def jac(t, y):
"""
This function defines Jacobian of the ordinary differential equations [ODEs] to be solved
input:
• t - time variable [internal to solver]
• y - array holding concentrations of all compounds in both gas and particulate [molecules/cc]
output:
dydt_dydt - the N_compounds x N_compounds matrix of Jacobian values
"""
# Different solvers might call jacobian at different stages, so we have to redo some calculations here
# Calculate time of day
time_of_day_seconds = start_time + t
# make sure the y array is not a list. Assimulo uses lists
y_asnumpy = numpy.array(y)
#Calculate the concentration of RO2 species, using an index file created during parsing
RO2 = numpy.sum(y[RO2_indices])
#Calculate reaction rate for each equation.
# Note that H2O will change in parcel mode
rates = evaluate_rates(time_of_day_seconds, RO2, H2O, temp,
numpy.zeros((equations)), numpy.zeros((63)))
#pdb.set_trace()
# Now use reaction rates with the loss_gain matrix to calculate the final dydt for each compound
# With the assimulo solvers we need to output numpy arrays
dydt_dydt = jacobian_function(
rates, y_asnumpy, numpy.zeros((len(y_asnumpy), len(y_asnumpy))))
#pdb.set_trace()
return dydt_dydt
def dydt_func(t, y):
"""
This function defines the right-hand side [RHS] of the ordinary differential equations [ODEs] to be solved
input:
• t - time variable [internal to solver]
• y - array holding concentrations of all compounds in both gas and particulate [molecules/cc]
output:
dydt - the dy_dt of each compound in both gas and particulate phase [molecules/cc.sec]
"""
#pdb.set_trace()
#Here we use the pre-created Numba based functions to arrive at our value for dydt
# Calculate time of day
time_of_day_seconds = start_time + t
# make sure the y array is not a list. Assimulo uses lists
y_asnumpy = numpy.array(y)
#pdb.set_trace()
# reactants=numpy.zeros((equations),)
#pdb.set_trace()
#Calculate the concentration of RO2 species, using an index file created during parsing
RO2 = numpy.sum(y[RO2_indices])
#Calculate reaction rate for each equation.
# Note that H2O will change in parcel mode [to be changed in the full aerosol mode]
# The time_of_day_seconds is used for photolysis rates - need to change this if want constant values
#pdb.set_trace()
rates = evaluate_rates(time_of_day_seconds, RO2, H2O, temp,
numpy.zeros((equations)), numpy.zeros((63)))
# Calculate product of all reactants and stochiometry for each reaction [A^a*B^b etc]
reactants = reactant_product(y_asnumpy, equations,
numpy.zeros((equations)))
#Multiply product of reactants with rate coefficient to get reaction rate
#pdb.set_trace()
reactants = numpy.multiply(reactants, rates)
# Now use reaction rates with the loss_gain information in a pre-created Numba file to calculate the final dydt for each compound
dydt = dydt_eval(numpy.zeros((len(y_asnumpy))), reactants)
#pdb.set_trace()
############ Development place-holder ##############
# ----------------------------------------------------------------------------------
# The following demonstrates the same procedure but using only Numpy and pure python
# For the full MCM this is too slow, but is useful for demonstrations and testing
#Calculate reaction rate for each equation.
## rates=test(time_of_day_seconds,RO2,H2O,temp)
# Calculate product of all reactants and stochiometry for each reaction [A^a*B^b etc]
# Take the approach of using sparse matrix operations from a python perspective
# This approach uses the rule of logarithms and sparse matrix multiplication
##temp_array=reactants_indices_sparse @ numpy.log(y_asnumpy)
##indices=numpy.where(temp_array > 0.0)
##reactants[indices]=numpy.exp(temp_array[indices])
#Multiply product of reactants with rate coefficient to get reaction rate
## reactants = numpy.multiply(reactants,rates)
# Now use reaction rates with the loss_gain matri to calculate the final dydt for each compound
# With the assimulo solvers we need to output numpy arrays
##dydt=numpy.array(loss_gain @ reactants)
# ----------------------------------------------------------------------------------
return dydt