diff options
| -rw-r--r-- | wqflask/wqflask/correlation/rust_correlation.py | 37 |
1 files changed, 19 insertions, 18 deletions
diff --git a/wqflask/wqflask/correlation/rust_correlation.py b/wqflask/wqflask/correlation/rust_correlation.py index 67bd5ff5..41dd77a1 100644 --- a/wqflask/wqflask/correlation/rust_correlation.py +++ b/wqflask/wqflask/correlation/rust_correlation.py @@ -13,6 +13,8 @@ from wqflask.correlation.correlation_gn3_api import do_lit_correlation from wqflask.correlation.pre_computes import fetch_text_file from wqflask.correlation.pre_computes import read_text_file from wqflask.correlation.pre_computes import write_db_to_textfile +from wqflask.correlation.pre_computes import read_trait_metadata +from wqflask.correlation.pre_computes import cache_trait_metadata from gn3.computations.correlations import compute_all_lit_correlation from gn3.computations.rust_correlation import run_correlation from gn3.computations.rust_correlation import get_sample_corr_data @@ -25,7 +27,7 @@ from wqflask.correlation.exceptions import WrongCorrelationType def query_probes_metadata(dataset, trait_list): """query traits metadata in bulk for probeset""" - if not bool(trait_list) or dataset.type!="ProbeSet": + if not bool(trait_list) or dataset.type != "ProbeSet": return [] with database_connection(SQL_URI) as conn: @@ -63,8 +65,11 @@ def get_metadata(dataset, traits): if probe_mb: return f"Chr{probe_chr}: {probe_mb:.6f}" return f"Chr{probe_chr}: ???" - - return {trait_name: { + cached_metadata = read_trait_metadata(dataset.name) + to_fetch_metadata = list( + set(traits).difference(list(cached_metadata.keys()))) + if to_fetch_metadata: + results = {**({trait_name: { "name": trait_name, "view": True, "symbol": symbol, @@ -77,13 +82,16 @@ def get_metadata(dataset, traits): "location": __location__(probe_chr, probe_mb), "chr": probe_chr, "mb": probe_mb, - "lrs_location":f'Chr{chr_score}: {mb:{".6f" if mb else ""}}', + "lrs_location": f'Chr{chr_score}: {mb:{".6f" if mb else ""}}', "lrs_chr": chr_score, "lrs_mb": mb - } for trait_name, probe_chr, probe_mb, symbol, mean, description, + } for trait_name, probe_chr, probe_mb, symbol, mean, description, additive, lrs, chr_score, mb - in query_probes_metadata(dataset, traits)} + in query_probes_metadata(dataset, to_fetch_metadata)}), **cached_metadata} + cache_trait_metadata(dataset.name, results) + return results + return cached_metadata def chunk_dataset(dataset, steps, name): @@ -235,21 +243,20 @@ def __compute_sample_corr__( """Compute the sample correlations""" (this_dataset, this_trait, target_dataset, sample_data) = target_trait_info - if this_dataset.group.f1list !=None: - this_dataset.group.samplelist+= this_dataset.group.f1list + if this_dataset.group.f1list != None: + this_dataset.group.samplelist += this_dataset.group.f1list - if this_dataset.group.parlist!= None: - this_dataset.group.samplelist+= this_dataset.group.parlist + if this_dataset.group.parlist != None: + this_dataset.group.samplelist += this_dataset.group.parlist sample_data = get_sample_corr_data( sample_type=start_vars["corr_samples_group"], - sample_data= json.loads(start_vars["sample_vals"]), + sample_data=json.loads(start_vars["sample_vals"]), dataset_samples=this_dataset.group.all_samples_ordered()) if not bool(sample_data): return {} - if target_dataset.type == "ProbeSet" and start_vars.get("use_cache") == "true": with database_connection(SQL_URI) as conn: file_path = fetch_text_file(target_dataset.name, conn) @@ -257,23 +264,18 @@ def __compute_sample_corr__( (sample_vals, target_data) = read_text_file( sample_data, file_path) - return run_correlation(target_data, sample_vals, method, ",", corr_type, n_top) - write_db_to_textfile(target_dataset.name, conn) file_path = fetch_text_file(target_dataset.name, conn) if file_path: (sample_vals, target_data) = read_text_file( sample_data, file_path) - return run_correlation(target_data, sample_vals, method, ",", corr_type, n_top) - - target_dataset.get_trait_data(list(sample_data.keys())) def __merge_key_and_values__(rows, current): @@ -288,7 +290,6 @@ def __compute_sample_corr__( if len(target_data) == 0: return {} - return run_correlation( target_data, list(sample_data.values()), method, ",", corr_type, n_top) |