def steady(constants, init):
o3 = np.zeros(constants["nlevs"])
o3_sum = np.zeros(constants["nlevs"])
o2 = np.zeros(constants["nlevs"])
o2_sum = np.zeros(constants["nlevs"])
o = np.zeros(constants["nlevs"])
J_o2 = np.zeros(constants["nlevs"])
J_o3 = np.zeros(constants["nlevs"])
k3M = np.zeros(constants["nlevs"])
k4 = np.zeros(constants["nlevs"])
q = np.zeros(constants["nlevs"])
o2_running = 0
o3_running = 0
for i in range(constants["nlevs"]):
o2[i] = constants["ratio"] * constants["M"][i]
optical_depth = constants["o2_c"] * o2_running + constants["o3_c"] * o3_running
I_factor = np.exp(-optical_depth)
I = np.array(constants["sol"] * I_factor)
J_o2[i] = j(I, "JO2", constants)
J_o3[i] = j(I, "JO3", constants)
k3M[i] = k("k3M", constants, i)
k4[i] = k("k4", constants, i)
o3[i], q[i] = ozone(J_o2[i], J_o3[i], k3M[i], k4[i], o2[i], constants["M"][i])
o[i] = otp(o3[i], J_o3[i], k3M[i], o2[i])
o2_running = o2_running + o2[i] * constants["box_h"][i]
o2_sum[i] = o2_running
o3_running = o3_running + o3[i] * constants["box_h"][i]
o3_sum[i] = o3_running
if init == True:
file = Dataset("netcdf/initial.nc", "w")
file.createDimension("height", constants["nlevs"])
spec_in_init = ["O3", "O", "O2", "M", "CH4", "N2O", "H2O"]
val_in_init = [
o3,
o,
o2,
constants["M"],
1.7e-6 * constants["M"],
3.2e-7 * constants["M"],
4.0e-6 * constants["M"],
]
for i in range(len(spec_in_init)):
file.createVariable(spec_in_init[i], np.float64, "height")
file.variables[spec_in_init[i]][:] = val_in_init[i]
# file.variables['O3'][:]=o3
# file.variables['O'][:]=o
# file.variables['O2'][:]=o2
# file.variables['M'][:]=constants['M']
# file.variables['CH4'][:]=1.7E-6*constants['M']
# file.variables['N2O'][:]=3.2E-7*constants['M']
# file.variables['H2O'][:]=4.E-6*constants['M']
# plt.plot(constants['heights'],o3)
# plt.savefig('ozone.png')
return o3, o2, o, J_o2, J_o3, o3_running
def solve(constants):
o2_running=0
o3_running=0
constants['M_surf']=2.5E19*(constants['ratio']+0.79)
constants['M']=constants['M_surf']*np.exp(-constants['heights']/constants['H'])
d1defs=np.genfromtxt("species.dat",dtype='str',skiprows=2)
bimol=np.genfromtxt("bimol.dat",dtype='str',skiprows=2)
photo=np.genfromtxt("photol.dat",dtype='str',skiprows=2)
nspec=len(d1defs[:,0])
d1=np.zeros([len(d1defs[:,0]),constants['nlevs']])
file=Dataset('netcdf/initial.nc')
## ##
# This Section Initialises Arrays To Your Input File. #
# Any Species With No Value In Input Are Set To Zero #
## ##
for i in range(len(d1defs[:,0])):
#if os.path.isfile('netcdf/'+d1defs[i,0]+'.nc')==True:
#file=Dataset('netcdf/initial.nc')
try:
species=file.variables[d1defs[i,0]][:]
except:
species=np.zeros(constants['nlevs'])
d1[i,:]=species
for i in range(len(bimol[:,0])):
for a in [0,1,3,4,5,6]:
if bimol[i,a] in d1defs[:,0] or bimol[i,a]=='X':
pass
else:
sys.exit("SPECIES "+bimol[i,a]+" IN BIMOL RXN BUT NOT IN SPECIES.DAT")
for i in range(len(photo[:,0])):
for a in [0,2,3,4,5]:
if photo[i,a] in d1defs[:,0] or photo[i,a]=='X':
pass
else:
sys.exit("SPECIES "+photo[i,a]+" IN PHOTOL RXN BUT NOT IN SPECIES.DAT")
#M=d1[np.where(d1defs=='M')[0][0],:]
d3=np.zeros([nspec,constants['nlevs']])
for i in range(constants['nlevs']):
# Create dictionary of rates
rates={}
for a in range(len(bimol)):
rates[bimol[a,2]]=k(bimol[a,2],constants,i)
optical_depth=constants['o2_c']*o2_running+constants['o3_c']*o3_running
I_factor=np.exp(-optical_depth)
I=np.array(I_factor*constants['sol'])
#I=od_chems(o2_running,o3_running,constants)
for a in range(len(photo)):
rates[photo[a,1]]=j(I,photo[a,1],constants)
# Define function for chemical tendencies
def f(y,t):
g=np.empty(nspec)
# Loop through chemical species
for spec in range(nspec):
# Set to zero if species constant, e.g. M, O2
if d1defs[spec,1]=='n':
g[spec]=0.0
else:
g[spec]=0.0
# Add chemical tendency for a bimolecular rxn
# np.where(bimol==d1defs[spec,0])[:][0].shape[0] is the number of times spec appears in bimol
for bi in range(np.where(bimol==d1defs[spec,0])[:][0].shape[0]):
rxno=np.where(bimol==d1defs[spec,0])[0][bi]
if np.where(bimol==d1defs[spec,0])[1][bi]<2:
# If appears on LHS of eqn, results in loss
g[spec]-=y[np.where(d1defs==bimol[rxno,0])[0][0]]\
*y[np.where(d1defs==bimol[rxno,1])[0][0]]\
*rates[bimol[rxno,2]]
else:
# If appears on RHS of eqn, results in production
g[spec]+=y[np.where(d1defs==bimol[rxno,0])[0][0]]\
*y[np.where(d1defs==bimol[rxno,1])[0][0]]\
*rates[bimol[rxno,2]]
for pho in range(np.where(photo==d1defs[spec,0])[:][0].shape[0]):
rxno=np.where(photo==d1defs[spec,0])[0][pho]
if np.where(photo==d1defs[spec,0])[1][pho]<1:
g[spec]-=y[np.where(d1defs==photo[rxno,0])[0][0]]\
*rates[photo[rxno,1]]
else:
g[spec]+=y[np.where(d1defs==photo[rxno,0])[0][0]]\
*rates[photo[rxno,1]]
return g
# Set up times
# t=np.linspace(0,5.174E8/60.,5.174E8/3600.)
#t=np.arange(60*60*24*360*10,step=60*60)
#solution provides a time series, take the final value
t_start=0.
t_step1=60.
t_step2=60.*60.*6.
t_end=t_step2*24.*360.*5.
t1=np.arange(t_start, t_end/100., t_step1)
t2=np.arange(t_start+t_end/100., t_end, t_step2)
t_interval=np.concatenate((t1,t2),axis=0)
soln,hi=spi.odeint(f,d1[:,i],t_interval,full_output=1)
soln=soln[len(t_interval)-1,:]
d3[:,i]=soln
o3_running=o3_running+d3[np.where(d1defs=='O3')[0][0],i]*constants['box_h'][i]
o2_running=o2_running+d1[np.where(d1defs=='O2')[0][0],i]*constants['box_h'][i]
return d1defs,d1,d3