def OnGenEvents(self, event):
from PYME.simulation import locify
#from PYME.Acquire.Hardware.Simulator import wormlike2
from PYME.IO import tabular
from PYME.IO.image import ImageBounds
# import pylab
import matplotlib.pyplot as plt
#wc = wormlike2.wormlikeChain(100)
pipeline = self.visFr.pipeline
pipeline.filename='Simulation'
plt.figure()
plt.plot(self.xp, self.yp, 'x') #, lw=2)
if isinstance(self.source, WormlikeSource):
plt.plot(self.xp, self.yp, lw=2)
if self.mode == 'STORM':
res = locify.eventify(self.xp, self.yp, self.meanIntensity, self.meanDuration, self.backgroundIntensity,
self.meanEventNumber, self.scaleFactor, self.meanTime, z=self.zp)
else:
res = locify.eventify2(self.xp, self.yp, self.meanIntensity, self.meanDuration, self.backgroundIntensity,
self.meanEventNumber, self.scaleFactor, self.meanTime, z=self.zp)
plt.plot(res['fitResults']['x0'],res['fitResults']['y0'], '+')
ds = tabular.MappingFilter(tabular.FitResultsSource(res))
if isinstance(self.source, ImageSource):
pipeline.imageBounds = image.openImages[self.source.image].imgBounds
else:
pipeline.imageBounds = ImageBounds.estimateFromSource(ds)
pipeline.addDataSource('Generated Points', ds)
pipeline.selectDataSource('Generated Points')
from PYME.IO.MetaDataHandler import NestedClassMDHandler
pipeline.mdh = NestedClassMDHandler()
pipeline.mdh['Camera.ElectronsPerCount'] = 1
pipeline.mdh['Camera.TrueEMGain'] = 1
pipeline.mdh['Camera.CycleTime'] = 1
pipeline.mdh['voxelsize.x'] = .110
try:
pipeline.filterKeys.pop('sig')
except:
pass
pipeline.Rebuild()
if len(self.visFr.layers) < 1:
self.visFr.add_pointcloud_layer() #TODO - move this logic so that layer added automatically when datasource is added?
#self.visFr.CreateFoldPanel()
self.visFr.SetFit()
def gaussian_blob_paint(fwhm=30,
n_fluors=18,
meanIntensity=1500,
meanDuration=3,
backGroundIntensity=100,
meanEventNumber=2):
from PYME.simulation import locify
nf = np.random.poisson(n_fluors) + 1
x = (fwhm / 2.35) * np.random.normal(size=nf)
y = (fwhm / 2.35) * np.random.normal(size=nf)
return locify.eventify2(x, y, meanIntensity, meanDuration,
backGroundIntensity, meanEventNumber), len(x)
def ring_paint(radius=30,
n_handles=18,
labeling_prob=0.5,
meanIntensity=1500,
meanDuration=3,
backGroundIntensity=100,
meanEventNumber=2):
from PYME.simulation import locify
x, y = ring(radius, n_handles)
mask = np.random.uniform(size=len(x)) < labeling_prob
x = x[mask]
y = y[mask]
#print len(x), mask
return locify.eventify2(x, y, meanIntensity, meanDuration,
backGroundIntensity, meanEventNumber), len(x)
def uniform_blob_paint(radius=30,
n_fluors=18,
meanIntensity=1500,
meanDuration=3,
backGroundIntensity=100,
meanEventNumber=2):
from PYME.simulation import locify
nf = np.random.poisson(4 * n_fluors / np.pi) + 1
x = radius * (2 * np.random.uniform(size=nf) - 1)
y = radius * (2 * np.random.uniform(size=nf) - 1)
ind = (x * x + y * y) < radius * radius
x = x[ind]
y = y[ind]
return locify.eventify2(x, y, meanIntensity, meanDuration,
backGroundIntensity, meanEventNumber), len(x)
def OnGenEvents(self, event):
from PYME.simulation import locify
#from PYME.Acquire.Hardware.Simulator import wormlike2
from PYME.IO import tabular
from PYME.IO.image import ImageBounds
# import pylab
import matplotlib.pyplot as plt
#wc = wormlike2.wormlikeChain(100)
pipeline = self.visFr.pipeline
pipeline.filename = 'Simulation'
plt.figure()
plt.plot(self.xp, self.yp, 'x') #, lw=2)
if isinstance(self.source, WormlikeSource):
plt.plot(self.xp, self.yp, lw=2)
if self.mode == 'STORM':
res = locify.eventify(self.xp,
self.yp,
self.meanIntensity,
self.meanDuration,
self.backgroundIntensity,
self.meanEventNumber,
self.scaleFactor,
self.meanTime,
z=self.zp)
else:
res = locify.eventify2(self.xp,
self.yp,
self.meanIntensity,
self.meanDuration,
self.backgroundIntensity,
self.meanEventNumber,
self.scaleFactor,
self.meanTime,
z=self.zp)
plt.plot(res['fitResults']['x0'], res['fitResults']['y0'], '+')
ds = tabular.MappingFilter(tabular.FitResultsSource(res))
try:
# some data sources (current ImageSource) have image bound info. Use this if available
# this could fail on either an AttributeError (if the data source doesn't implement bounds
# or another error if something fails in get_bounds(). Only catch the AttributeError, as we have
# should not be handling other errors here.
pipeline.imageBounds = self.source.get_bounds()
except AttributeError:
pipeline.imageBounds = ImageBounds.estimateFromSource(ds)
pipeline.addDataSource('Generated Points', ds)
pipeline.selectDataSource('Generated Points')
from PYME.IO.MetaDataHandler import NestedClassMDHandler
pipeline.mdh = NestedClassMDHandler()
pipeline.mdh['Camera.ElectronsPerCount'] = 1
pipeline.mdh['Camera.TrueEMGain'] = 1
pipeline.mdh['Camera.CycleTime'] = 1
pipeline.mdh['voxelsize.x'] = .110
# some info about the parameters
pipeline.mdh['GeneratedPoints.MeanIntensity'] = self.meanIntensity
pipeline.mdh['GeneratedPoints.MeanDuration'] = self.meanDuration
pipeline.mdh['GeneratedPoints.MeanEventNumber'] = self.meanEventNumber
pipeline.mdh[
'GeneratedPoints.BackgroundIntensity'] = self.backgroundIntensity
pipeline.mdh['GeneratedPoints.ScaleFactor'] = self.scaleFactor
pipeline.mdh['GeneratedPoints.MeanTime'] = self.meanTime
pipeline.mdh['GeneratedPoints.Mode'] = self.mode
# the source info
self.source.genMetaData(pipeline.mdh)
try:
pipeline.filterKeys.pop('sig')
except:
pass
pipeline.Rebuild()
if len(self.visFr.layers) < 1:
self.visFr.add_pointcloud_layer(
) #TODO - move this logic so that layer added automatically when datasource is added?
#self.visFr.CreateFoldPanel()
self.visFr.SetFit()