def keep_spectra(keepfile,spectra,names,spect_index,comps):
keepdata,keepcols=ccam.read_csv_cols(keepfile,1,labelrow=True)
keepindex=numpy.array(keepdata[0],dtype='int')-1
fullindex=range(len(names))
matchindex=numpy.in1d(fullindex,keepindex)
spectra=spectra[matchindex,:]
comps=comps[matchindex,:]
names=names[matchindex]
spect_index=spect_index[matchindex]
return spectra,names,spect_index,comps
def remove_spectra(removefile, spectra, names, spect_index, comps):
#read the list of sample names and spectrum indices from the file
data = ccam.read_csv_cols(removefile, 0, labelrow=False)
removenames = numpy.array(data[0], dtype='string')
removeinds = numpy.array(data[1], dtype='int')
#define an array to hold the indices for each row in the file
index = numpy.empty([len(names), len(removenames)])
for i in range(len(removenames)):
#for each row, find the indices that correspond to the matching
#name AND spectral index
index[:,
i] = (names == removenames[i]) & (spect_index == removeinds[i])
#combine the indices for each row to a single array that indicates which
#spectra to remove, then invert it to indicate which spectra to keep
index = numpy.invert(numpy.any(index, axis=1))
spectra = spectra[index, :]
names = names[index]
spect_index = spect_index[index]
comps = comps[index, :]
return spectra, names, spect_index, comps
imgfile_full = outpath + '\\' + which_elem + '_final_model_predictions_1to1_' + str(
xminmax[0]) + '-' + str(
xminmax[1]) + '_full (' + str(fullmin) + '-' + str(fullmax) + ').png'
imgfile_low = outpath + '\\' + which_elem + '_final_model_predictions_1to1_' + str(
xminmax[0]) + '-' + str(
xminmax[1]) + '_low (' + str(lowmin) + '-' + str(lowmax) + ').png'
imgfile_mid = outpath + '\\' + which_elem + '_final_model_predictions_1to1_' + str(
xminmax[0]) + '-' + str(
xminmax[1]) + '_mid (' + str(midmin) + '-' + str(midmax) + ').png'
imgfile_high = outpath + '\\' + which_elem + '_final_model_predictions_1to1_' + str(
xminmax[0]) + '-' + str(
xminmax[1]) + '_high (' + str(highmin) + '-' + str(highmax) + ').png'
print 'Making outlier check plots'
Q_res_full, Q_labels_full = ccam.read_csv_cols(full_Qres_file,
0,
labelrow=True)
Q_res_low, Q_labels_low = ccam.read_csv_cols(low_Qres_file, 0, labelrow=True)
Q_res_mid, Q_labels_mid = ccam.read_csv_cols(mid_Qres_file, 0, labelrow=True)
Q_res_high, Q_labels_high = ccam.read_csv_cols(high_Qres_file,
0,
labelrow=True)
Q_res_full = numpy.array(Q_res_full[4:], dtype='float')
Q_res_low = numpy.array(Q_res_low[4:], dtype='float')
Q_res_mid = numpy.array(Q_res_mid[4:], dtype='float')
Q_res_high = numpy.array(Q_res_high[4:], dtype='float')
T2_res_full, T2_labels_full = ccam.read_csv_cols(full_T2_file,
0,
labelrow=True)
T2_res_low, T2_labels_low = ccam.read_csv_cols(low_T2_file, 0, labelrow=True)
""" Created on Tue Feb 10 10:31:21 2015 @author: rbanderson """ import ccam import numpy ica_db_file=r'C:\Users\rbanderson\Documents\MSL\ChemCam\DataProcessing\ICA_1500mm_db.csv' uni_db_file=r'C:\Users\rbanderson\Documents\MSL\ChemCam\DataProcessing\Univariate_1500mm_db.csv' dbfile='C:\\Users\\rbanderson\\Documents\\MSL\\ChemCam\\DataProcessing\\Working\\Input\\full_db_mars_corrected.csv' which_elem='K2O' ica_db=ccam.read_csv_cols(ica_db_file,0,labelrow=False) targetnames=numpy.array(ica_db[0],dtype='string') Si_scores=numpy.array(ica_db[1],dtype='float') Ti_scores=numpy.array(ica_db[2],dtype='float') Al_scores=numpy.array(ica_db[3],dtype='float') Fe_scores=numpy.array(ica_db[4],dtype='float') Mg_scores=numpy.array(ica_db[5],dtype='float') Ca_scores=numpy.array(ica_db[6],dtype='float') Na_scores=numpy.array(ica_db[7],dtype='float') K_scores=numpy.array(ica_db[8],dtype='float') ICA_MgO=0.2943*numpy.exp(8.5948*Mg_scores) uni_db=ccam.read_csv_cols(uni_db_file,1,labelrow=False) uni_targetnames=numpy.array(uni_db[0],dtype='string')