sigma_A = sigma_y * Numeric.sqrt(Numeric.sum(x**2) / DELTA)
sigma_B = sigma_y * Numeric.sqrt(N / DELTA)
return A, B, sigma_y, sigma_A, sigma_B
# Test program
if __name__ == '__main__':
# Use latex text formatting
matplotlib.rc('text', usetex=True)
# Create a test data set
x = Numeric.arange(20)
dy = RandomArray.standard_normal(20)
y = 0.5 * x + 3 + dy
A, B, dy, dA, dB = lregress(x, y)
y2 = A + B * x
print A, B, dy, dA, dB
# pylab.plot(x,y,'o')
# pylab.plot(x,y2,linewidth=3)
# pylab.xlabel('x',fontsize='large')
# pylab.ylabel('y',fontsize='large')
# pylab.text(2, 13, 'A = %.1f $\pm$ %.1f (true: 3.0)' % (A,dA) )
# pylab.text(2, 12, 'B = %.2f $\pm$ %.2f (true: 0.5)' % (B,dB) )
# pylab.text(2, 11, 'dy = %.1f (true: 1.0)' % (dy) )
def _maxwellboltzmanndistribution(masses, temp):
xi = RandomArray.standard_normal(shape=(len(masses), 3))
momenta = xi * Numeric.sqrt(masses * temp)[:, Numeric.NewAxis]
return momenta
def _maxwellboltzmanndistribution(masses, temp):
xi = RandomArray.standard_normal(shape=(len(masses),3))
momenta = xi * Numeric.sqrt(masses * temp)[:,Numeric.NewAxis]
return momenta
sigma_A = sigma_y * Numeric.sqrt( Numeric.sum(x**2)/DELTA )
sigma_B = sigma_y * Numeric.sqrt( N/DELTA )
return A,B,sigma_y,sigma_A,sigma_B
# Test program
if __name__== '__main__':
# Use latex text formatting
matplotlib.rc('text', usetex=True)
# Create a test data set
x = Numeric.arange(20)
dy = RandomArray.standard_normal(20)
y = 0.5*x+3 + dy
A,B,dy,dA,dB = lregress(x,y)
y2 = A + B*x
print A, B, dy ,dA,dB
# pylab.plot(x,y,'o')
# pylab.plot(x,y2,linewidth=3)
# pylab.xlabel('x',fontsize='large')
# pylab.ylabel('y',fontsize='large')
# pylab.text(2, 13, 'A = %.1f $\pm$ %.1f (true: 3.0)' % (A,dA) )
# pylab.text(2, 12, 'B = %.2f $\pm$ %.2f (true: 0.5)' % (B,dB) )
# pylab.text(2, 11, 'dy = %.1f (true: 1.0)' % (dy) )
