本文整理汇总了Python中chemreac.ReactionDiffusion._as_odesys方法的典型用法代码示例。如果您正苦于以下问题：Python ReactionDiffusion._as_odesys方法的具体用法？Python ReactionDiffusion._as_odesys怎么用？Python ReactionDiffusion._as_odesys使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类chemreac.ReactionDiffusion的用法示例。
在下文中一共展示了ReactionDiffusion._as_odesys方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_chained_parameter_variation
# 需要导入模块: from chemreac import ReactionDiffusion [as 别名]
# 或者: from chemreac.ReactionDiffusion import _as_odesys [as 别名]
def test_chained_parameter_variation():
# A -> B
names = ['A', 'B']
rd = ReactionDiffusion(len(names), [], [], k=[],
substance_names=names, g_value_parents=[0], g_values=[[0, 1]],
param_names=['doserate'])
durations = [1., 3., 2.]
y0 = [13., 7.]
ic = dict(zip(names, y0))
doserates = [.3, .11, .7]
npoints = 3
odesys = rd._as_odesys(variables_from_params=dict(
density=lambda self, params: 1.0
))
res = odesys.chained_parameter_variation(
durations, ic, {'doserate': doserates}, npoints=npoints,
integrate_kwargs=dict(atol={k: 1e-8 for k in odesys.names}))
assert res.xout.size == npoints*len(durations) + 1
assert res.xout[0] == 0
assert np.all(res.yout[0, :] == y0)
expected = [.3]*npoints + [.11]*npoints + [.7]*(npoints+1)
assert np.all(res.params[:, odesys.param_names.index('doserate')] == expected)
cumulative = 0.0
for dr, dur in zip(doserates, durations):
mask = (cumulative <= res.xout) & (res.xout <= cumulative + dur)
cumulative += dur
t, y = res.xout[mask], res.yout[mask, :]
a, b = y[:, 0], y[:, 1]
refa = a[0]
refb = b[0] + (t - t[0])*dr*a[0]
assert np.allclose(refa, a)
assert np.allclose(refb, b)
res.extend_by_integration(np.sum(durations)+1, dict(doserate=doserates[-1]), integrator='cvode')
assert abs(res.yout[-1, 1] - (refb[-1] + doserates[-1]*a[0])) < 1e-8示例2: _get_odesys
# 需要导入模块: from chemreac import ReactionDiffusion [as 别名]
# 或者: from chemreac.ReactionDiffusion import _as_odesys [as 别名]
def _get_odesys():
names = ['A', 'B', 'C']
pns = ['kA', 'kB']
rd = ReactionDiffusion(len(names), [[0], [1]], [[1], [2]], k=[0, 0],
substance_names=names, param_names=pns)
return rd._as_odesys(k_from_params=lambda self, p: [p[k] for k in self.param_names])