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fastrk-0.0.5
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مشاهده بیشترتوضیحات
FastRK, a generator of fast jit-compiled code for ODE propagation by ERK methods with adaptive step and events
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | fastrk-0.0.5 |
| نام | fastrk |
| نسخه کتابخانه | 0.0.5 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Bober S.A. |
| ایمیل نویسنده | stas.bober@gmail.com |
| آدرس صفحه اصلی | https://github.com/BoberSA/fastrk |
| آدرس اینترنتی | https://pypi.org/project/fastrk/ |
| مجوز | MIT |
# FastRK
- developed as **fast alternative** for subset of [scipy.integrate.ode](https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.ode.html) methods (i.e. DOP853);
- is a python code generator for Ordinary Differential Equations (ODE) propagation;
- uses explicit **embedded Runge-Kutta** (ERK) methods with adaptive step technique;
- calculates events using **event functions** (like [scipy.integrate.solve_ivp](https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.solve_ivp.html));
- is **jit-compiled** by [numba](https://pypi.org/project/numba/);
- compiled code **cached** on SSD/HDD to prevent unnecessary recompilation;
- reentry, i.e. can be used in **multithreaded** applications;
- OS-independent (to the same extent as [numba](https://pypi.org/project/numba/));
- contains Butcher Tables for several ERK methods:
- Dormand and Prince 6(5)8M;
- Dormand and Prince 8(7)13M;
- **1.5x - 4.5x faster** than `DOP853` from `scipy.integrate.ode`
- Verner's 8(9)16;
- user-defined Butcher Tables also supported;
- generated code is open and user-modifiable;
Butcher Tables was adapted from [TrackerComponentLibrary](https://github.com/USNavalResearchLaboratory/TrackerComponentLibrary).
## Installation
pip install fastrk
## Fast example
```python
import numpy as np
import matplotlib.pyplot as plt
from fastrk import BT8713M, RKCodeGen
# CRTBP ODE, https://github.com/BoberSA/fastrk/tree/master/examples/model_crtbp.py
from model_crtbp import crtbp
rk_module = RKCodeGen(BT8713M, autonomous=True).save_and_import()
rk_prop = rk_module.rk_prop
t0, t1 = 0., 3 * np.pi
# initial state for halo orbit
s0 = np.zeros(6)
s0[[0, 2, 4]] = 9.949942666080747733e-01, 4.732924802139452415e-03, -1.973768492871211949e-02
mc = np.array([3.001348389698916e-06]) # CRTBP constant
rtol, atol = 1e-12, 1e-12
# integrate CRTBP ODE from t0, s0 to t1
arr = rk_prop(crtbp, s0, t0, t1, np.inf, rtol, atol, mc)
plt.plot(arr[:, 1], arr[:, 2], 'r')
plt.axis('equal')
plt.show()
```
## Performance measurement
```python
import numpy as np
import pandas as pd
from scipy.integrate import ode
from numba import jit
from timeit import timeit
from fastrk import BT8713M, RKCodeGen, default_jitkwargs
from model_crtbp import crtbp
rk_prop = RKCodeGen(BT8713M, autonomous=False).save_and_import().rk_prop
#%%
# scipy.integrate.ode's fortran implementation of DOP853 works
# significantly faster with @jit-compiled function rather than @cfunc-compiled
# default_jitkwargs = {'nopython': True, 'nogil': True, 'fastmath': True, 'cache': True}
jit_crtbp = jit(**default_jitkwargs)(crtbp._pyfunc).compile('f8[:](f8, f8[:], f8[:])')
#%% integration parameters
rtol, atol = 1e-12, 1e-12
max_step = np.inf
#%% CTRBP constant
mc = np.array([3.001348389698916e-06]) # CRTBP constant
#%% initial states
s0 = [0.9919060293647325, 0., 0.0016194537148125807, 0., -0.010581643111837302, 0.]
s1 = [0.9966271059324971, 0., 0.0050402173579027045, 0., -0.024398902561093703, 0.]
s2 = [0.4999857344807682, -0.866005893551121, 0., 3.902066111769351e-05, 2.252789194673211e-05, 0.]
s3 = [0.4966615415563801, -0.8602481879501589, 0., 0.011597147217611577, 0.0066415463209149195, 0.]
#%% tests
# name t initial state
tests = {'halo (small)': (4*np.pi, np.array(s0)),
'halo (big)': (4*np.pi, np.array(s1)),
'stable L5 (small)': (100*np.pi, np.array(s2)),
'stable L5 (big)': (100*np.pi, np.array(s3)),
}
#%%
# define DOP853 integrator with same parameters
dop853_prop = ode(jit_crtbp)
dop853_prop.set_integrator('DOP853', max_step=np.inf, rtol=rtol, atol=atol, nsteps=100000)
dop853_prop.set_f_params(mc)
# to retrieve all integrator steps a callback with side effect needed
lst = []
def solout(t, s):
lst.append([t, *s])
dop853_prop.set_solout(solout)
#%% measure execution time
loops = 1000
res = []
for i, name in enumerate(tests):
print(name)
t, s = tests[name]
lst = []
dop853_prop.set_initial_value(s, 0.).integrate(t)
steps0 = len(lst)
steps1 = rk_prop(crtbp, s, 0., t, max_step, rtol, atol, mc).shape[0]
r0 = timeit("dop853_prop.set_initial_value(s, 0.).integrate(t)",
number=loops, globals=globals())
r1 = timeit("rk_prop(crtbp, s, 0., t, max_step, rtol, atol, mc)",
number=loops, globals=globals())
res.append([t, steps0, r0, steps1, r1])
#%% print results
columns = pd.MultiIndex.from_tuples([('integration', 'time'),
('dop853', 'steps'),
('dop853', 'time'),
('fastrk', 'steps'),
('fastrk', 'time')], names=['', ''])
df = pd.DataFrame(res, columns=columns, index=tests.keys())
df['speedup'] = df[('dop853', 'time')] / df[('fastrk', 'time')]
print(df)
```
Output for `AMD Ryzen 7 4700U @ 4GHz`:
integration dop853 fastrk speedup
time steps time steps time
halo (small) 12.566371 96 1.422070 235 0.964003 1.475171
halo (big) 12.566371 145 2.212505 151 0.611676 3.617117
stable L5 (small) 314.159265 452 5.898220 422 1.333944 4.421639
stable L5 (big) 314.159265 916 12.021953 856 2.701954 4.449355
## Detailed examples
#### [Example 0: Propagate spacecraft motion in Circular Restricted Three Body Problem](https://github.com/BoberSA/fastrk/blob/master/examples/ex0_propagate_crtbp.ipynb)
#### [Example 1: Calculate events in Circular Restricted Three Body Problem](https://github.com/BoberSA/fastrk/blob/master/examples/ex1_calculate_events.ipynb)
Required modules:
- [model_crtbp.py](https://github.com/BoberSA/fastrk/blob/master/examples/model_crtbp.py)
#### [Parallel (OpenMP) example](https://github.com/BoberSA/fastrk/blob/master/examples/parallel_example.py)
Required modules:
- [model_crtbp.py](https://github.com/BoberSA/fastrk/blob/master/examples/model_crtbp.py)
- [map_vy_funcs.py](https://github.com/BoberSA/fastrk/blob/master/examples/map_vy_funcs.py)
Output for `AMD Ryzen 7 4700U @ 4GHz`:
```
compiling sequential...
compiling parallel...
states count: 2500
sequential calculation started (Ms)
map calculation time: 46.31 s
parallel calculation started (Mp)
map calculation time: 22.19 s
speedup x 2.09
||Ms - Mp|| = 0.0
```
## Core Developer
Stanislav Bober, [MIEM NRU HSE](https://miem.hse.ru/), [IKI RAS](http://iki.rssi.ru/)
نیازمندی
| مقدار | نام |
|---|---|
| >=0.51.2 | numba |
| >=1.19.2 | numpy |
| >=1.8 | sympy |
| >=1.2 | scipy |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl fastrk-0.0.5:
pip install fastrk-0.0.5.whl
نصب پکیج tar.gz fastrk-0.0.5:
pip install fastrk-0.0.5.tar.gz