ax1.set_ylabel(r"Depth $(cm)$")
for tl in ax1.get_xticklabels():
tl.set_color('g')
h2 = ax2.plot(X1,Y)
ax2.set_xlabel(r"$\Delta14C$ ("+ u"\u2030)"+ "(solid line)")
plt.gca().invert_yaxis()
pylab.text(0.8, 0.1,orderlabel[i]+"\n"+"N = "+str(len(set(biomedataid))),
horizontalalignment='center',verticalalignment='center',
transform = ax1.transAxes,fontsize=16)
dum = dum + 1
fig.tight_layout() # Or equivalently, "plt.tight_layout()"
matplotlib.rcParams.update({'font.size': 14})
#fig.savefig('../figures/biome_profiles/withGCBdata/%s_%s.png'%(order[pltorder[0]],order[pltorder[1]]))
#%% plot C-averaged D14C and depth/SOC content
filename = 'Non_peat_data_synthesis.csv'
Cave14C = prep.getCweightedD14C2(filename)
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10,6))
nsmp = Cave14C[~np.isnan(Cave14C[:,4]),4].shape[0]
axes[0].scatter(Cave14C[:,5],Cave14C[:,4])
axes[0].set_xlabel(r'Total C Content$(kg \ m^{-2})$')
axes[0].set_ylabel(r'SOC averaged $\Delta 14C$ ('+ u"\u2030)")
ylimm = axes[0].get_ylim()
axes[0].plot(np.array([7.02,7.02]),ylimm,'r:',lw=2)
axes[0].set_ylim(ylimm)
axes[0].set_xlim(0,axes[0].get_xlim()[1])
axes[0].annotate('HadGEM equilibrium global mean',xy=(7.3,-750),xytext=(15,-750),\
arrowprops=dict(facecolor='black',shrink=0.05)) # global average SOC stock in HadGEM is 7.02 kg/m2
axes[0].text(100,0.8,'n = %d'%nsmp)
axes[1].scatter(Cave14C[:,2],Cave14C[:,4])
axes[1].set_xlabel(r'Depth (cm)')
axes[1].set_ylabel(r'SOC averaged $\Delta 14C$ ('+ u"\u2030)")
import D14Cpreprocess as prep
import scipy.stats as stats
import statsmodels.api as sm
import mystats as mysm
import myplot
import pylab
import random
from collections import Counter
#%% prepare data
# get 14C and SOC of total profiles
filename = 'Non_peat_data_synthesis.csv'
data = pd.read_csv(filename,encoding='iso-8859-1',index_col='ProfileID', skiprows=[1])
layerbot = prep.getvarxls(data, 'Layer_bottom_norm', data.index.unique(), -1)
plt.hist(layerbot, 60)
cutdep = 40.
Cave14C = prep.getCweightedD14C2(filename, cutdep=cutdep)
nprof = Cave14C[(Cave14C[:,1]==0) & (Cave14C[:,2]==cutdep)].shape[0] # n = 138
totprofid = Cave14C[(Cave14C[:,1]==0) & (Cave14C[:,2]==cutdep), 3]
totprof14C = Cave14C[(Cave14C[:,1]==0) & (Cave14C[:,2]==cutdep), 4]
totprofSOC = Cave14C[(Cave14C[:,1]==0) & (Cave14C[:,2]==cutdep), 5]
totprofveg = prep.getvarxls(data,'VegTypeCode_Local',totprofid,0)
dum = list(totprofveg); Counter(dum)
# get 14C and SOC of profiles selected for modeling
pltorisitesonly = 0
filename = 'sitegridid2.txt'
data = np.loadtxt(filename,unpack=True,delimiter=',')[:,:].T
mdlprofid = data[:,2].astype(float)
if pltorisitesonly == 0: # including extra sites
filename = 'extrasitegridid.txt'
data = np.loadtxt(filename,unpack=True,delimiter=',')[:,:].T
besttau.append(taufast[np.argmin(np.asarray(cost))])
else:
# use prebomb_FM
for n, tau in enumerate(tauslow):
#print 'tauslow: %d, #%d'%(tau, n)
dum = obs - FMtoD14C(cal_prebomb_FM(1./tau))
cost.append(dum**2)
besttau.append(tauslow[np.argmin(np.asarray(cost))])
return besttau
#%% test cal_tau
import D14Cpreprocess as prep
import pandas as pd
import C14tools
filename = 'Non_peat_data_synthesis.csv'
Cave14C = prep.getCweightedD14C2(filename)
data = pd.read_csv(filename,encoding='iso-8859-1',index_col='ProfileID', skiprows=[1])
profid = Cave14C[:,3]
d14C = prep.getvarxls(data,'D14C_BulkLayer', profid, ':')
sampleyr = prep.getvarxls(data, 'SampleYear', profid, ':')
n0 = 40
nend = 60
%timeit tau, cost = C14tools.cal_tau(d14C[n0:nend], sampleyr[n0:nend])
#%%
import numba as nb
#@nb.jit(nb.f8(nb.f8[:]))
#@nb.autojit
def summ(arr):
summ = 0.
import pylab
from matplotlib.ticker import FuncFormatter
# All profiles
fromsitegrididfile = 1
if fromsitegrididfile == 1:
filename = 'sitegridid2.txt'
dum = np.loadtxt(filename, delimiter=',')
proftotSOC = dum[:,6]
profD14C= dum[:,5]
proflon = dum[:,0]
proflat = dum[:,1]
else:
filename = 'Non_peat_data_synthesis.csv'
cutdep = 100.
profdata = prep.getCweightedD14C2(filename,cutdep=cutdep)
proftotSOC = profdata[profdata[:,2]==cutdep,5]
profD14C = profdata[profdata[:,2]==cutdep,4]
# HWSD
sawtcfn = '..\\AncillaryData\\HWSD\\Regridded_ORNLDAAC\\AWT_S_SOC.nc4'
tawtcfn = '..\\AncillaryData\\HWSD\\Regridded_ORNLDAAC\\AWT_T_SOC.nc4'
ncfid = Dataset(sawtcfn, 'r')
nc_attrs, nc_dims, nc_vars = mync.ncdump(ncfid)
sawtc = ncfid.variables['SUM_s_c_1'][:]
ncfid = Dataset(tawtcfn, 'r')
nc_attrs, nc_dims, nc_vars = mync.ncdump(ncfid)
hwsdlat = ncfid.variables['lat'][:]
hwsdlon = ncfid.variables['lon'][:]
tawtc = ncfid.variables['SUM_t_c_12'][:]
hwsdsoc = sawtc + tawtc
hwsdsoc = np.ravel(hwsdsoc)