def main(args=None):
args = parse_arguments().parse_args(args)
if args.plotFile is None and args.outFileCorMatrix is None:
sys.exit("At least one of --plotFile and --outFileCorMatrix must be specified!\n")
corr = Correlation(args.corData,
args.corMethod,
labels=args.labels,
remove_outliers=args.removeOutliers,
skip_zeros=args.skipZeros)
if args.corMethod == 'pearson':
# test if there are outliers and write a message recommending the removal
if len(corr.get_outlier_indices(np.asarray(corr.matrix).flatten())) > 0:
if args.removeOutliers:
sys.stderr.write("\nOutliers were detected in the data. They "
"will be removed to avoid bias "
"in the pearson correlation.\n")
else:
sys.stderr.write("\nOutliers were detected in the data. Consider "
"using the --removeOutliers parameter to avoid a bias "
"in the pearson correlation.\n")
if args.colorMap:
try:
plt.get_cmap(args.colorMap)
except ValueError as error:
sys.stderr.write(
"A problem was found. Message: {}\n".format(error))
exit()
if args.plotFile is not None:
if args.whatToPlot == 'scatterplot':
corr.plot_scatter(args.plotFile,
plot_title=args.plotTitle,
image_format=args.plotFileFormat,
xRange=args.xRange,
yRange=args.yRange,
log1p=args.log1p)
else:
corr.plot_correlation(args.plotFile,
vmax=args.zMax,
vmin=args.zMin,
colormap=args.colorMap,
plot_title=args.plotTitle,
image_format=args.plotFileFormat,
plot_numbers=args.plotNumbers,
plotWidth=args.plotWidth,
plotHeight=args.plotHeight)
if args.outFileCorMatrix:
o = open(args.outFileCorMatrix, "w")
o.write("#plotCorrelation --outFileCorMatrix\n")
corr.save_corr_matrix(o)
o.close()
def main(args=None):
args = parse_arguments().parse_args(args)
if args.plotFile is None and args.outFileCorMatrix is None:
sys.exit("At least one of --plotFile and --outFileCorMatrix must be specified!\n")
corr = Correlation(args.corData,
args.corMethod,
labels=args.labels,
remove_outliers=args.removeOutliers,
skip_zeros=args.skipZeros)
if args.corMethod == 'pearson':
# test if there are outliers and write a message recommending the removal
if len(corr.get_outlier_indices(np.asarray(corr.matrix).flatten())) > 0:
if args.removeOutliers:
sys.stderr.write("\nOutliers were detected in the data. They "
"will be removed to avoid bias "
"in the pearson correlation.\n")
else:
sys.stderr.write("\nOutliers were detected in the data. Consider "
"using the --removeOutliers parameter to avoid a bias "
"in the pearson correlation.\n")
if args.colorMap:
try:
plt.get_cmap(args.colorMap)
except ValueError as error:
sys.stderr.write(
"A problem was found. Message: {}\n".format(error))
exit()
if args.plotFile is not None:
if args.whatToPlot == 'scatterplot':
corr.plot_scatter(args.plotFile,
plot_title=args.plotTitle,
image_format=args.plotFileFormat,
xRange=args.xRange,
yRange=args.yRange,
log1p=args.log1p)
else:
corr.plot_correlation(args.plotFile,
vmax=args.zMax,
vmin=args.zMin,
colormap=args.colorMap,
plot_title=args.plotTitle,
image_format=args.plotFileFormat,
plot_numbers=args.plotNumbers,
plotWidth=args.plotWidth,
plotHeight=args.plotHeight)
if args.outFileCorMatrix:
o = open(args.outFileCorMatrix, "w")
o.write("#plotCorrelation --outFileCorMatrix\n")
corr.save_corr_matrix(o)
o.close()
def main(args=None):
args = parse_arguments().parse_args(args)
corr = Correlation(
args.corData,
labels=args.labels,
)
if args.outFileCorMatrix:
corr.save_corr_matrix(args.outFileCorMatrix)
args.plotFile.close()
corr.plot_pca(args.plotFile.name,
plot_title=args.plotTitle,
image_format=args.plotFileFormat)
