one-color | based JavaScript color parser/computation toolkit | Runtime Evironment library

You can pass the color array in, but it seems to work with 1D array only:

In modern you can use itemConfig to configure Ext.grid.Row.

Add the code bellow to a Sencha Fiddle:

You can use background: repeating-linear-gradient(90deg, #DF0000, #DF0000 50px, #000000 50px, #000000 100px) to have multiple colors inside 1 div (or td) no need to make it harder

Well, Using three different (and all rather elderly, even the newest is 6 years old) versions of Ghostscript certainly isn't going to help.

First thing to note is that Ghostscript does not 'merge' PDF files. The actual process is described here

So all the input files will be completely interpreted, broken down into graphics primitives and then a new file re-assembled. Now in general, unless otherwise instructed, the pdfwrite device will attempt to maintain colour specifications in the original colour space. You are correct in your point that a PDF file may contain multiple different colour spaces, so I would treat the 'identify' results with a large degree of caution.

You haven't supplied the input file, nor any of the output files, so I can't perform any analysis of them, so its not really possible to tell you what's going on. Ghostscript itself, and the pdfwrite device does not have any 'configuration' in the way I think you are expecting. All the configuration is done through the command line, so if you are running the same command line for all the installations, then you are running the same 'configuration'.

I'm not sure why you are having trouble 'appying the exact options'. Surely you can determine the Ghostscript command line that your own code is creating ?

Anyway older versions are, obviously, less feature-rich and capable than newer versions. Its conceivable that there's a bug specific to your 8.70 version which is due to a new feature, that had a problem in that version (note the minor version number bump here indicates something significant changed). That could also explain why you get sRGB from your (very) old version and CMYK from your newer version.

If you supply the files to look at I'll tell you what the differences are. My advice would be to use the same version on all three platforms though, and I'd suggest that using software which is less than 6 years old would probably help. Not least because you are leaving yourself open to a number of known, published security exploits. At least one of which has been seen 'in the wild'.

I also would not use some of the command line switches you have supplied, -dUseCIEColor is a terrible idea, don't do that (newer versions of Ghostscript will specifically warn you if you do). Don't change the ColorConversionStrategy without a good reason. The colour management changed **totally* between the 8.x and 9.x series, prior to that -sOutptuICCProfile will have no effect whatsoever, so you are trying to apply controls that are not supported in the earlier version.

There is a third party library, can give slanted shape Textview. you can set a listener to Textview and perform your desired function. You can change the direction of SlantedTextView by changing the value of app:slantedMode="left" and also can increase and decrease the size of SlantedTextView by changing the value of app:slantedLength="55dp".

Library:

The question Matplotlib 2 Subplots, 1 Colorbar is probably more what you are looking for. The problem is however that you do not directly have access to the mappable that is created in the pandas scatter plot.

The solution here would be to distill this mappable (in this case a PatchCollection) from the axes using it plt.gca().get_children()[0], which takes the first child artist from the axes.

This method is save as long as all scatterplots share the same colors and as long as there are no other artists in the axes.

ImageFilter is what you sought. GrayFilter could be used.

I'm guessing the loop is the bottleneck in your code that is taking so long? You could remove the loop and create the plot with one call to bar, then call set to modify the hggroup object and its child patch object:

If you want to be able to tell them apart, you can set the linetype to IndID. Note, however, that you will need to change the NA to some other value to (easily) get it to plot.

I also expanded your data a little bit to give enough values per individual to show meaningful lines. I also used geom_line(stat = "density") instead of geom_density() because it omits the line along the bottom and gives legends with lines instead of boxes.