size = 10000 tco2_store = np.empty(size) talk_store = np.empty(size) ph_store = np.empty(size) co3_store = np.empty(size) co2_store = np.empty(size) for i in range(size): tmp_tco2 = random.randint(1600,2300)/1.0e6 tmp_alk = random.randint(1800,2500)/1.0e6 tco2_store[i] = tmp_tco2 talk_store[i] = tmp_alk TCO2.fill(tmp_tco2) TALK.fill(tmp_alk) ph_store[i] = carbchem.carbchem(2,mdi,T,S,TCO2,TALK)[0] co3_store[i] = carbchem.carbchem(5,mdi,T,S,TCO2,TALK)[0] co2_store[i] = carbchem.carbchem(1,mdi,T,S,TCO2,TALK)[0] x = ph_store y = co3_store z = co2_store # define grid. xi = np.linspace(np.min(x),np.max(x),100) yi = np.linspace(np.min(y),np.max(y),100) # grid the data. zi = griddata((x, y), z, (xi[None,:], yi[:,None]), method='cubic') # contour the gridded data, plotting dots at the randomly spaced data points. #CS = plt.contour(xi,yi,zi,51,linewidths=0.5,colors='k')
import carbchem import numpy as np import matplotlib.pyplot as plt mdi=-999.0 sizing=(100) T = np.empty(sizing) S = np.empty(sizing) TCO2 = np.empty(sizing) TALK = np.empty(sizing) T.fill(15.0) S.fill(32.0) tco2_tmp = 0.005 talk_tmp = 0.005 for i in np.arange(100): TCO2[i] = tco2_tmp TALK[i] = talk_tmp tco2_tmp = tco2_tmp*0.99 talk_tmp = talk_tmp*0.99 plt.plot(TALK,carbchem.carbchem(1,mdi,T,S,TCO2,TALK)) plt.show()
# plt.show()
mlr_tco2.data = mlr_tco2_tmp
mlr_alk.data = mlr_alk_tmp
import carbchem
pco2_tmp = pco2.data
pH_tmp = pco2.data
for i in np.arange(pco2_tmp.shape[0]):
tco2_in2 = tco2_in[i]
tco2_in2.transpose()
alk_in2 = alk_in[i]
alk_in2.transpose()
pco2_tmp[i,:,:] = carbchem.carbchem(1,0.0,sst_in[0][i].data,sss_in[0][i].data,mlr_tco2_tmp[i,:,:]/(1026.*1000.),mlr_alk_tmp[i,:,:]/(1026.*1000.))
pH_tmp[i,:,:] = carbchem.carbchem(2,0.0,sst_in[0][i].data,sss_in[0][i].data,mlr_tco2_tmp[i,:,:]/(1026.*1000.),mlr_alk_tmp[i,:,:]/(1026.*1000.))
#CARBCHEM.carbchem(1,0.0,sst_in[0][i].data,sss_in[0][i].data,tco2_in2.data/(1000.*1000.),alk_in2.data/(1000.*1000.))
#note that his is only applicable near the surface because I've not introducsed the pressure term...
#note that the pCO2 we calculate using glodap and woa is high when before I start MLRing. Why is this? Glodap is paracticle alkalinity, as the carbem script uses.
pco2.data = pco2_tmp
pH = pco2
pH.data = pH_tmp
qplt.contourf(pco2[0])
plt.show()
s_temp=s[(years == yr_temp) & (months == month_temp) & (tco2 != mdi) & (t != mdi) & (s != mdi) & (talk != mdi) & (depth <= 20.0)]
tco2_temp=tco2[(years == yr_temp) & (months == month_temp) & (tco2 != mdi) & (t != mdi) & (s != mdi) & (talk != mdi) & (depth <= 20.0)]
talk_temp=talk[(years == yr_temp) & (months == month_temp) & (tco2 != mdi) & (t != mdi) & (s != mdi) & (talk != mdi) & (depth <= 20.0)]
#pulls out only those lats and longs corresponding to the subpolar gyre
test1=np.in1d(lats_temp,spg_lats)
test2=np.in1d(lons_temp,spg_lons)
#note - I can't find a simple way of combining the two masks - this is the best I can manage!
combined_mask=np.copy(test1)
combined_mask.fill(False)
loc=[i for i,x in enumerate(test1) if (test1[i] == True) & (test2[i] == True)]
combined_mask[loc]=True
#calculate the mean pco2 for the spg in that year and month
if t_temp[combined_mask].size > 1:
temp_co2_data = carbchem.carbchem(1,mdi,t_temp[combined_mask],s_temp[combined_mask],tco2_temp[combined_mask],talk_temp[combined_mask])
# print 'lats: '+str(lats_temp[combined_mask[0:10]])
# print 'lons: '+str(lons_temp[combined_mask[0:10]])
# print 'T: '+str(t_temp[combined_mask[0:10]])
# print 'S: '+str(s_temp[combined_mask[0:10]])
# print 'TALK: '+str(tco2_temp[combined_mask[0:10]])
# print 'TCO2: '+str(talk_temp[combined_mask[0:10]])
# print np.mean(temp_co2_data)
# print 'year: '+str(yr_temp)
# print 'month: '+str(month_temp)
# print '\n'
co2_obs.append(np.mean(temp_co2_data))
year_co2.append(yr_temp)
month_co2.append(month_temp)
lats_obs.append(lats_temp[combined_mask])
lons_obs.append(lons_temp[combined_mask])
plt.figure()
plt.pcolor(alk_field2,cmap='jet',vmin = vmin,vmax = vmax)
plt.colorbar()
plt.title('mean alkalinity = '+np.str(np.mean(alk_field1))+' (mol kg$^{-1}$)')
#plt.show(block = False)
plt.savefig('/home/ph290/Documents/figures/alkalinity_nonlinearity/alk_2.pdf',transparent = True)
plt.figure()
plt.pcolor(alk_field3,cmap='jet',vmin = vmin,vmax = vmax)
plt.colorbar()
plt.title('mean alkalinity = '+np.str(np.mean(alk_field1))+' (mol kg$^{-1}$)')
#plt.show(block = False)
plt.savefig('/home/ph290/Documents/figures/alkalinity_nonlinearity/alk_3.pdf',transparent = True)
co2_1 = carbchem.carbchem(1,-99.9,t,s,dic,alk_field1)
co2_2 = carbchem.carbchem(1,-99.9,t,s,dic,alk_field2)
co2_3 = carbchem.carbchem(1,-99.9,t,s,dic,alk_field3)
vmin1 = 100
vmax1 = 3000
plt.figure()
plt.pcolor(co2_1,cmap='jet',vmin = vmin1,vmax = vmax1)
plt.colorbar()
plt.title('mean pCO$_2$ = '+np.str(np.mean(co2_1))+' (uatm.)')
#plt.show(block = False)
plt.savefig('/home/ph290/Documents/figures/alkalinity_nonlinearity/co2_1.pdf',transparent = True)
plt.figure()
rcp85=['ajnji', 'kaadf', 'kaaef', 'kaaff']
co2_stab=['alytb']
geo_e=['amreb','amrec']
#runs=[rcp26,rcp45,rcp60,rcp85,co2_stab,geo_e]
runs=[g3,g4]
for i,dummy in enumerate(runs):
size=np.size(dummy)
for j in range(size):
print 'processing '+runs[i][j]
cube=iris.load(dir+runs[i][j]+'/*.pp')
s101 = cube.extract(c_stash_101)
s101=s101[0]
s102 = cube.extract(c_stash_102)
s102=s102[0]
s103 = cube.extract(c_stash_103)
s103=s103[0]
s104 = cube.extract(c_stash_104)
s104=s104[0]
arag_ss_data = carbchem.carbchem(9,s101.data.fill_value,s101.data,s102.data*1000.0+35.0,s103.data/(1026.0*1000.0),s104.data/(1026.0*1000.0))
arag_ss=s101.copy(data=arag_ss_data)
arag_ss.long_name='aragnoite_saturation_state'
iris.fileformats.netcdf.save(arag_ss, '/data/local/hador/arag_ss/'+runs[i][j]+'.nc', netcdf_format='NETCDF3_CLASSIC')
def answer(TALK,TCO2,t,s):
H2CO3 = cc.carbchem(3,-99.0,np.array([t,t]),np.array([s,s]),np.array([TCO2,TCO2]),np.array([TALK,TALK]))[0]
HCO3 = cc.carbchem(4,-99.0,np.array([t,t]),np.array([s,s]),np.array([TCO2,TCO2]),np.array([TALK,TALK]))[0]
CO3 = cc.carbchem(5,-99.0,np.array([t,t]),np.array([s,s]),np.array([TCO2,TCO2]),np.array([TALK,TALK]))[0]
return H2CO3*10000.0,HCO3*10000.0,CO3*10000.0
import carbchem
import matplotlib.pyplot as plt
import numpy as np
sizing = 1000
mdi=-999.0
T = np.empty(sizing)
S = np.empty(sizing)
TCO2 = np.empty(sizing)
TALK = np.empty(sizing)
T.fill(15.0)
S.fill(32.0)
TCO2 = np.linspace(0.0016,0.0023,1000)
TALK.fill(0.0024)
co2 = carbchem.carbchem(1,mdi,T,S,TCO2,TALK)
plt.plot(co2,TCO2,'r')
TALK.fill(0.0020)
co2 = carbchem.carbchem(1,mdi,T,S,TCO2,TALK)
plt.plot(co2,TCO2,'b')
plt.xlim([200,1000])
#plt.show(block = True)
plt.savefig('/home/ph290/Documents/figures/co2_dic.pdf')
import iris import carbchem import iris.quickplot as qplt import matplotlib.pyplot as plt directory = '/home/ph290/data0/lester_geoeng/' files = ['anfea.pp','anfeb.pp','anfec.pp','anfed.pp'] for i,file in enumerate(files): cube = iris.load(directory+files[i]) aragonite_sat_state = cube[2].copy() aragonite_sat_state.data = carbchem.carbchem(9,cube[0].data.fill_value,cube[2].data,cube[3].data*1000+35.0,cube[0].data/(1026.*1000.),cube[1].data/(1026.*1000.)) iris.fileformats.netcdf.save(aragonite_sat_state, directory+files[i][0:5]+'_aragonite_sat_state.nc', netcdf_format='NETCDF3_CLASSIC') iris.fileformats.netcdf.save(cube[2], directory+files[i][0:5]+'_sst.nc', netcdf_format='NETCDF3_CLASSIC')
