800x600 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from Embedding import embedding_plots\n", "\n", @@ -522,11 +1888,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Load 1000 files located in data/export/processed/test/velo-sim10b-nospillover\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7ed00f4159294597a9bd98ba0d010cf2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1000 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<IPython.core.display.Image object>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "metric_learning_model.load_partition(\"velo-sim10b-nospillover\")\n", "graphplot.plot_predicted_graph(metric_learning_model, CONFIG)\n" @@ -534,9 +1932,175 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Load 20 files located in data/export/processed/test/velo-sim10b-nospillover\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "bca3673af1764a97a36756418be9a143", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/20 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "32f50cda642e4ba9ab7ec59221a5aa50", + 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+ "text/plain": [ + "<Figure size 2400x600 with 3 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from Embedding.embedding_plots import plot_best_performances_radius\n", "plot_best_performances_radius(\n", @@ -560,9 +2124,41 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Load 1000 files located in data/export/processed/test/velo-sim10b-nospillover\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4ade8e4ad4304caab854c4206ace19b7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1000 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<IPython.core.display.Image object>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "metric_learning_model.load_partition(\"velo-sim10b-nospillover\")\n", "perfplot.plot_track_lengths(metric_learning_model, CONFIG)" @@ -570,9 +2166,76 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Load 100 files located in data/export/processed/train\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6337c971d5c84150a72d76c6ab4c4e07", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Load 100 files located in data/export/processed/val\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "25703e3e61d649e3a293264427714e3c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6b5aff99014641a687f36887b0f05168", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x500 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "metric_learning_model.setup(stage=\"fit\") # load train and val datasets\n", "\n", @@ -591,9 +2254,81 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:------------- Step 2: Constructing graphs from metric learning model -------------\n", + "INFO:---------------------------- a) Loading trained model ----------------------------\n", + "INFO:Load model from artifacts/metric_learning/export/version_0/checkpoints/epoch=0-step=100.ckpt.\n", + "INFO:----------------------------- b) Running inferencing -----------------------------\n", + "INFO:Use radius 0.02\n", + "INFO:Use the following parameters for train: {'building': None, 'filtering': 'edges_at_least_3_hits'}\n", + "INFO:Inference from data/export/processed/train to data/export/metric_learning_processed/train\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "96e5113edb3a4685ae3fe183e3d7b3dc", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Use the following parameters for val: {'building': None, 'filtering': 'edges_at_least_3_hits'}\n", + "INFO:Inference from data/export/processed/val to data/export/metric_learning_processed/val\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7977a70f3b794d518747eba029433a05", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Use the following parameters for test: {'building': None}\n", + "INFO:Inference from data/export/processed/test/velo-sim10b-nospillover to data/export/metric_learning_processed/test/velo-sim10b-nospillover\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e432ddc8c8cf4446a7e6ff574b74adc2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1000 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "graph_builder = run_metric_learning_inference(\n", " CONFIG,\n", @@ -621,9 +2356,144 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:------------------------- Step 3: Running GNN training -------------------------\n", + "INFO:----------------------------- a) Initialising model -----------------------------\n", + "INFO:------------------------------ b) Running training ------------------------------\n", + "Missing logger folder: /home/fgias/etx4velo-2/LHCb_Pipeline/artifacts/gnn/export\n", + "INFO:Save hyperparameters, metrics and artifacts in /home/fgias/etx4velo-2/LHCb_Pipeline/artifacts/gnn/export/version_0\n", + "/home/fgias/.conda/envs/etx4velo/lib/python3.10/site-packages/lightning_fabric/plugins/environments/slurm.py:165: PossibleUserWarning: The `srun` command is available on your system but is not used. HINT: If your intention is to run Lightning on SLURM, prepend your python command with `srun` like so: srun python /home/fgias/.conda/envs/etx4velo/lib/python3.10/site ...\n", + " rank_zero_warn(\n", + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "You are using a CUDA device ('NVIDIA A100 80GB PCIe MIG 3g.40gb') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n", + "INFO:Load 100 files located in data/export/metric_learning_processed/train\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "950ddaa3cfff4586adbc44d630b46c02", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Load 100 files located in data/export/metric_learning_processed/val\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e991f107d56c4bc7ae12f7186fd1698f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", + "\n", + " | Name | Type | Params\n", + "------------------------------------------------------\n", + "0 | node_encoder | Sequential | 332 K \n", + "1 | edge_encoder | Sequential | 462 K \n", + "2 | edge_network | Sequential | 793 K \n", + "3 | node_network | Sequential | 528 K \n", + "4 | output_edge_classifier | Sequential | 529 K \n", + "------------------------------------------------------\n", + "2.6 M Trainable params\n", + "0 Non-trainable params\n", + "2.6 M Total params\n", + "10.586 Total estimated model params size (MB)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/fgias/.conda/envs/etx4velo/lib/python3.10/site-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 8, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n", + " warnings.warn(_create_warning_msg(\n", + "/home/fgias/.conda/envs/etx4velo/lib/python3.10/site-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 8, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n", + " warnings.warn(_create_warning_msg(\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a16756e84bc649928a22a0d937b956fe", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "`Trainer.fit` stopped: `max_epochs=1` reached.\n", + "INFO:-------------------------------- c) Saving model --------------------------------\n" + ] + } + ], "source": [ "if run_training:\n", " send_telegram_message('Started GNN training.', chat_id, api_key)\n", @@ -646,9 +2516,18 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "gnn_metric_path='artifacts/gnn/export/version_0/metrics.csv'\n", + "gnn_artifact_path='artifacts/gnn/export/version_0/checkpoints/epoch=0-step=100.ckpt'\n" + ] + } + ], "source": [ "from utils.modelutils.checkpoint_utils import (\n", " get_last_version_dir_from_config,\n", @@ -665,7 +2544,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -708,7 +2587,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -729,9 +2608,62 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>epoch</th>\n", + " <th>train_loss</th>\n", + " <th>val_loss</th>\n", + " <th>eff</th>\n", + " <th>pur</th>\n", + " <th>current_lr</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0</td>\n", + " <td>0.201887</td>\n", + " <td>0.166563</td>\n", + " <td>0.25814</td>\n", + " <td>0.90355</td>\n", + " <td>0.00002</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " epoch train_loss val_loss eff pur current_lr\n", + "0 0 0.201887 0.166563 0.25814 0.90355 0.00002" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# gnn_metrics = checkpoint_utils.get_training_metrics(gnn_trainer)\n", "\n", @@ -742,18 +2674,89 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Figure was saved in output/export/loss_gnn.pdf\n", + "Figure was saved in output/export/loss_gnn.png\n" + ] + }, + { + "data": { + "text/plain": [ + "(<Figure size 800x600 with 1 Axes>, <Axes: xlabel='Epoch', ylabel='Loss'>)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 800x600 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "perfplot_mpl.plot_loss(gnn_metrics, CONFIG, \"gnn\")\n" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Figure was saved in output/export/eff_gnn.pdf\n", + "Figure was saved in output/export/eff_gnn.png\n", + "Figure was saved in output/export/pur_gnn.pdf\n", + "Figure was saved in output/export/pur_gnn.png\n" + ] + }, + { + "data": { + "text/plain": [ + "(<Figure size 800x600 with 1 Axes>,\n", + " <Axes: xlabel='Epoch', ylabel='Edge Purity'>)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 800x600 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 800x600 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "perfplot_mpl.plot_metric_epochs(\n", " \"eff\",\n", @@ -785,9 +2788,49 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Load 1000 files located in data/export/metric_learning_processed/test/velo-sim10b-nospillover\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "bac2873d25514d41a4daac53933b8181", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1000 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/fgias/.conda/envs/etx4velo/lib/python3.10/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n", + " warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<IPython.core.display.Image object>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "gnn_model.load_partition(\"velo-sim10b-nospillover\")\n", "perfplot.plot_edge_performance(gnn_model, CONFIG)\n" @@ -795,9 +2838,311 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Load 50 files located in data/export/metric_learning_processed/test/velo-sim10b-nospillover\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1e28029d73194847bb0cabf57c0d2f28", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/50 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7f85f85dddea4ac7857bae91a578eb23", + 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Gradients will be None\")\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Figure was saved in output/export/performance_given_score_cut.pdf\n", + "Figure was saved in output/export/performance_given_score_cut.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 2400x600 with 3 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from GNN.gnn_plots import plot_best_performances_score_cut\n", "\n", @@ -821,9 +3166,80 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:--------------------- Step 4: Scoring graph edges using GNN ---------------------\n", + "INFO:---------------------------- a) Loading trained model ----------------------------\n", + "INFO:Load model from artifacts/gnn/export/version_0/checkpoints/epoch=0-step=100.ckpt.\n", + "INFO:Load model from artifacts/gnn/export/version_0/checkpoints/epoch=0-step=100.ckpt.\n", + "INFO:----------------------------- b) Running inferencing -----------------------------\n", + "INFO:Inference from data/export/metric_learning_processed/train to data/export/gnn_processed/train\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ad9925c26a664e9483fbf05e82c12492", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/fgias/.conda/envs/etx4velo/lib/python3.10/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n", + " warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n", + "INFO:Inference from data/export/metric_learning_processed/val to data/export/gnn_processed/val\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "816f430d3fef4d84ab9317394481602d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Inference from data/export/metric_learning_processed/test/velo-sim10b-nospillover to data/export/gnn_processed/test/velo-sim10b-nospillover\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4700080f2f654a40a6d3d16ea9b4e5bf", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1000 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "run_gnn_inference(CONFIG, checkpoint=gnn_artifact_path)" ] @@ -838,9 +3254,75 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:----------- Step 5: Building track candidates from the scored graph -----------\n", + "INFO:Score cut: 0.7\n", + "INFO:Inference from data/export/gnn_processed/train to data/export/track_building_processed/train\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cf42f0d1c6624aac98c6701a991b748e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Inference from data/export/gnn_processed/val to data/export/track_building_processed/val\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b69c5c9c67654de39e30d28a64aeec3d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Inference from data/export/gnn_processed/test/velo-sim10b-nospillover to data/export/track_building_processed/test/velo-sim10b-nospillover\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ef894aecc5bc47d49e8d720dd35d2f7d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1000 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "build_track_candidates(CONFIG)" ] @@ -855,11 +3337,146 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:------------------------------ Evaluation for train ------------------------------\n", + "INFO:1) Load dataframe of tracks, hits-particles and particles\n", + "INFO:Load tracks in data/export/track_building_processed/train.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d0f9fa11e96948d3a50967b0ab098f2c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Load truncated paths for train in data/export/processed/splitting.json\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e1300faec84b485fa943eda8294e86dc", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e05737e1c32648c5a8d3266f0b01f279", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Compute plat stats\n", + "INFO:2) Matching\n", + "INFO:3) Evaluation\n", + "INFO:Report was saved in output/export/report-2023.10.06-15.37.38-train.txt\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TrackChecker output : 5/ 167 2.99% ghosts\n", + "01_velo : 126/ 11107 1.13% ( 1.29%), 0 ( 0.00%) clones, pur 100.00%, hit eff 51.93%\n", + "02_long : 73/ 6331 1.15% ( 1.36%), 0 ( 0.00%) clones, pur 100.00%, hit eff 51.96%\n", + "03_long_P>5GeV : 71/ 4124 1.72% ( 2.11%), 0 ( 0.00%) clones, pur 100.00%, hit eff 51.91%\n", + "04_long_strange : 0/ 348 0.00% ( 0.00%), 0 ( nan%) clones, pur nan%, hit eff nan%\n", + "05_long_strange_P>5GeV : 0/ 161 0.00% ( 0.00%), 0 ( nan%) clones, pur nan%, hit eff nan%\n", + "06_long_fromB : 0/ 15 0.00% ( 0.00%), 0 ( nan%) clones, pur nan%, hit eff nan%\n", + "07_long_fromB_P>5GeV : 0/ 8 0.00% ( 0.00%), 0 ( nan%) clones, pur nan%, hit eff nan%\n", + "08_long_electrons : 2/ 437 0.46% ( 0.26%), 0 ( 0.00%) clones, pur 100.00%, hit eff 46.67%\n", + "09_long_fromB_electrons : 0/ 0 nan% ( nan%), 0 ( nan%) clones, pur nan%, hit eff nan%\n", + "10_long_fromB_electrons_P>5GeV : 0/ 0 nan% ( nan%), 0 ( nan%) clones, pur nan%, hit eff nan%\n", + "\n", + "| Categories | Efficiency | Average efficiency | % clones | Average hit purity | Average hit efficiency |\n", + "|:---------------------|:-------------|:---------------------|:-----------|:---------------------|:-------------------------|\n", + "| Velo | 1.02% | 1.17% | 0.00% | 100.00% | 52.14% |\n", + "| Long | 1.11% | 1.30% | 0.00% | 100.00% | 51.82% |\n", + "| Velo, no electrons | 1.13% | 1.29% | 0.00% | 100.00% | 51.93% |\n", + "| Velo, only electrons | 0.33% | 0.34% | 0.00% | 100.00% | 56.51% |\n", + "| Long, only electrons | 0.46% | 0.26% | 0.00% | 100.00% | 46.67% |\n", + "| Categories | # ghosts | # tracks | % ghosts |\n", + "|:-------------|-----------:|-----------:|:-----------|\n", + "| Everything | 5 | 167 | 2.99% |\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Plot basic for category velo_reconstructible_acceptance saved in output/export/hist1d_basic_velo_reconstructible_acceptance-train.pdf\n", + "INFO:Plot basic for category long_reconstructible_acceptance_only_electrons saved in output/export/hist1d_basic_long_reconstructible_acceptance_only_electrons-train.pdf\n" + ] + }, + { + "data": { + "text/plain": [ + "<montetracko.evaluation.trackevaluator.TrackEvaluator at 0x7f47bce2f790>" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 3200x1800 with 24 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 3200x1800 with 24 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "evaluate_candidates_montetracko(\n", " CONFIG,\n", @@ -871,9 +3488,116 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:------------------------------- Evaluation for val -------------------------------\n", + "INFO:1) Load dataframe of tracks, hits-particles and particles\n", + "INFO:Load tracks in data/export/track_building_processed/val.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7db1c90e628d43b2908176d00038bf9e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Load truncated paths for val in data/export/processed/splitting.json\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "382da7b43dca4ef98f1188120dad4080", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "190a6032f2854105b62f8b0a38e9c706", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Compute plat stats\n", + "INFO:2) Matching\n", + "INFO:3) Evaluation\n", + "INFO:Report was saved in output/export/report-2023.10.06-15.37.50-val.txt\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TrackChecker output : 5/ 194 2.58% ghosts\n", + "01_velo : 139/ 11510 1.21% ( 1.34%), 0 ( 0.00%) clones, pur 99.82%, hit eff 52.06%\n", + "02_long : 86/ 6621 1.30% ( 1.44%), 0 ( 0.00%) clones, pur 100.00%, hit eff 51.60%\n", + "03_long_P>5GeV : 81/ 4286 1.89% ( 2.14%), 0 ( 0.00%) clones, pur 100.00%, hit eff 51.49%\n", + "04_long_strange : 1/ 323 0.31% ( 0.39%), 0 ( 0.00%) clones, pur 100.00%, hit eff 50.00%\n", + "05_long_strange_P>5GeV : 0/ 148 0.00% ( 0.00%), 0 ( nan%) clones, pur nan%, hit eff nan%\n", + "06_long_fromB : 0/ 10 0.00% ( 0.00%), 0 ( nan%) clones, pur nan%, hit eff nan%\n", + "07_long_fromB_P>5GeV : 0/ 6 0.00% ( 0.00%), 0 ( nan%) clones, pur nan%, hit eff nan%\n", + "08_long_electrons : 5/ 431 1.16% ( 1.99%), 0 ( 0.00%) clones, pur 100.00%, hit eff 48.07%\n", + "09_long_fromB_electrons : 0/ 3 0.00% ( 0.00%), 0 ( nan%) clones, pur nan%, hit eff nan%\n", + "10_long_fromB_electrons_P>5GeV : 0/ 2 0.00% ( 0.00%), 0 ( nan%) clones, pur nan%, hit eff nan%\n", + "\n", + "| Categories | Efficiency | Average efficiency | % clones | Average hit purity | Average hit efficiency |\n", + "|:---------------------|:-------------|:---------------------|:-----------|:---------------------|:-------------------------|\n", + "| Velo | 1.11% | 1.23% | 0.00% | 99.83% | 52.28% |\n", + "| Long | 1.29% | 1.44% | 0.00% | 100.00% | 51.41% |\n", + "| Velo, no electrons | 1.21% | 1.34% | 0.00% | 99.82% | 52.06% |\n", + "| Velo, only electrons | 0.53% | 0.63% | 0.00% | 100.00% | 55.32% |\n", + "| Long, only electrons | 1.16% | 1.99% | 0.00% | 100.00% | 48.07% |\n", + "| Categories | # ghosts | # tracks | % ghosts |\n", + "|:-------------|-----------:|-----------:|:-----------|\n", + "| Everything | 5 | 194 | 2.58% |\n" + ] + }, + { + "data": { + "text/plain": [ + "<montetracko.evaluation.trackevaluator.TrackEvaluator at 0x7f47bcf4c580>" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "evaluate_candidates_montetracko(\n", " CONFIG,\n", @@ -894,9 +3618,107 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:---------------------------- velo-sim10b-nospillover ----------------------------\n", + "INFO:--------------------- Evaluation for velo-sim10b-nospillover ---------------------\n", + "INFO:1) Load dataframe of tracks, hits-particles and particles\n", + "INFO:Load tracks in data/export/track_building_processed/test/velo-sim10b-nospillover.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "df8e322fea9040179b78e32a75e1def3", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1000 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Load pre-processed test datasets in data/__test__/velo-sim10b-nospillover.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c2b093cb300540d5a575efa5e71aa560", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1000 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b64f53f24516461d8edb5e8567c34902", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1000 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Compute plat stats\n", + "INFO:2) Matching\n", + "INFO:3) Evaluation\n", + "INFO:Report was saved in output/export/report-2023.10.06-15.38.14-velo-sim10b-nospillover.txt\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TrackChecker output : 58/ 1864 3.11% ghosts\n", + "01_velo : 1408/ 104668 1.35% ( 1.57%), 0 ( 0.00%) clones, pur 99.77%, hit eff 51.69%\n", + "02_long : 852/ 59775 1.43% ( 1.64%), 0 ( 0.00%) clones, pur 99.78%, hit eff 51.85%\n", + "03_long_P>5GeV : 801/ 38230 2.10% ( 2.40%), 0 ( 0.00%) clones, pur 99.76%, hit eff 51.95%\n", + "04_long_strange : 25/ 3031 0.82% ( 0.95%), 0 ( 0.00%) clones, pur 100.00%, hit eff 60.32%\n", + "05_long_strange_P>5GeV : 20/ 1462 1.37% ( 1.67%), 0 ( 0.00%) clones, pur 100.00%, hit eff 61.39%\n", + "06_long_fromB : 2/ 114 1.75% ( 0.46%), 0 ( 0.00%) clones, pur 100.00%, hit eff 40.18%\n", + "07_long_fromB_P>5GeV : 2/ 80 2.50% ( 0.58%), 0 ( 0.00%) clones, pur 100.00%, hit eff 40.18%\n", + "08_long_electrons : 13/ 4354 0.30% ( 0.38%), 0 ( 0.00%) clones, pur 100.00%, hit eff 45.96%\n", + "09_long_fromB_electrons : 0/ 15 0.00% ( 0.00%), 0 ( nan%) clones, pur nan%, hit eff nan%\n", + "10_long_fromB_electrons_P>5GeV : 0/ 12 0.00% ( 0.00%), 0 ( nan%) clones, pur nan%, hit eff nan%\n", + "\n", + "| Categories | Efficiency | Average efficiency | % clones | Average hit purity | Average hit efficiency |\n", + "|:---------------------|:-------------|:---------------------|:-----------|:---------------------|:-------------------------|\n", + "| Velo | 1.15% | 1.35% | 0.00% | 99.75% | 51.74% |\n", + "| Long | 1.35% | 1.56% | 0.00% | 99.78% | 51.77% |\n", + "| Velo, no electrons | 1.35% | 1.57% | 0.00% | 99.77% | 51.69% |\n", + "| Velo, only electrons | 0.19% | 0.26% | 0.00% | 99.38% | 53.34% |\n", + "| Long, only electrons | 0.30% | 0.38% | 0.00% | 100.00% | 45.96% |\n", + "| Categories | # ghosts | # tracks | % ghosts |\n", + "|:-------------|-----------:|:-----------|:-----------|\n", + "| Everything | 58 | 1,864 | 3.11% |\n" + ] + } + ], "source": [ "for test_dataset_name in get_required_test_dataset_names(CONFIG):\n", " logging.info(headline(test_dataset_name))\n", @@ -911,9 +3733,134 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:--------------------- Evaluation for velo-sim10b-nospillover ---------------------\n", + "INFO:1) Load dataframe of tracks, hits-particles and particles\n", + "INFO:Load tracks in data/export/track_building_processed/test/velo-sim10b-nospillover.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "42c04e9f18034d72bf6b5f5773e46092", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1000 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Load pre-processed test datasets in data/__test__/velo-sim10b-nospillover.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3a7c0b70d8954adf873b3c959d64f464", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1000 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8b5d7eaf792b4e7396621b07ed922e2d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1000 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Compute plat stats\n", + "INFO:2) Matching\n", + "INFO:3) Evaluation\n", + "INFO:Report was saved in output/export/report-2023.10.06-15.38.39-velo-sim10b-nospillover.txt\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TrackChecker output : 58/ 1864 3.11% ghosts\n", + "01_velo : 1408/ 104668 1.35% ( 1.57%), 0 ( 0.00%) clones, pur 99.77%, hit eff 51.69%\n", + "02_long : 852/ 59775 1.43% ( 1.64%), 0 ( 0.00%) clones, pur 99.78%, hit eff 51.85%\n", + "03_long_P>5GeV : 801/ 38230 2.10% ( 2.40%), 0 ( 0.00%) clones, pur 99.76%, hit eff 51.95%\n", + "04_long_strange : 25/ 3031 0.82% ( 0.95%), 0 ( 0.00%) clones, pur 100.00%, hit eff 60.32%\n", + "05_long_strange_P>5GeV : 20/ 1462 1.37% ( 1.67%), 0 ( 0.00%) clones, pur 100.00%, hit eff 61.39%\n", + "06_long_fromB : 2/ 114 1.75% ( 0.46%), 0 ( 0.00%) clones, pur 100.00%, hit eff 40.18%\n", + "07_long_fromB_P>5GeV : 2/ 80 2.50% ( 0.58%), 0 ( 0.00%) clones, pur 100.00%, hit eff 40.18%\n", + "08_long_electrons : 13/ 4354 0.30% ( 0.38%), 0 ( 0.00%) clones, pur 100.00%, hit eff 45.96%\n", + "09_long_fromB_electrons : 0/ 15 0.00% ( 0.00%), 0 ( nan%) clones, pur nan%, hit eff nan%\n", + "10_long_fromB_electrons_P>5GeV : 0/ 12 0.00% ( 0.00%), 0 ( nan%) clones, pur nan%, hit eff nan%\n", + "\n", + "| Categories | Efficiency | Average efficiency | % clones | Average hit purity | Average hit efficiency |\n", + "|:---------------------|:-------------|:---------------------|:-----------|:---------------------|:-------------------------|\n", + "| Velo | 1.15% | 1.35% | 0.00% | 99.75% | 51.74% |\n", + "| Long | 1.35% | 1.56% | 0.00% | 99.78% | 51.77% |\n", + "| Velo, no electrons | 1.35% | 1.57% | 0.00% | 99.77% | 51.69% |\n", + "| Velo, only electrons | 0.19% | 0.26% | 0.00% | 99.38% | 53.34% |\n", + "| Long, only electrons | 0.30% | 0.38% | 0.00% | 100.00% | 45.96% |\n", + "| Categories | # ghosts | # tracks | % ghosts |\n", + "|:-------------|-----------:|:-----------|:-----------|\n", + "| Everything | 58 | 1,864 | 3.11% |\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Plot basic for category velo_reconstructible_acceptance saved in output/export/hist1d_basic_velo_reconstructible_acceptance-velo-sim10b-nospillover.pdf\n", + "INFO:Plot basic for category long_reconstructible_acceptance_only_electrons saved in output/export/hist1d_basic_long_reconstructible_acceptance_only_electrons-velo-sim10b-nospillover.pdf\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 3200x1800 with 24 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 3200x1800 with 24 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "trackEvaluator = evaluate_candidates_montetracko(\n", " CONFIG,\n", @@ -922,13 +3869,6 @@ " table_report=True,\n", ")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -947,7 +3887,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.10" + "version": "3.10.11" }, "vscode": { "interpreter": { diff --git a/LHCb_Pipeline/gnn_3dim.onnx b/LHCb_Pipeline/gnn_3dim.onnx new file mode 100644 index 0000000000000000000000000000000000000000..d673d4b7439274929ec62694413a4eb5395e70d1 Binary files /dev/null and b/LHCb_Pipeline/gnn_3dim.onnx differ diff --git a/LHCb_Pipeline/ml_3dim.onnx b/LHCb_Pipeline/ml_3dim.onnx new file mode 100644 index 0000000000000000000000000000000000000000..4468f3b092812a0c0466570f33a5d9f2c998b5f9 Binary files /dev/null and b/LHCb_Pipeline/ml_3dim.onnx differ diff --git a/LHCb_Pipeline/pipeline_configs/pipeline_config_export.yaml b/LHCb_Pipeline/pipeline_configs/pipeline_config_export.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8d9ddfdf1d91afe9a66771872cd9cc782c82163f --- /dev/null +++ b/LHCb_Pipeline/pipeline_configs/pipeline_config_export.yaml @@ -0,0 +1,122 @@ +common: + experiment_name: export + data_directory: data # where the data are saved + artifact_directory: artifacts # where the checkpoints are saved + performance_directory: output # where the plots and reports are saved + gpus: 1 + # Name of the test datasets to use (defined in `test_samples.yaml`) + test_dataset_names: + - velo-sim10b-nospillover + +preprocessing: + input_dir: data/datasets/minbias-sim10b-xdigi-nospillover + # Can be + # - Integer: Last subdirectory that can be used (starting from `0`). `-1` for all. + # - String or list of strings: sub-directories that can be used + # - `null`: use `input_dir` directly + # - Dictionary with keys `start` and `stop` + subdirs: 10 + output_subdirectory: "preprocessed" + selection: triplets_first_selection # Selection function, defined in `Preprocessing/selecting.py` + n_events: null # if `null`, default to `n_train_events + n_test_events` + num_true_hits_threshold: 500 # Minimal number of genuine hits + # Columns to keep in the dataframes of hits-particles and particles + # (excluding `event`, `particle_id` and `lhcbid`) + # `null` means keep everything + hits_particles_columns: ["x", "y", "z", "plane"] + particles_columns: null + +processing: + input_subdirectory: "preprocessed" + output_subdirectory: "processed" + n_workers: 32 # Number of processes in parallel in the processing stage + features: ["r", "phi", "z"] # Name of the features to use + feature_means: [18., 0.0, 281.0] # Means for normalising the features + feature_scales: [9.75, 1.82, 287.0] # Scales for normalising the features + # List of the columns to keep in the PyTorch batches, in the dataframe of hits + kept_hits_columns: ["plane", {"un_z": "z"}] + # List of columns in the dataframe of particles that are merged to the dataframe + # of hits and stored in the PyTorch batches + kept_particles_columns: ["nhits_velo"] + n_train_events: 100 # Number of training events + n_val_events: 100 # Number of validation events + split_seed: 0 # Seed used for the splitting train-val + # How the true edges are computed + # - sortwise: sort by z + # - modulewise: sort by distance to production vertex + # - planewise: hits belonging to same particle and belonging to adjacent planes + true_edges_column: planewise + +metric_learning: + # Dataset parameters + input_subdirectory: "processed" + output_subdirectory: "metric_learning_processed" + + # Model parameters + feature_indices: 3 + emb_hidden: 256 # Number of hidden units / layer in the MPL + nb_layer: 4 # Number of layers + emb_dim: 3 # Embedding dimension + activation: Tanh # Action function used in the MLP + weight: 2 # Weight for positive examples + randomisation: 2 # Number of random pairs per hit + points_per_batch: 100000 # Number of query points to consider + r: 0.015 # Maximum distance for hard-mining + r_inference: 0.020 # Maximum distance for inference + knn: 2 # Maximal number of neighbours during training and inference + warmup: 8 # Start with small increasing learning rate for `warmup` epochs + margin: 0.1 # Loss for negative examples is max(0.1**2 - d²) + # Multiply the initial learning rate by ``factor`` every ``patience`` epochs + lr: 0.001 + factor: 0.7 + patience: 4 + # Available regimes + # - rp: random pairs + # - hnm: hard negative mining + # - norm: perform L2 normalisation + regime: [rp, hnm, norm] + bidir: true # Whether to use a bi-directional graph + max_epochs: 1 + + filtering: edges_at_least_3_hits + +gnn: + # Dataset parameters + input_subdirectory: "metric_learning_processed" + output_subdirectory: "gnn_processed" + edge_cut: 0.5 # Edge cut for validation + + # Model parameters + feature_indices: 3 + hidden: 256 # Number of hidden units per layer in the node encoder + # Number of layers in each MLP of the GNN + n_graph_iters: 8 + nb_node_layers: 6 + nb_node_encoder_layers: 6 # = `nb_node_layers` if not specified + nb_edge_layers: 10 + nb_edge_encoder_layers: 6 # = `nb_edge_layers` if not specified + nb_edge_classifier_layers: 6 # = `nb_edge_layers` if not specified + layernorm: True # Whether to use layer normalisation + aggregation: sum_max # Message-passing aggregation + hidden_activation: SiLU # hidden activation function + weight: 0.25 # if focal loss, `alpha`. Otherwise, weight of positive samples + warmup: 10 # Start with small increasing learning rate for `warmup` epochs + lr: 0.0002 # initial learning rate + # Multiply the learning rate by ``factor`` every ``patience`` epochs + factor: 0.7 + patience: 8 + # Existing regimes + # - weighting: use `edge_weights` as weights + # - pid: any edge belonging to the same particle is considered as a true edge + # - triplet: use the loss for triplets, with penalty term + regime: [pid] + max_epochs: 1 # Number of training epochs + gradient_clip_val: 0.5 # Gradient clipping value. Avoid exploiding gradients. + focal_loss: false # Whether to use the focal loss + bidir: true # whether to use a bi-directional graph + export: true # To be set to true when you are exporting the model with the export notebook + +track_building: + score_cut: 0.7 + input_subdirectory: "gnn_processed" + output_subdirectory: "track_building_processed" diff --git a/LHCb_Pipeline/test_samples.yaml b/LHCb_Pipeline/test_samples.yaml index b76aeb12dc4fea9c89b8f6bba0f3ef7dd6f9ec58..dc37f5380cd5091063c51e6346533024c4185bb0 100644 --- a/LHCb_Pipeline/test_samples.yaml +++ b/LHCb_Pipeline/test_samples.yaml @@ -1,5 +1,5 @@ velo-sim10b-nospillover: - input_dir: /scratch/acorreia/data_validation/minbias-sim10b-xdigi-nospillover/500 + input_dir: /home/fgias/data/minbias-sim10b-xdigi-nospillover/500 selection: null n_events: 1000 num_true_hits_threshold: null diff --git a/gpu_environment.yaml b/gpu_environment.yaml index 9f1570f6581b4e6a38abf39949c9abf7899f48b2..b7ac66f4e43e9fb6782563ae620c1ea0bb23fefc 100644 --- a/gpu_environment.yaml +++ b/gpu_environment.yaml @@ -19,6 +19,7 @@ dependencies: - pytorch - pytorch-lightning - pytorch-scatter +- pytorch-cluster - pyg - faiss-gpu - cudf @@ -31,4 +32,6 @@ dependencies: - tabulate - black - numba +- uncertainties +- onnx>=1.13 - python>=3.9 diff --git a/gpu_environment_explicit.yaml b/gpu_environment_explicit.yaml index 7ff7ff5d14889333fc26cbf1ae6355fef5fa89c0..9c6a55eee39677a4df59fe002e801ff5a5716fa2 100644 --- a/gpu_environment_explicit.yaml +++ b/gpu_environment_explicit.yaml @@ -1,377 +1,435 @@ name: etx4velo channels: -- pytorch -- pyg -- rapidsai -- nvidia -- conda-forge + - pytorch + - pyg + - nvidia + - rapidsai + - conda-forge dependencies: -- _libgcc_mutex=0.1 -- _openmp_mutex=4.5 -- abseil-cpp=20211102.0 -- aiofiles=22.1.0 -- aiosqlite=0.18.0 -- alsa-lib=1.2.8 -- anyio=3.6.2 -- apache-arrow-proc=4.0.0 -- argon2-cffi=21.3.0 -- argon2-cffi-bindings=21.2.0 -- arrow-cpp=5.0.0 -- arrow-cpp-proc=4.0.0 -- asttokens=2.2.1 -- async_generator=1.10 -- attr=2.5.1 -- attrs=22.2.0 -- aws-c-cal=0.5.11 -- aws-c-common=0.6.2 -- aws-c-event-stream=0.2.7 -- aws-c-io=0.10.5 -- aws-checksums=0.1.11 -- aws-sdk-cpp=1.8.186 -- babel=2.12.1 -- backcall=0.2.0 -- backports=1.0 -- backports.functools_lru_cache=1.6.4 -- beautifulsoup4=4.12.2 -- black=23.3.0 -- blas=1.0 -- bleach=6.0.0 -- bokeh=3.1.0 -- brotli=1.0.9 -- brotli-bin=1.0.9 -- brotlipy=0.7.0 -- bzip2=1.0.8 -- c-ares=1.18.1 -- ca-certificates=2022.12.7 -- cachetools=5.3.0 -- cairo=1.16.0 -- certifi=2022.12.7 -- cffi=1.15.1 -- charset-normalizer=3.1.0 -- click=8.0.4 -- cloudpickle=2.2.1 -- colorama=0.4.6 -- comm=0.1.3 -- contourpy=1.0.7 -- cryptography=39.0.0 -- cudatoolkit=11.3.1 -- cudf=21.12.02 -- cugraph=21.12.00 -- cupy=9.6.0 -- cycler=0.11.0 -- cytoolz=0.12.0 -- dask=2021.11.2 -- dask-core=2021.11.2 -- dask-cuda=21.12.00 -- dask-cudf=21.12.02 -- dbus=1.13.6 -- debugpy=1.6.7 -- decorator=5.1.1 -- defusedxml=0.7.1 -- distributed=2021.11.2 -- dlpack=0.5 -- entrypoints=0.4 -- exceptiongroup=1.1.1 -- executing=1.2.0 -- expat=2.5.0 -- faiss=1.7.0 -- faiss-gpu=1.7.0 -- faiss-proc=1.0.0 -- fastavro=1.7.3 -- fastrlock=0.8 -- fftw=3.3.10 -- firefox=112.0 -- flit-core=3.8.0 -- font-ttf-dejavu-sans-mono=2.37 -- font-ttf-inconsolata=3.000 -- font-ttf-source-code-pro=2.038 -- font-ttf-ubuntu=0.83 -- fontconfig=2.14.2 -- fonts-conda-ecosystem=1 -- fonts-conda-forge=1 -- fonttools=4.39.3 -- freetype=2.12.1 -- fsspec=2023.4.0 -- geckodriver=0.33.0 -- gettext=0.21.1 -- gflags=2.2.2 -- glib=2.74.1 -- glib-tools=2.74.1 -- glog=0.6.0 -- graphite2=1.3.13 -- grpc-cpp=1.45.2 -- gst-plugins-base=1.21.3 -- gstreamer=1.21.3 -- gstreamer-orc=0.4.33 -- h11=0.14.0 -- harfbuzz=6.0.0 -- heapdict=1.0.1 -- icu=70.1 -- idna=3.4 -- importlib-metadata=6.4.1 -- importlib-resources=5.12.0 -- importlib_metadata=6.4.1 -- importlib_resources=5.12.0 -- ipykernel=6.22.0 -- ipython=8.12.0 -- ipython_genutils=0.2.0 -- ipywidgets=8.0.6 -- jack=1.9.22 -- jedi=0.18.2 -- jinja2=3.1.2 -- joblib=1.2.0 -- jpeg=9e -- json5=0.9.5 -- jsonschema=4.17.3 -- jupyter_client=7.3.4 -- jupyter_core=5.3.0 -- jupyter_events=0.6.3 -- jupyter_server=1.23.6 -- jupyter_server_fileid=0.9.0 -- jupyter_server_ydoc=0.8.0 -- jupyter_ydoc=0.2.3 -- jupyterlab=3.6.3 -- jupyterlab_pygments=0.2.2 -- jupyterlab_server=2.22.1 -- jupyterlab_widgets=3.0.7 -- keyutils=1.6.1 -- kiwisolver=1.4.4 -- krb5=1.20.1 -- lame=3.100 -- lcms2=2.15 -- ld_impl_linux-64=2.40 -- lerc=4.0.0 -- libabseil=20211102.0 -- libblas=3.9.0 -- libbrotlicommon=1.0.9 -- libbrotlidec=1.0.9 -- libbrotlienc=1.0.9 -- libcap=2.66 -- libcblas=3.9.0 -- libclang=15.0.7 -- libclang13=15.0.7 -- libcudf=21.12.02 -- libcugraph=21.12.00 -- libcups=2.3.3 -- libcurl=7.87.0 -- libcusolver=11.4.4.55 -- libdb=6.2.32 -- libdeflate=1.17 -- libedit=3.1.20191231 -- libev=4.33 -- libevent=2.1.10 -- libexpat=2.5.0 -- libfaiss=1.7.0 -- libfaiss-avx2=1.7.0 -- libffi=3.4.2 -- libflac=1.4.2 -- libgcc-ng=12.2.0 -- libgcrypt=1.10.1 -- libgfortran-ng=12.2.0 -- libgfortran5=12.2.0 -- libglib=2.74.1 -- libgpg-error=1.46 -- libhwloc=2.3.0 -- libiconv=1.17 -- liblapack=3.9.0 -- libllvm11=11.1.0 -- libllvm15=15.0.7 -- libnghttp2=1.51.0 -- libnsl=2.0.0 -- libogg=1.3.4 -- libopus=1.3.1 -- libpng=1.6.39 -- libpq=15.1 -- libprotobuf=3.20.3 -- librmm=21.12.00 -- libsndfile=1.2.0 -- libsodium=1.0.18 -- libsqlite=3.40.0 -- libssh2=1.10.0 -- libstdcxx-devel_linux-64=12.2.0 -- libstdcxx-ng=12.2.0 -- libsystemd0=252 -- libthrift=0.16.0 -- libtiff=4.5.0 -- libtool=2.4.7 -- libudev1=253 -- libutf8proc=2.8.0 -- libuuid=2.38.1 -- libuv=1.44.2 -- libvorbis=1.3.7 -- libwebp-base=1.3.0 -- libxcb=1.13 -- libxkbcommon=1.5.0 -- libxml2=2.10.3 -- libzlib=1.2.13 -- lightning-utilities=0.8.0 -- llvm-openmp=16.0.1 -- llvmlite=0.39.1 -- locket=1.0.0 -- lz4-c=1.9.4 -- markupsafe=2.1.2 -- matplotlib=3.7.1 -- matplotlib-base=3.7.1 -- matplotlib-inline=0.1.6 -- mistune=2.0.5 -- mkl=2022.2.1 -- mpg123=1.31.3 -- msgpack-python=1.0.5 -- munkres=1.1.4 -- mypy_extensions=1.0.0 -- mysql-common=8.0.32 -- mysql-libs=8.0.32 -- nbclassic=0.5.5 -- nbclient=0.7.3 -- nbconvert=7.3.1 -- nbconvert-core=7.3.1 -- nbconvert-pandoc=7.3.1 -- nbformat=5.8.0 -- nccl=2.15.5.1 -- ncurses=6.3 -- nest-asyncio=1.5.6 -- networkx=3.1 -- notebook=6.5.4 -- notebook-shim=0.2.2 -- nspr=4.35 -- nss=3.89 -- numba=0.56.4 -- numpy=1.23.5 -- nvtx=0.2.3 -- openjpeg=2.5.0 -- openssl=1.1.1t -- orc=1.7.5 -- outcome=1.2.0 -- packaging=23.1 -- pandas=1.3.5 -- pandoc=2.19.2 -- pandocfilters=1.5.0 -- parquet-cpp=1.5.1 -- parso=0.8.3 -- partd=1.4.0 -- pathspec=0.11.1 -- pcre2=10.40 -- pexpect=4.8.0 -- pickleshare=0.7.5 -- pillow=9.4.0 -- pip=23.1 -- pixman=0.40.0 -- pkgutil-resolve-name=1.3.10 -- platformdirs=3.2.0 -- ply=3.11 -- pooch=1.7.0 -- prometheus_client=0.16.0 -- prompt-toolkit=3.0.38 -- prompt_toolkit=3.0.38 -- protobuf=3.20.3 -- psutil=5.9.4 -- pthread-stubs=0.4 -- ptxcompiler=0.7.0 -- ptyprocess=0.7.0 -- pulseaudio=16.1 -- pure_eval=0.2.2 -- pyarrow=5.0.0 -- pycparser=2.21 -- pyg=2.0.4 -- pygments=2.15.0 -- pynvml=11.5.0 -- pyopenssl=23.1.1 -- pyparsing=3.0.9 -- pyqt=5.15.7 -- pyqt5-sip=12.11.0 -- pyrsistent=0.19.3 -- pysocks=1.7.1 -- python=3.8.15 -- python-dateutil=2.8.2 -- python-fastjsonschema=2.16.3 -- python-json-logger=2.0.7 -- python-louvain=0.16 -- python_abi=3.8 -- pytorch=1.10.2 -- pytorch-cluster=1.6.0 -- pytorch-lightning=1.9.2 -- pytorch-mutex=1.0 -- pytorch-scatter=2.0.9 -- pytorch-sparse=0.6.13 -- pytorch-spline-conv=1.2.1 -- pytz=2023.3 -- pyyaml=6.0 -- pyzmq=25.0.2 -- qt-main=5.15.6 -- re2=2022.04.01 -- readline=8.2 -- requests=2.28.2 -- rfc3339-validator=0.1.4 -- rfc3986-validator=0.1.1 -- rmm=21.12.00 -- s2n=1.0.10 -- scikit-learn=1.2.2 -- scipy=1.10.1 -- selenium=4.8.3 -- send2trash=1.8.0 -- setuptools=59.8.0 -- sip=6.7.8 -- six=1.16.0 -- snappy=1.1.10 -- sniffio=1.3.0 -- sortedcontainers=2.4.0 -- soupsieve=2.3.2.post1 -- spdlog=1.8.5 -- stack_data=0.6.2 -- tabulate=0.9.0 -- tbb=2021.7.0 -- tblib=1.7.0 -- terminado=0.17.1 -- threadpoolctl=3.1.0 -- tinycss2=1.2.1 -- tk=8.6.12 -- toml=0.10.2 -- tomli=2.0.1 -- toolz=0.12.0 -- torchmetrics=0.11.4 -- tornado=6.1 -- tqdm=4.65.0 -- traitlets=5.9.0 -- trio=0.21.0 -- trio-websocket=0.10.2 -- typing-extensions=4.5.0 -- typing_extensions=4.5.0 -- ucx=1.11.2+gef2bbcf -- ucx-proc=1.0.0 -- ucx-py=0.23.0 -- unicodedata2=15.0.0 -- urllib3=1.26.15 -- wcwidth=0.2.6 -- webencodings=0.5.1 -- websocket-client=1.5.1 -- wheel=0.40.0 -- widgetsnbextension=4.0.7 -- wsproto=1.2.0 -- xcb-util=0.4.0 -- xcb-util-image=0.4.0 -- xcb-util-keysyms=0.4.0 -- xcb-util-renderutil=0.3.9 -- xcb-util-wm=0.4.1 -- xkeyboard-config=2.38 -- xorg-kbproto=1.0.7 -- xorg-libice=1.0.10 -- xorg-libsm=1.2.3 -- xorg-libx11=1.8.4 -- xorg-libxau=1.0.9 -- xorg-libxdmcp=1.1.3 -- xorg-libxext=1.3.4 -- xorg-libxrender=0.9.10 -- xorg-renderproto=0.11.1 -- xorg-xextproto=7.3.0 -- xorg-xproto=7.0.31 -- xyzservices=2023.2.0 -- xz=5.2.6 -- y-py=0.5.9 -- yacs=0.1.8 -- yaml=0.2.5 -- ypy-websocket=0.8.2 -- zeromq=4.3.4 -- zict=2.2.0 -- zipp=3.15.0 -- zlib=1.2.13 -- zstd=1.5.2 + - _libgcc_mutex=0.1=conda_forge + - _openmp_mutex=4.5=2_kmp_llvm + - alsa-lib=1.2.8=h166bdaf_0 + - anyio=3.6.1=pyhd8ed1ab_1 + - argon2-cffi=21.3.0=pyhd8ed1ab_0 + - argon2-cffi-bindings=21.2.0=py310h5764c6d_3 + - arrow-cpp=10.0.1=ha770c72_17_cpu + - asttokens=2.2.1=pyhd8ed1ab_0 + - async-lru=2.0.2=pyhd8ed1ab_0 + - async_generator=1.10=py_0 + - attr=2.5.1=h166bdaf_1 + - attrs=23.1.0=pyh71513ae_1 + - aws-c-auth=0.6.26=h987a71b_2 + - aws-c-cal=0.5.21=h48707d8_2 + - aws-c-common=0.8.14=h0b41bf4_0 + - aws-c-compression=0.2.16=h03acc5a_5 + - aws-c-event-stream=0.2.20=h00877a2_4 + - aws-c-http=0.7.6=hf342b9f_0 + - aws-c-io=0.13.19=h5b20300_3 + - aws-c-mqtt=0.8.6=hc4349f7_12 + - aws-c-s3=0.2.7=h909e904_1 + - aws-c-sdkutils=0.1.9=h03acc5a_0 + - aws-checksums=0.1.14=h03acc5a_5 + - aws-crt-cpp=0.19.8=hf7fbfca_12 + - aws-sdk-cpp=1.10.57=h17c43bd_8 + - babel=2.12.1=pyhd8ed1ab_1 + - backcall=0.2.0=pyh9f0ad1d_0 + - backports=1.0=pyhd8ed1ab_3 + - backports.functools_lru_cache=1.6.4=pyhd8ed1ab_0 + - beautifulsoup4=4.12.2=pyha770c72_0 + - black=23.3.0=py310hff52083_1 + - blas=1.0=mkl + - bleach=6.0.0=pyhd8ed1ab_0 + - bokeh=2.4.3=pyhd8ed1ab_3 + - brotli=1.0.9=h166bdaf_8 + - brotli-bin=1.0.9=h166bdaf_8 + - brotlipy=0.7.0=py310h5764c6d_1005 + - bzip2=1.0.8=h7f98852_4 + - c-ares=1.19.0=hd590300_0 + - ca-certificates=2023.5.7=hbcca054_0 + - cachetools=5.3.0=pyhd8ed1ab_0 + - cairo=1.16.0=h35add3b_1015 + - certifi=2023.5.7=pyhd8ed1ab_0 + - cffi=1.15.1=py310h255011f_3 + - charset-normalizer=3.1.0=pyhd8ed1ab_0 + - click=8.1.3=unix_pyhd8ed1ab_2 + - cloudpickle=2.2.1=pyhd8ed1ab_0 + - colorama=0.4.6=pyhd8ed1ab_0 + - comm=0.1.3=pyhd8ed1ab_0 + - contourpy=1.0.7=py310hdf3cbec_0 + - cryptography=40.0.2=py310h34c0648_0 + - cubinlinker=0.2.2=py310hf09951c_0 + - cuda-cccl=12.1.109=0 + - cuda-command-line-tools=11.8.0=0 + - cuda-compiler=12.1.1=0 + - cuda-cudart=11.8.89=0 + - cuda-cudart-dev=11.8.89=0 + - cuda-cuobjdump=12.1.111=0 + - cuda-cupti=11.8.87=0 + - cuda-cuxxfilt=12.1.105=0 + - cuda-documentation=12.1.105=0 + - cuda-driver-dev=12.1.105=0 + - cuda-gdb=12.1.105=0 + - cuda-libraries=11.8.0=0 + - cuda-libraries-dev=11.8.0=0 + - cuda-memcheck=11.8.86=0 + - cuda-nsight=12.1.105=0 + - cuda-nsight-compute=12.1.1=0 + - cuda-nvcc=12.1.105=0 + - cuda-nvdisasm=12.1.105=0 + - cuda-nvml-dev=12.1.105=0 + - cuda-nvprof=12.1.105=0 + - cuda-nvprune=12.1.105=0 + - cuda-nvrtc=11.8.89=0 + - cuda-nvrtc-dev=11.8.89=0 + - cuda-nvtx=11.8.86=0 + - cuda-nvvp=12.1.105=0 + - cuda-profiler-api=11.8.86=0 + - cuda-python=11.8.1=py310h3fd9d12_0 + - cuda-runtime=11.8.0=0 + - cuda-sanitizer-api=12.1.105=0 + - cuda-toolkit=11.8.0=0 + - cuda-tools=11.8.0=0 + - cuda-visual-tools=11.8.0=0 + - cudatoolkit=11.7.0=hd8887f6_10 + - cudf=23.02.00=cuda_11_py310_g5ad4a85b9d_0 + - cugraph=23.02.00=cuda11_py310_g450c25b8_0 + - cupy=11.6.0=py310h9216885_0 + - cycler=0.11.0=pyhd8ed1ab_0 + - cytoolz=0.12.0=py310h5764c6d_1 + - dask=2023.1.1=pyhd8ed1ab_0 + - dask-core=2023.1.1=pyhd8ed1ab_0 + - dask-cuda=23.02.01=py310_g2c50668_0 + - dask-cudf=23.02.00=cuda_11_py310_g5ad4a85b9d_0 + - dbus=1.13.6=h5008d03_3 + - debugpy=1.6.7=py310heca2aa9_0 + - decorator=5.1.1=pyhd8ed1ab_0 + - defusedxml=0.7.1=pyhd8ed1ab_0 + - distributed=2023.1.1=pyhd8ed1ab_0 + - dlpack=0.5=h9c3ff4c_0 + - entrypoints=0.4=pyhd8ed1ab_0 + - exceptiongroup=1.1.1=pyhd8ed1ab_0 + - executing=1.2.0=pyhd8ed1ab_0 + - expat=2.5.0=hcb278e6_1 + - faiss=1.7.2=py310cuda112hae2f2aa_3_cuda + - faiss-gpu=1.7.2=h0240f8b_4 + - fastavro=1.7.4=py310h2372a71_0 + - fastrlock=0.8=py310hd8f1fbe_3 + - filelock=3.12.0=pyhd8ed1ab_0 + - firefox=113.0=h59595ed_0 + - flit-core=3.9.0=pyhd8ed1ab_0 + - font-ttf-dejavu-sans-mono=2.37=hab24e00_0 + - font-ttf-inconsolata=3.000=h77eed37_0 + - font-ttf-source-code-pro=2.038=h77eed37_0 + - font-ttf-ubuntu=0.83=hab24e00_0 + - fontconfig=2.14.2=h14ed4e7_0 + - fonts-conda-ecosystem=1=0 + - fonts-conda-forge=1=0 + - fonttools=4.39.4=py310h2372a71_0 + - freetype=2.12.1=hca18f0e_1 + - fsspec=2023.5.0=pyh1a96a4e_0 + - future=0.18.3=pyhd8ed1ab_0 + - gds-tools=1.6.1.9=0 + - geckodriver=0.33.0=hd2f7af9_0 + - gettext=0.21.1=h27087fc_0 + - gflags=2.2.2=he1b5a44_1004 + - glib=2.76.2=hfc55251_0 + - glib-tools=2.76.2=hfc55251_0 + - glog=0.6.0=h6f12383_0 + - gmp=6.2.1=h58526e2_0 + - gmpy2=2.1.2=py310h3ec546c_1 + - graphite2=1.3.13=h58526e2_1001 + - gst-plugins-base=1.22.0=h4243ec0_2 + - gstreamer=1.22.0=h25f0c4b_2 + - h11=0.14.0=pyhd8ed1ab_0 + - harfbuzz=7.3.0=hdb3a94d_0 + - icu=72.1=hcb278e6_0 + - idna=3.4=pyhd8ed1ab_0 + - importlib-metadata=6.6.0=pyha770c72_0 + - importlib_metadata=6.6.0=hd8ed1ab_0 + - importlib_resources=5.12.0=pyhd8ed1ab_0 + - ipykernel=6.23.1=pyh210e3f2_0 + - ipython=8.13.2=pyh41d4057_0 + - ipywidgets=8.0.6=pyhd8ed1ab_0 + - jedi=0.18.2=pyhd8ed1ab_0 + - jinja2=3.1.2=pyhd8ed1ab_1 + - joblib=1.2.0=pyhd8ed1ab_0 + - json5=0.9.5=pyh9f0ad1d_0 + - jsonschema=4.17.3=pyhd8ed1ab_0 + - jupyter-lsp=2.1.0=pyhd8ed1ab_0 + - jupyter_client=8.2.0=pyhd8ed1ab_0 + - jupyter_core=5.3.0=py310hff52083_0 + - jupyter_events=0.6.3=pyhd8ed1ab_0 + - jupyter_server=2.5.0=pyhd8ed1ab_0 + - jupyter_server_terminals=0.4.4=pyhd8ed1ab_1 + - jupyterlab=4.0.0=pyhd8ed1ab_1 + - jupyterlab_pygments=0.2.2=pyhd8ed1ab_0 + - jupyterlab_server=2.22.1=pyhd8ed1ab_0 + - jupyterlab_widgets=3.0.7=pyhd8ed1ab_1 + - keyutils=1.6.1=h166bdaf_0 + - kiwisolver=1.4.4=py310hbf28c38_1 + - krb5=1.20.1=h81ceb04_0 + - lame=3.100=h166bdaf_1003 + - lcms2=2.15=haa2dc70_1 + - ld_impl_linux-64=2.40=h41732ed_0 + - lerc=4.0.0=h27087fc_0 + - libabseil=20230125.0=cxx17_hcb278e6_1 + - libarrow=10.0.1=h51ec05e_17_cpu + - libblas=3.9.0=16_linux64_mkl + - libbrotlicommon=1.0.9=h166bdaf_8 + - libbrotlidec=1.0.9=h166bdaf_8 + - libbrotlienc=1.0.9=h166bdaf_8 + - libcap=2.67=he9d0100_0 + - libcblas=3.9.0=16_linux64_mkl + - libclang=16.0.3=default_h1cdf331_2 + - libclang13=16.0.3=default_h4d60ac6_2 + - libcrc32c=1.1.2=h9c3ff4c_0 + - libcublas=11.11.3.6=0 + - libcublas-dev=11.11.3.6=0 + - libcudf=23.02.00=cuda11_g5ad4a85b9d_0 + - libcufft=10.9.0.58=0 + - libcufft-dev=10.9.0.58=0 + - libcufile=1.6.1.9=0 + - libcufile-dev=1.6.1.9=0 + - libcugraph=23.02.00=cuda11_g450c25b8_0 + - libcugraphops=23.02.01=cuda11_g505ea813_0 + - libcups=2.3.3=h36d4200_3 + - libcurand=10.3.0.86=0 + - libcurand-dev=10.3.0.86=0 + - libcurl=8.1.0=h409715c_0 + - libcusolver=11.4.1.48=0 + - libcusolver-dev=11.4.1.48=0 + - libcusparse=11.7.5.86=0 + - libcusparse-dev=11.7.5.86=0 + - libdeflate=1.18=h0b41bf4_0 + - libedit=3.1.20191231=he28a2e2_2 + - libev=4.33=h516909a_1 + - libevent=2.1.12=h3358134_0 + - libexpat=2.5.0=hcb278e6_1 + - libfaiss=1.7.2=cuda112hb18a002_3_cuda + - libfaiss-avx2=1.7.2=cuda112h1234567_3_cuda + - libffi=3.4.2=h7f98852_5 + - libflac=1.4.2=h27087fc_0 + - libgcc-ng=12.2.0=h65d4601_19 + - libgcrypt=1.10.1=h166bdaf_0 + - libgfortran-ng=12.2.0=h69a702a_19 + - libgfortran5=12.2.0=h337968e_19 + - libglib=2.76.2=hebfc3b9_0 + - libgoogle-cloud=2.8.0=h0bc5f78_1 + - libgpg-error=1.46=h620e276_0 + - libgrpc=1.52.1=hcf146ea_1 + - libhwloc=2.9.1=hd6dc26d_0 + - libiconv=1.17=h166bdaf_0 + - libjpeg-turbo=2.1.5.1=h0b41bf4_0 + - liblapack=3.9.0=16_linux64_mkl + - libllvm14=14.0.6=hcd5def8_2 + - libllvm16=16.0.3=hbf9e925_1 + - libnghttp2=1.52.0=h61bc06f_0 + - libnpp=11.8.0.86=0 + - libnpp-dev=11.8.0.86=0 + - libnsl=2.0.0=h7f98852_0 + - libnvjpeg=11.9.0.86=0 + - libnvjpeg-dev=11.9.0.86=0 + - libogg=1.3.4=h7f98852_1 + - libopus=1.3.1=h7f98852_1 + - libpng=1.6.39=h753d276_0 + - libpq=15.3=hbcd7760_1 + - libprotobuf=3.21.12=h3eb15da_0 + - libraft-distance=23.02.00=cuda11_g69dce2d4_0 + - libraft-headers=23.02.00=cuda11_g69dce2d4_0 + - librmm=23.02.00=cuda11_g48e8f2a8_0 + - libsndfile=1.2.0=hb75c966_0 + - libsodium=1.0.18=h36c2ea0_1 + - libsqlite=3.42.0=h2797004_0 + - libssh2=1.10.0=hf14f497_3 + - libstdcxx-ng=12.2.0=h46fd767_19 + - libsystemd0=253=h8c4010b_1 + - libthrift=0.18.1=h8fd135c_1 + - libtiff=4.5.0=ha587672_6 + - libutf8proc=2.8.0=h166bdaf_0 + - libuuid=2.38.1=h0b41bf4_0 + - libvorbis=1.3.7=h9c3ff4c_0 + - libwebp-base=1.3.0=h0b41bf4_0 + - libxcb=1.13=h7f98852_1004 + - libxkbcommon=1.5.0=h79f4944_1 + - libxml2=2.10.4=hfdac1af_0 + - libzlib=1.2.13=h166bdaf_4 + - lightning-utilities=0.8.0=pyhd8ed1ab_0 + - llvm-openmp=16.0.4=h4dfa4b3_0 + - llvmlite=0.40.0=py310h1b8f574_0 + - locket=1.0.0=pyhd8ed1ab_0 + - lz4=4.3.2=py310h0cfdcf0_0 + - lz4-c=1.9.4=hcb278e6_0 + - markupsafe=2.1.2=py310h1fa729e_0 + - matplotlib=3.7.1=py310hff52083_0 + - matplotlib-base=3.7.1=py310he60537e_0 + - matplotlib-inline=0.1.6=pyhd8ed1ab_0 + - mistune=2.0.5=pyhd8ed1ab_0 + - mkl=2022.2.1=h84fe81f_16997 + - mpc=1.3.1=hfe3b2da_0 + - mpfr=4.2.0=hb012696_0 + - mpg123=1.31.3=hcb278e6_0 + - mpmath=1.3.0=pyhd8ed1ab_0 + - msgpack-python=1.0.5=py310hdf3cbec_0 + - munkres=1.1.4=pyh9f0ad1d_0 + - mypy_extensions=1.0.0=pyha770c72_0 + - mysql-common=8.0.32=hf1915f5_2 + - mysql-libs=8.0.32=hca2cd23_2 + - nbclient=0.7.4=pyhd8ed1ab_0 + - nbconvert-core=7.4.0=pyhd8ed1ab_0 + - nbformat=5.8.0=pyhd8ed1ab_0 + - nccl=2.15.5.1=h0800d71_0 + - ncurses=6.3=h27087fc_1 + - nest-asyncio=1.5.6=pyhd8ed1ab_0 + - networkx=3.1=pyhd8ed1ab_0 + - notebook-shim=0.2.3=pyhd8ed1ab_0 + - nsight-compute=2023.1.1.4=0 + - nspr=4.35=h27087fc_0 + - nss=3.89=he45b914_0 + - numba=0.57.0=py310h0f6aa51_0 + - numpy=1.24.3=py310ha4c1d20_0 + - nvtx=0.2.5=py310h1fa729e_0 + - onnx=1.14.0=py310ha3deec4_0 + - openjpeg=2.5.0=hfec8fc6_2 + - openssl=3.1.1=hd590300_1 + - orc=1.8.3=hfdbbad2_0 + - outcome=1.2.0=pyhd8ed1ab_0 + - packaging=23.1=pyhd8ed1ab_0 + - pandas=1.5.3=py310h9b08913_1 + - pandocfilters=1.5.0=pyhd8ed1ab_0 + - parquet-cpp=1.5.1=2 + - parso=0.8.3=pyhd8ed1ab_0 + - partd=1.4.0=pyhd8ed1ab_0 + - pathspec=0.11.1=pyhd8ed1ab_0 + - pcre2=10.40=hc3806b6_0 + - pexpect=4.8.0=pyh1a96a4e_2 + - pickleshare=0.7.5=py_1003 + - pillow=9.5.0=py310h065c6d2_0 + - pip=23.1.2=pyhd8ed1ab_0 + - pixman=0.40.0=h36c2ea0_0 + - pkgutil-resolve-name=1.3.10=pyhd8ed1ab_0 + - platformdirs=3.5.1=pyhd8ed1ab_0 + - ply=3.11=py_1 + - pooch=1.7.0=pyha770c72_3 + - prometheus_client=0.16.0=pyhd8ed1ab_0 + - prompt-toolkit=3.0.38=pyha770c72_0 + - prompt_toolkit=3.0.38=hd8ed1ab_0 + - protobuf=4.21.12=py310heca2aa9_0 + - psutil=5.9.5=py310h1fa729e_0 + - pthread-stubs=0.4=h36c2ea0_1001 + - ptxcompiler=0.8.1=py310h01a121a_0 + - ptyprocess=0.7.0=pyhd3deb0d_0 + - pulseaudio-client=16.1=h5195f5e_3 + - pure_eval=0.2.2=pyhd8ed1ab_0 + - pyarrow=10.0.1=py310h633f555_17_cpu + - pycparser=2.21=pyhd8ed1ab_0 + - pyg=2.3.0=py310_torch_2.0.0_cu118 + - pygments=2.15.1=pyhd8ed1ab_0 + - pylibcugraph=23.02.00=cuda11_py310_g450c25b8_0 + - pylibraft=23.02.00=cuda11_py310_g69dce2d4_0 + - pynvml=11.4.1=pyhd8ed1ab_0 + - pyopenssl=23.1.1=pyhd8ed1ab_0 + - pyparsing=3.0.9=pyhd8ed1ab_0 + - pyqt=5.15.7=py310hab646b1_3 + - pyqt5-sip=12.11.0=py310heca2aa9_3 + - pyrsistent=0.19.3=py310h1fa729e_0 + - pysocks=1.7.1=pyha2e5f31_6 + - python=3.10.11=he550d4f_0_cpython + - python-dateutil=2.8.2=pyhd8ed1ab_0 + - python-fastjsonschema=2.16.3=pyhd8ed1ab_0 + - python-json-logger=2.0.7=pyhd8ed1ab_0 + - python_abi=3.10=3_cp310 + - pytorch=2.0.1=py3.10_cuda11.8_cudnn8.7.0_0 + - pytorch-cuda=11.8=h7e8668a_5 + - pytorch-lightning=2.0.2=pyhd8ed1ab_0 + - pytorch-mutex=1.0=cuda + - pytorch-scatter=2.1.1=py310_torch_2.0.0_cu118 + - pytz=2023.3=pyhd8ed1ab_0 + - pyyaml=6.0=py310h5764c6d_5 + - pyzmq=25.0.2=py310h059b190_0 + - qt-main=5.15.8=haa3a1c2_11 + - raft-dask=23.02.00=cuda11_py310_g69dce2d4_0 + - re2=2023.02.02=hcb278e6_0 + - readline=8.2=h8228510_1 + - requests=2.29.0=pyhd8ed1ab_0 + - rfc3339-validator=0.1.4=pyhd8ed1ab_0 + - rfc3986-validator=0.1.1=pyh9f0ad1d_0 + - rmm=23.02.00=cuda11_py310_g48e8f2a8_0 + - s2n=1.3.41=h3358134_0 + - scikit-learn=1.2.2=py310h41b6a48_1 + - scipy=1.10.1=py310ha4c1d20_3 + - selenium=4.9.1=pyhd8ed1ab_0 + - send2trash=1.8.2=pyh41d4057_0 + - setuptools=67.7.2=pyhd8ed1ab_0 + - sip=6.7.9=py310hc6cd4ac_0 + - six=1.16.0=pyh6c4a22f_0 + - snappy=1.1.10=h9fff704_0 + - sniffio=1.3.0=pyhd8ed1ab_0 + - sortedcontainers=2.4.0=pyhd8ed1ab_0 + - soupsieve=2.3.2.post1=pyhd8ed1ab_0 + - spdlog=1.8.5=h4bd325d_1 + - stack_data=0.6.2=pyhd8ed1ab_0 + - sympy=1.12=pypyh9d50eac_103 + - tabulate=0.9.0=pyhd8ed1ab_1 + - tbb=2021.9.0=hf52228f_0 + - tblib=1.7.0=pyhd8ed1ab_0 + - terminado=0.17.1=pyh41d4057_0 + - threadpoolctl=3.1.0=pyh8a188c0_0 + - tinycss2=1.2.1=pyhd8ed1ab_0 + - tk=8.6.12=h27826a3_0 + - toml=0.10.2=pyhd8ed1ab_0 + - tomli=2.0.1=pyhd8ed1ab_0 + - toolz=0.12.0=pyhd8ed1ab_0 + - torchmetrics=0.11.4=pyhd8ed1ab_0 + - torchtriton=2.0.0=py310 + - tornado=6.3.2=py310h2372a71_0 + - tqdm=4.65.0=pyhd8ed1ab_1 + - traitlets=5.9.0=pyhd8ed1ab_0 + - trio=0.22.0=py310hff52083_1 + - trio-websocket=0.10.2=pyhd8ed1ab_0 + - typing-extensions=4.5.0=hd8ed1ab_0 + - typing_extensions=4.5.0=pyha770c72_0 + - tzdata=2023c=h71feb2d_0 + - ucx=1.13.1=h538f049_1 + - ucx-proc=1.0.0=gpu + - ucx-py=0.30.00=py310_gc2651fc_0 + - uncertainties=3.1.7=pyhd8ed1ab_0 + - unicodedata2=15.0.0=py310h5764c6d_0 + - urllib3=1.26.15=pyhd8ed1ab_0 + - wcwidth=0.2.6=pyhd8ed1ab_0 + - webencodings=0.5.1=py_1 + - websocket-client=1.5.2=pyhd8ed1ab_0 + - wheel=0.40.0=pyhd8ed1ab_0 + - widgetsnbextension=4.0.7=pyhd8ed1ab_0 + - wsproto=1.2.0=pyhd8ed1ab_0 + - xcb-util=0.4.0=h516909a_0 + - xcb-util-image=0.4.0=h166bdaf_0 + - xcb-util-keysyms=0.4.0=h516909a_0 + - xcb-util-renderutil=0.3.9=h166bdaf_0 + - xcb-util-wm=0.4.1=h516909a_0 + - xkeyboard-config=2.38=h0b41bf4_0 + - xorg-kbproto=1.0.7=h7f98852_1002 + - xorg-libice=1.0.10=h7f98852_0 + - xorg-libsm=1.2.3=hd9c2040_1000 + - xorg-libx11=1.8.4=h0b41bf4_0 + - xorg-libxau=1.0.11=hd590300_0 + - xorg-libxdmcp=1.1.3=h7f98852_0 + - xorg-libxext=1.3.4=h0b41bf4_2 + - xorg-libxrender=0.9.10=h7f98852_1003 + - xorg-renderproto=0.11.1=h7f98852_1002 + - xorg-xextproto=7.3.0=h0b41bf4_1003 + - xorg-xf86vidmodeproto=2.3.1=h7f98852_1002 + - xorg-xproto=7.0.31=h7f98852_1007 + - xz=5.2.6=h166bdaf_0 + - yaml=0.2.5=h7f98852_2 + - zeromq=4.3.4=h9c3ff4c_1 + - zict=3.0.0=pyhd8ed1ab_0 + - zipp=3.15.0=pyhd8ed1ab_0 + - zlib=1.2.13=h166bdaf_4 + - zstd=1.5.2=h3eb15da_6 + - pip: + - coloredlogs==15.0.1 + - flatbuffers==23.5.26 + - humanfriendly==10.0 + - onnxruntime==1.15.1 + - torch-cluster==1.6.1 +prefix: /home/fgias/.conda/envs/etx4velo diff --git a/notebook-gpu.sh b/notebook-gpu.sh index 3e84f6ac448c006db7f26190c836d832c7f992c2..e7cf3c09b134ed65dfba5fd4c5e58b1762e45de9 100644 --- a/notebook-gpu.sh +++ b/notebook-gpu.sh @@ -2,7 +2,7 @@ #SBATCH --job-name=etx4velo #SBATCH --nodes=1 -#SBATCH --gpus-per-node=1 +#SBATCH --gpus=a100_3g.40gb:1 #SBATCH --time=14400 #SBATCH --output=%x-%j.out #SBATCH --error=%x-%j.err