1 files changed, 118 insertions, 0 deletions
diff --git a/gn2/wqflask/wgcna/gn3_wgcna.py b/gn2/wqflask/wgcna/gn3_wgcna.py
new file mode 100644
index 00000000..2cae4f18
--- /dev/null
+++ b/gn2/wqflask/wgcna/gn3_wgcna.py
@@ -0,0 +1,118 @@
+"""module contains code to consume gn3-wgcna api
+and process data to be rendered by datatables
+"""
+
+import requests
+from types import SimpleNamespace
+
+from gn2.utility.helper_functions import get_trait_db_obs
+from gn2.utility.tools import GN3_LOCAL_URL
+
+
+def fetch_trait_data(requestform):
+    """fetch trait data"""
+    db_obj = SimpleNamespace()
+    get_trait_db_obs(db_obj,
+                     [trait.strip()
+                      for trait in requestform['trait_list'].split(',')])
+
+    return process_dataset(db_obj.trait_list)
+
+
+def process_dataset(trait_list):
+    """process datasets and strains"""
+
+    input_data = {}
+    traits = []
+    strains = []
+
+    for trait in trait_list:
+        traits.append(trait[0].name)
+
+        input_data[trait[0].name] = {}
+        for strain in trait[0].data:
+            strains.append(strain)
+            input_data[trait[0].name][strain] = trait[0].data[strain].value
+
+    return {
+        "input": input_data,
+        "trait_names": traits,
+        "sample_names": strains
+    }
+
+
+def process_wgcna_data(response):
+    """function for processing modeigene genes
+    for create row data for datataba"""
+    mod_eigens = response["output"]["ModEigens"]
+
+    sample_names = response["input"]["sample_names"]
+
+    mod_dataset = [[sample] for sample in sample_names]
+
+    for _, mod_values in mod_eigens.items():
+        for (index, _sample) in enumerate(sample_names):
+            mod_dataset[index].append(round(mod_values[index], 3))
+
+    return {
+        "col_names": ["sample_names", *mod_eigens.keys()],
+        "mod_dataset": mod_dataset
+    }
+
+
+def process_image(response):
+    """function to process image check if byte string is empty"""
+    image_data = response["output"]["image_data"]
+    return ({
+        "image_generated": True,
+        "image_data": image_data
+    } if image_data else {
+        "image_generated": False
+    })
+
+
+def run_wgcna(form_data):
+    """function to run wgcna"""
+
+    wgcna_api = f"{GN3_LOCAL_URL}/api/wgcna/run_wgcna"
+
+    trait_dataset = fetch_trait_data(form_data)
+    form_data["minModuleSize"] = int(form_data["MinModuleSize"])
+
+    form_data["SoftThresholds"] = [int(threshold.strip())
+                                   for threshold in form_data['SoftThresholds'].rstrip().split(",")]
+
+    try:
+
+        unique_strains = list(set(trait_dataset["sample_names"]))
+
+        response = requests.post(wgcna_api, json={
+            "sample_names": unique_strains,
+            "trait_names": trait_dataset["trait_names"],
+            "trait_sample_data": list(trait_dataset["input"].values()),
+            **form_data
+
+        }
+        )
+
+        status_code = response.status_code
+        response = response.json()
+
+        parameters = {
+            "nstrains": len(unique_strains),
+            "nphe": len(trait_dataset["trait_names"]),
+            **{key: val for key, val in form_data.items() if key not in ["trait_list"]}
+        }
+
+        return {"error": response} if status_code != 200 else {
+            "error": 'null',
+            "parameters": parameters,
+            "results": response,
+            "data": process_wgcna_data(response["data"]),
+            "image": process_image(response["data"])
+        }
+
+    except requests.exceptions.ConnectionError:
+        return {
+            "error": "A connection error to perform computation occurred"
+        }