jpivarski

import numpy as np

class System:
    G = 3
    min_distance = 0.1

    @classmethod
    def random(cls, num_particles, num_dimensions, mass_mean, mass_width, x_width, p_width):
        m = np.random.gamma(mass_mean / mass_width, mass_width, num_particles)
        x = np.random.normal(0, x_width, (num_particles, num_dimensions))

jpivarski

jpivarski

jpivarski

jpivarski

jpivarski

# see https://gist.github.com/jpivarski/001867b9da51a47b93913a0b9809db3a

import concurrent.futures
import time
import glob
import tarfile
import json
import ast
import re
import gzip

jpivarski

# see https://gist.github.com/jpivarski/001867b9da51a47b93913a0b9809db3a

import concurrent.futures
import time
import glob
import tarfile
import json
import ast
import re
import gzip

jpivarski

import yaml

import awkward as ak

arg2key = {
    "List[bool]": "np.bool_",
    "Const[List[bool]]": "np.bool_",
    "List[int8_t]": "np.int8",
    "Const[List[int8_t]]": "np.int8",
    "List[uint8_t]": "np.uint8",

jpivarski

jpivarski

import time
import numpy as np
import numba as nb

# This could also be done without a class, using @nb.jit directly.
# It just seems natural to use a class. (That *might* slow it down a little; haven't tested it.)

spec = [
  ("width", nb.int32),
  ("height", nb.int32),