def test_crop(input, size, offset, result):
im1 = AstroImage(data=input)
im1.crop(size[0], size[1], offset[0], offset[1])
verifyData(im1.data, result)
# Compute uncertainty array for this image
tmpImg.sigma = np.sqrt((bkg + tmpImg.arr)/effective_gain)
# Create a copy of the temporary image and replace its array
# with the x and y positions of the Mimir pixels
yImg = tmpImg.copy()
xImg = tmpImg.copy()
ny, nx = tmpImg.arr.shape
yImg.arr, xImg.arr = np.mgrid[0:ny, 0:nx]
# Apply the Kokopelli mask to the image
tmpImg.arr[np.where(kokopelliMask.arr)] = np.nan
# Crop all images (and position images) to avoid edges
tmpImg.crop(12,1012, 13, 1013)
xImg.crop(12, 1012, 13, 1013)
yImg.crop(12, 1012, 13, 1013)
# Append each image to its respective list
imgList.append(tmpImg)
xPosList.append(xImg)
yPosList.append(yImg)
# Append scale factor and background level to their lists
scaleFactors.append(thisScale)
backgroundLevels.append(bkg)
# # Loop through each image in the image list and compute its uncertainty
# imgList1 = imgList.copy()
# for imgNum, img in enumerate(imgList):
