ax = fig.add_axes([0.05, 0.05, 0.9, 0.9])
ax.scatter(rlm.predict(), rlm.resid)
ax.set_ylabel('residual')
ax.set_xlabel('yhat')
#%% OLS model on profile
# indicate if want to use deltaDelta
ddelta = 1
filename = 'Non_peat_data_synthesis.csv'
cutdep = 40.
Cave14C = prep.getCweightedD14C2(filename)
data = pd.read_csv(filename,encoding='iso-8859-1',index_col='ProfileID', skiprows=[1])
profid = data[data['Start_of_Profile']==1].index # index of profile start
d14C = prep.getvarxls(data,'D14C_BulkLayer', profid, ':')
sampleyr = prep.getvarxls(data, 'SampleYear', profid, ':')
dd14C = prep.getDDelta14C(sampleyr, d14C)
tau, cost = C14tools.cal_tau(d14C, sampleyr)
data['tau'] = pd.Series(tau[:,0], index=data.index)
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, ':')
vegiduniq = np.unique(vegid[~np.isnan(vegid)])
soilorder = prep.getvarxls(data, 'LEN Order', profid, ':')
soilorder = np.array([str(i) for i in soilorder])
soilorderuniq = np.unique(soilorder[soilorder != 'nan'])
lon = prep.getvarxls(data,'Lon',profid,':')
lat = prep.getvarxls(data,'Lat',profid,':')
clayon = 0
nppon = 0
plott = 0
