For some reason I cannot get xlab and ylab to work in mosaic plots in vcd package.
I tried:
mosaic(~Sex +Survived,
data=Titanic,
shade=T,
legend=T,
main="myplot",
labeling_args=list(abbreviate = c( Sex=2, Survived=1)) ,
ylab="Gender",
xlab="survival")
this labels the axes "Sex" and "Survived" respectively.
mosaic(margin.table(Titanic, c(2,4)),
ylab="Gender",
xlab="survival",
shade=T, legend=T,
main="myplot",
labeling_args=list(abbreviate = c( Sex=2, Survived=1)))
this also labels the axes "Sex" and "Survived" respectively.
mosaicplot(margin.table(Titanic, c(2,4)),
shade=T,
legend=T,
main="myplot",
labeling_args=list(abbreviate = c(Sex=2, Survived=1)),
ylab="Gender",
xlab="survival")
this labels the axes correctly, but disregards ‘labeling_args’
Does anyone have any suggestions?
There is a huge part written on labels in the vcd documentation.
What you were looking for is this:
labeling_args = list(set_varnames = c(Sex="Gender", Survived="survival"))
It's been a while since you asked, but maybe it helps someone else ;)
The mosaic function and the mosaicplot function use very different graphic options. So it's no wonder that what works with one doesn't work with the other.
Related
I am using a Swin Transformer for a hierarchical problem of multi calss multi label classification. I would like to visualize the self attention maps on my input image trying to extract them from the model, unfortunately I am not succeeding in this task. Could you give me a hint on how to do it?
I share you the part of the code in which I am trying to do this task.
attention_maps = []
for module in model.modules():
#print(module)
if hasattr(module,'attention_patches'): #controlla se la variabile ha l' attributo
print(module.attention_patches.shape)
if module.attention_patches.numel() == 224*224:
attention_maps.append(module.attention_patches)
for attention_map in attention_maps:
attention_map = attention_map.reshape(224, 224, 1)
plt.imshow(sample['image'].permute(1, 2, 0), interpolation='nearest')
plt.imshow(attention_map, alpha=0.7, cmap=plt.cm.Greys)
plt.show()
``
In addition if you know about some explainability techniques, like Grad-CAM, which could be used with a hierarchical Swin Transformer, feel free to attach a link, it would be very helpful for me.
I am also researching the same, while I don't have anything specific to SWIN. Here are some resources related to Vision Transformers. I hope it helps:
https://jacobgil.github.io/deeplearning/vision-transformer-explainability
https://github.com/jacobgil/vit-explain
https://github.com/hila-chefer/Transformer-Explainability
https://github.com/hila-chefer/Transformer-Explainability/blob/main/Transformer_explainability.ipynb
#I'm trying to do a taste wheel, like the Meilgaard wheel.
I'm not trying to do anything complex I only want to plot together a pie chart and a PCA plot.
I would like to have the piechart bigger than the PCA graph.
I would like to plot the pie graph with both the two PCA graphs or with the fviz_pca_biplot.
The following is the PCA:
principal4<-PCA(ExpertWine2,scale.unit = T,ind.sup = NULL,quanti.sup =29:30,quali.sup =1,graph = T,axes = c(1,2),ncp = 3)
fviz_pca_biplot(principal4)
The following is the pie chart:
slices <- c(10, 10, 10, 10)
lbls <- c("acidity", "freshness", "sweetness", "corposity")
coloris = c("gold", "cyan", "indianred1", "burlywood4")
cake=pie(slices, labels = lbls, init.angle=45, main="Pie Chart of tastes",col=coloris)
There are multiple packages aimed at solving this. It will depend on whether you as using a base r plotting implementation or something like ggplot.
I would reccomend looking at bentobox (strict positioning), patchwork (auto positioning) and cowplot (somewhere in the middle). Patchwork is primarily aimed at ggplot but the other two should work with any plotting method.
bento box:
https://github.com/PhanstielLab/BentoBox
patchwork:
https://patchwork.data-imaginist.com/
cowplot:
https://cran.r-project.org/web/packages/cowplot/vignettes/introduction.html
I use code like the example below to do basic plotting of a list of values from F# Interactive. When plotting more points, the time taken to display increases dramatically. In the examples below, 10^4 points display in 4 seconds whereas 4.10^4 points take a patience-testing 53 seconds to display. Overall it's roughly as if the time to plot N points is in N^2.
The result is that I'll probably add an interpolation layer in front of this code, but
1) I wonder if someone who knows the workings of FSharpChart and Windows.Forms could explain what is causing this behaviour? (The data is bounded so one thing that seems to rule out is the display needing to adjust scale.)
2)Is there a simple remedy other than interpolating the data myself?
let plotl (f:float list) =
let chart = FSharpChart.Line(f, Name = "")
|> FSharpChart.WithSeries.Style(Color = System.Drawing.Color.Red, BorderWidth = 2)
let form = new Form(Visible = true, TopMost = true, Width = 700, Height = 500)
let ctl = new ChartControl(chart, Dock = DockStyle.Fill)
form.Controls.Add(ctl)
let z1 = [for i in 1 .. 10000 do yield sin(float(i * i))]
let z2 = [for i in 1 .. 20000 do yield sin(float(i * i))]
plotl z1
plotl z2
First of all, FSharpChart is a name used in an older version of the library. The latest version is called F# Charting, comes with a new documentation and uses just Chart.
To answer your question, Chart.Line and Chart.Points are quite slow for large number of points. The library also has Chart.FastLine and Chart.FastPoints (which do not support as many features, but are faster). So, try getting the latest version of F# Charting and using the "Fast" version of the method.
I'm having a problem where Octave will render figures just fine in the figure box, but then refuses to properly export to PNG when I use the print() command. This is also true when I try other formats like EPS or JPG.
My current version of Octave is 3.8.1-1ubuntu1, which is up to date at the time of this post. My Ubuntu version is also 14.04. I do not receive any error messages when the code runs.
The script commands used to plot are pretty basic. For example:
linewidth = 4;
xStr = 'Particle Diameter (\mum)';
yStr = 'Scattering Cross-Section (\mum^2)';
FontName = 'Times New Roman';
LabelFontSize = 22;
AxisFontSize = 18;
F1 = figure(1);
clf('reset');
plot(diameter*1e6,sigma_0*1e12,'k','linewidth',linewidth);
hold on
plot(diameter*1e6,sigma_1*1e12,'r','linewidth',linewidth);
X = xlabel(xStr);
set(X,'FontName',FontName,'fontsize',LabelFontSize);
Y = ylabel(yStr);
set(Y,'FontName',FontName,'fontsize',LabelFontSize);
axis([xMin xMax sigMin sigMax]);
set(gca,'fontsize',AxisFontSize,'linewidth',2);
legend('2.0 \mum','3.8 \mum',4);
print(F1,'Mie.png','-dpng');
The strange thing is that I have other images from months ago that rendered the LaTex bits just fine, and even used nearly identical code. That almost seems like some recent software upgrade may have killed my plotting.
I appreciate any help you can give me. This issue is driving me nuts.
This is a known problem when using the OpenGL toolkits (graphics_toolkit FLTK) which is default in octave3.8.x. Previous versions used gnuplot for printing.
So you have two choices:
Switch back to gnuplot with "graphics_toolkit gnuplot" before doing any plotting. You may also add this to your .octaverc so it's set every time you start octave
Use LaTex output: http://wiki.octave.org/Printing_with_FLTK
I am using Maya to do some procedural work, and I have a lot of textures that I need to load into Maya, and they all have transparencies (alpha channels). I would very much like to be able to automate this process. Using PyMEL, I can create my textures and hook them up to a shader, but the alpha doesn't set properly by default. There is an attribute in the psdFileTex node called "Alpha to Use", and it must be set to "Transparency" in order for my alpha channel to work. My question is this - how do I use PyMEL scripting to set the "Alpha to Use" attribute properly?
Here is the code I am using to set up my textures:
import pymel.core as pm
pm.shadingNode('lambert', asShader=True, name='myShader1')
pm.sets(renderable=True, noSurfaceShader=True, empty=True, name='myShader1SG')
pm.connectAttr('myShader1.outColor', 'myShader1SG.surfaceShader', f=True)
pm.shadingNode('psdFileTex', asTexture=True, name='myShader1PSD')
pm.connectAttr('myShader1PSD.outColor', 'myShader1.color')
pm.connectAttr('myShader1PSD.outTransparency', 'myShader1.transparency')
pm.setAttr('myShader1ColorPSD.fileTextureName', '<pathway>/myShader1_texture.psd', type='string')
If anyone can help me, I would really appreciate it.
Thanks
With any node, you can use listAttr() to get the available editable attributes. Run listAttr('myShaderPSD'), note in it's output, there will be two attributes called 'alpha' and 'alphaList'. Alpha, will return you the current selected alpha channel. AlphaList will return you however many alpha channels you have in your psd.
Example
pm.PyNode('myShader1PSD').alphaList.get()
# Result: [u'Alpha 1', u'Alpha 2'] #
If you know you'll only ever be using just the one alpha, or the first alpha channel, you can simply do this.
psdShader = pm.PyNode('myShader1PSD')
alphaList = psdShader.alphaList.get()
if (len(alphaList) > 0):
psdShader.alpha.set(alphaList[0])
else:
// No alpha channel
pass
Remember that lists start iterating from 0, so our first alpha channel will be located at position 0.
Additionally and unrelated, while you're still using derivative commands of the maya.core converted for Pymel, there's still some commands you can use to help make your code read nicer.
pm.setAttr('myShader1ColorPSD.fileTextureName', '<pathway>/myShader1_texture.psd', type='string')
We can convert this to pymel like so:
pm.PyNode('myShader1ColorPSD').fileTextureName.set('<pathway>/myShader1_texture.psd')
And:
pm.connectAttr('myShader1PSD.outColor', 'myShader1.color')
Can be converted to:
pm.connect('myShader1PSD.outColor', 'myShader1.color')
While they may only be small changes, it reads just the little bit nicer, and it's native PyMel.
Anyway, I hope I have helped you!