def test_simple_band_pattern(tmpdir):
png_path = os.path.join(str(tmpdir), "test.png")
patterns = [
BandsPattern([100, 500, 3500], ladder, label="C1"),
BandsPattern([300, 400, 1500], ladder, label="C2"),
BandsPattern([100, 1200, 1400, 3000], ladder, label="C3"),
BandsPattern([100, 700], ladder, label="C4"),
BandsPattern([200], ladder, label="C4", band_is_uncut=True),
]
patterns_set = BandsPatternsSet(patterns=[ladder] + patterns,
ladder=ladder,
label="Digests",
ladder_ticks=3)
ax = patterns_set.plot()
ax.figure.savefig(png_path, bbox_inches="tight", dpi=200)
assert os.path.exists(png_path)
def test_mixed_digestions(tmpdir):
png_path = os.path.join(str(tmpdir), "test.png")
enzymes = "EcoRI", "EcoRV", "BamHI", "XhoI"
mixes = [[e] for e in enzymes] + list(itertools.combinations(enzymes, 2))
patterns = [
BandsPattern(
compute_digestion_bands(example_sequence, mix, linear=True),
ladder=LADDER_100_to_4k,
label=" + ".join(mix),
label_fontdict={
"rotation": 40,
"size": 9
},
) for mix in mixes
]
patterns_set = BandsPatternsSet(
patterns=[LADDER_100_to_4k] + patterns,
ladder=LADDER_100_to_4k,
label="Digestion results",
ladder_ticks=3,
)
ax = patterns_set.plot()
ax.figure.savefig(png_path, bbox_inches="tight", dpi=200)
assert os.path.exists(png_path)
def draw_gel(self, digest_metadata, xlabel, output_dir):
strain_name = digest_metadata['ID']
cpn60UT_ladder = custom_ladder(
"cpn60UT", {
25: 250,
50: 225,
100: 205,
200: 186,
300: 171,
400: 158,
500: 149,
600: 139
})
LADDER_100_to_1k = custom_ladder(
"cpn60UT", {
25: 249,
50: 226,
100: 205,
200: 186,
300: 171,
400: 158,
500: 149,
650: 139,
850: 128,
1000: 121
})
# ladder = cpn60UT_ladder.modified(background_color="#E2EDFF", label_fontdict={"rotation": -90}, label="MW")
ladder = LADDER_100_to_1k.modified(background_color="#E2EDFF",
label_fontdict={"rotation": -90},
label="MW")
patterns = []
for renzyme in sorted(self.renzymes):
patterns.append(
BandsPattern(digest_metadata[str((renzyme, 'Band Sizes'))],
ladder,
label_fontdict={"rotation": -90},
label=renzyme))
# patterns_set = BandsPatternsSet(patterns=[ladder] + patterns, ladder=ladder, label_fontdict={"size": 7}, label="Migration Distance", ladder_ticks=3)
patterns_set = BandsPatternsSet(patterns=[ladder] + patterns,
ladder=ladder,
ladder_ticks=3)
ax = patterns_set.plot()
ax.set_xlabel(xlabel)
strain_filename = strain_name.replace("/", "-").replace("(",
"_").replace(
")", "_")
outfile = os.path.join(output_dir, strain_filename + ".png")
if not os.path.exists(outfile):
ax.figure.savefig(outfile, bbox_inches="tight", dpi=600)
return outfile
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,
)
def test_simple_digestions_matplotlib_plot(tmpdir):
png_path = os.path.join(str(tmpdir), "test.png")
patterns = [
BandsPattern(
compute_digestion_bands(example_sequence, [enzyme]),
ladder=LADDER_100_to_4k,
label=enzyme,
global_bands_props={"label": "=size"},
) for enzyme in ["BamHI", "EcoRI", "EcoRV", "PstI", "SpeI", "XbaI"]
]
patterns_set = BandsPatternsSet(
patterns=[LADDER_100_to_4k] + patterns,
ladder=LADDER_100_to_4k,
label="Digestion results",
ladder_ticks=3,
)
ax = patterns_set.plot()
ax.figure.savefig(png_path, bbox_inches="tight", dpi=200)
assert os.path.exists(png_path)
def test_customized_band_pattern(tmpdir):
band_image_path = os.path.join("tests", "data", "band_image.png")
png_path = os.path.join(str(tmpdir), "test.png")
gel_image = mpimg.imread(band_image_path)
patterns = [
BandsPattern(
[100, 1500, 2000, 1000, 3500],
ladder,
label="C1",
corner_note="corner note",
corner_note_fontdict=None,
background_color="#eeeeff",
gel_image=gel_image,
)
]
patterns_set = BandsPatternsSet(patterns=[ladder] + patterns,
ladder=ladder,
label="Digests",
ladder_ticks=3)
ax = patterns_set.plot()
ax.figure.savefig(png_path, bbox_inches="tight", dpi=200)
assert os.path.exists(png_path)
def test_simple_digestions_bokeh_plot(tmpdir):
patterns = [
BandsPattern(
compute_digestion_bands(example_sequence, [enzyme]),
ladder=LADDER_100_to_4k,
label=enzyme,
global_bands_props={"label": "=size"},
) for enzyme in ["BamHI", "EcoRI", "EcoRV", "PstI", "SpeI", "XbaI"]
]
patterns_set = BandsPatternsSet(
patterns=[LADDER_100_to_4k] + patterns,
ladder=LADDER_100_to_4k,
label="Digestion results",
ladder_ticks=3,
)
plot = patterns_set.plot_with_bokeh()
target_file = os.path.join(str(tmpdir), "plot_with_bokeh.html")
with open(target_file, "w+") as f:
f.write(file_html(plot, CDN, "Example Sequence"))
with open(target_file, "r") as f:
assert len(f.read()) > 9400
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon May 27 09:46:13 2019
@author: bjorn
"""
from bandwagon import BandsPattern, BandsPatternsSet, LADDER_100_to_4k
ladder = LADDER_100_to_4k.modified(label="Ladder", background_color="#ffffaf")
patterns = [
BandsPattern([100, 500, 3500], ladder, label="C1"),
BandsPattern([300, 400, 1500], ladder, label="C2"),
BandsPattern([100, 1200, 1400, 3000], ladder, label="C3"),
BandsPattern([100, 700], ladder, label="C4"),
]
patterns_set = BandsPatternsSet(patterns=[ladder] + patterns,
ladder=ladder,
label="Test pattern",
ladder_ticks=3)
ax = patterns_set.plot()
#ax.figure.savefig("simple_band_patterns.png", bbox_inches="tight", dpi=200)
"""Simple gel simulation with gelsimulation.py.
This example shows how to plot the the digestion patterns produced by different
restriction enzymes on a same DNA sequence.
"""
import itertools
from bandwagon import (BandsPattern, BandsPatternsSet, LADDER_100_to_4k,
compute_digestion_bands)
with open("example_sequence.txt", "r") as f:
sequence = f.read()
enzymes = "EcoRI", "EcoRV", "BamHI", "XhoI"
mixes = [[e] for e in enzymes] + list(itertools.combinations(enzymes, 2))
patterns = [
BandsPattern(compute_digestion_bands(sequence, mix, linear=True),
ladder=LADDER_100_to_4k, label=" + ".join(mix),
label_fontdict={"rotation": 40, "size": 9})
for mix in mixes
]
patterns_set = BandsPatternsSet(patterns=[LADDER_100_to_4k] + patterns,
ladder=LADDER_100_to_4k,
label="Digestion results", ladder_ticks=3)
ax = patterns_set.plot()
ax.figure.savefig("mixed_digestions.png", bbox_inches="tight", dpi=120)
"""Simple gel simulation with gelsimulation.py.
This example shows how to plot the digestion patterns produced by different
restriction enzymes on a same DNA sequence.
"""
from bandwagon import (BandsPattern, BandsPatternsSet, LADDER_100_to_4k,
compute_digestion_bands)
with open("example_sequence.txt", "r") as f:
sequence = f.read()
patterns = [
BandsPattern(compute_digestion_bands(sequence, [enzyme], linear=True),
ladder=LADDER_100_to_4k, label=enzyme)
for enzyme in ["BamHI", "EcoRI", "EcoRV", "PstI", "SpeI", "XbaI"]
]
patterns_set = BandsPatternsSet(patterns=[LADDER_100_to_4k] + patterns,
ladder=LADDER_100_to_4k,
label="Digestion results", ladder_ticks=3)
ax = patterns_set.plot()
ax.figure.savefig("simple_digestions.png", bbox_inches="tight", dpi=120)
def write_identification_report(
self,
target_file=None,
relative_tolerance=0.05,
min_band_cutoff=None,
max_band_cutoff=None,
):
"""Plot a Graphic report of the gel validation.
The report displays the patterns, with green and red backgrounds
depending on whether they passed the validation test.
Parameters
----------
target_file
File object or file path where to save the figure. If provided, the
function returns none, else the function returns the axes array of
the final figure
relative_tolerance
Relative error tolerated on each band for the ovserved patterns to
be considered similar to the expected patterns.
min_band_cutoff
Bands with a size below this value will not be considered
max_band_cutoff
Bands with a size above this value will not be considered
"""
bands_validities = self.compute_all_bands_validities(
relative_tolerance=relative_tolerance,
min_band_cutoff=min_band_cutoff,
max_band_cutoff=max_band_cutoff,
)
L = len(self.constructs_records)
max_x = (
max(len(measures)
for measures in self.observed_bands.values()) + 1)
fig, axes = plt.subplots(L, 1, figsize=(2.2 * max_x, 3 * L))
axes_validities = zip(axes, bands_validities.items())
for ax, (construct_id, validities) in axes_validities:
reference = BandsPattern(
self.expected_bands[construct_id],
ladder=self.ladder,
label="exp.",
background_color="#c6dcff",
corner_note="Total: %d bp" %
sum(self.expected_bands[construct_id]),
global_bands_props={"label_fontdict": {
"size": 5
}},
)
sorted_bands = sorted(reference.bands, key=lambda b: -b.dna_size)
for band_name, band in zip("abcdefghijklm", sorted_bands):
band.label = band_name
patterns = [
BandsPattern(
self.observed_bands[construct_id][measure_name],
corner_note="Total: %d bp" %
sum(self.observed_bands[construct_id][measure_name]),
ladder=self.ladder,
label=measure_name,
gel_image=self.migration_images[measure_name],
background_color="#aaffaa"
if validities[measure_name] else "#ffaaaa",
) for measure_name in self.observed_bands[construct_id]
]
patterns_set = BandsPatternsSet(
[reference] + patterns,
ladder=self.ladder,
label=construct_id,
ladder_ticks=5,
global_patterns_props={"label_fontdict": {
"rotation": 60
}},
)
ax.set_xlim(0.5, max_x + 2)
patterns_set.plot(ax)
fig.subplots_adjust(hspace=0.3)
if target_file is not None:
fig.savefig(target_file, bbox_inches="tight")
plt.close(fig)
else:
return axes
def plot_all_validations_patterns(self,
validations,
target=None,
per_digestion_discrepancy=False):
"""Plot a Graphic report of the gel validation.
The report displays the patterns, with green and red backgrounds
depending on whether they passed the validation test.
Parameters
----------
target_file
File object or file path where to save the figure. If provided, the
function returns none, else the function returns the axes array of
the final figure
relative_tolerance
Relative error tolerated on each band for the ovserved patterns to
be considered similar to the expected patterns.
min_band_cutoff
Bands with a size below this value will not be considered
max_band_cutoff
Bands with a size above this value will not be considered
"""
summary = self.validations_summary(validations)
max_x = (
Counter([clone.construct_id
for clone in self.clones.values()]).most_common(1)[0][1] +
2)
digestions_by_construct = {construct: [] for construct in summary}
for construct, validations in summary.items():
for validation in validations:
for digestion in validation.discrepancies:
if digestion not in digestions_by_construct[construct]:
digestions_by_construct[construct].append(digestion)
# ladder = list(validations[0].clone.digestions.values())[0].ladder
pdf_io = BytesIO()
with PdfPages(pdf_io) as pdf:
for construct_id, validations in summary.items():
ladder = list(
validations[0].clone.digestions.values())[0].ladder
digestions = digestions_by_construct[construct_id]
band_patterns = OrderedDict()
seen_clones = set()
for i, digestion in enumerate(digestions):
reference_bands = self.get_digestion_bands_for_construct(
construct_id, digestion)
reference = BandsPattern(
bands=reference_bands,
ladder=ladder,
label="exp." if (i == 0) else None,
background_color="#c6dcff",
corner_note="Total: %d bp" % sum(reference_bands),
global_bands_props={"label_fontdict": {
"size": 5
}},
)
sorted_bands = sorted(reference.bands,
key=lambda b: -b.dna_size)
for band_name, band in zip("abcdefghijklm", sorted_bands):
band.label = band_name
patterns = []
for validation in validations:
clone_name = validation.clone.name
label = None if clone_name in seen_clones else "auto"
pattern = validation.to_bandwagon_bandpattern(
digestion=digestion,
label=label,
per_digestion_discrepancy=per_digestion_discrepancy,
)
if pattern is not None:
seen_clones.add(clone_name)
patterns.append(pattern)
band_patterns[digestion] = BandsPatternsSet(
[reference] + patterns,
ladder=ladder,
label=" + ".join(digestion),
ladder_ticks=5,
global_patterns_props={
"label_fontdict": {
"rotation": 60
}
},
)
digestions = digestions_by_construct[construct_id]
fig, axes = plt.subplots(
len(digestions),
1,
sharex=True,
figsize=(1.1 * max_x, 3 * len(digestions)),
)
if len(digestions) == 1:
axes = [axes]
axes[-1].set_xlim(right=max_x)
for ax, (dig, pattern_set) in zip(axes, band_patterns.items()):
pattern_set.plot(ax)
axes[-1].set_xlabel(
construct_id,
fontdict=dict(size=14, weight="bold"),
ha="left",
position=(0.0, 0.1),
)
pdf.attach_note(construct_id)
# Temporary fix because of Matplotlib bug #12634
pdf.savefig(fig, bbox_inches="tight")
plt.close(fig)
pdf_data = pdf_io.getvalue()
if target is None:
return pdf_data
elif target == "base64":
return "data:application/pdf;base64," + pdf_data.decode("utf-8")
else:
with open(target, "wb") as f:
f.write(pdf_data)