def test_circle(nbpoints, expected):
"""
test with parametrize to run several tests
"""
theta, path = spl.circle([0, 0], 1, npoints=nbpoints)
assert path == approx(expected)
def circle(img):
def xs_func():
nsamples = 500
return (np.random.random() + 2 * np.pi * np.linspace(0, 1, nsamples))%(2*np.pi)
theta, path = spl.circle([.5, .5], .3, npoints=40)
spl.update_img(img, path, xs_func, nrep=4000, x=theta, scale_value=.00005)
def test_circle_10():
"""
test with 10 points
"""
theta, path = spl.circle([0, 0], 1, npoints=10)
assert theta == approx(np.linspace(0, 2 * np.pi, 10))
def test_circle_1():
theta, path = spl.circle(
[0, 0], 1, npoints=2
) # create a splinned circle with center [0,0] radius 1 and 2 points
print(theta)
assert theta == approx(
[0, 2 * np.pi]) # the resulting vector of theta angles for each point
#assert path == approx(np.array([[1, 0], [1, 0]]))
print(path)
assert path == approx(np.array([[1, 0], [1, 0]
])) # and the resulting path must be this
def test_case(tmpdir, load_json):
datadir, js = load_json
np.random.seed(js["seed"])
img_size, channels = 1000, 4
img = np.ones((img_size, img_size, channels), dtype=np.float32)
theta, path = spl.circle(js["center"], js["radius"])
def xs_func():
nsamples = 500
return (np.random.random() + 2 * np.pi * np.linspace(0, 1, nsamples))%(2*np.pi)
spl.update_img(img, path, xs_func, nrep=js["nrep"], x=theta)
spl.save_img(img, tmpdir.dirname, 'output.png')
assert filecmp.cmp(tmpdir.dirname + '/output.png', datadir + "/" + js["output"])
def test_circle_case():
np.random.seed(42)
img_size, channels = 1000, 4
img = np.ones((img_size, img_size, channels), dtype=np.float32)
theta, path = spl.circle([0.5, 0.5], 0.3)
def xs_func():
nsamples = 500
return (np.random.random() +
2 * np.pi * np.linspace(0, 1, nsamples)) % (2 * np.pi)
spl.update_img(img, path, xs_func, nrep=4000, x=theta)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.imshow(img)
return fig
def test_spline_circle(center, radius, nbpoints):
theta, path = spl.circle(center, radius, npoints=nbpoints)
y2s = spl.spline.spline(theta, path)
y_new = np.zeros_like(path)
spl.spline.splint(theta, path, y2s, theta, y_new)
assert path == pytest.approx(y_new)
import numpy as np
import splinart as spl
import json
SEED = 42
CENTER = [.5, .5]
RADIUS = .3
NREP = 4000
np.random.seed(SEED)
img_size, channels = 1000, 4
img = np.ones((img_size, img_size, channels), dtype=np.float32)
theta, path = spl.circle(CENTER, RADIUS)
def xs_func():
nsamples = 500
return (np.random.random() +
2 * np.pi * np.linspace(0, 1, nsamples)) % (2 * np.pi)
spl.update_img(img, path, xs_func, nrep=NREP, x=theta)
spl.save_img(img, '.', 'circle.png')
js = json.dumps(
{
"circle": {
"seed": SEED,
"center": CENTER,
from __future__ import print_function, division, absolute_import
import numpy as np
from six.moves import range
import splinart as spl
img_size, channels = 1000, 4
img = np.ones((img_size, img_size, channels), dtype=np.float32)
nb_circles = 10
theta_circles = []
path_circles = []
color_circles = []
for i in range(nb_circles):
radius = .1 + np.random.random() * .1
center = .2 + np.random.random(2) * .6
theta, path = spl.circle(center, radius, npoints=75)
theta_circles.append(theta)
path_circles.append(path)
color = np.random.random(4) * .3
color[-1] = 1.
color_circles.append(color)
def xs_func():
nsamples = 500
return (np.random.random() +
2 * np.pi * np.linspace(0, 1, nsamples)) % (2 * np.pi)
for i in range(nb_circles):
img1 = np.ones_like(img)
from __future__ import print_function, division, absolute_import
import numpy as np
import splinart as spl
img_size, channels = 1000, 4
img = np.ones((img_size, img_size, channels), dtype=np.float32)
theta, path = spl.circle([.5, .5], .3, npoints=40)
def xs_func():
nsamples = 500
return (np.random.random() +
2 * np.pi * np.linspace(0, 1, nsamples)) % (2 * np.pi)
spl.update_img(img, path, xs_func, nrep=4000, x=theta, scale_value=.00005)
spl.save_img(img, './output', 'circle.png')
def test_circle_2():
theta, path = spl.circle([0, 0], 1, npoints=5)
assert theta == approx(np.linspace(0, 2 * np.pi, 5))
assert path == approx(np.array([[1, 0], [0, 1], [-1, 0], [0, -1], [1, 0]]))
def test_circle_1():
theta, path = spl.circle([0, 0], 1, npoints=2)
assert theta == approx([0, 2 * np.pi])
assert path == approx(np.array([[1, 0], [1, 0]]))
def test_circle(nbpoints, expected):
theta, path = spl.circle([0, 0], 1, npoints=nbpoints)
assert path == approx(expected)
