tried to make logic self-explanatory
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@ -116,7 +116,7 @@ def work(backend_address, accumulator_host, accumulator_port, visualisation_host
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calc_peakfinder_analysis(results, pfdata, pixel_mask_pf)
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# ???
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data, force_send_visualisation = calc_force_send(results, data, pixel_mask_pf, image, aggregator)
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data, aggregation_is_ready = calc_force_send(results, data, pixel_mask_pf, image, aggregator)
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results["type"] = str(data.dtype)
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results["shape"] = data.shape
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@ -126,12 +126,17 @@ def work(backend_address, accumulator_host, accumulator_port, visualisation_host
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apply_aggregation = results.get("apply_aggregation", False)
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aggregation_is_enabled = (apply_aggregation and "aggregation_max" in results)
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aggregation_is_enabled_but_not_ready = (aggregation_is_enabled and not aggregation_is_ready)
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send_empty_cond1 = (apply_aggregation and "aggregation_max" in results and not force_send_visualisation)
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send_empty_cond2 = (not results["is_good_frame"])
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send_empty_cond3 = (not results["is_hit_frame"] and randint(1, skip_frames_rate) != 1)
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is_bad_frame = (not results["is_good_frame"])
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if send_empty_cond1 or send_empty_cond2 or send_empty_cond3:
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# hits are sent at full rate, but no-hits are sent at reduced frequency
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is_no_hit_frame = (not results["is_hit_frame"])
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random_skip = (randint(1, skip_frames_rate) != 1)
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is_no_hit_frame_and_skipped = (is_no_hit_frame and random_skip)
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if aggregation_is_enabled_but_not_ready or is_bad_frame or is_no_hit_frame_and_skipped:
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data = np.empty((2, 2), dtype=np.uint16)
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results["type"] = str(data.dtype)
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results["shape"] = data.shape
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