I have a graph where each edge (not nodes) has a capacity and a load. I want to draw this graph using a color map which is a gradient from blue to red, red being the most loaded and blue being the less loaded. I tried to understand the official documentation of Edge Colormap, but it doesn't help me. Can you help me?
The key is to set edge_color to be a sequence of edges coinciding with edgelist, both keyword arguments of nx.draw_networkx. Here is a minimal working example using pandas and networkx.
import networkx as nx
import pandas as pd
edge_df = pd.DataFrame({"source": [0, 1, 2],
"target": [1, 2, 0],
"capacity": [.1, .2, .3],
"load": [40, 20, 10]})
G = nx.from_pandas_edgelist(edge_df,
source="source",
target="target",
edge_attr=["capacity", "load"])
nx.draw_networkx(G,
edgelist=list(zip(edge_df['source'], edge_df['target'])),
edge_color=edge_df['capacity'],
edge_cmap=plt.cm.bwr)
Should get you something like this:
Related
I'm looking for a working example to have a .png picture as the background of a scatter chart.
Currently, I use mark_image to draw the background image:
source = pd.DataFrame.from_records([
{"x": 0, "y": 0,
"img": "http://localhost:8888/files/BARTStreamlit/assets/BARTtracksmap.png?_xsrf=2%7Ce13c35be%7Ce013c83479b892363239e5b6a77d97dc%7C1652400559"}
])
tracksmap = alt.Chart(source).mark_image(
width=500,
height=500
).encode(
x='x',
y='y',
url='img'
)
tracksmap
Here is the resulted image drown:
and draw the scater chart,
chart = alt.Chart(maptable).mark_circle(size=60).encode(
x= 'x',
y= 'y',
tooltip=['short_name', 'ENTRY']
).interactive()
chart
I have scaled the x, y channel values for the scatter chart to be in the range of [0, 500]. 500 is the width and height of the background image that I guessed.
Here is the resulted scatter plot:
then I combined the two chart with layer mechanism:
geovizvega = alt.layer(tracksmap, chart)
geovizvega
resulting the following:
The two charts do not align. I'd like to have the scatter dots aligning with the tracks on the background image. How can I achieve that?
To have them aligned, I might need to have the background image's top left corner at the coordinates (0, 0), how can I achieve that? (It seems that the x, y channel values for mark_image is the coordinates of the center of the image? With accurate definition of the x, y channel values, it might be possible to calculate the proper value of x, and y for the top left coroner to be at (0, 0)).
I might need to to have precise dimension of the background image. How?
My above approach may not be the right one. Please show me a working example.
Yes, if you change the values of x and y in your image plot to something like y=-200 and x=200, the image should be more centered in the scatter plot.
You can also change the anchor point of the image using align and baseline:
import altair as alt
import pandas as pd
source = pd.DataFrame.from_records([
{"x": 2, "y": 2, "img": "https://vega.github.io/vega-datasets/data/7zip.png"}
])
imgs = alt.Chart(source).mark_image(
width=100,
height=100
).encode(
x='x',
y='y',
url='img'
)
imgs + imgs.mark_circle(size=200, color='red', opacity=1)
imgs = alt.Chart(source).mark_image(
width=100,
height=100,
align='right',
baseline='top'
).encode(
x='x',
y='y',
url='img'
)
imgs + imgs.mark_circle(size=200, color='red', opacity=1)
After this, you would still need to change the dimensions of the chart so that it has the same size as the image. The default is width=400 and height=300. You can get the dimensions of your image in most image editing software or using the file <imagename> command (at least on linux). But even after getting these dimensions, you would have to do some manual adjustments due to axes taking up some of that space in the chart.
Hello Stackoverflow forks,
I'm a enthusiastic python learner.
I have studied python to visualiza my personal project about population density.
I have gone through tutorials about matplotlib and basemap in python.
I came across with the idea about
mapping my 3dimensional graph on top of the basemap which allows me to use geographycal coordinate information.
Can anyone let me know how I could use basemap as a base plane for the 3dimensional graph?
Please let me know which tutorial or references I could go with for developing this.
Best,
Thank you always Stackoverflow forks.
The basemap documentation has a small section on 3D plotting. Here's a simple script to get you started:
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
plt.close('all')
fig = plt.figure()
ax = fig.gca(projection='3d')
extent = [-127, -65, 25, 51]
# make the map and axis.
m = Basemap(llcrnrlon=extent[0], llcrnrlat=extent[2],
urcrnrlon=extent[1], urcrnrlat=extent[3],
projection='cyl', resolution='l', fix_aspect=False, ax=ax)
ax.add_collection3d(m.drawcoastlines(linewidth=0.25))
ax.add_collection3d(m.drawcountries(linewidth=0.25))
ax.add_collection3d(m.drawstates(linewidth=0.25))
ax.view_init(azim = 230, elev = 15)
ax.set_xlabel(u'Longitude (°E)', labelpad=10)
ax.set_ylabel(u'Latitude (°N)', labelpad=10)
ax.set_zlabel(u'Altitude (ft)', labelpad=20)
# values to plot - change as needed. Plots 2 dots, one at elevation 0 and another 100.
# also draws a line between the two.
x, y = m(-85.4808, 32.6099)
ax.plot3D([x, x], [y, y], [0, 100], color = 'green', lw = 0.5)
ax.scatter3D(x, y, 100, s = 5, c = 'k', zorder = 4)
ax.scatter3D(x, y, 0, s = 2, c = 'k', zorder = 4)
ax.set_zlim(0., 400.)
plt.show()
I have multiple coordinates (latitude and longitude) and I would like to create a heatmap. I have checked all the documentation online and examples and cannot find anything which helps my to create a heatmap on an ipyleaflet map.
Please could someone advise how I generate and add a heatmap layer onto an ipyleaflet map.
I am working inside a jupyter notebook.
Thanks
Since the last version of ipyleaflet it is now possible to create a HeatMap:
from ipyleaflet import Map, Heatmap
from random import uniform
m = Map(center=[0, 0], zoom=2)
locations = [
[uniform(-80, 80), uniform(-180, 180), uniform(0, 1000)] # lat, lng, intensity
for i in range(1000)
]
heat = Heatmap(locations=locations, radius=20, blur=10)
m.add_layer(heat)
# Change some attributes of the heatmap
heat.radius = 30
heat.blur = 50
heat.max = 0.5
heat.gradient = {0.4: 'red', 0.6: 'yellow', 0.7: 'lime', 0.8: 'cyan', 1.0: 'blue'}
m
I'm trying to model voting dynamics on networks, and would like to be able to create a graph in NetworkX where I can iterate the voter process on nodes, having their colour change corresponding to their vote 'labels'.
I've managed to get this code to let me see the attributes for each node, but how do I go about using those in a for loop to designate colour?
H = nx.Graph()
H.add_node(1,vote='labour')
H.add_node(2,vote='labour')
H.add_node(3,vote='conservative')
h=nx.get_node_attributes(H,'vote')
h.items()
Gives me the result:
[(1, 'labour'), (2, 'labour'), (3, 'conservative')]
I've got a for loop to do this type of colour coding based on the node number as follows, but haven't managed to make it work for my 'vote' status.
S=nx.star_graph(10)
colour_map=[]
for node in S:
if node % 2 ==0:
colour_map.append('blue')
else: colour_map.append('yellow')
nx.draw(S, node_color = colour_map,with_labels = True)
plt.show()
You can iterate the node attributes with H.nodes(data=True) which returns the node name and the node attributes in a dictionary. Here's a full example using your graph.
import networkx as nx
import matplotlib.pyplot as plt
H = nx.Graph()
H.add_node(1, vote='labour')
H.add_node(2, vote='labour')
H.add_node(3, vote='conservative')
color_map = []
for node, data in H.nodes(data=True):
if data['vote'] == 'labour':
color_map.append(0.25) # blue color
elif data['vote'] == 'conservative':
color_map.append(0.7) # yellow color
nx.draw(H, vmin=0, vmax=1, cmap=plt.cm.jet, node_color=color_map, with_labels=True)
plt.show()
This code will draw a different layout of nodes each time you run it (some layouts, as e.g. draw_spring, are available here).
Regarding colors, I use 0.25 for blue and 0.7 for yellow. Note that I use the jet matplotlib colormap and that I set vmin=0 and vmax=1 so that the color values are absolute (and not relative to eachother).
Output of the code above:
UPDATE:
I wasn't aware that you could simply use color names in matplotlib. Here's the updated for loop:
for node, data in H.nodes(data=True):
if data['vote'] == 'labour':
color_map.append("blue")
elif data['vote'] == 'conservative':
color_map.append("yellow")
And the updated draw command:
nx.draw(H, node_color=color_map, with_labels=True)
Note that this way you get different shades of blue and yellow than in the image above.
I have created a graph with networkx and have wrote the graph representation to a dot file to be displayed with graphviz. Now, the nodes have color attributes and I would like graphviz to place nodes with the same color closer to each other.
For example, if node "soccer" and node "football" both have color 'blue' then they should be close together, whereas node "baseball" with color 'green' would not be near nodes "soccer" and "football"
How can I get nodes with the same color to be drawn closer together in Graphviz; hence forming clusters of colors?
Thanks for all the help and let me know if you need more information :)
You could use PyGraphviz to do the layout using dot with "clusters".
e.g.
import networkx as nx
G = nx.Graph()
G.add_node(1, color='blue', style='filled')
G.add_node(2, color='red', style='filled')
G.add_edge(1,2)
G.add_node(3, color='blue',style='filled')
G.add_node(4, color='red',style='filled')
G.add_edge(3,4)
G.add_edge(4,10)
G.add_path([10,20,30,40,50])
A = nx.to_agraph(G) # uses pygraphviz
red_nodes = [n for n,d in G.node.items() if d.get('color')=='red']
blue_nodes = [n for n,d in G.node.items() if d.get('color')=='blue']
A.add_subgraph(red_nodes, name = 'cluster1', color='red')
A.add_subgraph(blue_nodes, name = 'cluster2', color='blue')
A.write('colors.dot')
A.layout('dot')
A.draw('colors.png')