primordial: | inflationary equation solver |
---|---|
Author: | Will Handley |
Version: | 0.0.14 |
Homepage: | https://github.com/williamjameshandley/primordial |
Documentation: | http://primordial.readthedocs.io/ |
primordial
is a python package for solving cosmological inflationary equations.
It is very much in beta stage, and currently being built for research purposes.
import numpy
import matplotlib.pyplot as plt
from primordial.solver import solve
from primordial.equations.inflation_potentials import ChaoticPotential
from primordial.equations.t.inflation import Equations, KD_initial_conditions
from primordial.equations.events import Inflation, Collapse
fig, ax = plt.subplots(3,sharex=True)
for K in [-1, 0, +1]:
m = 1
V = ChaoticPotential(m)
equations = Equations(K, V)
events= [Inflation(equations), # Record inflation entry and exit
Inflation(equations, -1, terminal=True), # Stop on inflation exit
Collapse(equations, terminal=True)] # Stop if universe stops expanding
N_p = -1.5
phi_p = 23
t_p = 1e-5
ic = KD_initial_conditions(t_p, N_p, phi_p)
t = numpy.logspace(-5,10,1e6)
sol = solve(equations, ic, t_eval=t, events=events)
ax[0].plot(sol.N(t),sol.phi(t))
ax[0].set_ylabel(r'$\phi$')
ax[1].plot(sol.N(t),sol.H(t))
ax[1].set_yscale('log')
ax[1].set_ylabel(r'$H$')
ax[2].plot(sol.N(t),1/(sol.H(t)*numpy.exp(sol.N(t))))
ax[2].set_yscale('log')
ax[2].set_ylabel(r'$1/aH$')
ax[-1].set_xlabel('$N$')
import numpy
import matplotlib.pyplot as plt
from primordial.solver import solve
from primordial.equations.inflation_potentials import ChaoticPotential
from primordial.equations.t.mukhanov_sasaki import Equations, KD_initial_conditions
from primordial.equations.events import Inflation, Collapse, ModeExit
fig, axes = plt.subplots(3,sharex=True)
for ax, K in zip(axes, [-1, 0, +1]):
ax2 = ax.twinx()
m = 1
V = ChaoticPotential(m)
k = 100
equations = Equations(K, V, k)
events= [
Inflation(equations), # Record inflation entry and exit
Collapse(equations, terminal=True), # Stop if universe stops expanding
ModeExit(equations, +1, terminal=True, value=1e1*k) # Stop on mode exit
]
N_p = -1.5
phi_p = 23
t_p = 1e-5
ic = KD_initial_conditions(t_p, N_p, phi_p)
t = numpy.logspace(-5,10,1e6)
sol = solve(equations, ic, t_eval=t, events=events)
N = sol.N(t)
ax.plot(N,sol.R1(t), 'k-')
ax2.plot(N,-numpy.log(sol.H(t))-N, 'b-')
ax.set_ylabel('$\mathcal{R}$')
ax2.set_ylabel('$-\log aH$')
ax.text(0.9, 0.9, r'$K=%i$' % K, transform=ax.transAxes)
axes[-1].set_xlabel('$N$')
Eventually would like to submit this to JOSS. Here are things to do before then:
- Slow roll initial conditions
- add \eta as independent variable
- add \phi as independent variable
- Documentation
- Tests
- 100% coverage
- interpolation