def get_data(cls, fname):
data = pd.read_excel(fname, 'angle_calibration')
delta_V = data['delta V (uV)'] * 1e-6
delta_V_err = np.ones_like(delta_V) * 2 * 1e-6
phi = data['dPhi Real (degree)/25'] / 25 * np.pi / 180
V_A = data['V_a (mV)'] * 1e-3
V_A_err = np.ones_like(V_A) * 2e-6
va = Value(V_A, V_A_err)
dv = Value(delta_V, delta_V_err)
return phi, va, dv
def __init__(self, data_file):
self.g = Value(9.8058, 2/1e5)
self.raw_data = self._load_raw(data_file)
self.analysis = None
self.I = None
self.m = None
def get_BL(self):
slope = self.get_slope()
g = self.g
bl = g.value/slope.value
blerr = np.sqrt((g.err/slope.value)**2
+ (slope.err*g.value/slope.value**2)**2)
return Value(bl, blerr, 'kgm/s^2A')
def get_k(self):
m = np.abs(self.analysis.model.params[0])
merr = self.analysis.model.param_errs[0]
v = 1 / m
e = merr * 1 / m**2
return Value(v, e)
def _f_phi_measured(self, dv, va):
v = 1 / 2 * dv.v / (2 * va.v - dv.v)
e = [
1 / 4 * (dv.e * (2 * va.v / (2 * va.v - dv.v)**2))**2,
1 / 4 * (va.e * (2 * dv.v / (2 * va.v - dv.v)**2))**2
]
return Value(v, np.sqrt(sum(e)))
def _f_verdet(m, gamma, k):
v = m.v * k.v / gamma.v
e = [(m.e * k.v / gamma.v)**2, (gamma.e * m.v * k.v / gamma.v**2)**2,
(k.e * m.v / gamma.v)**2]
e = np.sqrt(sum(e))
v = v * 180 / np.pi * 60
e = e * 180 / np.pi * 60
return Value(v, e)
def get_gamma(self):
model = self.analysis.model
a = Value(model.params[0], model.param_errs[0])
b = Value(model.params[1], model.param_errs[1])
c = Value(model.params[2], model.param_errs[2])
print('a', a)
print('b', b)
print('c', c)
alpha = self._f_alpha(self.L, self.delta_x)
beta = self._f_beta(self.L, self.delta_x)
print('alpha', alpha)
print('beta', beta)
gamma = self._f_gamma(a, b, c, alpha, beta)
return gamma
def _f_gamma(a, b, c, alpha, beta):
e = [(a.e / 3 * (beta.v**3 - alpha.v**3))**2,
(b.e / 2 * (beta.v**2 - alpha.v**2))**2,
(c.e * (beta.v - alpha.v))**2,
(alpha.e * (a.v * alpha.v**2 + b.v * alpha.v + c.v))**2,
(beta.e * (a.v * beta.v**2 + b.v * beta.v + c.v))**2]
e = np.sqrt(sum(e))
def f(x):
return a.v / 3 * x**3 + b.v / 2 * x**2 + c.v * x
v = f(beta.v) - f(alpha.v)
return Value(v, e)
def _get_I(raw_data):
I1 = np.mean(raw_data[['I1', 'I3']], axis=1)
I2 = np.mean(raw_data[['I2', 'I4']], axis=1)
I1err = np.std(raw_data[['I1', 'I3']], ddof=1, axis=1)
I2err = np.std(raw_data[['I2', 'I4']], ddof=1, axis=1)
I1err = np.max([I1err, np.ones_like(I1)*0.01], axis=0)
I2err = np.max([I2err, np.ones_like(I2)*0.01], axis=0)
# I1err = np.sqrt(np.sum(raw_data[['I1 err', 'I3 err']], axis=1))
# I2err = np.sqrt(np.sum(raw_data[['I2 err', 'I4 err']], axis=1))
I = I2 - I1
Ierr = np.sqrt(I1err**2+I2err**2)
return Value(I, Ierr)
def _get_m(raw_data):
return Value(raw_data['mass'], 0.0001)
def get_slope(self):
v = self.analysis.model.params[0]
e = self.analysis.model.param_errs[0]
return Value(v, e)
def _f_slope(m_water, m_air):
v = m_water.v - m_air.v
e = np.sqrt(m_water.e**2 + m_air.e**2)
return Value(v, e)
def _f_alpha(L, delta_x):
v = -L.v / 2 + delta_x.v
e = np.sqrt((L.e**2 / 4 + delta_x.e**2))
return Value(v, e)
class Magnet:
L = Value(9.4673, 0.0024, 'cm')
delta_x = Value(0, .2, 'cm')
def __init__(self, fname):
x, y, yerr = self.get_data(fname)
self.x = x
self.y = y
self.yerr = yerr
self.analysis = self._analysis()
def __str__(self):
return 'Magnet:\n' \
'--------\n' \
'Analysis\n' \
'--------\n' \
'{}\n\n' \
'gamma: {}'.format(self.analysis, self.get_gamma())
def get_gamma(self):
model = self.analysis.model
a = Value(model.params[0], model.param_errs[0])
b = Value(model.params[1], model.param_errs[1])
c = Value(model.params[2], model.param_errs[2])
print('a', a)
print('b', b)
print('c', c)
alpha = self._f_alpha(self.L, self.delta_x)
beta = self._f_beta(self.L, self.delta_x)
print('alpha', alpha)
print('beta', beta)
gamma = self._f_gamma(a, b, c, alpha, beta)
return gamma
@staticmethod
def _f_beta(L, delta_x):
v = L.v / 2 + delta_x.v
e = np.sqrt((L.e**2 / 4 + delta_x.e**2))
return Value(v, e)
@staticmethod
def _f_alpha(L, delta_x):
v = -L.v / 2 + delta_x.v
e = np.sqrt((L.e**2 / 4 + delta_x.e**2))
return Value(v, e)
@staticmethod
def _f_gamma(a, b, c, alpha, beta):
e = [(a.e / 3 * (beta.v**3 - alpha.v**3))**2,
(b.e / 2 * (beta.v**2 - alpha.v**2))**2,
(c.e * (beta.v - alpha.v))**2,
(alpha.e * (a.v * alpha.v**2 + b.v * alpha.v + c.v))**2,
(beta.e * (a.v * beta.v**2 + b.v * beta.v + c.v))**2]
e = np.sqrt(sum(e))
def f(x):
return a.v / 3 * x**3 + b.v / 2 * x**2 + c.v * x
v = f(beta.v) - f(alpha.v)
return Value(v, e)
def plot(self, fig):
fig.suptitle('Magnetic Field Calibration')
ax = fig.add_subplot(121)
self.analysis.plot(ax)
ax = fig.add_subplot(122)
self.analysis.plot_chi(ax)
ax.grid()
def _analysis(self):
x, y, yerr = self.x, self.y, self.yerr
def fit(x, a, b, c):
return a * x**2 + b * x + c
popt, pcov = scipy.optimize.curve_fit(fit, x, y, sigma=yerr)
perr = np.sqrt(np.diag(pcov))
model = Model(fit, popt, perr)
analysis = Analysis(model, x, y, yerr)
return analysis
@classmethod
def get_data(cls, fname):
data = pd.read_excel(fname, 'coil_calibration')
x = data['x (cm)']
y = data['B (Gauss)']
yerr = np.ones_like(y) * 0.1
i = np.where(np.abs(x) < 5.25)[0]
x = x[i]
y = y[i]
yerr = yerr[i]
return x, y, yerr
