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-rw-r--r--wqflask/wqflask/correlation_matrix/show_corr_matrix.py24
1 files changed, 10 insertions, 14 deletions
diff --git a/wqflask/wqflask/correlation_matrix/show_corr_matrix.py b/wqflask/wqflask/correlation_matrix/show_corr_matrix.py
index 9b4cb2eb..88d62045 100644
--- a/wqflask/wqflask/correlation_matrix/show_corr_matrix.py
+++ b/wqflask/wqflask/correlation_matrix/show_corr_matrix.py
@@ -21,27 +21,23 @@
import datetime
import random
import string
-
-
import numpy as np
import scipy
-from base import data_set
+from base.data_set import create_dataset
from base.webqtlConfig import GENERATED_TEXT_DIR
-from utility import helper_functions
-from utility import corr_result_helpers
+
+
+from utility.helper_functions import get_trait_db_obs
+from utility.corr_result_helpers import normalize_values
from utility.redis_tools import get_redis_conn
from gn3.computations.pca import compute_pca
-
from gn3.computations.pca import process_factor_loadings_tdata
from gn3.computations.pca import generate_pca_temp_traits
from gn3.computations.pca import cache_pca_dataset
-Redis = get_redis_conn()
-THIRTY_DAYS = 60 * 60 * 24 * 30
-
class CorrelationMatrix:
@@ -49,7 +45,7 @@ class CorrelationMatrix:
trait_db_list = [trait.strip()
for trait in start_vars['trait_list'].split(',')]
- helper_functions.get_trait_db_obs(self, trait_db_list)
+ get_trait_db_obs(self, trait_db_list)
self.all_sample_list = []
self.traits = []
@@ -117,7 +113,7 @@ class CorrelationMatrix:
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, num_overlap = normalize_values(
this_trait_vals, target_vals)
if num_overlap < self.lowest_overlap:
@@ -193,8 +189,9 @@ class CorrelationMatrix:
self.scores = pca["scores"]
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 = create_dataset(
+ dataset_name="Temp", dataset_type="Temp",
+ group_name=this_group_name)
temp_dataset.group.get_samplelist()
pca_temp_traits = generate_pca_temp_traits(species=temp_dataset.group.species, group=this_group_name,
@@ -203,7 +200,6 @@ class CorrelationMatrix:
shared_samples=self.shared_samples_list,
create_time=datetime.datetime.now().strftime("%m%d%H%M%S"))
-
cache_pca_dataset(redis_conn=get_redis_conn(
), exp_days=60 * 60 * 24 * 30, pca_trait_dict=pca_temp_traits)