def plot(self, hardcopy=None): if hardcopy: R.png(hardcopy, width=1024, height=768, type="cairo") R.require('qvalue') # build a qobj R.assign("pval", self.mPValues) R.assign("pi0", self.mPi0) R.assign("qval", self.mQValues) R.assign("lambda", self.mLambda) R("""qobj <-list( pi0=pi0, qvalues=qval, pvalues=pval, lambda=lambda)""" ) R(""" class(qobj) <- "qvalue" """) R("""qplot(qobj)""") if hardcopy: R.dev_off()
def main(): parser = E.OptionParser( version = "%prog version: $Id: rates2rates.py 2781 2009-09-10 11:33:14Z andreas $", usage = globals()["__doc__"]) parser.add_option( "--output-filename-pattern", dest="output_filename_pattern", type="string", help="pattern for additional output files [%default]." ) parser.add_option( "--input-filename-neutral", dest="input_filename_neutral", type="string", help="a tab-separated file with rates and G+C content in neutrally evolving regions [%default]." ) parser.set_defaults( input_filename_neutral = None, output_filename_pattern = "%s", normalize = True, hardcopy = None, ) (options, args) = E.Start( parser, add_csv_options = True ) if not options.input_filename_neutral: raise ValueError( "please supply a file with neutral rates." ) lines = options.stdin.readlines() if len(lines) == 0: raise IOError ( "no input" ) from rpy import r as R import rpy R.png( options.output_filename_pattern % "fit" + ".png", width=1024, height=768, type="cairo") matrix, headers = readRates( open( options.input_filename_neutral, "r" ) ) R.assign("matrix", matrix) R.assign("headers", headers) nref = R( """length( matrix[,1] )""" ) dat = R("""dat <- data.frame(x = matrix[,2], y = matrix[,3])""") mod = R("""mod <- lm( y ~ x, dat)""") R("""plot( matrix[,2], matrix[,3], cex=%s, col="blue", pch="o", xlab="%s", ylab="%s" %s)""" % (0.3, headers[1], headers[2], "") ) R("""new <- data.frame(x = seq( min(matrix[,2]), max(matrix[,2]), (max(matrix[,2]) - min(matrix[,2])) / 100))""") R("""predict(mod, new, se.fit = TRUE)""") R("""pred.w.plim <- predict(mod, new, interval="prediction")""") R("""pred.w.clim <- predict(mod, new, interval="confidence")""") R("""matpoints(new$x,cbind(pred.w.clim, pred.w.plim[,-1]), lty=c(1,2,2,3,3), type="l")""") R.mtext( "y = %f * x + %f, r=%6.4f, n=%i" % (mod["coefficients"]["x"], mod["coefficients"]["(Intercept)"], R("""cor( dat )[2]"""), nref ), 3, cex = 1.0) R("""mean_rate <- mean( matrix[,3] )""") data_matrix, data_headers = readRates( lines ) R.assign("data_matrix", data_matrix) R.assign("data_headers", data_headers) ndata = R( """length( data_matrix[,1] )""" ) R("""points( data_matrix[,2], data_matrix[,3], cex=%s, col="red", pch="o" %s)""" % (0.3, "") ) R("""topred <- data.frame( x = data_matrix[,2] )""") R("""corrected_rates <- predict( mod, topred, se.fit = TRUE )""") uncorrected = R("""uncorrected <- data_matrix[,3] / mean_rate """) corrected = R("""corrected <- as.vector(data_matrix[,3] / corrected_rates$fit)""") R.dev_off() R.png( options.output_filename_pattern % "correction" + ".png", width=1024, height=768, type="cairo") R("""plot( uncorrected, corrected, cex=%s, col="blue", pch="o", xlab="uncorrected rate", ylab="corrected rate" %s)""" % (0.3, "") ) R.dev_off() E.Stop()
def plot(outfile, data, out_format='png'): w = int(round(len(data) / 4.0)) if out_format == 'png': r.png(outfile, width=w * 100, height=1000, res=72) elif out_format == 'pdf': r.pdf(outfile, width=w, height=10) else: raise Exception('Unrecognised format: ' + str(out_format)) print("total: " + str(len(data))) series = [] points = {'translate': [], 'preprocessing': []} for dat in data: points['translate'].append(float(dat['translate'])) points['preprocessing'].append(float(dat['preprocessing'])) xlabels = [] for k, v in data[0].iteritems(): if k not in ["problem", 'translate', 'preprocessing']: series.append(k) points[k] = [] index = 0 for dat in data: for k in series: if dat[k] != 'no-plan': points[k].append(float(dat[k]) + \ points['translate'][index] + \ points['preprocessing'][index]) else: points[k].append(-1000) xlabels.append(dat['problem']) index += 1 max_value = max(iter([max(iter(points[k])) for k in series])) yrange = (0, max_value) legend_labels = [] x = [i for i in range(1, len(points['translate']) + 1)] y = [-1000 for i in x] r.par(mar=(7, 5, 4, 2)) r.plot(x, y, main='', xlab="", ylab='', xaxt='n', yaxt='n', pch=0, ylim=yrange, mgp=(5, 1, 0)) r.mtext("Problem", side=1, line=5) r.mtext("CPU Time (s)", side=2, line=3) pch_start = 1 pch_index = pch_start # plotting "translate" #r.plot(x, points['translate'], main='', # xlab='', ylab='Time (s)', # xaxt='n', yaxt='n', # pch=0, ylim=yrange) #legend_labels.append('translate') r.lines(x, points['translate'], lty=1) # preprocessing -- Removed since it's insignificant #r.points(x, points['preprocessing'], pch=pch_index) #pch_index =+ 1 # planner output for k in series: if k != 'translate' and k != 'preporcessing': r.points(x, points[k], pch=pch_index) pch_index += 1 legend_labels.append("FD+" + k.upper()) # put x-axis labels for i in range(0, len(xlabels)): r.axis(side=1, at=i + 1, labels=xlabels[i], las=2) # put y-axis labels base, step = get_y_step(max_value) print("base: " + str(base) + " -- step: " + str(step)) y = base for i in range(0, step): r.axis(side=2, at=y, labels=str(y), las=2) y += base # legend r.legend(1, max_value, legend_labels, pch=[i for i in range(pch_start, pch_index)]) r.dev_off()