def plot(dirname_sets_u, dirname_sets_D, cs, labels, ax):
ejm_rcparams.prettify_axes(ax)
for dirnames_u, dirnames_D, c, label in zip(dirname_sets_u,
dirname_sets_D, cs, labels):
pfs, uds, uds_err = utils.pf_uds_x(dirnames_u)
i_sort = np.argsort(pfs)
pfs, uds, uds_err = pfs[i_sort], uds[i_sort], uds_err[i_sort]
pfs, Ds, Ds_err = utils.pf_Ds_scalar(dirnames_D)
i_sort = np.argsort(pfs)
pfs, Ds, Ds_err = pfs[i_sort], Ds[i_sort], Ds_err[i_sort]
uds_norm = uds / u_x_0
uds_norm_err = uds_err / u_x_0
Ds_norm = Ds / D_0
Ds_norm_err = Ds_err / D_0
ls = Ds_norm / uds_norm
ls_err = ls * np.sqrt(np.sum(np.square([Ds_norm_err / Ds_norm,
uds_norm_err / uds_norm])))
ax.errorbar(pfs, ls, yerr=ls_err, label=label, c=c)
ax.set_xlim(0.0, 1.0)
ax.set_ylim(0.5, 6.5)
ax.set_xlabel(r'$\phi$', fontsize=35)
ax.set_ylabel(r'$l_x$', fontsize=35)
ax.tick_params(axis='both', labelsize=26, pad=10.0)
def plot(dirname_sets, cs, labels, ax):
ejm_rcparams.prettify_axes(ax)
for dirnames, c, label in zip(dirname_sets, cs, labels):
pfs, Ds, Ds_err = utils.pf_Ds_scalar(dirnames)
i_sort = np.argsort(pfs)
pfs, Ds, Ds_err = pfs[i_sort], Ds[i_sort], Ds_err[i_sort]
ax.errorbar(pfs, Ds / Ds[0], yerr=Ds_err / Ds[0], label=label, c=c)
ax.set_xlim(0.0, 1.0)
ax.set_ylim(0.0, 1.0)
ax.set_xlabel(r'$\phi$', fontsize=35)
ax.set_ylabel(r'$\mathrm{D} / \mathrm{D}_0$', fontsize=35)
ax.tick_params(axis='both', labelsize=26, pad=10.0)
(dirnames_p_1side_S, dirnames_p_a),
(dirnames_Dr_1side_S, dirnames_Dr_a),
(dirnames_p_2side_S, dirnames_p_a),
(dirnames_Dr_2side_S, dirnames_Dr_a),
]
def f(x, m):
return 1.0 + m * x
for dirnames_u, dirnames_D in dirname_sets:
pfs, uds, uds_err = utils.pf_uds_x(dirnames_u)
i_sort = np.argsort(pfs)
pfs, uds, uds_err = pfs[i_sort], uds[i_sort], uds_err[i_sort]
pfs, Ds, Ds_err = utils.pf_Ds_scalar(dirnames_D)
i_sort = np.argsort(pfs)
pfs, Ds, Ds_err = pfs[i_sort], Ds[i_sort], Ds_err[i_sort]
uds_norm = uds / u_x_0
uds_norm_err = uds_err / u_x_0
Ds_norm = Ds / D_0
Ds_norm_err = Ds_err / D_0
ls = Ds_norm / uds_norm
ls_err = ls * np.sqrt(np.sum(np.square([Ds_norm_err / Ds_norm,
uds_norm_err / uds_norm])))
# pfs, ls, ls_err = pfs[:-3], ls[:-3], ls_err[:-3]
popt, pcov = curve_fit(f, pfs, ls, sigma=ls_err)
def plot_pf_Ds_scalar(dirnames, ax):
pfs, Ds, Ds_err = utils.pf_Ds_scalar(dirnames)
i_sort = np.argsort(pfs)
pfs, Ds = pfs[i_sort], Ds[i_sort]
ax.plot(pfs, Ds)
ax.set_xlim(-0.02, 1.0)
