def pdf_to_quantile(f,precision=0.00001):
def tmp(x):
return x*f(x)
mn=integ(tmp,-10000,10000)[0]
val=integ(f,-10000,mn)[0]
max_iter=100
def quantile(x,precision=precision):
cur=mn
quant=val
incr=0
while (abs(quant-x)>precision)&(incr<max_iter):
past_cur=cur
cur=(x-quant)/f(past_cur)+cur
quant=quant+(integ(f,past_cur,cur)[0])
incr+=1
quant=integ(f,-10000,cur)[0]
while (abs(quant-x)>precision)&(incr<max_iter):
past_cur=cur
cur=(x-quant)/f(past_cur)+cur
quant=quant+integ(f,past_cur,cur)[0]
incr+=1
print('nb iteration; %d, quantile: %f'%(incr,quant))
return float(cur)
return quantile
def main(lamm,flum,lamt,flut): ##--------------------------------------------------------- #Import packages import os #from scipy.interpolate import splprep, splev from scipy.interpolate import interp1d from scipy.integrate import trapz as integ from numpy import loadtxt, arange #,pi, log, linspace, convolve ##--------------------------------------------------------- ## define lambda vector = the same as transmission lambda ln=len(lamt) #----------------------------------------------------------- ## interpolate transmisison to cover same lambda intervals ## interpolate model to cover same lambda intervals ## Create interpolating function #tti=interp1d(lamt,flut) mmi=interp1d(lamm,flum) moint=[] trint=[] for i in lamt: moint.append(float(mmi(i))) ## length must be the same print 'length of interpolated model is ', len(moint) print 'length of transmission file is', len(lamt) print 'COMPUTE INTEGRAL' # filter characteristics: ltr=flut*lamt tint = integ(flut,lamt) # surface lef = integ(ltr,lamt) /tint # effective wavelength delta=flut*((lamt-lef)**2) dint = 2*(integ(delta,lamt) /tint)**(0.5) # whole bandpass # Compute flux averaged in the transmission : ymt=moint*flut imt = integ(ymt,lamt) #flav=1 flav=imt/tint# averaged flux # result res=[0*x for x in range (0,3)] res[0]=lef res[1]=dint res[2]=flav return res
def extract_coefs(
_x: np.ndarray,
signal: np.ndarray,
nb: int = 1,
act: int = None,
plot: bool = True,
graph_len: int = 200,
):
if plot:
fig, ax = plt.subplots()
ax.plot(signal[:graph_len], "+")
if act is None:
act = 1
freqs: list = []
amps: list = []
for n in range(nb):
freqs.append(signal.argmax() / end)
mi = int(signal.argmax() - act)
if mi < 0:
mi = 0
ma = int(signal.argmax() + act + 1)
amps.append(integ(signal[mi:ma], _x[mi:ma]))
signal[mi:ma] = np.zeros(ma - mi)
if plot:
ax.plot(signal[:graph_len])
if plot:
fig.show()
return amps, freqs
def quantile(x,precision=precision):
cur=mn
quant=val
incr=0
while (abs(quant-x)>precision)&(incr<max_iter):
past_cur=cur
cur=(x-quant)/f(past_cur)+cur
quant=quant+(integ(f,past_cur,cur)[0])
incr+=1
quant=integ(f,-10000,cur)[0]
while (abs(quant-x)>precision)&(incr<max_iter):
past_cur=cur
cur=(x-quant)/f(past_cur)+cur
quant=quant+integ(f,past_cur,cur)[0]
incr+=1
print('nb iteration; %d, quantile: %f'%(incr,quant))
return float(cur)
def trasm(lamt,flut): ##--------------------------------------------------------- #Import packages import os #from scipy.interpolate import splprep, splev from scipy.interpolate import interp1d from scipy.integrate import trapz as integ from numpy import loadtxt, arange #,pi, log, linspace, convolve ln=len(lamt) # filter characteristics: ltr=flut*lamt tint = integ(flut,lamt) # surface lef = integ(ltr,lamt) /tint # effective wavelength delta=flut*((lamt-lef)**2) dint = 2*(integ(delta,lamt) /tint)**(0.5) # whole bandpass # result res=[0*x for x in range (0,3)] res[0]=lef res[1]=dint res[2]=tint return res
def calculate_pvalue_gev(original_score: float, scores: list) -> float:
"""Calculates a p-value to a target/query combination by int. with a given amount of shuffle iterations by
fitting a generalized extreme value distribution and integrating from -inf to the original score
>>> i = IntaRNApvalue(['-q', 'AGGAUG', '-t', 'UUUAUCGUU', '--scores', '10', '-m', 'b', '--threads', '3'])
>>> i.calculate_pvalue_gev(-10.0, [-1.235, -1.435645, -6.234234, -12.999, -15.23, -6.98, -6.23, -2.78])
0.17611816922560236
"""
shape, loc, scale = gev.fit(scores)
def f(x):
return gev.pdf(x, shape, loc=loc, scale=scale)
return integ(f, -np.inf, original_score)[0]
def calculate_pvalue_gumbel(original_score: float, scores: list) -> float:
"""Calculates a p-value to a target/query combination by int. with a given amount of shuffle iterations by
fitting a gumbel distribution and integrating from -inf to the original score
>>> i = IntaRNApvalue(['-q', 'AGGAUG', '-t', 'UUUAUCGUU', '--scores', '10', '-m', 'b', '--threads', '3'])
>>> i.calculate_pvalue_gumbel(-10.0, [-1.235, -1.435645, -6.234234, -12.999, -15.23, -6.98, -6.23, -2.78])
0.19721934073203196
"""
loc, scale = gum.fit(scores)
def f(x):
return gum.pdf(x, loc=loc, scale=scale)
return integ(f, -np.inf, original_score)[0]
def ext_grf_steady(
rad,
r_ref,
conductivity,
dim=2,
lat_ext=1.0,
rate=-1e-4,
h_ref=0.0,
arg_dict=None,
**kwargs
):
"""
The extended "General radial flow" model for steady flow.
The general radial flow (GRF) model by Barker introduces an arbitrary
dimension for radial groundwater flow. We introduced the possibility to
define radial dependet conductivity.
This solution is based on the grf model presented in [Barker88]_.
Parameters
----------
rad : :class:`numpy.ndarray`
Array with all radii where the function should be evaluated
r_ref : :class:`float`
Radius of the reference head.
conductivity : :class:`float` or :any:`callable`
Conductivity. Either callable function taking kwargs or float.
dim : :class:`float`, optional
Fractional dimension of the aquifer. Default: ``2.0``
lat_ext : :class:`float`, optional
Lateral extend of the aquifer. Default: ``1.0``
rate : :class:`float`, optional
Pumpingrate at the well. Default: -1e-4
h_ref : :class:`float`, optional
Reference head at the reference-radius `r_ref`. Default: ``0.0``
arg_dict : :class:`dict` or :any:`None`, optional
Keyword-arguments given as a dictionary that are forwarded to the
conductivity function. Will be merged with ``**kwargs``.
This is designed for overlapping keywords in ``grf_steady`` and
``conductivity``.
Default: ``None``
**kwargs
Keyword-arguments that are forwarded to the conductivity function.
Will be merged with ``arg_dict``
Returns
-------
:class:`numpy.ndarray`
Array with all heads at the given radii and time-points.
References
----------
.. [Barker88] Barker, J.
''A generalized radial flow model for hydraulic tests
in fractured rock.'',
Water Resources Research 24.10, 1796-1804, 1988
"""
arg_dict = {} if arg_dict is None else arg_dict
kwargs.update(arg_dict)
Input = Shaper(rad=rad)
q_fac = rate / (sph_surf(dim) * lat_ext ** (3.0 - dim)) # pumping factor
if not r_ref > 0.0:
raise ValueError("The reference radius needs to be positive.")
if not Input.rad_min > 0.0:
raise ValueError("The given radii need to be positive.")
if not dim > 0.0 or dim > 3.0:
raise ValueError("The dimension needs to be positiv and <= 3.")
if not lat_ext > 0.0:
raise ValueError("The lateral extend needs to be positiv.")
if callable(conductivity):
res = np.zeros(Input.rad_no)
def integrand(val):
"""Integrand."""
return val ** (1 - dim) / conductivity(val, **kwargs)
for ri, re in enumerate(Input.rad):
res[ri] = integ(integrand, re, r_ref)[0]
else:
con = float(conductivity)
if not con > 0:
raise ValueError("The Conductivity needs to be positive.")
if np.isclose(dim, 2):
res = np.log(r_ref / Input.rad) / con
else:
res = (
(r_ref ** (2 - dim) - Input.rad ** (2 - dim)) / (2 - dim) / con
)
res = Input.reshape(res)
# rescale by pumping rate
res *= q_fac
# add the reference head
res += h_ref
return res
def res(y):
def g(x,y=y):
return cost(y-x)*density(x)
# ut.curve(g,inter=[-10,10])
return integ(g,-10,10)[0]
def Compute(self):
if self.log_active:
print(
'===========================================================')
print('SOLVER INSIDE THE STAR')
print(
'===========================================================\n'
)
print('Initial density: ', self.initDensity, ' MeV/fm^3')
print('Initial pressure: ', self.initPressure / 10**12, ' GPa')
print('Initial mass: ', self.initMass / massSun, ' solar mass')
print('Initial phi: ', self.initPhi)
print('Initial psi: ', self.initPsi)
print('Number of points: ', self.Npoint)
print('Radius max: ', self.radiusMax_in / 1000, ' km')
y0 = [self.initPressure, self.initMass, self.initPhi, self.initPsi]
if self.log_active:
print('y0 = ', y0, '\n')
r_min = 0.000000001
r = np.linspace(r_min, self.radiusMax_in, self.Npoint)
if self.log_active:
print('radius min ', r_min)
print('radius max ', self.radiusMax_in)
sol = solve_ivp(dy_dr, [r_min, self.radiusMax_in],
y0,
method='RK45',
t_eval=r,
args=(self.option, self.dilaton_active))
# condition for Pressure = 0
'''
self.g_rr = b(sol.t, sol.y[1])
a_dot_a = adota(sol.t, sol.y[0], sol.y[1], sol.y[3], sol.y[2])
self.g_tt = np.exp(np.concatenate([[0.0], integcum(a_dot_a,sol.t)])-integ(a_dot_a,sol.t))
plt.plot(self.g_tt/self.g_rr)
plt.show()
'''
if sol.t[-1] < self.radiusMax_in:
self.pressure = sol.y[0][0:-2]
self.mass = sol.y[1][0:-2]
self.Phi = sol.y[2][0:-2]
self.Psi = sol.y[3][0:-2]
self.radius = sol.t[0:-2]
# Value at the radius of star
self.massStar = sol.y[1][-1]
self.radiusStar = sol.t[-1]
self.pressureStar = sol.y[0][-1]
self.phiStar = sol.y[2][-1]
n_star = len(self.radius)
if self.log_active:
print('Star radius: ', self.radiusStar / 1000, ' km')
print('Star Mass: ', self.massStar / massSun, ' solar mass')
print('Star Mass: ', self.massStar, ' kg')
print('Star pressure: ', self.pressureStar, ' Pa\n')
print(
'==========================================================='
)
print('SOLVER OUTSIDE THE STAR')
print(
'===========================================================\n'
)
y0 = [self.massStar, sol.y[2][-1], sol.y[3][-1]]
if self.log_active:
print('y0 = ', y0, '\n')
r = np.logspace(
np.log(self.radiusStar) / np.log(10),
np.log(self.radiusMax_out) / np.log(10), self.Npoint)
if self.log_active:
print('radius min ', self.radiusStar)
print('radius max ', self.radiusMax_out)
sol = solve_ivp(dy_dr_out, [r[0], self.radiusMax_out],
y0,
method='DOP853',
t_eval=r,
args=(0, self.option, self.dilaton_active))
self.pressure = np.concatenate(
[self.pressure, np.zeros(self.Npoint)])
self.mass = np.concatenate([self.mass, sol.y[0]])
self.Phi = np.concatenate([self.Phi, sol.y[1]])
self.Psi = np.concatenate([self.Psi, sol.y[2]])
self.radius = np.concatenate([self.radius, r])
self.phi_inf = self.Phi[-1]
if self.log_active:
print('Phi at infinity ', self.phi_inf)
# Compute metrics
self.g_rr = b(self.radius, self.mass)
a_dot_a = adota(self.radius, self.pressure, self.mass, self.Psi,
self.Phi)
b_dot_b = bdotb(self.radius, self.pressure, self.mass, self.Psi,
self.Phi, self.option)
self.g_tt = np.exp(
np.concatenate([[0.0], integcum(a_dot_a, self.radius)]) -
integ(a_dot_a, self.radius))
self.Lm = Lagrangian(self.pressure, self.option)
#compute Ricci scalar
a_dot = a_dot_a * self.g_tt
a_2dot = (a_dot[1:-1] - a_dot[0:-2]) / (self.radius[1:-1] -
self.radius[0:-2])
A = self.g_tt[0:-2]
B = self.g_rr[0:-2]
r = self.radius[0:-2]
a_dot_a = a_dot_a[0:-2]
b_dot_b = b_dot_b[0:-2]
R = -(2 / B) * (a_2dot / (2 * A) - 0.5 * a_dot_a**2 + 0.5 *
(0.5 * a_dot_a + 2 / r) * (a_dot_a - b_dot_b) +
(1 - B) / (r**2))
R_interpol = interp1d(self.radius[0:-2],
R,
fill_value="extrapolate")
self.R = R_interpol(self.radius)
self.massADM = self.mass[-1]
self.g_tt_ext = np.array(self.g_tt[n_star:-1])
self.g_rr_ext = np.array(self.g_rr[n_star:-1])
self.r_ext = np.array(self.radius[n_star:-1])
self.r_ext[0] = self.radiusStar
if self.log_active:
print('Star Mass ADM: ', self.massADM, ' kg')
print(
'==========================================================='
)
print('END')
print(
'===========================================================\n'
)
else:
print('Pressure=0 not reached')
def Compute(self):
if self.log_active:
print(
'===========================================================')
print('SOLVER INSIDE THE STAR')
print(
'===========================================================\n'
)
print('Initial density: ', self.initDensity, ' MeV/fm^3')
print('Initial pressure: ', self.initPressure / 10**12, ' GPa')
print('Initial mass: ', self.initMass / massSun, ' solar mass')
print('Initial phi: ', self.initPhi)
print('Initial psi: ', self.initPsi)
print('Number of points: ', self.Npoint)
print('Radius max: ', self.radiusMax_in / 1000, ' km')
y0 = [self.initPressure, self.initMass, self.initPhi, self.initPsi]
if self.log_active:
print('y0 = ', y0, '\n')
r = np.linspace(0.01, self.radiusMax_in, self.Npoint)
if self.log_active:
print('radius min ', 0.01)
print('radius max ', self.radiusMax_in)
sol = solve_ivp(dy_dr, [0.01, self.radiusMax_in],
y0,
t_eval=r,
args=(self.option, self.dilaton_active))
# condition for Pressure = 0
if sol.t[-1] < self.radiusMax_in:
self.pressure = sol.y[0]
self.mass = sol.y[1]
self.Phi = sol.y[2]
self.Psi = sol.y[3]
self.radius = sol.t
# Value at the radius of star
self.massStar = self.mass[-1]
self.radiusStar = self.radius[-1]
self.pressureStar = self.pressure[-1]
if self.log_active:
print('Star radius: ', self.radiusStar / 1000, ' km')
print('Star Mass: ', self.massStar / massSun, ' solar mass')
print('Star pressure: ', self.pressureStar, ' Pa\n')
print(
'==========================================================='
)
print('SOLVER OUTSIDE THE STAR')
print(
'===========================================================\n'
)
y0 = [self.massStar, self.Phi[-1], self.Psi[-1]]
if self.log_active:
print('y0 = ', y0, '\n')
r = np.linspace(self.radiusStar, self.radiusMax_out, self.Npoint)
if self.log_active:
print('radius min ', self.radiusStar)
print('radius max ', self.radiusMax_out)
sol = solve_ivp(dy_dr_out, [self.radiusStar, self.radiusMax_out],
y0,
t_eval=r,
args=(0, self.option, self.dilaton_active))
self.pressure = np.concatenate(
[self.pressure, np.zeros(self.Npoint)])
self.mass = np.concatenate([self.mass, sol.y[0]])
self.Phi = np.concatenate([self.Phi, sol.y[1]])
self.Psi = np.concatenate([self.Psi, sol.y[2]])
self.radius = np.concatenate([self.radius, sol.t])
self.phi_inf = self.Phi[-1]
if self.log_active:
print('Phi at infinity ', self.phi_inf)
# Compute metrics
self.g_rr = b(self.radius, self.mass)
a_dot_a = adota(self.radius, self.pressure, self.mass, self.Psi,
self.Phi)
self.g_tt = np.exp(
np.concatenate([[0.0], integcum(a_dot_a, self.radius)]) -
integ(a_dot_a, self.radius))
self.massADM = self.mass[-1]
if self.log_active:
print(
'==========================================================='
)
print('END')
print(
'===========================================================\n'
)
else:
print('Pressure=0 not reached')
def annular_fmean(func,
val_arr,
f_def,
f_inv,
ann_dim=2,
arg_dict=None,
**kwargs):
r"""
Calculating the annular generalized f-mean.
Calculating the annular generalized f-mean of a radial symmetric function
for given consecutive radii defining annuli by the following formula
.. math::
\varphi_i=f^{-1}\left(\frac{d}{r_{i+1}^d-r_i^d}
\intop^{r_{i+1}}_{r_i} r^{d-1}\cdot f(\varphi(r))\, dr \right)
Parameters
----------
func : :any:`callable`
Function that should be used (:math:`\varphi` in the formula).
The first argument needs to be the radial variable:
``func(r, **kwargs)``
val_arr : :class:`numpy.ndarray`
Radii defining the annuli.
ann_dim : :class:`float`, optional
The dimension of the annuli.
Default: ``2.0``
f_def : :any:`callable`
Function defining the f-mean.
f_inv : :any:`callable`
Inverse of the function defining the f-mean.
arg_dict : :class:`dict` or :any:`None`, optional
Keyword-arguments given as a dictionary that are forwarded to the
function given in ``func``. Will be merged with ``**kwargs``.
This is designed for overlapping keywords in ``annular_fmean`` and
``func``.
Default: ``None``
**kwargs
Keyword-arguments that are forwarded to the function given in ``func``.
Will be merged with ``arg_dict``
Returns
-------
:class:`numpy.ndarray`
Array with all calculated arithmetic means
Raises
------
ValueError
If `func` is not callable.
ValueError
If `f_def` is not callable.
ValueError
If `f_inv` is not callable.
ValueError
If ``val_arr`` has less than 2 values.
ValueError
If ``val_arr`` is not sorted in incresing order.
Notes
-----
If the last value in val_arr is "inf", the given function should provide
a value for "inf" as input: ``func(float("inf"))``
"""
arg_dict = {} if arg_dict is None else arg_dict
kwargs.update(arg_dict)
val_arr = np.array(val_arr, dtype=np.double).reshape(-1)
parts = len(val_arr) - 1
if not callable(func):
raise ValueError("The given function needs to be callable")
if not callable(f_def):
raise ValueError("The f-mean function needs to be callable")
if not callable(f_inv):
raise ValueError("The inverse f-mean function needs to be callable")
if not np.all(
np.isclose(f_inv(f_def(func(val_arr, **kwargs))),
func(val_arr, **kwargs))):
raise ValueError("f_def and f_inv need to be inverse to each other")
if len(val_arr) < 2:
raise ValueError("To few input values in val_arr. Need at least 2.")
if not all(val_arr[i] < val_arr[i + 1] for i in range(len(val_arr) - 1)):
raise ValueError("The input values need to be sorted")
def integrand(val):
"""Integrand for the f-mean ``r^(d-1)*f(phi(r))``."""
return val**(ann_dim - 1) * f_def(func(val, **kwargs))
# creating the output array
func_arr = np.zeros_like(val_arr[:-1], dtype=np.double)
# iterating over the input values
for i in range(parts):
# if one side is infinity, the function is evaluated at infinity
if val_arr[i + 1] == np.inf:
func_arr[i] = func(np.inf, **kwargs)
else:
func_arr[i] = f_inv(
ann_dim * integ(integrand, val_arr[i], val_arr[i + 1])[0] /
(val_arr[i + 1]**ann_dim - val_arr[i]**ann_dim))
return func_arr
def rad_amean_func(func, val_arr, arg_dict=None, **kwargs):
'''
Calculating the arithmetic mean of a radial symmetric function
for given consecutive radii defining 2D disks by the following formula
.. math::
f_i=\\frac{2}{r_{i+1}^2-r_i^2}
\\intop^{r_{i+1}}_{r_i} r\\cdot f(r)\\, dr
Parameters
----------
func : :any:`callable`
function that should be used
The first argument needs to be the radial variable:
``func(r, **kwargs)``
val_arr : :class:`numpy.ndarray`
given radii defining the disks
arg_dict : :class:`dict` or :any:`None`, optional
Keyword-arguments given as a dictionary that are forwarded to the
function given in ``func``. Will be merged with ``**kwargs``.
This is designed for overlapping keywords in ``rad_amean_func`` and
``func``.
Default: ``None``
**kwargs
Keyword-arguments that are forwarded to the function given in ``func``.
Will be merged with ``arg_dict``
Returns
-------
:class:`numpy.ndarray`
Array with all calculated arithmetic means
Raises
------
ValueError
If `func` is not callable.
ValueError
If ``val_arr`` has less than 2 values.
ValueError
If ``val_arr`` is not sorted in incresing order.
Notes
-----
If the last value in val_arr is "inf", the given function should provide
a value for "inf" as input: ``func(float("inf"))``
Example
-------
>>> f = lambda x: x**2
>>> rad_amean_func(f, [1,2,3])
array([ 2.33588885, 6.33423311])
'''
if arg_dict is None:
arg_dict = {}
kwargs.update(arg_dict)
val_arr = np.array(val_arr, dtype=float).reshape(-1)
parts = len(val_arr) - 1
if not callable(func):
raise ValueError("The given function needs to be callable")
if len(val_arr) < 2:
raise ValueError("To few input values in val_arr. Need at least 2.")
if not all(val_arr[i] < val_arr[i + 1] for i in range(len(val_arr) - 1)):
raise ValueError("The input values need to be sorted")
# dummy function defining the needed integrand
def _step(func, kwargs):
"""
dummy function providing the integrand
"""
def integrand(val):
'''
Integrand for the geometric mean ``r*log(f(r))``
'''
return 2 * val * func(val, **kwargs)
return integrand
# creating the output array
func_arr = np.zeros_like(val_arr[:-1], dtype=float)
# iterating over the input values
for i in range(parts):
# if one side is infinity, the function is evaluated at infinity
if val_arr[i + 1] == np.inf:
func_arr[i] = func(np.inf, **kwargs)
else:
func_arr[i] = integ(_step(func, kwargs), val_arr[i],
val_arr[i + 1])[0]
func_arr[i] = func_arr[i] / (val_arr[i + 1]**2 - val_arr[i]**2)
return func_arr
def Compute(self):
if self.log_active:
print(
'===========================================================')
print('SOLVER INSIDE THE STAR')
print(
'===========================================================\n'
)
print('Initial density: ', self.initDensity, ' MeV/fm^3')
print('Initial pressure: ', self.initPressure / 10**12, ' GPa')
print('Initial mass: ', self.initMass / massSun, ' solar mass')
print('Initial phi: ', self.initPhi)
print('Initial psi: ', self.initPsi)
print('Number of points: ', self.Npoint)
print('Radius max: ', self.radiusMax_in / 1000, ' km')
y0 = [self.initPressure, self.initMass, self.initPhi, self.initPsi]
if self.log_active:
print('y0 = ', y0, '\n')
r = np.linspace(0.01, self.radiusMax_in, self.Npoint)
if self.log_active:
print('radius min ', 0.01)
print('radius max ', self.radiusMax_in)
sol = solve_ivp(dy_dr, [0.01, self.radiusMax_in],
y0,
method='RK45',
t_eval=r,
args=(self.option, self.dilaton_active))
# condition for Pressure = 0
'''
self.g_rr = b(sol.t, sol.y[1])
a_dot_a = adota(sol.t, sol.y[0], sol.y[1], sol.y[3], sol.y[2])
self.g_tt = np.exp(np.concatenate([[0.0], integcum(a_dot_a,sol.t)])-integ(a_dot_a,sol.t))
plt.plot(self.g_tt/self.g_rr)
plt.show()
'''
if sol.t[-1] < self.radiusMax_in:
self.pressure = sol.y[0][0:-2]
self.mass = sol.y[1][0:-2]
self.Phi = sol.y[2][0:-2]
self.Psi = sol.y[3][0:-2]
self.radius = sol.t[0:-2]
# Value at the radius of star
self.massStar = sol.y[1][-1]
self.radiusStar = sol.t[-1]
self.pressureStar = sol.y[0][-1]
self.phiStar = sol.y[2][-1]
n_star = len(self.radius)
if self.log_active:
print('Star radius: ', self.radiusStar / 1000, ' km')
print('Star Mass: ', self.massStar / massSun, ' solar mass')
print('Star Mass: ', self.massStar, ' kg')
print('Star pressure: ', self.pressureStar, ' Pa\n')
print(
'==========================================================='
)
print('SOLVER OUTSIDE THE STAR')
print(
'===========================================================\n'
)
y0 = [self.massStar, sol.y[2][-1], sol.y[3][-1]]
if self.log_active:
print('y0 = ', y0, '\n')
r = np.logspace(
np.log(self.radiusStar) / np.log(10),
np.log(self.radiusMax_out) / np.log(10), self.Npoint)
if self.log_active:
print('radius min ', self.radiusStar)
print('radius max ', self.radiusMax_out)
sol = solve_ivp(dy_dr_out, [r[0], self.radiusMax_out],
y0,
method='DOP853',
t_eval=r,
args=(0, self.option, self.dilaton_active))
self.pressure = np.concatenate(
[self.pressure, np.zeros(self.Npoint)])
self.mass = np.concatenate([self.mass, sol.y[0]])
self.Phi = np.concatenate([self.Phi, sol.y[1]])
self.Psi = np.concatenate([self.Psi, sol.y[2]])
self.radius = np.concatenate([self.radius, r])
self.phi_inf = self.Phi[-1]
if self.log_active:
print('Phi at infinity ', self.phi_inf)
# Compute metrics
self.g_rr = b(self.radius, self.mass)
a_dot_a = adota(self.radius, self.pressure, self.mass, self.Psi,
self.Phi)
#plt.plot(self.radius, np.concatenate([[0.0], integcum(a_dot_a,self.radius)]))
#plt.show()
self.g_tt = np.exp(
np.concatenate([[0.0], integcum(a_dot_a, self.radius)]) -
integ(a_dot_a, self.radius))
self.massADM = self.mass[-1]
self.g_tt_ext = np.array(self.g_tt[n_star:-1])
self.g_rr_ext = np.array(self.g_rr[n_star:-1])
self.r_ext = np.array(self.radius[n_star:-1])
self.r_ext[0] = self.radiusStar
if self.log_active:
print('Star Mass ADM: ', self.massADM, ' kg')
print(
'==========================================================='
)
print('END')
print(
'===========================================================\n'
)
else:
print('Pressure=0 not reached')
