def propertyChange(self, evt):
if self.ignoreChanges: return
change = False
if not self.isTemporal:
if evt.getSource() == self.fst and evt.getPropertyName() == 'value':
new = float(evt.getNewValue())
old = float(evt.getOldValue())
if new > 0.99:
evt.getSource().setValue(old)
if new < -0.2:
evt.getSource().setValue(old)
elif new< 0.0:
self.systemPanel.force.setSelected(False)
self.systemPanel.force.setEnabled(False)
else:
self.systemPanel.force.setEnabled(True)
else:
if evt.getSource() == self.ne and evt.getPropertyName() == 'value':
new = int(evt.getNewValue())
old = int(evt.getOldValue())
if new < 0:
evt.getSource().setValue(old)
if self.isDominant and evt.getSource() in [self.theta, self.beta1,
self.beta2, self.crit] and evt.getPropertyName() == 'value':
new = float(evt.getNewValue())
old = float(evt.getOldValue())
if new > 0.99 or new < -0.2:
evt.getSource().setValue(old)
if evt.getSource() == self.sampleSize and evt.getPropertyName() == 'value':
change = True
new = int(evt.getNewValue())
old = int(evt.getOldValue())
if new > 50 and old > 4:
warn(self, 'A sample size bigger than 50 is not expected to produce better results, while it slows the computation')
if new < 5 and (new!=0 and not self.isDominant):
evt.getSource().setValue(old)
if evt.getSource() == self.pops and evt.getPropertyName() == 'value':
change = True
new = int(evt.getNewValue())
old = int(evt.getOldValue())
if new > 100:
error(self, 'Maximum value for total populations is 100')
evt.getSource().setValue(100)
if new < self.knownPops:
error(self,
'Expected total populations has to be at least the same as the number of populations under study.'
)
evt.getSource().setValue(self.knownPops)
if not self.isTemporal:
if evt.getSource() == self.mut:
change = True
if not change: return
if self.menuHandles['item5000'].isEnabled():
self.menuHandles['item5000'].setEnabled(False)
warn(self, "Changed parameter will require restarting simulations")
def actionPerformed(self, e):
if e.getID() <> ActionEvent.ACTION_PERFORMED:
return
if e.getSource() == self.neutral:
if self.isTemporal:
warn(self, """
Using a "neutral" mean Ne means doing a first simulation run to
remove potential selected loci for computing the initial mean Ne.
This effectively doubles the computation time, but it is the recommended
option.
If you are not sure of what you want, please check this option.
""")
else:
warn(self, """
Using a "neutral" mean Fst means doing a first simulation run to
remove potential selected loci for computing the initial mean Fst.
This effectively doubles the computation time, but it is the recommended
option.
If you are not sure of what you want, please check this option.
""")
elif e.getSource() == self.force:
warn(self, """
Simulating a PRECISE mean Fst is not possible outside ideal theoretical
conditions (infinite populations, infinite allele model). SELWB can try to
approximate a desired Fst, by running a bisection algorithm over repeated
simulations.
Checking this option will increase the computation time by a certain amount
of time (in most cases it will double the computation time), this is still,
the recommended option.
If you are not sure of what you want, please check this option.
""")
def actionPerformed(self, e):
if e.getID() <> ActionEvent.ACTION_PERFORMED:
return
if e.getSource() == self.neutral:
if self.isTemporal:
warn(self, """
Using a "neutral" mean Ne means doing a first simulation run to
remove potential selected loci for computing the initial mean Ne.
This effectively doubles the computation time, but it is the recommended
option.
If you are not sure of what you want, please check this option.
""")
else:
warn(self, """
Using a "neutral" mean Fst means doing a first simulation run to
remove potential selected loci for computing the initial mean Fst.
This effectively doubles the computation time, but it is the recommended
option.
If you are not sure of what you want, please check this option.
""")
elif e.getSource() == self.force:
warn(self, """
Simulating a PRECISE mean Fst is not possible outside ideal theoretical
conditions (infinite populations, infinite allele model). SELWB can try to
approximate a desired Fst, by running a bisection algorithm over repeated
simulations.
Checking this option will increase the computation time by a certain amount
of time (in most cases it will double the computation time), this is still,
the recommended option.
If you are not sure of what you want, please check this option.
""")
def propertyChange(self, evt):
if self.ignoreChanges:
return
change = False
if not self.isTemporal:
if evt.getSource() == self.fst and evt.getPropertyName(
) == 'value':
new = float(evt.getNewValue())
old = float(evt.getOldValue())
if new > 0.99:
evt.getSource().setValue(old)
if new < -0.2:
evt.getSource().setValue(old)
elif new < 0.0:
self.systemPanel.force.setSelected(False)
self.systemPanel.force.setEnabled(False)
else:
self.systemPanel.force.setEnabled(True)
else:
if evt.getSource() == self.ne and evt.getPropertyName() == 'value':
new = int(evt.getNewValue())
old = int(evt.getOldValue())
if new < 0:
evt.getSource().setValue(old)
if self.isDominant and \
evt.getSource() in [self.theta, self.beta1, self.beta2, self.crit] and \
evt.getPropertyName() == 'value':
new = float(evt.getNewValue())
old = float(evt.getOldValue())
if new > 0.99 or new < -0.2:
evt.getSource().setValue(old)
# Subsample size
if evt.getSource() == self.sampleSize and evt.getPropertyName(
) == 'value':
change = True
new = int(evt.getNewValue())
old = int(evt.getOldValue())
if new > 50 and old > 4:
warn(
self,
'A sample size bigger than 50 is not expected to produce better results, '
+ 'while it slows the computation')
if new < 5 and (new != 0 and not self.isDominant):
evt.getSource().setValue(old)
if evt.getSource() == self.pops and evt.getPropertyName() == 'value':
change = True
new = int(evt.getNewValue())
old = int(evt.getOldValue())
if new > 100:
error(self, 'Maximum value for total populations is 100')
evt.getSource().setValue(100)
if new < self.knownPops:
error(
self,
'Expected total populations has to be at least the same as the number of populations under study.'
)
evt.getSource().setValue(self.knownPops)
if not self.isTemporal:
if evt.getSource() == self.mut:
change = True
if not change:
return
if self.menuHandles['item5000'].isEnabled():
self.menuHandles['item5000'].setEnabled(False)
warn(self, "Changed parameter will require restarting simulations")