diff --git a/metadata_interface/.ipynb_checkpoints/Fine-grained_metadata_interface-checkpoint.ipynb b/metadata_interface/.ipynb_checkpoints/Fine-grained_metadata_interface-checkpoint.ipynb
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@@ -0,0 +1,167 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fine-grained containerized metadata interface"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ipywidgets as widgets\n",
+    "import json\n",
+    "import networkx as nx\n",
+    "import matplotlib.pyplot as plt\n",
+    "from networkx_viewer import Viewer\n",
+    "import rglob\n",
+    "from ipyfilechooser import FileChooser\n",
+    "import utils"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selection of metadata directory \n",
+    "The user can select the directory where the metadata is located"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "741cffcbb7ca4b53b198c9f90cb74d4a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "FileChooser(path='Metadata selection', filename='', title='HTML(value='', layout=Layout(display='none'))', sho…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create and display a FileChooser widget\n",
+    "fc = FileChooser('Metadata selection')\n",
+    "display(fc)\n",
+    "\n",
+    "\n",
+    "# Change defaults and reset the dialog\n",
+    "fc.default_path = '/home/polaya/git-repos/Project_Sandia_Paula/metadata_visualization/'\n",
+    "fc.reset()\n",
+    "\n",
+    "# Change hidden files\n",
+    "fc.show_hidden = True\n",
+    "\n",
+    "# Show or hide folder icons\n",
+    "fc.use_dir_icons = True\n",
+    "\n",
+    "# Set multiple file filter patterns (uses https://docs.python.org/3/library/fnmatch.html)\n",
+    "#fc.filter_pattern = ['*.json']\n",
+    "\n",
+    "# Change the title (use '' to hide)\n",
+    "fc.title = '<b>Select the directory where the metadata is located</b>'\n",
+    "\n",
+    "# Sample callback function\n",
+    "def change_title(chooser):\n",
+    "    chooser.title = '<b>Metadata files captured</b>'\n",
+    "\n",
+    "# Register callback function\n",
+    "fc.register_callback(change_title)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'read_json_file' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-9-cd2949f7d089>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mgraphs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfile\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiles\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mread_json_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0mgraphs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfrom_json_to_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mplot_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraphs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midentification\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'read_json_file' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "files = rglob.rglob(fc.selected_path, \"*\")\n",
+    "\n",
+    "data=[]\n",
+    "graphs=[]\n",
+    "for i,file in enumerate(files):\n",
+    "    data.append(read_json_file(file))\n",
+    "    graphs.append(from_json_to_graph(data[i]))\n",
+    "    plot_graph(graphs[i], identification)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "merged_graphs=graphs[0]\n",
+    "for i in range(1,len(graphs)):\n",
+    "    merged_graphs = nx.compose(merged_graphs,graphs[i])\n",
+    "plot_graph(merged_graphs, 'Container_name')\n",
+    "#print(merged_graphs.nodes(data=True))\n",
+    "app = Viewer(merged_graphs)\n",
+    "app.mainloop()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/metadata_interface/Examples_of_metadata/Explainability/a_output_oklahoma.json b/metadata_interface/Examples_of_metadata/Explainability/a_output_oklahoma.json
new file mode 100644
index 0000000..31e5224
--- /dev/null
+++ b/metadata_interface/Examples_of_metadata/Explainability/a_output_oklahoma.json
@@ -0,0 +1,16 @@
+[
+	{
+	"org.label-schema.build-container_uuid": "0ba80a99-8cad-4ece-aff9-332ec8e7f1ef",
+	"org.label-schema.build-container_name": "output_oklahoma.sif",
+	"org.label-schema.build-date": "2021-09-11 04:34:17 -0400 EDT"
+	},
+	{"Record trail": {
+		"Container_name":["output_oklahoma.sif","visualization.sif","predictions_oklahoma.sif"],
+		"UUID":["0ba80a99-8cad-4ece-aff9-332ec8e7f1ef","66752397-25bc-4fb6-aa8d-f0426d4f045d","24d4798f-7669-434a-8f88-dd01f9d86229"],
+		"Creation_time":["2021-09-11T04:34:17-04:00","2021-09-11T04:29:08-04:00","2021-09-09T13:40:06-04:00"],
+		"Modification_time":["2021-09-11T04:34:17-04:00","2021-09-11T04:29:08-04:00","2021-09-09T13:40:08-04:00"]
+		}
+	},
+	{"Command_line":"exec python3 visualization.py Predictions/predictions_oklahoma.csv Output/out_oklahoma.png 0.175 0.35"
+	}
+]
diff --git a/metadata_interface/Examples_of_metadata/Explainability/a_predictions_oklahoma.json b/metadata_interface/Examples_of_metadata/Explainability/a_predictions_oklahoma.json
new file mode 100644
index 0000000..0d7386a
--- /dev/null
+++ b/metadata_interface/Examples_of_metadata/Explainability/a_predictions_oklahoma.json
@@ -0,0 +1,17 @@
+[
+	{
+	"org.label-schema.build-container_uuid": "24d4798f-7669-434a-8f88-dd01f9d86229",
+	"org.label-schema.build-container_name": "predictions_oklahoma.sif",
+	"org.label-schema.build-date": "2021-09-09 13:40:06 -0400 EDT"
+	},
+	{"Record trail": {
+		"Container_name":["predictions_oklahoma.sif","knn.sif","train_27km.sif","eval_250m.sif"],
+		"UUID":["24d4798f-7669-434a-8f88-dd01f9d86229", "f3edaf15-d37d-4abd-a7a6-9dd5bbc527b7", "641e1a52-7757-42f2-85c5-fb4f9b4c2cfc", "38aa530a-fa24-4807-969d-b5a4e40c7cb6"],
+		"Creation_time":["2021-09-09T13:40:06-04:00","2021-09-01T12:14:54-04:00","2021-09-01T12:07:26-04:00","2021-09-09T13:40:02-04:00"],
+		"Modification_time":["2021-09-09T13:40:06-04:00","2021-09-01T12:14:54-04:00","2021-09-01T12:07:26-04:00","2021-09-09T13:40:03-04:00"]
+		}
+	},
+	{"Command_line":"exec python3 knn.py Train/train.csv Eval/eval_250m.csv Predictions/predictions_oklahoma.csv"
+	}
+]
+
diff --git a/metadata_interface/Examples_of_metadata/Explainability/b_output_oklahoma.json b/metadata_interface/Examples_of_metadata/Explainability/b_output_oklahoma.json
new file mode 100644
index 0000000..5664ae0
--- /dev/null
+++ b/metadata_interface/Examples_of_metadata/Explainability/b_output_oklahoma.json
@@ -0,0 +1,16 @@
+[
+	{
+	"org.label-schema.build-container_uuid": "3dfb182b-7fdc-4d6f-9348-8477c6e77f3e",
+	"org.label-schema.build-container_name": "output_oklahoma.sif",
+	"org.label-schema.build-date": "2021-09-11 06:08:37 -0400 EDT"
+	},
+	{"Record trail": {
+		"Container_name":["output_oklahoma.sif","visualization.sif","predictions_oklahoma.sif"],
+		"UUID":["3dfb182b-7fdc-4d6f-9348-8477c6e77f3e","66752397-25bc-4fb6-aa8d-f0426d4f045d","52bf0ba4-0812-43c7-bee9-876d90abb49c"],
+		"Creation_time":["2021-09-11T06:08:37-04:00","2021-09-11T04:29:08-04:00","2021-09-09T22:37:47-04:00"],
+		"Modification_time":["2021-09-11T06:08:37-04:00","2021-09-11T04:29:08-04:00","2021-09-09T22:37:50-04:00"]
+		}
+	},
+	{"Command_line":"exec python3 visualization.py Predictions/predictions_oklahoma.csv Output/out_oklahoma.png 0.175 0.35"
+	}
+]
diff --git a/metadata_interface/Examples_of_metadata/Explainability/b_predictions_oklahoma.json b/metadata_interface/Examples_of_metadata/Explainability/b_predictions_oklahoma.json
new file mode 100644
index 0000000..a6797f5
--- /dev/null
+++ b/metadata_interface/Examples_of_metadata/Explainability/b_predictions_oklahoma.json
@@ -0,0 +1,17 @@
+[
+	{
+	"org.label-schema.build-container_uuid": "52bf0ba4-0812-43c7-bee9-876d90abb49c",
+	"org.label-schema.build-container_name": "predictions_oklahoma.sif",
+	"org.label-schema.build-date": "2021-09-09 22:37:47 -0400 EDT"
+	},
+	{"Record trail": {
+		"Container_name":["predictions_oklahoma.sif","rf.sif","train_27km.sif","eval_250m.sif"],
+		"UUID":["52bf0ba4-0812-43c7-bee9-876d90abb49c", "bc9bb5be-bd6f-42f6-b8ae-1556c5c042b9", "641e1a52-7757-42f2-85c5-fb4f9b4c2cfc", "38aa530a-fa24-4807-969d-b5a4e40c7cb6"],
+		"Creation_time":["2021-09-09T22:37:47-04:00","2021-09-09T21:08:55-04:00","2021-09-01T12:07:26-04:00","2021-09-09T13:40:02-04:00"],
+		"Modification_time":["2021-09-09T22:37:48-04:00","2021-09-09T21:08:55-04:00","2021-09-01T12:07:26-04:00","2021-09-09T13:40:03-04:00"]
+		}
+	},
+	{"Command_line":"exec python3 rf.py Train/train.csv Eval/eval_250m.csv Predictions/predictions_oklahoma.csv"
+	}
+]
+
diff --git a/metadata_interface/Examples_of_metadata/Explainability/c_output_oklahoma.json b/metadata_interface/Examples_of_metadata/Explainability/c_output_oklahoma.json
new file mode 100644
index 0000000..e8d55cc
--- /dev/null
+++ b/metadata_interface/Examples_of_metadata/Explainability/c_output_oklahoma.json
@@ -0,0 +1,16 @@
+[
+	{
+	"org.label-schema.build-container_uuid": "da671285-f40c-4ed7-ab82-527a7fb6309a",
+	"org.label-schema.build-container_name": "output_oklahoma.sif",
+	"org.label-schema.build-date": "2021-09-11 06:19:46 -0400 EDT"
+	},
+	{"Record trail": {
+		"Container_name":["output_oklahoma.sif","visualization.sif","predictions_oklahoma.sif"],
+		"UUID":["da671285-f40c-4ed7-ab82-527a7fb6309a","66752397-25bc-4fb6-aa8d-f0426d4f045d","54b39d78-d002-4efb-a64c-1283b0727300"],
+		"Creation_time":["2021-09-11T06:08:37-04:00","2021-09-11T04:29:08-04:00","2021-09-08T08:22:50-04:00"],
+		"Modification_time":["2021-09-11T06:08:37-04:00","2021-09-11T04:29:08-04:00","2021-09-08T08:22:52-04:00"]
+		}
+	},
+	{"Command_line":"exec python3 visualization.py Predictions/predictions_oklahoma.csv Output/out_oklahoma.png 0.175 0.35"
+	}
+]
diff --git a/metadata_interface/Examples_of_metadata/Explainability/c_predictions_oklahoma.json b/metadata_interface/Examples_of_metadata/Explainability/c_predictions_oklahoma.json
new file mode 100644
index 0000000..de6fdda
--- /dev/null
+++ b/metadata_interface/Examples_of_metadata/Explainability/c_predictions_oklahoma.json
@@ -0,0 +1,17 @@
+[
+	{
+	"org.label-schema.build-container_uuid": "54b39d78-d002-4efb-a64c-1283b0727300",
+	"org.label-schema.build-container_name": "predictions_oklahoma.sif",
+	"org.label-schema.build-date": "2021-09-09 22:37:47 -0400 EDT"
+	},
+	{"Record trail": {
+		"Container_name":["predictions_oklahoma.sif","sbm.sif","train_27km.sif","eval_250m.sif"],
+		"UUID":["54b39d78-d002-4efb-a64c-1283b0727300", "3805c76b-a805-49fa-970b-09a489e4f13e", "641e1a52-7757-42f2-85c5-fb4f9b4c2cfc", "38aa530a-fa24-4807-969d-b5a4e40c7cb6"],
+		"Creation_time":["2021-09-09T22:37:47-04:00","2021-09-07T10:34:56-04:00","2021-09-01T12:07:26-04:00","2021-09-09T13:40:02-04:00"],
+		"Modification_time":["2021-09-09T22:37:48-04:00","2021-09-07T10:34:56-04:00","2021-09-01T12:07:26-04:00","2021-09-09T13:40:03-04:00"]
+		}
+	},
+	{"Command_line":"exec python3 sbm.py Train/train.csv Eval/eval_250m.csv Predictions/predictions_oklahoma.csv"
+	}
+]
+
diff --git a/metadata_interface/Examples_of_metadata/Traceability/a_output_oklahoma.json b/metadata_interface/Examples_of_metadata/Traceability/a_output_oklahoma.json
new file mode 100644
index 0000000..fbfe2aa
--- /dev/null
+++ b/metadata_interface/Examples_of_metadata/Traceability/a_output_oklahoma.json
@@ -0,0 +1,16 @@
+[
+	{
+	"org.label-schema.build-container_uuid": "3b41ccde-7997-4713-b3f5-123188975427",
+	"org.label-schema.build-container_name": "output_oklahoma.sif",
+	"org.label-schema.build-date": "2021-09-11 04:29:25 -0400 EDT"
+	},
+	{"Record trail": {
+		"Container_name":["output_oklahoma.sif","visualization.sif","predictions_oklahoma.sif"],
+		"UUID":["3b41ccde-7997-4713-b3f5-123188975427","66752397-25bc-4fb6-aa8d-f0426d4f045d","a5a7a0df-39b2-4822-bc4a-b9d54406b7ae"],
+		"Creation_time":["2021-09-11T04:29:25-04:00","2021-09-11T04:29:08-04:00","2021-09-09T12:48:57-04:00"],
+		"Modification_time":["2021-09-11T04:29:25-04:00","2021-09-11T04:29:08-04:00","2021-09-09T12:48:57-04:00"]
+		}
+	},
+	{"Command_line":"exec python3 visualization.py Predictions/predictions_oklahoma.csv Output/out_oklahoma.png 0.175 0.35"
+	}
+]
diff --git a/metadata_interface/Examples_of_metadata/Traceability/a_predictions_oklahoma.json b/metadata_interface/Examples_of_metadata/Traceability/a_predictions_oklahoma.json
new file mode 100644
index 0000000..53ca4a7
--- /dev/null
+++ b/metadata_interface/Examples_of_metadata/Traceability/a_predictions_oklahoma.json
@@ -0,0 +1,17 @@
+[
+	{
+	"org.label-schema.build-container_uuid": "a5a7a0df-39b2-4822-bc4a-b9d54406b7ae",
+	"org.label-schema.build-container_name": "predictions_oklahoma.sif",
+	"org.label-schema.build-date": "2021-09-09 13:40:06 -0400 EDT"
+	},
+	{"Record trail": {
+		"Container_name":["predictions_oklahoma.sif","knn.sif","train_27km.sif","eval_1km.sif"],
+		"UUID":["a5a7a0df-39b2-4822-bc4a-b9d54406b7ae", "f3edaf15-d37d-4abd-a7a6-9dd5bbc527b7", "641e1a52-7757-42f2-85c5-fb4f9b4c2cfc", "cf2089ac-f3c0-493a-b646-16fdeb0c5f1e"],
+		"Creation_time":["2021-09-09T13:40:06-04:00","2021-09-01T12:14:54-04:00","2021-09-01T12:07:26-04:00","2021-09-09T13:40:02-04:00"],
+		"Modification_time":["2021-09-09T13:40:06-04:00","2021-09-01T12:14:54-04:00","2021-09-01T12:07:26-04:00","2021-09-09T13:40:03-04:00"]
+		}
+	},
+	{"Command_line":"exec python3 knn.py Train/train.csv Eval/eval_1km.csv Predictions/predictions_oklahoma.csv"
+	}
+]
+
diff --git a/metadata_interface/Examples_of_metadata/Traceability/b_output_oklahoma.json b/metadata_interface/Examples_of_metadata/Traceability/b_output_oklahoma.json
new file mode 100644
index 0000000..31e5224
--- /dev/null
+++ b/metadata_interface/Examples_of_metadata/Traceability/b_output_oklahoma.json
@@ -0,0 +1,16 @@
+[
+	{
+	"org.label-schema.build-container_uuid": "0ba80a99-8cad-4ece-aff9-332ec8e7f1ef",
+	"org.label-schema.build-container_name": "output_oklahoma.sif",
+	"org.label-schema.build-date": "2021-09-11 04:34:17 -0400 EDT"
+	},
+	{"Record trail": {
+		"Container_name":["output_oklahoma.sif","visualization.sif","predictions_oklahoma.sif"],
+		"UUID":["0ba80a99-8cad-4ece-aff9-332ec8e7f1ef","66752397-25bc-4fb6-aa8d-f0426d4f045d","24d4798f-7669-434a-8f88-dd01f9d86229"],
+		"Creation_time":["2021-09-11T04:34:17-04:00","2021-09-11T04:29:08-04:00","2021-09-09T13:40:06-04:00"],
+		"Modification_time":["2021-09-11T04:34:17-04:00","2021-09-11T04:29:08-04:00","2021-09-09T13:40:08-04:00"]
+		}
+	},
+	{"Command_line":"exec python3 visualization.py Predictions/predictions_oklahoma.csv Output/out_oklahoma.png 0.175 0.35"
+	}
+]
diff --git a/metadata_interface/Examples_of_metadata/Traceability/b_predictions_oklahoma.json b/metadata_interface/Examples_of_metadata/Traceability/b_predictions_oklahoma.json
new file mode 100644
index 0000000..0d7386a
--- /dev/null
+++ b/metadata_interface/Examples_of_metadata/Traceability/b_predictions_oklahoma.json
@@ -0,0 +1,17 @@
+[
+	{
+	"org.label-schema.build-container_uuid": "24d4798f-7669-434a-8f88-dd01f9d86229",
+	"org.label-schema.build-container_name": "predictions_oklahoma.sif",
+	"org.label-schema.build-date": "2021-09-09 13:40:06 -0400 EDT"
+	},
+	{"Record trail": {
+		"Container_name":["predictions_oklahoma.sif","knn.sif","train_27km.sif","eval_250m.sif"],
+		"UUID":["24d4798f-7669-434a-8f88-dd01f9d86229", "f3edaf15-d37d-4abd-a7a6-9dd5bbc527b7", "641e1a52-7757-42f2-85c5-fb4f9b4c2cfc", "38aa530a-fa24-4807-969d-b5a4e40c7cb6"],
+		"Creation_time":["2021-09-09T13:40:06-04:00","2021-09-01T12:14:54-04:00","2021-09-01T12:07:26-04:00","2021-09-09T13:40:02-04:00"],
+		"Modification_time":["2021-09-09T13:40:06-04:00","2021-09-01T12:14:54-04:00","2021-09-01T12:07:26-04:00","2021-09-09T13:40:03-04:00"]
+		}
+	},
+	{"Command_line":"exec python3 knn.py Train/train.csv Eval/eval_250m.csv Predictions/predictions_oklahoma.csv"
+	}
+]
+
diff --git a/metadata_interface/Examples_of_metadata/Traceability/c_output_oklahoma.json b/metadata_interface/Examples_of_metadata/Traceability/c_output_oklahoma.json
new file mode 100644
index 0000000..52ee585
--- /dev/null
+++ b/metadata_interface/Examples_of_metadata/Traceability/c_output_oklahoma.json
@@ -0,0 +1,16 @@
+[
+	{
+	"org.label-schema.build-container_uuid": "819a32ee-d74c-4c42-979b-dce8aa7320c3",
+	"org.label-schema.build-container_name": "output_oklahoma.sif",
+	"org.label-schema.build-date": "2021-09-11 04:51:12 -0400 EDT"
+	},
+	{"Record trail": {
+		"Container_name":["output_oklahoma.sif","visualization.sif","predictions_oklahoma.sif"],
+		"UUID":["819a32ee-d74c-4c42-979b-dce8aa7320c3","66752397-25bc-4fb6-aa8d-f0426d4f045d","b82baffb-c1dc-48fe-9c14-61239fb03292"],
+		"Creation_time":["2021-09-11T04:51:12-04:00","2021-09-11T04:29:08-04:00","2021-09-09T17:39:22-04:00"],
+		"Modification_time":["2021-09-11T04:51:12-04:00","2021-09-11T04:29:08-04:00","2021-09-09T17:39:31-04:00"]
+		}
+	},
+	{"Command_line":"exec python3 visualization.py Predictions/predictions_oklahoma.csv Output/out_oklahoma.png 0.175 0.35"
+	}
+]
diff --git a/metadata_interface/Examples_of_metadata/Traceability/c_predictions_oklahoma.json b/metadata_interface/Examples_of_metadata/Traceability/c_predictions_oklahoma.json
new file mode 100644
index 0000000..13424dd
--- /dev/null
+++ b/metadata_interface/Examples_of_metadata/Traceability/c_predictions_oklahoma.json
@@ -0,0 +1,16 @@
+[
+	{
+	"org.label-schema.build-container_uuid": "b82baffb-c1dc-48fe-9c14-61239fb03292",
+	"org.label-schema.build-container_name": "predictions_oklahoma.sif",
+	"org.label-schema.build-date": "2021-09-09 17:39:22 -0400 EDT"
+	},
+	{"Record trail": {
+		"Container_name":["predictions_oklahoma.sif","knn.sif","train_27km.sif","eval_90m.sif"],
+		"UUID":["b82baffb-c1dc-48fe-9c14-61239fb03292", "f3edaf15-d37d-4abd-a7a6-9dd5bbc527b7", "641e1a52-7757-42f2-85c5-fb4f9b4c2cfc", "0b22b5d9-3914-429b-abfc-b3a239e96a95"],
+		"Creation_time":["2021-09-09T17:39:22-04:00","2021-09-01T12:14:54-04:00","2021-09-01T12:07:26-04:00","2021-09-09T17:39:04-04:00"],
+		"Modification_time":["2021-09-09T17:39:25-04:00","2021-09-01T12:14:54-04:00","2021-09-01T12:07:26-04:00","2021-09-09T17:39:25-04:00"]
+		}
+	},
+	{"Command_line":"exec python3 knn.py Train/train.csv Eval/eval_90m.csv Predictions/predictions_oklahoma.csv"
+	}
+]
diff --git a/metadata_interface/Figures_IPDPS_paper/explainability_output.png b/metadata_interface/Figures_IPDPS_paper/explainability_output.png
new file mode 100644
index 0000000..b781bab
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diff --git a/metadata_interface/Figures_IPDPS_paper/explainability_predictions.png b/metadata_interface/Figures_IPDPS_paper/explainability_predictions.png
new file mode 100644
index 0000000..c11f0ba
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diff --git a/metadata_interface/Figures_IPDPS_paper/explainabilyit.png b/metadata_interface/Figures_IPDPS_paper/explainabilyit.png
new file mode 100644
index 0000000..5e7ff06
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diff --git a/metadata_interface/Figures_IPDPS_paper/traceability.png b/metadata_interface/Figures_IPDPS_paper/traceability.png
new file mode 100644
index 0000000..dd0255f
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diff --git a/metadata_interface/Figures_IPDPS_paper/traceability_output.png b/metadata_interface/Figures_IPDPS_paper/traceability_output.png
new file mode 100644
index 0000000..686c106
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diff --git a/metadata_interface/Figures_IPDPS_paper/traceability_predictions.png b/metadata_interface/Figures_IPDPS_paper/traceability_predictions.png
new file mode 100644
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diff --git a/metadata_interface/Fine-grained_metadata_interface.ipynb b/metadata_interface/Fine-grained_metadata_interface.ipynb
new file mode 100644
index 0000000..ea0840d
--- /dev/null
+++ b/metadata_interface/Fine-grained_metadata_interface.ipynb
@@ -0,0 +1,232 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fine-grained containerized metadata interface"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ipywidgets as widgets\n",
+    "import networkx as nx\n",
+    "import matplotlib.pyplot as plt\n",
+    "from networkx_viewer import Viewer\n",
+    "import rglob\n",
+    "from ipyfilechooser import FileChooser\n",
+    "import sys\n",
+    "from utils import read_json_file, from_json_to_graph, plot_graph \n",
+    "from ViewerApp import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selection of metadata directory \n",
+    "The user can select the directory with the metadata or single metadata file which is aimed to be analyzed "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "753ba4d9aa7e407f9cdb447c8b86a19b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "FileChooser(path='Metadata selection', filename='', title='HTML(value='', layout=Layout(display='none'))', sho…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create and display a FileChooser widget\n",
+    "fc = FileChooser('Metadata selection')\n",
+    "display(fc)\n",
+    "\n",
+    "\n",
+    "# Change defaults and reset the dialog\n",
+    "fc.default_path = '/'\n",
+    "fc.reset()\n",
+    "\n",
+    "# Change hidden files\n",
+    "fc.show_hidden = False\n",
+    "\n",
+    "# Show or hide folder icons\n",
+    "fc.use_dir_icons = True\n",
+    "\n",
+    "# Set multiple file filter patterns (uses https://docs.python.org/3/library/fnmatch.html)\n",
+    "#fc.filter_pattern = ['*.json']\n",
+    "\n",
+    "# Change the title (use '' to hide)\n",
+    "fc.title = '<b>Select the directory where the metadata is located</b>'\n",
+    "\n",
+    "# Sample callback function\n",
+    "def change_title(chooser):\n",
+    "    chooser.title = '<b>Metadata files captured</b>'\n",
+    "\n",
+    "# Register callback function\n",
+    "fc.register_callback(change_title)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Graph representation of the workflow based on the metadata\n",
+    "Once the metadata is selected, it is read and processed to build a graph where the nodes are the workflow components (data and application containers) and the edges are the execution paths og how the results were generated based on the record trail"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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QwKFD+d2fJ0/md38aGwN16wKjRgGDB1fui9ACUviEEOKRPOieLNhqK6l78sG/anRPipJJ4RNCiGLk5OQgJiamSHF7uHuyYAuuMnZPiqKk8AkharR79+7hr7/+KtKCK6570tvbG40bN65y3ZOiMCl8Qohq7+HuyYJFrqTuSQ8PD5iYmKgdXTwBUviEENVGwe7JgsXt/PnzMDMzKzJy0tvbG05OTtI9WcNI4RNCVDkPd08++Fe6J0V5SOETQlRKpXVP3rt3D40bNy7SgpPuSVEeUviEEKrKzs7GpUuXinz37a+//pLuSfFESOETQjwV9+7dK3TO7eHuyYd/vUS6J8WTIoVPCFFhSOL69evF/nqJVqtVuicLFjnpnhRPmxQ+IcQje9A9Wdyvl5ibmxd7aRzpnhSVhRQ+IUSJyts9WbAFZ2Njo3ZsIUolhU+IGq5g9+TDLbjiuicfXBpHuidFVSWFT4gaQronhcgnhU+IaqZg92TBInflyhU0aNCg2Ct3S/ekqEmk8InqizrgTjiQeRNgDmBkDdRuARhX8ouFlUNx3ZMP/n3QPVnclbule1IIKXyiOspMAi6tBM4vBPIyAD19AASgB+iyAOdegNcHQG1ftZOW6eHuyYItOHNz82IHl0j3pBClk8InqpfYtcCJkQD08otecfQMAH0TwOE5oP1mwND0aSYsVnHdk9HR0YiLi1O6Jx++crd0TwrxeKTwieojegFw9mMgL7188xuYAlbPAM8ffSrF70H3ZHGDS6R7Uoinp8YWvoYNG2LlypV4/vnnK2R9V65cgaurK3JycmBoaFhk+vTp0xETE4MNGzY88rrXrl2LlStX4rfffquIqNDT08PFixfh7u5e5rxlPa+H/fjjjxg3bhzu3r2Lo0ePwte3YrsTp06dimXLlsHQ0BA3btz4e8K1n4E/+pTcygNw5Tbg+h6Qsw4wNPjfgwamgMMLQKefKyxjdnZ2iZfGke7J8nvUY0+I8pJ32iO4fv06evbsidq1a8PJyQnLli2rsHUHBASgVq1a0Gg00Gg0aNy4cYWtu6IdOHAALVq0gLm5OZydnbFlyxZl2vjx47FkyRKkpaWVWfS6dOkCPT095ObmAgBu3bqFPn36wNHREVZWVmjfvj3CwsKU+a9du4b58+fj3LlzStHLy8vD1ClT4OjTCxaDM+A7GUi5/whPJi8DuHEAuHsGJDF16lTUr18fVlZWCAgIQFRUVImL3rt3D2FhYVi7di0++ugjBAYGonHjxrC0tERgYCDWrl0LrVaLLl264Ouvv8bVq1dx48YNHDp0CKNHj8bu3bvRt29fuLi4FCl6pR0PoaGh0NfXV6ZpNBp8//33hZbftGkTvL29YW5ujkaNGuHo0aNl7orBgwfD2Ni40Hrz8vLKXC4rKwtDhw6FpaUlHBwcsGDBgmLnmzFjBvT09HDgwAHlsfHjx8PDwwMWFhbw8vLCunXrytxewe2+//77cHR0hI2NDUaNGoWcnJxyLy9qLvkz6hH0798fPj4+2LZtG86dO4fOnTujcePG6Ny5c4Wsf8mSJRg2bFiFrOtJOXfuHPr27Yvvv/8eL7zwAu7du4eUlBRlelxcHJo0aVLmeoKCgpSC90BaWhr8/PywYMEC1K1bF6tWrcLLL7+MK1euQKPRIC4uDra2tqhbt66yzLRp0/DH4RAc+8wEDazTERUP1DJ6xCelywKiF2BrfHesXr0av/32G1xcXDB16lQMGDAAO3fuLHZwycPdkwMGDICXl1e5uieNjIzwxhtvYNSoUQgMDCx2ntKOB0dHR8THxxc7bf/+/Zg4cSI2b96M1q1bIzExsdy74sMPP8TMmTPLPT+Q35tx8eJFxMXF4caNG+jcuTOeeeYZdOvWTZnn0qVL2LZtG+rVq1doWXNzc/zyyy/w9PTEiRMn0K1bN7i7u8Pf37/M7c6ZMwfh4eGIjIxEXl4eXnnlFcycORMzZsx4pPyiBmIN5eLiwlmzZtHb25vW1tYcPHgwMzIySJI//fQTfXx8aGFhQTc3N4aEhDA1NZUAeOvWLWUdw4cPZ//+/UmSly9fJgAuX76c9erVo4ODA+fNm6fMO23aNPbq1YtvvPEGNRoNfX19eebMGWV6p06d+N133xWbdc2aNfT39+eYMWNoaWnJxo0b88CBA8r0lJQUDh06lA4ODnR0dOSUKVOYm5tb4nMHwMWLF9PV1ZW2trYcP3488/LySJK5ubn84IMPaGtrS1dXVy5ZsoQAmJOTQ5Ls06cPp06dWmSdmZmZNDc3JwCamZnRzc2txO2npKTQw8ODx44dK7Tu4lhYWDA8PJz79+9nrVq1qKenR3Nzcw4aNIjJyck0NzdnzIYAMkiPDEKpt8uLkP8avQXWswYdrMF5ffOn6X6oxf+8O4rt2rXjzJkz2b9/fzZp0oQA6ODgwICAAAYEBNDe3p6mpqZ0dnbmt99+WyjrL7/8Qh8fH1pZWbFdu3aMiIgo8Xk9cPHiRRb3NizteDh06BDr169f4jrbtWvHlStXlji9uOObJAcNGsQpU6Y88nKOjo7cu3evMt/UqVPZu3fvQst269aNu3fvpouLC/fv31/iNl555RXlfVPWe6ply5bcsmWLcj8oKIhOTk4lrluIB2p04WvSpAmvXr3KO3fu0N/fn1OmTGFYWBgtLS25b98+5uXlMT4+ntHR0dRqtQTAmzdvKusYNmwYmzdvTvLvN+mbb77JtLQ0nj17lnZ2dsqbfNq0aTQ0NOTWrVuZnZ3NL7/8kg0bNmR2djbJ/A86Ozs72tra0t/fn4cOHVK2s2bNGhoYGHDBggXMzs7mpk2baGlpyTt37pAke/bsyREjRjAtLY03b96kn58fly1bVuJzB8CAgADeuXOHcXFx9PDwUD5kly5dysaNGyv7JSAgoFBxcnV15dSpU9m0aVM6ODiwX79+So4H67548WKp+37UqFFcsGCBss9KKnynT5+miYkJU1JSSBb9wD98+DCtrKw4p28t2luBHg7gkkFlF74324Fpq8Czs0E7C3D/JDD9e2P27GBPS0tLDhs2jCtWrGC/fv340ksvKdvbtWsXY2JiqNPpGBoaSlNTU548eZIkefLkSdapU4fHjx9nbm4u165dSxcXF2ZmZpa6L0orfCUdD4cOHaKRkRHr1q3Lhg0b8r333mNaWhrJ/D9cjIyMOHv2bDZq1Ij169fn6NGjmZ6eTpIlHt9kfuGzsbGhjY0NW7RowW3btinbLGm55ORkAuCNGzeUebdu3cqmTZsq97ds2cIePXqQZKmFLz09nQ4ODkpBLes91aJFC27evFlZfsOGDQSgHC9ClKRGF76lS5cq93fv3k03NzeOGDGC7733XrHLtG/fnmPGjGFGRgZPnjxJGxsbenp6kvz7TfrgQ4QkJ0yYwKFDh5LML3xt2rRRpuXl5dHBwYFHjhwhSR4/fpxarZaZmZlcu3YtNRoNY2JiSOYXvnr16lGn0ynL+/n5cd26dbxx4waNjY2VDzaS3LhxIwMCAkp87gCUDxeS/Oabb9ilSxeSZOfOnQvtl7179xYqTkZGRnRxceFff/3F1NRUvvrqq+zbt2+hdZdW+E6cOEEfHx/m5OSUWvju3bvHpk2bctasWcpjDxe+oKAgAuDQAD2mrwEj/lfI9n1UeuGL/vLvxya8DA7tBHKzJbNiNnPcuHEEQAMDAzZs2JCxsbElPpeePXty0aJFJMl33nmnSEvY09OToaGhJS5Pllz4SjseEhMTGRUVxby8PMbGxrJjx44cMWIESfL69esEwJYtWzIhIYG3b9+mv78/J0+eTJKlHt8nT55kUlISc3JyuHv3bmo0Gv7222+lLnf16lUCUHpLSHLfvn10cXEhSaamptLd3V3Zj6UVvoEDB7Jr167KcV7We2rKlCn09/fnrVu3mJiYyNatWxMAExISStjbQuSr0YNbnJ2dlf+7uLggISEB165dQ6NGjYqdPygoCJcvX4azszNGjhyJfv36wcnJqcx1FjdNX18fTk5OyvQ2bdrAwsICJiYmGDRoENq3b4/g4GBl/vr160NPT6/IuuPi4pCTk4N69erB2toa1tbWePvtt3Hr1i0AQJMmTZSBCgUHOJSUMyEhoci0gkxNTTFkyBB4enpCo9Fg8uTJhXI+vL8ebPvFF1+ETqfDqFGjsHjx4lJH6WVkZOCVV15B27ZtMWnSpBLnMzXN/wrCJ68aw9QYaNYAeLMdEHwmf7pm6N+3q0l/L+dc4NqmLnZAwl0AenqYsXg7Tpw4gWvXriEzMxPTpk1Dly5dkJ6e//WIkJAQtG3bFrVr14a1tTWCg4ORlJS/4ri4OMyfP195DaytrXHt2jUkJCQU2Q/lUdrx4ODggGeeeQb6+vpwdXXF3LlzsW3btkL7ZOzYsahXrx7s7Ozwn//8R1m2tOO7RYsWsLW1haGhIV566SX069cPO3bsKHU5jUYDANBqtcpjWq0WFhYWAPLPwQ4YMACurq6lPt8JEyYgMjISW7ZsKXScAyUfq1OmTIGvry+aN28Of39/BAYGwsjIqNA5YCGKU6ML37Vr15T/X716FY6OjnB2dsalS5eKnd/FxQW7du3C7du3ERYWhjt37qB169ZlrrO4aTqdDvHx8YWmF6SnpwcW+KbJ9evXC90vmNfExARJSUlISUlBSkoKtFqtMhoxKioKaWlpSEtLQ8eOHcvMWa9evSLTCmrWrFmRD6aS9OvXT9l2SEgItFotwsPD0bt3bzg4OMDPzw8A4OTkpBTlrKwsBAYGon79+li+fHmp62/WrFn+vjK2KnZ62uq/bw3s/n78WvLf/796B3C0AcA8RJy/jt69e8PJyQmGhoYYPHgw7t69i3PnziErKwu9evXC+PHjcfPmTaSkpOCll15SXhNnZ2dMmTJFeQ1SUlKQnp6OPn36FNkPj+Ph46GkaTY2NnBycirxNSrt+C5tvSUtZ2Njg3r16iEiIkJ5LCIiQhngdPDgQXz11VdwcHCAg4MDrl27hjfeeANffPGFMv+0adMQEhKCffv2wdLSssg2SjpWTU1NsWTJEly/fh2xsbGwtbVFy5YtYWBgUGQdQhSiZnNTTS4uLmzatCmvXbvGO3fusEOHDpw0aRLDwsJoZWXFAwcOFDkHcu7cOWq1WmZlZXH9+vW0tbVVBrs86Jbp27cv79+/z8jISNapU0c56f/gHN/27duZk5PD+fPn08XFhdnZ2bx79y737NnDjIwM5uTkcMOGDTQzM+P58+dJ/n2Ob9GiRczOzuaWLVtoYWHBpKQkkmSPHj04btw43rt3j3l5eYyJiSm1iw0Au3TpwuTkZF69epWNGzfm8uXLSZLffvstvb29ee3aNSYnJ7NLly6FuiNXrVrFhg0b8tKlS7x//z5ff/11ZYDPg3WX1NWp0+mYmJio3P78808CYHx8PLOyspidnc3u3buzZ8+exXZ/Fjeoo2PHjhzRqzkz1xnz3FywjiV4YFLpXZ19/cH7q8HIL/Ln3zsR5HYHTp82je3bt+eNGzeYl5fHdevW0czMjHfv3qVWq6W+vj5DQ0Op0+kYHBxMU1NTZTDIiRMn6OTkxOPHj1On0zEtLY27du2iVqstcV9kZGQwKipK6Sp8cD6wrOPh0KFDjIuLo06n49WrVxkQEMDBgwcr6/7444/ZqlUr3rx5k8nJyezQoYPSDVva8b1161ampqYyLy+Pe/fupUajUc4tlrbcxIkT+eyzzzI5OZnR0dGFztMlJSUVes2dnJy4ZcsWpqamkiRnzZpFd3f3Yrsny3pPxcfH8/r169TpdDx27BidnJwKDbIRoiQ1uvA9GNVpZWXFgQMH8v79+yTJHTt28F//+hc1Gg0bNWrEPXv2kCQXLlxIOzs7mpmZsX379jxx4oSyvodHoNnb2/OLL75Qpj88qrN58+bKwIhbt26xVatW1Gg0tLKyYps2bbhv3z5l2QejOkePHk1LS0t6eHgUeoOnpKTwnXfeYf369WlpacnmzZvzhx9+KPG5o8Coztq1a/M///mPMgo0JyeH7733HmvXrs2GDRsWGdVJkp988gnt7OxoZ2fH/v37Mzk5udC6yxrc8vA+e7Du0NBQAqCpqSnNzc2V24PzoMUVvvj4eHZ9vhPNTUDXOuCyoUG9lTcAACAASURBVOUf1WlvBX7xJshNpmTUXGZkZHDUqFF0cHCghYUFfX19C50LXbJkCevWrUsrKyv279+fvXv3LjQKMiQkhK1ataKVlRUdHBz42muvlVj4Hjz3grcH58XKOh7mz59PR0dHmpqa0snJiWPGjCm0nezsbI4cOZJWVla0t7fn2LFjC52DK+n47tChAy0tLWlhYcFmzZoVOYZKWi4zM5NDhgyhhYUF69aty/nz55f4mj98jg8AjY2NC73en3/+eaF9VNJ76vDhw3RxcaGpqSk9PT25YcOGErcrREE19pdbRDXza1fg5kGAZX/huggDUyDwGmBiW/G5hBCVTo0+xyeqkbar8i879KgMzIDWy6XoCVGDSOET1YOZE/D8YcDYNv/qC+VhYAr4zAJcBzzZbEKISkUKn6g2Ltwywv9954q8Op0B/Vr5lx4qQg8wNAfMGwIdtgBe7z7tmEIIlUnhE1VeUlIS3n77bTRp0gQ/7QtHdvudQI+LgPcHgKljfstO3wgwsgHqv4xEr9VwHJmJi+mV94fAhRBPjgxuEVXa0qVLMX78eOTk5CAnJwc2NjZITk4udZn//ve/aNasGWrVqoWtW7eie/fuTymtEKIykBafqNIyMzOh0+mUy9GU5xqDubm5MDU1RWZmJt544w1MmTIFOp3uSUcVQlQSUvhElfb+++/jjTfegKmpKQwMDMp1SaTs7GwYGxsDyP+lmFmzZuHPP/980lGFEJWEFD5RpR05cgT79+/H+fPnMXr0aPTq1avMZbKzs3H//n0YGRmhTp06iIqKQtu2bZ9CWiFEZSCFT1RZWq0WgwYNwooVK9CgQQMsXry4XOfrPDw8MGHCBERFRcHAwABZWVlPIa0QorKQwS2iyho6dCgMDQ2xYsWKx17HvHnzEB4ejk2bNlVgMiFEZSaFT1RJP/30E8aPH48zZ84ol8Z5HKmpqXB1dcXx48fLNTBGCFH1SeETVc7Nmzfh4+ODHTt2wN/f/x+v7+OPP8bNmzf/UctRCFF1SOETVQpJ9OjRA82aNcPnn39eIeu8ffs2PD09ERUVVeL1EYUQ1YcMbhFVyqpVq3D9+nVMmzatwtZZp04dDBw4EAsXLqywdQohKi9p8Ykq49KlS2jbti1CQ0PL9X29R3H16lX4+voiJiYGNjY2FbpuIUTlIi0+USXk5eVh4MCBmDJlSoUXPQBo0KABevTogSVLllT4uoUQlYu0+ESVMHv2bBw4cAD79++Hvv6T+XstOjoanTp1wuXLl2Fubv5EtiGEUJ8UPlHpnT59Gl27dkV4eDgaNGjwRLf16quvIiAgAOPGjXui2xFCqEcKn6jUMjMz0bJlS0yaNAn9+/d/4ts7ceIEevXqhZiYGOX3PIUQ1Yuc4xOV2uTJk9GkSRP069fvqWzPz88Pnp6e2Lhx41PZnhDi6ZMWn6i0fv31VwwYMABnz56Fra3tU9vugQMHMHbsWERFRT2x84lCCPXIu1pUSikpKRgyZAhWrVr1VIseADz33HPQaDT4+eefn+p2hRBPh7T4RKU0cOBAWFhY4JtvvlFl+zt27MCcOXMQFhYGPT09VTIIIZ4MafGJSmfbtm04fvw45s6dq1qGwMBAaLVa/Prrr6plEEI8GdLiE5VKYmIifH198fPPP6NNmzaqZlmzZg2CgoJw4MABVXMIISqWFD5RaZDEyy+/DD8/P8yYMUPtOMjOzoa7uzu2b98OPz8/teMIISqIdHWKSmP58uW4ffs2pk6dqnYUAICxsTE++OADzJkzR+0oQogKJC0+USlcuHAB/v7++O233+Dl5aV2HMX9+/fh5uaGw4cPV6pcQojHJy0+obrc3FwMGDAA06dPr3TFxdzcHGPGjMEXX3yhdhQhRAWRFp9Q3WeffYajR49iz549lfIL48nJyXB3d0dERAScnZ3VjiOE+Iek8AlVhYeH4+WXX8apU6dQv359teOUaPz48cjNzcWiRYvUjiKE+Iek8AnVpKeno2XLlpg+fTp69+6tdpxSJSQkoGnTprhw4QLs7OzUjiOE+Aek8AnVjBs3DklJSVXmB6FHjBgBBwcHfPrpp2pHEUL8A1L4hCr279+Pt956CxEREbCxsVE7TrlcvHgR/v7+iI2NhYWFhdpxhBCPqfKNJBDVXnJyMoYOHYrVq1dXmaIHAB4eHujSpQtWrFihdhQhxD8gLT7x1PXp0wd169bF4sWL1Y7yyE6fPo3u3bsjNjYWJiYmascRQjwGafGJp+qHH37AmTNnquyvofj6+qJZs2ZYt26d2lGEEI9JWnziqYmPj0eLFi0QHByMVq1aqR3nsR0+fBjDhg3D+fPnYWBgoHYcIcQjkhafeCp0Oh2GDh2KcePGVemiBwDPPvss6tSpg+3bt6sdRQjxGKTwiafim2++QWpqKj766CO1o/xjenp6mDRpEmbPng3pMBGi6pHCJ5648+fP49NPP8W6detgaGiodpwK8fLLLyM3Nxd79+5VO4oQ4hFJ4RNPVE5ODvr374/PPvsMHh4easepMPr6+pg4cWKVHaQjRE0mhU88UTNnzkTdunXx9ttvqx2lwr355puIi4vDsWPH1I4ihHgEMqpTPDHHjx9HYGAgTp8+jXr16qkd54n49ttvsWfPHuzcuVPtKEKIcpLCJ56I+/fvo3nz5pgzZw569eqldpwnJiMjA25ubti/fz+aNm2qdhwhRDlI4RNPxKhRo5CWllYjvug9Z84cREVFYf369WpHEUKUgxQ+UeFCQkLwzjvv4OzZs7CyslI7zhN37949uLm5ITw8HK6urmrHEUKUQQa3iAp1584dDBs2DN9//32NKHoAYGVlhREjRmDevHlqRxFClIO0+ESFIYk33ngDDRo0wPz589WO81TdvHkT3t7eiI6Ohr29vdpxhBClkBafqDBBQUGIjo7G559/rnaUp87e3h59+vSpklecEKKmkRafqBBXr15Fy5YtsX//fjRv3lztOKq4fPky/Pz8cOnSpRrTzStEVSQtPvGP6XQ6DB48GB988EGNLXoA4Orqim7dumHp0qVqRxFClEJafOIfW7hwIbZv347Dhw/X+Mv0REZG4oUXXkBsbCxMTU3VjiOEKIYUPvGPREVFISAgAGFhYXBzc1M7TqXQo0cPvPjiixg5cqTaUYQQxZDCJx5bdnY22rRpgzFjxuCtt95SO06l8ccff6B///64cOFCtbkahRDViZzjE49t+vTpcHZ2xtChQ9WOUqn4+/vD2dkZmzdvVjuKEKIY0uITj+X333/Ha6+9hjNnzsj31oqxZ88eTJgwAWfPnoWenp7acYQQBUiLTzyy1NRUDBw4EMuWLZOiV4KuXbvC0NAQu3fvVjuKEOIh0uITj2z48OHQ6XRYtWqV2lEqtc2bN+Orr77Cb7/9Jq0+ISoRafGJR/LLL7/g4MGDWLhwodpRKr3XXnsNt27dwtGjR9WOIoQoQFp8otxu374NHx8fbN68GR07dlQ7TpXw3Xff4ccff0RwcLDaUYQQ/yOFT5QLSbz66qvw9PTEF198oXacKiMrKwuNGjXCrl27avSv2ghRmUhXpyiXtWvXIjY2Fp9++qnaUaoUExMTvP/++5gzZ47aUYQQ/yMtPlGmy5cvo3Xr1vj111/xr3/9S+04VU5qairc3Nxw7NgxuLu7qx1HiBpPWnyiVHl5eRg0aBAmTpwoRe8xWVhYYOTIkfjyyy/VjiKEgLT4RBm+/PJL7N69GwcPHqzxP0D9TyQlJcHT0xORkZFwdHRUO44QNZoUPlGiiIgIPP/88wgPD4eLi4vacaq89957D0ZGRtLyE0JlUvhEsbKysuDn54cPPvgAgwYNUjtOtXD16lX4+voiJiYGNjY2ascRosaSc3yiWB9//DHc3d0xcOBAtaNUGw0aNECPHj3wzTffqB1FiBpNWnyiiMOHD6NPnz6IiIhAnTp11I5TrZw/fx6dOnVCbGwszM3N1Y4jRI0kLT5RiFarxeDBg/Hdd99J0XsCvLy80KFDB/mdUyFUJC0+UciQIUNgbGyM5cuXqx2l2jpx4gR69eqFmJgYGBsbqx1HiBpHWnxC8eOPP+Lo0aOYP3++2lGqNT8/P3h6euKHH35QO4oQNZK0+AQA4MaNG2jevDl+/PFHtGvXTu041d7BgwcxduxYREZGQl9f/v4U4mmSd5wASQwbNgzDhg2ToveUdOnSBRqNBj///LPaUYSocaTwCaxcuRIJCQn45JNP1I5SY+jp6eGjjz7C7NmzIZ0uQjxdUvhquEuXLmHy5MnYsGGDDLR4ygIDA5GamopDhw6pHUWIGkUKXw2Wm5uLAQMGYOrUqXjmmWfUjlPj6OvrY+LEiZg9e7baUYSoUaTw1WBz586FmZkZxo4dq3aUGqtv377466+/EB4ernYUIWoMGdVZQ50+fRpdu3bFyZMn4ezsrHacGm3x4sU4cuQItm/frnYUIWoEKXw1UGZmJlq2bInJkyejX79+asep8e7fvw83NzccPnwYXl5eascRotqTwlcD/ec//8H169exadMm6OnpqR1HAPjss89w+fJlrF69Wu0oQlR7UvhqmIMHD2LQoEE4e/YsateurXYc8T93796Fu7s7zpw5I13PQjxhMrilBklJScGQIUOwatUqKXqVjI2NDYYMGSI/FyfEUyAtvhpkwIABsLS0lOvBVVIJCQlo2rQpLly4ADs7O7XjCFFtSYuvhti6dSv+/PNPzJ07V+0oogSOjo547bXX8PXXX6sdRYhqTVp8NUBCQgJ8fX3xyy+/oHXr1mrHEaWIiYlBu3btEBsbCwsLC7XjCFEtSYuvmiOJoUOHYuTIkVL0qgB3d3d06dIFK1asUDuKENWWtPiquaVLl2LNmjX4/fffYWRkpHYcUQ6nT59G9+7dERsbCxMTE7XjCFHtSIuvGrtw4QI+/vhjrF+/XopeFeLr64tmzZph/fr1akcRolqSFl81lZubi/bt22PgwIEYPXq02nHEIzpy5AiGDRuG6OhoGBgYqB1HiGpFWnzV1KxZs2BtbY1Ro0apHUU8ho4dO8LOzk5+v1OIJ0BafNXQiRMn0L17d5w6dQr169dXO454TL/88gs++eQTnDp1Sn5aTogKJC2+aiY9PR0DBgzA119/LUWvinv55ZeRm5uLffv2qR1FiGpFWnzVzNixY5GcnIygoCC1o4gKEBQUhO+++w6hoaFqRxGi2pDCV43s27cPw4YNQ0REBGxsbNSOIypAbm4uPDw8sHHjRrRr107tOEJUC9LVWU0kJyfjrbfewpo1a6ToVSOGhoaYMGECZs+erXYUIaoNafFVE2+++Sbs7e2xePFitaOICpaRkQE3Nzfs378fTZs2VTuOEFWetPiqgR9++AFnz57FnDlz1I4ingBTU1O8++67+OKLL9SOIkS1IC2+Ki4+Ph4tWrRASEgIWrZsqXYc8YTcu3cPbm5uCA8Ph6urq9pxhKjSpMVXhel0OgwePBjjxo2TolfNWVlZYcSIEZg3b57aUYSo8qTFV4V99dVX+OGHH3D06FEYGhqqHUc8YTdv3oS3tzeio6Nhb2+vdhwhqiwpfFVUdHQ0nn32WRw7dgzu7u5qxxFPyejRo2FlZYVZs2apHUWIKksKXxWUk5ODdu3aYfjw4Xj77bfVjiOeosuXL8PPzw+XLl2ClZWV2nGEqJLkHF8V9Nlnn8He3h4jRoxQO4p4ylxdXdGtWzcsXbpU7ShCVFnS4qtijh8/jsDAQJw5cwYODg5qxxEqiIyMxAsvvIDY2FiYmpqqHUeIKkdafFXI/fv3MWDAAHz77bdS9Gqwpk2bws/PD2vXrlU7ihBVkrT4qpCRI0ciPT0d33//vdpRhMr++OMP9O/fHxcuXJARvUI8ImnxVRHBwcEICQnBV199pXYUUQn4+/vD2dkZmzdvVjuKEFWOtPiqgKSkJPj4+GDjxo3o1KmT2nFEJbFnzx5MmDABERER0NeXv2GFKC95t1RyJPHOO++gT58+UvREIV27doWhoSGCg4PVjiJElSKFr5LbsGED/vrrL8ycOVPtKKKS0dPTw0cffYTZs2dDOm6EKD/p6qzE4uLi0KpVKxw4cAA+Pj5qxxGVUF5eHry8vLBq1So8++yzascRokqQFl8l9eAHqMePHy9FT5TIwMAAH374oVyoVohHIIWvklq0aBFyc3Mxfvx4taOISm7gwIE4e/Yszpw5o3YUIaoE6eqshCIjI9G5c2eEhYXBzc1N7TiiCpg3bx7Cw8OxadMmtaMIUelJ4atksrKy0KZNG4wdOxZvvfWW2nFEFZGamgo3Nze5WocQ5SBdnZXM9OnT4eLigqFDh6odRVQhFhYWGDlyJObOnat2FCEqPWnxVSK//fYbXn/9dURERKBu3bpqxxFVTFJSEjw9PREZGQlHR0e14whRaUmLr5JITU3FoEGDsHz5cil64rHY2dlhwIABWLhwodpRhKjUpMVXSQwfPhwksXLlSrWjiCrs6tWr8PX1RUxMDGxsbNSOI0SlJC2+SmDnzp04ePCg/KUu/rEGDRqgR48eWLJkidpRhKi0pMWnslu3bsHHxwdbt25Fhw4d1I4jqoHo6Gh06tQJly9fhrm5udpxhKh0pMWnIpIYMWIEBg0aJEVPVBhvb2906NABq1atUjuKEJWStPhUtHr1anz11VcICwuDiYmJ2nFENXLixAn06tULMTExMDY2VjuOEJWKtPhUcvnyZUycOBEbNmyQoicqnJ+fHzw9PbFx40a1owhR6UiLTwV5eXkICAhAYGAgPvjgA7XjiGrqwIEDGDt2LKKiouRCtUIUIO8GFcyfPx+GhoZ4//331Y4iqrHnnnsOGo0GP//8s9pRhKhUpMX3lEVEROD5559HeHg4XFxc1I4jqrkdO3Zgzpw5CAsLg56entpxhKgUpMX3FGVmZmLAgAGYP3++FD3xVAQGBkKr1eLXX39VO4oQlYa0+J6iCRMmIDY2Ftu2bZO/vsVTs2bNGgQFBeHAgQNqRxGiUpDC95QcPnwYffv2RUREBOzs7NSOI2qQ7OxsuLu7Y/v27fDz81M7jhCqk67Op+DevXsYNGgQVqxYIUVPPHXGxsb44IMPMGfOHLWjCFEpSIvvKRg8eDBq1aqFZcuWqR1F1FD379+Hq6srjhw5Ai8vL7XjCKEqafE9YTt27MDvv/+OefPmqR1F1GDm5uYYM2YMvvjiC7WjCKE6afE9QTdu3EDz5s3x008/oW3btmrHETVccnIy3N3dceZMBC5edMbhw8CtW4CJCeDkBLz+OtCwodophXjypPA9ISTxyiuvwNfXF5999pnacYSAVgv06PEzTp0KAGmFtLS/pxkbA/r6QNu2wIcfAt26ATLwWFRXUviekBUrVmD58uU4fvw4jIyM1I4jarhLl4CAAODOHSIjo/SKZm4OvPoqsHo1YGj4dPIJ8TTJOb4nICYmBlOmTMH69eul6FUxv//+Ozw8PKDRaPDTTz+pHadCXL0KtG4NJCSgzKIHAPfvA9u353d96nRPPl/Dhg1L/I7hlStXoKenh9zc3CcfpArT09NDTEyM2jGqDCl8FSw3NxcDBw7E1KlT8cwzz6gd55H1798f9erVg6WlJTw9PbFy5Upl2pYtW+Dt7Q0LCws888wzFVYY1q5dCwMDA2g0GuUWGhqqTO/cuTPq1KkDS0tL+Pj4FPntyY0bN8LFxQXm5uYIDAxEcnLyY2f55JNPMGbMGKSlpSEwMLDUeY8fP44XXngBtWvXRp06dfD6668jMTGxyHzZ2dnw8vKCk5NTuTIkJyejTp06Ra7RWNb+X7hwIRwcHGBlZYWhQ4ciKysLeXlAx45XkZysgU6nAfDgpgdgfokZ0tP/wM6drWFqaoFmzZrht99+KzT99u3b6Nu3L6ytrWFjY4N+/fop065fv46ePXuidu3acHJyeqKjmcs6dsqrS5cuj1VgS3ptz5w5g5YtW8LMzAwtW7bEmTNnylzX0aNHCz0PjUYDPT09bN++/ZEyFae016tGoqhQM2fO5HPPPce8vDy1ozyWyMhIZmZmkiSjo6Npb2/P8PBwxsfH08jIiMHBwdTpdNy1axdNTU158+bNf7zNNWvWsH379iVOj4iIYE5ODkny+PHj1Gg0TEhIUPJqNBoePnyYqamp7NOnD3v37v3YWRo1asT9+/eXa97g4GBu2bKF9+7d4/379zlkyBB27dq1yHwzZ85kx44dWb9+/XKtd9iwYezYsWOhfVLW/t+zZw/r1q3LyMhIJicns1OnTpw4cSJ37yYtLEig4C2WgD6Byw89/uB2h4AtgS20tMzlmjXraW1tzeTkZCVPhw4d+P777zMlJYXZ2dk8deqUMi0gIIDvvvsus7OzeebMGdrY2PDXX38t8fm6uLiUuM8vX75MAMrr/7Cyjp3y2LBhAzt27FjqdkpS3GublZXFBg0acMGCBczMzOTixYvZoEEDZmVlPdK6Dx06RI1Gw7S0tDLnBcCLFy+WOL2016smksJXgU6ePMk6derw2rVrakepEOfPn6eDgwM3b97M48ePs06dOoWm29nZ8Y8//iBJhoWFsW3btrSysqKDgwNHjx5d6I0+btw4Ojk50cLCgi1atOCRI0eUaY/y4RUWFkYTExOGhYWRJCdNmsQ+ffoo02NiYmhkZEStVkuSnD17Nt3c3KjRaOjt7c0dO3aUuG43Nzfq6emxVq1aNDc3Z2ZmJlevXk0vLy9qNBq6urpy2bJlJS5/8uRJajSaQo/FxsbSy8uLwcHB5Sp8f/zxB9u2bcvVq1cX2idl7f8+ffpw0qRJyrQDBw7Q3t6enToVV9imEwgooeiRwC8EniFAajTkpk2kh4cHV65cSZLcu3cvXVxcmJubWyR/amoqAfDWrVvKY8OHD2f//v2V++vWrWODBg1Yu3Ztzpw5s1yFb/ny5axXrx4dHBw4b948ZXpZx05pxx1JpqSk0MPDg8eOHStS+KKjo/n888/TxsaGnp6e3Lx5c6FlS3pt9+7dS0dHR+p0OuUxZ2dnhoSEkCTT09P5n//8hw0aNKClpSXbt2/P9PT0ItkHDx7MwYMHK/dzc3P5+eefK8dzixYtePXqVZL5hW/x4sV0dXWlra0tx48fr/zxXdrrVVNJ4asg6enp9Pb2ZlBQkNpR/rGRI0fS1NSUAOjr68vU1FTm5uby2Wef5c8//8zc3Fz++OOPrF+/vvLXaHh4OI8dO8acnBxevnyZXl5eXLhwobLO9evXMykpiTk5OZw3bx7t7e2ZkZFBMv/Dy8zMjLa2tvTw8OCnn35a5C/vl19+mSYmJgTArl27Km/qHj16cM6cOYXmNTc3Z3h4OElyy5YtvH79OvPy8rhp0yaamZkprcXiPPwhvGvXLsbExFCn0zE0NJSmpqY8efJkscsuXLiQbdq0KZJ7x44dPHToUJmFLzc3l76+vgwPDy/ygV7W/m/WrBk3bdqkzH/79m0CoIlJUjGFzY3AmlIK304C3sr9Fi1Id3d3vvfeeyTJGTNm8N///jf79evH2rVrs1WrVgwNDSVJarVaAijUEzBs2DA2b96cJBkVFUVzc3MePnyYmZmZfP/992lgYFBm4XvzzTeZlpbGs2fP0s7OTpm/rGOntOOOJEeNGsUFCxYUaVmmpaXRycmJq1evZk5ODk+ePElbW1tGRkaW+douWLCA3bp1K3IcPCjYo0aNYqdOnRgfH8/c3Fz+/vvvSi/LA/fv36dGo+GhQ4eUx+bOncumTZvy/Pnz1Ol0PHPmDJOSkkjmF76AgADeuXOHcXFx9PDw4HfffVfm61VTSeGrIO+99x579+5d6K+8qiw3N5dHjx7lZ599xuzsbJLkypUraW5uTgMDA5qamnLXrl0lLr9w4UIGBgaWON3a2ppnzpwhSV66dImxsbHMy8vj2bNn6e3tzVmzZhVZJjs7m8HBwVywYIHyWJcuXbh06dJC8zk6Ohb6wCjIx8eHP/30U4m5Smt9kGTPnj25aNGiIo9HRETQxsamUItix44dStdneQrfggUL+M4775AsviVT2v53c3NTWhRk/r4CQAuLh7szjxAwJ5BaSuFLImBFYCOBbNaqtZZ6enocMWIEyfwWHACuXLmS2dnZ/OGHH2hlZcXbt2+TJNu3b88xY8YwIyODJ0+eVFpMZP6HcMGu6LS0NBoZGZVZ+KKjo5XHJkyYwKFDh5Is/7HzQMHj7sSJE/Tx8VH+WCtY+DZt2sQOHToUWnbEiBGcPn06ydJf208//bRId3vfvn05bdo05uXlsVatWkqGkqxbt44NGzYs9Hni6elZ4rELoNDr/80337BLly4ky369aiIZ3FIBDh48iK1bt+Lbb7+tNlddMDAwQIcOHRAfH4+lS5fiwIED+PDDDxEaGors7GwcPnwYw4YNU07aX7hwAd27d4eDgwMsLS0xefJkJCUlKeubP38+vL29YWVlBWtra9y7d0+Z7ubmBldXV+jr6+Nf//oXPvnkE2zbtq1IJiMjI7z44ovYu3cvdu7cCQDQaDTQarWF5tNqtbCwsAAArFu3Ds2bN4e1tTWsra0RGRmpbLdJkybKIIKjR48Wux9CQkLQtm1b1K5dG9bW1ggODi70vID8UbwvvvgiFi9ejI4dOwLI/4mwDz/8EF9//XWx633nnXeUbc+aNQsJCQn46quv8Pnnnxc7f1n7/+H98OD/pMVDa/oeQC/kD3B5oAn+HvRyFIAtgJ8BLABgj6ysPXj++eeVARympqZo2LAh3nrrLRgZGeHNN9+Es7Mzfv/9dwBAUFAQLl++DGdnZ4wcORL9+vVTlk1ISICzs7OyZXNzc9ja2ir3Cw7suHr1qvJ4wWVcXFyQkJAAoOxjp6TjTqfTYdSoUVi8eDEMi/nORlxcHMLCwpTjxtraGkFBQbhx40aZr21px2RSUhIyMzPRqFGjYpd94Pvvv8fAgQMLfZ5cu3at1OVK2kdlvV41kRS+fyglJQVDBLPLmAAAFTZJREFUhgzBqlWrULt2bbXjVLjc3FxcunQJZ86cwbPPPotWrVpBX18ffn5+aNOmjTIMfeTIkfDy8sLFixeh1Woxa9Ys8H9fET169Ci++OILbNmyBXfv3kVKSgqsrKyU6Q/T09MrcVrBTEB+8YqIiFCmxcbGIisrC56enoiLi8Pw4cOxZMkS3LlzBykpKWjatKmy7qioKKSlpSEtLU0pWAVlZWWhV69eGD9+PG7evImUlBS89NJLhbLFxcXh+eefx8cff4wBAwYoj1+8eBFXrlxBx44d4eDggFdffRWJiYlwcHDAlStXsGzZMmXbkydPxp9//onExEQ888wzcHBwwLvvvos///wTDg4OyMvLK3P/P7wfIiIiYGNjDwODv4sKkAFgK4BBDz3TKABp/7s92A+dAJwAkAwzs/X466+/0Lp1awBAs2bNSv0Dz8XFBbt27cLt27cRFhaGO3fuKMvWq1cP165dU+ZNT0/HnTt3lPsP9klaWhoaNGigPF5wmatXr8LR0bHYbRc8dko77rRaLcLDw9G7d284ODgoV61wcnLC0aNH4ezsjE6dOiElJUW5paWlYenSpWW+tk2aNMHZs2cLHSdnz55FkyZNYGdnh1q1ainHb3GuXbuG0NBQDBw4sNDjzs7OZS5X3D4q6/WqkdRrbFYP/fr14+jRo9WOUSFu3rzJH374QTmnt2fPHpqZmfGnn35iaGgobW1tefr0aZLkqVOnWLt2be7du5ck6efnxxkzZlCn0zE6Opqenp5KV93u3btZr149JiYmMisrizNmzKC+vr7SvRUcHMwbN26QzB9Q0KRJE6VLKTo6msHBwUxPT2d2djbXr19PIyMj5TxbZGQkLSwseOTIEaalpbFfv35KN1NUVBRNTEx4/vx55ubmcvXq1TQwMFDOfRSnYFenVqulvr4+Q0NDqdPpGBwcTFNTU06ZMoVk/khLNzc3zp07t8h6cnJymJiYqNy2b9+u7IPiBhlkZmYWmn/RokVs3bo1ExMTSbLM/R8SEkJ7e3tGRUUxOTmZnTt35vDhE2lmVrALM4hAAwK6Uro5H9xOEcgmcI82Nu/S399fyXrnzh1aW1tz7dq1zM3N5datW2ljY6N0nZ07d45arZZZWVlcv349bW1tlcEukZGRNDc359H/b+/eg6I67z6AfwmWy7KIAgGEXfHSyEy84IVIEiSKJmnqm4iOqTFW8P4mNbbRMEZlopPU0Yy3YKuT1DpvqlXpZJK2aZPGS+qgopKAjRiVkEujgMYbUIJcF9jv+8fGLSvLgooust/PzM6wu+fZ57cHZr4853nOOdnZrK+vZ1paWrvm+KZNm8bq6mqeOnWK9957r/17u/rbcfV3Z7VaHfZ3bm4uAfDcuXOsr69nZWUle/fuzT/+8Y+0WCy0WCzMzc1lQUFBm7/ba6s6N27cyLq6Om7atMlhVef8+fM5duxYnj9/no2NjTx69KjDHN+qVauYmJjYYl+sXbuWgwcP5ldffUWr1coTJ044zPGNHTuW5eXlLC4uZkxMDLds2dKu35cnUvDdgnfeeYcxMTGsrq52dykd4vLly3zkkUcYFBTEwMBADho0iL///e/t72/atIn9+/e3r3Bsvrru4MGDjImJYUBAAEeNGsXly5fbg6+xsZGzZ89mYGAgIyIiuGbNGoeASUtLY1hYGA0GA/v27cvly5fb5xULCgo4cuRIGo1GBgUFMS4ursXKzF27dtFsNtNgMHDChAksKyuzv5eens6ePXsyJCSEixYt4iOPPNLu4CPJzZs3MywsjEFBQZw+fTqfeeYZe/C9+uqrBMCAgACHhzPtmeNrztkcn6v9T5IbNmxgWFgYAwMDOXPmTNbV1XHIkOZh9jiBV9oReiQwlUB3At05YsSUFqetHDp0iIMGDWJAQABHjBjhMLeZkZHB0NBQGgwGJiQkMC8vz6Httm3baDabb2pVZ3h4ONesWWN/39XfTlt/d876ab4wprCwkOPHj2doaCiDg4OZlJRk/8ejOWe/288++4zDhw+nn58fhw0b5nD6QE1NDV988UVGRkaye/fuTExMdFjVGRMTY19B21xjYyNXrlzJPn360Gg0Mi4uzr6CHM1WdQYHB/Oll15y+AfL1e/LE+mSZTfp/PnzGD58OD788EPd3FM6rcxM4Lnn4HBdzhvh72+7kLXR2Pa2IncLzfHdBJKYM2cO5s+fr9CTTm3yZMDP7+ba+vsDs2Yp9KTrUfDdhLfeegv/+c9/kJ6e7u5SRFzy9QX27rVdePpG291/P/DGG7enLhF30qHOG/Tll19i1KhROHLkCAYMGODuckTa5ehR4Kc/tV2AuqnJ9bYGAxAbC+zeDQQF3Zn6RO4kjfhuQENDA1JSUvDaa68p9OSu8vDDQH4+MHOm7RCmsxGg0QhERQGrVwMHDij0pOvSiO8GvPbaa8jJycHu3bt1Xozcta5eBXbssI3oysr+ewf2WbOApCTdgFa6PgVfO+Xm5uKpp57C8ePHWz15VkREOj8d6myHmpoapKSkYNOmTQo9EZG7nEZ87bBgwQJUVFRg586d7i5FRERuUcurs4qDvXv34oMPPnC4DqKIiNy9NOJzoby8HEOGDMH27dsxbtw4d5cjIiIdQMHXCpKYOnUqIiMjkZGR4e5yRESkg+hQZyv+9Kc/4eTJk9i2bZu7SxERkQ6kEZ8TJSUlGDFiBPbs2YPhw4e7uxwREelAOp3hOlarFbNmzcKLL76o0BMR6YIUfNfZtGkTampqsGTJEneXIiIit4EOdTZTUFCA0aNHIycnBz/+8Y/dXY6IiNwGGvH9wGKxICUlBatWrVLoiYh0YQq+H6xcuRK9evXCvHnz3F2KiIjcRjqdAUBOTg62bt2K/Px83XVBRKSL8/gRX1VVFVJTU/Hmm28iIiLC3eWIiMht5vGLW55//nnU1dXpRHUREQ/h0Yc6//GPf2DPnj26ALWIiAfx2BFfaWkpYmNjkZmZidGjR7u7HBERuUM8MvhI4umnn0a/fv2wbt06d5cjIiJ3kEce6tyxYwe+/vprZGZmursUERG5wzxuxFdUVIS4uDj885//RGxsrLvLERGRO8yjTmewWq2YMWMGFi9erNATEfFQHhV8GRkZsFqtSEtLc3cpIiLiJh5zqPPUqVNISkpCbm4u+vbt6+5yRETETTxixFdfX4/p06djzZo1Cj0REQ/nESO+pUuXorCwEH/96191LU4REQ/X5U9nOHz4MLZv344TJ04o9EREpGsf6rx69SpSU1OxZcsWhIWFubscERHpBLr0oc65c+fCy8sLW7dudXcpIiLSSXTZQ51/+9vfkJWVhfz8fHeXIiIinUiXHPFdvnwZsbGxeO+995CQkODuckREpBPpcsFHEhMnTsT999+P119/3d3liIhIJ9PlDnW+/fbbKCoqwrvvvuvuUkREpBPqUiO+b7/9FvHx8cjKysKgQYPcXY6IiHRCXeZ0hqamJqSmpmLZsmUKPRERadVdc6jzwgXg88+B778H/PwAkwkYNgy4dk76+vXr4ePjg4ULF7q3UBER6dQ69aFOEsjKAtatAw4cAHx9AasVuOceoKkJCAkBXn4ZiI39HJMmPYpjx46hd+/e7i5bREQ6sU4bfKWlwE9+Anz1FVBV1fp2AQFAXV0NFi06gnXrHrtzBYqIyF2pUwbf5ctAXBxw6RJgsbSvjb8/sGMHMHny7a1NRETubp1ucUtDAzBuHHDxImCxvAUgHIARQJnLdrW1QEoKkJd3J6q8Ma+88gpCQ0MRERGBs2fPwsvLC42NjTf1WUeOHMF9990Ho9GI999/3+W2t9rX7dB8X4iIuIPL4DMajYiJibE/X716NYxGo/3h7++Pe+65B6WlpW12NGvWLHh5eeGbb74BYLtH3pw5cxAdHY3AwEAMGzYMu3fvxt//Dpw9CzQ0NAB4CcA+AFUAmgA8CyASQBCABACfOvRRWwssWfLf5/n5+UhMTERQUBBMJhN+/etf29+7FgrNv8/KlSvb/B432q6kpAQbNmxAQUEBLl682ObnNzd27NgWwbVixQosWLAAVVVVmDhxIs6fP4/k5GQEBwfDZDLhd7/73Q314aqv1mzfvh0jRoxA9+7dYTKZ8PLLL7er3a3sCxGRjuJyVWfVdZNr6enpSE9Ptz9/9dVXcejQIYSGhrrs5PDhw/j3v//t8FpjYyPMZjMOHjyI3r1746OPPsKUKVPQr99JVFX1AXAJQB2AgdeqAfAAgDcAhAH4PwD/A+AsbCNCm5wcW3D26QNMmzYNkyZNwoEDB3D27FmMGjUKQ4cOxYQJE+zbV1RUoFu3G1/c2t52RUVFCAkJueG7Q+zatctpmBQVFWHgwIH259OnT7dfnq2goABJSUmIiYlBUlLSLffVmpqaGmzcuBHx8fG4cuUKJkyYgPXr12Pp0qUu293svhAR6VC8SVarlf369eO2bdtcbtfQ0MChQ4fyxIkTBMCvv/661W0HDBhMH5/3CHxJwEAABAIIJNG2xvP6RyCBYw6v+fiQaWm2z/P39+fp06ftn//0009z9erVJMkzZ84QABsaGpzWUlZWxpkzZ7JXr17s0aMHk5OT29WuuY8//ph+fn708vJiQEAAZ8yYYW+/ZcsW9urVixEREVy/fr1Du4qKCt53333Myclx6Ktfv3708vKin58fAwICWFFRQQC8fPmyve28efM4ffp0h1pvpi+SPHXqFB999FH27NmTYWFhXLVqldPvuWHDBj755JP258XFxZw0aRJDQ0MZHBzMF154wem+IMns7Gw+9NBDDAoKoslk4h/+8Ic296uIyK1wGXwPP/wws7KynL538OBBBgQE8OrVqy47WLt2LX/1q1/ZOnMRfBcvXmS3br708/vihxA780PwNbQSescJ+BKoaPHeoEG2z1y2bBmXLFlCi8XCwsJCRkVFMTc3l+R/QyEyMpJRUVGcOXMmr1y5Yq9n/PjxnDJlCsvLy2mxWHjgwIF2tbteVlYWo6Ki7M+vtZ86dSqrqqr4+eefMzQ0lB9//LF9m/nz5/ONN95wGrLR0dH2bSsrKwmAly5dsr8/d+5cDh069Jb7qqystAdlbW0tKysr+cknnzj9jsnJyVyyZAlJsrGxkUOGDOHChQtZVVXF2tpaZmdnO90XRUVFNBqNzMzMpMViYWlpKY8fP97qvhQR6Qgug2/btm00Go385ptvWrw3e/Zs+3/trSkuLmb//v1ZUVFh66yV4LNYLBw3bhzj4/+XP/oR2xF83xMYRGC101A0m22fe+TIEfbv35/e3t4EwBUrVtj7vHr1KvPy8tjQ0MCLFy9y8uTJfPzxx0mS3333Hb28vFheXt6iVlftnGkt+L744gv7a4sXL+bs2bNJknl5eYyNjWVDQ0ObwUeSCQkJXLBgAWtra/mvf/2LPXv25IABA265r8zMTHuAuvL2228zKirKHv5Hjx5laGio0xHx9fti9erVnDhxYpt9iIh0JJeLW2bMmIGEhAR89NFHDq/X1tbi3XffxYwZM+yvZWdn2xd7XJuDWrhwIVasWIGgoKBW+7BarUhJSYGPjw+mTNmMe9pcZ1oL4CkADwJY1uz1gbDN9RnR2JiN8vJyPPHEE1ixYgXq6upQUlKCvXv34s033wRgW7gTFxeHbt26ITw8HJs3b8a+fftQWVmJkpISBAcHo2fPni16d9XO2T5ojdlstv8cHR2N7777DlarFfPnz8dvfvObds877tq1C2fOnIHZbMYvfvEL/PznP4fJZLrlvkpKStC/f3+Xfb///vtYunQpdu/ebZ/nLSkpQXR0dLvqb08fIiIdrc2Y8fLyAq871e8vf/kLgoODMWbMGPtriYmJqKqqQlVVFU6fPg0A2L9/PxYvXoyIiAj78vWHHnoImZmZAGy3EJozZw4uXbqEP//5z4iK+hF8fFxVUw9gIoAoAFuue+80bAtgqtC3byK+/fZbeHt7IzU1Fd26dYPJZMLUqVNbhHjz73mtJrPZjPLyclRUVLS1exzaOdsHrSkpKbH/XFxcjMjISFRWVuLYsWN45plnEBERgQceeAAAYDKZkJ2d7fRzoqOj8eGHH+LKlSv49NNPUVZWhpEjR95yX2azucWCpOb27NmDefPm4YMPPsDgwYPtr5vNZhQXF7drsUxbfYiI3BauhoM7d+6kwWBgYWGhw+uPPfYYly9f3uZw8tKlS7xw4YL9AYA5OTmsqakhST733HOMj4+3zxNWVpL+/q0d6rQQeJJAsot5P9JoJLdvJ7///nsGBQVx165dbGpq4oULF/jggw8yPT2dJPnJJ5+wsLCQTU1NLC0t5ZQpUzhmzBh77ePHj+ezzz5rn+M7ePBgu9pdr7VDndOmTWN1dTVPnTrFe++9l3v37qXVanXYX7m5uQTAc+fOsb6+nmTLQ50FBQWsrKxkfX09d+zYwZCQEPtil1vp69ocX0ZGBuvq6hzm+Pbv38/g4GD7Pmnu2hxfWlqafY7v8OHDTvfFtTm+d955hw0NDZrjE5E7wmXwxcfHc9++fQ6vnTt3jt7e3i5XZ7baWbM5vrNnzxIAfX19GRAQYH+MHr2T3t7Ogu/AD8/9f1jpee1xqEXw1dba+tu/fz/j4uLYvXt3hoeHc+7cuayuriZpm8Pq06cPDQYDIyIimJKSwgsXLthrLSsrY2pqKsPCwtijRw9OmjSpXe2u11rwXVtpGR4ezjVr1jht2545voyMDIaGhtJgMDAhIYF5eXkd1tfJkyc5duxY9ujRg+Hh4Xz99ddJkmPGjKG3t7fD7+2JJ56wtysqKmJycjKDg4MZEhLCX/7yl073BUkeOnSII0eOZGBgIE0mU5urhEVEblWnu2TZ6dPAAw/YTka/Ub6+wPPPAxs3dnxdIiLSNXS6S5YNHGi744LBcGPtunUDoqOBdlx8RUREPFinG/EBtoOWixYBW7cCNTVtb+/ra7s/X3Y20KvX7a9PRETuXp1uxAfYbi67cSPw298C4eGA0eh8O4PBdlPan/0M+OwzhZ6IiLStU474mrNagX37gLVrgfx8oLoa8PEBwsKAF14AZs0CnJxuJyIi4lSnDz4REZGO1CkPdYqIiNwuCj4REfEoCj4REfEoCj4REfEoCj4REfEoCj4REfEoCj4REfEoCj4REfEoCj4REfEoCj4REfEoCj4REfEoCj4REfEoCj4REfEoCj4REfEoCj4REfEoCj4REfEoCj4REfEoCj4REfEoCj4REfEoCj4REfEoCj4REfEoCj4REfEo/w8FZrU5T7gmoAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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358dn8/ppIiurnN/lGzoU+M9/cn6fryRi4SMiKsO0Wi0OHToEtVqN4OBgjBgxAp6enmjatOlLbU8ECArK6f48cyan+9PcHKhcGZg6FRg/vmT/CC3AwkdEVCbFx8dj/fr1WLNmDRQKBZRKJUaNGqV3oX95VfAl/kREVGo8PVenVquxa9cuDBgwAJs3b0a7du307mRT3rHFR0RUyiUnJ2Pz5s1Qq9VIT0+Hp6cnxo8fDwcHB0NHK5FY+IiISqnz589DpVLB398fPXv2hFKp1P0ILeWPXZ1ERKVIWloa/P39oVKpEBMTg8mTJ+PChQuoVq2aoaOVGmzxERGVAleuXIFarYavry/atm0LT09P9O7du9BfY6DceMSIiEqorKws7Ny5EyqVCpGRkfjggw9w8uRJ1KlTx9DRSjUWPiKiEubOnTtYu3Yt1q1bh3r16sHT0xODBw+GhYWFoaOVCSx8REQlQHZ2Ng4cOACVSoXw8HCMHj0aBw8e1PudQSoeLHxERAZ0//59+Pj4YM2aNXB0dIRSqcTWrVthbW1t6GhlFgsfEdEbJiIIDQ2FSqXCvn37MHjwYPz0009o3bq1oaOVCxzVSUT0hjx69AibNm2CWq2GVquFUqnE2LFjUbFiRUNHK1fY4iMies3OnDkDlUqFgIAAuLu7Y/Xq1ejatSsvNDcQFj4iotcgNTUV27Ztg0qlwoMHDzBlyhRcunQJVapUMXS0co9dnURExejPP//EmjVrsHnzZnTs2BFKpRLu7u4wMTExdDT6C1t8RESvKDMzEz///DPUajUuX76MiRMn4uzZs3B1dTV0NMoDW3xERC/p1q1b8PLygo+PDxo3bgylUokBAwbA3Nzc0NGoAGzxERG9gOzsbOzduxdqtRonTpzA2LFjERwcjIYNGxo6GhURCx8RURHcu3cP3t7e8PLyQtWqVaFUKrF9+3ZYWloaOhq9IBY+IqJ8iAiCgoKgUqlw6NAhDB06FDt27EDLli0NHY1eAc/xERE9JzExERs3boRarYaZmRmUSiXGjBkDOzs7Q0ejYsAWHxERclp3J0+ehEqlwo4dO9C3b1+sW7cOnTp14oXmZQxbfERUrqWkpGDLli1Qq9VISkrClClTMGHCBFSqVMnQ0eg1YeEjonIpMjISKpUKW7duhZubGzw9PfH3v/8dxsbGho5Grxm7Oomo3MjIyMD27duhUqlw8+ZNTJo0Cb///jtcXFwMHY3eILb4iKjMu379OtasWYMNGzagefPmUCqV6Nu3L8zMzAwdjQyAbXoiKpM0Gg127NgBd3d3tG/fHiKC8PBwHDhwAIMGDWLRK8fY1UlEZUpMTAy8vb2xdu1auLq6QqlUYufOnahQoYKho1EJwcJHRKWeVqvF4cOHoVKpEBwcjBEjRmDv3r1o2rSpoaNRCcRzfERUasXHx2PDhg1Ys2YNrK2toVQqMWrUKNjY2Bg6GpVgbPERUakiIjh27BjUajV27dqFAQMGYNOmTWjXrh0vNKciYYuPiEqF5ORk+Pn5QaVSIT09HZ6enhg3bhwcHR0NHY1KGRY+IirRzp8/D7VajR9//BE9e/aEUqlE9+7deaE5vTR2dRJRiZOWlgZ/f3+o1WpER0dj8uTJuHDhAqpVq2boaFQGsMVHRCXGlStXoFar4evrizZt2kCpVKJ3794wNeW/0an48NNERAaVlZWFnTt3Qq1W448//sCECRNw8uRJ1KlTx9DRqIxi4SMig4iKisLatWvh7e2NevXqwdPTE4MHD4aFhYWho1EZx8JHRG9MdnY2Dhw4AJVKhbCwMIwePRoHDx5EkyZNDB2NyhEWPiJ67e7fvw8fHx+sWbMGDg4OUCqV2Lp1K6ytrQ0djcohFj4iei1EBKGhoVCpVNi3bx8GDx4Mf39/tGnTxtDRqJzjqE4iKlZJSUnw9fWFWq2GVquFp6cn3n//fVSsWNHQ0YgAsPARUTE5c+YMVCoVAgIC4O7uDk9PT7i5ufE2YlTisKuTiF5aamoqtm3bBrVajfv372PKlCm4dOkSqlSpYuhoRPlii4+IXtjFixehVquxefNmdOzYEZ6envDw8ICJiYmhoxEVii0+IiqSzMxM/PLLL1CpVLh8+TImTpyIs2fPwtXV1dDRiF4IW3xEVKBbt27By8sLPj4+aNy4MTw9PTFw4ECYm5sbOhrRS2GLj4hyyc7ORmBgIFQqFU6cOIGxY8ciODgYDRs2NHQ0olfGwkdEOvfu3cO6devg5eUFZ2dnKJVK/PTTT7CysjJ0NKJiw8JHVM6JCIKDg6FSqXDw4EEMHToUv/zyC1q2bGnoaESvBc/xEZVTDx8+xMaNG6FWq2FqagqlUokxY8bAzs7O0NGIXiu2+IjKERHBqVOnoFKp8Msvv6BPnz7w9vZGp06deKE5lRts8RGVAykpKdi6dStUKhWSkpIwZcoUTJgwAZUqVTJ0NKI3joWPqAyLjIyEWq3Gli1b0LVrVyiVSvz973+HsbGxoaMRGQy7OonKmIyMDAQEBEClUuHGjRv48MMP8fvvv8PFxcXQ0YhKBLb4iMqI69evw8vLC+vXr0ezZs2gVCrRr18/mJmZGToaUYnCFh9RKabRaLB7926o1WqcOXMG48aNQ3h4OOrVq2foaEQlFgsfUSkUExMDb29vrF27FjVr1oRSqcSOHTtQoUIFQ0cjKvFY+IhKCa1Wi8OHD0OtVuPIkSMYMWIE9uzZg2bNmhk6GlGpwnN8RCVcQkICNmzYALVaDSsrKyiVSowePRo2NjaGjkZUKrHFR1QCiQiOHz8OtVqNX3/9Ff3794evry/at2/PC82JXhFbfEQlyOPHj7F582ao1WqkpqbC09MT48ePh6Ojo6GjEZUZLHxEJUBERATUajV+/PFH9OjRA56enujRowcvNCd6DdjVSWQgaWlp+Omnn6BWqxEVFYVJkyYhMjIS1apVM3Q0ojKNLT6iN+zq1atQq9Xw9fVF69at4enpiT59+sDUlP8OJXoT+H8a0RuQlZWFX3/9FWq1GhEREZgwYQJOnDiBOnXqGDoaUbnDwkf0GkVFRWHt2rXw9vbG3/72N3h6euK9996DhYWFoaMRlVssfETFTKvV4sCBA1CpVAgNDcXo0aNx4MABvPXWW4aORkTgOT6iYvPgwQP4+PhgzZo1sLe3h1KpxMiRI6FQKAwdjYiewRYf0SsQEYSFhUGlUiEwMBCDBg3Ctm3b0KZNG15oTlRCscVH9BKSkpKwadMmqNVqaDQaKJVKvP/++6hYsaKhoxFRIVj4iF7A2bNnoVKpsH37drz77rvw9PREt27d2LojKkXY1UlUiNTUVPz4449QqVSIi4vDlClTcPHiRTg7Oxs6GhG9BLb4iPJx6dIlqNVqbN68Ge3bt4enpyd69eoFExMTQ0cjolfAFh+VTkmXgCe3gewngJktYNsIsKr+ypvNzMzEjh07oFKpcPHiRUycOBGnT59GrVq1Xj0zEZUILHxUemSnA3e2A38uBVKuA8bmgAhgZARkZwCVOgGN5wDO7wBGL3Zz51u3bmHt2rXw8fFBw4YNoVQqMXDgQJibm7+mnSEiQ+Gt36l0SDgF/FIdOKUEkiKB7DQgKwnQJOf8V5sOxB0GQt8D9jQBUmNzbeLGjRuYNm0anvbuZ2dnY8+ePejbty9atWqFJ0+e4MiRIwgKCsKwYcNY9IjKKJ7jo5Lv/lEgqBeQnVq05Y1MAfOKgMdpwLomgJxfMW/evDnu3buHgIAAREZGwsvLC5UrV4ZSqcTw4cNhZWX1GneCiEoKtvgol3/+858YM2bMa38dEcGECRNQsWJFtG3bFsHBwXBxcdFf6PE1ILhv0YseAIgGyEwEDnUDNE+Qnp6Od955B/fu3YNGo8GQIUNw8+ZNBAQE4OTJk5gwYcIrF73x48dj/vz5+c6/fPkyWrRoARsbG/zwww+v9FrF6datWzAyMoJGoynS8iqVClWqVIFCoUBCQsJrTvc/eX42iF5SmS5827ZtQ6NGjWBtbY26desiNDQUQM7w9KlTp8LJyQl2dnbo2rWrbp1evXpBoVDoHubm5nj77bd182vVqgVLS0vd/HfffVc3b+PGjWjVqhVsbW3h4uKCOXPm6H2hjBkzBlWrVoWtrS3q168Pb29vvbxPb2SsUCjg4eGB2Nj/ddcVlut53333Hd566y3Y2Nigdu3a+O677/TmF7Qfr0NISAiMjIz0ikNYWBgOHjyI6OhonDx5Mu8Vf1+oV/TO3gS6fgkoPgCqKIGV+/J5QckG0u9Dbviibdu2OH/+vN578fXXX6NVq1b55s3IyICnpyeqVKkCBwcH9OvXDzExMS+0z8/69ttv0a1bNzx+/BgfffQRRARz586Fo6MjHB0dMWfOHOTV+bJx40YYGRnpfVYKe28BYOXKlahduzasra3RqFEjXLly5YXyJiYmolKlSujcubNuWlZWFv7xj3/gwIEDSElJgaOjI27duoXu3bvDysoKDRs2xKFDh/LcXo8ePXIV2Df9GSR6qswWvoMHD2Lu3LlYv349Hj9+jKNHj+p+Amby5MlITEzExYsXkZiYiH//+9+69QIDA5GSkqJ7dOzYEUOHDtXb9q5du3TzDxw4oJuempqKFStWID4+HidOnMDhw4fx/fff6+Z//vnnuHXrFpKTk/Hrr79i/vz5OHPmDICcwvDFF19g586dSExMRO3atTFy5MgXyvUsEYGvry8ePnyIffv2YdWqVdi2bVuR9qO4ZWVl4eOPP0a7du30pt++fRu1atWCtbV13itmPgSif84pYgDiHwMe3wJTegIJa4Bry4F386/9OSM+L34HY2MjNGnSBPXr10flypVhZmaGs2fPFph55cqVOH78OH7//XfExsbC3t4eM2bMeJHd1nP79m00adJE99zLyws7duxAREQEfv/9d+zevRtr1qzRW+fhw4f4+uuv9dYDCn9vvb29sW7dOuzZswcpKSnYvXs3nJycXijv3Llz0ahRI71pcXFxSE9P18szcuRItGjRAgkJCVi8eDGGDBmCBw8e6K3n5+eXb4vyTX0GifRIGdWhQwfx9vbONf3SpUtiY2MjSUlJhW7j5s2bYmxsLDdu3NBNc3V1lYMHDxYpw7Jly6Rv3755zrt06ZI4OzvLjz/+KCIis2bNkqlTp+rmx8TECAC5du1akXIVZsaMGTJ9+vQi7cfChQvlvffek2HDholCoZAWLVrI+fPndfPv3LkjgwYNEicnJ3FwcJBp06YV+Npff/21zJ49W8aNGyfz5s0TERFvb2+xsLAQY2Njsba2lgULFkhQUJBUr15dFi9eLI6OjuJaraJsnm4u4gcRP8jn/SFjOkH3vLDH7k8hzWsZi43CSlxcXGThwoV6uYYMGSJVqlQRW1tb6dKli0RGRurmeXp6yuzZs3XPd+/eLfXr19c9P3v2rLRo0UIUCoUMGzZMhg8frtu353Xv3l2MjY3FwsJCrK2t5fLly9KhQwdZs2aNbhlvb29p166d3npTpkyR//73v+Lm5iZr167N9/g++95mZ2eLi4uLHDp0KM9lNRqNzJo1SxwdHaV27dqyatUqASBZWVm6ZY4dOybt27cXHx8f6dSpk4iIXL58WaysrASAWFtbS/fu3eXy5ctibm4uycnJunU7d+4sKpVK9/zRo0dSr149OX78eK7XKegzmJqaKuPGjRN7e3tp1KiRfPvtt1K9evV8jwHRiyiTLb7s7GycPn0aDx48wN/+9je4uLhg+vTpSEtLw4kTJ+Dq6oqFCxfCyckJb7/9NgICAvLcjq+vL7p06YLatWvrTR89ejQqVaqEd999FxEREfnmOHr0aK5/rU+dOlXXLVS1alX07t0bQM6/4uWZrq6nf0dGRhY5V35EBKGhobmyFLQfO3fuxNChQ5GYmIhRo0Zh4MCByMrKQnZ2Nvr27QtXV1fcunULMTExGDFiRL6vffv2bfj4+GDBggV60ydOnAi1Wo0OHTogJSUFixYtAgDcu3cP8fHxiImJwcZZtTF5bSYu/9Xj+9s1wEEBdPwnUFkJ9PseuBOf/35bWwC+nkZ4dGwe9uzZA5VKhR07dujm9+rVC1evXsX9+/fRsmVLjB49Wi9feHg4YmNjkZqaCj8/P/Tq1QtAzrV+A3Z02qoAACAASURBVAcOxNixY5GYmIihQ4fm+xkCgCNHjqBLly5YtWoVUlJSUL9+fVy4cAHNmjXTLdOsWTNcuHBB9/zkyZM4ffo0PD09899B5H5vo6OjER0djcjISNSoUQO1a9fGwoULodVqAQBr167F7t27ce7cOZw+fRrbt2/X2152djamTZuGVatW6d2G7WlmAHj06BGOHDmCCxcuoE6dOrCxscl3P7744gsolcp873KT32dw0aJFuH79Oq5fv479+/dj48aNBR4Hohdi0LL7mjxtLbVq1UpiY2PlwYMH0rFjR/niiy9k8eLFAkAWLlwoGRkZEhwcLNbW1vLnn3/m2k7dunVl/fr1etPCwsIkNTVVnjx5IkuWLJEqVarIw4cPc63r4+Mj1atXlwcPHuSap9FoJDQ0VP71r39JZmamiIgcOnRIHB0dJSIiQlJTU2Xy5MliZGQkW7ZsKVKugixYsECaNm0q6enpRdqPhQsX6rU+srOzxdnZWY4ePSrHjh0TJycnvX+5F6R///6ybds2ERG9Fp+IyPr163UtChGRoKAgMTExkZSUlJwJe1vI0HaQL4fktODqOUPsrCAnv4SkrYfMeBfSsV4RWn9n/iEiIh9//LHMnDkzz5wPHz4UAPLo0SMREUlKSpIRI0YIADExMZHmzZtLQkKCiIiEhIRI1apVRavV6tbv0KFDvi0+EcnVajM2NpaLFy/qnl+5ckUAiFarFY1GI61atZJjx47lue6znn9vw8PDBYD07t1bHj58KDdv3pR69eqJl5eXiOS0Pp9tke3fv1+vJbZ8+XLx9PTM8/25efOm3rK+vr65WqlffPGFjBs3TkRETp06Jc2aNZOsrKxc64oU/BmsXbu2BAYG6pZds2YNW3xUbMpki8/S0hIAMGPGDFStWhVOTk74xz/+gb1798LS0hJmZmaYP38+zM3N4ebmhu7du+c6vxAWFoZ79+5hyJAhetM7deoES0tLWFlZ4fPPP4e9vb1u0MxTO3bswGeffYbAwMA8z62YmJigc+fOiI6OhkqlAgD07NkTixYtwnvvvQdXV1fUqlULNjY2uUay5ZVryZIlugECz7cQVq1aBV9fX+zZs0fvV78L248aNWro/jY2NoaLiwtiY2MRFRUFV1dXmJrmvvdBkyZNdDlCQ0Oxa9cuPH78GMOHD8+1bH4qVqz4v3N+xuZwdQJiH+Y8tTQHBrUG2tQFKpgDCwcDx64CSanAkp05A14UHwCe63KWP3EN6P4VUKmnCnZ2dlCr1YiPz2kiZmdn47PPPkPdunVha2uruzPL0/lKpRLp6elISEjAkydPMHjwYF2LLzY2FtWrV9drEbm6uur+fnYgkp+fX577qVAokJycrHuenJwMhUIBIyMjrF69Gk2bNkWHDh0KPFZ5vbdPP/tz5syBvb09atWqhSlTpmDv3r267M++t8/mjo2NxQ8//IDFixcX+Lr57cPT/bCxsYFWq8XUqVOxcuXKPD8rQMGfwYJyEr2qMnnnlooVK8LFxSXPO+Y3bdq0SNvYuHEjBg8eXOiPiBoZGel1Ue7btw+TJk3Cnj17Chx1CQAajQbXr1/XPZ82bRqmTZsGALhy5Qq++uqrXL/anVeuL774Al988UWu7fv4+OCbb77B0aNHCx0K/vx+REVF6f7WarWIjo5GtWrVYGpqijt37kCj0eT6Qnu2iwsAZs6cidOnT+u6uZKSkmBiYoI//vgDO3fuzDPHw4cP8eTJk5ziZ1kdd+KBt/76/mtaI+cmLf/LnPNfEeCLATmPZ436LzDd3QyBn36FCk3/gZkzZ+oK25YtW7Bz504cOnQItWrVQlJSEipWrKg7BhEREVi8eDEcHBwA5PwjasGCBYiPj0fVqlURExMDEdF9xu7cuYO6desCyBmIVJgmTZogIiICbdu21b3e0+7Kw4cPIyQkRFesEhMTce7cOZw/fx6rVq0CkP9726BBA5ibm+f7axFVq1bVe2/v3Lmj+/vkyZO4e/cuGjduDABIS0tDWloanJ2d8xzR2qRJE9y4cQOPHz/WdXdGRERg1KhRSE5OxunTp3X/6MnOzhmg5OLigp9++gldunTJtb1nP4NPcz49Js/mJHplBm1vvkb/93//J61bt5a4uDhJTEyUzp07y/z58yUzM1Pq1q0rX375pWRlZUlYWJgoFAq9bqfU1FSxs7OTw4cP623z9u3bEhYWJhkZGZKWlibffvutODk5SXx8vIiIHD58WBwcHCQkJCRXnri4ONm6das8fvxYNBqN7Nu3T6ysrGTHjh0iIpKWliZ//PGHaLVauX37tri5ucnnn3+ut438cuVl8+bNUqVKlTy7cAvbj4ULF4qpqakEBARIVlaWLFu2TFxdXSUzM1M0Go00bdpUZs2aJSkpKZKWliZhYWF5ZkhOTpa7d+/qHsOGDZOZM2fqugzz6+qcNWuWZGRkyNGAb8TKAnLxu5wuy8NfQOytIOcWQzI3QmZ6QDo3yL+Ls5ItZIOnqciTaDlx4oRUqlRJRo8eLSIi//3vf6VZs2aSlJQkKSkpolQqBYBcvXpVRETGjx8vgwcPlkePHklmZqYsXrxYqlWrJiIiGRkZUqNGDVmxYoVkZWVJQECAmJqavlBXp0qlkoYNG0p0dLTExMRI48aNdV2QDx8+1DtuHTp0kGXLlum6YQt6b0VExo4dK3369JHk5GSJioqSBg0a6AZ6rV69Who1aiRRUVGSmJgoPXr00HVBpqen673uihUrpG3btnL37l0Ryd3VKSLSrl07mTVrlqSlpcnPP/8sdnZ2cv/+fdFqtXrbOnnypACQ6OhoycjIKPQzOGfOHOnataskJiZKVFSUvP322+zqpGJTZgtfZmamKJVKsbOzkypVqsiMGTMkLS1NREQiIyOlffv2YmVlJY0aNZKff/5Zb90tW7ZIzZo19c7hPF3v7bffFisrK3FwcJAePXrIqVOndPO7desmJiYmYm1trXt4eHiIiMj9+/ela9euYmdnJzY2NvLWW2/pzruI5HzZPd12lSpV5LPPPhONRlOkXHmpVauWmJqa6mWZMmVKkfbj+VGdzZs3lzNnzujm3759WwYMGCAODg7i6OgoM2bMKDSPSNHO8VWvXl2++uorcXR0lBo1aojvDBu9YrZ6AqRaxZwC2LcF5M4P+Re+nz6C1KxcQRQKhfTp00emTZumK3yPHz+W/v37i0KhkJo1a8rGjRv1Cl98fLyMGjVKKlWqJHZ2dtKpUyc5ceKELuupU6ekefPmulGdw4YNe6HCp9VqZfbs2VKxYkWpWLGizJ49O9/39fl1C3pvRXLOTw4fPlwUCoW4uLjIokWLdNvOysqSmTNnioODg9SqVSvPUZ35vT95Fb6bN2+Km5ubVKhQQerXr5/vKM3n1y3sM/jkyRMZO3as2NnZcVQnFTvesoxKtgtfA5H/yrk354sysQbcfgWcexR/LiIqtcrk4BYqQxp+Atg2ghiZvdh6JlZArZFAle6vJxcRlVosfFSymVTA7rSPcTFWIMYViriONVCtD9BGrT8ahogILHxUwoWGhuIDz0+R4RYCo9pjAZMKOYUtL6Y2gJkd8Pb/AZ1/BIz5S+lElBvP8VGJFRkZiZ49e8LPzw/vvPNOzsSsZOCGL3B5JZAWC2gzc4qhbcOcH6F1GQgYv2C3KBGVKyx8VCLduXMHnTp1wnfffVfgLdGIiF4UuzqpxElISIC7uztmzZrFokdExY4tPipRUlNT0bNnT3Tt2hVLly41dBwiKoNY+KjE0Gg0GDRoEBwcHLBhw4Z8b7tFRPQq2NVJJYKIYMqUKcjOzoa3tzeLHhG9NmXyJtVU+syfPx+RkZE4cuQIzMw4KpOIXh8WPjK4//znP9i+fTvCw8P/95NERESvCQsfGZS/vz+WLl2KsLCwPH+7kIiouHFwCxlMUFAQRowYgYMHDxb5dxKJiF4VW3xkEOfPn8fw4cPh7+/PokdEbxRHddIbd/PmTfTp0werV69Gt27dDB2HiMoZFj56ox48eAB3d3fMmzcPQ4YMMXQcIiqHeI6P3piUlBT06NED7u7u+Ne//mXoOERUTrHw0RuRmZmJ/v37w8XFBWvXruUF6kRkMCx89NpptVqMGzcOSUlJ+Pnnn2FqyjFVRGQ4/Aai127u3Lm4fv06Dh06xKJHRAbHbyF6rZYvX449e/YgLCwMVlZWho5DRMTCR6+Pn58fVqxYgfDwcDg4OBg6DhERAJ7jo9fkwIEDGDt2LI4cOYImTZoYOg4RkQ5bfFTsTp8+jTFjxuDnn39m0SOiEocXsFOxunr1Kvr374+1a9eic+fOho5DRJQLCx8Vm3v37sHDwwOLFi3CgAEDDB2HiChPLHxULJKTk9GrVy+MGzcOkyZNMnQcIqJ8cXALvbKMjAz07t0b9evXx+rVq3lXFiIq0Vj46JVotVqMHDkSGo0G/v7+MDExMXQkIqICcVQnvTQRwSeffIJ79+5h//79LHpEVCqw8NFLW7p0KYKCgnD06FFUqFDB0HGIiIqEhY9eyoYNG6BWq3Hs2DHY29sbOg4RUZHxHB+9sD179mDixIkIDg5Gw4YNDR2HiOiFsPDRCzlx4gT69u2LXbt2oX379oaOQ0T0wngdHxXZ5cuXMWDAAGzYsIFFj4hKLRY+KpLY2Fh4eHjgm2++QZ8+fQwdh4jopbHwUaEePXoEDw8PTJkyBePHjzd0HCKiV8JzfFSg9PR0uLu7o3nz5lixYgXvykJEpR4LH+UrOzsbw4YNg6mpKbZu3QpjY3YQEFHpx+v4KE8ighkzZuDRo0fYu3cvix4RlRksfJSnr776CsePH0dISAgsLCwMHYeIqNiw8FEua9euxfr163Hs2DHY2toaOg4RUbHiOT7Ss3PnTnh6euLo0aOoV6+eoeMQERU7Fj7SCQ8Px6BBg7B37160bt3a0HGIiF4LjlggAMCFCxcwePBgbN68mUWPiMo0Fj5CVFQUevXqheXLl+Pdd981dBwioteKha+cS0xMhLu7Oz7++GOMHj3a0HGIiF47nuMrx1JTU/H3v/8dHTp0wPfff2/oOEREbwQLXzml0WgwePBg2NrawtfXlxeoE1G5wW+7ckhE4OnpiYyMDPj4+LDoEVG5wgvYy6EFCxYgIiICQUFBMDc3N3QcIqI3ioWvnFm9ejV+/PFHhIWFQaFQGDoOEdEbx8JXjmzfvh2LFy9GaGgoKleubOg4REQGwcEt5URwcDCGDRuG/fv3o0WLFoaOQ0RkMBzVUA5ERERg2LBh2LZtG4seEZV7LHxl3K1bt9CnTx+sWrUKPXr0MHQcIiKDY+Erwx48eAB3d3fMnTsXw4YNM3QcIqISgef4yqgnT56gR48e6NmzJ5YsWWLoOEREJQYLXxmUlZWFAQMGwNnZGevWrYORkZGhIxERlRgsfGWMiGD8+PFISEjAjh07YGrKK1aIiJ7Fb8Uy5rPPPsOVK1dw+PBhFj0iojzwm7EMWbFiBXbu3ImwsDBYWVkZOg4RUYnEwldGbN26FcuWLUNYWBicnJwMHYeIqMTiOb4y4ODBgxg9ejQOHz6Mt99+29BxiIhKNLb4SrkzZ85g1KhRCAgIYNEjIioCXsBeil2/fh39+vXDmjVr0LVrV0PHISIqFVj4Sqm4uDi4u7tjwYIFGDx4sKHjEBGVGjzHVwo9fvwY3bp1Q9++fbFo0SJDxyEiKlVY+EqZzMxM9OnTB3Xq1IFareZdWYiIXhALXymi1WoxZswYpKamYvv27bxAnYjoJfCbs5QQEXz66aeIiorCgQMHWPSIiF4Svz1Lie+//x4HDhxAaGgoLC0tDR2HiKjUYuErBTZt2oRVq1YhPDwcFStWNHQcIqJSjef4Srh9+/Zh/PjxCAoKQqNGjQwdh4io1GOLrwQ7efIkxo4di507d7LoEREVE17AXkJduXIFAwYMgI+PDzp27GjoOEREZQYLXwl09+5duLu746uvvkK/fv0MHYeIqExh4SthkpKS0KtXL3z44YeYOHGioeMQEZU5HNxSgqSnp6NXr15o0qQJ/vOf//CuLERErwELXwmRnZ2NESNGAAC2bdsGExMTAyciIiqbOKqzBBARfPzxx4iPj0dgYCCLHhHRa8TCVwIsWbIEYWFhCAkJQYUKFQwdh4ioTGPhM7B169Zh3bp1CA8Ph52dnaHjEBGVeTzHZ0C7du3C5MmTERISgvr16xs6DhFRucDCZyDHjh3DgAEDsGfPHrRt29bQcYiIyg1ex2cAf/75JwYNGoRNmzax6BERvWEsfG9YdHQ0evXqhe+//x4eHh6GjkNEVO6w8L1BDx8+hIeHB6ZPn46xY8caOg4RUbnEc3xvSFpaGt599120adMGy5Yt411ZiIgMhIXvDdBoNBgyZAisra2xadMmGBuzoU1EZCi8ju81ExFMnToVqamp8Pf3Z9EjIjIwFr7XbNGiRTh79iyCgoJgbm5u6DhEROUeC99rpFar4efnh/DwcNjY2Bg6DhERgef4XptffvkF06dPx9GjR1G3bl1DxyEior+w8L0GR48exZAhQ7Bv3z60bNnS0HGIiOgZHGlRzP744w8MHToUW7ZsYdEjIiqBWPiK0e3bt9G7d2+sXLkS77zzjqHjEBFRHlj4iklCQgI8PDwwa9Ys3S+pExFRycNzfMXgyZMneOedd9C1a1csXbrU0HGIiKgALHyvKCsrC4MGDYKjoyM2bNjAW5EREZVw7Op8BSKCKVOmQKvVwtvbm0WPiKgU4AXsr2DevHm4cOECjhw5AjMzM0PHISKiImDhe0n/+c9/EBAQgPDwcFhbWxs6DhERFREL30vw9/fH0qVLERYWBicnJ0PHISKiF8DBLS/oyJEjGDFiBA4dOoSmTZsaOg4REb0gDm55AefOncOIESPg7+/PokdEVEqx8BXRjRs30LdvX6xevRrdunUzdBwiInpJLHxFcP/+fbi7u2PevHkYMmSIoeMQEdEr4Dm+QqSkpKB79+7w8PDAv/71L0PHISKiV8TCV4DMzEz069cPNWvWhJeXFy9QJyIqA1j48qHVavH+++/j8ePHCAgIgKkpr/wgIioL+G2ejzlz5uDmzZs4ePAgix4RURnCb/Q8LFu2DIGBgQgNDYWVlZWh4xARUTFi4XvO5s2bsXLlSoSHh8PBwcHQcYiIqJjxHN8z9u/fj/fffx9HjhxBkyZNDB2HiIheAxa+v5w6dQp9+vTBL7/8gk6dOhk6DhERvSa8gB3A1atX0b9/f3h7e7PoERGVceW+8N27d093cXr//v0NHYeIiF6zcl34kpOT0atXL4wfPx4ffvihoeMQEdEbUG7P8WVkZKB3795o2LAhVq1axbuyEBGVE+Wy8Gm1WowcORLZ2dn48ccfYWJiYuhIRET0hpS76/hEBJ988gni4uKwb98+Fj0ionKm3BW+pUuXIigoCEePHkWFChUMHYeIiN6wclX4NmzYgDVr1iA8PBz29vaGjkNERAZQbs7x7dmzBxMnTkRISAgaNGhg6DhERGQg5aLw/fbbb+jfvz927dqFdu3aGToOEREZUJm/ju/SpUsYOHAgNm7cyKJHRERlu/DFxMTAw8MDS5cuRa9evQwdh4iISoAyV/hu3rwJAHj06BE8PDygVCoxbtw4A6ciIqKSokyd47t48SIaN26M//u//0NwcDBatmyJf//737wrCxER6ZSpyxkCAwNhbm6OJUuWoFatWjhy5AiLHhER6Sk1Lb67d4HffweSkoAKFQAXF6BFC+DZutahQwf89ttvAAAzMzMMHToUfn5+BkpMREQlUYlu8YkAQUHAd98BwcGAhQWg1QLGxkB2NuDoCMyZA4wdC5iapuHkyZMAACsrKzg7O8PDw8OwO0BERCVOiW3xxccD7u7AlStASkr+y1lb57T6Pv/8NObPb4uhQ4dizpw5aNmyJbs5iYgolxJZ+O7fB1q3BuLigMzMoq1jaQn4+moxZEiZG6hKRETF6I1WifHjx2P+/PkFLpOVBfTsCdy9m4bMzH4A7AAMLXTbaWnA++8b49Sp4slKL+/y5cto0aIFbGxs8MMPPxg6DhGRnmIrfBMmTICRkRGuXbtW5HVSU1MxdepUODk5wc7ODl27dsWvvwK3bgEazXYAcQASAPyUzxaOAWgLwAZAU6SlhWHu3Jw5QUFBePvtt2Fvbw9HR0cMGjQIMTExubaQmJiISpUqoXPnznrT/f390ahRI9jY2KBx48bYsWNHgfty5MgRtGzZEra2tqhTpw68vLzyXC6v46RQKPQeJiYmmDFjBgDAz89Pb56VlRWMjIxw5syZAvMAQHZ2NubPn49q1arBxsYGLVq0wKNHjwpdLzQ0NFcmIyMjBAQEAAA8PT315llYWMDGxka3/rfffotu3brh8ePH+Oijj7BixQrUqVMHtra2qFatGj755BNoNJpCcxARvRZSDEJDQ6VLly4CQK5evZrvcuPGjZN58+bpno8ePVqGDx8u9+/fF41GI6dPn5Y2bURyhrX8S4DRf/2d1yNBAEcB/AXQCLBJAHuxsEiUmzdF7t27JzExMSIikp6eLrNnz5Z+/frlyvThhx9Kly5dpFOnTrpp0dHRYmZmJnv37hWtViu7d+8WS0tLiYuLy3O/MjMzxdbWVtRqtWi1Wjl58qRYW1vL+fPnX/g4paSkiLW1tYSEhOQ5f/369VKnTh3RarX5Huen5s2bJ927d5dbt26JVquVP/74Q9LS0gpd73lBQUGiUCgkJSUlz/njxo2TCRMm6J737NlT1q5dq3t+7do1efjwoYiIJCQkSPfu3WXZsmUvnIOIqDgUWPhsbGykTp06EhgYmO8yWVlZ0rx5c4mIiMj1hX727Flp0aKFKBQKGTZsmAwfPlxX+C5duiQ2NjaSlJSkW/7iRRFLSxFggQBmApgKYC2Adx6Fb5cAjZ+bVk9MTLxl1iz9jOnp6fLZZ59Jo0aN9KYfO3ZM2rdvLz4+PnqF77fffpNKlSrpLevk5CTHjh3L8xjcu3dPAMiTJ09001q3bi1btmwp0nF61oYNG6R27dr5FrZu3brJP//5T93z1NRU+cc//iE1a9YUW1tb6dSpk6SmpkpiYqJYW1vLtWvX8txOdna2fP3111KnTh1xcHCQoUOHSkJCQp7Ljh8/XsaPH5/nvJSUFFEoFBIcHCwiIt27dxdjY2OxsLAQa2truXz5st7y8fHx0rNnT1EqlbppFy9elHfeeUcqVqwo9evXlx9//DHP1yIiKg4FFr7s7GyJjo6Wixcv5rvMt99+Kx999FHOxp75Qs/IyJCaNWvK8uXLJTMzU3766ScxNTXVFb6NGzfKW2+9JTNnzhRHR0d56623xNNzu1hZPS1iCwtp8f0qQKPnpv1NgJny1ls52W7fvi12dnZiZGQkpqamsn79el1ujUYjLVq0kNOnT8v69ev1Cp9Go5GuXbvKzp07RaPRyC+//CLVq1fPt8UjIjJy5EhZtWqVaDQaOXbsmFSqVEnu3LlT6HF6Xvfu3WXhwoV5zrt165YYGxvLjRs3dNOmTp0qbm5uEh0dLRqNRsLDwyU9PV1CQkLEzs5OvvnmG6lSpYrUq1dPVq1apVvv3//+t7Rr106ioqIkPT1dJk+eLCNGjMj1mk+ePBGFQiFBQUF5Ztq4cWOuQu3m5qbX4hMR8fPzExsbGwEgTk5OutZwSkqKuLi4iI+Pj2RlZcmZM2fE0dFRIiMj83w9IqJX9UpdnXfu3JG6devKo0ePcjb2zBd6SEiIVK1aVe8LsUOHDrrCt3jxYgEgCxculIyMDAkODhZzc2sxNf2ziIUvXgA7AbYIkCnABgGMBJgsNWro50xISJBvvvlGjh8/rpu2fPly8fT0FBHJVfhERLy9vcXa2lpMTEzE0tJSdu/eXeCx+PXXX6Vy5cpiYmIiJiYm4uXlVaTj9Kzbt2/nKmzP+vLLL8XNzU33PDs7WypUqJCrS1Ukp9AAkA8++EBSU1MlIiJCnJyc5MCBAyIi0rBhQzl06JBu+djYWDE1NZWsrCy97fj6+kqtWrXybYH26NEjV6HOq/A9deXKFZk/f77cvXtXRES2bdsmnTt31ltm8uTJeq1aIqLiVOTBLc8OeGjSpAkAYObMmViwYAHs7OxyLR8bG4vq1avrXUvn6uqq+9vS0hJmZmaYP38+zM3N4ebmhnr1ukPkQD4JmgBQ/PUIBeAIYCeA5QCqANgH4B0ALjAz01/TwcEB48aNw4ABA6DRaBAbG4sffvgBixcvzvOVDh06hDlz5iA4OBiZmZkICQnBhx9+iPPnzwPQH4xy584dXLp0CcOHD4evry8yMzNx4cIFfPvtt9izZ0+hx+lZvr6+6Ny5M2rXrp3v/GdvuB0fH4/09HTUrVs317KWlpYAgAULFsDS0hJNmzbFiBEjsHfvXgDA7du3MWjQINjb28Pe3h6NGjWCiYkJ4uLi9LazceNGvP/++3leExkVFYWQkBC8//77Be7Xs+rVq4cmTZpg6tSpuhwnTpzQ5bC3t4efnx/u3btX5G0SEb2IIhe+Ll26ICUlBSkpKbhw4QIA4PDhw5g9ezacnZ3h7OwMIOe2YVu2bEHVqlURExMDeeYywTt37uj+btq0aa7XsLAATPO9l8wFACl/Pbr8Nc0NwCkAiQA2AbgMoC3+iqJHo9Hg/v37SE5OxsmTJ3H37l00btwYzs7O+Pjjj3Hy5Ek4OzsjOzsb58+fR9euXdG6dWsYGxujTZs2aNeuHQ4dOgQAuuOQkpKCmjVrIjIyEg0aNIC7uzuMjY3RoEED9OnTB4GBgYUep2c9X9ieFR4ejtjYWAwZMkQ3zcnJCRUqVMD169dzLf/0+OZ3EX+NGjUQGBiIR48e6R7p6emoXr26bpmoqCgEBwfnW9h8fX3RsWNH1KlTJ8/5+dFoNLrMNWrUgJubm16OlJQUqFSqF9omEVGRFdQcLOwcX1xcnNy9e1f3ACDHjx+X1NRUNmOj6wAAA7lJREFUycjIkBo1asiKFSskKytLAgIC9M7xZWZmSt26deXLL7+UrKwsCQsLE4VCIRYWF4vY1SkCnP2rmzNJgI8F6CgKhcjGjSIBAQFy6dIlyc7Olvv378vQoUOlRYsWIpIz2OXZ3CtWrJC2bdvqut+Cg4PF0dFRzp07JyI5g3QcHBxk//79eR6Ha9euibW1tRw+fFi0Wq1cu3ZN6tatq+vuLOg4PRUeHi5WVlaSnJyc52tMmjRJxo4dm2v61KlTpUePHhITE6M7v5ieni4iIl26dJHJkydLenq6/Pnnn1KpUiVd9+by5cvFzc1Nbt26JSIi9+/flx07duhte/HixdKlS5c884iI1K9fX9atW5dr+vNdnWvX/n97d8/SSBSGYfgFxcB4iCiSoIIxBhRNYyPYWFgJ9sYtLNRKsJCACNZ2ohBiI2hlZ5NabAULBbEw4g+w2VYwgUB8tshu2Ej8YpUdPfcF6WYmJylyZ+bMx379jNhisajR0VFls1lJ0v39vfr7+3V4eKhKpaJKpaLz83Pd3Nw8+74A8C9eDJ9zTqlUSsfHx2/b2JO5q4uLC42NjdXP6sxkMg2XM1xfX2tiYkJBEGhkZESFQkFLS1JLy1vD90Nm0d+vjMx+yjmpXJby+bwGBgYUBIHi8bjm5ubqP/JPNZvj293dVSqVknNOyWRS29vbL372o6MjpdNpOefU19en9fV1VavVN31PUm1ea35+vuny5XJZHR0dDXNyf5RKJa2urqq3t1fRaFSTk5P1oN7d3Wl6elrt7e1KJpPa29urr1etVrWzs6OhoSE55zQ4OKiNjY2GbQ8PD+vg4KDpmM7Ozp4N9dPwLSwsKBaLKQgCJRIJra2tNVxWcXt7q5mZGXV3d6urq0tTU1P1Px0A8NFCd8uyYtFsfLx2J5b3ikTMlpfNcrmPHxcA4HsI3Y0t0+naExeC4H3rtbaaJRJmm5ufMy4AwPcQuj0+s9pBzGzWbH/frFR6fflIpPZ8vtNTs56ezx8fAODrCt0en1ntMUO5nFk+bxaPmznXfLkgqD2UdnbW7PKS6AEAXhfKPb6/PT6anZyYbW2ZXV2ZPTyYtbWZxWJmKytmi4tmnZ3/e5QAgK8i9OEDAOAjhfJQJwAAn4XwAQC8QvgAAF4hfAAArxA+AIBXCB8AwCuEDwDgFcIHAPAK4QMAeIXwAQC8QvgAAF4hfAAArxA+AIBXCB8AwCuEDwDgFcIHAPAK4QMAeIXwAQC8QvgAAF4hfAAArxA+AIBXCB8AwCu/AGelUxm+H7tqAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "files = rglob.rglob(fc.selected_path, \"*\")\n",
+    "\n",
+    "data=[]\n",
+    "graphs=[]\n",
+    "identification = 'UUID'\n",
+    "#Read the metadata from all the containers available and create independent graphs with each execution\n",
+    "for i,file in enumerate(files):\n",
+    "    #Read metadata files\n",
+    "    data.append(read_json_file(file))\n",
+    "    #Conver to independent graphs\n",
+    "    graphs.append(from_json_to_graph(data[i], identification))\n",
+    "    #Graph of each execution and file\n",
+    "    plot_graph(graphs[i], identification)\n",
+    "    \n",
+    "#Merge all the graphs to overlap the repetitive components and build the main workflow\n",
+    "merged_graphs=graphs[0]\n",
+    "for i in range(1,len(graphs)):\n",
+    "    merged_graphs = nx.compose(merged_graphs,graphs[i])\n",
+    "plot_graph(merged_graphs, identification)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interactive graph and metadata analysis\n",
+    "One the main graph is built, it is visualized through an interactive interface. The user can reorganize and see the attributes of each component to have an easier traceability and explainability of the workflow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Visualize plot in the interactive interface\n",
+    "app = ViewerApp(merged_graphs)\n",
+    "app.mainloop()"
+   ]
+  }
+ ],
+ "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/metadata_interface/ViewerApp.py b/metadata_interface/ViewerApp.py
new file mode 100644
index 0000000..eeec6dd
--- /dev/null
+++ b/metadata_interface/ViewerApp.py
@@ -0,0 +1,484 @@
+try:
+    # Python 3
+    import tkinter as tk
+    import tkinter.messagebox as tkm
+    import tkinter.simpledialog as tkd
+except ImportError:
+    # Python 2
+    import Tkinter as tk
+    import tkMessageBox as tkm
+    import tkSimpleDialog as tkd
+
+
+
+import networkx as nx
+
+from networkx_viewer.tokens import TkPassthroughEdgeToken, TkPassthroughNodeToken
+from networkx_viewer.autocomplete_entry import AutocompleteEntry
+from networkx_viewer import NodeToken, GraphCanvas
+
+
+class ViewerApp(tk.Tk):
+    """Example simple GUI to plot a NetworkX Graph"""
+    def __init__(self, graph, **kwargs):
+        """Additional keyword arguments beyond graph are passed down to the
+        GraphCanvas.  See it's docs for details"""
+        tk.Tk.__init__(self)
+        self.geometry('1000x600')
+        self.title('NetworkX Viewer')
+
+        bottom_row = 10
+        self.columnconfigure(0, weight=1)
+        self.rowconfigure(bottom_row, weight=1)
+
+        self.canvas = GraphCanvas(graph, width=400, height=400, NodeTokenClass=CustomNodeToken, **kwargs)
+        self.canvas.grid(row=0, column=0, rowspan=bottom_row+2, sticky='NESW')
+        self.canvas.onNodeSelected = self.onNodeSelected
+        self.canvas.onEdgeSelected = self.onEdgeSelected
+
+        r = 0   # Current row
+        tk.Label(self, text='Nodes:').grid(row=r, column=1, sticky='W')
+        self.node_entry = AutocompleteEntry(self.canvas.dataG.nodes)
+        self.node_entry.bind('<Return>',self.add_node, add='+')
+        self.node_entry.bind('<Control-Return>', self.buildNewShortcut, add='+')
+        self.node_entry.grid(row=r, column=2, columnspan=2, sticky='NESW', pady=2)
+        tk.Button(self, text='+', command=self.add_node, width=2).grid(
+            row=r, column=4,sticky=tk.NW,padx=2,pady=2)
+
+        r += 1
+        nlsb = tk.Scrollbar(self, orient=tk.VERTICAL)
+        self.node_list = tk.Listbox(self, yscrollcommand=nlsb.set, height=5)
+        self.node_list.grid(row=r, column=1, columnspan=3, sticky='NESW')
+        self.node_list.bind('<Delete>',lambda e: self.node_list.delete(tk.ANCHOR))
+        nlsb.config(command=self.node_list.yview)
+        nlsb.grid(row=r, column=4, sticky='NWS')
+
+        r += 1
+        tk.Label(self, text='Neighbors Levels:').grid(row=r, column=1,
+                                                    columnspan=2, sticky=tk.NW)
+        self.level_entry = tk.Entry(self, width=4)
+        self.level_entry.insert(0,'1')
+        self.level_entry.grid(row=r, column=3, sticky=tk.NW, padx=5)
+
+        r += 1
+        tk.Button(self, text='Build New', command=self.onBuildNew).grid(
+            row=r, column=1)
+        tk.Button(self, text='Add to Existing', command=self.onAddToExisting
+                  ).grid(row=r, column=2, columnspan=2)
+
+        r += 1
+        line = tk.Canvas(self, height=15, width=200)
+        line.create_line(0,13,250,13)
+        line.create_line(0,15,250,15)
+        line.grid(row=r, column=1, columnspan=4, sticky='NESW')
+
+        r += 1
+        tk.Label(self, text='Filters:').grid(row=r, column=1, sticky=tk.W)
+        self.filter_entry = tk.Entry(self)
+        self.filter_entry.bind('<Return>',self.add_filter, add='+')
+        self.filter_entry.grid(row=r, column=2, columnspan=2, sticky='NESW', pady=2)
+        tk.Button(self, text='+', command=self.add_filter, width=2).grid(
+            row=r, column=4,sticky=tk.NW,padx=2,pady=2)
+
+        r += 1
+        flsb = tk.Scrollbar(self, orient=tk.VERTICAL)
+        self.filter_list = tk.Listbox(self, yscrollcommand=flsb.set, height=5)
+        self.filter_list.grid(row=r, column=1, columnspan=3, sticky='NESW')
+        self.filter_list.bind('<Delete>',self.remove_filter)
+        flsb.config(command=self.node_list.yview)
+        flsb.grid(row=r, column=4, sticky='NWS')
+
+        r += 1
+        tk.Button(self, text='Clear',command=self.remove_filter).grid(
+                    row=r, column=1, sticky='W')
+        tk.Button(self, text='?', command=self.filter_help
+                  ).grid(row=r, column=4, stick='NESW', padx=2)
+
+
+        r += 1
+        line2 = tk.Canvas(self, height=15, width=200)
+        line2.create_line(0,13,250,13)
+        line2.create_line(0,15,250,15)
+        line2.grid(row=r, column=1, columnspan=4, sticky='NESW')
+
+        r += 1
+        self.lbl_attr = tk.Label(self, text='Attributes',
+                                 wraplength=200, anchor=tk.SW, justify=tk.LEFT)
+        self.lbl_attr.grid(row=r, column=1, columnspan=4, sticky='NW')
+
+        r += 1
+        self.tbl_attr = PropertyTable(self, {})
+        self.tbl_attr.grid(row=r, column=1, columnspan=4, sticky='NESW')
+
+        assert r == bottom_row, "Set bottom_row to %d" % r
+
+        self._build_menu()
+
+    def _build_menu(self):
+        self.menubar = tk.Menu(self)
+        self.config(menu=self.menubar)
+
+        view = tk.Menu(self.menubar, tearoff=0)
+        view.add_command(label='Undo', command=self.canvas.undo, accelerator="Ctrl+Z")
+        self.bind_all("<Control-z>", lambda e: self.canvas.undo())  # Implement accelerator
+        view.add_command(label='Redo', command=self.canvas.redo)
+        view.add_separator()
+        view.add_command(label='Center on node...', command=self.center_on_node)
+        view.add_separator()
+        view.add_command(label='Reset Node Marks', command=self.reset_node_markings)
+        view.add_command(label='Reset Edge Marks', command=self.reset_edge_markings)
+        view.add_command(label='Redraw Plot', command=self.canvas.replot)
+        view.add_separator()
+        view.add_command(label='Grow display one level...', command=self.grow_all)
+
+        self.menubar.add_cascade(label='View', menu=view)
+
+    def center_on_node(self):
+        node = NodeDialog(self, "Name of node to center on:").result
+        if node is None: return
+        self.canvas.center_on_node(node)
+
+    def reset_edge_markings(self):
+        for u,v,k,d in self.canvas.dispG.edges(data=True, keys=True):
+            token = d['token']
+            if token.is_marked:
+                self.canvas.mark_edge(u,v,k)
+
+    def reset_node_markings(self):
+        for u,d in self.canvas.dispG.nodes(data=True):
+            token = d['token']
+            if token.is_marked:
+                self.canvas.mark_node(u)
+
+    def add_node(self, event=None):
+        node = self.node_entry.get()
+
+        if node.isdigit() and self.canvas.dataG.has_node(int(node)):
+                node = int(node)
+
+        if self.canvas.dataG.has_node(node):
+            self.node_list.insert(tk.END, node)
+            self.node_entry.delete(0, tk.END)
+        else:
+            tkm.showerror("Node not found", "Node '%s' not in graph."%node)
+
+    def add_filter(self, event=None, filter_lambda=None):
+        if filter_lambda is None:
+            filter_lambda = self.filter_entry.get()
+
+        if self.canvas.add_filter(filter_lambda):
+            # We successfully added the filter; add to list and clear entry
+            self.filter_list.insert(tk.END, filter_lambda)
+            self.filter_entry.delete(0, tk.END)
+
+    def filter_help(self, event=None):
+        msg = ("Enter a lambda function which returns True if you wish\n"
+               "to show nodes with ONLY a given property.\n"
+               "Parameters are:\n"
+               "  - u, the node's name, and \n"
+               "  - d, the data dictionary.\n\n"
+               "Example: \n"
+               " d.get('color',None)=='red'\n"
+               "would show only red nodes.\n"
+               "Example 2:\n"
+               " str(u).is_digit()\n"
+               "would show only nodes which have a numerical name.\n\n"
+               "Multiple filters are ANDed together.")
+        tkm.showinfo("Filter Condition", msg)
+    def remove_filter(self, event=None):
+        all_items = self.filter_list.get(0, tk.END)
+        if event is None:
+            # When no event passed, this function was called via the "clear"
+            # button.
+            items = all_items
+        else:
+            # Remove currently selected item
+            items = (self.filter_list.get(tk.ANCHOR),)
+
+        for item in items:
+            self.canvas.remove_filter(item)
+            idx = all_items.index(item)
+            self.filter_list.delete(idx)
+            all_items = self.filter_list.get(0, tk.END)
+
+
+    def grow_all(self):
+        """Grow all visible nodes one level"""
+        for u, d in self.canvas.dispG.copy().nodes.items():
+            if not d['token'].is_complete:
+                self.canvas.grow_node(u)
+
+    def get_node_list(self):
+        """Get nodes in the node list and clear"""
+        # See if we forgot to hit the plus sign
+        if len(self.node_entry.get()) != 0:
+            self.add_node()
+        nodes = self.node_list.get(0, tk.END)
+        self.node_list.delete(0, tk.END)
+        return nodes
+
+
+    def onBuildNew(self):
+        nodes = self.get_node_list()
+
+        if len(nodes) == 2:
+            self.canvas.plot_path(nodes[0], nodes[1], levels=self.level)
+        else:
+            self.canvas.plot(nodes, levels=self.level)
+
+    def onAddToExisting(self):
+        """Add nodes to existing plot.  Prompt to include link to existing
+        if possible"""
+        home_nodes = set(self.get_node_list())
+        self.canvas.plot_additional(home_nodes, levels=self.level)
+
+    def buildNewShortcut(self, event=None):
+        # Add node intelligently then doe a build new
+        self.node_entry.event_generate('<Return>') # Resolve current
+        self.onBuildNew()
+
+    def goto_path(self, event):
+        frm = self.node_entry.get()
+        to = self.node_entry2.get()
+        self.node_entry.delete(0, tk.END)
+        self.node_entry2.delete(0, tk.END)
+
+        if frm == '':
+            tkm.showerror("No From Node", "Please enter a node in both "
+                "boxes to plot a path.  Enter a node in only the first box "
+                "to bring up nodes immediately adjacent.")
+            return
+
+        if frm.isdigit() and int(frm) in self.canvas.dataG.nodes():
+            frm = int(frm)
+        if to.isdigit() and int(to) in self.canvas.dataG.nodes():
+            to = int(to)
+
+        self.canvas.plot_path(frm, to, levels=self.level)
+
+    def onNodeSelected(self, node_name, node_dict):
+        self.tbl_attr.build(node_dict)
+        self.lbl_attr.config(text="Attributes of node '%s'"%node_name)
+
+    def onEdgeSelected(self, edge_name, edge_dict):
+        self.tbl_attr.build(edge_dict)
+        self.lbl_attr.config(text="Attributes of edge between '%s' and '%s'"%
+                                    edge_name[:2])
+
+    @property
+    def level(self):
+        try:
+            l = int(self.level_entry.get())
+        except ValueError:
+            tkm.showerror("Invalid Level", "Please specify a level between "
+                "greater than or equal to 0")
+            raise
+        return l
+
+
+
+class CustomNodeToken(NodeToken):
+    def render(self, data, node_name):
+        """Example of custom Node Token
+        Draw a circle if the node's data says we are a circle, otherwise
+        draw us as a rectangle.  Also, if data contains a color key,
+        use that as our color (default, red)
+        """
+
+        # Set color and other options
+        marker_options = {'fill':       data.get('color'),
+                          'outline':    'black'}
+
+        self.label = self.create_text(5, 5, text=node_name)
+        # Draw circle or square, depending on what the node said to do
+        self.marker = self.create_oval(5,5,20,20, **marker_options)
+        
+         # Modify marker using options from data
+        cfg = self.itemconfig(self.marker)
+        for k,v in cfg.copy().items():
+            cfg[k] = data.get(k, cfg[k][-1])
+        self.itemconfig(self.marker, **cfg)
+        self._default_outline_color = 'black'
+
+        # Modify the text label using options from data
+        cfg = self.itemconfig(self.label)
+        for k,v in cfg.copy().items():
+            cfg[k] = data.get('label_'+k, cfg[k][-1])
+        self.itemconfig(self.label, **cfg)
+        self._default_label_color = 'black'
+
+        # Figure out how big we really need to be
+        bbox = self.bbox(self.label)
+        bbox = [abs(x) for x in bbox]
+        br = ( max((bbox[0] + bbox[2]),20), max((bbox[1]+bbox[3]),20) )
+        
+        self.config(width=br[0], height=br[1]+7)
+
+        # Place label and marker
+        mid = ( int(br[0]/2.0), int(br[1]/2.0)+7 )
+        self.coords(self.label, mid)
+        self.coords(self.marker, mid[0]-5,0, mid[0]+5,10)
+
+
+class TkPassthroughViewerApp(ViewerApp):
+    def __init__(self, graph, **kwargs):
+        ViewerApp.__init__(self, graph,
+            NodeTokenClass=TkPassthroughNodeToken,
+            EdgeTokenClass=TkPassthroughEdgeToken, **kwargs)
+
+
+class PropertyTable(tk.Frame):
+    """A pure Tkinter scrollable frame that actually works!
+    * Use the 'interior' attribute to place widgets inside the scrollable frame
+    * Construct and pack/place/grid normally
+    * This frame only allows vertical scrolling
+    """
+    def __init__(self, parent, property_dict, *args, **kw):
+        tk.Frame.__init__(self, parent, *args, **kw)
+
+        # create a canvas object and a vertical scrollbar for scrolling it
+        self.vscrollbar = vscrollbar = tk.Scrollbar(self, orient=tk.VERTICAL)
+        vscrollbar.pack(fill=tk.Y, side=tk.RIGHT, expand=tk.FALSE)
+        self.canvas = canvas = tk.Canvas(self, bd=0, highlightthickness=0,
+                        yscrollcommand=vscrollbar.set)
+        canvas.pack(side=tk.LEFT, fill=tk.BOTH, expand=tk.TRUE)
+        vscrollbar.config(command=canvas.yview)
+
+        # reset the view
+        canvas.xview_moveto(0)
+        canvas.yview_moveto(0)
+
+        # create a frame inside the canvas which will be scrolled with it
+        self.interior = interior = tk.Frame(canvas)
+        self.interior_id = canvas.create_window(0, 0, window=interior,
+                                           anchor='nw')
+
+        self.interior.bind('<Configure>', self._configure_interior)
+        self.canvas.bind('<Configure>', self._configure_canvas)
+
+        self.build(property_dict)
+
+    def build(self, property_dict):
+        for c in self.interior.winfo_children():
+            c.destroy()
+
+
+        # Filter property dict
+        property_dict = {k: v for k, v in property_dict.items()
+                         if self._key_filter_function(k)}
+
+        # Prettify key/value pairs for display
+        property_dict = {self._make_key_pretty(k): self._make_value_pretty(v)
+                            for k, v in property_dict.items()}
+
+        # Sort by key and filter
+        dict_values = sorted(property_dict.items(), key=lambda x: x[0])
+
+        for n,(k,v) in enumerate(dict_values):
+            tk.Label(self.interior, text=k, borderwidth=1, relief=tk.SOLID,
+                wraplength=75, anchor=tk.E, justify=tk.RIGHT).grid(
+                row=n, column=0, sticky='nesw', padx=1, pady=1, ipadx=1)
+            tk.Label(self.interior, text=v, borderwidth=1,
+                wraplength=125, anchor=tk.W, justify=tk.LEFT).grid(
+                row=n, column=1, sticky='nesw', padx=1, pady=1, ipadx=1)
+
+    def _make_key_pretty(self, key):
+        """Make key of property dictionary displayable
+        Used by build function to make key displayable on the table.
+        Args:
+            key (object)
+            Key of property dictionary from dataG
+        Returns:
+            label (str)
+            String representation of key.  Might be made shorter or with
+            different name if desired.
+        """
+        return str(key)
+
+    def _make_value_pretty(self, value):
+        """Make key of property dictionary displayable
+        Used by build function to make key displayable on the table.
+        Args:
+            key (object)
+            Key of property dictionary from dataG
+        Returns:
+            label (str)
+            String representation of key.  Might be made shorter or with
+            different name if desired.
+        """
+        label = str(value)
+        if len(label) > 255:
+            label = label[:253] + '...'
+        return label
+
+    def _key_filter_function(self, key):
+        """Function to determine if key should be displayed.
+        Called by build for each key in the propery dict.  Overwrite
+        with your implementation if you want to hide specific keys (all
+        starting "_" for example).
+        Args:
+            key (object)
+            Key of property dictionary from dataG
+        Returns:
+            display (bool)
+            True if the key-value pair associate with this key should
+            be displayed
+        """
+        # Should be more specifically implemented when subclassed
+        return True # Show all keys
+
+
+    def _configure_interior(self, event):
+        """
+        track changes to the canvas and frame width and sync them,
+        also updating the scrollbar
+        """
+        # update the scrollbars to match the size of the inner frame
+        size = (self.interior.winfo_reqwidth(), self.interior.winfo_reqheight())
+        self.canvas.config(scrollregion="0 0 %s %s" % size)
+        if self.interior.winfo_reqwidth() != self.canvas.winfo_width():
+            # update the canvas's width to fit the inner frame
+            self.canvas.config(width=self.interior.winfo_reqwidth())
+
+
+    def _configure_canvas(self, event):
+        if self.interior.winfo_reqwidth() != self.canvas.winfo_width():
+            # update the inner frame's width to fill the canvas
+            self.canvas.itemconfigure(self.interior_id, width=self.canvas.winfo_width())
+
+
+class NodeDialog(tk.Toplevel):
+    def __init__(self, main_window, msg='Please enter a node:'):
+        tk.Toplevel.__init__(self)
+        self.main_window = main_window
+        self.title('Node Entry')
+        self.geometry('170x160')
+        self.rowconfigure(3, weight=1)
+
+        tk.Label(self, text=msg).grid(row=0, column=0, columnspan=2,
+                                      sticky='NESW',padx=5,pady=5)
+        self.posibilities = [d['dataG_id'] for n,d in
+                    main_window.canvas.dispG.nodes(data=True)]
+        self.entry = AutocompleteEntry(self.posibilities, self)
+        self.entry.bind('<Return>', lambda e: self.destroy(), add='+')
+        self.entry.grid(row=1, column=0, columnspan=2, sticky='NESW',padx=5,pady=5)
+
+        tk.Button(self, text='Ok', command=self.destroy).grid(
+            row=3, column=0, sticky='ESW',padx=5,pady=5)
+        tk.Button(self, text='Cancel', command=self.cancel).grid(
+            row=3, column=1, sticky='ESW',padx=5,pady=5)
+
+        # Make modal
+        self.winfo_toplevel().wait_window(self)
+
+
+    def destroy(self):
+        res = self.entry.get()
+        if res not in self.posibilities:
+            res = None
+        self.result = res
+        tk.Toplevel.destroy(self)
+
+    def cancel(self):
+        self.entry.delete(0,tk.END)
+        self.destroy()
diff --git a/metadata_interface/__pycache__/ViewerApp.cpython-37.pyc b/metadata_interface/__pycache__/ViewerApp.cpython-37.pyc
new file mode 100644
index 0000000..0b366c0
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diff --git a/metadata_interface/__pycache__/utils.cpython-37.pyc b/metadata_interface/__pycache__/utils.cpython-37.pyc
new file mode 100644
index 0000000..f880518
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diff --git a/metadata_interface/utils.py b/metadata_interface/utils.py
new file mode 100644
index 0000000..f729d9d
--- /dev/null
+++ b/metadata_interface/utils.py
@@ -0,0 +1,47 @@
+import json
+import networkx as nx
+import matplotlib.pyplot as plt
+from networkx_viewer import Viewer
+import rglob
+
+def read_json_file(file):
+    with open(file) as f:
+      data = json.load(f)
+    return data
+
+def from_json_to_graph (data, identification):
+    record_trail=data[1]['Record trail']
+    #print(data)
+    #print (record_trail)
+    G = nx.DiGraph()
+    record=[]
+    #print(len(record_trail['Container_name']))
+    color_map=['blue']*len(record_trail['Container_name'])
+    color_map[1]='orange'
+    for i in range(0,len(record_trail['Container_name'])):
+        G.add_node(record_trail[identification][i])
+        G.nodes[record_trail[identification][i]]['1--UUID']=record_trail['UUID'][i]
+        G.nodes[record_trail[identification][i]]['2--Container_name']=record_trail['Container_name'][i]
+        G.nodes[record_trail[identification][i]]['5--Creation_time']=record_trail['Creation_time'][i]
+        G.nodes[record_trail[identification][i]]['6--Modification_time']=record_trail['Modification_time'][i]
+        record.append('['+record_trail['UUID'][i]+' '+record_trail['Container_name'][i]+']')
+        G.nodes[record_trail[identification][i]]['color']=color_map[i]
+
+    G.nodes[data[0]["org.label-schema.build-container_uuid"]]['4--Command_line']=data[-1]['Command_line']
+    G.nodes[data[0]["org.label-schema.build-container_uuid"]]['3--Record_trail']=record
+
+    list_G=list(G.nodes)
+    #print(list_G)
+    for i in range(len(list_G)-2):
+        G.add_edge(list_G[i+1],list_G[i])
+    G.add_edge(list_G[-1],list_G[1])
+    return G
+
+def plot_graph (G, attribute):
+    pos = nx.spring_layout(G)
+    labels = nx.get_node_attributes(G,attribute)
+    color_map=['blue']*len(G)
+    color_map[1]='orange'
+    nx.draw(G, pos, node_color=color_map, with_labels=True)
+    plt.show()
+