def compute(x0, y0, x1, y1):
# Selected square
sel = 255 - average(img[y0:y1,x0:x1],2)
# Average across x’s
ysel = average(sel,1)
line = ysel
xs = mgrid[0:line.shape[0]]
# Pass the data to R
rxs = robjects.FloatVector(xs)
rys = robjects.FloatVector(line)
rdf = robjects.DataFrame({'x': rxs, 'y': rys})
robjects.globalenv['xs'] = rxs
robjects.globalenv['df'] = rdf
#print(rys.r_repr())
# Fir an R model
robjects.r('''fit <- nls(y ~ (off + c1 * exp(-(x-mu1)**2/(2*sg1**2))
+ c2 * exp(-(x-mu2)**2/(2*sg2**2))),
data=df,
start=list(off = 90, c1=120, mu1=30, sg1=10,
c2=120, mu2=60, sg2=10),
algorithm='port')''')
# Get fit results
robjects.r('''k <- coef(fit)
fitdat <- data.frame(x=xs)
fitdat$y <- predict(fit, newdata=fitdat)
## Independent Gaussians
fitg1 <- data.frame(x=xs)
fitg1$y <- k[['off']] + k[['c1']] * exp(-(xs-k[['mu1']])**2/(2*k[['sg1']]**2))
fitg2 <- data.frame(x=xs)
fitg2$y <- k[['off']] + k[['c2']] * exp(-(xs-k[['mu2']])**2/(2*k[['sg2']]**2))''')
# Plot R fits
fitdat = robjects.globalenv['fitdat']
fitg1 = robjects.globalenv['fitg1']
fitg2 = robjects.globalenv['fitg2']
pp = ggplot2.ggplot(rdf) \
+ ggplot2.aes_string(x='x', y='y') \
+ ggplot2.geom_point() \
+ ggplot2.geom_smooth(data=fitdat, stat="identity", size=1.5) \
+ ggplot2.geom_smooth(data=fitg1, stat="identity") \
+ ggplot2.geom_smooth(data=fitg2, stat="identity")
pp.plot()
# Compute the ratio of the gaussian integrals
ratio = robjects.r('''k[['c1']]*k[['sg1']]/(k[['c1']]*k[['sg1']]+k[['c2']]*k[['sg2']])''')
title('ratio='+str(ratio))
show()
def main():
usage = 'usage: %prog [options] <raw file>'
parser = OptionParser(usage)
parser.add_option('-d', dest='downstream', default=2000, type='int', help='TSS downstream [Default: %default]')
parser.add_option('-o', dest='out_prefix', default='tss', help='Output prefix [Default: %default]')
parser.add_option('-u', dest='upstream', default=5000, type='int', help='TSS upstream [Default: %default]')
parser.add_option('--ymax', dest='ymax', default=None, type='float', help='Y-coordinate limit [Default: %default]')
(options,args) = parser.parse_args()
if len(args) != 1:
parser.error('Must provide raw file')
else:
raw_file = args[0]
# collect data
coords = []
main_cov = []
control_cov = []
for line in open(raw_file):
a = line.split()
coords.append(int(a[0]))
main_cov.append(float(a[1]))
control_cov.append(float(a[2]))
# data structures
tss_i = ro.IntVector(range(-options.upstream,options.downstream+1))
labels = ro.StrVector(['Main']*(options.upstream+options.downstream+1)+['Control']*(options.upstream+options.downstream+1))
cov = ro.FloatVector(main_cov + control_cov)
df = ro.DataFrame({'tss_i':tss_i, 'cov':cov, 'label':labels})
# plot
'''
gp = ggplot2.ggplot(df) + \
ggplot2.aes_string(x='tss_i', y='cov', colour='label') + \
ggplot2.geom_point() + \
ggplot2.scale_x_continuous('TSS index') + \
ggplot2.scale_colour_discrete('')
'''
gp = ggplot2.ggplot(df) + \
ggplot2.aes_string(x='tss_i', y='cov', colour='label') + \
ggplot2.geom_smooth(method='loess', size=1, span=0.2, se=False) + \
ggplot2.scale_x_continuous('TSS Position') + \
ggplot2.scale_colour_discrete('') + \
ggplot2.theme_bw()
if options.ymax == None:
gp += ggplot2.scale_y_continuous('Coverage')
else:
gp += ggplot2.scale_y_continuous('Coverage', limits=ro.FloatVector([0,options.ymax]))
# save to file
grdevices.pdf(file='%s_and.pdf' % options.out_prefix)
gp.plot()
grdevices.dev_off()
ggplot2.stat_smooth(method = 'lm', fill = 'blue',
color = 'red', size = 3)
pp.plot()
#-- ggplot2smoothblue-end
grdevices.dev_off()
grdevices.png('../../_static/graphics_ggplot2smoothbycyl.png',
width=612,
height=612,
antialias="subpixel",
type="cairo")
#-- ggplot2smoothbycyl-begin
pp = gp + \
ggplot2.aes_string(x='wt', y='mpg') + \
ggplot2.geom_point() + \
ggplot2.geom_smooth(ggplot2.aes_string(group = 'cyl'),
method = 'lm')
pp.plot()
#-- ggplot2smoothbycyl-end
grdevices.dev_off()
grdevices.png('../../_static/graphics_ggplot2_smoothbycylwithcolours.png',
width=612,
height=612,
antialias="subpixel",
type="cairo")
#-- ggplot2smoothbycylwithcolours-begin
pp = ggplot2.ggplot(mtcars) + \
ggplot2.aes_string(x='wt', y='mpg', col='factor(cyl)') + \
ggplot2.geom_point() + \
ggplot2.geom_smooth(ggplot2.aes_string(group = 'cyl'),
method = 'lm')
type="cairo")
#-- ggplot2smoothblue-begin
pp = (gp + ggplot2.aes_string(x='wt', y='mpg') + ggplot2.geom_point() +
ggplot2.stat_smooth(method='lm', fill='blue', color='red', size=3))
pp.plot()
#-- ggplot2smoothblue-end
grdevices.dev_off()
grdevices.png('../../_static/graphics_ggplot2smoothbycyl.png',
width=612,
height=612,
antialias=ANTIALIAS,
type="cairo")
#-- ggplot2smoothbycyl-begin
pp = (gp + ggplot2.aes_string(x='wt', y='mpg') + ggplot2.geom_point() +
ggplot2.geom_smooth(ggplot2.aes_string(group='cyl'), method='lm'))
pp.plot()
#-- ggplot2smoothbycyl-end
grdevices.dev_off()
grdevices.png('../../_static/graphics_ggplot2_smoothbycylwithcolours.png',
width=612,
height=612,
antialias=ANTIALIAS,
type="cairo")
#-- ggplot2smoothbycylwithcolours-begin
pp = (ggplot2.ggplot(mtcars) +
ggplot2.aes_string(x='wt', y='mpg', col='factor(cyl)') +
ggplot2.geom_point() +
ggplot2.geom_smooth(ggplot2.aes_string(group='cyl'), method='lm'))
pp.plot()
import math, datetime
import time
import rpy2.robjects.lib.ggplot2 as ggplot2
import rpy2.robjects as ro
from rpy2.robjects.packages import importr
from rpy2.interactive import process_revents
grdevices = importr('grDevices')
process_revents.start()
base = importr('base')
datasets= importr('datasets')
mtcars = datasets.__rdata__.fetch('mtcars')['mtcars']
pp = ggplot2.ggplot(mtcars) + ggplot2.aes_string(x='wt', y='mpg', col='factor(cyl)') + ggplot2.geom_point() + ggplot2.geom_smooth(ggplot2.aes_string(group = 'cyl'), method = 'lm')
#pp.plot()
#process_revents.start()
print(pp)
process_revents.process_revents()
while True:
time.sleep(1)
process_revents.stop()
pp = gp + \
ggplot2.aes_string(x='wt', y='mpg') + \
ggplot2.geom_point() + \
ggplot2.stat_smooth(method = 'lm', fill = 'blue',
color = 'red', size = 3)
pp.plot()
#-- ggplot2smoothblue-end
grdevices.dev_off()
grdevices.png('../../_static/graphics_ggplot2smoothbycyl.png',
width = 612, height = 612, antialias="subpixel", type="cairo")
#-- ggplot2smoothbycyl-begin
pp = gp + \
ggplot2.aes_string(x='wt', y='mpg') + \
ggplot2.geom_point() + \
ggplot2.geom_smooth(ggplot2.aes_string(group = 'cyl'),
method = 'lm')
pp.plot()
#-- ggplot2smoothbycyl-end
grdevices.dev_off()
grdevices.png('../../_static/graphics_ggplot2_smoothbycylwithcolours.png',
width = 612, height = 612, antialias="subpixel", type="cairo")
#-- ggplot2smoothbycylwithcolours-begin
pp = ggplot2.ggplot(mtcars) + \
ggplot2.aes_string(x='wt', y='mpg', col='factor(cyl)') + \
ggplot2.geom_point() + \
ggplot2.geom_smooth(ggplot2.aes_string(group = 'cyl'),
method = 'lm')
pp.plot()
#-- ggplot2smoothbycylwithcolours-end
grdevices.dev_off()
def main():
usage = 'usage: %prog [options] <raw file>'
parser = OptionParser(usage)
parser.add_option('-d',
dest='downstream',
default=2000,
type='int',
help='TSS downstream [Default: %default]')
parser.add_option('-o',
dest='out_prefix',
default='tss',
help='Output prefix [Default: %default]')
parser.add_option('-u',
dest='upstream',
default=5000,
type='int',
help='TSS upstream [Default: %default]')
parser.add_option('--ymax',
dest='ymax',
default=None,
type='float',
help='Y-coordinate limit [Default: %default]')
(options, args) = parser.parse_args()
if len(args) != 1:
parser.error('Must provide raw file')
else:
raw_file = args[0]
# collect data
coords = []
main_cov = []
control_cov = []
for line in open(raw_file):
a = line.split()
coords.append(int(a[0]))
main_cov.append(float(a[1]))
control_cov.append(float(a[2]))
# data structures
tss_i = ro.IntVector(range(-options.upstream, options.downstream + 1))
labels = ro.StrVector(['Main'] *
(options.upstream + options.downstream + 1) +
['Control'] *
(options.upstream + options.downstream + 1))
cov = ro.FloatVector(main_cov + control_cov)
df = ro.DataFrame({'tss_i': tss_i, 'cov': cov, 'label': labels})
# plot
'''
gp = ggplot2.ggplot(df) + \
ggplot2.aes_string(x='tss_i', y='cov', colour='label') + \
ggplot2.geom_point() + \
ggplot2.scale_x_continuous('TSS index') + \
ggplot2.scale_colour_discrete('')
'''
gp = ggplot2.ggplot(df) + \
ggplot2.aes_string(x='tss_i', y='cov', colour='label') + \
ggplot2.geom_smooth(method='loess', size=1, span=0.2, se=False) + \
ggplot2.scale_x_continuous('TSS Position') + \
ggplot2.scale_colour_discrete('') + \
ggplot2.theme_bw()
if options.ymax == None:
gp += ggplot2.scale_y_continuous('Coverage')
else:
gp += ggplot2.scale_y_continuous('Coverage',
limits=ro.FloatVector(
[0, options.ymax]))
# save to file
grdevices.pdf(file='%s_and.pdf' % options.out_prefix)
gp.plot()
grdevices.dev_off()
import math, datetime
import rpy2.robjects.lib.ggplot2 as ggplot2
import rpy2.robjects as ro
from rpy2.robjects.packages import importr
base = importr("base")
datasets = importr("datasets")
mtcars = datasets.data.fetch("mtcars")["mtcars"]
pp = (
ggplot2.ggplot(mtcars)
+ ggplot2.aes_string(x="wt", y="mpg", col="factor(cyl)")
+ ggplot2.geom_point()
+ ggplot2.geom_smooth(ggplot2.aes_string(group="cyl"), method="lm")
)
pp.plot()
#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date : 2013-09-21 22:43:00 # @Author : Fengce [email protected] ''' python 调用 R语言，并使用 ggplot2 ''' import rpy2.robjects.lib.ggplot2 as ggplot2 import rpy2.robjects as ro from rpy2.robjects.packages import importr datasets = importr('datasets') mtcars = datasets.__rdata__.fetch('mtcars')['mtcars'] gp = ggplot2.ggplot(mtcars) pp = ggplot2.ggplot(mtcars) + \ ggplot2.aes_string(x='wt', y='mpg', col='factor(cyl)') + \ ggplot2.geom_point() + \ ggplot2.geom_smooth(ggplot2.aes_string(group = 'cyl'), method = 'lm') #pp.plot() ro.r.pdf('ggplot2inPython.pdf') pp.plot() ro.r('dev.off')
import numpy as np
import pandas as pd
import rpy2.robjects.packages as packages
import rpy2.robjects.lib.ggplot2 as ggplot2
import rpy2.robjects as ro
# Importando o dataset do R, o mtcars
R = ro.r
datasets = packages.importr('datasets')
mtcars = packages.data(datasets).fetch('mtcars')['mtcars']
# Gerando o gráfico com ggplot
gp = ggplot2.ggplot(mtcars)
pyplot = (gp
+ ggplot2.aes_string(x = 'wt', y = 'mpg')
+ ggplot2.geom_point(ggplot2.aes_string(colour = 'qsec'))
+ ggplot2.scale_colour_gradient(low = "yellow", high = "red")
+ ggplot2.geom_smooth(method = 'auto')
+ ggplot2.labs(title = "mtcars", x = 'wt', y = 'mpg'))
pyplot.plot()
print("\nAnálise de Variância")
print("--------------------")
import rpy2.robjects as robjects
r = robjects.r
controle = robjects.FloatVector([4.17,5.58,5.18,6.11,4.50,4.61,
5.17,4.53,5.33,5.14])
tratamento = robjects.FloatVector([4.81,4.17,4.41,3.59,5.87,3.83,
6.03,4.89,4.32,4.69])
import math, datetime
import time
import rpy2.robjects.lib.ggplot2 as ggplot2
import rpy2.robjects as ro
from rpy2.robjects.packages import importr
from rpy2.interactive import process_revents
grdevices = importr('grDevices')
process_revents.start()
base = importr('base')
datasets = importr('datasets')
mtcars = datasets.__rdata__.fetch('mtcars')['mtcars']
pp = ggplot2.ggplot(mtcars) + ggplot2.aes_string(
x='wt', y='mpg',
col='factor(cyl)') + ggplot2.geom_point() + ggplot2.geom_smooth(
ggplot2.aes_string(group='cyl'), method='lm')
#pp.plot()
#process_revents.start()
print(pp)
process_revents.process_revents()
while True:
time.sleep(1)
process_revents.stop()
#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date : 2013-09-21 22:43:00 # @Author : Fengce [email protected] ''' python 调用 R语言，并使用 ggplot2 ''' import rpy2.robjects.lib.ggplot2 as ggplot2 import rpy2.robjects as ro from rpy2.robjects.packages import importr datasets = importr('datasets') mtcars = datasets.__rdata__.fetch('mtcars')['mtcars'] gp = ggplot2.ggplot(mtcars) pp = ggplot2.ggplot(mtcars) + \ ggplot2.aes_string(x='wt', y='mpg', col='factor(cyl)') + \ ggplot2.geom_point() + \ ggplot2.geom_smooth(ggplot2.aes_string(group = 'cyl'), method = 'lm') #pp.plot() ro.r.pdf('ggplot2inPython.pdf') pp.plot() ro.r('dev.off')
# Restructure date data
# TODO: make this less hacky
start = base.as_Date(min(date_hits.elements()))
end = base.as_Date(max(date_hits.elements()))
dates = base.seq(start, end, by='day')
hits_arr = [0] * len(dates)
for date in date_hits:
hits_arr[int(base.as_Date(date)[0]) - int(start[0])] = date_hits[date]
hits = IntVector(hits_arr)
grdevices.png('analytics_out/hits_by_date.png')
df = robjects.DataFrame({'date': dates, 'hits': hits})
pp = ggplot.ggplot(df) + \
ggplot.aes_string(x = 'date', y = 'hits') + \
ggplot.geom_point() + \
ggplot.geom_smooth(method = 'loess')
pp.plot()
grdevices.dev_off()
# Restructure hour data
hour_arr = []
hits_arr = []
for hour in hour_hits:
hour_arr.append(datetime.min + timedelta(hours=int(hour[0:2])))
hits_arr.append(hour_hits[hour])
hour = POSIXct(hour_arr)
hits = IntVector(hits_arr)
grdevices.png('analytics_out/hits_by_time.png')
df = robjects.DataFrame({'hour': POSIXct(hour), 'hits': IntVector(hits)})
pp = ggplot.ggplot(df) + \
