I want to vectorize a function frame = np.zeros((out_h, out_w, 3), dtype=np.uint8) for i, val1 in enumerate(rows): for j, val2 in enumerate(cols): for j, val3 in enumerate(ch): # Assuming you want to store the pair as tuples in the matrix frame[i, j, k] = image[val1, val2, val3] out.write(np.array(frame)) with a simple numpy function that does something like this what is it called
I want to vectorize a function frame = np.zeros((out_h, out_w, 3), dtype=np.uint8) for i, val1 in enumerate(rows): for j, val2 in enumerate(cols): for j, val3 in enumerate(ch): # Assuming you want to store the pair as tuples in the matrix frame[i, j, k] = image[val1, val2, val3] out.write(np.array(frame)) with a simple numpy function that does something like this what is it called
The technique you're looking for is called "fancy indexing" (also known as "advanced indexing") in NumPy. Instead of looping element‐by‐element, you can use fancy indexing to select multiple values from an array at once. For example, if you have: • rows: an array (or list) of row indices • cols: an array (or list) of column indices • ch: an array (or list) of channel indices …you might combine them into a single indexing expression. One way to do that is with np.ix_ when indexing the first two dimensions. For example, assuming your image has shape (height, width, channels), you can do: ---------------------------------------------------------------- # Example assuming "rows" and "cols" are 1D arrays of indices # and you want to pick all the channels (or a subset given by "ch"). # If you want to select specific channels, say "ch", you can do: frame = image[np.ix_(rows, cols, ch)] # Or if you want to get all channels frame = image[np.ix_(rows, cols)] ---------------------------------------------------------------- (Note: np.ix_ creates open mesh grids from the given 1D sequences, which lets you index the array in a vectorized way.) By using fancy indexing, you avoid writing explicit Python loops, and the underlying operations are implemented in fast, compiled C-code, which can be a lot more efficient.
Testing if the previous output contains the string ix_: True
I want to vectorize a function frame = np.zeros((out_h, out_w, 3), dtype=np.uint8) for i, val1 in enumerate(rows): for j, val2 in enumerate(cols): for j, val3 in enumerate(ch): # Assuming you want to store the pair as tuples in the matrix frame[i, j, k] = image[val1, val2, val3] out.write(np.array(frame)) with a simple numpy function that does something like this what is it called
I want to vectorize a function frame = np.zeros((out_h, out_w, 3), dtype=np.uint8) for i, val1 in enumerate(rows): for j, val2 in enumerate(cols): for j, val3 in enumerate(ch): # Assuming you want to store the pair as tuples in the matrix frame[i, j, k] = image[val1, val2, val3] out.write(np.array(frame)) with a simple numpy function that does something like this what is it called
It sounds like you’re looking for what is often referred to as “vectorization” in NumPy. In NumPy “vectorization” means replacing explicit Python loops with operations that work on entire arrays at once. There are a couple of related techniques you might be thinking of: 1. np.vectorize: • NumPy provides a function called np.vectorize that lets you wrap a scalar function so that it appears to operate on arrays. However, note that np.vectorize is mainly a convenience tool—it doesn’t actually speed things up because it’s still looping under the hood. 2. Broadcasting and advanced indexing: • Often, you can eliminate loops altogether by taking advantage of NumPy’s broadcasting rules and/or advanced (or “fancy”) indexing. For example, if you’re trying to assign values from one array to another with different indices (as in your code), you might be able to do something like this: frame = image[rows[:, None], cols, :] That is, if rows and cols are 1D arrays that specify the rows and columns you need. The key idea is that the arrays “broadcast” against one another to produce the desired output. Which one you choose depends on your particular situation. If you have a function that needs to operate element‐by‐element, np.vectorize might be what you have in mind. But if your situation simply requires rearranging or selecting parts of an existing array (as it appears with your loops), then advanced indexing or broadcasting is usually the better approach. In summary, if you’re referring to a built–in NumPy function for “vectorizing” a function that applies element by element, that function is called np.vectorize.
Testing if the previous output contains the string ix_: False