diff options
Diffstat (limited to 'wqflask')
| -rw-r--r-- | wqflask/wqflask/my_pylmm/pyLMM/lmm.py | 4 | ||||
| -rw-r--r-- | wqflask/wqflask/my_pylmm/pyLMM/lmm2.py | 12 |
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) |