if output: plt.subplots(num=2) plt.xlabel(r'$t$ / s') plt.ylabel(r'$U$ / V') plt.xlim(*t_range) plt.plot(t, U) i_U_max = find_peaks_cwt(U, range(1,30), noise_perc=20) i_U_max = i_U_max[U[i_U_max] > 0.0] i_U_max = i_U_max[t[i_U_max] > t_range[0]] i_U_max = i_U_max[t[i_U_max] < t_range[1]] popt, pcov = curve_fit(gauss, t[i_U_max], U[i_U_max], p0=(2.5e-3, 0.015, 0.015)) d_popt = sqrt(np.diag(pcov)) if output: x_fit = dpl.x_fit_like(t[i_U_max]) y_fit = gauss(x_fit, *popt) data_pts, *_ = plt.plot(t[i_U_max], U[i_U_max], marker='o', ls='None') plt.plot(x_fit, y_fit, color=data_pts.get_color()) if output: print(dpr.val(popt[2], d_popt[2], name='Δt', unit='s')) delta_s = v_mov * popt[2] d_delta_s = v_mov * d_popt[2] if output: print(dpr.val(delta_s, d_delta_s, name='Δs', unit='m')) L = 2 * pi * delta_s
plt.subplots(num=2)
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
omega_F = 2 * pi * f_F
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:
## Evaluation
def omega_fit(t, delta, omega_0):
return omega_0 * exp(-delta * t)
popt, pcov = curve_fit(omega_fit, t, omega, p0=(1 / 1000, 80), sigma=d_omega)
if output:
plt.subplots(num=1)
plt.xlabel(r'$t$ / s')
plt.ylabel(r'$\omega$ / (1/s)')
plt.yscale('log')
lines, *_ = plt.errorbar(t, omega, d_omega, fmt='o')
x_fit = dp.x_fit_like(t)
y_fit = omega_fit(x_fit, *popt)
plt.plot(x_fit, y_fit, color=lines.get_color())
delta, *_ = popt
d_delta = sqrt(pcov[0, 0])
t_h = log(2) / delta
d_t_h = t_h * d_delta / delta
if output:
print(
dpr.val(delta * cs.day,
d_delta * cs.day,
name='δ',
unit='1/d',
T = 25.0 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)
omega_F = 2 * pi * f
d_omega_F = omega_F * d_f / f
Omega = 2 * pi / t
d_Omega = Omega * d_t / t
## Evaluation
if output:
plt.subplots(num=4)
plt.xlabel(r'$\Omega$ / (1/s)')
plt.ylabel(r'$\omega_F$ / (1/s)')
s, d_s, b, d_b = dpl.linreg(Omega, omega_F, d_omega_F, d_Omega)
if output:
print(dpr.val(s, d_s, name='s'))
if output:
x_fit = dpl.x_fit_like(Omega)
y_fit, y_u_fit = dpl.linreg_lines(x_fit, s, d_s, b, d_b)
dataPts, *_ = plt.errorbar(Omega, omega_F, d_omega_F, d_Omega, 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()
delta_I = I_z_ / (s - 1)
d_delta_I = delta_I * sqrt((d_I_z_ / I_z_)**2 + (d_s / (s - 1))**2)
if output:
]
]))
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(
if output:
plt.subplots(num=1)
plt.xlabel(r'$r^2$ / mm$^2$')
plt.ylabel(r'$\frac{v}{\varrho_K - \varrho_\textnormal{PEG}} / \frac{\textnormal{cm}^4}{\textnormal{g} \, \textnormal{s}}$')
plt.errorbar(*dp.to_units(r2, v_rho_ratio, d_v_rho_ratio, d_r2, x_unit=cs.milli**2, y_unit=(cs.centi**4 / cs.gram)), fmt='o')
v[:7] = v_c[:7]
d_v[:7] = d_v_c[:7]
v_rho_ratio = v / (rho_K - rho_peg)
d_v_rho_ratio = v_rho_ratio * sqrt((d_v / v)**2 + (d_rho_K**2 + d_rho_peg**2) / (rho_K - rho_peg)**2)
slope1, d_slope1, itc1, d_itc1 = dp.linreg(r2, v_rho_ratio, d_v_rho_ratio, d_r2)
if output:
lines, *_ = plt.errorbar(*dp.to_units(r2, v_rho_ratio, d_v_rho_ratio, d_r2, x_unit=cs.milli**2, y_unit=(cs.centi**4 / cs.gram)), fmt='o')
x_line = dp.x_fit_like(r2)
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.milli**2), y_unit=(cs.centi**4 / cs.gram)),
label='Fit', color=lines.get_color())
plt.plot(*dp.to_units(x_line, y_uline, x_unit=(cs.milli**2), y_unit=(cs.centi**4 / cs.gram)),
label='Fit uncertainty', color=lines.get_color(), ls='dashed')
plt.legend()
if output:
print(dpr.val(slope1 / (cs.centi**2 / cs.gram), d_slope1 / (cs.centi**2 / cs.gram),
name='slope1', unit='cm^2 / g', prefix=False))
print()
eta = 2 / 9 * g / slope1
d_eta = eta * sqrt((d_g / g)**2 + (d_slope1 / slope1)**2)
import datproc.plot as dpl import datproc.print as dpr import stokes as st from stokes import a, d_a, T, d_T, eta, d_eta, t, r2_ ## General output = __name__ == '__main__' ## Evaluation r2_cum = np.cumsum(r2_) t_ = np.append(t[:67], t[68:-1] - (t[67] - t[66])) popt, pcov = curve_fit(lambda x, s, b: s * x + b, t_, r2_cum) d_popt = sqrt(np.diag(pcov)) if output: x_fit = dpl.x_fit_like(t_) y_fit, y_u_fit = dpl.linreg_lines(x_fit, popt[0], d_popt[0], popt[1], d_popt[1]) plt.subplots(num=3) plt.xlabel(r'$t$ / s') plt.ylabel(r'$r^2$ / $\mu$m$^2$') plt.plot(t_, r2_cum / cs.micro**2, marker='o', ls='None') plt.plot(x_fit, y_fit / cs.micro**2) if output: print(dpr.val(popt[0] / cs.micro**2, d_popt[0] / cs.micro**2, name='slope', unit='μm² / s')) print() D = popt[0] / 4 d_D = d_popt[0] / 4