diff options
| -rw-r--r-- | wqflask/wqflask/correlation_matrix/show_corr_matrix.py | 63 |
1 files changed, 32 insertions, 31 deletions
diff --git a/wqflask/wqflask/correlation_matrix/show_corr_matrix.py b/wqflask/wqflask/correlation_matrix/show_corr_matrix.py index bcd73436..d5ec738b 100644 --- a/wqflask/wqflask/correlation_matrix/show_corr_matrix.py +++ b/wqflask/wqflask/correlation_matrix/show_corr_matrix.py @@ -19,7 +19,6 @@ # This module is used by GeneNetwork project (www.genenetwork.org) import datetime -import math import random import string @@ -29,9 +28,6 @@ import scipy from base import data_set from base.webqtlConfig import GENERATED_TEXT_DIR -from functools import reduce -from functools import cmp_to_key -from utility import webqtlUtil from utility import helper_functions from utility import corr_result_helpers from utility.redis_tools import get_redis_conn @@ -42,6 +38,8 @@ from gn3.computations.principal_component_analysis import compute_pca from gn3.computations.principal_component_analysis import process_factor_loadings_tdata from gn3.computations.principal_component_analysis import generate_pca_traits_vals +from gn3.computations.principal_component_analysis import generate_pca_temp_dataset +from gn3.computations.principal_component_analysis import cache_pca_dataset Redis = get_redis_conn() THIRTY_DAYS = 60 * 60 * 24 * 30 @@ -171,11 +169,11 @@ class CorrelationMatrix: self.pca_works = "False" try: + if self.do_PCA == True: self.pca_works = "True" self.pca_trait_ids = [] - pca = self.calculate_pca( - list(range(len(self.traits)))) + pca = self.calculate_pca() self.loadings_array = process_factor_loadings_tdata(self.loadings,len(self.trait_list)) else: self.pca_works = "False" @@ -189,7 +187,7 @@ class CorrelationMatrix: samples=self.all_sample_list, sample_data=self.sample_data,) - def calculate_pca(self, cols): + def calculate_pca(self): pca = compute_pca(self.pca_corr_results) @@ -197,36 +195,37 @@ class CorrelationMatrix: self.loadings = pca["components"] self.scores = pca["scores"] - trait_array_vectors = generate_pca_traits_vals(self.trait_data_array,self.pca_corr_results) - - - - pca_traits = [] - for i, vector in enumerate(trait_array_vectors): - # ZS: Check if below check is necessary - # if corr_eigen_value[i-1] > 100.0/len(self.trait_list): - pca_traits.append((vector * -1.0).tolist()) this_group_name = self.trait_list[0][1].group.name temp_dataset = data_set.create_dataset( dataset_name="Temp", dataset_type="Temp", group_name=this_group_name) temp_dataset.group.get_samplelist() - for i, pca_trait in enumerate(pca_traits): - trait_id = "PCA" + str(i + 1) + "_" + temp_dataset.group.species + "_" + \ - this_group_name + "_" + datetime.datetime.now().strftime("%m%d%H%M%S") - this_vals_string = "" - position = 0 - for sample in temp_dataset.group.all_samples_ordered(): - if sample in self.shared_samples_list: - this_vals_string += str(pca_trait[position]) - this_vals_string += " " - position += 1 - else: - this_vals_string += "x " - this_vals_string = this_vals_string[:-1] - Redis.set(trait_id, this_vals_string, ex=THIRTY_DAYS) - self.pca_trait_ids.append(trait_id) + + species = temp_dataset.group.species + + group =this_group_name + + trait_data_array = self.trait_data_array + + pca_corr = self.pca_corr_results + + sample_list = temp_dataset.group.all_samples_ordered() + + + shared = self.shared_samples_list + + dt_time = datetime.datetime.now().strftime("%m%d%H%M%S") + + + + results = generate_pca_temp_dataset(species = species, group= group,traits_data = self.trait_data_array,corr_array = self.pca_corr_results,dataset_samples = sample_list, shared_samples=shared,create_time=dt_time) + + + + cache_pca_dataset(Redis,THIRTY_DAYS,results) + + self.pca_trait_ids = list(results.keys()) return pca @@ -269,3 +268,5 @@ def export_corr_matrix(corr_results): return corr_matrix_filename, matrix_export_path + + |