def __init__(self, tag, field='Br', precision='Float32', avg=False):
"""
:param tag: if you specify a pattern, it tries to read the corresponding
files and stack them.
:type tag: str
:param field: nature of the radial spectra. Possible choices are
'Bt' or 'Bp'
:type field: str
:param precision: single or double precision (default single, i.e. 'Float32')
:type precision: str
:param avg: when set to True, display time averaged quantities
:type avg: bool
"""
logFiles = scanDir('log.*')
if len(logFiles) != 0:
MagicSetup.__init__(self, quiet=True, nml=logFiles[-1])
self.n_r_max = int(self.n_r_max)
self.n_r_ic_max = int(self.n_r_ic_max)
else:
n_r_max = 'n_r_max ?\n'
self.n_r_max = int(input(str1))
n_r_ic_max = 'n_r_ic_max ?\n'
self.n_r_ic_max = int(input(str1))
str1 = 'Aspect ratio ?\n'
self.radratio = float(input(str1))
self.n_r_tot = self.n_r_max+self.n_r_ic_max
self.ricb = self.radratio/(1.-self.radratio)
self.rcmb = 1./(1.-self.radratio)
outerCoreGrid = chebgrid(self.n_r_max-1, self.rcmb, self.ricb)
n_r_ic_tot = 2*self.n_r_ic_max-1
innerCoreGrid = chebgrid(n_r_ic_tot-1, self.ricb, -self.ricb)
self.radius = np.zeros((self.n_r_tot-1), dtype=precision)
self.radius[:self.n_r_max] = outerCoreGrid
self.radius[self.n_r_max-1:] = innerCoreGrid[:self.n_r_ic_max]
pattern = 'r%s' % field +'Spec'
files = scanDir('%s.%s' % (pattern, tag))
# Read the rB[rp]Spec.TAG files (stack them)
data = []
for k, file in enumerate(files):
print('Reading %s' % file)
f = npfile(file, endian='B')
while 1:
try:
data.append(f.fort_read(precision))
except TypeError:
break
data = np.array(data, dtype=precision)
# Time (every two lines only)
self.time = data[::2, 0]
# Poloidal/Toroidal energy for all radii for the 6 first spherical harmonic
# degrees
self.e_pol = np.zeros((len(self.time), self.n_r_tot-1, 6), dtype=precision)
self.e_pol_axi = np.zeros_like(self.e_pol)
self.e_pol[:, :, 0] = data[::2, 1:self.n_r_tot]
self.e_pol[:, :, 1] = data[::2, self.n_r_tot-1:2*(self.n_r_tot-1)]
self.e_pol[:, :, 2] = data[::2, 2*(self.n_r_tot-1):3*(self.n_r_tot-1)]
self.e_pol[:, :, 3] = data[::2, 3*(self.n_r_tot-1):4*(self.n_r_tot-1)]
self.e_pol[:, :, 4] = data[::2, 4*(self.n_r_tot-1):5*(self.n_r_tot-1)]
self.e_pol[:, :, 5] = data[::2, 5*(self.n_r_tot-1):6*(self.n_r_tot-1)]
self.e_pol_axi[:, :, 0] = data[1::2, 1:self.n_r_tot]
self.e_pol_axi[:, :, 1] = data[1::2, self.n_r_tot-1:2*(self.n_r_tot-1)]
self.e_pol_axi[:, :, 2] = data[1::2, 2*(self.n_r_tot-1):3*(self.n_r_tot-1)]
self.e_pol_axi[:, :, 3] = data[1::2, 3*(self.n_r_tot-1):4*(self.n_r_tot-1)]
self.e_pol_axi[:, :, 4] = data[1::2, 4*(self.n_r_tot-1):5*(self.n_r_tot-1)]
self.e_pol_axi[:, :, 5] = data[1::2, 5*(self.n_r_tot-1):6*(self.n_r_tot-1)]
if avg:
self.plotAvg()
def __init__(self,
tag,
ratio_cmb_surface=1,
scale_b=1,
iplot=True,
field='B',
r=1,
precision='Float64',
lCut=None,
quiet=False):
"""
:param tag: if you specify a pattern, it tries to read the corresponding files
:type tag: str
:param ratio_cmb_surface: ratio of surface ratio to CMB radius (default is 1)
:type ratio_cmb_surface: float
:param scale_b: magnetic field unit (default is 1)
:type scale_b: float
:param iplot: a logical to toggle the plot (default is True)
:type iplot: bool
:param field: 'B', 'V' or 'T' (magnetic field, velocity field or temperature)
:type field: str
:param r: an integer to characterise which file we want to plot
:type r: int
:param precision: single or double precision
:type precision: str
:param lCut: reduce the spherical harmonic truncation to l <= lCut
:type lCut: int
:param quiet: verbose when toggled to True (default is True)
:type quiet: bool
"""
logFiles = scanDir('log.*')
if len(logFiles) != 0:
MagicSetup.__init__(self, quiet=True, nml=logFiles[-1])
else:
str1 = 'Aspect ratio ?\n'
self.radratio = float(input(str1))
self.rcmb = 1. / (1. - self.radratio)
ricb = self.radratio / (1. - self.radratio)
files = scanDir('%s_coeff_r%i.%s' % (field, r, tag))
# Read the B_coeff files (by stacking the different tags)
data = []
for k, file in enumerate(files):
if not quiet: print('Reading %s' % file)
f = npfile(file, endian='B')
out = f.fort_read('3i4,%s' % precision)[0]
self.l_max_r, self.minc, n_data = out[0]
self.m_max_r = int((self.l_max_r / self.minc) * self.minc)
self.radius = out[1]
while 1:
try:
data.append(f.fort_read(precision))
except TypeError:
break
self.lm_max_r = self.m_max_r*(self.l_max_r+1)//self.minc - \
self.m_max_r*(self.m_max_r-self.minc)//(2*self.minc) + \
self.l_max_r-self.m_max_r+1
# Get indices location
self.idx = np.zeros((self.l_max_r + 1, self.m_max_r + 1), 'i')
self.ell = np.zeros(self.lm_max_r, 'i')
self.ms = np.zeros(self.lm_max_r, 'i')
self.idx[0:self.l_max_r + 2, 0] = np.arange(self.l_max_r + 1)
self.ell[0:self.l_max_r + 2] = np.arange(self.l_max_r + 2)
k = self.l_max_r + 1
for m in range(self.minc, self.l_max_r + 1, self.minc):
for l in range(m, self.l_max_r + 1):
self.idx[l, m] = k
self.ell[self.idx[l, m]] = l
self.ms[self.idx[l, m]] = m
k += 1
# Rearange data
data = np.array(data, dtype=precision)
self.nstep = data.shape[0]
self.wlm = np.zeros((self.nstep, self.lm_max_r), 'Complex64')
self.dwlm = np.zeros((self.nstep, self.lm_max_r), 'Complex64')
self.zlm = np.zeros((self.nstep, self.lm_max_r), 'Complex64')
# Get time
self.time = np.zeros(self.nstep, dtype=precision)
self.time = data[:, 0]
# wlm
self.wlm[:, 1:self.l_max_r + 1] = data[:, 1:self.l_max_r + 1]
k = self.l_max_r + 1
for m in range(self.minc, self.l_max_r + 1, self.minc):
for l in range(m, self.l_max_r + 1):
self.wlm[:, self.idx[l, m]] = data[:, k] + 1j * data[:, k + 1]
k += 2
# dwlm
self.dwlm[:, 1:self.l_max_r + 1] = data[:, k:k + self.l_max_r]
k += self.l_max_r
for m in range(self.minc, self.l_max_r + 1, self.minc):
for l in range(m, self.l_max_r + 1):
self.dwlm[:, self.idx[l, m]] = data[:, k] + 1j * data[:, k + 1]
k += 2
# zlm
self.zlm[:, 1:self.l_max_r + 1] = data[:, k:k + self.l_max_r]
k += self.l_max_r
for m in range(self.minc, self.l_max_r + 1, self.minc):
for l in range(m, self.l_max_r + 1):
self.zlm[:, self.idx[l, m]] = data[:, k] + 1j * data[:, k + 1]
k += 2
# ddw in case B is stored
if field == 'B':
self.ddwlm = np.zeros((self.nstep, self.lm_max_r), 'Complex64')
self.ddwlm[:, 1:self.l_max_r + 1] = data[:, k:k + self.l_max_r]
k += self.l_max_r
for m in range(self.minc, self.l_max_r + 1, self.minc):
for l in range(m, self.l_max_r + 1):
self.ddwlm[:,
self.idx[l,
m]] = data[:, k] + 1j * data[:, k + 1]
k += 2
# Truncate!
if lCut is not None:
if lCut < self.l_max_r:
self.truncate(lCut, field=field)
self.e_pol_axi_l = np.zeros((self.nstep, self.l_max_r + 1), precision)
self.e_tor_axi_l = np.zeros((self.nstep, self.l_max_r + 1), precision)
self.e_pol_l = np.zeros((self.nstep, self.l_max_r + 1), precision)
self.e_tor_l = np.zeros((self.nstep, self.l_max_r + 1), precision)
for l in range(1, self.l_max_r + 1):
self.e_pol_l[:, l] = 0.
self.e_tor_l[:, l] = 0.
self.e_pol_axi_l[:, l] = 0.
self.e_tor_axi_l[:, l] = 0.
for m in range(0, l + 1, self.minc):
lm = self.idx[l, m]
if m == 0:
epol = 0.5*self.ell[lm]*(self.ell[lm]+1)*( \
self.ell[lm]*(self.ell[lm]+1)/self.radius**2* \
abs(self.wlm[:,lm])**2+ abs(self.dwlm[:,lm])**2 )
etor = 0.5 * self.ell[lm] * (self.ell[lm] + 1) * abs(
self.zlm[:, lm])**2
self.e_pol_axi_l[:, l] += epol
self.e_tor_axi_l[:, l] += etor
else:
epol = self.ell[lm]*(self.ell[lm]+1)*( \
self.ell[lm]*(self.ell[lm]+1)/self.radius**2* \
abs(self.wlm[:,lm])**2+ abs(self.dwlm[:,lm])**2 )
etor = self.ell[lm] * (self.ell[lm] + 1) * abs(
self.zlm[:, lm])**2
self.e_pol_l[:, l] += epol
self.e_tor_l[:, l] += etor
# Time-averaged energy
facT = 1. / (self.time[-1] - self.time[0])
self.e_pol_lM = facT * np.trapz(self.e_pol_l, self.time, axis=0)
self.e_tor_lM = facT * np.trapz(self.e_tor_l, self.time, axis=0)
self.e_pol_axi_lM = facT * np.trapz(
self.e_pol_axi_l, self.time, axis=0)
self.e_tor_axi_lM = facT * np.trapz(
self.e_tor_axi_l, self.time, axis=0)
def __init__(self,
tag=None,
datadir='.',
ratio_cmb_surface=1,
scale_b=1,
iplot=True,
lCut=None,
precision='Float64',
ave=False,
sv=False,
quiet=False):
"""
A class to read the B_coeff_cmb files
:param tag: if you specify a pattern, it tries to read the corresponding files
:type tag: str
:param ratio_cmb_surface: ratio of surface ratio to CMB radius (default is 1)
:type ratio_cmb_surface: float
:param scale_b: magnetic field unit (default is 1)
:type scale_b: float
:param iplot: a logical to toggle the plot (default is True)
:type iplot: int
:param precision: single or double precision
:type precision: char
:param ave: load a time-averaged CMB file when set to True
:type ave: bool
:param sv: load a dt_b CMB file when set to True
:type sv: bool
:param quiet: verbose when toggled to True (default is True)
:type quiet: bool
:param lCut: reduce the spherical harmonic truncation to l <= lCut
:type lCut: int
:param datadir: working directory
:type datadir: str
"""
pattern = os.path.join(datadir, 'log.*')
logFiles = scanDir(pattern)
if ave:
self.name = 'B_coeff_cmb_ave'
elif sv:
self.name = 'B_coeff_dt_cmb'
else:
self.name = 'B_coeff_cmb'
if tag is not None:
pattern = os.path.join(datadir, '%s.%s' % (self.name, tag))
files = scanDir(pattern)
# Either the log.tag directly exists and the setup is easy to obtain
if os.path.exists(os.path.join(datadir, 'log.%s' % tag)):
MagicSetup.__init__(self,
datadir=datadir,
quiet=True,
nml='log.%s' % tag)
# Or the tag is a bit more complicated and we need to find
# the corresponding log file
else:
pattern = os.path.join(datadir, '%s' % self.name)
mask = re.compile(r'%s\.(.*)' % pattern)
if mask.match(files[-1]):
ending = mask.search(files[-1]).groups(0)[0]
pattern = os.path.join(datadir, 'log.%s' % ending)
if logFiles.__contains__(pattern):
MagicSetup.__init__(self,
datadir=datadir,
quiet=True,
nml='log.%s' % ending)
else:
pattern = os.path.join(datadir, '%s.*' % self.name)
files = scanDir(pattern)
filename = files[-1]
# Determine the setup
mask = re.compile(r'%s\.(.*)' % self.name)
ending = mask.search(files[-1]).groups(0)[0]
if os.path.exists('log.%s' % ending):
try:
MagicSetup.__init__(self,
datadir=datadir,
quiet=True,
nml='log.%s' % ending)
except AttributeError:
pass
self.rcmb = 1. / (1. - self.radratio)
ricb = self.radratio / (1. - self.radratio)
# Read the B_coeff files (by stacking the different tags)
data = []
for k, file in enumerate(files):
if not quiet: print('Reading %s' % file)
f = npfile(file, endian='B')
self.l_max_cmb, self.minc, n_data = f.fort_read('i')
self.m_max_cmb = int((self.l_max_cmb / self.minc) * self.minc)
while 1:
try:
data.append(f.fort_read(precision))
except TypeError:
break
self.lm_max_cmb = self.m_max_cmb*(self.l_max_cmb+1)//self.minc - \
self.m_max_cmb*(self.m_max_cmb-self.minc)//(2*self.minc) + \
self.l_max_cmb-self.m_max_cmb+1
# Get indices location
self.idx = np.zeros((self.l_max_cmb + 1, self.m_max_cmb + 1), 'i')
self.ell = np.zeros(self.lm_max_cmb, 'i')
self.ms = np.zeros(self.lm_max_cmb, 'i')
self.idx[0:self.l_max_cmb + 2, 0] = np.arange(self.l_max_cmb + 1)
self.ell[0:self.l_max_cmb + 2] = np.arange(self.l_max_cmb + 2)
k = self.l_max_cmb + 1
for m in range(self.minc, self.l_max_cmb + 1, self.minc):
for l in range(m, self.l_max_cmb + 1):
self.idx[l, m] = k
self.ell[self.idx[l, m]] = l
self.ms[self.idx[l, m]] = m
k += 1
# Rearange data
data = np.array(data, dtype=precision)
self.nstep = data.shape[0]
self.blm = np.zeros((self.nstep, self.lm_max_cmb), 'Complex64')
self.blm[:, 1:self.l_max_cmb + 1] = data[:, 1:self.l_max_cmb + 1]
self.blm[:, self.l_max_cmb+1:] = data[:, self.l_max_cmb+1::2]+\
1j*data[:, self.l_max_cmb+2::2]
# Truncate!
if lCut is not None:
if lCut < self.l_max_cmb:
self.truncate(lCut)
# Get time
self.time = np.zeros(self.nstep, precision)
self.time = data[:, 0]
# Get Gauss coefficients
self.glm = np.zeros((self.nstep, self.lm_max_cmb), precision)
self.hlm = np.zeros((self.nstep, self.lm_max_cmb), precision)
self.glm, self.hlm = getGauss(self.blm.real, self.blm.imag, self.ell,
self.ms, scale_b, ratio_cmb_surface,
self.rcmb)
# Time-averaged Gauss coefficient
if not ave:
facT = 1. / (self.time[-1] - self.time[0])
self.glmM = facT * np.trapz(self.glm, self.time, axis=0)
self.hlmM = facT * np.trapz(self.hlm, self.time, axis=0)
if len(self.time) > 3:
self.dglmdt = deriv(self.time, self.glm.T, axis=1)
self.dhlmdt = deriv(self.time, self.hlm.T, axis=1)
self.dglmdt = self.dglmdt.T
self.dhlmdt = self.dhlmdt.T
else:
self.dglmdt = np.zeros_like(self.glm)
self.dhlmdt = np.zeros_like(self.hlm)
# Magnetic energy (Lowes)
self.El = np.zeros((self.nstep, self.l_max_cmb + 1), precision)
self.Em = np.zeros((self.nstep, self.m_max_cmb + 1), precision)
self.ESVl = np.zeros((self.nstep, self.l_max_cmb + 1), precision)
E = 0.
for l in range(1, self.l_max_cmb + 1):
self.El[:, l] = 0.
self.ESVl[:, l] = 0.
for m in range(0, l + 1, self.minc):
lm = self.idx[l, m]
self.El[:, l] += (self.ell[lm]+1)*\
(self.glm[:,lm]**2+self.hlm[:,lm]**2)
self.Em[:, m] += (self.ell[lm]+1)*\
(self.glm[:,lm]**2+self.hlm[:,lm]**2)
if not ave:
self.ESVl[:, l] += (self.ell[lm]+1)*\
(self.dglmdt[:, lm]**2+self.dhlmdt[:, lm]**2)
if not ave:
# Time-averaged energy
self.ElM = facT * np.trapz(self.El, self.time, axis=0)
self.EmM = facT * np.trapz(self.Em, self.time, axis=0)
# Secular variation
self.ESVlM = facT * np.trapz(self.ESVl, self.time, axis=0)
self.taul = np.sqrt(self.ElM[1:] / self.ESVlM[1:])
if iplot:
self.plot()
def __init__(self, tag, field='Br', precision=np.float32, avg=False):
"""
:param tag: if you specify a pattern, it tries to read the corresponding
files and stack them.
:type tag: str
:param field: nature of the radial spectra. Possible choices are
'Bt' or 'Bp'
:type field: str
:param precision: single or double precision (default single, i.e.
np.float32)
:type precision: str
:param avg: when set to True, display time averaged quantities
:type avg: bool
"""
logFiles = scanDir('log.*')
if len(logFiles) != 0:
MagicSetup.__init__(self, quiet=True, nml=logFiles[-1])
self.n_r_max = int(self.n_r_max)
self.n_r_ic_max = int(self.n_r_ic_max)
else:
n_r_max = 'n_r_max ?\n'
self.n_r_max = int(input(str1))
n_r_ic_max = 'n_r_ic_max ?\n'
self.n_r_ic_max = int(input(str1))
str1 = 'Aspect ratio ?\n'
self.radratio = float(input(str1))
self.n_r_tot = self.n_r_max + self.n_r_ic_max
self.ricb = self.radratio / (1. - self.radratio)
self.rcmb = 1. / (1. - self.radratio)
outerCoreGrid = chebgrid(self.n_r_max - 1, self.rcmb, self.ricb)
n_r_ic_tot = 2 * self.n_r_ic_max - 1
innerCoreGrid = chebgrid(n_r_ic_tot - 1, self.ricb, -self.ricb)
self.radius = np.zeros((self.n_r_tot - 1), dtype=precision)
self.radius[:self.n_r_max] = outerCoreGrid
self.radius[self.n_r_max - 1:] = innerCoreGrid[:self.n_r_ic_max]
pattern = 'r{}'.format(field) + 'Spec'
files = scanDir('{}.{}'.format(pattern, tag))
# Read the rB[rp]Spec.TAG files (stack them)
data = []
for k, file in enumerate(files):
print('Reading {}'.format(file))
f = npfile(file, endian='B')
while 1:
try:
data.append(f.fort_read(precision))
except TypeError:
break
data = np.array(data, dtype=precision)
# Time (every two lines only)
self.time = data[::2, 0]
# Poloidal/Toroidal energy for all radii for the 6 first spherical harmonic
# degrees
self.e_pol = np.zeros((len(self.time), self.n_r_tot - 1, 6),
dtype=precision)
self.e_pol_axi = np.zeros_like(self.e_pol)
self.e_pol[:, :, 0] = data[::2, 1:self.n_r_tot]
self.e_pol[:, :, 1] = data[::2,
self.n_r_tot - 1:2 * (self.n_r_tot - 1)]
self.e_pol[:, :,
2] = data[::2,
2 * (self.n_r_tot - 1):3 * (self.n_r_tot - 1)]
self.e_pol[:, :,
3] = data[::2,
3 * (self.n_r_tot - 1):4 * (self.n_r_tot - 1)]
self.e_pol[:, :,
4] = data[::2,
4 * (self.n_r_tot - 1):5 * (self.n_r_tot - 1)]
self.e_pol[:, :,
5] = data[::2,
5 * (self.n_r_tot - 1):6 * (self.n_r_tot - 1)]
self.e_pol_axi[:, :, 0] = data[1::2, 1:self.n_r_tot]
self.e_pol_axi[:, :, 1] = data[1::2,
self.n_r_tot - 1:2 * (self.n_r_tot - 1)]
self.e_pol_axi[:, :,
2] = data[1::2,
2 * (self.n_r_tot - 1):3 * (self.n_r_tot - 1)]
self.e_pol_axi[:, :,
3] = data[1::2,
3 * (self.n_r_tot - 1):4 * (self.n_r_tot - 1)]
self.e_pol_axi[:, :,
4] = data[1::2,
4 * (self.n_r_tot - 1):5 * (self.n_r_tot - 1)]
self.e_pol_axi[:, :,
5] = data[1::2,
5 * (self.n_r_tot - 1):6 * (self.n_r_tot - 1)]
if avg:
self.plotAvg()
def __init__(self, tag, ratio_cmb_surface=1, scale_b=1, iplot=True,
precision='Float64', ave=False, sv=False):
"""
A class to read the B_coeff_cmb files
:param tag: if you specify a pattern, it tries to read the corresponding files
:type tag: str
:param ratio_cmb_surface: ratio of surface ratio to CMB radius (default is 1)
:type ratio_cmb_surface: float
:param scale_b: magnetic field unit (default is 1)
:type scale_b: float
:param iplot: a logical to toggle the plot (default is True)
:type iplot: int
:param precision: single or double precision
:type precision: char
:param ave: load a time-averaged CMB file when set to True
:type ave: bool
:param sv: load a dt_b CMB file when set to True
:type sv: bool
"""
logFiles = scanDir('log.*')
if len(logFiles) != 0:
MagicSetup.__init__(self, quiet=True, nml=logFiles[-1])
else:
str1 = 'Aspect ratio ?\n'
self.radratio = float(input(str1))
rcmb = 1./(1.-self.radratio)
ricb = self.radratio/(1.-self.radratio)
if ave:
files = scanDir('B_coeff_cmb_ave.%s' % tag)
elif sv:
files = scanDir('B_coeff_dt_cmb.%s' % tag)
else:
files = scanDir('B_coeff_cmb.%s' % tag)
# Read the B_coeff files (by stacking the different tags)
data = []
for k, file in enumerate(files):
print('Reading %s' % file)
f = npfile(file, endian='B')
self.l_max_cmb, self.minc, n_data = f.fort_read('i')
self.m_max_cmb = int((self.l_max_cmb/self.minc)*self.minc)
while 1:
try:
data.append(f.fort_read(precision))
except TypeError:
break
data = N.array(data, dtype=precision)
self.ell = N.arange(self.l_max_cmb+1)
self.nstep = data.shape[0]
# Get time
self.time = N.zeros(self.nstep, precision)
self.time = data[:, 0]
# Rearange and get Gauss coefficients
self.alm = N.zeros((self.nstep, self.l_max_cmb+1, self.m_max_cmb+1), precision)
self.blm = N.zeros((self.nstep, self.l_max_cmb+1, self.m_max_cmb+1), precision)
self.glm = N.zeros((self.nstep, self.l_max_cmb+1, self.m_max_cmb+1), precision)
self.hlm = N.zeros((self.nstep, self.l_max_cmb+1, self.m_max_cmb+1), precision)
# Axisymmetric coefficients (m=0)
self.alm[:, 1:, 0] = data[:, 1:self.l_max_cmb+1]
self.glm[:, 1:, 0], self.hlm[:, 1:, 0] = getGauss(self.alm[:, 1:, 0],
self.blm[:, 1:, 0], self.ell[1:], 0, scale_b,
ratio_cmb_surface, rcmb)
# Other coefficients (m!=0)
k = self.l_max_cmb+1
for m in range(self.minc, self.l_max_cmb+1, self.minc):
for l in range(m, self.l_max_cmb+1):
self.alm[:, l, m] = data[:, k]
self.blm[:, l, m] = data[:, k+1]
self.glm[:, l, m], self.hlm[:, l, m] = getGauss(self.alm[:, l, m],
self.blm[:, l, m], l, m,
scale_b, ratio_cmb_surface, rcmb)
k += 2
# Time-averaged Gauss coefficient
facT = 1./(self.time[-1]-self.time[0])
self.glmM = facT * N.trapz(self.glm, self.time, axis=0)
self.hlmM = facT * N.trapz(self.hlm, self.time, axis=0)
if len(self.time) > 3:
self.dglmdt = deriv(self.time, self.glm.T, axis=2)
self.dhlmdt = deriv(self.time, self.hlm.T, axis=2)
self.dglmdt = self.dglmdt.T
self.dhlmdt = self.dhlmdt.T
else:
self.dglmdt = N.zeros_like(self.glm)
self.dhlmdt = N.zeros_like(self.hlm)
# Magnetic energy (Lowes)
self.El = (self.ell+1)*(self.glm**2+self.hlm**2).sum(axis=2)
self.Em = N.zeros((self.nstep, self.m_max_cmb+1), precision)
# For m, we need to unfold the loop in case of minc != 1
for m in range(0, self.m_max_cmb+1, self.minc):
self.Em[:,m] = ((self.ell+1)*(self.glm[:, :, m]**2+self.hlm[:, :, m]**2)).sum(axis=1)
#self.Em = ((self.ell+1)*(self.glm**2+self.hlm**2)).sum(axis=1)
# Time-averaged energy
self.ElM = facT * N.trapz(self.El, self.time, axis=0)
self.EmM = facT * N.trapz(self.Em, self.time, axis=0)
# Secular variation
self.ESVl = (self.ell+1)*(self.dglmdt**2+self.dhlmdt**2).sum(axis=2)
self.ESVlM = facT * N.trapz(self.ESVl, self.time, axis=0)
self.taul = N.sqrt(self.ElM[1:]/self.ESVlM[1:])
if iplot:
self.plot()
def __init__(self, tag, ratio_cmb_surface=1, scale_b=1, iplot=True,
field='B', r=1, precision='Float64'):
"""
:param tag: if you specify a pattern, it tries to read the corresponding files
:type tag: str
:param ratio_cmb_surface: ratio of surface ratio to CMB radius (default is 1)
:type ratio_cmb_surface: float
:param scale_b: magnetic field unit (default is 1)
:type scale_b: float
:param iplot: a logical to toggle the plot (default is True)
:type iplot: bool
:param field: 'B', 'V' or 'T' (magnetic field, velocity field or temperature)
:type field: str
:param r: an integer to characterise which file we want to plot
:type r: int
:param precision: single or double precision
:type precision: str
"""
logFiles = scanDir('log.*')
if len(logFiles) != 0:
MagicSetup.__init__(self, quiet=True, nml=logFiles[-1])
else:
str1 = 'Aspect ratio ?\n'
self.radratio = float(input(str1))
rcmb = 1./(1.-self.radratio)
ricb = self.radratio/(1.-self.radratio)
files = scanDir('%s_coeff_r%i.%s' % (field,r,tag))
# Read the B_coeff files (by stacking the different tags)
data = []
for k, file in enumerate(files):
print('Reading %s' % file)
f = npfile(file, endian='B')
out = f.fort_read('3i4,%s' % precision)[0]
self.l_max_cmb, self.minc, n_data = out[0]
self.m_max_cmb = int((self.l_max_cmb/self.minc)*self.minc)
self.radius = out[1]
while 1:
try:
data.append(f.fort_read(precision))
except TypeError:
break
data = N.array(data, dtype=precision)
self.ell = N.arange(self.l_max_cmb+1)
self.nstep = data.shape[0]
# Get time
self.time = N.zeros(self.nstep, dtype=precision)
self.time = data[:, 0]
# Rearange and get Gauss coefficients
self.wlm = N.zeros((self.nstep, self.l_max_cmb+1, self.m_max_cmb+1), 'Complex64')
self.dwlm = N.zeros((self.nstep, self.l_max_cmb+1, self.m_max_cmb+1), 'Complex64')
self.zlm = N.zeros((self.nstep, self.l_max_cmb+1, self.m_max_cmb+1), 'Complex64')
# wlm
# Axisymmetric coefficients (m=0)
self.wlm[:, 1:, 0] = data[:, 1:self.l_max_cmb+1]
# Other coefficients (m!=0)
k = self.l_max_cmb+1
for m in range(self.minc, self.l_max_cmb+1, self.minc):
for l in range(m, self.l_max_cmb+1):
self.wlm[:, l, m] = data[:, k]+1j*data[:, k+1]
k += 2
# dwlm
self.dwlm[:, 1:, 0] = data[:, k:k+self.l_max_cmb]
k += self.l_max_cmb
for m in range(self.minc, self.l_max_cmb+1, self.minc):
for l in range(m, self.l_max_cmb+1):
self.dwlm[:, l, m] = data[:, k]+1j*data[:, k+1]
k += 2
# zlm
self.zlm[:, 1:, 0] = data[:, k:k+self.l_max_cmb]
k += self.l_max_cmb
for m in range(self.minc, self.l_max_cmb+1, self.minc):
for l in range(m, self.l_max_cmb+1):
self.zlm[:, l, m] = data[:, k]+1j*data[:, k+1]
k += 2
# ddw in case B is stored
if field == 'B':
self.ddwlm = N.zeros((self.nstep, self.l_max_cmb+1, self.m_max_cmb+1), 'Complex64')
self.ddwlm[:, 1:, 0] = data[:, k:k+self.l_max_cmb]
k += self.l_max_cmb
for m in range(self.minc, self.l_max_cmb+1, self.minc):
for l in range(m, self.l_max_cmb+1):
self.ddwlm[:, l, m] = data[:, k]+1j*data[:, k+1]
k += 2
#self.epolLM = 0.5*self.ell*(self.ell+1)* (self.ell*(self.ell+1)* \
# abs(self.wlm)**2+abs(self.dwlm)**2)
self.epolAxiL = 0.5*self.ell*(self.ell+1)*(self.ell*(self.ell+1)* \
abs(self.wlm[:,:,0])**2+abs(self.dwlm[:,:,0])**2)
#self.etorLM = 0.5*self.ell*(self.ell+1)*abs(self.zlm)**2
self.etorAxiL = 0.5*self.ell*(self.ell+1)*abs(self.zlm[:,:,0])**2
#epolTot = self.epolLM.sum(axis=1).sum(axis=1)
#etorTot = self.etorLM.sum(axis=1).sum(axis=1)
etorAxiTot = self.etorAxiL.sum(axis=1)
epolAxiTot = self.epolAxiL.sum(axis=1)
if iplot:
P.plot(self.time, epolTot)
P.plot(self.time, etorTot)
P.plot(self.time, epolAxiTot)
P.plot(self.time, etorAxiTot)
"""
def __init__(self, tag, ratio_cmb_surface=1, scale_b=1, iplot=True,
precision='Float64', ave=False, sv=False, quiet=False):
"""
A class to read the B_coeff_cmb files
:param tag: if you specify a pattern, it tries to read the corresponding files
:type tag: str
:param ratio_cmb_surface: ratio of surface ratio to CMB radius (default is 1)
:type ratio_cmb_surface: float
:param scale_b: magnetic field unit (default is 1)
:type scale_b: float
:param iplot: a logical to toggle the plot (default is True)
:type iplot: int
:param precision: single or double precision
:type precision: char
:param ave: load a time-averaged CMB file when set to True
:type ave: bool
:param sv: load a dt_b CMB file when set to True
:type sv: bool
:param quiet: verbose when toggled to True (default is True)
:type quiet: bool
"""
logFiles = scanDir('log.*')
if len(logFiles) != 0:
MagicSetup.__init__(self, quiet=True, nml=logFiles[-1])
else:
str1 = 'Aspect ratio ?\n'
self.radratio = float(input(str1))
self.rcmb = 1./(1.-self.radratio)
ricb = self.radratio/(1.-self.radratio)
if ave:
files = scanDir('B_coeff_cmb_ave.%s' % tag)
elif sv:
files = scanDir('B_coeff_dt_cmb.%s' % tag)
else:
files = scanDir('B_coeff_cmb.%s' % tag)
# Read the B_coeff files (by stacking the different tags)
data = []
for k, file in enumerate(files):
if not quiet: print('Reading %s' % file)
f = npfile(file, endian='B')
self.l_max_cmb, self.minc, n_data = f.fort_read('i')
self.m_max_cmb = int((self.l_max_cmb/self.minc)*self.minc)
while 1:
try:
data.append(f.fort_read(precision))
except TypeError:
break
self.lm_max_cmb = self.m_max_cmb*(self.l_max_cmb+1)//self.minc - \
self.m_max_cmb*(self.m_max_cmb-self.minc)//(2*self.minc) + \
self.l_max_cmb-self.m_max_cmb+1
# Get indices location
self.idx = np.zeros((self.l_max_cmb+1, self.m_max_cmb+1), 'i')
self.ell = np.zeros(self.lm_max_cmb, 'i')
self.ms = np.zeros(self.lm_max_cmb, 'i')
self.idx[0:self.l_max_cmb+2, 0] = np.arange(self.l_max_cmb+1)
self.ell[0:self.l_max_cmb+2] = np.arange(self.l_max_cmb+2)
k = self.l_max_cmb+1
for m in range(self.minc, self.l_max_cmb+1, self.minc):
for l in range(m, self.l_max_cmb+1):
self.idx[l, m] = k
self.ell[self.idx[l,m]] = l
self.ms[self.idx[l,m]] = m
k +=1
# Rearange data
data = np.array(data, dtype=precision)
self.nstep = data.shape[0]
self.blm = np.zeros((self.nstep, self.lm_max_cmb), 'Complex64')
self.blm[:, 1:self.l_max_cmb+1] = data[:, 1:self.l_max_cmb+1]
self.blm[:, self.l_max_cmb+1:] = data[:, self.l_max_cmb+1::2]+\
1j*data[:, self.l_max_cmb+2::2]
# Get time
self.time = np.zeros(self.nstep, precision)
self.time = data[:, 0]
# Get Gauss coefficients
self.glm = np.zeros((self.nstep, self.lm_max_cmb), precision)
self.hlm = np.zeros((self.nstep, self.lm_max_cmb), precision)
self.glm, self.hlm = getGauss(self.blm.real, self.blm.imag,
self.ell, self.ms, scale_b,
ratio_cmb_surface, self.rcmb)
# Time-averaged Gauss coefficient
if not ave:
facT = 1./(self.time[-1]-self.time[0])
self.glmM = facT * np.trapz(self.glm, self.time, axis=0)
self.hlmM = facT * np.trapz(self.hlm, self.time, axis=0)
if len(self.time) > 3:
self.dglmdt = deriv(self.time, self.glm.T, axis=1)
self.dhlmdt = deriv(self.time, self.hlm.T, axis=1)
self.dglmdt = self.dglmdt.T
self.dhlmdt = self.dhlmdt.T
else:
self.dglmdt = np.zeros_like(self.glm)
self.dhlmdt = np.zeros_like(self.hlm)
# Magnetic energy (Lowes)
self.El = np.zeros((self.nstep, self.l_max_cmb+1), precision)
self.Em = np.zeros((self.nstep, self.m_max_cmb+1), precision)
self.ESVl = np.zeros((self.nstep, self.l_max_cmb+1), precision)
E = 0.
for l in range(1, self.l_max_cmb+1):
self.El[:, l] = 0.
self.ESVl[:, l] = 0.
for m in range(0, l+1, self.minc):
lm = self.idx[l, m]
self.El[:, l] += (self.ell[lm]+1)*\
(self.glm[:,lm]**2+self.hlm[:,lm]**2)
self.Em[:, m] += (self.ell[lm]+1)*\
(self.glm[:,lm]**2+self.hlm[:,lm]**2)
if not ave:
self.ESVl[:, l] += (self.ell[lm]+1)*\
(self.dglmdt[:, lm]**2+self.dhlmdt[:, lm]**2)
if not ave:
# Time-averaged energy
self.ElM = facT * np.trapz(self.El, self.time, axis=0)
self.EmM = facT * np.trapz(self.Em, self.time, axis=0)
# Secular variation
self.ESVlM = facT * np.trapz(self.ESVl, self.time, axis=0)
self.taul = np.sqrt(self.ElM[1:]/self.ESVlM[1:])
if iplot:
self.plot()
def __init__(self, tag, ratio_cmb_surface=1, scale_b=1, iplot=True,
field='B', r=1, precision='Float64', lCut=None, quiet=False):
"""
:param tag: if you specify a pattern, it tries to read the corresponding files
:type tag: str
:param ratio_cmb_surface: ratio of surface ratio to CMB radius (default is 1)
:type ratio_cmb_surface: float
:param scale_b: magnetic field unit (default is 1)
:type scale_b: float
:param iplot: a logical to toggle the plot (default is True)
:type iplot: bool
:param field: 'B', 'V' or 'T' (magnetic field, velocity field or temperature)
:type field: str
:param r: an integer to characterise which file we want to plot
:type r: int
:param precision: single or double precision
:type precision: str
:param lCut: reduce the spherical harmonic truncation to l <= lCut
:type lCut: int
:param quiet: verbose when toggled to True (default is True)
:type quiet: bool
"""
logFiles = scanDir('log.*')
if len(logFiles) != 0:
MagicSetup.__init__(self, quiet=True, nml=logFiles[-1])
else:
str1 = 'Aspect ratio ?\n'
self.radratio = float(input(str1))
self.rcmb = 1./(1.-self.radratio)
ricb = self.radratio/(1.-self.radratio)
files = scanDir('%s_coeff_r%i.%s' % (field,r,tag))
# Read the B_coeff files (by stacking the different tags)
data = []
for k, file in enumerate(files):
if not quiet: print('Reading %s' % file)
f = npfile(file, endian='B')
out = f.fort_read('3i4,%s' % precision)[0]
self.l_max_r, self.minc, n_data = out[0]
self.m_max_r = int((self.l_max_r/self.minc)*self.minc)
self.radius = out[1]
while 1:
try:
data.append(f.fort_read(precision))
except TypeError:
break
self.lm_max_r = self.m_max_r*(self.l_max_r+1)//self.minc - \
self.m_max_r*(self.m_max_r-self.minc)//(2*self.minc) + \
self.l_max_r-self.m_max_r+1
# Get indices location
self.idx = np.zeros((self.l_max_r+1, self.m_max_r+1), 'i')
self.ell = np.zeros(self.lm_max_r, 'i')
self.ms = np.zeros(self.lm_max_r, 'i')
self.idx[0:self.l_max_r+2, 0] = np.arange(self.l_max_r+1)
self.ell[0:self.l_max_r+2] = np.arange(self.l_max_r+2)
k = self.l_max_r+1
for m in range(self.minc, self.l_max_r+1, self.minc):
for l in range(m, self.l_max_r+1):
self.idx[l, m] = k
self.ell[self.idx[l,m]] = l
self.ms[self.idx[l,m]] = m
k +=1
# Rearange data
data = np.array(data, dtype=precision)
self.nstep = data.shape[0]
self.wlm = np.zeros((self.nstep, self.lm_max_r), 'Complex64')
self.dwlm = np.zeros((self.nstep, self.lm_max_r), 'Complex64')
self.zlm = np.zeros((self.nstep, self.lm_max_r), 'Complex64')
# Get time
self.time = np.zeros(self.nstep, dtype=precision)
self.time = data[:, 0]
# wlm
self.wlm[:, 1:self.l_max_r+1] = data[:, 1:self.l_max_r+1]
k = self.l_max_r+1
for m in range(self.minc, self.l_max_r+1, self.minc):
for l in range(m, self.l_max_r+1):
self.wlm[:, self.idx[l, m]] = data[:, k]+1j*data[:, k+1]
k += 2
# dwlm
self.dwlm[:, 1:self.l_max_r+1] = data[:, k:k+self.l_max_r]
k += self.l_max_r
for m in range(self.minc, self.l_max_r+1, self.minc):
for l in range(m, self.l_max_r+1):
self.dwlm[:, self.idx[l, m]] = data[:, k]+1j*data[:, k+1]
k += 2
# zlm
self.zlm[:, 1:self.l_max_r+1] = data[:, k:k+self.l_max_r]
k += self.l_max_r
for m in range(self.minc, self.l_max_r+1, self.minc):
for l in range(m, self.l_max_r+1):
self.zlm[:, self.idx[l, m]] = data[:, k]+1j*data[:, k+1]
k += 2
# ddw in case B is stored
if field == 'B':
self.ddwlm = np.zeros((self.nstep, self.lm_max_r), 'Complex64')
self.ddwlm[:, 1:self.l_max_r+1] = data[:, k:k+self.l_max_r]
k += self.l_max_r
for m in range(self.minc, self.l_max_r+1, self.minc):
for l in range(m, self.l_max_r+1):
self.ddwlm[:, self.idx[l, m]] = data[:, k]+1j*data[:, k+1]
k += 2
# Truncate!
if lCut is not None:
if lCut < self.l_max_r:
self.truncate(lCut, field=field)
self.e_pol_axi_l = np.zeros((self.nstep, self.l_max_r+1), precision)
self.e_tor_axi_l = np.zeros((self.nstep, self.l_max_r+1), precision)
self.e_pol_l = np.zeros((self.nstep, self.l_max_r+1), precision)
self.e_tor_l = np.zeros((self.nstep, self.l_max_r+1), precision)
for l in range(1, self.l_max_r+1):
self.e_pol_l[:, l] = 0.
self.e_tor_l[:, l] = 0.
self.e_pol_axi_l[:, l] = 0.
self.e_tor_axi_l[:, l] = 0.
for m in range(0, l+1, self.minc):
lm = self.idx[l, m]
if m == 0:
epol = 0.5*self.ell[lm]*(self.ell[lm]+1)*( \
self.ell[lm]*(self.ell[lm]+1)/self.radius**2* \
abs(self.wlm[:,lm])**2+ abs(self.dwlm[:,lm])**2 )
etor = 0.5*self.ell[lm]*(self.ell[lm]+1)*abs(self.zlm[:, lm])**2
self.e_pol_axi_l[:, l] += epol
self.e_tor_axi_l[:, l] += etor
else:
epol = self.ell[lm]*(self.ell[lm]+1)*( \
self.ell[lm]*(self.ell[lm]+1)/self.radius**2* \
abs(self.wlm[:,lm])**2+ abs(self.dwlm[:,lm])**2 )
etor = self.ell[lm]*(self.ell[lm]+1)*abs(self.zlm[:, lm])**2
self.e_pol_l[:, l] += epol
self.e_tor_l[:, l] += etor
# Time-averaged energy
facT = 1./(self.time[-1]-self.time[0])
self.e_pol_lM = facT * np.trapz(self.e_pol_l, self.time, axis=0)
self.e_tor_lM = facT * np.trapz(self.e_tor_l, self.time, axis=0)
self.e_pol_axi_lM = facT * np.trapz(self.e_pol_axi_l, self.time, axis=0)
self.e_tor_axi_lM = facT * np.trapz(self.e_tor_axi_l, self.time, axis=0)