Reenable PCA for correlation matrix

1 files changed, 63 insertions, 61 deletions

diff --git a/wqflask/wqflask/correlation_matrix/show_corr_matrix.py b/wqflask/wqflask/correlation_matrix/show_corr_matrix.py
index 9ac02ac5..e7b16e77 100644
--- a/wqflask/wqflask/correlation_matrix/show_corr_matrix.py
+++ b/wqflask/wqflask/correlation_matrix/show_corr_matrix.py
@@ -23,6 +23,9 @@ import math
 import random
 import string
 
+import rpy2.robjects as ro
+from rpy2.robjects.packages import importr
+
 import numpy as np
 import scipy
 
@@ -160,23 +163,22 @@ class CorrelationMatrix:
         for sample in self.all_sample_list:
             groups.append(1)
 
-        # Not doing PCA until rpy2 is excised
         self.pca_works = "False"
-        # try:
-        #     corr_result_eigen = np.linalg.eig(np.array(self.pca_corr_results))
-        #     corr_eigen_value, corr_eigen_vectors = sortEigenVectors(
-        #         corr_result_eigen)
-
-        #     if self.do_PCA == True:
-        #         self.pca_works = "True"
-        #         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()
-        #     else:
-        #         self.pca_works = "False"
-        # except:
-        #     self.pca_works = "False"
+        try:
+            corr_result_eigen = np.linalg.eig(np.array(self.pca_corr_results))
+            corr_eigen_value, corr_eigen_vectors = sortEigenVectors(
+                corr_result_eigen)
+
+            if self.do_PCA == True:
+                self.pca_works = "True"
+                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()
+            else:
+                self.pca_works = "False"
+        except:
+            self.pca_works = "False"
 
         self.js_data = dict(traits=[trait.name for trait in self.traits],
                             groups=groups,
@@ -185,51 +187,51 @@ class CorrelationMatrix:
                             samples=self.all_sample_list,
                             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 = robjects.FloatVector(
-    #         [item for sublist in self.pca_corr_results for item in sublist])
-
-    #     m = robjects.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')
-
-    #     trait_array = zScore(self.trait_data_array)
-    #     trait_array_vectors = np.dot(corr_eigen_vectors, trait_array)
-
-    #     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)
-
-    #     return pca
+    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')
+
+        trait_array = zScore(self.trait_data_array)
+        trait_array_vectors = np.dot(corr_eigen_vectors, trait_array)
+
+        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)
+
+        return pca
 
     def process_loadings(self):
         loadings_array = []