Result for 15C741851BA9F4FBF158F86A1C57BA4C7E8384F9

Query result

Key Value
FileName./usr/lib/python3/dist-packages/pyspectral/tests/test_reflectance.py
FileSize9048
MD5563A9E653AE9185618E830A903B41AE7
SHA-115C741851BA9F4FBF158F86A1C57BA4C7E8384F9
SHA-25625F03ECE4B5F194AC567E2A9E03C666B682EB62CE3097A87037EBE5CA0B35F8E
SSDEEP192:sfsa77ietKfF+1RSdNtLG/zjO0DSS7lW645+HDI0Dn9EqvSnxwwqpE:sfR7OekfsLmym0DSS7cOHDI0D9MqpE
TLSHT16812C79F83C74E75E7435ACAC487A697852348370D2C600A7A2C7A445F2F27997F2CA7
hashlookup:parent-total2
hashlookup:trust60

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Parents (Total: 2)

The searched file hash is included in 2 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
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