matthewturk

import yt
import h5py
import glob
import numpy as np
from collections import defaultdict

from yt.geometry.oct_container import EnzoEOctreeContainer

nroot = 16
octree = EnzoEOctreeContainer([16, 16, 16], [0.0, 0.0, 0.0], [1.0, 1.0, 1.0], 16)

matthewturk

matthewturk

matthewturk

matthewturk

yt 4.0 highlights

This is a manually curated list of pull requests that went in to yt 4.0, representing a subset of the full list.

New Functions

• yt.load_sample (PR #2417, PR #2496, PR #2875, PR #2877, PR #2894, PR #3262, PR #3263, PR #3277, PR #3309, and PR #3336)
• yt.set_log_level (PR #2869 and PR #3094)

matthewturk

{
  "$schema": "https://aka.ms/codetour-schema",
  "title": "particle indexing",
  "steps": [
    {
      "title": "Introduction",
      "description": "We're going to walk through how particle indexing works in yt 4.0.\n\nImagine you've got a bunch of LEGO blocks that you know build up a model.  (For fun, let's pretend it's the Arendelle castle.)  They're all separated into little bags, and you don't exactly know which pieces are in which bags, but maybe you vaguely know that there's some organizational scheme to them.\n\nWhen you build your castle, you *could* read the instructions step by step and inspect every single piece in every single bag until you find the one you're looking for.  But that wouldn't be terribly efficient!  Wouldn't it be easier if you had some way of saying, \"I know that this piece is in one of *these* bags, so I'll only look in those bags until I find it.\"\n\nThat's what we do with indexing in yt, and why particle indexing is particularly tricky -- because when we want to select da

matthewturk

[
  {
    "redshift": 25.751117757703,
    "m": 12917.596499489899,
    "x": 0.842004501419555,
    "y": 0.16423811137914449,
    "z": 0.749396924313566
  },
  {
    "redshift": 25.751117757703,

matthewturk

# particle_position_x, particle_position_y, particle_position_z, particle_velocity_x, particle_velocity_y, particle_velocity_z, virial_radius, vrms, particle_mass, spin, vmax, rvmax, energy, kin_to_pot
4.6957e-01, 4.9250e-01, 5.0414e-01, 2.9442e-02, -6.2840e-04, 4.3845e-02, 1.2281e-04, 3.9032e-03, 3.3128e+02, 7.0283e-02, 2.7754e-03, 1.4261e-03, 3.3593e+07, 1.4581e+00
4.6945e-01, 4.9315e-01, 5.0462e-01, 2.7579e-02, -3.8346e-04, 4.3776e-02, 3.1911e-05, 1.8293e-02, 5.7614e+00, 0.0000e+00, 8.4188e-04, 2.7192e-04, 1.2574e+02, 0.0000e+00
4.6927e-01, 4.9356e-01, 5.0481e-01, 2.8701e-02, 1.9386e-04, 4.5392e-02, 3.6529e-05, 1.4660e-02, 8.6421e+00, 4.0685e-01, 8.4173e-04, 4.0802e-04, 2.1872e+05, 8.3015e+00
4.6917e-01, 4.9441e-01, 5.0378e-01, 2.7373e-02, -4.1200e-03, 4.7373e-02, 7.3059e-05, 3.1449e-03, 6.9137e+01, 6.4265e-02, 1.6662e-03, 8.3301e-04, 2.4131e+06, 1.4974e+00
4.5829e-01, 4.9516e-01, 4.3323e-01, 3.0024e-02, -2.6513e-02, 7.1958e-02, 6.3823e-05, 5.2920e-03, 4.6091e+01, 3.8393e-02, 1.4468e-03, 7.3660e-04, 1.1614

matthewturk

# coding: utf-8
import pprint
import yt
import numpy as np
import re

import netCDF4 as nc4

poor_regex = re.compile(r'([a-zA-Z]+)(.*)')
def poor_parser(s):

matthewturk