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-rw-r--r--wqflask/wqflask/my_pylmm/pyLMM/kinship.py14
-rw-r--r--wqflask/wqflask/my_pylmm/pyLMM/lmm.py35
-rw-r--r--wqflask/wqflask/my_pylmm/pyLMM/phenotype.py2
-rw-r--r--wqflask/wqflask/my_pylmm/pyLMM/runlmm.py4
4 files changed, 29 insertions, 26 deletions
diff --git a/wqflask/wqflask/my_pylmm/pyLMM/kinship.py b/wqflask/wqflask/my_pylmm/pyLMM/kinship.py
index 62f7be47..be12417e 100644
--- a/wqflask/wqflask/my_pylmm/pyLMM/kinship.py
+++ b/wqflask/wqflask/my_pylmm/pyLMM/kinship.py
@@ -28,17 +28,21 @@ import time
from optmatrix import matrix_initialize, matrixMultT
-def kinship_full(G):
+def kinship_full(G,uses):
"""
Calculate the Kinship matrix using a full dot multiplication
"""
- print G.shape
+ info,mprint = uses('info','mprint')
+
+ # mprint("kinship_full G",G)
m = G.shape[0] # snps
n = G.shape[1] # inds
- sys.stderr.write(str(m)+" SNPs\n")
- assert m>n, "n should be larger than m (snps>inds)"
- m = np.dot(G.T,G)
+ info("%d SNPs",m)
+ assert m>n, "n should be larger than m (%d snps > %d inds)" % (m,n)
+ # m = np.dot(G.T,G)
+ m = matrixMultT(G.T)
m = m/G.shape[0]
+ # mprint("kinship_full K",m)
return m
def compute_W(job,G,n,snps,compute_size):
diff --git a/wqflask/wqflask/my_pylmm/pyLMM/lmm.py b/wqflask/wqflask/my_pylmm/pyLMM/lmm.py
index e0fc8305..c040e3c2 100644
--- a/wqflask/wqflask/my_pylmm/pyLMM/lmm.py
+++ b/wqflask/wqflask/my_pylmm/pyLMM/lmm.py
@@ -65,7 +65,7 @@ except ImportError:
sys.stderr.write("WARNING: LMM standalone version missing the Genenetwork2 environment\n")
pass
-progress,debug,info = uses('progress','debug','info')
+progress,mprint,debug,info = uses('progress','mprint','debug','info')
#np.seterr('raise')
@@ -277,7 +277,7 @@ 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(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))
@@ -331,7 +331,7 @@ def run_other_new(pheno_vector,
# G = np.apply_along_axis( genotype.normalize, axis=1, arr=G)
with Bench("Calculate Kinship"):
- K,G = calculate_kinship(G, tempdata)
+ K,G = calculate_kinship_new(G, tempdata)
print("kinship_matrix: ", pf(K))
print("kinship_matrix.shape: ", pf(K.shape))
@@ -393,9 +393,9 @@ def calculate_kinship_new(genotype_matrix, temp_data=None):
Call the new kinship calculation where genotype_matrix contains
inds (columns) by snps (rows).
"""
- print("call genotype.normalize")
+ info("call genotype.normalize")
G = np.apply_along_axis( genotype.normalize, axis=0, arr=genotype_matrix)
- print("call calculate_kinship_new")
+ info("call calculate_kinship_new")
return kinship(G.T,uses),G # G gets transposed, we'll turn this into an iterator (FIXME)
def calculate_kinship_old(genotype_matrix, temp_data=None):
@@ -406,11 +406,11 @@ def calculate_kinship_old(genotype_matrix, temp_data=None):
normalizes the resulting vectors and returns the RRM matrix.
"""
- print("call calculate_kinship_old")
+ info("call calculate_kinship_old")
n = genotype_matrix.shape[0]
m = genotype_matrix.shape[1]
- print("genotype 2D matrix n (inds) is:", n)
- print("genotype 2D matrix m (snps) is:", m)
+ info("genotype 2D matrix n (inds) is: %d" % (n))
+ info("genotype 2D matrix m (snps) is: %d" % (m))
assert m>n, "n should be larger than m (snps>inds)"
keep = []
for counter in range(m):
@@ -431,14 +431,13 @@ def calculate_kinship_old(genotype_matrix, temp_data=None):
continue
keep.append(counter)
genotype_matrix[:,counter] = (genotype_matrix[:,counter] - values_mean) / np.sqrt(vr)
- progress('kinship_old',counter,m)
+ progress('kinship_old normalize genotype',counter,m)
genotype_matrix = genotype_matrix[:,keep]
- print("After kinship (old) genotype_matrix: ", pf(genotype_matrix))
- kinship_matrix = np.dot(genotype_matrix, genotype_matrix.T) * 1.0/float(m)
- return kinship_matrix,genotype_matrix
-
-calculate_kinship = calculate_kinship_new # alias
+ mprint("After kinship (old) genotype_matrix: ", genotype_matrix)
+ # kinship_matrix = np.dot(genotype_matrix, genotype_matrix.T) * 1.0/float(m)
+ # return kinship_matrix,genotype_matrix
+ return kinship_full(genotype_matrix.T,uses),genotype_matrix
def GWAS(pheno_vector,
genotype_matrix,
@@ -464,9 +463,9 @@ def GWAS(pheno_vector,
refit - refit the variance component for each SNP
"""
- if kinship_eigen_vals == None:
+ if kinship_eigen_vals is None:
kinship_eigen_vals = []
- if kinship_eigen_vectors == None:
+ if kinship_eigen_vectors is None:
kinship_eigen_vectors = []
n = genotype_matrix.shape[0]
@@ -570,7 +569,7 @@ class LMM:
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: X0 = np.ones(len(Y)).reshape(len(Y),1)
+ if X0 is None: X0 = np.ones(len(Y)).reshape(len(Y),1)
self.verbose = verbose
#x = Y != -9
@@ -663,7 +662,7 @@ class LMM:
REML is computed by adding additional terms to the standard LL and can be computed by setting REML=True.
"""
- if X == None:
+ if X is None:
X = self.X0t
elif stack:
self.X0t_stack[:,(self.q)] = matrixMult(self.Kve.T,X)[:,0]
diff --git a/wqflask/wqflask/my_pylmm/pyLMM/phenotype.py b/wqflask/wqflask/my_pylmm/pyLMM/phenotype.py
index 682ba371..4c8175f7 100644
--- a/wqflask/wqflask/my_pylmm/pyLMM/phenotype.py
+++ b/wqflask/wqflask/my_pylmm/pyLMM/phenotype.py
@@ -24,7 +24,7 @@ def remove_missing(y,g,verbose=False):
Remove missing data from matrices, make sure the genotype data has
individuals as rows
"""
- assert(y!=None)
+ assert(y is not None)
assert(y.shape[0] == g.shape[0])
y1 = y
diff --git a/wqflask/wqflask/my_pylmm/pyLMM/runlmm.py b/wqflask/wqflask/my_pylmm/pyLMM/runlmm.py
index e3e8659c..6a38da56 100644
--- a/wqflask/wqflask/my_pylmm/pyLMM/runlmm.py
+++ b/wqflask/wqflask/my_pylmm/pyLMM/runlmm.py
@@ -134,7 +134,7 @@ elif cmd == 'redis':
# Emulating the redis setup of GN2
G = g
print "Original G",G.shape, "\n", G
- if y != None and options.remove_missing_phenotypes:
+ if y is not None and options.remove_missing_phenotypes:
gnt = np.array(g).T
Y,g,keep = phenotype.remove_missing(y,g.T,options.verbose)
G = g.T
@@ -165,7 +165,7 @@ elif cmd == 'redis':
assert p1==0.0897, "p1=%f" % p1
assert p2==0.0405, "p2=%f" % p2
if options.geno == 'data/test8000.geno':
- assert round(sum(ps)) == 4070
+ assert int(sum(ps)) == 4070
assert len(ps) == 8000
elif cmd == 'kinship':
G = g