I want to copy the visualization i made on R studio using ggplot2 to kaggle. I am very new with the entire interface.
I ran my R programming code on R studio and copied to kaggle using ctrl c and ctrl v. Now i'm doing my visualizations to round off my capstone project. I'm having real difficulty copying the visualization to R. Ctl C and Ctrl V cant do it. Pls is there a way out. I dont want to start all over with my project. I've been at it for over 2 weeks. Please find attached the screen shot, the image is to the lower right.enter image description here
I am plotting multiples signals overlapping in one figure using matplotlib and then saving it in pdf/eps using matplotlib.pyplot.savefig, the problem is that when I try to insert the figure in a pdf-latex file, it takes like 1.5 min to load because it starts rendering every plot. Is there any way to properly save or create these plots in one figure?
The final pdf works perfectly when I use preview on macOS, regularly when I use edge or chrome navigators and it is a disaster if I use adobe-reader. I am looking for a multiplatform solution.
I am running a python-tensorflow-keras jupyter notebook on Colab, training a CNN on Caltech-256 images. The data is loaded from the Caltech site directly to the Colab area with a wget, and never appears on my PC. The notebook includes Tensorboard, and some Callbacks. Obviously, as I first upload the notebook to Colab, that will use some internet bandwidth. I would expect that the rest of the time there should be very little traffic - only enough to update my screen at the end of each epoch (every 600 seconds) or as I click on it. However, there is actually quite a lot of traffic - enough to impact the other people in my house significantly. I believe that the problem is with our upload speed (ie data going from my PC to Colab). I am using Firefox web browser. When I switch to Colab Playground mode, the issue disappears.
What is being uploaded, and why?
Is there any workaround?
I've found the answer here https://github.com/tensorflow/tensorboard/issues/3196
I don't totally understand it, but the workaround listed there works for me:
While the notebook is running, in the tensorboard display, click the settings cog (top right), and uncheck the auto-update box. (You then need to click the refresh icon whenever you want to update your graphs.)
This has to be repeated every time you open the notebook, but it's a small price to pay for family harmony.
I have a Squeak 4.3 image that I have updated through 4.4 by using the trunk update stream, which pulls down source code updates from the Monticello repository here
How do I get the new 4.4 desktop? In particular, how do I replace the brown antique paper background with the new grey Ulam Spiral background?
You can download the wallpaper itself here. (This URL points to the wallpaper of the current Squeak release, but you ought to have at least 6 months from today before it changes :) )
Wallpaper in hand, and assuming that the image is in your current directory, you need just evaluate Form openAsBackground: (FileDirectory default / 'wallpaper.png') pathName.
(I should add that the background image is intentionally not part of the update stream: it would bloat images, and change people's existing backgrounds. Thus, it's applied (along with a few other tweaks) "on the side" by the release process.)
I generate a figure in MATLAB with large amount of elements (100000+) and want to save it into a PDF file. With zbuffer or painters renderer I've got very large and slowly opened file (over 4 Mb) - all points are in vector format. Using OpenGL renderer rasterize the figure in PDF, ok for the plot, but not good for text labels. The file size is about 150 Kb.
Try this simplified code, for example:
x=linspace(1,10,100000);
y=sin(x)+randn(size(x));
plot(x,y,'.')
set(gcf,'Renderer','zbuffer')
print -dpdf -r300 testpdf_zb
set(gcf,'Renderer','painters')
print -dpdf -r300 testpdf_pa
set(gcf,'Renderer','opengl')
print -dpdf -r300 testpdf_op
The actual figure is much more complex with several axes and different types of plots.
Is there a way to rasterize the figure, but keep text labels as vectors?
Another problem with OpenGL is that is does not work in terminal mode (-nosplash -nodesktop -nodisplay) under Mac OSX. Looks like OpenGL is not supported. I have to use terminal mode for automation. The MATLAB version I run is 2007b. Mac OSX server 10.4.
This is a funny one. Your problem is not Matlab, it's Ghostscript (Matlab creates PDFs by calling Ghostscript, at least on Windows). When I run
x=linspace(1,10,100000);
y=sin(x)+randn(size(x));
plot(x,y,'.')
print -dpsc2 test.ps
I've got a 2Mb PS file (all vector, of course), which when compressed became a 164Kb ZIP. One would expect to get more-or-less the same result when converting PS to PDF, but ps2pdf test.ps produced your 4Mb file!
Since you are on a Mac, you probably have Distiller. I'd give it a try — generate PS files as above, and then run them through Distiller; you should get a 150K vector PDF.
If you insist on rasterizing, I can suggest printing the figure without any axes or labels to a tiff, opening the tiff, and recreating axes and labels on top of it.
If you don't want to go with a 2D histogram (i.e. an image where pixel brightness corresponds to density of points) as BlessedKey suggests, it looks like the only good way is to do the rasterizing yourself, as mentioned by AB.
getframe followed by frame2im seems to be the way to go for that. Unfortunately, getframe returns empty if you run with -nodisplay. Therefore, you'd have to save the figure as .fig, and on another computer run a script that
opens the figure, gets the content of the axes with getframe, displays the image from getframe and then saves to pdf.
As an alternative to simple plotting or a 2D histogram, you may want to look into scattercloud, which combines plotting the points with density information, by the way.
If at all possible you should try to subsample your problem before building the illustration. If you are plotting points on a curve then 10,000 is probably more than you need. A modern printer is only about 600 DPI afterall.
If the points are illustrating a cloud with some density properties, a better solution may be to build a two dimensional histogram first, and illustrate that with imshow or imagesc.
If multiple clouds are being illustrated with different colors you may be interested in building one such image for each cloud and the combining them with transparency.