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''' Some tests for filters '''
from __future__ import division, print_function, absolute_import
import sys
import numpy as np
from numpy.testing import (assert_equal, assert_raises, assert_allclose,
assert_array_equal, TestCase, run_module_suite)
import scipy.ndimage as sndi
def test_ticket_701():
# Test generic filter sizes
arr = np.arange(4).reshape((2,2))
func = lambda x: np.min(x)
res = sndi.generic_filter(arr, func, size=(1,1))
# The following raises an error unless ticket 701 is fixed
res2 = sndi.generic_filter(arr, func, size=1)
assert_equal(res, res2)
def test_gh_5430():
# At least one of these raises an error unless gh-5430 is
# fixed. In py2k an int is implemented using a C long, so
# which one fails depends on your system. In py3k there is only
# one arbitrary precision integer type, so both should fail.
sigma = np.int32(1)
out = sndi._ni_support._normalize_sequence(sigma, 1)
assert_equal(out, [sigma])
sigma = np.int64(1)
out = sndi._ni_support._normalize_sequence(sigma, 1)
assert_equal(out, [sigma])
# This worked before; make sure it still works
sigma = 1
out = sndi._ni_support._normalize_sequence(sigma, 1)
assert_equal(out, [sigma])
# This worked before; make sure it still works
sigma = [1, 1]
out = sndi._ni_support._normalize_sequence(sigma, 2)
assert_equal(out, sigma)
# Also include the OPs original example to make sure we fixed the issue
x = np.random.normal(size=(256, 256))
perlin = np.zeros_like(x)
for i in 2**np.arange(6):
perlin += sndi.filters.gaussian_filter(x, i, mode="wrap") * i**2
# This also fixes gh-4106, show that the OPs example now runs.
x = np.int64(21)
sndi._ni_support._normalize_sequence(x, 0)
def test_orders_gauss():
# Check order inputs to Gaussians
arr = np.zeros((1,))
yield assert_equal, 0, sndi.gaussian_filter(arr, 1, order=0)
yield assert_equal, 0, sndi.gaussian_filter(arr, 1, order=3)
yield assert_raises, ValueError, sndi.gaussian_filter, arr, 1, -1
yield assert_raises, ValueError, sndi.gaussian_filter, arr, 1, 4
yield assert_equal, 0, sndi.gaussian_filter1d(arr, 1, axis=-1, order=0)
yield assert_equal, 0, sndi.gaussian_filter1d(arr, 1, axis=-1, order=3)
yield assert_raises, ValueError, sndi.gaussian_filter1d, arr, 1, -1, -1
yield assert_raises, ValueError, sndi.gaussian_filter1d, arr, 1, -1, 4
def test_valid_origins():
"""Regression test for #1311."""
func = lambda x: np.mean(x)
data = np.array([1,2,3,4,5], dtype=np.float64)
assert_raises(ValueError, sndi.generic_filter, data, func, size=3,
origin=2)
func2 = lambda x, y: np.mean(x + y)
assert_raises(ValueError, sndi.generic_filter1d, data, func,
filter_size=3, origin=2)
assert_raises(ValueError, sndi.percentile_filter, data, 0.2, size=3,
origin=2)
for filter in [sndi.uniform_filter, sndi.minimum_filter,
sndi.maximum_filter, sndi.maximum_filter1d,
sndi.median_filter, sndi.minimum_filter1d]:
# This should work, since for size == 3, the valid range for origin is
# -1 to 1.
list(filter(data, 3, origin=-1))
list(filter(data, 3, origin=1))
# Just check this raises an error instead of silently accepting or
# segfaulting.
assert_raises(ValueError, filter, data, 3, origin=2)
def test_gaussian_truncate():
# Test that Gaussian filters can be truncated at different widths.
# These tests only check that the result has the expected number
# of nonzero elements.
arr = np.zeros((100, 100), float)
arr[50, 50] = 1
num_nonzeros_2 = (sndi.gaussian_filter(arr, 5, truncate=2) > 0).sum()
assert_equal(num_nonzeros_2, 21**2)
num_nonzeros_5 = (sndi.gaussian_filter(arr, 5, truncate=5) > 0).sum()
assert_equal(num_nonzeros_5, 51**2)
# Test truncate when sigma is a sequence.
f = sndi.gaussian_filter(arr, [0.5, 2.5], truncate=3.5)
fpos = f > 0
n0 = fpos.any(axis=0).sum()
# n0 should be 2*int(2.5*3.5 + 0.5) + 1
assert_equal(n0, 19)
n1 = fpos.any(axis=1).sum()
# n1 should be 2*int(0.5*3.5 + 0.5) + 1
assert_equal(n1, 5)
# Test gaussian_filter1d.
x = np.zeros(51)
x[25] = 1
f = sndi.gaussian_filter1d(x, sigma=2, truncate=3.5)
n = (f > 0).sum()
assert_equal(n, 15)
# Test gaussian_laplace
y = sndi.gaussian_laplace(x, sigma=2, truncate=3.5)
nonzero_indices = np.where(y != 0)[0]
n = nonzero_indices.ptp() + 1
assert_equal(n, 15)
# Test gaussian_gradient_magnitude
y = sndi.gaussian_gradient_magnitude(x, sigma=2, truncate=3.5)
nonzero_indices = np.where(y != 0)[0]
n = nonzero_indices.ptp() + 1
assert_equal(n, 15)
class TestThreading(TestCase):
def check_func_thread(self, n, fun, args, out):
from threading import Thread
thrds = [Thread(target=fun, args=args, kwargs={'output': out[x]}) for x in range(n)]
[t.start() for t in thrds]
[t.join() for t in thrds]
def check_func_serial(self, n, fun, args, out):
for i in range(n):
fun(*args, output=out[i])
def test_correlate1d(self):
d = np.random.randn(5000)
os = np.empty((4, d.size))
ot = np.empty_like(os)
self.check_func_serial(4, sndi.correlate1d, (d, np.arange(5)), os)
self.check_func_thread(4, sndi.correlate1d, (d, np.arange(5)), ot)
assert_array_equal(os, ot)
def test_correlate(self):
d = np.random.randn(500, 500)
k = np.random.randn(10, 10)
os = np.empty([4] + list(d.shape))
ot = np.empty_like(os)
self.check_func_serial(4, sndi.correlate, (d, k), os)
self.check_func_thread(4, sndi.correlate, (d, k), ot)
assert_array_equal(os, ot)
def test_median_filter(self):
d = np.random.randn(500, 500)
os = np.empty([4] + list(d.shape))
ot = np.empty_like(os)
self.check_func_serial(4, sndi.median_filter, (d, 3), os)
self.check_func_thread(4, sndi.median_filter, (d, 3), ot)
assert_array_equal(os, ot)
def test_uniform_filter1d(self):
d = np.random.randn(5000)
os = np.empty((4, d.size))
ot = np.empty_like(os)
self.check_func_serial(4, sndi.uniform_filter1d, (d, 5), os)
self.check_func_thread(4, sndi.uniform_filter1d, (d, 5), ot)
assert_array_equal(os, ot)
def test_minmax_filter(self):
d = np.random.randn(500, 500)
os = np.empty([4] + list(d.shape))
ot = np.empty_like(os)
self.check_func_serial(4, sndi.maximum_filter, (d, 3), os)
self.check_func_thread(4, sndi.maximum_filter, (d, 3), ot)
assert_array_equal(os, ot)
self.check_func_serial(4, sndi.minimum_filter, (d, 3), os)
self.check_func_thread(4, sndi.minimum_filter, (d, 3), ot)
assert_array_equal(os, ot)
def test_minmaximum_filter1d():
# Regression gh-3898
in_ = np.arange(10)
out = sndi.minimum_filter1d(in_, 1)
assert_equal(in_, out)
out = sndi.maximum_filter1d(in_, 1)
assert_equal(in_, out)
# Test reflect
out = sndi.minimum_filter1d(in_, 5, mode='reflect')
assert_equal([0, 0, 0, 1, 2, 3, 4, 5, 6, 7], out)
out = sndi.maximum_filter1d(in_, 5, mode='reflect')
assert_equal([2, 3, 4, 5, 6, 7, 8, 9, 9, 9], out)
#Test constant
out = sndi.minimum_filter1d(in_, 5, mode='constant', cval=-1)
assert_equal([-1, -1, 0, 1, 2, 3, 4, 5, -1, -1], out)
out = sndi.maximum_filter1d(in_, 5, mode='constant', cval=10)
assert_equal([10, 10, 4, 5, 6, 7, 8, 9, 10, 10], out)
# Test nearest
out = sndi.minimum_filter1d(in_, 5, mode='nearest')
assert_equal([0, 0, 0, 1, 2, 3, 4, 5, 6, 7], out)
out = sndi.maximum_filter1d(in_, 5, mode='nearest')
assert_equal([2, 3, 4, 5, 6, 7, 8, 9, 9, 9], out)
# Test wrap
out = sndi.minimum_filter1d(in_, 5, mode='wrap')
assert_equal([0, 0, 0, 1, 2, 3, 4, 5, 0, 0], out)
out = sndi.maximum_filter1d(in_, 5, mode='wrap')
assert_equal([9, 9, 4, 5, 6, 7, 8, 9, 9, 9], out)
if __name__ == "__main__":
run_module_suite(argv=sys.argv)
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