def scatter(x, y, names, path, plots, color="#4CB391", figformat="png",
stat=None, log=False, minvalx=0, minvaly=0, title=None,
plot_settings={}, xmax=None, ymax=None):
"""Create bivariate plots.
Create four types of bivariate plots of x vs y, containing marginal summaries
-A scatter plot with histograms on axes
-A hexagonal binned plot with histograms on axes
-A kernel density plot with density curves on axes
-A pauvre-style plot using code from https://github.com/conchoecia/pauvre
"""
logging.info("Nanoplotter: Creating {} vs {} plots using statistics from {} reads.".format(
names[0], names[1], x.size))
if not contains_variance([x, y], names):
return []
sns.set(style="ticks", **plot_settings)
maxvalx = xmax or np.amax(x)
maxvaly = ymax or np.amax(y)
plots_made = []
if plots["hex"]:
hex_plot = Plot(
path=path + "_hex." + figformat,
title="{} vs {} plot using hexagonal bins".format(names[0], names[1]))
plot = sns.jointplot(
x=x,
y=y,
kind="hex",
color=color,
stat_func=stat,
space=0,
xlim=(minvalx, maxvalx),
ylim=(minvaly, maxvaly),
height=10)
plot.set_axis_labels(names[0], names[1])
if log:
hex_plot.title = hex_plot.title + " after log transformation of read lengths"
ticks = [10**i for i in range(10) if not 10**i > 10 * (10**maxvalx)]
plot.ax_joint.set_xticks(np.log10(ticks))
plot.ax_marg_x.set_xticks(np.log10(ticks))
plot.ax_joint.set_xticklabels(ticks)
plt.subplots_adjust(top=0.90)
plot.fig.suptitle(title or "{} vs {} plot".format(names[0], names[1]), fontsize=25)
hex_plot.fig = plot
hex_plot.save(format=figformat)
plots_made.append(hex_plot)
sns.set(style="darkgrid", **plot_settings)
if plots["dot"]:
dot_plot = Plot(
path=path + "_dot." + figformat,
title="{} vs {} plot using dots".format(names[0], names[1]))
plot = sns.jointplot(
x=x,
y=y,
kind="scatter",
color=color,
stat_func=stat,
xlim=(minvalx, maxvalx),
ylim=(minvaly, maxvaly),
space=0,
height=10,
joint_kws={"s": 1})
plot.set_axis_labels(names[0], names[1])
if log:
dot_plot.title = dot_plot.title + " after log transformation of read lengths"
ticks = [10**i for i in range(10) if not 10**i > 10 * (10**maxvalx)]
plot.ax_joint.set_xticks(np.log10(ticks))
plot.ax_marg_x.set_xticks(np.log10(ticks))
plot.ax_joint.set_xticklabels(ticks)
plt.subplots_adjust(top=0.90)
plot.fig.suptitle(title or "{} vs {} plot".format(names[0], names[1]), fontsize=25)
dot_plot.fig = plot
dot_plot.save(format=figformat)
plots_made.append(dot_plot)
if plots["kde"]:
idx = np.random.choice(x.index, min(2000, len(x)), replace=False)
kde_plot = Plot(
path=path + "_kde." + figformat,
title="{} vs {} plot using a kernel density estimation".format(names[0], names[1]))
plot = sns.jointplot(
x=x[idx],
y=y[idx],
kind="kde",
clip=((0, np.Inf), (0, np.Inf)),
xlim=(minvalx, maxvalx),
ylim=(minvaly, maxvaly),
space=0,
color=color,
stat_func=stat,
shade_lowest=False,
height=10)
plot.set_axis_labels(names[0], names[1])
if log:
kde_plot.title = kde_plot.title + " after log transformation of read lengths"
ticks = [10**i for i in range(10) if not 10**i > 10 * (10**maxvalx)]
plot.ax_joint.set_xticks(np.log10(ticks))
plot.ax_marg_x.set_xticks(np.log10(ticks))
plot.ax_joint.set_xticklabels(ticks)
plt.subplots_adjust(top=0.90)
plot.fig.suptitle(title or "{} vs {} plot".format(names[0], names[1]), fontsize=25)
kde_plot.fig = plot
kde_plot.save(format=figformat)
plots_made.append(kde_plot)
if plots["pauvre"] and names == ['Read lengths', 'Average read quality'] and log is False:
pauvre_plot = Plot(
path=path + "_pauvre." + figformat,
title="{} vs {} plot using pauvre-style @conchoecia".format(names[0], names[1]))
sns.set(style="white", **plot_settings)
margin_plot(df=pd.DataFrame({"length": x, "meanQual": y}),
Y_AXES=False,
title=title or "Length vs Quality in Pauvre-style",
plot_maxlen=None,
plot_minlen=0,
plot_maxqual=None,
plot_minqual=0,
lengthbin=None,
qualbin=None,
BASENAME="whatever",
path=pauvre_plot.path,
fileform=[figformat],
dpi=600,
TRANSPARENT=True,
QUIET=True)
plots_made.append(pauvre_plot)
plt.close("all")
return plots_made
def scatter(x,
y,
names,
path,
plots,
color="#4CB391",
figformat="png",
stat=None,
log=False,
minvalx=0,
minvaly=0):
"""Create bivariate plots.
Create four types of bivariate plots of x vs y, containing marginal summaries
-A scatter plot with histograms on axes
-A hexagonal binned plot with histograms on axes
-A kernel density plot with density curves on axes, subsampled to 10000 reads if required
-A pauvre-style plot using code from https://github.com/conchoecia/pauvre
"""
logging.info(
"Nanoplotter: Creating {} vs {} plots using statistics from {} reads.".
format(names[0], names[1], x.size))
sns.set(style="ticks")
maxvalx = np.amax(x)
maxvaly = np.amax(y)
plots_made = []
if plots["hex"]:
hex_plot = Plot(path=path + "_hex." + figformat,
title="{} vs {} plot using hexagonal bins".format(
names[0], names[1]))
plot = sns.jointplot(x=x,
y=y,
kind="hex",
color=color,
stat_func=stat,
space=0,
xlim=(minvalx, maxvalx),
ylim=(minvaly, maxvaly),
size=10)
plot.set_axis_labels(names[0], names[1])
if log:
hex_plot.title = hex_plot.title + " after log transformation of read lengths"
ticks = [
10**i for i in range(10) if not 10**i > 10 * (10**maxvalx)
]
plot.ax_joint.set_xticks(np.log10(ticks))
plot.ax_joint.set_xticklabels(ticks)
plot.savefig(hex_plot.path, format=figformat, dpi=100)
plots_made.append(hex_plot)
sns.set(style="darkgrid")
if plots["dot"]:
dot_plot = Plot(path=path + "_dot." + figformat,
title="{} vs {} plot using dots".format(
names[0], names[1]))
plot = sns.jointplot(x=x,
y=y,
kind="scatter",
color=color,
stat_func=stat,
xlim=(minvalx, maxvalx),
ylim=(minvaly, maxvaly),
space=0,
size=10,
joint_kws={"s": 1})
plot.set_axis_labels(names[0], names[1])
if log:
dot_plot.title = dot_plot.title + " after log transformation of read lengths"
ticks = [
10**i for i in range(10) if not 10**i > 10 * (10**maxvalx)
]
plot.ax_joint.set_xticks(np.log10(ticks))
plot.ax_joint.set_xticklabels(ticks)
plot.savefig(dot_plot.path, format=figformat, dpi=100)
plots_made.append(dot_plot)
if plots["kde"]:
kde_plot = Plot(
path=path + "_kde." + figformat,
title="{} vs {} plot using a kernel density estimation".format(
names[0], names[1]))
plot = sns.jointplot(x=x,
y=y,
kind="kde",
clip=((0, np.Inf), (0, np.Inf)),
xlim=(minvalx, maxvalx),
ylim=(minvaly, maxvaly),
space=0,
color=color,
stat_func=stat,
shade_lowest=False,
size=10)
plot.set_axis_labels(names[0], names[1])
if log:
kde_plot.title = kde_plot.title + " after log transformation of read lengths"
ticks = [
10**i for i in range(10) if not 10**i > 10 * (10**maxvalx)
]
plot.ax_joint.set_xticks(np.log10(ticks))
plot.ax_joint.set_xticklabels(ticks)
plot.savefig(kde_plot.path, format=figformat, dpi=100)
plots_made.append(kde_plot)
if plots["pauvre"] and names == ['Read lengths', 'Average read quality']:
pauvre_plot = Plot(
path=path + "_pauvre." + figformat,
title="{} vs {} plot using pauvre-style @conchoecia".format(
names[0], names[1]))
sns.set_style("white")
margin_plot(df=pd.DataFrame({
"length": x,
"meanQual": y
}),
Y_AXES=False,
title="Length vs Quality",
plot_maxlen=None,
plot_minlen=0,
plot_maxqual=None,
plot_minqual=0,
lengthbin=None,
qualbin=None,
BASENAME="whatever",
path=pauvre_plot.path,
fileform=[figformat],
dpi=600,
TRANSPARENT=True,
QUIET=True)
if log:
pauvre_plot.title = pauvre_plot.title + " after log transformation of read lengths"
plots_made.append(pauvre_plot)
plt.close("all")
return plots_made