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‎docs/gallery/networks.ipynb‎

Lines changed: 140 additions & 9 deletions
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@@ -28,7 +28,37 @@
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"id": "cell-2",
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"metadata": {},
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"outputs": [],
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"source": "import numpy as np\nimport pandas as pd\nfrom ggplotly import *\n\n# Create nodes in a circular layout\nn_nodes = 20\nangles = np.linspace(0, 2 * np.pi, n_nodes, endpoint=False)\nradius = 10\nnode_x = radius * np.cos(angles)\nnode_y = radius * np.sin(angles)\n\n# Create edges across the circle\nedges = []\nfor i in range(n_nodes):\n for offset in [5, 7, 10]:\n j = (i + offset) % n_nodes\n edges.append({\n 'x': node_x[i], 'y': node_y[i],\n 'xend': node_x[j], 'yend': node_y[j]\n })\n\nedges_df = pd.DataFrame(edges)\nnodes_df = pd.DataFrame({'x': node_x, 'y': node_y})\n\n(ggplot(edges_df, aes(x='x', y='y', xend='xend', yend='yend'))\n + geom_edgebundle(compatibility_threshold=0.6)\n + geom_point(data=nodes_df, mapping=aes(x='x', y='y'), color='white', size=4)\n + theme_dark()\n + labs(title='Circular Network with Edge Bundling'))"
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"from ggplotly import *\n",
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"\n",
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"# Create nodes in a circular layout\n",
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"n_nodes = 20\n",
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"angles = np.linspace(0, 2 * np.pi, n_nodes, endpoint=False)\n",
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"radius = 10\n",
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"node_x = radius * np.cos(angles)\n",
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"node_y = radius * np.sin(angles)\n",
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"\n",
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"# Create edges across the circle\n",
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"edges = []\n",
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"for i in range(n_nodes):\n",
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" for offset in [5, 7, 10]:\n",
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" j = (i + offset) % n_nodes\n",
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" edges.append({\n",
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" 'x': node_x[i], 'y': node_y[i],\n",
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" 'xend': node_x[j], 'yend': node_y[j]\n",
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" })\n",
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"\n",
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"edges_df = pd.DataFrame(edges)\n",
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"nodes_df = pd.DataFrame({'x': node_x, 'y': node_y})\n",
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"\n",
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"(ggplot(edges_df, aes(x='x', y='y', xend='xend', yend='yend'))\n",
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" + geom_edgebundle(compatibility_threshold=0.6)\n",
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" + geom_point(data=nodes_df, mapping=aes(x='x', y='y'), color='white', size=4)\n",
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" + theme_dark()\n",
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" + labs(title='Circular Network with Edge Bundling'))"
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]
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},
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{
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"cell_type": "markdown",
@@ -44,7 +74,31 @@
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"id": "cell-4",
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"metadata": {},
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"outputs": [],
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"source": "np.random.seed(42)\nn_nodes = 30\nn_edges = 80\n\nnode_x = np.random.uniform(0, 100, n_nodes)\nnode_y = np.random.uniform(0, 100, n_nodes)\n\nedges = []\nfor _ in range(n_edges):\n i, j = np.random.choice(n_nodes, 2, replace=False)\n edges.append({\n 'x': node_x[i], 'y': node_y[i],\n 'xend': node_x[j], 'yend': node_y[j]\n })\n\nedges_df = pd.DataFrame(edges)\nnodes_df = pd.DataFrame({'x': node_x, 'y': node_y})\n\n(ggplot(edges_df, aes(x='x', y='y', xend='xend', yend='yend'))\n + geom_edgebundle(C=5, compatibility_threshold=0.5)\n + geom_point(data=nodes_df, mapping=aes(x='x', y='y'), color='#00ff00', size=5)\n + theme_dark()\n + labs(title='Random Network with Edge Bundling'))"
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"source": [
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"np.random.seed(42)\n",
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"n_nodes = 30\n",
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"n_edges = 80\n",
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"\n",
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"node_x = np.random.uniform(0, 100, n_nodes)\n",
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"node_y = np.random.uniform(0, 100, n_nodes)\n",
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"\n",
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"edges = []\n",
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"for _ in range(n_edges):\n",
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" i, j = np.random.choice(n_nodes, 2, replace=False)\n",
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" edges.append({\n",
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" 'x': node_x[i], 'y': node_y[i],\n",
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" 'xend': node_x[j], 'yend': node_y[j]\n",
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" })\n",
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"\n",
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"edges_df = pd.DataFrame(edges)\n",
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"nodes_df = pd.DataFrame({'x': node_x, 'y': node_y})\n",
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"\n",
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"(ggplot(edges_df, aes(x='x', y='y', xend='xend', yend='yend'))\n",
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" + geom_edgebundle(C=5, compatibility_threshold=0.5)\n",
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" + geom_point(data=nodes_df, mapping=aes(x='x', y='y'), color='#00ff00', size=5)\n",
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" + theme_dark()\n",
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" + labs(title='Random Network with Edge Bundling'))"
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]
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},
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{
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"cell_type": "markdown",
@@ -81,7 +135,20 @@
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"id": "cell-6",
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"metadata": {},
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"outputs": [],
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"source": "# Edges with weights - heavier edges attract lighter ones\nedges_df = pd.DataFrame({\n 'x': [0, 0, 0],\n 'y': [0, 1, 2],\n 'xend': [10, 10, 10],\n 'yend': [0, 1, 2],\n 'traffic': [100, 10, 10] # First edge is 10x heavier\n})\n\n(ggplot(edges_df, aes(x='x', y='y', xend='xend', yend='yend', weight='traffic'))\n + geom_edgebundle(C=4, compatibility_threshold=0.5)\n + theme_dark())"
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"source": [
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"# Edges with weights - heavier edges attract lighter ones\n",
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"edges_df = pd.DataFrame({\n",
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" 'x': [0, 0, 0],\n",
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" 'y': [0, 1, 2],\n",
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" 'xend': [10, 10, 10],\n",
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" 'yend': [0, 1, 2],\n",
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" 'traffic': [100, 10, 10] # First edge is 10x heavier\n",
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"})\n",
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"\n",
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"(ggplot(edges_df, aes(x='x', y='y', xend='xend', yend='yend', weight='traffic'))\n",
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" + geom_edgebundle(C=4, compatibility_threshold=0.5)\n",
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" + theme_dark())"
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]
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},
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{
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"cell_type": "markdown",
@@ -99,7 +166,19 @@
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"id": "cell-8",
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"metadata": {},
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"outputs": [],
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"source": "import igraph as ig\nfrom ggplotly import data\n\n# Load built-in US flights network\ng = data('us_flights')\n\n(ggplot()\n + geom_map(map_type='usa')\n + geom_edgebundle(graph=g, show_nodes=True, node_color='white', node_size=3)\n + theme_dark()\n + labs(title='US Flight Network'))"
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"source": [
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"import igraph as ig\n",
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"from ggplotly import data\n",
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"\n",
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"# Load built-in US flights network\n",
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"g = data('us_flights')\n",
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"\n",
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"(ggplot()\n",
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" + geom_map(map_type='usa')\n",
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" + geom_edgebundle(graph=g, show_nodes=True, node_color='white', node_size=3)\n",
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" + theme_dark()\n",
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" + labs(title='US Flight Network'))"
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]
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},
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{
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"cell_type": "markdown",
@@ -119,7 +198,27 @@
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"id": "cell-10",
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"metadata": {},
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"outputs": [],
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"source": "airports = pd.DataFrame({\n 'lon': [-122.4, -73.8, -87.6, -118.4, -95.3, -84.4],\n 'lat': [37.8, 40.6, 41.9, 34.0, 29.8, 33.6],\n 'name': ['SFO', 'JFK', 'ORD', 'LAX', 'IAH', 'ATL']\n})\n\nflights = pd.DataFrame({\n 'src_lon': [-122.4, -73.8, -87.6, -118.4, -95.3, -84.4, -122.4, -73.8],\n 'src_lat': [37.8, 40.6, 41.9, 34.0, 29.8, 33.6, 37.8, 40.6],\n 'dst_lon': [-73.8, -87.6, -118.4, -95.3, -84.4, -122.4, -84.4, -118.4],\n 'dst_lat': [40.6, 41.9, 34.0, 29.8, 33.6, 37.8, 33.6, 34.0]\n})\n\n(ggplot(flights, aes(x='src_lon', y='src_lat', xend='dst_lon', yend='dst_lat'))\n + geom_map(map_type='usa')\n + geom_point(data=airports, mapping=aes(x='lon', y='lat'), color='white', size=8)\n + geom_edgebundle(C=4, compatibility_threshold=0.5, verbose=False)\n + theme_dark()\n + labs(title='US Flights with Edge Bundling'))"
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"source": [
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"airports = pd.DataFrame({\n",
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" 'lon': [-122.4, -73.8, -87.6, -118.4, -95.3, -84.4],\n",
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" 'lat': [37.8, 40.6, 41.9, 34.0, 29.8, 33.6],\n",
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" 'name': ['SFO', 'JFK', 'ORD', 'LAX', 'IAH', 'ATL']\n",
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"})\n",
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"\n",
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"flights = pd.DataFrame({\n",
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" 'src_lon': [-122.4, -73.8, -87.6, -118.4, -95.3, -84.4, -122.4, -73.8],\n",
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" 'src_lat': [37.8, 40.6, 41.9, 34.0, 29.8, 33.6, 37.8, 40.6],\n",
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" 'dst_lon': [-73.8, -87.6, -118.4, -95.3, -84.4, -122.4, -84.4, -118.4],\n",
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" 'dst_lat': [40.6, 41.9, 34.0, 29.8, 33.6, 37.8, 33.6, 34.0]\n",
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"})\n",
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"\n",
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"(ggplot(flights, aes(x='src_lon', y='src_lat', xend='dst_lon', yend='dst_lat'))\n",
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" + geom_map(map_type='usa')\n",
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" + geom_point(data=airports, mapping=aes(x='lon', y='lat'), color='white', size=8)\n",
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" + geom_edgebundle(C=4, compatibility_threshold=0.5, verbose=False)\n",
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" + theme_dark()\n",
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" + labs(title='US Flights with Edge Bundling'))"
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]
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},
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{
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"cell_type": "markdown",
@@ -141,7 +240,21 @@
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"id": "cell-12",
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"metadata": {},
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"outputs": [],
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"source": "shipping_routes = pd.DataFrame({\n 'origin': ['Rotterdam', 'Shanghai', 'Los Angeles'],\n 'x': [4.48, 121.47, -118.24], # origin longitude\n 'y': [51.92, 31.23, 33.73], # origin latitude\n 'xend': [121.47, -74.01, 4.48], # destination longitude\n 'yend': [31.23, 40.71, 51.92] # destination latitude\n})\n\n(ggplot(shipping_routes, aes(x='x', y='y', xend='xend', yend='yend'))\n + geom_map(map_type='world')\n + geom_searoute(color='steelblue', linewidth=1.0)\n + theme_dark()\n + labs(title='Maritime Shipping Routes'))"
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"source": [
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"shipping_routes = pd.DataFrame({\n",
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" 'origin': ['Rotterdam', 'Shanghai', 'Los Angeles'],\n",
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" 'x': [4.48, 121.47, -118.24], # origin longitude\n",
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" 'y': [51.92, 31.23, 33.73], # origin latitude\n",
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" 'xend': [121.47, -74.01, 4.48], # destination longitude\n",
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" 'yend': [31.23, 40.71, 51.92] # destination latitude\n",
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"})\n",
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"\n",
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"(ggplot(shipping_routes, aes(x='x', y='y', xend='xend', yend='yend'))\n",
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" + geom_map(map_type='world')\n",
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" + geom_searoute(color='steelblue', linewidth=1.0)\n",
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" + theme_dark()\n",
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" + labs(title='Maritime Shipping Routes'))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cell-14",
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"metadata": {},
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"outputs": [],
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"source": "(ggplot(shipping_routes, aes(x='x', y='y', xend='xend', yend='yend'))\n + geom_map(map_type='world')\n + geom_searoute(\n restrictions=['suez'], # Routes go around Cape of Good Hope\n color='#ff6b35',\n show_highlight=True,\n show_ports=True,\n port_color='#00ff88',\n verbose=True # Shows route distances\n )\n + theme_dark()\n + labs(title='Shipping Routes Avoiding Suez Canal'))"
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"source": [
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"(ggplot(shipping_routes, aes(x='x', y='y', xend='xend', yend='yend'))\n",
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" + geom_map(map_type='world')\n",
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" + geom_searoute(\n",
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" restrictions=['suez'], \n",
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" color='#ff6b35',\n",
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" show_highlight=True,\n",
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" show_ports=True,\n",
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" port_color='#00ff88',\n",
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" verbose=True # Shows route distances\n",
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" )\n",
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" + theme_dark()\n",
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" + labs(title='Shipping Routes Avoiding Suez Canal'))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cell-16",
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"metadata": {},
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"outputs": [],
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"source": "from ggplotly.stats.stat_edgebundle import clear_bundling_cache\n\n# Clear cache if needed (e.g., memory constraints)\nclear_bundling_cache()"
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"source": [
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"from ggplotly.stats.stat_edgebundle import clear_bundling_cache\n",
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"\n",
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"# Clear cache if needed (e.g., memory constraints)\n",
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"clear_bundling_cache()"
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]
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},
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{
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"cell_type": "markdown",
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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}

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