further eye catcher plot work
This commit is contained in:
parent
e42e151c17
commit
eecee47b9e
5 changed files with 794 additions and 114 deletions
4
.gitignore
vendored
4
.gitignore
vendored
|
|
@ -9,8 +9,10 @@ cache/
|
|||
*.svg
|
||||
*.gv
|
||||
|
||||
*.png
|
||||
|
||||
traces/
|
||||
venv310/
|
||||
**/build/
|
||||
**/install/
|
||||
**/log/
|
||||
**/log/
|
||||
|
|
|
|||
Binary file not shown.
|
Before Width: | Height: | Size: 539 KiB |
|
|
@ -1,113 +0,0 @@
|
|||
experiment_type,experiment_dir,chain,mean_latency_ms,std_latency_ms,actual_runs,theoretical_max_runs,completion_percentage,input_delay_ms
|
||||
edf_single_timed_20_boosted_10,edf_single_timed_20_boosted_10,/input/baroA/alt --> /output/flight/cmd,35.42897435897436,6.192051282051281,4.102564102564102,200,2.051282051282051,100
|
||||
edf_single_timed_20_boosted_10,edf_single_timed_20_boosted_10,/input/cameraA/raw --> /output/cameraA/mapped,74.52780000000001,6.6764,26.0,100,26.0,200
|
||||
edf_single_timed_20_boosted_10,edf_single_timed_20_boosted_10,/input/cameraB/raw --> /output/classifier/classification,98.844,31.873800000000003,32.7,133,24.586466165413537,150
|
||||
edf_single_timed_20_boosted_10,edf_single_timed_20_boosted_10,/input/gpsA/fix --> /output/flight/cmd,30.827368421052633,5.925526315789473,4.105263157894737,200,2.0526315789473686,100
|
||||
edf_single_timed_20_boosted_10,edf_single_timed_20_boosted_10,/input/imuA/data --> /output/flight/cmd,33.086052631578944,6.046052631578946,4.105263157894737,200,2.0526315789473686,100
|
||||
edf_single_timed_20_boosted_10,edf_single_timed_20_boosted_10,/input/lidar/scan --> /output/flight/cmd,23.061794871794874,5.396410256410257,4.102564102564102,200,2.051282051282051,100
|
||||
edf_single_timed_20_boosted_10,edf_single_timed_20_boosted_10,/input/operator/commands --> /output/flight/cmd,29.039743589743587,5.063333333333334,4.102564102564102,200,2.051282051282051,100
|
||||
edf_single_timed_20,edf_single_timed_20,/input/baroA/alt --> /output/flight/cmd,35.124418604651154,6.056976744186046,3.8372093023255816,200,1.9186046511627908,100
|
||||
edf_single_timed_20,edf_single_timed_20,/input/cameraA/raw --> /output/cameraA/mapped,74.8638,6.913800000000001,25.96,100,25.96,200
|
||||
edf_single_timed_20,edf_single_timed_20,/input/cameraB/raw --> /output/classifier/classification,99.209,32.5044,32.6,133,24.51127819548872,150
|
||||
edf_single_timed_20,edf_single_timed_20,/input/gpsA/fix --> /output/flight/cmd,30.65209302325581,5.489767441860465,3.8372093023255816,200,1.9186046511627908,100
|
||||
edf_single_timed_20,edf_single_timed_20,/input/imuA/data --> /output/flight/cmd,32.79976744186047,5.736976744186046,3.8372093023255816,200,1.9186046511627908,100
|
||||
edf_single_timed_20,edf_single_timed_20,/input/lidar/scan --> /output/flight/cmd,23.58,4.84953488372093,3.8372093023255816,200,1.9186046511627908,100
|
||||
edf_single_timed_20,edf_single_timed_20,/input/operator/commands --> /output/flight/cmd,27.936046511627907,4.471627906976744,3.8372093023255816,200,1.9186046511627908,100
|
||||
ros_multi_timed_20,ros_multi_timed_20,/input/baroA/alt --> /output/flight/cmd,77.7224,38.486999999999995,1073.12,200,105.0,100
|
||||
ros_multi_timed_20,ros_multi_timed_20,/input/baroB/alt --> /output/telemetry/radio,67.8986,38.959799999999994,3197.82,200,105.0,100
|
||||
ros_multi_timed_20,ros_multi_timed_20,/input/cameraA/raw --> /output/cameraA/mapped,76.226,8.531600000000001,96.86,100,96.86,200
|
||||
ros_multi_timed_20,ros_multi_timed_20,/input/cameraA/raw --> /output/telemetry/radio,183.4386,68.3676,3184.16,100,105.0,200
|
||||
ros_multi_timed_20,ros_multi_timed_20,/input/cameraB/raw --> /output/classifier/classification,81.2846,9.4038,128.34,133,96.49624060150376,150
|
||||
ros_multi_timed_20,ros_multi_timed_20,/input/gpsA/fix --> /output/flight/cmd,77.76100000000001,37.5182,1073.06,200,105.0,100
|
||||
ros_multi_timed_20,ros_multi_timed_20,/input/gpsB/fix --> /output/telemetry/radio,67.8846,36.48500000000001,3200.64,200,105.0,100
|
||||
ros_multi_timed_20,ros_multi_timed_20,/input/imuA/data --> /output/flight/cmd,77.796,38.1552,1073.18,200,105.0,100
|
||||
ros_multi_timed_20,ros_multi_timed_20,/input/imuB/data --> /output/telemetry/radio,67.9932,37.3536,3199.1,200,105.0,100
|
||||
ros_multi_timed_20,ros_multi_timed_20,/input/lidar/scan --> /output/flight/cmd,68.4622,37.3688,1073.76,200,105.0,100
|
||||
ros_multi_timed_20,ros_multi_timed_20,/input/operator/commands --> /output/flight/cmd,68.36500000000001,35.9894,1073.48,200,105.0,100
|
||||
edf_multi_timed_20_boosted_500,edf_multi_timed_20_boosted_500,/input/baroA/alt --> /output/flight/cmd,59.1838775510204,29.148571428571433,769.0204081632653,200,105.0,100
|
||||
edf_multi_timed_20_boosted_500,edf_multi_timed_20_boosted_500,/input/baroB/alt --> /output/telemetry/radio,35.7862,16.4298,174.42,200,87.21,100
|
||||
edf_multi_timed_20_boosted_500,edf_multi_timed_20_boosted_500,/input/cameraA/raw --> /output/cameraA/mapped,74.4708,6.9518,98.46,100,98.46,200
|
||||
edf_multi_timed_20_boosted_500,edf_multi_timed_20_boosted_500,/input/cameraA/raw --> /output/telemetry/radio,161.5576,33.4002,174.2,100,105.0,200
|
||||
edf_multi_timed_20_boosted_500,edf_multi_timed_20_boosted_500,/input/cameraB/raw --> /output/classifier/classification,80.88102040816325,9.17,130.55102040816325,133,98.158661961025,150
|
||||
edf_multi_timed_20_boosted_500,edf_multi_timed_20_boosted_500,/input/gpsA/fix --> /output/flight/cmd,63.50000000000001,31.609387755102045,769.0,200,105.0,100
|
||||
edf_multi_timed_20_boosted_500,edf_multi_timed_20_boosted_500,/input/gpsB/fix --> /output/telemetry/radio,31.027199999999997,17.3174,174.42,200,87.21,100
|
||||
edf_multi_timed_20_boosted_500,edf_multi_timed_20_boosted_500,/input/imuA/data --> /output/flight/cmd,59.050399999999996,28.727199999999996,753.64,200,105.0,100
|
||||
edf_multi_timed_20_boosted_500,edf_multi_timed_20_boosted_500,/input/imuB/data --> /output/telemetry/radio,33.2768,18.6738,174.42,200,87.21,100
|
||||
edf_multi_timed_20_boosted_500,edf_multi_timed_20_boosted_500,/input/lidar/scan --> /output/flight/cmd,53.76862745098039,29.196470588235297,753.8627450980392,200,105.0,100
|
||||
edf_multi_timed_20_boosted_500,edf_multi_timed_20_boosted_500,/input/operator/commands --> /output/flight/cmd,58.61078431372549,29.589607843137255,753.8823529411765,200,105.0,100
|
||||
edf_single_timed_20_boosted_50,edf_single_timed_20_boosted_50,/input/baroA/alt --> /output/flight/cmd,35.738139534883715,5.748372093023256,4.0,200,2.0,100
|
||||
edf_single_timed_20_boosted_50,edf_single_timed_20_boosted_50,/input/cameraA/raw --> /output/cameraA/mapped,74.54119999999999,6.5826,26.0,100,26.0,200
|
||||
edf_single_timed_20_boosted_50,edf_single_timed_20_boosted_50,/input/cameraB/raw --> /output/classifier/classification,99.0342,32.3718,32.74,133,24.61654135338346,150
|
||||
edf_single_timed_20_boosted_50,edf_single_timed_20_boosted_50,/input/gpsA/fix --> /output/flight/cmd,30.709534883720924,5.705581395348838,4.0,200,2.0,100
|
||||
edf_single_timed_20_boosted_50,edf_single_timed_20_boosted_50,/input/imuA/data --> /output/flight/cmd,33.36255813953488,5.513488372093024,4.0,200,2.0,100
|
||||
edf_single_timed_20_boosted_50,edf_single_timed_20_boosted_50,/input/lidar/scan --> /output/flight/cmd,23.437441860465114,4.923255813953489,4.0,200,2.0,100
|
||||
edf_single_timed_20_boosted_50,edf_single_timed_20_boosted_50,/input/operator/commands --> /output/flight/cmd,28.58860465116279,3.7818604651162793,4.0,200,2.0,100
|
||||
edf_single_timed_20_boosted_500,edf_single_timed_20_boosted_500,/input/baroA/alt --> /output/flight/cmd,35.773250000000004,5.296,3.9,200,1.95,100
|
||||
edf_single_timed_20_boosted_500,edf_single_timed_20_boosted_500,/input/cameraA/raw --> /output/cameraA/mapped,74.5806,6.636,26.0,100,26.0,200
|
||||
edf_single_timed_20_boosted_500,edf_single_timed_20_boosted_500,/input/cameraB/raw --> /output/classifier/classification,99.0724,32.465999999999994,32.84,133,24.691729323308273,150
|
||||
edf_single_timed_20_boosted_500,edf_single_timed_20_boosted_500,/input/gpsA/fix --> /output/flight/cmd,30.87375,5.2219999999999995,3.9,200,1.95,100
|
||||
edf_single_timed_20_boosted_500,edf_single_timed_20_boosted_500,/input/imuA/data --> /output/flight/cmd,33.338,5.1255,3.9,200,1.95,100
|
||||
edf_single_timed_20_boosted_500,edf_single_timed_20_boosted_500,/input/lidar/scan --> /output/flight/cmd,23.27475,4.28075,3.9,200,1.95,100
|
||||
edf_single_timed_20_boosted_500,edf_single_timed_20_boosted_500,/input/operator/commands --> /output/flight/cmd,29.66375,4.34175,3.9,200,1.95,100
|
||||
edf_multi_timed_20_boosted_10,edf_multi_timed_20_boosted_10,/input/baroA/alt --> /output/flight/cmd,59.2382,28.2522,774.5,200,105.0,100
|
||||
edf_multi_timed_20_boosted_10,edf_multi_timed_20_boosted_10,/input/baroB/alt --> /output/telemetry/radio,34.864599999999996,14.1428,171.72,200,85.86,100
|
||||
edf_multi_timed_20_boosted_10,edf_multi_timed_20_boosted_10,/input/cameraA/raw --> /output/cameraA/mapped,74.5012,6.878599999999999,98.72,100,98.72,200
|
||||
edf_multi_timed_20_boosted_10,edf_multi_timed_20_boosted_10,/input/cameraA/raw --> /output/telemetry/radio,159.89939999999996,31.602999999999998,171.68,100,105.0,200
|
||||
edf_multi_timed_20_boosted_10,edf_multi_timed_20_boosted_10,/input/cameraB/raw --> /output/classifier/classification,80.8814,8.9892,130.94,133,98.45112781954887,150
|
||||
edf_multi_timed_20_boosted_10,edf_multi_timed_20_boosted_10,/input/gpsA/fix --> /output/flight/cmd,63.6704,30.875800000000005,774.5,200,105.0,100
|
||||
edf_multi_timed_20_boosted_10,edf_multi_timed_20_boosted_10,/input/gpsB/fix --> /output/telemetry/radio,30.210000000000004,15.1086,171.72,200,85.86,100
|
||||
edf_multi_timed_20_boosted_10,edf_multi_timed_20_boosted_10,/input/imuA/data --> /output/flight/cmd,59.562,28.5752,774.5,200,105.0,100
|
||||
edf_multi_timed_20_boosted_10,edf_multi_timed_20_boosted_10,/input/imuB/data --> /output/telemetry/radio,32.63,16.3702,171.72,200,85.86,100
|
||||
edf_multi_timed_20_boosted_10,edf_multi_timed_20_boosted_10,/input/lidar/scan --> /output/flight/cmd,54.1478,28.7682,774.5,200,105.0,100
|
||||
edf_multi_timed_20_boosted_10,edf_multi_timed_20_boosted_10,/input/operator/commands --> /output/flight/cmd,58.743599999999994,29.187400000000004,774.5,200,105.0,100
|
||||
ros_single_timed_20,ros_single_timed_20,/input/baroA/alt --> /output/flight/cmd,483.0948,63.2838,118.36,200,59.18,100
|
||||
ros_single_timed_20,ros_single_timed_20,/input/baroB/alt --> /output/telemetry/radio,467.39779999999996,59.0954,117.08,200,58.540000000000006,100
|
||||
ros_single_timed_20,ros_single_timed_20,/input/cameraA/raw --> /output/cameraA/mapped,385.3639999999999,237.4138,94.42,100,94.42,200
|
||||
ros_single_timed_20,ros_single_timed_20,/input/cameraA/raw --> /output/telemetry/radio,844.8803999999999,321.32239999999996,115.9,100,105.0,200
|
||||
ros_single_timed_20,ros_single_timed_20,/input/cameraB/raw --> /output/classifier/classification,169.2946,66.2818,112.24,133,84.39097744360902,150
|
||||
ros_single_timed_20,ros_single_timed_20,/input/gpsA/fix --> /output/flight/cmd,486.62440000000004,64.31439999999999,118.48,200,59.24,100
|
||||
ros_single_timed_20,ros_single_timed_20,/input/gpsB/fix --> /output/telemetry/radio,470.49940000000004,61.70079999999999,117.46,200,58.72999999999999,100
|
||||
ros_single_timed_20,ros_single_timed_20,/input/imuA/data --> /output/flight/cmd,484.7596,63.57379999999999,118.38,200,59.19,100
|
||||
ros_single_timed_20,ros_single_timed_20,/input/imuB/data --> /output/telemetry/radio,468.7872,60.461400000000005,117.16,200,58.58,100
|
||||
ros_single_timed_20,ros_single_timed_20,/input/lidar/scan --> /output/flight/cmd,311.30480000000006,43.138400000000004,119.2,200,59.599999999999994,100
|
||||
ros_single_timed_20,ros_single_timed_20,/input/operator/commands --> /output/flight/cmd,309.44019999999995,42.80259999999999,119.06,200,59.53000000000001,100
|
||||
edf_multi_timed_20,edf_multi_timed_20,/input/baroA/alt --> /output/flight/cmd,59.171400000000006,28.876400000000004,769.32,200,105.0,100
|
||||
edf_multi_timed_20,edf_multi_timed_20,/input/baroB/alt --> /output/telemetry/radio,35.613,14.744599999999998,173.64,200,86.82,100
|
||||
edf_multi_timed_20,edf_multi_timed_20,/input/cameraA/raw --> /output/cameraA/mapped,74.48700000000001,7.1496,98.68,100,98.68,200
|
||||
edf_multi_timed_20,edf_multi_timed_20,/input/cameraA/raw --> /output/telemetry/radio,161.5632,31.481800000000003,173.54,100,105.0,200
|
||||
edf_multi_timed_20,edf_multi_timed_20,/input/cameraB/raw --> /output/classifier/classification,80.9862,9.209,130.7,133,98.27067669172932,150
|
||||
edf_multi_timed_20,edf_multi_timed_20,/input/gpsA/fix --> /output/flight/cmd,64.0672,32.28,769.32,200,105.0,100
|
||||
edf_multi_timed_20,edf_multi_timed_20,/input/gpsB/fix --> /output/telemetry/radio,30.8428,15.6802,173.64,200,86.82,100
|
||||
edf_multi_timed_20,edf_multi_timed_20,/input/imuA/data --> /output/flight/cmd,60.100199999999994,29.397199999999994,769.32,200,105.0,100
|
||||
edf_multi_timed_20,edf_multi_timed_20,/input/imuB/data --> /output/telemetry/radio,32.967600000000004,16.905,173.64,200,86.82,100
|
||||
edf_multi_timed_20,edf_multi_timed_20,/input/lidar/scan --> /output/flight/cmd,53.48519999999999,28.731399999999997,769.28,200,105.0,100
|
||||
edf_multi_timed_20,edf_multi_timed_20,/input/operator/commands --> /output/flight/cmd,58.02719999999999,29.204199999999997,769.32,200,105.0,100
|
||||
edf_multi_timed_20_boosted_50,edf_multi_timed_20_boosted_50,/input/baroA/alt --> /output/flight/cmd,59.22893617021277,28.212553191489366,771.1702127659574,200,105.0,100
|
||||
edf_multi_timed_20_boosted_50,edf_multi_timed_20_boosted_50,/input/baroB/alt --> /output/telemetry/radio,34.84583333333333,15.932291666666666,174.875,200,87.4375,100
|
||||
edf_multi_timed_20_boosted_50,edf_multi_timed_20_boosted_50,/input/cameraA/raw --> /output/cameraA/mapped,74.66879999999999,8.4206,98.52,100,98.52,200
|
||||
edf_multi_timed_20_boosted_50,edf_multi_timed_20_boosted_50,/input/cameraA/raw --> /output/telemetry/radio,160.73208333333335,32.815000000000005,174.85416666666666,100,105.0,200
|
||||
edf_multi_timed_20_boosted_50,edf_multi_timed_20_boosted_50,/input/cameraB/raw --> /output/classifier/classification,81.05958333333332,10.574375,130.64583333333334,133,98.22994987468672,150
|
||||
edf_multi_timed_20_boosted_50,edf_multi_timed_20_boosted_50,/input/gpsA/fix --> /output/flight/cmd,62.287659574468094,30.74404255319149,771.1702127659574,200,105.0,100
|
||||
edf_multi_timed_20_boosted_50,edf_multi_timed_20_boosted_50,/input/gpsB/fix --> /output/telemetry/radio,30.258750000000003,16.552916666666665,174.875,200,87.4375,100
|
||||
edf_multi_timed_20_boosted_50,edf_multi_timed_20_boosted_50,/input/imuA/data --> /output/flight/cmd,58.75000000000001,28.383829787234042,771.1702127659574,200,105.0,100
|
||||
edf_multi_timed_20_boosted_50,edf_multi_timed_20_boosted_50,/input/imuB/data --> /output/telemetry/radio,32.87978723404255,17.99340425531915,175.2340425531915,200,87.61702127659575,100
|
||||
edf_multi_timed_20_boosted_50,edf_multi_timed_20_boosted_50,/input/lidar/scan --> /output/flight/cmd,53.87255319148936,29.093617021276597,771.1702127659574,200,105.0,100
|
||||
edf_multi_timed_20_boosted_50,edf_multi_timed_20_boosted_50,/input/operator/commands --> /output/flight/cmd,58.9159574468085,29.80680851063829,771.1702127659574,200,105.0,100
|
||||
edf_multi_timed_20_boosted_100,edf_multi_timed_20_boosted_100,/input/baroA/alt --> /output/flight/cmd,59.09428571428571,28.03642857142857,771.4285714285714,200,105.0,100
|
||||
edf_multi_timed_20_boosted_100,edf_multi_timed_20_boosted_100,/input/baroB/alt --> /output/telemetry/radio,34.47714285714286,13.967142857142857,167.28571428571428,200,83.64285714285714,100
|
||||
edf_multi_timed_20_boosted_100,edf_multi_timed_20_boosted_100,/input/cameraA/raw --> /output/cameraA/mapped,74.82000000000001,8.075714285714286,98.71428571428571,100,98.71428571428571,200
|
||||
edf_multi_timed_20_boosted_100,edf_multi_timed_20_boosted_100,/input/cameraA/raw --> /output/telemetry/radio,159.91142857142856,31.444999999999997,167.0,100,105.0,200
|
||||
edf_multi_timed_20_boosted_100,edf_multi_timed_20_boosted_100,/input/cameraB/raw --> /output/classifier/classification,81.09785714285715,9.535,130.92857142857142,133,98.44253490870031,150
|
||||
edf_multi_timed_20_boosted_100,edf_multi_timed_20_boosted_100,/input/gpsA/fix --> /output/flight/cmd,63.925000000000004,30.982857142857142,771.4285714285714,200,105.0,100
|
||||
edf_multi_timed_20_boosted_100,edf_multi_timed_20_boosted_100,/input/gpsB/fix --> /output/telemetry/radio,30.61857142857142,15.20142857142857,167.28571428571428,200,83.64285714285714,100
|
||||
edf_multi_timed_20_boosted_100,edf_multi_timed_20_boosted_100,/input/imuA/data --> /output/flight/cmd,59.98,28.602857142857147,771.4285714285714,200,105.0,100
|
||||
edf_multi_timed_20_boosted_100,edf_multi_timed_20_boosted_100,/input/imuB/data --> /output/telemetry/radio,32.915000000000006,16.017857142857142,167.28571428571428,200,83.64285714285714,100
|
||||
edf_multi_timed_20_boosted_100,edf_multi_timed_20_boosted_100,/input/lidar/scan --> /output/flight/cmd,53.777142857142856,28.602142857142848,771.4285714285714,200,105.0,100
|
||||
edf_multi_timed_20_boosted_100,edf_multi_timed_20_boosted_100,/input/operator/commands --> /output/flight/cmd,58.76928571428572,29.2,771.4285714285714,200,105.0,100
|
||||
edf_single_timed_20_boosted_100,edf_single_timed_20_boosted_100,/input/baroA/alt --> /output/flight/cmd,35.05540540540541,5.996756756756756,4.378378378378378,200,2.189189189189189,100
|
||||
edf_single_timed_20_boosted_100,edf_single_timed_20_boosted_100,/input/cameraA/raw --> /output/cameraA/mapped,74.62279999999998,6.7632,25.98,100,25.980000000000004,200
|
||||
edf_single_timed_20_boosted_100,edf_single_timed_20_boosted_100,/input/cameraB/raw --> /output/classifier/classification,99.44580000000002,32.4518,32.7,133,24.586466165413537,150
|
||||
edf_single_timed_20_boosted_100,edf_single_timed_20_boosted_100,/input/gpsA/fix --> /output/flight/cmd,30.72783783783784,5.941081081081082,4.378378378378378,200,2.189189189189189,100
|
||||
edf_single_timed_20_boosted_100,edf_single_timed_20_boosted_100,/input/imuA/data --> /output/flight/cmd,32.395135135135135,5.7843243243243245,4.378378378378378,200,2.189189189189189,100
|
||||
edf_single_timed_20_boosted_100,edf_single_timed_20_boosted_100,/input/lidar/scan --> /output/flight/cmd,23.244864864864862,5.153243243243244,4.378378378378378,200,2.189189189189189,100
|
||||
edf_single_timed_20_boosted_100,edf_single_timed_20_boosted_100,/input/operator/commands --> /output/flight/cmd,28.249999999999993,4.603783783783783,4.378378378378378,200,2.189189189189189,100
|
||||
|
424
eye_catcher_plot_dis.py
Normal file
424
eye_catcher_plot_dis.py
Normal file
|
|
@ -0,0 +1,424 @@
|
|||
import pandas as pd
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sns
|
||||
import os
|
||||
import glob
|
||||
import argparse
|
||||
from pathlib import Path
|
||||
|
||||
def parse_arguments():
|
||||
parser = argparse.ArgumentParser(description='Cross-experiment analysis of chain performance.')
|
||||
parser.add_argument('--experiments-dir', '-e', required=True,
|
||||
help='Path to directory containing experiment subdirectories')
|
||||
parser.add_argument('--supplementary', '-s', required=True,
|
||||
help='Path to supplementary.csv file with input delays')
|
||||
parser.add_argument('--output', '-o', default='cross_experiment_analysis',
|
||||
help='Output filename prefix for the plots (will add chain name and .png)')
|
||||
parser.add_argument('--experiment-duration', '-d', type=int, default=20,
|
||||
help='Duration of each experiment in seconds (default: 20)')
|
||||
return parser.parse_args()
|
||||
|
||||
def load_supplementary_data(supplementary_path):
|
||||
"""Load the supplementary data with input delays and theoretical perfect times for each chain."""
|
||||
supp_df = pd.read_csv(supplementary_path)
|
||||
# Create dictionaries for quick lookup
|
||||
delay_dict = dict(zip(supp_df['chain'], supp_df['input_delay']))
|
||||
|
||||
# Load theoretical perfect e2e time (assuming the third column exists)
|
||||
if len(supp_df.columns) >= 3:
|
||||
perfect_time_dict = dict(zip(supp_df['chain'], supp_df.iloc[:, 2])) # Third column
|
||||
return delay_dict, perfect_time_dict
|
||||
else:
|
||||
print("Warning: No third column found for theoretical perfect times. Using input_delay as fallback.")
|
||||
perfect_time_dict = delay_dict.copy() # Fallback to input_delay
|
||||
return delay_dict, perfect_time_dict
|
||||
|
||||
def calculate_theoretical_max_runs(chain, input_delay_ms, experiment_duration_s):
|
||||
"""Calculate the theoretical maximum number of runs for a chain."""
|
||||
runs_per_second = 1000 / input_delay_ms # Convert ms to runs per second
|
||||
max_runs = runs_per_second * experiment_duration_s
|
||||
return int(max_runs)
|
||||
|
||||
def parse_experiment_subtypes(exp_name):
|
||||
"""Parse experiment name to extract sub-types."""
|
||||
exp_name_lower = exp_name.lower()
|
||||
|
||||
# Extract scheduler type
|
||||
scheduler = 'edf' if 'edf' in exp_name_lower else 'ros' if 'ros' in exp_name_lower else 'unknown'
|
||||
|
||||
# Extract threading type
|
||||
threading = 'multi' if 'multi' in exp_name_lower else 'single' if 'single' in exp_name_lower else 'unknown'
|
||||
|
||||
# Extract timing type
|
||||
timing = 'direct' if 'direct' in exp_name_lower else 'timed' if 'timed' in exp_name_lower else 'unknown'
|
||||
|
||||
return scheduler, threading, timing
|
||||
|
||||
def load_experiment_data(experiments_dir, delay_dict, perfect_time_dict, experiment_duration):
|
||||
"""Load all experiment data and calculate performance metrics."""
|
||||
all_data = []
|
||||
|
||||
# Find all subdirectories containing results.csv
|
||||
experiment_dirs = [d for d in Path(experiments_dir).iterdir()
|
||||
if d.is_dir() and (d / 'results.csv').exists()]
|
||||
|
||||
print(f"Found {len(experiment_dirs)} experiment directories")
|
||||
|
||||
for exp_dir in experiment_dirs:
|
||||
results_path = exp_dir / 'results.csv'
|
||||
|
||||
try:
|
||||
df = pd.read_csv(results_path)
|
||||
|
||||
# Extract experiment name (remove timestamp if present)
|
||||
if 'experiment_name' in df.columns:
|
||||
exp_name = df['experiment_name'].iloc[0]
|
||||
exp_name = exp_name.split('-')[0] if '-' in exp_name else exp_name
|
||||
else:
|
||||
exp_name = exp_dir.name
|
||||
|
||||
# Parse experiment sub-types
|
||||
scheduler, threading, timing = parse_experiment_subtypes(exp_name)
|
||||
|
||||
# Group by chain and calculate metrics
|
||||
for chain, chain_data in df.groupby('chain'):
|
||||
if chain in delay_dict and chain in perfect_time_dict:
|
||||
# Calculate theoretical maximum runs
|
||||
input_delay = delay_dict[chain]
|
||||
perfect_time = perfect_time_dict[chain]
|
||||
theoretical_max = calculate_theoretical_max_runs(
|
||||
chain, input_delay, experiment_duration
|
||||
)
|
||||
|
||||
# Calculate actual performance metrics
|
||||
actual_runs = chain_data['count'].mean()
|
||||
mean_latency = chain_data['mean'].mean()
|
||||
std_latency = chain_data['std'].mean()
|
||||
|
||||
# Normalize latency by theoretical perfect time
|
||||
normalized_latency = mean_latency / perfect_time
|
||||
|
||||
# Calculate percentage of theoretical maximum
|
||||
completion_percentage = (actual_runs / theoretical_max) * 100
|
||||
|
||||
if completion_percentage > 100:
|
||||
print(f"Warning: Completion percentage for {chain} in {exp_name} exceeds 100%: {completion_percentage:.2f}%")
|
||||
# Cap at 105% for visualization purposes
|
||||
# This is to avoid visual clutter in the plot
|
||||
# and to handle cases where the actual runs exceed theoretical max.
|
||||
# This is a safeguard and should be adjusted based on actual data characteristics.
|
||||
# In practice, this might indicate an issue with the data or the calculation.
|
||||
completion_percentage = 105
|
||||
|
||||
all_data.append({
|
||||
'experiment_type': exp_name,
|
||||
'experiment_dir': exp_dir.name,
|
||||
'scheduler': scheduler,
|
||||
'threading': threading,
|
||||
'timing': timing,
|
||||
'chain': chain,
|
||||
'mean_latency_ms': mean_latency,
|
||||
'normalized_latency': normalized_latency,
|
||||
'std_latency_ms': std_latency,
|
||||
'actual_runs': actual_runs,
|
||||
'theoretical_max_runs': theoretical_max,
|
||||
'completion_percentage': completion_percentage,
|
||||
'input_delay_ms': input_delay,
|
||||
'perfect_time_ms': perfect_time
|
||||
})
|
||||
else:
|
||||
missing_info = []
|
||||
if chain not in delay_dict:
|
||||
missing_info.append("input delay")
|
||||
if chain not in perfect_time_dict:
|
||||
missing_info.append("perfect time")
|
||||
print(f"Warning: Chain '{chain}' missing {', '.join(missing_info)} in supplementary data")
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error processing {results_path}: {e}")
|
||||
|
||||
return pd.DataFrame(all_data)
|
||||
|
||||
def create_visualizations(data_df, output_prefix):
|
||||
"""Create separate visualization plots for each chain showing all experiment types."""
|
||||
plt.style.use('seaborn-v0_8-darkgrid')
|
||||
|
||||
# Get unique chains
|
||||
chains = sorted(data_df['chain'].unique())
|
||||
|
||||
print(f"Creating {len(chains)} separate plots for chains: {chains}")
|
||||
|
||||
created_files = []
|
||||
|
||||
for chain in chains:
|
||||
# Filter data for this chain
|
||||
chain_data = data_df[data_df['chain'] == chain]
|
||||
|
||||
# Get unique experiment types for this chain
|
||||
experiment_types = sorted(chain_data['experiment_type'].unique())
|
||||
|
||||
# Create a more sophisticated color mapping based on sub-types
|
||||
# Define base colors for each scheduler type
|
||||
scheduler_colors = {'edf-direct': 'blue', 'edf-timed': 'cyan', 'ros-direct': 'red', 'ros-timed': 'orange', 'unknown': 'gray'}
|
||||
|
||||
# Create markers for threading type
|
||||
threading_markers = {'multi': 'o', 'single': 's', 'unknown': '^'}
|
||||
|
||||
# Create alpha values for timing type
|
||||
timing_alpha = {'direct': 0.9, 'timed': 0.9, 'unknown': 0.4}
|
||||
|
||||
# Set up the figure
|
||||
fig, ax = plt.subplots(figsize=(16, 10))
|
||||
|
||||
# Plot data points for each experiment type
|
||||
for exp_type in experiment_types:
|
||||
exp_data = chain_data[chain_data['experiment_type'] == exp_type]
|
||||
|
||||
# Get the first row to extract sub-types (should be same for all rows with same exp_type)
|
||||
first_row = exp_data.iloc[0]
|
||||
scheduler = first_row['scheduler']
|
||||
threading = first_row['threading']
|
||||
timing = first_row['timing']
|
||||
|
||||
# Create label with sub-type information
|
||||
label = f"{exp_type} ({scheduler.upper()}-{threading.upper()}-{timing.upper()})"
|
||||
|
||||
ax.scatter(
|
||||
exp_data['completion_percentage'],
|
||||
exp_data['normalized_latency'],
|
||||
color=scheduler_colors.get(f"{scheduler}-{timing}", 'gray'),
|
||||
marker=threading_markers.get(threading, 'o'),
|
||||
alpha=timing_alpha.get(timing, 0.7),
|
||||
label=label,
|
||||
s=120,
|
||||
edgecolors='black',
|
||||
linewidth=0.8
|
||||
)
|
||||
|
||||
# Set labels and title
|
||||
ax.set_xlabel('Completion Rate (% of Theoretical Maximum)', fontsize=14, fontweight='bold')
|
||||
ax.set_ylabel('Normalized Latency (Actual / Theoretical Perfect)', fontsize=14, fontweight='bold')
|
||||
ax.set_title(f'Performance Analysis: {chain}\nNormalized Latency vs Completion Rate Across Experiments\n' +
|
||||
f'Colors: EDF(Blue)/ROS(Red), Markers: Multi(○)/Single(□), Alpha: Direct(High)/Timed(Low)',
|
||||
fontsize=16, fontweight='bold', pad=20)
|
||||
|
||||
# Add grid for better readability
|
||||
ax.grid(True, alpha=0.3)
|
||||
|
||||
# Set axis limits
|
||||
ax.set_xlim(0, 107)
|
||||
ax.set_ylim(bottom=1)
|
||||
|
||||
# Create legend for experiment types
|
||||
legend = ax.legend(title='Experiment Configuration',
|
||||
loc='best',
|
||||
fontsize=9,
|
||||
title_fontsize=11,
|
||||
framealpha=0.9,
|
||||
fancybox=True,
|
||||
shadow=True,
|
||||
bbox_to_anchor=(1.05, 1))
|
||||
|
||||
# Add a second legend for the encoding
|
||||
from matplotlib.lines import Line2D
|
||||
legend_elements = [
|
||||
Line2D([0], [0], marker='o', color='blue', linestyle='None', markersize=10, alpha=0.9, label='EDF-Multi-Direct'),
|
||||
Line2D([0], [0], marker='s', color='blue', linestyle='None', markersize=10, alpha=0.9, label='EDF-Single-Direct'),
|
||||
Line2D([0], [0], marker='o', color='red', linestyle='None', markersize=10, alpha=0.9, label='ROS-Multi-Direct'),
|
||||
Line2D([0], [0], marker='s', color='red', linestyle='None', markersize=10, alpha=0.9, label='ROS-Single-Direct'),
|
||||
Line2D([0], [0], marker='o', color='cyan', linestyle='None', markersize=10, alpha=0.9, label='EDF-Multi-Timed'),
|
||||
Line2D([0], [0], marker='s', color='cyan', linestyle='None', markersize=10, alpha=0.9, label='EDF-Single-Timed'),
|
||||
Line2D([0], [0], marker='o', color='orange', linestyle='None', markersize=10, alpha=0.9, label='ROS-Multi-Timed'),
|
||||
Line2D([0], [0], marker='s', color='orange', linestyle='None', markersize=10, alpha=0.9, label='ROS-Single-Timed'),
|
||||
]
|
||||
|
||||
# Add encoding legend in a separate box
|
||||
encoding_legend = ax.legend(handles=legend_elements, title='Encoding Guide',
|
||||
loc='upper left', fontsize=8, title_fontsize=10,
|
||||
framealpha=0.9, fancybox=True, shadow=True)
|
||||
ax.add_artist(encoding_legend) # Keep both legends
|
||||
|
||||
# Adjust layout to accommodate legend
|
||||
plt.tight_layout()
|
||||
|
||||
# Save the plot
|
||||
safe_chain_name = chain.replace('/', '_').replace(' ', '_')
|
||||
output_path = f"{output_prefix}_{safe_chain_name}.png"
|
||||
plt.savefig(output_path, dpi=300, bbox_inches='tight')
|
||||
created_files.append(output_path)
|
||||
|
||||
# Show the plot
|
||||
plt.show()
|
||||
|
||||
# Close the figure to free memory
|
||||
plt.close()
|
||||
|
||||
return created_files
|
||||
|
||||
def create_combined_summary_plot(data_df, output_prefix):
|
||||
"""Create a combined summary plot showing all chains in subplots."""
|
||||
chains = sorted(data_df['chain'].unique())
|
||||
n_chains = len(chains)
|
||||
|
||||
# Calculate subplot grid dimensions
|
||||
n_cols = min(3, n_chains) # Max 3 columns
|
||||
n_rows = (n_chains + n_cols - 1) // n_cols # Ceiling division
|
||||
|
||||
fig, axes = plt.subplots(n_rows, n_cols, figsize=(6*n_cols, 5*n_rows))
|
||||
|
||||
# Ensure axes is always a 2D array
|
||||
if n_rows == 1 and n_cols == 1:
|
||||
axes = np.array([[axes]])
|
||||
elif n_rows == 1:
|
||||
axes = axes.reshape(1, -1)
|
||||
elif n_cols == 1:
|
||||
axes = axes.reshape(-1, 1)
|
||||
|
||||
plt.style.use('seaborn-v0_8-darkgrid')
|
||||
|
||||
for i, chain in enumerate(chains):
|
||||
row = i // n_cols
|
||||
col = i % n_cols
|
||||
ax = axes[row, col]
|
||||
|
||||
# Filter data for this chain
|
||||
chain_data = data_df[data_df['chain'] == chain]
|
||||
experiment_types = sorted(chain_data['experiment_type'].unique())
|
||||
|
||||
# Create color palette for experiment types
|
||||
exp_colors = sns.color_palette("husl", len(experiment_types))
|
||||
exp_color_map = dict(zip(experiment_types, exp_colors))
|
||||
|
||||
# Plot data points
|
||||
for exp_type in experiment_types:
|
||||
exp_data = chain_data[chain_data['experiment_type'] == exp_type]
|
||||
ax.scatter(
|
||||
exp_data['completion_percentage'],
|
||||
exp_data['normalized_latency'],
|
||||
color=exp_color_map[exp_type],
|
||||
s=60,
|
||||
alpha=0.7,
|
||||
edgecolors='black',
|
||||
linewidth=0.5
|
||||
)
|
||||
|
||||
ax.set_title(chain, fontsize=12, fontweight='bold')
|
||||
ax.set_xlabel('Completion Rate (%)', fontsize=10)
|
||||
ax.set_ylabel('Normalized Latency', fontsize=10)
|
||||
ax.grid(True, alpha=0.3)
|
||||
|
||||
# Set axis limits for consistency
|
||||
ax.set_xlim(0, 107)
|
||||
ax.set_ylim(bottom=1)
|
||||
|
||||
# Hide unused subplots
|
||||
for i in range(n_chains, n_rows * n_cols):
|
||||
row = i // n_cols
|
||||
col = i % n_cols
|
||||
axes[row, col].set_visible(False)
|
||||
|
||||
plt.suptitle('Performance Analysis Summary - All Chains\n(Normalized Latency vs Completion Rate)',
|
||||
fontsize=16, fontweight='bold', y=0.98)
|
||||
plt.tight_layout()
|
||||
|
||||
summary_output = f"{output_prefix}_summary.png"
|
||||
plt.savefig(summary_output, dpi=300, bbox_inches='tight')
|
||||
plt.show()
|
||||
plt.close()
|
||||
|
||||
return summary_output
|
||||
|
||||
def print_summary_statistics(data_df):
|
||||
"""Print summary statistics for the analysis."""
|
||||
print("\n" + "="*80)
|
||||
print("CROSS-EXPERIMENT ANALYSIS SUMMARY")
|
||||
print("="*80)
|
||||
|
||||
print(f"\nTotal experiments analyzed: {data_df['experiment_type'].nunique()}")
|
||||
print(f"Total chains analyzed: {data_df['chain'].nunique()}")
|
||||
print(f"Total data points: {len(data_df)}")
|
||||
|
||||
print("\nPer Chain Summary:")
|
||||
chain_summary = data_df.groupby('chain').agg({
|
||||
'completion_percentage': ['mean', 'std', 'min', 'max'],
|
||||
'normalized_latency': ['mean', 'std', 'min', 'max'],
|
||||
'mean_latency_ms': ['mean', 'std', 'min', 'max'],
|
||||
'experiment_type': 'count'
|
||||
}).round(2)
|
||||
print(chain_summary)
|
||||
|
||||
print("\nPer Experiment Type Summary:")
|
||||
exp_summary = data_df.groupby('experiment_type').agg({
|
||||
'completion_percentage': ['mean', 'std'],
|
||||
'normalized_latency': ['mean', 'std'],
|
||||
'mean_latency_ms': ['mean', 'std'],
|
||||
'chain': 'count'
|
||||
}).round(2)
|
||||
print(exp_summary)
|
||||
|
||||
# Find best and worst performing combinations
|
||||
print("\nBest Performance (highest completion rate):")
|
||||
best_completion = data_df.loc[data_df['completion_percentage'].idxmax()]
|
||||
print(f" {best_completion['chain']} - {best_completion['experiment_type']}")
|
||||
print(f" Completion: {best_completion['completion_percentage']:.1f}%, Normalized Latency: {best_completion['normalized_latency']:.2f}x, Raw Latency: {best_completion['mean_latency_ms']:.1f}ms")
|
||||
|
||||
print("\nWorst Performance (lowest completion rate):")
|
||||
worst_completion = data_df.loc[data_df['completion_percentage'].idxmin()]
|
||||
print(f" {worst_completion['chain']} - {worst_completion['experiment_type']}")
|
||||
print(f" Completion: {worst_completion['completion_percentage']:.1f}%, Normalized Latency: {worst_completion['normalized_latency']:.2f}x, Raw Latency: {worst_completion['mean_latency_ms']:.1f}ms")
|
||||
|
||||
print("\nBest Normalized Latency (closest to theoretical perfect):")
|
||||
best_latency = data_df.loc[data_df['normalized_latency'].idxmin()]
|
||||
print(f" {best_latency['chain']} - {best_latency['experiment_type']}")
|
||||
print(f" Normalized Latency: {best_latency['normalized_latency']:.2f}x, Completion: {best_latency['completion_percentage']:.1f}%, Raw Latency: {best_latency['mean_latency_ms']:.1f}ms")
|
||||
|
||||
print("\nWorst Normalized Latency (furthest from theoretical perfect):")
|
||||
worst_latency = data_df.loc[data_df['normalized_latency'].idxmax()]
|
||||
print(f" {worst_latency['chain']} - {worst_latency['experiment_type']}")
|
||||
print(f" Normalized Latency: {worst_latency['normalized_latency']:.2f}x, Completion: {worst_latency['completion_percentage']:.1f}%, Raw Latency: {worst_latency['mean_latency_ms']:.1f}ms")
|
||||
|
||||
def main():
|
||||
args = parse_arguments()
|
||||
|
||||
print("Starting cross-experiment analysis...")
|
||||
|
||||
# Load supplementary data
|
||||
print(f"Loading supplementary data from: {args.supplementary}")
|
||||
delay_dict, perfect_time_dict = load_supplementary_data(args.supplementary)
|
||||
print(f"Found delay information for {len(delay_dict)} chains")
|
||||
print(f"Found perfect time information for {len(perfect_time_dict)} chains")
|
||||
|
||||
# Load all experiment data
|
||||
print(f"Loading experiment data from: {args.experiments_dir}")
|
||||
data_df = load_experiment_data(args.experiments_dir, delay_dict, perfect_time_dict, args.experiment_duration)
|
||||
|
||||
if data_df.empty:
|
||||
print("No data found! Please check your paths and file formats.")
|
||||
return
|
||||
|
||||
print(f"Loaded data for {len(data_df)} experiment-chain combinations")
|
||||
|
||||
# Create individual visualizations for each chain
|
||||
print(f"Creating individual visualizations...")
|
||||
created_files = create_visualizations(data_df, args.output)
|
||||
|
||||
# Create combined summary plot
|
||||
print(f"Creating combined summary plot...")
|
||||
summary_file = create_combined_summary_plot(data_df, args.output)
|
||||
created_files.append(summary_file)
|
||||
|
||||
# Print summary statistics
|
||||
print_summary_statistics(data_df)
|
||||
|
||||
# Save detailed data to CSV for further analysis
|
||||
csv_output = f"{args.output}_detailed_data.csv"
|
||||
data_df.to_csv(csv_output, index=False)
|
||||
print(f"\nDetailed data saved to: {csv_output}")
|
||||
|
||||
print(f"\nCreated visualization files:")
|
||||
for file in created_files:
|
||||
print(f" - {file}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
367
eye_catcher_plot_inverse.py
Normal file
367
eye_catcher_plot_inverse.py
Normal file
|
|
@ -0,0 +1,367 @@
|
|||
import pandas as pd
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sns
|
||||
import os
|
||||
import glob
|
||||
import argparse
|
||||
from pathlib import Path
|
||||
|
||||
def parse_arguments():
|
||||
parser = argparse.ArgumentParser(description='Cross-experiment analysis of chain performance.')
|
||||
parser.add_argument('--experiments-dir', '-e', required=True,
|
||||
help='Path to directory containing experiment subdirectories')
|
||||
parser.add_argument('--supplementary', '-s', required=True,
|
||||
help='Path to supplementary.csv file with input delays')
|
||||
parser.add_argument('--output', '-o', default='cross_experiment_analysis',
|
||||
help='Output filename prefix for the plots (will add chain name and .png)')
|
||||
parser.add_argument('--experiment-duration', '-d', type=int, default=20,
|
||||
help='Duration of each experiment in seconds (default: 20)')
|
||||
return parser.parse_args()
|
||||
|
||||
def load_supplementary_data(supplementary_path):
|
||||
"""Load the supplementary data with input delays and theoretical perfect times for each chain."""
|
||||
supp_df = pd.read_csv(supplementary_path)
|
||||
# Create dictionaries for quick lookup
|
||||
delay_dict = dict(zip(supp_df['chain'], supp_df['input_delay']))
|
||||
|
||||
# Load theoretical perfect e2e time (assuming the third column exists)
|
||||
if len(supp_df.columns) >= 3:
|
||||
perfect_time_dict = dict(zip(supp_df['chain'], supp_df.iloc[:, 2])) # Third column
|
||||
return delay_dict, perfect_time_dict
|
||||
else:
|
||||
print("Warning: No third column found for theoretical perfect times. Using input_delay as fallback.")
|
||||
perfect_time_dict = delay_dict.copy() # Fallback to input_delay
|
||||
return delay_dict, perfect_time_dict
|
||||
|
||||
def calculate_theoretical_max_runs(chain, input_delay_ms, experiment_duration_s):
|
||||
"""Calculate the theoretical maximum number of runs for a chain."""
|
||||
runs_per_second = 1000 / input_delay_ms # Convert ms to runs per second
|
||||
max_runs = runs_per_second * experiment_duration_s
|
||||
return int(max_runs)
|
||||
|
||||
def load_experiment_data(experiments_dir, delay_dict, perfect_time_dict, experiment_duration):
|
||||
"""Load all experiment data and calculate performance metrics."""
|
||||
all_data = []
|
||||
|
||||
# Find all subdirectories containing results.csv
|
||||
experiment_dirs = [d for d in Path(experiments_dir).iterdir()
|
||||
if d.is_dir() and (d / 'results.csv').exists()]
|
||||
|
||||
print(f"Found {len(experiment_dirs)} experiment directories")
|
||||
|
||||
for exp_dir in experiment_dirs:
|
||||
results_path = exp_dir / 'results.csv'
|
||||
|
||||
try:
|
||||
df = pd.read_csv(results_path)
|
||||
|
||||
# Extract experiment name (remove timestamp if present)
|
||||
if 'experiment_name' in df.columns:
|
||||
exp_name = df['experiment_name'].iloc[0]
|
||||
exp_name = exp_name.split('-')[0] if '-' in exp_name else exp_name
|
||||
else:
|
||||
exp_name = exp_dir.name
|
||||
|
||||
# Group by chain and calculate metrics
|
||||
for chain, chain_data in df.groupby('chain'):
|
||||
if chain in delay_dict and chain in perfect_time_dict:
|
||||
# Calculate theoretical maximum runs
|
||||
input_delay = delay_dict[chain]
|
||||
perfect_time = perfect_time_dict[chain]
|
||||
theoretical_max = calculate_theoretical_max_runs(
|
||||
chain, input_delay, experiment_duration
|
||||
)
|
||||
|
||||
# Calculate actual performance metrics
|
||||
actual_runs = chain_data['count'].mean()
|
||||
mean_latency = chain_data['mean'].mean()
|
||||
std_latency = chain_data['std'].mean()
|
||||
|
||||
# Normalize latency by theoretical perfect time
|
||||
normalized_latency = mean_latency / perfect_time
|
||||
|
||||
# Calculate percentage of theoretical maximum
|
||||
completion_percentage = (actual_runs / theoretical_max) * 100
|
||||
|
||||
if completion_percentage > 100:
|
||||
print(f"Warning: Completion percentage for {chain} in {exp_name} exceeds 100%: {completion_percentage:.2f}%")
|
||||
# Cap at 105% for visualization purposes
|
||||
# This is to avoid visual clutter in the plot
|
||||
# and to handle cases where the actual runs exceed theoretical max.
|
||||
# This is a safeguard and should be adjusted based on actual data characteristics.
|
||||
# In practice, this might indicate an issue with the data or the calculation.
|
||||
completion_percentage = 105
|
||||
|
||||
all_data.append({
|
||||
'experiment_type': exp_name,
|
||||
'experiment_dir': exp_dir.name,
|
||||
'chain': chain,
|
||||
'mean_latency_ms': mean_latency,
|
||||
'normalized_latency': normalized_latency,
|
||||
'std_latency_ms': std_latency,
|
||||
'actual_runs': actual_runs,
|
||||
'theoretical_max_runs': theoretical_max,
|
||||
'completion_percentage': completion_percentage,
|
||||
'input_delay_ms': input_delay,
|
||||
'perfect_time_ms': perfect_time
|
||||
})
|
||||
else:
|
||||
missing_info = []
|
||||
if chain not in delay_dict:
|
||||
missing_info.append("input delay")
|
||||
if chain not in perfect_time_dict:
|
||||
missing_info.append("perfect time")
|
||||
print(f"Warning: Chain '{chain}' missing {', '.join(missing_info)} in supplementary data")
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error processing {results_path}: {e}")
|
||||
|
||||
return pd.DataFrame(all_data)
|
||||
|
||||
def create_visualizations(data_df, output_prefix):
|
||||
"""Create separate visualization plots for each chain showing all experiment types."""
|
||||
plt.style.use('seaborn-v0_8-darkgrid')
|
||||
|
||||
# Get unique chains
|
||||
chains = sorted(data_df['chain'].unique())
|
||||
|
||||
print(f"Creating {len(chains)} separate plots for chains: {chains}")
|
||||
|
||||
created_files = []
|
||||
|
||||
for chain in chains:
|
||||
# Filter data for this chain
|
||||
chain_data = data_df[data_df['chain'] == chain]
|
||||
|
||||
# Get unique experiment types for this chain
|
||||
experiment_types = sorted(chain_data['experiment_type'].unique())
|
||||
|
||||
# Create color palette for experiment types
|
||||
exp_colors = sns.color_palette("husl", len(experiment_types))
|
||||
exp_color_map = dict(zip(experiment_types, exp_colors))
|
||||
|
||||
# Set up the figure
|
||||
fig, ax = plt.subplots(figsize=(14, 10))
|
||||
|
||||
# Plot data points for each experiment type
|
||||
for exp_type in experiment_types:
|
||||
exp_data = chain_data[chain_data['experiment_type'] == exp_type]
|
||||
|
||||
ax.scatter(
|
||||
exp_data['completion_percentage'],
|
||||
exp_data['normalized_latency'],
|
||||
color=exp_color_map[exp_type],
|
||||
label=exp_type,
|
||||
s=120,
|
||||
alpha=0.8,
|
||||
edgecolors='black',
|
||||
linewidth=0.8
|
||||
)
|
||||
|
||||
# Set labels and title
|
||||
ax.set_xlabel('Completion Rate (% of Theoretical Maximum)', fontsize=14, fontweight='bold')
|
||||
ax.set_ylabel('Normalized Latency (Actual / Theoretical Perfect)', fontsize=14, fontweight='bold')
|
||||
ax.set_title(f'Performance Analysis: {chain}\nNormalized Latency vs Completion Rate Across Experiments',
|
||||
fontsize=16, fontweight='bold', pad=20)
|
||||
|
||||
# Add grid for better readability
|
||||
ax.grid(True, alpha=0.3)
|
||||
|
||||
# Set axis limits
|
||||
ax.set_xlim(0, 107)
|
||||
ax.set_ylim(bottom=1)
|
||||
|
||||
# Create legend for experiment types
|
||||
legend = ax.legend(title='Experiment Type',
|
||||
loc='best',
|
||||
fontsize=10,
|
||||
title_fontsize=12,
|
||||
framealpha=0.9,
|
||||
fancybox=True,
|
||||
shadow=True,
|
||||
bbox_to_anchor=(1.05, 1))
|
||||
|
||||
# Adjust layout to accommodate legend
|
||||
plt.tight_layout()
|
||||
|
||||
# Save the plot
|
||||
safe_chain_name = chain.replace('/', '_').replace(' ', '_')
|
||||
output_path = f"{output_prefix}_{safe_chain_name}.png"
|
||||
plt.savefig(output_path, dpi=300, bbox_inches='tight')
|
||||
created_files.append(output_path)
|
||||
|
||||
# Show the plot
|
||||
plt.show()
|
||||
|
||||
# Close the figure to free memory
|
||||
plt.close()
|
||||
|
||||
return created_files
|
||||
|
||||
def create_combined_summary_plot(data_df, output_prefix):
|
||||
"""Create a combined summary plot showing all chains in subplots."""
|
||||
chains = sorted(data_df['chain'].unique())
|
||||
n_chains = len(chains)
|
||||
|
||||
# Calculate subplot grid dimensions
|
||||
n_cols = min(3, n_chains) # Max 3 columns
|
||||
n_rows = (n_chains + n_cols - 1) // n_cols # Ceiling division
|
||||
|
||||
fig, axes = plt.subplots(n_rows, n_cols, figsize=(6*n_cols, 5*n_rows))
|
||||
|
||||
# Ensure axes is always a 2D array
|
||||
if n_rows == 1 and n_cols == 1:
|
||||
axes = np.array([[axes]])
|
||||
elif n_rows == 1:
|
||||
axes = axes.reshape(1, -1)
|
||||
elif n_cols == 1:
|
||||
axes = axes.reshape(-1, 1)
|
||||
|
||||
plt.style.use('seaborn-v0_8-darkgrid')
|
||||
|
||||
for i, chain in enumerate(chains):
|
||||
row = i // n_cols
|
||||
col = i % n_cols
|
||||
ax = axes[row, col]
|
||||
|
||||
# Filter data for this chain
|
||||
chain_data = data_df[data_df['chain'] == chain]
|
||||
experiment_types = sorted(chain_data['experiment_type'].unique())
|
||||
|
||||
# Create color palette for experiment types
|
||||
exp_colors = sns.color_palette("husl", len(experiment_types))
|
||||
exp_color_map = dict(zip(experiment_types, exp_colors))
|
||||
|
||||
# Plot data points
|
||||
for exp_type in experiment_types:
|
||||
exp_data = chain_data[chain_data['experiment_type'] == exp_type]
|
||||
ax.scatter(
|
||||
exp_data['completion_percentage'],
|
||||
exp_data['normalized_latency'],
|
||||
color=exp_color_map[exp_type],
|
||||
s=60,
|
||||
alpha=0.7,
|
||||
edgecolors='black',
|
||||
linewidth=0.5
|
||||
)
|
||||
|
||||
ax.set_title(chain, fontsize=12, fontweight='bold')
|
||||
ax.set_xlabel('Completion Rate (%)', fontsize=10)
|
||||
ax.set_ylabel('Normalized Latency', fontsize=10)
|
||||
ax.grid(True, alpha=0.3)
|
||||
|
||||
# Set axis limits for consistency
|
||||
ax.set_xlim(0, 107)
|
||||
ax.set_ylim(bottom=1)
|
||||
|
||||
# Hide unused subplots
|
||||
for i in range(n_chains, n_rows * n_cols):
|
||||
row = i // n_cols
|
||||
col = i % n_cols
|
||||
axes[row, col].set_visible(False)
|
||||
|
||||
plt.suptitle('Performance Analysis Summary - All Chains\n(Normalized Latency vs Completion Rate)',
|
||||
fontsize=16, fontweight='bold', y=0.98)
|
||||
plt.tight_layout()
|
||||
|
||||
summary_output = f"{output_prefix}_summary.png"
|
||||
plt.savefig(summary_output, dpi=300, bbox_inches='tight')
|
||||
plt.show()
|
||||
plt.close()
|
||||
|
||||
return summary_output
|
||||
|
||||
def print_summary_statistics(data_df):
|
||||
"""Print summary statistics for the analysis."""
|
||||
print("\n" + "="*80)
|
||||
print("CROSS-EXPERIMENT ANALYSIS SUMMARY")
|
||||
print("="*80)
|
||||
|
||||
print(f"\nTotal experiments analyzed: {data_df['experiment_type'].nunique()}")
|
||||
print(f"Total chains analyzed: {data_df['chain'].nunique()}")
|
||||
print(f"Total data points: {len(data_df)}")
|
||||
|
||||
print("\nPer Chain Summary:")
|
||||
chain_summary = data_df.groupby('chain').agg({
|
||||
'completion_percentage': ['mean', 'std', 'min', 'max'],
|
||||
'normalized_latency': ['mean', 'std', 'min', 'max'],
|
||||
'mean_latency_ms': ['mean', 'std', 'min', 'max'],
|
||||
'experiment_type': 'count'
|
||||
}).round(2)
|
||||
print(chain_summary)
|
||||
|
||||
print("\nPer Experiment Type Summary:")
|
||||
exp_summary = data_df.groupby('experiment_type').agg({
|
||||
'completion_percentage': ['mean', 'std'],
|
||||
'normalized_latency': ['mean', 'std'],
|
||||
'mean_latency_ms': ['mean', 'std'],
|
||||
'chain': 'count'
|
||||
}).round(2)
|
||||
print(exp_summary)
|
||||
|
||||
# Find best and worst performing combinations
|
||||
print("\nBest Performance (highest completion rate):")
|
||||
best_completion = data_df.loc[data_df['completion_percentage'].idxmax()]
|
||||
print(f" {best_completion['chain']} - {best_completion['experiment_type']}")
|
||||
print(f" Completion: {best_completion['completion_percentage']:.1f}%, Normalized Latency: {best_completion['normalized_latency']:.2f}x, Raw Latency: {best_completion['mean_latency_ms']:.1f}ms")
|
||||
|
||||
print("\nWorst Performance (lowest completion rate):")
|
||||
worst_completion = data_df.loc[data_df['completion_percentage'].idxmin()]
|
||||
print(f" {worst_completion['chain']} - {worst_completion['experiment_type']}")
|
||||
print(f" Completion: {worst_completion['completion_percentage']:.1f}%, Normalized Latency: {worst_completion['normalized_latency']:.2f}x, Raw Latency: {worst_completion['mean_latency_ms']:.1f}ms")
|
||||
|
||||
print("\nBest Normalized Latency (closest to theoretical perfect):")
|
||||
best_latency = data_df.loc[data_df['normalized_latency'].idxmin()]
|
||||
print(f" {best_latency['chain']} - {best_latency['experiment_type']}")
|
||||
print(f" Normalized Latency: {best_latency['normalized_latency']:.2f}x, Completion: {best_latency['completion_percentage']:.1f}%, Raw Latency: {best_latency['mean_latency_ms']:.1f}ms")
|
||||
|
||||
print("\nWorst Normalized Latency (furthest from theoretical perfect):")
|
||||
worst_latency = data_df.loc[data_df['normalized_latency'].idxmax()]
|
||||
print(f" {worst_latency['chain']} - {worst_latency['experiment_type']}")
|
||||
print(f" Normalized Latency: {worst_latency['normalized_latency']:.2f}x, Completion: {worst_latency['completion_percentage']:.1f}%, Raw Latency: {worst_latency['mean_latency_ms']:.1f}ms")
|
||||
|
||||
def main():
|
||||
args = parse_arguments()
|
||||
|
||||
print("Starting cross-experiment analysis...")
|
||||
|
||||
# Load supplementary data
|
||||
print(f"Loading supplementary data from: {args.supplementary}")
|
||||
delay_dict, perfect_time_dict = load_supplementary_data(args.supplementary)
|
||||
print(f"Found delay information for {len(delay_dict)} chains")
|
||||
print(f"Found perfect time information for {len(perfect_time_dict)} chains")
|
||||
|
||||
# Load all experiment data
|
||||
print(f"Loading experiment data from: {args.experiments_dir}")
|
||||
data_df = load_experiment_data(args.experiments_dir, delay_dict, perfect_time_dict, args.experiment_duration)
|
||||
|
||||
if data_df.empty:
|
||||
print("No data found! Please check your paths and file formats.")
|
||||
return
|
||||
|
||||
print(f"Loaded data for {len(data_df)} experiment-chain combinations")
|
||||
|
||||
# Create individual visualizations for each chain
|
||||
print(f"Creating individual visualizations...")
|
||||
created_files = create_visualizations(data_df, args.output)
|
||||
|
||||
# Create combined summary plot
|
||||
print(f"Creating combined summary plot...")
|
||||
summary_file = create_combined_summary_plot(data_df, args.output)
|
||||
created_files.append(summary_file)
|
||||
|
||||
# Print summary statistics
|
||||
print_summary_statistics(data_df)
|
||||
|
||||
# Save detailed data to CSV for further analysis
|
||||
csv_output = f"{args.output}_detailed_data.csv"
|
||||
data_df.to_csv(csv_output, index=False)
|
||||
print(f"\nDetailed data saved to: {csv_output}")
|
||||
|
||||
print(f"\nCreated visualization files:")
|
||||
for file in created_files:
|
||||
print(f" - {file}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Add table
Add a link
Reference in a new issue