def test_ReactionSystem__to_ReactionDiffusion():
sbstncs = mk_sn_dict_from_names('AB')
r1 = Reaction({'A': 2}, {'B': 1}, 3.0)
rsys = ReactionSystem([r1], sbstncs)
rd = ReactionDiffusion.from_ReactionSystem(rsys)
assert rd.stoich_active == [[0, 0]]
assert rd.stoich_prod == [[1]]
assert rd.k == [3.0]
def test_autobinary():
from chemreac.chemistry import (
Reaction, ReactionSystem, mk_sn_dict_from_names
)
sbstncs = mk_sn_dict_from_names('AB')
k = 3.0
r1 = Reaction({'A': 2}, {'B': 1}, k)
rsys = ReactionSystem([r1], sbstncs)
rd = ReactionDiffusion.from_ReactionSystem(rsys)
_test_f_and_dense_jac_rmaj(rd, 0, [1, 37], [-2*3, 3])
def test_chemistry():
sbstncs = mk_sn_dict_from_names('ABC', D=[.1, .2, .3])
r1 = Reaction({'A': 1, 'B': 1}, {'C': 1}, 0.3)
rsys = ReactionSystem([r1], sbstncs)
rd = ReactionDiffusion.from_ReactionSystem(rsys)
serialized_rd = load(JSON_PATH)
assert rd.stoich_active == serialized_rd.stoich_active
assert rd.stoich_prod == serialized_rd.stoich_prod
assert rd.stoich_inact == serialized_rd.stoich_inact
assert np.allclose(rd.k, serialized_rd.k)
assert np.allclose(rd.D, serialized_rd.D)
def test_autodimerization(arrhenius):
# A + A -> B
from chemreac.chemistry import (
Reaction, ReactionSystem, mk_sn_dict_from_names
)
sbstncs = mk_sn_dict_from_names('AB')
if arrhenius:
from chempy.kinetics.arrhenius import ArrheniusParam
k = ArrheniusParam(7e11, -8.314472*298.15*(np.log(3) - np.log(7) - 11*np.log(10)))
variables = {'temperature': 298.15}
param = 3.0
else:
param = k = 3.0
variables = None
r1 = Reaction({'A': 2}, {'B': 1}, k)
rsys = ReactionSystem([r1], sbstncs)
rd = ReactionDiffusion.from_ReactionSystem(rsys, variables=variables)
t = np.linspace(0, 5, 3)
A0, B0 = 1.0, 0.0
integr = run(rd, [A0, B0], t)
Aref = 1/(1/A0+2*param*t)
yref = np.vstack((Aref, (A0-Aref)/2)).transpose()
assert np.allclose(integr.yout[:, 0, :], yref)
def plot_per_reaction_contribution(integr, substances, equilibria=None, field_yields=False, **kwargs):
"""
Plots contributions to concentration derivatives of selected
substances from individual reactions.
Parameters
----------
integr: Integration instance
substances: sequence of Substance instances
equilibria: set of tuples of reaction indices (optional)
When passed only net effect of equilibria reaction will be plotted
field_yields: bool (default: False)
If ``True`` contributions from g_values times field will be shown
**kwargs: kwargs passed on to _plot_analysis
Returns
-------
list of matplotlib.axes.Axes instances
"""
rd = integr.rd
if rd.N != 1:
# should be quite straight forward to implement
raise NotImplementedError
indices = [ri if isinstance(ri, int) else rd.substance_names.index(ri) for ri in substances]
if rd.substance_latex_names is not None:
print_names = rd.substance_latex_names
use_tex = True
else:
print_names = rd.substance_names
use_tex = False
substances = mk_sn_dict_from_names(rd.substance_names, latex_name=rd.substance_latex_names)
if field_yields:
# Let's use negative reaction indices for each field -1: 0, -2: 1
rxn_indices = range(-len(rd.fields), 0)
else:
rxn_indices = []
if equilibria is not None:
equilibria_participants = []
for rxnidxs in equilibria:
equilibria_participants.extend(rxnidxs)
rxn_indices.append(rxnidxs)
for ri in range(rd.nr):
if ri not in equilibria_participants:
rxn_indices.append(ri)
else:
rxn_indices += range(rd.nr)
labels = []
for rxni in rxn_indices:
if isinstance(rxni, int):
if rxni >= 0:
rxn = rd.to_Reaction(rxni)
labels.append("R" + str(rxni) + ": " + ("$" + rxn.latex(substances) + "$") if use_tex else str(rxn))
else:
# Field production!
fi = -rxni - 1
if rd.g_value_parents[fi] == -1:
# No parents
parent = ""
else:
parent = print_names[rd.g_value_parents[fi]] + "$\\rightsquigarrow$" if use_tex else " ~~~> "
labels.append(
"G_"
+ str(fi)
+ ": "
+ parent
+ ", ".join([print_names[si] for si in range(rd.n) if rd.g_values[fi][si] != 0])
)
else:
rxn = rd.to_Reaction(rxni[0])
labels.append(
"R("
+ ", ".join(map(str, rxni))
+ "): "
+ ("$" + rxn.latex(dict(zip(rd.substance_names, print_names))) + "$")
if use_tex
else str(rxn)
)
def cb(rd_, tout_, yout_, specie_indices_):
bi = 0 # bin index, N=1 only implemented for now
per_rxn = _get_per_rxn_out(rd_, tout_, yout_, specie_indices_)
out = np.zeros((yout_.shape[0], len(specie_indices_), len(rxn_indices)))
for i, rxns in enumerate(rxn_indices):
if isinstance(rxns, int):
if rxns >= 0:
out[:, :, i] = per_rxn[:, :, rxns]
else:
fi = -rxns - 1
for sii, si in enumerate(specie_indices_):
out[:, sii, i] = rd.g_values[fi][si] * rd.fields[fi][bi]
else:
for rxn_idx in rxns:
out[:, :, i] += per_rxn[:, :, rxn_idx]
return out
axes = _plot_analysis(
cb,
labels,
rd,
integr.tout,
integr.yout,
indices,
titles=["$" + print_names[i] + "$" for i in indices],
**kwargs
)
for ax in axes:
ax.set_ylabel(r"Reaction rate / $M\cdot s^{-1}$")
return axes
