def get_data(sim_file, halo_index): HUBBLE_CONST = 0.688062 sdf_data = load_sdf(sim_file) x = float(sdf_data['x'][halo_index] / HUBBLE_CONST) y = float(sdf_data['y'][halo_index] / HUBBLE_CONST) z = float(sdf_data['z'][halo_index] / HUBBLE_CONST) vx = float(sdf_data['vx'][halo_index]) vy = float(sdf_data['vy'][halo_index]) vz = float(sdf_data['vz'][halo_index]) mvir = float(sdf_data['mvir'][halo_index] / HUBBLE_CONST) r200b = float(sdf_data['r200b'][halo_index] / HUBBLE_CONST) hid = int(sdf_data['id'][halo_index]) hpid = int(sdf_data['pid'][halo_index]) data = { 'x': x, 'y': y, 'z': z, 'vx': vx, 'vy': vy, 'vz': vz, 'mvir': mvir, 'r200b': r200b, 'id': hid, 'pid': hpid } return data
from yt.utilities.sdf import load_sdf import math HUBBLE_CONST = 0.688062 sim_file = '/media/jsnguyen/JK-PEXHD/ds14_a_halos_1.0000' save_dir = '/home/jsnguyen/DSS_data/' load_data_fn = 'reduced_5Mpc_mass_filter_subhalos_1e+14.txt' save_data_fn = 'full_data_' + load_data_fn sdf_data = load_sdf(sim_file) f_pairs_data = open(save_dir + save_data_fn, 'w') #header describes the format of how a pair is stored f_pairs_data.write('# pair_id\n') f_pairs_data.write('# ax ay az avx avy avz amvir ar200b aid apid\n') f_pairs_data.write('# bx by bz bvx bvy bvz bmvir br200b bid bpid\n') f_pairs_data.close() f_pairs = open(save_dir + load_data_fn, 'r') f_pairs.next() #skip header line i = 0 for line in f_pairs: halo_a = int(line.split()[0]) halo_b = int(line.split()[1])
import time import numpy as np import yt import sklearn from sklearn.cluster import MiniBatchKMeans import sys for arg in sys.argv: k=arg k = int(k) yt.funcs.mylog.setLevel(50) #coerce output null print "{} clusters test".format(k) num =100000000 print "Loading {} Particles".format(num) from yt.utilities.sdf import load_sdf path = 'http://darksky.slac.stanford.edu/simulations/ds14_a/ds14_a_1.0000' data = load_sdf(path) x = data['x'][:num] print x.nbytes y = data['y'][:num] z = data['z'][:num] idx = data['ident'][:num] #ds = yt.load("../../ds14_scivis_0128_e4_dt04_1.0000") #ad = ds.all_data() #x = ad[("all","particle_position_x")] #y = ad[("all","particle_position_y")] #z = ad[("all","particle_position_z")] #idx = ad[("all","particle_index")] train = np.array([idx,x,y,z]).T np.savetxt("train.txt",train) avrg = open('avrg_dens.txt', 'a') timef = open('time.txt','a')
import struct from random import * from math import * import sys if len(sys.argv) < 2: print "usage: %s <filename>" % sys.argv[0] quit() print "data%s.xyzb" % sys.argv[1][-4:] fileToOpen = sys.argv[1] fileToSave = "data%s.xyzb" % sys.argv[1][-4:] sdfdata = load_sdf(fileToOpen) file = open(fileToSave, 'wb') ''' for ix in range(0,sdfdata['x'].size): if (sdfdata['vx'][ix] > 0): maxVX = minVX = sdfdata['vx'][ix] print (ix) break for iy in range(0,sdfdata['x'].size): if (sdfdata['vy'][iy] > 0): maxVY = minVY = sdfdata['vy'][iy] print (iy)