I have compiled a heat map with the following code:
compounds=uiimport('matrix.txt');
names=uiimport('names.txt');
stages=['Stage ' '3';'Stage ' '6';'Stage ' '9';];
imagesc(compounds.matrix);
colorbar;
colormap('winter');
title('Heat map of ...')
ylabel('Compounds');
xlabel('Developmental stage');
set(gca,'YTick',1:21,'YTickLabel',names.names)
set(gca,'XTick',1:3,'XTickLabel',stages)
Everything works fine and i do not want to change the code as it comes from uplading my data, however i cannot figure out how to add a code (or should i say what kind of code) that would adjust my heatmap colors at a particular scale numbers.
What i would like, is for it to be red to green (0 to 10), with a value 1 as black.
Additionally, my scale is 0 to 100, but i would like the scale to be visible from 1 to 10 and then have a gap and see just 100 at the top.
Many thanks in advance.
There isn't a language requirement listed here, but can I assume you are using matplotlib or matlab.
You can specify the first part of your request by doing breaks. Setting certain ranges. They have a good explanation here
Color a heatmap in Python/Matplotlib according to requirement
A more technical method is to actually create your own colormap
colors = [('white')] + [(cm.jet(i)) for i in xrange(1,256)]
new_map = matplotlib.colors.LinearSegmentedColormap.from_list('new_map', colors, N=256)
Here you can specify color for particular ranges in this example white is 0 and the rest is the standard flowing range
Credit:
Make reverse diagonals white in heatmap
Related
I have three columns with RGB colors. In another set of columns, I need to convert them to X,Y coordinates per the following examples:
How?
The math is eluding me, mostly because it has to wrap back around to red.
Of note: no VB please, and I'd prefer the map jump from the second red directly to pure grayscale (i.e. no bleeding).
UPDATE: I came up with the following:
X —
=IF(AND(MAX([#R]:[#B])=[#R],MAX([#R]:[#B])<>[#B]),[#R]+[#G]-[#B],
IF(MAX([#R]:[#B])=[#G],512+[#G]+[#B]-[#R],
IF(MAX([#R]:[#B])=[#B],1024+[#B]+[#R]-[#G])))
-(256*(MAX([#R]:[#B])/256))
Y —
=ROUNDUP(MAX(Table1[#[R]:[B]])/2*IF(MIN(Table1[#[R]:[B]])=0,1,1+MIN(Table1[#[R]:[B]])/256),0)
Plus a separate quick calculation to wrap around negative X values. I'll also write a quick check for grayscale exceptions after I resolve colors.
But first, tell me why the above is wrong.
I am on day #2 of searching the web and, while I have found plenty of hits that seem like they should work, none of them seem to apply to my particular situation.
I have an Excel chart with two series displayed. One is a sort of exponential decay curve, and one is a constant that intersects with the exponential curve, but does not continue past it (the final x-value of the orange line is estimated to make it look like it intersects the blue curve):
The raw data for the blue curve is as follows (leaving off data labels for confidentiality reasons, but x-values are on the left and y-values are on the right):
The orange line is simply set at 24 all the way across until it intersects with the blue curve.
So here's the problem I need to solve: I need to fill in all of the area below the blue curve with one color, and I need to fill in the area below the orange line with another color. Everything above the blue curve needs to be blank (transparent). Here's an illustration of what I want:
I know in order to get the coloring/shading I need to use an area chart. However, when I try to change the chart type to Area the scales of the axes change for each series and they no longer match up, and I am unable to edit the axes (can't set min, max, etc) to make them match up again. Additionally, only the area directly beneath the constant line fills in (as expected), but I am looking for a way to fill in the area between the orange line, the blue curve, and the axes:
How might one go about doing what I need to do?
If there's any other information I could provide that would be of help, please let me know and I'll be sure to add it in.
EDIT:
I can extend the orange line to follow the blue line off to the right, which may help fill in the lower area. However, when I switch to an area chart I still get the issue with mismatched axes with scale I can't edit:
Notice how the "567" point (the x-value where the orange line should intersect the blue curve) is spaced evenly between "500" and "600", rather than scaling slightly to the right of center as I would have expected.
How do I keep the spacing of one tick every 100 units on the x-axis but keep the datapoint for 567?
You could find the intersection point's coordinates (graphically or analitically), then split your data in two separate series within the same graph as follows :
Edit post comment section :
For some reason x-values are considered by default as text.
Righ click the x-axis > format > Select date on the axis
Then play with the principal and base in days/months to have the intervals you want.
Good parameters for this data :
main : 100 in days
base : in days
I would just have two identical charts : one does the blue and the other the orange then lay the orange chart on top of the blue and make it transparent ... worked a treat in the past...
In MS Excel, I applied conditional formatting to a cell A1 so that it's color changes from blue at value 0 and red at value 100. I copied that cell and pasted it as linked picture(I) specially.(Paste Special). The color of the rectangle picture now changes if value in original cell is changed. The problem is, the shape is only rectangular, it can be converted into square and diamond shape at best.If same rectangle can be transformed into different shapes(maybe by adding extra anchor on rectangle shape), a heat map can be created easily. Please share your insights about this thing if there is a way.
Following pictures may help understand the problem:
[Example][1]
If you want a heat map, please consider using a CHART not gazillions of picture objects, colored/placed/rotated/etc to ... mimic a chart.
First, google anything about "surface chart" in Excel, see how it looks in Excel, whatever. Just see it and think about it so you can compare it to your current approach.
You will observe some things, like:
it's 3D
it has just a few layers/colors
etc.
But really, all of them can be solved. Probably easier than your current approach.
you can easily turn off both axes and you can rotate it so the camera is totally straight top-down - then it looks flat as paper and noone can see it as 3D anymore
you can add more layers, you can set each of them to specific colors
etc.
Some resources:
multiple colors in surface chart
how to change rotation of 3d chart
geesh, I just found even a whole article/tutorial dedicated to creating heat map charts.
Please, read that last link and I'm pretty sure you will want to use that approach instead of doing picture puzzles.
I have an image and I am picking colors by RGB (data sampling). I select N points from a specific region in the image which has the "same" color. By "same" I mean, that part of the image belongs to an object, (let's say a yellow object). Each picked point in the RGB case has three values [R,G,B]. For example: [120,150,225]. And the maximum and minimum for each field are 255 and 0 respectively.
Let's assume that I picked N points from the region of the object in the image. The points obviously have different RGB values but from the same family (a gradient of the specific color).
Question:
I want to find a range for each RGB field that when I apply a color filter on the image the pixels related to that specific object remain (to be considered as inliers). Is it correct to find the maximum and minimum from the sampled points and consider them as the filter range? For example if the max and min of the field R are 120 ,170 respectively, can it be used as a the range that should be kept.
In my opinion, the idea is not true. Because when choosing the max and min of a set of sampled data some points will be out of that range and also there will be some point on the object that doesn't fit in this range.
What is a better solution to include more points as inliers?
If anybody needs to see collected data samples, please let me know.
I am not sure I fully grasp what you are asking for, but in my opinion filtering in RGB is not the way to go. You should use a different color space than RGB if you want to compare pixels of similar color. RGB is good for representing colors on a screen, but you actually want to look at the hue, saturation and intensity (lightness, or luminance) for analysing visible similarities in colors.
For example, you should convert your pixels to HSI or HSL color space first, then compare the different parameters you get. At that point, it is more natural to compare the resulting hue in a hue range, saturation in a saturation range, and so on.
Go here for further information on how to convert to and from RGB.
What happens here is that you implicitly try to reinvent either color indexing or histogram back-projection. You call it color filter but it is better to focus on probabilities than on colors and color spaces. Colors of course not super reliable and change with lighting (though hue tends to stay the same given non-colored illumination) that's why some color spaces are better than others. You can handle this separately but it seems that you are more interested in the principles of calculating "filtering operation" that will do segmentation of the foreground object from background. Hopefully.
In short, a histogram back-projection works by first creating a histogram for R, G, B within object area and then back-projecting them into the image in the following way. For each pixel in the image find its bin in the histogram, calculate its relative weight (probability) given overall sum of the bins and put this probability into the image. In such a way each pixel would have probability that it belongs to the object. You can improve it by dividing with probability of background if you want to model background too.
The result will be messy but somewhat resemble an object segment plus some background noise. It has to be cleaned and then reconnected into object using separate methods such as connected components, grab cut, morphological operation, blur, etc.
I have more then 1 week reading about selective color change of an image. It meand selcting a color from a color picker and then select a part of image in which I want to change the color and apply the changing of color form original color to color of color picker.
E.g. if I select a blue color in color picker and I also select a red part in the image I should be able to change red color to blue color in all the image.
Another example. If I have an image with red apples and oranges and if I select an apple on the image and a blue color in the color picket, then all apples should be changing the color from red to blue.
I have some ideas but of course I need something more concrete on how to do this
Thank you for reading
As a starting point, consider clustering the colors of your image. If you don't know how many clusters you want, then you will need methods to determine whether to merge or not two given clusters. For the moment, let us suppose that we know that number. For example, given the following image at left, I mapped its colors to 3 clusters, which have the mean colors as shown in the middle, and representing each cluster by its mean color gives the figure at right.
With the output at right, now what you need is a method to replace colors. Suppose the user clicks (a single point) somewhere in your image, then you know the positions in the original image that you will need to modify. For the next image, the user (me) clicked on a point that is contained by the "orange" cluster. Then he clicked on some blue hue. From that, you make a mask representing the points in the "orange" cluster and play with that. I considered a simple gaussian filter followed by a flat dilation 3x5. Then you replace the hues in the original image according to the produced mask (after the low pass filtering, the values on it are also considered as a alpha value for compositing the images).
Not perfect at all, but you could have a better clustering than me and also a much-less-primitive color replacement method. I intentionally skipped the details about clustering method, color space, and others, because I used only basic k-means on RGB without any pre-processing of the input. So you can consider the results above as a baseline for anything else you can do.
Given the image, a selected color, and a target new color - you can't do much that isn't ugly. You also need a range, some amount of variation in color, so you can say one pixel's color is "close enough" while another is clearly "different".
First step of processing: You create a mask image, which is grayscale and varying from 0.0 to 1.0 (or from zero to some maximum value we'll treat as 1.0), and the same size as the input image. For each input pixel, test if its color is sufficiently near the selected color. If it's "the same" or "close enough" put 1.0 in the mask. If it's different, put 0.0. If is sorta borderline, put an in-between value. Exactly how to do this depends on the details of the image.
This might work best in LAB space, and testing for sameness according to the angle of the A,B coordinates relative to their origin.
Once you have the mask, put it aside. Now color-transform the whole image. This might be best done in HSV space. Don't touch the V channel. Add a constant to S, modulo 360deg (or mod 256, if S is stored as bytes) and multiply S by a constant chosen so that the coordinates in HSV corresponding to the selected color is moved to the HSV coordinates for the target color. Convert the transformed S and H, with the unchanged L, back to RGB.
Finally, use the mask to blend the original image with the color-transformed one. Apply this to each channel - red, green, blue:
output = (1-mask)*original + mask*transformed
If you're doing it all in byte arrays, 0 is 0.0 and 255 is 1.0, and be careful of overflow and signed/unsigned problems.