from thunder import ThunderContext, ICA, export
if __name__ == "__main__":
parser = optparse.OptionParser(description="do independent components analysis",
usage="%prog datafile outputdir k c [options]")
parser.add_option("--svdmethod", choices=("direct", "em"), default="direct")
parser.add_option("--maxiter", type=float, default=100)
parser.add_option("--tol", type=float, default=0.000001)
parser.add_option("--seed", type=int, default=0)
opts, args = parser.parse_args()
try:
datafile = args[0]
outputdir = args[1]
k = int(args[2])
c = int(args[3])
except IndexError:
parser.print_usage()
raise Exception("too few arguments")
tsc = ThunderContext.start(appName="ica")
data = tsc.loadSeries(datafile).cache()
model = ICA(k=k, c=c, svdmethod=opts.svdmethod, maxiter=opts.maxiter, tol=opts.tol, seed=opts.seed)
result = model.fit(data)
outputdir += "-ica"
export(result.a, outputdir, "a", "matlab")
export(result.sigs, outputdir, "sigs", "matlab")
"""
Example standalone app for principal component analysis
"""
import argparse
from thunder import ThunderContext, PCA, export
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="do principal components analysis")
parser.add_argument("datafile", type=str)
parser.add_argument("outputdir", type=str)
parser.add_argument("k", type=int)
parser.add_argument("--svdmethod", choices=("direct", "em"), default="direct", required=False)
args = parser.parse_args()
tsc = ThunderContext.start(appName="pca")
data = tsc.loadSeries(args.datafile).cache()
model = PCA(args.k, args.svdmethod)
model.fit(data)
outputdir = args.outputdir + "-pca"
export(model.comps, outputdir, "comps", "matlab")
export(model.latent, outputdir, "latent", "matlab")
export(model.scores, outputdir, "scores", "matlab")
if __name__ == "__main__":
parser = optparse.OptionParser(
description="fit a regression model",
usage="%prog datafile modelfile outputdir [options]")
parser.add_option("--regressmode",
choices=("mean", "linear", "bilinear"),
help="form of regression")
parser.add_option("--k", type=int, default=2)
opts, args = parser.parse_args()
try:
datafile = args[0]
modelfile = args[1]
outputdir = args[2]
except IndexError:
parser.print_usage()
raise Exception("too few arguments")
tsc = ThunderContext.start(appName="regresswithpca")
data = tsc.loadSeries(datafile)
model = RegressionModel.load(modelfile, opts.regressmode) # do regression
betas, stats, resid = model.fit(data)
pca = PCA(opts.k).fit(betas) # do PCA
traj = model.fit(data, pca.comps) # get trajectories
outputdir += "-regress"
export(pca.comps, outputdir, "comps", "matlab")
export(pca.latent, outputdir, "latent", "matlab")
export(pca.scores, outputdir, "scores", "matlab")
export(traj, outputdir, "traj", "matlab")
parser = argparse.ArgumentParser(
description="do independent components analysis")
parser.add_argument("datafile", type=str)
parser.add_argument("outputdir", type=str)
parser.add_argument("k", type=int)
parser.add_argument("c", type=int)
parser.add_argument("--svdmethod",
choices=("direct", "em"),
default="direct",
required=False)
parser.add_argument("--maxiter", type=float, default=100, required=False)
parser.add_argument("--tol", type=float, default=0.000001, required=False)
parser.add_argument("--seed", type=int, default=0, required=False)
args = parser.parse_args()
tsc = ThunderContext.start(appName="ica")
data = tsc.loadSeries(args.datafile).cache()
model = ICA(k=args.k,
c=args.c,
svdmethod=args.svdmethod,
maxiter=args.maxiter,
tol=args.tol,
seed=args.seed)
result = model.fit(data)
outputdir = args.outputdir + "-ica"
export(result.a, outputdir, "a", "matlab")
export(result.sigs, outputdir, "sigs", "matlab")
import optparse
from thunder import ThunderContext, PCA, export
if __name__ == "__main__":
parser = optparse.OptionParser(
description="do principal components analysis",
usage="%prog datafile outputdir k [options]")
parser.add_option("--svdmethod",
choices=("direct", "em"),
default="direct")
opts, args = parser.parse_args()
try:
datafile = args[0]
outputdir = args[1]
k = int(args[2])
except IndexError:
parser.print_usage()
raise Exception("too few arguments")
tsc = ThunderContext.start(appName="pca")
data = tsc.loadSeries(datafile).cache()
model = PCA(k, opts.svdmethod)
model.fit(data)
outputdir += "-pca"
export(model.comps, outputdir, "comps", "matlab")
export(model.latent, outputdir, "latent", "matlab")
export(model.scores, outputdir, "scores", "matlab")
"""
Example standalone app for kmeans clustering
"""
import argparse
from thunder import ThunderContext, KMeans, export
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="do kmeans clustering")
parser.add_argument("datafile", type=str)
parser.add_argument("outputdir", type=str)
parser.add_argument("k", type=int)
parser.add_argument("--maxiter", type=float, default=20, required=False)
parser.add_argument("--tol", type=float, default=0.001, required=False)
args = parser.parse_args()
tsc = ThunderContext.start(appName="kmeans")
data = tsc.loadSeries(args.datafile).cache()
model = KMeans(k=args.k, maxIterations=args.maxiter).fit(data)
labels = model.predict(data)
outputdir = args.outputdir + "-kmeans"
export(model.centers, outputdir, "centers", "matlab")
export(labels, outputdir, "labels", "matlab")
"""
Example standalone app for mass-unvariate regression combined with PCA
"""
import argparse
from thunder import ThunderContext, RegressionModel, PCA, export
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="fit a regression model")
parser.add_argument("datafile", type=str)
parser.add_argument("modelfile", type=str)
parser.add_argument("outputdir", type=str)
parser.add_argument("regressmode", choices=("mean", "linear", "bilinear"), help="form of regression")
parser.add_argument("--k", type=int, default=2)
args = parser.parse_args()
tsc = ThunderContext.start(appName="regresswithpca")
data = tsc.loadSeries(args.datafile)
model = RegressionModel.load(args.modelfile, args.regressmode) # do regression
betas, stats, resid = model.fit(data)
pca = PCA(args.k).fit(betas) # do PCA
traj = model.fit(data, pca.comps) # get trajectories
outputdir = args.outputdir + "-regress"
export(pca.comps, outputdir, "comps", "matlab")
export(pca.latent, outputdir, "latent", "matlab")
export(pca.scores, outputdir, "scores", "matlab")
export(traj, outputdir, "traj", "matlab")
from thunder import ThunderContext, NMF, export
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="do non-negative matrix factorization")
parser.add_argument("datafile", type=str)
parser.add_argument("outputdir", type=str)
parser.add_argument("k", type=int)
parser.add_argument("--nmfmethod", choices="als", default="als", required=False)
parser.add_argument("--maxiter", type=float, default=20, required=False)
parser.add_argument("--tol", type=float, default=0.001, required=False)
parser.add_argument("--w_hist", type=bool, default=False, required=False)
parser.add_argument("--recon_hist", type=bool, default=False, required=False)
args = parser.parse_args()
tsc = ThunderContext.start(appName="nmf")
data = tsc.loadSeries(args.datafile).cache()
nmf = NMF(k=args.k, method=args.nmfmethod, maxiter=args.maxiter, tol=args.tol, w_hist=args.w_hist,
recon_hist=args.recon_hist)
nmf.fit(data)
outputdir = args.outputdir + "-nmf"
export(nmf.w, outputdir, "w", "matlab")
export(nmf.h, outputdir, "h", "matlab")
if args.w_hist:
export(nmf.w_convergence, outputdir, "w_convergence", "matlab")
if args.recon_hist:
export(nmf.recon_err, outputdir, "rec_err", "matlab")
"""
Example standalone app for mass-univariate regression
"""
import argparse
from thunder import ThunderContext, RegressionModel, export
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="fit a regression model")
parser.add_argument("datafile", type=str)
parser.add_argument("modelfile", type=str)
parser.add_argument("outputdir", type=str)
parser.add_argument("regressmode", choices=("mean", "linear", "bilinear"), help="form of regression")
args = parser.parse_args()
tsc = ThunderContext.start(appName="regress")
data = tsc.loadText(args.datafile, args.preprocess)
result = RegressionModel.load(args.modelfile, args.regressmode).fit(data)
outputdir = args.outputdir + "-regress"
export(result.select('stats'), outputdir, "stats", "matlab")
export(result.select('betas'), outputdir, "betas", "matlab")
"""
Example standalone app for calculating series statistics
"""
import optparse
from thunder import ThunderContext, export
if __name__ == "__main__":
parser = optparse.OptionParser(
description="compute summary statistics on time series data",
usage="%prog datafile outputdir mode [options]")
parser.add_option("--preprocess", action="store_true", default=False)
opts, args = parser.parse_args()
try:
datafile = args[0]
outputdir = args[1]
mode = args[2]
except IndexError:
parser.print_usage()
raise Exception("too few arguments")
tsc = ThunderContext.start(appName="stats")
data = tsc.loadSeries(datafile).cache()
vals = data.seriesStat(mode)
outputdir += "-stats"
export(vals, outputdir, "stats_" + mode, "matlab")
"""
Example standalone app for calculating series statistics
"""
import optparse
from thunder import ThunderContext, export
if __name__ == "__main__":
parser = optparse.OptionParser(description="compute summary statistics on time series data",
usage="%prog datafile outputdir mode [options]")
parser.add_option("--preprocess", action="store_true", default=False)
opts, args = parser.parse_args()
try:
datafile = args[0]
outputdir = args[1]
mode = args[2]
except IndexError:
parser.print_usage()
raise Exception("too few arguments")
tsc = ThunderContext.start(appName="stats")
data = tsc.loadSeries(datafile).cache()
vals = data.seriesStat(mode)
outputdir += "-stats"
export(vals, outputdir, "stats_" + mode, "matlab")
