dstack | source tool for managing dev environments | Data Visualization library

According to matplotlib's colormap documentation, the lightness values of their default colormaps are not constant. However, I would like to create a colormap from the HSL color space that has a constant lightness. How can I do that?

I get that generally, it's not that hard to create your own colormaps, but I don't know how to do this while satisfying the lightness criterion. Maybe this can be done by reverse-engineering the code from the colormap documentation?

I think I found a way to do that, based on this post. First of all, working in the HSL color space turned out to be not the best idea for my overal goal, so I switched to HSV instead. With that, I can load the preferred colormap from matplotlib, create a set of RGB colors from it, transform them into HSV, set their color value constant, transform them back into RGB and finally create a colormap from them again (which I can then use for a 2d histogram e.g.).

I need a colormap in HSV with a constant color value because then I can uniquely map colors to the RGB space from the pallet that is spanned by hue and saturation. This in turn would allow me to create a 2d histogram where I could color-code both the counts (via the saturation) and a third variable (via the hue).

In the MWE below for example (slightly changed from here), with a colormap with constant color value, in each bin I could use the saturation to indicate the number of counts (e.g. the lighter the color, the lower the number), and use the hue to indicate the the average z value. This would allow me to essentially combine the two plots below into one. (There is also this tutorial on adding alpha values to a 2d histogram, but this wouldn't work in this case I think.)

Currently, you still need both plots to get the full picture, because without the histogram for example, you wouldn't be able to tell how significant a certain z value in a bin might be, as the same color is used independently of how many data points contributed to it (so judging by the color, a bin with only one data point might look just as significant as a bin with the same color but that contains many more data points; thus there is a bias in favor of outliers).

I want to recognize the tray is empty or not in the given image using OpenCV in python.

below is what I have tried

1. detect the biggest rectangle and cropped by using the below code

I want to split a 3d matrix into smaller matrixes of equal size. In this case a 4x4x3 matrix into 4 individual matrices of 2x2x3. I am trying this:

I'm trying to calibrate HD Camera, resolution 1280x720.

I need it to be resized after applying undistort function to size 640x480. So it's changing aspect ratio from 16:9 to 4:3.

In my opencv application anything works flawless besides saving (y, x) positions of calibrated camera. I need it because, binary output file is working with flashplayer application that reads this changed camera (y, x) positions and applyies it to original camera view that is being grabbed by flashplayer.

Binary file with cv2.resize():

I am dealing with a dataset on Kaggle.com: https://www.kaggle.com/sorour/38cloud-cloud-segmentation-in-satellite-images And what I'm trying to do is combine 3 grayscale image represent 3 channels (R, G, B) to one natural color image. So here is what I've tried:

I have two numpy arrays (4x4 each). I would like to concatenate them to a tensor of (4x4x2) in which the first 'sheet' is the first array, second 'sheet' is the second array, etc. However, when I try np.stack the output of d[1] is not showing the correct values of the first matrix.

I need to generate multiple temperature plots for every month of every year, spanning from 2002 to 2018. I have managed to develop one plot for all of the data in 2002 (about 6 months). I had import my netcdf files into an array and slice the 3d array to a 2d array of lat lon lst. Using pcolormesh I plotted one singular lon, lat, data_2d plot but can't figure out how to define the months so I can plot them separately. The data is not available online but I am looking for a general function or command that can iterate through the months and plot them separately onto a map.

I have numpy 3D arrays with the following shape : (688, 549, 3).
Each dimension is an image. All the dimentions dtype is 'float64', however, the 3rd one was has only integers (but because of the dtype they are saved as float, for example. isntad of 3 it will be 3.0 ).

I'm trying to transform the array to dataframe. I use this script:

I am trying to find a way to create a trajectory between two points with specified speeds in a vectorized way. For example start point is [0, 0] and end point is [25, 15]. Next I need to specified speeds [25, 4, 8, 2] and their corresponding probabilities [0.4, 0.2, 0.3, 0.1]. So for each time interval, speed can be 25 m/s (with probability 40%), or 4 m/s (probability 20%), etc.

Here is an example of desired output: