def plot_squiggle(args, filename, start_times, mean_signals): """ Use rpy2 to create a squiggle plot of the read """ r = robjects.r r.library("ggplot2") grdevices = importr('grDevices') # set t_0 as the first measured time for the read. t_0 = start_times[0] total_time = start_times[-1] - start_times[0] # adjust times to be relative to t_0 r_start_times = robjects.FloatVector([t - t_0 for t in start_times]) r_mean_signals = robjects.FloatVector(mean_signals) # infer the appropriate number of events given the number of facets num_events = len(r_mean_signals) events_per_facet = (num_events / args.num_facets) + 1 # dummy variable to control faceting facet_category = robjects.FloatVector([(i / events_per_facet) + 1 for i in range(len(start_times))]) # make a data frame of the start times and mean signals d = {'start': r_start_times, 'mean': r_mean_signals, 'cat': facet_category} df = robjects.DataFrame(d) gp = ggplot2.ggplot(df) if not args.theme_bw: pp = gp + ggplot2.aes_string(x='start', y='mean') \ + ggplot2.geom_step(size=0.25) \ + ggplot2.facet_wrap(robjects.Formula('~cat'), ncol=1, scales="free_x") \ + ggplot2.scale_x_continuous('Time (seconds)') \ + ggplot2.scale_y_continuous('Mean signal (picoamps)') \ + ggplot2.ggtitle('Squiggle plot for read: ' + filename + "\nTotal time (sec): " + str(total_time)) \ + ggplot2.theme(**{'plot.title': ggplot2.element_text(size=11)}) else: pp = gp + ggplot2.aes_string(x='start', y='mean') \ + ggplot2.geom_step(size=0.25) \ + ggplot2.facet_wrap(robjects.Formula('~cat'), ncol=1, scales="free_x") \ + ggplot2.scale_x_continuous('Time (seconds)') \ + ggplot2.scale_y_continuous('Mean signal (picoamps)') \ + ggplot2.ggtitle('Squiggle plot for read: ' + filename + "\nTotal time (sec): " + str(total_time)) \ + ggplot2.theme(**{'plot.title': ggplot2.element_text(size=11)}) \ + ggplot2.theme_bw() if args.saveas is not None: plot_file = os.path.basename(filename) + "." + args.saveas if os.path.isfile(plot_file): raise Exception('Cannot create plot for %s: plot file %s already exists' % (filename, plot_file)) if args.saveas == "pdf": grdevices.pdf(plot_file, width = 8.5, height = 11) elif args.saveas == "png": grdevices.png(plot_file, width = 8.5, height = 11, units = "in", res = 300) pp.plot() grdevices.dev_off() else: pp.plot() # keep the plot open until user hits enter print('Type enter to exit.') raw_input()
def plot_collectors_curve(args, start_times, read_lengths): """ Use rpy2 to create a collectors curve of the run """ r = robjects.r r.library("ggplot2") grdevices = importr('grDevices') # set t_0 as the first measured time for the read. t_0 = start_times[0] # adjust times to be relative to t_0 r_start_times = robjects.FloatVector([float(t - t_0) / float(3600) + 0.00000001 \ for t in start_times]) r_read_lengths = robjects.IntVector(read_lengths) # compute the cumulative based on reads or total base pairs if args.plot_type == 'reads': y_label = "Total reads" cumulative = \ r.cumsum(robjects.IntVector([1] * len(start_times))) elif args.plot_type == 'basepairs': y_label = "Total base pairs" cumulative = r.cumsum(r_read_lengths) step = args.skip # make a data frame of the lists d = { 'start': robjects.FloatVector( [r_start_times[n] for n in xrange(0, len(r_start_times), step)]), 'lengths': robjects.IntVector( [r_read_lengths[n] for n in xrange(0, len(r_read_lengths), step)]), 'cumul': robjects.IntVector( [cumulative[n] for n in xrange(0, len(cumulative), step)]) } df = robjects.DataFrame(d) if args.savedf: robjects.r("write.table")(df, file=args.savedf, sep="\t") # title total_reads = len(read_lengths) total_bp = sum(read_lengths) plot_title = "Yield: " \ + str(total_reads) + " reads and " \ + str(total_bp) + " base pairs." # plot gp = ggplot2.ggplot(df) pp = gp + ggplot2.aes_string(x='start', y='cumul') \ + ggplot2.geom_step(size=2) \ + ggplot2.scale_x_continuous('Time (hours)') \ + ggplot2.scale_y_continuous(y_label) \ + ggplot2.ggtitle(plot_title) # extrapolation if args.extrapolate: start = robjects.ListVector({'a': 1, 'b': 1}) pp = pp + ggplot2.stat_smooth(fullrange='TRUE', method='nls', formula='y~a*I((x*3600)^b)', se='FALSE', start=start) \ + ggplot2.xlim(0, float(args.extrapolate)) if args.theme_bw: pp = pp + ggplot2.theme_bw() if args.saveas is not None: plot_file = args.saveas if plot_file.endswith(".pdf"): grdevices.pdf(plot_file, width=8.5, height=8.5) elif plot_file.endswith(".png"): grdevices.png(plot_file, width=8.5, height=8.5, units="in", res=300) else: logger.error("Unrecognized extension for %s!" % (plot_file)) sys.exit() pp.plot() grdevices.dev_off() else: pp.plot() # keep the plot open until user hits enter print('Type enter to exit.') raw_input()
def plot_collectors_curve(args, start_times, read_lengths): """ Use rpy2 to create a collectors curve of the run """ r = robjects.r r.library("ggplot2") grdevices = importr('grDevices') # set t_0 as the first measured time for the read. t_0 = start_times[0] # adjust times to be relative to t_0 r_start_times = robjects.FloatVector([float(t - t_0) / float(3600) + 0.00000001 \ for t in start_times]) r_read_lengths = robjects.IntVector(read_lengths) # compute the cumulative based on reads or total base pairs if args.plot_type == 'reads': y_label = "Total reads" cumulative = \ r.cumsum(robjects.IntVector([1] * len(start_times))) elif args.plot_type == 'basepairs': y_label = "Total base pairs" cumulative = r.cumsum(r_read_lengths) # make a data frame of the lists d = {'start': r_start_times, 'lengths': r_read_lengths, 'cumul': cumulative} df = robjects.DataFrame(d) if args.savedf: robjects.r("write.table")(df, file=args.savedf, sep="\t") # title total_reads = len(read_lengths) total_bp = sum(read_lengths) plot_title = "Yield: " \ + str(total_reads) + " reads and " \ + str(total_bp) + " base pairs." # plot gp = ggplot2.ggplot(df) pp = gp + ggplot2.aes_string(x='start', y='cumul') \ + ggplot2.geom_step(size=2) \ + ggplot2.scale_x_continuous('Time (hours)') \ + ggplot2.scale_y_continuous(y_label) \ + ggplot2.ggtitle(plot_title) # extrapolation if args.extrapolate: start = robjects.ListVector({'a': 1, 'b': 1}) pp = pp + ggplot2.stat_smooth(fullrange='TRUE', method='nls', formula='y~a*I((x*3600)^b)', se='FALSE', start=start) \ + ggplot2.xlim(0, float(args.extrapolate)) if args.theme_bw: pp = pp + ggplot2.theme_bw() if args.saveas is not None: plot_file = args.saveas if plot_file.endswith(".pdf"): grdevices.pdf(plot_file, width = 8.5, height = 8.5) elif plot_file.endswith(".png"): grdevices.png(plot_file, width = 8.5, height = 8.5, units = "in", res = 300) else: logger.error("Unrecognized extension for %s!" % (plot_file)) sys.exit() pp.plot() grdevices.dev_off() else: pp.plot() # keep the plot open until user hits enter print('Type enter to exit.') raw_input()
def plot_squiggle(args, filename, start_times, mean_signals): """ Use rpy2 to create a squiggle plot of the read """ r = robjects.r r.library("ggplot2") grdevices = importr('grDevices') # set t_0 as the first measured time for the read. t_0 = start_times[0] total_time = start_times[-1] - start_times[0] # adjust times to be relative to t_0 r_start_times = robjects.FloatVector([t - t_0 for t in start_times]) r_mean_signals = robjects.FloatVector(mean_signals) # infer the appropriate number of events given the number of facets num_events = len(r_mean_signals) events_per_facet = (num_events / args.num_facets) + 1 # dummy variable to control faceting facet_category = robjects.FloatVector([(i / events_per_facet) + 1 for i in range(len(start_times))]) # make a data frame of the start times and mean signals d = {'start': r_start_times, 'mean': r_mean_signals, 'cat': facet_category} df = robjects.DataFrame(d) gp = ggplot2.ggplot(df) if not args.theme_bw: pp = gp + ggplot2.aes_string(x='start', y='mean') \ + ggplot2.geom_step(size=0.25) \ + ggplot2.facet_wrap(robjects.Formula('~cat'), ncol=1, scales="free_x") \ + ggplot2.scale_x_continuous('Time (seconds)') \ + ggplot2.scale_y_continuous('Mean signal (picoamps)') \ + ggplot2.ggtitle('Squiggle plot for read: ' + filename + "\nTotal time (sec): " + str(total_time)) \ + ggplot2.theme(**{'plot.title': ggplot2.element_text(size=11)}) else: pp = gp + ggplot2.aes_string(x='start', y='mean') \ + ggplot2.geom_step(size=0.25) \ + ggplot2.facet_wrap(robjects.Formula('~cat'), ncol=1, scales="free_x") \ + ggplot2.scale_x_continuous('Time (seconds)') \ + ggplot2.scale_y_continuous('Mean signal (picoamps)') \ + ggplot2.ggtitle('Squiggle plot for read: ' + filename + "\nTotal time (sec): " + str(total_time)) \ + ggplot2.theme(**{'plot.title': ggplot2.element_text(size=11)}) \ + ggplot2.theme_bw() if args.saveas is not None: plot_file = os.path.basename(filename) + "." + args.saveas if os.path.isfile(plot_file): raise Exception( 'Cannot create plot for %s: plot file %s already exists' % (filename, plot_file)) if args.saveas == "pdf": grdevices.pdf(plot_file, width=8.5, height=11) elif args.saveas == "png": grdevices.png(plot_file, width=8.5, height=11, units="in", res=300) pp.plot() grdevices.dev_off() else: pp.plot() # keep the plot open until user hits enter print('Type enter to exit.') raw_input()