def testVisualization(self):
X, LAMB = numpy.meshgrid(numpy.linspace(-1.5, 1.5, self.n),
numpy.linspace(-1.0, 1.0, self.m))
self.lambDim = numpy.linspace(-1.0, 1.0, self.m)
self.xDim = numpy.linspace(-1.5, 1.5, self.n)
lambField = DC.FieldContainer(self.lambDim,
unit='1 V / m**3',
longname='parameter',
shortname='\lambda')
xField = DC.FieldContainer(self.xDim,
unit='1 m',
longname='position',
shortname='x')
#Prepare potential
V = []
for i in xrange(len(lambField.data)):
u = X[i]
V.append(-lambField.data[i] / 2 * u**2 + u**4 / 4 -
u * self.kappa1)
self.V = DC.FieldContainer(numpy.array(V),
unit='1 V',
dimensions=[lambField, xField],
longname='electric potential',
shortname=r'\varphi')
self.V.seal()
#Visualise result
visualizer = ImageVisualizer(self.V, show=False)
filename = os.path.join(self.tmpdir,
'pyphant-' + DC.parseId(self.V.id)[0] + '.pdf')
visualizer.figure.savefig(filename)
def simulation(recipe, curvNo, noIndicators):
worker = recipe.getWorker("ThicknessModeller")
simulation = worker.plugCalcAbsorption.getResult()
worker = recipe.getWorker("AddColumn")
table = worker.plugCompute.getResult(
subscriber=TextSubscriber("Add Column"))
thickness = table[u"thickness"]
index = curvNo2Index(table[u"pixel"], curvNo)
worker = recipe.getWorker("MRA Exp")
minimaPos = worker.plugMra.getResult()[r'\lambda_{min}'].inUnitsOf(
simulation.dimensions[1])
maximaPos = worker.plugMra.getResult()[r'\lambda_{max}'].inUnitsOf(
simulation.dimensions[1])
visualizer = ImageVisualizer(simulation, False)
ordinate = simulation.dimensions[1].data
if not noIndicators:
pylab.hlines(thickness.data[index],
ordinate.min(),
ordinate.max(),
label="$%s$" % minimaPos.shortname)
abscissae = simulation.dimensions[0].data
pylab.vlines(minimaPos.data[:, index],
abscissae.min(),
abscissae.max(),
label="$%s$" % minimaPos.shortname)
pylab.vlines(maximaPos.data[:, index],
abscissae.min(),
abscissae.max(),
label="$%s$" % maximaPos.shortname)
def thicknessMap(recipe, curvNo):
worker = recipe.getWorker("OscMapper")
oscMap = worker.plugMapHeights.getResult()
worker = recipe.getWorker("AddColumn")
table = worker.plugCompute.getResult(
subscriber=TextSubscriber("Add Column"))
xPos = table[u"x-position"]
yPos = table[u"y-position"]
index = curvNo2Index(table[u"pixel"], curvNo)
visualizer = ImageVisualizer(oscMap, False)
pylab.plot([xPos.data[index]], [yPos.data[index]],
'xk',
scalex=False,
scaley=False)
# OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import pkg_resources
pkg_resources.require('pyphant')
pkg_resources.require('pyphant.imageprocessing') # for demo.h5
from pyphant.core.H5FileHandler import H5FileHandler
from pyphant.visualizers.ImageVisualizer import ImageVisualizer
#Load recipe from hdf file
with H5FileHandler('demo.h5', 'r') as handler:
recipe = handler.loadRecipe()
#Configure ImageLoaderWorker
inputWorker = recipe.getWorker('Load Image')
imageName = 'demo.png'
inputWorker.getParam('filename').value = imageName
#Fetch Result
worker = recipe.getWorker('Invert')
result = worker.plugInvert.getResult()
#Visualise result
visualizer = ImageVisualizer(result)
visualizer.figure.savefig('result-' + imageName)
# TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER
# OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import os
import ImageProcessing.ImageLoaderWorker as IL
from pyphant.visualizers.ImageVisualizer import ImageVisualizer
#Initialize ImageLoaderWorker
worker = IL.ImageLoaderWorker(None)
#Configure ImageLoaderWorker
demoFileName = os.path.join(
os.path.dirname(__file__),
"pyphant-shade-Information-Analysis-Framework.png")
worker.paramFilename.value = demoFileName
worker.paramXScale.value = '10m'
#Fetch Image as FieldContainer
fieldContainer = worker.plugLoadImageAsGreyScale.getResult()
#Visualise FieldContainer
visualizer = ImageVisualizer(fieldContainer)
visualizer.figure.savefig('annotatedLogo.png')