![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
"
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "899447e4-f4dc-4491-be22-46ed1591fc71",
+ "metadata": {
+ "id": "899447e4-f4dc-4491-be22-46ed1591fc71"
+ },
+ "source": [
+ "# yFiles Graphs for Jupyter "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "08285758-8025-40d1-9cfd-3de06ba59693",
+ "metadata": {
+ "id": "08285758-8025-40d1-9cfd-3de06ba59693"
+ },
+ "source": [
+ "### Before using the widget, make sure to install the required packages\n",
+ "\n",
+ "Ensure you have the necessary packages installed by running the following commands:\n",
+ "- ```%pip install yfiles_jupyter_graphs --quiet```\n",
+ "- ```from yfiles_jupyter_graphs import GraphWidget```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "40c51747-64bc-49f2-90c0-c0ff79d24048",
+ "metadata": {
+ "id": "40c51747-64bc-49f2-90c0-c0ff79d24048"
+ },
+ "outputs": [],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "from yfiles_jupyter_graphs import GraphWidget"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "651c2e90-4f3b-4b25-9404-735288b25771",
+ "metadata": {
+ "id": "651c2e90-4f3b-4b25-9404-735288b25771"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "01400028-72b5-47ff-a5bc-5d9f64f0df31",
+ "metadata": {
+ "id": "01400028-72b5-47ff-a5bc-5d9f64f0df31"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7a4c051-be35-4269-a322-32a147cf02cd",
+ "metadata": {
+ "id": "e7a4c051-be35-4269-a322-32a147cf02cd"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f0823ca8-ea68-4bdd-863c-09b0524aa96e",
+ "metadata": {
+ "id": "f0823ca8-ea68-4bdd-863c-09b0524aa96e"
+ },
+ "source": [
+ "## How to create a new graph\n",
+ "Before specifying nodes or edges, initialize a new graph widget. You can do this by creating a new instance, for example, `w = GraphWidget()`\n",
+ "\n",
+ "- Nodes always have an \"id,\" and any additional data can be added to the \"properties\" dictionary. The \"id\" is used by the edges to identify which nodes to connect.\n",
+ " \n",
+ "- Edges always consist of an \"id,\" \"start,\" and \"end.\" Edges have a customizable \"properties\" dictionary that can store additional data.\n",
+ "\n",
+ "- Edges are visualized undirected unless specified otherwise by setting the `directed` property on the widget or defining the `directed_mapping` per edge."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "e09589a6-ce29-4da1-9578-90dc4b4394ba",
+ "metadata": {
+ "id": "e09589a6-ce29-4da1-9578-90dc4b4394ba"
+ },
+ "outputs": [],
+ "source": [
+ "w = GraphWidget()\n",
+ "w.nodes = [\n",
+ " {\"id\": 0, \"properties\": {\"firstName\": \"Alpha\", \"label\": \"Person A\"}},\n",
+ " {\"id\": \"one\", \"properties\": {\"firstName\": \"Bravo\", \"label\": \"Person B\"}},\n",
+ " {\"id\": 2.0, \"properties\": {\"firstName\": \"Charlie\", \"label\": \"Person C\", \"has_hat\": False}},\n",
+ " {\"id\": True, \"properties\": {\"firstName\": \"Delta\", \"label\": \"Person D\", \"likes_pizza\": True}}\n",
+ "]\n",
+ "w.edges = [\n",
+ " {\"id\": \"zero\", \"start\": 0, \"end\": \"one\", \"properties\": {\"since\": \"1992\", \"label\": \"knows\"}},\n",
+ " {\"id\": 1, \"start\": \"one\", \"end\": True, \"properties\": {\"label\": \"knows\", \"since\": \"1992\"}},\n",
+ " {\"id\": 2.0, \"start\": 2.0, \"end\": True, \"properties\": {\"label\": \"knows\", \"since\": \"1992\"}},\n",
+ " {\"id\": False, \"start\": 0, \"end\": 2.0, \"properties\": {\"label\": \"knows\", \"since\": 234}}\n",
+ "]\n",
+ "w.directed = True"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9d9ea06a-130f-4103-bd1a-6fda3ae084bb",
+ "metadata": {
+ "id": "9d9ea06a-130f-4103-bd1a-6fda3ae084bb"
+ },
+ "source": [
+ "To display the current state of the widget, you can either use ```display(w)```, ```w.show()``` or ```w```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "055f895e-6934-45e4-8ed5-e1e423c9ce09",
+ "metadata": {
+ "id": "055f895e-6934-45e4-8ed5-e1e423c9ce09",
+ "outputId": "69c2c53b-b072-4f1c-db6f-4b24288c093f",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 517,
+ "referenced_widgets": [
+ "fdfdd5c491d3456eb6346141a833e381",
+ "d3b86df3ae9942ca8de32531bcb6b2b4"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='500px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "fdfdd5c491d3456eb6346141a833e381"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "display(w)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0e3f207e-b1ac-48a3-89f3-75f5dbb03013",
+ "metadata": {
+ "id": "0e3f207e-b1ac-48a3-89f3-75f5dbb03013"
+ },
+ "source": [
+ "## Nodes\n",
+ "\n",
+ "Nodes in this graph come with a variety of style properties that can be customized through mapping functions. These properties can be altered to change the appearance and attributes of each node, providing flexible graph representation.\n",
+ "\n",
+ "If no explicit values are set, the default mappings are used, resulting in the following values."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "6f5c56bf-c4d3-4dc1-a6dd-5e640d913d21",
+ "metadata": {
+ "id": "6f5c56bf-c4d3-4dc1-a6dd-5e640d913d21",
+ "outputId": "a903d426-cfa2-4f8c-ee1f-4776138bc188",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'id': 0,\n",
+ " 'properties': {'firstName': 'Alpha', 'label': 'Person A'},\n",
+ " 'color': '#15AFAC',\n",
+ " 'styles': {},\n",
+ " 'label': 'Person A',\n",
+ " 'scale_factor': 1.0,\n",
+ " 'type': '#15AFAC',\n",
+ " 'size': (55.0, 55.0),\n",
+ " 'position': (0.0, 0.0)},\n",
+ " {'id': 'one',\n",
+ " 'properties': {'firstName': 'Bravo', 'label': 'Person B'},\n",
+ " 'color': '#15AFAC',\n",
+ " 'styles': {},\n",
+ " 'label': 'Person B',\n",
+ " 'scale_factor': 1.0,\n",
+ " 'type': '#15AFAC',\n",
+ " 'size': (55.0, 55.0),\n",
+ " 'position': (0.0, 0.0)},\n",
+ " {'id': 2.0,\n",
+ " 'properties': {'firstName': 'Charlie',\n",
+ " 'label': 'Person C',\n",
+ " 'has_hat': False},\n",
+ " 'color': '#15AFAC',\n",
+ " 'styles': {},\n",
+ " 'label': 'Person C',\n",
+ " 'scale_factor': 1.0,\n",
+ " 'type': '#15AFAC',\n",
+ " 'size': (55.0, 55.0),\n",
+ " 'position': (0.0, 0.0)},\n",
+ " {'id': True,\n",
+ " 'properties': {'firstName': 'Delta',\n",
+ " 'label': 'Person D',\n",
+ " 'likes_pizza': True},\n",
+ " 'color': '#15AFAC',\n",
+ " 'styles': {},\n",
+ " 'label': 'Person D',\n",
+ " 'scale_factor': 1.0,\n",
+ " 'type': '#15AFAC',\n",
+ " 'size': (55.0, 55.0),\n",
+ " 'position': (0.0, 0.0)}]"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 17
+ }
+ ],
+ "source": [
+ "w.nodes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1f5d3a78-75fa-4f0d-a8b0-fe60fc69012a",
+ "metadata": {
+ "id": "1f5d3a78-75fa-4f0d-a8b0-fe60fc69012a"
+ },
+ "source": [
+ "## Edges\n",
+ "\n",
+ "Similar to nodes, edges come with customizable properties. These values can be adjusted to suit specific graph visualization needs, while default values are as follows."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "43a47347-36c1-49af-a6bc-3207329fd54a",
+ "metadata": {
+ "id": "43a47347-36c1-49af-a6bc-3207329fd54a",
+ "outputId": "a62c84e7-4838-48f1-e4eb-feccdda01ae8",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'id': 'zero',\n",
+ " 'start': 0,\n",
+ " 'end': 'one',\n",
+ " 'properties': {'since': '1992', 'label': 'knows'},\n",
+ " 'label': 'knows',\n",
+ " 'color': '#15AFAC',\n",
+ " 'thickness_factor': 1.0,\n",
+ " 'directed': True},\n",
+ " {'id': 1,\n",
+ " 'start': 'one',\n",
+ " 'end': True,\n",
+ " 'properties': {'label': 'knows', 'since': '1992'},\n",
+ " 'label': 'knows',\n",
+ " 'color': '#15AFAC',\n",
+ " 'thickness_factor': 1.0,\n",
+ " 'directed': True},\n",
+ " {'id': 2.0,\n",
+ " 'start': 2.0,\n",
+ " 'end': True,\n",
+ " 'properties': {'label': 'knows', 'since': '1992'},\n",
+ " 'label': 'knows',\n",
+ " 'color': '#15AFAC',\n",
+ " 'thickness_factor': 1.0,\n",
+ " 'directed': True},\n",
+ " {'id': False,\n",
+ " 'start': 0,\n",
+ " 'end': 2.0,\n",
+ " 'properties': {'label': 'knows', 'since': 234},\n",
+ " 'label': 'knows',\n",
+ " 'color': '#15AFAC',\n",
+ " 'thickness_factor': 1.0,\n",
+ " 'directed': True}]"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 18
+ }
+ ],
+ "source": [
+ "w.edges"
+ ]
+ }
+ ],
+ "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": "3.11.3"
+ },
+ "colab": {
+ "provenance": []
+ },
+ "widgets": {
+ "application/vnd.jupyter.widget-state+json": {
+ "fdfdd5c491d3456eb6346141a833e381": {
+ "model_module": "yfiles-jupyter-graphs",
+ "model_name": "GraphModel",
+ "model_module_version": "^1.8.1",
+ "state": {
+ "_context_pane_mapping": [
+ {
+ "id": "Neighborhood",
+ "title": "Neighborhood"
+ },
+ {
+ "id": "Data",
+ "title": "Data"
+ },
+ {
+ "id": "Search",
+ "title": "Search"
+ },
+ {
+ "id": "About",
+ "title": "About"
+ }
+ ],
+ "_data_importer": "unknown",
+ "_directed": true,
+ "_dom_classes": [],
+ "_edges": [
+ {
+ "id": "zero",
+ "start": 0,
+ "end": "one",
+ "properties": {
+ "since": "1992",
+ "label": "knows"
+ },
+ "label": "knows",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "id": 1,
+ "start": "one",
+ "end": true,
+ "properties": {
+ "label": "knows",
+ "since": "1992"
+ },
+ "label": "knows",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "id": 2,
+ "start": 2,
+ "end": true,
+ "properties": {
+ "label": "knows",
+ "since": "1992"
+ },
+ "label": "knows",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "id": false,
+ "start": 0,
+ "end": 2,
+ "properties": {
+ "label": "knows",
+ "since": 234
+ },
+ "label": "knows",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ }
+ ],
+ "_graph_layout": {},
+ "_highlight": [],
+ "_license": {},
+ "_model_module": "yfiles-jupyter-graphs",
+ "_model_module_version": "^1.8.1",
+ "_model_name": "GraphModel",
+ "_neighborhood": {},
+ "_nodes": [
+ {
+ "id": 0,
+ "properties": {
+ "firstName": "Alpha",
+ "label": "Person A"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Person A",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": "one",
+ "properties": {
+ "firstName": "Bravo",
+ "label": "Person B"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Person B",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 2,
+ "properties": {
+ "firstName": "Charlie",
+ "label": "Person C",
+ "has_hat": false
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Person C",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": true,
+ "properties": {
+ "firstName": "Delta",
+ "label": "Person D",
+ "likes_pizza": true
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Person D",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ }
+ ],
+ "_overview": {
+ "enabled": null,
+ "overview_set": false
+ },
+ "_selected_graph": [
+ [],
+ []
+ ],
+ "_sidebar": {
+ "enabled": false,
+ "start_with": null
+ },
+ "_view_count": null,
+ "_view_module": "yfiles-jupyter-graphs",
+ "_view_module_version": "^1.8.1",
+ "_view_name": "GraphView",
+ "layout": "IPY_MODEL_d3b86df3ae9942ca8de32531bcb6b2b4",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "d3b86df3ae9942ca8de32531bcb6b2b4": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "2.0.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "2.0.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border_bottom": null,
+ "border_left": null,
+ "border_right": null,
+ "border_top": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": "500px",
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": "100%"
+ }
+ }
+ }
+ }
},
- {
- "cell_type": "markdown",
- "id": "08285758-8025-40d1-9cfd-3de06ba59693",
- "metadata": {},
- "source": [
- "### Before using the widget, make sure to install the required packages\n",
- "\n",
- "Ensure you have the necessary packages installed by running the following commands:\n",
- "- ```%pip install yfiles_jupyter_graphs --quiet```\n",
- "- ```from yfiles_jupyter_graphs import GraphWidget``` "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "40c51747-64bc-49f2-90c0-c0ff79d24048",
- "metadata": {},
- "outputs": [],
- "source": [
- "%pip install yfiles_jupyter_graphs --quiet\n",
- "from yfiles_jupyter_graphs import GraphWidget"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "651c2e90-4f3b-4b25-9404-735288b25771",
- "metadata": {},
- "source": [
- "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "01400028-72b5-47ff-a5bc-5d9f64f0df31",
- "metadata": {},
- "outputs": [],
- "source": [
- "try:\n",
- " import google.colab\n",
- " from google.colab import output\n",
- " output.enable_custom_widget_manager()\n",
- "except:\n",
- " pass"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e7a4c051-be35-4269-a322-32a147cf02cd",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f0823ca8-ea68-4bdd-863c-09b0524aa96e",
- "metadata": {},
- "source": [
- "## How to create a new graph\n",
- "Before specifying nodes or edges, initialize a new graph widget. You can do this by creating a new instance, for example, `w = GraphWidget()`\n",
- "\n",
- "- Nodes always have an \"id,\" and any additional data can be added to the \"properties\" dictionary. The \"id\" is used by the edges to identify which nodes to connect.\n",
- " \n",
- "- Edges always consist of an \"id,\" \"start,\" and \"end.\" Edges have a customizable \"properties\" dictionary that can store additional data.\n",
- "\n",
- "- Edges are visualized undirected unless specified otherwise by setting the `directed` property on the widget or defining the `directed_mapping` per edge."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e09589a6-ce29-4da1-9578-90dc4b4394ba",
- "metadata": {},
- "outputs": [],
- "source": [
- "w = GraphWidget()\n",
- "w.nodes = [\n",
- " {\"id\": 0, \"properties\": {\"firstName\": \"Alpha\", \"label\": \"Person A\"}},\n",
- " {\"id\": \"one\", \"properties\": {\"firstName\": \"Bravo\", \"label\": \"Person B\"}},\n",
- " {\"id\": 2.0, \"properties\": {\"firstName\": \"Charlie\", \"label\": \"Person C\", \"has_hat\": False}},\n",
- " {\"id\": True, \"properties\": {\"firstName\": \"Delta\", \"label\": \"Person D\", \"likes_pizza\": True}}\n",
- "]\n",
- "w.edges = [\n",
- " {\"id\": \"zero\", \"start\": 0, \"end\": \"one\", \"properties\": {\"since\": \"1992\", \"label\": \"knows\"}},\n",
- " {\"id\": 1, \"start\": \"one\", \"end\": True, \"properties\": {\"label\": \"knows\", \"since\": \"1992\"}},\n",
- " {\"id\": 2.0, \"start\": 2.0, \"end\": True, \"properties\": {\"label\": \"knows\", \"since\": \"1992\"}},\n",
- " {\"id\": False, \"start\": 0, \"end\": 2.0, \"properties\": {\"label\": \"knows\", \"since\": 234}}\n",
- "]\n",
- "w.directed = True"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9d9ea06a-130f-4103-bd1a-6fda3ae084bb",
- "metadata": {},
- "source": [
- "To display the current state of the widget, you can either use ```display(w)```, ```w.show()``` or ```w```"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "055f895e-6934-45e4-8ed5-e1e423c9ce09",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "0e3f207e-b1ac-48a3-89f3-75f5dbb03013",
- "metadata": {},
- "source": [
- "## Nodes\n",
- "\n",
- "Nodes in this graph come with a variety of style properties that can be customized through mapping functions. These properties can be altered to change the appearance and attributes of each node, providing flexible graph representation. \n",
- "\n",
- "If no explicit values are set, the default mappings are used, resulting in the following values."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6f5c56bf-c4d3-4dc1-a6dd-5e640d913d21",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.nodes"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1f5d3a78-75fa-4f0d-a8b0-fe60fc69012a",
- "metadata": {},
- "source": [
- "## Edges \n",
- "\n",
- "Similar to nodes, edges come with customizable properties. These values can be adjusted to suit specific graph visualization needs, while default values are as follows."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "43a47347-36c1-49af-a6bc-3207329fd54a",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.edges"
- ]
- }
- ],
- "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": "3.11.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/02_label_mapping.ipynb b/examples/02_label_mapping.ipynb
index 6793ef0..38b7b9a 100644
--- a/examples/02_label_mapping.ipynb
+++ b/examples/02_label_mapping.ipynb
@@ -1,359 +1,2133 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "2bc80da9-831a-403f-9307-7f08fd20599c",
- "metadata": {},
- "source": [
- "# Element Label Mapping "
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "2bc80da9-831a-403f-9307-7f08fd20599c",
+ "metadata": {
+ "id": "2bc80da9-831a-403f-9307-7f08fd20599c"
+ },
+ "source": [
+ "# Element Label Mapping "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dffe6f9e-b5bc-4338-aa02-2ea826c8875d",
+ "metadata": {
+ "id": "dffe6f9e-b5bc-4338-aa02-2ea826c8875d"
+ },
+ "source": [
+ "This notebook covers the basics of customizing node and edge labels.\n",
+ "\n",
+ "For mapping demonstrations, we'll use the same graph, ```erdos_renyi_graph```, imported from the NetworkX package.\n",
+ "\n",
+ "For more details on how to import graph data, explore the other example notebooks or refer to the full widget [documentation](https://yworks.github.io/yfiles-jupyter-graphs/).\n",
+ "\n",
+ "Before utilizing the graph widget, ensure all necessary packages are installed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "3d1e6aef-8aeb-4b37-b7b8-5ae0279ff1f2",
+ "metadata": {
+ "id": "3d1e6aef-8aeb-4b37-b7b8-5ae0279ff1f2",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 86
+ },
+ "outputId": "972dddf9-fc67-4cdd-d46d-c1c21a217a77"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m38.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m24.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "from yfiles_jupyter_graphs import GraphWidget\n",
+ "%pip install networkx --quiet\n",
+ "from typing import Dict\n",
+ "from networkx import erdos_renyi_graph, set_node_attributes, set_edge_attributes\n",
+ "\n",
+ "g = erdos_renyi_graph(10, 0.3, 2)\n",
+ "# We will use this additional attribute as a label later on\n",
+ "set_node_attributes(g, {node: {\"NodeName\": f\"Node {node}\"} for node in g.nodes})\n",
+ "set_edge_attributes(g, {edge: {\"EdgeName\": f\"Edge {edge}\"} for edge in g.edges})\n",
+ "w = GraphWidget(graph=g)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bb2a8969-59b4-4fc2-8ac2-e6bef9420a22",
+ "metadata": {
+ "id": "bb2a8969-59b4-4fc2-8ac2-e6bef9420a22"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "ac38b394-68c0-417d-b054-d5cff09e436f",
+ "metadata": {
+ "id": "ac38b394-68c0-417d-b054-d5cff09e436f"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "42c6792a-87fd-4f42-b6c1-7d3b04a8e12c",
+ "metadata": {
+ "id": "42c6792a-87fd-4f42-b6c1-7d3b04a8e12c"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6136a30d-5a9e-41f4-b650-d35ed3bcc146",
+ "metadata": {
+ "id": "6136a30d-5a9e-41f4-b650-d35ed3bcc146"
+ },
+ "source": [
+ "This is the graph we will be working on:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "4a65a738-fa76-489b-b444-1cef7f00ea1c",
+ "metadata": {
+ "id": "4a65a738-fa76-489b-b444-1cef7f00ea1c",
+ "outputId": "9e78ffb4-446e-475a-e4f1-3fd6199eaee5",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 517,
+ "referenced_widgets": [
+ "42eaed2229e6451588fcd836003bea69",
+ "8045061ce63d4de39e96b821f52a976e"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='500px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "42eaed2229e6451588fcd836003bea69"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "display(GraphWidget(graph=g))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8af7687d-bd70-48e4-85b1-3a1fa55b92a4",
+ "metadata": {
+ "id": "8af7687d-bd70-48e4-85b1-3a1fa55b92a4"
+ },
+ "source": [
+ "## Node Label Mapping\n",
+ "### Property key mapping\n",
+ "\n",
+ "To only reflect the data of a property on the node, you can easily assign this using the respective key. Check out the [mapping overloads notebook](./12_mapping_overloads.ipynb) for a more detailed explanation.\n",
+ "\n",
+ "This is a shorter alternative to a local lambda function, e.g., `lambda node: node['properties']['NodeName']`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "0057e94d-fc4a-417a-ad94-a81689ec6f16",
+ "metadata": {
+ "id": "0057e94d-fc4a-417a-ad94-a81689ec6f16",
+ "outputId": "9454cad6-df54-4342-f926-3275654f418f",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 517,
+ "referenced_widgets": [
+ "7ad33d6f022e47bcbf25415fa73dce57",
+ "8277125789534576855d61f41200ff83"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='500px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "7ad33d6f022e47bcbf25415fa73dce57"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w.node_label_mapping = 'NodeName'\n",
+ "display(w)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "12bd0dd5-77c8-4aa3-bd0d-46c80f7ec83b",
+ "metadata": {
+ "id": "12bd0dd5-77c8-4aa3-bd0d-46c80f7ec83b"
+ },
+ "source": [
+ "### Function mapping\n",
+ "\n",
+ "For a more versatile node label computation you can define a mapping function:\n",
+ "\n",
+ "The node label mapping is a function that is supposed to return a label string for each given node object which is then displayed in the widget.\n",
+ "\n",
+ "Optionally, the index can be used as the first function parameter.\n",
+ "\n",
+ "We will use the index of each node to compute our new label which will be the negated index. \\\n",
+ "For this, we first define a new mapping function and then set this function as our current label mapping."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "70de2c54-0a34-4d1c-9eb4-9a186946eac2",
+ "metadata": {
+ "id": "70de2c54-0a34-4d1c-9eb4-9a186946eac2"
+ },
+ "outputs": [],
+ "source": [
+ "def custom_node_label_mapping(index: int, node: Dict):\n",
+ " \"\"\"let the label be the negated index\"\"\"\n",
+ " return '-' + str(index)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d4b098da-f7f3-4d97-8814-74cb7484c714",
+ "metadata": {
+ "id": "d4b098da-f7f3-4d97-8814-74cb7484c714"
+ },
+ "source": [
+ "### Custom node mappings\n",
+ "\n",
+ "There are get and set methods for each customizable node property.\n",
+ "- you can set a new node mapping with ```w.set_node_[binding]_mapping```\n",
+ "- you can get the current node mapping with ```w.get_node_[binding]_mapping```\n",
+ "- you can delete a custom node mapping with ```w.del_node_[binding]_mapping```\n",
+ "\n",
+ "You can find more details in the dedicated function documentation, available at ```w.[function_name].__doc__``` or in the [documentation](https://yworks.github.io/yfiles-jupyter-graphs/02_graph_widget/#methods).\n",
+ "\n",
+ "If no custom mapping is set the default mappings are used."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "a4d6f729-9089-4004-a229-7e547b9d2d77",
+ "metadata": {
+ "id": "a4d6f729-9089-4004-a229-7e547b9d2d77",
+ "outputId": "3e33d07d-bca0-48a2-9e2e-2959d29c9ef4",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "The default label mapping for graph elements.\n",
+ "\n",
+ " Element (dict) should have key properties which itself should be a dict.\n",
+ " Then one of the following values (in descending priority) is used as label if the label is a string:\n",
+ "\n",
+ " - properties[\"label\"]\n",
+ " - properties[\"yf_label\"]\n",
+ "\n",
+ " When importing a Neo4j graph, the following properties are values are used as labels (in descending priority):\n",
+ "\n",
+ " - properties['name']\n",
+ " - properties['title']\n",
+ " - properties['label']\n",
+ " - properties['description']\n",
+ " - properties['caption']\n",
+ " - properties['text']\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " index: int (optional)\n",
+ " element: typing.Dict\n",
+ "\n",
+ " Notes\n",
+ " -----\n",
+ " This is the default value for the {`node|edge`}_label_mapping property.\n",
+ " Can be 'overwritten' by setting the property\n",
+ " with a function of the same signature.\n",
+ "\n",
+ " If the given mapping function has only one parameter (that is not typed as int),\n",
+ " then it will be called with the element (typing.Dict) as first parameter.\n",
+ "\n",
+ " When a string is provided as the function argument, the key will be searched for in both the properties\n",
+ " dictionary and the element keys.\n",
+ "\n",
+ " Example\n",
+ " -------\n",
+ "\n",
+ " .. code::\n",
+ "\n",
+ " from yfiles_jupyter_graphs import GraphWidget\n",
+ " w = GraphWidget()\n",
+ " w.{node|edge}_label_mapping = 'id'\n",
+ "\n",
+ " .. code::\n",
+ "\n",
+ " from yfiles_jupyter_graphs import GraphWidget\n",
+ " w = GraphWidget()\n",
+ " def custom_element_label_mapping(element: typing.Dict):\n",
+ " ...\n",
+ " w.set_{node|edge}_label_mapping(custom_element_label_mapping)\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " label: str\n",
+ "\n",
+ " \n"
+ ]
+ }
+ ],
+ "source": [
+ "print(w.default_element_label_mapping.__doc__)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "40028f47-0a6f-4853-9189-47210fa5e2ec",
+ "metadata": {
+ "id": "40028f47-0a6f-4853-9189-47210fa5e2ec"
+ },
+ "source": [
+ "Let's set new negated node labels:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "79200fe1-2020-4682-9fdb-6dd7c853358b",
+ "metadata": {
+ "id": "79200fe1-2020-4682-9fdb-6dd7c853358b",
+ "outputId": "b64c6c81-03b3-45fc-fc34-9fd7557f3fbb",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 105
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "custom_node_label_mapping
def custom_node_label_mapping(index: int, node: Dict)
let the label be the negated index
yfiles_jupyter_graphs.widget.GraphWidget.default_node_label_mapping
def default_node_label_mapping(index: int, node: TDict)
See default element label mapping.\n", + " \n", + "
custom_edge_label_mapping
def custom_edge_label_mapping(edge: Dict)
let the label be the negated index
yfiles_jupyter_graphs.widget.GraphWidget.default_edge_label_mapping
def default_edge_label_mapping(index: int, edge: TDict)
See default element label mapping.\n", + " \n", + "
"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6136a30d-5a9e-41f4-b650-d35ed3bcc146",
- "metadata": {},
- "source": [
- "This is the graph we will be working on: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4a65a738-fa76-489b-b444-1cef7f00ea1c",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8af7687d-bd70-48e4-85b1-3a1fa55b92a4",
- "metadata": {},
- "source": [
- "## Node Label Mapping\n",
- "### Property key mapping\n",
- "\n",
- "To only reflect the data of a property on the node, you can easily assign this using the respective key. Check out the [mapping overloads notebook](./12_mapping_overloads.ipynb) for a more detailed explanation.\n",
- "\n",
- "This is a shorter alternative to a local lambda function, e.g., `lambda node: node['properties']['NodeName']`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0057e94d-fc4a-417a-ad94-a81689ec6f16",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.node_label_mapping = 'NodeName'\n",
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "12bd0dd5-77c8-4aa3-bd0d-46c80f7ec83b",
- "metadata": {},
- "source": [
- "### Function mapping\n",
- "\n",
- "For a more versatile node label computation you can define a mapping function:\n",
- "\n",
- "The node label mapping is a function that is supposed to return a label string for each given node object which is then displayed in the widget.\n",
- "\n",
- "Optionally, the index can be used as the first function parameter.\n",
- "\n",
- "We will use the index of each node to compute our new label which will be the negated index. \\\n",
- "For this, we first define a new mapping function and then set this function as our current label mapping."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "70de2c54-0a34-4d1c-9eb4-9a186946eac2",
- "metadata": {},
- "outputs": [],
- "source": [
- "def custom_node_label_mapping(index: int, node: Dict):\n",
- " \"\"\"let the label be the negated index\"\"\"\n",
- " return '-' + str(index)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d4b098da-f7f3-4d97-8814-74cb7484c714",
- "metadata": {},
- "source": [
- "### Custom node mappings\n",
- "\n",
- "There are get and set methods for each customizable node property.\n",
- "- you can set a new node mapping with ```w.set_node_[binding]_mapping```\n",
- "- you can get the current node mapping with ```w.get_node_[binding]_mapping```\n",
- "- you can delete a custom node mapping with ```w.del_node_[binding]_mapping```\n",
- "\n",
- "You can find more details in the dedicated function documentation, available at ```w.[function_name].__doc__``` or in the [documentation](https://yworks.github.io/yfiles-jupyter-graphs/02_graph_widget/#methods).\n",
- "\n",
- "If no custom mapping is set the default mappings are used."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a4d6f729-9089-4004-a229-7e547b9d2d77",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(w.default_element_label_mapping.__doc__)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "40028f47-0a6f-4853-9189-47210fa5e2ec",
- "metadata": {},
- "source": [
- "Let's set new negated node labels: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "79200fe1-2020-4682-9fdb-6dd7c853358b",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.set_node_label_mapping(custom_node_label_mapping)\n",
- "w.get_node_label_mapping()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a379f670-653d-4a25-a380-7dd53369bde9",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "12c4eaed-a2ca-415e-8eb6-e8c64891eb4c",
- "metadata": {},
- "source": [
- "If a node label mapping is deleted, the label mapping reverts back to the default mapping."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6c4c2164-f1af-472e-bb93-b2edd48a53ba",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.del_node_label_mapping()\n",
- "w.get_node_label_mapping()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2a48e8b8-d8e2-4d3e-9891-ddaa73826f48",
- "metadata": {},
- "source": [
- "## Edge Label Mapping\n",
- "### Property key mapping\n",
- "\n",
- "Similar to node mappings, edges also allow for a short version assigning properties by key:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d320734c-8a53-4bbe-8468-d0de06423bcd",
- "metadata": {},
- "outputs": [],
- "source": [
- "w2 = GraphWidget(graph=g)\n",
- "w2.edge_label_mapping = 'EdgeName'\n",
- "display(w2)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4aeec1ca-34e6-4cb5-9e0a-a361606046fa",
- "metadata": {},
- "source": [
- "### Function mapping\n",
- "\n",
- "Similar to node labels, you can define an edge label mapping function for more control over the visualized label. The edge label mapping is a function that is supposed to return a label string for each given edge object which is then displayed in the widget.\n",
- "\n",
- "We will use a similar mapping function as for the node labels. We negate every edge index and use this as our new edge label. \\\n",
- "For this, we first a define a new mapping function and then set this function as our current edge label mapping. \\\n",
- "In the case of the node label mapping, we used the optional index parameter. Here we only use the edge dictionary."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "782a5ab9-7268-42ee-a88f-1f6dfb70e1e4",
- "metadata": {},
- "outputs": [],
- "source": [
- "edges = w2.get_edges()\n",
- "def custom_edge_label_mapping(edge: Dict):\n",
- " \"\"\"let the label be the negated index\"\"\"\n",
- " return '-' + str(edges.index(edge))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5864b497-c542-4946-a58e-e0b043652005",
- "metadata": {},
- "source": [
- "### Custom edge mappings\n",
- "\n",
- "There are get and set methods for each customizable edge property.\n",
- "- you can set a new edge mapping with ```w.set_edge_[binging]_mapping```\n",
- "- you can get the current edge mapping with ```w.get_edge_[binding]_mapping```\n",
- "- you can delete a custom edge mapping with ```w.del_edge_[binding]_mapping```\n",
- "\n",
- "You can find more details in the dedicated function documentation, available at ```w.[function_name].__doc__``` or in the [documentation](https://yworks.github.io/yfiles-jupyter-graphs/02_graph_widget/#methods).\n",
- "\n",
- "If no custom mapping is set the default mappings are used."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1fa27ff0-de0e-4e4c-8857-0616dea901be",
- "metadata": {},
- "source": [
- "Let's set the new negated edge labels: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "eca341ce-1276-4d76-b01c-181083365fa2",
- "metadata": {},
- "outputs": [],
- "source": [
- "w2.set_edge_label_mapping(custom_edge_label_mapping)\n",
- "w2.get_edge_label_mapping()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "08ceda9c-14ff-4396-954d-9dfbdbc31389",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w2)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1f25992e-2e98-4a97-8d32-d070aa854ea4",
- "metadata": {},
- "source": [
- "If a edge label mapping is deleted, the label mapping reverts back to the default mapping."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "270efae3-3485-4088-a667-569d984108ce",
- "metadata": {},
- "outputs": [],
- "source": [
- "w2.del_edge_label_mapping()\n",
- "w2.get_edge_label_mapping()"
- ]
- }
- ],
- "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": "3.12.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/02a_label_styles_mapping.ipynb b/examples/02a_label_styles_mapping.ipynb
index 929ade3..e75f420 100644
--- a/examples/02a_label_styles_mapping.ipynb
+++ b/examples/02a_label_styles_mapping.ipynb
@@ -1,264 +1,1989 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "8b919f4a-fd0e-49b2-90c7-f538cdb68288",
- "metadata": {},
- "source": [
- "# Element Label Mapping "
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "8b919f4a-fd0e-49b2-90c7-f538cdb68288",
+ "metadata": {
+ "id": "8b919f4a-fd0e-49b2-90c7-f538cdb68288"
+ },
+ "source": [
+ "# Element Label Mapping "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "08db2296-9111-4446-b82d-6afa54a3c13b",
+ "metadata": {
+ "id": "08db2296-9111-4446-b82d-6afa54a3c13b"
+ },
+ "source": [
+ "This notebook covers the basics of customizing label visualizations for nodes and edges.\n",
+ "\n",
+ "For mapping demonstrations, we'll use the same graph, ```erdos_renyi_graph```, imported from the NetworkX package.\n",
+ "\n",
+ "For more details on how to import graph data, explore the other example notebooks or refer to the full widget [documentation](https://yworks.github.io/yfiles-jupyter-graphs/).\n",
+ "\n",
+ "Before utilizing the graph widget, ensure all necessary packages are installed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "3ce520a0-0c33-48ae-ab02-54d7aba460a8",
+ "metadata": {
+ "id": "3ce520a0-0c33-48ae-ab02-54d7aba460a8",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 86
+ },
+ "outputId": "91891e34-fd3c-4526-8a76-3335601410cb"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m20.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m25.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m23.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "from yfiles_jupyter_graphs import GraphWidget\n",
+ "%pip install networkx --quiet\n",
+ "from typing import Dict\n",
+ "from networkx import erdos_renyi_graph\n",
+ "\n",
+ "g = erdos_renyi_graph(10, 0.3, 2)\n",
+ "w = GraphWidget(graph=g)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e090cf46-b02f-42af-9a91-279b3440ecf6",
+ "metadata": {
+ "id": "e090cf46-b02f-42af-9a91-279b3440ecf6"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "d99602b7-b89b-46bb-a477-00172006ac05",
+ "metadata": {
+ "id": "d99602b7-b89b-46bb-a477-00172006ac05"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1addb8c7-84ee-47ac-aa5b-6d1b69e4d0c7",
+ "metadata": {
+ "id": "1addb8c7-84ee-47ac-aa5b-6d1b69e4d0c7"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "aab103ba-9d3b-4d39-bb4e-3bdfac2566d6",
+ "metadata": {
+ "id": "aab103ba-9d3b-4d39-bb4e-3bdfac2566d6"
+ },
+ "source": [
+ "This is the graph we will be working on:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "1a87056a-a345-4221-ad19-d1440a63f08e",
+ "metadata": {
+ "id": "1a87056a-a345-4221-ad19-d1440a63f08e",
+ "outputId": "285f922f-3d19-4ba5-9660-089a3bcce75d",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 517,
+ "referenced_widgets": [
+ "47950c99710940eda99817ee3c84d775",
+ "7bed0c5d681548249a094889eadafd1b"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='500px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "47950c99710940eda99817ee3c84d775"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "display(GraphWidget(graph=g))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "81e2d7a1-6653-467a-8e87-c5f888145774",
+ "metadata": {
+ "id": "81e2d7a1-6653-467a-8e87-c5f888145774"
+ },
+ "source": [
+ "## Node Label Styles Mapping\n",
+ "\n",
+ "Additionally to the shown label text mapping in [02 Label Mapping](./02_label_mapping.ipynb), the mapping function may also return styling properties alongside the `text`.\n",
+ "\n",
+ "Supported visualization properties:\n",
+ "* `fontSize: number`: The text size of the label.\n",
+ "* `color: string`: The text color of the label.\n",
+ "* `backgroundColor: string`: A color string that is used as the label's background.\n",
+ "* `position: 'center' | 'north' | 'east' | 'south' | 'west'`: The label position at the node.\n",
+ "* `maximumWidth: number`: The maximum width of the label. By default, the label is clipped at the given size, or wrapped when `wrapping` is set.\n",
+ "* `maximumHeight: number`: The maximum height of the label. Clips the label at the given height. May be combined with `wrapping`.\n",
+ "* `wrapping: 'character' | 'character_ellipsis' | 'none' | 'word' | 'word_ellipsis'`: Text wrapping for the label. Must be set in combination with `maximumWidth`.\n",
+ "* `textAlignment: 'center' | 'left' | 'right'`: The horizontal text alignment when `wrapping` is enabled.\n",
+ "\n",
+ "For this, we define a new label mapping function and, instead of just returning a text string, we return a dictionary with a `text` property and optional styling properties:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "371c5053-6019-4a20-af67-067f38fcbf39",
+ "metadata": {
+ "id": "371c5053-6019-4a20-af67-067f38fcbf39"
+ },
+ "outputs": [],
+ "source": [
+ "from datetime import datetime\n",
+ "\n",
+ "def custom_label_styles_mapping(node: Dict):\n",
+ " \"\"\"let the label be the negated purple big index\"\"\"\n",
+ " return {\n",
+ " 'text' : datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"),\n",
+ " 'backgroundColor': '#5C268B',\n",
+ " 'fontSize': 20,\n",
+ " 'color': '#FF6EC7',\n",
+ " 'position': 'north',\n",
+ " 'maximumWidth': 130,\n",
+ " 'wrapping': 'word',\n",
+ " 'textAlignment': 'center'\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d306b597-678e-4a07-90f1-401bff0bc7c5",
+ "metadata": {
+ "id": "d306b597-678e-4a07-90f1-401bff0bc7c5"
+ },
+ "source": [
+ "Let's set our new label styles:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "938719a0-d739-4ddf-8f7f-da00c1d05012",
+ "metadata": {
+ "id": "938719a0-d739-4ddf-8f7f-da00c1d05012",
+ "outputId": "c1bb94a7-c9ac-4815-d58c-a922b4695e79",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 105
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "custom_label_styles_mapping
def custom_label_styles_mapping(node: Dict)
let the label be the negated purple big index
yfiles_jupyter_graphs.widget.GraphWidget.default_node_label_mapping
def default_node_label_mapping(index: int, node: TDict)
See default element label mapping.\n", + " \n", + "
custom_label_styles_mapping
def custom_label_styles_mapping(node: Dict)
let the label be the negated purple big index
yfiles_jupyter_graphs.widget.GraphWidget.default_edge_label_mapping
def default_edge_label_mapping(index: int, edge: TDict)
See default element label mapping.\n", + " \n", + "
"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "aab103ba-9d3b-4d39-bb4e-3bdfac2566d6",
- "metadata": {},
- "source": [
- "This is the graph we will be working on: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1a87056a-a345-4221-ad19-d1440a63f08e",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "81e2d7a1-6653-467a-8e87-c5f888145774",
- "metadata": {},
- "source": [
- "## Node Label Styles Mapping\n",
- "\n",
- "Additionally to the shown label text mapping in [02 Label Mapping](./02_label_mapping.ipynb), the mapping function may also return styling properties alongside the `text`. \n",
- "\n",
- "Supported visualization properties:\n",
- "* `fontSize: number`: The text size of the label.\n",
- "* `color: string`: The text color of the label.\n",
- "* `backgroundColor: string`: A color string that is used as the label's background.\n",
- "* `position: 'center' | 'north' | 'east' | 'south' | 'west'`: The label position at the node.\n",
- "* `maximumWidth: number`: The maximum width of the label. By default, the label is clipped at the given size, or wrapped when `wrapping` is set.\n",
- "* `maximumHeight: number`: The maximum height of the label. Clips the label at the given height. May be combined with `wrapping`.\n",
- "* `wrapping: 'character' | 'character_ellipsis' | 'none' | 'word' | 'word_ellipsis'`: Text wrapping for the label. Must be set in combination with `maximumWidth`.\n",
- "* `textAlignment: 'center' | 'left' | 'right'`: The horizontal text alignment when `wrapping` is enabled.\n",
- "\n",
- "For this, we define a new label mapping function and, instead of just returning a text string, we return a dictionary with a `text` property and optional styling properties:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "371c5053-6019-4a20-af67-067f38fcbf39",
- "metadata": {},
- "outputs": [],
- "source": [
- "from datetime import datetime\n",
- "\n",
- "def custom_label_styles_mapping(node: Dict):\n",
- " \"\"\"let the label be the negated purple big index\"\"\"\n",
- " return {\n",
- " 'text' : datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"), \n",
- " 'backgroundColor': '#5C268B', \n",
- " 'fontSize': 20, \n",
- " 'color': '#FF6EC7', \n",
- " 'position': 'north', \n",
- " 'maximumWidth': 130, \n",
- " 'wrapping': 'word', \n",
- " 'textAlignment': 'center'\n",
- " }"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d306b597-678e-4a07-90f1-401bff0bc7c5",
- "metadata": {},
- "source": [
- "Let's set our new label styles:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "938719a0-d739-4ddf-8f7f-da00c1d05012",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.set_node_label_mapping(custom_label_styles_mapping)\n",
- "w.get_node_label_mapping()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a45c9447-74ba-48a1-bdcb-acb6e3bec468",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "711a763a-e897-4e2b-bca0-4a4640c5f235",
- "metadata": {},
- "source": [
- "If the mapping is deleted, it reverts back to the default mapping."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5f64587f-1035-4649-a751-32022baac797",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.del_node_label_mapping()\n",
- "w.get_node_label_mapping()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1d216797-d219-4d9e-a4f4-808dcb8bd390",
- "metadata": {},
- "source": [
- "## Edge Label Mapping\n",
- "\n",
- "Similar to the node labels mapping function, the edge label mapping function may also return styling properties alongside the `text`. \n",
- "\n",
- "It supports the same optional visualization properties:\n",
- "* `fontSize: number`: The text size of the label.\n",
- "* `color: string`: The text color of the label.\n",
- "* `backgroundColor: string`: A color string that is used as the label's background\n",
- "\n",
- "Here, we will use the same mapping function as for the nodes. Let's set our new edge styles:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3cb41d68-d190-4e48-b91d-58a8973463c7",
- "metadata": {},
- "outputs": [],
- "source": [
- "w2 = GraphWidget(graph=g)\n",
- "w2.set_edge_label_mapping(custom_label_styles_mapping)\n",
- "w2.get_edge_label_mapping()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "70a11fd8-e8ff-40ce-ba01-94762d3fd78d",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w2)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f80d8bcd-6f6f-48f7-8c6a-aed259425365",
- "metadata": {},
- "source": [
- "If a edge label mapping is deleted, the label mapping reverts back to the default mapping."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1b31186d-0cbb-4e2b-903e-83a9a3659640",
- "metadata": {},
- "outputs": [],
- "source": [
- "w2.del_edge_label_mapping()\n",
- "w2.get_edge_label_mapping()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "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": "3.6.15"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/03_color_mapping.ipynb b/examples/03_color_mapping.ipynb
index 965e6d4..694de97 100644
--- a/examples/03_color_mapping.ipynb
+++ b/examples/03_color_mapping.ipynb
@@ -1,334 +1,2242 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "e9aec846-1e8b-4930-9e05-6e12621ad1ee",
- "metadata": {},
- "source": [
- "# Element Color Mapping \n",
- "\n",
- "This notebook covers the basics of customizing node and edge colors. \n",
- "\n",
- "For the purpose of mapping demonstrations, the same graph, ```erdos_renyi_graph```, will be used. For this, we will import the graph from the NetworkX package. \\\n",
- "For more details on how to import graph data, explore the other example notebooks or refer to the full widget [documentation](https://yworks.github.io/yfiles-jupyter-graphs/).\n",
- "\n",
- "Before using the graph widget, install all necessary packages."
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "e9aec846-1e8b-4930-9e05-6e12621ad1ee",
+ "metadata": {
+ "id": "e9aec846-1e8b-4930-9e05-6e12621ad1ee"
+ },
+ "source": [
+ "# Element Color Mapping \n",
+ "\n",
+ "This notebook covers the basics of customizing node and edge colors.\n",
+ "\n",
+ "For the purpose of mapping demonstrations, the same graph, ```erdos_renyi_graph```, will be used. For this, we will import the graph from the NetworkX package. \\\n",
+ "For more details on how to import graph data, explore the other example notebooks or refer to the full widget [documentation](https://yworks.github.io/yfiles-jupyter-graphs/).\n",
+ "\n",
+ "Before using the graph widget, install all necessary packages."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "d56c690f-ea18-4f92-a6e2-63593f06e3b3",
+ "metadata": {
+ "id": "d56c690f-ea18-4f92-a6e2-63593f06e3b3",
+ "outputId": "a650c4b5-c2f4-4c82-ccb7-6853fc234241",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 86
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m29.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m20.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m35.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "from yfiles_jupyter_graphs import GraphWidget\n",
+ "%pip install networkx --quiet\n",
+ "from typing import Dict\n",
+ "from networkx import erdos_renyi_graph\n",
+ "\n",
+ "g = erdos_renyi_graph(10, 0.3, 2)\n",
+ "w = GraphWidget(graph=g)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4fabf87e-26e0-4f5f-b4ba-0d2c377ebc1a",
+ "metadata": {
+ "id": "4fabf87e-26e0-4f5f-b4ba-0d2c377ebc1a"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "28440f01-73ee-467a-b99d-7293cc8015d6",
+ "metadata": {
+ "id": "28440f01-73ee-467a-b99d-7293cc8015d6"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d42543fc-6da2-4c88-9a1f-bcdcf6d9d129",
+ "metadata": {
+ "id": "d42543fc-6da2-4c88-9a1f-bcdcf6d9d129"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c59d77a7-987c-4a6f-895e-e41817d3cffb",
+ "metadata": {
+ "id": "c59d77a7-987c-4a6f-895e-e41817d3cffb"
+ },
+ "source": [
+ "This is the graph we will be working on:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "3c234cb0-fa25-40ce-85b3-b81b54d6e522",
+ "metadata": {
+ "id": "3c234cb0-fa25-40ce-85b3-b81b54d6e522",
+ "outputId": "91cb0264-f6a1-4ea7-a731-df947cb842f8",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 517,
+ "referenced_widgets": [
+ "d60ce03297f949468da42187a0463ee8",
+ "7ca3c5ce3646409cac69432128cf8590"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='500px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "d60ce03297f949468da42187a0463ee8"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "display(GraphWidget(graph=g))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a89c07b2-1314-4f5b-a584-c9910b9a51c9",
+ "metadata": {
+ "id": "a89c07b2-1314-4f5b-a584-c9910b9a51c9"
+ },
+ "source": [
+ "## Node Color Mapping\n",
+ "\n",
+ "The node color mapping is a function that is supposed to return a CSS color string for each given node object which is then used as node color in the widget.\n",
+ "\n",
+ "Optionally, the index can be used as the first function parameter.\n",
+ "\n",
+ "We will color every node which has an odd label blue and nodes with an even label orange. \\\n",
+ "For this we first define a new mapping function and then set this function as our current color mapping."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "e3bb8793-db19-470a-9ce2-8f6823dac3a0",
+ "metadata": {
+ "id": "e3bb8793-db19-470a-9ce2-8f6823dac3a0"
+ },
+ "outputs": [],
+ "source": [
+ "def custom_node_color_mapping(node: Dict):\n",
+ " \"\"\"let the color be orange or blue if the index is even or odd respectively\"\"\"\n",
+ " return (\"#ff8800\" if int(node['properties']['label']) % 2 == 0 else \"#0096C7\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6029f59d-e25d-4e9f-b6e4-5f66e519a6b2",
+ "metadata": {
+ "id": "6029f59d-e25d-4e9f-b6e4-5f66e519a6b2"
+ },
+ "source": [
+ "### Custom node mappings\n",
+ "\n",
+ "There are get and set methods for each customizable node property.\n",
+ "- you can set a new node mapping with ```w.set_node_[binding]_mapping```\n",
+ "- you can get the current node mapping with ```w.get_node_[binding]_mapping```\n",
+ "- you can delete a custom node mapping with ```w.del_node_[binding]_mapping```\n",
+ "\n",
+ "You can find more details in the dedicated function documentation, available at ```w.[function_name].__doc__``` or in the [documentation](https://yworks.github.io/yfiles-jupyter-graphs/02_graph_widget/#methods).\n",
+ "\n",
+ "If no custom mapping is set the default mappings are used."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "f86ee9b9-f7da-41e7-9a1e-51269c787ef3",
+ "metadata": {
+ "id": "f86ee9b9-f7da-41e7-9a1e-51269c787ef3",
+ "outputId": "ba3b5d8f-d4d0-4f86-b808-815000c938ad",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "The default color mapping for nodes.\n",
+ "\n",
+ " Provides constant value of '#15AFAC' for all nodes, or different colors per label/type when importing a Neo4j\n",
+ " graph.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " index: int (optional)\n",
+ " node: typing.Dict\n",
+ "\n",
+ " Notes\n",
+ " -----\n",
+ " This is the default value for the `node_color_mapping` property.\n",
+ " Can be 'overwritten' by setting the property\n",
+ " with a function of the same signature.\n",
+ "\n",
+ " If the given mapping function has only one parameter (that is not typed as int),\n",
+ " then it will be called with the element (typing.Dict) as first parameter.\n",
+ "\n",
+ " Example\n",
+ " -------\n",
+ " .. code::\n",
+ "\n",
+ " from yfiles_jupyter_graphs import GraphWidget\n",
+ " w = GraphWidget()\n",
+ " def custom_node_color_mapping(node: typing.Dict):\n",
+ " ...\n",
+ " w.set_node_color_mapping(custom_node_color_mapping)\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " color: str\n",
+ " css color value\n",
+ "\n",
+ " References\n",
+ " ----------\n",
+ " css color value yfiles_jupyter_graphs.widget.GraphWidget.default_node_color_mapping
def default_node_color_mapping(index: int, node: TDict)
The default color mapping for nodes.\n", + "\n", + "Provides constant value of '#15AFAC' for all nodes, or different colors per label/type when importing a Neo4j\n", + "graph.\n", + "\n", + "Parameters\n", + "----------\n", + "index: int (optional)\n", + "node: typing.Dict\n", + "\n", + "Notes\n", + "-----\n", + "This is the default value for the `node_color_mapping` property.\n", + "Can be 'overwritten' by setting the property\n", + "with a function of the same signature.\n", + "\n", + "If the given mapping function has only one parameter (that is not typed as int),\n", + "then it will be called with the element (typing.Dict) as first parameter.\n", + "\n", + "Example\n", + "-------\n", + ".. code::\n", + "\n", + " from yfiles_jupyter_graphs import GraphWidget\n", + " w = GraphWidget()\n", + " def custom_node_color_mapping(node: typing.Dict):\n", + " ...\n", + " w.set_node_color_mapping(custom_node_color_mapping)\n", + "\n", + "Returns\n", + "-------\n", + "color: str\n", + " css color value\n", + "\n", + "References\n", + "----------\n", + "css color value <https://developer.mozilla.org/en-US/docs/Web/CSS/color_value>\n", + "\n", + "yFiles docs Fill api <https://docs.yworks.com/yfileshtml/#/api/Fill>\n", + " \n", + "
custom_node_color_mapping
def custom_node_color_mapping(node: Dict)
let the color be orange or blue if the index is even or odd respectively
yfiles_jupyter_graphs.widget.GraphWidget.default_node_color_mapping
def default_node_color_mapping(index: int, node: TDict)
The default color mapping for nodes.\n", + "\n", + "Provides constant value of '#15AFAC' for all nodes, or different colors per label/type when importing a Neo4j\n", + "graph.\n", + "\n", + "Parameters\n", + "----------\n", + "index: int (optional)\n", + "node: typing.Dict\n", + "\n", + "Notes\n", + "-----\n", + "This is the default value for the `node_color_mapping` property.\n", + "Can be 'overwritten' by setting the property\n", + "with a function of the same signature.\n", + "\n", + "If the given mapping function has only one parameter (that is not typed as int),\n", + "then it will be called with the element (typing.Dict) as first parameter.\n", + "\n", + "Example\n", + "-------\n", + ".. code::\n", + "\n", + " from yfiles_jupyter_graphs import GraphWidget\n", + " w = GraphWidget()\n", + " def custom_node_color_mapping(node: typing.Dict):\n", + " ...\n", + " w.set_node_color_mapping(custom_node_color_mapping)\n", + "\n", + "Returns\n", + "-------\n", + "color: str\n", + " css color value\n", + "\n", + "References\n", + "----------\n", + "css color value <https://developer.mozilla.org/en-US/docs/Web/CSS/color_value>\n", + "\n", + "yFiles docs Fill api <https://docs.yworks.com/yfileshtml/#/api/Fill>\n", + " \n", + "
yfiles_jupyter_graphs.widget.GraphWidget.default_edge_color_mapping
def default_edge_color_mapping(index: int, edge: TDict)
The default color mapping for edges.\n", + "\n", + "Provides constant value of '#15AFAC' for all edges.\n", + "\n", + "Parameters\n", + "----------\n", + "index: int (optional)\n", + "edge: typing.Dict\n", + "\n", + "Notes\n", + "-----\n", + "This is the default value for the `edge_color_mapping` property.\n", + "Can be 'overwritten' by setting the property\n", + "with a function of the same signature.\n", + "\n", + "If the given mapping function has only one parameter (that is not typed as int),\n", + "then it will be called with the element (typing.Dict) as first parameter.\n", + "\n", + "Example\n", + "-------\n", + ".. code::\n", + "\n", + " from yfiles_jupyter_graphs import GraphWidget\n", + " w = GraphWidget()\n", + " def custom_edge_color_mapping(edge: typing.Dict):\n", + " ...\n", + " w.set_edge_color_mapping(custom_edge_color_mapping)\n", + "\n", + "Returns\n", + "-------\n", + "color: str\n", + " css color value\n", + "\n", + "References\n", + "----------\n", + "css color value <https://developer.mozilla.org/en-US/docs/Web/CSS/color_value>\n", + "\n", + "yFiles docs Fill api <https://docs.yworks.com/yfileshtml/#/api/Fill>\n", + " \n", + "
custom_edge_color_mapping
def custom_edge_color_mapping(edge: Dict)
let the edge be purple if the starting node has an even index
yfiles_jupyter_graphs.widget.GraphWidget.default_edge_color_mapping
def default_edge_color_mapping(index: int, edge: TDict)
The default color mapping for edges.\n", + "\n", + "Provides constant value of '#15AFAC' for all edges.\n", + "\n", + "Parameters\n", + "----------\n", + "index: int (optional)\n", + "edge: typing.Dict\n", + "\n", + "Notes\n", + "-----\n", + "This is the default value for the `edge_color_mapping` property.\n", + "Can be 'overwritten' by setting the property\n", + "with a function of the same signature.\n", + "\n", + "If the given mapping function has only one parameter (that is not typed as int),\n", + "then it will be called with the element (typing.Dict) as first parameter.\n", + "\n", + "Example\n", + "-------\n", + ".. code::\n", + "\n", + " from yfiles_jupyter_graphs import GraphWidget\n", + " w = GraphWidget()\n", + " def custom_edge_color_mapping(edge: typing.Dict):\n", + " ...\n", + " w.set_edge_color_mapping(custom_edge_color_mapping)\n", + "\n", + "Returns\n", + "-------\n", + "color: str\n", + " css color value\n", + "\n", + "References\n", + "----------\n", + "css color value <https://developer.mozilla.org/en-US/docs/Web/CSS/color_value>\n", + "\n", + "yFiles docs Fill api <https://docs.yworks.com/yfileshtml/#/api/Fill>\n", + " \n", + "
"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c59d77a7-987c-4a6f-895e-e41817d3cffb",
- "metadata": {},
- "source": [
- "This is the graph we will be working on: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3c234cb0-fa25-40ce-85b3-b81b54d6e522",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a89c07b2-1314-4f5b-a584-c9910b9a51c9",
- "metadata": {},
- "source": [
- "## Node Color Mapping\n",
- "\n",
- "The node color mapping is a function that is supposed to return a CSS color string for each given node object which is then used as node color in the widget.\n",
- "\n",
- "Optionally, the index can be used as the first function parameter.\n",
- "\n",
- "We will color every node which has an odd label blue and nodes with an even label orange. \\\n",
- "For this we first define a new mapping function and then set this function as our current color mapping."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e3bb8793-db19-470a-9ce2-8f6823dac3a0",
- "metadata": {},
- "outputs": [],
- "source": [
- "def custom_node_color_mapping(node: Dict):\n",
- " \"\"\"let the color be orange or blue if the index is even or odd respectively\"\"\"\n",
- " return (\"#ff8800\" if int(node['properties']['label']) % 2 == 0 else \"#0096C7\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6029f59d-e25d-4e9f-b6e4-5f66e519a6b2",
- "metadata": {},
- "source": [
- "### Custom node mappings\n",
- "\n",
- "There are get and set methods for each customizable node property.\n",
- "- you can set a new node mapping with ```w.set_node_[binding]_mapping```\n",
- "- you can get the current node mapping with ```w.get_node_[binding]_mapping```\n",
- "- you can delete a custom node mapping with ```w.del_node_[binding]_mapping```\n",
- "\n",
- "You can find more details in the dedicated function documentation, available at ```w.[function_name].__doc__``` or in the [documentation](https://yworks.github.io/yfiles-jupyter-graphs/02_graph_widget/#methods).\n",
- "\n",
- "If no custom mapping is set the default mappings are used."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f86ee9b9-f7da-41e7-9a1e-51269c787ef3",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(w.default_node_color_mapping.__doc__)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d9b6afce-9faa-466c-b6f2-b7fc5d7407b6",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.get_node_color_mapping()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "93d66078-f9ba-4f7f-ab7a-67388456de2c",
- "metadata": {},
- "source": [
- "Let's use the new node color mapping: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "673c731a-9d65-4811-b148-327a40b23856",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.set_node_color_mapping(custom_node_color_mapping)\n",
- "w.get_node_color_mapping()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "18520a26-0421-4291-aa84-4c92666b2104",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2bc8c19a-9a18-4a31-b87c-9b3b3657def0",
- "metadata": {},
- "source": [
- "If a node color mapping is deleted, the color mapping reverts back to the default mapping."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "30962f59-7a19-4d22-b4f2-4fcc3f2e83da",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.del_node_color_mapping()\n",
- "w.get_node_color_mapping()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d1991d12-1032-4761-9ad8-af5e619e84e6",
- "metadata": {},
- "source": [
- "## Edge Color Mapping\n",
- "\n",
- "The edge color mapping is a function that is supposed to return a CSS color string for each given edge object which is then used as node color in the widget.\n",
- "\n",
- "If the index is used, it can be optionally given as the first function parameter. \n",
- "Edge mappings generally work the same as node mappings.\n",
- "\n",
- "We will color every edge which starts from an evenly indexed node purple. \\\n",
- "For this we first define a new mapping function and then set this function as our current color mapping."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "bd6681e6-6998-45b0-b6b2-859908c34a4c",
- "metadata": {},
- "outputs": [],
- "source": [
- "w2 = GraphWidget(graph=g)\n",
- "\n",
- "def custom_edge_color_mapping(edge: Dict):\n",
- " \"\"\"let the edge be purple if the starting node has an even index\"\"\"\n",
- " return (\"#D6B4FC\" if int(edge['start']) % 2 == 0 else \"#15AFAC\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d23bb323-bdf4-4365-a7c9-f3736a1526c3",
- "metadata": {},
- "source": [
- "### Custom edge mappings\n",
- "\n",
- "There are get and set methods for each customizable edge property.\n",
- "- you can set a new edge mapping with ```w.set_edge_[binding]_mapping```\n",
- "- you can get the current edge mapping with ```w.get_edge_[binding]_mapping```\n",
- "- you can delete a custom edge mapping with ```w.del_edge_[binding]_mapping```\n",
- "\n",
- "You can find more details in the dedicated function documentation, available at ```w.[function_name].__doc__``` or in the [documentation](https://yworks.github.io/yfiles-jupyter-graphs/02_graph_widget/#methods).\n",
- "\n",
- "If no custom mapping is set the default mappings are used."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1ca048f0-f794-4778-b691-c2794544deaf",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(w2.default_edge_color_mapping.__doc__)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6b18d49e-9873-476d-aaf3-311c3f9c80a6",
- "metadata": {},
- "outputs": [],
- "source": [
- "w2.get_edge_color_mapping()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b8538547-4710-4497-976d-d61a1b2f85e8",
- "metadata": {},
- "source": [
- "Let's use the new edge color mapping: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4b574f9d-aca9-4623-9bcc-18515121e7fb",
- "metadata": {},
- "outputs": [],
- "source": [
- "w2 = GraphWidget(graph=g)\n",
- "w2.set_edge_color_mapping(custom_edge_color_mapping)\n",
- "w2.get_edge_color_mapping()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1542fcdf-8974-46ab-8720-3bdda965112c",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w2)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "200d7806-000e-4c3f-8636-10719302a1e4",
- "metadata": {},
- "source": [
- "If a edge color mapping is deleted, the color mapping reverts back to the default mapping."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "25f29cd1-1b09-40ab-a154-4ff0b461ab25",
- "metadata": {},
- "outputs": [],
- "source": [
- "w2.del_edge_color_mapping()\n",
- "w2.get_edge_color_mapping()"
- ]
- }
- ],
- "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": "3.11.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/04_layout_mapping.ipynb b/examples/04_layout_mapping.ipynb
index 48d7357..60f02d4 100644
--- a/examples/04_layout_mapping.ipynb
+++ b/examples/04_layout_mapping.ipynb
@@ -1,266 +1,1560 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "d1bf7f01-9c4d-4101-bedf-2092ceaedd0f",
- "metadata": {},
- "source": [
- "# Node Layout Mapping "
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "d1bf7f01-9c4d-4101-bedf-2092ceaedd0f",
+ "metadata": {
+ "id": "d1bf7f01-9c4d-4101-bedf-2092ceaedd0f"
+ },
+ "source": [
+ "# Node Layout Mapping "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c3a6b6ae-2a9d-4a0b-b4c8-c6c98a62a70d",
+ "metadata": {
+ "id": "c3a6b6ae-2a9d-4a0b-b4c8-c6c98a62a70d"
+ },
+ "source": [
+ "This notebook covers the basics of customizing node layouts.\n",
+ "\n",
+ "A node layout is consists of 4 values:\n",
+ "- x-position\n",
+ "- y-position\n",
+ "- width\n",
+ "- height.\n",
+ "\n",
+ "Edges do not have a layout property.\n",
+ "\n",
+ "For the purpose of mapping demonstrations, the same graph, ```erdos_renyi_graph```, will be used. For this, we will import the graph from the networkx database. \\\n",
+ "For more details on how to import graph data, explore the other example notebooks or refer to the full widget [documentation](https://yworks.github.io/yfiles-jupyter-graphs/).\n",
+ "\n",
+ "Before using the graph widget, install all necessary packages."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "94cdaddf-2f9c-4848-85bb-f51b1bf76bb5",
+ "metadata": {
+ "id": "94cdaddf-2f9c-4848-85bb-f51b1bf76bb5",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 92
+ },
+ "outputId": "31c86c99-29a3-4d6c-cf96-0a505bf2bf49"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m39.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m51.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m23.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "from yfiles_jupyter_graphs import GraphWidget\n",
+ "%pip install networkx --quiet\n",
+ "from typing import Dict\n",
+ "from networkx import erdos_renyi_graph\n",
+ "\n",
+ "g = erdos_renyi_graph(10, 0.3, 2)\n",
+ "w = GraphWidget(graph=g)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2a1af9bc-a9fb-48ae-b158-dfb7a2c6c328",
+ "metadata": {
+ "id": "2a1af9bc-a9fb-48ae-b158-dfb7a2c6c328"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "d3d2e9e0-9190-4b18-8e17-9a4bdd3458fc",
+ "metadata": {
+ "id": "d3d2e9e0-9190-4b18-8e17-9a4bdd3458fc"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d4560226-e2e5-4595-93e6-4ecb081ddac1",
+ "metadata": {
+ "id": "d4560226-e2e5-4595-93e6-4ecb081ddac1"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f0e7cd9c-eeff-4245-b028-2057675f19a4",
+ "metadata": {
+ "id": "f0e7cd9c-eeff-4245-b028-2057675f19a4"
+ },
+ "source": [
+ "This is the graph we will be working on:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "1c67daeb-332b-4b55-9d32-afeeb04d9b1c",
+ "metadata": {
+ "id": "1c67daeb-332b-4b55-9d32-afeeb04d9b1c",
+ "outputId": "a4a0704d-eba8-4a59-b491-c94430988b7b",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 517,
+ "referenced_widgets": [
+ "eea6fbd959374264886245e05f1c244a",
+ "3898e66a62a24068b44e1a10e8171397"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='500px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "eea6fbd959374264886245e05f1c244a"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "display(GraphWidget(graph=g))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c4450211-c989-4e0b-bdb9-05e22ad4f837",
+ "metadata": {
+ "id": "c4450211-c989-4e0b-bdb9-05e22ad4f837"
+ },
+ "source": [
+ "## Mapping Function\n",
+ "\n",
+ "The node layout mapping is a function that is supposed to return a 4-tuple of numbers `(x, y, width, height)` for each given node object which is then used in the widget.\n",
+ "\n",
+ "Optionally, the index can be used as the first function parameter.\n",
+ "\n",
+ "If no size, position or layout mapping is set, the default position (0, 0) and default sizes are used (55.0, 55.0). Note that the given position is also adjusted by an automatic layout algorithm which runs per default in the widget. As shown later in this example, you need to either disable the automtatic layout or only use an edge-router which does not change the node positions in order to see the specified positions from the layout mapping.\n",
+ "\n",
+ "Let's order the nodes from smallest to largest index, by changing their positions. \\\n",
+ "Additionally change the shape of the nodes to an ellipse by making the height bigger than the width:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "1c28cd03-5237-4aee-99eb-a76b33c29d2a",
+ "metadata": {
+ "id": "1c28cd03-5237-4aee-99eb-a76b33c29d2a"
+ },
+ "outputs": [],
+ "source": [
+ "nodes = w.nodes\n",
+ "def custom_node_layout_mapping(node: Dict):\n",
+ " index = nodes.index(node)\n",
+ " return index*100, 0, 60, 80"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e5225ab9-e3b7-4c3d-9073-cea92a90f228",
+ "metadata": {
+ "id": "e5225ab9-e3b7-4c3d-9073-cea92a90f228"
+ },
+ "source": [
+ "### Custom node mappings\n",
+ "\n",
+ "There are get and set methods for each customizable node property.\n",
+ "- you can set a new node mapping with ```w.set_node_[binding]_mapping```\n",
+ "- you can get the current node mapping with ```w.get_node_[binding]_mapping```\n",
+ "- you can delete a custom node mapping with ```w.del_node_[binding]_mapping```\n",
+ "\n",
+ "You can find more details in the dedicated function documentation, available at ```w.[function_name].__doc__``` or in the [documentation](https://yworks.github.io/yfiles-jupyter-graphs/02_graph_widget/#methods).\n",
+ "\n",
+ "If no size, position or layout mapping is set, the default position and default sizes are used."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "7a1dab5a-a36d-440b-96a0-1dfffb67f071",
+ "metadata": {
+ "id": "7a1dab5a-a36d-440b-96a0-1dfffb67f071",
+ "outputId": "2cd31241-0628-4e7e-98d1-b979c4767a53",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "The default layout mapping for nodes.\n",
+ "\n",
+ " Provides constant value None for all nodes.\n",
+ " Position and size mappings are used instead.\n",
+ " Default position and size mappings are a constant value of 0.0, 0.0 and 55.0,55.0 respectively.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " index: int (optional)\n",
+ " node: typing.Dict\n",
+ "\n",
+ " Notes\n",
+ " -----\n",
+ " This is the default value for the `node_layout_mapping` property.\n",
+ " Can be 'overwritten' by setting the property\n",
+ " with a function returning a float 4-tuple.\n",
+ "\n",
+ " The layout overwrites position and size mappings if not None.\n",
+ "\n",
+ " If the given mapping function has only one parameter (that is not typed as int),\n",
+ " then it will be called with the element (typing.Dict) as first parameter.\n",
+ "\n",
+ " Example\n",
+ " -------\n",
+ " .. code::\n",
+ "\n",
+ " from yfiles_jupyter_graphs import GraphWidget\n",
+ " w = GraphWidget()\n",
+ " def custom_node_layout_mapping(node: typing.Dict):\n",
+ " ...\n",
+ " w.set_node_layout_mapping(custom_node_layout_mapping)\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " layout: None | float 4-tuple\n",
+ "\n",
+ " \n"
+ ]
+ }
+ ],
+ "source": [
+ "print(w.node_layout_mapping.__doc__)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "b59b54b5-bf11-4b7f-82c4-23c6bfd3e835",
+ "metadata": {
+ "id": "b59b54b5-bf11-4b7f-82c4-23c6bfd3e835",
+ "outputId": "3eb91c75-5a5d-48f8-dc2a-4b9ffca8cb72",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 188
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "yfiles_jupyter_graphs.widget.GraphWidget.default_node_layout_mapping
def default_node_layout_mapping(index: int, node: TDict)
The default layout mapping for nodes.\n", + "\n", + "Provides constant value None for all nodes.\n", + "Position and size mappings are used instead.\n", + "Default position and size mappings are a constant value of 0.0, 0.0 and 55.0,55.0 respectively.\n", + "\n", + "Parameters\n", + "----------\n", + "index: int (optional)\n", + "node: typing.Dict\n", + "\n", + "Notes\n", + "-----\n", + "This is the default value for the `node_layout_mapping` property.\n", + "Can be 'overwritten' by setting the property\n", + "with a function returning a float 4-tuple.\n", + "\n", + "The layout overwrites position and size mappings if not None.\n", + "\n", + "If the given mapping function has only one parameter (that is not typed as int),\n", + "then it will be called with the element (typing.Dict) as first parameter.\n", + "\n", + "Example\n", + "-------\n", + ".. code::\n", + "\n", + " from yfiles_jupyter_graphs import GraphWidget\n", + " w = GraphWidget()\n", + " def custom_node_layout_mapping(node: typing.Dict):\n", + " ...\n", + " w.set_node_layout_mapping(custom_node_layout_mapping)\n", + "\n", + "Returns\n", + "-------\n", + "layout: None | float 4-tuple\n", + " \n", + "
custom_node_layout_mapping
def custom_node_layout_mapping(node: Dict)
<no docstring>
yfiles_jupyter_graphs.widget.GraphWidget.default_node_layout_mapping
def default_node_layout_mapping(index: int, node: TDict)
The default layout mapping for nodes.\n", + "\n", + "Provides constant value None for all nodes.\n", + "Position and size mappings are used instead.\n", + "Default position and size mappings are a constant value of 0.0, 0.0 and 55.0,55.0 respectively.\n", + "\n", + "Parameters\n", + "----------\n", + "index: int (optional)\n", + "node: typing.Dict\n", + "\n", + "Notes\n", + "-----\n", + "This is the default value for the `node_layout_mapping` property.\n", + "Can be 'overwritten' by setting the property\n", + "with a function returning a float 4-tuple.\n", + "\n", + "The layout overwrites position and size mappings if not None.\n", + "\n", + "If the given mapping function has only one parameter (that is not typed as int),\n", + "then it will be called with the element (typing.Dict) as first parameter.\n", + "\n", + "Example\n", + "-------\n", + ".. code::\n", + "\n", + " from yfiles_jupyter_graphs import GraphWidget\n", + " w = GraphWidget()\n", + " def custom_node_layout_mapping(node: typing.Dict):\n", + " ...\n", + " w.set_node_layout_mapping(custom_node_layout_mapping)\n", + "\n", + "Returns\n", + "-------\n", + "layout: None | float 4-tuple\n", + " \n", + "
"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f0e7cd9c-eeff-4245-b028-2057675f19a4",
- "metadata": {},
- "source": [
- "This is the graph we will be working on: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1c67daeb-332b-4b55-9d32-afeeb04d9b1c",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c4450211-c989-4e0b-bdb9-05e22ad4f837",
- "metadata": {},
- "source": [
- "## Mapping Function\n",
- "\n",
- "The node layout mapping is a function that is supposed to return a 4-tuple of numbers `(x, y, width, height)` for each given node object which is then used in the widget.\n",
- "\n",
- "Optionally, the index can be used as the first function parameter.\n",
- "\n",
- "If no size, position or layout mapping is set, the default position (0, 0) and default sizes are used (55.0, 55.0). Note that the given position is also adjusted by an automatic layout algorithm which runs per default in the widget. As shown later in this example, you need to either disable the automtatic layout or only use an edge-router which does not change the node positions in order to see the specified positions from the layout mapping.\n",
- "\n",
- "Let's order the nodes from smallest to largest index, by changing their positions. \\\n",
- "Additionally change the shape of the nodes to an ellipse by making the height bigger than the width:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1c28cd03-5237-4aee-99eb-a76b33c29d2a",
- "metadata": {},
- "outputs": [],
- "source": [
- "nodes = w.nodes\n",
- "def custom_node_layout_mapping(node: Dict):\n",
- " index = nodes.index(node)\n",
- " return index*100, 0, 60, 80"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e5225ab9-e3b7-4c3d-9073-cea92a90f228",
- "metadata": {},
- "source": [
- "### Custom node mappings\n",
- "\n",
- "There are get and set methods for each customizable node property.\n",
- "- you can set a new node mapping with ```w.set_node_[binding]_mapping```\n",
- "- you can get the current node mapping with ```w.get_node_[binding]_mapping```\n",
- "- you can delete a custom node mapping with ```w.del_node_[binding]_mapping```\n",
- "\n",
- "You can find more details in the dedicated function documentation, available at ```w.[function_name].__doc__``` or in the [documentation](https://yworks.github.io/yfiles-jupyter-graphs/02_graph_widget/#methods).\n",
- "\n",
- "If no size, position or layout mapping is set, the default position and default sizes are used."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7a1dab5a-a36d-440b-96a0-1dfffb67f071",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(w.node_layout_mapping.__doc__)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b59b54b5-bf11-4b7f-82c4-23c6bfd3e835",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.get_node_layout_mapping()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7269741e-5d5c-4748-82c7-d1b91c124b51",
- "metadata": {},
- "source": [
- "Let's use our new node layouts:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "bbf09bdd-2996-41ad-92d9-cb4df2911e0f",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.set_node_layout_mapping(custom_node_layout_mapping)\n",
- "w.get_node_layout_mapping()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a874ea2d-e7ac-4f7c-8c91-c1e9111467c0",
- "metadata": {},
- "source": [
- "To actually see the custom positioning, use a layout algorithm that does not arrange graph items, e.g. an edge router layout. \\\n",
- "If the layout is changed from the toolbar to a layout changing node positions, the original positions cannot be retrieved by changing the layout again. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e16ff7b0-486e-49e7-89a9-d436468d30e7",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.orthogonal_edge_router()\n",
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a4a26bb8-4d3b-49bf-b615-98ff56b3a1a8",
- "metadata": {},
- "source": [
- "The layout property is not part of the node keys. It overwrites any size or position mapping. \\\n",
- "You can find more about the position size mappings in the next example notebooks [05_size_mapping.ipynb](./05_size_mapping.ipynb) and [06_position_mapping.ipynb](./06_position_mapping.ipynb)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a3ab0b29-1b4b-4a4d-ac16-cc7a5ac88894",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.nodes"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "cf8ed865-7595-4b50-98fd-e0156f59f058",
- "metadata": {},
- "source": [
- "If a node layout mapping is deleted, the layout mapping reverts back to the default mapping.\n",
- "\n",
- "The default layout mapping is ```None```, hence the position and size mappings are used."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1f81aa8b-29a8-4c81-a1af-b550b3df6170",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.del_node_layout_mapping()\n",
- "w.get_node_layout_mapping()"
- ]
- }
- ],
- "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": "3.11.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/05_size_mapping.ipynb b/examples/05_size_mapping.ipynb
index a232f7e..3a0a472 100644
--- a/examples/05_size_mapping.ipynb
+++ b/examples/05_size_mapping.ipynb
@@ -1,431 +1,2892 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "234a60b7-7d2e-4365-8189-8ead06599838",
- "metadata": {},
- "source": [
- "# Node Dimension Mapping \n",
- "\n",
- "This notebook covers the basics of customizing node size and node scale factors.\n",
- "\n",
- "A node size has 2 values:\n",
- "- width\n",
- "- height\n",
- "\n",
- "The size binding is closely related to the scale factor mapping and layout mapping\n",
- "\n",
- "- The scale factor is multiplied with the size of a node to retrieve the final dimensions of a node. \n",
- "- It achieves the same result either by doubling the size of a node or by doubling the scaling factor.\n",
- "- The size mapping is overwritten by any custom node layout mapping\n",
- "\n",
- "Edges do not have a size or scale factor mapping, however they have an edge-thickness mapping, see notebook [11_thickness_mapping](./11_thickness_mapping.ipynb).\n",
- "\n",
- "For the purpose of mapping demonstrations, the same graph, ```erdos_renyi_graph```, will be used. For this, we will import the graph from the NetworkX package. \\\n",
- "For more details on how to import graph data, explore the other example notebooks or refer to the full widget [documentation](https://yworks.github.io/yfiles-jupyter-graphs/).\n",
- "\n",
- "Before using the graph widget, install all necessary packages."
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "234a60b7-7d2e-4365-8189-8ead06599838",
+ "metadata": {
+ "id": "234a60b7-7d2e-4365-8189-8ead06599838"
+ },
+ "source": [
+ "# Node Dimension Mapping \n",
+ "\n",
+ "This notebook covers the basics of customizing node size and node scale factors.\n",
+ "\n",
+ "A node size has 2 values:\n",
+ "- width\n",
+ "- height\n",
+ "\n",
+ "The size binding is closely related to the scale factor mapping and layout mapping\n",
+ "\n",
+ "- The scale factor is multiplied with the size of a node to retrieve the final dimensions of a node.\n",
+ "- It achieves the same result either by doubling the size of a node or by doubling the scaling factor.\n",
+ "- The size mapping is overwritten by any custom node layout mapping\n",
+ "\n",
+ "Edges do not have a size or scale factor mapping, however they have an edge-thickness mapping, see notebook [11_thickness_mapping](./11_thickness_mapping.ipynb).\n",
+ "\n",
+ "For the purpose of mapping demonstrations, the same graph, ```erdos_renyi_graph```, will be used. For this, we will import the graph from the NetworkX package. \\\n",
+ "For more details on how to import graph data, explore the other example notebooks or refer to the full widget [documentation](https://yworks.github.io/yfiles-jupyter-graphs/).\n",
+ "\n",
+ "Before using the graph widget, install all necessary packages."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "15aa5228-6042-47ab-acc7-a47a82d5495e",
+ "metadata": {
+ "id": "15aa5228-6042-47ab-acc7-a47a82d5495e",
+ "outputId": "d73c2db1-9477-48dc-921f-c7cfb8907d0b",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 92
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m21.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m24.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m17.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "from yfiles_jupyter_graphs import GraphWidget\n",
+ "%pip install networkx --quiet\n",
+ "from typing import Dict\n",
+ "from networkx import erdos_renyi_graph\n",
+ "from random import random, seed\n",
+ "seed(0)\n",
+ "\n",
+ "g = erdos_renyi_graph(10, 0.3, 2)\n",
+ "w = GraphWidget(graph=g)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "01742ec4-8a41-4a0b-a650-39bede7e76fe",
+ "metadata": {
+ "id": "01742ec4-8a41-4a0b-a650-39bede7e76fe"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "8670e9e2-440a-42f8-8585-b257d0d0258a",
+ "metadata": {
+ "id": "8670e9e2-440a-42f8-8585-b257d0d0258a"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c50d207e-8022-4f10-9c36-e26657495a0d",
+ "metadata": {
+ "id": "c50d207e-8022-4f10-9c36-e26657495a0d"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c122c011-624f-4649-ab1d-6be38e21de7c",
+ "metadata": {
+ "id": "c122c011-624f-4649-ab1d-6be38e21de7c"
+ },
+ "source": [
+ "This is the graph we will be working on:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "7e503c4a-451e-4370-b32b-2b443dd9ec67",
+ "metadata": {
+ "id": "7e503c4a-451e-4370-b32b-2b443dd9ec67",
+ "outputId": "c43c2eb4-111f-4e4b-f26b-91d0a9e0615b",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 517,
+ "referenced_widgets": [
+ "732c5229e0784e24b1c0b8120474e173",
+ "af87bc3cf1f0429ea03ce55cea5caab4"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='500px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "732c5229e0784e24b1c0b8120474e173"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "display(GraphWidget(graph=g))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "876439be-e751-40f5-aa7c-c5981419fd63",
+ "metadata": {
+ "id": "876439be-e751-40f5-aa7c-c5981419fd63"
+ },
+ "source": [
+ "### Custom node mappings\n",
+ "\n",
+ "There are get and set methods for each customizable node property.\n",
+ "- you can set a new node mapping with ```w.set_node_[binding]_mapping```\n",
+ "- you can get the current node mapping with ```w.get_node_[binding]_mapping```\n",
+ "- you can delete a custom node mapping with ```w.del_node_[binding]_mapping```\n",
+ "\n",
+ "You can find more details in the dedicated function documentation, available at ```w.[function_name].__doc__``` or in the [documentation](https://yworks.github.io/yfiles-jupyter-graphs/02_graph_widget/#methods)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "23afed3e-a0da-4307-bc03-9b73134fa550",
+ "metadata": {
+ "id": "23afed3e-a0da-4307-bc03-9b73134fa550",
+ "outputId": "0fa1e2af-9c3c-4615-914c-28ef80d24c2d",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "The default scale factor mapping for nodes.\n",
+ "\n",
+ " Provides constant value of 1.0 for all nodes.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " index: int (optional)\n",
+ " node: typing.Dict\n",
+ "\n",
+ " Notes\n",
+ " -----\n",
+ " This is the default value for the `node_scale_factor_mapping` property.\n",
+ " Can be 'overwritten' by setting the property\n",
+ " with a function of the same signature.\n",
+ "\n",
+ " If the given mapping function has only one parameter (that is not typed as int),\n",
+ " then it will be called with the element (typing.Dict) as first parameter.\n",
+ "\n",
+ " Example\n",
+ " -------\n",
+ " .. code::\n",
+ "\n",
+ " from yfiles_jupyter_graphs import GraphWidget\n",
+ " w = GraphWidget()\n",
+ " def custom_node_scale_factor_mapping(node: typing.Dict):\n",
+ " ...\n",
+ " w.set_node_scale_factor_mapping(custom_node_scale_factor_mapping)\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " node_scale_factor: float\n",
+ "\n",
+ " \n"
+ ]
+ }
+ ],
+ "source": [
+ "print(w.node_scale_factor_mapping.__doc__)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "69cd6b33-acee-4891-8e14-958dc87763d8",
+ "metadata": {
+ "id": "69cd6b33-acee-4891-8e14-958dc87763d8",
+ "outputId": "c44782c5-e338-4fdc-b201-96c163ba69b0",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "The default size mapping for nodes.\n",
+ "\n",
+ " Provides constant value 55.0, 55.0 for the width and height of all nodes.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " index: int (optional)\n",
+ " node: typing.Dict\n",
+ "\n",
+ " Notes\n",
+ " -----\n",
+ " This is the default value for the `node_size_mapping` property.\n",
+ " Can be 'overwritten' by setting the property\n",
+ " with a function of the same signature.\n",
+ "\n",
+ " If the given mapping function has only one parameter (that is not typed as int),\n",
+ " then it will be called with the element (typing.Dict) as first parameter.\n",
+ "\n",
+ " Example\n",
+ " -------\n",
+ " .. code::\n",
+ "\n",
+ " from yfiles_jupyter_graphs import GraphWidget\n",
+ " w = GraphWidget()\n",
+ " def custom_node_size_mapping(node: typing.Dict):\n",
+ " ...\n",
+ " w.set_node_size_mapping(custom_node_size_mapping)\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " size: float 2-tuple\n",
+ "\n",
+ " \n"
+ ]
+ }
+ ],
+ "source": [
+ "print(w.node_size_mapping.__doc__)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a720eb6b-03a9-4e5e-ac6c-6d9074667fca",
+ "metadata": {
+ "id": "a720eb6b-03a9-4e5e-ac6c-6d9074667fca"
+ },
+ "source": [
+ "## Node Scale Factor Mapping\n",
+ "\n",
+ "The node scale factor mapping is a function that is supposed to return a scale factor for each given node object which is then used in the widget.\n",
+ "\n",
+ "Optionally, the index can be used as the first function parameter.\n",
+ "\n",
+ "We will randomly assign a new scale factor for each node. \\\n",
+ "For this we first define a new mapping function and then set this function as our current scale factor mapping."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "3e4ec82e-a92c-4230-a355-36e64cbe6356",
+ "metadata": {
+ "id": "3e4ec82e-a92c-4230-a355-36e64cbe6356"
+ },
+ "outputs": [],
+ "source": [
+ "def custom_factor_mapping(node: Dict):\n",
+ " \"\"\"choose random factor\"\"\"\n",
+ " return 2 * random()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "780ef19a-9fb6-4af8-95a5-5afd3e35a182",
+ "metadata": {
+ "id": "780ef19a-9fb6-4af8-95a5-5afd3e35a182"
+ },
+ "source": [
+ "If no custom mapping is set the default mappings are used. \\\n",
+ "The default node scale factor is 1.0. When this scale factor is multiplied with the size, the size remains unchanged if the scale factor is 1. \\\n",
+ "The default node scale factor does not affect the node size."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "33e2dc95-7f1c-4858-8979-abefc10d8d7d",
+ "metadata": {
+ "id": "33e2dc95-7f1c-4858-8979-abefc10d8d7d",
+ "outputId": "0055f109-9b77-47ee-b754-26c53b52430e",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 188
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "yfiles_jupyter_graphs.widget.GraphWidget.default_node_scale_factor_mapping
def default_node_scale_factor_mapping(index: int, node: TDict)
The default scale factor mapping for nodes.\n", + "\n", + "Provides constant value of 1.0 for all nodes.\n", + "\n", + "Parameters\n", + "----------\n", + "index: int (optional)\n", + "node: typing.Dict\n", + "\n", + "Notes\n", + "-----\n", + "This is the default value for the `node_scale_factor_mapping` property.\n", + "Can be 'overwritten' by setting the property\n", + "with a function of the same signature.\n", + "\n", + "If the given mapping function has only one parameter (that is not typed as int),\n", + "then it will be called with the element (typing.Dict) as first parameter.\n", + "\n", + "Example\n", + "-------\n", + ".. code::\n", + "\n", + " from yfiles_jupyter_graphs import GraphWidget\n", + " w = GraphWidget()\n", + " def custom_node_scale_factor_mapping(node: typing.Dict):\n", + " ...\n", + " w.set_node_scale_factor_mapping(custom_node_scale_factor_mapping)\n", + "\n", + "Returns\n", + "-------\n", + "node_scale_factor: float\n", + " \n", + "
custom_factor_mapping
def custom_factor_mapping(node: Dict)
choose random factor
yfiles_jupyter_graphs.widget.GraphWidget.default_node_scale_factor_mapping
def default_node_scale_factor_mapping(index: int, node: TDict)
The default scale factor mapping for nodes.\n", + "\n", + "Provides constant value of 1.0 for all nodes.\n", + "\n", + "Parameters\n", + "----------\n", + "index: int (optional)\n", + "node: typing.Dict\n", + "\n", + "Notes\n", + "-----\n", + "This is the default value for the `node_scale_factor_mapping` property.\n", + "Can be 'overwritten' by setting the property\n", + "with a function of the same signature.\n", + "\n", + "If the given mapping function has only one parameter (that is not typed as int),\n", + "then it will be called with the element (typing.Dict) as first parameter.\n", + "\n", + "Example\n", + "-------\n", + ".. code::\n", + "\n", + " from yfiles_jupyter_graphs import GraphWidget\n", + " w = GraphWidget()\n", + " def custom_node_scale_factor_mapping(node: typing.Dict):\n", + " ...\n", + " w.set_node_scale_factor_mapping(custom_node_scale_factor_mapping)\n", + "\n", + "Returns\n", + "-------\n", + "node_scale_factor: float\n", + " \n", + "
custom_size_mapping
def custom_size_mapping(index: int, node: Dict)
Select a width and height for the node
yfiles_jupyter_graphs.widget.GraphWidget.default_node_size_mapping
def default_node_size_mapping(index: int, node: TDict)
The default size mapping for nodes.\n", + "\n", + "Provides constant value 55.0, 55.0 for the width and height of all nodes.\n", + "\n", + "Parameters\n", + "----------\n", + "index: int (optional)\n", + "node: typing.Dict\n", + "\n", + "Notes\n", + "-----\n", + "This is the default value for the `node_size_mapping` property.\n", + "Can be 'overwritten' by setting the property\n", + "with a function of the same signature.\n", + "\n", + "If the given mapping function has only one parameter (that is not typed as int),\n", + "then it will be called with the element (typing.Dict) as first parameter.\n", + "\n", + "Example\n", + "-------\n", + ".. code::\n", + "\n", + " from yfiles_jupyter_graphs import GraphWidget\n", + " w = GraphWidget()\n", + " def custom_node_size_mapping(node: typing.Dict):\n", + " ...\n", + " w.set_node_size_mapping(custom_node_size_mapping)\n", + "\n", + "Returns\n", + "-------\n", + "size: float 2-tuple\n", + " \n", + "
yfiles_jupyter_graphs.widget.GraphWidget.default_node_size_mapping
def default_node_size_mapping(index: int, node: TDict)
The default size mapping for nodes.\n", + "\n", + "Provides constant value 55.0, 55.0 for the width and height of all nodes.\n", + "\n", + "Parameters\n", + "----------\n", + "index: int (optional)\n", + "node: typing.Dict\n", + "\n", + "Notes\n", + "-----\n", + "This is the default value for the `node_size_mapping` property.\n", + "Can be 'overwritten' by setting the property\n", + "with a function of the same signature.\n", + "\n", + "If the given mapping function has only one parameter (that is not typed as int),\n", + "then it will be called with the element (typing.Dict) as first parameter.\n", + "\n", + "Example\n", + "-------\n", + ".. code::\n", + "\n", + " from yfiles_jupyter_graphs import GraphWidget\n", + " w = GraphWidget()\n", + " def custom_node_size_mapping(node: typing.Dict):\n", + " ...\n", + " w.set_node_size_mapping(custom_node_size_mapping)\n", + "\n", + "Returns\n", + "-------\n", + "size: float 2-tuple\n", + " \n", + "
yfiles_jupyter_graphs.widget.GraphWidget.default_node_scale_factor_mapping
def default_node_scale_factor_mapping(index: int, node: TDict)
The default scale factor mapping for nodes.\n", + "\n", + "Provides constant value of 1.0 for all nodes.\n", + "\n", + "Parameters\n", + "----------\n", + "index: int (optional)\n", + "node: typing.Dict\n", + "\n", + "Notes\n", + "-----\n", + "This is the default value for the `node_scale_factor_mapping` property.\n", + "Can be 'overwritten' by setting the property\n", + "with a function of the same signature.\n", + "\n", + "If the given mapping function has only one parameter (that is not typed as int),\n", + "then it will be called with the element (typing.Dict) as first parameter.\n", + "\n", + "Example\n", + "-------\n", + ".. code::\n", + "\n", + " from yfiles_jupyter_graphs import GraphWidget\n", + " w = GraphWidget()\n", + " def custom_node_scale_factor_mapping(node: typing.Dict):\n", + " ...\n", + " w.set_node_scale_factor_mapping(custom_node_scale_factor_mapping)\n", + "\n", + "Returns\n", + "-------\n", + "node_scale_factor: float\n", + " \n", + "
"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c122c011-624f-4649-ab1d-6be38e21de7c",
- "metadata": {},
- "source": [
- "This is the graph we will be working on: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7e503c4a-451e-4370-b32b-2b443dd9ec67",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "876439be-e751-40f5-aa7c-c5981419fd63",
- "metadata": {},
- "source": [
- "### Custom node mappings\n",
- "\n",
- "There are get and set methods for each customizable node property.\n",
- "- you can set a new node mapping with ```w.set_node_[binding]_mapping```\n",
- "- you can get the current node mapping with ```w.get_node_[binding]_mapping```\n",
- "- you can delete a custom node mapping with ```w.del_node_[binding]_mapping```\n",
- "\n",
- "You can find more details in the dedicated function documentation, available at ```w.[function_name].__doc__``` or in the [documentation](https://yworks.github.io/yfiles-jupyter-graphs/02_graph_widget/#methods)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "23afed3e-a0da-4307-bc03-9b73134fa550",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(w.node_scale_factor_mapping.__doc__) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "69cd6b33-acee-4891-8e14-958dc87763d8",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(w.node_size_mapping.__doc__)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a720eb6b-03a9-4e5e-ac6c-6d9074667fca",
- "metadata": {},
- "source": [
- "## Node Scale Factor Mapping\n",
- "\n",
- "The node scale factor mapping is a function that is supposed to return a scale factor for each given node object which is then used in the widget.\n",
- "\n",
- "Optionally, the index can be used as the first function parameter.\n",
- "\n",
- "We will randomly assign a new scale factor for each node. \\\n",
- "For this we first define a new mapping function and then set this function as our current scale factor mapping."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3e4ec82e-a92c-4230-a355-36e64cbe6356",
- "metadata": {},
- "outputs": [],
- "source": [
- "def custom_factor_mapping(node: Dict):\n",
- " \"\"\"choose random factor\"\"\"\n",
- " return 2 * random()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "780ef19a-9fb6-4af8-95a5-5afd3e35a182",
- "metadata": {},
- "source": [
- "If no custom mapping is set the default mappings are used. \\\n",
- "The default node scale factor is 1.0. When this scale factor is multiplied with the size, the size remains unchanged if the scale factor is 1. \\\n",
- "The default node scale factor does not affect the node size."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "33e2dc95-7f1c-4858-8979-abefc10d8d7d",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.get_node_scale_factor_mapping()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "36bad77e-c5da-4f8c-9ec1-b6ec6c136432",
- "metadata": {},
- "source": [
- "Let's assign a random scale factor for each node: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d83085e2-5b4c-4410-928e-b844c4f05172",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.set_node_scale_factor_mapping(custom_factor_mapping)\n",
- "w.get_node_scale_factor_mapping()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5b0d9975-d7e1-4189-8340-f041b038e491",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "76b18d80-975c-48ba-be65-39868092e582",
- "metadata": {},
- "source": [
- "If a node scale factor mapping is deleted, the scale factor mapping reverts back to the default mapping."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "64a6c991-dad7-4c47-8495-5a6976d8c260",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.del_node_scale_factor_mapping()\n",
- "w.get_node_scale_factor_mapping()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "202fa308-47b9-4c7f-9da1-a11299a53677",
- "metadata": {},
- "source": [
- "## Node Size Mapping"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7feca230-5286-4157-8159-1fc46177c0ec",
- "metadata": {},
- "source": [
- "The node size mapping is a function that is supposed to return an explicit width and height for each given node object which is then used in the widget.\n",
- "\n",
- "Optionally, the index can be used as the first function parameter.\n",
- "\n",
- "We will compute a new size derived from the node index. \\\n",
- "For this we first define a new mapping function and then set this function as our current size mapping."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3d63176a-feb0-4192-96b5-5e2956d3f41a",
- "metadata": {},
- "outputs": [],
- "source": [
- "w2 = GraphWidget(graph=g)\n",
- "def custom_size_mapping(index: int, node: Dict):\n",
- " \"\"\"Select a width and height for the node\"\"\"\n",
- " return 55 + 10 * (index % 5), 55 - 10 * (index % 5)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5ffb2f31-ced4-40f7-9bff-e7ded3a9b217",
- "metadata": {},
- "source": [
- "If no custom mapping is set the default mappings are used."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3f59708c-db00-453e-9e35-d880ef6c991a",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(w2.node_size_mapping.__doc__)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9468b414-1162-46e3-9e56-2f2126f1b926",
- "metadata": {},
- "source": [
- "Let's use the new size mapping function: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b33d4b93-d84c-473b-b567-e13b44860df5",
- "metadata": {},
- "outputs": [],
- "source": [
- "w2.set_node_size_mapping(custom_size_mapping)\n",
- "w2.get_node_size_mapping()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a5a22d4d-8798-4a00-94d7-b6aa6f9a5d42",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w2)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d905c025-027b-4b94-b4a0-b20da4df3e8f",
- "metadata": {},
- "source": [
- "If a node size mapping is deleted, the size mapping reverts back to the default mapping."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1396933f-0c18-44ed-bb1d-0a0db3c9df2f",
- "metadata": {},
- "outputs": [],
- "source": [
- "w2.del_node_size_mapping()\n",
- "w2.get_node_size_mapping()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "932b357e-0c07-4199-a16b-d25868f6902f",
- "metadata": {},
- "source": [
- "## Combining Size and Scale Factor Mapping\n",
- "\n",
- "When applying both the `node_scale_factor_mapping` and the `node_size_mapping`, \\\n",
- "the scaling factor will be applied on the size mapping. \n",
- "\n",
- "\n",
- "For example, with a scaling factor of 2 and a size mapping of (45, 65), the resulting size will be (90, 130).\n",
- "\n",
- "Let's use the same scale factor mapping and size mapping as above.\n",
- "- random scale factor\n",
- "- the bigger the index mod 5 is, the shape gets more oval"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ae982d4f-31f4-44c4-a3a7-f455e14d0259",
- "metadata": {},
- "outputs": [],
- "source": [
- "w3 = GraphWidget(graph=g)\n",
- "w3.set_node_size_mapping(custom_size_mapping)\n",
- "w3.set_node_scale_factor_mapping(custom_factor_mapping)\n",
- "display(w3)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d64cd16e-3cdf-47f8-b56e-c579e9e43b8c",
- "metadata": {},
- "source": [
- "Everytime the widget is displayed, the mappings are newly calculated. As we use a random scale factor, everytime we apply the mapping and display the widget, we get a new scale factor:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6afb83c9-afd3-48c2-bba6-5b78ee0434ba",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w3)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "645dd996-9207-40b9-ac1a-6d8c56e62798",
- "metadata": {},
- "outputs": [],
- "source": [
- "w3.del_node_size_mapping()\n",
- "w3.get_node_size_mapping()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4ef144f7-a17d-40e9-95d8-9446dfdca9b2",
- "metadata": {},
- "outputs": [],
- "source": [
- "w3.del_node_scale_factor_mapping()\n",
- "w3.get_node_scale_factor_mapping()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b65bff85-b163-4cdf-a36d-d1b2fbcfe3a5",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w3)"
- ]
- }
- ],
- "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": "3.11.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/06_position_mapping.ipynb b/examples/06_position_mapping.ipynb
index b0556f7..c851c8a 100644
--- a/examples/06_position_mapping.ipynb
+++ b/examples/06_position_mapping.ipynb
@@ -1,275 +1,1854 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "1c0f1f16-e86d-4a34-8c73-fad6b2639260",
- "metadata": {},
- "source": [
- "# Node Position Mapping "
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "1c0f1f16-e86d-4a34-8c73-fad6b2639260",
+ "metadata": {
+ "id": "1c0f1f16-e86d-4a34-8c73-fad6b2639260"
+ },
+ "source": [
+ "# Node Position Mapping "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8c775bba-7db3-4934-ac78-dd4fcf4c6e8b",
+ "metadata": {
+ "id": "8c775bba-7db3-4934-ac78-dd4fcf4c6e8b"
+ },
+ "source": [
+ "This notebook covers the basics of customizing node positions.\n",
+ "\n",
+ "A node position has 2 values:\n",
+ "- x-position\n",
+ "- y-position\n",
+ "\n",
+ "The position mapping is only used if an automatic layout algorithm does not change the node positions. \\\n",
+ "The position mapping is overwritten by any custom node layout mapping.\n",
+ "\n",
+ "Edges do not have a position mapping.\n",
+ "\n",
+ "For the purpose of mapping demonstrations, the same graph, ```erdos_renyi_graph```, will be used. For this, we will import the graph from the NetworkX package. \\\n",
+ "For more details on how to import graph data, explore the other example notebooks or refer to the full widget [documentation](https://yworks.github.io/yfiles-jupyter-graphs/).\n",
+ "\n",
+ "Before using the graph widget, install all necessary packages."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "b20bf47e-6de7-41ec-a5ef-42a119c12f06",
+ "metadata": {
+ "id": "b20bf47e-6de7-41ec-a5ef-42a119c12f06"
+ },
+ "outputs": [],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "from yfiles_jupyter_graphs import GraphWidget\n",
+ "%pip install networkx --quiet\n",
+ "from typing import Dict\n",
+ "from networkx import erdos_renyi_graph\n",
+ "from random import random, seed\n",
+ "seed(0)\n",
+ "\n",
+ "g = erdos_renyi_graph(10, 0.3, 2)\n",
+ "w = GraphWidget(graph=g)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0eb632e8-7dd8-4f92-b6d2-ffe77ac3dbab",
+ "metadata": {
+ "id": "0eb632e8-7dd8-4f92-b6d2-ffe77ac3dbab"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "79d57154-17bc-48b9-83de-a9efe7522bcd",
+ "metadata": {
+ "id": "79d57154-17bc-48b9-83de-a9efe7522bcd"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "98456b58-1196-4752-aecf-c8652c739a84",
+ "metadata": {
+ "id": "98456b58-1196-4752-aecf-c8652c739a84"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "242f5733-ad8c-4cd4-8e72-1faeefc49b6f",
+ "metadata": {
+ "id": "242f5733-ad8c-4cd4-8e72-1faeefc49b6f"
+ },
+ "source": [
+ "This is the graph we will be working on :"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "b16592e9-2c9b-4afa-bd66-e1e6997024bb",
+ "metadata": {
+ "id": "b16592e9-2c9b-4afa-bd66-e1e6997024bb",
+ "outputId": "b275b428-e470-4336-f807-afd035e6616a",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 517,
+ "referenced_widgets": [
+ "145b17879c434861a629f4ac24bbc3a8",
+ "a077451e7a58478a8a02946f3cf9cd4b"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='500px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "145b17879c434861a629f4ac24bbc3a8"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "display(w)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dd2e9679-180b-4180-90b6-73432736f1d5",
+ "metadata": {
+ "id": "dd2e9679-180b-4180-90b6-73432736f1d5"
+ },
+ "source": [
+ "## Mapping Function\n",
+ "\n",
+ "The node position mapping is a function that is supposed to return an (x, y)-tuple for each given node object which is then used in the widget.\n",
+ "\n",
+ "Optionally, the index can be used as the first function parameter.\n",
+ "\n",
+ "Let's assign a random position to each node.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "001ee109-3e2e-43fc-af01-bc2f67e49bb1",
+ "metadata": {
+ "id": "001ee109-3e2e-43fc-af01-bc2f67e49bb1"
+ },
+ "outputs": [],
+ "source": [
+ "w2 = GraphWidget(graph=g)\n",
+ "node_positions = [((random() - 0.5) * 1000, (random() - 0.5) * 1000) for _ in g.nodes]\n",
+ "nodes = w2.get_nodes()\n",
+ "def custom_node_position_mapping(node: Dict):\n",
+ " \"\"\"choose position randomly\"\"\"\n",
+ " return node_positions[nodes.index(node)]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "43ce21b7-f065-40be-bbfc-0d8e973d2e0b",
+ "metadata": {
+ "id": "43ce21b7-f065-40be-bbfc-0d8e973d2e0b"
+ },
+ "source": [
+ "### Custom node mappings\n",
+ "\n",
+ "There are get and set methods for each customizable node property.\n",
+ "- you can set a new node mapping with ```w.set_node_[binding]_mapping```\n",
+ "- you can get the current node mapping with ```w.get_node_[binding]_mapping```\n",
+ "- you can delete a custom node mapping with ```w.del_node_[binding]_mapping```\n",
+ "\n",
+ "You can find more details in the dedicated function documentation, available at ```w.[function_name].__doc__``` or in the [documentation](https://yworks.github.io/yfiles-jupyter-graphs/02_graph_widget/#methods).\n",
+ "\n",
+ "If no custom mapping is set the default mappings are used. \\\n",
+ "The default node position are 0.0, 0.0. However, as most layouts compute their own node positions, this is only relevant for edge router layouts."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "92ebba37-0c90-4112-b27f-25212574dbaf",
+ "metadata": {
+ "id": "92ebba37-0c90-4112-b27f-25212574dbaf",
+ "outputId": "d78b200c-642d-4866-df21-83c989101c13",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "The default position mapping for nodes.\n",
+ "\n",
+ " Provides constant value of 0.0, 0.0 for all nodes.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " index: int (optional)\n",
+ " node: typing.Dict\n",
+ "\n",
+ " Notes\n",
+ " -----\n",
+ " This is the default value for the `node_position_mapping` property.\n",
+ " Can be 'overwritten' by setting the property\n",
+ " with a function of the same signature.\n",
+ "\n",
+ " If the given mapping function has only one parameter (that is not typed as int),\n",
+ " then it will be called with the element (typing.Dict) as first parameter.\n",
+ "\n",
+ " Example\n",
+ " -------\n",
+ " .. code::\n",
+ "\n",
+ " from yfiles_jupyter_graphs import GraphWidget\n",
+ " w = GraphWidget()\n",
+ " def custom_node_position_mapping(node: typing.Dict):\n",
+ " ...\n",
+ " w.set_node_position_mapping(custom_node_position_mapping)\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " position: float 2-tuple\n",
+ "\n",
+ " \n"
+ ]
+ }
+ ],
+ "source": [
+ "print(w2.node_position_mapping.__doc__)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "bf86f21c-d629-4861-9a1f-7a458c848c0f",
+ "metadata": {
+ "id": "bf86f21c-d629-4861-9a1f-7a458c848c0f",
+ "outputId": "c11798c5-fa14-4c0d-94b2-9c401be36e9e",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 188
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "yfiles_jupyter_graphs.widget.GraphWidget.default_node_position_mapping
def default_node_position_mapping(index: int, node: TDict)
The default position mapping for nodes.\n", + "\n", + "Provides constant value of 0.0, 0.0 for all nodes.\n", + "\n", + "Parameters\n", + "----------\n", + "index: int (optional)\n", + "node: typing.Dict\n", + "\n", + "Notes\n", + "-----\n", + "This is the default value for the `node_position_mapping` property.\n", + "Can be 'overwritten' by setting the property\n", + "with a function of the same signature.\n", + "\n", + "If the given mapping function has only one parameter (that is not typed as int),\n", + "then it will be called with the element (typing.Dict) as first parameter.\n", + "\n", + "Example\n", + "-------\n", + ".. code::\n", + "\n", + " from yfiles_jupyter_graphs import GraphWidget\n", + " w = GraphWidget()\n", + " def custom_node_position_mapping(node: typing.Dict):\n", + " ...\n", + " w.set_node_position_mapping(custom_node_position_mapping)\n", + "\n", + "Returns\n", + "-------\n", + "position: float 2-tuple\n", + " \n", + "
custom_node_position_mapping
def custom_node_position_mapping(node: Dict)
choose position randomly
yfiles_jupyter_graphs.widget.GraphWidget.default_node_position_mapping
def default_node_position_mapping(index: int, node: TDict)
The default position mapping for nodes.\n", + "\n", + "Provides constant value of 0.0, 0.0 for all nodes.\n", + "\n", + "Parameters\n", + "----------\n", + "index: int (optional)\n", + "node: typing.Dict\n", + "\n", + "Notes\n", + "-----\n", + "This is the default value for the `node_position_mapping` property.\n", + "Can be 'overwritten' by setting the property\n", + "with a function of the same signature.\n", + "\n", + "If the given mapping function has only one parameter (that is not typed as int),\n", + "then it will be called with the element (typing.Dict) as first parameter.\n", + "\n", + "Example\n", + "-------\n", + ".. code::\n", + "\n", + " from yfiles_jupyter_graphs import GraphWidget\n", + " w = GraphWidget()\n", + " def custom_node_position_mapping(node: typing.Dict):\n", + " ...\n", + " w.set_node_position_mapping(custom_node_position_mapping)\n", + "\n", + "Returns\n", + "-------\n", + "position: float 2-tuple\n", + " \n", + "
"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "242f5733-ad8c-4cd4-8e72-1faeefc49b6f",
- "metadata": {},
- "source": [
- "This is the graph we will be working on : "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b16592e9-2c9b-4afa-bd66-e1e6997024bb",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "dd2e9679-180b-4180-90b6-73432736f1d5",
- "metadata": {},
- "source": [
- "## Mapping Function\n",
- "\n",
- "The node position mapping is a function that is supposed to return an (x, y)-tuple for each given node object which is then used in the widget.\n",
- "\n",
- "Optionally, the index can be used as the first function parameter.\n",
- "\n",
- "Let's assign a random position to each node.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "001ee109-3e2e-43fc-af01-bc2f67e49bb1",
- "metadata": {},
- "outputs": [],
- "source": [
- "node_positions = [((random() - 0.5) * 1000, (random() - 0.5) * 1000) for _ in g.nodes]\n",
- "nodes = w.get_nodes()\n",
- "def custom_node_position_mapping(node: Dict):\n",
- " \"\"\"choose position randomly\"\"\"\n",
- " return node_positions[nodes.index(node)]"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "43ce21b7-f065-40be-bbfc-0d8e973d2e0b",
- "metadata": {},
- "source": [
- "### Custom node mappings\n",
- "\n",
- "There are get and set methods for each customizable node property.\n",
- "- you can set a new node mapping with ```w.set_node_[binding]_mapping```\n",
- "- you can get the current node mapping with ```w.get_node_[binding]_mapping```\n",
- "- you can delete a custom node mapping with ```w.del_node_[binding]_mapping```\n",
- "\n",
- "You can find more details in the dedicated function documentation, available at ```w.[function_name].__doc__``` or in the [documentation](https://yworks.github.io/yfiles-jupyter-graphs/02_graph_widget/#methods).\n",
- "\n",
- "If no custom mapping is set the default mappings are used. \\\n",
- "The default node position are 0.0, 0.0. However, as most layouts compute their own node positions, this is only relevant for edge router layouts."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "92ebba37-0c90-4112-b27f-25212574dbaf",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(w.node_position_mapping.__doc__)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "bf86f21c-d629-4861-9a1f-7a458c848c0f",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.get_node_position_mapping()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "454c1367-531d-4618-96d4-b0cd17bd96e3",
- "metadata": {},
- "source": [
- "Let's set a random node position:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c868086f-d1e4-4b9e-a583-f91bd1ae925c",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.set_node_position_mapping(custom_node_position_mapping)\n",
- "w.get_node_position_mapping()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5992a237-bab8-42c5-9c5d-5d1158337e59",
- "metadata": {},
- "source": [
- "Choose one of two edge router layouts to see your custom node positioning:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "91099565-cfd2-4cc4-a8fe-d793d814a42f",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.orthogonal_edge_router()\n",
- "display(w)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "976df720-b661-44c8-a488-3fb2f9ed3667",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.organic_edge_router()\n",
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f2743a52-215e-486b-bae3-23174f58cada",
- "metadata": {},
- "source": [
- "If the node mapping is deleted, the default mapping is used: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "db069018-cb62-4403-8035-0f2f1df546c9",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.del_node_position_mapping()\n",
- "w.get_node_position_mapping()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9eb18bef-de04-48c2-91ed-d455162e960e",
- "metadata": {},
- "source": [
- "To see all nodes, choose a different layout in the toolbar:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9063960f-bde4-41d0-83d9-a03c89b2d105",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w)"
- ]
- }
- ],
- "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": "3.11.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/07_property_mapping.ipynb b/examples/07_property_mapping.ipynb
index f104717..19530c1 100644
--- a/examples/07_property_mapping.ipynb
+++ b/examples/07_property_mapping.ipynb
@@ -1,335 +1,2075 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "e0da67b7-ae24-4ce9-8141-14b8e8d9bd45",
- "metadata": {},
- "source": [
- "# Element Property Mapping "
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "e0da67b7-ae24-4ce9-8141-14b8e8d9bd45",
+ "metadata": {
+ "id": "e0da67b7-ae24-4ce9-8141-14b8e8d9bd45"
+ },
+ "source": [
+ "# Element Property Mapping "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7aa134ad-24e5-458b-8d9a-6a500aeec10f",
+ "metadata": {
+ "id": "7aa134ad-24e5-458b-8d9a-6a500aeec10f"
+ },
+ "source": [
+ "This notebook covers the basics of customizing node and edge properties.\n",
+ "\n",
+ "For the purpose of mapping demonstrations, the same graph, ```erdos_renyi_graph```, will be used. For this, we will import the graph from the NetworkX package. \\\n",
+ "For more details on how to import graph data, explore the other example notebooks or refer to the full widget [documentation](https://yworks.github.io/yfiles-jupyter-graphs/).\n",
+ "\n",
+ "Before using the graph widget, install all necessary packages."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "a882a7fc-c9c2-4df9-8bc5-8a3eff951c21",
+ "metadata": {
+ "id": "a882a7fc-c9c2-4df9-8bc5-8a3eff951c21",
+ "outputId": "177ed981-0b71-4c46-de5f-0eaeb3e15465",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 714
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m19.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m29.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m17.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hThe default property mapping for graph elements.\n",
+ "\n",
+ " Simply selects the properties value of element dictionary.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " index: int (optional)\n",
+ " element: typing.Dict\n",
+ "\n",
+ " Notes\n",
+ " -----\n",
+ " This is the default value for the {`node|edge`}_property_mapping property.\n",
+ " Can be 'overwritten' by setting the property\n",
+ " with a function of the same signature.\n",
+ "\n",
+ " If the given mapping function has only one parameter (that is not typed as int),\n",
+ " then it will be called with the element (typing.Dict) as first parameter.\n",
+ "\n",
+ " Example\n",
+ " -------\n",
+ " .. code::\n",
+ "\n",
+ " from yfiles_jupyter_graphs import GraphWidget\n",
+ " w = GraphWidget()\n",
+ " def custom_element_property_mapping(element: typing.Dict):\n",
+ " ...\n",
+ " w.set_{node|edge}_property_mapping(custom_element_property_mapping)\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " properties: typing.Dict\n",
+ "\n",
+ " \n"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "from yfiles_jupyter_graphs import GraphWidget\n",
+ "%pip install networkx --quiet\n",
+ "from typing import Dict\n",
+ "from networkx import degree, edge_load_centrality, erdos_renyi_graph\n",
+ "\n",
+ "g = erdos_renyi_graph(10, 0.3, 2)\n",
+ "w = GraphWidget(graph=g)\n",
+ "\n",
+ "print(w.default_element_property_mapping.__doc__)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7bec868b-a50c-438c-bac6-48a01bafe2fe",
+ "metadata": {
+ "id": "7bec868b-a50c-438c-bac6-48a01bafe2fe"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "729a617b-f0cd-469b-a1da-aee55cecda02",
+ "metadata": {
+ "id": "729a617b-f0cd-469b-a1da-aee55cecda02"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9604ccc3-2330-439e-b790-d543daa08dbc",
+ "metadata": {
+ "id": "9604ccc3-2330-439e-b790-d543daa08dbc"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cc865b3e-513d-40f9-842c-74c6507f72d4",
+ "metadata": {
+ "id": "cc865b3e-513d-40f9-842c-74c6507f72d4"
+ },
+ "source": [
+ "This is the graph we will be working with:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "45ec7216-fa80-4211-bab7-e7ef47836ef4",
+ "metadata": {
+ "id": "45ec7216-fa80-4211-bab7-e7ef47836ef4",
+ "outputId": "fe1c20c7-7c3c-41dc-ba9d-823f511e0bb2",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 517,
+ "referenced_widgets": [
+ "4ee7647f3620427293202cfad8f54f13",
+ "30ebe0a3ab9f41f5a3b24584ff2152cf"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='500px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "4ee7647f3620427293202cfad8f54f13"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "display(GraphWidget(graph=g))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "92dd9bd8-1bc9-48f4-877f-edaf3a9854ed",
+ "metadata": {
+ "id": "92dd9bd8-1bc9-48f4-877f-edaf3a9854ed"
+ },
+ "source": [
+ "## Node Property Mapping\n",
+ "\n",
+ "The node property mapping is a function that is supposed to return a dictionary for each given node object which is then available on the nodes.\n",
+ "\n",
+ "Optionally, the index can be used as the first function parameter.\n",
+ "\n",
+ "We will use the the inbuild ```degree()``` function of NetworkX to determine the degree of each node and then add this to the node property. \\\n",
+ "For this we first define a new mapping function and then set this function as our current label mapping.\n",
+ "\n",
+ "But be careful as properties are changed inplace. Therefore, deleting mappings does not restore anything."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "812fdd7e-fd92-4beb-a927-0403c2cfe2a7",
+ "metadata": {
+ "id": "812fdd7e-fd92-4beb-a927-0403c2cfe2a7",
+ "outputId": "15c13e13-d6d8-4ce1-98f2-06452a1ffe56",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "[(0, 2), (1, 1), (2, 3), (3, 3), (4, 3), (5, 2), (6, 3), (7, 1), (8, 1), (9, 1)]\n"
+ ]
+ }
+ ],
+ "source": [
+ "degree_mapping = degree(g)\n",
+ "print(degree_mapping)\n",
+ "\n",
+ "nodes = w.get_nodes()\n",
+ "def custom_node_property_mapping(node: Dict):\n",
+ " \"\"\"use degree mapping to determine degree property\"\"\"\n",
+ " node['properties'].update({'degree': degree_mapping[nodes.index(node)]})\n",
+ " return node['properties']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "90111598-0f47-4f8a-8f59-7e074407f23f",
+ "metadata": {
+ "id": "90111598-0f47-4f8a-8f59-7e074407f23f"
+ },
+ "source": [
+ "### Custom node mappings\n",
+ "\n",
+ "There are get and set methods for each customizable node property.\n",
+ "- you can set a new node mapping with ```w.set_node_[binding]_mapping```\n",
+ "- you can get the current node mapping with ```w.get_node_[binding]_mapping```\n",
+ "- you can delete a custom node mapping with ```w.del_node_[binding]_mapping```\n",
+ "\n",
+ "You can find more details in the dedicated function documentation, available at ```w.[function_name].__doc__``` or in the [documentation](https://yworks.github.io/yfiles-jupyter-graphs/02_graph_widget/#methods).\n",
+ "\n",
+ "If no custom mapping is set the default mappings are used."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "ac0d520e-464e-40c3-883a-71238af0a0e4",
+ "metadata": {
+ "id": "ac0d520e-464e-40c3-883a-71238af0a0e4",
+ "outputId": "bfe3f35a-13f5-4a06-e6f3-43a9008bfb5e",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 123
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "yfiles_jupyter_graphs.widget.GraphWidget.default_node_property_mapping
def default_node_property_mapping(index: int, node: TDict)
See default element property mapping.\n", + " \n", + "
custom_node_property_mapping
def custom_node_property_mapping(node: Dict)
use degree mapping to determine degree property
yfiles_jupyter_graphs.widget.GraphWidget.default_node_property_mapping
def default_node_property_mapping(index: int, node: TDict)
See default element property mapping.\n", + " \n", + "
yfiles_jupyter_graphs.widget.GraphWidget.default_edge_property_mapping
def default_edge_property_mapping(index: int, edge: TDict)
See default element property mapping.\n", + " \n", + "
custom_edge_property_mapping
def custom_edge_property_mapping(edge: Dict)
use edge load centrality mapping to determine edge load centrality property
yfiles_jupyter_graphs.widget.GraphWidget.default_edge_property_mapping
def default_edge_property_mapping(index: int, edge: TDict)
See default element property mapping.\n", + " \n", + "
"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "cc865b3e-513d-40f9-842c-74c6507f72d4",
- "metadata": {},
- "source": [
- "This is the graph we will be working with:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "45ec7216-fa80-4211-bab7-e7ef47836ef4",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "92dd9bd8-1bc9-48f4-877f-edaf3a9854ed",
- "metadata": {},
- "source": [
- "## Node Property Mapping\n",
- "\n",
- "The node property mapping is a function that is supposed to return a dictionary for each given node object which is then available on the nodes.\n",
- "\n",
- "Optionally, the index can be used as the first function parameter.\n",
- "\n",
- "We will use the the inbuild ```degree()``` function of NetworkX to determine the degree of each node and then add this to the node property. \\\n",
- "For this we first define a new mapping function and then set this function as our current label mapping. \n",
- "\n",
- "But be careful as properties are changed inplace. Therefore, deleting mappings does not restore anything."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "812fdd7e-fd92-4beb-a927-0403c2cfe2a7",
- "metadata": {},
- "outputs": [],
- "source": [
- "degree_mapping = degree(g)\n",
- "print(degree_mapping)\n",
- "\n",
- "nodes = w.get_nodes()\n",
- "def custom_node_property_mapping(node: Dict):\n",
- " \"\"\"use degree mapping to determine degree property\"\"\"\n",
- " node['properties'].update({'degree': degree_mapping[nodes.index(node)]})\n",
- " return node['properties']"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "90111598-0f47-4f8a-8f59-7e074407f23f",
- "metadata": {},
- "source": [
- "### Custom node mappings\n",
- "\n",
- "There are get and set methods for each customizable node property.\n",
- "- you can set a new node mapping with ```w.set_node_[binding]_mapping```\n",
- "- you can get the current node mapping with ```w.get_node_[binding]_mapping```\n",
- "- you can delete a custom node mapping with ```w.del_node_[binding]_mapping```\n",
- "\n",
- "You can find more details in the dedicated function documentation, available at ```w.[function_name].__doc__``` or in the [documentation](https://yworks.github.io/yfiles-jupyter-graphs/02_graph_widget/#methods).\n",
- "\n",
- "If no custom mapping is set the default mappings are used."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ac0d520e-464e-40c3-883a-71238af0a0e4",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.get_node_property_mapping()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f699ded9-4523-4ea9-afa0-305bf5c650c4",
- "metadata": {},
- "source": [
- "Let's update the property binding:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "48cbb4fe-84c6-4acb-877b-d3a0371a2101",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.set_node_property_mapping(custom_node_property_mapping)\n",
- "w.get_node_property_mapping()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "56d5ff90-3b86-40fe-a87a-0323f53d5f36",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "284ba080-ffaf-49db-92ac-4f44466cf2dd",
- "metadata": {},
- "source": [
- "If a node property mapping is deleted, the property mapping reverts back to the default mapping."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f9dcd3d4-3fbf-4f08-bae7-1b7933aed0e5",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.del_node_property_mapping()\n",
- "w.get_node_property_mapping()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8b4ff087-142f-4833-8776-09619cf5cfea",
- "metadata": {},
- "source": [
- "## Edge Property Mapping\n",
- "\n",
- "The edge property mapping is a function that is supposed to return a dictionary for each given edge object which is then available on the edges.\n",
- "\n",
- "If the index is used, it can be optionally given as the first function parameter. \n",
- "\n",
- "We will use the networkx function ```edge_load_centrality()```, to determine the edge centrality of each edge and then add this to the edge property.\n",
- "\n",
- "For this we first a define a new mapping function and then set this function as our current edge property mapping. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4737ee59-2829-4c66-bf8d-c5deb00fd129",
- "metadata": {},
- "outputs": [],
- "source": [
- "w2 = GraphWidget(graph= erdos_renyi_graph(10, 0.3, 2))\n",
- "\n",
- "edge_load_centrality_mapping = edge_load_centrality(g)\n",
- "print(edge_load_centrality_mapping)\n",
- "\n",
- "def custom_edge_property_mapping(edge: Dict):\n",
- " \"\"\"use edge load centrality mapping to determine edge load centrality property\"\"\"\n",
- " # works because node labels are same as index\n",
- " # otherwise another way of indexing mapping has to be found\n",
- " c = edge_load_centrality_mapping[(edge['start'], edge['end'])]\n",
- " edge['properties'].update({'load_centrality': c})\n",
- " return edge['properties'] "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5e7b9d2b-3e49-46c0-91d0-14204f3dd7af",
- "metadata": {},
- "source": [
- "### Custom edge mappings\n",
- "\n",
- "There are get and set methods for each customizable edge property. In this case property = property.\n",
- "- you can set a new edge mapping with ```w.set_edge_[property]_mapping```\n",
- "- you can get the current edge mapping with ```w.get_edge_[property]_mapping```\n",
- "- you can delete a custom edge mapping with ```w.del_edge_[property]_mapping```\n",
- "\n",
- "You can find more details in the dedicated function documentation, available at ```w.[function_name].__doc__``` or in the [documentation](https://yworks.github.io/yfiles-jupyter-graphs/02_graph_widget/#methods).\n",
- "\n",
- "If no custom mapping is set the default mappings are used."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5906db97-8ea8-49e3-bc9c-d080683cc155",
- "metadata": {},
- "outputs": [],
- "source": [
- "w2.get_edge_property_mapping()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "01db1080-bb12-4ae9-95ef-47f3242f8a6b",
- "metadata": {},
- "source": [
- "Let's set the new edge property: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "825e27e9-b9f8-4b8d-8b35-160d30b95812",
- "metadata": {},
- "outputs": [],
- "source": [
- "w2.set_edge_property_mapping(custom_edge_property_mapping)\n",
- "w2.get_edge_property_mapping()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f46689dc-341a-4b0a-b35d-95b813d1afb2",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w2)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "405a1da7-ab20-44f8-b89c-e7ab11b7b839",
- "metadata": {},
- "source": [
- "If a edge property mapping is deleted, the property mapping reverts back to the default mapping."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e20453b7-6ece-443e-a913-b3efe3223860",
- "metadata": {},
- "outputs": [],
- "source": [
- "w2.del_edge_property_mapping()\n",
- "w2.get_edge_property_mapping()"
- ]
- }
- ],
- "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": "3.11.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/08_styles_mapping.ipynb b/examples/08_styles_mapping.ipynb
index 8c74c2a..66c580e 100644
--- a/examples/08_styles_mapping.ipynb
+++ b/examples/08_styles_mapping.ipynb
@@ -1,411 +1,3013 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "eecc07af-a3c7-4106-83b3-4b3cf188bf00",
- "metadata": {},
- "source": [
- "# Node Styles Mapping "
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "eecc07af-a3c7-4106-83b3-4b3cf188bf00",
+ "metadata": {
+ "id": "eecc07af-a3c7-4106-83b3-4b3cf188bf00"
+ },
+ "source": [
+ "# Node Styles Mapping "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "568394f4-3172-4727-bab4-7b185bbf3475",
+ "metadata": {
+ "id": "568394f4-3172-4727-bab4-7b185bbf3475"
+ },
+ "source": [
+ "This notebook covers the basics of customizing node styles.\n",
+ "\n",
+ "Although edges do not have a styles property, their color may be adjusted as demonstrated in [03_color_mapping.ipynb](./03_color_mapping.ipynb).\n",
+ "\n",
+ "For the purpose of mapping demonstrations, the same graph, ```erdos_renyi_graph```, will be used. For this, we will import the graph from the networkx database. \\\n",
+ "For more details on how to import graph data, explore the other example notebooks or refer to the full widget [documentation](https://yworks.github.io/yfiles-jupyter-graphs/).\n",
+ "\n",
+ "Before using the graph widget, install all necessary packages."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "90ecbac8-b17b-4334-b047-90bf2c660789",
+ "metadata": {
+ "id": "90ecbac8-b17b-4334-b047-90bf2c660789",
+ "outputId": "fb2282a5-81df-43a1-aa97-d41d40ad5812",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 90
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m15.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m17.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "from yfiles_jupyter_graphs import GraphWidget\n",
+ "%pip install networkx --quiet\n",
+ "from typing import Dict\n",
+ "from networkx import erdos_renyi_graph\n",
+ "from random import random, seed, choice\n",
+ "seed(0)\n",
+ "\n",
+ "g = erdos_renyi_graph(10, 0.3, 2)\n",
+ "w = GraphWidget(graph=g)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "77fabc96-6d28-4236-a7e7-43947de3ea9c",
+ "metadata": {
+ "id": "77fabc96-6d28-4236-a7e7-43947de3ea9c"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "193f755e-f9bc-4e02-ba8d-12b58b060073",
+ "metadata": {
+ "id": "193f755e-f9bc-4e02-ba8d-12b58b060073"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2de88426-f109-447a-ac84-6bff394d9d8c",
+ "metadata": {
+ "id": "2de88426-f109-447a-ac84-6bff394d9d8c"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "398a965a-f48f-44bd-abf3-80881bbc0b10",
+ "metadata": {
+ "id": "398a965a-f48f-44bd-abf3-80881bbc0b10"
+ },
+ "source": [
+ "This is the graph we will be using:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "89d498d9-36c7-4b5d-9306-118bfbd23ed1",
+ "metadata": {
+ "id": "89d498d9-36c7-4b5d-9306-118bfbd23ed1",
+ "outputId": "851b518b-4f4f-4d19-fb2c-7eccf3ce9715",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 517,
+ "referenced_widgets": [
+ "1cf4097c1f8e47df9389c58c864f67c4",
+ "528d5624eb3d4492b2d7e635a6f08ce9"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='500px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "1cf4097c1f8e47df9389c58c864f67c4"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "display(w)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "db627c1c-f274-49e7-bc20-430cca14648c",
+ "metadata": {
+ "id": "db627c1c-f274-49e7-bc20-430cca14648c"
+ },
+ "source": [
+ "## Mapping Function\n",
+ "\n",
+ "The node styles mapping is a function that is supposed to return a dictionary of style properties for each given node object which is then used in the widget.\n",
+ "\n",
+ "Optionally, the index can be used as the first function parameter.\n",
+ "\n",
+ "There are three style options: `shape`, `color` and `image`.\n",
+ "\n",
+ "### Custom node mappings\n",
+ "\n",
+ "There are get and set methods for each customizable node property.\n",
+ "- you can set a new node mapping with ```w.set_node_[binding]_mapping```\n",
+ "- you can get the current node mapping with ```w.get_node_[binding]_mapping```\n",
+ "- you can delete a custom node mapping with ```w.del_node_[binding]_mapping```\n",
+ "\n",
+ "You can find more details in the dedicated function documentation, available at ```w.[function_name].__doc__``` or in the [documentation](https://yworks.github.io/yfiles-jupyter-graphs/02_graph_widget/#methods).\n",
+ "\n",
+ "If no style mapping is set, the default mapping is used."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "365ca4b5-2c1d-44d4-8e8b-c564fc1384ac",
+ "metadata": {
+ "id": "365ca4b5-2c1d-44d4-8e8b-c564fc1384ac",
+ "outputId": "d8266e7c-25c5-48c5-94a0-3b14300a0aca",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "The default styles mapping for nodes.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " index: int (optional)\n",
+ " node: typing.Dict\n",
+ "\n",
+ " Notes\n",
+ " -----\n",
+ " This is the default value for the `node_styles_mapping` property.\n",
+ " Can be 'overwritten' by setting the property\n",
+ " with a function of the same signature.\n",
+ "\n",
+ " If the given mapping function has only one parameter (that is not typed as int),\n",
+ " then it will be called with the element (typing.Dict) as first parameter.\n",
+ "\n",
+ " Example\n",
+ " -------\n",
+ " .. code::\n",
+ "\n",
+ " from yfiles_jupyter_graphs import GraphWidget\n",
+ " w = GraphWidget()\n",
+ " def custom_node_styles_mapping(node: typing.Dict):\n",
+ " ...\n",
+ " w.set_node_styles_mapping(custom_node_styles_mapping)\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " \n",
+ " styles: typing.Dict\n",
+ " can contain the following key-value-pairs:\n",
+ " \"color\": str\n",
+ " css color value\n",
+ " \"shape\": str\n",
+ " possible values: 'ellipse', 'hexagon', 'hexagon2', 'octagon', 'pill', 'rectangle', 'round-rectangle' or 'triangle'\n",
+ " \"image\": str\n",
+ " url or data URL of the image\n",
+ "\n",
+ " References\n",
+ " ----------\n",
+ " css color value yfiles_jupyter_graphs.widget.GraphWidget.default_node_styles_mapping
def default_node_styles_mapping(index: int, node: TDict)
The default styles mapping for nodes.\n", + "\n", + "Parameters\n", + "----------\n", + "index: int (optional)\n", + "node: typing.Dict\n", + "\n", + "Notes\n", + "-----\n", + "This is the default value for the `node_styles_mapping` property.\n", + "Can be 'overwritten' by setting the property\n", + "with a function of the same signature.\n", + "\n", + "If the given mapping function has only one parameter (that is not typed as int),\n", + "then it will be called with the element (typing.Dict) as first parameter.\n", + "\n", + "Example\n", + "-------\n", + ".. code::\n", + "\n", + " from yfiles_jupyter_graphs import GraphWidget\n", + " w = GraphWidget()\n", + " def custom_node_styles_mapping(node: typing.Dict):\n", + " ...\n", + " w.set_node_styles_mapping(custom_node_styles_mapping)\n", + "\n", + "Returns\n", + "-------\n", + "\n", + "styles: typing.Dict\n", + " can contain the following key-value-pairs:\n", + " "color": str\n", + " css color value\n", + " "shape": str\n", + " possible values: 'ellipse', 'hexagon', 'hexagon2', 'octagon', 'pill', 'rectangle', 'round-rectangle' or 'triangle'\n", + " "image": str\n", + " url or data URL of the image\n", + "\n", + "References\n", + "----------\n", + "css color value <https://developer.mozilla.org/en-US/docs/Web/CSS/color_value>\n", + "\n", + "Data URL <https://developer.mozilla.org/en-US/docs/Web/HTTP/Basics_of_HTTP/Data_URLs>\n", + " \n", + "
custom_styles_mapping
def custom_styles_mapping(node: Dict)
Select a shape and a random color for the node
custom_styles_mapping_image
def custom_styles_mapping_image(item: Dict)
<no docstring>
"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "398a965a-f48f-44bd-abf3-80881bbc0b10",
- "metadata": {},
- "source": [
- "This is the graph we will be using: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "89d498d9-36c7-4b5d-9306-118bfbd23ed1",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "db627c1c-f274-49e7-bc20-430cca14648c",
- "metadata": {},
- "source": [
- "## Mapping Function\n",
- "\n",
- "The node styles mapping is a function that is supposed to return a dictionary of style properties for each given node object which is then used in the widget.\n",
- "\n",
- "Optionally, the index can be used as the first function parameter.\n",
- "\n",
- "There are three style options: `shape`, `color` and `image`.\n",
- "\n",
- "### Custom node mappings\n",
- "\n",
- "There are get and set methods for each customizable node property.\n",
- "- you can set a new node mapping with ```w.set_node_[binding]_mapping```\n",
- "- you can get the current node mapping with ```w.get_node_[binding]_mapping```\n",
- "- you can delete a custom node mapping with ```w.del_node_[binding]_mapping```\n",
- "\n",
- "You can find more details in the dedicated function documentation, available at ```w.[function_name].__doc__``` or in the [documentation](https://yworks.github.io/yfiles-jupyter-graphs/02_graph_widget/#methods).\n",
- "\n",
- "If no style mapping is set, the default mapping is used."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "365ca4b5-2c1d-44d4-8e8b-c564fc1384ac",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(w.node_styles_mapping.__doc__)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9870a144-9afa-4be5-98f6-c8dbf2c6da71",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.get_node_styles_mapping()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "26d23e41-d42b-4248-b138-10460adc5916",
- "metadata": {},
- "source": [
- "## Node Styles Mapping with 'shape' and 'color'\n",
- "\n",
- "First, change the node styles with a new random shape and a new random color"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2b2c8788-b50f-43da-a646-93f1153f99af",
- "metadata": {},
- "outputs": [],
- "source": [
- "nodes = w.get_nodes()\n",
- "node_shapes = [\"rectangle\", \"round-rectangle\", \"ellipse\", \"hexagon\", \"hexagon2\", \"octagon\", \"triangle\", \"pill\"]\n",
- "def custom_styles_mapping(node: Dict):\n",
- " \"\"\"Select a shape and a random color for the node\"\"\"\n",
- " return {\n",
- " 'shape': node_shapes[nodes.index(node) % len(node_shapes)],\n",
- " 'color': \"#\"+''.join([choice('0123456789abcdef') for j in range(6)])\n",
- " }"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b5fa3218-3440-4fa9-861c-83b1e7fbca61",
- "metadata": {},
- "source": [
- "Let's change the node styles:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "15c4086d-d686-4da2-a9af-ec50b5c0f292",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.set_node_styles_mapping(custom_styles_mapping)\n",
- "w.get_node_styles_mapping()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "950b2ab0-7865-43e6-8bc4-de58d0a9a702",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9e8b6f9c-35d4-4726-8247-779a851464e3",
- "metadata": {},
- "source": [
- "## Node Styles Mapping with 'image''\n",
- "\n",
- "Now we change the node styles by setting them as an image"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9ccd8aa6-c69b-4385-bf88-392652874345",
- "metadata": {},
- "outputs": [],
- "source": [
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fill='%23fff'/%3E%3Ccircle cx='20.36' cy='32.99' r='5.43' fill='%23fff'/%3E%3Ccircle cx='68.39' cy='60.72' r='5.43' fill='%23fff'/%3E%3C/g%3E%3C/g%3E%3C/svg%3E\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "dff70d4c-ff48-4ff6-8ba3-ad4193b3adec",
- "metadata": {},
- "outputs": [],
- "source": [
- "w2 = GraphWidget(graph=g)\n",
- "\n",
- "def custom_styles_mapping_image(item: Dict):\n",
- " if item['id'] % 2 == 0:\n",
- " return {\n",
- " 'image': dataURI\n",
- " }\n",
- " else:\n",
- " return {}"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "263c96f8-126d-4383-a85d-d7bb4c09a14b",
- "metadata": {},
- "source": [
- "Let's change the node styles to the image:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b599853f-5cc7-4725-a891-88e5acd7b40d",
- "metadata": {},
- "outputs": [],
- "source": [
- "w2.set_node_styles_mapping(custom_styles_mapping_image)\n",
- "w2.get_node_styles_mapping()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "02079d78-6373-4789-b007-444d9ffcd659",
- "metadata": {},
- "source": [
- "Note that you can inspect the styles property in the data tab by selecting a node."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "aabb11c1-e4cf-4ddc-890f-68c96e0d0115",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w2)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "27fd6b08-ec37-46a0-9ebd-d9457cf83eb0",
- "metadata": {},
- "source": [
- " Note that the 'image' property can also be set to a url, but only if it allows cross-origin access:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "de3e8a7d-d641-4cfd-8c3c-ee0cba130f53",
- "metadata": {},
- "outputs": [],
- "source": [
- "w3 = GraphWidget(graph=g)\n",
- "\n",
- "def custom_styles_mapping_image_url(item: Dict):\n",
- " if item['id'] % 2 == 0:\n",
- " return {\n",
- " 'image': 'https://gist.githubusercontent.com/fskpf/b5c5b765139056ddc7e72ea28d4f44e4/raw/f4483469a9d4f638a8acae39aa6adfd76b61f587/yfiles-jupyter-graphs-icon.svg'\n",
- " }\n",
- " else:\n",
- " return {}\n",
- "\n",
- "w3.set_node_styles_mapping(custom_styles_mapping_image_url)\n",
- "display(w3)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1af88e4b-8fd4-476f-a54c-405518744b20",
- "metadata": {},
- "source": [
- "## Node Styles Mapping with 'image', 'shape' and 'color'\n",
- "If the styles property contains values for ```'image'```, but for ```'shape'``` or ```'color'``` as well, the former will be used for the nodes and ```'shape'``` and ```'color'``` will be ignored."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "26d87382-08af-4e4d-a8ca-f7ae77c7e86c",
- "metadata": {},
- "outputs": [],
- "source": [
- "w4 = GraphWidget(graph=g)\n",
- "\n",
- "def custom_styles_mapping_image_shape_color(item: Dict):\n",
- " return {\n",
- " 'shape': 'triangle',\n",
- " 'color': 'red',\n",
- " 'image': dataURI\n",
- " }\n",
- " \n",
- "w4.set_node_styles_mapping(custom_styles_mapping_image_shape_color)\n",
- "display(w4)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9fdc003c-8200-4c59-8f78-1c871752ad36",
- "metadata": {},
- "source": [
- "## Node Styles Mapping and Node Color Mapping\n",
- "\n",
- "There is a seperate color binding apart from the styles binding. The styles binding takes precedence if both are defined."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1322ffd9-bec4-4ecc-8442-ae0e28d77427",
- "metadata": {},
- "outputs": [],
- "source": [
- "w5 = GraphWidget(graph=g)\n",
- "def custom_styles_mapping_red(item: Dict):\n",
- " if item['id'] % 2 == 0:\n",
- " return {\n",
- " 'color': 'red'\n",
- " }\n",
- " else:\n",
- " return {}\n",
- " \n",
- "def custom_color_mapping_blue(item: Dict):\n",
- " return 'blue'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a3d56af6-5600-4374-adec-98c7aaaf5192",
- "metadata": {},
- "outputs": [],
- "source": [
- "w5.set_node_styles_mapping(custom_styles_mapping_red)\n",
- "w5.set_node_color_mapping(custom_color_mapping_blue)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "40bba8c5-3424-4087-a10f-e1ad16379c84",
- "metadata": {},
- "source": [
- "Only every second node is blue, although all of them are set to blue by the ```node_color_mapping```. \\\n",
- "The api tries to get the ```color``` property on ```styles```. \\\n",
- "If it doesn't exist, it will use the ```color``` from ```custom_color_mapping``` instead."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2c6638e6-e841-410c-8d00-d59475813263",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w5)"
- ]
- }
- ],
- "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": "3.11.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/09_type_mapping.ipynb b/examples/09_type_mapping.ipynb
index 4c8963d..c857faa 100644
--- a/examples/09_type_mapping.ipynb
+++ b/examples/09_type_mapping.ipynb
@@ -1,286 +1,1669 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "7e7c5cf8-4f63-470e-a5c9-420270530286",
- "metadata": {},
- "source": [
- "# Node Type Mapping "
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "7e7c5cf8-4f63-470e-a5c9-420270530286",
+ "metadata": {
+ "id": "7e7c5cf8-4f63-470e-a5c9-420270530286"
+ },
+ "source": [
+ "# Node Type Mapping "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e6777b35-da2a-443c-b579-75278d94ba72",
+ "metadata": {
+ "id": "e6777b35-da2a-443c-b579-75278d94ba72"
+ },
+ "source": [
+ "This notebook covers the basics of customizing node types. Node types are considered by the automatic layout algorithms to arrange similar types together.\n",
+ "\n",
+ "Edges do not have a type mapping.\n",
+ "\n",
+ "Before using the graph widget, install all necessary packages."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "bf28ad28-0b03-4020-9510-b01fcc554a97",
+ "metadata": {
+ "id": "bf28ad28-0b03-4020-9510-b01fcc554a97"
+ },
+ "outputs": [],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "from typing import Dict\n",
+ "from yfiles_jupyter_graphs import GraphWidget\n",
+ "\n",
+ "w=GraphWidget()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "97362274-1e46-4b96-ab69-4f40bf0c3fd9",
+ "metadata": {
+ "id": "97362274-1e46-4b96-ab69-4f40bf0c3fd9"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "cc77c3ba-a83e-4dd6-9dcf-e6c901a5965e",
+ "metadata": {
+ "id": "cc77c3ba-a83e-4dd6-9dcf-e6c901a5965e"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "869e0212-3330-45b8-bee9-6c802e580ee0",
+ "metadata": {
+ "id": "869e0212-3330-45b8-bee9-6c802e580ee0"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "f3b74f7b-8b05-4bad-95db-6ead02b4d552",
+ "metadata": {
+ "id": "f3b74f7b-8b05-4bad-95db-6ead02b4d552"
+ },
+ "outputs": [],
+ "source": [
+ "w.nodes = [\n",
+ " {'id': 0, 'properties': {'type': 0}},\n",
+ " {'id': 1, 'properties': {'type': 1}}, {'id': 2, 'properties': {'type': 1}}, {'id': 3, 'properties': {'type': 1}}, {'id': 4, 'properties': {'type': 1}}, {'id': 5, 'properties': {'type': 1}}, {'id': 6, 'properties': {'type': 1}},\n",
+ " {'id': 7, 'properties': {'type': 2}}, {'id': 8, 'properties': {'type': 2}}, {'id': 9, 'properties': {'type': 2}}, {'id': 10, 'properties': {'type': 2}}, {'id': 11, 'properties': {'type': 2}}, {'id': 12, 'properties': {'type': 2}},\n",
+ " {'id': 13, 'properties': {'type': 3}}, {'id': 14, 'properties': {'type': 3}}, {'id': 15, 'properties': {'type': 3}}, {'id': 16, 'properties': {'type': 3}}, {'id': 17, 'properties': {'type': 3}}, {'id': 18, 'properties': {'type': 3}},\n",
+ " {'id': 19, 'properties': {'type': 4}}\n",
+ "]\n",
+ "w.edges = [\n",
+ " {'id': 0, 'start': 0, 'end': 1, 'properties': {}}, {'id': 1, 'start': 0, 'end': 2, 'properties': {}}, {'id': 2, 'start': 0, 'end': 3, 'properties': {}},\n",
+ " {'id': 3, 'start': 0, 'end': 4, 'properties': {}}, {'id': 4, 'start': 0, 'end': 5, 'properties': {}}, {'id': 5, 'start': 0, 'end': 6, 'properties': {}},\n",
+ " {'id': 6, 'start': 0, 'end': 7, 'properties': {}}, {'id': 7, 'start': 0, 'end': 8, 'properties': {}}, {'id': 8, 'start': 0, 'end': 9, 'properties': {}},\n",
+ " {'id': 9, 'start': 0, 'end': 10, 'properties': {}}, {'id': 10, 'start': 0, 'end': 11, 'properties': {}}, {'id': 11, 'start': 0, 'end': 12, 'properties': {}},\n",
+ " {'id': 12, 'start': 0, 'end': 13, 'properties': {}}, {'id': 13, 'start': 0, 'end': 14, 'properties': {}}, {'id': 14, 'start': 0, 'end': 15, 'properties': {}},\n",
+ " {'id': 15, 'start': 0, 'end': 16, 'properties': {}}, {'id': 16, 'start': 0, 'end': 17, 'properties': {}}, {'id': 17, 'start': 0, 'end': 18, 'properties': {}},\n",
+ " {'id': 18, 'start': 1, 'end': 19, 'properties': {}}, {'id': 19, 'start': 2, 'end': 19, 'properties': {}}, {'id': 20, 'start': 3, 'end': 19, 'properties': {}},\n",
+ " {'id': 21, 'start': 4, 'end': 19, 'properties': {}}, {'id': 22, 'start': 5, 'end': 19, 'properties': {}}, {'id': 23, 'start': 6, 'end': 19, 'properties': {}},\n",
+ " {'id': 24, 'start': 7, 'end': 19, 'properties': {}}, {'id': 25, 'start': 8, 'end': 19, 'properties': {}}, {'id': 26, 'start': 9, 'end': 19, 'properties': {}},\n",
+ " {'id': 27, 'start': 10, 'end': 19, 'properties': {}}, {'id': 28, 'start': 11, 'end': 19, 'properties': {}}, {'id': 29, 'start': 12, 'end': 19, 'properties': {}},\n",
+ " {'id': 30, 'start': 13, 'end': 19, 'properties': {}}, {'id': 31, 'start': 14, 'end': 19, 'properties': {}}, {'id': 32, 'start': 15, 'end': 19, 'properties': {}},\n",
+ " {'id': 33, 'start': 16, 'end': 19, 'properties': {}}, {'id': 34, 'start': 17, 'end': 19, 'properties': {}}, {'id': 35, 'start': 18, 'end': 19, 'properties': {}}\n",
+ "]\n",
+ "w.directed = True"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "18e38511-c624-48ef-b386-964fed8e669b",
+ "metadata": {
+ "id": "18e38511-c624-48ef-b386-964fed8e669b"
+ },
+ "source": [
+ "We will work with this graph:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "a5f66fea-8935-40a5-88dc-6587636ff87e",
+ "metadata": {
+ "id": "a5f66fea-8935-40a5-88dc-6587636ff87e",
+ "outputId": "312f3030-bd10-47ba-fd87-ecc0db374030",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 717,
+ "referenced_widgets": [
+ "d2e8ec9345bf4a89873812c53b8a0daa",
+ "71ea6d5cb33e4b8890b6220a76667783",
+ "88716a0dd5a64a599202eb3b6ff1783e",
+ "5445a9c311014a19b606efccdb4809ff"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='700px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "d2e8ec9345bf4a89873812c53b8a0daa"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "display(w)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7b9e0269-aa70-4d8c-9cc8-8dfabce121a0",
+ "metadata": {
+ "id": "7b9e0269-aa70-4d8c-9cc8-8dfabce121a0"
+ },
+ "source": [
+ "## Mapping function\n",
+ "\n",
+ "The node type mapping is a function that is supposed to return a value for each given node object that is considered as its `type`.\n",
+ "\n",
+ "Optionally, the index can be used as the first function parameter.\n",
+ "\n",
+ "Let's assign the type as specified in properties:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "6148c0ba-405b-4325-b0e4-290ea852e56a",
+ "metadata": {
+ "id": "6148c0ba-405b-4325-b0e4-290ea852e56a"
+ },
+ "outputs": [],
+ "source": [
+ "def custom_node_type_mapping(node: Dict):\n",
+ " \"\"\"assign type accordingly\"\"\"\n",
+ " return node['properties']['type']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7b9fe48c-e1d8-4b6c-bd2f-ac1283a34d55",
+ "metadata": {
+ "id": "7b9fe48c-e1d8-4b6c-bd2f-ac1283a34d55"
+ },
+ "source": [
+ "### Custom node mappings\n",
+ "\n",
+ "There are get and set methods for each customizable node property.\n",
+ "- you can set a new node mapping with ```w.set_node_[binding]_mapping```\n",
+ "- you can get the current node mapping with ```w.get_node_[binding]_mapping```\n",
+ "- you can delete a custom node mapping with ```w.del_node_[binding]_mapping```\n",
+ "\n",
+ "You can find more details in the dedicated function documentation, available at ```w.[function_name].__doc__``` or in the [documentation](https://yworks.github.io/yfiles-jupyter-graphs/02_graph_widget/#methods).\n",
+ "\n",
+ "If no custom mapping is set the default mappings are used."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "fd420dad-31a1-4b1d-bd98-28861d859698",
+ "metadata": {
+ "id": "fd420dad-31a1-4b1d-bd98-28861d859698",
+ "outputId": "dab3e540-63fb-493d-f654-ff258730e677",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "The default type mapping for nodes.\n",
+ "\n",
+ " Provides the mapped node color to distinguish different node types\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " index: int (optional)\n",
+ " node: typing.Dict\n",
+ "\n",
+ " Notes\n",
+ " -----\n",
+ " This is the default value for the `node_type_mapping` property.\n",
+ " Can be 'overwritten' by setting the property\n",
+ " with a function of the same signature.\n",
+ "\n",
+ " If the given mapping function has only one parameter (that is not typed as int),\n",
+ " then it will be called with the element (typing.Dict) as first parameter.\n",
+ "\n",
+ " Example\n",
+ " -------\n",
+ " .. code::\n",
+ "\n",
+ " from yfiles_jupyter_graphs import GraphWidget\n",
+ " w = GraphWidget()\n",
+ " def custom_node_type_mapping(node: typing.Dict):\n",
+ " ...\n",
+ " w.set_node_type_mapping(custom_node_type_mapping)\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " type: None\n",
+ "\n",
+ " \n"
+ ]
+ }
+ ],
+ "source": [
+ "print(w.node_type_mapping.__doc__)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "7b6a071d-b8fa-4ac4-a29f-2ee914cdc5e1",
+ "metadata": {
+ "id": "7b6a071d-b8fa-4ac4-a29f-2ee914cdc5e1",
+ "outputId": "12b28263-a525-4427-fdb0-0c4ce1de357e",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 186
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "yfiles_jupyter_graphs.widget.GraphWidget.default_node_type_mapping
def default_node_type_mapping(index: int, node: TDict)
The default type mapping for nodes.\n", + "\n", + "Provides the mapped node color to distinguish different node types\n", + "\n", + "Parameters\n", + "----------\n", + "index: int (optional)\n", + "node: typing.Dict\n", + "\n", + "Notes\n", + "-----\n", + "This is the default value for the `node_type_mapping` property.\n", + "Can be 'overwritten' by setting the property\n", + "with a function of the same signature.\n", + "\n", + "If the given mapping function has only one parameter (that is not typed as int),\n", + "then it will be called with the element (typing.Dict) as first parameter.\n", + "\n", + "Example\n", + "-------\n", + ".. code::\n", + "\n", + " from yfiles_jupyter_graphs import GraphWidget\n", + " w = GraphWidget()\n", + " def custom_node_type_mapping(node: typing.Dict):\n", + " ...\n", + " w.set_node_type_mapping(custom_node_type_mapping)\n", + "\n", + "Returns\n", + "-------\n", + "type: None\n", + " \n", + "
custom_node_type_mapping
def custom_node_type_mapping(node: Dict)
assign type accordingly
custom_node_type_mapping
def custom_node_type_mapping(node: Dict)
assign type accordingly
yfiles_jupyter_graphs.widget.GraphWidget.default_node_type_mapping
def default_node_type_mapping(index: int, node: TDict)
The default type mapping for nodes.\n", + "\n", + "Provides the mapped node color to distinguish different node types\n", + "\n", + "Parameters\n", + "----------\n", + "index: int (optional)\n", + "node: typing.Dict\n", + "\n", + "Notes\n", + "-----\n", + "This is the default value for the `node_type_mapping` property.\n", + "Can be 'overwritten' by setting the property\n", + "with a function of the same signature.\n", + "\n", + "If the given mapping function has only one parameter (that is not typed as int),\n", + "then it will be called with the element (typing.Dict) as first parameter.\n", + "\n", + "Example\n", + "-------\n", + ".. code::\n", + "\n", + " from yfiles_jupyter_graphs import GraphWidget\n", + " w = GraphWidget()\n", + " def custom_node_type_mapping(node: typing.Dict):\n", + " ...\n", + " w.set_node_type_mapping(custom_node_type_mapping)\n", + "\n", + "Returns\n", + "-------\n", + "type: None\n", + " \n", + "
"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f3b74f7b-8b05-4bad-95db-6ead02b4d552",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.nodes = [\n",
- " {'id': 0, 'properties': {'type': 0}},\n",
- " {'id': 1, 'properties': {'type': 1}}, {'id': 2, 'properties': {'type': 1}}, {'id': 3, 'properties': {'type': 1}}, {'id': 4, 'properties': {'type': 1}}, {'id': 5, 'properties': {'type': 1}}, {'id': 6, 'properties': {'type': 1}},\n",
- " {'id': 7, 'properties': {'type': 2}}, {'id': 8, 'properties': {'type': 2}}, {'id': 9, 'properties': {'type': 2}}, {'id': 10, 'properties': {'type': 2}}, {'id': 11, 'properties': {'type': 2}}, {'id': 12, 'properties': {'type': 2}},\n",
- " {'id': 13, 'properties': {'type': 3}}, {'id': 14, 'properties': {'type': 3}}, {'id': 15, 'properties': {'type': 3}}, {'id': 16, 'properties': {'type': 3}}, {'id': 17, 'properties': {'type': 3}}, {'id': 18, 'properties': {'type': 3}},\n",
- " {'id': 19, 'properties': {'type': 4}}\n",
- "]\n",
- "w.edges = [\n",
- " {'id': 0, 'start': 0, 'end': 1, 'properties': {}}, {'id': 1, 'start': 0, 'end': 2, 'properties': {}}, {'id': 2, 'start': 0, 'end': 3, 'properties': {}},\n",
- " {'id': 3, 'start': 0, 'end': 4, 'properties': {}}, {'id': 4, 'start': 0, 'end': 5, 'properties': {}}, {'id': 5, 'start': 0, 'end': 6, 'properties': {}},\n",
- " {'id': 6, 'start': 0, 'end': 7, 'properties': {}}, {'id': 7, 'start': 0, 'end': 8, 'properties': {}}, {'id': 8, 'start': 0, 'end': 9, 'properties': {}},\n",
- " {'id': 9, 'start': 0, 'end': 10, 'properties': {}}, {'id': 10, 'start': 0, 'end': 11, 'properties': {}}, {'id': 11, 'start': 0, 'end': 12, 'properties': {}},\n",
- " {'id': 12, 'start': 0, 'end': 13, 'properties': {}}, {'id': 13, 'start': 0, 'end': 14, 'properties': {}}, {'id': 14, 'start': 0, 'end': 15, 'properties': {}},\n",
- " {'id': 15, 'start': 0, 'end': 16, 'properties': {}}, {'id': 16, 'start': 0, 'end': 17, 'properties': {}}, {'id': 17, 'start': 0, 'end': 18, 'properties': {}},\n",
- " {'id': 18, 'start': 1, 'end': 19, 'properties': {}}, {'id': 19, 'start': 2, 'end': 19, 'properties': {}}, {'id': 20, 'start': 3, 'end': 19, 'properties': {}},\n",
- " {'id': 21, 'start': 4, 'end': 19, 'properties': {}}, {'id': 22, 'start': 5, 'end': 19, 'properties': {}}, {'id': 23, 'start': 6, 'end': 19, 'properties': {}},\n",
- " {'id': 24, 'start': 7, 'end': 19, 'properties': {}}, {'id': 25, 'start': 8, 'end': 19, 'properties': {}}, {'id': 26, 'start': 9, 'end': 19, 'properties': {}},\n",
- " {'id': 27, 'start': 10, 'end': 19, 'properties': {}}, {'id': 28, 'start': 11, 'end': 19, 'properties': {}}, {'id': 29, 'start': 12, 'end': 19, 'properties': {}},\n",
- " {'id': 30, 'start': 13, 'end': 19, 'properties': {}}, {'id': 31, 'start': 14, 'end': 19, 'properties': {}}, {'id': 32, 'start': 15, 'end': 19, 'properties': {}},\n",
- " {'id': 33, 'start': 16, 'end': 19, 'properties': {}}, {'id': 34, 'start': 17, 'end': 19, 'properties': {}}, {'id': 35, 'start': 18, 'end': 19, 'properties': {}}\n",
- "]\n",
- "w.directed = True"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "18e38511-c624-48ef-b386-964fed8e669b",
- "metadata": {},
- "source": [
- "We will work with this graph:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a5f66fea-8935-40a5-88dc-6587636ff87e",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7b9e0269-aa70-4d8c-9cc8-8dfabce121a0",
- "metadata": {},
- "source": [
- "## Mapping function\n",
- "\n",
- "The node type mapping is a function that is supposed to return a value for each given node object that is considered as its `type`.\n",
- "\n",
- "Optionally, the index can be used as the first function parameter.\n",
- "\n",
- "Let's assign the type as specified in properties:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6148c0ba-405b-4325-b0e4-290ea852e56a",
- "metadata": {},
- "outputs": [],
- "source": [
- "def custom_node_type_mapping(node: Dict):\n",
- " \"\"\"assign type accordingly\"\"\"\n",
- " return node['properties']['type']"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7b9fe48c-e1d8-4b6c-bd2f-ac1283a34d55",
- "metadata": {},
- "source": [
- "### Custom node mappings\n",
- "\n",
- "There are get and set methods for each customizable node property.\n",
- "- you can set a new node mapping with ```w.set_node_[binding]_mapping```\n",
- "- you can get the current node mapping with ```w.get_node_[binding]_mapping```\n",
- "- you can delete a custom node mapping with ```w.del_node_[binding]_mapping```\n",
- "\n",
- "You can find more details in the dedicated function documentation, available at ```w.[function_name].__doc__``` or in the [documentation](https://yworks.github.io/yfiles-jupyter-graphs/02_graph_widget/#methods).\n",
- "\n",
- "If no custom mapping is set the default mappings are used. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fd420dad-31a1-4b1d-bd98-28861d859698",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(w.node_type_mapping.__doc__)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7b6a071d-b8fa-4ac4-a29f-2ee914cdc5e1",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.get_node_type_mapping()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a3c026cb-f807-4f48-b5b1-e3f53381bab4",
- "metadata": {},
- "source": [
- "Let's set the new type mapping: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "99dcc6fb-0296-4256-bee8-69f918617028",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.set_node_type_mapping(custom_node_type_mapping)\n",
- "w.get_node_type_mapping()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7b0dd16a-bf95-40ba-8d66-d00667e27f2d",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b22ae496-c6b6-473b-ac64-be2578006021",
- "metadata": {},
- "source": [
- "To enhance the optical seperation of types we can assign each type a different color. \\\n",
- "Check out [03_color_mapping.ipynb](./03_color_mapping.ipynb), to get more information on the node color mapping."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "110fcecc-2cd7-4ffd-aa58-7569ae1de44a",
- "metadata": {},
- "outputs": [],
- "source": [
- "colors = [\"#17bebb\", \"#ffc914\", \"#0b7189\", \"#ff6c00\", '#76b041']\n",
- "\n",
- "def custom_node_color_mapping(node: Dict):\n",
- " \"\"\"assign colors based on type\"\"\"\n",
- " return colors[node['properties']['type']]\n",
- "\n",
- "w.set_node_color_mapping(custom_node_color_mapping)\n",
- "w.get_node_type_mapping()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "077f46ef-64ac-4582-95e2-da18252a14db",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9421f3dc-61ff-4144-b53e-4bdc1a90c6e0",
- "metadata": {},
- "source": [
- "If a node type mapping is deleted, the layout mapping reverts back to the default mapping."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "07caa339-a63f-467c-8d8c-7b422948a632",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.del_node_type_mapping()\n",
- "w.get_node_type_mapping()"
- ]
- }
- ],
- "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": "3.11.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/10_direction_mapping.ipynb b/examples/10_direction_mapping.ipynb
index 1996629..4e80b40 100644
--- a/examples/10_direction_mapping.ipynb
+++ b/examples/10_direction_mapping.ipynb
@@ -1,238 +1,1409 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "70865006-0292-4cdf-a82e-f34b1a0376f5",
- "metadata": {},
- "source": [
- "# Edge Direction Mapping "
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "70865006-0292-4cdf-a82e-f34b1a0376f5",
+ "metadata": {
+ "id": "70865006-0292-4cdf-a82e-f34b1a0376f5"
+ },
+ "source": [
+ "# Edge Direction Mapping "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "581c3ddd-7a60-4842-a4e2-c3af65d4d9b4",
+ "metadata": {
+ "id": "581c3ddd-7a60-4842-a4e2-c3af65d4d9b4"
+ },
+ "source": [
+ "This notebook covers the basics of customizing edge directions.\n",
+ "\n",
+ "A direction is a boolean, it can only have one of two values:\n",
+ "- `True` (The edge is directed from start to end)\n",
+ "- `False` (The edge is undirected)\n",
+ "\n",
+ "For the purpose of mapping demonstrations, the same graph, ```erdos_renyi_graph```, will be used. For this, we will import the graph from the NetworkX package. \\\n",
+ "For more details on how to import graph data, explore the other example notebooks or refer to the full widget [documentation](https://yworks.github.io/yfiles-jupyter-graphs/).\n",
+ "\n",
+ "Before using the graph widget, install all necessary packages."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "398b6aa8-c4ed-4870-a469-bc1b4e7b7e19",
+ "metadata": {
+ "id": "398b6aa8-c4ed-4870-a469-bc1b4e7b7e19",
+ "outputId": "291501ef-15c4-4f08-d72e-e491cb65f945",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 92
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m39.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m17.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m25.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "from yfiles_jupyter_graphs import GraphWidget\n",
+ "%pip install networkx --quiet\n",
+ "from typing import Dict\n",
+ "from networkx import erdos_renyi_graph\n",
+ "from random import random, seed\n",
+ "seed(0)\n",
+ "\n",
+ "g = erdos_renyi_graph(10, 0.3, 2)\n",
+ "w = GraphWidget(graph=g)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0b575a84-7aaa-46cf-853d-5de53ea437d3",
+ "metadata": {
+ "id": "0b575a84-7aaa-46cf-853d-5de53ea437d3"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "93c169ac-0019-4a89-84d3-c4b602bbc153",
+ "metadata": {
+ "id": "93c169ac-0019-4a89-84d3-c4b602bbc153"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d34c3b6c-1da4-444b-9c18-42fc5279572c",
+ "metadata": {
+ "id": "d34c3b6c-1da4-444b-9c18-42fc5279572c"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1e84ed97-cff3-49ac-a4cc-eb6aba76f1f7",
+ "metadata": {
+ "id": "1e84ed97-cff3-49ac-a4cc-eb6aba76f1f7"
+ },
+ "source": [
+ "This is the graph we will be working with:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "d7cafa9b-b5fd-47ae-b3b6-01a883520eca",
+ "metadata": {
+ "id": "d7cafa9b-b5fd-47ae-b3b6-01a883520eca",
+ "outputId": "81720e60-d1c3-41cb-9755-cba25727750a",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 517,
+ "referenced_widgets": [
+ "4c64a8c9bc3347d5be625d3faa4258b9",
+ "4fbd92119b6b4a93b00468de614ff45b"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='500px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "4c64a8c9bc3347d5be625d3faa4258b9"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "display(GraphWidget(graph=g))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "51174db6-5416-4a04-887e-1235a0c45382",
+ "metadata": {
+ "id": "51174db6-5416-4a04-887e-1235a0c45382"
+ },
+ "source": [
+ "## Mapping function\n",
+ "\n",
+ "The edge direction mapping is a function that determines for each edge whether it should visualize its direction.\n",
+ "\n",
+ "If the index is used, it can be optionally given as the first function parameter.\n",
+ "\n",
+ "We will randomly direct some edges in the graph.\\\n",
+ "For this we first define a new mapping function and then set this function as our current direction mapping."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "0d918c4a-5745-497a-a42b-55efda0b40c9",
+ "metadata": {
+ "id": "0d918c4a-5745-497a-a42b-55efda0b40c9"
+ },
+ "outputs": [],
+ "source": [
+ "def custom_directed_mapping(edge: Dict):\n",
+ " \"\"\"choose randomly if edge is directed\"\"\"\n",
+ " return random() < 0.5"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9a779ab1-fed3-45bd-952e-c5642928aa9a",
+ "metadata": {
+ "id": "9a779ab1-fed3-45bd-952e-c5642928aa9a"
+ },
+ "source": [
+ "### Custom edge mappings\n",
+ "\n",
+ "There are get and set methods for each customizable edge property.\n",
+ "- you can set a new edge mapping with ```w.set_edge_[binding]_mapping```\n",
+ "- you can get the current edge mapping with ```w.get_edge_[binding]_mapping```\n",
+ "- you can delete a custom edge mapping with ```w.del_edge_[binding]_mapping```\n",
+ "\n",
+ "You can find more details in the dedicated function documentation, available at ```w.[function_name].__doc__``` or in the [documentation](https://yworks.github.io/yfiles-jupyter-graphs/02_graph_widget/#methods).\n",
+ "\n",
+ "If no custom mapping is set the default mappings are used."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "4ab2314d-c58a-43d1-9955-5216b21ed4fd",
+ "metadata": {
+ "id": "4ab2314d-c58a-43d1-9955-5216b21ed4fd",
+ "outputId": "26af8fd9-c2b7-4f96-ce94-f11aa762dee6",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "The default directed mapping for edges.\n",
+ "\n",
+ " Uses the graph wide directed attribute for all edges.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " index: int (optional)\n",
+ " edge: typing.Dict\n",
+ "\n",
+ " Notes\n",
+ " -----\n",
+ " This is the default value for the `directed_mapping` property.\n",
+ " Can be 'overwritten' by setting the property\n",
+ " with a function of the same signature.\n",
+ "\n",
+ " If the given mapping function has only one parameter (that is not typed as int),\n",
+ " then it will be called with the element (typing.Dict) as first parameter.\n",
+ "\n",
+ " Example\n",
+ " -------\n",
+ " .. code::\n",
+ "\n",
+ " from yfiles_jupyter_graphs import GraphWidget\n",
+ " w = GraphWidget()\n",
+ " def custom_directed_mapping(edge: typing.Dict):\n",
+ " ...\n",
+ " w.set_directed_mapping(custom_directed_mapping)\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " directed: bool\n",
+ "\n",
+ " \n"
+ ]
+ }
+ ],
+ "source": [
+ "print(w.directed_mapping.__doc__)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "bc44f305-b640-41fc-a013-4631aebbc67d",
+ "metadata": {
+ "id": "bc44f305-b640-41fc-a013-4631aebbc67d",
+ "outputId": "2bb2ba79-1e2f-4006-e8f8-0ca11e4791d3",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 188
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "yfiles_jupyter_graphs.widget.GraphWidget.default_directed_mapping
def default_directed_mapping(index: int, edge: TDict)
The default directed mapping for edges.\n", + "\n", + "Uses the graph wide directed attribute for all edges.\n", + "\n", + "Parameters\n", + "----------\n", + "index: int (optional)\n", + "edge: typing.Dict\n", + "\n", + "Notes\n", + "-----\n", + "This is the default value for the `directed_mapping` property.\n", + "Can be 'overwritten' by setting the property\n", + "with a function of the same signature.\n", + "\n", + "If the given mapping function has only one parameter (that is not typed as int),\n", + "then it will be called with the element (typing.Dict) as first parameter.\n", + "\n", + "Example\n", + "-------\n", + ".. code::\n", + "\n", + " from yfiles_jupyter_graphs import GraphWidget\n", + " w = GraphWidget()\n", + " def custom_directed_mapping(edge: typing.Dict):\n", + " ...\n", + " w.set_directed_mapping(custom_directed_mapping)\n", + "\n", + "Returns\n", + "-------\n", + "directed: bool\n", + " \n", + "
custom_directed_mapping
def custom_directed_mapping(edge: Dict)
choose randomly if edge is directed
yfiles_jupyter_graphs.widget.GraphWidget.default_directed_mapping
def default_directed_mapping(index: int, edge: TDict)
The default directed mapping for edges.\n", + "\n", + "Uses the graph wide directed attribute for all edges.\n", + "\n", + "Parameters\n", + "----------\n", + "index: int (optional)\n", + "edge: typing.Dict\n", + "\n", + "Notes\n", + "-----\n", + "This is the default value for the `directed_mapping` property.\n", + "Can be 'overwritten' by setting the property\n", + "with a function of the same signature.\n", + "\n", + "If the given mapping function has only one parameter (that is not typed as int),\n", + "then it will be called with the element (typing.Dict) as first parameter.\n", + "\n", + "Example\n", + "-------\n", + ".. code::\n", + "\n", + " from yfiles_jupyter_graphs import GraphWidget\n", + " w = GraphWidget()\n", + " def custom_directed_mapping(edge: typing.Dict):\n", + " ...\n", + " w.set_directed_mapping(custom_directed_mapping)\n", + "\n", + "Returns\n", + "-------\n", + "directed: bool\n", + " \n", + "
"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1e84ed97-cff3-49ac-a4cc-eb6aba76f1f7",
- "metadata": {},
- "source": [
- "This is the graph we will be working with: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d7cafa9b-b5fd-47ae-b3b6-01a883520eca",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "51174db6-5416-4a04-887e-1235a0c45382",
- "metadata": {},
- "source": [
- "## Mapping function\n",
- "\n",
- "The edge direction mapping is a function that determines for each edge whether it should visualize its direction.\n",
- "\n",
- "If the index is used, it can be optionally given as the first function parameter. \n",
- "\n",
- "We will randomly direct some edges in the graph.\\\n",
- "For this we first define a new mapping function and then set this function as our current direction mapping."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0d918c4a-5745-497a-a42b-55efda0b40c9",
- "metadata": {},
- "outputs": [],
- "source": [
- "def custom_directed_mapping(edge: Dict):\n",
- " \"\"\"choose randomly if edge is directed\"\"\"\n",
- " return random() < 0.5"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9a779ab1-fed3-45bd-952e-c5642928aa9a",
- "metadata": {},
- "source": [
- "### Custom edge mappings\n",
- "\n",
- "There are get and set methods for each customizable edge property.\n",
- "- you can set a new edge mapping with ```w.set_edge_[binding]_mapping```\n",
- "- you can get the current edge mapping with ```w.get_edge_[binding]_mapping```\n",
- "- you can delete a custom edge mapping with ```w.del_edge_[binding]_mapping```\n",
- "\n",
- "You can find more details in the dedicated function documentation, available at ```w.[function_name].__doc__``` or in the [documentation](https://yworks.github.io/yfiles-jupyter-graphs/02_graph_widget/#methods).\n",
- "\n",
- "If no custom mapping is set the default mappings are used."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4ab2314d-c58a-43d1-9955-5216b21ed4fd",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(w.directed_mapping.__doc__)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "bc44f305-b640-41fc-a013-4631aebbc67d",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.get_directed_mapping()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fb7d9982-8773-4b15-8762-0eec2fc94b58",
- "metadata": {},
- "source": [
- "Let's change the direction of some edges: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "cb210e8c-f561-4497-8bfe-df3f8828d4fb",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.set_directed_mapping(custom_directed_mapping)\n",
- "w.get_directed_mapping()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3b65b05a-3bda-4aa5-90a6-10aa8208ddf1",
- "metadata": {},
- "source": [
- "You may have to zoom in to clearly differentiate the directed from the undirected edges"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b8e47f79-044a-4cff-91ca-05e82b5a67a5",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "cb0eed96-e523-4875-b0dd-d68a08555603",
- "metadata": {},
- "source": [
- "If a edge direction mapping is deleted, the direction mapping reverts back to the default mapping."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3f953cac-c781-43e4-9b43-2b8a5537a384",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.del_directed_mapping()\n",
- "w.get_directed_mapping()"
- ]
- }
- ],
- "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": "3.11.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/11_thickness_mapping.ipynb b/examples/11_thickness_mapping.ipynb
index ace330e..101f715 100644
--- a/examples/11_thickness_mapping.ipynb
+++ b/examples/11_thickness_mapping.ipynb
@@ -1,220 +1,1387 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "99e52022-4e7f-49b7-a61f-20dcd1391719",
- "metadata": {},
- "source": [
- "# Edge Thickness Factor Mapping \n",
- "\n",
- "This notebook covers the basics of customizing the edge thickness factor.\n",
- "\n",
- "For the purpose of mapping demonstrations, the same graph, ```erdos_renyi_graph```, will be used. For this, we will import the graph from the NetworkX package. \\\n",
- "For more details on how to import graph data, explore the other example notebooks or refer to the full widget [documentation](https://yworks.github.io/yfiles-jupyter-graphs/). \n",
- "\n",
- "Before using the graph widget, install all necessary packages."
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "99e52022-4e7f-49b7-a61f-20dcd1391719",
+ "metadata": {
+ "id": "99e52022-4e7f-49b7-a61f-20dcd1391719"
+ },
+ "source": [
+ "# Edge Thickness Factor Mapping \n",
+ "\n",
+ "This notebook covers the basics of customizing the edge thickness factor.\n",
+ "\n",
+ "For the purpose of mapping demonstrations, the same graph, ```erdos_renyi_graph```, will be used. For this, we will import the graph from the NetworkX package. \\\n",
+ "For more details on how to import graph data, explore the other example notebooks or refer to the full widget [documentation](https://yworks.github.io/yfiles-jupyter-graphs/).\n",
+ "\n",
+ "Before using the graph widget, install all necessary packages."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "0d390b87-a3c4-4364-8bd8-c0d786a98a42",
+ "metadata": {
+ "id": "0d390b87-a3c4-4364-8bd8-c0d786a98a42",
+ "outputId": "bc06ea67-49ae-4617-86c5-94755ec70d5f",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 92
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m34.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m20.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m35.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "from yfiles_jupyter_graphs import GraphWidget\n",
+ "%pip install networkx --quiet\n",
+ "from typing import Dict\n",
+ "from networkx import erdos_renyi_graph\n",
+ "from random import random, seed\n",
+ "seed(0)\n",
+ "\n",
+ "g = erdos_renyi_graph(10, 0.3, 2)\n",
+ "w = GraphWidget(graph=g)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8b19b29c-903f-4eea-b1a1-cad0618b7589",
+ "metadata": {
+ "id": "8b19b29c-903f-4eea-b1a1-cad0618b7589"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "02024196-72ea-4481-b832-a04fdf5724e5",
+ "metadata": {
+ "id": "02024196-72ea-4481-b832-a04fdf5724e5"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f01b692b-7dab-46b7-ab92-3fe7dc31a6fb",
+ "metadata": {
+ "id": "f01b692b-7dab-46b7-ab92-3fe7dc31a6fb"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6cf50fe-6251-4993-89d0-cd1da76945b4",
+ "metadata": {
+ "id": "b6cf50fe-6251-4993-89d0-cd1da76945b4"
+ },
+ "source": [
+ "This is the graph we will be working on:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "62d381bd-271b-40c5-b1bf-2973f4e28e4b",
+ "metadata": {
+ "id": "62d381bd-271b-40c5-b1bf-2973f4e28e4b",
+ "outputId": "72fc3bc3-7138-4e66-dcf5-0f54b2357cab",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 517,
+ "referenced_widgets": [
+ "b16658beba7740d99badf44eeb6e59ce",
+ "0493abe556f641629cc66383060d53e9"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='500px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "b16658beba7740d99badf44eeb6e59ce"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "display(GraphWidget(graph=g))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fc4ec447-7424-4a11-bf8a-a167fcc5d1ef",
+ "metadata": {
+ "id": "fc4ec447-7424-4a11-bf8a-a167fcc5d1ef"
+ },
+ "source": [
+ "## Mapping function\n",
+ "\n",
+ "The edge thickness factor mapping is a function that is supposed to return a thickness factor for each given edge object which is then used in the widget.\n",
+ "\n",
+ "If the index is used, it can be optionally given as the first function parameter.\n",
+ "\n",
+ "We will randomly change the thickness of edges.\n",
+ "For this we first define a new mapping function and then set this function as our current thickness factor mapping."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "badccc44-2896-45fc-b3be-4a875c6e3a55",
+ "metadata": {
+ "id": "badccc44-2896-45fc-b3be-4a875c6e3a55"
+ },
+ "outputs": [],
+ "source": [
+ "def custom_factor_mapping(node: Dict):\n",
+ " \"\"\"choose random factor\"\"\"\n",
+ " return 2 * random()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "686bbf44-f138-4951-a7f7-f07b5b9a485b",
+ "metadata": {
+ "id": "686bbf44-f138-4951-a7f7-f07b5b9a485b"
+ },
+ "source": [
+ "### Custom edge mappings\n",
+ "\n",
+ "There are get and set methods for each customizable edge property.\n",
+ "- you can set a new edge mapping with ```w.set_edge_[binding]_mapping```\n",
+ "- you can get the current edge mapping with ```w.get_edge_[binding]_mapping```\n",
+ "- you can delete a custom edge mapping with ```w.del_edge_[binding]_mapping```\n",
+ "\n",
+ "You can find more details in the dedicated function documentation, available at ```w.[function_name].__doc__``` or in the [documentation](https://yworks.github.io/yfiles-jupyter-graphs/02_graph_widget/#methods).\n",
+ "\n",
+ "If no custom mapping is set the default mappings are used."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "a0936353-5e1f-4459-b9a2-944909d14337",
+ "metadata": {
+ "id": "a0936353-5e1f-4459-b9a2-944909d14337",
+ "outputId": "00d1eefc-05b3-4fbe-c5a5-775c1a2ad7a0",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "The default thickness factor mapping for edges.\n",
+ "\n",
+ " Provides constant value of 1.0 for all edges.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " index: int (optional)\n",
+ " edge: typing.Dict\n",
+ "\n",
+ " Notes\n",
+ " -----\n",
+ " This is the default value for the `edge_thickness_factor_mapping` property.\n",
+ " Can be 'overwritten' by setting the property\n",
+ " with a function of the same signature.\n",
+ "\n",
+ " If the given mapping function has only one parameter (that is not typed as int),\n",
+ " then it will be called with the element (typing.Dict) as first parameter.\n",
+ "\n",
+ " Example\n",
+ " -------\n",
+ " .. code::\n",
+ "\n",
+ " from yfiles_jupyter_graphs import GraphWidget\n",
+ " w = GraphWidget()\n",
+ " def custom_edge_thickness_factor_mapping(edge: typing.Dict):\n",
+ " ...\n",
+ " w.set_edge_thickness_factor_mapping(custom_edge_thickness_factor_mapping)\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " edge_thickness_factor: float\n",
+ "\n",
+ " \n"
+ ]
+ }
+ ],
+ "source": [
+ "print(w.edge_thickness_factor_mapping.__doc__)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "b9c0fcc7-55b4-495b-b103-f5bea2178220",
+ "metadata": {
+ "id": "b9c0fcc7-55b4-495b-b103-f5bea2178220",
+ "outputId": "7628349a-2eb1-4493-9bdd-f922df77d396",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 188
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "yfiles_jupyter_graphs.widget.GraphWidget.default_edge_thickness_factor_mapping
def default_edge_thickness_factor_mapping(index: int, edge: TDict)
The default thickness factor mapping for edges.\n", + "\n", + "Provides constant value of 1.0 for all edges.\n", + "\n", + "Parameters\n", + "----------\n", + "index: int (optional)\n", + "edge: typing.Dict\n", + "\n", + "Notes\n", + "-----\n", + "This is the default value for the `edge_thickness_factor_mapping` property.\n", + "Can be 'overwritten' by setting the property\n", + "with a function of the same signature.\n", + "\n", + "If the given mapping function has only one parameter (that is not typed as int),\n", + "then it will be called with the element (typing.Dict) as first parameter.\n", + "\n", + "Example\n", + "-------\n", + ".. code::\n", + "\n", + " from yfiles_jupyter_graphs import GraphWidget\n", + " w = GraphWidget()\n", + " def custom_edge_thickness_factor_mapping(edge: typing.Dict):\n", + " ...\n", + " w.set_edge_thickness_factor_mapping(custom_edge_thickness_factor_mapping)\n", + "\n", + "Returns\n", + "-------\n", + "edge_thickness_factor: float\n", + " \n", + "
custom_factor_mapping
def custom_factor_mapping(node: Dict)
choose random factor
yfiles_jupyter_graphs.widget.GraphWidget.default_edge_thickness_factor_mapping
def default_edge_thickness_factor_mapping(index: int, edge: TDict)
The default thickness factor mapping for edges.\n", + "\n", + "Provides constant value of 1.0 for all edges.\n", + "\n", + "Parameters\n", + "----------\n", + "index: int (optional)\n", + "edge: typing.Dict\n", + "\n", + "Notes\n", + "-----\n", + "This is the default value for the `edge_thickness_factor_mapping` property.\n", + "Can be 'overwritten' by setting the property\n", + "with a function of the same signature.\n", + "\n", + "If the given mapping function has only one parameter (that is not typed as int),\n", + "then it will be called with the element (typing.Dict) as first parameter.\n", + "\n", + "Example\n", + "-------\n", + ".. code::\n", + "\n", + " from yfiles_jupyter_graphs import GraphWidget\n", + " w = GraphWidget()\n", + " def custom_edge_thickness_factor_mapping(edge: typing.Dict):\n", + " ...\n", + " w.set_edge_thickness_factor_mapping(custom_edge_thickness_factor_mapping)\n", + "\n", + "Returns\n", + "-------\n", + "edge_thickness_factor: float\n", + " \n", + "
"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b6cf50fe-6251-4993-89d0-cd1da76945b4",
- "metadata": {},
- "source": [
- "This is the graph we will be working on: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "62d381bd-271b-40c5-b1bf-2973f4e28e4b",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fc4ec447-7424-4a11-bf8a-a167fcc5d1ef",
- "metadata": {},
- "source": [
- "## Mapping function\n",
- "\n",
- "The edge thickness factor mapping is a function that is supposed to return a thickness factor for each given edge object which is then used in the widget.\n",
- "\n",
- "If the index is used, it can be optionally given as the first function parameter. \n",
- "\n",
- "We will randomly change the thickness of edges.\n",
- "For this we first define a new mapping function and then set this function as our current thickness factor mapping."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "badccc44-2896-45fc-b3be-4a875c6e3a55",
- "metadata": {},
- "outputs": [],
- "source": [
- "def custom_factor_mapping(node: Dict):\n",
- " \"\"\"choose random factor\"\"\"\n",
- " return 2 * random()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "686bbf44-f138-4951-a7f7-f07b5b9a485b",
- "metadata": {},
- "source": [
- "### Custom edge mappings\n",
- "\n",
- "There are get and set methods for each customizable edge property.\n",
- "- you can set a new edge mapping with ```w.set_edge_[binding]_mapping```\n",
- "- you can get the current edge mapping with ```w.get_edge_[binding]_mapping```\n",
- "- you can delete a custom edge mapping with ```w.del_edge_[binding]_mapping```\n",
- "\n",
- "You can find more details in the dedicated function documentation, available at ```w.[function_name].__doc__``` or in the [documentation](https://yworks.github.io/yfiles-jupyter-graphs/02_graph_widget/#methods).\n",
- "\n",
- "If no custom mapping is set the default mappings are used."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a0936353-5e1f-4459-b9a2-944909d14337",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(w.edge_thickness_factor_mapping.__doc__)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b9c0fcc7-55b4-495b-b103-f5bea2178220",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.get_edge_thickness_factor_mapping()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b961fcd0-d4d6-44db-ab8a-e7f8d1f69fb4",
- "metadata": {},
- "source": [
- "Let's change the thickness factor:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8cb56a04-1351-4e4a-8e84-f38e8ac7ce96",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.set_edge_thickness_factor_mapping(custom_factor_mapping)\n",
- "w.get_edge_thickness_factor_mapping()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e321a3bf-aefe-4e55-8f36-b199f956ed53",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f685e88c-2bcb-4191-9603-fbc9621768ed",
- "metadata": {},
- "source": [
- "If a edge thickness factor mapping is deleted, the thickness factor mapping reverts back to the default mapping."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c1e5dd8c-89d9-4d64-a3a4-a3779c4f2dc5",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.del_edge_thickness_factor_mapping()\n",
- "w.get_edge_thickness_factor_mapping()"
- ]
- }
- ],
- "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": "3.11.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/12_mapping_overloads.ipynb b/examples/12_mapping_overloads.ipynb
index 340f59d..c78db07 100644
--- a/examples/12_mapping_overloads.ipynb
+++ b/examples/12_mapping_overloads.ipynb
@@ -1,331 +1,1644 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "7ca6fa41-0560-4136-a3fa-1ca1e637cb6b",
- "metadata": {},
- "source": [
- "# Mapping Overloads "
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "7ca6fa41-0560-4136-a3fa-1ca1e637cb6b",
+ "metadata": {
+ "id": "7ca6fa41-0560-4136-a3fa-1ca1e637cb6b"
+ },
+ "source": [
+ "# Mapping Overloads "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3c667362-1228-4d03-b2a3-d49537d8d9b4",
+ "metadata": {
+ "id": "3c667362-1228-4d03-b2a3-d49537d8d9b4"
+ },
+ "source": [
+ "This notebook covers the different methods to change node or edge mappings.\n",
+ "\n",
+ "Before using the graph widget, install all necessary packages."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "7304ae55-3dc2-4a77-8275-f44a8122601f",
+ "metadata": {
+ "id": "7304ae55-3dc2-4a77-8275-f44a8122601f",
+ "outputId": "0431ff14-db0e-4f62-c9ac-5ea22c95e76a",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 90
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m36.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m29.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m15.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "%pip install networkx --quiet\n",
+ "from random import choice, seed\n",
+ "from typing import Dict\n",
+ "from yfiles_jupyter_graphs import GraphWidget\n",
+ "seed(0)\n",
+ "w = GraphWidget()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1704d4f6-798b-4d6d-8977-a5ba94376a7e",
+ "metadata": {
+ "id": "1704d4f6-798b-4d6d-8977-a5ba94376a7e"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "7014c58b-cdc9-4703-b69c-601c3318bc0b",
+ "metadata": {
+ "id": "7014c58b-cdc9-4703-b69c-601c3318bc0b"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "48cd4f3e-f5f4-4b0c-8dbf-12fd27da82d8",
+ "metadata": {
+ "id": "48cd4f3e-f5f4-4b0c-8dbf-12fd27da82d8"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "4c5413fd-98a3-4b8c-9224-c7ca75b0afda",
+ "metadata": {
+ "id": "4c5413fd-98a3-4b8c-9224-c7ca75b0afda"
+ },
+ "outputs": [],
+ "source": [
+ "w.nodes = [\n",
+ " {\"id\": 0, \"properties\": {\"firstName\": \"Alpha\", \"label\": \"Person A\", \"age\": 31}},\n",
+ " {\"id\": \"one\", \"properties\": {\"firstName\": \"Bravo\", \"label\": \"Person B\", \"age\": 56}},\n",
+ " {\"id\": 2.0, \"properties\": {\"firstName\": \"Charlie\", \"label\": \"Person C\", \"age\": 27, \"has_hat\": False}},\n",
+ " {\"id\": True, \"properties\": {\"firstName\": \"Delta\", \"label\": \"Person D\", \"age\": 43, \"likes_pizza\": True}}\n",
+ "]\n",
+ "w.edges = [\n",
+ " {\"id\": \"zero\", \"start\": 0, \"end\": \"one\", \"properties\": {\"since\": \"1992\", \"label\": \"knows\"}},\n",
+ " {\"id\": 1, \"start\": \"one\", \"end\": True, \"properties\": {\"label\": \"knows\", \"since\": \"1992\"}},\n",
+ " {\"id\": 2.0, \"start\": 2.0, \"end\": True, \"properties\": {\"label\": \"knows\", \"since\": \"1992\"}},\n",
+ " {\"id\": False, \"start\": 0, \"end\": 2.0, \"properties\": {\"label\": \"knows\", \"since\": 234}}\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9564bafb-6d2a-44f7-ac81-4b5e73fc2ed2",
+ "metadata": {
+ "id": "9564bafb-6d2a-44f7-ac81-4b5e73fc2ed2"
+ },
+ "source": [
+ "We will use this graph:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "5d8516f3-66c4-4281-8a95-84865bef2bd3",
+ "metadata": {
+ "id": "5d8516f3-66c4-4281-8a95-84865bef2bd3",
+ "outputId": "ef162ca8-101d-4682-a013-a4006c150b4a",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 517,
+ "referenced_widgets": [
+ "c68b6f2e6dc641cd8973fdf0f85999a6",
+ "dd8a7ec042694fadaeb55258bfd8e735"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='500px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "c68b6f2e6dc641cd8973fdf0f85999a6"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "display(w)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ac801d6f-7fed-4b89-a977-173066410df5",
+ "metadata": {
+ "id": "ac801d6f-7fed-4b89-a977-173066410df5"
+ },
+ "source": [
+ "### Using Properties data\n",
+ "\n",
+ "Let's set the size of the nodes based on the age:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "8c6ba18c-4232-4b01-8ebf-f97e18f1dc90",
+ "metadata": {
+ "id": "8c6ba18c-4232-4b01-8ebf-f97e18f1dc90",
+ "outputId": "e1349538-e6d6-41de-ed1a-17489793773a",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 517,
+ "referenced_widgets": [
+ "c68b6f2e6dc641cd8973fdf0f85999a6",
+ "dd8a7ec042694fadaeb55258bfd8e735"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='500px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "c68b6f2e6dc641cd8973fdf0f85999a6"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "def custom_node_scale_mapping_age(item: Dict):\n",
+ " return item['properties']['age'] / 35\n",
+ "w.set_node_scale_factor_mapping(custom_node_scale_mapping_age)\n",
+ "display(w)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dfb0eed3-dd82-42bb-aaaa-8be807dd50d5",
+ "metadata": {
+ "id": "dfb0eed3-dd82-42bb-aaaa-8be807dd50d5"
+ },
+ "source": [
+ "### There is a shorter way for that:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "846a94ba-411c-4fec-967f-77a0ef29eb0f",
+ "metadata": {
+ "id": "846a94ba-411c-4fec-967f-77a0ef29eb0f"
+ },
+ "outputs": [],
+ "source": [
+ "w2 = GraphWidget()\n",
+ "w2.nodes = [\n",
+ " {\"id\": 0, \"properties\": {\"firstName\": \"Alpha\", \"label\": \"Person A\", \"age\": 31}},\n",
+ " {\"id\": \"one\", \"properties\": {\"firstName\": \"Bravo\", \"label\": \"Person B\", \"age\": 56}},\n",
+ " {\"id\": 2.0, \"properties\": {\"firstName\": \"Charlie\", \"label\": \"Person C\", \"age\": 27, \"has_hat\": False}},\n",
+ " {\"id\": True, \"properties\": {\"firstName\": \"Delta\", \"label\": \"Person D\", \"age\": 43, \"likes_pizza\": True}}\n",
+ "]\n",
+ "w2.edges = [\n",
+ " {\"id\": \"zero\", \"start\": 0, \"end\": \"one\", \"properties\": {\"since\": \"1992\", \"label\": \"knows\"}},\n",
+ " {\"id\": 1, \"start\": \"one\", \"end\": True, \"properties\": {\"label\": \"knows\", \"since\": \"1992\"}},\n",
+ " {\"id\": 2.0, \"start\": 2.0, \"end\": True, \"properties\": {\"label\": \"knows\", \"since\": \"1992\"}},\n",
+ " {\"id\": False, \"start\": 0, \"end\": 2.0, \"properties\": {\"label\": \"knows\", \"since\": 234}}\n",
+ "]\n",
+ "\n",
+ "w2.node_scale_factor_mapping = lambda item: item['properties']['age'] / 35"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "999f44ca-823c-4d09-b136-44473da1765a",
+ "metadata": {
+ "id": "999f44ca-823c-4d09-b136-44473da1765a"
+ },
+ "source": [
+ "Both approaches produce the same ouptut"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "cc72cb90-7941-472e-bac0-1a759052955d",
+ "metadata": {
+ "id": "cc72cb90-7941-472e-bac0-1a759052955d",
+ "outputId": "2942cf2b-dbca-4a17-a4ed-fac780e5a779",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 517,
+ "referenced_widgets": [
+ "fdba3142cf444a89a6f0a4e299853f8a",
+ "6c2c76dd92da45c5be113d0ca8ddcc15"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='500px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "fdba3142cf444a89a6f0a4e299853f8a"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "display(w2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4fa81220-5164-4864-9499-85f8051b1ba5",
+ "metadata": {
+ "id": "4fa81220-5164-4864-9499-85f8051b1ba5"
+ },
+ "source": [
+ "## Functions with less arguments\n",
+ "Next, Let's set the colors of the edges:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "5776d087-dd85-4254-97a1-bdc0b4e67c09",
+ "metadata": {
+ "id": "5776d087-dd85-4254-97a1-bdc0b4e67c09",
+ "outputId": "5c4fd331-5116-4dfd-bdad-856abbbdd3e7",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 517,
+ "referenced_widgets": [
+ "b981d56da27e4c088430ea72c28f613e",
+ "cf099fca2a3246b5a3ef0a6941d53580"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='500px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "b981d56da27e4c088430ea72c28f613e"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w3 = GraphWidget()\n",
+ "w3.nodes = [\n",
+ " {\"id\": 0, \"properties\": {\"firstName\": \"Alpha\", \"label\": \"Person A\", \"age\": 31}},\n",
+ " {\"id\": \"one\", \"properties\": {\"firstName\": \"Bravo\", \"label\": \"Person B\", \"age\": 56}},\n",
+ " {\"id\": 2.0, \"properties\": {\"firstName\": \"Charlie\", \"label\": \"Person C\", \"age\": 27, \"has_hat\": False}},\n",
+ " {\"id\": True, \"properties\": {\"firstName\": \"Delta\", \"label\": \"Person D\", \"age\": 43, \"likes_pizza\": True}}\n",
+ "]\n",
+ "w3.edges = [\n",
+ " {\"id\": \"zero\", \"start\": 0, \"end\": \"one\", \"properties\": {\"since\": \"1992\", \"label\": \"knows\"}},\n",
+ " {\"id\": 1, \"start\": \"one\", \"end\": True, \"properties\": {\"label\": \"knows\", \"since\": \"1992\"}},\n",
+ " {\"id\": 2.0, \"start\": 2.0, \"end\": True, \"properties\": {\"label\": \"knows\", \"since\": \"1992\"}},\n",
+ " {\"id\": False, \"start\": 0, \"end\": 2.0, \"properties\": {\"label\": \"knows\", \"since\": 234}}\n",
+ "]\n",
+ "\n",
+ "custom_colors = [ '#956518', '#d13471', '#3fa670', '#8375ba' ]\n",
+ "edges = w3.get_edges()\n",
+ "w3.edge_color_mapping = lambda edge : custom_colors[edges.index(edge) % len(custom_colors)]\n",
+ "display(w3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d47da27c-a5f2-4f9d-bc1e-06a4c3a52809",
+ "metadata": {
+ "id": "d47da27c-a5f2-4f9d-bc1e-06a4c3a52809"
+ },
+ "source": [
+ "Sometimes, one doesn't even need the item argument. \\\n",
+ "The following syntax is especially useful for constant values:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "b7d5ce38-01ac-4144-b616-b33614365912",
+ "metadata": {
+ "id": "b7d5ce38-01ac-4144-b616-b33614365912",
+ "outputId": "64acb82b-f4bd-4bb1-c60b-6cba60447972",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 517,
+ "referenced_widgets": [
+ "b981d56da27e4c088430ea72c28f613e",
+ "cf099fca2a3246b5a3ef0a6941d53580"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='500px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "b981d56da27e4c088430ea72c28f613e"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w3.node_color_mapping = lambda : \"#f22441\"\n",
+ "display(w3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d3f0f63b-9ee9-45fc-a7f8-796f18bcd7a4",
+ "metadata": {
+ "id": "d3f0f63b-9ee9-45fc-a7f8-796f18bcd7a4"
+ },
+ "source": [
+ "## Overloading with strings\n",
+ "Now, Let's set the labels of the nodes to the first names."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "45608997-4fd1-42ac-92a1-1fd2a5e90b98",
+ "metadata": {
+ "id": "45608997-4fd1-42ac-92a1-1fd2a5e90b98",
+ "outputId": "604d84c3-f630-4b1f-d8b5-44721e838618",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 517,
+ "referenced_widgets": [
+ "45f4ef913fdf43d9ba2f690225c39eed",
+ "840fb60cac5e4fd19fb80209f95c9eff"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='500px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "45f4ef913fdf43d9ba2f690225c39eed"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w4 = GraphWidget()\n",
+ "w4.nodes = [\n",
+ " {\"id\": 0, \"properties\": {\"firstName\": \"Alpha\", \"label\": \"Person A\", \"age\": 31}},\n",
+ " {\"id\": \"one\", \"properties\": {\"firstName\": \"Bravo\", \"label\": \"Person B\", \"age\": 56}},\n",
+ " {\"id\": 2.0, \"properties\": {\"firstName\": \"Charlie\", \"label\": \"Person C\", \"age\": 27, \"has_hat\": False}},\n",
+ " {\"id\": True, \"properties\": {\"firstName\": \"Delta\", \"label\": \"Person D\", \"age\": 43, \"likes_pizza\": True}}\n",
+ "]\n",
+ "w4.edges = [\n",
+ " {\"id\": \"zero\", \"start\": 0, \"end\": \"one\", \"properties\": {\"since\": \"1992\", \"label\": \"knows\"}},\n",
+ " {\"id\": 1, \"start\": \"one\", \"end\": True, \"properties\": {\"label\": \"knows\", \"since\": \"1992\"}},\n",
+ " {\"id\": 2.0, \"start\": 2.0, \"end\": True, \"properties\": {\"label\": \"knows\", \"since\": \"1992\"}},\n",
+ " {\"id\": False, \"start\": 0, \"end\": 2.0, \"properties\": {\"label\": \"knows\", \"since\": 234}}\n",
+ "]\n",
+ "\n",
+ "def custom_label_mapping_name(item: Dict):\n",
+ " return item['properties']['firstName']\n",
+ "w4.set_node_label_mapping(custom_label_mapping_name)\n",
+ "display(w4)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a486b223-b89c-4cf2-8e26-4d907c890f2f",
+ "metadata": {
+ "id": "a486b223-b89c-4cf2-8e26-4d907c890f2f"
+ },
+ "source": [
+ "For binding to properties, the api provides an easier syntax:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "fd258b86-fe20-496f-a812-1718bc2ef1d3",
+ "metadata": {
+ "id": "fd258b86-fe20-496f-a812-1718bc2ef1d3",
+ "outputId": "6d43cb0d-5467-443a-8058-3f6659141dac",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 36
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "'firstName'"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ }
+ },
+ "metadata": {},
+ "execution_count": 11
+ }
+ ],
+ "source": [
+ "w4.node_label_mapping = 'firstName'\n",
+ "w4.get_node_label_mapping()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "34c290df-2d1a-4bab-a2f2-56b2a3c73377",
+ "metadata": {
+ "id": "34c290df-2d1a-4bab-a2f2-56b2a3c73377"
+ },
+ "source": [
+ "Now the labels are bound to the ```'firstName'``` property, which produces the same output as before"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "a2f4b41d-ebae-4b97-8b87-4b80a76c412d",
+ "metadata": {
+ "id": "a2f4b41d-ebae-4b97-8b87-4b80a76c412d",
+ "outputId": "cc11cc58-d117-4a6a-b4b7-959d4d7110b2",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 517,
+ "referenced_widgets": [
+ "45f4ef913fdf43d9ba2f690225c39eed",
+ "840fb60cac5e4fd19fb80209f95c9eff"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='500px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "45f4ef913fdf43d9ba2f690225c39eed"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "display(w4)"
+ ]
+ }
+ ],
+ "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": "3.11.3"
+ },
+ "colab": {
+ "provenance": []
+ },
+ "widgets": {
+ "application/vnd.jupyter.widget-state+json": {
+ "c68b6f2e6dc641cd8973fdf0f85999a6": {
+ "model_module": "yfiles-jupyter-graphs",
+ "model_name": "GraphModel",
+ "model_module_version": "^1.8.1",
+ "state": {
+ "_context_pane_mapping": [
+ {
+ "id": "Neighborhood",
+ "title": "Neighborhood"
+ },
+ {
+ "id": "Data",
+ "title": "Data"
+ },
+ {
+ "id": "Search",
+ "title": "Search"
+ },
+ {
+ "id": "About",
+ "title": "About"
+ }
+ ],
+ "_data_importer": "unknown",
+ "_directed": false,
+ "_dom_classes": [],
+ "_edges": [
+ {
+ "id": "zero",
+ "start": 0,
+ "end": "one",
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+ }
},
- {
- "cell_type": "markdown",
- "id": "3c667362-1228-4d03-b2a3-d49537d8d9b4",
- "metadata": {},
- "source": [
- "This notebook covers the different methods to change node or edge mappings.\n",
- "\n",
- "Before using the graph widget, install all necessary packages."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7304ae55-3dc2-4a77-8275-f44a8122601f",
- "metadata": {},
- "outputs": [],
- "source": [
- "%pip install yfiles_jupyter_graphs --quiet\n",
- "%pip install networkx --quiet\n",
- "from random import choice, seed\n",
- "from typing import Dict\n",
- "from yfiles_jupyter_graphs import GraphWidget\n",
- "seed(0)\n",
- "w = GraphWidget()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1704d4f6-798b-4d6d-8977-a5ba94376a7e",
- "metadata": {},
- "source": [
- "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7014c58b-cdc9-4703-b69c-601c3318bc0b",
- "metadata": {},
- "outputs": [],
- "source": [
- "try:\n",
- " import google.colab\n",
- " from google.colab import output\n",
- " output.enable_custom_widget_manager()\n",
- "except:\n",
- " pass"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "48cd4f3e-f5f4-4b0c-8dbf-12fd27da82d8",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4c5413fd-98a3-4b8c-9224-c7ca75b0afda",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.nodes = [\n",
- " {\"id\": 0, \"properties\": {\"firstName\": \"Alpha\", \"label\": \"Person A\", \"age\": 31}},\n",
- " {\"id\": \"one\", \"properties\": {\"firstName\": \"Bravo\", \"label\": \"Person B\", \"age\": 56}},\n",
- " {\"id\": 2.0, \"properties\": {\"firstName\": \"Charlie\", \"label\": \"Person C\", \"age\": 27, \"has_hat\": False}},\n",
- " {\"id\": True, \"properties\": {\"firstName\": \"Delta\", \"label\": \"Person D\", \"age\": 43, \"likes_pizza\": True}}\n",
- "]\n",
- "w.edges = [\n",
- " {\"id\": \"zero\", \"start\": 0, \"end\": \"one\", \"properties\": {\"since\": \"1992\", \"label\": \"knows\"}},\n",
- " {\"id\": 1, \"start\": \"one\", \"end\": True, \"properties\": {\"label\": \"knows\", \"since\": \"1992\"}},\n",
- " {\"id\": 2.0, \"start\": 2.0, \"end\": True, \"properties\": {\"label\": \"knows\", \"since\": \"1992\"}},\n",
- " {\"id\": False, \"start\": 0, \"end\": 2.0, \"properties\": {\"label\": \"knows\", \"since\": 234}}\n",
- "]"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9564bafb-6d2a-44f7-ac81-4b5e73fc2ed2",
- "metadata": {},
- "source": [
- "We will use this graph: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5d8516f3-66c4-4281-8a95-84865bef2bd3",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ac801d6f-7fed-4b89-a977-173066410df5",
- "metadata": {},
- "source": [
- "### Using Properties data\n",
- "\n",
- "Let's set the size of the nodes based on the age:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8c6ba18c-4232-4b01-8ebf-f97e18f1dc90",
- "metadata": {},
- "outputs": [],
- "source": [
- "def custom_node_scale_mapping_age(item: Dict):\n",
- " return item['properties']['age'] / 35\n",
- "w.set_node_scale_factor_mapping(custom_node_scale_mapping_age)\n",
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "dfb0eed3-dd82-42bb-aaaa-8be807dd50d5",
- "metadata": {},
- "source": [
- "### There is a shorter way for that:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "846a94ba-411c-4fec-967f-77a0ef29eb0f",
- "metadata": {},
- "outputs": [],
- "source": [
- "w2 = GraphWidget()\n",
- "w2.nodes = [\n",
- " {\"id\": 0, \"properties\": {\"firstName\": \"Alpha\", \"label\": \"Person A\", \"age\": 31}},\n",
- " {\"id\": \"one\", \"properties\": {\"firstName\": \"Bravo\", \"label\": \"Person B\", \"age\": 56}},\n",
- " {\"id\": 2.0, \"properties\": {\"firstName\": \"Charlie\", \"label\": \"Person C\", \"age\": 27, \"has_hat\": False}},\n",
- " {\"id\": True, \"properties\": {\"firstName\": \"Delta\", \"label\": \"Person D\", \"age\": 43, \"likes_pizza\": True}}\n",
- "]\n",
- "w2.edges = [\n",
- " {\"id\": \"zero\", \"start\": 0, \"end\": \"one\", \"properties\": {\"since\": \"1992\", \"label\": \"knows\"}},\n",
- " {\"id\": 1, \"start\": \"one\", \"end\": True, \"properties\": {\"label\": \"knows\", \"since\": \"1992\"}},\n",
- " {\"id\": 2.0, \"start\": 2.0, \"end\": True, \"properties\": {\"label\": \"knows\", \"since\": \"1992\"}},\n",
- " {\"id\": False, \"start\": 0, \"end\": 2.0, \"properties\": {\"label\": \"knows\", \"since\": 234}}\n",
- "]\n",
- "\n",
- "w2.node_scale_factor_mapping = lambda item: item['properties']['age'] / 35"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "999f44ca-823c-4d09-b136-44473da1765a",
- "metadata": {},
- "source": [
- "Both approaches produce the same ouptut"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "cc72cb90-7941-472e-bac0-1a759052955d",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w2)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4fa81220-5164-4864-9499-85f8051b1ba5",
- "metadata": {},
- "source": [
- "## Functions with less arguments\n",
- "Next, Let's set the colors of the edges: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5776d087-dd85-4254-97a1-bdc0b4e67c09",
- "metadata": {},
- "outputs": [],
- "source": [
- "w3 = GraphWidget()\n",
- "w3.nodes = [\n",
- " {\"id\": 0, \"properties\": {\"firstName\": \"Alpha\", \"label\": \"Person A\", \"age\": 31}},\n",
- " {\"id\": \"one\", \"properties\": {\"firstName\": \"Bravo\", \"label\": \"Person B\", \"age\": 56}},\n",
- " {\"id\": 2.0, \"properties\": {\"firstName\": \"Charlie\", \"label\": \"Person C\", \"age\": 27, \"has_hat\": False}},\n",
- " {\"id\": True, \"properties\": {\"firstName\": \"Delta\", \"label\": \"Person D\", \"age\": 43, \"likes_pizza\": True}}\n",
- "]\n",
- "w3.edges = [\n",
- " {\"id\": \"zero\", \"start\": 0, \"end\": \"one\", \"properties\": {\"since\": \"1992\", \"label\": \"knows\"}},\n",
- " {\"id\": 1, \"start\": \"one\", \"end\": True, \"properties\": {\"label\": \"knows\", \"since\": \"1992\"}},\n",
- " {\"id\": 2.0, \"start\": 2.0, \"end\": True, \"properties\": {\"label\": \"knows\", \"since\": \"1992\"}},\n",
- " {\"id\": False, \"start\": 0, \"end\": 2.0, \"properties\": {\"label\": \"knows\", \"since\": 234}}\n",
- "]\n",
- "\n",
- "custom_colors = [ '#956518', '#d13471', '#3fa670', '#8375ba' ]\n",
- "edges = w3.get_edges()\n",
- "w3.edge_color_mapping = lambda edge : custom_colors[edges.index(edge) % len(custom_colors)]\n",
- "display(w3)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d47da27c-a5f2-4f9d-bc1e-06a4c3a52809",
- "metadata": {},
- "source": [
- "Sometimes, one doesn't even need the item argument. \\\n",
- "The following syntax is especially useful for constant values:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b7d5ce38-01ac-4144-b616-b33614365912",
- "metadata": {},
- "outputs": [],
- "source": [
- "w3.node_color_mapping = lambda : \"#f22441\"\n",
- "display(w3)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d3f0f63b-9ee9-45fc-a7f8-796f18bcd7a4",
- "metadata": {},
- "source": [
- "## Overloading with strings\n",
- "Now, Let's set the labels of the nodes to the first names."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "45608997-4fd1-42ac-92a1-1fd2a5e90b98",
- "metadata": {},
- "outputs": [],
- "source": [
- "w4 = GraphWidget()\n",
- "w4.nodes = [\n",
- " {\"id\": 0, \"properties\": {\"firstName\": \"Alpha\", \"label\": \"Person A\", \"age\": 31}},\n",
- " {\"id\": \"one\", \"properties\": {\"firstName\": \"Bravo\", \"label\": \"Person B\", \"age\": 56}},\n",
- " {\"id\": 2.0, \"properties\": {\"firstName\": \"Charlie\", \"label\": \"Person C\", \"age\": 27, \"has_hat\": False}},\n",
- " {\"id\": True, \"properties\": {\"firstName\": \"Delta\", \"label\": \"Person D\", \"age\": 43, \"likes_pizza\": True}}\n",
- "]\n",
- "w4.edges = [\n",
- " {\"id\": \"zero\", \"start\": 0, \"end\": \"one\", \"properties\": {\"since\": \"1992\", \"label\": \"knows\"}},\n",
- " {\"id\": 1, \"start\": \"one\", \"end\": True, \"properties\": {\"label\": \"knows\", \"since\": \"1992\"}},\n",
- " {\"id\": 2.0, \"start\": 2.0, \"end\": True, \"properties\": {\"label\": \"knows\", \"since\": \"1992\"}},\n",
- " {\"id\": False, \"start\": 0, \"end\": 2.0, \"properties\": {\"label\": \"knows\", \"since\": 234}}\n",
- "]\n",
- "\n",
- "def custom_label_mapping_name(item: Dict):\n",
- " return item['properties']['firstName']\n",
- "w4.set_node_label_mapping(custom_label_mapping_name)\n",
- "display(w4)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a486b223-b89c-4cf2-8e26-4d907c890f2f",
- "metadata": {},
- "source": [
- "For binding to properties, the api provides an easier syntax:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fd258b86-fe20-496f-a812-1718bc2ef1d3",
- "metadata": {},
- "outputs": [],
- "source": [
- "w4.node_label_mapping = 'firstName'\n",
- "w4.get_node_label_mapping()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "34c290df-2d1a-4bab-a2f2-56b2a3c73377",
- "metadata": {},
- "source": [
- "Now the labels are bound to the ```'firstName'``` property, which produces the same output as before"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a2f4b41d-ebae-4b97-8b87-4b80a76c412d",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w4)"
- ]
- }
- ],
- "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": "3.11.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/13_networkx_import.ipynb b/examples/13_networkx_import.ipynb
index e450a96..ef9f6df 100644
--- a/examples/13_networkx_import.ipynb
+++ b/examples/13_networkx_import.ipynb
@@ -1,287 +1,3470 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "afbb43bc-a9ab-4322-9a07-5e34ec791d77",
- "metadata": {},
- "source": [
- "# Visualizing Networkx graphs in yFiles Graphs for Jupyter "
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "afbb43bc-a9ab-4322-9a07-5e34ec791d77",
+ "metadata": {
+ "id": "afbb43bc-a9ab-4322-9a07-5e34ec791d77"
+ },
+ "source": [
+ "# Visualizing Networkx graphs in yFiles Graphs for Jupyter "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa5ae97-32d4-49c3-a914-899fd359e299",
+ "metadata": {
+ "id": "9aa5ae97-32d4-49c3-a914-899fd359e299"
+ },
+ "source": [
+ "Before using the graph widget, install all necessary packages."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "4d4bbd0b-30b9-4dbd-8d36-2115a4e4af7d",
+ "metadata": {
+ "id": "4d4bbd0b-30b9-4dbd-8d36-2115a4e4af7d",
+ "outputId": "63512e32-d0ea-4286-c020-6d755bed09c5",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m37.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m18.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m15.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "%pip install networkx --quiet\n",
+ "from networkx import Graph, DiGraph, MultiGraph, MultiDiGraph, path_graph, karate_club_graph\n",
+ "from yfiles_jupyter_graphs import GraphWidget"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "16e47873-e31e-46e0-80f9-6514819a44fe",
+ "metadata": {
+ "id": "16e47873-e31e-46e0-80f9-6514819a44fe"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "64516534-c09e-4f96-984f-7d6b26dde6f7",
+ "metadata": {
+ "id": "64516534-c09e-4f96-984f-7d6b26dde6f7"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3d367f22-0f34-49c7-b224-5e0a1e3937d1",
+ "metadata": {
+ "id": "3d367f22-0f34-49c7-b224-5e0a1e3937d1"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d27114d0-2133-4c6f-87c1-f049e56448c6",
+ "metadata": {
+ "id": "d27114d0-2133-4c6f-87c1-f049e56448c6"
+ },
+ "source": [
+ "## How to import a graph\n",
+ "- either import the graph directly when initilizing: ```GraphWidget(graph=your_graph)```\n",
+ "- or use the ```w.import_graph(your_graph)``` function, if you already initilized a Widget called ```w```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4450f8a7-3444-4e4b-bc39-678ac09ddedf",
+ "metadata": {
+ "id": "4450f8a7-3444-4e4b-bc39-678ac09ddedf"
+ },
+ "source": [
+ "## Notes about NetworkX importer\n",
+ "- graph attributes are ignored\n",
+ "- node identifiers are saved under property `label` (or `yf_label` if key `label` already exists)\n",
+ "- subgraphs (graph as node, see networx docs) are not supported\n",
+ "- any additional data is stored in ```properties```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "864ff6dd-fa00-4a3e-a32d-6781c00e7202",
+ "metadata": {
+ "id": "864ff6dd-fa00-4a3e-a32d-6781c00e7202"
+ },
+ "source": [
+ "### Import undirected graph with self loops\n",
+ "https://networkx.org/documentation/stable/reference/classes/graph.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "da205fc6-af88-4dbe-9d2d-23beb6f66e3e",
+ "metadata": {
+ "id": "da205fc6-af88-4dbe-9d2d-23beb6f66e3e",
+ "outputId": "5e2f89fd-593a-45bc-8c08-b918ba066dcf",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 627,
+ "referenced_widgets": [
+ "22382074164944e280e20f779e7df34c",
+ "c4c6adaae9f4492e8ccc5a6191ee735c"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='610px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "22382074164944e280e20f779e7df34c"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "def make_graph(graph_class):\n",
+ " G = graph_class(day=\"Friday\")\n",
+ " G.add_node(\"origin\")\n",
+ " G.add_node(1)\n",
+ " G.add_nodes_from([2, 3])\n",
+ " H = path_graph(10)\n",
+ " G.add_nodes_from(H)\n",
+ " G.add_edge(1, 2)\n",
+ " G.add_edges_from([(1, 2), (1, 3)])\n",
+ " G.add_edges_from(H.edges)\n",
+ " G.add_node(1, time=\"5pm\")\n",
+ " G.add_nodes_from([3], time=\"2pm\")\n",
+ " for n in G.nodes:\n",
+ " G.add_edge(\"origin\", n)\n",
+ " G.nodes[1][\"room\"] = 714\n",
+ " G.add_edge(1, 2, weight=4.7)\n",
+ " G.add_edges_from([(3, 4), (4, 5)], color=\"red\")\n",
+ " G.add_edges_from([(1, 2, {\"color\": \"blue\"}), (2, 3, {\"weight\": 8})])\n",
+ " G[1][2][\"weight\"] = 4.7\n",
+ " G.edges[1, 2][\"weight\"] = 4\n",
+ " return G\n",
+ "display(GraphWidget(graph=make_graph(Graph)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "40d439fc-7406-4435-b043-32534ebc23ce",
+ "metadata": {
+ "id": "40d439fc-7406-4435-b043-32534ebc23ce"
+ },
+ "source": [
+ "### import directed graph with self loops\n",
+ "https://networkx.org/documentation/stable/reference/classes/digraph.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "37dfb8b1-a22d-4ca8-922d-be5de5455e0e",
+ "metadata": {
+ "id": "37dfb8b1-a22d-4ca8-922d-be5de5455e0e",
+ "outputId": "5fdef107-b338-433f-87a5-a9eb1df1605d",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 627,
+ "referenced_widgets": [
+ "301b75c7175046eebabd968fbaea0c36",
+ "6ba22dd53a5e4cd790773be845554efc"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='610px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "301b75c7175046eebabd968fbaea0c36"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "display(GraphWidget(graph=make_graph(DiGraph)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c4a3a547-4e02-4f29-9343-5dbd1b9ea0f3",
+ "metadata": {
+ "id": "c4a3a547-4e02-4f29-9343-5dbd1b9ea0f3"
+ },
+ "source": [
+ "### import undirected graph with self loops and parallel edges\n",
+ "https://networkx.org/documentation/stable/reference/classes/multigraph.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "487e2f91-9be8-41e7-b26f-23cd0de5291e",
+ "metadata": {
+ "id": "487e2f91-9be8-41e7-b26f-23cd0de5291e",
+ "outputId": "af02e1c2-19bb-46d2-ac26-d179deac075c",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 627,
+ "referenced_widgets": [
+ "e05042e0527a4d07baeb8a54469dab29",
+ "0284de82dd1a49eeac8971f8d977ed09"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='610px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "e05042e0527a4d07baeb8a54469dab29"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "def make_graph_(graph_class):\n",
+ " G = graph_class(day=\"Friday\")\n",
+ " G.add_node(\"origin\")\n",
+ " G.add_node(1)\n",
+ " G.add_nodes_from([2, 3])\n",
+ " H = path_graph(10)\n",
+ " G.add_nodes_from(H)\n",
+ " G.add_edge(1, 2)\n",
+ " G.add_edges_from([(1, 2), (1, 3)])\n",
+ " G.add_edges_from(H.edges)\n",
+ " G.add_node(1, time=\"5pm\")\n",
+ " G.add_nodes_from([3], time=\"2pm\")\n",
+ " for n in G.nodes:\n",
+ " G.add_edge(\"origin\", n)\n",
+ " G.add_edges_from([(4, 5, dict(route=282)), (4, 5, dict(route=37))])\n",
+ " G.add_edge(1, 2, weight=4.7)\n",
+ " G.add_edges_from([(3, 4), (4, 5)], color=\"red\")\n",
+ " G.add_edges_from([(1, 2, {\"color\": \"blue\"}), (2, 3, {\"weight\": 8})])\n",
+ " G[1][2][0][\"weight\"] = 4.7\n",
+ " G.edges[1, 2, 0][\"weight\"] = 4\n",
+ " return G\n",
+ "display(GraphWidget(graph=make_graph_(MultiGraph)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f940c0c5-8d34-4a39-ac52-2a884e69ad47",
+ "metadata": {
+ "id": "f940c0c5-8d34-4a39-ac52-2a884e69ad47"
+ },
+ "source": [
+ "### Import directed graph with self loops and parallel edges\n",
+ "https://networkx.org/documentation/stable/reference/classes/multidigraph.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "d50c0882-e673-4997-89bf-eb60632064cd",
+ "metadata": {
+ "id": "d50c0882-e673-4997-89bf-eb60632064cd",
+ "outputId": "347575cc-e444-4425-d8df-44795403db2c",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 627,
+ "referenced_widgets": [
+ "97b071e333f248858951f54c3f2b6f2f",
+ "ec1e4f0af77441e296cacbcee124b4ba"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='610px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "97b071e333f248858951f54c3f2b6f2f"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "display(GraphWidget(graph=make_graph_(MultiDiGraph)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5e702e7d-67d7-4815-b305-2b78c5871c0c",
+ "metadata": {
+ "id": "5e702e7d-67d7-4815-b305-2b78c5871c0c"
+ },
+ "source": [
+ "The resulting graph has properties that are not displayed by simply importing the graph. \\\n",
+ "You can access this data by looking at the nodes and edges data:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "9b9a4ea1-eccf-4ad0-b266-828d96f0320b",
+ "metadata": {
+ "id": "9b9a4ea1-eccf-4ad0-b266-828d96f0320b",
+ "outputId": "d205fd09-9bbf-48a6-e8d7-e420d8f235d3",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 455
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[{'id': 0, 'start': 0, 'end': 0, 'properties': {}},\n",
+ " {'id': 1, 'start': 0, 'end': 1, 'properties': {}},\n",
+ " {'id': 2, 'start': 0, 'end': 2, 'properties': {}},\n",
+ " {'id': 3, 'start': 0, 'end': 3, 'properties': {}},\n",
+ " {'id': 4, 'start': 0, 'end': 4, 'properties': {}},\n",
+ " {'id': 5, 'start': 0, 'end': 5, 'properties': {}},\n",
+ " {'id': 6, 'start': 0, 'end': 6, 'properties': {}},\n",
+ " {'id': 7, 'start': 0, 'end': 7, 'properties': {}},\n",
+ " {'id': 8, 'start': 0, 'end': 8, 'properties': {}},\n",
+ " {'id': 9, 'start': 0, 'end': 9, 'properties': {}},\n",
+ " {'id': 10, 'start': 0, 'end': 10, 'properties': {}},\n",
+ " {'id': 11,\n",
+ " 'start': 1,\n",
+ " 'end': 2,\n",
+ " 'properties': {'weight': 4, 'color': 'blue'}},\n",
+ " {'id': 12, 'start': 1, 'end': 3, 'properties': {}},\n",
+ " {'id': 13, 'start': 1, 'end': 4, 'properties': {}},\n",
+ " {'id': 14, 'start': 2, 'end': 3, 'properties': {'weight': 8}},\n",
+ " {'id': 15, 'start': 3, 'end': 5, 'properties': {'color': 'red'}},\n",
+ " {'id': 16, 'start': 5, 'end': 6, 'properties': {'color': 'red'}},\n",
+ " {'id': 17, 'start': 6, 'end': 7, 'properties': {}},\n",
+ " {'id': 18, 'start': 7, 'end': 8, 'properties': {}},\n",
+ " {'id': 19, 'start': 8, 'end': 9, 'properties': {}},\n",
+ " {'id': 20, 'start': 9, 'end': 10, 'properties': {}}]"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 7
+ }
+ ],
+ "source": [
+ "w = GraphWidget(graph=make_graph(Graph))\n",
+ "w.edges"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0a5d25c2-643b-48f3-8136-28aefac4cafe",
+ "metadata": {
+ "id": "0a5d25c2-643b-48f3-8136-28aefac4cafe"
+ },
+ "source": [
+ "### Using graph data\n",
+ "\n",
+ "To access the 'properties' data, you can use the data key in squared brackets: ```['properties']['key'] ```\n",
+ "\n",
+ "Possible keys in this example are 'weight' and 'color'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "56147fb6-5a95-40cc-94c1-8962075ce774",
+ "metadata": {
+ "id": "56147fb6-5a95-40cc-94c1-8962075ce774",
+ "outputId": "4c3c7855-6352-43a3-99e0-25b8c2a8dda5",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Edge 0: {'id': 0, 'start': 0, 'end': 0, 'properties': {}}\n",
+ "Edge 1: {'id': 1, 'start': 0, 'end': 1, 'properties': {}}\n",
+ "Edge 2: {'id': 2, 'start': 0, 'end': 2, 'properties': {}}\n",
+ "Edge 3: {'id': 3, 'start': 0, 'end': 3, 'properties': {}}\n",
+ "Edge 4: {'id': 4, 'start': 0, 'end': 4, 'properties': {}}\n",
+ "Edge 5: {'id': 5, 'start': 0, 'end': 5, 'properties': {}}\n",
+ "Edge 6: {'id': 6, 'start': 0, 'end': 6, 'properties': {}}\n",
+ "Edge 7: {'id': 7, 'start': 0, 'end': 7, 'properties': {}}\n",
+ "Edge 8: {'id': 8, 'start': 0, 'end': 8, 'properties': {}}\n",
+ "Edge 9: {'id': 9, 'start': 0, 'end': 9, 'properties': {}}\n",
+ "Edge 10: {'id': 10, 'start': 0, 'end': 10, 'properties': {}}\n",
+ "Edge 11: {'id': 11, 'start': 1, 'end': 2, 'properties': {'weight': 4, 'color': 'blue'}}\n",
+ "Edge 12: {'id': 12, 'start': 1, 'end': 3, 'properties': {}}\n",
+ "Edge 13: {'id': 13, 'start': 1, 'end': 4, 'properties': {}}\n",
+ "Edge 14: {'id': 14, 'start': 2, 'end': 3, 'properties': {'weight': 8}}\n",
+ "Edge 15: {'id': 15, 'start': 3, 'end': 5, 'properties': {'color': 'red'}}\n",
+ "Edge 16: {'id': 16, 'start': 5, 'end': 6, 'properties': {'color': 'red'}}\n",
+ "Edge 17: {'id': 17, 'start': 6, 'end': 7, 'properties': {}}\n",
+ "Edge 18: {'id': 18, 'start': 7, 'end': 8, 'properties': {}}\n",
+ "Edge 19: {'id': 19, 'start': 8, 'end': 9, 'properties': {}}\n",
+ "Edge 20: {'id': 20, 'start': 9, 'end': 10, 'properties': {}}\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "formattedProperties = ''.join(f\"Edge {edge['id']}: {edge}\\n\" for edge in w.edges)\n",
+ "print(formattedProperties)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b65f203c-280c-4664-8da8-a9ad211cde8b",
+ "metadata": {
+ "id": "b65f203c-280c-4664-8da8-a9ad211cde8b"
+ },
+ "source": [
+ "Let's use the 'color' key in properties to turn some edges blue and red."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "6139a3ee-ce54-41d8-9951-469db637ace7",
+ "metadata": {
+ "id": "6139a3ee-ce54-41d8-9951-469db637ace7",
+ "outputId": "3b6a337c-2422-4393-81be-f2183b66e88b",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 627,
+ "referenced_widgets": [
+ "0b452d259a4e4fab9d4f5e52ab3ebe82",
+ "fc6880b76db24349a5e0e103ba7a22a8"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='610px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "0b452d259a4e4fab9d4f5e52ab3ebe82"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w.edge_color_mapping = 'color'\n",
+ "display(w)"
+ ]
+ }
+ ],
+ "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": "3.11.3"
+ },
+ "colab": {
+ "provenance": []
+ },
+ "widgets": {
+ "application/vnd.jupyter.widget-state+json": {
+ "22382074164944e280e20f779e7df34c": {
+ "model_module": "yfiles-jupyter-graphs",
+ "model_name": "GraphModel",
+ "model_module_version": "^1.8.1",
+ "state": {
+ "_context_pane_mapping": [
+ {
+ "id": "Neighborhood",
+ "title": "Neighborhood"
+ },
+ {
+ "id": "Data",
+ "title": "Data"
+ },
+ {
+ "id": "Search",
+ "title": "Search"
+ },
+ {
+ "id": "About",
+ "title": "About"
+ }
+ ],
+ "_data_importer": "networkx",
+ "_directed": false,
+ "_dom_classes": [],
+ "_edges": [
+ {
+ "id": 0,
+ "start": 0,
+ "end": 0,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 1,
+ "start": 0,
+ "end": 1,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 2,
+ "start": 0,
+ "end": 2,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 3,
+ "start": 0,
+ "end": 3,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 4,
+ "start": 0,
+ "end": 4,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 5,
+ "start": 0,
+ "end": 5,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 6,
+ "start": 0,
+ "end": 6,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 7,
+ "start": 0,
+ "end": 7,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 8,
+ "start": 0,
+ "end": 8,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 9,
+ "start": 0,
+ "end": 9,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 10,
+ "start": 0,
+ "end": 10,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 11,
+ "start": 1,
+ "end": 2,
+ "properties": {
+ "weight": 4,
+ "color": "blue"
+ },
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 12,
+ "start": 1,
+ "end": 3,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 13,
+ "start": 1,
+ "end": 4,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 14,
+ "start": 2,
+ "end": 3,
+ "properties": {
+ "weight": 8
+ },
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 15,
+ "start": 3,
+ "end": 5,
+ "properties": {
+ "color": "red"
+ },
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 16,
+ "start": 5,
+ "end": 6,
+ "properties": {
+ "color": "red"
+ },
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 17,
+ "start": 6,
+ "end": 7,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 18,
+ "start": 7,
+ "end": 8,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 19,
+ "start": 8,
+ "end": 9,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 20,
+ "start": 9,
+ "end": 10,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ }
+ ],
+ "_graph_layout": {},
+ "_highlight": [],
+ "_license": {},
+ "_model_module": "yfiles-jupyter-graphs",
+ "_model_module_version": "^1.8.1",
+ "_model_name": "GraphModel",
+ "_neighborhood": {},
+ "_nodes": [
+ {
+ "id": 0,
+ "properties": {
+ "label": "origin"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "origin",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 1,
+ "properties": {
+ "time": "5pm",
+ "room": 714,
+ "label": "1"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "1",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 2,
+ "properties": {
+ "label": "2"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "2",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 3,
+ "properties": {
+ "time": "2pm",
+ "label": "3"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "3",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 4,
+ "properties": {
+ "label": "0"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "0",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 5,
+ "properties": {
+ "label": "4"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "4",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 6,
+ "properties": {
+ "label": "5"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "5",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 7,
+ "properties": {
+ "label": "6"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "6",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 8,
+ "properties": {
+ "label": "7"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "7",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
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+ "_license": {},
+ "_model_module": "yfiles-jupyter-graphs",
+ "_model_module_version": "^1.8.1",
+ "_model_name": "GraphModel",
+ "_neighborhood": {},
+ "_nodes": [
+ {
+ "id": 0,
+ "properties": {
+ "label": "origin"
+ },
+ "color": "#15AFAC",
+ "styles": {},
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+ "_overview": {
+ "enabled": null,
+ "overview_set": false
+ },
+ "_selected_graph": [
+ [],
+ []
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+ "_sidebar": {
+ "enabled": false,
+ "start_with": null
+ },
+ "_view_count": null,
+ "_view_module": "yfiles-jupyter-graphs",
+ "_view_module_version": "^1.8.1",
+ "_view_name": "GraphView",
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+ "tabbable": null,
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+ "model_module_version": "2.0.0",
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+ "_model_module": "@jupyter-widgets/base",
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+ "_model_name": "LayoutModel",
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+ "height": "610px",
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+ }
+ }
+ }
+ }
},
- {
- "cell_type": "markdown",
- "id": "9aa5ae97-32d4-49c3-a914-899fd359e299",
- "metadata": {},
- "source": [
- "Before using the graph widget, install all necessary packages."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4d4bbd0b-30b9-4dbd-8d36-2115a4e4af7d",
- "metadata": {},
- "outputs": [],
- "source": [
- "%pip install yfiles_jupyter_graphs --quiet\n",
- "%pip install networkx --quiet\n",
- "from networkx import Graph, DiGraph, MultiGraph, MultiDiGraph, path_graph, karate_club_graph\n",
- "from yfiles_jupyter_graphs import GraphWidget"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "16e47873-e31e-46e0-80f9-6514819a44fe",
- "metadata": {},
- "source": [
- "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "64516534-c09e-4f96-984f-7d6b26dde6f7",
- "metadata": {},
- "outputs": [],
- "source": [
- "try:\n",
- " import google.colab\n",
- " from google.colab import output\n",
- " output.enable_custom_widget_manager()\n",
- "except:\n",
- " pass"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3d367f22-0f34-49c7-b224-5e0a1e3937d1",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d27114d0-2133-4c6f-87c1-f049e56448c6",
- "metadata": {},
- "source": [
- "## How to import a graph\n",
- "- either import the graph directly when initilizing: ```GraphWidget(graph=your_graph)```\n",
- "- or use the ```w.import_graph(your_graph)``` function, if you already initilized a Widget called ```w```"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4450f8a7-3444-4e4b-bc39-678ac09ddedf",
- "metadata": {},
- "source": [
- "## Notes about NetworkX importer \n",
- "- graph attributes are ignored\n",
- "- node identifiers are saved under property `label` (or `yf_label` if key `label` already exists)\n",
- "- subgraphs (graph as node, see networx docs) are not supported\n",
- "- any additional data is stored in ```properties``` "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "864ff6dd-fa00-4a3e-a32d-6781c00e7202",
- "metadata": {},
- "source": [
- "### Import undirected graph with self loops\n",
- "https://networkx.org/documentation/stable/reference/classes/graph.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "da205fc6-af88-4dbe-9d2d-23beb6f66e3e",
- "metadata": {},
- "outputs": [],
- "source": [
- "def make_graph(graph_class):\n",
- " G = graph_class(day=\"Friday\")\n",
- " G.add_node(\"origin\")\n",
- " G.add_node(1)\n",
- " G.add_nodes_from([2, 3])\n",
- " H = path_graph(10)\n",
- " G.add_nodes_from(H)\n",
- " G.add_edge(1, 2)\n",
- " G.add_edges_from([(1, 2), (1, 3)])\n",
- " G.add_edges_from(H.edges)\n",
- " G.add_node(1, time=\"5pm\")\n",
- " G.add_nodes_from([3], time=\"2pm\")\n",
- " for n in G.nodes:\n",
- " G.add_edge(\"origin\", n)\n",
- " G.nodes[1][\"room\"] = 714\n",
- " G.add_edge(1, 2, weight=4.7)\n",
- " G.add_edges_from([(3, 4), (4, 5)], color=\"red\")\n",
- " G.add_edges_from([(1, 2, {\"color\": \"blue\"}), (2, 3, {\"weight\": 8})])\n",
- " G[1][2][\"weight\"] = 4.7\n",
- " G.edges[1, 2][\"weight\"] = 4\n",
- " return G\n",
- "display(GraphWidget(graph=make_graph(Graph)))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "40d439fc-7406-4435-b043-32534ebc23ce",
- "metadata": {},
- "source": [
- "### import directed graph with self loops\n",
- "https://networkx.org/documentation/stable/reference/classes/digraph.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "37dfb8b1-a22d-4ca8-922d-be5de5455e0e",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(GraphWidget(graph=make_graph(DiGraph)))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c4a3a547-4e02-4f29-9343-5dbd1b9ea0f3",
- "metadata": {},
- "source": [
- "### import undirected graph with self loops and parallel edges\n",
- "https://networkx.org/documentation/stable/reference/classes/multigraph.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "487e2f91-9be8-41e7-b26f-23cd0de5291e",
- "metadata": {},
- "outputs": [],
- "source": [
- "def make_graph_(graph_class):\n",
- " G = graph_class(day=\"Friday\")\n",
- " G.add_node(\"origin\")\n",
- " G.add_node(1)\n",
- " G.add_nodes_from([2, 3])\n",
- " H = path_graph(10)\n",
- " G.add_nodes_from(H)\n",
- " G.add_edge(1, 2)\n",
- " G.add_edges_from([(1, 2), (1, 3)])\n",
- " G.add_edges_from(H.edges)\n",
- " G.add_node(1, time=\"5pm\")\n",
- " G.add_nodes_from([3], time=\"2pm\")\n",
- " for n in G.nodes:\n",
- " G.add_edge(\"origin\", n)\n",
- " G.add_edges_from([(4, 5, dict(route=282)), (4, 5, dict(route=37))])\n",
- " G.add_edge(1, 2, weight=4.7)\n",
- " G.add_edges_from([(3, 4), (4, 5)], color=\"red\")\n",
- " G.add_edges_from([(1, 2, {\"color\": \"blue\"}), (2, 3, {\"weight\": 8})])\n",
- " G[1][2][0][\"weight\"] = 4.7\n",
- " G.edges[1, 2, 0][\"weight\"] = 4\n",
- " return G\n",
- "display(GraphWidget(graph=make_graph_(MultiGraph)))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f940c0c5-8d34-4a39-ac52-2a884e69ad47",
- "metadata": {},
- "source": [
- "### Import directed graph with self loops and parallel edges\n",
- "https://networkx.org/documentation/stable/reference/classes/multidigraph.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d50c0882-e673-4997-89bf-eb60632064cd",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(GraphWidget(graph=make_graph_(MultiDiGraph)))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5e702e7d-67d7-4815-b305-2b78c5871c0c",
- "metadata": {},
- "source": [
- "The resulting graph has properties that are not displayed by simply importing the graph. \\\n",
- "You can access this data by looking at the nodes and edges data: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9b9a4ea1-eccf-4ad0-b266-828d96f0320b",
- "metadata": {},
- "outputs": [],
- "source": [
- "w = GraphWidget(graph=make_graph(Graph))\n",
- "w.edges"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "0a5d25c2-643b-48f3-8136-28aefac4cafe",
- "metadata": {},
- "source": [
- "### Using graph data\n",
- "\n",
- "To access the 'properties' data, you can use the data key in squared brackets: ```['properties']['key'] ```\n",
- "\n",
- "Possible keys in this example are 'weight' and 'color'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "56147fb6-5a95-40cc-94c1-8962075ce774",
- "metadata": {},
- "outputs": [],
- "source": [
- "formattedProperties = ''.join(f\"Edge {edge['id']}: {edge}\\n\" for edge in w.edges)\n",
- "print(formattedProperties)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b65f203c-280c-4664-8da8-a9ad211cde8b",
- "metadata": {},
- "source": [
- "Let's use the 'color' key in properties to turn some edges blue and red. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6139a3ee-ce54-41d8-9951-469db637ace7",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.edge_color_mapping = 'color'\n",
- "display(w)"
- ]
- }
- ],
- "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": "3.11.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/14_pandas_import.ipynb b/examples/14_pandas_import.ipynb
index 7ff7652..7650f39 100644
--- a/examples/14_pandas_import.ipynb
+++ b/examples/14_pandas_import.ipynb
@@ -1,201 +1,1202 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "32d03e09-094d-40f1-bdb1-5ee752ed94b8",
- "metadata": {},
- "source": [
- "# Visualizing Pandas DataFrames in yFiles Graphs for Jupyter "
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "32d03e09-094d-40f1-bdb1-5ee752ed94b8",
+ "metadata": {
+ "id": "32d03e09-094d-40f1-bdb1-5ee752ed94b8"
+ },
+ "source": [
+ "# Visualizing Pandas DataFrames in yFiles Graphs for Jupyter "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "87dc4803-39b0-434f-bba4-eac433d6016b",
+ "metadata": {
+ "id": "87dc4803-39b0-434f-bba4-eac433d6016b"
+ },
+ "source": [
+ "Before using the graph widget, install all necessary packages."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "5c411c64-1368-454d-b725-c949e09330f6",
+ "metadata": {
+ "id": "5c411c64-1368-454d-b725-c949e09330f6",
+ "outputId": "e018a407-a68c-430e-e917-59b1beb96a79",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m14.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m22.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m25.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "%pip install pandas --quiet\n",
+ "import pandas as pd\n",
+ "from yfiles_jupyter_graphs import GraphWidget"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c6ea79b9-6287-475e-9c3b-094b63177e56",
+ "metadata": {
+ "id": "c6ea79b9-6287-475e-9c3b-094b63177e56"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "03e06677-a479-4977-83ac-7e8382f0ca08",
+ "metadata": {
+ "id": "03e06677-a479-4977-83ac-7e8382f0ca08"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c5c6deb9-87cc-4f04-8576-e01e2452976d",
+ "metadata": {
+ "id": "c5c6deb9-87cc-4f04-8576-e01e2452976d"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dfb0c218-6da5-48d2-8eba-3a0edacde699",
+ "metadata": {
+ "id": "dfb0c218-6da5-48d2-8eba-3a0edacde699"
+ },
+ "source": [
+ "## How to import a graph\n",
+ "- either import the graph directly when initilizing: `GraphWidget(graph=your_graph)`\n",
+ "- or use the `w.import_graph(your_graph)` function, if you already initilized a Widget called `w`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2e31422e-3de0-452c-8147-26780b1b1fab",
+ "metadata": {
+ "id": "2e31422e-3de0-452c-8147-26780b1b1fab"
+ },
+ "source": [
+ "## Notes about pandas importer\n",
+ "- each row corresponds to an edge\n",
+ "- the edges are defined by pairs of 'source' and 'target' indices\n",
+ "- if you have a 'label' column in your DataFrame, the edges automatically have this label\n",
+ "- the default edge is always directed\n",
+ "- nodes are created for every id used in `source` and `target`\n",
+ "- any additional DataFrame columns are stored in `properties` under the same name"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fb7a4ebd-c29e-47be-9445-d13cbee5cbec",
+ "metadata": {
+ "id": "fb7a4ebd-c29e-47be-9445-d13cbee5cbec"
+ },
+ "source": [
+ "## Sample data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "e810300a-7e22-48d7-a1ac-d1106364aba0",
+ "metadata": {
+ "id": "e810300a-7e22-48d7-a1ac-d1106364aba0",
+ "outputId": "1b093aad-eb4c-4951-ba2a-58b2644eb4b5",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 363
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " source target label id age color\n",
+ "0 Node 0 Node 3 Row 0 0 31 red\n",
+ "1 Node 0 Node 4 Row 1 1 56 blue\n",
+ "2 Node 1 Node 4 Row 2 2 27 green\n",
+ "3 Node 2 Node 5 Row 3 3 43 orange\n",
+ "4 Node 2 Node 6 Row 4 4 19 purple\n",
+ "5 Node 2 Node 7 Row 5 5 84 yellow\n",
+ "6 Node 3 Node 8 Row 6 6 38 grey\n",
+ "7 Node 3 Node 9 Row 7 7 70 pink\n",
+ "8 Node 4 Node 6 Row 8 8 5 black\n",
+ "9 Node 5 Node 6 Row 9 9 92 brown"
+ ],
+ "text/html": [
+ "\n",
+ " | \n", + " | source | \n", + "target | \n", + "label | \n", + "id | \n", + "age | \n", + "color | \n", + "
|---|---|---|---|---|---|---|
| 0 | \n", + "Node 0 | \n", + "Node 3 | \n", + "Row 0 | \n", + "0 | \n", + "31 | \n", + "red | \n", + "
| 1 | \n", + "Node 0 | \n", + "Node 4 | \n", + "Row 1 | \n", + "1 | \n", + "56 | \n", + "blue | \n", + "
| 2 | \n", + "Node 1 | \n", + "Node 4 | \n", + "Row 2 | \n", + "2 | \n", + "27 | \n", + "green | \n", + "
| 3 | \n", + "Node 2 | \n", + "Node 5 | \n", + "Row 3 | \n", + "3 | \n", + "43 | \n", + "orange | \n", + "
| 4 | \n", + "Node 2 | \n", + "Node 6 | \n", + "Row 4 | \n", + "4 | \n", + "19 | \n", + "purple | \n", + "
| 5 | \n", + "Node 2 | \n", + "Node 7 | \n", + "Row 5 | \n", + "5 | \n", + "84 | \n", + "yellow | \n", + "
| 6 | \n", + "Node 3 | \n", + "Node 8 | \n", + "Row 6 | \n", + "6 | \n", + "38 | \n", + "grey | \n", + "
| 7 | \n", + "Node 3 | \n", + "Node 9 | \n", + "Row 7 | \n", + "7 | \n", + "70 | \n", + "pink | \n", + "
| 8 | \n", + "Node 4 | \n", + "Node 6 | \n", + "Row 8 | \n", + "8 | \n", + "5 | \n", + "black | \n", + "
| 9 | \n", + "Node 5 | \n", + "Node 6 | \n", + "Row 9 | \n", + "9 | \n", + "92 | \n", + "brown | \n", + "
"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "dfb0c218-6da5-48d2-8eba-3a0edacde699",
- "metadata": {},
- "source": [
- "## How to import a graph\n",
- "- either import the graph directly when initilizing: `GraphWidget(graph=your_graph)`\n",
- "- or use the `w.import_graph(your_graph)` function, if you already initilized a Widget called `w`"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2e31422e-3de0-452c-8147-26780b1b1fab",
- "metadata": {},
- "source": [
- "## Notes about pandas importer\n",
- "- each row corresponds to an edge\n",
- "- the edges are defined by pairs of 'source' and 'target' indices\n",
- "- if you have a 'label' column in your DataFrame, the edges automatically have this label\n",
- "- the default edge is always directed\n",
- "- nodes are created for every id used in `source` and `target`\n",
- "- any additional DataFrame columns are stored in `properties` under the same name"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fb7a4ebd-c29e-47be-9445-d13cbee5cbec",
- "metadata": {},
- "source": [
- "## Sample data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e810300a-7e22-48d7-a1ac-d1106364aba0",
- "metadata": {},
- "outputs": [],
- "source": [
- "data = {'source': ['Node 0','Node 0','Node 1','Node 2','Node 2','Node 2','Node 3','Node 3','Node 4','Node 5'],\n",
- " 'target': ['Node 3','Node 4','Node 4', 'Node 5', 'Node 6','Node 7','Node 8','Node 9','Node 6','Node 6'],\n",
- " 'label': ['Row 0','Row 1','Row 2','Row 3','Row 4','Row 5','Row 6','Row 7','Row 8','Row 9'],\n",
- " 'id': ['0','1','2','3','4','5','6','7','8','9'],\n",
- " 'age': [31, 56, 27, 43, 19, 84, 38, 70, 5, 92],\n",
- " 'color': ['red','blue','green','orange','purple','yellow','grey','pink','black','brown']}\n",
- "df = pd.DataFrame(data)\n",
- "df"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f13f87af-def7-42bd-8222-25cff4666150",
- "metadata": {},
- "source": [
- "## Visualizing the sample data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0e640a88-6512-4638-97ba-ce34e7c93a14",
- "metadata": {},
- "outputs": [],
- "source": [
- "w = GraphWidget(graph = df)\n",
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b13b64ac-25b1-4cf6-8ad6-378f4892a59d",
- "metadata": {},
- "source": [
- "When hovering over a edge, you can see the age and color data for each edge. You can look into the edge data as well.\n",
- "\n",
- "To access the 'properties' data, you can use the data key in squared brackets: `['properties']['key'] `\n",
- "\n",
- "Possible edge keys in this example are 'label', 'age' and 'color' \n",
- "\n",
- "To display all properties, we remove any additional edge data except the properties"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "50579e0a-88c0-4bd5-94c6-7d7113c6b0e4",
- "metadata": {},
- "outputs": [],
- "source": [
- "properties = [edge['properties'] for edge in w.edges]\n",
- "formattedProperties = ''.join(f\"Edge {edge['label']}: {edge}\\n\" for edge in properties)\n",
- "print(formattedProperties)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "361a0f2f-ff40-4e14-bb5e-cb0c08904b01",
- "metadata": {},
- "source": [
- "### Using column data stored in 'properties'\n",
- "\n",
- "To utilize the age and color data, we set the thickness factor of the edge as the age and the color as the edge color"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "31594712-81a6-4811-9cb8-1fc606f967d9",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.edge_color_mapping = 'color'\n",
- "w.edge_thickness_factor_mapping = lambda item: item['properties']['age'] / 35\n",
- "display(w)"
- ]
- }
- ],
- "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": "3.12.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/16_neo4j_import.ipynb b/examples/16_neo4j_import.ipynb
index 9f7b81c..d4dc188 100644
--- a/examples/16_neo4j_import.ipynb
+++ b/examples/16_neo4j_import.ipynb
@@ -1,255 +1,2475 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "3f6f41b8-4449-45bb-8fec-72598dc80e18",
- "metadata": {},
- "source": [
- "# Visualizing Neo4j graphs in yFiles Graphs for Jupyter "
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "3f6f41b8-4449-45bb-8fec-72598dc80e18",
+ "metadata": {
+ "id": "3f6f41b8-4449-45bb-8fec-72598dc80e18"
+ },
+ "source": [
+ "# Visualizing Neo4j graphs in yFiles Graphs for Jupyter "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f04acd58-3dcb-454a-9a92-79e59aab3721",
+ "metadata": {
+ "id": "f04acd58-3dcb-454a-9a92-79e59aab3721"
+ },
+ "source": [
+ "## Note `yfiles-jupyter-graphs-for-neo4j`\n",
+ "\n",
+ "We have built a specifcally tailored extension for working with Neo4j databases: [yfiles-jupyter-graphs-for-neo4j](https://github.com/yWorks/yfiles-jupyter-graphs-for-neo4j/) on top of the `yfiles-jupyter-graphs` widget. This extension provides an easier Python interface for the driver and allows direct configuration of data mappings depending on the label or type of the node or relationship."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "95c4bb2b-5952-43b5-8f95-6683a2d05309",
+ "metadata": {
+ "tags": [],
+ "id": "95c4bb2b-5952-43b5-8f95-6683a2d05309"
+ },
+ "source": [
+ "## Using `yfiles-jupyter-graphs`\n",
+ "\n",
+ "Before using the graph widget, install all necessary packages."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "9bbc5e34-b83a-48eb-b918-f08bbd149275",
+ "metadata": {
+ "id": "9bbc5e34-b83a-48eb-b918-f08bbd149275",
+ "outputId": "474ee031-72fb-486a-9ef2-c1b58860489d",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m24.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m7.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m38.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m16.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m296.6/296.6 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "%pip install neo4j --quiet\n",
+ "from neo4j import GraphDatabase\n",
+ "from yfiles_jupyter_graphs import GraphWidget"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9b50bd83-a689-46d5-ad25-b8afde5bd45f",
+ "metadata": {
+ "id": "9b50bd83-a689-46d5-ad25-b8afde5bd45f"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "84373484-d7f0-4c6a-bdb6-40ebcb66ca0c",
+ "metadata": {
+ "id": "84373484-d7f0-4c6a-bdb6-40ebcb66ca0c"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8884af78-e001-45fd-8cf5-28479946415d",
+ "metadata": {
+ "id": "8884af78-e001-45fd-8cf5-28479946415d"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9df19056-475f-4615-8c81-3354c6f3fc8f",
+ "metadata": {
+ "id": "9df19056-475f-4615-8c81-3354c6f3fc8f"
+ },
+ "source": [
+ "## How to import a graph\n",
+ "- either import the graph directly when initilizing: ```GraphWidget(graph=your_graph)```\n",
+ "- or use the ```w.import_graph(your_graph)``` function, if you already initilized a Widget called ```w```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "181df1a8-125d-4561-b682-1f833b1195f3",
+ "metadata": {
+ "id": "181df1a8-125d-4561-b682-1f833b1195f3"
+ },
+ "source": [
+ "### Helper function to directly show a Cypher query as graph"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "e604df90-9727-4bcf-9e67-b7a3f469ec11",
+ "metadata": {
+ "id": "e604df90-9727-4bcf-9e67-b7a3f469ec11"
+ },
+ "outputs": [],
+ "source": [
+ "NEO4J_URI = \"neo4j+ssc://demo.neo4jlabs.com\"\n",
+ "NEO4J_USERNAME = \"movies\"\n",
+ "NEO4J_PASSWORD = \"movies\"\n",
+ "\n",
+ "# create a neo4j session to run queries\n",
+ "driver = GraphDatabase.driver(uri = NEO4J_URI, auth = (NEO4J_USERNAME, NEO4J_PASSWORD), database = 'movies')\n",
+ "session = driver.session()\n",
+ "\n",
+ "# directly show the graph resulting from the given Cypher query\n",
+ "def showGraph(cypher: str):\n",
+ " widget = GraphWidget(graph = session.run(cypher).graph())\n",
+ " display(widget)\n",
+ " return widget"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d8b50c96-0e49-4182-a2b8-19abd0f21557",
+ "metadata": {
+ "id": "d8b50c96-0e49-4182-a2b8-19abd0f21557"
+ },
+ "source": [
+ "### Notes about Neo4j importer\n",
+ "- By default, the widget displays one of the following properties in descending priority as item text: `['name', 'title', 'label', 'description', 'caption', 'text']`. If no such property is available, the node's label or relationship's type is displayed. The visualized label can be adjusted by setting a different label-mapping, e.g., see [02_label_mapping](./02_label_mapping.ipynb).\n",
+ "- Node labels are combined with ':' and added as `label` property on the item\n",
+ "- Node and relationship properties are available on the graph item's `properties`.\n",
+ "- Each node label or relationship type is assigned a specific color."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "93400164-96cb-4458-acad-3050d5345952",
+ "metadata": {
+ "id": "93400164-96cb-4458-acad-3050d5345952"
+ },
+ "source": [
+ "## Use Cypher to display a graph"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "cfcc7b8e-d0b9-438d-b608-e9161330dc2e",
+ "metadata": {
+ "id": "cfcc7b8e-d0b9-438d-b608-e9161330dc2e",
+ "outputId": "33484150-fd27-4b5a-fe34-40910b387a49",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 627,
+ "referenced_widgets": [
+ "2d825dc3b71a46b7882bbbe43c944f84",
+ "6a8234d53db94d7193490228d97ff19e"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='610px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "2d825dc3b71a46b7882bbbe43c944f84"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w = showGraph(\"MATCH (s)-[r]->(t) RETURN s,r,t LIMIT 20\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "31804019-3a47-49f2-950d-5b2f4b57b224",
+ "metadata": {
+ "id": "31804019-3a47-49f2-950d-5b2f4b57b224"
+ },
+ "source": [
+ "# Map data to visualization\n",
+ "\n",
+ "The yFiles Graphs for Jupyter widget provides different mapping function with which we can display node and edge properties in the diagram.\n",
+ "\n",
+ "To access the 'properties' data, you can use the data key in squared brackets: ```['properties']['key'] ```\n",
+ "\n",
+ "Possible node keys in this example are 'born', 'name', 'tagline', 'votes', 'title', 'released' and 'label'\n",
+ "\n",
+ "To visualize all properties, we remove any additional node data except the properties"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "4e9a1739-4f66-4f30-8b0c-c2f9582be562",
+ "metadata": {
+ "id": "4e9a1739-4f66-4f30-8b0c-c2f9582be562",
+ "outputId": "c419d7b5-25a9-4551-cc18-faa7b2a24b2c",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "{'born': 1964, 'name': 'Keanu Reeves', 'label': 'Person'}\n",
+ "{'tagline': 'Welcome to the Real World', 'votes': 5941, 'title': 'The Matrix', 'released': 1999, 'label': 'Movie'}\n",
+ "{'born': 1967, 'name': 'Carrie-Anne Moss', 'label': 'Person'}\n",
+ "{'born': 1961, 'name': 'Laurence Fishburne', 'label': 'Person'}\n",
+ "{'born': 1960, 'name': 'Hugo Weaving', 'label': 'Person'}\n",
+ "{'born': 1967, 'name': 'Lilly Wachowski', 'label': 'Person'}\n",
+ "{'born': 1965, 'name': 'Lana Wachowski', 'label': 'Person'}\n",
+ "{'born': 1952, 'name': 'Joel Silver', 'label': 'Person'}\n",
+ "{'born': 1978, 'name': 'Emil Eifrem', 'label': 'Person'}\n",
+ "{'tagline': 'Free your mind', 'votes': 1743, 'title': 'The Matrix Reloaded', 'released': 2003, 'label': 'Movie'}\n",
+ "{'tagline': 'Everything that has a beginning has an end', 'votes': 1407, 'title': 'The Matrix Revolutions', 'released': 2003, 'label': 'Movie'}\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "properties = [node['properties'] for node in w.nodes]\n",
+ "formattedProperties = ''.join(f\"{node}\\n\" for node in properties)\n",
+ "print(formattedProperties)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "ac965084-deff-4721-9afe-c7319f1f5579",
+ "metadata": {
+ "id": "ac965084-deff-4721-9afe-c7319f1f5579",
+ "outputId": "9db575b5-2016-4b16-e79d-f42ef6ae6c9f",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 627,
+ "referenced_widgets": [
+ "d909dbb40e434b378c9b2e320d4c6b40",
+ "21b50b528ab34ae8aec3853163ba1972"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='610px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "d909dbb40e434b378c9b2e320d4c6b40"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w2 = GraphWidget(graph = session.run(\"MATCH (s)-[r]->(t) RETURN s,r,t LIMIT 20\").graph())\n",
+ "\n",
+ "def custom_node_label_mapping(index, node):\n",
+ " \"\"\"let the label be the name or the title\"\"\"\n",
+ " properties = node.get('properties', {})\n",
+ " return {\n",
+ " 'text': properties.get('name', 'no name') + (\"\\n ({})\".format(properties.get('label', ''))),\n",
+ " 'textAlignment': 'center'\n",
+ " }\n",
+ "w2.node_label_mapping = custom_node_label_mapping\n",
+ "\n",
+ "def custom_relationship_label_mapping(index, node):\n",
+ " \"\"\"let the label be the role\"\"\"\n",
+ " properties = node.get('properties', {})\n",
+ " return {\n",
+ " 'text': properties.get('roles', ['no role'])[0] + (\"\\n ({})\".format(properties.get('label', ''))),\n",
+ " 'fontSize': 8,\n",
+ " 'textAlignment': 'center'\n",
+ " }\n",
+ "w2.edge_label_mapping = custom_relationship_label_mapping\n",
+ "\n",
+ "w2.hierarchic_layout()\n",
+ "display(w2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "093939d9-a9bd-4d16-8a44-60c5356b9ef2",
+ "metadata": {
+ "id": "093939d9-a9bd-4d16-8a44-60c5356b9ef2"
+ },
+ "source": [
+ "An alternative method for changing the names and titles to labels, utilizing the \"title\" and \"name\" properties stored in the node properties:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "b2ccec0c-0e29-490c-941a-458baa03bc5e",
+ "metadata": {
+ "id": "b2ccec0c-0e29-490c-941a-458baa03bc5e",
+ "outputId": "8b54666b-cfc1-4bb9-a2b7-888f25956a91",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 627,
+ "referenced_widgets": [
+ "fd374611111b4466873f35adb23d9a91",
+ "74086eff078649289940a145b928b0ab"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='610px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
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+ "model_id": "fd374611111b4466873f35adb23d9a91"
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+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w3 = GraphWidget(graph = session.run(\"MATCH (s)-[r]->(t) RETURN s,r,t LIMIT 20\").graph())\n",
+ "\n",
+ "w3.node_label_mapping = lambda node: node['properties']['name'] if 'name' in node['properties'] else node['properties']['title']\n",
+ "\n",
+ "w3.hierarchic_layout()\n",
+ "\n",
+ "w3.set_heat_mapping(lambda element: element['properties']['votes']/ 7000 if 'votes' in element['properties'] else 0)\n",
+ "\n",
+ "display(w3)"
+ ]
+ }
+ ],
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+ "label": "Lana Wachowski",
+ "scale_factor": 1,
+ "type": "#2196F3",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ],
+ "heat": 0
+ },
+ {
+ "id": 7,
+ "properties": {
+ "born": 1952,
+ "name": "Joel Silver",
+ "label": "Person"
+ },
+ "color": "#2196F3",
+ "styles": {},
+ "label": "Joel Silver",
+ "scale_factor": 1,
+ "type": "#2196F3",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ],
+ "heat": 0
+ },
+ {
+ "id": 8,
+ "properties": {
+ "born": 1978,
+ "name": "Emil Eifrem",
+ "label": "Person"
+ },
+ "color": "#2196F3",
+ "styles": {},
+ "label": "Emil Eifrem",
+ "scale_factor": 1,
+ "type": "#2196F3",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ],
+ "heat": 0
+ },
+ {
+ "id": 9,
+ "properties": {
+ "tagline": "Free your mind",
+ "votes": 1743,
+ "title": "The Matrix Reloaded",
+ "released": 2003,
+ "label": "Movie"
+ },
+ "color": "#4CAF50",
+ "styles": {},
+ "label": "The Matrix Reloaded",
+ "scale_factor": 1,
+ "type": "#4CAF50",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ],
+ "heat": 0.249
+ },
+ {
+ "id": 10,
+ "properties": {
+ "tagline": "Everything that has a beginning has an end",
+ "votes": 1407,
+ "title": "The Matrix Revolutions",
+ "released": 2003,
+ "label": "Movie"
+ },
+ "color": "#4CAF50",
+ "styles": {},
+ "label": "The Matrix Revolutions",
+ "scale_factor": 1,
+ "type": "#4CAF50",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ],
+ "heat": 0.201
+ }
+ ],
+ "_overview": {
+ "enabled": null,
+ "overview_set": false
+ },
+ "_selected_graph": [
+ [],
+ []
+ ],
+ "_sidebar": {
+ "enabled": false,
+ "start_with": null
+ },
+ "_view_count": null,
+ "_view_module": "yfiles-jupyter-graphs",
+ "_view_module_version": "^1.8.1",
+ "_view_name": "GraphView",
+ "layout": "IPY_MODEL_74086eff078649289940a145b928b0ab",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "74086eff078649289940a145b928b0ab": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "2.0.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "2.0.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border_bottom": null,
+ "border_left": null,
+ "border_right": null,
+ "border_top": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": "610px",
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": "100%"
+ }
+ }
+ }
+ }
},
- {
- "cell_type": "markdown",
- "id": "f04acd58-3dcb-454a-9a92-79e59aab3721",
- "metadata": {},
- "source": [
- "## Note `yfiles-jupyter-graphs-for-neo4j`\n",
- "\n",
- "We have built a specifcally tailored extension for working with Neo4j databases: [yfiles-jupyter-graphs-for-neo4j](https://github.com/yWorks/yfiles-jupyter-graphs-for-neo4j/) on top of the `yfiles-jupyter-graphs` widget. This extension provides an easier Python interface for the driver and allows direct configuration of data mappings depending on the label or type of the node or relationship."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "95c4bb2b-5952-43b5-8f95-6683a2d05309",
- "metadata": {
- "tags": []
- },
- "source": [
- "## Using `yfiles-jupyter-graphs`\n",
- "\n",
- "Before using the graph widget, install all necessary packages."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9bbc5e34-b83a-48eb-b918-f08bbd149275",
- "metadata": {},
- "outputs": [],
- "source": [
- "%pip install yfiles_jupyter_graphs --quiet\n",
- "%pip install neo4j --quiet\n",
- "from neo4j import GraphDatabase\n",
- "from yfiles_jupyter_graphs import GraphWidget"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9b50bd83-a689-46d5-ad25-b8afde5bd45f",
- "metadata": {},
- "source": [
- "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "84373484-d7f0-4c6a-bdb6-40ebcb66ca0c",
- "metadata": {},
- "outputs": [],
- "source": [
- "try:\n",
- " import google.colab\n",
- " from google.colab import output\n",
- " output.enable_custom_widget_manager()\n",
- "except:\n",
- " pass"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8884af78-e001-45fd-8cf5-28479946415d",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9df19056-475f-4615-8c81-3354c6f3fc8f",
- "metadata": {},
- "source": [
- "## How to import a graph\n",
- "- either import the graph directly when initilizing: ```GraphWidget(graph=your_graph)```\n",
- "- or use the ```w.import_graph(your_graph)``` function, if you already initilized a Widget called ```w```"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "181df1a8-125d-4561-b682-1f833b1195f3",
- "metadata": {},
- "source": [
- "### Helper function to directly show a Cypher query as graph"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e604df90-9727-4bcf-9e67-b7a3f469ec11",
- "metadata": {},
- "outputs": [],
- "source": [
- "NEO4J_URI = \"neo4j+ssc://demo.neo4jlabs.com\" \n",
- "NEO4J_USERNAME = \"movies\"\n",
- "NEO4J_PASSWORD = \"movies\"\n",
- "\n",
- "# create a neo4j session to run queries\n",
- "driver = GraphDatabase.driver(uri = NEO4J_URI, auth = (NEO4J_USERNAME, NEO4J_PASSWORD), database = 'movies')\n",
- "session = driver.session()\n",
- "\n",
- "# directly show the graph resulting from the given Cypher query\n",
- "def showGraph(cypher: str):\n",
- " widget = GraphWidget(graph = session.run(cypher).graph()) \n",
- " display(widget)\n",
- " return widget"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d8b50c96-0e49-4182-a2b8-19abd0f21557",
- "metadata": {},
- "source": [
- "### Notes about Neo4j importer\n",
- "- By default, the widget displays one of the following properties in descending priority as item text: `['name', 'title', 'label', 'description', 'caption', 'text']`. If no such property is available, the node's label or relationship's type is displayed. The visualized label can be adjusted by setting a different label-mapping, e.g., see [02_label_mapping](./02_label_mapping.ipynb).\n",
- "- Node labels are combined with ':' and added as `label` property on the item\n",
- "- Node and relationship properties are available on the graph item's `properties`.\n",
- "- Each node label or relationship type is assigned a specific color."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "93400164-96cb-4458-acad-3050d5345952",
- "metadata": {},
- "source": [
- "## Use Cypher to display a graph"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "cfcc7b8e-d0b9-438d-b608-e9161330dc2e",
- "metadata": {},
- "outputs": [],
- "source": [
- "w = showGraph(\"MATCH (s)-[r]->(t) RETURN s,r,t LIMIT 20\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "31804019-3a47-49f2-950d-5b2f4b57b224",
- "metadata": {},
- "source": [
- "# Map data to visualization\n",
- "\n",
- "The yFiles Graphs for Jupyter widget provides different mapping function with which we can display node and edge properties in the diagram.\n",
- "\n",
- "To access the 'properties' data, you can use the data key in squared brackets: ```['properties']['key'] ```\n",
- "\n",
- "Possible node keys in this example are 'born', 'name', 'tagline', 'votes', 'title', 'released' and 'label' \n",
- "\n",
- "To visualize all properties, we remove any additional node data except the properties"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4e9a1739-4f66-4f30-8b0c-c2f9582be562",
- "metadata": {},
- "outputs": [],
- "source": [
- "properties = [node['properties'] for node in w.nodes]\n",
- "formattedProperties = ''.join(f\"{node}\\n\" for node in properties)\n",
- "print(formattedProperties)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ac965084-deff-4721-9afe-c7319f1f5579",
- "metadata": {},
- "outputs": [],
- "source": [
- "w2 = GraphWidget(graph = session.run(\"MATCH (s)-[r]->(t) RETURN s,r,t LIMIT 20\").graph())\n",
- "\n",
- "def custom_node_label_mapping(index, node):\n",
- " \"\"\"let the label be the name or the title\"\"\"\n",
- " properties = node.get('properties', {})\n",
- " return {\n",
- " 'text': properties.get('name', 'no name') + (\"\\n ({})\".format(properties.get('label', ''))),\n",
- " 'textAlignment': 'center'\n",
- " }\n",
- "w2.node_label_mapping = custom_node_label_mapping\n",
- "\n",
- "def custom_relationship_label_mapping(index, node):\n",
- " \"\"\"let the label be the role\"\"\"\n",
- " properties = node.get('properties', {})\n",
- " return {\n",
- " 'text': properties.get('roles', ['no role'])[0] + (\"\\n ({})\".format(properties.get('label', ''))),\n",
- " 'fontSize': 8, \n",
- " 'textAlignment': 'center'\n",
- " }\n",
- "w2.edge_label_mapping = custom_relationship_label_mapping\n",
- "\n",
- "w2.hierarchic_layout()\n",
- "display(w2)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "093939d9-a9bd-4d16-8a44-60c5356b9ef2",
- "metadata": {},
- "source": [
- "An alternative method for changing the names and titles to labels, utilizing the \"title\" and \"name\" properties stored in the node properties:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b2ccec0c-0e29-490c-941a-458baa03bc5e",
- "metadata": {},
- "outputs": [],
- "source": [
- "w3 = GraphWidget(graph = session.run(\"MATCH (s)-[r]->(t) RETURN s,r,t LIMIT 20\").graph())\n",
- "\n",
- "w3.node_label_mapping = lambda node: node['properties']['name'] if 'name' in node['properties'] else node['properties']['title']\n",
- "\n",
- "w3.hierarchic_layout()\n",
- "\n",
- "w3.set_heat_mapping(lambda element: element['properties']['votes']/ 7000 if 'votes' in element['properties'] else 0)\n",
- "\n",
- "display(w3)"
- ]
- }
- ],
- "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": "3.11.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/17_igraph_import.ipynb b/examples/17_igraph_import.ipynb
index b99b7c6..176bd8b 100644
--- a/examples/17_igraph_import.ipynb
+++ b/examples/17_igraph_import.ipynb
@@ -1,190 +1,1004 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "4cc79916-98aa-4d1e-bd5f-94eb03ef51cc",
- "metadata": {},
- "source": [
- "# Visualizing IGraph graphs in yFiles Graphs for Jupyter "
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "4cc79916-98aa-4d1e-bd5f-94eb03ef51cc",
+ "metadata": {
+ "id": "4cc79916-98aa-4d1e-bd5f-94eb03ef51cc"
+ },
+ "source": [
+ "# Visualizing IGraph graphs in yFiles Graphs for Jupyter "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c6588ec8-b229-4ca1-9cde-cc3aa8e50cbb",
+ "metadata": {
+ "id": "c6588ec8-b229-4ca1-9cde-cc3aa8e50cbb"
+ },
+ "source": [
+ "Before using the graph widget, install all necessary packages."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "308bda31-f1e5-441c-afe8-291743b19970",
+ "metadata": {
+ "id": "308bda31-f1e5-441c-afe8-291743b19970",
+ "outputId": "04c69b1a-dc94-4aee-eedc-58a42a683743",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m31.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m28.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m34.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m23.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "%pip install igraph --quiet\n",
+ "from typing import Dict\n",
+ "from igraph import Graph\n",
+ "from yfiles_jupyter_graphs import GraphWidget"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ae5b3eea-3fca-4c9b-b2bf-5f4b7b4b399e",
+ "metadata": {
+ "id": "ae5b3eea-3fca-4c9b-b2bf-5f4b7b4b399e"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "dc224c8f-49f5-42e7-b015-b96fbbd27970",
+ "metadata": {
+ "id": "dc224c8f-49f5-42e7-b015-b96fbbd27970"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b8226994-22e2-4e91-a7ec-3b9dd535c5e4",
+ "metadata": {
+ "id": "b8226994-22e2-4e91-a7ec-3b9dd535c5e4"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2b2ad4bf-a516-4eaa-80a7-7dfd98bcf88c",
+ "metadata": {
+ "id": "2b2ad4bf-a516-4eaa-80a7-7dfd98bcf88c"
+ },
+ "source": [
+ "## How to import a graph\n",
+ "- either import the graph directly when initilizing: ```GraphWidget(graph=your_graph)```\n",
+ "- or use the ```w.import_graph(your_graph)``` function, if you already initilized a Widget called ```w```\n",
+ "\n",
+ "## Notes about IGraph importer\n",
+ "- Nodes and edges are identified by index attribute.\n",
+ "- Node and edge properties are provided through attributes method.\n",
+ "- Edges are determined by source and target attribute.\n",
+ "- Any additional data is stored in ```properties```\n",
+ "\n",
+ "## Import graph from tutorial\n",
+ "https://igraph.org/python/tutorial/latest/tutorial.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "2b183c0f-a452-4d37-a759-88e0207b24e0",
+ "metadata": {
+ "id": "2b183c0f-a452-4d37-a759-88e0207b24e0",
+ "outputId": "1a76fc31-4e50-4e30-c0b8-1bbe74d228c7",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 517,
+ "referenced_widgets": [
+ "454cba81523e47a3be869822e0f53963",
+ "aaafda1ea8654b4ab3443ae7356fd06b"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='500px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "454cba81523e47a3be869822e0f53963"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "g = Graph()\n",
+ "g.add_vertices(3)\n",
+ "g.add_edges([(0,1), (1,2)])\n",
+ "g.add_edges([(2, 0)])\n",
+ "g.add_vertices(3)\n",
+ "g.add_edges([(2, 3), (3, 4), (4, 5), (5, 3)])\n",
+ "display(GraphWidget(graph=g))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "af0601c1-f95e-4ac5-8c04-1ffc1659e3d2",
+ "metadata": {
+ "id": "af0601c1-f95e-4ac5-8c04-1ffc1659e3d2",
+ "outputId": "68afd383-e1d5-41e8-a8d4-6f9261653ecd",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 517,
+ "referenced_widgets": [
+ "b78bc982997e4251b6ac56acf7ddc0e2",
+ "75a4239e84eb4ea6b258d459f994387f"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='500px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
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+ }
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+ }
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+ }
+ }
+ }
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+ "source": [
+ "f = Graph([(0,1), (0,2), (2,3), (3,4), (4,2), (2,5), (5,0), (6,3), (5,6)])\n",
+ "f.vs[\"name\"] = [\"Alice\", \"Bob\", \"Claire\", \"Dennis\", \"Esther\", \"Frank\", \"George\"]\n",
+ "f.vs[\"age\"] = [25, 31, 18, 47, 22, 23, 50]\n",
+ "f.vs[\"gender\"] = [\"f\", \"m\", \"f\", \"m\", \"f\", \"m\", \"m\"]\n",
+ "f.es[\"is_formal\"] = [False, False, True, True, True, False, True, False, False]\n",
+ "\n",
+ "w = GraphWidget(graph=f)\n",
+ "display(w)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ddab99cf-3d72-4714-a91c-6bdacbe40d8b",
+ "metadata": {
+ "id": "ddab99cf-3d72-4714-a91c-6bdacbe40d8b"
+ },
+ "source": [
+ "### Using imported data\n",
+ "\n",
+ "To use the additional data in ```properties``` we will use the ```is_formal``` key to determine edge directions and the ```gender``` for the node type mapping.\n",
+ "\n",
+ "To access the 'properties' data, you can use the data key in squared brackets: ```['properties']['key'] ```\n",
+ "\n",
+ "Possible keys for nodes in this example are 'name', 'age' and 'gender'\n",
+ "\n",
+ "To visualize all properties, we remove any additional node data except the properties"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "b34a0545-c78b-43c3-9900-17070cf641f9",
+ "metadata": {
+ "id": "b34a0545-c78b-43c3-9900-17070cf641f9",
+ "outputId": "1d259cb9-ab76-4e80-b572-d9f2bdcd71f3",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "{'name': 'Alice', 'age': 25, 'gender': 'f'}\n",
+ "{'name': 'Bob', 'age': 31, 'gender': 'm'}\n",
+ "{'name': 'Claire', 'age': 18, 'gender': 'f'}\n",
+ "{'name': 'Dennis', 'age': 47, 'gender': 'm'}\n",
+ "{'name': 'Esther', 'age': 22, 'gender': 'f'}\n",
+ "{'name': 'Frank', 'age': 23, 'gender': 'm'}\n",
+ "{'name': 'George', 'age': 50, 'gender': 'm'}\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "properties = [node['properties'] for node in w.nodes]\n",
+ "formattedProperties = ''.join(f\"{node}\\n\" for node in properties)\n",
+ "print(formattedProperties)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c888eb11-585b-40da-b9b9-3d3a3c66524a",
+ "metadata": {
+ "id": "c888eb11-585b-40da-b9b9-3d3a3c66524a"
+ },
+ "source": [
+ "Since the type binding only accepts numbers, we'll need to convert the gender to a numerical representation:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "94220416-3397-4c20-88db-5ea3776078e5",
+ "metadata": {
+ "id": "94220416-3397-4c20-88db-5ea3776078e5",
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+ "height": 517,
+ "referenced_widgets": [
+ "b78bc982997e4251b6ac56acf7ddc0e2",
+ "75a4239e84eb4ea6b258d459f994387f"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='500px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
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+ "source": [
+ "w.node_type_mapping = lambda node: 1 if node['properties']['gender'] == 'f' else (2 if node['properties']['gender'] == 'm' else 3)\n",
+ "\n",
+ "w.directed_mapping = 'is_formal'\n",
+ "\n",
+ "w.node_label_mapping = lambda node: node['properties'][\"name\"]\n",
+ "\n",
+ "display(w)"
+ ]
+ }
+ ],
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},
- {
- "cell_type": "markdown",
- "id": "c6588ec8-b229-4ca1-9cde-cc3aa8e50cbb",
- "metadata": {},
- "source": [
- "Before using the graph widget, install all necessary packages."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "308bda31-f1e5-441c-afe8-291743b19970",
- "metadata": {},
- "outputs": [],
- "source": [
- "%pip install yfiles_jupyter_graphs --quiet\n",
- "%pip install igraph --quiet\n",
- "from typing import Dict\n",
- "from igraph import Graph\n",
- "from yfiles_jupyter_graphs import GraphWidget"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ae5b3eea-3fca-4c9b-b2bf-5f4b7b4b399e",
- "metadata": {},
- "source": [
- "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "dc224c8f-49f5-42e7-b015-b96fbbd27970",
- "metadata": {},
- "outputs": [],
- "source": [
- "try:\n",
- " import google.colab\n",
- " from google.colab import output\n",
- " output.enable_custom_widget_manager()\n",
- "except:\n",
- " pass"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b8226994-22e2-4e91-a7ec-3b9dd535c5e4",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2b2ad4bf-a516-4eaa-80a7-7dfd98bcf88c",
- "metadata": {},
- "source": [
- "## How to import a graph\n",
- "- either import the graph directly when initilizing: ```GraphWidget(graph=your_graph)```\n",
- "- or use the ```w.import_graph(your_graph)``` function, if you already initilized a Widget called ```w```\n",
- "\n",
- "## Notes about IGraph importer\n",
- "- Nodes and edges are identified by index attribute.\n",
- "- Node and edge properties are provided through attributes method.\n",
- "- Edges are determined by source and target attribute.\n",
- "- Any additional data is stored in ```properties```\n",
- "\n",
- "## Import graph from tutorial\n",
- "https://igraph.org/python/tutorial/latest/tutorial.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2b183c0f-a452-4d37-a759-88e0207b24e0",
- "metadata": {},
- "outputs": [],
- "source": [
- "g = Graph()\n",
- "g.add_vertices(3)\n",
- "g.add_edges([(0,1), (1,2)])\n",
- "g.add_edges([(2, 0)])\n",
- "g.add_vertices(3)\n",
- "g.add_edges([(2, 3), (3, 4), (4, 5), (5, 3)])\n",
- "display(GraphWidget(graph=g))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "af0601c1-f95e-4ac5-8c04-1ffc1659e3d2",
- "metadata": {},
- "outputs": [],
- "source": [
- "f = Graph([(0,1), (0,2), (2,3), (3,4), (4,2), (2,5), (5,0), (6,3), (5,6)])\n",
- "f.vs[\"name\"] = [\"Alice\", \"Bob\", \"Claire\", \"Dennis\", \"Esther\", \"Frank\", \"George\"]\n",
- "f.vs[\"age\"] = [25, 31, 18, 47, 22, 23, 50]\n",
- "f.vs[\"gender\"] = [\"f\", \"m\", \"f\", \"m\", \"f\", \"m\", \"m\"]\n",
- "f.es[\"is_formal\"] = [False, False, True, True, True, False, True, False, False]\n",
- "\n",
- "w = GraphWidget(graph=f)\n",
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ddab99cf-3d72-4714-a91c-6bdacbe40d8b",
- "metadata": {},
- "source": [
- "### Using imported data\n",
- "\n",
- "To use the additional data in ```properties``` we will use the ```is_formal``` key to determine edge directions and the ```gender``` for the node type mapping. \n",
- "\n",
- "To access the 'properties' data, you can use the data key in squared brackets: ```['properties']['key'] ```\n",
- "\n",
- "Possible keys for nodes in this example are 'name', 'age' and 'gender'\n",
- "\n",
- "To visualize all properties, we remove any additional node data except the properties"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b34a0545-c78b-43c3-9900-17070cf641f9",
- "metadata": {},
- "outputs": [],
- "source": [
- "properties = [node['properties'] for node in w.nodes]\n",
- "formattedProperties = ''.join(f\"{node}\\n\" for node in properties)\n",
- "print(formattedProperties)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c888eb11-585b-40da-b9b9-3d3a3c66524a",
- "metadata": {},
- "source": [
- "Since the type binding only accepts numbers, we'll need to convert the gender to a numerical representation:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "94220416-3397-4c20-88db-5ea3776078e5",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.node_type_mapping = lambda node: 1 if node['properties']['gender'] == 'f' else (2 if node['properties']['gender'] == 'm' else 3)\n",
- "\n",
- "w.directed_mapping = 'is_formal'\n",
- "\n",
- "w.node_label_mapping = lambda node: node['properties'][\"name\"]\n",
- "\n",
- "display(w)"
- ]
- }
- ],
- "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": "3.11.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/19_rdflib_import.ipynb b/examples/19_rdflib_import.ipynb
index a21c282..70ee589 100644
--- a/examples/19_rdflib_import.ipynb
+++ b/examples/19_rdflib_import.ipynb
@@ -1,148 +1,459 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "7136fd9d-cc2c-4dbf-9138-7fb4477a616b",
- "metadata": {},
- "source": [
- "# Visualizing RDF graphs in yFiles Graphs for Jupyter \n",
- "\n",
- "Before using the graph widget, install all necessary packages."
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "7136fd9d-cc2c-4dbf-9138-7fb4477a616b",
+ "metadata": {
+ "id": "7136fd9d-cc2c-4dbf-9138-7fb4477a616b"
+ },
+ "source": [
+ "# Visualizing RDF graphs in yFiles Graphs for Jupyter \n",
+ "\n",
+ "Before using the graph widget, install all necessary packages."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "c6cb45fc-e442-4631-afa7-f93d646e03af",
+ "metadata": {
+ "id": "c6cb45fc-e442-4631-afa7-f93d646e03af",
+ "outputId": "8154efde-2955-4431-b810-0d95333effe3",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m25.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m23.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m26.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m562.1/562.1 kB\u001b[0m \u001b[31m21.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.2/116.2 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "%pip install rdflib networkx --quiet\n",
+ "from rdflib import Graph\n",
+ "from yfiles_jupyter_graphs import GraphWidget\n",
+ "from rdflib.extras.external_graph_libs import rdflib_to_networkx_graph"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "222eab1d-14cc-418b-a109-a63b9f1ca7d1",
+ "metadata": {
+ "id": "222eab1d-14cc-418b-a109-a63b9f1ca7d1"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "bb5491e4-0c8c-4031-b7f4-6f84b9d65c2d",
+ "metadata": {
+ "id": "bb5491e4-0c8c-4031-b7f4-6f84b9d65c2d"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4b69e398-3b70-40f7-ae7b-8e4a2d186807",
+ "metadata": {
+ "id": "4b69e398-3b70-40f7-ae7b-8e4a2d186807"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04acaf9-3737-4b85-8c10-80b44b3e01ce",
+ "metadata": {
+ "id": "a04acaf9-3737-4b85-8c10-80b44b3e01ce"
+ },
+ "source": [
+ "# Requirements on rdflib\n",
+ "\n",
+ "This notebook requires rdflib version 6+ due to a [bug](https://github.com/RDFLib/rdflib/issues/1488) in the rdflib library.\n",
+ "\n",
+ "Subsequently, rdflib version 6+ requires at least Python 3.7+. So make sure to met these requirements for running this notebook."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6340f7a0-4019-4856-aee4-e4a2cd117b0c",
+ "metadata": {
+ "id": "6340f7a0-4019-4856-aee4-e4a2cd117b0c"
+ },
+ "source": [
+ "## How to import a graph\n",
+ "- either import the graph directly when initilizing: ```GraphWidget(graph=your_graph)```\n",
+ "- or use the ```w.import_graph(your_graph)``` function, if you already initilized a Widget called ```w```\n",
+ "\n",
+ "## Notes about RDF\n",
+ "- We use the Networkx importer to import RDF graph data\n",
+ "- a lot of different libraries support Networkx or pandas\n",
+ "- convert your graph to one of the supported import libraries, then you can use the widget on your data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9ffac35e-1d64-47b2-b034-d91ad7ef8a98",
+ "metadata": {
+ "id": "9ffac35e-1d64-47b2-b034-d91ad7ef8a98"
+ },
+ "source": [
+ "Shows how to load an RDF graph into a yFiles Jupyter Graph widget via networkx"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "1c2155ce-03bc-4eca-88e9-312c5c4e2533",
+ "metadata": {
+ "id": "1c2155ce-03bc-4eca-88e9-312c5c4e2533",
+ "outputId": "edd10973-136b-4212-ece9-6c2cc4037b97",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 517,
+ "referenced_widgets": [
+ "7e217c0b832f4c288bd623d84d18e0e1",
+ "fdecde4a1c3641a5bac4cf5bb77d20af"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='500px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "7e217c0b832f4c288bd623d84d18e0e1"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "# Create a Graph, add in some test data\n",
+ "g = Graph()\n",
+ "g.parse(\n",
+ " data=\"\"\"\n",
+ " "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a04acaf9-3737-4b85-8c10-80b44b3e01ce",
- "metadata": {},
- "source": [
- "# Requirements on rdflib\n",
- "\n",
- "This notebook requires rdflib version 6+ due to a [bug](https://github.com/RDFLib/rdflib/issues/1488) in the rdflib library.\n",
- "\n",
- "Subsequently, rdflib version 6+ requires at least Python 3.7+. So make sure to met these requirements for running this notebook."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6340f7a0-4019-4856-aee4-e4a2cd117b0c",
- "metadata": {},
- "source": [
- "## How to import a graph\n",
- "- either import the graph directly when initilizing: ```GraphWidget(graph=your_graph)```\n",
- "- or use the ```w.import_graph(your_graph)``` function, if you already initilized a Widget called ```w```\n",
- "\n",
- "## Notes about RDF\n",
- "- We use the Networkx importer to import RDF graph data\n",
- "- a lot of different libraries support Networkx or pandas \n",
- "- convert your graph to one of the supported import libraries, then you can use the widget on your data"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9ffac35e-1d64-47b2-b034-d91ad7ef8a98",
- "metadata": {},
- "source": [
- "Shows how to load an RDF graph into a yFiles Jupyter Graph widget via networkx"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1c2155ce-03bc-4eca-88e9-312c5c4e2533",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Create a Graph, add in some test data\n",
- "g = Graph()\n",
- "g.parse(\n",
- " data=\"\"\"\n",
- " \n",
- "\n",
- "The widget can support very large diagrams by switching to WebGL rendering for the diagram.\n",
- "\n",
- "However note that automatic layouts may take some time to calculate on dense graphs.\n",
- "\n",
- "Before using the graph widget, install all necessary packages."
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "8fd43bf8-ff9d-4cf3-b4e5-9a07aca3a5df",
+ "metadata": {
+ "id": "8fd43bf8-ff9d-4cf3-b4e5-9a07aca3a5df"
+ },
+ "source": [
+ "# Large Diagrams \n",
+ "\n",
+ "The widget can support very large diagrams by switching to WebGL rendering for the diagram.\n",
+ "\n",
+ "However note that automatic layouts may take some time to calculate on dense graphs.\n",
+ "\n",
+ "Before using the graph widget, install all necessary packages."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "91ecf78a-82c0-47be-82cd-705a761984be",
+ "metadata": {
+ "id": "91ecf78a-82c0-47be-82cd-705a761984be",
+ "outputId": "b4868775-6d48-4ebc-a8a8-878aa8f4211c",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m42.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m18.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "%pip install networkx --quiet\n",
+ "import networkx as nx\n",
+ "from yfiles_jupyter_graphs import GraphWidget"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7601cfbf-f60e-4f01-a3c7-5f776ebd442b",
+ "metadata": {
+ "id": "7601cfbf-f60e-4f01-a3c7-5f776ebd442b"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "61972097-52c4-49e6-82f4-8a1b6b616cdf",
+ "metadata": {
+ "id": "61972097-52c4-49e6-82f4-8a1b6b616cdf"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bb08acb6-10eb-4491-ae28-842bc2b1cd89",
+ "metadata": {
+ "id": "bb08acb6-10eb-4491-ae28-842bc2b1cd89"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5631520b-b527-4180-96b7-ed580fc1d2b3",
+ "metadata": {
+ "id": "5631520b-b527-4180-96b7-ed580fc1d2b3"
+ },
+ "source": [
+ "To showcase a large graph, we'll use the 'lobster' graph from Networkx. This graph initially consists of 100 nodes, however, each of these 100 nodes has a 90% probability of having a new node added, and the process repeats for the next level with the same 90% probability:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "6c897a65-6355-4c2e-910f-db91a81e404b",
+ "metadata": {
+ "id": "6c897a65-6355-4c2e-910f-db91a81e404b",
+ "outputId": "e0b9b794-4081-4fc0-aece-a3bd5805da32",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "nodes: 2577 edges: 2576\n"
+ ]
+ }
+ ],
+ "source": [
+ "DG = nx.random_lobster(100, 0.9, 0.9)\n",
+ "print('nodes:', len(list(DG.nodes)), 'edges:', len(list(DG.edges)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2c66a927-05fe-4b7c-923a-395b446cfe04",
+ "metadata": {
+ "id": "2c66a927-05fe-4b7c-923a-395b446cfe04"
+ },
+ "source": [
+ "Rendering and calculating the node positions can take a while:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "5fc502cf-c929-473a-9189-f8c999882ceb",
+ "metadata": {
+ "id": "5fc502cf-c929-473a-9189-f8c999882ceb",
+ "outputId": "46df6d5d-2db3-4fe9-be94-c0acd48acf21",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 817,
+ "referenced_widgets": [
+ "2e1da4ea7b1b41a09eefc926bcc52a59",
+ "b956351b3fa542beb240ef406520fff6"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='800px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "2e1da4ea7b1b41a09eefc926bcc52a59"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
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+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
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+ ]
+ },
+ {
+ "id": 2575,
+ "properties": {
+ "label": "2575"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "2575",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 2576,
+ "properties": {
+ "label": "2576"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "2576",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
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+ }
+ ],
+ "_overview": {
+ "enabled": null,
+ "overview_set": false
+ },
+ "_selected_graph": [
+ [],
+ []
+ ],
+ "_sidebar": {
+ "enabled": false,
+ "start_with": null
+ },
+ "_view_count": null,
+ "_view_module": "yfiles-jupyter-graphs",
+ "_view_module_version": "^1.8.1",
+ "_view_name": "GraphView",
+ "layout": "IPY_MODEL_b956351b3fa542beb240ef406520fff6",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "b956351b3fa542beb240ef406520fff6": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "2.0.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "2.0.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border_bottom": null,
+ "border_left": null,
+ "border_right": null,
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+ "grid_auto_flow": null,
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+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": "800px",
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
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+ "order": null,
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+ "width": "100%"
+ }
+ }
+ }
+ }
},
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "91ecf78a-82c0-47be-82cd-705a761984be",
- "metadata": {},
- "outputs": [],
- "source": [
- "%pip install yfiles_jupyter_graphs --quiet\n",
- "%pip install networkx --quiet\n",
- "import networkx as nx\n",
- "from yfiles_jupyter_graphs import GraphWidget"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7601cfbf-f60e-4f01-a3c7-5f776ebd442b",
- "metadata": {},
- "source": [
- "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "61972097-52c4-49e6-82f4-8a1b6b616cdf",
- "metadata": {},
- "outputs": [],
- "source": [
- "try:\n",
- " import google.colab\n",
- " from google.colab import output\n",
- " output.enable_custom_widget_manager()\n",
- "except:\n",
- " pass"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "bb08acb6-10eb-4491-ae28-842bc2b1cd89",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5631520b-b527-4180-96b7-ed580fc1d2b3",
- "metadata": {},
- "source": [
- "To showcase a large graph, we'll use the 'lobster' graph from Networkx. This graph initially consists of 100 nodes, however, each of these 100 nodes has a 90% probability of having a new node added, and the process repeats for the next level with the same 90% probability:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6c897a65-6355-4c2e-910f-db91a81e404b",
- "metadata": {},
- "outputs": [],
- "source": [
- "DG = nx.random_lobster(100, 0.9, 0.9)\n",
- "print('nodes:', len(list(DG.nodes)), 'edges:', len(list(DG.edges)))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2c66a927-05fe-4b7c-923a-395b446cfe04",
- "metadata": {},
- "source": [
- "Rendering and calculating the node positions can take a while: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5fc502cf-c929-473a-9189-f8c999882ceb",
- "metadata": {},
- "outputs": [],
- "source": [
- "w = GraphWidget(graph=DG)\n",
- "display(w)"
- ]
- }
- ],
- "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": "3.11.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/21_selection_export.ipynb b/examples/21_selection_export.ipynb
index 124e2b7..e45cc4f 100644
--- a/examples/21_selection_export.ipynb
+++ b/examples/21_selection_export.ipynb
@@ -1,135 +1,645 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "25d207f9-1f19-40b7-b79e-7ae4d1352cbb",
- "metadata": {},
- "source": [
- "# Export a selection of items to Jupyter cell "
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "25d207f9-1f19-40b7-b79e-7ae4d1352cbb",
+ "metadata": {
+ "id": "25d207f9-1f19-40b7-b79e-7ae4d1352cbb"
+ },
+ "source": [
+ "# Export a selection of items to Jupyter cell "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "84c0bb57-48fe-49bc-b444-86e0c3922fbe",
+ "metadata": {
+ "id": "84c0bb57-48fe-49bc-b444-86e0c3922fbe"
+ },
+ "source": [
+ "Before using the graph widget, install all necessary packages."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "28a97638-ff06-444e-8dab-bb90b3e905b0",
+ "metadata": {
+ "id": "28a97638-ff06-444e-8dab-bb90b3e905b0",
+ "outputId": "5c5ed3bc-0f56-4cd2-efe7-3448c9eda93f",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m27.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m14.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m20.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "%pip install networkx --quiet\n",
+ "from networkx import erdos_renyi_graph\n",
+ "from yfiles_jupyter_graphs import GraphWidget"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c52d12f7-c8aa-4f52-b408-5d196337e391",
+ "metadata": {
+ "id": "c52d12f7-c8aa-4f52-b408-5d196337e391"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "468d3484-2c8f-4243-a968-52225160a858",
+ "metadata": {
+ "id": "468d3484-2c8f-4243-a968-52225160a858"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "273eafa0-5a0d-460b-bd5a-679e1010b3e2",
+ "metadata": {
+ "id": "273eafa0-5a0d-460b-bd5a-679e1010b3e2"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "51b81aa3-9235-422f-8354-201ba43357a0",
+ "metadata": {
+ "id": "51b81aa3-9235-422f-8354-201ba43357a0",
+ "outputId": "53e0c904-f0e8-4973-8503-3d5ebefe4256",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 517,
+ "referenced_widgets": [
+ "a5102af6189d44b7a0c604fbb19c576e",
+ "a93b185cbac442c69206c228e023e144"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='500px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "a5102af6189d44b7a0c604fbb19c576e"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w = GraphWidget(graph=erdos_renyi_graph(10,0.3,2))\n",
+ "display(w)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "202ac600-8d65-461f-b1b0-ef22fb388349",
+ "metadata": {
+ "id": "202ac600-8d65-461f-b1b0-ef22fb388349"
+ },
+ "source": [
+ "## How to select items\n",
+ "- Select nodes/edges by left click\n",
+ "- Expand the current selection by holding CTRL while clicking nodes/edges\n",
+ "- Marquee select multiple graph items by holding SHIFT while dragging with left mouse button\n",
+ "\n",
+ "## How to get the selected data\n",
+ "\n",
+ "The `get_selection` function returns a tuple of the currently selected nodes and edges of the widget."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "cd4ba091-c85e-4285-8bee-d1c7e84d810f",
+ "metadata": {
+ "id": "cd4ba091-c85e-4285-8bee-d1c7e84d810f"
+ },
+ "outputs": [],
+ "source": [
+ "nodes, edges = w.get_selection()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "67ae5046-b9c8-4df8-966a-12beddc7dc1d",
+ "metadata": {
+ "id": "67ae5046-b9c8-4df8-966a-12beddc7dc1d",
+ "outputId": "18bb9d16-a70b-4eac-f794-5e5cd953825b",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "selected nodes:\n",
+ "[{'id': 1, 'properties': {'label': '1'}}, {'id': 6, 'properties': {'label': '6'}}, {'id': 5, 'properties': {'label': '5'}}]\n",
+ "selected edges:\n",
+ "[]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('selected nodes:')\n",
+ "print(nodes)\n",
+ "\n",
+ "print('selected edges:')\n",
+ "print(edges)"
+ ]
+ }
+ ],
+ "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": "3.11.3"
+ },
+ "colab": {
+ "provenance": []
+ },
+ "widgets": {
+ "application/vnd.jupyter.widget-state+json": {
+ "a5102af6189d44b7a0c604fbb19c576e": {
+ "model_module": "yfiles-jupyter-graphs",
+ "model_name": "GraphModel",
+ "model_module_version": "^1.8.1",
+ "state": {
+ "_context_pane_mapping": [
+ {
+ "id": "Neighborhood",
+ "title": "Neighborhood"
+ },
+ {
+ "id": "Data",
+ "title": "Data"
+ },
+ {
+ "id": "Search",
+ "title": "Search"
+ },
+ {
+ "id": "About",
+ "title": "About"
+ }
+ ],
+ "_data_importer": "networkx",
+ "_directed": false,
+ "_dom_classes": [],
+ "_edges": [
+ {
+ "id": 0,
+ "start": 0,
+ "end": 3,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 1,
+ "start": 0,
+ "end": 4,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 2,
+ "start": 1,
+ "end": 4,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 3,
+ "start": 2,
+ "end": 5,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
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+ "start": 2,
+ "end": 6,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
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+ "end": 7,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
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+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
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+ "directed": false
+ },
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+ "end": 9,
+ "properties": {},
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+ "color": "#15AFAC",
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+ },
+ {
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+ "properties": {},
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+ "directed": false
+ },
+ {
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+ "end": 6,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
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+ "directed": false
+ }
+ ],
+ "_graph_layout": {},
+ "_highlight": [],
+ "_license": {},
+ "_model_module": "yfiles-jupyter-graphs",
+ "_model_module_version": "^1.8.1",
+ "_model_name": "GraphModel",
+ "_neighborhood": {},
+ "_nodes": [
+ {
+ "id": 0,
+ "properties": {
+ "label": "0"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "0",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
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+ {
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+ "position": [
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+ "position": [
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+ ]
+ },
+ {
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+ "properties": {
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+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "6",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 7,
+ "properties": {
+ "label": "7"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "7",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
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+ ]
+ },
+ {
+ "id": 8,
+ "properties": {
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+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "8",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 9,
+ "properties": {
+ "label": "9"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "9",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ }
+ ],
+ "_overview": {
+ "enabled": null,
+ "overview_set": false
+ },
+ "_selected_graph": [
+ [
+ {
+ "id": 1,
+ "properties": {
+ "label": "1"
+ }
+ },
+ {
+ "id": 6,
+ "properties": {
+ "label": "6"
+ }
+ },
+ {
+ "id": 5,
+ "properties": {
+ "label": "5"
+ }
+ }
+ ],
+ []
+ ],
+ "_sidebar": {
+ "enabled": false,
+ "start_with": null
+ },
+ "_view_count": null,
+ "_view_module": "yfiles-jupyter-graphs",
+ "_view_module_version": "^1.8.1",
+ "_view_name": "GraphView",
+ "layout": "IPY_MODEL_a93b185cbac442c69206c228e023e144",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "a93b185cbac442c69206c228e023e144": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "2.0.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "2.0.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border_bottom": null,
+ "border_left": null,
+ "border_right": null,
+ "border_top": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": "500px",
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": "100%"
+ }
+ }
+ }
+ }
},
- {
- "cell_type": "markdown",
- "id": "84c0bb57-48fe-49bc-b444-86e0c3922fbe",
- "metadata": {},
- "source": [
- "Before using the graph widget, install all necessary packages."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "28a97638-ff06-444e-8dab-bb90b3e905b0",
- "metadata": {},
- "outputs": [],
- "source": [
- "%pip install yfiles_jupyter_graphs --quiet\n",
- "%pip install networkx --quiet\n",
- "from networkx import erdos_renyi_graph\n",
- "from yfiles_jupyter_graphs import GraphWidget"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c52d12f7-c8aa-4f52-b408-5d196337e391",
- "metadata": {},
- "source": [
- "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "468d3484-2c8f-4243-a968-52225160a858",
- "metadata": {},
- "outputs": [],
- "source": [
- "try:\n",
- " import google.colab\n",
- " from google.colab import output\n",
- " output.enable_custom_widget_manager()\n",
- "except:\n",
- " pass"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "273eafa0-5a0d-460b-bd5a-679e1010b3e2",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "51b81aa3-9235-422f-8354-201ba43357a0",
- "metadata": {},
- "outputs": [],
- "source": [
- "w = GraphWidget(graph=erdos_renyi_graph(10,0.3,2))\n",
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "202ac600-8d65-461f-b1b0-ef22fb388349",
- "metadata": {},
- "source": [
- "## How to select items\n",
- "- Select nodes/edges by left click\n",
- "- Expand the current selection by holding CTRL while clicking nodes/edges\n",
- "- Marquee select multiple graph items by holding SHIFT while dragging with left mouse button\n",
- "\n",
- "## How to get the selected data\n",
- "\n",
- "The `get_selection` function returns a tuple of the currently selected nodes and edges of the widget."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "cd4ba091-c85e-4285-8bee-d1c7e84d810f",
- "metadata": {},
- "outputs": [],
- "source": [
- "nodes, edges = w.get_selection()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "67ae5046-b9c8-4df8-966a-12beddc7dc1d",
- "metadata": {},
- "outputs": [],
- "source": [
- "print('selected nodes:')\n",
- "print(nodes)\n",
- "\n",
- "print('selected edges:')\n",
- "print(edges)"
- ]
- }
- ],
- "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": "3.11.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/22_layouts.ipynb b/examples/22_layouts.ipynb
index f12cddf..02124c5 100644
--- a/examples/22_layouts.ipynb
+++ b/examples/22_layouts.ipynb
@@ -1,306 +1,6687 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "11164a0f-1e27-4d4f-907b-28be64dfef46",
- "metadata": {},
- "source": [
- "# Graph Layouts "
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "11164a0f-1e27-4d4f-907b-28be64dfef46",
+ "metadata": {
+ "id": "11164a0f-1e27-4d4f-907b-28be64dfef46"
+ },
+ "source": [
+ "# Graph Layouts "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d5dda599-a06c-42a5-8758-1fc3bc23a76b",
+ "metadata": {
+ "id": "d5dda599-a06c-42a5-8758-1fc3bc23a76b"
+ },
+ "source": [
+ "There are 8 different Layouts. A layout can either be selected in the toolbar or by setting the layout beforehand via ```w.[layout_name]()```. See also the [documentation](https://yworks.github.io/yfiles-jupyter-graphs/02_graph_widget/#methods).\n",
+ "\n",
+ "Available automatic layout algorithms:\n",
+ "* Circular\n",
+ "* Hierarchic\n",
+ "* Organic (default)\n",
+ "* Orthogonal\n",
+ "* Radial\n",
+ "* Tree\n",
+ "\n",
+ "Additionally, the following edge routers are available:\n",
+ "* Orthogonal edge router\n",
+ "* Organic edge router\n",
+ "\n",
+ "In contrast to automatic layout algorithms, edge routers only adjust the edge path while keeping the current node positions. This works well in conjuction with setting explicit node positions as demonstrated in [04_layout_mapping.ipynb](./04_layout_mapping.ipynb) and [06_position_mapping.ipynb](./06_position_mapping.ipynb).\n",
+ "\n",
+ "Before using the graph widget, install all necessary packages."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "67b85a4c-50a4-44e5-bea5-c47dcf6ac8cc",
+ "metadata": {
+ "id": "67b85a4c-50a4-44e5-bea5-c47dcf6ac8cc",
+ "outputId": "351935ae-efb4-4917-be0d-e9cfb337b441",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m31.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m29.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "%pip install networkx --quiet\n",
+ "from networkx import florentine_families_graph\n",
+ "from yfiles_jupyter_graphs import GraphWidget"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5f3dca5a-cc7b-406f-91b0-4f8395001dee",
+ "metadata": {
+ "id": "5f3dca5a-cc7b-406f-91b0-4f8395001dee"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "e268314d-7540-48b0-b03c-aa76d748b34f",
+ "metadata": {
+ "id": "e268314d-7540-48b0-b03c-aa76d748b34f"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a17981f1-d4dc-48ce-8a9a-7db8634bb02d",
+ "metadata": {
+ "id": "a17981f1-d4dc-48ce-8a9a-7db8634bb02d"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "bcb18821-fdb3-4795-bd13-b5cbd8ea9129",
+ "metadata": {
+ "id": "bcb18821-fdb3-4795-bd13-b5cbd8ea9129",
+ "outputId": "018dd91a-2bde-45a8-ec18-e63b418120db",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 667,
+ "referenced_widgets": [
+ "51c46a1e46f74ce1acbd9b72bbf4fd77",
+ "331bfbfbc8404c57addeae086e030101"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='650px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "51c46a1e46f74ce1acbd9b72bbf4fd77"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w = GraphWidget(graph = florentine_families_graph())\n",
+ "display(w)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f8e67ec2-de96-449b-b99f-401635017425",
+ "metadata": {
+ "id": "f8e67ec2-de96-449b-b99f-401635017425"
+ },
+ "source": [
+ "## Circular Layout"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "91766d81-92b6-45d1-9b99-b98e14641137",
+ "metadata": {
+ "id": "91766d81-92b6-45d1-9b99-b98e14641137",
+ "outputId": "9b360326-5598-414c-e98b-cf1c76725a18",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 667,
+ "referenced_widgets": [
+ "a040da624dba4a94af7e32cd3ea5812a",
+ "d198d78e5ccf4d4d9bd91bd65d60d036"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='650px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "a040da624dba4a94af7e32cd3ea5812a"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w2 = GraphWidget(graph = florentine_families_graph())\n",
+ "w2.circular_layout()\n",
+ "display(w2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7749b671-d15c-41c4-86b4-73e7d7c6a0bb",
+ "metadata": {
+ "id": "7749b671-d15c-41c4-86b4-73e7d7c6a0bb"
+ },
+ "source": [
+ "## Hierarchic Layout"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "a60d734a-c841-49b9-a3da-e2f7f59d4319",
+ "metadata": {
+ "id": "a60d734a-c841-49b9-a3da-e2f7f59d4319",
+ "outputId": "92ca2d7f-ba12-477c-cde2-7a6b0c28fd43",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 667,
+ "referenced_widgets": [
+ "c0f18f58779d4c20a1751cfc3812f96e",
+ "c51e180d5b3740bfa9fbbcf79caf394b"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='650px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "c0f18f58779d4c20a1751cfc3812f96e"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w3 = GraphWidget(graph = florentine_families_graph())\n",
+ "w3.hierarchic_layout()\n",
+ "display(w3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e8b2f5f0-686b-46d2-8ff0-70adf4934562",
+ "metadata": {
+ "id": "e8b2f5f0-686b-46d2-8ff0-70adf4934562"
+ },
+ "source": [
+ "## Organic Layout"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "df9a073f-fa14-483b-be07-4e9dc06af3ae",
+ "metadata": {
+ "id": "df9a073f-fa14-483b-be07-4e9dc06af3ae",
+ "outputId": "2fd90c61-3d85-41a8-dfd2-0ccccd38bdc6",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 667,
+ "referenced_widgets": [
+ "94ca6faed8d844a39db11367643ceec9",
+ "802f4c2946f94fe69ab1cde9f83ecba7"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='650px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "94ca6faed8d844a39db11367643ceec9"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w4 = GraphWidget(graph = florentine_families_graph())\n",
+ "w4.organic_layout()\n",
+ "display(w4)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a5068b61-9995-4f11-a70e-1cc59c50a2e2",
+ "metadata": {
+ "id": "a5068b61-9995-4f11-a70e-1cc59c50a2e2"
+ },
+ "source": [
+ "## Orthogonal Layout"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "f75dabe7-f795-4ecc-b0d1-4a036701be64",
+ "metadata": {
+ "id": "f75dabe7-f795-4ecc-b0d1-4a036701be64",
+ "outputId": "772745e7-9884-4765-8b36-9556fedaf06c",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 667,
+ "referenced_widgets": [
+ "b1a929a44aca4624b7c8922f0fdc7a8b",
+ "10bbb236f2344ee791fcc729dffabd96"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='650px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "b1a929a44aca4624b7c8922f0fdc7a8b"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w5 = GraphWidget(graph = florentine_families_graph())\n",
+ "w5.orthogonal_layout()\n",
+ "display(w5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "64afbae3-1c3e-4bea-864d-19a4e78e376f",
+ "metadata": {
+ "id": "64afbae3-1c3e-4bea-864d-19a4e78e376f"
+ },
+ "source": [
+ "## Radial Layout"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "8bb347df-6b53-448e-aa62-abea08b1261c",
+ "metadata": {
+ "id": "8bb347df-6b53-448e-aa62-abea08b1261c",
+ "outputId": "e2fcac08-ef81-4efb-f7e9-b7ad7862fdf1",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 667,
+ "referenced_widgets": [
+ "d699925978df48f1815316f0d760e47c",
+ "38052be4350947d085cac5f3ab341864"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='650px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "d699925978df48f1815316f0d760e47c"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w6 = GraphWidget(graph = florentine_families_graph())\n",
+ "w6.radial_layout()\n",
+ "display(w6)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6f0cc35-f9bc-4c1b-a6e1-1f64957d29cb",
+ "metadata": {
+ "id": "b6f0cc35-f9bc-4c1b-a6e1-1f64957d29cb"
+ },
+ "source": [
+ "## Tree Layout"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "0c0a80e3-69e8-481a-b8f8-c6a1e6a986b2",
+ "metadata": {
+ "id": "0c0a80e3-69e8-481a-b8f8-c6a1e6a986b2",
+ "outputId": "58a36dee-cf16-4349-f718-544b579e4860",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 667,
+ "referenced_widgets": [
+ "89e50cf7d8484770a38c3e7571f555c5",
+ "10fa84a70b89465a89844cb34140330e"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='650px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "89e50cf7d8484770a38c3e7571f555c5"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w7 = GraphWidget(graph = florentine_families_graph())\n",
+ "w7.tree_layout()\n",
+ "display(w7)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5825b7ea-6b56-45d2-8c2d-ad7f2690c21f",
+ "metadata": {
+ "id": "5825b7ea-6b56-45d2-8c2d-ad7f2690c21f"
+ },
+ "source": [
+ "## Orthogonal Edge Router\n",
+ "\n",
+ "The edge router requires node positions, see the node position mapping example, notebook 06"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "53b023c3-9b97-4999-a122-07857e6d15ea",
+ "metadata": {
+ "id": "53b023c3-9b97-4999-a122-07857e6d15ea",
+ "outputId": "cb79c5cc-3755-4ee3-d595-4f4e60035509",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 667,
+ "referenced_widgets": [
+ "234fee7a595243bdad5b10b1e482d8f9",
+ "fdd55a31696a4f3eb914b5ebbf04f3aa"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='650px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "234fee7a595243bdad5b10b1e482d8f9"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w8 = GraphWidget(graph = florentine_families_graph())\n",
+ "w8.orthogonal_edge_router()\n",
+ "display(w8)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1c7f8754-8229-436a-b556-5f8f4bdf4966",
+ "metadata": {
+ "id": "1c7f8754-8229-436a-b556-5f8f4bdf4966"
+ },
+ "source": [
+ "## Organic Edge Router\n",
+ "\n",
+ "The edge router requires node positions, see the node position mapping example, notebook 06"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "a28a9d39-2c66-4994-b298-0e73e7525965",
+ "metadata": {
+ "id": "a28a9d39-2c66-4994-b298-0e73e7525965",
+ "outputId": "30906eea-6d83-44e5-8bc3-bb2e19f7633c",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 667,
+ "referenced_widgets": [
+ "993b4995fdb649d0a6133befdf448b91",
+ "a2f3969b40f94b839f59b51b5d54d167"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='650px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "993b4995fdb649d0a6133befdf448b91"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w9 = GraphWidget(graph = florentine_families_graph())\n",
+ "w9.organic_edge_router()\n",
+ "display(w9)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cc731bba-3fb9-416b-8ea8-2c2b8d8567bb",
+ "metadata": {
+ "id": "cc731bba-3fb9-416b-8ea8-2c2b8d8567bb"
+ },
+ "source": [
+ "## Interactive Organic Layout\n",
+ "\n",
+ "The interactive organic layout allows node dragging"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "d66c4239-3b93-4a0a-a678-596e73b833f9",
+ "metadata": {
+ "id": "d66c4239-3b93-4a0a-a678-596e73b833f9",
+ "outputId": "34f0dda6-8654-49e6-f1ee-f76f7b544adb",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 667,
+ "referenced_widgets": [
+ "cc11b7fcd1b847c299a1d09fd33536dc",
+ "c6bc41e99beb4ca1a81e3a5832a5782c"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='650px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "cc11b7fcd1b847c299a1d09fd33536dc"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w10 = GraphWidget(graph=florentine_families_graph())\n",
+ "w10.interactive_organic_layout()\n",
+ "display(w10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8abec923-5234-4654-9817-821582d9562b",
+ "metadata": {
+ "id": "8abec923-5234-4654-9817-821582d9562b"
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+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
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- "id": "d5dda599-a06c-42a5-8758-1fc3bc23a76b",
- "metadata": {},
- "source": [
- "There are 8 different Layouts. A layout can either be selected in the toolbar or by setting the layout beforehand via ```w.[layout_name]()```. See also the [documentation](https://yworks.github.io/yfiles-jupyter-graphs/02_graph_widget/#methods).\n",
- "\n",
- "Available automatic layout algorithms:\n",
- "* Circular\n",
- "* Hierarchic\n",
- "* Organic (default)\n",
- "* Orthogonal\n",
- "* Radial\n",
- "* Tree\n",
- "\n",
- "Additionally, the following edge routers are available:\n",
- "* Orthogonal edge router\n",
- "* Organic edge router\n",
- "\n",
- "In contrast to automatic layout algorithms, edge routers only adjust the edge path while keeping the current node positions. This works well in conjuction with setting explicit node positions as demonstrated in [04_layout_mapping.ipynb](./04_layout_mapping.ipynb) and [06_position_mapping.ipynb](./06_position_mapping.ipynb).\n",
- "\n",
- "Before using the graph widget, install all necessary packages."
- ]
- },
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- "execution_count": null,
- "id": "67b85a4c-50a4-44e5-bea5-c47dcf6ac8cc",
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- "outputs": [],
- "source": [
- "%pip install yfiles_jupyter_graphs --quiet\n",
- "%pip install networkx --quiet\n",
- "from networkx import florentine_families_graph\n",
- "from yfiles_jupyter_graphs import GraphWidget"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5f3dca5a-cc7b-406f-91b0-4f8395001dee",
- "metadata": {},
- "source": [
- "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e268314d-7540-48b0-b03c-aa76d748b34f",
- "metadata": {},
- "outputs": [],
- "source": [
- "try:\n",
- " import google.colab\n",
- " from google.colab import output\n",
- " output.enable_custom_widget_manager()\n",
- "except:\n",
- " pass"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a17981f1-d4dc-48ce-8a9a-7db8634bb02d",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "bcb18821-fdb3-4795-bd13-b5cbd8ea9129",
- "metadata": {},
- "outputs": [],
- "source": [
- "w = GraphWidget(graph = florentine_families_graph())\n",
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f8e67ec2-de96-449b-b99f-401635017425",
- "metadata": {},
- "source": [
- "## Circular Layout "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "91766d81-92b6-45d1-9b99-b98e14641137",
- "metadata": {},
- "outputs": [],
- "source": [
- "w2 = GraphWidget(graph = florentine_families_graph())\n",
- "w2.circular_layout()\n",
- "display(w2)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7749b671-d15c-41c4-86b4-73e7d7c6a0bb",
- "metadata": {},
- "source": [
- "## Hierarchic Layout "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a60d734a-c841-49b9-a3da-e2f7f59d4319",
- "metadata": {},
- "outputs": [],
- "source": [
- "w3 = GraphWidget(graph = florentine_families_graph())\n",
- "w3.hierarchic_layout()\n",
- "display(w3)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e8b2f5f0-686b-46d2-8ff0-70adf4934562",
- "metadata": {},
- "source": [
- "## Organic Layout"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "df9a073f-fa14-483b-be07-4e9dc06af3ae",
- "metadata": {},
- "outputs": [],
- "source": [
- "w4 = GraphWidget(graph = florentine_families_graph())\n",
- "w4.organic_layout()\n",
- "display(w4)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a5068b61-9995-4f11-a70e-1cc59c50a2e2",
- "metadata": {},
- "source": [
- "## Orthogonal Layout"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f75dabe7-f795-4ecc-b0d1-4a036701be64",
- "metadata": {},
- "outputs": [],
- "source": [
- "w5 = GraphWidget(graph = florentine_families_graph())\n",
- "w5.orthogonal_layout()\n",
- "display(w5)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "64afbae3-1c3e-4bea-864d-19a4e78e376f",
- "metadata": {},
- "source": [
- "## Radial Layout"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8bb347df-6b53-448e-aa62-abea08b1261c",
- "metadata": {},
- "outputs": [],
- "source": [
- "w6 = GraphWidget(graph = florentine_families_graph())\n",
- "w6.radial_layout()\n",
- "display(w6)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b6f0cc35-f9bc-4c1b-a6e1-1f64957d29cb",
- "metadata": {},
- "source": [
- "## Tree Layout "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0c0a80e3-69e8-481a-b8f8-c6a1e6a986b2",
- "metadata": {},
- "outputs": [],
- "source": [
- "w7 = GraphWidget(graph = florentine_families_graph())\n",
- "w7.tree_layout()\n",
- "display(w7)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5825b7ea-6b56-45d2-8c2d-ad7f2690c21f",
- "metadata": {},
- "source": [
- "## Orthogonal Edge Router\n",
- "\n",
- "The edge router requires node positions, see the node position mapping example, notebook 06"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "53b023c3-9b97-4999-a122-07857e6d15ea",
- "metadata": {},
- "outputs": [],
- "source": [
- "w8 = GraphWidget(graph = florentine_families_graph())\n",
- "w8.orthogonal_edge_router()\n",
- "display(w8)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1c7f8754-8229-436a-b556-5f8f4bdf4966",
- "metadata": {},
- "source": [
- "## Organic Edge Router\n",
- "\n",
- "The edge router requires node positions, see the node position mapping example, notebook 06"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a28a9d39-2c66-4994-b298-0e73e7525965",
- "metadata": {},
- "outputs": [],
- "source": [
- "w9 = GraphWidget(graph = florentine_families_graph())\n",
- "w9.organic_edge_router()\n",
- "display(w9)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "cc731bba-3fb9-416b-8ea8-2c2b8d8567bb",
- "metadata": {},
- "source": [
- "## Interactive Organic Layout\n",
- "\n",
- "The interactive organic layout allows node dragging"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d66c4239-3b93-4a0a-a678-596e73b833f9",
- "metadata": {},
- "outputs": [],
- "source": [
- "w10 = GraphWidget(graph=florentine_families_graph())\n",
- "w10.interactive_organic_layout()\n",
- "display(w10)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8abec923-5234-4654-9817-821582d9562b",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/23_sidebar.ipynb b/examples/23_sidebar.ipynb
index e2251cd..6eb1814 100644
--- a/examples/23_sidebar.ipynb
+++ b/examples/23_sidebar.ipynb
@@ -1,4646 +1,5337 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "969f40cf-3ea9-4e67-94eb-0b4c4b321544",
- "metadata": {},
- "source": [
- "# Configure Sidebar "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9d18951c-0e4f-49eb-bce5-be6c57f59196",
- "metadata": {},
- "source": [
- "Before using the graph widget, install all necessary packages."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "47aaa060-e80e-4fd7-ac35-f1bc10243eb8",
- "metadata": {},
- "outputs": [],
- "source": [
- "%pip install yfiles_jupyter_graphs --quiet\n",
- "%pip install networkx --quiet\n",
- "from networkx import florentine_families_graph\n",
- "from yfiles_jupyter_graphs import GraphWidget"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "59bd3bb4-24e6-4f3e-81ab-38d197894e6f",
- "metadata": {},
- "source": [
- "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "329e3d8f-c816-48ff-8c3f-913fb5355735",
- "metadata": {},
- "outputs": [],
- "source": [
- "try:\n",
- " import google.colab\n",
- " from google.colab import output\n",
- " output.enable_custom_widget_manager()\n",
- "except:\n",
- " pass"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e7b8471c-e7e7-4b11-8cbd-378414de7bf4",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b6aabea3-0047-44cf-b7f0-bf22269e917b",
- "metadata": {},
- "source": [
- "## Default sidebar configuration\n",
- "\n",
- "#### The sidebar is collapsed but starts with the 'About' panel when opened:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "09f665b8-bf19-4649-86a8-6c7570e3e6b9",
- "metadata": {},
- "outputs": [],
- "source": [
- "w = GraphWidget(graph=florentine_families_graph())\n",
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2835c438-ee79-46e8-979d-f28bae00610e",
- "metadata": {},
- "source": [
- "#### Let's open the sidebar when displaying the widget"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "90c05a29-ee44-474d-b1b3-8236f7171be8",
- "metadata": {},
- "outputs": [],
- "source": [
- "w2 = GraphWidget(graph=florentine_families_graph())\n",
- "w2.set_sidebar(enabled=True)\n",
- "display(w2)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "0f3637c1-d319-45a9-a743-accf7f4809e5",
- "metadata": {},
- "source": [
- "#### Let's start with the 'Neighborhood' panel"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e3e427c9-8619-4cdc-b270-71fff5cd4655",
- "metadata": {},
- "outputs": [],
- "source": [
- "w3 = GraphWidget(graph=florentine_families_graph())\n",
- "w3.set_sidebar(start_with='Neighborhood')\n",
- "display(w3)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2b92dab0-c21d-4858-b55c-03460bae7533",
- "metadata": {},
- "source": [
- "#### Let's hide the sidebar again, but when enabling it again via the toolbar, still starting with the 'Neighborhood' panel"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "24f083d5-f2d1-44b0-8b1c-cf68e18dc268",
- "metadata": {},
- "outputs": [],
- "source": [
- "w4 = GraphWidget(graph=florentine_families_graph())\n",
- "w4.set_sidebar(enabled=False, start_with='Neighborhood')\n",
- "display(w4)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6876668e-db46-4743-bfff-0239cf918399",
- "metadata": {},
- "source": [
- "#### Let's start with the 'Data' panel:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d9d10e29-0282-42d3-86a2-b63f4ecd215b",
- "metadata": {},
- "outputs": [],
- "source": [
- "w5 = GraphWidget(graph=florentine_families_graph())\n",
- "w5.set_sidebar(start_with='Data')\n",
- "display(w5)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "17ebc6cd-9947-4ce2-ab56-f454d9f24dcf",
- "metadata": {},
- "source": [
- "#### Hide the sidebar, but still start with the Data panel:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "cd663b46-a3f1-4561-b062-5c7d23fe4e60",
- "metadata": {},
- "outputs": [],
- "source": [
- "w6 = GraphWidget(graph=florentine_families_graph())\n",
- "w6.set_sidebar(enabled=False, start_with='Data')\n",
- "display(w6)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fd119e05-c946-43f2-b640-1dd99a99b47b",
- "metadata": {},
- "source": [
- "#### Let's start with the 'Search' panel"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5a08591d-4bc4-49cf-b8dc-cc90e021ea3e",
- "metadata": {},
- "outputs": [],
- "source": [
- "w7 = GraphWidget(graph=florentine_families_graph())\n",
- "w7.set_sidebar(start_with='Search')\n",
- "display(w7)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "0fa81375-3653-40f5-9bc7-28d18c97b2db",
- "metadata": {},
- "source": [
- "#### Hide the sidebar whilst starting with the 'Search' panel:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "486ac062-d796-4a90-9d99-798a6416edba",
- "metadata": {},
- "outputs": [],
- "source": [
- "w8 = GraphWidget(graph=florentine_families_graph())\n",
- "w8.set_sidebar(enabled=False, start_with='Search')\n",
- "display(w8)"
- ]
- }
- ],
- "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": "3.12.3"
- },
- "widgets": {
- "application/vnd.jupyter.widget-state+json": {
- "state": {
- "04a2dd1d47334ce384d007bacb794afc": {
- "model_module": "@jupyter-widgets/base",
- "model_module_version": "2.0.0",
- "model_name": "LayoutModel",
- "state": {
- "height": "500px",
- "width": "100%"
- }
- },
- "0a66c2d8409a44028d62a3570f2643d8": {
- "model_module": "@jupyter-widgets/base",
- "model_module_version": "2.0.0",
- "model_name": "LayoutModel",
- "state": {
- "height": "650px",
- "width": "100%"
- }
- },
- "0adce6c7b8c1455487f5db57995f7d2b": {
- "model_module": "@jupyter-widgets/base",
- "model_module_version": "2.0.0",
- "model_name": "LayoutModel",
- "state": {
- "height": "650px",
- "width": "100%"
- }
- },
- "22312ae5293849438d2da70ae1aafae9": {
- "model_module": "@jupyter-widgets/base",
- "model_module_version": "2.0.0",
- "model_name": "LayoutModel",
- "state": {
- "height": "500px",
- "width": "100%"
- }
- },
- "2ae7be09416247888147c0e23510f5d4": {
- "model_module": "@jupyter-widgets/base",
- "model_module_version": "2.0.0",
- "model_name": "LayoutModel",
- "state": {
- "height": "650px",
- "width": "100%"
- }
- },
- "2c877674b78d4bf99e4cdb80bc2b9b0e": {
- "model_module": "@jupyter-widgets/base",
- "model_module_version": "2.0.0",
- "model_name": "LayoutModel",
- "state": {
- "height": "500px",
- "width": "100%"
- }
- },
- "2d3573c5259c47d297851fc8922b4f5b": {
- "model_module": "yfiles-jupyter-graphs",
- "model_module_version": "^1.7.3",
- "model_name": "GraphModel",
- "state": {
- "_context_pane_mapping": [
- {
- "id": "Neighborhood",
- "title": "Neighborhood"
- },
- {
- "id": "Data",
- "title": "Data"
- },
- {
- "id": "Search",
- "title": "Search"
- },
- {
- "id": "About",
- "title": "About"
- }
- ],
- "_data_importer": "networkx",
- "_directed": false,
- "_edges": [
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 1,
- "id": 0,
- "label": "",
- "properties": {},
- "start": 0,
- "thickness_factor": 1
- },
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 5,
- "id": 1,
- "label": "",
- "properties": {},
- "start": 1,
- "thickness_factor": 1
- },
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 6,
- "id": 2,
- "label": "",
- "properties": {},
- "start": 1,
- "thickness_factor": 1
- },
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 7,
- "id": 3,
- "label": "",
- "properties": {},
- "start": 1,
- "thickness_factor": 1
- },
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 8,
- "id": 4,
- "label": "",
- "properties": {},
- "start": 1,
- "thickness_factor": 1
- },
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 9,
- "id": 5,
- "label": "",
- "properties": {},
- "start": 1,
- "thickness_factor": 1
- },
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 3,
- "id": 6,
- "label": "",
- "properties": {},
- "start": 2,
- "thickness_factor": 1
- },
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 4,
- "id": 7,
- "label": "",
- "properties": {},
- "start": 2,
- "thickness_factor": 1
- },
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 5,
- "id": 8,
- "label": "",
- "properties": {},
- "start": 2,
- "thickness_factor": 1
- },
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 4,
- "id": 9,
- "label": "",
- "properties": {},
- "start": 3,
- "thickness_factor": 1
- },
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 11,
- "id": 10,
- "label": "",
- "properties": {},
- "start": 3,
- "thickness_factor": 1
- },
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 6,
- "id": 11,
- "label": "",
- "properties": {},
- "start": 4,
- "thickness_factor": 1
- },
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 11,
- "id": 12,
- "label": "",
- "properties": {},
- "start": 4,
- "thickness_factor": 1
- },
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 7,
- "id": 13,
- "label": "",
- "properties": {},
- "start": 6,
- "thickness_factor": 1
- },
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 12,
- "id": 14,
- "label": "",
- "properties": {},
- "start": 7,
- "thickness_factor": 1
- },
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 13,
- "id": 15,
- "label": "",
- "properties": {},
- "start": 8,
- "thickness_factor": 1
- },
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 12,
- "id": 16,
- "label": "",
- "properties": {},
- "start": 8,
- "thickness_factor": 1
- },
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 10,
- "id": 17,
- "label": "",
- "properties": {},
- "start": 9,
- "thickness_factor": 1
- },
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 12,
- "id": 18,
- "label": "",
- "properties": {},
- "start": 11,
- "thickness_factor": 1
- },
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 14,
- "id": 19,
- "label": "",
- "properties": {},
- "start": 12,
- "thickness_factor": 1
- }
- ],
- "_model_module_version": "^1.7.3",
- "_nodes": [
- {
- "color": "#15AFAC",
- "id": 0,
- "label": "Acciaiuoli",
- "position": [
- 0,
- 0
- ],
- "properties": {
- "label": "Acciaiuoli"
- },
- "scale_factor": 1,
- "size": [
- 55,
- 55
- ],
- "styles": {},
- "type": "#15AFAC"
- },
- {
- "color": "#15AFAC",
- "id": 1,
- "label": "Medici",
- "position": [
- 0,
- 0
- ],
- "properties": {
- "label": "Medici"
- },
- "scale_factor": 1,
- "size": [
- 55,
- 55
- ],
- "styles": {},
- "type": "#15AFAC"
- },
- {
- "color": "#15AFAC",
- "id": 2,
- "label": "Castellani",
- "position": [
- 0,
- 0
- ],
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m21.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m15.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m22.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
}
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- },
- "scale_factor": 1,
- "size": [
- 55,
- 55
- ],
- "styles": {},
- "type": "#15AFAC"
+ ],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "%pip install networkx --quiet\n",
+ "from networkx import florentine_families_graph\n",
+ "from yfiles_jupyter_graphs import GraphWidget"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "59bd3bb4-24e6-4f3e-81ab-38d197894e6f",
+ "metadata": {
+ "id": "59bd3bb4-24e6-4f3e-81ab-38d197894e6f"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "329e3d8f-c816-48ff-8c3f-913fb5355735",
+ "metadata": {
+ "id": "329e3d8f-c816-48ff-8c3f-913fb5355735"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7b8471c-e7e7-4b11-8cbd-378414de7bf4",
+ "metadata": {
+ "id": "e7b8471c-e7e7-4b11-8cbd-378414de7bf4"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6aabea3-0047-44cf-b7f0-bf22269e917b",
+ "metadata": {
+ "id": "b6aabea3-0047-44cf-b7f0-bf22269e917b"
+ },
+ "source": [
+ "## Default sidebar configuration\n",
+ "\n",
+ "#### The sidebar is collapsed but starts with the 'About' panel when opened:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "09f665b8-bf19-4649-86a8-6c7570e3e6b9",
+ "metadata": {
+ "id": "09f665b8-bf19-4649-86a8-6c7570e3e6b9",
+ "outputId": "84c4e6c8-c68f-4219-f9fb-46bfcb527e3e",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 667,
+ "referenced_widgets": [
+ "25ba1345deae48469ecac5d9457ab3bb",
+ "77bc43e9e3a6484596c649f0d820ab61"
+ ]
}
- ],
- "_overview": {
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- "_selected_graph": [
- [],
- []
- ],
- "_sidebar": {
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- "start_with": "Neighborhood"
- },
- "_view_module_version": "^1.7.3",
- "layout": "IPY_MODEL_2ae7be09416247888147c0e23510f5d4"
- }
- },
- "a6cda817ac6240abae822b3b48544aee": {
- "model_module": "yfiles-jupyter-graphs",
- "model_module_version": "^1.7.3",
- "model_name": "GraphModel",
- "state": {
- "_context_pane_mapping": [
- {
- "id": "Neighborhood",
- "title": "Neighborhood"
- },
- {
- "id": "Data",
- "title": "Data"
- },
- {
- "id": "Search",
- "title": "Search"
- },
- {
- "id": "About",
- "title": "About"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='650px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "25ba1345deae48469ecac5d9457ab3bb"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
}
- ],
- "_data_importer": "networkx",
- "_directed": false,
- "_edges": [
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 1,
- "id": 0,
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- "thickness_factor": 1
- },
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- "label": "",
- "properties": {},
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- "thickness_factor": 1
- },
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- "properties": {},
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- },
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- "directed": false,
- "end": 3,
- "id": 6,
- "label": "",
- "properties": {},
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- },
- {
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- "end": 4,
- "id": 7,
- "label": "",
- "properties": {},
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- },
- {
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- "end": 5,
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- "label": "",
- "properties": {},
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- },
- {
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- "end": 4,
- "id": 9,
- "label": "",
- "properties": {},
- "start": 3,
- "thickness_factor": 1
- },
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 11,
- "id": 10,
- "label": "",
- "properties": {},
- "start": 3,
- "thickness_factor": 1
- },
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 6,
- "id": 11,
- "label": "",
- "properties": {},
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- },
- {
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- "label": "",
- "properties": {},
- "start": 4,
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- },
- {
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- "directed": false,
- "end": 7,
- "id": 13,
- "label": "",
- "properties": {},
- "start": 6,
- "thickness_factor": 1
- },
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 12,
- "id": 14,
- "label": "",
- "properties": {},
- "start": 7,
- "thickness_factor": 1
- },
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 13,
- "id": 15,
- "label": "",
- "properties": {},
- "start": 8,
- "thickness_factor": 1
- },
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 12,
- "id": 16,
- "label": "",
- "properties": {},
- "start": 8,
- "thickness_factor": 1
- },
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 10,
- "id": 17,
- "label": "",
- "properties": {},
- "start": 9,
- "thickness_factor": 1
- },
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 12,
- "id": 18,
- "label": "",
- "properties": {},
- "start": 11,
- "thickness_factor": 1
- },
- {
- "color": "#15AFAC",
- "directed": false,
- "end": 14,
- "id": 19,
- "label": "",
- "properties": {},
- "start": 12,
- "thickness_factor": 1
+ ],
+ "source": [
+ "w = GraphWidget(graph=florentine_families_graph())\n",
+ "display(w)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2835c438-ee79-46e8-979d-f28bae00610e",
+ "metadata": {
+ "id": "2835c438-ee79-46e8-979d-f28bae00610e"
+ },
+ "source": [
+ "#### Let's open the sidebar when displaying the widget"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "90c05a29-ee44-474d-b1b3-8236f7171be8",
+ "metadata": {
+ "id": "90c05a29-ee44-474d-b1b3-8236f7171be8",
+ "outputId": "5165bde1-bf06-4baf-b600-522ebf4c15d7",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 667,
+ "referenced_widgets": [
+ "83bb974127d94cd19f8eb564d118902d",
+ "b34cf5cebf5446118cd34186bdba9132"
+ ]
}
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- "label": "Ridolfi",
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- "properties": {
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- {
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- "properties": {
- "label": "Tornabuoni"
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- "size": [
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- "styles": {},
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- {
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- "position": [
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- "properties": {
- "label": "Albizzi"
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- {
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- "properties": {
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- "styles": {},
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- },
- {
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- "id": 12,
- "label": "Guadagni",
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- "properties": {
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- "styles": {},
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- {
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- "label": "Ginori",
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- "label": "Ginori"
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- "styles": {},
- "type": "#15AFAC"
- },
- {
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- "id": 14,
- "label": "Lamberteschi",
- "position": [
- 0,
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- "properties": {
- "label": "Lamberteschi"
- },
- "scale_factor": 1,
- "size": [
- 55,
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- ],
- "styles": {},
- "type": "#15AFAC"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='650px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "83bb974127d94cd19f8eb564d118902d"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
}
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- "overview_set": false
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- "id": "Data",
- "title": "Data"
- },
- {
- "id": "Search",
- "title": "Search"
- },
- {
- "id": "About",
- "title": "About"
+ ],
+ "source": [
+ "w2 = GraphWidget(graph=florentine_families_graph())\n",
+ "w2.set_sidebar(enabled=True)\n",
+ "display(w2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f3637c1-d319-45a9-a743-accf7f4809e5",
+ "metadata": {
+ "id": "0f3637c1-d319-45a9-a743-accf7f4809e5"
+ },
+ "source": [
+ "#### Let's start with the 'Neighborhood' panel"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "e3e427c9-8619-4cdc-b270-71fff5cd4655",
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+ "referenced_widgets": [
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- "directed": false,
- "end": 14,
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- "label": "",
- "properties": {},
- "start": 12,
- "thickness_factor": 1
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='650px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
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+ }
+ }
+ }
+ }
}
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- "properties": {
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- },
- "scale_factor": 1,
- "size": [
- 55,
- 55
- ],
- "styles": {},
- "type": "#15AFAC"
+ ],
+ "source": [
+ "w3 = GraphWidget(graph=florentine_families_graph())\n",
+ "w3.set_sidebar(start_with='Neighborhood')\n",
+ "display(w3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2b92dab0-c21d-4858-b55c-03460bae7533",
+ "metadata": {
+ "id": "2b92dab0-c21d-4858-b55c-03460bae7533"
+ },
+ "source": [
+ "#### Let's hide the sidebar again, but when enabling it again via the toolbar, still starting with the 'Neighborhood' panel"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "24f083d5-f2d1-44b0-8b1c-cf68e18dc268",
+ "metadata": {
+ "id": "24f083d5-f2d1-44b0-8b1c-cf68e18dc268",
+ "outputId": "8a08bdcf-3509-4cb9-dc5b-6bceb5d1a2a5",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 667,
+ "referenced_widgets": [
+ "d588aabef3d44c75a6b430b6f6e1f772",
+ "68d97ba5d74344e7a701d3ad95f51568"
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- {
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+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
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+ "application/vnd.jupyter.widget-view+json": {
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+ ],
+ "source": [
+ "w4 = GraphWidget(graph=florentine_families_graph())\n",
+ "w4.set_sidebar(enabled=False, start_with='Neighborhood')\n",
+ "display(w4)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6876668e-db46-4743-bfff-0239cf918399",
+ "metadata": {
+ "id": "6876668e-db46-4743-bfff-0239cf918399"
+ },
+ "source": [
+ "#### Let's start with the 'Data' panel:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "d9d10e29-0282-42d3-86a2-b63f4ecd215b",
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+ "outputId": "3607d336-f89e-4abb-e16e-74640cec084f",
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+ "height": 667,
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- 55
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- "styles": {},
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- "label": "Strozzi",
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- 55,
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- "label": "Tornabuoni",
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- 0
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- "properties": {
- "label": "Tornabuoni"
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- 55
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- "styles": {},
- "type": "#15AFAC"
- },
- {
- "color": "#15AFAC",
- "id": 8,
- "label": "Albizzi",
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- 0
- ],
- "properties": {
- "label": "Albizzi"
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- "scale_factor": 1,
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- ],
- "styles": {},
- "type": "#15AFAC"
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- {
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- "label": "Salviati",
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- "scale_factor": 1,
- "size": [
- 55,
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- ],
- "styles": {},
- "type": "#15AFAC"
- },
- {
- "color": "#15AFAC",
- "id": 10,
- "label": "Pazzi",
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- 0,
- 0
- ],
- "properties": {
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- 55,
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- ],
- "styles": {},
- "type": "#15AFAC"
- },
- {
- "color": "#15AFAC",
- "id": 11,
- "label": "Bischeri",
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- "properties": {
- "label": "Bischeri"
- },
- "scale_factor": 1,
- "size": [
- 55,
- 55
- ],
- "styles": {},
- "type": "#15AFAC"
- },
- {
- "color": "#15AFAC",
- "id": 12,
- "label": "Guadagni",
- "position": [
- 0,
- 0
- ],
- "properties": {
- "label": "Guadagni"
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- "scale_factor": 1,
- "size": [
- 55,
- 55
- ],
- "styles": {},
- "type": "#15AFAC"
- },
- {
- "color": "#15AFAC",
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- 0,
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- 55,
- 55
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- "styles": {},
- "type": "#15AFAC"
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- {
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- "id": 14,
- "label": "Lamberteschi",
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- 0,
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- "properties": {
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- },
- "scale_factor": 1,
- "size": [
- 55,
- 55
- ],
- "styles": {},
- "type": "#15AFAC"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='650px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "0b691005451e428d9f031ec1ab445b82"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w5 = GraphWidget(graph=florentine_families_graph())\n",
+ "w5.set_sidebar(start_with='Data')\n",
+ "display(w5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "17ebc6cd-9947-4ce2-ab56-f454d9f24dcf",
+ "metadata": {
+ "id": "17ebc6cd-9947-4ce2-ab56-f454d9f24dcf"
+ },
+ "source": [
+ "#### Hide the sidebar, but still start with the Data panel:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "cd663b46-a3f1-4561-b062-5c7d23fe4e60",
+ "metadata": {
+ "id": "cd663b46-a3f1-4561-b062-5c7d23fe4e60",
+ "outputId": "b41b0025-d592-420f-d031-92f79db0e7c2",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 667,
+ "referenced_widgets": [
+ "04f6effb8755414f8f62e7a90e612980",
+ "e23678f18ff147aa9fc2bbb8f485c669"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='650px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "04f6effb8755414f8f62e7a90e612980"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w6 = GraphWidget(graph=florentine_families_graph())\n",
+ "w6.set_sidebar(enabled=False, start_with='Data')\n",
+ "display(w6)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fd119e05-c946-43f2-b640-1dd99a99b47b",
+ "metadata": {
+ "id": "fd119e05-c946-43f2-b640-1dd99a99b47b"
+ },
+ "source": [
+ "#### Let's start with the 'Search' panel"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "5a08591d-4bc4-49cf-b8dc-cc90e021ea3e",
+ "metadata": {
+ "id": "5a08591d-4bc4-49cf-b8dc-cc90e021ea3e",
+ "outputId": "9f45a45b-e16d-42cb-c066-c3cbdee4cc64",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 667,
+ "referenced_widgets": [
+ "2c4d7155716c440dbdf580a476428240",
+ "67f7e6c2d84141beab28f72a66385e5b"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='650px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "2c4d7155716c440dbdf580a476428240"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w7 = GraphWidget(graph=florentine_families_graph())\n",
+ "w7.set_sidebar(start_with='Search')\n",
+ "display(w7)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0fa81375-3653-40f5-9bc7-28d18c97b2db",
+ "metadata": {
+ "id": "0fa81375-3653-40f5-9bc7-28d18c97b2db"
+ },
+ "source": [
+ "#### Hide the sidebar whilst starting with the 'Search' panel:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "486ac062-d796-4a90-9d99-798a6416edba",
+ "metadata": {
+ "id": "486ac062-d796-4a90-9d99-798a6416edba",
+ "outputId": "38947c2f-c760-4e08-cde8-73f91b7bbee9",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 667,
+ "referenced_widgets": [
+ "df03c64bf649486bbe1c44197de96339",
+ "dd6a4fb337774d79b5444072afb17895"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='650px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "df03c64bf649486bbe1c44197de96339"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w8 = GraphWidget(graph=florentine_families_graph())\n",
+ "w8.set_sidebar(enabled=False, start_with='Search')\n",
+ "display(w8)"
+ ]
+ }
+ ],
+ "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": "3.12.3"
+ },
+ "widgets": {
+ "application/vnd.jupyter.widget-state+json": {
+ "25ba1345deae48469ecac5d9457ab3bb": {
+ "model_module": "yfiles-jupyter-graphs",
+ "model_name": "GraphModel",
+ "model_module_version": "^1.8.1",
+ "state": {
+ "_context_pane_mapping": [
+ {
+ "id": "Neighborhood",
+ "title": "Neighborhood"
+ },
+ {
+ "id": "Data",
+ "title": "Data"
+ },
+ {
+ "id": "Search",
+ "title": "Search"
+ },
+ {
+ "id": "About",
+ "title": "About"
+ }
+ ],
+ "_data_importer": "networkx",
+ "_directed": false,
+ "_dom_classes": [],
+ "_edges": [
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+ "start": 0,
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+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
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+ "start": 1,
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+ "color": "#15AFAC",
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+ "directed": false
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+ "color": "#15AFAC",
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+ "label": "",
+ "color": "#15AFAC",
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+ "color": "#15AFAC",
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+ "color": "#15AFAC",
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+ "label": "",
+ "color": "#15AFAC",
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+ "label": "",
+ "color": "#15AFAC",
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+ },
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+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
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+ "label": "",
+ "color": "#15AFAC",
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+ "color": "#15AFAC",
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+ ],
+ "_graph_layout": {},
+ "_highlight": [],
+ "_license": {},
+ "_model_module": "yfiles-jupyter-graphs",
+ "_model_module_version": "^1.8.1",
+ "_model_name": "GraphModel",
+ "_neighborhood": {},
+ "_nodes": [
+ {
+ "id": 0,
+ "properties": {
+ "label": "Acciaiuoli"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Acciaiuoli",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
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+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 1,
+ "properties": {
+ "label": "Medici"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Medici",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 2,
+ "properties": {
+ "label": "Castellani"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Castellani",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
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+ ],
+ "position": [
+ 0,
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+ ]
+ },
+ {
+ "id": 3,
+ "properties": {
+ "label": "Peruzzi"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Peruzzi",
+ "scale_factor": 1,
+ "type": "#15AFAC",
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+ "position": [
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+ "properties": {
+ "label": "Strozzi"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Strozzi",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
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+ ],
+ "position": [
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+ ]
+ },
+ {
+ "id": 5,
+ "properties": {
+ "label": "Barbadori"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Barbadori",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
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+ ],
+ "position": [
+ 0,
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+ ]
+ },
+ {
+ "id": 6,
+ "properties": {
+ "label": "Ridolfi"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Ridolfi",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
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+ "position": [
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+ ]
+ },
+ {
+ "id": 7,
+ "properties": {
+ "label": "Tornabuoni"
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+ "color": "#15AFAC",
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+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Albizzi",
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+ "type": "#15AFAC",
+ "size": [
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+ ],
+ "position": [
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+ "color": "#15AFAC",
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+ "color": "#15AFAC",
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+ ]
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+ "properties": {
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+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Lamberteschi",
+ "scale_factor": 1,
+ "type": "#15AFAC",
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+ ],
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+ "overview_set": false
+ },
+ "_selected_graph": [
+ [],
+ []
+ ],
+ "_sidebar": {
+ "enabled": false,
+ "start_with": null
+ },
+ "_view_count": null,
+ "_view_module": "yfiles-jupyter-graphs",
+ "_view_module_version": "^1.8.1",
+ "_view_name": "GraphView",
+ "layout": "IPY_MODEL_77bc43e9e3a6484596c649f0d820ab61",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "77bc43e9e3a6484596c649f0d820ab61": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "2.0.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "2.0.0",
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diff --git a/examples/24_neighborhood.ipynb b/examples/24_neighborhood.ipynb
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- "id": "d9132ee5-a932-4ce7-97fc-c6d6e431ce66",
- "metadata": {},
- "source": [
- "# Configure Neighborhood "
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "d9132ee5-a932-4ce7-97fc-c6d6e431ce66",
+ "metadata": {
+ "id": "d9132ee5-a932-4ce7-97fc-c6d6e431ce66"
+ },
+ "source": [
+ "# Configure Neighborhood "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "379d347b-e610-42d8-aabf-43374883aa36",
+ "metadata": {
+ "id": "379d347b-e610-42d8-aabf-43374883aa36"
+ },
+ "source": [
+ "Before using the graph widget, install all necessary packages."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "41a8c112-93cb-4f9c-b2f8-d867b09f061c",
+ "metadata": {
+ "id": "41a8c112-93cb-4f9c-b2f8-d867b09f061c",
+ "outputId": "c853c541-e074-4f16-90a1-6c958943a68a",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m23.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m34.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "%pip install networkx --quiet\n",
+ "from networkx import connected_caveman_graph\n",
+ "from yfiles_jupyter_graphs import GraphWidget\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bd55f8ea-57d7-47fb-8bf4-2c1c3ac28a93",
+ "metadata": {
+ "id": "bd55f8ea-57d7-47fb-8bf4-2c1c3ac28a93"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "232377de-03a6-45bc-8d83-fe0d8ea04a9e",
+ "metadata": {
+ "id": "232377de-03a6-45bc-8d83-fe0d8ea04a9e"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f6cf813d-1814-444f-9d85-16a85fee9ceb",
+ "metadata": {
+ "id": "f6cf813d-1814-444f-9d85-16a85fee9ceb"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "48ed1ee8-da66-4a61-93f1-83bfd421ef6e",
+ "metadata": {
+ "id": "48ed1ee8-da66-4a61-93f1-83bfd421ef6e"
+ },
+ "source": [
+ "## Default Neighborhood Configuration\n",
+ "\n",
+ "The Neighborhood starts with the depth = 1 and no selected node"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "68e2bb8a-5f32-4df0-b59a-57a204eaf7dd",
+ "metadata": {
+ "id": "68e2bb8a-5f32-4df0-b59a-57a204eaf7dd",
+ "outputId": "1a132b13-26f7-4db4-8cae-4565a8013d56",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 637,
+ "referenced_widgets": [
+ "a898da7ecf11456791a7b57c903b291a",
+ "80ed30cba46c4eadb8fcb49fc5f151c7"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='620px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "a898da7ecf11456791a7b57c903b291a"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w = GraphWidget(graph = connected_caveman_graph(3,4))\n",
+ "display(w)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1b26d61c-3848-42e5-9b56-d1bf2f5241d3",
+ "metadata": {
+ "id": "1b26d61c-3848-42e5-9b56-d1bf2f5241d3"
+ },
+ "source": [
+ "Let's change the depth of the Neighborhood and start with node 1:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "4404f7eb-6c56-48f4-a60f-8fc92d136c7d",
+ "metadata": {
+ "id": "4404f7eb-6c56-48f4-a60f-8fc92d136c7d",
+ "outputId": "05840288-feda-4f06-8ae9-46def5fca945",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 637,
+ "referenced_widgets": [
+ "303ddc55ccf144c7956ebd50f8af49e3",
+ "20550a75bde646e59fac027d05148b66"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='620px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "303ddc55ccf144c7956ebd50f8af49e3"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w2 = GraphWidget(graph = connected_caveman_graph(3,4))\n",
+ "w2.set_neighborhood(3, [1])\n",
+ "w2.set_sidebar(start_with = 'Neighborhood')\n",
+ "display(w2)"
+ ]
+ }
+ ],
+ "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": "3.11.3"
+ },
+ "colab": {
+ "provenance": []
+ },
+ "widgets": {
+ "application/vnd.jupyter.widget-state+json": {
+ "a898da7ecf11456791a7b57c903b291a": {
+ "model_module": "yfiles-jupyter-graphs",
+ "model_name": "GraphModel",
+ "model_module_version": "^1.8.1",
+ "state": {
+ "_context_pane_mapping": [
+ {
+ "id": "Neighborhood",
+ "title": "Neighborhood"
+ },
+ {
+ "id": "Data",
+ "title": "Data"
+ },
+ {
+ "id": "Search",
+ "title": "Search"
+ },
+ {
+ "id": "About",
+ "title": "About"
+ }
+ ],
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+ "type": "#15AFAC",
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+ "color": "#15AFAC",
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+ "type": "#15AFAC",
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+ ]
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+ {
+ "id": 8,
+ "properties": {
+ "label": "8"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "8",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 9,
+ "properties": {
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+ "color": "#15AFAC",
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+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
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+ {
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+ "label": "10"
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+ "color": "#15AFAC",
+ "styles": {},
+ "label": "10",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 11,
+ "properties": {
+ "label": "11"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "11",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
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+ ]
+ }
+ ],
+ "_overview": {
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+ "overview_set": false
+ },
+ "_selected_graph": [
+ [
+ {
+ "id": 1,
+ "properties": {
+ "label": "1"
+ }
+ }
+ ],
+ []
+ ],
+ "_sidebar": {
+ "enabled": true,
+ "start_with": "Neighborhood"
+ },
+ "_view_count": null,
+ "_view_module": "yfiles-jupyter-graphs",
+ "_view_module_version": "^1.8.1",
+ "_view_name": "GraphView",
+ "layout": "IPY_MODEL_20550a75bde646e59fac027d05148b66",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "20550a75bde646e59fac027d05148b66": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "2.0.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "2.0.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border_bottom": null,
+ "border_left": null,
+ "border_right": null,
+ "border_top": null,
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+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": "620px",
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": "100%"
+ }
+ }
+ }
+ }
},
- {
- "cell_type": "markdown",
- "id": "379d347b-e610-42d8-aabf-43374883aa36",
- "metadata": {},
- "source": [
- "Before using the graph widget, install all necessary packages."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "41a8c112-93cb-4f9c-b2f8-d867b09f061c",
- "metadata": {},
- "outputs": [],
- "source": [
- "%pip install yfiles_jupyter_graphs --quiet\n",
- "%pip install networkx --quiet\n",
- "from networkx import connected_caveman_graph\n",
- "from yfiles_jupyter_graphs import GraphWidget\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "bd55f8ea-57d7-47fb-8bf4-2c1c3ac28a93",
- "metadata": {},
- "source": [
- "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "232377de-03a6-45bc-8d83-fe0d8ea04a9e",
- "metadata": {},
- "outputs": [],
- "source": [
- "try:\n",
- " import google.colab\n",
- " from google.colab import output\n",
- " output.enable_custom_widget_manager()\n",
- "except:\n",
- " pass"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f6cf813d-1814-444f-9d85-16a85fee9ceb",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "48ed1ee8-da66-4a61-93f1-83bfd421ef6e",
- "metadata": {},
- "source": [
- "## Default Neighborhood Configuration\n",
- "\n",
- "The Neighborhood starts with the depth = 1 and no selected node"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "68e2bb8a-5f32-4df0-b59a-57a204eaf7dd",
- "metadata": {},
- "outputs": [],
- "source": [
- "w = GraphWidget(graph = connected_caveman_graph(3,4))\n",
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1b26d61c-3848-42e5-9b56-d1bf2f5241d3",
- "metadata": {},
- "source": [
- "Let's change the depth of the Neighborhood and start with node 1:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4404f7eb-6c56-48f4-a60f-8fc92d136c7d",
- "metadata": {},
- "outputs": [],
- "source": [
- "w2 = GraphWidget(graph = connected_caveman_graph(3,4))\n",
- "w2.set_neighborhood(3, [1])\n",
- "w2.set_sidebar(start_with = 'Neighborhood')\n",
- "display(w2)"
- ]
- }
- ],
- "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": "3.11.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/25_overview.ipynb b/examples/25_overview.ipynb
index c642500..594faf4 100644
--- a/examples/25_overview.ipynb
+++ b/examples/25_overview.ipynb
@@ -1,133 +1,1278 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "74201b09-c615-4434-bb14-577287d42120",
- "metadata": {},
- "source": [
- "# Configure Overview "
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "74201b09-c615-4434-bb14-577287d42120",
+ "metadata": {
+ "id": "74201b09-c615-4434-bb14-577287d42120"
+ },
+ "source": [
+ "# Configure Overview "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0750295b-b694-48eb-9457-d865b79d7f21",
+ "metadata": {
+ "id": "0750295b-b694-48eb-9457-d865b79d7f21"
+ },
+ "source": [
+ "Before using the graph widget, install all necessary packages."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "2fa129dc-da82-474e-95be-456b12ac8f6a",
+ "metadata": {
+ "id": "2fa129dc-da82-474e-95be-456b12ac8f6a",
+ "outputId": "556b8e5c-0f88-47b7-a150-8106e33de0e5",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m30.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m18.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m38.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "%pip install networkx --quiet\n",
+ "from ipywidgets import Layout\n",
+ "from networkx import connected_caveman_graph\n",
+ "from yfiles_jupyter_graphs import GraphWidget"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b5732966-2173-4b46-bc38-9ffdd17963ab",
+ "metadata": {
+ "id": "b5732966-2173-4b46-bc38-9ffdd17963ab"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "d5c7d51c-dc56-49af-8c4c-a580366e3412",
+ "metadata": {
+ "id": "d5c7d51c-dc56-49af-8c4c-a580366e3412"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cc908e3b-cb79-4a5a-959d-476441f2bda4",
+ "metadata": {
+ "id": "cc908e3b-cb79-4a5a-959d-476441f2bda4"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9ae2ca63-7a08-4cdd-80c8-489099e577b8",
+ "metadata": {
+ "id": "9ae2ca63-7a08-4cdd-80c8-489099e577b8"
+ },
+ "source": [
+ "## Default Overview Configuration\n",
+ "\n",
+ "The Overview starts enabled. When fitting the diagram through the toolbar, the overview matches the visible graph exactly."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "7f761434-c07f-4f16-9011-c19d847bc97b",
+ "metadata": {
+ "id": "7f761434-c07f-4f16-9011-c19d847bc97b",
+ "outputId": "3e2423f8-50af-47be-e804-5c0e3e43f121",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 637,
+ "referenced_widgets": [
+ "26a37ff3fe60407781fa4bad1c83f524",
+ "57dc5adc344c4b6bb260c11ed92b5668"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='620px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "26a37ff3fe60407781fa4bad1c83f524"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "def get_widget(widget_layout=None):\n",
+ " w = GraphWidget(widget_layout=widget_layout)\n",
+ " g = connected_caveman_graph(3, 4)\n",
+ " w.import_graph(g)\n",
+ " return w\n",
+ "\n",
+ "w = get_widget()\n",
+ "display(w)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "067b4d7b-db94-4a0b-aa17-81bba9d0fcb9",
+ "metadata": {
+ "id": "067b4d7b-db94-4a0b-aa17-81bba9d0fcb9"
+ },
+ "source": [
+ "Let's hide the Overview"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "bfdaf5c0-2aa2-43da-8a8a-36458e4c2d9c",
+ "metadata": {
+ "id": "bfdaf5c0-2aa2-43da-8a8a-36458e4c2d9c",
+ "outputId": "8cff4a57-274e-46ac-9c58-2277a1360305",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 637,
+ "referenced_widgets": [
+ "f73210c0bb5a4763a19df8261c89c67b",
+ "057fdb38d86046eca05cd790206f8796"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='620px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "f73210c0bb5a4763a19df8261c89c67b"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w2 = get_widget()\n",
+ "w2.set_overview(enabled=False)\n",
+ "display(w2)"
+ ]
+ }
+ ],
+ "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": "3.11.3"
+ },
+ "colab": {
+ "provenance": []
+ },
+ "widgets": {
+ "application/vnd.jupyter.widget-state+json": {
+ "26a37ff3fe60407781fa4bad1c83f524": {
+ "model_module": "yfiles-jupyter-graphs",
+ "model_name": "GraphModel",
+ "model_module_version": "^1.8.1",
+ "state": {
+ "_context_pane_mapping": [
+ {
+ "id": "Neighborhood",
+ "title": "Neighborhood"
+ },
+ {
+ "id": "Data",
+ "title": "Data"
+ },
+ {
+ "id": "Search",
+ "title": "Search"
+ },
+ {
+ "id": "About",
+ "title": "About"
+ }
+ ],
+ "_data_importer": "networkx",
+ "_directed": false,
+ "_dom_classes": [],
+ "_edges": [
+ {
+ "id": 0,
+ "start": 0,
+ "end": 2,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 1,
+ "start": 0,
+ "end": 3,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 2,
+ "start": 0,
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},
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- "cell_type": "markdown",
- "id": "0750295b-b694-48eb-9457-d865b79d7f21",
- "metadata": {},
- "source": [
- "Before using the graph widget, install all necessary packages."
- ]
- },
- {
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- "execution_count": null,
- "id": "2fa129dc-da82-474e-95be-456b12ac8f6a",
- "metadata": {},
- "outputs": [],
- "source": [
- "%pip install yfiles_jupyter_graphs --quiet\n",
- "%pip install networkx --quiet\n",
- "from ipywidgets import Layout\n",
- "from networkx import connected_caveman_graph\n",
- "from yfiles_jupyter_graphs import GraphWidget"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b5732966-2173-4b46-bc38-9ffdd17963ab",
- "metadata": {},
- "source": [
- "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d5c7d51c-dc56-49af-8c4c-a580366e3412",
- "metadata": {},
- "outputs": [],
- "source": [
- "try:\n",
- " import google.colab\n",
- " from google.colab import output\n",
- " output.enable_custom_widget_manager()\n",
- "except:\n",
- " pass"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "cc908e3b-cb79-4a5a-959d-476441f2bda4",
- "metadata": {},
- "source": [
- ""
- ]
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- {
- "cell_type": "markdown",
- "id": "9ae2ca63-7a08-4cdd-80c8-489099e577b8",
- "metadata": {},
- "source": [
- "## Default Overview Configuration\n",
- "\n",
- "The Overview starts enabled. When fitting the diagram through the toolbar, the overview matches the visible graph exactly."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7f761434-c07f-4f16-9011-c19d847bc97b",
- "metadata": {},
- "outputs": [],
- "source": [
- "def get_widget(widget_layout=None):\n",
- " w = GraphWidget(widget_layout=widget_layout)\n",
- " g = connected_caveman_graph(3, 4)\n",
- " w.import_graph(g)\n",
- " return w\n",
- "\n",
- "w = get_widget()\n",
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "067b4d7b-db94-4a0b-aa17-81bba9d0fcb9",
- "metadata": {},
- "source": [
- "Let's hide the Overview "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "bfdaf5c0-2aa2-43da-8a8a-36458e4c2d9c",
- "metadata": {},
- "outputs": [],
- "source": [
- "w2 = get_widget()\n",
- "w2.set_overview(enabled=False)\n",
- "display(w2)"
- ]
- }
- ],
- "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": "3.11.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/26_widget_layout.ipynb b/examples/26_widget_layout.ipynb
index 7d8423f..0d3e852 100644
--- a/examples/26_widget_layout.ipynb
+++ b/examples/26_widget_layout.ipynb
@@ -1,141 +1,2069 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "bb044a7c-5aaf-4f13-b5e8-dfb50d40bdd9",
- "metadata": {},
- "source": [
- "# Configure Widget Layout \n",
- "\n",
- "**This layout does not change the graph but the height and width of the output window.**\n",
- "\n",
- "Before using the graph widget, install all necessary packages."
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "bb044a7c-5aaf-4f13-b5e8-dfb50d40bdd9",
+ "metadata": {
+ "id": "bb044a7c-5aaf-4f13-b5e8-dfb50d40bdd9"
+ },
+ "source": [
+ "# Configure Widget Layout \n",
+ "\n",
+ "**This layout does not change the graph but the height and width of the output window.**\n",
+ "\n",
+ "Before using the graph widget, install all necessary packages."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "0c4efcb9-22f6-48b4-9683-24398177f026",
+ "metadata": {
+ "id": "0c4efcb9-22f6-48b4-9683-24398177f026",
+ "outputId": "4156e622-10b5-4fcd-fd39-1ef3cd42f842",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m27.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m18.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "%pip install networkx --quiet\n",
+ "from ipywidgets import Layout\n",
+ "from networkx import florentine_families_graph\n",
+ "from yfiles_jupyter_graphs import GraphWidget"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "901a1da5-766d-47e0-a2ce-c75082cdbd80",
+ "metadata": {
+ "id": "901a1da5-766d-47e0-a2ce-c75082cdbd80"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "4db4f3d3-da8b-49f8-87c4-42f8bce8df0a",
+ "metadata": {
+ "id": "4db4f3d3-da8b-49f8-87c4-42f8bce8df0a"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ca5461a0-1301-4458-ba81-41b5fc1df90f",
+ "metadata": {
+ "id": "ca5461a0-1301-4458-ba81-41b5fc1df90f"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7490b70-c535-451b-bb93-0c26b0a0aec2",
+ "metadata": {
+ "id": "e7490b70-c535-451b-bb93-0c26b0a0aec2"
+ },
+ "source": [
+ "## Default Widget Layout Configuration\n",
+ "\n",
+ "The Layout starts with 500px height and 100% width:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "a2c43b9f-f0f8-4625-ae7c-ae5a756c4323",
+ "metadata": {
+ "id": "a2c43b9f-f0f8-4625-ae7c-ae5a756c4323",
+ "outputId": "85dfd3d9-b535-46ec-a60f-e219242c9674",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 667,
+ "referenced_widgets": [
+ "00bc33f185b644169f3a9be9572e2bef",
+ "99d7d16dc3304a6195112233a0a0cf46"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='650px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "00bc33f185b644169f3a9be9572e2bef"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w = GraphWidget(graph=florentine_families_graph())\n",
+ "display(w)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "db19566f-9d2d-4071-9098-97a5d16fca6c",
+ "metadata": {
+ "id": "db19566f-9d2d-4071-9098-97a5d16fca6c"
+ },
+ "source": [
+ "Let's make the layout smaller:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "c95b6a40-4fd2-40b8-b2c5-31cf96b5bcc9",
+ "metadata": {
+ "id": "c95b6a40-4fd2-40b8-b2c5-31cf96b5bcc9",
+ "outputId": "8a71e039-1d58-4f13-a6d8-30e5438871cf",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 217,
+ "referenced_widgets": [
+ "2bf351ac940248d6a46044321011dc63",
+ "71732c8d6c43403697690676ffe0dcb6"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='200px', width='80%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "2bf351ac940248d6a46044321011dc63"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w2 = GraphWidget(Layout(height='200px', width='80%'), graph = florentine_families_graph())\n",
+ "display(w2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1ca20f1c-e549-4f89-a826-b6759cc7c34b",
+ "metadata": {
+ "id": "1ca20f1c-e549-4f89-a826-b6759cc7c34b"
+ },
+ "source": [
+ "Note: The `height` cannot be specified by percentage because parent DOM element has no specified height itself. Therefore the height has to be specified in pixels:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "1c5f974c-2b68-46b1-a98c-94e52858f585",
+ "metadata": {
+ "id": "1c5f974c-2b68-46b1-a98c-94e52858f585",
+ "outputId": "2dca88ec-91b9-47ab-ec10-e3eca86764ad",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 17,
+ "referenced_widgets": [
+ "809c41d4be34422385afb210c5f179fd",
+ "8996a82dbbb04fa1aa103bfa97560957"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='120%', width='700px'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "809c41d4be34422385afb210c5f179fd"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w3 = GraphWidget(Layout(height='120%', width='700px'), graph = florentine_families_graph())\n",
+ "display(w3)"
+ ]
+ }
+ ],
+ "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": "3.11.3"
+ },
+ "colab": {
+ "provenance": []
+ },
+ "widgets": {
+ "application/vnd.jupyter.widget-state+json": {
+ "00bc33f185b644169f3a9be9572e2bef": {
+ "model_module": "yfiles-jupyter-graphs",
+ "model_name": "GraphModel",
+ "model_module_version": "^1.8.1",
+ "state": {
+ "_context_pane_mapping": [
+ {
+ "id": "Neighborhood",
+ "title": "Neighborhood"
+ },
+ {
+ "id": "Data",
+ "title": "Data"
+ },
+ {
+ "id": "Search",
+ "title": "Search"
+ },
+ {
+ "id": "About",
+ "title": "About"
+ }
+ ],
+ "_data_importer": "networkx",
+ "_directed": false,
+ "_dom_classes": [],
+ "_edges": [
+ {
+ "id": 0,
+ "start": 0,
+ "end": 1,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 1,
+ "start": 1,
+ "end": 5,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 2,
+ "start": 1,
+ "end": 6,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 3,
+ "start": 1,
+ "end": 7,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 4,
+ "start": 1,
+ "end": 8,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 5,
+ "start": 1,
+ "end": 9,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 6,
+ "start": 2,
+ "end": 3,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 7,
+ "start": 2,
+ "end": 4,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 8,
+ "start": 2,
+ "end": 5,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 9,
+ "start": 3,
+ "end": 4,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 10,
+ "start": 3,
+ "end": 11,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 11,
+ "start": 4,
+ "end": 6,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 12,
+ "start": 4,
+ "end": 11,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 13,
+ "start": 6,
+ "end": 7,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 14,
+ "start": 7,
+ "end": 12,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 15,
+ "start": 8,
+ "end": 13,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 16,
+ "start": 8,
+ "end": 12,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 17,
+ "start": 9,
+ "end": 10,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 18,
+ "start": 11,
+ "end": 12,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ },
+ {
+ "id": 19,
+ "start": 12,
+ "end": 14,
+ "properties": {},
+ "label": "",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": false
+ }
+ ],
+ "_graph_layout": {},
+ "_highlight": [],
+ "_license": {},
+ "_model_module": "yfiles-jupyter-graphs",
+ "_model_module_version": "^1.8.1",
+ "_model_name": "GraphModel",
+ "_neighborhood": {},
+ "_nodes": [
+ {
+ "id": 0,
+ "properties": {
+ "label": "Acciaiuoli"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Acciaiuoli",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 1,
+ "properties": {
+ "label": "Medici"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Medici",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 2,
+ "properties": {
+ "label": "Castellani"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Castellani",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 3,
+ "properties": {
+ "label": "Peruzzi"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Peruzzi",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 4,
+ "properties": {
+ "label": "Strozzi"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Strozzi",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 5,
+ "properties": {
+ "label": "Barbadori"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Barbadori",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 6,
+ "properties": {
+ "label": "Ridolfi"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Ridolfi",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 7,
+ "properties": {
+ "label": "Tornabuoni"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Tornabuoni",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 8,
+ "properties": {
+ "label": "Albizzi"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Albizzi",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 9,
+ "properties": {
+ "label": "Salviati"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Salviati",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 10,
+ "properties": {
+ "label": "Pazzi"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Pazzi",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 11,
+ "properties": {
+ "label": "Bischeri"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Bischeri",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 12,
+ "properties": {
+ "label": "Guadagni"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Guadagni",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 13,
+ "properties": {
+ "label": "Ginori"
+ },
+ "color": "#15AFAC",
+ "styles": {},
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+ 55,
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+ ],
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+ 0,
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+ ]
+ },
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+ "id": 7,
+ "properties": {
+ "label": "Tornabuoni"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Tornabuoni",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
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+ ]
+ },
+ {
+ "id": 8,
+ "properties": {
+ "label": "Albizzi"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Albizzi",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
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+ ],
+ "position": [
+ 0,
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+ ]
+ },
+ {
+ "id": 9,
+ "properties": {
+ "label": "Salviati"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Salviati",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 10,
+ "properties": {
+ "label": "Pazzi"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Pazzi",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 11,
+ "properties": {
+ "label": "Bischeri"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Bischeri",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 12,
+ "properties": {
+ "label": "Guadagni"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Guadagni",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 13,
+ "properties": {
+ "label": "Ginori"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Ginori",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ },
+ {
+ "id": 14,
+ "properties": {
+ "label": "Lamberteschi"
+ },
+ "color": "#15AFAC",
+ "styles": {},
+ "label": "Lamberteschi",
+ "scale_factor": 1,
+ "type": "#15AFAC",
+ "size": [
+ 55,
+ 55
+ ],
+ "position": [
+ 0,
+ 0
+ ]
+ }
+ ],
+ "_overview": {
+ "enabled": null,
+ "overview_set": false
+ },
+ "_selected_graph": [
+ [],
+ []
+ ],
+ "_sidebar": {
+ "enabled": false,
+ "start_with": null
+ },
+ "_view_count": null,
+ "_view_module": "yfiles-jupyter-graphs",
+ "_view_module_version": "^1.8.1",
+ "_view_name": "GraphView",
+ "layout": "IPY_MODEL_8996a82dbbb04fa1aa103bfa97560957",
+ "tabbable": null,
+ "tooltip": null
+ }
+ },
+ "8996a82dbbb04fa1aa103bfa97560957": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "2.0.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "2.0.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "2.0.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border_bottom": null,
+ "border_left": null,
+ "border_right": null,
+ "border_top": null,
+ "bottom": null,
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+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
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+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": "120%",
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": "700px"
+ }
+ }
+ }
+ }
},
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0c4efcb9-22f6-48b4-9683-24398177f026",
- "metadata": {},
- "outputs": [],
- "source": [
- "%pip install yfiles_jupyter_graphs --quiet\n",
- "%pip install networkx --quiet\n",
- "from ipywidgets import Layout\n",
- "from networkx import florentine_families_graph\n",
- "from yfiles_jupyter_graphs import GraphWidget"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "901a1da5-766d-47e0-a2ce-c75082cdbd80",
- "metadata": {},
- "source": [
- "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4db4f3d3-da8b-49f8-87c4-42f8bce8df0a",
- "metadata": {},
- "outputs": [],
- "source": [
- "try:\n",
- " import google.colab\n",
- " from google.colab import output\n",
- " output.enable_custom_widget_manager()\n",
- "except:\n",
- " pass"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ca5461a0-1301-4458-ba81-41b5fc1df90f",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e7490b70-c535-451b-bb93-0c26b0a0aec2",
- "metadata": {},
- "source": [
- "## Default Widget Layout Configuration\n",
- "\n",
- "The Layout starts with 500px height and 100% width:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a2c43b9f-f0f8-4625-ae7c-ae5a756c4323",
- "metadata": {},
- "outputs": [],
- "source": [
- "w = GraphWidget(graph=florentine_families_graph())\n",
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "db19566f-9d2d-4071-9098-97a5d16fca6c",
- "metadata": {},
- "source": [
- "Let's make the layout smaller:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c95b6a40-4fd2-40b8-b2c5-31cf96b5bcc9",
- "metadata": {},
- "outputs": [],
- "source": [
- "w2 = GraphWidget(Layout(height='200px', width='80%'), graph = florentine_families_graph())\n",
- "display(w2)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1ca20f1c-e549-4f89-a826-b6759cc7c34b",
- "metadata": {},
- "source": [
- "Note: The `height` cannot be specified by percentage because parent DOM element has no specified height itself. Therefore the height has to be specified in pixels:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1c5f974c-2b68-46b1-a98c-94e52858f585",
- "metadata": {},
- "outputs": [],
- "source": [
- "w3 = GraphWidget(Layout(height='120%', width='700px'), graph = florentine_families_graph())\n",
- "display(w3)"
- ]
- }
- ],
- "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": "3.11.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/28_little-alchemy_example.ipynb b/examples/28_little-alchemy_example.ipynb
index df68a59..750ced7 100644
--- a/examples/28_little-alchemy_example.ipynb
+++ b/examples/28_little-alchemy_example.ipynb
@@ -1,294 +1,6123 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "473ff031-02a0-4081-86cc-6317944d79e8",
- "metadata": {},
- "source": [
- "# Example using Little Alchemy 2 "
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "473ff031-02a0-4081-86cc-6317944d79e8",
+ "metadata": {
+ "id": "473ff031-02a0-4081-86cc-6317944d79e8"
+ },
+ "source": [
+ "# Example using Little Alchemy 2 "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f144d219-1189-402e-a067-cd643832d7fb",
+ "metadata": {
+ "id": "f144d219-1189-402e-a067-cd643832d7fb"
+ },
+ "source": [
+ "Little Alchemy 2 is a game, where the goal is to guess recipes to create over 600 elements just using the 4 prime elements air, earth, fire and water.\n",
+ "\n",
+ "\n",
+ "In this example, we create a small bill of material (BoM) graph using these recipes\n",
+ "\n",
+ "Before using the graph widget, install all necessary packages."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "0f48d90d-4800-4776-9909-2b346846f24f",
+ "metadata": {
+ "id": "0f48d90d-4800-4776-9909-2b346846f24f",
+ "outputId": "dbc175b7-ef5c-4103-84ea-472b154e0048",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m19.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m27.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "from yfiles_jupyter_graphs import GraphWidget"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "122b620f-f292-4131-8a03-4667b5ccfe97",
+ "metadata": {
+ "id": "122b620f-f292-4131-8a03-4667b5ccfe97"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "342cc1c8-6bd3-4d27-ba34-7c1a264209b6",
+ "metadata": {
+ "tags": [],
+ "id": "342cc1c8-6bd3-4d27-ba34-7c1a264209b6"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5c84fd6f-46df-45c4-8a97-95a4f8f6eea0",
+ "metadata": {
+ "tags": [],
+ "id": "5c84fd6f-46df-45c4-8a97-95a4f8f6eea0"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "98a951aa-1f51-4d00-9151-01cec6f9b6bf",
+ "metadata": {
+ "id": "98a951aa-1f51-4d00-9151-01cec6f9b6bf"
+ },
+ "source": [
+ "This is the library containing all recipe information:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "6d27665f-9313-42bf-b1f3-941158830b2e",
+ "metadata": {
+ "id": "6d27665f-9313-42bf-b1f3-941158830b2e"
+ },
+ "outputs": [],
+ "source": [
+ "import urllib.request, json\n",
+ "with urllib.request.urlopen(\"https://unpkg.com/little-alchemy-2@0.0.1/dist/alchemy.json\") as url:\n",
+ " data = json.load(url)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5d73654c-2b9c-4885-af26-36b805e84176",
+ "metadata": {
+ "id": "5d73654c-2b9c-4885-af26-36b805e84176"
+ },
+ "source": [
+ "We parse the resulting data to create our nodes and edges.\n",
+ "In the given data, each element has a name and parent information.\n",
+ "\n",
+ "We only use a small sample of elements, but you can adjust the constant ```num_elements``` to see more recipes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "bac9a02a-5386-4dd2-8322-9afb931f56ef",
+ "metadata": {
+ "id": "bac9a02a-5386-4dd2-8322-9afb931f56ef"
+ },
+ "outputs": [],
+ "source": [
+ "import itertools\n",
+ "\n",
+ "# we'll use a subset of the graph here\n",
+ "num_elements = 50\n",
+ "dataset = dict(itertools.islice(data.items(), num_elements))\n",
+ "\n",
+ "nodes = []\n",
+ "edges = []\n",
+ "\n",
+ "for key, item in dataset.items():\n",
+ " nodes.append({\"id\": key, 'properties': {'label': item['n']}})\n",
+ " # for simplicity, we just examine the parents of the items\n",
+ " if 'p' in item:\n",
+ " # each element has exactly two (or none) source elements in Little Alchemy 2\n",
+ " for source1, source2 in item['p']:\n",
+ " if source1 in dataset and source2 in dataset:\n",
+ " if not source1 == source2:\n",
+ " edges.append({\"start\": source1, \"end\": key, \"properties\": {'label': ('+ ' + data[source2]['n'])}})\n",
+ " edges.append({\"start\": source2, \"end\": key, \"properties\": {'label': ('+ ' + data[source1]['n'])}})\n",
+ " else:\n",
+ " edges.append({\"start\": source1, \"end\": key, \"properties\": {'label': ('+ ' + data[source2]['n'])}})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "fbfd87ea-7e6d-4263-8e4a-7db33b254dd5",
+ "metadata": {
+ "id": "fbfd87ea-7e6d-4263-8e4a-7db33b254dd5",
+ "outputId": "a08e58ae-a1a6-4f27-d9b6-998a14fce632",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 817,
+ "referenced_widgets": [
+ "4c3581a96a73445a8c82bcaa5109bd0b",
+ "4cf51a0aed384a45a6912f9ef5b132dd"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='800px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "4c3581a96a73445a8c82bcaa5109bd0b"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w = GraphWidget()\n",
+ "w.nodes = nodes\n",
+ "w.edges = edges\n",
+ "w.directed = True\n",
+ "display(w)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e8fadf33-ffa4-42a8-b21c-3a92bf29cce0",
+ "metadata": {
+ "id": "e8fadf33-ffa4-42a8-b21c-3a92bf29cce0"
+ },
+ "source": [
+ "To make the graph easier to read, we add the game images and make the prime elements bigger as well as use the hierarchic layout.\n",
+ "\n",
+ "You can check out the other examples like [size mappings](./05_size_mapping.ipynb) and [styles mappings](./08_styles_mapping.ipynb) to learn more about graph customization."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "63c59e05-3bda-43de-8b3d-a6c576f0685b",
+ "metadata": {
+ "id": "63c59e05-3bda-43de-8b3d-a6c576f0685b",
+ "outputId": "f9a75f3b-359f-46ac-dca1-9b7c87f5f4db",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 817,
+ "referenced_widgets": [
+ "fb360f3ed7b6416bad8fbdf69eb0fbe1",
+ "2519a5ce09d44ca5a0536a6f196888f4"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='800px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "fb360f3ed7b6416bad8fbdf69eb0fbe1"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w3 = GraphWidget()\n",
+ "w3.nodes = nodes\n",
+ "w3.edges = edges\n",
+ "w3.directed = True\n",
+ "\n",
+ "def custom_images(index, node):\n",
+ " return {'image': \"https://littlealchemy2.com/static/icons/\" + node['id'] + \".svg\"}\n",
+ "\n",
+ "w3.set_node_styles_mapping(custom_images)\n",
+ "\n",
+ "def custom_size(index, node):\n",
+ " if 'prime' in data[str(index+1)]:\n",
+ " return 80,80\n",
+ " return 55,55\n",
+ "\n",
+ "w3.set_node_size_mapping(custom_size)\n",
+ "w3.hierarchic_layout()\n",
+ "w3.edge_color_mapping = lambda : 'grey'\n",
+ "w3.set_overview(enabled=False)\n",
+ "w3.set_sidebar(enabled=False)\n",
+ "display(w3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fdfd148b-9ab1-4ac8-96d6-24f9dbd00ceb",
+ "metadata": {
+ "id": "fdfd148b-9ab1-4ac8-96d6-24f9dbd00ceb"
+ },
+ "source": [
+ "You can use the Neighborhood Tab in the sidebar to explore single elements.\n",
+ "\n",
+ "However to focus on just one element you're interested in, you can use the following code.\n",
+ "\n",
+ "Change the constant ```element_name``` to only see the defined element with its children and parent nodes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "d6606494-1829-4177-b19b-a03246974033",
+ "metadata": {
+ "id": "d6606494-1829-4177-b19b-a03246974033"
+ },
+ "outputs": [],
+ "source": [
+ "# change this to focus on a different element\n",
+ "element_name = 'cat'\n",
+ "\n",
+ "nodes2 = []\n",
+ "edges2 = []\n",
+ "parentSet = set()\n",
+ "element_id = None\n",
+ "\n",
+ "\n",
+ "#This function generates edge labels\n",
+ "def getCombinations(id, target_id):\n",
+ " result = []\n",
+ " item = data[target_id]\n",
+ " for source1, source2 in item['p']:\n",
+ " if source1 == id:\n",
+ " result.append(data[source2]['n'])\n",
+ " if source2 == id:\n",
+ " result.append(data[source1]['n'])\n",
+ " return result\n",
+ "\n",
+ "for key, item in data.items():\n",
+ " if item['n'] == element_name:\n",
+ " nodes2.append({\"id\": key, 'properties': {'label': item['n']}})\n",
+ " element_id = key\n",
+ " if 'p' in item:\n",
+ " for source1, source2 in item['p']:\n",
+ " parentSet.add(source1)\n",
+ " parentSet.add(source2)\n",
+ " if 'c' in item:\n",
+ " for child in item['c']:\n",
+ " if child not in parentSet:\n",
+ " nodes2.append({\"id\":child, 'properties': {'label': data[child]['n']}})\n",
+ " edges2.append({\"start\": key, \"end\": child, \"properties\": {'label': '+ ' + str(getCombinations(element_id, child))[1:-1].replace(\"\\'\", \"\")}})\n",
+ " else:\n",
+ " edges2.append({\"start\": key, \"end\": child, \"properties\": {'label': '+ ' + str(getCombinations(element_id, child))[1:-1].replace(\"\\'\", \"\")}})\n",
+ "\n",
+ "\n",
+ "for source in parentSet:\n",
+ " if not source == element_id:\n",
+ " nodes2.append({\"id\": source, 'properties': {'label': data[source]['n']}})\n",
+ " edges2.append({\"start\": source, \"end\": element_id, \"properties\": {'label': '+ ' + str(getCombinations(source, element_id))[1:-1].replace(\"\\'\", \"\")}})\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f3affbf-745b-490e-832e-67245d8a43ba",
+ "metadata": {
+ "id": "0f3affbf-745b-490e-832e-67245d8a43ba"
+ },
+ "source": [
+ "We use some of the mappings above:\n",
+ "\n",
+ "- Game image node styling\n",
+ "- directed, grey edges\n",
+ "- hierarchic layout\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "c02fb72d-a09e-449e-9325-63b8b3302db8",
+ "metadata": {
+ "id": "c02fb72d-a09e-449e-9325-63b8b3302db8",
+ "outputId": "360d2d8b-c1b0-464c-ad8e-a2bf87e9d29a",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 517,
+ "referenced_widgets": [
+ "1ac612c14dbd49c7b487caeaf192c63a",
+ "007c47d5a7bd40759b94ead98defc2fe"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='500px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "1ac612c14dbd49c7b487caeaf192c63a"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w2 = GraphWidget()\n",
+ "w2.nodes = nodes2\n",
+ "w2.edges = edges2\n",
+ "w2.set_node_styles_mapping(custom_images)\n",
+ "w2.directed = True\n",
+ "w2.hierarchic_layout()\n",
+ "w2.edge_color_mapping = lambda : 'grey'\n",
+ "display(w2)"
+ ]
+ }
+ ],
+ "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": "3.11.3"
+ },
+ "colab": {
+ "provenance": []
+ },
+ "widgets": {
+ "application/vnd.jupyter.widget-state+json": {
+ "4c3581a96a73445a8c82bcaa5109bd0b": {
+ "model_module": "yfiles-jupyter-graphs",
+ "model_name": "GraphModel",
+ "model_module_version": "^1.8.1",
+ "state": {
+ "_context_pane_mapping": [
+ {
+ "id": "Neighborhood",
+ "title": "Neighborhood"
+ },
+ {
+ "id": "Data",
+ "title": "Data"
+ },
+ {
+ "id": "Search",
+ "title": "Search"
+ },
+ {
+ "id": "About",
+ "title": "About"
+ }
+ ],
+ "_data_importer": "unknown",
+ "_directed": true,
+ "_dom_classes": [],
+ "_edges": [
+ {
+ "start": "2",
+ "end": "2",
+ "properties": {
+ "label": "+ coal"
+ },
+ "label": "+ coal",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "30",
+ "end": "2",
+ "properties": {
+ "label": "+ fire"
+ },
+ "label": "+ fire",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "1",
+ "end": "5",
+ "properties": {
+ "label": "+ fire"
+ },
+ "label": "+ fire",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "2",
+ "end": "5",
+ "properties": {
+ "label": "+ water"
+ },
+ "label": "+ water",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "1",
+ "end": "5",
+ "properties": {
+ "label": "+ lava"
+ },
+ "label": "+ lava",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "6",
+ "end": "5",
+ "properties": {
+ "label": "+ water"
+ },
+ "label": "+ water",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "2",
+ "end": "6",
+ "properties": {
+ "label": "+ earth"
+ },
+ "label": "+ earth",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "3",
+ "end": "6",
+ "properties": {
+ "label": "+ fire"
+ },
+ "label": "+ fire",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "4",
+ "end": "7",
+ "properties": {
+ "label": "+ air"
+ },
+ "label": "+ air",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "10",
+ "end": "7",
+ "properties": {
+ "label": "+ ocean"
+ },
+ "label": "+ ocean",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "3",
+ "end": "8",
+ "properties": {
+ "label": "+ lava"
+ },
+ "label": "+ lava",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "6",
+ "end": "8",
+ "properties": {
+ "label": "+ earth"
+ },
+ "label": "+ earth",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "1",
+ "end": "10",
+ "properties": {
+ "label": "+ sea"
+ },
+ "label": "+ sea",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "9",
+ "end": "10",
+ "properties": {
+ "label": "+ water"
+ },
+ "label": "+ water",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "9",
+ "end": "10",
+ "properties": {
+ "label": "+ sea"
+ },
+ "label": "+ sea",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "2",
+ "end": "11",
+ "properties": {
+ "label": "+ fire"
+ },
+ "label": "+ fire",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "1",
+ "end": "12",
+ "properties": {
+ "label": "+ earth"
+ },
+ "label": "+ earth",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "3",
+ "end": "12",
+ "properties": {
+ "label": "+ water"
+ },
+ "label": "+ water",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "7",
+ "end": "13",
+ "properties": {
+ "label": "+ cloud"
+ },
+ "label": "+ cloud",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "15",
+ "end": "13",
+ "properties": {
+ "label": "+ pressure"
+ },
+ "label": "+ pressure",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "1",
+ "end": "13",
+ "properties": {
+ "label": "+ cloud"
+ },
+ "label": "+ cloud",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "15",
+ "end": "13",
+ "properties": {
+ "label": "+ water"
+ },
+ "label": "+ water",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "3",
+ "end": "14",
+ "properties": {
+ "label": "+ air"
+ },
+ "label": "+ air",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "4",
+ "end": "14",
+ "properties": {
+ "label": "+ earth"
+ },
+ "label": "+ earth",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "1",
+ "end": "15",
+ "properties": {
+ "label": "+ sky"
+ },
+ "label": "+ sky",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "22",
+ "end": "15",
+ "properties": {
+ "label": "+ water"
+ },
+ "label": "+ water",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "15",
+ "end": "16",
+ "properties": {
+ "label": "+ cloud"
+ },
+ "label": "+ cloud",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "13",
+ "end": "16",
+ "properties": {
+ "label": "+ wind"
+ },
+ "label": "+ wind",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "20",
+ "end": "16",
+ "properties": {
+ "label": "+ rain"
+ },
+ "label": "+ rain",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "3",
+ "end": "17",
+ "properties": {
+ "label": "+ steam"
+ },
+ "label": "+ steam",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "5",
+ "end": "17",
+ "properties": {
+ "label": "+ earth"
+ },
+ "label": "+ earth",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "5",
+ "end": "17",
+ "properties": {
+ "label": "+ pressure"
+ },
+ "label": "+ pressure",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "7",
+ "end": "17",
+ "properties": {
+ "label": "+ steam"
+ },
+ "label": "+ steam",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "8",
+ "end": "18",
+ "properties": {
+ "label": "+ ocean"
+ },
+ "label": "+ ocean",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "10",
+ "end": "18",
+ "properties": {
+ "label": "+ volcano"
+ },
+ "label": "+ volcano",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "8",
+ "end": "18",
+ "properties": {
+ "label": "+ sea"
+ },
+ "label": "+ sea",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "9",
+ "end": "18",
+ "properties": {
+ "label": "+ volcano"
+ },
+ "label": "+ volcano",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "3",
+ "end": "19",
+ "properties": {
+ "label": "+ energy"
+ },
+ "label": "+ energy",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "11",
+ "end": "19",
+ "properties": {
+ "label": "+ earth"
+ },
+ "label": "+ earth",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "4",
+ "end": "20",
+ "properties": {
+ "label": "+ pressure"
+ },
+ "label": "+ pressure",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "7",
+ "end": "20",
+ "properties": {
+ "label": "+ air"
+ },
+ "label": "+ air",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "7",
+ "end": "21",
+ "properties": {
+ "label": "+ volcano"
+ },
+ "label": "+ volcano",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "8",
+ "end": "21",
+ "properties": {
+ "label": "+ pressure"
+ },
+ "label": "+ pressure",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "6",
+ "end": "21",
+ "properties": {
+ "label": "+ pressure"
+ },
+ "label": "+ pressure",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "7",
+ "end": "21",
+ "properties": {
+ "label": "+ lava"
+ },
+ "label": "+ lava",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "8",
+ "end": "21",
+ "properties": {
+ "label": "+ time"
+ },
+ "label": "+ time",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "41",
+ "end": "21",
+ "properties": {
+ "label": "+ volcano"
+ },
+ "label": "+ volcano",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "4",
+ "end": "22",
+ "properties": {
+ "label": "+ cloud"
+ },
+ "label": "+ cloud",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "15",
+ "end": "22",
+ "properties": {
+ "label": "+ air"
+ },
+ "label": "+ air",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "16",
+ "end": "23",
+ "properties": {
+ "label": "+ storm"
+ },
+ "label": "+ storm",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "16",
+ "end": "23",
+ "properties": {
+ "label": "+ wind"
+ },
+ "label": "+ wind",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "20",
+ "end": "23",
+ "properties": {
+ "label": "+ storm"
+ },
+ "label": "+ storm",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "20",
+ "end": "23",
+ "properties": {
+ "label": "+ wind"
+ },
+ "label": "+ wind",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "2",
+ "end": "25",
+ "properties": {
+ "label": "+ dust"
+ },
+ "label": "+ dust",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "14",
+ "end": "25",
+ "properties": {
+ "label": "+ fire"
+ },
+ "label": "+ fire",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "11",
+ "end": "25",
+ "properties": {
+ "label": "+ dust"
+ },
+ "label": "+ dust",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "14",
+ "end": "25",
+ "properties": {
+ "label": "+ energy"
+ },
+ "label": "+ energy",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "2",
+ "end": "26",
+ "properties": {
+ "label": "+ gunpowder"
+ },
+ "label": "+ gunpowder",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "25",
+ "end": "26",
+ "properties": {
+ "label": "+ fire"
+ },
+ "label": "+ fire",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "7",
+ "end": "26",
+ "properties": {
+ "label": "+ volcano"
+ },
+ "label": "+ volcano",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "8",
+ "end": "26",
+ "properties": {
+ "label": "+ pressure"
+ },
+ "label": "+ pressure",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "3",
+ "end": "27",
+ "properties": {
+ "label": "+ pressure"
+ },
+ "label": "+ pressure",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "7",
+ "end": "27",
+ "properties": {
+ "label": "+ earth"
+ },
+ "label": "+ earth",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "4",
+ "end": "27",
+ "properties": {
+ "label": "+ lava"
+ },
+ "label": "+ lava",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "6",
+ "end": "27",
+ "properties": {
+ "label": "+ air"
+ },
+ "label": "+ air",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "4",
+ "end": "28",
+ "properties": {
+ "label": "+ stone"
+ },
+ "label": "+ stone",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "27",
+ "end": "28",
+ "properties": {
+ "label": "+ air"
+ },
+ "label": "+ air",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "20",
+ "end": "28",
+ "properties": {
+ "label": "+ stone"
+ },
+ "label": "+ stone",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "27",
+ "end": "28",
+ "properties": {
+ "label": "+ wind"
+ },
+ "label": "+ wind",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "2",
+ "end": "29",
+ "properties": {
+ "label": "+ plant"
+ },
+ "label": "+ plant",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "24",
+ "end": "29",
+ "properties": {
+ "label": "+ fire"
+ },
+ "label": "+ fire",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "2",
+ "end": "29",
+ "properties": {
+ "label": "+ tree"
+ },
+ "label": "+ tree",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "42",
+ "end": "29",
+ "properties": {
+ "label": "+ fire"
+ },
+ "label": "+ fire",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "7",
+ "end": "31",
+ "properties": {
+ "label": "+ coal"
+ },
+ "label": "+ coal",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
+ },
+ {
+ "start": "30",
+ "end": "31",
+ "properties": {
+ "label": "+ pressure"
+ },
+ "label": "+ pressure",
+ "color": "#15AFAC",
+ "thickness_factor": 1,
+ "directed": true
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+ {
+ "id": "238",
+ "properties": {
+ "label": "cat"
+ },
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+ "label": "armadillo"
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+ "label": "catnip"
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+ },
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+ "type": "#15AFAC",
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+ 55,
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+ ],
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+ 0,
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+ "id": "77",
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+ "label": "milk"
+ },
+ "color": "#15AFAC",
+ "styles": {
+ "image": "https://littlealchemy2.com/static/icons/77.svg"
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+ "label": "animal"
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+ 55,
+ 55
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+ ]
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+ "label": "coconut milk"
+ },
+ "color": "#15AFAC",
+ "styles": {
+ "image": "https://littlealchemy2.com/static/icons/262.svg"
+ },
+ "label": "coconut milk",
+ "scale_factor": 1,
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+ "tabbable": null,
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},
- {
- "cell_type": "markdown",
- "id": "f144d219-1189-402e-a067-cd643832d7fb",
- "metadata": {},
- "source": [
- "Little Alchemy 2 is a game, where the goal is to guess recipes to create over 600 elements just using the 4 prime elements air, earth, fire and water.\n",
- "\n",
- "\n",
- "In this example, we create a small bill of material (BoM) graph using these recipes\n",
- "\n",
- "Before using the graph widget, install all necessary packages."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0f48d90d-4800-4776-9909-2b346846f24f",
- "metadata": {},
- "outputs": [],
- "source": [
- "%pip install yfiles_jupyter_graphs --quiet\n",
- "from yfiles_jupyter_graphs import GraphWidget"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "122b620f-f292-4131-8a03-4667b5ccfe97",
- "metadata": {},
- "source": [
- "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "342cc1c8-6bd3-4d27-ba34-7c1a264209b6",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "try:\n",
- " import google.colab\n",
- " from google.colab import output\n",
- " output.enable_custom_widget_manager()\n",
- "except:\n",
- " pass"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5c84fd6f-46df-45c4-8a97-95a4f8f6eea0",
- "metadata": {
- "tags": []
- },
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "98a951aa-1f51-4d00-9151-01cec6f9b6bf",
- "metadata": {},
- "source": [
- "This is the library containing all recipe information:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6d27665f-9313-42bf-b1f3-941158830b2e",
- "metadata": {},
- "outputs": [],
- "source": [
- "import urllib.request, json \n",
- "with urllib.request.urlopen(\"https://unpkg.com/little-alchemy-2@0.0.1/dist/alchemy.json\") as url:\n",
- " data = json.load(url)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5d73654c-2b9c-4885-af26-36b805e84176",
- "metadata": {},
- "source": [
- "We parse the resulting data to create our nodes and edges.\n",
- "In the given data, each element has a name and parent information. \n",
- "\n",
- "We only use a small sample of elements, but you can adjust the constant ```num_elements``` to see more recipes."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "bac9a02a-5386-4dd2-8322-9afb931f56ef",
- "metadata": {},
- "outputs": [],
- "source": [
- "import itertools\n",
- "\n",
- "# we'll use a subset of the graph here\n",
- "num_elements = 50\n",
- "dataset = dict(itertools.islice(data.items(), num_elements))\n",
- "\n",
- "nodes = []\n",
- "edges = []\n",
- "\n",
- "for key, item in dataset.items():\n",
- " nodes.append({\"id\": key, 'properties': {'label': item['n']}})\n",
- " # for simplicity, we just examine the parents of the items\n",
- " if 'p' in item:\n",
- " # each element has exactly two (or none) source elements in Little Alchemy 2\n",
- " for source1, source2 in item['p']:\n",
- " if source1 in dataset and source2 in dataset:\n",
- " if not source1 == source2:\n",
- " edges.append({\"start\": source1, \"end\": key, \"properties\": {'label': ('+ ' + data[source2]['n'])}})\n",
- " edges.append({\"start\": source2, \"end\": key, \"properties\": {'label': ('+ ' + data[source1]['n'])}})\n",
- " else:\n",
- " edges.append({\"start\": source1, \"end\": key, \"properties\": {'label': ('+ ' + data[source2]['n'])}})"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fbfd87ea-7e6d-4263-8e4a-7db33b254dd5",
- "metadata": {},
- "outputs": [],
- "source": [
- "w = GraphWidget()\n",
- "w.nodes = nodes\n",
- "w.edges = edges\n",
- "w.directed = True\n",
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e8fadf33-ffa4-42a8-b21c-3a92bf29cce0",
- "metadata": {},
- "source": [
- "To make the graph easier to read, we add the game images and make the prime elements bigger as well as use the hierarchic layout.\n",
- "\n",
- "You can check out the other examples like [size mappings](./05_size_mapping.ipynb) and [styles mappings](./08_styles_mapping.ipynb) to learn more about graph customization."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "63c59e05-3bda-43de-8b3d-a6c576f0685b",
- "metadata": {},
- "outputs": [],
- "source": [
- "def custom_images(index, node):\n",
- " return {'image': \"https://littlealchemy2.com/static/icons/\" + node['id'] + \".svg\"}\n",
- "\n",
- "w.set_node_styles_mapping(custom_images)\n",
- "\n",
- "def custom_size(index, node):\n",
- " if 'prime' in data[str(index+1)]:\n",
- " return 80,80\n",
- " return 55,55\n",
- "\n",
- "w.set_node_size_mapping(custom_size)\n",
- "w.hierarchic_layout()\n",
- "w.edge_color_mapping = lambda : 'grey'\n",
- "w.set_overview(enabled=False)\n",
- "w.set_sidebar(enabled=False)\n",
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fdfd148b-9ab1-4ac8-96d6-24f9dbd00ceb",
- "metadata": {},
- "source": [
- "You can use the Neighborhood Tab in the sidebar to explore single elements.\n",
- "\n",
- "However to focus on just one element you're interested in, you can use the following code.\n",
- "\n",
- "Change the constant ```element_name``` to only see the defined element with its children and parent nodes"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d6606494-1829-4177-b19b-a03246974033",
- "metadata": {},
- "outputs": [],
- "source": [
- "# change this to focus on a different element\n",
- "element_name = 'cat'\n",
- "\n",
- "nodes2 = []\n",
- "edges2 = []\n",
- "parentSet = set()\n",
- "element_id = None\n",
- "\n",
- "\n",
- "#This function generates edge labels \n",
- "def getCombinations(id, target_id):\n",
- " result = []\n",
- " item = data[target_id]\n",
- " for source1, source2 in item['p']:\n",
- " if source1 == id:\n",
- " result.append(data[source2]['n'])\n",
- " if source2 == id:\n",
- " result.append(data[source1]['n'])\n",
- " return result\n",
- "\n",
- "for key, item in data.items():\n",
- " if item['n'] == element_name:\n",
- " nodes2.append({\"id\": key, 'properties': {'label': item['n']}})\n",
- " element_id = key\n",
- " if 'p' in item:\n",
- " for source1, source2 in item['p']:\n",
- " parentSet.add(source1)\n",
- " parentSet.add(source2)\n",
- " if 'c' in item:\n",
- " for child in item['c']:\n",
- " if child not in parentSet:\n",
- " nodes2.append({\"id\":child, 'properties': {'label': data[child]['n']}})\n",
- " edges2.append({\"start\": key, \"end\": child, \"properties\": {'label': '+ ' + str(getCombinations(element_id, child))[1:-1].replace(\"\\'\", \"\")}})\n",
- " else:\n",
- " edges2.append({\"start\": key, \"end\": child, \"properties\": {'label': '+ ' + str(getCombinations(element_id, child))[1:-1].replace(\"\\'\", \"\")}})\n",
- "\n",
- "\n",
- "for source in parentSet:\n",
- " if not source == element_id:\n",
- " nodes2.append({\"id\": source, 'properties': {'label': data[source]['n']}})\n",
- " edges2.append({\"start\": source, \"end\": element_id, \"properties\": {'label': '+ ' + str(getCombinations(source, element_id))[1:-1].replace(\"\\'\", \"\")}})\n",
- " "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "0f3affbf-745b-490e-832e-67245d8a43ba",
- "metadata": {},
- "source": [
- "We use some of the mappings above:\n",
- "\n",
- "- Game image node styling\n",
- "- directed, grey edges\n",
- "- hierarchic layout\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c02fb72d-a09e-449e-9325-63b8b3302db8",
- "metadata": {},
- "outputs": [],
- "source": [
- "w2 = GraphWidget()\n",
- "w2.nodes = nodes2\n",
- "w2.edges = edges2\n",
- "w2.set_node_styles_mapping(custom_images)\n",
- "w2.directed = True\n",
- "w2.hierarchic_layout()\n",
- "w2.edge_color_mapping = lambda : 'grey'\n",
- "display(w2)"
- ]
- }
- ],
- "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": "3.11.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/29_heat_mapping.ipynb b/examples/29_heat_mapping.ipynb
index 3798d49..e413413 100644
--- a/examples/29_heat_mapping.ipynb
+++ b/examples/29_heat_mapping.ipynb
@@ -1,238 +1,1355 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "675ab9b2-219d-490d-8efd-45dbd77c26b9",
- "metadata": {},
- "source": [
- "# Heat Mapping \n",
- "\n",
- "Before using the graph widget, install all necessary packages and initialize your widget."
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "675ab9b2-219d-490d-8efd-45dbd77c26b9",
+ "metadata": {
+ "id": "675ab9b2-219d-490d-8efd-45dbd77c26b9"
+ },
+ "source": [
+ "# Heat Mapping \n",
+ "\n",
+ "Before using the graph widget, install all necessary packages and initialize your widget."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "acb80d9e-112d-4e03-9915-c074c03a871a",
+ "metadata": {
+ "id": "acb80d9e-112d-4e03-9915-c074c03a871a",
+ "outputId": "c73ac286-8340-4b85-cc40-6b5e68393754",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 90
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m56.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m54.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m46.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "from yfiles_jupyter_graphs import GraphWidget\n",
+ "w = GraphWidget()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "26a2bbb9-7f41-485c-82d5-541b21539996",
+ "metadata": {
+ "id": "26a2bbb9-7f41-485c-82d5-541b21539996"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "bb9670f9-698b-4a8d-a961-3263dc709e44",
+ "metadata": {
+ "id": "bb9670f9-698b-4a8d-a961-3263dc709e44"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8a1cc311-e290-47fe-8b8f-df0c91c4d14b",
+ "metadata": {
+ "id": "8a1cc311-e290-47fe-8b8f-df0c91c4d14b"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e9b0ac00-6de6-47c3-b599-f14389831c06",
+ "metadata": {
+ "id": "e9b0ac00-6de6-47c3-b599-f14389831c06"
+ },
+ "source": [
+ "## Mapping Function\n",
+ "\n",
+ "The heatmapping is slightly different than other mappings, as there is one combined mapping for edges and nodes\n",
+ "\n",
+ "There are get, set and delete methods for the heatmap.\n",
+ "- you can set a new heat mapping with ```w.set_heat_mapping```\n",
+ "- you can get the current heat mapping with ```w.get_heat_mapping```\n",
+ "- you can delete a custom heat mapping with ```w.del_heat_mapping```\n",
+ "\n",
+ "If no custom mapping is set the default mapping is used."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "69c45788-84d3-4b1b-8e8b-05dbd74bd8ab",
+ "metadata": {
+ "id": "69c45788-84d3-4b1b-8e8b-05dbd74bd8ab",
+ "outputId": "ef396d93-4234-44ed-e72f-ac995dd24934",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "The default scale factor mapping for nodes.\n",
+ "\n",
+ " Provides constant value of None for all elements.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " index: int (optional)\n",
+ " element: typing.Dict\n",
+ "\n",
+ " Notes\n",
+ " -----\n",
+ " This is the default value for the `heat_mapping` property.\n",
+ " Can be 'overwritten' by setting the property\n",
+ " with a function of the same signature.\n",
+ "\n",
+ " If the given mapping function has only one parameter (that is not typed as int),\n",
+ " then it will be called with the element (typing.Dict) as first parameter.\n",
+ "\n",
+ " Example\n",
+ " -------\n",
+ " .. code::\n",
+ "\n",
+ " from yfiles_jupyter_graphs import GraphWidget\n",
+ " w = GraphWidget()\n",
+ " def custom_heat_mapping(element: typing.Dict):\n",
+ " ...\n",
+ " w.set_heat_mapping(custom_heat_mapping)\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " heat: float | None\n",
+ "\n",
+ " \n"
+ ]
+ }
+ ],
+ "source": [
+ "print(w.default_heat_mapping.__doc__)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "d7a90608-4c1e-486a-8789-04ac80f0d395",
+ "metadata": {
+ "id": "d7a90608-4c1e-486a-8789-04ac80f0d395"
+ },
+ "outputs": [],
+ "source": [
+ "def heatmap(element):\n",
+ " load = 0.5\n",
+ " if 'start' in element:\n",
+ " #edge case\n",
+ " return 0\n",
+ " else:\n",
+ " # Calculate load based on number of edges\n",
+ " load += sum(0.1 for edge in w.edges if edge['end'] == element['id'])\n",
+ " load += sum(-0.1 for edge in w.edges if edge['start'] == element['id'])\n",
+ " # Calculate load based on capacity (if available)\n",
+ " if 'properties' in element and 'capacity' in element['properties']:\n",
+ " load += 1 - (element['properties']['capacity'] / 100)\n",
+ " # Calculate load based on duration (if available)\n",
+ " if 'properties' in element and 'duration' in element['properties']:\n",
+ " load += element['properties']['duration'] / 10\n",
+ " load = min(1, max(0, load))\n",
+ " return load\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4cc6bc9c-b259-46cc-9604-9c9cb250a375",
+ "metadata": {
+ "id": "4cc6bc9c-b259-46cc-9604-9c9cb250a375"
+ },
+ "source": [
+ "We use a graph representing a production chain and add our heatmap according to the expected step load:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "6732964b-649c-4ebc-a580-005464bf681d",
+ "metadata": {
+ "id": "6732964b-649c-4ebc-a580-005464bf681d",
+ "outputId": "5652cc8e-09fc-42a3-e1c9-7ef9be433513",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 104
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "heatmap
def heatmap(element)
<no docstring>
yfiles_jupyter_graphs.widget.GraphWidget.default_heat_mapping
def default_heat_mapping(element: TDict)
The default scale factor mapping for nodes.\n", + "\n", + "Provides constant value of None for all elements.\n", + "\n", + "Parameters\n", + "----------\n", + "index: int (optional)\n", + "element: typing.Dict\n", + "\n", + "Notes\n", + "-----\n", + "This is the default value for the `heat_mapping` property.\n", + "Can be 'overwritten' by setting the property\n", + "with a function of the same signature.\n", + "\n", + "If the given mapping function has only one parameter (that is not typed as int),\n", + "then it will be called with the element (typing.Dict) as first parameter.\n", + "\n", + "Example\n", + "-------\n", + ".. code::\n", + "\n", + " from yfiles_jupyter_graphs import GraphWidget\n", + " w = GraphWidget()\n", + " def custom_heat_mapping(element: typing.Dict):\n", + " ...\n", + " w.set_heat_mapping(custom_heat_mapping)\n", + "\n", + "Returns\n", + "-------\n", + "heat: float | None\n", + " \n", + "
"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e9b0ac00-6de6-47c3-b599-f14389831c06",
- "metadata": {},
- "source": [
- "## Mapping Function\n",
- "\n",
- "The heatmapping is slightly different than other mappings, as there is one combined mapping for edges and nodes\n",
- "\n",
- "There are get, set and delete methods for the heatmap.\n",
- "- you can set a new heat mapping with ```w.set_heat_mapping```\n",
- "- you can get the current heat mapping with ```w.get_heat_mapping```\n",
- "- you can delete a custom heat mapping with ```w.del_heat_mapping```\n",
- "\n",
- "If no custom mapping is set the default mapping is used."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "69c45788-84d3-4b1b-8e8b-05dbd74bd8ab",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(w.default_heat_mapping.__doc__)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d7a90608-4c1e-486a-8789-04ac80f0d395",
- "metadata": {},
- "outputs": [],
- "source": [
- "def heatmap(element):\n",
- " load = 0.5\n",
- " if 'start' in element:\n",
- " #edge case\n",
- " return 0\n",
- " else:\n",
- " # Calculate load based on number of edges\n",
- " load += sum(0.1 for edge in w.edges if edge['end'] == element['id'])\n",
- " load += sum(-0.1 for edge in w.edges if edge['start'] == element['id'])\n",
- " # Calculate load based on capacity (if available)\n",
- " if 'properties' in element and 'capacity' in element['properties']:\n",
- " load += 1 - (element['properties']['capacity'] / 100)\n",
- " # Calculate load based on duration (if available)\n",
- " if 'properties' in element and 'duration' in element['properties']:\n",
- " load += element['properties']['duration'] / 10\n",
- " load = min(1, max(0, load))\n",
- " return load\n",
- " "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4cc6bc9c-b259-46cc-9604-9c9cb250a375",
- "metadata": {},
- "source": [
- "We use a graph representing a production chain and add our heatmap according to the expected step load:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6732964b-649c-4ebc-a580-005464bf681d",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.set_heat_mapping(heatmap)\n",
- "w.nodes = [\n",
- " { \"id\": 1, \"properties\": {\"label\": \"Start\", \"capacity\": 50 }},\n",
- " { \"id\": 3, \"properties\": {\"label\": \"Evaluation\", \"capacity\": 20 }},\n",
- " { \"id\": 4, \"properties\": {\"label\": \"Move to Backlog\"}},\n",
- " { \"id\": 5, \"properties\": {\"label\": \"Prepare\", \"capacity\": 20 }},\n",
- " { \"id\": 6, \"properties\": {\"label\": \"Prepare Shortcut\"}},\n",
- " { \"id\": 7, \"properties\": {\"label\": \"Step A-1\"}},\n",
- " { \"id\": 9, \"properties\": {\"label\": \"Advance\", \"duration\": 3 }},\n",
- " { \"id\": 10, \"properties\": {\"label\": \"Early Discard\"}},\n",
- " { \"id\": 11, \"properties\": {\"label\": \"Step A-2\"}},\n",
- " { \"id\": 12, \"properties\": {\"label\": \"Quick Preparation\"}},\n",
- " { \"id\": 13, \"properties\": {\"label\": \"Backlog\", \"capacity\": 100 }},\n",
- " { \"id\": 14, \"properties\": {\"label\": \"Step B\"}},\n",
- " { \"id\": 16, \"properties\": {\"label\": \"End of Preparation\"}},\n",
- " { \"id\": 17, \"properties\": {\"label\": \"Buffer\", \"capacity\": 30, \"duration\": 10 }},\n",
- " { \"id\": 18, \"properties\": {\"label\": \"Main Processing\", \"capacity\": 70, \"duration\": 2 }},\n",
- " { \"id\": 19, \"properties\": {\"label\": \"Refinement 1\", \"capacity\": 20, \"duration\": 1 }},\n",
- " { \"id\": 20, \"properties\": {\"label\": \"Refinement 2\", \"capacity\": 60, \"duration\": 4 }},\n",
- " { \"id\": 21, \"properties\": {\"label\": \"Testing\", \"capacity\": 70, \"duration\": 1 }},\n",
- " { \"id\": 22, \"properties\": {\"label\": \"Delivery\", \"capacity\": 20 }},\n",
- " { \"id\": 23, \"properties\": {\"label\": \"Rejection\", \"capacity\": 100 }},\n",
- " { \"id\": 24, \"properties\": {\"label\": \"Store\", \"capacity\": 50, \"duration\": 5 }}\n",
- "]\n",
- "\n",
- "w.edges = [\n",
- " { \"start\": 1, \"end\": 5, \"properties\": {} },\n",
- " { \"start\": 1, \"end\": 2, \"properties\": {} },\n",
- " { \"start\": 1, \"end\": 3 , \"properties\": {} },\n",
- " { \"start\": 1, \"end\": 4, \"properties\": { \"probability\": 0.1 }},\n",
- " { \"start\": 16, \"end\": 17, \"properties\": {} },\n",
- " { \"start\": 16, \"end\": 18, \"properties\": {} },\n",
- " { \"start\": 18, \"end\": 19, \"properties\": {} },\n",
- " { \"start\": 5, \"end\": 11, \"properties\": {} },\n",
- " { \"start\": 9, \"end\": 6, \"properties\": { \"probability\": 0.1 }},\n",
- " { \"start\": 5, \"end\": 7, \"properties\": { \"probability\": 3 }},\n",
- " { \"start\": 2, \"end\": 8 , \"properties\": {} },\n",
- " { \"start\": 6, \"end\": 12 , \"properties\": {} },\n",
- " { \"start\": 3, \"end\": 9 , \"properties\": {} },\n",
- " { \"start\": 3, \"end\": 10, \"properties\": { \"probability\": 0.1 }},\n",
- " { \"start\": 4, \"end\": 13 , \"properties\": {} },\n",
- " { \"start\": 11, \"end\": 14 , \"properties\": {} },\n",
- " { \"start\": 7, \"end\": 14, \"properties\": {} },\n",
- " { \"start\": 5, \"end\": 16, \"properties\": { \"probability\": 0.1 }},\n",
- " { \"start\": 14, \"end\": 16 , \"properties\": {} },\n",
- " { \"start\": 18, \"end\": 21 , \"properties\": {} },\n",
- " { \"start\": 21, \"end\": 23, \"properties\": { \"probability\": 0.1 }},\n",
- " { \"start\": 21, \"end\": 24 , \"properties\": {} },\n",
- " { \"start\": 21, \"end\": 22 , \"properties\": {} },\n",
- " { \"start\": 13, \"end\": 15 , \"properties\": {} },\n",
- " { \"start\": 12, \"end\": 17 , \"properties\": {} },\n",
- " { \"start\": 18, \"end\": 22 , \"properties\": {} },\n",
- " { \"start\": 10, \"end\": 15 , \"properties\": {} },\n",
- " { \"start\": 9, \"end\": 20, \"properties\": {} },\n",
- " { \"start\": 19, \"end\": 21 , \"properties\": {} },\n",
- " { \"start\": 17, \"end\": 18 , \"properties\": {} },\n",
- " { \"start\": 20, \"end\": 21 , \"properties\": {} }\n",
- "]\n",
- "w.hierarchic_layout()\n",
- "w.get_heat_mapping()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "773513de-4b4e-492d-959d-e88116b4d4bb",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9390607f-9f13-49bf-b77e-b9f4939df12e",
- "metadata": {},
- "source": [
- "If the heat mapping is deleted, the heatmap mapping reverts back to the default mapping."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "45a96e73-f5e2-474e-b1c1-4092f857bdd1",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.del_heat_mapping()\n",
- "w.get_heat_mapping()"
- ]
- }
- ],
- "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": "3.12.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/30_leaflet_mapping.ipynb b/examples/30_leaflet_mapping.ipynb
index 7bb4147..5114ce5 100644
--- a/examples/30_leaflet_mapping.ipynb
+++ b/examples/30_leaflet_mapping.ipynb
@@ -1,250 +1,6203 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "2dc9a13a-8eda-41a2-b82e-0fa6dcdba165",
- "metadata": {},
- "source": [
- "# Leaflet Integration \n",
- "\n",
- "Before using the graph widget, install all necessary packages and initialize your widget."
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "2dc9a13a-8eda-41a2-b82e-0fa6dcdba165",
+ "metadata": {
+ "id": "2dc9a13a-8eda-41a2-b82e-0fa6dcdba165"
+ },
+ "source": [
+ "# Leaflet Integration \n",
+ "\n",
+ "Before using the graph widget, install all necessary packages and initialize your widget."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "7e2c1065-4f28-4ae1-b743-9166aa647e4e",
+ "metadata": {
+ "id": "7e2c1065-4f28-4ae1-b743-9166aa647e4e",
+ "outputId": "29bd4bdb-9e7a-474c-fda0-40215e86c3bc",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 90
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m12.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m11.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "from yfiles_jupyter_graphs import GraphWidget\n",
+ "w = GraphWidget()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8730a384-1ece-4615-a86e-68f289cef3a6",
+ "metadata": {
+ "id": "8730a384-1ece-4615-a86e-68f289cef3a6"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "aa6b295b-3b11-4fce-ba09-81f8c70b2bb5",
+ "metadata": {
+ "id": "aa6b295b-3b11-4fce-ba09-81f8c70b2bb5"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "26eead6d-66c5-4f14-9a5c-cc8a633dcb95",
+ "metadata": {
+ "id": "26eead6d-66c5-4f14-9a5c-cc8a633dcb95"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "516f8b11-6f66-46b7-bfea-4d4f9f4e548f",
+ "metadata": {
+ "id": "516f8b11-6f66-46b7-bfea-4d4f9f4e548f"
+ },
+ "source": [
+ "## Mapping Function\n",
+ "\n",
+ "The node coordinate mapping is a function that is supposed to return a 2-tuple of numbers `(lat, lng)` for each given node object which is then used in the widget.\n",
+ "\n",
+ "Optionally, the index can be used as the first function parameter."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "899cb5a5-c81a-4598-b534-2375c1857774",
+ "metadata": {
+ "id": "899cb5a5-c81a-4598-b534-2375c1857774",
+ "outputId": "b28ae8e4-ee13-4c96-e760-c871ab4e2777",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "The default coordinate mapping for nodes.\n",
+ "\n",
+ " Provides constant value of None for all nodes.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " index: int (optional)\n",
+ " node: typing.Dict\n",
+ "\n",
+ " Notes\n",
+ " -----\n",
+ " This is the default value for the `node_coordinate_mapping` property.\n",
+ " Can be 'overwritten' by setting the property\n",
+ " with a function of the same signature.\n",
+ "\n",
+ " If the given mapping function has only one parameter (that is not typed as int),\n",
+ " then it will be called with the element (typing.Dict) as first parameter.\n",
+ "\n",
+ " Example\n",
+ " -------\n",
+ " .. code::\n",
+ "\n",
+ " from yfiles_jupyter_graphs import GraphWidget\n",
+ " w = GraphWidget()\n",
+ " def custom_node_coordinate_mapping(node: typing.Dict):\n",
+ " ...\n",
+ " w.set_node_coordinate_mapping(custom_node_coordinate_mapping)\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " coordinate: float 2-tuple\n",
+ "\n",
+ " \n"
+ ]
+ }
+ ],
+ "source": [
+ "print(w.default_node_coordinate_mapping.__doc__)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "642533bd-2922-4641-b71b-314de376ac87",
+ "metadata": {
+ "id": "642533bd-2922-4641-b71b-314de376ac87"
+ },
+ "outputs": [],
+ "source": [
+ "flightData = {\n",
+ " 'airports': [\n",
+ " { 'name': 'Los Angeles', 'iata': 'LAX','lat': 33.942536,'lng': -118.408075, 'passengers': 65000000 }, { 'name': 'Rio de Janeiro', 'iata': 'GIG','lat': -22.808903,'lng': -43.243647, 'passengers': 5000000 }, {'name': 'Lima', 'iata': 'LIM','lat': -12.021889,'lng': -77.114319, 'passengers': 18000000 }, { 'name': 'London', 'iata': 'LHR','lat': 51.4775,'lng': -0.461389, 'passengers': 61000000 },\n",
+ " {'name': 'Frankfurt', 'iata': 'FRA','lat': 50.033333,'lng': 8.570556, 'passengers': 48000000 }, {'name': 'Moscow', 'iata': 'SVO','lat': 55.972642,'lng': 37.414589, 'passengers': 49000000 }, {'name': 'New Delhi', 'iata': 'DEL','lat': 28.5665,'lng': 77.103089, 'passengers': 39000000 }, {'name': 'Shanghai', 'iata': 'PVG','lat': 31.143378,'lng': 121.805214, 'passengers': 32000000 },\n",
+ " {'name': 'Hongkong', 'iata': 'HKG','lat': 22.308919,'lng': 113.914603, 'passengers': 1000000 }, {'name': 'Tokio', 'iata': 'NRT','lat': 35.764722,'lng': 140.386389, 'passengers': 15000000 }, {'name': 'Dubai', 'iata': 'DXB','lat': 25.252778,'lng': 55.364444, 'passengers': 29000000 }, {'name': 'Dakar', 'iata': 'DKR','lat': 14.670833,'lng': -17.072778, 'passengers': 2000000 },\n",
+ " {'name': 'Johannesburg', 'iata': 'JNB','lat': -26.133694,'lng': 28.242317, 'passengers': 9000000 }, {'name': 'Sydney', 'iata': 'SYD','lat': -33.946111,'lng': 151.177222, 'passengers': 44000000 }, {'name': 'Nairobi', 'iata': 'NBO','lat': -1.319167,'lng': 36.927778, 'passengers': 900000 }, {'name': 'Atlanta', 'iata': 'ATL','lat': 33.639167,'lng': -84.427778, 'passengers': 93000000 },\n",
+ " {'name': 'New York City', 'iata': 'JFK','lat': 40.63975,'lng': -73.778925, 'passengers': 55000000 }, {'name': 'Cairo', 'iata': 'CAI','lat': 30.121944,'lng': 31.405556, 'passengers': 14000000 }, {'name': 'Casablanca', 'iata': 'CMN','lat': 33.367467,'lng': -7.589967, 'passengers': 7000000 }, {'name': 'Lagos', 'iata': 'LOS','lat': 6.577222,'lng': 3.321111, 'passengers': 5000000 },\n",
+ " {'name': 'Cape Town', 'iata': 'CPT','lat': -33.969444,'lng': 18.597222, 'passengers': 5000000 }, {'name': 'Chengdu', 'iata': 'CTU','lat': 30.578333,'lng': 103.946944, 'passengers': 40000000 }, {'name': 'Jakarta', 'iata': 'CGK','lat': -6.125567,'lng': 106.655897, 'passengers': 54000000 }, {'name': 'Teheran', 'iata': 'IKA','lat': 35.416111,'lng': 51.152222, 'passengers': 8000000 },\n",
+ " {'name': 'Tel Aviv', 'iata': 'TLV','lat': 32.011389,'lng': 34.886667, 'passengers': 20000000 }, {'name': 'Kuala Lumpur', 'iata': 'KUL','lat': 2.745578,'lng': 101.709917, 'passengers': 25000000 }, {'name': 'Manila', 'iata': 'MNL','lat': 14.508647,'lng': 121.019581, 'passengers': 8000000 }, {'name': 'Singapur', 'iata': 'SIN','lat': 1.350189,'lng': 103.994433, 'passengers': 32000000 },\n",
+ " {'name': 'Taipeh', 'iata': 'TPE','lat': 25.077732,'lng': 121.232822, 'passengers': 800000 }, {'name': 'Bangkok', 'iata': 'BKK','lat': 13.681108,'lng': 100.747283, 'passengers': 65000000 }, {'name': 'Istanbul', 'iata': 'IST','lat': 40.976922,'lng': 28.814606, 'passengers': 64000000 }, {'name': 'Ulaanbaatar', 'iata': 'ULN','lat': 47.843056,'lng': 106.766639, 'passengers': 1000000 },\n",
+ " {'name': 'Melbourne', 'iata': 'MEL','lat': -37.673333,'lng': 144.843333, 'passengers': 12000000 }, {'name': 'Brisbane', 'iata': 'BNE','lat': -27.383333,'lng': 153.118056, 'passengers': 23000000 }, {'name': 'Nadi', 'iata': 'NAN','lat': -17.755392,'lng': 177.443378, 'passengers': 2000000 }, {'name': 'Auckland', 'iata': 'AKL','lat': -37.008056,'lng': 174.791667, 'passengers': 21000000 },\n",
+ " {'name': 'Paris', 'iata': 'CDG','lat': 49.009722,'lng': 2.547778, 'passengers': 57000000 }, {'name': 'Madrid', 'iata': 'MAD','lat': 40.4675,'lng': -3.551944, 'passengers': 50000000 }, {'name': 'Barcelona', 'iata': 'BCN','lat': 41.297078,'lng': 2.078464, 'passengers': 41000000 }, {'name': 'Rome', 'iata': 'FCO','lat': 41.804444,'lng': 12.250833, 'passengers': 29000000 },\n",
+ " {'name': 'Copenhagen', 'iata': 'CPH','lat': 55.617917,'lng': 12.655972, 'passengers': 30000000 }, {'name': 'Helsinki', 'iata': 'HEL','lat': 60.317222,'lng': 24.963333, 'passengers': 5000000 }, {'name': 'Athens', 'iata': 'ATH','lat': 37.936358,'lng': 23.944467, 'passengers': 22000000 }, {'name': 'Dublin', 'iata': 'DUB','lat': 53.421333,'lng': -6.270075, 'passengers': 32000000 },\n",
+ " {'name': 'Reykjavik', 'iata': 'RKV','lat': 64.13,'lng': -21.940556, 'passengers': 400000 }, {'name': 'Oslo', 'iata': 'OSL','lat': 60.193917,'lng': 11.100361, 'passengers': 9000000 }, {'name': 'Vienna', 'iata': 'VIE','lat': 48.110833,'lng': 16.570833, 'passengers': 10000000 }, {'name': 'Lisbon', 'iata': 'LIS','lat': 38.774167,'lng': -9.134167, 'passengers': 28000000 },\n",
+ " {'name': 'Stockholm', 'iata': 'ARN','lat': 59.651944,'lng': 17.918611, 'passengers': 7000000 }, {'name': 'Edinburgh', 'iata': 'EDI','lat': 55.95,'lng': -3.3725, 'passengers': 14000000 }, {'name': 'Chicago', 'iata': 'ORD','lat': 41.978603,'lng': -87.904842, 'passengers': 54000000 }, {'name': 'Dallas', 'iata': 'DFW','lat': 32.896828,'lng': -97.037997, 'passengers': 73000000 },\n",
+ " {'name': 'San Francisco', 'iata': 'SFO','lat': 37.618972,'lng': -122.374889, 'passengers': 42000000 }, {'name': 'Las Vegas', 'iata': 'LAS','lat': 36.080056,'lng': -115.15225, 'passengers': 52000000 }, {'name': 'Miami', 'iata': 'MIA','lat': 25.79325,'lng': -80.290556, 'passengers': 50000000 }, {'name': 'Toronto', 'iata': 'YYZ','lat': 43.677222,'lng': -79.630556, 'passengers': 12000000 },\n",
+ " {'name': 'Vancouver', 'iata': 'YVR','lat': 49.193889,'lng': -123.184444, 'passengers': 19000000 }, {'name': 'Montreal', 'iata': 'YUL','lat': 45.47175,'lng': -73.736569, 'passengers': 15000000 }, {'name': 'Mexico-City', 'iata': 'MEX','lat': 19.436303,'lng': -99.072097, 'passengers': 46000000 }, {'name': 'Guatemala-City', 'iata': 'GUA','lat': 14.583272,'lng': -90.527475, 'passengers': 2000000 },\n",
+ " {'name': 'Buenos Aires', 'iata': 'EZE','lat': -34.822222,'lng': -58.535833, 'passengers': 5000000 }, {'name': 'Sao Paulo', 'iata': 'GRU','lat': -23.432075,'lng': -46.469511, 'passengers': 34000000 }, {'name': 'Santiago de Chile', 'iata': 'SCL','lat': -33.392975,'lng': -70.785803, 'passengers': 20000000 }, {'name': 'Brasilia', 'iata': 'BSB','lat': -15.871111,'lng': -47.918611, 'passengers': 13000000 },\n",
+ " {'name': 'Bogota', 'iata': 'BOG','lat': 4.701594,'lng': -74.146947, 'passengers': 36000000 }, {'name': 'Caracas', 'iata': 'CCS','lat': 10.601194,'lng': -66.991222, 'passengers': 8000000 } ],\n",
+ " 'connections': [\n",
+ " {'from': 'LAX','to': 'JFK' }, {'from': 'JFK','to': 'GIG' }, {'from': 'JFK','to': 'LIM' }, {'from': 'JFK','to': 'LHR' }, {'from': 'GIG','to': 'FRA' }, {'from': 'LIM','to': 'GIG' }, {'from': 'FRA','to': 'JFK' }, {'from': 'LHR','to': 'FRA' }, {'from': 'FRA','to': 'SVO' }, {'from': 'FRA','to': 'DXB' }, {'from': 'SVO','to': 'DEL' },\n",
+ " {'from': 'SVO','to': 'PVG' }, {'from': 'DEL','to': 'HKG' }, {'from': 'PVG','to': 'HKG' }, {'from': 'PVG','to': 'NRT' }, {'from': 'HKG','to': 'SYD' }, {'from': 'NRT','to': 'SYD' }, {'from': 'DXB','to': 'SVO' }, {'from': 'DXB','to': 'DEL' }, {'from': 'DXB','to': 'DKR' }, {'from': 'DXB','to': 'JNB' }, {'from': 'JNB','to': 'LHR' },\n",
+ " {'from': 'JNB','to': 'DKR' }, {'from': 'SYD','to': 'DXB' }, {'from': 'NBO','to': 'JNB' }, {'from': 'NBO','to': 'DXB' }, {'from': 'ATL','to': 'JFK' }, {'from': 'LAX','to': 'ATL' }, {'from': 'ATL','to': 'LHR' }, {'from': 'ATL','to': 'LIM' }, {'from': 'SCL','to': 'LIM' }, {'from': 'EZE','to': 'SCL' }, {'from': 'SCL','to': 'GRU' },\n",
+ " {'from': 'GIG','to': 'EZE' }, {'from': 'GIG','to': 'GRU' }, {'from': 'BSB','to': 'GIG' }, {'from': 'SCL','to': 'BSB' }, {'from': 'LIM','to': 'BSB' }, {'from': 'BOG','to': 'BSB' }, {'from': 'CCS','to': 'BSB' }, {'from': 'BOG','to': 'GUA' }, {'from': 'CCS','to': 'MIA' }, {'from': 'GUA','to': 'MIA' }, {'from': 'GUA','to': 'MEX' },\n",
+ " {'from': 'MEX','to': 'LAX' }, {'from': 'MEX','to': 'LAX' }, {'from': 'LAX','to': 'SFO' }, {'from': 'SFO','to': 'YVR' }, {'from': 'LAX','to': 'LAS' }, {'from': 'LAX','to': 'DFW' }, {'from': 'LAX','to': 'ORD' }, {'from': 'SFO','to': 'LAS' }, {'from': 'DFW','to': 'ATL' }, {'from': 'ATL','to': 'YYZ' }, {'from': 'ORD','to': 'YYZ' },\n",
+ " {'from': 'YYZ','to': 'YUL' }, {'from': 'YYZ','to': 'JFK' }, {'from': 'YUL','to': 'JFK' }, {'from': 'JNB','to': 'CPT' }, {'from': 'LOS','to': 'DKR' }, {'from': 'NBO','to': 'LOS' }, {'from': 'DKR','to': 'CMN' }, {'from': 'DKR','to': 'CAI' }, {'from': 'NBO','to': 'CAI' }, {'from': 'DXB','to': 'CAI' }, {'from': 'IKA','to': 'DXB' },\n",
+ " {'from': 'IST','to': 'IKA' }, {'from': 'TLV','to': 'ATH' }, {'from': 'CAI','to': 'TLV' }, {'from': 'ATH','to': 'IST' }, {'from': 'FCO','to': 'ATH' }, {'from': 'LIS','to': 'LHR' }, {'from': 'LIS','to': 'MAD' }, {'from': 'MAD','to': 'BCN' }, {'from': 'CDG','to': 'LHR' }, {'from': 'DUB','to': 'LHR' }, {'from': 'EDI','to': 'LHR' },\n",
+ " {'from': 'CDG','to': 'BCN' }, {'from': 'BCN','to': 'FCO' }, {'from': 'VIE','to': 'IST' }, {'from': 'VIE','to': 'SVO' }, {'from': 'CMN','to': 'LIS' }, {'from': 'MAD','to': 'CMN' }, {'from': 'FRA','to': 'CPH' }, {'from': 'LHR','to': 'CPH' }, {'from': 'CPH','to': 'OSL' }, {'from': 'CPH','to': 'ARN' }, {'from': 'ARN','to': 'HEL' },\n",
+ " {'from': 'HEL','to': 'SVO' }, {'from': 'ULN','to': 'PVG' }, {'from': 'CTU','to': 'PVG' }, {'from': 'PVG','to': 'TPE' }, {'from': 'CTU','to': 'HKG' }, {'from': 'TPE','to': 'HKG' }, {'from': 'HKG','to': 'MNL' }, {'from': 'BKK','to': 'HKG' }, {'from': 'SIN','to': 'KUL' }, {'from': 'SIN','to': 'BKK' }, {'from': 'CGK','to': 'SIN' },\n",
+ " {'from': 'MNL','to': 'SIN' }, {'from': 'SIN','to': 'SYD' }, {'from': 'BNE','to': 'SYD' }, {'from': 'SYD','to': 'MEL' }, {'from': 'NAN','to': 'SYD' }, {'from': 'AKL','to': 'SYD' }, {'from': 'NAN','to': 'AKL' }, {'from': 'RKV','to': 'LHR' }, {'from': 'BCN','to': 'CDG' }, {'from': 'BCN','to': 'FRA' }, {'from': 'FCO','to': 'FRA' },\n",
+ " {'from': 'BOG','to': 'MEX' }, {'from': 'BOG','to': 'GRU' }, {'from': 'ATL','to': 'MIA' }, {'from': 'FRA','to': 'IST' }, {'from': 'IST','to': 'DEL' }, {'from': 'PVG','to': 'BKK' }, {'from': 'DEL','to': 'BKK' },\n",
+ " ],\n",
+ "}\n",
+ "\n",
+ "w.edges = edges = [\n",
+ " {\"start\": connection['from'], \"end\": connection['to'], \"label\": \"\", \"properties\": {}, \"directed\": False}\n",
+ " for connection in flightData['connections']\n",
+ "]\n",
+ "\n",
+ "w.nodes = nodes = [\n",
+ " {\"id\": airport['iata'], \"properties\": {\"label\": airport['name'], 'passengers': airport['passengers']}, \"coordinates\": [airport['lat'], airport['lng']]}\n",
+ " for airport in flightData['airports']\n",
+ "]\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4e296c92-22b3-4765-9c5e-0d2bf9e98742",
+ "metadata": {
+ "id": "4e296c92-22b3-4765-9c5e-0d2bf9e98742"
+ },
+ "source": [
+ "We'll use the given latitude and longitute for our coordinate mapping."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "7448ef35-a12d-4d4d-a6a8-e2f96b97dd99",
+ "metadata": {
+ "id": "7448ef35-a12d-4d4d-a6a8-e2f96b97dd99",
+ "outputId": "591f9e2c-3d51-45b1-daa8-4f4977d25956",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 36
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "'coordinates'"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ }
+ },
+ "metadata": {},
+ "execution_count": 5
+ }
+ ],
+ "source": [
+ "w.node_coordinate_mapping = 'coordinates'\n",
+ "w.get_node_coordinate_mapping()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "515b21f4-5a9d-42c2-a463-1594f6a727d4",
+ "metadata": {
+ "id": "515b21f4-5a9d-42c2-a463-1594f6a727d4"
+ },
+ "source": [
+ "To show the map-view, either pick the `Show on Map` layout in the toolbar, or pre-configure the layout with `w.map_layout()`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "20a5689d-30dc-4a40-a8c4-b64f5e3ae630",
+ "metadata": {
+ "id": "20a5689d-30dc-4a40-a8c4-b64f5e3ae630"
+ },
+ "outputs": [],
+ "source": [
+ "w.map_layout()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "0acf19fd-a301-4ece-9354-cd8474427d1e",
+ "metadata": {
+ "id": "0acf19fd-a301-4ece-9354-cd8474427d1e",
+ "outputId": "f5463f01-0a7f-46a5-d928-f2cc969ff415",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 817,
+ "referenced_widgets": [
+ "06130374d95a42e097ca7b403a9a8cf0",
+ "6264ee641bc8411c90a739c64b5c37ba"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='800px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "06130374d95a42e097ca7b403a9a8cf0"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "display(w)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e64bce42-f3d8-4153-bb46-a349ebb3f10c",
+ "metadata": {
+ "id": "e64bce42-f3d8-4153-bb46-a349ebb3f10c"
+ },
+ "source": [
+ "If the coordinate mapping is deleted, the heatmap mapping reverts back to the default mapping."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "8d4b5fda-947d-4eed-b624-70c19726bde4",
+ "metadata": {
+ "id": "8d4b5fda-947d-4eed-b624-70c19726bde4",
+ "outputId": "1e8574c4-af1f-424b-9fa5-e422935e265e",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 186
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "yfiles_jupyter_graphs.widget.GraphWidget.default_node_coordinate_mapping
def default_node_coordinate_mapping(index: int, node: TDict)
The default coordinate mapping for nodes.\n", + "\n", + "Provides constant value of None for all nodes.\n", + "\n", + "Parameters\n", + "----------\n", + "index: int (optional)\n", + "node: typing.Dict\n", + "\n", + "Notes\n", + "-----\n", + "This is the default value for the `node_coordinate_mapping` property.\n", + "Can be 'overwritten' by setting the property\n", + "with a function of the same signature.\n", + "\n", + "If the given mapping function has only one parameter (that is not typed as int),\n", + "then it will be called with the element (typing.Dict) as first parameter.\n", + "\n", + "Example\n", + "-------\n", + ".. code::\n", + "\n", + " from yfiles_jupyter_graphs import GraphWidget\n", + " w = GraphWidget()\n", + " def custom_node_coordinate_mapping(node: typing.Dict):\n", + " ...\n", + " w.set_node_coordinate_mapping(custom_node_coordinate_mapping)\n", + "\n", + "Returns\n", + "-------\n", + "coordinate: float 2-tuple\n", + " \n", + "
"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "516f8b11-6f66-46b7-bfea-4d4f9f4e548f",
- "metadata": {},
- "source": [
- "## Mapping Function\n",
- "\n",
- "The node coordinate mapping is a function that is supposed to return a 2-tuple of numbers `(lat, lng)` for each given node object which is then used in the widget.\n",
- "\n",
- "Optionally, the index can be used as the first function parameter."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "899cb5a5-c81a-4598-b534-2375c1857774",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(w.default_node_coordinate_mapping.__doc__)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "642533bd-2922-4641-b71b-314de376ac87",
- "metadata": {},
- "outputs": [],
- "source": [
- "flightData = {\n",
- " 'airports': [\n",
- " { 'name': 'Los Angeles', 'iata': 'LAX','lat': 33.942536,'lng': -118.408075, 'passengers': 65000000 }, { 'name': 'Rio de Janeiro', 'iata': 'GIG','lat': -22.808903,'lng': -43.243647, 'passengers': 5000000 }, {'name': 'Lima', 'iata': 'LIM','lat': -12.021889,'lng': -77.114319, 'passengers': 18000000 }, { 'name': 'London', 'iata': 'LHR','lat': 51.4775,'lng': -0.461389, 'passengers': 61000000 },\n",
- " {'name': 'Frankfurt', 'iata': 'FRA','lat': 50.033333,'lng': 8.570556, 'passengers': 48000000 }, {'name': 'Moscow', 'iata': 'SVO','lat': 55.972642,'lng': 37.414589, 'passengers': 49000000 }, {'name': 'New Delhi', 'iata': 'DEL','lat': 28.5665,'lng': 77.103089, 'passengers': 39000000 }, {'name': 'Shanghai', 'iata': 'PVG','lat': 31.143378,'lng': 121.805214, 'passengers': 32000000 },\n",
- " {'name': 'Hongkong', 'iata': 'HKG','lat': 22.308919,'lng': 113.914603, 'passengers': 1000000 }, {'name': 'Tokio', 'iata': 'NRT','lat': 35.764722,'lng': 140.386389, 'passengers': 15000000 }, {'name': 'Dubai', 'iata': 'DXB','lat': 25.252778,'lng': 55.364444, 'passengers': 29000000 }, {'name': 'Dakar', 'iata': 'DKR','lat': 14.670833,'lng': -17.072778, 'passengers': 2000000 },\n",
- " {'name': 'Johannesburg', 'iata': 'JNB','lat': -26.133694,'lng': 28.242317, 'passengers': 9000000 }, {'name': 'Sydney', 'iata': 'SYD','lat': -33.946111,'lng': 151.177222, 'passengers': 44000000 }, {'name': 'Nairobi', 'iata': 'NBO','lat': -1.319167,'lng': 36.927778, 'passengers': 900000 }, {'name': 'Atlanta', 'iata': 'ATL','lat': 33.639167,'lng': -84.427778, 'passengers': 93000000 },\n",
- " {'name': 'New York City', 'iata': 'JFK','lat': 40.63975,'lng': -73.778925, 'passengers': 55000000 }, {'name': 'Cairo', 'iata': 'CAI','lat': 30.121944,'lng': 31.405556, 'passengers': 14000000 }, {'name': 'Casablanca', 'iata': 'CMN','lat': 33.367467,'lng': -7.589967, 'passengers': 7000000 }, {'name': 'Lagos', 'iata': 'LOS','lat': 6.577222,'lng': 3.321111, 'passengers': 5000000 },\n",
- " {'name': 'Cape Town', 'iata': 'CPT','lat': -33.969444,'lng': 18.597222, 'passengers': 5000000 }, {'name': 'Chengdu', 'iata': 'CTU','lat': 30.578333,'lng': 103.946944, 'passengers': 40000000 }, {'name': 'Jakarta', 'iata': 'CGK','lat': -6.125567,'lng': 106.655897, 'passengers': 54000000 }, {'name': 'Teheran', 'iata': 'IKA','lat': 35.416111,'lng': 51.152222, 'passengers': 8000000 },\n",
- " {'name': 'Tel Aviv', 'iata': 'TLV','lat': 32.011389,'lng': 34.886667, 'passengers': 20000000 }, {'name': 'Kuala Lumpur', 'iata': 'KUL','lat': 2.745578,'lng': 101.709917, 'passengers': 25000000 }, {'name': 'Manila', 'iata': 'MNL','lat': 14.508647,'lng': 121.019581, 'passengers': 8000000 }, {'name': 'Singapur', 'iata': 'SIN','lat': 1.350189,'lng': 103.994433, 'passengers': 32000000 },\n",
- " {'name': 'Taipeh', 'iata': 'TPE','lat': 25.077732,'lng': 121.232822, 'passengers': 800000 }, {'name': 'Bangkok', 'iata': 'BKK','lat': 13.681108,'lng': 100.747283, 'passengers': 65000000 }, {'name': 'Istanbul', 'iata': 'IST','lat': 40.976922,'lng': 28.814606, 'passengers': 64000000 }, {'name': 'Ulaanbaatar', 'iata': 'ULN','lat': 47.843056,'lng': 106.766639, 'passengers': 1000000 },\n",
- " {'name': 'Melbourne', 'iata': 'MEL','lat': -37.673333,'lng': 144.843333, 'passengers': 12000000 }, {'name': 'Brisbane', 'iata': 'BNE','lat': -27.383333,'lng': 153.118056, 'passengers': 23000000 }, {'name': 'Nadi', 'iata': 'NAN','lat': -17.755392,'lng': 177.443378, 'passengers': 2000000 }, {'name': 'Auckland', 'iata': 'AKL','lat': -37.008056,'lng': 174.791667, 'passengers': 21000000 },\n",
- " {'name': 'Paris', 'iata': 'CDG','lat': 49.009722,'lng': 2.547778, 'passengers': 57000000 }, {'name': 'Madrid', 'iata': 'MAD','lat': 40.4675,'lng': -3.551944, 'passengers': 50000000 }, {'name': 'Barcelona', 'iata': 'BCN','lat': 41.297078,'lng': 2.078464, 'passengers': 41000000 }, {'name': 'Rome', 'iata': 'FCO','lat': 41.804444,'lng': 12.250833, 'passengers': 29000000 },\n",
- " {'name': 'Copenhagen', 'iata': 'CPH','lat': 55.617917,'lng': 12.655972, 'passengers': 30000000 }, {'name': 'Helsinki', 'iata': 'HEL','lat': 60.317222,'lng': 24.963333, 'passengers': 5000000 }, {'name': 'Athens', 'iata': 'ATH','lat': 37.936358,'lng': 23.944467, 'passengers': 22000000 }, {'name': 'Dublin', 'iata': 'DUB','lat': 53.421333,'lng': -6.270075, 'passengers': 32000000 },\n",
- " {'name': 'Reykjavik', 'iata': 'RKV','lat': 64.13,'lng': -21.940556, 'passengers': 400000 }, {'name': 'Oslo', 'iata': 'OSL','lat': 60.193917,'lng': 11.100361, 'passengers': 9000000 }, {'name': 'Vienna', 'iata': 'VIE','lat': 48.110833,'lng': 16.570833, 'passengers': 10000000 }, {'name': 'Lisbon', 'iata': 'LIS','lat': 38.774167,'lng': -9.134167, 'passengers': 28000000 },\n",
- " {'name': 'Stockholm', 'iata': 'ARN','lat': 59.651944,'lng': 17.918611, 'passengers': 7000000 }, {'name': 'Edinburgh', 'iata': 'EDI','lat': 55.95,'lng': -3.3725, 'passengers': 14000000 }, {'name': 'Chicago', 'iata': 'ORD','lat': 41.978603,'lng': -87.904842, 'passengers': 54000000 }, {'name': 'Dallas', 'iata': 'DFW','lat': 32.896828,'lng': -97.037997, 'passengers': 73000000 },\n",
- " {'name': 'San Francisco', 'iata': 'SFO','lat': 37.618972,'lng': -122.374889, 'passengers': 42000000 }, {'name': 'Las Vegas', 'iata': 'LAS','lat': 36.080056,'lng': -115.15225, 'passengers': 52000000 }, {'name': 'Miami', 'iata': 'MIA','lat': 25.79325,'lng': -80.290556, 'passengers': 50000000 }, {'name': 'Toronto', 'iata': 'YYZ','lat': 43.677222,'lng': -79.630556, 'passengers': 12000000 },\n",
- " {'name': 'Vancouver', 'iata': 'YVR','lat': 49.193889,'lng': -123.184444, 'passengers': 19000000 }, {'name': 'Montreal', 'iata': 'YUL','lat': 45.47175,'lng': -73.736569, 'passengers': 15000000 }, {'name': 'Mexico-City', 'iata': 'MEX','lat': 19.436303,'lng': -99.072097, 'passengers': 46000000 }, {'name': 'Guatemala-City', 'iata': 'GUA','lat': 14.583272,'lng': -90.527475, 'passengers': 2000000 },\n",
- " {'name': 'Buenos Aires', 'iata': 'EZE','lat': -34.822222,'lng': -58.535833, 'passengers': 5000000 }, {'name': 'Sao Paulo', 'iata': 'GRU','lat': -23.432075,'lng': -46.469511, 'passengers': 34000000 }, {'name': 'Santiago de Chile', 'iata': 'SCL','lat': -33.392975,'lng': -70.785803, 'passengers': 20000000 }, {'name': 'Brasilia', 'iata': 'BSB','lat': -15.871111,'lng': -47.918611, 'passengers': 13000000 },\n",
- " {'name': 'Bogota', 'iata': 'BOG','lat': 4.701594,'lng': -74.146947, 'passengers': 36000000 }, {'name': 'Caracas', 'iata': 'CCS','lat': 10.601194,'lng': -66.991222, 'passengers': 8000000 } ],\n",
- " 'connections': [\n",
- " {'from': 'LAX','to': 'JFK' }, {'from': 'JFK','to': 'GIG' }, {'from': 'JFK','to': 'LIM' }, {'from': 'JFK','to': 'LHR' }, {'from': 'GIG','to': 'FRA' }, {'from': 'LIM','to': 'GIG' }, {'from': 'FRA','to': 'JFK' }, {'from': 'LHR','to': 'FRA' }, {'from': 'FRA','to': 'SVO' }, {'from': 'FRA','to': 'DXB' }, {'from': 'SVO','to': 'DEL' },\n",
- " {'from': 'SVO','to': 'PVG' }, {'from': 'DEL','to': 'HKG' }, {'from': 'PVG','to': 'HKG' }, {'from': 'PVG','to': 'NRT' }, {'from': 'HKG','to': 'SYD' }, {'from': 'NRT','to': 'SYD' }, {'from': 'DXB','to': 'SVO' }, {'from': 'DXB','to': 'DEL' }, {'from': 'DXB','to': 'DKR' }, {'from': 'DXB','to': 'JNB' }, {'from': 'JNB','to': 'LHR' },\n",
- " {'from': 'JNB','to': 'DKR' }, {'from': 'SYD','to': 'DXB' }, {'from': 'NBO','to': 'JNB' }, {'from': 'NBO','to': 'DXB' }, {'from': 'ATL','to': 'JFK' }, {'from': 'LAX','to': 'ATL' }, {'from': 'ATL','to': 'LHR' }, {'from': 'ATL','to': 'LIM' }, {'from': 'SCL','to': 'LIM' }, {'from': 'EZE','to': 'SCL' }, {'from': 'SCL','to': 'GRU' },\n",
- " {'from': 'GIG','to': 'EZE' }, {'from': 'GIG','to': 'GRU' }, {'from': 'BSB','to': 'GIG' }, {'from': 'SCL','to': 'BSB' }, {'from': 'LIM','to': 'BSB' }, {'from': 'BOG','to': 'BSB' }, {'from': 'CCS','to': 'BSB' }, {'from': 'BOG','to': 'GUA' }, {'from': 'CCS','to': 'MIA' }, {'from': 'GUA','to': 'MIA' }, {'from': 'GUA','to': 'MEX' },\n",
- " {'from': 'MEX','to': 'LAX' }, {'from': 'MEX','to': 'LAX' }, {'from': 'LAX','to': 'SFO' }, {'from': 'SFO','to': 'YVR' }, {'from': 'LAX','to': 'LAS' }, {'from': 'LAX','to': 'DFW' }, {'from': 'LAX','to': 'ORD' }, {'from': 'SFO','to': 'LAS' }, {'from': 'DFW','to': 'ATL' }, {'from': 'ATL','to': 'YYZ' }, {'from': 'ORD','to': 'YYZ' }, \n",
- " {'from': 'YYZ','to': 'YUL' }, {'from': 'YYZ','to': 'JFK' }, {'from': 'YUL','to': 'JFK' }, {'from': 'JNB','to': 'CPT' }, {'from': 'LOS','to': 'DKR' }, {'from': 'NBO','to': 'LOS' }, {'from': 'DKR','to': 'CMN' }, {'from': 'DKR','to': 'CAI' }, {'from': 'NBO','to': 'CAI' }, {'from': 'DXB','to': 'CAI' }, {'from': 'IKA','to': 'DXB' }, \n",
- " {'from': 'IST','to': 'IKA' }, {'from': 'TLV','to': 'ATH' }, {'from': 'CAI','to': 'TLV' }, {'from': 'ATH','to': 'IST' }, {'from': 'FCO','to': 'ATH' }, {'from': 'LIS','to': 'LHR' }, {'from': 'LIS','to': 'MAD' }, {'from': 'MAD','to': 'BCN' }, {'from': 'CDG','to': 'LHR' }, {'from': 'DUB','to': 'LHR' }, {'from': 'EDI','to': 'LHR' }, \n",
- " {'from': 'CDG','to': 'BCN' }, {'from': 'BCN','to': 'FCO' }, {'from': 'VIE','to': 'IST' }, {'from': 'VIE','to': 'SVO' }, {'from': 'CMN','to': 'LIS' }, {'from': 'MAD','to': 'CMN' }, {'from': 'FRA','to': 'CPH' }, {'from': 'LHR','to': 'CPH' }, {'from': 'CPH','to': 'OSL' }, {'from': 'CPH','to': 'ARN' }, {'from': 'ARN','to': 'HEL' },\n",
- " {'from': 'HEL','to': 'SVO' }, {'from': 'ULN','to': 'PVG' }, {'from': 'CTU','to': 'PVG' }, {'from': 'PVG','to': 'TPE' }, {'from': 'CTU','to': 'HKG' }, {'from': 'TPE','to': 'HKG' }, {'from': 'HKG','to': 'MNL' }, {'from': 'BKK','to': 'HKG' }, {'from': 'SIN','to': 'KUL' }, {'from': 'SIN','to': 'BKK' }, {'from': 'CGK','to': 'SIN' },\n",
- " {'from': 'MNL','to': 'SIN' }, {'from': 'SIN','to': 'SYD' }, {'from': 'BNE','to': 'SYD' }, {'from': 'SYD','to': 'MEL' }, {'from': 'NAN','to': 'SYD' }, {'from': 'AKL','to': 'SYD' }, {'from': 'NAN','to': 'AKL' }, {'from': 'RKV','to': 'LHR' }, {'from': 'BCN','to': 'CDG' }, {'from': 'BCN','to': 'FRA' }, {'from': 'FCO','to': 'FRA' }, \n",
- " {'from': 'BOG','to': 'MEX' }, {'from': 'BOG','to': 'GRU' }, {'from': 'ATL','to': 'MIA' }, {'from': 'FRA','to': 'IST' }, {'from': 'IST','to': 'DEL' }, {'from': 'PVG','to': 'BKK' }, {'from': 'DEL','to': 'BKK' },\n",
- " ],\n",
- "}\n",
- "\n",
- "w.edges = edges = [\n",
- " {\"start\": connection['from'], \"end\": connection['to'], \"label\": \"\", \"properties\": {}, \"directed\": False}\n",
- " for connection in flightData['connections']\n",
- "]\n",
- "\n",
- "w.nodes = nodes = [\n",
- " {\"id\": airport['iata'], \"properties\": {\"label\": airport['name'], 'passengers': airport['passengers']}, \"coordinates\": [airport['lat'], airport['lng']]}\n",
- " for airport in flightData['airports']\n",
- "]\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4e296c92-22b3-4765-9c5e-0d2bf9e98742",
- "metadata": {},
- "source": [
- "We'll use the given latitude and longitute for our coordinate mapping."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7448ef35-a12d-4d4d-a6a8-e2f96b97dd99",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.node_coordinate_mapping = 'coordinates' \n",
- "w.get_node_coordinate_mapping()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "515b21f4-5a9d-42c2-a463-1594f6a727d4",
- "metadata": {},
- "source": [
- "To show the map-view, either pick the `Show on Map` layout in the toolbar, or pre-configure the layout with `w.map_layout()`:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "20a5689d-30dc-4a40-a8c4-b64f5e3ae630",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.map_layout()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0acf19fd-a301-4ece-9354-cd8474427d1e",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e64bce42-f3d8-4153-bb46-a349ebb3f10c",
- "metadata": {},
- "source": [
- "If the coordinate mapping is deleted, the heatmap mapping reverts back to the default mapping."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8d4b5fda-947d-4eed-b624-70c19726bde4",
- "metadata": {},
- "outputs": [],
- "source": [
- "w.del_node_coordinate_mapping()\n",
- "w.get_node_coordinate_mapping()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c087955a-d94d-4a5b-bb38-d366bfb5f36b",
- "metadata": {},
- "source": [
- "Add an additional heat mapping:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fc71eb02-da8f-4d9f-8b68-52e82f1d76c8",
- "metadata": {},
- "outputs": [],
- "source": [
- "w2 = GraphWidget()\n",
- "w2.nodes = w.nodes\n",
- "w2.edges = w.edges\n",
- "\n",
- "w2.node_coordinate_mapping = 'coordinates' \n",
- "\n",
- "def heat(element):\n",
- " if 'start' in element:\n",
- " #edge case\n",
- " return 0\n",
- " else: return (element['properties']['passengers'] - 800000)/92200000\n",
- "\n",
- "w2.set_heat_mapping(heat)\n",
- "w2.map_layout()\n",
- "display(w2)"
- ]
- }
- ],
- "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": "3.12.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/examples/31_nested_graphs.ipynb b/examples/31_nested_graphs.ipynb
index b3eea07..5b5bfa5 100644
--- a/examples/31_nested_graphs.ipynb
+++ b/examples/31_nested_graphs.ipynb
@@ -1,19338 +1,19766 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "95c7a78b-917a-42b6-a1a9-b3255c0667ce",
- "metadata": {},
- "source": [
- "# Nested Graphs \n",
- "\n",
- "Before using the graph widget, install all necessary packages and initialize your widget."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "86ccab84-0881-4e14-b33d-51313ec2b85e",
- "metadata": {},
- "outputs": [],
- "source": [
- "%pip install yfiles_jupyter_graphs --quiet\n",
- "from yfiles_jupyter_graphs import GraphWidget"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "99c91625-6682-4dcc-86ed-ff80a85d435d",
- "metadata": {},
- "source": [
- "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "837b3dc2-a656-4bd1-856d-b2e4ed12b7a0",
- "metadata": {},
- "outputs": [],
- "source": [
- "try:\n",
- " import google.colab\n",
- " from google.colab import output\n",
- " output.enable_custom_widget_manager()\n",
- "except:\n",
- " pass"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e8af92d1-a099-4f27-b42b-7ffebff1954d",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8cea83a9-2373-4dce-a9a2-353022fb1676",
- "metadata": {},
- "source": [
- "## Sample Data\n",
- "\n",
- "The dataset for this sample is the following airport and flight routes data:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0eb47bdd-d943-4820-bbc3-29d8396204ac",
- "metadata": {},
- "outputs": [],
- "source": [
- "airports = [\n",
- " {\"name\": \"Los Angeles\", \"iata\": \"LAX\", \"lat\": 33.942536, \"lng\": -118.408075, \"passengers\": 65000000, \"country\": \"USA\", \"id\": \"LAX\"}, {\"name\": \"Rio de Janeiro\", \"iata\": \"GIG\", \"lat\": -22.808903, \"lng\": -43.243647, \"passengers\": 5000000, \"country\": \"Brazil\", \"id\": \"GIG\"}, {\"name\": \"Lima\", \"iata\": \"LIM\", \"lat\": -12.021889, \"lng\": -77.114319, \"passengers\": 18000000, \"country\": \"Peru\", \"id\": \"LIM\"}, {\"name\": \"London\", \"iata\": \"LHR\", \"lat\": 51.4775, \"lng\": -0.461389, \"passengers\": 61000000, \"country\": \"UK\", \"id\": \"LHR\"},\n",
- " {\"name\": \"Frankfurt\", \"iata\": \"FRA\", \"lat\": 50.033333, \"lng\": 8.570556, \"passengers\": 48000000, \"country\": \"Germany\", \"id\": \"FRA\"}, {\"name\": \"Moscow\", \"iata\": \"SVO\", \"lat\": 55.972642, \"lng\": 37.414589, \"passengers\": 49000000, \"country\": \"Russia\", \"id\": \"SVO\"}, {\"name\": \"New Delhi\", \"iata\": \"DEL\", \"lat\": 28.5665, \"lng\": 77.103089, \"passengers\": 39000000, \"country\": \"India\", \"id\": \"DEL\"}, {\"name\": \"Shanghai\", \"iata\": \"PVG\", \"lat\": 31.143378, \"lng\": 121.805214, \"passengers\": 32000000, \"country\": \"China\", \"id\": \"PVG\"},\n",
- " {\"name\": \"Hongkong\", \"iata\": \"HKG\", \"lat\": 22.308919, \"lng\": 113.914603, \"passengers\": 1000000, \"country\": \"China\", \"id\": \"HKG\"}, {\"name\": \"Tokio\", \"iata\": \"NRT\", \"lat\": 35.764722, \"lng\": 140.386389, \"passengers\": 15000000, \"country\": \"Japan\", \"id\": \"NRT\"}, {\"name\": \"Dubai\", \"iata\": \"DXB\", \"lat\": 25.252778, \"lng\": 55.364444, \"passengers\": 29000000, \"country\": \"UAE\", \"id\": \"DXB\"}, {\"name\": \"Dakar\", \"iata\": \"DKR\", \"lat\": 14.670833, \"lng\": -17.072778, \"passengers\": 2000000, \"country\": \"Senegal\", \"id\": \"DKR\"},\n",
- " {\"name\": \"Johannesburg\", \"iata\": \"JNB\", \"lat\": -26.133694, \"lng\": 28.242317, \"passengers\": 9000000, \"country\": \"South Africa\", \"id\": \"JNB\"}, {\"name\": \"Sydney\", \"iata\": \"SYD\", \"lat\": -33.946111, \"lng\": 151.177222, \"passengers\": 44000000, \"country\": \"Australia\", \"id\": \"SYD\"}, {\"name\": \"Nairobi\", \"iata\": \"NBO\", \"lat\": -1.319167, \"lng\": 36.927778, \"passengers\": 900000, \"country\": \"Kenya\", \"id\": \"NBO\"}, {\"name\": \"Atlanta\", \"iata\": \"ATL\", \"lat\": 33.639167, \"lng\": -84.427778, \"passengers\": 93000000, \"country\": \"USA\", \"id\": \"ATL\"},\n",
- " {\"name\": \"New York City\", \"iata\": \"JFK\", \"lat\": 40.63975, \"lng\": -73.778925, \"passengers\": 55000000, \"country\": \"USA\", \"id\": \"JFK\"}, {\"name\": \"Cairo\", \"iata\": \"CAI\", \"lat\": 30.121944, \"lng\": 31.405556, \"passengers\": 14000000, \"country\": \"Egypt\", \"id\": \"CAI\"}, {\"name\": \"Casablanca\", \"iata\": \"CMN\", \"lat\": 33.367467, \"lng\": -7.589967, \"passengers\": 7000000, \"country\": \"Morocco\", \"id\": \"CMN\"}, {\"name\": \"Lagos\", \"iata\": \"LOS\", \"lat\": 6.577222, \"lng\": 3.321111, \"passengers\": 5000000, \"country\": \"Nigeria\", \"id\": \"LOS\"},\n",
- " {\"name\": \"Cape Town\", \"iata\": \"CPT\", \"lat\": -33.969444, \"lng\": 18.597222, \"passengers\": 5000000, \"country\": \"South Africa\", \"id\": \"CPT\"}, {\"name\": \"Chengdu\", \"iata\": \"CTU\", \"lat\": 30.578333, \"lng\": 103.946944, \"passengers\": 40000000, \"country\": \"China\", \"id\": \"CTU\"}, {\"name\": \"Jakarta\", \"iata\": \"CGK\", \"lat\": -6.125567, \"lng\": 106.655897, \"passengers\": 54000000, \"country\": \"Indonesia\", \"id\": \"CGK\"}, {\"name\": \"Teheran\", \"iata\": \"IKA\", \"lat\": 35.416111, \"lng\": 51.152222, \"passengers\": 8000000, \"country\": \"Iran\", \"id\": \"IKA\"},\n",
- " {\"name\": \"Tel Aviv\", \"iata\": \"TLV\", \"lat\": 32.011389, \"lng\": 34.886667, \"passengers\": 20000000, \"country\": \"Israel\", \"id\": \"TLV\"}, {\"name\": \"Kuala Lumpur\", \"iata\": \"KUL\", \"lat\": 2.745578, \"lng\": 101.709917, \"passengers\": 25000000, \"country\": \"Malaysia\", \"id\": \"KUL\"}, {\"name\": \"Manila\", \"iata\": \"MNL\", \"lat\": 14.508647, \"lng\": 121.019581, \"passengers\": 8000000, \"country\": \"Philippines\", \"id\": \"MNL\"}, {\"name\": \"Singapur\", \"iata\": \"SIN\", \"lat\": 1.350189, \"lng\": 103.994433, \"passengers\": 32000000, \"country\": \"Singapore\", \"id\": \"SIN\"},\n",
- " {\"name\": \"Taipeh\", \"iata\": \"TPE\", \"lat\": 25.077732, \"lng\": 121.232822, \"passengers\": 800000, \"country\": \"Taiwan\", \"id\": \"TPE\"}, {\"name\": \"Bangkok\", \"iata\": \"BKK\", \"lat\": 13.681108, \"lng\": 100.747283, \"passengers\": 65000000, \"country\": \"Thailand\", \"id\": \"BKK\"}, {\"name\": \"Istanbul\", \"iata\": \"IST\", \"lat\": 40.976922, \"lng\": 28.814606, \"passengers\": 64000000, \"country\": \"Turkey\", \"id\": \"IST\"}, {\"name\": \"Ulaanbaatar\", \"iata\": \"ULN\", \"lat\": 47.843056, \"lng\": 106.766639, \"passengers\": 1000000, \"country\": \"Mongolia\", \"id\": \"ULN\"},\n",
- " {\"name\": \"Melbourne\", \"iata\": \"MEL\", \"lat\": -37.673333, \"lng\": 144.843333, \"passengers\": 12000000, \"country\": \"Australia\", \"id\": \"MEL\"}, {\"name\": \"Brisbane\", \"iata\": \"BNE\", \"lat\": -27.383333, \"lng\": 153.118056, \"passengers\": 23000000, \"country\": \"Australia\", \"id\": \"BNE\"}, {\"name\": \"Nadi\", \"iata\": \"NAN\", \"lat\": -17.755392, \"lng\": 177.443378, \"passengers\": 2000000, \"country\": \"Fiji\", \"id\": \"NAN\"}, {\"name\": \"Auckland\", \"iata\": \"AKL\", \"lat\": -37.008056, \"lng\": 174.791667, \"passengers\": 21000000, \"country\": \"New Zealand\", \"id\": \"AKL\"},\n",
- " {\"name\": \"Paris\", \"iata\": \"CDG\", \"lat\": 49.009722, \"lng\": 2.547778, \"passengers\": 57000000, \"country\": \"France\", \"id\": \"CDG\"}, {\"name\": \"Madrid\", \"iata\": \"MAD\", \"lat\": 40.4675, \"lng\": -3.551944, \"passengers\": 50000000, \"country\": \"Spain\", \"id\": \"MAD\"}, {\"name\": \"Barcelona\", \"iata\": \"BCN\", \"lat\": 41.297078, \"lng\": 2.078464, \"passengers\": 41000000, \"country\": \"Spain\", \"id\": \"BCN\"}, {\"name\": \"Rome\", \"iata\": \"FCO\", \"lat\": 41.804444, \"lng\": 12.250833, \"passengers\": 29000000, \"country\": \"Italy\", \"id\": \"FCO\"},\n",
- " {\"name\": \"Copenhagen\", \"iata\": \"CPH\", \"lat\": 55.617917, \"lng\": 12.655972, \"passengers\": 30000000, \"country\": \"Denmark\", \"id\": \"CPH\"}, {\"name\": \"Helsinki\", \"iata\": \"HEL\", \"lat\": 60.317222, \"lng\": 24.963333, \"passengers\": 5000000, \"country\": \"Finland\", \"id\": \"HEL\"}, {\"name\": \"Athens\", \"iata\": \"ATH\", \"lat\": 37.936358, \"lng\": 23.944467, \"passengers\": 22000000, \"country\": \"Greece\", \"id\": \"ATH\"}, {\"name\": \"Dublin\", \"iata\": \"DUB\", \"lat\": 53.421333, \"lng\": -6.270075, \"passengers\": 32000000, \"country\": \"Ireland\", \"id\": \"DUB\"},\n",
- " {\"name\": \"Reykjavik\", \"iata\": \"RKV\", \"lat\": 64.13, \"lng\": -21.940556, \"passengers\": 400000, \"country\": \"Iceland\", \"id\": \"RKV\"}, {\"name\": \"Oslo\", \"iata\": \"OSL\", \"lat\": 60.193917, \"lng\": 11.100361, \"passengers\": 9000000, \"country\": \"Norway\", \"id\": \"OSL\"}, {\"name\": \"Vienna\", \"iata\": \"VIE\", \"lat\": 48.110833, \"lng\": 16.570833, \"passengers\": 10000000, \"country\": \"Austria\", \"id\": \"VIE\"}, {\"name\": \"Lisbon\", \"iata\": \"LIS\", \"lat\": 38.774167, \"lng\": -9.134167, \"passengers\": 28000000, \"country\": \"Portugal\", \"id\": \"LIS\"},\n",
- " {\"name\": \"Stockholm\", \"iata\": \"ARN\", \"lat\": 59.651944, \"lng\": 17.918611, \"passengers\": 7000000, \"country\": \"Sweden\", \"id\": \"ARN\"}, {\"name\": \"Edinburgh\", \"iata\": \"EDI\", \"lat\": 55.95, \"lng\": -3.3725, \"passengers\": 14000000, \"country\": \"UK\", \"id\": \"EDI\"}, {\"name\": \"Chicago\", \"iata\": \"ORD\", \"lat\": 41.978603, \"lng\": -87.904842, \"passengers\": 54000000, \"country\": \"USA\", \"id\": \"ORD\"}, {\"name\": \"Dallas\", \"iata\": \"DFW\", \"lat\": 32.896828, \"lng\": -97.037997, \"passengers\": 73000000, \"country\": \"USA\", \"id\": \"DFW\"},\n",
- " {\"name\": \"San Francisco\", \"iata\": \"SFO\", \"lat\": 37.618972, \"lng\": -122.374889, \"passengers\": 42000000, \"country\": \"USA\", \"id\": \"SFO\"}, {\"name\": \"Las Vegas\", \"iata\": \"LAS\", \"lat\": 36.080056, \"lng\": -115.15225, \"passengers\": 52000000, \"country\": \"USA\", \"id\": \"LAS\"}, {\"name\": \"Miami\", \"iata\": \"MIA\", \"lat\": 25.79325, \"lng\": -80.290556, \"passengers\": 50000000, \"country\": \"USA\", \"id\": \"MIA\"}, {\"name\": \"Toronto\", \"iata\": \"YYZ\", \"lat\": 43.677222, \"lng\": -79.630556, \"passengers\": 12000000, \"country\": \"Canada\", \"id\": \"YYZ\"},\n",
- " {\"name\": \"Vancouver\", \"iata\": \"YVR\", \"lat\": 49.193889, \"lng\": -123.184444, \"passengers\": 19000000, \"country\": \"Canada\", \"id\": \"YVR\"}, {\"name\": \"Montreal\", \"iata\": \"YUL\", \"lat\": 45.47175, \"lng\": -73.736569, \"passengers\": 15000000, \"country\": \"Canada\", \"id\": \"YUL\"}, {\"name\": \"Mexico-City\", \"iata\": \"MEX\", \"lat\": 19.436303, \"lng\": -99.072097, \"passengers\": 46000000, \"country\": \"Mexico\", \"id\": \"MEX\"}, {\"name\": \"Guatemala-City\", \"iata\": \"GUA\", \"lat\": 14.583272, \"lng\": -90.527475, \"passengers\": 2000000, \"country\": \"Guatemala\", \"id\": \"GUA\"},\n",
- " {\"name\": \"Buenos Aires\", \"iata\": \"EZE\", \"lat\": -34.822222, \"lng\": -58.535833, \"passengers\": 5000000, \"country\": \"Argentina\", \"id\": \"EZE\"}, {\"name\": \"Sao Paulo\", \"iata\": \"GRU\", \"lat\": -23.432075, \"lng\": -46.469511, \"passengers\": 34000000, \"country\": \"Brazil\", \"id\": \"GRU\"}, {\"name\": \"Santiago de Chile\", \"iata\": \"SCL\", \"lat\": -33.392975, \"lng\": -70.785803, \"passengers\": 20000000, \"country\": \"Chile\", \"id\": \"SCL\"}, {\"name\": \"Brasilia\", \"iata\": \"BSB\", \"lat\": -15.871111, \"lng\": -47.918611, \"passengers\": 13000000, \"country\": \"Brazil\", \"id\": \"BSB\"},\n",
- " {\"name\": \"Bogota\", \"iata\": \"BOG\", \"lat\": 4.701594, \"lng\": -74.146947, \"passengers\": 36000000, \"country\": \"Colombia\", \"id\": \"BOG\"}, {\"name\": \"Caracas\", \"iata\": \"CCS\", \"lat\": 10.601194, \"lng\": -66.991222, \"passengers\": 8000000, \"country\": \"Venezuela\", \"id\": \"CCS\"}\n",
- " ]\n",
- "connections = [\n",
- " {'from': 'LAX','to': 'JFK' }, {'from': 'JFK','to': 'GIG' }, {'from': 'JFK','to': 'LIM' }, {'from': 'JFK','to': 'LHR' }, {'from': 'GIG','to': 'FRA' }, {'from': 'LIM','to': 'GIG' }, {'from': 'FRA','to': 'JFK' }, {'from': 'LHR','to': 'FRA' }, {'from': 'FRA','to': 'SVO' }, {'from': 'FRA','to': 'DXB' }, {'from': 'SVO','to': 'DEL' },\n",
- " {'from': 'SVO','to': 'PVG' }, {'from': 'DEL','to': 'HKG' }, {'from': 'PVG','to': 'HKG' }, {'from': 'PVG','to': 'NRT' }, {'from': 'HKG','to': 'SYD' }, {'from': 'NRT','to': 'SYD' }, {'from': 'DXB','to': 'SVO' }, {'from': 'DXB','to': 'DEL' }, {'from': 'DXB','to': 'DKR' }, {'from': 'DXB','to': 'JNB' }, {'from': 'JNB','to': 'LHR' },\n",
- " {'from': 'JNB','to': 'DKR' }, {'from': 'SYD','to': 'DXB' }, {'from': 'NBO','to': 'JNB' }, {'from': 'NBO','to': 'DXB' }, {'from': 'ATL','to': 'JFK' }, {'from': 'LAX','to': 'ATL' }, {'from': 'ATL','to': 'LHR' }, {'from': 'ATL','to': 'LIM' }, {'from': 'SCL','to': 'LIM' }, {'from': 'EZE','to': 'SCL' }, {'from': 'SCL','to': 'GRU' },\n",
- " {'from': 'GIG','to': 'EZE' }, {'from': 'GIG','to': 'GRU' }, {'from': 'BSB','to': 'GIG' }, {'from': 'SCL','to': 'BSB' }, {'from': 'LIM','to': 'BSB' }, {'from': 'BOG','to': 'BSB' }, {'from': 'CCS','to': 'BSB' }, {'from': 'BOG','to': 'GUA' }, {'from': 'CCS','to': 'MIA' }, {'from': 'GUA','to': 'MIA' }, {'from': 'GUA','to': 'MEX' },\n",
- " {'from': 'MEX','to': 'LAX' }, {'from': 'MEX','to': 'LAX' }, {'from': 'LAX','to': 'SFO' }, {'from': 'SFO','to': 'YVR' }, {'from': 'LAX','to': 'LAS' }, {'from': 'LAX','to': 'DFW' }, {'from': 'LAX','to': 'ORD' }, {'from': 'SFO','to': 'LAS' }, {'from': 'DFW','to': 'ATL' }, {'from': 'ATL','to': 'YYZ' }, {'from': 'ORD','to': 'YYZ' }, \n",
- " {'from': 'YYZ','to': 'YUL' }, {'from': 'YYZ','to': 'JFK' }, {'from': 'YUL','to': 'JFK' }, {'from': 'JNB','to': 'CPT' }, {'from': 'LOS','to': 'DKR' }, {'from': 'NBO','to': 'LOS' }, {'from': 'DKR','to': 'CMN' }, {'from': 'DKR','to': 'CAI' }, {'from': 'NBO','to': 'CAI' }, {'from': 'DXB','to': 'CAI' }, {'from': 'IKA','to': 'DXB' }, \n",
- " {'from': 'IST','to': 'IKA' }, {'from': 'TLV','to': 'ATH' }, {'from': 'CAI','to': 'TLV' }, {'from': 'ATH','to': 'IST' }, {'from': 'FCO','to': 'ATH' }, {'from': 'LIS','to': 'LHR' }, {'from': 'LIS','to': 'MAD' }, {'from': 'MAD','to': 'BCN' }, {'from': 'CDG','to': 'LHR' }, {'from': 'DUB','to': 'LHR' }, {'from': 'EDI','to': 'LHR' }, \n",
- " {'from': 'CDG','to': 'BCN' }, {'from': 'BCN','to': 'FCO' }, {'from': 'VIE','to': 'IST' }, {'from': 'VIE','to': 'SVO' }, {'from': 'CMN','to': 'LIS' }, {'from': 'MAD','to': 'CMN' }, {'from': 'FRA','to': 'CPH' }, {'from': 'LHR','to': 'CPH' }, {'from': 'CPH','to': 'OSL' }, {'from': 'CPH','to': 'ARN' }, {'from': 'ARN','to': 'HEL' },\n",
- " {'from': 'HEL','to': 'SVO' }, {'from': 'ULN','to': 'PVG' }, {'from': 'CTU','to': 'PVG' }, {'from': 'PVG','to': 'TPE' }, {'from': 'CTU','to': 'HKG' }, {'from': 'TPE','to': 'HKG' }, {'from': 'HKG','to': 'MNL' }, {'from': 'BKK','to': 'HKG' }, {'from': 'SIN','to': 'KUL' }, {'from': 'SIN','to': 'BKK' }, {'from': 'CGK','to': 'SIN' },\n",
- " {'from': 'MNL','to': 'SIN' }, {'from': 'SIN','to': 'SYD' }, {'from': 'BNE','to': 'SYD' }, {'from': 'SYD','to': 'MEL' }, {'from': 'NAN','to': 'SYD' }, {'from': 'AKL','to': 'SYD' }, {'from': 'NAN','to': 'AKL' }, {'from': 'RKV','to': 'LHR' }, {'from': 'BCN','to': 'CDG' }, {'from': 'BCN','to': 'FRA' }, {'from': 'FCO','to': 'FRA' }, \n",
- " {'from': 'BOG','to': 'MEX' }, {'from': 'BOG','to': 'GRU' }, {'from': 'ATL','to': 'MIA' }, {'from': 'FRA','to': 'IST' }, {'from': 'IST','to': 'DEL' }, {'from': 'PVG','to': 'BKK' }, {'from': 'DEL','to': 'BKK' },\n",
- " ]"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1970927b-c195-4135-8b10-c0ad7902abab",
- "metadata": {},
- "source": [
- "Provided as data to yFiles Jupyter Graphs results in this graph:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "cd760fb5-88da-4343-9fc0-4dbaccae7202",
- "metadata": {},
- "outputs": [],
- "source": [
- "w = GraphWidget()\n",
- "w.edges = edges = [\n",
- " {\"start\": connection['from'], \"end\": connection['to'], \"label\": \"\", \"properties\": {}, \"directed\": False}\n",
- " for connection in connections\n",
- "]\n",
- "\n",
- "w.nodes = nodes = [\n",
- " {\"id\": airport['iata'], \"properties\": {\"label\": airport['name'], 'passengers': airport['passengers'], 'country': airport['country']}, \"coordinates\": [airport['lat'], airport['lng']]}\n",
- " for airport in airports\n",
- "]\n",
- "w.node_coordinate_mapping = 'coordinates' # also map the geo-coordinate data because they are available in the dataset\n",
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "66223c0a-fcfa-468e-a04b-6f29ff15055a",
- "metadata": {},
- "source": [
- "Among other properties, each airport entry contains a `country` property which we can use as grouping key, such that the airports are grouped by country."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c0b30cf2-74c3-462f-9e62-37c6fd209887",
- "metadata": {},
- "source": [
- "## Different ways to specify node grouping\n",
- "\n",
- "A different parent mapping must be used, depending on whether the group nodes are already part of the data set or not.\n",
- "\n",
- "node_parent_mappingnode_parent_group_mapping\n",
+ "\n",
+ "Before using the graph widget, install all necessary packages and initialize your widget."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "86ccab84-0881-4e14-b33d-51313ec2b85e",
+ "metadata": {
+ "id": "86ccab84-0881-4e14-b33d-51313ec2b85e",
+ "outputId": "dcd553d5-178a-42c1-e094-4d191b3d537a",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m27.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m24.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "from yfiles_jupyter_graphs import GraphWidget"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "99c91625-6682-4dcc-86ed-ff80a85d435d",
+ "metadata": {
+ "id": "99c91625-6682-4dcc-86ed-ff80a85d435d"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "837b3dc2-a656-4bd1-856d-b2e4ed12b7a0",
+ "metadata": {
+ "id": "837b3dc2-a656-4bd1-856d-b2e4ed12b7a0"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e8af92d1-a099-4f27-b42b-7ffebff1954d",
+ "metadata": {
+ "id": "e8af92d1-a099-4f27-b42b-7ffebff1954d"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8cea83a9-2373-4dce-a9a2-353022fb1676",
+ "metadata": {
+ "id": "8cea83a9-2373-4dce-a9a2-353022fb1676"
+ },
+ "source": [
+ "## Sample Data\n",
+ "\n",
+ "The dataset for this sample is the following airport and flight routes data:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "0eb47bdd-d943-4820-bbc3-29d8396204ac",
+ "metadata": {
+ "id": "0eb47bdd-d943-4820-bbc3-29d8396204ac"
+ },
+ "outputs": [],
+ "source": [
+ "airports = [\n",
+ " {\"name\": \"Los Angeles\", \"iata\": \"LAX\", \"lat\": 33.942536, \"lng\": -118.408075, \"passengers\": 65000000, \"country\": \"USA\", \"id\": \"LAX\"}, {\"name\": \"Rio de Janeiro\", \"iata\": \"GIG\", \"lat\": -22.808903, \"lng\": -43.243647, \"passengers\": 5000000, \"country\": \"Brazil\", \"id\": \"GIG\"}, {\"name\": \"Lima\", \"iata\": \"LIM\", \"lat\": -12.021889, \"lng\": -77.114319, \"passengers\": 18000000, \"country\": \"Peru\", \"id\": \"LIM\"}, {\"name\": \"London\", \"iata\": \"LHR\", \"lat\": 51.4775, \"lng\": -0.461389, \"passengers\": 61000000, \"country\": \"UK\", \"id\": \"LHR\"},\n",
+ " {\"name\": \"Frankfurt\", \"iata\": \"FRA\", \"lat\": 50.033333, \"lng\": 8.570556, \"passengers\": 48000000, \"country\": \"Germany\", \"id\": \"FRA\"}, {\"name\": \"Moscow\", \"iata\": \"SVO\", \"lat\": 55.972642, \"lng\": 37.414589, \"passengers\": 49000000, \"country\": \"Russia\", \"id\": \"SVO\"}, {\"name\": \"New Delhi\", \"iata\": \"DEL\", \"lat\": 28.5665, \"lng\": 77.103089, \"passengers\": 39000000, \"country\": \"India\", \"id\": \"DEL\"}, {\"name\": \"Shanghai\", \"iata\": \"PVG\", \"lat\": 31.143378, \"lng\": 121.805214, \"passengers\": 32000000, \"country\": \"China\", \"id\": \"PVG\"},\n",
+ " {\"name\": \"Hongkong\", \"iata\": \"HKG\", \"lat\": 22.308919, \"lng\": 113.914603, \"passengers\": 1000000, \"country\": \"China\", \"id\": \"HKG\"}, {\"name\": \"Tokio\", \"iata\": \"NRT\", \"lat\": 35.764722, \"lng\": 140.386389, \"passengers\": 15000000, \"country\": \"Japan\", \"id\": \"NRT\"}, {\"name\": \"Dubai\", \"iata\": \"DXB\", \"lat\": 25.252778, \"lng\": 55.364444, \"passengers\": 29000000, \"country\": \"UAE\", \"id\": \"DXB\"}, {\"name\": \"Dakar\", \"iata\": \"DKR\", \"lat\": 14.670833, \"lng\": -17.072778, \"passengers\": 2000000, \"country\": \"Senegal\", \"id\": \"DKR\"},\n",
+ " {\"name\": \"Johannesburg\", \"iata\": \"JNB\", \"lat\": -26.133694, \"lng\": 28.242317, \"passengers\": 9000000, \"country\": \"South Africa\", \"id\": \"JNB\"}, {\"name\": \"Sydney\", \"iata\": \"SYD\", \"lat\": -33.946111, \"lng\": 151.177222, \"passengers\": 44000000, \"country\": \"Australia\", \"id\": \"SYD\"}, {\"name\": \"Nairobi\", \"iata\": \"NBO\", \"lat\": -1.319167, \"lng\": 36.927778, \"passengers\": 900000, \"country\": \"Kenya\", \"id\": \"NBO\"}, {\"name\": \"Atlanta\", \"iata\": \"ATL\", \"lat\": 33.639167, \"lng\": -84.427778, \"passengers\": 93000000, \"country\": \"USA\", \"id\": \"ATL\"},\n",
+ " {\"name\": \"New York City\", \"iata\": \"JFK\", \"lat\": 40.63975, \"lng\": -73.778925, \"passengers\": 55000000, \"country\": \"USA\", \"id\": \"JFK\"}, {\"name\": \"Cairo\", \"iata\": \"CAI\", \"lat\": 30.121944, \"lng\": 31.405556, \"passengers\": 14000000, \"country\": \"Egypt\", \"id\": \"CAI\"}, {\"name\": \"Casablanca\", \"iata\": \"CMN\", \"lat\": 33.367467, \"lng\": -7.589967, \"passengers\": 7000000, \"country\": \"Morocco\", \"id\": \"CMN\"}, {\"name\": \"Lagos\", \"iata\": \"LOS\", \"lat\": 6.577222, \"lng\": 3.321111, \"passengers\": 5000000, \"country\": \"Nigeria\", \"id\": \"LOS\"},\n",
+ " {\"name\": \"Cape Town\", \"iata\": \"CPT\", \"lat\": -33.969444, \"lng\": 18.597222, \"passengers\": 5000000, \"country\": \"South Africa\", \"id\": \"CPT\"}, {\"name\": \"Chengdu\", \"iata\": \"CTU\", \"lat\": 30.578333, \"lng\": 103.946944, \"passengers\": 40000000, \"country\": \"China\", \"id\": \"CTU\"}, {\"name\": \"Jakarta\", \"iata\": \"CGK\", \"lat\": -6.125567, \"lng\": 106.655897, \"passengers\": 54000000, \"country\": \"Indonesia\", \"id\": \"CGK\"}, {\"name\": \"Teheran\", \"iata\": \"IKA\", \"lat\": 35.416111, \"lng\": 51.152222, \"passengers\": 8000000, \"country\": \"Iran\", \"id\": \"IKA\"},\n",
+ " {\"name\": \"Tel Aviv\", \"iata\": \"TLV\", \"lat\": 32.011389, \"lng\": 34.886667, \"passengers\": 20000000, \"country\": \"Israel\", \"id\": \"TLV\"}, {\"name\": \"Kuala Lumpur\", \"iata\": \"KUL\", \"lat\": 2.745578, \"lng\": 101.709917, \"passengers\": 25000000, \"country\": \"Malaysia\", \"id\": \"KUL\"}, {\"name\": \"Manila\", \"iata\": \"MNL\", \"lat\": 14.508647, \"lng\": 121.019581, \"passengers\": 8000000, \"country\": \"Philippines\", \"id\": \"MNL\"}, {\"name\": \"Singapur\", \"iata\": \"SIN\", \"lat\": 1.350189, \"lng\": 103.994433, \"passengers\": 32000000, \"country\": \"Singapore\", \"id\": \"SIN\"},\n",
+ " {\"name\": \"Taipeh\", \"iata\": \"TPE\", \"lat\": 25.077732, \"lng\": 121.232822, \"passengers\": 800000, \"country\": \"Taiwan\", \"id\": \"TPE\"}, {\"name\": \"Bangkok\", \"iata\": \"BKK\", \"lat\": 13.681108, \"lng\": 100.747283, \"passengers\": 65000000, \"country\": \"Thailand\", \"id\": \"BKK\"}, {\"name\": \"Istanbul\", \"iata\": \"IST\", \"lat\": 40.976922, \"lng\": 28.814606, \"passengers\": 64000000, \"country\": \"Turkey\", \"id\": \"IST\"}, {\"name\": \"Ulaanbaatar\", \"iata\": \"ULN\", \"lat\": 47.843056, \"lng\": 106.766639, \"passengers\": 1000000, \"country\": \"Mongolia\", \"id\": \"ULN\"},\n",
+ " {\"name\": \"Melbourne\", \"iata\": \"MEL\", \"lat\": -37.673333, \"lng\": 144.843333, \"passengers\": 12000000, \"country\": \"Australia\", \"id\": \"MEL\"}, {\"name\": \"Brisbane\", \"iata\": \"BNE\", \"lat\": -27.383333, \"lng\": 153.118056, \"passengers\": 23000000, \"country\": \"Australia\", \"id\": \"BNE\"}, {\"name\": \"Nadi\", \"iata\": \"NAN\", \"lat\": -17.755392, \"lng\": 177.443378, \"passengers\": 2000000, \"country\": \"Fiji\", \"id\": \"NAN\"}, {\"name\": \"Auckland\", \"iata\": \"AKL\", \"lat\": -37.008056, \"lng\": 174.791667, \"passengers\": 21000000, \"country\": \"New Zealand\", \"id\": \"AKL\"},\n",
+ " {\"name\": \"Paris\", \"iata\": \"CDG\", \"lat\": 49.009722, \"lng\": 2.547778, \"passengers\": 57000000, \"country\": \"France\", \"id\": \"CDG\"}, {\"name\": \"Madrid\", \"iata\": \"MAD\", \"lat\": 40.4675, \"lng\": -3.551944, \"passengers\": 50000000, \"country\": \"Spain\", \"id\": \"MAD\"}, {\"name\": \"Barcelona\", \"iata\": \"BCN\", \"lat\": 41.297078, \"lng\": 2.078464, \"passengers\": 41000000, \"country\": \"Spain\", \"id\": \"BCN\"}, {\"name\": \"Rome\", \"iata\": \"FCO\", \"lat\": 41.804444, \"lng\": 12.250833, \"passengers\": 29000000, \"country\": \"Italy\", \"id\": \"FCO\"},\n",
+ " {\"name\": \"Copenhagen\", \"iata\": \"CPH\", \"lat\": 55.617917, \"lng\": 12.655972, \"passengers\": 30000000, \"country\": \"Denmark\", \"id\": \"CPH\"}, {\"name\": \"Helsinki\", \"iata\": \"HEL\", \"lat\": 60.317222, \"lng\": 24.963333, \"passengers\": 5000000, \"country\": \"Finland\", \"id\": \"HEL\"}, {\"name\": \"Athens\", \"iata\": \"ATH\", \"lat\": 37.936358, \"lng\": 23.944467, \"passengers\": 22000000, \"country\": \"Greece\", \"id\": \"ATH\"}, {\"name\": \"Dublin\", \"iata\": \"DUB\", \"lat\": 53.421333, \"lng\": -6.270075, \"passengers\": 32000000, \"country\": \"Ireland\", \"id\": \"DUB\"},\n",
+ " {\"name\": \"Reykjavik\", \"iata\": \"RKV\", \"lat\": 64.13, \"lng\": -21.940556, \"passengers\": 400000, \"country\": \"Iceland\", \"id\": \"RKV\"}, {\"name\": \"Oslo\", \"iata\": \"OSL\", \"lat\": 60.193917, \"lng\": 11.100361, \"passengers\": 9000000, \"country\": \"Norway\", \"id\": \"OSL\"}, {\"name\": \"Vienna\", \"iata\": \"VIE\", \"lat\": 48.110833, \"lng\": 16.570833, \"passengers\": 10000000, \"country\": \"Austria\", \"id\": \"VIE\"}, {\"name\": \"Lisbon\", \"iata\": \"LIS\", \"lat\": 38.774167, \"lng\": -9.134167, \"passengers\": 28000000, \"country\": \"Portugal\", \"id\": \"LIS\"},\n",
+ " {\"name\": \"Stockholm\", \"iata\": \"ARN\", \"lat\": 59.651944, \"lng\": 17.918611, \"passengers\": 7000000, \"country\": \"Sweden\", \"id\": \"ARN\"}, {\"name\": \"Edinburgh\", \"iata\": \"EDI\", \"lat\": 55.95, \"lng\": -3.3725, \"passengers\": 14000000, \"country\": \"UK\", \"id\": \"EDI\"}, {\"name\": \"Chicago\", \"iata\": \"ORD\", \"lat\": 41.978603, \"lng\": -87.904842, \"passengers\": 54000000, \"country\": \"USA\", \"id\": \"ORD\"}, {\"name\": \"Dallas\", \"iata\": \"DFW\", \"lat\": 32.896828, \"lng\": -97.037997, \"passengers\": 73000000, \"country\": \"USA\", \"id\": \"DFW\"},\n",
+ " {\"name\": \"San Francisco\", \"iata\": \"SFO\", \"lat\": 37.618972, \"lng\": -122.374889, \"passengers\": 42000000, \"country\": \"USA\", \"id\": \"SFO\"}, {\"name\": \"Las Vegas\", \"iata\": \"LAS\", \"lat\": 36.080056, \"lng\": -115.15225, \"passengers\": 52000000, \"country\": \"USA\", \"id\": \"LAS\"}, {\"name\": \"Miami\", \"iata\": \"MIA\", \"lat\": 25.79325, \"lng\": -80.290556, \"passengers\": 50000000, \"country\": \"USA\", \"id\": \"MIA\"}, {\"name\": \"Toronto\", \"iata\": \"YYZ\", \"lat\": 43.677222, \"lng\": -79.630556, \"passengers\": 12000000, \"country\": \"Canada\", \"id\": \"YYZ\"},\n",
+ " {\"name\": \"Vancouver\", \"iata\": \"YVR\", \"lat\": 49.193889, \"lng\": -123.184444, \"passengers\": 19000000, \"country\": \"Canada\", \"id\": \"YVR\"}, {\"name\": \"Montreal\", \"iata\": \"YUL\", \"lat\": 45.47175, \"lng\": -73.736569, \"passengers\": 15000000, \"country\": \"Canada\", \"id\": \"YUL\"}, {\"name\": \"Mexico-City\", \"iata\": \"MEX\", \"lat\": 19.436303, \"lng\": -99.072097, \"passengers\": 46000000, \"country\": \"Mexico\", \"id\": \"MEX\"}, {\"name\": \"Guatemala-City\", \"iata\": \"GUA\", \"lat\": 14.583272, \"lng\": -90.527475, \"passengers\": 2000000, \"country\": \"Guatemala\", \"id\": \"GUA\"},\n",
+ " {\"name\": \"Buenos Aires\", \"iata\": \"EZE\", \"lat\": -34.822222, \"lng\": -58.535833, \"passengers\": 5000000, \"country\": \"Argentina\", \"id\": \"EZE\"}, {\"name\": \"Sao Paulo\", \"iata\": \"GRU\", \"lat\": -23.432075, \"lng\": -46.469511, \"passengers\": 34000000, \"country\": \"Brazil\", \"id\": \"GRU\"}, {\"name\": \"Santiago de Chile\", \"iata\": \"SCL\", \"lat\": -33.392975, \"lng\": -70.785803, \"passengers\": 20000000, \"country\": \"Chile\", \"id\": \"SCL\"}, {\"name\": \"Brasilia\", \"iata\": \"BSB\", \"lat\": -15.871111, \"lng\": -47.918611, \"passengers\": 13000000, \"country\": \"Brazil\", \"id\": \"BSB\"},\n",
+ " {\"name\": \"Bogota\", \"iata\": \"BOG\", \"lat\": 4.701594, \"lng\": -74.146947, \"passengers\": 36000000, \"country\": \"Colombia\", \"id\": \"BOG\"}, {\"name\": \"Caracas\", \"iata\": \"CCS\", \"lat\": 10.601194, \"lng\": -66.991222, \"passengers\": 8000000, \"country\": \"Venezuela\", \"id\": \"CCS\"}\n",
+ " ]\n",
+ "connections = [\n",
+ " {'from': 'LAX','to': 'JFK' }, {'from': 'JFK','to': 'GIG' }, {'from': 'JFK','to': 'LIM' }, {'from': 'JFK','to': 'LHR' }, {'from': 'GIG','to': 'FRA' }, {'from': 'LIM','to': 'GIG' }, {'from': 'FRA','to': 'JFK' }, {'from': 'LHR','to': 'FRA' }, {'from': 'FRA','to': 'SVO' }, {'from': 'FRA','to': 'DXB' }, {'from': 'SVO','to': 'DEL' },\n",
+ " {'from': 'SVO','to': 'PVG' }, {'from': 'DEL','to': 'HKG' }, {'from': 'PVG','to': 'HKG' }, {'from': 'PVG','to': 'NRT' }, {'from': 'HKG','to': 'SYD' }, {'from': 'NRT','to': 'SYD' }, {'from': 'DXB','to': 'SVO' }, {'from': 'DXB','to': 'DEL' }, {'from': 'DXB','to': 'DKR' }, {'from': 'DXB','to': 'JNB' }, {'from': 'JNB','to': 'LHR' },\n",
+ " {'from': 'JNB','to': 'DKR' }, {'from': 'SYD','to': 'DXB' }, {'from': 'NBO','to': 'JNB' }, {'from': 'NBO','to': 'DXB' }, {'from': 'ATL','to': 'JFK' }, {'from': 'LAX','to': 'ATL' }, {'from': 'ATL','to': 'LHR' }, {'from': 'ATL','to': 'LIM' }, {'from': 'SCL','to': 'LIM' }, {'from': 'EZE','to': 'SCL' }, {'from': 'SCL','to': 'GRU' },\n",
+ " {'from': 'GIG','to': 'EZE' }, {'from': 'GIG','to': 'GRU' }, {'from': 'BSB','to': 'GIG' }, {'from': 'SCL','to': 'BSB' }, {'from': 'LIM','to': 'BSB' }, {'from': 'BOG','to': 'BSB' }, {'from': 'CCS','to': 'BSB' }, {'from': 'BOG','to': 'GUA' }, {'from': 'CCS','to': 'MIA' }, {'from': 'GUA','to': 'MIA' }, {'from': 'GUA','to': 'MEX' },\n",
+ " {'from': 'MEX','to': 'LAX' }, {'from': 'MEX','to': 'LAX' }, {'from': 'LAX','to': 'SFO' }, {'from': 'SFO','to': 'YVR' }, {'from': 'LAX','to': 'LAS' }, {'from': 'LAX','to': 'DFW' }, {'from': 'LAX','to': 'ORD' }, {'from': 'SFO','to': 'LAS' }, {'from': 'DFW','to': 'ATL' }, {'from': 'ATL','to': 'YYZ' }, {'from': 'ORD','to': 'YYZ' },\n",
+ " {'from': 'YYZ','to': 'YUL' }, {'from': 'YYZ','to': 'JFK' }, {'from': 'YUL','to': 'JFK' }, {'from': 'JNB','to': 'CPT' }, {'from': 'LOS','to': 'DKR' }, {'from': 'NBO','to': 'LOS' }, {'from': 'DKR','to': 'CMN' }, {'from': 'DKR','to': 'CAI' }, {'from': 'NBO','to': 'CAI' }, {'from': 'DXB','to': 'CAI' }, {'from': 'IKA','to': 'DXB' },\n",
+ " {'from': 'IST','to': 'IKA' }, {'from': 'TLV','to': 'ATH' }, {'from': 'CAI','to': 'TLV' }, {'from': 'ATH','to': 'IST' }, {'from': 'FCO','to': 'ATH' }, {'from': 'LIS','to': 'LHR' }, {'from': 'LIS','to': 'MAD' }, {'from': 'MAD','to': 'BCN' }, {'from': 'CDG','to': 'LHR' }, {'from': 'DUB','to': 'LHR' }, {'from': 'EDI','to': 'LHR' },\n",
+ " {'from': 'CDG','to': 'BCN' }, {'from': 'BCN','to': 'FCO' }, {'from': 'VIE','to': 'IST' }, {'from': 'VIE','to': 'SVO' }, {'from': 'CMN','to': 'LIS' }, {'from': 'MAD','to': 'CMN' }, {'from': 'FRA','to': 'CPH' }, {'from': 'LHR','to': 'CPH' }, {'from': 'CPH','to': 'OSL' }, {'from': 'CPH','to': 'ARN' }, {'from': 'ARN','to': 'HEL' },\n",
+ " {'from': 'HEL','to': 'SVO' }, {'from': 'ULN','to': 'PVG' }, {'from': 'CTU','to': 'PVG' }, {'from': 'PVG','to': 'TPE' }, {'from': 'CTU','to': 'HKG' }, {'from': 'TPE','to': 'HKG' }, {'from': 'HKG','to': 'MNL' }, {'from': 'BKK','to': 'HKG' }, {'from': 'SIN','to': 'KUL' }, {'from': 'SIN','to': 'BKK' }, {'from': 'CGK','to': 'SIN' },\n",
+ " {'from': 'MNL','to': 'SIN' }, {'from': 'SIN','to': 'SYD' }, {'from': 'BNE','to': 'SYD' }, {'from': 'SYD','to': 'MEL' }, {'from': 'NAN','to': 'SYD' }, {'from': 'AKL','to': 'SYD' }, {'from': 'NAN','to': 'AKL' }, {'from': 'RKV','to': 'LHR' }, {'from': 'BCN','to': 'CDG' }, {'from': 'BCN','to': 'FRA' }, {'from': 'FCO','to': 'FRA' },\n",
+ " {'from': 'BOG','to': 'MEX' }, {'from': 'BOG','to': 'GRU' }, {'from': 'ATL','to': 'MIA' }, {'from': 'FRA','to': 'IST' }, {'from': 'IST','to': 'DEL' }, {'from': 'PVG','to': 'BKK' }, {'from': 'DEL','to': 'BKK' },\n",
+ " ]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1970927b-c195-4135-8b10-c0ad7902abab",
+ "metadata": {
+ "id": "1970927b-c195-4135-8b10-c0ad7902abab"
+ },
+ "source": [
+ "Provided as data to yFiles Jupyter Graphs results in this graph:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "cd760fb5-88da-4343-9fc0-4dbaccae7202",
+ "metadata": {
+ "id": "cd760fb5-88da-4343-9fc0-4dbaccae7202",
+ "outputId": "6c088327-c62a-4ddb-dd97-61d9b766c09a",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 817,
+ "referenced_widgets": [
+ "8e7a45940cb04c3f9dbd8d6026e6ad43",
+ "3d2f57cdcd26430c8233bd0f301510bb"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='800px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "8e7a45940cb04c3f9dbd8d6026e6ad43"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w = GraphWidget()\n",
+ "w.edges = edges = [\n",
+ " {\"start\": connection['from'], \"end\": connection['to'], \"label\": \"\", \"properties\": {}, \"directed\": False}\n",
+ " for connection in connections\n",
+ "]\n",
+ "\n",
+ "w.nodes = nodes = [\n",
+ " {\"id\": airport['iata'], \"properties\": {\"label\": airport['name'], 'passengers': airport['passengers'], 'country': airport['country']}, \"coordinates\": [airport['lat'], airport['lng']]}\n",
+ " for airport in airports\n",
+ "]\n",
+ "w.node_coordinate_mapping = 'coordinates' # also map the geo-coordinate data because they are available in the dataset\n",
+ "display(w)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "66223c0a-fcfa-468e-a04b-6f29ff15055a",
+ "metadata": {
+ "id": "66223c0a-fcfa-468e-a04b-6f29ff15055a"
+ },
+ "source": [
+ "Among other properties, each airport entry contains a `country` property which we can use as grouping key, such that the airports are grouped by country."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c0b30cf2-74c3-462f-9e62-37c6fd209887",
+ "metadata": {
+ "id": "c0b30cf2-74c3-462f-9e62-37c6fd209887"
+ },
+ "source": [
+ "## Different ways to specify node grouping\n",
+ "\n",
+ "A different parent mapping must be used, depending on whether the group nodes are already part of the data set or not.\n",
+ "\n",
+ "node_parent_mappingnode_parent_group_mapping\n",
- "\n",
- "Before using the graph widget, install all necessary packages and initialize your widget."
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "95c7a78b-917a-42b6-a1a9-b3255c0667ce",
+ "metadata": {
+ "id": "95c7a78b-917a-42b6-a1a9-b3255c0667ce"
+ },
+ "source": [
+ "# yFiles Graphs for Jupyter v1.7 \n",
+ "\n",
+ "Before using the graph widget, install all necessary packages and initialize your widget."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "86ccab84-0881-4e14-b33d-51313ec2b85e",
+ "metadata": {
+ "id": "86ccab84-0881-4e14-b33d-51313ec2b85e",
+ "outputId": "e68bc112-ca79-453a-8c01-b2d602322e40",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m20.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m24.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m24.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install yfiles_jupyter_graphs --quiet\n",
+ "from yfiles_jupyter_graphs import GraphWidget"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "99c91625-6682-4dcc-86ed-ff80a85d435d",
+ "metadata": {
+ "id": "99c91625-6682-4dcc-86ed-ff80a85d435d"
+ },
+ "source": [
+ "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "f0b1c0be-758f-47b3-be83-f4508d5bbf3b",
+ "metadata": {
+ "id": "f0b1c0be-758f-47b3-be83-f4508d5bbf3b"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import google.colab\n",
+ " from google.colab import output\n",
+ " output.enable_custom_widget_manager()\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "29c27896-cc64-4c03-846f-a69a8887526b",
+ "metadata": {
+ "id": "29c27896-cc64-4c03-846f-a69a8887526b"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8cea83a9-2373-4dce-a9a2-353022fb1676",
+ "metadata": {
+ "id": "8cea83a9-2373-4dce-a9a2-353022fb1676"
+ },
+ "source": [
+ "## Sample Data\n",
+ "\n",
+ "The dataset for this sample is the following airport and flight routes data:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "0eb47bdd-d943-4820-bbc3-29d8396204ac",
+ "metadata": {
+ "id": "0eb47bdd-d943-4820-bbc3-29d8396204ac"
+ },
+ "outputs": [],
+ "source": [
+ "airports_json = [\n",
+ " {\"name\": \"Los Angeles\", \"iata\": \"LAX\", \"lat\": 33.942536, \"lng\": -118.408075, \"passengers\": 65000000, \"country\": \"USA\", \"id\": \"LAX\"}, {\"name\": \"Rio de Janeiro\", \"iata\": \"GIG\", \"lat\": -22.808903, \"lng\": -43.243647, \"passengers\": 5000000, \"country\": \"Brazil\", \"id\": \"GIG\"}, {\"name\": \"Lima\", \"iata\": \"LIM\", \"lat\": -12.021889, \"lng\": -77.114319, \"passengers\": 18000000, \"country\": \"Peru\", \"id\": \"LIM\"}, {\"name\": \"London\", \"iata\": \"LHR\", \"lat\": 51.4775, \"lng\": -0.461389, \"passengers\": 61000000, \"country\": \"UK\", \"id\": \"LHR\"},\n",
+ " {\"name\": \"Frankfurt\", \"iata\": \"FRA\", \"lat\": 50.033333, \"lng\": 8.570556, \"passengers\": 48000000, \"country\": \"Germany\", \"id\": \"FRA\"}, {\"name\": \"Moscow\", \"iata\": \"SVO\", \"lat\": 55.972642, \"lng\": 37.414589, \"passengers\": 49000000, \"country\": \"Russia\", \"id\": \"SVO\"}, {\"name\": \"New Delhi\", \"iata\": \"DEL\", \"lat\": 28.5665, \"lng\": 77.103089, \"passengers\": 39000000, \"country\": \"India\", \"id\": \"DEL\"}, {\"name\": \"Shanghai\", \"iata\": \"PVG\", \"lat\": 31.143378, \"lng\": 121.805214, \"passengers\": 32000000, \"country\": \"China\", \"id\": \"PVG\"},\n",
+ " {\"name\": \"Hongkong\", \"iata\": \"HKG\", \"lat\": 22.308919, \"lng\": 113.914603, \"passengers\": 1000000, \"country\": \"China\", \"id\": \"HKG\"}, {\"name\": \"Tokio\", \"iata\": \"NRT\", \"lat\": 35.764722, \"lng\": 140.386389, \"passengers\": 15000000, \"country\": \"Japan\", \"id\": \"NRT\"}, {\"name\": \"Dubai\", \"iata\": \"DXB\", \"lat\": 25.252778, \"lng\": 55.364444, \"passengers\": 29000000, \"country\": \"UAE\", \"id\": \"DXB\"}, {\"name\": \"Dakar\", \"iata\": \"DKR\", \"lat\": 14.670833, \"lng\": -17.072778, \"passengers\": 2000000, \"country\": \"Senegal\", \"id\": \"DKR\"},\n",
+ " {\"name\": \"Johannesburg\", \"iata\": \"JNB\", \"lat\": -26.133694, \"lng\": 28.242317, \"passengers\": 9000000, \"country\": \"South Africa\", \"id\": \"JNB\"}, {\"name\": \"Sydney\", \"iata\": \"SYD\", \"lat\": -33.946111, \"lng\": 151.177222, \"passengers\": 44000000, \"country\": \"Australia\", \"id\": \"SYD\"}, {\"name\": \"Nairobi\", \"iata\": \"NBO\", \"lat\": -1.319167, \"lng\": 36.927778, \"passengers\": 900000, \"country\": \"Kenya\", \"id\": \"NBO\"}, {\"name\": \"Atlanta\", \"iata\": \"ATL\", \"lat\": 33.639167, \"lng\": -84.427778, \"passengers\": 93000000, \"country\": \"USA\", \"id\": \"ATL\"},\n",
+ " {\"name\": \"New York City\", \"iata\": \"JFK\", \"lat\": 40.63975, \"lng\": -73.778925, \"passengers\": 55000000, \"country\": \"USA\", \"id\": \"JFK\"}, {\"name\": \"Cairo\", \"iata\": \"CAI\", \"lat\": 30.121944, \"lng\": 31.405556, \"passengers\": 14000000, \"country\": \"Egypt\", \"id\": \"CAI\"}, {\"name\": \"Casablanca\", \"iata\": \"CMN\", \"lat\": 33.367467, \"lng\": -7.589967, \"passengers\": 7000000, \"country\": \"Morocco\", \"id\": \"CMN\"}, {\"name\": \"Lagos\", \"iata\": \"LOS\", \"lat\": 6.577222, \"lng\": 3.321111, \"passengers\": 5000000, \"country\": \"Nigeria\", \"id\": \"LOS\"},\n",
+ " {\"name\": \"Cape Town\", \"iata\": \"CPT\", \"lat\": -33.969444, \"lng\": 18.597222, \"passengers\": 5000000, \"country\": \"South Africa\", \"id\": \"CPT\"}, {\"name\": \"Chengdu\", \"iata\": \"CTU\", \"lat\": 30.578333, \"lng\": 103.946944, \"passengers\": 40000000, \"country\": \"China\", \"id\": \"CTU\"}, {\"name\": \"Jakarta\", \"iata\": \"CGK\", \"lat\": -6.125567, \"lng\": 106.655897, \"passengers\": 54000000, \"country\": \"Indonesia\", \"id\": \"CGK\"}, {\"name\": \"Teheran\", \"iata\": \"IKA\", \"lat\": 35.416111, \"lng\": 51.152222, \"passengers\": 8000000, \"country\": \"Iran\", \"id\": \"IKA\"},\n",
+ " {\"name\": \"Tel Aviv\", \"iata\": \"TLV\", \"lat\": 32.011389, \"lng\": 34.886667, \"passengers\": 20000000, \"country\": \"Israel\", \"id\": \"TLV\"}, {\"name\": \"Kuala Lumpur\", \"iata\": \"KUL\", \"lat\": 2.745578, \"lng\": 101.709917, \"passengers\": 25000000, \"country\": \"Malaysia\", \"id\": \"KUL\"}, {\"name\": \"Manila\", \"iata\": \"MNL\", \"lat\": 14.508647, \"lng\": 121.019581, \"passengers\": 8000000, \"country\": \"Philippines\", \"id\": \"MNL\"}, {\"name\": \"Singapur\", \"iata\": \"SIN\", \"lat\": 1.350189, \"lng\": 103.994433, \"passengers\": 32000000, \"country\": \"Singapore\", \"id\": \"SIN\"},\n",
+ " {\"name\": \"Taipeh\", \"iata\": \"TPE\", \"lat\": 25.077732, \"lng\": 121.232822, \"passengers\": 800000, \"country\": \"Taiwan\", \"id\": \"TPE\"}, {\"name\": \"Bangkok\", \"iata\": \"BKK\", \"lat\": 13.681108, \"lng\": 100.747283, \"passengers\": 65000000, \"country\": \"Thailand\", \"id\": \"BKK\"}, {\"name\": \"Istanbul\", \"iata\": \"IST\", \"lat\": 40.976922, \"lng\": 28.814606, \"passengers\": 64000000, \"country\": \"Turkey\", \"id\": \"IST\"}, {\"name\": \"Ulaanbaatar\", \"iata\": \"ULN\", \"lat\": 47.843056, \"lng\": 106.766639, \"passengers\": 1000000, \"country\": \"Mongolia\", \"id\": \"ULN\"},\n",
+ " {\"name\": \"Melbourne\", \"iata\": \"MEL\", \"lat\": -37.673333, \"lng\": 144.843333, \"passengers\": 12000000, \"country\": \"Australia\", \"id\": \"MEL\"}, {\"name\": \"Brisbane\", \"iata\": \"BNE\", \"lat\": -27.383333, \"lng\": 153.118056, \"passengers\": 23000000, \"country\": \"Australia\", \"id\": \"BNE\"}, {\"name\": \"Nadi\", \"iata\": \"NAN\", \"lat\": -17.755392, \"lng\": 177.443378, \"passengers\": 2000000, \"country\": \"Fiji\", \"id\": \"NAN\"}, {\"name\": \"Auckland\", \"iata\": \"AKL\", \"lat\": -37.008056, \"lng\": 174.791667, \"passengers\": 21000000, \"country\": \"New Zealand\", \"id\": \"AKL\"},\n",
+ " {\"name\": \"Paris\", \"iata\": \"CDG\", \"lat\": 49.009722, \"lng\": 2.547778, \"passengers\": 57000000, \"country\": \"France\", \"id\": \"CDG\"}, {\"name\": \"Madrid\", \"iata\": \"MAD\", \"lat\": 40.4675, \"lng\": -3.551944, \"passengers\": 50000000, \"country\": \"Spain\", \"id\": \"MAD\"}, {\"name\": \"Barcelona\", \"iata\": \"BCN\", \"lat\": 41.297078, \"lng\": 2.078464, \"passengers\": 41000000, \"country\": \"Spain\", \"id\": \"BCN\"}, {\"name\": \"Rome\", \"iata\": \"FCO\", \"lat\": 41.804444, \"lng\": 12.250833, \"passengers\": 29000000, \"country\": \"Italy\", \"id\": \"FCO\"},\n",
+ " {\"name\": \"Copenhagen\", \"iata\": \"CPH\", \"lat\": 55.617917, \"lng\": 12.655972, \"passengers\": 30000000, \"country\": \"Denmark\", \"id\": \"CPH\"}, {\"name\": \"Helsinki\", \"iata\": \"HEL\", \"lat\": 60.317222, \"lng\": 24.963333, \"passengers\": 5000000, \"country\": \"Finland\", \"id\": \"HEL\"}, {\"name\": \"Athens\", \"iata\": \"ATH\", \"lat\": 37.936358, \"lng\": 23.944467, \"passengers\": 22000000, \"country\": \"Greece\", \"id\": \"ATH\"}, {\"name\": \"Dublin\", \"iata\": \"DUB\", \"lat\": 53.421333, \"lng\": -6.270075, \"passengers\": 32000000, \"country\": \"Ireland\", \"id\": \"DUB\"},\n",
+ " {\"name\": \"Reykjavik\", \"iata\": \"RKV\", \"lat\": 64.13, \"lng\": -21.940556, \"passengers\": 400000, \"country\": \"Iceland\", \"id\": \"RKV\"}, {\"name\": \"Oslo\", \"iata\": \"OSL\", \"lat\": 60.193917, \"lng\": 11.100361, \"passengers\": 9000000, \"country\": \"Norway\", \"id\": \"OSL\"}, {\"name\": \"Vienna\", \"iata\": \"VIE\", \"lat\": 48.110833, \"lng\": 16.570833, \"passengers\": 10000000, \"country\": \"Austria\", \"id\": \"VIE\"}, {\"name\": \"Lisbon\", \"iata\": \"LIS\", \"lat\": 38.774167, \"lng\": -9.134167, \"passengers\": 28000000, \"country\": \"Portugal\", \"id\": \"LIS\"},\n",
+ " {\"name\": \"Stockholm\", \"iata\": \"ARN\", \"lat\": 59.651944, \"lng\": 17.918611, \"passengers\": 7000000, \"country\": \"Sweden\", \"id\": \"ARN\"}, {\"name\": \"Edinburgh\", \"iata\": \"EDI\", \"lat\": 55.95, \"lng\": -3.3725, \"passengers\": 14000000, \"country\": \"UK\", \"id\": \"EDI\"}, {\"name\": \"Chicago\", \"iata\": \"ORD\", \"lat\": 41.978603, \"lng\": -87.904842, \"passengers\": 54000000, \"country\": \"USA\", \"id\": \"ORD\"}, {\"name\": \"Dallas\", \"iata\": \"DFW\", \"lat\": 32.896828, \"lng\": -97.037997, \"passengers\": 73000000, \"country\": \"USA\", \"id\": \"DFW\"},\n",
+ " {\"name\": \"San Francisco\", \"iata\": \"SFO\", \"lat\": 37.618972, \"lng\": -122.374889, \"passengers\": 42000000, \"country\": \"USA\", \"id\": \"SFO\"}, {\"name\": \"Las Vegas\", \"iata\": \"LAS\", \"lat\": 36.080056, \"lng\": -115.15225, \"passengers\": 52000000, \"country\": \"USA\", \"id\": \"LAS\"}, {\"name\": \"Miami\", \"iata\": \"MIA\", \"lat\": 25.79325, \"lng\": -80.290556, \"passengers\": 50000000, \"country\": \"USA\", \"id\": \"MIA\"}, {\"name\": \"Toronto\", \"iata\": \"YYZ\", \"lat\": 43.677222, \"lng\": -79.630556, \"passengers\": 12000000, \"country\": \"Canada\", \"id\": \"YYZ\"},\n",
+ " {\"name\": \"Vancouver\", \"iata\": \"YVR\", \"lat\": 49.193889, \"lng\": -123.184444, \"passengers\": 19000000, \"country\": \"Canada\", \"id\": \"YVR\"}, {\"name\": \"Montreal\", \"iata\": \"YUL\", \"lat\": 45.47175, \"lng\": -73.736569, \"passengers\": 15000000, \"country\": \"Canada\", \"id\": \"YUL\"}, {\"name\": \"Mexico-City\", \"iata\": \"MEX\", \"lat\": 19.436303, \"lng\": -99.072097, \"passengers\": 46000000, \"country\": \"Mexico\", \"id\": \"MEX\"}, {\"name\": \"Guatemala-City\", \"iata\": \"GUA\", \"lat\": 14.583272, \"lng\": -90.527475, \"passengers\": 2000000, \"country\": \"Guatemala\", \"id\": \"GUA\"},\n",
+ " {\"name\": \"Buenos Aires\", \"iata\": \"EZE\", \"lat\": -34.822222, \"lng\": -58.535833, \"passengers\": 5000000, \"country\": \"Argentina\", \"id\": \"EZE\"}, {\"name\": \"Sao Paulo\", \"iata\": \"GRU\", \"lat\": -23.432075, \"lng\": -46.469511, \"passengers\": 34000000, \"country\": \"Brazil\", \"id\": \"GRU\"}, {\"name\": \"Santiago de Chile\", \"iata\": \"SCL\", \"lat\": -33.392975, \"lng\": -70.785803, \"passengers\": 20000000, \"country\": \"Chile\", \"id\": \"SCL\"}, {\"name\": \"Brasilia\", \"iata\": \"BSB\", \"lat\": -15.871111, \"lng\": -47.918611, \"passengers\": 13000000, \"country\": \"Brazil\", \"id\": \"BSB\"},\n",
+ " {\"name\": \"Bogota\", \"iata\": \"BOG\", \"lat\": 4.701594, \"lng\": -74.146947, \"passengers\": 36000000, \"country\": \"Colombia\", \"id\": \"BOG\"}, {\"name\": \"Caracas\", \"iata\": \"CCS\", \"lat\": 10.601194, \"lng\": -66.991222, \"passengers\": 8000000, \"country\": \"Venezuela\", \"id\": \"CCS\"}\n",
+ " ]\n",
+ "connections_json = [\n",
+ " {'from': 'LAX','to': 'JFK' }, {'from': 'JFK','to': 'GIG' }, {'from': 'JFK','to': 'LIM' }, {'from': 'JFK','to': 'LHR' }, {'from': 'GIG','to': 'FRA' }, {'from': 'LIM','to': 'GIG' }, {'from': 'FRA','to': 'JFK' }, {'from': 'LHR','to': 'FRA' }, {'from': 'FRA','to': 'SVO' }, {'from': 'FRA','to': 'DXB' }, {'from': 'SVO','to': 'DEL' },\n",
+ " {'from': 'SVO','to': 'PVG' }, {'from': 'DEL','to': 'HKG' }, {'from': 'PVG','to': 'HKG' }, {'from': 'PVG','to': 'NRT' }, {'from': 'HKG','to': 'SYD' }, {'from': 'NRT','to': 'SYD' }, {'from': 'DXB','to': 'SVO' }, {'from': 'DXB','to': 'DEL' }, {'from': 'DXB','to': 'DKR' }, {'from': 'DXB','to': 'JNB' }, {'from': 'JNB','to': 'LHR' },\n",
+ " {'from': 'JNB','to': 'DKR' }, {'from': 'SYD','to': 'DXB' }, {'from': 'NBO','to': 'JNB' }, {'from': 'NBO','to': 'DXB' }, {'from': 'ATL','to': 'JFK' }, {'from': 'LAX','to': 'ATL' }, {'from': 'ATL','to': 'LHR' }, {'from': 'ATL','to': 'LIM' }, {'from': 'SCL','to': 'LIM' }, {'from': 'EZE','to': 'SCL' }, {'from': 'SCL','to': 'GRU' },\n",
+ " {'from': 'GIG','to': 'EZE' }, {'from': 'GIG','to': 'GRU' }, {'from': 'BSB','to': 'GIG' }, {'from': 'SCL','to': 'BSB' }, {'from': 'LIM','to': 'BSB' }, {'from': 'BOG','to': 'BSB' }, {'from': 'CCS','to': 'BSB' }, {'from': 'BOG','to': 'GUA' }, {'from': 'CCS','to': 'MIA' }, {'from': 'GUA','to': 'MIA' }, {'from': 'GUA','to': 'MEX' },\n",
+ " {'from': 'MEX','to': 'LAX' }, {'from': 'MEX','to': 'LAX' }, {'from': 'LAX','to': 'SFO' }, {'from': 'SFO','to': 'YVR' }, {'from': 'LAX','to': 'LAS' }, {'from': 'LAX','to': 'DFW' }, {'from': 'LAX','to': 'ORD' }, {'from': 'SFO','to': 'LAS' }, {'from': 'DFW','to': 'ATL' }, {'from': 'ATL','to': 'YYZ' }, {'from': 'ORD','to': 'YYZ' },\n",
+ " {'from': 'YYZ','to': 'YUL' }, {'from': 'YYZ','to': 'JFK' }, {'from': 'YUL','to': 'JFK' }, {'from': 'JNB','to': 'CPT' }, {'from': 'LOS','to': 'DKR' }, {'from': 'NBO','to': 'LOS' }, {'from': 'DKR','to': 'CMN' }, {'from': 'DKR','to': 'CAI' }, {'from': 'NBO','to': 'CAI' }, {'from': 'DXB','to': 'CAI' }, {'from': 'IKA','to': 'DXB' },\n",
+ " {'from': 'IST','to': 'IKA' }, {'from': 'TLV','to': 'ATH' }, {'from': 'CAI','to': 'TLV' }, {'from': 'ATH','to': 'IST' }, {'from': 'FCO','to': 'ATH' }, {'from': 'LIS','to': 'LHR' }, {'from': 'LIS','to': 'MAD' }, {'from': 'MAD','to': 'BCN' }, {'from': 'CDG','to': 'LHR' }, {'from': 'DUB','to': 'LHR' }, {'from': 'EDI','to': 'LHR' },\n",
+ " {'from': 'CDG','to': 'BCN' }, {'from': 'BCN','to': 'FCO' }, {'from': 'VIE','to': 'IST' }, {'from': 'VIE','to': 'SVO' }, {'from': 'CMN','to': 'LIS' }, {'from': 'MAD','to': 'CMN' }, {'from': 'FRA','to': 'CPH' }, {'from': 'LHR','to': 'CPH' }, {'from': 'CPH','to': 'OSL' }, {'from': 'CPH','to': 'ARN' }, {'from': 'ARN','to': 'HEL' },\n",
+ " {'from': 'HEL','to': 'SVO' }, {'from': 'ULN','to': 'PVG' }, {'from': 'CTU','to': 'PVG' }, {'from': 'PVG','to': 'TPE' }, {'from': 'CTU','to': 'HKG' }, {'from': 'TPE','to': 'HKG' }, {'from': 'HKG','to': 'MNL' }, {'from': 'BKK','to': 'HKG' }, {'from': 'SIN','to': 'KUL' }, {'from': 'SIN','to': 'BKK' }, {'from': 'CGK','to': 'SIN' },\n",
+ " {'from': 'MNL','to': 'SIN' }, {'from': 'SIN','to': 'SYD' }, {'from': 'BNE','to': 'SYD' }, {'from': 'SYD','to': 'MEL' }, {'from': 'NAN','to': 'SYD' }, {'from': 'AKL','to': 'SYD' }, {'from': 'NAN','to': 'AKL' }, {'from': 'RKV','to': 'LHR' }, {'from': 'BCN','to': 'CDG' }, {'from': 'BCN','to': 'FRA' }, {'from': 'FCO','to': 'FRA' },\n",
+ " {'from': 'BOG','to': 'MEX' }, {'from': 'BOG','to': 'GRU' }, {'from': 'ATL','to': 'MIA' }, {'from': 'FRA','to': 'IST' }, {'from': 'IST','to': 'DEL' }, {'from': 'PVG','to': 'BKK' }, {'from': 'DEL','to': 'BKK' },\n",
+ " ]\n",
+ "\n",
+ "def initData():\n",
+ " return ([\n",
+ " {\"id\": airport['iata'], \"properties\": {\"label\": airport['name'], 'passengers': airport['passengers'], 'country': airport['country']}, \"coordinates\": [airport['lat'], airport['lng']]}\n",
+ " for airport in airports_json\n",
+ " ], [\n",
+ " {\"start\": connection['from'], \"end\": connection['to'], \"label\": \"\", \"properties\": {}, \"directed\": False}\n",
+ " for connection in connections_json\n",
+ " ])\n",
+ "\n",
+ "airports, connections = initData()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1970927b-c195-4135-8b10-c0ad7902abab",
+ "metadata": {
+ "id": "1970927b-c195-4135-8b10-c0ad7902abab"
+ },
+ "source": [
+ "Provided as data to yFiles Jupyter Graphs results in this graph:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "cd760fb5-88da-4343-9fc0-4dbaccae7202",
+ "metadata": {
+ "id": "cd760fb5-88da-4343-9fc0-4dbaccae7202",
+ "outputId": "4d11478a-2306-4072-e1f4-4500f283f4d7",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 817,
+ "referenced_widgets": [
+ "febfa41892574fbaa2135c052e0ef0de",
+ "2ce456beccf34503b071fea64d98f5c4"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='800px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "febfa41892574fbaa2135c052e0ef0de"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "w = GraphWidget()\n",
+ "w.set_overview(False)\n",
+ "w.edges = connections\n",
+ "w.nodes = airports\n",
+ "display(w)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4398de48-e075-4537-a591-ed46e5f07f94",
+ "metadata": {
+ "id": "4398de48-e075-4537-a591-ed46e5f07f94"
+ },
+ "source": [
+ "# Visualize data as heatmap"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "466813b3-fae0-4500-a208-0022bb08e777",
+ "metadata": {
+ "id": "466813b3-fae0-4500-a208-0022bb08e777",
+ "outputId": "4736346d-942f-4f1e-e205-aa4a7dfa232a",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 817,
+ "referenced_widgets": [
+ "9038e3e42e8245a5a7a969a870acd148",
+ "5c4472284ced449db80e1db8ed6ae20e"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='800px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "9038e3e42e8245a5a7a969a870acd148"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "# Map passengers at the airport to a heat value\n",
+ "w.set_heat_mapping(lambda item : (item['properties']['passengers'] - 800000)/92200000 if 'passengers' in item['properties'] else 0)\n",
+ "display(w)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c785842e-5d8e-43fd-926c-ebbc13ce5ca6",
+ "metadata": {
+ "id": "c785842e-5d8e-43fd-926c-ebbc13ce5ca6"
+ },
+ "source": [
+ "# Geospatial data mapping"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "ec4c84bf-fa9b-4e0f-bf97-ba9daaeeaaec",
+ "metadata": {
+ "id": "ec4c84bf-fa9b-4e0f-bf97-ba9daaeeaaec",
+ "outputId": "b09aa39e-f310-41d4-cae2-730e783c9547",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 817,
+ "referenced_widgets": [
+ "9038e3e42e8245a5a7a969a870acd148",
+ "5c4472284ced449db80e1db8ed6ae20e"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='800px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "9038e3e42e8245a5a7a969a870acd148"
+ }
+ },
+ "metadata": {
+ "application/vnd.jupyter.widget-view+json": {
+ "colab": {
+ "custom_widget_manager": {
+ "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
+ }
+ }
+ }
+ }
+ }
+ ],
+ "source": [
+ "# provide a geolocation mapping for the airports\n",
+ "w.node_coordinate_mapping = 'coordinates'\n",
+ "# start widget directly in geospatial layout mode\n",
+ "w.map_layout()\n",
+ "display(w)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "507a16ad-4712-4e46-8220-44220d434e5c",
+ "metadata": {
+ "id": "507a16ad-4712-4e46-8220-44220d434e5c"
+ },
+ "source": [
+ "# Group related data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "6c3615c0-6f70-4525-94e2-0d674f1e24d9",
+ "metadata": {
+ "id": "6c3615c0-6f70-4525-94e2-0d674f1e24d9",
+ "outputId": "c5cf7578-cd97-45fa-a077-acb19bd7fefa",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 817,
+ "referenced_widgets": [
+ "c967cd6898d94823aa59dfeb4122cadd",
+ "3d83a711027745d7b822ce9bb103397e"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "GraphWidget(layout=Layout(height='800px', width='100%'))"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "c967cd6898d94823aa59dfeb4122cadd"
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+ "source": [
+ "airports, connections = initData()\n",
+ "# add parent nodes to the dataset\n",
+ "countries = []\n",
+ "for airport_node in airports:\n",
+ " # collect unique countries among airports\n",
+ " airport_country = airport_node[\"properties\"][\"country\"]\n",
+ " if not any(country[\"id\"] == airport_country for country in countries):\n",
+ " countries.append({\"id\": airport_country, \"properties\": {\"label\": airport_country}})\n",
+ "\n",
+ "# create a nodes source with both, the unique countries (reprsenting our group nodes) and airports\n",
+ "w = GraphWidget()\n",
+ "w.set_overview(False)\n",
+ "w.nodes = airports + countries\n",
+ "w.edges = connections\n",
+ "# use the country property as parent-id for each node\n",
+ "w.node_parent_mapping = \"country\"\n",
+ "# assign a color mapping to style country nodes differently, otherwise use the widget's default color\n",
+ "w.node_color_mapping = lambda node : None if \"passengers\" in node[\"properties\"] else \"gray\"\n",
+ "display(w)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
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},
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "86ccab84-0881-4e14-b33d-51313ec2b85e",
- "metadata": {},
- "outputs": [],
- "source": [
- "%pip install yfiles_jupyter_graphs --quiet\n",
- "from yfiles_jupyter_graphs import GraphWidget"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "99c91625-6682-4dcc-86ed-ff80a85d435d",
- "metadata": {},
- "source": [
- "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f0b1c0be-758f-47b3-be83-f4508d5bbf3b",
- "metadata": {},
- "outputs": [],
- "source": [
- "try:\n",
- " import google.colab\n",
- " from google.colab import output\n",
- " output.enable_custom_widget_manager()\n",
- "except:\n",
- " pass"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "29c27896-cc64-4c03-846f-a69a8887526b",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8cea83a9-2373-4dce-a9a2-353022fb1676",
- "metadata": {},
- "source": [
- "## Sample Data\n",
- "\n",
- "The dataset for this sample is the following airport and flight routes data:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0eb47bdd-d943-4820-bbc3-29d8396204ac",
- "metadata": {},
- "outputs": [],
- "source": [
- "airports_json = [\n",
- " {\"name\": \"Los Angeles\", \"iata\": \"LAX\", \"lat\": 33.942536, \"lng\": -118.408075, \"passengers\": 65000000, \"country\": \"USA\", \"id\": \"LAX\"}, {\"name\": \"Rio de Janeiro\", \"iata\": \"GIG\", \"lat\": -22.808903, \"lng\": -43.243647, \"passengers\": 5000000, \"country\": \"Brazil\", \"id\": \"GIG\"}, {\"name\": \"Lima\", \"iata\": \"LIM\", \"lat\": -12.021889, \"lng\": -77.114319, \"passengers\": 18000000, \"country\": \"Peru\", \"id\": \"LIM\"}, {\"name\": \"London\", \"iata\": \"LHR\", \"lat\": 51.4775, \"lng\": -0.461389, \"passengers\": 61000000, \"country\": \"UK\", \"id\": \"LHR\"},\n",
- " {\"name\": \"Frankfurt\", \"iata\": \"FRA\", \"lat\": 50.033333, \"lng\": 8.570556, \"passengers\": 48000000, \"country\": \"Germany\", \"id\": \"FRA\"}, {\"name\": \"Moscow\", \"iata\": \"SVO\", \"lat\": 55.972642, \"lng\": 37.414589, \"passengers\": 49000000, \"country\": \"Russia\", \"id\": \"SVO\"}, {\"name\": \"New Delhi\", \"iata\": \"DEL\", \"lat\": 28.5665, \"lng\": 77.103089, \"passengers\": 39000000, \"country\": \"India\", \"id\": \"DEL\"}, {\"name\": \"Shanghai\", \"iata\": \"PVG\", \"lat\": 31.143378, \"lng\": 121.805214, \"passengers\": 32000000, \"country\": \"China\", \"id\": \"PVG\"},\n",
- " {\"name\": \"Hongkong\", \"iata\": \"HKG\", \"lat\": 22.308919, \"lng\": 113.914603, \"passengers\": 1000000, \"country\": \"China\", \"id\": \"HKG\"}, {\"name\": \"Tokio\", \"iata\": \"NRT\", \"lat\": 35.764722, \"lng\": 140.386389, \"passengers\": 15000000, \"country\": \"Japan\", \"id\": \"NRT\"}, {\"name\": \"Dubai\", \"iata\": \"DXB\", \"lat\": 25.252778, \"lng\": 55.364444, \"passengers\": 29000000, \"country\": \"UAE\", \"id\": \"DXB\"}, {\"name\": \"Dakar\", \"iata\": \"DKR\", \"lat\": 14.670833, \"lng\": -17.072778, \"passengers\": 2000000, \"country\": \"Senegal\", \"id\": \"DKR\"},\n",
- " {\"name\": \"Johannesburg\", \"iata\": \"JNB\", \"lat\": -26.133694, \"lng\": 28.242317, \"passengers\": 9000000, \"country\": \"South Africa\", \"id\": \"JNB\"}, {\"name\": \"Sydney\", \"iata\": \"SYD\", \"lat\": -33.946111, \"lng\": 151.177222, \"passengers\": 44000000, \"country\": \"Australia\", \"id\": \"SYD\"}, {\"name\": \"Nairobi\", \"iata\": \"NBO\", \"lat\": -1.319167, \"lng\": 36.927778, \"passengers\": 900000, \"country\": \"Kenya\", \"id\": \"NBO\"}, {\"name\": \"Atlanta\", \"iata\": \"ATL\", \"lat\": 33.639167, \"lng\": -84.427778, \"passengers\": 93000000, \"country\": \"USA\", \"id\": \"ATL\"},\n",
- " {\"name\": \"New York City\", \"iata\": \"JFK\", \"lat\": 40.63975, \"lng\": -73.778925, \"passengers\": 55000000, \"country\": \"USA\", \"id\": \"JFK\"}, {\"name\": \"Cairo\", \"iata\": \"CAI\", \"lat\": 30.121944, \"lng\": 31.405556, \"passengers\": 14000000, \"country\": \"Egypt\", \"id\": \"CAI\"}, {\"name\": \"Casablanca\", \"iata\": \"CMN\", \"lat\": 33.367467, \"lng\": -7.589967, \"passengers\": 7000000, \"country\": \"Morocco\", \"id\": \"CMN\"}, {\"name\": \"Lagos\", \"iata\": \"LOS\", \"lat\": 6.577222, \"lng\": 3.321111, \"passengers\": 5000000, \"country\": \"Nigeria\", \"id\": \"LOS\"},\n",
- " {\"name\": \"Cape Town\", \"iata\": \"CPT\", \"lat\": -33.969444, \"lng\": 18.597222, \"passengers\": 5000000, \"country\": \"South Africa\", \"id\": \"CPT\"}, {\"name\": \"Chengdu\", \"iata\": \"CTU\", \"lat\": 30.578333, \"lng\": 103.946944, \"passengers\": 40000000, \"country\": \"China\", \"id\": \"CTU\"}, {\"name\": \"Jakarta\", \"iata\": \"CGK\", \"lat\": -6.125567, \"lng\": 106.655897, \"passengers\": 54000000, \"country\": \"Indonesia\", \"id\": \"CGK\"}, {\"name\": \"Teheran\", \"iata\": \"IKA\", \"lat\": 35.416111, \"lng\": 51.152222, \"passengers\": 8000000, \"country\": \"Iran\", \"id\": \"IKA\"},\n",
- " {\"name\": \"Tel Aviv\", \"iata\": \"TLV\", \"lat\": 32.011389, \"lng\": 34.886667, \"passengers\": 20000000, \"country\": \"Israel\", \"id\": \"TLV\"}, {\"name\": \"Kuala Lumpur\", \"iata\": \"KUL\", \"lat\": 2.745578, \"lng\": 101.709917, \"passengers\": 25000000, \"country\": \"Malaysia\", \"id\": \"KUL\"}, {\"name\": \"Manila\", \"iata\": \"MNL\", \"lat\": 14.508647, \"lng\": 121.019581, \"passengers\": 8000000, \"country\": \"Philippines\", \"id\": \"MNL\"}, {\"name\": \"Singapur\", \"iata\": \"SIN\", \"lat\": 1.350189, \"lng\": 103.994433, \"passengers\": 32000000, \"country\": \"Singapore\", \"id\": \"SIN\"},\n",
- " {\"name\": \"Taipeh\", \"iata\": \"TPE\", \"lat\": 25.077732, \"lng\": 121.232822, \"passengers\": 800000, \"country\": \"Taiwan\", \"id\": \"TPE\"}, {\"name\": \"Bangkok\", \"iata\": \"BKK\", \"lat\": 13.681108, \"lng\": 100.747283, \"passengers\": 65000000, \"country\": \"Thailand\", \"id\": \"BKK\"}, {\"name\": \"Istanbul\", \"iata\": \"IST\", \"lat\": 40.976922, \"lng\": 28.814606, \"passengers\": 64000000, \"country\": \"Turkey\", \"id\": \"IST\"}, {\"name\": \"Ulaanbaatar\", \"iata\": \"ULN\", \"lat\": 47.843056, \"lng\": 106.766639, \"passengers\": 1000000, \"country\": \"Mongolia\", \"id\": \"ULN\"},\n",
- " {\"name\": \"Melbourne\", \"iata\": \"MEL\", \"lat\": -37.673333, \"lng\": 144.843333, \"passengers\": 12000000, \"country\": \"Australia\", \"id\": \"MEL\"}, {\"name\": \"Brisbane\", \"iata\": \"BNE\", \"lat\": -27.383333, \"lng\": 153.118056, \"passengers\": 23000000, \"country\": \"Australia\", \"id\": \"BNE\"}, {\"name\": \"Nadi\", \"iata\": \"NAN\", \"lat\": -17.755392, \"lng\": 177.443378, \"passengers\": 2000000, \"country\": \"Fiji\", \"id\": \"NAN\"}, {\"name\": \"Auckland\", \"iata\": \"AKL\", \"lat\": -37.008056, \"lng\": 174.791667, \"passengers\": 21000000, \"country\": \"New Zealand\", \"id\": \"AKL\"},\n",
- " {\"name\": \"Paris\", \"iata\": \"CDG\", \"lat\": 49.009722, \"lng\": 2.547778, \"passengers\": 57000000, \"country\": \"France\", \"id\": \"CDG\"}, {\"name\": \"Madrid\", \"iata\": \"MAD\", \"lat\": 40.4675, \"lng\": -3.551944, \"passengers\": 50000000, \"country\": \"Spain\", \"id\": \"MAD\"}, {\"name\": \"Barcelona\", \"iata\": \"BCN\", \"lat\": 41.297078, \"lng\": 2.078464, \"passengers\": 41000000, \"country\": \"Spain\", \"id\": \"BCN\"}, {\"name\": \"Rome\", \"iata\": \"FCO\", \"lat\": 41.804444, \"lng\": 12.250833, \"passengers\": 29000000, \"country\": \"Italy\", \"id\": \"FCO\"},\n",
- " {\"name\": \"Copenhagen\", \"iata\": \"CPH\", \"lat\": 55.617917, \"lng\": 12.655972, \"passengers\": 30000000, \"country\": \"Denmark\", \"id\": \"CPH\"}, {\"name\": \"Helsinki\", \"iata\": \"HEL\", \"lat\": 60.317222, \"lng\": 24.963333, \"passengers\": 5000000, \"country\": \"Finland\", \"id\": \"HEL\"}, {\"name\": \"Athens\", \"iata\": \"ATH\", \"lat\": 37.936358, \"lng\": 23.944467, \"passengers\": 22000000, \"country\": \"Greece\", \"id\": \"ATH\"}, {\"name\": \"Dublin\", \"iata\": \"DUB\", \"lat\": 53.421333, \"lng\": -6.270075, \"passengers\": 32000000, \"country\": \"Ireland\", \"id\": \"DUB\"},\n",
- " {\"name\": \"Reykjavik\", \"iata\": \"RKV\", \"lat\": 64.13, \"lng\": -21.940556, \"passengers\": 400000, \"country\": \"Iceland\", \"id\": \"RKV\"}, {\"name\": \"Oslo\", \"iata\": \"OSL\", \"lat\": 60.193917, \"lng\": 11.100361, \"passengers\": 9000000, \"country\": \"Norway\", \"id\": \"OSL\"}, {\"name\": \"Vienna\", \"iata\": \"VIE\", \"lat\": 48.110833, \"lng\": 16.570833, \"passengers\": 10000000, \"country\": \"Austria\", \"id\": \"VIE\"}, {\"name\": \"Lisbon\", \"iata\": \"LIS\", \"lat\": 38.774167, \"lng\": -9.134167, \"passengers\": 28000000, \"country\": \"Portugal\", \"id\": \"LIS\"},\n",
- " {\"name\": \"Stockholm\", \"iata\": \"ARN\", \"lat\": 59.651944, \"lng\": 17.918611, \"passengers\": 7000000, \"country\": \"Sweden\", \"id\": \"ARN\"}, {\"name\": \"Edinburgh\", \"iata\": \"EDI\", \"lat\": 55.95, \"lng\": -3.3725, \"passengers\": 14000000, \"country\": \"UK\", \"id\": \"EDI\"}, {\"name\": \"Chicago\", \"iata\": \"ORD\", \"lat\": 41.978603, \"lng\": -87.904842, \"passengers\": 54000000, \"country\": \"USA\", \"id\": \"ORD\"}, {\"name\": \"Dallas\", \"iata\": \"DFW\", \"lat\": 32.896828, \"lng\": -97.037997, \"passengers\": 73000000, \"country\": \"USA\", \"id\": \"DFW\"},\n",
- " {\"name\": \"San Francisco\", \"iata\": \"SFO\", \"lat\": 37.618972, \"lng\": -122.374889, \"passengers\": 42000000, \"country\": \"USA\", \"id\": \"SFO\"}, {\"name\": \"Las Vegas\", \"iata\": \"LAS\", \"lat\": 36.080056, \"lng\": -115.15225, \"passengers\": 52000000, \"country\": \"USA\", \"id\": \"LAS\"}, {\"name\": \"Miami\", \"iata\": \"MIA\", \"lat\": 25.79325, \"lng\": -80.290556, \"passengers\": 50000000, \"country\": \"USA\", \"id\": \"MIA\"}, {\"name\": \"Toronto\", \"iata\": \"YYZ\", \"lat\": 43.677222, \"lng\": -79.630556, \"passengers\": 12000000, \"country\": \"Canada\", \"id\": \"YYZ\"},\n",
- " {\"name\": \"Vancouver\", \"iata\": \"YVR\", \"lat\": 49.193889, \"lng\": -123.184444, \"passengers\": 19000000, \"country\": \"Canada\", \"id\": \"YVR\"}, {\"name\": \"Montreal\", \"iata\": \"YUL\", \"lat\": 45.47175, \"lng\": -73.736569, \"passengers\": 15000000, \"country\": \"Canada\", \"id\": \"YUL\"}, {\"name\": \"Mexico-City\", \"iata\": \"MEX\", \"lat\": 19.436303, \"lng\": -99.072097, \"passengers\": 46000000, \"country\": \"Mexico\", \"id\": \"MEX\"}, {\"name\": \"Guatemala-City\", \"iata\": \"GUA\", \"lat\": 14.583272, \"lng\": -90.527475, \"passengers\": 2000000, \"country\": \"Guatemala\", \"id\": \"GUA\"},\n",
- " {\"name\": \"Buenos Aires\", \"iata\": \"EZE\", \"lat\": -34.822222, \"lng\": -58.535833, \"passengers\": 5000000, \"country\": \"Argentina\", \"id\": \"EZE\"}, {\"name\": \"Sao Paulo\", \"iata\": \"GRU\", \"lat\": -23.432075, \"lng\": -46.469511, \"passengers\": 34000000, \"country\": \"Brazil\", \"id\": \"GRU\"}, {\"name\": \"Santiago de Chile\", \"iata\": \"SCL\", \"lat\": -33.392975, \"lng\": -70.785803, \"passengers\": 20000000, \"country\": \"Chile\", \"id\": \"SCL\"}, {\"name\": \"Brasilia\", \"iata\": \"BSB\", \"lat\": -15.871111, \"lng\": -47.918611, \"passengers\": 13000000, \"country\": \"Brazil\", \"id\": \"BSB\"},\n",
- " {\"name\": \"Bogota\", \"iata\": \"BOG\", \"lat\": 4.701594, \"lng\": -74.146947, \"passengers\": 36000000, \"country\": \"Colombia\", \"id\": \"BOG\"}, {\"name\": \"Caracas\", \"iata\": \"CCS\", \"lat\": 10.601194, \"lng\": -66.991222, \"passengers\": 8000000, \"country\": \"Venezuela\", \"id\": \"CCS\"}\n",
- " ]\n",
- "connections_json = [\n",
- " {'from': 'LAX','to': 'JFK' }, {'from': 'JFK','to': 'GIG' }, {'from': 'JFK','to': 'LIM' }, {'from': 'JFK','to': 'LHR' }, {'from': 'GIG','to': 'FRA' }, {'from': 'LIM','to': 'GIG' }, {'from': 'FRA','to': 'JFK' }, {'from': 'LHR','to': 'FRA' }, {'from': 'FRA','to': 'SVO' }, {'from': 'FRA','to': 'DXB' }, {'from': 'SVO','to': 'DEL' },\n",
- " {'from': 'SVO','to': 'PVG' }, {'from': 'DEL','to': 'HKG' }, {'from': 'PVG','to': 'HKG' }, {'from': 'PVG','to': 'NRT' }, {'from': 'HKG','to': 'SYD' }, {'from': 'NRT','to': 'SYD' }, {'from': 'DXB','to': 'SVO' }, {'from': 'DXB','to': 'DEL' }, {'from': 'DXB','to': 'DKR' }, {'from': 'DXB','to': 'JNB' }, {'from': 'JNB','to': 'LHR' },\n",
- " {'from': 'JNB','to': 'DKR' }, {'from': 'SYD','to': 'DXB' }, {'from': 'NBO','to': 'JNB' }, {'from': 'NBO','to': 'DXB' }, {'from': 'ATL','to': 'JFK' }, {'from': 'LAX','to': 'ATL' }, {'from': 'ATL','to': 'LHR' }, {'from': 'ATL','to': 'LIM' }, {'from': 'SCL','to': 'LIM' }, {'from': 'EZE','to': 'SCL' }, {'from': 'SCL','to': 'GRU' },\n",
- " {'from': 'GIG','to': 'EZE' }, {'from': 'GIG','to': 'GRU' }, {'from': 'BSB','to': 'GIG' }, {'from': 'SCL','to': 'BSB' }, {'from': 'LIM','to': 'BSB' }, {'from': 'BOG','to': 'BSB' }, {'from': 'CCS','to': 'BSB' }, {'from': 'BOG','to': 'GUA' }, {'from': 'CCS','to': 'MIA' }, {'from': 'GUA','to': 'MIA' }, {'from': 'GUA','to': 'MEX' },\n",
- " {'from': 'MEX','to': 'LAX' }, {'from': 'MEX','to': 'LAX' }, {'from': 'LAX','to': 'SFO' }, {'from': 'SFO','to': 'YVR' }, {'from': 'LAX','to': 'LAS' }, {'from': 'LAX','to': 'DFW' }, {'from': 'LAX','to': 'ORD' }, {'from': 'SFO','to': 'LAS' }, {'from': 'DFW','to': 'ATL' }, {'from': 'ATL','to': 'YYZ' }, {'from': 'ORD','to': 'YYZ' }, \n",
- " {'from': 'YYZ','to': 'YUL' }, {'from': 'YYZ','to': 'JFK' }, {'from': 'YUL','to': 'JFK' }, {'from': 'JNB','to': 'CPT' }, {'from': 'LOS','to': 'DKR' }, {'from': 'NBO','to': 'LOS' }, {'from': 'DKR','to': 'CMN' }, {'from': 'DKR','to': 'CAI' }, {'from': 'NBO','to': 'CAI' }, {'from': 'DXB','to': 'CAI' }, {'from': 'IKA','to': 'DXB' }, \n",
- " {'from': 'IST','to': 'IKA' }, {'from': 'TLV','to': 'ATH' }, {'from': 'CAI','to': 'TLV' }, {'from': 'ATH','to': 'IST' }, {'from': 'FCO','to': 'ATH' }, {'from': 'LIS','to': 'LHR' }, {'from': 'LIS','to': 'MAD' }, {'from': 'MAD','to': 'BCN' }, {'from': 'CDG','to': 'LHR' }, {'from': 'DUB','to': 'LHR' }, {'from': 'EDI','to': 'LHR' }, \n",
- " {'from': 'CDG','to': 'BCN' }, {'from': 'BCN','to': 'FCO' }, {'from': 'VIE','to': 'IST' }, {'from': 'VIE','to': 'SVO' }, {'from': 'CMN','to': 'LIS' }, {'from': 'MAD','to': 'CMN' }, {'from': 'FRA','to': 'CPH' }, {'from': 'LHR','to': 'CPH' }, {'from': 'CPH','to': 'OSL' }, {'from': 'CPH','to': 'ARN' }, {'from': 'ARN','to': 'HEL' },\n",
- " {'from': 'HEL','to': 'SVO' }, {'from': 'ULN','to': 'PVG' }, {'from': 'CTU','to': 'PVG' }, {'from': 'PVG','to': 'TPE' }, {'from': 'CTU','to': 'HKG' }, {'from': 'TPE','to': 'HKG' }, {'from': 'HKG','to': 'MNL' }, {'from': 'BKK','to': 'HKG' }, {'from': 'SIN','to': 'KUL' }, {'from': 'SIN','to': 'BKK' }, {'from': 'CGK','to': 'SIN' },\n",
- " {'from': 'MNL','to': 'SIN' }, {'from': 'SIN','to': 'SYD' }, {'from': 'BNE','to': 'SYD' }, {'from': 'SYD','to': 'MEL' }, {'from': 'NAN','to': 'SYD' }, {'from': 'AKL','to': 'SYD' }, {'from': 'NAN','to': 'AKL' }, {'from': 'RKV','to': 'LHR' }, {'from': 'BCN','to': 'CDG' }, {'from': 'BCN','to': 'FRA' }, {'from': 'FCO','to': 'FRA' }, \n",
- " {'from': 'BOG','to': 'MEX' }, {'from': 'BOG','to': 'GRU' }, {'from': 'ATL','to': 'MIA' }, {'from': 'FRA','to': 'IST' }, {'from': 'IST','to': 'DEL' }, {'from': 'PVG','to': 'BKK' }, {'from': 'DEL','to': 'BKK' },\n",
- " ]\n",
- "\n",
- "def initData():\n",
- " return ([\n",
- " {\"id\": airport['iata'], \"properties\": {\"label\": airport['name'], 'passengers': airport['passengers'], 'country': airport['country']}, \"coordinates\": [airport['lat'], airport['lng']]}\n",
- " for airport in airports_json\n",
- " ], [\n",
- " {\"start\": connection['from'], \"end\": connection['to'], \"label\": \"\", \"properties\": {}, \"directed\": False}\n",
- " for connection in connections_json\n",
- " ])\n",
- " \n",
- "airports, connections = initData()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1970927b-c195-4135-8b10-c0ad7902abab",
- "metadata": {},
- "source": [
- "Provided as data to yFiles Jupyter Graphs results in this graph:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "cd760fb5-88da-4343-9fc0-4dbaccae7202",
- "metadata": {},
- "outputs": [],
- "source": [
- "w = GraphWidget()\n",
- "w.set_overview(False)\n",
- "w.edges = connections\n",
- "w.nodes = airports\n",
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4398de48-e075-4537-a591-ed46e5f07f94",
- "metadata": {},
- "source": [
- "# Visualize data as heatmap"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "466813b3-fae0-4500-a208-0022bb08e777",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Map passengers at the airport to a heat value\n",
- "w.set_heat_mapping(lambda item : (item['properties']['passengers'] - 800000)/92200000 if 'passengers' in item['properties'] else 0)\n",
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c785842e-5d8e-43fd-926c-ebbc13ce5ca6",
- "metadata": {},
- "source": [
- "# Geospatial data mapping"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ec4c84bf-fa9b-4e0f-bf97-ba9daaeeaaec",
- "metadata": {},
- "outputs": [],
- "source": [
- "# provide a geolocation mapping for the airports\n",
- "w.node_coordinate_mapping = 'coordinates' \n",
- "# start widget directly in geospatial layout mode\n",
- "w.map_layout()\n",
- "display(w)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "507a16ad-4712-4e46-8220-44220d434e5c",
- "metadata": {},
- "source": [
- "# Group related data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6c3615c0-6f70-4525-94e2-0d674f1e24d9",
- "metadata": {},
- "outputs": [],
- "source": [
- "airports, connections = initData()\n",
- "# add parent nodes to the dataset\n",
- "countries = []\n",
- "for airport_node in airports:\n",
- " # collect unique countries among airports\n",
- " airport_country = airport_node[\"properties\"][\"country\"]\n",
- " if not any(country[\"id\"] == airport_country for country in countries):\n",
- " countries.append({\"id\": airport_country, \"properties\": {\"label\": airport_country}})\n",
- "\n",
- "# create a nodes source with both, the unique countries (reprsenting our group nodes) and airports\n",
- "w = GraphWidget()\n",
- "w.set_overview(False)\n",
- "w.nodes = airports + countries\n",
- "w.edges = connections\n",
- "# use the country property as parent-id for each node\n",
- "w.node_parent_mapping = \"country\"\n",
- "# assign a color mapping to style country nodes differently, otherwise use the widget's default color\n",
- "w.node_color_mapping = lambda node : None if \"passengers\" in node[\"properties\"] else \"gray\"\n",
- "display(w)"
- ]
- }
- ],
- "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": "3.12.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file