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
list_domain_power = np.linspace(domain_power_min,domain_power_max,num_domain_power)
list_semblance = semb.findListSemblanceForSignalGainedWithListOfDomainPower(
np.squeeze(trace),
domain_time_sample,
list_domain_power)
output_file_base_name = "%dsemblancevsdomainpower" % trace_index
plot_helper.plotSemblanceVsDomainPower(list_domain_power,
list_semblance,
local_output_directory,
output_file_base_name,
figure_output_settings)
#log log linear fit
output_logloglinear = lll.findDomainPowerViaLogLogLinearRegresstion(
np.abs(trace),
domain_time_sample)
unique_to_function_call_logger = misc.createUniqueToFunctionCallLogger()
log_file = local_output_directory + \
os.path.sep + \
unique_to_function_call_logger.name + \
"logloglinear.log"
file_console_handler = logging.FileHandler(log_file)
misc.setupHandlerAndAddToLogger(file_console_handler, unique_to_function_call_logger)
unique_to_function_call_logger.info("slope=%g,intercept=%g,r_value=%g,p_value=%g,std_err=%g" %
(output_logloglinear.slope,
output_logloglinear.intercept,
output_logloglinear.r_value,
output_logloglinear.p_value,
output_logloglinear.std_err))
#END FOR
