diff options
Diffstat (limited to 'wqflask')
| -rw-r--r-- | wqflask/wqflask/correlation_matrix/show_corr_matrix.py | 35 |
1 files changed, 20 insertions, 15 deletions
diff --git a/wqflask/wqflask/correlation_matrix/show_corr_matrix.py b/wqflask/wqflask/correlation_matrix/show_corr_matrix.py index efe4ecea..1bc2fb2f 100644 --- a/wqflask/wqflask/correlation_matrix/show_corr_matrix.py +++ b/wqflask/wqflask/correlation_matrix/show_corr_matrix.py @@ -33,8 +33,6 @@ import math import collections import resource -import numarray -import numarray.linear_algebra as la import numpy as np import scipy @@ -117,7 +115,6 @@ class CorrelationMatrix(object): self.corr_results = [] self.pca_corr_results = [] - self.trait_data_array = [] self.shared_samples_list = self.all_sample_list for trait_db in self.trait_list: this_trait = trait_db[0] @@ -126,13 +123,6 @@ class CorrelationMatrix(object): this_db_samples = this_db.group.all_samples_ordered() this_sample_data = this_trait.data - this_trait_vals = [] - for index, sample in enumerate(this_db_samples): - if (sample in this_sample_data): - sample_value = this_sample_data[sample].value - this_trait_vals.append(sample_value) - self.trait_data_array.append(this_trait_vals) - corr_result_row = [] pca_corr_result_row = [] is_spearman = False #ZS: To determine if it's above or below the diagonal @@ -146,12 +136,13 @@ class CorrelationMatrix(object): target_vals = [] for index, sample in enumerate(target_samples): if (sample in this_sample_data) and (sample in target_sample_data): - if sample not in self.shared_samples_list: - self.shared_samples_list.remove(sample) sample_value = this_sample_data[sample].value target_sample_value = target_sample_data[sample].value this_trait_vals.append(sample_value) target_vals.append(target_sample_value) + else: + if sample in self.shared_samples_list: + self.shared_samples_list.remove(sample) this_trait_vals, target_vals, num_overlap = corr_result_helpers.normalize_values(this_trait_vals, target_vals) @@ -175,7 +166,21 @@ class CorrelationMatrix(object): self.corr_results.append(corr_result_row) self.pca_corr_results.append(pca_corr_result_row) - corr_result_eigen = la.eigenvectors(numarray.array(self.pca_corr_results)) + self.trait_data_array = [] + for trait_db in self.trait_list: + this_trait = trait_db[0] + this_db = trait_db[1] + this_db_samples = this_db.group.all_samples_ordered() + this_sample_data = this_trait.data + + this_trait_vals = [] + for index, sample in enumerate(this_db_samples): + if (sample in this_sample_data) and (sample in self.shared_samples_list): + sample_value = this_sample_data[sample].value + this_trait_vals.append(sample_value) + self.trait_data_array.append(this_trait_vals) + + corr_result_eigen = np.linalg.eig(np.array(self.pca_corr_results)) corr_eigen_value, corr_eigen_vectors = sortEigenVectors(corr_result_eigen) groups = [] @@ -226,7 +231,7 @@ class CorrelationMatrix(object): self.scale = pca.rx('scale') trait_array = zScore(self.trait_data_array) - trait_array = np.array(trait_array) + trait_array = trait_array trait_array_vectors = np.dot(corr_eigen_vectors, trait_array) pca_traits = [] @@ -307,6 +312,6 @@ def sortEigenVectors(vector): B.append(item[1]) sum = reduce(lambda x,y: x+y, A, 0.0) A = map(lambda x:x*100.0/sum, A) - return [A,B] + return [A, B] except: return []
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