In [9]:
%matplotlib inline
import seaborn
import matplotlib.pyplot as plt
plt.style.use('seaborn-ticks')
from matplotlib import transforms
import matplotlib.patheffects
import numpy as np
COLOR_SCHEME = {'G': 'orange',
'A': 'red',
'C': 'blue',
'T': 'darkgreen'}
BASES = list(COLOR_SCHEME.keys())
ALL_SCORES1 = [[('C', 0.02247014831444764),
('T', 0.057903843733384308),
('A', 0.10370837683591219),
('G', 0.24803586793255664)],
[('T', 0.046608227674354567),
('G', 0.048827667087419063),
('A', 0.084338697696451109),
('C', 0.92994511407402669)],
[('G', 0.0),
('T', 0.011098351287382456),
('A', 0.022196702574764911),
('C', 1.8164301607015951)],
[('C', 0.020803153636453006),
('T', 0.078011826136698756),
('G', 0.11268374886412044),
('A', 0.65529933954826969)],
[('T', 0.017393530660176126),
('A', 0.030438678655308221),
('G', 0.22611589858228964),
('C', 0.45078233627623127)],
[('G', 0.022364103549245576),
('A', 0.043412671595594352),
('T', 0.097349627214363091),
('C', 0.1657574733649966)],
[('C', 0.03264675899941203),
('T', 0.045203204768416654),
('G', 0.082872542075430544),
('A', 1.0949220710572034)],
[('C', 0.0),
('T', 0.0076232429756614498),
('A', 0.011434864463492175),
('G', 1.8867526364762088)],
[('C', 0.0018955903000026028),
('T', 0.0094779515000130137),
('A', 0.35637097640048931),
('G', 0.58005063180079641)],
[('A', 0.01594690817903021),
('C', 0.017541598996933229),
('T', 0.2774762023151256),
('G', 0.48638069946042134)],
[('A', 0.003770051401807444),
('C', 0.0075401028036148881),
('T', 0.011310154205422331),
('G', 1.8624053924928772)],
[('C', 0.036479877757360731),
('A', 0.041691288865555121),
('T', 0.072959755514721461),
('G', 1.1517218549109602)],
[('G', 0.011831087684038642),
('T', 0.068620308567424126),
('A', 0.10174735408273231),
('C', 1.0009100180696691)],
[('C', 0.015871770937774379),
('T', 0.018757547471915176),
('A', 0.32176408355669878),
('G', 0.36505073156881074)],
[('A', 0.022798100897300954),
('T', 0.024064662058262118),
('G', 0.24571286522646588),
('C', 0.34070495229855319)]]
ALL_SCORES2 = [[('A', 0.01653482213365913),
('G', 0.026710097292833978),
('C', 0.035613463057111966),
('T', 0.057235922770358522)],
[('C', 0.020055669245080433),
('G', 0.023816107228533015),
('A', 0.031336983195438178),
('T', 0.058913528407423782)],
[('T', 0.018666958185377256),
('G', 0.084001311834197651),
('A', 0.093334790926886277),
('C', 0.30333807051238043)],
[('C', 0.0),
('G', 0.0),
('A', 0.32027512306044359),
('T', 0.82203948252180525)],
[('C', 0.012698627658037786),
('A', 0.053334236163758708),
('T', 0.096509570201087178),
('G', 0.10920819785912497)],
[('C', 0.0),
('G', 0.089472611853783468),
('A', 0.1930724782107959),
('T', 0.22132698721725386)],
[('C', 0.020962390607965918),
('A', 0.026202988259957396),
('G', 0.066380903591892068),
('T', 0.07336836712788071)],
[('G', 0.0),
('A', 0.10236420974570831),
('C', 0.15354631461856247),
('T', 0.29173799777526871)],
[('G', 0.027681850851852024),
('C', 0.089966015268519078),
('A', 0.089966015268519078),
('T', 0.53287562889815143)],
[('A', 0.034165612000664765),
('C', 0.06833122400132953),
('G', 0.072601925501412631),
('T', 0.28186629900548432)],
[('G', 0.0),
('A', 0.037325935579058833),
('C', 0.23328709736911771),
('T', 0.72785574379164719)],
[('A', 0.017470244196759552),
('C', 0.062892879108334396),
('G', 0.094339318662501587),
('T', 0.19916078384305891)],
[('G', 0.0),
('A', 0.096447131567581681),
('C', 0.15844885900388422),
('T', 0.48223565783790845)],
[('G', 0.0),
('A', 0.069291952024925829),
('C', 0.20787585607477749),
('T', 0.46425607856700307)],
[('G', 0.0),
('A', 0.0),
('C', 0.21713201856318373),
('T', 1.1495224512168551)],
[('G', 0.0),
('A', 0.048934292002649343),
('T', 0.27263391258618919),
('C', 0.42642740173737281)],
[('A', 0.0),
('G', 0.053607190685875404),
('C', 0.2054942309625224),
('T', 0.69689347891638032)],
[('G', 0.0),
('A', 0.0),
('C', 0.31312908494534769),
('T', 0.84220926295645249)],
[('G', 0.0),
('C', 0.068079835765814778),
('A', 0.068079835765814778),
('T', 1.3207488138568066)],
[('G', 0.020257705570431345),
('A', 0.020257705570431345),
('C', 0.048618493369035232),
('T', 0.055371061892512348)],
[('G', 0.0),
('A', 0.076286510680262556),
('C', 0.20538675952378382),
('T', 0.34622339462580698)]]
In [10]:
class Scale(matplotlib.patheffects.RendererBase):
def __init__(self, sx, sy=None):
self._sx = sx
self._sy = sy
def draw_path(self, renderer, gc, tpath, affine, rgbFace):
affine = affine.identity().scale(self._sx, self._sy)+affine
renderer.draw_path(gc, tpath, affine, rgbFace)
def draw_logo(all_scores):
fig = plt.figure()
fig.set_size_inches(len(all_scores),2.5)
ax = fig.add_subplot(111)
ax.set_xticks(range(len(all_scores)))
xshift = 0
trans_offset = transforms.offset_copy(ax.transAxes,
fig=fig,
x=0,
y=0,
units='points')
for scores in all_scores:
yshift = 0
for base, score in scores:
txt = ax.text(0,
0,
base,
transform=trans_offset,
fontsize=80,
color=COLOR_SCHEME[base],
weight='bold',
ha='center',
family='sans-serif'
)
txt.set_clip_on(False)
txt.set_path_effects([Scale(1.0, score)])
fig.canvas.draw()
window_ext = txt.get_window_extent(txt._renderer)
yshift = window_ext.height*score
trans_offset = transforms.offset_copy(txt._transform, fig=fig, y=yshift, units='points')
xshift += window_ext.width
trans_offset = transforms.offset_copy(ax.transAxes, fig=fig, x=xshift, units='points')
ax.set_yticks(range(0,3))
seaborn.despine(ax=ax, offset=30, trim=True)
ax.set_xticklabels(range(1,len(all_scores)+1), rotation=90)
ax.set_yticklabels(np.arange(0,3,1))
plt.show()
In [12]:
draw_logo(ALL_SCORES1)
In [14]:
draw_logo(ALL_SCORES2)
Plotted using meme-suite
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