def get_stat(pickle_path, alg):
superset = pickle_load(pickle_path)
eta_0, eta_0_err = superset.get_eta_0()
f_peak, f_peak_err = superset.get_f_peak(alg, dr)
return eta_0, eta_0_err, f_peak, f_peak_err
def get_stat(pickle_path):
superset = pickle_load(pickle_path)
R_var, R_var_err = superset.get_var()
vf, vf_err = superset.get_vf()
return vf, vf_err, R_var, R_var_err
superset = pickle_load(pickle_path)
eta_0, eta_0_err = superset.get_eta_0()
f_peak, f_peak_err = superset.get_f_peak(alg, dr)
return eta_0, eta_0_err, f_peak, f_peak_err
eta_0, eta_0_err, f_peak, f_peak_err = get_stat(paths.exp_pickle_path)
ax.errorbar(eta_0,
f_peak,
xerr=eta_0_err,
yerr=f_peak_err,
ls='none',
label=r'Experiment',
c=color_exp)
superset = pickle_load(paths.exp_pickle_path)
gamma_fit, gamma_fit_err, k_fit, k_fit_err = superset.fit_to_model(alg, dr)
f_peak_model = superset.get_f_peak_model(gamma_fit, k_fit)
# ch = np.sqrt(np.sum(np.square(f_peak_model - f_peak))) / len(f_peak_model)
gamma_fit = 0.14
k_fit = 0.08
if use_latex:
label = r'Model fit, $\tau^{-1} = \SI{%.2g}{\per\s}, k = %.2g$' % (
gamma_fit, k_fit)
else:
label = r'Model fit, $\tau^{-1} = %.2g s^{-1}, k = %.2g$' % (gamma_fit,
k_fit)
ax.scatter(eta_0, f_peak_model, label=label, c=color_model)
# eta_0, eta_0_err, f_peak, f_peak_err = get_stat(paths.exp_reproduction_Dr_0_05_Drc_0_pickle_path)
# ax.errorbar(eta_0, f_peak, xerr=eta_0_err, yerr=f_peak_err, ls='none', label=r'$D_r^c = 0$', c=color_0)
from dataset import Superset, pickle_load
import numpy as np
import paths
res = 0.7
# superset = Superset(paths.exp_dset_paths)
# print(superset.fit_to_model('mean', res))
# superset = Superset(paths.exp_dset_paths)
# print(superset.fit_to_model('median', res))
# superset = Superset(paths.exp_reproduction_Dr_0_05_Drc_10_dset_paths)
superset = pickle_load(paths.exp_reproduction_Dr_0_05_Drc_10_pickle_path)
print(superset.fit_to_model('mean', res))
from dataset import Superset, pickle_load
import numpy as np
import paths
res = 0.7
# superset = Superset(paths.exp_dset_paths)
# print(superset.fit_to_model('mean', res))
# superset = Superset(paths.exp_dset_paths)
# print(superset.fit_to_model('median', res))
# superset = Superset(paths.exp_reproduction_Dr_0_05_Drc_10_dset_paths)
superset = pickle_load(paths.exp_reproduction_Dr_0_05_Drc_10_pickle_path)
print(superset.fit_to_model('mean', res))
def get_stat(pickle_path):
superset = pickle_load(pickle_path)
R_var, R_var_err = superset.get_var()
vf, vf_err = superset.get_vf()
return vf, vf_err, R_var, R_var_err
def get_stat(pickle_path):
superset = pickle_load(pickle_path)
R_mean, R_mean_err = superset.get_mean()
vf, vf_err = superset.get_vf()
return vf, vf_err, R_mean, R_mean_err
ejm_rcparams.prettify_axes(ax)
dr = 0.7
alg = 'mean'
def get_stat(pickle_path):
superset = pickle_load(pickle_path)
eta_0, eta_0_err = superset.get_eta_0()
f_peak, f_peak_err = superset.get_f_peak(alg, dr)
return eta_0, eta_0_err, f_peak, f_peak_err
eta_0, eta_0_err, f_peak, f_peak_err = get_stat(paths.exp_pickle_path)
ax.errorbar(eta_0, f_peak, xerr=eta_0_err, yerr=f_peak_err, ls='none', label=r'Experiment', c=color_exp)
superset = pickle_load(paths.exp_pickle_path)
gamma_fit, gamma_fit_err, k_fit, k_fit_err = superset.fit_to_model(alg, dr)
f_peak_model = superset.get_f_peak_model(gamma_fit, k_fit)
# ch = np.sqrt(np.sum(np.square(f_peak_model - f_peak))) / len(f_peak_model)
gamma_fit = 0.14
k_fit = 0.08
if use_latex:
label = r'Model fit, $\tau^{-1} = \SI{%.2g}{\per\s}, k = %.2g$' % (gamma_fit, k_fit)
else:
label = r'Model fit, $\tau^{-1} = %.2g s^{-1}, k = %.2g$' % (gamma_fit, k_fit)
ax.scatter(eta_0, f_peak_model, label=label, c=color_model)
# eta_0, eta_0_err, f_peak, f_peak_err = get_stat(paths.exp_reproduction_Dr_0_05_Drc_0_pickle_path)
# ax.errorbar(eta_0, f_peak, xerr=eta_0_err, yerr=f_peak_err, ls='none', label=r'$D_r^c = 0$', c=color_0)
# eta_0, eta_0_err, f_peak, f_peak_err = get_stat(paths.exp_reproduction_Dr_0_05_Drc_10_pickle_path)
def get_stat(pickle_path):
superset = pickle_load(pickle_path)
R = superset.get_R()
f_peak, f_peak_err = superset.get_f_peak(alg, dr)
return R, f_peak, f_peak_err
def get_stat(pickle_path):
superset = pickle_load(pickle_path)
R_mean, R_mean_err = superset.get_mean()
vf, vf_err = superset.get_vf()
return vf, vf_err, R_mean, R_mean_err