ADMIRALTY e-Reader 1.3 – User Guide Introduction to ADMIRALTY e-Reader 1.3 ADMIRALTY e-Reader 1.3 is a standalone application that can be used to update and view ADMIRALTY e-Nautical Publications (AENPs). Keeping up-to-date To keep your ADMIRALTY e-Reader 1.3 installation up-to-date, you will need to regularly update the AENP Catalogue. Palette v 11.10 Update Download. Palette v 11.04 Update Download. Palette v11.01. For current owners of Palette v10.0 or higher. This file is only for those who have deleted the installation files on their registered Palette 11.0 dongle. Palette PTS Version 1.07 Update and Installation Files.
# ColorBrewer Accent # # for use with qualitative/categorical data # provides 8 colors, 4 pale and 4 saturated # compatible with gnuplot =4.2 # author: Anna Schneider # pale green - pale purple - pale orange - pale yellow - blue - magenta - brown - grey # palette set palette maxcolors 8 set palette defined ( 0 ' #7FC97F', 1 '#BEAED4', 2 '#FDC086', 3 '#FFFF99', 4 '#386CB0', 5 '#F0027F', 6 '#BF5B17', 7 '#666666' )Zbiory palet:.Sprawdzamy jaki gradient ma aktualna paleta:show palette gradientOtrzymujemy:0. Gray=0.0000, (r,g,b)=(1.0000,1.0000,1.0000), #ffffff = 255 255 2551. Gray=1.0000, (r,g,b)=(0.0000,0.0000,0.0000), #000000 = 0 0 0References.
/src/command.c.
Some weeks ago, I was working on a dataviz to show results coming from an analysis I had performed, and I found myself looking at that default ggplot2 palette, which is optimal in term of discrimination among categories, but nevertheless can not be compared to some wonderful palettes you can see employed within art masterpieces like Monet or Michelangelo’s. Those palettes are coming from years and years of studies around colour theory and pictorial techniques and, I started thinking, would do a great service if employed as plot’s palettes, with their complementary colours or their balanced set of hues. Behind the scenes: k-means for image processingI have to be honest: my first thinking when dealing with this task was not the one I am going to show you. I was actually getting lost following a bad path made of triples of Red Green and Blues, trying to select from frequency tables the most representative ones.
But suddenly, after reading a blog post about k-means algorithm the idea was born: let k-means divide the picture into homogeneous clusters, having each one a “center” constituted by an RGB triple and by this way define a palette representative of the picture itself. The k-means algorithmTo let you maximize the fun coming from the rest of the post we have to see here briefly what the k-means clustering algorithm is. This algo is a way to divide a given set of data into k homogeneous groups/clusters, where k stands for a number, like usually n does. How do we do that? The basic and elegant idea behind the algorythm is: measure the difference between data points and look for the clusterisation which minimize this difference within clusters.
The typical measure employed here is the euclidean distance, and particularly the squared euclidean distance. We measure with it the distance between points, and we can expand it from 1 to n dimensions.
That is why it turns to be effective with such kind of problems where populations can be pertaining to multidimensional spaces. Making it less wordy, let us look at the formula behind the distance.
Given two points of a three dimension space and this distance is measured as the square of the distance between the three couples of coordinates raised to the the second:Now that we know the measure we want to minimize let us have a look at how to find the best possible clusterisation. Read the imageAs is often the case within the R world, we have a package to help us with this task. The jpeg package, developed by provides us functions to import an image and decomposing it into points each of which codified in the terms of the RGB system. Once the package is installed you can run the readJPEG function to import and decompose every kind of.jpeg image. Within our example we are going to employ the Sacra Famiglia Canigiana painting by Raffaello, which contains a wonderful set of vivid colours coming from Raffaello’s studies on the unione painting technique. Run k-meanswe are now ready to apply k-means algorithm to our data. It is actually really easy to apply this algo in R.
As is often the case with R this quite advanced algo is already made available within the base R package, stored within the function conveniently named kmeans. We just have to pass to this function the dataset over which we want to apply the algo, specifying the number of clusters. We could also specify some other settings, like the maximum number of iterations of above introduced steps (b) (c) and (d). ## List of 9## $ cluster: int 1:247950 9 9 9 9 9 9 9 9 9 9.## $ centers: num 1:20, 1:3 0.3981 0.7117 0.7668 0.6133 0.0958.##.- attr(., 'dimnames')=List of 2##.$: chr 1:20 '1' '2' '3' '4'.##.$: chr 1:3 'R' 'G' 'B'## $ totss: num 60652## $ withinss: num 1:20 85 36.6 75.2 43 28.6.## $ tot.withinss: num 1048## $ betweenss: num 59604## $ size: int 1:20 9924 0 3 2297 4.## $ iter: int 10## $ ifault: int 0## - attr(., 'class')= chr 'kmeans'. Showing the palettewithout analysing every single attribute we can easily locate the centers vector, showing the above mentioned centroids. As you would imagine, those centroids have got three dimensions: one for the R, one for the G and one for the B.
And those centroids are points whose minimise the distance from the other points within their cluster. So they are RGB triples representative of homogenous clusters of other RGB triples composing our image. What do you think, is this enough to be a representative palette? Let us check it in the best possible way, i.e. letting our eyes scrutinize them. To do this we are going to leverage the showcol function from the scales package (once more, thank you Hadley Whickam). Paletter package to replicate the processOne of the best gifts every R user receives from the R community is the ton of packages available to accomplish nearly every data mining task. This is why I often try to translate my code into packages, especially when it contains some functions which could be useful for someone.
This I think is the case for the processing steps we have seen before, as we will see later on. For this reason you can find, which provides you the palettemaker function, to let you easily apply the above introduced algo on your custom image. Paletter is freely available (of course), is not so badly documented, and you can install it running the following. Alternative use casesabstracting from this use case, what we have here? A flexible tool to derive a set of representative colours from an image. We could employ this in several fields:. web design: dinamically generate the set of colour to fill web pages based on a main image which should set the tone of all the website.
graphics: strarting from something defining the graphical identity of a company, for instance a logo or some kind of picture, easily derive a representative palette to be employed on marketing materials and similar stuffs. images placeholder: have you noticed that on google images the actual loading of the picture is preceeded from a coloured square filled with a colour representative of the incoming picture? We could do this with paletter, just specifying 1 when setting the number of coloursHave you got any other idea? Feel free to comment here suggesting further developments or use cases.
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January 2023
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