def heatmap3py(numDataR, ColSideColors, annoColDicList, fileName=None, outPath=None):
from rpy2.robjects.packages import importr
heatmap = importr("heatmap3")
grdevices = importr("grDevices")
from rpy2.robjects.functions import SignatureTranslatedFunction
# explicitly translate the R argument to legal python name
heatmap.showLegend = SignatureTranslatedFunction(heatmap.showLegend,
init_prm_translate = {'pt_bg': 'pt.bg'})
# annoName = ColSideColors.colnames
# draw heatmap in file
if (fileName!=None):
grdevices.pdf(file = fileName )
heatmap.heatmap3(numDataR,ColSideColors=ColSideColors,showRowDendro=False)
grdevices.dev_off()
for i in range(len(annoColDicList)):
anno = robjects.StrVector(annoColDicList[i].keys())
col = robjects.StrVector(annoColDicList[i].values())
fileName = outPath +"/heatmapLegend" + str(i) + ".pdf"
grdevices.pdf(file = fileName )
heatmap.showLegend(legend= anno,col=col,cex=1.5, title="Annotation Legend: "+ColSideColors.colnames[i], pch=22, lwd = robjects.NA_Integer, pt_bg=col)
grdevices.dev_off()
else:
# Draw heatmap in R window
heatmap.heatmap3(numDataR,ColSideColors=ColSideColors,showRowDendro=False)
# Plot legends in another window
for i in range(len(annoColDicList)):
grdevices.dev_new()
anno = robjects.StrVector(annoColDicList[i].keys())
col = robjects.StrVector(annoColDicList[i].values())
heatmap.showLegend(legend= anno,col=col,cex=1.5, title="Annotation Legend: "+ ColSideColors.colnames[i], pch=22, lwd = robjects.NA_Integer, pt_bg=col)
def training_step(self, train=None, opt_Q=True, opt_sg=True):
# self.nb_students = len(train)
# self.nb_questions = len(train[0])
# raw_data = list(map(int, reduce(lambda x, y: x + y, train)))
# self.data = r.matrix(robjects.IntVector(raw_data), nrow=self.nb_students, byrow=True)
if os.path.isfile(self.get_backup_path()):
print('Cool, already found!', self.checksum)
r.load(self.get_backup_path())
#self.r_scores = r('scores')
self.load()
else:
model = ltm.rasch(self.r_data)
self.coeff = ltm.coef_rasch(model)
ltm.factor_scores = SignatureTranslatedFunction(ltm.factor_scores, init_prm_translate={'resp_patterns': 'resp.patterns'}) # Mais dans quel monde vivons-nous ma p'tite dame
self.r_scores = ltm.factor_scores(model, resp_patterns=self.r_data).rx('score.dat')[0].rx('z1')[0]
robjects.globalenv['scores'] = self.r_scores
self.scores = np.array(self.r_scores)
r('data <- %s' % self.r_data.r_repr())
#r('data[1][1] <- NA')
r('coeff <- coef(rasch(data))')
r('one <- rep(1, %d)' % self.nb_questions)
r('itembank <- cbind(coeff[,2:1], 1 - one, one)')
self.itembank = np.array(r('itembank'))
# self.compute_all_errors()
r('save(itembank, scores, file="{:s}")'.format(self.get_backup_path()))
self.save() # To pickle file
def default_ri2ro(o):
""" Convert an :class:`rpy2.rinterface.Sexp` object to a higher-level object,
without copying the R object.
:param o: object
:rtype: :class:`rpy2.robjects.RObject` (and subclasses)
"""
res = None
try:
rcls = o.do_slot("class")
except LookupError as le:
rcls = [None]
if isinstance(o, RObject):
res = o
elif isinstance(o, SexpVector):
if 'data.frame' in rcls:
res = vectors.DataFrame(o)
if res is None:
try:
dim = o.do_slot("dim")
if len(dim) == 2:
res = vectors.Matrix(o)
else:
res = vectors.Array(o)
except LookupError as le:
if o.typeof == rinterface.INTSXP:
if 'factor' in rcls:
res = vectors.FactorVector(o)
else:
res = vectors.IntVector(o)
elif o.typeof == rinterface.REALSXP:
if o.rclass[0] == 'POSIXct':
res = vectors.POSIXct(o)
else:
res = vectors.FloatVector(o)
elif o.typeof == rinterface.STRSXP:
res = vectors.StrVector(o)
elif o.typeof == rinterface.VECSXP:
res = vectors.ListVector(o)
elif o.typeof == rinterface.LANGSXP and 'formula' in rcls:
res = Formula(o)
else:
res = vectors.Vector(o)
elif isinstance(o, SexpClosure):
res = SignatureTranslatedFunction(o)
elif isinstance(o, SexpEnvironment):
res = Environment(o)
elif isinstance(o, SexpS4):
res = RS4(o)
elif isinstance(o, SexpExtPtr):
res = o
elif o is NULL:
res = o
else:
res = RObject(o)
return res
def __fill_rpy2r__(self):
super(SignatureTranslatedPackage, self).__fill_rpy2r__()
for name, robj in self.__dict__.iteritems():
if isinstance(
robj,
rinterface.Sexp) and robj.typeof == rinterface.CLOSXP:
self.__dict__[name] = SignatureTranslatedFunction(
self.__dict__[name])
for k,v in sorted(fullhash.items()):
for k1,v1 in sorted(v.items()):
newhash[k]["mean"].append(meanstdv(v1)[0])
for k,v in sorted(fullhash.items()):
for k1,v1 in sorted(v.items()):
newhash[k]["stdabw"].append(meanstdv(v1)[1])
for k,v in sorted(fullhash.items()):
for k1,v1 in sorted(v.items()):
newhash[k]["se"].append(meanstdv(v1)[2])
#print newhash
r=robjects.r
graphics = importr('graphics')
grdevices = importr('grDevices')
gplot=importr('gplots')
graphics.par = SignatureTranslatedFunction(graphics.par,init_prm_translate = {'cex_axis': 'cex.axis'})
graphics.par1 = SignatureTranslatedFunction(graphics.par,init_prm_translate = {'cex_lab': 'cex.lab'})
popnames = robjects.StrVector(pop)
print popnames
#sys.exit()
grdevices.pdf(file=out,width=30,height=10)
a_bf15=[0.5,1.67,2.84,4.01]
candr=robjects.vectors.StrVector(cand)
colr=robjects.vectors.StrVector(["green","red","blue"])
base_bf15=robjects.vectors.FloatVector(fullhash["bf15"][pops[0]])
f15_bf15=robjects.vectors.FloatVector(fullhash["bf15"][pops[1]])
f27_bf15=robjects.vectors.FloatVector(fullhash["bf15"][pops[2]])
f37_bf15=robjects.vectors.FloatVector(fullhash["bf15"][pops[3]])
a_bf37=[0.75,1.92,3.09,4.26]
base_bf37=robjects.vectors.FloatVector(fullhash["bf37"][pops[0]])
def _rpy2py_sexpclosure(obj):
return SignatureTranslatedFunction(obj)
def _(obj):
return SignatureTranslatedFunction(obj)
res = vectors.FactorVector(o)
else:
res = vectors.IntVector(o)
elif o.typeof == rinterface.REALSXP:
res = vectors.FloatVector(o)
elif o.typeof == rinterface.STRSXP:
res = vectors.StrVector(o)
elif o.typeof == rinterface.VECSXP:
res = vectors.ListVector(o)
elif o.typeof == rinterface.LANGSXP and 'formula' in rcls:
res = Formula(o)
else:
res = vectors.Vector(o)
elif isinstance(o, SexpClosure):
res = SignatureTranslatedFunction(o)
elif isinstance(o, SexpEnvironment):
res = Environment(o)
elif isinstance(o, SexpS4):
res = RS4(o)
elif isinstance(o, SexpExtPtr):
res = o
elif o is NULL:
res = o
else:
res = RObject(o)
return res
conversion.ri2py = default_ri2py
import numpy as np
from multiprocessing import Pool
import pickle
import os
from sklearn.metrics import pairwise_distances
import rpy2.robjects.numpy2ri
from rpy2.robjects.packages import importr
from rpy2.robjects.functions import SignatureTranslatedFunction
rpy2.robjects.numpy2ri.activate()
Rsession = rpy2.robjects.r
DTW = importr('dtw')
Rsession.dtw = SignatureTranslatedFunction(
Rsession.dtw, init_prm_translate={'window_size': 'window.size'})
def interpolate(x, maxlen):
if len(x) == maxlen:
return x
else:
return np.interp(np.linspace(0, 1, maxlen), np.linspace(0, 1, len(x)),
x)
def compute_dtw_R_sakoechiba(tup):
s1, s2, wsize = tup
alignment = Rsession.dtw(gdata[s1],
gdata[s2],
keep_internals=False,
distance_only=True,
window_type='sakoechiba',
