data-visualization

I currently have this graph implemented and I'm hoping to keep the structure and collapsibility while depicting a child node having multiple parents. Is there a way to do this? I looked into force graphs but I'm also wanting to preserve a set hierarchy (meaning a parent at level 1 can have a child and level 3 shown). After a lot of research and tinkering with my current code, I am unable to figure my issues out. Is there anyone who has any experience with displaying a D3 tree node with multiple parents? Just in case anyone was wondering what I did to answer my question. I ended up making a

In the conv-nets model, I know how to visualize the filters, we can do itorch.image(model:get(1).weight) But how could I efficiently visualize the output images after the convolution? especially those images in the second or third layer in a deep neural network? Thanks. Similarly to weight, you can use: itorch.image(model:get(1).output) To visualize the weights: -- visualizing weights n = nn.SpatialConvolution(1,64,16,16) itorch.image(n.weight) To visualize the feature maps: -- initialize a simple conv layer n = nn.SpatialConvolution(1,16,12,12) -- push lena through net :) res = n:forward

How does one create a grouped bar chart in Altair? I'm trying the following but it is just producing two graphs side by side. Chart(data).mark_bar().encode( column='Gender', x='Genre', y='Rating', color='Gender' ) titipata Example of group bar chart I show a simplified example of Grouped Bar Chart from Altair's documentation. You can also see the full documentation here . Basically, you have to specify x-axis Gender (F or M in each subplot), y-axis as Rating and Genre as Column . from altair import * import pandas as pd # create dataframe df = pd.DataFrame([['Action', 5, 'F'], ['Crime', 10, 'F

I'm using Highcharts/Highstock to plot a fairly large amount of data (~10,000 points). The data consists of Date objects on the X axis and floats on the Y, formatted as such: [[(date), 1.728], [(date), 0.346], ...] . The dates are always 1 hour apart and there are no gaps in the data. When the chart's range is >= 21 days (such that at least 21 days of data is graphed), the chart appears correctly. Whenever the range is less than that, though, the chart becomes blank and the tooltip displays each point as having a Y-value of 0.0. The Y values for those points do exist in the array (I can see

I have a matrix with hundreds of rows and tens of columns and wish to plot a heatmap. If I use the native R function: heatmap(matrix(sample(1:10000),nrow=1000,ncol=10)) I get a figure with illegible row titles. I assume the image is produced to match the specifications of the current plotting device. I'd like control over the height of the rows, even if this can't be displayed on my current device. Simply writing to a tall PNG just adds whitespace above/below the image: png( '/looks_the_same_with_whitespace.png', width=500, height=1500) heatmap(matrix(sample(1:10000),nrow=1000,ncol=10)) dev

So I am using the react-native-audio package to play preloaded audio files and capture the user's recorded audio. What I would like to do is convert the audio into some sort of data for visualization and analysis. There seems to be several options for web but not much in this direction specifically for React Native. How would I achieve this? Thank you. Juanan Jimenez I've just bump with this post, I am building a React Native Waveform visualiser, still work in progres with the android side, but its working on the iOS side. Pretty much is a port from WaveForm on IOS ,using Igor Shubin's

I've taken some of the things I learned in a Flowing Data great circle mapping tutorial and combined them with code linked in the comments to prevent weird things from happening when R plots trans-equatorial great circles. That gives me this: airports <- read.csv("/home/geoff/Desktop/DissertationData/airports.csv", header=TRUE) flights <- read.csv("/home/geoff/Desktop/DissertationData/ATL.csv", header=TRUE, as.is=TRUE) library(maps) library(geosphere) checkDateLine <- function(l){ n<-0 k<-length(l) k<-k-1 for (j in 1:k){ n[j] <- l[j+1] - l[j] } n <- abs(n) m<-max(n, rm.na=TRUE) ifelse(m > 30,