From 974699dd81837248b9a1c34e1f7a30cab4192b13 Mon Sep 17 00:00:00 2001
From: bolade <babawale030@gmail.com>
Date: Fri, 21 Nov 2025 14:20:15 +0100
Subject: [PATCH] sighh

---
 .../context_generator.cpython-312.pyc         | Bin 46085 -> 46089 bytes
 .../graph_generator.cpython-312.pyc           | Bin 57999 -> 57999 bytes
 app/services/context_generator.py             |  34 ++++++++++++------
 3 files changed, 23 insertions(+), 11 deletions(-)

diff --git a/app/services/__pycache__/context_generator.cpython-312.pyc b/app/services/__pycache__/context_generator.cpython-312.pyc
index 2fc12bcedf82be2d9aaca30d2af9711456df108e..2e8fc75b5a9b5c035970589f4a254c752b46c042 100644
GIT binary patch
delta 161
zcmZpD!PNPJiT5-wFBbz4)Tb$A_HX3fTF7{D^Wj2YX7vYR(sP^_a9@_uxgf2(BI<&L
z_X)>~7Je7Z{V#|Fd}L-2l>WfMAgeRkxYU<Dmytou`h&~n_ELWi#)8e)Ypq!r-)|Of
zs^(zi+}z)lD#Pfv`RlSh+~ReNtSTE^J}`jj9U-5=^alr)%_lbLG4f}LGD>`60Fgz)
HKo<c37B)6p

delta 157
zcmeBt!PNSKiT5-wFBbz4l&2_Urf=lkTF7{I^Wj2YX5}05DocV_NMBYpy`W;YA?t!;
z$c50bi;m$J93n2rMo#2<Afr9mywsOHlaWEq`h&yf?oxja#=On9Ypq!r-)<Ifs^(y1
z-#oc1Rff@L^Uq~_xW#K2SyeW;d|&|4J3>B#=?^u`o6l_0W8}{eWt8~D03wTofvy1n
D-D5WI

diff --git a/app/services/__pycache__/graph_generator.cpython-312.pyc b/app/services/__pycache__/graph_generator.cpython-312.pyc
index b61675b59930110003f559f1da004c2e085c974d..480e77bb7407ec08e48422ce5a8a4d7d660b4058 100644
GIT binary patch
delta 24
ecmeA_%G`gHnfEj=FBbz4u%;<we%{F2^auc3ln1l`

delta 24
ecmeA_%G`gHnfEj=FBbz4*d{4t{@%#j^auc5ln3wt

diff --git a/app/services/context_generator.py b/app/services/context_generator.py
index 925a216..de02bf4 100644
--- a/app/services/context_generator.py
+++ b/app/services/context_generator.py
@@ -235,19 +235,27 @@ class ContextGenerator:
         return metrics
 
     def _detect_thresholds(self) -> Tuple[Optional[Dict], Optional[Dict]]:
-        """Detect VT1 and VT2 thresholds"""
+        """Detect VT1 and VT2 thresholds (matching notebook logic)"""
+        # VT1: First index where carb burn > fat burn AND remains higher
         condition = self.pnoe_df["CHO_smoothed"] > self.pnoe_df["FAT_smoothed"]
         crossover_indices = condition[condition].index
 
         vt1 = None
         if len(crossover_indices) > 0:
-            vt1_idx = crossover_indices[0]
-            vt1_row = self.pnoe_df.loc[vt1_idx]
-            vt1 = {
-                "HeartRate": vt1_row["HR(bpm)_smoothed"],
-                "Speed": vt1_row["Speed"],
-                "Time": vt1_row["T(sec)"],
-            }
+            # Find first crossover where carbs remain higher for the rest
+            for idx in crossover_indices:
+                if all(
+                    self.pnoe_df.loc[idx:]["CHO_smoothed"]
+                    > self.pnoe_df.loc[idx:]["FAT_smoothed"]
+                ):
+                    vt1_idx = idx
+                    vt1_row = self.pnoe_df.loc[vt1_idx]
+                    vt1 = {
+                        "HeartRate": vt1_row["HR(bpm)_smoothed"],
+                        "Speed": vt1_row["Speed"],
+                        "Time": vt1_row["T(sec)"],
+                    }
+                    break
 
         ve_slope = self.pnoe_df["VE(l/min)_smoothed"].diff()
         second_derivative = ve_slope.diff()
@@ -745,9 +753,13 @@ class ContextGenerator:
         """Calculate detailed metrics for each heart rate zone based on actual data"""
         import math
 
-        # Get zone boundaries
-        fat_max_idx = self.pnoe_df["FAT_smoothed"].idxmax()
-        optimal_row = self.pnoe_df.loc[fat_max_idx]
+        # Get zone boundaries - use optimal fat burning zone (highest fat:carb ratio)
+        # matching notebook logic
+        self.pnoe_df["fat_carb_ratio"] = self.pnoe_df["FAT_smoothed"] / (
+            self.pnoe_df["CHO_smoothed"] + 0.00000001
+        )
+        optimal_fat_idx = self.pnoe_df["fat_carb_ratio"].idxmax()
+        optimal_row = self.pnoe_df.loc[optimal_fat_idx]
 
         # Detect VT1 and VT2
         vt1 = pnoe_metrics.get("vt1")