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Diffstat (limited to '_downloads/d071301e92e895067d308e076bb57540/plot_knuth_miles.ipynb')
| -rw-r--r-- | _downloads/d071301e92e895067d308e076bb57540/plot_knuth_miles.ipynb | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/_downloads/d071301e92e895067d308e076bb57540/plot_knuth_miles.ipynb b/_downloads/d071301e92e895067d308e076bb57540/plot_knuth_miles.ipynb index eeff6586..45362fcd 100644 --- a/_downloads/d071301e92e895067d308e076bb57540/plot_knuth_miles.ipynb +++ b/_downloads/d071301e92e895067d308e076bb57540/plot_knuth_miles.ipynb @@ -26,7 +26,7 @@ }, "outputs": [], "source": [ - "import gzip\nimport re\n\n# Ignore any warnings related to downloading shpfiles with cartopy\nimport warnings\n\nwarnings.simplefilter(\"ignore\")\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport networkx as nx\n\n\ndef miles_graph():\n \"\"\"Return the cites example graph in miles_dat.txt\n from the Stanford GraphBase.\n \"\"\"\n # open file miles_dat.txt.gz (or miles_dat.txt)\n\n fh = gzip.open(\"knuth_miles.txt.gz\", \"r\")\n\n G = nx.Graph()\n G.position = {}\n G.population = {}\n\n cities = []\n for line in fh.readlines():\n line = line.decode()\n if line.startswith(\"*\"): # skip comments\n continue\n\n numfind = re.compile(r\"^\\d+\")\n\n if numfind.match(line): # this line is distances\n dist = line.split()\n for d in dist:\n G.add_edge(city, cities[i], weight=int(d))\n i = i + 1\n else: # this line is a city, position, population\n i = 1\n (city, coordpop) = line.split(\"[\")\n cities.insert(0, city)\n (coord, pop) = coordpop.split(\"]\")\n (y, x) = coord.split(\",\")\n\n G.add_node(city)\n # assign position - Convert string to lat/long\n G.position[city] = (-float(x) / 100, float(y) / 100)\n G.population[city] = float(pop) / 1000\n return G\n\n\nG = miles_graph()\n\nprint(\"Loaded miles_dat.txt containing 128 cities.\")\nprint(G)\n\n# make new graph of cites, edge if less then 300 miles between them\nH = nx.Graph()\nfor v in G:\n H.add_node(v)\nfor (u, v, d) in G.edges(data=True):\n if d[\"weight\"] < 300:\n H.add_edge(u, v)\n\n# draw with matplotlib/pylab\nfig = plt.figure(figsize=(8, 6))\n\n# nodes colored by degree sized by population\nnode_color = [float(H.degree(v)) for v in H]\n\n# Use cartopy to provide a backdrop for the visualization\ntry:\n import cartopy.crs as ccrs\n import cartopy.io.shapereader as shpreader\n\n ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.LambertConformal(), frameon=False)\n ax.set_extent([-125, -66.5, 20, 50], ccrs.Geodetic())\n # Add map of countries & US states as a backdrop\n for shapename in (\"admin_1_states_provinces_lakes_shp\", \"admin_0_countries\"):\n shp = shpreader.natural_earth(\n resolution=\"110m\", category=\"cultural\", name=shapename\n )\n ax.add_geometries(\n shpreader.Reader(shp).geometries(),\n ccrs.PlateCarree(),\n facecolor=\"none\",\n edgecolor=\"k\",\n )\n # NOTE: When using cartopy, use matplotlib directly rather than nx.draw\n # to take advantage of the cartopy transforms\n ax.scatter(\n *np.array([v for v in G.position.values()]).T,\n s=[G.population[v] for v in H],\n c=node_color,\n transform=ccrs.PlateCarree(),\n zorder=100 # Ensure nodes lie on top of edges/state lines\n )\n # Plot edges between the cities\n for edge in H.edges():\n edge_coords = np.array([G.position[v] for v in edge])\n ax.plot(\n edge_coords[:, 0],\n edge_coords[:, 1],\n transform=ccrs.PlateCarree(),\n linewidth=0.75,\n color=\"k\",\n )\n\nexcept ImportError:\n # If cartopy is unavailable, the backdrop for the plot will be blank;\n # though you should still be able to discern the general shape of the US\n # from graph nodes and edges!\n nx.draw(\n H,\n G.position,\n node_size=[G.population[v] for v in H],\n node_color=node_color,\n with_labels=False,\n )\n\nplt.show()" + "import gzip\nimport re\n\n# Ignore any warnings related to downloading shpfiles with cartopy\nimport warnings\n\nwarnings.simplefilter(\"ignore\")\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport networkx as nx\n\n\ndef miles_graph():\n \"\"\"Return the cites example graph in miles_dat.txt\n from the Stanford GraphBase.\n \"\"\"\n # open file miles_dat.txt.gz (or miles_dat.txt)\n\n fh = gzip.open(\"knuth_miles.txt.gz\", \"r\")\n\n G = nx.Graph()\n G.position = {}\n G.population = {}\n\n cities = []\n for line in fh.readlines():\n line = line.decode()\n if line.startswith(\"*\"): # skip comments\n continue\n\n numfind = re.compile(r\"^\\d+\")\n\n if numfind.match(line): # this line is distances\n dist = line.split()\n for d in dist:\n G.add_edge(city, cities[i], weight=int(d))\n i = i + 1\n else: # this line is a city, position, population\n i = 1\n (city, coordpop) = line.split(\"[\")\n cities.insert(0, city)\n (coord, pop) = coordpop.split(\"]\")\n (y, x) = coord.split(\",\")\n\n G.add_node(city)\n # assign position - Convert string to lat/long\n G.position[city] = (-float(x) / 100, float(y) / 100)\n G.population[city] = float(pop) / 1000\n return G\n\n\nG = miles_graph()\n\nprint(\"Loaded miles_dat.txt containing 128 cities.\")\nprint(G)\n\n# make new graph of cites, edge if less then 300 miles between them\nH = nx.Graph()\nfor v in G:\n H.add_node(v)\nfor u, v, d in G.edges(data=True):\n if d[\"weight\"] < 300:\n H.add_edge(u, v)\n\n# draw with matplotlib/pylab\nfig = plt.figure(figsize=(8, 6))\n\n# nodes colored by degree sized by population\nnode_color = [float(H.degree(v)) for v in H]\n\n# Use cartopy to provide a backdrop for the visualization\ntry:\n import cartopy.crs as ccrs\n import cartopy.io.shapereader as shpreader\n\n ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.LambertConformal(), frameon=False)\n ax.set_extent([-125, -66.5, 20, 50], ccrs.Geodetic())\n # Add map of countries & US states as a backdrop\n for shapename in (\"admin_1_states_provinces_lakes_shp\", \"admin_0_countries\"):\n shp = shpreader.natural_earth(\n resolution=\"110m\", category=\"cultural\", name=shapename\n )\n ax.add_geometries(\n shpreader.Reader(shp).geometries(),\n ccrs.PlateCarree(),\n facecolor=\"none\",\n edgecolor=\"k\",\n )\n # NOTE: When using cartopy, use matplotlib directly rather than nx.draw\n # to take advantage of the cartopy transforms\n ax.scatter(\n *np.array(list(G.position.values())).T,\n s=[G.population[v] for v in H],\n c=node_color,\n transform=ccrs.PlateCarree(),\n zorder=100 # Ensure nodes lie on top of edges/state lines\n )\n # Plot edges between the cities\n for edge in H.edges():\n edge_coords = np.array([G.position[v] for v in edge])\n ax.plot(\n edge_coords[:, 0],\n edge_coords[:, 1],\n transform=ccrs.PlateCarree(),\n linewidth=0.75,\n color=\"k\",\n )\n\nexcept ImportError:\n # If cartopy is unavailable, the backdrop for the plot will be blank;\n # though you should still be able to discern the general shape of the US\n # from graph nodes and edges!\n nx.draw(\n H,\n G.position,\n node_size=[G.population[v] for v in H],\n node_color=node_color,\n with_labels=False,\n )\n\nplt.show()" ] } ], |