我有一个特定的python问题，非常需要通过避免使用循环来加快速度，但是，我对如何执行此操作感到困惑。我需要读取适合图像，将其转换为一个numpy数组（大小约为2000 x 2000个元素），然后为每个元素计算围绕它的元素环的统计信息。如我现在的代码所示，围绕元素的环的统计信息是通过使用掩码的函数来计算的。这很快，但是，我当然将此函数调用了2000x2000次（最慢的部分）。我对python比较陌生。我认为使用遮罩功能很聪明，但是我找不到单独解决每个元素的方法。感谢您提供的任何帮助。
# First, the function computing the statistics within a ring
around the central pixel:<br/>
# flux = image intensity at pixel (i,j)<br/>
# rad1, rad2 = inner and outer radii<br/>
# array = image array<br/>_
def snr(flux, i, j, rad1, rad2, array):
a, b = i, j
nx, ny = array.shape
y, x = np.ogrid[-a:nx-a, -b:ny-b]
mask = (x*x + y*y >= rad1*rad1) & (x*x + y*y <= rad2*rad2)
Nmask = np.count_nonzero(mask)
noise = 0.6052697 * abs(Nmask * flux - sum(array[mask]))
return noise
# Now, the call to snr for each pixel in the array data1:<br/>_
frame1 = fits.open(in_frame, mode='readonly') # read in fits file
data1 = frame1[ext].data # convert to np array
ny, nx = data1.shape # array dimensions
noise1 = zeros((ny, nx), float) # empty array
r1 = 5 # inner radius (pixels)
r2 = 7 # outer radius (pixels)
# The function is fast, but calling it 2k x 2k times is not:
for j in range(ny):
for i in range(nx):
noise1[i,j] = der_snr(data1[i,j], i, j, r1, r2, data1)
您尝试执行的操作可以表示为图像卷积。尝试这样的事情：
import numpy as np
import scipy.ndimage
from astropy.io import fits
def make_kernel(inner_radius, outer_radius):
if inner_radius > outer_radius:
raise ValueError
x, y = np.ogrid[-outer_radius:outer_radius + 1, -outer_radius:outer_radius + 1]
r2 = x * x + y * y
kernel = (r2 >= inner_radius * inner_radius) & (r2 <= outer_radius * outer_radius)
return kernel
in_frame = '<file path>'
ext = '...'
frame1 = fits.open(in_frame, mode='readonly')
data1 = frame1[ext].data
inner_radius = 5
outer_radius = 7
kernel = make_kernel(inner_radius, outer_radius)
n_kernel = np.count_nonzero(kernel)
conv = scipy.ndimage.convolve(data1, kernel, mode='constant')
noise1 = 0.6052697 * np.abs(n_kernel * data1 - conv)
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