“Autogalaxy”的版本间差异
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==学习笔记== |
==学习笔记== |
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*Array_2d/Mask2D/Kernal2D |
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*array_2d |
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:为图像数据定义的一类二维数组类,包含了pixelsize信息,并可以同时包含mask信息 |
:为图像数据定义的一类二维数组类,包含了pixelsize信息,并可以同时包含mask信息 |
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: 注意Mask2D中 True值是指被mask掉 (下面例子中mask是一个booltype的ndarray,True是指保留) |
: 注意Mask2D中 True值是指被mask掉 (下面例子中mask是一个booltype的ndarray,True是指保留) |
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noise_2d = aa.Array2D.no_mask(values=vflux**0.5+0.01,shape_native=Agemap[0].shape,pixel_scales=0.5) |
noise_2d = aa.Array2D.no_mask(values=vflux**0.5+0.01,shape_native=Agemap[0].shape,pixel_scales=0.5) |
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#noise_2d = noise_2d.apply_mask(mask=mask_2d) |
#noise_2d = noise_2d.apply_mask(mask=mask_2d) |
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# psf是Kernel2D类型 |
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psf_2d = ag.Kernel2D.from_gaussian(shape_native=(10, 10), sigma=1.5, pixel_scales=0.5) |
psf_2d = ag.Kernel2D.from_gaussian(shape_native=(10, 10), sigma=1.5, pixel_scales=0.5) |
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2023年2月21日 (二) 03:02的版本
PyAutoGalaxy is an open-source Python 3.8+ package for analysing the morphologies and structures of large multi-wavelength galaxy samples.
详见 https://pyautogalaxy.readthedocs.io/
学习笔记
- Array_2d/Mask2D/Kernal2D
- 为图像数据定义的一类二维数组类,包含了pixelsize信息,并可以同时包含mask信息
- 注意Mask2D中 True值是指被mask掉 (下面例子中mask是一个booltype的ndarray,True是指保留)
ima_2d = aa.Array2D.no_mask(values=Agemap[0],shape_native=Agemap[0].shape,pixel_scales=0.5) mask_2d = aa.Mask2D(mask=~mask,shape_native=Agemap[0].shape,pixel_scales=0.5) #ima_2d = ima_2d.apply_mask(mask=mask_2d) noise_2d = aa.Array2D.no_mask(values=vflux**0.5+0.01,shape_native=Agemap[0].shape,pixel_scales=0.5) #noise_2d = noise_2d.apply_mask(mask=mask_2d)
- psf是Kernel2D类型
psf_2d = ag.Kernel2D.from_gaussian(shape_native=(10, 10), sigma=1.5, pixel_scales=0.5)
参见 https://pyautogalaxy.readthedocs.io/en/latest/api/_autosummary/autogalaxy.Array2D.html
- imaging
- 基于array_2d生成的图像类,必须同时具有原始图像数据,和noisemap,同时psf信息可选
imaging=ag.Imaging(image=ima_2d,noise_map=noise_2d,psf=psf_2d)
- Grid
grid_2d = ag.Grid2D.uniform(shape_native=Agemap[0].shape, pixel_scales=0.5).5)