process_plink.py: remove unused file

1 files changed, 0 insertions, 127 deletions

diff --git a/wqflask/wqflask/my_pylmm/pyLMM/process_plink.py b/wqflask/wqflask/my_pylmm/pyLMM/process_plink.py
deleted file mode 100644
index e47c18e1..00000000
--- a/wqflask/wqflask/my_pylmm/pyLMM/process_plink.py
+++ /dev/null
@@ -1,127 +0,0 @@
-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)
-    
-
-
-