[Timeout Error still exists] COCO (obj det) Submission Error

I still got the "timeout" error @ozendelait @tylin
the scoring output logs as follows
Annotation Type: bbox
Loading and spliting submission results ...
Evaluating test-dev ...
Some images have more than 100 detections. Results were cut to 100 detections per images on 1980 images.
loading annotations into memory...
Done (t=4.27s)
creating index...
index created!
Loading and preparing results...
DONE (t=25.80s)
creating index...
index created!
evaluate category: person
Running per image evaluation...
useSegm (deprecated) is not None. Running bbox evaluation
Evaluate annotation type bbox
DONE (t=43.81s).
Accumulating evaluation results...
DONE (t=4.64s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.614
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.856
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.678
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.426
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.695
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.799
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.204
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.615
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.704
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.542
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.780
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.875
evaluate category: bicycle
Running per image evaluation...
useSegm (deprecated) is not None. Running bbox evaluation
Evaluate annotation type bbox
DONE (t=1.97s).
Accumulating evaluation results...
DONE (t=0.32s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.388
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.650
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.390
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.252
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.450
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.601
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.264
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.491
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.528
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.376
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.600
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.752
evaluate category: car
Running per image evaluation...
useSegm (deprecated) is not None. Running bbox evaluation
Evaluate annotation type bbox
DONE (t=7.71s).
Accumulating evaluation results...
DONE (t=1.32s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.509
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.748
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.557
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.400
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.665
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.693
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.212
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.593
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.650
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.551
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.784
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.844
evaluate category: motorcycle
Running per image evaluation...
useSegm (deprecated) is not None. Running bbox evaluation
Evaluate annotation type bbox
DONE (t=2.08s).
Accumulating evaluation results...
DONE (t=0.26s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.478
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.768
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.517
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.254
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.456
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.665
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.278
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.557
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.598
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.357
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.591
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.786
evaluate category: airplane
Running per image evaluation...
useSegm (deprecated) is not None. Running bbox evaluation
Evaluate annotation type bbox
DONE (t=1.41s).
Accumulating evaluation results...
DONE (t=0.17s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.648
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.861
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.723
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.484
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.649
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.792
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.452
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.714
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.727
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.564
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.722
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.863
evaluate category: bus
Running per image evaluation...
useSegm (deprecated) is not None. Running bbox evaluation
Evaluate annotation type bbox
DONE (t=1.82s).
Accumulating evaluation results...
DONE (t=0.37s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.734
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.875
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.810
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.262
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.636
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.847
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.532
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.821
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.827
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.513
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.772
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.910
evaluate category: train
Running per image evaluation...
useSegm (deprecated) is not None. Running bbox evaluation
Evaluate annotation type bbox
DONE (t=1.59s).
Accumulating evaluation results...
DONE (t=0.28s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.701
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.888
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.783
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.205
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.489
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.761
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.644
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.798
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.806
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.567
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.622
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.857
evaluate category: truck
Running per image evaluation...
useSegm (deprecated) is not None. Running bbox evaluation
Evaluate annotation type bbox
DONE (t=2.27s).
Accumulating evaluation results...
DONE (t=0.58s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.404
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.566
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.442
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.214
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.402
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.549
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.388
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.680
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.690
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.506
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.720
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.796
evaluate category: boat
Running per image evaluation...
useSegm (deprecated) is not None. Running bbox evaluation
Evaluate annotation type bbox
DONE (t=4.19s).
Accumulating evaluation results...
DONE (t=0.58s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.340
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.610
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.330
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.260
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.425
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.457
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.155
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.436
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.545
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.443
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.627
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.716
evaluate category: traffic light
Running per image evaluation...
useSegm (deprecated) is not None. Running bbox evaluation
Evaluate annotation type bbox
DONE (t=2.76s).
Accumulating evaluation results...
DONE (t=0.49s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.343
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.600
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.350
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.300
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.505
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.435
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.179
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.491
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.524
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.471
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.696
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.688
evaluate category: fire hydrant
Running per image evaluation...
useSegm (deprecated) is not None. Running bbox evaluation
Evaluate annotation type bbox
DONE (t=1.23s).
Accumulating evaluation results...
DONE (t=0.18s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.677
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.838
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.757
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.431
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.667
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.808
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.716
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.769
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.769
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.558
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.745
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.889
evaluate category: stop sign
Running per image evaluation...
useSegm (deprecated) is not None. Running bbox evaluation
Evaluate annotation type bbox
DONE (t=1.43s).
Accumulating evaluation results...
DONE (t=0.30s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.701
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.784
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.760
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.357
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.693
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.869
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.727
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.833
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.833
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.724
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.800
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.926
evaluate category: parking meter
Running per image evaluation...
useSegm (deprecated) is not None. Running bbox evaluation
Evaluate annotation type bbox
DONE (t=1.56s).
Accumulating evaluation results...
DONE (t=0.42s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.454
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.619
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.477
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.145
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.511
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.684
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.430
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.641
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.644
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.329
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.712
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.858
evaluate category: bench
Running per image evaluation...
useSegm (deprecated) is not None. Running bbox evaluation
Evaluate annotation type bbox
DONE (t=3.10s).
Accumulating evaluation results...
DONE (t=0.97s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.298
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.440
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.315
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.150
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.293
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.479
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.294
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.475
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.500
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.326
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.511
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.702
evaluate category: bird
Running per image evaluation...
useSegm (deprecated) is not None. Running bbox evaluation
Evaluate annotation type bbox
DONE (t=8.76s).
Accumulating evaluation results...
DONE (t=0.91s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.482
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.699
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.526
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.343
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.668
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.732
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.212
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.499
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.610
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.487
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.775
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.855
evaluate category: cat
Running per image evaluation...
useSegm (deprecated) is not None. Running bbox evaluation
Evaluate annotation type bbox
DONE (t=1.53s).
Accumulating evaluation results...
DONE (t=0.22s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.752
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.925
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.836
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.315
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.687
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.792
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.668
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.838
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.841
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.479
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.782
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.874
evaluate category: dog
Running per image evaluation...
useSegm (deprecated) is not None. Running bbox evaluation
Evaluate annotation type bbox
DONE (t=1.71s).
Accumulating evaluation results...
DONE (t=0.32s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.735
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.896
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.824
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.371
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.731
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.809
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.669
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.829
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.831
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.574
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.823
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.892
evaluate category: horse
Running per image evaluation...
useSegm (deprecated) is not None. Running bbox evaluation
Evaluate annotation type bbox
DONE (t=1.87s).
Accumulating evaluation results...
DONE (t=0.29s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.648
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.856
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.715
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.323
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.651
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.782
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.391
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.741
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.762
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.541
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.755
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.870
evaluate category: sheep
Running per image evaluation...
useSegm (deprecated) is not None. Running bbox evaluation
Evaluate annotation type bbox
DONE (t=2.97s).
Accumulating evaluation results...
DONE (t=0.29s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.589
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.822
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.650
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.475
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.665
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.749
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.131
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.580
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.703
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.582
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.768
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.869
evaluate category: cow
Running per image evaluation...
useSegm (deprecated) is not None. Running bbox evaluation
Evaluate annotation type bbox
DONE (t=3.48s).
Accumulating evaluation results...
DONE (t=0.48s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.612
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.831
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.673
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.448
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.657
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.829
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.201
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.634
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.736
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.612
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.778
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.907
evaluate category: elephant
Running per image evaluation...
useSegm (deprecated) is not None. Running bbox evaluation
Evaluate annotation type bbox
DONE (t=1.83s).
Accumulating evaluation results...
DONE (t=0.22s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.749
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.918
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.830
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.516
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.695
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.838
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.351
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.774
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.832
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.667
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.788
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.900
evaluate category: bear
Running per image evaluation...
useSegm (deprecated) is not None. Running bbox evaluation
Evaluate annotation type bbox
DONE (t=1.17s).
Accumulating evaluation results...
DONE (t=0.15s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.804
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.934
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.882
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.459
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.760
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.838
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.657
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.873
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.874
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.630
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.819
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.905
evaluate category: zebra
Running per image evaluation...
useSegm (deprecated) is not None. Running bbox evaluation
Evaluate annotation type bbox
DONE (t=1.85s).
Accumulating evaluation results...
DONE (t=0.17s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.692
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.912
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.751
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.420
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.678
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.809
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.301
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.725
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.780
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.593
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.759
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.881
evaluate category: giraffe
Running per image evaluation...
useSegm (deprecated) is not None. Running bbox evaluation
Evaluate annotation type bbox

the scoring error log as follows
WARNING: Your kernel does not support swap limit capabilities or the cgroup is not mounted. Memory limited without swap.
/opt/conda/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
Execution time limit exceeded!

I tried to reduce the number of detections of each img (100 -> 75 -> 50 -> 25 -> 10), but still failed
Here are your submissions to date
1 | --- | coco.test.zip | 09/29/2022 20:11:33 | 80362466 | Failed |   |
2 | --- | coco.test100.zip | 09/29/2022 20:48:29 | 50793473 | Failed |   |
3 | --- | coco.test100.zip | 09/29/2022 21:12:20 | 50825518 | Failed |   |
4 | --- | coco.test100.zip | 09/30/2022 02:04:07 | 50809187 | Failed |   |
5 | --- | coco.test100.zip | 09/30/2022 07:44:00 | 50817353 | Failed |   |
6 | --- | coco.test100.zip | 09/30/2022 11:24:58 | 50805602 | Failed |   |
7 | --- | coco.test100.zip | 09/30/2022 14:26:50 | 50823228 | Failed |   |
8 | --- | 20221002_coco.test.zip | 10/01/2022 19:38:59 | 45555971 | Failed |   |
9 | --- | 20221002_coco.test.zip | 10/01/2022 20:06:25 | 45567731 | Failed |   |
10 | --- | 20221002_coco.test100.zip | 10/01/2022 20:33:38 | 45041369 | Failed |   |
11 | --- | 20221002_coco.test75.zip | 10/01/2022 20:52:02 | 41358681 | Failed |   |
12 | --- | 20221002_coco.test50.zip | 10/01/2022 21:00:52 | 31428913 | Failed |   |
13 | --- | 20221002_coco.test25.zip | 10/01/2022 21:08:17 | 24523058 | Failed |   |
14 | --- | 20221002_coco.test25.zip | 10/01/2022 21:16:29 | 17862323 | Failed |   |
15 | --- | 20221002_coco.test10.zip | 10/01/2022 21:33:49 | 12518230 | Failed
need some helps! many thanks

it's ok now


Error

{"error":"ReferenceError: fetchOptions is not defined"}

This is a weird error. I tried in multiple machines but cannot reproduce this bug.
fetchOptions is explicitly declared here which aims to support http proxy.
https://github.com/wuuconix/DifferentDimensionMeAPI/blob/22dbc5b833f932c02b6a5ff95e8d719a363f2e08/index.mjs#L8
If you still occur this error. Just delete the fetchOptions in https://github.com/wuuconix/DifferentDimensionMeAPI/blob/22dbc5b833f932c02b6a5ff95e8d719a363f2e08/index.mjs#L17
which may look this.
let res = await fetch(url)

However, after you change, the program will lose the ability of proxy which is important in China.
If you don't live in China, feel free to remove fetchOptions in program.

fixed, thankyou


Feature request: disable loopback

In ros1 we used to use the socketcan bridge to convert the canbusses to can_msgs/Frame ros messages. All messages we put on /to_canbus were never on the /from_canbus topic.
However, for ROS2 this package is recommended it seems.
Now we do see all messages we send on the /to_canbus back on the /from_canbus topic. Not only led this to (solvable) issues, but this actually almost doubles the frequency of the /from_canbus topic. And (thus) doubles the handling effort on the subscribing nodes as these messages need to be discarded.
Is there a way to get the 'old' behavior back?

This could be implemented by adding

// Check if it's a TX frame
if (frame.can_id & (CAN_ERR_TX_TIMEOUT | CAN_ERR_CRTL_TX_WARNING | CAN_ERR_CRTL_TX_PASSIVE))
{
  // This is a TX frame, ignore it
  continue;
}

After the read here: https://github.com/autowarefoundation/ros2_socketcan/blob/d25a55528eb7b27ab3c22c1b888a7276ae4af6f9/ros2_socketcan/src/socket_can_receiver.cpp#L140C48-L140C53
Would such a PR be accepted if not made optional? As I can't think of a usecase where anyone wants this. As it then also could subscribe to the /to_can_bus_topic topic.

I'm also interested by this!


CSV Import broken on macOS

Hi Everyone,
I wanted to try out your software but the CSV import is not working on macOS. Im working with a dump of a sqlite3 db (comma separated, utf8 with special characters). I can create the input node, but after dragging in the file, no data shows up and clicking the "Update" button doesn't do anything.
Im happy to provide further information as needed.

My Setup:

Im using the current version, downloaded today from the official website.

@maxmunzel Is it possible to provide a small CSV sample file so that I can reproduce it on my side?

Thanks for the quick reply!
I created a csv source node, dragged the file in the file text field, pressed "Update" and then "Explore".
The only thing that happenes now is a red "Failed to get data [new line] null"-popup spawning in the bottom left.
My Test File:
https://gist.github.com/maxmunzel/5ba22c68e240e184dae4a79d31136124

I can reproduce this issue, it is now fixed in release 0.8.2 https://github.com/ananas-analytics/ananas-desktop/releases/tag/v0.8.2

@maxmunzel could you please download version 0.8.2 (https://ananasanalytics.com/docs/downloads/v0.8.2) and see if it helps?

@bhou thanks for the quick reaction. I downloaded the version you linked, but sadly the error persists. To rule out some usage error from my side, I did a small screen recording:
https://www.icloud.com/iclouddrive/0FEp_FZhL74Wjb7oMmhsksYtQ#Screen_Recording480

@maxmunzel  I can't reproduce the issue with 0.8.2 following your recorded video instruction
But I think the issue you are facing is due to a 0.8.1 ananas process still running. Please go to Activity Monitor and force quit the ananas process, and rerun 0.8.2

The reason that ananas process is still running here, is because Ananas is designed for both small data analysis and big data analysis. In big data case,  jobs could last hours,  we need to keep the process running to support such long-run job (We have jobs on production processing terabytes data in half-hour).
The issue you are facing does remind me to rethink the way we handle the app quit event. Probably only keep the ananas process when there are jobs running.

That resolved it. Thanks again for the quick and helpful reaction!
Have a nice day :)

On 24. Jul 2019, at 10:47, Bo HOU<EMAIL_ADDRESS>wrote:
@maxmunzel I can't reproduce the issue with 0.8.2 following your recorded video instruction
But I think the issue you are facing is due to a 0.8.1 ananas process still running. Please go to Activity Monitor and force quit the ananas process, and rerun 0.8.2
The reason that ananas process is still running here, is because Ananas is designed for both small data analysis and big data analysis. In big data case, jobs could last hours, we need to keep the process running to support such long-run job (We have jobs on production processing terabytes data in half-hour).
The issue you are facing does remind me to rethink the way we handle the app quit event. Probably only keep the ananas process when there are jobs running.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub, or mute the thread.


[BUG]: Documentation contains invalid interface description

Опишите баг
In documentation we see
`private val vkAuthCallback = object : VKIDAuthCallback {
override fun onSuccess(accessToken: AccessToken) {
val token = accessToken.token
    }

    override fun onFail(fail: VKIDAuthFail) {
        when (fail) {
            is VKIDAuthFail.Canceled -> {
                println("Canceled")
            }
            else -> {
                println("Other error ${fail.description}")
            }
        }
    }
}`

But correct interface is
`public interface VKIDAuthCallback {
/**
 * Called upon successful auth.
 * Won't be called if you passed [VKIDAuthParams.codeChallenge] because in that case we can't validate it.
 * You will receive auth code in [onAuthCode] and can exchange it to the access token yourself.
 *
 * @param accessToken access token and other useful data.
 */
public fun onAuth(accessToken: AccessToken)

/**
 * Called upon successful first step of auth - receiving auth code which can later be exchanged to access token.
 *
 * @param data auth code that can be exchanged for access token
 * @param isCompletion true if [onAuth] won't be called.
 * This will happen if you passed auth parameters and implement their validation yourself.
 * In that case we can't exchange auth code for access token and you should do this yourself.
 */
public fun onAuthCode(data: AuthCodeData, isCompletion: Boolean): Unit = Unit

/**
 * Called upon any failure during auth.
 *
 * @param fail Information about a failure.
 */
public fun onFail(fail: VKIDAuthFail)

}`
So consider to replace documentation to this code
`private val vkAuthCallback = object : VKIDAuthCallback {
override fun onAuth(accessToken: AccessToken) {
val token = accessToken.token
}
    override fun onFail(fail: VKIDAuthFail) {
        when (fail) {
            is VKIDAuthFail.Canceled -> {
            }
            else -> {
            }
        }
    }
}`

Вопроизведение

Go to documentation
Copy it to your sample
See error

Ожидаемое поведение
Not to see error
Фактическое поведение
Saw compiler error
Используемые модули и их версии
2.1.0
Скриншоты
No response
Смартфон или эмулятор
Any
Внутренний или внешний сервис
No
Обновляли ли вы сдк
Yes
Исправлена ли ошибка
No
Где инициализиурете SDK
Doesn't matter
Версия клиента авторизации
2.1.0
Дополнительные заметки
No response

Hello! What exactly do you think should be fixed in the documentation? From what i see the code in the documentation confines to the protocol you've specified. However you seem to have sent an old version of the code, in the new version there should be on OAuth parameter in the onAuth and onFail callbacks


New install seems to install multiple versions of some packages

See https://github.com/google/codeworld/discussions/1473, reported by @smmohash

Duplicate packages are Cabal (2.4 and 3.0) and cabal-doctest (1.0.7 and 1.0.8).  Neither are directly listed in codeworld-available-pkgs, and can probably be hidden outright.


listOsTypes respond returns isuserdefined as string

ISSUE TYPE

Bug Report
Enhancement

COMPONENT NAME

API

CLOUDSTACK VERSION

4.11

CONFIGURATION

N/A
OS / ENVIRONMENT

N/A
SUMMARY

listOsTypes returns isuserdefined as json string, instead of json bool
STEPS TO REPRODUCE

 $ cs listOsTypes id=1ee61d39-4b9a-11e7-8fea-0242ac110003 --region simulator

EXPECTED RESULTS

{
  "count": 1,
  "ostype": [
    {
      "description": "Windows XP SP3 (32-bit)",
      "id": "1ee61d39-4b9a-11e7-8fea-0242ac110003",
      "isuserdefined": false,
      "oscategoryid": "1ede7e9a-4b9a-11e7-8fea-0242ac110003"
    }
  ]
}

ACTUAL RESULTS

{
  "count": 1,
  "ostype": [
    {
      "description": "Windows XP SP3 (32-bit)",
      "id": "1ee61d39-4b9a-11e7-8fea-0242ac110003",
      "isuserdefined": "false",
      "oscategoryid": "1ede7e9a-4b9a-11e7-8fea-0242ac110003"
    }
  ]
}

Closing as Fixed in PR #2632

no need to close it manually, github will close it after PR is merged.


Implement MultiColumnMerger to support querying data of one timeseries distributed on different DataNodes

Coverage increased (+0.03%) to 54.292% when pulling 49653eddd14a8ace4846265a56009a3da0f700c5 on stable-mpp into 3d040c9855ec6c96459c92838e7c8c74f0be09ee on master.


add IoT (connected power meter) options; create user accounts to allow for people to monitor their appliance utilization and calculate based on actual usage

http://bit.ly/2pxJLyx


Pytorch implementation of MV-MWM

Hi Karl,
are there plans to add a pytorch implementation of https://github.com/younggyoseo/MV-MWM ?
Would be really great!

Hi David, unfortunately, I don't have the plan to implement MV-MWM for now because of limited bandwidth. However, if you have concrete/interesting research ideas based on that, I will consider collaborating and implementing it.


add 2 fields to status to support showing result of kubectl get

Ⅰ. Describe what this PR does
if we run kubectl get rollout rollouts-demo, we get result like this:

the CANARY_STEP and CANARY_STATE are read from rollout.status.CanaryStatus, however, if we support blue-green, we cannot read the values from this path anymore, because the CanaryStatus will be nil. Therefore, i add copies of these two fields to rollout.status. They are only used to showing the brief result returned from kubectl get command.
Ⅱ. Does this pull request fix one issue?

no
Ⅲ. Special notes for reviews

/lgtm

/approve


Update documentation based on UAT

@IrinaRevutska Please comment on Eugene remarks

@Eugene-Yesakov ,

I added more info into https://edenlab.atlassian.net/wiki/spaces/EH/pages/659325157
Changed this one a little bit, but don`t want to overwhelm it with a business logic.
https://edenlab.atlassian.net/wiki/spaces/EH/pages/608272488/Submit+Encounter+Data+Package


Updating to newer python

Pull Request Description
What issue does this change request address?
#1748
see #1806 and #1764
What are the significant changes in functionality due to this change request?
Updating dependencies to update python version.  It also regolds some tensorflow files and adds a new min_python_version to test specifications.

For Change Control Board: Change Request Review
The following review must be completed by an authorized member of the Change Control Board.

[x] 1. Review all computer code.
[x] 2. If any changes occur to the input syntax, there must be an accompanying change to the user manual and xsd schema. If the input syntax change deprecates existing input files, a conversion script needs to be added (see Conversion Scripts).
[x] 3. Make sure the Python code and commenting standards are respected (camelBack, etc.) - See on the wiki for details.
[x] 4. Automated Tests should pass, including run_tests, pylint, manual building and xsd tests. If there are changes to Simulation.py or JobHandler.py the qsub tests must pass.
[x] 5. If significant functionality is added, there must  be tests added to check this. Tests should cover all possible options.  Multiple short tests are preferred over one large test. If new development on the internal JobHandler parallel system is performed, a cluster test must be added setting, in  XML block, the node <internalParallel> to True.
[x] 6. If the change modifies or adds a requirement or a requirement based test case, the Change Control Board's Chair or designee also needs to approve the change.  The requirements and the requirements test shall be in sync.
[x] 7. The merge request must reference an issue.  If the issue is closed, the issue close checklist shall be done.
[x] 8. If an analytic test is changed/added is the the analytic documentation updated/added?
[x] 9. If any test used as a basis for documentation examples (currently found in raven/tests/framework/user_guide and raven/docs/workshop) have been changed, the associated documentation must be reviewed and assured the text matches the example.

Hm, the windows tests are failing because cbcpy from LOGOS is not available for python > 3.7.

The typical other failures are:
FAILED:
Diff tests/framework/Samplers/AdaptiveBatch/ThickenedBand
Failed tests/framework/unit_tests/utils/Debugging
Diff tests/framework/ROM/tensorflow_keras/tf_cnn1d
Diff tests/framework/ROM/tensorflow_keras/tf_lstm
Diff tests/framework/ROM/tensorflow_keras/tf_mlpr
Diff tests/framework/ROM/tensorflow_keras/LSTMFromFile

Basically, these need to be regolded.

Hm, the windows tests are failing because cbcpy from LOGOS is not available for python > 3.7.

I couldn't find anywhere that cpcpy is actually used. I think it was intended to be a workaround to installing the CBC solver on Windows to work with pyomo since conda doesn't support a Windows installation. I think the dependency should be removed (easier) and abandon the use of CBC on Windows (unless the user goes through the process of installing CBC from the binaries like me, not recommended), or additional work done to integrate cbcpy with pyomo (more work).

Job Precheck on 1147bea : invalidated by @joshua-cogliati-inl
Could not resolve host: github.com

Job Precheck on 1147bea : invalidated by @joshua-cogliati-inl
Could not resolve host: github.com

Job Test CentOS 7 on 26af206 : invalidated by @joshua-cogliati-inl
failed in set python environment

Job Mingw Test on 26af206 : invalidated by @joshua-cogliati-inl
failed in fetch

Hm, the windows tests are failing because cbcpy from LOGOS is not available for python > 3.7.

I couldn't find anywhere that cpcpy is actually used. I think it was intended to be a workaround to installing the CBC solver on Windows to work with pyomo since conda doesn't support a Windows installation. I think the dependency should be removed (easier) and abandon the use of CBC on Windows (unless the user goes through the process of installing CBC from the binaries like me, not recommended), or additional work done to integrate cbcpy with pyomo (more work).

Considering that the result of running the LOGOS windows tests with cbcpy is:
08-Aug-22 09:57:38 pyomo.opt            ERROR    Solver (cbc) returned non-zero return code<PHONE_NUMBER>)

Traceback (most recent call last):

  File "C:\Git\opt\build_root\raven\plugins\LOGOS\src\logos_main.py", line 72, in <module>

    modelInstance.run()

  File "C:\Git\opt\build_root\raven\plugins\LOGOS\src\CapitalInvestments\PyomoModels\ModelBase.py", line 294, in run

    results = opt.solve(model, load_solutions=False, tee=self.tee, **{'use_signal_handling':False})

  File "C:\Users\cogljj\.conda\envs\raven_libraries\lib\site-packages\pyomo\opt\base\solvers.py", line 597, in solve

    "Solver (%s) did not exit normally" % self.name)

pyomo.common.errors.ApplicationError: Solver (cbc) did not exit normally

dropping it is definitely worth trying.

Job Mingw Test on fa9eb41 : invalidated by @joshua-cogliati-inl
failed in fetch

This pull request depends on idaholab/LOGOS#42

Which now has been merged, so this is ready to review.

Job Test qsubs sawtooth on 3b8e016 : invalidated by @joshua-cogliati-inl
FAILED: Diff tests/cluster_tests/AdaptiveSobol/test_parallel_adaptive_sobol

The tensorflow data still follows the general trend:

I agree that it would be nice if it was stabler.
I am not sure why the Adaptive batch is different, but the sampler returned different values, which resulted in the limit surface being somewhat different.

The result for tensorflow looks good to me. For the other test, which one is the original results for the limit surface test, the orange color? I'm curious why the samples get changed, Do you see any libraries versions difference?

The result for tensorflow looks good to me. For the other test, which one is the original results for the limit surface test, the orange color? I'm curious why the samples get changed, Do you see any libraries versions difference?

Yes, the orange were the original.   I am not sure why the samples got changed. The adaptive batch doesn't seem to be using tensorflow, and other libraries (including scipy 1.5.3 and scikit-learn 0.24.2) are generally the same.

Both results look reasonable for me. But I think if you have some extra time I would recommend to take a deep look at the code and figure out the cause for the difference.

Both results look reasonable for me. But I think if you have some extra time I would recommend to take a deep look at the code and figure out the cause for the difference.

I haven't figured out the exact cause yet, but it is in Samplers/LimitSurfaceSearch.py

More details.  In  Samplers/LimitSurfaceSearch.py it is finding different values for self.surfPoint[maxGridId][maxId,:]
    if self.surfPoint is not None and len(self.surfPoint) > 0:
      if self.batchStrategy == 'none':
        self.__scoreCandidates()
        maxDistance, maxGridId, maxId =  0.0, "", 0
        for key, value in sorted(self.invPointPersistence.items()):
          if key != self.exceptionGrid and self.surfPoint[key] is not None:
            localMax = np.max(self.scores[key])
            if localMax > maxDistance:
              maxDistance, maxGridId, maxId  = localMax, key,  np.argmax(self.scores[key])
        if maxDistance > 0.0:
          for varIndex, _ in enumerate([key.replace('<distribution>','') for key in self.axisName]):
            self.values[self.axisName[varIndex]] = copy.copy(float(self.surfPoint[maxGridId][maxId,varIndex]))
            self.inputInfo['SampledVarsPb'][self.axisName[varIndex]] = self.distDict[self.axisName[varIndex]].pdf(self.values[self.axisName[varIndex]])
            self.inputInfo['ProbabilityWeight-'+self.axisName[varIndex]] = self.distDict[self.axisName[varIndex]].pdf(self.values[self.axisName[varIndex]])
          varSet=True
        else:
          self.raiseADebug('Maximum score is 0.0')

checklist is good, tests are green.


Suppress exception

boto3 throws when we try to copy an object in s3 to itself without changing the metadata, storage class, etc. Suppress this exception because copy_object should work even when its source and destination are the same.

It seems like if no metadata is specified, then the metadata is copied from the source object. So we could no-op if src and dest are equal


VTM build for releases

@mar-v-in: Could you please compile also the vtm build for each release until the map api separation is done?

It's online on GitHub.

Thanks :)

Plz F-Droid repo also


2024 oled switches

had a few run-ins with some oled switches that just glitches too slow - takes a long time.
glitch pattern is
instant
instant
instant
40 seconds
60 seconds
80 seconds
instant
80 seconds
60 seconds
best guess is it's either glitching too slow or too fast (that it couldn't catch the last saved record)
upping the resistance doesn't change the result
changing mosfet setup doesn't change the result
above is using 2.75
if using 2.74 - it will always timeout and cycle back to writing the bct and training
my hypothesis is that these new 2024 emmc's (new samsung ones) needs new timings like how the toshiba/kioxia fix was done before on the early days.
or, another fix could be to have several records of known good glitch patters
try the first success pattern twice.
then the 2nd, then the 3rd etc before attempting to use the random array pattern.

I might look into this issue once either I find such console or someone gives me a high-speed logic analyzer dump of the eMMC lines behavior of this specific case

I might look into this issue once either I find such console or someone gives me a high-speed logic analyzer dump of the eMMC lines behavior of this specific case

or we could a similar fix you did with toshiba. i noticed when i flashed the no overclock file, it always reach the number of attempts, writes the emmc again and tries to glitch.

I also have this problem, I was on 2.7.3 for weeks and it has the same behaviour as 2.8.0. But 2.8.0 sometimes boots straight into stock fw with error code *== (short long long).

I also have this problem, I was on 2.7.3 for weeks and it has the same behaviour as 2.8.0. But 2.8.0 sometimes boots straight into HOS with error code *== (short long long).


Export Data should work at the resource level as well (leveraging local storage)

When users open a resource view, they should find a button to "export" (use) the resource.
This button will work similarly to the "Export Data" in Search, storing to local storage a Dataset/Resource, that can be re-used in the Workflow Data tab.

Implement through Data Cart. Users can now add a resource to the data cart directly from the admin plugin.


configure chef client windows service to the correct chef directory

If a user installs chef to a directory other than c:\opscode\chef via MSI and installs the chef-client service as well., the service will not run because it points to the default location.
I have validated this fix by building a chef client with the changed wix.

dunno enough about windows to tell if this is right, but you tested it and the code change looks reasonable. :+1:

:+1: Good catch!

travis is having issues unrelated to this change so merging. This change would not affect travis anyways


Add files via upload

This PR fixes #

@robinburen Is this PR work in progress? I only see the sonar file.


H3: body buffer size configuration

Make it possible for the user to specify body initial buffer size and max size for receiving it. Replacing the static values at quinn-h3/src/server.rs:296, and quinn-h3/src/client.rs:320 with fields settable by the user.
Receiving the body from the user code would then look like:
response.set_max_capacity(n);
response.set_capacity(m);
response.body().and_then(...)

The lines pointed in this issue don't exist anymore. Is this issue still valid?

The initial concern was about removing body::ReadToEnd::new() params
capacity and size_limit and offer a request / connection / endpoint level
configuration interface to set those.
body::ReadToEnd will soon be removed in an effort to simplify the public API,
I started to draft it in #518. I'm currently studying hyper::h2 API and some
crates I'd like to see quinn-h3 integrated into, namely hyper and actix-web.
Still, there is some work to do with Body chunks size management, even if it is
less trivial than I first thought:
Bodies are transported via an arbitrary number of DataFrames, with a header
indicating the size of the payload for each of them. But what if this Data
frame size is 1G? We don't want to grow a buffer until the whole payload is
received, because it would be too easy for a malicious peer to make our peer go
out of memory.
I came up with a quick solution via proto::frame::PartialData, which cuts big
DataFrames into smaller ones, then permits frame::FrameStream to yield their
payloads to the user. The problem is that the largest size for a chunk is now
determined by the internal buffer size of tokio_codec::FramedRead.
If the buffer is too small, the throughput performance will be limited. We also
want to limit it's growth to prevent it from draining the memory, and having a
way to make it grow by user-determined setting would be a nice feature.
For example, a content-length header could be present in the response and we
would like to let the user choose how he wants to handle the body.
So now the issue is more about: what should we do to maximize throughput and let
the user influence the memory management via some settings. Keeping in mind that
we want to stay close to what h2 does so quinn-h3 integrates nicely with
crates using it.
Definitely not a trivial issue, but it could bring a good grasp on what's this
project problematics are about.
@djc @Ralith Maybe you have some thoughts about all this?

How do DataFrames relate to QUIC packets? I would assume a DataFrame will always be contained within a QUIC packet. QUIC packets can not be longer than 65536 bytes, so we can simply use that as a cutoff.
Basically, I think we should deliver data frames as they come in, except in order. The interface ought to be similar to what hyper does; I think it currently uses a HttpBody trait, which even lives in a separate crate, so we could reuse that? hyper then provides a simple default implementation.

Fixed by #662


#68: proper ci cd for npm package publishing

[!CAUTION]
Intentionally targetting V2 branch:
If accepting this PR, please ensure that you either REBASE the commits or follow the below steps for first release

CLOSES: #68

New Features

Automated dependency updates with Dependabot for improved package management.
Automated release management setup for streamlined versioning and deployment.
Enhanced workflow execution triggers for better CI/CD pipeline control.
Security enhancements including CodeQL analysis, Microsoft Defender for DevOps integration, dependency review for vulnerabilities, open-source static analysis (OSSAR), and supply-chain security checks.

Chores

Configuration for automated dependency updates, release management, and security workflows.

Documentation

Added configuration files and workflows for dependency management, release automation, and security analysis.

Examples

Example PR to security steps in action: https://github.com/Eventiva/zenstack/pull/2
Example PR for Monorepo: https://github.com/Eventiva/zenstack/pull/4
Example PR for Language: https://github.com/Eventiva/zenstack/pull/6
Example Release: https://github.com/Eventiva/zenstack/releases/tag/Monorepo-v2.0.0
Example Changelog: https://github.com/Eventiva/zenstack/blob/v2/CHANGELOG.md

How should I write my commits?
Release Please assumes you are using Conventional Commit messages.
The most important prefixes you should have in mind are:

fix: which represents bug fixes, and correlates to a SemVer
patch.
feat: which represents a new feature, and correlates to a SemVer minor.
feat!:,  or fix!:, refactor!:, etc., which represent a breaking change
(indicated by the !) and will result in a SemVer major.

Linear git commit history (use squash-merge)
We highly recommend that you use squash-merges when merging pull requests.
Further info found here: https://github.com/googleapis/release-please
I am ready to release V2.
Amazing, we are all looking forward to it. To start using release-please all you need to do is REBASE this PR onto the v2 branch. This will ensure that the "chore: release 2.0.0"  commit is the last commit on your branch.
If you then plan on merging V2 into Main you should follow one of these below steps:
REBASE - Keeps the commit at the top, ensuring that release-please knows to release at V2.
SQUASH - Name the PR "chore: release 2.0.0" and include "Release-As: 2.0.0" within the message.
POST-PR COMMIT - Make a new commit post PR merge to main to link to V2.
git commit --allow-empty -m "chore: release 2.0.0" -m "Release-As: 2.0.0"

I try to follow the OpenSSF Best Practices Gold Level. Files I create automatically generate a License Statement. Please feel free to remove these if you desire - or ask me to :)

Thanks for working on this @TGTGamer ! I've wanted to have such a nice workflow for a long time.
I'm new to release-please and still checking documentation, but it looks very neat. Let me dig into your change and ask questions in the review.

Thanks for working on this @TGTGamer ! I've wanted to have such a nice workflow for a long time.
I'm new to release-please and still checking documentation, but it looks very neat. Let me dig into your change and ask questions in the review.

I've added a bunch of comments (questions more precisely 😄). You're definitely at an expert level with properly configuring an OSS project. I'm learning a lot from this PR!


Add board metadata to support Quick Connect provisioning.

This change adds a KEY_IMPORT_PROVISIONING metadata tag to distinguish boards that can and can't support Quick Connect provisioning.

/bot run checks


GH1156: Fixes assembly loading on .NET Core.

Closes #1156

LGTM :+1: Tested both #addin and #r without issue, I'll do some more testing and then I'll merge.


[BUG] alpha24 crash - looks to be light related

Updated to the new alpha hoping my thermostats would work and the bridge no longer starts due to the error below. No changes to devices between alpha 23 and 24.
Behaviors have errors
      at file:///usr/local/lib/node_modules/home-assistant-matter-hub/node_modules/@matter/node/dist/esm/endpoint/properties/Behaviors.js:128:21
      at all (file:///usr/local/lib/node_modules/home-assistant-matter-hub/node_modules/@matter/general/dist/esm/util/Construction.js:375:17)
      at file:///usr/local/lib/node_modules/home-assistant-matter-hub/node_modules/@matter/general/dist/esm/util/Construction.js:365:11
      Cause #0: Error initializing 02ef6e4c0bdf4eb8a78a85f442104c61.aggregator.light_dining_room_lights.colorControl
        at crash (file:///usr/local/lib/node_modules/home-assistant-matter-hub/node_modules/@matter/node/dist/esm/behavior/internal/BehaviorBacking.js:48:13)
      Caused by: Validating 02ef6e4c0bdf4eb8a78a85f442104c61.aggregator.light_dining_room_lights.colorControl.state: Constraint "colorTempPhysicalMinMireds to colorTemperatureMireds": Value 154 is not within bounds defined by constraint (135)
        at file:///usr/local/lib/node_modules/home-assistant-matter-hub/node_modules/@matter/node/dist/esm/behavior/state/validation/constraint.js:35:17
        at file:///usr/local/lib/node_modules/home-assistant-matter-hub/node_modules/@matter/node/dist/esm/behavior/state/validation/ValueValidator.js:154:25
        at file:///usr/local/lib/node_modules/home-assistant-matter-hub/node_modules/@matter/node/dist/esm/behavior/state/validation/conformance.js:15:22
        at file:///usr/local/lib/node_modules/home-assistant-matter-hub/node_modules/@matter/node/dist/esm/behavior/state/managed/values/StructManager.js:135:13
        at Object.change (file:///usr/local/lib/node_modules/home-assistant-matter-hub/node_modules/@matter/node/dist/esm/behavior/state/managed/Datasource.js:155:7)
        at ColorControl$State.set [as coupleColorTempToLevelMinMireds] (file:///usr/local/lib/node_modules/home-assistant-matter-hub/node_modules/@matter/node/dist/esm/behavior/state/managed/values/StructManager.js:105:32)
        at _ExtendedColorControlServer.initialize (file:///usr/local/lib/node_modules/home-assistant-matter-hub/dist/backend/cli.js:1200:52)

i changed the way i am rounding those min and max values. i hope this should be fixed now. if not feel free to reopen this issue.

Getting a new crash now. Will raise a new bug.

Someone else already raised it #103.


The jpg data is not readable on Mac

The jpg file generated by the sample code can't be opened on Mac, or safari. But It is viewable in Chrome.
Is it because the jpg file does not contain meta header data?
I also tried to encode that data in base64 and embedded in HTML page. The image is not able to show in safari.

I am guessing it's because its an MJPEG frame, which excludes certain elements needed for JPEG decompression (and are assumed to be standard in MJPEG) Some browsers/fileviewers will fix this and some wont in my experience. See also https://github.com/jcupitt/libvips/issues/253

true. Is there a way to add the header info so that the captured data can be viewed on IPhone and Mac?

Thanks for the link! It seems to be working. I tested on Mac.
Adapted type script:
let jpeg:Buffer = this.transform(  cam.frameRaw() );

transform =  (buffer:Buffer):Buffer => {
		let patchData = this.patch();
			let newBuffer = new Buffer( buffer.length + patchData.length );
			let input_skip:number = (buffer[4] << 8) + buffer[5] + 4;
			buffer.copy(  newBuffer, 0, 0, input_skip );
			patchData.copy( newBuffer, input_skip, 0, patchData.length);
			buffer.copy(  newBuffer, input_skip  + patchData.length, input_skip );
			return newBuffer;
	};

	patch = (): Buffer => {
		return new Buffer([

			// dht_segment_head[]
			0xFF, 0xC4, 0x01, 0xA2, 0x00,

			// avpriv_mjpeg_bits_dc_luminance[17] +1
			0, 1, 5, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,

			// dht_segment_frag[]
			0x00, 0x01, 0x02, 0x03, 0x04, 0x05, 0x06, 0x07, 0x08, 0x09,
			0x0a, 0x0b, 0x01, 0x00, 0x03, 0x01, 0x01, 0x01, 0x01, 0x01,
			0x01, 0x01, 0x01, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00,

			// avpriv_mjpeg_val_dc[12]
			0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,

			0x10,

			// avpriv_mjpeg_bits_ac_luminance[17] +1
			0, 2, 1, 3, 3, 2, 4, 3, 5, 5, 4, 4, 0, 0, 1, 0x7d,

			// avpriv_mjpeg_val_ac_luminance[]
			0x01, 0x02, 0x03, 0x00, 0x04, 0x11, 0x05, 0x12,
			0x21, 0x31, 0x41, 0x06, 0x13, 0x51, 0x61, 0x07,
			0x22, 0x71, 0x14, 0x32, 0x81, 0x91, 0xa1, 0x08,
			0x23, 0x42, 0xb1, 0xc1, 0x15, 0x52, 0xd1, 0xf0,
			0x24, 0x33, 0x62, 0x72, 0x82, 0x09, 0x0a, 0x16,
			0x17, 0x18, 0x19, 0x1a, 0x25, 0x26, 0x27, 0x28,
			0x29, 0x2a, 0x34, 0x35, 0x36, 0x37, 0x38, 0x39,
			0x3a, 0x43, 0x44, 0x45, 0x46, 0x47, 0x48, 0x49,
			0x4a, 0x53, 0x54, 0x55, 0x56, 0x57, 0x58, 0x59,
			0x5a, 0x63, 0x64, 0x65, 0x66, 0x67, 0x68, 0x69,
			0x6a, 0x73, 0x74, 0x75, 0x76, 0x77, 0x78, 0x79,
			0x7a, 0x83, 0x84, 0x85, 0x86, 0x87, 0x88, 0x89,
			0x8a, 0x92, 0x93, 0x94, 0x95, 0x96, 0x97, 0x98,
			0x99, 0x9a, 0xa2, 0xa3, 0xa4, 0xa5, 0xa6, 0xa7,
			0xa8, 0xa9, 0xaa, 0xb2, 0xb3, 0xb4, 0xb5, 0xb6,
			0xb7, 0xb8, 0xb9, 0xba, 0xc2, 0xc3, 0xc4, 0xc5,
			0xc6, 0xc7, 0xc8, 0xc9, 0xca, 0xd2, 0xd3, 0xd4,
			0xd5, 0xd6, 0xd7, 0xd8, 0xd9, 0xda, 0xe1, 0xe2,
			0xe3, 0xe4, 0xe5, 0xe6, 0xe7, 0xe8, 0xe9, 0xea,
			0xf1, 0xf2, 0xf3, 0xf4, 0xf5, 0xf6, 0xf7, 0xf8,
			0xf9, 0xfa,

			0x11,

			// avpriv_mjpeg_bits_ac_chrominance[17] +1
			0, 2, 1, 2, 4, 4, 3, 4, 7, 5, 4, 4, 0, 1, 2, 0x77,

			// avpriv_mjpeg_val_ac_chrominance[]
			0x00, 0x01, 0x02, 0x03, 0x11, 0x04, 0x05, 0x21,
			0x31, 0x06, 0x12, 0x41, 0x51, 0x07, 0x61, 0x71,
			0x13, 0x22, 0x32, 0x81, 0x08, 0x14, 0x42, 0x91,
			0xa1, 0xb1, 0xc1, 0x09, 0x23, 0x33, 0x52, 0xf0,
			0x15, 0x62, 0x72, 0xd1, 0x0a, 0x16, 0x24, 0x34,
			0xe1, 0x25, 0xf1, 0x17, 0x18, 0x19, 0x1a, 0x26,
			0x27, 0x28, 0x29, 0x2a, 0x35, 0x36, 0x37, 0x38,
			0x39, 0x3a, 0x43, 0x44, 0x45, 0x46, 0x47, 0x48,
			0x49, 0x4a, 0x53, 0x54, 0x55, 0x56, 0x57, 0x58,
			0x59, 0x5a, 0x63, 0x64, 0x65, 0x66, 0x67, 0x68,
			0x69, 0x6a, 0x73, 0x74, 0x75, 0x76, 0x77, 0x78,
			0x79, 0x7a, 0x82, 0x83, 0x84, 0x85, 0x86, 0x87,
			0x88, 0x89, 0x8a, 0x92, 0x93, 0x94, 0x95, 0x96,
			0x97, 0x98, 0x99, 0x9a, 0xa2, 0xa3, 0xa4, 0xa5,
			0xa6, 0xa7, 0xa8, 0xa9, 0xaa, 0xb2, 0xb3, 0xb4,
			0xb5, 0xb6, 0xb7, 0xb8, 0xb9, 0xba, 0xc2, 0xc3,
			0xc4, 0xc5, 0xc6, 0xc7, 0xc8, 0xc9, 0xca, 0xd2,
			0xd3, 0xd4, 0xd5, 0xd6, 0xd7, 0xd8, 0xd9, 0xda,
			0xe2, 0xe3, 0xe4, 0xe5, 0xe6, 0xe7, 0xe8, 0xe9,
			0xea, 0xf2, 0xf3, 0xf4, 0xf5, 0xf6, 0xf7, 0xf8,
			0xf9, 0xfa
		]);
	};


Update LNbits install to new frameworks

Does not fix all the problems but supports the new Quart framework and hypercorn server needed for newer LNbits releases. Install is set to tag raspiblitz in the lnbits repo ... this needs still to be set to a new framework commit and also later on updated to commit that maes voucher work.

+cp into dev branch


Fix typo in dequeueInput

This was breaking tcp messages that were sent in chunks, since only the first chunk would be processed.
As you can imagine, this one took quite a while to figure out... (in my case, I could only reproduce the error on a local network and not over a tcp tunnel, so I totally misdiagnosed it at first).

ah, apologize on the time it took to track this down, can't believe I did that. Thank you much!

No problem, got to explore the codebase 😃


chore: tweak markdown-it types

Pull Request Test Coverage Report for Build<PHONE_NUMBER>7
Details

1 of 1 (100.0%)  changed or added relevant line in 1 file are covered.
No unchanged relevant lines lost coverage.
Overall coverage remained the same at 58.636%

Totals

Change from base Build<PHONE_NUMBER>1:
0.0%

Covered Lines:
1168

Relevant Lines:
1794

💛  - Coveralls


Repositories

Hi @AskerNC,

Is it correctly understood that we have to create the repository on our own profile, and then you fork it?
Is it possible to get invited to the NumEconCopenhagen-organization from our personal profiles?

Best,

Hi Sekor1
Thank you for your questions

If you are talking about your group repositories for handing in the assignments, then no: when you follow the invitation link: https://classroom.github.com/a/4JNBSrgL  (from the guide ) and create a new team, then a new repository will automatically be created under https://github.com/NumEconCopenhagen/projects-2024-YOURGROUPNAME of which you will be admin, so you can clone it and commit changes.
It shouldn't be a problem, try following the guide using your personal profiles and let me know if it doesn't work.
For DataCamp it is however important for you to register using your alumni.ku.dk emails to get free access to all the courses

Best regards,
Asker


Minimal bundle update.

Bundle install fails with mimemagic 3.something having been yanked.
This seems innocent enough where I can still run rails s and get an error other than what's described in #6.

Close #6


Start writing some tests using pytest

Per https://github.com/LukePrior/nbn-upgrade-map/issues/89

Working on improving coverage now
Name                  Stmts   Miss  Cover
-----------------------------------------
code/adhoc_tools.py     114    114     0%
code/data.py             32      3    91%
code/db.py               62     48    23%
code/geojson.py          31     12    61%
code/main.py            132     85    36%
code/nbn.py              45     28    38%
code/suburbs.py          89     37    58%
code/utils.py            14      6    57%
-----------------------------------------
TOTAL                   519    333    36%

❯ PYTHONPATH=code pytest tests --cov=code
======================================================================== test session starts =========================================================================
platform darwin -- Python 3.10.12, pytest-7.4.0, pluggy-1.2.0
rootdir: /Users/simonroberts/src/github/lyricnz/nbn-upgrade-map
plugins: cov-4.1.0
collected 9 items

tests/test_combined_suburbs.py ....                                                                                [ 44%]
tests/test_geojson.py ...                                                                                [ 77%]
tests/test_utils.py .s                                                                                [100%]

--------- coverage: platform darwin, python 3.10.12-final-0 ----------
Name                  Stmts   Miss  Cover
-----------------------------------------
code/adhoc_tools.py     114    114     0%
code/data.py             32      0   100%
code/db.py               62     48    23%
code/geojson.py          33      0   100%
code/main.py            132     79    40%
code/nbn.py              45     28    38%
code/suburbs.py          89     37    58%
code/utils.py            14      6    57%
-----------------------------------------
TOTAL                   521    312    40%

Module | statements | missing | excluded | coverage
-- | -- | -- | -- | --
code/adhoc_tools.py | 114 | 114 | 0 | 0%
code/data.py | 32 | 0 | 0 | 100%
code/db.py | 62 | 48 | 0 | 23%
code/geojson.py | 33 | 0 | 0 | 100%
code/main.py | 132 | 68 | 0 | 48%
code/nbn.py | 45 | 19 | 0 | 58%
code/suburbs.py | 89 | 25 | 0 | 72%
code/utils.py | 14 | 0 | 0 | 100%
Total | 521 | 274 | 0 | 47%

Module	statements	missing	excluded	coverage
[code/adhoc_tools.py](https://github.com/LukePrior/nbn-upgrade-map/pull/d_1c76e98fcac0e60a_adhoc_tools_py.html)	114	114	0	0%
[code/data.py](https://github.com/LukePrior/nbn-upgrade-map/pull/d_1c76e98fcac0e60a_data_py.html)	32	0	0	100%
[code/db.py](https://github.com/LukePrior/nbn-upgrade-map/pull/d_1c76e98fcac0e60a_db_py.html)	62	48	0	23%
[code/geojson.py](https://github.com/LukePrior/nbn-upgrade-map/pull/d_1c76e98fcac0e60a_geojson_py.html)	33	0	0	100%
[code/main.py](https://github.com/LukePrior/nbn-upgrade-map/pull/d_1c76e98fcac0e60a_main_py.html)	132	68	0	48%
[code/nbn.py](https://github.com/LukePrior/nbn-upgrade-map/pull/d_1c76e98fcac0e60a_nbn_py.html)	45	19	0	58%
[code/suburbs.py](https://github.com/LukePrior/nbn-upgrade-map/pull/d_1c76e98fcac0e60a_suburbs_py.html)	89	25	0	72%
[code/utils.py](https://github.com/LukePrior/nbn-upgrade-map/pull/d_1c76e98fcac0e60a_utils_py.html)	14	0	0	100%
Total	521	274	0	47%

Name                  Stmts   Miss  Cover
-----------------------------------------
code/adhoc_tools.py     114    114     0%
code/data.py             32      0   100%
code/db.py               62     48    23%
code/geojson.py          33      0   100%
code/main.py            132     68    48%
code/nbn.py              45      9    80%
code/suburbs.py          89      0   100%
code/utils.py            14      0   100%
-----------------------------------------
TOTAL                   521    239    54%

OK, I'm bored of writing unit tests now. Please review.

Wow this looks great, the tests all look good just those few comments about the GitHub Action.

The GHA is pretty much copied from https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python

those few comments about the GitHub Action.

Which comments?

Which comments?

I did a review on GitHub maybe it isn't showing:

for the paths the two site/** can be removed as there won't be any python code in that directory
we don't need to check using both python 3.10 and 3.11, just 3.10 should be fine as that's the target?

I was allowing the possibility that the python code consumed html/js from that directory.  Can remove if you want.
I was adding 3.11 for "forward compatibility", so when the time comes, code will be ready

Alright then that should all be fine


Update with official JupyterLab 3.0 release

Fixes #1076
Developer's Certificate of Origin 1.1
   By making a contribution to this project, I certify that:

   (a) The contribution was created in whole or in part by me and I
       have the right to submit it under the Apache License 2.0; or

   (b) The contribution is based upon previous work that, to the best
       of my knowledge, is covered under an appropriate open source
       license and I have the right under that license to submit that
       work with modifications, whether created in whole or in part
       by me, under the same open source license (unless I am
       permitted to submit under a different license), as indicated
       in the file; or

   (c) The contribution was provided directly to me by some other
       person who certified (a), (b) or (c) and I have not modified
       it.

   (d) I understand and agree that this project and the contribution
       are public and that a record of the contribution (including all
       personal information I submit with it, including my sign-off) is
       maintained indefinitely and may be redistributed consistent with
       this project or the open source license(s) involved.

After updating to JupyterLab 3.0, I am seeing this:
lerna ERR! yarn run build exited 1 in '@elyra/pipeline-editor-extension'
lerna ERR! yarn run build stdout:
yarn run v1.22.4
$ tsc
../../node_modules/@types/enzyme/index.d.ts(384,15): error TS2503: Cannot find namespace 'cheerio'.
../../node_modules/@types/enzyme/index.d.ts(728,69): error TS2503: Cannot find namespace 'cheerio'.
info Visit https://yarnpkg.com/en/docs/cli/run for documentation about this command.

lerna ERR! yarn run build stderr:
error Command failed with exit code 1.

lerna ERR! yarn run build exited 1 in '@elyra/pipeline-editor-extension'

I have tried multiple different suggestions around trying to add @types/cheerio, etc without success.
Ideas and suggestions are welcomed.


Model selection.

Hi! I am using Bayesian MCMC to analyze spectra. Which statistical measurement values (AIC, BIC, DIC) should I use to select the model?

In general, even though we report all of these values... one should steer away from the BIC. The AIC should not be used with bayesian fits.
DIC is a predictive measure. It can only tell you that a model relatively better predicts the data than another one... but not how much better. It also has caveats as it is slow to converge.
In general, posterior predictive checks are are good way to assess the quality of fits. There is a package that does this for some data with 3ML: https://twopc.readthedocs.io/en/latest/
Model selection is a tricky business within the world of statistics and literature is a good place to start.
I recommend this article: https://projecteuclid.org/journals/statistics-surveys/volume-6/issue-none/A-survey-of-Bayesian-predictive-methods-for-model-assessment-selection/10.1214/12-SS102.full

Thank you very much for your reply. Sometimes DIC and PDIC are negative, how to compare with other models?

if PDIC (the effective number of parameters) is negative, this is a sign that the median of the posterior is far from the mode. Thus, it will be difficult to do model comparison.

Thank you very much, your answer is of great help to me !

In general, even though we report all of these values... one should steer away from the BIC. The AIC should not be used with bayesian fits.
DIC is a predictive measure. It can only tell you that a model relatively better predicts the data than another one... but not how much better. It also has caveats as it is slow to converge.
In general, posterior predictive checks are are good way to assess the quality of fits. There is a package that does this for some data with 3ML: https://twopc.readthedocs.io/en/latest/
Model selection is a tricky business within the world of statistics and literature is a good place to start.
I recommend this article: https://projecteuclid.org/journals/statistics-surveys/volume-6/issue-none/A-survey-of-Bayesian-predictive-methods-for-model-assessment-selection/10.1214/12-SS102.full

It seems like PPC is quite similar to parametric bootstrapping with MCMC simulations (using the goodness of fit plugin of threeML), except that GoF bootstrap takes the current model while PPCs sample directly over the posterior. Am I right is thinking so? Moreover, the PPC check with "twopc" seems to be more of a visual check (like cumulative QQ plots of observed vs model predicted counts in BXA). It can get ugly if the fits involve multiple instruments and/or spectra. It is also not clear why testing twice (once with source+background and once with the background-subtracted spectrum) is necessary for a visual model selection. Could it be used if backgrounds are modeled separately instead of assuming a joint fit?
Given it is a visual alternative to AIC, BIC or DIC, it complicates the model comparison perspective since numbers aren't involved. DIC is only applicable if the posterior is multi-normal for sure (like one uses with MCMC over XSPEC or emcee on python), and AIC has a frequentist base. https://stats.stackexchange.com/questions/115157/what-are-posterior-predictive-checks-and-what-makes-them-useful suggests PPCs can be use to check if the model fits the data like a Monte Carlo GoF bootstrapping (as aforementioned).
In light of all this, how would a crowded (multiple spectra-instruments), visual PPC be indicative of a better fit and serve as model comparison (other than the evidence)?

Indeed, PPCs are not really a selection tool, but a model fitness tool. There are ways to compress PPCs into numerical checks by computing Bayesian tail probabilities, for example. However, these are not well understood as their distribution is not guaranteed to be uniform ( https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-7/issue-none/Two-simple-examples-for-understanding-posterior-p-values-whose-distributions/10.1214/13-EJS854.full )
Model selection is very tricky. One thing that is known for sure is that marginal likelihoods are not a quick alternative, but there are appropriate ways to use them (https://arxiv.org/abs/2103.08744)
PPCs are related to bootstrap goodness-of-fit checks... but derived from a mathematically generic and robust approach. Two good resources for this are: https://arxiv.org/abs/1709.01449 and https://betanalpha.github.io/assets/case_studies/principled_bayesian_workflow.html

Indeed, PPCs are not really a selection tool, but a model fitness tool. There are ways to compress PPCs into numerical checks by computing Bayesian tail probabilities, for example. However, these are not well understood as their distribution is not guaranteed to be uniform ( https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-7/issue-none/Two-simple-examples-for-understanding-posterior-p-values-whose-distributions/10.1214/13-EJS854.full )
Model selection is very tricky. One thing that is known for sure is that marginal likelihoods are not a quick alternative, but there are appropriate ways to use them (https://arxiv.org/abs/2103.08744)
PPCs are related to bootstrap goodness-of-fit checks... but derived from a mathematically generic and robust approach. Two good resources for this are: https://arxiv.org/abs/1709.01449 and https://betanalpha.github.io/assets/case_studies/principled_bayesian_workflow.html

I understand PPCs are a crude visualization tool like BXA's QQ plots. But why is there a need to check once with no background invokved and again wity the background subtracted spectrum? How much of a deviation of the PPC fit against the posterior data is significant enough to compare two models (nested and non-nested alike) and rule out one over the other? There should be some simple factor or normalization, right? Bayes Factor (taking the ratio of evidences Z) is good to compare on a crude basis for model comparison of quality. But it'd require additional support from the residual plots and the PPC plots (if not AIC).

On the residuals, I can see they're unbinned in the plot_counts commands. Since threeML does not require specifying the data likelihood, it is not trivial for one to know how the residuals (in terms of sigma) are being calculated. So, kindly assist on that information. It'd also help to rebin just the plot (and print out just that plot information to a file for comparative plots outside threeML) to be able to check the features better.

@kchoX14 I'm not sure what you mean by a two step process.

@kchoX14 I'm not sure what you mean by a two step process.

 That is what I meant by a two-step process.

These are just options. For very dominant backgrounds, the uncertainties in the background can overwhelm the uncertainties in the source. Thus, in those cases, deviations from the model are easier to see via subtraction.

So, my original query remains:  how much of a deviation seen in the PPC plots are significant to visually test the goodness of the model versus another?


feat: nullified note retrieval in get_notes and view_notes

Supercedes https://github.com/AztecProtocol/aztec-packages/pull/4208, which was closed as that one was created from a fork and we therefore cannot run CI there.
Fixes https://github.com/AztecProtocol/aztec-packages/issues/3755.

Benchmark results
No metrics with a significant change found.

Detailed results
All benchmarks are run on txs on the Benchmarking contract on the repository. Each tx consists of a batch call  to create_note and increment_balance, which guarantees that each tx has a private call, a nested private call, a public call, and a nested public call, as well as an emitted private note, an unencrypted log, and public storage read and write.
This benchmark source data is available in JSON format on S3 here.
Values are compared against data from master at commit 1c72c0d2 and shown if the difference exceeds 1%.
L2 block published to L1
Each column represents the number of txs on an L2 block published to L1.

Metric
8 txs
32 txs
128 txs

l1_rollup_calldata_size_in_bytes
45,412
179,524
716,068

l1_rollup_calldata_gas
221,668
866,536
3,446,680

l1_rollup_execution_gas
314,087
984,208
3,666,269

l2_block_processing_time_in_ms
1,143 (-1%)
4,342
17,136

note_successful_decrypting_time_in_ms
260 (+3%)
865 (-1%)
3,306 (-1%)

note_trial_decrypting_time_in_ms
70.7 (+17%)
89.9 (-10%)
139 (+1%)

l2_block_building_time_in_ms
15,762
62,492
250,943

l2_block_rollup_simulation_time_in_ms
11,320
44,915
180,834

l2_block_public_tx_process_time_in_ms
4,412
17,491
69,867

L2 chain processing
Each column represents the number of blocks on the L2 chain where each block has 16 txs.

Metric
5 blocks
10 blocks

node_history_sync_time_in_ms
13,406 (-5%)
25,649 (-5%)

note_history_successful_decrypting_time_in_ms
2,202 (+3%)
4,454 (+6%)

note_history_trial_decrypting_time_in_ms
160 (+25%)
200 (+16%)

node_database_size_in_bytes
17,305,680
33,128,528

pxe_database_size_in_bytes
29,923
59,478

Circuits stats
Stats on running time and I/O sizes collected for every circuit run across all benchmarks.

Circuit
circuit_simulation_time_in_ms
circuit_input_size_in_bytes
circuit_output_size_in_bytes

private-kernel-init
214
44,777
26,073

private-kernel-ordering
170 (-1%)
44,393
14,809

base-rollup
1,209 (+1%)
128,850
881

root-rollup
79.2
4,088
677

private-kernel-inner
301
71,816
26,073

public-kernel-private-input
212 (+1%)
32,375
26,073

public-kernel-non-first-iteration
209
32,417
26,073

merge-rollup
7.67 (+1%)
2,608
881

Tree insertion stats
The duration to insert a fixed batch of leaves into each tree type.

Metric
0 deployed contracts
1 deployed contracts

tx_size_in_bytes
15,455
32,706

Transaction processing duration by data writes.

Metric
0 new commitments
1 new commitments

tx_pxe_processing_time_ms
384 (+1%)
1,092

Metric
1 public data writes

tx_sequencer_processing_time_ms
549 (+1%)

CI fails due to tests shared with MemoryDB, which is not being used. I created https://github.com/AztecProtocol/aztec-packages/pull/4241 to delete it, once that's merged we can update this branch.

Nice! Should we update documentation anywhere regarding this change?

Yes! I was not aware of those docs (or rather forgot :sweat_smile:), will open a PR soon and tag you.


Delete old Ahbap Multi-Sig adresses while they have been changed with new BTCTürk NGO adresses

source:
1- https://twitter.com/TheBullduck/status/1624389815016751105
2- https://donation.btcturk.com/

Ahbap has shared a tweet: https://discord.com/channels/1073302047618760765/1073324124509847602/1074029701384913007
The multisig addresses are not deprecated. We've just had a call on this and the consensus was to keep the existing addresses.
@tervelix you could close this issue for now if you agree.

I agree


Fix configuration format for filter processor overrides

The metrics/logs/traces sections are blocks, not statements. Also, fix a missing closing bracket that caused the configuration to break if the user had configured any extra log filters.

 Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.You have signed the CLA already but the status is still pending? Let us recheck it.


Updated for newer releases of dependent modules

Have used ~syntax so that any later minor version will be usable by the module too

Now in 1.0.3


In v1.1.0, steps are printed in html reports when calling the feature file (which is @report=false) using afterScenario

I have marked my reusable file as @report=false, but when I call some scenarios present in my reusable file using afterScenario. The scenario which is present in the reusable file is printed in HTML reports of my original feature file.
But, it works fine for afterFeature, called scenario steps are not printed in HTML reports when a feature file is marked as @report=false (This was the behavior for afterScenario as well before this release happened).

@MounarajElangovan follow this process please to re-open: https://github.com/intuit/karate/wiki/How-to-Submit-an-Issue
ideally please contribute code to fix this also, I don't consider this a priority at all


Hello, world...

If a massive storage device was created that could hold ~4.2x10^55 yottabytes, would the compiler be able to run the program, or would it take like 10 years to execute?

The entire program does not have to fit in main memory, but it does have to be read from disk completely. The load time would depend on the speed of this hypothetical storage.

If the storage device was capable of, say, DDR5 RAM, holding such a file would be possible and loading it to the interpreter would be fast enough, right?


LuaEntity name is  "inserter" | "filter-inserter"

Hey. I'm new to Typescript and Lua so I might be mistaken but I thought I'd write a quick issue just in case.
LuaEntity has a name field, but I think it should be any string, not just limited to inserter or filter-inserter.
node_modules\typed-factorio\generated\classes.d.ts line 2394
  /**
   * Name of the entity prototype. E.g. "inserter" or "filter-inserter".
   *
   * {@link https://lua-api.factorio.com/next/LuaEntity.html#LuaEntity.name View documentation}
   */
  readonly name: "inserter" | "filter-inserter"

I have typed-factorio version 0.7.3

It's getting that from the description when it shouldn't... Fixed in 0.7.4


Does not load folders which have large number of files

I have uploaded a large number of files(11GB of images) from ipfs desktop app. It went fine until I have uploaded 14000 images. But after that, the app keeps loading and returning to the main folder. All the files are on my pc. A small number of files uploaded fine and can be viewed inside the folder

@sawsrinath

Were those files (14000  of them) all in a single directory?
Are you able to do some CLI debugging?

does ipfs files ls /path/to/your/big/dir work, or does it hang too?
what is the number returned by ipfs files ls /path/to/your/big/dir | wc -l

-yes

i cannot type anything on CLI

@sawsrinath you should be able to start ipfs daemon in one terminal and then run ipfs files commands i listed in another one.

Can we reopen. I've tried everything and it seems there is no way to upload a large folder (70k files) reliably.
Here is the CLI output
ls /Users/admin/Git_Downloads/AfricaRare/AfricarareLandDataParser/output/metadata | wc -l
   71504

Things I have tried

IPFS Desktop importing folder (hangs saying importing 1 item at 0%, or crashes the UI)
Pinata CLI, errors at end after uploading all files (it works intermittently even with error, but now not at all)
NFT UP app [mac m1 version] (hangs at 71,502 files, 0.01B of 20.75MB)
Pinata, NFT.Storage and more websites (they all crash)

Is there a recommended and reliable way to upload a large # of files in a folder? I'm all out of options here it seems.
I managed to upload 80k file folders in past, but only ever got the pinataCLI to work. Now I am at a dead end it seems. Appreciate the help!


Messages is a required property in Chat GPT 3.5 Turbo

When I try to execute the notebooks to introduce to a project I get the error:
openai.error.InvalidRequestError: 'messages' is a required property
completion = openai.Completion.create(
    engine=AZURE_OPENAI_CHATGPT_DEPLOYMENT,
    prompt=prompt,
    temperature=0.7,
    max_tokens=1024,
    stop=["<|im_end|>", "<|im_start|>"])

According to what was published on the Azure page, the new versions of Chat GPT do not work with prompt
https://learn.microsoft.com/en-us/azure/cognitive-services/openai/how-to/chatgpt?pivots=programming-language-chat-completions

We have since resolved this by migrating to the ChatCompletion API.


qcom-sdxmdm: add the machine conf governing SDX and MDM platforms

Follow the qcom-armv8a and qcom-armv7a lead and provide a unified machine configuration for Qualcomm modem platforms (including, but not limited to sdx55, sdx65 and mdm9615).
Signed-off-by: Dmitry Baryshkov<EMAIL_ADDRESS>

@Mani-Sadhasivam , @superna9999 , could you please also give it a glance?

if we do such a generic machine, I think we at least need to include the underlying arch, eg. armv7a in the name. We also need to pick sdx or mdm, i believe they are just 2 marketing names for the same thing, no?

These names were used for different generations of modems. I'm a little bit hesistant to include armv7a into the machine name. But to be fully future proof, maybe it should be done. What about qcom-armv7a-modem?

LGTM!

Where are planning to set PREFERRED_PROVIDER_virtual/kernel ?

Where are planning to set PREFERRED_PROVIDER_virtual/kernel ?

The preference for the virtual/kernel is set in the qcom-common.inc

Where are planning to set PREFERRED_PROVIDER_virtual/kernel ?

The preference for the virtual/kernel is set in the qcom-common.inc

ouch.. sorry, i missed that you included this file!


Suggestion: basic error handling in fastaFromBed

So my text file is also used by another language which can read types based on basic semantics, e.g. all ints in the file is interpreted as an int type, and characters wrapped in quotes are taken as strings, etc. Previously running fastaFromBed with such a file (e.g. "chr1" 100 150 ...) worked, but now it does not take "chr1" but just chr1.
It would be nice if bedtools auto-stripped quotes

Bedtools will not support this, but it is simple to fix:
sed -e 's/\"//g' file.fa > file.noquotes.fa