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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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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) |
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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) |
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return x, y, z |
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