From db6eb175aa7c25948677a9b64258afd377016d8c Mon Sep 17 00:00:00 2001
From: Alexander Thomas <77535027+alethomas@users.noreply.github.com>
Date: Sat, 18 Jun 2022 17:37:09 +0200
Subject: [PATCH] fix: voc probability (#541)

---
 resources/lineage-variant-table-formatter.js       | 14 ++++++++++++--
 workflow/scripts/generate-lineage-variant-table.py | 14 +++++++++++---
 2 files changed, 23 insertions(+), 5 deletions(-)

diff --git a/resources/lineage-variant-table-formatter.js b/resources/lineage-variant-table-formatter.js
index c980d5682..862fe9d67 100644
--- a/resources/lineage-variant-table-formatter.js
+++ b/resources/lineage-variant-table-formatter.js
@@ -117,13 +117,23 @@
   Probability: function (prob) {
     prob = parseFloat(prob)
     if (!isNaN(prob)) {
-      var lighting = (0.9 - prob * 0.4) * 100;
+      if (prob < 0.5) {  
+        var lighting = (0.5 + (prob * 0.8)) * 100;
+        var color = "10, 85%, " + lighting + "%"
+
+      } else if (prob < 0.95 && prob >= 0.5){
+        var lighting = ((0.9 * prob) + 0.05) * 100;
+        var color = "47, 100%, " + lighting + "%"
+      } else {
+        var lighting = (8.5 - (prob * 8)) * 100;
+        var color = "138, 72%, " + lighting + "%"
+      }
       if (prob < 0.1) {  
         prob = prob.toExponential(2)
       } else {
         prob = prob.toFixed(2)
       }
-      return `<span class="badge p-0 m-1"><span class="p-1 rounded" style="background:hsl(138, 72%, ${lighting}%);">${prob}</span></span>`;
+      return `<span class="badge p-0 m-1"><span class="p-1 rounded" style="background:hsl(${color});">${prob}</span></span>`;
     } else {
       return "&nbsp;"
     }
diff --git a/workflow/scripts/generate-lineage-variant-table.py b/workflow/scripts/generate-lineage-variant-table.py
index 6784c48a0..6a7491fed 100644
--- a/workflow/scripts/generate-lineage-variant-table.py
+++ b/workflow/scripts/generate-lineage-variant-table.py
@@ -21,11 +21,19 @@ def phred_to_prob(phred):
 
 
 # np.prod returns 1's as values for a pd series with NaN's. A list would return NaN's
-def prod_prob_not_present(probs):
+# switched for use of minimum
+# def prod_prob_not_present(probs):
+#     if pd.isna(probs).any():
+#         return pd.NA
+#     else:
+#         return np.prod(probs)
+
+
+def min_prob_not_present(probs):
     if pd.isna(probs).any():
         return pd.NA
     else:
-        return np.prod(probs)
+        return np.min(probs)
 
 
 def has_numbers(inputString):
@@ -89,7 +97,7 @@ def rename_enumeration(list_length):
 variants_df = variants_df.groupby(["Mutations"]).agg(
     func={
         "Frequency": lambda x: min(sum(x), 1.0),
-        "Prob_not_present": prod_prob_not_present,
+        "Prob_not_present": min_prob_not_present,
         "ReadDepth": np.min,
     },
     axis=1,