def gen_view_data_tables_from_atomic_data(self):
essential_fields = [f for f in self.atomic_data_fields if constants.IS_ESSENTIAL_FIELD(f)]
auxiliary_fields = [f for f in self.atomic_data_fields if constants.IS_AUXILIARY_FIELD(f)]
# setting standard view table structure and types
view_table_structure = ['contig'] + self.sample_ids_found_in_input_dbs + auxiliary_fields
view_table_types = ['text'] + ['numeric'] * len(self.sample_ids_found_in_input_dbs) + ['text']
# generate a dictionary for normalized coverage of each contig across samples per target
self.normalized_coverages = {'contigs': {}, 'splits': {}}
for target in ['contigs', 'splits']:
for split_name in self.split_names:
self.normalized_coverages[target][split_name] = {}
for input_profile_db_path in self.profile_dbs_info_dict:
self.normalized_coverages[target][split_name][input_profile_db_path] = self.get_normalized_coverage_of_split(target, input_profile_db_path, split_name)
# generate a dictionary for max normalized ratio of each contig across samples per target
self.max_normalized_ratios = {'contigs': {}, 'splits': {}}
for target in ['contigs', 'splits']:
for split_name in self.split_names:
self.max_normalized_ratios[target][split_name] = self.get_max_normalized_ratio_of_split(target, split_name)
self.progress.new('Generating view data tables')
for target in ['contigs', 'splits']:
for essential_field in essential_fields:
self.progress.update('Processing %s for %s ...' % (essential_field, target))
data_dict = {}
for split_name in self.split_names:
data_dict[split_name] = {'__parent__': self.split_parents[split_name]}
for input_profile_db_path in self.profile_dbs_info_dict:
sample_id = self.profile_dbs_info_dict[input_profile_db_path]['sample_id']
if essential_field == 'normalized_coverage':
data_dict[split_name][sample_id] = self.normalized_coverages[target][split_name][input_profile_db_path]
elif essential_field == 'max_normalized_ratio':
data_dict[split_name][sample_id] = self.max_normalized_ratios[target][split_name][input_profile_db_path]
elif essential_field == 'relative_abundance':
data_dict[split_name][sample_id] = self.get_relative_abundance_of_split(target, input_profile_db_path, split_name)
else:
data_dict[split_name][sample_id] = self.atomic_data_for_each_run[target][input_profile_db_path][split_name][essential_field]
# time to store the data for this view in the profile database
table_name = '_'.join([essential_field, target])
TablesForViews(self.merged_profile_db_path).create_new_view(
data_dict=data_dict,
table_name=table_name,
table_structure=view_table_structure,
table_types=view_table_types,
view_name=essential_field if target == 'splits' else None)
# if SNVs were not profiled, remove all entries from variability tables:
if not self.SNVs_profiled:
TablesForViews(self.merged_profile_db_path).remove(view_name='variability', table_names_to_blank=['variability_splits', 'variability_contigs'])
self.progress.end()
def gen_view_data_tables_from_atomic_data(self):
essential_fields = [
f for f in self.atomic_data_fields
if constants.IS_ESSENTIAL_FIELD(f)
]
auxiliary_fields = [
f for f in self.atomic_data_fields
if constants.IS_AUXILIARY_FIELD(f)
]
views_table = dbops.TableForViews(self.profile_db_path,
anvio.__profile__version__,
progress=self.progress)
# setting standard view table structure and types
view_table_structure = ['contig'
] + self.merged_sample_ids + auxiliary_fields
view_table_types = [
'text'
] + ['numeric'] * len(self.merged_sample_ids) + ['text']
# generate a dictionary for normalized coverage of each contig across samples per target
self.normalized_coverages = {'contigs': {}, 'splits': {}}
for target in ['contigs', 'splits']:
for split_name in self.split_names:
self.normalized_coverages[target][split_name] = {}
for sample_id in self.merged_sample_ids:
self.normalized_coverages[target][split_name][
sample_id] = self.get_normalized_coverage_of_split(
target, sample_id, split_name)
# generate a dictionary for max normalized ratio of each contig across samples per target
self.max_normalized_ratios = {'contigs': {}, 'splits': {}}
for target in ['contigs', 'splits']:
for split_name in self.split_names:
self.max_normalized_ratios[target][
split_name] = self.get_max_normalized_ratio_of_split(
target, split_name)
self.progress.new('Generating view data tables')
profile_db = dbops.ProfileDatabase(self.profile_db_path, quiet=True)
for target in ['contigs', 'splits']:
for essential_field in essential_fields:
self.progress.update('Processing %s for %s ...' %
(essential_field, target))
target_table = '_'.join([essential_field, target])
m = {}
for split_name in self.split_names:
m[split_name] = {
'__parent__': self.split_parents[split_name]
}
for sample_id in self.merged_sample_ids:
if essential_field == 'normalized_coverage':
m[split_name][
sample_id] = self.normalized_coverages[target][
split_name][sample_id]
elif essential_field == 'max_normalized_ratio':
m[split_name][
sample_id] = self.max_normalized_ratios[
target][split_name][sample_id]
elif essential_field == 'relative_abundance':
m[split_name][
sample_id] = self.get_relative_abundance_of_split(
target, sample_id, split_name)
else:
m[split_name][
sample_id] = self.atomic_data_for_each_run[
target][sample_id][split_name][
essential_field]
# variable 'm' for the essential field is now ready to be its own table:
profile_db.db.create_table(target_table, view_table_structure,
view_table_types)
db_entries = [
tuple([split_name] +
[m[split_name][h] for h in view_table_structure[1:]])
for split_name in self.split_names
]
profile_db.db._exec_many(
'''INSERT INTO %s VALUES (%s)''' % (target_table, ','.join(
['?'] * len(view_table_structure))), db_entries)
if target == 'splits':
views_table.append(essential_field, target_table)
profile_db.disconnect()
self.progress.end()
# store views in the database
views_table.store()