def diagnostics(self, fn=None):
"""
Plot diagnostics for the regression. If `fn` is provided, then save
the results to file. The filetype to be saved is determined by the
extension.
Additional kwargs are passed to the saving function (e.g., width=10)
"""
d = {
'.pdf': grdevices.pdf,
'.png': grdevices.png
}
if fn:
ext = os.path.splitext(fn)[1]
try:
saver_func = d[ext]
except KeyError:
raise ValueError('extension "%s" not supported, '
'please use one of %s' % (ext, d.keys()))
saver_func(file=fn)
r.layout(r.matrix([1, 2, 3, 4], 2, 2))
r.plot(self.lm)
if fn:
rclose()
return
def __call__(self, track, slice = None):
s = [random.randint(0,20) for x in range(40)]
random.shuffle( s )
# do the plotting
R.x11()
R.plot( s, s )
return odict( (("text", "#$rpl %i$#" % getCurrentRDevice()),) )
def plot_matrix_as_image(mat,
x1=None,
y1=None,
x2=None,
y2=None,
maxval=None,
main="",
xlab="",
ylab=""):
""" adds the image to the current plot if x1, x2, y1 and y2 are defined;
otherwise, create a new image with the dimensions of the matrix """
from rpy2.robjects import r
r.plot(numpy.array([0]),
xlim=numpy.array([x1, x2]),
ylim=numpy.array([y1, y2]),
type="n",
bty="n",
main=main,
xlab=xlab,
ylab=ylab)
if maxval is None:
maxval = mat.max()
r.rasterImage(r["as.raster"](mat, max=maxval), x1, y1, x2, y2)
def do_km(name, time, censor, split, outdir):
"""Given three clean (pre-processed) lists, make a kmplot of the data, and save it to outdir"""
data = {
'time': robjects.IntVector(np.array(time)),
'censor': robjects.IntVector(np.array(censor)),
'split': robjects.IntVector(np.array(split))
}
df = robjects.DataFrame(data)
surv = importr('survival')
grdevices = importr('grDevices')
km = surv.survfit(robjects.Formula('Surv(time, censor) ~ split'), data=df)
grdevices.png(file=os.path.join(outdir, name + '_km.png'),
width=512,
height=512)
r.plot(km,
xlab='Time',
ylab='Cumulative Hazard',
col=robjects.StrVector(['Red', 'Blue']))
r.legend(1000,
1,
robjects.StrVector(['<= Mean', '> Mean']),
lty=robjects.IntVector([1, 1]),
col=robjects.StrVector(['Red', 'Blue']))
grdevices.dev_off()
def plot_scatter(target, source, env):
"""
"""
mat = npz_to_df(source[0].rstr())
module["grDevices"].png(target[0].rstr())
r.plot(mat)
module["grDevices"].dev_off()
return None
def main():
in_fname = sys.argv[1]
out_fname = sys.argv[2]
try:
columns = int( sys.argv[3] ) - 1, int( sys.argv[4] ) - 1
except:
stop_err( "Columns not specified, your query does not contain a column of numerical data." )
title = sys.argv[5]
xlab = sys.argv[6]
ylab = sys.argv[7]
matrix = []
skipped_lines = 0
first_invalid_line = 0
invalid_value = ''
invalid_column = 0
i = 0
for i, line in enumerate( file( in_fname ) ):
valid = True
line = line.rstrip( '\r\n' )
if line and not line.startswith( '#' ):
row = []
fields = line.split( "\t" )
for column in columns:
try:
val = fields[column]
if val.lower() == "na":
row.append( float( "nan" ) )
else:
row.append( float( fields[column] ) )
except:
valid = False
skipped_lines += 1
if not first_invalid_line:
first_invalid_line = i + 1
try:
invalid_value = fields[column]
except:
invalid_value = ''
invalid_column = column + 1
break
else:
valid = False
skipped_lines += 1
if not first_invalid_line:
first_invalid_line = i+1
if valid:
matrix.append( row )
if skipped_lines < i:
try:
r.pdf( out_fname, 8, 8 )
r.plot( array(matrix), type="p", main=title, xlab=xlab, ylab=ylab, col="blue", pch=19 )
r['dev.off']()
except Exception, exc:
stop_err( "%s" %str( exc ) )
def r_plot(obj, fn, w=800, h=800, xlabel="", ylabel="", label=""):
from rpy2.robjects.packages import importr
from rpy2.robjects import r
grdevices = importr('grDevices')
grdevices.png(file=fn, width=w, height=h)
r.plot(obj)
grdevices.dev_off()
print ">> saved: " + fn
def r_plot(obj,fn,w=800,h=800,xlabel="",ylabel="",label=""):
from rpy2.robjects.packages import importr
from rpy2.robjects import r
grdevices = importr('grDevices')
grdevices.png(file=fn, width=w, height=h)
r.plot(obj)
grdevices.dev_off()
print ">> saved: "+fn
def __call__(self, track, slice=None):
if not R:
return
s = [random.randint(0, 20) for x in range(40)]
random.shuffle(s)
# do the plotting
R.x11()
R.plot(s, s)
return {"text": "#$rpl %i$#" % R["dev.cur"]()[0]}
def plotASProfile(tcc, cName, directory = None, min = 0, extra = "0"):
if not directory:
fN = extra + '.' + tcc + '.png'
else:
fN = directory + '/' + extra + '.' + tcc + '.png'
#Get S Profile
tccStretch = cgPeaks.stretch(tcc, cName)
highest = tccStretch.getHighestLevel()
if highest < min:
return 0
sortedX = tccStretch.profile.keys()
sortedX.sort()
sortedY = []
for X in sortedX:
sortedY.append(tccStretch.profile[X])
#Get AS Profile
chr, strand, start, end = tcc.strip().split(':')
if strand == '1':
strand = '-1'
else:
strand = '1'
tcc = cg.makeTcc(chr, strand, start, end)
tccStretchAS = cgPeaks.stretch(tcc, cName)
highest = tccStretchAS.getHighestLevel()
if highest < min:
return 0 #AS can have minimum I guess...
sortedXAS = tccStretchAS.profile.keys()
sortedXAS.sort()
sortedYAS = []
for X in sortedXAS:
sortedYAS.append(tccStretchAS.profile[X])
#Plot them
gDevice = importr('grDevices')
gDevice.png(file=fN, width=1680, height=1050)
r('split.screen(c(2,1))')
r('screen(1)')
r.plot(sortedX, sortedY, xlab = "Coordinates", ylab = "(Syn) Expression Level" )
r.lines(sortedX, sortedY, type = "b")
r('screen(2)')
r.plot(sortedXAS, sortedYAS, xlab = "Coordinates", ylab = "(Anti) Expression Level")
r.lines(sortedXAS, sortedYAS, type = "b")
gDevice.dev_off()
def plot_me(sub_f, label):
if (get_vec_type(sub_f) == 'real') and (len(sub_f.unique()) > 10):
sub_f = to_quants(sub_f, q=q, std=std)
m = get_cox_ph(surv, sub_f, formula=fmla)
r_data = m.rx2('call')[2]
p = log_rank(sub_f, surv)['p']
ls = r.c(*colors)
r.plot(survival.survfit(fmla, r_data), lty=1, col=ls, lwd=4, cex=1.25,
xlab='Years to Event', ylab='Survival');
r.title(label, cex=3.)
if ann == 'p':
r.text(.2, 0, labels='logrank p = {0:.1e}'.format(p), pos=4)
elif ann != None:
r.text(0, labels=ann, pos=4)
def clusterize_r_em(*args):
""" Clustering and plotting with EM GMM"""
try:
from rpy2.robjects import r
import rpy2.robjects.numpy2ri
rpy2.robjects.numpy2ri.activate()
except:
print "You need rpy2"
sys.exit(-1)
r.library("mclust")
for arg in args:
model = r.Mclust(arg)
print model
print r.summary(model)
r.quartz("plot")
r.plot(model, arg)
print raw_input("any key to pass")
def clusterize_r_em(*args):
""" Clustering and plotting with EM GMM"""
try:
from rpy2.robjects import r
import rpy2.robjects.numpy2ri
rpy2.robjects.numpy2ri.activate()
except:
print "You need rpy2"
sys.exit(-1)
r.library("mclust")
for arg in args:
model = r.Mclust(arg)
print model
print r.summary(model)
r.quartz("plot")
r.plot(model, arg)
print raw_input("any key to pass")
def render(self, work, path ):
# R.graphics_off()
self.legend = []
self.xlabels = []
nplotted = 0
for line, data in work.iteritems():
for label, coords in data.iteritems():
# sanity check on data
try: keys = coords.keys()
except AttributeError:
self.warn("could not plot %s - coords is not a dict: %s" % (label, str(coords) ))
continue
if len(keys) <= 1:
self.warn("could not plot %s: not enough columns: %s" % (label, str(coords) ))
continue
xlabel, ylabels = self.initCoords( label, coords)
xvals = coords[xlabel]
for ylabel in ylabels:
yvals = coords[ylabel]
R.plot( xvals, yvals )
# self.addData( line, label, xlabel, ylabel, xvals, yvals, nplotted )
nplotted += 1
if len(ylabels) > 1:
self.legend.append( "/".join((line,label,ylabel) ))
else:
self.legend.append( "/".join((line,label) ))
self.xlabels.append(xlabel)
figid = getCurrentRDevice()
blocks = ResultBlocks( ResultBlock( "\n".join( ("#$rpl %i$#" % (figid), "")),
title = "/".join(path) ) )
return blocks
def plot_me(sub_f, label):
if (get_vec_type(sub_f) == 'real') and (len(sub_f.unique()) > 10):
sub_f = to_quants(sub_f, q=q, std=std)
m = get_cox_ph(surv, sub_f, formula=fmla)
r_data = m.rx2('call')[2]
p = log_rank(sub_f, surv)['p']
ls = r.c(*colors)
r.plot(survival.survfit(fmla, r_data),
lty=1,
col=ls,
lwd=4,
cex=1.25,
xlab='Years to Event',
ylab='Survival')
r.title(label, cex=3.)
if ann == 'p':
r.text(.2, 0, labels='logrank p = {0:.1e}'.format(p), pos=4)
elif ann != None:
r.text(0, labels=ann, pos=4)
def render(self, dataframe, path):
fig = self.startPlot()
labels = dataframe.index.levels
paths = list(itertools.product(*labels))
self.initPlot(fig, dataframe, path)
nplotted = 0
for idx in range(0, len(paths), 2):
self.initLine(path, dataframe)
xpath = paths[idx]
ypath = paths[idx + 1]
xvalues, yvalues = dataframe.ix[xpath], dataframe.ix[ypath]
if len(xvalues) != len(yvalues):
raise ValueError(
"length of x,y tuples not consistent: %i != %i" %
len(xvalues), len(yvalues))
R.plot(xvalues, yvalues)
self.initCoords(xvalues, yvalues)
nplotted += 1
self.finishPlot(fig, dataframe, path)
figid = getCurrentRDevice()
blocks = ResultBlocks(
ResultBlock("\n".join(("#$rpl %i$#" % (figid), "")),
path2str(path)))
return blocks
def clusterize_r_em(*args, **kwargs):
""" Clustering and plotting with EM GMM"""
try:
from rpy2.robjects import r
import rpy2.robjects.numpy2ri
rpy2.robjects.numpy2ri.activate()
from sklearn.decomposition import PCA
except:
print "You need rpy2"
sys.exit(-1)
r.library("mclust")
for arg in args:
if kwargs.get('clf_on_pca', False):
pca = PCA(2)
arg = pca.fit(arg).transform(arg)
model = r.Mclust(arg)
print model
print r.summary(model)
r.quartz("plot")
r.plot(model, arg)
print raw_input("press any key to pass")
def plotSmallDeg(tcc, smallCName, degCName, outDir = None, description = "None", nameNum = "0"):
if not outDir:
fN = nameNum + "." + tcc + '.png'
else:
fN = outDir + '/' + nameNum + "." + tcc + '.png'
#Get deg Profile
tccStretch = cgPeaks.stretch(tcc, degCName)
sortedX = tccStretch.profile.keys()
sortedX.sort()
sortedY = []
for X in sortedX:
sortedY.append(tccStretch.profile[X])
#Get small
tccStretchSmall = cgPeaks.stretch(tcc, smallCName)
sortedXAS = tccStretchSmall.profile.keys()
sortedXAS.sort()
sortedYAS = []
for X in sortedXAS:
sortedYAS.append(tccStretchSmall.profile[X])
#Plot them
gDevice = importr('grDevices')
gDevice.png(file=fN, width=1680, height=1050)
r('split.screen(c(2,1))')
r('screen(1)')
r.plot(sortedX, sortedY, xlab = "Coordinates", ylab = "Degradome Expression" )
r.lines(sortedX, sortedY, type = "b")
r('screen(2)')
r.plot(sortedXAS, sortedYAS, xlab = description, ylab = "Small Expression")
r.lines(sortedXAS, sortedYAS, type = "b")
gDevice.dev_off()
def plotProfile(tcc, cName, directory = None, min = 0):
if not directory:
fN = tcc + '.png'
else:
fN = directory + '/' + tcc + '.png'
tccStretch = cgPeaks.stretch(tcc, cName)
highest = tccStretch.getHighestLevel()
if highest < min:
return 0
sortedX = tccStretch.profile.keys()
sortedX.sort()
sortedY = []
for X in sortedX:
sortedY.append(tccStretch.profile[X])
gDevice = importr('grDevices')
gDevice.png(file=fN, width=1680, height=1050)
r.plot(sortedX, sortedY, xlab = "Coordinates", ylab = "Expression Level")
r.lines(sortedX, sortedY, type = "b")
gDevice.dev_off()
def val(self):
""" Estimate value functions with b-splines and compare """
new_data = pd.DataFrame({'OverallRank': np.linspace(1, 194, 1000)})
fit_a = self.spline_est(self.policy_a['value'], new_data)
fit_b = self.spline_est(self.policy_b['value'], new_data)
r.pdf(os.path.join(os.path.dirname(self.out_dir), 'value.pdf'))
r.plot(new_data['OverallRank'], fit_a, type='l', xlab='Rank_M',
ylab='V(Rank)')
r.lines(new_data['OverallRank'], fit_b, col='red')
r.points(self.policy_a['value']['OverallRank'],
self.policy_a['value']['val'],
col='black')
r.points(self.policy_b['value']['OverallRank'],
self.policy_b['value']['val'],
col='red')
r.legend('topright', np.array(['No Info', 'Info']),
lty=np.array([1, 1]), col=np.array(['black', 'red']))
r('dev.off()')
diff = np.array(fit_b) - np.array(fit_a)
r.pdf(os.path.join(os.path.dirname(self.out_dir), 'value_diff.pdf'))
r.plot(new_data['OverallRank'], diff, type='l', xlab='Rank',
ylab='V(Rank|info=1) - V(Rank|info=0)')
r.abline(h=0, lty=2)
r('dev.off()')
diff = (np.array(fit_b) - np.array(fit_a)) / np.array(fit_a)
r.pdf(os.path.join(os.path.dirname(self.out_dir),
'value_percent_diff.pdf'))
r.plot(new_data['OverallRank'], diff, type='l', xlab='Rank',
ylab='(V(Rank|info=1) - V(Rank|info=0)) / V(Rank|info=0)')
r.abline(h=0, lty=2)
r('dev.off()')
data_path = dirname(dirname(__file__))
data_path = join(data_path, 'data', 'lawData.csv')
data = pd.read_csv(data_path)
new_data = deepcopy(data.loc[data['year'] == 2013, 'OverallRank'])
#new_data = np.concatenate((
# new_data, np.zeros(lc.N_SCHOOLS - len(new_data))
#))
new_data = pd.DataFrame({'OverallRank': np.array(new_data)})
fit_a = self.spline_est(self.policy_a['value'], new_data)
fit_b = self.spline_est(self.policy_b['value'], new_data)
diff = np.sum(np.array(fit_b) - np.array(fit_a))
pdiff = diff / np.sum(fit_a)
print(" - Change in Producer Surplus: {0}".format(diff))
print(" - Percent change in Producer Surplus: {0}".format(pdiff))
return diff
>>> import rpy2.robjects as robjects
>>> from rpy2.robjects import r
>>> x= np.range(1,30)
Traceback (most recent call last):
File "<pyshell#65>", line 1, in <module>
x= np.range(1,30)
AttributeError: 'module' object has no attribute 'range'
>>> x= np.arange(1,30)
>>> y=np.arange(3,33)
>>> len(x)
29
>>> len(y)
30
>>> y=np.arange(3,32)
>>> r.plot(x,y)
Traceback (most recent call last):
File "<pyshell#71>", line 1, in <module>
r.plot(x,y)
File "/usr/local/lib/python2.7/dist-packages/rpy2/robjects/functions.py", line 178, in __call__
return super(SignatureTranslatedFunction, self).__call__(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/rpy2/robjects/functions.py", line 102, in __call__
new_args = [conversion.py2ri(a) for a in args]
File "/usr/local/lib/python2.7/dist-packages/singledispatch.py", line 210, in wrapper
return dispatch(args[0].__class__)(*args, **kw)
File "/usr/local/lib/python2.7/dist-packages/rpy2/robjects/conversion.py", line 60, in _py2ri
raise NotImplementedError("Conversion 'py2ri' not defined for objects of type '%s'" % str(type(obj)))
NotImplementedError: Conversion 'py2ri' not defined for objects of type '<type 'numpy.ndarray'>'
>>> pplot=r.plot(x,y)
# -*- coding: utf-8 -*-
from rpy2.robjects import r
from rpy2.robjects import IntVector
x = IntVector(range(9))
y = IntVector(range(9))
import ipdb
ipdb.set_trace()
r.png('figura1.png')
r.plot(x, y)
r['dev.off']()
r.png('figura2.jpeg')
r.plot(x, y, xlab='x', ylab='y', main='Minha plotagem', type='l')
r['dev.off']()
r.png('figura3.pdf')
normal = r.rnorm(500, 0, 1)
r.hist(normal)
r['dev.off']()
# -*- coding: utf-8 -*-
from rpy2.robjects import r
from rpy2.robjects import IntVector
x = IntVector(range(9))
y = IntVector(range(9))
import ipdb; ipdb.set_trace()
r.png('figura1.png')
r.plot(x, y)
r['dev.off']()
r.png('figura2.jpeg')
r.plot(x, y, xlab='x', ylab='y', main='Minha plotagem', type='l')
r['dev.off']()
r.png('figura3.pdf')
normal = r.rnorm(500, 0, 1)
r.hist(normal)
r['dev.off']()
def plot_forecast(data, fcast):
index = pd.date_range(start=data.index.max(),
periods=len(fcast[3]) + 1,
freq='W')[1:]
forecast = pd.Series(fcast[3], index=index)
lowerpi = pd.Series(fcast[4], index=index)
upperpi = pd.Series(fcast[5], index=index)
plt.plot(data.index, data.casos_est, color='b', alpha=0.5)
plt.plot(forecast.index, forecast.values, color='red')
plt.fill_between(forecast.index,
lowerpi.values,
upperpi.values,
alpha=0.2,
color='red')
if __name__ == "__main__":
data = get_alerta_table(3304557) # Nova Iguaçu: 3303609
tscount = importr('tscount')
tsglm = r('tsglm')
model = build_model(data)
print(r.summary(model))
r.plot(model)
# fcast = forecast.forecast(model, h=5, level=95.0)
# print(fcast[3], fcast[4], fcast[5])
# plot_forecast(data=data, fcast=fcast)
# plt.show()
import os
os.environ[
'R_USER'] = '******' #path depends on where you installed Python. Mine is the Anaconda distribution
from rpy2.robjects import r
print(r)
r('.libPaths("C:/Users/Uwe/Documents/R/win-library/3.3")')
r('.libPaths()')
from rpy2.robjects.packages import importr
gr = importr('grDevices')
#importr("ggplot2")
r.pi
r.plot(1, 1)
gr.dev_off()
r("plot(mtcars$cyl, mtcars$gear)")
gr.dev_off()
import rpy2.robjects.lib.ggplot2 as ggplot2
p = r("p=ggplot(mtcars) + aes(cyl,gear) + geom_line();print(p)")
p.plot()
r('dev.off()')
boxcore = importr("BoxCore")
#ggplot2 = importr('ggplot2')
from rpy2.robjects.vectors import IntVector, FloatVector
blub = boxcore.plot_distr(FloatVector(df.age.values),
FloatVector(df.fare.values))
def plot(self, names=None):
r.plot(model._estimate, names=names)
from rpy2.robjects import r
from rpy2.robjects.packages import importr
gDevice = importr('grDevices')
gDevice.png(file="file.png", width=512, height=512)
# plotting code here
x = [1,2,3]
y = [1,2,3]
r.plot(x,y, xlab = "X")
gDevice.dev_off()
from rpy2.robjects import r
from rpy2.robjects.packages import importr
gDevice = importr('grDevices')
gDevice.png(file="file.png", width=512, height=512)
# plotting code here
x = [1, 2, 3]
y = [1, 2, 3]
r.plot(x, y, xlab="X")
gDevice.dev_off()
import pandas as pd
from rpy2 import robjects as ro
from rpy2.robjects import r
from rpy2.robjects import pandas2ri
from rpy2.robjects import FloatVector as c
import psycopg2
pandas2ri.activate()
con=psycopg2.connect("dbname='stars' user='******' host='localhost'")
cur=con.cursor()
cur.execute("select Jmag, kmag from alphaperprevmembers")
cur.execute("select jmag, ksmag from sheikhi where pm=0")
df=pd.DataFrame(cur.fetchall(),columns=['j','k'])
r.plot(df['j']-df['k'],df['j'],xlim=c([min(df['j']-df['k']),max(df['j']-df['k'])]), ylim=c([max(df['j']), min(df['j'])]), xlab='color J-K', ylab='J_mag', main='')
rW=raw_input()
import pandas as pd
import rpy2.robjects as ro
from rpy2.robjects import r
from rpy2.robjects import pandas2ri
pandas2ri.activate()
import psycopg2
con=psycopg2.connect("dbname='stars' user='******' host='localhost'")
cur=con.cursor()
cur.execute("select jmag, ksmag from sheikhi")
df=pd.DataFrame(cur.fetchall(),columns=['j','ks'])
r.png("isochroneofsheikhidata.png")
r.plot(df['j']-df['ks'],df['j'],xlim=ro.FloatVector([min(df['j']-df['ks']),max(df['j']-df['ks'])]),ylim=ro.FloatVector([max(df['j']),min(df['j'])]),xlab='color [J-Ks]', ylab='Jmag', main='cmd from Sheikhi data')
v=raw_input()
def buildCuffdiffPlots(infile, outfile):
'''create summaries of cufflinks results (including some diagnostic plots)
Plots are created in the <exportdir>/cuffdiff directory.
Plots are:
<geneset>_<method>_<level>_<track1>_vs_<track2>_significance.png
fold change against expression level
'''
###########################################
###########################################
# create diagnostic plots
###########################################
outdir = os.path.join(PARAMS["exportdir"], "cuffdiff")
dbhandle = sqlite3.connect(PARAMS["database"])
prefix = P.snip(infile, ".load")
geneset, method = prefix.split("_")
for level in CUFFDIFF_LEVELS:
tablename_diff = prefix + "_%s_diff" % level
tablename_levels = prefix + "_%s_levels" % level
# note that the ordering of EXPERIMENTS and the _diff table
# needs to be the same as only one triangle is stored of the
# pairwise results. do not plot "undefined" lfold values
# (where treatment_mean or control_mean = 0) do not plot lfold
# values where the confidence bounds contain 0.
for track1, track2 in itertools.combinations(EXPERIMENTS, 2):
statement = """
SELECT CASE WHEN d.treatment_mean < d.control_mean
THEN d.treatment_mean
ELSE d.control_mean END,
d.l2fold, d.significant
FROM %(tablename_diff)s AS d
WHERE treatment_name = '%(track1)s' AND
control_name = '%(track2)s' AND
status = 'OK' AND
treatment_mean > 0 AND
control_mean > 0
""" % locals()
data = zip(*Database.executewait(dbhandle, statement))
pngfile = "%(outdir)s/%(geneset)s_%(method)s_%(level)s_%(track1)s_vs_%(track2)s_significance.png" % locals(
)
# ian: Bug fix: moved R.png to after data check so that no
# plot is started if there is no data this was leading
# to R falling over from too many open devices
if len(data) == 0:
E.warn("no plot for %s - %s -%s vs %s" %
(pngfile, level, track1, track2))
continue
R.png(pngfile)
R.plot(ro.FloatVector(data[0]),
ro.FloatVector(data[1]),
xlab='min(FPKM)',
ylab='log2fold',
log="x",
pch=20,
cex=.1,
col=R.ifelse(ro.IntVector(data[2]), "red", "black"))
R['dev.off']()
P.touch(outfile)
def plot(self, names=None):
r.plot(model._estimate, names=names)
def main(argv=None, **kwargs):
'''main function for test.py.
Long-form of command line arguments can also be supplied as kwargs.
If argv is not None, command line parsing will be performed.
'''
parser = optparse.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option("-t", "--tracker", dest="tracker", type="string",
help="tracker to use [default=%default]")
parser.add_option("-p", "--page", dest="page", type="string",
help="render an rst page [default=%default]")
parser.add_option("-a", "--tracks", dest="tracks", type="string",
help="tracks to use [default=%default]")
parser.add_option("-m", "--transformer", dest="transformers",
type="string", action="append",
help="add transformation [default=%default]")
parser.add_option("-s", "--slices", dest="slices", type="string",
help="slices to use [default=%default]")
parser.add_option("-r", "--renderer", dest="renderer", type="string",
help="renderer to use [default=%default]")
parser.add_option("-w", "--path", "--trackerdir",
dest="trackerdir", type="string",
help="path to trackers [default=%default]")
parser.add_option("-f", "--force", dest="force", action="store_true",
help="force recomputation of data by deleting cached "
"results [default=%default]")
parser.add_option("-o", "--option", dest="options", type="string",
action="append",
help="renderer options - supply as key=value pairs "
"(without spaces). [default=%default]")
parser.add_option("-l", "--language", dest="language", type="choice",
choices=("rst", "notebook"),
help="output language for snippet. Use ``rst`` "
"to create a snippet to paste "
"into a cgatreport document. Use ``notebook`` to "
"create a snippet to paste "
"into an ipython notebook [default=%default]")
parser.add_option("--no-print", dest="do_print", action="store_false",
help="do not print an rst text element to create "
"the displayed plots [default=%default].")
parser.add_option("--no-show", dest="do_show", action="store_false",
help="do not show a plot [default=%default].")
parser.add_option("--layout", dest="layout", type="string",
help="output rst with layout [default=%default].")
parser.add_option("-i", "--start-interpreter", dest="start_interpreter",
action="store_true",
help="do not render, but start python interpreter "
"[default=%default].")
parser.add_option("-I", "--ii", "--start-ipython", dest="start_ipython",
action="store_true",
help="do not render, start ipython interpreter "
"[default=%default].")
parser.add_option(
"--workdir", dest="workdir", type="string",
help="working directory - change to this directory "
"before executing "
"[default=%default]")
parser.add_option(
"--hardcopy", dest="hardcopy", type="string",
help="output images of plots. The parameter should "
"contain one or more %s "
"The suffix determines the type of plot. "
"[default=%default].")
parser.set_defaults(
loglevel=1,
tracker=None,
transformers=[],
tracks=None,
slices=None,
options=[],
renderer="table",
do_show=True,
do_print=True,
force=False,
trackerdir=TRACKERDIR,
caption="add caption here",
start_interpreter=False,
start_ipython=False,
language="rst",
workdir=None,
layout=None,
dpi=100)
if argv is None and len(kwargs) == 0:
argv = sys.argv
if argv:
(options, args) = parser.parse_args(argv)
if len(args) == 2:
options.tracker, options.renderer = args
else:
(options, args) = parser.parse_args([])
######################################################
# set keyword arguments as options
for keyword, value in kwargs.items():
if hasattr(options, keyword):
setattr(options, keyword, value)
del kwargs[keyword]
# change some kwarguments
if options.transformers:
for keyword, value in kwargs.items():
if keyword.startswith("tf"):
kwargs["tf-{}".format(keyword[2:])] = value
if options.workdir is not None:
savedir = os.getcwd()
os.chdir(options.workdir)
Utils.getParameters(sorted(glob.glob("*.ini")))
else:
savedir = None
######################################################
# configure options
options.trackerdir = os.path.abspath(
os.path.expanduser(options.trackerdir))
if os.path.exists(options.trackerdir):
sys.path.insert(0, options.trackerdir)
else:
L.warn("directory %s does not exist" % options.trackerdir)
######################################################
# test plugins
for x in options.options:
if "=" in x:
data = x.split("=")
key, val = [y.strip() for y in (data[0], "=".join(data[1:]))]
else:
key, val = x.strip(), None
kwargs[key] = val
if options.tracks:
kwargs["tracks"] = options.tracks
if options.slices:
kwargs["slices"] = options.slices
kwargs = Utils.updateOptions(kwargs)
option_map = getOptionMap()
renderer_options = Utils.selectAndDeleteOptions(
kwargs, option_map["render"])
transformer_options = Utils.selectAndDeleteOptions(
kwargs, option_map["transform"])
display_options = Utils.selectAndDeleteOptions(
kwargs, option_map["display"])
######################################################
# decide whether to render or not
if options.renderer == "none" or options.start_interpreter or \
options.start_ipython or options.language == "notebook":
renderer = None
else:
renderer = Utils.getRenderer(options.renderer, renderer_options)
transformers = Utils.getTransformers(
options.transformers, transformer_options)
exclude = set(("Tracker",
"TrackerSQL",
"returnLabeledData",
"returnMultipleColumnData",
"returnMultipleColumns",
"returnSingleColumn",
"returnSingleColumnData",
"SQLError",
"MultipleColumns",
"MultipleColumnData",
"LabeledData",
"DataSimple",
"Data"))
######################################################
# build from tracker
if options.tracker:
if "." in options.tracker:
parts = options.tracker.split(".")
tracker_modulename = ".".join(parts[:-1])
tracker_name = parts[-1]
else:
tracker_modulename = None
tracker_name = options.tracker
try:
_code, tracker, tracker_path = Utils.makeTracker(
options.tracker, (), kwargs)
except ImportError:
# try to find class in module
trackers = []
for filename in glob.glob(
os.path.join(options.trackerdir, "*.py")):
modulename = os.path.basename(filename)
trackers.extend(
[x for x in getTrackers(modulename)
if x[0] not in exclude])
for name, tracker_class, modulename, is_derived in trackers:
if name == tracker_name:
if tracker_modulename is not None:
if modulename == tracker_modulename:
break
else:
tracker_modulename = modulename
break
else:
available_trackers = set([x[0] for x in trackers if x[3]])
print(
"unknown tracker '%s': possible trackers are\n %s" %
(options.tracker, "\n ".join(sorted(available_trackers))))
print(
"(the list above does not contain functions).")
sys.exit(1)
# instantiate functors
if is_derived:
tracker = tracker_class(**kwargs)
# but not functions
else:
tracker = tracker_class
# remove everything related to that tracker for a clean slate
if options.force:
removed = CGATReport.clean.removeTracker(tracker_name)
print("removed all data for tracker %s: %i files" %
(tracker_name, len(removed)))
dispatcher = Dispatcher(tracker, renderer, transformers)
if renderer is None:
# dispatcher.parseArguments(**kwargs)
# result = dispatcher.collect()
# result = dispatcher.transform()
result = dispatcher(**kwargs)
options.do_print = options.language == "notebook"
options.do_show = False
options.hardcopy = False
else:
# needs to be resolved between renderer and dispatcher options
result = dispatcher(**kwargs)
if options.do_print:
sys.stdout.write(".. ---- TEMPLATE START --------\n\n")
if options.language == "rst":
writeRST(sys.stdout,
options,
kwargs,
renderer_options,
transformer_options,
display_options,
tracker_modulename,
tracker_name)
elif options.language == "notebook":
writeNotebook(sys.stdout,
options,
kwargs,
renderer_options,
transformer_options,
display_options,
tracker_modulename,
tracker_name)
sys.stdout.write("\n.. ---- TEMPLATE END ----------\n")
sys.stdout.write("\n.. ---- OUTPUT-----------------\n")
if result and renderer is not None:
if options.layout is not None:
lines = Utils.layoutBlocks(result, layout=options.layout)
print "\n".join(lines)
else:
for r in result:
if r.title:
print ("")
print ("title: %s" % r.title)
print ("")
for s in r:
print(str(s))
if options.hardcopy:
fig_managers = _pylab_helpers.Gcf.get_all_fig_managers()
# create all the images
for figman in fig_managers:
# create all images
figid = figman.num
outfile = re.sub("%s", str(figid), options.hardcopy)
figman.canvas.figure.savefig(outfile, dpi=options.dpi)
if result and options.do_show:
if options.renderer.startswith("r-"):
for rr in result:
for r in rr:
if hasattr(r, 'rggplot'):
from rpy2.robjects import r as R
import rpy2.rinterface
try:
R.plot(r.rggplot)
except rpy2.rinterface.RRuntimeError, msg:
if re.search("object.*not found", str(msg)):
print '%s: available columns in dataframe=%s' % \
(msg,
R('''colnames(rframe)'''))
print("press Ctrl-c to stop")
while 1:
pass
elif len(_pylab_helpers.Gcf.get_all_fig_managers()) > 0:
plt.show()
else:
for rr in result:
for r in rr:
if hasattr(r, 'xls'):
tmpfile, outpath = tempfile.mkstemp(
dir='.', suffix='.xlsx')
os.close(tmpfile)
print ('saving xlsx to %s' % outpath)
r.xls.save(outpath)
elif hasattr(r, 'bokeh'):
import bokeh.plotting as bk
bk.show()
def buildCuffdiffPlots(infile, outfile):
'''create summaries of cufflinks results (including some diagnostic plots)
Plots are created in the <exportdir>/cuffdiff directory.
Plots are:
<geneset>_<method>_<level>_<track1>_vs_<track2>_significance.png
fold change against expression level
'''
###########################################
###########################################
# create diagnostic plots
###########################################
outdir = os.path.join(PARAMS["exportdir"], "cuffdiff")
dbhandle = sqlite3.connect(PARAMS["database"])
prefix = P.snip(infile, ".load")
geneset, method = prefix.split("_")
for level in CUFFDIFF_LEVELS:
tablename_diff = prefix + "_%s_diff" % level
tablename_levels = prefix + "_%s_levels" % level
# note that the ordering of EXPERIMENTS and the _diff table
# needs to be the same as only one triangle is stored of the
# pairwise results. do not plot "undefined" lfold values
# (where treatment_mean or control_mean = 0) do not plot lfold
# values where the confidence bounds contain 0.
for track1, track2 in itertools.combinations(EXPERIMENTS, 2):
statement = """
SELECT CASE WHEN d.treatment_mean < d.control_mean
THEN d.treatment_mean
ELSE d.control_mean END,
d.l2fold, d.significant
FROM %(tablename_diff)s AS d
WHERE treatment_name = '%(track1)s' AND
control_name = '%(track2)s' AND
status = 'OK' AND
treatment_mean > 0 AND
control_mean > 0
""" % locals()
data = zip(*Database.executewait(dbhandle, statement))
pngfile = "%(outdir)s/%(geneset)s_%(method)s_%(level)s_%(track1)s_vs_%(track2)s_significance.png" % locals()
# ian: Bug fix: moved R.png to after data check so that no
# plot is started if there is no data this was leading
# to R falling over from too many open devices
if len(data) == 0:
E.warn("no plot for %s - %s -%s vs %s" %
(pngfile, level, track1, track2))
continue
R.png(pngfile)
R.plot(ro.FloatVector(data[0]),
ro.FloatVector(data[1]),
xlab='min(FPKM)',
ylab='log2fold',
log="x", pch=20, cex=.1,
col=R.ifelse(ro.IntVector(data[2]), "red", "black"))
R['dev.off']()
P.touch(outfile)
from rpy2.robjects import Formula
from rpy2.robjects.packages import importr
from rpy2.robjects import r
grdevices=importr('grDevices')
#accessing pi in R:
pi=r['pi']
print pi
stats=importr('stats')
graphics=importr('graphics')
base=importr('base')
##
sum=r.sum
print sum(np.array([1,2,3,4]))
#######plot1
d=stats.rnorm(10000)
hist=graphics.hist
grdevices.png(file='/Users/behrouzmadahian/Dropbox/Research_and_Development/Python/DEVELOPMENTS/R_int_hist.png',width=512
,height=800)
hist(d,col='blue',ylab='Freg',xlab='rnorm',main='')
grdevices.dev_off()
#####plot2
lattice=importr('lattice')
xyplot=lattice.xyplot
x=range(10)
y=range(10,20)
r.plot(x,y)
raw_input()
