def print_normalized(profid, tosmpyr):
prof1 = data.loc[profid,['Layer_bottom','D14C_BulkLayer','SampleYear']]
mod = C14.cal_D14Ctosmpyr(tau[:,0], tosmpyr)
newdata['D14C_normalized'] = mod
prof = newdata.loc[profid,['Layer_bottom','D14C_BulkLayer','D14C_normalized','SampleYear']]
print prof
import pylab
import C14tools
import matplotlib
#%% fill Layer_top, Layer_bottom using Layer_top_norm Layer_bottom_norm
filename = 'Non_peat_data_synthesis.csv'
data = pd.read_csv(filename,encoding='iso-8859-1',index_col='ProfileID', skiprows=[1])
newdata = prep.get_originalLayerdepth(data)
newdata.to_csv('Non_peat_data_synthesis2.csv',encoding='iso-8859-1')
#%% plot 14C and SOC profile at each site
filename = 'Non_peat_data_synthesis.csv'
data = pd.read_csv(filename,encoding='iso-8859-1',index_col='ProfileID', skiprows=[1])
pid = data[data['Start_of_Profile']==1].index # index of profile start
d14C = prep.getvarxls(data,'D14C_BulkLayer', pid, ':')
sampleyr = prep.getvarxls(data, 'SampleYear', pid, ':')
tau, cost = C14tools.cal_tau(d14C, sampleyr)
data['tau'] = pd.Series(tau[:,0], index=data.index)
#%% plot individual profile
fig, axes = plt.subplots(nrows=4, ncols=4, figsize=(16,10))
for fign in range(15):
i = fign + 135
ax1 = fig.axes[fign]
plt.gca().invert_yaxis()
ax2 = ax1.twiny()
Y = data.loc[pid[i]:pid[i+1]-1,['Layer_bottom']]
X1 = data.loc[pid[i]:pid[i+1]-1,['D14C_BulkLayer']]
# total SOC
X2 = np.array(data.loc[pid[i]:pid[i+1]-1,['BulkDensity']]).astype(float)* \
(np.array(data.loc[pid[i]:pid[i+1]-1,['Layer_bottom']].astype(float)) \
-np.array(data.loc[pid[i]:pid[i+1]-1,['Layer_top']].astype(float))) * \
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.
for i in arr:
summ = summ + i
return summ
%timeit out = summ(np.arange(100000,dtype='float'))
#%% calculate turnover time for d14C of each layer
import C14tools as C14
import D14Cpreprocess as prep
import mystats as mysm
