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import ufo.numpy
import numpy as np
import tifffile
import contextlib
from common import disable, tempdir
a, b = 1.5, 2.5
ones = np.ones((512, 512))
zeros = np.zeros((512, 512))
small = np.ones((256, 256))
random = np.random.random((512, 512))
def have_camera_plugin():
from gi.repository import Ufo
return 'camera' in Ufo.PluginManager().get_all_task_names()
@contextlib.contextmanager
def single_tiff_setup(n_images, fmt='foo-{:05}.tif'):
with tempdir() as d:
data = np.ones((512, 512), dtype=np.float32)
for i in range(n_images):
tifffile.imsave(d.path(fmt.format(i)), data)
yield d
def test_read_single_tiffs():
from ufo import Read, Null
with single_tiff_setup(32) as d:
read = Read(path=d.root)
null = Null()
null(read()).run().join()
assert(null.task.props.num_processed == 32)
def test_read_single_tiffs_stepped():
from ufo import Read, Null
with single_tiff_setup(32) as d:
read = Read(path=d.root, step=2)
null = Null()
null(read()).run().join()
assert(null.task.props.num_processed == 32 / 2)
def test_read_single_tiffs_start_modified():
from ufo import Read, Null
with single_tiff_setup(32) as d:
read = Read(path=d.root, start=15)
null = Null()
null(read()).run().join()
assert(null.task.props.num_processed == 32 - 15)
@disable
def test_read_multi_tiffs():
from ufo import Read, Null
with tempdir() as d:
n_images = 32
data = np.ones((512, 512, n_images))
tifffile.imsave(d.path('foo.tif'), data)
read = Read(path=d.path('foo.tif'))
null = Null()
null(read()).run().join()
assert(null.task.props.num_processed == n_images)
def test_average():
from ufo import Average
average = Average()
for x in average([a * ones, b * ones]):
expected = (a + b) / 2
assert(np.all(x == expected))
def test_buffer():
from ufo import DummyData, Buffer
data = DummyData(number=10, width=512, height=256)
buffr = Buffer()
result = list(buffr(data()))
assert(len(result) == 10)
for r in result:
assert(r.shape[0] == 256)
assert(r.shape[1] == 512)
def test_rescale():
from ufo import Rescale
rescale = Rescale(factor=0.5)
result = list(rescale([a * ones, b * small]))
assert(np.mean(result[0]) == a)
assert(np.mean(result[1]) == b)
@disable
def test_roi():
from ufo import CutRoi
x, y = 10, 20
w, h = 256, 128
roi = CutRoi(x=x, y=y, width=w, height=h)
result = list(roi([random, random]))
ref = random[y:y+h, x:x+w]
assert(ref.shape[0] == h)
assert(ref.shape[1] == w)
assert(np.all(ref == result[0]))
def test_stack():
from ufo import Stack
stack = Stack(number=2)
for x in stack([a * ones, b * ones]):
assert(x.shape[0] == 2)
assert(np.all(x[0,:,:] == a))
assert(np.all(x[1,:,:] == b))
def test_flatten():
from ufo import FlattenInplace
summing = FlattenInplace(mode="sum")
result = list(summing([a * ones, b * ones]).items())
assert(np.all(result[0] == a + b))
def test_fft_1d():
from ufo import Fft, Ifft
fft = Fft(dimensions=1)
ifft = Ifft(dimensions=1)
orig_a = a * ones
orig_b = b * random
result = list(ifft(fft([orig_a, orig_b])))
assert(np.sum(orig_a - result[0]) < 0.001)
assert(np.sum(orig_b - result[1]) < 0.01)
def test_fft_2d():
from ufo import Fft, Ifft
fft = Fft(dimensions=2)
ifft = Ifft(dimensions=2)
orig_a = a * ones
orig_b = b * random
result = list(ifft(fft([orig_a, orig_b])))
assert(np.sum(orig_a - result[0]) < 0.001)
assert(np.sum(orig_b - result[1]) < 0.1)
def test_flatfield_correction():
from ufo import FlatFieldCorrect
darks = np.ones((512, 512)) * 1.5
flats = np.ones((512, 512)) * 11.5
projs = np.ones((512, 512)) * 100.0
ffc = FlatFieldCorrect()
expected = (projs - darks) / (flats - darks)
result = list(ffc([projs, projs], [darks, darks], [flats, flats]))[0]
assert(np.sum(np.abs(expected - result)) < 1)
expected = - np.log((projs - darks) / (flats - darks))
ffc = FlatFieldCorrect(absorption_correct=True)
result = list(ffc([projs, projs], [darks, darks], [flats, flats]))[0]
assert(np.sum(np.abs(expected - result)) < 1)
def test_measure():
from ufo import Measure
measures = []
def measure_callback(m, a):
measures.append(ufo.numpy.asarray(a))
measure = Measure(metric='mean', axis=-1)
measure.connect('result', measure_callback)
measure([a * ones, b * ones]).run().join()
assert(len(measures) == 2)
def test_dummy_data():
from ufo import DummyData
data = DummyData(number=10, width=256, height=128)
result = list(data())
assert(len(result) == 10)
assert(all(r.shape[0] == 128 and r.shape[1] == 256 for r in result))
def test_metaballs():
from ufo import Metaballs
metaballs = Metaballs(number=5, number_balls=5, width=512, height=256)
result = list(metaballs())
assert(len(result) == 5)
assert(all(r.shape[0] == 256 and r.shape[1] == 512 for r in result))
def test_transpose():
from ufo import Transpose
transpose = Transpose()
ones = np.ones((256, 512))
zeros = np.zeros((256, 128))
result = list(transpose([ones, zeros]))
assert(np.all(result[0] == ones.transpose()))
assert(np.all(result[1] == zeros.transpose()))
def test_uca():
if have_camera_plugin():
from ufo import Camera
camera = Camera(name='mock', number=2)
result = list(camera())
assert(len(result) == 2)
def test_uca_direct():
try:
from gi.repository import Ufo, Uca
if have_camera_plugin():
from ufo import Camera
uca_pm = Uca.PluginManager()
mock = uca_pm.get_camerav('mock', [])
camera = Camera(camera=mock, count=3)
result = list(camera())
assert(len(result) == 3)
except ImportError:
pass
def test_memory_in():
with tempdir() as d:
from ufo import MemoryIn, Write
ref = random.astype(np.float32)
read = MemoryIn(pointer=ref.__array_interface__['data'][0], number=1,
width=ref.shape[1], height=ref.shape[0])
write = Write(filename=d.path('foo.tif'))
write(read()).run().join()
result = tifffile.imread(d.path('foo.tif'))
assert(np.all(ref == result))
def test_memory_out():
with tempdir() as d:
from ufo import MemoryOut, Read
ref = random.astype(np.float32)
out = np.zeros_like(ref).astype(np.float32)
tifffile.imsave(d.path('foo.tif'), ref)
read = Read(path=d.path('foo.tif'))
write = MemoryOut(pointer=out.__array_interface__['data'][0], max_size=ref.nbytes)
write(read()).run().join()
assert(np.all(out == ref))
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