def gridDensity(fname):
''' Calculates the density squared within 15 M on (r, theta) grid
:returns: r, th, annihilationRates, sqrt(dispersions)
'''
# Calculate density from the data file
data = pp.loadpdata(fname)
Ntotal = data["Ntotal"]
Eps_1 = data["Eps_1"]
Lz_1 = data["Lz_1"]
K_1 = data["K_1"]
M_1 = data["M_1"]
a_1 = data["a_1"]
rMin_1 = data["rMin_1"]
rMax_1 = data["rMax_1"]
g_1 = np.ravel(data["g_1"])
Eps_2 = data["Eps_2"]
Lz_2 = data["Lz_2"]
K_2 = data["K_2"]
M_2 = data["M_2"]
a_2 = data["a_2"]
rMin_2 = data["rMin_2"]
rMax_2 = data["rMax_2"]
thMin_2 = data["thMin_2"]
thMax_2 = data["thMax_2"]
g_2 = np.ravel(data["g_2"])
N_1 = np.ones(len(Eps_1)) * 1. / Ntotal
f_1 = N_1 / g_1
f_2 = f_1 # Constancy of phase space
R = np.linspace(M_2, 15. * M_2, 80)
TH = np.linspace(np.pi / 3., np.pi * (1. - 1. / 3.), 10)
TH = [np.pi / 2. + 0.02]
rV = []
thV = []
Densitys = []
sqrtdispersions = []
nSamples = []
# Calculate annihilation rate for each "Grid" point
for r in R:
for th in TH:
cond = ((rMin_2 < r) & (r < rMax_2) & (thMin_2 < th) &
(th < thMax_2))
Density = GR.ZAMODensity(f_2, Eps_2, Lz_2, K_2, M_2, a_2, rMin_2,
rMax_2, thMin_2, thMax_2, r, th)**2
Densitys.append(Density * 1.e45)
sqrtdispersions.append(
np.sqrt(
GR.ZAMODispersion(f_2, Eps_2, Lz_2, K_2, M_2, a_2, rMin_2,
rMax_2, thMin_2, thMax_2, r, th)))
rV.append(r)
thV.append(th)
nSamples.append(np.sum(cond))
print "r, th: %f %f" % (r, th)
print "Max annihilation rate %lf" % np.max(Densitys)
print "Mean value of degeneracy: %lf" % np.mean(g_2)
return rV, thV, Densitys, sqrtdispersions, nSamples, M_2, a_2
#matplotlib.rcParams['mathtext.rm'] = 'Bitstream Vera Sans'
#matplotlib.rcParams['mathtext.it'] = 'Bitstream Vera Sans:italic'
#matplotlib.rcParams['mathtext.bf'] = 'Bitstream Vera Sans:bold'
#matplotlib.rc('font', **{'family': 'serif', 'serif': ['Computer Modern']})
#matplotlib.rc('text', usetex=True)
import numpy as np
import paris as pp
from paris import GR
import pylab as plt
print "python binDegeneracies.py ProductionData/M10_r2_spin0998"
plt.figure(figsize=(16, 9))
print np.array(sys.argv)[1:]
for fname in np.array(sys.argv)[1:]:
data = pp.loadpdata(fname)
g_1 = np.ravel(data["g_1"])
g_2 = np.ravel(data["g_2"])
Ntotal = np.ravel(data["Ntotal"])
plt.hist(g_1,
color='red',
label="Degeneracy before growth, fname = " + str(fname),
alpha=0.5)
plt.hist(g_2,
color='black',
label="Degeneracy after growth, fname = " + str(fname),
alpha=0.5)
plt.yscale("log")
plt.xscale("log")
plt.ylabel("Counts")
plt.xlabel("Degeneracy")
def generate3DVTKUltra(fin, fout):
# Read in data:
data = pp.loadpdata(fin)
Ntotal = data["Ntotal"]
Eps_1 = data["Eps_1"]
Lz_1 = data["Lz_1"]
K_1 = data["K_1"]
M_1 = data["M_1"]
a_1 = data["a_1"]
rMin_1 = data["rMin_1"]
rMax_1 = data["rMax_1"]
g_1 = data["g_1"]
Eps_2 = data["Eps_2"]
Lz_2 = data["Lz_2"]
K_2 = data["K_2"]
M_2 = data["M_2"]
a_2 = data["a_2"]
rMin_2 = data["rMin_2"]
rMax_2 = data["rMax_2"]
thMin_2 = data["thMin_2"]
thMax_2 = data["thMax_2"]
g_2 = data["g_2"]
N_1 = np.ones(len(Eps_1)) * 1. / Ntotal
f_1 = N_1 / g_1
f_2 = f_1 # Constancy of phase space
fOut = fout
rMin = 1. * M_2
rMax = 25. * M_2
rDimensions = 200
thMin = 0
thMax = np.pi
thDimensions = 30
phMin = 0
phMax = 2. * np.pi
phDimensions2 = 30
r, theta, phi = np.mgrid[rMin:rMax:np.complex(0, rDimensions),
thMin:thMax:complex(0, thDimensions),
phMin:phMax:complex(0, phDimensions2)]
th = theta
x = r * np.sin(theta) * np.cos(phi)
y = r * np.sin(theta) * np.sin(phi)
z = r * np.cos(theta)
FFIODensity = np.zeros(np.shape(r))
ZAMODensity = np.zeros(np.shape(r))
sqrtdisp = np.zeros((np.shape(r)[0], np.shape(r)[1], np.shape(r)[2], 4))
Px = np.zeros(np.shape(r))
Py = np.zeros(np.shape(r))
Pz = np.zeros(np.shape(r))
for i in range(len(r)):
for j in range(len(r[0])):
for k in range(len(r[0][0])):
FFIODensity[i][j][k] = GR.FDensity(f_2, Eps_2, Lz_2, K_2, M_2,
a_2, rMin_2, rMax_2,
thMin_2, thMax_2,
r[i][j][k], th[i][j][k])
ZAMODensity[i][j][k] = GR.ZAMODensity(f_2, Eps_2, Lz_2, K_2,
M_2, a_2, rMin_2, rMax_2,
thMin_2, thMax_2,
r[i][j][k], th[i][j][k])
sqrtdisp[i][j][k] = np.sqrt(
GR.ZAMODispersion(f_2, Eps_2, Lz_2, K_2, M_2, a_2, rMin_2,
rMax_2, thMin_2, thMax_2, r[i][j][k],
th[i][j][k]))
Nmu = GR.Nmu(f_2, Eps_2, Lz_2, K_2, M_2, a_2, rMin_2, rMax_2,
thMin_2, thMax_2, r[i][j][k], th[i][j][k])
sinth = np.sin(th[i][j][k])
costh = np.cos(th[i][j][k])
sinphi = np.sin(phi[i][j][k])
cosphi = np.cos(phi[i][j][k])
Px[i][j][k] = Nmu[1] * sinth * cosphi + Nmu[
2] * costh * cosphi + Nmu[3] * (-1. * sinphi)
Py[i][j][k] = Nmu[1] * sinth * sinphi + Nmu[
2] * costh * sinphi + Nmu[3] * cosphi
Px[i][j][k] = Nmu[1] * costh + Nmu[2] * (-1. *
sinth) + Nmu[3] * 0.
FFIODensity = np.array(FFIODensity)
ZAMODensity = np.array(ZAMODensity)
sqrtdisp = np.array(sqrtdisp)
Px = np.array(Px)
Py = np.array(Py)
Pz = np.array(Pz)
pointData = {
"FFIODensity": FFIODensity,
"ZAMODensity": ZAMODensity,
"Px": Px,
"Py": Py,
"Pz": Pz
} #, "sigma_Px": sqrtdisp[:,1], "sigma_Py": sqrtdisp[:,2], "sigma_Pz": sqrtdisp[:,3]}
gridToVTK(fOut, x, y, z, pointData=pointData)
return rMin, rMax, thMin, thMax, phMin, phMax, rDimensions, thDimensions, phDimensions2, pointData
#matplotlib.rcParams['mathtext.it'] = 'Bitstream Vera Sans:italic'
#matplotlib.rcParams['mathtext.bf'] = 'Bitstream Vera Sans:bold'
#matplotlib.rc('font', **{'family': 'serif', 'serif': ['Computer Modern']})
#matplotlib.rc('text', usetex=True)
import paris as pp
import numpy as np
import pylab as plt
import sys
from paris import GR
fname1 = sys.argv[1]
fname2 = sys.argv[2]
fout = sys.argv[3]
data = pp.loadpdata(fname2)
Eps_1 = data["Eps_1"]
Lz_1 = data["Lz_1"]
K_1 = data["K_1"]
M_1 = data["M_1"]
a_1 = data["a_1"]
rMin_1 = data["rMin_1"]
rMax_1 = data["rMax_1"]
g_1 = data["g_1"]
Eps_2 = data["Eps_2"]
Lz_2 = data["Lz_2"]
K_2 = data["K_2"]
M_2 = data["M_2"]
a_2 = data["a_2"]
rMin_2 = data["rMin_2"]
"a": a,
"M": M,
"a_2": a_2,
"M_2": M_2,
"Eps_1": Eps_1,
"Lz_1": Lz_1,
"K_1": K_1,
"Q_1": Q_1,
"rMin_1": rMin_1,
"rMax_1": rMax_1,
"thMin_1": thMin_1,
"thMax_1": thMax_1,
"g_1": g_1,
"M_1": M_1,
"a_1": a_1,
"Eps_2": Eps_2,
"Lz_2": Lz_2,
"K_2": K_2,
"Q_2": Q_2,
"rMin_2": rMin_2,
"rMax_2": rMax_2,
"thMin_2": thMin_2,
"thMax_2": thMax_2,
"g_2": g_2,
"M_2": M_2,
"a_2": a_2
}
pp.savepdata(fout, data)
data2 = pp.loadpdata(fout)
def calculateVariables(fname, average=False):
# Load the data files
data = pp.loadpdata(fname)
Ntotal = data["Ntotal"]
Eps_1 = data["Eps_1"]
Lz_1 = data["Lz_1"]
K_1 = data["K_1"]
M_1 = data["M_1"]
a_1 = data["a_1"]
rMin_1 = data["rMin_1"]
rMax_1 = data["rMax_1"]
g_1 = np.ravel(data["g_1"])
Eps_2 = data["Eps_2"]
Lz_2 = data["Lz_2"]
K_2 = data["K_2"]
M_2 = data["M_2"]
a_2 = data["a_2"]
rMin_2 = data["rMin_2"]
rMax_2 = data["rMax_2"]
thMin_2 = data["thMin_2"]
thMax_2 = data["thMax_2"]
g_2 = np.ravel(data["g_2"])
#if GR.tests.isBound(Eps_1,Lz_1,K_1,M_1,a_1) == False:
# raise ValueError("Eps_1,Lz_1,Q_1 not bound; something wrong")
#if GR.tests.isBound(Eps_2,Lz_2,K_2,M_2,a_2) == False:
# raise ValueError("Eps_2,Lz_2,Q_2 not bound; something wrong")
# Data loaded; now calculate the 4-current
N_1 = np.ones(len(Eps_1)) * 1. / Ntotal
f_1 = N_1 / g_1
f_2 = f_1
th = np.pi / 2. + 0.01
FFIODensity = []
ZAMODensity = []
ZAMODispersion = []
nSamples = []
R = np.linspace(1. * M_2, 50 * M_2, 1000)
router = 1. * M_2 + np.sqrt(M_2**2 - a_2**2)
if np.sum(rMin_2 <= router) != 0:
raise ValueError(
"Bad limits; we are inside the inner horizon with rmin router %lf %lf"
% (np.min(rMin_2), router))
for r in R:
if average == True:
TH = np.linspace(np.pi * (1. / 2. - 1. / 7.),
np.pi * (1. / 2. + 1. / 7.), 10)
else:
TH = [np.pi / 2. + 0.01]
# Average over TH:
NTH = len(TH)
samples = 0.
dispersion = np.zeros(4) * 1.
FDensity = 0.
ZDensity = 0.
for i in np.arange(len(TH)):
th = TH[i]
samples = samples + np.sum(((r < rMax_2) & (rMin_2 < r) &
(th < thMax_2) & (thMin_2 < th)))
# Get the ZAMO Dispersion
dispersion = dispersion + GR.ZAMODispersion(
f_2, Eps_2, Lz_2, K_2, M_2, a_2, rMin_2, rMax_2, thMin_2,
thMax_2, r, th)
FDensity = FDensity + GR.FDensity(f_2, Eps_2, Lz_2, K_2, M_2, a_2,
rMin_2, rMax_2, thMin_2, thMax_2,
r, th)
ZDensity = ZDensity + GR.ZAMODensity(f_2, Eps_2, Lz_2, K_2, M_2,
a_2, rMin_2, rMax_2, thMin_2,
thMax_2, r, th)
# Calculate mean:
samples = samples / float(NTH)
dispersion = dispersion / float(NTH)
FDensity = FDensity / float(NTH)
ZDensity = ZDensity / float(NTH)
# Insert:
FFIODensity.append(FDensity)
# Note that J^0 = \rho_0 U^0 = \rho = (density at rest) in the ZAMO frame
ZAMODensity.append(ZDensity)
ZAMODispersion.append(dispersion)
nSamples.append(samples)
FFIODensity = np.array(FFIODensity)
ZAMODensity = np.array(ZAMODensity)
ZAMODispersion = np.array(ZAMODispersion)
nSamples = np.array(nSamples)
return R, FFIODensity, ZAMODensity, ZAMODispersion[:,
1], ZAMODispersion[:,
2], ZAMODispersion[:,
3], nSamples