def plotHeatmap(data, ax, cmap=False, xlbls=True, ylbls=True):
"""
This function creates and plots a heatmap or a hcheatmap (hiearchical
cluster heat map)
:Arguments:
:type data: pandas data frame.
:param data: Data that is going to be used to plot
:type ax: matplotlib axis
:param ax: axis to be drawn on.
:Returns:
:return ax: matplotlib axis.
:rtype ax: axis with the drawn figure.
"""
# Create a custom colormap for the heatmap values
# You can have two possible ways to create a pallete.
# 1) sns.diverging_palette (a seaborn function)
# 2) palettable.colorbrewer.diverging.[color].mpl_colors
if not cmap:
colors = colorHandler(pal="diverging", col="Spectral_10")
cmap = colors.mpl_colormap
# Draw the full plot
sns.heatmap(data,
linewidths=0.0,
ax=ax,
cmap=cmap,
xticklabels=xlbls,
yticklabels=ylbls)
def _artVenn2(venn, innerLabels):
"""
Modifies the colors, text and alpha for the 2 cicles venn plot.
:Arguments:
:type data: matplotlib.figure
:param data: Figure with the venn diagram
:type innerLabels: list
:param innerLabels: List of the labels to be printed inside the circles
[Ab,AB,aB]
"""
# Subset innerLabels
venn.get_label_by_id('10').set_text(innerLabels[0])
venn.get_label_by_id('01').set_text(innerLabels[1])
venn.get_label_by_id('11').set_text(innerLabels[2])
#Selecting colours from colour manager
palette = colorHandler(pal="tableau", col="BlueRed_6")
# Subset colors
venn.get_patch_by_id('10').set_color(palette.mpl_colors[3])
venn.get_patch_by_id('01').set_color(palette.mpl_colors[4])
venn.get_patch_by_id('11').set_color(palette.mpl_colors[5])
# Subset alphas
venn.get_patch_by_id('10').set_alpha(1.0)
venn.get_patch_by_id('01').set_alpha(1.0)
venn.get_patch_by_id('11').set_alpha(1.0)
def main(args):
"""
Main Script
"""
#Getting palettes for data and cutoffs
global cutPalette
cutPalette = ch.colorHandler(pal="tableau", col="TrafficLight_9")
# Checking if levels
if args.levels and args.group:
levels = [args.group] + args.levels
elif args.group and not args.levels:
levels = [args.group]
else:
levels = []
#Parsing data with interface
dat = wideToDesign(args.input,
args.design,
args.uniqID,
group=args.group,
anno=args.levels,
logger=logger,
runOrder=args.order)
#Dropping missing values and remove groups with just one sample
dat.dropMissing()
if args.group:
dat.removeSingle()
#Select colors for data
dataPalette.getColors(design=dat.design, groups=levels)
dat.design = dataPalette.design
#Open pdfPages Calculate SED
with PdfPages(os.path.abspath(args.figure)) as pdf:
SEDtoMean, SEDpairwise = calculateSED(dat, dataPalette.ugColors,
dataPalette.combName, pdf,
args.p)
#Outputing files for tsv files
SEDtoMean.to_csv(os.path.abspath(args.toMean),
index_label="sampleID",
columns=["SED_to_Mean"],
sep='\t')
SEDpairwise.drop(["colors"], axis=1, inplace=True)
if args.group:
SEDpairwise.drop(["colors_x", "colors_y"], axis=1, inplace=True)
SEDpairwise.to_csv(os.path.abspath(args.pairwise),
index_label="sampleID",
sep='\t')
#Ending script
logger.info("Script complete.")
def plotHCHeatmap(data, hcheatmap=True, cmap=False, xlbls=True, ylbls=True):
"""
This function creates and plots a heatmap or a hcheatmap (hiearchical
cluster heat map)
:Arguments:
:type data: pandas data frame.
:param data: Data that is going to be used to plot
:type hcheatmap: boolean
:param hcheatmap: boolean to determinate wether you want to plot a normal heatmap or a hcheatmap.
:Returns:
:return hmap: Instance of the heatmap
:rtype hmap: seaborn.matrix.ClusterGrid
"""
# Create a custom colormap for the heatmap values
# You can have two possible ways to create a pallete.
# 1) sns.diverging_palette (a seaborn function)
# 2) palettable.colorbrewer.diverging.[color].mpl_colors
if not cmap:
colors = colorHandler(pal="diverging", col="Spectral_10")
cmap = colors.mpl_colormap
# Draw the full plot
hmap = sns.clustermap(data,
row_cluster=hcheatmap,
col_cluster=hcheatmap,
linewidths=0.0,
figsize=(13, 13),
cmap=cmap,
xticklabels=xlbls,
yticklabels=ylbls)
#Rotate axis
plt.setp(hmap.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
plt.setp(hmap.ax_heatmap.xaxis.get_majorticklabels(), rotation=90)
#Return Plot
return hmap
# Iterating over groups
for name, group in dat.design.groupby(args.group):
logger.info(u"Plotting for group {0}".format(name))
# Plotting Density and Box plot for the group
plotDensity(data=wide.T[group.index], name=name, pdf=pdf)
# Get colors for each feature for "All groups"
logger.info(u"Plotting for group {0}".format("samples"))
palette.getColors(design=dat.design, groups=[])
# Plotting density and boxplots for all
plotDensity(data=wide, name="samples", pdf=pdf)
#Ending script
logger.info(u"Ending script")
if __name__ == '__main__':
args = getOptions()
logger = logging.getLogger()
sl.setLogger(logger)
logger.info("Importing data with following parameters: "\
"\n\tWide: {0}"\
"\n\tDesign: {1}"\
"\n\tUnique ID: {2}".format(args.input, args.design, args.uniqID))
palette = colorHandler(pal=args.palette, col=args.color)
logger.info(u"Using {0} color scheme from {1} palette".format(
args.color, args.palette))
main(args)
# Ending script
logger.info("Script complete.")
if __name__ == '__main__':
# Turn on Logging if option -g was given
args = getOptions()
# Turn on logger
logger = logging.getLogger()
sl.setLogger(logger)
# Standar logging
logger.info(u"""Importing data with following parameters:
\tWide: {0}
\tDesign: {1}
\tUnique ID: {2}
\tGroup: {3}
\tRun Order: {4}
""".format(args.input, args.design, args.uniqID, args.group,
args.order))
# Stablishing color palette for adata and cutoffs
dataPalette = colorHandler(pal=args.palette, col=args.color)
cutPalette = colorHandler(pal="tableau", col="TrafficLight_9")
logger.info(u"Using {0} color scheme from {1} palette".format(
args.color, args.palette))
#Main script
main(args)
