repository_name stringlengths 5 67 | func_path_in_repository stringlengths 4 234 | func_name stringlengths 0 314 | whole_func_string stringlengths 52 3.87M | language stringclasses 6
values | func_code_string stringlengths 52 3.87M | func_documentation_string stringlengths 1 47.2k | func_code_url stringlengths 85 339 |
|---|---|---|---|---|---|---|---|
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) + \
... | This function returns the real-valued form of the Overlap Reduction Functions,
see Eqs 47 in Mingarelli et al, 2013. | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/spharmORFbasis.py#L281-L297 |
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]
... | Return the total chisq for the current timing solution,
removing noise-averaged mean residual, and ignoring deleted points. | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/fit.py#L4-L15 |
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()
res, err = psr.residuals(removemean=False)[psr.deleted == 0], psr.toa... | Return gradient of total chisq for the current timing solution,
after removing noise-averaged mean residual, and ignoring deleted points. | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/fit.py#L17-L41 |
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,
passing through all extra options. Resets psr[...].val to the final solution,
and returns the final chisq. Will use chisq gradient if ... | 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 method requires it.
Ignores deleted points. | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/fit.py#L43-L101 |
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... | 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. | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/fit.py#L103-L132 |
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
... | 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
:param freq: option to output frequencies
:param Tspan: option to some other Tspan
:param logf: use log frequency spacing
:param fmin... | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/utils.py#L31-L74 |
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) | 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 | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/utils.py#L77-L86 |
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):
"""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... | 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 binaries,
'seed' (a negative integer) reseeds the GWbkgrd pseudorandom-number-generator,
'flow' and 'fhigh' [Hz] determine ... | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/toasim.py#L32-L56 |
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):
"""Add a stochastic background from inspiraling binaries distributed
accord... | Add a stochastic background from inspiraling binaries distributed
according to a pure dipole distribution, using the tempo2
code that underlies the GWdipolebkgrd plugin.
The basic use is identical to that of 'add_gwb':
Here 'dist' is the pulsar distance [in kpc]; 'ngw' is the number of binaries,
's... | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/toasim.py#L58-L95 |
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... | 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 list) 'obstimes', with
measurement errors given by toaerr (us).
A new timfile can then be saved with pulsar.sa... | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/toasim.py#L100-L145 |
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)
# check that efac... | Add nominal TOA errors, multiplied by `efac` factor.
Optionally take a pseudorandom-number-generator seed. | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/toasim.py#L153-L176 |
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... | Add quadrature noise of rms `equad` [s].
Optionally take a pseudorandom-number-generator seed. | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/toasim.py#L178-L201 |
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].
Optionally take a pseudorandom-number-generator seed."""
if seed is not None:
N.random.seed(... | Add correlated quadrature noise of rms `ecorr` [s],
with coarse-graining time `coarsegrain` [days].
Optionally take a pseudorandom-number-generator seed. | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/toasim.py#L255-L287 |
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()
minx, maxx = N.min... | 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. | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/toasim.py#L290-L317 |
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 | Add a line of frequency `f` [Hz] and amplitude `A` [s],
with origin at a fraction `offset` through the dataset. | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/toasim.py#L350-L360 |
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
... | 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 | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/toasim.py#L362-L379 |
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
defined in Ellis et. al 2012,2013. Tries to be sm... | Function to create GW-induced residuals from a SMBMB as
defined in Ellis et. al 2012,2013. Tries to be smart about it...
:param psr: pulsar object
:param gwtheta: Polar angle of GW source in celestial coords [radians]
:param gwphi: Azimuthal angle of GW source in celestial coords [radians]
:param ... | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/toasim.py#L381-L521 |
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
Taylor et al. (2015) and Barack and Cutler (20... | Simulate GW from eccentric SMBHB. Waveform models from
Taylor et al. (2015) and Barack and Cutler (2004).
WARNING: This residual waveform is only accurate if the
GW frequency is not significantly evolving over the
observation time of the pulsar.
:param psr: pulsar object
:param gwtheta: Polar... | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/toasim.py#L523-L674 |
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):
... | Function to extend an interpolation function to an
extrapolation function.
:param interpolator: scipy interp1d object
:returns ufunclike: extension of function to extrapolation | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/toasim.py#L677-L701 |
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):
"""
Function to create GW-induced residuals from a stochastic GWB as defined
in Chamberlin, Creighton, Demorest, et ... | Function to create GW-induced residuals from a stochastic GWB as defined
in Chamberlin, Creighton, Demorest, et al. (2014).
:param psr: pulsar object for single pulsar
:param Amp: Amplitude of red noise in GW units
:param gam: Red noise power law spectral index
:param noCorr: Add red noise wit... | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/toasim.py#L704-L842 |
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.
"""
# begin loop over all pulsar pairs and calculat... | 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. | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/toasim.py#L844-L879 |
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]
... | Plot residuals, compute unweighted rms residual. | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/plot.py#L7-L38 |
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.... | Plot a GWB source population as a mollweide projection. | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/plot.py#L304-L324 |
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.
... | 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.
If efac, equad, and/or Ared are set, will compute the likelihood assuming
the corresponding noise model. EFAC multiplies measurement noise;
... | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/like.py#L74-L148 |
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:
... | Rewrite a list of parameters by expanding ranges (e.g., log10_efac{1-10}) into
individual parameters. | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/like.py#L242-L264 |
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... | Rewrite a list of parameters name by detecting ranges (e.g., JUMP1, JUMP2, ...)
and compressing them. | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/like.py#L267-L291 |
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(... | Merge one every 'skip' clouds into a single emcee population,
using the later 'fraction' of the run. | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/emcee.py#L46-L55 |
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,:]
... | Sieve an emcee clouds by excluding walkers with search variable 'index'
smaller than 'min' or larger than 'max'. | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/emcee.py#L57-L69 |
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'),
'libstempo/ecc_vs_nharm.txt')
... | 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'),
'libstempo/ecc_vs_nharm.txt')
... | Make interpolation function from eccentricity file to
determine number of harmonics to use for a given
eccentricity.
:returns: interpolant | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/eccUtils.py#L28-L41 |
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
dedt = -304/(15*mc) * (2*np.pi*... | 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 | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/eccUtils.py#L45-L63 |
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... | 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 | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/eccUtils.py#L65-L83 |
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
"""
# chirp mass
mc *= SOLAR2S
... | 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 | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/eccUtils.py#L85-L107 |
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... | 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]
phase0: Orbital phase [rad]
:param y: Vector of... | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/eccUtils.py#L109-L141 |
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
a given time.
:param F0: Initial orbital frequency [Hz]
:param e0: Initial orbital eccentricity
:param gam... | 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 gamma0: Initial angle of precession of periastron [rad]
:param mc: Chirp mass... | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/eccUtils.py#L143-L169 |
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
... | 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 | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/eccUtils.py#L171-L197 |
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
... | 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 | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/eccUtils.py#L199-L224 |
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
... | 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 | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/eccUtils.py#L226-L250 |
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)
in that it includes the time dependence of the advance of periastron.
:param nmax: Total numbe... | 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 number of harmonics to use
:param mc: Chirp mass of binary [Solar Mass]
:param dl: Lum... | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/eccUtils.py#L252-L305 |
vallis/libstempo | libstempo/multinest.py | run | def run(LogLikelihood,
Prior,
n_dims,
n_params = None,
n_clustering_params = None, wrapped_params = None,
importance_nested_sampling = True,
multimodal = True, const_efficiency_mode = False, n_live_points = 400,
evidence_tolerance = 0.5, sampling_efficiency = 0.8,
n_iter_before_update = ... | python | def run(LogLikelihood,
Prior,
n_dims,
n_params = None,
n_clustering_params = None, wrapped_params = None,
importance_nested_sampling = True,
multimodal = True, const_efficiency_mode = False, n_live_points = 400,
evidence_tolerance = 0.5, sampling_efficiency = 0.8,
n_iter_before_update = ... | Runs MultiNest
The most important parameters are the two log-probability functions Prior
and LogLikelihood. They are called by MultiNest.
Prior should transform the unit cube into the parameter cube. Here
is an example for a uniform prior::
def Prior(cube, ndim, nparams):
for i in... | https://github.com/vallis/libstempo/blob/0b19300a9b24d64c9ddc25cd6ddbfd12b6231990/libstempo/multinest.py#L22-L208 |
gagneurlab/concise | concise/legacy/kmer.py | best_kmers | def best_kmers(dt, response, sequence, k=6, consider_shift=True, n_cores=1,
seq_align="start", trim_seq_len=None):
"""
Find best k-mers for CONCISE initialization.
Args:
dt (pd.DataFrame): Table containing response variable and sequence.
response (str): Name of the column use... | python | def best_kmers(dt, response, sequence, k=6, consider_shift=True, n_cores=1,
seq_align="start", trim_seq_len=None):
"""
Find best k-mers for CONCISE initialization.
Args:
dt (pd.DataFrame): Table containing response variable and sequence.
response (str): Name of the column use... | Find best k-mers for CONCISE initialization.
Args:
dt (pd.DataFrame): Table containing response variable and sequence.
response (str): Name of the column used as the reponse variable.
sequence (str): Name of the column storing the DNA/RNA sequences.
k (int): Desired k-mer length.
... | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/legacy/kmer.py#L28-L102 |
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.
Returns:
pandas.DataFrame: Count matrix for seach sequence in seq_list
Example:
>>> 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})
k (int): K in k-mer.
Returns:
pandas.DataFrame: Count matrix for seach sequence in seq_list
Example:
>>> kmer... | 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.
Returns:
pandas.DataFrame: Count matrix for seach sequence in seq_list
Example:
>>> kmer_count(["ACGTTAT", "GACGCGA"], 2)
... | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/legacy/kmer.py#L107-L128 |
gagneurlab/concise | concise/legacy/kmer.py | generate_all_kmers | def generate_all_kmers(k):
"""
Generate all possible k-mers
Example:
>>> generate_all_kmers(2)
['AA', 'AC', 'AG', 'AT', 'CA', 'CC', 'CG', 'CT', 'GA', 'GC', 'GG', 'GT', 'TA', 'TC', 'TG', 'TT']
"""
bases = ['A', 'C', 'G', 'T']
return [''.join(p) for p in itertools.product(bases, repeat=k)... | python | def generate_all_kmers(k):
"""
Generate all possible k-mers
Example:
>>> generate_all_kmers(2)
['AA', 'AC', 'AG', 'AT', 'CA', 'CC', 'CG', 'CT', 'GA', 'GC', 'GG', 'GT', 'TA', 'TC', 'TG', 'TT']
"""
bases = ['A', 'C', 'G', 'T']
return [''.join(p) for p in itertools.product(bases, repeat=k)... | Generate all possible k-mers
Example:
>>> generate_all_kmers(2)
['AA', 'AC', 'AG', 'AT', 'CA', 'CC', 'CG', 'CT', 'GA', 'GC', 'GG', 'GT', 'TA', 'TC', 'TG', 'TT'] | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/legacy/kmer.py#L130-L139 |
gagneurlab/concise | concise/utils/helper.py | dict_to_numpy_dict | def dict_to_numpy_dict(obj_dict):
"""
Convert a dictionary of lists into a dictionary of numpy arrays
"""
return {key: np.asarray(value) if value is not None else None for key, value in obj_dict.items()} | python | def dict_to_numpy_dict(obj_dict):
"""
Convert a dictionary of lists into a dictionary of numpy arrays
"""
return {key: np.asarray(value) if value is not None else None for key, value in obj_dict.items()} | Convert a dictionary of lists into a dictionary of numpy arrays | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/utils/helper.py#L65-L69 |
gagneurlab/concise | concise/utils/helper.py | rec_dict_to_numpy_dict | def rec_dict_to_numpy_dict(obj_dict):
"""
Same as dict_to_numpy_dict, but recursive
"""
if type(obj_dict) == dict:
return {key: rec_dict_to_numpy_dict(value) if value is not None else None for key, value in obj_dict.items()}
elif obj_dict is None:
return None
else:
return... | python | def rec_dict_to_numpy_dict(obj_dict):
"""
Same as dict_to_numpy_dict, but recursive
"""
if type(obj_dict) == dict:
return {key: rec_dict_to_numpy_dict(value) if value is not None else None for key, value in obj_dict.items()}
elif obj_dict is None:
return None
else:
return... | Same as dict_to_numpy_dict, but recursive | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/utils/helper.py#L72-L81 |
gagneurlab/concise | concise/utils/helper.py | compare_numpy_dict | def compare_numpy_dict(a, b, exact=True):
"""
Compare two recursive numpy dictionaries
"""
if type(a) != type(b) and type(a) != np.ndarray and type(b) != np.ndarray:
return False
# go through a dictionary
if type(a) == dict and type(b) == dict:
if not a.keys() == b.keys():
... | python | def compare_numpy_dict(a, b, exact=True):
"""
Compare two recursive numpy dictionaries
"""
if type(a) != type(b) and type(a) != np.ndarray and type(b) != np.ndarray:
return False
# go through a dictionary
if type(a) == dict and type(b) == dict:
if not a.keys() == b.keys():
... | Compare two recursive numpy dictionaries | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/utils/helper.py#L84-L111 |
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):
"""Main function required by (TF)Concise class
"""
# make sure n_bases is an int
assert type(n_bases) == int
x = np.arange(start, end + 1)
knots = get_knots(start, end, n_bases, spline_order)
X_splines =... | python | def get_gam_splines(start=0, end=100, n_bases=10, spline_order=3, add_intercept=True):
"""Main function required by (TF)Concise class
"""
# make sure n_bases is an int
assert type(n_bases) == int
x = np.arange(start, end + 1)
knots = get_knots(start, end, n_bases, spline_order)
X_splines =... | Main function required by (TF)Concise class | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/utils/splines.py#L102-L116 |
gagneurlab/concise | concise/utils/splines.py | get_knots | def get_knots(start, end, n_bases=10, spline_order=3):
"""
Arguments:
x; np.array of dim 1
"""
x_range = end - start
start = start - x_range * 0.001
end = end + x_range * 0.001
# mgcv annotation
m = spline_order - 1
nk = n_bases - m # number of interior knots
... | python | def get_knots(start, end, n_bases=10, spline_order=3):
"""
Arguments:
x; np.array of dim 1
"""
x_range = end - start
start = start - x_range * 0.001
end = end + x_range * 0.001
# mgcv annotation
m = spline_order - 1
nk = n_bases - m # number of interior knots
... | Arguments:
x; np.array of dim 1 | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/utils/splines.py#L123-L140 |
gagneurlab/concise | concise/utils/splines.py | get_X_spline | def get_X_spline(x, knots, n_bases=10, spline_order=3, add_intercept=True):
"""
Returns:
np.array of shape [len(x), n_bases + (add_intercept)]
# BSpline formula
https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.BSpline.html#scipy.interpolate.BSpline
Fortran code:
h... | python | def get_X_spline(x, knots, n_bases=10, spline_order=3, add_intercept=True):
"""
Returns:
np.array of shape [len(x), n_bases + (add_intercept)]
# BSpline formula
https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.BSpline.html#scipy.interpolate.BSpline
Fortran code:
h... | Returns:
np.array of shape [len(x), n_bases + (add_intercept)]
# BSpline formula
https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.BSpline.html#scipy.interpolate.BSpline
Fortran code:
https://github.com/scipy/scipy/blob/v0.19.0/scipy/interpolate/fitpack/splev.f | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/utils/splines.py#L144-L173 |
gagneurlab/concise | concise/utils/splines.py | BSpline.getS | def getS(self, add_intercept=False):
"""Get the penalty matrix S
Returns
np.array, of shape (n_bases + add_intercept, n_bases + add_intercept)
"""
S = self.S
if add_intercept is True:
# S <- cbind(0, rbind(0, S)) # in R
zeros = np.zeros_like(S... | python | def getS(self, add_intercept=False):
"""Get the penalty matrix S
Returns
np.array, of shape (n_bases + add_intercept, n_bases + add_intercept)
"""
S = self.S
if add_intercept is True:
# S <- cbind(0, rbind(0, S)) # in R
zeros = np.zeros_like(S... | Get the penalty matrix S
Returns
np.array, of shape (n_bases + add_intercept, n_bases + add_intercept) | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/utils/splines.py#L49-L63 |
gagneurlab/concise | concise/utils/splines.py | BSpline.predict | def predict(self, x, add_intercept=False):
"""For some x, predict the bn(x) for each base
Arguments:
x: np.array; Vector of dimension 1
add_intercept: bool; should we add the intercept to the final array
Returns:
np.array, of shape (len(x), n_bases + (add_in... | python | def predict(self, x, add_intercept=False):
"""For some x, predict the bn(x) for each base
Arguments:
x: np.array; Vector of dimension 1
add_intercept: bool; should we add the intercept to the final array
Returns:
np.array, of shape (len(x), n_bases + (add_in... | For some x, predict the bn(x) for each base
Arguments:
x: np.array; Vector of dimension 1
add_intercept: bool; should we add the intercept to the final array
Returns:
np.array, of shape (len(x), n_bases + (add_intercept)) | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/utils/splines.py#L65-L85 |
gagneurlab/concise | concise/data/encode.py | get_metadata | def get_metadata():
"""Get pandas.DataFrame with metadata about the PWM's. Columns:
- PWM_id (id of the PWM - pass to get_pwm_list() for getting the pwm
- 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:
- PWM_id (id of the PWM - pass to get_pwm_list() for getting the pwm
- info1 - additional information about the motifs
- info2
- consensus: PWM consensus sequence
"""
motifs = _load_motifs()
motif_names... | Get pandas.DataFrame with metadata about the PWM's. Columns:
- PWM_id (id of the PWM - pass to get_pwm_list() for getting the pwm
- info1 - additional information about the motifs
- info2
- consensus: PWM consensus sequence | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/data/encode.py#L14-L31 |
gagneurlab/concise | concise/data/encode.py | get_pwm_list | def get_pwm_list(motif_name_list, pseudocountProb=0.0001):
"""Get a list of ENCODE PWM's.
# Arguments
pwm_id_list: List of id's from the `PWM_id` column in `get_metadata()` table
pseudocountProb: Added pseudocount probabilities to the PWM
# Returns
List of `concise.utils.pwm.PWM` i... | python | def get_pwm_list(motif_name_list, pseudocountProb=0.0001):
"""Get a list of ENCODE PWM's.
# Arguments
pwm_id_list: List of id's from the `PWM_id` column in `get_metadata()` table
pseudocountProb: Added pseudocount probabilities to the PWM
# Returns
List of `concise.utils.pwm.PWM` i... | Get a list of ENCODE PWM's.
# Arguments
pwm_id_list: List of id's from the `PWM_id` column in `get_metadata()` table
pseudocountProb: Added pseudocount probabilities to the PWM
# Returns
List of `concise.utils.pwm.PWM` instances. | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/data/encode.py#L34-L47 |
gagneurlab/concise | concise/eval_metrics.py | auc | def auc(y_true, y_pred, round=True):
"""Area under the ROC curve
"""
y_true, y_pred = _mask_value_nan(y_true, y_pred)
if round:
y_true = y_true.round()
if len(y_true) == 0 or len(np.unique(y_true)) < 2:
return np.nan
return skm.roc_auc_score(y_true, y_pred) | python | def auc(y_true, y_pred, round=True):
"""Area under the ROC curve
"""
y_true, y_pred = _mask_value_nan(y_true, y_pred)
if round:
y_true = y_true.round()
if len(y_true) == 0 or len(np.unique(y_true)) < 2:
return np.nan
return skm.roc_auc_score(y_true, y_pred) | Area under the ROC curve | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/eval_metrics.py#L37-L46 |
gagneurlab/concise | concise/eval_metrics.py | auprc | def auprc(y_true, y_pred):
"""Area under the precision-recall curve
"""
y_true, y_pred = _mask_value_nan(y_true, y_pred)
precision, recall, _ = skm.precision_recall_curve(y_true, y_pred)
return skm.auc(recall, precision) | python | def auprc(y_true, y_pred):
"""Area under the precision-recall curve
"""
y_true, y_pred = _mask_value_nan(y_true, y_pred)
precision, recall, _ = skm.precision_recall_curve(y_true, y_pred)
return skm.auc(recall, precision) | Area under the precision-recall curve | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/eval_metrics.py#L49-L54 |
gagneurlab/concise | concise/eval_metrics.py | recall_at_precision | def recall_at_precision(y_true, y_pred, precision):
"""Recall at a certain precision threshold
Args:
y_true: true labels
y_pred: predicted labels
precision: resired precision level at which where to compute the recall
"""
y_true, y_pred = _mask_value_nan(y_true, y_pred)
precision,... | python | def recall_at_precision(y_true, y_pred, precision):
"""Recall at a certain precision threshold
Args:
y_true: true labels
y_pred: predicted labels
precision: resired precision level at which where to compute the recall
"""
y_true, y_pred = _mask_value_nan(y_true, y_pred)
precision,... | Recall at a certain precision threshold
Args:
y_true: true labels
y_pred: predicted labels
precision: resired precision level at which where to compute the recall | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/eval_metrics.py#L57-L67 |
gagneurlab/concise | concise/eval_metrics.py | accuracy | def accuracy(y_true, y_pred, round=True):
"""Classification accuracy
"""
y_true, y_pred = _mask_value_nan(y_true, y_pred)
if round:
y_true = np.round(y_true)
y_pred = np.round(y_pred)
return skm.accuracy_score(y_true, y_pred) | python | def accuracy(y_true, y_pred, round=True):
"""Classification accuracy
"""
y_true, y_pred = _mask_value_nan(y_true, y_pred)
if round:
y_true = np.round(y_true)
y_pred = np.round(y_pred)
return skm.accuracy_score(y_true, y_pred) | Classification accuracy | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/eval_metrics.py#L70-L77 |
gagneurlab/concise | concise/eval_metrics.py | tpr | def tpr(y_true, y_pred, round=True):
"""True positive rate `tp / (tp + fn)`
"""
y_true, y_pred = _mask_value_nan(y_true, y_pred)
if round:
y_true = np.round(y_true)
y_pred = np.round(y_pred)
return skm.recall_score(y_true, y_pred) | python | def tpr(y_true, y_pred, round=True):
"""True positive rate `tp / (tp + fn)`
"""
y_true, y_pred = _mask_value_nan(y_true, y_pred)
if round:
y_true = np.round(y_true)
y_pred = np.round(y_pred)
return skm.recall_score(y_true, y_pred) | True positive rate `tp / (tp + fn)` | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/eval_metrics.py#L80-L87 |
gagneurlab/concise | concise/eval_metrics.py | tnr | def tnr(y_true, y_pred, round=True):
"""True negative rate `tn / (tn + fp)`
"""
y_true, y_pred = _mask_value_nan(y_true, y_pred)
if round:
y_true = np.round(y_true)
y_pred = np.round(y_pred)
c = skm.confusion_matrix(y_true, y_pred)
return c[0, 0] / c[0].sum() | python | def tnr(y_true, y_pred, round=True):
"""True negative rate `tn / (tn + fp)`
"""
y_true, y_pred = _mask_value_nan(y_true, y_pred)
if round:
y_true = np.round(y_true)
y_pred = np.round(y_pred)
c = skm.confusion_matrix(y_true, y_pred)
return c[0, 0] / c[0].sum() | True negative rate `tn / (tn + fp)` | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/eval_metrics.py#L90-L98 |
gagneurlab/concise | concise/eval_metrics.py | mcc | def mcc(y_true, y_pred, round=True):
"""Matthews correlation coefficient
"""
y_true, y_pred = _mask_value_nan(y_true, y_pred)
if round:
y_true = np.round(y_true)
y_pred = np.round(y_pred)
return skm.matthews_corrcoef(y_true, y_pred) | python | def mcc(y_true, y_pred, round=True):
"""Matthews correlation coefficient
"""
y_true, y_pred = _mask_value_nan(y_true, y_pred)
if round:
y_true = np.round(y_true)
y_pred = np.round(y_pred)
return skm.matthews_corrcoef(y_true, y_pred) | Matthews correlation coefficient | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/eval_metrics.py#L101-L108 |
gagneurlab/concise | concise/eval_metrics.py | f1 | def f1(y_true, y_pred, round=True):
"""F1 score: `2 * (p * r) / (p + r)`, where p=precision and r=recall.
"""
y_true, y_pred = _mask_value_nan(y_true, y_pred)
if round:
y_true = np.round(y_true)
y_pred = np.round(y_pred)
return skm.f1_score(y_true, y_pred) | python | def f1(y_true, y_pred, round=True):
"""F1 score: `2 * (p * r) / (p + r)`, where p=precision and r=recall.
"""
y_true, y_pred = _mask_value_nan(y_true, y_pred)
if round:
y_true = np.round(y_true)
y_pred = np.round(y_pred)
return skm.f1_score(y_true, y_pred) | F1 score: `2 * (p * r) / (p + r)`, where p=precision and r=recall. | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/eval_metrics.py#L111-L118 |
gagneurlab/concise | concise/eval_metrics.py | cat_acc | def cat_acc(y_true, y_pred):
"""Categorical accuracy
"""
return np.mean(y_true.argmax(axis=1) == y_pred.argmax(axis=1)) | python | def cat_acc(y_true, y_pred):
"""Categorical accuracy
"""
return np.mean(y_true.argmax(axis=1) == y_pred.argmax(axis=1)) | Categorical accuracy | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/eval_metrics.py#L123-L126 |
gagneurlab/concise | concise/eval_metrics.py | cor | def cor(y_true, y_pred):
"""Compute Pearson correlation coefficient.
"""
y_true, y_pred = _mask_nan(y_true, y_pred)
return np.corrcoef(y_true, y_pred)[0, 1] | python | def cor(y_true, y_pred):
"""Compute Pearson correlation coefficient.
"""
y_true, y_pred = _mask_nan(y_true, y_pred)
return np.corrcoef(y_true, y_pred)[0, 1] | Compute Pearson correlation coefficient. | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/eval_metrics.py#L131-L135 |
gagneurlab/concise | concise/eval_metrics.py | kendall | def kendall(y_true, y_pred, nb_sample=100000):
"""Kendall's tau coefficient, Kendall rank correlation coefficient
"""
y_true, y_pred = _mask_nan(y_true, y_pred)
if len(y_true) > nb_sample:
idx = np.arange(len(y_true))
np.random.shuffle(idx)
idx = idx[:nb_sample]
y_true = ... | python | def kendall(y_true, y_pred, nb_sample=100000):
"""Kendall's tau coefficient, Kendall rank correlation coefficient
"""
y_true, y_pred = _mask_nan(y_true, y_pred)
if len(y_true) > nb_sample:
idx = np.arange(len(y_true))
np.random.shuffle(idx)
idx = idx[:nb_sample]
y_true = ... | Kendall's tau coefficient, Kendall rank correlation coefficient | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/eval_metrics.py#L138-L148 |
gagneurlab/concise | concise/eval_metrics.py | mad | def mad(y_true, y_pred):
"""Median absolute deviation
"""
y_true, y_pred = _mask_nan(y_true, y_pred)
return np.mean(np.abs(y_true - y_pred)) | python | def mad(y_true, y_pred):
"""Median absolute deviation
"""
y_true, y_pred = _mask_nan(y_true, y_pred)
return np.mean(np.abs(y_true - y_pred)) | Median absolute deviation | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/eval_metrics.py#L151-L155 |
gagneurlab/concise | concise/eval_metrics.py | mse | def mse(y_true, y_pred):
"""Mean squared error
"""
y_true, y_pred = _mask_nan(y_true, y_pred)
return ((y_true - y_pred) ** 2).mean(axis=None) | python | def mse(y_true, y_pred):
"""Mean squared error
"""
y_true, y_pred = _mask_nan(y_true, y_pred)
return ((y_true - y_pred) ** 2).mean(axis=None) | Mean squared error | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/eval_metrics.py#L170-L174 |
gagneurlab/concise | concise/eval_metrics.py | var_explained | def var_explained(y_true, y_pred):
"""Fraction of variance explained.
"""
y_true, y_pred = _mask_nan(y_true, y_pred)
var_resid = np.var(y_true - y_pred)
var_y_true = np.var(y_true)
return 1 - var_resid / var_y_true | python | def var_explained(y_true, y_pred):
"""Fraction of variance explained.
"""
y_true, y_pred = _mask_nan(y_true, y_pred)
var_resid = np.var(y_true - y_pred)
var_y_true = np.var(y_true)
return 1 - var_resid / var_y_true | Fraction of variance explained. | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/eval_metrics.py#L183-L189 |
gagneurlab/concise | concise/legacy/args_sampler.py | sample_params | def sample_params(params):
"""Randomly sample hyper-parameters stored in a dictionary on a predefined range and scale.
Useful for hyper-parameter random search.
Args:
params (dict): hyper-parameters to sample. Dictionary value-type parsing:
- :python:`[1e3, 1e7]` - uniformly ... | python | def sample_params(params):
"""Randomly sample hyper-parameters stored in a dictionary on a predefined range and scale.
Useful for hyper-parameter random search.
Args:
params (dict): hyper-parameters to sample. Dictionary value-type parsing:
- :python:`[1e3, 1e7]` - uniformly ... | Randomly sample hyper-parameters stored in a dictionary on a predefined range and scale.
Useful for hyper-parameter random search.
Args:
params (dict): hyper-parameters to sample. Dictionary value-type parsing:
- :python:`[1e3, 1e7]` - uniformly sample on a **log10** scale from t... | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/legacy/args_sampler.py#L8-L68 |
gagneurlab/concise | concise/metrics.py | contingency_table | def contingency_table(y, z):
"""Note: if y and z are not rounded to 0 or 1, they are ignored
"""
y = K.cast(K.round(y), K.floatx())
z = K.cast(K.round(z), K.floatx())
def count_matches(y, z):
return K.sum(K.cast(y, K.floatx()) * K.cast(z, K.floatx()))
ones = K.ones_like(y)
zeros =... | python | def contingency_table(y, z):
"""Note: if y and z are not rounded to 0 or 1, they are ignored
"""
y = K.cast(K.round(y), K.floatx())
z = K.cast(K.round(z), K.floatx())
def count_matches(y, z):
return K.sum(K.cast(y, K.floatx()) * K.cast(z, K.floatx()))
ones = K.ones_like(y)
zeros =... | Note: if y and z are not rounded to 0 or 1, they are ignored | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/metrics.py#L17-L38 |
gagneurlab/concise | concise/metrics.py | tpr | def tpr(y, z):
"""True positive rate `tp / (tp + fn)`
"""
tp, tn, fp, fn = contingency_table(y, z)
return tp / (tp + fn) | python | def tpr(y, z):
"""True positive rate `tp / (tp + fn)`
"""
tp, tn, fp, fn = contingency_table(y, z)
return tp / (tp + fn) | True positive rate `tp / (tp + fn)` | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/metrics.py#L41-L45 |
gagneurlab/concise | concise/metrics.py | tnr | def tnr(y, z):
"""True negative rate `tn / (tn + fp)`
"""
tp, tn, fp, fn = contingency_table(y, z)
return tn / (tn + fp) | python | def tnr(y, z):
"""True negative rate `tn / (tn + fp)`
"""
tp, tn, fp, fn = contingency_table(y, z)
return tn / (tn + fp) | True negative rate `tn / (tn + fp)` | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/metrics.py#L48-L52 |
gagneurlab/concise | concise/metrics.py | fpr | def fpr(y, z):
"""False positive rate `fp / (fp + tn)`
"""
tp, tn, fp, fn = contingency_table(y, z)
return fp / (fp + tn) | python | def fpr(y, z):
"""False positive rate `fp / (fp + tn)`
"""
tp, tn, fp, fn = contingency_table(y, z)
return fp / (fp + tn) | False positive rate `fp / (fp + tn)` | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/metrics.py#L55-L59 |
gagneurlab/concise | concise/metrics.py | fnr | def fnr(y, z):
"""False negative rate `fn / (fn + tp)`
"""
tp, tn, fp, fn = contingency_table(y, z)
return fn / (fn + tp) | python | def fnr(y, z):
"""False negative rate `fn / (fn + tp)`
"""
tp, tn, fp, fn = contingency_table(y, z)
return fn / (fn + tp) | False negative rate `fn / (fn + tp)` | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/metrics.py#L62-L66 |
gagneurlab/concise | concise/metrics.py | precision | def precision(y, z):
"""Precision `tp / (tp + fp)`
"""
tp, tn, fp, fn = contingency_table(y, z)
return tp / (tp + fp) | python | def precision(y, z):
"""Precision `tp / (tp + fp)`
"""
tp, tn, fp, fn = contingency_table(y, z)
return tp / (tp + fp) | Precision `tp / (tp + fp)` | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/metrics.py#L69-L73 |
gagneurlab/concise | concise/metrics.py | fdr | def fdr(y, z):
"""False discovery rate `fp / (tp + fp)`
"""
tp, tn, fp, fn = contingency_table(y, z)
return fp / (tp + fp) | python | def fdr(y, z):
"""False discovery rate `fp / (tp + fp)`
"""
tp, tn, fp, fn = contingency_table(y, z)
return fp / (tp + fp) | False discovery rate `fp / (tp + fp)` | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/metrics.py#L76-L80 |
gagneurlab/concise | concise/metrics.py | accuracy | def accuracy(y, z):
"""Classification accuracy `(tp + tn) / (tp + tn + fp + fn)`
"""
tp, tn, fp, fn = contingency_table(y, z)
return (tp + tn) / (tp + tn + fp + fn) | python | def accuracy(y, z):
"""Classification accuracy `(tp + tn) / (tp + tn + fp + fn)`
"""
tp, tn, fp, fn = contingency_table(y, z)
return (tp + tn) / (tp + tn + fp + fn) | Classification accuracy `(tp + tn) / (tp + tn + fp + fn)` | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/metrics.py#L83-L87 |
gagneurlab/concise | concise/metrics.py | f1 | def f1(y, z):
"""F1 score: `2 * (p * r) / (p + r)`, where p=precision and r=recall.
"""
_recall = recall(y, z)
_prec = precision(y, z)
return 2 * (_prec * _recall) / (_prec + _recall) | python | def f1(y, z):
"""F1 score: `2 * (p * r) / (p + r)`, where p=precision and r=recall.
"""
_recall = recall(y, z)
_prec = precision(y, z)
return 2 * (_prec * _recall) / (_prec + _recall) | F1 score: `2 * (p * r) / (p + r)`, where p=precision and r=recall. | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/metrics.py#L94-L99 |
gagneurlab/concise | concise/metrics.py | mcc | def mcc(y, z):
"""Matthews correlation coefficient
"""
tp, tn, fp, fn = contingency_table(y, z)
return (tp * tn - fp * fn) / K.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn)) | python | def mcc(y, z):
"""Matthews correlation coefficient
"""
tp, tn, fp, fn = contingency_table(y, z)
return (tp * tn - fp * fn) / K.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn)) | Matthews correlation coefficient | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/metrics.py#L102-L106 |
gagneurlab/concise | concise/metrics.py | cat_acc | def cat_acc(y, z):
"""Classification accuracy for multi-categorical case
"""
weights = _cat_sample_weights(y)
_acc = K.cast(K.equal(K.argmax(y, axis=-1),
K.argmax(z, axis=-1)),
K.floatx())
_acc = K.sum(_acc * weights) / K.sum(weights)
return _acc | python | def cat_acc(y, z):
"""Classification accuracy for multi-categorical case
"""
weights = _cat_sample_weights(y)
_acc = K.cast(K.equal(K.argmax(y, axis=-1),
K.argmax(z, axis=-1)),
K.floatx())
_acc = K.sum(_acc * weights) / K.sum(weights)
return _acc | Classification accuracy for multi-categorical case | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/metrics.py#L126-L134 |
gagneurlab/concise | concise/metrics.py | var_explained | def var_explained(y_true, y_pred):
"""Fraction of variance explained.
"""
var_resid = K.var(y_true - y_pred)
var_y_true = K.var(y_true)
return 1 - var_resid / var_y_true | python | def var_explained(y_true, y_pred):
"""Fraction of variance explained.
"""
var_resid = K.var(y_true - y_pred)
var_y_true = K.var(y_true)
return 1 - var_resid / var_y_true | Fraction of variance explained. | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/metrics.py#L152-L157 |
gagneurlab/concise | concise/utils/model_data.py | split_KFold_idx | def split_KFold_idx(train, cv_n_folds=5, stratified=False, random_state=None):
"""Get k-fold indices generator
"""
test_len(train)
y = train[1]
n_rows = y.shape[0]
if stratified:
if len(y.shape) > 1:
if y.shape[1] > 1:
raise ValueError("Can't use stratified K-... | python | def split_KFold_idx(train, cv_n_folds=5, stratified=False, random_state=None):
"""Get k-fold indices generator
"""
test_len(train)
y = train[1]
n_rows = y.shape[0]
if stratified:
if len(y.shape) > 1:
if y.shape[1] > 1:
raise ValueError("Can't use stratified K-... | Get k-fold indices generator | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/utils/model_data.py#L38-L55 |
gagneurlab/concise | concise/utils/model_data.py | subset | def subset(train, idx, keep_other=True):
"""Subset the `train=(x, y)` data tuple, each of the form:
- list, np.ndarray
- tuple, np.ndarray
- dictionary, np.ndarray
- np.ndarray, np.ndarray
# Note
In case there are other data present in the tuple:
`(x, y, other1, other2, ...)`, ... | python | def subset(train, idx, keep_other=True):
"""Subset the `train=(x, y)` data tuple, each of the form:
- list, np.ndarray
- tuple, np.ndarray
- dictionary, np.ndarray
- np.ndarray, np.ndarray
# Note
In case there are other data present in the tuple:
`(x, y, other1, other2, ...)`, ... | Subset the `train=(x, y)` data tuple, each of the form:
- list, np.ndarray
- tuple, np.ndarray
- dictionary, np.ndarray
- np.ndarray, np.ndarray
# Note
In case there are other data present in the tuple:
`(x, y, other1, other2, ...)`, these get passed on as:
`(x_sub, y_sub, ... | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/utils/model_data.py#L58-L92 |
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):
"""
Prepare data for Concise.train or ConciseCV.train.
Args:
dt: A pandas DataFrame containing all the required data.
features (List of strings): Column names of `dt` used to produce the ... | python | def prepare_data(dt, features, response, sequence, id_column=None, seq_align="end", trim_seq_len=None):
"""
Prepare data for Concise.train or ConciseCV.train.
Args:
dt: A pandas DataFrame containing all the required data.
features (List of strings): Column names of `dt` used to produce the ... | Prepare data for Concise.train or ConciseCV.train.
Args:
dt: A pandas DataFrame containing all the required data.
features (List of strings): Column names of `dt` used to produce the features design matrix. These columns should be numeric.
response (str or list of strings): Name(s) of colum... | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/legacy/get_data.py#L10-L59 |
gagneurlab/concise | concise/preprocessing/splines.py | _trunc | def _trunc(x, minval=None, maxval=None):
"""Truncate vector values to have values on range [minval, maxval]
"""
x = np.copy(x)
if minval is not None:
x[x < minval] = minval
if maxval is not None:
x[x > maxval] = maxval
return x | python | def _trunc(x, minval=None, maxval=None):
"""Truncate vector values to have values on range [minval, maxval]
"""
x = np.copy(x)
if minval is not None:
x[x < minval] = minval
if maxval is not None:
x[x > maxval] = maxval
return x | Truncate vector values to have values on range [minval, maxval] | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/preprocessing/splines.py#L8-L16 |
gagneurlab/concise | concise/preprocessing/splines.py | encodeSplines | def encodeSplines(x, n_bases=10, spline_order=3, start=None, end=None, warn=True):
"""**Deprecated**. Function version of the transformer class `EncodeSplines`.
Get B-spline base-function expansion
# Details
First, the knots for B-spline basis functions are placed
equidistantly on the [star... | python | def encodeSplines(x, n_bases=10, spline_order=3, start=None, end=None, warn=True):
"""**Deprecated**. Function version of the transformer class `EncodeSplines`.
Get B-spline base-function expansion
# Details
First, the knots for B-spline basis functions are placed
equidistantly on the [star... | **Deprecated**. Function version of the transformer class `EncodeSplines`.
Get B-spline base-function expansion
# Details
First, the knots for B-spline basis functions are placed
equidistantly on the [start, end] range.
(inferred from the data if None). Next, b_n(x) value is
is ... | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/preprocessing/splines.py#L93-L149 |
gagneurlab/concise | concise/preprocessing/splines.py | EncodeSplines.fit | def fit(self, x):
"""Calculate the knot placement from the values ranges.
# Arguments
x: numpy array, either N x D or N x L x D dimensional.
"""
assert x.ndim > 1
self.data_min_ = np.min(x, axis=tuple(range(x.ndim - 1)))
self.data_max_ = np.max(x, axis=tuple(... | python | def fit(self, x):
"""Calculate the knot placement from the values ranges.
# Arguments
x: numpy array, either N x D or N x L x D dimensional.
"""
assert x.ndim > 1
self.data_min_ = np.min(x, axis=tuple(range(x.ndim - 1)))
self.data_max_ = np.max(x, axis=tuple(... | Calculate the knot placement from the values ranges.
# Arguments
x: numpy array, either N x D or N x L x D dimensional. | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/preprocessing/splines.py#L52-L64 |
gagneurlab/concise | concise/preprocessing/splines.py | EncodeSplines.transform | def transform(self, x, warn=True):
"""Obtain the transformed values
"""
# 1. split across last dimension
# 2. re-use ranges
# 3. Merge
array_list = [encodeSplines(x[..., i].reshape((-1, 1)),
n_bases=self.n_bases,
... | python | def transform(self, x, warn=True):
"""Obtain the transformed values
"""
# 1. split across last dimension
# 2. re-use ranges
# 3. Merge
array_list = [encodeSplines(x[..., i].reshape((-1, 1)),
n_bases=self.n_bases,
... | Obtain the transformed values | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/preprocessing/splines.py#L66-L79 |
gagneurlab/concise | concise/layers.py | InputCodon | def InputCodon(seq_length, ignore_stop_codons=True, name=None, **kwargs):
"""Input placeholder for array returned by `encodeCodon`
Note: The seq_length is divided by 3
Wrapper for: `keras.layers.Input((seq_length / 3, 61 or 61), name=name, **kwargs)`
"""
if ignore_stop_codons:
vocab = CODO... | python | def InputCodon(seq_length, ignore_stop_codons=True, name=None, **kwargs):
"""Input placeholder for array returned by `encodeCodon`
Note: The seq_length is divided by 3
Wrapper for: `keras.layers.Input((seq_length / 3, 61 or 61), name=name, **kwargs)`
"""
if ignore_stop_codons:
vocab = CODO... | Input placeholder for array returned by `encodeCodon`
Note: The seq_length is divided by 3
Wrapper for: `keras.layers.Input((seq_length / 3, 61 or 61), name=name, **kwargs)` | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/layers.py#L40-L53 |
gagneurlab/concise | concise/layers.py | InputAA | def InputAA(seq_length, name=None, **kwargs):
"""Input placeholder for array returned by `encodeAA`
Wrapper for: `keras.layers.Input((seq_length, 22), name=name, **kwargs)`
"""
return Input((seq_length, len(AMINO_ACIDS)), name=name, **kwargs) | python | def InputAA(seq_length, name=None, **kwargs):
"""Input placeholder for array returned by `encodeAA`
Wrapper for: `keras.layers.Input((seq_length, 22), name=name, **kwargs)`
"""
return Input((seq_length, len(AMINO_ACIDS)), name=name, **kwargs) | Input placeholder for array returned by `encodeAA`
Wrapper for: `keras.layers.Input((seq_length, 22), name=name, **kwargs)` | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/layers.py#L56-L61 |
gagneurlab/concise | concise/layers.py | InputRNAStructure | def InputRNAStructure(seq_length, name=None, **kwargs):
"""Input placeholder for array returned by `encodeRNAStructure`
Wrapper for: `keras.layers.Input((seq_length, 5), name=name, **kwargs)`
"""
return Input((seq_length, len(RNAplfold_PROFILES)), name=name, **kwargs) | python | def InputRNAStructure(seq_length, name=None, **kwargs):
"""Input placeholder for array returned by `encodeRNAStructure`
Wrapper for: `keras.layers.Input((seq_length, 5), name=name, **kwargs)`
"""
return Input((seq_length, len(RNAplfold_PROFILES)), name=name, **kwargs) | Input placeholder for array returned by `encodeRNAStructure`
Wrapper for: `keras.layers.Input((seq_length, 5), name=name, **kwargs)` | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/layers.py#L64-L69 |
gagneurlab/concise | concise/layers.py | InputSplines | def InputSplines(seq_length, n_bases=10, name=None, **kwargs):
"""Input placeholder for array returned by `encodeSplines`
Wrapper for: `keras.layers.Input((seq_length, n_bases), name=name, **kwargs)`
"""
return Input((seq_length, n_bases), name=name, **kwargs) | python | def InputSplines(seq_length, n_bases=10, name=None, **kwargs):
"""Input placeholder for array returned by `encodeSplines`
Wrapper for: `keras.layers.Input((seq_length, n_bases), name=name, **kwargs)`
"""
return Input((seq_length, n_bases), name=name, **kwargs) | Input placeholder for array returned by `encodeSplines`
Wrapper for: `keras.layers.Input((seq_length, n_bases), name=name, **kwargs)` | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/layers.py#L73-L78 |
gagneurlab/concise | concise/layers.py | InputSplines1D | def InputSplines1D(seq_length, n_bases=10, name=None, **kwargs):
"""Input placeholder for array returned by `encodeSplines`
Wrapper for: `keras.layers.Input((seq_length, n_bases), name=name, **kwargs)`
"""
return Input((seq_length, n_bases), name=name, **kwargs) | python | def InputSplines1D(seq_length, n_bases=10, name=None, **kwargs):
"""Input placeholder for array returned by `encodeSplines`
Wrapper for: `keras.layers.Input((seq_length, n_bases), name=name, **kwargs)`
"""
return Input((seq_length, n_bases), name=name, **kwargs) | Input placeholder for array returned by `encodeSplines`
Wrapper for: `keras.layers.Input((seq_length, n_bases), name=name, **kwargs)` | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/layers.py#L81-L86 |
gagneurlab/concise | concise/layers.py | InputDNAQuantity | def InputDNAQuantity(seq_length, n_features=1, name=None, **kwargs):
"""Convenience wrapper around `keras.layers.Input`:
`Input((seq_length, n_features), name=name, **kwargs)`
"""
return Input((seq_length, n_features), name=name, **kwargs) | python | def InputDNAQuantity(seq_length, n_features=1, name=None, **kwargs):
"""Convenience wrapper around `keras.layers.Input`:
`Input((seq_length, n_features), name=name, **kwargs)`
"""
return Input((seq_length, n_features), name=name, **kwargs) | Convenience wrapper around `keras.layers.Input`:
`Input((seq_length, n_features), name=name, **kwargs)` | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/layers.py#L90-L95 |
gagneurlab/concise | concise/layers.py | InputDNAQuantitySplines | def InputDNAQuantitySplines(seq_length, n_bases=10, name="DNASmoothPosition", **kwargs):
"""Convenience wrapper around keras.layers.Input:
`Input((seq_length, n_bases), name=name, **kwargs)`
"""
return Input((seq_length, n_bases), name=name, **kwargs) | python | def InputDNAQuantitySplines(seq_length, n_bases=10, name="DNASmoothPosition", **kwargs):
"""Convenience wrapper around keras.layers.Input:
`Input((seq_length, n_bases), name=name, **kwargs)`
"""
return Input((seq_length, n_bases), name=name, **kwargs) | Convenience wrapper around keras.layers.Input:
`Input((seq_length, n_bases), name=name, **kwargs)` | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/layers.py#L99-L104 |
gagneurlab/concise | concise/layers.py | ConvSequence._plot_weights_heatmap | def _plot_weights_heatmap(self, index=None, figsize=None, **kwargs):
"""Plot weights as a heatmap
index = can be a particular index or a list of indicies
**kwargs - additional arguments to concise.utils.plot.heatmap
"""
W = self.get_weights()[0]
if index is None:
... | python | def _plot_weights_heatmap(self, index=None, figsize=None, **kwargs):
"""Plot weights as a heatmap
index = can be a particular index or a list of indicies
**kwargs - additional arguments to concise.utils.plot.heatmap
"""
W = self.get_weights()[0]
if index is None:
... | Plot weights as a heatmap
index = can be a particular index or a list of indicies
**kwargs - additional arguments to concise.utils.plot.heatmap | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/layers.py#L197-L210 |
gagneurlab/concise | concise/layers.py | ConvSequence._plot_weights_motif | def _plot_weights_motif(self, index, plot_type="motif_raw",
background_probs=DEFAULT_BASE_BACKGROUND,
ncol=1,
figsize=None):
"""Index can only be a single int
"""
w_all = self.get_weights()
if len(w_all... | python | def _plot_weights_motif(self, index, plot_type="motif_raw",
background_probs=DEFAULT_BASE_BACKGROUND,
ncol=1,
figsize=None):
"""Index can only be a single int
"""
w_all = self.get_weights()
if len(w_all... | Index can only be a single int | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/layers.py#L212-L243 |
gagneurlab/concise | concise/layers.py | ConvSequence.plot_weights | def plot_weights(self, index=None, plot_type="motif_raw", figsize=None, ncol=1, **kwargs):
"""Plot filters as heatmap or motifs
index = can be a particular index or a list of indicies
**kwargs - additional arguments to concise.utils.plot.heatmap
"""
if "heatmap" in self.AVAILAB... | python | def plot_weights(self, index=None, plot_type="motif_raw", figsize=None, ncol=1, **kwargs):
"""Plot filters as heatmap or motifs
index = can be a particular index or a list of indicies
**kwargs - additional arguments to concise.utils.plot.heatmap
"""
if "heatmap" in self.AVAILAB... | Plot filters as heatmap or motifs
index = can be a particular index or a list of indicies
**kwargs - additional arguments to concise.utils.plot.heatmap | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/layers.py#L245-L257 |
gagneurlab/concise | concise/initializers.py | _check_pwm_list | def _check_pwm_list(pwm_list):
"""Check the input validity
"""
for pwm in pwm_list:
if not isinstance(pwm, PWM):
raise TypeError("element {0} of pwm_list is not of type PWM".format(pwm))
return True | python | def _check_pwm_list(pwm_list):
"""Check the input validity
"""
for pwm in pwm_list:
if not isinstance(pwm, PWM):
raise TypeError("element {0} of pwm_list is not of type PWM".format(pwm))
return True | Check the input validity | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/initializers.py#L22-L28 |
gagneurlab/concise | concise/initializers.py | _truncated_normal | def _truncated_normal(mean,
stddev,
seed=None,
normalize=True,
alpha=0.01):
''' Add noise with truncnorm from numpy.
Bounded (0.001,0.999)
'''
# within range ()
# provide entry to chose which adding noise way to ... | python | def _truncated_normal(mean,
stddev,
seed=None,
normalize=True,
alpha=0.01):
''' Add noise with truncnorm from numpy.
Bounded (0.001,0.999)
'''
# within range ()
# provide entry to chose which adding noise way to ... | Add noise with truncnorm from numpy.
Bounded (0.001,0.999) | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/initializers.py#L31-L54 |
gagneurlab/concise | concise/utils/plot.py | heatmap | def heatmap(w, vmin=None, vmax=None, diverge_color=False,
ncol=1,
plot_name=None, vocab=["A", "C", "G", "T"], figsize=(6, 2)):
"""Plot a heatmap from weight matrix w
vmin, vmax = z axis range
diverge_color = Should we use diverging colors?
plot_name = plot_title
vocab = voca... | python | def heatmap(w, vmin=None, vmax=None, diverge_color=False,
ncol=1,
plot_name=None, vocab=["A", "C", "G", "T"], figsize=(6, 2)):
"""Plot a heatmap from weight matrix w
vmin, vmax = z axis range
diverge_color = Should we use diverging colors?
plot_name = plot_title
vocab = voca... | 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) | 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)
path_s... | Given a POLYGON string, standardize the coordinates to a 1x1 grid.
Input : data_str (taken from above)
Output: tuple of polygon objects | https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/utils/plot.py#L98-L126 |
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