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vallis/libstempo | libstempo/spharmORFbasis.py | real_rotated_Gammas | def real_rotated_Gammas(m,l,phi1,phi2,theta1,theta2,gamma_ml):
"""
This function returns the real-valued form of the Overlap Reduction Functions,
see Eqs 47 in Mingarelli et al, 2013.
"""
if m>0:
ans=(1./sqrt(2))*(rotated_Gamma_ml(m,l,phi1,phi2,theta1,theta2,gamma_ml) + \
... | python | def real_rotated_Gammas(m,l,phi1,phi2,theta1,theta2,gamma_ml):
"""
This function returns the real-valued form of the Overlap Reduction Functions,
see Eqs 47 in Mingarelli et al, 2013.
"""
if m>0:
ans=(1./sqrt(2))*(rotated_Gamma_ml(m,l,phi1,phi2,theta1,theta2,gamma_ml) + \
... | [
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vallis/libstempo | libstempo/fit.py | chisq | def chisq(psr,formbats=False):
"""Return the total chisq for the current timing solution,
removing noise-averaged mean residual, and ignoring deleted points."""
if formbats:
psr.formbats()
res, err = psr.residuals(removemean=False)[psr.deleted == 0], psr.toaerrs[psr.deleted == 0]
... | python | def chisq(psr,formbats=False):
"""Return the total chisq for the current timing solution,
removing noise-averaged mean residual, and ignoring deleted points."""
if formbats:
psr.formbats()
res, err = psr.residuals(removemean=False)[psr.deleted == 0], psr.toaerrs[psr.deleted == 0]
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vallis/libstempo | libstempo/fit.py | dchisq | def dchisq(psr,formbats=False,renormalize=True):
"""Return gradient of total chisq for the current timing solution,
after removing noise-averaged mean residual, and ignoring deleted points."""
if formbats:
psr.formbats()
res, err = psr.residuals(removemean=False)[psr.deleted == 0], psr.toa... | python | def dchisq(psr,formbats=False,renormalize=True):
"""Return gradient of total chisq for the current timing solution,
after removing noise-averaged mean residual, and ignoring deleted points."""
if formbats:
psr.formbats()
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vallis/libstempo | libstempo/fit.py | findmin | def findmin(psr,method='Nelder-Mead',history=False,formbats=False,renormalize=True,bounds={},**kwargs):
"""Use scipy.optimize.minimize to find minimum-chisq timing solution,
passing through all extra options. Resets psr[...].val to the final solution,
and returns the final chisq. Will use chisq gradient if ... | python | def findmin(psr,method='Nelder-Mead',history=False,formbats=False,renormalize=True,bounds={},**kwargs):
"""Use scipy.optimize.minimize to find minimum-chisq timing solution,
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vallis/libstempo | libstempo/fit.py | glsfit | def glsfit(psr,renormalize=True):
"""Solve local GLS problem using scipy.linalg.cholesky.
Update psr[...].val and psr[...].err from solution.
If renormalize=True, normalize each design-matrix column by its norm."""
mask = psr.deleted == 0
res, err = psr.residuals(removemean=False)[mask], psr.to... | python | def glsfit(psr,renormalize=True):
"""Solve local GLS problem using scipy.linalg.cholesky.
Update psr[...].val and psr[...].err from solution.
If renormalize=True, normalize each design-matrix column by its norm."""
mask = psr.deleted == 0
res, err = psr.residuals(removemean=False)[mask], psr.to... | [
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vallis/libstempo | libstempo/utils.py | create_fourier_design_matrix | def create_fourier_design_matrix(t, nmodes, freq=False, Tspan=None,
logf=False, fmin=None, fmax=None):
"""
Construct fourier design matrix from eq 11 of Lentati et al, 2013
:param t: vector of time series in seconds
:param nmodes: number of fourier coefficients to use
... | python | def create_fourier_design_matrix(t, nmodes, freq=False, Tspan=None,
logf=False, fmin=None, fmax=None):
"""
Construct fourier design matrix from eq 11 of Lentati et al, 2013
:param t: vector of time series in seconds
:param nmodes: number of fourier coefficients to use
... | [
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vallis/libstempo | libstempo/utils.py | powerlaw | def powerlaw(f, log10_A=-16, gamma=5):
"""Power-law PSD.
:param f: Sampling frequencies
:param log10_A: log10 of red noise Amplitude [GW units]
:param gamma: Spectral index of red noise process
"""
fyr = 1 / 3.16e7
return (10**log10_A)**2 / 12.0 / np.pi**2 * fyr**(gamma-3) * f**(-gamma) | python | def powerlaw(f, log10_A=-16, gamma=5):
"""Power-law PSD.
:param f: Sampling frequencies
:param log10_A: log10 of red noise Amplitude [GW units]
:param gamma: Spectral index of red noise process
"""
fyr = 1 / 3.16e7
return (10**log10_A)**2 / 12.0 / np.pi**2 * fyr**(gamma-3) * f**(-gamma) | [
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vallis/libstempo | libstempo/toasim.py | add_gwb | def add_gwb(psr, dist=1, ngw=1000, seed=None, flow=1e-8, fhigh=1e-5,
gwAmp=1e-20, alpha=-0.66, logspacing=True):
"""Add a stochastic background from inspiraling binaries, using the tempo2
code that underlies the GWbkgrd plugin.
Here 'dist' is the pulsar distance [in kpc]; 'ngw' is the number of... | python | def add_gwb(psr, dist=1, ngw=1000, seed=None, flow=1e-8, fhigh=1e-5,
gwAmp=1e-20, alpha=-0.66, logspacing=True):
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vallis/libstempo | libstempo/toasim.py | add_dipole_gwb | def add_dipole_gwb(psr, dist=1, ngw=1000, seed=None, flow=1e-8,
fhigh=1e-5, gwAmp=1e-20, alpha=-0.66,
logspacing=True, dipoleamps=None,
dipoledir=None, dipolemag=None):
"""Add a stochastic background from inspiraling binaries distributed
accord... | python | def add_dipole_gwb(psr, dist=1, ngw=1000, seed=None, flow=1e-8,
fhigh=1e-5, gwAmp=1e-20, alpha=-0.66,
logspacing=True, dipoleamps=None,
dipoledir=None, dipolemag=None):
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vallis/libstempo | libstempo/toasim.py | fakepulsar | def fakepulsar(parfile, obstimes, toaerr, freq=1440.0, observatory='AXIS',
flags='', iters=3):
"""Returns a libstempo tempopulsar object corresponding to a noiseless set
of observations for the pulsar specified in 'parfile', with observations
happening at times (MJD) given in the array (or li... | python | def fakepulsar(parfile, obstimes, toaerr, freq=1440.0, observatory='AXIS',
flags='', iters=3):
"""Returns a libstempo tempopulsar object corresponding to a noiseless set
of observations for the pulsar specified in 'parfile', with observations
happening at times (MJD) given in the array (or li... | [
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vallis/libstempo | libstempo/toasim.py | add_efac | def add_efac(psr, efac=1.0, flagid=None, flags=None, seed=None):
"""Add nominal TOA errors, multiplied by `efac` factor.
Optionally take a pseudorandom-number-generator seed."""
if seed is not None:
N.random.seed(seed)
# default efacvec
efacvec = N.ones(psr.nobs)
# check that efac... | python | def add_efac(psr, efac=1.0, flagid=None, flags=None, seed=None):
"""Add nominal TOA errors, multiplied by `efac` factor.
Optionally take a pseudorandom-number-generator seed."""
if seed is not None:
N.random.seed(seed)
# default efacvec
efacvec = N.ones(psr.nobs)
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vallis/libstempo | libstempo/toasim.py | add_equad | def add_equad(psr, equad, flagid=None, flags=None, seed=None):
"""Add quadrature noise of rms `equad` [s].
Optionally take a pseudorandom-number-generator seed."""
if seed is not None:
N.random.seed(seed)
# default equadvec
equadvec = N.zeros(psr.nobs)
# check that equad is scalar... | python | def add_equad(psr, equad, flagid=None, flags=None, seed=None):
"""Add quadrature noise of rms `equad` [s].
Optionally take a pseudorandom-number-generator seed."""
if seed is not None:
N.random.seed(seed)
# default equadvec
equadvec = N.zeros(psr.nobs)
# check that equad is scalar... | [
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vallis/libstempo | libstempo/toasim.py | add_jitter | def add_jitter(psr, ecorr ,flagid=None, flags=None, coarsegrain=0.1,
seed=None):
"""Add correlated quadrature noise of rms `ecorr` [s],
with coarse-graining time `coarsegrain` [days].
Optionally take a pseudorandom-number-generator seed."""
if seed is not None:
N.random.seed(... | python | def add_jitter(psr, ecorr ,flagid=None, flags=None, coarsegrain=0.1,
seed=None):
"""Add correlated quadrature noise of rms `ecorr` [s],
with coarse-graining time `coarsegrain` [days].
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vallis/libstempo | libstempo/toasim.py | add_rednoise | def add_rednoise(psr,A,gamma,components=10,seed=None):
"""Add red noise with P(f) = A^2 / (12 pi^2) (f year)^-gamma,
using `components` Fourier bases.
Optionally take a pseudorandom-number-generator seed."""
if seed is not None:
N.random.seed(seed)
t = psr.toas()
minx, maxx = N.min... | python | def add_rednoise(psr,A,gamma,components=10,seed=None):
"""Add red noise with P(f) = A^2 / (12 pi^2) (f year)^-gamma,
using `components` Fourier bases.
Optionally take a pseudorandom-number-generator seed."""
if seed is not None:
N.random.seed(seed)
t = psr.toas()
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vallis/libstempo | libstempo/toasim.py | add_line | def add_line(psr,f,A,offset=0.5):
"""
Add a line of frequency `f` [Hz] and amplitude `A` [s],
with origin at a fraction `offset` through the dataset.
"""
t = psr.toas()
t0 = offset * (N.max(t) - N.min(t))
sine = A * N.cos(2 * math.pi * f * day * (t - t0))
psr.stoas[:] += sine / day | python | def add_line(psr,f,A,offset=0.5):
"""
Add a line of frequency `f` [Hz] and amplitude `A` [s],
with origin at a fraction `offset` through the dataset.
"""
t = psr.toas()
t0 = offset * (N.max(t) - N.min(t))
sine = A * N.cos(2 * math.pi * f * day * (t - t0))
psr.stoas[:] += sine / day | [
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vallis/libstempo | libstempo/toasim.py | add_glitch | def add_glitch(psr, epoch, amp):
"""
Like pulsar term BWM event, but now differently parameterized: just an
amplitude (not log-amp) parameter, and an epoch. [source: piccard]
:param psr: pulsar object
:param epoch: TOA time (MJD) the burst hits the earth
:param amp: amplitude of the glitch
... | python | def add_glitch(psr, epoch, amp):
"""
Like pulsar term BWM event, but now differently parameterized: just an
amplitude (not log-amp) parameter, and an epoch. [source: piccard]
:param psr: pulsar object
:param epoch: TOA time (MJD) the burst hits the earth
:param amp: amplitude of the glitch
... | [
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vallis/libstempo | libstempo/toasim.py | add_cgw | def add_cgw(psr, gwtheta, gwphi, mc, dist, fgw, phase0, psi, inc, pdist=1.0, \
pphase=None, psrTerm=True, evolve=True, \
phase_approx=False, tref=0):
"""
Function to create GW-induced residuals from a SMBMB as
defined in Ellis et. al 2012,2013. Tries to be sm... | python | def add_cgw(psr, gwtheta, gwphi, mc, dist, fgw, phase0, psi, inc, pdist=1.0, \
pphase=None, psrTerm=True, evolve=True, \
phase_approx=False, tref=0):
"""
Function to create GW-induced residuals from a SMBMB as
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vallis/libstempo | libstempo/toasim.py | add_ecc_cgw | def add_ecc_cgw(psr, gwtheta, gwphi, mc, dist, F, inc, psi, gamma0,
e0, l0, q, nmax=100, nset=None, pd=None, periEv=True,
psrTerm=True, tref=0, check=True, useFile=True):
"""
Simulate GW from eccentric SMBHB. Waveform models from
Taylor et al. (2015) and Barack and Cutler (20... | python | def add_ecc_cgw(psr, gwtheta, gwphi, mc, dist, F, inc, psi, gamma0,
e0, l0, q, nmax=100, nset=None, pd=None, periEv=True,
psrTerm=True, tref=0, check=True, useFile=True):
"""
Simulate GW from eccentric SMBHB. Waveform models from
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vallis/libstempo | libstempo/toasim.py | extrap1d | def extrap1d(interpolator):
"""
Function to extend an interpolation function to an
extrapolation function.
:param interpolator: scipy interp1d object
:returns ufunclike: extension of function to extrapolation
"""
xs = interpolator.x
ys = interpolator.y
def pointwise(x):
... | python | def extrap1d(interpolator):
"""
Function to extend an interpolation function to an
extrapolation function.
:param interpolator: scipy interp1d object
:returns ufunclike: extension of function to extrapolation
"""
xs = interpolator.x
ys = interpolator.y
def pointwise(x):
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vallis/libstempo | libstempo/toasim.py | createGWB | def createGWB(psr, Amp, gam, noCorr=False, seed=None, turnover=False,
clm=[N.sqrt(4.0*N.pi)], lmax=0, f0=1e-9, beta=1,
power=1, userSpec=None, npts=600, howml=10):
"""
Function to create GW-induced residuals from a stochastic GWB as defined
in Chamberlin, Creighton, Demorest, et ... | python | def createGWB(psr, Amp, gam, noCorr=False, seed=None, turnover=False,
clm=[N.sqrt(4.0*N.pi)], lmax=0, f0=1e-9, beta=1,
power=1, userSpec=None, npts=600, howml=10):
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Function to create GW-induced residuals from a stochastic GWB as defined
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vallis/libstempo | libstempo/toasim.py | computeORFMatrix | def computeORFMatrix(psr):
"""
Compute ORF matrix.
:param psr: List of pulsar object instances
:returns: Matrix that has the ORF values for every pulsar
pair with 2 on the diagonals to account for the
pulsar term.
"""
# begin loop over all pulsar pairs and calculat... | python | def computeORFMatrix(psr):
"""
Compute ORF matrix.
:param psr: List of pulsar object instances
:returns: Matrix that has the ORF values for every pulsar
pair with 2 on the diagonals to account for the
pulsar term.
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vallis/libstempo | libstempo/plot.py | plotres | def plotres(psr,deleted=False,group=None,**kwargs):
"""Plot residuals, compute unweighted rms residual."""
res, t, errs = psr.residuals(), psr.toas(), psr.toaerrs
if (not deleted) and N.any(psr.deleted != 0):
res, t, errs = res[psr.deleted == 0], t[psr.deleted == 0], errs[psr.deleted == 0]
... | python | def plotres(psr,deleted=False,group=None,**kwargs):
"""Plot residuals, compute unweighted rms residual."""
res, t, errs = psr.residuals(), psr.toas(), psr.toaerrs
if (not deleted) and N.any(psr.deleted != 0):
res, t, errs = res[psr.deleted == 0], t[psr.deleted == 0], errs[psr.deleted == 0]
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vallis/libstempo | libstempo/plot.py | plotgwsrc | def plotgwsrc(gwb):
"""
Plot a GWB source population as a mollweide projection.
"""
theta, phi, omega, polarization = gwb.gw_dist()
rho = phi-N.pi
eta = 0.5*N.pi - theta
# I don't know how to get rid of the RuntimeWarning -- RvH, Oct 10, 2014:
# /Users/vhaaster/env/dev/lib/python2.... | python | def plotgwsrc(gwb):
"""
Plot a GWB source population as a mollweide projection.
"""
theta, phi, omega, polarization = gwb.gw_dist()
rho = phi-N.pi
eta = 0.5*N.pi - theta
# I don't know how to get rid of the RuntimeWarning -- RvH, Oct 10, 2014:
# /Users/vhaaster/env/dev/lib/python2.... | [
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vallis/libstempo | libstempo/like.py | loglike | def loglike(pulsar,efac=1.0,equad=None,jitter=None,Ared=None,gammared=None,marginalize=True,normalize=True,redcomponents=10,usedeleted=True):
"""Returns the Gaussian-process likelihood for 'pulsar'.
The likelihood is evaluated at the current value of the pulsar parameters,
as given by pulsar[parname].val.
... | python | def loglike(pulsar,efac=1.0,equad=None,jitter=None,Ared=None,gammared=None,marginalize=True,normalize=True,redcomponents=10,usedeleted=True):
"""Returns the Gaussian-process likelihood for 'pulsar'.
The likelihood is evaluated at the current value of the pulsar parameters,
as given by pulsar[parname].val.
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vallis/libstempo | libstempo/like.py | expandranges | def expandranges(parlist):
"""Rewrite a list of parameters by expanding ranges (e.g., log10_efac{1-10}) into
individual parameters."""
ret = []
for par in parlist:
# match anything of the form XXX{number1-number2}
m = re.match('(.*)\{([0-9]+)\-([0-9]+)\}',par)
if m is None:
... | python | def expandranges(parlist):
"""Rewrite a list of parameters by expanding ranges (e.g., log10_efac{1-10}) into
individual parameters."""
ret = []
for par in parlist:
# match anything of the form XXX{number1-number2}
m = re.match('(.*)\{([0-9]+)\-([0-9]+)\}',par)
if m is None:
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vallis/libstempo | libstempo/like.py | _findrange | def _findrange(parlist,roots=['JUMP','DMXR1_','DMXR2_','DMX_','efac','log10_efac']):
"""Rewrite a list of parameters name by detecting ranges (e.g., JUMP1, JUMP2, ...)
and compressing them."""
rootdict = {root: [] for root in roots}
res = []
for par in parlist:
found = False
for ro... | python | def _findrange(parlist,roots=['JUMP','DMXR1_','DMXR2_','DMX_','efac','log10_efac']):
"""Rewrite a list of parameters name by detecting ranges (e.g., JUMP1, JUMP2, ...)
and compressing them."""
rootdict = {root: [] for root in roots}
res = []
for par in parlist:
found = False
for ro... | [
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vallis/libstempo | libstempo/emcee.py | merge | def merge(data,skip=50,fraction=1.0):
"""Merge one every 'skip' clouds into a single emcee population,
using the later 'fraction' of the run."""
w,s,d = data.chains.shape
start = int((1.0 - fraction) * s)
total = int((s - start) / skip)
return data.chains[:,start::skip,:].reshape(... | python | def merge(data,skip=50,fraction=1.0):
"""Merge one every 'skip' clouds into a single emcee population,
using the later 'fraction' of the run."""
w,s,d = data.chains.shape
start = int((1.0 - fraction) * s)
total = int((s - start) / skip)
return data.chains[:,start::skip,:].reshape(... | [
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vallis/libstempo | libstempo/emcee.py | cull | def cull(data,index,min=None,max=None):
"""Sieve an emcee clouds by excluding walkers with search variable 'index'
smaller than 'min' or larger than 'max'."""
ret = data
if min is not None:
ret = ret[ret[:,index] > min,:]
if max is not None:
ret = ret[ret[:,index] < max,:]
... | python | def cull(data,index,min=None,max=None):
"""Sieve an emcee clouds by excluding walkers with search variable 'index'
smaller than 'min' or larger than 'max'."""
ret = data
if min is not None:
ret = ret[ret[:,index] > min,:]
if max is not None:
ret = ret[ret[:,index] < max,:]
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vallis/libstempo | libstempo/eccUtils.py | make_ecc_interpolant | def make_ecc_interpolant():
"""
Make interpolation function from eccentricity file to
determine number of harmonics to use for a given
eccentricity.
:returns: interpolant
"""
pth = resource_filename(Requirement.parse('libstempo'),
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... | python | def make_ecc_interpolant():
"""
Make interpolation function from eccentricity file to
determine number of harmonics to use for a given
eccentricity.
:returns: interpolant
"""
pth = resource_filename(Requirement.parse('libstempo'),
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vallis/libstempo | libstempo/eccUtils.py | get_edot | def get_edot(F, mc, e):
"""
Compute eccentricity derivative from Taylor et al. (2015)
:param F: Orbital frequency [Hz]
:param mc: Chirp mass of binary [Solar Mass]
:param e: Eccentricity of binary
:returns: de/dt
"""
# chirp mass
mc *= SOLAR2S
dedt = -304/(15*mc) * (2*np.pi*... | python | def get_edot(F, mc, e):
"""
Compute eccentricity derivative from Taylor et al. (2015)
:param F: Orbital frequency [Hz]
:param mc: Chirp mass of binary [Solar Mass]
:param e: Eccentricity of binary
:returns: de/dt
"""
# chirp mass
mc *= SOLAR2S
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vallis/libstempo | libstempo/eccUtils.py | get_Fdot | def get_Fdot(F, mc, e):
"""
Compute frequency derivative from Taylor et al. (2015)
:param F: Orbital frequency [Hz]
:param mc: Chirp mass of binary [Solar Mass]
:param e: Eccentricity of binary
:returns: dF/dt
"""
# chirp mass
mc *= SOLAR2S
dFdt = 48 / (5*np.pi*mc**2) * (2*n... | python | def get_Fdot(F, mc, e):
"""
Compute frequency derivative from Taylor et al. (2015)
:param F: Orbital frequency [Hz]
:param mc: Chirp mass of binary [Solar Mass]
:param e: Eccentricity of binary
:returns: dF/dt
"""
# chirp mass
mc *= SOLAR2S
dFdt = 48 / (5*np.pi*mc**2) * (2*n... | [
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vallis/libstempo | libstempo/eccUtils.py | get_gammadot | def get_gammadot(F, mc, q, e):
"""
Compute gamma dot from Barack and Cutler (2004)
:param F: Orbital frequency [Hz]
:param mc: Chirp mass of binary [Solar Mass]
:param q: Mass ratio of binary
:param e: Eccentricity of binary
:returns: dgamma/dt
"""
# chirp mass
mc *= SOLAR2S
... | python | def get_gammadot(F, mc, q, e):
"""
Compute gamma dot from Barack and Cutler (2004)
:param F: Orbital frequency [Hz]
:param mc: Chirp mass of binary [Solar Mass]
:param q: Mass ratio of binary
:param e: Eccentricity of binary
:returns: dgamma/dt
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# chirp mass
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vallis/libstempo | libstempo/eccUtils.py | get_coupled_ecc_eqns | def get_coupled_ecc_eqns(y, t, mc, q):
"""
Computes the coupled system of differential
equations from Peters (1964) and Barack &
Cutler (2004). This is a system of three variables:
F: Orbital frequency [Hz]
e: Orbital eccentricity
gamma: Angle of precession of periastron [rad]
phase... | python | def get_coupled_ecc_eqns(y, t, mc, q):
"""
Computes the coupled system of differential
equations from Peters (1964) and Barack &
Cutler (2004). This is a system of three variables:
F: Orbital frequency [Hz]
e: Orbital eccentricity
gamma: Angle of precession of periastron [rad]
phase... | [
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vallis/libstempo | libstempo/eccUtils.py | solve_coupled_ecc_solution | def solve_coupled_ecc_solution(F0, e0, gamma0, phase0, mc, q, t):
"""
Compute the solution to the coupled system of equations
from from Peters (1964) and Barack & Cutler (2004) at
a given time.
:param F0: Initial orbital frequency [Hz]
:param e0: Initial orbital eccentricity
:param gam... | python | def solve_coupled_ecc_solution(F0, e0, gamma0, phase0, mc, q, t):
"""
Compute the solution to the coupled system of equations
from from Peters (1964) and Barack & Cutler (2004) at
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:param e0: Initial orbital eccentricity
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vallis/libstempo | libstempo/eccUtils.py | get_an | def get_an(n, mc, dl, F, e):
"""
Compute a_n from Eq. 22 of Taylor et al. (2015).
:param n: Harmonic number
:param mc: Chirp mass of binary [Solar Mass]
:param dl: Luminosity distance [Mpc]
:param F: Orbital frequency of binary [Hz]
:param e: Orbital Eccentricity
:returns: a_n
... | python | def get_an(n, mc, dl, F, e):
"""
Compute a_n from Eq. 22 of Taylor et al. (2015).
:param n: Harmonic number
:param mc: Chirp mass of binary [Solar Mass]
:param dl: Luminosity distance [Mpc]
:param F: Orbital frequency of binary [Hz]
:param e: Orbital Eccentricity
:returns: a_n
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vallis/libstempo | libstempo/eccUtils.py | get_bn | def get_bn(n, mc, dl, F, e):
"""
Compute b_n from Eq. 22 of Taylor et al. (2015).
:param n: Harmonic number
:param mc: Chirp mass of binary [Solar Mass]
:param dl: Luminosity distance [Mpc]
:param F: Orbital frequency of binary [Hz]
:param e: Orbital Eccentricity
:returns: b_n
... | python | def get_bn(n, mc, dl, F, e):
"""
Compute b_n from Eq. 22 of Taylor et al. (2015).
:param n: Harmonic number
:param mc: Chirp mass of binary [Solar Mass]
:param dl: Luminosity distance [Mpc]
:param F: Orbital frequency of binary [Hz]
:param e: Orbital Eccentricity
:returns: b_n
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vallis/libstempo | libstempo/eccUtils.py | get_cn | def get_cn(n, mc, dl, F, e):
"""
Compute c_n from Eq. 22 of Taylor et al. (2015).
:param n: Harmonic number
:param mc: Chirp mass of binary [Solar Mass]
:param dl: Luminosity distance [Mpc]
:param F: Orbital frequency of binary [Hz]
:param e: Orbital Eccentricity
:returns: c_n
... | python | def get_cn(n, mc, dl, F, e):
"""
Compute c_n from Eq. 22 of Taylor et al. (2015).
:param n: Harmonic number
:param mc: Chirp mass of binary [Solar Mass]
:param dl: Luminosity distance [Mpc]
:param F: Orbital frequency of binary [Hz]
:param e: Orbital Eccentricity
:returns: c_n
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vallis/libstempo | libstempo/eccUtils.py | calculate_splus_scross | def calculate_splus_scross(nmax, mc, dl, F, e, t, l0, gamma, gammadot, inc):
"""
Calculate splus and scross summed over all harmonics.
This waveform differs slightly from that in Taylor et al (2015)
in that it includes the time dependence of the advance of periastron.
:param nmax: Total numbe... | python | def calculate_splus_scross(nmax, mc, dl, F, e, t, l0, gamma, gammadot, inc):
"""
Calculate splus and scross summed over all harmonics.
This waveform differs slightly from that in Taylor et al (2015)
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vallis/libstempo | libstempo/multinest.py | run | def run(LogLikelihood,
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n_clustering_params = None, wrapped_params = None,
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n_clustering_params = None, wrapped_params = None,
importance_nested_sampling = True,
multimodal = True, const_efficiency_mode = False, n_live_points = 400,
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gagneurlab/concise | concise/legacy/kmer.py | best_kmers | def best_kmers(dt, response, sequence, k=6, consider_shift=True, n_cores=1,
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gagneurlab/concise | concise/legacy/kmer.py | kmer_count | def kmer_count(seq_list, k):
"""
Generate k-mer counts from a set of sequences
Args:
seq_list (iterable): List of DNA sequences (with letters from {A, C, G, T})
k (int): K in k-mer.
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>>> kmer... | python | def kmer_count(seq_list, k):
"""
Generate k-mer counts from a set of sequences
Args:
seq_list (iterable): List of DNA sequences (with letters from {A, C, G, T})
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['AA', 'AC', 'AG', 'AT', 'CA', 'CC', 'CG', 'CT', 'GA', 'GC', 'GG', 'GT', 'TA', 'TC', 'TG', 'TT']
"""
bases = ['A', 'C', 'G', 'T']
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"""
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Example:
>>> generate_all_kmers(2)
['AA', 'AC', 'AG', 'AT', 'CA', 'CC', 'CG', 'CT', 'GA', 'GC', 'GG', 'GT', 'TA', 'TC', 'TG', 'TT']
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gagneurlab/concise | concise/utils/helper.py | dict_to_numpy_dict | def dict_to_numpy_dict(obj_dict):
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gagneurlab/concise | concise/utils/splines.py | get_gam_splines | def get_gam_splines(start=0, end=100, n_bases=10, spline_order=3, add_intercept=True):
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np.array of shape [len(x), n_bases + (add_intercept)]
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gagneurlab/concise | concise/utils/splines.py | BSpline.getS | def getS(self, add_intercept=False):
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Returns
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"""
S = self.S
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zeros = np.zeros_like(S... | python | def getS(self, add_intercept=False):
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Returns
np.array, of shape (n_bases + add_intercept, n_bases + add_intercept)
"""
S = self.S
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gagneurlab/concise | concise/data/encode.py | get_metadata | def get_metadata():
"""Get pandas.DataFrame with metadata about the PWM's. Columns:
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- info1 - additional information about the motifs
- info2
- consensus: PWM consensus sequence
"""
motifs = _load_motifs()
motif_names... | python | def get_metadata():
"""Get pandas.DataFrame with metadata about the PWM's. Columns:
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- info1 - additional information about the motifs
- info2
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y_true: true labels
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gagneurlab/concise | concise/eval_metrics.py | var_explained | def var_explained(y_true, y_pred):
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gagneurlab/concise | concise/metrics.py | contingency_table | def contingency_table(y, z):
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gagneurlab/concise | concise/metrics.py | f1 | def f1(y, z):
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gagneurlab/concise | concise/metrics.py | mcc | def mcc(y, z):
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gagneurlab/concise | concise/utils/model_data.py | split_KFold_idx | def split_KFold_idx(train, cv_n_folds=5, stratified=False, random_state=None):
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gagneurlab/concise | concise/legacy/get_data.py | prepare_data | def prepare_data(dt, features, response, sequence, id_column=None, seq_align="end", trim_seq_len=None):
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gagneurlab/concise | concise/preprocessing/splines.py | _trunc | def _trunc(x, minval=None, maxval=None):
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gagneurlab/concise | concise/preprocessing/splines.py | encodeSplines | def encodeSplines(x, n_bases=10, spline_order=3, start=None, end=None, warn=True):
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Get B-spline base-function expansion
# Details
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gagneurlab/concise | concise/layers.py | InputCodon | def InputCodon(seq_length, ignore_stop_codons=True, name=None, **kwargs):
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gagneurlab/concise | concise/layers.py | InputDNAQuantity | def InputDNAQuantity(seq_length, n_features=1, name=None, **kwargs):
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gagneurlab/concise | concise/layers.py | ConvSequence._plot_weights_heatmap | def _plot_weights_heatmap(self, index=None, figsize=None, **kwargs):
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... | python | def _plot_weights_heatmap(self, index=None, figsize=None, **kwargs):
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"... | Plot a heatmap from weight matrix w
vmin, vmax = z axis range
diverge_color = Should we use diverging colors?
plot_name = plot_title
vocab = vocabulary (corresponds to the first axis) | [
"Plot",
"a",
"heatmap",
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"weight",
"matrix",
"w"
] | train | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/utils/plot.py#L22-L89 |
gagneurlab/concise | concise/utils/plot.py | standardize_polygons_str | def standardize_polygons_str(data_str):
"""Given a POLYGON string, standardize the coordinates to a 1x1 grid.
Input : data_str (taken from above)
Output: tuple of polygon objects
"""
# find all of the polygons in the letter (for instance an A
# needs to be constructed from 2 polygons)
path_s... | python | def standardize_polygons_str(data_str):
"""Given a POLYGON string, standardize the coordinates to a 1x1 grid.
Input : data_str (taken from above)
Output: tuple of polygon objects
"""
# find all of the polygons in the letter (for instance an A
# needs to be constructed from 2 polygons)
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Input : data_str (taken from above)
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