Ubuntu
pyspectral package

pyspectral 0.13.2+ds-1 source package in Ubuntu

Changelog

pyspectral (0.13.2+ds-1) unstable; urgency=medium

  * New upstream release.

 -- Antonio Valentino <email address hidden>  Sat, 29 Jun 2024 09:41:58 +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

Builds

Oracular: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
pyspectral_0.13.2+ds-1.dsc 3.7 KiB e8bf39ca905bba964cdcb9c1224976245b838f4ac515289ac3c35f1180e8d20f
pyspectral_0.13.2+ds.orig.tar.xz 3.4 MiB 6aef410ade424bda1725d715de7565d8bcb8b7396612035ae26d71eea7d370ea
pyspectral_0.13.2+ds-1.debian.tar.xz 116.9 KiB 5b51945ae2ccac81929b75e72169d71cde9bd53edf575f83a0b891710aa15ec6

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.