def complex_eval(expression):
print expression
expression = expression.replace(" ", "")
expression = expression.replace("^", "**")
for bincomplex in re.findall(constants.get("REGEX_BINOM"), expression):
print bincomplex
bincomplex = bincomplex[0]
bincomplex_list = bincomplex.strip("(").strip(")").split(",")
expression = expression.replace(
bincomplex, "({}+{}j)".format(bincomplex_list[0],
bincomplex_list[1]))
for polcomplex in re.findall(constants.get("REGEX_POLAR"), expression):
print polcomplex
polcomplex = polcomplex[0]
polcomplex_list = polcomplex.strip("[").strip("]").split(";")
real = math.cos(float(polcomplex_list[1])) * float(polcomplex_list[0])
imag = math.sin(float(polcomplex_list[1])) * float(polcomplex_list[0])
expression = expression.replace(polcomplex,
"({}+{}j)".format(real, imag))
print expression
return eval(expression)
def getRIR(self, mic, Fs, t0=0.0, t_max=None):
"""
Compute the room impulse response between the source
and the microphone whose position is given as an
argument.
"""
# compute the distance
dist = self.distance(mic)
time = dist / constants.get("c") + t0
alpha = self.damping / (4.0 * np.pi * dist)
# the number of samples needed
if t_max is None:
# we give a little bit of time to the sinc to decay anyway
N = np.ceil((1.05 * time.max() - t0) * Fs)
else:
N = np.ceil((t_max - t0) * Fs)
t = np.arange(N) / float(Fs)
ir = np.zeros(t.shape)
# from utilities import lowPassDirac
import utilities as u
return u.lowPassDirac(time[:, np.newaxis], alpha[:, np.newaxis], Fs, N).sum(axis=0)
def getRIR(self, mic, Fs, t0=0., t_max=None):
'''
Compute the room impulse response between the source
and the microphone whose position is given as an
argument.
'''
# compute the distance
dist = self.distance(mic)
time = dist / constants.get('c') + t0
alpha = self.damping / (4. * np.pi * dist)
# the number of samples needed
if t_max is None:
# we give a little bit of time to the sinc to decay anyway
N = np.ceil((1.05 * time.max() - t0) * Fs)
else:
N = np.ceil((t_max - t0) * Fs)
t = np.arange(N) / float(Fs)
ir = np.zeros(t.shape)
#from utilities import lowPassDirac
import utilities as u
return u.lowPassDirac(time[:, np.newaxis], alpha[:, np.newaxis], Fs,
N).sum(axis=0)
def test_background(elID):
const = constants.get()
planck = const["planck"]
elvolt = const["elvolt"]
nurev, Jnurev = np.loadtxt("test_continuum3.dat", unpack=True)
#Jnurev /= (2.0)
print "Using test background radiation field (table power law -1)"
ediff = 1.0E-10
ekeys = elID.keys()
Jnuadd = np.zeros(
2 * len(ekeys) +
2 * 7) # 7 additional points for secondary heat/ionizations
nuadd = np.zeros(
2 * len(ekeys) +
2 * 7) # 7 additional points for secondary heat/ionizations
for i in range(len(ekeys)):
nup = (elID[ekeys[i]].ip + ediff) * elvolt / planck
num = (elID[ekeys[i]].ip - ediff) * elvolt / planck
Jnuvp = np.interp(nup, nurev, Jnurev)
Jnuvm = np.interp(nup, nurev, Jnurev)
nuadd[2 * i] = nup
nuadd[2 * i + 1] = num
Jnuadd[2 * i] = Jnuvp
Jnuadd[2 * i + 1] = Jnuvm
# Now include the additional points for secondary heat/ionizations
ekeysA = ["H I", "D I", "He I", "He II"]
# ekeysA = ["H I", "He I", "He II"]
extra = 28.0
cntr = 2 * len(ekeys)
for i in range(len(ekeysA)):
nup = (elID[ekeysA[i]].ip + extra + ediff) * elvolt / planck
num = (elID[ekeysA[i]].ip + extra - ediff) * elvolt / planck
Jnuvp = np.interp(nup, nurev, Jnurev)
Jnuvm = np.interp(nup, nurev, Jnurev)
nuadd[2 * i + cntr] = nup
nuadd[2 * i + cntr + 1] = num
Jnuadd[2 * i + cntr] = Jnuvp
Jnuadd[2 * i + cntr + 1] = Jnuvm
ekeysB = ["H I", "D I", "He I"]
# ekeysB = ["H I", "He I"]
extra = 11.0
cntr = 2 * len(ekeys) + 2 * 4
for i in range(len(ekeysB)):
nup = (elID[ekeysB[i]].ip + extra + ediff) * elvolt / planck
num = (elID[ekeysB[i]].ip + extra - ediff) * elvolt / planck
Jnuvp = np.interp(nup, nurev, Jnurev)
Jnuvm = np.interp(nup, nurev, Jnurev)
nuadd[2 * i + cntr] = nup
nuadd[2 * i + cntr + 1] = num
Jnuadd[2 * i + cntr] = Jnuvp
Jnuadd[2 * i + cntr + 1] = Jnuvm
# Append to the original arrays
Jnut = np.append(Jnurev, Jnuadd)
nut = np.append(nurev, nuadd)
argsrt = np.argsort(nut, kind='mergesort')
#plt.plot(np.log10(nu[::-1]),np.log10(Jnu[::-1]),'bo')
#plt.plot(np.log10(nut[argsrt]),np.log10(Jnut[argsrt]),'rx')
#plt.show()
return Jnut[argsrt], nut[argsrt]
def initialize_matrix_for_robot(map_width, map_length, shelf_pos, charging_pos,
unload_pos):
n = int(map_width / constants.get('robot_radius'))
m = int(map_length / constants.get('robot_radius'))
# Create map nxm
map = []
for i in range(n):
map.append([])
for j in range(m):
map[i].append(0)
for i in shelf_pos:
map[i[0]][i[1]] = 1
for i in charging_pos:
map[i[0]][i[1]] = 1
for i in unload_pos:
map[i[0]][i[1]] = 1
return map
def _HM_background_impl(elID, redshift, version, alpha_UV):
if version in {'12', '15'}:
usecols = tuple(range(60))
elif version == '05':
usecols = tuple(range(50))
data = np.loadtxt(
os.path.join(os.path.dirname(__file__),
"data/radfields/HM{:s}_UVB.dat").format(version),
usecols=usecols)
rdshlist = data[0, :]
amin = np.argmin(np.abs(rdshlist - redshift))
waveAt, Jnut = data[1:, 0], data[1:, amin + 1]
waveA = waveAt[1:] * 1.0E-10
#w = np.where(waveAt < 912.0)
Jnu = Jnut[1:]
nu = 299792458.0 / waveA
# Interpolate the Haardt & Madau background around each of the ionization potentials
nut, Jnut, egyt = extra_interp(elID, nu, Jnu)
# Shape parameter (Crighton et al 2015, https://arxiv.org/pdf/1406.4239.pdf)
if alpha_UV != 0:
const = constants.get()
elvolt = const["elvolt"]
logJ = np.log10(Jnut)
e0 = elID["H I"].ip * elvolt
e1 = 10 * elID["H I"].ip * elvolt
rge0 = np.logical_and(e0 <= egyt, egyt <= e1)
rge1 = egyt > e1
idx1 = np.searchsorted(egyt, e1)
logJ[rge0] = logJ[rge0] + alpha_UV * np.log10(egyt[rge0] / e0)
logJ[rge1] = logJ[rge1] + alpha_UV * np.log10(e1 / e0)
Jnut = 10**logJ
#plt.figure()
#plt.plot(np.log10(nut), np.log10(Jnut))
#plt.show()
return Jnut, nut, rdshlist[amin]
def buildRIRMatrix(mics, sources, Lg, Fs, epsilon=5e-3, unit_damping=False):
"""
A function to build the channel matrix for many sources and microphones
mics is a dim-by-M ndarray where each column is the position of a microphone
sources is a list of SoundSource objects
Lg is the length of the beamforming filters
Fs is the sampling frequency
epsilon determines how long the sinc is let to decay. Defaults to epsilon=5e-3
unit_damping determines if the wall damping parameters are used or not. Default to false.
returns the RIR matrix H =
--------------------
| H_{11} H_{12} ...
| ...
|
--------------------
where H_{ij} is channel matrix between microphone i and source j.
H is of type (M*Lg)x((Lg+Lh-1)*S) where Lh is the channel length (determined by epsilon),
and M, S are the number of microphones, sources, respectively.
"""
from beamforming import distance
from utilities import lowPassDirac, convmtx
from scipy.linalg import toeplitz
# set the boundaries of RIR filter for given epsilon
d_min = np.inf
d_max = 0.0
dmp_max = 0.0
for s in xrange(len(sources)):
dist_mat = distance(mics, sources[s].images)
if unit_damping == True:
dmp_max = np.maximum((1.0 / (4 * np.pi * dist_mat)).max(), dmp_max)
else:
dmp_max = np.maximum((sources[s].damping[np.newaxis, :] / (4 * np.pi * dist_mat)).max(), dmp_max)
d_min = np.minimum(dist_mat.min(), d_min)
d_max = np.maximum(dist_mat.max(), d_max)
t_max = d_max / constants.get("c")
t_min = d_min / constants.get("c")
offset = dmp_max / (np.pi * Fs * epsilon)
# RIR length
Lh = int((t_max - t_min + 2 * offset) * float(Fs))
# build the channel matrix
L = Lg + Lh - 1
H = np.zeros((Lg * mics.shape[1], len(sources) * L))
for s in xrange(len(sources)):
for r in np.arange(mics.shape[1]):
dist = sources[s].distance(mics[:, r])
time = dist / constants.get("c") - t_min + offset
if unit_damping == True:
dmp = 1.0 / (4 * np.pi * dist)
else:
dmp = sources[s].damping / (4 * np.pi * dist)
h = lowPassDirac(time[:, np.newaxis], dmp[:, np.newaxis], Fs, Lh).sum(axis=0)
H[r * Lg : (r + 1) * Lg, s * L : (s + 1) * L] = convmtx(h, Lg).T
return H
def extra_interp(elID, nu, Jnu):
"""add finer interpolation to radiation field around ionisation potentials"""
const = constants.get()
planck = const["planck"]
elvolt = const["elvolt"]
ediff = 1.0E-10
ekeys = elID.keys()
Jnurev = Jnu[::-1]
nurev = nu[::-1]
Jnuadd = np.zeros(
2 * len(ekeys) +
2 * 7) # 7 additional points for secondary heat/ionizations
nuadd = np.zeros(
2 * len(ekeys) +
2 * 7) # 7 additional points for secondary heat/ionizations
for i in range(len(ekeys)):
nup = (elID[ekeys[i]].ip + ediff) * elvolt / planck
num = (elID[ekeys[i]].ip - ediff) * elvolt / planck
Jnuvp = np.interp(nup, nurev, Jnurev)
Jnuvm = np.interp(num, nurev, Jnurev)
nuadd[2 * i] = nup
nuadd[2 * i + 1] = num
Jnuadd[2 * i] = Jnuvp
Jnuadd[2 * i + 1] = Jnuvm
# Now include the additional points for secondary heat/ionizations
ekeysA = ["H I", "D I", "He I", "He II"]
# ekeysA = ["H I", "He I", "He II"]
extra = 28.0
cntr = 2 * len(ekeys)
for i in range(len(ekeysA)):
nup = (elID[ekeysA[i]].ip + extra + ediff) * elvolt / planck
num = (elID[ekeysA[i]].ip + extra - ediff) * elvolt / planck
Jnuvp = np.interp(nup, nurev, Jnurev)
Jnuvm = np.interp(num, nurev, Jnurev)
nuadd[2 * i + cntr] = nup
nuadd[2 * i + cntr + 1] = num
Jnuadd[2 * i + cntr] = Jnuvp
Jnuadd[2 * i + cntr + 1] = Jnuvm
ekeysB = ["H I", "D I", "He I"]
# ekeysB = ["H I", "He I"]
extra = 11.0
cntr = 2 * len(ekeys) + 2 * 4
for i in range(len(ekeysB)):
nup = (elID[ekeysB[i]].ip + extra + ediff) * elvolt / planck
num = (elID[ekeysB[i]].ip + extra - ediff) * elvolt / planck
Jnuvp = np.interp(nup, nurev, Jnurev)
Jnuvm = np.interp(num, nurev, Jnurev)
nuadd[2 * i + cntr] = nup
nuadd[2 * i + cntr + 1] = num
Jnuadd[2 * i + cntr] = Jnuvp
Jnuadd[2 * i + cntr + 1] = Jnuvm
# Append to the original arrays
Jnut = np.append(Jnurev, Jnuadd)
nut = np.append(nurev, nuadd)
argsrt = np.argsort(nut, kind='mergesort')
Jnut = Jnut[argsrt]
nut = nut[argsrt]
egyt = nut * planck # energies
return nut, Jnut, egyt
def buildRIRMatrix(mics, sources, Lg, Fs, epsilon=5e-3, unit_damping=False):
'''
A function to build the channel matrix for many sources and microphones
mics is a dim-by-M ndarray where each column is the position of a microphone
sources is a list of SoundSource objects
Lg is the length of the beamforming filters
Fs is the sampling frequency
epsilon determines how long the sinc is let to decay. Defaults to epsilon=5e-3
unit_damping determines if the wall damping parameters are used or not. Default to false.
returns the RIR matrix H =
--------------------
| H_{11} H_{12} ...
| ...
|
--------------------
where H_{ij} is channel matrix between microphone i and source j.
H is of type (M*Lg)x((Lg+Lh-1)*S) where Lh is the channel length (determined by epsilon),
and M, S are the number of microphones, sources, respectively.
'''
from beamforming import distance
from utilities import lowPassDirac, convmtx
from scipy.linalg import toeplitz
# set the boundaries of RIR filter for given epsilon
d_min = np.inf
d_max = 0.
dmp_max = 0.
for s in xrange(len(sources)):
dist_mat = distance(mics, sources[s].images)
if unit_damping == True:
dmp_max = np.maximum((1. / (4 * np.pi * dist_mat)).max(), dmp_max)
else:
dmp_max = np.maximum((sources[s].damping[np.newaxis, :] /
(4 * np.pi * dist_mat)).max(), dmp_max)
d_min = np.minimum(dist_mat.min(), d_min)
d_max = np.maximum(dist_mat.max(), d_max)
t_max = d_max / constants.get('c')
t_min = d_min / constants.get('c')
offset = dmp_max / (np.pi * Fs * epsilon)
# RIR length
Lh = int((t_max - t_min + 2 * offset) * float(Fs))
# build the channel matrix
L = Lg + Lh - 1
H = np.zeros((Lg * mics.shape[1], len(sources) * L))
for s in xrange(len(sources)):
for r in np.arange(mics.shape[1]):
dist = sources[s].distance(mics[:, r])
time = dist / constants.get('c') - t_min + offset
if unit_damping == True:
dmp = 1. / (4 * np.pi * dist)
else:
dmp = sources[s].damping / (4 * np.pi * dist)
h = lowPassDirac(time[:, np.newaxis], dmp[:, np.newaxis], Fs,
Lh).sum(axis=0)
H[r * Lg:(r + 1) * Lg, s * L:(s + 1) * L] = convmtx(h, Lg).T
return H
def is_binom(expression):
match = re.match(constants.get("REGEX_BINOM"), expression)
if match:
return len(match.group(0))
return 0
def is_polar(expression):
match = re.match(constants.get("REGEX_POLAR"), expression)
if match:
return len(match.group(0))
return 0
def run_grid(opt, cosmopar, ions, dryrun=False):
# Get arrays defining the grid of models to run
gridparams = init_grid(opt)
virialm = gridparams['virialm' ]
redshift = gridparams['redshift' ]
baryscale = gridparams['baryscale']
nummvir, numreds, numbary = map(len, [virialm, redshift, baryscale])
prev_fname, (smvir, sbary, sreds) = init_resume(opt, [nummvir, numreds, numbary])
# build list of parameters for each model to run
models = []
for i in range(sreds, numreds):
for j in range(sbary, numbary):
for k in range(smvir, nummvir):
models.append((i, j, k))
models.reverse()
# Load baryon fraction as a function of halo mass
halomass, barymass = np.loadtxt('data/baryfrac.dat', unpack=True)
baryfracvals = 10.0**barymass / 10.0**halomass
baryfrac = np.interp(virialm, halomass, baryfracvals)
# Get some constants needed to define the halo model
const = constants.get()
hztos = const['hztos' ]
Gcons = const['Gcons' ]
somtog = const['somtog']
while models:
j, k, l = models.pop()
logger.log('info', "###########################")
logger.log('info', "###########################")
logger.log('info', " virialm 10**{2:.3f} ({0:d}/{1:d})".format(l+1,nummvir, virialm[l]))
logger.log('info', " redshift {2:.2f} ({0:d}/{1:d})".format(k+1,numreds, redshift[k]))
logger.log('info', " baryon scale {2:.2f} ({0:d}/{1:d})".format(j+1,numbary, baryscale[j]))
logger.log('info', "###########################")
logger.log('info', "###########################")
if opt['geometry']['concrel'] == "Prada":
concentration = cosmo.massconc_Prada12(10**virialm[l], cosmopar, redshift[k])
elif opt['geometry']['concrel'] == "Ludlow":
concentration = cosmo.massconc_Ludlow16(10**virialm[l], cosmopar, redshift[k])
elif opt['geometry']['concrel'] == "Bose":
concentration = cosmo.massconc_Bose16(10**virialm[l], cosmopar, redshift[k])
else:
raise ValueError("Unknown concentration relation")
hubpar = cosmo.hubblepar(redshift[k], cosmopar)
rhocrit = 3.0*(hubpar*hztos)**2/(8.0*np.pi*Gcons)
hmodel = halomodel.make_halo(opt['geometry']['profile'],
10**virialm[l] * somtog,
baryfrac[l] * baryscale[j],
rhocrit,
concentration)
#acore=opt['geometry']['acore'])
# Let's go!
if not dryrun:
ok, res = gethalo.get_halo(hmodel, redshift[k], cosmopar, ions, prevfile=prev_fname, options=opt)
if ok == True:
# model complete
prev_fname = res
else:
# something went wrong with the model
logger.log('error', res)
# move onto next grid
# (keep popping elements till mass counter wraps back to 0)
while models and (models[-1][3] > l):
models.pop()
# once a run over increasing halo masses is complete, clear the previous filename
# if doing subsequent runs varying other parameters, don't want to load this run's output!
if l == nummvir - 1:
prev_fname = None
return