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-rwxr-xr-xwqflask/base/data_set.py2
-rwxr-xr-xwqflask/wqflask/marker_regression/marker_regression.py5
-rw-r--r--wqflask/wqflask/my_pylmm/pyLMM/lmm.py24
3 files changed, 21 insertions, 10 deletions
diff --git a/wqflask/base/data_set.py b/wqflask/base/data_set.py
index 1520b180..d7328441 100755
--- a/wqflask/base/data_set.py
+++ b/wqflask/base/data_set.py
@@ -385,7 +385,7 @@ class PhenotypeDataSet(DataSet):
continue # for now
if not webqtlUtil.hasAccessToConfidentialPhenotypeTrait(privilege=self.privilege, userName=self.userName, authorized_users=this_trait.authorized_users):
description = this_trait.pre_publication_description
- this_trait.description_display = description
+ this_trait.description_display = unicode(description, "utf8")
if not this_trait.year.isdigit():
this_trait.pubmed_text = "N/A"
diff --git a/wqflask/wqflask/marker_regression/marker_regression.py b/wqflask/wqflask/marker_regression/marker_regression.py
index 6ae1318e..334ce631 100755
--- a/wqflask/wqflask/marker_regression/marker_regression.py
+++ b/wqflask/wqflask/marker_regression/marker_regression.py
@@ -78,8 +78,9 @@ class MarkerRegression(object):
genotype_matrix = np.array(trimmed_genotype_data).T
- print("pheno_vector is: ", pf(pheno_vector))
- print("genotype_matrix is: ", pf(genotype_matrix))
+ print("pheno_vector: ", pf(pheno_vector))
+ print("genotype_matrix: ", pf(genotype_matrix))
+ print("genotype_matrix.shape: ", pf(genotype_matrix.shape))
t_stats, p_values = lmm.run(
pheno_vector,
diff --git a/wqflask/wqflask/my_pylmm/pyLMM/lmm.py b/wqflask/wqflask/my_pylmm/pyLMM/lmm.py
index fc021a0b..62fb0fbd 100644
--- a/wqflask/wqflask/my_pylmm/pyLMM/lmm.py
+++ b/wqflask/wqflask/my_pylmm/pyLMM/lmm.py
@@ -58,6 +58,10 @@ def run_human(pheno_vector,
identifier = str(uuid.uuid4())
+ print("pheno_vector: ", pf(pheno_vector))
+ print("kinship_matrix: ", pf(kinship_matrix))
+ print("kinship_matrix.shape: ", pf(kinship_matrix.shape))
+
lmm_vars = pickle.dumps(dict(
pheno_vector = pheno_vector,
covariate_matrix = covariate_matrix,
@@ -70,12 +74,12 @@ def run_human(pheno_vector,
pheno_vector = pheno_vector[keep]
#print("pheno_vector shape is now: ", pf(pheno_vector.shape))
covariate_matrix = covariate_matrix[keep,:]
- print("kinship_matrix shape is: ", pf(kinship_matrix.shape))
+ #print("kinship_matrix shape is: ", pf(kinship_matrix.shape))
#print("len(keep) is: ", pf(keep.shape))
kinship_matrix = kinship_matrix[keep,:][:,keep]
n = kinship_matrix.shape[0]
- print("n is:", n)
+ #print("n is:", n)
lmm_ob = LMM(pheno_vector,
kinship_matrix,
covariate_matrix)
@@ -86,7 +90,7 @@ def run_human(pheno_vector,
p_values = []
t_stats = []
- print("input_file: ", plink_input_file)
+ #print("input_file: ", plink_input_file)
with Bench("Opening and loading pickle file"):
with gzip.open(plink_input_file, "rb") as input_file:
@@ -103,6 +107,8 @@ def run_human(pheno_vector,
with Bench("Create list of inputs"):
inputs = list(plink_input)
+
+ print("len(genotypes): ", len(inputs))
with Bench("Divide into chunks"):
results = chunks.divide_into_chunks(inputs, 64)
@@ -116,7 +122,7 @@ def run_human(pheno_vector,
timestamp = datetime.datetime.utcnow().isoformat()
- print("Starting adding loop")
+ #print("Starting adding loop")
for part, result in enumerate(results):
#data = pickle.dumps(result, pickle.HIGHEST_PROTOCOL)
holder = pickle.dumps(dict(
@@ -126,10 +132,10 @@ def run_human(pheno_vector,
result = result
), pickle.HIGHEST_PROTOCOL)
- print("Adding:", part)
+ #print("Adding:", part)
Redis.rpush(key, zlib.compress(holder))
- print("End adding loop")
- print("***** Added to {} queue *****".format(key))
+ #print("End adding loop")
+ #print("***** Added to {} queue *****".format(key))
for snp, this_id in plink_input:
#with Bench("part before association"):
if count > 2000:
@@ -157,6 +163,10 @@ def run_human(pheno_vector,
return p_values, t_stats
+#class HumanAssociation(object):
+# def __init__(self):
+#
+
def human_association(snp,
n,
keep,