Merge branch 'chores/code-refactoring' of github.com:Alexanderlacuna/genenetwork2 into Alexanderlacuna-chores/code-refactoring

1 files changed, 208 insertions, 138 deletions

diff --git a/wqflask/wqflask/correlation/show_corr_results.py b/wqflask/wqflask/correlation/show_corr_results.py
index 5da8a6b9..56378d27 100644
--- a/wqflask/wqflask/correlation/show_corr_results.py
+++ b/wqflask/wqflask/correlation/show_corr_results.py
@@ -44,13 +44,10 @@ def set_template_vars(start_vars, correlation_data):
 
     target_dataset_ob = create_dataset(correlation_data['target_dataset'])
     correlation_data['target_dataset'] = target_dataset_ob.as_monadic_dict().data
-
-    table_json = correlation_json_for_table(correlation_data,
-                                            correlation_data['this_trait'],
-                                            correlation_data['this_dataset'],
-                                            target_dataset_ob)
-
-    correlation_data['table_json'] = table_json
+    correlation_data['table_json'] = correlation_json_for_table(
+        start_vars,
+        correlation_data,
+        target_dataset_ob.as_monadic_dict().data)
 
     if target_dataset_ob.type == "ProbeSet":
         filter_cols = [7, 6]
@@ -69,151 +66,224 @@ def set_template_vars(start_vars, correlation_data):
     return correlation_data
 
 
-def correlation_json_for_table(start_vars, correlation_data, this_trait, this_dataset, target_dataset_ob):
-    """Return JSON data for use with the DataTable in the correlation result page
+def apply_filters(trait, target_trait, target_dataset, **filters):
+    def __p_val_filter__(p_lower, p_upper):
 
-    Keyword arguments:
-    correlation_data -- Correlation results
-    this_trait -- Trait being correlated against a dataset, as a dict
-    this_dataset -- Dataset of this_trait, as a monadic dict
-    target_dataset_ob - Target dataset, as a Dataset ob
-    """
-    this_trait = correlation_data['this_trait']
-    this_dataset = correlation_data['this_dataset']
-    target_dataset = target_dataset_ob.as_monadic_dict().data
+        return  not  (p_lower <= float(trait.get("corr_coefficient",0.0)) <= p_upper)
+
+    def __min_filter__(min_expr):
+        if (target_dataset['type'] in ["ProbeSet", "Publish"] and target_trait['mean']):
+            return (min_expr != None) and (float(target_trait['mean']) < min_expr)
+
+        return False
 
-    corr_results = correlation_data['correlation_results']
-    results_list = []
+    def __location_filter__(location_type, location_chr,
+                            min_location_mb, max_location_mb):
 
-    new_traits_metadata = {}
+        if target_dataset["type"] in ["ProbeSet", "'Geno"] and location_type == "gene":
 
-    dataset_metadata = correlation_data["traits_metadata"]
+            return (
+                ((location_chr!=None) and (target_trait["chr"]!=location_chr))
+                     or
+                ((min_location_mb!= None) and (
+                    float(target_trait['mb']) < min_location_mb)
+                    )
 
-    min_expr = get_float(start_vars, 'min_expr')
-    p_range_lower = get_float(start_vars, 'p_range_lower', -1.0)
-    p_range_upper = get_float(start_vars, 'p_range_upper', 1.0)
+                     or
+                    ((max_location_mb != None) and
+                    (float(target_trait['mb']) > float(max_location_mb)
+                     ))
 
-    if ('loc_chr' in start_vars and
-        'min_loc_mb' in start_vars and
-        'max_loc_mb' in start_vars):
+                )
+        elif target_dataset["type"] in ["ProbeSet", "Publish"]:
+
+            return ((location_chr!=None) and (target_trait["lrs_chr"] != location_chr)
+                  or 
+                  ((min_location_mb != None) and (
+                         float(target_trait['lrs_mb']) < float(min_location_mb)))
+                  or
+                ((max_location_mb != None) and (
+                float(target_trait['lrs_mb']) > float(max_location_mb))
+            )
+
+                )
+            
+        return True
+
+    # check if one of the condition is not met i.e One is True
+
+    return (__p_val_filter__(
+        filters.get("p_range_lower"),
+        filters.get("p_range_upper")
+    )
+        or
+        (
+            __min_filter__(
+                filters.get("min_expr")
+            )
+    )
+        or
+        __location_filter__(
+            filters.get("location_type"),
+            filters.get("location_chr"),
+            filters.get("min_location_mb"),
+            filters.get("max_location_mb")
+
+
+    )
+    )
+
+
+def get_user_filters(start_vars):
+    (min_expr, p_min, p_max) = (
+        get_float(start_vars, 'min_expr'),
+        get_float(start_vars, 'p_range_lower', -1.0),
+        get_float(start_vars, 'p_range_upper', 1.0)
+    )
+
+    if all(keys in start_vars for keys in ["loc_chr",
+                                           "min_loc_mb",
+                                           "max_location_mb"]):
+
+        location_chr = get_string(start_vars, "loc_chr")
+        min_location_mb = get_int(start_vars, "min_loc_mb")
+        max_location_mb = get_int(start_vars, "max_loc_mb")
 
-        location_chr = get_string(start_vars, 'loc_chr')
-        min_location_mb = get_int(start_vars, 'min_loc_mb')
-        max_location_mb = get_int(start_vars, 'max_loc_mb')
     else:
         location_chr = min_location_mb = max_location_mb = None
 
-    for i, trait_dict in enumerate(corr_results):
-        trait_name = list(trait_dict.keys())[0]
-        trait = trait_dict[trait_name]
+    return {
+
+        "min_expr": min_expr,
+        "p_range_lower": p_min,
+        "p_range_upper": p_max,
+        "location_chr": location_chr,
+        "location_type": start_vars['location_type'],
+        "min_location_mb": min_location_mb,
+        "max_location_mb": max_location_mb
+
+    }
 
+
+def generate_table_metadata(all_traits, dataset_metadata, dataset_obj):
+
+    def __fetch_trait_data__(trait, dataset_obj):
+        target_trait_ob = create_trait(dataset=dataset_obj,
+                                       name=trait,
+                                       get_qtl_info=True)
+        return jsonable(target_trait_ob, dataset_obj)
+
+    metadata = [__fetch_trait_data__(trait, dataset_obj) for
+                trait in (all_traits ^ dataset_metadata.keys())]
+    return (dataset_metadata | ({trait["name"]: trait for trait in metadata}))
+
+
+def populate_table(dataset_metadata, target_dataset, this_dataset, corr_results, filters):
+
+    def __populate_trait__(idx, trait):
+
+        trait_name = list(trait.keys())[0]
         target_trait = dataset_metadata.get(trait_name)
-        if target_trait is None:
-            target_trait_ob = create_trait(dataset=target_dataset_ob,
-                                           name=trait_name,
-                                           get_qtl_info=True)
-            target_trait = jsonable(target_trait_ob, target_dataset_ob)
-            new_traits_metadata[trait_name] = target_trait
-
-        if (float(trait.get('corr_coefficient',0.0)) >= p_range_lower and
-            float(trait.get('corr_coefficient',0.0)) <= p_range_upper):
-
-            if (target_dataset['type'] == "ProbeSet" or target_dataset['type'] == "Publish") and bool(target_trait['mean']):
-                if (min_expr != None) and (float(target_trait['mean']) < min_expr):
-                    continue
-
-            if start_vars['location_type'] == "gene" and (target_dataset['type'] == "ProbeSet" or target_dataset['type'] == "Geno"):
-                if location_chr != None and (target_trait['chr'] != location_chr):
-                    continue
-                if (min_location_mb != None) and (float(target_trait['mb']) < float(min_location_mb)):
-                    continue
-                if (max_location_mb != None) and (float(target_trait['mb']) > float(max_location_mb)):
-                    continue
-            elif target_dataset['type'] == "ProbeSet" or target_dataset['type'] == "Publish":
-                if location_chr != None and (target_trait['lrs_chr'] != location_chr):
-                    continue
-                if (min_location_mb != None) and (float(target_trait['lrs_mb']) < float(min_location_mb)):
-                    continue
-                if (max_location_mb != None) and (float(target_trait['lrs_mb']) > float(max_location_mb)):
-                    continue
-            else:
-                continue
-        else:
-            continue
-
-        results_dict = {}
-        results_dict['index'] = i + 1
-        results_dict['trait_id'] = target_trait['name']
-        results_dict['dataset'] = target_dataset['name']
-        results_dict['hmac'] = hmac.data_hmac(
-            '{}:{}'.format(target_trait['name'], target_dataset['name']))
-        results_dict['sample_r'] = f"{float(trait.get('corr_coefficient',0.0)):.3f}"
-        results_dict['num_overlap'] = trait.get('num_overlap',0)
-        results_dict['sample_p'] = f"{float(trait.get('p_value',0)):.3e}"
-        if target_dataset['type'] == "ProbeSet":
-            results_dict['symbol'] = target_trait['symbol']
-            results_dict['description'] = "N/A"
-            results_dict['location'] = target_trait['location']
-            results_dict['mean'] = "N/A"
-            results_dict['additive'] = "N/A"
-            if bool(target_trait['description']):
-                results_dict['description'] = target_trait['description']
-            if bool(target_trait['mean']):
-                results_dict['mean'] = f"{float(target_trait['mean']):.3f}"
-            try:
-                results_dict['lod_score'] = f"{float(target_trait['lrs_score']) / 4.61:.1f}"
-            except:
-                results_dict['lod_score'] = "N/A"
-            results_dict['lrs_location'] = target_trait['lrs_location']
-            if bool(target_trait['additive']):
-                results_dict['additive'] = f"{float(target_trait['additive']):.3f}"
-            results_dict['lit_corr'] = "--"
-            results_dict['tissue_corr'] = "--"
-            results_dict['tissue_pvalue'] = "--"
-            if this_dataset['type'] == "ProbeSet":
-                if 'lit_corr' in trait:
-                    results_dict['lit_corr'] = f"{float(trait['lit_corr']):.3f}"
-                if 'tissue_corr' in trait:
-                    results_dict['tissue_corr'] = f"{float(trait['tissue_corr']):.3f}"
-                    results_dict['tissue_pvalue'] = f"{float(trait['tissue_p_val']):.3e}"
-        elif target_dataset['type'] == "Publish":
-            results_dict['abbreviation_display'] = "N/A"
-            results_dict['description'] = "N/A"
-            results_dict['mean'] = "N/A"
-            results_dict['authors_display'] = "N/A"
-            results_dict['additive'] = "N/A"
-            results_dict['pubmed_link'] = "N/A"
-            results_dict['pubmed_text'] = target_trait["pubmed_text"]
-
-            if bool(target_trait['abbreviation']):
-                results_dict['abbreviation_display'] = target_trait['abbreviation']
-            if bool(target_trait['description']):
-                results_dict['description'] = target_trait['description']
-            if bool(target_trait['mean']):
-                results_dict['mean'] = f"{float(target_trait['mean']):.3f}"
-            if bool(target_trait['authors']):
-                authors_list = target_trait['authors'].split(',')
-                if len(authors_list) > 6:
+        trait = trait[trait_name]
+        if not apply_filters(trait, target_trait, target_dataset, **filters):
+            results_dict = {}
+            results_dict['index'] = idx + 1  #
+            results_dict['trait_id'] = target_trait['name']
+            results_dict['dataset'] = target_dataset['name']
+            results_dict['hmac'] = hmac.data_hmac(
+                '{}:{}'.format(target_trait['name'], target_dataset['name']))
+            results_dict['sample_r'] = f"{float(trait.get('corr_coefficient',0.0)):.3f}"
+            results_dict['num_overlap'] = trait.get('num_overlap', 0)
+            results_dict['sample_p'] = f"{float(trait.get('p_value',0)):.3e}"
+            if target_dataset['type'] == "ProbeSet":
+                results_dict['symbol'] = target_trait['symbol']
+                results_dict['description'] = "N/A"
+                results_dict['location'] = target_trait['location']
+                results_dict['mean'] = "N/A"
+                results_dict['additive'] = "N/A"
+                if target_trait['description']:
+                    results_dict['description'] = target_trait['description']
+                if target_trait['mean']:
+                    results_dict['mean'] = f"{float(target_trait['mean']):.3f}"
+                try:
+                    results_dict['lod_score'] = f"{float(target_trait['lrs_score']) / 4.61:.1f}"
+                except:
+                    results_dict['lod_score'] = "N/A"
+                results_dict['lrs_location'] = target_trait['lrs_location']
+                if target_trait['additive']:
+                    results_dict['additive'] = f"{float(target_trait['additive']):.3f}"
+                results_dict['lit_corr'] = "--"
+                results_dict['tissue_corr'] = "--"
+                results_dict['tissue_pvalue'] = "--"
+                if this_dataset['type'] == "ProbeSet":
+                    if 'lit_corr' in trait:
+                        results_dict['lit_corr'] = f"{float(trait['lit_corr']):.3f}"
+                    if 'tissue_corr' in trait:
+                        results_dict['tissue_corr'] = f"{float(trait['tissue_corr']):.3f}"
+                        results_dict['tissue_pvalue'] = f"{float(trait['tissue_p_val']):.3e}"
+            elif target_dataset['type'] == "Publish":
+                results_dict['abbreviation_display'] = "N/A"
+                results_dict['description'] = "N/A"
+                results_dict['mean'] = "N/A"
+                results_dict['authors_display'] = "N/A"
+                results_dict['additive'] = "N/A"
+                results_dict['pubmed_link'] = "N/A"
+                results_dict['pubmed_text'] = target_trait["pubmed_text"]
+
+                if target_trait["abbreviation"]:
+                    results_dict = target_trait['abbreviation']
+
+                if target_trait["description"] == target_trait['description']:
+                    results_dict['description'] = target_trait['description']
+
+                if target_trait["mean"]:
+                    results_dict['mean'] = f"{float(target_trait['mean']):.3f}"
+
+                if target_trait["authors"]:
+                    authors_list = target_trait['authors'].split(',')
                     results_dict['authors_display'] = ", ".join(
-                        authors_list[:6]) + ", et al."
-                else:
-                    results_dict['authors_display'] = target_trait['authors']
-            if 'pubmed_id' in target_trait:
-                results_dict['pubmed_link'] = target_trait['pubmed_link']
-                results_dict['pubmed_text'] = target_trait['pubmed_text']
-            try:
-                results_dict['lod_score'] = f"{float(target_trait['lrs_score']) / 4.61:.1f}"
-            except:
-                results_dict['lod_score'] = "N/A"
-            results_dict['lrs_location'] = target_trait['lrs_location']
-            if bool(target_trait['additive']):
-                results_dict['additive'] = f"{float(target_trait['additive']):.3f}"
-        else:
-            results_dict['location'] = target_trait['location']
+                        authors_list[:6]) + ", et al." if len(authors_list) > 6 else target_trait['authors']
+
+                if "pubmed_id" in target_trait:
+                    results_dict['pubmed_link'] = target_trait['pubmed_link']
+                    results_dict['pubmed_text'] = target_trait['pubmed_text']
+                try:
+                    results_dict["lod_score"] = f"{float(target_trait['lrs_score']) / 4.61:.1f}"
+                except ValueError:
+                    results_dict['lod_score'] = "N/A"
+            else:
+                results_dict['lrs_location'] = target_trait['lrs_location']
+
+            return results_dict
+
+    return [__populate_trait__(idx, trait)
+            for (idx, trait) in enumerate(corr_results)]
+
+
+def correlation_json_for_table(start_vars, correlation_data, target_dataset_ob):
+    """Return JSON data for use with the DataTable in the correlation result page
+
+    Keyword arguments:
+    correlation_data -- Correlation results
+    this_trait -- Trait being correlated against a dataset, as a dict
+    this_dataset -- Dataset of this_trait, as a monadic dict
+    target_dataset_ob - Target dataset, as a Dataset ob
+    """
+    this_dataset = correlation_data['this_dataset']
 
-        results_list.append(results_dict)
+    traits = set()
+    for trait in correlation_data["correlation_results"]:
+        traits.add(list(trait)[0])
 
-    return json.dumps(results_list)
+    dataset_metadata = generate_table_metadata(traits,
+                                               correlation_data["traits_metadata"],
+                                               target_dataset_ob)
+    return json.dumps([result for result in (
+        populate_table(dataset_metadata=dataset_metadata,
+                       target_dataset=target_dataset_ob.as_dict(),
+                       this_dataset=correlation_data['this_dataset'],
+                       corr_results=correlation_data['correlation_results'],
+                       filters=get_user_filters(start_vars))) if result])
 
 
 def get_formatted_corr_type(corr_type, corr_method):
@@ -315,4 +385,4 @@ def get_header_fields(data_type, corr_method):
                              'N',
                              'Sample p(r)']
 
-    return header_fields
+    return header_fields
\ No newline at end of file