def __truediv__(self, other):
if isinstance(other,Measurement):
if (not self.variance is None) and hasattr(other,'variance') and not other.variance is None:
return Measurement(*err1d.div(self.x,self.variance,other.x,other.variance))
elif type(self)==numpy.ndarray:
return Measurement(self/other.x, self.variance/other.x**2)
else:
return Measurement(self.x/other.x, self.x/other.x**2) #maybe revisit this--we claim that other is a measurement, but if the variance is None, then what does it mean to divide by this--the current solution is practical.
else:
if not self.variance is None:
return Measurement(self.x/other, self.variance/other**2)
else:
return Measurement(self.x/other,None)
def __truediv__(self, other):
if isinstance(other, Uncertainty):
return Uncertainty(
*err1d.div(self.x, self.variance, other.x, other.variance))
else:
return Uncertainty(self.x / other, self.variance / other**2)
def __truediv__(self, other):
if isinstance(other,Measurement):
return Measurement(*err1d.div(self.x,self.variance,other.x,other.variance))
else:
return Measurement(self.x/other, self.variance/other**2)
def __truediv__(self, other):
if isinstance(other,Uncertainty):
return Uncertainty(*err1d.div(self.x,self.variance,other.x,other.variance))
else:
return Uncertainty(self.x/other, self.variance/other**2)
def __truediv__(self, other):
if isinstance(other, Measurement):
return Measurement(
*err1d.div(self.x, self.variance, other.x, other.variance))
else:
return Measurement(self.x / other, self.variance / other**2)