#!/usr/bin/python
from datetime import datetime
import sys
import subprocess
import os
import math
import rpy2.robjects as robjects
robjects.r('library("scales")')
import rpy2.robjects.lib.ggplot2 as ggplot2
ggplot2.theme_set(ggplot2.theme_bw())
#print ggplot2.theme_get()
from rpy2.robjects.packages import importr
from rpy2.robjects import FloatVector, StrVector, IntVector, DataFrame
def ggplot2_options():
return ggplot2.opts(
**{
'axis.title.x':
ggplot2.theme_blank(),
'axis.title.y':
ggplot2.theme_text(
family='serif', face='bold', size=15, angle=90, vjust=0.2),
'axis.text.x':
ggplot2.theme_text(family='serif', size=15),
'axis.text.y':
ggplot2.theme_text(family='serif', size=15),
'legend.title':
ggplot2.theme_text(family='serif', face='bold', size=15),
def plot_qc_reads(qc_df):
"""
Plot number of reads part of a pipeline QC file.
"""
# Record NA values as 0
qc_df = qc_df.fillna(0)#.set_index("sample")
cols = ["sample",
"num_reads",
"num_mapped",
"num_unique_mapped",
"num_junctions"]
qc_df = qc_df[cols]
melted_qc = pandas.melt(qc_df, id_vars=["sample"])
qc_r = conversion_pydataframe(melted_qc)
labels = tuple(["num_reads",
"num_mapped",
"num_unique_mapped",
"num_junctions"])
labels = robj.StrVector(labels)
variable_i = qc_r.names.index('variable')
qc_r[variable_i] = robj.FactorVector(qc_r[variable_i],
levels = labels)
ggplot2.theme_set(ggplot2.theme_bw(12))
scales = importr("scales")
r_opts = r.options(scipen=4)
p = ggplot2.ggplot(qc_r) + \
ggplot2.geom_point(aes_string(x="sample", y="value")) + \
ggplot2.scale_y_continuous(trans=scales.log10_trans(),
breaks=scales.trans_breaks("log10",
robj.r('function(x) 10^x')),
labels=scales.trans_format("log10",
robj.r('math_format(10^.x)'))) + \
r.xlab("CLIP-Seq samples") + \
r.ylab("No. reads") + \
ggplot2.coord_flip() + \
ggplot2.facet_wrap(Formula("~ variable"), ncol=1) + \
theme(**{"panel.grid.major.x": element_blank(),
"panel.grid.minor.x": element_blank(),
"panel.grid.major.y": theme_line(size=0.5,colour="grey66",linetype=3)})
p.plot()
return
r.par(mfrow=np.array([1,2]))
num_samples = len(qc_df.num_reads)
r.par(bty="n", lwd=1.7, lty=2)
r_opts = r.options(scipen=4)
r.options(r_opts)
r.dotchart(convert_to_r_matrix(qc_df[["num_reads",
"num_mapped",
"num_unique_mapped"]]),
xlab="No. reads",
lcolor="black",
pch=19,
gcolor="darkblue",
cex=0.8)
r.par(bty="n")
r.dotchart(convert_to_r_matrix(qc_df[["num_ribosub_mapped",
"num_ribo",
"num_junctions"]]),
xlab="No. reads",
lcolor="black",
pch=19,
gcolor="darkblue",
cex=0.8)
#!/usr/bin/env python
from problems import *
from utils import *
from config import *
import sys
import rpy2.robjects as robjects
robjects.r('library("scales")')
import rpy2.robjects.lib.ggplot2 as ggplot2
ggplot2.theme_set(ggplot2.theme_bw ())
from rpy2.robjects.packages import importr
from rpy2.robjects import FloatVector, StrVector, IntVector, DataFrame
def ggplot2_options ():
def normal_text():
return ggplot2.theme_text(family = 'serif', size = 15)
def bold_text():
return ggplot2.theme_text(family = 'serif', face = 'bold', size = 15)
def rotated_text():
return ggplot2.theme_text(family = 'serif', face = 'bold',
size = 15, angle=90, vjust=0.2)
return ggplot2.opts (**{'axis.title.x' : ggplot2.theme_blank(),
'axis.title.y' : rotated_text(),
'axis.text.x' : normal_text(),
'axis.text.y' : normal_text(),
'legend.title' : bold_text(),
'legend.text' : normal_text(),
'aspect.ratio' : 0.6180339888,
'strip.text.x' : normal_text(),
})
iris_py = pandas.read_csv("/home/yarden/iris.csv")
iris_py = iris_py.rename(columns={"Name": "Species"})
corrs = []
from scipy.stats import spearmanr
for species in set(iris_py.Species):
entries = iris_py[iris_py["Species"] == species]
c = spearmanr(entries["SepalLength"], entries["SepalWidth"])
print "c: ", c
# compute r.cor(x, y) and divide up by Species
# Assume we get a vector of length Species saying what the
# correlation is for each Species' Petal Length/Width
p = ggplot2.ggplot(iris) + \
ggplot2.geom_point(ggplot2.aes_string(x="Sepal.Length", y="Sepal.Width")) + \
ggplot2.facet_wrap(Formula("~Species"))
p.plot()
r["dev.off"]()
sys.exit(1)
grdevices = importr('grDevices')
ggplot2.theme_set(ggplot2.theme_bw(12))
p = ggplot2.ggplot(iris) + \
ggplot2.geom_point(ggplot2.aes_string(x="Sepal.Length", y="Sepal.Width")) + \
ggplot2.facet_wrap(Formula('~ Species'), ncol=2, nrow = 2) + \
ggplot2.geom_text(aes_string(x="Sepal.Length", y="Sepal.Width"), label="t") + \
ggplot2.GBaseObject(r('ggplot2::coord_fixed')()) # aspect ratio
p.plot()