new file module for preprocessing input

1 files changed, 57 insertions, 0 deletions

diff --git a/wqflask/wqflask/correlation/rust_correlation.py b/wqflask/wqflask/correlation/rust_correlation.py
new file mode 100644
index 00000000..ebb079fa
--- /dev/null
+++ b/wqflask/wqflask/correlation/rust_correlation.py
@@ -0,0 +1,57 @@
+"""module contains integration code for rust-gn3"""
+import json
+from wqflask.correlation.correlation_functions import get_trait_symbol_and_tissue_values
+from wqflask.correlation.correlation_gn3_api import create_target_this_trait
+from gn3.computations.rust_correlation import run_correlation
+from gn3.computations.rust_correlation import get_sample_corr_data
+from gn3.computations.rust_correlation import parse_tissue_corr_data
+
+
+def compute_correlation_rust(start_vars: dict, corr_type: str,
+                             method: str = "pearson", n_top: int = 500):
+    """function to compute correlation"""
+
+    (this_dataset, this_trait, target_dataset,
+     sample_data) = create_target_this_trait(start_vars)
+
+    if corr_type == "sample":
+
+        all_samples = json.loads(start_vars["sample_vals"])
+        sample_data = get_sample_corr_data(sample_type=start_vars["corr_samples_group"],
+                                           all_samples=all_samples,
+                                           dataset_samples=this_dataset.group.all_samples_ordered())
+
+        target_dataset.get_trait_data(list(sample_data.keys()))
+
+        target_data = []
+        for (key, val) in target_dataset.trait_data.items():
+            lts = [key] + [str(x) for x in val]
+            r = ",".join(lts)
+            target_data.append(r)
+        # breakpoint()
+
+        results = run_correlation(target_data, ",".join(
+            [str(x) for x in list(sample_data.values())]), method, ",")
+
+
+    if corr_type == "tissue":
+
+        trait_symbol_dict = this_dataset.retrieve_genes("Symbol")
+        corr_result_tissue_vals_dict = get_trait_symbol_and_tissue_values(
+            symbol_list=list(trait_symbol_dict.values()))
+
+        data = parse_tissue_corr_data(symbol_name=this_trait.symbol,
+                                      symbol_dict=get_trait_symbol_and_tissue_values(
+                                          symbol_list=[this_trait.symbol]),
+                                      dataset_symbols=trait_symbol_dict,
+                                      dataset_vals=corr_result_tissue_vals_dict)
+
+        if data:
+            results = run_correlation(
+                data[1], data[0], method, ",")
+
+    return {"correlation_results": results[0:n_top],
+            "this_trait": this_trait.name,
+            "target_dataset": start_vars['corr_dataset'],
+            "return_results": n_top
+            }