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-rw-r--r--wqflask/wqflask/correlation_matrix/show_corr_matrix.py124
1 files changed, 62 insertions, 62 deletions
diff --git a/wqflask/wqflask/correlation_matrix/show_corr_matrix.py b/wqflask/wqflask/correlation_matrix/show_corr_matrix.py
index 155b725f..9ac02ac5 100644
--- a/wqflask/wqflask/correlation_matrix/show_corr_matrix.py
+++ b/wqflask/wqflask/correlation_matrix/show_corr_matrix.py
@@ -25,8 +25,6 @@ import string
import numpy as np
import scipy
-import rpy2.robjects as robjects
-from rpy2.robjects.packages import importr
from base import data_set
from base.webqtlConfig import GENERATED_TEXT_DIR
@@ -162,21 +160,23 @@ class CorrelationMatrix:
for sample in self.all_sample_list:
groups.append(1)
- 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"
+ # 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"
self.js_data = dict(traits=[trait.name for trait in self.traits],
groups=groups,
@@ -185,51 +185,51 @@ 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 = 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 process_loadings(self):
loadings_array = []