def bisector(rv, ccf, low_high_cut = 0.1, figure_title = '', doplot = False, ccf_plot_file = '', showplot=False):
# use the props from the CCF determination code
# Could be per-order or with the mean
#rv = props['RV_CCF']
#ccf = props['MEAN_CCF']
# get minima
imin = int(np.argmin(ccf))
#print(imin,type(imin))
# get point where the derivative changes sign at the edge of the line
# the bisector is ambiguous passed this poind
width_blue = imin - np.max(np.where(np.gradient(ccf[:imin])>0))
#print(width_blue)
width_red = np.min(np.where(np.gradient(ccf[imin:])<0))
#print(width_red)
# get the width from the side of the center that reaches
# that point first
width = int(np.min([width_blue, width_red]))
# set depth to zero
ccf -= np.min(ccf)
# set continuum to one
ccf /= np.min( ccf[ [imin - width,imin + width] ])
# interpolate each side of the ccf slope at a range of depths
depth = np.arange(low_high_cut,1-low_high_cut,0.001)
# blue and red side of line
g1 = (ccf[imin:imin - width:-1]>low_high_cut) & (ccf[imin:imin - width:-1]<(1-low_high_cut))
spline1 = ius(ccf[imin:imin - width:-1][g1],rv[imin:imin - width:-1 ][g1], k=2)
g2 = (ccf[imin : imin + width]>low_high_cut) & (ccf[imin : imin + width]<(1-low_high_cut))
spline2 = ius(ccf[imin : imin + width][g2],rv[imin : imin + width][g2], k=2)
# get midpoint
bisector_position = (spline2(depth)+spline1(depth))/2
# get bisector widht
width_ccf = (spline2(depth)-spline1(depth))
if doplot:
# some nice plots
plt.plot(rv[imin - width : imin+ width],ccf[imin - width : imin+ width],label = 'ccf')
plt.plot(bisector_position,depth,label = 'bisector')
plt.plot((bisector_position-np.mean(bisector_position))*100+np.mean(bisector_position),depth, label = 'bisector * 100',
)
plt.legend()
plt.title(figure_title)
plt.xlabel('Velocity (km/s)')
plt.ylabel('Depth')
if ccf_plot_file !='':
plt.savefig(ccf_plot_file)
if showplot :
plt.show()
# define depth in the same way as Perryman, 0 is top, 1 is bottom
return 1-depth, bisector_position, width_ccf
def set_cosmology(self, cosmo):
self.cosmo_now = cosmo
params = cosmo.get_cosmology()
self.sM.set_cosmology(cosmo)
self.sigs0 = self.sM.get_sigma_in_default_binning()
Om = 1 - params[0, 2]
dlnsdlnm = np.gradient(np.log(self.sigs0)) / \
np.gradient(np.log(self.Mlist))
self.f_to_num = -(Om * hmf_gp.rho_cr) * dlnsdlnm / self.Mlist**2
flag, params_out = cosmo_util.test_cosm_range(params,
return_edges=True)
if flag:
self.coeff_table = (np.dot(
self.gps.predict(np.atleast_2d(params_out)), self.eigdata) +
self.ymean).reshape(21, 2)
else:
self.coeff_table = (
np.dot(self.gps.predict(np.atleast_2d(params)), self.eigdata) +
self.ymean).reshape(21, 2)
self.coeff_table[:, 0] /= 10
self.coeff_Anorm_spl = ius(-self.redshift_list, self.coeff_table[:, 0])
self.coeff_a_spl = ius(-self.redshift_list, self.coeff_table[:, 1])
def Mass_nu(emu, nu, z):
# TODO: This does both interpolation and evaluation, could split up?
# Import
from scipy.interpolate import InterpolatedUnivariateSpline as ius
# Options
log_interp = log_interp_sigma # Should sigma(M) be interpolated logarithmically?
# Get internal M vs sigma arrays
Ms_internal = emu.massfunc.Mlist
sig0s_internal = emu.massfunc.sigs0
sigs_internal = sig0s_internal * emu.Dgrowth_from_z(z)
nus_internal = dc / sigs_internal
# Make an interpolator for sigma(M)
if log_interp:
mass_interpolator = ius(nus_internal, np.log(Ms_internal))
else:
mass_interpolator = ius(nus_internal, Ms_internal)
# Get sigma(M) from the interpolator at the desired masses
if log_interp:
Mass = np.exp(mass_interpolator(nu))
else:
Mass = mass_interpolator(nu)
return Mass
def get_p_gg(self, k, redshift):
"""get_p_gg
Compute galaxy power spectrum :math:`\\P_\mathrm{gg}(k)`.
Args:
k (numpy array): 3 dimensional separation in :math:`h^{-1}\mathrm{Mpc}`
redshift (float): redshift at which the galaxies are located
Returns:
numpy array: galaxy power spectrum
"""
from scipy.interpolate import InterpolatedUnivariateSpline as ius
self._check_update_redshift(redshift)
self._compute_p_1hcs(redshift)
self._compute_p_1hss(redshift)
self._compute_p_2hcc(redshift)
self._compute_p_2hcs(redshift)
self._compute_p_2hss(redshift)
p_tot_1h = 2. * self.p_1hcs + self.p_1hss
p_tot_2h = self.p_2hcc + 2. * self.p_2hcs + self.p_2hss
p_gg = ius(self.fftlog_1h.k, p_tot_1h)(k) + ius(self.fftlog_2h.k,
p_tot_2h)(k)
return p_gg
def get_ilpk(zz, mode='real'):
'''return log-interpolated pk at redshift z(aa) for Hankel transform'''
aa = 1 / (zz + 1)
al, b1, b2 = alphad['%0.1f' % zz], b1lstd['%0.1f' % zz], b2lstd['%0.1f' %
zz],
pkd = np.loadtxt(dpath + "HI_bias_{:06.4f}.txt".format(aa))[1:, :]
bb = pkd[1:6, 2].mean()
print(1 + b1, bb, (1 + b1) / bb)
if mode == 'real':
kk, pk = pkd[:, 0], pkd[:, 2]**2 * pkd[:, 3]
elif mode == 'red':
pks = np.loadtxt(dpath + "HI_pks_1d_{:06.4f}.txt".format(aa))[1:, :]
kk, pk = pks[:, 0], pks[:, 1]
bbs = (pk / ius(pkd[:, 0], pkd[:, 3])(kk))**0.5
bb = bbs[1:6].mean()
#pk *= bb**2/bbs**2
#on large scales, extend with b^2*P_lin
klin, plin = np.loadtxt('../../data/pklin_{:06.4f}.txt'.format(aa),
unpack=True)
plin *= bb**2
ipklin = ius(klin, plin)
kt = np.concatenate((klin[(klin < kk[0])], kk))
pt = np.concatenate((plin[(klin < kk[0])] * pk[0] / ipklin(kk[0]), pk))
#On small scales, truncate at k=1
pt = pt[kt < 1]
kt = kt[kt < 1]
ilpk = loginterp(kt, pt)
return ilpk
def convolve(self, k, x, y):
#FIXME most naive implementation, could do better with just about anything else for the integral
#Do the N^2 operation, assume (probably bad) that not zero padding is fine
#It would be better (for all N>100) to do the fft convolution
A, B = ius(k, x, ext=1), ius(k, y, ext=1)
N = len(k)
res = np.zeros(N)
for i in range(N):
res[i] = np.trapz(k**2 * A(abs(k - k[i])) * B(k),
x=k) / (2 * np.pi**2)
return res
def __init__(self, parameters):
NSProblem.__init__(self, parameters=parameters)
self.prm['viscosity'] = 1. / self.prm['Re']
self.L = 30.
f = open('../data/Re395/statistics_streamwise.txt')
a = loadtxt(f, comments='%')
sz = a.shape
a2 = zeros((sz[0] + 2, 2))
a2[1:-1, :] = a[:, :2]
a2[1:-1, 0] += 10. # Bump starts at x = 10
a2[-1, 0] = self.L
self.bump = ius(a2[:, 0], a2[:, 1]) # Create bump-spline
# Use Expressions for 'complicated' profiles
self.inlet_velocity = {
'u': U0(self.bump),
'p': ('0'),
'up': UP0(self.bump)
}
# Otherwise just initialize to zero
self.zero_velocity = Initdict(u=("0.0", "0.0"), p="0.0")
self.mesh, self.boundaries = self.create_mesh_and_boundaries()
self.prm['dt'] = self.timestep()
transient = self.prm['time_integration'] == 'Transient'
self.q0 = self.zero_velocity if transient else self.inlet_velocity
def get_p_gm(self, k, redshift):
"""get_p_gm
Compute galaxy matter power spectrum P_gm.
Args:
k (numpy array): 2 dimensional projected separation in :math:`h^{-1}\mathrm{Mpc}`
redshift (float): redshift at which the lens galaxies are located
Returns:
numpy array: excess surface density in :math:`h M_\odot \mathrm{pc}^{-2}`
"""
from scipy.interpolate import InterpolatedUnivariateSpline as ius
self._check_update_redshift(redshift)
self._compute_p_cen(redshift)
self._compute_p_cen_off(redshift)
self._compute_p_sat(redshift)
p_tot = self.p_cen + self.p_cen_off + self.p_sat
p_gm = ius(self.fftlog_1h.k, p_tot)(k)
return p_gm
### ###
def ilogcdfnfw(c=7):
'''Inverse cdf of nfw in log-scale
'''
rr = np.logspace(-4, 0, 1000)
cdf = cumnfw(rr, c=c)
lrr, lcdf = np.log(rr), np.log(cdf)
return ius(lcdf, lrr)
def derivativesFromSpline(df, objName='obj', xName='rs_au', tName='time'):
"""Calculate first and second derivatives of a colum in a DataFrame
via splines.
Parameters:
-----------
df ... input dataframe
objName ... name of individual object/trajectory in dataframe
xName ... column name for dependent variable in spline interpolation
tname ... column name for independent variable (such as time).
Returns:
--------
x,xdot,xddot ... numpy array, returns x,dx/dt,d^2x/dt^2 for all values of t in df
"""
i = 0
objlist = df[objName].unique()
xdot = []
xddot = []
x = []
for o in objlist:
dat = df[df[objName] == o]
s = ius(dat[tName].values, dat[xName].values)
for index, row in dat.iterrows():
x.append(s.derivatives(row[tName])[0])
xdot.append(s.derivatives(row[tName])[1])
xddot.append(s.derivatives(row[tName])[2])
return np.array(x), np.array(xdot), np.array(xddot)
def get(self, rs, redshift, logdens):
xic = np.array([
rbs(-self.logdens_list, -self.redshift_list,
self.pred_table[:, :, i])(-logdens, -redshift)
for i in range(66)
])
return np.exp(ius(self.logrscale, xic, ext=3)(np.log(rs)))
def tospline(problem):
"""Store the results in a dictionary of splines and save to file.
The channel solution is very often used to initialize other problems
that have a VelocityInlet."""
NS_solver = problem.pdesystems['Navier-Stokes']
Turb_solver = problem.pdesystems['Turbulence model']
f = open(
'../data/channel_' + problem.prm['turbulence_model'] + '_' +
str(problem.prm['Re_tau']) + '.ius', 'w')
N = 1000
xy = zeros(N)
xy = zeros((N, 2))
xy[:, 1] = linspace(0., 1., 1000) # y-direction
spl = {}
for s in (NS_solver, Turb_solver):
for name in s.names:
vals = zeros((N, s.V[name].num_sub_spaces() or 1))
for i in range(N):
getattr(s, name + '_').eval(vals[i, :], xy[i, :])
spl[name] = []
# Create spline for all components
# Scalar has zero subspaces, hence the or
for i in range(s.V[name].num_sub_spaces() or 1):
spl[name].append(ius(xy[:, 1], vals[:, i]))
cPickle.dump(spl, f)
f.close()
return spl
def set_cosmology(self, cosmo):
cparams = cosmo.get_cosmology()
self.ns = cparams[0, 4]
As = np.exp(cparams[0, 3]) / 1e10
k0 = 0.05
h = np.sqrt(
(cparams[0, 0] + cparams[0, 1] + 0.00064) / (1 - cparams[0, 2]))
gp_rec = np.array([
self.gps[i].predict(self.ydata[:800, i],
np.atleast_2d(cparams[0, :3]),
return_cov=False)[0] for i in range(20)
])
tk_rec = np.exp(
np.dot(gp_rec * self.ystd + self.yavg, self.eigdata) + self.ymean)
if not np.isclose(cparams[0, 5], -1):
growth_wcdm = _linearGrowth(
cparams[0, 2], cparams[0, 5], 0.) / _linearGrowth(
cparams[0, 2], cparams[0, 5], 1000.)
growth_lcdm = _linearGrowth(cparams[0, 2], -1.,
0.) / _linearGrowth(
cparams[0, 2], -1., 1000.)
tk_rec *= growth_wcdm / growth_lcdm
pzeta = (2.*np.pi**2)/self.klist**3 * As * \
(self.klist/(k0/h))**(self.ns-1.)
self.pred_table = np.log(pzeta * tk_rec**2)
self.pklin_spl = ius(self.logklist, self.pred_table, ext=3)
def create_boundaries(self):
# Define the spline for the heart beat
self.inflow_t_spline = ius(MCAtime, MCAval)
# Preassemble normal vector on inlet
n = self.n = FacetNormal(self.mesh)
self.normal = [
assemble(-n[i] * ds(4), mesh=self.mesh) for i in range(3)
]
# Area of inlet
self.A0 = assemble(Constant(1.) * ds(4), mesh=self.mesh)
# Create dictionary used for Dirichlet inlet conditions. Values are assigned in prepare
self.inflow = {
'u': Constant((0, 0, 0)),
'u0': Constant(0),
'u1': Constant(0),
'u2': Constant(0)
}
# Pressures on outlets are specified by DirichletBCs
self.p_out1 = Constant(0)
# Specify the boundary subdomains and hook up dictionaries for DirichletBCs
walls = MeshSubDomain(3, 'Wall')
inlet = MeshSubDomain(4, 'VelocityInlet', self.inflow)
pressure1 = MeshSubDomain(5, 'ConstantPressure', {'p': self.p_out1})
return [walls, inlet, pressure1]
def mass_avg(emu, Mmin, Mmax, z, pow=1):
'''
Calculate the average halo mass between two limits, weighted by the halo mass function [Msun/h]
'''
from scipy.interpolate import InterpolatedUnivariateSpline as ius
from scipy.integrate import quad
# Parameters
epsabs = 1e-5 # Integration accuracy
# Construct an interpolator for n(M) (or dn/dM) from the emulator internals
Ms = emu.massfunc.Mlist
dndM = emu.massfunc.get_dndM(z)
log_dndM_interp = ius(np.log(Ms), np.log(dndM), ext='extrapolate')
# Number density of haloes in the mass range
n = ndenshalo(emu, Mmin, Mmax, z)
# Integrate to get the average mass
Mav, _ = quad(lambda M: (M**pow) * np.exp(log_dndM_interp(np.log(M))),
Mmin,
Mmax,
epsabs=epsabs)
return Mav / n
def wp(self,
rp,
pi=np.linspace(0, 100, 100 + 1),
rMin=0.1,
wantGrad=False):
"FIXME: Lazy copy from AutoCorrelator - should make this function accessible by both. - general correlator class..."
"""Projected correlation function.
Parameters:
-----------
rp: array (float)
2D abscissa values for wp
pi_bins: array (float)
Array must be of size that divides evenly into r - projection window, tophat for now
wantGrad : boolean,optional
Whether to return the function value, or the tuple (val,grad).
Returns:
----------
(array (float), array (float), [array (float)])
projected radius rp, projected correlation function wp, gradient if wanted
"""
if (wantGrad):
wp_grad = np.zeros((len(rp), len(self.params)))
xir, grad_xir = self.Xi(wantGrad=wantGrad)
else:
xir = self.Xi(wantGrad=wantGrad)
dpi = pi[1] - pi[0] #bin width
wp = np.zeros(len(rp))
for i in range(len(wp)):
rev = np.sqrt(rp[i]**2 + pi**2)
wp[i] = 2 * ius(rev,
xir(rev) / np.sqrt(1 -
(rp[i] /
(rev + rMin))**2)).integral(
rev.min(), rev.max())
if (wantGrad):
wp_grad[i] = 2 * ius(
rev,
grad_xir(rev) / np.sqrt(1 - (rp[i] /
(rev + rMin))**2)).integral(
rev.min(), rev.max())
if (wantGrad):
return wp, wp_grad
else:
return wp
def get_nhalo_tinker(self, Mmin, Mmax, vol, redshift):
if Mmax > 1e16:
Mmax = 1e16
dndM = self.get_dndM_tinker(redshift)
dndM_interp = ius(np.log(self.Mlist), np.log(dndM))
return vol * integrate.quad(
lambda t: np.exp(dndM_interp(np.log(t))), Mmin, Mmax,
epsabs=1e-5)[0]
def _make_psfs(self):
""" Produces PSF arrays"""
self.psf2D = self.models[self.psffunct](self.rr, *self.psf) * \
self.dr * self.dr
self.psf2D /= np.sum(self.psf2D)
self.psf1D = ius(self.rr.diagonal()[self.rsize:],
self.psf2D.diagonal()[self.rsize:])
self.fft_psf2D = np.fft.fft2(self.psf2D)
return
def set_cosmology(self, cosmo):
self.cosmo_now = cosmo
cpara = cosmo.get_cosmology()
self.coeff_rec = np.dot(self.gps.predict(np.atleast_2d(cpara)),
self.eigdata).reshape(21, 13, 3)
self.coeff_rec_dm = np.dot(self.gps_dm.predict(np.atleast_2d(cpara)),
self.eigdata_dm).reshape(21, 2)
self.sd0 = self.sd.get(cosmo)
# self.D0s = np.array([cosmo.Dgrowth_from_z(z) for z in self.redshift_list])
self.coeff1_spline = rbs(-self.redshift_list, -self.logdens_list,
self.coeff_rec[:, :, 0])
self.coeff2_spline = rbs(-self.redshift_list, -self.logdens_list,
self.coeff_rec[:, :, 1])
self.coeff3_spline = rbs(-self.redshift_list, -self.logdens_list,
self.coeff_rec[:, :, 2])
self.coeff2_spline_dm = ius(-self.redshift_list, self.coeff_rec_dm[:,
0])
self.coeff3_spline_dm = ius(-self.redshift_list, self.coeff_rec_dm[:,
1])
def dens_to_mass(self, dens, redshift, nint=20, integration="quad"):
mlist = np.linspace(12., 15.95, nint)
dlist = np.log(
np.array([
self.mass_to_dens(10**mlist[i],
redshift,
integration=integration) for i in range(nint)
]))
d_to_m_interp = ius(-dlist, mlist)
return 10**d_to_m_interp(-np.log(dens))
def make_omHI_plot(fname, fsize=12):
"""Does the work of making the distribution figure."""
zlist = [2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0]
clist = ['b', 'c', 'g', 'm', 'r']
# Now make the figure.
fig, axis = plt.subplots(figsize=(6, 5))
# Read in the data and convert to "normal" OmegaHI convention.
dd = np.loadtxt("../../data/omega_HI_obs.txt")
#Ez = np.sqrt( 0.3*(1+dd[:,0])**3+0.7 )
#axis.errorbar(dd[:,0],1e-3*dd[:,1]/Ez**2,yerr=1e-3*dd[:,2]/Ez**2,\
# fmt='s',mfc='None')
axis.errorbar(dd[:, 0],
1e-3 * dd[:, 1],
yerr=1e-3 * dd[:, 2],
fmt='s',
mfc='None',
color='m')
# Plot the fit line.
zz = np.linspace(0, 7, 100)
Ez = np.sqrt(0.3 * (1 + zz)**3 + 0.7)
axis.plot(zz, 4e-4 * (1 + zz)**0.6, 'k-')
#for im, model in enumerate(['ModelA', 'ModelB', 'ModelC']):
for im, model in enumerate(models):
dpath = '../../data/outputs/%s/%s/' % (suff, model)
omz = []
for iz, zz in enumerate(zlist):
# Read the data from file.
aa = 1.0 / (1.0 + zz)
omHI = np.loadtxt(dpath + "HI_dist_{:06.4f}.txt".format(aa)).T
omHI = (omHI[1] * omHI[2]).sum() / bs**3 / 27.754e10
omHI *= (1 + zz)**3
if iz == 0: axis.plot(zz, omHI, 'C%do' % im, label=model)
else: axis.plot(zz, omHI, 'C%do' % im)
omz.append(omHI)
ss = ius(zlist, omz)
axis.plot(np.linspace(2, 6, 100), ss(np.linspace(2, 6, 100)),
'C%d' % im)
axis.set_yscale('log')
axis.legend(fontsize=fsize)
for tick in axis.xaxis.get_major_ticks():
tick.label.set_fontsize(fsize)
for tick in axis.yaxis.get_major_ticks():
tick.label.set_fontsize(fsize)
# Put on some more labels.
axis.set_xlabel(r'$z$')
axis.set_ylabel(r'$\Omega_{HI}$')
# and finish up.
plt.tight_layout()
plt.savefig(fname)
def loginterp(x, y, yint = None, side = "both", lorder = 15, rorder = 15, lp = 1, rp = -1, \
ldx = 1e-6, rdx = 1e-6, k=5):
if yint is None:
yint = ius(x, y, k=k)
if side == "both":
side = "lr"
l = lp
r = rp
lneff, rneff = 0, 0
niter = 0
while (lneff <= 0) or (lneff > 1):
lneff = derivative(yint, x[l], dx=x[l] * ldx,
order=lorder) * x[l] / y[l]
l += 1
if niter > 100: continue
print('Left slope = %0.3f at point ' % lneff, l)
niter = 0
while (rneff < -3) or (rneff > -2):
rneff = derivative(yint, x[r], dx=x[r] * rdx,
order=rorder) * x[r] / y[r]
r -= 1
if niter > 100: continue
print('Rigth slope = %0.3f at point ' % rneff, r)
xl = np.logspace(-18, np.log10(x[l]), 10**6.)
xr = np.logspace(np.log10(x[r]), 10., 10**6.)
yl = y[l] * (xl / x[l])**lneff
yr = y[r] * (xr / x[r])**rneff
xint = x[l + 1:r].copy()
yint = y[l + 1:r].copy()
if side.find("l") > -1:
xint = np.concatenate((xl, xint))
yint = np.concatenate((yl, yint))
if side.find("r") > -1:
xint = np.concatenate((xint, xr))
yint = np.concatenate((yint, yr))
yint2 = ius(xint, yint, k=k)
return yint2
def wave2wave(e2ds_data_input, wave1, wave2):
# transform e2ds data from one wavelength grid to another.
e2ds_data = np.array(e2ds_data_input)
for iord in range(49):
keep = np.isfinite(e2ds_data[iord])
spl = ius(wave1[iord][keep],e2ds_data[iord][keep],k=3, ext=1)
e2ds_data[iord][keep] = spl(wave2[iord][keep])
return e2ds_data
def fast_med(vect,w):
#
# inputs : vect -> vector to be median-filtered
# w -> width of the filtering. Needs to be much shorter than
# the length of vect
#
# performs a fast median filter for a box width that is too long to
# be used with the normal numpy median filter.
# The code performs a 'true' median with numpy, but does not
# shift by 1 pixel as would be done with a true boxcar.
# It moves by 1/2 of the width and splines in between.
#
# index of points along vector
index = np.arange(len(vect),dtype=float)
# index only on non-nans, all others are set to nans.
# this ensures that the splining is properly centered if there is a
# big bunch of NaNs on a width close to w
index[np.isfinite(vect)==False]=np.nan
# reshaping the vector in a form so that we can use the axis
# keyword to do the job of a running boxcar. For a 1000-long vect and w=10
# this would be equivalent to reformating into a 100x10 array and then taking
# the median with axis=1.
# This is the same a taking a running median with a size of 10, and getting
# the value every 10 pixels
y1 = np.nanmedian(np.reshape( vect[0:w*(len(vect)//w)],[len(vect)//w,w]),axis=1)
# same but with an offset of 0.5*w
y2 = np.nanmedian(np.reshape( (np.roll(vect,w//2))[0:w*(len(vect)//w)],[len(vect)//w,w]),axis=1)
# same median as for the vect value, but for the position along vect
x1 = np.nanmedian(np.reshape( index[0:w*(len(vect)//w)],[len(vect)//w,w]),axis=1)
x2 = np.nanmedian(np.reshape( (np.roll(index,w//2))[0:w*(len(vect)//w)],[len(vect)//w,w]),axis=1)
# append the median-binned at each w*integer with w*(integer+0.5)
x=np.append(x1,x2)
y=np.append(y1,y2)
ind = np.argsort(x)
# get the binned vectors in order of index
x=x[ind]
y=y[ind]
# remove NaNs. There may be stretches of vect larger than w that are full
# of NaNs. We also use np.roll to remove points of identical x
keep = np.isfinite(x) & (x != np.roll(x,1)) & np.isfinite(y)
x=x[keep]
y=y[keep]
# spline the whole thing onto the vect grid
spline = ius(x,y,k=2,ext=1)
med_filt_vect = spline( np.arange(len(vect),dtype=float))
med_filt_vect[np.isfinite(vect) == False]=np.nan
#output median-filtered vector
return med_filt_vect
def _set_cosmology_param(self, cparams):
gp_rec = np.array([self.gps[i].predict(self.ydata[:800, i], np.atleast_2d(
cparams[[0, 1, 2, 4]]), return_cov=False)[0] for i in range(4)])
self.pred_table = np.sqrt(np.exp(cparams[3]) / self.As_fid * np.exp(
np.dot(gp_rec*self.ystd+self.yavg, self.eigdata) + self.ymean))
if not np.isclose(cparams[5], -1):
growth_wcdm = _linearGrowth(
cparams[2], cparams[5], 0.)/_linearGrowth(cparams[2], cparams[5], 1000.)
growth_lcdm = _linearGrowth(
cparams[2], -1., 0.)/_linearGrowth(cparams[2], -1., 1000.)
self.pred_table *= growth_wcdm/growth_lcdm
self.sigma_M = ius(self.Mlist, self.pred_table)
def extrapolate(xvals: np.ndarray,
yvals: np.ndarray,
x: float,
grouping: int = 5,
order: int = 3):
"""Given equal-length arrays xvals and yvals, with the
xvals in ascending order, produce an interpolated or extrapolated
estimate for y at corresponding x. If there are enough points to
use grouping to smooth out noise in the yvals, average over
consecutive points up to groups of size grouping.
"""
assert len(xvals) == len(yvals)
n = len(xvals)
if n == 1:
return yvals[0]
if n == 0:
raise IndexError
# Do we have enough points to form (order+1) groups of size
# grouping?
while True:
points = grouping * (order + 1)
if points <= n:
break
if order > 1:
order -= 1
elif grouping > 1:
grouping -= 1
else:
break
if grouping > 1:
pts = grouping * (order + 1)
for array in (xvals, yvals):
if x <= xvals[0]:
tmp = array[:pts]
else:
tmp = array[-pts:]
tmp = np.mean(np.reshape(tmp, (grouping, -1)), axis=1)
if array == xvals:
xvals = tmp
else:
yvals = tmp
try:
spline = ius(xvals, yvals, k=order)
return float(spline(x))
except Exception as eeps:
print(eeps)
return yvals[0] # ???
def sigma_M(emu):
'''
Create an interpolator for sigma(M)
TODO: Attach this to emu class?
'''
from scipy.interpolate import InterpolatedUnivariateSpline as ius
log_interp = log_interp_sigma # Should sigma(M) be interpolated logarithmically?
# Get internal M vs sigma arrays
Ms_internal = emu.massfunc.Mlist
sigs_internal = emu.massfunc.sigs0
g = emu.Dgrowth_from_z # This is a function
# Make an interpolator for sigma(M)
if log_interp:
sigma_interpolator = ius(np.log(Ms_internal),
np.log(sigs_internal),
ext='extrapolate')
else:
sigma_interpolator = ius(Ms_internal, sigs_internal, ext='extrapolate')
return lambda M, z: g(z) * sigma_interpolator(M)
def running_rms(sp1):
sp1b = np.zeros(4096)+np.nan
sp1b[4:-4] = sp1
with warnings.catch_warnings(record=True) as _:
b1 = np.nanpercentile(np.reshape(sp1b, [16, 256]),[16,84], axis=1)
rms = (b1[1]-b1[0])/2
index = np.arange(16)*256+128
keep = np.isfinite(rms)
index = index[keep]
rms = rms[keep]
return ius(index,rms,k=2,ext=3)(np.arange(len(sp1)))
def psfconvolve(self, model1D_unc, model2D_unc):
""" Returns a PSF convolved profile. """
model2D = np.fft.ifft2(np.fft.fft2(model2D_unc) * self.fft_psf2D).real
model2D = np.fft.fftshift(model2D)
model1D = np.column_stack((self.rr.diagonal(), model2D.diagonal()))
index = np.where(model1D[:,0] == np.min(model1D[:,0]))[0][0]
x, y = model1D[index:].T
s = ius(x, y)
rbox = x[-1] / np.sqrt(2)
profile = model1D_unc
profile[np.where(self.sma<rbox)] = s(self.sma[np.where(self.sma<rbox)])
return profile
def ISI_from_poisson(times, log=False, bw_PSTH=BW_PSTH, window=WINDOW, step=STEP, total_times=(0, 2.5), res=500, res_2=40, **kwargs):
start, end = total_times
time_bins = generate_time_bins(start, end, window=window, step=step)
PSTH_func = PSTH(times, bw_PSTH=bw_PSTH, mirror=True, trial_time=total_times, res=res)
delta_1 = (end - start) / (res - 1.)
PSTH_list = [PSTH_func(y) for y in numpy.linspace(start, end, res)]
int_firing_list = cumtrapz(PSTH_list, dx=delta_1)
def prob(ISI, a, b):
start_bin = int(a / delta_1)
end_bin = int(b / delta_1)
step = int(ISI / delta_1)
Y = [PSTH_list[i] * PSTH_list[i + step] * numpy.exp(-int_firing_list[i + step] + int_firing_list[i]) for i in range(start_bin, end_bin - step)]
value = trapz(Y, dx=delta_1) * ISI
return value
#num_trials = len(times)
times = flatten(times)
if log:
ISI_short = -3.
ISI_long = numpy.log10(window)
else:
ISI_short = .001
ISI_long = window
X = numpy.linspace(ISI_short, ISI_long, res_2)
delta_2 = (ISI_long - ISI_short) / (res_2 - 1.)
prob_dict = {}
for i, time_bin in enumerate(time_bins):
if log:
Y = numpy.array([prob(10**log_ISI, time_bin[0], time_bin[1]) * 10**log_ISI for log_ISI in X])
else:
Y = numpy.array([prob(ISI, time_bin[0], time_bin[1]) for ISI in X])
norm = trapz(Y, dx=delta_2)
Y = Y / norm
prob_dict[tuple(time_bin)] = ius(X, Y)
def prob_time_wrapper(ISI, time):
try:
value = prob_dict[current_bin(time, time_bins)](ISI)
if value > 0:
return(value)
else:
return(numpy.nan)
except:
return(numpy.nan)
return prob_time_wrapper
def get_xiauto_mass_avg(emu, rs, M1min, M1max, M2min, M2max, z):
'''
Averages the halo-halo correlation function over mass ranges to return the weighted-by-mass-function mean version
'''
from scipy.interpolate import InterpolatedUnivariateSpline as ius
from scipy.interpolate import RectBivariateSpline as rbs
from scipy.integrate import dblquad
# Parameters
epsabs = 1e-3 # Integration accuracy
nM = 6 # Number of halo-mass bins in each of M1 and M2 directions
# Calculations
nr = len(rs)
# Number densities of haloes in each sample
n1 = ndenshalo(emu, M1min, M1max, z)
n2 = ndenshalo(emu, M2min, M2max, z)
# Arrays for halo masses
M1s = mead.logspace(M1min, M1max, nM)
M2s = mead.logspace(M2min, M2max, nM)
# Get mass function interpolation
Ms = emu.massfunc.Mlist
dndM = emu.massfunc.get_dndM(z)
log_dndM_interp = ius(np.log(Ms), np.log(dndM))
# Loop over radii
xiauto_avg = np.zeros((nr))
for ir, r in enumerate(rs):
# Get correlation function interpolation
# Note that this is not necessarily symmetric because M1, M2 run over different ranges
xiauto_mass = np.zeros((nM, nM))
for iM1, M1 in enumerate(M1s):
for iM2, M2 in enumerate(M2s):
xiauto_mass[iM1, iM2] = emu.get_xiauto_mass(r, M1, M2, z)
xiauto_interp = rbs(np.log(M1s), np.log(M2s), xiauto_mass)
# Integrate interpolated functions
xiauto_avg[ir], _ = dblquad(
lambda M1, M2: xiauto_interp(np.log(M1), np.log(M2)) * np.exp(
log_dndM_interp(np.log(M1)) + log_dndM_interp(np.log(M2))),
M1min,
M1max,
lambda M1: M2min,
lambda M1: M2max,
epsabs=epsabs)
return xiauto_avg / (n1 * n2)
def get_dndM_mass(emu, Ms, z):
'''
Return an array of n(M) (dn/dM in Dark Quest notation) at user-specified halo masses
Extrapolates if necessary, which is perhaps dangerous
'''
from scipy.interpolate import InterpolatedUnivariateSpline as ius
# Construct an interpolator for n(M) (or dn/dM) from the emulator internals
Ms = emu.massfunc.Mlist
dndM = emu.massfunc.get_dndM(z)
dndM_interp = ius(np.log(Ms), np.log(dndM), ext='extrapolate')
# Evaluate the interpolator at the desired mass points
return np.exp(dndM_interp(np.log(Ms)))
def fit_spline(coords,wvln,flux):
sx,sy=np.array([]),np.array([])
for i in range(len(coords)):
if coords[i][0] < 1.1: #hacky bug fix; make boolean?
sx=np.append(sx,np.nan)
sy=np.append(sy,np.nan)
else:
sx=np.append(sx,find_nearest(wvln, coords[i][0]))
ind = np.where(wvln == sx[i])
sy=np.append(sy,flux[ind])
gv = np.isfinite(sy)
sx,sy=sx[gv],sy[gv]
sx=np.hstack((sx,wf))
sy=np.hstack((sy,nf))
indz = np.argsort(sx)
sx,sy = sx[indz],sy[indz]
# splfit=interpolate.splrep(sx,sy,s=0.7) #default deg=3
# cont = interpolate.splev(wvln,splfit)
splfit = ius(sx, sy, k=3)
cont = splfit(wvln)
return cont, sx, sy
def match_x(ref_x, x_array, y_array):
temp = ius(x_array, y_array)
new_y = temp(ref_x)
return new_y
