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authorPjotr Prins2016-10-05 07:02:35 +0000
committerPjotr Prins2016-10-05 07:02:35 +0000
commitd99da978111aa90f154be84bf40f09953239ac48 (patch)
treee8f255939f463ecee73ad9cd28269386e68eee14 /wqflask
parentff48bdbce4b4f5cb4c561b06268fd91d8fe546a4 (diff)
downloadgenenetwork2-d99da978111aa90f154be84bf40f09953239ac48.tar.gz
print statements should be logger
Diffstat (limited to 'wqflask')
-rw-r--r--wqflask/wqflask/marker_regression/marker_regression.py64
1 files changed, 32 insertions, 32 deletions
diff --git a/wqflask/wqflask/marker_regression/marker_regression.py b/wqflask/wqflask/marker_regression/marker_regression.py
index 324f128c..19e2d50a 100644
--- a/wqflask/wqflask/marker_regression/marker_regression.py
+++ b/wqflask/wqflask/marker_regression/marker_regression.py
@@ -205,12 +205,12 @@ class MarkerRegression(object):
elif self.mapping_method == "plink":
results = self.run_plink()
elif self.mapping_method == "pylmm":
- print("RUNNING PYLMM")
+ logger.debug("RUNNING PYLMM")
if self.num_perm > 0:
self.run_permutations(str(temp_uuid))
results = self.gen_data(str(temp_uuid))
else:
- print("RUNNING NOTHING")
+ logger.debug("RUNNING NOTHING")
if self.pair_scan == True:
self.qtl_results = []
@@ -264,9 +264,9 @@ class MarkerRegression(object):
#Need to convert the QTL objects that qtl reaper returns into a json serializable dictionary
for index, qtl in enumerate(self.qtl_results):
#if index<40:
- # print("lod score is:", qtl['lod_score'])
+ # logger.debug("lod score is:", qtl['lod_score'])
if qtl['chr'] == highest_chr and highest_chr != "X" and highest_chr != "X/Y":
- #print("changing to X")
+ #logger.debug("changing to X")
self.json_data['chr'].append("X")
else:
self.json_data['chr'].append(str(qtl['chr']))
@@ -284,7 +284,7 @@ class MarkerRegression(object):
self.json_data['chrnames'].append([self.species.chromosomes.chromosomes[key].name, self.species.chromosomes.chromosomes[key].mb_length])
chromosome_mb_lengths[key] = self.species.chromosomes.chromosomes[key].mb_length
- # print("json_data:", self.json_data)
+ # logger.debug("json_data:", self.json_data)
self.js_data = dict(
result_score_type = self.score_type,
@@ -312,7 +312,7 @@ class MarkerRegression(object):
self.dataset.group.name,
self.dataset.group.name,
self.dataset.group.name)
- #print("gemma_command:" + gemma_command)
+ #logger.debug("gemma_command:" + gemma_command)
os.system(gemma_command)
@@ -334,7 +334,7 @@ class MarkerRegression(object):
included_markers.append(line.split("\t")[1])
p_values.append(float(line.split("\t")[10]))
#p_values[line.split("\t")[1]] = float(line.split("\t")[10])
- #print("p_values: ", p_values)
+ #logger.debug("p_values: ", p_values)
return included_markers, p_values
def gen_pheno_txt_file(self):
@@ -362,7 +362,7 @@ class MarkerRegression(object):
self.gen_pheno_txt_file_plink(pheno_filename = plink_output_filename)
plink_command = PLINK_COMMAND + ' --noweb --ped %s/%s.ped --no-fid --no-parents --no-sex --no-pheno --map %s/%s.map --pheno %s%s.txt --pheno-name %s --maf %s --missing-phenotype -9999 --out %s%s --assoc ' % (PLINK_PATH, self.dataset.group.name, PLINK_PATH, self.dataset.group.name, TMPDIR, plink_output_filename, self.this_trait.name, self.maf, TMPDIR, plink_output_filename)
- print("plink_command:", plink_command)
+ logger.debug("plink_command:", plink_command)
os.system(plink_command)
@@ -370,11 +370,11 @@ class MarkerRegression(object):
#for marker in self.dataset.group.markers.markers:
# if marker['name'] not in included_markers:
- # print("marker:", marker)
+ # logger.debug("marker:", marker)
# self.dataset.group.markers.markers.remove(marker)
# #del self.dataset.group.markers.markers[marker]
- print("p_values:", pf(p_values))
+ logger.debug("p_values:", pf(p_values))
self.dataset.group.markers.add_pvalues(p_values)
@@ -641,7 +641,7 @@ class MarkerRegression(object):
top_lod_scores = []
- #print("self.num_perm:", self.num_perm)
+ #logger.debug("self.num_perm:", self.num_perm)
for permutation in range(self.num_perm):
@@ -686,10 +686,10 @@ class MarkerRegression(object):
if p_value < lowest_p_value:
lowest_p_value = p_value
- #print("lowest_p_value:", lowest_p_value)
+ #logger.debug("lowest_p_value:", lowest_p_value)
top_lod_scores.append(-math.log10(lowest_p_value))
- #print("top_lod_scores:", top_lod_scores)
+ #logger.debug("top_lod_scores:", top_lod_scores)
self.suggestive = np.percentile(top_lod_scores, 67)
self.significant = np.percentile(top_lod_scores, 95)
@@ -698,13 +698,13 @@ class MarkerRegression(object):
"""Generates p-values for each marker"""
- print("self.vals is:", self.vals)
+ logger.debug("self.vals is:", self.vals)
pheno_vector = np.array([(val == "x" or val == "") and np.nan or float(val) for val in self.vals])
#lmm_uuid = str(uuid.uuid4())
key = "pylmm:input:" + temp_uuid
- print("key is:", pf(key))
+ logger.debug("key is:", pf(key))
#with Bench("Loading cache"):
# result = Redis.get(key)
@@ -713,7 +713,7 @@ class MarkerRegression(object):
#p_values = self.trim_results(p_values)
else:
- print("NOW CWD IS:", os.getcwd())
+ logger.debug("NOW CWD IS:", os.getcwd())
genotype_data = [marker['genotypes'] for marker in self.dataset.group.markers.markers]
no_val_samples = self.identify_empty_samples()
@@ -721,9 +721,9 @@ class MarkerRegression(object):
genotype_matrix = np.array(genotype_data).T
- #print("pheno_vector: ", pf(pheno_vector))
- #print("genotype_matrix: ", pf(genotype_matrix))
- #print("genotype_matrix.shape: ", pf(genotype_matrix.shape))
+ #logger.debug("pheno_vector: ", pf(pheno_vector))
+ #logger.debug("genotype_matrix: ", pf(genotype_matrix))
+ #logger.debug("genotype_matrix.shape: ", pf(genotype_matrix.shape))
#params = {"pheno_vector": pheno_vector,
# "genotype_matrix": genotype_matrix,
@@ -731,8 +731,8 @@ class MarkerRegression(object):
# "refit": False,
# "temp_data": tempdata}
- # print("genotype_matrix:", str(genotype_matrix.tolist()))
- # print("pheno_vector:", str(pheno_vector.tolist()))
+ # logger.debug("genotype_matrix:", str(genotype_matrix.tolist()))
+ # logger.debug("pheno_vector:", str(pheno_vector.tolist()))
params = dict(pheno_vector = pheno_vector.tolist(),
genotype_matrix = genotype_matrix.tolist(),
@@ -745,14 +745,14 @@ class MarkerRegression(object):
)
json_params = json.dumps(params)
- #print("json_params:", json_params)
+ #logger.debug("json_params:", json_params)
Redis.set(key, json_params)
Redis.expire(key, 60*60)
- print("before printing command")
+ logger.debug("before printing command")
command = PYLMM_COMMAND + ' --key {} --species {}'.format(key, "other")
- print("command is:", command)
- print("after printing command")
+ logger.debug("command is:", command)
+ logger.debug("after printing command")
shell(command)
@@ -762,7 +762,7 @@ class MarkerRegression(object):
json_results = Redis.blpop("pylmm:results:" + temp_uuid, 45*60)
results = json.loads(json_results[1])
p_values = [float(result) for result in results['p_values']]
- #print("p_values:", p_values[:10])
+ #logger.debug("p_values:", p_values[:10])
#p_values = self.trim_results(p_values)
t_stats = results['t_stats']
@@ -773,7 +773,7 @@ class MarkerRegression(object):
# refit=False,
# temp_data=tempdata
#)
- #print("p_values:", p_values)
+ #logger.debug("p_values:", p_values)
self.dataset.group.markers.add_pvalues(p_values)
@@ -782,7 +782,7 @@ class MarkerRegression(object):
return self.dataset.group.markers.markers
def trim_results(self, p_values):
- print("len_p_values:", len(p_values))
+ logger.debug("len_p_values:", len(p_values))
if len(p_values) > 500:
p_values.sort(reverse=True)
trimmed_values = p_values[:500]
@@ -801,7 +801,7 @@ class MarkerRegression(object):
kinship_matrix = np.fromfile(open(file_base + '.kin','r'),sep=" ")
kinship_matrix.resize((len(plink_input.indivs),len(plink_input.indivs)))
- print("Before creating params")
+ logger.debug("Before creating params")
params = dict(pheno_vector = pheno_vector.tolist(),
covariate_matrix = covariate_matrix.tolist(),
@@ -820,12 +820,12 @@ class MarkerRegression(object):
Redis.set(key, json_params)
Redis.expire(key, 60*60)
- print("Before creating the command")
+ logger.debug("Before creating the command")
command = PYLMM_COMMAND+' --key {} --species {}'.format(key,
"human")
- print("command is:", command)
+ logger.debug("command is:", command)
os.system(command)
@@ -848,7 +848,7 @@ class MarkerRegression(object):
return p_values, t_stats
def get_lod_score_cutoff(self):
- print("INSIDE GET LOD CUTOFF")
+ logger.debug("INSIDE GET LOD CUTOFF")
high_qtl_count = 0
for marker in self.dataset.group.markers.markers:
if marker['lod_score'] > 1: