from collections import OrderedDict
from bandwagon.ladders import ladder_from_aati_fa_calibration_table
from bandwagon import BandsPattern
from .band_patterns_discrepancy import band_patterns_discrepancy
try:
from plateo.parsers import plate_from_aati_fragment_analyzer_zip
PLATEO_AVAILABLE = True
except ImportError:
PLATEO_AVAILABLE = False
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class BandsObservation:
"""One observation of a bands pattern.
Parameters
----------
name : str
Name of the observation (used for the top label in plots).
bands : BandPattern
A BandPattern object.
ladder : BandPattern
A BandPattern object representing a ladder.
migration_image
Optional RGB array (HxWx3) representing the gel "image" (which will be
displayed on the side of the plot).
"""
def __init__(self, name, bands, ladder, migration_image=None):
"""Initialize."""
self.name = name
self.bands = sorted(bands)
self.ladder = ladder
self.migration_image = migration_image
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@staticmethod
def from_aati_fa_archive(
archive_path,
min_rfu_size_ratio=0.3,
ignore_bands_under=None,
direction="column",
):
"""Return a dictionary of all band observations in AATI output files.
Parameters
----------
archive_path : str
A path to a ZIP file containing all files output by the AATI fragment
analyzer.
min_rfu_size_ratio : float
Cut-off ratio to filter out bands whose intensity is below some
threshold. The higher the value, the more bands will be filtered out.
Returns
-------
dict
A dictionary ``{'A1': BandsObservation(), 'A2': ...}`` containing the
measured pattern information for a whole 96-well microplate.
"""
if not PLATEO_AVAILABLE:
raise ImportError("Plateo must be installed to parse AATI zips.")
ignore_bands_under = ignore_bands_under or 0
plate = plate_from_aati_fragment_analyzer_zip(archive_path)
ladder_data = plate.data["ladder"]
ladder = ladder_from_aati_fa_calibration_table(dataframe=ladder_data)
# get the set of constructs sorted by order of columnwise appearance:
def band_is_strong_enough(band):
"""Return True if the band's intensity is above the set level."""
return 1.0 * band["RFU"] / band["Size (bp)"] > min_rfu_size_ratio
ordered_list = []
for well in plate.iter_wells(direction=direction):
bands_list = []
for band in well.data["bands"].values():
if band_is_strong_enough(band) and (
band["Size (bp)"] > ignore_bands_under
):
bands_list += [band["Size (bp)"]]
bands_observation = BandsObservation(
name=well.name,
ladder=ladder,
bands=bands_list,
migration_image=well.data["migration_image"],
)
entry = (well.name, bands_observation)
ordered_list += [entry]
ordered_dict = OrderedDict(ordered_list)
return ordered_dict
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def patterns_discrepancy(
self,
other_bands,
relative_tolerance=0.1,
min_band_cutoff=None,
max_band_cutoff=None,
):
"""Return the maximal discrepancy between two band patterns.
The discrepancy is defined as the largest distance between a band in
one pattern and the closest band in the other pattern.
Parameters
----------
other_bands : list of int
A list of bands (integers) to be compared with the current bands.
relative_tolerance : float
Tolerance, as a ratio of the full ladder span. If =0.1, then the
discrepancy will have a value of 1 when a band's nearest
correspondent in the other pattern is more than 10% of the ladder
span apart.
min_band_cutoff : int, optional
Discrepancies involving at least one band below this minimal band
size will be ignored. By default, it will be set to the smallest
band size in the ladder.
max_band_cutoff : int, optional
Discrepancies involving at least one band above this minimum band
size will be ignored. By default, it will be set to the largest
band size in the ladder.
"""
ladder_min, ladder_max = self.ladder.dna_size_span
if min_band_cutoff is None:
min_band_cutoff = ladder_min
if max_band_cutoff is None:
max_band_cutoff = ladder_max
return band_patterns_discrepancy(
other_bands,
self.bands,
ladder=self.ladder,
relative_tolerance=relative_tolerance,
zone=[min_band_cutoff, max_band_cutoff],
reference_and_gel=True,
)
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def to_bandwagon_bandpattern(self, background_color=None, label="auto"):
"""Return a pattern version for the plotting library BandWagon.
If label is left to 'auto', it will be the pattern's name.
"""
if label == "auto":
label = self.name
return BandsPattern(
self.bands,
corner_note="Total: %d bp." % sum(self.bands),
ladder=self.ladder,
label=label,
gel_image=self.migration_image,
background_color=background_color,
)