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Merged
networmix merged 1 commit into from
Dec 8, 2024
Merged

Added a notebook with examples and further improved resilience of run.sh #27

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331 changes: 331 additions & 0 deletions notebooks/lib_examples.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# NetGraph Library Examples\n",
"\n",
"This notebook contains examples of using the NetGraph library to create and manipulate graphs, calculate maximum flow, and place traffic demands."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Required imports\n",
"from ngraph.lib.graph import MultiDiGraph\n",
"from ngraph.lib.max_flow import calc_max_flow\n",
"\n",
"# Create a graph with parallel edges\n",
"# Metric:\n",
"# [1,1] [1,1]\n",
"# ┌────────►B─────────┐\n",
"# │ │\n",
"# │ ▼\n",
"# A C\n",
"# │ ▲\n",
"# │ [2] [2] │\n",
"# └────────►D─────────┘\n",
"#\n",
"# Capacity:\n",
"# [1,2] [1,2]\n",
"# ┌────────►B─────────┐\n",
"# │ │\n",
"# │ ▼\n",
"# A C\n",
"# │ ▲\n",
"# │ [3] [3] │\n",
"# └────────►D─────────┘\n",
"\n",
"g = MultiDiGraph()\n",
"g.add_edge(\"A\", \"B\", metric=1, capacity=1)\n",
"g.add_edge(\"B\", \"C\", metric=1, capacity=1)\n",
"g.add_edge(\"A\", \"B\", metric=1, capacity=2)\n",
"g.add_edge(\"B\", \"C\", metric=1, capacity=2)\n",
"g.add_edge(\"A\", \"D\", metric=2, capacity=3)\n",
"g.add_edge(\"D\", \"C\", metric=2, capacity=3)\n",
"\n",
"# Calculate MaxFlow between the source and destination nodes\n",
"max_flow = calc_max_flow(g, \"A\", \"C\")\n",
"\n",
"# We can verify that the result is as expected\n",
"assert max_flow == 6.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Required imports\n",
"from ngraph.lib.graph import MultiDiGraph\n",
"from ngraph.lib.max_flow import calc_max_flow\n",
"\n",
"# Create a graph with parallel edges\n",
"# Metric:\n",
"# [1,1] [1,1]\n",
"# ┌────────►B─────────┐\n",
"# │ │\n",
"# │ ▼\n",
"# A C\n",
"# │ ▲\n",
"# │ [2] [2] │\n",
"# └────────►D─────────┘\n",
"#\n",
"# Capacity:\n",
"# [1,2] [1,2]\n",
"# ┌────────►B─────────┐\n",
"# │ │\n",
"# │ ▼\n",
"# A C\n",
"# │ ▲\n",
"# │ [3] [3] │\n",
"# └────────►D─────────┘\n",
"g = MultiDiGraph()\n",
"g.add_edge(\"A\", \"B\", metric=1, capacity=1)\n",
"g.add_edge(\"B\", \"C\", metric=1, capacity=1)\n",
"g.add_edge(\"A\", \"B\", metric=1, capacity=2)\n",
"g.add_edge(\"B\", \"C\", metric=1, capacity=2)\n",
"g.add_edge(\"A\", \"D\", metric=2, capacity=3)\n",
"g.add_edge(\"D\", \"C\", metric=2, capacity=3)\n",
"\n",
"# Calculate MaxFlow between the source and destination nodes\n",
"# Flows will be placed only on the shortest paths\n",
"max_flow = calc_max_flow(g, \"A\", \"C\", shortest_path=True)\n",
"\n",
"# We can verify that the result is as expected\n",
"assert max_flow == 3.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Required imports\n",
"from ngraph.lib.graph import MultiDiGraph\n",
"from ngraph.lib.max_flow import calc_max_flow\n",
"from ngraph.lib.common import FlowPlacement\n",
"\n",
"# Create a graph with parallel edges\n",
"# Metric:\n",
"# [1,1] [1,1]\n",
"# ┌────────►B─────────┐\n",
"# │ │\n",
"# │ ▼\n",
"# A C\n",
"# │ ▲\n",
"# │ [2] [2] │\n",
"# └────────►D─────────┘\n",
"#\n",
"# Capacity:\n",
"# [1,2] [1,2]\n",
"# ┌────────►B─────────┐\n",
"# │ │\n",
"# │ ▼\n",
"# A C\n",
"# │ ▲\n",
"# │ [3] [3] │\n",
"# └────────►D─────────┘\n",
"g = MultiDiGraph()\n",
"g.add_edge(\"A\", \"B\", metric=1, capacity=1)\n",
"g.add_edge(\"B\", \"C\", metric=1, capacity=1)\n",
"g.add_edge(\"A\", \"B\", metric=1, capacity=2)\n",
"g.add_edge(\"B\", \"C\", metric=1, capacity=2)\n",
"g.add_edge(\"A\", \"D\", metric=2, capacity=3)\n",
"g.add_edge(\"D\", \"C\", metric=2, capacity=3)\n",
"\n",
"# Calculate MaxFlow between the source and destination nodes\n",
"# Flows will be equally balanced across the shortest paths\n",
"max_flow = calc_max_flow(\n",
" g, \"A\", \"C\", shortest_path=True, flow_placement=FlowPlacement.EQUAL_BALANCED\n",
")\n",
"\n",
"# We can verify that the result is as expected\n",
"assert max_flow == 2.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Required imports\n",
"from ngraph.lib.graph import MultiDiGraph\n",
"from ngraph.lib.common import init_flow_graph\n",
"from ngraph.lib.demand import FlowPolicyConfig, Demand, get_flow_policy\n",
"from ngraph.lib.flow import FlowIndex\n",
"\n",
"# Create a graph\n",
"# Metric:\n",
"# [1] [1]\n",
"# ┌──────►B◄──────┐\n",
"# │ │\n",
"# │ │\n",
"# │ │\n",
"# ▼ [1] ▼\n",
"# A◄─────────────►C\n",
"#\n",
"# Capacity:\n",
"# [15] [15]\n",
"# ┌──────►B◄──────┐\n",
"# │ │\n",
"# │ │\n",
"# │ │\n",
"# ▼ [5] ▼\n",
"# A◄─────────────►C\n",
"g = MultiDiGraph()\n",
"g.add_edge(\"A\", \"B\", metric=1, capacity=15, label=\"1\")\n",
"g.add_edge(\"B\", \"A\", metric=1, capacity=15, label=\"1\")\n",
"g.add_edge(\"B\", \"C\", metric=1, capacity=15, label=\"2\")\n",
"g.add_edge(\"C\", \"B\", metric=1, capacity=15, label=\"2\")\n",
"g.add_edge(\"A\", \"C\", metric=1, capacity=5, label=\"3\")\n",
"g.add_edge(\"C\", \"A\", metric=1, capacity=5, label=\"3\")\n",
"\n",
"# Initialize a flow graph\n",
"r = init_flow_graph(g)\n",
"\n",
"# Create traffic demands\n",
"demands = [\n",
" Demand(\n",
" \"A\",\n",
" \"C\",\n",
" 20,\n",
" ),\n",
" Demand(\n",
" \"C\",\n",
" \"A\",\n",
" 20,\n",
" ),\n",
"]\n",
"\n",
"# Place traffic demands onto the flow graph\n",
"for demand in demands:\n",
" # Create a flow policy with required parameters or\n",
" # use one of the predefined policies from FlowPolicyConfig\n",
" flow_policy = get_flow_policy(FlowPolicyConfig.TE_UCMP_UNLIM)\n",
"\n",
" # Place demand using the flow policy\n",
" demand.place(r, flow_policy)\n",
"\n",
"# We can verify that all demands were placed as expected\n",
"for demand in demands:\n",
" assert demand.placed_demand == 20\n",
"\n",
"assert r.get_edges() == {\n",
" 0: (\n",
" \"A\",\n",
" \"B\",\n",
" 0,\n",
" {\n",
" \"capacity\": 15,\n",
" \"flow\": 15.0,\n",
" \"flows\": {\n",
" FlowIndex(src_node=\"A\", dst_node=\"C\", flow_class=0, flow_id=1): 15.0\n",
" },\n",
" \"label\": \"1\",\n",
" \"metric\": 1,\n",
" },\n",
" ),\n",
" 1: (\n",
" \"B\",\n",
" \"A\",\n",
" 1,\n",
" {\n",
" \"capacity\": 15,\n",
" \"flow\": 15.0,\n",
" \"flows\": {\n",
" FlowIndex(src_node=\"C\", dst_node=\"A\", flow_class=0, flow_id=1): 15.0\n",
" },\n",
" \"label\": \"1\",\n",
" \"metric\": 1,\n",
" },\n",
" ),\n",
" 2: (\n",
" \"B\",\n",
" \"C\",\n",
" 2,\n",
" {\n",
" \"capacity\": 15,\n",
" \"flow\": 15.0,\n",
" \"flows\": {\n",
" FlowIndex(src_node=\"A\", dst_node=\"C\", flow_class=0, flow_id=1): 15.0\n",
" },\n",
" \"label\": \"2\",\n",
" \"metric\": 1,\n",
" },\n",
" ),\n",
" 3: (\n",
" \"C\",\n",
" \"B\",\n",
" 3,\n",
" {\n",
" \"capacity\": 15,\n",
" \"flow\": 15.0,\n",
" \"flows\": {\n",
" FlowIndex(src_node=\"C\", dst_node=\"A\", flow_class=0, flow_id=1): 15.0\n",
" },\n",
" \"label\": \"2\",\n",
" \"metric\": 1,\n",
" },\n",
" ),\n",
" 4: (\n",
" \"A\",\n",
" \"C\",\n",
" 4,\n",
" {\n",
" \"capacity\": 5,\n",
" \"flow\": 5.0,\n",
" \"flows\": {\n",
" FlowIndex(src_node=\"A\", dst_node=\"C\", flow_class=0, flow_id=0): 5.0\n",
" },\n",
" \"label\": \"3\",\n",
" \"metric\": 1,\n",
" },\n",
" ),\n",
" 5: (\n",
" \"C\",\n",
" \"A\",\n",
" 5,\n",
" {\n",
" \"capacity\": 5,\n",
" \"flow\": 5.0,\n",
" \"flows\": {\n",
" FlowIndex(src_node=\"C\", dst_node=\"A\", flow_class=0, flow_id=0): 5.0\n",
" },\n",
" \"label\": \"3\",\n",
" \"metric\": 1,\n",
" },\n",
" ),\n",
"}"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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