delete dead code

1 files changed, 0 insertions, 116 deletions

diff --git a/wqflask/wqflask/correlation/pre_computes.py b/wqflask/wqflask/correlation/pre_computes.py
index d5916673..2831bd39 100644
--- a/wqflask/wqflask/correlation/pre_computes.py
+++ b/wqflask/wqflask/correlation/pre_computes.py
@@ -82,122 +82,6 @@ def generate_filename(*args, suffix="", file_ext="json"):
     return f"{hashlib.md5(string_unicode).hexdigest()}_{suffix}.{file_ext}"
 
 
-def cache_compute_results(base_dataset_type,
-                          base_dataset_name,
-                          target_dataset_name,
-                          corr_method,
-                          correlation_results,
-                          trait_name):
-    """function to cache correlation results for heavy computations"""
-
-    base_timestamp = query_table_timestamp(base_dataset_type)
-
-    target_dataset_timestamp = base_timestamp
-
-    file_name = generate_filename(
-        base_dataset_name, target_dataset_name,
-        base_timestamp, target_dataset_timestamp,
-        suffix="corr_precomputes")
-
-    file_path = os.path.join(TMPDIR, file_name)
-
-    try:
-        with open(file_path, "r+") as json_file_handler:
-            data = json.load(json_file_handler)
-
-            data[trait_name] = correlation_results
-
-            json_file_handler.seek(0)
-
-            json.dump(data, json_file_handler)
-
-            json_file_handler.truncate()
-
-    except FileNotFoundError:
-        with open(file_path, "w+") as file_handler:
-            data = {}
-            data[trait_name] = correlation_results
-
-            json.dump(data, file_handler)
-
-
-def fetch_precompute_results(base_dataset_name,
-                             target_dataset_name,
-                             dataset_type,
-                             trait_name):
-    """function to check for precomputed  results"""
-
-    base_timestamp = target_dataset_timestamp = query_table_timestamp(
-        dataset_type)
-    file_name = generate_filename(
-        base_dataset_name, target_dataset_name,
-        base_timestamp, target_dataset_timestamp,
-        suffix="corr_precomputes")
-
-    file_path = os.path.join(TMPDIR, file_name)
-
-    try:
-        with open(file_path, "r+") as json_handler:
-            correlation_results = json.load(json_handler)
-
-        return correlation_results.get(trait_name)
-
-    except FileNotFoundError:
-        pass
-
-
-def pre_compute_dataset_vs_dataset(base_dataset,
-                                   target_dataset,
-                                   corr_method):
-    """compute sample correlation between dataset vs dataset
-    wn:heavy function should be invoked less frequently
-    input:datasets_data(two dicts),corr_method
-
-    output:correlation results for entire dataset against entire dataset
-    """
-    dataset_correlation_results = {}
-
-    target_traits_data, base_traits_data = get_datasets_data(
-        base_dataset, target_dataset_data)
-
-    for (primary_trait_name, strain_values) in base_traits_data:
-
-        this_trait_data = {
-            "trait_sample_data": strain_values,
-            "trait_id": primary_trait_name
-        }
-
-        trait_correlation_result = compute_all_sample_correlation(
-            corr_method=corr_method,
-            this_trait=this_trait_data,
-            target_dataset=target_traits_data)
-
-        dataset_correlation_results[primary_trait_name] = trait_correlation_result
-
-    return dataset_correlation_results
-
-
-def get_datasets_data(base_dataset, target_dataset_data):
-    """required to pass data in a given format to the pre compute
-    function
-
-    (works for bxd only probeset datasets)
-
-    output:two dicts for datasets with key==trait and value==strains
-    """
-    samples_fetched = base_dataset.group.all_samples_ordered()
-    target_traits_data = target_dataset.get_trait_data(
-        samples_fetched)
-
-    base_traits_data = base_dataset.get_trait_data(
-        samples_fetched)
-
-    target_results = map_shared_keys_to_values(
-        samples_fetched, target_traits_data)
-    base_results = map_shared_keys_to_values(
-        samples_fetched, base_traits_data)
-
-    return (target_results, base_results)
 
 
 def fetch_text_file(dataset_name, conn, text_dir=TMPDIR):