## Is it possible to isolate a BGR color gradient with cv2.inRange()?

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

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

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

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?

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

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?

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?

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

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

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

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