libpyhat.derived.crism package
Submodules
libpyhat.derived.crism.crism_algs module
- libpyhat.derived.crism.crism_algs.bd1300(data, kernel=(5, 15, 5))[source]
” NAME: BD1300 PARAMETER: 1.3 μm absorption associated with Fe2+ substitution in plagioclase FORMULATION (with kernels): 1 - ( R1320 / (a * R1080 + b * R1750) ) NOTE: Viviano-Beck et al (2014) list different wavelengths for the kernel widths (1370, 1432, 1470) than for the formulation for this parameter. We use the wavelengths listed in the formulation, as these match more closely with the wavelength in the parameter name.
RATIONALE: Plagioclase with Fe2+ substitution
Parameters
data : PyHAT SpectralData object kernel: Tuple of windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.bd1400(data, kernel=(5, 3, 5))[source]
NAME: BD1400 PARAMETER: 1.4 micron H2O and OH band depth FORMULATION: 1 - ( R1395 / (a * R1330 + b * R1467) ) NOTE: Viviano-Beck et al (2014) list different wavelengths for the kernel widths (1370, 1432, 1470) than for the formulation for this parameter. We use the wavelengths listed in the formulation, as these match more closely with the wavelength in the parameter name. RATIONALE: Hydrated or hydroxylated minerals
Parameters
data : PyHAT SpectralData object kernel: Tuple of windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.bd1435(data, kernel=(3, 1, 3))[source]
NAME: BD1435 PARAMETER: 1.435 micron band depth FORMULATION: 1 - ( R1435 / (a * R1370 + b * R1470) ) NOTE: Viviano-Beck et al (2014) list a different wavelength for one of the kernel widths (1432) than for the formulation for this parameter. We use the wavelengths listed in the formulation. RATIONALE: CO2 ice, some hydrated minerals
Parameters
data : PyHAT SpectralData object kernel: Tuple of windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.bd1500_2(data, kernel=(5, 11, 5))[source]
NAME: BD1500 PARAMETER: 1.5 micron H2O ice band depth FORMULATION (with kernels): 1.0 - (R1525 / (b * R1808 + a * R1367)) RATIONALE: H2O surface ice Algorithm differs from published - coded as per CAT (reduced instrument noise)
- Parameters
data : PyHAT SpectralData object
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.bd1750_2(data, kernel=(5, 3, 5))[source]
NAME: BD1750 PARAMETER: 1.7 micron band depth FORMULATION (with kernels): 1 - ( R1750 / (a * R1690 + b * R1815) ) RATIONALE: gypsum
- Parameters
data : PyHAT SpectralData object kernel: Tuple of windows in # of spectral channels around the specified wvls
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.bd1900_2(data, kernel=(5, 5, 5))[source]
NAME: BD1900_2 PARAMETER: 1.9 micron band depth FORMULATION (with kernels): .5 * (1 - (R1930 / (a * R1850 + b * R2067))) + .5 * (1 - (R1985 / (a * R1850 + b * R2067))) RATIONALE: H2O, chemically bound or adsorbed
Algorithm differs from published - coded as per CAT (reduced instrument noise)
Parameters
wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.bd1900r2(data)[source]
NAME: BD1900r2 PARAMETER: 1.9 micron band depth FORMULATION:1 - ((R1908 / RC1908 + R1914 / RC1914 + R1921 / RC1921 + R1928 / RC1928 +
- R1934 / RC1934 + R1941 / RC1941) / (R1862 / RC1862 + R1869 / RC1869 + R1875 / RC1875 + R2112 / RC2112 +
R2120 / RC2120 + R2126 / RC2126))
RATIONALE: H2O, chemically bound or adsorbed
Algorithm differs from published - coded as per CAT (reduced instrument noise)
data : PyHAT SpectralData object
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.bd2100_2(data, kernel=(3, 5, 3))[source]
NAME: BD2100 PARAMETER: 2.1 micron band depth FORMULATION (with kernels): 1 - ( R2132 / (a * R1930 + b * R2250) ) RATIONALE: monohydrated minerals
Parameters
data : PyHAT SpectralData object kernel: Tuple of windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.bd2165(data, kernel=(5, 3, 3))[source]
NAME: BD2165 PARAMETER: 2.165 micron Al-OH band depth FORMULATION (with kernels): 1 - ( R2165 / (a * R2120 + b * R2230) ) RATIONALE: Pyrophyllite Kaolinite group
Parameters
data : PyHAT SpectralData object kernel: Tuple of windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.bd2190(data, kernel=(5, 3, 3))[source]
NAME: BD2190 PARAMETER: 2.190 micron Al-OH band depth FORMULATION (with kernels): 1 - ( R2185 / (a * R2120 + b * R2250) ) RATIONALE: Beidellite Allophane Imogolite
Parameters
data : PyHAT SpectralData object kernel: Tuple of windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.bd2210_2(data, kernel=5)[source]
NAME: BD2210 PARAMETER: 2.21 micron band depth FORMULATION: 1 - ( R2210 / (a*R2165+b*R2250) ) RATIONALE: Al-OH minerals: monohydrated minerals
Parameters
data : PyHAT SpectralData object kernel: Tuple of windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.bd2230(data, kernel=(3, 3, 3))[source]
NAME: BD2230 PARAMETER: 2.23 μm band depth FORMULATION (with kernels): 1 - (R2235 / (a * R2210 + b * R2252)) RATIONALE: Hydroxylated ferric sulfates
Parameters
data : PyHAT SpectralData object kernel: Tuple of windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.bd2250(data, kernel=(5, 7, 3))[source]
NAME: BD2250 PARAMETER: 2.25 μm band depth FORMULATION (with kernels): 1 - (R2245 / (a * R2120 + b * R2340)) RATIONALE: 2.25 μm broad Al-OH and Si-OH band depth
Parameters
wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.bd2265(data, kernel=(5, 3, 5))[source]
NAME: BD2265 PARAMETER: 2.265 micron band depth FORMULATION: 1 - ( R2265 / (a*R2210+b*R2340) ) RATIONALE: Jarosite Gibbsite Acid-leached nontronite
Parameters
data : PyHAT SpectralData object kernel: Tuple of windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.bd2290(data, kernel=(5, 5, 5))[source]
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
Parameters
data : PyHAT SpectralData object kernel: Tuple of windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.bd2355(data, kernel=(5, 5, 5))[source]
NAME: BD2355 PARAMETER: 2.35 micron band depth FORMULATION: 1 - ( R2355 / (a * R2300+b * R2450) ) RATIONALE: Chlorite Prehnite Pumpellyite
Parameters
wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.bd2500h_2(data, kernel=(5, 5, 5))[source]
NAME: BD2500h PARAMETER: Mg Carbonate overtone band depth FORMULATION (with kernels): 1 - (R2480 / ((a * R2364) + (b * R2570))) RATIONALE: Mg carbonates
Parameters
data : PyHAT SpectralData object kernel: Tuple of windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.bd2600(data, kernel=(5, 5, 5))[source]
NAME: BD2600 PARAMETER: 2.6 μm H 2 O band depth FORMULATION: 1 - (R2600 / (a * R2530 + b * R2630)) RATIONALE: H 2 O vapor (accounts for spectral slope)
Parameters
data : PyHAT SpectralData object kernel: Tuple of windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.bd3100(data, kernel=(5, 5, 5))[source]
NAME: BD3100 PARAMETER: 3.1 micron band depth FORMULATION: 1 - ( R3120 / (a*R3000+b*R3250) ) RATIONALE: H2O ice
Parameters
data : PyHAT SpectralData object kernel: Tuple of windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.bd3200(data, kernel=(5, 5, 5))[source]
NAME: BD3200 PARAMETER: 3.2 micron band depth FORMULATION: 1 - ( R3320 / (a*R3250+b*R3390) ) RATIONALE: CO2 ice
Parameters
data : PyHAT SpectralData object kernel: Tuple of windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.bd3400_2(data, kernel=(10, 15, 10))[source]
NAME: BD3400 PARAMETER: 3.4 micron band depth FORMULATION: 1 - ( R3420 / (a*R3250+b*R3630) ) RATIONALE: carbonates; organics
Parameters
data : PyHAT SpectralData object kernel: Tuple of windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.bd530_2(data, kernel=(5, 5, 5))[source]
NAME: BD530_2 PARAMETER: 0.53 micron band depth FORMULATION: 1 - (R530/(a*R614+b*R440)) RATIONALE: Crystalline ferric minerals
Parameters
wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.bd640_2(data, kernel=(5, 5, 5))[source]
NAME: BD640 PARAMETER: 0.64 micron band depth FORMULATION (with kernels): 1 - (R624 / (a * R600 + b * R760)) RATIONALE: select ferric minerals, especially maghemite
Parameters
data : PyHAT SpectralData object kernel: Tuple of windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.bd860_2(data, kernel=(5, 5, 5))[source]
NAME: BD860 PARAMETER: 0.86 micron band depth FORMULATION (with kernels): 1 - (R860 / (a * R755 + b * R977)) RATIONALE: select ferric minerals (‘hematite band’)
Parameters
data : PyHAT SpectralData object kernel: Tuple of windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.bd920_2(data, kernel=(5, 5, 5))[source]
NAME: BD920 PARAMETER: 0.92 micron band depth FORMULATION (with kernels): 1 - ( R920 / (a * R807 + b * R984) ) RATIONALE: select ferric minerals (‘Pseudo BDI1000 VIS’)
Parameters
data : PyHAT SpectralData object kernel: Tuple of windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.bdi1000IR(data)[source]
NAME: BDI1000IR PARAMETER: 1 micron integrated band depth; IR wavelengths FORMULATION: divide reflectances between 1020 and 1255 by linear fit from the 75th percentile R in 1330-1870 range to the median R in the range 2430-2600. Then integrate over (1 - normalized values) Note: We follow the implementation in the CAT IDL code, which differs slightly from that described in Viviano-Beck et al, 2014 RATIONALE: crystalline Fe+2 minerals; corrected for overlying aerosol induced slope
- libpyhat.derived.crism.crism_algs.bdi1000VIS(data)[source]
NAME: BDI1000VIS PARAMETER: 1 micron integrated band depth; VIS wavelengths FORMULATION: Divide reflectances from R833 to R1023 by the modeled reflectance at RPEAK1, then integrate over (1 - normalized radiances) to get integrated band depth RATIONALE: crystalline Fe+2 or Fe+3 minerals
- libpyhat.derived.crism.crism_algs.cindex2(data, kernel=(9, 11, 7))[source]
NAME: CINDEX PARAMETER: Inverse lever rule to detect convexity at 3.6 μm due to 3.4 μm and 3.9 μm absorptions FORMULATION (with kernels): 1 - ((a * R3450 + b * R3875) / 3610) RATIONALE: carbonates
Parameters
data : PyHAT SpectralData object kernel: Tuple of windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.crism_bd3000(data, kernel=(5, 5, 5))[source]
NAME: BD3000 PARAMETER: 3 micron band depth FORMULATION: 1 - ( R3000 / (R2530*(R2530/R2210)) ) RATIONALE: H2O, chemically bound or adsorbed
Parameters
data : PyHAT SpectralData object kernel: Tuple of windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.crism_bdi2000(data)[source]
NAME: BDI2000 PARAMETER: 2 micron integrated band depth FORMULATION: divide reflectances between 1660 and 2390 by linear fit from the 75th percentile R in 1330-1870 range to the median R in the range 2430-2600. Then integrate over (1 - normalized values) Note: We follow the implementation in the CAT IDL code, which differs slightly from that described in Viviano-Beck et al, 2014 RATIONALE: pyroxene abundance and particle size
- libpyhat.derived.crism.crism_algs.d2200(data, kernel=(7, 5, 7, 7, 7))[source]
NAME: D2200 PARAMETER: 2.2 micron dropoff FORMULATION (with kernels): 1 - (((R2210 / RC2210) + (R2230 / RC2230)) / (2 * (R2165 / RC2165))) Slope for RC#### anchored at R1815 and R2430. RATIONALE: Al-OH minerals
Parameters
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.d2300(data, kernel=(5, 5, 5, 5, 3, 3, 3, 5))[source]
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
Parameters
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.doub2200h(data, kernel=(5, 3, 3, 5))[source]
NAME: DOUB2200H PARAMETER: 2.16 micron Si-OH band depth and 2.21 micron H-bound Si-OH band depth (doublet) FORMULATION (with kernels): 1 - ((R2205 + R2258) / (R2172 + R2311)) RATIONALE: Opal and other Al-OH minerals
Parameters
data : PyHAT SpectralData object kernel: windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.hcp_index2(data, kernel=(7, 5, 7, 7, 7, 7, 7, 7))[source]
NAME: HCPINDEX2 PARAMETER: pyroxene index FORMULATION: RB2120 * 0.10 + RB2140 * 0.10 + RB2230 * 0.15 + RB2250 * 0.30 + RB2430 * 0.20 + RB2460 * 0.15 RB # # # # = (RC # # # #-R # # # #)/RC # # # # Slope for RC#### anchored at R1690 and R2530
RATIONALE: pyroxene is strongly +; favors high-Ca pyroxene
Parameters
data : PyHAT SpectralData object kernel: tuple contaning windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.icer1_2(data)[source]
NAME: ICER1_2 PARAMETER: 1.5 micron and 1.43 micron band ratio FORMULATION: 1 - ((1 - bd1435) / (1 - bd1500)) RATIONALE: CO2, H20 ice mixtures
Parameters
data : PyHAT SpectralData object
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.irr1(data, kernel=(5, 5))[source]
Name: IRR1 Parameter: IR ratio 1 FORMULATION (with kernels): R800 / R997 Rationale: Aphelion ice clouds (>1) versus seasonal or
Parameters
data : PyHAT SpectralData object kernel: windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.irr2(data, kernel=(5, 5))[source]
Name: IRR2 Parameter: IR ratio 2 FORMULATION (with kernels): R2530 / R2210 Rationale: Aphelion ice clouds versus seasonal or dust
Parameters
data : PyHAT SpectralData object kernel: windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.irr3(data, kernel=(7, 7))[source]
Name: IRR3 Parameter: IR ratio 3 FORMULATION (with kernels): R3500 / R3390 Rationale: Aphelion ice clouds (higher values) versus seasonal or dust
Parameters
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.islope1(data, kernel=5)[source]
NAME: ISLOPE1 PARAMETER: -1 * spectral slope1 FORMULATION: (R1815-R2530) / (2530-1815) RATIONALE: ferric coating on dark rock
Parameters
data : PyHAT SpectralData object kernel: window in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.lcp_index2(data, kernel=7)[source]
NAME: LCPINDEX2 PARAMETER: Detect broad absorption centered at 1.81 μm FORMULATION (with kernels): RB1690 * 0.20 + RB1750 * 0.20 + RB1810 * 0.30 + RB1870 * 0.30 Anchored at R1560 and R2450 RATIONALE: Pyroxene is strongly +; favors LCP
Algorithm differs from published - coded as per CAT <— What?
Parameters
data : PyHAT SpectralData object kernel: window in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.min2200(data, kernel=(5, 3, 5))[source]
NAME: MIN2200 PARAMETER: 2.16 μm Si-OH band depth and 2.21 μm H-bound Si-OH band depth (doublet) FORMULATION (with kernels): minimum( 1 - (R2165 / (a * R2120 + b * R2350)), 1 - (R2210 / (a * R2120 + b * R2350))) RATIONALE: Kaolinite group
Parameters
data : PyHAT SpectralData object kernel: windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.min2250(data, kernel=(5, 3, 5))[source]
NAME: MIN2250 PARAMETER: 2.21 μm Si-OH band depth and 2.26 μm H-bound Si-OH band depth FORMULATION (with kernels): minimum( 1 - (R2210 / (a * R2165 + b * R2350)), 1 - (R2265 / (a * R2165 + b * R2350))) RATIONALE: Opal
Parameters
data : PyHAT SpectralData object kernel: windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.min2295_2480(data, kernel=(5, 5, 5))[source]
NAME: MIN2295_2480 PARAMETER: Mg Carbonate overtone band depth and metal-OH band FORMULATION (with kernels): minimum( 1 - (R2295 / (a * R2165 + b * R2364)), 1 - (R2480 / (a * R2364 + b * R2570))) RATIONALE: Mg carbonates; both overtones must be present
Parameters
data : PyHAT SpectralData object kernel: windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.min2345_2537(data, kernel=(5, 5, 5))[source]
NAME: MIN2345_2537 PARAMETER: Ca/Fe Carbonate overtone band depth and metal-OH band FORMULATION (with kernels): minimum( 1 - (R2345 / (a * R2250 + b * R2430)), 1 - (R2537 / (a * R2430 + b * R2602))) RATIONALE: Ca/Fe carbonates; both overtones must be present
Parameters
data : PyHAT SpectralData object kernel: windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.olivine_index3(data, kernel=7)[source]
NAME: OLINDEX3 PARAMETER: olivine index with less sensitivity to illumination FORMULATION: RB1210 * 0.1 + RB1250 * 0.1 + RB1263 * 0.2 + RB1276 * 0.2 + RB1330 * 0.4
Slope for RC anchored at R1750 and R1862 RB#### = (RC#### - R####)/RC####
- RATIONALE: detect broad absorption centered at 1 um
Parameters
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.r1080(data, kernel=5)[source]
Name: R1080 Parameter: 1.08 micron reflectance FORMULATION (with kernels): R1080 Rationale: FAL browse product component
Parameters
data : PyHAT SpectralData object kernel: Window in # of spectral channels around the specified central wvl
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.r1300(data, kernel=5)[source]
Name: R1300 Parameter: 1.30 micron reflectance FORMULATION (with kernels): R1300 Rationale: IRA browse product component
Parameters
data : PyHAT SpectralData object kernel: Window in # of spectral channels around the specified central wvl
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.r1330(data, kernel=5)[source]
NAME: R1330 PARAMETER: IR albedo FORMULATION: R1330 RATIONALE: IR albedo (ices > dust > unaltered mafics)
Parameters
data : PyHAT SpectralData object kernel: Window in # of spectral channels around the specified central wvl
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.r1506(data, kernel=5)[source]
Name: R1506 Parameter: 1.51 micron reflectance FORMULATION (with kernels): R1506 Rationale: TRU browse product component
Parameters
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.r2529(data, kernel=5)[source]
Name: R2529 Parameter: 2.53 micron reflectance Formulation: R2529 Rationale: TRU browse product component
Parameters
data : PyHAT SpectralData object kernel: Window in # of spectral channels around the specified central wvl
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.r3920(data, kernel=5)[source]
Name: R3920 Parameter: 3.92 micron reflectance Formulation: R3920 Rationale: IC2 browse product component
Parameters
data : PyHAT SpectralData object kernel: Window in # of spectral channels around the specified central wvl
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.r440(data, kernel=5)[source]
Name: R440 Parameter: 0.44 micron reflectance FORMULATION (with kernels): R440 Rationale: Clouds/Hazes
Parameters
data : PyHAT SpectralData object kernel: Window in # of spectral channels around the specified central wvl
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.r530(data, kernel=5)[source]
Name: R530 Parameter: 0.53 micron reflectance FORMULATION (with kernels): R530 Rationale: TRU browse product component
Parameters
data : PyHAT SpectralData object kernel: Window in # of spectral channels around the specified central wvl
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.r600(data, kernel=5)[source]
Name: R600 Parameter: 0.60 micron reflectance FORMULATION (with kernels): R600 Rationale: TRU browse product component
Parameters
data : PyHAT SpectralData object kernel: Window in # of spectral channels around the specified central wvl
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.r770(data, kernel=5)[source]
Name: R770 Parameter: 0.77micron reflectance Formulation: R770 Rationale: Higher value more dusty or icy Caveats: Sensitive to slope effects, clouds
Parameters
data : PyHAT SpectralData object kernel: Window in # of spectral channels around the specified central wvl
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.rbr(data, kernel=5)[source]
Name: RBR Parameter: Red/Blue Ratio Formulation: R770 / R440 Rationale: Higher value indicates more npFeOx Caveats: Sensitive to dust in atmosphere
Parameters
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.rpeak1(data)[source]
NAME: RPEAK1 PARAMETER: Reflectance peak 1 FORMULATION: Wavelength where first derivative = 0 of fifth-order polynomial fit to reflectances at all valid VNIR wavelengths RATIONALE: crystalline Fe+2 or Fe+3 minerals
Parameters
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.sh600_2(data, kernel=(5, 5, 3))[source]
NAME: SH600 PARAMETER: 0.60 micron shoulder height FORMULATION (with kernels): 1 - (a * R533 + b * R716) / R600 RATIONALE: select ferric minerals
Parameters
wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.sh770(data, kernel=(3, 5, 5))[source]
NAME: SH770 PARAMETER: 0.77 micron shoulder height FORMULATION (with kernels): 1 - (a * R716 + b * R860) / R775 RATIONALE: select ferric minerals
Parameters
data : PyHAT SpectralData object kernel: Tuple of windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter
- libpyhat.derived.crism.crism_algs.sindex2(data, kernel=(5, 7, 3))[source]
NAME: SINDEX2 PARAMETER: Inverse lever rule to detect convexity at 2.29 μm due to 2.1 μm and 2.4 μm absorptions FORMULATION (with kernels): 1 - (a * R2120 + b * R2400) / R2290 RATIONALE: Hydrated sulfates (mono and polyhydrated sulfates) will be strongly > 0
Parameters
data : PyHAT SpectralData object kernel: Tuple of windows in # of spectral channels around the specified wvls
Returns
data: PyHAT SpectralData object with a new column added for the derived parameter