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| from __future__ import annotations | |
| from typing import TYPE_CHECKING, Any | |
| import numpy as np | |
| if TYPE_CHECKING: | |
| from contourpy._contourpy import CoordinateArray | |
| def simple( | |
| shape: tuple[int, int], | |
| want_mask: bool = False, | |
| ) -> tuple[ | |
| CoordinateArray, CoordinateArray, CoordinateArray | np.ma.MaskedArray[Any, Any] | |
| ]: | |
| """Return simple test data consisting of the sum of two gaussians. | |
| Args: | |
| shape (tuple(int, int)): 2D shape of data to return. | |
| want_mask (bool, optional): Whether test data should be masked or not, default ``False``. | |
| Return: | |
| Tuple of 3 arrays: ``x``, ``y``, ``z`` test data, ``z`` will be masked if | |
| ``want_mask=True``. | |
| """ | |
| ny, nx = shape | |
| x = np.arange(nx, dtype=np.float64) | |
| y = np.arange(ny, dtype=np.float64) | |
| x, y = np.meshgrid(x, y) | |
| xscale = nx - 1.0 | |
| yscale = ny - 1.0 | |
| # z is sum of 2D gaussians. | |
| amp = np.asarray([1.0, -1.0, 0.8, -0.9, 0.7]) | |
| mid = np.asarray([[0.4, 0.2], [0.3, 0.8], [0.9, 0.75], [0.7, 0.3], [0.05, 0.7]]) | |
| width = np.asarray([0.4, 0.2, 0.2, 0.2, 0.1]) | |
| z = np.zeros_like(x) | |
| for i in range(len(amp)): | |
| z += amp[i] * np.exp( | |
| -((x / xscale - mid[i, 0]) ** 2 + (y / yscale - mid[i, 1]) ** 2) | |
| / width[i] ** 2 | |
| ) | |
| if want_mask: | |
| mask = np.logical_or( | |
| ((x / xscale - 1.0) ** 2 / 0.2 + (y / yscale - 0.0) ** 2 / 0.1) < 1.0, | |
| ((x / xscale - 0.2) ** 2 / 0.02 + (y / yscale - 0.45) ** 2 / 0.08) < 1.0, | |
| ) | |
| z = np.ma.array(z, mask=mask) # type: ignore[no-untyped-call] | |
| return x, y, z | |
| def random( | |
| shape: tuple[int, int], | |
| seed: int = 2187, | |
| mask_fraction: float = 0.0, | |
| ) -> tuple[ | |
| CoordinateArray, CoordinateArray, CoordinateArray | np.ma.MaskedArray[Any, Any] | |
| ]: | |
| """Return random test data.. | |
| Args: | |
| shape (tuple(int, int)): 2D shape of data to return. | |
| seed (int, optional): Seed for random number generator, default 2187. | |
| mask_fraction (float, optional): Fraction of elements to mask, default 0. | |
| Return: | |
| Tuple of 3 arrays: ``x``, ``y``, ``z`` test data, ``z`` will be masked if | |
| ``mask_fraction`` is greater than zero. | |
| """ | |
| ny, nx = shape | |
| x = np.arange(nx, dtype=np.float64) | |
| y = np.arange(ny, dtype=np.float64) | |
| x, y = np.meshgrid(x, y) | |
| rng = np.random.default_rng(seed) | |
| z = rng.uniform(size=shape) | |
| if mask_fraction > 0.0: | |
| mask_fraction = min(mask_fraction, 0.99) | |
| mask = rng.uniform(size=shape) < mask_fraction | |
| z = np.ma.array(z, mask=mask) # type: ignore[no-untyped-call] | |
| return x, y, z | |