def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
parser = E.OptionParser(
version="%prog version: $Id: r_compare_distributions.py 2782 2009-09-10 11:40:29Z andreas $")
parser.add_option("-m", "--method", dest="method", type="choice",
help="method to use: ks=Kolmogorov-Smirnov, mwu=Mann-WhitneyU, shapiro=Shapiro-Wilk, paired-mwu=paired Mann-WhitneyU, paired-t=paired t-test [default=%default]",
choices=("ks", "mwu", "shapiro", "paired-mwu", "paired-t"))
parser.add_option("-a", "--hardcopy", dest="hardcopy", type="string",
help="write hardcopy to file.", metavar="FILE")
parser.add_option("-1", "--infile1", dest="filename_input1", type="string",
help="input filename for distribution 1.")
parser.add_option("-2", "--infile2", dest="filename_input2", type="string",
help="input filename for distribution 2.")
parser.add_option("--plot-legend", dest="legend", type="string",
help="legend for histograms.""")
parser.add_option("-f", "--infile-map", dest="filename_input_map", type="string",
help="input filename for mapping categories to values.")
parser.add_option("-n", "--norm-test", dest="norm_test", action="store_true",
help="""test if a set of values is normally distributed. Mean and variance
are calculated from the data.""")
parser.add_option("-b", "--num-bins", dest="num_bins", type="int",
help="""number of bins (for plotting purposes only).""")
parser.add_option("--bin-size", dest="bin_size", type="float",
help="""bin size for plot.""")
parser.add_option("--min-value", dest="min_value", type="float",
help="""minimum_value for plot.""")
parser.add_option("--max-value", dest="max_value", type="float",
help="""maximum_value for plot.""")
parser.add_option("--skip-plot", dest="plot", action="store_false",
help="""skipping plotting.""")
parser.add_option("--header-names", dest="header", type="string",
help="""header of value column [default=%default].""")
parser.add_option("--title", dest="title", type="string",
help="""plot title [default=%default].""")
parser.set_defaults(
method="ks",
filename_input1=None,
filename_input2=None,
filename_input_map=None,
legend=None,
norm_test=False,
num_bins=0,
legend_range="2,2",
bin_size=None,
min_value=None,
plot=True,
header="value",
title=None,
)
(options, args) = E.Start(parser,
add_pipe_options=True)
kwargs = {}
xargs = []
for arg in args:
if "=" in arg:
key, value = arg.split("=")
kwargs[key] = value
else:
xargs.append(arg)
if options.legend:
options.legend = options.legend.split(",")
map_category2value = {}
if options.filename_input_map:
map_category2value = IOTools.ReadMap(open(options.filename_input_map, "r"),
map_functions=(str, float))
f = str
else:
f = float
if options.filename_input1:
infile1 = IOTools.openFile(options.filename_input1, "r")
else:
infile1 = sys.stdin
values1, errors1 = IOTools.ReadList(infile1,
map_function=f,
map_category=map_category2value)
if options.filename_input1:
infile1.close()
if errors1 and options.loglevel >= 3:
options.stdlog.write("# errors in input1: %s\n" %
";".join(map(str, errors1)))
if options.norm_test:
mean = R.mean(values1)
stddev = R.sd(values1)
options.stdlog.write("# creating %i samples from normal distribution with mean %f and stddev %f\n" % (
len(values1), mean, stddev))
values2 = R.rnorm(len(values1), mean, stddev)
errors2 = ()
else:
values2, errors2 = IOTools.ReadList(open(options.filename_input2, "r"),
map_function=f,
map_category=map_category2value)
if errors2 and options.loglevel >= 3:
options.stdlog.write("# errors in input2: %s\n" %
";".join(map(str, errors2)))
if options.loglevel >= 1:
options.stdlog.write("# ninput1=%i, nerrors1=%i, ninput2=%i, nerrors2=%i\n" % (len(values1), len(errors1),
len(values2), len(errors2)))
if options.method in ("paired-mwu", "paired-t"):
if len(values1) != len(values2):
raise ValueError(
"number of values must be equal for paired tests.")
if options.hardcopy:
R.png(options.hardcopy, width=1024, height=768)
if options.method == "ks":
result = R.ks_test(values1, values2, *xargs, **kwargs)
elif options.method == "mwu":
result = R.wilcox_test(
values1, values2, paired=False, correct=True, *xargs, **kwargs)
elif options.method == "paired-mwu":
result = R.wilcox_test(
values1, values2, paired=True, correct=True, *xargs, **kwargs)
elif options.method == "paired-t":
result = R.t_test(values1, values2, paired=True, *xargs, **kwargs)
elif options.method == "shapiro":
if len(values1) > 5000:
E.warn(
"shapiro-wilk test only accepts < 5000 values, a random sample has been created.")
values1 = random.sample(values1, 5000)
result = R.shapiro_test(values1, *xargs, **kwargs)
if options.plot:
R.assign("v1", values1)
R.assign("v2", values2)
if options.title:
# set the size of the outer margins - the title needs to be added at the end
# after plots have been created
R.par(oma=R.c(0, 0, 4, 0))
R.layout(R.matrix((1, 2, 3, 4), 2, 2, byrow=True))
R.boxplot(values1, values2, col=('white', 'red'), main="Boxplot")
R("""qqplot( v1, v2, main ='Quantile-quantile plot' ); lines( c(0,1), c(0,1) );""")
# compute breaks:
min_value = min(min(values1), min(values2))
if options.min_value is not None:
min_value = min(min_value, options.min_value)
max_value = max(max(values1), max(values2))
if options.max_value is not None:
max_value = max(max_value, options.max_value)
extra_options = ""
if options.num_bins and not (options.min_value or options.max_value):
extra_options += ", breaks=%i" % options.num_bins
elif options.num_bins and (options.min_value or options.max_value):
bin_size = float((max_value - min_value)) / (options.num_bins + 1)
breaks = [
min_value + x * bin_size for x in range(options.num_bins)]
extra_options += ", breaks=c(%s)" % ",".join(map(str, breaks))
elif options.bin_size is not None:
num_bins = int(((max_value - min_value) / options.bin_size)) + 1
breaks = [
min_value + x * options.bin_size for x in range(num_bins + 1)]
extra_options += ", breaks=c(%s)" % ",".join(map(str, breaks))
R("""h1 <- hist( v1, freq=FALSE, density=20, main='Relative frequency histogram' %s)""" %
extra_options)
R("""h2 <- hist( v2, freq=FALSE, add=TRUE, density=20, col='red', offset=0.5, angle=135 %s)""" %
extra_options)
if options.legend:
R("""legend( ( max(c(h1$breaks[-1], h2$breaks[-1])) - min(c(h1$breaks[1], h2$breaks[1]) ) ) / 2,
max( max(h1$density), max(h2$density)) / 2, c('%s'), fill=c('white','red'))""" % (
"','".join(options.legend)))
R("""h1 <- hist( v1, freq=TRUE, density=20, main='Absolute frequency histogram' %s)""" %
extra_options)
R("""h2 <- hist( v2, freq=TRUE, add=TRUE, density=20, col='red', offset=0.5, angle=135 %s )""" %
extra_options)
if options.legend:
R("""legend( ( max(c(h1$breaks[-1], h2$breaks[-1])) - min(c(h1$breaks[1], h2$breaks[1]) ) ) / 2,
max( max(h1$counts), max(h2$counts)) / 2, c('%s'), fill=c('white','red'))""" % (
"','".join(options.legend)))
if options.title:
R.mtext(options.title, 3, outer=True, line=1, cex=1.5)
if options.loglevel >= 1:
options.stdout.write("## Results for %s\n" % result['method'])
options.stdout.write("%s\t%s\n" % ("key", options.header))
for key in list(result.keys()):
if key == "data.name":
continue
options.stdout.write("\t".join((key, str(result[key]))) + "\n")
stat = Stats.Summary(values1)
for key, value in list(stat.items()):
options.stdout.write("%s1\t%s\n" % (str(key), str(value)))
stat = Stats.Summary(values2)
for key, value in list(stat.items()):
options.stdout.write("%s2\t%s\n" % (str(key), str(value)))
if options.plot:
if options.hardcopy:
R.dev_off()
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
parser = E.OptionParser(
version=
"%prog version: $Id: r_compare_distributions.py 2782 2009-09-10 11:40:29Z andreas $"
)
parser.add_option(
"-m",
"--method",
dest="method",
type="choice",
help=
"method to use: ks=Kolmogorov-Smirnov, mwu=Mann-WhitneyU, shapiro=Shapiro-Wilk, paired-mwu=paired Mann-WhitneyU, paired-t=paired t-test [default=%default]",
choices=("ks", "mwu", "shapiro", "paired-mwu", "paired-t"))
parser.add_option("-a",
"--hardcopy",
dest="hardcopy",
type="string",
help="write hardcopy to file.",
metavar="FILE")
parser.add_option("-1",
"--infile1",
dest="filename_input1",
type="string",
help="input filename for distribution 1.")
parser.add_option("-2",
"--infile2",
dest="filename_input2",
type="string",
help="input filename for distribution 2.")
parser.add_option("--plot-legend",
dest="legend",
type="string",
help="legend for histograms."
"")
parser.add_option("-f",
"--infile-map",
dest="filename_input_map",
type="string",
help="input filename for mapping categories to values.")
parser.add_option(
"-n",
"--norm-test",
dest="norm_test",
action="store_true",
help=
"""test if a set of values is normally distributed. Mean and variance
are calculated from the data.""")
parser.add_option("-b",
"--num-bins",
dest="num_bins",
type="int",
help="""number of bins (for plotting purposes only).""")
parser.add_option("--bin-size",
dest="bin_size",
type="float",
help="""bin size for plot.""")
parser.add_option("--min-value",
dest="min_value",
type="float",
help="""minimum_value for plot.""")
parser.add_option("--max-value",
dest="max_value",
type="float",
help="""maximum_value for plot.""")
parser.add_option("--skip-plot",
dest="plot",
action="store_false",
help="""skipping plotting.""")
parser.add_option("--header-names",
dest="header",
type="string",
help="""header of value column [default=%default].""")
parser.add_option("--title",
dest="title",
type="string",
help="""plot title [default=%default].""")
parser.set_defaults(
method="ks",
filename_input1=None,
filename_input2=None,
filename_input_map=None,
legend=None,
norm_test=False,
num_bins=0,
legend_range="2,2",
bin_size=None,
min_value=None,
plot=True,
header="value",
title=None,
)
(options, args) = E.Start(parser, add_pipe_options=True)
kwargs = {}
xargs = []
for arg in args:
if "=" in arg:
key, value = arg.split("=")
kwargs[key] = value
else:
xargs.append(arg)
if options.legend:
options.legend = options.legend.split(",")
map_category2value = {}
if options.filename_input_map:
map_category2value = IOTools.ReadMap(open(options.filename_input_map,
"r"),
map_functions=(str, float))
f = str
else:
f = float
if options.filename_input1:
infile1 = IOTools.openFile(options.filename_input1, "r")
else:
infile1 = sys.stdin
values1, errors1 = IOTools.ReadList(infile1,
map_function=f,
map_category=map_category2value)
if options.filename_input1:
infile1.close()
if errors1 and options.loglevel >= 3:
options.stdlog.write("# errors in input1: %s\n" %
";".join(map(str, errors1)))
if options.norm_test:
mean = R.mean(values1)
stddev = R.sd(values1)
options.stdlog.write(
"# creating %i samples from normal distribution with mean %f and stddev %f\n"
% (len(values1), mean, stddev))
values2 = R.rnorm(len(values1), mean, stddev)
errors2 = ()
else:
values2, errors2 = IOTools.ReadList(open(options.filename_input2, "r"),
map_function=f,
map_category=map_category2value)
if errors2 and options.loglevel >= 3:
options.stdlog.write("# errors in input2: %s\n" %
";".join(map(str, errors2)))
if options.loglevel >= 1:
options.stdlog.write(
"# ninput1=%i, nerrors1=%i, ninput2=%i, nerrors2=%i\n" %
(len(values1), len(errors1), len(values2), len(errors2)))
if options.method in ("paired-mwu", "paired-t"):
if len(values1) != len(values2):
raise ValueError(
"number of values must be equal for paired tests.")
if options.hardcopy:
R.png(options.hardcopy, width=1024, height=768)
if options.method == "ks":
result = R.ks_test(values1, values2, *xargs, **kwargs)
elif options.method == "mwu":
result = R.wilcox_test(values1,
values2,
paired=False,
correct=True,
*xargs,
**kwargs)
elif options.method == "paired-mwu":
result = R.wilcox_test(values1,
values2,
paired=True,
correct=True,
*xargs,
**kwargs)
elif options.method == "paired-t":
result = R.t_test(values1, values2, paired=True, *xargs, **kwargs)
elif options.method == "shapiro":
if len(values1) > 5000:
E.warn(
"shapiro-wilk test only accepts < 5000 values, a random sample has been created."
)
values1 = random.sample(values1, 5000)
result = R.shapiro_test(values1, *xargs, **kwargs)
if options.plot:
R.assign("v1", values1)
R.assign("v2", values2)
if options.title:
# set the size of the outer margins - the title needs to be added at the end
# after plots have been created
R.par(oma=R.c(0, 0, 4, 0))
R.layout(R.matrix((1, 2, 3, 4), 2, 2, byrow=True))
R.boxplot(values1, values2, col=('white', 'red'), main="Boxplot")
R("""qqplot( v1, v2, main ='Quantile-quantile plot' ); lines( c(0,1), c(0,1) );"""
)
# compute breaks:
min_value = min(min(values1), min(values2))
if options.min_value is not None:
min_value = min(min_value, options.min_value)
max_value = max(max(values1), max(values2))
if options.max_value is not None:
max_value = max(max_value, options.max_value)
extra_options = ""
if options.num_bins and not (options.min_value or options.max_value):
extra_options += ", breaks=%i" % options.num_bins
elif options.num_bins and (options.min_value or options.max_value):
bin_size = float((max_value - min_value)) / (options.num_bins + 1)
breaks = [
min_value + x * bin_size for x in range(options.num_bins)
]
extra_options += ", breaks=c(%s)" % ",".join(map(str, breaks))
elif options.bin_size is not None:
num_bins = int(((max_value - min_value) / options.bin_size)) + 1
breaks = [
min_value + x * options.bin_size for x in range(num_bins + 1)
]
extra_options += ", breaks=c(%s)" % ",".join(map(str, breaks))
R("""h1 <- hist( v1, freq=FALSE, density=20, main='Relative frequency histogram' %s)"""
% extra_options)
R("""h2 <- hist( v2, freq=FALSE, add=TRUE, density=20, col='red', offset=0.5, angle=135 %s)"""
% extra_options)
if options.legend:
R("""legend( ( max(c(h1$breaks[-1], h2$breaks[-1])) - min(c(h1$breaks[1], h2$breaks[1]) ) ) / 2,
max( max(h1$density), max(h2$density)) / 2, c('%s'), fill=c('white','red'))"""
% ("','".join(options.legend)))
R("""h1 <- hist( v1, freq=TRUE, density=20, main='Absolute frequency histogram' %s)"""
% extra_options)
R("""h2 <- hist( v2, freq=TRUE, add=TRUE, density=20, col='red', offset=0.5, angle=135 %s )"""
% extra_options)
if options.legend:
R("""legend( ( max(c(h1$breaks[-1], h2$breaks[-1])) - min(c(h1$breaks[1], h2$breaks[1]) ) ) / 2,
max( max(h1$counts), max(h2$counts)) / 2, c('%s'), fill=c('white','red'))"""
% ("','".join(options.legend)))
if options.title:
R.mtext(options.title, 3, outer=True, line=1, cex=1.5)
if options.loglevel >= 1:
options.stdout.write("## Results for %s\n" % result['method'])
options.stdout.write("%s\t%s\n" % ("key", options.header))
for key in list(result.keys()):
if key == "data.name":
continue
options.stdout.write("\t".join((key, str(result[key]))) + "\n")
stat = Stats.Summary(values1)
for key, value in list(stat.items()):
options.stdout.write("%s1\t%s\n" % (str(key), str(value)))
stat = Stats.Summary(values2)
for key, value in list(stat.items()):
options.stdout.write("%s2\t%s\n" % (str(key), str(value)))
if options.plot:
if options.hardcopy:
R.dev_off()
E.Stop()
valores = IntVector([6, 7, 4, 3, 2, 0, 0, 6])
valores[4] # importante notar que python começa de 0
valores[3] # e o R começa de 1
len(valores)
max(valores)
min(valores)
import ipdb; ipdb.set_trace()
print r.sum(valores)[0]
print r.prod(valores)[0]
print r.sort(valores)
print r.mean(valores)[0]
print r.median(valores)[0]
print r.sd(valores)[0]
print r.var(valores)[0]
valores_python = list(valores)
he = IntVector([10, 2, 23, 11, 14, 35, 46, 32, 13, 51, 27, 49])
ha = he
print r.var(he)[0]
print r.cov(ha, he)[0]
print r.cor(ha, he)[0]
# funções
sqr = robjects.r('function(x) x^2')
print(sqr)
print(sqr(2))
valores = IntVector([6, 7, 4, 3, 2, 0, 0, 6])
valores[4] # importante notar que python começa de 0
valores[3] # e o R começa de 1
len(valores)
max(valores)
min(valores)
import ipdb
ipdb.set_trace()
print r.sum(valores)[0]
print r.prod(valores)[0]
print r.sort(valores)
print r.mean(valores)[0]
print r.median(valores)[0]
print r.sd(valores)[0]
print r.var(valores)[0]
valores_python = list(valores)
he = IntVector([10, 2, 23, 11, 14, 35, 46, 32, 13, 51, 27, 49])
ha = he
print r.var(he)[0]
print r.cov(ha, he)[0]
print r.cor(ha, he)[0]
# funções
sqr = robjects.r('function(x) x^2')
print(sqr)
print(sqr(2))