def disruption_mask(cc_satellites, criteria, M, X, Y, Z):
"""Returns np bool array of shape `cc_satellites` with disruption criteria (None, int, or list) implemented on satellite cores."""
if criteria is None:
criteria = []
elif type(criteria) is int:
criteria = [criteria]
# criterion: (1) core radius is less than 20 kpc/h
radius_mask = cc_satellites['radius'] < 20e-3
# criterion: (2) remove merged cores
merged_mask = cc_satellites['merged'] != 1
# criterion: (3) cores are distance r >= 0.05*Rvir_0 from central core
Rvir_0 = getRvir_0(M)
x, y, z = periodic_bcs(cc_satellites['x'], X, BOXSIZE), periodic_bcs(
cc_satellites['y'], Y, BOXSIZE), periodic_bcs(cc_satellites['z'], Z,
BOXSIZE)
distance_mask = dist(x, y, z, X, Y, Z) >= (0.05 * Rvir_0)
masks_dict = {1: radius_mask, 2: merged_mask, 3: distance_mask}
mask = np.full_like(cc_satellites['x'], True, bool)
for i in criteria:
mask = mask & masks_dict[i]
return mask
def disruption_mask(cc_satellites, criteria, M, X, Y, Z, z):
"""Returns np bool array of shape `cc_satellites` with disruption criteria (None, int, or list) implemented on satellite cores."""
if criteria is None:
criteria = []
elif type(criteria) is int:
criteria = [criteria]
# criterion: (1) core radius is less than 20 kpc/h
radius_mask = cc_satellites['radius'] < CORERADIUSCUT
# criterion: (2) remove merged cores
merged_mask = cc_satellites['merged'] != 1
# criterion: (3) cores are distance r >= COREDISTANCEPERCENT*Rvir(z) from central core
Rvir = getRvir(M, z)
x, y, z = periodic_bcs(cc_satellites['x'], X, BOXSIZE), periodic_bcs(cc_satellites['y'], Y, BOXSIZE), periodic_bcs(cc_satellites['z'], Z, BOXSIZE)
distance_mask = dist(x, y, z, X, Y, Z) >= (COREDISTANCEPERCENT*Rvir)
masks_dict = { 1:radius_mask, 2:merged_mask, 3:distance_mask}#, 4:mergedCoreTag_mask }
# criterion: (4) cores with a nonzero `mergedCoreTag` are removed
if 4 in criteria:
mergedCoreTag_mask = cc_satellites['mergedCoreTag']==0
masks_dict[4] = mergedCoreTag_mask
mask = np.full_like(cc_satellites['x'], True, bool)
for i in criteria:
mask = mask&masks_dict[i]
return mask