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Added a notebook with examples and further improved resilience of run.sh
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‎notebooks/lib_examples.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# NetGraph Library Examples\n",
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"\n",
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"This notebook contains examples of using the NetGraph library to create and manipulate graphs, calculate maximum flow, and place traffic demands."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Required imports\n",
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"from ngraph.lib.graph import MultiDiGraph\n",
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"from ngraph.lib.max_flow import calc_max_flow\n",
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"\n",
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"# Create a graph with parallel edges\n",
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"# Metric:\n",
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"# [1,1] [1,1]\n",
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"# ┌────────►B─────────┐\n",
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"# │ │\n",
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"# │ ▼\n",
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"# A C\n",
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"# │ ▲\n",
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"# │ [2] [2] │\n",
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"# └────────►D─────────┘\n",
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"#\n",
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"# Capacity:\n",
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"# [1,2] [1,2]\n",
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"# ┌────────►B─────────┐\n",
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"# │ │\n",
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"# │ ▼\n",
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"# A C\n",
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"# │ ▲\n",
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"# │ [3] [3] │\n",
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"# └────────►D─────────┘\n",
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"\n",
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"g = MultiDiGraph()\n",
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"g.add_edge(\"A\", \"B\", metric=1, capacity=1)\n",
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"g.add_edge(\"B\", \"C\", metric=1, capacity=1)\n",
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"g.add_edge(\"A\", \"B\", metric=1, capacity=2)\n",
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"g.add_edge(\"B\", \"C\", metric=1, capacity=2)\n",
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"g.add_edge(\"A\", \"D\", metric=2, capacity=3)\n",
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"g.add_edge(\"D\", \"C\", metric=2, capacity=3)\n",
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"\n",
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"# Calculate MaxFlow between the source and destination nodes\n",
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"max_flow = calc_max_flow(g, \"A\", \"C\")\n",
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"\n",
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"# We can verify that the result is as expected\n",
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"assert max_flow == 6.0"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Required imports\n",
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"from ngraph.lib.graph import MultiDiGraph\n",
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"from ngraph.lib.max_flow import calc_max_flow\n",
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"\n",
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"# Create a graph with parallel edges\n",
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"# Metric:\n",
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"# [1,1] [1,1]\n",
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"# ┌────────►B─────────┐\n",
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"# │ │\n",
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"# │ ▼\n",
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"# A C\n",
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"# │ ▲\n",
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"# │ [2] [2] │\n",
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"# └────────►D─────────┘\n",
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"#\n",
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"# Capacity:\n",
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"# [1,2] [1,2]\n",
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"# ┌────────►B─────────┐\n",
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"# │ │\n",
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"# │ ▼\n",
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"# A C\n",
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"# │ ▲\n",
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"# │ [3] [3] │\n",
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"# └────────►D─────────┘\n",
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"g = MultiDiGraph()\n",
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"g.add_edge(\"A\", \"B\", metric=1, capacity=1)\n",
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"g.add_edge(\"B\", \"C\", metric=1, capacity=1)\n",
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"g.add_edge(\"A\", \"B\", metric=1, capacity=2)\n",
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"g.add_edge(\"B\", \"C\", metric=1, capacity=2)\n",
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"g.add_edge(\"A\", \"D\", metric=2, capacity=3)\n",
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"g.add_edge(\"D\", \"C\", metric=2, capacity=3)\n",
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"\n",
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"# Calculate MaxFlow between the source and destination nodes\n",
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"# Flows will be placed only on the shortest paths\n",
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"max_flow = calc_max_flow(g, \"A\", \"C\", shortest_path=True)\n",
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"\n",
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"# We can verify that the result is as expected\n",
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"assert max_flow == 3.0"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Required imports\n",
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"from ngraph.lib.graph import MultiDiGraph\n",
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"from ngraph.lib.max_flow import calc_max_flow\n",
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"from ngraph.lib.common import FlowPlacement\n",
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"\n",
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"# Create a graph with parallel edges\n",
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"# Metric:\n",
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"# [1,1] [1,1]\n",
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"# ┌────────►B─────────┐\n",
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"# │ │\n",
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"# │ ▼\n",
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"# A C\n",
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"# │ ▲\n",
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"# │ [2] [2] │\n",
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"# └────────►D─────────┘\n",
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"#\n",
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"# Capacity:\n",
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"# [1,2] [1,2]\n",
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"# ┌────────►B─────────┐\n",
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"# │ │\n",
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"# │ ▼\n",
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"# A C\n",
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"# │ ▲\n",
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"# │ [3] [3] │\n",
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"# └────────►D─────────┘\n",
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"g = MultiDiGraph()\n",
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"g.add_edge(\"A\", \"B\", metric=1, capacity=1)\n",
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"g.add_edge(\"B\", \"C\", metric=1, capacity=1)\n",
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"g.add_edge(\"A\", \"B\", metric=1, capacity=2)\n",
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"g.add_edge(\"B\", \"C\", metric=1, capacity=2)\n",
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"g.add_edge(\"A\", \"D\", metric=2, capacity=3)\n",
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"g.add_edge(\"D\", \"C\", metric=2, capacity=3)\n",
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"\n",
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"# Calculate MaxFlow between the source and destination nodes\n",
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"# Flows will be equally balanced across the shortest paths\n",
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"max_flow = calc_max_flow(\n",
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" g, \"A\", \"C\", shortest_path=True, flow_placement=FlowPlacement.EQUAL_BALANCED\n",
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")\n",
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"\n",
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"# We can verify that the result is as expected\n",
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"assert max_flow == 2.0"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Required imports\n",
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"from ngraph.lib.graph import MultiDiGraph\n",
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"from ngraph.lib.common import init_flow_graph\n",
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"from ngraph.lib.demand import FlowPolicyConfig, Demand, get_flow_policy\n",
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"from ngraph.lib.flow import FlowIndex\n",
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"\n",
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"# Create a graph\n",
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"# Metric:\n",
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"# [1] [1]\n",
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"# ┌──────►B◄──────┐\n",
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"# │ │\n",
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"# │ │\n",
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"# │ │\n",
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"# ▼ [1] ▼\n",
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"# A◄─────────────►C\n",
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"#\n",
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"# Capacity:\n",
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"# [15] [15]\n",
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"# ┌──────►B◄──────┐\n",
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"# │ │\n",
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"# │ │\n",
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"# │ │\n",
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"# ▼ [5] ▼\n",
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"# A◄─────────────►C\n",
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"g = MultiDiGraph()\n",
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"g.add_edge(\"A\", \"B\", metric=1, capacity=15, label=\"1\")\n",
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"g.add_edge(\"B\", \"A\", metric=1, capacity=15, label=\"1\")\n",
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"g.add_edge(\"B\", \"C\", metric=1, capacity=15, label=\"2\")\n",
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"g.add_edge(\"C\", \"B\", metric=1, capacity=15, label=\"2\")\n",
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"g.add_edge(\"A\", \"C\", metric=1, capacity=5, label=\"3\")\n",
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"g.add_edge(\"C\", \"A\", metric=1, capacity=5, label=\"3\")\n",
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"\n",
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"# Initialize a flow graph\n",
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"r = init_flow_graph(g)\n",
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"\n",
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"# Create traffic demands\n",
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"demands = [\n",
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" Demand(\n",
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" \"A\",\n",
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" \"C\",\n",
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" 20,\n",
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" ),\n",
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" Demand(\n",
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" \"C\",\n",
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" \"A\",\n",
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" 20,\n",
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" ),\n",
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"]\n",
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"\n",
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"# Place traffic demands onto the flow graph\n",
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"for demand in demands:\n",
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" # Create a flow policy with required parameters or\n",
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" # use one of the predefined policies from FlowPolicyConfig\n",
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" flow_policy = get_flow_policy(FlowPolicyConfig.TE_UCMP_UNLIM)\n",
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"\n",
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" # Place demand using the flow policy\n",
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" demand.place(r, flow_policy)\n",
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"\n",
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"# We can verify that all demands were placed as expected\n",
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"for demand in demands:\n",
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" assert demand.placed_demand == 20\n",
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"\n",
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"assert r.get_edges() == {\n",
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" 0: (\n",
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" \"A\",\n",
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" \"B\",\n",
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" 0,\n",
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" {\n",
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" \"capacity\": 15,\n",
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" \"flow\": 15.0,\n",
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" \"flows\": {\n",
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" FlowIndex(src_node=\"A\", dst_node=\"C\", flow_class=0, flow_id=1): 15.0\n",
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" },\n",
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" \"label\": \"1\",\n",
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" \"metric\": 1,\n",
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" },\n",
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" ),\n",
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" 1: (\n",
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" \"B\",\n",
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" \"A\",\n",
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" 1,\n",
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" {\n",
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" \"capacity\": 15,\n",
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" \"flow\": 15.0,\n",
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" \"flows\": {\n",
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" FlowIndex(src_node=\"C\", dst_node=\"A\", flow_class=0, flow_id=1): 15.0\n",
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" },\n",
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" \"label\": \"1\",\n",
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" \"metric\": 1,\n",
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" },\n",
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" ),\n",
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" 2: (\n",
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" \"B\",\n",
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" \"C\",\n",
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" 2,\n",
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" {\n",
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" \"capacity\": 15,\n",
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" \"flow\": 15.0,\n",
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" \"flows\": {\n",
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" FlowIndex(src_node=\"A\", dst_node=\"C\", flow_class=0, flow_id=1): 15.0\n",
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" },\n",
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" \"label\": \"2\",\n",
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" \"metric\": 1,\n",
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" },\n",
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" ),\n",
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" 3: (\n",
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" \"C\",\n",
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" \"B\",\n",
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" 3,\n",
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" {\n",
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" \"capacity\": 15,\n",
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" \"flow\": 15.0,\n",
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" \"flows\": {\n",
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" FlowIndex(src_node=\"C\", dst_node=\"A\", flow_class=0, flow_id=1): 15.0\n",
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" },\n",
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" \"label\": \"2\",\n",
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" \"metric\": 1,\n",
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" },\n",
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" ),\n",
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" 4: (\n",
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" \"A\",\n",
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" \"C\",\n",
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" 4,\n",
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" {\n",
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" \"capacity\": 5,\n",
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" \"flow\": 5.0,\n",
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" \"flows\": {\n",
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" FlowIndex(src_node=\"A\", dst_node=\"C\", flow_class=0, flow_id=0): 5.0\n",
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" },\n",
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" \"label\": \"3\",\n",
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" \"metric\": 1,\n",
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" },\n",
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" ),\n",
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" 5: (\n",
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" \"C\",\n",
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" \"A\",\n",
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" 5,\n",
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" {\n",
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" \"capacity\": 5,\n",
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" \"flow\": 5.0,\n",
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" \"flows\": {\n",
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" FlowIndex(src_node=\"C\", dst_node=\"A\", flow_class=0, flow_id=0): 5.0\n",
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" },\n",
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" \"label\": \"3\",\n",
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" \"metric\": 1,\n",
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" },\n",
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" ),\n",
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"}"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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