def test_binned_outlier(mask_method):
b = map_to_binner(*generate_map_bin(geo, (2048, 2048)))
img = np.ones((2048, 2048))
bad = np.unique(np.random.randint(0, 2048 * 2048, 1000))
urbad = np.unravel_index(bad, (2048, 2048))
img[urbad] = 100
mask = binned_outlier(img, b, mask_method=mask_method)
assert_equal(np.where(mask.ravel() == 0)[0], bad)
def test_z_score_image():
b = map_to_binner(*generate_map_bin(geo, (2048, 2048)))
img = np.ones((2048, 2048))
bad = np.unique(np.random.randint(0, 2048 * 2048, 1000))
urbad = np.unravel_index(bad, (2048, 2048))
img[urbad] = 10
z_score = z_score_image(img, b)
assert all(z_score[urbad] > 2)
def test_mask_img(mask_method):
b = map_to_binner(*generate_map_bin(geo, (2048, 2048)))
img = np.ones((2048, 2048))
r = np.random.RandomState(42)
bad = np.unique(r.randint(0, 2048 * 2048, 1000))
urbad = np.unravel_index(bad, (2048, 2048))
img[urbad] = 10
mask = mask_img(
img,
b,
auto_type=mask_method,
edge=None,
lower_thresh=None,
upper_thresh=None,
)
assert_equal(np.where(mask.ravel() == 0)[0], bad)
def test_generate_binner_mask():
b = map_to_binner(
*generate_map_bin(geo, (2048, 2048)),
np.random.randint(0, 2, 2048 * 2048, dtype=bool).reshape((2048, 2048))
)
assert b
def test_generate_binner():
a = generate_binner(geo, (2048, 2048))
b = map_to_binner(*generate_map_bin(geo, (2048, 2048)))
assert a
assert b
assert_equal(a.flatcount, b.flatcount)
from tifffile import imread
from xpdtools.tools import map_to_binner, generate_map_bin
from profilehooks import profile
import pyFAI
from numba import jit
import numpy as np
geo = pyFAI.load("test.poni")
img = imread("test.tiff")
bo = map_to_binner
binner = bo(*generate_map_bin(geo, img.shape))
f = profile(binner.__call__)
a = binner.xy_argsort
@jit(nopython=True, cache=True)
def b(data):
return np.max(data)
f(img.flatten(), statistic=np.max)
# median
# standard .255
# numba .2
def total(geo, img_shape):
return generate_map_bin(geo, img_shape)
