dimension_type,
end_locations_list[ii],
topo_xyz,
num_rx_per_src,
station_separation,
)
dc_survey_original = dc.survey.Survey(source_list)
# Write out data at their original electrode locations (not shifted)
data_obj = data.Data(dc_survey_original, dobs=dobs, standard_deviation=std)
fname = dir_path + "dc_data.xyz"
write_dcip_xyz(
fname,
data_obj,
data_header='V/A',
uncertainties_header='UNCERT',
out_dict=out_dict
)
# Add Gaussian noise with a standard deviation of 5e-3 V/V
np.random.seed(444)
std = 5e-3 * np.ones_like(dpred_ip)
noise = std * np.random.rand(len(dpred_ip))
dobs = dpred_ip + noise
# Create a survey with the original electrode locations
# and not the shifted ones.
source_list = []
for ii in range(0, len(end_locations_list)):
dimension_type,
end_locations_list[ii],
topo_xyz,
num_rx_per_src,
station_separation,
)
dc_survey_original = dc.survey.Survey(source_list)
# Write out data at their original electrode locations (not shifted)
data_obj = data.Data(dc_survey_original, dobs=dobs, standard_deviation=std)
fname = dir_path + "dc_data.xyz"
write_dcip_xyz(
fname,
data_obj,
data_header="V/A",
uncertainties_header="UNCERT",
out_dict=out_dict,
)
# Add Gaussian noise with a standard deviation of 5e-3 V/V
np.random.seed(444)
std = 5e-3 * np.ones_like(dpred_ip)
noise = std * np.random.rand(len(dpred_ip))
dobs = dpred_ip + noise
# Create a survey with the original electrode locations
# and not the shifted ones.
source_list = []
for ii in range(0, len(end_locations_list)):
source_list += generate_dcip_sources_line(