def createGraphSeries(cohort):
hm = ['bothR5','bothDX','esR5','esDX','trofileR5','trofileDX','mixedR5DX']
stat_data = DataFrame.from_csvfile(cohort+'.seq.out', sep = "\t")
# Begin witing graphs to pdf
grdevices.pdf(file=cohort+".stats.pdf",width=7,height=7)
# graph 1 scatter plot
scatterPlot(stat_data,'bothR5','bothDX',cohort.upper()+' cohort\n bothR5/bothDX correlation')
scatterPlot(stat_data,'esR5','esDX',cohort.upper()+' cohort\n esR5/esDX correlation')
scatterPlot(stat_data,'trofileR5','trofileDX',cohort.upper()+' cohort\n trofileR5/trofileDX correlation')
scatterPlot(stat_data,'bothR5','esR5',cohort.upper()+' cohort\n bothR5/esR5 correlation')
scatterPlot(stat_data,'trofileR5','esR5',cohort.upper()+' cohort\n trofileR5/esR5 correlation')
scatterPlot(stat_data,'bothDX','esDX',cohort.upper()+' cohort\n bothDX/esDX correlation')
scatterPlot(stat_data,'trofileDX','esDX',cohort.upper()+' cohort\n trofileDX/esDX correlation')
scatterPlot(stat_data,'bothDX','mixedR5DX',cohort.upper()+' cohort\n bothDX/mixedR5DX correlation')
for hmm in hm:
hmm_scores = DataFrame.from_csvfile(cohort+'.seq.'+hmm+'.stats',sep="\t")
# graph 3
plot(hmm_scores.rx2("Cutoff"),hmm_scores.rx2("Accuracy"),type='o',main='Accuracy vs. Cutoff '+cohort.upper()+' Cohort\n'+hmm+'.hmm',xlab='Cutoff',ylab='Accuracy')
# graph 4
plot(hmm_scores.rx2("FPP"),hmm_scores.rx2("TPP"),type='o',xlim=base.c(0,1),ylim=base.c(0,1),main=cohort.upper()+' Cohort ROC\n'+hmm+'.hmm',xlab='% False Pos',ylab='% False Neg')
# graph 5
plot(hmm_scores.rx2("Cutoff"),hmm_scores.rx2("Phi"),type='o',xlim=base.c(50,100),ylim=base.c(0,0.75),main=cohort.upper()+' Cohort Association Coeff\n'+hmm+'.hmm',xlab='Cutoff',ylab='Association Coefficient')
# close pdf file
grdevices.dev_off()
return
def analyzeR():
mypatchData = DataFrame.from_csvfile(inputfile,header=True, sep = "\t")
ici = list(mypatchData.colnames).index('ic50_val')
starti = ici + 1
lasti = mypatchData.nrow
myData = mypatchData[,ici:lasti]
def loadfiles(self):
"""
Load files into R environment
"""
rcount = 0
names = robjects.r['names']
# Set the default parameter for reading from csv
param = {'sep': '\t', 'header': True, 'as_is': True,
'row.names': ri.NULL}
# Check the correct parameter and set the default
for p in param.keys():
if p in self.param:
if self.param[p] is not None:
param[p] = self.param[p]
self.param.update(param)
# Read all the files in the R-environment
for f, s in zip(self.filelist, self.seplist):
try:
tmpdata = DataFrame.from_csvfile(f,
sep=str(s),
header=param['header'],
as_is=param['as_is'],
row_names=param['row.names'])
self.mylist.append(tmpdata)
fdir, fname = os.path.split(os.path.splitext(f)[0])
self.listname.append(fname)
rcount += 1
except IOError, e:
self.error += e
def clusterPop(admix, indexes):
if len(indexes)==1:
return [indexes[0]]
subf = "adm.%d" % (os.getpid(),)
w = open(subf, "w")
f = open(admix)
fPos = 0
oldOrder = []
for l in f:
if fPos in indexes:
w.write(l)
oldOrder.append(fPos)
fPos += 1
f.close()
w.close()
df = DataFrame.from_csvfile(subf, sep=" ", header=False)
d=robjects.r.hclust(robjects.r.dist(df))
for name, value in d.items():
if name=="order":
order = value
break
os.remove(subf)
reOrder = []
for pos in order:
reOrder.append(oldOrder[pos-1])
return reOrder
def hae_ennakkoilmoitukset(tiedosto):
if not os.path.exists(tiedosto):
tiedosto = os.path.join(DATADIR, tiedosto)
if not os.path.exists(tiedosto):
raise IOError("Annettua tiedostoa %s ei löydy" % tiedosto)
return DataFrame.from_csvfile(tiedosto, header=True, sep=',',
as_is=True)
def clusterAll(admix, myPop):
f = open(admix)
ls = f.readlines()
f.close()
numK = len(ls[0].split(" "))
pops = {}
for i in range(len(ls)):
vals = [float(x) for x in ls[i].rstrip().split(" ")]
cnt, accu = pops.get(myPop[i], (0, [0.0]*numK))
cnt += 1
for i2 in range(numK): accu[i2] += vals[i2]
pops[myPop[i]] = cnt, accu
popNames = list(pops.keys())
popNames.sort()
subf = "accu.%d" % (os.getpid(),)
w = open(subf, "w")
for popName in popNames:
myVals = pops[popName][1]
cnt = pops[popName][0]
myVals = [x/cnt for x in myVals]
w.write(" ".join([str(x) for x in myVals]))
w.write("\n")
w.close()
df = DataFrame.from_csvfile(subf, sep=" ", header=False)
d=robjects.r.hclust(robjects.r.dist(df, method="max"), method="complete")
for name, value in d.items():
if name=="order":
order = value
break
os.remove(subf)
popOrder = []
for pos in order:
popOrder.append(popNames[pos-1])
return popOrder
def createGraphSeries(cohort,t,sfiles,ofile):
hm = ['bothR5','bothDX','esR5','esDX','trofileR5','trofileDX','mixedR5DX']
# Begin witing graphs to pdf
grdevices.pdf(file=cohort+".stats.pdf",width=7,height=7)
if t == 'both' or t =='out':
stat_data = DataFrame.from_csvfile(ofile, sep = "\t")
# graph 1 scatter plot
scatterPlot(stat_data,'bothR5','bothDX',cohort.upper()+' cohort\n bothR5/bothDX correlation')
scatterPlot(stat_data,'esR5','esDX',cohort.upper()+' cohort\n esR5/esDX correlation')
scatterPlot(stat_data,'trofileR5','trofileDX',cohort.upper()+' cohort\n trofileR5/trofileDX correlation')
scatterPlot(stat_data,'bothR5','esR5',cohort.upper()+' cohort\n bothR5/esR5 correlation')
scatterPlot(stat_data,'trofileR5','esR5',cohort.upper()+' cohort\n trofileR5/esR5 correlation')
scatterPlot(stat_data,'bothDX','esDX',cohort.upper()+' cohort\n bothDX/esDX correlation')
scatterPlot(stat_data,'trofileDX','esDX',cohort.upper()+' cohort\n trofileDX/esDX correlation')
scatterPlot(stat_data,'bothDX','mixedR5DX',cohort.upper()+' cohort\n bothDX/mixedR5DX correlation')
if t == 'both' or t =='stats':
for hmm in hm:
if sfiles.has_key(hmm):
f = sfiles[hmm]
hmm_scores = DataFrame.from_csvfile(f,sep="\t")
# graph 3
plot(hmm_scores.rx2("Cutoff"),hmm_scores.rx2("Accuracy"),type='o',main='Accuracy vs. Cutoff '+cohort.upper()+' Cohort\n'+hmm+'.hmm',xlab='Cutoff',ylab='Accuracy')
# graph 4
plot(hmm_scores.rx2("FPP"),hmm_scores.rx2("TPP"),type='o',xlim=base.c(0,1),ylim=base.c(0,1),main=cohort.upper()+' Cohort ROC\n'+hmm+'.hmm',xlab='% False Pos',ylab='% False Neg')
# graph 5
plot(hmm_scores.rx2("Cutoff"),hmm_scores.rx2("Phi"),type='o',xlim=base.c(50,100),ylim=base.c(0,0.75),main=cohort.upper()+' Cohort Association Coeff\n'+hmm+'.hmm',xlab='Cutoff',ylab='Association Coefficient')
# graph 6
plot(hmm_scores.rx2("Cutoff"),hmm_scores.rx2("Specificity"),type='o',main='Specificity vs. Cutoff '+cohort.upper()+' Cohort\n'+hmm+'.hmm',xlab='Cutoff',ylab='Specificity')
# graph 7
plot(hmm_scores.rx2("Cutoff"),hmm_scores.rx2("Sensitivity"),type='o',main='Sensitivity vs. Cutoff '+cohort.upper()+' Cohort\n'+hmm+'.hmm',xlab='Cutoff',ylab='Sensitivity')
# graph 8
plot(hmm_scores.rx2("Sensitivity"),hmm_scores.rx2("Specificity"),type='o',main='Sensitivity vs. Specificity '+cohort.upper()+' Cohort\n'+hmm+'.hmm',xlab='Sensitivity',ylab='Specificity')
# close pdf file
grdevices.dev_off()
return
def loadfiles(self):
"""
Load files into R environment
"""
rcount = 0
asmatrix = robjects.r['as.matrix']
diag = robjects.r['diag']
names = robjects.r['names']
## Set the default parameter for reading from csv
param = {'header': True, 'as_is': True, 'row.names': ri.RNULLArg}
## Check the correct parameter and set the default
for p in param.keys():
if p in self.param:
if self.param[p] is not None:
param[p] = self.param[p]
for f, s in zip(self.filelist, self.seplist):
try:
dataf = DataFrame.from_csvfile(f,
sep=str(s),
header=param['header'],
as_is=param['as_is'],
row_names=param['row.names'])
dataf = asmatrix(dataf)
# Should be the diagonal set to 0?
# Do it for all the inputs, just to be sure
zcount = 0
for i in xrange(dataf.ncol):
if (dataf.rx(i+1,i+1)[0] - 0.0 >= 1e-8):
zcount += 1
dataf.rx[i+1,i+1] = 0
if zcount:
self.e += f
self.mylist.append(dataf)
fdir, fname = os.path.split(os.path.splitext(f)[0])
self.listname.append(fname)
rcount += 1
except IOError, e:
self.error += e
except RRuntimeError, e:
self.error += e
def stepwise_regression(data, d_v, i_vs):
# __file = tempfile.NamedTemporaryFile(delete=False)
__file = open('/home/foodfan/haha','wb')
__file.writelines(data)
stats = importr('stats')
pat = '%s~%s' % (d_v, '+'.join(i_vs))
print pat
# return None
__file.close()
data_from_input = DataFrame.from_csvfile(__file.name)
reg = stats.lm(pat, data_from_input)
st = stats.step(reg, direction = 'backward')
ret = str(st[0])
# print '------------------------------------------------------------'
#
# print '------------------------------------------------------------'
return ret
def csv2graph(csvfiles, seplist=[], param={},filepath='.', graph_format='gml'):
"""
Utility to convert from csv file to igraph format file
"""
igraph = importr('igraph')
gadj = igraph.graph_adjacency
wgraph = igraph.write_graph
if len(seplist) != len(csvfiles):
raise IOError('Not enought separators')
for i,f in enumerate(csvfiles):
myfname = f + ".%s" % format
tmpdata = DataFrame.from_csvfile(f,
sep=seplist[i],
header=param['header'] if param.has_key('header') else True,
as_is=True,
row_names=param['row.names'] if param.has_key('row_names') else False)
g = gadj(reslist, mode='undirected', weighted=True)
wgraph(g, file=os.path.join(filepath,myfname), format=format)
return True
def from_csv(cls, data):
return cls(DataFrame.from_csvfile(str(data)))
def show4():
open4()
r.source('D:/Postgraduate/Course/2-semester/R-language/TimeAnalyze/Programe/R/end.R',encoding="utf-8")
data = DataFrame.from_csvfile('D:/Postgraduate/Course/2-semester/R-language/TimeAnalyze/Programe/temp/project2.csv')
pp = ggplot2.ggplot(data)+ggplot2.aes_string(x='day', y='time',fill = 'factor(project)')+ggplot2.geom_bar(stat ='identity',position = 'dodge')+ggplot2.ggtitle("两项目时间对比图")+ggplot2.labs(x='日期',y='时间 (min)')+ggplot2.theme(**{'axis.text.x': ggplot2.element_text(angle = 45)})
pp.plot()
def hae_ehdokkaat():
in_tiedosto = os.path.join(DATADIR, 'e2011ehd.csv')
return DataFrame.from_csvfile(in_tiedosto, header=True, sep='\t',
as_is=True)
def main():
try:
datafile = sys.argv[1]
outfile_name = sys.argv[2]
expression = sys.argv[3]
except:
stop_err('Usage: python gsummary.py input_file ouput_file expression')
math_allowed = S3_METHODS()['Math']
ops_allowed = S3_METHODS()['Ops']
# Check for invalid expressions
for word in re.compile('[a-zA-Z]+').findall(expression):
if word and not word in math_allowed:
stop_err(
"Invalid expression '%s': term '%s' is not recognized or allowed"
% (expression, word))
symbols = set()
for symbol in re.compile('[^a-z0-9\s]+').findall(expression):
if symbol and not symbol in ops_allowed:
stop_err(
"Invalid expression '%s': operator '%s' is not recognized or allowed"
% (expression, symbol))
else:
symbols.add(symbol)
if len(symbols) == 1 and ',' in symbols:
# User may have entered a comma-separated list r_data_frame columns
stop_err(
"Invalid columns '%s': this tool requires a single column or expression"
% expression)
# Find all column references in the expression
cols = []
for col in re.compile('c[0-9]+').findall(expression):
try:
cols.append(int(col[1:]) - 1)
except:
pass
tmp_file = tempfile.NamedTemporaryFile('w+b')
# Write the R header row to the temporary file
hdr_str = "\t".join("c%s" % str(col + 1) for col in cols)
tmp_file.write("%s\n" % hdr_str)
skipped_lines = 0
first_invalid_line = 0
i = 0
for i, line in enumerate(file(datafile)):
line = line.rstrip('\r\n')
if line and not line.startswith('#'):
valid = True
fields = line.split('\t')
# Write the R data row to the temporary file
for col in cols:
try:
float(fields[col])
except:
skipped_lines += 1
if not first_invalid_line:
first_invalid_line = i + 1
valid = False
break
if valid:
data_str = "\t".join(fields[col] for col in cols)
tmp_file.write("%s\n" % data_str)
tmp_file.flush()
if skipped_lines == i + 1:
stop_err(
"Invalid column or column data values invalid for computation. See tool tips and syntax for data requirements."
)
else:
# summary function and return labels
summary_func = r(
"function( x ) { c( sum=sum( as.numeric( x ), na.rm=T ), mean=mean( as.numeric( x ), na.rm=T ), stdev=sd( as.numeric( x ), na.rm=T ), quantile( as.numeric( x ), na.rm=TRUE ) ) }"
)
headings = ['sum', 'mean', 'stdev', '0%', '25%', '50%', '75%', '100%']
headings_str = "\t".join(headings)
#r.set_default_mode( NO_CONVERSION )
#r_data_frame = r.read_table( tmp_file.name, header=True, sep="\t" )
r_data_frame = DataFrame.from_csvfile(tmp_file.name,
header=True,
sep="\t")
outfile = open(outfile_name, 'w')
for col in re.compile('c[0-9]+').findall(expression):
r.assign(col, r["$"](r_data_frame, col))
try:
summary = summary_func(r(expression))
except RException, s:
outfile.close()
stop_err("Computation resulted in the following error: %s" %
str(s))
#summary = summary.as_py( BASIC_CONVERSION )
outfile.write("#%s\n" % headings_str)
print summary
print summary.r_repr()
outfile.write(
"%s\n" % "\t".join(["%g" % (summary.rx2(k)[0]) for k in headings]))
outfile.close()
if skipped_lines:
print "Skipped %d invalid lines beginning with line #%d. See tool tips for data requirements." % (
skipped_lines, first_invalid_line)
def main():
try:
datafile = sys.argv[1]
outfile_name = sys.argv[2]
expression = sys.argv[3]
except:
stop_err( 'Usage: python gsummary.py input_file ouput_file expression' )
math_allowed = S3_METHODS()[ 'Math' ]
ops_allowed = S3_METHODS()[ 'Ops' ]
# Check for invalid expressions
for word in re.compile( '[a-zA-Z]+' ).findall( expression ):
if word and not word in math_allowed:
stop_err( "Invalid expression '%s': term '%s' is not recognized or allowed" %( expression, word ) )
symbols = set()
for symbol in re.compile( '[^a-z0-9\s]+' ).findall( expression ):
if symbol and not symbol in ops_allowed:
stop_err( "Invalid expression '%s': operator '%s' is not recognized or allowed" % ( expression, symbol ) )
else:
symbols.add( symbol )
if len( symbols ) == 1 and ',' in symbols:
# User may have entered a comma-separated list r_data_frame columns
stop_err( "Invalid columns '%s': this tool requires a single column or expression" % expression )
# Find all column references in the expression
cols = []
for col in re.compile( 'c[0-9]+' ).findall( expression ):
try:
cols.append( int( col[1:] ) - 1 )
except:
pass
tmp_file = tempfile.NamedTemporaryFile( 'w+b' )
# Write the R header row to the temporary file
hdr_str = "\t".join( "c%s" % str( col+1 ) for col in cols )
tmp_file.write( "%s\n" % hdr_str )
skipped_lines = 0
first_invalid_line = 0
i = 0
for i, line in enumerate( file( datafile ) ):
line = line.rstrip( '\r\n' )
if line and not line.startswith( '#' ):
valid = True
fields = line.split( '\t' )
# Write the R data row to the temporary file
for col in cols:
try:
float( fields[ col ] )
except:
skipped_lines += 1
if not first_invalid_line:
first_invalid_line = i + 1
valid = False
break
if valid:
data_str = "\t".join( fields[ col ] for col in cols )
tmp_file.write( "%s\n" % data_str )
tmp_file.flush()
if skipped_lines == i + 1:
stop_err( "Invalid column or column data values invalid for computation. See tool tips and syntax for data requirements." )
else:
# summary function and return labels
summary_func = r( "function( x ) { c( sum=sum( as.numeric( x ), na.rm=T ), mean=mean( as.numeric( x ), na.rm=T ), stdev=sd( as.numeric( x ), na.rm=T ), quantile( as.numeric( x ), na.rm=TRUE ) ) }" )
headings = [ 'sum', 'mean', 'stdev', '0%', '25%', '50%', '75%', '100%' ]
headings_str = "\t".join( headings )
#r.set_default_mode( NO_CONVERSION )
#r_data_frame = r.read_table( tmp_file.name, header=True, sep="\t" )
r_data_frame = DataFrame.from_csvfile( tmp_file.name, header=True, sep="\t" )
outfile = open( outfile_name, 'w' )
for col in re.compile( 'c[0-9]+' ).findall( expression ):
r.assign( col, r[ "$" ]( r_data_frame, col ) )
try:
summary = summary_func( r( expression ) )
except RException, s:
outfile.close()
stop_err( "Computation resulted in the following error: %s" % str( s ) )
#summary = summary.as_py( BASIC_CONVERSION )
outfile.write( "#%s\n" % headings_str )
print summary
print summary.r_repr()
outfile.write( "%s\n" % "\t".join( [ "%g" % ( summary.rx2( k )[0] ) for k in headings ] ) )
outfile.close()
if skipped_lines:
print "Skipped %d invalid lines beginning with line #%d. See tool tips for data requirements." % ( skipped_lines, first_invalid_line )
distEisen = robjects.r('''
distEisen <- function(x, use = "pairwise.complete.obs") {
co.x <- cor(x, use = use)
dist.co.x <- 1 - co.x
return(as.dist(dist.co.x))
}
''')
listToDF = robjects.r('''
listToDF <- function(inputList, fill = NA){
# Use fill = NULL for regular recycling behavior
maxLen = max(sapply(inputList, length))
for(i in seq_along(inputList))
inputList[[i]] <- c(inputList[[i]], rep(fill, maxLen -
length(inputList[[i]])))
return(as.data.frame(inputList))
}
''')
annotations = DataFrame.from_csvfile(annotation_classes_input_file,
header=True,
sep='\t',
quote='"',
row_names=1)
R = robjects.r
R["library"]("utils")
R["library"]("tools")
def show1():
open1()
r.source('D:/Postgraduate/Course/2-semester/R-language/TimeAnalyze/Programe/R/head1.r',encoding="utf-8")
data = DataFrame.from_csvfile('D:/Postgraduate/Course/2-semester/R-language/TimeAnalyze/Programe/temp/day1.csv')
pp = ggplot2.ggplot(data)+ggplot2.aes_string(x='project', y='time',fill = 'project')+ggplot2.geom_bar(stat ='identity')+ggplot2.ggtitle("今日项目时间分布图")+ggplot2.labs(x='项目',y='时间 (min)')+ggplot2.theme(**{'axis.text.x': ggplot2.element_text(angle = 45)})
pp.plot()
def __init__(self, dex_name):
self.dexcom_data = DataFrame.from_csvfile(dex_name)
import rpy2.robjects.pandas2ri
from rpy2.robjects.vectors import DataFrame
import math
import datetime
parser = argparse.ArgumentParser()
parser.add_argument("-in_csv", help="")
parser.add_argument("-out", help="")
args=parser.parse_args()
dataf = DataFrame.from_csvfile(args.in_csv, sep = ",",header=True)
# Get statistics for investigated seqs
rmean = robjects.r['mean']
rmed = robjects.r['median']
rmax = robjects.r['max']
rsd = robjects.r['sd']
rsum = robjects.r['sum']
ma=rmax(dataf.rx('hitlen'))
as_vec = robjects.r['as.vector']
as_num = robjects.r['as.numeric']
as_mat = robjects.r['as.matrix']
#test22=as_vec(dataf.rx('Length'))
## print pat
## return None
# __file.close()
# data_from_input = DataFrame.from_csvfile(__file.name)
# reg = stats.lm(pat, data_from_input)
# st = stats.step(reg, direction = 'backward')
## ret = ''
## for key, value in st.iteritems():
## ret += key + ',' + str(value) + '\n'
## return ret
# return ''
# stepwise_regression()
d_v = "inflat"
i_vs = ["money", "output", "initial", "poprate", "inv", "school"]
data_from_input = DataFrame.from_csvfile("/home/foodfan/money.csv")
stats = importr("stats")
pat = "%s~%s" % (d_v, "+".join(i_vs))
# print pat
# return None
reg = stats.lm(pat, data_from_input)
st = stats.step(reg, direction="backward")
ret = ""
print "------------------------------------"
print str(st[0])
# print st[1]
# for key, value in st.iteritems():
# ret += key + ',' + str(value) + '\n'
print ret
from rpy2.robjects.vectors import DataFrame
import math
import datetime
parser = argparse.ArgumentParser()
parser.add_argument("-in_csv", help="")
parser.add_argument("-out", help="")
args=parser.parse_args()
infile="/Users/security/science/bigoutput.csv"
dataf = DataFrame.from_csvfile(infile, sep = ",",header=True)
# Get statistics for investigated seqs
#rmean = robjects.r['mean']
#rmed = robjects.r['median']
#rmax = robjects.r['max']
#rsd = robjects.r['sd']
#rsum = robjects.r['sum']
#
#ma=rmax(dataf.rx('Length'))
#
#as_vec = robjects.r['as.vector']
#as_num = robjects.r['as.numeric']
#as_mat = robjects.r['as.matrix']
#
#test22=as_vec(dataf.rx('Length'))
