{"id":18106848,"url":"https://github.com/rune75/coupledgraphs","last_synced_at":"2026-04-17T01:02:41.081Z","repository":{"id":258015009,"uuid":"873279813","full_name":"Rune75/coupledGraphs","owner":"Rune75","description":"Example of coupled adjacency lists in python","archived":false,"fork":false,"pushed_at":"2024-10-20T16:33:31.000Z","size":94,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-06T06:45:50.509Z","etag":null,"topics":["adjacency-lists","edges","graph","jupyter-notebook","python","vertex"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Rune75.png","metadata":{"files":{"readme":"README.ipynb","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-10-15T22:38:34.000Z","updated_at":"2024-10-20T16:33:34.000Z","dependencies_parsed_at":"2024-12-19T14:48:21.714Z","dependency_job_id":"6a7c4896-a6c4-4f33-b273-37fd45a4d8bb","html_url":"https://github.com/Rune75/coupledGraphs","commit_stats":null,"previous_names":["rune75/coupledgraphs"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Rune75%2FcoupledGraphs","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Rune75%2FcoupledGraphs/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Rune75%2FcoupledGraphs/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Rune75%2FcoupledGraphs/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Rune75","download_url":"https://codeload.github.com/Rune75/coupledGraphs/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247445649,"owners_count":20939953,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["adjacency-lists","edges","graph","jupyter-notebook","python","vertex"],"created_at":"2024-10-31T23:08:14.258Z","updated_at":"2026-04-17T01:02:41.033Z","avatar_url":"https://github.com/Rune75.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Adventures of coupled adjacency Lists and handling of graphs in Python\\n\",\n    \"======================================================================\\n\",\n    \"\\n\",\n    \"This notebook is an investigation of the use of coupled adjacency lists in Python. The usecase could for example be to find combined paths over multiple connected circuits in a system.\\n\",\n    \"\\n\",\n    \"The interface between different circuits would be common global vertices defined in connectors or cables between the circuits. \\n\",\n    \"\\n\",\n    \"Table 1: Vertices\\n\",\n    \"-----------------\\n\",\n    \"| Global Vertex | Adjacent vertices |\\n\",\n    \"|--------|-------------------|\\n\",\n    \"| A      | tbl1.A, tbl1.B    |\\n\",\n    \"| B      | tbl1.C, tbl1.A    |\\n\",\n    \"| C      | tbl1.D, tbl1.B    |\\n\",\n    \"| D      | tbl1.C, tbl1.D    |\\n\",\n    \"\\n\",\n    \"Table 2: Vertices\\n\",\n    \"-----------------\\n\",\n    \"| Global Vertex | Adjacent vertices |\\n\",\n    \"|--------|-------------------|\\n\",\n    \"| A      | tbl2.A, tbl2.B    |\\n\",\n    \"| B      | tbl2.B, tbl2.A    |\\n\",\n    \"| C      | tbl2.C, tbl2.D    |\\n\",\n    \"| D      | tbl2.D, tbl2.C    |\\n\",\n    \"\\n\",\n    \"The adjacency lists are coupled by the global vertices. Now let see how this can be implemented in Python.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 92,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"dict1:\\n\",\n      \"A ['tbl1.A', 'tbl1.B']\\n\",\n      \"B ['tbl1.C', 'tbl1.D', 'tbl1.E']\\n\",\n      \"C ['tbl1.E', 'tbl1.B', 'tbl1.A']\\n\",\n      \"D ['tbl1.B', 'tbl1.F']\\n\",\n      \"\\n\",\n      \"dict2:\\n\",\n      \"A ['tbl2.A', 'tbl2.B']\\n\",\n      \"B ['tbl2.C', 'tbl2.D']\\n\",\n      \"C ['tbl2.E', 'tbl2.F']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# set up two lists\\n\",\n    \"list1 = [['A', 'tbl1.A', 'tbl1.B'],\\n\",\n    \"         ['B', 'tbl1.C', 'tbl1.D', 'tbl1.E'],\\n\",\n    \"         ['C', 'tbl1.E', 'tbl1.B', 'tbl1.A'],\\n\",\n    \"            ['D', 'tbl1.B', 'tbl1.F']]\\n\",\n    \"\\n\",\n    \"list2 = [['A', 'tbl2.A', 'tbl2.B'],\\n\",\n    \"         ['B', 'tbl2.C', 'tbl2.D'],\\n\",\n    \"         ['C', 'tbl2.E', 'tbl2.F']]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Convert the lists to dictionaries, Yes we could also set up the dictionaries directly, but this is a more general approach\\n\",\n    \"# since data is often imported from files or databases in list format\\n\",\n    \"dict1 = {x[0]: x[1:] for x in list1}\\n\",\n    \"dict2 = {x[0]: x[1:] for x in list2}\\n\",\n    \"\\n\",\n    \"#print the dictionaries row by row\\n\",\n    \"print('dict1:')\\n\",\n    \"for key in dict1:\\n\",\n    \"    print(key, dict1[key])\\n\",\n    \"     \\n\",\n    \"print('\\\\ndict2:')\\n\",\n    \"for key in dict2:\\n\",\n    \"   print(key, dict2[key])\\n\",\n    \"   \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can now convert the dictionaries to adjacency lists. and plot the graphs. The folowing code will plot the two diffrent Lists with diffren colors green an blue.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 93,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"\u003cFigure size 640x480 with 1 Axes\u003e\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Convert the dictionaries to graphs\\n\",\n    \"import networkx as nx\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"G1 = nx.Graph()\\n\",\n    \"G2 = nx.Graph()\\n\",\n    \"\\n\",\n    \"# build the graph from the dictionaries\\n\",\n    \"for key in dict1:\\n\",\n    \"    for value in dict1[key]:\\n\",\n    \"        G1.add_edge(key, value)\\n\",\n    \"        \\n\",\n    \"for key in dict2:\\n\",\n    \"      for value in dict2[key]:\\n\",\n    \"         G2.add_edge(key, value)\\n\",\n    \"         \\n\",\n    \"# draw the graph\\n\",\n    \"nx.draw(G1, with_labels=True, node_color='b')\\n\",\n    \"nx.draw(G2, with_labels=True, node_color='g')\\n\",\n    \"plt.show()\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now lest merge the two graphs with their common vertices and plot the merged graph.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 94,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"\u003cFigure size 640x480 with 1 Axes\u003e\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# merge the two graphs\\n\",\n    \"G = nx.compose(G1, G2)\\n\",\n    \"nx.draw(G, with_labels=True, node_color='r')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"At this point we have a merged graph with two adjacency lists. We can now perform operations on the graph, such as finding the shortest path between two nodes.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 95,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"All paths from tbl1.F to tbl2.C:\\n\",\n      \"['tbl1.F', 'D', 'tbl1.B', 'A', 'tbl1.A', 'C', 'tbl1.E', 'B', 'tbl2.C']\\n\",\n      \"['tbl1.F', 'D', 'tbl1.B', 'C', 'tbl1.E', 'B', 'tbl2.C']\\n\",\n      \"\\n\",\n      \"Shortest path from tbl1.F to tbl2.C:\\n\",\n      \"['tbl1.F', 'D', 'tbl1.B', 'C', 'tbl1.E', 'B', 'tbl2.C']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"        \\n\",\n    \"# find paths between two nodes\\n\",\n    \"print('\\\\nAll paths from tbl1.F to tbl2.C:')\\n\",\n    \"for path in nx.all_simple_paths(G, source='tbl1.F', target='tbl2.C'):\\n\",\n    \"    print(path)\\n\",\n    \"    \\n\",\n    \"# find the shortest path between two nodes\\n\",\n    \"print('\\\\nShortest path from tbl1.F to tbl2.C:')\\n\",\n    \"print(nx.shortest_path(G, source='tbl1.F', target='tbl2.C'))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 96,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frune75%2Fcoupledgraphs","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frune75%2Fcoupledgraphs","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frune75%2Fcoupledgraphs/lists"}