| def local_peaks( | |
| log_spectrogram: np.ndarray, amp_min: float, p_nn: int | |
| ) -> List[Tuple[int, int]]: | |
| """ | |
| Defines a local neighborhood and finds the local peaks | |
| in the spectrogram, which must be larger than the | |
| specified `amp_min`. | |
| Parameters | |
| ---------- |
Download OBS: https://obsproject.com/
OBS VirtualCam (for use with Zoom): https://obsproject.com/forum/resources/obs-virtualcam.539/
Using OBS, we can do lectures live on Zoom with a face + document cam, as well as record the lectures without recording the entire Zoom meeting and everyone in it.
| def plot_jobs(*jobs, fn): | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| (jobs,) = jobs | |
| x, y = np.arange(-2, 2, 0.1), np.arange(-2, 2, 0.1) | |
| X, Y = np.meshgrid(x, y) | |
| V = parabaloid(X, Y) | |
| fig, ax = plt.subplots() |
| def get_fourier_components( | |
| func, num_samples, domain_length, sort_by_descending_magnitude=False | |
| ): | |
| """ | |
| Returns the N // 2 + 1 amplitude-phase terms of the inverse D series | |
| of func(t), where funct t is evaluated on [0, T) in N evenly-spaced points. | |
| I.e each component | |
| A[k] cos(2*pi * f[k] * t + phi[k]) | |
| import numpy as np | |
| def ecdf(data): | |
| """Returns (x) the sorted data and (y) the empirical cumulative-proportion | |
| of each datum. | |
| Parameters | |
| ---------- | |
| data : numpy.ndarray, size-N | |
