def prettyplot():
reds = mpl.cm.Reds
reds.set_bad("white")
reds.set_under("white")
blues_r = mpl.cm.Blues_r
blues_r.set_bad("white")
blues_r.set_under("white")
# Need to 'reverse' red to blue so that blue=cold=small numbers,
# and red=hot=large numbers with '_r' suffix
blue_red = get_map("RdBu", "diverging", 11, reverse=True).mpl_colormap
green_purple = get_map("PRGn", "diverging", 11).mpl_colormap
red_purple = get_map("RdPu", "sequential", 9).mpl_colormap
return blues_r, reds, blue_red, green_purple, red_purple
def main():
SIZE = 20
PLOIDY = 2
MUTATIONS = 2
indices = range(SIZE)
# Build fake data
seqA = list("0" * SIZE)
allseqs = [seqA[:] for x in range(PLOIDY)] # Hexaploid
for s in allseqs:
for i in [choice(indices) for x in range(MUTATIONS)]:
s[i] = "1"
allseqs = [make_sequence(s, name=name) for (s, name) in \
zip(allseqs, [str(x) for x in range(PLOIDY)])]
# Build graph structure
G = Graph("Assembly graph", filename="graph")
G.attr(rankdir="LR", fontname="Arial", splines="true")
G.attr(ranksep=".2", nodesep="0.02")
G.attr('node', shape='point')
G.attr('edge', dir='none', penwidth='4')
colorset = get_map('Set2', 'qualitative', 8).mpl_colors
colorset = [to_hex(x) for x in colorset]
colors = sample(colorset, PLOIDY)
for s, color in zip(allseqs, colors):
sequence_to_graph(G, s, color=color)
zip_sequences(G, allseqs)
# Output graph
G.view()
def main():
SIZE = 20
PLOIDY = 2
MUTATIONS = 2
indices = range(SIZE)
# Build fake data
seqA = list("0" * SIZE)
allseqs = [seqA[:] for x in range(PLOIDY)] # Hexaploid
for s in allseqs:
for i in [choice(indices) for x in range(MUTATIONS)]:
s[i] = "1"
allseqs = [make_sequence(s, name=name) for (s, name) in \
zip(allseqs, [str(x) for x in range(PLOIDY)])]
# Build graph structure
G = Graph("Assembly graph", filename="graph")
G.attr(rankdir="LR", fontname="Helvetica", splines="true")
G.attr(ranksep=".2", nodesep="0.02")
G.attr('node', shape='point')
G.attr('edge', dir='none', penwidth='4')
colorset = get_map('Set2', 'qualitative', 8).mpl_colors
colorset = [to_hex(x) for x in colorset]
colors = sample(colorset, PLOIDY)
for s, color in zip(allseqs, colors):
sequence_to_graph(G, s, color=color)
zip_sequences(G, allseqs)
# Output graph
G.view()
def plot(self, samplekey, chrs=allsomes, color=None, dx=None, ymax=8,
ms=2, alpha=.7):
import matplotlib.pyplot as plt
from jcvi.utils.brewer2mpl import get_map
props = dict(boxstyle='round', facecolor='wheat', alpha=0.2)
if isinstance(chrs, str):
chrs = [chrs]
f, axs = plt.subplots(1, len(chrs), sharey=True)
if not isinstance(axs, np.ndarray):
axs = np.array([axs])
plt.tight_layout()
if color is None:
color = choice(get_map('Set2', 'qualitative', 8).mpl_colors)
for region, ax in zip(chrs, axs):
chr, start, end = parse_region(region)
X, Z, clen, events = self.run_one(samplekey, chr)
ax.plot(X, ".", label="observations",
ms=ms, mfc=color, alpha=alpha)
ax.plot(Z, "k.", label="hidden", ms=6)
if start is None and end is None:
ax.set_xlim(0, clen)
else:
ax.set_xlim(start / 1000, end / 1000)
ax.set_ylim(0, ymax)
ax.set_xlabel("1Kb bins")
title = "{} {}".format(samplekey.split("_")[1], chr)
if dx:
title += " ({})".format(dx)
ax.set_title(title)
# The final calls
yy = .9
abnormal = [x for x in events if x[-1]]
if len(abnormal) > 5:
yinterval = .02
size = 10
else:
yinterval = .05
size = 12
for mean_cn, rr, event in events:
if mean_cn > ymax:
continue
ax.text(np.mean(rr), mean_cn + .2, mean_cn,
ha="center", bbox=props)
if event is None:
continue
ax.text(.5, yy, str(event).rsplit(" ", 1)[0],
color='r', ha="center",
transform=ax.transAxes, size=size)
yy -= yinterval
axs[0].set_ylabel("Copy number")
def plot(self, samplekey, chrs=allsomes, color=None, dx=None, ymax=8,
ms=2, alpha=.7):
import matplotlib.pyplot as plt
from jcvi.utils.brewer2mpl import get_map
props = dict(boxstyle='round', facecolor='wheat', alpha=0.2)
if isinstance(chrs, str):
chrs = [chrs]
f, axs = plt.subplots(1, len(chrs), sharey=True)
if not isinstance(axs, np.ndarray):
axs = np.array([axs])
plt.tight_layout()
if color is None:
color = choice(get_map('Set2', 'qualitative', 8).mpl_colors)
for region, ax in zip(chrs, axs):
chr, start, end = parse_region(region)
X, Z, clen, events = self.run_one(samplekey, chr)
ax.plot(X, ".", label="observations",
ms=ms, mfc=color, alpha=alpha)
ax.plot(Z, "k.", label="hidden", ms=6)
if start is None and end is None:
ax.set_xlim(0, clen)
else:
ax.set_xlim(start / 1000, end / 1000)
ax.set_ylim(0, ymax)
ax.set_xlabel("1Kb bins")
title = "{} {}".format(samplekey.split("_")[1], chr)
if dx:
title += " ({})".format(dx)
ax.set_title(title)
# The final calls
yy = .9
abnormal = [x for x in events if x[-1]]
if len(abnormal) > 5:
yinterval = .02
size = 10
else:
yinterval = .05
size = 12
for mean_cn, rr, event in events:
if mean_cn > ymax:
continue
ax.text(np.mean(rr), mean_cn + .2, mean_cn,
ha="center", bbox=props)
if event is None:
continue
ax.text(.5, yy, str(event).rsplit(" ", 1)[0],
color='r', ha="center",
transform=ax.transAxes, size=size)
yy -= yinterval
axs[0].set_ylabel("Copy number")
def prettyplot():
# Get Set2 from ColorBrewer, a set of colors deemed colorblind-safe and
# pleasant to look at by Drs. Cynthia Brewer and Mark Harrower of Pennsylvania
# State University. These colors look lovely together, and are less
# saturated than those colors in Set1. For more on ColorBrewer, see:
set2 = get_map('Set2', 'qualitative', 8).mpl_colors
set1 = get_map('Set1', 'qualitative', 9).mpl_colors
reds = mpl.cm.Reds
reds.set_bad('white')
reds.set_under('white')
blues_r = mpl.cm.Blues_r
blues_r.set_bad('white')
blues_r.set_under('white')
# Need to 'reverse' red to blue so that blue=cold=small numbers,
# and red=hot=large numbers with '_r' suffix
blue_red = get_map('RdBu', 'diverging', 11, reverse=True).mpl_colormap
green_purple = get_map('PRGn', 'diverging', 11).mpl_colormap
red_purple = get_map('RdPu', 'sequential', 9).mpl_colormap
return blues_r, reds, blue_red, set1, set2, green_purple, red_purple
def prettyplot():
# Get Set2 from ColorBrewer, a set of colors deemed colorblind-safe and
# pleasant to look at by Drs. Cynthia Brewer and Mark Harrower of Pennsylvania
# State University. These colors look lovely together, and are less
# saturated than those colors in Set1. For more on ColorBrewer, see:
set2 = get_map('Set2', 'qualitative', 8).mpl_colors
set1 = get_map('Set1', 'qualitative', 9).mpl_colors
reds = mpl.cm.Reds
reds.set_bad('white')
reds.set_under('white')
blues_r = mpl.cm.Blues_r
blues_r.set_bad('white')
blues_r.set_under('white')
# Need to 'reverse' red to blue so that blue=cold=small numbers,
# and red=hot=large numbers with '_r' suffix
blue_red = get_map('RdBu', 'Diverging', 11,
reverse=True).mpl_colormap
green_purple = get_map('PRGn', 'diverging', 11).mpl_colormap
red_purple = get_map('RdPu', 'Sequential', 9).mpl_colormap
return blues_r, reds, blue_red, set1, set2, green_purple, red_purple
def assign_colors(self):
colorset = get_map('Set2', 'qualitative', len(self)).mpl_colors
self.assign_array("color", colorset)
def diverge(self):
colors = get_map(self.opts.diverge, 'diverging', 5).mpl_colors
return colors[0], colors[-1]
def diverge(self):
colors = get_map(self.opts.diverge, "diverging", 5).mpl_colors
return colors[0], colors[-1]
def set3_n(number=12):
return get_map("Set3", "qualitative", number).hex_colors
def set2_n(number=8):
# Get Set2 from ColorBrewer, a set of colors deemed colorblind-safe and
# pleasant to look at by Drs. Cynthia Brewer and Mark Harrower of Pennsylvania
# State University. These colors look lovely together, and are less
# saturated than those colors in Set1. For more on ColorBrewer, see:
return get_map("Set2", "qualitative", number).hex_colors
def set1_n(number=9):
return get_map("Set1", "qualitative", number).hex_colors
def assign_colors(self):
colorset = get_map('Set2', 'qualitative', 8).mpl_colors
colorset = sample_N(colorset, len(self))
self.assign_array("color", colorset)
def assign_colors(self):
colorset = get_map('Set2', 'qualitative', len(self)).mpl_colors
self.assign_array("color", colorset)
def assign_colors(self):
colorset = get_map('Set2', 'qualitative', 8).mpl_colors
colorset = sample_N(colorset, len(self))
self.assign_array("color", colorset)
