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 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
df_transf.to_csv(args.oname, index=False, sep='\t', float_format="%.4f")
df_rev.to_csv(args.oname2, index=False, sep='\t')
if __name__ == '__main__':
# Command line options
args = getOptions()
# Activate Logger
logger = logging.getLogger()
sl.setLogger(logger)
# Starting script
logger.info(u"Importing data with following parameters: "\
"\n\tWide: {0}"\
"\n\tDesign: {1}"\
"\n\tUnique ID: {2}"\
"\n\tGroup Column: {3}".format(args.input, args.design,
args.uniqID, args.group))
# Stablishing color palette
palette = colorHandler(pal=args.palette, col=args.color)
logger.info(u"Using {0} color scheme from {1} palette".format(
args.color, args.palette))
# Getting rid of warnings.
warnings.filterwarnings("ignore", category=DeprecationWarning)
# Main Code
main(args)
