diff options
| -rw-r--r-- | wqflask/wqflask/correlation_matrix/show_corr_matrix.py | 28 |
1 files changed, 13 insertions, 15 deletions
diff --git a/wqflask/wqflask/correlation_matrix/show_corr_matrix.py b/wqflask/wqflask/correlation_matrix/show_corr_matrix.py index e7b16e77..9462f973 100644 --- a/wqflask/wqflask/correlation_matrix/show_corr_matrix.py +++ b/wqflask/wqflask/correlation_matrix/show_corr_matrix.py @@ -23,8 +23,6 @@ import math import random import string -import rpy2.robjects as ro -from rpy2.robjects.packages import importr import numpy as np import scipy @@ -38,6 +36,12 @@ from utility import helper_functions from utility import corr_result_helpers from utility.redis_tools import get_redis_conn + + +from gn3.computations.principal_component_analysis import compute_pca + +from gn3.computations.principal_component_analysis import process_factor_loadings_tdata + Redis = get_redis_conn() THIRTY_DAYS = 60 * 60 * 24 * 30 @@ -174,7 +178,7 @@ class CorrelationMatrix: self.pca_trait_ids = [] pca = self.calculate_pca( list(range(len(self.traits))), corr_eigen_value, corr_eigen_vectors) - self.loadings_array = self.process_loadings() + self.loadings_array = process_factor_loadings_tdata(self.loadings,len(self.trait_list)) else: self.pca_works = "False" except: @@ -188,18 +192,12 @@ class CorrelationMatrix: sample_data=self.sample_data,) def calculate_pca(self, cols, corr_eigen_value, corr_eigen_vectors): - base = importr('base') - stats = importr('stats') - - corr_results_to_list = ro.FloatVector( - [item for sublist in self.pca_corr_results for item in sublist]) - - m = ro.r.matrix(corr_results_to_list, nrow=len(cols)) - eigen = base.eigen(m) - pca = stats.princomp(m, cor="TRUE") - self.loadings = pca.rx('loadings') - self.scores = pca.rx('scores') - self.scale = pca.rx('scale') + + + pca = compute_pca(self.pca_corr_results) + + self.loadings = pca["components"] + self.scores = pca["scores"] trait_array = zScore(self.trait_data_array) trait_array_vectors = np.dot(corr_eigen_vectors, trait_array) |