Is it possible to isolate a BGR color gradient with cv2.inRange()? Problem Description: I am writing a program that observes an area on the screen that displays a number between 0 and 100. When the number is 100, it has green text. When the number is 0, it has red text. As the number decreases … Read more
Create 2D numpy array from buffer Problem Description: Consider a system with n_channels transmitting n_samples at a given sampling rate. The 1D buffer containing the timestamps and the 2D buffer containing (n_channels, n_samples) is: from ctypes import c_double, c_float # Assume a 2-second window, 3 channels, sampled at 1024 Hz # data: (n_channels, n_samples) = … Read more
Replace all off diagonal elements in numpy array Problem Description: I have below numpy array import numpy as np np.identity(13) Now I like to replace all off-diagonal elements with some other number, say 0.45. Is there any direct method available to perform this? Solution – 1 What about the following? import numpy as np n … Read more
Most efficient way to convert numpy array to dict Problem Description: I have 2 numpy arrays: import numpy as np a = np.array([1, 2, 3]) b = np.array([10, 20, 30]) I need to create a list of dicts: res = [{"a": 1, "b": 10}, {"a": 2, "b": 20}, {"a": 3, "b": 30}] in most optimal … Read more
How to get a maximally performant NumPy installation from Pip? Problem Description: While conda does a great job of installing a maximally performant NumPy, pip leaves me with a 6x performance reduction by comparison. How can I get pip to install NumPy, such that it yields the same performance as when installed via conda? Solution … Read more
how to arrange list of lists of dictionaries as a table with key name as header in python Problem Description: I want to create a data frame as shown in the last section of my question. the explanation is as follows, I have this: results df = pd.DataFrame(results) df output : results: farm_power_output farm_unwaked_power_output farm_wind_direction … Read more
Numpys pinv seem slow? Problem Description: I was playing around with numpys pinv, and it seems like it is quite slow. Compared to a homebrewed function: def moore_penrose_inverse(data: np.ndarray) -> np.ndarray: if data.shape[0] > data.shape[1]: results = np.linalg.inv(np.dot(data.T, data)) @ data.T elif data.shape[0] < data.shape[1]: results = data.T @ np.linalg.inv(np.dot(data, data.T)) else: results = np.linalg.inv(data) … Read more
Numpys pinv seem slow? Problem Description: I was playing around with numpys pinv, and it seems like it is quite slow. Compared to a homebrewed function: def moore_penrose_inverse(data: np.ndarray) -> np.ndarray: if data.shape[0] > data.shape[1]: results = np.linalg.inv(np.dot(data.T, data)) @ data.T elif data.shape[0] < data.shape[1]: results = data.T @ np.linalg.inv(np.dot(data, data.T)) else: results = np.linalg.inv(data) … Read more
How to split and read raw data into different numpy arrays based on delimeter parameter Problem Description: I have a raw data in the following form ####### ####### #col1 #col2 #col3 1 10 100 2 11 150 3 14 155 ####### ####### ####### ####### #col1 #col2 #col3 1 14 100 2 17 180 3 14 … Read more
How to split and read raw data into different numpy arrays based on delimeter parameter Problem Description: I have a raw data in the following form ####### ####### #col1 #col2 #col3 1 10 100 2 11 150 3 14 155 ####### ####### ####### ####### #col1 #col2 #col3 1 14 100 2 17 180 3 14 … Read more