def __init__(self, args, run=run, progress=progress):
self.run = run
self.progress = progress
A = lambda x: args.__dict__[x] if x in args.__dict__ else None
self.gene_coverages_data_file_path = A('data_file')
self.gene_detection_data_file_path = A('gene_detection_data_file')
self.profile_db_path = A('profile_db')
self.output_file_prefix = A('output_file_prefix')
self.alpha = A('alpha')
self.beta = A('beta')
self.gamma = A('gamma')
self.eta = A('eta')
self.zeta = A('zeta')
self.additional_layers_to_append = A('additional_layers_to_append')
self.samples_information_to_append = A('samples_information_to_append')
self.number_of_positive_samples = None
self.collection_name = A('collection_name')
self.bin_id = A('bin_id')
self.bin_ids_file_path = A('bin_ids_file')
self.store_gene_detections_and_gene_coverages_tables = A(
'store_gene_detections_and_gene_coverages_tables')
self.gene_coverages = {}
self.gene_detection = {}
self.samples = {}
self.positive_samples = {}
self.negative_samples = {}
self.gene_class_information = {}
self.samples_information = {}
self.profile_db = {}
self.sanity_check()
if self.profile_db_path is None:
self.get_data_from_txt_file()
else:
# load sample list and gene_coverage_dict from the merged profile db
args.init_gene_coverages = True
if self.collection_name:
self.summary = summarizer.ProfileSummarizer(args)
self.summary.init()
else:
self.profile_db = ProfileSuperclass(args)
self.profile_db.init_gene_coverages_and_detection_dicts()
self.gene_coverages = self.profile_db.gene_coverages_dict
self.gene_detection = self.profile_db.gene_detection_dict
self.samples = set(
next(iter(self.gene_coverages.values())).keys())
def __init__(self, args, run=run, progress=progress):
self.run = run
self.progress = progress
A = lambda x: args.__dict__[x] if x in args.__dict__ else None
self.gene_coverages_data_file_path = A('data_file')
self.gene_detections_data_file_path = A('gene_detection_data_file')
self.profile_db_path = A('profile_db')
self.output_file_prefix = A('output_file_prefix')
self.alpha = A('alpha')
self.additional_layers_to_append = A('additional_layers_to_append')
self.samples_information_to_append = A('samples_information_to_append')
self.collection_name = A('collection_name')
self.bin_id = A('bin_id')
self.bin_ids_file_path = A('bin_ids_file')
self.exclude_samples = A('exclude_samples')
self.include_samples = A('include_samples')
self.profile_db = {}
self.coverage_values_per_nt = {}
self.gene_coverages = pd.DataFrame.empty
self.gene_detections = pd.DataFrame.empty
self.samples = {}
self.sample_detection_information_was_initiated = False
self.positive_samples = []
self.number_of_positive_samples = None
self.negative_samples = pd.DataFrame.empty
self.number_of_negative_samples = None
self.gene_class_information = pd.DataFrame.empty
self.samples_detection_information = pd.DataFrame.empty
self.gene_presence_absence_in_samples = pd.DataFrame.empty
self.gene_coverages_filtered = pd.DataFrame.empty
self.additional_description = ''
self.total_length = None
self.samples_coverage_stats_dicts_was_initiated = False
self.samples_coverage_stats_dicts = pd.DataFrame.empty
self.non_outlier_indices = {}
if self.exclude_samples:
# check that there is a file like this
filesnpaths.is_file_exists(self.exclude_samples)
self.samples_to_exclude = set([
l.split('\t')[0].strip()
for l in open(self.exclude_samples, 'rU').readlines()
])
if not self.samples_to_exclude:
raise ConfigError(
"You asked to exclude samples, but provided an empty list."
)
run.info(
'Excluding Samples',
'The following samples will be excluded: %s' %
self.samples_to_exclude,
)
else:
self.samples_to_exclude = set([])
if self.include_samples:
# check that there is a file like this
filesnpaths.is_file_exists(self.include_samples)
self.samples_to_include = set([
l.split('\t')[0].strip()
for l in open(self.include_samples, 'rU').readlines()
])
if not self.samples_to_include:
raise ConfigError(
"You provided an empty list of samples to include.")
run.info(
'Including Samples',
'The following samples will be included: %s' %
self.samples_to_include,
)
else:
self.samples_to_include = set([])
# run sanity check on all input arguments
self.sanity_check()
if self.profile_db_path is None:
# TODO: this will probably be removed because we don't save the coverage information in nucleotide level.
pass
else:
# load sample list and gene_coverage_dict from the merged profile db
args.init_gene_coverages = True
if self.collection_name:
self.summary = summarizer.ProfileSummarizer(args)
self.summary.init()
self.init_samples(self.summary.p_meta['samples'])
else:
self.profile_db = ProfileSuperclass(args)
self.init_samples(self.profile_db.p_meta['samples'])
self.profile_db.init_split_coverage_values_per_nt_dict()
self.profile_db.init_gene_level_coverage_stats_dicts()
self.coverage_values_per_nt = get_coverage_values_per_nucleotide(
self.profile_db.split_coverage_values_per_nt_dict,
self.samples)
# comply with the new design and get gene_coverages and gene_detection dicsts from
# gene_level_coverage_stats_dict.
gene_coverages, gene_detection = self.get_gene_coverages_and_gene_detection_dicts(
)
self.init_coverage_and_detection_dataframes(
gene_coverages, gene_detection)
# getting the total length of all contigs
self.total_length = self.profile_db.p_meta['total_length']
def __init__(self, args, run=run, progress=progress):
self.run = run
self.progress = progress
A = lambda x: args.__dict__[x] if x in args.__dict__ else None
self.gene_coverages_data_file_path = A('data_file')
self.gene_detections_data_file_path = A('gene_detection_data_file')
self.profile_db_path = A('profile_db')
self.output_file_prefix = A('output_file_prefix')
self.alpha = A('alpha')
self.beta = A('beta')
self.gamma = A('gamma')
self.eta = A('eta')
self.zeta = A('zeta')
self.additional_layers_to_append = A('additional_layers_to_append')
self.samples_information_to_append = A('samples_information_to_append')
self.collection_name = A('collection_name')
self.bin_id = A('bin_id')
self.bin_ids_file_path = A('bin_ids_file')
self.store_gene_detections_and_gene_coverages_tables = A(
'store_gene_detections_and_gene_coverages_tables')
self.exclude_samples = A('exclude_samples')
self.gene_coverages = pd.DataFrame.empty
self.gene_detections = pd.DataFrame.empty
self.samples = {}
self.positive_samples = pd.DataFrame.empty
self.number_of_positive_samples = None
self.negative_samples = pd.DataFrame.empty
self.number_of_negative_samples = None
self.gene_class_information = pd.DataFrame.empty
self.samples_information = pd.DataFrame.empty
self.profile_db = {}
self.gene_presence_absence_in_samples = pd.DataFrame.empty
self.gene_coverages_filtered = pd.DataFrame.empty
# check that there is a file like this
if self.exclude_samples:
filesnpaths.is_file_exists(self.exclude_samples)
self.samples_to_exclude = set([
l.split('\t')[0].strip()
for l in open(args.exclude_samples, 'rU').readlines()
])
run.info(
'Excluding Samples',
'The following samples will be excluded: %s' %
self.samples_to_exclude,
)
else:
self.samples_to_exclude = set([])
self.sanity_check()
if self.profile_db_path is None:
self.get_data_from_txt_file()
else:
# load sample list and gene_coverage_dict from the merged profile db
args.init_gene_coverages = True
if self.collection_name:
self.summary = summarizer.ProfileSummarizer(args)
self.summary.init()
else:
self.profile_db = ProfileSuperclass(args)
self.profile_db.init_gene_coverages_and_detection_dicts()
self.gene_coverages = pd.DataFrame.from_dict(
self.profile_db.gene_coverages_dict,
orient='index',
dtype=float)
self.gene_coverages.drop(self.samples_to_exclude,
axis=1,
inplace=True)
self.Ng = len(self.gene_coverages.index)
self.gene_detections = pd.DataFrame.from_dict(
self.profile_db.gene_detection_dict,
orient='index',
dtype=float)
self.gene_detections.drop(self.samples_to_exclude,
axis=1,
inplace=True)
self.samples = set(self.gene_coverages.columns)
