add performance profiling again

05_PerformanceProfiling/.gitattributes

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+* annex.largefiles=largerthan=100kb
+*.md annex.largefiles=nothing
+*.txt annex.largefiles=nothing
+*.yaml annex.largefiles=nothing
+*.json annex.largefiles=nothing
+LICENSE annex.largefiles=nothing
+*.ipynb annex.largefiles=nothing
+*.png annex.largefiles=anything
+*.svg annex.largefiles=anything
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05_PerformanceProfiling/.gitignore

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+*.ipynb_checkpoints/
+

05_PerformanceProfiling/LICENSE

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+

05_PerformanceProfiling/README.md

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+# Performance-profiling data for the real-time DeepLabCut project
+
+Latency and accuracy profiling for the real-time DeepLabCut project.
+
+#### IMPORTANT NOTE
+
+The code reflects the organization of files when the dataset was generated,
+and it is very likely that it does not work as-is.
+Please change the output path etc. for the code to work properly.
+
+## Subdirectories
+
+Generated data, along with the code, can be found on each directory.
+
+1. **frame-capture** contains the code and data used to profile the time spent for capturing a video frame.
+2. **position-estimation** contains the code and data used to profile the time spent for the body-part estimation step.
+3. **trigger-output** contains the code and data used to profile the time spent for trigger output generation.
+4. **spike2-profiling** contains the code and the summary data of the hardware-recorded frame rate and trigger latency.
+5. **realtime-accuracy** contains the code and the summary data of computing the real-time accuracy of trigger output generation.
+
+

05_PerformanceProfiling/frame-capture/README.md

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+# Latency profiling of the frame-capture step
+
+The [timedcapture](https://github.com/gwappa/python-timedcapture) python library 
+was used to capture from the ImagingSource DMK37BUX287 camera.
+
+Software triggers were generated for 2000 times for each exposure condition,
+and latency for the python call to obtain the frame was measured.
+
+In addition to the library above, python libraries such as `numpy`,
+`matplotlib` and `pandas` will be required to run the code.
+
+**PLEASE NOTE**: the code reflects the file organization when it was run,
+and it is very likely that it does not run properly with the paths specified in it.
+Please update it according to your needs in case of re-uses.
+

05_PerformanceProfiling/frame-capture/triggered_buffered_raw.csv

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05_PerformanceProfiling/frame-capture/triggered_buffered_summary.csv

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+ExposureUs,nFrames,LatencyMeanMs,LatencyStdMs,LatencyMedianMs,LatencyLowMs,Latency25pMs,Latency75pMs,LatencyHighMs
+5,2000,1.974905014038086,0.08747594177661133,1.9824504852294922,1.9464492797851562,1.9536018371582031,1.9867420196533203,2.004152536392212
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+1350,2000,3.303798198699951,0.02031117921567594,3.3111572265625,3.2765865325927734,3.285646438598633,3.318309783935547,3.3380985260009766
+1700,2000,3.661833167076111,0.018425376828025617,3.6693811416625977,3.6346912384033203,3.6439895629882812,3.675222396850586,3.6950111389160156
+2000,2000,4.24800431728363,0.01772997147535539,4.250645637512207,4.2228639125823975,4.230976104736328,4.261016845703125,4.283905029296875
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05_PerformanceProfiling/frame-capture/untriggered_buffered_raw.csv

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05_PerformanceProfiling/frame-capture/untriggered_buffered_summary.csv

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+ExposureUs,nFrames,LatencyMeanMs,LatencyStdMs,LatencyMedianMs,LatencyLowMs,Latency25pMs,Latency75pMs,LatencyHighMs
+5,2000,1.663352370262146,0.05693409776829187,1.661539077758789,1.6305387020111084,1.657724380493164,1.6646385192871094,1.697540283203125
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+100,2000,1.9657169580459595,13.485266913220872,1.661539077758789,1.6295850276947021,1.6571879386901855,1.6651153564453125,1.699209213256836
+200,2000,1.661782145500183,0.013972942842689755,1.661539077758789,1.6307830810546875,1.6582012176513672,1.6644001007080078,1.697540283203125
+400,2000,1.661793828010559,0.01424656669297923,1.661539077758789,1.6314983367919922,1.6582012176513672,1.6644001007080078,1.6975462436676025
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+1700,2000,1.7309179306030273,0.013394032398383689,1.7309188842773438,1.7011165618896484,1.7275810241699219,1.7333030700683594,1.7662107944488525
+2000,2000,2.035907745361328,0.015356001021713117,2.035856246948242,2.0058155059814453,2.0322799682617188,2.038717269897461,2.0694732666015625
+2500,2000,2.5523399114608765,0.018540894051138262,2.552509307861328,2.520555257797241,2.5491714477539062,2.5548934936523438,2.592092752456665
+3000,2000,3.061824917793274,0.015188100304324844,3.0617713928222656,3.0291080474853516,3.0579566955566406,3.064393997192383,3.1001687049865723

05_PerformanceProfiling/position-estimation/README.md

@@ -0,0 +1,38 @@
+# Profiling of the position-estimation step
+
+Latency and accuracy position estimation using [DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) (DLC) was calculated.
+
+Body parts (3 body-parts per frame x 20 per video x 3 per animal x 3 animals = 540 body-parts) were manually annotated using ImageJ.
+
+The frames were then subsampled to various sizes, and pose-estimation was performed using 
+the [dlclib](https://github.com/gwappa/python-dlclib) library (this corresponds to the pose-estimation part of DLC).
+
+For the corresponding DLC projects, refer to another repository.
+
+In addition to the libraries above, python libraries such as `numpy`,
+`matplotlib` and `pandas` will be required to run the code.
+
+**PLEASE NOTE**: the code reflects the file organization when it was run,
+and it is very likely that it does not run properly with the paths specified in it.
+Please update it according to your needs in case of re-uses.
+
+## Annotation data
+
+The annotations are found in the `annotations` directory.
+
+## Base data
+
+The `profiling-frame-annotation.csv` contains the base data.
+
+The `*_anno` columns refer to the manually annotated positions, whereas the `*_pred` columns refer to the positions estimated using DLC.
+The values are in mm.
+
+The `Latency` column contains the net time required for subsampling and position estimation in seconds.
+
+## Summary data
+
+The `*summary` file corresponds to the summary figures for different subsampling factors.
+The `lower` and `upper` represent the bounds of the 5% confidence intervals.
+
+Note the difference in the unit: here, latency is written in milliseconds.
+

05_PerformanceProfiling/position-estimation/annotations/MLA-041630_2020-09-05_run121738/coords.tsv

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