domain constraints
时间: 2023-11-25 07:31:49 浏览: 24
Domain constraints refer to the limitations or rules that apply to a specific domain, such as a particular industry, field of study, or area of expertise. These constraints may include legal regulations, technical requirements, ethical standards, or other guidelines that dictate how a particular domain should operate. For example, in the healthcare domain, there may be regulations governing the privacy and security of patient data, while in the financial domain, there may be rules about how financial transactions are conducted. Adhering to domain constraints is important for ensuring compliance, avoiding legal or ethical violations, and promoting best practices within a particular field.
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import numpy as np import numpy.fft as fft import scipy.ndimage as nd import imageio import scipy.misc as misc from math import pi #Read in source image #source = nd.imread("einstein.bmp", flatten=True) source = imageio.imread("einstein.bmp", as_gray=True) #Pad image to simulate oversampling pad_len = len(source) padded = np.pad(source, ((pad_len, pad_len),(pad_len, pad_len)), 'constant', constant_values=((0,0),(0,0))) ft = fft.fft2(padded) #simulate diffraction pattern diffract = np.abs(ft) l = len(padded) #keep track of where the image is vs the padding mask = np.ones((pad_len+2,pad_len+2)) mask = np.pad(mask, ((pad_len-1, pad_len-1),(pad_len-1, pad_len-1)), 'constant', constant_values=((0,0),(0,0))) #Initial guess using random phase info guess = diffract * np.exp(1j * np.random.rand(l,l) * 2 * pi) #number of iterations r = 801 #step size parameter beta = 0.8 #previous result prev = None for s in range(0,r): #apply fourier domain constraints update = diffract * np.exp(1j * np.angle(guess)) inv = fft.ifft2(update) inv = np.real(inv) if prev is None: prev = inv #apply real-space constraints temp = inv for i in range(0,l): for j in range(0,l): #image region must be positive if inv[i,j] < 0 and mask[i,j] == 1: inv[i,j] = prev[i,j] - beta*inv[i,j] #push support region intensity toward zero if mask[i,j] == 0: inv[i,j] = prev[i,j] - beta*inv[i,j] prev = temp guess = fft.fft2(inv) #save an image of the progress if s % 10 == 0: #imageio.imsave("/Users/chasegoddard/Stuff/CDI/code/save/progress" + str(s) + # ".bmp", prev) imageio.imsave("C:/Users/Administrator/Desktop/仿真结果/结果" + str(s) + ".bmp", prev) print(s)
这段代码是用于模拟计算机辅助衍射成像的过程。它使用了傅里叶变换和反傅里叶变换来处理图像。在代码中,首先读入源图像,然后对图像进行填充以模拟过采样。接下来,对填充后的图像进行傅里叶变换,得到衍射图样。然后,使用随机相位信息初始化重建图像的猜测,并使用一定的迭代次数和步长参数来逐步优化猜测。在每次迭代中,根据傅里叶域和实空间的约束条件,更新重建图像。最后,保存优化过程中的图像并打印迭代次数。
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The application of adaptive mesh constraints requires the definition of an adaptive mesh domain.
Yes, that's correct. In order to apply adaptive mesh constraints, you need to define a domain in which the mesh will be refined or coarsened based on certain criteria. This domain can be defined in terms of geometric boundaries or physical properties, such as regions of high gradient or areas of interest in the simulation. Once the domain is defined, the adaptive mesh algorithm will use this information to selectively refine or coarsen the mesh in order to improve the accuracy and efficiency of the simulation.