def setup():
testing.init_blank_db_for_testing()
creator = tangos.testing.simulation_generator.TestSimulationGenerator()
halo_offset = 0
for ts in range(1, 4):
creator.add_timestep()
creator.add_objects_to_timestep(4)
creator.link_last_halos()
tangos.get_default_session().commit()
tangos.get_item("sim/ts1/halo_1")['test_image'] = np.zeros((500, 500, 3))
for ts in tangos.get_simulation(1).timesteps:
for h in ts.halos:
h['test_value'] = 1.0
tangos.get_default_session().commit()
creator = tangos.testing.simulation_generator.TestSimulationGenerator(
"simname/has/slashes")
creator.add_timestep()
creator.add_objects_to_timestep(1)
creator.add_timestep()
creator.add_objects_to_timestep(1)
creator.link_last_halos()
tangos.get_simulation(2).timesteps[0].halos[0]['test_value'] = 2.0
tangos.get_default_session().commit()
global app
app = TestApp(tangos.web.main({}))
def test_readd_simulation():
manager = add_simulation.SimulationAdderUpdater(output_testing.TestInputHandler("dummy_sim_1"))
with log.LogCapturer():
manager.scan_simulation_and_add_all_descendants()
assert db.core.get_default_session().query(db.core.Simulation).count()==1
assert len(db.get_simulation("dummy_sim_1").timesteps)==2
assert db.get_simulation("dummy_sim_1").properties.count()==3
def test_custom_delta_t():
try:
db.get_simulation("sim")["histogram_delta_t_Gyr"] = 0.01
reconstructed_lc = db.get_halo("sim/ts2/1").calculate("reassemble(dummy_histogram, 'place')")
assert len(reconstructed_lc) == int(db.get_timestep("sim/ts2").time_gyr/0.01)
finally:
# restore to default
db.get_simulation("sim")["histogram_delta_t_Gyr"] = DummyHistogramProperty.pixel_delta_t_Gyr
def Tangos_load(sim, redshift):
global path
print('Loading %s at z=%.2f' % (sim, redshift))
tangos.core.init_db('{}/{}.db'.format(path, sim.split('_')[0]))
session = tangos.core.get_default_session()
# Find the output closest to this redshift:
times = tangos.get_simulation('%s' % sim, session).timesteps
final_time = times[-1].__dict__['extension']
h = tangos.get_halo('{}/{}/halo_{:d}'.format(sim, final_time, 1), session)
halo, z = h.calculate_for_progenitors('halo_number()', 'z()')
z_ = np.array([times[i].__dict__['redshift'] for i in range(len(times))])[-len(halo):]
output = np.array([times[i].__dict__['extension'] for i in range(len(times))])[-len(halo):]
output = output[abs(z_ - redshift).argmin()]
redshift = z_[abs(z_ - redshift).argmin()]
# Find the progenitor halo number closest to this redshift:
halo = halo[abs(z - redshift).argmin()]
h = tangos.get_halo('{}/{}/halo_{:d}'.format(sim, output, halo))
return h, session, output, halo, redshift
def find_halos_with_stars(timestep, output):
halos = tangos.get_simulation("h1.cosmo50").timesteps[timestep].halos
#halos = tangos.get_simulation("cosmo25").timesteps[110].halos
halo_id = []
Rvir = []
dist_to_cluster = []
Mvir = []
Mstar = []
Mgas = []
MColdGas = []
MHIGas = []
Rvir = []
R200 = []
center = []
Contam = []
cluster_center = halos[0]['shrink_center']
for i in range(len(halos[:1000])):
halo = halos[i]
mstar = halo['Mstar']
mgas = halo['Mgas']
if i % 50 == 0:
print(i)
if mstar > 1e8:
print(i, mstar / 1e12)
halo_id.append(i)
Contam.append(halo['contamination_fraction'])
Rvir.append(halo['max_radius'])
r200 = halo.calculate('radius(200)')
R200.append(r200)
center.append(halo['shrink_center'])
dist_to_cluster.append(
np.linalg.norm(
np.subtract(halo['shrink_center'], cluster_center)))
Mvir.append(halo['Mvir'])
Mstar.append(mstar)
Mgas.append(mgas)
MColdGas.append(halo['MColdGas'])
MHIGas.append(halo['MHIGas'])
h5file = h5.File('/nobackup/ibutsky/data/romulusC_halo_data_%i' % (output),
'w')
h5file.create_dataset('contamination', data=np.array(Contam))
h5file.create_dataset('r200', data=np.array(R200))
h5file.create_dataset('halo_id', data=np.array(halo_id))
h5file.create_dataset('rvir', data=np.array(Rvir))
h5file.create_dataset('mvir', data=np.array(Mvir))
h5file.create_dataset('mstar', data=np.array(Mstar))
h5file.create_dataset('mgas', data=np.array(Mgas))
h5file.create_dataset('mcoldgas', data=np.array(MColdGas))
h5file.create_dataset('mhigas', data=np.array(MHIGas))
h5file.create_dataset('center', data=np.array(center))
h5file.create_dataset('dist_to_cluster', data=np.array(dist_to_cluster))
h5file.flush()
def setup():
testing.init_blank_db_for_testing()
creator = tangos.testing.simulation_generator.TestSimulationGenerator()
halo_offset = 0
for ts in range(1,4):
creator.add_timestep()
creator.add_objects_to_timestep(4)
creator.link_last_halos()
tangos.get_default_session().commit()
tangos.get_item("sim/ts1/halo_1")['test_image'] = np.zeros((500,500,3))
for ts in tangos.get_simulation(1).timesteps:
for h in ts.halos:
h['test_value'] = 1.0
tangos.get_default_session().commit()
creator.add_timestep() # add ts 4
creator.add_objects_to_timestep(3)
tangos.get_default_session().commit()
# now make the object have halo numbers which have a different ordering to the database IDs
tangos.get_item("sim/ts4/1")['test_value'] = 1.0
tangos.get_item("sim/ts4/2")['test_value'] = 2.0
tangos.get_item("sim/ts4/3")['test_value'] = 3.0
tangos.get_item("sim/ts4/1").halo_number = 10
tangos.get_default_session().commit()
creator = tangos.testing.simulation_generator.TestSimulationGenerator("simname/has/slashes")
creator.add_timestep()
creator.add_objects_to_timestep(1)
creator.add_timestep()
creator.add_objects_to_timestep(1)
creator.link_last_halos()
tangos.get_simulation(2).timesteps[0].halos[0]['test_value'] = 2.0
tangos.get_default_session().commit()
global app
app = TestApp(tangos.web.main({}))
def add_test_simulation_to_db():
global _added_to_db
if not _added_to_db:
with log.LogCapturer():
add.SimulationAdderUpdater(
output_manager).scan_simulation_and_add_all_descendants()
tx = db.core.tracking.TrackData(db.get_simulation("test_tipsy"))
tx.particles = tracked_particles
tx.use_iord = False
tx = db.core.get_default_session().merge(tx)
tx.create_objects()
tx = db.core.tracking.TrackData(db.get_simulation("test_tipsy"))
tx.particles = tracked_iord
tx.use_iord = True
tx = db.core.get_default_session().merge(tx)
tx.create_objects()
_added_to_db = True
def __init__(self, sim_name, center_halo, latest_timestep, earliest_timestep, region_size, box_size=25e3, periodic=True): import numpy as np import tangos from scipy.spatial import KDTree self.simulation = tangos.get_simulation(str(sim_name)) self.region_size = 5*float(region_size) self.box_size = float(box_size) self.timestep, self.num_timesteps = self.find_timesteps(int(latest_timestep), int(earliest_timestep)) self.center_halo = self.timestep[center_halo] self.relevant_halos = self.gather_relevant_halos()
def _setup_dummy_histogram_data(ts1, ts2):
global test_histogram
test_histogram = np.arange(0.0,1000.0,1.0)
ts1_h1 = db.get_halo("sim/ts1/1")
ts1_h2 = db.get_halo("sim/ts1/2")
ts2_h1 = db.get_halo("sim/ts2/1")
property = DummyHistogramProperty(db.get_simulation("sim"))
ts1_h1['dummy_histogram'] = test_histogram[property.store_slice(ts1.time_gyr)]
ts1_h2['dummy_histogram'] = test_histogram[property.store_slice(ts1.time_gyr)] * 0.5
ts2_h1['dummy_histogram'] = test_histogram[property.store_slice(ts2.time_gyr)]
db.core.get_default_session().commit()
def update_tracker_halos(sim=None):
from tangos import get_simulation
from tangos.core import get_default_session
from tangos.util.terminalcontroller import heading
if sim is None:
x = get_default_session().query(TrackData).all()
else:
x = get_simulation(sim).trackers.all()
for y in x:
heading(repr(y))
y.create_objects()
def test_update_simulation():
with testing.autorevert():
assert db.get_simulation("dummy_sim_1")['dummy_sim_property_2'] == 'banana'
_perform_simulation_update()
assert db.get_simulation("dummy_sim_1").properties.count() == 4
assert db.get_simulation("dummy_sim_1")['dummy_sim_property_2']=='orange'
assert db.get_simulation("dummy_sim_1")['dummy_sim_property_new'] == 'fruits'
assert len(db.get_simulation("dummy_sim_1").timesteps) == 3
assert db.get_timestep("dummy_sim_1/step.3").halos.count()==7
assert db.get_simulation("dummy_sim_1")['dummy_sim_property_2'] == 'banana'
def test_histogram_written_as_expected():
dumhistprop = DummyHistogramProperty(db.get_simulation("sim"))
ts1 = db.get_timestep("sim/ts1")
ts2 = db.get_timestep("sim/ts2")
ts1_h1 = db.get_halo("sim/ts1/1")
ts1_h2 = db.get_halo("sim/ts1/2")
ts2_h1 = db.get_halo("sim/ts2/1")
npt.assert_almost_equal(
ts1_h1.calculate('raw(dummy_histogram)'),
test_histogram[dumhistprop.store_slice(ts1.time_gyr)])
npt.assert_almost_equal(
ts1_h2.calculate('raw(dummy_histogram)'),
test_histogram[dumhistprop.store_slice(ts1.time_gyr)] * 0.5)
npt.assert_almost_equal(
ts2_h1.calculate('raw(dummy_histogram)'),
test_histogram[dumhistprop.store_slice(ts2.time_gyr)])
def test_handler():
assert isinstance(
db.get_simulation("test_tipsy_yt").get_output_handler(),
yt_outputs.YtChangaAHFInputHandler)
def wrap(relpos,scale=1,boxsize=25e3):
#author: Michael Tremmel
#scale = 1/1+z
bphys = boxsize*scale
bad = np.where(np.abs(relpos) > bphys/2.)
if type(bphys) == np.ndarray:
relpos[bad] = -1.0 * (relpos[bad] / np.abs(relpos[bad])) * np.abs(bphys[bad] - np.abs(relpos[bad]))
else:
relpos[bad] = -1.0 * (relpos[bad]/np.abs(relpos[bad])) * np.abs(bphys - np.abs(relpos[bad]))
return
#Load in Romulus25 and the desired halo properties
print('Loading Database...')
os.environ['TANGOS_DB_CONNECTION'] = '/myhome2/users/munshi/Romulus/data_romulus25.working.db'
import tangos
rom = tangos.get_simulation('cosmo25')
hnum, cen, mvir, rvir, vmag, rmag, kmag, bmag, mstar, csfh, sfr = rom[-1].calculate_all(
'halo_number()','shrink_center','Mvir','max_radius',
'AB_V','AB_R','AB_K','AB_B','Mstar','CumSFH','SFR_encl_250Myr')
myprint('Database Loaded',clear=True)
#Initialize Data output Dictionaries
MilkyWays = {}
Satellites = {}
LargeHalos = {}
#Find Large Halos (Mvir > 5e11)
print('Searching for Large Halos...')
for i in np.arange(len(hnum)):
if mvir[i] > 5e11:
LargeHalos[str(hnum[i])] = {}
import numpy as np
import pynbody, tangos, glob, gc
sim = tangos.get_simulation('h1.cosmo50')
steps = sim.timesteps
stepnum = -1
halonum = 0 #generalize these later
halo = steps[stepnum].halos[halonum]
steps = [0, 8, 9, 10, 11, 12, 16, 17, 18, 25, 26, 27, 37, 38, 39]
ts = halo.calculate_for_progenitors('t()')[0]
times = np.array([ts[i] for i in steps])
def vdisp(profile):
return np.sqrt(profile['vx_disp']**2 + profile['vy_disp']**2 +
profile['vz_disp']**2)
def get_profile(snap, weight='mass', rmin=5, rmax=2e3, tmax=None, tmin=None):
if tmin:
if tmax:
ptcls = snap.g[(pynbody.filt.HighPass('temp', tmin))
& (pynbody.filt.LowPass('temp', tmax))]
elif tmax:
ptcls = snap.g[pynbody.filt.LowPass('temp', tmax)]
else:
ptcls = snap.g
return pynbody.analysis.profile.Profile(ptcls,
min=rmin,
max=rmax,
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import tangos as db
import pynbody
from matplotlib import rc
import gc
#Halo numbers
#numbers = [18, 23, 24, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 134, 135, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 151, 152, 153, 154, 155, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 186, 187, 188, 189, 190, 191, 192, 193, 194, 196, 197, 198, 199, 200, 201, 202, 204, 206, 207, 208, 209, 210, 213, 214, 215, 216]
numbers = [38]
#Simulation data
sim = db.get_simulation('cosmo25')
step1 = db.get_timestep('cosmo25%/%8192')
s = pynbody.load('/oasis/scratch/comet/mjt29/temp_project/Romulus25/cosmo25p.768sg1bwK1BHe75.008192')
s.physical_units()
gal = s.halos(dosort=True)
#Skirt data & comparison data
data = np.genfromtxt('skirt_faceon.dat', names = ['halo', 'IRX', 'beta', 'neg_ebeta', 'pos_ebeta', 'intercept', 'neg_eint', 'pos_eint'])
#data = data_angles[data_angles['angle'] == 90.]
compare_data = np.genfromtxt('BF_IRXBdata_comparison.dat', names = ['IRX', 'e_IRX', 'beta', 'e_beta'], skip_header =11)
#Fit from Forrest et al.
B = np.linspace(-2.5, 2.5, 100)
y = np.log10(10**(0.4*(5.05 + 2.39*B)) - 1) + np.log10(1.68)
#Grabbing my variables
halomvir = []
def test_simulation_properties(fresh_database):
assert db.get_simulation("dummy_sim_1").properties.count() == 3
assert db.get_simulation("dummy_sim_1")['dummy_sim_property'] == '42'
import numpy as np
from scipy.stats import binned_statistic
import tangos as db
import glob, gc, pynbody
#load particles
basename = '/nobackupp2/mtremmel/Romulus/'
datadir = basename + 'h1.cosmo50/h1.cosmo50PLK.1536gst1bwK1BH.004096'
# basename= '/gpfs/project/fas/nagai/etl28/Romulus/'
# datadir = basename+'RomulusC_Snapshots/h1.cosmo50PLK.1536gst1bwK1BH.004096'
sim = db.get_simulation('h1.cosmo50')
steps = sim.timesteps
halonum = 0
halo = steps[-1].halos[halonum]
steptime = halo.reverse_property_cascade('t()')[0]
keytimes = [9.2, 9.7, 11.3, 11.65, 11.97, 12.20, 12.50, 12.80, 13.25,
13.55] #Gyr
snapnums = [
np.argmin(abs(np.array(steptime) - keytime)) for keytime in keytimes
]
snapinds = [1, 3, 5, 7, 9] #
rcore = 30
radius = str(rcore) + ' kpc'
nbins = 25
allgas = True
nosubs = True
def filter(snap, halo_center=[0, 0, 0], radius='1 kpc'):
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import tangos as db
import pynbody
from matplotlib import rc
import gc
#Halo numbers
#numbers = [18, 23, 24, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 134, 135, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 151, 152, 153, 154, 155, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 186, 187, 188, 189, 190, 191, 192, 193, 194, 196, 197, 198, 199, 200, 201, 202, 204, 206, 207, 208, 209, 210, 213, 214, 215, 216]
numbers = [38]
#Simulation data
sim = db.get_simulation('cosmo25')
step1 = db.get_timestep('cosmo25%/%8192')
s = pynbody.load(
'/oasis/scratch/comet/mjt29/temp_project/Romulus25/cosmo25p.768sg1bwK1BHe75.008192'
)
s.physical_units()
gal = s.halos(dosort=True)
#Skirt data & comparison data
data = np.genfromtxt('skirt_faceon.dat',
names=[
'halo', 'IRX', 'beta', 'neg_ebeta', 'pos_ebeta',
'intercept', 'neg_eint', 'pos_eint'
])
#data = data_angles[data_angles['angle'] == 90.]
compare_data = np.genfromtxt('BF_IRXBdata_comparison.dat',
names=['IRX', 'e_IRX', 'beta', 'e_beta'],
#this analysis is officially easier to run on Grace
#because both the snapshots and database files now live here
#and pynbody and tangos work
import tangos as db
import numpy as np
import matplotlib.pylab as plt
from matplotlib import cm
import os
from scipy.stats import binned_statistic
from astropy import units, constants
from tangos.examples import mergers
# from astropy.stats import LombScargle
omega_m = 0.3
simname='h1.cosmo50'
sim = db.get_simulation(simname)
steps = sim.timesteps
#black holes count as halos
#get local copies of h1.cosmo50 and the database to the office desktop
haloind = 0
startstep = -1
h = steps[startstep].halos[0]
zs = np.array(h.calculate_for_progenitors("z()")[0])
ts = np.array(h.calculate_for_progenitors("t()")[0])
#total mass is in Msol
ind00 = np.argmin(abs(ts - 13.80)) #sim end
ind01 = np.argmin(abs(ts - 12.50)) #disruption end
ind02 = np.argmin(abs(ts - 12)) # disruption start
ind03 = np.argmin(abs(ts - 11.1)) #1st pericenter passage done
ind04 = np.argmin(abs(ts - 10.8)) #merger start
ind05 = np.argmin(abs(ts - 9.7))#post quenching end
tracedvy = np.interp(tracedTime,
np.flipud(time),
np.flipud(vel[:, 1]),
left=0)
tracedvz = np.interp(tracedTime,
np.flipud(time),
np.flipud(vel[:, 2]),
left=0)
tracedVelocities = np.vstack((tracedvx, tracedvy, tracedvz))
#Combine everything into a dictionary
if hasBH:
historyBook = {"time": tracedTime, "haloNumber": np.array(haloNumber), "Mstar": tracedMstar, "SFR": combinedSFR, "Mvir": tracedMvir, \
"R200": tracedR200, "Mgas": tracedMgas, "Mcold": tracedMcold, "SSC": tracedCoordinates, "Vcom": tracedVelocities, "t_slice": time, \
"Mbh": tracedMbh, "BHAR": combinedBHAR, "Dbh": tracedDbh}
else:
historyBook = {"time": tracedTime, "haloNumber": np.array(haloNumber), "Mstar": tracedMstar, "SFR": combinedSFR, "Mvir": tracedMvir, \
"R200": tracedR200, "Mgas": tracedMgas, "Mcold": tracedMcold, "SSC": tracedCoordinates, "Vcom": tracedVelocities, "t_slice": time}
return historyBook
if __name__ == '__main__':
simulationName = 'h1.cosmo50'
sim = db.get_simulation(simulationName)
#This is z=0.14
startingStep = 59
step = sim.timesteps[startingStep]
book = makeHistory(step.halos[2])
