def run(self):
"""
06-23-05
only import rpy when in type 2 because import rpy doesn't work in qsubbed jobs
run --dstruc_loadin
--[type==1]
--reformat --get_weight
--[type==2]
--draw_graph
--visualize_clusters --draw_graph
"""
self.dstruc_loadin()
if self.type == 1:
if self.output_file:
self.reformat(self.input_graph, self.output_file, self.no_of_genes)
else:
sys.stderr.write("Please the output file.\n")
sys.exit(2)
elif self.type == 2:
from rpy import r
if self.r_fname:
if self.cluster_file:
self.visualize_clusters(self.cluster_file, self.input_graph, self.label_dict[self.label],\
self.r_fname, self.functioncolor, self.plot_type)
else:
self.draw_graph(self.input_graph, self.label_dict[self.label], self.r_fname,\
self.functioncolor, self.plot_type)
r.source(self.r_fname)
stop = raw_input("Exit? Y/n:")
else:
sys.stderr.write("Please give the R_FILE.\n")
def calcKinship(snps):
"""
Requires EMMA to be installed.
"""
a = array(snps)
#r.library("emma")
r.source("emma.R")
return r.emma_kinship(a)
def calcKinship(snps):
"""
Requires EMMA to be installed.
"""
a = array(snps)
#r.library("emma")
print r.source("emma.R")
print a
#print r.emma_kinship(a)
return r.emma_kinship(a)
def runEmma(phed, p_i, k, snps):
#Assume that the accessions are ordered.
i = phed.getPhenIndex(p_i)
#r.library("emma")
r.source("emma.R")
phenValues = []
for vals in phed.phenotypeValues:
phenValues.append(float(vals[i]))
phenArray = array([phenValues])
snpsArray = array(snps)
res = r.emma_REML_t(phenArray, snpsArray, k)
#print res
return res
def runEmma(phed,p_i,k,snps):
#Assume that the accessions are ordered.
i = phed.getPhenIndex(p_i)
#r.library("emma")
r.source("emma.R")
phenValues = []
for vals in phed.phenotypeValues:
phenValues.append(float(vals[i]))
phenArray = array([phenValues])
snpsArray = array(snps)
res = r.emma_REML_t(phenArray,snpsArray,k)
#print res
return res
def getRobject():
global robj
if robj:
log.debug("manalysis.getRobject(): R already imported.")
return robj
else:
log.debug("manalysis.getRobject(): R not found. Importing.")
from rpy import r as robj
# Set up R interface...
manalysis_file=config.get('analysis','manalysis_file')
robj.source(manalysis_file)
log.debug("R interface to manalysis set up via file %s" % manalysis_file)
return robj
def testLapackLoad(self):
from rpy import r
fi = 'logit.r'
#--load the functions
r.source(fi)
#--test a example
x =50000
n = [x, x-500,x+460,x-400,x-100,x-4]
y = [12, 24, 23, 2,4,5]
f = [2,2,2,1,1,1]
jk = r.logit_1fact(n,y,f)
def run(self):
"""
03-09-05
04-01-05
"""
(conn, curs) = db_connect(self.hostname, self.dbname, self.schema)
self.dstruc_loadin(curs)
if self.r_fname:
self.r_f = open(self.r_fname, 'w')
if self.type == 1:
subgraph = self.get_subgraph(curs, self.table, self.mcl_table, self.mcl_id)
#unweighted
weighted=0
self.subgraph_output(self.r_f, subgraph, self.label_dict[self.label], self.global_gene_to_go_dict, \
self.centralnode, self.function, self.functioncolor, self.plot_type, weighted)
self.r_f.close()
r.source(self.r_fname)
raw_input("Pause:\t")
elif self.type == 2:
if self.gene_table==None or self.gene_p_table==None:
sys.stderr.write("Error: Please specify both the gene_p_table and gene_table.\n")
sys.exit(2)
subgraph = self.context_subgraph(curs, self.table, self.mcl_table, self.gene_p_table, self.gene_table, \
self.centralnode, self.function)
self.subgraph_output(self.r_f, subgraph, self.label_dict[self.label], self.global_gene_to_go_dict, \
self.centralnode, self.function, self.functioncolor, self.plot_type)
self.r_f.close()
r.source(self.r_fname)
raw_input("Pause:\t")
elif self.type == 3:
for i in range(self.no_of_datasets):
sys.stdout.write("Dataset %s\n"%(i+1))
if self.edge_table==None:
sys.stderr.write("Error: Please specify both the edge_table.\n")
sys.exit(2)
sub_subgraph = self.subgraph_in_one_dataset(curs, self.table, self.mcl_table, self.edge_table, self.mcl_id, i)
self.subgraph_output(self.r_f, sub_subgraph, self.label_dict[self.label], self.global_gene_to_go_dict, \
self.centralnode, self.function, self.functioncolor, self.plot_type)
self.r_f.close()
r.source(self.r_fname)
#asking to continue or not
no_stop = raw_input("Continue? Y/n:\t")
if no_stop == 'n' or no_stop == 'N':
sys.exit(3)
#open it again for the next dataset
self.r_f = open(self.r_fname, 'w')
elif self.type==4:
subgraph = self.get_subgraph(curs, self.table, self.mcl_table, self.mcl_id)
original_subgraph = self.get_original_graph(curs, subgraph)
self.subgraph_output(self.r_f, original_subgraph, self.label_dict[self.label], self.global_gene_to_go_dict, \
self.centralnode, self.function, self.functioncolor, self.plot_type)
self.r_f.close()
r.source(self.r_fname)
raw_input("Pause:\t")
def __call__(self, gen, semis, morpho_f, phyllo_f, sen_f, prev_f, alpha_f, plan_f, fname):
os.chdir(path)
r.source('writeparsGL.R')
r.readExcell.local_mode(0)
r.as_data_frame.local_mode(0)
bloc = "B1" #B1 par defaut (changer si bcp de variabilite entre blocs)
# dimensions des organes (en, gaine, longueur largeur de feuilles) f(gen,semis)
morpho = r.readExcell(morpho_f, gen+'_'+semis)
# tableau angles des feuilles f(gen,semis)
anglesPrev = r.readExcell(prev_f, gen+'_'+semis)
# dynamique nbvis et nblig
dev = r.readExcell(phyllo_f, gen+'_'+semis)
n_max = round(dev.as_py()['nbvis'][-1])
# dynamique de senescence
r.readExcell.local_mode(1)
sen = r.readExcell(sen_f, gen+'_'+semis)
r.readExcell.local_mode(0)
sen['TT'].append(3000.)
sen['nb_sen'].append(n_max+1)
sen = r.as_data_frame(sen)
# formes de limbes
alpha = r.readExcell(alpha_f,gen)
#plan parcelle
plan = r.readExcell(plan_f, gen+'_'+semis+'_'+bloc)
#fname = "_05_geomF2_S1B1.h"
txt1 = r.writenumf (fname,dev,morpho,anglesPrev)
print txt1
r.writePO (fname)
r.writecarto(fname, plan, 0.8, 0.125)
txt2 = r.writedimkin(fname, dev, sen, morpho)
print txt2
r.writeprev(fname, alpha, anglesPrev, dev)
os.chdir(path_ini)
return join(join(path, 'temp'),fname)
def __call__(self, gen, semis, morpho_f, phyllo_f, sen_f, prev_f, alpha_f,
plan_f, fname):
os.chdir(path)
r.source('writeparsGL.R')
r.readExcell.local_mode(0)
r.as_data_frame.local_mode(0)
bloc = "B1" #B1 par defaut (changer si bcp de variabilite entre blocs)
# dimensions des organes (en, gaine, longueur largeur de feuilles) f(gen,semis)
morpho = r.readExcell(morpho_f, gen + '_' + semis)
# tableau angles des feuilles f(gen,semis)
anglesPrev = r.readExcell(prev_f, gen + '_' + semis)
# dynamique nbvis et nblig
dev = r.readExcell(phyllo_f, gen + '_' + semis)
n_max = round(dev.as_py()['nbvis'][-1])
# dynamique de senescence
r.readExcell.local_mode(1)
sen = r.readExcell(sen_f, gen + '_' + semis)
r.readExcell.local_mode(0)
sen['TT'].append(3000.)
sen['nb_sen'].append(n_max + 1)
sen = r.as_data_frame(sen)
# formes de limbes
alpha = r.readExcell(alpha_f, gen)
#plan parcelle
plan = r.readExcell(plan_f, gen + '_' + semis + '_' + bloc)
#fname = "_05_geomF2_S1B1.h"
txt1 = r.writenumf(fname, dev, morpho, anglesPrev)
print txt1
r.writePO(fname)
r.writecarto(fname, plan, 0.8, 0.125)
txt2 = r.writedimkin(fname, dev, sen, morpho)
print txt2
r.writeprev(fname, alpha, anglesPrev, dev)
os.chdir(path_ini)
return join(join(path, 'temp'), fname)
def run_emma_w_missing_data(snpsd, phed, p_i, chromosome, k, missing_val='NA'):
"""
Runs Emma w. missing data.
"""
#Assume that the accessions are ordered.
snps = snpsd.snps
r.source("emma.R")
phen_vals = phed.getPhenVals(p_i, noNAs=False)
if phen_vals.count('NA'):
print "Coordinating SNPs and phenotypes, removing %d accessions which were missing phenotypic values." % phen_vals.count(
'NA')
ai_to_keep = [i for i in range(len(phen_vals)) if phen_vals[i] != 'NA']
#print ai_to_keep
new_snps = []
for snp in snps:
new_snps.append([snp[i] for i in ai_to_keep])
snps = new_snps
#print snps[0]
phen_vals = [phen_vals[i] for i in ai_to_keep]
#print phen_vals
new_k = zeros((len(ai_to_keep), len(ai_to_keep)))
for j1 in range(len(ai_to_keep)):
for j2 in range(len(ai_to_keep)):
new_k[j1, j2] = k[ai_to_keep[j1], ai_to_keep[j2]]
k = new_k
p_vals = []
for snp in snps:
if snp.count(missing_val):
acc_to_keep = []
new_snp = []
for j, nt in enumerate(snp):
if not nt == missing_val:
acc_to_keep.append(j)
new_snp.append(nt)
new_k = zeros((len(acc_to_keep), len(acc_to_keep)))
new_phen_vals = []
for j1 in range(len(acc_to_keep)):
for j2 in range(len(acc_to_keep)):
new_k[j1, j2] = k[acc_to_keep[j1], acc_to_keep[j2]]
new_phen_vals.append(phen_vals[acc_to_keep[j1]])
if len(set(snp)) == 1:
p_vals.append(1)
print phenArray
print snpsArray
print new_k
else:
phenArray = array([new_phen_vals])
snpsArray = array([new_snp])
#print "Running EMMA w. missing data."
try:
p_vals.extend(
r.emma_REML_t(phenArray, snpsArray, new_k)['ps'])
except Exception, err_str:
print err_str
print phenArray
print snpsArray
print new_k
else:
phenArray = array([phen_vals])
snpsArray = array([snp])
#print "Running EMMA w.o. missing data."
try:
p_vals.extend(r.emma_REML_t(phenArray, snpsArray, k)['ps'])
except Exception, err_str:
print err_str
print phenArray
print snpsArray
print k
from fast_krige import *
import st_cov_fun
from parse_and_check import *
import time
import copy as cp
from mbgw.master_grid import *
from mbgw import auxiliary_data
import os
curpath = os.getcwd()
import mbgw
mbgw_root = __root__ = mbgw.__path__[0]
r_path = mbgw_root+'/joint_simulation/CONDSIMalgorithm'
os.chdir(r_path)
from examineRealization import *
from rpy import r
r.source("CONDSIMpreloop.R")
r.source("CONDSIMmonthloop.R")
r.source('MVRNORM.R')
mvrnormPY=r['MVRNORM']
os.chdir(curpath)
import scipy
from scipy import ndimage, mgrid
from map_utils import grid_convert
#####################TEMP
#from map_utils import getEmpiricalCovarianceFunction_STmarginals
#from map_utils import plotEmpiricalCovarianceFunction
#####################TEMP
from IPython.Debugger import Pdb
return data, is_correct_list
known_fname = '/tmp/hs_fim_92m5x25bfsdfl10q0_7gf1.known'
unknown_fname = '/tmp/hs_fim_92m5x25bfsdfl10q0_7gf1.unknown'
known_data, known_is_correct_list = read_data(known_fname)
unknown_data, unknown_is_correct_list = read_data(unknown_fname)
from numarray import array
from rpy import r, set_default_mode,NO_CONVERSION,BASIC_CONVERSION
set_default_mode(NO_CONVERSION)
#pack data into data_frame
known_data = array(known_data)
known_data_frame = r.as_data_frame({"p_value":known_data[:,0], "recurrence":known_data[:,1], "connectivity":known_data[:,2], \
"cluster_size":known_data[:,3], "gradient":known_data[:,4]})
unknown_data = array(unknown_data)
unknown_data_frame = r.as_data_frame({"p_value":unknown_data[:,0], "recurrence":unknown_data[:,1], "connectivity":unknown_data[:,2], \
"cluster_size":unknown_data[:,3], "gradient":unknown_data[:,4]})
#start to call randomF.r to run randomForest
r.library('randomForest')
r.source('randomF.r')
#rf_model still needs to be in pure R object
rf_model = r.randomF(known_data_frame, known_data[:,-1])
set_default_mode(BASIC_CONVERSION)
unknown_pred = r.predictRandomF(rf_model, unknown_data_frame)
rf_model= rf_model.as_py(BASIC_CONVERSION)
print rf_model.keys()
print rf_model['confusion']
from fast_krige import *
import st_cov_fun
from parse_and_check import *
import time
import copy as cp
from mbgw.master_grid import *
from mbgw import auxiliary_data
import os
curpath = os.getcwd()
import mbgw
mbgw_root = __root__ = mbgw.__path__[0]
r_path = mbgw_root + '/joint_simulation/CONDSIMalgorithm'
os.chdir(r_path)
from examineRealization import *
from rpy import r
r.source("CONDSIMpreloop.R")
r.source("CONDSIMmonthloop.R")
r.source('MVRNORM.R')
mvrnormPY = r['MVRNORM']
os.chdir(curpath)
import scipy
from scipy import ndimage, mgrid
from map_utils import grid_convert
#####################TEMP
#from map_utils import getEmpiricalCovarianceFunction_STmarginals
#from map_utils import plotEmpiricalCovarianceFunction
#####################TEMP
from IPython.Debugger import Pdb
