diff options
Diffstat (limited to 'wqflask')
| -rw-r--r-- | wqflask/wqflask/marker_regression/marker_regression.py | 64 |
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: |