ggplot2.scale_fill_gradient(high = 'blue', low = 'red') + \
ggplot2.scale_fill_continuous(name = "Obama Vote Share") + \
ggplot2.scale_colour_continuous(name = "Obama Vote Share") + \
ggplot2.opts(**{'legend.position': 'left', 'legend.key.size': robjects.r.unit(2, 'lines'), 'legend.title' : ggplot2.theme_text(size = 14, hjust=0), \
'legend.text': ggplot2.theme_text(size = 12), 'title' : "Obama Vote Share and Distance to Railroads in IL", \
'plot.title': ggplot2.theme_text(size = 24), 'plot.margin': robjects.r.unit(robjects.r.rep(0,4),'lines'), \
'panel.background': ggplot2.theme_blank(), 'panel.grid.minor': ggplot2.theme_blank(), 'panel.grid.major': ggplot2.theme_blank(), \
'axis.ticks': ggplot2.theme_blank(), 'axis.title.x': ggplot2.theme_blank(), 'axis.title.y': ggplot2.theme_blank(), \
'axis.title.x': ggplot2.theme_blank(), 'axis.title.x': ggplot2.theme_blank(), 'axis.text.x': ggplot2.theme_blank(), \
'axis.text.y': ggplot2.theme_blank()} ) + \
ggplot2.geom_line(ggplot2.aes(x='long', y='lat', group='group'), data=IL_railroads, color='grey', size=0.2) + \
ggplot2.coord_equal()
p_map.plot()
## add the scatterplot
## define layout of subplot with viewports
vp_sub = grid.viewport(x = 0.19, y = 0.2, width = 0.32, height = 0.4)
p_sub = ggplot2.ggplot(RR_distance) + \
ggplot2.aes_string(x = 'OBAMA_SHAR', y= 'NEAR_DIST') + \
ggplot2.geom_point(ggplot2.aes(color='OBAMA_SHAR')) + \
ggplot2.stat_smooth(color="black") + \
ggplot2.opts(**{'legend.position': 'none'}) + \
ggplot2.scale_x_continuous("Obama Vote Share") + \
ggplot2.scale_y_continuous("Distance to nearest Railroad")
p_sub.plot(vp=vp_sub)
grdevices.dev_off()
ggplot2.geom_abline(intercept = 30) + \
ggplot2.geom_abline(intercept = 15)
pp.plot()
#-- qplot3addline-end
grdevices.dev_off()
grdevices.png('../../_static/graphics_ggplot2addsmooth.png',
width=612,
height=612,
antialias="subpixel",
type="cairo")
#-- ggplot2addsmooth-begin
pp = gp + \
ggplot2.aes_string(x='wt', y='mpg') + \
ggplot2.geom_point() + \
ggplot2.stat_smooth(method = 'lm')
pp.plot()
#-- ggplot2addsmooth-end
grdevices.dev_off()
grdevices.png('../../_static/graphics_ggplot2addsmoothmethods.png',
width=1024,
height=340,
antialias="subpixel",
type="cairo")
#-- ggplot2addsmoothmethods-begin
grid.newpage()
grid.viewport(layout=grid.layout(1, 3)).push()
ggplot2.aes_string(x='wt', y='mpg') + \
ggplot2.geom_point() + \
ggplot2.geom_abline(intercept = 30) + \
ggplot2.geom_abline(intercept = 15)
pp.plot()
#-- qplot3addline-end
grdevices.dev_off()
grdevices.png('../../_static/graphics_ggplot2addsmooth.png',
width = 612, height = 612, antialias="subpixel", type="cairo")
#-- ggplot2addsmooth-begin
pp = gp + \
ggplot2.aes_string(x='wt', y='mpg') + \
ggplot2.geom_point() + \
ggplot2.stat_smooth(method = 'lm')
pp.plot()
#-- ggplot2addsmooth-end
grdevices.dev_off()
grdevices.png('../../_static/graphics_ggplot2addsmoothmethods.png',
width = 1024, height = 340, antialias="subpixel", type="cairo")
#-- ggplot2addsmoothmethods-begin
grid.newpage()
grid.viewport(layout=grid.layout(1, 3)).push()
params = (('lm', 'y ~ x'),
('lm', 'y ~ poly(x, 2)'),
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()
y_lab = r("expression(Discharge (m^{3}/s))")
x_lab = r("expression(Area (km^{2}))")
annotate1 = r('annotate("text", x = '+str(max(areas)-30)+', y = 0.5, color = "red", label = "Mean Annual", parse=FALSE)')
annotate2 = r('annotate("text", x = '+str(max(areas)-30)+', y = 0.42, label = "'+r_sq_lab+'", color = "red", parse=TRUE)')
annotate3 = r('annotate("text", x = '+str(max(areas)-30)+', y = 0.34, label = "slope~'+sl+'", color = "red", parse=TRUE)')
annotate4 = r('annotate("text", x = '+str(max(areas)-150)+', y = 0.7, color = "blue", label = "LGM", parse=FALSE)')
annotate5 = r('annotate("text", x = '+str(max(areas)-150)+', y = 0.6, color = "blue", label = "'+r_sq_lab_lgm+'", parse=TRUE)')
annotate6 = r('annotate("text", x = '+str(max(areas)-150)+', y = 0.5, color = "blue", label = "slope~'+sl_lgm+'", parse=TRUE)')
pp = ggplot2.ggplot(dat_frame) + \
ggplot2.aes_string(y='discharge', x='areas') + \
ggplot2.ggtitle('Area vs. Sediment Flux') + \
ggplot2.scale_x_log10(x_lab) + \
ggplot2.theme_bw() + \
ggplot2.stat_smooth(method = "lm", formula = 'y ~ x') + \
ggplot2.scale_y_log10(y_lab) + \
annotate1 + \
annotate2 + \
annotate3 + \
annotate4 + \
annotate5 + \
annotate6 + \
ggplot2.geom_point(color='blue') + \
ggplot2.geom_errorbar(ggplot2.aes_string(ymin='min',ymax='max'), data=dat_frame, width=.02, alpha=.3) + \
ggplot2.geom_point(data=dat_frame2,color='red',show_guide='FALSE' ) + \
ggplot2.stat_smooth(data=dat_frame2, method = "lm", formula = 'y ~ x', color='red')
grdevices = importr('grDevices')
grdevices.pdf(file="area_qs.pdf")
color='grey',
size=0.2) + \
ggplot2.coord_equal()
p_map.plot()
## add the scatterplot
## define layout of subplot with viewports
vp_sub = grid.viewport(x = 0.19, y = 0.2,
width = 0.32, height = 0.4)
p_sub = ggplot2.ggplot(RR_distance) + \
ggplot2.aes_string(x = 'OBAMA_SHAR',
y = 'NEAR_DIST') + \
ggplot2.geom_point(ggplot2.aes(color='OBAMA_SHAR')) + \
ggplot2.stat_smooth(color="black",
method='auto') + \
ggplot2.theme(**{ 'legend.position' : 'none' }) + \
ggplot2.scale_x_continuous("Obama Vote Share") + \
ggplot2.scale_y_continuous("Distance to nearest Railroad")
p_sub.plot(vp=vp_sub)
grdevices.dev_off()
""" ----------------------------------------------------------------------------
End note:
(end note starts here)
============================================================================ """
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(self,
fn,
x='x',
y='y',
col=None,
group=None,
w=1100,
h=800,
size=2,
smooth=True,
point=True,
jitter=False,
boxplot=False,
boxplot2=False,
title=False,
flip=False,
se=False,
density=False,
line=False):
df = self.df
#import math, datetime
grdevices = importr('grDevices')
if not title:
title = fn.split("/")[-1]
grdevices.png(file=fn, width=w, height=h)
gp = ggplot2.ggplot(df)
pp = gp
if col and group:
pp += ggplot2.aes_string(x=x, y=y, col=col, group=group)
elif col:
pp += ggplot2.aes_string(x=x, y=y, col=col)
elif group:
pp += ggplot2.aes_string(x=x, y=y, group=group)
else:
pp += ggplot2.aes_string(x=x, y=y)
if boxplot:
if col:
pp += ggplot2.geom_boxplot(ggplot2.aes_string(fill=col),
color='blue')
else:
pp += ggplot2.geom_boxplot(color='blue')
if point:
if jitter:
if col:
pp += ggplot2.geom_point(ggplot2.aes_string(fill=col,
col=col),
size=size,
position='jitter')
else:
pp += ggplot2.geom_point(size=size, position='jitter')
else:
if col:
pp += ggplot2.geom_point(ggplot2.aes_string(fill=col,
col=col),
size=size)
else:
pp += ggplot2.geom_point(size=size)
if boxplot2:
if col:
pp += ggplot2.geom_boxplot(ggplot2.aes_string(fill=col),
color='blue',
outlier_colour="NA")
else:
pp += ggplot2.geom_boxplot(color='blue')
if smooth:
if smooth == 'lm':
if col:
pp += ggplot2.stat_smooth(ggplot2.aes_string(col=col),
size=1,
method='lm',
se=se)
else:
pp += ggplot2.stat_smooth(col='blue',
size=1,
method='lm',
se=se)
else:
if col:
pp += ggplot2.stat_smooth(ggplot2.aes_string(col=col),
size=1,
se=se)
else:
pp += ggplot2.stat_smooth(col='blue', size=1, se=se)
if density:
pp += ggplot2.geom_density(ggplot2.aes_string(x=x, y='..count..'))
if line:
pp += ggplot2.geom_line(position='jitter')
pp += ggplot2.opts(
**{
'title': title,
'axis.text.x': ggplot2.theme_text(size=24),
'axis.text.y': ggplot2.theme_text(size=24, hjust=1)
})
#pp+=ggplot2.scale_colour_brewer(palette="Set1")
pp += ggplot2.scale_colour_hue()
if flip:
pp += ggplot2.coord_flip()
pp.plot()
grdevices.dev_off()
print ">> saved: " + fn
ggplot2.scale_fill_gradient(high = 'blue', low = 'red') + \
ggplot2.scale_fill_continuous(name = "Obama Vote Share") + \
ggplot2.scale_colour_continuous(name = "Obama Vote Share") + \
ggplot2.opts(**{'legend.position': 'left', 'legend.key.size': robjects.r.unit(2, 'lines'), 'legend.title' : ggplot2.theme_text(size = 14, hjust=0), \
'legend.text': ggplot2.theme_text(size = 12), 'title' : "Obama Vote Share and Distance to Railroads in IL", \
'plot.title': ggplot2.theme_text(size = 24), 'plot.margin': robjects.r.unit(robjects.r.rep(0,4),'lines'), \
'panel.background': ggplot2.theme_blank(), 'panel.grid.minor': ggplot2.theme_blank(), 'panel.grid.major': ggplot2.theme_blank(), \
'axis.ticks': ggplot2.theme_blank(), 'axis.title.x': ggplot2.theme_blank(), 'axis.title.y': ggplot2.theme_blank(), \
'axis.title.x': ggplot2.theme_blank(), 'axis.title.x': ggplot2.theme_blank(), 'axis.text.x': ggplot2.theme_blank(), \
'axis.text.y': ggplot2.theme_blank()} ) + \
ggplot2.geom_line(ggplot2.aes(x='long', y='lat', group='group'), data=IL_railroads, color='grey', size=0.2) + \
ggplot2.coord_equal()
p_map.plot()
## add the scatterplot
## define layout of subplot with viewports
vp_sub = grid.viewport(x=0.19, y=0.2, width=0.32, height=0.4)
p_sub = ggplot2.ggplot(RR_distance) + \
ggplot2.aes_string(x = 'OBAMA_SHAR', y= 'NEAR_DIST') + \
ggplot2.geom_point(ggplot2.aes(color='OBAMA_SHAR')) + \
ggplot2.stat_smooth(color="black") + \
ggplot2.opts(**{'legend.position': 'none'}) + \
ggplot2.scale_x_continuous("Obama Vote Share") + \
ggplot2.scale_y_continuous("Distance to nearest Railroad")
p_sub.plot(vp=vp_sub)
grdevices.dev_off()
def plot(self, fn, x='x', y='y', col=None, group=None, w=1100, h=800, size=2, smooth=True, point=True, jitter=False, boxplot=False, boxplot2=False, title=False, flip=False, se=False, density=False, line=False):
df=self.df
#import math, datetime
grdevices = importr('grDevices')
if not title:
title=fn.split("/")[-1]
grdevices.png(file=fn, width=w, height=h)
gp = ggplot2.ggplot(df)
pp = gp
if col and group:
pp+=ggplot2.aes_string(x=x, y=y,col=col,group=group)
elif col:
pp+=ggplot2.aes_string(x=x, y=y,col=col)
elif group:
pp+=ggplot2.aes_string(x=x, y=y,group=group)
else:
pp+=ggplot2.aes_string(x=x, y=y)
if boxplot:
if col:
pp+=ggplot2.geom_boxplot(ggplot2.aes_string(fill=col),color='blue')
else:
pp+=ggplot2.geom_boxplot(color='blue')
if point:
if jitter:
if col:
pp+=ggplot2.geom_point(ggplot2.aes_string(fill=col,col=col),size=size,position='jitter')
else:
pp+=ggplot2.geom_point(size=size,position='jitter')
else:
if col:
pp+=ggplot2.geom_point(ggplot2.aes_string(fill=col,col=col),size=size)
else:
pp+=ggplot2.geom_point(size=size)
if boxplot2:
if col:
pp+=ggplot2.geom_boxplot(ggplot2.aes_string(fill=col),color='blue',outlier_colour="NA")
else:
pp+=ggplot2.geom_boxplot(color='blue')
if smooth:
if smooth=='lm':
if col:
pp+=ggplot2.stat_smooth(ggplot2.aes_string(col=col),size=1,method='lm',se=se)
else:
pp+=ggplot2.stat_smooth(col='blue',size=1,method='lm',se=se)
else:
if col:
pp+=ggplot2.stat_smooth(ggplot2.aes_string(col=col),size=1,se=se)
else:
pp+=ggplot2.stat_smooth(col='blue',size=1,se=se)
if density:
pp+=ggplot2.geom_density(ggplot2.aes_string(x=x,y='..count..'))
if line:
pp+=ggplot2.geom_line(position='jitter')
pp+=ggplot2.opts(**{'title' : title, 'axis.text.x': ggplot2.theme_text(size=24), 'axis.text.y': ggplot2.theme_text(size=24,hjust=1)} )
#pp+=ggplot2.scale_colour_brewer(palette="Set1")
pp+=ggplot2.scale_colour_hue()
if flip:
pp+=ggplot2.coord_flip()
pp.plot()
grdevices.dev_off()
print ">> saved: "+fn
#!/usr/bin/env python2
import cPickle
import numpy
from rpy2.robjects import FloatVector, DataFrame
from rpy2.robjects.lib import ggplot2
from rpy2.robjects.packages import importr
pscores = cPickle.load(open('bootstrap.pickle'))
pscores.sort()
proportion = numpy.linspace(1, len(pscores), len(pscores)) / len(pscores)
dataf = DataFrame({
'pscore': FloatVector(pscores),
'proportion': FloatVector(proportion),
})
grdevices = importr('grDevices')
#grdevices.postscript(file="pscores.eps", width=512, height=512)
grdevices.postscript(file='pscores.eps')
(
ggplot2.ggplot(dataf)
+ ggplot2.aes_string(y='pscore', x='proportion')
+ ggplot2.geom_point()
+ ggplot2.scale_x_log10()
+ ggplot2.scale_y_log10()
+ ggplot2.stat_smooth(method='lm')
).plot()
grdevices.dev_off()
