def bdcarb(raster, wavelengths):
from math import sqrt
'''
NAME: BDCARB
PARAMETER: overtone band depth
FORMULATION *: 1 - ( sqrt [ ( R2330 / (a*R2230+b*R2390) ) *
( R2530/(c*R2390+d*R2600) ) ] )
RATIONALE: carbonate overtones
'''
bands = csas.getbandnumbers(wavelengths, 2230, 2330, 2390, 2530, 2600)
band2230 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
band2330 = csas.getarray(raster.GetRasterBand(bands[1] + 1))
band2390 = csas.getarray(raster.GetRasterBand(bands[2] + 1))
band2530 = csas.getarray(raster.GetRasterBand(bands[3] + 1))
band2600 = csas.getarray(raster.GetRasterBand(bands[4] + 1))
wv2230 = wavelengths[bands[0]]
wv2330 = wavelengths[bands[1]]
wv2390 = wavelengths[bands[2]]
wv2530 = wavelengths[bands[3]]
wv2600 = wavelengths[bands[4]]
#Algorithm
a = (((wv2330 + wv2120) * .5) - wv2230) / (wv2390 - wv2230)
b = 1.0 - a
c = (((wv2530 + wv2120) * .5) - wv2390) / (wv2600 - wv2390)
d = 1.0 - c
array_out = 1 - (sqrt(band2330 / ((b * band2230) + (a * band2390))) *
(band2530 / ((d * band2230) + (c * band2600))))
return array_out
def bdcarb(raster, wavelengths):
from math import sqrt
'''
NAME: BDCARB
PARAMETER: overtone band depth
FORMULATION *: 1 - ( sqrt [ ( R2330 / (a*R2230+b*R2390) ) *
( R2530/(c*R2390+d*R2600) ) ] )
RATIONALE: carbonate overtones
'''
bands = csas.getbandnumbers(wavelengths, 2230, 2330, 2390, 2530, 2600)
band2230 = csas.getarray(raster.GetRasterBand(bands[0]+1))
band2330 = csas.getarray(raster.GetRasterBand(bands[1]+1))
band2390 = csas.getarray(raster.GetRasterBand(bands[2]+1))
band2530 = csas.getarray(raster.GetRasterBand(bands[3]+1))
band2600 = csas.getarray(raster.GetRasterBand(bands[4]+1))
wv2230 = wavelengths[bands[0]]
wv2330 = wavelengths[bands[1]]
wv2390 = wavelengths[bands[2]]
wv2530 = wavelengths[bands[3]]
wv2600 = wavelengths[bands[4]]
#Algorithm
a = (((wv2330 + wv2120)*.5) - wv2230)/ (wv2390 - wv2230)
b = 1.0 - a
c = (((wv2530 + wv2120)*.5) - wv2390)/ (wv2600 - wv2390)
d = 1.0 - c
array_out = 1 - (sqrt(band2330 / ((b*band2230)+(a*band2390)))* (band2530 / ((d*band2230)+(c*band2600))))
return array_out
def icer2(raster, wavelengths):
'''
NAME: ICER2
PARAMETER: gauge 2.7 micron band
FORMULATION *: R2530 / R2600
RATIONALE: CO2 ice will be >>1, H2O ice and soil will be about 1
'''
bands = csas.getbandnumbers(wavelengths, 2530, 2600)
band2530 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
band2600 = csas.getarray(raster.GetRasterBand(bands[1] + 1))
#Algorithm
array_out = band2530 / band2600
return array_out
def icer2(raster, wavelengths):
'''
NAME: ICER2
PARAMETER: gauge 2.7 micron band
FORMULATION *: R2530 / R2600
RATIONALE: CO2 ice will be >>1, H2O ice and soil will be about 1
'''
bands = csas.getbandnumbers(wavelengths, 2530, 2600)
band2530 = csas.getarray(raster.GetRasterBand(bands[0]+1))
band2600 = csas.getarray(raster.GetRasterBand(bands[1]+1))
#Algorithm
array_out = band2530 / band2600
return array_out
def icer1(raster, wavelengths):
'''
NAME: ICER1
PARAMETER: 1.5 micron and 1.43 micron band ratio
FORMULATION *: R1510 / R1430
RATIONALE: CO2, H20 ice mixtures
'''
bands = csas.getbandnumbers(wavelengths, 1430, 1510)
band1430 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
band1510 = csas.getarray(raster.GetRasterBand(bands[1] + 1))
#Algorithm
array_out = band1430 / band1510
return array_out
def rockdust2(raster, wavelengths):
'''
Name: RBR
Parameter: Red/Blue Ratio
Formulation: R770 / R440
Rationale: rock/dust
'''
bands = csas.getbandnumbers(wavelengths, 440, 770)
band440 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
band770 = csas.getarray(raster.GetRasterBand(bands[1] + 1))
#Algorithm
array_out = band770 / band440
return array_out
def bd3000(raster, wavelengths):
'''
NAME: BD3000
PARAMETER: 3 micron band depth
FORMULATION *: 1 - ( R3000 / (R2530*(R2530/R2210)) )
RATIONALE: H2O, chemically bound or adsorbed
'''
bands = csas.getbandnumbers(wavelengths, 2210, 2530, 3000)
band2210 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
band2530 = csas.getarray(raster.GetRasterBand(bands[1] + 1))
band3000 = csas.getarray(raster.GetRasterBand(bands[2] + 1))
#Algorithm
array_out = 1 - (band3000 / (band2530 * (band2530 / band2210)))
return array_out
def rockdust2(raster, wavelengths):
'''
Name: RBR
Parameter: Red/Blue Ratio
Formulation: R770 / R440
Rationale: rock/dust
'''
bands = csas.getbandnumbers(wavelengths, 440,770)
band440 = csas.getarray(raster.GetRasterBand(bands[0]+1))
band770 = csas.getarray(raster.GetRasterBand(bands[1]+1))
#Algorithm
array_out = band770 / band440
return array_out
def icer1(raster, wavelengths):
'''
NAME: ICER1
PARAMETER: 1.5 micron and 1.43 micron band ratio
FORMULATION *: R1510 / R1430
RATIONALE: CO2, H20 ice mixtures
'''
bands = csas.getbandnumbers(wavelengths, 1430, 1510)
band1430 = csas.getarray(raster.GetRasterBand(bands[0]+1))
band1510 = csas.getarray(raster.GetRasterBand(bands[1]+1))
#Algorithm
array_out = band1430 / band1510
return array_out
def bd3000(raster, wavelengths):
'''
NAME: BD3000
PARAMETER: 3 micron band depth
FORMULATION *: 1 - ( R3000 / (R2530*(R2530/R2210)) )
RATIONALE: H2O, chemically bound or adsorbed
'''
bands = csas.getbandnumbers(wavelengths, 2210, 2530, 3000)
band2210 = csas.getarray(raster.GetRasterBand(bands[0]+1))
band2530 = csas.getarray(raster.GetRasterBand(bands[1]+1))
band3000 = csas.getarray(raster.GetRasterBand(bands[2]+1))
#Algorithm
array_out = 1 - (band3000 / (band2530 * (band2530 / band2210)))
return array_out
def bd1500(raster, wavelengths):
'''
NAME: BD1500
PARAMETER: 1.5 micron band depth
FORMULATION *: 1.0 - ((R1558 + R1505)/(R1808 + R1367))
RATIONALE: H2O surface ice
Algorithm differs from published - coded as per CAT (reduced instrument noise)
'''
bands = csas.getbandnumbers(wavelengths, 1367, 1505, 1558,1808)
band1367 = csas.getarray(raster.GetRasterBand(bands[0]+1))
band1505 = csas.getarray(raster.GetRasterBand(bands[1]+1))
band1558 = csas.getarray(raster.GetRasterBand(bands[2]+1))
band1808 = csas.getarray(raster.GetRasterBand(bands[3]+1))
#Algorithm
array_out = 1.0 - ((band1558 + band1505)/(band1808 + band1367))
return array_out
def islope1(raster, wavelengths):
'''
NAME: ISLOPE1
PARAMETER: -1 * spectral slope1
FORMULATION *: (R1815-R2530) / (2530-1815)
RATIONALE: ferric coating on dark rock
'''
bands = csas.getbandnumbers(wavelengths, 1815,2530)
band1080 = csas.getarray(raster.GetRasterBand(bands[0]+1))
band1330 = csas.getarray(raster.GetRasterBand(bands[1]+1))
wv1815 = wavelengths[bands[0]]
wv2530 = wavelengths[bands[1]]
#Algorithm
array_out = (band1815-band2530)/(wv2530-wv1815)
return array_out
def islope1(raster, wavelengths):
'''
NAME: ISLOPE1
PARAMETER: -1 * spectral slope1
FORMULATION *: (R1815-R2530) / (2530-1815)
RATIONALE: ferric coating on dark rock
'''
bands = csas.getbandnumbers(wavelengths, 1815, 2530)
band1080 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
band1330 = csas.getarray(raster.GetRasterBand(bands[1] + 1))
wv1815 = wavelengths[bands[0]]
wv2530 = wavelengths[bands[1]]
#Algorithm
array_out = (band1815 - band2530) / (wv2530 - wv1815)
return array_out
def bd1500(raster, wavelengths):
'''
NAME: BD1500
PARAMETER: 1.5 micron band depth
FORMULATION *: 1.0 - ((R1558 + R1505)/(R1808 + R1367))
RATIONALE: H2O surface ice
Algorithm differs from published - coded as per CAT (reduced instrument noise)
'''
bands = csas.getbandnumbers(wavelengths, 1367, 1505, 1558, 1808)
band1367 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
band1505 = csas.getarray(raster.GetRasterBand(bands[1] + 1))
band1558 = csas.getarray(raster.GetRasterBand(bands[2] + 1))
band1808 = csas.getarray(raster.GetRasterBand(bands[3] + 1))
#Algorithm
array_out = 1.0 - ((band1558 + band1505) / (band1808 + band1367))
return array_out
def bd1900(raster, wavelengths):
'''
NAME: BD1900
PARAMETER: 1.9 micron band depth
FORMULATION *: 1.0 - ((R1972 + R1927)/(R2006 + R1874))
RATIONALE: H2O, chemically bound or adsorbed
Algorithm differs from published - coded as per CAT (reduced instrument noise)
'''
bands = csas.getbandnumbers(wavelengths, 1874, 1927, 1973, 2006)
band1874 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
band1927 = csas.getarray(raster.GetRasterBand(bands[1] + 1))
band1973 = csas.getarray(raster.GetRasterBand(bands[2] + 1))
band2006 = csas.getarray(raster.GetRasterBand(bands[3] + 1))
#Algorithm
array_out = 1.0 - ((band1972 + band1927) / (band2006 + band1874))
return array_out
def bd1900(raster, wavelengths):
'''
NAME: BD1900
PARAMETER: 1.9 micron band depth
FORMULATION *: 1.0 - ((R1972 + R1927)/(R2006 + R1874))
RATIONALE: H2O, chemically bound or adsorbed
Algorithm differs from published - coded as per CAT (reduced instrument noise)
'''
bands = csas.getbandnumbers(wavelengths, 1874, 1927, 1973, 2006)
band1874= csas.getarray(raster.GetRasterBand(bands[0]+1))
band1927 = csas.getarray(raster.GetRasterBand(bands[1]+1))
band1973 = csas.getarray(raster.GetRasterBand(bands[2]+1))
band2006 = csas.getarray(raster.GetRasterBand(bands[3]+1))
#Algorithm
array_out = 1.0 - ((band1972 + band1927)/(band2006 + band1874))
return array_out
def hcp_index(raster, wavelengths):
'''
NAME: HCPXINDEX
PARAMETER: pyroxene index
FORMULATION *: 100 * ((R1470 - R1080)/(R1470 + R1080)) * ((R1470 - R2067)/(R1470+R2067))
RATIONALE: pyroxene is strongly +; favors high-Ca pyroxene
Algorithm differs from published - coded as per CAT
'''
bands = csas.getbandnumbers(wavelengths, 1080,1470, 2067)
band1080 = csas.getarray(raster.GetRasterBand(bands[0]+1))
band1470 = csas.getarray(raster.GetRasterBand(bands[1]+1))
band2067 = csas.getarray(raster.GetRasterBand(bands[2]+1))
#Algorithm
array_out = 100 * ((band1470 - band1080)/(band1470 + band1080)) * ((band1470 - band2067)/(band1470+band2067))
return array_out
def lcp_index(raster, wavelengths):
'''
NAME: LCPINDEX
PARAMETER: pyroxene index
FORMULATION *: 100 * ((R1330 - R1080)/(R1330 + R1080)) * ((R1330 - R1815)/(R1330+R1815))
RATIONALE: pyroxene is strongly +; favors low-Ca pyroxene
Algorithm differs from published - coded as per CAT
'''
bands = csas.getbandnumbers(wavelengths, 1080,1330,1815)
band1080 = csas.getarray(raster.GetRasterBand(bands[0]+1))
band1330 = csas.getarray(raster.GetRasterBand(bands[1]+1))
band1815 = csas.getarray(raster.GetRasterBand(bands[2]+1))
#Algorithm
array_out = 100 * ((band1330 - band1080)/(band1330 + band1080)) * ((band1330 - band1815)/(band1330+band1815))
return array_out
def sindex(raster, wavelengths):
'''
NAME: SINDEX
PARAMETER: Convexity at 2.29 microns due to absorptions at
1.9/2.1 microns and 2.4 microns
FORMULATION *: 1 - (R2100 + R2400) / (2 * R2290) CR
values are observed R values divided by values fit along the
slope as determined between 1.8 - 2.53 microns (essentially
continuum corrected))
RATIONALE: hydrated minerals; particularly sulfates
'''
bands = csas.getbandnumbers(wavelengths, 2100, 2400, 2290)
band2100 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
band2400 = csas.getarray(raster.GetRasterBand(bands[1] + 1))
band2290 = csas.getarray(raster.GetRasterBand(bands[2] + 1))
#Algorithm
array_out = 1.0 - ((band2100 + band2400) / (2 * band2290))
return array_out
def olivine_index(raster, wavelengths):
'''
NAME: OLINDEX (prior to TRDR version 3)
PARAMETER: olivine index
FORMULATION *: (R1695 / (0.1*R1080 + 0.1*R1210 + 0.4*R1330 +
0.4*R1470)) - 1
RATIONALE: olivine will be strongly +; based on fayalite
'''
bands = csas.getbandnumbers(wavelengths, 1080,1210, 1470, 1695)
band1080 = csas.getarray(raster.GetRasterBand(bands[0]+1))
band1210 = csas.getarray(raster.GetRasterBand(bands[1]+1))
band1470 = csas.getarray(raster.GetRasterBand(bands[2]+1))
band1695 = csas.getarray(raster.GetRasterBand(bands[3]+1))
#Algorithm
array_out = (band1695 / (0.1*band1080 + 0.1*band1210 + 0.4*band1330 +
0.4*band1470)) - 1
return array_out
def sindex(raster, wavelengths):
'''
NAME: SINDEX
PARAMETER: Convexity at 2.29 microns due to absorptions at
1.9/2.1 microns and 2.4 microns
FORMULATION *: 1 - (R2100 + R2400) / (2 * R2290) CR
values are observed R values divided by values fit along the
slope as determined between 1.8 - 2.53 microns (essentially
continuum corrected))
RATIONALE: hydrated minerals; particularly sulfates
'''
bands = csas.getbandnumbers(wavelengths, 2100, 2400, 2290)
band2100 = csas.getarray(raster.GetRasterBand(bands[0]+1))
band2400 = csas.getarray(raster.GetRasterBand(bands[1]+1))
band2290 = csas.getarray(raster.GetRasterBand(bands[2]+1))
#Algorithm
array_out = 1.0 - ((band2100 + band2400)/(2*band2290))
return array_out
def olivine_index(raster, wavelengths):
'''
NAME: OLINDEX (prior to TRDR version 3)
PARAMETER: olivine index
FORMULATION *: (R1695 / (0.1*R1080 + 0.1*R1210 + 0.4*R1330 +
0.4*R1470)) - 1
RATIONALE: olivine will be strongly +; based on fayalite
'''
bands = csas.getbandnumbers(wavelengths, 1080, 1210, 1470, 1695)
band1080 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
band1210 = csas.getarray(raster.GetRasterBand(bands[1] + 1))
band1470 = csas.getarray(raster.GetRasterBand(bands[2] + 1))
band1695 = csas.getarray(raster.GetRasterBand(bands[3] + 1))
#Algorithm
array_out = (band1695 / (0.1 * band1080 + 0.1 * band1210 + 0.4 * band1330 +
0.4 * band1470)) - 1
return array_out
def bd920(raster, wavelengths):
'''
NAME: BD920
PARAMETER: 0.92 micron band depth
FORMULATION *: 1 - ( R920 / (a*R800+b*R984) )
RATIONALE: select ferric minerals ('Pseudo BDI1000 VIS')
'''
bands = csas.getbandnumbers(wavelengths, 800, 920, 984)
band800 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
band920 = csas.getarray(raster.GetRasterBand(bands[1] + 1))
band984 = csas.getarray(raster.GetRasterBand(bands[2] + 1))
wv800 = wavelengths[bands[0]]
wv920 = wavelengths[bands[1]]
wv984 = wavelengths[bands[2]]
#Algorithm
a = (wv920 - wv800) / (wv984 - wv800)
b = 1.0 - a
array_out = 1.0 - (band920 / ((b * band800) + (a * band984)))
return array_out
def bd860(raster, wavelengths):
'''
NAME: BD860
PARAMETER: 0.86 micron band depth
FORMULATION *: 1 - (R860/(a*R800+b*R984))
RATIONALE: select ferric minerals ('hematite band')
'''
bands = csas.getbandnumbers(wavelengths, 800, 860, 984)
band800 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
band860 = csas.getarray(raster.GetRasterBand(bands[1] + 1))
band984 = csas.getarray(raster.GetRasterBand(bands[2] + 1))
wv800 = wavelengths[bands[0]]
wv860 = wavelengths[bands[1]]
wv984 = wavelengths[bands[2]]
#Algorithm
a = (wv860 - wv800) / (wv984 - wv800)
b = 1.0 - a
array_out = 1.0 - (band860 / ((b * band800) + (a * band984)))
return array_out
def bd860(raster, wavelengths):
'''
NAME: BD860
PARAMETER: 0.86 micron band depth
FORMULATION *: 1 - (R860/(a*R800+b*R984))
RATIONALE: select ferric minerals ('hematite band')
'''
bands = csas.getbandnumbers(wavelengths, 800, 860, 984)
band800 = csas.getarray(raster.GetRasterBand(bands[0]+1))
band860 = csas.getarray(raster.GetRasterBand(bands[1]+1))
band984 = csas.getarray(raster.GetRasterBand(bands[2]+1))
wv800 = wavelengths[bands[0]]
wv860 = wavelengths[bands[1]]
wv984 = wavelengths[bands[2]]
#Algorithm
a = (wv860 - wv800) / (wv984 - wv800)
b = 1.0 - a
array_out = 1.0 - (band860/((b*band800)+(a*band984)))
return array_out
def bd530(raster, wavelengths):
'''
NAME: BD530
PARAMETER: 0.53 micron band depth
FORMULATION *: 1 - (R530/(a*R709+b*R440))
RATIONALE: Crystalline ferric minerals
'''
bands = csas.getbandnumbers(wavelengths, 440, 530, 709)
band440 = csas.getarray(raster.GetRasterBand(bands[0]+1))
band530 = csas.getarray(raster.GetRasterBand(bands[1]+1))
band709 = csas.getarray(raster.GetRasterBand(bands[2]+1))
wv440 = wavelengths[bands[0]]
wv530 = wavelengths[bands[1]]
wv709 = wavelengths[bands[2]]
#Algorithm
a = (wv530 - wv440) / (wv709 - wv440)
b = 1.0 - a
array_out = 1.0 - (band530/((a*band709)+(b*band440)))
return array_out
def cindex(raster, wavelengths):
'''
NAME: CINDEX
PARAMETER: gauge 3.9 micron band
FORMULATION *: ( R3750 + (R3750-R3630) / (3750-3630) *
(3920-3750) ) / R3920 - 1
RATIONALE: carbonates
Algorithm differs from published - coded as per CAT
'''
bands = csas.getbandnumbers(wavelengths, 3630, 3750, 3950)
band3630 = csas.getarray(raster.GetRasterBand(bands[0]+1))
band3750 = csas.getarray(raster.GetRasterBand(bands[1]+1))
band3950 = csas.getarray(raster.GetRasterBand(bands[2]+1))
wv3630 = wavelengths[bands[0]] / 1000 #Need in microns
wv3750 = wavelengths[bands[1]]
wv3950 = wavelengths[bands[2]]
#Algorithm
array_out = ((band3750+((band3750-band3630)/((wv3750-wv3630)*(wv3920-wv3750))))) / band3920 - 1
return array_out
def bd640(raster, wavelengths):
'''
NAME: BD640
PARAMETER: 0.64 micron band depth
FORMULATION *: 1 - (R648/(a*R600+b*R709))
RATIONALE: select ferric minerals, especially maghemite
'''
bands = csas.getbandnumbers(wavelengths, 600, 648, 709)
band600 = csas.getarray(raster.GetRasterBand(bands[0]+1))
band648 = csas.getarray(raster.GetRasterBand(bands[1]+1))
band709 = csas.getarray(raster.GetRasterBand(bands[2]+1))
wv600 = wavelengths[bands[0]]
wv648 = wavelengths[bands[1]]
wv709 = wavelengths[bands[2]]
#Algorithm
a = (wv648 - wv600) / (wv709 - wv600)
b = 1.0 - a
array_out = 1.0 - (band648/((b*band600)+(a*band709)))
return array_out
def bd1435(raster, wavelengths):
'''
NAME: BD1435
PARAMETER: 1.435 micron band depth
FORMULATION *: 1 - ( R1430 / (a*R1370+b*R1470) )
RATIONALE: CO2 surface ice
'''
bands = csas.getbandnumbers(wavelengths, 1370, 1430,1470)
band1370 = csas.getarray(raster.GetRasterBand(bands[0]+1))
band1430 = csas.getarray(raster.GetRasterBand(bands[1]+1))
band1470 = csas.getarray(raster.GetRasterBand(bands[2]+1))
wv1370 = wavelengths[bands[0]]
wv1430 = wavelengths[bands[1]]
wv1470 = wavelengths[bands[2]]
#Algorithm
a = (wv1430 - wv1370) / (wv1470 - wv1370)
b = 1.0 - a
array_out = 1.0 - (band1430/((b*band1370)+(a*band1470)))
return array_out
def bd3200(raster, wavelengths):
'''
NAME: BD3200
PARAMETER: 3.2 micron band depth
FORMULATION *: 1 - ( R3320 / (a*R3250+b*R3390) )
RATIONALE: CO2 ice
'''
bands = csas.getbandnumbers(wavelengths, 3250, 3320, 3390)
band3250 = csas.getarray(raster.GetRasterBand(bands[0]+1))
band3320 = csas.getarray(raster.GetRasterBand(bands[1]+1))
band3390 = csas.getarray(raster.GetRasterBand(bands[2]+1))
wv3250 = wavelengths[bands[0]]
wv3320 = wavelengths[bands[1]]
wv3390 = wavelengths[bands[2]]
#Algorithm
a = (wv3320 - wv3250)/ (wv3390 - wv3250)
b = 1.0 - a
array_out = 1.0 - (band3320/((b*band3250)+(a*band3390)))
return array_out
def bd1750(raster, wavelengths):
'''
NAME: BD1750
PARAMETER: 1.75 micron band depth
FORMULATION *: 1 - ( R1750 / (a*R1660+b*R1815) )
RATIONALE: gypsum
'''
bands = csas.getbandnumbers(wavelengths, 1557, 1750, 1815)
band1557 = csas.getarray(raster.GetRasterBand(bands[0]+1))
band1750 = csas.getarray(raster.GetRasterBand(bands[1]+1))
band1815 = csas.getarray(raster.GetRasterBand(bands[2]+1))
wv1557 = wavelengths[bands[0]]
wv1750 = wavelengths[bands[1]]
wv1815 = wavelengths[bands[2]]
#Algorithm
a = (wv1750 - wv1557) / (wv1815 - wv1557)
b = 1.0 - a
array_out = 1.0 - (band1750/((b*band1557)+(a*band1815)))
return array_out
def bd2210(raster, wavelengths):
'''
NAME: BD2210
PARAMETER: 2.21 micron band depth
FORMULATION *: 1 - ( R2210 / (a*R2140+b*R2250) )
RATIONALE: Al-OH minerals: monohydrated minerals
'''
bands = csas.getbandnumbers(wavelengths, 2140, 2210, 2250)
band2140 = csas.getarray(raster.GetRasterBand(bands[0]+1))
band2210 = csas.getarray(raster.GetRasterBand(bands[1]+1))
band2250 = csas.getarray(raster.GetRasterBand(bands[2]+1))
wv2140 = wavelengths[bands[0]]
wv2210 = wavelengths[bands[1]]
wv2250 = wavelengths[bands[2]]
#Algorithm
a = (wv2210 - wv2140) / (wv2250 - wv2140)
b = 1.0 - a
array_out = 1.0 - ((band2210)/((b*band2140)+(a*band2250)))
return array_out
def sh600(raster, wavelengths):
'''
NAME: SH600
PARAMETER: 0.60 micron shoulder height
FORMULATION *: R600/(a*R530+b*R709)
RATIONALE: select ferric minerals
'''
bands = csas.getbandnumbers(wavelengths, 533, 600, 710)
band533 = csas.getarray(raster.GetRasterBand(bands[0]+1))
band600 = csas.getarray(raster.GetRasterBand(bands[1]+1))
band710 = csas.getarray(raster.GetRasterBand(bands[2]+1))
wv533 = wavelengths[bands[0]]
wv600 = wavelengths[bands[1]]
wv710 = wavelengths[bands[2]]
#Algorithm
a = (wv600 - wv533) / (wv710 - wv533)
b = 1.0 - a
array_out = 1.0 - (((b * band533)+(a*band710))/band600)
return array_out
def bd640(raster, wavelengths):
'''
NAME: BD640
PARAMETER: 0.64 micron band depth
FORMULATION *: 1 - (R648/(a*R600+b*R709))
RATIONALE: select ferric minerals, especially maghemite
'''
bands = csas.getbandnumbers(wavelengths, 600, 648, 709)
band600 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
band648 = csas.getarray(raster.GetRasterBand(bands[1] + 1))
band709 = csas.getarray(raster.GetRasterBand(bands[2] + 1))
wv600 = wavelengths[bands[0]]
wv648 = wavelengths[bands[1]]
wv709 = wavelengths[bands[2]]
#Algorithm
a = (wv648 - wv600) / (wv709 - wv600)
b = 1.0 - a
array_out = 1.0 - (band648 / ((b * band600) + (a * band709)))
return array_out
def bd2210(raster, wavelengths):
'''
NAME: BD2210
PARAMETER: 2.21 micron band depth
FORMULATION *: 1 - ( R2210 / (a*R2140+b*R2250) )
RATIONALE: Al-OH minerals: monohydrated minerals
'''
bands = csas.getbandnumbers(wavelengths, 2140, 2210, 2250)
band2140 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
band2210 = csas.getarray(raster.GetRasterBand(bands[1] + 1))
band2250 = csas.getarray(raster.GetRasterBand(bands[2] + 1))
wv2140 = wavelengths[bands[0]]
wv2210 = wavelengths[bands[1]]
wv2250 = wavelengths[bands[2]]
#Algorithm
a = (wv2210 - wv2140) / (wv2250 - wv2140)
b = 1.0 - a
array_out = 1.0 - ((band2210) / ((b * band2140) + (a * band2250)))
return array_out
def bd920(raster, wavelengths):
'''
NAME: BD920
PARAMETER: 0.92 micron band depth
FORMULATION *: 1 - ( R920 / (a*R800+b*R984) )
RATIONALE: select ferric minerals ('Pseudo BDI1000 VIS')
'''
bands = csas.getbandnumbers(wavelengths, 800, 920, 984)
band800 = csas.getarray(raster.GetRasterBand(bands[0]+1))
band920 = csas.getarray(raster.GetRasterBand(bands[1]+1))
band984 = csas.getarray(raster.GetRasterBand(bands[2]+1))
wv800 = wavelengths[bands[0]]
wv920 = wavelengths[bands[1]]
wv984 = wavelengths[bands[2]]
#Algorithm
a = (wv920 - wv800) / (wv984 - wv800)
b = 1.0 - a
array_out = 1.0 - (band920/((b*band800)+(a*band984)))
return array_out
def hcp_index(raster, wavelengths):
'''
NAME: HCPXINDEX
PARAMETER: pyroxene index
FORMULATION *: 100 * ((R1470 - R1080)/(R1470 + R1080)) * ((R1470 - R2067)/(R1470+R2067))
RATIONALE: pyroxene is strongly +; favors high-Ca pyroxene
Algorithm differs from published - coded as per CAT
'''
bands = csas.getbandnumbers(wavelengths, 1080, 1470, 2067)
band1080 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
band1470 = csas.getarray(raster.GetRasterBand(bands[1] + 1))
band2067 = csas.getarray(raster.GetRasterBand(bands[2] + 1))
#Algorithm
array_out = 100 * ((band1470 - band1080) /
(band1470 + band1080)) * ((band1470 - band2067) /
(band1470 + band2067))
return array_out
def bd1750(raster, wavelengths):
'''
NAME: BD1750
PARAMETER: 1.75 micron band depth
FORMULATION *: 1 - ( R1750 / (a*R1660+b*R1815) )
RATIONALE: gypsum
'''
bands = csas.getbandnumbers(wavelengths, 1557, 1750, 1815)
band1557 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
band1750 = csas.getarray(raster.GetRasterBand(bands[1] + 1))
band1815 = csas.getarray(raster.GetRasterBand(bands[2] + 1))
wv1557 = wavelengths[bands[0]]
wv1750 = wavelengths[bands[1]]
wv1815 = wavelengths[bands[2]]
#Algorithm
a = (wv1750 - wv1557) / (wv1815 - wv1557)
b = 1.0 - a
array_out = 1.0 - (band1750 / ((b * band1557) + (a * band1815)))
return array_out
def bd1435(raster, wavelengths):
'''
NAME: BD1435
PARAMETER: 1.435 micron band depth
FORMULATION *: 1 - ( R1430 / (a*R1370+b*R1470) )
RATIONALE: CO2 surface ice
'''
bands = csas.getbandnumbers(wavelengths, 1370, 1430, 1470)
band1370 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
band1430 = csas.getarray(raster.GetRasterBand(bands[1] + 1))
band1470 = csas.getarray(raster.GetRasterBand(bands[2] + 1))
wv1370 = wavelengths[bands[0]]
wv1430 = wavelengths[bands[1]]
wv1470 = wavelengths[bands[2]]
#Algorithm
a = (wv1430 - wv1370) / (wv1470 - wv1370)
b = 1.0 - a
array_out = 1.0 - (band1430 / ((b * band1370) + (a * band1470)))
return array_out
def sh600(raster, wavelengths):
'''
NAME: SH600
PARAMETER: 0.60 micron shoulder height
FORMULATION *: R600/(a*R530+b*R709)
RATIONALE: select ferric minerals
'''
bands = csas.getbandnumbers(wavelengths, 533, 600, 710)
band533 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
band600 = csas.getarray(raster.GetRasterBand(bands[1] + 1))
band710 = csas.getarray(raster.GetRasterBand(bands[2] + 1))
wv533 = wavelengths[bands[0]]
wv600 = wavelengths[bands[1]]
wv710 = wavelengths[bands[2]]
#Algorithm
a = (wv600 - wv533) / (wv710 - wv533)
b = 1.0 - a
array_out = 1.0 - (((b * band533) + (a * band710)) / band600)
return array_out
def bd3200(raster, wavelengths):
'''
NAME: BD3200
PARAMETER: 3.2 micron band depth
FORMULATION *: 1 - ( R3320 / (a*R3250+b*R3390) )
RATIONALE: CO2 ice
'''
bands = csas.getbandnumbers(wavelengths, 3250, 3320, 3390)
band3250 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
band3320 = csas.getarray(raster.GetRasterBand(bands[1] + 1))
band3390 = csas.getarray(raster.GetRasterBand(bands[2] + 1))
wv3250 = wavelengths[bands[0]]
wv3320 = wavelengths[bands[1]]
wv3390 = wavelengths[bands[2]]
#Algorithm
a = (wv3320 - wv3250) / (wv3390 - wv3250)
b = 1.0 - a
array_out = 1.0 - (band3320 / ((b * band3250) + (a * band3390)))
return array_out
def bd530(raster, wavelengths):
'''
NAME: BD530
PARAMETER: 0.53 micron band depth
FORMULATION *: 1 - (R530/(a*R709+b*R440))
RATIONALE: Crystalline ferric minerals
'''
bands = csas.getbandnumbers(wavelengths, 440, 530, 709)
band440 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
band530 = csas.getarray(raster.GetRasterBand(bands[1] + 1))
band709 = csas.getarray(raster.GetRasterBand(bands[2] + 1))
wv440 = wavelengths[bands[0]]
wv530 = wavelengths[bands[1]]
wv709 = wavelengths[bands[2]]
#Algorithm
a = (wv530 - wv440) / (wv709 - wv440)
b = 1.0 - a
array_out = 1.0 - (band530 / ((a * band709) + (b * band440)))
return array_out
def lcp_index(raster, wavelengths):
'''
NAME: LCPINDEX
PARAMETER: pyroxene index
FORMULATION *: 100 * ((R1330 - R1080)/(R1330 + R1080)) * ((R1330 - R1815)/(R1330+R1815))
RATIONALE: pyroxene is strongly +; favors low-Ca pyroxene
Algorithm differs from published - coded as per CAT
'''
bands = csas.getbandnumbers(wavelengths, 1080, 1330, 1815)
band1080 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
band1330 = csas.getarray(raster.GetRasterBand(bands[1] + 1))
band1815 = csas.getarray(raster.GetRasterBand(bands[2] + 1))
#Algorithm
array_out = 100 * ((band1330 - band1080) /
(band1330 + band1080)) * ((band1330 - band1815) /
(band1330 + band1815))
return array_out
def bd2290(raster, wavelengths):
'''
NAME: BD2290
PARAMETER: 2.29 micron band depth
FORMULATION *: 1 - ( R2290 / (a*R2250+b*R2350) )
RATIONALE: Mg,Fe-OH minerals (at 2.3); also CO2 ice
(at 2.292 microns)
'''
bands = csas.getbandnumbers(wavelengths, 2250, 2290, 2350)
band2250 = csas.getarray(raster.GetRasterBand(bands[0]+1))
band2290 = csas.getarray(raster.GetRasterBand(bands[1]+1))
band2350 = csas.getarray(raster.GetRasterBand(bands[2]+1))
wv2250 = wavelengths[bands[0]]
wv2290 = wavelengths[bands[1]]
wv2350 = wavelengths[bands[2]]
#Algorithm
a = (wv2290 - wv2250) / (wv2350 - wv2250)
b = 1.0 - a
array_out = 1.0 - ((band2290)/((b*band2250)+(a*band2350)))
return array_out
def ira(raster, wavelengths):
'''
NAME: IRA
PARAMETER: 1.3 micron reflectance
FORMULATION *: R1330
RATIONALE: IR albedo
'''
bands = csas.getbandnumbers(wavelengths, 1330)
array1330 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
return array1330
def bd2290(raster, wavelengths):
'''
NAME: BD2290
PARAMETER: 2.29 micron band depth
FORMULATION *: 1 - ( R2290 / (a*R2250+b*R2350) )
RATIONALE: Mg,Fe-OH minerals (at 2.3); also CO2 ice
(at 2.292 microns)
'''
bands = csas.getbandnumbers(wavelengths, 2250, 2290, 2350)
band2250 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
band2290 = csas.getarray(raster.GetRasterBand(bands[1] + 1))
band2350 = csas.getarray(raster.GetRasterBand(bands[2] + 1))
wv2250 = wavelengths[bands[0]]
wv2290 = wavelengths[bands[1]]
wv2350 = wavelengths[bands[2]]
#Algorithm
a = (wv2290 - wv2250) / (wv2350 - wv2250)
b = 1.0 - a
array_out = 1.0 - ((band2290) / ((b * band2250) + (a * band2350)))
return array_out
def ira(raster, wavelengths):
'''
NAME: IRA
PARAMETER: 1.3 micron reflectance
FORMULATION *: R1330
RATIONALE: IR albedo
'''
bands = csas.getbandnumbers(wavelengths, 1330)
array1330 = csas.getarray(raster.GetRasterBand(bands[0]+1))
return array1330
def rockdust1(raster, wavelengths):
'''
Name: R770
Parameter: 0.77micron reflectance
Formulation: R770
Rationale: rock/dust
'''
bands = csas.getbandnumbers(wavelengths, 770)
print raster, raster.GetRasterBand(247).ReadRaster(0,0,320,450,1,gdal.GDT_Float64)
array770 = csas.getarray(raster.GetRasterBand(bands[0]+1))
return array770
def bd3400(raster, wavelengths):
'''
NAME: BD3400
PARAMETER: 3.4 micron band depth
FORMULATION *: 1 - ( (a*R3390+b*R3500) / (c*R3250+d*R3630) )
RATIONALE: carbonates; organics
'''
bands = csas.getbandnumbers(wavelengths, 3250, 3390, 3500, 3630)
band3250 = csas.getarray(raster.GetRasterBand(bands[0]+1))
band3390 = csas.getarray(raster.GetRasterBand(bands[1]+1))
band3500 = csas.getarray(raster.GetRasterBand(bands[2]+1))
band3630 = csas.getarray(raster.GetRasterBand(bands[3]+1))
wv3250 = wavelengths[bands[0]]
wv3390 = wavelengths[bands[1]]
wv3500 = wavelengths[bands[2]]
wv3630 = wavelengths[bands[3]]
#Algorithm
c = (((wv3390+wv3500)*0.5)- wv3250)/ (wv3630 - wv3250)
d = 1.0 - c
array_out = 1.0 - (((band3390 + band3500)*0.5)/((d*band3250)+(c*band3630)))
return array_out
def bd2100(raster, wavelengths):
'''
NAME: BD2100
PARAMETER: 2.1 micron band depth
FORMULATION *: 1 - ( ((R2120+R2140)*0.5) / (a*R1930+b*R2250) )
RATIONALE: monohydrated minerals
'''
bands = csas.getbandnumbers(wavelengths, 1930, 2120, 2140, 2250)
band1930 = csas.getarray(raster.GetRasterBand(bands[0]+1))
band2120 = csas.getarray(raster.GetRasterBand(bands[1]+1))
band2140 = csas.getarray(raster.GetRasterBand(bands[2]+1))
band2250 = csas.getarray(raster.GetRasterBand(bands[3]+1))
wv1930 = wavelengths[bands[0]]
wv2120 = wavelengths[bands[1]]
wv2140 = wavelengths[bands[2]]
wv2250 = wavelengths[bands[2]]
#Algorithm
a = (((wv2120 + wv2140) / 2) - wv1930) / (wv2250 - wv1930)
b = 1.0 - a
array_out = 1.0 - (((band2120 + band2140)*0.5)/((b*band1930)+(a*band2250)))
return array_out
def cindex(raster, wavelengths):
'''
NAME: CINDEX
PARAMETER: gauge 3.9 micron band
FORMULATION *: ( R3750 + (R3750-R3630) / (3750-3630) *
(3920-3750) ) / R3920 - 1
RATIONALE: carbonates
Algorithm differs from published - coded as per CAT
'''
bands = csas.getbandnumbers(wavelengths, 3630, 3750, 3950)
band3630 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
band3750 = csas.getarray(raster.GetRasterBand(bands[1] + 1))
band3950 = csas.getarray(raster.GetRasterBand(bands[2] + 1))
wv3630 = wavelengths[bands[0]] / 1000 #Need in microns
wv3750 = wavelengths[bands[1]]
wv3950 = wavelengths[bands[2]]
#Algorithm
array_out = ((band3750 + ((band3750 - band3630) /
((wv3750 - wv3630) *
(wv3920 - wv3750))))) / band3920 - 1
return array_out
def bdi1000VIS(raster, wavelengths):
import numpy as np
from scipy import integrate
'''
NAME: BDI1000VIS
PARAMETER: 1 micron integrated band depth; VIS wavelengths
FORMULATION *: divide R830, R860, R890, R915 by RPEAK1 then
integrate over (1 - normalized radiances)
RATIONALE: crystalline Fe+2 or Fe+3 minerals
'''
bands = csas.getbandnumbers(wavelengths, 833, 860, 892, 925, 951, 984, 1023)
band833= csas.getarray(raster.GetRasterBand(bands[0]+1))
band860 = csas.getarray(raster.GetRasterBand(bands[1]+1))
band892 = csas.getarray(raster.GetRasterBand(bands[2]+1))
band925 = csas.getarray(raster.GetRasterBand(bands[3]+1))
band951 = csas.getarray(raster.GetRasterBand(bands[4]+1))
band984 = csas.getarray(raster.GetRasterBand(bands[5]+1))
band1023 = csas.getarray(raster.GetRasterBand(bands[6]+1))
wavelength_index = np.array([833, 860, 892, 925, 951, 984, 1023])
wavelength_vector = np.arange(len(wavelength_index), dtype=np.float64)
for index,band in enumerate(bands):
wavelength_vector[index] = wavelengths[band]
wavelength_vector_um = wavelength_vector / 1000.0
bdivis_value = np.zeros(shape=(band984.shape),dtype=np.float64)
bdi1000_cube = np.dstack((band883,band860,band892,band925,band951,band984,band1023))
rpeak_value_cube = np.zeros(shape=(band984.shape),dtype=np.float64)
print "Computing the rpeak value for each cell."
for x in range(0,rpeak_cube.shape[1]):
sys.stdout.write("Processed column %i of %i \r" %(x,rpeak_cube.shape[1]))
sys.stdout.flush()
for y in range(0,rpeak_cube.shape[0]):
spec_vec = rpeak_cube[x][y]
rpeak_params = np.polyfit(wavelength_vector,spec_vec,poly_degree)
#model_spec = np.polyval(model_wv_vector, rpeak_params)
model_spec = np.zeros(num_model_points)
for m in range(0,int(poly_degree)+1):
model_spec = model_spec + rpeak_params[m] * (model_wv_vector**float(m))
model_spec_max = model_spec.max()
model_spec_max_index = np.argmax(model_spec)
model_spec_max_wv = model_wv_vector[model_spec_max_index]
rpeak_wavelength[x][y] = model_spec_max_wv
rpeak_value_cube[x][y] = model_spec_max
print "Finished computing rpeak values. Now computing Integrated Band Depth."
bdi1000_normalized_cube = bdi1000_cube / rpeak_value_cube
for x in range(0,rpeak_cube.shape[1]+1):
sys.stdout.write("Processed column %i of %i \r" %(x,rpeak_cube.shape[1]))
sys.stdout.flush()
for y in range(0,rpeak_cube.shape[0]+1):
spec_vec = bdi1000_normalized_cube[x][y]
check_vec = bdi1000_cube[x][y]
bdivis_value[x][y] = scipy.integrate.newton_cotes(wavelength_vecor_um, (1.0-spec_vec))
return bdvis_value
def bd3400(raster, wavelengths):
'''
NAME: BD3400
PARAMETER: 3.4 micron band depth
FORMULATION *: 1 - ( (a*R3390+b*R3500) / (c*R3250+d*R3630) )
RATIONALE: carbonates; organics
'''
bands = csas.getbandnumbers(wavelengths, 3250, 3390, 3500, 3630)
band3250 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
band3390 = csas.getarray(raster.GetRasterBand(bands[1] + 1))
band3500 = csas.getarray(raster.GetRasterBand(bands[2] + 1))
band3630 = csas.getarray(raster.GetRasterBand(bands[3] + 1))
wv3250 = wavelengths[bands[0]]
wv3390 = wavelengths[bands[1]]
wv3500 = wavelengths[bands[2]]
wv3630 = wavelengths[bands[3]]
#Algorithm
c = (((wv3390 + wv3500) * 0.5) - wv3250) / (wv3630 - wv3250)
d = 1.0 - c
array_out = 1.0 - (((band3390 + band3500) * 0.5) / ((d * band3250) +
(c * band3630)))
return array_out
def rockdust1(raster, wavelengths):
'''
Name: R770
Parameter: 0.77micron reflectance
Formulation: R770
Rationale: rock/dust
'''
bands = csas.getbandnumbers(wavelengths, 770)
print raster, raster.GetRasterBand(247).ReadRaster(0, 0, 320, 450, 1,
gdal.GDT_Float64)
array770 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
return array770
def bd2100(raster, wavelengths):
'''
NAME: BD2100
PARAMETER: 2.1 micron band depth
FORMULATION *: 1 - ( ((R2120+R2140)*0.5) / (a*R1930+b*R2250) )
RATIONALE: monohydrated minerals
'''
bands = csas.getbandnumbers(wavelengths, 1930, 2120, 2140, 2250)
band1930 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
band2120 = csas.getarray(raster.GetRasterBand(bands[1] + 1))
band2140 = csas.getarray(raster.GetRasterBand(bands[2] + 1))
band2250 = csas.getarray(raster.GetRasterBand(bands[3] + 1))
wv1930 = wavelengths[bands[0]]
wv2120 = wavelengths[bands[1]]
wv2140 = wavelengths[bands[2]]
wv2250 = wavelengths[bands[2]]
#Algorithm
a = (((wv2120 + wv2140) / 2) - wv1930) / (wv2250 - wv1930)
b = 1.0 - a
array_out = 1.0 - (((band2120 + band2140) * 0.5) / ((b * band1930) +
(a * band2250)))
return array_out
def d2300(raster, wavelengths):
'''
NAME: D2300
PARAMETER: 2.3 micron drop
FORMULATION *: 1 - ( (CR2290+CR2320+CR2330) /
(CR2140+CR2170+CR2210) ) (CR values are observed R values
divided by values fit along the slope as determined between 1.8
and 2.53 microns - essentially continuum corrected))
RATIONALE: hydrated minerals; particularly clays
'''
bands = csas.getbandnumbers(wavelengths, 1815, 2120, 2170, 2210, 2290,
2320, 2330, 2530)
band1815 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
band2120 = csas.getarray(raster.GetRasterBand(bands[1] + 1))
band2170 = csas.getarray(raster.GetRasterBand(bands[2] + 1))
band2210 = csas.getarray(raster.GetRasterBand(bands[3] + 1))
band2290 = csas.getarray(raster.GetRasterBand(bands[4] + 1))
band2320 = csas.getarray(raster.GetRasterBand(bands[5] + 1))
band2330 = csas.getarray(raster.GetRasterBand(bands[6] + 1))
band2530 = csas.getarray(raster.GetRasterBand(bands[7] + 1))
wv1815 = wavelengths[bands[0]]
wv2110 = wavelengths[bands[1]]
wv2170 = wavelengths[bands[2]]
wv2210 = wavelengths[bands[3]]
wv2290 = wavelengths[bands[4]]
wv2320 = wavelengths[bands[5]]
wv2330 = wavelengths[bands[6]]
wv2530 = wavelengths[bands[7]]
#Algorithm
#Continuum removal phase
slope = (band2530 - band1815) / (wv2530 - wv1815)
cr2290 = band1815 + slope * (wv2290 - wv1815)
cr2320 = band1815 + slope * (wv2320 - wv1815)
cr2330 = band1815 + slope * (wv2330 - wv1815)
cr2120 = band1815 + slope * (wv2120 - wv1815)
cr2170 = band1815 + slope * (wv2170 - wv1815)
cr2210 = band1815 + slope * (wv2210 - wv1815)
#Computation phase
array_out = 1.0 - (((band2290 / cr2290) + (band2320 / cr2320) +
(band2330 / cr2330)) /
((band2120 / cr2120) + (band2170 / cr2170) +
(band2210 / cr2210)))
return array_out
def d2300(raster, wavelengths):
'''
NAME: D2300
PARAMETER: 2.3 micron drop
FORMULATION *: 1 - ( (CR2290+CR2320+CR2330) /
(CR2140+CR2170+CR2210) ) (CR values are observed R values
divided by values fit along the slope as determined between 1.8
and 2.53 microns - essentially continuum corrected))
RATIONALE: hydrated minerals; particularly clays
'''
bands = csas.getbandnumbers(wavelengths, 1815, 2120, 2170, 2210, 2290, 2320,2330, 2530 )
band1815 = csas.getarray(raster.GetRasterBand(bands[0]+1))
band2120 = csas.getarray(raster.GetRasterBand(bands[1]+1))
band2170 = csas.getarray(raster.GetRasterBand(bands[2]+1))
band2210 = csas.getarray(raster.GetRasterBand(bands[3]+1))
band2290 = csas.getarray(raster.GetRasterBand(bands[4]+1))
band2320 = csas.getarray(raster.GetRasterBand(bands[5]+1))
band2330 = csas.getarray(raster.GetRasterBand(bands[6]+1))
band2530 = csas.getarray(raster.GetRasterBand(bands[7]+1))
wv1815 = wavelengths[bands[0]]
wv2110 = wavelengths[bands[1]]
wv2170 = wavelengths[bands[2]]
wv2210 = wavelengths[bands[3]]
wv2290 = wavelengths[bands[4]]
wv2320 = wavelengths[bands[5]]
wv2330 = wavelengths[bands[6]]
wv2530 = wavelengths[bands[7]]
#Algorithm
#Continuum removal phase
slope = (band2530-band1815) / (wv2530 - wv1815)
cr2290 = band1815 + slope * (wv2290 - wv1815)
cr2320 = band1815 + slope * (wv2320 - wv1815)
cr2330 = band1815 + slope * (wv2330 - wv1815)
cr2120 = band1815 + slope * (wv2120 - wv1815)
cr2170 = band1815 + slope * (wv2170 - wv1815)
cr2210 = band1815 + slope * (wv2210 - wv1815)
#Computation phase
array_out = 1.0 - (((band2290/cr2290)+(band2320/cr2320)+(band2330/cr2330))/((band2120/cr2120)+(band2170/cr2170)+(band2210/cr2210)))
return array_out
def rpeak1(raster, wavelengths):
import numpy as np
import sys
import time
import multiprocessing as mp
starttime = time.clock()
'''
NAME: RPEAK1
PARAMETER: reflectance peak 1
FORMULATION *: wavelength where 1st derivative=0 of 5th order
polynomial fit to R600, R648, R680, R710, R740, R770, R800, R830
RATIONALE: Fe mineralogy
'''
bands = csas.getbandnumbers(wavelengths, 442,533,600,710,740,775,800,833,860,893,925)
band442 = csas.getarray(raster.GetRasterBand(bands[0]+1))
band533 = csas.getarray(raster.GetRasterBand(bands[1]+1))
band600 = csas.getarray(raster.GetRasterBand(bands[2]+1))
band710 = csas.getarray(raster.GetRasterBand(bands[3]+1))
band740 = csas.getarray(raster.GetRasterBand(bands[4]+1))
band775 = csas.getarray(raster.GetRasterBand(bands[5]+1))
band800 = csas.getarray(raster.GetRasterBand(bands[6]+1))
band833 = csas.getarray(raster.GetRasterBand(bands[7]+1))
band860 = csas.getarray(raster.GetRasterBand(bands[8]+1))
band893 = csas.getarray(raster.GetRasterBand(bands[9]+1))
band925 = csas.getarray(raster.GetRasterBand(bands[10]+1))
wavelength_index = np.array([442,533,600,710,740,775,800,833,860,893,925])
wavelength_vector = np.arange(len(wavelength_index), dtype=np.float64)
for index,band in enumerate(bands):
wavelength_vector[index] = wavelengths[band]
num_model_points = 1001
poly_degree = 4.0 #4th order polynomial fit
model_wv_vector = np.arange(num_model_points, dtype=np.float64) / (num_model_points - 1.0)*((wavelength_vector.max() - wavelength_vector.min())+wavelength_vector.min())
#Create a multi dimensional data cube from the selected bands
rpeak_cube = np.dstack((band442,band533,band600,band710,band740,band775, band800, band833, band860, band893, band925))
#Create output arrays.
rpeak_wavelength = np.ones(shape=(band442.shape),dtype=np.float32)
rpeak_value = np.ones(shape=(band442.shape),dtype=np.float32)
#Now we need to iterate over each pixel in the band depth dimension (11)
for x in range(0,rpeak_cube.shape[1]):
sys.stdout.write("Processed column %i of %i \r" %(x,rpeak_cube.shape[1]))
sys.stdout.flush()
for y in range(0,rpeak_cube.shape[0]):
spec_vec = rpeak_cube[y][x]
rpeak_params = np.polyfit(wavelength_vector,spec_vec,poly_degree)
#model_spec = np.polyval(model_wv_vector, rpeak_params)
model_spec = np.zeros(num_model_points)
for m in range(0,int(poly_degree)+1):
model_spec = model_spec + rpeak_params[m] * (model_wv_vector**float(m))
model_spec_max = model_spec.max()
model_spec_max_index = np.argmax(model_spec)
model_spec_max_wv = model_wv_vector[model_spec_max_index]
rpeak_wavelength[y][x] = model_spec_max_wv
print rpeak_wavelength[y][x]
rpeak_value[y][x] = model_spec_max
array_out = rpeak_wavelength / 1000.0
stoptime = time.clock()
print "Total time to perform Rpeak1 %s" %(stoptime-starttime)
return array_out
def bdi1000VIS(raster, wavelengths):
import numpy as np
from scipy import integrate
'''
NAME: BDI1000VIS
PARAMETER: 1 micron integrated band depth; VIS wavelengths
FORMULATION *: divide R830, R860, R890, R915 by RPEAK1 then
integrate over (1 - normalized radiances)
RATIONALE: crystalline Fe+2 or Fe+3 minerals
'''
bands = csas.getbandnumbers(wavelengths, 833, 860, 892, 925, 951, 984,
1023)
band833 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
band860 = csas.getarray(raster.GetRasterBand(bands[1] + 1))
band892 = csas.getarray(raster.GetRasterBand(bands[2] + 1))
band925 = csas.getarray(raster.GetRasterBand(bands[3] + 1))
band951 = csas.getarray(raster.GetRasterBand(bands[4] + 1))
band984 = csas.getarray(raster.GetRasterBand(bands[5] + 1))
band1023 = csas.getarray(raster.GetRasterBand(bands[6] + 1))
wavelength_index = np.array([833, 860, 892, 925, 951, 984, 1023])
wavelength_vector = np.arange(len(wavelength_index), dtype=np.float64)
for index, band in enumerate(bands):
wavelength_vector[index] = wavelengths[band]
wavelength_vector_um = wavelength_vector / 1000.0
bdivis_value = np.zeros(shape=(band984.shape), dtype=np.float64)
bdi1000_cube = np.dstack(
(band883, band860, band892, band925, band951, band984, band1023))
rpeak_value_cube = np.zeros(shape=(band984.shape), dtype=np.float64)
print "Computing the rpeak value for each cell."
for x in range(0, rpeak_cube.shape[1]):
sys.stdout.write("Processed column %i of %i \r" %
(x, rpeak_cube.shape[1]))
sys.stdout.flush()
for y in range(0, rpeak_cube.shape[0]):
spec_vec = rpeak_cube[x][y]
rpeak_params = np.polyfit(wavelength_vector, spec_vec, poly_degree)
#model_spec = np.polyval(model_wv_vector, rpeak_params)
model_spec = np.zeros(num_model_points)
for m in range(0, int(poly_degree) + 1):
model_spec = model_spec + rpeak_params[m] * (model_wv_vector**
float(m))
model_spec_max = model_spec.max()
model_spec_max_index = np.argmax(model_spec)
model_spec_max_wv = model_wv_vector[model_spec_max_index]
rpeak_wavelength[x][y] = model_spec_max_wv
rpeak_value_cube[x][y] = model_spec_max
print "Finished computing rpeak values. Now computing Integrated Band Depth."
bdi1000_normalized_cube = bdi1000_cube / rpeak_value_cube
for x in range(0, rpeak_cube.shape[1] + 1):
sys.stdout.write("Processed column %i of %i \r" %
(x, rpeak_cube.shape[1]))
sys.stdout.flush()
for y in range(0, rpeak_cube.shape[0] + 1):
spec_vec = bdi1000_normalized_cube[x][y]
check_vec = bdi1000_cube[x][y]
bdivis_value[x][y] = scipy.integrate.newton_cotes(
wavelength_vecor_um, (1.0 - spec_vec))
return bdvis_value
def rpeak1(raster, wavelengths):
import numpy as np
import sys
import time
import multiprocessing as mp
starttime = time.clock()
'''
NAME: RPEAK1
PARAMETER: reflectance peak 1
FORMULATION *: wavelength where 1st derivative=0 of 5th order
polynomial fit to R600, R648, R680, R710, R740, R770, R800, R830
RATIONALE: Fe mineralogy
'''
bands = csas.getbandnumbers(wavelengths, 442, 533, 600, 710, 740, 775, 800,
833, 860, 893, 925)
band442 = csas.getarray(raster.GetRasterBand(bands[0] + 1))
band533 = csas.getarray(raster.GetRasterBand(bands[1] + 1))
band600 = csas.getarray(raster.GetRasterBand(bands[2] + 1))
band710 = csas.getarray(raster.GetRasterBand(bands[3] + 1))
band740 = csas.getarray(raster.GetRasterBand(bands[4] + 1))
band775 = csas.getarray(raster.GetRasterBand(bands[5] + 1))
band800 = csas.getarray(raster.GetRasterBand(bands[6] + 1))
band833 = csas.getarray(raster.GetRasterBand(bands[7] + 1))
band860 = csas.getarray(raster.GetRasterBand(bands[8] + 1))
band893 = csas.getarray(raster.GetRasterBand(bands[9] + 1))
band925 = csas.getarray(raster.GetRasterBand(bands[10] + 1))
wavelength_index = np.array(
[442, 533, 600, 710, 740, 775, 800, 833, 860, 893, 925])
wavelength_vector = np.arange(len(wavelength_index), dtype=np.float64)
for index, band in enumerate(bands):
wavelength_vector[index] = wavelengths[band]
num_model_points = 1001
poly_degree = 4.0 #4th order polynomial fit
model_wv_vector = np.arange(
num_model_points, dtype=np.float64) / (num_model_points - 1.0) * (
(wavelength_vector.max() - wavelength_vector.min()) +
wavelength_vector.min())
#Create a multi dimensional data cube from the selected bands
rpeak_cube = np.dstack(
(band442, band533, band600, band710, band740, band775, band800,
band833, band860, band893, band925))
#Create output arrays.
rpeak_wavelength = np.ones(shape=(band442.shape), dtype=np.float32)
rpeak_value = np.ones(shape=(band442.shape), dtype=np.float32)
#Now we need to iterate over each pixel in the band depth dimension (11)
for x in range(0, rpeak_cube.shape[1]):
sys.stdout.write("Processed column %i of %i \r" %
(x, rpeak_cube.shape[1]))
sys.stdout.flush()
for y in range(0, rpeak_cube.shape[0]):
spec_vec = rpeak_cube[y][x]
rpeak_params = np.polyfit(wavelength_vector, spec_vec, poly_degree)
#model_spec = np.polyval(model_wv_vector, rpeak_params)
model_spec = np.zeros(num_model_points)
for m in range(0, int(poly_degree) + 1):
model_spec = model_spec + rpeak_params[m] * (model_wv_vector**
float(m))
model_spec_max = model_spec.max()
model_spec_max_index = np.argmax(model_spec)
model_spec_max_wv = model_wv_vector[model_spec_max_index]
rpeak_wavelength[y][x] = model_spec_max_wv
print rpeak_wavelength[y][x]
rpeak_value[y][x] = model_spec_max
array_out = rpeak_wavelength / 1000.0
stoptime = time.clock()
print "Total time to perform Rpeak1 %s" % (stoptime - starttime)
return array_out
