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ICICLE.py
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ICICLE.py
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from numpy import random, cos, sin, sqrt, log
import sys
sys.path.insert(0,'../ProfileFiles/')
#sys.path.insert(0,'ProfileFiles/')
import pandas as pd
import os
from ICs_NFW import *
from ICs_NFWX import *
from ICs_Hernquist import *
from ICs_King import *
from ICs_Einasto import *
import matplotlib.pyplot as plt
h = 0.6766
rhom = 8.634164473613977e-09
rhoc = 2.7753662724570803e-08
#PLOT PARAMS
# Set the font size for axis labels and tick labels
plt.rcParams['font.size'] = 18
# Set the font family for all text in the plot
plt.rcParams['font.family'] = 'serif'
# Set the figure size to 6 x 4 inches
plt.rcParams['figure.figsize'] = [8, 6]
# Set the linewidth for lines in the plot
plt.rcParams['lines.linewidth'] = 1.5
# Set the color cycle for multiple lines in the same plot
plt.rcParams['axes.prop_cycle'] = plt.cycler('color', ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf', '#a55194'])
# Set the tick direction to 'in'
plt.rcParams['xtick.direction'] = 'in'
plt.rcParams['ytick.direction'] = 'in'
# Set the tick length to 4 points
plt.rcParams['xtick.major.size'] = 4
plt.rcParams['ytick.major.size'] = 4
# Set the number of minor ticks between major ticks to 5
plt.rcParams['xtick.minor.visible'] = True
plt.rcParams['ytick.minor.visible'] = True
plt.rcParams['xtick.minor.size'] = 2
plt.rcParams['ytick.minor.size'] = 2
plt.rcParams['xtick.minor.width'] = 0.5
plt.rcParams['ytick.minor.width'] = 0.5
plt.rcParams['xtick.minor.pad'] = 2.0
plt.rcParams['ytick.minor.pad'] = 2.0
plt.rcParams['xtick.minor.top'] = True
plt.rcParams['ytick.minor.right'] = True
plt.rcParams['xtick.minor.bottom'] = True
plt.rcParams['ytick.minor.left'] = True
# Set the default dpi of figures to 150
plt.rcParams['figure.dpi'] = 300
# Set the default file format to PDF
plt.rcParams['savefig.format'] = 'pdf'
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''
READ PARAMETER FILE
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''
def radii(rads,denz):
a= rads.min()
b= rads.max()
global m
radius = np.sort(rads)
while (b - a)/2.0 > 1E-12:
midpoint = (a + b)/2.0
partii = radius[radius <= midpoint]
volume = (4.0/3.0) * np.pi * midpoint**3
dens = m * partii.size / volume
if dens < denz: # Increasing but below 0 case
b = midpoint
else:
a = midpoint
print(midpoint, m * partii.size)
return midpoint
def new_n(n,m):
if m < 0.01:
new_n_part = 145000 #360 26
elif m < 0.1:
new_n_part = 151700 #775 70
else:
new_n_part = 167600 #1670 220
return new_n_part
def read_paramfile(paramfile):
#########################################################
##Read in Param File
#########################################################
d = {} #create dictionary
with open(paramfile) as f:
for line in f:
myline = line.split()
if len(myline)>=2 and line[0]!='#': #make sure not blank line or comment
(key,val) = myline[0:2] #extract first two items
if key == 'Model':
d[key] = val
elif val == 'True':
d[key] = True
elif val == 'False':
d[key] = False
else:
d[key] = float(val)
model = d['Model']
n = int(d['n'])
trun = d['truncate']
m_OG = d['m']
trun = d['truncate']
if model == 'Hernquist':
if m_OG <= 0.1:
d['m'] = d['m']/1.105
else:
d['m'] = d['m']/1.13
if trun and model == 'NFW':
d['m'] = d['m']*(n/new_n(n,m_OG))
if model == 'Einasto':
d['m'] = d['m']/1.5
if model == 'NFWX':
d['m'] = d['m']/1.01
rvir = d['r_vir']
m = d['m']
G = d['G']
params = d
return model,n,m,G,params,m_OG,rvir
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''
MAIN PART OF PROGRAM
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''
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'''''''''''''''''''''''''''''''''''''''''''''''''''''''''
def ICs(model,n,m,G,params):
#########################################################
##Outputs positions and velocities of n particles in a
##profile specifed by model
#########################################################
good_n = False
target_n = n
trun = params['truncate']
global m_OG
while not good_n:
if trun and model == 'NFW':
n=new_n(target_n,m_OG)
if model == 'Einasto':
n=1.5*target_n
if model == 'Hernquist':
if m_OG <= 0.01:
n=1.105*target_n
else:
n=1.13*target_n
if model == 'NFWX':
n=1.01*n
#Find R
rand = random.rand(int(n)).astype('f8')
initialguess = eval(model+'_RootGuess')(params)
res = optimize.root(eval(model+'_MD'),initialguess*rand,args=(rand,params),tol=1e-4,method='krylov')
R = res.x
#Find V
P = eval(model+'_P')(R,params) #potential
E = pick_E(model,P,params) #energy
V = sqrt(2*(P-E)) #velocity magnitude
#Convert dimensionless R,V to r,v
r,v = eval(model+'_dimens')(R,V,n,m,G,params)
print('try! '+str(r.size))
if r.size >= target_n:
good_n = True
print('Used Particles: '+str(100-(float((r.size-target_n))/target_n*100))+'%')
#x,y,z components of positions and velocities
sort_radius = np.argsort(r)
sort_velos = np.argsort(v)
#sort_radius = np.random.shuffle(sort_radius)
r = r[sort_radius]
v = v[sort_radius]
# r = r[sort_velos]
# v = v[sort_velos]
# x,y,z = isotrop(r[:])
# vx,vy,vz = isotrop(v[:])
x,y,z = isotrop(r[:target_n])
vx,vy,vz = isotrop(v[:target_n])
return x, y, z, vx, vy, vz
def pick_E(model,P,params):
#########################################################
##Given points with potentials P, and a distribution
##function, DF, picks random energies from the distribution
#########################################################
#Generate distribution function
E = np.linspace(0.0,max(P),int(1e3))
f = np.zeros(E.shape[0])
for i in range(1,E.shape[0]):
f[i] = eval(model+'_DF')(E[i],params)
#Make sure no nans
E = E[~np.isnan(f)]
f = f[~np.isnan(f)]
#Choose E from distribution function
n = P.shape[0]
rand = random.rand(n)
ans = np.ones(n)
for i in range(n):
newE = E[E<P[i]] #newE goes to maxE(R)=P(R)
F = integrate.cumtrapz(f[E<P[i]]*sqrt(P[i]-newE),newE, initial=0) #cummulative distribution
newE = newE[~np.isnan(F)]; F = F[~np.isnan(F)];
ans[i] = np.interp(rand[i]*F[-1], F, newE) #find at which energy F=rand
return ans
def isotrop(m):
#########################################################
##Given n points with radius or velocity magnitude, m,
##outputs components in 3D for an isotropic distribution
#########################################################
#Generate random numbers
n = m.shape[0]
u = 2*random.rand(n)-1 # uniform in -1, 1
theta = 2*pi*random.rand(n) # uniform in 0, 2*pi
#Find x,y,z components
x = m*sqrt(1-u*u)*cos(theta)
y = m*sqrt(1-u*u)*sin(theta)
z = m*u
return x, y, z
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''
MAKE OUTPUT FILE
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''
#Initialize
paramfile = sys.argv[1]
#paramfile = 'params.txt'
model,n,m,G,params,m_OG,rvir = read_paramfile(paramfile)
eval(model+'_Initialize')(params)
# import the builtin time module
import time
# Grab Currrent Time Before Running the Code
start = time.time()
print('loading...')
#Run Program
x, y, z ,vx, vy, vz = ICs(model,n,m,G,params)
#Write File
filename = sys.argv[2]
#filename = 'out.txt'
if filename[-4:] == '.txt':
filename = filename[:-4]+'.csv'
try:
os.remove(filename)
except (OSError,TypeError,ValueError) as e:
pass
#print(x.dtype)
d = {'x': x, 'y': y,'z': z, 'vx': vx,'vy': vy, 'vz': vz}
df = pd.DataFrame(data=d)
#print(df.dtypes)
#df = df*1000
posi = df[['x','y','z']]
min_pos = posi[['x','y','z']].min().min()
max_pos = posi[['x','y','z']].max().max()
max_box = round((abs(min_pos)+abs(max_pos))*2,6)
centre_box = max_box/2
df[['x','y','z']] = df[['x','y','z']] + centre_box
#print(df.shape)
df.to_csv(filename,sep=',',index=False)
df['r'] = np.sqrt(x**2+y**2+z**2)
rads = df.r
print(rads.min(),rads.max())
m = m_OG
if model == 'NFW':
m = m_OG * n/np.sum(df.r<rvir)/1.014
if model == 'Hernquist':
m = m_OG * n/np.sum(df.r<rvir)/1.001
if model == 'King':
m = m_OG * n/np.sum(df.r<radii(rads,rhom*200))
if model == 'Einasto':
if m_OG == 0.001:
m = m_OG * n/np.sum(df.r<rvir)/1.16
if m_OG == 0.01:
m = m_OG * n/np.sum(df.r<rvir)/1.2
if m_OG == 0.1:
m = m_OG * n/np.sum(df.r<rvir)/1.61
print(f'rvir = {radii(rads,rhom*200)}')
print(f'r< = {np.sum(rads<rvir)}')
print(f'm = {m}')
with open('Params.txt', 'w') as f:
f.write(str(max_box)+" "+str(m))
f.close()
# Grab Currrent Time After Running the Code
end = time.time()
#Subtract Start Time from The End Time
total_time = end - start
print("\n"+ str(int(total_time/60))+'m '+str(round(total_time%60,2))+'s')
#%%
fig = plt.figure()
plt.plot(x,y,'.',alpha=0.2,label='xy')
plt.plot(x,z,'.',alpha=0.2,label='xz')
plt.plot(y,z,'.',alpha=0.2,label='yz')
plt.xlabel(r'Dimension [kpc]')
plt.ylabel(r'Dimension [kpc]')
plt.legend(loc='best')
plt.tight_layout()
# plt.show()
plt.savefig(f'Figures/IC_Dimensions ({m_OG*n}).png')
plt.close(fig)
fig1 = plt.figure()
plt.xlabel(r'Radius [kpc]')
plt.ylabel(r'Counts [#]')
plt.hist(rads,bins=100)
# plt.legend(loc='best')
plt.tight_layout()
# plt.show()
plt.savefig(f'Figures/IC_Histogram ({m_OG*n}).png')
plt.close(fig1)
#%%
import numpy as np
def density_profile(r, m, dr):
"""
Calculates the density profile of a set of shells with mass m and radii r.
Parameters:
r (array): Radii of the shells.
m (float): Total mass within each shell.
dr (float): Width of each shell.
Returns:
density (array): Density profile of the shells.
"""
volume = 4.0/3 * np.pi * (r**3) # Calculate volume of a shell with radius r and width dr
# volume = 4.0 * np.pi * (r**2) * dr # Calculate volume of a shell with radius r and width dr
density = m / volume # Calculate density as mass / volume
return density
# raddd = np.arange(rads.min(),rads.max(),1) # Radii of the shells
raddd = np.logspace(np.log10(rads.min()),np.log10(rads.max()),1000) # Radii of the shells
aa = np.ones_like(raddd)
for xi,kk in enumerate(raddd):
aa[xi] *= np.sum(rads<kk)*m
mass = np.diff(aa) # Total mass within each shell
dr = 0.5 # Width of each shell
# density = density_profile(raddd[1:], mass, dr) # Calculate density profile
density = density_profile(raddd, aa, dr) # Calculate density profile
maskr = density > 0
density = np.compress(maskr, density)
raddd = np.compress(maskr, raddd)
mean_density = np.ones_like(raddd)*(rhom*200)
mean_cdensity = np.ones_like(raddd)*(rhoc*500)
#print(raddd[np.argwhere(np.diff(np.sign(density - mean_density)))][0])
#print(raddd[np.argwhere(np.diff(np.sign(density - mean_cdensity)))][0])
maskr[::25] = maskr[::25] * 0
density = np.compress(~maskr, density)
raddd = np.compress(~maskr, raddd)
mean_density = np.compress(~maskr, mean_density)
mean_cdensity = np.compress(~maskr, mean_cdensity)
#%%
fig2 = plt.figure()
plt.xlabel(r'Radius [kpc]')
plt.ylabel(r'$\rho$(<r) [M$_{\odot}$ kpc$^{-3}$]')
plt.loglog(raddd,mean_cdensity,'--',c='g',label=r'$\rho_{500c}$')
plt.loglog(raddd,mean_density,'--',c='r',label=r'$\rho_{200m}$')
plt.loglog(raddd,density,'o-',c='black',label=r'$\rho$ $_{IC}$')
plt.text(x=1,y=mean_density[0]*1.3,s=r'R$_{200m}$ = '+f'{radii(rads,rhom*200):.2f} kpc',c='r',size=14)
plt.text(x=1,y=mean_cdensity[0]*1.3,s=r'R$_{500c}$ = '+f'{radii(rads,rhoc*500):.2f} kpc',c='g',size=14)
plt.legend(loc='upper right')
plt.tight_layout()
# plt.show()
plt.savefig(f'Figures/IC_Density Profile ({m_OG*n}).png')
plt.close(fig2)
# Writing to file
with open(f"Figures/Data ({m_OG*n}).txt", "w") as file1:
# Writing data to a file
file1.write(f'R200m = {radii(rads,rhom*200)}\n')
file1.write(f'R500c = {radii(rads,rhoc*500)}\n')