| Key | Value |
|---|---|
| FileName | ./usr/lib/python3/dist-packages/pyspectral/data/MSG_SEVIRI_Spectral_Response_Characterisation.XLS |
| FileSize | 744960 |
| MD5 | DAF15DF58D8BFB3F4CF8733BD0BD70E7 |
| SHA-1 | 08A5658AE5B1037FAE018537BA430F954DB6936A |
| SHA-256 | 3A2D812AE94A106AD11DC3FDACB28C785423597D8FD301D05FCB0B21373CB28F |
| SSDEEP | 12288:R+qsqXCwgRJxSV3RvJBMsmRMbS9r+as81WE4GbXpu1ToFI365ER+1/VOEfzl9sTF:UoTSI365ERm9Bzl9sTiBwHDJ+lZS43YA |
| TLSH | T186F46011FB745E58C234D23958F792A0627D6D41FB1B8F6FE1807E223F926E0AE06395 |
| hashlookup:parent-total | 6 |
| hashlookup:trust | 80 |
The searched file hash is included in 6 parent files which include package known and seen by metalookup. A sample is included below:
| Key | Value |
|---|---|
| FileSize | 192076 |
| MD5 | C57211383ED1B11A34E937A3EE38A64D |
| PackageDescription | Reading 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. |
| PackageMaintainer | Debian GIS Project <pkg-grass-devel@lists.alioth.debian.org> |
| PackageName | python3-pyspectral |
| PackageSection | python |
| PackageVersion | 0.10.5+ds-1 |
| SHA-1 | 79607BD5837F39F11192AA60417F5A0A8386EB56 |
| SHA-256 | BB8540AD728FAC51DCDDD050B0407F6298A39193D472C7BA325F167AD9D4B7CE |
| Key | Value |
|---|---|
| FileSize | 172096 |
| MD5 | 638515C3A05034D18DD0E5D428B3E7CA |
| PackageDescription | Reading 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. |
| PackageMaintainer | Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com> |
| PackageName | python3-pyspectral |
| PackageSection | python |
| PackageVersion | 0.9.5+ds-1 |
| SHA-1 | 987C45577B9C254F644BB75981518844F9EE34B3 |
| SHA-256 | AEA8177DBE8D3E880C1D06CFB50F4F27362C8EF7316E2EB41985F429DF5A773E |
| Key | Value |
|---|---|
| FileSize | 191112 |
| MD5 | 77CB94E47E2630EF5F66981CC1AEFDA8 |
| PackageDescription | Reading 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. |
| PackageMaintainer | Debian GIS Project <pkg-grass-devel@lists.alioth.debian.org> |
| PackageName | python3-pyspectral |
| PackageSection | python |
| PackageVersion | 0.10.4+ds-1 |
| SHA-1 | DE099204FB9DEC401194A1C579478F0D44864F53 |
| SHA-256 | 9AA2CD4A52C0F7C87B270BA2E0B42DDC2F1E9D0E47DE2860EF578E9F7B73072C |
| Key | Value |
|---|---|
| FileSize | 185880 |
| MD5 | 0916E097D0356A42C436CE0966AF9955 |
| PackageDescription | Reading 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. |
| PackageMaintainer | Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com> |
| PackageName | python3-pyspectral |
| PackageSection | python |
| PackageVersion | 0.10.0+ds-1 |
| SHA-1 | 6360BE7F1423D6E8D9DDCC4228F466C8D3B82019 |
| SHA-256 | CDE1AD967F6C32FA2A11F150B174E8D812AE262A842DE45BB9B62624B1C31174 |
| Key | Value |
|---|---|
| FileSize | 173500 |
| MD5 | 27743AC76F88459F8894AB50C277942D |
| PackageDescription | Reading 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. |
| PackageMaintainer | Debian GIS Project <pkg-grass-devel@lists.alioth.debian.org> |
| PackageName | python3-pyspectral |
| PackageSection | python |
| PackageVersion | 0.8.6+ds-1 |
| SHA-1 | 754B980D8FDCD6623D9813C6F79D8F5BBA9E88E0 |
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| Key | Value |
|---|---|
| FileSize | 188328 |
| MD5 | 1E6C02EDC007B0EDF0C30E05C4AD4FC9 |
| PackageDescription | Reading 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. |
| PackageMaintainer | Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com> |
| PackageName | python3-pyspectral |
| PackageSection | python |
| PackageVersion | 0.10.4+ds-1 |
| SHA-1 | 4F17C5DD51EFDDCE7750D01D3442B066B7CCC448 |
| SHA-256 | F7123A82BEC388C49E7C0A6F7681F1D1ABB885E9D0753E067F146616D6AAAD43 |