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Needed indentation
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@ishita9
ishita9 committed Sep 11, 2024
1 parent 6cd56b2 commit 630bc20
Showing 1 changed file with 93 additions and 89 deletions.
182 changes: 93 additions & 89 deletions metcalcpy/agg_stat.py
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Original file line number Diff line number Diff line change
Expand Up @@ -990,99 +990,103 @@ def _get_bootstrapped_stats_for_derived(self, series, distributions, axis="1"):
if perm in self.group_to_value:
permute_for_second_series[i] = self.group_to_value[perm]

logger.debug(f"First series components: {permute_for_first_series}")
logger.debug(f"Second series components: {permute_for_second_series}")

ds_1 = ds_2 = None

# for each component find its BootstrapDistributionResult object
for series_to_distrib_key in distributions.keys():
if all(elem in permute_for_first_series for elem in series_to_distrib_key):
ds_1 = distributions[series_to_distrib_key]
if all(elem in permute_for_second_series for elem in series_to_distrib_key):
ds_2 = distributions[series_to_distrib_key]
if ds_1 is not None and ds_2 is not None:
break

if ds_1 is None or ds_2 is None:
logger.warning("Could not find BootstrapDistributionResult objects for one or both series.")
except KeyError as err:
logger.error(f"Error during derived component lookup: {err}", exc_info=True)
return BootstrapResults(None, None, None)

# if BootstrapDistributionResult object doesn't exist or the original series data size is 0, return empty object
if ds_1.values is None or ds_2.values is None or ds_1.values.size == 0 or ds_2.values.size == 0:
logger.warning("One or both series have no values. Returning empty BootstrapResults object.")
return BootstrapResults(lower_bound=None, value=None, upper_bound=None)

# calculate the number of values in the group if the series has a group, needed for validation
num_diff_vals_first = sum(len(val.split(GROUP_SEPARATOR)) for val in permute_for_first_series if len(val.split(GROUP_SEPARATOR)) > 1)
num_diff_vals_second = sum(len(val.split(GROUP_SEPARATOR)) for val in permute_for_second_series if len(val.split(GROUP_SEPARATOR)) > 1)
num_diff_vals_first = max(num_diff_vals_first, 1)
num_diff_vals_second = max(num_diff_vals_second, 1)

# validate data
if derived_curve_component.derived_operation != 'SINGLE':
logger.debug("Validating series for derived operation.")
self._validate_series_cases_for_derived_operation(ds_1.values, axis, num_diff_vals_first)
self._validate_series_cases_for_derived_operation(ds_2.values, axis, num_diff_vals_second)

# handle bootstrapping
if self.params['num_iterations'] == 1 or derived_curve_component.derived_operation == 'ETB':
logger.debug("No bootstrapping required; calculating derived statistic.")
if derived_curve_component.derived_operation == 'ETB':
index_array = np.where(self.column_names == 'stat_value')[0]
func_name = f'calculate_{self.statistic}'
for row in ds_1.values:
stat = [globals()[func_name](row[np.newaxis, ...], self.column_names)]
row[index_array] = stat
for row in ds_2.values:
stat = [globals()[func_name](row[np.newaxis, ...], self.column_names)]
row[index_array] = stat

ds_1_value = ds_1.values[:, index_array].flatten().tolist()
ds_2_value = ds_2.values[:, index_array].flatten().tolist()
else:
ds_1_value = [ds_1.value]
ds_2_value = [ds_2.value]
logger.debug(f"First series components: {permute_for_first_series}")
logger.debug(f"Second series components: {permute_for_second_series}")

ds_1 = ds_2 = None

# for each component find its BootstrapDistributionResult object
for series_to_distrib_key in distributions.keys():
if all(elem in permute_for_first_series for elem in series_to_distrib_key):
ds_1 = distributions[series_to_distrib_key]
if all(elem in permute_for_second_series for elem in series_to_distrib_key):
ds_2 = distributions[series_to_distrib_key]
if ds_1 is not None and ds_2 is not None:
break

if ds_1 is None or ds_2 is None:
logger.warning("Could not find BootstrapDistributionResult objects for one or both series.")

# if BootstrapDistributionResult object doesn't exist or the original series data size is 0, return empty object
if ds_1.values is None or ds_2.values is None or ds_1.values.size == 0 or ds_2.values.size == 0:
logger.warning("One or both series have no values. Returning empty BootstrapResults object.")
return BootstrapResults(lower_bound=None, value=None, upper_bound=None)

stat_val = calc_derived_curve_value(ds_1_value, ds_2_value, derived_curve_component.derived_operation)
if stat_val is not None:
results = BootstrapResults(lower_bound=None, value=round_half_up(stat_val[0], 5), upper_bound=None)
else:
results = BootstrapResults(lower_bound=None, value=None, upper_bound=None)
results.set_distributions([results.value])
# calculate the number of values in the group if the series has a group, needed for validation
num_diff_vals_first = sum(len(val.split(GROUP_SEPARATOR)) for val in permute_for_first_series if len(val.split(GROUP_SEPARATOR)) > 1)
num_diff_vals_second = sum(len(val.split(GROUP_SEPARATOR)) for val in permute_for_second_series if len(val.split(GROUP_SEPARATOR)) > 1)
num_diff_vals_first = max(num_diff_vals_first, 1)
num_diff_vals_second = max(num_diff_vals_second, 1)

# validate data
if derived_curve_component.derived_operation != 'SINGLE':
logger.debug("Validating series for derived operation.")
self._validate_series_cases_for_derived_operation(ds_1.values, axis, num_diff_vals_first)
self._validate_series_cases_for_derived_operation(ds_2.values, axis, num_diff_vals_second)

# handle bootstrapping
if self.params['num_iterations'] == 1 or derived_curve_component.derived_operation == 'ETB':
logger.debug("No bootstrapping required; calculating derived statistic.")
if derived_curve_component.derived_operation == 'ETB':
index_array = np.where(self.column_names == 'stat_value')[0]
func_name = f'calculate_{self.statistic}'
for row in ds_1.values:
stat = [globals()[func_name](row[np.newaxis, ...], self.column_names)]
row[index_array] = stat
for row in ds_2.values:
stat = [globals()[func_name](row[np.newaxis, ...], self.column_names)]
row[index_array] = stat

ds_1_value = ds_1.values[:, index_array].flatten().tolist()
ds_2_value = ds_2.values[:, index_array].flatten().tolist()
else:
logger.debug("Performing bootstrapping with CI calculation.")
operation = np.full((len(ds_1.values), 1), derived_curve_component.derived_operation)
values_both_arrays = np.concatenate((ds_1.values, ds_2.values, operation), axis=1)
ds_1_value = [ds_1.value]
ds_2_value = [ds_2.value]

try:
block_length = int(math.sqrt(len(values_both_arrays))) if 'circular_block_bootstrap' in self.params and parse_bool(self.params['circular_block_bootstrap']) else 1
results = bootstrap_and_value(
values_both_arrays,
stat_func=self._calc_stats_derived,
num_iterations=self.params['num_iterations'],
num_threads=self.params['num_threads'],
ci_method=self.params['method'],
alpha=self.params['alpha'],
save_data=False,
save_distributions=(derived_curve_component.derived_operation == 'DIFF_SIG'),
block_length=block_length)
except KeyError as err:
logger.error(f"Error during bootstrapping: {err}", exc_info=True)
return BootstrapResults(None, None, None)

# Post-processing for DIFF_SIG
if derived_curve_component.derived_operation == 'DIFF_SIG':
logger.debug("Processing DIFF_SIG operation for derived statistics.")
distributions = [i for i in results.distributions if i is not None]
if distributions and results.value is not None:
distribution_mean = np.mean(distributions)
distribution_under_h0 = distributions - distribution_mean
pval = np.mean(np.absolute(distribution_under_h0) <= np.absolute(results.value))
diff_sig = perfect_score_adjustment(ds_1.value, ds_2.value, self.statistic, pval)
results.value = diff_sig

logger.info("Completed derived statistics calculation.")
return results
stat_val = calc_derived_curve_value(ds_1_value, ds_2_value, derived_curve_component.derived_operation)
if stat_val is not None:
results = BootstrapResults(lower_bound=None, value=round_half_up(stat_val[0], 5), upper_bound=None)
else:
results = BootstrapResults(lower_bound=None, value=None, upper_bound=None)
results.set_distributions([results.value])
else:
logger.debug("Performing bootstrapping with CI calculation.")
operation = np.full((len(ds_1.values), 1), derived_curve_component.derived_operation)
values_both_arrays = np.concatenate((ds_1.values, ds_2.values, operation), axis=1)

try:
block_length = int(math.sqrt(len(values_both_arrays))) if 'circular_block_bootstrap' in self.params and parse_bool(self.params['circular_block_bootstrap']) else 1
results = bootstrap_and_value(
values_both_arrays,
stat_func=self._calc_stats_derived,
num_iterations=self.params['num_iterations'],
num_threads=self.params['num_threads'],
ci_method=self.params['method'],
alpha=self.params['alpha'],
save_data=False,
save_distributions=(derived_curve_component.derived_operation == 'DIFF_SIG'),
block_length=block_length)
except KeyError as err:
logger.error(f"Error during bootstrapping: {err}", exc_info=True)
return BootstrapResults(None, None, None)

# Post-processing for DIFF_SIG
if derived_curve_component.derived_operation == 'DIFF_SIG':
logger.debug("Processing DIFF_SIG operation for derived statistics.")
distributions = [i for i in results.distributions if i is not None]
if distributions and results.value is not None:
distribution_mean = np.mean(distributions)
distribution_under_h0 = distributions - distribution_mean
pval = np.mean(np.absolute(distribution_under_h0) <= np.absolute(results.value))
diff_sig = perfect_score_adjustment(ds_1.value, ds_2.value, self.statistic, pval)
results.value = diff_sig

logger.info("Completed derived statistics calculation.")
return results

def _get_bootstrapped_stats(self, series_data, axis="1"):
""" Calculates aggregation statistic value and CI intervals if needed for input data
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