def compareMedianBalanceVsRegressionGivenGroundTruth(
list_of_power,
num_time_sample=2000,
origin_time_sample = 0.01,
delta_time_sample = 0.002,
num_point_ricker_wavelet=100,
width_parameter_ricker_wavelet=10,
noise_level=1,
initial_exponent_power=2,
max_iteration=60,
delta_exponent_power_tolerance=1e-12,
output_directory='/tmp'):
output = collections.namedtuple('compareMedianBalanceVsRegression',
['list_median_balance_recovered_power',
'list_regression_recovered_power',
'data_by_trace_time'
'domain_time_sample'])
output.domain_time_sample = math_op.calculateLinearlySpacedValues(origin_time_sample,
delta_time_sample,
num_time_sample)
output.data_by_trace_time = get_ricker_data(list_of_power,
num_time_sample,
num_point_ricker_wavelet,
width_parameter_ricker_wavelet,
output.domain_time_sample,
noise_level)
output.list_median_balance_recovered_power = []
output.list_regression_recovered_power = []
for power, trace in zip(list_of_power, output.data_by_trace_time):
#median_balance
normalized_trace = np.squeeze(math_op.divideEachRowByItsMaxAbsValue(np.array([trace])))
median_result = mb.recoverDomainWeightedGainViaMedianBalancing( normalized_trace,
output.domain_time_sample,
initial_exponent_power,
max_iteration,
delta_exponent_power_tolerance,
output_directory,
print_to_stdout = 0,
logging_level=logging.CRITICAL)
output.list_median_balance_recovered_power.append(median_result.domain_exponent_power)
#regression
output_logloglinear = lll.findDomainPowerViaLogLogLinearRegresstion(
np.abs(trace),
output.domain_time_sample)
output.list_regression_recovered_power.append(-output_logloglinear.slope)
l2_median_balance_powers_error = np.linalg.norm(output.list_median_balance_recovered_power - list_of_power)
l2_regression_powers_error = np.linalg.norm(output.list_regression_recovered_power - list_of_power)
print 'l2_median_balance_powers_error= %g' % l2_median_balance_powers_error
print 'l2_regression_powers_error = %g' % l2_regression_powers_error
print 'l2_median_balance_powers_error / l2_regression_powers_error = %g' % (l2_median_balance_powers_error/l2_regression_powers_error)
return output
break
if case('median_balance'):
###################################################################
# USER CONFIG: choose run parameters
###################################################################
#--> input data_type
input_data_type = 'rsf'
num_trace = 3
num_time_sample = 2000
delta_time_sample = 0.002
origin_time_sample = delta_time_sample
origin_trace = -2
delta_trace = 0.05
#--> powers to attenuate synthetic data with
list_of_power = math_op.calculateLinearlySpacedValues(0.25, 0.5, num_trace)
#-->location of config file
config_file = "/home/mlai/geo/mlai/median_balance/user_settings.cfg"
#-->change these values for code speedup
logging_level = logging.DEBUG
print_to_stdout = 0
#-->view input as family of traces or individual traces
treat_traces_individually = 1
###################################################################
domain_time_sample = math_op.calculateLinearlySpacedValues(
origin_time_sample,
delta_time_sample,
num_time_sample)
domain_time_sample_with_end_point = math_op.calculateLinearlySpacedValues(
origin_time_sample,
