test/test_tomopy/test_prep/test_stripe.py
#!/usr/bin/env python
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from __future__ import (absolute_import, division, print_function,
unicode_literals)
import numpy as np
import unittest
from ..util import read_file
from numpy.testing import assert_allclose
import tomopy.prep.stripe as srm
__author__ = "Doga Gursoy, Nghia Vo"
__copyright__ = "Copyright (c) 2015, UChicago Argonne, LLC."
__docformat__ = 'restructuredtext en'
class StripeRemovalTestCase(unittest.TestCase):
def setUp(self):
self.eps = 10 ** (-6)
self.size = 64
self.mat = np.random.rand(self.size, self.size)
(self.b, self.e) = (30, 31)
self.mat[:, self.b:self.e] = 0.0
def test_remove_stripe_fw(self):
assert_allclose(
srm.remove_stripe_fw(read_file('proj.npy')),
read_file('remove_stripe_fw.npy'), rtol=1e-2)
def test_remove_stripe_ti(self):
assert_allclose(
srm.remove_stripe_ti(read_file('proj.npy')),
read_file('remove_stripe_ti.npy'), rtol=1e-2)
def test_remove_stripe_based_sorting(self):
mat_corr = srm.remove_stripe_based_sorting(
np.expand_dims(self.mat, 1), 3, dim=1)[:, 0, :]
num = np.mean(mat_corr[:, self.b:self.e])
self.assertTrue(num > self.eps)
def test_remove_stripe_based_filtering(self):
mat_corr = srm.remove_stripe_based_filtering(
np.expand_dims(self.mat, 1), 3, 3, dim=1)[:, 0, :]
num = np.mean(mat_corr[:, self.b:self.e])
self.assertTrue(num > self.eps)
def test_remove_stripe_based_fitting(self):
mat = np.random.rand(self.size, self.size)
mat[:, self.b:self.e] = 1.0
mat_corr = srm.remove_stripe_based_fitting(
np.expand_dims(mat, 1), 1, (5, 20))[:, 0, :]
num = np.abs(np.mean(mat_corr[:, self.b:self.e]) - 1.0)
self.assertTrue(num > self.eps)
def test_detect_stripe(self):
lis = np.random.rand(self.size)
lis_off = np.linspace(0, 1, len(lis))
lis = lis + lis_off
lis[self.b:self.e] = 6.0
lis_bin = srm._detect_stripe(lis, 1.5)
pos = np.where(lis_bin == 1.0)
self.assertTrue(len(pos) > 0 and pos[0] == self.b)
def test_remove_large_stripe(self):
mat = np.random.rand(self.size, self.size)
lis_off = np.linspace(0, 1, self.size)
mat_off = np.tile(lis_off, (self.size, 1))
mat[:, self.b:self.e] = 6.0
mat_corr = srm.remove_large_stripe(
np.expand_dims(mat, 1), 1.5, 5)[:, 0, :]
num = np.abs(np.mean(mat_corr[:, self.b:self.e]) - 6.0)
self.assertTrue(num > self.eps)
def test_remove_dead_stripe(self):
mat = np.random.rand(self.size, self.size)
lis_off = np.linspace(0, 1, self.size)
mat[:, self.b:self.e] = 6.0
mat_corr = srm.remove_dead_stripe(
np.expand_dims(mat, 1), 1.5, 5)[:, 0, :]
num = np.abs(np.mean(mat_corr[:, self.b:self.e]) - 6.0)
self.assertTrue(num > self.eps)
def test_remove_all_stripe(self):
mat = np.random.rand(self.size, self.size)
lis_off = np.linspace(0, 1, self.size)
mat_off = np.tile(lis_off, (self.size, 1))
mat[:, self.b:self.e] = 6.0
mat_corr = srm.remove_all_stripe(
np.expand_dims(mat, 1), 1.5, 5, 3)[:, 0, :]
num = np.abs(np.mean(mat_corr[:, self.b:self.e]) - 6.0)
self.assertTrue(num > self.eps)
def test_remove_stripe_based_interpolation(self):
mat = np.random.rand(self.size, self.size)
lis_off = np.linspace(0, 1, self.size)
mat[:, self.b:self.e] = 6.0
mat_corr = srm.remove_stripe_based_interpolation(
np.expand_dims(mat, 1), 1.5, 5)[:, 0, :]
num = np.abs(np.mean(mat_corr[:, self.b:self.e]) - 6.0)
self.assertTrue(num > self.eps)
def test_stripe_detection(self):
assert_allclose(
srm.stripes_detect3d(read_file('test_stripe_data.npy'),
size=10,
radius=1),
read_file('stripes_detect3d.npy'), rtol=1e-6)
def test_stripe_mask(self):
assert_allclose(
srm.stripes_mask3d(read_file('stripes_detect3d.npy'),
threshold=0.6,
min_stripe_length = 10,
min_stripe_depth = 0,
min_stripe_width = 5,
sensitivity_perc=85.0),
read_file('stripes_mask3d.npy'), rtol=1e-6)