is_smlzn1m = data.loc[kk:kk, 'Layer_bottom'].values<100.
if data.loc[kk:kk, 'Layer_bottom'].values[-1] > 100.:
lastlyrCcnt = data.loc[kk:kk, 'pct_C'].values[wr_smlzn1m[-1]+1] * \
data.loc[kk:kk, 'BulkDensity'].values[wr_smlzn1m[-1]+1] * \
(100. - data.loc[kk:kk, 'Layer_top'].values[wr_smlzn1m[-1]+1])
cumC = data.loc[kk:kk, 'cumC'].values
cumC[wr_smlzn1m[-1]+1] = cumC[wr_smlzn1m[-1]] + lastlyrCcnt
is_bigznD0 = data.loc[kk:kk, 'D14C_BulkLayer'].values>0.
is_smlznD500 = data.loc[kk:kk, 'D14C_BulkLayer'].values<=-500.
cfast = cumC[is_bigznD0 & is_smlzn1m]
cpasv = cumC[is_smlznD500 & is_smlzn1m][-1] - \
cumC[np.where(cumC==cumC[is_smlznD500 & is_smlzn1m][0])[-1]][0]
cfastfrac[-1] += [cfast],
cpasvfrac[-1] += [cpasv],
df = prep.getprofSOCD14C_interp(filename, 100)
#%% calculate # profiles that start from top and are at least 1m deep
newdf = prep.cal_pfNumber(0, 0.)
filename = 'Non_peat_data_synthesis.csv'
data = pd.read_csv(filename,encoding='iso-8859-1',index_col='ProfileID', skiprows=[1])
profid = data.index.unique()
Cave14C = prep.getCweightedD14C2(data, cutdep=100.)
top0 = prep.getvarxls(data, 'Layer_top', profid, 0)
plt.hist(top0,cumulative=True,bins=100)
top0bottom = prep.getvarxls(newdf, 'Layer_bottom', newdf.index.unique(), -1)
plt.hist(top0bottom,cumulative=True,bins=100)
#%% plot total SOC vs. D14C
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import D14Cpreprocess as prep
import pylab
filename = 'tot48prof.txt'
dum = np.loadtxt(filename, delimiter=',')
profid = dum[:,2]
#%% plot cumuSOC vs cumuC-averaged D14C
cm = plt.get_cmap('gist_rainbow')
numcolr = len(profid) # no repeat in color
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(8,6))
axes.set_color_cycle([cm(1.*jj/numcolr) for jj in range(numcolr)])
for i in profid:
data = prep.getprofSOCD14C_interp('Non_peat_data_synthesis.csv',i,cutdep=100.)
cumuC = np.cumsum(data[:,2])
cumufracC = cumuC/cumuC[-1]
cumuD14C = np.cumsum(data[:,1] * data[:,2])/cumuC
axes.plot(cumufracC, cumuD14C, ':', lw=2)
axes.set_xlabel('Cumulative SOC fraction (%)')
axes.set_ylabel(r"Cumulative $\Delta^{14}C$ ("+ u"\u2030)")
#%% plot cumuSOC vs D14C
cm = plt.get_cmap('gist_rainbow')
numcolr = len(profid) # no repeat in color
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(8,6))
axes.set_color_cycle([cm(1.*jj/numcolr) for jj in range(numcolr)])
for i in profid:
if i==144.:
continue