plt.xlabel(r'$\omega_F / (1/s)$')
plt.ylabel(r'$T_P / s$')
s, d_s = np.zeros(len(m)), np.zeros(len(m))
b, d_b = np.zeros(len(m)), np.zeros(len(m))
for i in range(len(m)):
par = [omega_F[i], T_P[i], d_T_P[i], d_omega_F[i]]
for j in range(len(par)):
par[j] = np.insert(par[j], 0, 0.0)
s[i], d_s[i], b[i], d_b[i] = dp.linreg(*par)
if output:
for i in range(len(m)):
x_fit = dp.x_fit_like(omega_F[i])
y_fit, y_u_fit = dp.linreg_lines(x_fit, s[i], d_s[i], b[i], d_b[i])
dataPts, *_ = plt.errorbar(omega_F[i], T_P[i], d_T_P[i], d_omega_F[i], fmt='o')
plt.plot(x_fit, y_fit, color=dataPts.get_color(), label='Fit')
plt.plot(x_fit, y_u_fit, color=dataPts.get_color(), label='Fit uncertainty', ls='dashed')
# plt.legend()
if output:
print(dpr.tbl([
dpr.lst(s, d_s, name='s', unit='s^2', prefix=False, exp_to_fix=0)
]))
I_z_ = m * g * l / (2 * pi) * s
d_I_z_ = I_z_ * d_s / s
if output:
d_T = 0.05 ## Data processing h_mean = 0.5 * (hI + hF) d_h_mean = 0.5 * (hI - hF) ## Evaluation slope1, d_slope1, itc1, d_itc1 = dp.linreg(V, t, d_t) if output: plt.subplots(num=3) plt.xlabel(r'V / cm$^3$') plt.ylabel(r't / s') lines, *_ = plt.errorbar(*dp.to_units(V, t, d_t, x_unit=cs.centi**3), fmt='o') x_line = dp.x_fit_like(V) y_line, y_uline = dp.linreg_lines(x_line, slope1, d_slope1, itc1, d_itc1) plt.plot(*dp.to_units(x_line, y_line, x_unit=cs.centi**3), label='Fit', color=lines.get_color()) plt.plot(*dp.to_units(x_line, y_uline, x_unit=cs.centi**3), label='Fit uncertainty', color=lines.get_color(), ls='dashed') if output: print(dpr.val(slope1 * cs.centi**3, d_slope1 * cs.centi**3, name='slope1', unit='s / cm^3')) J = 1 / slope1 d_J = J * d_slope1 / slope1 if output: print(dpr.val(J / cs.milli**3, d_J / cs.milli**3, name='J', unit='mm^3 / s')) p_tube = h_mean * rho_peg * g d_p_tube = p_tube * sqrt((d_h_mean / h_mean)**2 + (d_rho_peg / rho_peg)**2 + (d_g / g)**2)
d_omega_F = 2 * pi * d_f_F
omega_N = 2 * pi * f_N
d_omega_N = 2 * pi * d_f_N
## Evaluation
if output:
plt.subplots(num=5)
plt.xlabel(r'$\omega_N$ / (1/s)')
plt.ylabel(r'$\omega_F$ / (1/s)')
s, d_s, b, d_b = dpl.linreg(omega_N, omega_F, d_omega_F, d_omega_N)
if output:
print(dpr.val(s, d_s, name='s'))
if output:
x_fit = dpl.x_fit_like(omega_N)
y_fit, y_u_fit = dpl.linreg_lines(x_fit, s, d_s, b, d_b)
dataPts, *_ = plt.errorbar(omega_N, omega_F, d_omega_F, d_omega_F, fmt='o')
plt.plot(x_fit, y_fit, label='Fit', color=dataPts.get_color())
plt.plot(x_fit, y_u_fit, label='Fit uncertainty', color=dataPts.get_color(), ls='dashed')
plt.legend()
I_x = I_z_ * s
d_I_x = I_x * sqrt((d_I_z_ / I_z_)**2 + (d_s / s)**2)
if output:
print(dpr.val(I_x / (cs.gram * cs.centi**2), d_I_x / (cs.gram * cs.centi**2),
name='I_x', unit='g cm^2'))
if output:
fig_folder_path = 'figures/spinning_top'
]))
popts = np.zeros((len(p), 2))
d_popts = np.zeros((len(p), 2))
for i in range(len(p)):
popt = dpl.linreg(m, p[i], d_p[i])
popts[i] = popt[0], popt[2]
d_popts[i] = popt[1], popt[3]
if output:
fig, axes = plt.subplots(num=1, nrows=2, ncols=2)
plt.subplots_adjust(hspace=0.25, wspace=0.25)
axes[-1][-1].axis('off')
for i in range(len(popts)):
x_fit = dpl.x_fit_like(m)
y_fit, y_u_fit = dpl.linreg_lines(x_fit, popts[i][0], d_popts[i][0],
popts[i][1], d_popts[i][1])
ax = axes[i // 2][i % 2]
ax.set_xlabel(r'$m$')
ax.set_ylabel(r'$p$ / kPa')
dataPts, *_ = ax.errorbar(m, p[i] / cs.kilo, d_p[i] / cs.kilo, fmt='o')
ax.plot(x_fit, y_fit / cs.kilo, color=dataPts.get_color())
ax.plot(x_fit,
y_u_fit / cs.kilo,
color=dataPts.get_color(),
ls='dashed')
if output:
print(
dpr.tbl([dpr.lst(popts[:, 0], d_popts[:, 0], name='slope',
unit='Pa')]))