1. Dark Sky Simulations
  2. Untitled project
  3. darksky_tour

Overview

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Dark Sky Simulations Examples

Welcome to the dark side. In this repository you'll find a number of example analysis scripts that can be used to analyze data from the Dark Sky Simulations Early Data Release. As opposed to a traditional data product servers, there are no server-side compute. All data is requested through the world wide web (WWW), and returned to your locally running analysis for further action. This allows you request any subset of the data that you'd like, and do whatever you want with it, without any of us getting in your way.

Before you get started check out the pre-requisites below.

Prerequisites

Use a recent version from of https://bitbucket.org/darkskysims/yt-dark. In the near future, all of these capabilities will be included in the main line of yt, but for now as we work out all the kinks, you'll need this one.

We manage our remote data access through thingking, a handy package that exposes Numpy memmap access to data on the WWW. Grab the latest using pip install thingking and you should be set. It can also be installed from source -- just grab a copy from https://bitbucket.org/zeropy/thingking and run python setup.py develop or python setup.py install.

If you install thingking from source, you'll need to also manually install the requests and functools32 packages.

Current Examples

  • rzplot.py: Load the cosmology table used for the ds14 simulations, and plot the conformal distance against redshift.
  • mass_function.py: Load the mass function histograms from the Dark Sky Simulations and plot the mass function and it's relative value to Tinker et al 2008.
  • load_remote_sdf.py: Load the SDF data directly and print out some information.
  • load_bbox.py: Load a region around the most massive galaxy cluster in ds14_a_1.0000 and create a projection of the dark matter density.
  • annotated_halo.py: Load the region around the largest halo in ds14_a_1.0000 into yt and make a projection of the dark matter density, annotated by the halo mass and R_200 radius.
  • plot_halos.py: For a sub-volume of the ds14_a dataset, load up all of the particles and halos, annotating all halos above 1e14 Msun/h.
  • splat_viz.py: Load up a (100 Mpc)**3 volume, and splat the particles using their line-of-sight velocity.
  • test_all_loads: Doesn't test all of the possible ways to load a dataset, but quite a few!
  • test_performance.py: Tests how long it takes to access 100 values of particle position, all separated by 10M particles. Good way to use a lot of bandwidth for very little useful information.
  • SDSS_photoz.py: Remotely SDSS catalog of photometric redshift data, and plot each objects RA and DEC.

Gotcha's

Many of these examples are designed to use a decent amount of data, but not a tremendous amount. If things are taking a while, feel free to modify the examples and either decrease the bounding boxes of the regions you are interested in, or decrease the number of halos you are attempting to load. Also, maybe don't use access this from the free coffee shop wireless.

There are some support files in here that will likely make it into a proper python package at some point, and a few examples that don't currently work, and will error saying so.

Contribution

Got an awesome script that you want to share with the world? Fork this repo and submit a pull request. We'd love to include it!