#Use matplotlib pre-2.0 version style
p.style.use('classic')
prefix = 'plot_data'
data_file_list = [
'cphase_fidelity_NAtoms_aE_a_opt_lambda_sagnac_g1d_0.05_OmegaScattering_1_OmegaStorageRetrieval_1_kd_0.5.txt',
'cphase_fidelity_NAtoms_aE_a_opt_dualv_sym_sagnac_g1d_0.05_OmegaScattering_1_OmegaStorageRetrieval_1_kd_0.266.txt',
]
usetex()
fig = p.figure(figsize=(3.375, 2.0))
data = []
column_dic = []
for file_name in data_file_list:
param_dict = extract_params_from_file_name(file_name)
full_path = os.path.join(prefix, file_name)
if not os.path.exists(full_path):
print('Path {} doesn\'t exist'.format(full_path))
continue
data.append(
np.loadtxt(full_path,
dtype=np.float64,
delimiter=';',
unpack=True,
skiprows=1))
column_names = read_column_names(full_path)
column_dic.append(dict(zip(column_names, range(len(column_names)))))
p.subplot(1, 2, 1)
handle1, = p.loglog(data[0][column_dic[0]['NAtoms']],
def dual_v_linear_dispersion_relation(Delta3, Deltac, Omega, g1d):
gprime = 1 - g1d
Delta = Delta3 + Deltac
tilde_Delta = Delta + 0.5j * gprime
eta = Omega**2 / (4 * Delta3 * tilde_Delta)
return -g1d / (2 * tilde_Delta) * (1 - eta) / (1 - 2 * eta)
prefix = './data_for_plots'
data_lambda_file_name = 'grating_dispersion_relation_lambda_N_10000_g1d_0.1_Deltac_-90_Omega_1_OmegaPeriods_5000_seed_12345.txt'
data_dualV_file_name = 'grating_dispersion_relation_dualV_N_10000_g1d_0.1_Deltac_-90_Omega_1_OmegaPeriods_5000_seed_12345.txt'
param_dict_dualV = extract_params_from_file_name(data_dualV_file_name)
full_path_dualV = os.path.join(prefix, data_dualV_file_name)
full_path_lambda = os.path.join(prefix, data_lambda_file_name)
data_dualV = p.loadtxt(full_path_dualV,
dtype=p.float64,
delimiter=';',
unpack=True,
skiprows=1)
data_lambda = p.loadtxt(full_path_lambda,
dtype=p.float64,
delimiter=';',
unpack=True,
skiprows=1)
column_names_lambda = read_column_names(full_path_lambda)
column_dic_lambda = dict(
zip(column_names_lambda, range(len(column_names_lambda))))