class Contig:
def __init__(self, name):
self.name = name
self.sequence = None
self.parent = None
self.splits = []
self.length = 0
self.abundance = 0.0
self.coverage = Coverage()
self.min_coverage_for_variability = 10
self.skip_SNV_profiling = False
self.report_variability_full = False
self.ignore_orphans = False
self.max_coverage_depth = constants.max_depth_for_coverage
self.codon_frequencies_dict = {}
def get_atomic_data_dict(self):
d = {'std_coverage': self.coverage.std,
'mean_coverage': self.coverage.mean,
'mean_coverage_Q2Q3': self.coverage.mean_Q2Q3,
'max_normalized_ratio': 1.0,
'relative_abundance': 1.0,
'detection': self.coverage.detection,
'abundance': self.abundance,
'variability': sum(s.auxiliary.variation_density for s in self.splits) if not self.skip_SNV_profiling else None,
'__parent__': None}
return d
def analyze_coverage(self, bam):
contig_coverage = []
counter = 1
for split in self.splits:
split.coverage = Coverage()
split.coverage.run(bam, split,
ignore_orphans=self.ignore_orphans,
max_coverage_depth=self.max_coverage_depth)
contig_coverage.extend(split.coverage.c)
counter += 1
self.coverage.process_c(contig_coverage)
def analyze_auxiliary(self, bam):
counter = 1
for split in self.splits:
split.auxiliary = Auxiliary(split,
bam,
parent_outlier_positions=self.coverage.outlier_positions,
min_coverage=self.min_coverage_for_variability,
report_variability_full=self.report_variability_full,
ignore_orphans=self.ignore_orphans,
max_coverage_depth=self.max_coverage_depth)
counter += 1
class Contig:
def __init__(self, name):
self.name = name
self.sequence = None
self.parent = None
self.splits = []
self.length = 0
self.abundance = 0.0
self.coverage = Coverage()
self.min_coverage_for_variability = 10
self.report_variability_full = False
def get_atomic_data_dict(self):
d = {'std_coverage': self.coverage.std,
'mean_coverage': self.coverage.mean,
'normalized_coverage': self.coverage.normalized,
'max_normalized_ratio': 1.0,
'relative_abundance': 1.0,
'portion_covered': self.coverage.portion_covered,
'abundance': self.abundance,
'variability': sum(s.auxiliary.variation_density for s in self.splits),
'__parent__': None}
return d
def analyze_coverage(self, bam, progress):
contig_coverage = []
num_splits = len(self.splits)
counter = 1
for split in self.splits:
progress.update('Coverage (split: %d of %d)' % (counter, num_splits))
split.coverage = Coverage()
split.coverage.run(bam, split)
contig_coverage.extend(split.coverage.c)
counter += 1
self.coverage.process_c(contig_coverage)
def analyze_auxiliary(self, bam, progress):
num_splits = len(self.splits)
counter = 1
for split in self.splits:
progress.update('Auxiliary stats (split: %d of %d) CMC: %.1f :: SMC: %.1f'\
% (counter, num_splits, self.coverage.mean, split.coverage.mean))
split.auxiliary = Auxiliary(split, bam, min_coverage = self.min_coverage_for_variability,
report_variability_full = self.report_variability_full)
counter += 1
class Contig:
def __init__(self, name):
self.name = name
self.sequence = None
self.parent = None
self.splits = []
self.length = 0
self.abundance = 0.0
self.coverage = Coverage()
self.min_coverage_for_variability = 10
self.skip_SNV_profiling = False
self.report_variability_full = False
def get_atomic_data_dict(self):
d = {'std_coverage': self.coverage.std,
'mean_coverage': self.coverage.mean,
'mean_coverage_Q2Q3': self.coverage.mean_Q2Q3,
'max_normalized_ratio': 1.0,
'relative_abundance': 1.0,
'detection': self.coverage.detection,
'abundance': self.abundance,
'variability': sum(s.auxiliary.variation_density for s in self.splits) if not self.skip_SNV_profiling else None,
'__parent__': None}
return d
def analyze_coverage(self, bam):
contig_coverage = []
counter = 1
for split in self.splits:
split.coverage = Coverage()
split.coverage.run(bam, split)
contig_coverage.extend(split.coverage.c)
counter += 1
self.coverage.process_c(contig_coverage)
def analyze_auxiliary(self, bam):
counter = 1
for split in self.splits:
split.auxiliary = Auxiliary(split,
bam,
parent_outlier_positions=self.coverage.outlier_positions,
min_coverage=self.min_coverage_for_variability,
report_variability_full=self.report_variability_full)
counter += 1
class Contig:
def __init__(self, name):
self.name = name
self.parent = None
self.splits = []
self.length = 0
self.abundance = 0.0
self.coverage = Coverage()
self.min_coverage_for_variability = 10
self.report_variability_full = False
def get_metadata_dict(self):
d = {
"std_coverage": self.coverage.std,
"mean_coverage": self.coverage.mean,
"normalized_coverage": self.coverage.normalized,
"max_normalized_ratio": 1.0,
"relative_abundance": 1.0,
"portion_covered": self.coverage.portion_covered,
"abundance": self.abundance,
"variability": sum(s.auxiliary.variability_score for s in self.splits),
"__parent__": None,
}
return d
def analyze_coverage(self, bam, progress):
contig_coverage = []
for split in self.splits:
progress.update("Coverage (split: %d of %d)" % (split.order, len(self.splits)))
split.coverage = Coverage()
split.coverage.run(bam, split)
contig_coverage.extend(split.coverage.c)
self.coverage.process_c(contig_coverage)
def analyze_auxiliary(self, bam, progress):
for split in self.splits:
progress.update(
"Auxiliary stats (split: %d of %d) CMC: %.1f :: SMC: %.1f"
% (split.order, len(self.splits), self.coverage.mean, split.coverage.mean)
)
split.auxiliary = Auxiliary(
split,
bam,
min_coverage=self.min_coverage_for_variability,
report_variability_full=self.report_variability_full,
)
class Contig:
def __init__(self, name):
self.name = name
self.parent = None
self.splits = []
self.length = 0
self.abundance = 0.0
self.coverage = Coverage()
self.min_coverage_for_variability = 10
self.report_variability_full = False
def get_atomic_data_dict(self):
d = {
'std_coverage': self.coverage.std,
'mean_coverage': self.coverage.mean,
'normalized_coverage': self.coverage.normalized,
'max_normalized_ratio': 1.0,
'relative_abundance': 1.0,
'portion_covered': self.coverage.portion_covered,
'abundance': self.abundance,
'variability':
sum(s.auxiliary.variation_density for s in self.splits),
'__parent__': None
}
return d
def analyze_coverage(self, bam, progress):
contig_coverage = []
for split in self.splits:
progress.update('Coverage (split: %d of %d)' %
(split.order, len(self.splits)))
split.coverage = Coverage()
split.coverage.run(bam, split)
contig_coverage.extend(split.coverage.c)
self.coverage.process_c(contig_coverage)
def analyze_auxiliary(self, bam, progress):
for split in self.splits:
progress.update('Auxiliary stats (split: %d of %d) CMC: %.1f :: SMC: %.1f'\
% (split.order, len(self.splits), self.coverage.mean, split.coverage.mean))
split.auxiliary = Auxiliary(
split,
bam,
min_coverage=self.min_coverage_for_variability,
report_variability_full=self.report_variability_full)
class Contig:
def __init__(self, name):
self.name = name
self.sequence = None
self.parent = None
self.splits = []
self.length = 0
self.abundance = 0.0
self.coverage = Coverage()
self.min_coverage_for_variability = 10
self.skip_SNV_profiling = False
self.report_variability_full = False
def get_atomic_data_dict(self):
d = {
'std_coverage':
self.coverage.std,
'mean_coverage':
self.coverage.mean,
'mean_coverage_Q1Q3':
self.coverage.mean_Q1Q3,
'max_normalized_ratio':
1.0,
'relative_abundance':
1.0,
'portion_covered':
self.coverage.portion_covered,
'abundance':
self.abundance,
'variability':
sum(s.auxiliary.variation_density
for s in self.splits) if not self.skip_SNV_profiling else None,
'__parent__':
None
}
return d
def analyze_coverage(self, bam, progress):
contig_coverage = []
num_splits = len(self.splits)
counter = 1
for split in self.splits:
progress.update('Coverage (split: %d of %d)' %
(counter, num_splits))
split.coverage = Coverage()
split.coverage.run(bam, split)
contig_coverage.extend(split.coverage.c)
counter += 1
self.coverage.process_c(contig_coverage)
def analyze_auxiliary(self, bam, progress):
num_splits = len(self.splits)
counter = 1
for split in self.splits:
progress.update('Auxiliary stats (split: %d of %d) CMC: %.1f :: SMC: %.1f'\
% (counter, num_splits, self.coverage.mean, split.coverage.mean))
split.auxiliary = Auxiliary(
split,
bam,
parent_outlier_positions=self.coverage.outlier_positions,
min_coverage=self.min_coverage_for_variability,
report_variability_full=self.report_variability_full)
counter += 1