If I have an array: x = np.array(np.random.rand(2000, 400), order='F'), and create a mask: y = (x > .5) (which also happens to become F-contiguous), then the following code:

I have a problem regarding numpy arrays. Here is a while loop solution I came up with but its run time is way too long. the array SG1_stresses has little under two million rows and the values are for example 34.3241. time_list contains the time values corresponding to the SG1_stresses.

I have the following code:

I’m using numpy.histogram on my data and then I want to perform a fit to some curve where the number of occurrences in each bin is dividing, so it cannot be zero. So I need the bins to contain at least one count. Is there anyway to do that with numpy.histogram or numpy.histogram_bin_edges?

I’m looking for a way to calculate the mean value from a series of arrays. The window should contain 3 values ​​and slide to the third last place in the array, then it saves the last 2 values ​​and if the new array arrives within a certain time (e.g. 2 seconds), the last two values ​​are added to the new array and the data is recalculated. If it takes longer than the time, the array starts again and the last two values ​​are discarded. The data is then plotted live (plotting already works, I’m just not getting any further with the calculation). Here is my attempt so far, unfortunately my window freezes.

I’m looking for a way to calculate the mean value from a series of arrays. The window should contain 3 values ​​and slide to the third last place in the array, then it saves the last 2 values ​​and if the new array arrives within a certain time (e.g. 2 seconds), the last two values ​​are added to the new array and the data is recalculated. If it takes longer than the time, the array starts again and the last two values ​​are discarded. The data is then plotted live (plotting already works, I’m just not getting any further with the calculation). Here is my attempt so far, unfortunately my window freezes.

I have the following script and sample data:

Please, consider the following lines, where a tuple is starred while being used to index a NumPy array:

I just started learning the machine and while learning np.zeros and np.ones, a question arose in my mind that what are these used for? I don’t know if they are used in illustration or algorithms, but I would like to know what they are used for and What makes them attractive?

I have two numpy array “point” and “r”. These array contains coordinates of point cloud [[x y z]]. Now i need to remove the values of “r” from “point”. I cannot do a for loop because i have thousands of coordinates and this take a lot of time. I tried using the numpy isin method but the problem it seems is that it also remove values where only one of the three dimension match inside the array. I need to remove point where all the x y z matches in both array.