Updating package files

Author: ewagner

PLSR_model_evaluation_report.xlsx

@@ -0,0 +1,53 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Nov 26 14:29:10 2018
+
+@author: zwang896
+
+Applying ASD fresh spectra models and predict traits
+"""
+
+import numpy as np, os,pandas as pd,glob
+
+#-----------------define inout and output
+outdir = 'D:/test/output/'
+plsrDir = 'D:/test/coefficients/'
+specCSV = 'D:/test/test_spectra.csv'
+
+plsrCSVs = glob.glob(plsrDir+'*.csv')
+plsrCSVs = sorted(plsrCSVs)
+
+df_spec = pd.read_csv(specCSV, sep=',')
+spec = df_spec.iloc[:,0:2151].values
+
+#---------------vector normalization
+spec_len = np.tile(np.linalg.norm(spec, axis=1), (spec.shape[1], 1))
+spec = spec/spec_len.T
+
+#---------------5nm resampling
+wl_step = 5
+wl = np.arange(350,2501)
+spec = spec[:,0::wl_step]
+wl = wl[0::wl_step]
+
+#----------------appyling PLSR coefficients
+df_all=pd.DataFrame()
+df_all=df_spec.iloc[:,2151:]
+
+for plsrCSV in plsrCSVs:
+    trait_model = pd.read_csv(plsrCSV, sep=',', index_col=0).values
+    intercept = trait_model[:, 0]
+    coefficients = np.array(trait_model[:, list(np.arange(1, trait_model.shape[1]))])
+    
+    traitPred = np.einsum('jl,ml->jm',spec,coefficients, optimize='optimal')
+    traitPred = traitPred + intercept
+    traitPred_mean = traitPred.mean(axis=1)
+    traitPred_std = traitPred.std(axis=1,ddof=1)
+
+    trait = os.path.basename(plsrCSV)[14:-4]
+    df_all[trait+'_mean'] = traitPred_mean
+    df_all[trait+'_std'] = traitPred_std
+
+#----------------write predicted traits to csv
+outfile = os.path.join(outdir,os.path.basename(specCSV)[:-4]+'_traits.csv')
+df_all.to_csv(outfile)

apply_PLSR_coeffs.py

@@ -0,0 +1,3 @@
+,sample_id,Carbon_mean,Carbon_std,Carotenoid_area_mean,Carotenoid_area_std,Carotenoid_mass_mean,Carotenoid_mass_std,Cellulose_mean,Cellulose_std,Chlorophyllab_area_mean,Chlorophyllab_area_std,Chlorophyllab_mass_mean,Chlorophyllab_mass_std,Fiber_mean,Fiber_std,LMA_gm2_V2_mean,LMA_gm2_V2_std,Lignin_mean,Lignin_std,Nitrogen_mean,Nitrogen_std
+0,sample_1,52.3092069602,1.26855142525,161.270009237,8.82692805479,881.311840857,89.4154982596,2.55836683574,1.61789827305,652.270615909,27.2613778985,5970.65691612,469.045440737,22.0749698735,4.93547616027,62.6207399326,8.05210275164,10.3145542589,4.14721982408,2.7684241229,0.251827221871
+1,sample_2,49.640218138,1.29392487234,145.260679035,7.62493883188,873.21020191,76.0677377237,5.21288224249,1.49148426045,546.990335136,24.9053065567,5739.3660338,432.557897992,27.277664893,4.46234772175,58.350221029,7.8131393889,12.9276500101,3.93899520858,2.20993918673,0.24194709589