Reproducible source for the Elaphrocentre paper

Copyright (C) 2018-2020 Mohammad Akhlaghi mohammad@akhlaghi.org Copyright (C) 2020-2021 Marius Peper, Boud Roukema See the end of the file for license conditions.

This is the reproducible project source for the research paper "The role of the elaphrocentre in void galaxy formation"", by Marius Peper and Boudewijn Roukema, submitted for peer review.

To reproduce the results and final paper, the only dependency is a minimal Unix-like building environment including a C compiler (already available on your system if you have ever built and installed software from source) and a downloader (Wget or cURL). Git is not mandatory: if you don't have Git to run the first command below, go to the URL given in the command on your browser, and download the project's source (there is a button to download a compressed tarball of the project). If you have received this source from arXiv, please see the respective section below. If you have received this source from ArXiv:2010.03742 or Zenodo.4062461 (without any .git directory inside), or SoftwareHeritage, please see the "Building project tarball" section below.

$ git clone https://codeberg.org/boud/elaphrocentre.git
$ cd elaphrocentre
$ ./project configure
$ ./project make

The 'configure' (download/compile/install) component of 'elaphrocentre' will take about 2-3 hours on 4 cores at 2.0-2.5 GHz and about 5 GB of disk space in the .build/software/ directory.

The time, disk and cpu resources requirements of the 'analysis' (calculate/analyse/plot) component of 'elaphrocentre' strongly depend on the particle resolution (Ncroot); time and cpu usage also depend on levelmax, which is effectively the softening length. Ncroot is not a direct constraint on RAMSES memory requirements: these are hardwired to the values of ngridtot and nparttot; you may decrease these to reduce the memory requirements - RAMSES will normally give a fatal error and explain that insufficient memory has been allocated if that is the case.

The following four calculation time estimates assume that you set 'compare_centres = NO' in 'reproduce/analysis/config/analyse-plot-params.conf'. This will remove calculations that generate Table 4 of version 2 of our paper, and will remove the corresponding, final paragraph of Section 4.2 from the paper. For maximising the simplicity of automated reproducibility, we intend to keep 'compare_centres = YES' in the second version of this paper in Zenodo. The YES option will give much longer calculation times than those below.

We recommend setting 'compare_centres = NO', although the (modest) scientific cost will be the omission of a small part of version 2 of the paper (see previous paragraph).

• A 32^3 particle simulation will be rather fast and is recommended for development and debugging. Many numerical results will be poor. Using 4 cores at 2.0-2.5 GHz, a full 32^3 particle run (levelmax=10) and its followup analysis will take about 15 minutes of wall time, a maximum of about 160 Mb of RAM and roughly 2 Gb for .build/n-body-init/ (altogether about 7 Gb of disk space, when the 5 Gb for .build/software/ are included).

• A 64^3 run, using 4 cores again, will take roughly 25 minutes, up to 1.6 Gb of RAM and 19 Gb of disk space for .build/n-body-init/, and 1.5 Gb for .build/haloes/, making about 26 Gb altogether.

• Using the parameters of our standard calculation, i.e. a 128^3 particle run and levelmax=12, will take, using 4 cores, roughly 8 hours, 14.8 Gb of RAM, about 145 Gb of disk space for .build/n-body-init/, 12 Gb for .build/haloes/, for a total of about 162 Gb of disk space.

• Do not try running the current version for a 256^3 particle run. The later steps in the pipeline are not parallelised, and a 20-core calculation would take you about four weeks.

To learn more about the purpose, principles and technicalities of maneage format reproducible papers, please see README-hacking.md, or the Maneage webpage at https://maneage.org . For a general introduction to reproducible science as implemented in this project, please see the principles of reproducible science, and a reproducible paper template that is based on it.

Building the project

This project was designed to have as few dependencies as possible without requiring root/administrator permissions.

1. Necessary dependencies:

   1.1: Minimal software building tools like C compiler, Make, and other tools found on any Unix-like operating system (GNU/Linux, BSD, Mac OS, and others). All necessary dependencies will be built from source (for use only within this project) by the ./project configure script (next step).

   1.2: (OPTIONAL) Tarball of dependencies. If they are already present (in a directory given at configuration time), they will be used. Otherwise, a downloader (wget or curl) will be necessary to download any necessary tarball. The necessary tarballs are also collected in the archived project at https://doi.org/10.5281/zenodo.4062461. Unpack the tarball and you should see most of the tarballs of this project's software (Maneage is a project that is still under development; some of the less critical software may not yet be included). When ./project configure asks for the "software tarball directory", give the address of the unpacked directory that has all the tarballs.

    Software Heritage: https://... [software heritage link]
   

2. Configure the environment (top-level directories in particular) and build all the necessary software for use in the next step. It is recommended to set directories that are outside the current directory. For example, create your build (temporary) directory outside the current directory and store the full name of that directory (with its full path).

   Please read the description of each necessary input clearly and set the best value. The configure script downloads, builds and locally installs many programs (project dependencies), specifically for this particular project. Please do this without root privileges. As of September 2020 on a typical desktop or laptop computer, this may take a few hours (see the estimates for the time and hardware usage above).

   $ ./project configure
   

   If there is an interruption and you wish to continue or seek help, try

   $ ./project configure --existing-conf
   

   to continue or

   $ ./project --help
   

   for help. We recommend creating log files for the main commands. The command

   (./project configure <options> 2>&1) | tee yyyymmdd<logname>
   

   will create a log file called yyyymmdd (where is whatever label you choose) and also print all lines (standard output and standard error) to the terminal. We recommend the equivalent syntax for the commands described in the following steps to again create log files. Creating logs will be very helpful in tracing bugs, and in helping other people to help you to trace bugs. The most powerful command for searching through long log files is probably grep. Try man grep or info grep or grep --help for help on grep.

2a. Optionally, as an alternative to the configure step in 2, you can instead add a parallelisation option:

```shell
$ ./project configure -j4
```

or

```shell
$ ./project configure --existing-conf -j4
```

The option -j4 is optional, meaning that you choose to build your "high-level" software with 4 parallel threads. If your CPU has a different number of threads, choose the number that you wish to use (you can see the number of threads available on your operating system by running ./.local/bin/nproc).

3. Run the following command to run the simulations and other galaxy pipeline steps:

    $ ./project make full-pipeline
   

   For help on redoing or removing individual steps:

    $ ./project --help-pipeline
   

   You will find information on these steps, most of which should be run chronologically; 'full-pipeline' should run all of these steps. See the paper for explanations.

   ./project make init-conditions        Run the initial conditions.
   ./project make run-simulation         Run the simulations (not run automatically).
   ./project make detect-haloes          Detect dark matter haloes.
   ./project make create-mergertree      Create a merger history tree.
   ./project make create-galaxies        Create galaxies from SFR histories.
   ./project make detect-voids           Detect voids in dark matter distribution.
   ./project make analyse-plot           Analyse and plot.
   
   
   
   

4. Run the following command to create the pdf file:

   $ ./project make
   

Overall directory structure

The main front-end script is `project`, which calls
`reproduce/software/shell/configure.sh` and 'configures'
(does the download, configure, compile, install cycle) if necessary.

The download, configure, compile, install cycle is coded
in the `reproduce/software/` directory tree, with, in particular,
`reproduce/software/make/` - makefiles
`reproduce/software/config/` - software configure files.

The calculate, analyse, plot, verify, LaTeX steps are coded
in the `reproduce/analysis/` directory tree, with, in particular,
`reproduce/analysis/make/` - makefiles
`reproduce/analysis/config/` - cosmological configure files.

Building project tarball (possibly from arXiv)

If the paper is also published on arXiv, it is highly likely that the authors also uploaded/published the full project there along with the LaTeX sources. If you have downloaded (or plan to download) this source from arXiv, some minor extra steps are necessary as listed below. This is because this tarball is mainly tailored to automatic creation of the final PDF without using Maneage (only calling LaTeX, not using the './project' command)!

You can directly run 'latex' on this directory and the paper will be built with no analysis (all necessary built products are already included in the tarball). One important feature of the tarball is that it has an extra Makefile to allow easy building of the PDF paper without worring about the exact LaTeX and bibliography software commands.

Only building PDF using tarball (no analysis)

1. If you got the tarball from arXiv and the arXiv code for the paper is 1234.56789, then the downloaded source will be called 1234.56789 (no suffix). However, it is actually a .tar.gz file. So take these steps to unpack it to see its contents.

   $ arxiv=1234.56789
   $ mv $arxiv $arxiv.tar.gz
   $ mkdir $arxiv
   $ cd $arxiv
   $ tar xf ../$arxiv.tar.gz
   

2. No matter how you got the tarball, if you just want to build the PDF paper, simply run the command below. Note that this won't actually install any software or do any analysis, it will just use your host operating system (assuming you already have a LaTeX installation and all the necessary LaTeX packages) to build the PDF using the already-present plots data.

   $ make              # Build PDF in tarball without doing analysis
   

3. If you want to re-build the figures from scratch, you need to make the following corrections to the paper's main LaTeX source (paper.tex): uncomment (remove the starting %) the line containing \newcommand{\makepdf}{}, see the comments above it for more.

Building full project from tarball (custom software and analysis)

As described above, the tarball is mainly geared to only building the final PDF. A few small tweaks are necessary to build the full project from scratch (download necessary software and data, build them and run the analysis and finally create the final paper).

1. If you got the tarball from arXiv, before following the standard procedure of projects described at the top of the file above (using the ./project script), its necessary to set its executable flag because arXiv removes the executable flag from the files (for its own security).

   $ chmod +x project
   

2. Make the following changes in two of the LaTeX files so LaTeX attempts to build the figures from scratch (to make the tarball; it was configured to avoid building the figures, just using the ones that came with the tarball).

   • paper.tex: uncomment (remove the starting %) of the line containing \newcommand{\makepdf}{}, see the comments above it for more.

   • tex/src/preamble-pgfplots.tex: set the tikzsetexternalprefix variable value to tikz/, so it looks like this: \tikzsetexternalprefix{tikz/}.

3. Remove extra files. In order to make sure arXiv can build the paper (resolve conflicts due to different versions of LaTeX packages), it is sometimes necessary to copy raw LaTeX package files in the tarball uploaded to arXiv. Later, we will implement a feature to automatically delete these extra files, but for now, the project's top directory should only have the following contents (where reproduce and tex are directories). You can safely remove any other file/directory.

   $ ls
   COPYING  paper.tex  project  README-hacking.md  README.md  reproduce/  tex/
   

Building in Docker containers

Docker containers are a common way to build projects in an independent filesystem, and an almost independent operating system. Containers thus allow using GNU/Linux operating systems within proprietary operating systems like macOS or Windows. But without the overhead and huge file size of virtual machines. Furthermore containers allow easy movement of built projects from one system to another without rebuilding. Just note that Docker images are large binary files (+1 Gigabytes) and may not be usable in the future (for example with new Docker versions not reading old images). Containers are thus good for temporary/testing phases of a project, but shouldn't be what you archive for the long term!

Hence if you want to save and move your maneaged project within a Docker image, be sure to commit all your project's source files and push them to your external Git repository (you can do these within the Docker image as explained below). This way, you can always recreate the container with future technologies too. Generally, if you are developing within a container, its good practice to recreate it from scratch every once in a while, to make sure you haven't forgot to include parts of your work in your project's version-controlled source. In the sections below we also describe how you can use the container only for the software environment and keep your data and project source on your host.

Dockerfile for a Maneaged project, and building a Docker image

Below is a series of recommendations on the various components of a Dockerfile optimized to store the built state of a maneaged project as a Docker image. Each component is also accompanied with explanations. Simply copy the code blocks under each item into a plain-text file called Dockerfile, in the same order of the items. Don't forget to implement the suggested corrections (in particular step 4).

NOTE: Internet for TeXLive installation: If you have the project software tarballs and input data (optional features described below) you can disable internet. In this situation, the configuration and analysis will be exactly reproduced, the final LaTeX macros will be created, and all results will be verified successfully. However, no final paper.pdf will be created to visualize/combine everything in one easy-to-read file. Until task 15267 is complete, we need internet to install TeXLive packages (using TeXLive's own package manager tlmgr) in the ./project configure phase. This won't stop the configuration, and it will finish successfully (since all the analysis can still be reproduced). We are working on completing this task as soon as possible, but until then, if you want to disable internet and you want to build the final PDF, please disable internet after the configuration phase. Note that only the necessary TeXLive packages are installed (~350 MB), not the full TeXLive collection!

0. Summary: If you are already familiar with Docker, then the full Dockerfile to get the project environment setup is shown here (without any comments or explanations, because explanations are done in the next items). Note that the last two COPY lines (to copy the directory containing software tarballs used by the project and the possible input databases) are optional because they will be downloaded if not available. You can also avoid copying over all, and simply mount your host directories within the image, we have a separate section on doing this below ("Only software environment in the Docker image"). Once you build the Docker image, your project's environment is setup and you can go into it to run ./project make manually.

   FROM debian:stable-slim
   RUN apt-get update && apt-get install -y gcc g++ wget
   RUN useradd -ms /bin/sh maneager
   USER maneager
   WORKDIR /home/maneager
   RUN mkdir build
   RUN mkdir software
   COPY --chown=maneager:maneager ./project-source /home/maneager/source
   COPY --chown=maneager:maneager ./software-dir   /home/maneager/software
   COPY --chown=maneager:maneager ./data-dir       /home/maneager/data
   RUN cd /home/maneager/source \
       && ./project configure --build-dir=/home/maneager/build \
                              --software-dir=/home/maneager/software \
                              --input-dir=/home/maneager/data
   

1. Choose the base operating system: The first step is to select the operating system that will be used in the docker image. Note that your choice of operating system also determines the commands of the next step to install core software.

   FROM debian:stable-slim
   

2. Maneage dependencies: By default the "slim" versions of the operating systems don't contain a compiler (needed by Maneage to compile precise versions of all the tools). You thus need to use the selected operating system's package manager to import them (below is the command for Debian). Optionally, if you don't have the project's software tarballs, and want the project to download them automatically, you also need a downloader.

   # C and C++ compiler.
   RUN apt-get update && apt-get install -y gcc g++
   
   # Uncomment this if you don't have 'software-XXXXXXX.tar.gz' (below).
   #RUN apt-get install -y wget
   

3. Define a user: Some core software packages will complain if you try to install them as the default (root) user. Generally, it is also good practice to avoid being the root user. After building the Docker image, you can always run it as root with this command: docker run -u 0 -it XXXXXXX (where XXXXXXX is the image identifier). Hence with the commands below we define a maneager user and activate it for the next steps.

   RUN useradd -ms /bin/sh maneager
   USER maneager
   WORKDIR /home/maneager
   

4. Copy project files into the container: these commands make the assumptions listed below. IMPORTANT: you can also avoid copying over all, and simply mount your host directories within the image, we have a separate section on doing this below ("Only software environment in the Docker image").

   • The project's source is in the maneaged/ sub-directory and this directory is in the same directory as the Dockerfile. The source can either be from cloned from Git (highly recommended!) or from a tarball. Both are described above (note that arXiv's tarball needs to be corrected as mentioned above).

   • (OPTIONAL) By default the project's necessary software source tarballs will be downloaded when necessary during the ./project configure phase. But if you already have the sources, its better to use them and not waste network traffic (and resulting carbon footprint!). Maneaged projects usually come with a software-XXXXXXX.tar.gz file that is published on Zenodo (link above). If you have this file, put it in the same directory as your Dockerfile and include the relevant lines below.

   • (OPTIONAL) The project's input data. The INPUT-FILES depends on the project, please look into the project's reproduce/analysis/config/INPUTS.conf for the URLs and the file names of input data. Similar to the software source files mentioned above, if you don't have them, the project will attempt to download its necessary data automatically in the ./project make phase.

   # Make the project's build directory and copy the project source
   RUN mkdir build
   COPY --chown=maneager:maneager ./maneaged /home/maneager/source
   
   # Optional (for software)
   COPY --chown=maneager:maneager ./software-XXXXXXX.tar.gz /home/maneager/
   RUN tar xf software-XXXXXXX.tar.gz && mv software-XXXXXXX software && rm software-XXXXXXX.tar.gz
   
   # Optional (for data)
   RUN mkdir data
   COPY --chown=maneager:maneager ./INPUT-FILES /home/maneager/data
   

5. Configure the project: With this line, the Docker image will configure the project (build all its necessary software). This will usually take about an hour on an 8-core system. You can also optionally avoid putting this step (and the next) in the Dockerfile and simply execute them in the Docker image in interactive mode (as explained in the sub-section below, in this case don't forget to preserve the build container after you are done).

   # Configure project (build full software environment).
   RUN cd /home/maneager/source \
          && ./project configure --build-dir=/home/maneager/build \
                                 --software-dir=/home/maneager/software \
                                 --input-dir=/home/maneager/data
   

6. Project's analysis: With this line, the Docker image will do the project's analysis and produce the final paper.pdf. The time it takes for this step to finish, and the storage/memory requirements highly depend on the particular project.

   # Run the project's analysis
   RUN cd /home/maneager/source && ./project make
   

7. Build the Docker image: The Dockerfile is now ready! In the terminal, go to its directory and run the command below to build the Docker image. We recommend to keep the Dockerfile in an empty directory and run it from inside that directory too. This is because Docker considers that directories contents to be part of the environment. Finally, just set a NAME for your project and note that Docker only runs as root.

   sudo su
   docker build -t NAME ./
   

Interactive tests on built container

If you later want to start a container with the built image and enter it in interactive mode (for example for temporary tests), please run the following command. Just replace NAME with the same name you specified when building the project. You can always exit the container with the exit command (note that all your changes will be discarded once you exit, see below if you want to preserve your changes after you exit).

docker run -it NAME

Running your own project's shell for same analysis environment

The default operating system only has minimal features: not having many of the tools you are accustomed to in your daily command-line operations. But your maneaged project has a very complete (for the project!) environment which is fully built and ready to use interactively with the commands below. For example the project also builds Git within itself, as well as many other high-level tools that are used in your project and aren't present in the container's operating system.

# Once you are in the docker container
cd source
./project shell

Preserving the state of a built container

All interactive changes in a container will be deleted as soon as you exit it. THIS IS A VERY GOOD FEATURE IN GENERAL! If you want to make persistent changes, you should do it in the project's plain-text source and commit them into your project's online Git repository. As described in the Docker introduction above, we strongly recommend to not rely on a built container for archival purposes.

But for temporary tests it is sometimes good to preserve the state of an interactive container. To do this, you need to commit the container (and thus save it as a Docker "image"). To do this, while the container is still running, open another terminal and run these commands:

# These two commands should be done in another terminal
docker container list

# Get 'XXXXXXX' of your desired container from the first column above.
# Give the new image a name by replacing 'NEW-IMAGE-NAME'.
docker commit XXXXXXX NEW-IMAGE-NAME

Copying files from the Docker image to host operating system

The Docker environment's file system is completely indepenent of your host operating system. One easy way to copy files to and from an open container is to use the docker cp command (very similar to the shell's cp command).

docker cp CONTAINER:/file/path/within/container /host/path/target

Only software environment in the Docker image

You can set the docker image to only contain the software environment and keep the project source and built analysis files (data and PDF) on your host operating system. This enables you to keep the size of the Docker image to a minimum (only containing the built software environment) to easily move it from one computer to another. Below we'll summarize the steps.

1. Get your user ID with this command: id -u.

2. Put the following lines into a Dockerfile of an otherwise empty directory. Just replacing UID with your user ID (found in the step above). This will build the basic directory structure. for the next steps.

FROM debian:stable-slim
RUN apt-get update && apt-get install -y gcc g++ wget
RUN useradd -ms /bin/sh --uid UID maneager
USER maneager
WORKDIR /home/maneager
RUN mkdir build

3. Create an image based on the Dockerfile above. Just replace PROJECT with your desired name.

docker build -t PROJECT ./

4. Run the command below to create a container based on the image and mount the desired directories on your host into the special directories of your container. Just don't forget to replace PROJECT and set the /PATHs to the respective paths in your host operating system.

docker run -v /PATH/TO/PROJECT/SOURCE:/home/maneager/source \
           -v /PATH/TO/PROJECT/ANALYSIS/OUTPUTS:/home/maneager/build/analysis \
           -v /PATH/TO/SOFTWARE/SOURCE/CODE/DIR:/home/maneager/software \
           -v /PATH/TO/RAW/INPUT/DATA:/home/maneager/data \
           -it PROJECT

5. After running the command above, you are within the container. Go into the project source directory and run these commands to build the software environment.

cd /home/maneager/source
./project configure --build-dir=/home/maneager/build \
                    --software-dir=/home/maneager/software \
                    --input-dir=/home/maneager/data

6. After the configuration finishes successfully, it will say so and ask you to run ./project make. But don't do that yet. Keep this Docker container open and don't exit the container or terminal. Open a new terminal, and follow the steps described in the sub-section above to preserve the built container as a Docker image. Let's assume you call it PROJECT-ENV. After the new image is made, you should be able to see the new image in the list of images with this command (in the same terminal that you created the image):

docker image list      # In the other terminal.

7. Now you can run ./project make in the initial container. You will see that all the built products (temporary or final datasets or PDFs), will be written in the /PATH/TO/PROJECT/ANALYSIS/OUTPUTS directory of your host. You can even change the source of your project on your host operating system an re-run Make to see the effect on the outputs and add/commit the changes to your Git history within your host. You can also exit the container any time. You can later load the PROJECT-ENV environment image into a new container with the same docker run -v ... command above, just use PROJECT-ENV instead of PROJECT.

8. In case you want to store the image as a single file as backup or to move to another computer, you can run the commands below. They will produce a single project-env.tar.gz file.

docker save -o project-env.tar PROJECT-ENV
gzip --best project-env.tar

9. To load the tarball above into a clean docker environment (either on the same system or in another system), and create a new container from the image like above (the docker run -v ... command). Just don't forget that if your /PATH/TO/PROJECT/ANALYSIS/OUTPUTS directory is empty on the new/clean system, you should first run ./project configure -e in the docker image so it builds the core file structure there. Don't worry, it won't build any software and should finish in a second or two. Afterwards, you can safely run ./project make.

docker load --input project-env.tar.gz

Deleting all Docker images

After doing your tests/work, you may no longer need the multi-gigabyte files images, so its best to just delete them. To do this, just run the two commands below to first stop all running containers and then to delete all the images:

docker ps -a -q | xargs docker rm
docker images -a -q | xargs docker rmi -f

Copyright information

This file and .file-metadata (a binary file, used by Metastore to store file dates when doing Git checkouts) are part of the reproducible project mentioned above and share the same copyright notice (at the start of this file) and license notice (below).

This project is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This project is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this project. If not, see https://www.gnu.org/licenses/.