diff --git a/grape/general_graph.py b/grape/general_graph.py index b090857..77eb5e5 100644 --- a/grape/general_graph.py +++ b/grape/general_graph.py @@ -9,6 +9,8 @@ import pandas as pd from matplotlib import cm from matplotlib.colors import ListedColormap +import matplotlib.patches as mpatches +import matplotlib.lines as mlines import matplotlib.pyplot as plt warnings.simplefilter(action='ignore', category=FutureWarning) @@ -1230,9 +1232,10 @@ def compute_service(self): return computed_service, splitting def print_graph(self, radius=None, initial_pos=None, fixed_nodes=None, - n_iter=500, thresh=0.0001, size=800, border='black', fsize=12, - fcolor='k', family='sans-serif', title='Graph', input_cmap=None, - save_to_file=None): + n_iter=500, thresh=0.0001, size=800, border='black', edge_width=1.0, + arrow_size=10, fsize=12, fcolor='k', family='sans-serif', title='Graph', + input_cmap=None, legend_loc='upper right', legend_ncol=1, + legend_anchor=None, legend_fsize=12, save_to_file=None): """ Print the graph. @@ -1256,7 +1259,7 @@ def print_graph(self, radius=None, initial_pos=None, fixed_nodes=None, :type radius: float, optional, default to 1/sqrt(n) where n is the number of nodes in the graph :param initial_pos: initial positions for nodes as a dictionary with - node as jeys and values as a coordinate list or tuple. If None, + node as keys and values as a coordinate list or tuple. If None, then use random initial positions. :type initial_pos: dict, optional, default to None :param fixed_nodes: nodes to keep fixed at initial position. ValueError @@ -1272,6 +1275,10 @@ def print_graph(self, radius=None, initial_pos=None, fixed_nodes=None, :type size: int, optional, default to 800 :param border: color of node borders. :type border: color, optional, default to 'black' + :param edge_width: width of edges. + :type edge_width: float, optional, default to 1.0 + :param arrow_size: size of the arrow head head’s length and width. + :type arrow_size: int, optional, default to 10 :param fsize: font size for text labels. :type fsize: int, optional, default to 12 :param fcolor: font color string for labels. @@ -1280,9 +1287,22 @@ def print_graph(self, radius=None, initial_pos=None, fixed_nodes=None, :type ffamily: string, optional, default to 'sans-serif' :param title: title for figure window. :type title: string, optional, defaut to 'Graph' - :param cmap: colormap for coloring the different areas with different - colors. If None, all nodes are colored as white. - :type cmap: Matplotlib colormap, optional, default to None + :param input_cmap: colormap for coloring the different areas with + different colors. If None, all nodes are colored as white. + :type input_cmap: Matplotlib colormap, optional, default to None + :param legend_loc: the location of the legend. + :type legend_loc: str, optional, default to 'upper right' + :param legend_ncol: the number of columns that the legend has. + :type legend_ncol: int, optional, default to 1 + :param legend_anchor: box that is used to position the legend in + conjunction with loc. + :type legend_anchor: 2-tuple, or 4-tuple of floats, optional, + defaults to axes.bbox (if called as a method to Axes.legend) + or figure.bbox (if Figure.legend). + This argument allows arbitrary placement of the legend + :param legend_fsize: the font size of the legend. The value must be + numeric, implying the size the absolute font size in points. + :type legend_fsize: int, optional, default to 12 :param save_to_file: name of the file where to save the graph drawing. The extension is guesses from the filename. Interactive window is rendered in any case. @@ -1318,6 +1338,16 @@ def print_graph(self, radius=None, initial_pos=None, fixed_nodes=None, node_shape=shapes[self.type[node]], node_size=size, edgecolors=border) + pert_resistant = [node for node in self.perturbation_resistant.keys() + if self.perturbation_resistant[node] == 1] + + for node in pert_resistant: + col = mymap(area_indices[self.area[node]]) + col = np.array([col]) + nx.draw_networkx_nodes(self, pos, nodelist=[node], node_color=col, + node_shape=shapes[self.type[node]], node_size=size, + edgecolors='red') + or_edges = [(u, v) for (u, v, d) in self.edges(data=True) if d['father_condition'] == 'OR'] and_edges = [(u, v) for (u, v, d) in self.edges(data=True) @@ -1325,12 +1355,15 @@ def print_graph(self, radius=None, initial_pos=None, fixed_nodes=None, single_edges = [(u, v) for (u, v, d) in self.edges(data=True) if d['father_condition'] == 'SINGLE'] - nx.draw_networkx_edges(self, pos, edgelist=or_edges, width=3, alpha=0.9, - edge_color='brown', style='dashed', node_size=size) - nx.draw_networkx_edges(self, pos, edgelist=and_edges, width=3, - alpha=0.9, edge_color='violet', node_size=size) - nx.draw_networkx_edges(self, pos, edgelist=single_edges, width=3, - alpha=0.9, edge_color='black', node_size=size) + nx.draw_networkx_edges(self, pos, edgelist=or_edges, width=edge_width, + arrowsize=arrow_size, alpha=0.9, edge_color='brown', style='dashed', + node_size=size) + nx.draw_networkx_edges(self, pos, edgelist=and_edges, width=edge_width, + arrowsize=arrow_size, alpha=0.9, edge_color='violet', + node_size=size) + nx.draw_networkx_edges(self, pos, edgelist=single_edges, + width=edge_width, arrowsize=arrow_size, alpha=0.9, + edge_color='black', node_size=size) nx.draw_networkx_labels(self, pos, labels=self.mark, font_size=fsize, font_color=fcolor) @@ -1338,7 +1371,44 @@ def print_graph(self, radius=None, initial_pos=None, fixed_nodes=None, plt.get_current_fig_manager().canvas.set_window_title(title) plt.tight_layout() plt.axis('off') + + handles = [] + plt.rcParams.update({"text.usetex": True}) + + source_key = mlines.Line2D([], [], color='white', marker='v', + markeredgecolor='black', markersize=legend_fsize, label='SOURCE') + handles.append(source_key) + user_key = mlines.Line2D([], [], color='white', marker='^', + markeredgecolor='black', markersize=legend_fsize, label='USER') + handles.append(user_key) + hub_key = mlines.Line2D([], [], color='white', marker='o', + markeredgecolor='black', markersize=legend_fsize, label='HUB') + handles.append(hub_key) + switch_key = mlines.Line2D([], [], color='white', marker='X', + markeredgecolor='black', markersize=legend_fsize, label='SWITCH') + handles.append(switch_key) + pr_key = mlines.Line2D([], [], color='white', marker='o', + markeredgecolor='red', markersize=legend_fsize, + label='Perturbation Resistant') + handles.append(pr_key) + + single_key = mlines.Line2D([], [], color='white', + marker=r'$\rightarrow$', markeredgecolor='black', + markersize=legend_fsize, label='SINGLE') + handles.append(single_key) + or_key = mlines.Line2D([], [], color='white', + marker=r'$-\rightarrow$', markeredgecolor='brown', + markersize=legend_fsize, label='OR') + handles.append(or_key) + and_key = mlines.Line2D([], [], color='white', + marker=r'$\rightarrow$', markeredgecolor='violet', + markersize=legend_fsize, label='AND') + handles.append(and_key) + + plt.legend(handles=handles, loc=legend_loc, ncol=legend_ncol, + bbox_to_anchor=legend_anchor, fontsize=legend_fsize) + if save_to_file: plt.savefig(save_to_file, orientation='landscape', transparent=True) else: - plt.show() \ No newline at end of file + plt.show() diff --git a/tutorials/tutorial01/01_element_perturbation.ipynb b/tutorials/tutorial01/01_element_perturbation.ipynb index 331ee41..f4b0864 100644 --- a/tutorials/tutorial01/01_element_perturbation.ipynb +++ b/tutorials/tutorial01/01_element_perturbation.ipynb @@ -45,50 +45,59 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Example\n", + "### The TOY graph\n", "\n", - "In the cartoon below it is represented the example input file `TOY_graph.csv`.\n", - "In this file are present 19 nodes/elements connected by direct edges that reflect the hierarchy of the system in a parent-child fashion.\n", - "\n", - "The nodes are distributed in adjacent areas.\n", - "\n", - "In area1 are present 5 nodes: 1, 2, 3, 4 and 5.\n", - "\n", - "In area2 are present 6 nodes: 11, 19, 12, 13, 14 and 18.\n", - "\n", - "In area3 are present 5 nodes: 15, 9, 16, 17 and 10.\n", - "\n", - "In area4 are present 3 nodes: 6, 7 and 8.\n", - "\n", - "A perturbation of one or multiple elements in one area may exceed the area boundaries and propagate to other systems connected to it, located in other areas. \n", - "\n", - "Nodes 2, 3, 4, 5 are perturbation resistant nodes (`perturbation_resistant` field = 1).\n", - "These nodes will not be affected by the simulated perturbation.\n", - "\n", - "Nodes 2 and 3 are isolating elements (they are a particular type of `HUB` nodes, called `SWITCH`). In the figure, perturbing node 1 would result in the breakage of all the nodes present in the graph except \n", - "node 15 in absence of perturbation resistant nodes. On the other hand, isolating elements 2 and 3 would stop the perturbation propagation cascade to node 1.\n", - "\n", - "\"TOY_graph\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Simulate a perturbation to element \"1\"\n", - "\n", - "Let us see how to generate a perturbation that propagates from element \"1\" of the TOY graph. First of all, we need to import GRAPE classes. We are also importing `pandas` package since we are going to use it for reading the `CSV` output files obtained." + "Let us see how to generate a perturbation that propagates from element \"1\" of the TOY graph. First of all, we need to import GRAPE classes. We are also importing pandas package since we are going to use it for reading the CSV output files obtained." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DEBUG:matplotlib.pyplot:Loaded backend module://ipykernel.pylab.backend_inline version unknown.\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import pandas as pd\n", "from grape.general_graph import GeneralGraph\n", - "from grape.fault_diagnosis import FaultDiagnosis" + "from grape.fault_diagnosis import FaultDiagnosis\n", + "%matplotlib inline\n", + "from IPython.core.display import HTML\n", + "HTML(\"\"\"\n", + "\n", + "\"\"\")" ] }, { @@ -111,13 +120,82 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let us check the input before perturbing the graph in any way. In order to do so we use `check_input_with_gephi` function on F, the `FaultDiagnosis` class, which prints out the list of edges and the list of nodes of the graph. The two output files generated can be used to visualize the input with Gephi." + "Let's have a look at the graph we are going to study." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "initial = { '11': (2., 2.), '19': (6., 2.), '12': (10., 2.),\n", + " '14': (6., 10.), '13': (10., 10.), '18': (2., 10.), '5': (-2., 2.),\n", + " '3': (-4., 6.), '1': (-6., 10.), '2': (-8., 6.), '4': (-10., 2.),\n", + " '6': (-6., -2.), '7': (-10, -10.), '8': (-2, -10.), '10': (2., -2.),\n", + " '17': (10., -2.), '16': (10., -10.), '9': (2., -10.), '15': (6., -6.)}\n", + "\n", + "F.G.print_graph(initial_pos=initial, size=800, edge_width=3., arrow_size=7, fsize=12,\n", + " fixed_nodes=list(F.G), title='TOY graph (integer)', input_cmap='Accent',\n", + " legend_loc='upper center', legend_ncol=4, legend_anchor=(0.5, 1.2),\n", + " legend_fsize=12)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the figure above it is represented the example input file `TOY_graph.csv`.\n", + "In this file are present 19 nodes/elements connected by direct edges that reflect the hierarchy of the system in a parent-child fashion.\n", + "\n", + "The nodes are distributed in adjacent areas, which have been depicted with different colors specifying the `input_cmap` argument of `print_graph` function.\n", + "\n", + "In area1 are present 5 nodes: 1, 2, 3, 4 and 5.\n", + "\n", + "In area2 are present 6 nodes: 11, 19, 12, 13, 14 and 18.\n", + "\n", + "In area3 are present 5 nodes: 15, 9, 16, 17 and 10.\n", + "\n", + "In area4 are present 3 nodes: 6, 7 and 8.\n", + "\n", + "A perturbation of one or multiple elements in one area may exceed the area boundaries and propagate to other systems connected to it, located in other areas. \n", + "\n", + "Nodes 2, 3, 4, 5 are perturbation resistant nodes (`perturbation_resistant` field = 1).\n", + "These nodes will not be affected by the simulated perturbation.\n", + "\n", + "Nodes 2 and 3 are isolating elements (they are a particular type of `HUB` nodes, called `SWITCH`). In the figure, perturbing node 1 would result in the breakage of all the nodes present in the graph except \n", + "node 15 in absence of perturbation resistant nodes. On the other hand, isolating elements 2 and 3 would stop the perturbation propagation cascade to node 1." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Simulate a perturbation to element \"1\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us check the input before perturbing the graph in any way. In order to do so we use `check_input_with_gephi` function on F, the `FaultDiagnosis` class, which prints out the list of edges and the list of nodes of the graph. The two output files generated can be used to visualize the input with Gephi." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, "outputs": [], "source": [ "F.check_input_with_gephi()" @@ -132,7 +210,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -343,7 +421,7 @@ "18 18 0 area2" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -355,7 +433,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -554,7 +632,7 @@ "26 18 14" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -575,7 +653,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -589,15 +667,23 @@ "DEBUG:root:Node: 1\n", "DEBUG:root:Predecessors: []\n", "DEBUG:root:Broken: ['1']\n", - "DEBUG:root:Visited: {'1', '3'}\n", - "DEBUG:root:Node: 3\n", - "DEBUG:root:Node 3 visited, fault resistant node\n", - "DEBUG:root:Visited: {'1', '3', '2'}\n", + "DEBUG:root:Visited: {'1', '2'}\n", "DEBUG:root:Node: 2\n", "DEBUG:root:Node 2 visited, fault resistant node\n", + "DEBUG:root:Visited: {'1', '2', '3'}\n", + "DEBUG:root:Node: 3\n", + "DEBUG:root:Node 3 visited, fault resistant node\n", "DEBUG:root:In the graph are present 18 nodes\n", "DEBUG:root:The graph is dense, density = 0.08169934640522876\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/u/m/mteruzzi/miniconda/envs/test/lib/python3.6/site-packages/grape-1.1.0-py3.6.egg/grape/fault_diagnosis.py:540: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n", + " params, weights, parallel))\n" + ] } ], "source": [ @@ -613,7 +699,32 @@ "\n", "The debug output gives us an idea of what happens when a node is perturbed. Node 1 has no fault resistance of any kind; hence, it gets broken. After that, the perturbation gets propagated on all neighboring nodes, namely nodes 2 and 3 in our case. They are visited, but not affected by the damage, being fault resistant.\n", "\n", - "Since node 2 and 3 are fault resistant, the best state identified for the switches by the genetic algorithm is still the one with switches closed, since it implies the smallest number of manual actions with respect to the initial condition. We are going to give a more detailed explanation on switches activation on a later tutorial. " + "Since node 2 and 3 are fault resistant, the best state identified for the switches by the genetic algorithm is still the one with switches closed, since it implies the smallest number of manual actions with respect to the initial condition. We are going to give a more detailed explanation on switches activation on a later tutorial.\n", + "\n", + "We can have a look at the final configuration using the function `print_graph`. The graph is missing node `1`, which has been broken." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "F.G.print_graph(initial_pos=initial, size=800, edge_width=3., arrow_size=7, fsize=12,\n", + " fixed_nodes=list(F.G), title='TOY graph (perturbed)', input_cmap='Accent',\n", + " legend_loc='upper center', legend_ncol=4, legend_anchor=(0.5, 1.2),\n", + " legend_fsize=12)" ] }, { @@ -635,8 +746,10 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, + "execution_count": 9, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -1295,7 +1408,7 @@ "[19 rows x 22 columns]" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1315,7 +1428,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -1466,7 +1579,7 @@ "18 ACTIVE AVAILABLE" ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -1484,7 +1597,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1583,7 +1696,7 @@ "1 [['15', '9', '16', '17', '10', '11', '19', '12... 0.125 " ] }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1602,7 +1715,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1613,7 +1726,7 @@ "Name: 18, dtype: object" ] }, - "execution_count": 10, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } diff --git a/tutorials/tutorial01/01_element_perturbation.py b/tutorials/tutorial01/01_element_perturbation.py index 0d442d6..0ca3be6 100644 --- a/tutorials/tutorial01/01_element_perturbation.py +++ b/tutorials/tutorial01/01_element_perturbation.py @@ -3,5 +3,21 @@ F = FaultDiagnosis("./input_files/TOY_graph.csv") +initial = { '11': (2., 2.), '19': (6., 2.), '12': (10., 2.), + '14': (6., 10.), '13': (10., 10.), '18': (2., 10.), '5': (-2., 2.), + '3': (-4., 6.), '1': (-6., 10.), '2': (-8., 6.), '4': (-10., 2.), + '6': (-6., -2.), '7': (-10, -10.), '8': (-2, -10.), '10': (2., -2.), + '17': (10., -2.), '16': (10., -10.), '9': (2., -10.), '15': (6., -6.)} + +F.G.print_graph(initial_pos=initial, size=800, edge_width=3., arrow_size=7, + fsize=12, fixed_nodes=list(F.G), title='TOY graph (integer)', + input_cmap='Accent', legend_loc='upper center', legend_ncol=4, + legend_anchor=(0.5, 1.2), legend_fsize=12) + F.check_input_with_gephi() F.simulate_element_perturbation(["1"]) + +F.G.print_graph(initial_pos=initial, size=800, edge_width=3., arrow_size=7, + fsize=12, fixed_nodes=list(F.G), title='TOY graph (perturbed)', + input_cmap='Accent', legend_loc='upper center', legend_ncol=4, + legend_anchor=(0.5, 1.2), legend_fsize=12) diff --git a/tutorials/tutorial01/input_files/TOY_graph.png b/tutorials/tutorial01/input_files/TOY_graph.png deleted file mode 100644 index 3fd64a6..0000000 Binary files a/tutorials/tutorial01/input_files/TOY_graph.png and /dev/null differ diff --git a/tutorials/tutorial02/02_area_perturbation.ipynb b/tutorials/tutorial02/02_area_perturbation.ipynb index b4751fe..f1011d7 100644 --- a/tutorials/tutorial02/02_area_perturbation.ipynb +++ b/tutorials/tutorial02/02_area_perturbation.ipynb @@ -45,66 +45,144 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Example\n", + "### The TOY graph\n", "\n", - "In the cartoon below it is represented the example input file `TOY_graph.csv`.\n", - "In this file are present 19 nodes/elements connected by direct edges that reflect the hierarchy of the system in a parent-child fashion.\n", - "\n", - "The nodes are distributed in adjacent areas.\n", - "\n", - "In area1 are present 5 nodes: 1, 2, 3, 4 and 5.\n", - "\n", - "In area2 are present 6 nodes: 11, 19, 12, 13, 14 and 18.\n", - "\n", - "In area3 are present 5 nodes: 15, 9, 16, 17 and 10.\n", - "\n", - "In area4 are present 3 nodes: 6, 7 and 8.\n", - "\n", - "A perturbation of one or multiple elements in one area may exceed the area boundaries and propagate to other systems connected to it, located in other areas. \n", - "\n", - "Nodes 2, 3, 4, 5 are perturbation resistant nodes (`perturbation_resistant` field = 1).\n", - "These nodes will not be affected by the simulated perturbation.\n", - "\n", - "Nodes 2 and 3 are isolating elements (they are a particular type of `HUB` nodes, called `SWITCH`). In the figure, perturbing node 1 would result in the breakage of all the nodes present in the graph except \n", - "node 15 in absence of perturbation resistant nodes. On the other hand, isolating elements 2 and 3 would stop the perturbation propagation cascade to node 1.\n", - "\n", - "\"TOY_graph\"" + "Let us see how to generate a perturbation that propagates from element \"1\" of the TOY graph. First of all, we need to import GRAPE classes. We are also importing pandas package since we are going to use it for reading the CSV output files obtained." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DEBUG:matplotlib.pyplot:Loaded backend module://ipykernel.pylab.backend_inline version unknown.\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "from grape.general_graph import GeneralGraph\n", + "from grape.fault_diagnosis import FaultDiagnosis\n", + "%matplotlib inline\n", + "from IPython.core.display import HTML\n", + "HTML(\"\"\"\n", + "\n", + "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Simulate a perturbation to \"area1\"\n", - "\n", - "Let us see how to generate a perturbation of an entire area, namely \"area1\" of the TOY graph. First of all, we need to import GRAPE classes. We are also importing `pandas` package since we are going to use it for reading the `CSV` output files obtained." + "Second of all, we define a FaultDiagnosis class and we load the nodes for the TOY graph from the input file." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "import pandas as pd\n", - "from grape.general_graph import GeneralGraph\n", - "from grape.fault_diagnosis import FaultDiagnosis" + "F = FaultDiagnosis(\"./input_files/TOY_graph.csv\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Second of all, we define a FaultDiagnosis and we load the nodes for the TOY graph from the input file." + "Let's have a look at the graph we are going to study." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "F = FaultDiagnosis(\"./input_files/TOY_graph.csv\")" + "initial = { '11': (2., 2.), '19': (6., 2.), '12': (10., 2.),\n", + " '14': (6., 10.), '13': (10., 10.), '18': (2., 10.), '5': (-2., 2.),\n", + " '3': (-4., 6.), '1': (-6., 10.), '2': (-8., 6.), '4': (-10., 2.),\n", + " '6': (-6., -2.), '7': (-10, -10.), '8': (-2, -10.), '10': (2., -2.),\n", + " '17': (10., -2.), '16': (10., -10.), '9': (2., -10.), '15': (6., -6.)}\n", + "\n", + "F.G.print_graph(initial_pos=initial, size=800, edge_width=3., arrow_size=7, fsize=12,\n", + " fixed_nodes=list(F.G), title='TOY graph (integer)', input_cmap='Accent',\n", + " legend_loc='upper center', legend_ncol=4, legend_anchor=(0.5, 1.2),\n", + " legend_fsize=12)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the figure above it is represented the example input file `TOY_graph.csv`.\n", + "In this file are present 19 nodes/elements connected by direct edges that reflect the hierarchy of the system in a parent-child fashion.\n", + "\n", + "The nodes are distributed in adjacent areas, which have been depicted with different colors specifying the `input_cmap` argument of `print_graph` function.\n", + "\n", + "In area1 are present 5 nodes: 1, 2, 3, 4 and 5.\n", + "\n", + "In area2 are present 6 nodes: 11, 19, 12, 13, 14 and 18.\n", + "\n", + "In area3 are present 5 nodes: 15, 9, 16, 17 and 10.\n", + "\n", + "In area4 are present 3 nodes: 6, 7 and 8.\n", + "\n", + "A perturbation of one or multiple elements in one area may exceed the area boundaries and propagate to other systems connected to it, located in other areas. \n", + "\n", + "Nodes 2, 3, 4, 5 are perturbation resistant nodes (`perturbation_resistant` field = 1).\n", + "These nodes will not be affected by the simulated perturbation.\n", + "\n", + "Nodes 2 and 3 are isolating elements (they are a particular type of `HUB` nodes, called `SWITCH`). In the figure, perturbing node 1 would result in the breakage of all the nodes present in the graph except \n", + "node 15 in absence of perturbation resistant nodes. On the other hand, isolating elements 2 and 3 would stop the perturbation propagation cascade to node 1." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Simulate a perturbation to \"area1\"" ] }, { @@ -116,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -132,7 +210,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -343,7 +421,7 @@ "18 18 0 area2" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -355,7 +433,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -554,7 +632,7 @@ "26 18 14" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -575,7 +653,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -592,7 +670,7 @@ "DEBUG:root:Visited: {'1', '2'}\n", "DEBUG:root:Node: 2\n", "DEBUG:root:Node 2 visited, fault resistant node\n", - "DEBUG:root:Visited: {'1', '2', '3'}\n", + "DEBUG:root:Visited: {'1', '3', '2'}\n", "DEBUG:root:Node: 3\n", "DEBUG:root:Node 3 visited, fault resistant node\n", "DEBUG:root:Visited: {'2'}\n", @@ -610,6 +688,14 @@ "DEBUG:root:In the graph are present 18 nodes\n", "DEBUG:root:The graph is dense, density = 0.08169934640522876\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/u/m/mteruzzi/miniconda/envs/test/lib/python3.6/site-packages/grape-1.1.0-py3.6.egg/grape/fault_diagnosis.py:540: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n", + " params, weights, parallel))\n" + ] } ], "source": [ @@ -625,7 +711,32 @@ "\n", "The debug output gives us an idea of what happens when a node is perturbed. Node 1 has no fault resistance of any kind; hence, it gets broken. After that, the perturbation gets propagated on all neighboring nodes, namely nodes 2 and 3 in our case. They are visited, but not affected by the damage, being fault resistant.\n", "\n", - "Since node 2 and 3 are fault resistant, the best state identified for the switches by the genetic algorithm is still the one with switches closed, since it implies the smallest number of manual actions with respect to the initial condition. We are going to give a more detailed explanation on switches activation on a later tutorial. " + "Since node 2 and 3 are fault resistant, the best state identified for the switches by the genetic algorithm is still the one with switches closed, since it implies the smallest number of manual actions with respect to the initial condition. We are going to give a more detailed explanation on switches activation on a later tutorial.\n", + "\n", + "We can have a look at the final configuration using the function `print_graph`. The graph is missing node `1`, which has been broken." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "F.G.print_graph(initial_pos=initial, size=800, edge_width=3., arrow_size=7, fsize=12,\n", + " fixed_nodes=list(F.G), title='TOY graph (perturbed)', input_cmap='Accent',\n", + " legend_loc='upper center', legend_ncol=4, legend_anchor=(0.5, 1.2),\n", + " legend_fsize=12)" ] }, { @@ -647,7 +758,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -1307,7 +1418,7 @@ "[19 rows x 22 columns]" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1327,7 +1438,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -1478,7 +1589,7 @@ "18 ACTIVE AVAILABLE" ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -1496,7 +1607,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1595,7 +1706,7 @@ "1 [['15', '9', '16', '17', '10', '11', '19', '12... 0.125 " ] }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1614,7 +1725,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1625,7 +1736,7 @@ "Name: 18, dtype: object" ] }, - "execution_count": 10, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } diff --git a/tutorials/tutorial02/02_area_perturbation.py b/tutorials/tutorial02/02_area_perturbation.py index 80133bd..f83119d 100644 --- a/tutorials/tutorial02/02_area_perturbation.py +++ b/tutorials/tutorial02/02_area_perturbation.py @@ -3,5 +3,21 @@ F = FaultDiagnosis("./input_files/TOY_graph.csv") +initial = { '11': (2., 2.), '19': (6., 2.), '12': (10., 2.), + '14': (6., 10.), '13': (10., 10.), '18': (2., 10.), '5': (-2., 2.), + '3': (-4., 6.), '1': (-6., 10.), '2': (-8., 6.), '4': (-10., 2.), + '6': (-6., -2.), '7': (-10, -10.), '8': (-2, -10.), '10': (2., -2.), + '17': (10., -2.), '16': (10., -10.), '9': (2., -10.), '15': (6., -6.)} + +F.G.print_graph(initial_pos=initial, size=800, edge_width=3., arrow_size=7, + fsize=12, fixed_nodes=list(F.G), title='TOY graph (integer)', + input_cmap='Accent', legend_loc='upper center', legend_ncol=4, + legend_anchor=(0.5, 1.2), legend_fsize=12) + F.check_input_with_gephi() F.simulate_area_perturbation(["area1"]) + +F.G.print_graph(initial_pos=initial, size=800, edge_width=3., arrow_size=7, + fsize=12, fixed_nodes=list(F.G), title='TOY graph (perturbed)', + input_cmap='Accent', legend_loc='upper center', legend_ncol=4, + legend_anchor=(0.5, 1.2), legend_fsize=12) diff --git a/tutorials/tutorial02/input_files/TOY_graph.png b/tutorials/tutorial02/input_files/TOY_graph.png deleted file mode 100644 index 3fd64a6..0000000 Binary files a/tutorials/tutorial02/input_files/TOY_graph.png and /dev/null differ diff --git a/tutorials/tutorial03/03_switch_activation.ipynb b/tutorials/tutorial03/03_switch_activation.ipynb index b1d7889..bea6edc 100644 --- a/tutorials/tutorial03/03_switch_activation.ipynb +++ b/tutorials/tutorial03/03_switch_activation.ipynb @@ -45,12 +45,95 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Example\n", + "### The switch line graph" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DEBUG:matplotlib.pyplot:Loaded backend module://ipykernel.pylab.backend_inline version unknown.\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from grape.general_graph import GeneralGraph\n", + "from grape.fault_diagnosis import FaultDiagnosis\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First of all, let us define a FaultDiagnosis variable and load the nodes for graph from the input file." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "F = FaultDiagnosis(\"./input_files/switch_line.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's have a look at the graph we are going to study." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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PP3x4DNB+oKysbPtTTz31cvp7cf78+SVdGRa7PyxYsKB4zpw5I5J6/mzU1dckHWStW7fuxf256iPbTqD39es+UD4DA6WdRLxXW5v38oMPHvb2H/84tLWlJenmdEm2HId25ZgRgOwyIA/IAPY3fV1VNxCr6TIPlo877rgeX9lbU1MzeNGiRSMjdg4z0tVhsJYuXbqmoKCgOSLisssuy+qrDjOHqPzLX/6yx0HXFVdcsav9v/jFL17dV+2iZ+6+++6iLVu27Nony83NjSuvvLJfnuvKK6+M3NwP3zJbtmzJ+elPf3pYvzwZHSotLd0WEdHQ0OCkCTvdfXdRZPQDkZsb0U/9QFx55c7tp23ZkhMDvB/IvHjn6quvTqQyfvHixcPnz59fUllZmbUX++xrXpP27a8n0AfK33ugtPNAV/df/zXkV5Mnn/KfN910zO+mTTv5mf/v/8vKUU8yZdtx6N6OGQHILoI6gJ4b3Fe34447LjVlypR3Mjfe06q69qrppk6d+s6xxx6b6ss2d7the5E539wdd9zR4yqTefPmHZH++eqrr17fnXUvuuiiDRE7r3DO5qq6f//3fy9M/9y2Cm7BggXF6YnKp0yZUqdKbp/o1Wdp2bJlh2Zu7Lzzzuv1HJUdKSkpiXPPPXe3ZcuXLz+kl78D3VBXV5ebrh6eMGFC494ez4DQ++/UNv1AnHdeRD/1A1FSEtGmH4j9oB8oKyvbHBHx4osvDku6LbA3TqCTsL48JuyX26u//OVHmjMuYHm5omLk1nfeGdJH2+8X2XYc2tkxIwDZZ1DSDQAYoAZHxKl9ucF58+bFgw8+uKsSLl1Vd9ttt3VrO22r6QYPHhzz5s07PCIO73itHnkhIvpsh7+4uLi5rKxsc01NzdDKysqiqVOnjvrRj37U6RCY7Vm2bNmuA5vMuQC64qKLLqpPXwX585///LDurr8vLF68uCh9kn/GjBl7HAD+7ne/23VAdsEFF7zT9n76XK/7gpUrV+72/4kTJ/Zmc3s1ceLEWLJkSebzHxoRh/Zik33aF+zP6urqcj/+8Y+fHBFRUFDQ/KMf/ajnYxyTLfpmf6BNPxD93A/ExIkRGf1A6Adgn3ICnQT1+XFsv2ht3WPR8wsXln3qu9+NVCrVzgrd0i/fWdl0HLq3Y0YAso+KOoAsUVpaGt/4xjd2W9bdqrr2qukuvfTSfqvO6Wt33HHH2vTPFRUVxcOHDz99zJgxJ8+cOfPIrlxZWFdXl1tbW5sf8eGV9d0xfvz4XetkHmhlg+XLlw+dNGnS6OnTp4+O2Dmcyu233/5m28c9+eSTu078ZGPQyO4aGhrilVde2W3Z2LFj+/U5227/5ZdfjsZGb5X+UlNTM3jJkiUFU6dOHTV8+PDTa2tr88vKyjY//fTTNfvzXEx0Q0NDRJt+IPq5H9hj+y+/HDGA+4G6urrc9JxAZ511VkN7j1myZEnBuHHjji8sLDw9lUqNTe9fdDTs4MyZM49MpVJjx40bd3x62Zw5c0aMGTPm5MLCwtMjdlaxp1KpsalUamz6+efPn1+SXtZ2/YiIqVOnjkqlUmPT22hPet05c+aM6Em7OrJ8+fKhU6dOHZV+bGFh4emTJk0a3dnQi23XSb927bWtp69J+ncqLS09paN21NTUDJ46deqo0tLSU9Kv37hx445fvHhxUUfrpF/r9PO19x7o6PfoT109gd7d92zb1yj9OqXXq6mp2a2SqLPXvavb6snfO6L776u0nv5Ne9pOOrbt3XfjyauvjgfGjNnr7fczZ0bDa691a91Xf/nLPZ7z1f/zf+Lfv/71aFi7do/7kpYtx6FdPWYEIPuoqAPIIt/5znfirrvu6nFVXXvVdN/5znf6pa39Yfz48ZuXLVu2csqUKaPTBzo1NTVDa2pqhqavMJw4cWL9tddeuz7zYCZt1apV+emfjzrqqB7Pcxexc9iR3qzfE5WVlUVtT5Zs2rRpUHooy7QZM2as7+iAK/3Y9DwHfamqqqowlUrt9exxSUnJtnXr1r3Y18+/P9qwYcMey0466aR+fc72tr9hw4YoKMiqbHqXrr7vskFFRUVxRUVFcUf3T5w4sf6b3/zmBiE6u2mnH4h+7gfa3f6GDRFZ2g/szXnnnTc6/fO11167R/Axc+bMI9P7ERE7vyPT+xf333//8Keffrqms6qm5cuXDz3nnHNOaPt9fPjhhzenT8imT/4XFBQ0p+ehjIg4+uij+61aqqN2tdXY2Jg7adKk0ZWVlUXpNqbXqaysLPr4xz8+9Nlnn13Z9uKB9l63kpKSbenX7t577x3edr3+eE3mzJkzYv78+buuOku3v6qqqrCqqqrwzjvvbHjkkUfWdHbxw9SpU0e17Z/Tv8eKFSuGZc5z2F+WL18+dP78+SPTf4fOTqB39z27fPnyoRMmTDg5/f+ysrLNDQ0NubW1tfnp9Q4//PAd8+bNe7sr7ezqtnry9+7J+6o93fmbJv1Z3R/98YYbYt2//VuXHvvnJ56I99atiy88+mikUqn443e/G+t++9sePe/bf/hDPP73fx/n/upXkTMoe05p7svj0L44ZgQg+6ioA8givamqG+jVdGnjx4/fvG7duhdvueWWteXl5XtcFV9ZWVk0YcKEkxcsWLDHyfDOrggfKGpra/Mzb5kHXDNmzFi/YcOG57pywLW3k3Y9VVJSsm1vt8yTHnQuM1hPGzJkSL8+Z3vb37Ytu/9kXXnf9Uc43ROdtamysrLojjvuGJ7Nc2CSgHb6gejnfqDd7Wd5P9Ce5cuXDx0zZszJmdVJbS/kyQwFbrnllrWtra0rGhoantuwYcNzU6ZMqWtsbMz9yle+cmxHz7Fu3br8CRMmnNzY2JhbVla2+e67716zbNmylRER06ZNq6+url5ZXV29sqSkZFtExOWXX/5Well1dfXKBx54oF9KPzprV3tefPHFoQ8//PDqzN9/4sSJ9RE79z0yw85MZWVlmzPXW7du3YvLli1bWVBQ0Nzeen39mmSGdNdee23thg0bnmtoaHiutbV1xcMPP7y6pKRkW1VVVWF6WOH2VFVVFVZUVBSn//6tra0rli1btqt9lZWVRW2rzXoqfQI985au9powYcLJ6ZBuxowZ65966qmX29tGT96z3/zmN0dF7LwgpLW1dUV1dfXKdevWvfjBzy/MmDFj/cSJE7t0kUh3ttXTv3d331dtdfdvmuRndX+1YcWKbj2+4bXXYvumTTvX/dOfevXc79fWxtZ3smuE/319HNpXx4wAZI/sufwEgIjoeVXdQK+ma2vWrFl1s2bNqovYeSLu/vvvL3riiScK01fBzp49e9Rxxx23LbMy5YQTTth1lvGNN97oVUVcEif9J06cWN/2iu62VzXv7erizKvka2pqBvflvCfl5eUNHZ1UomcGD97zvOCWLVti2LBh/facW7Zs2WNZfv4+LyDtsu6875KuvJsyZUpdeyf66urqcpcvXz70jjvuGF5ZWVlUWVlZVF5evtcKEA4Q7fQDsWVLRD/2A9FOPxBZ2g88+eSThZlD0m3atGlQuronvaygoKB57ty5ten9hrTly5fvqsh/+OGHV2fuMxQXFzc/8MADa+vr63MrKyuLli9fPrS9av3089xyyy1r224/Sd1tV9sKpeLi4ualS5euSVclVVVVFbZ9DW666ab1xcXFe5zoHT9+/ObLL7/8rfnz55dUVVUV1tXV5fZHX/ZBBVpJRMTdd9+9Ztq0afWZ90+ePLlx/PjxK0ePHn1qbW1t/syZM4/s6MT0jBkz1me+TuPHj99cUVGxJr2P9etf/7qwrKysT/6+nVXDzJgxY/0Hr2u7r1dP37Pr1q3Lj4i44IIL6ttus6ysbHt3Ttj35bba01fvq335N2VPw8eO7XJFXURE4ejRMfiQQ3au+4lP9LiiLiLi4NLSOOiww3q8fn/Yl8ehfXHMCED2UVEHkGV6UlW3v1TTdWT8+PGbb7/99jerq6tX3nLLLbtOgl922WWjMh+XGUqlA73uWL58+a51sqUqbPz48ZunTJlSFxGxaNGikZltbM8pp5zyfvrn1atXZ+dZV3b5yEc+sseyVatW9etztrf94cOH9+tzHuiKi4ubJ0+e3Lh06dI16YqBqqqqws985jMnJN02skA7/UD0cz/Q7vaztB/IHOKwqqqqsKamZmhtbW1+QUFBc3l5ecOMGTPWr1mz5oX2wqqf/OQnwyN2Bv4dDTmbHirz/vvv73CusylTptRlU0iX1p12dXTSNvPigravQWcnejOrs/a2b9JTV1999ZERO6uv2oZ0acXFxc1z586tjdi5n9RRVctll122xxizmaHku+++2yfVMOkqtMxb20rHzl7Xnr5n0/utc+fOPXLx4sVFvanu6ctttaev3lf76m9K+z793e/GqC98oUuP/ehnPhNn/+hHkUqldq57ww1dXretkeXl8Vc//WlWDXsZkfxxaHePGQHIPtn1zQYwcGyPiBf6a+NXXnll3p133nliU1NTKmLvVXVtq+ny8vJar7zyypcioqm/2hg7X4N9btasWXWvvvpq/qJFi0bW1tbmt736u6ysbHP64Kijq+M7knnC4+yzz95j2M1MmzZt6tJ3aF+c3HjggQfWPvbYY0WNjY25U6ZMGd3Z/G9f+tKX6tNDgN1xxx3DzYXV73rVFxQUFMSoUaNOXLt27a6SmhUrVsTYsf1XGLaizVBFo0aN2l5QUPBSLzY5IOd02Zef4Uzjx4/ffN999716/vnnn1BTUzN0zpw5I7oyZxBZq/f7AwUFEaNGnRgZ/UCsWBHRj/1AtB2ybNSo7ZGl/UBZWdnm6urqDod07Mzzzz8/NKJrc12+/vrrHV7c8q1vfaudiQST11ftKikp2VZbW5uffr264rDDDtuR/rm+vr5fziuk92f2tk92zjnnNMyePTsiIh599NHC9kK9vhxhoLvSJ9ArKiqKFy1aNPKiiy6q72j/tKfv2fnz59eef/75J9TW1uZPnz59dMTOv+uECRMaL7jggne6sz/Yl9vqru68r5L8mw5wfXIcO/iQQ+Ksm2+Os26+uV/WfXLWrJK1S5fudvFA6d/8zbsTFi5c1/3W7qFf3jv76ji0I905ZgQg+6ioA+i57f11O/bYY9+/8MILd7tCuqOquvaq6b761a/WjR49+v3+bGO3X60+dNFFF+06AfPyyy/vdmJt2rRpu05azZ8/f2R0w/3337+rnKC9q3R7cqXkM888s+txmUOidNfChQvXRuwcTmnOnDkjOnrcrFmz6tLDpfTlnCt0qlefpVNPPfW9zI1VVlb2a2Pbbv+D5x+QfUF3JfkZzjR58uTG9Of03//93wv7YpskqvffqW36gejnfmCP7e+n/UD6c15WVra5vLy8obPb0Ucf3eHnuzsnW/elvmpXunqjowsYFi9eXDR16tRRY8aMOTk959qYMWNO7Yvn7kjmRRLHHntsp33vRz7ykV0VWq+++uoe+z3ZMIfpAw88sDbdjilTpnQ4/1pP37OTJ09uXLZs2cqysrJd74na2tr8ioqK4vPPP/+EzOFj96Yvt9WZ3ryvsuFvOsD15zFin9xyhwzZ44LTM+bMWdtH2+8X/Xkc2lVdPWYEIPsI6gCy1PXXX/9WXl5ea/r/6aq6ttqrprv++uvf2jetTEbmyZuioqIdmfe1Daq6OuzHzJkzj0zP7TZx4sT6jq7SzTxp0ZUQbMWKFcMidp5Q6M2Vv9OmTasvLy9viIiYP39+SWfPnR4CKiLiK1/5yrE9fU72jbPPPnu3K9MfeeSRToe67Y3a2tp45JFHOn3+/V1Sn+FMdXV1uen+5pBDDnGykYi2n8NHHonop34gamt3br+z599PpPcHpk2btuGpp556ubNbR/NuZWsg0JftSs9JdtRRR+0WiC1YsKC4sLDw9OnTp4+uqKgobmhoyC0sLGw+5ZRT3k/vk/SXzOER//SnP3U6YeOqVat2XbR16KGHZuXfK6JrJ9B7854dP3785urq6pXV1dUv3HLLLWsnTpxYn95eVVVV4aRJkzoMCNvqy221leT7ioHjuK98pS53yJCW9P+PnzJl/ZDhw3d0tk7S+vs4tCu6c8wIQHYR1AFkqeOOO65pb1V1HVXTHXvssf055GW/WL58+dCZM2ce2ZXH3nzzzSMjdp7MaG/4nfvuu+/V9M/nnHPOCXs7QFm8eHHRokWLdm3zn//5n9d29NhZs2atT/981VVXdToJ4PLly4dWVlYWRURcfvnlvQ5PH3nkkV2ThncWwM2aNasuPUdBTU3N0DFjxpzcleH7XHWZjOnTp9cPyTgR0dzcHLfeemu/PNett94aLS27niqGDBnS8vWvf/2dfnmyLJXkZzjt4osv3jW/5je/+c2sHFKPfWz69PrI6AeiuTmin/qBuPXWiIx+IIYMaYn9tB8466yzGiIi/uVf/qXD+eeyTV8Pt9uV56utrc2PiBg7duyueW4XLFhQPHv27FGNjY25d99995rW1tYV69ate7G6unrlU0899fJdd93V4b5SX0lfWLFs2bKCzh5XWVm56/5zzjkna4OerpxA74v3bFlZ2fZZs2bVLV26dE1DQ8Nz6X3C9PdZUtuKyI73FQND8cc+tuULS5a8+Mnrrnvtb+69d+UZ/+t/tXsxRbbpz+PQrurqMSMA2UVQB5DF9lZVtz9V01VWVhYsWrRoZGlp6SkLFiwobu9EVV1dXe6kSZNGp+csSV+Z3NbkyZMbr7322tqIiMbGxtwzzzyzbPHixe2eUJgzZ86I9PwbERG//vWvV3c2yf20adPqJ06cWP9Bm4sWLFhQ3N7jampqBqeHNiorK9vcF3NQFRcXN6d/r5qamqEdPXfEziGWMsO64cOHnz5z5swj217ZuXz58qFz5swZUVhYePr8+fM7DS02bdo0qKamZnBXb739fQ8Uhx9+ePPkyZM3Zi5buHBh/OEPf+jT53n66adj4cKFuy07//zzNx522GEtHayyX0ryM7x48eKiMWPGnJw+wXnttdfWmkeSiIg4/PDmaNMPxMKFEX3cD8TTT+/cbqbzz98Y+2k/8IMf/KA2YmcFUEf7AWl99b21Zs2ag7ryuHT1RFuPPvpovwyH29Hvd9555+3aB5o6dequocV/97vfFUZElJeXN7Q359vq1as7nNOvra6+Jm2lL6zorAKtpqZmcHr/pby8vCHb5y3b2wn0nr5nOwt4L7jggm4F8b3dVmd/7758X/VWT9+X7DsHl5Q0Hf/Vr77zkTPO2JzKGRinL/vzOLSrunPMCED2GBjfdAAHqM6q6vanarqID4cqqq2tzZ89e/ao4cOHn/7BXBUnT5o0afSYMWNOHj58+OnpE9wzZsxY394Bftq8efPevvvuu9dE7DxImj59+ujS0tJTpk6dOmrmzJlHTpo0aXRmOFVQUNBcXV39Qlfme1m6dOma9In+2bNnjxo3btzxCxYsKF6+fPnQxYsXF82cOfPIMWPGnFpbW5tfVla2+T/+4z9W98VrlP690leYz549e1RnJ1MeeOCBtXffffeakpKSbRERixYtGjlhwoSTU6nU2PRtwoQJJ8+fP7+ksbExd2/DDX1QnXdqV2/Cuq675ppr3h40aNCuUL6lpSW+9rWvRX19h2/xbqmvr49LLrlkt2q6QYMGtV599dW9Dp8Gov76DD/22GNFkyZNGp15Gzdu3PGlpaWnpFKpsdOnTx9dU1MztKCgoPmWW25Z2xfhH/uRa655OzL6gWhpifja1yL6qB+I+vqISy7ZvZpu0KDW2I/7gbKysu3pk5XTp08fPXPmzCMzvzfr6upy08Pw/fjHPx7e8Zb2Lj3P22OPPVaUfo4lS5YUtB0icPTo0VvTP7f9nly+fPnQK664YlT0g89//vMnZF6sU1NTM3jcuHHHpy9+mjFjxvrMkCtdXddeYLRkyZKCyy67bK/t7Opr0pFp06bVpy86mj9/fsnUqVNHpV+z9N/uzDPPLIvYuR+XGYJlq72dQO/pe3b06NGnjhkz5uTFixfvNj9xTU3N4Guvvbbkg213aU7Dnm6rK3/vvnhf9VZv35ewN/15HNqdNnT1mBGA7NDuZNEAZI/rr7/+rYceeqi4qakpFfFhVV1ra+t+U00XsXO4xksuuaT+uuuuG3n//fcPb2xszG1sbMytqakZWlNTs+vEUklJybYf//jHa7tShTJt2rT6c88997nLL7+8pKKiori2tja/oqJityt1CwoKmi+//PK3unvCfOnSpWsWL15cNHfu3COrqqoK0ye6ervdrrjjjjvWTpgw4eSInVfCP/XUUy939Nhp06bVT5s2rX7x4sVFP//5z4tefPHFoZs2bRrU2NiYW1BQ0FxaWrrt7LPPbrjssss2ZPtV6Puz0047bduVV17555tvvnnX8K8vvfRSnHPOOfHrX/86iop6PmpbfX19TJo0KV566aXdll911VV/Pu2007Z1sNp+rz8+w42NjbkdDQdWVla2+aijjtr213/91w2zZs2qa+8xHOBOO21bXHnlnyOjH4iXXoo455yIX/86ohf9QNTXR0yatHN7ma666s+xn/cD6c/w/PnzSxYtWjRy0aJFIwsKCprbVrQdfvjhvZr76Etf+lJ9VVVVYWNjY+7w4cNPz7yvpqZmcPo7duLEiY3z58+PiIgxY8acmr5IZt26dfm1tbX55eXlDW37o75QWFjYnN53aGvixIn1bec7+5//83/W/ehHPzoifZJ5+vTpkfm6dWWOvK6+Jp154IEH1h5yyCE7Fi1aNLKioqK4oqJij8qQsrKyzb/4xS9e7YtKlH1h3rx5bz/88MOH1dTUDJ09e/aoSy65pD6z7T15z6b3mTOrc9qaP39+lya+7Om2uvL37ov3VW/1xfsS9qY/j0O7qjvHjAAkT1AHkOXSVXU/+9nPdl01e+edd+7xuIFcTZdWXFzcfPvtt795++23v1lTUzP4D3/4w7CNGzfmvvvuu7mHHnpo85lnnrm5u1caFhcXNz/wwANrH3jggbVLliwpeOWVV/J7s71M6RBs+fLlQ59++umh6e0ed9xx27o6nF1ZWdn21tbWFd153vHjx2/u7jrptnZnnYietY+emTdv3vpf/epXRdXV1buC6aeffjrKy8vj3nvvjU9/+tPd3ubTTz8dX/va12L16t0LwsaMGbP5pptuWt/Baonr6fuup5+L3nyGi4uLm31G6DPz5q2PX/2qKDL6gXj66Yjy8oh7743oQT8QTz+9szKvTT8QY8ZsjizuB/rSvHnz3p46dWr9vHnzjli2bFlBbW1tfl9frDJr1qy6P/3pT8Mee+yxooidVTunnXba5m9961u7bXv8+PGbb7nllrULFy4cWVtbm//iiy8OKy0t3TZhwoTGCy64YO3kyZMbU6nU2N7+zm1VV1evXLBgQfHixYuHr1u3Lj8i4pRTTnn/0ksvrWtv/6C4uLj56aefrvnGN74x6sUXXxwWEXHIIYfsOOussxr++q//uuGSSy6pbxtytNXV12Rvbr/99jcvu+yyDfPmzTvi+eefH1pTUzO0pKRk2ymnnLJ5oF78sLcT6N19z7a2tq5YsGBB8e9+97vCN954I3/dunX5jY2NuSUlJdsmTJjQOGfOnLe6+pr3dFtd+Xv3xfuqt/rqfQl705fHofvqmBGA5KRaW1v3/iiAA8CKFSuKIuK3EREHHXTQyRERJ5xwwuq8vLzEr8595ZVX8srKyk5NV9W1lZeX17py5coXBnpQBwe6l156afCECRNO2rBhQ17m8pycnLjiiiviyiuvjJKSTqcSjIiI2trauPXWW2PhwoW7DXcZETF8+PCmZcuWrTrxxBOdjIJs9NJLg2PChJOiTT8QOTkRV1wRceWVEV3oB6K2NuLWW3fOSdemH4jhw5ti2bJVoR8AAPaBpqam3NWrV58QEbF169aVHyz+27Fjx/bRGN8AA5s56gAGgPbmqsu0P1TTAREnnnji9srKytXDhw/f7fPc0tIS//AP/xBHH310nH/++XHHHXfEM888E++99140NzfHe++9F88880zccccdcf7558fRRx8d//AP/9BuSFdZWblaSAdZ7MQTt0dl5epo0w9ES0vEP/xDxNFHR5x/fsQdd0Q880zEe+9FNDfv/PeZZ3YuP//8nY/7h39oP6SrrFwtpAMAAMgOKuoAPpDNFXURHVfVqaaD/c+qVasGf/nLXz42cxjM3hozZszmX/7yl68K6WCAWLVqcHz5y8dGH/YDMWbM5vjlL18V0gEA+5KKOoDOqagDGCA6qqpTTQf7n5NOOmn7s88+u/Kaa655c9CgQb26qmrQoEGt11xzzZvPPvvsSiEdDCAnnbQ9nn12ZVxzzZvRy34gBg1qjWuueTOefXalkA4AACC7COoABpDrr7/+rby8vF0n6/Ly8lqvv/76t5JsE9A/8vLy4nvf+976Z555pnrq1KkbhgwZ0rL3tT40ZMiQlqlTp2545plnqr/3ve+tz8vL2/tKQHbJy4v43vfWxzPPVMfUqRuim/1ADBnSElOnbohnnqmO731vfegHAAAAss6gpBsAQNelq+p+9rOfDY9QTQcHgtNOO23b/fff/8Y777xT+9Of/vSwJ554ouCFF14Ytnbt2vy2jx01atS2U0899f2zzz678etf//o7hx12WPdO6gPZ6bTTtsX9978R77xTGz/96WHxxBMF8cILw6KdfiBGjdoWp576fpx9dmN8/evvhH4AAAAgq5mjDuAD2T5HXVp6rrqICHPTwYGrvr4+56233hq0devWnIMOOqjliCOO2FFUVOSEPBxI6utz4q23BsXWrTlx0EEtccQRO0I/AABkGXPUAXRORR3AAJOuqkulUiGkgwNXUVFRS1FRkbmm4EBWVNQS+gEAAIABTVAHMABdf/31b6VSqaSbAQAAAABALwjqAAag4447TiUdAAAAAMAAl5N0AwAAAAAAAOBAJKgDAAAAAACABAjqAAagV155Je/VV1/NS7odAAAAAAD0nDnqAAagG2644YhUKhX33XffG0m3Beh/LS0tkZPTveurerIOAAAAAPuWszcAA8wrr7yS99BDDxU/+OCDxarqYP93/fXXj7jkkkuOamlp6fI6LS0t8bWvfe2o66+/fkQ/Ng0AAACAXlJRBzDA3HDDDUc0NTWl0j+rqoP91/XXXz/ixhtvLImIOProo7fdeOONb3d1vZ/97GfD0/+/4YYburQeAAAAAPuWijqAASRdTZf+v6o62H9lhnQRETfddFPJXXfdVbS39e68886iefPm7VrvxhtvLFFZBwAAAJCdBHUAA0hmNV1ERFNTU+qGG244Isk2AX2vbUiX9q1vfeuYysrKgztar7Ky8uBvf/vbx7RdLqwDAAAAyE6COoABom01XZqqOti/dBTSRewM5y+88MLjnn/++fy29z377LMHXXjhhcdlhvmZhHUAAAAA2UdQBzBAtK2mS1NVB/uPzkK6tIaGhtwvfOELJ6xbt27XXMPr1q0b9MUvfvH4hoaG3M7WFdYBAAAAZBdBHcAA0FE1XZqqOhj4Ogrpbr755jj33HN3W/bmm28OPuecc45vaGjIaWhoyJk0adLxf/7znwdnPubcc8+Nm2++eY/nEdYBAAAAZA9BHcAA0LaabvDgwTF48Ifn5PeHqrqamprBqVRqbCqVGjtu3Ljju7LO8uXLh6bXWbJkSUFnj128eHHRuHHjji8sLDw9lUqNLSwsPH3MmDEnT5o0afTixYuLutKu7tza+x26sq3S0tJTxo0bd/zMmTOPrKur67Q6iv1HRyHdggULYvbs2VFRURFnnHHGbve9+OKLQydPnjx68uTJo6urq4dm3vfJT34yKioqYvbs2bFgwYI9nm+ghXXd/fwuWLCgOP2Z6uzznWncuHHHp9fp6mdvzJgxJ6dSqbFz5szZ9VrurS9L/w69vRUWFp7elTYuXry4aNKkSaNLS0tPyVy3tLT0lPTr197v25M+OSKiu+2DruiL7/CO3sd1dXW5bT9fNTU1g9t7bKapU6eOSn9vd/d3mTp16qjS0tJT0r9P+rt/6tSpo5YvXz60o3V7+rnsaBu93a8BAAD6hqAOIMu1V0136aWXxje+8Y3dHqeqrn3Lly8fOmbMmJOnT58+uqqqqrCxsTE3IqKxsTG3pqZmaGVlZdH06dNHFxYWnt7ZybG+VlBQ0Dxx4sT6zFt5eXlDRERVVVXhokWLRg4fPvz0qVOnjhLY7d86C+muuuqqiIgYNmxYPPbYY3H00Ufv9pjHH3/8kMcff/yQzGXHHHNMPProozFs2LCIiLjqqqsGbFjX08/vJZdcUp/++ec//3mXgrqqqqrC9M+PPvpoYWePjdh5cr+mpmZoRMTUqVPr9/b4tEMOOWRHVx/bG4sXLy4qLCw8ffr06aMrKyuLamtrd81r2NjYmFtbW5uffv0uvvjiUfuiTex799xzz6H//b//96O//vWvl/7xj38cknR7uiup7/Crrrqq0yGIe2Lx4sVFpaWlp0yfPn10RUVFcW1tbX7696mtrc2vqqoqrKioKJ4wYcLJpaWlp/juBw40257fduh7v3jv6Pcffb90x5s7Btx3VmcWL15clHlhZnfW7a8LSrp64UZpaekpU6dOHbW3C2MB6J1Be38IAElqr5ruO9/5TrS2tsZdd90V27dvj4gPq+ruu+++NxJrbJapq6vLPeecc05obGzMLSgoaL788svfmjhxYuNJJ520LWLnCcDf/OY3hYsWLRrZ2NiYm17ekfLy8oannnrq5b5o21lnndWwdOnSNR21+4c//GHx/PnzSyoqKoqXLVtW8Oyzz64sLi5u7ovnJnt0JaRLGzFiRCxdujTGjRsX9fXt50KHHXZYLF26NEaM2D1/S29r1qxZuy1PP/cNN9zwdi9+jX7Rm89vcXFxc1lZ2eb0ify9PVe6IqekpGRbbW1t/s9//vOiadOmdRq+3XPPPbvWKSsr297V32vdunUvdnZ/KpUam/65tbV1RVe3m2nq1KmjKioqdl3gMWPGjPWXXXbZhnQ7a2pqBq9evTr/jjvuGF5ZWVk0duzY93vyPGS3f/qnfyr65je/OTr9/5/97GfDn3nmmepTTz210++6bNHX3+Fdke4DKisri5YvXz50/Pjxm3v/m+z+mSwoKGi+6KKLNmR+Juvq6nKXL18+NP2ZrK2tzd8X3/l9uV8D0Bvb/rStaPOvNu/6ztr+wvbhhd8orM79SO6A+M7amwULFoxM/1xbW5u/ZMmSgsmTJzf2dHtXXXVVSUfHkj1RUFDQfNZZZzVkLtu0aVPupk2bBtXU1AytqKjIr6ioKC4vL2+466671nZn3xeArlFRB5DFOqqmKykpidLSUlV1e3HeeeeNTp/gW7NmzQvz5s17e/z48ZuLi4ubi4uLmydPntx4++23v1ldXf3CxIkT67MlCCsuLm6eN2/e28uWLVsZsfNgTsXL/qezOenahnRpJ510Uvzrv/7rbkPfpuXn58e//uu/xoknntjuulddddWAmrOut5/f888//530z3u7AjhddXfFFVesj4joSrj3L//yL0UREV/84he7XE23L2QGAmVlZZs3bNjw3O233/5m5gmVsrKy7ZMnT25cunTpmurq6he6UxHIwPGLX/zisMz/b9++PXXTTTeN7Ojx2SaJ7/ArrrhifUFBQXNExNVXX31k73+LPT+Ta9aseaHtZzL9+yxdunTNsmXLVk6ZMqWuL54bYKDYXrN9t++saI7Ulie2DJjvrM7U1NQMTo/CkHbHHXcM78m2SkpKtkXs3Ffty0ry9EWkmbennnrq5erq6pUbNmx47tprr62N2DkCxZgxY05VXQfQ9wR1AFmso2q6tO985zv73Vx1fSk9lN3cuXNrOzuBV1ZWtr0vr0jsK+PHj988ceLE+oidB2OGwdp/tLS0xJo1a/Lbu2/ZsmXx/vsdFzhNmDAh7rvvvj2W33fffTF+/PgO13v//ffjiSeeaPe+1157Lb+lpWVvzd6nevv5zQyffv7znx/W9v5MTz75ZGFJScm2zCEz93YCIt2+iy66KGtCrsWLFxdlBgLV1dV7rcQtKyvb7qro/dOWLVv2ONb7/e9/f0hzc1Zck7JXSXyHH3744c2XX375W+nn7+2JyMzPZElJybaufCbHjx+/+YEHHljbm+cFGGhad7Tu8Z21Y+2OQ1pbWpNoTp/68Y9/PDxiZxVzWVnZ5oieH9v1xwUle5O+iLS6uvqFdFB4/vnnn9CV4TcB6DpBHUCW6qyaLk1VXccyrzA87rjjBuyQKZlD0v3lL38R1O0ncnJy4t57731jzpw5tW3ve/TRR+Nzn/tcvP12x6NRXnjhhbtVx91yyy1xwQUXdPj4t99+Oz772c/GY489tsd91113Xe0999zzRk5O9uwW9sXnt6ysbHv6ZMJjjz3WYYXckiVLChobG3MnTJjQmB4yM6LzcC89VGZBQUFzXw2N1xeuuOKKXZW3v/jFL15Nsi0k7xOf+MQeif+GDRvybrvttsOTaE93JPkdPm/evLfTJ0GvvfbaXs1Vl/mZ/PGPfyx8A+jAoJGD9vjOat3cmrftP7dl/XfW3tx///3DIyIuvfTSumnTpm1IL//hD39Y3PFa7evrC0q6o6ysbPtvfvOb1en/f/7znz9hXz03wIEge87IALCbvVXTpamqa1/mXDU9HVokG/z7v/97YfpnVS/7l5ycnLjpppve/qd/+qc1eXl5u10u/J//+Z9RXl4eL730Uofrz5o1K771rW/Ft7/97Q6HyoyIWLVqVZx55pnxzDPP7LY8Ly+v9c4771xz4403vp1NIV1E331+08NSNjY25nY0PFA6kLvgggveiYg4++yzGyI6D/fSQ2Vm07CXCxYsKG5sbMyNiJgyZUqd/oLLLrusLjc3d49ShO9+97sl69evz+oLP5L+Dp87d25tRERNTc3QdDDfXZmfyfLy8obezEUEsL/L/1R+XaRij++srf+xtaTlvZas/s7qzOLFi4vSwzhPmzatPnP0hnvvvbdH3299eUFJd5WVlW1PD4NZW1ubv2DBgm6HjQC0L7vOygAMLIP76/bqq68O21s1XVpHVXVr1qwZ1p9t7PartY9lVsZUVlYWTZ06ddRAGzpy8eLFRemhv2bMmLE+6fbQoV59li699NL3H3300TWFhYW7DYf22muvRXl5eSxbtqzdJ02lUrFw4cJYuHBhpFKpdh/zxBNPxLhx4+L111/fbXlhYWHzo48+uuYb3/jG+71tfxdfo27pq89v5rCU999/f7sn25ctW1YQEZE+iZ5ep7NwLz2HXTrcywa/+93vdoX62dSuA0R/ftf2+Hb88cfHl770pXfbNvbdd98d9Fd/9VcnrV69ui/3E/pU0t/hs2bNqktX5M6dO7dHQ4ul57GMiLj66qt9hwPZJPHvqLa33MNyI++kvHfbNrR1W+ugxn9uPKl5Y3PWfmd15s477yyO+PDiruLi4uby8vKGiJ1BV0/nmeuLC0p6at68ebuG/Vi4cOF+MY8gQDYYlHQDAAaowRFxan9t/NZbb42mpqYPn6yDarq073znO3HXXXfF9u07CyiamppSt95660m33XZbfzUxIuKFiMjqio077rhj7YQJE06OiKioqCiuqKgoLisr23z22Wc3fP7zn8/aq9uXL18+dP78+SPTYUB5eXnD7bff/mbS7aJdfdIXfP7zn48//OEPMWnSpN1Ctfr6+vibv/mbuO++++LCCy/cY71BgzrelXvwwQfja1/72q5+Ie2YY46JpUuX5p544onH9rbdH+iXvqAvPr/jx4/fXFBQ0NzY2Jj72GOPFbX9HNXU1Ayura3NT58wabvO/fffX9R2aMv0EEMFBQXN2dSHPPnkk7uCumxq1wGgz/cHGhoa4pVXXom+mDfyvPPOi1/84hd7LF+5cuVB48aNO+mee+6JL3zhCx2G/d3Q5/1A0t/hP/7xj9eef/75J6QrBmbNmlXXnfVffPHFYemfTzjhhAE7BDew3+nT763Wba3R/E5z7FkL1315J+RF08qmPZa31LUc1Li48aSh5w2NvOPzsvI7qz01NTWD0xddfutb39o15OWll15al14+f/78kT2ZZ3XWrFl1CxcuHFlbW5s/d+7cI6dNm7ZPR3koLy9vqKqqKqytrc2vqakZbCQHgN4T1AFkmXXr1sVdd92127KOqunS0lV1P/nJT3Ytu/POO+M73/lOp+vt78aPH7952bJlK6dMmTK6trY2P2LnVYc1NTVDFy1aNDIiYuLEifXXXnvt+q7MM1VVVVWYSqXG7u1xJSUl29atW/diZ4+prKwsKi0tPSVz2aZNmwalh8lKmzFjxnoh3YHhpJNOiqeffjq++MUv7jZM5fbt2+OrX/1qvPHGGzFr1qy9npxobW2NW265Ja655po97vvkJz8Zjz76aIwYMaLP29/X+urz+8UvfrG+oqKiuLa2Nr+uri63uLh4V+XiAw88UBQR8aUvfam+vXXaC/fSQ2WeddZZDZFF0n1HeiikvtTVvo/eaW1tjeuvvz6+//3v7xGw94eNGzfGueeeG6eddlrcddddccYZZ/T7c3ZHX3+Hd9fkyZMby8rKNtfU1Ay98cYbS7ob1GV+n2frCcy+3K8BDiytra2x9T+2xtantkb0+Z5HO8+3pTXef+j9yB2RG0O/ODQGfTT7T2f++Mc/Hh6xsw/N/J6aNm1a/fTp0yNi5zFh2/3Tbmy/VxeU9MZf/dVfNaTDxj/84Q/DsvV7DmAgMfQlQJb53ve+t9sJur1V06W1natu+/bt8b3vfa9f2jiQjB8/fvO6detevOWWW9ZmVs2kVVZWFk2YMOHkro6vX1JSsm1vt9LS0i5dOV9bW5ufecs8qTdjxoz1GzZseE5Id2AZMWJE/P73v49zzz13j/uuvvrq+B//43/Ejh07Olx/x44d8e1vf7vdkO7cc8+Nxx9/fECEdGl98fnNHAbynnvu2W1ooIcffviwiIhzzjmnob110lcJZ96XnrvuggsuyJr56TK1Dfv7Slf6vv4ICQ8ky5Yti5tuummfhHSZnn/++Tj//PM77VuS0tff4d11xx13rI3Y+bmaM2fOwOk8u6Ev92uAA8eON3bE1mX7JqTL1Px2c7z3i/eitaUPSvj62f333z88ov05jSdOnLhr2Q9/+MMefYelLyiJiLjxxhv36dW5hx566K6//MaNGwfU9BIA2UpQB5BFelJNl9beXHV33nln1NbW9mkbB6pZs2bVPfXUUy+3trauWLZs2coZM2asTx/YRETMnj17VHpIu46Ul5c3rFu37sW93Z566qmX99aeiRMn1re2tq7IvC1btmxl5mN6cmUl+4fW1o5PPnRWUZdKpfpiOKCs05vP7+TJkxvTAVLmnFF1dXW5NTU1Q0tKSra1vQo4c0i9dNVdxM4hjNJB2L4eYmhvMkOytuFib3W172toaHiuL5/3QPPss88m9ty1tbXxl7/8JbHn35u++A7vifHjx29OB4Tz588v6c48eV39TJaWlp7S3q2/wsdMfblfAxxYmtcnd5jS2tAare9nd1C3ePHiovQ+42WXXbah7f3f/OY3dy279957h/f0ebLhgpJ3331XUAfQBwR1AFmkp9V0aarqumb8+PGbb7/99jerq6tX3nLLLWvTyy+77LJRSbdrypQpdRERixYtGtnTycUZuNavXx+f/exn47HHHtvjvltuuSVuu+22yM3t+Fg4Nzc3brvttrj55pv3uO/RRx+Nz372s/H222+3s+bA0ZPPb3qYyvQQPREfVte1d5VzxIdXOqer7iI+DO0yr4LOFqeccsr76Z9Xr16dn2Rb6JmPf/zjiT13SUlJFBf3ey7UJ/b1d/hdd9216zmuu+66kV1dL3N43M4+k22r69M3Jz6BbJY7MrkuKlWYitTQ7L4w7c477yyOiCgrK9vc3rCQmReS1dbW5vf0uK83F5T0RuZ31LHHHmvYS4A+IKgDyBK9qaZL21+q6jZt2tSlSQf64kBk1qxZdTNmzFgf0buDpL7ywAMPrE0ftE2ZMmV0km1h31q1alWUl5fvNj9dxM7A/qGHHurS/HQRO6vqZs+eHQ8++OBuwX1ExDPPPBNnnnlmrFq1qk/bnpSufn4zh6lMV9387ne/K4yIuOiii9oN3f76r/+6IWLnnFjpZenQLhuHvcycZ++OO+7o8ZXZJGfChAlx3XXX7fG57W9jx46NJUuW7PPn7Qv74ju8rKxse+ZFNF3d98jsJ9JzW7Yns7J+w4YNz/W6wQD7wKCjBsVBEw6K2Md5Xe4RuXHwBQdHKjd7g7qamprB6YvDpk2btkc1XVrmxWLz58/v8oUgbfX0gpLeWLFixbD0z8cff7zhkQH6QPbPvgqQnbZHxAt9ucE5c+Z8dPv27Yen/9/darq073znO3HXXXftqszbvn17zJkzZ+M999zz575r7c5N9+XGMq80zDwx3plnnnlm1+NOOOGEHh8gXHTRRfWLFi0aGRHx8ssv52dO9p2EhQsXrp0+ffro2tra/Dlz5oyYN2/ewC6B2r/1SV9QWVk57MILLxzV0NCw2+mOoqKi+Nd//deYMGFCu+vt2LEjUqlUu1V2F154YXz0ox+Nv/u7v4v6+g9zpddffz0+/elPN//85z9//fOf/3xfvNcTvYq2K5/fadOm1U+fPj0iIn7zm98UTp48ufHJJ58sLCgoaO7o837JJZfUz549e1TEznBv8uTJjem+6dxzz91jrqykzZo1q+7GG28saWxszK2srCyqqakZ3N4V3PS5PtsfSKVSceONN8b/+//+vzk1NTWDW1paen0Wsrq6On/mzJml7d13xBFHNP3kJz+pPe+8897Lyen19ZuJvdf2xXf4j370o9qKioriiIiLL7541NKlS9fsbZ1p06bVX3HFFc2NjY25FRUVxXPmzHnLZxLIAn3yvZVKpWLIZ4dE/qfzc1rqWga3trb2+jurZUNL/uZfb273Oyt1cKpp6DlDa/NOyHuvD4Z579e++Mc//vGuC6Zmz549Kr0/2ZnKysqiurq63J5MfZC+oKSioqJ40aJFI2+66ab1/T2FwpNPPlkYsXOY56SPnQH2F4I6gJ7rsx38V155Ja+iomK3q627W02Xlq6q+8lPfrJr2QMPPHDYdddd9+axxx7b1PvW9p+ysrLN6RPhXTnJnL6Sr6CgoLk3J78yr44vKira0dPt9JVp06bV33nnnQ1VVVWF8+fPL5k6dWq9k3tZrVd/m3/6p38q+h//438c09TUtNtZh2OOOSaWLl0aJ554Yrvrtba2xuWXXx6pVCpuu+22dqvtJkyYEE899VScc8458dprr+1a3tDQkHvuueeO/sd//MfXLr300qyrDuuOrn5+J06cWF9ZWVn0xBNPFNbU1GxobGzMTVfJtKe4uLg53Sf95je/2TVkZnl5eUO2zh85d+7c2vTJoK985SvHVldXr9zbOvSJPu2fi4qK4qyzztraF9u6/vrrj2tv+RlnnPHeY4899sqIESOy8r3cHfviO7y4uLh5xowZ6xctWjQyHYR3Zb30hTcRPpNAVumz762cITmRU5rTJ99Zjb9vbPc7K/eI3PcOnnLwKznDcgbEd9b9998/PCKipKRk2ymnnNJpiPXiiy8Ora2tzY+I+OEPf1jc0ws0e3JBSU9lzr930UUXdVgxCED3GPoSIAvccMMNR2SepO9pNV1a27nqmpqaUjfccMMRvWtl/5s1a9b69M9XXXVVpynl8uXLh1ZWVhZFRFx++eVvtXf/zJkzj+zK8958880jI3YGfpMnT27sXqv7xyOPPLLr4OorX/nKsUm2hf7R0tISc+bMGfHNb35zdNuQ7pOf/GRUVVV1GNJFRCxYsCBuv/32+MlPfhI/+MEPOnzcSSedFFVVVXHGGWfstrypqSn193//96Ovu+66ES0tLb38bfpWf3x+08PQ1dTUDP31r39d+MGydzrb9vnnn/9ORMQTTzxRmA7rMoeYzDazZs2qS4ePNTU1Q8eMGXNyV4bpmzNnzoj+bx372quvvpr3+OO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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib as mpl\n", + "dpi = 300\n", + "mpl.rcParams['figure.dpi']= dpi\n", + "import math\n", "\n", - "In the cartoon below it is represented the example input file `switch_line.csv`.\n", - "In this file are present 25 elements connected by direct edges that reflect the hierarchy of the system in a parent-child fashion.\n", + "initial = {'10': (0., 0.), '1': (0., 2.), 'A': (0., 4.),\n", + " 'S1': (2., 2.), '2': (4., 2.), 'S2': (6., 2.), '3': (8., 2.),\n", + " '11': (8., 0.), 'S3': (10., 2.), '4': (12., 2.), 'S4': (14., 2.),\n", + " '5': (16., 2.), '12': (16., 0.), 'B': (16., 4.), 'S5': (18., 2.),\n", + " '6': (20., 2.), 'S6': (22., 2.), '7': (24., 2.), '13': (24., 0.),\n", + " 'C': (24., 4.), 'S7': (26., 2.), '8': (28., 2.), 'S8': (30., 2.),\n", + " '9': (32., 2.), '14': (32., 0.)}\n", + "\n", + "F.G.print_graph(initial_pos=initial, size=150, arrow_size=4, fsize=5, fixed_nodes=list(F.G),\n", + " title='Switch line (integer)', input_cmap='Accent', legend_loc='upper center',\n", + " legend_ncol=4, legend_anchor=(0.5, 1.1), legend_fsize=7)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the figure above it is represented the example input file `switch_line.csv`.\n", + "The graph contains 25 elements connected by direct edges that reflect the hierarchy of the system in a parent-child fashion.\n", "\n", - "The nodes are distributed in adjacent areas.\n", + "The nodes are distributed in adjacent areas, which have been depicted with different colors specifying the `input_cmap` argument of `print_graph function`.\n", "\n", "In area1 are present 6 nodes: A, 1, S1, 2, S2, and 10.\n", "\n", @@ -64,11 +147,9 @@ "\n", "A perturbation of one or multiple elements in one area may exceed the area boundaries and propagate to other systems connected to it, located in other areas. \n", "\n", - "Nodes 10, 11, 12, 13, 14 are perturbation resistant nodes (`perturbation_resistant` field = 1). These nodes will not be affected by the simulated perturbation.\n", + "Nodes 10, 11, 12, 13, 14 are perturbation resistant nodes (`perturbation_resistant` field = 1). These nodes will not be affected by the simulated perturbation. The resistance is specified by the red border of these nodes.\n", "\n", - "Nodes S1, S2, S3, S4, S5, S6, S7 and S8 are isolating elements (they are a particular type of `HUB` nodes, called `SWITCH`).\n", - "\n", - "\"switch_line\"" + "Nodes S1, S2, S3, S4, S5, S6, S7 and S8 are isolating elements (they are a particular type of `HUB` nodes, called `SWITCH`)." ] }, { @@ -98,7 +179,7 @@ "- *total final service at USERs*: the sum of the total final service\n", "- *final graph size*: the total number of surviving nodes after the perturbation, for that given configuration of switches\n", "\n", - "The final fitness is easily given by: $(# actions) - (total final service) - (final graph size)$." + "These three quantities can be weighted in different ways, specifying a their multiplicative coefficients. For this tutorial, we choose $w_1 = w_2 = w_3 = 1.0$. In this way, the final fitness is easily given by: $(# actions) - (total final service) - (final graph size)$." ] }, { @@ -111,33 +192,6 @@ "We are now going to simulate different possible kinds of perturbations for the graph described in the input file `switch_line.csv`. For every one of them, we are going to comment about the best configuration for the switches." ] }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from grape.general_graph import GeneralGraph\n", - "from grape.fault_diagnosis import FaultDiagnosis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First of all, let us define a FaultDiagnosis variable and load the nodes for graph from the input file." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "F = FaultDiagnosis(\"./input_files/switch_line.csv\")" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -147,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -163,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -437,7 +491,7 @@ "25 C 0 area4" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -449,7 +503,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -726,7 +780,7 @@ "39 9 S8" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -756,7 +810,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "scrolled": true }, @@ -779,6 +833,14 @@ "DEBUG:root:In the graph are present 24 nodes\n", "DEBUG:root:The graph is dense, density = 0.06340579710144928\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/u/m/mteruzzi/miniconda/envs/test/lib/python3.6/site-packages/grape-1.1.0-py3.6.egg/grape/fault_diagnosis.py:540: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n", + " params, weights, parallel))\n" + ] } ], "source": [ @@ -798,12 +860,34 @@ "\n", "The final fitness is, then: $1 - 2 - 24 = -25$.\n", "\n", - "The activation of one switch cuts off the edges between the switch and its predecessors. We can see that inspecting the graph." + "The activation of one switch cuts off the edges between the switch and *all* its predecessors. We can see that inspecting the graph." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "F.G.print_graph(initial_pos=initial, size=150, arrow_size=4, fsize=5, fixed_nodes=list(F.G),\n", + " title='Switch line (node 1 perturbed)', input_cmap='Accent', legend_loc='upper center',\n", + " legend_ncol=4, legend_anchor=(0.5, 1.1), legend_fsize=7)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -833,16 +917,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/martina/miniconda/envs/test/lib/python3.6/site-packages/deap/creator.py:141: RuntimeWarning: A class named 'FitnessMin' has already been created and it will be overwritten. Consider deleting previous creation of that class or rename it.\n", + "/u/m/mteruzzi/miniconda/envs/test/lib/python3.6/site-packages/deap/creator.py:141: RuntimeWarning: A class named 'FitnessMin' has already been created and it will be overwritten. Consider deleting previous creation of that class or rename it.\n", " RuntimeWarning)\n", - "/home/martina/miniconda/envs/test/lib/python3.6/site-packages/deap/creator.py:141: RuntimeWarning: A class named 'Individual' has already been created and it will be overwritten. Consider deleting previous creation of that class or rename it.\n", + "/u/m/mteruzzi/miniconda/envs/test/lib/python3.6/site-packages/deap/creator.py:141: RuntimeWarning: A class named 'Individual' has already been created and it will be overwritten. Consider deleting previous creation of that class or rename it.\n", " RuntimeWarning)\n" ] }, @@ -878,7 +962,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -951,7 +1035,7 @@ "4 14 0.6 0.5" ] }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -965,7 +1049,29 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Finally, in fact, just the USER \"10\" is connected to SOURCE \"A\", receiving all its service. Regarding SOURCEs \"B\" and \"C\", instead, the sum of their service is evenly split between \"11\", \"12, \"13\" and \"14\"." + "Finally, in fact, just the USER \"10\" is connected to SOURCE \"A\", receiving all its service. Regarding SOURCEs \"B\" and \"C\", instead, the sum of their service is evenly split between \"11\", \"12, \"13\" and \"14\". As before, we can inspect the final situation looking at the graph." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "D.G.print_graph(initial_pos=initial, size=150, arrow_size=4, fsize=5, fixed_nodes=list(D.G),\n", + " title='Switch line (node 2)', input_cmap='Accent', legend_loc='upper center',\n", + " legend_ncol=4, legend_anchor=(0.5, 1.1), legend_fsize=7)" ] }, { @@ -979,16 +1085,16 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/martina/miniconda/envs/test/lib/python3.6/site-packages/deap/creator.py:141: RuntimeWarning: A class named 'FitnessMin' has already been created and it will be overwritten. Consider deleting previous creation of that class or rename it.\n", + "/u/m/mteruzzi/miniconda/envs/test/lib/python3.6/site-packages/deap/creator.py:141: RuntimeWarning: A class named 'FitnessMin' has already been created and it will be overwritten. Consider deleting previous creation of that class or rename it.\n", " RuntimeWarning)\n", - "/home/martina/miniconda/envs/test/lib/python3.6/site-packages/deap/creator.py:141: RuntimeWarning: A class named 'Individual' has already been created and it will be overwritten. Consider deleting previous creation of that class or rename it.\n", + "/u/m/mteruzzi/miniconda/envs/test/lib/python3.6/site-packages/deap/creator.py:141: RuntimeWarning: A class named 'Individual' has already been created and it will be overwritten. Consider deleting previous creation of that class or rename it.\n", " RuntimeWarning)\n" ] }, @@ -1010,12 +1116,20 @@ "DEBUG:root:Node: 3\n", "DEBUG:root:Predecessors: ['S2', 'S3']\n", "DEBUG:root:Broken: ['3']\n", - "DEBUG:root:Visited: {'11', '3'}\n", + "DEBUG:root:Visited: {'3', '11'}\n", "DEBUG:root:Node: 11\n", "DEBUG:root:Node 11 visited, fault resistant node\n", "DEBUG:root:In the graph are present 23 nodes\n", "DEBUG:root:The graph is dense, density = 0.05731225296442688\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/u/m/mteruzzi/miniconda/envs/test/lib/python3.6/site-packages/grape-1.1.0-py3.6.egg/grape/fault_diagnosis.py:540: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n", + " params, weights, parallel))\n" + ] } ], "source": [ @@ -1031,7 +1145,31 @@ "- $\\{S_1: False, S_2: False, S_3: False, S_4: True, S_5: True, S_6: True, S_7: True, S_8: True\\}$, with fitness: $3 - 3 - 23 = -23$\n", "- $\\{S_1: False, S_2: True, S_3: False, S_4: True, S_5: True, S_6: True, S_7: True, S_8: True\\}$, with fitness: $2 - 3 - 22 = -23$.\n", "\n", - "In this case, both outcomes are possible as best final states." + "In this case, both outcomes are possible as best final states. In order to make one final state with respect to another,the weights $\\{w_1, w_2, w_3\\}$ for the fitness evaluation can be specified.\n", + "\n", + "As usual, we inspect the final configuration of the graph printing it." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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04IOHxADtB0pLS7c9+eSTL6U/F+fPn1/SlWGx+8OCBQuK58yZc2hSz5+NuvqapIOsDRs2vLAvV31k2wX0vn7dB8o+MFDaScS7NTV5L/3kJwe/+fvfD21pbk66OV2SLeehXTlnBCC7DMgTMoB9TV9X1Q3EarrMk+Vjjjmmx3f2VldXD160aNGIiJ3DjHR1GKxly5atLygoaIqIuPjii7P6rsPMISr/8pe/7HHSdemll+5q/89+9rN1e6td9Mzdd99dtGXLll3HZLm5uXHZZZf1y3NddtllkZv7wVtmy5YtOT/60Y8O7pcno10jR45sjIior6930YSd7r67KDL6gcjNjeinfiAuu2zn9tO2bMmJAd4PZN68c8UVVyRSGb948eLh8+fPL6moqMjam332Nq9J2/bVC+gD5e89UNq5v6v97/8e8ovJk0/6ww03fOTX06ad+PT/+T9ZOepJpmw7D+3snBGA7CKoA+i5wX31dcwxx6SmTJnydubGe1pV11Y13dSpU98++uijU33Z5m43rBOZ883dcccdPa4ymTdv3mHp76+44oo3urPu+eefvzFi5x3O2VxV91//9V+F6e9bV8EtWLCgOD1R+ZQpU2pVye0VvdqXli9fflDmxs4+++xez1HZnpKSkjjrrLN2W7ZixYoDe/k70A21tbW56erhCRMmNHT2eAaE3n+mtuoH4uyzI/qpH4iSkohW/UDsA/1AaWnp5oiIF154YVjSbYHOuIBOwvrynLBfvtb9/Ocfbsq4geWl8vIRW99+e0gfbb9fZNt5aEfnjABkn0FJNwBggBocER/tyw3OmzcvfvKTn+yqhEtX1d12223d2k7rarrBgwfHvHnzDomIQ9pfq0eej4g+O+AvLi5uKi0t3VxdXT20oqKiaOrUqaNuueWWDofAbMvy5ct3ndhkzgXQFeeff35d+i7In/70pwd3d/29YfHixUXpi/wzZszY4wTw17/+9a4TsnPPPfft1j+nz/W6L1i1atVu/584cWJvNtepiRMnxtKlSzOf/6CIOKgXm+zTvmBfVltbm/vxj3/8xIiIgoKCpltuuaXnYxyTLfrmeKBVPxD93A/ExIkRGf1A6Adgr3IBnQT1+Xlsv2hp2WPRcwsXln7y29+OVCrVxgrd0i+fWdl0HtrZOSMA2UdFHUCWGDlyZHz961/fbVl3q+raqqa76KKL+q06p6/dcccdr6S/Ly8vLx4+fPgpY8aMOXHmzJlHdOXOwtra2tyampr8iA/urO+O8ePH71on80QrG6xYsWLopEmTRk+fPn10xM7hVG6//fbXWj/uiSee2HXhJxuDRnZXX18fa9eu3W3Z2LFj+/U5W2//pZdeioYGb5X+Ul1dPXjp0qUFU6dOHTV8+PBTampq8ktLSzc/9dRT1fvyXEx0Q319RKt+IPq5H9hj+y+9FDGA+4Ha2trc9JxAp59+en1bj1m6dGnBuHHjji0sLDwllUqNTR9ftDfs4MyZM49IpVJjx40bd2x62Zw5cw4dM2bMiYWFhadE7KxiT6VSY1Op1Nj088+fP78kvaz1+hERU6dOHZVKpcamt9GW9Lpz5sw5tCftas+KFSuGTp06dVT6sYWFhadMmjRpdEdDL7ZeJ/3atdW2nr4m6d9p5MiRJ7XXjurq6sFTp04dNXLkyJPSr9+4ceOOXbx4cVF766Rf6/TztfUeaO/36E9dvYDe3fds69co/Tql16uurt6tkqij172r2+rJ3zui+++rtJ7+TXvaTtrX+M478cQVV8SSMWM6/frNzJlR//LL3Vp33c9/vsdzrvv//r/4r699LepfeWWPnyUtW85Du3rOCED2UVEHkEWuuuqquOuuu3pcVddWNd1VV13VL23tD+PHj9+8fPnyVVOmTBmdPtGprq4eWl1dPTR9h+HEiRPrrr766jcyT2bSVq9enZ/+/sgjj+zxPHcRO4cd6c36PVFRUVHU+mLJpk2bBqWHskybMWPGG+2dcKUfm57noC9VVlYWplKpTq8el5SUNG7YsOGFvn7+fdHGjRv3WHbCCSf063O2tf2NGzdGQUFWZdO7dPV9lw3Ky8uLy8vLi9v7+cSJE+u+8Y1vbBSis5s2+oHo536gze1v3BiRpf1AZ84+++zR6e+vvvrqPYKPmTNnHpE+jojY+RmZPr64//77hz/11FPVHVU1rVixYuiZZ555XOvP40MOOaQpfUE2ffG/oKCgKT0PZUTEUUcd1W/VUu21q7WGhobcSZMmja6oqChKtzG9TkVFRdHHP/7xoc8888yq1jcPtPW6lZSUNKZfu3vvvXd46/X64zWZM2fOofPnz99111m6/ZWVlYWVlZWFd955Z/1DDz20vqObH6ZOnTqqdf+c/j1Wrlw5LHOew/6yYsWKofPnzx+R/jt0dAG9u+/ZFStWDJ0wYcKJ6f+XlpZurq+vz62pqclPr3fIIYfsmDdv3ptdaWdXt9WTv3dP3ldt6c7fNOl9dV/0++uuiw3/+Z9deuyfH3883t2wIT7/8MORSqXi99/+dmz41a969Lxv/u538dg//mOc9YtfRM6g7LmkuTfPQ/vinBGA7KOiDiCL9KaqbqBX06WNHz9+84YNG1646aabXikrK9vjrviKioqiCRMmnLhgwYI9LoZ3dEf4QFFTU5Of+ZV5wjVjxow3Nm7c+GxXTrg6u2jXUyUlJY2dfWVe9KBjmcF62pAhQ/r1OdvafmNjdv/JuvK+649wuic6alNFRUXRHXfcMTyb58AkAW30A9HP/UCb28/yfqAtK1asGDpmzJgTM6uTWt/IkxkK3HTTTa+0tLSsrK+vf3bjxo3PTpkypbahoSH3y1/+8tHtPceGDRvyJ0yYcGJDQ0NuaWnp5rvvvnv98uXLV0VETJs2ra6qqmpVVVXVqpKSksaIiEsuueT19LKqqqpVS5Ys6ZfSj47a1ZYXXnhh6IMPPrgm8/efOHFiXcTOY4/MsDNTaWnp5sz1NmzY8MLy5ctXFRQUNLW1Xl+/Jpkh3dVXX12zcePGZ+vr659taWlZ+eCDD64pKSlprKysLEwPK9yWysrKwvLy8uL037+lpWXl8uXLd7WvoqKiqHW1WU+lL6BnfqWrvSZMmHBiOqSbMWPGG08++eRLbW2jJ+/Zb3zjG6Midt4Q0tLSsrKqqmrVhg0bXnj/++dnzJjxxsSJE7t0k0h3ttXTv3d331etdfdvmuS+uq/auHJltx5f//LLsW3Tpp3r/vGPvXru92pqYuvb2TXC/94+D+2rc0YAskf23H4CQET0vKpuoFfTtTZr1qzaWbNm1UbsvBB3//33Fz3++OOF6btgZ8+ePeqYY45pzKxMOe6443ZdZXz11Vd7VRGXxEX/iRMn1rW+o7v1Xc2d3V2ceZd8dXX14L6c96SsrKy+vYtK9MzgwXteF9yyZUsMGzas355zy5YteyzLz9/rBaRd1p33XdKVd1OmTKlt60JfbW1t7ooVK4becccdwysqKooqKiqKysrKOq0AYT/RRj8QW7ZE9GM/EG30A5Gl/cATTzxRmDkk3aZNmwalq3vSywoKCprmzp1bkz5uSFuxYsWuivwHH3xwTeYxQ3FxcdOSJUteqaury62oqChasWLF0Laq9dPPc9NNN73SevtJ6m67WlcoFRcXNy1btmx9uiqpsrKysPVrcMMNN7xRXFy8x4Xe8ePHb77kkktenz9/fkllZWVhbW1tbn/0Ze9XoJVERNx9993rp02bVpf588mTJzeMHz9+1ejRoz9aU1OTP3PmzCPauzA9Y8aMNzJfp/Hjx28uLy9fnz7GevTRRwtLS0v75O/bUTXMjBkz3nj/dW3z9erpe3bDhg35ERHnnntuXettlpaWbuvOBfu+3FZb+up9tTf/puxp+NixXa6oi4goHD06Bh944M51/+qvelxRFxHxoZEj44CDD+7x+v1hb56H9sU5IwDZR0UdQJbpSVXdvlJN157x48dvvv3221+rqqpaddNNN+26CH7xxRePynxcZiiVDvS6Y8WKFbvWyZaqsPHjx2+eMmVKbUTEokWLRmS2sS0nnXTSe+nv16xZk51XXdnlwx/+8B7LVq9e3a/P2db2hw8f3q/Pub8rLi5umjx5csOyZcvWpysGKisrCz/96U8fl3TbyAJt9APRz/1Am9vP0n4gc4jDysrKwurq6qE1NTX5BQUFTWVlZfUzZsx4Y/369c+3FVb94Ac/GB6xM/Bvb8jZ9FCZ999/f7tznU2ZMqU2m0K6tO60q72Ltpk3F7R+DTq60JtZndXZsUlPXXHFFUdE7Ky+ah3SpRUXFzfNnTu3JmLncVJ7VS0XX3zxHmPMZoaS77zzTp9Uw6Sr0DK/Wlc6dvS69vQ9mz5unTt37hGLFy8u6k11T19uqy199b7aW39T2vapb387Rn3+81167OGf/nScccstkUqldq573XVdXre1EWVl8dc/+lFWDXsZkfx5aHfPGQHIPtn1yQYwcGyLiOf7a+OXXXZZ3p133nn89u3bUxGdV9W1rqbLy8trueyyy16MiO391cbY+RrsdbNmzapdt25d/qJFi0bU1NTkt777u7S0dHP65Ki9u+Pbk3nB44wzzthj2M1MmzZt6tJnaF9c3FiyZMkrjzzySFFDQ0PulClTRnc0/9sXv/jFuvQQYHfcccdwc2H1u171BQUFBTFq1KjjX3nllV0lNStXroyxY/uvMGxlq6GKRo0ata2goODFXmxyQM7psjf34Uzjx4/ffN99960755xzjquurh46Z86cQ7syZxBZq/fHAwUFEaNGHR8Z/UCsXBnRj/1AtB6ybNSobZGl/UBpaenmqqqqdod07Mhzzz03NKJrc13+6U9/avfmlm9+85ttTCSYvL5qV0lJSWNNTU1++vXqioMPPnhH+vu6urp+ua6QPp7p7JjszDPPrJ89e3ZERDz88MOFbYV6fTnCQHelL6CXl5cXL1q0aMT5559f197xaU/fs/Pnz68555xzjqupqcmfPn366Iidf9cJEyY0nHvuuW9353iwL7fVXd15XyX5Nx3g+uQ8dvCBB8bpN94Yp994Y7+s+8SsWSWvLFu2280DI//2b9+ZsHDhhu63dg/98t7ZW+eh7enOOSMA2UdFHUDPbeuvr6OPPvq98847b7c7pNurqmurmu4rX/lK7ejRo9/rzzZ2+9XqQ+eff/6uCzAvvfTSbhfWpk2btuui1fz580dEN9x///27ygnauku3J3dKPv3007selzkkSnctXLjwlYidwynNmTPn0PYeN2vWrNr0cCl9OecKHerVvvTRj3703cyNVVRU9GtjW2///ecfkH1BdyW5D2eaPHlyQ3o//a//+q/Cvtgmier9Z2qrfiD6uR/YY/v7aD+Q3s9LS0s3l5WV1Xf0ddRRR7W7f3fnYuve1FftSldvtHcDw+LFi4umTp06asyYMSem51wbM2bMR/viuduTeZPE0Ucf3WHf++EPf3hXhda6dev2OO7JhjlMlyxZ8kq6HVOmTGl3/rWevmcnT57csHz58lWlpaW73hM1NTX55eXlxeecc85xmcPHdqYvt9WR3ryvsuFvOsD15zlin3zlDhmyxw2np86Z80ofbb9f9Od5aFd19ZwRgOwjqAPIUtdee+3reXl5Len/p6vqWmurmu7aa699fe+0MhmZF2+Kiop2ZP6sdVDV1WE/Zs6ceUR6breJEyfWtXeXbuZFi66EYCtXrhwWsfOCQm/u/J02bVpdWVlZfUTE/PnzSzp67vQQUBERX/7yl4/u6XOyd5xxxhm73Zn+0EMPdTjUbW/U1NTEQw891OHz7+uS2ocz1dbW5qb7mwMPPNDFRiJa74cPPRTRT/1A1NTs3H5Hz7+PSB8PTJs2beOTTz75Ukdf7c27la2BQF+2Kz0n2ZFHHrlbILZgwYLiwsLCU6ZPnz66vLy8uL6+PrewsLDppJNOei99TNJfModH/OMf/9jhhI2rV6/eddPWQQcdlJV/r4iuXUDvzXt2/Pjxm6uqqlZVVVU9f9NNN70yceLEuvT2KisrCydNmtRuQNhaX26rtSTfVwwcx3z5y7W5Q4Y0p/9/7JQpbwwZPnxHR+skrb/PQ7uiO+eMAGQXQR1AljrmmGO2d1ZV11413dFHH92fQ172ixUrVgydOXPmEV157I033jgiYufFjLaG37nvvvvWpb8/88wzj+vsBGXx4sVFixYt2rXNf/u3f3ulvcfOmjXrjfT3l19+eYeTAK5YsWJoRUVFUUTEJZdc0uvw9KGHHto1aXhHAdysWbNq03MUVFdXDx0zZsyJXRm+z12XyZg+fXrdkIwLEU1NTXHzzTf3y3PdfPPN0dy866liyJAhzV/72tfe7pcny1JJ7sNpF1xwwa75Nb/xjW9k5ZB67GXTp9dFRj8QTU0R/dQPxM03R2T0AzFkSHPso/3A6aefXh8R8e///u/tzj+Xbfp6uN2uPF9NTU1+RMTYsWN3zXO7YMGC4tmzZ49qaGjIvfvuu9e3tLSs3LBhwwtVVVWrnnzyyZfuuuuudo+V+kr6xorly5cXdPS4ioqKXT8/88wzszbo6coF9L54z5aWlm6bNWtW7bJly9bX19c/mz4mTH+eJbWtiOx4XzEwFH/sY1s+v3TpC5+45pqX//bee1ed+r//d5s3U2Sb/jwP7aqunjMCkF0EdQBZrLOqun2pmq6ioqJg0aJFI0aOHHnSggULitu6UFVbW5s7adKk0ek5S9J3Jrc2efLkhquvvromIqKhoSH3tNNOK128eHGbFxTmzJlzaHr+jYiIRx99dE1Hk9xPmzatbuLEiXXvt7lowYIFxW09rrq6enB6aKPS0tLNfTEHVXFxcVP696qurh7a3nNH7BxiKTOsGz58+CkzZ848ovWdnStWrBg6Z86cQwsLC0+ZP39+h6HFpk2bBlVXVw/u6ldvf9/9xSGHHNI0efLktzKXLVy4MH73u9/16fM89dRTsXDhwt2WnXPOOW8dfPDBze2ssk9Kch9evHhx0ZgxY05MX+C8+uqra8wjSUREHHJIU7TqB2Lhwog+7gfiqad2bjfTOee8FftoP/C9732vJmJnBVB7xwFpffW5tX79+gO68rh09URrDz/8cL8Mh9ve73f22WfvOgaaOnXqrqHFf/3rXxdGRJSVldW3NefbmjVr2p3Tr7WuviatpW+s6KgCrbq6enD6+KWsrKw+2+ct6+wCek/fsx0FvOeee263gvjebqujv3dfvq96q6fvS/aeD5WUbD/2K195+8Onnro5lTMwLl/253loV3XnnBGA7DEwPukA9lMdVdXtS9V0ER8MVVRTU5M/e/bsUcOHDz/l/bkqTpw0adLoMWPGnDh8+PBT0he4Z8yY8UZbJ/hp8+bNe/Puu+9eH7HzJGn69OmjR44cedLUqVNHzZw584hJkyaNzgynCgoKmqqqqp7vynwvy5YtW5++0D979uxR48aNO3bBggXFK1asGLp48eKimTNnHjFmzJiP1tTU5JeWlm7+7W9/u6YvXqP075W+w3z27NmjOrqYsmTJklfuvvvu9SUlJY0REYsWLRoxYcKEE1Op1Nj014QJE06cP39+SUNDQ25nww29X5330a5+Ceu67sorr3xz0KBBu0L55ubm+OpXvxp1de2+xbulrq4uLrzwwt2q6QYNGtRyxRVX9Dp8Goj6ax9+5JFHiiZNmjQ682vcuHHHjhw58qRUKjV2+vTpo6urq4cWFBQ03XTTTa/0RfjHPuTKK9+MjH4gmpsjvvrViD7qB6KuLuLCC3evphs0qCX24X6gtLR0W/pi5fTp00fPnDnziMzPzdra2tz0MHy33nrr8Pa31Ln0PG+PPPJIUfo5li5dWtB6iMDRo0dvTX/f+nNyxYoVQy+99NJR0Q8+97nPHZd5s051dfXgcePGHZu++WnGjBlvZIZc6eq6tgKjpUuXFlx88cWdtrOrr0l7pk2bVpe+6Wj+/PklU6dOHZV+zdJ/u9NOO600YudxXGYIlq06u4De0/fs6NGjPzpmzJgTFy9evNv8xNXV1YOvvvrqkve33aU5DXu6ra78vfvifdVbvX1fQmf68zy0O23o6jkjANmhzcmiAcge11577esPPPBA8fbt21MRH1TVtbS07DPVdBE7h2u88MIL66655poR999///CGhobchoaG3Orq6qHV1dW7LiyVlJQ03nrrra90pQpl2rRpdWedddazl1xySUl5eXlxTU1Nfnl5+W536hYUFDRdcsklr3f3gvmyZcvWL168uGju3LlHVFZWFqYvdPV2u11xxx13vDJhwoQTI3beCf/kk0++1N5jp02bVjdt2rS6xYsXF/30pz8teuGFF4Zu2rRpUENDQ25BQUHTyJEjG88444z6iy++eGO234W+Lzv55JMbL7vssj/feOONu4Z/ffHFF+PMM8+MRx99NIqKej5qW11dXUyaNClefPHF3ZZffvnlfz755JMb21ltn9cf+3BDQ0Nue8OBlZaWbj7yyCMb/+Zv/qZ+1qxZtW09hv3cySc3xmWX/Tky+oF48cWIM8+MePTRiF70A1FXFzFp0s7tZbr88j/HPt4PpPfh+fPnlyxatGjEokWLRhQUFDS1rmg75JBDejX30Re/+MW6ysrKwoaGhtzhw4efkvmz6urqwenP2IkTJzbMnz8/IiLGjBnz0fRNMhs2bMivqanJLysrq2/dH/WFwsLCpvSxQ2sTJ06saz3f2T//8z/X3nLLLYelLzJPnz49Ml+3rsyR19XXpCNLlix55cADD9yxaNGiEeXl5cXl5eV7VIaUlpZu/tnPfrauLypR9oZ58+a9+eCDDx5cXV09dPbs2aMuvPDCusy29+Q9mz5mzqzOaW3+/Pldmviyp9vqyt+7L95XvdUX70voTH+eh3ZVd84ZAUieoA4gy6Wr6n784x/vumv2zjvv3ONxA7maLq24uLjp9ttvf+32229/rbq6evDvfve7YW+99VbuO++8k3vQQQc1nXbaaZu7e6dhcXFx05IlS15ZsmTJK0uXLi1Yu3Ztfm+2lykdgq1YsWLoU089NTS93WOOOaaxq8PZlZaWbmtpaVnZnecdP3785u6uk25rd9aJ6Fn76Jl58+a98Ytf/KKoqqpqVzD91FNPRVlZWdx7773xqU99qtvbfOqpp+KrX/1qrFmze0HYmDFjNt9www1vtLNa4nr6vuvpftGbfbi4uLjJPkKfmTfvjfjFL4oiox+Ip56KKCuLuPfeiB70A/HUUzsr81r1AzFmzObI4n6gL82bN+/NqVOn1s2bN++w5cuXF9TU1OT39c0qs2bNqv3jH/847JFHHimK2Fm1c/LJJ2/+5je/udu2x48fv/mmm256ZeHChSNqamryX3jhhWEjR45snDBhQsO55577yuTJkxtSqdTY3v7OrVVVVa1asGBB8eLFi4dv2LAhPyLipJNOeu+iiy6qbev4oLi4uOmpp56q/vrXvz7qhRdeGBYRceCBB+44/fTT6//mb/6m/sILL6xrHXK01tXXpDO33377axdffPHGefPmHfbcc88Nra6uHlpSUtJ40kknbR6oNz90dgG9u+/ZlpaWlQsWLCj+9a9/Xfjqq6/mb9iwIb+hoSG3pKSkccKECQ1z5sx5vauveU+31ZW/d1+8r3qrr96X0Jm+PA/dW+eMACQn1dLS0vmjAPYDK1euLIqIX0VEHHDAASdGRBx33HFr8vLyEr87d+3atXmlpaUfTVfVtZaXl9eyatWq5wd6UAf7uxdffHHwhAkTTti4cWNe5vKcnJy49NJL47LLLouSkg6nEoyIiJqamrj55ptj4cKFuw13GRExfPjw7cuXL199/PHHuxgF2ejFFwfHhAknRKt+IHJyIi69NOKyyyK60A9ETU3EzTfvnJOuVT8Qw4dvj+XLV4d+AADYC7Zv3567Zs2a4yIitm7duur9xX83duzYPhrjG2BgM0cdwADQ1lx1mfaFajog4vjjj99WUVGxZvjw4bvtz83NzfEv//IvcdRRR8U555wTd9xxRzz99NPx7rvvRlNTU7z77rvx9NNPxx133BHnnHNOHHXUUfEv//IvbYZ0FRUVa4R0kMWOP35bVFSsiVb9QDQ3R/zLv0QcdVTEOedE3HFHxNNPR7z7bkRT085/n3565/Jzztn5uH/5l7ZDuoqKNUI6AACA7KCiDuB92VxRF9F+VZ1qOtj3rF69evCXvvSlozOHweytMWPGbP75z3++TkgHA8Tq1YPjS186OvqwH4gxYzbHz3++TkgHAOxNKuoAOqaiDmCAaK+qTjUd7HtOOOGEbc8888yqK6+88rVBgwb16q6qQYMGtVx55ZWvPfPMM6uEdDCAnHDCtnjmmVVx5ZWvRS/7gRg0qCWuvPK1eOaZVUI6AACA7CKoAxhArr322tfz8vJ2XazLy8trufbaa19Psk1A/8jLy4vvfOc7bzz99NNVU6dO3ThkyJDmztf6wJAhQ5qnTp268emnn676zne+80ZeXl7nKwHZJS8v4jvfeSOefroqpk7dGN3sB2LIkOaYOnVjPP10VXznO2+EfgAAACDrDEq6AQB0Xbqq7sc//vHwCNV0sD84+eSTG++///5X33777Zof/ehHBz/++OMFzz///LBXXnklv/VjR40a1fjRj370vTPOOKPha1/72tsHH3xw9y7qA9np5JMb4/77X423366JH/3o4Hj88YJ4/vlh0UY/EKNGNcZHP/penHFGQ3zta2+HfgAAACCrmaMO4H3ZPkddWnquuogIc9PB/quuri7n9ddfH7R169acAw44oPmwww7bUVRU5II87E/q6nLi9dcHxdatOXHAAc1x2GE7Qj8AAGQZc9QBdExFHcAAk66qS6VSIaSD/VdRUVFzUVGRuaZgf1ZU1Bz6AQAAgAFNUAcwAF177bWvp1KppJsBAAAAAEAvCOoABqBjjjlGJR0AAAAAwACXk3QDAAAAAAAAYH8kqAMAAAAAAIAECOoABqC1a9fmrVu3Li/pdgAAAAAA0HPmqAMYgK677rrDUqlU3Hfffa8m3Rag/zU3N0dOTvfur+rJOgAAAADsXa7eAAwwa9euzXvggQeKf/KTnxSrqoN937XXXnvohRdeeGRzc3OX12lubo6vfvWrR1577bWH9mPTAAAAAOglFXUAA8x111132Pbt21Pp71XVwb7r2muvPfT6668viYg46qijGq+//vo3u7rej3/84+Hp/1933XVdWg8AAACAvUtFHcAAkq6mS/9fVR3suzJDuoiIG264oeSuu+4q6my9O++8s2jevHm71rv++utLVNYBAAAAZCdBHcAAkllNFxGxffv21HXXXXdYkm0C+l7rkC7tm9/85kcqKio+1N56FRUVH/rWt771kdbLhXUAAAAA2UlQBzBAtK6mS1NVB/uW9kK6iJ3h/HnnnXfMc889l9/6Z88888wB55133jGZYX4mYR0AAABA9hHUAQwQravp0lTVwb6jo5Aurb6+Pvfzn//8cRs2bNg11/CGDRsGfeELXzi2vr4+t6N1hXUAAAAA2UVQBzAAtFdNl6aqDga+9kK6G2+8Mc4666zdlr322muDzzzzzGPr6+tz6uvrcyZNmnTsn//858GZjznrrLPixhtv3ON5hHUAAAAA2UNQBzAAtK6mGzx4cAwe/ME1+X2hqq66unpwKpUam0qlxo4bN+7YrqyzYsWKoel1li5dWtDRYxcvXlw0bty4YwsLC09JpVJjCwsLTxkzZsyJkyZNGr148eKirrSrO19t/Q5d2dbIkSNPGjdu3LEzZ848ora2tsPqKPYd7YV0CxYsiNmzZ0d5eXmceuqpu/3shRdeGDp58uTRkydPHl1VVTU082ef+MQnory8PGbPnh0LFizY4/kGWljX3f13wYIFxel9qqP9O9O4ceOOTa/T1X1vzJgxJ6ZSqbFz5szZ9Vp21pelf4fefhUWFp7SlTYuXry4aNKkSaNHjhx5Uua6I0eOPCn9+rX1+/akT46I6G77oCv64jO8vfdxbW1tbuv9q7q6enBbj800derUUenP7e7+LlOnTh01cuTIk9K/T/qzf+rUqaNWrFgxtL11e7pftreN3h7XAAAAfUNQB5Dl2qqmu+iii+LrX//6bo9TVde2FStWDB0zZsyJ06dPH11ZWVnY0NCQGxHR0NCQW11dPbSioqJo+vTpowsLC0/p6OJYXysoKGiaOHFiXeZXWVlZfUREZWVl4aJFi0YMHz78lKlTp44S2O3bOgrpLr/88oiIGDZsWDzyyCNx1FFH7faYxx577MDHHnvswMxlH/nIR+Lhhx+OYcOGRUTE5ZdfPmDDup7uvxdeeGFd+vuf/vSnXQrqKisrC9PfP/zww4UdPTZi58X96urqoRERU6dOrevs8WkHHnjgjq4+tjcWL15cVFhYeMr06dNHV1RUFNXU1Oya17ChoSG3pqYmP/36XXDBBaP2RpvY++65556D/uf//J9Hfe1rXxv5+9//fkjS7emupD7DL7/88g6HIO6JxYsXF40cOfKk6dOnjy4vLy+uqanJT/8+NTU1+ZWVlYXl5eXFEyZMOHHkyJEn+ewH9jeNzzUe9O7P3j3qvYffG7njtR0D7jOrI4sXLy7KvDGzO+v21w0lXb1xY+TIkSdNnTp1VGc3xgLQO4M6fwgASWqrmu6qq66KlpaWuOuuu2Lbtm0R8UFV3X333fdqYo3NMrW1tblnnnnmcQ0NDbkFBQVNl1xyyesTJ05sOOGEExojdl4A/OUvf1m4aNGiEQ0NDbnp5e0pKyurf/LJJ1/qi7adfvrp9cuWLVvfXru///3vF8+fP7+kvLy8ePny5QXPPPPMquLi4qa+eG6yR1dCurRDDz00li1bFuPGjYu6urZzoYMPPjiWLVsWhx66e/6W3tasWbN2W55+7uuuu+7NXvwa/aI3+29xcXFTaWnp5vSF/M6eK12RU1JS0lhTU5P/05/+tGjatGkdhm/33HPPrnVKS0u3dfX32rBhwwsd/TyVSo1Nf9/S0rKyq9vNNHXq1FHl5eW7bvCYMWPGGxdffPHGdDurq6sHr1mzJv+OO+4YXlFRUTR27Nj3evI8ZLcf/vCHRd/4xjdGp///4x//ePjTTz9d9dGPfrTDz7ps0def4V2R7gMqKiqKVqxYMXT8+PGbe/+b7L5PFhQUNJ1//vkbM/fJ2tra3BUrVgxN75M1NTX5e+Mzvy+PawB6o/GPjUWbf7F512fWtue3DS/8emFV7odzB8RnVmcWLFgwIv19TU1N/tKlSwsmT57c0NPtXX755SXtnUv2REFBQdPpp59en7ls06ZNuZs2bRpUXV09tLy8PL+8vLy4rKys/q677nqlO8e+AHSNijqALNZeNV1JSUmMHDlSVV0nzj777NHpC3zr169/ft68eW+OHz9+c3FxcVNxcXHT5MmTG26//fbXqqqqnp84cWJdtgRhxcXFTfPmzXtz+fLlqyJ2nsypeNn3dDQnXeuQLu2EE06I//iP/9ht6Nu0/Pz8+I//+I84/vjj21z38ssvH1Bz1vV2/z3nnHPeTn/f2R3A6aq7Sy+99I2IiK6Ee//+7/9eFBHxhS98ocvVdHtDZiBQWlq6eePGjc/efvvtr2VeUCktLd02efLkhmXLlq2vqqp6vjsVgQwcP/vZzw7O/P+2bdtSN9xww4j2Hp9tkvgMv/TSS98oKChoioi44oorjuj9b7HnPrl+/frnW++T6d9n2bJl65cvX75qypQptX3x3AADxbbqbbt9ZkVTpLY8vmXAfGZ1pLq6enB6FIa0O+64Y3hPtlVSUtIYsfNYtS8rydM3kWZ+Pfnkky9VVVWt2rhx47NXX311TcTOESjGjBnzUdV1AH1PUAeQxdqrpku76qqr9rm56vpSeii7uXPn1nR0Aa+0tHRbX96R2FfGjx+/eeLEiXURO0/GDIO172hubo7169fnt/Wz5cuXx3vvtV/gNGHChLjvvvv2WH7ffffF+PHj213vvffei8cff7zNn7388sv5zc3NnTV7r+rt/psZPv30pz89uPXPMz3xxBOFJSUljZlDZnZ2ASLdvvPPPz9rQq7FixcXZQYCVVVVnVbilpaWbnNX9L5py5Yte5zr/eY3vzmwqSkr7knpVBKf4YccckjTJZdc8nr6+Xt7ITJznywpKWnsyj45fvz4zUuWLHmlN88LMNC07GjZ4zNrxys7DmxpbkmiOX3q1ltvHR6xs4q5tLR0c0TPz+3644aSzqRvIq2qqno+HRSec845x3Vl+E0Auk5QB5ClOqqmS1NV177MOwyPOeaYATtkSuaQdH/5y18EdfuInJycuPfee1+dM2dOTeufPfzww/HZz3423nyz/dEozzvvvN2q42666aY499xz2338m2++GZ/5zGfikUce2eNn11xzTc0999zzak5O9hwW9sX+W1paui19MeGRRx5pt0Ju6dKlBQ0NDbkTJkxoSA+ZGdFxuJceKrOgoKCpr4bG6wuXXnrprsrbn/3sZ+uSbAvJ+6u/+qs9Ev+NGzfm3XbbbYck0Z7uSPIzfN68eW+mL4JeffXVvZqrLnOfvPXWW4VvAO0YNGLQHp9ZLZtb8hr/0Jj1n1mduf/++4dHRFx00UW106ZN25he/v3vf7+4/bXa1tc3lHRHaWnptl/+8pdr0v//3Oc+d9zeem6A/UH2XJEBYDedVdOlqaprW+ZcNT0dWiQb/Nd//Vdh+ntVL/uWnJycuOGGG9784Q9/uD4vL2+324X/8Ic/RFlZWbz44ovtrj9r1qz45je/Gd/61rfaHSozImL16tVx2mmnxdNPP73b8ry8vJY777xz/fXXX/9mNoV0EX23/6aHpWxoaMhtb3igdCB37rnnvh0RccYZZ9RHdBzupYfKzKZhLxcsWFDc0NCQGxExZcqUWv0FF198cW1ubu4epQjf/va3S954442svvEj6c/wuXPn1kREVFdXD00H892VuU+WlZXV92YuIoB9Xf4n82sjFXt8Zm397daS5nebs/ozqyOLFy8uSg/jPG3atLrM0RvuvffeHn2+9eUNJd1VWlq6LT0MZk1NTf6CBQu6HTYC0LbsuioDMLAM7q+vdevWDeusmi6tvaq69evXD+vPNnb71drLMitjKioqiqZOnTpqoA0duXjx4qL00F8zZsx4I+n20K5e7UsXXXTRew8//PD6wsLC3YZDe/nll6OsrCyWL1/e5pOmUqlYuHBhLFy4MFKpVJuPefzxx2PcuHHxpz/9abflhYWFTQ8//PD6r3/96+/1tv1dfI26pa/238xhKe+///42L7YvX768ICIifRE9vU5H4V56Drt0uJcNfv3rX+8K9bOpXfuJ/vys7fHXscceG1/84hffad3Yd955Z9Bf//Vfn7BmzZq+PE7oU0l/hs+aNas2XZE7d+7cHg0tlp7HMiLiiiuu8BkOZJPEP6Naf+UenBt5J+S907qhLY0tgxr+reGEpreasvYzqyN33nlnccQHN3cVFxc3lZWV1UfsDLp6Os9cX9xQ0lPz5s3bNezHwoUL94l5BAGywaCkGwAwQA2OiI/218Zvvvnm2L59+wdP1k41XdpVV10Vd911V2zbtrOAYvv27ambb775hNtuu62/mhgR8XxEZHXFxh133PHKhAkTToyIKC8vLy4vLy8uLS3dfMYZZ9R/7nOfy9q721esWDF0/vz5I9JhQFlZWf3tt9/+WtLtok190hd87nOfi9/97ncxadKk3UK1urq6+Nu//du477774rzzzttjvUGD2j+U+8lPfhJf/epXd/ULaR/5yEdi2bJluccff/zRvW33+/qlL+iL/Xf8+PGbCwoKmhoaGnIfeeSRotb7UXV19eCampr89AWT1uvcf//9Ra2HtkwPMVRQUNCUTX3IE088sSuoy6Z27Qf6/Higvr4+1q5dG30xb+TZZ58dP/vZz/ZYvmrVqgPGjRt3wj333BOf//zn2w37u6HP+4GkP8NvvfXWV84555zj0hUDs2bNqu3O+i+88MKw9PfHHXfcgB2CG9jn9OnnVktjSzS93RR71sJ1X95xebF91fY9ljfXNh/QsLjhhKFnD428Y/Oy8jOrLdXV1YPTN11+85vf3DXk5UUXXVSbXj5//vwRPZlnddasWbULFy4cUVNTkz937twjpk2btldHeSgrK6uvrKwsrKmpya+urh5sJAeA3hPUAWSZDRs2xF133bXbsvaq6dLSVXU/+MEPdi27884746qrrupwvX3d+PHjNy9fvnzVlClTRtfU1ORH7LzrsLq6euiiRYtGRERMnDix7uqrr36jK/NMVVZWFqZSqbGdPa6kpKRxw4YNL3T0mIqKiqKRI0eelLls06ZNg9LDZKXNmDHjDSHd/uGEE06Ip556Kr7whS/sNkzltm3b4itf+Uq8+uqrMWvWrE4vTrS0tMRNN90UV1555R4/+8QnPhEPP/xwHHrooX3e/r7WV/vvF77whbry8vLimpqa/Nra2tzi4uJdlYtLliwpioj44he/WNfWOm2Fe+mhMk8//fT6yCLpviM9FFJf6mrfR++0tLTEtddeG9/97nf3CNj7w1tvvRVnnXVWnHzyyXHXXXfFqaee2u/P2R19/RneXZMnT24oLS3dXF1dPfT6668v6W5Ql/l5nq0XMPvyuAbYv7S0tMTW326NrU9ujejzI482nm9LS7z3wHuRe2huDP3C0Bh0ePZfzrz11luHR+zsQzM/p6ZNm1Y3ffr0iNh5Ttj6+LQb2+/VDSW98dd//df16bDxd7/73bBs/ZwDGEgMfQmQZb7zne/sdoGus2q6tNZz1W3bti2+853v9EsbB5Lx48dv3rBhwws33XTTK5lVM2kVFRVFEyZMOLGr4+uXlJQ0dvY1cuTILt05X1NTk5/5lXlRb8aMGW9s3LjxWSHd/uXQQw+N3/zmN3HWWWft8bMrrrgi/umf/il27NjR7vo7duyIb33rW22GdGeddVY89thjAyKkS+uL/TdzGMh77rlnt6GBHnzwwYMjIs4888z6ttZJ3yWc+bP03HXnnntu1sxPl6l12N9XutL39UdIuD9Zvnx53HDDDXslpMv03HPPxTnnnNNh35KUvv4M76477rjjlYid+9WcOXMGTufZDX15XAPsP3a8uiO2Lt87IV2mpjeb4t2fvRstzX1QwtfP7r///uERbc9pPHHixF3Lvv/97/foMyx9Q0lExPXXX79X78496KCDdv3l33rrrQE1vQRAthLUAWSRnlTTpbU1V92dd94ZNTU1fdrGgWrWrFm1Tz755EstLS0rly9fvmrGjBlvpE9sIiJmz549Kj2kXXvKysrqN2zY8EJnX08++eRLnbVn4sSJdS0tLSszv5YvX74q8zE9ubOSfUNLS/sXHzqqqEulUn0xHFDW6c3+O3ny5IZ0gJQ5Z1RtbW1udXX10JKSksbWdwFnDqmXrrqL2DmEUToI29tDDHUmMyRrHS72Vlf7vvr6+mf78nn3N88880xiz11TUxN/+ctfEnv+zvTFZ3hPjB8/fnM6IJw/f35Jd+bJ6+o+OXLkyJPa+uqv8DFTXx7XAPuXpjeSO01pqW+JlveyO6hbvHhxUfqY8eKLL97Y+uff+MY3di279957h/f0ebLhhpJ33nlHUAfQBwR1AFmkp9V0aarqumb8+PGbb7/99teqqqpW3XTTTa+kl1988cWjkm7XlClTaiMiFi1aNKKnk4szcL3xxhvxmc98Jh555JE9fnbTTTfFbbfdFrm57Z8L5+bmxm233RY33njjHj97+OGH4zOf+Uy8+eabbaw5cPRk/00PU5keoifig+q6tu5yjvjgTud01V3EB6Fd5l3Q2eKkk056L/39mjVr8pNsCz3z8Y9/PLHnLikpieLifs+F+sTe/gy/6667dj3HNddcM6Kr62UOj9vRPtm6uj795cInkM1yRyTXRaUKU5Eamt03pt15553FERGlpaWb2xoWMvNGspqamvyenvf15oaS3sj8jDr66KMNewnQBwR1AFmiN9V0aftKVd2mTZu6NOlAX5yIzJo1q3bGjBlvRPTuJKmvLFmy5JX0SduUKVNGJ9kW9q7Vq1dHWVnZbvPTRewM7B944IEuzU8XsbOqbvbs2fGTn/xkt+A+IuLpp5+O0047LVavXt2nbU9KV/ffzGEq01U3v/71rwsjIs4///w2Q7e/+Zu/qY/YOSdWelk6tMvGYS8z59m74447enxnNsmZMGFCXHPNNXvst/1t7NixsXTp0r3+vH1hb3yGl5aWbsu8iaarxx6Z/UR6bsu2ZFbWb9y48dleNxhgLxh05KA4YMIBEXs5r8s9LDc+dO6HIpWbvUFddXX14PTNYdOmTdujmi4t82ax+fPnd/lGkNZ6ekNJb6xcuXJY+vtjjz3W8MgAfSD7Z18FyE7bIuL5vtzgnDlzDt+2bdsh6f93t5ou7aqrroq77rprV2Xetm3bYs6cOW/dc889f+671u7cdF9uLPNOw8wL4x15+umndz3uuOOO6/EJwvnnn1+3aNGiERERL730Un7mZN9JWLhw4SvTp08fXVNTkz9nzpxD582bN7BLoPZtfdIXVFRUDDvvvPNG1dfX73a5o6ioKP7jP/4jJkyY0OZ6O3bsiFQq1WaV3XnnnReHH354/P3f/33U1X2QK/3pT3+KT33qU00//elP//S5z32uL97rid5F25X9d9q0aXXTp0+PiIhf/vKXhZMnT2544oknCgsKCpra298vvPDCutmzZ4+K2BnuTZ48uSHdN5111ll7zJWVtFmzZtVef/31JQ0NDbkVFRVF1dXVg9u6g5s+12fHA6lUKq6//vr4X//rf+VUV1cPbm5u7vVVyKqqqvyZM2eObOtnhx122PYf/OAHNWefffa7OTm9vn8zsffa3vgMv+WWW2rKy8uLIyIuuOCCUcuWLVvf2TrTpk2ru/TSS5saGhpyy8vLi+fMmfO6fRLIAn3yuZVKpWLIZ4ZE/qfyc5prmwe3tLT0+jOreWNz/uZHN7f5mZX6UGr70DOH1uQdl/duHwzz3q998a233rrrhqnZs2ePSh9PdqSioqKotrY2tydTH6RvKCkvLy9etGjRiBtuuOGN/p5C4YknniiM2DnMc9LnzgD7CkEdQM/12QH+2rVr88rLy3e727q71XRp6aq6H/zgB7uWLVmy5OBrrrnmtaOPPnp771vbf0pLSzenL4R35SJz+k6+goKCpt5c/Mq8O76oqGhHT7fTV6ZNm1Z355131ldWVhbOnz+/ZOrUqXUu7mW1Xv1tfvjDHxb90z/900e2b9++21WHj3zkI7Fs2bI4/vjj21yvpaUlLrnkkkilUnHbbbe1WW03YcKEePLJJ+PMM8+Ml19+edfy+vr63LPOOmv0v/7rv7580UUXZV11WHd0df+dOHFiXUVFRdHjjz9eWF1dvbGhoSE3XSXTluLi4qZ0n/TLX/5y15CZZWVl9dk6f+TcuXNr0heDvvzlLx9dVVW1qrN16BN92j8XFRXF6aefvrUvtnXttdce09byU0899d1HHnlk7aGHHpqV7+Xu2Buf4cXFxU0zZsx4Y9GiRSPSQXhX1kvfeBNhnwSySp99buUMyYmckTl98pnV8JuGNj+zcg/LffdDUz60NmdYzoD4zLr//vuHR0SUlJQ0nnTSSR2GWC+88MLQmpqa/IiI73//+8U9vUGzJzeU9FTm/Hvnn39+uxWDAHSPoS8BssB11113WOZF+p5W06W1nqtu+/btqeuuu+6w3rWy/82aNeuN9PeXX355hynlihUrhlZUVBRFRFxyySWvt/XzmTNnHtGV573xxhtHROwM/CZPntzQvVb3j4ceemjXydWXv/zlo5NsC/2jubk55syZc+g3vvGN0a1Duk984hNRWVnZbkgXEbFgwYK4/fbb4wc/+EF873vfa/dxJ5xwQlRWVsapp5662/Lt27en/vEf/3H0Nddcc2hzc3Mvf5u+1R/7b3oYuurq6qGPPvpo4fvL3u5o2+ecc87bERGPP/54YTqsyxxiMtvMmjWrNh0+VldXDx0zZsyJXRmmb86cOYf2f+vY29atW5f32GO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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "T.G.print_graph(initial_pos=initial, size=150, arrow_size=4, fsize=5, fixed_nodes=list(T.G),\n", + " title='Switch line (nodes 2 and 3 perturbed)', input_cmap='Accent', legend_loc='upper center',\n", + " legend_ncol=4, legend_anchor=(0.5, 1.1), legend_fsize=7)" ] } ], diff --git a/tutorials/tutorial03/03_switch_activation.py b/tutorials/tutorial03/03_switch_activation.py index fa0a6fa..dd44b24 100644 --- a/tutorials/tutorial03/03_switch_activation.py +++ b/tutorials/tutorial03/03_switch_activation.py @@ -3,12 +3,41 @@ F = FaultDiagnosis("./input_files/switch_line.csv") F.check_input_with_gephi() + +initial = {'10': (0., 0.), '1': (0., 2.), 'A': (0., 4.), + 'S1': (2., 2.), '2': (4., 2.), 'S2': (6., 2.), '3': (8., 2.), + '11': (8., 0.), 'S3': (10., 2.), '4': (12., 2.), 'S4': (14., 2.), + '5': (16., 2.), '12': (16., 0.), 'B': (16., 4.), 'S5': (18., 2.), + '6': (20., 2.), 'S6': (22., 2.), '7': (24., 2.), '13': (24., 0.), + 'C': (24., 4.), 'S7': (26., 2.), '8': (28., 2.), 'S8': (30., 2.), + '9': (32., 2.), '14': (32., 0.)} + +F.G.print_graph(initial_pos=initial, size=200, arrow_size=4, fsize=10, + fixed_nodes=list(F.G), title='Switch line (integer)', input_cmap='Accent', + legend_loc='upper center', legend_ncol=8, legend_anchor=(0.5, 1.05), + legend_fsize=7) + F.simulate_element_perturbation(["1"]) print("\nPredecessors of S1: ", list(F.G.predecessors('S1'))) print("\nSuccessors of S1: ", list(F.G.successors('S1'))) +F.G.print_graph(initial_pos=initial, size=200, arrow_size=4, fsize=10, + fixed_nodes=list(F.G), title='Switch line (node 1 perturbed)', + input_cmap='Accent', legend_loc='upper center', legend_ncol=8, + legend_anchor=(0.5, 1.05), legend_fsize=7) + D = FaultDiagnosis("./input_files/switch_line.csv") D.simulate_element_perturbation(["2"]) +D.G.print_graph(initial_pos=initial, size=200, arrow_size=4, fsize=10, + fixed_nodes=list(D.G), title='Switch line (node 2 perturbed)', + input_cmap='Accent', legend_loc='upper center', legend_ncol=8, + legend_anchor=(0.5, 1.05), legend_fsize=7) + T = FaultDiagnosis("./input_files/switch_line.csv") T.simulate_element_perturbation(["2", "3"]) + +T.G.print_graph(initial_pos=initial, size=200, arrow_size=4, fsize=10, + fixed_nodes=list(T.G), title='Switch line (nodes 2 and 3 perturbed)', + input_cmap='Accent', legend_loc='upper center', legend_ncol=8, + legend_anchor=(0.5, 1.05), legend_fsize=7) diff --git a/tutorials/tutorial03/input_files/switch_line.png b/tutorials/tutorial03/input_files/switch_line.png deleted file mode 100644 index 2f919a9..0000000 Binary files a/tutorials/tutorial03/input_files/switch_line.png and /dev/null differ