Ubuntu
pyspectral package

pyspectral 0.13.3+ds-1 source package in Ubuntu

Changelog

pyspectral (0.13.3+ds-1) unstable; urgency=medium

  * New upstream release. 

 -- Antonio Valentino <email address hidden>  Sat, 20 Jul 2024 10:09:22 +0000

Upload details

Uploaded by:
Debian GIS Project
Uploaded to:
Sid
Original maintainer:
Debian GIS Project
Architectures:
all
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Oracular release universe misc

Builds

Oracular: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
pyspectral_0.13.3+ds-1.dsc 3.7 KiB 30aa39086307313bd1941e93b5a91fa7f810f8b5d54e5cc416752abf7e047dae
pyspectral_0.13.3+ds.orig.tar.xz 3.4 MiB 6327c626f3d5eb3d18e1ca949c9e48565cb64bb93a69ac2c352494c15ba76acb
pyspectral_0.13.3+ds-1.debian.tar.xz 116.9 KiB ca35f2654f5faa6ecdb8149b414e3630502c51fc4a92c670892ee189447adcd1

Available diffs

No changes file available.

Binary packages built by this source

pyspectral-bin: Reading and manipulaing satellite sensor spectral responses -- scripts

 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.
 .
 This package provides utilities and executable scripts.

python3-pyspectral: 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.

python3-pyspectral-doc: Reading and manipulaing satellite sensor spectral responses -- documentation

 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.
 .
 This package includes the PySpectral documentation in HTML format.