-
Notifications
You must be signed in to change notification settings - Fork 1
/
bulk.py
133 lines (124 loc) · 5.74 KB
/
bulk.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
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
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
import pickle
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rcParams['font.family'] = 'sans-serif'
mpl.rcParams['font.sans-serif'] = ['Tahoma','Lucida Grande','Verdana', 'DejaVu Sans']
def read_file(simpath,simname):
### leave this section as is
data = []
with open(simpath + '.data', 'rb') as f:
while True:
try:
data.append(pickle.load(f,encoding='latin1'))
except EOFError:
break
data1 = pd.DataFrame(data)
data1['sim'] = [simname]*len(data1)
data1['g-r'] = 1.646*(data1['V_mag'] - data1['R_mag']) - 0.139
return data1
def read_all():
sim1 = '/home/akinshol/Data/Sims/h148.cosmo50PLK.3072g3HbwK1BH/h148.cosmo50PLK.3072g3HbwK1BH.004096/h148.cosmo50PLK.3072g3HbwK1BH.004096'
sim2 = '/home/akinshol/Data/Sims/h229.cosmo50PLK.3072gst5HbwK1BH/h229.cosmo50PLK.3072gst5HbwK1BH.004096/h229.cosmo50PLK.3072gst5HbwK1BH.004096'
sim3 = '/home/akinshol/Data/Sims/h242.cosmo50PLK.3072gst5HbwK1BH/h242.cosmo50PLK.3072gst5HbwK1BH.004096/h242.cosmo50PLK.3072gst5HbwK1BH.004096'
sim4 = '/home/akinshol/Data/Sims/h329.cosmo50PLK.3072gst5HbwK1BH/h329.cosmo50PLK.3072gst5HbwK1BH.004096/h329.cosmo50PLK.3072gst5HbwK1BH.004096'
data = read_file(sim1,'h148')
data = data.append(read_file(sim2,'h229'))
data = data.append(read_file(sim3,'h242'))
data = data.append(read_file(sim4,'h329'))
return data
def quenched(data):
return np.array((data['sSFR'] <= 1e-10) & (data['HIgasfrac'] <= 0.2))
# output = []
# for i in range(len(data)):
# if data['sSFR'].tolist()[i] <= 1e-10 and data['HIgasfrac'].tolist()[i] <= 0.2:
# output.append(True)
# else:
# output.append(False)
# return np.array(output)
def sat(data,whichhost=False):
output = []
hostid = []
for i in range(len(data)):
if data['sim'].tolist()[i]=='h148' or data['sim'].tolist()[i]=='h242':
if data['hostHalo'].tolist()[i]==-1:
output.append(False)
hostid.append(-1)
else:
output.append(True)
hostid.append(data['haloid'][np.array(data['id2'])==np.array(data['hostHalo'].tolist()[i])].tolist()[0])
elif data['sim'].tolist()[i]=='h229' or data['sim'].tolist()[i]=='h329':
if data['hostHalo'].tolist()[i]==0:
output.append(False)
hostid.append(-1)
else:
output.append(True)
hostid.append(data['haloid'][np.array(data['id2'])==np.array(data['hostHalo'].tolist()[i])].tolist()[0])
else:
raise Exception("simname must be either 'h148','h229','h242','h329'")
if whichhost:
return np.array(hostid)
else:
return np.array(output)
def whichHost(data):
return sat(data,whichhost=True)
def distance_to_nearest_halo(data):
distances = []
for i in range(len(data)):
halocoords = np.array([data['Xc'].tolist()[i],data['Yc'].tolist()[i],data['Zc'].tolist()[i]])
nstars = np.delete(data['n_star'].tolist(),i)
x = np.delete(data['Xc'].tolist(),i)
y = np.delete(data['Yc'].tolist(),i)
z = np.delete(data['Zc'].tolist(),i)
x = x[nstars >= 100].tolist()
y = y[nstars >= 100].tolist()
z = z[nstars >= 100].tolist()
coords = np.array([x,y,z])
coords = np.transpose(coords)
dist = np.min(np.sqrt(np.sum((halocoords - coords)**2, axis=1)))
distances.append(dist)
return np.array(distances)
def distance_to_host(data,rvir=True):
'''
Returns an array of the distances to host galaxies for all halos in `data`.
Optional argument `rvir` changes whether or not to divide distance by host virial radius.
'''
distances = []
for i in range(len(data)):
host = whichHost(data)[i]
if host == -1:
distances.append(0)
else:
halocoords = np.array([data['Xc'].tolist()[i],data['Yc'].tolist()[i],data['Zc'].tolist()[i]])
hostcoords = np.array([data['Xc'][data['haloid']==host].tolist()[0],data['Yc'][data['haloid']==host].tolist()[0],data['Zc'][data['haloid']==host].tolist()[0]])
if rvir:
if data['sim'].tolist()[i]=='h148':
r = data['Rvir'][(data['haloid']==host) & (data['sim']=='h148')].tolist()[0]
elif data['sim'].tolist()[i]=='h229':
r = data['Rvir'][(data['haloid']==host) & (data['sim']=='h229')].tolist()[0]
elif data['sim'].tolist()[i]=='h242':
r = data['Rvir'][(data['haloid']==host) & (data['sim']=='h242')].tolist()[0]
elif data['sim'].tolist()[i]=='h329':
r = data['Rvir'][(data['haloid']==host) & (data['sim']=='h329')].tolist()[0]
distances.append(np.sqrt(np.sum((hostcoords - halocoords)**2))/r)
else:
distances.append(np.sqrt(np.sum((hostcoords - halocoords)**2)))
return np.array(distances)
def distance_to_nearest_host(data):
distances = []
for i in range(len(data)):
halocoords = np.array([data['Xc'].tolist()[i],data['Yc'].tolist()[i],data['Zc'].tolist()[i]])
nstars = np.delete(data['n_star'].tolist(),i)
notsat = np.delete(~np.array(sat(data)),i)
x = np.delete(data['Xc'].tolist(),i)
y = np.delete(data['Yc'].tolist(),i)
z = np.delete(data['Zc'].tolist(),i)
x = x[nstars >= 100 & notsat].tolist()
y = y[nstars >= 100 & notsat].tolist()
z = z[nstars >= 100 & notsat].tolist()
coords = np.array([x,y,z])
coords = np.transpose(coords)
dist = np.min(np.sqrt(np.sum((halocoords - coords)**2, axis=1)))
distances.append(dist)
return np.array(distances)