diff --git a/.gitignore b/.gitignore
new file mode 100644
index 000000000..e43b0f988
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+.DS_Store
diff --git a/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/.gitignore b/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/.gitignore
new file mode 100644
index 000000000..8adbb37c5
--- /dev/null
+++ b/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/.gitignore
@@ -0,0 +1,12 @@
+.venv/
+examples/heatmaps/
+__pycache__/
+*.pyc
+.venv/
+__pycache__/
+*.pyc
+examples/heatmaps/
+.venv/
+__pycache__/
+*.pyc
+examples/heatmaps/
diff --git a/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/README b/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/README
new file mode 100644
index 000000000..cf0eefdae
--- /dev/null
+++ b/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/README
@@ -0,0 +1,56 @@
+# AFL Workload & Heatmap Pipeline
+
+This repo provides **workload metrics** and **heatmap visualisations** from AFL player tracking data.  
+It processes coordinate-based tracking files (`tracking2.0.csv`) and delivers outputs consumable by the frontend.
+
+---
+
+## Project structure
+
+afl-workload-pipeline/
+├─ config.yaml                 # thresholds & settings (edit here, no code changes needed)
+├─ examples/
+│  ├─ tracking2.0.csv          # sample input (~200 frames)
+│  ├─ out_workload.json        # sample workload output
+│  ├─ out_workload.csv         # same data in CSV format
+│  └─ heatmaps/                # sample generated PNGs
+├─ scripts/
+│  ├─ compute_workload.py      # workload metrics CLI
+│  └─ generate_heatmaps.py     # heatmap generation CLI
+├─ workload/                   # workload metric logic (distance, speed, HSR, ratios)
+├─ heatmaps/                   # heatmap logic (grid, blur, render)
+└─ tests/                      # unit tests
+
+## Setup
+
+Clone and set up environment:
+
+```bash
+git clone https://github.com/<USERNAME>/afl-workload-pipeline.git
+cd afl-workload-pipeline
+
+python3 -m venv .venv
+source .venv/bin/activate
+
+pip install -r requirements.txt   # or: pip install pandas numpy pyyaml scipy matplotlib imageio
+
+python scripts/compute_workload.py \
+  --tracking examples/tracking2.0.csv \
+  --config config.yaml \
+  --out-json examples/out_workload.json \
+  --out-csv  examples/out_workload.csv
+
+Outputs:
+	•	JSON: per-player workload indicators (distance, speed, HSR, work/rest, workload score, fatigue risk).
+	•	CSV: tidy table for spreadsheets / BI tools.
+
+python scripts/generate_heatmaps.py \
+  --tracking examples/tracking2.0.csv \
+  --config config.yaml \
+  --out-dir examples/heatmaps \
+  --group-by player_id
+
+Outputs:
+	•	PNG heatmaps in examples/heatmaps/
+	•	Configurable grid size (nx, ny), blur (sigma), and weighting mode (conf).
+
diff --git a/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/config.yaml b/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/config.yaml
new file mode 100644
index 000000000..95dd6f773
--- /dev/null
+++ b/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/config.yaml
@@ -0,0 +1,48 @@
+# === AFL Workload & Heatmap Config ===
+
+# Tracking video parameters
+fps: 25.0     # frames per second of the dataset
+
+field:
+  length_m: 165.0   # AFL oval length (metres)
+  width_m: 135.0    # AFL oval width (metres)
+
+# Data smoothing
+speed_smoothing_window: 3
+accel_smoothing_window: 3
+
+# Thresholds for workload metrics
+thresholds:
+  hsr_kmh: 18.0       # high-speed running (HSR) threshold in km/h
+  sprint_kmh: 24.0    # sprint threshold (optional future use)
+  accel_ms2: 3.0      # high acceleration threshold (m/s²)
+  decel_ms2: -3.0     # high deceleration threshold (m/s²)
+  min_sprint_dur_s: 1.0        # sprint must last at lease this long
+  repeat_sprint_window_s: 60   # time window for repeated sprints (seconds)
+
+# Weights for workload score calculation
+weights:
+  distance: 0.25
+  hsr: 0.25
+  repeat_sprints: 0.20
+  accels_decels: 0.20
+  work_rest: 0.10
+
+# Risk bands for fatigue risk classification
+risk_bands:
+  - {max: 40, label: "Low"}
+  - {max: 70, label: "Moderate"}
+  - {max: 100, label: "High"}
+
+# Quarter segmentation (seconds from game start)
+quarters:
+  # Default: standard 20-min quarters → edges at 1200s, 2400s, 3600s
+  edges_s: [1200, 2400, 3600]
+
+# Heatmap rendering parameters
+heatmap:
+  nx: 200          # grid cells in x-direction
+  ny: 150          # grid cells in y-direction
+  sigma: 2.0       # Gaussian blur (controls smoothing)
+  weight_mode: conf   # "conf" = confidence-weighted, or None for uniform
+  vmax_percentile: 99 # scaling for consistent colour intensity
\ No newline at end of file
diff --git a/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/examples/out_workload.csv b/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/examples/out_workload.csv
new file mode 100644
index 000000000..65cb5aa88
--- /dev/null
+++ b/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/examples/out_workload.csv
@@ -0,0 +1,581 @@
+player_id,distance_m,hsr_m,mean_speed_mps,max_speed_mps,total_time_s,num_sprints,repeated_sprint_bouts,work_rest_ratio,workload_score,fatigue_risk
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+281,0.0,0.0,0.0,0.0,0.08,0.0,0.0,0.0,0.0,Low
+291,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+297,0.2662923746375315,0.2662923746375315,6.657309365938288,6.657309365938288,0.04,0.0,0.0,40000000.0,3.7624735778108334,Low
+299,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+301,0.10646687697160928,0.0,0.8872239747634106,2.661671924290232,0.9600000000000001,0.0,0.0,0.0,0.0829940461247926,Low
+312,0.0,0.0,0.0,0.0,0.08,0.0,0.0,0.0,0.0,Low
+316,0.0,0.0,0.0,0.0,0.9600000000000001,0.0,0.0,0.0,0.0,Low
+317,1.6548704163208818,1.6548704163208818,0.8802502214472776,10.646687697160928,1.8800000000000001,0.0,0.0,0.11904761904761904,2.666886344931868,Low
+321,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,Low
+322,0.0,0.0,0.0,0.0,0.52,0.0,0.0,0.0,0.0,Low
+324,0.4258675078864371,0.4258675078864371,5.323343848580464,10.646687697160928,0.8400000000000001,0.0,0.0,0.049999999999999996,0.6863016179405188,Low
+353,0.4019865618734011,0.0,0.8374720039029189,2.5124160117087566,0.76,0.0,0.0,0.0,0.31336028825720474,Low
+354,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+355,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+356,0.0,0.0,0.0,0.0,1.04,0.0,0.0,0.0,0.0,Low
+361,0.0,0.0,0.0,0.0,0.39999999999999997,0.0,0.0,0.0,0.0,Low
+362,1.248004281810729,1.248004281810729,3.065730936593829,12.0,1.6,0.0,0.0,0.08108108108108109,2.0112061606476495,Low
+364,0.0,0.0,0.0,0.0,0.6000000000000001,0.0,0.0,0.0,0.0,Low
+374,0.0,0.0,0.0,0.0,0.48,0.0,0.0,0.0,0.0,Low
+402,0.2662923746375315,0.2662923746375315,3.328654682969144,6.657309365938288,0.56,0.0,0.0,0.07692307692307693,0.429140250887756,Low
+404,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+405,0.24408284023668614,0.24408284023668614,3.0510355029585767,6.1020710059171535,0.9600000000000001,0.0,0.0,0.043478260869565216,0.3933487424840381,Low
+406,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+407,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+409,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+410,0.0,0.0,0.0,0.0,0.76,0.0,0.0,0.0,0.0,Low
+411,0.10646687697160928,0.0,0.08065672497849188,0.24197017493547565,0.56,0.0,0.0,0.0,0.0829940461247926,Low
+412,0.0,0.0,0.0,0.0,0.16,0.0,0.0,0.0,0.0,Low
+413,0.0,0.0,0.0,0.0,0.6000000000000001,0.0,0.0,0.0,0.0,Low
+426,0.0,0.0,0.0,0.0,0.48,0.0,0.0,0.0,0.0,Low
+431,2.321457896398577,2.321457896398577,2.1212603188459362,12.0,1.08,0.0,0.0,0.2857142857142857,3.741117322270451,Low
+452,0.0,0.0,0.0,0.0,0.6,0.0,0.0,0.0,0.0,Low
+453,2.1618827631496886,2.1618827631496886,1.9030232242006417,12.0,1.12,0.0,0.0,0.2727272727272727,3.4839559518918755,Low
+454,1.7756988220114707,1.7756988220114707,1.607793726939564,12.0,1.12,0.0,0.0,0.21739130434782605,2.861605903878358,Low
+455,0.8075663113882001,0.8075663113882001,1.110278654048372,12.0,1.12,0.0,0.0,0.07692307692307693,1.3014236944623447,Low
+456,0.0,0.0,0.0,0.0,0.16,0.0,0.0,0.0,0.0,Low
+457,0.4019865618734011,0.4019865618734011,2.5124160117087566,10.049664046835026,0.8,0.0,0.0,0.05263157894736842,0.647816569444705,Low
+458,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+471,1.7139604278832499,1.7139604278832499,1.7542529585798814,12.0,1.0,0.0,0.0,0.1904761904761905,2.7621121418689483,Low
+472,1.425149104803177,1.425149104803177,1.5063777259635167,12.0,0.9600000000000001,0.0,0.0,0.14285714285714285,2.296681755362868,Low
+482,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+483,0.4258675078864371,0.4258675078864371,0.9678806997419026,10.646687697160928,0.48,0.0,0.0,0.09090909090909091,0.6863016213496097,Low
+484,0.0,0.0,0.0,0.0,0.24,0.0,0.0,0.0,0.0,Low
+485,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+486,0.0,0.0,0.0,0.0,0.27999999999999997,0.0,0.0,0.0,0.0,Low
+487,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+488,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+502,0.4996407984583709,0.4996407984583709,0.75,12.0,0.64,0.0,0.0,0.06666666666666667,0.805190070397684,Low
+503,0.0,0.0,0.0,0.0,0.12,0.0,0.0,0.0,0.0,Low
+504,0.32390927196725755,0.32390927196725755,1.1568188284544914,8.097731799181439,0.28,0.0,0.0,0.16666666666666666,0.5219920699879923,Low
+505,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+506,1.371965235950746,1.371965235950746,4.287391362346081,10.049664046835026,0.32,0.0,0.0,1.0,2.210974008410767,Low
+513,0.8190414293731987,0.8190414293731987,1.665417981072558,12.0,0.48,0.0,0.0,0.2,1.319916291839184,Low
+521,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+522,0.4881656804733723,0.4881656804733723,6.0,12.0,0.12,0.0,0.0,0.5000000000000001,0.7866975193883661,Low
+524,0.2662923746375183,0.2662923746375183,1.6643273414844895,6.657309365937958,0.16,0.0,0.0,0.33333333333333337,0.4291402722552561,Low
+527,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+531,0.0,0.0,0.0,0.0,0.04,0.0,0.0,0.0,0.0,Low
+533,0.0,0.0,0.0,0.0,0.04,0.0,0.0,0.0,0.0,Low
+537,0.9790216340599741,0.9790216340599741,1.8461538461538463,12.0,0.6,0.0,0.0,0.15384615384615385,1.5777304452489136,Low
+538,9.573066099474426,9.147198591587989,3.908645057405455,12.0,3.7600000000000002,0.0,0.0,0.492063492063492,15.073033100382974,Low
+539,0.31940063091482696,0.31940063091482696,0.9981269716088342,7.985015772870674,0.32,0.0,0.0,0.14285714285714285,0.5147262222351495,Low
+543,0.0,0.0,0.0,0.0,0.04,0.0,0.0,0.0,0.0,Low
+544,5.885968103235316,5.885968103235316,2.0849044043962817,12.0,3.44,0.0,0.0,0.24637681159420294,9.485460481978517,Low
+545,0.0,0.0,0.0,0.0,0.04,0.0,0.0,0.0,0.0,Low
+546,5.714435803754923,5.501502049811704,1.678327990307461,12.0,3.4,0.0,0.0,0.2142857142857143,9.031866969248908,Low
+554,9.662454377258292,9.343053746343466,2.9305843984009012,12.0,3.2800000000000002,0.0,0.0,0.5471698113207547,15.305667056491497,Low
+557,0.0,0.0,0.0,0.0,0.36,0.0,0.0,0.0,0.0,Low
+558,0.4996407984583709,0.4996407984583709,3.0,12.0,0.2,0.0,0.0,0.25,0.8051900856754618,Low
+559,0.32390927196725755,0.32390927196725755,2.6992439330604796,8.097731799181439,0.36,0.0,0.0,0.125,0.52199206651577,Low
+562,3.4423460196652167,3.0164785117787796,2.386903788420927,12.0,2.56,0.0,0.0,0.14285714285714285,5.193145532431259,Low
+564,1.2823615291287578,1.2823615291287578,0.942912889065263,10.646687697160928,1.36,0.0,0.0,0.1333333333333333,2.0665741699390656,Low
+567,0.31940063091482696,0.31940063091482696,1.9962539432176685,7.985015772870674,0.16,0.0,0.0,0.33333333333333337,0.5147262381081654,Low
+574,7.008023450685131,6.901556573713521,2.6742421758538617,12.0,2.68,0.0,0.0,0.3958333333333333,11.205113814421134,Low
+578,0.2662923746375185,0.2662923746375185,1.1095515609896605,6.657309365937962,0.28,0.0,0.0,0.16666666666666666,0.4291402583663674,Low
+579,3.5412270534090466,3.328293299465828,1.762447846231151,12.0,2.0,0.0,0.0,0.28205128205128205,5.529658788523782,Low
+581,2.2415280565780042,2.135061179606409,1.1068607888231305,12.0,2.44,0.0,0.0,0.0892857142857143,3.5237259852920673,Low
+589,0.0,0.0,0.0,0.0,0.12,0.0,0.0,0.0,0.0,Low
+591,0.0,0.0,0.0,0.0,0.04,0.0,0.0,0.0,0.0,Low
+594,2.5587212160297237,2.5587212160297237,1.9926215121533997,12.0,1.28,0.0,0.0,0.33333333333333337,4.123476149433123,Low
+600,2.1637011310888887,1.8443005001740609,3.828679165411639,12.0,0.56,0.0,0.0,0.7499999999999998,3.2211422884923033,Low
+602,0.0,0.0,0.0,0.0,0.16,0.0,0.0,0.0,0.0,Low
+604,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+614,0.0,0.0,0.0,0.0,0.64,0.0,0.0,0.0,0.0,Low
+629,0.4881656804733723,0.4881656804733723,3.0,12.0,0.64,0.0,0.0,0.06666666666666667,0.786697483277255,Low
+642,0.4881656804733723,0.4881656804733723,6.0,12.0,0.68,0.0,0.0,0.0625,0.7866974829300328,Low
+647,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+648,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+649,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+650,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+651,0.0,0.0,0.0,0.0,0.6,0.0,0.0,0.0,0.0,Low
+652,0.0,0.0,0.0,0.0,0.04,0.0,0.0,0.0,0.0,Low
+653,0.0,0.0,0.0,0.0,0.16,0.0,0.0,0.0,0.0,Low
+654,0.0,0.0,0.0,0.0,0.76,0.0,0.0,0.0,0.0,Low
+655,0.0,0.0,0.0,0.0,0.08,0.0,0.0,0.0,0.0,Low
+656,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+657,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+665,0.0,0.0,0.0,0.0,0.72,0.0,0.0,0.0,0.0,Low
+675,0.4996407984583709,0.4996407984583709,4.0,12.0,0.6,0.0,0.0,0.07142857142857144,0.8051900707945094,Low
+690,0.0,0.0,0.0,0.0,0.6000000000000001,0.0,0.0,0.0,0.0,Low
+693,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+694,0.0,0.0,0.0,0.0,0.08,0.0,0.0,0.0,0.0,Low
+695,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+696,0.0,0.0,0.0,0.0,0.16,0.0,0.0,0.0,0.0,Low
+697,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+698,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+699,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+724,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+725,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+726,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+727,0.7571483282241303,0.2662923746375315,3.5825918819422036,6.657309365938288,0.24,0.0,0.0,0.2,0.8117768408144664,Low
+728,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+745,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+746,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+747,0.0,0.0,0.0,0.0,0.04,0.0,0.0,0.0,0.0,Low
+760,0.3505497172082954,0.24408284023668614,0.5477339331379616,6.1020710059171535,0.64,0.0,0.0,0.06666666666666667,0.47634279054119794,Low
+772,0.0,0.0,0.0,0.0,0.56,0.0,0.0,0.0,0.0,Low
+778,0.0,0.0,0.0,0.0,0.64,0.0,0.0,0.0,0.0,Low
+779,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+781,4.424099357438324,4.211165603495106,2.6447380994704073,12.0,1.72,0.0,0.0,0.34375,6.952440940203525,Low
+785,0.0,0.0,0.0,0.0,0.28,0.0,0.0,0.0,0.0,Low
+790,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+793,0.590201646604789,0.590201646604789,1.4755041165119729,8.097731799181439,1.2,0.0,0.0,0.07142857142857144,0.9511323065289836,Low
+794,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+795,3.13362850623537,3.0271616292637606,1.0178772728021808,12.0,3.72,0.0,0.0,0.10714285714285712,4.961379639407467,Low
+796,3.906865365820426,3.800398488848817,2.247751060806543,12.0,1.76,0.0,0.0,0.2941176470588236,6.207480131628397,Low
+797,4.723133158811436,4.510199404868217,1.1650499499665077,12.0,5.36,0.0,0.0,0.10743801652892562,7.434345213876256,Low
+798,2.4312876414156146,2.3248207644440053,1.3814134326225083,10.646687697160928,1.76,0.0,0.0,0.22222222222222224,3.8295307522892466,Low
+799,2.600450256473867,2.600450256473867,2.6748774955354784,12.0,0.96,0.0,0.0,0.3333333333333333,4.190724078114469,Low
+800,2.924980824442448,2.924980824442448,1.3009486030606463,12.0,2.24,0.0,0.0,0.19148936170212763,4.713717364423786,Low
+801,0.0,0.0,0.0,0.0,0.44,0.0,0.0,0.0,0.0,Low
+802,0.8102565845014211,0.8102565845014211,0.9516674177557389,12.0,0.84,0.0,0.0,0.10526315789473684,1.3057591738090066,Low
+804,0.5323343848580464,0.5323343848580464,0.47529855790897,7.985015772870696,1.1600000000000001,0.0,0.0,0.07407407407407407,0.8578770233901545,Low
+805,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+808,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+809,0.0,0.0,0.0,0.0,0.32,0.0,0.0,0.0,0.0,Low
+813,4.473753135617272,3.409084365901179,3.527510752285259,12.0,2.3600000000000003,0.0,0.0,0.22916666666666663,6.323809015704479,Low
+817,0.0,0.0,0.0,0.0,0.2,0.0,0.0,0.0,0.0,Low
+820,5.007914637267638,4.901447760296029,1.8730492516904795,12.0,3.3200000000000003,0.0,0.0,0.16901408450704222,7.98186271166889,Low
+821,0.0,0.0,0.0,0.0,0.32,0.0,0.0,0.0,0.0,Low
+822,0.0,0.0,0.0,0.0,0.12,0.0,0.0,0.0,0.0,Low
+824,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+825,0.0,0.0,0.0,0.0,0.12,0.0,0.0,0.0,0.0,Low
+826,0.0,0.0,0.0,0.0,0.12,0.0,0.0,0.0,0.0,Low
+827,0.0,0.0,0.0,0.0,0.76,0.0,0.0,0.0,0.0,Low
+836,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+837,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+843,0.0,0.0,0.0,0.0,0.4,0.0,0.0,0.0,0.0,Low
+844,0.0,0.0,0.0,0.0,0.84,0.0,0.0,0.0,0.0,Low
+848,0.4996407984583709,0.4996407984583709,1.0,12.0,0.92,0.0,0.0,0.045454545454545456,0.8051900686300073,Low
+854,0.4996407984583709,0.4996407984583709,1.5,12.0,0.48,0.0,0.0,0.09090909090909091,0.805190072417886,Low
+855,0.2662923746375315,0.2662923746375315,0.9510441951340411,6.657309365938288,0.4,0.0,0.0,0.1111111111111111,0.4291402537367588,Low
+856,0.0,0.0,0.0,0.0,0.08,0.0,0.0,0.0,0.0,Low
+858,1.2122431463748269,1.2122431463748269,5.005779969950944,12.0,0.24,0.0,0.0,1.0,1.9535758134291579,Low
+861,0.0,0.0,0.0,0.0,0.2,0.0,0.0,0.0,0.0,Low
+863,0.2662923746375315,0.2662923746375315,0.6052099423580262,6.657309365938288,0.44,0.0,0.0,0.09999999999999999,0.42914025281083285,Low
+864,0.7258958338406587,0.7258958338406587,0.864161706953165,10.049664046835026,0.84,0.0,0.0,0.10526315789473684,1.1698086299297734,Low
+866,0.10646687697160928,0.0,0.24197017493547565,2.661671924290232,0.52,0.0,0.0,0.0,0.0829940461247926,Low
+867,0.7809258661471588,0.7809258661471588,1.3945104752627835,8.097731799181439,1.4,0.0,0.0,0.09375000000000001,1.2584916096593735,Low
+868,0.4258675078864371,0.4258675078864371,1.330835962145116,10.646687697160928,0.32,0.0,0.0,0.14285714285714285,0.6863016256786141,Low
+872,0.2662923746375315,0.2662923746375315,1.664327341484572,6.657309365938288,1.36,0.0,0.0,0.030303030303030304,0.429140247002752,Low
+874,0.4019865618734011,0.4019865618734011,0.33498880156116756,10.049664046835026,1.48,0.0,0.0,0.02777777777777778,0.647816567373555,Low
+875,0.7037897075298204,0.7037897075298204,2.165417981072558,12.0,0.32,0.0,0.0,0.33333333333333337,1.1341837893714055,Low
+881,1.1830158361105876,1.1830158361105876,1.9535998042066363,12.0,0.76,0.0,0.0,0.1875,1.9064748285830806,Low
+894,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+901,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+902,3.783878454768643,3.5709447008254243,1.8315839104796714,12.0,2.56,0.0,0.0,0.18518518518518517,5.920700701362805,Low
+903,0.4570165941799047,0.24408284023668614,1.752748586041477,6.1020710059171535,1.4400000000000002,0.0,0.0,0.02857142857142857,0.5593368334913872,Low
+904,0.0,0.0,0.0,0.0,1.08,0.0,0.0,0.0,0.0,Low
+916,0.0,0.0,0.0,0.0,1.04,0.0,0.0,0.0,0.0,Low
+920,1.5731222085611032,1.5731222085611032,1.2290017254383618,10.646687697160928,2.3200000000000003,0.0,0.0,0.07407407407407407,2.535146009109673,Low
+921,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+922,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+923,3.536546022907498,3.430079145935889,3.1188591147866993,12.0,2.32,0.0,0.0,0.1836734693877551,5.610696479751072,Low
+924,0.0,0.0,0.0,0.0,0.8,0.0,0.0,0.0,0.0,Low
+926,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,Low
+941,0.0,0.0,0.0,0.0,0.12,0.0,0.0,0.0,0.0,Low
+942,0.0,0.0,0.0,0.0,0.88,0.0,0.0,0.0,0.0,Low
+943,2.090592929510966,2.090592929510966,2.1777009682405892,10.049664046835026,1.92,0.0,0.0,0.14285714285714285,3.369069674881216,Low
+944,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+945,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+971,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+973,0.10646687697160839,0.0,1.330835962145105,2.66167192429021,0.48,0.0,0.0,0.0,0.0829940461247919,Low
+974,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+975,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+976,0.0,0.0,0.0,0.0,0.08,0.0,0.0,0.0,0.0,Low
+977,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1014,3.9781466806586683,3.6587460497438475,2.9988680925759827,12.0,1.6,0.0,0.0,0.4285714285714285,6.1451899660141684,Low
+1016,0.0,0.0,0.0,0.0,0.08,0.0,0.0,0.0,0.0,Low
+1017,5.3453079565478046,5.3453079565478046,4.700601476785258,12.0,1.6,0.0,0.0,0.6,8.614166213799516,Low
+1018,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1019,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1066,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1067,0.0,0.0,0.0,0.0,0.7200000000000001,0.0,0.0,0.0,0.0,Low
+1068,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1069,5.426883530102803,5.320416653131193,4.311491312543943,12.0,1.36,0.0,0.0,0.7000000000000001,8.657046990331049,Low
+1070,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1071,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1072,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1112,1.6105290777314283,1.6105290777314283,1.4379723908316324,10.646687697160928,1.12,0.0,0.0,0.21739130434782605,2.5954286070740302,Low
+1113,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1114,0.590201646604789,0.590201646604789,0.922190072819983,8.097731799181439,0.68,0.0,0.0,0.1333333333333333,0.9511323116877138,Low
+1115,0.0,0.0,0.0,0.0,1.12,0.0,0.0,0.0,0.0,Low
+1116,3.864086454521109,3.544685823606283,3.430691560132061,12.0,1.12,0.0,0.0,0.6470588235294116,5.961377611086627,Low
+1120,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1132,0.5323343848580464,0.5323343848580464,1.0237199708808584,7.985015772870696,0.52,0.0,0.0,0.18181818181818182,0.8578770323688302,Low
+1135,1.9595800175323126,1.746646263589094,4.211620774001599,10.049664046835026,0.48,0.0,0.0,1.0,2.9807747634174606,Low
+1138,1.9019173975037897,1.9019173975037897,4.685277296430117,12.0,0.4,0.0,0.0,1.0,3.065011982572867,Low
+1139,0.2662923746375301,0.2662923746375301,6.657309365938252,6.657309365938252,0.04,0.0,0.0,40000000.0,3.762473577810831,Low
+1141,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1142,0.9817119071732037,0.9817119071732037,6.0,12.0,0.16,0.0,0.0,1.0,1.5820659927467364,Low
+1149,1.6800440916679444,1.6800440916679444,1.0377520582559834,12.0,1.6,0.0,0.0,0.14285714285714285,2.7074546779000888,Low
+1150,0.5946325574449816,0.4881656804733723,0.5864668769716093,12.0,1.0,0.0,0.0,0.04166666666666667,0.8696915273187142,Low
+1151,0.9673918090541215,0.9673918090541215,0.3285020988290237,12.0,3.0,0.0,0.0,0.041666666666666664,1.5589885325583457,Low
+1152,0.32390927196725755,0.32390927196725755,0.31145122304543993,8.097731799181439,1.04,0.0,0.0,0.04,0.5219920594324367,Low
+1153,3.3449498981911017,3.3449498981911017,1.0231704662174481,12.0,3.2800000000000002,0.0,0.0,0.12328767123287669,5.390513422566036,Low
+1154,0.0,0.0,0.0,0.0,0.4,0.0,0.0,0.0,0.0,Low
+1156,0.5084534388450104,0.4019865618734011,6.355667985562629,10.049664046835026,0.08,0.0,0.0,1.0,0.7308106945168661,Low
+1158,0.0,0.0,0.0,0.0,1.04,0.0,0.0,0.0,0.0,Low
+1161,0.0,0.0,0.0,0.0,0.92,0.0,0.0,0.0,0.0,Low
+1162,0.0,0.0,0.0,0.0,0.64,0.0,0.0,0.0,0.0,Low
+1163,0.40198656187340037,0.40198656187340037,0.5911567086373535,10.049664046835009,0.68,0.0,0.0,0.0625,0.6478165702670722,Low
+1164,2.3143367124451677,2.2078698354735584,1.247117344558042,12.0,1.84,0.0,0.0,0.1794871794871795,3.6410598985259814,Low
+1170,0.4996407984583709,0.4996407984583709,0.42857142857142855,12.0,1.6,0.0,0.0,0.02564102564102564,0.8051900669788805,Low
+1171,0.0,0.0,0.0,0.0,0.12,0.0,0.0,0.0,0.0,Low
+1172,0.0,0.0,0.0,0.0,0.08,0.0,0.0,0.0,0.0,Low
+1176,0.21293375394321856,0.21293375394321856,0.2801759920305507,5.323343848580464,0.88,0.0,0.0,0.04761904761904762,0.34315081085518007,Low
+1192,0.0,0.0,0.0,0.0,0.44,0.0,0.0,0.0,0.0,Low
+1193,8.997510527980914,8.25224238917965,3.4767753376897863,12.0,2.56,0.0,0.0,0.6,13.879759441437885,Low
+1194,12.782079950110083,12.143278688280427,3.081846740048159,12.0,4.2,0.0,0.0,0.5217391304347826,20.06731771286676,Low
+1195,0.0,0.0,0.0,0.0,0.12,0.0,0.0,0.0,0.0,Low
+1196,0.4881656804733723,0.4881656804733723,0.8,12.0,0.6,0.0,0.0,0.07142857142857144,0.7866974836740804,Low
+1197,0.21293375394321856,0.21293375394321856,0.332708990536279,5.323343848580464,0.64,0.0,0.0,0.06666666666666667,0.34315081244248163,Low
+1198,2.555084480151289,2.3421507262080845,2.0430423975629606,12.0,1.24,0.0,0.0,0.24,3.9404526894505354,Low
+1199,0.0,0.0,0.0,0.0,0.08,0.0,0.0,0.0,0.0,Low
+1200,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1208,8.637673062604984,8.105338677746937,3.5013924886160432,12.0,2.64,0.0,0.0,0.5348837209302325,13.477030450506188,Low
+1221,0.8102565845014368,0.8102565845014368,1.427501126633621,12.0,0.56,0.0,0.0,0.16666666666666666,1.305759178925991,Low
+1232,5.9836145891264385,5.664213958211611,2.7854016015532803,12.0,2.6,0.0,0.0,0.38297872340425526,9.377077430652783,Low
+1235,6.844416901801862,6.5250162708870345,2.7510907236405884,12.0,3.44,0.0,0.0,0.34375,10.764292947209064,Low
+1239,5.151893463892238,4.513092202062582,3.748596439848624,12.0,2.12,0.0,0.0,0.325,7.770983268715606,Low
+1241,0.0,0.0,0.0,0.0,0.12,0.0,0.0,0.0,0.0,Low
+1257,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1260,5.906041552046609,5.693107798103391,5.084375751356239,12.0,1.16,0.0,0.0,1.9000000000000004,9.340647027952615,Low
+1266,0.0,0.0,0.0,0.0,0.12,0.0,0.0,0.0,0.0,Low
+1273,0.7233089688174362,0.5103752148742177,5.140350765381892,6.657309365938288,0.16,0.0,0.0,1.0,0.9884771589212678,Low
+1275,0.7322485207100442,0.7322485207100442,3.6204142011833595,12.0,0.2,0.0,0.0,0.6666666666666666,1.1800462721380818,Low
+1278,0.26629237463754457,0.26629237463754457,3.328654682969307,6.657309365938614,0.2,0.0,0.0,0.25,0.4291402653108539,Low
+1290,0.7571483282241263,0.7571483282241263,0.6433554953771824,12.0,1.44,0.0,0.0,0.05882352941176471,1.2201732040861506,Low
+1295,0.24408284023667193,0.24408284023667193,0.5085059171597331,6.102071005916798,0.48,0.0,0.0,0.09090909090909091,0.39334874643658435,Low
+1306,0.2662923746375315,0.2662923746375315,0.832163670742286,6.657309365938288,0.32,0.0,0.0,0.14285714285714285,0.42914025638226144,Low
+1308,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1309,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1311,0.0,0.0,0.0,0.0,0.32,0.0,0.0,0.0,0.0,Low
+1313,0.0,0.0,0.0,0.0,0.36,0.0,0.0,0.0,0.0,Low
+1315,0.4996407984583709,0.4996407984583709,0.8,12.0,0.6,0.0,0.0,0.07142857142857144,0.8051900707945094,Low
+1319,0.0,0.0,0.0,0.0,0.04,0.0,0.0,0.0,0.0,Low
+1320,0.0,0.0,0.0,0.0,0.08,0.0,0.0,0.0,0.0,Low
+1321,0.0,0.0,0.0,0.0,0.16,0.0,0.0,0.0,0.0,Low
+1334,0.0,0.0,0.0,0.0,0.12,0.0,0.0,0.0,0.0,Low
+1335,0.0,0.0,0.0,0.0,0.32,0.0,0.0,0.0,0.0,Low
+1336,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1345,0.31940063091482784,0.31940063091482784,0.24953174290220925,7.985015772870696,2.3600000000000003,0.0,0.0,0.017241379310344827,0.5147262117671707,Low
+1346,2.6545570846754933,2.6545570846754933,1.5208689838122955,12.0,1.84,0.0,0.0,0.1794871794871795,4.277919267612286,Low
+1348,1.540428622161425,1.540428622161425,2.534646372838423,12.0,0.68,0.0,0.0,0.3076923076923077,2.482459076202668,Low
+1350,0.0,0.0,0.0,0.0,0.08,0.0,0.0,0.0,0.0,Low
+1351,4.9838012540215155,4.877334377049906,1.3347695098763532,12.0,4.2,0.0,0.0,0.12903225806451613,7.943003078219962,Low
+1354,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,Low
+1356,0.0,0.0,0.0,0.0,0.24000000000000002,0.0,0.0,0.0,0.0,Low
+1360,2.2346649877355196,1.4893968489342546,1.1582338719525576,12.0,1.92,0.0,0.0,0.11627906976744187,2.9811777483303223,Low
+1361,0.0,0.0,0.0,0.0,0.56,0.0,0.0,0.0,0.0,Low
+1362,11.71765497041968,9.694784307959118,3.4062950495406046,10.049664046835026,3.48,0.0,0.0,0.74,17.200398928334966,Low
+1363,11.15850192988378,9.135631267423204,3.5764429262448014,10.049664046835026,3.16,0.0,0.0,0.7954545454545455,16.29930263666705,Low
+1364,2.9288936026156422,2.822426725644033,3.853807371862687,8.097731799181439,0.76,0.0,0.0,1.375,4.631441695908402,Low
+1365,12.255111786393332,10.338708000904393,4.190229790388686,12.0,2.92,0.0,0.0,1.0857142857142859,18.155112323376223,Low
+1367,11.027599381321508,8.898261841889322,3.6693195276210457,12.0,3.0,0.0,0.0,0.7441860465116279,15.99976686164526,Low
+1368,13.040730943329612,11.976062173613519,3.53482692461136,12.0,3.72,0.0,0.0,0.6909090909090908,20.129818166682874,Low
+1372,17.36583703929891,16.194701392611222,4.371605371557045,12.0,3.96,0.0,0.0,0.98,27.011309070566007,Low
+1373,10.77347074433978,8.750600081879233,3.489926526921969,12.0,3.44,0.0,0.0,0.5925925925925926,15.678810285647119,Low
+1374,3.989028298157221,3.989028298157221,5.205868996575546,12.0,0.76,0.0,0.0,1.1111111111111112,6.428470233463022,Low
+1375,12.789258439607572,10.872854654118616,4.259467495340323,12.0,3.0,0.0,0.0,1.2058823529411766,19.015909889682277,Low
+1376,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1377,3.6298586195880755,3.6298586195880755,4.100166718653264,12.0,1.0,0.0,0.0,0.6666666666666667,5.849654660007151,Low
+1378,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1379,12.889692355333064,11.399156077730549,4.655945926962436,12.0,2.8000000000000003,0.0,0.0,1.3333333333333333,19.53208838754447,Low
+1380,11.092973376989484,9.389503345443735,3.598774954922861,12.0,3.04,0.0,0.0,0.7272727272727273,16.459444958814732,Low
+1381,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1382,11.766693811796832,11.127892549967177,4.794071568538325,12.0,2.68,0.0,0.0,1.09375,18.43098455261604,Low
+1383,16.48292443043576,16.05705692254933,3.306495769994476,12.0,5.0,0.0,0.0,0.6447368421052632,26.208531444554044,Low
+1384,14.297456175373707,12.593986143827959,2.9955449039612105,12.0,5.0,0.0,0.0,0.48809523809523814,21.623590301266958,Low
+1387,13.958758193630942,13.107023177858082,5.196339676444459,12.0,2.84,0.0,0.0,1.2187500000000002,21.786416598850263,Low
+1388,15.94134584564711,14.770210198959408,3.5434993156689782,12.0,4.6000000000000005,0.0,0.0,0.6197183098591548,24.715687545815644,Low
+1390,16.099877988288355,15.03520921857226,3.7507125876861265,12.0,4.32,0.0,0.0,0.8305084745762711,25.05974947114104,Low
+1391,17.424130277033125,16.67886213823186,3.549121507909175,12.0,4.96,0.0,0.0,0.5897435897435898,27.459576227111327,Low
+1392,17.149062989258237,15.339126080740884,3.4736058117648625,12.0,4.92,0.0,0.0,0.6621621621621622,26.13048131071264,Low
+1393,10.923713928232573,10.284912666402917,4.679580108786245,12.0,2.8,0.0,0.0,0.9444444444444446,17.072490539679936,Low
+1397,8.110947246573913,5.768675953198527,2.8559673403429273,8.097731799181439,3.68,0.0,0.0,0.3142857142857143,11.122308761512782,Low
+1404,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1405,0.49085595358660183,0.49085595358660183,4.0,12.0,0.12,0.0,0.0,0.5000000000000001,0.7910329963733682,Low
+1406,10.870308696344336,9.166838664798595,2.9456010718204797,12.0,3.8000000000000003,0.0,0.0,0.5079365079365079,16.100612376823136,Low
+1407,1.0480559639834999,1.0480559639834999,8.733799699862496,10.049664046835135,0.12,0.0,0.0,119999999.99999999,11.688981868978349,Low
+1414,7.21030879246954,6.677974407611494,2.834857095642642,12.0,3.08,0.0,0.0,0.37499999999999994,11.17677887091768,Low
+1436,10.765593846940881,10.339726339054444,5.257284463582207,12.0,2.56,0.0,0.0,1.0,16.9948367609729,Low
+1437,8.735558207534224,8.096756945704568,3.5768002160085457,12.0,2.44,0.0,0.0,0.9062499999999999,13.546194741457873,Low
+1443,8.978505172658666,8.233237033857401,4.664996492440351,12.0,2.0,0.0,0.0,1.173913043478261,13.849131639553768,Low
+1446,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1449,0.3727592516091408,0.2662923746375315,4.659490645114261,6.657309365938288,0.08,0.0,0.0,1.0,0.5121343739356254,Low
+1450,0.0,0.0,0.0,0.0,0.12,0.0,0.0,0.0,0.0,Low
+1465,8.34958459401047,8.13665084006725,6.308030491852025,12.0,1.32,0.0,0.0,2.3,13.278509194390642,Low
+1470,0.3727592516091408,0.2662923746375315,4.659490645114261,6.657309365938288,0.08,0.0,0.0,1.0,0.5121343739356254,Low
+1472,0.31940063091482784,0.21293375394321856,2.661671924290232,5.323343848580464,0.32,0.0,0.0,0.14285714285714285,0.4261448649164805,Low
+1477,5.3256256846306735,5.219158807659064,6.030231875648522,12.0,0.88,0.0,0.0,3.400000000000001,8.49386636338092,Low
+1480,0.4881656804733723,0.4881656804733723,12.0,12.0,0.04,0.0,0.0,40000000.0,4.120030811055034,Low
+1504,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1505,0.24408284023668614,0.24408284023668614,3.0510355029585767,6.1020710059171535,0.48,0.0,0.0,0.09090909090909091,0.3933487464366073,Low
+1535,0.2662923746375315,0.2662923746375315,3.328654682969144,6.657309365938288,0.12,0.0,0.0,0.5000000000000001,0.42914028614416616,Low
+1538,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1539,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1540,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1542,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1543,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1544,0.916723461473039,0.916723461473039,0.5146974476627484,12.0,1.96,0.0,0.0,0.04255319148936171,1.4773345720266529,Low
+1546,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1547,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1548,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1597,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1600,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1601,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1602,0.0,0.0,0.0,0.0,0.24000000000000002,0.0,0.0,0.0,0.0,Low
+1603,0.0,0.0,0.0,0.0,0.28,0.0,0.0,0.0,0.0,Low
+1604,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1649,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1650,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1651,0.0,0.0,0.0,0.0,1.48,0.0,0.0,0.0,0.0,Low
+1652,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1653,0.10646687697160928,0.0,0.8872239747634106,2.661671924290232,0.8,0.0,0.0,0.0,0.0829940461247926,Low
+1654,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1655,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1695,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1696,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1697,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1698,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1699,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1701,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1702,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1703,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1714,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1725,1.0231903384446344,1.0231903384446344,1.581772476340689,12.0,0.8,0.0,0.0,0.1764705882352941,1.6489099866298766,Low
+1741,2.027379303512997,2.027379303512997,2.027379303512997,10.646687697160928,1.0,0.0,0.0,0.3157894736842105,3.2671985375102865,Low
+1742,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1743,2.2283123008660493,2.2283123008660493,2.2174563472794504,12.0,1.0,0.0,0.0,0.3157894736842105,3.5910096707620625,Low
+1744,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1745,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1746,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1747,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1748,3.466323987307882,3.466323987307882,3.602248236285948,12.0,1.0,0.0,0.0,0.923076923076923,5.5861124294690905,Low
+1773,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1774,4.2602694715462865,3.9408688406314596,5.605617725718798,10.646687697160884,0.76,0.0,0.0,2.166666666666667,6.599841670514187,Low
+1775,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1776,1.3836984690466083,1.3836984690466083,1.7920875814123698,12.0,0.76,0.0,0.0,0.1875,2.2298824900971486,Low
+1777,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1781,1.1472547006746625,1.1472547006746625,1.687139265698033,10.646687697160928,0.68,0.0,0.0,0.21428571428571425,1.8488444070201184,Low
+1789,0.0,0.0,0.0,0.0,0.52,0.0,0.0,0.0,0.0,Low
+1790,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1797,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1805,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1806,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1810,1.4918290069365554,1.4918290069365554,0.8178823380434358,12.0,1.8,0.0,0.0,0.09756097560975609,2.4041389386075434,Low
+1811,0.0,0.0,0.0,0.0,0.68,0.0,0.0,0.0,0.0,Low
+1813,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1818,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1822,2.5095736078253763,2.5095736078253763,2.3054933420064865,12.0,1.08,0.0,0.0,0.2857142857142857,4.044272913945353,Low
+1827,0.40198656187339543,0.40198656187339543,10.049664046834886,10.049664046834886,0.04,0.0,0.0,40000000.0,3.9811498983920646,Low
+1832,0.32390927196724684,0.32390927196724684,4.048865899590585,8.09773179918117,0.08,0.0,0.0,1.0,0.5219921394324195,Low
+1833,0.0,0.0,0.0,0.0,0.48,0.0,0.0,0.0,0.0,Low
+1834,0.0,0.0,0.0,0.0,0.68,0.0,0.0,0.0,0.0,Low
+1835,22.887117133321496,21.503047732690575,2.5081404151997093,12.0,9.08,0.0,0.0,0.3757575757575758,35.7318982318282,Low
+1836,27.436569330043586,24.349029897866917,3.1177143862121444,12.0,9.08,0.0,0.0,0.5547945205479452,41.64621100264153,Moderate
+1837,0.0,0.0,0.0,0.0,0.2,0.0,0.0,0.0,0.0,Low
+1838,0.0,0.0,0.0,0.0,0.28,0.0,0.0,0.0,0.0,Low
+1840,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1842,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1844,0.0,0.0,0.0,0.0,1.28,0.0,0.0,0.0,0.0,Low
+1851,1.1517633417270816,1.1517633417270816,0.5537323758303276,10.646687697160573,2.12,0.0,0.0,0.06,1.8561102399316725,Low
+1862,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1863,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1864,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1865,32.07063697422408,30.047766311763517,3.6286725687610732,12.0,8.92,0.0,0.0,0.7153846153846155,50.00000005961538,Moderate
+1866,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1880,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1881,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1882,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1883,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1884,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1903,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1904,25.202628962156176,22.328023283922725,2.9729884136051656,12.0,8.44,0.0,0.0,0.5179856115107914,38.223294226662205,Low
+1905,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1906,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1911,28.473652957115952,25.066712894024462,3.3799852312227436,12.0,8.36,0.0,0.0,0.6076923076923078,43.05176646768915,Moderate
+1913,31.829900073008176,28.635893763859904,3.9216361784719673,12.0,8.32,0.0,0.0,0.793103448275862,48.637648504146526,Moderate
+1923,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1924,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1930,24.916945100804767,21.616471914684897,3.085042653917495,12.0,8.040000000000001,0.0,0.0,0.5227272727272727,37.408578438237086,Low
+1935,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1936,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1945,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1955,25.47418428583991,22.280177976691647,3.8395971703141556,12.0,6.84,0.0,0.0,0.7448979591836735,38.39517185975445,Low
+1956,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1965,0.49085595358660183,0.49085595358660183,1.2,12.0,0.4,0.0,0.0,0.1111111111111111,0.7910329639659609,Low
+1970,1.5096568117909595,1.5096568117909595,5.251340053122206,12.0,0.4,0.0,0.0,0.6666666666666666,2.4328691685781614,Low
+1973,0.0,0.0,0.0,0.0,0.04,0.0,0.0,0.0,0.0,Low
+1974,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+1975,0.5856930055523594,0.5856930055523594,1.6269250154232207,7.985015772870696,0.6,0.0,0.0,0.15384615384615385,0.9438664676284014,Low
+1984,17.97684934693126,17.444514962073214,3.307183507529888,12.0,6.04,0.0,0.0,0.5408163265306123,28.5274666466654,Low
+1985,4.890821948355076,4.571421317440248,1.3555461096579096,12.0,3.6,0.0,0.0,0.1842105263157895,7.616000696581667,Low
+1987,0.49964079845837434,0.49964079845837434,3.0,12.0,0.4,0.0,0.0,0.1111111111111111,0.8051900741013933,Low
+1999,4.740149671748415,4.203306645837938,4.052601284685754,12.0,3.2,0.0,0.0,0.21212121212121213,7.1922726774131345,Low
+2007,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+2009,14.990817206742616,13.71321468308331,4.848218823300617,12.0,3.08,0.0,0.0,1.3333333333333333,23.09529335607277,Low
+2010,0.0,0.0,0.0,0.0,0.04,0.0,0.0,0.0,0.0,Low
+2023,4.786386355085692,3.8281844623412153,3.717640528245267,12.0,1.32,0.0,0.0,0.65,6.91621067863953,Low
+2024,5.895603467502127,5.789136590530518,1.9507265510519114,12.0,3.0,0.0,0.0,0.2931034482758621,9.41240688289787,Low
+2026,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+2029,13.979045857041436,13.340244595211788,5.690733455351642,12.0,2.44,0.0,0.0,1.6521739130434785,21.99627368735328,Low
+2032,4.5968688606203125,4.490401983648703,2.32790178411536,12.0,2.04,0.0,0.0,0.30769230769230765,7.319446890545326,Low
+2035,0.0,0.0,0.0,0.0,0.16,0.0,0.0,0.0,0.0,Low
+2039,0.5103752148742177,0.5103752148742177,6.379690185927721,6.657309365938288,0.08,0.0,0.0,80000000.0,7.489155650005016,Low
+2044,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+2051,0.0,0.0,0.0,0.0,0.68,0.0,0.0,0.0,0.0,Low
+2061,0.674458989175553,0.32390927196725755,4.603479742143416,8.097731799181439,0.16,0.0,0.0,0.33333333333333337,0.7952558465283998,Low
+2076,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+2077,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+2078,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+2079,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+2091,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+2099,0.8031354005480076,0.8031354005480076,6.69279500456673,8.097731799181439,0.12,0.0,0.0,119999999.99999999,11.29428310746353,Low
+2104,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+2109,3.978430969737431,3.978430969737431,2.0311811332833076,12.0,1.92,0.0,0.0,0.2631578947368421,6.411392166800304,Low
+2112,0.10646687697160928,0.0,0.095059711581794,2.661671924290232,1.12,0.0,0.0,0.0,0.0829940461247926,Low
+2113,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,Low
+2116,0.0,0.0,0.0,0.0,0.52,0.0,0.0,0.0,0.0,Low
+2117,1.1517633417270958,1.1517633417270958,4.113440506168199,10.646687697160928,0.28,0.0,0.0,0.7499999999999998,1.8561102974316952,Low
+2118,11.424708789775641,10.998841281889204,4.887009121156391,12.0,2.32,0.0,0.0,0.9333333333333337,18.057025442163976,Low
+2119,10.00683906838131,8.516302790778788,4.306253322170072,12.0,2.32,0.0,0.0,1.230769230769231,14.886261152291452,Low
+2120,13.21548982052982,12.789622312643383,5.649538915803387,12.0,2.32,0.0,0.0,1.5217391304347831,20.9429369274136,Low
+2121,14.472847874595809,13.30171222790811,6.3429789127233365,12.0,2.2800000000000002,0.0,0.0,2.7999999999999985,22.349147644751017,Low
+2122,6.813593024393913,5.535990500734601,4.584990909096061,12.0,1.48,0.0,0.0,1.0555555555555556,9.917386945116782,Low
+2123,6.949096420188407,5.56502701955749,2.9917804912564807,12.0,2.32,0.0,0.0,0.5675675675675675,10.047174400081888,Low
+2124,17.401364502868798,16.549629487095924,7.4678331154149085,12.0,2.32,0.0,0.0,4.272727272727276,27.33430726965131,Low
+2125,9.739951290969865,8.249415013367335,4.193575576458303,12.0,2.32,0.0,0.0,0.9333333333333337,14.456161368913456,Low
+2126,11.75582529117153,9.515512233715324,3.7723491637225153,12.0,3.2,0.0,0.0,0.6666666666666666,17.080997896605705,Low
+2128,12.117398801498663,10.413928769952928,5.197619140570496,12.0,2.32,0.0,0.0,1.761904761904762,18.11034540491571,Low
+2129,12.611871743737783,12.186004235851346,5.494340010420978,12.0,2.2800000000000002,0.0,0.0,1.5909090909090908,19.970183547362026,Low
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diff --git a/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/examples/out_workload.json b/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/examples/out_workload.json
new file mode 100644
index 000000000..cd3663ccd
--- /dev/null
+++ b/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/examples/out_workload.json
@@ -0,0 +1,17426 @@
+{
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+          "workload_score": 0.6478165858920735,
+          "fatigue_risk": "Low"
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/examples/tracking.csv b/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/examples/tracking.csv
new file mode 100644
index 000000000..13d227369
--- /dev/null
+++ b/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/examples/tracking.csv
@@ -0,0 +1,762 @@
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\ No newline at end of file
diff --git a/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/examples/tracking2.0.csv b/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/examples/tracking2.0.csv
new file mode 100644
index 000000000..51035b4aa
--- /dev/null
+++ b/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/examples/tracking2.0.csv
@@ -0,0 +1,12351 @@
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diff --git a/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/heatmappipeline_xuan.py b/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/heatmappipeline_xuan.py
new file mode 100644
index 000000000..7508209b9
--- /dev/null
+++ b/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/heatmappipeline_xuan.py
@@ -0,0 +1,419 @@
+""" 
+---------------------------------------------------
+REQUIREMENTS
+---------------------------------------------------
+pip install numpy pandas matplotlib scipy
+
+
+---------------------------------------------------
+headers required
+---------------------------------------------------
+frame_id, player_id, timestamp_s, x1, y1, x2, y2, cx, cy, w, h, confidence
+
+
+---------------------------------------------------
+BASIC USAGE
+---------------------------------------------------
+# CMD or terminal 
+py .\\heatmappipeline.py --inputs "Data.csv:Data" --out-dir outputs
+
+
+"""
+import os
+import argparse
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from matplotlib.colors import Normalize
+from scipy.ndimage import gaussian_filter
+
+# -------------------------
+# Defaults and schema
+# -------------------------
+
+# These column names MUST exist in each CSV.
+# If your source uses different names, rename the columns in a pre-step or here.
+REQUIRED_COLS = [
+    "frame_id","player_id","timestamp_s","x1","y1","x2","y2","cx","cy","w","h","confidence" # ADJUSTED FOR XUAN CSV --- LUCAS
+]
+
+# AFL ground (typical) dimensions in metres.
+# Length = tip-to-tip, Width = wing-to-wing. Adjust via CLI if needed.
+DEFAULT_FIELD_LENGTH_M = 165.0
+DEFAULT_FIELD_WIDTH_M  = 135.0
+
+# Grid resolution and smoothing.
+# nx/ny = number of bins; sigma = Gaussian blur in “bins”.
+DEFAULT_NX = 200
+DEFAULT_NY = 150
+DEFAULT_SIGMA = 2.0
+
+# -------------------------
+# Schema + loading
+# -------------------------
+
+def assert_schema(df: pd.DataFrame, path: str):
+
+    missing = [c for c in REQUIRED_COLS if c not in df.columns]
+    if missing:
+        raise ValueError(
+            f"{path} is missing required columns: {missing}\n"
+            f"Found columns: {list(df.columns)}\n"
+            f"Tip: ensure your header row is exactly: {REQUIRED_COLS}"
+        )
+
+def load_events_csv(path: str) -> pd.DataFrame:
+
+    df = pd.read_csv(path)
+    assert_schema(df, path)
+
+    # Ensure numeric, drop bad rows (NaN/inf in x/y).
+    for c in ["frame_id","player_id","timestamp_s","x1","y1","x2","y2","cx","cy","w","h","confidence"]:
+        df[c] = pd.to_numeric(df[c], errors="coerce")
+    df = df.dropna(subset=["cx", "cy"])
+    df = df[np.isfinite(df["cx"]) & np.isfinite(df["cy"])]
+    return df.reset_index(drop=True)
+
+def choose_weights(df: pd.DataFrame, mode: str | None): # HAVE ADJUSTED TO TAKE DATASET WHERE VISIBILITY COLUMN DOES NOT EXIST --- LUCAS
+
+    if not mode:
+        return None
+    m = mode.lower().strip()
+
+    # Use 'confidence' for weights with Xuans csv --- LUCAS
+    conf_col = "confidence" if "confidence" in df.columns else ("conf" if "conf" in df.columns else None)
+
+    if m in ("conf", "confidence"):
+        if conf_col is None:  # no confidence column at all
+            return None
+        return pd.to_numeric(df[conf_col], errors="coerce").fillna(0).to_numpy(dtype=float)
+
+    if m == "visibility":
+        if "visibility" not in df.columns:
+            # no visibility in Xuan's CSV unweighted
+            return None
+        return pd.to_numeric(df["visibility"], errors="coerce").fillna(0).to_numpy(dtype=float)
+
+    if m in ("conf*visibility", "visibility*conf"):
+        # conf * visibility; if visibility missing, treat as 1s (i.e. just conf)
+        conf = (pd.to_numeric(df[conf_col], errors="coerce").fillna(0).to_numpy(dtype=float)
+                if conf_col else np.zeros(len(df), dtype=float))
+        vis = (pd.to_numeric(df["visibility"], errors="coerce").fillna(1).to_numpy(dtype=float)
+               if "visibility" in df.columns else np.ones(len(df), dtype=float))
+        return conf * vis
+    # If someone passes an unexpected string, fail gracefully to unweighted.
+    return None
+
+# -------------------------
+# Field + mapping helpers
+# -------------------------
+
+def raw_bbox(xs, ys, pad_ratio=0.02):
+
+    xmin, xmax = float(np.min(xs)), float(np.max(xs))
+    ymin, ymax = float(np.min(ys)), float(np.max(ys))
+    dx, dy = max(xmax - xmin, 1e-9), max(ymax - ymin, 1e-9)
+    return (xmin - dx*pad_ratio, xmax + dx*pad_ratio,
+            ymin - dy*pad_ratio, ymax + dy*pad_ratio)
+
+def raw_to_metres(x, y, bbox_raw, a, b):
+
+    xmin, xmax, ymin, ymax = bbox_raw
+    x_m = ((x - xmin) / max(1e-9, (xmax - xmin))) * (2*a) - a
+    y_m = ((y - ymin) / max(1e-9, (ymax - ymin))) * (2*b) - b
+    return x_m, y_m
+
+def make_oval_mask_metres(nx, ny, a, b):
+
+    x_edges = np.linspace(-a, a, nx + 1)
+    y_edges = np.linspace(-b, b, ny + 1)
+    xc = (x_edges[:-1] + x_edges[1:]) / 2
+    yc = (y_edges[:-1] + y_edges[1:]) / 2
+    Xc, Yc = np.meshgrid(xc, yc)
+    mask = (Xc**2) / (a**2) + (Yc**2) / (b**2) <= 1.0
+    return x_edges, y_edges, mask
+
+def heatmap_in_metres(x_raw, y_raw, bbox_raw, a, b, nx, ny, sigma, weights=None):
+
+    x_m, y_m = raw_to_metres(np.asarray(x_raw, float), np.asarray(y_raw, float), bbox_raw, a, b)
+    x_edges, y_edges, mask = make_oval_mask_metres(nx, ny, a, b)
+
+    # Note: np.histogram2d expects bins=[x_edges, y_edges] but returns H with shape (nx, ny),
+    # so we transpose to align with imshow(origin="lower") which expects [y, x].
+    H, _, _ = np.histogram2d(x_m, y_m, bins=[x_edges, y_edges], weights=weights)
+    H = H.T
+    Hs = gaussian_filter(H, sigma=sigma)  # smooth in bin-space
+    Hs_masked = np.where(mask, Hs, np.nan)
+    return Hs_masked, x_edges, y_edges
+
+# -------------------------
+# AFL field drawing
+# -------------------------
+
+def draw_afl_field_metres(ax, a, b,
+                          centre_square=50.0,
+                          centre_inner_d=3.0,
+                          centre_outer_d=10.0,
+                          goal_square_depth=9.0,
+                          goal_square_width=6.4,
+                          arc_r=50.0,
+                          line_color="white", lw=2.0, alpha=0.95,
+                          show_ticks=False):
+    """Draw an AFL ground overlay (lines only) in metres on the given axes."""
+    t = np.linspace(0, 2*np.pi, 800)
+
+    # Oval boundary
+    ax.plot(a*np.cos(t), b*np.sin(t), color=line_color, lw=lw, alpha=alpha)
+
+    # Centre line
+    ax.plot([0, 0], [-b, b], color=line_color, lw=lw, alpha=alpha*0.9)
+
+    # Centre square (50 x 50)
+    cs = centre_square/2.0
+    ax.plot([-cs,  cs,  cs, -cs, -cs], [-cs, -cs, cs, cs, -cs],
+            color=line_color, lw=lw-0.5, alpha=alpha*0.9)
+
+    # Centre circles (outer 10 m, inner 3 m)
+    th = np.linspace(0, 2*np.pi, 400)
+    ax.plot((centre_outer_d/2.0)*np.cos(th), (centre_outer_d/2.0)*np.sin(th),
+            color=line_color, lw=lw-0.5, alpha=alpha*0.9)
+    ax.plot((centre_inner_d/2.0)*np.cos(th), (centre_inner_d/2.0)*np.sin(th),
+            color=line_color, lw=lw-0.5, alpha=alpha*0.6)
+
+    # 50 m arcs (left/right)
+    phi_L = np.linspace(-np.pi/2, np.pi/2, 400)
+    phi_R = np.linspace(np.pi/2, 3*np.pi/2, 400)
+    ax.plot(-a + arc_r*np.cos(phi_L), 0 + arc_r*np.sin(phi_L),
+            color=line_color, lw=lw-0.5, alpha=alpha*0.6)
+    ax.plot( a + arc_r*np.cos(phi_R), 0 + arc_r*np.sin(phi_R),
+            color=line_color, lw=lw-0.5, alpha=alpha*0.6)
+
+    # Goal squares: 9 m deep x 6.4 m wide
+    gs_w = goal_square_width/2.0
+    ax.plot([-a, -a+goal_square_depth, -a+goal_square_depth, -a, -a],
+            [-gs_w, -gs_w, gs_w, gs_w, -gs_w],
+            color=line_color, lw=lw-0.5, alpha=alpha*0.9)
+    ax.plot([ a,  a-goal_square_depth,  a-goal_square_depth,  a,  a],
+            [-gs_w, -gs_w, gs_w, gs_w, -gs_w],
+            color=line_color, lw=lw-0.5, alpha=alpha*0.9)
+
+    # Goal line ticks (simple markers near tips)
+    tick = 4.0
+    for xg in (-a, a):
+        ax.plot([xg, xg], [-tick, tick], color=line_color, lw=lw, alpha=alpha)
+
+    # Axes cosmetic setup
+    if show_ticks:
+        ax.set_xlabel("Metres (X)")
+        ax.set_ylabel("Metres (Y)")
+        ax.set_xticks(np.arange(-a, a+1e-6, 20))
+        ax.set_yticks(np.arange(-b, b+1e-6, 20))
+        ax.grid(alpha=0.15, linewidth=0.8)
+    else:
+        ax.set_axis_off()
+
+    ax.set_aspect('equal')
+
+def plot_heatmap_on_field_metres(
+    H, x_edges, y_edges, a, b, title="", alpha_img=0.88, out_path=None
+):
+    """Render a heatmap on a green oval with AFL field lines and save PNG.
+
+    - Uses a robust upper bound (99th percentile) for colour scaling.
+    - Adds a small “20 m” scale bar.
+    """
+    fig, ax = plt.subplots(figsize=(11, 8))
+
+    # Green oval background (grass vibe) beneath the heatmap
+    t = np.linspace(0, 2*np.pi, 600)
+    ax.fill(a*np.cos(t), b*np.sin(t), color=(0.05, 0.35, 0.05), alpha=0.95, zorder=0)
+
+    extent = [x_edges.min(), x_edges.max(), y_edges.min(), y_edges.max()]
+    finite_vals = H[np.isfinite(H)]
+    vmax = (np.nanpercentile(finite_vals, 99) if finite_vals.size else np.nanmax(H)) or 1.0
+
+    # Heatmap layer
+    im = ax.imshow(
+        H, origin="lower", extent=extent, aspect="equal",
+        interpolation="bilinear", alpha=alpha_img,
+        norm=Normalize(vmin=0.0, vmax=vmax), zorder=2
+    )
+
+    # Field lines drawn over the grass, under the heatmap for soft blending
+    draw_afl_field_metres(ax, a, b, show_ticks=False)
+
+    # 20 m scale bar (bottom-left)
+    sb_y = -b + 8
+    sb_x0, sb_x1 = -a + 12, -a + 32
+    ax.plot([sb_x0, sb_x1], [sb_y, sb_y], color="white", lw=4, alpha=0.95)
+    ax.text((sb_x0+sb_x1)/2, sb_y-4, "20 m", ha="center", va="top",
+            color="white", fontsize=11)
+
+    ax.set_title(title, color="white")
+
+    # Colorbar on the right
+    cbar = plt.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
+    cbar.set_label("Intensity")
+
+    # Clean frame
+    for spine in ax.spines.values():
+        spine.set_visible(False)
+
+    plt.tight_layout()
+    if out_path:
+        os.makedirs(os.path.dirname(out_path), exist_ok=True)
+        fig.savefig(out_path, dpi=220)
+    plt.close(fig)
+
+def save_density_csv(H, x_edges, y_edges, out_csv_path):
+    """Export the smoothed density as a tidy CSV: (x_m, y_m, density)."""
+    os.makedirs(os.path.dirname(out_csv_path), exist_ok=True)
+    xc = (x_edges[:-1] + x_edges[1:]) / 2
+    yc = (y_edges[:-1] + y_edges[1:]) / 2
+    Xc, Yc = np.meshgrid(xc, yc)
+    df = pd.DataFrame({
+        "x_m": Xc.flatten(),
+        "y_m": Yc.flatten(),
+        "density": np.nan_to_num(H, nan=0.0).flatten()
+    })
+    df.to_csv(out_csv_path, index=False)
+
+# -------------------------
+# Main
+# -------------------------
+
+def parse_inputs(pairs):
+    """Parse ["path:label", ...] into a list of (path, label).
+
+    If ":label" is omitted, the filename stem is used as the label.
+    """
+    out = []
+    for p in pairs:
+        if ":" in p:
+            path, label = p.split(":", 1)
+        else:
+            path, label = p, os.path.splitext(os.path.basename(p))[0]
+        out.append((path, label))
+    return out
+
+def main():
+    """Command-line entry point.
+
+    Key choices your teammates might tweak:
+    - --weight-mode: how to weight each plotted point (conf, visibility, both, or none)
+    - --nx/--ny: spatial resolution (bigger = finer grid, slower)
+    - --sigma: smoothing; try 1.0–4.0 depending on point sparsity
+    - --group-by class_id: produce one heatmap per class within each CSV
+    """
+    ap = argparse.ArgumentParser(description="AFL oval heatmap generator")
+    ap.add_argument(
+        "--inputs", nargs="+", required=True,
+        help='List like "file.csv:label". Label optional; defaults to file stem.'
+    )
+    ap.add_argument("--out-dir", default="heatmap_outputs", help="Output directory")
+    ap.add_argument("--weight-mode", default="conf*visibility",
+                    choices=["none", "conf", "visibility", "conf*visibility"],
+                    help="Weighting for histogram bins")
+    ap.add_argument("--field-length", type=float, default=DEFAULT_FIELD_LENGTH_M,
+                    help="Oval length in metres (tip-to-tip)")
+    ap.add_argument("--field-width", type=float, default=DEFAULT_FIELD_WIDTH_M,
+                    help="Oval width in metres (wing-to-wing)")
+    ap.add_argument("--nx", type=int, default=DEFAULT_NX, help="Grid bins in X")
+    ap.add_argument("--ny", type=int, default=DEFAULT_NY, help="Grid bins in Y")
+    ap.add_argument("--sigma", type=float, default=DEFAULT_SIGMA,
+                    help="Gaussian smoothing sigma (bins)")
+    """ap.add_argument("--group-by", default=None,
+                    choices=[None, "class_id"],
+                    help="Optional grouping to produce one heatmap per group")""" # COMMENTED OUT --- LUCAS
+    ap.add_argument("--group-by", default=None,
+                    choices=[None, "class_id", "player_id"],
+                    help="Optional grouping to produce one heatmap per group") # ALLOWS GROUPING BY player_id IN THE CLI --- LUCAS
+    args = ap.parse_args()
+    print("DEBUG group-by:", args.group_by) # DEBUG --- LUCAS
+
+    inputs = parse_inputs(args.inputs)
+    out_dir = args.out_dir
+    os.makedirs(out_dir, exist_ok=True)
+
+    # Half-dimensions in metres
+    a = args.field_length / 2.0
+    b = args.field_width  / 2.0
+
+    # Load all inputs first so we can compute a single shared raw bbox across them.
+    # This ensures all figures align spatially and are directly comparable.
+    loaded = []
+    all_x, all_y = [], []
+
+    for path, label in inputs:
+        df = load_events_csv(path)
+        loaded.append((df, label, path))
+        all_x.append(df["cx"].to_numpy())
+        all_y.append(df["cy"].to_numpy())
+
+    # If no data at all (edge-case), invent a tiny bbox to avoid division-by-zero.
+    all_x = np.concatenate(all_x) if len(all_x) else np.array([0.0])
+    all_y = np.concatenate(all_y) if len(all_y) else np.array([0.0])
+    shared_bbox_raw = raw_bbox(all_x, all_y)
+
+    # Generate heatmaps per file (and optionally per class)
+    for df, label, path in loaded:
+        # Pick base weights once per file; subsetting below recomputes if needed.
+        weights_base = None if args.weight_mode == "none" else choose_weights(df, args.weight_mode)
+
+        if args.group_by == "class_id":
+            # One output per class_id present in the file.
+            classes = sorted(df["class_id"].dropna().unique().tolist())
+            for cid in classes:
+                sub = df[df["class_id"] == cid].reset_index(drop=True)
+                if sub.empty:
+                    continue
+                # Recompute weights on the subset to keep alignment correct.
+                w = None if weights_base is None else choose_weights(sub, args.weight_mode)
+                H, xe, ye = heatmap_in_metres(
+                    sub["cx"], sub["cy"], shared_bbox_raw, a, b,
+                    args.nx, args.ny, args.sigma, w
+                )
+                title = f"{label} — class_id {cid} ({int(args.field_length)}×{int(args.field_width)} m)"
+                img_out = os.path.join(out_dir, f"heatmap_{label}_class{int(cid)}.png")
+                csv_out = os.path.join(out_dir, f"density_{label}_class{int(cid)}.csv")
+                plot_heatmap_on_field_metres(H, xe, ye, a, b, title, out_path=img_out)
+                save_density_csv(H, xe, ye, csv_out)
+        
+        elif args.group_by == "player_id": # ADDING PLAYER BRANCH --- LUCAS
+            players = sorted(
+                pd.to_numeric(df["player_id"], errors="coerce")
+                .dropna().unique().astype(int).tolist())
+            
+            for pid in players:
+                sub = df[df["player_id"] == pid].reset_index(drop=True)
+                if sub.empty:
+                    continue
+
+                w = None if weights_base is None else choose_weights(sub, args.weight_mode)
+                H, xe, ye = heatmap_in_metres(
+                    sub["cx"], sub["cy"], shared_bbox_raw, a, b,
+                    args.nx, args.ny, args.sigma, w
+                )
+
+                title = f"{label} - player_id {pid} ({int(args.field_length)}x{int(args.field_width)} m)"
+                img_out = os.path.join(out_dir, f"heatmap_{pid}.png")
+                csv_out = os.path.join(out_dir, f"density_player_{pid}.csv")
+                plot_heatmap_on_field_metres(H, xe, ye, a, b, title, out_path=img_out)
+                save_density_csv(H, xe, ye, csv_out)
+
+        else:
+            # Single heatmap for the entire file.
+            H, xe, ye = heatmap_in_metres(
+                df["cx"], df["cy"], shared_bbox_raw, a, b,
+                args.nx, args.ny, args.sigma, weights_base
+            )
+            title = f"{label} — Density ({int(args.field_length)}×{int(args.field_width)} m)"
+            img_out = os.path.join(out_dir, f"heatmap_{label}.png")
+            csv_out = os.path.join(out_dir, f"density_{label}.csv")
+            plot_heatmap_on_field_metres(H, xe, ye, a, b, title, out_path=img_out)
+            save_density_csv(H, xe, ye, csv_out)
+
+    print(f"Done. Outputs in: {os.path.abspath(out_dir)}")
+
+if __name__ == "__main__":
+    main()
diff --git a/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/scripts/compute_workload.py b/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/scripts/compute_workload.py
new file mode 100644
index 000000000..a389ad27a
--- /dev/null
+++ b/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/scripts/compute_workload.py
@@ -0,0 +1,350 @@
+#!/usr/bin/env python3
+import argparse, json
+import numpy as np
+import pandas as pd
+import yaml                             # Allows loading of config.yaml
+from typing import Dict, List, Any      # typing for readbility          
+
+def _minmax(series: pd.Series) -> np.ndarray:   # Scales 0-1 (handles NaN)
+    x = series.to_numpy(dtype=float)
+    if x.size == 0:
+        return x
+    lo = np.nanmin(x)
+    hi = np.nanmax(x)
+    denom = (hi - lo) if (hi - lo) > 0 else 1.0
+    return (x - lo) / denom
+
+def _get(cfg: Dict, path: str, default):        # Allows _get(cfg: "thresholds.hsr_kmh", 18.0) without error
+    cur = cfg
+    for key in path.split("."):
+        if isinstance(cur, dict) and key in cur:
+            cur = cur[key]
+        else:
+            return default
+    return cur
+
+def _clean_float(v) -> float:                   # Cleaner: turns NaN into 0.0
+    try:
+        fv = float(v)
+        return fv if np.isfinite(fv) else 0.0
+    except Exception:
+        return 0.0
+
+def _band_label(s: float, bands: List[Dict[str, Any]]) -> str:  # Talks with config.yaml file, converts 0-100 score into low, moderate or high
+    for b in bands:
+        if s <= float(b["max"]):
+            return str(b["label"])
+    return str(bands[-1]["label"])
+
+def _ensure_timestamps_and_quarters(df: pd.DataFrame, fps: float, edges_s: list[int|float]) -> pd.DataFrame:   # Safety net for time and quart labels
+    """
+    Ensures df has a reliable 'timestamp_s' and a 'quarter' column.
+    - If timestamp_s is missing not possible time jumps, synthesise from frame_id/fps.
+    - Assign quarters by edges_s; if still impossible, fall back to Q1.
+    """
+    df = df.copy()
+
+    def _is_mono_increasing(x: pd.Series) -> bool:  # Ensures timestamp_s increases over time for each player
+        if x.isna().any(): 
+            return False
+        d = x.diff().dropna()
+        if d.empty:
+            return False
+        return (d >= -1e-6).mean() >= 0.95  # Allows tiny negative time diff 
+
+    use_estimated = False                   # Decides whether to estimate timestamps 
+    has_ts = "timestamp_s" in df.columns    
+    if not has_ts:
+        use_estimated = True                # If no timestamp; create one from frame number
+    else:                                   # Loop through each player's data, check if it is monotonically increasing (always going foward)
+        ok = True
+        for _, g in df.groupby("player_id", sort=False):
+            if not _is_mono_increasing(g["timestamp_s"]):
+                ok = False
+                break
+        if not ok:                          # If not always monotonically increasing ignore timestamp and create new one
+            use_estimated = True
+
+    if use_estimated:                       # Runs through this if we decide from above that a timestamp is missing or unreliable
+        df["timestamp_s"] = (
+            (df["frame_id"] - df.groupby("player_id")["frame_id"].transform("min")) / float(max(fps, 1e-9))
+        )
+
+    # Assigns quarters using edges_s from config.yaml
+    try:
+        bins = [-np.inf] + list(edges_s) + [np.inf]
+        df["quarter"] = pd.cut(df["timestamp_s"], bins=bins, labels=[1,2,3,4]).astype(int)
+    except Exception:
+        # last resort: put everything in Q1
+        df["quarter"] = 1
+
+    return df, ("estimated" if use_estimated else "provided")
+
+def main():     # Script input and output paths
+    ap = argparse.ArgumentParser(description="Compute AFL workload metrics (game total + per quarter).")
+    ap.add_argument(
+        "--tracking",
+        default="examples/tracking2.0.csv",
+        help="Path to tracking2.0.csv (default: examples/tracking2.0.csv)"
+    )
+    ap.add_argument("--config", default="config.yaml", help="Path to config.yaml")
+    ap.add_argument("--out-json", required=True, help="Output JSON path")
+    ap.add_argument("--out-csv", default="", help="(Optional) Output CSV path for game totals")
+    args = ap.parse_args()
+
+    # - Config - Loads configuration from config.yaml
+    cfg = yaml.safe_load(open(args.config))
+    fps = float(_get(cfg, "fps", 25.0))                     # frames per second
+    L   = float(_get(cfg, "field.length_m", 165.0))         # AFL oval length (m)
+    W   = float(_get(cfg, "field.width_m", 135.0))          # AFL oval width (m)
+    hsr_kmh = float(_get(cfg, "thresholds.hsr_kmh", 15.0))  # High-speed running threshold (km/h)
+    vmax_ms = float(_get(cfg, "thresholds.vmax_ms", 12.0))  # Max plausible speed clamp (m/s)
+    weights = _get(cfg, "weights", {                        # scoring weights
+        "distance": 0.3,
+        "hsr": 0.4,
+        "work_rest": 0.3,
+        "repeat_sprints": 0.2,
+    })
+    risk_bands = _get(cfg, "risk_bands", [                  # default risk bands
+        {"max": 33, "label": "low"},
+        {"max": 66, "label": "moderate"},
+        {"max": 100, "label": "high"},
+    ])
+
+    # Quarter start and finish in seconds (20min qtrs)
+    quarter_edges_s = _get(cfg, "quarters.edges_s", [1200, 2400, 3600])
+
+    # - Load tracking data
+    df = pd.read_csv(args.tracking).sort_values(["player_id", "frame_id"]).reset_index(drop=True)
+    required = {"frame_id", "player_id", "cx", "cy"}
+    missing = required - set(df.columns)
+    if missing:
+        raise ValueError(f"Missing required columns: {missing}")
+
+    # - Scale pixels to metres
+    sx = W / max(df["cx"].max() - df["cx"].min(), 1e-9)
+    sy = L / max(df["cy"].max() - df["cy"].min(), 1e-9)
+    df["cx_m"] = (df["cx"] - df["cx"].min()) * sx
+    df["cy_m"] = (df["cy"] - df["cy"].min()) * sy
+
+    # - Per-step distance with clamp (anti-teleport)
+    dx = df.groupby("player_id")["cx_m"].diff()             # X- axis diff between one player_id frame to the next
+    dy = df.groupby("player_id")["cy_m"].diff()             # Y- axis
+    step = np.hypot(dx, dy)                                 # Euclidean distance (total) between frames
+    df["step_dist_m"] = np.where(np.isfinite(step) & (step <= 0.5), step, 0.0).astype(float)    # If value is not NaN or 12.5 m/s keep (0.5 x 25 fps = 12.5)
+
+    # - delta time (dt, time difference between each frame) & speed 
+    dt = df.groupby("player_id")["frame_id"].diff() / fps
+    df["dt_s"] = np.where((dt > 0) & np.isfinite(dt), dt, 0.0).astype(float)        # Keeps legitate scores sets duplicate or NaN to 0.0
+    speed_mps = np.where(df["dt_s"] > 0, df["step_dist_m"] / df["dt_s"], np.nan)    # Speed = distance / time
+    df["speed_mps"] = np.clip(speed_mps, 0.0, vmax_ms)                              # Clips speed 0 - 12 m/s
+    kmh = (df["speed_mps"] * 3.6).astype(float)                                     # Convert to km/h
+
+    # - Sprint detection (events) 
+    sprint_kmh = float(_get(cfg, "thresholds.sprint_kmh", 20.0))         # sprint threshold (km/h)
+    min_sprint_dur_s = float(_get(cfg, "thresholds.min_sprint_dur_s", 1.0))  # min sprint duration (s)
+    rs_window_s = float(_get(cfg, "thresholds.repeat_sprint_window_s", 30.0)) # repeated-sprint window (s)
+
+    df["is_sprint"] = kmh >= sprint_kmh                                             # Boolean checks if player performed a sprint
+
+    # Run-length encode per player to get sprint segments
+    def _sprint_segments(g: pd.DataFrame):
+        # g is sorted by frame_id already
+        mask = g["is_sprint"].to_numpy()
+        if mask.size == 0:
+            return pd.DataFrame(columns=["player_id","seg_id","t_start","t_end","dur_s",
+                                        "dist_m","mean_speed_mps"])
+        # segment ids
+        boundaries = np.where(np.diff(mask.astype(int)) != 0)[0] + 1
+        seg_ids = np.zeros(len(mask), dtype=int)
+        seg_ids[boundaries] = 1
+        seg_ids = np.cumsum(seg_ids)
+        g = g.copy()
+        g["seg_id"] = seg_ids
+
+        # keep only sprint segments (mask True)
+        sprint_rows = g[mask]
+        if sprint_rows.empty:
+            return pd.DataFrame(columns=["player_id","seg_id","t_start","t_end","dur_s",
+                                        "dist_m","mean_speed_mps"])
+
+        agg = (sprint_rows.groupby("seg_id")
+                .agg(player_id=("player_id","first"),
+                    t_start=("timestamp_s","min"),
+                    t_end=("timestamp_s","max"),
+                    dur_s=("dt_s","sum"),
+                    dist_m=("step_dist_m","sum"),
+                    mean_speed_mps=("speed_mps","mean"))
+                .reset_index(drop=True))
+        # keep segments that last long enough
+        agg = agg[agg["dur_s"] >= min_sprint_dur_s].reset_index(drop=True)
+        return agg
+
+    sprint_segments = (
+        df.groupby("player_id", group_keys=False)
+        .apply(_sprint_segments)
+        .reset_index(drop=True)
+    )
+
+    # - Repeated Sprint Bouts (clusters of sprints within rs_window_s)
+    def _count_repeated_bouts(agg: pd.DataFrame) -> pd.DataFrame:
+        if agg.empty:
+            return pd.DataFrame({"player_id": [], "num_sprints": [], "repeated_sprint_bouts": []})
+        out = []
+        for pid, gseg in agg.groupby("player_id"):
+            gseg = gseg.sort_values("t_start")
+            num_sprints = len(gseg)
+
+            # cluster by start-time gaps
+            starts = gseg["t_start"].to_numpy()
+            gaps = np.diff(starts)
+            # new cluster whenever gap > window
+            cluster_break = np.insert(gaps > rs_window_s, 0, True)
+            cluster_id = np.cumsum(cluster_break)
+
+            sizes = pd.Series(cluster_id).value_counts()
+            repeated_bouts = int((sizes >= 2).sum())  # clusters with >=2 sprints
+
+            out.append({
+                "player_id": int(pid),
+                "num_sprints": int(num_sprints),
+                "repeated_sprint_bouts": repeated_bouts,
+                "mean_sprint_speed_mps": float(gseg["mean_speed_mps"].mean()) if num_sprints else 0.0,
+                "mean_sprint_dist_m": float(gseg["dist_m"].mean()) if num_sprints else 0.0,
+            })
+        return pd.DataFrame(out)
+
+    sprint_summary = _count_repeated_bouts(sprint_segments)
+
+    # - HSR metres
+    df["hsr_m"] = np.where(kmh >= hsr_kmh, df["step_dist_m"], 0.0).astype(float)
+
+    # - Quarter segmentation (robust)
+    df, ts_mode = _ensure_timestamps_and_quarters(df, fps=fps, edges_s=quarter_edges_s) 
+
+    # - GAME TOTAL (per player)
+    high_time_s = np.where(kmh >= hsr_kmh, df["dt_s"], 0.0)
+    per_player_high_s = pd.Series(high_time_s).groupby(df["player_id"]).sum()
+    per_player_total_s = df.groupby("player_id")["dt_s"].sum()
+
+    g = (df.groupby("player_id")
+           .agg(distance_m=("step_dist_m", "sum"),
+                hsr_m=("hsr_m", "sum"),
+                mean_speed_mps=("speed_mps", "mean"),
+                max_speed_mps=("speed_mps", "max"),
+                total_time_s=("dt_s", "sum"))
+           .reset_index())
+    
+    # Merge sprint summary into game totals
+    g = g.merge(sprint_summary, on="player_id", how="left").fillna({
+        "num_sprints": 0,
+        "repeated_sprint_bouts": 0,
+        "mean_sprint_speed_mps": 0.0,
+        "mean_sprint_dist_m": 0.0
+    })
+
+    high_aligned = per_player_high_s.reindex(g["player_id"]).fillna(0.0).to_numpy()
+    total_aligned = g["total_time_s"].to_numpy(dtype=float)
+    denom = np.clip(total_aligned - high_aligned, 1e-9, None)
+    g["work_rest_ratio"] = (high_aligned / denom)
+
+    # Score & risk (game-total)
+    parts = []
+    if "distance" in weights:   
+        parts.append(weights["distance"]  * _minmax(g["distance_m"]))
+    if "hsr" in weights:        
+        parts.append(weights["hsr"]       * _minmax(g["hsr_m"]))
+    if "work_rest" in weights:  
+        parts.append(weights["work_rest"] * _minmax(g["work_rest_ratio"]))
+    if "repeat_sprints" in weights and "num_sprints" in g:
+        parts.append(weights["repeat_sprints"] * _minmax(g["num_sprints"]))
+
+    score_game = 100.0 * np.clip(
+        np.sum(parts, axis=0) if parts else np.zeros(len(g)), 0.0, 1.0
+    )
+    g["workload_score"] = score_game
+    g["fatigue_risk"] = g["workload_score"].apply(lambda s: _band_label(s, risk_bands))
+
+    # - PER QUARTER (per player, per quarter)
+    # Aggregations
+    gq = (df.groupby(["player_id","quarter"])
+            .agg(distance_m=("step_dist_m","sum"),
+                 hsr_m=("hsr_m","sum"),
+                 mean_speed_mps=("speed_mps","mean"),
+                 max_speed_mps=("speed_mps","max"),
+                 total_time_s=("dt_s","sum"))
+            .reset_index())
+
+    # Work/rest per quarter
+    high_time_q = pd.Series(np.where(kmh >= hsr_kmh, df["dt_s"], 0.0)).groupby([df["player_id"], df["quarter"]]).sum()
+    total_time_q = df.groupby(["player_id","quarter"])["dt_s"].sum()
+    wr_q = (high_time_q / (total_time_q - high_time_q).clip(lower=1e-9)).reset_index(name="work_rest_ratio")
+    gq = gq.merge(wr_q, on=["player_id","quarter"], how="left").fillna(0.0)
+
+    # Quarter scores are computed across ALL (player, quarter) rows so they’re comparable
+    parts_q = []
+    if "distance" in weights:   parts_q.append(weights["distance"]  * _minmax(gq["distance_m"]))
+    if "hsr" in weights:        parts_q.append(weights["hsr"]       * _minmax(gq["hsr_m"]))
+    if "work_rest" in weights:  parts_q.append(weights["work_rest"] * _minmax(gq["work_rest_ratio"]))
+    score_q = 100.0 * np.clip(np.sum(parts_q, axis=0) if parts_q else np.zeros(len(gq)), 0.0, 1.0)
+    gq["workload_score"] = score_q
+    gq["fatigue_risk"] = gq["workload_score"].apply(lambda s: _band_label(s, risk_bands))
+
+    # CSV of game totals
+    if args.out_csv:
+        g.to_csv(args.out_csv, index=False)
+
+    # - Build JSON payload with quarters nested under each player 
+    players_payload = []
+    for pid, row in g.set_index("player_id").iterrows():
+        # quarters for this player
+        qrows = gq[gq["player_id"] == pid].sort_values("quarter")
+        quarters_payload = [
+            {
+                "q":          int(qr.quarter),
+                "distance_m": _clean_float(qr.distance_m),
+                "hsr_m":      _clean_float(qr.hsr_m),
+                "mean_speed_mps": _clean_float(qr.mean_speed_mps),
+                "max_speed_mps":  _clean_float(qr.max_speed_mps),
+                "total_time_s":   _clean_float(qr.total_time_s),
+                "work_rest_ratio": _clean_float(qr.work_rest_ratio),
+                "workload_score":  _clean_float(qr.workload_score),
+                "fatigue_risk":    str(qr.fatigue_risk),
+            }
+            for _, qr in qrows.iterrows()
+        ]
+
+        players_payload.append({
+            "player_id": int(pid),
+            "game_total": {
+                "distance_m":     _clean_float(row.distance_m),
+                "hsr_m":          _clean_float(row.hsr_m),
+                "mean_speed_mps": _clean_float(row.mean_speed_mps),
+                "max_speed_mps":  _clean_float(row.max_speed_mps),
+                "total_time_s":   _clean_float(row.total_time_s),
+                "work_rest_ratio": _clean_float(row.work_rest_ratio),
+                "workload_score":  _clean_float(row.workload_score),
+                "fatigue_risk":    str(row.fatigue_risk),
+                "num_sprints":          int(row.get("num_sprints", 0)),
+                "repeated_sprint_bouts": int(row.get("repeated_sprint_bouts", 0)),
+                "mean_sprint_speed_mps": _clean_float(row.get("mean_sprint_speed_mps", 0.0)),
+                "mean_sprint_dist_m":    _clean_float(row.get("mean_sprint_dist_m", 0.0)),
+            },
+            "quarters": quarters_payload
+        })
+
+    payload = {
+    "meta": {
+        "fps": fps,
+        "field": {"length_m": L, "width_m": W},
+        "hsr_kmh": hsr_kmh,
+        "quarters": {"edges_s": quarter_edges_s, "labels": [1,2,3,4], "timestamp_mode": ts_mode}
+    },
+    "players": players_payload
+}
+
+    with open(args.out_json, "w") as f:
+        json.dump(payload, f, indent=2)
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/scripts/generate_heatmaps.py b/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/scripts/generate_heatmaps.py
new file mode 100644
index 000000000..7aa986bba
--- /dev/null
+++ b/Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/player_tracking_logic_heatmaps_workload/scripts/generate_heatmaps.py
@@ -0,0 +1,84 @@
+#!/usr/bin/env python3
+import argparse
+import subprocess
+import sys
+from pathlib import Path
+import yaml
+
+# Wrapper: call heatmappipeline_xuan.py directly so we don't depend on a non-existent
+# `heatmaps` package. Pulls defaults from config.yaml when present.
+
+def _get(cfg, path, default=None):
+    cur = cfg
+    for k in path.split('.'):
+        if isinstance(cur, dict) and k in cur:
+            cur = cur[k]
+        else:
+            return default
+    return cur
+
+
+def main():
+    ap = argparse.ArgumentParser(description="Generate heatmaps from tracking data (wrapper)")
+    ap.add_argument(
+        "--tracking",
+        default="examples/tracking2.0.csv",
+        help="Path to tracking CSV (default: examples/tracking2.0.csv)"
+    )
+    ap.add_argument("--config", default="config.yaml", help="Path to config.yaml")
+    ap.add_argument("--out-dir", required=True, help="Directory for saving heatmaps")
+    ap.add_argument("--group-by", default="player_id", help="Column to group by (e.g. player_id)")
+
+    # Optional overrides; if omitted we'll pull from config or fall back to sensible defaults
+    ap.add_argument("--nx", type=int, default=None, help="Grid bins in X (override)")
+    ap.add_argument("--ny", type=int, default=None, help="Grid bins in Y (override)")
+    ap.add_argument("--sigma", type=float, default=None, help="Gaussian blur sigma in bins (override)")
+    ap.add_argument("--weight-mode", default=None, help="Weighting: none|conf|visibility|conf*visibility (override)")
+
+    args = ap.parse_args()
+
+    # Load config (best-effort)
+    try:
+        with open(args.config, "r") as f:
+            cfg = yaml.safe_load(f) or {}
+    except FileNotFoundError:
+        cfg = {}
+
+    # Field dimensions with defaults
+    L = float(_get(cfg, "field.length_m", 165.0))
+    W = float(_get(cfg, "field.width_m", 135.0))
+
+    # Heatmap tunables from config if present
+    nx = args.nx if args.nx is not None else int(_get(cfg, "heatmap.nx", 200))
+    ny = args.ny if args.ny is not None else int(_get(cfg, "heatmap.ny", 150))
+    sigma = args.sigma if args.sigma is not None else float(_get(cfg, "heatmap.sigma", 2.0))
+
+    weight_mode = args.weight_mode if args.weight_mode is not None else _get(cfg, "heatmap.weight_mode", "conf")
+
+    # Build command to call the canonical pipeline
+    repo_root = Path(__file__).resolve().parents[1]
+    pipeline_py = repo_root / "heatmappipeline_xuan.py"
+
+    cmd = [
+        sys.executable, str(pipeline_py),
+        "--inputs", f"{args.tracking}:Xuan",
+        "--out-dir", args.out_dir,
+        "--field-length", str(L),
+        "--field-width", str(W),
+        "--nx", str(nx),
+        "--ny", str(ny),
+        "--sigma", str(sigma),
+        "--weight-mode", str(weight_mode),
+    ]
+
+    if args.group_by:
+        cmd += ["--group-by", args.group_by]
+
+    # Run and stream output
+    print("Running:", " ".join(cmd))
+    subprocess.run(cmd, check=True)
+    print(f"Done. Outputs in {args.out_dir}")
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file