I am trying to build a visualization where I am using networkx to build a graph in bokeh. I want to add a tooltip to the connecting nodes of an edge.
Currently, the tooltip shows up on the single node showing the labels of both nodes. How can I separate the tooltips, such that each node has a single label? Thanks.
P.S Something like this:
plot = Plot(plot_width=700, plot_height=700,
x_range=Range1d(-1.1,1.1), y_range=Range1d(-1.1,1.1))
plot.title.text = "Graph Interaction Demonstration"
plot.add_tools(HoverTool(tooltips=[("Bigram","#start+#end")]))
nodes = list(G.nodes())
edges = list(G.edges())
edges_start = [edge[0] for edge in edges]
edges_end = [edge[1] for edge in edges]
node_source = ColumnDataSource(data={'index':nodes})
edge_source = ColumnDataSource(data=dict(
start=edges_start,
end=edges_end,
))
graph_renderer = GraphRenderer()
graph_renderer.node_renderer.data_source.data = node_source.data
graph_renderer.edge_renderer.data_source.data = edge_source.data
graph_layout=nx.circular_layout(G,scale=1,center=(0,0))
graph_renderer.layout_provider = StaticLayoutProvider(graph_layout=graph_layout)
graph_renderer.node_renderer.glyph = Circle(size=15, fill_color=Spectral4[0])
graph_renderer.node_renderer.selection_glyph = Circle(size=15, fill_color=Spectral4[2])
graph_renderer.node_renderer.hover_glyph = Circle(size=25, fill_color=Spectral4[1])
graph_renderer.edge_renderer.glyph = MultiLine(line_color="#CCCCCC", line_alpha=0.8, line_width=5)
graph_renderer.edge_renderer.selection_glyph = MultiLine(line_color=Spectral4[2], line_width=5)
graph_renderer.edge_renderer.hover_glyph = MultiLine(line_color=Spectral4[1], line_width=5)
#graph_renderer.selection_policy = NodesAndLinkedEdges()
#graph_renderer.inspection_policy = EdgesAndLinkedNodes()
plot.renderers.append(graph_renderer)
Related
I have created an alluvial plot but, for visibility purposes I would like to move one lode in one of the axes: more specifically I would like the "NA" of the "Type of surgery" to be at the top so the last 4 axes are aligned.
This is the code I used on R:
aes(y = ID, axis1 = Reason, axis2 = Response, axis3=Type_of_surgery, axis4=Margins, axis5=RT_post_op, axis6=Chemo_post_op)) +
geom_alluvium(aes(fill = Type_of_surgery), width = 1/12,aes.bind = TRUE) +
geom_flow(aes.bind = TRUE) +
geom_stratum(width = 1/3, fill = "grey", color = "white") +
geom_label(stat = "stratum", aes(label = after_stat(stratum))) +
scale_x_discrete(limits = c("Reason", "Response","Type of surgery", "Margins","RT post op", "Chemo post-op"), expand = c(0.1,0.1)) +
scale_fill_brewer(type = "qual", palette = "Pastel1") +
ggtitle("TBC") ```
This is the plot I obtained:
[Alluvial plot][1]
[1]: https://i.stack.imgur.com/nDCIZ.png
I am beginning on the world of coding so any help would be most welcome,
Thank you all for your help,
JB
i am reading the document https://plotly.com/r/reference/sankey/, and want to change the links color for a sankey chart. But i can't quite understand the parameters in add_trace() function
where should i specify the color value?
add_trace(p,type='sankey', color=????)
You haven't provided a minimal reproducible example, so I can't jump right into your code. But I think I can point you in the right direction.
In the documentation you screenshotted, it's saying that the color argument is one key of the list link that defines links in the plot. Using this example from the R plotly documentation for adding links, let's take a look at where that goes:
library(plotly)
library(rjson)
json_file <- "https://raw.githubusercontent.com/plotly/plotly.js/master/test/image/mocks/sankey_energy.json"
json_data <- fromJSON(paste(readLines(json_file), collapse=""))
fig <- plot_ly(
type = "sankey",
domain = list(
x = c(0,1),
y = c(0,1)
),
orientation = "h",
valueformat = ".0f",
valuesuffix = "TWh",
node = list(
label = json_data$data[[1]]$node$label,
color = json_data$data[[1]]$node$color,
pad = 15,
thickness = 15,
line = list(
color = "black",
width = 0.5
)
),
link = list(
source = json_data$data[[1]]$link$source,
target = json_data$data[[1]]$link$target,
value = json_data$data[[1]]$link$value,
label = json_data$data[[1]]$link$label,
#### Color goes here! ####
color = "yellow"
)
)
fig <- fig %>% layout(
title = "Energy forecast for 2050<br>Source: Department of Energy & Climate Change, Tom Counsell via <a href='https://bost.ocks.org/mike/sankey/'>Mike Bostock</a>",
font = list(
size = 10
),
xaxis = list(showgrid = F, zeroline = F),
yaxis = list(showgrid = F, zeroline = F)
)
fig
The plotly documentation can be a bit opaque at times. I have found it helpful to sometimes review the documentation for python. For example, this part of the python documentation does give some more guidance about changing link colors.
I'm trying to implement the Louvain algorihtm in pyspark using dataframes. The problem is that my implementation is reaaaally slow. This is how I do it:
I collect all vertices and communityIds into simple python lists
For each vertex - communityId pair I calculate the modularity gain using dataframes (just a fancy formula involving edge weights sums/differences)
Repeat untill no change
What am I doing wrong?
I suppose that if I could somehow parallelize the for each loop the performance would increase, but how can I do that?
LATER EDIT:
I could use vertices.foreach(changeCommunityId) instead of the for each loop, but then I'd have to compute the modularity gain (that fancy formula) without dataframes.
See the code sample below:
def louvain(self):
oldModularity = 0 # since intially each node represents a community
graph = self.graph
# retrieve graph vertices and edges dataframes
vertices = verticesDf = self.graph.vertices
aij = edgesDf = self.graph.edges
canOptimize = True
allCommunityIds = [row['communityId'] for row in verticesDf.select('communityId').distinct().collect()]
verticesIdsCommunityIds = [(row['id'], row['communityId']) for row in verticesDf.select('id', 'communityId').collect()]
allEdgesSum = self.graph.edges.groupBy().sum('weight').collect()
m = allEdgesSum[0]['sum(weight)']/2
def computeModularityGain(vertexId, newCommunityId):
# the sum of all weights of the edges within C
sourceNodesNewCommunity = vertices.join(aij, vertices.id == aij.src) \
.select('weight', 'src', 'communityId') \
.where(vertices.communityId == newCommunityId);
destinationNodesNewCommunity = vertices.join(aij, vertices.id == aij.dst) \
.select('weight', 'dst', 'communityId') \
.where(vertices.communityId == newCommunityId);
k_in = sourceNodesNewCommunity.join(destinationNodesNewCommunity, sourceNodesNewCommunity.communityId == destinationNodesNewCommunity.communityId) \
.count()
# the rest of the formula computation goes here, I just wanted to show you an example
# just return some value for the modularity
return 0.9
def changeCommunityId(vertexId, currentCommunityId):
maxModularityGain = 0
maxModularityGainCommunityId = None
for newCommunityId in allCommunityIds:
if (newCommunityId != currentCommunityId):
modularityGain = computeModularityGain(vertexId, newCommunityId)
if (modularityGain > maxModularityGain):
maxModularityGain = modularityGain
maxModularityGainCommunityId = newCommunityId
if (maxModularityGain > 0):
return maxModularityGainCommunityId
return currentCommunityId
while canOptimize:
while self.changeInModularity:
self.changeInModularity = False
for vertexCommunityIdPair in verticesIdsCommunityIds:
vertexId = vertexCommunityIdPair[0]
currentCommunityId = vertexCommunityIdPair[1]
newCommunityId = changeCommunityId(vertexId, currentCommunityId)
self.changeInModularity = False
canOptimize = False
I have three monitors in a horizontal line. Sometimes i want to maximize a window to three monitors at once, by pressing a combination of keys (and then returning it all back if necessary). How can I do that?
Untested, but the basic idea is to make the window floating and resize it to cover everything:
function(c)
c.floating = true
local geo = screen[1].geometry
geo.x2 = geo.x + geo.width
geo.y2 = geo.y + geo.height
for s in screen do
local geo2 = s.geometry
geo.x = math.min(geo.x, geo2.x)
geo.y = math.min(geo.y, geo2.y)
geo.x2 = math.max(geo.x2, geo2.x + geo2.width)
geo.y2 = math.max(geo.y2, geo2.y + geo2.height)
end
c:geometry{
x = geo.x,
y = geo.y,
width = geo.x2 - geo.x,
height = geo.y2 - geo.y
}
end
To undo the above, make the client no longer floating, i.e. c.floating = false.
Wiring the above to a keybinding is left as an exercise for the reader.
I am quite new to Matlab and I am trying to use this code I found online.
I am trying to fit a graph described by the HydrodynamicSpectrum. But instead of having it fit after inputting fvA and fmA, I am trying to obtain the fitted parameters for this value also.
I have tried removing them, changing them. But none is working. I was wondering if any one here will be able to point me into the right direction of fixing this.
specFunc = #(f, para)HydrodynamicSpectrum(f, [para fvA fmA]);
[fit.AXfc, fit.AXD] = NonLinearFit(fit.f(indXY), fit.AXSpec(indXY), specFunc, [iguess_AXfc iguess_AXD]);
[fit.AYfc, fit.AYD] = NonLinearFit(fit.f(indXY), fit.AYSpec(indXY), specFunc, [iguess_AYfc iguess_AYD]);
[fit.ASumfc, fit.ASumD] = NonLinearFit(fit.f(indSum), fit.ASumSpec(indSum), specFunc, [iguess_ASumfc iguess_ASumD]);
predictedAX = HydrodynamicSpectrum(fit.f, [fit.AXfc fit.AXD fvA fmA]);
predictedAY = HydrodynamicSpectrum(fit.f, [fit.AYfc fit.AYD fvA fmA]);
predictedASum = HydrodynamicSpectrum(fit.f, [fit.ASumfc fit.ASumD fvA fmA]);
function spec = HydrodynamicSpectrum(f, para);
fc = para(1);
D = para(2);
fv = para(3);
fm = para(4);
f = abs(f); %Kludge!
spec = D/pi^2*(1+sqrt(f/fv))./((fc - f.*sqrt(f./fv) - (f.^2)/fm).^2 + (f + f.*sqrt(f./fv)).^2);
function [fc, D, sfc, sD] = NonLinearFit(f, spec, specFunc, init);
func = #(para, f)spec./specFunc(f, para);
[paraFit, resid, J] = nlinfit(f, ones(1, length(spec)), func, init);
fc = paraFit(1);
D = paraFit(2);
ci = nlparci(real(paraFit), real(resid), real(J)); % Kludge!!
sfc = (ci(1,2) - ci(1,1))/4;
sD = (ci(2,2) - ci(2,1))/4;
[paraFit, resid, J] = nlinfit(f, ones(1, length(spec)), func, init);
It looks like you get your fitted parameter using this line. And you are further processing them to get other stuff out from the function. You can modify your second function to get them out as well.
As there are very few comments, and questions seems to be application specific, there is not much help I can give with what you have presented.