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Remove Shadow&Ambience plugin SaveWhen.EXPLICIT #982

Merged
merged 7 commits into from
Mar 25, 2022
Merged

Remove Shadow&Ambience plugin SaveWhen.EXPLICIT #982

merged 7 commits into from
Mar 25, 2022

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dachengx
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What does the code in this PR do / what does it improve?

Remove save_when attribute of PeakShadow, EventShadow, PeakAmbience, EventAmbience plugins.

Motivation: When analysts are loading event_shadow of calibration data, the save_when = strax.SaveWhen.EXPLICIT PeakShadow plugins will be stored in RAM and RAM can not hold that large amount of data.

Can you briefly describe how it works?

Can you give a minimal working example (or illustrate with a figure)?

Please include the following if applicable:

  • Update the docstring(s)
  • Update the documentation
  • Tests to check the (new) code is working as desired.
  • Does it solve one of the open issues on github?

@dachengx dachengx marked this pull request as ready for review March 17, 2022 16:33
@coveralls
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coveralls commented Mar 17, 2022 •
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Coverage Status

Coverage increased (+0.01%) to 93.782% when pulling 0747b02 on shadow_always into eec7a93 on master.

@WenzDaniel
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Can you please be a bit more specific and give an example? Because, if you load all data it does not make any difference if it is first stored to disk. Further, I am wondering how you can run into any RAM issues. All the data_types you are using are small. Could it be that for one of the runs peak_basics were not available so you started making peaks first?

@ershockley
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If you check e.g. run 24075, it was going to require O(50-100) GB to load a single run. Peak basics is available, it's just calculating the peak_shadow I guess.

@WenzDaniel
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I doubt that unless there is a bug. Is peak_positions available too? At least for v6 it is not. I just looked at peak_basics which is 2.2 GB and peak_positions should be smaller I think. So there is no way that these two data_types require 50 - 100 GB memory if they are both available.

@ershockley
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I guess it's during the computation of peak_shadow that we hit extreme memory consumption (not during loading of the lower level data)? Did you try yourself? Both @skazama and I hit this problem

@WenzDaniel
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But do you agree that if you load two data_types which are in total less than 10 GB it should not occupy 100 GB unless there is a bug in code? I am not arguing that I do not believe you that the memory consumption is so high. All I want to say that making it a save_when.ALWAYS plugin does not solve the issue. It sounds like a bug to me.

@WenzDaniel
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I am sorry, but I cannot confirm to observe the high memory consumption for 24075. I processed peak_shadow for 024075, peak_basics was stored and peak_positions was not (but the individual pos rec data_types were stored.) peak_basics and peak_positions have a size of 2219 MB and 1638 MB respectively. See attached image:

example

I also did some additional checks. I compared things with the previous straxen v1.2.8
for which my memory consumption was slightly smaller around 2 GB - 3 GB during processing. I compared the output data_types of the old and new plugin version and its byte-size doubled which explains the lower amount of memory before.

@ershockley
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We were doing st.get_df(runid, 'event_shadow'). I think this loads all of peak_shadow into memory, whereas st.make handles memory better? Or am I misunderstanding?

What we are trying to do is have peak_shadow be written to disk always, rather than explicitly as you are doing, so that you don't have to go out of your way to call st.make before st.get_df. But maybe i'm wrong.

@WenzDaniel
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Okay let me check with get_df still sounds like a bug to me. I checked the number of bytes of the peak_shadow data_type which is 88 per row and compared it to peaklets which was something like 3100ish. Peaklets for 24075 was about 118 GB. So peakshaodws should be actually below 10 GB. But, I must admit I am not working a lot with DataRames I prefer the numpy.structured arryas. So might be that some funky is going on like converting everything to 64 bit for the dataframe.

@skazama
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skazama commented Mar 25, 2022

sorry @WenzDaniel, do you think we can merge this PR or should wait a bit to investigate potential dataframe bugs?

@JoranAngevaare
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JoranAngevaare commented Mar 25, 2022 •
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@skazama @ershockley @WenzDaniel I must admit that I think what you observed is simply because you tried loading the data when much lower level processing was still ongoing. Specifically the neural nets can take a large amount of memory.
afbeelding

By the way this is not what is happening (you clear peak_shadow from your memory as soon as it's processed up to event_shadow):

We were doing st.get_df(runid, 'event_shadow'). I think this loads all of peak_shadow into memory, whereas st.make handles memory better? Or am I misunderstanding?

I don't think that loading in a dataframe can cause a memory usage that is that high (you only buffer each of the chunks output of event_shadow). When I made 8k runs last friday, it was super easy to compute.

JoranAngevaare
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@dachengx I think the explicit saving of the peaks info is fine, could you revert that? I would definitely approve on removing the explicit saving at the events level, there is a clear speed benefit to be gained from not having to load so much data at that level

@WenzDaniel
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@JoranAngevaare can you reproduce the error? I simply cannot and I real would like to understand whats going wrong here.

@WenzDaniel
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I must admit that I think what you observed is simply because you tried loading the data when much lower level processing was still ongoing

This is/was my suspicion too. But according to the others the low level data was already stored.

@JoranAngevaare
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So for this specific - very high rate run - I can definitely imagine that if peak_basics is stored but the pos-rec algo's are not, it's going to take a lot of resources.
afbeelding

I'll double check the memory, but the plugin does take some time to compute (since it's a very high rate run)

@dachengx
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@dachengx I think the explicit saving of the peaks info is fine, could you revert that? I would definitely approve on removing the explicit saving at the events level, there is a clear speed benefit to be gained from not having to load so much data at that level

Hey. I just reverted it.

@JoranAngevaare
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JoranAngevaare commented Mar 25, 2022 •
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@WenzDaniel , I've checked but the max usage for this run was ~6.5 GB and doesn't depend on if it's either a dataframe or array:

afbeelding

@ershockley
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Hmm okay something weird is going on then, because both Shingo and I could load the other event-level data for this run just fine, it was just the event_shadow giving problems. But good if it is fine now

@JoranAngevaare JoranAngevaare merged commit 97bbfbd into master Mar 25, 2022
@JoranAngevaare JoranAngevaare deleted the shadow_always branch March 25, 2022 16:17
JoranAngevaare added a commit that referenced this pull request Apr 1, 2022
@JoranAngevaare @dachengx
@tuankhaibui @dependabot @ershockley @WenzDaniel @ahiguera-mx @RoBGlaBe @skazama
update development (#1004)
d2e8d0e 
* Update Shadow plugins and add Ambience plugins (#912)

* Update Shadow plugins
Add Ambience plugins

* Update context

* Update online_monitor.py (#958)

* Update online_monitor.py

I removed the ADC_to_PE hardcoded value 170.

* Update online_monitor.py

change the version of muon_veto monitor 0.0.1 -> 0.0.2

* fix ref to PR in docs (#957)

* fix ref to PR in docs

* Update online_monitor.rst

* Bump actions/checkout from 2 to 3 (#962)

Bumps [actions/checkout](https://github.com/actions/checkout) from 2 to 3.
- [Release notes](https://github.com/actions/checkout/releases)
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- [Commits](actions/checkout@v2...v3)

---
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* sneaky commit to master

* bump

* Bump version: 1.4.0 → 1.5.0

* Patch corrected areas (#964)

* Patch corrected areas

* Add avg_se_gain to single_value_corrections

* Patch in EventShadow (#967)

* Patch in EventShadow

* Minor change of the dtype decryption

* Bump version of merged S2s (#919)

* Bump version of merged S2s

* Added test for peaklet area_fraction

* Add Bayes peak probability (#954)

* Bayes plugin

* fix hit sorting, add raw-record-aqm overlap check (#961)

* fix hit sorting, add raw-record-aqm overlap check

* infer n_channels instead of hardcoding

* Make v1 6 0 (#968)

* Updated history

* Bump version: 1.5.0 → 1.6.0

* Fix href datakind page (#969)

* Bump wemake-services/wemake-python-styleguide from 0.16.0 to 0.16.1 (#971)

Bumps [wemake-services/wemake-python-styleguide](https://github.com/wemake-services/wemake-python-styleguide) from 0.16.0 to 0.16.1.
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* Exit 0 on existing data in straxer (#970)

* Exit 0 on existing data in straxer

* Update straxer

* Allow unused rucio local (#976)

* allow useless frontends

* update tests

* add a warning

* Revert "add a warning"

This reverts commit 3b4221a.

* Position shadow sigma set to nan when S2 not positive (#980)

* Position shadow sigma set to nan when S2 not positive

To suppress warning

* Use numpy.errstate to suppress warnings in PeakShadow cut

* Keep version of PeakShadow unchange

* Fix small bug if GPS has larger delay (#986)

* Pin Jinja2 (#988)

* Update requirements-tests.txt

* Update requirements-tests.txt

* Update requirements-tests.txt

* Remove records not hits. (#987)

Co-authored-by: Joran R. Angevaare <jorana@nikhef.nl>

* Remove Shadow&Ambience plugin SaveWhen.EXPLICIT (#982)

* Remove Shadow&Ambience plugin SaveWhen.EXPLICIT

* Revert SaveWhen.EXPLICIT for event_shadow

* Still save peak_shadow EXPLICITly

Co-authored-by: Shingo Kazama <kazama@isee.nagoya-u.ac.jp>
Co-authored-by: Joran R. Angevaare <jorana@nikhef.nl>

* Pin Jinja2==3.0.3 (#990)

* fix issue 977   (#984)

* fix this issue #977

* Update straxen/plugins/bayes_peak_classification.py

Co-authored-by: Joran R. Angevaare <jorana@nikhef.nl>

Co-authored-by: ahiguera-mx <ahiguera-mx@fried.rice.edu>
Co-authored-by: Joran R. Angevaare <jorana@nikhef.nl>

Co-authored-by: Dacheng Xu <xudctron@gmail.com>
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JoranAngevaare added a commit that referenced this pull request Apr 1, 2022
@JoranAngevaare @dependabot
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Updates from development branch (#1006)
ce4863e 
* Bump zstd from 1.5.0.4 to 1.5.1.0 in /extra_requirements (#909)

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* Bump wfsim from 0.5.12 to 0.5.13 in /extra_requirements (#907)

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* Bump xarray from 0.20.2 to 0.21.1 in /extra_requirements (#908)

Bumps [xarray](https://github.com/pydata/xarray) from 0.20.2 to 0.21.1.
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* Bump dask from 2022.01.1 to 2022.2.1 in /extra_requirements (#941)

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* Bump pytest from 6.2.5 to 7.0.1 in /extra_requirements (#944)

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* Bump coverage from 6.3 to 6.3.2 in /extra_requirements (#947)

Bumps [coverage](https://github.com/nedbat/coveragepy) from 6.3 to 6.3.2.
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* Bump typing-extensions from 4.0.1 to 4.1.1 in /extra_requirements (#948)

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* Bump tensorflow from 2.8.0rc1 to 2.8.0 in /extra_requirements (#949)

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* Bump pandas from 1.3.5 to 1.4.1 in /extra_requirements (#950)

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* Bump scipy from 1.7.3 to 1.8.0 in /extra_requirements (#942)

Bumps [scipy](https://github.com/scipy/scipy) from 1.7.3 to 1.8.0.
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* Bump hypothesis from 6.36.1 to 6.38.0 in /extra_requirements (#943)

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* Bump tqdm from 4.62.2 to 4.63.0 in /extra_requirements (#945)

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* Update requirements-tests.txt

* Update requirements-tests.txt

* Bump panel from 0.12.6 to 0.12.7 in /extra_requirements (#1002)

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* update development (#1004)

* Update Shadow plugins and add Ambience plugins (#912)

* Update Shadow plugins
Add Ambience plugins

* Update context

* Update online_monitor.py (#958)

* Update online_monitor.py

I removed the ADC_to_PE hardcoded value 170.

* Update online_monitor.py

change the version of muon_veto monitor 0.0.1 -> 0.0.2

* fix ref to PR in docs (#957)

* fix ref to PR in docs

* Update online_monitor.rst

* Bump actions/checkout from 2 to 3 (#962)

Bumps [actions/checkout](https://github.com/actions/checkout) from 2 to 3.
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- [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md)
- [Commits](actions/checkout@v2...v3)

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* sneaky commit to master

* bump

* Bump version: 1.4.0 → 1.5.0

* Patch corrected areas (#964)

* Patch corrected areas

* Add avg_se_gain to single_value_corrections

* Patch in EventShadow (#967)

* Patch in EventShadow

* Minor change of the dtype decryption

* Bump version of merged S2s (#919)

* Bump version of merged S2s

* Added test for peaklet area_fraction

* Add Bayes peak probability (#954)

* Bayes plugin

* fix hit sorting, add raw-record-aqm overlap check (#961)

* fix hit sorting, add raw-record-aqm overlap check

* infer n_channels instead of hardcoding

* Make v1 6 0 (#968)

* Updated history

* Bump version: 1.5.0 → 1.6.0

* Fix href datakind page (#969)

* Bump wemake-services/wemake-python-styleguide from 0.16.0 to 0.16.1 (#971)

Bumps [wemake-services/wemake-python-styleguide](https://github.com/wemake-services/wemake-python-styleguide) from 0.16.0 to 0.16.1.
- [Release notes](https://github.com/wemake-services/wemake-python-styleguide/releases)
- [Changelog](https://github.com/wemake-services/wemake-python-styleguide/blob/master/CHANGELOG.md)
- [Commits](wemake-services/wemake-python-styleguide@0.16.0...0.16.1)

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* Exit 0 on existing data in straxer (#970)

* Exit 0 on existing data in straxer

* Update straxer

* Allow unused rucio local (#976)

* allow useless frontends

* update tests

* add a warning

* Revert "add a warning"

This reverts commit 3b4221a.

* Position shadow sigma set to nan when S2 not positive (#980)

* Position shadow sigma set to nan when S2 not positive

To suppress warning

* Use numpy.errstate to suppress warnings in PeakShadow cut

* Keep version of PeakShadow unchange

* Fix small bug if GPS has larger delay (#986)

* Pin Jinja2 (#988)

* Update requirements-tests.txt

* Update requirements-tests.txt

* Update requirements-tests.txt

* Remove records not hits. (#987)

Co-authored-by: Joran R. Angevaare <jorana@nikhef.nl>

* Remove Shadow&Ambience plugin SaveWhen.EXPLICIT (#982)

* Remove Shadow&Ambience plugin SaveWhen.EXPLICIT

* Revert SaveWhen.EXPLICIT for event_shadow

* Still save peak_shadow EXPLICITly

Co-authored-by: Shingo Kazama <kazama@isee.nagoya-u.ac.jp>
Co-authored-by: Joran R. Angevaare <jorana@nikhef.nl>

* Pin Jinja2==3.0.3 (#990)

* fix issue 977   (#984)

* fix this issue #977

* Update straxen/plugins/bayes_peak_classification.py

Co-authored-by: Joran R. Angevaare <jorana@nikhef.nl>

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* Bump pytest from 7.0.1 to 7.1.1 in /extra_requirements (#1003)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.0.1 to 7.1.1.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](pytest-dev/pytest@7.0.1...7.1.1)

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* Bump xarray from 0.21.1 to 2022.3.0 in /extra_requirements (#1000)

Bumps [xarray](https://github.com/pydata/xarray) from 0.21.1 to 2022.3.0.
- [Release notes](https://github.com/pydata/xarray/releases)
- [Changelog](https://github.com/pydata/xarray/blob/main/HOW_TO_RELEASE.md)
- [Commits](pydata/xarray@v0.21.1...v2022.03.0)

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update development

* Bump dask from 2022.2.1 to 2022.3.0 in /extra_requirements (#998)

Bumps [dask](https://github.com/dask/dask) from 2022.2.1 to 2022.3.0.
- [Release notes](https://github.com/dask/dask/releases)
- [Changelog](https://github.com/dask/dask/blob/main/docs/release-procedure.md)
- [Commits](dask/dask@2022.02.1...2022.03.0)

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* Bump ipywidgets from 7.6.5 to 7.7.0 in /extra_requirements (#997)

Bumps [ipywidgets](http://ipython.org) from 7.6.5 to 7.7.0.

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* Bump sphinx from 4.4.0 to 4.5.0 in /extra_requirements (#993)

Bumps [sphinx](https://github.com/sphinx-doc/sphinx) from 4.4.0 to 4.5.0.
- [Release notes](https://github.com/sphinx-doc/sphinx/releases)
- [Changelog](https://github.com/sphinx-doc/sphinx/blob/4.x/CHANGES)
- [Commits](sphinx-doc/sphinx@v4.4.0...v4.5.0)

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* Bump utilix from 0.6.6 to 0.6.7 in /extra_requirements (#992)

Bumps [utilix](https://github.com/XENONnT/utilix) from 0.6.6 to 0.6.7.
- [Release notes](https://github.com/XENONnT/utilix/releases)
- [Changelog](https://github.com/XENONnT/utilix/blob/master/HISTORY.md)
- [Commits](XENONnT/utilix@v0.6.6...v0.6.7)

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* Bump tqdm from 4.63.0 to 4.63.1 in /extra_requirements (#999)

Bumps [tqdm](https://github.com/tqdm/tqdm) from 4.63.0 to 4.63.1.
- [Release notes](https://github.com/tqdm/tqdm/releases)
- [Commits](tqdm/tqdm@v4.63.0...v4.63.1)

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  update-type: version-update:semver-patch
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* Bump jupyter-client from 7.1.2 to 7.2.1 in /extra_requirements (#1001)

Bumps [jupyter-client](https://github.com/jupyter/jupyter_client) from 7.1.2 to 7.2.1.
- [Release notes](https://github.com/jupyter/jupyter_client/releases)
- [Changelog](https://github.com/jupyter/jupyter_client/blob/main/CHANGELOG.md)
- [Commits](jupyter/jupyter_client@v7.1.2...v7.2.1)

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* Bump hypothesis from 6.38.0 to 6.40.1 in /extra_requirements (#1005)

Bumps [hypothesis](https://github.com/HypothesisWorks/hypothesis) from 6.38.0 to 6.40.1.
- [Release notes](https://github.com/HypothesisWorks/hypothesis/releases)
- [Commits](HypothesisWorks/hypothesis@hypothesis-python-6.38.0...hypothesis-python-6.40.1)

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Co-authored-by: ahiguera-mx <ahiguera-mx@fried.rice.edu>
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