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
data_by_trace_time[idx_power,:] = \
attenuation_by_time_sample * true_data_by_trace_time[idx_power,:]
break
if case(): # default, could also just omit condition or 'if True'
print "unknown input_data_type = %s \n" % input_data_type
processed_data_by_trace_time = np.zeros((num_trace,num_time_sample),'f')
if (treat_traces_individually):
list_power_by_traceindex = np.zeros(num_trace)
list_iterationcount_by_traceindex = np.zeros(num_trace)
list_traceindex = range(num_trace)
for trace_index in list_traceindex:
trace = data_by_trace_time[[trace_index],:]
#normalize values
normalized_trace = math_op.divideEachRowByItsMaxAbsValue(trace)
#setup output directory
local_output_directory = output_directory + os.sep + "data" + str(trace_index)
file_operation.makeDirectoryIfNotExist(local_output_directory)
median_balance_output = \
mb.recoverDomainWeightedGainViaMedianBalancing( normalized_trace,
domain_time_sample,
initial_exponent_power,
max_iteration,
delta_exponent_power_tolerance,
local_output_directory,
print_to_stdout = 1,
logging_level=logging.DEBUG)
weighted_trace = math_op.weightSignalByDomainExponentiated(