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