def _plt_distr(dat,
col,
title='',
splitBy_pfill=True,
pfill='label',
independentpdf=False,
fname='xdistr.pdf'):
df = dat[dat[pfill] != 'NA'] ## remove invalid pairs
n = len(df)
df = {
col: robjects.FloatVector(list(df[col])),
pfill: robjects.StrVector(list(df[pfill]))
}
df = robjects.DataFrame(df)
pp = ggplot2.ggplot(df) + \
ggplot2.ggtitle('%s [Total = %s]' % (title, n))
## Plot1: counts
if splitBy_pfill:
p1 = pp + ggplot2.aes_string(x=col, fill=pfill)
else:
p1 = pp + ggplot2.aes_string(x=col)
## Plot2: density
if splitBy_pfill:
p2 = pp + ggplot2.aes_string(x=col, fill=pfill, y='..density..')
else:
p2 = pp + ggplot2.aes_string(x=col, y='..density..')
p2 = p2 + ggplot2.geom_density(alpha=.5, origin=-500)
if col == 'distance':
p1 = p1 + \
ggplot2.geom_histogram(binwidth=1000, alpha=.5, position='identity', origin=-500) + \
ggplot2.xlim(-1000, 51000)
p2 = p2 + \
ggplot2.geom_histogram(binwidth=1000, alpha=.33, position='identity', origin=-500) + \
ggplot2.xlim(-1000, 51000)
else:
p1 = p1 + \
ggplot2.geom_histogram(alpha=.5, position='identity')
p2 = p2 + \
ggplot2.geom_histogram(alpha=.33, position='identity')
if col == 'correlation':
p1 = p1 + ggplot2.xlim(-1.1, 1.1)
p2 = p2 + ggplot2.xlim(-1.1, 1.1)
if independentpdf:
grdevices = importr('grDevices')
grdevices.pdf(file=fname)
p1.plot()
p2.plot()
grdevices.dev_off()
else:
p1.plot()
p2.plot()
return
dataf = DataFrame(d)
from rpy2.robjects.lib import ggplot2
p = ggplot2.ggplot(dataf) + \
ggplot2.geom_line(ggplot2.aes_string(x="n_loop",
y="time",
colour="code")) + \
ggplot2.geom_point(ggplot2.aes_string(x="n_loop",
y="time",
colour="code")) + \
ggplot2.facet_wrap(Formula('~sequence')) + \
ggplot2.scale_y_continuous('running time') + \
ggplot2.scale_x_continuous('repeated n times', ) + \
ggplot2.xlim(0, max(n_loops)) + \
ggplot2.labs(title = "Benchmark (running time)")
from rpy2.robjects.packages import importr
grdevices = importr('grDevices')
grdevices.png('../../_static/benchmark_sum.png',
width = 712, height = 512)
p.plot()
grdevices.dev_off()
#base = importr("base")
stats = importr('stats')
nlme = importr("nlme")
fit = nlme.lmList(Formula('time ~ n_loop | group'), data = dataf,
na_action = stats.na_exclude)
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()
d['group'] = StrVector(
[d['code'][x] + ':' + d['sequence'][x] for x in xrange(len(d['n_loop']))])
dataf = DataFrame(d)
from rpy2.robjects.lib import ggplot2
p = ggplot2.ggplot(dataf) + \
ggplot2.geom_line(ggplot2.aes_string(x="n_loop",
y="time",
colour="code")) + \
ggplot2.geom_point(ggplot2.aes_string(x="n_loop",
y="time",
colour="code")) + \
ggplot2.facet_wrap(Formula('~sequence')) + \
ggplot2.scale_y_continuous('running time') + \
ggplot2.scale_x_continuous('repeated n times', ) + \
ggplot2.xlim(0, max(n_loops)) + \
ggplot2.opts(title = "Benchmark (running time)")
from rpy2.robjects.packages import importr
grdevices = importr('grDevices')
grdevices.png('../../_static/benchmark_sum.png', width=712, height=512)
p.plot()
grdevices.dev_off()
#base = importr("base")
stats = importr('stats')
nlme = importr("nlme")
fit = nlme.lmList(Formula('time ~ n_loop | group'),
data=dataf,
na_action=stats.na_exclude)
def _plt_percountr(dat, independentpdf=False, fname='xpercount.pdf'):
def _filt_dat(dat, item, getlabel=True):
df = pd.DataFrame(dat[item].value_counts())
df.columns = ['count']
if getlabel:
df['label'] = [
list(dat[dat[item] == i]['label'])[0] for i in df.index
]
n = len(df)
mx = max(df['count'])
return df, n, mx
dat = dat[dat['label'] != 'NA']
## NUMBER OF MIRNA PER TSS
df, n, mx = _filt_dat(dat, 'tss', False)
df = {'count': robjects.IntVector(df['count'])}
df = robjects.DataFrame(df)
pt = ggplot2.ggplot(df) + \
ggplot2.geom_histogram(binwidth=1, origin=-.5, alpha=.5, position="identity") + \
ggplot2.xlim(-.5, mx+1) + \
ggplot2.aes_string(x='count') + \
ggplot2.ggtitle('TSS [Total = %s]' % n) + \
ggplot2.labs(x='Number of miRNA per TSS (max = %s)' % mx)
pt_den = ggplot2.ggplot(df) + \
ggplot2.aes_string(x='count', y='..density..') + \
ggplot2.geom_density(binwidth=1, alpha=.5, origin=-.5) + \
ggplot2.geom_histogram(binwidth=1, alpha=.33, position='identity', origin=-.5) + \
ggplot2.ggtitle('TSS [Total = %s]' % n) + \
ggplot2.labs(x='Number of miRNA per TSS (max = %s)' % mx)
## NUMBER OF TSS PER MIRNA
df, n, mx = _filt_dat(dat, 'mirna')
df = {
'count': robjects.IntVector(df['count']),
'label': robjects.StrVector(df['label'])
}
df = robjects.DataFrame(df)
_pm = ggplot2.ggplot(df) + \
ggplot2.geom_histogram(binwidth=1, origin=-.5, alpha=.5, position="identity") + \
ggplot2.xlim(-.5, mx+1) + \
ggplot2.ggtitle('miRNA [Total = %s]' % n)
_pm_den = ggplot2.ggplot(df) + \
ggplot2.geom_density(binwidth=1, alpha=.5, origin=-.5) + \
ggplot2.geom_histogram(binwidth=1, alpha=.33, position='identity', origin=-.5) + \
ggplot2.ggtitle('miRNA [Total = %s]' % n)
## not split by label
pm = _pm + ggplot2.aes_string(x='count')
pm_den = _pm_den + ggplot2.aes_string(x='count', y='..density..')
## split by label
pms = _pm + ggplot2.aes_string(x='count', fill='label')
pm_dens = _pm_den + ggplot2.aes_string(
x='count', fill='label', y='..density..')
## add xlabelling (need to be added after aes_string)
_xlab = ggplot2.labs(x='Number of TSS per miRNA (max = %s)' % mx)
pm += _xlab
pm_den += _xlab
pms += _xlab
pm_dens += _xlab
if independentpdf:
grdevices = importr('grDevices')
grdevices.pdf(fname)
pt.plot()
pt_den.plot()
pm.plot()
pm_den.plot()
pms.plot()
pm_dens.plot()
grdevices.dev_off()
else:
pt.plot()
pt_den.plot()
pm.plot()
pm_den.plot()
pms.plot()
pm_dens.plot()
return
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()
hour_speed['mph'] = 3600 * (hour_speed.trip_distance / hour_speed.trip_time_in_secs)
hour_speed
# Pickups by hour (aggregated across all days)
max_passenger_count = np.max(np.log1p(pickups_hr['passenger_count']))
min_passenger_count = np.log1p(1)
for i in range(0,24):
temp = pickups_hr[(pickups_hr.hour==i)].reset_index()
temp.passenger_count = np.log1p(temp.passenger_count)
temp_r = pandas2ri.py2ri(temp)
p = ggplot2.ggplot(temp_r) + \
ggplot2.aes_string(x='rounded_lon', y='rounded_lat', color='passenger_count') + \
ggplot2.geom_point(size=0.5) + \
ggplot2.scale_color_gradient(low='black', high='white', limits=np.array([min_passenger_count, max_passenger_count])) + \
ggplot2.xlim(-74.2, -73.7) + ggplot2.ylim(40.56, 40.93) + \
ggplot2.labs(x=' ', y=' ', title='NYC taxi pickups %02i:00' % i) + \
ggplot2.guides(color=False)
p.save('./plots/taxi_pickups%02i.png' % i, width=4, height=4.5)
# Create animated .gif of pickups by hour
import imageio
file_names = sorted((fn for fn in os.listdir('./plots') if fn.startswith('taxi_pickups')))
file_names = ['plots/' + s for s in file_names]
images = []
for filename in file_names:
images.append(imageio.imread(filename))
imageio.mimsave('./plots/pickups_movie.gif', images, duration=0.4)
# total pickups by date, color
