Version 3.1.2 # NOOOOO! My local files have been
# rejected due to recent changes!
11.623 s # Timing for your command
5.184 s # Timing with lower verbosity
PS - doesn’t appear to represent your latest 2 commits!
PPS - updated gmic and now the command is zippy (0.019 s). Doesn’t have stdout. Instead, I get a display window.
I.e., with
tic +rep_find_permutations 8 toc
@afre Thanks, afre. That seem to confirm that it is useful in some cases other than building strings with it.
In light of permutations thing, I have released two permutations commands rep_extract_permutation_order, and rep_find_permutation_lexicographic_index.
Here how they work, let’s use rep_find_permutations and see what it yields:
$ +rep_find_permutations 5 echo {I(#-1,80)}
=> 3,1,2,0,4
The command rep_find_permutations creates all the 720 possible order, and they are all sorted in lexicographic order. The math parser access index 80, and the resulting output is what it yields.
As for the 5, well, that is the number of items variable.
Let’s try rep_extract_permutation_order and see what it yields:
$ rep_extract_permutation_order 5,80 echo ${}
=> 3,1,2,0,4
The result are the same? Huh, so what’s the difference? The difference is that this command doesn’t generate all of the permutation, it goes straight into finding out what could be the order at index n. And I know there’s the question of what the 5 means. Well, that is the number of items.
Let’s try rep_find_permutation_lexicographic_index and see what it yields. (To use this command, just reorder a list of integer starting from 0, and the number of integer is the number of items):
$ rep_find_permutation_lexicographic_index 3,1,2,0,4 echo ${}
=> 80
With the above note, this is self-explanatory. It finds which index contains the following order.
These new commands does not generate all of the combinations, so you can literally access billions of index.
This means in theory, you can use those commands to reorder images, and blend them multiple ways.
EDIT: I pushed Bit-Plane Shuffler upgrade. This demonstrates the use of those commands. Preview still needs work, but I will get to that later. Regardless, it is accurate.
On Ordered Dithering:
I finally figured how to make it look nice:
Here’s the 1-bit version:
I’m inching closer to adding hundreds of new palettes to +pal command. I’m actually using Python to create the G’MIC code. Possibly even thousand is possible.
EDIT: I settled to 379 palette. I’m actually going to sort palettes with a image editor, then export to ora to get the index. It is incredibly boring to do that though as I’m sorting 130+ new palettes, and some few existing ones. But, the reward is worth it.
EDIT: I have finished sorting it the way that I like. That was tedious, but this image shows there’s more flexibility within the palette list.
If you look closely, you’d note that there are some deliberate sorting of tone/coloring, and there was also naming order, etc. Basically a mixture of orders.
Now, I finished up and added 155 palettes to gmic-community. So, users can now choose up to 379 palettes.
Seems like a huge effort you put in!
Is this all rolled into the cli command pal? If so, two suggestions:
gmic h pal
Yes, it’s rolled in one command.
This is already added. It’s gmic h +pal. David_Tschumperle has added the specialization of +command.
This I don’t know about. With my Premade Palette GUI filter, the help documentation provided there might look ugly. And that’s also why I have added so many palettes. There’s rainbow palettes, there’s gradients palettes, there’s hardware-based palette, there’s software palette, there’s existing game palette, there’s desaturated palette, there’s palette of each color, and there’s speciality palette like Human Faces. Basically just about everything though I wish I could do some more.
I see, that does seem confusing to me to define +pal without also defining pal. Even if it’s only a stub which calls the other (take a look at mse for example).
The thing I consider about taking short command names is it reduces options for others. Example:
# in community file
long_command_name_pal : skip 0
# in local file
pal : long_command_name_pal # I can shorthand alias it
pal : e "do something else" # OR use my own pal AND the community one
With short names:
# in community file
pal : skip 0
# local file
# I want to use pal myself locally, but it's taken!
pal : e "hi" # redefined, but now I can't use community pal any more!
It’s not a disaster, but it limits what other people can do (unless there’s a workaround I didn’t think of!).
Edit: I guess I should add that since commands are redefined silently, if you’re also using pal inside your other commands, it could cause them to break in unexpected ways if somebody has their own pal command. The more unique you can make them the better.
Community commands should have a prefix. This has another benefit not yet mentioned: we know who the author is. Instead of having users bother David about an error or request, we know who to direct it. If David sees it fit, he can always link your command to a non-prefixed name or integrate it into core.
Whatever you decide to do, your hard work is appreciated for sure!
As far as I see, this “+pal” custom function comprises of more than 760 (sub-) custom functions!
Maybe a “long_command_name_pal” with “long_command_ name” sub-custom functions could be some test for the implementation quality of the gmic script processor!
Actually there are about 4800 custom functions in update313.gmic shown by my lightweight grep ^[a-z_+][a-z0-9_]* *:{1,1}[^=].*$ with about 2300 custom functions from gmic_stdlib and about 450 pal related functions.
update313.gmic seems to be not actual, it does not reflect the actual gmic-community files!
It would be better indeed not to define too much subfunctions for a single commands. Because all these sub-commands are added to the pool of commands the interpreter has to check when parsing any item of a gmic script.
To be clear : the more commands, the more time spent to parse and analyze a gmic script. Having lot of commands slows down everything.
So, to reduce the number of subcommands, I should switch to if else format or use command merge within these command? Or maybe a new command that acts like a switch statement based on number rather than if/else? On the new command idea, basically G’MIC finds the position argument then parse command that matches position argument. Or a use arg and a lot of base64 codes?
In your case, I would suggest a completely different approach.
As you basically have a (big) image list of palettes, it could be interesting to compress the whole list, and encode it as a base64 string.
After that, command pal would only have to decompress the list and return the $1^th index of the list as an image.
To compress your list of palettes:
repeat 223 pal $> done serialize auto,1,1 img2base64 e ${}
Then, copy/paste the displayed string, and put it somewhere in your reptorian_b64_repository.gmic file:
+_pal_b64 :
base642img "..your base64 string here.."
Then, pal becomes basically:
+pal : l[] { _pal_b64 k[$1] }
That’s clearly the more efficient way of storing all your palettes, without requiring to access to an external data file (something that clut does for instance).
More precisely, something like this:
+pal :
l[] {
l[] $_pal onfail
base642img "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"
+store. _pal
}
k[{$1%$!}]
}
EDIT: This whole command takes around 30Kb.
Very good and efficient. There should be an “unserialize” anywhere! (befor keep)
Hmm, I would have to test that once I am able to update G’MIC again.
Meanwhile, I released a new command that would make importing file with the use of ‘it’ command easier. This is similar to python_string.split(“var”)
See results of the new command:
# Default value for rep_split_it is \n or newline.
C:\Users\User\Downloads>gmic it test.txt rep_split_it , foreach echo {t} done rm
[gmic]-0./ Start G'MIC interpreter.
[gmic]-0./ Input text-data file 'test.txt'.
[gmic]-1./*foreach/ ; paint.net Palette File: Rainbow
[gmic]-1./*foreach/ ; Made by: BoltBait
[gmic]-1./*foreach/ FFFFD8D8
[gmic]-1./*foreach/ FFFFA8A8
[gmic]-1./*foreach/ FFFF7777
[gmic]-1./*foreach/ FFFF3A3A
[gmic]-1./*foreach/ FFFF0000
[gmic]-1./*foreach/ FFC10000
[gmic]-1./*foreach/ FF890000
[gmic]-1./*foreach/ FF4C0000
[gmic]-1./*foreach/ FFFFD8FF
[gmic]-1./*foreach/ FFFFA8FF
[gmic]-1./*foreach/ FFFF77FF
[gmic]-1./*foreach/ FFFF3AFF
[gmic]-1./*foreach/ FFFF00FF
[gmic]-1./*foreach/ FFBC00BC
[gmic]-1./*foreach/ FF840084
[gmic]-1./*foreach/ FF4F004F
[gmic]-1./*foreach/ FFD8FFDF
[gmic]-1./*foreach/ FFA8FFB8
[gmic]-1./*foreach/ FF77FF90
[gmic]-1./*foreach/ FF3AFF5E
[gmic]-1./*foreach/ FF00FF00
[gmic]-1./*foreach/ FF00BF23
[gmic]-1./*foreach/ FF008718
[gmic]-1./*foreach/ FF00510E
[gmic]-1./*foreach/ FFD8D8FF
[gmic]-1./*foreach/ FFA8A8FF
[gmic]-1./*foreach/ FF7777FF
[gmic]-1./*foreach/ FF3A3AFF
[gmic]-1./*foreach/ FF0000FF
[gmic]-1./*foreach/ FF0000BF
[gmic]-1./*foreach/ FF000087
[gmic]-1./*foreach/ FF000051
[gmic]-1./*foreach/ FFF7FFD8
[gmic]-1./*foreach/ FFEDFFA8
[gmic]-1./*foreach/ FFE3FF77
[gmic]-1./*foreach/ FFD7FF3A
[gmic]-1./*foreach/ FFCCFF00
[gmic]-1./*foreach/ FF96BC00
[gmic]-1./*foreach/ FF6A8400
[gmic]-1./*foreach/ FF3F4F00
[gmic]-1./*foreach/ FFD8F1FF
[gmic]-1./*foreach/ FFA8E0FF
[gmic]-1./*foreach/ FF77CFFF
[gmic]-1./*foreach/ FF3ABAFF
[gmic]-1./*foreach/ FF00A5FF
[gmic]-1./*foreach/ FF0077B7
[gmic]-1./*foreach/ FF00527F
[gmic]-1./*foreach/ FF002F47
[gmic]-1./*foreach/ FFFFFFD8
[gmic]-1./*foreach/ FFFFFFA8
[gmic]-1./*foreach/ FFFFFF77
[gmic]-1./*foreach/ FFFFFF3A
[gmic]-1./*foreach/ FFFFFF00
[gmic]-1./*foreach/ FFBCBC00
[gmic]-1./*foreach/ FF848400
[gmic]-1./*foreach/ FF4F4F00
[gmic]-1./*foreach/ FFD8FFFF
[gmic]-1./*foreach/ FFA8FFFF
[gmic]-1./*foreach/ FF77FFFF
[gmic]-1./*foreach/ FF3AFFFF
[gmic]-1./*foreach/ FF00FFFF
[gmic]-1./*foreach/ FF00BABA
[gmic]-1./*foreach/ FF008282
[gmic]-1./*foreach/ FF004444
[gmic]-1./*foreach/ FFFFF3CC
[gmic]-1./*foreach/ FFFFE89E
[gmic]-1./*foreach/ FFFFDB66
[gmic]-1./*foreach/ FFFFCE30
[gmic]-1./*foreach/ FFFFC300
[gmic]-1./*foreach/ FFE0AC00
[gmic]-1./*foreach/ FFB78C00
[gmic]-1./*foreach/ FF876700
[gmic]-1./*foreach/ FFFAF3EC
[gmic]-1./*foreach/ FFF6E0DA
[gmic]-1./*foreach/ FFF0D4C5
[gmic]-1./*foreach/ FFEDC7BD
[gmic]-1./*foreach/ FFEBB9AF
[gmic]-1./*foreach/ FFD09385
[gmic]-1./*foreach/ FFC5887D
[gmic]-1./*foreach/ FFAA6D64
[gmic]-1./*foreach/ FFFFE2CC
[gmic]-1./*foreach/ FFFFD0A8
[gmic]-1./*foreach/ FFFFB677
[gmic]-1./*foreach/ FFFF9D47
[gmic]-1./*foreach/ FFFF7700
[gmic]-1./*foreach/ FFBF5900
[gmic]-1./*foreach/ FF7F3B00
[gmic]-1./*foreach/ FF492100
[gmic]-1./*foreach/ FFE0E0E0
[gmic]-1./*foreach/ FFC0C0C0
[gmic]-1./*foreach/ FFA0A0A0
[gmic]-1./*foreach/ FF808080
[gmic]-1./*foreach/ FF606060
[gmic]-1./*foreach/ FF404040
[gmic]-1./*foreach/ FF202020
[gmic]-1./*foreach/ FF000000
C:\Users\User\Downloads>gmic ('"Hi, I am Reptorian\nLove GMIC."') rep_split_it "m " foreach echo {t} done rm
[gmic]-0./ Start G'MIC interpreter. (1 image 29x1x1x1).
[gmic]-1./*foreach/ Hi, I a
[gmic]-1./*foreach/ Reptorian
Love GMIC.
Not sure if you know this already, but command split has a special mode to split an image according to a sequence of values specified as a separator. That’s actually very useful to process text buffers.
For instance:
foo :
('"Hello my friends\nHow are you today?\nI like the \'split\' command\n"')
split. -,{'\n'}
repeat $! echo {$>,t} done
~~~
gives:
~~~
$ gmic foo
[gmic]-0./ Start G'MIC interpreter.
[gmic]-3./foo/*repeat#152/ Hello my friends
[gmic]-3./foo/*repeat#152/ How are you today?
[gmic]-3./foo/*repeat#152/ I like the 'split' command
~~~EDIT: Checked the license of original code, and it came from Unreal Engine 4. Looks like I’ll have to remove this for obvious reason.
Not really much of news:
I pushed the combinations version of the permutations commands I made. Before that, I made a importer for Paint.NET palette files. The import is slow, but it works for the vast majority of cases.
Bad news is that I’m still stuck on cubic spline, but I think for length, I will go for a naive approach as it seems the two non-naive approach has their problems (one of them has the problem of being part of UE4 code).
That being said, I released a command which shows a usage of rep_full_combinations (I added a option to it recently, so that will show up when the new command shows up on update). Here’s the output of a new command:
You see what’s going there? These are symbols used for the Wii U. I wanted to figure out if I can enter symbol-password without having connection to TV. There are 256 possible combination. The video showcase these possible entries.
Another update. I pushed nCr combinations version of the combinations/permutations command. I will figure out implementing this algorithm for the final part of nCr → Revisions to Fast way to get a position of combination (without repetitions) - Mathematics Stack Exchange . That being said, this is the first time built-in permut() is being used.
EDIT: This didn’t work for the last nCr command I pushed. I will have to find another solution before resorting to finding my own (which I did for first time)
EDIT: Maybe this will work - indexing - Find the index of a given combination (of natural numbers) among those returned by `itertools` Python module - Stack Overflow