class Lab:
"""The batch experiments simulations"""
def __init__(self, tend, dt, tstart=0):
""" init function
initialize the lab class
Arguments:
tend {float} -- end time of computation
dt {float} -- timestep
Keyword Arguments:
tstart {float} -- strart time of computation (default: {0})
"""
self.tend = tend
self.dt = dt
self.time = np.linspace(tstart, tend, round(tend / dt) + 1)
self.species = DotDict({})
self.dynamic_functions = DotDict({})
self.profiles = DotDict({})
self.dcdt = DotDict({})
self.rates = DotDict({})
self.estimated_rates = DotDict({})
self.constants = DotDict({})
self.henry_law_equations = []
self.acid_base_components = []
self.acid_base_system = phcalc.System()
self.ode_method = 'scipy'
def __getattr__(self, attr):
"""dot notation for species
you can use lab.element and get
species dictionary
Arguments:
attr {str} -- name of the species
Returns:
DotDict -- returns DotDict of species
"""
return self.species[attr]
def solve(self, verbose=True):
""" solves coupled PDEs
Keyword Arguments:
verbose {bool} -- if true verbose output (default: {True})
with estimation of computational time etc.
"""
self.reset()
with np.errstate(invalid='raise'):
for i in np.arange(
1,
len(
np.linspace(0, self.tend,
round(self.tend / self.dt) + 1))):
# try:
self.integrate_one_timestep(i)
if verbose:
self.estimate_time_of_computation(i)
# except FloatingPointError as inst:
# print(
# '\nABORT!!!: Numerical instability... Please, adjust dt and dx manually...')
# traceback.print_exc()
# sys.exit()
def estimate_time_of_computation(self, i):
if i == 1:
self.tstart = time.time()
print("Simulation started:\n\t",
time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
if i == 100:
total_t = len(self.time) * (time.time() -
self.tstart) / 100 * self.dt / self.dt
m, s = divmod(total_t, 60)
h, m = divmod(m, 60)
print(
"\n\nEstimated time of the code execution:\n\t %dh:%02dm:%02ds"
% (h, m, s))
print(
"Will finish approx.:\n\t",
time.strftime("%Y-%m-%d %H:%M:%S",
time.localtime(time.time() + total_t)))
def henry_equilibrium_integrate(self, i):
for eq in self.henry_law_equations:
self.species[eq['gas']]['concentration'][:, i], self.species[
eq['aq']][
'concentration'][:, i] = equilibriumsolver.solve_henry_law(
self.species[eq['aq']]['concentration'][:, i] +
self.species[eq['gas']]['concentration'][:, i],
eq['Hcc'])
for elem in [eq['gas'], eq['aq']]:
self.profiles[elem] = self.species[elem]['concentration'][:, i]
if self.species[elem]['int_transport']:
self.update_matrices_due_to_bc(elem, i)
def acid_base_solve_ph(self, i):
# initial guess from previous time-step
res = self.species['pH']['concentration'][0, i - 1]
for idx_j in range(self.N):
for c in self.acid_base_components:
init_conc = 0
for element in c['species']:
init_conc += self.species[element]['concentration'][idx_j,
i]
c['pH_object'].conc = init_conc
if idx_j == 0:
self.acid_base_system.pHsolve(guess=7, tol=1e-4)
res = self.acid_base_system.pH
else:
phs = np.linspace(res - 0.1, res + 0.1, 201)
idx = self.acid_base_system._diff_pos_neg(phs).argmin()
res = phs[idx]
self.species['pH']['concentration'][idx_j, i] = res
self.profiles['pH'][idx_j] = res
def add_partition_equilibrium(self, aq, gas, Hcc):
""" For partition reactions between 2 species
Args:
aq (string): name of aquatic species
gas (string): name of gaseous species
Hcc (double): Henry Law Constant
"""
self.henry_law_equations.append({'aq': aq, 'gas': gas, 'Hcc': Hcc})
def add_ion(self, element, charge):
ion = phcalc.Neutral(charge=charge, conc=np.nan)
self.acid_base_components.append({
'species': [element],
'pH_object': ion
})
def add_acid(self, species, pKa, charge=0):
acid = phcalc.Acid(pKa=pKa, charge=charge, conc=np.nan)
self.acid_base_components.append({
'species': species,
'pH_object': acid
})
def acid_base_equilibrium_solve(self, i):
self.acid_base_solve_ph(i)
self.acid_base_update_concentrations(i)
def init_rates_arrays(self):
for rate in self.rates:
self.estimated_rates[rate] = np.zeros((self.N, self.time.size))
def create_dynamic_functions(self):
fun_str = desolver.create_ode_function(self.species, self.constants,
self.rates, self.dcdt)
exec(fun_str)
self.dynamic_functions['dydt_str'] = fun_str
self.dynamic_functions['dydt'] = locals()['f']
self.dynamic_functions['solver'] = desolver.create_solver(
locals()['f'])
def reset(self):
"""lab.reset()
resets the solution for re-run
"""
for element in self.species:
self.profiles[element] = self.species[element]['concentration'][:,
0]
def pre_run_methods(self):
if len(self.acid_base_components) > 0:
self.create_acid_base_system()
self.acid_base_equilibrium_solve(0)
if self.ode_method is 'scipy':
self.create_dynamic_functions()
self.init_rates_arrays()
def change_concentration_profile(self, element, i, new_profile):
self.profiles[element] = new_profile
self.update_matrices_due_to_bc(element, i)
def reactions_integrate_scipy(self, i):
# C_new, rates_per_elem, rates_per_rate = desolver.ode_integrate(self.profiles, self.dcdt, self.rates, self.constants, self.dt, solver='rk4')
# C_new, rates_per_elem = desolver.ode_integrate(self.profiles, self.dcdt, self.rates, self.constants, self.dt, solver='rk4')
# for idx_j in range(self.N):
for idx_j in range(self.N):
yinit = np.zeros(len(self.species))
for idx, s in enumerate(self.species):
yinit[idx] = self.profiles[s][idx_j]
ynew = desolver.ode_integrate_scipy(
self.dynamic_functions['solver'], yinit, self.dt)
for idx, s in enumerate(self.species):
self.species[s]['concentration'][idx_j, i] = ynew[idx]
for element in self.species:
self.profiles[element] = self.species[element]['concentration'][:,
i]
if self.species[element]['int_transport']:
self.update_matrices_due_to_bc(element, i)
def reconstruct_rates(self):
for idx_t in range(len(self.time)):
for name, rate in self.rates.items():
conc = {}
for s in self.species:
conc[s] = self.species[s]['concentration'][:, idx_t]
r = ne.evaluate(rate, {**self.constants, **conc})
self.estimated_rates[name][:, idx_t] = r * (r > 0)
for s in self.species:
self.species[s]['rates'] = (
self.species[s]['concentration'][:, 1:] -
self.species[s]['concentration'][:, :-1]) / self.dt
class Lab:
"""The batch experiments simulations"""
def __init__(self, tend, dt, tstart=0):
""" init function
initialize the lab class
Arguments:
tend {float} -- end time of computation
dt {float} -- timestep
Keyword Arguments:
tstart {float} -- start time of computation (default: {0})
"""
self.tend = tend
self.dt = dt
self.time = np.linspace(tstart, tend, round(tend / dt) + 1)
self.species = DotDict({})
self.dynamic_functions = DotDict({})
self.profiles = DotDict({})
self.dcdt = DotDict({})
self.rates = DotDict({})
self.estimated_rates = DotDict({})
self.constants = DotDict({})
self.functions = DotDict({})
self.henry_law_equations = []
self.acid_base_components = []
self.acid_base_system = phcalc.System()
self.ode_method = 'scipy'
def __getattr__(self, attr):
"""dot notation for species
you can use lab.element and get
species dictionary
Arguments:
attr {str} -- name of the species
Returns:
DotDict -- returns DotDict of species
"""
return self.species[attr]
def solve(self, verbose=True):
""" solves coupled PDEs
Keyword Arguments:
verbose {bool} -- if true verbose output (default: {True})
with estimation of computational time etc.
"""
self.reset()
with np.errstate(invalid='raise'):
for i in np.arange(1, len(self.time)):
try:
self.integrate_one_timestep(i)
if verbose:
self.estimate_time_of_computation(i)
except FloatingPointError as inst:
print(
'\nABORT!!!: Numerical instability... Please, adjust dt and dx manually...'
)
traceback.print_exc()
sys.exit()
# temporal hack for time dependent variables
if 'TIME' in self.species:
self.species.pop('TIME', None)
def estimate_time_of_computation(self, i):
""" function estimates time required for computation
uses first hundread of steps to estimate approximate
time for computation of all steps
Arguments:
i {int} -- index of time
"""
if i == 1:
self.start_computation_time = time.time()
print("Simulation started:\n\t",
time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
if i == 100:
total_t = len(self.time) * (
time.time() -
self.start_computation_time) / 100 * self.dt / self.dt
m, s = divmod(total_t, 60)
h, m = divmod(m, 60)
print(
"\n\nEstimated time of the code execution:\n\t %dh:%02dm:%02ds"
% (h, m, s))
print(
"Will finish approx.:\n\t",
time.strftime("%Y-%m-%d %H:%M:%S",
time.localtime(time.time() + total_t)))
def henry_equilibrium_integrate(self, i):
"""integrates Henry equlibrium reactions
Estimates the destribution of species using functions from
equilibriumsolver, and, then, updated the profiles with new
concentrations
Arguments:
i {int} -- index of time
"""
for eq in self.henry_law_equations:
self.species[eq['gas']]['concentration'][:, i], self.species[
eq['aq']][
'concentration'][:, i] = equilibriumsolver.solve_henry_law(
self.species[eq['aq']]['concentration'][:, i] +
self.species[eq['gas']]['concentration'][:, i],
eq['Hcc'])
for elem in [eq['gas'], eq['aq']]:
self.profiles[elem] = self.species[elem]['concentration'][:, i]
if self.species[elem]['int_transport']:
self.update_matrices_due_to_bc(elem, i)
def acid_base_solve_ph(self, i):
"""solves acid base reactions
solves acid-base using function from phcalc. First, it sums the total
concentration for particular species, then, estimates pH. if it idx=0,
then it uses "greedy" algorithm, else, uses +-0.1 of previous pH and
finds minimum around it.
Arguments:
i {[type]} -- [description]
"""
# initial guess from previous time-step
res = self.species['pH']['concentration'][0, i - 1]
for idx_j in range(self.N):
for c in self.acid_base_components:
init_conc = 0
for element in c['species']:
init_conc += self.species[element]['concentration'][idx_j,
i]
c['pH_object'].conc = init_conc
if idx_j == 0:
self.acid_base_system.pHsolve(guess=7, tol=1e-4)
res = self.acid_base_system.pH
else:
phs = np.linspace(res - 0.1, res + 0.1, 201)
idx = self.acid_base_system._diff_pos_neg(phs).argmin()
res = phs[idx]
self.species['pH']['concentration'][idx_j, i] = res
self.profiles['pH'][idx_j] = res
def add_partition_equilibrium(self, aq, gas, Hcc):
""" For partition reactions between 2 species
Args:
aq (string): name of aquatic species
gas (string): name of gaseous species
Hcc (double): Henry Law Constant
"""
self.henry_law_equations.append({'aq': aq, 'gas': gas, 'Hcc': Hcc})
def henry_equilibrium(self, aq, gas, Hcc):
""" For partition reactions between 2 species
Args:
aq (string): name of aquatic species
gas (string): name of gaseous species
Hcc (double): Henry Law Constant
"""
self.add_partition_equilibrium(aq, gas, Hcc)
def add_ion(self, name, charge):
"""add non-dissociative ion in acid-base system
Arguments:
name {str} -- name of the chemical element
charge {float} -- charge of chemical element
"""
ion = phcalc.Neutral(charge=charge, conc=np.nan)
self.acid_base_components.append({'species': [name], 'pH_object': ion})
def add_acid(self, species, pKa, charge=0):
"""add acid in acid-base system
Arguments:
species {list} -- list of species, e.g. ['H3PO4', 'H2PO4', 'HPO4', 'PO4']
pKa {list} -- list of floats with pKs, e.g. [2.148, 7.198, 12.375]
Keyword Arguments:
charge {float} -- highest charge in the acid (default: {0})
"""
acid = phcalc.Acid(pKa=pKa, charge=charge, conc=np.nan)
self.acid_base_components.append({
'species': species,
'pH_object': acid
})
def acid_base_equilibrium_solve(self, i):
"""solves acid-base equilibrium equations
Arguments:
i {int} -- step in time
"""
self.acid_base_solve_ph(i)
self.acid_base_update_concentrations(i)
def init_rates_arrays(self):
"""allocates zero matrices for rates
"""
for rate in self.rates:
self.estimated_rates[rate] = np.zeros((self.N, self.time.size))
def create_dynamic_functions(self):
"""create strings of dynamic functions for scipy solver and later execute
them using exec(), potentially not safe but haven't found better approach yet.
"""
fun_str = desolver.create_ode_function(self.species, self.functions,
self.constants, self.rates,
self.dcdt)
exec(fun_str)
self.dynamic_functions['dydt_str'] = fun_str
self.dynamic_functions['dydt'] = locals()['f']
self.dynamic_functions['solver'] = desolver.create_solver(
locals()['f'])
def reset(self):
"""resets the solution for re-run
"""
for element in self.species:
self.profiles[element] = self.species[element]['concentration'][:,
0]
def pre_run_methods(self):
"""pre-run before solve
initiates acid-base system and creates dynamic functions (strings of ODE)
for reaction solver
"""
self.add_time_variable()
if len(self.acid_base_components) > 0:
self.create_acid_base_system()
self.acid_base_equilibrium_solve(0)
if self.ode_method is 'scipy':
self.create_dynamic_functions()
self.init_rates_arrays()
def change_concentration_profile(self, element, i, new_profile):
"""change concentration in profile vectors
Arguments:
element {str} -- name of the element
i {int} -- step in time
new_profile {np.array} -- vector of new concetrations
"""
self.profiles[element] = new_profile
self.update_matrices_due_to_bc(element, i)
def reactions_integrate_scipy(self, i):
"""integrates ODE of reactions
Arguments:
i {int} -- step in time
"""
# C_new, rates_per_elem, rates_per_rate = desolver.ode_integrate(self.profiles, self.dcdt, self.rates, self.constants, self.dt, solver='rk4')
# C_new, rates_per_elem = desolver.ode_integrate(self.profiles, self.dcdt, self.rates, self.constants, self.dt, solver='rk4')
# for idx_j in range(self.N):
for idx_j in range(self.N):
yinit = np.zeros(len(self.species))
for idx, s in enumerate(self.species):
yinit[idx] = self.profiles[s][idx_j]
ynew = desolver.ode_integrate_scipy(
self.dynamic_functions['solver'], yinit, self.dt)
for idx, s in enumerate(self.species):
self.species[s]['concentration'][idx_j, i] = ynew[idx]
for element in self.species:
self.profiles[element] = self.species[element]['concentration'][:,
i]
if self.species[element]['int_transport']:
self.update_matrices_due_to_bc(element, i)
def reconstruct_rates(self):
"""reconstructs rates after model run
1. estimates rates;
2. estimates changes of concentrations
not sure if it works with dynamic functions of "scipy",
only with rk4 and butcher 5?
"""
if self.ode_method == 'scipy':
rates_str = desolver.create_rate_function(self.species,
self.functions,
self.constants,
self.rates, self.dcdt)
exec(rates_str, globals())
self.dynamic_functions['rates_str'] = rates_str
# from IPython.core.debugger import set_trace
# set_trace()
self.dynamic_functions['rates'] = globals()['rates']
yinit = np.zeros(len(self.species))
for idx_t in range(len(self.time)):
for idx_j in range(self.N):
for idx, s in enumerate(self.species):
yinit[idx] = self.species[s]['concentration'][idx_j,
idx_t]
rates = self.dynamic_functions['rates'](yinit)
for idx, r in enumerate(self.rates):
self.estimated_rates[r][idx_j, idx_t] = rates[idx]
else:
for idx_t in range(len(self.time)):
for name, rate in self.rates.items():
conc = {}
for spc in self.species:
conc[spc] = self.species[spc]['concentration'][:,
idx_t]
r = ne.evaluate(rate, {**self.constants, **conc})
self.estimated_rates[name][:, idx_t] = r * (r > 0)
for spc in self.species:
self.species[spc]['rates'] = (
self.species[spc]['concentration'][:, 1:] -
self.species[spc]['concentration'][:, :-1]) / self.dt