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from __future__ import absolute_import, print_function, division
import sys
sys.path.append("../../..")
print("sys.path: ", sys.path)
import numpy as np
import zlib
import cPickle as pickle
import redis
Redis = redis.Redis()
import lmm
class ProcessLmmChunk(object):
def __init__(self):
self.get_snp_data()
self.get_lmm_vars()
keep = self.trim_matrices()
self.do_association(keep)
print("p_values is: ", self.p_values)
def get_snp_data(self):
plink_pickled = zlib.decompress(Redis.lpop("plink_inputs"))
plink_data = pickle.loads(plink_pickled)
self.snps = np.array(plink_data['result'])
self.identifier = plink_data['identifier']
def get_lmm_vars(self):
lmm_vars_pickled = Redis.hget(self.identifier, "lmm_vars")
lmm_vars = pickle.loads(lmm_vars_pickled)
self.pheno_vector = np.array(lmm_vars['pheno_vector'])
self.covariate_matrix = np.array(lmm_vars['covariate_matrix'])
self.kinship_matrix = np.array(lmm_vars['kinship_matrix'])
def trim_matrices(self):
v = np.isnan(self.pheno_vector)
keep = True - v
keep = keep.reshape((len(keep),))
if v.sum():
self.pheno_vector = self.pheno_vector[keep]
self.covariate_matrix = self.covariate_matrix[keep,:]
self.kinship_matrix = self.kinship_matrix[keep,:][:,keep]
return keep
def do_association(self, keep):
n = self.kinship_matrix.shape[0]
lmm_ob = lmm.LMM(self.pheno_vector,
self.kinship_matrix,
self.covariate_matrix)
lmm_ob.fit()
self.p_values = []
for snp in self.snps:
snp = snp[0]
p_value, t_stat = lmm.human_association(snp,
n,
keep,
lmm_ob,
self.pheno_vector,
self.covariate_matrix,
self.kinship_matrix,
False)
self.p_values.append(p_value)
#plink_pickled = zlib.decompress(Redis.lpop("plink_inputs"))
#
#plink_data = pickle.loads(plink_pickled)
#result = np.array(plink_data['result'])
#print("snp size is: ", result.shape)
#identifier = plink_data['identifier']
#
#lmm_vars_pickled = Redis.hget(identifier, "lmm_vars")
#lmm_vars = pickle.loads(lmm_vars_pickled)
#
#pheno_vector = np.array(lmm_vars['pheno_vector'])
#covariate_matrix = np.array(lmm_vars['covariate_matrix'])
#kinship_matrix = np.array(lmm_vars['kinship_matrix'])
#
#v = np.isnan(pheno_vector)
#keep = True - v
#keep = keep.reshape((len(keep),))
#print("keep is: ", keep)
#
#if v.sum():
# pheno_vector = pheno_vector[keep]
# covariate_matrix = covariate_matrix[keep,:]
# kinship_matrix = kinship_matrix[keep,:][:,keep]
#
#n = kinship_matrix.shape[0]
#print("n is: ", n)
#lmm_ob = lmm.LMM(pheno_vector,
# kinship_matrix,
# covariate_matrix)
#lmm_ob.fit()
#
#p_values = []
#
#for snp in result:
# snp = snp[0]
# p_value, t_stat = lmm.human_association(snp,
# n,
# keep,
# lmm_ob,
# pheno_vector,
# covariate_matrix,
# kinship_matrix,
# False)
#
# p_values.append(p_value)
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