-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathrun0_eris.bak_mean
executable file
·162 lines (115 loc) · 5.45 KB
/
run0_eris.bak_mean
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
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
"""
run0_eris.py
============
Serves as first draft for getting peculiar velocity plots to Jo Bovy for paper.
Note email dated 8/10/2014 from D. Weinberg
"""
from __future__ import print_function
import config
import sim_tools as st
import survey_sims as ss
import numpy as np
import simtopandas as stp
import pandas as pd
import scipy
import pynbody
from scipy import interpolate as interpolate
import matplotlib as mpl
from matplotlib import pyplot as plt
import decomp
def gcCoords(obsDataDict, sunpos):
"""
Function to calculate rectagular galacto-centric coordinates given l, b,
distance2d, distance3d.
obsDataDict is a dictionary object with the following keys:
'l', 'b', 'd2', 'd3'
Assume you want Sun at (xgc=8,ygc=0, zgc=0)
sunpos a tuple or array (3d)
"""
try:
xGC = obsDataDict['d2'] * np.cos(np.radians(obsDataDict['l']))
yGC = obsDataDict['d2'] * np.sin(np.radians(obsDataDict['l']))
zGC = obsDataDict['d3'] * np.sin(np.radians(obsDataDict['b']))
return (np.vstack([8.0 - xGC, yGC, zGC])).transpose()
except KeyError:
print("Data missing from Dictionary.\nMake sure object has l,b,d2,d3 with correct naming.")
originalsim = st.load_faceon(400, config.params)
## Let's just look at subSim centered on disk
decomp.decomp(originalsim, config)
#sim = originalsim[pynbody.filt.Disc('17 kpc', '0.5 kpc') & pynbody.filt.LowPass('tform', 10.0)]
sim = originalsim[pynbody.filt.Disc('17 kpc', '0.5 kpc') & pynbody.filt.LowPass('decomp', 1.5)]
#rint(pynbody.analysis.angmom.ang_mom_vec(sim))
#pynbody.analysis.angmom.faceon(sim)
#print(pynbody.analysis.angmom.ang_mom_vec(sim))
#2D velocities
sim['vcxy']
sim['vrxy']
#osim = ss.ObsSim(sim, 5.565,5.565)
osim = ss.ObsSim(sim, 8.0, 0.0)
obsdata = ss.lbdrv(sim['pos'], sim['vel'], osim.sunpos)
galCoords = gcCoords(obsdata, osim.sunpos)
gcDict = {i:galCoords[:,j] for (j,i) in enumerate(['xgc', 'ygc', 'zgc'])}
##pynSim = stp.SimPynbody(sim, sid=400)
##dfSim = stp.sim2df(pynSim)
#dfObsdata = pd.DataFrame(obsdata)
#dfGalcoords = pd.DataFrame(galCoords)
#d = [obsdata,gcDict]
dfObsDataCoords = pd.DataFrame(obsdata)
for i in gcDict.keys():
dfObsDataCoords[i] = pd.Series(gcDict[i], index=dfObsDataCoords.index)
extentx = [-15.0,15.0]
extenty = [-15.0,15.0]
###extentx = [5.0,12.5]
###extenty = [-3.0,4.0]
nbinsx,nbinsy = 40,40
###nbinsx,nbinsy = 15,14
binsx = np.arange(extentx[0], extentx[1]+0.001, (extentx[1]-extentx[0])/nbinsx)
binsy = np.arange(extenty[0], extenty[1]+0.001, (extenty[1]-extenty[0])/nbinsy)
#Zcut in GC coords, also needed for original sim for GC vels
decomp.decomp(sim, config) # Disk and Thick disk are 1 and 2
#dfzcut = dfObsDataCoords[(np.abs(dfObsDataCoords.zgc) < 0.25)]
#ubSim = sim[np.asarray(np.abs(dfObsDataCoords.zgc) < 0.25)]
dfzcut = dfObsDataCoords[(np.abs(sim['z']) < 0.25)]
subSim = sim[np.asarray(np.abs(sim['z']) < 0.25)]
#dfzcut = dfObsDataCoords[(np.abs(dfObsDataCoords.zgc) < 0.25) & (sim.s['decomp']<1.5)]
#subSim = sim[np.asarray(np.abs(dfObsDataCoords.zgc) < 0.25) & (sim.s['decomp']<1.5)]
#Need to decide if Vr and vt should be mass averaged or not; for now, yes, so
#use pynbody; otherwise can use pandas to do a straight number average
# We need to make the cuts for disk stars only. Halo stars only have low Vc and therefore make a very non-gaussian tail
radprofileSim = pynbody.analysis.profile.Profile(subSim, min=0.00, max=16.5, nbins=45)
vcxyRfit = interpolate.InterpolatedUnivariateSpline(radprofileSim['rbins'], radprofileSim['vcxy'])
vrxyRfit = interpolate.InterpolatedUnivariateSpline(radprofileSim['rbins'], radprofileSim['vrxy'])
##rcfile = np.load('stargasdm.rotcurve.00400.npz')
##rad = rcfile['radius']
##vc = rcfile['jz_max']/rcfile['radius']
##radVcFit = interpolate.InterpolatedUnivariateSpline(rad[1:],vc[1:])
rgc = np.sqrt(dfzcut.xgc**2 + dfzcut.ygc**2)
dfzcut['rgc'] = pd.Series(np.asarray(rgc), index = dfzcut.index)
deltaVr = subSim['vrxy'] - vrxyRfit(rgc)
deltaVt = subSim['vcxy'] - vcxyRfit(rgc)
#deltaV = dfzcut.rv/np.cos(np.radians(dfzcut.b)) - vcDerived*np.sin(np.radians(dfzcut.l + dfzcut.az))
histres = np.histogram2d(np.asarray(dfzcut.xgc), np.asarray(dfzcut.ygc), bins=[binsx,binsy], range=[extentx, extenty])
hist1d = np.ravel(histres[0])
emptybinMask = hist1d == 0 # where are there NO stars, can later update for minimum star #
dfzcut['deltaVr'] = pd.Series(np.asarray(deltaVr), index = dfzcut.index)
dfzcut['deltaVt'] = pd.Series(np.asarray(deltaVt), index = dfzcut.index)
binned = dfzcut.groupby([pd.cut(dfzcut.xgc, binsx), pd.cut(dfzcut.ygc, binsy)])
medDeltaVr = binned.median().deltaVr
medDeltaVt = binned.median().deltaVt
hist1d[emptybinMask] = np.nan # set the emptpy bins not to be plotted
hist1d[~emptybinMask] = np.asarray(medDeltaVr)
minmax_cm = 24 #N used for vmax, vmin
deltaVmap = hist1d.reshape(nbinsx,nbinsy)
fig = plt.figure()
ax = fig.add_subplot(111)
im = ax.imshow(deltaVmap.T, aspect='auto', interpolation='none', extent=[extentx[0], extentx[1], extenty[0], extenty[1]], cmap='Spectral', origin='lower', vmax=minmax_cm, vmin=-minmax_cm)
plt.colorbar(im)
plt.savefig('vrmap.png', format='png')
hist1d[~emptybinMask] = np.asarray(medDeltaVt)
deltaV2map = hist1d.reshape(nbinsx,nbinsy)
fig2 = plt.figure()
ax2 = fig2.add_subplot(111)
im2 = ax2.imshow(deltaV2map.T, aspect='auto', interpolation='none', extent=[extentx[0], extentx[1], extenty[0], extenty[1]], cmap='Spectral', origin='lower', vmax=minmax_cm, vmin=-minmax_cm)
plt.colorbar(im2)
plt.savefig('vtmap.png', format='png')
#plt.show()