diff options
Diffstat (limited to 'wqflask')
| -rw-r--r-- | wqflask/wqflask/correlation/correlation_gn3_api.py | 15 |
1 files changed, 10 insertions, 5 deletions
diff --git a/wqflask/wqflask/correlation/correlation_gn3_api.py b/wqflask/wqflask/correlation/correlation_gn3_api.py index 1e3a40f2..7b828016 100644 --- a/wqflask/wqflask/correlation/correlation_gn3_api.py +++ b/wqflask/wqflask/correlation/correlation_gn3_api.py @@ -1,5 +1,7 @@ """module that calls the gn3 api's to do the correlation """ import json +import time +from functools import wraps from wqflask.correlation import correlation_functions @@ -9,6 +11,7 @@ from base.trait import create_trait from base.trait import retrieve_sample_data from gn3.computations.correlations import compute_all_sample_correlation +from gn3.computations.correlations import fast_compute_all_sample_correlation from gn3.computations.correlations import map_shared_keys_to_values from gn3.computations.correlations import compute_all_lit_correlation from gn3.computations.correlations import compute_tissue_correlation @@ -19,9 +22,11 @@ def create_target_this_trait(start_vars): """this function creates the required trait and target dataset for correlation""" if start_vars['dataset'] == "Temp": - this_dataset = data_set.create_dataset(dataset_name="Temp", dataset_type="Temp", group_name=start_vars['group']) + this_dataset = data_set.create_dataset( + dataset_name="Temp", dataset_type="Temp", group_name=start_vars['group']) else: - this_dataset = data_set.create_dataset(dataset_name=start_vars['dataset']) + this_dataset = data_set.create_dataset( + dataset_name=start_vars['dataset']) target_dataset = data_set.create_dataset( dataset_name=start_vars['corr_dataset']) this_trait = create_trait(dataset=this_dataset, @@ -187,10 +192,10 @@ def compute_correlation(start_vars, method="pearson", compute_all=False): if corr_type == "sample": (this_trait_data, target_dataset_data) = fetch_sample_data( start_vars, this_trait, this_dataset, target_dataset) - correlation_results = compute_all_sample_correlation(corr_method=method, - this_trait=this_trait_data, - target_dataset=target_dataset_data) + correlation_results = fast_compute_all_sample_correlation(corr_method=method, + this_trait=this_trait_data, + target_dataset=target_dataset_data) elif corr_type == "tissue": trait_symbol_dict = this_dataset.retrieve_genes("Symbol") tissue_input = get_tissue_correlation_input( |