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-rw-r--r--wqflask/wqflask/my_pylmm/pyLMM/lmm.py4
-rw-r--r--wqflask/wqflask/my_pylmm/pyLMM/lmm2.py12
2 files changed, 8 insertions, 8 deletions
diff --git a/wqflask/wqflask/my_pylmm/pyLMM/lmm.py b/wqflask/wqflask/my_pylmm/pyLMM/lmm.py
index 200424ba..f0473f99 100644
--- a/wqflask/wqflask/my_pylmm/pyLMM/lmm.py
+++ b/wqflask/wqflask/my_pylmm/pyLMM/lmm.py
@@ -278,7 +278,7 @@ def run_other_old(pheno_vector,
print("Running the original LMM engine in run_other (old)")
print("REML=",restricted_max_likelihood," REFIT=",refit)
with Bench("Calculate Kinship"):
- kinship_matrix,genotype_matrix = calculate_kinship_new(genotype_matrix, tempdata)
+ kinship_matrix,genotype_matrix = calculate_kinship_old(genotype_matrix, tempdata)
print("kinship_matrix: ", pf(kinship_matrix))
print("kinship_matrix.shape: ", pf(kinship_matrix.shape))
@@ -880,7 +880,7 @@ def gn2_load_redis(key,species,kinship,pheno,geno,new_code=True):
k = kinship.tolist()
params = dict(pheno_vector = pheno.tolist(),
genotype_matrix = geno.tolist(),
- kinship_matrix= k,
+ kinship_matrix = k,
restricted_max_likelihood = True,
refit = False,
temp_uuid = "testrun_temp_uuid",
diff --git a/wqflask/wqflask/my_pylmm/pyLMM/lmm2.py b/wqflask/wqflask/my_pylmm/pyLMM/lmm2.py
index aa6b473d..d67e1205 100644
--- a/wqflask/wqflask/my_pylmm/pyLMM/lmm2.py
+++ b/wqflask/wqflask/my_pylmm/pyLMM/lmm2.py
@@ -85,7 +85,7 @@ def GWAS(Y, X, K, Kva=[], Kve=[], X0=None, REML=True, refit=False):
print("genotype matrix n is:", n)
print("genotype matrix m is:", m)
- if X0 == None:
+ if X0 is None:
X0 = np.ones((n,1))
# Remove missing values in Y and adjust associated parameters
@@ -173,7 +173,7 @@ class LMM2:
When this parameter is not provided, the constructor will set X0 to an n x 1 matrix of all ones to represent a mean effect.
"""
- if X0 == None:
+ if X0 is None:
X0 = np.ones(len(Y)).reshape(len(Y),1)
self.verbose = verbose
@@ -260,7 +260,7 @@ class LMM2:
REML is computed by adding additional terms to the standard LL and can be computed by setting REML=True.
"""
- if X == None: X = self.X0t
+ if X is None: X = self.X0t
elif stack:
self.X0t_stack[:,(self.q)] = matrixMult(self.Kve.T,X)[:,0]
X = self.X0t_stack
@@ -316,7 +316,7 @@ class LMM2:
Given this optimum, the function computes the LL and associated ML solutions.
"""
- if X == None: X = self.X0t
+ if X is None: X = self.X0t
else:
#X = np.hstack([self.X0t,matrixMult(self.Kve.T, X)])
self.X0t_stack[:,(self.q)] = matrixMult(self.Kve.T,X)[:,0]
@@ -340,7 +340,7 @@ class LMM2:
def association(self,X,h=None,stack=True,REML=True,returnBeta=False):
"""
Calculates association statitics for the SNPs encoded in the vector X of size n.
- If h == None, the optimal h stored in optH is used.
+ If h is None, the optimal h stored in optH is used.
"""
if False:
@@ -358,7 +358,7 @@ class LMM2:
self.X0t_stack[:,(self.q)] = m
X = self.X0t_stack
- if h == None: h = self.optH
+ if h is None: h = self.optH
L,beta,sigma,betaVAR = self.LL(h,X,stack=False,REML=REML)
q = len(beta)