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testOpArrayPiper.py
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from builtins import object
###############################################################################
# lazyflow: data flow based lazy parallel computation framework
#
# Copyright (C) 2011-2014, the ilastik developers
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the Lesser GNU General Public License
# as published by the Free Software Foundation; either version 2.1
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# See the files LICENSE.lgpl2 and LICENSE.lgpl3 for full text of the
# GNU Lesser General Public License version 2.1 and 3 respectively.
# This information is also available on the ilastik web site at:
# http://ilastik.org/license/
###############################################################################
__author__ = "John Kirkham <[email protected]>"
__date__ = "$Feb 06, 2015 12:28:04 EST$"
import nose
import numpy
import vigra
from lazyflow.graph import Graph
from lazyflow.operators.opArrayPiper import OpArrayPiper
from lazyflow.roi import roiFromShape, roiToSlice
class AllowMaskException(Exception):
pass
class TestOpArrayPiper(object):
def setUp(self):
self.graph = Graph()
self.operator_identity = OpArrayPiper(graph=self.graph)
self.operator_identity.Input.meta.axistags = vigra.AxisTags("txyzc")
def test1(self):
# Generate a random dataset and see if it we get the right masking from the operator.
data = numpy.random.random((4, 5, 6, 7, 3)).astype(numpy.float32)
# Provide input read all output.
self.operator_identity.Input.setValue(data)
output = self.operator_identity.Output[None].wait()
assert((data == output).all())
def test2(self):
# Generate a dataset and grab chunks of it from the operator. The result should be the same as above.
data = numpy.random.random((4, 5, 6, 7, 3)).astype(numpy.float32)
# Create array to store results. Don't keep original data.
output = data.copy()
output[:] = 0
# Provide input and grab chunks.
self.operator_identity.Input.setValue(data)
output[:2] = self.operator_identity.Output[:2].wait()
output[2:] = self.operator_identity.Output[2:].wait()
assert((data == output).all())
def test3(self):
# Generate a random dataset and see if it we get the right masking from the operator.
data = numpy.random.random((4, 5, 6, 7, 3)).astype(numpy.float32)
# Provide input read all output.
self.operator_identity.Input.setValue(numpy.zeros_like(data))
output = self.operator_identity.Output[None].wait()
assert((output == 0).all())
# Try setInSlot
data_shape_roi = roiFromShape(data.shape)
data_shape_slice = roiToSlice(*data_shape_roi)
self.operator_identity.Input[data_shape_slice] = data
output = self.operator_identity.Output[None].wait()
assert((data == output).all())
def tearDown(self):
# Take down operators
self.operator_identity.Input.disconnect()
self.operator_identity.Output.disconnect()
self.operator_identity.cleanUp()
class TestOpArrayPiper2(object):
def setUp(self):
self.graph = Graph()
self.operator_identity = OpArrayPiper(graph=self.graph)
self.operator_identity.Input.meta.axistags = vigra.AxisTags("txyzc")
self.operator_identity.Input.meta.has_mask = True
def test1(self):
# Generate a random dataset and see if it we get the right masking from the operator.
data = numpy.random.random((4, 5, 6, 7, 3)).astype(numpy.float32)
data = numpy.ma.masked_array(
data,
mask=numpy.zeros(data.shape, dtype=bool),
shrink=False
)
# Provide input read all output.
self.operator_identity.Input.setValue(data)
output = self.operator_identity.Output[None].wait()
assert((data == output).all())
assert(data.mask.shape == output.mask.shape)
assert((data.mask == output.mask).all())
def test2(self):
# Generate a dataset and grab chunks of it from the operator. The result should be the same as above.
data = numpy.random.random((4, 5, 6, 7, 3)).astype(numpy.float32)
data = numpy.ma.masked_array(
data,
mask=numpy.zeros(data.shape, dtype=bool),
shrink=False
)
# Create array to store results. Don't keep original data.
output = data.copy()
output[:] = 0
output[:] = numpy.ma.nomask
# Provide input and grab chunks.
self.operator_identity.Input.setValue(data)
output[:2] = self.operator_identity.Output[:2].wait()
output[2:] = self.operator_identity.Output[2:].wait()
assert((data == output).all())
assert(data.mask.shape == output.mask.shape)
assert((data.mask == output.mask).all())
def test3(self):
# Generate a random dataset and see if it we get the right masking from the operator.
data = numpy.random.random((4, 5, 6, 7, 3)).astype(numpy.float32)
data = numpy.ma.masked_array(
data,
mask=numpy.zeros(data.shape, dtype=bool),
shrink=False
)
# Provide input read all output.
self.operator_identity.Input.setValue(numpy.zeros_like(data))
output = self.operator_identity.Output[None].wait()
assert((output == 0).all())
assert(data.mask.shape == output.mask.shape)
assert((output.mask == False).all())
# Try setInSlot
data_shape_roi = roiFromShape(data.shape)
data_shape_slice = roiToSlice(*data_shape_roi)
self.operator_identity.Input[data_shape_slice] = data
output = self.operator_identity.Output[None].wait()
assert((data == output).all())
assert(data.mask.shape == output.mask.shape)
assert((data.mask == output.mask).all())
def tearDown(self):
# Take down operators
self.operator_identity.Input.disconnect()
self.operator_identity.Output.disconnect()
self.operator_identity.cleanUp()
class TestOpArrayPiper3(object):
def setUp(self):
self.graph = Graph()
self.operator_identity = OpArrayPiper(graph=self.graph)
self.operator_identity.Input.meta.axistags = vigra.AxisTags("txyzc")
def test1(self):
# Generate a random dataset and see if it we get the right masking from the operator.
data = numpy.random.random((4, 5, 6, 7, 3)).astype(numpy.float32)
data = numpy.ma.masked_array(
data,
mask=numpy.zeros(data.shape, dtype=bool),
shrink=False
)
# Provide input read all output.
self.operator_identity.Input.setValue(data)
assert(self.operator_identity.Input.meta.has_mask)
assert(self.operator_identity.Output.meta.has_mask)
output = self.operator_identity.Output[None].wait()
assert((data == output).all())
assert(data.mask.shape == output.mask.shape)
assert((data.mask == output.mask).all())
def test2(self):
# Generate a dataset and grab chunks of it from the operator. The result should be the same as above.
data = numpy.random.random((4, 5, 6, 7, 3)).astype(numpy.float32)
data = numpy.ma.masked_array(
data,
mask=numpy.zeros(data.shape, dtype=bool),
shrink=False
)
# Create array to store results. Don't keep original data.
output = data.copy()
output[:] = 0
output[:] = numpy.ma.nomask
# Provide input and grab chunks.
self.operator_identity.Input.setValue(data)
assert(self.operator_identity.Input.meta.has_mask)
assert(self.operator_identity.Output.meta.has_mask)
output[:2] = self.operator_identity.Output[:2].wait()
output[2:] = self.operator_identity.Output[2:].wait()
assert((data == output).all())
assert(data.mask.shape == output.mask.shape)
assert((data.mask == output.mask).all())
def test3(self):
# Generate a random dataset and see if it we get the right masking from the operator.
data = numpy.random.random((4, 5, 6, 7, 3)).astype(numpy.float32)
data = numpy.ma.masked_array(
data,
mask=numpy.zeros(data.shape, dtype=bool),
shrink=False
)
# Provide input read all output.
self.operator_identity.Input.setValue(numpy.zeros_like(data))
assert(self.operator_identity.Input.meta.has_mask)
assert(self.operator_identity.Output.meta.has_mask)
output = self.operator_identity.Output[None].wait()
assert((output == 0).all())
assert(data.mask.shape == output.mask.shape)
assert((output.mask == False).all())
# Try setInSlot
data_shape_roi = roiFromShape(data.shape)
data_shape_slice = roiToSlice(*data_shape_roi)
self.operator_identity.Input[data_shape_slice] = data
output = self.operator_identity.Output[None].wait()
assert((data == output).all())
assert(data.mask.shape == output.mask.shape)
assert((data.mask == output.mask).all())
def tearDown(self):
# Take down operators
self.operator_identity.Input.disconnect()
self.operator_identity.Output.disconnect()
self.operator_identity.cleanUp()
class TestOpArrayPiper4(object):
def setUp(self):
self.graph = Graph()
self.operator_identity = OpArrayPiper(graph=self.graph)
self.operator_identity.Input.allow_mask = False
self.operator_identity.Output.allow_mask = False
self.operator_identity.Input.meta.has_mask = False
self.operator_identity.Output.meta.has_mask = False
self.operator_identity.Input.meta.axistags = vigra.AxisTags("txyzc")
@nose.tools.raises(AllowMaskException)
def test1(self):
# Generate a random dataset and see if it we get the right masking from the operator.
data = numpy.random.random((4, 5, 6, 7, 3)).astype(numpy.float32)
data = numpy.ma.masked_array(
data,
mask=numpy.zeros(data.shape, dtype=bool),
shrink=False
)
# Provide input read all output.
try:
self.operator_identity.Input.setValue(data)
except AssertionError as e:
raise AllowMaskException(str(e))
@nose.tools.raises(AllowMaskException)
def test2(self):
# Generate a dataset and grab chunks of it from the operator. The result should be the same as above.
data = numpy.random.random((4, 5, 6, 7, 3)).astype(numpy.float32)
data = numpy.ma.masked_array(
data,
mask=numpy.zeros(data.shape, dtype=bool),
shrink=False
)
# Create array to store results. Don't keep original data.
output = data.copy()
output[:] = 0
output[:] = numpy.ma.nomask
# Provide input and grab chunks.
try:
self.operator_identity.Input.setValue(data)
except AssertionError as e:
raise AllowMaskException(str(e))
@nose.tools.raises(AllowMaskException)
def test3(self):
# Generate a random dataset and see if it we get the right masking from the operator.
data = numpy.random.random((4, 5, 6, 7, 3)).astype(numpy.float32)
data = numpy.ma.masked_array(
data,
mask=numpy.zeros(data.shape, dtype=bool),
shrink=False
)
# Provide input read all output.
try:
self.operator_identity.Input.setValue(numpy.zeros_like(data))
except AssertionError as e:
raise AllowMaskException(str(e))
def tearDown(self):
# Take down operators
self.operator_identity.Input.disconnect()
self.operator_identity.Output.disconnect()
self.operator_identity.cleanUp()
class TestOpArrayPiper5(object):
def setUp(self):
self.graph = Graph()
self.operator_identity = OpArrayPiper(graph=self.graph)
self.operator_identity.Input.allow_mask = False
self.operator_identity.Output.allow_mask = False
self.operator_identity.Input.meta.axistags = vigra.AxisTags("txyzc")
@nose.tools.raises(AllowMaskException)
def test1(self):
# Generate a random dataset and see if it we get the right masking from the operator.
data = numpy.random.random((4, 5, 6, 7, 3)).astype(numpy.float32)
data = numpy.ma.masked_array(
data,
mask=numpy.zeros(data.shape, dtype=bool),
shrink=False
)
# Provide input read all output.
try:
self.operator_identity.Input.setValue(data)
except AssertionError as e:
raise AllowMaskException(str(e))
@nose.tools.raises(AllowMaskException)
def test2(self):
# Generate a dataset and grab chunks of it from the operator. The result should be the same as above.
data = numpy.random.random((4, 5, 6, 7, 3)).astype(numpy.float32)
data = numpy.ma.masked_array(
data,
mask=numpy.zeros(data.shape, dtype=bool),
shrink=False
)
# Create array to store results. Don't keep original data.
output = data.copy()
output[:] = 0
output[:] = numpy.ma.nomask
# Provide input and grab chunks.
try:
self.operator_identity.Input.setValue(data)
except AssertionError as e:
raise AllowMaskException(str(e))
@nose.tools.raises(AllowMaskException)
def test3(self):
# Generate a random dataset and see if it we get the right masking from the operator.
data = numpy.random.random((4, 5, 6, 7, 3)).astype(numpy.float32)
data = numpy.ma.masked_array(
data,
mask=numpy.zeros(data.shape, dtype=bool),
shrink=False
)
# Provide input read all output.
try:
self.operator_identity.Input.setValue(numpy.zeros_like(data))
except AssertionError as e:
raise AllowMaskException(str(e))
def tearDown(self):
# Take down operators
self.operator_identity.Input.disconnect()
self.operator_identity.Output.disconnect()
self.operator_identity.cleanUp()
class TestOpArrayPiper6(object):
def setUp(self):
self.graph = Graph()
self.operator_identity_1 = OpArrayPiper(graph=self.graph)
self.operator_identity_2 = OpArrayPiper(graph=self.graph)
self.operator_identity_2.Input.allow_mask = False
self.operator_identity_2.Output.allow_mask = False
self.operator_identity_1.Input.meta.axistags = vigra.AxisTags("txyzc")
self.operator_identity_2.Input.meta.axistags = vigra.AxisTags("txyzc")
@nose.tools.raises(AllowMaskException)
def test1(self):
# Explicitly set has_mask for the input
self.operator_identity_1.Input.meta.has_mask = True
self.operator_identity_1.Output.meta.has_mask = True
# Try to connect the incompatible operators.
try:
self.operator_identity_2.Input.connect(self.operator_identity_1.Output)
except AssertionError as e:
raise AllowMaskException(str(e))
@nose.tools.raises(AllowMaskException)
def test2(self):
# Generate a dataset and grab chunks of it from the operator. The result should be the same as above.
data = numpy.random.random((4, 5, 6, 7, 3)).astype(numpy.float32)
data = numpy.ma.masked_array(
data,
mask=numpy.zeros(data.shape, dtype=bool),
shrink=False
)
# Implicitly set has_mask for the input by setting the value.
self.operator_identity_1.Input.setValue(data)
# Try to connect the incompatible operators.
try:
self.operator_identity_2.Input.connect(self.operator_identity_1.Output)
except AssertionError as e:
raise AllowMaskException(str(e))
def tearDown(self):
# Take down operators
self.operator_identity_2.Input.disconnect()
self.operator_identity_2.Output.disconnect()
self.operator_identity_2.cleanUp()
self.operator_identity_1.Input.disconnect()
self.operator_identity_1.Output.disconnect()
self.operator_identity_1.cleanUp()
class TestOpArrayPiper7(object):
def setUp(self):
self.graph = Graph()
self.operator_identity_1 = OpArrayPiper(graph=self.graph)
self.operator_identity_2 = OpArrayPiper(graph=self.graph)
self.operator_identity_1.Input.meta.axistags = vigra.AxisTags("txyzc")
self.operator_identity_2.Input.meta.axistags = vigra.AxisTags("txyzc")
def test1(self):
# Explicitly set has_mask for the input
self.operator_identity_1.Input.meta.has_mask = True
self.operator_identity_1.Output.meta.has_mask = True
# Generate a dataset and grab chunks of it from the operator. The result should be the same as above.
data = numpy.random.random((4, 5, 6, 7, 3)).astype(numpy.float32)
data = numpy.ma.masked_array(
data,
mask=numpy.zeros(data.shape, dtype=bool),
shrink=False
)
# Try to connect the compatible operators.
self.operator_identity_2.Input.connect(self.operator_identity_1.Output)
self.operator_identity_1.Input.setValue(data)
output = self.operator_identity_2.Output[None].wait()
assert((data == output).all())
assert(data.mask.shape == output.mask.shape)
assert((data.mask == output.mask).all())
def test2(self):
# Generate a dataset and grab chunks of it from the operator. The result should be the same as above.
data = numpy.random.random((4, 5, 6, 7, 3)).astype(numpy.float32)
data = numpy.ma.masked_array(
data,
mask=numpy.zeros(data.shape, dtype=bool),
shrink=False
)
# Try to connect the compatible operators.
self.operator_identity_1.Input.setValue(data)
self.operator_identity_2.Input.connect(self.operator_identity_1.Output)
output = self.operator_identity_2.Output[None].wait()
assert((data == output).all())
assert(data.mask.shape == output.mask.shape)
assert((data.mask == output.mask).all())
def tearDown(self):
# Take down operators
self.operator_identity_2.Input.disconnect()
self.operator_identity_2.Output.disconnect()
self.operator_identity_2.cleanUp()
self.operator_identity_1.Input.disconnect()
self.operator_identity_1.Output.disconnect()
self.operator_identity_1.cleanUp()