def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option("-t", "--test", dest="test", type="string",
help="supply help")
parser.add_option("-p", "--plot-type", dest="plot_type", type="choice",
choices=["histogram", "barplot", "density",
"boxplot", "scatter", "map",
"pca"],
help="the plot type to generate")
parser.add_option("--plot-n-pc", dest="n_pcs", type="int",
help="The number of principal components to "
"plot")
parser.add_option("-g", "--group-by", dest="group_by", type="string",
help="column header to group observations by")
parser.add_option("-x", "--x-column", dest="x_col", type="string",
help="column to plot on X axis")
parser.add_option("-y", "--y-column", dest="y_col", type="string",
help="column to plot on y axis")
parser.add_option("-i", "--index_column", dest="indx", type="string",
help="column number that refers to the row index")
parser.add_option("--output-file", dest="outfile", type="string",
help="path and filename to save plot to")
parser.add_option("--labels", dest="labels", type="string",
help="a comma-separated list of axis labels. "
"The first 2 correspond to the X and Y-axis, "
"respectively, and the third is the plot title")
parser.add_option("--metadata-file", dest="meta_file", type="string",
help="file containing metadata for annotating "
"plots with. Use `--group-labels` to define table "
"columns to use")
parser.add_option("--fam-file", dest="fam_file", type="string",
help="Plink .fam file containing file IDs")
parser.add_option("--xvar-labels", dest="xvar_labs", type="string",
help="a comma-separated list of variable X labels"
"only applies when X is a discrete or categorical "
"variable. The labels must be in the correct order")
parser.add_option("--group-labels", dest="group_labs", type="string",
help="a comma-separated list of grouping variable "
"labels. Can only apply when the grouping variable "
"is discrete or categorical. The labels must be "
"input in the order of the data")
parser.add_option("--yvar-labels", dest="yvar_labs", type="string",
help="a comma-separated list of variable Y labels"
"only applies when Y is a discrete or categorical "
"variable")
parser.add_option("--var-type", dest="var_type", type="choice",
choices=["continuous", "categorical", "integer"],
help="The data type of the variables to be plotted."
"The default is continuous")
parser.add_option("--coordinate-file", dest="coordinates", type="string",
help="file containing co-ordinates data")
parser.add_option("--coords-id-col", dest="coord_ids", type="string",
help="column header containing individual IDs")
parser.add_option("--lattitude-column", dest="lat_col", type="string",
help="column header containing lattitude co-ordinates")
parser.add_option("--longitude-column", dest="long_col", type="string",
help="column header containing longitude co-ordinates")
parser.add_option("--reference-value", dest="ref_val", type="string",
help="categorical variable level to dichotomise on")
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
parser.set_defaults(y_col=None,
group_by=None,
indx=None,
labels="X,Y,title",
xvar_labs=None,
yvar_labs=None,
var_type="continuous")
infile = argv[-1]
df = pd.read_table(infile, sep="\t", index_col=options.indx,
header=0)
if options.plot_type == "map":
df = pd.read_table(infile, sep="\t", index_col=options.indx,
header=0)
coords_df = pd.read_table(options.coordinates, sep="\t",
header=0, index_col=options.indx)
gwas.plotMapPhenotype(data=df,
coords=coords_df,
coord_id_col=options.coord_ids,
lat_col=options.lat_col,
long_col=options.long_col,
save_path=options.outfile,
xvar=options.x_col,
var_type=options.var_type,
xlabels=options.xvar_labs,
level=options.ref_val)
elif options.plot_type == "pca":
data = gwas.parseFlashPCA(pcs_file=infile,
fam_file=options.fam_file)
gwas.plotPCA(data=data,
nPCs=options.n_pcs,
point_labels=options.group_labs,
save_path=options.outfile,
headers=False,
metadata=options.meta_file,
multiplot=True)
else:
df = pd.read_table(infile, sep="\t", index_col=options.indx,
header=0)
gwas.plotPhenotype(data=df,
plot_type=options.plot_type,
x=options.x_col,
y=options.y_col,
group=options.group_by,
save_path=options.outfile,
labels=options.labels,
xlabels=options.xvar_labs,
ylabels=options.yvar_labs,
glabels=options.group_labs,
var_type=options.var_type)
# write footer and output benchmark information.
E.Stop()
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option("-t",
"--test",
dest="test",
type="string",
help="supply help")
parser.add_option("-p",
"--plot-type",
dest="plot_type",
type="choice",
choices=[
"histogram", "barplot", "density", "boxplot",
"scatter", "map", "pca"
],
help="the plot type to generate")
parser.add_option("--plot-n-pc",
dest="n_pcs",
type="int",
help="The number of principal components to "
"plot")
parser.add_option("-g",
"--group-by",
dest="group_by",
type="string",
help="column header to group observations by")
parser.add_option("-x",
"--x-column",
dest="x_col",
type="string",
help="column to plot on X axis")
parser.add_option("-y",
"--y-column",
dest="y_col",
type="string",
help="column to plot on y axis")
parser.add_option("-i",
"--index_column",
dest="indx",
type="string",
help="column number that refers to the row index")
parser.add_option("--output-file",
dest="outfile",
type="string",
help="path and filename to save plot to")
parser.add_option("--labels",
dest="labels",
type="string",
help="a comma-separated list of axis labels. "
"The first 2 correspond to the X and Y-axis, "
"respectively, and the third is the plot title")
parser.add_option("--metadata-file",
dest="meta_file",
type="string",
help="file containing metadata for annotating "
"plots with. Use `--group-labels` to define table "
"columns to use")
parser.add_option("--fam-file",
dest="fam_file",
type="string",
help="Plink .fam file containing file IDs")
parser.add_option("--xvar-labels",
dest="xvar_labs",
type="string",
help="a comma-separated list of variable X labels"
"only applies when X is a discrete or categorical "
"variable. The labels must be in the correct order")
parser.add_option("--group-labels",
dest="group_labs",
type="string",
help="a comma-separated list of grouping variable "
"labels. Can only apply when the grouping variable "
"is discrete or categorical. The labels must be "
"input in the order of the data")
parser.add_option("--yvar-labels",
dest="yvar_labs",
type="string",
help="a comma-separated list of variable Y labels"
"only applies when Y is a discrete or categorical "
"variable")
parser.add_option("--var-type",
dest="var_type",
type="choice",
choices=["continuous", "categorical", "integer"],
help="The data type of the variables to be plotted."
"The default is continuous")
parser.add_option("--coordinate-file",
dest="coordinates",
type="string",
help="file containing co-ordinates data")
parser.add_option("--coords-id-col",
dest="coord_ids",
type="string",
help="column header containing individual IDs")
parser.add_option("--lattitude-column",
dest="lat_col",
type="string",
help="column header containing lattitude co-ordinates")
parser.add_option("--longitude-column",
dest="long_col",
type="string",
help="column header containing longitude co-ordinates")
parser.add_option("--reference-value",
dest="ref_val",
type="string",
help="categorical variable level to dichotomise on")
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
parser.set_defaults(y_col=None,
group_by=None,
indx=None,
labels="X,Y,title",
xvar_labs=None,
yvar_labs=None,
var_type="continuous")
infile = argv[-1]
df = pd.read_table(infile, sep="\t", index_col=options.indx, header=0)
if options.plot_type == "map":
df = pd.read_table(infile, sep="\t", index_col=options.indx, header=0)
coords_df = pd.read_table(options.coordinates,
sep="\t",
header=0,
index_col=options.indx)
gwas.plotMapPhenotype(data=df,
coords=coords_df,
coord_id_col=options.coord_ids,
lat_col=options.lat_col,
long_col=options.long_col,
save_path=options.outfile,
xvar=options.x_col,
var_type=options.var_type,
xlabels=options.xvar_labs,
level=options.ref_val)
elif options.plot_type == "pca":
data = gwas.parseFlashPCA(pcs_file=infile, fam_file=options.fam_file)
gwas.plotPCA(data=data,
nPCs=options.n_pcs,
point_labels=options.group_labs,
save_path=options.outfile,
headers=False,
metadata=options.meta_file,
multiplot=True)
else:
df = pd.read_table(infile, sep="\t", index_col=options.indx, header=0)
gwas.plotPhenotype(data=df,
plot_type=options.plot_type,
x=options.x_col,
y=options.y_col,
group=options.group_by,
save_path=options.outfile,
labels=options.labels,
xlabels=options.xvar_labs,
ylabels=options.yvar_labs,
glabels=options.group_labs,
var_type=options.var_type)
# write footer and output benchmark information.
E.Stop()
