diff options
Diffstat (limited to 'wqflask')
| -rw-r--r-- | wqflask/wqflask/correlation_matrix/show_corr_matrix.py | 124 |
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 = [] |