expzstar = expnpzfiles['out_prop_exp'][:,1]
expcsurf = expnpzfiles['out_prop_exp'][:,2]
lmprofid = lmnpzfiles['out_prop_lm'][:,0]
lmzstar = lmnpzfiles['out_prop_lm'][:,1]
lmcsurf = lmnpzfiles['out_prop_lm'][:,2]
# that are fitted using exp vs. lm
expzstarcl = filter(lambda x:~np.isinf(x) and ~np.isnan(x), expzstar)
lmzstarcl = filter(lambda x:~np.isinf(x) and ~np.isnan(x), lmzstar)
lm_df = pd.DataFrame(data=expnpzfiles['out_prop_exp'][:,1:],
index=expnpzfiles['out_prop_exp'][:,0],columns=['zstar','csurf'])
exp_df = pd.DataFrame(data=lmnpzfiles['out_prop_lm'][:,1:],
index=lmnpzfiles['out_prop_lm'][:,0],columns=['zstar','csurf'])
join_df = exp_df.join(lm_df, how='inner', lsuffix='_exp', rsuffix='_lm')
fig, axes = plt.subplots(figsize=(10,8))
axes.scatter(join_df['zstar_exp'], join_df['zstar_lm'])
axes.set_ylim([-500,1000])
axes.set_xlim([-500,1000])
axes.set_xlabel('zstar (cm, exp)')
axes.set_ylabel('zstar (cm, lm)')
myplt.refline()
fig, axes = plt.subplots(figsize=(8,6))
axes.scatter(join_df['csurf_exp'], join_df['csurf_lm'])
axes.set_ylim([0,60])
axes.set_xlim([0,60])
axes.set_xlabel('csurf (%, exp)')
axes.set_ylabel('csurf (%, lm)')
myplt.refline()
fig, axes = plt.subplots(nrows=1,ncols=1)
#plt.hist(y,30,alpha=0.3, normed=True)
plt.scatter(X[:,1],y)
if plotres == 1:
fig = plt.figure()
ax = fig.add_axes([0.05, 0.05, 0.9, 0.9])
ax.scatter(model.predict(), model.resid)
ax.set_ylabel('residual')
ax.set_xlabel('yhat')
if plot_y_yhat:
fig = plt.figure()
ax = fig.add_axes([0.05, 0.05, 0.9, 0.9])
ax.scatter(y, yhat)
ax.set_ylabel('yhat')
ax.set_xlabel('y')
myplot.refline()
boolist = yhat < -1000.
idx = [i for i, elem in enumerate(boolist) if elem]
#%% Linear Mixed Effects Model on profile
filename = 'Non_peat_data_synthesis.csv'
cutdep = 110.
Cave14C = prep.getCweightedD14C2(filename)
data = pd.read_csv(filename,encoding='iso-8859-1',skiprows=[1],index_col='ProfileID')
profid = Cave14C[:,3]
d14C = prep.getvarxls(data,'D14C_BulkLayer', profid, ':')
mat = prep.getvarxls(data,'MAT', profid, ':')
mapp = prep.getvarxls(data,'MAP', profid, ':')
layerbot = prep.getvarxls(data, 'Layer_bottom_norm', profid, ':')
vegid = prep.getvarxls(data, 'VegTypeCode_Local', profid, ':')
