def testUnits(self):
data = (np.arange(0, 256, .01)).reshape((80, 320))
image = FieldContainer(data, unit=Quantity('1 mJ'))
for dim in image.dimensions:
dim.unit = Quantity('1 cm')
image.seal()
gradient = Gradient()
result = gradient.gradientWorker(image)
self.assertEqual(result.dimensions, image.dimensions)
self.assertEqual(result.unit, Quantity('1 mJ / cm'))
def testUnits(self):
from ImageProcessing.Gradient import Gradient
from pyphant.core.DataContainer import FieldContainer
from pyphant.quantities import Quantity
data = (numpy.arange(0, 256, .01)).reshape((80, 320))
image = FieldContainer(data, unit=Quantity('1 mJ'))
for dim in image.dimensions:
dim.unit = Quantity('1 cm')
image.seal()
gradient = Gradient()
result = gradient.gradientWorker(image)
self.assertEqual(result.dimensions, image.dimensions)
self.assertEqual(result.unit, Quantity('1 mJ / cm'))
def testNonUniformAxes(self):
im = np.array(
[
[0., 1., 2.],
[30., 10., 50.],
[8., 9., 6.],
[-10., 0., 22.]
]
)
x = FieldContainer(np.array([1., 10., 200.]), unit=Quantity('1 m'))
y = FieldContainer(np.array([0., 2., 4., 8.]), unit=Quantity('1 cm'))
fc = FieldContainer(im, unit=Quantity('5 V'), dimensions=[y, x])
fc.seal()
grad_y, grad_x = np.gradient(fc.data)
grad_y /= np.gradient(y.data).reshape((4, 1))
grad_x /= np.gradient(x.data).reshape((1, 3))
grad_y = FieldContainer(
grad_y, unit=fc.unit / y.unit,
dimensions=copy.deepcopy(fc.dimensions)
)
grad_x = FieldContainer(
grad_x, unit=fc.unit / x.unit,
dimensions=copy.deepcopy(fc.dimensions)
)
grad_x = grad_x.inUnitsOf(grad_y)
expected_result = FieldContainer(
(grad_x.data ** 2 + grad_y.data ** 2) ** 0.5,
unit=copy.deepcopy(grad_y.unit),
dimensions=copy.deepcopy(fc.dimensions)
)
result = Gradient().gradientWorker(fc)
self.assertEqual(expected_result, result)
from pyphant.core.CompositeWorker import CompositeWorker from ImageProcessing.ImageLoaderWorker import ImageLoaderWorker from ImageProcessing.Gradient import Gradient from Statistics.Histogram import Histogram # create a recipe aka CompositeWorker recipe = CompositeWorker() # instantiate an ImageLoader worker, insert it into the recipe # and set the filename parameter loader = ImageLoaderWorker() recipe.addWorker(loader) loader.paramFilename.value = "1.5.01.tiff" # instantiate a Gradient worker, insert it into the recipe # and connect it to the ImageLoader worker gradient = Gradient() recipe.addWorker(gradient) gradient.getSockets()[0].insert(loader.getPlugs()[0]) # instantiate a Histogram worker, insert it into the recipe, # set its bins parameter and connect it to the Gradient worker histogram = Histogram() recipe.addWorker(histogram) histogram.paramBins.value = 50 histogram.getSockets()[0].insert(gradient.getPlugs()[0]) # request the result from the Histogram worker's plug result = histogram.getPlugs()[0].getResult() print result.data.mean()
