Result for 109A4C74B973AB325AF149739EC98160229FBB2D

Query result

Key Value
FileName./usr/lib/python3/dist-packages/pyspectral/blackbody.py
FileSize8356
MD5CC866B940757AB0D3641E72FF1384FA3
SHA-1109A4C74B973AB325AF149739EC98160229FBB2D
SHA-2561B92C0EB71481E89C8D57A404B6F10A577F90BE5BF8D16A110847A0992F7EDD5
SSDEEP96:+JzfPOK/K4C6yyLszs7GrvAxtMU/K4mKmHssDdgp9xlp3OXDQ5NN7WntbmlY7k/s:OfPOQJwjkxtMiMhBWxl4DeyN4/L1MmC/
TLSHT14402B66A76910229E20182FE0C53C2533ABD6D974F4C2576780CF657AF01B3597F69E8
hashlookup:parent-total3
hashlookup:trust65

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Parents (Total: 3)

The searched file hash is included in 3 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
FileSize191112
MD577CB94E47E2630EF5F66981CC1AEFDA8
PackageDescriptionReading and manipulaing satellite sensor spectral responses Reading and manipulaing satellite sensor spectral responses and the solar spectrum, to perform various corrections to VIS and NIR band data. . Given a passive sensor on a meteorological satellite PySpectral provides the relative spectral response (rsr) function(s) and offer some basic operations like convolution with the solar spectrum to derive the in band solar flux, for instance. . The focus is on imaging sensors like AVHRR, VIIRS, MODIS, ABI, AHI, OLCI and SEVIRI. But more sensors are included and if others are needed they can be easily added. With PySpectral it is possible to derive the reflective and emissive parts of the signal observed in any NIR band around 3-4 microns where both passive terrestrial emission and solar backscatter mix the information received by the satellite. Furthermore PySpectral allows correcting true color imagery for the background (climatological) atmospheric signal due to Rayleigh scattering of molecules, absorption by atmospheric gases and aerosols, and Mie scattering of aerosols.
PackageMaintainerDebian GIS Project <pkg-grass-devel@lists.alioth.debian.org>
PackageNamepython3-pyspectral
PackageSectionpython
PackageVersion0.10.4+ds-1
SHA-1DE099204FB9DEC401194A1C579478F0D44864F53
SHA-2569AA2CD4A52C0F7C87B270BA2E0B42DDC2F1E9D0E47DE2860EF578E9F7B73072C
Key Value
FileSize192076
MD5C57211383ED1B11A34E937A3EE38A64D
PackageDescriptionReading and manipulaing satellite sensor spectral responses Reading and manipulaing satellite sensor spectral responses and the solar spectrum, to perform various corrections to VIS and NIR band data. . Given a passive sensor on a meteorological satellite PySpectral provides the relative spectral response (rsr) function(s) and offer some basic operations like convolution with the solar spectrum to derive the in band solar flux, for instance. . The focus is on imaging sensors like AVHRR, VIIRS, MODIS, ABI, AHI, OLCI and SEVIRI. But more sensors are included and if others are needed they can be easily added. With PySpectral it is possible to derive the reflective and emissive parts of the signal observed in any NIR band around 3-4 microns where both passive terrestrial emission and solar backscatter mix the information received by the satellite. Furthermore PySpectral allows correcting true color imagery for the background (climatological) atmospheric signal due to Rayleigh scattering of molecules, absorption by atmospheric gases and aerosols, and Mie scattering of aerosols.
PackageMaintainerDebian GIS Project <pkg-grass-devel@lists.alioth.debian.org>
PackageNamepython3-pyspectral
PackageSectionpython
PackageVersion0.10.5+ds-1
SHA-179607BD5837F39F11192AA60417F5A0A8386EB56
SHA-256BB8540AD728FAC51DCDDD050B0407F6298A39193D472C7BA325F167AD9D4B7CE
Key Value
FileSize188328
MD51E6C02EDC007B0EDF0C30E05C4AD4FC9
PackageDescriptionReading and manipulaing satellite sensor spectral responses Reading and manipulaing satellite sensor spectral responses and the solar spectrum, to perform various corrections to VIS and NIR band data. . Given a passive sensor on a meteorological satellite PySpectral provides the relative spectral response (rsr) function(s) and offer some basic operations like convolution with the solar spectrum to derive the in band solar flux, for instance. . The focus is on imaging sensors like AVHRR, VIIRS, MODIS, ABI, AHI, OLCI and SEVIRI. But more sensors are included and if others are needed they can be easily added. With PySpectral it is possible to derive the reflective and emissive parts of the signal observed in any NIR band around 3-4 microns where both passive terrestrial emission and solar backscatter mix the information received by the satellite. Furthermore PySpectral allows correcting true color imagery for the background (climatological) atmospheric signal due to Rayleigh scattering of molecules, absorption by atmospheric gases and aerosols, and Mie scattering of aerosols.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamepython3-pyspectral
PackageSectionpython
PackageVersion0.10.4+ds-1
SHA-14F17C5DD51EFDDCE7750D01D3442B066B7CCC448
SHA-256F7123A82BEC388C49E7C0A6F7681F1D1ABB885E9D0753E067F146616D6AAAD43