Live Code, Satellite Example Part B Now We Will Do Something Even More Interesting Have you ever wondered how to determine the Euclidean distance between every point and the circle's center? In this post, we'll show you how to draw a circle with a radius equal to 50% of the total row index and figure out the Euclidean distance between each of the circle's points. We'll describe how to build a filter that makes all the points outside of that radius line and circle true and all the points inside of that circle false. To start, we create two mesh grids, x and y, which will be used to calculate the center point of the circle. We will also determine the shape of our photo data, an ndarray which is a three-layer image. The number of rows will be assigned to total rows, the number of columns to total columns, and the three layers RGB to total layers. If we print the shape, we will see that it still has the original shape of the image. Using the ogrid function, we will vectorize the distance from the center, which will be a function of the two variables, x and y. The ogrid function returns an ndarray that has the range of total rows and total columns as its arguments. The function then gives us two vectors, x and y, which will have the total number of rows and the total number of columns in the original image, respectively. Ogrid is a compact method of creating multidimensional ndarray operations in single lines. After that, we get the center point, x and y, by dividing the total number of rows and columns by 2. To determine the locations that are farther than the radius of the circle we are attempting to generate, we use the two vectors, x and y. Using the x vector and our center row, we generate a distance from the center matrix. With our circular mask, we have set true for each of the circular values that are outside of the circle. That is what we give the circular mask. Only the borders of the circular mask will be visible if we print it. Then, we can filter out all the points outside of the circle using this mask. All values that are less than or equal to the radius will be set to false, while all values greater than the radius will be set to true. We will now determine the Euclidean distance between each point and the center. The broadcasting feature of NumPy can be used to determine the Euclidean distance. NumPy can conduct operations on arrays of various shapes thanks to broadcasting. In this instance, the center column and row will be subtracted from the x vector and y vector, respectively. The x and y vectors will then be squared and added. The outcome will be a distance matrix that we can use to determine how far each point is from the center using the Euclidean distance formula. We can calculate the Euclidean distance using the following formula: def euclidean_distance(x, y): return np.sqrt(x ** 2 + y ** 2) To determine the Euclidean distance between each point inside the circle, we can now apply the circular mask to the distance matrix. To use the circular mask, we can create another function: def apply_circular_mask(mask, data): return np.ma.masked_array(data, mask=mask) This function will mask all the points outside of the circle in our distance matrix. We can then use the masked distance matrix to get the Euclidean distance of all the points within the circle.